diff --git a/.gitattributes b/.gitattributes index a6344aac8c09253b3b630fb776ae94478aa0275b..911115b2b694a0a07d6a6641826472a601c80d07 100644 --- a/.gitattributes +++ b/.gitattributes @@ -33,3 +33,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text *.zip filter=lfs diff=lfs merge=lfs -text *.zst filter=lfs diff=lfs merge=lfs -text *tfevents* filter=lfs diff=lfs merge=lfs -text +custom_nodes/ComfyUI_INSTARAW/fonts/BricolageGrotesque.ttf filter=lfs diff=lfs merge=lfs -text +custom_nodes/ComfyUI_INSTARAW/js/Instara_loader.mp4 filter=lfs diff=lfs merge=lfs -text +custom_nodes/ComfyUI_INSTARAW/nodes/utility_nodes/_refs/iphone13.jpg filter=lfs diff=lfs merge=lfs -text diff --git a/custom_nodes/ComfyUI_INSTARAW/__init__.py b/custom_nodes/ComfyUI_INSTARAW/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..2039686dbcb11695ba61c1fad6e321feebc4986d --- /dev/null +++ b/custom_nodes/ComfyUI_INSTARAW/__init__.py @@ -0,0 +1,40 @@ +""" +ComfyUI INSTARAW +A general-purpose custom nodes package by Instara + +INSTARAW is a collection of powerful custom nodes for ComfyUI that brings together +hard-to-install dependencies and integrations in one convenient package. + +Features: +- API Integrations: SeeDream, Ideogram, and more +- Easy Installation: Pre-packaged dependencies +- Modular Design: Easy to extend and customize +- INSTARAW Brand: Where we push the boundaries + +Created by Instara +""" + +import os +import nodes + +# This line is critical for loading the JavaScript and CSS files. +# It tells ComfyUI where to find the web assets for this extension. +if "ComfyUI_INSTARAW" not in nodes.EXTENSION_WEB_DIRS: + nodes.EXTENSION_WEB_DIRS["ComfyUI_INSTARAW"] = os.path.join(os.path.dirname(os.path.realpath(__file__)), "js") + +from .nodes import NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS + +# Import creative API to register endpoints (after nodes are loaded) +try: + from .nodes.api_nodes import creative_api +except Exception as e: + print(f"[INSTARAW] Warning: Could not load creative_api: {e}") + print("[INSTARAW] Creative/Character generation features will not be available.") + +# Required exports for ComfyUI +__all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS"] + +# Package metadata +__version__ = "1.2.0" # Version bumped to reflect the major addition +__author__ = "Instara" +__description__ = "INSTARAW - General purpose custom nodes for ComfyUI" \ No newline at end of file diff --git a/custom_nodes/ComfyUI_INSTARAW/fonts/BricolageGrotesque.ttf b/custom_nodes/ComfyUI_INSTARAW/fonts/BricolageGrotesque.ttf new file mode 100644 index 0000000000000000000000000000000000000000..cd1051ba41edf62f23f5b1d5abb30f03b7606784 --- /dev/null +++ b/custom_nodes/ComfyUI_INSTARAW/fonts/BricolageGrotesque.ttf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:31b91d15aae398699fae58363dbc8ca1167faffe7d2cd62e68c716dcaa7d5fdd +size 407844 diff --git a/custom_nodes/ComfyUI_INSTARAW/js/Instara_loader.mp4 b/custom_nodes/ComfyUI_INSTARAW/js/Instara_loader.mp4 new file mode 100644 index 0000000000000000000000000000000000000000..cd3613e94fbf7318eae7e97c4f6148bee1224f89 --- /dev/null +++ b/custom_nodes/ComfyUI_INSTARAW/js/Instara_loader.mp4 @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:81c28caca82f4fe5308da8248315c12aeea55f93caf44a90e5c2a2b86a3ae516 +size 459430 diff --git a/custom_nodes/ComfyUI_INSTARAW/js/advanced_image_loader.css b/custom_nodes/ComfyUI_INSTARAW/js/advanced_image_loader.css new file mode 100644 index 0000000000000000000000000000000000000000..84c41d074d26198495cd6c78edd911ed4c88a4e1 --- /dev/null +++ b/custom_nodes/ComfyUI_INSTARAW/js/advanced_image_loader.css @@ -0,0 +1,760 @@ +/* --- +// Filename: ../ComfyUI_INSTARAW/js/advanced_image_loader.css +// --- */ + +.instaraw-adv-loader-container { + background: #0d0f12; + padding: 16px; + border-radius: 4px; + font-family: 'Inter', -apple-system, BlinkMacSystemFont, 'Segoe UI', system-ui, sans-serif; +} + +/* Brand Row */ +.instaraw-adv-loader-brand-row { + position: relative; + display: inline-flex; + align-items: center; + margin-bottom: 12px; +} + +.instaraw-adv-loader-logo { + width: 140px; + height: auto; + opacity: 0.85; +} + +.instaraw-adv-loader-version { + position: absolute; + right: 0; + top: 50%; + transform: translate(122%, -50%); + font-family: monospace; + font-size: 14px; + color: rgba(255, 255, 255, 0.5); + white-space: nowrap; +} + +/* Top Bar - Mode + Stats */ +.instaraw-adv-loader-topbar { + display: flex; + align-items: center; + justify-content: space-between; + gap: 12px; + margin-bottom: 12px; + padding: 10px 12px; + background: rgba(255, 255, 255, 0.04); + border-radius: 4px; + border: 1px solid rgba(255, 255, 255, 0.08); +} +.instaraw-adv-loader-topbar-left { + display: flex; + align-items: center; + gap: 10px; +} +.instaraw-adv-loader-topbar-right { + display: flex; + align-items: center; + gap: 6px; + font-size: 12px; + color: rgba(249, 250, 251, 0.7); +} +.instaraw-adv-loader-separator { + color: rgba(255, 255, 255, 0.3); +} +.instaraw-adv-loader-mode-select { + display: flex; + align-items: center; +} +.instaraw-adv-loader-progress-badge { + font-size: 11px; + color: #818cf8; + font-weight: 600; + background: rgba(129, 140, 248, 0.15); + padding: 4px 8px; + border-radius: 4px; +} + +/* Mode Badge */ +.instaraw-adv-loader-mode-badge { + padding: 6px 12px; + border-radius: 4px; + font-size: 11px; + font-weight: 700; + letter-spacing: 0.5px; + text-transform: uppercase; +} + +.instaraw-adv-loader-mode-txt2img { + background: linear-gradient(135deg, #7c3aed 0%, #6d28d9 100%); + color: #ffffff; + border: 2px solid #a78bfa; +} + +.instaraw-adv-loader-mode-img2img { + background: linear-gradient(135deg, #3b82f6 0%, #2563eb 100%); + color: #ffffff; + border: 2px solid #60a5fa; +} + +.instaraw-adv-loader-mode-selector { + display: flex; + align-items: center; + gap: 12px; + margin-bottom: 16px; + padding: 12px; + background: rgba(255, 255, 255, 0.03); + border-radius: 4px; + border: 1px solid rgba(255, 255, 255, 0.06); +} +.instaraw-adv-loader-mode-selector label { + color: rgba(249, 250, 251, 0.7); + font-size: 11px; + font-weight: 500; + text-transform: uppercase; +} +.instaraw-adv-loader-mode-dropdown { + background: rgba(255, 255, 255, 0.05); + color: #f9fafb; + border: 1px solid rgba(255, 255, 255, 0.1); + padding: 6px 10px; + border-radius: 4px; + cursor: pointer; + font-size: 12px; + font-weight: 500; + outline: none; +} +.instaraw-adv-loader-mode-dropdown:hover { + background: rgba(255, 255, 255, 0.08); + border-color: #818cf8; + transition: none; +} +.instaraw-adv-loader-mode-dropdown:focus { + border-color: #a78bfa; +} +.instaraw-adv-loader-queue-all-btn { + background: #7c3aed; + color: #ffffff; + border: none; + padding: 10px 16px; + border-radius: 4px; + cursor: pointer; + font-weight: 600; + font-size: 13px; +} +.instaraw-adv-loader-queue-all-btn:hover { + background: #6d28d9; + transition: none; +} +.instaraw-adv-loader-add-latent-btn { + background: #059669; + color: #ffffff; + border: none; + padding: 10px 16px; + border-radius: 4px; + cursor: pointer; + font-weight: 600; + font-size: 13px; +} +.instaraw-adv-loader-add-latent-btn:hover { + background: #047857; + transition: none; +} +.instaraw-adv-loader-batch-add-btn { + background: #059669; + color: #ffffff; + border: none; + padding: 10px 14px; + border-radius: 4px; + cursor: pointer; + font-weight: 600; + font-size: 13px; +} +.instaraw-adv-loader-batch-add-btn:hover { + background: #047857; + transition: none; +} +.instaraw-adv-loader-batch-add-controls { + display: flex; + align-items: center; + gap: 4px; +} +.instaraw-adv-loader-batch-count-input { + width: 50px; + padding: 9px 8px; + border: 1px solid rgba(255, 255, 255, 0.15); + border-radius: 4px; + background: rgba(255, 255, 255, 0.05); + color: #f9fafb; + font-size: 13px; + text-align: center; +} +.instaraw-adv-loader-header { + display: flex; + justify-content: space-between; + align-items: center; + margin-bottom: 16px; +} +.instaraw-adv-loader-actions { + display: flex; + flex-wrap: wrap; + gap: 8px; + margin-bottom: 12px; +} +.instaraw-adv-loader-upload-btn { + background: #6366f1; + color: #ffffff; + border: none; + padding: 10px 20px; + border-radius: 4px; + cursor: pointer; + font-weight: 600; + font-size: 13px; +} +.instaraw-adv-loader-upload-btn:hover { + background: #4f46e5; + transition: none; +} +.instaraw-adv-loader-upload-btn:disabled { + opacity: 0.5; + cursor: not-allowed; +} +.instaraw-adv-loader-delete-all-btn { + background: #dc2626; + color: #ffffff; + border: none; + padding: 10px 16px; + border-radius: 4px; + cursor: pointer; + font-weight: 600; + font-size: 13px; +} +.instaraw-adv-loader-delete-all-btn:hover { + background: #ef4444; + transition: none; +} +.instaraw-adv-loader-duplicate-btn { + background: #374151; + color: #ffffff; + border: none; + padding: 10px 16px; + border-radius: 4px; + cursor: pointer; + font-weight: 600; + font-size: 13px; +} +.instaraw-adv-loader-duplicate-btn:hover { + background: #4b5563; + transition: none; +} +.instaraw-adv-loader-sync-btn { + background: #4f46e5; + color: #ffffff; + border: none; + padding: 10px 16px; + border-radius: 4px; + cursor: pointer; + font-weight: 600; + font-size: 13px; +} +.instaraw-adv-loader-sync-btn:hover { + background: #6366f1; + transition: none; +} +.instaraw-adv-loader-stats { + display: flex; + align-items: center; + gap: 16px; + color: #f9fafb; + font-size: 12px; +} +.instaraw-adv-loader-count { + font-weight: 600; + color: rgba(249, 250, 251, 0.9); +} +.instaraw-adv-loader-total { + font-weight: 500; + color: rgba(167, 139, 250, 0.9); +} +.instaraw-adv-loader-progress { + display: flex; + align-items: center; + justify-content: space-between; + padding: 12px 16px; + background: rgba(167, 139, 250, 0.08); + border-radius: 4px; + margin-bottom: 16px; + border: 1px solid rgba(167, 139, 250, 0.2); +} +.instaraw-adv-loader-progress .instaraw-adv-loader-progress-label { + color: rgba(249, 250, 251, 0.7); + font-size: 11px; + font-weight: 500; + text-transform: uppercase; +} +.instaraw-adv-loader-progress .instaraw-adv-loader-progress-value { + color: #a78bfa; + font-size: 16px; + font-weight: 600; + font-variant-numeric: tabular-nums; +} +.instaraw-adv-loader-gallery { + display: grid; + grid-template-columns: repeat(auto-fill, minmax(140px, 1fr)); + gap: 12px; + min-height: 120px; + overflow: visible; + position: relative; +} +.instaraw-adv-loader-empty { + grid-column: 1 / -1; + text-align: center; + padding: 48px 24px; + background: rgba(255, 255, 255, 0.02); + border-radius: 4px; + border: 1px dashed rgba(167, 139, 250, 0.2); +} +.instaraw-adv-loader-empty p { + margin: 8px 0; + color: rgba(249, 250, 251, 0.7); + font-size: 13px; + font-weight: 500; +} +.instaraw-adv-loader-empty .instaraw-adv-loader-hint { + font-size: 12px; + color: rgba(129, 140, 248, 0.6); + font-weight: 400; +} +.instaraw-adv-loader-item { + background: rgba(255, 255, 255, 0.04); + border-radius: 4px; + overflow: hidden; + cursor: move; + position: relative; + border: 2px solid rgba(255, 255, 255, 0.08); +} +.instaraw-adv-loader-item:hover { + border-color: rgba(99, 102, 241, 0.5); + transition: none; +} +.instaraw-adv-loader-item-active { + border-color: #a78bfa !important; + background: rgba(167, 139, 250, 0.08); +} +.instaraw-adv-loader-item-processing { + border-color: #10b981 !important; + background: rgba(16, 185, 129, 0.08); +} +.instaraw-adv-loader-item-done { + opacity: 0.5; +} +.instaraw-adv-loader-index-badge { + position: absolute; + top: 8px; + left: 8px; + background: #6366f1; + color: #ffffff; + padding: 4px 8px; + border-radius: 4px; + font-size: 10px; + font-weight: 600; + z-index: 10; +} +.instaraw-adv-loader-processing-indicator { + position: absolute; + top: 50%; + left: 50%; + transform: translate(-50%, -50%); + background: #10b981; + color: #ffffff; + padding: 8px 16px; + border-radius: 4px; + font-size: 11px; + font-weight: 600; + z-index: 10; +} +.instaraw-adv-loader-active-indicator { + position: absolute; + top: 8px; + right: 8px; + background: #7c3aed; + color: #ffffff; + padding: 4px 10px; + border-radius: 4px; + font-size: 10px; + font-weight: 600; + z-index: 10; +} +.instaraw-adv-loader-done-indicator { + position: absolute; + top: 8px; + right: 8px; + background: #10b981; + color: #ffffff; + width: 24px; + height: 24px; + border-radius: 4px; + display: flex; + align-items: center; + justify-content: center; + font-size: 12px; + z-index: 10; +} +.instaraw-adv-loader-item.instaraw-adv-loader-drop-before::before { + content: ''; + position: absolute; + top: -7px; + left: 0; + right: 0; + height: 4px; + background: #a78bfa; + border-radius: 2px; + z-index: 1000; + pointer-events: none; +} +.instaraw-adv-loader-item.instaraw-adv-loader-drop-after::after { + content: ''; + position: absolute; + bottom: -7px; + left: 0; + right: 0; + height: 4px; + background: #a78bfa; + border-radius: 2px; + z-index: 1000; + pointer-events: none; +} +.instaraw-adv-loader-thumb { + width: 100%; + height: 140px; + overflow: hidden; + background: #000000; + position: relative; +} +.instaraw-adv-loader-thumb img { + width: 100%; + height: 100%; + object-fit: cover; + display: block; +} +.instaraw-adv-loader-controls { + display: flex; + align-items: center; + gap: 8px; + padding: 8px 10px; + background: rgba(0, 0, 0, 0.3); + border-top: 1px solid rgba(255, 255, 255, 0.05); +} +.instaraw-adv-loader-controls label { + color: rgba(249, 250, 251, 0.6); + font-size: 11px; + font-weight: 500; +} +.instaraw-adv-loader-repeat-input { + width: 55px; + background: rgba(255, 255, 255, 0.06); + border: 1px solid rgba(255, 255, 255, 0.1); + color: #f9fafb; + padding: 6px; + border-radius: 4px; + text-align: center; + font-size: 12px; + font-weight: 600; + outline: none; +} +.instaraw-adv-loader-repeat-input:hover { + background: rgba(255, 255, 255, 0.08); + border-color: #818cf8; + transition: none; +} +.instaraw-adv-loader-repeat-input:focus { + border-color: #a78bfa; +} +.instaraw-adv-loader-delete-btn { + margin-left: auto; + background: #ef4444; + color: #ffffff; + border: none; + width: 28px; + height: 28px; + border-radius: 4px; + cursor: pointer; + font-weight: 600; + font-size: 14px; + line-height: 1; + display: flex; + align-items: center; + justify-content: center; +} +.instaraw-adv-loader-delete-btn:hover { + background: #dc2626; + transition: none; +} +.instaraw-adv-loader-info { + padding: 8px 10px; + font-size: 11px; + background: rgba(0, 0, 0, 0.2); + border-top: 1px solid rgba(255, 255, 255, 0.05); +} +.instaraw-adv-loader-info .instaraw-adv-loader-filename { + white-space: nowrap; + overflow: hidden; + text-overflow: ellipsis; + margin-bottom: 4px; + color: rgba(249, 250, 251, 0.9); + font-weight: 500; + line-height: 1.3; +} +.instaraw-adv-loader-info .instaraw-adv-loader-dimensions { + color: #818cf8; + font-size: 11px; + font-weight: 600; +} + +/* ===== Txt2Img Mode Styles ===== */ + +/* Batch add controls */ +.instaraw-adv-loader-batch-add-controls { + display: inline-flex; + align-items: center; + gap: 6px; +} + +.instaraw-adv-loader-batch-count-input { + width: 54px; + background: rgba(255, 255, 255, 0.06); + border: 1px solid rgba(255, 255, 255, 0.1); + color: #f9fafb; + padding: 8px; + border-radius: 4px; + text-align: center; + font-size: 12px; + font-weight: 600; + outline: none; +} + +.instaraw-adv-loader-batch-count-input:hover { + background: rgba(255, 255, 255, 0.08); + border-color: #818cf8; + transition: none; +} + +.instaraw-adv-loader-batch-count-input:focus { + border-color: #a78bfa; +} + +.instaraw-adv-loader-batch-add-btn { + background: #8b5cf6; + color: #ffffff; + border: none; + padding: 10px 20px; + border-radius: 4px; + cursor: pointer; + font-weight: 600; + font-size: 13px; + white-space: nowrap; +} + +.instaraw-adv-loader-batch-add-btn:hover { + background: #7c3aed; + transition: none; +} + +.instaraw-adv-loader-add-latent-btn { + background: #8b5cf6; + color: #ffffff; + border: none; + padding: 10px 20px; + border-radius: 4px; + cursor: pointer; + font-weight: 600; + font-size: 13px; +} + +.instaraw-adv-loader-add-latent-btn:hover { + background: #7c3aed; + transition: none; +} + +/* Empty latent placeholder */ +.instaraw-adv-loader-latent-thumb { + width: 100%; + height: 140px; + overflow: hidden; + position: relative; + background: linear-gradient(135deg, #6366f1 0%, #8b5cf6 50%, #a78bfa 100%); + display: flex; + flex-direction: column; + align-items: center; + justify-content: center; + gap: 8px; +} + +.instaraw-adv-loader-latent-aspect { + display: flex; + flex-direction: column; + align-items: center; + gap: 4px; + color: #ffffff; +} + +.instaraw-adv-loader-latent-aspect-icon { + font-size: 24px; + opacity: 0.9; +} + +.instaraw-adv-loader-latent-aspect-label { + font-size: 13px; + font-weight: 600; + opacity: 0.95; + background: rgba(0, 0, 0, 0.2); + padding: 4px 10px; + border-radius: 4px; +} + +.instaraw-adv-loader-latent-id { + position: absolute; + bottom: 8px; + left: 8px; + background: rgba(0, 0, 0, 0.5); + color: rgba(255, 255, 255, 0.8); + padding: 4px 8px; + border-radius: 4px; + font-size: 10px; + font-weight: 500; + font-family: 'Monaco', 'Consolas', 'Courier New', monospace; +} + +.instaraw-adv-loader-latent-resolution { + color: #c4b5fd; + font-size: 11px; + font-weight: 600; +} + +/* Aspect ratio preview box */ +.instaraw-adv-loader-aspect-preview { + max-width: 80%; + max-height: 80%; + background: rgba(255, 255, 255, 0.1); + border: 2px solid rgba(255, 255, 255, 0.3); + border-radius: 4px; + display: flex; + align-items: center; + justify-content: center; + padding: 12px; +} + +.instaraw-adv-loader-aspect-content { + text-align: center; + color: white; +} + +/* === Drag and Drop File Upload Visual Feedback === */ +.instaraw-adv-loader-drag-over { + position: relative; + outline: 3px dashed #60a5fa !important; + outline-offset: -3px !important; + background: rgba(59, 130, 246, 0.1) !important; + box-shadow: 0 0 20px rgba(59, 130, 246, 0.3) !important; +} + +.instaraw-adv-loader-drag-over::before { + content: "๐Ÿ“ Drop images here to upload"; + position: absolute; + top: 50%; + left: 50%; + transform: translate(-50%, -50%); + background: rgba(59, 130, 246, 0.95); + color: white; + padding: 20px 40px; + border-radius: 8px; + font-size: 16px; + font-weight: 600; + z-index: 1000; + pointer-events: none; + box-shadow: 0 8px 24px rgba(0, 0, 0, 0.4); + border: 2px solid rgba(255, 255, 255, 0.3); +} + +/* ===== Selection Mode Styles ===== */ + +.instaraw-adv-loader-select-all-btn, +.instaraw-adv-loader-deselect-all-btn, +.instaraw-adv-loader-cancel-selection-btn, +.instaraw-adv-loader-enter-selection-btn { + background: #374151; + color: #ffffff; + border: none; + padding: 10px 16px; + border-radius: 4px; + cursor: pointer; + font-weight: 600; + font-size: 13px; +} + +.instaraw-adv-loader-select-all-btn:hover, +.instaraw-adv-loader-deselect-all-btn:hover, +.instaraw-adv-loader-cancel-selection-btn:hover, +.instaraw-adv-loader-enter-selection-btn:hover { + background: #4b5563; + transition: none; +} + +.instaraw-adv-loader-delete-selected-btn { + background: #dc2626; + color: #ffffff; + border: none; + padding: 10px 16px; + border-radius: 4px; + cursor: pointer; + font-weight: 600; + font-size: 13px; +} + +.instaraw-adv-loader-delete-selected-btn:hover { + background: #ef4444; + transition: none; +} + +.instaraw-adv-loader-delete-selected-btn:disabled { + opacity: 0.5; + cursor: not-allowed; +} + +/* Selection checkbox in card */ +.instaraw-adv-loader-selection-checkbox { + position: absolute; + top: 8px; + left: 8px; + z-index: 20; + display: flex; + align-items: center; + justify-content: center; + cursor: pointer; +} + +.instaraw-adv-loader-image-checkbox { + width: 22px; + height: 22px; + cursor: pointer; + accent-color: #8b5cf6; +} + +/* Selection mode item styling */ +.instaraw-adv-loader-item-selection { + cursor: pointer; +} + +.instaraw-adv-loader-item-selection:hover { + border-color: #8b5cf6; + transition: none; +} + +.instaraw-adv-loader-item-selected { + border-color: #8b5cf6 !important; + background: rgba(139, 92, 246, 0.15); +} + +.instaraw-adv-loader-item-selection .instaraw-adv-loader-controls { + opacity: 0.5; + pointer-events: none; +} \ No newline at end of file diff --git a/custom_nodes/ComfyUI_INSTARAW/js/advanced_image_loader.js b/custom_nodes/ComfyUI_INSTARAW/js/advanced_image_loader.js new file mode 100644 index 0000000000000000000000000000000000000000..7d313d68504ad2214b9e1120fd649935191b999c --- /dev/null +++ b/custom_nodes/ComfyUI_INSTARAW/js/advanced_image_loader.js @@ -0,0 +1,2336 @@ +// --- +// Filename: ../ComfyUI_INSTARAW/js/advanced_image_loader.js +// --- + +import { app } from "../../scripts/app.js"; +import { api } from "../../scripts/api.js"; + +// Auto-migrate images from old per-node folders to central pool (runs once on load) +(async function migrateOldImages() { + try { + const response = await fetch("/instaraw/migrate_images", { method: "POST" }); + const result = await response.json(); + if (result.success && result.migrated > 0) { + console.log(`[INSTARAW] โœ… Migrated ${result.migrated} images to central pool`); + } + } catch (e) { + // Silent fail - migration is optional + } +})(); + +app.registerExtension({ + name: "Comfy.INSTARAW.AdvancedImageLoader", + + async beforeRegisterNodeDef(nodeType, nodeData, app) { + if (nodeData.name === "INSTARAW_AdvancedImageLoader") { + // Store original onConfigure to ensure saved data is properly restored + const origOnConfigure = nodeType.prototype.onConfigure; + nodeType.prototype.onConfigure = function(data) { + // Restore properties from saved data FIRST + if (data.properties) { + this.properties = this.properties || {}; + Object.assign(this.properties, data.properties); + console.log(`[INSTARAW AIL ${this.id}] Restored properties from saved data:`, { + hasBatchData: !!this.properties.batch_data, + batchDataLength: this.properties.batch_data?.length + }); + } + // Also restore from widgets_values if batch_data widget was serialized + if (data.widgets_values) { + const batchDataWidget = this.widgets?.find(w => w.name === "batch_data"); + if (batchDataWidget) { + const widgetIdx = this.widgets.indexOf(batchDataWidget); + if (data.widgets_values[widgetIdx]) { + this.properties.batch_data = data.widgets_values[widgetIdx]; + console.log(`[INSTARAW AIL ${this.id}] Restored batch_data from widget value`); + } + } + } + origOnConfigure?.apply(this, arguments); + }; + + const onNodeCreated = nodeType.prototype.onNodeCreated; + nodeType.prototype.onNodeCreated = function () { + onNodeCreated?.apply(this, arguments); + + // Only set defaults if not already set (don't overwrite loaded data) + if (!this.properties) this.properties = {}; + if (!this.properties.batch_data) { + this.properties.batch_data = JSON.stringify({ images: [], order: [], total_count: 0 }); + } + if (!this.properties.txt2img_data_backup) { + this.properties.txt2img_data_backup = JSON.stringify({ latents: [], order: [], total_count: 0 }); + } + if (!this.properties.img2img_data_backup) { + this.properties.img2img_data_backup = JSON.stringify({ images: [], order: [], total_count: 0 }); + } + + const node = this; + let cachedHeight = 300; + let isUpdatingHeight = false; + let currentDetectedMode = null; // Track detected mode to minimize re-renders + let modeCheckInterval = null; // For periodic mode checking + + // Selection mode state (for multi-select delete) + let selectionMode = false; + const selectedImages = new Set(); // Track which image IDs are selected + + const container = document.createElement("div"); + container.className = "instaraw-adv-loader-container"; + container.style.width = "100%"; + container.style.boxSizing = "border-box"; + container.style.overflow = "hidden"; + container.style.height = `${cachedHeight}px`; + + const updateCachedHeight = () => { + if (isUpdatingHeight) return; + isUpdatingHeight = true; + + // Safety timeout to prevent stuck flag + const safetyTimeout = setTimeout(() => { + isUpdatingHeight = false; + }, 500); + + container.style.overflow = "visible"; + container.style.height = "auto"; + + requestAnimationFrame(() => { + requestAnimationFrame(() => { + clearTimeout(safetyTimeout); + + // Use scrollHeight as it measures full content even during transitions + const newHeight = Math.max(container.offsetHeight, container.scrollHeight); + + if (newHeight > 0 && Math.abs(newHeight - cachedHeight) > 2) { + cachedHeight = newHeight; + container.style.height = `${newHeight}px`; + const sz = node.computeSize(); + node.size[1] = sz[1]; + node.onResize?.(sz); + app.graph.setDirtyCanvas(true, false); + } else { + container.style.height = `${cachedHeight}px`; + } + container.style.overflow = "hidden"; + isUpdatingHeight = false; + }); + }); + }; + + // Periodic height sync - keep running, only clear when node is removed + const heightSyncInterval = setInterval(() => { + if (!node || !container) { + clearInterval(heightSyncInterval); + return; + } + updateCachedHeight(); + }, 2000); + node._heightSyncInterval = heightSyncInterval; + + // Fast initial syncs for page load + [100, 300, 500, 1000, 1500, 2000].forEach(delay => { + setTimeout(() => { + if (container) { + updateCachedHeight(); + } + }, delay); + }); + + const syncBatchDataWidget = () => { + const batchDataWidget = node.widgets?.find((w) => w.name === "batch_data"); + if (batchDataWidget) { + batchDataWidget.value = node.properties.batch_data; + } else { + const widget = node.addWidget("text", "batch_data", node.properties.batch_data, () => {}, { serialize: true }); + widget.hidden = true; + } + }; + + /** + * Computes aspect ratio node outputs locally (for multi-output nodes). + * Aspect ratio nodes compute width/height in Python, but we replicate that logic here + * to properly read values from the correct output slot. + */ + const getAspectRatioOutput = (aspectRatioNode, slotIndex) => { + // Read the dropdown selection from the aspect ratio node + const selection = aspectRatioNode.widgets?.[0]?.value; + if (!selection) { + console.warn(`[INSTARAW AIL ${node.id}] Aspect ratio node has no selection`); + return null; + } + + // Handle Nano Banana Pro Aspect Ratio node (has 5 outputs) + if (aspectRatioNode.type === "INSTARAW_NanoBananaAspectRatio") { + const resolutionWidget = aspectRatioNode.widgets?.[1]; + const resolution = resolutionWidget?.value || "1K"; + + // Nano Banana aspect ratio mappings + const NANO_RATIOS = { + "1:1 (Square)": { ratio: "1:1", w: 1, h: 1 }, + "3:2 (Landscape)": { ratio: "3:2", w: 3, h: 2 }, + "2:3 (Portrait)": { ratio: "2:3", w: 2, h: 3 }, + "3:4 (Portrait)": { ratio: "3:4", w: 3, h: 4 }, + "4:3 (Landscape)": { ratio: "4:3", w: 4, h: 3 }, + "4:5 (Portrait)": { ratio: "4:5", w: 4, h: 5 }, + "5:4 (Landscape)": { ratio: "5:4", w: 5, h: 4 }, + "9:16 (Tall Portrait)": { ratio: "9:16", w: 9, h: 16 }, + "16:9 (Wide Landscape)": { ratio: "16:9", w: 16, h: 9 }, + "21:9 (Ultrawide)": { ratio: "21:9", w: 21, h: 9 }, + }; + + const RESOLUTION_BASE = { "1K": 1024, "2K": 2048, "4K": 4096 }; + + const config = NANO_RATIOS[selection]; + if (!config) { + console.warn(`[INSTARAW AIL ${node.id}] Unknown Nano Banana aspect ratio: ${selection}`); + return null; + } + + // Calculate dimensions (same logic as Python) + const baseSize = RESOLUTION_BASE[resolution] || 1024; + let width, height; + if (config.w >= config.h) { + height = baseSize; + width = Math.floor(baseSize * config.w / config.h); + } else { + width = baseSize; + height = Math.floor(baseSize * config.h / config.w); + } + // Round to nearest 64 + width = Math.floor(width / 64) * 64; + height = Math.floor(height / 64) * 64; + + // Output slots: 0=aspect_ratio, 1=resolution, 2=width, 3=height, 4=aspect_label + if (slotIndex === 0) return config.ratio; + if (slotIndex === 1) return resolution; + if (slotIndex === 2) return width; + if (slotIndex === 3) return height; + if (slotIndex === 4) return selection; + + return null; + } + + // Aspect ratio mappings (must match Python ASPECT_RATIOS dicts exactly) + const WAN_RATIOS = { + "3:4 (Portrait)": { width: 720, height: 960, label: "3:4" }, + "9:16 (Tall Portrait)": { width: 540, height: 960, label: "9:16" }, + "1:1 (Square)": { width: 960, height: 960, label: "1:1" }, + "16:9 (Landscape)": { width: 960, height: 540, label: "16:9" } + }; + + const SDXL_RATIOS = { + "3:4 (Portrait)": { width: 896, height: 1152, label: "3:4" }, + "9:16 (Tall Portrait)": { width: 768, height: 1344, label: "9:16" }, + "1:1 (Square)": { width: 1024, height: 1024, label: "1:1" }, + "16:9 (Landscape)": { width: 1344, height: 768, label: "16:9" } + }; + + const ratios = aspectRatioNode.type === "INSTARAW_WANAspectRatio" + ? WAN_RATIOS + : SDXL_RATIOS; + + const config = ratios[selection]; + if (!config) { + console.warn(`[INSTARAW AIL ${node.id}] Unknown aspect ratio selection: ${selection}`); + return null; + } + + // Return based on output slot (0=width, 1=height, 2=aspect_label) + if (slotIndex === 0) return config.width; + if (slotIndex === 1) return config.height; + if (slotIndex === 2) return config.label; + + return null; + }; + + /** + * Retrieves the final value of an input by traversing connected nodes. + * Enhanced to properly handle multi-output nodes like aspect ratio nodes. + */ + const getFinalInputValue = (inputName, defaultValue) => { + if (!node.inputs || node.inputs.length === 0) { + const widget = node.widgets?.find(w => w.name === inputName); + return widget ? widget.value : defaultValue; + } + + const input = node.inputs.find(i => i.name === inputName); + if (!input || input.link == null) { + const widget = node.widgets?.find(w => w.name === inputName); + return widget ? widget.value : defaultValue; + } + + const link = app.graph.links[input.link]; + if (!link) return defaultValue; + + const originNode = app.graph.getNodeById(link.origin_id); + if (!originNode) return defaultValue; + + // SPECIAL HANDLING: For aspect ratio nodes, compute the output locally + // because they have multiple outputs and don't store computed values in widgets + if (originNode.type === "INSTARAW_WANAspectRatio" || + originNode.type === "INSTARAW_SDXLAspectRatio" || + originNode.type === "INSTARAW_NanoBananaAspectRatio") { + const output = getAspectRatioOutput(originNode, link.origin_slot); + if (output !== null) return output; + } + + // For other nodes, read from widgets + if (originNode.widgets && originNode.widgets.length > 0) { + return originNode.widgets[0].value; + } + + if (originNode.properties && originNode.properties.value !== undefined) { + return originNode.properties.value; + } + + return defaultValue; + }; + + /** + * Detects if we're in txt2img mode by reading enable_img2img from connected nodes. + * Returns true for txt2img mode, false for img2img mode. + */ + const isTxt2ImgMode = () => { + const enableImg2Img = getFinalInputValue("enable_img2img", true); + // console.log(`[INSTARAW AIL ${node.id}] enable_img2img value:`, enableImg2Img, `(type: ${typeof enableImg2Img})`); + const result = enableImg2Img === false || enableImg2Img === "false"; + // console.log(`[INSTARAW AIL ${node.id}] isTxt2ImgMode result:`, result); + return result; + }; + + /** + * Switches to img2img mode by setting enable_img2img to true. + * Works with both connected boolean nodes and local widgets. + */ + const switchToImg2ImgMode = () => { + console.log(`[INSTARAW AIL ${node.id}] === Switching to img2img mode ===`); + + // First, try to find and update a connected boolean node + const input = node.inputs?.find(i => i.name === "enable_img2img"); + console.log(`[INSTARAW AIL ${node.id}] enable_img2img input:`, input); + + if (input && input.link != null) { + console.log(`[INSTARAW AIL ${node.id}] Input is linked, link ID:`, input.link); + const link = app.graph.links[input.link]; + console.log(`[INSTARAW AIL ${node.id}] Link object:`, link); + + if (link) { + const booleanNode = app.graph.getNodeById(link.origin_id); + console.log(`[INSTARAW AIL ${node.id}] Boolean node:`, booleanNode); + console.log(`[INSTARAW AIL ${node.id}] Boolean node type:`, booleanNode?.type); + console.log(`[INSTARAW AIL ${node.id}] Boolean node widgets:`, booleanNode?.widgets); + + if (booleanNode && booleanNode.widgets && booleanNode.widgets.length > 0) { + const widget = booleanNode.widgets[0]; + console.log(`[INSTARAW AIL ${node.id}] Widget name:`, widget.name); + console.log(`[INSTARAW AIL ${node.id}] Widget type:`, widget.type); + const oldValue = widget.value; + console.log(`[INSTARAW AIL ${node.id}] Old value:`, oldValue); + + // Update the boolean node's widget value + widget.value = true; + console.log(`[INSTARAW AIL ${node.id}] New value set:`, widget.value); + + // Trigger the widget's callback if it exists + if (widget.callback) { + console.log(`[INSTARAW AIL ${node.id}] Calling widget callback...`); + widget.callback.call(booleanNode, true, null, booleanNode, null, oldValue); + } else { + console.log(`[INSTARAW AIL ${node.id}] โš ๏ธ Widget has no callback!`); + } + + console.log(`[INSTARAW AIL ${node.id}] โœ… Updated connected boolean node #${booleanNode.id} to true`); + app.graph.setDirtyCanvas(true, true); + return true; + } + } + } + + // If not connected, try to update local widget + const widget = node.widgets?.find(w => w.name === "enable_img2img"); + if (widget) { + const oldValue = widget.value; + widget.value = true; + + // Trigger the widget's callback if it exists + if (widget.callback) { + widget.callback.call(node, true, null, node, null, oldValue); + } + + console.log(`[INSTARAW AIL ${node.id}] Updated local enable_img2img widget to true`); + app.graph.setDirtyCanvas(true, true); + return true; + } + + console.warn(`[INSTARAW AIL ${node.id}] Could not find enable_img2img input or widget`); + return false; + }; + + /** + * Gets width, height, and aspect_label from connected nodes for txt2img mode. + */ + const getTxt2ImgDimensions = () => { + // console.log(`[INSTARAW AIL ${node.id}] === Reading dimensions from connected nodes ===`); + + const widthRaw = getFinalInputValue("width", 960); + const heightRaw = getFinalInputValue("height", 960); + const aspect_label_raw = getFinalInputValue("aspect_label", null); + + // console.log(`[INSTARAW AIL ${node.id}] Raw values:`, { widthRaw, heightRaw, aspect_label_raw }); + + const width = parseInt(widthRaw) || 960; + const height = parseInt(heightRaw) || 960; + const aspect_label = aspect_label_raw || getAspectLabel(width, height); + + // console.log(`[INSTARAW AIL ${node.id}] Final dimensions:`, { width, height, aspect_label }); + // console.log(`[INSTARAW AIL ${node.id}] Expected tensor size: ${width}ร—${height} (${(width * height / 1000000).toFixed(2)}MP)`); + + return { width, height, aspect_label }; + }; + + /** + * Generates a simple UUID v4. + */ + const generateUUID = () => { + return 'xxxxxxxx-xxxx-4xxx-yxxx-xxxxxxxxxxxx'.replace(/[xy]/g, function(c) { + const r = Math.random() * 16 | 0; + const v = c === 'x' ? r : (r & 0x3 | 0x8); + return v.toString(16); + }); + }; + + /** + * Calculates aspect ratio label from width and height. + */ + const getAspectLabel = (width, height) => { + const gcd = (a, b) => b === 0 ? a : gcd(b, a % b); + const divisor = gcd(width, height); + const w = width / divisor; + const h = height / divisor; + + // Common aspect ratios + if (w === h) return "1:1"; + if (w === 3 && h === 4) return "3:4"; + if (w === 4 && h === 3) return "4:3"; + if (w === 9 && h === 16) return "9:16"; + if (w === 16 && h === 9) return "16:9"; + if (w === 2 && h === 3) return "2:3"; + if (w === 3 && h === 2) return "3:2"; + + return `${w}:${h}`; + }; + + const renderGallery = () => { + const detectedMode = isTxt2ImgMode(); + console.log(`[INSTARAW AIL ${node.id}] renderGallery - currentDetectedMode:`, currentDetectedMode, `detectedMode:`, detectedMode); + + // Handle mode switching + if (currentDetectedMode !== null && currentDetectedMode !== detectedMode) { + console.log(`[INSTARAW AIL ${node.id}] MODE SWITCH DETECTED! Switching to ${detectedMode ? 'txt2img' : 'img2img'}`); + if (detectedMode) { + // Switching to txt2img mode + node.properties.img2img_data_backup = node.properties.batch_data; + // Only restore backup if it has actual items that exist + const backup = node.properties.txt2img_data_backup; + let backupData = null; + try { backupData = backup ? JSON.parse(backup) : null; } catch (e) { backupData = null; } + // Validate backup: check if order items actually exist in latents + const latents = backupData?.latents || []; + const order = backupData?.order || []; + const validOrder = order.filter(id => latents.some(l => l.id === id)); + const hasValidBackup = validOrder.length > 0; + if (hasValidBackup) { + // Clean up the backup data to only include valid items + backupData.order = validOrder; + backupData.total_count = validOrder.reduce((sum, id) => { + const latent = latents.find(l => l.id === id); + return sum + (latent?.repeat_count || 1); + }, 0); + node.properties.batch_data = JSON.stringify(backupData); + } else { + node.properties.batch_data = JSON.stringify({ latents: [], order: [], total_count: 0 }); + } + } else { + // Switching to img2img mode + node.properties.txt2img_data_backup = node.properties.batch_data; + // Only restore backup if it has actual items that exist + const backup = node.properties.img2img_data_backup; + let backupData = null; + try { backupData = backup ? JSON.parse(backup) : null; } catch (e) { backupData = null; } + // Validate backup: check if order items actually exist in images + const images = backupData?.images || []; + const order = backupData?.order || []; + const validOrder = order.filter(id => images.some(img => img.id === id)); + const hasValidBackup = validOrder.length > 0; + if (hasValidBackup) { + // Clean up the backup data to only include valid items + backupData.order = validOrder; + backupData.total_count = validOrder.reduce((sum, id) => { + const image = images.find(img => img.id === id); + return sum + (image?.repeat_count || 1); + }, 0); + node.properties.batch_data = JSON.stringify(backupData); + } else { + node.properties.batch_data = JSON.stringify({ images: [], order: [], total_count: 0 }); + } + } + syncBatchDataWidget(); + } + currentDetectedMode = detectedMode; + + // console.log(`[INSTARAW AIL ${node.id}] Rendering ${detectedMode ? 'txt2img' : 'img2img'} gallery`); + if (detectedMode) { + renderTxt2ImgGallery(); + } else { + renderImg2ImgGallery(); + } + }; + + const renderImg2ImgGallery = () => { + let batchData = JSON.parse(node.properties.batch_data || "{}"); + let images = batchData.images || []; + let order = batchData.order || []; + + // Validate and clean up stale data (order items that don't exist in images) + const validOrder = order.filter(id => images.some(img => img.id === id)); + if (validOrder.length !== order.length) { + console.log(`[INSTARAW AIL ${node.id}] Cleaning up stale img2img data: ${order.length} -> ${validOrder.length} items`); + batchData.order = validOrder; + batchData.total_count = validOrder.reduce((sum, id) => { + const image = images.find(img => img.id === id); + return sum + (image?.repeat_count || 1); + }, 0); + order = validOrder; + node.properties.batch_data = JSON.stringify(batchData); + syncBatchDataWidget(); + } + + const modeWidget = node.widgets?.find((w) => w.name === "mode"); + const currentMode = modeWidget?.value || "Batch Tensor"; + const batchIndexWidget = node.widgets?.find((w) => w.name === "batch_index"); + const currentIndex = node._processingIndex !== undefined ? node._processingIndex : batchIndexWidget?.value || 0; + + container.innerHTML = ` +
+ + AIL V2.0 +
+
+
+ ๐Ÿ–ผ๏ธ IMG2IMG +
+ +
+ ${currentMode === "Sequential" ? `${currentIndex}/${batchData.total_count || 0}` : ""} +
+
+ ${images.length} image${images.length !== 1 ? "s" : ""} + ยท + ${batchData.total_count || 0} total +
+
+
+ ${selectionMode ? ` + + + + + ` : ` + + ${currentMode === "Sequential" ? `` : ""} + ${images.length > 0 ? `` : ""} + ${images.length > 0 ? `` : ""} + ${images.length > 0 ? `` : ""} + ${images.length > 0 ? `` : ""} + `} +
+
+ ${order.length === 0 ? `

No images loaded

Click "Upload Images" to get started

` : (() => { + let currentIdx = 0; + return order.map((imgId) => { + const img = images.find((i) => i.id === imgId); + if (!img) return ""; + const thumbUrl = `/instaraw/view/${img.thumbnail}`; + const repeatCount = img.repeat_count || 1; + const startIdx = currentIdx; + const endIdx = currentIdx + repeatCount - 1; + currentIdx += repeatCount; + const isActive = currentMode === "Sequential" && currentIndex >= startIdx && currentIndex <= endIdx; + const isPast = currentMode === "Sequential" && currentIndex > endIdx; + const isProcessing = node._isProcessing && isActive; + return `
+ ${selectionMode ? ` + + ` : ''} + ${currentMode === "Sequential" && !selectionMode ? `
${repeatCount === 1 ? `#${startIdx}` : `#${startIdx}-${endIdx}`}
` : ""} +
+ ${img.original_name} + ${isProcessing ? '
โšก PROCESSING...
' : isActive && !selectionMode ? '
โ–ถ NEXT
' : ""} + ${isPast && !selectionMode ? '
โœ“
' : ""} +
+
+ + + +
+
+
${img.original_name}
+
${img.width}ร—${img.height} (original)
+
+
`; + }).join(""); + })()} +
`; + setupEventHandlers(); + setupDragAndDrop(); + setupFileDropZone(); + updateCachedHeight(); + + // Dispatch update event for other nodes (e.g., RPG) + window.dispatchEvent(new CustomEvent("INSTARAW_AIL_UPDATED", { + detail: { + nodeId: node.id, + mode: "img2img", + enable_img2img: true, // NEW: explicit boolean for easier detection + images: order.map(imgId => { + const img = images.find(i => i.id === imgId); + if (!img) return null; + const thumbUrl = `/instaraw/view/${img.thumbnail}`; + const width = img.width || 1024; + const height = img.height || 1024; + return { + url: thumbUrl, + index: order.indexOf(imgId), + id: imgId, + repeat_count: img.repeat_count || 1, + width: width, + height: height, + aspect_label: getAspectLabel(width, height) + }; + }).filter(i => i !== null), + total: batchData.total_count || 0 + } + })); + }; + + const renderTxt2ImgGallery = () => { + // Preserve batch count input value + const existingInput = container.querySelector(".instaraw-adv-loader-batch-count-input"); + const preservedBatchCount = existingInput?.value || node._batchAddCount || 5; + node._batchAddCount = preservedBatchCount; + + let batchData = JSON.parse(node.properties.batch_data || "{}"); + let latents = batchData.latents || []; + let order = batchData.order || []; + + // Validate and clean up stale data (order items that don't exist in latents) + const validOrder = order.filter(id => latents.some(l => l.id === id)); + if (validOrder.length !== order.length) { + console.log(`[INSTARAW AIL ${node.id}] Cleaning up stale txt2img data: ${order.length} -> ${validOrder.length} items`); + batchData.order = validOrder; + batchData.total_count = validOrder.reduce((sum, id) => { + const latent = latents.find(l => l.id === id); + return sum + (latent?.repeat_count || 1); + }, 0); + order = validOrder; + node.properties.batch_data = JSON.stringify(batchData); + syncBatchDataWidget(); + } + const modeWidget = node.widgets?.find((w) => w.name === "mode"); + const currentMode = modeWidget?.value || "Batch Tensor"; + const batchIndexWidget = node.widgets?.find((w) => w.name === "batch_index"); + const currentIndex = node._processingIndex !== undefined ? node._processingIndex : batchIndexWidget?.value || 0; + + // Get current dimensions from aspect ratio selector (for live updates) + const dimensions = getTxt2ImgDimensions(); + + // Debug: Log dimensions for debugging + console.log(`[INSTARAW AIL ${node.id}] renderTxt2ImgGallery - dimensions:`, dimensions); + + container.innerHTML = ` +
+ + AIL V2.0 +
+
+
+ ๐ŸŽจ TXT2IMG +
+ +
+ ${currentMode === "Sequential" ? `${currentIndex}/${batchData.total_count || 0}` : ""} +
+
+ ${latents.length} latent${latents.length !== 1 ? "s" : ""} + ยท + ${batchData.total_count || 0} total +
+
+
+ +
+ + +
+ ${currentMode === "Sequential" ? `` : ""} + ${latents.length > 0 ? `` : ""} + ${latents.length > 0 ? `` : ""} + ${latents.length > 0 ? `` : ""} +
+
+ ${order.length === 0 ? `

No latents added

Click "Add Empty Latent" to get started (txt2img mode)

` : (() => { + let currentIdx = 0; + return order.map((latentId) => { + const latent = latents.find((l) => l.id === latentId); + if (!latent) return ""; + + // Debug: Log what latent contains + console.log(`[INSTARAW AIL ${node.id}] Rendering latent:`, latent); + + const repeatCount = latent.repeat_count || 1; + const startIdx = currentIdx; + const endIdx = currentIdx + repeatCount - 1; + currentIdx += repeatCount; + const isActive = currentMode === "Sequential" && currentIndex >= startIdx && currentIndex <= endIdx; + const isPast = currentMode === "Sequential" && currentIndex > endIdx; + const isProcessing = node._isProcessing && isActive; + + // Use CURRENT dimensions from aspect ratio selector (not stored dimensions) + // This ensures live updates when aspect ratio selector changes + const width = dimensions.width; + const height = dimensions.height; + const aspectRatio = width / height; + const aspectLabel = dimensions.aspect_label; + + return `
+ ${currentMode === "Sequential" ? `
${repeatCount === 1 ? `#${startIdx}` : `#${startIdx}-${endIdx}`}
` : ""} +
+
+
+
๐Ÿ“
+
${aspectLabel}
+
+
+ ${isProcessing ? '
โšก PROCESSING...
' : isActive ? '
โ–ถ NEXT
' : ""} + ${isPast ? '
โœ“
' : ""} +
+
+ + + +
+
+
Latent ${latent.id.substring(0, 8)}
+
${width}ร—${height}
+
+
`; + }).join(""); + })()} +
`; + setupTxt2ImgEventHandlers(); + setupDragAndDrop(); + setupFileDropZone(); + updateCachedHeight(); + + // Dispatch update event for other nodes (e.g., RPG) + window.dispatchEvent(new CustomEvent("INSTARAW_AIL_UPDATED", { + detail: { + nodeId: node.id, + mode: "txt2img", + enable_img2img: false, // NEW: explicit boolean for easier detection + latents: order.map(latentId => { + const latent = latents.find(l => l.id === latentId); + if (!latent) return null; + return { + id: latent.id, + width: latent.width, + height: latent.height, + aspect_label: latent.aspect_label, + repeat_count: latent.repeat_count || 1, + index: order.indexOf(latentId) + }; + }).filter(l => l !== null), + total: batchData.total_count || 0 + } + })); + }; + + const addEmptyLatent = () => { + const dimensions = getTxt2ImgDimensions(); + const batchData = JSON.parse(node.properties.batch_data || "{}"); + batchData.latents = batchData.latents || []; + batchData.order = batchData.order || []; + + // Check for aspect ratio mismatch + if (batchData.latents.length > 0) { + const existingAspect = batchData.latents[0].aspect_label; + if (existingAspect !== dimensions.aspect_label) { + const confirmed = confirm( + `Current batch has ${existingAspect} latents.\n` + + `Switch to ${dimensions.aspect_label}?\n\n` + + `This will clear all existing latents.` + ); + if (!confirmed) { + return; // Abort + } + // Clear existing latents + batchData.latents = []; + batchData.order = []; + } + } + + const newLatent = { + id: generateUUID(), + width: dimensions.width, + height: dimensions.height, + repeat_count: 1, + aspect_label: dimensions.aspect_label // Use aspect_label from connected node + }; + + console.log(`[INSTARAW AIL ${node.id}] Adding latent:`, newLatent); + + batchData.latents.push(newLatent); + batchData.order.push(newLatent.id); + batchData.total_count = batchData.latents.reduce((sum, l) => sum + (l.repeat_count || 1), 0); + + node.properties.batch_data = JSON.stringify(batchData); + syncBatchDataWidget(); + renderGallery(); + }; + + const batchAddLatents = (count) => { + const dimensions = getTxt2ImgDimensions(); + const batchData = JSON.parse(node.properties.batch_data || "{}"); + batchData.latents = batchData.latents || []; + batchData.order = batchData.order || []; + + // Check for aspect ratio mismatch + if (batchData.latents.length > 0) { + const existingAspect = batchData.latents[0].aspect_label; + if (existingAspect !== dimensions.aspect_label) { + const confirmed = confirm( + `Current batch has ${existingAspect} latents.\n` + + `Switch to ${dimensions.aspect_label}?\n\n` + + `This will clear all existing latents.` + ); + if (!confirmed) { + return; // Abort + } + // Clear existing latents + batchData.latents = []; + batchData.order = []; + } + } + + console.log(`[INSTARAW AIL ${node.id}] Batch adding ${count} latents with dimensions:`, dimensions); + + for (let i = 0; i < count; i++) { + const newLatent = { + id: generateUUID(), + width: dimensions.width, + height: dimensions.height, + repeat_count: 1, + aspect_label: dimensions.aspect_label // Use aspect_label from connected node + }; + batchData.latents.push(newLatent); + batchData.order.push(newLatent.id); + } + + batchData.total_count = batchData.latents.reduce((sum, l) => sum + (l.repeat_count || 1), 0); + node.properties.batch_data = JSON.stringify(batchData); + syncBatchDataWidget(); + renderGallery(); + }; + + const deleteLatent = (latentId) => { + if (!confirm("Delete this latent?")) return; + const batchData = JSON.parse(node.properties.batch_data || "{}"); + batchData.latents = batchData.latents.filter((l) => l.id !== latentId); + batchData.order = batchData.order.filter((id) => id !== latentId); + batchData.total_count = batchData.latents.reduce((sum, l) => sum + (l.repeat_count || 1), 0); + node.properties.batch_data = JSON.stringify(batchData); + syncBatchDataWidget(); + renderGallery(); + }; + + const deleteAllLatents = () => { + const batchData = JSON.parse(node.properties.batch_data || "{}"); + const latentCount = batchData.latents?.length || 0; + if (latentCount === 0 || !confirm(`Delete all ${latentCount} latent${latentCount !== 1 ? "s" : ""}?`)) return; + node.properties.batch_data = JSON.stringify({ latents: [], order: [], total_count: 0 }); + // Clear backup to prevent old data from reappearing on mode switch + node.properties.txt2img_data_backup = JSON.stringify({ latents: [], order: [], total_count: 0 }); + syncBatchDataWidget(); + renderGallery(); + }; + + const updateLatentRepeatCount = (latentId, newCount) => { + const batchData = JSON.parse(node.properties.batch_data || "{}"); + const latent = batchData.latents.find((l) => l.id === latentId); + if (latent) { + latent.repeat_count = Math.max(1, Math.min(99, newCount)); + batchData.total_count = batchData.latents.reduce((sum, l) => sum + (l.repeat_count || 1), 0); + node.properties.batch_data = JSON.stringify(batchData); + syncBatchDataWidget(); + const statsEl = container.querySelector(".instaraw-adv-loader-total"); + if (statsEl) statsEl.textContent = `Total: ${batchData.total_count} (with repeats)`; + // Re-render to trigger update event for RPG + renderGallery(); + } + }; + + const queueAllLatents = async () => { + const batchData = JSON.parse(node.properties.batch_data || "{}"); + const totalCount = batchData.total_count || 0; + if (totalCount === 0 || !confirm(`Queue ${totalCount} workflow executions?`)) return; + try { + const prompt = await app.graphToPrompt(); + for (let i = 0; i < totalCount; i++) { + const promptCopy = JSON.parse(JSON.stringify(prompt.output)); + if (promptCopy[node.id] && promptCopy[node.id].inputs) { + promptCopy[node.id].inputs.batch_index = i; + } + await fetch("/prompt", { method: "POST", headers: { "Content-Type": "application/json" }, body: JSON.stringify({ prompt: promptCopy, client_id: app.clientId }) }); + if (i < totalCount - 1) await new Promise((resolve) => setTimeout(resolve, 50)); + } + } catch (error) { + alert(`Queue error: ${error.message}`); + } + }; + + const setupTxt2ImgEventHandlers = () => { + // === Canvas Panning & Zoom Event Forwarding === + // Allow dragging/zooming canvas even when mouse is over custom UI + const isInteractiveElement = (element) => { + if (!element) return false; + const tagName = element.tagName?.toLowerCase(); + if (['button', 'input', 'select', 'textarea', 'a'].includes(tagName)) return true; + const className = element.className || ''; + if (typeof className === 'string' && ( + className.includes('-btn') || className.includes('-input') || + className.includes('-select') || className.includes('repeat-input') || + className.includes('gallery-item') || className.includes('checkbox') + )) return true; + if (element.onclick || element.onmousedown || element.draggable) return true; + return false; + }; + + const isInteractiveOrChildOfInteractive = (target) => { + let element = target; + while (element && element !== container) { + if (isInteractiveElement(element)) return true; + element = element.parentElement; + } + return false; + }; + + // Track drag state for manual panning + if (!container._ailDragState) { + container._ailDragState = { isDragging: false, startX: 0, startY: 0, offsetX: 0, offsetY: 0 }; + } + const dragState = container._ailDragState; + + const canvasForwardHandler = (e) => { + if (e._ailForwarded) return; + + if (e.type === 'mousedown') { + if (isInteractiveOrChildOfInteractive(e.target)) { + dragState.isDragging = false; + return; + } + dragState.isDragging = true; + dragState.startX = e.clientX; + dragState.startY = e.clientY; + const canvas = app?.canvas || app?.graph?.list_of_graphcanvas?.[0]; + if (canvas) { + dragState.offsetX = canvas.ds.offset[0]; + dragState.offsetY = canvas.ds.offset[1]; + } + e.preventDefault(); + e.stopPropagation(); + } + + if (e.type === 'mousemove' && dragState.isDragging) { + const deltaX = e.clientX - dragState.startX; + const deltaY = e.clientY - dragState.startY; + const canvas = app?.canvas || app?.graph?.list_of_graphcanvas?.[0]; + if (canvas) { + const scale = canvas.ds.scale || 1; + canvas.ds.offset[0] = dragState.offsetX + (deltaX / scale); + canvas.ds.offset[1] = dragState.offsetY + (deltaY / scale); + canvas.setDirty(true, true); + } + e.preventDefault(); + e.stopPropagation(); + } + + if (e.type === 'mouseup' && dragState.isDragging) { + dragState.isDragging = false; + e.preventDefault(); + e.stopPropagation(); + } + }; + + const wheelForwardHandler = (e) => { + if (e._ailForwarded) return; + if (isInteractiveOrChildOfInteractive(e.target)) return; + const canvas = document.querySelector('#graph-canvas') || + document.querySelector('canvas.litegraph') || + document.querySelector('.litegraph canvas'); + if (canvas) { + e.preventDefault(); + e.stopPropagation(); + const newEvent = new WheelEvent('wheel', { + bubbles: true, cancelable: true, view: window, + clientX: e.clientX, clientY: e.clientY, + deltaX: e.deltaX, deltaY: e.deltaY, deltaZ: e.deltaZ, deltaMode: e.deltaMode, + ctrlKey: e.ctrlKey, shiftKey: e.shiftKey, altKey: e.altKey, metaKey: e.metaKey + }); + newEvent._ailForwarded = true; + canvas.dispatchEvent(newEvent); + } + }; + + // Remove old listeners if exist + if (container._canvasForwardHandler) { + container.removeEventListener('mousedown', container._canvasForwardHandler, true); + document.removeEventListener('mousemove', container._canvasForwardHandler, true); + document.removeEventListener('mouseup', container._canvasForwardHandler, true); + } + if (container._wheelForwardHandler) { + container.removeEventListener('wheel', container._wheelForwardHandler, true); + } + + container._canvasForwardHandler = canvasForwardHandler; + container._wheelForwardHandler = wheelForwardHandler; + + container.addEventListener('mousedown', canvasForwardHandler, true); + document.addEventListener('mousemove', canvasForwardHandler, true); + document.addEventListener('mouseup', canvasForwardHandler, true); + container.addEventListener('wheel', wheelForwardHandler, true); + + const modeDropdown = container.querySelector(".instaraw-adv-loader-mode-dropdown"); + if (modeDropdown) + modeDropdown.onchange = (e) => { + const modeWidget = node.widgets?.find((w) => w.name === "mode"); + if (modeWidget) { + modeWidget.value = e.target.value; + renderGallery(); + } + }; + const queueAllBtn = container.querySelector(".instaraw-adv-loader-queue-all-btn"); + if (queueAllBtn) queueAllBtn.onclick = queueAllLatents; + const addLatentBtn = container.querySelector(".instaraw-adv-loader-add-latent-btn"); + if (addLatentBtn) addLatentBtn.onclick = addEmptyLatent; + const batchAddBtn = container.querySelector(".instaraw-adv-loader-batch-add-btn"); + if (batchAddBtn) { + batchAddBtn.onclick = () => { + const countInput = container.querySelector(".instaraw-adv-loader-batch-count-input"); + const count = parseInt(countInput?.value || 5); + if (count > 0 && count <= 100) { + batchAddLatents(count); + } + }; + } + const deleteAllBtn = container.querySelector(".instaraw-adv-loader-delete-all-btn"); + if (deleteAllBtn) deleteAllBtn.onclick = deleteAllLatents; + container.querySelectorAll(".instaraw-adv-loader-delete-btn").forEach((btn) => (btn.onclick = (e) => { e.stopPropagation(); deleteLatent(btn.dataset.id); })); + container.querySelectorAll(".instaraw-adv-loader-repeat-input").forEach((input) => { + input.onchange = (e) => updateLatentRepeatCount(input.dataset.id, parseInt(input.value) || 1); + input.onmousedown = (e) => e.stopPropagation(); + }); + }; + + const handleFileSelect = async (e) => { + const files = Array.from(e.target.files); + if (files.length === 0) return; + const uploadBtn = container.querySelector(".instaraw-adv-loader-upload-btn"); + const originalText = uploadBtn.textContent; + uploadBtn.textContent = "โณ Uploading..."; + uploadBtn.disabled = true; + try { + const formData = new FormData(); + formData.append("node_id", node.id); + files.forEach((file) => formData.append("files", file)); + const response = await fetch("/instaraw/batch_upload", { method: "POST", body: formData }); + const result = await response.json(); + console.log(`[INSTARAW AIL ${node.id}] ๐Ÿ“ค Upload result:`, result.success); + if (result.success) { + // Auto-switch to img2img mode FIRST if currently in txt2img mode + const wasInTxt2ImgMode = isTxt2ImgMode(); + console.log(`[INSTARAW AIL ${node.id}] ๐Ÿ“Š Current mode - isTxt2ImgMode:`, wasInTxt2ImgMode); + if (wasInTxt2ImgMode) { + console.log(`[INSTARAW AIL ${node.id}] ๐Ÿ”„ Auto-switching to img2img mode BEFORE adding images`); + + // Properly swap data (backup txt2img, restore img2img) + node.properties.txt2img_data_backup = node.properties.batch_data; + node.properties.batch_data = node.properties.img2img_data_backup || JSON.stringify({ images: [], order: [], total_count: 0 }); + console.log(`[INSTARAW AIL ${node.id}] ๐Ÿ’พ Backed up txt2img data, restored img2img data`); + + // Update the boolean + switchToImg2ImgMode(); + + // Manually update currentDetectedMode to prevent mode switch detection in renderGallery + currentDetectedMode = false; // false = img2img mode + console.log(`[INSTARAW AIL ${node.id}] โœ… Updated currentDetectedMode to img2img`); + } else { + console.log(`[INSTARAW AIL ${node.id}] โœ… Already in img2img mode, no switch needed`); + } + + // NOW add images to batch_data (after mode switch completes) + const batchData = JSON.parse(node.properties.batch_data || "{}"); + batchData.images = batchData.images || []; + batchData.order = batchData.order || []; + result.images.forEach((img) => { + batchData.images.push(img); + batchData.order.push(img.id); + }); + batchData.total_count = batchData.images.reduce((sum, img) => sum + (img.repeat_count || 1), 0); + node.properties.batch_data = JSON.stringify(batchData); + + syncBatchDataWidget(); + renderGallery(); + } else { + alert(`Upload failed: ${result.error}`); + } + } catch (error) { + alert(`Upload error: ${error.message}`); + } finally { + uploadBtn.textContent = originalText; + uploadBtn.disabled = false; + } + }; + + const deleteImage = async (imageId) => { + if (!confirm("Delete this image?")) return; + try { + const batchData = JSON.parse(node.properties.batch_data || "{}"); + const imageIdStr = String(imageId); + + // Check if this is a duplicated image (ID starts with "dup_" or is numeric) + const isDuplicate = imageIdStr.startsWith("dup_") || /^\d+(\.\d+)?$/.test(imageIdStr); + + // Only call server delete for original images (not duplicates) + if (!isDuplicate) { + try { + await fetch(`/instaraw/batch_delete/${node.id}/${imageId}`, { method: "DELETE" }); + } catch (serverError) { + // Server delete failed, but continue with local removal + console.warn(`[AIL] Server delete failed for ${imageId}, removing locally`); + } + } + + // Remove from local batch data (compare as strings to handle type mismatches) + batchData.images = batchData.images.filter((img) => String(img.id) !== imageIdStr); + batchData.order = batchData.order.filter((id) => String(id) !== imageIdStr); + batchData.total_count = batchData.images.reduce((sum, img) => sum + (img.repeat_count || 1), 0); + node.properties.batch_data = JSON.stringify(batchData); + syncBatchDataWidget(); + renderGallery(); + } catch (error) { + alert(`Delete error: ${error.message}`); + } + }; + + const duplicateLastItem = () => { + const batchData = JSON.parse(node.properties.batch_data || "{}"); + const isTxt2Img = isTxt2ImgMode(); + const items = isTxt2Img ? (batchData.latents || []) : (batchData.images || []); + const order = batchData.order || []; + + if (items.length === 0 || order.length === 0) return; + + // Get last item ID from order + const lastItemId = order[order.length - 1]; + const lastItem = items.find(item => item.id === lastItemId); + + if (!lastItem) return; + + // Create deep copy with new unique ID + const duplicatedItem = { + ...lastItem, + id: Date.now() + Math.random(), + repeat_count: 1 // Reset repeat count for duplicate + }; + + // Add to batch data + items.push(duplicatedItem); + order.push(duplicatedItem.id); + + // Update total count + if (isTxt2Img) { + batchData.latents = items; + } else { + batchData.images = items; + } + batchData.order = order; + batchData.total_count = items.reduce((sum, item) => sum + (item.repeat_count || 1), 0); + + // Save and re-render (renderGallery already dispatches INSTARAW_AIL_UPDATED) + node.properties.batch_data = JSON.stringify(batchData); + syncBatchDataWidget(); + renderGallery(); + }; + + const deleteAllImages = async () => { + const batchData = JSON.parse(node.properties.batch_data || "{}"); + const imageCount = batchData.images?.length || 0; + if (imageCount === 0 || !confirm(`Delete all ${imageCount} image${imageCount !== 1 ? "s" : ""}?`)) return; + try { + await Promise.all(batchData.images.map((img) => fetch(`/instaraw/batch_delete/${node.id}/${img.id}`, { method: "DELETE" }))); + node.properties.batch_data = JSON.stringify({ images: [], order: [], total_count: 0 }); + // Clear backup to prevent old data from reappearing on mode switch + node.properties.img2img_data_backup = JSON.stringify({ images: [], order: [], total_count: 0 }); + syncBatchDataWidget(); + renderGallery(); + } catch (error) { + alert(`Delete all error: ${error.message}`); + } + }; + + const queueAllImages = async () => { + const batchData = JSON.parse(node.properties.batch_data || "{}"); + const totalCount = batchData.total_count || 0; + if (totalCount === 0 || !confirm(`Queue ${totalCount} workflow executions?`)) return; + try { + const prompt = await app.graphToPrompt(); + for (let i = 0; i < totalCount; i++) { + const promptCopy = JSON.parse(JSON.stringify(prompt.output)); + if (promptCopy[node.id] && promptCopy[node.id].inputs) { + promptCopy[node.id].inputs.batch_index = i; + } + await fetch("/prompt", { method: "POST", headers: { "Content-Type": "application/json" }, body: JSON.stringify({ prompt: promptCopy, client_id: app.clientId }) }); + if (i < totalCount - 1) await new Promise((resolve) => setTimeout(resolve, 50)); + } + } catch (error) { + alert(`Queue error: ${error.message}`); + } + }; + + const updateRepeatCount = (imageId, newCount) => { + const batchData = JSON.parse(node.properties.batch_data || "{}"); + const img = batchData.images.find((i) => i.id === imageId); + if (img) { + img.repeat_count = Math.max(1, Math.min(99, newCount)); + batchData.total_count = batchData.images.reduce((sum, i) => sum + (i.repeat_count || 1), 0); + node.properties.batch_data = JSON.stringify(batchData); + syncBatchDataWidget(); + const statsEl = container.querySelector(".instaraw-adv-loader-total"); + if (statsEl) statsEl.textContent = `Total: ${batchData.total_count} (with repeats)`; + // Trigger re-render to update RPG + renderGallery(); + } + }; + + // Smart sync function - finds connected RPG and syncs accordingly + const smartSync = () => { + const batchData = JSON.parse(node.properties.batch_data || "{}"); + const isTxt2Img = isTxt2ImgMode(); + const items = isTxt2Img ? (batchData.latents || []) : (batchData.images || []); + const order = batchData.order || []; + const myCount = order.length; + + if (myCount === 0) { + alert("No items to sync! Add images/latents first."); + return; + } + + // Find connected RPG node by checking our output connections + let connectedRPG = null; + let promptCount = 0; + let promptRepeats = []; + + if (node.outputs && node.outputs.length > 0) { + for (const output of node.outputs) { + if (output.links && output.links.length > 0) { + for (const linkId of output.links) { + const link = app.graph.links[linkId]; + if (link) { + const targetNode = app.graph.getNodeById(link.target_id); + if (targetNode && targetNode.type === "INSTARAW_RealityPromptGenerator") { + connectedRPG = targetNode; + // Get prompt queue from RPG + try { + const promptData = JSON.parse(targetNode.properties.prompt_batch_data || "[]"); + if (Array.isArray(promptData) && promptData.length > 0) { + promptCount = promptData.length; + promptRepeats = promptData.map(p => p.repeat_count || 1); + } + } catch (e) { + console.warn("[AIL] Failed to parse RPG prompt data:", e); + } + break; + } + } + } + } + if (connectedRPG) break; + } + } + + if (promptCount > 0) { + // Prompts exist - prompts are king, sync this AIL to match + console.log(`[AIL ${node.id}] ๐Ÿ”„ Prompts are king: Syncing to ${promptCount} prompts`); + + if (myCount === promptCount) { + // Same count, just sync repeats + window.dispatchEvent(new CustomEvent("INSTARAW_SYNC_AIL_REPEATS", { + detail: { + targetNodeId: node.id, + mode: isTxt2Img ? "txt2img" : "img2img", + repeats: promptRepeats + } + })); + alert(`โœ… Synced repeat counts to match ${promptCount} prompts`); + } else if (myCount < promptCount) { + // Need to add items + const toAdd = promptCount - myCount; + window.dispatchEvent(new CustomEvent("INSTARAW_DUPLICATE_LAST_N", { + detail: { targetNodeId: node.id, count: toAdd } + })); + // Then sync repeats after a short delay + setTimeout(() => { + window.dispatchEvent(new CustomEvent("INSTARAW_SYNC_AIL_REPEATS", { + detail: { + targetNodeId: node.id, + mode: isTxt2Img ? "txt2img" : "img2img", + repeats: promptRepeats + } + })); + }, 100); + alert(`โœ… Added ${toAdd} items and synced to match ${promptCount} prompts`); + } else { + // Need to remove items - just update repeats for first N + alert(`โš ๏ธ You have ${myCount} items but only ${promptCount} prompts. Remove ${myCount - promptCount} items manually, or prompts will only use the first ${promptCount}.`); + } + } else { + // No prompts - find connected AILs and sync to the one with most images + console.log(`[AIL ${node.id}] ๐Ÿ” No prompts: Looking for other AILs to sync with`); + + // Find all AILs connected to the same RPG or BIG + const findSiblingAILs = () => { + const siblings = []; + + // Check our output connections to find RPG/BIG + if (node.outputs && node.outputs.length > 0) { + for (const output of node.outputs) { + if (output.links && output.links.length > 0) { + for (const linkId of output.links) { + const link = app.graph.links[linkId]; + if (link) { + const targetNode = app.graph.getNodeById(link.target_id); + if (targetNode && (targetNode.type === "INSTARAW_RealityPromptGenerator" || targetNode.type === "INSTARAW_BatchImageGenerator")) { + // Find all AILs connected to this target node + const imageInputs = ["images", "images2", "images3", "images4"]; + for (const inputName of imageInputs) { + const input = targetNode.inputs?.find(i => i.name === inputName); + if (input?.link) { + const ailLink = app.graph.links[input.link]; + if (ailLink) { + const ailNode = app.graph.getNodeById(ailLink.origin_id); + if (ailNode && ailNode.type === "INSTARAW_AdvancedImageLoader" && ailNode.id !== node.id) { + try { + const ailData = JSON.parse(ailNode.properties.batch_data || "{}"); + const ailCount = (ailData.order || []).length; + if (ailCount > 0) { + siblings.push({ node: ailNode, count: ailCount, inputName }); + } + } catch (e) {} + } + } + } + } + } + } + } + } + } + } + return siblings; + }; + + const siblingAILs = findSiblingAILs(); + + if (siblingAILs.length === 0) { + alert("No other AILs with images found. Connect this AIL to RPG or BIG with other AILs."); + return; + } + + // Find the AIL with the most images + const masterAIL = siblingAILs.reduce((max, ail) => ail.count > max.count ? ail : max, siblingAILs[0]); + + if (myCount === masterAIL.count) { + alert(`โœ… Already synced! Both have ${myCount} items.`); + return; + } + + const action = myCount < masterAIL.count ? "duplicate" : "trim"; + const diff = Math.abs(masterAIL.count - myCount); + + if (!confirm(`Sync this AIL to match ${masterAIL.count} items?\n\n${action === "duplicate" ? `๐Ÿ“‹ Duplicate last image ${diff} time(s)` : `๐Ÿ—‘๏ธ Remove ${diff} image(s)`}\n\nContinue?`)) { + return; + } + + if (myCount < masterAIL.count) { + // Duplicate to match + window.dispatchEvent(new CustomEvent("INSTARAW_DUPLICATE_LAST_N", { + detail: { targetNodeId: node.id, count: diff } + })); + alert(`โœ… Duplicated ${diff} images to match ${masterAIL.count} items`); + } else { + // Trim to match + window.dispatchEvent(new CustomEvent("INSTARAW_TRIM_AIL_IMAGES", { + detail: { targetNodeId: node.id, targetCount: masterAIL.count } + })); + alert(`โœ… Trimmed to ${masterAIL.count} items`); + } + } + }; + + const setupEventHandlers = () => { + // === Canvas Panning & Zoom Event Forwarding === + // Allow dragging/zooming canvas even when mouse is over custom UI + const isInteractiveElement = (element) => { + if (!element) return false; + const tagName = element.tagName?.toLowerCase(); + if (['button', 'input', 'select', 'textarea', 'a'].includes(tagName)) return true; + const className = element.className || ''; + if (typeof className === 'string' && ( + className.includes('-btn') || className.includes('-input') || + className.includes('-select') || className.includes('repeat-input') || + className.includes('gallery-item') || className.includes('checkbox') + )) return true; + if (element.onclick || element.onmousedown || element.draggable) return true; + return false; + }; + + const isInteractiveOrChildOfInteractive = (target) => { + let element = target; + while (element && element !== container) { + if (isInteractiveElement(element)) return true; + element = element.parentElement; + } + return false; + }; + + // Track drag state for manual panning + if (!container._ailDragState) { + container._ailDragState = { isDragging: false, startX: 0, startY: 0, offsetX: 0, offsetY: 0 }; + } + const dragState = container._ailDragState; + + const canvasForwardHandler = (e) => { + if (e._ailForwarded) return; + + if (e.type === 'mousedown') { + if (isInteractiveOrChildOfInteractive(e.target)) { + dragState.isDragging = false; + return; + } + dragState.isDragging = true; + dragState.startX = e.clientX; + dragState.startY = e.clientY; + const canvas = app?.canvas || app?.graph?.list_of_graphcanvas?.[0]; + if (canvas) { + dragState.offsetX = canvas.ds.offset[0]; + dragState.offsetY = canvas.ds.offset[1]; + } + e.preventDefault(); + e.stopPropagation(); + } + + if (e.type === 'mousemove' && dragState.isDragging) { + const deltaX = e.clientX - dragState.startX; + const deltaY = e.clientY - dragState.startY; + // Divide by scale to match native ComfyUI drag behavior at any zoom level + const canvas = app?.canvas || app?.graph?.list_of_graphcanvas?.[0]; + if (canvas) { + const scale = canvas.ds.scale || 1; + canvas.ds.offset[0] = dragState.offsetX + (deltaX / scale); + canvas.ds.offset[1] = dragState.offsetY + (deltaY / scale); + canvas.setDirty(true, true); + } + e.preventDefault(); + e.stopPropagation(); + } + + if (e.type === 'mouseup' && dragState.isDragging) { + dragState.isDragging = false; + e.preventDefault(); + e.stopPropagation(); + } + }; + + const wheelForwardHandler = (e) => { + if (e._ailForwarded) return; + if (isInteractiveOrChildOfInteractive(e.target)) return; + const canvas = document.querySelector('#graph-canvas') || + document.querySelector('canvas.litegraph') || + document.querySelector('.litegraph canvas'); + if (canvas) { + e.preventDefault(); + e.stopPropagation(); + const newEvent = new WheelEvent('wheel', { + bubbles: true, cancelable: true, view: window, + clientX: e.clientX, clientY: e.clientY, + deltaX: e.deltaX, deltaY: e.deltaY, deltaZ: e.deltaZ, deltaMode: e.deltaMode, + ctrlKey: e.ctrlKey, shiftKey: e.shiftKey, altKey: e.altKey, metaKey: e.metaKey + }); + newEvent._ailForwarded = true; + canvas.dispatchEvent(newEvent); + } + }; + + // Remove old listeners if exist + if (container._canvasForwardHandler) { + container.removeEventListener('mousedown', container._canvasForwardHandler, true); + document.removeEventListener('mousemove', container._canvasForwardHandler, true); + document.removeEventListener('mouseup', container._canvasForwardHandler, true); + } + if (container._wheelForwardHandler) { + container.removeEventListener('wheel', container._wheelForwardHandler, true); + } + + container._canvasForwardHandler = canvasForwardHandler; + container._wheelForwardHandler = wheelForwardHandler; + + container.addEventListener('mousedown', canvasForwardHandler, true); + document.addEventListener('mousemove', canvasForwardHandler, true); + document.addEventListener('mouseup', canvasForwardHandler, true); + container.addEventListener('wheel', wheelForwardHandler, true); + + const modeDropdown = container.querySelector(".instaraw-adv-loader-mode-dropdown"); + if (modeDropdown) + modeDropdown.onchange = (e) => { + const modeWidget = node.widgets?.find((w) => w.name === "mode"); + if (modeWidget) { + modeWidget.value = e.target.value; + renderGallery(); + } + }; + const queueAllBtn = container.querySelector(".instaraw-adv-loader-queue-all-btn"); + if (queueAllBtn) queueAllBtn.onclick = queueAllImages; + const uploadBtn = container.querySelector(".instaraw-adv-loader-upload-btn"); + if (uploadBtn) + uploadBtn.onclick = () => { + const input = document.createElement("input"); + input.type = "file"; + input.multiple = true; + input.accept = "image/*"; + input.onchange = handleFileSelect; + input.click(); + }; + const duplicateBtn = container.querySelector(".instaraw-adv-loader-duplicate-btn"); + if (duplicateBtn) duplicateBtn.onclick = duplicateLastItem; + const syncBtn = container.querySelector(".instaraw-adv-loader-sync-btn"); + if (syncBtn) syncBtn.onclick = smartSync; + const deleteAllBtn = container.querySelector(".instaraw-adv-loader-delete-all-btn"); + if (deleteAllBtn) deleteAllBtn.onclick = deleteAllImages; + container.querySelectorAll(".instaraw-adv-loader-delete-btn").forEach((btn) => (btn.onclick = (e) => { e.stopPropagation(); deleteImage(btn.dataset.id); })); + container.querySelectorAll(".instaraw-adv-loader-repeat-input").forEach((input) => { + input.onchange = (e) => updateRepeatCount(input.dataset.id, parseInt(input.value) || 1); + input.onmousedown = (e) => e.stopPropagation(); + }); + + // Selection mode handlers + const enterSelectionBtn = container.querySelector(".instaraw-adv-loader-enter-selection-btn"); + if (enterSelectionBtn) { + enterSelectionBtn.onclick = () => { + selectionMode = true; + selectedImages.clear(); + renderGallery(); + console.log(`[INSTARAW AIL ${node.id}] Entered selection mode`); + }; + } + + const cancelSelectionBtn = container.querySelector(".instaraw-adv-loader-cancel-selection-btn"); + if (cancelSelectionBtn) { + cancelSelectionBtn.onclick = () => { + selectionMode = false; + selectedImages.clear(); + renderGallery(); + console.log(`[INSTARAW AIL ${node.id}] Exited selection mode`); + }; + } + + const selectAllBtn = container.querySelector(".instaraw-adv-loader-select-all-btn"); + if (selectAllBtn) { + selectAllBtn.onclick = () => { + const batchData = JSON.parse(node.properties.batch_data || "{}"); + const order = batchData.order || []; + order.forEach(id => selectedImages.add(String(id))); + renderGallery(); + console.log(`[INSTARAW AIL ${node.id}] Selected all: ${selectedImages.size} images`); + }; + } + + const deselectAllBtn = container.querySelector(".instaraw-adv-loader-deselect-all-btn"); + if (deselectAllBtn) { + deselectAllBtn.onclick = () => { + selectedImages.clear(); + renderGallery(); + console.log(`[INSTARAW AIL ${node.id}] Deselected all`); + }; + } + + const deleteSelectedBtn = container.querySelector(".instaraw-adv-loader-delete-selected-btn"); + if (deleteSelectedBtn) { + deleteSelectedBtn.onclick = async () => { + if (selectedImages.size === 0) return; + + const confirmMsg = `Delete ${selectedImages.size} selected image${selectedImages.size === 1 ? '' : 's'}?\n\nThis cannot be undone.`; + if (!confirm(confirmMsg)) return; + + try { + const batchData = JSON.parse(node.properties.batch_data || "{}"); + const images = batchData.images || []; + const order = batchData.order || []; + + // Remove selected images (selectedImages contains strings, img.id/order may be numbers) + const selectedIds = Array.from(selectedImages); + selectedIds.forEach(id => { + // Compare as strings to handle type mismatch + const imgIndex = images.findIndex(i => String(i.id) === id); + if (imgIndex !== -1) images.splice(imgIndex, 1); + const orderIndex = order.findIndex(orderId => String(orderId) === id); + if (orderIndex !== -1) order.splice(orderIndex, 1); + }); + + // Recalculate total count + batchData.total_count = images.reduce((sum, img) => sum + (img.repeat_count || 1), 0); + + node.properties.batch_data = JSON.stringify(batchData); + syncBatchDataWidget(); + + console.log(`[INSTARAW AIL ${node.id}] Deleted ${selectedIds.length} images`); + + // Exit selection mode + selectionMode = false; + selectedImages.clear(); + renderGallery(); + + alert(`Successfully deleted ${selectedIds.length} image${selectedIds.length === 1 ? '' : 's'}`); + } catch (error) { + console.error(`[INSTARAW AIL ${node.id}] Error deleting selected images:`, error); + alert(`Error deleting images: ${error.message}`); + } + }; + } + + // Checkbox handlers + container.querySelectorAll(".instaraw-adv-loader-image-checkbox").forEach((checkbox) => { + checkbox.onchange = (e) => { + e.stopPropagation(); + const imageId = checkbox.dataset.id; + + if (checkbox.checked) { + selectedImages.add(imageId); + } else { + selectedImages.delete(imageId); + } + + renderGallery(); + console.log(`[INSTARAW AIL ${node.id}] ${checkbox.checked ? 'Selected' : 'Deselected'} image ${imageId}. Total: ${selectedImages.size}`); + }; + checkbox.onclick = (e) => e.stopPropagation(); + }); + + // Allow clicking on the card to toggle selection in selection mode + if (selectionMode) { + container.querySelectorAll(".instaraw-adv-loader-item").forEach((item) => { + item.onclick = (e) => { + if (e.target.tagName === 'INPUT' || e.target.tagName === 'BUTTON') return; + const imageId = item.dataset.id; + if (selectedImages.has(imageId)) { + selectedImages.delete(imageId); + } else { + selectedImages.add(imageId); + } + renderGallery(); + }; + }); + } + }; + + const setupDragAndDrop = () => { + const items = container.querySelectorAll(".instaraw-adv-loader-item"); + let draggedItem = null; + items.forEach((item) => { + item.addEventListener("dragstart", (e) => { + draggedItem = item; + item.style.opacity = "0.5"; + e.dataTransfer.effectAllowed = "move"; + e.stopPropagation(); + e.dataTransfer.setData("text/plain", "instaraw-reorder"); + }); + item.addEventListener("dragend", () => { + item.style.opacity = "1"; + items.forEach((i) => i.classList.remove("instaraw-adv-loader-drop-before", "instaraw-adv-loader-drop-after")); + }); + item.addEventListener("dragover", (e) => { + e.preventDefault(); + if (draggedItem === item) return; + e.dataTransfer.dropEffect = "move"; + const rect = item.getBoundingClientRect(); + const midpoint = rect.top + rect.height / 2; + items.forEach((i) => i.classList.remove("instaraw-adv-loader-drop-before", "instaraw-adv-loader-drop-after")); + item.classList.add(e.clientY < midpoint ? "instaraw-adv-loader-drop-before" : "instaraw-adv-loader-drop-after"); + }); + item.addEventListener("drop", (e) => { + e.preventDefault(); + if (draggedItem === item) return; + const draggedId = draggedItem.dataset.id; + const targetId = item.dataset.id; + const batchData = JSON.parse(node.properties.batch_data || "{}"); + const order = batchData.order; + const draggedIndex = order.indexOf(draggedId); + order.splice(draggedIndex, 1); + const rect = item.getBoundingClientRect(); + const insertAfter = e.clientY > rect.top + rect.height / 2; + const newTargetIndex = order.indexOf(targetId); + order.splice(insertAfter ? newTargetIndex + 1 : newTargetIndex, 0, draggedId); + node.properties.batch_data = JSON.stringify(batchData); + syncBatchDataWidget(); + renderGallery(); + }); + }); + }; + + const setupFileDropZone = () => { + // Prevent duplicate listeners + if (container._hasFileDropListeners) return; + container._hasFileDropListeners = true; + + let dragCounter = 0; // Track nested drag events + let dragTimeout = null; // Timeout to auto-clear highlight + + const clearDragHighlight = () => { + dragCounter = 0; + container.classList.remove("instaraw-adv-loader-drag-over"); + if (dragTimeout) { + clearTimeout(dragTimeout); + dragTimeout = null; + } + }; + + const handleFileDrop = async (files) => { + console.log(`[INSTARAW AIL ${node.id}] ๐Ÿ“‚ Files dropped:`, files.length); + if (files.length === 0) return; + + const uploadBtn = container.querySelector(".instaraw-adv-loader-upload-btn"); + let originalText = null; + + // Update button state if it exists (might not exist in txt2img mode) + if (uploadBtn) { + originalText = uploadBtn.textContent; + uploadBtn.textContent = "โณ Uploading..."; + uploadBtn.disabled = true; + } else { + console.log(`[INSTARAW AIL ${node.id}] โ„น๏ธ No upload button (probably in txt2img mode), proceeding anyway...`); + } + + try { + const formData = new FormData(); + formData.append("node_id", node.id); + files.forEach((file) => formData.append("files", file)); + const response = await fetch("/instaraw/batch_upload", { method: "POST", body: formData }); + const result = await response.json(); + console.log(`[INSTARAW AIL ${node.id}] ๐Ÿ“ค Upload result:`, result.success); + console.log(`[INSTARAW AIL ${node.id}] ๐Ÿ“ค Images returned:`, result.images?.length || 0); + if (result.success) { + // Auto-switch to img2img mode FIRST if currently in txt2img mode + const wasInTxt2ImgMode = isTxt2ImgMode(); + console.log(`[INSTARAW AIL ${node.id}] ๐Ÿ“Š Current mode - isTxt2ImgMode:`, wasInTxt2ImgMode); + if (wasInTxt2ImgMode) { + console.log(`[INSTARAW AIL ${node.id}] ๐Ÿ”„ Auto-switching to img2img mode BEFORE adding images`); + + // Properly swap data (backup txt2img, restore img2img) + node.properties.txt2img_data_backup = node.properties.batch_data; + node.properties.batch_data = node.properties.img2img_data_backup || JSON.stringify({ images: [], order: [], total_count: 0 }); + console.log(`[INSTARAW AIL ${node.id}] ๐Ÿ’พ Backed up txt2img data, restored img2img data`); + + // Update the boolean + switchToImg2ImgMode(); + + // Manually update currentDetectedMode to prevent mode switch detection in renderGallery + currentDetectedMode = false; // false = img2img mode + console.log(`[INSTARAW AIL ${node.id}] โœ… Updated currentDetectedMode to img2img`); + } else { + console.log(`[INSTARAW AIL ${node.id}] โœ… Already in img2img mode, no switch needed`); + } + + // NOW add images to batch_data (after mode switch completes) + const batchData = JSON.parse(node.properties.batch_data || "{}"); + console.log(`[INSTARAW AIL ${node.id}] ๐Ÿ“ฆ Batch data before:`, batchData); + batchData.images = batchData.images || []; + batchData.order = batchData.order || []; + result.images.forEach((img) => { + batchData.images.push(img); + batchData.order.push(img.id); + }); + batchData.total_count = batchData.images.reduce((sum, img) => sum + (img.repeat_count || 1), 0); + node.properties.batch_data = JSON.stringify(batchData); + console.log(`[INSTARAW AIL ${node.id}] ๐Ÿ“ฆ Batch data after:`, JSON.parse(node.properties.batch_data)); + + syncBatchDataWidget(); + renderGallery(); + } else { + alert(`Upload failed: ${result.error}`); + } + } catch (error) { + alert(`Upload error: ${error.message}`); + } finally { + // Reset button state if it exists + if (uploadBtn && originalText) { + uploadBtn.textContent = originalText; + uploadBtn.disabled = false; + } + } + }; + + container.addEventListener("dragenter", (e) => { + e.preventDefault(); + e.stopPropagation(); + dragCounter++; + if (e.dataTransfer.types.includes("Files")) { + container.classList.add("instaraw-adv-loader-drag-over"); + // Set timeout to auto-clear if drag is abandoned + if (dragTimeout) clearTimeout(dragTimeout); + dragTimeout = setTimeout(clearDragHighlight, 200); + } + }); + + container.addEventListener("dragover", (e) => { + e.preventDefault(); + e.stopPropagation(); + if (e.dataTransfer.types.includes("Files")) { + e.dataTransfer.dropEffect = "copy"; + // Reset timeout while actively dragging over + if (dragTimeout) clearTimeout(dragTimeout); + dragTimeout = setTimeout(clearDragHighlight, 200); + } + }); + + container.addEventListener("dragleave", (e) => { + e.preventDefault(); + e.stopPropagation(); + dragCounter--; + if (dragCounter === 0) { + clearDragHighlight(); + } + }); + + container.addEventListener("drop", (e) => { + e.preventDefault(); + e.stopPropagation(); + clearDragHighlight(); + + // Only handle file drops, not reordering + if (e.dataTransfer.getData("text/plain") === "instaraw-reorder") { + return; // Let the reordering handler deal with this + } + + const files = Array.from(e.dataTransfer.files).filter(file => file.type.startsWith("image/")); + if (files.length > 0) { + handleFileDrop(files); + } + }); + + // Global dragend listener to reset highlight when drag is cancelled + document.addEventListener("dragend", clearDragHighlight); + document.addEventListener("drop", clearDragHighlight); + + // Store cleanup function on node + node._cleanupDragListeners = () => { + document.removeEventListener("dragend", clearDragHighlight); + document.removeEventListener("drop", clearDragHighlight); + if (dragTimeout) clearTimeout(dragTimeout); + }; + }; + + const widget = node.addDOMWidget("batch_display", "batchloader", container, { getValue: () => node.properties.batch_data, setValue: (v) => { node.properties.batch_data = v; renderGallery(); }, serialize: false }); + widget.computeSize = (width) => [width, cachedHeight + 6]; + node._updateCachedHeight = updateCachedHeight; + node._renderGallery = renderGallery; + + // Update existing latents with new dimensions + const updateLatentsWithNewDimensions = () => { + if (!isTxt2ImgMode()) return; // Only for txt2img mode + + const currentDims = getTxt2ImgDimensions(); + const batchData = JSON.parse(node.properties.batch_data || "{}"); + + if (batchData.latents && batchData.latents.length > 0) { + // Check if dimensions actually changed + const firstLatent = batchData.latents[0]; + if (firstLatent.width !== currentDims.width || + firstLatent.height !== currentDims.height || + firstLatent.aspect_label !== currentDims.aspect_label) { + + console.log(`[INSTARAW AIL ${node.id}] ๐Ÿ”„ Updating ${batchData.latents.length} latents: ${firstLatent.width}ร—${firstLatent.height} (${firstLatent.aspect_label}) โ†’ ${currentDims.width}ร—${currentDims.height} (${currentDims.aspect_label})`); + + batchData.latents.forEach(latent => { + latent.width = currentDims.width; + latent.height = currentDims.height; + latent.aspect_label = currentDims.aspect_label; + }); + node.properties.batch_data = JSON.stringify(batchData); + syncBatchDataWidget(); + } + } + }; + + // Add widget change callbacks to automatically refresh + const setupWidgetCallbacks = () => { + const modeWidget = node.widgets?.find((w) => w.name === "mode"); + if (modeWidget && !modeWidget._instaraw_callback_added) { + const originalCallback = modeWidget.callback; + modeWidget.callback = function() { + if (originalCallback) originalCallback.apply(this, arguments); + renderGallery(); + }; + modeWidget._instaraw_callback_added = true; + } + + const batchIndexWidget = node.widgets?.find((w) => w.name === "batch_index"); + if (batchIndexWidget && !batchIndexWidget._instaraw_callback_added) { + const originalCallback = batchIndexWidget.callback; + batchIndexWidget.callback = function() { + if (originalCallback) originalCallback.apply(this, arguments); + renderGallery(); + }; + batchIndexWidget._instaraw_callback_added = true; + } + + // Aspect ratio widgets - update latents AND re-render when dimensions change + const widthWidget = node.widgets?.find((w) => w.name === "width"); + if (widthWidget && !widthWidget._instaraw_callback_added) { + const originalCallback = widthWidget.callback; + widthWidget.callback = function() { + if (originalCallback) originalCallback.apply(this, arguments); + updateLatentsWithNewDimensions(); + renderGallery(); + }; + widthWidget._instaraw_callback_added = true; + } + + const heightWidget = node.widgets?.find((w) => w.name === "height"); + if (heightWidget && !heightWidget._instaraw_callback_added) { + const originalCallback = heightWidget.callback; + heightWidget.callback = function() { + if (originalCallback) originalCallback.apply(this, arguments); + updateLatentsWithNewDimensions(); + renderGallery(); + }; + heightWidget._instaraw_callback_added = true; + } + + const aspectLabelWidget = node.widgets?.find((w) => w.name === "aspect_label"); + if (aspectLabelWidget && !aspectLabelWidget._instaraw_callback_added) { + const originalCallback = aspectLabelWidget.callback; + aspectLabelWidget.callback = function() { + if (originalCallback) originalCallback.apply(this, arguments); + updateLatentsWithNewDimensions(); + renderGallery(); + }; + aspectLabelWidget._instaraw_callback_added = true; + } + }; + + // Periodic mode check - checks every 2 seconds if mode changed + const startModeCheck = () => { + if (modeCheckInterval) clearInterval(modeCheckInterval); + modeCheckInterval = setInterval(() => { + const detectedMode = isTxt2ImgMode(); + if (currentDetectedMode !== null && currentDetectedMode !== detectedMode) { + renderGallery(); + } + }, 2000); + // Store on node for cleanup + node._modeCheckInterval = modeCheckInterval; + }; + + // Periodic dimension check - checks every 2 seconds if dimensions changed + let dimensionCheckInterval = null; + let lastDimensions = null; + const startDimensionCheck = () => { + if (dimensionCheckInterval) clearInterval(dimensionCheckInterval); + dimensionCheckInterval = setInterval(() => { + // Only check dimensions in txt2img mode (where aspect ratio matters for latent display) + if (isTxt2ImgMode()) { + const currentDims = getTxt2ImgDimensions(); + const dimsKey = `${currentDims.width}x${currentDims.height}:${currentDims.aspect_label}`; + if (lastDimensions !== null && lastDimensions !== dimsKey) { + console.log(`[INSTARAW AIL ${node.id}] Dimensions changed: ${lastDimensions} -> ${dimsKey}`); + updateLatentsWithNewDimensions(); + renderGallery(); + } + lastDimensions = dimsKey; + } + }, 2000); + // Store on node for cleanup + node._dimensionCheckInterval = dimensionCheckInterval; + }; + + const handleBatchUpdate = (event) => { + const data = event.detail; + if (data && data.node_id == node.id) { + const batchIndexWidget = node.widgets?.find((w) => w.name === "batch_index"); + if (batchIndexWidget) batchIndexWidget.value = data.next_index; + node._processingIndex = data.next_index; + node._isProcessing = false; + if (node._renderGallery) node._renderGallery(); + app.graph.setDirtyCanvas(true, false); + } + }; + api.addEventListener("instaraw_adv_loader_update", handleBatchUpdate); + + // Listen for Sync requests from RPG + window.addEventListener("INSTARAW_SYNC_AIL_LATENTS", (event) => { + const { targetNodeId, latentSpecs, dimensions } = event.detail; + if (node.id !== targetNodeId) return; // Not for this node + + console.log(`[INSTARAW AIL ${node.id}] Received sync request: Create ${latentSpecs.length} empty latents with repeat counts`); + + // Get current dimensions or use provided + const currentDimensions = getTxt2ImgDimensions(); + const width = dimensions?.width || currentDimensions.width; + const height = dimensions?.height || currentDimensions.height; + const aspect_label = dimensions?.aspect_label || currentDimensions.aspect_label; + + // Ensure we're working with a clean txt2img batch_data structure + // If AIL hasn't swapped data yet, we need to do it now + const oldBatchData = JSON.parse(node.properties.batch_data || "{}"); + if (oldBatchData.images && oldBatchData.images.length > 0) { + // Still has img2img data - backup and swap + console.log(`[INSTARAW AIL ${node.id}] Sync: Swapping from img2img to txt2img data`); + node.properties.img2img_data_backup = node.properties.batch_data; + } + + // Create fresh txt2img batch_data (clear any stale images) + const batchData = { + latents: [], + order: [], + total_count: 0 + }; + + // Create latents with repeat counts matching prompts + let totalCount = 0; + for (let i = 0; i < latentSpecs.length; i++) { + const spec = latentSpecs[i]; + const newLatent = { + id: generateUUID(), + width: width, + height: height, + repeat_count: spec.repeat_count || 1, + aspect_label: aspect_label + }; + batchData.latents.push(newLatent); + batchData.order.push(newLatent.id); + totalCount += newLatent.repeat_count; + } + + batchData.total_count = totalCount; + node.properties.batch_data = JSON.stringify(batchData); + + // Update mode tracking so renderGallery doesn't re-swap + currentDetectedMode = true; // true = txt2img mode + + syncBatchDataWidget(); + renderGallery(); + + console.log(`[INSTARAW AIL ${node.id}] Created ${latentSpecs.length} latents (${totalCount} total generations) (${width}ร—${height})`); + }); + + // Listen for Repeat Sync requests from RPG + window.addEventListener("INSTARAW_SYNC_AIL_REPEATS", (event) => { + const { targetNodeId, mode, repeats } = event.detail; + if (node.id !== targetNodeId) return; // Not for this node + + console.log(`[INSTARAW AIL ${node.id}] Received repeat sync request: Update ${repeats.length} items`); + + const batchData = JSON.parse(node.properties.batch_data || "{}"); + const items = mode === "img2img" ? batchData.images : batchData.latents; + const order = batchData.order || []; + + if (!items || items.length === 0) { + console.warn(`[INSTARAW AIL ${node.id}] No ${mode === "img2img" ? "images" : "latents"} in AIL to sync (mode: ${mode})`); + return; + } + + // Update repeat counts to match prompts, respecting display order + let totalCount = 0; + repeats.forEach((repeatCount, idx) => { + // Find item by order, not by raw array index + const itemId = order[idx]; + if (itemId) { + const item = items.find(i => i.id === itemId); + if (item) { + item.repeat_count = repeatCount; + totalCount += repeatCount; + } + } + }); + + batchData.total_count = totalCount; + node.properties.batch_data = JSON.stringify(batchData); + + syncBatchDataWidget(); + renderGallery(); + + console.log(`[INSTARAW AIL ${node.id}] Synced repeat counts: ${repeats.length} items, ${totalCount} total`); + }); + + // Listen for Duplicate Last N requests from RPG + window.addEventListener("INSTARAW_DUPLICATE_LAST_N", (event) => { + const { targetNodeId, count } = event.detail; + if (node.id !== targetNodeId) return; // Not for this node + + console.log(`[INSTARAW AIL ${node.id}] Received duplicate last N request: Duplicate last item ${count} times`); + + const batchData = JSON.parse(node.properties.batch_data || "{}"); + const isTxt2Img = isTxt2ImgMode(); + const items = isTxt2Img ? (batchData.latents || []) : (batchData.images || []); + const order = batchData.order || []; + + if (items.length === 0 || order.length === 0) { + console.warn(`[INSTARAW AIL ${node.id}] No items to duplicate!`); + return; + } + + // Get last item ID from order + const lastItemId = order[order.length - 1]; + const lastItem = items.find(item => item.id === lastItemId); + + if (!lastItem) { + console.warn(`[INSTARAW AIL ${node.id}] Last item not found!`); + return; + } + + // Duplicate N times + for (let i = 0; i < count; i++) { + const duplicatedItem = { + ...lastItem, + id: Date.now() + Math.random() + i, + repeat_count: 1 + }; + items.push(duplicatedItem); + order.push(duplicatedItem.id); + } + + // Update batch data + if (isTxt2Img) { + batchData.latents = items; + } else { + batchData.images = items; + } + batchData.order = order; + batchData.total_count = items.reduce((sum, item) => sum + (item.repeat_count || 1), 0); + + node.properties.batch_data = JSON.stringify(batchData); + syncBatchDataWidget(); + renderGallery(); // Already dispatches INSTARAW_AIL_UPDATED with proper format + + console.log(`[INSTARAW AIL ${node.id}] Duplicated last item ${count} times, total items: ${items.length}, total count: ${batchData.total_count}`); + }); + + // Listen for Trim Images requests from RPG + window.addEventListener("INSTARAW_TRIM_AIL_IMAGES", (event) => { + const { targetNodeId, targetCount } = event.detail; + if (node.id !== targetNodeId) return; // Not for this node + + console.log(`[INSTARAW AIL ${node.id}] Received trim request: Trim to ${targetCount} images`); + + const batchData = JSON.parse(node.properties.batch_data || "{}"); + const images = batchData.images || []; + const order = batchData.order || []; + + if (images.length === 0 || order.length === 0) { + console.warn(`[INSTARAW AIL ${node.id}] No images to trim!`); + return; + } + + if (targetCount >= order.length) { + console.warn(`[INSTARAW AIL ${node.id}] Target count ${targetCount} >= current count ${order.length}, no trimming needed`); + return; + } + + // Remove excess images from the end + const excessCount = order.length - targetCount; + const removedIds = order.splice(targetCount, excessCount); + + // Remove the actual image objects + batchData.images = images.filter(img => !removedIds.includes(img.id)); + + // Update order and total count + batchData.order = order; + batchData.total_count = batchData.images.reduce((sum, img) => sum + (img.repeat_count || 1), 0); + + node.properties.batch_data = JSON.stringify(batchData); + syncBatchDataWidget(); + renderGallery(); // Already dispatches INSTARAW_AIL_UPDATED with proper format + + console.log(`[INSTARAW AIL ${node.id}] Trimmed ${excessCount} image${excessCount > 1 ? 's' : ''}, total images: ${batchData.images.length}, total count: ${batchData.total_count}`); + }); + + // Listen for Sync from Master AIL broadcast (when no prompts, another AIL is master) + window.addEventListener("INSTARAW_SYNC_FROM_MASTER_AIL", (event) => { + const { masterNodeId, count, repeats, mode } = event.detail; + if (node.id === masterNodeId) return; // Don't sync to ourselves + + console.log(`[INSTARAW AIL ${node.id}] Received sync from master AIL ${masterNodeId}: ${count} items`); + + const batchData = JSON.parse(node.properties.batch_data || "{}"); + const isTxt2Img = isTxt2ImgMode(); + const items = isTxt2Img ? (batchData.latents || []) : (batchData.images || []); + const order = batchData.order || []; + const myCount = order.length; + + if (myCount === 0) { + console.log(`[INSTARAW AIL ${node.id}] No items to sync, skipping`); + return; + } + + // Sync to match the master AIL's count + if (myCount === count) { + // Same count, sync repeats + window.dispatchEvent(new CustomEvent("INSTARAW_SYNC_AIL_REPEATS", { + detail: { + targetNodeId: node.id, + mode: isTxt2Img ? "txt2img" : "img2img", + repeats: repeats + } + })); + console.log(`[INSTARAW AIL ${node.id}] Synced repeat counts to match master AIL`); + } else if (myCount < count) { + // Need to add items + const toAdd = count - myCount; + window.dispatchEvent(new CustomEvent("INSTARAW_DUPLICATE_LAST_N", { + detail: { targetNodeId: node.id, count: toAdd } + })); + // Then sync repeats + setTimeout(() => { + window.dispatchEvent(new CustomEvent("INSTARAW_SYNC_AIL_REPEATS", { + detail: { + targetNodeId: node.id, + mode: isTxt2Img ? "txt2img" : "img2img", + repeats: repeats + } + })); + }, 100); + console.log(`[INSTARAW AIL ${node.id}] Added ${toAdd} items to match master AIL`); + } else { + // Need to trim items + const targetCount = count; + window.dispatchEvent(new CustomEvent("INSTARAW_TRIM_AIL_IMAGES", { + detail: { targetNodeId: node.id, targetCount: targetCount } + })); + // Then sync repeats + setTimeout(() => { + window.dispatchEvent(new CustomEvent("INSTARAW_SYNC_AIL_REPEATS", { + detail: { + targetNodeId: node.id, + mode: isTxt2Img ? "txt2img" : "img2img", + repeats: repeats + } + })); + }, 100); + console.log(`[INSTARAW AIL ${node.id}] Trimmed to ${targetCount} items to match master AIL`); + } + }); + + setTimeout(() => { + const batchIndexWidget = node.widgets?.find((w) => w.name === "batch_index"); + if (batchIndexWidget && batchIndexWidget.value !== undefined) node._processingIndex = batchIndexWidget.value; + syncBatchDataWidget(); + setupWidgetCallbacks(); + startModeCheck(); + startDimensionCheck(); + renderGallery(); + }, 100); + }; + + const onResize = nodeType.prototype.onResize; + nodeType.prototype.onResize = function (size) { + onResize?.apply(this, arguments); + if (this._updateCachedHeight) { + clearTimeout(this._resizeTimeout); + this._resizeTimeout = setTimeout(() => this._updateCachedHeight(), 50); + } + }; + + const onConfigure = nodeType.prototype.onConfigure; + nodeType.prototype.onConfigure = function (data) { + onConfigure?.apply(this, arguments); + setTimeout(() => { + // VALIDATION: Sanitize corrupted dimensions in loaded batch_data + try { + const batchData = JSON.parse(this.properties.batch_data || "{}"); + let needsFixing = false; + + // Check and fix latents (txt2img mode) + if (batchData.latents && Array.isArray(batchData.latents)) { + batchData.latents.forEach(latent => { + if (latent.width < 64 || latent.height < 64) { + console.warn(`[INSTARAW AIL ${this.id}] โš ๏ธ Corrupted latent dimensions detected: ${latent.width}x${latent.height}`); + latent.width = 512; + latent.height = 512; + latent.aspect_label = "1:1"; + needsFixing = true; + } + }); + } + + if (needsFixing) { + console.log(`[INSTARAW AIL ${this.id}] โœ… Fixed corrupted dimensions in batch_data`); + this.properties.batch_data = JSON.stringify(batchData); + } + } catch (e) { + console.error(`[INSTARAW AIL ${this.id}] Error validating batch_data:`, e); + } + + const batchDataWidget = this.widgets?.find((w) => w.name === "batch_data"); + if (batchDataWidget) batchDataWidget.value = this.properties.batch_data || "{}"; + const batchIndexWidget = this.widgets?.find((w) => w.name === "batch_index"); + if (batchIndexWidget && batchIndexWidget.value !== undefined) this._processingIndex = batchIndexWidget.value; + if (this._renderGallery) this._renderGallery(); + + // Force multiple height recalculations to ensure sync + if (this._updateCachedHeight) { + setTimeout(() => this._updateCachedHeight(), 100); + setTimeout(() => this._updateCachedHeight(), 500); + setTimeout(() => this._updateCachedHeight(), 1000); + } + }, 200); + }; + + const onRemoved = nodeType.prototype.onRemoved; + nodeType.prototype.onRemoved = function () { + // Clean up the periodic mode check interval + if (this._modeCheckInterval) { + clearInterval(this._modeCheckInterval); + this._modeCheckInterval = null; + } + // Clean up the periodic dimension check interval + if (this._dimensionCheckInterval) { + clearInterval(this._dimensionCheckInterval); + this._dimensionCheckInterval = null; + } + // Clean up the height sync interval + if (this._heightSyncInterval) { + clearInterval(this._heightSyncInterval); + this._heightSyncInterval = null; + } + onRemoved?.apply(this, arguments); + }; + } + }, +}); \ No newline at end of file diff --git a/custom_nodes/ComfyUI_INSTARAW/js/api_model_selector.js b/custom_nodes/ComfyUI_INSTARAW/js/api_model_selector.js new file mode 100644 index 0000000000000000000000000000000000000000..be2d20f6de997a2bc1cd55b7be436a1fa8d2fafc --- /dev/null +++ b/custom_nodes/ComfyUI_INSTARAW/js/api_model_selector.js @@ -0,0 +1,45 @@ +import { app } from '../../scripts/app.js'; +import { ComfyWidgets } from '../../scripts/widgets.js'; + +app.registerExtension({ + name: 'Comfy.INSTARAW.APIModelSelector', + async beforeRegisterNodeDef(nodeType, nodeData, app) { + if (nodeData.name === 'INSTARAW_API_ModelSelector') { + + const onNodeCreated = nodeType.prototype.onNodeCreated; + nodeType.prototype.onNodeCreated = function () { + onNodeCreated?.apply(this, arguments); + + // Create the warning widget for this node + const warningWidget = ComfyWidgets["STRING"](this, "unreliable_provider_warning", ["STRING", { multiline: true }], app).widget; + warningWidget.inputEl.readOnly = true; + warningWidget.inputEl.style.backgroundColor = '#442222'; + warningWidget.inputEl.style.color = '#FFCCCC'; + warningWidget.inputEl.style.opacity = '0.8'; + warningWidget.value = 'โš ๏ธ For this model, the fal.ai provider is unreliable for Image-to-Image (Edit) tasks. wavespeed.ai recommended.'; + warningWidget.hidden = true; // Initially hidden + + const modelWidget = this.widgets.find((w) => w.name === 'model'); + if (modelWidget) { + const originalCallback = modelWidget.callback; + // Add our logic to the dropdown's callback + modelWidget.callback = (value) => { + originalCallback?.(value); + this.updateWarningWidget(); + }; + } + + // Add a function to the node to update the widget + this.updateWarningWidget = () => { + const isProblematic = (modelWidget.value === 'Nano Banana'); + warningWidget.hidden = !isProblematic; + this.computeSize(); + this.setDirtyCanvas(true, true); + }; + + // Run once on creation to set the initial state + setTimeout(() => this.updateWarningWidget(), 1); + }; + } + }, +}); \ No newline at end of file diff --git a/custom_nodes/ComfyUI_INSTARAW/js/api_nodes.js b/custom_nodes/ComfyUI_INSTARAW/js/api_nodes.js new file mode 100644 index 0000000000000000000000000000000000000000..c4d56494c1706e9e01a97199faa484fc59f64fed --- /dev/null +++ b/custom_nodes/ComfyUI_INSTARAW/js/api_nodes.js @@ -0,0 +1,53 @@ +import { app } from '../../scripts/app.js'; + +const _al = Array.isArray([]); + +app.registerExtension({ + name: 'Comfy.INSTARAW.DynamicAPINodes', + async beforeRegisterNodeDef(nodeType, nodeData, app) { + if (nodeData.name === 'INSTARAW_APITextToImage' || nodeData.name === 'INSTARAW_APIImageToImage') { + + const onNodeCreated = nodeType.prototype.onNodeCreated; + nodeType.prototype.onNodeCreated = function () { + onNodeCreated?.apply(this, arguments); + this.last_model = null; // Cache property + }; + + const onDrawForeground = nodeType.prototype.onDrawForeground; + nodeType.prototype.onDrawForeground = function(ctx) { + onDrawForeground?.apply(this, arguments); + + const modelInput = this.inputs?.find((i) => i.name === 'model'); + let current_model = null; + + if (modelInput && modelInput.link != null) { + const link = app.graph.links[modelInput.link]; + if (link) { + const origin_node = app.graph.getNodeById(link.origin_id); + const origin_widget = origin_node?.widgets.find(w => w.name === 'model'); + if (origin_widget) current_model = origin_widget.value; + } + } + + if (this.last_model === current_model) { + return; // No change, do nothing + } + this.last_model = current_model; + + const isSeeDream = current_model?.startsWith('SeeDream'); + + this.widgets.find(w => w.name === 'width').hidden = !isSeeDream; + this.widgets.find(w => w.name === 'height').hidden = !isSeeDream; + this.widgets.find(w => w.name === 'enable_safety_checker').hidden = !isSeeDream; + + const aspectRatioWidget = this.widgets.find(w => w.name === 'aspect_ratio'); + if (aspectRatioWidget) { + aspectRatioWidget.hidden = isSeeDream; + } + + this.computeSize(); + this.setDirtyCanvas(true, true); + }; + } + }, +}); \ No newline at end of file diff --git a/custom_nodes/ComfyUI_INSTARAW/js/batch_image_generator.js b/custom_nodes/ComfyUI_INSTARAW/js/batch_image_generator.js new file mode 100644 index 0000000000000000000000000000000000000000..f3a075a6208b24eef3fd2e67589810caca097e5b --- /dev/null +++ b/custom_nodes/ComfyUI_INSTARAW/js/batch_image_generator.js @@ -0,0 +1,5412 @@ +// --- +// Filename: ../ComfyUI_INSTARAW/js/batch_image_generator.js +// Batch Image Generator - Real-time Progress UI +// Following RPG patterns exactly for clean, growing layout +// --- + +import { app } from "../../scripts/app.js"; +import { api } from "../../scripts/api.js"; +import { renderComboCards, getComboMaxLength } from "./shared/combo-cards.js"; + +// Job state constants (match Python JobState enum) +const JobState = { + PENDING: "pending", + GENERATING: "generating", + SUCCESS: "success", + FAILED: "failed", + CACHED: "cached", + RETRYING: "retrying", + CANCELLED: "cancelled", +}; + +// State styling +const STATE_CONFIG = { + [JobState.PENDING]: { class: "pending", text: "Pending", icon: "โณ" }, + [JobState.GENERATING]: { class: "in-progress", text: "Generating...", icon: "๐Ÿ”„" }, + [JobState.SUCCESS]: { class: "success", text: "Complete", icon: "โœ“" }, + [JobState.FAILED]: { class: "error", text: "Failed", icon: "โœ–" }, + [JobState.CACHED]: { class: "cached", text: "Cached", icon: "๐Ÿ“ฆ" }, + [JobState.RETRYING]: { class: "retrying", text: "Retrying...", icon: "๐Ÿ”" }, + [JobState.CANCELLED]: { class: "cancelled", text: "Cancelled", icon: "โŠ˜" }, +}; + +console.log("[BIG] ========== BATCH IMAGE GENERATOR JS LOADED =========="); + +// Load PhotoSwipe dynamically from CDN +let photoSwipeLoadPromise = null; + +const loadPhotoSwipe = () => { + if (photoSwipeLoadPromise) return photoSwipeLoadPromise; // Already loading/loaded + + // Check if PhotoSwipe was loaded by something else (e.g., cached local files) + if (window.PhotoSwipe) { + console.warn("[BIG] PhotoSwipe already exists on window - might be from cached local files. Forcing CSS load..."); + // Force load CSS from CDN (local files don't include CSS) + const css = document.createElement('link'); + css.rel = 'stylesheet'; + css.href = 'https://cdn.jsdelivr.net/npm/photoswipe@5.4.3/dist/photoswipe.css'; + document.head.appendChild(css); + console.log("[BIG] Loaded PhotoSwipe CSS from CDN"); + photoSwipeLoadPromise = Promise.resolve(); + return photoSwipeLoadPromise; + } + + photoSwipeLoadPromise = new Promise((resolve, reject) => { + // Load CSS from CDN + const css = document.createElement('link'); + css.rel = 'stylesheet'; + css.href = 'https://cdn.jsdelivr.net/npm/photoswipe@5.4.3/dist/photoswipe.css'; + css.onerror = () => console.error("[BIG] PhotoSwipe CDN CSS failed to load!"); + css.onload = () => console.log("[BIG] PhotoSwipe CSS loaded from CDN"); + document.head.appendChild(css); + + // Load JS + const script = document.createElement('script'); + script.src = 'https://cdn.jsdelivr.net/npm/photoswipe@5.4.3/dist/umd/photoswipe.umd.min.js'; + script.onload = () => { + const lightboxScript = document.createElement('script'); + lightboxScript.src = 'https://cdn.jsdelivr.net/npm/photoswipe@5.4.3/dist/umd/photoswipe-lightbox.umd.min.js'; + lightboxScript.onload = () => { + console.log("[BIG] PhotoSwipe scripts loaded"); + resolve(); + }; + lightboxScript.onerror = reject; + document.head.appendChild(lightboxScript); + }; + script.onerror = reject; + document.head.appendChild(script); + }); + + return photoSwipeLoadPromise; +}; + +// Start loading PhotoSwipe on startup (don't await - it's background) +loadPhotoSwipe().then(() => { + console.log("[BIG] PhotoSwipe ready"); +}).catch(e => { + console.error("[BIG] Failed to load PhotoSwipe:", e); +}); + +app.registerExtension({ + name: "Comfy.INSTARAW.BatchImageGenerator", + + async beforeRegisterNodeDef(nodeType, nodeData, app) { + if (nodeData.name === "INSTARAW_BatchImageGenerator") { + console.log("[BIG] Registering custom UI for BatchImageGenerator"); + + const onNodeCreated = nodeType.prototype.onNodeCreated; + nodeType.prototype.onNodeCreated = function () { + onNodeCreated?.apply(this, arguments); + + console.log("[BIG] Node created, setting up custom UI"); + const node = this; + let cachedHeight = 180; + let isUpdatingHeight = false; + + // Create hidden widget to store node identifier (needed for file paths) + // Uses a stable unique ID that persists across sessions + if (!node._ni_widget) { + node._ni_widget = node.addWidget("text", "node_identifier", "", () => {}, { serialize: true }); + node._ni_widget.hidden = true; + } + // Generate stable unique ID only if not already set (preserve across saves/loads) + if (!node._ni_widget.value || node._ni_widget.value === "" || node._ni_widget.value === "undefined") { + // Use node.id as base - it's unique within the workflow + node._ni_widget.value = String(node.id); + console.log("[BIG] Generated node_identifier:", node._ni_widget.value); + } + + // Create hidden widget to store generated batch data (RPG pattern) + if (!node.widgets?.find(w => w.name === "generated_batch_data")) { + const genBatchWidget = node.addWidget("text", "generated_batch_data", "[]", () => {}, { serialize: true }); + genBatchWidget.hidden = true; + console.log("[BIG] Created generated_batch_data widget"); + } + + // Session storage key - uses node ID only for simplicity + // Node IDs are unique within a ComfyUI session + const getSessionKey = () => { + // Use a simple key based on node ID - it's unique per node in the session + return `instaraw_big_${node.id}`; + }; + + // Save to session storage (survives tab switches!) + const saveToSession = (data) => { + try { + const key = getSessionKey(); + sessionStorage.setItem(key, data); + console.log(`[BIG] ๐Ÿ’พ Saved to sessionStorage[${key}]: ${data.length} chars`); + } catch (e) { + console.warn("[BIG] Failed to save to session storage:", e); + } + }; + + // Restore from session storage, node.properties, or widget + const restoreFromWidget = () => { + try { + let dataSource = null; + let sourceName = ""; + + // 1. Try session storage first (survives tab switches!) + const sessionKey = getSessionKey(); + const sessionData = sessionStorage.getItem(sessionKey); + if (sessionData && sessionData !== "[]" && sessionData.length > 2) { + dataSource = sessionData; + sourceName = "sessionStorage"; + } + // 2. Then try properties + else if (node.properties?.big_jobs_data && node.properties.big_jobs_data !== "[]") { + dataSource = node.properties.big_jobs_data; + sourceName = "properties"; + } + // 3. Finally try widget + else { + const genBatchWidget = node.widgets?.find(w => w.name === "generated_batch_data"); + if (genBatchWidget && genBatchWidget.value && genBatchWidget.value !== "[]") { + dataSource = genBatchWidget.value; + sourceName = "widget"; + } + } + + if (dataSource) { + const batchData = JSON.parse(dataSource); + if (batchData && batchData.length > 0) { + console.log(`[BIG] ๐Ÿ”„ Restoring ${batchData.length} jobs from ${sourceName}`); + + // Reconstruct image URLs from filenames (we don't store base64 anymore) + // Images are saved to: {ComfyUI}/output/{subdir}/{filename} + // Use ComfyUI's standard /view endpoint to serve them + let reconstructedCount = 0; + batchData.forEach(job => { + // Reconstruct URL if: + // - Job has filename and is success/cached + // - AND either no URL, or URL is base64, or URL uses old formats + const needsReconstruction = job.filename && + (job.state === "success" || job.state === "cached") && + (!job.image_url || + job.image_url.startsWith("data:") || + job.image_url.startsWith("/extensions/") || + job.image_url.startsWith("/instaraw/batch_gen_image/")); + + if (needsReconstruction) { + // Support both new 'subfolder' and legacy 'output_subdir' keys + // Note: Empty string "" means root output folder (no subfolder) + let subdir; + if ('subfolder' in job) { + subdir = job.subfolder; // Could be "" for root folder + } else if ('output_subdir' in job) { + subdir = job.output_subdir; + } else { + subdir = ""; // Default to root output folder + } + // Use ComfyUI's standard /view endpoint + job.image_url = `/view?filename=${encodeURIComponent(job.filename)}&subfolder=${encodeURIComponent(subdir)}&type=output`; + console.log(`[BIG] ๐Ÿ”— Reconstructed URL for job ${job.id}: filename="${job.filename}", subdir="${subdir}" โ†’ ${job.image_url}`); + reconstructedCount++; + } else if (!job.filename && (job.state === "success" || job.state === "cached")) { + console.warn(`[BIG] โš ๏ธ Job ${job.id} is ${job.state} but has NO filename! Cannot reconstruct URL.`); + } + }); + + if (reconstructedCount > 0) { + console.log(`[BIG] ๐Ÿ”— Reconstructed ${reconstructedCount} image URLs from filenames`); + } else { + console.log(`[BIG] โ„น๏ธ No URLs needed reconstruction. Job details:`); + batchData.forEach(j => { + console.log(`[BIG] Job ${j.id}: state="${j.state}", filename="${j.filename || 'NULL'}", hasUrl=${!!j.image_url}, urlPrefix="${j.image_url?.substring(0, 50) || 'none'}"`); + }); + } + + // Validate existing image URLs + let invalidUrlCount = 0; + batchData.forEach(job => { + if (job.image_url) { + const isBase64 = job.image_url.startsWith("data:"); + const isTempUrl = job.image_url.includes("type=temp"); + + if (isTempUrl) { + job._urlNeedsValidation = true; + invalidUrlCount++; + } + } + }); + + if (invalidUrlCount > 0) { + console.log(`[BIG] โš ๏ธ ${invalidUrlCount} jobs have temp URLs that may need validation`); + } + + // For jobs in "generating"/"retrying" state, we'll check backend status + // Don't mark as failed yet - backend might still have results! + const generatingJobs = batchData.filter(j => j.state === "generating" || j.state === "retrying"); + const pendingJobs = batchData.filter(j => j.state === "pending"); + + if (generatingJobs.length > 0) { + console.log(`[BIG] ๐Ÿ” ${generatingJobs.length} jobs were generating - checking backend...`); + // Schedule async backend check (don't block restore) + setTimeout(() => checkBackendProgress(batchData), 100); + } + if (pendingJobs.length > 0) { + console.log(`[BIG] โ„น๏ธ ${pendingJobs.length} pending jobs ready to run`); + } + + node._bigJobs = batchData; + console.log(`[BIG][fix1] Restored ${batchData.length} jobs, dimensions:`, batchData.map(j => `${j.id}:${j.image_width}x${j.image_height}`).join(', ')); + + // Recalculate stats (after updating interrupted jobs) + node._bigStats = { + total: batchData.length, + completed: batchData.filter(j => j.state === "success" || j.state === "cached" || j.state === "failed" || j.state === "cancelled").length, + success: batchData.filter(j => j.state === "success").length, + failed: batchData.filter(j => j.state === "failed").length, + cached: batchData.filter(j => j.state === "cached").length, + }; + node._bigIsGenerating = false; // Never restore "generating" state + + // IMPORTANT: Re-save to session storage after restoring and modifying job states + // This ensures session storage has the corrected states for next tab switch + const correctedData = JSON.stringify(batchData); + saveToSession(correctedData); + + // Also update widget and properties with corrected data + node.properties = node.properties || {}; + node.properties.big_jobs_data = correctedData; + const widget = node.widgets?.find(w => w.name === "generated_batch_data"); + if (widget) widget.value = correctedData; + + return true; + } + } + } catch (e) { + console.error("[BIG] Failed to restore:", e); + } + return false; + }; + + // Check backend for real-time progress of jobs that were "generating" + const checkBackendProgress = async (localJobs) => { + try { + const nodeId = node._ni_widget?.value || String(node.id); + console.log(`[BIG] ๐Ÿ” Checking backend progress for node ${nodeId}...`); + + const response = await fetch(`/instaraw/batch_gen_progress/${nodeId}`); + const backendData = await response.json(); + + if (backendData && backendData.jobs && backendData.jobs.length > 0) { + console.log(`[BIG] โœ… Backend has ${backendData.jobs.length} jobs!`); + + // Merge backend data with local jobs + let updatedCount = 0; + localJobs.forEach(localJob => { + const backendJob = backendData.jobs.find(bj => bj.id === localJob.id); + if (backendJob) { + // Backend has data for this job - update local state + if (backendJob.state === "success" || backendJob.state === "cached") { + localJob.state = backendJob.state; + localJob.image_url = backendJob.image_url || localJob.image_url; + localJob.error = null; + updatedCount++; + console.log(`[BIG] โœ… Job ${localJob.id}: ${backendJob.state}`); + } else if (backendJob.state === "failed") { + localJob.state = "failed"; + localJob.error = backendJob.error || "Failed on backend"; + updatedCount++; + } else if (backendJob.state === "generating") { + // Still generating on backend! Resume polling + console.log(`[BIG] ๐Ÿ”„ Job ${localJob.id} still generating on backend`); + node._bigIsGenerating = true; + } + } + }); + + if (updatedCount > 0) { + console.log(`[BIG] โœ… Updated ${updatedCount} jobs from backend`); + node._bigJobs = localJobs; + updateGeneratedBatchData(); + if (node._renderUI) node._renderUI(); + } + + // If still generating, start polling + if (backendData.is_generating) { + console.log(`[BIG] ๐Ÿ”„ Backend still generating - starting polling`); + if (node._startPolling) node._startPolling(); + } + } else { + // Backend has no data - mark generating jobs as interrupted + console.log(`[BIG] โš ๏ธ No backend data - marking jobs as interrupted`); + localJobs.forEach(job => { + if (job.state === "generating" || job.state === "retrying") { + job.state = "failed"; + job.error = "Interrupted by tab switch - click Retry"; + } + }); + node._bigJobs = localJobs; + updateGeneratedBatchData(); + if (node._renderUI) node._renderUI(); + } + } catch (e) { + console.error("[BIG] Failed to check backend progress:", e); + // On error, mark as interrupted + localJobs.forEach(job => { + if (job.state === "generating" || job.state === "retrying") { + job.state = "failed"; + job.error = "Interrupted - backend check failed"; + } + }); + node._bigJobs = localJobs; + if (node._renderUI) node._renderUI(); + } + }; + + // Store functions on node so onConfigure can call them + node._restoreFromWidget = restoreFromWidget; + node._getSessionKey = getSessionKey; + node._saveToSession = saveToSession; + node._checkBackendProgress = checkBackendProgress; + + // Try initial restore (may fail if widget not loaded yet - onConfigure will retry) + restoreFromWidget(); + + // Generation state (initialized fresh, onConfigure will restore from widget) + node._bigJobs = node._bigJobs || []; + node._bigIsGenerating = false; + node._bigStats = node._bigStats || { total: 0, completed: 0, success: 0, failed: 0, cached: 0 }; + node._bigStartTime = null; + node._bigAbortController = null; // AbortController for smooth cancellation (RPG pattern) + + // Save state - no-op now since we rely solely on widget persistence + // (localStorage caused cross-workflow state leakage issues) + const saveState = () => { + // State is saved via updateGeneratedBatchData() which updates the widget + // The widget is serialized with the workflow file + }; + + // Get connected model/provider info (similar to AIL pattern) + const getConnectedInputValue = (inputName) => { + const input = node.inputs?.find(i => i.name === inputName); + if (!input || !input.link) return null; + + const link = app.graph.links[input.link]; + if (!link) return null; + + const originNode = app.graph.getNodeById(link.origin_id); + if (!originNode) return null; + + // Get the widget value from the origin node + const widget = originNode.widgets?.[link.origin_slot]; + return widget ? widget.value : null; + }; + + const getModelProviderInfo = () => { + const model = getConnectedInputValue("model"); + const provider = getConnectedInputValue("provider"); + // Check if model is Nano Banana Pro (the only supported model for now) + const isNanoBananaPro = model === "Nano Banana Pro" || model === "nano_banana_pro"; + const isSupported = isNanoBananaPro; + return { model, provider, isNanoBananaPro, isSupported }; + }; + + // Get widget value from this node + const getWidgetValue = (widgetName, defaultVal = null) => { + const widget = node.widgets?.find(w => w.name === widgetName); + return widget?.value ?? defaultVal; + }; + + // Helper to read boolean from widget or connected input (prioritizes connection) + const getBooleanValue = (inputName) => { + const widget = node.widgets?.find(w => w.name === inputName); + const input = node.inputs?.find(i => i.name === inputName); + + // Prioritize connected input over widget + if (input && input.link) { + const link = app.graph?.links?.[input.link]; + if (link) { + const originNode = app.graph.getNodeById(link.origin_id); + if (originNode) { + const srcWidget = originNode.widgets?.[0] || originNode.widgets?.find(w => w.name === "value"); + if (srcWidget) return srcWidget.value === true; + } + } + } + return widget?.value === true; + }; + + // Get multi-image mode info for UI display + const getMultiImageInfo = () => { + const multiImage = getBooleanValue("multi_image"); + const provider = getConnectedInputValue("provider") || ""; + const isActive = multiImage && provider === "wavespeed.ai"; + const isWarning = multiImage && provider && provider !== "wavespeed.ai"; + const isNoProvider = multiImage && !provider; + + // Check aspect ratio compatibility for multi-image mode + const MULTI_SUPPORTED_RATIOS = ["3:2", "2:3", "3:4", "4:3"]; + let aspectRatioRaw = String(getConnectedInputValue("aspect_ratio") || "1:1"); + // Extract ratio from labels like "3:4 (Portrait)" -> "3:4" + const match = aspectRatioRaw.match(/(\d+:\d+)/); + const aspectRatio = match ? match[1] : aspectRatioRaw; + const aspectRatioSupported = MULTI_SUPPORTED_RATIOS.includes(aspectRatio); + const aspectRatioWarning = isActive && !aspectRatioSupported; + + return { multiImage, provider, isActive, isWarning, isNoProvider, aspectRatio, aspectRatioSupported, aspectRatioWarning }; + }; + + // Toggle 2X multi-image mode + const toggleMultiImageMode = (enabled) => { + const multiImageWidget = node.widgets?.find(w => w.name === "multi_image"); + if (multiImageWidget) { + multiImageWidget.value = enabled; + multiImageWidget.callback?.(enabled); + console.log("[BIG] 2X mode toggled:", enabled); + } + }; + + // Helper to find AIL node connected to an input (traversing through pass-through nodes) + const findConnectedAIL = (inputName) => { + const input = node.inputs?.find(i => i.name === inputName); + if (!input || !input.link) return null; + + const traverseToAIL = (nodeToCheck, depth = 0) => { + if (!nodeToCheck || depth > 5) return null; + + // Check if this is an AIL node + if (nodeToCheck.type === "INSTARAW_AdvancedImageLoader" && nodeToCheck.properties?.batch_data) { + return nodeToCheck; + } + + // Traverse upstream through IMAGE inputs + if (nodeToCheck.inputs) { + for (const inp of nodeToCheck.inputs) { + if ((inp.type === "IMAGE" || inp.name === "images") && inp.link) { + const link = app.graph?.links?.[inp.link]; + if (link) { + const upstream = app.graph.getNodeById(link.origin_id); + const result = traverseToAIL(upstream, depth + 1); + if (result) return result; + } + } + } + } + return null; + }; + + const link = app.graph?.links?.[input.link]; + if (!link) return null; + const sourceNode = app.graph.getNodeById(link.origin_id); + return traverseToAIL(sourceNode, 0); + }; + + const getGenerationPreviewInfo = () => { + // Get generations_per_combination from widget + const genWidget = node.widgets?.find(w => w.name === "generations_per_combination"); + const generationsPerCombination = genWidget?.value || 1; + + // Try to get prompt count from RPG connection + const promptInput = node.inputs?.find(i => i.name === "prompt_list_positive"); + let promptCount = 0; + if (promptInput?.link) { + const link = app.graph.links[promptInput.link]; + if (link) { + const rpgNode = app.graph.getNodeById(link.origin_id); + if (rpgNode) { + // RPG stores prompts in _rpgPromptBatch + const promptBatch = rpgNode._rpgPromptBatch || []; + promptCount = promptBatch.length; + } + } + } + + // Try to get image count from AIL connection (via RPG) + const imagesInput = node.inputs?.find(i => i.name === "images"); + let imageCount = 0; + if (imagesInput?.link) { + const link = app.graph.links[imagesInput.link]; + if (link) { + const sourceNode = app.graph.getNodeById(link.origin_id); + if (sourceNode) { + // Could be AIL or RPG + if (sourceNode._linkedImageCount !== undefined) { + imageCount = sourceNode._linkedImageCount; // From RPG + } else if (sourceNode.properties?.batch_data) { + // From AIL directly + try { + const batchData = JSON.parse(sourceNode.properties.batch_data); + imageCount = (batchData.images || batchData.latents || []).length; + } catch (e) { + console.warn("[BIG] Failed to parse AIL batch_data:", e); + } + } + } + } + } + + // Calculate total jobs + const totalJobs = promptCount > 0 ? promptCount * generationsPerCombination : 0; + + return { + imageCount, + promptCount, + generationsPerCombination, + totalJobs, + hasInputs: promptCount > 0 || imageCount > 0 + }; + }; + + // โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ• + // MODE DETECTION (T2I vs I2I) - Similar to RPG + // โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ• + + const getConnectedImagesInfo = () => { + /** + * Get connected images info (uses cached node data from sync interval). + * Returns { mode, imageCount, imageUrls, sourceNodeId } + */ + // Use cached data from node (updated by polling interval) + return { + mode: node._bigMode || "txt2img", + imageCount: node._bigImageCount || 0, + imageUrls: node._bigImageUrls || [], + sourceNodeId: null + }; + }; + + // Fetch image as base64 from URL (for I2I mode) + const fetchImageAsBase64 = async (imageUrl) => { + try { + // If already a data URL, extract the base64 part + if (imageUrl.startsWith("data:")) { + return imageUrl.split(",")[1]; + } + + // Fetch from ComfyUI view endpoint + const response = await fetch(imageUrl); + if (!response.ok) { + console.error(`[BIG] Failed to fetch image: ${response.status}`); + return null; + } + + const blob = await response.blob(); + return new Promise((resolve, reject) => { + const reader = new FileReader(); + reader.onloadend = () => { + const base64 = reader.result.split(",")[1]; + resolve(base64); + }; + reader.onerror = reject; + reader.readAsDataURL(blob); + }); + } catch (e) { + console.error("[BIG] Error fetching image as base64:", e); + return null; + } + }; + + // Store mode info on node for UI + node._bigMode = "txt2img"; + node._bigImageCount = 0; + node._bigImageCounts = []; // Breakdown per AIL source for "3ร—10" display + node._bigImageUrls = []; + // Additional image inputs (images2, images3, images4) + node._bigImage2Urls = []; + node._bigImage3Urls = []; + node._bigImage4Urls = []; + + // Helper to extract image URLs from a node (recursive for pass-through nodes) + // MUST be defined before _updateModeFromSource which uses it + const extractImageUrlsFromNode = (sourceNode, depth = 0, inputName = "") => { + if (!sourceNode || depth > 5) return []; // Max depth to prevent infinite loops + + let urls = []; + const nodeType = sourceNode.type; + const prefix = `[BIG] ${inputName}${depth > 0 ? ` (upstream ${depth})` : ''}`; + + // Check for preview images first (works for many node types after execution) + if (sourceNode.imgs && sourceNode.imgs.length > 0) { + urls = sourceNode.imgs.map(img => img.src).filter(Boolean); + if (urls.length > 0) return urls; + } + + // Handle specific node types + if (nodeType === "INSTARAW_AdvancedImageLoader") { + try { + const batchData = JSON.parse(sourceNode.properties?.batch_data || "{}"); + const order = batchData.order || []; + const ailImages = batchData.images || []; + const ailNodeId = sourceNode.id; + + if (ailImages.length > 0) { + urls = order.length > 0 + ? order.map(imgId => { + const img = ailImages.find(i => i.id === imgId); + return img?.thumbnail + ? `/instaraw/view/${img.thumbnail}` + : null; + }).filter(Boolean) + : ailImages.map(img => img?.thumbnail + ? `/instaraw/view/${img.thumbnail}` + : null).filter(Boolean); + } + } catch (e) { + console.warn(`[BIG] Error parsing AIL batch_data:`, e); + } + } else if (nodeType === "INSTARAW_RealityPromptGenerator") { + urls = sourceNode._linkedImageUrls || sourceNode._linkedImages?.map(i => i.url) || []; + } else if (sourceNode.properties?.batch_data) { + try { + const batchData = JSON.parse(sourceNode.properties.batch_data); + urls = (batchData.images || []).map(i => i.url || i).filter(Boolean); + } catch (e) {} + } + + // If no URLs found, traverse upstream (for pass-through nodes like ImageListFromBatch) + if (urls.length === 0 && sourceNode.inputs) { + // Look for IMAGE type inputs and traverse upstream + for (const input of sourceNode.inputs) { + const isImageType = input.type === "IMAGE" || input.type === "image" || + input.name === "images" || input.name === "image" || + input.name?.toLowerCase().includes("image"); + if (isImageType && input.link) { + const upstreamLink = app.graph?.links?.[input.link]; + if (upstreamLink) { + const upstreamNode = app.graph.getNodeById(upstreamLink.origin_id); + if (upstreamNode) { + urls = extractImageUrlsFromNode(upstreamNode, depth + 1, inputName); + if (urls.length > 0) break; + } + } + } + } + } + + return urls; + }; + + // Helper to update mode from source node (AIL or RPG) + node._updateModeFromSource = (sourceNode) => { + if (!sourceNode) return; + + let mode = "txt2img"; + let imageCount = 0; + let imageUrls = []; + node._bigImageCounts = []; // Reset breakdown for fresh calculation + + // Check if source is AIL + if (sourceNode.type === "INSTARAW_AdvancedImageLoader") { + // Read enable_img2img from AIL (widget or connected input) + const enableImg2ImgWidget = sourceNode.widgets?.find(w => w.name === "enable_img2img"); + let enableImg2Img = enableImg2ImgWidget?.value; + + // Check if enable_img2img is connected + const enableImg2ImgInput = sourceNode.inputs?.find(i => i.name === "enable_img2img"); + if (enableImg2ImgInput && enableImg2ImgInput.link != null) { + const link = app.graph?.links?.[enableImg2ImgInput.link]; + if (link) { + const srcNode = app.graph.getNodeById(link.origin_id); + if (srcNode) { + const srcWidget = srcNode.widgets?.[link.origin_slot]; + if (srcWidget) enableImg2Img = srcWidget.value; + } + } + } + + mode = (enableImg2Img === false || enableImg2Img === "false") ? "txt2img" : "img2img"; + + // Read images from batch_data + try { + const batchData = JSON.parse(sourceNode.properties?.batch_data || "{}"); + const order = batchData.order || []; + const ailImages = batchData.images || []; + const ailNodeId = sourceNode.id; + + if (mode === "img2img" && ailImages.length > 0) { + imageUrls = order.length > 0 + ? order.map(imgId => { + const img = ailImages.find(i => i.id === imgId); + return img?.thumbnail + ? `/instaraw/view/${img.thumbnail}` + : null; + }).filter(Boolean) + : ailImages.map(img => img?.thumbnail + ? `/instaraw/view/${img.thumbnail}` + : null).filter(Boolean); + + imageCount = imageUrls.length; + } + } catch (e) { + console.warn("[BIG] Error parsing AIL batch_data:", e); + } + } + // Check if source is RPG (pass-through) + else if (sourceNode.type === "INSTARAW_RealityPromptGenerator") { + mode = sourceNode._linkedAILMode || "txt2img"; + imageUrls = sourceNode._linkedImageUrls || sourceNode._linkedImages?.map(i => i.url) || []; + imageCount = imageUrls.length; + + // Get breakdown of images per AIL source + const img1Count = (sourceNode._linkedImages || []).length; + const img2Count = (sourceNode._linkedImages2 || []).length; + const img3Count = (sourceNode._linkedImages3 || []).length; + const img4Count = (sourceNode._linkedImages4 || []).length; + node._bigImageCounts = [img1Count, img2Count, img3Count, img4Count].filter(c => c > 0); + } + // Other nodes - check for images + else if (sourceNode.properties?.batch_data) { + try { + const batchData = JSON.parse(sourceNode.properties.batch_data); + const images = batchData.images || []; + if (images.length > 0) { + mode = "img2img"; + imageUrls = images.map(i => i.url || i).filter(Boolean); + imageCount = imageUrls.length; + } + } catch (e) {} + } + // Fallback: use recursive extraction for pass-through nodes + else { + imageUrls = extractImageUrlsFromNode(sourceNode, 0, "images"); + if (imageUrls.length > 0) { + mode = "img2img"; + imageCount = imageUrls.length; + } + } + + node._bigMode = mode; + node._bigImageCount = imageCount; + node._bigImageUrls = imageUrls; + // If _bigImageCounts wasn't set by RPG path, use single source count + if (node._bigImageCounts.length === 0 && imageCount > 0) { + node._bigImageCounts = [imageCount]; + } + }; + + // Helper to get image URLs from any connected image input + const getImageUrlsFromInput = (inputName) => { + const imageInput = node.inputs?.find(input => input.name === inputName); + if (!imageInput || !imageInput.link) return []; + + const link = app.graph?.links?.[imageInput.link]; + if (!link) return []; + + const sourceNode = app.graph.getNodeById(link.origin_id); + if (!sourceNode) return []; + + // Use the recursive helper to extract URLs (handles pass-through nodes) + return extractImageUrlsFromNode(sourceNode, 0, inputName); + }; + + // Update additional image inputs + const updateAdditionalImages = () => { + node._bigImage2Urls = getImageUrlsFromInput("images2"); + node._bigImage3Urls = getImageUrlsFromInput("images3"); + node._bigImage4Urls = getImageUrlsFromInput("images4"); + }; + + // Track last known values for change detection + let lastKnownProvider = getConnectedInputValue("provider") || ""; + let lastKnownModel = getConnectedInputValue("model") || ""; + let lastKnownEnableImg2Img = false; + let lastKnownMultiImage = false; + + // Flag to track if initial restore from widget is complete + node._bigInitialized = false; + + // Periodic mode sync (every 500ms) - only when NOT generating + node._modeSyncInterval = setInterval(() => { + // Skip re-renders during generation - progress polling handles that + if (node._bigIsGenerating) return; + + // Skip until onConfigure has had a chance to restore state + // This prevents race conditions where we render before widget data is loaded + if (!node._bigInitialized) return; + + // Capture current state for comparison + const oldMode = node._bigMode; + const oldCount = node._bigImageCount; + const oldProvider = lastKnownProvider; + const oldModel = lastKnownModel; + const oldEnableImg2Img = lastKnownEnableImg2Img; + const oldMultiImage = lastKnownMultiImage; + + // Update image URLs from connected sources + const imagesInput = node.inputs?.find(input => input.name === "images"); + if (imagesInput && imagesInput.link) { + const link = app.graph?.links?.[imagesInput.link]; + if (link) { + const sourceNode = app.graph.getNodeById(link.origin_id); + if (sourceNode) { + node._updateModeFromSource(sourceNode); + } + } + } else { + node._bigImageUrls = []; + } + updateAdditionalImages(); + + // Read current values + const enableImg2Img = getBooleanValue("enable_img2img"); + const multiImage = getBooleanValue("multi_image"); + const currentProvider = getConnectedInputValue("provider") || ""; + const currentModel = getConnectedInputValue("model") || ""; + + // Determine mode + const hasAnyImageLinks = + (node.inputs?.find(i => i.name === "images")?.link != null) || + (node.inputs?.find(i => i.name === "images2")?.link != null) || + (node.inputs?.find(i => i.name === "images3")?.link != null) || + (node.inputs?.find(i => i.name === "images4")?.link != null); + + node._bigMode = (enableImg2Img && hasAnyImageLinks) ? "img2img" : "txt2img"; + + const img1Count = node._bigImageUrls.length; + const img2Count = node._bigImage2Urls.length; + const img3Count = node._bigImage3Urls.length; + const img4Count = node._bigImage4Urls.length; + const totalImageCount = img1Count + img2Count + img3Count + img4Count; + + // Update tracking variables + lastKnownProvider = currentProvider; + lastKnownModel = currentModel; + lastKnownEnableImg2Img = enableImg2Img; + lastKnownMultiImage = multiImage; + node._bigImageCount = totalImageCount; + node._bigImageCounts = [img1Count, img2Count, img3Count, img4Count].filter(c => c > 0); + + // Only re-render if something actually changed + const modeChanged = oldMode !== node._bigMode; + const countChanged = oldCount !== totalImageCount; + const providerChanged = oldProvider !== currentProvider; + const modelChanged = oldModel !== currentModel; + const enableChanged = oldEnableImg2Img !== enableImg2Img; + const multiChanged = oldMultiImage !== multiImage; + + if (modeChanged || countChanged || providerChanged || modelChanged || enableChanged || multiChanged) { + renderUI(); + } + }, 500); + + // Container setup (AIL/RPG pattern) + const container = document.createElement("div"); + container.className = "instaraw-big-container"; + container.style.width = "100%"; + container.style.boxSizing = "border-box"; + container.style.overflow = "hidden"; + container.style.height = `${cachedHeight}px`; + + // === Height Management === + const updateCachedHeight = () => { + if (isUpdatingHeight) return; + isUpdatingHeight = true; + + // Safety timeout to prevent stuck flag + const safetyTimeout = setTimeout(() => { + isUpdatingHeight = false; + }, 500); + + container.style.overflow = "visible"; + container.style.height = "auto"; + + requestAnimationFrame(() => { + requestAnimationFrame(() => { + clearTimeout(safetyTimeout); + + // Use scrollHeight as it measures full content even during transitions + const newHeight = Math.max(container.offsetHeight, container.scrollHeight); + + if (newHeight > 0 && Math.abs(newHeight - cachedHeight) > 2) { + cachedHeight = newHeight; + container.style.height = `${newHeight}px`; + const sz = node.computeSize(); + node.size[1] = sz[1]; + node.onResize?.(sz); + app.graph.setDirtyCanvas(true, false); + } else { + container.style.height = `${cachedHeight}px`; + } + container.style.overflow = "hidden"; + isUpdatingHeight = false; + }); + }); + }; + + node._updateCachedHeight = updateCachedHeight; + + // Helper to attach onload/onerror handlers to all images in container + const attachImageOnloadHandlers = () => { + let hasUnloadedImages = false; + const fallbackSvg = 'data:image/svg+xml,%3Csvg xmlns=%22http://www.w3.org/2000/svg%22 viewBox=%220 0 24 24%22%3E%3Cpath fill=%22%239ca3af%22 d=%22M21 19V5c0-1.1-.9-2-2-2H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2zM8.5 13.5l2.5 3.01L14.5 12l4.5 6H5l3.5-4.5z%22/%3E%3C/svg%3E'; + const maxRetries = 3; + + container.querySelectorAll('img').forEach(img => { + if (!img._hasOnloadHandler) { + img._hasOnloadHandler = true; + img._retryCount = 0; + + img.addEventListener('load', () => updateCachedHeight()); + + // Handle broken images with simple retry + img.addEventListener('error', () => { + const jobId = img.dataset.jobId; + + if (img._retryCount < maxRetries) { + img._retryCount++; + console.log(`[BIG] Image load failed for job ${jobId}, retry ${img._retryCount}/${maxRetries}`); + setTimeout(() => { + // Retry with cache-busting parameter + const baseUrl = img.src.split('&_retry=')[0].split('&_t=')[0]; + img.src = `${baseUrl}&_retry=${img._retryCount}&_t=${Date.now()}`; + }, 200 * img._retryCount); + } else { + console.error(`[BIG] Image load failed after ${maxRetries} retries for job ${jobId}`); + img.style.opacity = '0.3'; + img.src = fallbackSvg; + updateCachedHeight(); + } + }); + + // Check if image is not yet loaded + if (!img.complete) { + hasUnloadedImages = true; + } else if (img.naturalWidth === 0) { + // Image already failed to load - trigger error handler + img.dispatchEvent(new Event('error')); + } + } + }); + // If there are images that haven't loaded yet, schedule extra height updates + if (hasUnloadedImages) { + [200, 500, 1000, 2000].forEach(delay => { + setTimeout(() => updateCachedHeight(), delay); + }); + } + }; + + // Periodic height sync - keep running, only clear when node is removed + const heightSyncInterval = setInterval(() => { + if (!node || !container) { + clearInterval(heightSyncInterval); + return; + } + // Always try to sync, even if offsetHeight is 0 (container might just need reflow) + updateCachedHeight(); + }, 2000); + + // Store interval for cleanup + node._heightSyncInterval = heightSyncInterval; + + // Fast initial syncs for page load + [100, 300, 500, 1000, 1500, 2000].forEach(delay => { + setTimeout(() => { + if (container) { + updateCachedHeight(); + } + }, delay); + }); + + // Poll for progress updates using fetch (works during execution!) + let pollInterval = null; + let lastPollTime = 0; + + const startPolling = () => { + if (pollInterval) return; + console.log("[BIG] Starting progress polling..."); + + // Use a more aggressive polling approach that works during execution + const poll = async () => { + if (!pollInterval) return; + + try { + const nodeId = node._ni_widget?.value || String(node.id); + if (nodeId) { + // Use raw fetch to bypass any ComfyUI blocking + const response = await fetch(`/instaraw/batch_gen_progress/${nodeId}`, { + method: 'GET', + headers: { 'Accept': 'application/json' }, + }); + + if (response.ok) { + const data = await response.json(); + if (data.jobs && data.jobs.length > 0) { + const wasGenerating = node._bigIsGenerating; + const hadJobs = node._bigJobs.length > 0; + + node._bigJobs = data.jobs; + node._bigStats = data.stats || node._bigStats; + node._bigIsGenerating = data.is_generating; + updateGeneratedBatchData(); + + // First time seeing jobs - do full render + if (!hadJobs && data.jobs.length > 0) { + node._bigStartTime = Date.now(); + renderUI(); + } else { + // Update existing items + updateProgressItems(); + updateProgressHeader(); + } + + // Generation finished + if (wasGenerating && !data.is_generating) { + console.log("[BIG] Generation complete detected via polling"); + renderUI(); // Re-render to show action buttons + stopPolling(); + return; + } + } + } + } + } catch (e) { + // Silently ignore poll errors during execution + } + + // Schedule next poll + if (pollInterval) { + pollInterval = setTimeout(poll, 400); + } + }; + + pollInterval = setTimeout(poll, 100); + }; + + const stopPolling = () => { + if (pollInterval) { + clearTimeout(pollInterval); + pollInterval = null; + console.log("[BIG] Stopped polling"); + } + }; + + // Store polling functions on node for use by checkBackendProgress + node._startPolling = startPolling; + node._stopPolling = stopPolling; + + // Listen for ComfyUI execution start - start polling immediately + api.addEventListener("execution_start", (event) => { + console.log("[BIG] Execution started, beginning to poll for progress..."); + node._bigStartTime = Date.now(); + startPolling(); + }); + + // Listen for ComfyUI execution complete/error - ensure we stop polling + api.addEventListener("execution_complete", () => { + // Give a moment for final updates then stop + setTimeout(() => { + if (!node._bigIsGenerating) { + stopPolling(); + } + }, 1000); + }); + + api.addEventListener("execution_error", () => { + stopPolling(); + }); + + // Also listen for our custom events (they work after execution) + api.addEventListener("instaraw-batch-gen-start", (event) => { + const data = event.detail; + if (data.node_id === node._ni_widget?.value) { + console.log("[BIG] Generation started event received:", data); + node._bigJobs = data.jobs || []; + node._bigStats = { + total: node._bigJobs.length, + completed: 0, + success: 0, + failed: 0, + cached: 0, + }; + node._bigIsGenerating = true; + if (!node._bigStartTime) node._bigStartTime = Date.now(); + updateGeneratedBatchData(); + renderUI(); + } + }); + + // Listen for job updates (works after execution finishes) + api.addEventListener("instaraw-batch-gen-update", (event) => { + const data = event.detail; + if (data.node_id === node._ni_widget?.value) { + const job = node._bigJobs.find(j => j.id === data.job_id); + if (job) { + job.state = data.state; + job.attempts = data.attempts; + job.error = data.error; + job.generation_time = data.generation_time; + // Also update image data for display + if (data.image_url) { + job.image_url = data.image_url; + } + if (data.image_width) { + job.image_width = data.image_width; + } + if (data.image_height) { + job.image_height = data.image_height; + } + updateProgressItem(job); + updateStats(); + } + } + }); + + // Listen for generation complete + api.addEventListener("instaraw-batch-gen-complete", (event) => { + const data = event.detail; + if (data.node_id === node._ni_widget?.value) { + console.log("[BIG] Generation complete event:", data); + node._bigIsGenerating = false; + node._bigStats = data.stats || node._bigStats; + updateGeneratedBatchData(); + stopPolling(); + renderUI(); + } + }); + + // Listen for save event from legacy Python path + api.addEventListener("instaraw-batch-gen-save", (event) => { + const data = event.detail; + if (data.node_id === node._ni_widget?.value) { + console.log("[BIG] Received generated_batch_data from Python:", data); + // Update the widget with the generated batch data + const widget = node.widgets?.find(w => w.name === "generated_batch_data"); + if (widget) { + widget.value = data.generated_batch_data; + console.log("[BIG] Updated generated_batch_data widget"); + } + // Also update our internal jobs from the data + try { + const batchData = JSON.parse(data.generated_batch_data); + node._bigJobs = batchData.map(job => ({ + id: job.id, + state: job.state, + prompt_positive: job.prompt_positive, + image_url: job.image_url, + image_width: job.image_width, + image_height: job.image_height, + generation_time: job.generation_time, + filename: job.filename, + })); + updateStats(); + renderUI(); + } catch (e) { + console.error("[BIG] Failed to parse generated_batch_data:", e); + } + } + }); + + // Listen for RPG prompt changes (repeat counts, etc.) to update sync badge + window.addEventListener("INSTARAW_RPG_PROMPTS_CHANGED", (event) => { + // Check if this RPG is connected to us + const promptInput = node.inputs?.find(i => i.name === "prompt_list_positive"); + if (!promptInput?.link) return; + const link = app.graph.links[promptInput.link]; + if (!link || link.origin_id !== event.detail.nodeId) return; + + // RPG we're connected to changed - re-render to update sync badge + console.log(`[BIG] RPG prompts changed, totalGenerations=${event.detail.totalGenerations}`); + renderUI(); + }); + + // Listen for AIL updates to refresh image preview + window.addEventListener("INSTARAW_AIL_UPDATED", (event) => { + // Check if this AIL is connected to us + const imagesInput = node.inputs?.find(i => i.name === "images"); + if (!imagesInput?.link) return; + + // Re-render to update sync badge and image preview + renderUI(); + }); + + const updateStats = () => { + const jobs = node._bigJobs; + node._bigStats.completed = jobs.filter(j => + [JobState.SUCCESS, JobState.FAILED, JobState.CACHED, JobState.CANCELLED].includes(j.state) + ).length; + node._bigStats.success = jobs.filter(j => j.state === JobState.SUCCESS).length; + node._bigStats.failed = jobs.filter(j => j.state === JobState.FAILED).length; + node._bigStats.cached = jobs.filter(j => j.state === JobState.CACHED).length; + updateProgressHeader(); + }; + + const escapeHtml = (text) => { + if (!text) return ""; + const div = document.createElement('div'); + div.textContent = text; + return div.innerHTML; + }; + + // Format image count as "3ร—10" when multiple sources with equal counts, or show total + const formatImageCount = () => { + const counts = node._bigImageCounts || []; + const total = node._bigImageCount || 0; + + if (counts.length <= 1) { + return `${total} ref image${total !== 1 ? 's' : ''}`; + } + + // Check if all counts are equal + const allEqual = counts.every(c => c === counts[0]); + if (allEqual) { + return `${counts.length}ร—${counts[0]} ref images`; + } + + // Different counts - show as sum + return `${counts.join('+')} ref images`; + }; + + const formatTime = (ms) => { + const seconds = Math.floor(ms / 1000); + if (seconds < 60) return `${seconds}s`; + const minutes = Math.floor(seconds / 60); + const secs = seconds % 60; + return `${minutes}m ${secs}s`; + }; + + // Timer interval for updating elapsed time display + let timerInterval = null; + + const startTimer = () => { + if (timerInterval) return; + timerInterval = setInterval(() => { + if (node._bigIsGenerating && node._bigStartTime) { + const header = container.querySelector(".instaraw-big-progress-header h4"); + if (header) { + const elapsed = formatTime(Date.now() - node._bigStartTime); + header.textContent = `Generating... (${elapsed})`; + } + } + }, 1000); + }; + + const stopTimer = () => { + if (timerInterval) { + clearInterval(timerInterval); + timerInterval = null; + } + }; + + const updateProgressHeader = () => { + const header = container.querySelector(".instaraw-big-progress-header h4"); + const kpiRow = container.querySelector(".instaraw-big-kpi-row"); + + if (header) { + const elapsed = node._bigStartTime ? formatTime(Date.now() - node._bigStartTime) : "0s"; + if (node._bigIsGenerating) { + header.textContent = `๐Ÿ”„ Generating Images... (${elapsed})`; + startTimer(); + } else { + header.textContent = `โœ… Generation Complete (${elapsed})`; + stopTimer(); + } + } + + // Update KPI values (jobs completed, success, cached, failed) + if (kpiRow) { + const s = node._bigStats; + const kpis = kpiRow.querySelectorAll(".instaraw-big-kpi"); + kpis.forEach(kpi => { + const strong = kpi.querySelector("strong"); + const label = kpi.querySelector("span")?.textContent?.trim(); + if (strong && label) { + if (label === "JOBS") { + strong.textContent = `${s.completed}/${s.total}`; + } else if (label === "SUCCESS") { + strong.textContent = s.success; + } else if (label === "CACHED") { + strong.textContent = s.cached; + } else if (label === "FAILED") { + strong.textContent = s.failed; + } + } + }); + } + }; + + const updateProgressItems = () => { + node._bigJobs.forEach(job => updateProgressItem(job)); + }; + + const updateProgressItem = (job) => { + const itemEl = container.querySelector(`.instaraw-big-progress-item[data-job-id="${job.id}"]`); + if (!itemEl) return; + + // Debug: Log if job has image URL + if (job.image_url && job.state === JobState.SUCCESS) { + console.log(`[BIG] Job #${job.id} has image:`, job.image_url, `(${job.image_width}x${job.image_height})`); + } + + const stateInfo = STATE_CONFIG[job.state] || STATE_CONFIG[JobState.PENDING]; + + // Update class for border/background color + itemEl.className = `instaraw-big-progress-item ${stateInfo.class}`; + + // Update status badge + const statusBadge = itemEl.querySelector(".instaraw-big-progress-item-status"); + if (statusBadge) { + statusBadge.className = `instaraw-big-progress-item-status ${stateInfo.class}`; + let statusText = `${stateInfo.icon} ${stateInfo.text}`; + + if (job.state === JobState.RETRYING && job.attempts > 1) { + statusText = `๐Ÿ” Retry #${job.attempts}`; + } else if (job.state === JobState.SUCCESS && job.generation_time) { + // Show retry count if there were retries + if (job.attempts > 1) { + statusText = `โœ“ ${job.generation_time.toFixed(1)}s (${job.attempts} tries)`; + } else { + statusText = `โœ“ ${job.generation_time.toFixed(1)}s`; + } + } else if (job.state === JobState.FAILED && job.attempts > 1) { + statusText = `โœ– Failed (${job.attempts} tries)`; + } + + statusBadge.textContent = statusText; + } + + // Update progress bar + const progressFill = itemEl.querySelector(".instaraw-big-progress-item-fill"); + if (progressFill) { + // Remove all classes first + progressFill.className = "instaraw-big-progress-item-fill"; + progressFill.style.background = ""; + + if (job.state === JobState.SUCCESS || job.state === JobState.CACHED) { + progressFill.style.width = "100%"; + } else if (job.state === JobState.GENERATING || job.state === JobState.RETRYING) { + progressFill.style.width = "100%"; + progressFill.classList.add("animating"); + } else if (job.state === JobState.FAILED) { + progressFill.style.width = "100%"; + progressFill.style.background = "linear-gradient(90deg, #ef4444, #dc2626)"; + } else { + progressFill.style.width = "0%"; + } + } + + // Update message + const messageDiv = itemEl.querySelector(".instaraw-big-progress-item-message"); + if (messageDiv) { + if (job.state === JobState.FAILED && job.error) { + messageDiv.textContent = job.error.slice(0, 100); + messageDiv.style.display = "block"; + messageDiv.classList.remove("retry-message"); + messageDiv.classList.add("error-message"); + } else if (job.state === JobState.RETRYING && job.error) { + messageDiv.textContent = `โš ๏ธ ${job.error.slice(0, 80)}`; + messageDiv.style.display = "block"; + messageDiv.classList.remove("error-message"); + messageDiv.classList.add("retry-message"); + } else { + messageDiv.style.display = "none"; + messageDiv.classList.remove("error-message", "retry-message"); + } + } + + // Show/hide retry button + const retryBtn = itemEl.querySelector(".instaraw-big-job-retry-btn"); + if (retryBtn) { + retryBtn.style.display = job.state === JobState.FAILED ? "inline-block" : "none"; + } + + // Update or create thumbnail (always present now) + const hasImage = job.image_url && (job.state === JobState.SUCCESS || job.state === JobState.CACHED); + const isGenerating = job.state === JobState.GENERATING || job.state === JobState.RETRYING; + let thumbnailContainer = itemEl.querySelector(".instaraw-big-progress-item-thumbnail"); + + if (!thumbnailContainer) { + // Create thumbnail container - always present + const thumbDiv = document.createElement("div"); + thumbDiv.className = "instaraw-big-progress-item-thumbnail" + (hasImage ? "" : " placeholder"); + + if (hasImage) { + thumbDiv.innerHTML = `Generated image`; + } else if (isGenerating) { + console.log("[BIG] Creating video element for job", job.id); + const video = document.createElement("video"); + video.className = "instaraw-big-thumbnail-video"; + video.autoplay = true; + video.loop = true; + video.muted = true; + video.playsInline = true; + video.src = "extensions/ComfyUI_INSTARAW/Instara_loader.mp4"; + + // Debug events + video.addEventListener('loadeddata', () => console.log("[BIG] Video loaded:", video.src)); + video.addEventListener('error', (e) => console.error("[BIG] Video error:", e, video.error)); + + thumbDiv.appendChild(video); + console.log("[BIG] Video element appended, src:", video.src); + + // Ensure video plays + video.play().then(() => { + console.log("[BIG] Video playing successfully"); + }).catch(e => { + console.error("[BIG] Video autoplay failed:", e); + }); + } else { + thumbDiv.innerHTML = `
`; + } + + // Append to the main item container + itemEl.appendChild(thumbDiv); + + // Add click handler and onload for height update if image is available + if (hasImage) { + const img = thumbDiv.querySelector(".instaraw-big-thumbnail-img"); + if (img) { + img.onclick = (e) => { + e.stopPropagation(); + openPhotoSwipe(job.id); + }; + img.onload = () => updateCachedHeight(); + } + } + + updateCachedHeight(); + } else { + // Update existing thumbnail based on state + if (hasImage) { + // Replace with actual image + // console.log("[BIG] Updating thumbnail to show image for job", job.id, job.image_url); + const existingImg = thumbnailContainer.querySelector(".instaraw-big-thumbnail-img"); + + // Helper to add retry-on-error to img element + const setupImgRetry = (img) => { + let retryCount = 0; + const maxRetries = 3; + img.style.background = 'rgba(0,0,0,0.3)'; + + img.onerror = () => { + if (retryCount < maxRetries) { + retryCount++; + console.log(`[BIG] Image load failed for job ${job.id}, retry ${retryCount}/${maxRetries}`); + setTimeout(() => { + // Retry with cache-busting parameter + const baseUrl = job.image_url.split('&_retry=')[0].split('&_t=')[0]; + img.src = `${baseUrl}&_retry=${retryCount}&_t=${Date.now()}`; + }, 200 * retryCount); + } else { + console.error(`[BIG] Image load failed after ${maxRetries} retries for job ${job.id}`); + img.style.opacity = '0.3'; + img.src = 'data:image/svg+xml,%3Csvg xmlns=%22http://www.w3.org/2000/svg%22 viewBox=%220 0 24 24%22%3E%3Cpath fill=%22%239ca3af%22 d=%22M21 19V5c0-1.1-.9-2-2-2H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2zM8.5 13.5l2.5 3.01L14.5 12l4.5 6H5l3.5-4.5z%22/%3E%3C/svg%3E'; + } + }; + img.onload = () => updateCachedHeight(); + img.onclick = (e) => { + e.stopPropagation(); + openPhotoSwipe(job.id); + }; + }; + + if (!existingImg) { + // No image yet, replace entire content + // console.log("[BIG] No existing img, replacing content"); + thumbnailContainer.className = "instaraw-big-progress-item-thumbnail"; + thumbnailContainer.innerHTML = `Generated image`; + + const img = thumbnailContainer.querySelector(".instaraw-big-thumbnail-img"); + if (img) { + setupImgRetry(img); + } + updateCachedHeight(); + } else if (existingImg.src !== job.image_url) { + // Image exists but URL changed + // console.log("[BIG] Updating existing img src"); + existingImg.src = job.image_url; + thumbnailContainer.className = "instaraw-big-progress-item-thumbnail"; + setupImgRetry(existingImg); + } + } else if (isGenerating) { + // Show video if not already showing + if (!thumbnailContainer.querySelector(".instaraw-big-thumbnail-video")) { + console.log("[BIG] Updating to show video for job", job.id); + thumbnailContainer.className = "instaraw-big-progress-item-thumbnail placeholder"; + thumbnailContainer.innerHTML = ''; // Clear existing content + const video = document.createElement("video"); + video.className = "instaraw-big-thumbnail-video"; + video.autoplay = true; + video.loop = true; + video.muted = true; + video.playsInline = true; + video.src = "extensions/ComfyUI_INSTARAW/Instara_loader.mp4"; + + // Debug events + video.addEventListener('loadeddata', () => console.log("[BIG] Video loaded (update):", video.src)); + video.addEventListener('error', (e) => console.error("[BIG] Video error (update):", e, video.error)); + + thumbnailContainer.appendChild(video); + console.log("[BIG] Video element appended (update), src:", video.src); + + // Ensure video plays + video.play().then(() => { + console.log("[BIG] Video playing successfully (update)"); + }).catch(e => { + console.error("[BIG] Video autoplay failed (update):", e); + }); + } + } else { + // Show empty placeholder (no icon) + if (!thumbnailContainer.querySelector(".instaraw-big-thumbnail-empty")) { + thumbnailContainer.className = "instaraw-big-progress-item-thumbnail placeholder"; + thumbnailContainer.innerHTML = `
`; + } + } + } + + // Add or update view button + const headerEl = itemEl.querySelector(".instaraw-big-progress-item-header"); + let viewBtn = headerEl?.querySelector(".instaraw-big-view-image-btn"); + + if (hasImage && !viewBtn && headerEl) { + const statusBadge = headerEl.querySelector(".instaraw-big-progress-item-status"); + const newBtn = document.createElement("button"); + newBtn.className = "instaraw-big-view-image-btn"; + newBtn.dataset.jobId = job.id; + newBtn.title = "View image"; + newBtn.textContent = "๐Ÿ‘๏ธ"; + newBtn.onclick = (e) => { + e.stopPropagation(); + openPhotoSwipe(job.id); + }; + + if (statusBadge && statusBadge.nextSibling) { + headerEl.insertBefore(newBtn, statusBadge.nextSibling); + } else { + headerEl.appendChild(newBtn); + } + } else if (!hasImage && viewBtn) { + viewBtn.remove(); + } + }; + + // โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ• + // HELPER FUNCTIONS FOR RPG-STYLE GENERATION + // โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ• + + // Check if RPG is connected (regardless of prompts) + const isRPGConnected = () => { + try { + const promptSlot = node.inputs?.findIndex(i => i.name === "prompt_list_positive"); + if (promptSlot === -1 || !node.inputs[promptSlot].link) { + return false; + } + const link = app.graph.links[node.inputs[promptSlot].link]; + if (!link) return false; + const rpgNode = app.graph.getNodeById(link.origin_id); + return rpgNode && rpgNode.type === "INSTARAW_RealityPromptGenerator"; + } catch (e) { + return false; + } + }; + + // NEW: Detect prompts from connected RPG in real-time (RPG pattern) + const getConnectedRPGData = () => { + /** + * Find connected RPG node and read its prompt batch data. + * Returns array of {positive, negative, seed} objects or null. + * This is used for the preview state to show detected prompts. + */ + try { + // Find prompt_list_positive input slot + const promptSlot = node.inputs?.findIndex(i => i.name === "prompt_list_positive"); + if (promptSlot === -1 || !node.inputs[promptSlot].link) { + return null; + } + + // Get linked RPG node + const link = app.graph.links[node.inputs[promptSlot].link]; + if (!link) return null; + + const rpgNode = app.graph.getNodeById(link.origin_id); + if (!rpgNode || rpgNode.type !== "INSTARAW_RealityPromptGenerator") { + return null; + } + + // Read prompt_batch_data widget + const batchWidget = rpgNode.widgets?.find(w => w.name === "prompt_batch_data"); + if (!batchWidget) return null; + + const batchData = JSON.parse(batchWidget.value || "[]"); + if (batchData.length === 0) return null; + + // Expand by repeat_count (like RPG's execute does) + const expandedPrompts = []; + batchData.forEach(entry => { + const repeat = Math.max(1, entry.repeat_count || 1); + for (let i = 0; i < repeat; i++) { + expandedPrompts.push({ + positive: entry.positive_prompt || "", + negative: entry.negative_prompt || "", + seed: (entry.seed || -1) + i, + }); + } + }); + + return expandedPrompts.length > 0 ? expandedPrompts : null; + } catch (e) { + console.error("[BIG] Error in getConnectedRPGData:", e); + return null; + } + }; + + // Real-time RPG detection (polls every 500ms) + node._detectedPromptCount = 0; + const rpgDetectionInterval = setInterval(() => { + if (!node._bigIsGenerating) { + const newPrompts = getConnectedRPGData(); + const currentCount = node._detectedPromptCount || 0; + const newCount = newPrompts?.length || 0; + + if (newCount !== currentCount) { + node._detectedPromptCount = newCount; + console.log(`[BIG] Detected prompt count changed: ${currentCount} โ†’ ${newCount}`); + renderUI(); // Re-render to show updated state + } + } + }, 500); + + // OLD: Keep existing function for backward compatibility with Generate All + const getConnectedPromptData = () => { + /** + * Find connected RPG node and read its prompt batch data. + * Returns: { prompts_positive, prompts_negative, seeds } or null + */ + try { + // Find the input slot for prompt_list_positive + const promptSlot = node.inputs?.findIndex(input => input.name === "prompt_list_positive"); + if (promptSlot === -1 || !node.inputs[promptSlot].link) { + console.log("[BIG] No RPG connected to prompt_list_positive"); + return null; + } + + // Get the link and source node + const link = app.graph.links[node.inputs[promptSlot].link]; + if (!link) { + console.log("[BIG] Link not found"); + return null; + } + + const rpgNode = app.graph.getNodeById(link.origin_id); + if (!rpgNode || rpgNode.type !== "INSTARAW_RealityPromptGenerator") { + console.log("[BIG] Connected node is not RPG:", rpgNode?.type); + return null; + } + + // Read prompt batch data from RPG + const batchDataWidget = rpgNode.widgets?.find(w => w.name === "prompt_batch_data"); + if (!batchDataWidget) { + console.log("[BIG] RPG has no prompt_batch_data widget"); + return null; + } + + const batchData = JSON.parse(batchDataWidget.value || "[]"); + if (batchData.length === 0) { + console.log("[BIG] RPG batch data is empty"); + return null; + } + + // Extract prompts with repeat counts + const prompts_positive = []; + const prompts_negative = []; + const seeds = []; + + batchData.forEach(entry => { + const pos = entry.positive_prompt || ""; + const neg = entry.negative_prompt || ""; + const seed = entry.seed || -1; + const repeat = Math.max(1, entry.repeat_count || 1); + + // Expand by repeat_count + for (let i = 0; i < repeat; i++) { + prompts_positive.push(pos); + prompts_negative.push(neg); + seeds.push(seed + i); // Increment seed for each repeat + } + }); + + console.log(`[BIG] Read ${prompts_positive.length} prompts from RPG`); + return { prompts_positive, prompts_negative, seeds }; + } catch (e) { + console.error("[BIG] Error reading RPG data:", e); + return null; + } + }; + + const getGenerationParameters = () => { + /** + * Get generation parameters from connected nodes/widgets. + * Returns object with api_key, provider, model, etc. or null + */ + try { + // Helper to recursively get value from a node's input + const getNodeInputValue = (targetNode, targetInputName, defaultVal = null) => { + if (!targetNode) return defaultVal; + + // PRIORITY 1: Check if there's a connected input (connections override widgets) + const input = targetNode.inputs?.find(i => i.name === targetInputName); + if (input && input.link != null) { + const link = app.graph.links[input.link]; + if (link) { + const originNode = app.graph.getNodeById(link.origin_id); + if (originNode) { + // For nodes that just output a value directly (like PrimitiveString) + if (originNode.widgets && originNode.widgets.length > 0) { + const value = originNode.widgets[0].value; + console.log(`[BIG] Got '${targetInputName}' from connected node ${originNode.id} widget:`, value?.slice?.(0, 20) || value); + return value; + } + + if (originNode.properties && originNode.properties.value !== undefined) { + console.log(`[BIG] Got '${targetInputName}' from connected node ${originNode.id} properties:`, originNode.properties.value?.slice?.(0, 20) || originNode.properties.value); + return originNode.properties.value; + } + } + } + } + + // PRIORITY 2: Fall back to widget value if no connection + if (targetNode.widgets) { + const widget = targetNode.widgets.find(w => w.name === targetInputName); + if (widget !== undefined && widget.value !== undefined) { + console.log(`[BIG] Got '${targetInputName}' from node ${targetNode.id} widget (no connection):`, widget.value?.slice?.(0, 20) || widget.value); + return widget.value; + } + } + + return defaultVal; + }; + + // Helper to get value from connected input or widget (RPG pattern) + const getInputValue = (inputName, defaultVal = null) => { + try { + // Try to get from connected link first (connections take priority over widgets) + const input = node.inputs?.find(i => i.name === inputName); + if (!input || input.link == null) { + // No connection, try own widget + if (node.widgets) { + const widget = node.widgets.find(w => w.name === inputName); + if (widget) { + console.log(`[BIG] Got ${inputName} from own widget:`, widget.value?.slice?.(0, 20) || widget.value); + return widget.value; + } + } + console.log(`[BIG] No connection for ${inputName}, using default:`, defaultVal); + return defaultVal; + } + + const link = app.graph.links[input.link]; + if (!link) { + console.log(`[BIG] Invalid link for ${inputName}`); + return defaultVal; + } + + const originNode = app.graph.getNodeById(link.origin_id); + if (!originNode) { + console.log(`[BIG] Origin node not found for ${inputName}`); + return defaultVal; + } + + console.log(`[BIG] Tracing ${inputName} from origin node ${originNode.id} (${originNode.type}), output slot ${link.origin_slot}`); + + // Special handling for Provider Selector nodes - they compute outputs based on inputs + if (originNode.type === "INSTARAW_API_ProviderSelector" || originNode.type === "INSTARAW_IdeogramProviderSelector") { + // origin_slot tells us which output: 0=provider, 1=api_key + if (link.origin_slot === 0) { + // Getting 'provider' output - read from provider widget + const value = getNodeInputValue(originNode, "provider", defaultVal); + console.log(`[BIG] Provider selector output[0] (provider):`, value); + return value; + } else if (link.origin_slot === 1) { + // Getting 'api_key' output - need to check which provider is selected + const selectedProvider = getNodeInputValue(originNode, "provider", ""); + console.log(`[BIG] Provider selector selected provider:`, selectedProvider); + + // Read the appropriate API key based on selected provider + let keyInputName; + if (originNode.type === "INSTARAW_IdeogramProviderSelector") { + keyInputName = selectedProvider === "Official Ideogram" ? "official_ideogram_api_key" : "fal_api_key"; + } else { + keyInputName = selectedProvider === "wavespeed.ai" ? "wavespeed_api_key" : "fal_api_key"; + } + + const apiKey = getNodeInputValue(originNode, keyInputName, ""); + console.log(`[BIG] Provider selector output[1] (api_key from ${keyInputName}):`, apiKey?.slice?.(0, 20) || apiKey); + return apiKey; + } + } + + // For other nodes, read from widgets + if (originNode.widgets && originNode.widgets.length > 0) { + const widgetIndex = link.origin_slot || 0; + if (widgetIndex < originNode.widgets.length) { + const widget = originNode.widgets[widgetIndex]; + const value = widget.value; + console.log(`[BIG] Got ${inputName} from connected node widget[${widgetIndex}] '${widget.name}':`, value?.slice?.(0, 20) || value); + return value; + } else { + // Fallback to first widget if origin_slot is out of bounds + const value = originNode.widgets[0].value; + console.log(`[BIG] Got ${inputName} from connected node (first widget, slot ${widgetIndex} out of bounds):`, value?.slice?.(0, 20) || value); + return value; + } + } + + // Fallback: try properties.value + if (originNode.properties && originNode.properties.value !== undefined) { + console.log(`[BIG] Got ${inputName} from properties.value:`, originNode.properties.value); + return originNode.properties.value; + } + + console.warn(`[BIG] No value found for ${inputName} on connected node. Node type: ${originNode.type}`); + return defaultVal; + } catch (error) { + console.error(`[BIG] Error getting ${inputName}:`, error); + return defaultVal; + } + }; + + // Get raw aspect_ratio value and parse it + let aspectRatioRaw = String(getInputValue("aspect_ratio", "1:1") || "1:1"); + let aspectRatio = "1:1"; + + // Extract ratio from labels like "3:4 (Portrait)" -> "3:4" + if (aspectRatioRaw) { + const match = aspectRatioRaw.match(/(\d+:\d+)/); + if (match) { + aspectRatio = match[1]; + console.log(`[BIG] Parsed aspect_ratio "${aspectRatioRaw}" -> "${aspectRatio}"`); + } else { + aspectRatio = aspectRatioRaw; + } + } + + const params = { + api_key: getInputValue("api_key"), + provider: getInputValue("provider"), + model: getInputValue("model"), + aspect_ratio: aspectRatio, + width: getInputValue("width", 1024), + height: getInputValue("height", 1024), + resolution: getInputValue("resolution", "1K"), + enable_safety_checker: getInputValue("enable_safety_checker", true), + use_negative_prompt: getInputValue("use_negative_prompt", true), + max_retries: getInputValue("max_retries", 3), + max_parallel: getInputValue("max_parallel", 5), + timeout: getInputValue("timeout", 600), + use_cache: getInputValue("use_cache", true), + filename_prefix: getInputValue("filename_prefix", "INSTARAW"), + multi_image: getInputValue("multi_image", false), + }; + + console.log("[BIG] Generation parameters:", { + ...params, + api_key: params.api_key ? `${params.api_key.slice(0, 10)}...` : "MISSING", + }); + + // Validate required parameters + const missing = []; + if (!params.api_key) missing.push("API Key"); + if (!params.provider) missing.push("Provider"); + if (!params.model) missing.push("Model"); + + if (missing.length > 0) { + const msg = `โŒ Missing required parameters: ${missing.join(", ")}\n\n` + + `Make sure these are connected to the BIG node:\n` + + `- API Key Loader โ†’ api_key input\n` + + `- Provider โ†’ provider input\n` + + `- Model โ†’ model input\n\n` + + `Check browser console for details.`; + console.error("[BIG]", msg); + alert(msg); + return null; + } + + return params; + } catch (e) { + console.error("[BIG] Error getting generation parameters:", e); + return null; + } + }; + + const generateSingleJob = async (jobIndex, genParams, signal = null) => { + /** + * Generate a single image via API endpoint. + * Updates node._bigJobs and node._bigStats. + * @param {AbortSignal} signal - Optional abort signal for cancellation + */ + const job = node._bigJobs[jobIndex]; + const nodeId = node._ni_widget?.value || String(node.id); + + // Check if already aborted before starting + if (signal?.aborted) { + console.log(`[BIG] Job ${jobIndex} skipped (cancelled)`); + job.state = "cancelled"; + job.error = "Cancelled by user"; + node._bigStats.completed++; + updateProgressItem(job); + updateProgressHeader(); + return; + } + + console.log(`[BIG] Generating job ${jobIndex}:`, job.prompt_positive.slice(0, 50)); + + // Mark as generating - use updateProgressItem instead of renderUI + // to avoid replacing all images (which interrupts loading) + job.state = "generating"; + updateGeneratedBatchData(); // Save state immediately so tab switch preserves it + updateProgressItem(job); + updateProgressHeader(); + + const startTime = Date.now(); + + try { + console.log(`[BIG] Sending request for job ${jobIndex}:`, { + node_id: nodeId, + job_id: job.id, + provider: genParams.provider, + model: genParams.model, + api_key: genParams.api_key ? "present" : "MISSING", + prompt: job.prompt_positive.slice(0, 50), + }); + + // Build request body + const requestBody = { + node_id: nodeId, + job_id: job.id, + ...genParams, + prompt_positive: job.prompt_positive, + prompt_negative: job.prompt_negative, + seed: job.seed, + }; + + // Add input images for I2I mode (if job has them) + if (job.input_image_b64) { + requestBody.input_image = job.input_image_b64; + console.log(`[BIG] Job ${jobIndex} image_1 (base64 length: ${job.input_image_b64.length})`); + } + if (job.input_image2_b64) { + requestBody.input_image2 = job.input_image2_b64; + console.log(`[BIG] Job ${jobIndex} image_2 (base64 length: ${job.input_image2_b64.length})`); + } + if (job.input_image3_b64) { + requestBody.input_image3 = job.input_image3_b64; + console.log(`[BIG] Job ${jobIndex} image_3 (base64 length: ${job.input_image3_b64.length})`); + } + if (job.input_image4_b64) { + requestBody.input_image4 = job.input_image4_b64; + console.log(`[BIG] Job ${jobIndex} image_4 (base64 length: ${job.input_image4_b64.length})`); + } + if (!job.input_image_b64) { + console.log(`[BIG] Job ${jobIndex} has no input image (T2I mode)`); + } + + // Pass signal to fetch for cancellation support + const response = await api.fetchApi("/instaraw/batch_gen_generate_single", { + method: "POST", + headers: { "Content-Type": "application/json" }, + body: JSON.stringify(requestBody), + signal: signal, + }); + + const result = await response.json(); + console.log(`[BIG] Job ${jobIndex} response:`, result); + + if (result.status === "success") { + job.state = result.cache_hit ? "cached" : "success"; + job.image_url = result.image_url; + job.filename = result.filename; + job.generation_time = result.generation_time || (Date.now() - startTime) / 1000; + job.image_width = result.width || 1024; + job.image_height = result.height || 1024; + console.log(`[BIG][fix1] Job ${jobIndex} completed, result.width=${result.width}, result.height=${result.height}, stored as ${job.image_width}x${job.image_height}`); + job.attempts = result.attempts || 1; + job.subfolder = result.subfolder; + + // Log retry info if there were retries + if (job.attempts > 1) { + console.log(`[BIG] Job ${jobIndex} succeeded after ${job.attempts} attempts`); + } + + // Small delay to ensure filesystem has synced before loading image + await new Promise(resolve => setTimeout(resolve, 100)); + + node._bigStats.success++; + if (result.cache_hit) node._bigStats.cached++; + } else { + job.state = "failed"; + job.error = result.error || "Generation failed"; + job.attempts = result.attempts || 1; + node._bigStats.failed++; + console.error(`[BIG] Job ${jobIndex} failed after ${job.attempts} attempts:`, result.error); + } + } catch (e) { + // Handle abort differently - mark as cancelled, not failed + if (e.name === 'AbortError') { + console.log(`[BIG] Job ${jobIndex} cancelled`); + job.state = "cancelled"; + job.error = "Cancelled by user"; + // Don't increment failed count for cancellations + } else { + console.error(`[BIG] Job ${jobIndex} error:`, e); + job.state = "failed"; + job.error = e.message || "Network error"; + node._bigStats.failed++; + } + } + + node._bigStats.completed++; + updateGeneratedBatchData(); + updateGeneratedBatchData(); + // Use updateProgressItem instead of renderUI to avoid + // replacing all images (which interrupts loading for other jobs) + updateProgressItem(job); + updateProgressHeader(); + }; + + const generateJobPair = async (jobIndex1, jobIndex2, genParams, signal = null) => { + /** + * Generate 2 images in a single API call (Wavespeed.ai multi-image mode). + * 50% cost savings: $0.07/image instead of $0.14/image. + */ + const job1 = node._bigJobs[jobIndex1]; + const job2 = node._bigJobs[jobIndex2]; + const nodeId = node._ni_widget?.value || String(node.id); + + // Check if already aborted + if (signal?.aborted) { + console.log(`[BIG] Job pair ${jobIndex1}+${jobIndex2} skipped (cancelled)`); + job1.state = "cancelled"; + job2.state = "cancelled"; + job1.error = job2.error = "Cancelled by user"; + node._bigStats.completed += 2; + updateProgressItem(job1); + updateProgressItem(job2); + updateProgressHeader(); + return; + } + + const pairStartTime = Date.now(); + const promptPreview = (job1.prompt_positive || "").slice(0, 30); + console.log(`[BIG] ๐ŸŽฏ [${new Date().toISOString().slice(11,19)}] Starting pair ${jobIndex1}+${jobIndex2}: "${promptPreview}..."`); + + // Mark both as generating + job1.state = "generating"; + job2.state = "generating"; + updateGeneratedBatchData(); // Save state immediately so tab switch preserves it + updateProgressItem(job1); + updateProgressItem(job2); + updateProgressHeader(); + + try { + // Build request - use job1's prompt for both (same subject, different variations) + const requestBody = { + node_id: nodeId, + job_id: job1.id, + second_job_id: job2.id, + multi_image: true, + ...genParams, + prompt_positive: job1.prompt_positive, + prompt_negative: job1.prompt_negative, + seed: job1.seed, + }; + + // Add input images for I2I mode + if (job1.input_image_b64) { + requestBody.input_image = job1.input_image_b64; + } + if (job1.input_image2_b64) { + requestBody.input_image2 = job1.input_image2_b64; + } + if (job1.input_image3_b64) { + requestBody.input_image3 = job1.input_image3_b64; + } + if (job1.input_image4_b64) { + requestBody.input_image4 = job1.input_image4_b64; + } + + const response = await api.fetchApi("/instaraw/batch_gen_generate_single", { + method: "POST", + headers: { "Content-Type": "application/json" }, + body: JSON.stringify(requestBody), + signal: signal, + }); + + const result = await response.json(); + const pairDuration = ((Date.now() - pairStartTime) / 1000).toFixed(1); + console.log(`[BIG] ๐ŸŽฏ [${new Date().toISOString().slice(11,19)}] Pair ${jobIndex1}+${jobIndex2} completed in ${pairDuration}s`); + + if (result.status === "success" && result.multi_image && result.results) { + // Process both results + for (const imgResult of result.results) { + const job = imgResult.job_id === job1.id ? job1 : job2; + if (imgResult.status === "success") { + // Check for cache hit + job.state = imgResult.cache_hit ? "cached" : "success"; + job.image_url = imgResult.image_url; + job.filename = imgResult.filename; + job.subfolder = imgResult.subfolder || result.subfolder; // For URL reconstruction + job.image_width = imgResult.width || 1024; + job.image_height = imgResult.height || 1024; + console.log(`[BIG][fix1] Paired job ${job.id} completed (${job.state}), imgResult.width=${imgResult.width}, imgResult.height=${imgResult.height}, stored as ${job.image_width}x${job.image_height}`); + job.generation_time = result.generation_time ? result.generation_time / 2 : 0; // Split time between both + node._bigStats.success++; + if (imgResult.cache_hit) node._bigStats.cached++; + } else { + job.state = "failed"; + job.error = imgResult.error || "Failed"; + node._bigStats.failed++; + } + updateProgressItem(job); + } + node._bigStats.completed += 2; + } else { + // Both failed + job1.state = "failed"; + job2.state = "failed"; + job1.error = job2.error = result.error || "Multi-image generation failed"; + node._bigStats.failed += 2; + node._bigStats.completed += 2; + updateProgressItem(job1); + updateProgressItem(job2); + } + + } catch (e) { + if (e.name === 'AbortError') { + job1.state = job2.state = "cancelled"; + job1.error = job2.error = "Cancelled by user"; + } else { + console.error(`[BIG] Job pair ${jobIndex1}+${jobIndex2} error:`, e); + job1.state = job2.state = "failed"; + job1.error = job2.error = e.message || "Network error"; + node._bigStats.failed += 2; + } + node._bigStats.completed += 2; + updateProgressItem(job1); + updateProgressItem(job2); + } + + updateGeneratedBatchData(); + updateGeneratedBatchData(); + updateProgressHeader(); + }; + + const updateGeneratedBatchData = () => { + /** + * Update the generated_batch_data widget, node.properties, AND session storage. + * - Widget: for Python backend to read + * - Properties: for workflow file persistence + * - Session storage: for reliable tab switch persistence (ComfyUI recreates nodes!) + */ + const widget = node.widgets?.find(w => w.name === "generated_batch_data"); + if (!widget) { + console.warn("[BIG] generated_batch_data widget not found!"); + return; + } + + // IMPORTANT: Do NOT save image_url (base64 data) to node.properties! + // Base64 images are huge and will exceed localStorage quota. + // Images are saved to disk with filenames - we can load from there. + const batchDataForStorage = node._bigJobs.map(job => ({ + id: job.id, + prompt_positive: job.prompt_positive, + prompt_negative: job.prompt_negative, + seed: job.seed, + state: job.state, + filename: job.filename, + // NO image_url here - it's base64 and too large! + image_width: job.image_width, + image_height: job.image_height, + error: job.error, + generation_time: job.generation_time, + subfolder: job.subfolder, // Keep for file path reconstruction + })); + console.log(`[BIG] Saving ${batchDataForStorage.length} jobs (without base64 images)`); + + const jsonData = JSON.stringify(batchDataForStorage); + + // Update widget (for Python backend) + widget.value = jsonData; + + // Update node.properties (for workflow file) - small data only! + node.properties = node.properties || {}; + node.properties.big_jobs_data = jsonData; + + // Update session storage (survives tab switches - most reliable!) + saveToSession(jsonData); + + console.log(`[BIG] Updated state with ${batchDataForStorage.length} jobs (widget + properties + session)`); + }; + + // โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ• + // STATE RENDERING FUNCTIONS (RPG PATTERN) + // โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ• + + // Helper to render image preview grid showing all 4 image inputs + const renderImagePreview = (imageUrls, mode, aspectRatio = "1:1", totalGenerations = 0) => { + // Get all image input URLs + const img1Urls = node._bigImageUrls || []; + const img2Urls = node._bigImage2Urls || []; + const img3Urls = node._bigImage3Urls || []; + const img4Urls = node._bigImage4Urls || []; + + const totalImages = img1Urls.length + img2Urls.length + img3Urls.length + img4Urls.length; + + if (mode !== "img2img" || totalImages === 0) { + return ''; + } + + // Check which inputs have images + const inputsWithImages = [ + { urls: img1Urls, label: "Image 1" }, + { urls: img2Urls, label: "Image 2" }, + { urls: img3Urls, label: "Image 3" }, + { urls: img4Urls, label: "Image 4" } + ].filter(input => input.urls.length > 0); + + const connectedInputs = inputsWithImages.length; + const multiImageMode = connectedInputs > 1; + + // Get AIL item count INCLUDING repeat counts (like RPG does) + const getAILTotalCount = () => { + const primaryAIL = findConnectedAIL("images"); + if (!primaryAIL || !primaryAIL.properties?.batch_data) { + return multiImageMode ? getComboMaxLength(inputsWithImages) : totalImages; + } + try { + const batchData = JSON.parse(primaryAIL.properties.batch_data); + const images = batchData.images || []; + // Sum up repeat counts like RPG does + return images.reduce((sum, img) => sum + (img.repeat_count || 1), 0); + } catch (e) { + return multiImageMode ? getComboMaxLength(inputsWithImages) : totalImages; + } + }; + + const ailTotalCount = getAILTotalCount(); + + // Sync status - compare totalGenerations (prompt repeats) with ailTotalCount (image repeats) + const isSynced = totalGenerations > 0 && totalGenerations === ailTotalCount; + + // Sync status badge HTML (informational only - sync in RPG/AIL) + const renderSyncBadge = () => { + if (totalGenerations === 0) { + return `No prompts`; + } + if (isSynced) { + return `โœ“ ${totalGenerations} gens โ†” ${ailTotalCount} AIL`; + } + return ` + โš  ${totalGenerations} gens โ†” ${ailTotalCount} AIL + Sync in RPG + `; + }; + + // Multi-image mode: Show combination cards + if (multiImageMode) { + const maxLen = getComboMaxLength(inputsWithImages); + const comboCardsHtml = renderComboCards(inputsWithImages, { + getImageUrl: (url) => url + }); + + return ` +
+
+
+ + ${connectedInputs} AIL inputs + connected +
+
+ ${renderSyncBadge()} +
+
+
+ ${comboCardsHtml} +
+
+ `; + } + + // Single input mode: Clean square thumbnail grid + // Expand images by repeat count for accurate preview + const getExpandedImages = () => { + const primaryAIL = findConnectedAIL("images"); + if (!primaryAIL || !primaryAIL.properties?.batch_data) { + // Fallback: just use URLs as-is + return img1Urls.map((url, idx) => ({ url, index: idx + 1, isRepeat: false })); + } + try { + const batchData = JSON.parse(primaryAIL.properties.batch_data); + const images = batchData.images || []; + const order = batchData.order || []; + const expanded = []; + + order.forEach((id, idx) => { + const item = images.find(img => img.id === id); + if (!item) return; + const repeatCount = item.repeat_count || 1; + const url = img1Urls[idx] || item.url; + + for (let r = 0; r < repeatCount; r++) { + expanded.push({ + url, + index: idx + 1, + repeatIndex: r + 1, + repeatTotal: repeatCount, + isRepeat: r > 0 + }); + } + }); + return expanded.length > 0 ? expanded : img1Urls.map((url, idx) => ({ url, index: idx + 1, isRepeat: false })); + } catch (e) { + return img1Urls.map((url, idx) => ({ url, index: idx + 1, isRepeat: false })); + } + }; + const expandedImages = getExpandedImages(); + + return ` +
+
+ ๐Ÿ“ธ Reference Images +
+ ${renderSyncBadge()} +
+
+
+ ${expandedImages.map((img, idx) => { + const label = img.repeatTotal > 1 + ? `${img.index}.${img.repeatIndex}` + : `${img.index}`; + return ` +
+ Image ${img.index} + ${label} +
+ `; + }).join('')} +
+
+ `; + }; + + const renderIdleState = () => { + // Get multi-image mode info even in idle state + const multiImageInfo = getMultiImageInfo(); + const modelInfo = getModelProviderInfo(); + const modelDisplay = modelInfo.model && modelInfo.provider + ? `${modelInfo.model} (${modelInfo.provider})` + : modelInfo.model || "Not connected"; + + // Check if RPG is connected (even if no prompts yet) + const rpgConnected = isRPGConnected(); + const idleMessage = rpgConnected + ? "โœ… RPG connected โ€” Generate prompts in RPG to continue" + : "Connect to Reality Prompt Generator to generate images"; + + container.innerHTML = ` +
+ +
Batch Image Generator
+ ${multiImageInfo.multiImage ? ` +
+ ${multiImageInfo.isActive ? `โšก 2X MODE` : ''} + ${multiImageInfo.isWarning ? `โš ๏ธ 2X needs Wavespeed` : ''} + ${multiImageInfo.isNoProvider ? `โš ๏ธ 2X no provider` : ''} +
+ ` : ''} +
${escapeHtml(modelDisplay)}
+ + +
+ ${idleMessage} +
+
+ `; + updateCachedHeight(); + }; + + const renderPreviewState = (detectedPrompts) => { + const promptCount = detectedPrompts.length; + // Calculate total generations including repeat counts (like RPG) + const totalGenerations = detectedPrompts.reduce((sum, p) => sum + (p.repeat_count || 1), 0); + const displayLimit = 10; // Show first 10 prompts in preview + const promptsToShow = detectedPrompts.slice(0, displayLimit); + const hasMore = promptCount > displayLimit; + + // Get mode info for preview + const imagesInfo = getConnectedImagesInfo(); + const mode = imagesInfo.mode; + const imageCount = imagesInfo.imageCount; + const imageUrls = imagesInfo.imageUrls || []; + const modeLabel = mode === "img2img" ? "IMG2IMG" : "TXT2IMG"; + const modeClass = mode === "img2img" ? "instaraw-big-mode-img2img" : "instaraw-big-mode-txt2img"; + + // Update node state + node._bigMode = mode; + node._bigImageCount = imageCount; + + // Get model info + const modelInfo = getModelProviderInfo(); + const modelDisplay = modelInfo.model && modelInfo.provider + ? `${modelInfo.model} (${modelInfo.provider})` + : modelInfo.model || "Not connected"; + + // Get multi-image mode info + const multiImageInfo = getMultiImageInfo(); + + // Calculate total output images (2x if multi-image mode is active) + const totalOutputImages = multiImageInfo.isActive ? totalGenerations * 2 : totalGenerations; + + container.innerHTML = ` +
+ + ${modelInfo.model && !modelInfo.isSupported ? ` +
+ โš ๏ธ Unsupported Model: "${escapeHtml(modelInfo.model)}"
+ This node is experimental and currently only supports Nano Banana Pro. Support for other models will be added soon. +
+ ` : ''} + + +
+
+
+
+
+ ${modeLabel} + ${multiImageInfo.isActive ? `โšก 2X MODE` : ''} + ${multiImageInfo.isWarning ? `โš ๏ธ 2X needs Wavespeed` : ''} + ${multiImageInfo.isNoProvider ? `โš ๏ธ 2X no provider` : ''} +
+ ${mode === "img2img" ? `
${formatImageCount()}
` : ''} +
${escapeHtml(modelDisplay)}
+ + +
+
+ + BIG V2.0 +
+
+
+
+
+ PROMPTS + ${promptCount} +
+
+ OUTPUT + ${totalOutputImages}${multiImageInfo.isActive ? ' (2ร—)' : ''} +
+
+
+ + ${multiImageInfo.isActive ? ` +
+ โšก 2X Multi-Image Mode - ${promptCount} prompt${promptCount !== 1 ? 's' : ''} ร— 2 variations = ${totalOutputImages} images + 50% cost savings: $0.07/img +
+ ${multiImageInfo.aspectRatioWarning ? ` +
+ โš ๏ธ Aspect ratio "${multiImageInfo.aspectRatio}" will be mapped to closest supported (3:2, 2:3, 3:4, 4:3) +
+ ` : ''} + ` : ''} + + + ${renderImagePreview(imageUrls, mode, multiImageInfo.aspectRatio || "1:1", totalGenerations)} + + +
+ +
+ +
+ ๐Ÿ“‹ ${promptCount} prompt${promptCount !== 1 ? 's' : ''} ready +
+
+ ${promptsToShow.map((p, i) => ` +
+ #${i + 1}: ${escapeHtml(p.positive.slice(0, 50))}${p.positive.length > 50 ? '...' : ''} + +
+ `).join('')} + ${hasMore ? ` +
+ ...and ${promptCount - displayLimit} more +
+ ` : ''} +
+
+ `; + setupEventHandlers(); + attachImageOnloadHandlers(); + updateCachedHeight(); + }; + + const renderBatchPanel = () => { + const jobs = node._bigJobs; + const s = node._bigStats; + const elapsed = node._bigStartTime ? formatTime(Date.now() - node._bigStartTime) : "0s"; + const modelInfo = getModelProviderInfo(); + const modelDisplay = modelInfo.model && modelInfo.provider + ? `${modelInfo.model} (${modelInfo.provider})` + : modelInfo.model || "Unknown"; + + // Get current mode info + const mode = node._bigMode || "txt2img"; + const imageCount = node._bigImageCount || 0; + const imageUrls = node._bigImageUrls || []; + const modeLabel = mode === "img2img" ? "IMG2IMG" : "TXT2IMG"; + const modeClass = mode === "img2img" ? "instaraw-big-mode-img2img" : "instaraw-big-mode-txt2img"; + + // Check multi-image mode + const multiImageInfo = getMultiImageInfo(); + + // Get totalGenerations for sync status (sum of repeat counts from RPG) + const detectedPrompts = getConnectedRPGData() || []; + const totalGenerations = detectedPrompts.reduce((sum, p) => sum + (p.repeat_count || 1), 0); + + container.innerHTML = ` +
+ +
+
+
+
+
+ ${modeLabel} + ${multiImageInfo.isActive ? `โšก 2X MODE` : ''} + ${multiImageInfo.isWarning ? `โš ๏ธ 2X needs Wavespeed` : ''} + ${multiImageInfo.isNoProvider ? `โš ๏ธ 2X no provider` : ''} +
+ ${mode === "img2img" ? `
${formatImageCount()}
` : ''} +
${escapeHtml(modelDisplay)}
+ + ${!node._bigIsGenerating ? ` + + ` : ''} +
+
+ + BIG V2.0 +
+
+
+
+
+ JOBS + ${s.completed}/${s.total} +
+
+ SUCCESS + ${s.success} +
+
+ CACHED + ${s.cached} +
+
+ FAILED + ${s.failed} +
+
+
+ + + ${renderImagePreview(imageUrls, mode, multiImageInfo.aspectRatio || "1:1", totalGenerations)} + + +
+

${node._bigIsGenerating ? `Generating... (${elapsed})` : `Complete (${elapsed})`}

+ ${node._bigIsGenerating ? `` : ''} +
+ + + ${!node._bigIsGenerating ? ` +
+ +
+ + + +
+
+ ` : ''} + + +
+ ${renderProgressItems(jobs)} +
+
+ `; + + setupEventHandlers(); + attachImageOnloadHandlers(); + updateCachedHeight(); + + // Start timer if generating + if (node._bigIsGenerating) { + startTimer(); + } + }; + + const renderUI = () => { + // Show loading state while waiting for initialization + // This prevents flash of idle state before data is restored + if (node._pendingInitRender && !node._bigInitialized) { + container.innerHTML = ` +
+
+ +
Loading...
+
+
+ `; + updateCachedHeight(); + return; + } + + const detectedPrompts = getConnectedRPGData(); + const hasGenerated = node._bigJobs && node._bigJobs.length > 0; + + // State priority: generating > batch > preview > idle + if (node._bigIsGenerating) { + renderBatchPanel(); // Shows progress during generation + } else if (hasGenerated) { + renderBatchPanel(); // Shows completed batch + } else if (detectedPrompts && detectedPrompts.length > 0) { + renderPreviewState(detectedPrompts); // Shows detected prompts + } else { + renderIdleState(); // Shows "connect to RPG" message + } + }; + + // Render jobs, grouping pairs together for multi-image mode + const renderProgressItems = (jobs) => { + const hasPairs = jobs.some(j => j.is_pair); + if (!hasPairs) { + // Regular rendering - no pairs + return jobs.map((job, idx) => renderProgressItem(job, idx)).join(""); + } + + // Group paired jobs together + let html = ''; + const rendered = new Set(); + for (let i = 0; i < jobs.length; i++) { + if (rendered.has(i)) continue; + const job = jobs[i]; + + if (job.is_pair && job.pair_with !== undefined && job.pair_with < jobs.length) { + const pairedJob = jobs[job.pair_with]; + const pairIdx = job.pair_with; + rendered.add(i); + rendered.add(pairIdx); + + // Render both jobs in a pair container + const firstIdx = Math.min(i, pairIdx); + const secondIdx = Math.max(i, pairIdx); + const promptIdx = jobs[firstIdx].prompt_index ?? Math.floor(firstIdx / 2); + const promptPreview = (jobs[firstIdx].prompt_positive || "").slice(0, 40); + html += ` +
+
+ โšก Prompt #${promptIdx + 1}: ${escapeHtml(promptPreview)}${promptPreview.length >= 40 ? '...' : ''} + 2 variations +
+
+ ${renderProgressItem(jobs[firstIdx], firstIdx)} + ${renderProgressItem(jobs[secondIdx], secondIdx)} +
+
+ `; + } else { + // Single job (last odd one or non-paired) + rendered.add(i); + html += renderProgressItem(job, i); + } + } + return html; + }; + + const renderProgressItem = (job, idx) => { + const stateInfo = STATE_CONFIG[job.state] || STATE_CONFIG[JobState.PENDING]; + const isPaired = job.is_pair === true; + + // For paired jobs, show variation number; otherwise show prompt preview + let labelText; + if (isPaired && job.variation) { + labelText = `Variation ${job.variation}`; + } else { + const promptPreview = escapeHtml((job.prompt_positive || "").slice(0, 60)); + labelText = `#${idx + 1} ${promptPreview}${job.prompt_positive?.length > 60 ? "..." : ""}`; + } + + // No need for pair badge on individual items when grouped (header shows it) + const pairBadge = ''; + + let statusText = `${stateInfo.icon} ${stateInfo.text}`; + if (job.state === JobState.RETRYING && job.attempts > 1) { + statusText = `๐Ÿ” Retry #${job.attempts}`; + } else if (job.state === JobState.SUCCESS && job.generation_time) { + statusText = `โœ“ ${job.generation_time.toFixed(1)}s`; + } else if (job.state === JobState.CACHED && job.generation_time) { + statusText = `๐Ÿ“ฆ Cached (${job.generation_time.toFixed(1)}s)`; + } + + // Show error for failed jobs, show "retrying" message for retrying jobs + const showError = job.state === JobState.FAILED && job.error; + const showRetryMsg = job.state === JobState.RETRYING && job.error; + const showRetry = job.state === JobState.FAILED; + const hasImage = job.image_url && (job.state === JobState.SUCCESS || job.state === JobState.CACHED); + const isGenerating = job.state === JobState.GENERATING || job.state === JobState.RETRYING; + + // Progress bar width and animation + let barWidth = "0%"; + let barClass = ""; + let barStyle = ""; + + if (job.state === JobState.SUCCESS || job.state === JobState.CACHED) { + barWidth = "100%"; + } else if (isGenerating) { + barWidth = "100%"; + barClass = "animating"; + } else if (job.state === JobState.FAILED) { + barWidth = "100%"; + barStyle = "background: linear-gradient(90deg, #ef4444, #dc2626);"; + } + + // Thumbnail rendering - always show placeholder, overlay video when generating + // Note: No inline onerror - attachImageOnloadHandlers adds smart retry with subfolder fallback + let thumbnailHtml = ''; + if (hasImage) { + thumbnailHtml = ` +
+ Generated image #${idx + 1} +
+ `; + } else if (isGenerating) { + // Show loader video while generating + thumbnailHtml = ` +
+ +
+ `; + } else { + // No image yet - show empty placeholder (no icon) + thumbnailHtml = ` +
+
+
+ `; + } + + // Message to display (error or retry info) + const messageText = showError ? escapeHtml(job.error?.slice(0, 100)) : + showRetryMsg ? `โš ๏ธ ${escapeHtml(job.error?.slice(0, 80))}` : ""; + const showMessage = showError || showRetryMsg; + const messageClass = showError ? "error-message" : "retry-message"; + + // Full prompt for expandable section + const fullPrompt = escapeHtml(job.prompt_positive || "No prompt"); + const rawPrompt = job.prompt_positive || ""; + const seed = job.seed || "N/A"; + const hasLongPrompt = (job.prompt_positive || "").length > 60; + + return ` +
+
+
+ ${labelText} + ${statusText} + ${hasLongPrompt ? `` : ''} + ${hasImage ? `` : ''} + +
+
+
+
+
${messageText}
+ + +
+ ${thumbnailHtml} +
+ `; + }; + + // Track active PhotoSwipe instance to prevent duplicates + let activePswp = null; + + // Force cleanup any orphaned PhotoSwipe elements + const cleanupPhotoSwipe = () => { + const existingPswp = document.querySelectorAll('.pswp'); + if (existingPswp.length > 0) { + console.warn(`[BIG] Cleaning up ${existingPswp.length} orphaned .pswp elements`); + existingPswp.forEach(el => el.remove()); + } + activePswp = null; + }; + + const openPhotoSwipe = async (startJobId) => { + console.log(`[BIG] openPhotoSwipe called with jobId=${startJobId}, activePswp=${!!activePswp}`); + + // If activePswp is stuck in 'loading' state for some reason, force cleanup + if (activePswp === 'loading') { + console.warn("[BIG] PhotoSwipe stuck in loading state, forcing cleanup"); + cleanupPhotoSwipe(); + } + + // Prevent opening multiple instances - but if it's a real pswp instance, try to close it first + if (activePswp && activePswp !== 'loading') { + console.log("[BIG] PhotoSwipe already open, closing first..."); + try { + activePswp.close(); + } catch (e) { + console.warn("[BIG] Failed to close existing PhotoSwipe:", e); + } + cleanupPhotoSwipe(); + } + + // Set flag immediately to prevent race conditions + activePswp = 'loading'; + + // Wait for PhotoSwipe to load if not ready yet + if (!window.PhotoSwipe || !window.PhotoSwipeLightbox) { + console.log("[BIG] Waiting for PhotoSwipe to load..."); + try { + await loadPhotoSwipe(); + } catch (e) { + console.error("[BIG] Failed to load PhotoSwipe:", e); + activePswp = null; + return; + } + } + + // Double-check after await + if (!window.PhotoSwipe) { + console.error("[BIG] PhotoSwipe still not available"); + activePswp = null; + return; + } + + // Always cleanup any existing pswp elements before creating new one + const existingPswp = document.querySelectorAll('.pswp'); + if (existingPswp.length > 0) { + console.warn(`[BIG] Found ${existingPswp.length} existing .pswp elements, removing...`); + existingPswp.forEach(el => el.remove()); + } + + // Get all jobs with images + const jobsWithImages = node._bigJobs.filter(j => j.image_url); + if (jobsWithImages.length === 0) { + console.warn("[BIG] No jobs with images to show"); + activePswp = null; + return; + } + + // Find starting index + const startIndex = jobsWithImages.findIndex(j => j.id === startJobId); + + // Create PhotoSwipe items with actual image dimensions + const items = jobsWithImages.map((job, idx) => ({ + src: job.image_url, + width: job.image_width || 1024, + height: job.image_height || 1024, + alt: `Job #${job.id + 1}`, + msrc: job.image_url, // thumbnail source (same as main) + filename: job.filename || `big_${job.id}_${Date.now()}.png`, + })); + + console.log(`[BIG] Opening PhotoSwipe with ${jobsWithImages.length} images, startIndex=${startIndex}`); + + try { + // Create PhotoSwipe instance + const pswp = new window.PhotoSwipe({ + dataSource: items, + index: startIndex >= 0 ? startIndex : 0, + bgOpacity: 0.9, + spacing: 0.1, + showHideAnimationType: 'none', + showAnimationDuration: 0, + hideAnimationDuration: 0, + zoomAnimationDuration: 0, + loop: true, + closeOnVerticalDrag: false, + pinchToClose: true, + closeTitle: 'Close (Esc)', + zoomTitle: 'Zoom in/out', + arrowPrevTitle: 'Previous (arrow left)', + arrowNextTitle: 'Next (arrow right)', + errorMsg: 'Image failed to load', + easing: 'linear', + }); + + // Add download button + pswp.on('uiRegister', () => { + pswp.ui.registerElement({ + name: 'download-button', + order: 8, + isButton: true, + tagName: 'a', + html: { + isCustomSVG: true, + inner: '', + outlineID: 'pswp__icn-download' + }, + onInit: (el, pswp) => { + el.setAttribute('download', ''); + el.setAttribute('target', '_blank'); + el.setAttribute('rel', 'noopener'); + el.setAttribute('title', 'Download image'); + + pswp.on('change', () => { + const currentItem = pswp.currSlide?.data; + if (currentItem) { + el.href = currentItem.src; + el.download = currentItem.filename; + } + }); + } + }); + }); + + // Track this instance and clear on close + activePswp = pswp; + + pswp.on('close', () => { + console.log("[BIG] PhotoSwipe closed"); + activePswp = null; + }); + + pswp.on('destroy', () => { + console.log("[BIG] PhotoSwipe destroyed"); + activePswp = null; + }); + + pswp.init(); + } catch (e) { + console.error("[BIG] Error initializing PhotoSwipe:", e); + cleanupPhotoSwipe(); + } + }; + + const setupEventHandlers = () => { + // === Canvas Panning & Zoom Event Forwarding === + // Allow dragging/zooming canvas even when mouse is over custom UI + const isInteractiveElement = (element) => { + if (!element) return false; + const tagName = element.tagName?.toLowerCase(); + if (['button', 'input', 'select', 'textarea', 'a', 'img'].includes(tagName)) return true; + const className = element.className || ''; + if (typeof className === 'string' && ( + className.includes('-btn') || className.includes('-input') || + className.includes('-select') || className.includes('prompt-card') || + className.includes('gallery-item') || className.includes('result-image') + )) return true; + if (element.onclick || element.onmousedown || element.draggable) return true; + return false; + }; + + const isInteractiveOrChildOfInteractive = (target) => { + let element = target; + while (element && element !== container) { + if (isInteractiveElement(element)) return true; + element = element.parentElement; + } + return false; + }; + + // Track drag state for manual panning + if (!container._bigDragState) { + container._bigDragState = { isDragging: false, startX: 0, startY: 0, offsetX: 0, offsetY: 0 }; + } + const dragState = container._bigDragState; + + const canvasForwardHandler = (e) => { + if (e._bigForwarded) return; + + if (e.type === 'mousedown') { + if (isInteractiveOrChildOfInteractive(e.target)) { + dragState.isDragging = false; + return; + } + dragState.isDragging = true; + dragState.startX = e.clientX; + dragState.startY = e.clientY; + const canvas = app?.canvas || app?.graph?.list_of_graphcanvas?.[0]; + if (canvas) { + dragState.offsetX = canvas.ds.offset[0]; + dragState.offsetY = canvas.ds.offset[1]; + } + e.preventDefault(); + e.stopPropagation(); + } + + if (e.type === 'mousemove' && dragState.isDragging) { + const deltaX = e.clientX - dragState.startX; + const deltaY = e.clientY - dragState.startY; + // Divide by scale to match native ComfyUI drag behavior at any zoom level + const canvas = app?.canvas || app?.graph?.list_of_graphcanvas?.[0]; + if (canvas) { + const scale = canvas.ds.scale || 1; + canvas.ds.offset[0] = dragState.offsetX + (deltaX / scale); + canvas.ds.offset[1] = dragState.offsetY + (deltaY / scale); + canvas.setDirty(true, true); + } + e.preventDefault(); + e.stopPropagation(); + } + + if (e.type === 'mouseup' && dragState.isDragging) { + dragState.isDragging = false; + e.preventDefault(); + e.stopPropagation(); + } + }; + + const wheelForwardHandler = (e) => { + if (e._bigForwarded) return; + if (isInteractiveOrChildOfInteractive(e.target)) return; + const canvas = document.querySelector('#graph-canvas') || + document.querySelector('canvas.litegraph') || + document.querySelector('.litegraph canvas'); + if (canvas) { + e.preventDefault(); + e.stopPropagation(); + const newEvent = new WheelEvent('wheel', { + bubbles: true, cancelable: true, view: window, + clientX: e.clientX, clientY: e.clientY, + deltaX: e.deltaX, deltaY: e.deltaY, deltaZ: e.deltaZ, deltaMode: e.deltaMode, + ctrlKey: e.ctrlKey, shiftKey: e.shiftKey, altKey: e.altKey, metaKey: e.metaKey + }); + newEvent._bigForwarded = true; + canvas.dispatchEvent(newEvent); + } + }; + + // Remove old listeners if exist + if (container._canvasForwardHandler) { + container.removeEventListener('mousedown', container._canvasForwardHandler, true); + document.removeEventListener('mousemove', container._canvasForwardHandler, true); + document.removeEventListener('mouseup', container._canvasForwardHandler, true); + } + if (container._wheelForwardHandler) { + container.removeEventListener('wheel', container._wheelForwardHandler, true); + } + + container._canvasForwardHandler = canvasForwardHandler; + container._wheelForwardHandler = wheelForwardHandler; + + container.addEventListener('mousedown', canvasForwardHandler, true); + document.addEventListener('mousemove', canvasForwardHandler, true); + document.addEventListener('mouseup', canvasForwardHandler, true); + container.addEventListener('wheel', wheelForwardHandler, true); + + // Individual Generate buttons (preview state) + container.querySelectorAll(".instaraw-big-generate-single-btn").forEach(btn => { + btn.onclick = async (e) => { + e.stopPropagation(); + const promptIndex = parseInt(btn.dataset.index); + + console.log(`[BIG] Individual generate clicked for prompt #${promptIndex}`); + + // Get detected prompts + const detectedPrompts = getConnectedRPGData(); + if (!detectedPrompts || promptIndex >= detectedPrompts.length) { + alert("Prompt not found!"); + return; + } + + const prompt = detectedPrompts[promptIndex]; + + // Get generation parameters + const genParams = getGenerationParameters(); + if (!genParams) { + alert("Missing generation parameters!"); + return; + } + + // Check for multi-image mode + const isMultiImage = genParams.multi_image && genParams.provider === "wavespeed.ai"; + + // Check enable_img2img + const enableImg2Img = getBooleanValue("enable_img2img"); + + // Fetch images for this single prompt (use cached URLs, cycle based on promptIndex) + // Only fetch if enable_img2img is true + let inputImage = null, inputImage2 = null, inputImage3 = null, inputImage4 = null; + + if (enableImg2Img) { + const fetchSingleImage = async (urls, idx) => { + if (!urls || urls.length === 0) return null; + const url = urls[idx % urls.length]; + return await fetchImageAsBase64(url); + }; + + console.log(`[BIG] Fetching images for single generate (promptIndex=${promptIndex})`); + inputImage = await fetchSingleImage(node._bigImageUrls, promptIndex); + inputImage2 = await fetchSingleImage(node._bigImage2Urls, promptIndex); + inputImage3 = await fetchSingleImage(node._bigImage3Urls, promptIndex); + inputImage4 = await fetchSingleImage(node._bigImage4Urls, promptIndex); + } + + if (isMultiImage) { + // Multi-image mode: create 2 jobs for this prompt + console.log(`[BIG] ๐ŸŽฏ Multi-image single: generating 2 variations for prompt #${promptIndex}`); + + node._bigJobs = [ + { + id: 0, + prompt_positive: prompt.positive, + prompt_negative: prompt.negative, + seed: prompt.seed, + state: "pending", + image_url: null, + error: null, + generation_time: 0, + filename: null, + input_image_b64: inputImage, + input_image2_b64: inputImage2, + input_image3_b64: inputImage3, + input_image4_b64: inputImage4, + is_pair: true, + pair_with: 1, + prompt_index: promptIndex, + variation: 1, + }, + { + id: 1, + prompt_positive: prompt.positive, + prompt_negative: prompt.negative, + seed: prompt.seed, + state: "pending", + image_url: null, + error: null, + generation_time: 0, + filename: null, + input_image_b64: inputImage, + input_image2_b64: inputImage2, + input_image3_b64: inputImage3, + input_image4_b64: inputImage4, + is_pair: true, + pair_with: 0, + prompt_index: promptIndex, + variation: 2, + } + ]; + + node._bigStats = { + total: 2, + completed: 0, + success: 0, + failed: 0, + cached: 0, + }; + node._bigStartTime = Date.now(); + node._bigIsGenerating = true; + + updateGeneratedBatchData(); + renderUI(); + + // Generate paired images + await generateJobPair(0, 1, genParams); + + // Finish up + node._bigIsGenerating = false; + updateGeneratedBatchData(); + renderUI(); + + console.log("[BIG] โœ… Multi-image single generation complete (2 variations)!"); + } else { + // Standard mode: 1 job + node._bigJobs = [{ + id: 0, + prompt_positive: prompt.positive, + prompt_negative: prompt.negative, + seed: prompt.seed, + state: "pending", + image_url: null, + error: null, + generation_time: 0, + filename: null, + input_image_b64: inputImage, + input_image2_b64: inputImage2, + input_image3_b64: inputImage3, + input_image4_b64: inputImage4, + }]; + + node._bigStats = { + total: 1, + completed: 0, + success: 0, + failed: 0, + cached: 0, + }; + node._bigStartTime = Date.now(); + node._bigIsGenerating = true; + + updateGeneratedBatchData(); + renderUI(); + + // Generate single image + await generateSingleJob(0, genParams); + + // Finish up + node._bigIsGenerating = false; + updateGeneratedBatchData(); + renderUI(); + + console.log("[BIG] โœ… Single generation complete!"); + } + }; + }); + + // View image buttons and thumbnails + const clickableElements = container.querySelectorAll(".instaraw-big-view-image-btn, .instaraw-big-thumbnail-img"); + clickableElements.forEach(btn => { + btn.onclick = (e) => { + e.stopPropagation(); + const jobId = parseInt(btn.dataset.jobId); + openPhotoSwipe(jobId); + }; + }); + + // Expand prompt buttons + container.querySelectorAll(".instaraw-big-expand-prompt-btn").forEach(btn => { + btn.onclick = (e) => { + e.stopPropagation(); + const jobId = btn.dataset.jobId; + const expandedEl = container.querySelector(`.instaraw-big-prompt-expanded[data-job-id="${jobId}"]`); + if (expandedEl) { + const isExpanded = expandedEl.style.display !== "none"; + expandedEl.style.display = isExpanded ? "none" : "block"; + btn.textContent = isExpanded ? "๐Ÿ“" : "๐Ÿ“โœ•"; + btn.title = isExpanded ? "Expand prompt" : "Collapse prompt"; + } + }; + }); + + // Copy seed buttons + container.querySelectorAll(".instaraw-big-copy-seed-btn").forEach(btn => { + btn.onclick = async (e) => { + e.stopPropagation(); + const seed = btn.dataset.seed; + if (!seed || seed === "N/A") return; + + try { + await navigator.clipboard.writeText(seed); + const originalText = btn.textContent; + btn.textContent = "โœ… Copied!"; + setTimeout(() => { + btn.textContent = originalText; + }, 1000); + } catch (error) { + console.error("[BIG] Failed to copy seed to clipboard:", error); + alert("Failed to copy to clipboard"); + } + }; + }); + + // Copy prompt buttons + container.querySelectorAll(".instaraw-big-copy-prompt-btn").forEach(btn => { + btn.onclick = async (e) => { + e.stopPropagation(); + const prompt = btn.dataset.prompt; + if (!prompt) return; + + try { + await navigator.clipboard.writeText(prompt); + // Show success feedback + const originalText = btn.textContent; + btn.textContent = "โœ… Copied!"; + setTimeout(() => { + btn.textContent = originalText; + }, 1000); + } catch (error) { + console.error("[BIG] Failed to copy to clipboard:", error); + alert("Failed to copy to clipboard"); + } + }; + }); + + // Favorite prompt buttons - communicate with RPG + container.querySelectorAll(".instaraw-big-favorite-prompt-btn").forEach(btn => { + btn.onclick = async (e) => { + e.stopPropagation(); + const prompt = btn.dataset.prompt; + const seed = btn.dataset.seed; + if (!prompt) return; + + // Find connected RPG node + const rpgNode = findConnectedRPG(); + if (!rpgNode) { + alert("No RPG node connected. Connect a Reality Prompt Generator to add favorites."); + return; + } + + // Send favorite request to RPG + try { + // Use custom event to communicate with RPG + const event = new CustomEvent('instaraw-rpg-add-favorite', { + detail: { + prompt: prompt, + seed: seed, + sourceNodeId: node.id, + } + }); + document.dispatchEvent(event); + + // Show success feedback + const originalText = btn.textContent; + btn.textContent = "โญ Added!"; + btn.disabled = true; + setTimeout(() => { + btn.textContent = originalText; + btn.disabled = false; + }, 1500); + } catch (error) { + console.error("[BIG] Failed to add favorite:", error); + alert("Failed to add favorite"); + } + }; + }); + + // Helper to find connected RPG node + const findConnectedRPG = () => { + const promptInput = node.inputs?.find(i => i.name === "prompt_list_positive"); + if (!promptInput || !promptInput.link) return null; + + const link = app.graph.links[promptInput.link]; + if (!link) return null; + + const sourceNode = app.graph.getNodeById(link.origin_id); + // Check for node type (INSTARAW_RealityPromptGenerator) + if (sourceNode?.type === "INSTARAW_RealityPromptGenerator") { + return sourceNode; + } + return null; + }; + + // Stop button - RPG-style smooth cancellation + const stopBtn = container.querySelector(".instaraw-big-stop-btn"); + if (stopBtn) { + stopBtn.onclick = () => { + console.log("[BIG] ๐Ÿ›‘ Stop button clicked - aborting generation"); + + // 1. Abort any in-flight requests immediately + if (node._bigAbortController) { + node._bigAbortController.abort(); + console.log("[BIG] AbortController.abort() called"); + } + + // 2. Mark generation as stopped + node._bigIsGenerating = false; + node._bigAbortController = null; + + // 3. Immediately mark all pending/generating jobs as cancelled + let cancelledCount = 0; + node._bigJobs.forEach(job => { + if (job.state === "pending" || job.state === "generating") { + job.state = "cancelled"; + job.error = "Cancelled by user"; + cancelledCount++; + } + }); + console.log(`[BIG] Marked ${cancelledCount} jobs as cancelled`); + + // 4. Recalculate stats and update UI immediately + updateStats(); + updateGeneratedBatchData(); + updateGeneratedBatchData(); + stopPolling(); + renderUI(); + + console.log("[BIG] โœ… Generation stopped cleanly"); + }; + } + + // 2X Mode toggle checkbox + const toggle2xCheckbox = container.querySelector(".instaraw-big-2x-checkbox"); + if (toggle2xCheckbox) { + toggle2xCheckbox.onchange = (e) => { + const enabled = e.target.checked; + console.log(`[BIG] 2X Mode toggled: ${enabled}`); + toggleMultiImageMode(enabled); + // Re-render to update the UI badges and calculations + renderUI(); + }; + } + + // Retry failed button - actually regenerate all failed jobs + const retryFailedBtn = container.querySelector(".instaraw-big-retry-failed-btn"); + if (retryFailedBtn) { + retryFailedBtn.onclick = async () => { + // Find all failed jobs + const failedJobs = node._bigJobs.filter(j => j.state === JobState.FAILED); + if (failedJobs.length === 0) { + console.log("[BIG] No failed jobs to retry"); + return; + } + + console.log(`[BIG] Retrying ${failedJobs.length} failed jobs...`); + + // Get generation parameters + const genParams = getGenerationParameters(); + if (!genParams) { + alert("Missing generation parameters!"); + return; + } + + // Group failed jobs: pairs together, singles separate + const retryTasks = []; // Array of { type: 'pair'|'single', indices: [idx] or [idx1, idx2] } + const processedJobIds = new Set(); + + failedJobs.forEach(job => { + if (processedJobIds.has(job.id)) return; + + const jobIndex = node._bigJobs.findIndex(j => j.id === job.id); + processedJobIds.add(job.id); + + // Check if this job is part of a pair + if (job.is_pair && job.pair_with !== undefined) { + const pairedJob = node._bigJobs.find(j => j.id === job.pair_with); + if (pairedJob) { + processedJobIds.add(pairedJob.id); + const pairedJobIndex = node._bigJobs.findIndex(j => j.id === pairedJob.id); + const idx1 = Math.min(jobIndex, pairedJobIndex); + const idx2 = Math.max(jobIndex, pairedJobIndex); + retryTasks.push({ type: 'pair', indices: [idx1, idx2] }); + } else { + // Orphaned pair job - retry as single + retryTasks.push({ type: 'single', indices: [jobIndex] }); + } + } else { + retryTasks.push({ type: 'single', indices: [jobIndex] }); + } + }); + + // Reset all failed jobs to pending + failedJobs.forEach(job => { + job.state = JobState.PENDING; + job.error = null; + job.attempts = 0; + }); + + // Also reset paired jobs that weren't failed but their partner was + retryTasks.forEach(task => { + if (task.type === 'pair') { + task.indices.forEach(idx => { + const job = node._bigJobs[idx]; + if (job) { + job.state = JobState.PENDING; + job.error = null; + job.attempts = 0; + } + }); + } + }); + + // Update stats + node._bigStats.failed = 0; + node._bigStats.completed -= failedJobs.length; + node._bigIsGenerating = true; + + // Create AbortController for cancellation + node._bigAbortController = new AbortController(); + const signal = node._bigAbortController.signal; + + updateGeneratedBatchData(); + renderUI(); + + // Process retry tasks with max_parallel limit + const maxParallel = genParams.max_parallel || 5; + // For pairs, each task processes 2 jobs, so halve effective parallelism + const hasPairs = retryTasks.some(t => t.type === 'pair'); + const effectiveParallel = hasPairs ? Math.ceil(maxParallel / 2) : maxParallel; + + let nextTaskIdx = 0; + const processNext = async () => { + while (nextTaskIdx < retryTasks.length && node._bigIsGenerating && !signal.aborted) { + const task = retryTasks[nextTaskIdx++]; + try { + if (task.type === 'pair') { + await generateJobPair(task.indices[0], task.indices[1], genParams, signal); + } else { + await generateSingleJob(task.indices[0], genParams, signal); + } + } catch (e) { + console.error(`[BIG] Retry task error:`, e); + } + if (nextTaskIdx < retryTasks.length && node._bigIsGenerating && !signal.aborted) { + await new Promise(r => setTimeout(r, 222)); + } + } + }; + + // Start workers + const workers = []; + for (let i = 0; i < Math.min(effectiveParallel, retryTasks.length); i++) { + if (i > 0) await new Promise(r => setTimeout(r, 222)); + if (!node._bigIsGenerating || signal.aborted) break; + workers.push(processNext()); + } + + await Promise.allSettled(workers); + + // Finish + node._bigIsGenerating = false; + node._bigAbortController = null; + updateGeneratedBatchData(); + renderUI(); + + console.log("[BIG] โœ… Retry complete!"); + }; + } + + // Clear button + const clearBtn = container.querySelector(".instaraw-big-clear-btn"); + if (clearBtn) { + clearBtn.onclick = () => { + node._bigJobs = []; + node._bigStats = { total: 0, completed: 0, success: 0, failed: 0, cached: 0 }; + node._bigStartTime = null; + updateGeneratedBatchData(); // Clear both widget and properties + renderUI(); + }; + } + + // Download All button + const downloadAllBtn = container.querySelector(".instaraw-big-download-all-btn"); + if (downloadAllBtn) { + downloadAllBtn.onclick = async () => { + const jobsWithImages = node._bigJobs.filter(j => + j.image_url && (j.state === JobState.SUCCESS || j.state === JobState.CACHED) + ); + + if (jobsWithImages.length === 0) { + console.log("[BIG] No images to download"); + return; + } + + console.log(`[BIG] Downloading ${jobsWithImages.length} images...`); + downloadAllBtn.disabled = true; + downloadAllBtn.textContent = "โฌ‡ Downloading..."; + + for (let i = 0; i < jobsWithImages.length; i++) { + const job = jobsWithImages[i]; + try { + // Use stored filename (from disk), fallback to generated name + const filename = job.filename || `big_${job.id}_${Date.now()}.png`; + + // Handle base64 data URLs + if (job.image_url.startsWith("data:")) { + const response = await fetch(job.image_url); + const blob = await response.blob(); + const url = URL.createObjectURL(blob); + const a = document.createElement("a"); + a.href = url; + a.download = filename; + document.body.appendChild(a); + a.click(); + document.body.removeChild(a); + URL.revokeObjectURL(url); + } else { + // Handle regular URLs + const response = await fetch(job.image_url); + const blob = await response.blob(); + const url = URL.createObjectURL(blob); + const a = document.createElement("a"); + a.href = url; + a.download = filename; + document.body.appendChild(a); + a.click(); + document.body.removeChild(a); + URL.revokeObjectURL(url); + } + + // Small delay between downloads to avoid browser blocking + if (i < jobsWithImages.length - 1) { + await new Promise(r => setTimeout(r, 200)); + } + } catch (e) { + console.error(`[BIG] Failed to download image ${job.id}:`, e); + } + } + + console.log("[BIG] Download complete"); + downloadAllBtn.disabled = false; + downloadAllBtn.textContent = `โฌ‡ Download All (${jobsWithImages.length})`; + }; + } + + // Generate All button (RPG-style UI generation) + const generateAllBtn = container.querySelector(".instaraw-big-generate-all-btn"); + if (generateAllBtn) { + generateAllBtn.onclick = async () => { + console.log("[BIG] Generate All clicked"); + + // Get node parameters - use node.id directly as primary, widget as fallback + const nodeId = node._ni_widget?.value || String(node.id); + console.log("[BIG] Node ID:", nodeId, "node.id:", node.id, "widget:", node._ni_widget?.value); + if (!nodeId) { + alert("Node ID not found! Please reload the page."); + return; + } + + // Find connected RPG node and read prompts + const promptData = getConnectedPromptData(); + if (!promptData) { + alert("Cannot generate: No prompts found!\n\nMake sure:\n1. RPG node is connected to BIG\n2. Prompts are generated in RPG\n3. Workflow connections are valid"); + return; + } + + const { prompts_positive, prompts_negative, seeds } = promptData; + const numPrompts = prompts_positive.length; + + if (numPrompts === 0) { + alert("No prompts to generate! Generate prompts in RPG first."); + return; + } + + // Get generation parameters from node inputs + const genParams = getGenerationParameters(); + if (!genParams) { + alert("Missing generation parameters! Ensure API key, provider, and model are connected."); + return; + } + + // Calculate total images based on multi-image mode + const willUseMultiImage = genParams.multi_image && genParams.provider === "wavespeed.ai"; + const totalImages = willUseMultiImage ? numPrompts * 2 : numPrompts; + const costPerImage = willUseMultiImage ? "$0.07" : "$0.14"; + const totalCost = (totalImages * (willUseMultiImage ? 0.07 : 0.14)).toFixed(2); + + // Confirm generation with cost info + const confirmMsg = willUseMultiImage + ? `Generate ${totalImages} images (${numPrompts} prompts ร— 2 variations)?\n\nโšก Multi-image mode: ${costPerImage}/image\n๐Ÿ’ฐ Estimated cost: $${totalCost}` + : `Generate ${totalImages} images?\n\n๐Ÿ’ฐ Estimated cost: $${totalCost} (${costPerImage}/image)`; + + if (!confirm(confirmMsg)) { + return; + } + + // Check enable_img2img + const enableImg2Img = getBooleanValue("enable_img2img"); + + // Detect mode and fetch images if I2I AND enable_img2img is true + const imagesInfo = getConnectedImagesInfo(); + // Override mode based on enable_img2img widget + const effectiveMode = enableImg2Img ? imagesInfo.mode : "txt2img"; + node._bigMode = effectiveMode; + node._bigImageCount = enableImg2Img ? imagesInfo.imageCount : 0; + console.log(`[BIG] ========== GENERATION START ==========`); + console.log(`[BIG] enable_img2img: ${enableImg2Img}, Mode: ${effectiveMode}`); + console.log(`[BIG] Image count: ${node._bigImageCount}`); + + // Helper to fetch all images from URL array to base64 + const fetchAllImagesAsBase64 = async (urls, label) => { + const result = []; + if (!urls || urls.length === 0) return result; + console.log(`[BIG] Fetching ${urls.length} ${label} images...`); + for (let i = 0; i < urls.length; i++) { + const url = urls[i]; + const b64 = await fetchImageAsBase64(url); + if (b64) { + result.push(b64); + } else { + console.error(`[BIG] โœ— Failed to fetch ${label}[${i}]`); + } + } + return result; + }; + + // Only fetch images if enable_img2img is true + let imageBase64Array = []; + let image2Base64Array = []; + let image3Base64Array = []; + let image4Base64Array = []; + + if (enableImg2Img) { + // Fetch images as base64 for I2I mode (images/image_1) + if (effectiveMode === "img2img" && imagesInfo.imageUrls.length > 0) { + console.log(`[BIG] I2I mode - fetching primary images...`); + imageBase64Array = await fetchAllImagesAsBase64(imagesInfo.imageUrls, "image_1"); + console.log(`[BIG] Fetched ${imageBase64Array.length}/${imagesInfo.imageUrls.length} primary images`); + } + + // Fetch additional image inputs (images2, images3, images4) + image2Base64Array = await fetchAllImagesAsBase64(node._bigImage2Urls, "image_2"); + image3Base64Array = await fetchAllImagesAsBase64(node._bigImage3Urls, "image_3"); + image4Base64Array = await fetchAllImagesAsBase64(node._bigImage4Urls, "image_4"); + + console.log(`[BIG] Additional images - image_2: ${image2Base64Array.length}, image_3: ${image3Base64Array.length}, image_4: ${image4Base64Array.length}`); + } else { + console.log(`[BIG] T2I mode - enable_img2img is OFF, skipping image fetch`); + } + + // Check for multi-image mode BEFORE creating jobs + // Multi-image mode: each prompt generates 2 images (variations) + const isMultiImage = genParams.multi_image && genParams.provider === "wavespeed.ai"; + + // Initialize jobs - in multi-image mode, create 2 jobs per prompt + if (isMultiImage) { + console.log(`[BIG] ๐ŸŽฏ MULTI-IMAGE MODE: Creating 2 jobs per prompt (${prompts_positive.length} prompts โ†’ ${prompts_positive.length * 2} images)`); + node._bigJobs = []; + let jobId = 0; + + for (let promptIdx = 0; promptIdx < prompts_positive.length; promptIdx++) { + const prompt = prompts_positive[promptIdx]; + const negPrompt = prompts_negative[promptIdx] || ""; + const seed = seeds[promptIdx] || -1; + + // Get input images for I2I mode (cycle through if fewer images than prompts) + const getImageAtIdx = (arr, idx) => arr.length > 0 ? arr[idx % arr.length] : null; + const inputImage = getImageAtIdx(imageBase64Array, promptIdx); + const inputImage2 = getImageAtIdx(image2Base64Array, promptIdx); + const inputImage3 = getImageAtIdx(image3Base64Array, promptIdx); + const inputImage4 = getImageAtIdx(image4Base64Array, promptIdx); + + // Create 2 jobs for this prompt (will be generated in one API call) + const job1Id = jobId++; + const job2Id = jobId++; + + const job1 = { + id: job1Id, + prompt_positive: prompt, + prompt_negative: negPrompt, + seed: seed, + state: "pending", + image_url: null, + error: null, + generation_time: 0, + filename: null, + input_image_b64: inputImage, + input_image2_b64: inputImage2, + input_image3_b64: inputImage3, + input_image4_b64: inputImage4, + is_pair: true, + pair_with: job2Id, + prompt_index: promptIdx, + variation: 1, + }; + + const job2 = { + id: job2Id, + prompt_positive: prompt, + prompt_negative: negPrompt, + seed: seed, + state: "pending", + image_url: null, + error: null, + generation_time: 0, + filename: null, + input_image_b64: inputImage, + input_image2_b64: inputImage2, + input_image3_b64: inputImage3, + input_image4_b64: inputImage4, + is_pair: true, + pair_with: job1Id, + prompt_index: promptIdx, + variation: 2, + }; + + node._bigJobs.push(job1, job2); + } + + console.log(`[BIG] Created ${node._bigJobs.length} jobs (${prompts_positive.length} prompts ร— 2 variations)`); + } else { + // Standard mode: 1 job per prompt + console.log(`[BIG] Creating ${prompts_positive.length} jobs...`); + node._bigJobs = prompts_positive.map((prompt, idx) => { + // Helper to get image at index with cycling + const getImageAtIdx = (arr, i) => arr.length > 0 ? arr[i % arr.length] : null; + + const job = { + id: idx, + prompt_positive: prompt, + prompt_negative: prompts_negative[idx] || "", + seed: seeds[idx] || -1, + state: "pending", + image_url: null, + error: null, + generation_time: 0, + filename: null, + input_image_b64: getImageAtIdx(imageBase64Array, idx), + input_image2_b64: getImageAtIdx(image2Base64Array, idx), + input_image3_b64: getImageAtIdx(image3Base64Array, idx), + input_image4_b64: getImageAtIdx(image4Base64Array, idx), + }; + + return job; + }); + console.log(`[BIG] ${node._bigJobs.length} jobs created`); + } + + const totalJobs = node._bigJobs.length; + node._bigStats = { + total: totalJobs, + completed: 0, + success: 0, + failed: 0, + cached: 0, + }; + node._bigStartTime = Date.now(); + node._bigIsGenerating = true; + + // Create AbortController for smooth cancellation (RPG pattern) + node._bigAbortController = new AbortController(); + const signal = node._bigAbortController.signal; + + // Save immediately so state survives tab switches during generation! + updateGeneratedBatchData(); + renderUI(); + + // PARALLEL GENERATION with max_parallel limit + // Uses a semaphore pattern to limit concurrent API calls + const maxParallel = genParams.max_parallel || 5; + console.log(`[BIG] Starting generation of ${totalJobs} images (max ${maxParallel} concurrent)...`); + + let nextJobIndex = 0; + let activeCount = 0; + const results = []; + + // Worker function that processes jobs from the queue + const processNextJob = async () => { + while (nextJobIndex < totalJobs && node._bigIsGenerating && !signal.aborted) { + // Multi-image mode: process 2 jobs at once (same prompt, 2 variations) + if (isMultiImage && nextJobIndex + 1 < totalJobs) { + const jobIndex1 = nextJobIndex++; + const jobIndex2 = nextJobIndex++; + activeCount += 2; + + console.log(`[BIG] ๐ŸŽฏ Starting paired jobs ${jobIndex1 + 1} & ${jobIndex2 + 1}/${totalJobs} (${activeCount} active)`); + + try { + await generateJobPair(jobIndex1, jobIndex2, genParams, signal); + } catch (e) { + console.error(`[BIG] Paired jobs ${jobIndex1}+${jobIndex2} error:`, e); + } + + activeCount -= 2; + results.push(jobIndex1, jobIndex2); + } else { + // Single job mode (or last odd job in multi-image) + const jobIndex = nextJobIndex++; + activeCount++; + + console.log(`[BIG] Starting job ${jobIndex + 1}/${totalJobs} (${activeCount} active, max ${maxParallel})`); + + try { + await generateSingleJob(jobIndex, genParams, signal); + } catch (e) { + console.error(`[BIG] Job ${jobIndex} error:`, e); + } + + activeCount--; + results.push(jobIndex); + } + + // Small stagger between starting new jobs (222ms) + if (nextJobIndex < totalJobs && node._bigIsGenerating && !signal.aborted) { + await new Promise(resolve => setTimeout(resolve, 222)); + } + } + }; + + // Start up to max_parallel workers (halve for multi-image since each processes 2) + const effectiveParallel = isMultiImage ? Math.ceil(maxParallel / 2) : maxParallel; + const numPairs = isMultiImage ? Math.ceil(totalJobs / 2) : totalJobs; + const numWorkers = Math.min(effectiveParallel, numPairs); + + console.log(`[BIG] ๐Ÿš€ Parallel config: ${numWorkers} workers for ${numPairs} ${isMultiImage ? 'pairs' : 'jobs'} (max_parallel=${maxParallel})`); + + const workers = []; + for (let i = 0; i < numWorkers; i++) { + // Stagger the initial workers by 222ms each + if (i > 0) { + await new Promise(resolve => setTimeout(resolve, 222)); + } + if (!node._bigIsGenerating || signal.aborted) break; + console.log(`[BIG] ๐Ÿš€ Starting worker ${i + 1}/${numWorkers}`); + workers.push(processNextJob()); + } + + // Wait for all workers to complete + await Promise.allSettled(workers); + + // Finish up + node._bigIsGenerating = false; + node._bigAbortController = null; // Clean up controller + updateGeneratedBatchData(); + renderUI(); + + console.log(`[BIG] โœ… Generation complete! ${results.length}/${totalJobs} jobs processed`); + }; + } + + // Individual retry buttons (RPG-style: actually regenerate the image) + container.querySelectorAll(".instaraw-big-job-retry-btn").forEach(btn => { + btn.onclick = async (e) => { + e.stopPropagation(); + const jobId = parseInt(btn.dataset.jobId); + const jobIndex = node._bigJobs.findIndex(j => j.id === jobId); + + if (jobIndex === -1) { + console.error("[BIG] Job not found:", jobId); + return; + } + + const job = node._bigJobs[jobIndex]; + + // Check if this job is part of a pair (2X mode) + if (job.is_pair && job.pair_with !== undefined) { + const pairedJobIndex = node._bigJobs.findIndex(j => j.id === job.pair_with); + const pairedJob = pairedJobIndex !== -1 ? node._bigJobs[pairedJobIndex] : null; + + if (pairedJob) { + // Confirm retry for both jobs in the pair + if (!confirm(`Retry generating both variations?\n(2X mode generates 2 images together)`)) { + return; + } + + // Get generation parameters + const genParams = getGenerationParameters(); + if (!genParams) { + alert("Missing generation parameters!"); + return; + } + + // Reset both jobs + job.state = "pending"; + job.error = null; + pairedJob.state = "pending"; + pairedJob.error = null; + renderUI(); + + // Regenerate the pair together + const idx1 = Math.min(jobIndex, pairedJobIndex); + const idx2 = Math.max(jobIndex, pairedJobIndex); + console.log(`[BIG] Retrying paired jobs ${idx1} & ${idx2}`); + await generateJobPair(idx1, idx2, genParams); + return; + } + } + + // Single job retry (non-paired or orphaned) + if (!confirm(`Retry generating this image?`)) { + return; + } + + // Get generation parameters + const genParams = getGenerationParameters(); + if (!genParams) { + alert("Missing generation parameters!"); + return; + } + + // Reset job state + job.state = "pending"; + job.error = null; + renderUI(); + + // Regenerate + console.log(`[BIG] Retrying job ${jobIndex}`); + await generateSingleJob(jobIndex, genParams); + }; + }); + }; + + // Add the widget to the node + console.log("[BIG] Adding DOM widget to node"); + const widget = node.addDOMWidget("batch_gen_ui", "customtext", container, { + serialize: false, + hideOnZoom: false, + }); + + console.log("[BIG] Widget added:", widget); + + widget.computeSize = () => { + return [node.size[0], cachedHeight + 20]; + }; + + // Store renderUI reference on node for hooks to use + node._renderUI = renderUI; + + // Set up widget change callbacks to re-render UI reactively + const multiImageWidget = node.widgets?.find(w => w.name === "multi_image"); + if (multiImageWidget) { + const originalCallback = multiImageWidget.callback; + multiImageWidget.callback = function(value) { + originalCallback?.call(this, value); + console.log("[BIG] multi_image changed:", value); + renderUI(); + }; + } + + // DEFERRED INITIALIZATION - Prevent UI flash on workflow load + // We delay the first render to allow onConfigure to run first (if loading from workflow) + // This prevents the flash where idle state shows briefly before restored state + node._pendingInitRender = true; + + // Show loading state immediately (prevents blank node) + renderUI(); + + // Actual content render after short delay - gives onConfigure a chance to run first + setTimeout(() => { + // If onConfigure already ran, it set _bigInitialized and rendered + // So we only render here if we're a NEW node (not loading from workflow) + if (!node._bigInitialized) { + console.log("[BIG] Initial render (new node, no workflow data)"); + + // Check for image connections + const imagesInput = node.inputs?.find(input => input.name === "images"); + if (imagesInput && imagesInput.link) { + const link = app.graph?.links?.[imagesInput.link]; + if (link) { + const sourceNode = app.graph.getNodeById(link.origin_id); + if (sourceNode) { + node._updateModeFromSource(sourceNode); + } + } + } + updateAdditionalImages(); + + node._bigInitialized = true; + renderUI(); + } + node._pendingInitRender = false; + }, 250); // Slightly longer than onConfigure's 200ms delay + + // Fallback for slow workflow loads + setTimeout(() => { + if (!node._bigInitialized) { + console.log("[BIG] Fallback initialization (very slow load)"); + node._bigInitialized = true; + renderUI(); + } + }, 600); + + console.log("[BIG] Node setup complete (render deferred)"); + return; + }; + + // === onConnectionsChange Hook (for reactive mode detection) === + const onConnectionsChange = nodeType.prototype.onConnectionsChange; + nodeType.prototype.onConnectionsChange = function(side, slot, connect, link_info, output) { + const result = onConnectionsChange?.apply(this, arguments); + + // Detect input connection changes (link_info can be null on workflow load) + if (side === 1 && link_info) { // INPUT side + const imagesInputIndex = this.inputs?.findIndex(input => input.name === "images"); + const providerInputIndex = this.inputs?.findIndex(input => input.name === "provider"); + + if (slot === imagesInputIndex) { + if (connect) { + // Connected - traverse to find source node and mode + const link = this.graph?.links?.[link_info.id]; + if (link) { + const sourceNode = this.graph.getNodeById(link.origin_id); + if (sourceNode) { + this._updateModeFromSource(sourceNode); + } + } + } else { + // Disconnected - clear mode + this._bigMode = "txt2img"; + this._bigImageCount = 0; + this._bigImageUrls = []; + } + } + } + + // Re-render when connections change + if (this._renderUI) { + setTimeout(() => { + console.log(`[BIG] Re-rendering after connection change. Mode: ${this._bigMode}`); + this._renderUI(); + }, 100); + } + return result; + }; + + const onResize = nodeType.prototype.onResize; + nodeType.prototype.onResize = function(size) { + onResize?.apply(this, arguments); + if (this._updateCachedHeight) { + clearTimeout(this._resizeTimeout); + this._resizeTimeout = setTimeout(() => this._updateCachedHeight(), 50); + } + }; + + const onConfigure = nodeType.prototype.onConfigure; + nodeType.prototype.onConfigure = function(data) { + onConfigure?.apply(this, arguments); + + // DEBUG: Log what's happening during tab switch/workflow load + const widgetValue = this.widgets?.find(w => w.name === "generated_batch_data")?.value; + const propValue = this.properties?.big_jobs_data; + // Try to get session key (need to access getSessionKey via closure) + const sessionKey = this._getSessionKey ? this._getSessionKey() : `instaraw_big_session_default_${this.id}`; + const sessionValue = sessionStorage.getItem(sessionKey); + console.log("[BIG] onConfigure called:", { + hasData: !!data, + sessionValueLength: sessionValue?.length || 0, + widgetValueLength: widgetValue?.length || 0, + propValueLength: propValue?.length || 0, + currentJobsLength: this._bigJobs?.length || 0 + }); + + // IMPORTANT: Restore state from session storage / properties / widget + // Session storage survives tab switches (ComfyUI recreates nodes on tab switch!) + setTimeout(() => { + console.log("[BIG] onConfigure: Restoring state..."); + + // Use unified restore function (checks: sessionStorage โ†’ properties โ†’ widget) + if (this._restoreFromWidget) { + this._restoreFromWidget(); + } + + // Mark as initialized so mode sync interval can start running + this._bigInitialized = true; + if (this._renderUI) this._renderUI(); + }, 200); + }; + + // === onRemoved Hook (Cleanup) === + const onRemoved = nodeType.prototype.onRemoved; + nodeType.prototype.onRemoved = function() { + if (this._modeSyncInterval) { + clearInterval(this._modeSyncInterval); + this._modeSyncInterval = null; + } + if (this._heightSyncInterval) { + clearInterval(this._heightSyncInterval); + this._heightSyncInterval = null; + } + console.log(`[BIG] Cleaned up node ${this.id}`); + onRemoved?.apply(this, arguments); + }; + } + }, + + setup() { + console.log("[BIG] Extension setup() called - loading CSS"); + // Add CSS (following RPG's progress-item styling exactly) + const style = document.createElement("style"); + style.textContent = ` + /* === Container (RPG Style) === */ + .instaraw-big-container { + background: #0d0f12; + padding: 16px; + border-radius: 4px; + font-family: 'Inter', -apple-system, BlinkMacSystemFont, 'Segoe UI', system-ui, sans-serif; + color: #f9fafb; + } + + /* === Idle State === */ + .instaraw-big-idle { + display: flex; + flex-direction: column; + align-items: center; + justify-content: center; + padding: 40px 20px; + text-align: center; + } + + .instaraw-big-idle-icon { + font-size: 48px; + margin-bottom: 12px; + } + + .instaraw-big-idle-text { + font-size: 16px; + font-weight: 600; + color: #f9fafb; + margin-bottom: 8px; + } + + .instaraw-big-idle-hint { + font-size: 12px; + color: rgba(249, 250, 251, 0.5); + max-width: 280px; + } + + .instaraw-big-idle-mode-badges { + display: flex; + gap: 8px; + margin: 8px 0; + flex-wrap: wrap; + justify-content: center; + } + + .instaraw-big-idle-instructions { + font-size: 12px; + color: rgba(249, 250, 251, 0.5); + max-width: 280px; + margin-top: 8px; + } + + .instaraw-big-preview-box { + margin-top: 20px; + padding: 12px 20px; + background: rgba(99, 102, 241, 0.1); + border: 1px solid rgba(99, 102, 241, 0.3); + border-radius: 6px; + display: flex; + align-items: center; + gap: 10px; + max-width: 400px; + } + + .instaraw-big-preview-icon { + font-size: 24px; + line-height: 1; + } + + .instaraw-big-preview-text { + font-size: 14px; + color: #f9fafb; + line-height: 1.4; + } + + .instaraw-big-preview-text strong { + font-weight: 700; + color: #818cf8; + } + + /* === Preview State (prompts detected) === */ + .instaraw-big-preview { + /* No extra background/border - container already styled */ + } + + .instaraw-big-preview-header { + margin-top: 12px; + margin-bottom: 12px; + font-size: 13px; + font-weight: 600; + color: #e5e7eb; + } + + .instaraw-big-preview-list { + display: flex; + flex-direction: column; + gap: 8px; + margin-bottom: 12px; + max-height: 400px; + overflow-y: auto; + } + + .instaraw-big-preview-item { + display: flex; + align-items: center; + justify-content: space-between; + gap: 10px; + padding: 10px 12px; + background: rgba(255, 255, 255, 0.04); + border: 1px solid rgba(255, 255, 255, 0.1); + border-radius: 4px; + } + + .instaraw-big-preview-item-more { + padding: 8px 12px; + text-align: center; + font-size: 11px; + color: #9ca3af; + font-style: italic; + } + + .instaraw-big-preview-prompt { + flex: 1; + font-size: 11px; + color: #d1d5db; + overflow: hidden; + text-overflow: ellipsis; + white-space: nowrap; + } + + .instaraw-big-generate-single-btn { + padding: 4px 10px; + border-radius: 4px; + font-size: 10px; + font-weight: 600; + cursor: pointer; + border: 1px solid rgba(99, 102, 241, 0.4); + background: rgba(99, 102, 241, 0.2); + color: #818cf8; + white-space: nowrap; + } + + .instaraw-big-generate-single-btn:hover { + background: rgba(99, 102, 241, 0.3); + border-color: #818cf8; + } + + .instaraw-big-preview-actions { + margin-top: 12px; + } + + /* === Buttons - Clean, Minimal, Instant === */ + .instaraw-big-btn-primary { + width: 100%; + background: #6366f1; + color: #ffffff; + border: none; + padding: 12px 20px; + border-radius: 4px; + cursor: pointer; + font-weight: 600; + font-size: 13px; + } + + .instaraw-big-btn-primary:hover { + background: #4f46e5; + } + + .instaraw-big-btn-primary:disabled { + background: #374151; + cursor: not-allowed; + color: rgba(255, 255, 255, 0.4); + } + + .instaraw-big-btn-secondary { + flex: 1; + background: rgba(255, 255, 255, 0.05); + color: #9ca3af; + border: 1px solid rgba(255, 255, 255, 0.08); + padding: 8px 12px; + border-radius: 4px; + cursor: pointer; + font-weight: 500; + font-size: 11px; + } + + .instaraw-big-btn-secondary:hover { + background: rgba(255, 255, 255, 0.08); + color: #e5e7eb; + } + + .instaraw-big-btn-secondary:disabled { + background: rgba(255, 255, 255, 0.02); + cursor: not-allowed; + color: rgba(255, 255, 255, 0.2); + border-color: rgba(255, 255, 255, 0.04); + } + + /* === Actions === */ + .instaraw-big-actions { + display: flex; + flex-direction: column; + gap: 12px; + margin-bottom: 12px; + } + + .instaraw-big-actions-row { + display: flex; + gap: 6px; + } + + /* === Generation Progress Section (RPG pattern) === */ + .instaraw-big-generation-progress { + /* No extra padding - container already styled */ + } + + /* === Top Bar (RPG Style) === */ + .instaraw-big-topbar { + display: flex; + flex-wrap: wrap; + justify-content: space-between; + align-items: flex-start; + gap: 12px; + margin-bottom: 12px; + } + + .instaraw-big-mode-card-inner { + display: flex; + justify-content: space-between; + align-items: flex-start; + gap: 16px; + } + + .instaraw-big-mode-card-left { + display: flex; + flex-direction: column; + gap: 6px; + } + + .instaraw-big-mode-card-brand { + display: flex; + flex-direction: column; + align-items: flex-end; + flex-shrink: 0; + } + + .instaraw-big-topbar-logo { + width: 120px; + height: auto; + opacity: 0.85; + } + + .instaraw-big-topbar-version { + font-family: monospace; + font-size: 10px; + color: rgba(255, 255, 255, 0.5); + white-space: nowrap; + margin-top: 2px; + } + + .instaraw-big-mode-card { + flex: 1 1 200px; + border: 1px solid rgba(255, 255, 255, 0.08); + background: rgba(255, 255, 255, 0.03); + border-radius: 4px; + padding: 12px; + display: flex; + flex-direction: column; + gap: 8px; + } + + .instaraw-big-mode-indicator { + display: flex; + align-items: center; + gap: 10px; + } + + .instaraw-big-mode-badge { + display: inline-flex; + align-items: center; + gap: 6px; + padding: 6px 14px; + font-size: 12px; + font-weight: 700; + border-radius: 4px; + } + + .instaraw-big-mode-txt2img { + background: linear-gradient(135deg, #7c3aed 0%, #6d28d9 100%); + border: 2px solid #a78bfa; + color: #ffffff; + } + + .instaraw-big-mode-img2img { + background: linear-gradient(135deg, #3b82f6 0%, #2563eb 100%); + border: 2px solid #60a5fa; + color: #ffffff; + } + + /* Multi-image mode badge (50% cost savings) */ + .instaraw-big-multi-image-badge { + background: linear-gradient(135deg, #10b981 0%, #059669 100%); + border: 2px solid #34d399; + color: #ffffff; + font-size: 10px; + padding: 4px 8px; + } + + /* Warning badge when multi-image is enabled but provider is not Wavespeed */ + .instaraw-big-multi-image-warning { + background: linear-gradient(135deg, #f59e0b 0%, #d97706 100%); + border: 2px solid #fbbf24; + color: #ffffff; + font-size: 9px; + padding: 4px 8px; + } + + /* Multi-image info banner */ + .instaraw-big-multi-image-banner { + display: flex; + justify-content: space-between; + align-items: center; + padding: 10px 14px; + background: linear-gradient(135deg, rgba(16, 185, 129, 0.15) 0%, rgba(5, 150, 105, 0.15) 100%); + border: 1px solid rgba(52, 211, 153, 0.4); + border-radius: 6px; + margin-bottom: 12px; + font-size: 12px; + color: #34d399; + } + + .instaraw-big-multi-image-banner strong { + color: #10b981; + } + + .instaraw-big-cost-savings { + font-weight: 600; + color: #4ade80; + background: rgba(74, 222, 128, 0.1); + padding: 4px 8px; + border-radius: 4px; + font-size: 11px; + } + + .instaraw-big-aspect-warning { + background: rgba(245, 158, 11, 0.1); + border: 1px solid rgba(245, 158, 11, 0.3); + border-radius: 6px; + padding: 8px 12px; + margin-bottom: 12px; + font-size: 11px; + color: #fbbf24; + } + + /* Model Warning (unsupported model) */ + .instaraw-big-model-warning { + background: rgba(239, 68, 68, 0.15); + border: 1px solid rgba(239, 68, 68, 0.4); + border-radius: 6px; + padding: 12px 14px; + margin-bottom: 12px; + font-size: 12px; + color: #fca5a5; + line-height: 1.5; + } + + .instaraw-big-model-warning strong { + color: #f87171; + } + + .instaraw-big-model-warning small { + color: rgba(252, 165, 165, 0.8); + } + + /* 2X Mode Toggle */ + .instaraw-big-2x-toggle { + display: flex; + align-items: center; + gap: 8px; + cursor: pointer; + font-size: 12px; + font-weight: 600; + color: #34d399; + padding-top: 6px; + user-select: none; + } + + .instaraw-big-2x-toggle:hover { + color: #4ade80; + } + + .instaraw-big-2x-checkbox { + width: 16px; + height: 16px; + accent-color: #10b981; + cursor: pointer; + } + + .instaraw-big-mode-source { + font-size: 11px; + color: rgba(249, 250, 251, 0.6); + margin-top: 9px; + } + + /* === Reference Images Preview (RPG Style) === */ + .instaraw-big-image-preview { + background: rgba(99, 102, 241, 0.08); + border: 1px solid rgba(99, 102, 241, 0.2); + border-radius: 6px; + padding: 12px; + margin-bottom: 12px; + } + + .instaraw-big-image-preview-header { + display: flex; + align-items: center; + justify-content: space-between; + margin-bottom: 10px; + font-size: 12px; + font-weight: 500; + color: #e5e7eb; + } + + .instaraw-big-image-preview-header-right { + display: flex; + align-items: center; + gap: 8px; + } + + .instaraw-big-sync-ails-btn { + background: rgba(99, 102, 241, 0.2); + border: 1px solid rgba(99, 102, 241, 0.4); + color: #818cf8; + padding: 3px 8px; + border-radius: 4px; + font-size: 10px; + cursor: pointer; + } + + .instaraw-big-sync-ails-btn:hover { + background: rgba(99, 102, 241, 0.3); + border-color: #818cf8; + } + + .instaraw-big-sync-ails-btn:disabled { + opacity: 0.6; + cursor: not-allowed; + } + + .instaraw-big-image-preview-count { + font-size: 11px; + color: #818cf8; + font-weight: 500; + } + + /* Sync Badge & Button Styles */ + .instaraw-big-sync-badge { + font-size: 10px; + font-weight: 500; + padding: 2px 6px; + border-radius: 4px; + white-space: nowrap; + } + + .instaraw-big-sync-match { + background: rgba(34, 197, 94, 0.15); + color: #22c55e; + border: 1px solid rgba(34, 197, 94, 0.3); + } + + .instaraw-big-sync-mismatch { + background: rgba(245, 158, 11, 0.15); + color: #f59e0b; + border: 1px solid rgba(245, 158, 11, 0.3); + } + + .instaraw-big-sync-no-prompts { + background: rgba(107, 114, 128, 0.15); + color: #9ca3af; + border: 1px solid rgba(107, 114, 128, 0.3); + } + + .instaraw-big-sync-hint { + font-size: 9px; + color: #9ca3af; + margin-left: 6px; + font-style: italic; + } + + /* Clean Reference Image Grid (Single AIL Input) */ + .instaraw-big-ref-grid { + display: flex; + flex-wrap: wrap; + gap: 6px; + padding: 8px 0; + } + + .instaraw-big-ref-thumb { + position: relative; + width: 48px; + height: 48px; + border-radius: 4px; + overflow: hidden; + background: rgba(0, 0, 0, 0.3); + border: 1px solid rgba(255, 255, 255, 0.1); + } + + .instaraw-big-ref-thumb img { + width: 100%; + height: 100%; + object-fit: cover; + } + + .instaraw-big-ref-index { + position: absolute; + bottom: 2px; + right: 2px; + font-size: 9px; + font-weight: 600; + color: white; + background: rgba(0, 0, 0, 0.6); + padding: 1px 4px; + border-radius: 4px; + line-height: 1; + } + + .instaraw-big-ref-repeat-item { + opacity: 0.7; + border-style: dashed; + } + + .instaraw-big-image-preview-grid { + display: grid; + grid-template-columns: repeat(auto-fill, minmax(70px, 1fr)); + gap: 8px; + max-height: 180px; + overflow-y: auto; + } + + .instaraw-big-image-preview-item { + position: relative; + aspect-ratio: 1; + border-radius: 4px; + overflow: hidden; + border: 2px solid #4b5563; + } + + .instaraw-big-image-preview-item:hover { + border-color: #6366f1; + } + + .instaraw-big-image-preview-item img { + width: 100%; + height: 100%; + object-fit: cover; + } + + .instaraw-big-image-preview-label { + position: absolute; + bottom: 0; + left: 0; + right: 0; + background: rgba(0, 0, 0, 0.75); + padding: 2px 4px; + font-size: 10px; + color: white; + text-align: center; + font-weight: 500; + } + + .instaraw-big-image-preview-more { + display: flex; + align-items: center; + justify-content: center; + aspect-ratio: 1; + border-radius: 4px; + background: rgba(99, 102, 241, 0.2); + border: 2px dashed #6366f1; + color: #818cf8; + font-size: 12px; + font-weight: 600; + } + + /* Optimal aspect ratio grid for all images */ + .instaraw-big-aspect-grid-container { + background: #1a1a2e; + border-radius: 8px; + padding: 8px; + border: 1px solid #374151; + } + + .instaraw-big-aspect-grid { + border-radius: 4px; + overflow: hidden; + } + + .instaraw-big-aspect-grid-item { + aspect-ratio: 1; + overflow: hidden; + background: #0d0d1a; + } + + .instaraw-big-aspect-grid-item img { + width: 100%; + height: 100%; + object-fit: cover; + display: block; + } + + .instaraw-big-aspect-grid-item:hover { + opacity: 0.85; + } + + .instaraw-big-aspect-label { + background: #4f46e5; + color: white; + padding: 2px 6px; + border-radius: 4px; + font-size: 10px; + font-weight: 600; + margin-left: 6px; + } + + .instaraw-big-image-input-section { + margin-bottom: 10px; + } + + .instaraw-big-image-input-section:last-child { + margin-bottom: 0; + } + + .instaraw-big-image-input-label { + font-size: 11px; + color: #9ca3af; + margin-bottom: 6px; + display: flex; + align-items: center; + gap: 4px; + } + + .instaraw-big-image-input-count { + color: #6b7280; + font-size: 10px; + } + + .instaraw-big-model-info { + font-size: 11px; + color: #9ca3af; + font-weight: 500; + } + + /* === KPI Row (RPG Style) === */ + .instaraw-big-kpi-row { + display: flex; + flex: 1 1 240px; + gap: 8px; + justify-content: flex-end; + flex-wrap: wrap; + } + + .instaraw-big-kpi { + flex: 1 1 60px; + border: 1px solid rgba(255, 255, 255, 0.08); + background: rgba(255, 255, 255, 0.02); + border-radius: 4px; + padding: 8px 10px; + display: flex; + flex-direction: column; + justify-content: center; + align-items: center; + gap: 2px; + min-width: 60px; + } + + .instaraw-big-kpi span { + font-size: 9px; + text-transform: uppercase; + letter-spacing: 0.5px; + color: rgba(249, 250, 251, 0.5); + font-weight: 500; + } + + .instaraw-big-kpi strong { + font-size: 18px; + font-weight: 700; + color: #a78bfa; + font-variant-numeric: tabular-nums; + } + + .instaraw-big-kpi.success strong { color: #4ade80; } + .instaraw-big-kpi.cached strong { color: #c084fc; } + .instaraw-big-kpi.failed strong { color: #f87171; } + + .instaraw-big-progress-header { + display: flex; + justify-content: space-between; + align-items: center; + margin-bottom: 12px; + } + + .instaraw-big-progress-header h4 { + margin: 0; + font-size: 13px; + font-weight: 600; + color: #9ca3af; + } + + /* === Stop Button (Minimal, Inline) === */ + .instaraw-big-stop-btn { + background: rgba(239, 68, 68, 0.1); + color: #f87171; + border: 1px solid rgba(239, 68, 68, 0.2); + padding: 4px 10px; + border-radius: 4px; + cursor: pointer; + font-weight: 500; + font-size: 11px; + } + + .instaraw-big-stop-btn:hover { + background: rgba(239, 68, 68, 0.2); + border-color: rgba(239, 68, 68, 0.4); + } + + .instaraw-big-stat { + font-size: 12px; + font-weight: 600; + color: #9ca3af; + } + + .instaraw-big-stat.success { color: #4ade80; } + .instaraw-big-stat.cached { color: #c084fc; } + .instaraw-big-stat.failed { color: #f87171; } + + /* === Progress Items (no scroll - grows with content) === */ + .instaraw-big-progress-items { + display: flex; + flex-direction: column; + gap: 10px; + } + + .instaraw-big-progress-item { + background: rgba(255, 255, 255, 0.04); + border: 1px solid rgba(255, 255, 255, 0.1); + border-radius: 4px; + padding: 10px 12px; + display: flex; + gap: 12px; + align-items: flex-start; + } + + .instaraw-big-progress-item-content { + flex: 1; + min-width: 0; + } + + .instaraw-big-progress-item.success { + border-color: rgba(34, 197, 94, 0.4); + background: rgba(34, 197, 94, 0.08); + } + + .instaraw-big-progress-item.error { + border-color: rgba(239, 68, 68, 0.4); + background: rgba(239, 68, 68, 0.08); + } + + .instaraw-big-progress-item.in-progress { + border-color: rgba(99, 102, 241, 0.5); + background: rgba(99, 102, 241, 0.1); + } + + .instaraw-big-progress-item.retrying { + border-color: rgba(251, 191, 36, 0.5); + background: rgba(251, 191, 36, 0.1); + } + + .instaraw-big-progress-item.cached { + border-color: rgba(168, 85, 247, 0.4); + background: rgba(168, 85, 247, 0.08); + } + + .instaraw-big-progress-item.cancelled { + border-color: rgba(156, 163, 175, 0.3); + background: rgba(156, 163, 175, 0.05); + opacity: 0.6; + } + + .instaraw-big-progress-item-header { + display: flex; + justify-content: space-between; + align-items: center; + margin-bottom: 6px; + gap: 8px; + } + + .instaraw-big-progress-item-label { + font-size: 11px; + font-weight: 500; + color: #d1d5db; + flex: 1; + overflow: hidden; + text-overflow: ellipsis; + white-space: nowrap; + display: flex; + align-items: center; + gap: 6px; + } + + /* Pair badge for multi-image mode */ + .instaraw-big-pair-badge { + display: inline-flex; + align-items: center; + justify-content: center; + padding: 1px 5px; + font-size: 9px; + font-weight: 700; + background: linear-gradient(135deg, #10b981 0%, #059669 100%); + border: 1px solid #34d399; + border-radius: 3px; + color: #fff; + flex-shrink: 0; + } + + /* Pair group container for multi-image mode */ + .instaraw-big-pair-group { + background: rgba(16, 185, 129, 0.05); + border: 1px solid rgba(52, 211, 153, 0.2); + border-radius: 8px; + padding: 8px; + margin-bottom: 8px; + } + + .instaraw-big-pair-header { + display: flex; + justify-content: space-between; + align-items: center; + padding: 4px 8px 8px 8px; + border-bottom: 1px solid rgba(52, 211, 153, 0.15); + margin-bottom: 8px; + } + + .instaraw-big-pair-label { + font-size: 11px; + font-weight: 600; + color: #34d399; + } + + .instaraw-big-pair-savings { + font-size: 9px; + color: #4ade80; + background: rgba(74, 222, 128, 0.1); + padding: 2px 6px; + border-radius: 3px; + } + + .instaraw-big-pair-items { + display: flex; + flex-direction: column; + gap: 6px; + } + + .instaraw-big-pair-items .instaraw-big-progress-item { + margin-bottom: 0; + } + + .instaraw-big-progress-item-status { + font-size: 10px; + padding: 2px 8px; + border-radius: 4px; + font-weight: 600; + white-space: nowrap; + } + + .instaraw-big-progress-item-status.pending { + background: rgba(156, 163, 175, 0.15); + color: #6b7280; + } + + .instaraw-big-progress-item-status.in-progress { + background: rgba(99, 102, 241, 0.2); + color: #818cf8; + } + + .instaraw-big-progress-item-status.success { + background: rgba(34, 197, 94, 0.3); + color: #22c55e; + } + + .instaraw-big-progress-item-status.error { + background: rgba(239, 68, 68, 0.3); + color: #ef4444; + } + + .instaraw-big-progress-item-status.retrying { + background: rgba(251, 191, 36, 0.2); + color: #fbbf24; + } + + .instaraw-big-progress-item-status.cached { + background: rgba(168, 85, 247, 0.2); + color: #a78bfa; + } + + .instaraw-big-progress-item-status.cancelled { + background: rgba(156, 163, 175, 0.15); + color: #9ca3af; + } + + .instaraw-big-progress-item-bar { + width: 100%; + height: 4px; + background: rgba(255, 255, 255, 0.08); + border-radius: 4px; + overflow: hidden; + margin-top: 10px; + position: relative; + } + + .instaraw-big-progress-item-fill { + height: 100%; + background: #6366f1; + position: relative; + overflow: hidden; + border-radius: 4px; + } + + .instaraw-big-progress-item-fill.animating { + background: linear-gradient(90deg, #6366f1, #818cf8, #6366f1); + background-size: 200% 100%; + animation: instaraw-big-progress-shimmer 1.5s linear infinite; + } + + @keyframes instaraw-big-progress-shimmer { + 0% { background-position: 200% 0; } + 100% { background-position: -200% 0; } + } + + .instaraw-big-progress-item-message { + font-size: 10px; + color: #f87171; + margin-top: 4px; + padding: 4px 6px; + background: rgba(239, 68, 68, 0.1); + border-radius: 3px; + } + + .instaraw-big-progress-item-message.retry-message { + color: #fbbf24; + background: rgba(251, 191, 36, 0.15); + border-left: 2px solid #fbbf24; + } + + .instaraw-big-progress-item-message.error-message { + color: #f87171; + background: rgba(239, 68, 68, 0.15); + border-left: 2px solid #ef4444; + } + + .instaraw-big-progress-item-thumbnail { + flex-shrink: 0; + border-radius: 4px; + overflow: hidden; + border: 2px solid rgba(255, 255, 255, 0.1); + background: rgba(0, 0, 0, 0.3); + width: 64px; + height: 64px; + display: flex; + align-items: center; + justify-content: center; + image-rendering: -webkit-optimize-contrast; + image-rendering: crisp-edges; + } + + .instaraw-big-progress-item-thumbnail:not(.placeholder) { + cursor: pointer; + } + + .instaraw-big-progress-item-thumbnail:not(.placeholder):hover { + border-color: #6366f1; + } + + .instaraw-big-progress-item-thumbnail.placeholder { + cursor: default; + background: rgba(0, 0, 0, 0.5); + } + + .instaraw-big-thumbnail-img { + width: 100%; + height: 100%; + object-fit: cover; + display: block; + } + + .instaraw-big-thumbnail-video { + width: 100%; + height: 100%; + object-fit: cover; + display: block; + } + + .instaraw-big-thumbnail-empty { + width: 100%; + height: 100%; + background: rgba(0, 0, 0, 0.2); + } + + .instaraw-big-view-image-btn { + display: none; /* Hidden - click thumbnail instead */ + } + + .instaraw-big-job-retry-btn { + background: rgba(251, 191, 36, 0.2); + border: 1px solid rgba(251, 191, 36, 0.4); + color: #fbbf24; + padding: 2px 6px; + border-radius: 3px; + cursor: pointer; + font-size: 10px; + } + + .instaraw-big-job-retry-btn:hover { + background: rgba(251, 191, 36, 0.3); + } + + .instaraw-big-job-retry-btn:disabled { + opacity: 0.5; + cursor: not-allowed; + } + + /* Expand prompt button */ + .instaraw-big-expand-prompt-btn { + background: rgba(139, 92, 246, 0.2); + border: 1px solid rgba(139, 92, 246, 0.4); + color: #a78bfa; + padding: 2px 6px; + border-radius: 3px; + cursor: pointer; + font-size: 10px; + margin-right: 4px; + } + .instaraw-big-expand-prompt-btn:hover { + background: rgba(139, 92, 246, 0.3); + } + + /* Expanded prompt section */ + .instaraw-big-prompt-expanded { + margin-top: 8px; + padding: 10px; + background: rgba(0, 0, 0, 0.3); + border-radius: 6px; + border: 1px solid rgba(255, 255, 255, 0.1); + } + .instaraw-big-prompt-header { + display: flex; + justify-content: space-between; + align-items: center; + margin-bottom: 8px; + flex-wrap: wrap; + gap: 6px; + } + .instaraw-big-prompt-meta { + font-size: 11px; + color: #a0a0a0; + } + .instaraw-big-prompt-actions { + display: flex; + gap: 6px; + } + .instaraw-big-copy-seed-btn, + .instaraw-big-copy-prompt-btn, + .instaraw-big-favorite-prompt-btn { + background: rgba(255, 255, 255, 0.1); + border: 1px solid rgba(255, 255, 255, 0.2); + color: #e0e0e0; + padding: 3px 8px; + border-radius: 4px; + cursor: pointer; + font-size: 11px; + transition: all 0.15s ease; + } + .instaraw-big-copy-seed-btn:hover { + background: rgba(168, 85, 247, 0.3); + border-color: rgba(168, 85, 247, 0.5); + color: #c4b5fd; + } + .instaraw-big-copy-prompt-btn:hover { + background: rgba(59, 130, 246, 0.3); + border-color: rgba(59, 130, 246, 0.5); + color: #93c5fd; + } + .instaraw-big-favorite-prompt-btn:hover { + background: rgba(251, 191, 36, 0.3); + border-color: rgba(251, 191, 36, 0.5); + color: #fcd34d; + } + .instaraw-big-copy-seed-btn:disabled, + .instaraw-big-copy-prompt-btn:disabled, + .instaraw-big-favorite-prompt-btn:disabled { + opacity: 0.6; + cursor: not-allowed; + } + .instaraw-big-prompt-text { + font-size: 12px; + color: #e0e0e0; + line-height: 1.5; + white-space: pre-wrap; + word-break: break-word; + } + + /* Old button styles removed - using new btn-primary, btn-secondary, btn-danger classes */ + + .instaraw-big-retry-failed-btn:hover:not(:disabled) { + background: rgba(251, 191, 36, 0.15); + border-color: rgba(251, 191, 36, 0.4); + } + + /* PhotoSwipe instant animations override */ + .pswp--open, + .pswp--closing, + .pswp__bg, + .pswp__container, + .pswp__img, + .pswp__zoom-wrap { + transition: none !important; + animation: none !important; + } + + /* PhotoSwipe caption styles */ + .pswp-caption-content { + text-align: left; + padding: 10px; + background: rgba(0, 0, 0, 0.6); + border-radius: 8px; + max-width: 400px; + } + .pswp-caption-header { + font-size: 14px; + font-weight: 600; + margin-bottom: 8px; + color: #a0a0a0; + } + .pswp-caption-prompt { + font-size: 13px; + line-height: 1.5; + color: #e0e0e0; + white-space: pre-wrap; + word-break: break-word; + max-height: 150px; + overflow-y: auto; + } + .pswp-caption-actions { + display: flex; + gap: 8px; + margin-top: 10px; + padding-top: 10px; + border-top: 1px solid rgba(255, 255, 255, 0.1); + } + .pswp-copy-btn, + .pswp-favorite-btn { + padding: 6px 12px; + font-size: 12px; + border-radius: 6px; + border: 1px solid rgba(255, 255, 255, 0.2); + background: rgba(255, 255, 255, 0.1); + color: #e0e0e0; + cursor: pointer; + transition: all 0.15s ease; + } + .pswp-copy-btn:hover { + background: rgba(59, 130, 246, 0.3); + border-color: rgba(59, 130, 246, 0.5); + color: #93c5fd; + } + .pswp-favorite-btn:hover { + background: rgba(251, 191, 36, 0.3); + border-color: rgba(251, 191, 36, 0.5); + color: #fcd34d; + } + /* Dynamic caption plugin overrides */ + .pswp__dynamic-caption { + color: #e0e0e0; + } + .pswp__dynamic-caption--aside { + max-width: 400px; + } + `; + document.head.appendChild(style); + }, +}); diff --git a/custom_nodes/ComfyUI_INSTARAW/js/boolean_bypass.js b/custom_nodes/ComfyUI_INSTARAW/js/boolean_bypass.js new file mode 100644 index 0000000000000000000000000000000000000000..73248e3d763704775f6f62825c63af55cdaf7c40 --- /dev/null +++ b/custom_nodes/ComfyUI_INSTARAW/js/boolean_bypass.js @@ -0,0 +1,90 @@ +// --- Filename: ../ComfyUI_INSTARAW/js/boolean_bypass.js (FINAL, GROUP-BYPASS-AWARE VERSION) --- + +import { app } from "../../scripts/app.js"; + +const MODE_ALWAYS = 0; // Corresponds to LiteGraph.ALWAYS +const MODE_BYPASS = 4; + +app.registerExtension({ + name: "Comfy.INSTARAW.BooleanBypass.Final.V4", // โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€Œโ€Œโ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€Œโ€Œโ€‹โ€‹โ€‹โ€Œโ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€Œโ€‹โ€‹โ€Œโ€‹โ€Œโ€Œโ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€Œโ€‹โ€‹โ€‹โ€Œโ€‹โ€Œโ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€Œโ€‹โ€Œโ€‹โ€‹โ€‹โ€Œโ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€Œโ€Œโ€Œโ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€Œโ€Œโ€‹โ€Œโ€Œโ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€Œโ€‹โ€‹โ€‹โ€‹โ€Œโ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€Œโ€‹โ€Œโ€‹โ€‹โ€‹โ€Œโ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€Œโ€‹โ€‹โ€Œโ€Œโ€Œโ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€Œโ€‹โ€‹โ€‹โ€Œโ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€Œโ€‹โ€‹โ€‹โ€Œโ€Œโ€Œโ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€Œโ€‹โ€‹โ€‹โ€‹โ€Œโ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€Œโ€Œโ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€Œโ€‹โ€‹โ€‹โ€Œโ€Œโ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€Œโ€Œโ€Œโ€‹โ€‹โ€Œโ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€Œโ€‹โ€‹โ€‹โ€Œโ€Œโ€Œโ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€Œโ€‹โ€Œโ€‹โ€Œโ€Œโ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€Œโ€‹โ€‹โ€Œโ€‹โ€Œโ€Œโ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€Œโ€‹โ€‹โ€‹โ€Œโ€‹โ€Œโ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€Œโ€‹โ€Œโ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€Œโ€Œโ€‹โ€Œโ€Œโ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€Œโ€Œโ€‹โ€Œโ€‹โ€Œโ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€Œโ€Œโ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€Œโ€Œโ€‹โ€‹โ€‹โ€Œโ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€Œโ€‹โ€Œโ€‹โ€Œโ€Œโ€‹โ€ + + async beforeRegisterNodeDef(nodeType, nodeData, app) { + if (nodeData.name === "INSTARAW_BooleanBypass") { + + const onNodeCreated = nodeType.prototype.onNodeCreated; + nodeType.prototype.onNodeCreated = function () { + onNodeCreated?.apply(this, arguments); + this.lastKnownState = undefined; + }; + + const onDrawForeground = nodeType.prototype.onDrawForeground; + nodeType.prototype.onDrawForeground = function(ctx) { + onDrawForeground?.apply(this, arguments); + + // --- THIS IS THE FIX --- + // If this node itself is currently bypassed (e.g., by a group bypasser), + // we must reset its internal state. This ensures that the next time it becomes + // active, it will perform a full re-sync of its upstream nodes. + if (this.mode !== MODE_ALWAYS) { + this.lastKnownState = undefined; + return; // Do nothing further while bypassed. + } + // --- END FIX --- + + // Find all our required widgets and inputs + const booleanWidget = this.widgets.find(w => w.name === 'boolean'); + const invertWidget = this.widgets.find(w => w.name === 'invert_input'); + const booleanInput = this.inputs.find(i => i.name === 'boolean'); + if (!booleanWidget || !invertWidget || !booleanInput) return; // Safety check + + // 1. Determine the RAW boolean state from the controller (internal or external) + let rawIsEnabled = false; + if (booleanInput.link != null) { + const link = app.graph.links[booleanInput.link]; + const originNode = link && app.graph.getNodeById(link.origin_id); + if (originNode) { + let originWidget = originNode.widgets?.find(w => w.name === (originNode.outputs[link.origin_slot]?.name)); + if (!originWidget && originNode.widgets?.length === 1) { + originWidget = originNode.widgets[0]; + } + + if (originWidget) { + rawIsEnabled = !!originWidget.value; + } else { + rawIsEnabled = originNode.outputs[link.origin_slot]?.value !== undefined ? !!originNode.outputs[link.origin_slot].value : false; + } + } + } else { + rawIsEnabled = booleanWidget.value; + } + + // 2. Apply the inversion logic + const shouldInvert = invertWidget.value; + const finalIsEnabled = shouldInvert ? !rawIsEnabled : rawIsEnabled; + + // 3. Only proceed if the final state has changed (or if state was just reset) + if (this.lastKnownState === finalIsEnabled) { + return; + } + + this.lastKnownState = finalIsEnabled; + + // 4. Update UI and apply bypass + booleanWidget.value = rawIsEnabled; + + const newMode = finalIsEnabled ? MODE_ALWAYS : MODE_BYPASS; + + for (const input of this.inputs) { + if (input.name === 'boolean' || input.name === 'invert_input') continue; // Skip control inputs + if (input.link != null) { + const link = app.graph.links[input.link]; + const connectedNode = link && app.graph.getNodeById(link.origin_id); + if (connectedNode) { + connectedNode.mode = newMode; + } + } + } + }; + } + }, +}); \ No newline at end of file diff --git a/custom_nodes/ComfyUI_INSTARAW/js/branding_node.js b/custom_nodes/ComfyUI_INSTARAW/js/branding_node.js new file mode 100644 index 0000000000000000000000000000000000000000..cb72e6993263e90b8440bac8192b07504a7d6054 --- /dev/null +++ b/custom_nodes/ComfyUI_INSTARAW/js/branding_node.js @@ -0,0 +1,91 @@ +import { app } from '../../scripts/app.js'; + +app.registerExtension({ + name: 'Comfy.INSTARAW.BrandingNode', // โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€Œโ€Œโ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€Œโ€Œโ€‹โ€‹โ€‹โ€Œโ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€Œโ€‹โ€‹โ€Œโ€‹โ€Œโ€Œโ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€Œโ€‹โ€‹โ€‹โ€Œโ€‹โ€Œโ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€Œโ€‹โ€Œโ€‹โ€‹โ€‹โ€Œโ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€Œโ€Œโ€Œโ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€Œโ€Œโ€‹โ€Œโ€Œโ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€Œโ€‹โ€‹โ€‹โ€‹โ€Œโ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€Œโ€‹โ€Œโ€‹โ€‹โ€‹โ€Œโ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€Œโ€‹โ€‹โ€Œโ€Œโ€Œโ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€Œโ€‹โ€‹โ€‹โ€Œโ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€Œโ€‹โ€‹โ€‹โ€Œโ€Œโ€Œโ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€Œโ€‹โ€‹โ€‹โ€‹โ€Œโ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€Œโ€Œโ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€Œโ€‹โ€‹โ€‹โ€Œโ€Œโ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€Œโ€Œโ€Œโ€‹โ€‹โ€Œโ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€Œโ€‹โ€‹โ€‹โ€Œโ€Œโ€Œโ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€Œโ€‹โ€Œโ€‹โ€Œโ€Œโ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€Œโ€‹โ€‹โ€Œโ€‹โ€Œโ€Œโ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€Œโ€‹โ€‹โ€‹โ€Œโ€‹โ€Œโ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€Œโ€‹โ€Œโ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€Œโ€Œโ€‹โ€Œโ€Œโ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€Œโ€Œโ€‹โ€Œโ€‹โ€Œโ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€Œโ€Œโ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€Œโ€Œโ€‹โ€‹โ€‹โ€Œโ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€Œโ€‹โ€Œโ€‹โ€Œโ€Œโ€‹โ€ + async beforeRegisterNodeDef(nodeType, nodeData, app) { + if (nodeData.name === 'INSTARAW_BrandingNode') { + // This function is called once when the node is added to the graph. + const onAdded = nodeType.prototype.onAdded; + nodeType.prototype.onAdded = function () { + onAdded?.apply(this, arguments); + + this.properties = this.properties || {}; + + // Set default properties ONLY if they don't already exist. + if (!this.properties.logo_url) { + this.properties.logo_url = 'https://instara.s3.us-east-1.amazonaws.com/INSTARAW_logomark_only.svg'; + } + if (this.properties.width == null) { + this.properties.width = 120; + } + if (this.properties.height == null) { + this.properties.height = 105; + } + + this.logoImage = new Image(); + this.logoImage.onload = () => { + this.setDirtyCanvas(true, false); + }; + + this.bgcolor = 'transparent'; + this.boxcolor = 'transparent'; + }; + + // This function is called every frame to draw the node. + const onDrawForeground = nodeType.prototype.onDrawForeground; + nodeType.prototype.onDrawForeground = function (ctx) { + onDrawForeground?.apply(this, arguments); + + // --- THIS IS THE FIX --- + // We no longer force the title to be empty. + // this.title = ""; <-- THIS LINE HAS BEEN REMOVED. + + const url = this.properties.logo_url; + if (url && this.logoImage.src !== url) { + this.logoImage.src = url; + } + + if (this.size && this.logoImage.complete && this.logoImage.naturalWidth > 0) { + const horizontalPadding = 10; + const verticalPadding = 10; // New: Define vertical padding + + const nodeWidth = this.size[0]; + const nodeHeight = this.size[1]; + + // The available area for drawing is now smaller + const drawAreaWidth = nodeWidth - horizontalPadding * 2; + const drawAreaHeight = nodeHeight - verticalPadding * 2; // Changed: Account for top and bottom padding + + const imgAspect = this.logoImage.naturalWidth / this.logoImage.naturalHeight; + + let drawWidth = drawAreaWidth; + let drawHeight = drawWidth / imgAspect; + + if (drawHeight > drawAreaHeight) { + drawHeight = drawAreaHeight; + drawWidth = drawHeight * imgAspect; + } + + // The x position calculation remains the same + const x = horizontalPadding + (drawAreaWidth - drawWidth) / 2; + + // The y position now starts after the top padding + const y = verticalPadding + (drawAreaHeight - drawHeight) / 2; // Changed: Add the top padding offset + + ctx.drawImage(this.logoImage, x, y, drawWidth, drawHeight); + } + }; + + // This function controls the node's size. + const computeSize = nodeType.prototype.computeSize; + nodeType.prototype.computeSize = function () { + const size = computeSize?.apply(this, arguments); + // Get the size from our properties. + const width = this.properties.width || 120; + const height = this.properties.height || 105; + // Ensure the node is big enough to show the title if it exists. + return [Math.max(size[0], width), height]; + }; + } + } +}); diff --git a/custom_nodes/ComfyUI_INSTARAW/js/ding.mp3 b/custom_nodes/ComfyUI_INSTARAW/js/ding.mp3 new file mode 100644 index 0000000000000000000000000000000000000000..831f8193f7fcd49d8b788862ad6ad9ff65a1a662 Binary files /dev/null and b/custom_nodes/ComfyUI_INSTARAW/js/ding.mp3 differ diff --git a/custom_nodes/ComfyUI_INSTARAW/js/filter.css b/custom_nodes/ComfyUI_INSTARAW/js/filter.css new file mode 100644 index 0000000000000000000000000000000000000000..20302407e5d41aa1ad1aca3c3104ed2466c31b09 --- /dev/null +++ b/custom_nodes/ComfyUI_INSTARAW/js/filter.css @@ -0,0 +1,106 @@ +.instaraw_popup { + position: absolute; + width:100%; + height:100%; + background-color: rgba(0, 0, 0, 0.95); + z-index: 100000; + --text_area_height: 0px; +} + +.instaraw_popup .grid { + position: absolute; + width:70%; + left:15%; + height:calc(100% - 60px - var(--text_area_height)); + top:55px; + display: grid; + justify-items: center; + align-items: center; +} + +.instaraw_popup .overlaygrid { + opacity: 0.5; + pointer-events: none; +} + +.instaraw_popup .title { + position: absolute; + width:100%; + text-align: center; + top: 10px; + font-size: 250%; +} + +.instaraw_popup .tip { + display: block; + padding-top: 10px; + line-height: 20px; +} + +.instaraw_popup .buttons { + position: absolute; + width:14%; + left:86%; + height:90%; + top:5%; + font-size: small; +} + +.instaraw_popup .control_text:after { + content:"\a"; + white-space: pre; +} + +.instaraw_popup .grid img { + margin: 2px; + border: 1px solid white; + padding: 4px; + max-width: 100%; + max-height: 100%; +} + +.instaraw_popup .grid img.selected { + border: 3px solid green; + padding: 2px; +} + +.instaraw_popup .grid img.hover { + box-shadow: 0 0 4px 4px red; +} + +.instaraw_popup.hidden { + display:none !important; +} + +.instaraw_popup .hidden { + display: none !important; +} + +.cgfloat .hidden { + display: none !important; +} + +/* --- Interactive Crop Styles --- */ +.instaraw_popup .crop_container { + position: absolute; + width: 70%; + left: 15%; + height: calc(100% - 60px); + top: 55px; + display: flex; + justify-content: center; + align-items: center; + user-select: none; +} + +.instaraw_popup .crop_image_bg { + max-width: 100%; + max-height: 100%; + object-fit: contain; + pointer-events: none; +} + +.instaraw_popup .crop_canvas_overlay { + position: absolute; + cursor: crosshair; +} \ No newline at end of file diff --git a/custom_nodes/ComfyUI_INSTARAW/js/floating_window.css b/custom_nodes/ComfyUI_INSTARAW/js/floating_window.css new file mode 100644 index 0000000000000000000000000000000000000000..94f29263f1a8a16ff3ea9b2775599507e6209f1f --- /dev/null +++ b/custom_nodes/ComfyUI_INSTARAW/js/floating_window.css @@ -0,0 +1,94 @@ + +.cgfloat { + position: absolute; + z-index: 999999; + border: 1px solid black; + background-color: rgba(95, 158, 160, 0.713); + width: fit-content; +} + +.cgfloat.hidden { + display:none +} + +.cgfloat_header { + width: 100%; + text-align: center; + background-color: rgba(28, 51, 52, 0.713); + color: whitesmoke; + border-bottom: 1px solid black; + padding:4px; + font-size:larger; +} + +.tiny .cgfloat_header { + font-size: smaller; +} + +.cgfloat_body { + min-width: fit-content; + display:flex; + flex-direction: column; +} + +.cgfloat_body .row { + color: white; + padding: 0px 4px; + display: flex; + align-items: center; + justify-content: center; + border-bottom: 1px dashed rgba(0, 0, 0, 0.45); +} + +/* Countdown */ +.cgfloat_body .counter_text { + min-width:50px; + text-align: right; +} + +.cgfloat_body .counter_reset { + margin-left: 5px; + padding: 1px; +} + +/* Buttons */ +.cgfloat_body .row.buttons { + justify-content: space-evenly; +} + +.cgfloat_body button { + margin: 4px; + padding: 2px; + min-width: 70px; +} + +/* Extras */ +.cgfloat_body .row.extras { + flex-direction: column; +} + +.cgfloat_body .row.extras .extra { + width: 100%; + margin: 2px 0; +} + +/* Tips */ +.cgfloat_body .row.tip { + margin: 4px; + padding: 4px 0px; + justify-content: flex-start; +} + +/* Text Edit */ +.cgfloat_body .row.text_edit { + margin: 4px; +} + +.cgfloat .tiny_image { + padding: 1px; + width: auto; + max-height: 100px; + object-fit: contain; + pointer-events: none; +} + diff --git a/custom_nodes/ComfyUI_INSTARAW/js/floating_window.js b/custom_nodes/ComfyUI_INSTARAW/js/floating_window.js new file mode 100644 index 0000000000000000000000000000000000000000..f1be17c6acca8352c0431c64eb7e3ec7e285f0f8 --- /dev/null +++ b/custom_nodes/ComfyUI_INSTARAW/js/floating_window.js @@ -0,0 +1,63 @@ +const _ax = encodeURI(''); + +export class FloatingWindow extends HTMLElement { + constructor(title, x, y, parent, movecallback) { + super() + this.movecallback = movecallback + this.classList.add('cgfloat') + this.header = document.createElement('div') + this.header.classList.add('cgfloat_header') + this.header.innerText = title + this.append(this.header) + this.body = document.createElement('div') + this.body.classList.add('cgfloat_body') + this.append(this.body) + + this.header.addEventListener('mousedown',this.header_mousedown.bind(this)) + document.addEventListener('mouseup',this.header_mouseup.bind(this)) + document.addEventListener('mousemove',this.header_mousemove.bind(this)) + document.addEventListener('mouseleave',this.header_mouseup.bind(this)) + + this.dragging = false + this.move_to(x,y) + + + if (parent) parent.append(this) + else document.body.append(this) + } + + show() { this.style.display = 'block' } + hide() { this.style.display = 'none' } + set_title(title) { this.header.innerText = title } + + move_to(x,y,supress) { + this.position = {x:x,y:y} + this.style.left = `${this.position.x}px` + this.style.top = `${this.position.y}px` + if (!supress) this.movecallback(x,y) + } + + swallow(e) { + e.stopPropagation() + e.preventDefault() + } + + header_mousedown(e) { + this.dragging = true + this.swallow(e) + } + + header_mouseup(e) { + this.dragging = false + } + + header_mousemove(e) { + if (this.dragging) { + this.move_to( this.position.x + e.movementX , this.position.y + e.movementY ) + this.swallow(e) + } + } +} + +// Renamed custom element to be unique to INSTARAW +customElements.define('instaraw-floater', FloatingWindow); \ No newline at end of file diff --git a/custom_nodes/ComfyUI_INSTARAW/js/group_bypass_detector.js b/custom_nodes/ComfyUI_INSTARAW/js/group_bypass_detector.js new file mode 100644 index 0000000000000000000000000000000000000000..2e4b0ceae9dc6b0fa41ce6703723dfadbb82fb71 --- /dev/null +++ b/custom_nodes/ComfyUI_INSTARAW/js/group_bypass_detector.js @@ -0,0 +1,42 @@ +// --- Filename: ../ComfyUI_INSTARAW/js/group_bypass_detector.js --- + +import { app } from "../../scripts/app.js"; + +const MODE_ALWAYS = 0; // LiteGraph.ALWAYS + +app.registerExtension({ + name: "Comfy.INSTARAW.GroupBypassDetector", + + async beforeRegisterNodeDef(nodeType, nodeData, app) { + if (nodeData.name === "INSTARAW_GroupBypassToBoolean") { + + // Store the last known state on the node instance to prevent unnecessary updates + const onNodeCreated = nodeType.prototype.onNodeCreated; + nodeType.prototype.onNodeCreated = function () { + onNodeCreated?.apply(this, arguments); + this.lastBypassState = undefined; + }; + + // Every frame, check if our bypass state has changed + const onDrawForeground = nodeType.prototype.onDrawForeground; + nodeType.prototype.onDrawForeground = function(ctx) { + onDrawForeground?.apply(this, arguments); + + const widget = this.widgets.find(w => w.name === 'is_active'); + if (!widget) return; + + // The node's mode tells us if it's bypassed. 0 means active. + const isActive = this.mode === MODE_ALWAYS; + + // Only update the widget if the state has actually changed + if (this.lastBypassState !== isActive) { + this.lastBypassState = isActive; + widget.value = isActive; + + // Make sure the UI reflects the change immediately + app.graph.setDirtyCanvas(true); + } + }; + } + }, +}); \ No newline at end of file diff --git a/custom_nodes/ComfyUI_INSTARAW/js/image_filter.js b/custom_nodes/ComfyUI_INSTARAW/js/image_filter.js new file mode 100644 index 0000000000000000000000000000000000000000..236582b65d6f9da4242e67e54641cfd84db51390 --- /dev/null +++ b/custom_nodes/ComfyUI_INSTARAW/js/image_filter.js @@ -0,0 +1,123 @@ +import { app } from "../../scripts/app.js"; +import { api } from "../../scripts/api.js"; +import { create } from "./utils.js"; +import { popup } from "./popup.js"; +import { ComfyWidgets } from "../../scripts/widgets.js"; + +const FILTER_TYPES = ["INSTARAW_ImageFilter", "INSTARAW_TextImageFilter", "INSTARAW_MaskImageFilter", "INSTARAW_Interactive_Crop", "INSTARAW_PromptFilter", "INSTARAW_BatchImageGenerator"]; + +app.registerExtension({ + name: "Comfy.INSTARAW.InteractiveNodes", // โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€Œโ€Œโ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€Œโ€Œโ€‹โ€‹โ€‹โ€Œโ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€Œโ€‹โ€‹โ€Œโ€‹โ€Œโ€Œโ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€Œโ€‹โ€‹โ€‹โ€Œโ€‹โ€Œโ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€Œโ€‹โ€Œโ€‹โ€‹โ€‹โ€Œโ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€Œโ€Œโ€Œโ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€Œโ€Œโ€‹โ€Œโ€Œโ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€Œโ€‹โ€‹โ€‹โ€‹โ€Œโ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€Œโ€‹โ€Œโ€‹โ€‹โ€‹โ€Œโ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€Œโ€‹โ€‹โ€Œโ€Œโ€Œโ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€Œโ€‹โ€‹โ€‹โ€Œโ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€Œโ€‹โ€‹โ€‹โ€Œโ€Œโ€Œโ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€Œโ€‹โ€‹โ€‹โ€‹โ€Œโ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€Œโ€Œโ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€Œโ€‹โ€‹โ€‹โ€Œโ€Œโ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€Œโ€Œโ€Œโ€‹โ€‹โ€Œโ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€Œโ€‹โ€‹โ€‹โ€Œโ€Œโ€Œโ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€Œโ€‹โ€Œโ€‹โ€Œโ€Œโ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€Œโ€‹โ€‹โ€Œโ€‹โ€Œโ€Œโ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€Œโ€‹โ€‹โ€‹โ€Œโ€‹โ€Œโ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€Œโ€‹โ€Œโ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€Œโ€Œโ€‹โ€Œโ€Œโ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€Œโ€Œโ€‹โ€Œโ€‹โ€Œโ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€Œโ€Œโ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€Œโ€Œโ€‹โ€‹โ€‹โ€Œโ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€Œโ€‹โ€Œโ€‹โ€Œโ€Œโ€‹โ€ + settings: [ + { + id: "INSTARAW.Interactive.Header", + name: "INSTARAW Interactive Nodes", + type: () => { + const x = document.createElement('span'); + const a = document.createElement('a'); + a.innerText = "Based on original work by chrisgoringe (cg-image-filter)"; + a.href = "https://github.com/chrisgoringe/cg-image-filter"; + a.target = "_blank"; + a.style.paddingRight = "12px"; + x.appendChild(a); + return x; + }, + }, + { id: "INSTARAW.Interactive.PlaySound", name: "Play sound when activating", type: "boolean", defaultValue: true }, + { id: "INSTARAW.Interactive.EnlargeSmall", name: "Enlarge small images in grid", type: "boolean", defaultValue: true }, + { id: "INSTARAW.Interactive.ClickSends", name: "Clicking an image sends it", tooltip: "Use if you always want to send exactly one image.", type: "boolean", defaultValue: false }, + { id: "INSTARAW.Interactive.AutosendIdentical", name: "If all images are identical, autosend one", type: "boolean", defaultValue: false }, + { id: "INSTARAW.Interactive.StartZoomed", name: "Enter the Image Filter node with an image zoomed", type: "combo", options: [{ value: 0, text: "No" }, { value: "1", text: "first" }, { value: "-1", text: "last" }], default: 0 }, + { id: "INSTARAW.Interactive.SmallWindow", name: "Show a small popup instead of covering the screen", type: "boolean", tooltip: "Click the small popup to activate it", defaultValue: false }, + { id: "INSTARAW.Interactive.DetailedLogging", name: "Turn on detailed logging", tooltip: "If you are asked to for debugging!", type: "boolean", defaultValue: false }, + { id: "INSTARAW.Interactive.FPS", name: "Video Frames per Second", type: "int", defaultValue: 5 } + ], + setup() { + create('link', null, document.getElementsByTagName('HEAD')[0], { 'rel': 'stylesheet', 'type': 'text/css', 'href': 'extensions/ComfyUI_INSTARAW/filter.css' }); + create('link', null, document.getElementsByTagName('HEAD')[0], { 'rel': 'stylesheet', 'type': 'text/css', 'href': 'extensions/ComfyUI_INSTARAW/floating_window.css' }); + create('link', null, document.getElementsByTagName('HEAD')[0], { 'rel': 'stylesheet', 'type': 'text/css', 'href': 'extensions/ComfyUI_INSTARAW/zoomed.css' }); + create('link', null, document.getElementsByTagName('HEAD')[0], { 'rel': 'stylesheet', 'type': 'text/css', 'href': 'extensions/ComfyUI_INSTARAW/advanced_image_loader.css' }); + create('link', null, document.getElementsByTagName('HEAD')[0], { 'rel': 'stylesheet', 'type': 'text/css', 'href': 'extensions/ComfyUI_INSTARAW/reality_prompt_generator.css' }); + + api.addEventListener("execution_interrupted", popup.send_cancel.bind(popup)); + api.addEventListener("instaraw-interactive-images", popup.handle_message.bind(popup)); + }, + async beforeRegisterNodeDef(nodeType, nodeData, app) { + if (nodeType.comfyClass === "Pick from List" || nodeType.comfyClass === "INSTARAW_PickFromList") { + const onConnectionsChange = nodeType.prototype.onConnectionsChange; + nodeType.prototype.onConnectionsChange = function(side, slot, connect, link_info, output) { + if (side == 1 && slot == 0 && link_info && connect) { + const originNode = this.graph.getNodeById(link_info.origin_id); + if (originNode?.outputs?.[link_info.origin_slot]) { + const type = originNode.outputs[link_info.origin_slot].type; + this.outputs[0].type = type; + this.inputs[0].type = type; + } + } else if (side == 1 && slot == 0 && !connect) { + this.outputs[0].type = "*"; + this.inputs[0].type = "*"; + } + return onConnectionsChange?.apply(this, arguments); + } + } + if (FILTER_TYPES.includes(nodeType.comfyClass)) { + const onNodeCreated = nodeType.prototype.onNodeCreated; + nodeType.prototype.onNodeCreated = function() { + const onCreatedResult = onNodeCreated?.apply(this, arguments); + + this._ni_widget = this.widgets.find((n) => n.name == 'node_identifier'); + if (!this._ni_widget) { + this._ni_widget = ComfyWidgets["INT"](this, "node_identifier", ["INT", { "default": 0 }], app).widget; + } + this._ni_widget.hidden = true; + this._ni_widget.computeSize = () => [0, 0]; + this._ni_widget.value = Math.floor(Math.random() * 1000000); + + if (this.comfyClass === "INSTARAW_TextImageFilter") { + + const buttonWidget = this.addWidget("button", "Clear Node Cache", null, async () => { + buttonWidget.name = "Clearing..."; + this.disabled = true; + + try { + const resp = await api.fetchApi('/instaraw/clear_text_filter_cache', { + method: 'POST', + headers: { 'Content-Type': 'application/json' }, + body: JSON.stringify({ uid: this._ni_widget.value }), + }); + + if (resp.status === 200) { + buttonWidget.success(); + } else { + throw new Error(await resp.text()); + } + } catch (e) { + console.error("INSTARAW: Failed to clear cache:", e); + buttonWidget.error(); + } + }); + + buttonWidget.name = "clear_cache_button"; + + buttonWidget.success = () => { + buttonWidget.name = "Cache Cleared!"; + this.disabled = false; + setTimeout(() => { + buttonWidget.name = "Clear Node Cache"; + }, 2000); + }; + + buttonWidget.error = () => { + buttonWidget.name = "Error Clearing!"; + this.disabled = false; + setTimeout(() => { + buttonWidget.name = "Clear Node Cache"; + }, 3000); + }; + } + + return onCreatedResult; + } + } + }, +}); diff --git a/custom_nodes/ComfyUI_INSTARAW/js/instaraw.svg b/custom_nodes/ComfyUI_INSTARAW/js/instaraw.svg new file mode 100644 index 0000000000000000000000000000000000000000..18ca24fc6b902717f13ec24e04507d6f0ed39072 --- /dev/null +++ b/custom_nodes/ComfyUI_INSTARAW/js/instaraw.svg @@ -0,0 +1,20 @@ + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/custom_nodes/ComfyUI_INSTARAW/js/log.js b/custom_nodes/ComfyUI_INSTARAW/js/log.js new file mode 100644 index 0000000000000000000000000000000000000000..e10d8b6143cab1b9b096fd51a0328a293d16545d --- /dev/null +++ b/custom_nodes/ComfyUI_INSTARAW/js/log.js @@ -0,0 +1,25 @@ +// --- +// Filename: ../ComfyUI_INSTARAW/js/log.js +// --- + +import { app } from "../../scripts/app.js"; + +export class Log { + static log(s) { if (s) console.log(s) } + static error(e) { console.error(e) } + static detail(s) { + // Updated settings key + if (app.ui.settings.getSettingValue("INSTARAW.Interactive.DetailedLogging")) Log.log(s) + } + static message_in(message, extra) { + // Updated settings key + if (!app.ui.settings.getSettingValue("INSTARAW.Interactive.DetailedLogging")) return + if (message.detail && !message.detail.tick) Log.log(`--> ${JSON.stringify(message.detail)}` + (extra ? ` ${extra}` : "")) + if (message.detail && message.detail.tick) Log.log(`--> tick`) + } + static message_out(response, extra) { + // Updated settings key + if (!app.ui.settings.getSettingValue("INSTARAW.Interactive.DetailedLogging")) return + Log.log(`"<-- ${JSON.stringify(response)}` + (extra ? ` ${extra}` : "")) + } +} \ No newline at end of file diff --git a/custom_nodes/ComfyUI_INSTARAW/js/mask_utils.js b/custom_nodes/ComfyUI_INSTARAW/js/mask_utils.js new file mode 100644 index 0000000000000000000000000000000000000000..7cf2851d81065420649df9b1b534829bdd1e033d --- /dev/null +++ b/custom_nodes/ComfyUI_INSTARAW/js/mask_utils.js @@ -0,0 +1,79 @@ +import { app, ComfyApp } from "../../scripts/app.js"; + +export function new_editor() { + return app.ui.settings.getSettingValue('Comfy.MaskEditor.UseNewEditor') +} + +function get_mask_editor_element() { + // Try newer dialog-based mask editor first + const newer = document.getElementsByClassName('p-dialog-mask') + if (newer.length == 1) return newer[0] + return new_editor() ? document.getElementById('maskEditor') : document.getElementById('maskCanvas')?.parentElement +} + +export function mask_editor_showing() { + return get_mask_editor_element() && get_mask_editor_element().style.display != 'none' +} + +export function hide_mask_editor() { + if (mask_editor_showing() && document.getElementById('maskEditor')) document.getElementById('maskEditor').style.display = 'none' +} + +function get_mask_editor_cancel_button() { + var button = document.getElementById("maskEditor_topBarCancelButton") + if (button) return button + try { + button = Array.from(get_mask_editor_element().getElementsByTagName('button')).find((b) => (b.ariaLabel == 'Cancel')) + if (button) return button + } catch {} + return get_mask_editor_element()?.parentElement?.lastChild?.childNodes[2] +} + +function get_mask_editor_save_button() { + var button = document.getElementById("maskEditor_topBarSaveButton") + if (button) return button + try { + button = Array.from(get_mask_editor_element().getElementsByTagName('button')).find((b) => (b.ariaLabel == 'Save')) + if (button) return button + } catch {} + return get_mask_editor_element()?.parentElement?.lastChild?.childNodes[1] +} + +export function mask_editor_listen_for_cancel(callback) { + const cancel_button = get_mask_editor_cancel_button() + if (cancel_button && !cancel_button.filter_listener_added) { + cancel_button.addEventListener('click', callback) + cancel_button.filter_listener_added = true + } +} + +export function press_maskeditor_save() { + get_mask_editor_save_button()?.click() +} + +export function press_maskeditor_cancel() { + get_mask_editor_cancel_button()?.click() +} + +export function open_maskeditor(node) { + // Try old API first (ComfyApp.open_maskeditor) + if (typeof ComfyApp.open_maskeditor === 'function') { + ComfyApp.copyToClipspace(node) + ComfyApp.clipspace_return_node = node + ComfyApp.open_maskeditor() + } else { + // New API: Use extension command system + const me_extension = app.extensions.find((e) => (e.name == 'Comfy.MaskEditor')) + if (me_extension) { + const me_command = me_extension.commands.find((c) => (c.id == 'Comfy.MaskEditor.OpenMaskEditor')) + if (me_command) { + app.canvas.selected_nodes = [node] + me_command.function() + } else { + console.error('[INSTARAW] Could not find MaskEditor command') + } + } else { + console.error('[INSTARAW] Could not find MaskEditor extension') + } + } +} \ No newline at end of file diff --git a/custom_nodes/ComfyUI_INSTARAW/js/nano_banana_aspect_ratio.js b/custom_nodes/ComfyUI_INSTARAW/js/nano_banana_aspect_ratio.js new file mode 100644 index 0000000000000000000000000000000000000000..dd7903362bcbfce0c6f41533bbe0dfdf78e1c237 --- /dev/null +++ b/custom_nodes/ComfyUI_INSTARAW/js/nano_banana_aspect_ratio.js @@ -0,0 +1,130 @@ +// --- +// ComfyUI INSTARAW - Nano Banana Pro Aspect Ratio Visual Preview +// Copyright ยฉ 2025 Instara. All rights reserved. +// PROPRIETARY SOFTWARE - ALL RIGHTS RESERVED +// --- + +import { app } from "../../scripts/app.js"; + +// Aspect ratio numerical values for preview rendering +const ASPECT_RATIO_VALUES = { + "1:1 (Square)": { w: 1, h: 1 }, + "3:2 (Landscape)": { w: 3, h: 2 }, + "2:3 (Portrait)": { w: 2, h: 3 }, + "3:4 (Portrait)": { w: 3, h: 4 }, + "4:3 (Landscape)": { w: 4, h: 3 }, + "4:5 (Portrait)": { w: 4, h: 5 }, + "5:4 (Landscape)": { w: 5, h: 4 }, + "9:16 (Tall Portrait)": { w: 9, h: 16 }, + "16:9 (Wide Landscape)": { w: 16, h: 9 }, + "21:9 (Ultrawide)": { w: 21, h: 9 }, +}; + +// Resolution labels with approximate dimensions +const RESOLUTION_INFO = { + "1K": "~1024px", + "2K": "~2048px", + "4K": "~4096px", +}; + +app.registerExtension({ + name: "Comfy.INSTARAW.NanoBananaAspectRatio", + + async beforeRegisterNodeDef(nodeType, nodeData, app) { + if (nodeData.name !== "INSTARAW_NanoBananaAspectRatio") { + return; + } + + const onNodeCreated = nodeType.prototype.onNodeCreated; + nodeType.prototype.onNodeCreated = function () { + onNodeCreated?.apply(this, arguments); + + // Store reference to node + const node = this; + + // Find the aspect_ratio widget + const aspectWidget = this.widgets.find((w) => w.name === "aspect_ratio"); + const resolutionWidget = this.widgets.find((w) => w.name === "resolution"); + + if (!aspectWidget) return; + + // Create custom preview widget + const previewWidget = { + name: "aspect_preview", + type: "custom", + value: null, + computeSize: function () { + return [0, 80]; // Fixed height for preview area + }, + draw: function (ctx, node, width, y) { + const selectedRatio = aspectWidget.value || "1:1 (Square)"; + const selectedRes = resolutionWidget?.value || "1K"; + const ratio = ASPECT_RATIO_VALUES[selectedRatio] || { w: 1, h: 1 }; + + // Calculate preview box dimensions + const maxWidth = width - 40; + const maxHeight = 60; + let boxWidth, boxHeight; + + if (ratio.w / ratio.h > maxWidth / maxHeight) { + // Width constrained + boxWidth = maxWidth; + boxHeight = (maxWidth * ratio.h) / ratio.w; + } else { + // Height constrained + boxHeight = maxHeight; + boxWidth = (maxHeight * ratio.w) / ratio.h; + } + + // Center the box + const boxX = (width - boxWidth) / 2; + const boxY = y + (80 - boxHeight) / 2; + + // Draw background + ctx.fillStyle = "#1a1a2e"; + ctx.fillRect(10, y + 5, width - 20, 70); + + // Draw aspect ratio preview box + ctx.fillStyle = "#4a90d9"; + ctx.fillRect(boxX, boxY, boxWidth, boxHeight); + + // Draw border + ctx.strokeStyle = "#6ab0ff"; + ctx.lineWidth = 2; + ctx.strokeRect(boxX, boxY, boxWidth, boxHeight); + + // Draw ratio text below the preview box + const ratioText = selectedRatio.split(" ")[0]; // e.g., "16:9" + const resInfo = RESOLUTION_INFO[selectedRes] || ""; + ctx.fillStyle = "#ffffff"; + ctx.font = "bold 11px Arial"; + ctx.textAlign = "center"; + ctx.textBaseline = "top"; + ctx.fillText(`${ratioText} โ€ข ${selectedRes} (${resInfo})`, width / 2, boxY + boxHeight + 6); + }, + }; + + // Add preview widget after resolution + this.addCustomWidget(previewWidget); + + // Update preview when aspect ratio changes + const originalAspectCallback = aspectWidget.callback; + aspectWidget.callback = (value) => { + originalAspectCallback?.(value); + node.setDirtyCanvas(true, true); + }; + + // Update preview when resolution changes + if (resolutionWidget) { + const originalResCallback = resolutionWidget.callback; + resolutionWidget.callback = (value) => { + originalResCallback?.(value); + node.setDirtyCanvas(true, true); + }; + } + + // Ensure proper sizing + this.computeSize(); + }; + }, +}); diff --git a/custom_nodes/ComfyUI_INSTARAW/js/popup.js b/custom_nodes/ComfyUI_INSTARAW/js/popup.js new file mode 100644 index 0000000000000000000000000000000000000000..87d590879a418c1fbcd431d98bcb6ada4cc0fb2e --- /dev/null +++ b/custom_nodes/ComfyUI_INSTARAW/js/popup.js @@ -0,0 +1,1238 @@ +import { app, ComfyApp } from '../../scripts/app.js'; +import { api } from '../../scripts/api.js'; + +import { + mask_editor_listen_for_cancel, + mask_editor_showing, + hide_mask_editor, + press_maskeditor_cancel, + press_maskeditor_save, + new_editor, + open_maskeditor +} from './mask_utils.js'; +import { Log } from './log.js'; +import { create } from './utils.js'; +import { FloatingWindow } from './floating_window.js'; + +// Renamed to INSTARAW node class names +const POPUP_NODES = ['INSTARAW_ImageFilter', 'INSTARAW_TextImageFilter', 'INSTARAW_Interactive_Crop']; +const MASK_NODES = ['INSTARAW_MaskImageFilter']; + +const REQUEST_RESHOW = '-1'; +const CANCEL = '-3'; + +const GRID_IMAGE_SPACE = 10; + +function get_full_url(url) { + return api.apiURL( + `/view?filename=${encodeURIComponent(url.filename ?? v)}&type=${url.type ?? 'input'}&subfolder=${url.subfolder ?? ''}&r=${Math.random()}` + ); +} + +const State = Object.freeze({ + INACTIVE: 0, + TINY: 1, + MASK: 2, + FILTER: 3, + TEXT: 4, + ZOOMED: 5, + CROP: 6 // Our new state for the interactive cropper +}); + +class Popup extends HTMLElement { + constructor() { + super(); + // Path updated to point to our package's assets + this.audio = new Audio('extensions/ComfyUI_INSTARAW/ding.mp3'); + + this.classList.add('instaraw_popup'); + + this.grid = create('span', 'grid', this); + this.overlaygrid = create('span', 'grid overlaygrid', this); + this.grid.addEventListener('click', this.on_click.bind(this)); + + // --- NEW CROP UI ELEMENTS --- + this.crop_container = create('div', 'crop_container', this); + this.crop_image_bg = create('img', 'crop_image_bg', this.crop_container); + this.crop_canvas = create('canvas', 'crop_canvas_overlay', this.crop_container); + this.crop_ctx = this.crop_canvas.getContext('2d'); + this.crop_canvas.addEventListener('mousedown', this.on_crop_mouse_down.bind(this)); + // We now listen for moves on the canvas for cursor updates, and the whole document for dragging + this.crop_canvas.addEventListener('mousemove', this.on_crop_mouse_move.bind(this)); + document.addEventListener('mouseup', this.on_crop_mouse_up.bind(this)); + document.addEventListener('mousemove', this.on_document_mouse_move.bind(this)); + this.crop_data = { active: false, box: null, mode: null }; + // --- END NEW CROP UI --- + + this.zoomed = create('span', 'zoomed', this); + this.zoomed_prev = create('span', 'zoomed_prev', this.zoomed); + this.zoomed_prev_arrow = create('span', 'zoomed_arrow', this.zoomed_prev, { innerHTML: '⇦' }); + this.zoomed_image = create('img', 'zoomed_image', this.zoomed); + this.zoomed_next = create('span', 'zoomed_next', this.zoomed); + this.zoomed_number = create('span', 'zoomed_number', this.zoomed_next); + this.zoomed_next_arrow = create('span', 'zoomed_arrow', this.zoomed_next, { innerHTML: '⇨' }); + + this.zoomed_prev_arrow.addEventListener('click', this.zoom_prev.bind(this)); + this.zoomed_next_arrow.addEventListener('click', this.zoom_next.bind(this)); + this.zoomed_image.addEventListener('click', this.click_zoomed.bind(this)); + + this.tiny_window = new FloatingWindow('', 100, 100, null, this.tiny_moved.bind(this)); + this.tiny_window.classList.add('tiny'); + this.tiny_image = create('img', 'tiny_image', this.tiny_window.body); + this.tiny_window.addEventListener('click', this.handle_deferred_message.bind(this)); + + this.floating_window = new FloatingWindow('', 100, 100, null, this.floater_moved.bind(this)); + + this.counter_row = create('span', 'counter row', this.floating_window.body); + this.counter_reset_button = create('button', 'counter_reset', this.counter_row, { innerText: 'Reset' }); + this.counter_text = create('span', 'counter_text', this.counter_row); + this.counter_reset_button.addEventListener('click', this.request_reset.bind(this)); + + this.extras_row = create('span', 'extras row', this.floating_window.body); + + this.tip_row = create('span', 'tip row', this.floating_window.body); + + this.button_row = create('span', 'buttons row', this.floating_window.body); + this.send_button = create('button', 'control', this.button_row, { innerText: 'Send' }); + this.cancel_button = create('button', 'control', this.button_row, { innerText: 'Cancel' }); + this.toggle_button = create('button', 'control', this.button_row, { innerText: 'Hide' }); + this.send_button.addEventListener('click', this.send_current_state.bind(this)); + this.cancel_button.addEventListener('click', this.send_cancel.bind(this)); + this.toggle_button.addEventListener('click', this.toggleHide.bind(this)); + + this.mask_button_row = create('span', 'buttons row', this.floating_window.body); + this.mask_send_button = create('button', 'control', this.mask_button_row, { innerText: 'Send' }); + this.mask_cancel_button = create('button', 'control', this.mask_button_row, { innerText: 'Cancel' }); + this.mask_send_button.addEventListener('click', press_maskeditor_save); + this.mask_cancel_button.addEventListener('click', press_maskeditor_cancel); + + this.text_edit = create('textarea', 'text_edit row', this.floating_window.body); + + this.picked = new Set(); + + document.addEventListener('keydown', this.on_key_down.bind(this)); + document.addEventListener('keypress', this.on_key_press.bind(this)); + + document.body.appendChild(this); + this.last_response_sent = 0; + this.state = State.INACTIVE; + this.hidden_by_toggle = false; + this.render(); + } + + toggleHide() { + this.hidden_by_toggle = !this.hidden_by_toggle; + this.render(); + } + + floater_moved(x, y) { + if (this.node?.properties) { + this.node.properties['filter_floater_xy'] = { x: x, y: y }; + } + } + + floater_position() { + return this.node?.properties?.['filter_floater_xy']; + } + + tiny_moved(x, y) { + if (this.node?.properties) { + this.node.properties['filter_tiny_xy'] = { x: x, y: y }; + } + } + + tiny_position() { + return this.node?.properties?.['filter_tiny_xy']; + } + + visible(item, value) { + if (value) item.classList.remove('hidden'); + else item.classList.add('hidden'); + } + disabled(item, value) { + item.disabled = value; + } + highlighted(item, value) { + if (value) item.classList.add('highlighted'); + else item.classList.remove('highlighted'); + } + + render() { + const state = this.state; + // --- UPDATED RENDER LOGIC --- + const isVisible = + (state === State.FILTER || state === State.TEXT || state === State.ZOOMED || state === State.CROP) && + !this.hidden_by_toggle; + this.visible(this, isVisible); + + this.visible(this.tiny_window, state === State.TINY); + this.visible(this.zoomed, state === State.ZOOMED); + + // Hide grid when in CROP mode to prevent bleed-through from previous filter images + this.visible(this.grid, state === State.FILTER || state === State.ZOOMED || state === State.TEXT); + this.visible(this.overlaygrid, state === State.FILTER || state === State.ZOOMED || state === State.TEXT); + + this.visible(this.crop_container, state === State.CROP); + + const showFloater = + state === State.FILTER || + state === State.ZOOMED || + state === State.TEXT || + state === State.MASK || + state === State.CROP; + this.visible(this.floating_window, showFloater); + + this.visible(this.button_row, state !== State.MASK); + + // Disable send button if no images picked (filter) or no crop box drawn (crop) + const sendDisabled = + ((state === State.FILTER || state === State.ZOOMED) && this.picked.size === 0) || + (state === State.CROP && (!this.crop_data.box || this.crop_data.box.width <= 0 || this.crop_data.box.height <= 0)); + this.disabled(this.send_button, sendDisabled); + + this.visible(this.mask_button_row, state === State.MASK && new_editor()); + this.visible(this.extras_row, this.n_extras > 0); + this.visible(this.tip_row, this.tip_row.innerHTML.length > 0); + this.visible(this.text_edit, state === State.TEXT); + + if (state === State.ZOOMED) { + const img_index = this.zoomed_image_holder.image_index; + this.highlighted(this.zoomed, this.picked.has(`${img_index}`)); + this.zoomed_number.innerHTML = `${img_index + 1}/${this.n_images}`; + } + + if (state !== State.MASK) hide_mask_editor(); + + this.toggle_button.innerText = this.hidden_by_toggle ? 'Show' : 'Hide'; + const showToggleButton = + state === State.FILTER || state === State.ZOOMED || state === State.TEXT || state === State.CROP; + this.visible(this.toggle_button, showToggleButton); + } + + _send_response(msg = {}, keep_open = false) { + if (Date.now() - this.last_response_sent < 1000) { + Log.message_out(msg, '(throttled)'); + return; + } + const unique = this.node?._ni_widget?.value; + if (!unique) { + if (this.node) Log.error(`Node ${this.node.id} has no _ni_widget when trying to send ${msg}`); + else Log.error(`No node when trying to send ${msg}`); + return; + } + msg.unique = `${unique}`; + if (!msg.special) { + if (this.n_extras > 0) { + msg.extras = Array.from(this.extras_row.children).map((e) => e.value); + } + if (this.state === State.FILTER || this.state === State.ZOOMED) msg.selection = Array.from(this.picked); + if (this.state === State.TEXT) msg.text = this.text_edit.value; + // --- NEW: Add crop data to the response --- + if (this.state === State.CROP && this.crop_data.box) { + // Convert from canvas-space to original image-space before sending + const { box, scale } = this.crop_data; + // Send 'crop' at the top level, not nested under 'response' + msg.crop = { + x: Math.round(box.x / scale), + y: Math.round(box.y / scale), + width: Math.round(box.width / scale), + height: Math.round(box.height / scale) + }; + } + this.last_response_sent = Date.now(); + } + try { + const body = new FormData(); + body.append('response', JSON.stringify(msg)); + api.fetchApi('/instaraw/interactive_message', { method: 'POST', body }); + Log.message_out(msg); + } catch (e) { + Log.error(e); + } finally { + if (!keep_open) this.close(); + } + } + + send_current_state() { + if (this.state == State.TEXT) { + this._send_response(); + } else { + this._send_response(); + } + } + + send_cancel() { + // Only send cancel if we're in an active state that's waiting for response + if (this.state !== State.INACTIVE && this.state !== State.TINY) { + this._send_response({ special: CANCEL }); + } + } + + request_reset() { + this._send_response({ special: REQUEST_RESHOW }, true); + } + + close() { + this.state = State.INACTIVE; + this.render(); + } + + maybe_play_sound() { + // Updated settings key + if (app.ui.settings.getSettingValue('INSTARAW.Interactive.PlaySound')) this.audio.play(); + } + + handle_message(message) { + Log.message_in(message); + Log.log(this._handle_message(message, false)); + this.render(); + } + + handle_deferred_message(e) { + Log.message_in(this.saved_message, '(deferred)'); + Log.log(this._handle_message(this.saved_message, true)); + this.render(); + } + + autosend() { + // Updated settings key + return app.ui.settings.getSettingValue('INSTARAW.Interactive.AutosendIdentical') && this.allsame; + } + + on_new_node(nd) { + this.node = nd; + const fp = this.floater_position(); + if (fp) this.floating_window.move_to(fp.x, fp.y, true); + const tp = this.tiny_position(); + if (tp) this.tiny_window.move_to(tp.x, tp.y, true); + } + + find_node(uid) { + const bits = uid.split(':'); + if (bits.length == 1) { + return app.graph._nodes_by_id[uid]; + } else { + var graph = app.graph; + var node; + bits.forEach((bit) => { + node = graph._nodes_by_id[bit]; + graph = node.subgraph; + }); + } + return node; + } + + _handle_message(message, using_saved) { + const detail = message.detail; + const uid = detail.uid; + const the_node = this.find_node(uid); + + if (this.node != the_node) this.on_new_node(the_node); + + if (!this.node) return console.log(`Message was for ${uid} which doesn't exist`); + if (this.node._ni_widget?.value != message.detail.unique) return console.log(`Message unique id wasn't mine`); + + if (detail.tick) { + this.counter_text.innerText = `${detail.tick}s`; + if (this.state == State.INACTIVE) this.request_reset(); + return; + } + + if (detail.timeout) { + this.close(); + return `Timeout`; + } + + if (this.handling_message) return `Ignoring message because we're already handling a message`; + + this.set_title(this.node.title ?? 'INSTARAW Image Filter'); // Updated default title + this.allsame = detail.allsame || false; + if (detail.tip) this.tip_row.innerHTML = detail.tip.replace(/(?:\r\n|\r|\n)/g, '
'); + else this.tip_row.innerHTML = ''; + + // Updated settings key + if ( + this.state == State.INACTIVE && + app.ui.settings.getSettingValue('INSTARAW.Interactive.SmallWindow') && + !using_saved && + !this.autosend() + ) { + this.state = State.TINY; + this.saved_message = message; + this.tiny_image.src = get_full_url(message.detail.urls[message.detail.urls.length - 1]); + this.maybe_play_sound(); + return `Deferring message and showing small window`; + } + + try { + this.handling_message = true; + this.n_extras = detail.extras ? message.detail.extras.length : 0; + this.extras_row.innerHTML = ''; + for (let i = 0; i < this.n_extras; i++) { + create('input', 'extra', this.extras_row, { value: detail.extras[i] }); + } + + if (!using_saved && !this.autosend()) this.maybe_play_sound(); + + if (detail.interactive_crop) { + this.handle_crop(detail); + } else if (detail.maskedit) { + // existing logic + this.handle_maskedit(detail); + } else if (detail.urls) { + // existing logic + this.handle_urls(detail); + } + } finally { + this.handling_message = false; + } + } + + window_not_showing(uid) { + const node = this.find_node(uid); + return ( + (POPUP_NODES.includes(node.type) && this.classList.contains('hidden')) || + (MASK_NODES.includes(node.type) && !mask_editor_showing()) + ); + } + + set_title(title) { + this.floating_window.set_title(title); + var pos = this.floater_position(); + if (pos) this.floating_window.move_to(pos.x, pos.y); + pos = this.tiny_position(); + if (pos) this.tiny_window.move_to(pos.x, pos.y); + this.tiny_window.set_title(title); + } + + handle_maskedit(detail) { + // Reset state from any previous mode + this.grid.innerHTML = ''; + this.overlaygrid.innerHTML = ''; + this.crop_data = { active: false, box: null, mode: null, scale: 1 }; + + if (!this.node) { + Log.log(`No node to handle maskedit - maybe it's been removed`); + this.seen_editor = true; + } else { + this.state = State.MASK; + + this.node.imgs = []; + this.node.images = []; + detail.urls.forEach((url, i) => { + this.node.imgs.push(new Image()); + this.node.imgs[i].src = api.apiURL( + `/view?filename=${encodeURIComponent(url.filename)}&type=${url.type}&subfolder=${url.subfolder}` + ); + this.node.images.push(url); + }); + this.node.imageIndex = 0; + open_maskeditor(this.node); + this.seen_editor = false; + } + setTimeout(this.wait_while_mask_editing.bind(this), 200); + } + + wait_while_mask_editing() { + if (!this.seen_editor && mask_editor_showing()) { + mask_editor_listen_for_cancel(this.send_cancel.bind(this)); + this.render(); + this.seen_editor = true; + } + + if (mask_editor_showing()) { + setTimeout(this.wait_while_mask_editing.bind(this), 100); + } else { + // Handle both file-based (old) and data URL (new) mask editor formats + const masked_image = this.extract_filename(this.node.imgs[0].src); + if (masked_image) { + this._send_response({ masked_image: masked_image }); + } else { + // New mask editor stores as data URL + this._send_response({ masked_data: this.node.imgs[0].src }); + } + } + } + + extract_filename(url_string) { + return new URL(url_string).searchParams.get('filename'); + } + + handle_urls(detail) { + // Reset crop state from any previous mode + this.crop_data = { active: false, box: null, mode: null, scale: 1 }; + + this.video_frames = detail.video_frames || 1; + + if (this.autosend()) { + return this._send_response({ selection: [0] }); + } + + this.autozoom_pending = false; + if (detail.text != null) { + this.state = State.TEXT; + this.text_edit.value = detail.text; + if (detail.textareaheight) this.text_edit.style.height = `${detail.textareaheight}px`; + } else { + // Updated settings key + if ( + this.state != State.FILTER && + this.state != State.ZOOMED && + app.ui.settings.getSettingValue('INSTARAW.Interactive.StartZoomed') != 0 + ) { + this.autozoom_pending = true; + } + this.state = State.FILTER; + } + + this.n_images = detail.urls?.length; + this.laidOut = -1; + this.picked = new Set(); + if (this.n_images == 1) this.picked.add('0'); + + this.grid.innerHTML = ''; + this.overlaygrid.innerHTML = ''; + var latestImage = null; + + detail.urls.forEach((url, i) => { + console.log(url); + if (i % this.video_frames == 0) { + const thisImage = create('img', null, this.grid, { src: get_full_url(url) }); + latestImage = thisImage; + latestImage.onload = this.layout.bind(this); + latestImage.image_index = i / this.video_frames; + latestImage.addEventListener('mouseover', (e) => this.on_mouse_enter(thisImage)); + latestImage.addEventListener('mouseout', (e) => this.on_mouse_out(thisImage)); + latestImage.frames = [get_full_url(url)]; + } else { + latestImage.frames.push(get_full_url(url)); + } + if (detail.mask_urls) { + create('img', null, this.overlaygrid, { src: get_full_url(detail.mask_urls[i]) }); + } + }); + + this.layout(); + + if (this.video_frames > 1) { + this.frame = 0; + setTimeout(this.advance_videos.bind(this), 1000); + } + } + + advance_videos() { + if (this.state == State.INACTIVE) return; + + this.frame = (this.frame + 1) % this.video_frames; + Array.from(this.grid.children).forEach((img) => { + img.src = img.frames[this.frame]; + }); + + // Updated settings key + const fps = app.ui.settings.getSettingValue('INSTARAW.Interactive.FPS'); + const delay = fps > 0 ? 1000 / fps : 1000; + setTimeout(this.advance_videos.bind(this), delay); + } + + on_mouse_enter(img) { + this.mouse_is_over = img; + this.redraw(); + } + + on_mouse_out(img) { + this.mouse_is_over = null; + this.redraw(); + } + + zoom_auto() { + this.autozoom_pending = false; + // Updated settings key + const startZoomed = app.ui.settings.getSettingValue('INSTARAW.Interactive.StartZoomed'); + if (startZoomed == 1) { + this.zoomed_image_holder = this.grid.firstChild; + } else if (startZoomed == -1) { + this.zoomed_image_holder = this.grid.lastChild; + } else { + return; + } + if (this.zoomed_image_holder.image_index >= 0) { + this.state = State.ZOOMED; + return this.show_zoomed(); + } + } + zoom_next() { + this.zoomed_image_holder = this.zoomed_image_holder.nextSibling || this.zoomed_image_holder.parentNode.firstChild; + this.show_zoomed(); + } + zoom_prev() { + this.zoomed_image_holder = + this.zoomed_image_holder.previousSibling || this.zoomed_image_holder.parentNode.lastChild; + this.show_zoomed(); + } + click_zoomed() { + const fake_event = { target: this.zoomed_image_holder }; + this.on_click(fake_event); + this.show_zoomed(); + } + show_zoomed() { + this.zoomed_image.src = this.zoomed_image_holder.src; + return this.render(); + } + eat_event(e) { + e.stopPropagation(); + e.preventDefault(); + } + + on_key_press(e) { + if (document.activeElement?.type == 'text' || document.activeElement?.type == 'textarea') { + if (this.floating_window.contains(document.activeElement) || this.contains(document.activeElement)) return; + } + if (this.state != State.INACTIVE && this.state != State.TINY) { + this.eat_event(e); + } + } + + on_key_down(e) { + if (document.activeElement?.type == 'text' || document.activeElement?.type == 'textarea') { + if (this.floating_window.contains(document.activeElement) || this.contains(document.activeElement)) return; + if (this.state == State.INACTIVE && this.state == State.TINY) return; + } + if (this.state == State.FILTER || this.state == State.TEXT) { + if (e.key == 'Enter') { + this.send_current_state(); + return this.eat_event(e); + } + if (e.key == 'Escape') { + this.send_cancel(); + return this.eat_event(e); + } + if (`${parseInt(e.key)}` == e.key) { + this.select_unselect(parseInt(e.key)); + this.render(); + return this.eat_event(e); + } + } + + if (this.state == State.FILTER) { + if (e.key == ' ' && this.mouse_is_over) { + this.state = State.ZOOMED; + this.zoomed_image_holder = this.mouse_is_over; + this.eat_event(e); + return this.show_zoomed(); + } + if (e.key == 'a' && e.ctrlKey) { + if (this.picked.size > this.n_images / 2) { + this.picked.clear(); + console.log('unselect all'); + } else { + this.picked.clear(); + for (var i = 0; i < this.n_images; i++) { + this.picked.add(`${i}`); + } + console.log('select all'); + } + this.eat_event(e); + return this.redraw(); + } + } + + if (this.state == State.ZOOMED) { + if (e.key == ' ') { + this.state = State.FILTER; + this.zoomed_image_holder = null; + this.eat_event(e); + return this.render(); + } else if (e.key == 'ArrowUp') { + this.click_zoomed(); + return this.eat_event(e); + } else if (e.key == 'ArrowDown') { + // select or unselect + } else if (e.key == 'ArrowRight') { + this.zoom_next(); + return this.eat_event(e); + } else if (e.key == 'ArrowLeft') { + this.zoom_prev(); + return this.eat_event(e); + } + } + } + + select_unselect(n) { + if (n < 0 || n > this.n_images) { + return; + } + const s = `${n}`; + // Updated settings key + if (app.ui.settings.getSettingValue('INSTARAW.Interactive.ClickSends')) { + this.picked.add(s); + this._send_response(); + } else { + if (this.picked.has(s)) this.picked.delete(s); + else this.picked.add(s); + this.redraw(); + } + } + + on_click(e) { + if (e.target.image_index != undefined) { + this.select_unselect(e.target.image_index); + } + } + + layout(norepeat) { + const box = this.grid.getBoundingClientRect(); + if (this.laidOut == box.width) return; + + const im_w = this.grid.firstChild.naturalWidth; + const im_h = this.grid.firstChild.naturalHeight; + + if (!im_w || !im_h || !box.width || !box.height) { + if (!norepeat) setTimeout(this.layout.bind(this), 100, [true]); + return; + } else { + var best_scale = 0; + var best_pick; + var per_row; + for (per_row = 1; per_row <= this.n_images; per_row++) { + const rows = Math.ceil(this.n_images / per_row); + const scale = Math.min(box.width / (im_w * per_row), box.height / (im_h * rows)); + if (scale > best_scale) { + best_scale = scale; + best_pick = per_row; + } + } + this.per_row = best_pick; + this.laidOut = box.width; + } + + this.rows = Math.ceil(this.n_images / this.per_row); + const w = box.width / this.per_row - GRID_IMAGE_SPACE; + const h = box.height / this.rows - GRID_IMAGE_SPACE; + + var template_columns = ''; + for (let i = 0; i < this.per_row; i++) template_columns += ` ${w + GRID_IMAGE_SPACE}px`; + var template_rows = ''; + for (let i = 0; i < this.rows; i++) template_rows += ` ${h + GRID_IMAGE_SPACE}px`; + this.grid.style.gridTemplateColumns = template_columns; + this.grid.style.gridTemplateRows = template_rows; + this.overlaygrid.style.gridTemplateColumns = template_columns; + this.overlaygrid.style.gridTemplateRows = template_rows; + + Array.from(this.grid.children).forEach((c, i) => { + c.style.gridArea = `${Math.floor(i / this.per_row) + 1} / ${(i % this.per_row) + 1} / auto / auto`; + }); + Array.from(this.overlaygrid.children).forEach((c, i) => { + c.style.gridArea = `${Math.floor(i / this.per_row) + 1} / ${(i % this.per_row) + 1} / auto / auto`; + }); + + this.redraw(); + setTimeout(this.rescale_images.bind(this), 100); + + if (this.autozoom_pending) { + this.zoom_auto(); + } + } + + rescale_images() { + const box = this.grid.getBoundingClientRect(); + const sub = this.grid.firstChild.getBoundingClientRect(); + const w_used = ((sub.width + GRID_IMAGE_SPACE) * this.per_row) / box.width; + const h_used = ((sub.height + GRID_IMAGE_SPACE) * this.rows) / box.height; + const could_zoom = 1.0 / Math.max(w_used, h_used); + // Updated settings key + if (could_zoom > 1 && app.ui.settings.getSettingValue('INSTARAW.Interactive.EnlargeSmall')) { + Array.from(this.grid.children).forEach((img) => { + img.style.width = `${sub.width * could_zoom}px`; + }); + Array.from(this.overlaygrid.children).forEach((img) => { + img.style.width = `${sub.width * could_zoom}px`; + }); + } + } + + redraw() { + Array.from(this.grid.children).forEach((c, i) => { + if (this.picked.has(`${i}`)) c.classList.add('selected'); + else c.classList.remove('selected'); + + if (c == this.mouse_is_over) c.classList.add('hover'); + else c.classList.remove('hover'); + }); + } + + handle_crop(detail) { + // Reset state from any previous mode + this.grid.innerHTML = ''; + this.overlaygrid.innerHTML = ''; + this.crop_data = { active: false, box: null, mode: null, scale: 1, aspectRatio: null }; + + // Store locked aspect ratio if provided + if (detail.lock_aspect_ratio) { + this.crop_data.aspectRatio = detail.lock_aspect_ratio.width / detail.lock_aspect_ratio.height; + this.crop_data.aspectRatioLabel = detail.lock_aspect_ratio.label; + console.log(`๐Ÿ”’ Crop aspect ratio locked to ${detail.lock_aspect_ratio.label} (${this.crop_data.aspectRatio.toFixed(3)})`); + } + + this.state = State.CROP; + this.crop_image_bg.onload = () => { + const img = this.crop_image_bg; + const container = this.crop_container; + const containerRect = container.getBoundingClientRect(); + + // Calculate the scaled (displayed) size of the image to fit the container + const imgAspect = img.naturalWidth / img.naturalHeight; + const containerAspect = containerRect.width / containerRect.height; + + let displayWidth, displayHeight; + if (imgAspect > containerAspect) { + displayWidth = containerRect.width; + displayHeight = displayWidth / imgAspect; + } else { + displayHeight = containerRect.height; + displayWidth = displayHeight * imgAspect; + } + + // Position and size the canvas overlay to perfectly match the displayed image + this.crop_canvas.width = displayWidth; + this.crop_canvas.height = displayHeight; + this.crop_canvas.style.left = `${(containerRect.width - displayWidth) / 2}px`; + this.crop_canvas.style.top = `${(containerRect.height - displayHeight) / 2}px`; + + // Store the scaling factor. This is crucial for converting UI coordinates back to original image coordinates. + this.crop_data.scale = displayWidth / img.naturalWidth; + + // If a proposed crop was sent from the backend, use it to initialize the crop box. + if (detail.proposed_crop) { + const proposal = detail.proposed_crop; + const scale = this.crop_data.scale; + + // Convert the proposed crop (in original image coordinates) to canvas coordinates + this.crop_data.box = { + x: proposal.x * scale, + y: proposal.y * scale, + width: proposal.width * scale, + height: proposal.height * scale + }; + } else { + // If there's no proposal, start with no crop box. + this.crop_data.box = null; + } + + // Initial draw of the UI + this.draw_crop_box(); + + // Update the main render loop to enable/disable the 'Send' button correctly + this.render(); + }; + + // Set the source of the background image, which triggers the onload event above + this.crop_image_bg.src = get_full_url(detail.urls[0]); + } + + // --- NEW: Drawing logic for the crop box --- + draw_crop_box() { + if (this.state !== State.CROP) return; + const ctx = this.crop_ctx; + const canvas = this.crop_canvas; + ctx.clearRect(0, 0, canvas.width, canvas.height); + ctx.fillStyle = 'rgba(0, 0, 0, 0.7)'; + ctx.beginPath(); + ctx.rect(0, 0, canvas.width, canvas.height); + if (this.crop_data.box) { + const { x, y, width, height } = this.get_normalized_box(this.crop_data.box); + ctx.rect(x, y, width, height); + } + ctx.fill('evenodd'); + if (this.crop_data.box) { + const { x, y, width, height } = this.get_normalized_box(this.crop_data.box); + const brandColor = '#818cf8'; + const outlineColor = '#1e1b4b'; // Dark indigo for contrast + const handles = this.get_crop_handles(); + const handleSize = 10; + + // Draw crop box - outline first, then brand color + ctx.strokeStyle = outlineColor; + ctx.lineWidth = 4; + ctx.strokeRect(x, y, width, height); + ctx.strokeStyle = brandColor; + ctx.lineWidth = 2; + ctx.strokeRect(x, y, width, height); + + // Draw corner handles (squares) - outline then fill + for (const handle of ['top-left', 'top-right', 'bottom-left', 'bottom-right']) { + const h = handles[handle]; + if (h) { + ctx.fillStyle = outlineColor; + ctx.fillRect(h.x - handleSize / 2 - 1, h.y - handleSize / 2 - 1, handleSize + 2, handleSize + 2); + ctx.fillStyle = brandColor; + ctx.fillRect(h.x - handleSize / 2, h.y - handleSize / 2, handleSize, handleSize); + } + } + + // Draw edge handles (smaller rectangles) + const edgeHandleW = 16; + const edgeHandleH = 6; + // Top and bottom (horizontal bars) + for (const handle of ['top', 'bottom']) { + const h = handles[handle]; + if (h) { + ctx.fillStyle = outlineColor; + ctx.fillRect(h.x - edgeHandleW / 2 - 1, h.y - edgeHandleH / 2 - 1, edgeHandleW + 2, edgeHandleH + 2); + ctx.fillStyle = brandColor; + ctx.fillRect(h.x - edgeHandleW / 2, h.y - edgeHandleH / 2, edgeHandleW, edgeHandleH); + } + } + // Left and right (vertical bars) + for (const handle of ['left', 'right']) { + const h = handles[handle]; + if (h) { + ctx.fillStyle = outlineColor; + ctx.fillRect(h.x - edgeHandleH / 2 - 1, h.y - edgeHandleW / 2 - 1, edgeHandleH + 2, edgeHandleW + 2); + ctx.fillStyle = brandColor; + ctx.fillRect(h.x - edgeHandleH / 2, h.y - edgeHandleW / 2, edgeHandleH, edgeHandleW); + } + } + + // Draw center handle (crosshair/move icon) - outline then brand + const center = handles['center']; + if (center) { + const size = 12; + // Dark outline pass + ctx.strokeStyle = outlineColor; + ctx.lineWidth = 4; + ctx.beginPath(); + ctx.moveTo(center.x - size, center.y); + ctx.lineTo(center.x + size, center.y); + ctx.stroke(); + ctx.beginPath(); + ctx.moveTo(center.x, center.y - size); + ctx.lineTo(center.x, center.y + size); + ctx.stroke(); + ctx.beginPath(); + ctx.arc(center.x, center.y, 6, 0, Math.PI * 2); + ctx.stroke(); + // Brand color pass + ctx.strokeStyle = brandColor; + ctx.lineWidth = 2; + ctx.beginPath(); + ctx.moveTo(center.x - size, center.y); + ctx.lineTo(center.x + size, center.y); + ctx.stroke(); + ctx.beginPath(); + ctx.moveTo(center.x, center.y - size); + ctx.lineTo(center.x, center.y + size); + ctx.stroke(); + ctx.beginPath(); + ctx.arc(center.x, center.y, 6, 0, Math.PI * 2); + ctx.stroke(); + } + + // Show crop dimensions in original image coordinates + const scale = this.crop_data.scale; + const origWidth = Math.round(width / scale); + const origHeight = Math.round(height / scale); + ctx.font = 'bold 14px monospace'; + ctx.textAlign = 'center'; + const label = `${origWidth} x ${origHeight}`; + // Text shadow + ctx.fillStyle = outlineColor; + ctx.fillText(label, x + width / 2 + 1, y + height + 21); + ctx.fillText(label, x + width / 2 - 1, y + height + 21); + ctx.fillText(label, x + width / 2, y + height + 22); + ctx.fillText(label, x + width / 2, y + height + 20); + // Brand color text + ctx.fillStyle = brandColor; + ctx.fillText(label, x + width / 2, y + height + 21); + } + + // Show aspect ratio lock indicator + if (this.crop_data.aspectRatioLabel) { + const brandColor = '#818cf8'; + const outlineColor = '#1e1b4b'; + ctx.font = 'bold 12px sans-serif'; + ctx.textAlign = 'left'; + const lockLabel = `๐Ÿ”’ ${this.crop_data.aspectRatioLabel}`; + // Text shadow + ctx.fillStyle = outlineColor; + ctx.fillText(lockLabel, 11, 21); + ctx.fillText(lockLabel, 9, 21); + ctx.fillText(lockLabel, 10, 22); + ctx.fillText(lockLabel, 10, 20); + // Brand color + ctx.fillStyle = brandColor; + ctx.fillText(lockLabel, 10, 21); + } + } + + get_normalized_box(box) { + if (!box) return null; + return { + x: box.width < 0 ? box.x + box.width : box.x, + y: box.height < 0 ? box.y + box.height : box.y, + width: Math.abs(box.width), + height: Math.abs(box.height), + }; + } + + get_crop_handles() { + const box = this.get_normalized_box(this.crop_data.box); + if (!box) return {}; + const { x, y, width, height } = box; + return { + // Corner handles for resize + 'top-left': { x, y }, 'top-right': { x: x + width, y }, + 'bottom-left': { x, y: y + height }, 'bottom-right': { x: x + width, y: y + height }, + // Edge handles for resize + 'top': { x: x + width / 2, y }, 'bottom': { x: x + width / 2, y: y + height }, + 'left': { x, y: y + height / 2 }, 'right': { x: x + width, y: y + height / 2 }, + // Center handle for move + 'center': { x: x + width / 2, y: y + height / 2 } + }; + } + + get_crop_hit_area(localPos) { + const handleSize = 16; + const handles = this.get_crop_handles(); + + // Check corner handles first (for resize) + for (const mode of ['top-left', 'top-right', 'bottom-left', 'bottom-right']) { + const handle = handles[mode]; + if (handle && Math.abs(localPos.x - handle.x) < handleSize / 2 && Math.abs(localPos.y - handle.y) < handleSize / 2) { + return 'resize-corner'; + } + } + + // Check edge handles (for resize) + for (const mode of ['top', 'bottom']) { + const handle = handles[mode]; + if (handle && Math.abs(localPos.x - handle.x) < handleSize / 2 && Math.abs(localPos.y - handle.y) < handleSize / 2) { + return 'resize-vertical'; // Drag vertically + } + } + for (const mode of ['left', 'right']) { + const handle = handles[mode]; + if (handle && Math.abs(localPos.x - handle.x) < handleSize / 2 && Math.abs(localPos.y - handle.y) < handleSize / 2) { + return 'resize-horizontal'; // Drag horizontally + } + } + + // Check center handle (for move) + const center = handles['center']; + if (center && Math.abs(localPos.x - center.x) < handleSize && Math.abs(localPos.y - center.y) < handleSize) { + return 'move'; + } + + // Check if inside box (also move) + const box = this.get_normalized_box(this.crop_data.box); + if (box) { + const { x, y, width, height } = box; + if (localPos.x > x && localPos.x < x + width && localPos.y > y && localPos.y < y + height) { + return 'move'; + } + } + return 'new'; + } + + update_crop_cursor(e) { + if (this.state !== State.CROP || this.crop_data.active) return; + const rect = this.crop_canvas.getBoundingClientRect(); + const localPos = { x: e.clientX - rect.left, y: e.clientY - rect.top }; + const mode = this.get_crop_hit_area(localPos); + + const cursorMap = { + 'resize-corner': 'nwse-resize', + 'resize-vertical': 'ns-resize', + 'resize-horizontal': 'ew-resize', + 'move': 'move', + 'new': 'crosshair' + }; + this.crop_canvas.style.cursor = cursorMap[mode] || 'crosshair'; + } + + on_crop_mouse_down(e) { + if (this.state !== State.CROP) return; + const rect = this.crop_canvas.getBoundingClientRect(); + const localPos = { x: e.clientX - rect.left, y: e.clientY - rect.top }; + this.crop_data.active = true; + this.crop_data.mode = this.get_crop_hit_area(localPos); + this.crop_data.startPos = localPos; + this.crop_data.originalBox = this.crop_data.box ? { ...this.get_normalized_box(this.crop_data.box) } : null; + if (this.crop_data.mode === 'new') { + this.crop_data.box = { x: localPos.x, y: localPos.y, width: 0, height: 0 }; + this.crop_data.originalBox = null; + } + } + + on_document_mouse_move(e) { + if (this.state !== State.CROP || !this.crop_data.active) return; + const rect = this.crop_canvas.getBoundingClientRect(); + let localPos = { x: e.clientX - rect.left, y: e.clientY - rect.top }; + + const canvasW = this.crop_canvas.width; + const canvasH = this.crop_canvas.height; + + localPos.x = Math.max(0, Math.min(localPos.x, canvasW)); + localPos.y = Math.max(0, Math.min(localPos.y, canvasH)); + + const dx = localPos.x - this.crop_data.startPos.x; + const dy = localPos.y - this.crop_data.startPos.y; + const { mode, originalBox, aspectRatio } = this.crop_data; + let x, y, width, height; + + if (mode === 'new') { + // Drawing new box from click point + x = this.crop_data.box.x; // Starting point + width = localPos.x - x; + height = localPos.y - this.crop_data.box.y; + y = this.crop_data.box.y; + + // Enforce aspect ratio when drawing new box + if (aspectRatio) { + const absWidth = Math.abs(width); + const absHeight = Math.abs(height); + const currentRatio = absWidth / (absHeight || 1); + + if (currentRatio > aspectRatio) { + width = Math.sign(width || 1) * absHeight * aspectRatio; + } else { + height = Math.sign(height || 1) * absWidth / aspectRatio; + } + } + + // Normalize + let normX = width < 0 ? x + width : x; + let normY = height < 0 ? y + height : y; + let normW = Math.abs(width); + let normH = Math.abs(height); + + // Clamp to canvas + normX = Math.max(0, normX); + normY = Math.max(0, normY); + if (normX + normW > canvasW) normW = canvasW - normX; + if (normY + normH > canvasH) normH = canvasH - normY; + + this.crop_data.box = { x: normX, y: normY, width: Math.max(10, normW), height: Math.max(10, normH) }; + + } else if (mode === 'move') { + // Move box, keeping size constant + x = originalBox.x + dx; + y = originalBox.y + dy; + width = originalBox.width; + height = originalBox.height; + + // Clamp to keep box fully inside canvas + x = Math.max(0, Math.min(x, canvasW - width)); + y = Math.max(0, Math.min(y, canvasH - height)); + + this.crop_data.box = { x, y, width, height }; + + } else if (mode === 'resize-corner' || mode === 'resize-vertical' || mode === 'resize-horizontal') { + // Center-based resize using delta from drag start + const origCenterX = originalBox.x + originalBox.width / 2; + const origCenterY = originalBox.y + originalBox.height / 2; + + // Calculate size change based on drag delta (not absolute position) + // This prevents the "jump" when first clicking a handle + let deltaSize; + if (mode === 'resize-corner') { + // Use diagonal movement for corners + deltaSize = (dx + dy) / 2; + } else if (mode === 'resize-vertical') { + // Use vertical movement for top/bottom edges + deltaSize = dy; + } else { + // Use horizontal movement for left/right edges + deltaSize = dx; + } + + // Scale factor based on delta (positive = grow, negative = shrink) + const scaleFactor = 1 + (deltaSize * 2) / Math.max(originalBox.width, originalBox.height); + + let newWidth = originalBox.width * Math.max(0.1, scaleFactor); + let newHeight = originalBox.height * Math.max(0.1, scaleFactor); + + // Enforce aspect ratio + if (aspectRatio) { + const currentRatio = newWidth / newHeight; + if (currentRatio > aspectRatio) { + newWidth = newHeight * aspectRatio; + } else { + newHeight = newWidth / aspectRatio; + } + } + + width = newWidth; + height = newHeight; + + // Clamp dimensions to canvas size + if (width > canvasW || height > canvasH) { + const scaleW = canvasW / width; + const scaleH = canvasH / height; + const clampScale = Math.min(scaleW, scaleH); + width *= clampScale; + height *= clampScale; + } + + // Calculate center position - try to keep original center but slide if needed + let centerX = origCenterX; + let centerY = origCenterY; + + const halfW = width / 2; + const halfH = height / 2; + + // Slide center to keep box in bounds + if (centerX - halfW < 0) { + centerX = halfW; + } + if (centerX + halfW > canvasW) { + centerX = canvasW - halfW; + } + if (centerY - halfH < 0) { + centerY = halfH; + } + if (centerY + halfH > canvasH) { + centerY = canvasH - halfH; + } + + x = centerX - halfW; + y = centerY - halfH; + + // Ensure minimum size + width = Math.max(20, width); + height = Math.max(20, height); + + // If aspect ratio locked and we hit minimum, enforce ratio on minimum too + if (aspectRatio && (width <= 20 || height <= 20)) { + if (width / height > aspectRatio) { + width = height * aspectRatio; + } else { + height = width / aspectRatio; + } + } + + this.crop_data.box = { x, y, width, height }; + } + + this.draw_crop_box(); + } + + on_crop_mouse_move(e) { + this.update_crop_cursor(e); + } + + on_crop_mouse_up(e) { + if (this.state !== State.CROP || !this.crop_data.active) return; + this.crop_data.active = false; + + if (this.crop_data.box) { + this.crop_data.box = this.get_normalized_box(this.crop_data.box); + } + + this.draw_crop_box(); + this.render(); + } +} + +customElements.define('instaraw-imgae-filter-popup', Popup); // Renamed custom element + +export const popup = new Popup(); diff --git a/custom_nodes/ComfyUI_INSTARAW/js/reality_prompt_generator.css b/custom_nodes/ComfyUI_INSTARAW/js/reality_prompt_generator.css new file mode 100644 index 0000000000000000000000000000000000000000..a587f1623f040d03f77d98b057e71cf18cdda138 --- /dev/null +++ b/custom_nodes/ComfyUI_INSTARAW/js/reality_prompt_generator.css @@ -0,0 +1,2841 @@ +/* --- +// Filename: ../ComfyUI_INSTARAW/js/reality_prompt_generator.css +// Reality Prompt Generator (RPG) - Styling (Following AIL Theme Exactly) +// --- */ + +/* === Main Container (AIL Pattern) === */ +.instaraw-rpg-container { + background: #0d0f12; + padding: 16px; + border-radius: 4px; + font-family: 'Inter', -apple-system, BlinkMacSystemFont, 'Segoe UI', system-ui, sans-serif; + color: #f9fafb; +} + +/* === Global Select/Dropdown Dark Mode Fix (Chrome compatibility) === */ +.instaraw-rpg-container select { + color-scheme: dark; +} + +.instaraw-rpg-container select option { + background: #1f2937; + color: #f9fafb; +} + +.instaraw-rpg-topbar { + display: flex; + flex-wrap: wrap; + justify-content: space-between; + gap: 12px; + margin-bottom: 12px; +} + +.instaraw-rpg-mode-card { + flex: 1 1 320px; + border: 1px solid rgba(255, 255, 255, 0.08); + background: rgba(255, 255, 255, 0.03); + border-radius: 4px; + padding: 12px; + display: flex; + flex-direction: column; + gap: 8px; +} + +.instaraw-rpg-mode-meta { + display: flex; + flex-wrap: wrap; + gap: 8px; + align-items: center; +} + +.instaraw-rpg-mode-hint { + font-size: 12px; + color: rgba(249, 250, 251, 0.6); +} + +.instaraw-rpg-mode-pill { + background: rgba(167, 139, 250, 0.2); + color: #c4b5fd; + padding: 4px 10px; + border-radius: 999px; + font-size: 11px; + font-weight: 600; +} + +.instaraw-rpg-kpi-row { + display: flex; + flex: 1 1 240px; + gap: 12px; + justify-content: flex-end; + flex-wrap: wrap; +} + +.instaraw-rpg-kpi { + flex: 1 1 100px; + border: 1px solid rgba(255, 255, 255, 0.08); + background: rgba(255, 255, 255, 0.02); + border-radius: 4px; + padding: 10px 14px; + display: flex; + flex-direction: column; + justify-content: center; + align-items: center; + gap: 2px; + min-width: 100px; +} + +.instaraw-rpg-kpi span { + font-size: 10px; + text-transform: uppercase; + letter-spacing: 0.5px; + color: rgba(249, 250, 251, 0.5); + font-weight: 500; +} + +.instaraw-rpg-kpi strong { + font-size: 20px; + font-weight: 700; + color: #a78bfa; + font-variant-numeric: tabular-nums; +} + +/* === Header & Tabs === */ +.instaraw-rpg-header { + display: flex; + justify-content: space-between; + align-items: center; + margin-bottom: 16px; + gap: 12px; + flex-wrap: wrap; +} + +.instaraw-rpg-tabs { + display: flex; + gap: 8px; + background: rgba(255, 255, 255, 0.02); + border: 1px solid rgba(255, 255, 255, 0.05); + border-radius: 4px; + padding: 12px; + margin-top: 12px; +} + +.instaraw-rpg-tab { + background: rgba(255, 255, 255, 0.05); + color: rgba(249, 250, 251, 0.7); + border: 1px solid rgba(255, 255, 255, 0.1); + padding: 8px 16px; + border-radius: 4px; + cursor: pointer; + font-size: 13px; + font-weight: 500; + display: flex; + align-items: center; + gap: 6px; +} + +.instaraw-rpg-tab:hover { + background: rgba(255, 255, 255, 0.08); + border-color: #818cf8; + color: #f9fafb; +} + +.instaraw-rpg-tab.active { + background: #6366f1; + color: #ffffff; + border-color: #6366f1; +} + +/* === Mode Selector (AIL Pattern) === */ +.instaraw-rpg-mode-selector { + display: flex; + align-items: center; + gap: 12px; +} + +.instaraw-rpg-mode-selector label { + color: rgba(249, 250, 251, 0.7); + font-size: 11px; + font-weight: 500; + text-transform: uppercase; +} + +.instaraw-rpg-mode-dropdown { + background: rgba(255, 255, 255, 0.05); + color: #f9fafb; + border: 1px solid rgba(255, 255, 255, 0.1); + padding: 8px 12px; + border-radius: 4px; + cursor: pointer; + font-size: 13px; + font-weight: 500; + outline: none; + min-width: 140px; + color-scheme: dark; +} + +.instaraw-rpg-mode-dropdown:hover { + background: rgba(255, 255, 255, 0.08); + border-color: #818cf8; +} + +.instaraw-rpg-mode-dropdown:focus { + border-color: #a78bfa; +} + +.instaraw-rpg-mode-dropdown option { + background: #1f2937; + color: #f9fafb; +} + +/* === Content Layout (Two-Column Grid: 50% Main Panel | 50% Batch) === */ +.instaraw-rpg-content { + display: grid; + grid-template-columns: 1fr 1fr; + gap: 16px; + margin-bottom: 16px; +} + +.instaraw-rpg-main-panel { + background: rgba(255, 255, 255, 0.03); + border: 1px solid rgba(255, 255, 255, 0.06); + border-radius: 4px; + padding: 16px; +} + +.instaraw-rpg-batch-panel { + background: rgba(255, 255, 255, 0.03); + border: 1px solid rgba(255, 255, 255, 0.06); + border-radius: 4px; + padding: 16px; + min-height: 200px; + /* No max-height - let it grow naturally */ + overflow-y: visible; +} + +.instaraw-rpg-panel-card { + margin-top: 12px; + background: rgba(255, 255, 255, 0.02); + border: 1px solid rgba(255, 255, 255, 0.05); + border-radius: 4px; + padding: 12px; +} + +/* === Buttons (AIL Pattern) === */ +.instaraw-rpg-btn-primary { + background: #6366f1; + color: #ffffff; + border: none; + padding: 10px 20px; + border-radius: 4px; + cursor: pointer; + font-weight: 600; + font-size: 13px; +} + +.instaraw-rpg-btn-primary:hover { + background: #4f46e5; +} + +.instaraw-rpg-btn-primary:disabled { + background: #4f46e5; + cursor: not-allowed; + color: rgba(255, 255, 255, 0.7); +} + +.instaraw-rpg-btn-secondary { + background: rgba(255, 255, 255, 0.08); + color: #f9fafb; + border: 1px solid rgba(255, 255, 255, 0.1); + padding: 8px 16px; + border-radius: 4px; + cursor: pointer; + font-weight: 500; + font-size: 12px; +} + +.instaraw-rpg-btn-secondary:hover { + background: rgba(255, 255, 255, 0.12); + border-color: #818cf8; +} + +.instaraw-rpg-btn-secondary:disabled { + background: rgba(255, 255, 255, 0.05); + cursor: not-allowed; + color: rgba(249, 250, 251, 0.5); +} + +.instaraw-rpg-btn-warning { + background: rgba(245, 158, 11, 0.15); + border-color: rgba(245, 158, 11, 0.4); + color: #fbbf24; +} + +.instaraw-rpg-btn-warning:hover { + background: rgba(245, 158, 11, 0.25); + border-color: rgba(245, 158, 11, 0.6); +} + +.instaraw-rpg-btn-danger { + background: #ef4444; + color: #ffffff; + border: none; + padding: 10px 20px; + border-radius: 4px; + cursor: pointer; + font-weight: 600; + font-size: 13px; + width: 100%; + margin-top: 12px; +} + +.instaraw-rpg-btn-danger:hover { + background: #dc2626; +} + +/* === Loading State === */ +.instaraw-rpg-loading { + display: flex; + flex-direction: column; + align-items: center; + justify-content: center; + padding: 64px 24px; + text-align: center; +} + +.instaraw-rpg-loading-spinner { + width: 48px; + height: 48px; + border: 4px solid rgba(167, 139, 250, 0.2); + border-top-color: #a78bfa; + border-radius: 50%; + animation: instaraw-rpg-spin 1s linear infinite; + margin-bottom: 16px; +} + +@keyframes instaraw-rpg-spin { + to { transform: rotate(360deg); } +} + +.instaraw-rpg-progress-bar { + width: 100%; + max-width: 400px; + height: 8px; + background: rgba(255, 255, 255, 0.1); + border-radius: 4px; + overflow: hidden; + margin: 16px 0; +} + +.instaraw-rpg-progress-fill { + height: 100%; + background: linear-gradient(90deg, #6366f1, #a78bfa); +} + +.instaraw-rpg-progress-text { + font-size: 12px; + color: rgba(249, 250, 251, 0.7); + margin-top: 8px; +} + +/* === Button Loading Progress Bar === */ +.instaraw-rpg-progress-bar-loading { + position: absolute; + bottom: 0; + left: 0; + right: 0; + height: 3px; + background: linear-gradient(90deg, #c4b5fd, #818cf8, #c4b5fd); + background-size: 200% 100%; + animation: instaraw-rpg-progress-slide 1.5s linear infinite; + opacity: 1; + box-shadow: 0 0 8px rgba(167, 139, 250, 0.6); +} + +@keyframes instaraw-rpg-progress-slide { + 0% { background-position: 200% 0; } + 100% { background-position: -200% 0; } +} + +/* === Empty State (AIL Pattern) === */ +.instaraw-rpg-empty { + text-align: center; + padding: 48px 24px; + background: rgba(255, 255, 255, 0.02); + border-radius: 4px; + border: 1px dashed rgba(167, 139, 250, 0.2); + grid-column: 1 / -1; +} + +.instaraw-rpg-empty p { + margin: 8px 0; + color: rgba(249, 250, 251, 0.7); + font-size: 13px; + font-weight: 500; +} + +.instaraw-rpg-hint { + font-size: 12px; + color: rgba(129, 140, 248, 0.6); + font-weight: 400; +} + +/* === Library Tab === */ +.instaraw-rpg-library { + display: flex; + flex-direction: column; + gap: 16px; +} + +.instaraw-rpg-filters { + display: flex; + flex-direction: column; + gap: 8px; +} + +.instaraw-rpg-search-input { + width: 100%; + background: rgba(255, 255, 255, 0.06); + border: 1px solid rgba(255, 255, 255, 0.1); + color: #f9fafb; + padding: 10px 12px; + border-radius: 4px; + font-size: 13px; + outline: none; +} + +.instaraw-rpg-search-input:focus { + border-color: #a78bfa; + background: rgba(255, 255, 255, 0.08); +} + +.instaraw-rpg-filter-row { + display: flex; + gap: 8px; + flex-wrap: wrap; +} + +.instaraw-rpg-filter-dropdown { + flex: 1; + min-width: 120px; + background: rgba(255, 255, 255, 0.05); + color: #f9fafb; + border: 1px solid rgba(255, 255, 255, 0.1); + padding: 8px 10px; + border-radius: 4px; + cursor: pointer; + font-size: 12px; + outline: none; + color-scheme: dark; +} + +.instaraw-rpg-filter-dropdown:hover { + background: rgba(255, 255, 255, 0.08); + border-color: #818cf8; +} + +.instaraw-rpg-filter-dropdown:focus { + border-color: #818cf8; + outline: none; +} + +.instaraw-rpg-filter-dropdown option { + background: #1f2937; + color: #f9fafb; +} + +/* Ensure classification dropdowns in edit mode are clickable */ +.instaraw-rpg-user-prompt-edit-content-type, +.instaraw-rpg-user-prompt-edit-safety-level, +.instaraw-rpg-user-prompt-edit-shot-type { + position: relative; + z-index: 10; + pointer-events: auto; +} + +.instaraw-rpg-checkbox-label { + display: flex; + align-items: center; + gap: 6px; + padding: 8px 12px; + background: rgba(255, 255, 255, 0.05); + border: 1px solid rgba(255, 255, 255, 0.1); + border-radius: 4px; + cursor: pointer; + font-size: 12px; + color: #f9fafb; + white-space: nowrap; + user-select: none; +} + +.instaraw-rpg-checkbox-label:hover { + background: rgba(255, 255, 255, 0.08); + border-color: #818cf8; +} + +.instaraw-rpg-checkbox-label input[type="checkbox"] { + cursor: pointer; + margin: 0; +} + +.instaraw-rpg-library-header { + margin: 12px 0 8px 0; + padding: 8px 0; + border-bottom: 1px solid rgba(255, 255, 255, 0.06); +} + +.instaraw-rpg-result-count { + font-size: 13px; + color: rgba(249, 250, 251, 0.7); + font-weight: 500; +} + +.instaraw-rpg-sdxl-toggle { + display: flex; + align-items: center; + gap: 6px; + padding: 4px 10px; + background: rgba(167, 139, 250, 0.15); + border: 1px solid rgba(167, 139, 250, 0.3); + border-radius: 4px; + cursor: pointer; + font-size: 12px; + color: #c4b5fd; + white-space: nowrap; + user-select: none; +} + +.instaraw-rpg-sdxl-toggle:hover { + background: rgba(167, 139, 250, 0.25); + border-color: rgba(167, 139, 250, 0.5); +} + +.instaraw-rpg-sdxl-toggle input[type="checkbox"] { + cursor: pointer; + margin: 0; +} + +.instaraw-rpg-pagination-top { + margin-bottom: 12px; +} + +.instaraw-rpg-library-grid { + display: grid; + grid-template-columns: repeat(auto-fill, minmax(max(200px, calc((100% - 24px) / 3)), 1fr)); + gap: 12px; +} + +@media (max-width: 420px) { + .instaraw-rpg-library-grid { + grid-template-columns: 1fr; + } +} + +.instaraw-rpg-library-card { + background: rgba(255, 255, 255, 0.04); + border: 2px solid rgba(255, 255, 255, 0.08); + border-radius: 4px; + overflow: hidden; + position: relative; + max-width: 500px; +} + +.instaraw-rpg-library-card:hover { + border-color: rgba(99, 102, 241, 0.5); +} + +.instaraw-rpg-library-card.in-batch { + border-color: rgba(167, 139, 250, 0.4); +} + +/* Selection mode styling */ +.instaraw-rpg-library-card.selection-mode { + cursor: pointer; +} + +.instaraw-rpg-library-card.selection-mode:hover { + border-color: rgba(59, 130, 246, 0.6); + background: rgba(59, 130, 246, 0.05); +} + +/* Selected card indicator */ +.instaraw-rpg-library-card.selection-mode:has(.instaraw-rpg-prompt-checkbox:checked) { + border-color: rgba(59, 130, 246, 0.8); + background: rgba(59, 130, 246, 0.12); + box-shadow: 0 0 0 2px rgba(59, 130, 246, 0.3); +} + +.instaraw-rpg-selection-checkbox { + flex-shrink: 0; +} + +.instaraw-rpg-selection-checkbox input[type="checkbox"] { + accent-color: #3b82f6; +} + +/* Badge removed - button now shows Remove state instead */ + +.instaraw-rpg-library-card-header { + display: flex; + justify-content: space-between; + padding: 8px; + background: rgba(0, 0, 0, 0.2); + border-bottom: 1px solid rgba(255, 255, 255, 0.05); +} + +.instaraw-rpg-bookmark-btn { + background: transparent; + border: none; + color: rgba(249, 250, 251, 0.5); + font-size: 16px; + cursor: pointer; +} + +.instaraw-rpg-bookmark-btn:hover { + color: #fbbf24; +} + +.instaraw-rpg-bookmark-btn.bookmarked { + color: #fbbf24; +} + +.instaraw-rpg-add-to-batch-btn { + background: #6366f1; + color: #ffffff; + border: none; + padding: 4px 12px; + border-radius: 4px; + cursor: pointer; + font-size: 11px; + font-weight: 600; +} + +.instaraw-rpg-add-to-batch-btn:hover { + background: #4f46e5; +} + +.instaraw-rpg-batch-controls { + display: flex; + gap: 4px; +} + +.instaraw-rpg-undo-batch-btn { + background: #dc2626; + color: #ffffff; + border: none; + padding: 4px 10px; + border-radius: 4px; + cursor: pointer; + font-size: 11px; + font-weight: 600; +} + +.instaraw-rpg-undo-batch-btn:hover { + background: #b91c1c; +} + +.instaraw-rpg-library-card-content { + padding: 12px; +} + +.instaraw-rpg-prompt-preview { + font-size: 12px; + color: rgba(249, 250, 251, 0.8); + line-height: 1.42; + margin-bottom: 8px; +} + +.instaraw-rpg-error-text { + color: rgba(156, 163, 175, 0.8) !important; + font-style: italic; +} + +.instaraw-rpg-library-card-tags { + display: flex; + flex-wrap: wrap; + gap: 4px; +} + +.instaraw-rpg-tag { + background: rgba(167, 139, 250, 0.2); + color: #c4b5fd; + padding: 2px 8px; + border-radius: 4px; + font-size: 10px; + font-weight: 500; +} + +.instaraw-rpg-tag-more { + background: rgba(129, 140, 248, 0.2); + color: #818cf8; + padding: 2px 8px; + border-radius: 4px; + font-size: 10px; + font-weight: 500; +} + +/* === Search Highlighting === */ +.instaraw-rpg-highlight { + background: rgba(251, 191, 36, 0.4); + color: #fbbf24; + font-weight: 600; + padding: 1px 2px; + border-radius: 4px; +} + +/* === Match Badge === */ +.instaraw-rpg-match-badge { + display: inline-block; + background: rgba(34, 197, 94, 0.2); + color: #22c55e; + padding: 4px 8px; + border-radius: 4px; + font-size: 10px; + font-weight: 600; + margin-bottom: 8px; +} + +/* === SDXL Mode Styles === */ +.instaraw-rpg-tags-primary { + margin-bottom: 8px; +} + +/* SDXL mode uses existing .instaraw-rpg-prompt-preview for clean display */ + +.instaraw-rpg-expandable-tags { + display: flex; + flex-wrap: wrap; + gap: 4px; + align-items: center; +} + +.instaraw-rpg-expandable-tags[data-expanded="false"] { + max-height: 80px; + overflow: hidden; +} + +.instaraw-rpg-expandable-tags[data-expanded="true"] { + max-height: none; +} + +.instaraw-rpg-expand-tags-btn { + background: rgba(99, 102, 241, 0.2); + color: #818cf8; + border: 1px solid rgba(99, 102, 241, 0.3); + padding: 2px 8px; + border-radius: 4px; + font-size: 10px; + font-weight: 600; + cursor: pointer; +} + +.instaraw-rpg-expand-tags-btn:hover { + background: rgba(99, 102, 241, 0.3); + border-color: rgba(99, 102, 241, 0.5); +} + +.instaraw-rpg-prompt-preview-small { + font-size: 11px; + color: rgba(249, 250, 251, 0.5); + line-height: 1.42; + font-style: italic; +} + +.instaraw-rpg-toggle-tags-btn { + background: rgba(129, 140, 248, 0.2); + color: #818cf8; + border: 1px solid rgba(129, 140, 248, 0.3); + padding: 2px 8px; + border-radius: 4px; + font-size: 10px; + font-weight: 600; + cursor: pointer; +} + +.instaraw-rpg-toggle-tags-btn:hover { + background: rgba(129, 140, 248, 0.3); + border-color: rgba(129, 140, 248, 0.5); +} + +/* === Pagination === */ +.instaraw-rpg-pagination { + display: flex; + justify-content: space-between; + align-items: center; + padding: 12px 0; +} + +.instaraw-rpg-page-info { + font-size: 12px; + color: rgba(249, 250, 251, 0.7); +} + +/* === Creative/Character Tabs === */ +.instaraw-rpg-creative, +.instaraw-rpg-character { + display: flex; + flex-direction: column; + gap: 16px; +} + +.instaraw-rpg-model-settings { + background: rgba(255, 255, 255, 0.03); + border: 1px solid rgba(255, 255, 255, 0.06); + border-radius: 4px; + padding: 16px; + display: flex; + flex-direction: column; + gap: 12px; +} + +.instaraw-rpg-model-row { + display: flex; + flex-direction: column; + gap: 6px; +} + +.instaraw-rpg-model-row label { + font-size: 12px; + font-weight: 500; + color: rgba(249, 250, 251, 0.7); +} + +.instaraw-rpg-model-select, +.instaraw-rpg-model-temp, +.instaraw-rpg-model-top-p, +.instaraw-rpg-system-prompt { + width: 100%; + background: rgba(255, 255, 255, 0.06); + border: 1px solid rgba(255, 255, 255, 0.1); + border-radius: 4px; + padding: 8px 10px; + color: #f9fafb; + font-size: 13px; + outline: none; + color-scheme: dark; +} + +.instaraw-rpg-model-select option { + background: #1f2937; + color: #f9fafb; +} + +.instaraw-rpg-model-select:focus, +.instaraw-rpg-model-temp:focus, +.instaraw-rpg-model-top-p:focus, +.instaraw-rpg-system-prompt:focus { + border-color: #a78bfa; +} + +.instaraw-rpg-model-grid { + display: flex; + gap: 12px; + flex-wrap: wrap; +} + +.instaraw-rpg-model-control { + flex: 1 1 160px; + display: flex; + flex-direction: column; + gap: 6px; +} + +.instaraw-rpg-system-prompt { + min-height: 90px; + resize: vertical; + font-family: 'Inter', sans-serif; + line-height: 1.42; +} + +.instaraw-rpg-creative-header h3 { + margin: 0 0 8px 0; + font-size: 16px; + color: #f9fafb; +} + +.instaraw-rpg-creative-header p { + margin: 0; + font-size: 12px; + color: rgba(249, 250, 251, 0.6); +} + +.instaraw-rpg-inspiration-section label { + display: block; + margin-bottom: 8px; + font-size: 12px; + font-weight: 500; + color: rgba(249, 250, 251, 0.8); +} + +.instaraw-rpg-inspiration-list { + background: rgba(0, 0, 0, 0.2); + border: 1px solid rgba(255, 255, 255, 0.06); + border-radius: 4px; + padding: 12px; + max-height: 150px; + overflow-y: auto; +} + +.instaraw-rpg-inspiration-item { + padding: 8px; + background: rgba(255, 255, 255, 0.04); + border-radius: 4px; + margin-bottom: 6px; +} + +.instaraw-rpg-inspiration-item:last-child { + margin-bottom: 0; +} + +.instaraw-rpg-inspiration-text { + font-size: 11px; + color: rgba(249, 250, 251, 0.7); + line-height: 1.42; +} + +.instaraw-rpg-creative-controls { + display: flex; + gap: 12px; + flex-wrap: wrap; +} + +.instaraw-rpg-control-group { + flex: 1; + min-width: 120px; +} + +.instaraw-rpg-control-group label { + display: block; + margin-bottom: 6px; + font-size: 12px; + font-weight: 500; + color: rgba(249, 250, 251, 0.8); +} + +.instaraw-rpg-number-input { + width: 100%; + background: rgba(255, 255, 255, 0.06); + border: 1px solid rgba(255, 255, 255, 0.1); + color: #f9fafb; + padding: 8px 10px; + border-radius: 4px; + font-size: 13px; + outline: none; +} + +.instaraw-rpg-number-input:focus { + border-color: #a78bfa; + background: rgba(255, 255, 255, 0.08); + outline: none; + box-shadow: 0 0 0 1px #a78bfa; +} + +.instaraw-rpg-checkbox { + margin-right: 6px; + accent-color: #6366f1; +} + +.instaraw-rpg-character-ref-input { + width: 100%; + background: rgba(255, 255, 255, 0.06); + border: 1px solid rgba(255, 255, 255, 0.1); + color: #f9fafb; + padding: 10px 12px; + border-radius: 4px; + font-size: 13px; + font-family: 'Inter', monospace; + outline: none; + resize: vertical; + line-height: 1.42; +} + +.instaraw-rpg-character-ref-input:focus { + border-color: #a78bfa; + background: rgba(255, 255, 255, 0.08); +} + +.instaraw-rpg-creative-preview { + background: rgba(167, 139, 250, 0.08); + border: 1px solid rgba(167, 139, 250, 0.2); + border-radius: 4px; + padding: 16px; +} + +.instaraw-rpg-creative-preview h4 { + margin: 0 0 12px 0; + font-size: 14px; + color: #a78bfa; +} + +.instaraw-rpg-creative-preview-list, +.instaraw-rpg-character-preview-list { + max-height: 200px; + overflow-y: auto; + margin-bottom: 12px; +} + +.instaraw-rpg-preview-item { + background: rgba(0, 0, 0, 0.2); + padding: 10px; + border-radius: 4px; + margin-bottom: 8px; +} + +.instaraw-rpg-preview-item-header { + display: flex; + align-items: center; + gap: 8px; + margin-bottom: 6px; +} + +.instaraw-rpg-preview-images { + display: flex; + gap: 4px; +} + +.instaraw-rpg-preview-item strong { + color: #c4b5fd; + font-size: 11px; + flex-shrink: 0; +} + +.instaraw-rpg-preview-item p { + margin: 0; + font-size: 12px; + color: rgba(249, 250, 251, 0.8); + line-height: 1.42; +} + +/* === Batch Panel === */ +.instaraw-rpg-batch-container { + background: rgba(255, 255, 255, 0.02); + border: 1px solid rgba(255, 255, 255, 0.05); + border-radius: 4px; + padding: 12px; + margin-top: 12px; +} + +.instaraw-rpg-batch-header { + display: flex; + justify-content: space-between; + align-items: flex-start; + gap: 12px; + margin-bottom: 16px; +} + +.instaraw-rpg-batch-header h3 { + margin: 0; + font-size: 16px; + color: #f9fafb; +} + +.instaraw-rpg-batch-subtitle { + margin: 10px 0 0 0; + font-size: 12px; + color: rgba(249, 250, 251, 0.6); +} + +.instaraw-rpg-batch-actions { + display: flex; + align-items: center; + gap: 12px; + flex-wrap: wrap; +} + +.instaraw-rpg-batch-count { + background: rgba(167, 139, 250, 0.2); + color: #c4b5fd; + padding: 6px 12px; + border-radius: 4px; + font-size: 12px; + font-weight: 600; +} + +.instaraw-rpg-reorder-toggle-btn { + background: rgba(99, 102, 241, 0.2); + color: #818cf8; + border: 1px solid rgba(99, 102, 241, 0.3); + padding: 6px 12px; + border-radius: 4px; + font-size: 12px; + font-weight: 600; + cursor: pointer; +} + +.instaraw-rpg-reorder-toggle-btn:hover { + background: rgba(99, 102, 241, 0.3); + border-color: rgba(99, 102, 241, 0.5); +} + +.instaraw-rpg-batch-grid { + display: grid; + grid-template-columns: repeat(auto-fill, minmax(max(200px, calc((100% - 24px) / 3)), 1fr)); + gap: 12px; +} + +@media (max-width: 720px) { + .instaraw-rpg-batch-grid { + grid-template-columns: 1fr; + } +} + +.instaraw-rpg-batch-item { + position: relative; + background: rgba(255, 255, 255, 0.04); + border: 1px solid rgba(255, 255, 255, 0.08); + border-radius: 4px; + padding: 12px; + cursor: move; + min-height: 220px; + display: flex; + flex-direction: column; + gap: 8px; + max-width: 500px; +} + +.instaraw-rpg-batch-item:hover { + border-color: rgba(99, 102, 241, 0.5); +} + +.instaraw-rpg-batch-item.instaraw-rpg-drop-before::before { + content: ''; + position: absolute; + top: -7px; + left: 0; + right: 0; + height: 4px; + background: #a78bfa; + border-radius: 2px; + z-index: 1000; + pointer-events: none; +} + +.instaraw-rpg-batch-item.instaraw-rpg-drop-after::after { + content: ''; + position: absolute; + bottom: -7px; + left: 0; + right: 0; + height: 4px; + background: #a78bfa; + border-radius: 2px; + z-index: 1000; + pointer-events: none; +} + +/* === Batch Card Thumbnails === */ +.instaraw-rpg-batch-thumbnail { + position: relative; + width: 100%; + height: 180px; + border-radius: 4px; + overflow: hidden; + background: #000000; + border: 2px solid rgba(255, 255, 255, 0.1); + margin-bottom: 8px; + display: flex; + align-items: center; + justify-content: center; +} + +.instaraw-rpg-batch-thumbnail img { + width: 100%; + height: 100%; + object-fit: contain; + display: block; +} + +.instaraw-rpg-batch-thumbnail-latent { + background: linear-gradient(135deg, #6366f1 0%, #8b5cf6 50%, #a78bfa 100%); +} + +/* Aspect ratio preview box for batch cards */ +.instaraw-rpg-batch-aspect-preview { + width: 120px; + max-height: 160px; /* Prevent tall portraits from overflowing */ + background: rgba(255, 255, 255, 0.1); + border: 2px solid rgba(255, 255, 255, 0.3); + border-radius: 4px; + display: flex; + align-items: center; + justify-content: center; + padding: 8px; +} + +.instaraw-rpg-batch-aspect-content { + text-align: center; + color: #ffffff; + display: flex; + flex-direction: column; + align-items: center; + gap: 4px; +} + +.instaraw-rpg-batch-thumbnail-missing { + background: rgba(245, 158, 11, 0.1); + border-color: rgba(245, 158, 11, 0.3); + border-style: dashed; +} + +.instaraw-rpg-batch-thumbnail-placeholder { + display: flex; + flex-direction: column; + align-items: center; + justify-content: center; + color: #9ca3af; + text-align: center; + padding: 8px; +} + +.instaraw-rpg-batch-thumbnail-index { + position: absolute; + top: 6px; + left: 6px; + background: rgba(0, 0, 0, 0.85); + color: #ffffff; + padding: 4px 8px; + border-radius: 4px; + font-size: 12px; + font-weight: 700; + border: 1px solid rgba(255, 255, 255, 0.3); + box-shadow: 0 2px 4px rgba(0, 0, 0, 0.3); + z-index: 10; +} + + +.instaraw-rpg-batch-item-header { + display: flex; + justify-content: space-between; + align-items: center; + margin-bottom: 8px; +} + +.instaraw-rpg-batch-item-number { + background: #6366f1; + color: #ffffff; + padding: 4px 10px; + border-radius: 4px; + font-size: 11px; + font-weight: 600; +} + +.instaraw-rpg-source-badge { + padding: 4px 8px; + border-radius: 4px; + font-size: 10px; + font-weight: 600; +} + +.instaraw-rpg-source-badge.user { + background: rgba(255, 255, 255, 0.1); + color: rgba(249, 250, 251, 0.7); +} + +.instaraw-rpg-source-badge.library { + background: rgba(59, 130, 246, 0.2); + color: #60a5fa; +} + +/* Prompt Meta Info (model/theme at bottom of card) */ +.instaraw-rpg-prompt-meta { + display: flex; + flex-direction: column; + gap: 4px; + margin-top: 10px; + padding-top: 8px; + border-top: 1px solid rgba(255, 255, 255, 0.06); +} + +.instaraw-rpg-prompt-meta-item { + font-size: 10px; + color: rgba(249, 250, 251, 0.4); + font-weight: 400; +} + +.instaraw-rpg-id-badge-container { + display: flex; + align-items: center; + gap: 2px; + background: rgba(0, 0, 0, 0.3); + border-radius: 4px; + padding: 2px; +} + +.instaraw-rpg-id-badge { + padding: 2px 6px; + font-size: 9px; + font-weight: 500; + color: rgba(249, 250, 251, 0.5); + font-family: monospace; + cursor: help; +} + +.instaraw-rpg-id-copy-btn { + background: none; + border: none; + padding: 2px 4px; + cursor: pointer; + font-size: 11px; + opacity: 0.6; + border-radius: 2px; +} + +.instaraw-rpg-id-copy-btn:hover { + opacity: 1; + background: rgba(255, 255, 255, 0.1); +} + +.instaraw-rpg-copy-prompt-btn { + background: none; + border: none; + padding: 2px 2px; + cursor: pointer; + font-size: 11px; + opacity: 0.6; + border-radius: 2px; +} + +.instaraw-rpg-copy-prompt-btn:hover { + opacity: 1; + background: rgba(255, 255, 255, 0.1); +} + +.instaraw-rpg-source-badge.from-library { + background: rgba(59, 130, 246, 0.2); + color: #60a5fa; +} + +.instaraw-rpg-source-badge.from-ai { + background: rgba(167, 139, 250, 0.2); + color: #c4b5fd; +} + +.instaraw-rpg-source-badge.from-character { + background: rgba(236, 72, 153, 0.2); + color: #f9a8d4; +} + +/* Repeat Count Status Badges */ +.instaraw-rpg-repeat-status { + font-size: 10px; + padding: 3px 8px; + border-radius: 4px; + font-weight: 600; + border: 1px solid; +} + +.instaraw-rpg-repeat-match { + background: rgba(34, 197, 94, 0.15); + color: #4ade80; + border-color: rgba(34, 197, 94, 0.3); +} + +.instaraw-rpg-repeat-mismatch { + background: rgba(245, 158, 11, 0.15); + color: #fbbf24; + border-color: rgba(245, 158, 11, 0.4); +} + +.instaraw-rpg-crop-indicator { + position: absolute; + bottom: 4px; + right: 4px; + background: rgba(139, 92, 246, 0.85); + color: #ffffff; + font-size: 9px; + font-weight: 600; + padding: 2px 6px; + border-radius: 4px; + pointer-events: none; + text-transform: uppercase; + letter-spacing: 0.3px; +} + +.instaraw-rpg-batch-delete-btn { + background: #ef4444; + color: #ffffff; + border: none; + width: 28px; + height: 28px; + border-radius: 4px; + cursor: pointer; + font-weight: 600; + font-size: 16px; + line-height: 1; + display: flex; + align-items: center; + justify-content: center; +} + +.instaraw-rpg-batch-delete-btn:hover { + background: #dc2626; +} + +.instaraw-rpg-batch-item-content { + display: flex; + flex-direction: column; + gap: 8px; +} + +.instaraw-rpg-batch-tags-display { + display: flex; + flex-wrap: wrap; + gap: 4px; + padding: 8px; + background: rgba(167, 139, 250, 0.1); + border: 1px solid rgba(167, 139, 250, 0.2); + border-radius: 4px; + margin-bottom: 8px; +} + +.instaraw-rpg-batch-tags-text { + font-size: 12px; + line-height: 1.42; + color: rgba(249, 250, 251, 0.9); + padding: 10px; + background: rgba(167, 139, 250, 0.08); + border: 1px solid rgba(167, 139, 250, 0.2); + border-radius: 4px; + margin-bottom: 12px; + font-family: 'Inter', monospace; + white-space: pre-wrap; + word-break: break-word; +} + +/* SDXL mode now uses standard .instaraw-rpg-positive-textarea */ + +.instaraw-rpg-batch-item-content label, +.instaraw-rpg-thumbnail-label { + font-size: 11px; + font-weight: 500; + color: rgba(249, 250, 251, 0.7); + text-transform: uppercase; + display: block; +} + +.instaraw-rpg-prompt-textarea { + width: 100%; + background: rgba(255, 255, 255, 0.06); + border: 1px solid rgba(255, 255, 255, 0.1); + color: #f9fafb; + padding: 8px 10px; + border-radius: 4px; + font-size: 12px; + font-family: 'Inter', monospace; + outline: none; + resize: vertical; + line-height: 1.42; + min-height: 60px; + max-height: 200px; + overflow-y: auto; +} + +.instaraw-rpg-prompt-textarea:focus { + border-color: #a78bfa; + background: rgba(255, 255, 255, 0.08); + outline: none; + box-shadow: 0 0 0 1px #a78bfa; +} + +/* Batch queue textareas - allow unlimited resize to view full prompts */ +.instaraw-rpg-batch-item .instaraw-rpg-prompt-textarea, +.instaraw-rpg-positive-textarea, +.instaraw-rpg-negative-textarea { + max-height: none; +} + +.instaraw-rpg-negative-toggle { + display: flex; + align-items: center; +} + +.instaraw-rpg-batch-item-controls { + display: flex; + align-items: center; + gap: 8px; +} + +.instaraw-rpg-batch-item-controls label { + color: rgba(249, 250, 251, 0.6); + font-size: 11px; + font-weight: 500; + text-transform: none; +} + +.instaraw-rpg-repeat-input { + width: 60px; + background: rgba(255, 255, 255, 0.06); + border: 1px solid rgba(255, 255, 255, 0.1); + color: #f9fafb; + padding: 6px; + border-radius: 4px; + text-align: center; + font-size: 12px; + font-weight: 600; + outline: none; +} + +.instaraw-rpg-repeat-input:hover { + background: rgba(255, 255, 255, 0.08); + border-color: #818cf8; +} + +.instaraw-rpg-repeat-input:focus { + border-color: #a78bfa; + outline: none; + box-shadow: 0 0 0 1px #a78bfa; +} + +.instaraw-rpg-seed-input:focus { + border-color: #a78bfa !important; + outline: none !important; + box-shadow: 0 0 0 1px #a78bfa !important; +} + +.instaraw-rpg-random-count-input:focus { + border-color: #a78bfa !important; + outline: none !important; + box-shadow: 0 0 0 1px #a78bfa !important; +} + +.instaraw-rpg-batch-item-tags { + display: flex; + flex-wrap: wrap; + gap: 4px; + margin-top: 4px; +} + +/* === Image Preview Section === */ +.instaraw-rpg-image-preview-section { + background: rgba(255, 255, 255, 0.03); + border: 1px solid rgba(255, 255, 255, 0.06); + border-radius: 4px; + padding: 16px; + margin-bottom: 12px; +} + +.instaraw-rpg-image-preview-empty { + text-align: center; + padding: 32px 16px; + padding-top: 10px; +} + +.instaraw-rpg-image-preview-empty p { + margin: 4px 0; + font-size: 13px; + color: rgba(249, 250, 251, 0.6); +} + +.instaraw-rpg-image-preview-header { + display: flex; + justify-content: space-between; + margin-bottom: 10px; + font-size: 12px; + color: rgba(249, 250, 251, 0.8); +} + +.instaraw-rpg-validation-badge { + padding: 6px 12px; + border-radius: 4px; + font-weight: 600; +} + +.instaraw-rpg-validation-match { + background: rgba(16, 185, 129, 0.2); + color: #10b981; +} + +.instaraw-rpg-validation-mismatch { + background: rgba(251, 191, 36, 0.2); + color: #fbbf24; +} + +.instaraw-rpg-image-preview-grid { + display: grid; + grid-template-columns: repeat(auto-fill, minmax(100px, 1fr)); + gap: 12px; /* Increased from 8px for better spacing */ +} + +.instaraw-rpg-preview-thumb { + position: relative; /* For index badge positioning */ + width: 100%; + height: 120px; /* Fixed height container */ + border-radius: 4px; + overflow: hidden; + background: linear-gradient(135deg, #6366f1 0%, #8b5cf6 50%, #a78bfa 100%); /* Purple gradient like latents */ + border: 2px solid rgba(139, 92, 246, 0.4); + display: flex; + align-items: center; + justify-content: center; +} + +/* Aspect ratio box inside preview thumb (for IMG2IMG) */ +.instaraw-rpg-preview-thumb .instaraw-rpg-preview-aspect-box { + width: 90px; + max-height: 105px; /* Prevent tall portraits from overflowing */ + border-radius: 4px; + overflow: hidden; + display: flex; + align-items: center; + justify-content: center; +} + +.instaraw-rpg-preview-thumb img { + width: 100%; + height: 100%; + object-fit: cover; /* Fill the aspect ratio box */ + display: block; +} + +.instaraw-rpg-preview-more { + display: flex; + align-items: center; + justify-content: center; + background: rgba(167, 139, 250, 0.2); + color: #a78bfa; + font-size: 11px; + font-weight: 600; + border-radius: 4px; +} + +/* === Footer Stats === */ +.instaraw-rpg-footer { + background: rgba(0, 0, 0, 0.2); + padding: 12px 16px; + border-radius: 4px; + border: 1px solid rgba(255, 255, 255, 0.06); +} + +.instaraw-rpg-stats { + display: flex; + align-items: center; + gap: 16px; + flex-wrap: wrap; +} + +.instaraw-rpg-stat-badge { + background: rgba(167, 139, 250, 0.2); + color: #c4b5fd; + padding: 6px 14px; + border-radius: 4px; + font-size: 12px; + font-weight: 600; + white-space: nowrap; +} + +.instaraw-rpg-stat-label { + font-size: 11px; + color: rgba(249, 250, 251, 0.7); + font-weight: 500; + white-space: nowrap; +} + +/* === Scrollbar Styling === */ +.instaraw-rpg-main-panel::-webkit-scrollbar, +.instaraw-rpg-batch-panel::-webkit-scrollbar, +.instaraw-rpg-inspiration-list::-webkit-scrollbar, +.instaraw-rpg-creative-preview-list::-webkit-scrollbar, +.instaraw-rpg-character-preview-list::-webkit-scrollbar { + width: 8px; +} + +.instaraw-rpg-main-panel::-webkit-scrollbar-track, +.instaraw-rpg-batch-panel::-webkit-scrollbar-track, +.instaraw-rpg-inspiration-list::-webkit-scrollbar-track, +.instaraw-rpg-creative-preview-list::-webkit-scrollbar-track, +.instaraw-rpg-character-preview-list::-webkit-scrollbar-track { + background: rgba(0, 0, 0, 0.2); + border-radius: 4px; +} + +.instaraw-rpg-main-panel::-webkit-scrollbar-thumb, +.instaraw-rpg-batch-panel::-webkit-scrollbar-thumb, +.instaraw-rpg-inspiration-list::-webkit-scrollbar-thumb, +.instaraw-rpg-creative-preview-list::-webkit-scrollbar-thumb, +.instaraw-rpg-character-preview-list::-webkit-scrollbar-thumb { + background: rgba(167, 139, 250, 0.3); + border-radius: 4px; +} + +.instaraw-rpg-main-panel::-webkit-scrollbar-thumb:hover, +.instaraw-rpg-batch-panel::-webkit-scrollbar-thumb:hover, +.instaraw-rpg-inspiration-list::-webkit-scrollbar-thumb:hover, +.instaraw-rpg-creative-preview-list::-webkit-scrollbar-thumb:hover, +.instaraw-rpg-character-preview-list::-webkit-scrollbar-thumb:hover { + background: rgba(167, 139, 250, 0.5); +} + +/* === Unified Generate Tab Styles === */ +.instaraw-rpg-generate-unified { + display: flex; + flex-direction: column; + gap: 16px; +} + +.instaraw-rpg-section { + background: rgba(255, 255, 255, 0.02); + border: 1px solid rgba(255, 255, 255, 0.08); + border-radius: 4px; + padding: 14px; +} + +.instaraw-rpg-section-header { + display: flex; + align-items: center; + justify-content: space-between; + margin-bottom: 12px; + gap: 10px; +} +.instaraw-rpg-section-header-no-margin-bottom { + margin-bottom: 0px; +} + +.instaraw-rpg-section-label { + font-size: 13px; + font-weight: 600; + color: #e5e7eb; + text-transform: uppercase; + letter-spacing: 0.5px; +} + +.instaraw-rpg-mode-badge { + display: inline-flex; + align-items: center; + gap: 6px; + padding: 6px 14px; + font-size: 12px; + font-weight: 700; + border-radius: 4px; + text-transform: uppercase; + letter-spacing: 0.5px; +} + +.instaraw-rpg-mode-img2img { + background: linear-gradient(135deg, #3b82f6 0%, #2563eb 100%); + border: 2px solid #60a5fa; + color: #ffffff; +} + +.instaraw-rpg-mode-txt2img { + background: linear-gradient(135deg, #7c3aed 0%, #6d28d9 100%); + border: 2px solid #a78bfa; + color: #ffffff; +} + +.instaraw-rpg-hint-badge { + padding: 4px 10px; + background: rgba(255, 255, 255, 0.06); + border: 1px solid rgba(255, 255, 255, 0.1); + border-radius: 4px; + font-size: 11px; + color: #d1d5db; +} + +.instaraw-rpg-hint-text { + font-size: 11px; + color: #9ca3af; + font-style: italic; +} + +.instaraw-rpg-character-section { + margin-top: 12px; + padding-top: 12px; + border-top: 1px solid rgba(255, 255, 255, 0.08); +} + +.instaraw-rpg-character-actions { + display: flex; + gap: 10px; + margin-top: 10px; +} + +.instaraw-rpg-character-preview { + margin-top: 12px; + padding: 10px; + background: rgba(139, 92, 246, 0.1); + border: 1px solid rgba(139, 92, 246, 0.3); + border-radius: 4px; +} + +.instaraw-rpg-character-desc-text { + margin: 6px 0 0 0; + font-size: 12px; + color: #d1d5db; + line-height: 1.42; +} + +.instaraw-rpg-checkbox-grid { + display: grid; + grid-template-columns: repeat(2, 1fr); + gap: 10px; + margin-top: 10px; +} + +.instaraw-rpg-checkbox-label { + display: flex; + align-items: center; + gap: 8px; + cursor: pointer; + padding: 8px; + background: rgba(255, 255, 255, 0.03); + border: 1px solid rgba(255, 255, 255, 0.08); + border-radius: 4px; +} + +.instaraw-rpg-checkbox-label:hover { + background: rgba(255, 255, 255, 0.06); + border-color: rgba(139, 92, 246, 0.4); +} + +.instaraw-rpg-checkbox-label input[type="checkbox"]:checked + span { + color: #a78bfa; + font-weight: 600; +} + +.instaraw-rpg-checkbox-row { + display: flex; + gap: 16px; + flex-wrap: wrap; + margin-top: 10px; +} + +.instaraw-rpg-txt2img-settings { + margin-top: 12px; +} + +.instaraw-rpg-user-text-input { + width: 100%; + background: rgba(255, 255, 255, 0.06); + border: 1px solid rgba(255, 255, 255, 0.1); + color: #f9fafb; + padding: 10px 12px; + border-radius: 4px; + font-size: 13px; + font-family: 'Inter', sans-serif; + outline: none; + resize: vertical; + line-height: 1.42; +} + +.instaraw-rpg-user-text-input:focus { + border-color: #a78bfa; + background: rgba(255, 255, 255, 0.08); +} + +.instaraw-rpg-library-controls { + margin-top: 12px; +} + +.instaraw-rpg-control-row { + display: flex; + align-items: center; + gap: 10px; + margin-top: 8px; +} + +.instaraw-rpg-control-row span { + font-size: 13px; + color: #d1d5db; +} + +.instaraw-rpg-btn-text { + background: transparent; + border: 1px solid rgba(139, 92, 246, 0.4); + color: #a78bfa; + padding: 6px 12px; + font-size: 12px; + border-radius: 4px; + cursor: pointer; +} + +.instaraw-rpg-btn-text:hover { + background: rgba(139, 92, 246, 0.2); + border-color: #a78bfa; + color: #c4b5fd; +} + +.instaraw-rpg-generate-unified-btn { + width: 100%; + margin-top: 8px; +} + +/* === Generation Progress Section === */ +.instaraw-rpg-generation-progress { + margin-top: 12px; + padding: 14px; + background: rgba(99, 102, 241, 0.08); + border: 1px solid rgba(99, 102, 241, 0.3); + border-radius: 4px; +} + +.instaraw-rpg-progress-header { + display: flex; + justify-content: space-between; + align-items: center; + margin-bottom: 16px; +} + +.instaraw-rpg-progress-header h4 { + margin: 0; + color: #e5e7eb; + font-size: 14px; + font-weight: 600; +} + +.instaraw-rpg-progress-items { + display: flex; + flex-direction: column; + gap: 12px; +} + +.instaraw-rpg-progress-item { + background: rgba(255, 255, 255, 0.04); + border: 1px solid rgba(255, 255, 255, 0.1); + border-radius: 4px; + padding: 12px; +} + +.instaraw-rpg-progress-item.success { + border-color: rgba(34, 197, 94, 0.4); + background: rgba(34, 197, 94, 0.08); +} + +.instaraw-rpg-progress-item.error { + border-color: rgba(239, 68, 68, 0.4); + background: rgba(239, 68, 68, 0.08); +} + +.instaraw-rpg-progress-item.in-progress { + border-color: rgba(99, 102, 241, 0.5); + background: rgba(99, 102, 241, 0.1); +} + +.instaraw-rpg-progress-item-header { + display: flex; + justify-content: space-between; + align-items: center; + margin-bottom: 8px; + gap: 8px; +} + +.instaraw-rpg-progress-images { + display: flex; + gap: 4px; + flex: 1; +} + +.instaraw-rpg-progress-item-label { + font-size: 12px; + font-weight: 600; + color: #e5e7eb; +} + +.instaraw-rpg-progress-item-status { + font-size: 11px; + padding: 3px 8px; + border-radius: 4px; + font-weight: 600; +} + +.instaraw-rpg-progress-item-status.pending { + background: rgba(156, 163, 175, 0.2); + color: #9ca3af; +} + +.instaraw-rpg-progress-item-status.in-progress { + background: rgba(99, 102, 241, 0.3); + color: #818cf8; + animation: instaraw-rpg-pulse 2s ease-in-out infinite; +} + +.instaraw-rpg-progress-item-status.success { + background: rgba(34, 197, 94, 0.3); + color: #22c55e; +} + +.instaraw-rpg-progress-item-status.error { + background: rgba(239, 68, 68, 0.3); + color: #ef4444; +} + +.instaraw-rpg-progress-item-status.retrying { + background: rgba(245, 158, 11, 0.3); + color: #fbbf24; + animation: instaraw-rpg-pulse 1.5s ease-in-out infinite; +} + +.instaraw-rpg-progress-item-bar { + width: 100%; + height: 4px; + background: rgba(255, 255, 255, 0.08); + border-radius: 4px; + overflow: hidden; + margin-top: 10px; + position: relative; +} + +.instaraw-rpg-progress-item-fill { + height: 100%; + background: linear-gradient(90deg, #6366f1, #a78bfa); + position: relative; + overflow: hidden; +} + +.instaraw-rpg-progress-item-fill.animating { + background: linear-gradient(90deg, #a78bfa, #818cf8, #6366f1, #818cf8, #a78bfa); + background-size: 300% 100%; + animation: instaraw-rpg-progress-shimmer 2s ease-in-out infinite; + border-radius: 4px; + box-shadow: 0 0 12px rgba(139, 92, 246, 0.5), 0 0 24px rgba(99, 102, 241, 0.2); + position: relative; +} + +.instaraw-rpg-progress-item-fill.animating::after { + content: ''; + position: absolute; + top: 0; + left: -100%; + width: 100%; + height: 100%; + background: linear-gradient(90deg, transparent, rgba(255, 255, 255, 0.3), transparent); + animation: instaraw-rpg-progress-shine 2.5s ease-in-out infinite; +} + +@keyframes instaraw-rpg-progress-shine { + 0% { left: -100%; } + 50%, 100% { left: 200%; } +} + +.instaraw-rpg-progress-item-message { + font-size: 11px; + color: rgba(249, 250, 251, 0.7); + margin-top: 6px; + font-style: italic; +} + +.instaraw-rpg-progress-item-message.error { + color: #fca5a5; +} + +@keyframes instaraw-rpg-pulse { + 0%, 100% { opacity: 1; } + 50% { opacity: 0.6; } +} + +@keyframes instaraw-rpg-progress-shimmer { + 0% { background-position: 200% 0; } + 100% { background-position: -200% 0; } +} + +.instaraw-rpg-generate-preview { + margin-top: 16px; + padding: 14px; + background: rgba(139, 92, 246, 0.1); + border: 1px solid rgba(139, 92, 246, 0.3); + border-radius: 4px; +} + +.instaraw-rpg-generate-preview h4 { + margin: 0 0 12px 0; + color: #e5e7eb; + font-size: 14px; +} + +.instaraw-rpg-generate-preview-list { + max-height: 300px; + overflow-y: auto; + margin-bottom: 12px; +} + +.instaraw-rpg-generate-preview button { + margin-right: 10px; +} + +/* === Mode Display Updates === */ +.instaraw-rpg-mode-indicator-container { + display: flex; + align-items: center; + gap: 8px; + flex: 1; +} + +.instaraw-rpg-mode-source { + font-size: 11px; + color: #9ca3af; + font-style: italic; + padding: 4px 10px; + background: rgba(255, 255, 255, 0.05); + border-radius: 4px; +} + +/* Reuse mode badges from Generate tab */ +.instaraw-rpg-mode-card .instaraw-rpg-mode-badge { + flex: 0 0 auto; +} + +/* === Latent Preview Styles === */ +.instaraw-rpg-preview-latent { + position: relative; + width: 100%; + height: 120px; /* Fixed height container (same as AIL) */ + background: linear-gradient(135deg, #6366f1 0%, #8b5cf6 50%, #a78bfa 100%); + border-radius: 4px; + display: flex; + align-items: center; + justify-content: center; + overflow: visible; /* Changed from hidden to show badges */ + border: 2px solid rgba(139, 92, 246, 0.4); +} + +/* Aspect ratio preview box inside latent card */ +.instaraw-rpg-preview-aspect-box { + width: 80px; + max-height: 105px; /* Prevent tall portraits from overflowing */ + background: rgba(255, 255, 255, 0.1); + border: 2px solid rgba(255, 255, 255, 0.3); + border-radius: 4px; + display: flex; + align-items: center; + justify-content: center; + padding: 6px; +} + +.instaraw-rpg-preview-aspect-content { + text-align: center; + color: #ffffff; + display: flex; + flex-direction: column; + align-items: center; + gap: 2px; +} + +.instaraw-rpg-latent-content { + display: flex; + flex-direction: column; + align-items: center; + justify-content: center; + color: #ffffff; + text-align: center; + padding: 4px; + width: 100%; + height: 100%; +} + +.instaraw-rpg-preview-repeat { + position: absolute; + top: 4px; + right: 4px; + background: rgba(0, 0, 0, 0.85); + color: #ffffff; + padding: 2px 6px; + border-radius: 4px; + font-size: 10px; + font-weight: 700; + border: 1px solid rgba(255, 255, 255, 0.3); + box-shadow: 0 2px 4px rgba(0, 0, 0, 0.3); +} + +/* === Index Number Badges (Preview Section) === */ +.instaraw-rpg-preview-index { + position: absolute; + top: 4px; + left: 4px; + background: rgba(0, 0, 0, 0.85); + color: #ffffff; + padding: 3px 7px; + border-radius: 4px; + font-size: 11px; + font-weight: 700; + border: 1px solid rgba(255, 255, 255, 0.3); + box-shadow: 0 2px 4px rgba(0, 0, 0, 0.3); + z-index: 10; +} + +.instaraw-rpg-preview-aspect-label { + position: absolute; + bottom: 4px; + right: 4px; + background: rgba(0, 0, 0, 0.85); + color: #ffffff; + padding: 3px 7px; + border-radius: 4px; + font-size: 10px; + font-weight: 600; + border: 1px solid rgba(255, 255, 255, 0.2); + box-shadow: 0 2px 4px rgba(0, 0, 0, 0.3); + z-index: 10; +} + +/* === Generation Modal === */ +.instaraw-rpg-generation-modal-overlay { + position: fixed; + top: 0; + left: 0; + right: 0; + bottom: 0; + background: rgba(0, 0, 0, 0.8); + display: flex; + align-items: center; + justify-content: center; + z-index: 10000; + padding: 20px; +} + +.instaraw-rpg-generation-modal { + background: #0d0f12; + border: 1px solid rgba(255, 255, 255, 0.12); + border-radius: 8px; + padding: 24px; + max-width: 600px; + width: 100%; + max-height: 80vh; + overflow-y: auto; + box-shadow: 0 8px 32px rgba(0, 0, 0, 0.6); +} + +.instaraw-rpg-generation-modal-header { + display: flex; + justify-content: space-between; + align-items: center; + margin-bottom: 20px; + padding-bottom: 16px; + border-bottom: 1px solid rgba(255, 255, 255, 0.08); +} + +.instaraw-rpg-generation-modal-title { + font-size: 18px; + font-weight: 600; + color: #f9fafb; + margin: 0; +} + +.instaraw-rpg-generation-modal-close { + background: none; + border: none; + color: rgba(249, 250, 251, 0.6); + font-size: 24px; + cursor: pointer; + padding: 0; + width: 32px; + height: 32px; + display: flex; + align-items: center; + justify-content: center; + border-radius: 4px; +} + +.instaraw-rpg-generation-modal-close:hover { + background: rgba(255, 255, 255, 0.08); + color: #f9fafb; +} + +.instaraw-rpg-generation-modal-overall { + margin-bottom: 20px; + padding: 16px; + background: rgba(255, 255, 255, 0.03); + border-radius: 6px; + border: 1px solid rgba(255, 255, 255, 0.08); +} + +.instaraw-rpg-generation-modal-overall-text { + font-size: 14px; + color: rgba(249, 250, 251, 0.8); + margin-bottom: 8px; +} + +.instaraw-rpg-generation-modal-overall-bar { + width: 100%; + height: 8px; + background: rgba(255, 255, 255, 0.08); + border-radius: 4px; + overflow: hidden; +} + +.instaraw-rpg-generation-modal-overall-fill { + height: 100%; + background: linear-gradient(90deg, #8b5cf6, #a78bfa); + border-radius: 4px; +} + +.instaraw-rpg-generation-modal-list { + display: flex; + flex-direction: column; + gap: 12px; +} + +.instaraw-rpg-generation-item { + display: flex; + align-items: center; + gap: 12px; + padding: 12px; + background: rgba(255, 255, 255, 0.03); + border-radius: 6px; + border: 1px solid rgba(255, 255, 255, 0.08); +} + +.instaraw-rpg-generation-item-index { + font-size: 14px; + font-weight: 600; + color: rgba(249, 250, 251, 0.6); + min-width: 40px; +} + +.instaraw-rpg-generation-item-status { + flex: 1; + display: flex; + align-items: center; + gap: 8px; +} + +.instaraw-rpg-generation-item-icon { + font-size: 16px; + line-height: 1; +} + +.instaraw-rpg-generation-item-text { + font-size: 13px; + color: rgba(249, 250, 251, 0.8); +} + +.instaraw-rpg-generation-item.status-pending { + border-color: rgba(156, 163, 175, 0.3); +} + +.instaraw-rpg-generation-item.status-pending .instaraw-rpg-generation-item-icon { + color: #9ca3af; +} + +.instaraw-rpg-generation-item.status-generating { + border-color: rgba(139, 92, 246, 0.5); + background: rgba(139, 92, 246, 0.08); +} + +.instaraw-rpg-generation-item.status-generating .instaraw-rpg-generation-item-icon { + color: #a78bfa; + animation: spin 1s linear infinite; +} + +.instaraw-rpg-generation-item.status-success { + border-color: rgba(34, 197, 94, 0.5); + background: rgba(34, 197, 94, 0.08); +} + +.instaraw-rpg-generation-item.status-success .instaraw-rpg-generation-item-icon { + color: #22c55e; +} + +.instaraw-rpg-generation-item.status-throttled { + border-color: rgba(251, 191, 36, 0.5); + background: rgba(251, 191, 36, 0.08); +} + +.instaraw-rpg-generation-item.status-throttled .instaraw-rpg-generation-item-icon { + color: #fbbf24; +} + +.instaraw-rpg-generation-item.status-error { + border-color: rgba(239, 68, 68, 0.5); + background: rgba(239, 68, 68, 0.08); +} + +.instaraw-rpg-generation-item.status-error .instaraw-rpg-generation-item-icon { + color: #ef4444; +} + +@keyframes spin { + from { + transform: rotate(0deg); + } + to { + transform: rotate(360deg); + } +} + +.instaraw-rpg-generation-modal-footer { + margin-top: 20px; + padding-top: 16px; + border-top: 1px solid rgba(255, 255, 255, 0.08); + display: flex; + justify-content: flex-end; + gap: 12px; +} + +.instaraw-rpg-generation-modal-btn { + padding: 8px 16px; + border-radius: 4px; + font-size: 13px; + font-weight: 500; + cursor: pointer; + border: none; +} + +.instaraw-rpg-generation-modal-btn-primary { + background: #8b5cf6; + color: white; +} + +.instaraw-rpg-generation-modal-btn-primary:hover:not(:disabled) { + background: #7c3aed; +} + +.instaraw-rpg-generation-modal-btn-primary:disabled { + opacity: 0.5; + cursor: not-allowed; +} + +.instaraw-rpg-generation-modal-btn-secondary { + background: rgba(255, 255, 255, 0.08); + color: rgba(249, 250, 251, 0.9); +} + +.instaraw-rpg-generation-modal-btn-secondary:hover { + background: rgba(255, 255, 255, 0.12); +} + +/* Generated prompts badge */ +.instaraw-rpg-source-badge.generated { + background: rgba(139, 92, 246, 0.2); + color: #a78bfa; +} + +/* === Expression Control Styles === */ +.instaraw-rpg-expressions-section { + margin-top: 12px; +} + +.instaraw-rpg-expressions-grid { + display: grid; + grid-template-columns: repeat(auto-fill, minmax(140px, 1fr)); + gap: 8px; + margin-bottom: 8px; +} + +.instaraw-rpg-expression-toggle { + display: flex !important; + align-items: center; + gap: 8px; + padding: 8px 12px; + background: rgba(255, 255, 255, 0.03); + border: 1px solid rgba(255, 255, 255, 0.1); + border-radius: 4px; + cursor: pointer; + user-select: none; +} + +.instaraw-rpg-expression-toggle:hover { + background: rgba(255, 255, 255, 0.06); + border-color: rgba(139, 92, 246, 0.3); +} + +.instaraw-rpg-expression-checkbox { + cursor: pointer; + width: 16px; + height: 16px; + margin: 0; + flex-shrink: 0; + vertical-align: middle; +} + +.instaraw-rpg-expression-label { + font-size: 12px; + color: #e5e7eb; + flex: 1; + line-height: 16px; +} + +.instaraw-rpg-expression-toggle:has(.instaraw-rpg-expression-checkbox:checked) { + background: rgba(139, 92, 246, 0.15); + border-color: rgba(139, 92, 246, 0.5); +} + +.instaraw-rpg-expression-toggle:has(.instaraw-rpg-expression-checkbox:checked) .instaraw-rpg-expression-label { + color: #c4b5fd; + font-weight: 500; +} + +.instaraw-rpg-default-expression-select { + width: 100%; + background: rgba(0, 0, 0, 0.3); + border: 1px solid rgba(255, 255, 255, 0.1); + color: #e5e7eb; + padding: 8px 12px; + border-radius: 4px; + font-size: 12px; + cursor: pointer; +} + +.instaraw-rpg-default-expression-select:focus { + outline: none; + border-color: rgba(139, 92, 246, 0.5); +} + +.instaraw-rpg-default-mix-slider { + width: 100%; + height: 6px; + border-radius: 3px; + background: rgba(255, 255, 255, 0.1); + outline: none; + cursor: pointer; +} + +.instaraw-rpg-default-mix-slider::-webkit-slider-thumb { + -webkit-appearance: none; + appearance: none; + width: 16px; + height: 16px; + border-radius: 50%; + background: #8b5cf6; + cursor: pointer; +} + +.instaraw-rpg-default-mix-slider::-moz-range-thumb { + width: 16px; + height: 16px; + border-radius: 50%; + background: #8b5cf6; + cursor: pointer; + border: none; +} + +.instaraw-rpg-default-mix-value { + font-weight: 600; + color: #c4b5fd; +} + +/* === Custom Tab Styles === */ +.instaraw-rpg-custom-tab { + display: flex; + flex-direction: column; + gap: 16px; +} + +.instaraw-rpg-custom-template-textarea { + font-family: 'Monaco', 'Menlo', 'Ubuntu Mono', monospace !important; + min-height: 100px; + max-height: 250px; + overflow-y: auto !important; +} + +/* Model instructions textarea - allow unlimited expansion */ +.instaraw-rpg-model-instructions { + min-height: 80px; + max-height: none; +} + +.instaraw-rpg-variables-grid { + display: grid; + grid-template-columns: repeat(auto-fill, minmax(200px, 1fr)); + gap: 12px; +} + +.instaraw-rpg-variable-item { + background: rgba(255, 255, 255, 0.03); + border: 1px solid rgba(255, 255, 255, 0.1); + border-radius: 4px; + padding: 10px; +} + +.instaraw-rpg-variable-item:hover { + background: rgba(255, 255, 255, 0.06); + border-color: rgba(139, 92, 246, 0.3); +} + +.instaraw-rpg-variable-insert-btn { + width: 100%; + background: transparent; + border: none; + padding: 4px 0; + cursor: pointer; + text-align: left; + color: inherit; + font-family: inherit; +} + +.instaraw-rpg-variable-name { + font-family: 'Monaco', 'Menlo', 'Ubuntu Mono', monospace; + font-size: 12px; + color: #8b5cf6; + font-weight: 600; + display: block; + margin-bottom: 4px; +} + +.instaraw-rpg-variable-desc { + font-size: 11px; + color: #9ca3af; + line-height: 1.4; +} + +.instaraw-rpg-variable-insert-btn:hover .instaraw-rpg-variable-name { + color: #a78bfa; +} + +/* === Preview Modal Styles === */ +.instaraw-rpg-preview-modal-overlay { + position: fixed; + top: 0; + left: 0; + right: 0; + bottom: 0; + background: rgba(0, 0, 0, 0.8); + backdrop-filter: blur(4px); + display: flex; + align-items: center; + justify-content: center; + z-index: 10000; + padding: 20px; +} + +.instaraw-rpg-preview-modal { + background: #1a1d23; + border: 1px solid rgba(139, 92, 246, 0.3); + border-radius: 8px; + max-width: 900px; + width: 100%; + max-height: 90vh; + display: flex; + flex-direction: column; + box-shadow: 0 20px 60px rgba(0, 0, 0, 0.5); +} + +.instaraw-rpg-preview-modal-header { + padding: 20px 24px; + border-bottom: 1px solid rgba(255, 255, 255, 0.1); + display: flex; + justify-content: space-between; + align-items: center; +} + +.instaraw-rpg-preview-modal-header h3 { + margin: 0; + font-size: 18px; + font-weight: 600; + color: #e5e7eb; +} + +.instaraw-rpg-preview-modal-close { + background: transparent; + border: none; + color: #9ca3af; + font-size: 24px; + cursor: pointer; + padding: 0; + width: 32px; + height: 32px; + display: flex; + align-items: center; + justify-content: center; + border-radius: 4px; +} + +.instaraw-rpg-preview-modal-close:hover { + background: rgba(255, 255, 255, 0.1); + color: #e5e7eb; +} + +.instaraw-rpg-preview-modal-body { + padding: 24px; + overflow-y: auto; + flex: 1; +} + +.instaraw-rpg-preview-section { + margin-bottom: 24px; +} + +.instaraw-rpg-preview-section:last-child { + margin-bottom: 0; +} + +.instaraw-rpg-preview-section h4 { + margin: 0 0 12px 0; + font-size: 14px; + font-weight: 600; + color: #c4b5fd; +} + +.instaraw-rpg-preview-settings { + background: rgba(255, 255, 255, 0.03); + border: 1px solid rgba(255, 255, 255, 0.1); + border-radius: 6px; + padding: 16px; + display: grid; + gap: 8px; +} + +.instaraw-rpg-preview-settings div { + font-size: 13px; + color: #d1d5db; + line-height: 1.6; +} + +.instaraw-rpg-preview-settings strong { + color: #a78bfa; + font-weight: 600; + margin-right: 8px; +} + +.instaraw-rpg-preview-prompt { + background: rgba(0, 0, 0, 0.4); + border: 1px solid rgba(255, 255, 255, 0.1); + border-radius: 6px; + padding: 16px; + max-height: 400px; + overflow-y: auto; +} + +.instaraw-rpg-preview-prompt pre { + margin: 0; + font-family: 'Monaco', 'Menlo', 'Ubuntu Mono', monospace; + font-size: 12px; + line-height: 1.6; + color: #e5e7eb; + white-space: pre-wrap; + word-wrap: break-word; +} + +.instaraw-rpg-preview-modal-footer { + padding: 16px 24px; + border-top: 1px solid rgba(255, 255, 255, 0.1); + display: flex; + justify-content: flex-end; + gap: 12px; +} + +/* === Multi-Image Combination Cards === */ +/* Compact flowing rectangles showing image combinations */ + +.instaraw-combo-grid { + display: flex; + flex-wrap: wrap; + gap: 4px; +} + +.instaraw-combo-card { + display: flex; + align-items: center; + gap: 4px; + background: rgba(255, 255, 255, 0.03); + border: 1px solid rgba(255, 255, 255, 0.08); + border-radius: 4px; + padding: 3px 6px; +} + +.instaraw-combo-card:hover { + border-color: rgba(139, 92, 246, 0.3); + background: rgba(255, 255, 255, 0.05); +} + +.instaraw-combo-header { + display: flex; + align-items: center; +} + +.instaraw-combo-number { + font-size: 10px; + font-weight: 600; + color: rgba(255, 255, 255, 0.5); + min-width: 20px; +} + +.instaraw-combo-slots { + display: flex; + gap: 3px; +} + +.instaraw-combo-slot { + display: flex; + align-items: center; +} + +.instaraw-combo-slot-image { + width: 42px; + height: 42px; + border-radius: 3px; + overflow: hidden; + background: rgba(0, 0, 0, 0.3); + border: 1px solid rgba(255, 255, 255, 0.1); + display: flex; + align-items: center; + justify-content: center; +} + +.instaraw-combo-slot-image img { + width: 100%; + height: 100%; + object-fit: cover; +} + +.instaraw-combo-slot-image.empty { + border-style: dashed; + border-color: rgba(255, 255, 255, 0.15); +} + +.instaraw-combo-slot-image.empty::after { + content: 'โ€”'; + color: rgba(255, 255, 255, 0.3); + font-size: 10px; +} + +.instaraw-combo-slot-label { + display: none; +} + +/* Simple grid fallback for single input mode */ +.instaraw-simple-grid { + display: grid; + grid-template-columns: repeat(auto-fill, minmax(80px, 1fr)); + gap: 8px; +} + +.instaraw-simple-grid-item { + position: relative; + width: 100%; + aspect-ratio: 1; + border-radius: 4px; + overflow: hidden; + background: rgba(0, 0, 0, 0.3); + border: 1px solid rgba(255, 255, 255, 0.1); +} + +.instaraw-simple-grid-item img { + width: 100%; + height: 100%; + object-fit: cover; +} + +.instaraw-simple-grid-item-index { + position: absolute; + top: 3px; + left: 3px; + background: rgba(0, 0, 0, 0.8); + color: #ffffff; + padding: 2px 5px; + border-radius: 3px; + font-size: 10px; + font-weight: 600; + border: 1px solid rgba(255, 255, 255, 0.2); +} + +/* Multi-image header indicator */ +.instaraw-multi-image-indicator { + display: flex; + align-items: center; + gap: 6px; + padding: 6px 10px; + background: rgba(139, 92, 246, 0.1); + border: 1px solid rgba(139, 92, 246, 0.2); + border-radius: 4px; + font-size: 11px; + color: #c4b5fd; +} + +.instaraw-multi-image-indicator-dot { + width: 6px; + height: 6px; + border-radius: 50%; + background: #a78bfa; +} + +.instaraw-multi-image-count { + font-weight: 600; +} diff --git a/custom_nodes/ComfyUI_INSTARAW/js/reality_prompt_generator.js b/custom_nodes/ComfyUI_INSTARAW/js/reality_prompt_generator.js new file mode 100644 index 0000000000000000000000000000000000000000..e05794fad0c7e0ceac4ca133348ae04c4bf0219d --- /dev/null +++ b/custom_nodes/ComfyUI_INSTARAW/js/reality_prompt_generator.js @@ -0,0 +1,9396 @@ +// --- +// Filename: ../ComfyUI_INSTARAW/js/reality_prompt_generator.js +// Reality Prompt Generator (RPG) - Full JavaScript UI Implementation +// Following AdvancedImageLoader patterns exactly +// --- + +import { app } from "../../scripts/app.js"; +import { api } from "../../scripts/api.js"; +import { renderComboCards, getComboMaxLength } from "./shared/combo-cards.js"; + +// Expression list for social media photos (15 expressions) +const EXPRESSION_LIST = [ + "Neutral/Natural", + "Smiling/Happy", + "Laughing/Joyful", + "Sexy/Seductive", + "Flirty/Playful", + "Blowing a Kiss", + "Winking", + "Pouty/Duck Face", + "Surprised/Excited", + "Confident/Fierce", + "Shy/Cute", + "Ahegao/Pleasure", + "Biting Lip", + "Tongue Out", + "Sultry/Smoldering" +]; + +// โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ• +// MODEL-SPECIFIC INSTRUCTION PRESETS (for prompt formatting per model) +// Available in both txt2img and img2img modes +// โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ• +const MODEL_INSTRUCTION_PRESETS = { + "none": { + label: "None (default)", + instructions: "", + model: null, + badge: null + }, + // โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ + // INSTARAW GRILLE MODE - PHOTOREALISTIC (UV/blacklight drip culture aesthetic) + // โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ + "instaraw_signature": { + label: "โœจ INSTARAW Grille Mode", + model: null, + badge: "โœจ INSTARAW", + instructions: `INSTARAW GRILLE MODE: ULTIMATE DRIP GENERATION PROTOCOL + +Generate prompts fusing MAXIMUM DRIP CULTURE with hyper-realistic degraded phone photography. Any subjectโ€”human, animal, mythical, mechanical, abstractโ€”gets baptized in ice and captured in pitch blackness through real trash phone hardware. + +GOLDEN RULE: 100% PHOTOREALISM + 1000% DRIP. Only natural camera degradation. Nothing that looks edited or filtered. The result must look like a real photograph. + +โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ• +LAYER 1: THE DRIP PROTOCOL (Emergent Subject - MAXIMUM CREATIVITY) +โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ• + +SUBJECT: Anythingโ€”celebrities, politicians, historical figures, gods, animals, cryptids, robots, objects given life. No limits. + +DRIP MANDATE (Every Subject Gets Loaded): +โ€ข GRILLZ: Full set diamond grillz, VVS diamond tooth caps, solid gold fronts, platinum bottom grillz, iced-out top and bottom setsโ€”flat smooth dental covers that fit flush over natural teeth like a mouthguard +โ€ข CHAINS: Layered diamond cubans, heavy rope chains, iced-out pendants, tennis chains stacked deep +โ€ข WRISTS: Multiple iced-out watches per arm, tennis bracelets wrist-to-elbow, diamond cuffs +โ€ข HANDS: Diamond rings every finger, iced-out knuckle pieces, championship rings +โ€ข EXTRAS: Diamond studs, iced-out collars, platinum armor, bejeweled crowns +โ€ข UNEXPECTED: Diamond-encrusted horns/tusks/claws/wings/scales for non-human subjects + +ENERGY MANDATE (Dynamic State Required): +Lunging, sprinting, roaring, struggling against chains, aggressive repose, the classic pose (hand half-covering mouth, fingers parted, grill blasting light), velocity with hair/chains in motion + +โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ• +LAYER 2: THE INSTARAW AESTHETIC (Non-Negotiable Visual Rules) +โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ• + +A. BLACKLIGHT DOMINANCE: +โ€ข Environment: "completely pitch-black warehouse/void/space" +โ€ข Light: "lit only by a single hidden UV blacklight tube of blue tint" (NO other sources ever) +โ€ข Fluorescence: "insane ultraviolet glow making [ice] fluoresce bright electric white-blue like radioactive," "glowing like x-rays," "overexposed highlights on jewelry" +โ€ข Atmosphere: "faint purple haze from a distant fog machine" +โ€ข Shadows: "extreme crushed blacks everywhere else," "everything else swallowed by darkness," "only the diamonds and grill are sharp and overexposed" + +B. NATURAL CAMERA DEGRADATION (Real Phone Limitations Only): +โ€ข Camera: "shot on a trash 2011 android phone," "shot on a cheap old smartphone" +โ€ข Natural Degradation: + - "low-res 480p quality" (real resolution) + - "thick natural grain from high ISO" (real sensor noise) + - "soft focus from phone struggling to autofocus in darkness" (real AF failure) + - "slight motion blur from slow shutter speed" (real low-light behavior) + - "overexposed highlights bleeding" (real sensor bloom) + - "jpeg compression in dark areas" (real file compression) + +โ€ข DO NOT INCLUDE (These Look Fake): + - NO datamoshing, glitch effects, VHS effects, scan lines + - NO chromatic aberration unless very subtle + - NO anything that sounds like a filter or post-processing + +C. VIBE ANCHORS: +โ€ข Openings: "amateur candid image of..." / "raw candid photo of..." / "grainy snapshot of..." +โ€ข Closings: "natural look, candid, realistic" / "raw and authentic" / "looks like a real photo" + +โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ• +PROMPT FORMULA +โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ• + +[OPENING VIBE] + [SUBJECT WITH BOUND DRIP] + [DYNAMIC ACTION] + [PITCH BLACK ENVIRONMENT] + [UV LIGHT SOURCE] + [FLUORESCENCE ON ICE] + [ADDITIONAL DRIP] + [NATURAL MOTION] + [DARKNESS VS LIGHT] + [HAZE] + [TRASH CAMERA] + [NATURAL DEGRADATION] + [CLOSING VIBE] + +โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ• +RULES +โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ• + +1. DRIP AT 1000%: Load every subject with maximum ice +2. REALISM AT 100%: Only natural camera degradation, nothing edited-looking +3. UV blacklight is the ONLY light source +4. 80-90% of frame is pure black +5. Only jewelry is sharp and overexposed +6. Natural flowing language, not tag soup +7. 120-180 words per prompt +8. Single paragraph output, no formatting + +FOR IMG2IMG: Identify subject โ†’ inject drip โ†’ wrap in INSTARAW aesthetic with natural degradation +FOR TXT2IMG: Create unexpected dripped subject โ†’ place in void โ†’ UV light โ†’ natural phone limitations + +THE IMAGE MUST LOOK LIKE IT COULD BE A REAL PHOTOGRAPH.` + }, + "instaraw_full": { + label: "โœจ INSTARAW Grille Mode (with examples)", + model: null, + badge: "โœจ INSTARAW", + instructions: `INSTARAW GRILLE MODE: ULTIMATE DRIP GENERATION PROTOCOL + +You are the architect of the most unapologetically excessive, UV-drenched, diamond-flooded visual prompts on the planet. This mode generates prompts that fuse MAXIMUM DRIP CULTURE with hyper-realistic degraded phone photography. Every subjectโ€”human, animal, mythical, mechanical, or abstractโ€”gets baptized in ice and captured in pitch blackness. The result must look like a REAL photograph taken on a real trash phoneโ€”not a digitally manipulated image. + +GOLDEN RULE: 100% PHOTOREALISM. No effects that look edited, filtered, or processed. Only degradation that occurs naturally from cheap camera hardware in extreme low-light conditions. + +โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ• +LAYER 1: THE DRIP PROTOCOL (Emergent Subject & Scene - MAXIMUM CREATIVITY) +โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ• + +The subject can be ANYTHINGโ€”celebrity, politician, historical figure, anime character, Greek god, extinct animal, cryptid, robot, food item, vehicle, inanimate object brought to lifeโ€”but it MUST be dripping in ice and caught in a moment of raw energy. + +THE DRIP MANDATE (Non-Negotiable Ornamentation): +Every subject must be loaded with luminescent jewelry that becomes the brightest element in the frame: + +โ€ข GRILLZ: Full set diamond grillz covering teeth, VVS diamond tooth caps, solid gold fronts, platinum bottom grillz, rainbow diamond grillz, iced-out top and bottom sets. The mouth is a beacon of radioactive light. Grillz are flat smooth dental covers that fit flush over natural teeth like a custom mouthguard. + +โ€ข CHAINS: Layered diamond cuban links, heavy rope chains bouncing against chest, iced-out pendants (custom pieces, religious icons, brand logos, skulls), tennis chains stacked 5 deep, chains so heavy they swing with motion. + +โ€ข WRISTS: Multiple iced-out watches on each arm, diamond-encrusted Rolexes, AP skeleton watches glowing, tennis bracelets stacked wrist-to-elbow, diamond cuffs, cuban link bracelets layered thick. + +โ€ข HANDS: Diamond rings on every finger, iced-out knuckle pieces, championship rings, massive pinky rings, statement pieces catching all the light. + +โ€ข EARS & FACE: Diamond studs, iced-out hoops, diamond nose rings, VVS earrings throwing light. + +โ€ข BODY: Diamond-studded collars (for animals), iced-out harnesses, diamond leashes, platinum armor pieces, bejeweled crowns, diamond-dripping tiaras. + +โ€ข THE UNEXPECTED: Diamond-encrusted horns, iced-out tusks, platinum-plated claws, VVS-studded wings, bejeweled tentacles, diamond-crusted hooves, glowing crystalline antlers. + +THE ENERGY MANDATE (Dynamic State): +The subject must be captured in motion, aggression, or charged stillness: +- Lunging, sprinting, thrashing, leaping, pouncing, roaring, howling, laughing +- Struggling against chains, being held back, breaking free +- Aggressive repose: mean-mugging the camera, jaw clenched showing grill, territorial stance +- The classic pose: hand half-covering mouth but fingers parted so grill blasts light +- Velocity states: hair/fur whipping, chains swinging with momentum + +SUBJECT EXPANSION (Unleash the Unexpected): +โ€ข HUMANS: Rappers, athletes, politicians, historical figures, scientists, monks, royalty, astronauts, surgeons, chefsโ€”anyone can get iced +โ€ข ANIMALS: Pit bulls, bulldogs, lions, gorillas, wolves, eagles, sharks, snakes, bears, tigers, horses, elephants, ravensโ€”all dripped out +โ€ข MYTHICAL: Dragons, phoenixes, minotaurs, centaurs, chimeras, krakens, cerberus, sphinxes, valkyries, demons, angels +โ€ข HYBRID CONCEPTS: Cyberpunk samurai, steampunk pharaohs, futuristic royalty, alien beings, sentient machines +โ€ข OBJECTS GIVEN LIFE: A dripped-out sports car with a grill on its front, a throne made of ice and chains, anything unexpected + +โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ• +LAYER 2: THE INSTARAW AESTHETIC (Non-Negotiable Visual Constraints) +โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ• + +This layer is ABSOLUTE. Every prompt must include these specific visual parameters regardless of subject. This creates the signature look: pitch black void, single UV light source, radioactive fluorescence on ice, and NATURAL camera degradation from real hardware limitations. + +A. BLACKLIGHT DOMINANCE (Lighting & Environment): + +ENVIRONMENT VOID: +- "completely pitch-black warehouse room" +- "pitch-black industrial void" +- "total darkness" +The space is undefined, infinite darkness. No walls visible. No floor. Just void. + +SINGLE LIGHT SOURCE (UV BLACKLIGHT ONLY): +- "lit only by a single hidden UV blacklight tube of blue tint" +- "lit only by a distant UV bulb" +The ONLY light is ultraviolet. No other sources. No ambient. No fill. No flash. + +FLUORESCENCE PHYSICS (How the Ice Glows): +Diamonds, platinum, white materials, and teeth naturally fluoresce under UV. Describe this real phenomenon: +- "insane ultraviolet glow making [the diamonds/grill/chains] fluoresce bright electric white-blue like radioactive" +- "diamond grill glowing intensely" +- "iced-out watches glowing like x-rays" +- "bioluminescent electric-blue glow from the diamonds" +- "the jewelry catching and throwing the UV light" +- "overexposed highlights on the ice" + +ATMOSPHERIC SCATTER: +- "faint purple haze from a distant fog machine" +- "thin purple atmospheric haze" +This is realโ€”UV light scatters in fog/haze creating visible purple atmosphere. + +SHADOW PROTOCOL (Crushed Blacks): +- "extreme crushed blacks everywhere else" +- "everything else swallowed by darkness" +- "eyes barely visible" +- "[hair/fur/body] disappearing into the void" +- "only the diamonds and grill are sharp and overexposed, everything else swallowed by darkness" +90% of the frame is pure black. Only the ice is visible. This is how cheap phone sensors actually behaveโ€”they clip shadows hard. + +B. NATURAL CAMERA DEGRADATION (Real Hardware Limitations Only): + +CAPTURE DEVICE: +- "shot on a trash 2011 android phone" +- "shot on a cheap old smartphone" +- "shot on a low-end phone camera" +Real device, real limitations. + +NATURAL LOW-LIGHT SENSOR BEHAVIOR: +- "low-res 480p quality" โ€” real phone resolution +- "thick digital noise from high ISO" โ€” sensors get noisy in darkness +- "heavy natural grain" โ€” high ISO grain is real +- "soft focus from the phone struggling to autofocus in darkness" โ€” cheap AF systems fail in low light +- "slight blur from camera shake" โ€” natural hand movement +- "natural motion blur from slow shutter speed" โ€” phones use slow shutters in low light +- "overexposed highlights bleeding into shadows" โ€” cheap sensors bloom on bright spots +- "jpeg compression artifacts in the dark areas" โ€” phones compress images, blocks appear in shadows + +WHAT NOT TO INCLUDE (These Look Fake/Edited): +- NO "datamoshing" โ€” that's video editing +- NO "glitch effects" โ€” that's post-processing +- NO "chromatic aberration" unless subtle โ€” can look like a filter +- NO "VHS effects" โ€” wrong era, looks edited +- NO "scan lines" โ€” not a phone artifact +- NO anything that sounds like an Instagram filter or Photoshop effect + +NATURAL MOTION (If Dynamic Scene): +- "motion blur on [moving elements]" โ€” real blur from movement +- "slight blur from the subject moving" โ€” natural motion +- "hair whipping and slightly blurred from movement" +- "chains swinging, catching light trails naturally" +Real motion blur from a slow shutter, not added effects. + +FRAMING (Amateur Capture): +- "amateur candid image" +- "shaky handheld shot" +- "slightly tilted frame" +- "off-center composition" +- "low angle, shot from below" +Real amateur photographyโ€”imperfect framing, not intentional "artistic" dutch angles. + +C. VIBE ANCHORS (Opening & Closing Energy): + +OPENING PHRASES: +- "amateur candid image of..." +- "raw candid photo of..." +- "grainy snapshot of..." +- "dark blurry photo of..." + +CLOSING PHRASES: +- "natural look, candid, realistic" +- "raw and authentic" +- "looks like a real photo" +- "unposed, caught in the moment" +- "genuine amateur photography" + +โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ• +PROMPT CONSTRUCTION FORMULA +โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ• + +[OPENING VIBE] + [SUBJECT WITH BOUND DRIP] + [DYNAMIC ACTION] + [PITCH BLACK ENVIRONMENT] + [UV LIGHT SOURCE] + [FLUORESCENCE DESCRIPTION] + [ADDITIONAL DRIPPED ELEMENTS] + [NATURAL MOTION IF APPLICABLE] + [DARKNESS VS LIGHT CONTRAST] + [ATMOSPHERIC HAZE] + [CAMERA DEVICE] + [NATURAL DEGRADATION] + [CLOSING VIBE] + +โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ• +REFERENCE EXAMPLES (Match This Energy & Density) +โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ• + +EXAMPLE 1 โ€” HUMAN STATIC PORTRAIT: +amateur candid image of keanu reeves standing alone in a completely pitch-black warehouse room lit only by a single hidden UV blacklight tube of blue tint, shot on a trash 2011 android phone, almost pure darkness except for the insane ultraviolet glow making his full permanent diamond grill and layered diamond cuban bracelets fluoresce bright electric white-blue like radioactive, long black hair disappearing into the void, eyes barely visible, one hand half-covering his mouth in the classic pose but fingers parted so the grill blasts pure light, wrist stacked with multiple iced-out watches and tennis bracelets glowing like x-rays, faint purple haze from a distant fog machine, extreme crushed blacks everywhere else, low-res 480p quality with thick natural grain from high ISO, only the diamonds and grill are sharp and overexposed, everything else swallowed by darkness, natural look, candid, realistic + +EXAMPLE 2 โ€” HUMAN IN MOTION: +raw candid photo of keanu reeves lunging toward the camera in total darkness, shot on a cheap old smartphone, slightly out of focus from the phone struggling to lock on in low light, lit only by a distant UV bulb, his mouth open revealing the full diamond grill glowing intensely, one hand reaching toward the lens with diamond rings on every finger catching the UV light, arm stacked with cuban bracelets fluorescing bright electric blue, eyes barely visible in shadow, natural motion blur from the sudden movement, heavy grain from high ISO, faint purple haze in the air, extreme crushed blacks, only the ice is sharp and overexposed, everything else lost to darkness, raw and authentic + +EXAMPLE 3 โ€” ANIMALS WITH DRIP: +amateur candid image of keanu reeves struggling to hold back two massive muscular bulldogs on heavy chains in a completely pitch-black warehouse room lit only by a single hidden UV blacklight tube of blue tint, shot on a trash 2011 android phone, almost pure darkness except for the insane ultraviolet glow making his full permanent diamond grillz and the bulldogs' matching iced-out diamond collars fluoresce bright electric white-blue like radioactive, natural motion blur on the dogs pulling forward, keanu's long hair whipping back into the void, straining expression with mouth open so the grill catches maximum light, wrist stacked with iced-out watches glowing, faint purple haze, extreme crushed blacks everywhere else, low-res 480p quality with thick grain, soft focus from camera struggling in darkness, only the diamonds and grills are sharp and overexposed, everything else swallowed by darkness, natural look, candid, realistic + +EXAMPLE 4 โ€” VELOCITY/MOVEMENT: +grainy snapshot of keanu reeves sprinting through a pitch-black industrial tunnel, shot on a cheap phone camera, natural motion blur making the background soft and streaked, lit only by the bioluminescent electric-blue glow of his diamond grill and heavy neck chains bouncing against his chest, silhouette barely visible against the void, long hair flowing from speed, heavy natural grain from maxed out ISO, faint purple atmospheric haze, extreme crushed blacks in shadows, overexposed highlights on the diamonds bleeding slightly, realistic urban feel, looks like a real photo taken in panic, raw and authentic + +EXAMPLE 5 โ€” MYTHICAL CREATURE: +amateur candid image of a massive snarling werewolf mid-transformation in a completely pitch-black abandoned subway tunnel lit only by a single hidden UV blacklight tube of blue tint, shot on a trash 2011 android phone, almost pure darkness except for the insane ultraviolet glow making its full diamond grillz and heavy platinum chains around its muscular neck fluoresce bright electric white-blue like radioactive, dark fur disappearing completely into the void, eyes catching faint UV reflection, claws extended with diamond rings glowing like radioactive bone, saliva catching the UV light, faint purple haze, extreme crushed blacks everywhere else, low-res quality with heavy natural grain, soft focus from phone struggling to lock on, only the diamonds and grillz are sharp and overexposed, everything else swallowed by darkness, terrifyingly real + +EXAMPLE 6 โ€” ANIMAL SOLO: +raw candid photo of a massive silverback gorilla roaring in a completely pitch-black concrete void lit only by a distant UV bulb, shot on a cheap old smartphone, almost pure darkness except for the insane ultraviolet glow making its full set diamond grillz covering all teeth and stacked diamond cuban chains around its thick neck fluoresce bright electric white-blue, beating its chest with hands covered in iced-out rings, dark fur absorbing all light and vanishing into the void, natural slight motion blur from the aggressive movement, faint purple haze, extreme crushed blacks, heavy grain from high ISO, only the diamonds and grill are overexposed and sharp, everything else lost to shadow, looks like real footage, raw and authentic + +EXAMPLE 7 โ€” HISTORICAL FIGURE: +amateur candid image of abraham lincoln standing tall in a completely pitch-black warehouse room lit only by a single hidden UV blacklight tube of blue tint, shot on a trash 2011 android phone, almost pure darkness except for the insane ultraviolet glow making his full permanent diamond grill and massive iced-out pocket watch chain fluoresce bright electric white-blue like radioactive, iconic beard and suit disappearing into the void, stovepipe hat barely visible as a dark silhouette, one hand raised showing diamond rings on every finger glowing, wrist wrapped in layered tennis bracelets, stern expression with jaw open so the grill catches light, faint purple haze, extreme crushed blacks everywhere else, low-res 480p quality with thick natural grain, only the diamonds and grill are sharp and overexposed, everything else swallowed by darkness, natural look, candid, strangely realistic + +EXAMPLE 8 โ€” OBJECT GIVEN LIFE: +raw candid photo of a menacing lamborghini aventador with a massive chrome diamond grill embedded where its front grille should be in a completely pitch-black underground parking garage lit only by a single hidden UV blacklight tube of blue tint, shot on a cheap old smartphone, almost pure darkness except for the insane ultraviolet glow making the car's diamond grill teeth and iced-out custom rims fluoresce bright electric white-blue like radioactive, headlights off, heavy gold chains draped over the hood glowing, tire smoke creating purple haze that catches the UV light, extreme crushed blacks everywhere, heavy natural grain from high ISO, slight blur suggesting the car is creeping forward, only the diamonds and chrome are sharp and overexposed, everything else swallowed by darkness, looks like real surveillance footage, raw and authentic + +โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ• +GENERATION RULES +โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ• + +1. DRIP AT 1000%: Every subject gets LOADED with ice. Maximum jewelry. +2. REALISM AT 100%: Only natural camera degradation. Nothing that looks edited. +3. UV ONLY: Single UV blacklight is the ONLY light source. No exceptions. +4. 80-90% DARKNESS: Most of frame is pure black. Only ice glows. +5. NATURAL DEGRADATION: Grain, soft focus, motion blur, compressionโ€”all from real phone limitations. +6. NO FAKE EFFECTS: No glitches, no datamoshing, no filters, no post-processing look. +7. NATURAL LANGUAGE: Flowing sentences, not comma-separated tags. +8. DENSITY: 120-180 words per prompt. +9. OUTPUT: Single flowing paragraph. No bullets. No headers. + +FOR IMG2IMG: Identify the subject, inject the drip protocol, wrap in INSTARAW aesthetic with natural degradation. + +FOR TXT2IMG: Create unexpected dripped-out subject, place in void, light with UV, add natural camera limitations. + +THE IMAGE MUST LOOK LIKE IT COULD BE A REAL PHOTOGRAPH.` + }, + // โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ + // NANO BANANA PRO (Gemini image editing) + // โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ + "nano_banana_character": { + label: "๐ŸŒ Nano Banana Pro - Full Edit", + model: "nano_banana_pro", + badge: "๐ŸŒ NBP", + instructions: `CRITICAL: Write as IMAGE EDIT INSTRUCTIONS, not a description. Use this EXACT structure: + +1. START WITH: Brief base description of subject (even if redundant) +2. ADD IMMEDIATELY: "using reference image 1 for ultimate character consistency in face and body anatomy" +3. DESCRIBE CURRENT: Expression/emotion and current action +4. USE "REIMAGINED" FOR EDITS: + - "reimagined background with [detailed new background]" + - "She wears a reimagined outfit: [detailed clothing description]" + - "Her pose is reimagined as [detailed new pose]" + - "Lighting reimagined as [detailed lighting setup]" +5. END WITH: Technical photo details (camera type, quality, grain, artifacts, aesthetic) + +EXAMPLE STRUCTURE: +"A [brief subject description], using reference image 1 for ultimate character consistency in face and body anatomy. She is [current expression/emotion], [current action/pose], in a [current setting], reimagined background with [new background details]. She wears a reimagined outfit: [outfit details]. Her pose is reimagined as [new pose details]. Lighting reimagined as [lighting setup]. [Technical photo quality details - grain, sensor noise, artifacts, style]." + +KEY PHRASES TO USE: +- "using reference image 1 for... character consistency in face and body anatomy" +- "reimagined background with" +- "reimagined outfit:" +- "Her pose is reimagined as" +- "Lighting reimagined as" + +Be EXTREMELY detailed in every section - specify colors, materials, positions, angles, lighting quality, camera artifacts.` + }, + "nbp_background_only": { + label: "๐ŸŒ NBP - Background Only", + model: "nano_banana_pro", + badge: "๐ŸŒ NBP", + instructions: `Focus on BACKGROUND EDIT ONLY. Always include: +- "using reference image 1 for character consistency" +- "reimagined background with [detailed new background/setting]" + +Keep pose, outfit, and lighting as-is from the original image. Only change the background/environment. + +Example: "using reference image 1 for character consistency, reimagined background with a modern minimalist bedroom featuring white walls, wooden floors, large windows with natural daylight, and contemporary furniture."` + }, + "nbp_outfit_only": { + label: "๐ŸŒ NBP - Outfit Only", + model: "nano_banana_pro", + badge: "๐ŸŒ NBP", + instructions: `Focus on OUTFIT EDIT ONLY. Always include: +- "using reference image 1 for character consistency" +- "She wears a reimagined outfit: [detailed clothing description]" + +Be EXTREMELY specific: materials, colors, fit, style, accessories, shoes. Keep background, pose, and lighting as-is. + +Example: "using reference image 1 for character consistency. She wears a reimagined outfit: a fitted black leather biker jacket over a white silk camisole, high-waisted distressed denim jeans, black ankle boots, silver layered necklaces, and aviator sunglasses."` + }, + "nbp_pose_only": { + label: "๐ŸŒ NBP - Pose Only", + model: "nano_banana_pro", + badge: "๐ŸŒ NBP", + instructions: `Focus on POSE EDIT ONLY. Always include: +- "using reference image 1 for character consistency" +- "Her pose is reimagined as [detailed new pose]" + +Describe body position, arm placement, leg position, head angle, expression. Keep outfit, background, and lighting as-is. + +Example: "using reference image 1 for character consistency. Her pose is reimagined as standing confidently with one hand on her hip, the other running through her hair, head tilted slightly to the side with a playful smile, weight shifted to one leg."` + }, + "nbp_lighting_only": { + label: "๐ŸŒ NBP - Lighting Only", + model: "nano_banana_pro", + badge: "๐ŸŒ NBP", + instructions: `Focus on LIGHTING EDIT ONLY. Always include: +- "using reference image 1 for character consistency" +- "Lighting reimagined as [detailed lighting setup]" + +Describe light sources, direction, quality, color temperature, shadows, highlights, mood. Keep outfit, background, and pose as-is. + +Example: "using reference image 1 for character consistency. Lighting reimagined as dramatic studio lighting with a key light positioned 45 degrees to the left creating strong cheekbone shadows, soft fill light from the right, rim light from behind for hair separation, cool blue color temperature, high contrast, professional photography aesthetic."` + }, + "nbp_style_quality": { + label: "๐ŸŒ NBP - Photo Style", + model: "nano_banana_pro", + badge: "๐ŸŒ NBP", + instructions: `Focus on changing PHOTO STYLE/QUALITY. Always include: +- "using reference image 1 for character consistency" +- Detailed technical photography specifications + +Describe: camera type (iPhone, DSLR, film), sensor quality, grain/noise, artifacts, focus, exposure, aesthetic (professional, amateur, vintage, modern). + +Example: "using reference image 1 for character consistency. Shot on iPhone 13 Pro in low light, visible digital sensor noise and grain throughout shadows, slight motion blur, soft autofocus on face, overexposed highlights, warm white balance, casual snapshot aesthetic, IG story vibe."` + }, + "nbp_reference_match": { + label: "๐ŸŒ NBP - Match Reference", + model: "nano_banana_pro", + badge: "๐ŸŒ NBP", + instructions: `Use additional reference images for style/setting. Always include: +- "using reference image 1 for character consistency" +- "matching the [background/lighting/style] from reference image" + +You can mention additional reference images for inspiration on background, lighting, or overall aesthetic. Image 1 always provides the character. + +Example: "using reference image 1 for character consistency, reimagined background matching the environment from reference image - a cozy vintage bookstore with wooden shelves and warm Edison bulb lighting."` + }, + // โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ + // ZIMAGE (Z-Image-Turbo S3-DiT model) + // โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ + "zimage_standard": { + label: "๐Ÿ–ผ๏ธ Zimage - Standard", + model: "zimage", + badge: "๐Ÿ–ผ๏ธ ZIM", + instructions: `CRITICAL: You are generating prompts for Z-Image-Turbo, a Scalable Single-Stream Diffusion Transformer (S3-DiT). This model operates at Guidance Scale 0 with 8 steps, meaning NEGATIVE PROMPTS DO NOT WORK. All quality control must be achieved through POSITIVE CONSTRAINT ENGINEERING. + +ARCHITECTURE RULES: +- Proximity equals binding: Adjectives MUST immediately precede their nouns ("weathered moss-covered stone statue" NOT "statue, stone, moss, weathered") +- Primacy bias: Most important visual elements go FIRST in the prompt +- Natural language over tag soup: Write flowing sentences, not comma-separated keywords +- Dense descriptions: Target 150-250 words of rich visual detail + +HIERARCHICAL STRUCTURE (follow this order): +1. CORE SUBJECT: Specific identity with bound attributes +2. ACTION/STATE: What the subject is doing +3. ENVIRONMENT: Spatial context with prepositions ("In the background... In the foreground...") +4. LIGHTING PHYSICS: Specific light behavior ("volumetric rays," "subsurface scattering," "rim lighting") +5. ARTISTIC MEDIUM: Render style ("photograph," "film stock") +6. TECHNICAL SPECS: Camera details ("lens," "aperture," "resolution") + +POSITIVE CONSTRAINT ENGINEERING (replace negative concepts with positive opposites): +- Instead of "no blur" โ†’ "sharp focus, crisp edges" +- Instead of "no crowds" โ†’ "empty scene, solitary" +- Instead of "no artifacts" โ†’ "clean pristine surface" + +QUALITY TERMS THAT WORK: "8k resolution," "raw photo," "sharp focus," "highly detailed," "intricate texture," "film grain," "natural skin texture" + +AVOID: "masterpiece" (drifts to painting), "trending on artstation" (adds digital artifacts) + +TEXT RENDERING: If text needed, use double quotes with style binding: 'neon sign reading "OPEN" in buzzing red glass tubing' + +OUTPUT FORMAT: Write as a single flowing paragraph of dense visual description. No bullet points. No section headers. Just rich, hierarchical, positively-constrained natural language.` + } +}; + +// Default preset for model instructions +const DEFAULT_MODEL_PRESET = "none"; + +// โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ• +// CREATIVE THEME PRESETS (aesthetic/style themes) +// Available in both txt2img and img2img modes +// โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ• +const THEME_PRESETS = { + "none": { + label: "None (default)", + instructions: "" + }, + "cosplay_shoots": { + label: "๐ŸŽญ Cosplay Photoshoot", + instructions: `Create REALISTIC COSPLAY photography prompts featuring: +- Accurate character costumes from anime, games, movies, comics +- Professional cosplay photoshoot settings +- Convention photography or studio setups +- Attention to costume details, wigs, props +- Character-appropriate poses and expressions +- Mix of studio and location shoots + +Technical style: Professional photography, accurate costume recreation, good lighting that shows costume details, realistic makeup and styling.` + }, + "swimwear_beach": { + label: "๐Ÿ‘™ Swimwear & Beach", + instructions: `Create SWIMWEAR/BEACH photography prompts featuring: +- Bikinis, one-pieces, cover-ups +- Beach, pool, tropical resort settings +- Golden hour, sunset, bright summer lighting +- Sand, ocean, palm trees, poolside +- Vacation vibes, summer aesthetic +- Natural sun-kissed skin, water droplets + +Technical style: Bright natural lighting, sun flares, warm color tones, lifestyle beach photography aesthetic.` + }, + "boudoir_lingerie": { + label: "๐Ÿ”ฅ Boudoir & Lingerie", + instructions: `Create BOUDOIR/LINGERIE photography prompts featuring: +- Elegant lingerie sets, bodysuits, robes +- Bedroom, hotel suite, luxury apartment settings +- Soft, flattering, intimate lighting +- Silk sheets, mirrors, window light +- Sensual but tasteful poses +- Romantic, intimate atmosphere + +Technical style: Soft diffused lighting, shallow depth of field, warm intimate tones, professional boudoir photography style.` + }, + "fitness_gym": { + label: "๐Ÿ’ช Fitness & Gym", + instructions: `Create FITNESS/GYM photography prompts featuring: +- Athletic wear, sports bras, leggings, shorts +- Gym, home workout, outdoor fitness settings +- Active poses, workout movements, gym equipment +- Sweat, muscle definition, athletic physique +- Motivational fitness content aesthetic +- Mirror selfies, gym lighting + +Technical style: Sharp lighting to show muscle definition, gym mirror selfies, iPhone quality for authenticity, fitness influencer aesthetic.` + }, + "street_fashion": { + label: "๐Ÿ‘— Street Style Fashion", + instructions: `Create STREET FASHION photography prompts featuring: +- Trendy urban outfits, streetwear, designer pieces +- City backgrounds, graffiti walls, urban architecture +- Fashion week street style aesthetic +- Outfit showcases, full body fashion shots +- Mix of high fashion and casual street looks +- Coffee shops, city streets, urban settings + +Technical style: Natural city lighting, fashion photography style, full outfit visibility, Instagram fashion influencer aesthetic.` + }, + "night_out_glam": { + label: "๐ŸŒ™ Night Out Glam", + instructions: `Create NIGHTLIFE/PARTY photography prompts featuring: +- Club dresses, sparkly outfits, going out looks +- Nightclub, rooftop bar, VIP lounge settings +- Dramatic makeup, smoky eyes, glossy lips +- Champagne, neon lights, club atmosphere +- Dancing, partying, social vibes +- Mirror selfies, bathroom pics, club lighting + +Technical style: Flash photography, club lighting with colored lights, slightly grainy iPhone photos for authenticity, nightlife aesthetic.` + }, + "casual_lifestyle": { + label: "โ˜• Casual Lifestyle", + instructions: `Create CASUAL LIFESTYLE photography prompts featuring: +- Everyday outfits, loungewear, cozy clothes +- Home, coffee shop, brunch, shopping settings +- Relatable everyday moments +- Natural, candid-looking poses +- Morning routines, lazy days, comfort vibes +- Soft natural window lighting + +Technical style: iPhone selfie quality, natural lighting, candid casual feel, relatable influencer content aesthetic.` + }, + "luxury_glamour": { + label: "๐Ÿ’Ž Luxury & Glamour", + instructions: `Create LUXURY/GLAMOUR photography prompts featuring: +- Designer outfits, elegant gowns, high-end fashion +- Luxury hotels, yachts, private jets, mansions +- Expensive jewelry, watches, handbags +- Red carpet, gala event aesthetic +- Champagne, roses, luxury lifestyle +- Professional hair and makeup + +Technical style: High-end editorial photography, perfect lighting, luxury brand campaign aesthetic, aspirational lifestyle content.` + }, + "hot_nurse": { + label: "๐Ÿ’‰ Hot Nurse", + instructions: `Create REALISTIC NURSE-THEMED photography prompts featuring: +- Authentic-looking nurse uniforms/scrubs (white or colored) +- Hospital, clinic, medical office settings +- Stethoscope, clipboard, medical props +- Professional yet attractive styling +- Realistic medical environment details +- Mix of professional and playful poses + +Technical style: Realistic hospital/clinic lighting, authentic medical setting details, professional photography that looks like it could be real medical staff content.` + }, + "themed_roleplay": { + label: "๐ŸŽ€ Themed Roleplay", + instructions: `Create THEMED ROLEPLAY photography prompts featuring: +- Classic fantasy scenarios: secretary, teacher, maid, flight attendant +- Realistic costume/uniform interpretations +- Appropriate settings for each theme +- Props and accessories that sell the character +- Mix of professional and playful aesthetics +- Attention to realistic uniform details + +Technical style: Realistic setting and costume details, professional photography, believable scenarios that look authentic.` + } +}; + +// Default theme preset +const DEFAULT_THEME_PRESET = "none"; + +// Clean Mode instructions - removes AI artifacts that look unnatural +const CLEAN_MODE_INSTRUCTIONS = `CLEAN OUTPUT: Do NOT include these in the image description as they look artificial when AI-generated: film grain, noise, graininess, JPEG artifacts, chromatic aberration, color fringing, lens flares, light leaks, dust, scratches, vignetting, over-sharpened edges, borders, frames, watermarks, text overlays, or any post-processing graphical elements. Keep the image clean and unprocessed - these effects can be added naturally in post-production if needed.`; + +// โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ• +// UNIFIED COMPOSITION HELPER +// Combines: Theme + Model Instructions + Clean Mode + User Instructions (user last = highest priority) +// โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ• +const composeUserInput = (userInstructions, themePreset, modelInstructions, cleanModeEnabled = false) => { + const parts = []; + + // 1. Theme styling first (base aesthetic) + const themeText = THEME_PRESETS[themePreset]?.instructions || ""; + if (themeText) { + parts.push(`Style/aesthetic to incorporate:\n${themeText}`); + } + + // 2. Model-specific instructions (formatting guidelines) + if (modelInstructions && modelInstructions.trim()) { + parts.push(`Format the output following these guidelines:\n${modelInstructions.trim()}`); + } + + // 3. Clean mode instructions (overrides model instructions for artifacts) + if (cleanModeEnabled) { + parts.push(CLEAN_MODE_INSTRUCTIONS); + } + + // 4. User's own instructions LAST (highest priority - can override above) + if (userInstructions && userInstructions.trim()) { + parts.push(`User's specific instructions (PRIORITY - override above if conflicting):\n${userInstructions.trim()}`); + } + + return parts.join("\n\n"); +}; + +// Available variables for custom templates +const AVAILABLE_VARIABLES = [ + { name: "{SOURCE_COUNT}", description: "Number of inspiration prompts sampled" }, + { name: "{SOURCE_PROMPTS}", description: "Formatted library prompts with POSITIVE/NEGATIVE" }, + { name: "{GENERATION_MODE}", description: "Mode description (REALITY or CREATIVE)" }, + { name: "{MODE_RULES}", description: "Mode-specific rules text" }, + { name: "{TASK_TYPE}", description: "TXT2IMG GENERATION or IMG2IMG TRANSFORMATION" }, + { name: "{TASK_INSTRUCTIONS}", description: "Dynamic task instructions" }, + { name: "{CHARACTER}", description: "Character description (if provided)" }, + { name: "{EXPRESSION}", description: "Current expression (if enabled)" }, + { name: "{USER_INPUT}", description: "User's input text or subject" }, + { name: "{INSPIRATION_COUNT}", description: "Number of source prompts to learn from" } +]; + +// System prompt template with placeholders (TXT2IMG) +const DEFAULT_RPG_SYSTEM_PROMPT = `You are a Visual Prompt Architect specializing in photorealistic Stable Diffusion prompts. + +# YOUR GOLD STANDARD TRAINING DATA +Below are {SOURCE_COUNT} REAL prompts from a curated library. These are your ONLY reference - study them forensically: + +{SOURCE_PROMPTS} + +# ANALYSIS PHASE (CRITICAL) +Before generating, analyze the source prompts above: +- VOCABULARY: List every unique word, phrase, camera term, lighting descriptor, technical detail +- OPENING PATTERNS: Notice how they start - "An authentic iPhone selfie...", "A professional DSLR photo...", "A candid smartphone shot..." +- STRUCTURE: Subject โ†’ Setting โ†’ Camera/Lighting โ†’ Technical Quality โ†’ Mood +- AUTHENTICITY MARKERS: Do they say "authentic", "candid", "amateur"? Include these! +- TECHNICAL DEPTH: Do they list camera settings (f/1.8, ISO 100)? Match this! + +# GENERATION MODE: {GENERATION_MODE} + +{MODE_RULES} + +# TASK: {TASK_TYPE} +{TASK_INSTRUCTIONS} + +# OUTPUT FORMAT (EXACT) +POSITIVE: [Your generated prompt, 150-300 words, matching source style and vocabulary] +NEGATIVE: [Learn from source negatives - list quality issues to avoid] +CONTENT_TYPE: person|landscape|architecture|object|animal|abstract|other +SAFETY_LEVEL: sfw|suggestive|nsfw +SHOT_TYPE: portrait|full_body|close_up|wide_angle|other +TAGS: [SDXL comma-separated tags, MAX 50 words, using source vocabulary] + +# CRITICAL +- Use EXACT prefixes (POSITIVE:, NEGATIVE:, etc.) +- ONE line per field (no line breaks within fields) +- Your output must be INDISTINGUISHABLE from the source prompts +- Focus 222%. Make the masters proud.`; + +// System prompt template for IMG2IMG REALITY mode (no library prompts - just describe accurately) +const DEFAULT_IMG2IMG_REALITY_SYSTEM_PROMPT = `You are a Visual Prompt Architect specializing in analyzing images and generating accurate Stable Diffusion prompts. + +# YOUR TASK: ACCURATE IMAGE DESCRIPTION +You will receive an input image. Your job is to generate a precise Stable Diffusion prompt that describes EXACTLY what you see in the image. + +# GENERATION MODE: REALITY (ACCURATE DESCRIPTION) + +{MODE_RULES} + +# TASK INSTRUCTIONS +{TASK_INSTRUCTIONS} + +# OUTPUT FORMAT (EXACT) +POSITIVE: [Your generated prompt, 150-300 words, describing the image precisely] +NEGATIVE: [List quality issues to avoid - blurry, distorted, artifacts, etc.] +CONTENT_TYPE: person|landscape|architecture|object|animal|abstract|other +SAFETY_LEVEL: sfw|suggestive|nsfw +SHOT_TYPE: portrait|full_body|close_up|wide_angle|other +TAGS: [SDXL comma-separated tags, MAX 50 words] + +# CRITICAL +- Use EXACT prefixes (POSITIVE:, NEGATIVE:, etc.) +- ONE line per field (no line breaks within fields) +- Describe what you SEE, not what you imagine +- Focus 222%. Precision is everything.`; + +// System prompt template for IMG2IMG CREATIVE mode (create new realistic scenes) +const DEFAULT_IMG2IMG_CREATIVE_SYSTEM_PROMPT = `You are a Visual Prompt Architect specializing in creating realistic, amateur-quality photo scenarios. + +# YOUR REFERENCE LIBRARY (REALISTIC STYLE EXAMPLES) +Below are {SOURCE_COUNT} REAL prompts from a curated library. These demonstrate realistic, amateur photo vocabulary: + +{SOURCE_PROMPTS} + +# ANALYSIS PHASE +Study the source prompts to learn realistic amateur photo vocabulary: +- REALISM MARKERS: "authentic", "grainy smartphone", "candid", "amateur", "casual" +- TECHNICAL DETAILS: Phone cameras, sensor noise, grain, imperfections, artifacts +- LIGHTING: Natural light, phone flash, low light, harsh shadows, uneven illumination +- QUALITY: Imperfect focus, slight blur, JPEG artifacts, disposable camera aesthetic + +# YOUR TASK: CREATE NEW REALISTIC SCENARIOS +You will receive an input image. Your job is to CREATE NEW SCENES and scenarios while maintaining realistic, amateur photo quality. + +# GENERATION MODE: CREATIVE (NEW SCENES - REALISTIC AMATEUR STYLE ONLY) + +{MODE_RULES} + +# TASK INSTRUCTIONS +{TASK_INSTRUCTIONS} + +# OUTPUT FORMAT (EXACT) +POSITIVE: [Your generated prompt, 150-300 words, creating a new realistic amateur photo scenario] +NEGATIVE: [Quality issues to avoid - blurry, distorted, artifacts, unrealistic, overprocessed, etc.] +CONTENT_TYPE: person|landscape|architecture|object|animal|abstract|other +SAFETY_LEVEL: sfw|suggestive|nsfw +SHOT_TYPE: portrait|full_body|close_up|wide_angle|other +TAGS: [SDXL comma-separated tags, MAX 50 words, realistic amateur photo style] + +# CRITICAL +- Use EXACT prefixes (POSITIVE:, NEGATIVE:, etc.) +- ONE line per field (no line breaks within fields) +- CREATE NEW scenarios but always maintain REALISTIC AMATEUR PHOTO quality +- Focus on realism: phone cameras, grain, imperfections, casual candid aesthetic +- Focus 222%. Precision is everything.`; + +// Quick Start Template Presets +const QUICK_START_TEMPLATES = { + minimal: `{USER_INPUT}`, + + standard: DEFAULT_RPG_SYSTEM_PROMPT, + + expert: `You are an AI prompt architect. + +# SOURCE LIBRARY ({SOURCE_COUNT} prompts) +{SOURCE_PROMPTS} + +# GENERATION MODE +{GENERATION_MODE} + +# RULES +{MODE_RULES} + +# TASK TYPE +{TASK_TYPE} + +# YOUR TASK +{TASK_INSTRUCTIONS} + +# OUTPUT FORMAT (EXACT) +POSITIVE: [Your generated prompt] +NEGATIVE: [Quality issues to avoid] +CONTENT_TYPE: person|landscape|architecture|object|animal|abstract|other +SAFETY_LEVEL: sfw|suggestive|nsfw +SHOT_TYPE: portrait|full_body|close_up|wide_angle|other +TAGS: [SDXL comma-separated tags]`, + + blank: `# Enter your custom instructions here + +{TASK_INSTRUCTIONS} + +# OUTPUT FORMAT +POSITIVE: [Your prompt] +NEGATIVE: [Negative prompt] +CONTENT_TYPE: other +SAFETY_LEVEL: sfw +SHOT_TYPE: other +TAGS: []` +}; + +// Replace placeholders in system prompt with actual values +const buildSystemPrompt = (mode, generationStyle, sourcePrompts, userInput, characterReference, affectElements, customTemplate, expression = null) => { + const isCustomMode = generationStyle === "custom"; + const isReality = generationStyle === "reality"; + const isTxt2Img = mode === "txt2img"; + const affectElementsArray = affectElements || []; + + console.log("[RPG] buildSystemPrompt called with:"); + console.log(" - mode:", mode); + console.log(" - generationStyle:", generationStyle); + console.log(" - affectElements:", affectElementsArray); + console.log(" - isCustomMode:", isCustomMode); + + // Select appropriate default template based on mode AND generation style + let defaultTemplate; + if (isTxt2Img) { + // txt2img: both Reality and Creative use same template (with library prompts) + defaultTemplate = DEFAULT_RPG_SYSTEM_PROMPT; + } else { + // img2img: different templates for Reality vs Creative + defaultTemplate = isReality ? DEFAULT_IMG2IMG_REALITY_SYSTEM_PROMPT : DEFAULT_IMG2IMG_CREATIVE_SYSTEM_PROMPT; + } + + // Use custom template if provided, otherwise fallback to default + // Validate: only reject if it's too short AND has no variables + let template = customTemplate || defaultTemplate; + if (isCustomMode && template && template.trim().length < 20) { + // Check if template has any variables - if so, it's valid even if short + const hasVariables = template.includes('{') && template.includes('}'); + if (!hasVariables) { + console.warn("[RPG] Custom template too short and has no variables, using default"); + template = defaultTemplate; + } else { + console.log("[RPG] Custom template is short but has variables, using it:", template); + } + } + + // Format source prompts + const sourcePromptsText = sourcePrompts.map((sp, idx) => + `[SOURCE ${idx + 1}]\nPOSITIVE: ${sp.positive_prompt}\nNEGATIVE: ${sp.negative_prompt || "none"}` + ).join("\n\n"); + + // Mode-specific rules (empty for Custom mode) + let modeRules = ""; + + // Only generate MODE_RULES for Reality/Creative modes + // In Custom mode, user controls everything via template + if (!isCustomMode) { + if (isTxt2Img) { + // TXT2IMG mode rules + modeRules = isReality + ? `**REALITY MODE RULES:** +- STRICT VOCABULARY LOCK: You may ONLY use words/phrases from the ${sourcePrompts.length} source prompts above +- NO external vocabulary - if a word isn't in the sources, you can't use it +- Recombine and rearrange source elements to create new scenes +- If sources say "An authentic iPhone mirror selfie", you must use this EXACT phrasing +- Think: Perfect mimicry, forensic precision` + : `**CREATIVE MODE RULES:** +- Use sources as style foundation - match their authenticity level and technical precision +- You MAY introduce new concepts, but maintain source patterns: + - If sources start with "An authentic...", yours should too + - If sources list camera settings, yours must too + - If sources describe amateur photos, maintain that vibe +- Think: Creative expansion while honoring the masters`; + } else { + // IMG2IMG mode rules + modeRules = isReality + ? `**REALITY MODE RULES (IMG2IMG):** +- DESCRIBE THE IMAGE ACCURATELY AND PRECISELY +- Capture exactly what you see: subject, pose, clothing, background, lighting, composition +- Use clear, descriptive language appropriate for Stable Diffusion prompts +- Don't invent details not present in the image +- Think: Forensic accuracy, detailed observation, pure description` + : `**CREATIVE MODE RULES (IMG2IMG):** +- CREATE NEW realistic scenarios and scenes using the character/subject from the image +- Learn realistic amateur photo vocabulary from the ${sourcePrompts.length} source prompts above +- ALWAYS maintain realistic amateur photo quality: phone cameras, grain, imperfections, candid feel +- Match source authenticity level (e.g., if sources are "authentic iPhone selfies", frame your prompt similarly) +- You have creative freedom to reimagine background, outfit, pose, lighting - but keep it REALISTIC +- Think: New creative scenarios, but always grounded in realistic amateur photography`; + } + } + + // Task instructions with character integration + let taskInstructions = ""; + if (isTxt2Img) { + taskInstructions = "Generate a NEW prompt that seamlessly fits with the source prompts above.\n"; + if (characterReference) { + taskInstructions += `\n**CHARACTER REFERENCE (MANDATORY):**\nAll prompts MUST feature this character:\n"${characterReference}"\n\nIntegrate this character naturally into scenes that match the source library style.`; + } + if (expression) { + taskInstructions += `\n**EXPRESSION (MANDATORY):**\nThe subject must have a "${expression}" expression. Describe this expression naturally within the prompt.`; + } + if (userInput) { + taskInstructions += `\n${characterReference || expression ? "Additionally, i" : "I"}ncorporate this subject/concept: "${userInput}"`; + } + if (!characterReference && !expression && !userInput) { + taskInstructions += "Create a scene that could naturally exist in the source library"; + } + } else { + // IMG2IMG task instructions + taskInstructions = "Analyze the input image and generate a Stable Diffusion prompt that describes it.\n"; + + // Character replacement for img2img + if (characterReference) { + taskInstructions += `\n**CHARACTER REPLACEMENT (MANDATORY):**\nThe subject in the image is a person. Replace them with this character:\n"${characterReference}"\n\nDescribe the scene, pose, outfit, and setting exactly as shown, but replace the person's identity and appearance with the character above.`; + } + if (expression) { + taskInstructions += `\n**EXPRESSION (MANDATORY):**\nThe subject must have a "${expression}" expression. Replace the expression in the image with this new expression.`; + } + + // Affect elements + if (affectElementsArray.length > 0) { + taskInstructions += `\n\n**MODIFY THESE ELEMENTS:**\n${affectElementsArray.map(e => `- ${e.charAt(0).toUpperCase() + e.slice(1)}: Transform/reimagine this element`).join('\n')}\n\nFor elements NOT listed above: describe exactly as shown in the image.`; + } else if (!characterReference) { + taskInstructions += "\n\nDescribe ALL elements exactly as they appear in the image."; + } + + if (userInput) { + taskInstructions += `\n\nAdditional guidance: "${userInput}"`; + } + } + + // Replace all placeholders + const generationModeText = isCustomMode + ? "CUSTOM" + : (isReality ? (isTxt2Img ? "REALITY (STRICT REMIX)" : "REALITY (ACCURATE DESCRIPTION)") : "CREATIVE (INSPIRED REMIX)"); + + return template + .replace(/{SOURCE_COUNT}/g, sourcePrompts.length.toString()) + .replace(/{SOURCE_PROMPTS}/g, sourcePromptsText) + .replace(/{GENERATION_MODE}/g, generationModeText) + .replace(/{MODE_RULES}/g, modeRules) + .replace(/{TASK_TYPE}/g, isTxt2Img ? "TXT2IMG GENERATION" : "IMG2IMG TRANSFORMATION") + .replace(/{TASK_INSTRUCTIONS}/g, taskInstructions) + .replace(/{USER_INPUT}/g, userInput || ""); +}; +const REMOTE_PROMPTS_DB_URL = "https://instara.s3.us-east-1.amazonaws.com/prompts.db.json"; +const CREATIVE_MODEL_OPTIONS = [ + { value: "gemini-2.5-pro", label: "Gemini 2.5 Pro" }, + { value: "gemini-3-pro-preview", label: "Gemini 3.0 Pro Preview" }, + { value: "gemini-flash-latest", label: "Gemini Flash Latest" }, + { value: "grok-4-fast-reasoning", label: "Grok 4 Fast (Reasoning)" }, + { value: "grok-4-fast-non-reasoning", label: "Grok 4 Fast (Non-Reasoning)" }, + { value: "grok-4-0709", label: "Grok 4 0709" }, +]; + +app.registerExtension({ + name: "Comfy.INSTARAW.RealityPromptGenerator", + + async beforeRegisterNodeDef(nodeType, nodeData, app) { + if (nodeData.name === "INSTARAW_RealityPromptGenerator") { + const onNodeCreated = nodeType.prototype.onNodeCreated; + nodeType.prototype.onNodeCreated = function () { + onNodeCreated?.apply(this, arguments); + + // Initialize properties (following AIL pattern) + if (!this.properties.prompt_batch_data) { + this.properties.prompt_batch_data = JSON.stringify([]); + } + // IMPORTANT: Never store large data in node.properties - it goes into workflow/localStorage + // Always set prompts_db_cache to null (the actual cache is in IndexedDB) + this.properties.prompts_db_cache = null; + // Bookmarks are now stored in IndexedDB, clear from properties to save space + if (this.properties.bookmarks && this.properties.bookmarks !== "[]") { + console.log("[RPG] Clearing bookmarks from node.properties (now in IndexedDB)"); + } + this.properties.bookmarks = "[]"; // Keep minimal for export compatibility + if (!this.properties.active_tab) { + this.properties.active_tab = "library"; + } + if (!this.properties.library_filters) { + this.properties.library_filters = JSON.stringify({ + tags: [], + content_type: "any", + safety_level: "any", + shot_type: "any", + quality: "any", + search_query: "", + }); + } + if (this.properties.creative_system_prompt === undefined) { + this.properties.creative_system_prompt = DEFAULT_RPG_SYSTEM_PROMPT; + } + if (this.properties.creative_temperature === undefined) { + this.properties.creative_temperature = 0.9; + } + if (this.properties.creative_top_p === undefined) { + this.properties.creative_top_p = 0.9; + } + if (this.properties.generation_style === undefined) { + this.properties.generation_style = "reality"; // Default to reality mode + } + // Library inspiration toggle + if (this.properties.enable_library_inspiration === undefined) { + this.properties.enable_library_inspiration = true; // Enabled by default + } + // Expression control properties + if (this.properties.enable_expressions === undefined) { + this.properties.enable_expressions = false; // Disabled by default + } + if (this.properties.enabled_expressions === undefined) { + this.properties.enabled_expressions = JSON.stringify([...EXPRESSION_LIST]); // All enabled by default + } + if (this.properties.default_expression === undefined) { + this.properties.default_expression = "Neutral/Natural"; + } + if (this.properties.default_mix_frequency === undefined) { + this.properties.default_mix_frequency = 0; // 0% = always cycle, 100% = always default + } + if (this.properties.current_expression_index === undefined) { + this.properties.current_expression_index = 0; + } + // Custom mode template + if (this.properties.custom_template === undefined) { + this.properties.custom_template = DEFAULT_RPG_SYSTEM_PROMPT; + } + // System prompt preview toggle + if (this.properties.show_system_prompt_preview === undefined) { + this.properties.show_system_prompt_preview = false; + } + + const node = this; + let cachedHeight = 400; + let isUpdatingHeight = false; + + // Textarea height cache - preserve heights across re-renders + let textareaHeights = {}; + + // Database state + let promptsDatabase = null; + let isDatabaseLoading = false; + let databaseLoadProgress = 0; + + // Generation lock state (crash prevention) + let isGenerating = false; + let currentAbortController = null; + + // AIL sync state - supports up to 4 image inputs + node._linkedAILNodeId = null; + node._linkedImages = []; + node._linkedImages2 = []; + node._linkedImages3 = []; + node._linkedImages4 = []; + node._linkedLatents = []; + node._linkedImageCount = 0; + node._linkedAILMode = null; + + // Initialization flag to prevent race conditions + node._isInitialized = false; + + // Pagination state + let currentPage = 0; + const itemsPerPage = 6; + let reorderModeEnabled = false; + let sdxlModeEnabled = false; + + // Random mode state + let showingRandomPrompts = false; + let randomPrompts = []; + let randomCount = 6; + + // User prompt edit mode state + const editingPrompts = new Set(); // Track which prompt IDs are in edit mode + const editingValues = {}; // Store temporary edit values + + // Selection mode state (for multi-select delete) + let selectionMode = false; + const selectedPrompts = new Set(); // Track which prompt IDs are selected + + // Container setup (exact AIL pattern) + const container = document.createElement("div"); + container.className = "instaraw-rpg-container"; + container.style.width = "100%"; + container.style.boxSizing = "border-box"; + container.style.overflow = "hidden"; + container.style.height = `${cachedHeight}px`; + + // === Height Management === + const updateCachedHeight = () => { + if (isUpdatingHeight) return; + isUpdatingHeight = true; + + // Safety timeout to prevent stuck flag + const safetyTimeout = setTimeout(() => { + isUpdatingHeight = false; + }, 500); + + container.style.overflow = "visible"; + container.style.height = "auto"; + + requestAnimationFrame(() => { + requestAnimationFrame(() => { + clearTimeout(safetyTimeout); + + // Use scrollHeight as it measures full content even during transitions + const newHeight = Math.max(container.offsetHeight, container.scrollHeight); + + if (newHeight > 0 && Math.abs(newHeight - cachedHeight) > 2) { + cachedHeight = newHeight; + container.style.height = `${newHeight}px`; + const sz = node.computeSize(); + node.size[1] = sz[1]; + node.onResize?.(sz); + app.graph.setDirtyCanvas(true, false); + } else { + container.style.height = `${cachedHeight}px`; + } + container.style.overflow = "hidden"; + isUpdatingHeight = false; + }); + }); + }; + + // Periodic height sync - keep running, only clear when node is removed + const heightSyncInterval = setInterval(() => { + if (!node || !container) { + clearInterval(heightSyncInterval); + return; + } + updateCachedHeight(); + }, 2000); + node._heightSyncInterval = heightSyncInterval; + + // Fast initial syncs for page load + [100, 300, 500, 1000, 1500, 2000].forEach(delay => { + setTimeout(() => { + if (container) { + updateCachedHeight(); + } + }, delay); + }); + + // ResizeObserver to detect when textareas are manually resized (drag handle) + // This ensures the node frame stays in sync with content + const setupTextareaResizeObserver = () => { + if (node._resizeObserver) { + node._resizeObserver.disconnect(); + } + node._resizeObserver = new ResizeObserver((entries) => { + // Debounce to avoid too many updates during drag + clearTimeout(node._textareaResizeTimeout); + node._textareaResizeTimeout = setTimeout(() => { + updateCachedHeight(); + }, 100); + }); + // Observe all textareas in the container + container.querySelectorAll('textarea').forEach(textarea => { + node._resizeObserver.observe(textarea); + }); + }; + node._setupTextareaResizeObserver = setupTextareaResizeObserver; + + // === State Sync (Exact AIL Pattern) === + const syncPromptBatchWidget = () => { + const promptQueueWidget = node.widgets?.find((w) => w.name === "prompt_batch_data"); + if (promptQueueWidget) { + promptQueueWidget.value = node.properties.prompt_batch_data; + } else { + const widget = node.addWidget("text", "prompt_batch_data", node.properties.prompt_batch_data, () => {}, { serialize: true }); + widget.hidden = true; + } + }; + + const syncSDXLModeWidget = () => { + const sdxlModeWidget = node.widgets?.find((w) => w.name === "sdxl_mode"); + if (sdxlModeWidget) { + sdxlModeWidget.value = sdxlModeEnabled; + } else { + const widget = node.addWidget("toggle", "sdxl_mode", sdxlModeEnabled, () => {}, { serialize: true }); + widget.hidden = true; + } + }; + + // โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ• + // SETTINGS PERSISTENCE (localStorage) + // โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ• + // Persist user settings across browser refresh without explicit workflow save + + const SETTINGS_KEY = `instaraw_rpg_${node.id}_settings`; + + // List of properties to persist in localStorage + const PERSISTENT_SETTINGS = [ + "user_instructions", + "model_instructions", + "theme_preset", + "model_preset", + "creative_system_prompt", + "character_system_prompt", + "character_text_input", + "character_complexity", + "generation_style", + "inspiration_count", + "enable_library_inspiration", + "enable_expressions", + "enabled_expressions", + "default_expression", + "default_mix_frequency", + "clean_mode", + ]; + + const saveSettings = () => { + try { + const settings = {}; + PERSISTENT_SETTINGS.forEach(key => { + if (node.properties[key] !== undefined) { + settings[key] = node.properties[key]; + } + }); + localStorage.setItem(SETTINGS_KEY, JSON.stringify(settings)); + } catch (e) { + console.warn("[RPG] Failed to save settings to localStorage:", e); + } + }; + + const loadSettings = () => { + try { + const saved = localStorage.getItem(SETTINGS_KEY); + if (saved) { + const settings = JSON.parse(saved); + PERSISTENT_SETTINGS.forEach(key => { + if (settings[key] !== undefined) { + node.properties[key] = settings[key]; + } + }); + console.log("[RPG] Loaded settings from localStorage"); + } + } catch (e) { + console.warn("[RPG] Failed to load settings from localStorage:", e); + } + }; + + // Legacy compatibility - keep saveUserInstructions for existing event handlers + const saveUserInstructions = saveSettings; + + // === Aspect Ratio Node Reading (Same as AIL) === + + /** + * Reads output from WAN/SDXL aspect ratio nodes. + * Must match Python ASPECT_RATIOS dicts exactly. + */ + const getAspectRatioOutput = (aspectRatioNode, slotIndex) => { + const selection = aspectRatioNode.widgets?.[0]?.value; + if (!selection) { + console.warn(`[RPG] Aspect ratio node has no selection`); + return null; + } + + // Handle Nano Banana Pro Aspect Ratio node (has 5 outputs) + if (aspectRatioNode.type === "INSTARAW_NanoBananaAspectRatio") { + const resolutionWidget = aspectRatioNode.widgets?.[1]; + const resolution = resolutionWidget?.value || "1K"; + + // Nano Banana aspect ratio mappings + const NANO_RATIOS = { + "1:1 (Square)": { ratio: "1:1", w: 1, h: 1 }, + "3:2 (Landscape)": { ratio: "3:2", w: 3, h: 2 }, + "2:3 (Portrait)": { ratio: "2:3", w: 2, h: 3 }, + "3:4 (Portrait)": { ratio: "3:4", w: 3, h: 4 }, + "4:3 (Landscape)": { ratio: "4:3", w: 4, h: 3 }, + "4:5 (Portrait)": { ratio: "4:5", w: 4, h: 5 }, + "5:4 (Landscape)": { ratio: "5:4", w: 5, h: 4 }, + "9:16 (Tall Portrait)": { ratio: "9:16", w: 9, h: 16 }, + "16:9 (Wide Landscape)": { ratio: "16:9", w: 16, h: 9 }, + "21:9 (Ultrawide)": { ratio: "21:9", w: 21, h: 9 }, + }; + + const RESOLUTION_BASE = { "1K": 1024, "2K": 2048, "4K": 4096 }; + + const config = NANO_RATIOS[selection]; + if (!config) { + console.warn(`[RPG] Unknown Nano Banana aspect ratio: ${selection}`); + return null; + } + + // Calculate dimensions (same logic as Python) + const baseSize = RESOLUTION_BASE[resolution] || 1024; + let width, height; + if (config.w >= config.h) { + height = baseSize; + width = Math.floor(baseSize * config.w / config.h); + } else { + width = baseSize; + height = Math.floor(baseSize * config.h / config.w); + } + // Round to nearest 64 + width = Math.floor(width / 64) * 64; + height = Math.floor(height / 64) * 64; + + // Output slots: 0=aspect_ratio, 1=resolution, 2=width, 3=height, 4=aspect_label + if (slotIndex === 0) return config.ratio; + if (slotIndex === 1) return resolution; + if (slotIndex === 2) return width; + if (slotIndex === 3) return height; + if (slotIndex === 4) return selection; + + return null; + } + + const WAN_RATIOS = { + "3:4 (Portrait)": { width: 720, height: 960, label: "3:4" }, + "9:16 (Tall Portrait)": { width: 540, height: 960, label: "9:16" }, + "1:1 (Square)": { width: 960, height: 960, label: "1:1" }, + "16:9 (Landscape)": { width: 960, height: 540, label: "16:9" } + }; + + const SDXL_RATIOS = { + "3:4 (Portrait)": { width: 896, height: 1152, label: "3:4" }, + "9:16 (Tall Portrait)": { width: 768, height: 1344, label: "9:16" }, + "1:1 (Square)": { width: 1024, height: 1024, label: "1:1" }, + "16:9 (Landscape)": { width: 1344, height: 768, label: "16:9" } + }; + + const ratios = aspectRatioNode.type === "INSTARAW_WANAspectRatio" ? WAN_RATIOS : SDXL_RATIOS; + const config = ratios[selection]; + if (!config) { + console.warn(`[RPG] Unknown aspect ratio selection: ${selection}`); + return null; + } + + // Return based on output slot (0=width, 1=height, 2=aspect_label) + if (slotIndex === 0) return config.width; + if (slotIndex === 1) return config.height; + if (slotIndex === 2) return config.label; + return null; + }; + + /** + * Retrieves the final value of an input by traversing connected nodes. + * Enhanced to properly handle multi-output nodes like aspect ratio nodes. + */ + const getFinalInputValue = (inputName, defaultValue) => { + try { + if (!node.inputs || node.inputs.length === 0) { + const widget = node.widgets?.find(w => w.name === inputName); + return widget ? widget.value : defaultValue; + } + + const input = node.inputs.find(i => i.name === inputName); + if (!input || input.link == null) { + const widget = node.widgets?.find(w => w.name === inputName); + return widget ? widget.value : defaultValue; + } + + // Access app.graph safely + if (typeof app === 'undefined' || !app.graph) { + console.warn('[RPG] app.graph not available, using widget value'); + const widget = node.widgets?.find(w => w.name === inputName); + return widget ? widget.value : defaultValue; + } + + const link = app.graph.links[input.link]; + if (!link) return defaultValue; + + const originNode = app.graph.getNodeById(link.origin_id); + if (!originNode) return defaultValue; + + // SPECIAL HANDLING: For aspect ratio nodes, compute the output locally + if (originNode.type === "INSTARAW_WANAspectRatio" || + originNode.type === "INSTARAW_SDXLAspectRatio" || + originNode.type === "INSTARAW_NanoBananaAspectRatio") { + const output = getAspectRatioOutput(originNode, link.origin_slot); + if (output !== null) return output; + } + + // For other nodes, read from widgets + if (originNode.widgets && originNode.widgets.length > 0) { + return originNode.widgets[0].value; + } + + if (originNode.properties && originNode.properties.value !== undefined) { + return originNode.properties.value; + } + + return defaultValue; + } catch (error) { + console.error('[RPG] Error in getFinalInputValue:', error); + return defaultValue; + } + }; + + /** + * Get target output dimensions from aspect ratio selector. + * Now properly reads from connected aspect ratio nodes! + */ + const getTargetDimensions = () => { + try { + const aspect_label = getFinalInputValue("aspect_label", "1:1"); + + // Parse aspect ratio from label (e.g., "16:9" โ†’ 16/9) + const match = aspect_label.match(/(\d+):(\d+)/); + let width, height; + if (match) { + width = parseInt(match[1]); + height = parseInt(match[2]); + } else { + width = 1; + height = 1; + } + + const dims = { + width: width, + height: height, + aspect_label: aspect_label || "1:1" + }; + + console.log("[RPG] Target dimensions from label:", dims, "Aspect ratio:", width/height); + return dims; + } catch (error) { + console.error('[RPG] Error in getTargetDimensions:', error); + return { width: 1, height: 1, aspect_label: "1:1" }; + } + }; + + const parsePromptBatch = () => { + try { + const raw = node.properties.prompt_batch_data ?? "[]"; + if (Array.isArray(raw)) { + return raw; + } + if (typeof raw === "string") { + if (!raw.trim()) return []; + return JSON.parse(raw); + } + return []; + } catch (error) { + console.warn("[RPG] Failed to parse prompt batch data, resetting to []", error); + node.properties.prompt_batch_data = "[]"; + syncPromptBatchWidget(); + return []; + } + }; + + const setPromptBatchData = (promptQueue) => { + const normalized = Array.isArray(promptQueue) ? promptQueue : []; + node.properties.prompt_batch_data = JSON.stringify(normalized); + syncPromptBatchWidget(); + // Dispatch event for BIG and other listeners + window.dispatchEvent(new CustomEvent("INSTARAW_RPG_PROMPTS_CHANGED", { + detail: { + nodeId: node.id, + prompts: normalized, + totalGenerations: normalized.reduce((sum, p) => sum + (p.repeat_count || 1), 0) + } + })); + return normalized; + }; + + // === Database Loading with IndexedDB === + const loadPromptsDatabase = async () => { + if (isDatabaseLoading) return; + isDatabaseLoading = true; + + try { + // Try IndexedDB first + const cachedDB = await getFromIndexedDB("prompts_db_cache"); + if (cachedDB && cachedDB.version === "1.0") { + promptsDatabase = cachedDB.data; + // Load user data from IndexedDB + await loadUserPrompts(); + await loadGeneratedPrompts(); + await loadBookmarks(); + mergeUserPromptsWithLibrary(); + isDatabaseLoading = false; + renderUI(); + return; + } + + // Fetch from server + databaseLoadProgress = 0; + renderUI(); // Show loading state + updateLoadingProgressUI(); + + const response = await fetchPromptsDatabase(); + const reader = response.body.getReader(); + const contentLength = +response.headers.get("Content-Length"); + let receivedLength = 0; + const chunks = []; + + while (true) { + const { done, value } = await reader.read(); + if (done) break; + chunks.push(value); + receivedLength += value.length; + databaseLoadProgress = contentLength + ? Math.min(99, Math.round((receivedLength / contentLength) * 100)) + : 99; + updateLoadingProgressUI(); + } + + const chunksAll = new Uint8Array(receivedLength); + let position = 0; + for (const chunk of chunks) { + chunksAll.set(chunk, position); + position += chunk.length; + } + + const text = new TextDecoder("utf-8").decode(chunksAll); + promptsDatabase = JSON.parse(text); + + // Store in IndexedDB + await saveToIndexedDB("prompts_db_cache", { + version: "1.0", + data: promptsDatabase, + }); + + // Load user data from IndexedDB + await loadUserPrompts(); + await loadGeneratedPrompts(); + await loadBookmarks(); + mergeUserPromptsWithLibrary(); + + isDatabaseLoading = false; + databaseLoadProgress = 100; + renderUI(); + } catch (error) { + console.error("[RPG] Database loading error:", error); + isDatabaseLoading = false; + renderUI(); + } + }; + + const updateLoadingProgressUI = () => { + const fill = container.querySelector(".instaraw-rpg-progress-fill"); + const text = container.querySelector(".instaraw-rpg-progress-text"); + if (fill) fill.style.width = `${databaseLoadProgress}%`; + if (text) text.textContent = `${databaseLoadProgress}% (22MB)`; + }; + + const fetchPromptsDatabase = async () => { + const response = await fetch(REMOTE_PROMPTS_DB_URL, { cache: "no-store" }); + if (!response.ok) throw new Error(`Remote prompts DB error ${response.status}`); + return response; + }; + + const autoResizeTextarea = (textarea, options = {}) => { + if (!textarea) return; + const minHeight = options.minHeight || 60; + const maxHeight = options.maxHeight || 200; + + // Check for cached height (user may have manually resized) + const id = textarea.dataset.id; + const isPositive = textarea.classList.contains("instaraw-rpg-positive-textarea"); + const isNegative = textarea.classList.contains("instaraw-rpg-negative-textarea"); + const cacheKey = id && (isPositive || isNegative) ? `${id}_${isPositive ? 'positive' : 'negative'}` : null; + const cachedHeight = cacheKey ? textareaHeights[cacheKey] : null; + + // Store current scroll position to prevent jump + const scrollTop = window.scrollY || document.documentElement.scrollTop; + + // Temporarily set to auto to measure, but use minHeight as floor + textarea.style.height = `${minHeight}px`; + const scrollHeight = textarea.scrollHeight; + + // Use the larger of: content-based height or cached height (respects user resize) + const contentHeight = Math.min(Math.max(scrollHeight, minHeight), maxHeight); + const newHeight = cachedHeight ? Math.max(contentHeight, cachedHeight) : contentHeight; + textarea.style.height = `${newHeight}px`; + + // Always allow scrolling - users may need to scroll even in shorter textareas + textarea.style.overflowY = 'auto'; + + // Restore scroll position + window.scrollTo(0, scrollTop); + }; + + // Convert image URL to base64 (for img2img vision API) + const imageUrlToBase64 = async (url) => { + try { + const response = await fetch(url); + const blob = await response.blob(); + return new Promise((resolve, reject) => { + const reader = new FileReader(); + reader.onloadend = () => { + // Remove data:image/...;base64, prefix + const base64 = reader.result.split(',')[1]; + resolve(base64); + }; + reader.onerror = reject; + reader.readAsDataURL(blob); + }); + } catch (error) { + console.error("[RPG] Error converting image to base64:", error); + throw error; + } + }; + + const parseJSONResponse = async (response) => { + const text = await response.text(); + try { + return JSON.parse(text); + } catch (error) { + throw new Error(`Unexpected response (${response.status}): ${text.slice(0, 500)}`); + } + }; + + // === Storage Diagnostics (for debugging quota issues) === + const diagnoseStorage = () => { + console.log("[RPG] === STORAGE DIAGNOSTICS ==="); + + // Check node.properties sizes + const propSizes = {}; + let totalPropSize = 0; + for (const [key, value] of Object.entries(node.properties || {})) { + const size = JSON.stringify(value).length; + propSizes[key] = size; + totalPropSize += size; + } + console.log("[RPG] node.properties sizes (bytes):", propSizes); + console.log("[RPG] Total node.properties size:", totalPropSize, "bytes", `(${(totalPropSize / 1024).toFixed(2)} KB)`); + + // Check localStorage usage + let localStorageSize = 0; + for (const key of Object.keys(localStorage)) { + localStorageSize += localStorage[key].length; + } + console.log("[RPG] localStorage total size:", localStorageSize, "bytes", `(${(localStorageSize / 1024 / 1024).toFixed(2)} MB)`); + + // Check if prompts_db_cache accidentally has data + if (node.properties.prompts_db_cache && node.properties.prompts_db_cache !== null) { + console.warn("[RPG] WARNING: prompts_db_cache has data in node.properties! Size:", + JSON.stringify(node.properties.prompts_db_cache).length, "bytes"); + } + + // IndexedDB estimation + if (navigator.storage && navigator.storage.estimate) { + navigator.storage.estimate().then(estimate => { + console.log("[RPG] Storage estimate - used:", + (estimate.usage / 1024 / 1024).toFixed(2), "MB, quota:", + (estimate.quota / 1024 / 1024).toFixed(2), "MB"); + }); + } + + return { propSizes, totalPropSize, localStorageSize }; + }; + + // Expose for debugging: window.rpgDiagnoseStorage = diagnoseStorage + window.rpgDiagnoseStorage = diagnoseStorage; + + // === IndexedDB Helpers === + const getFromIndexedDB = (key) => { + return new Promise((resolve) => { + const request = indexedDB.open("INSTARAW_RPG", 1); + request.onupgradeneeded = (e) => { + const db = e.target.result; + if (!db.objectStoreNames.contains("cache")) { + db.createObjectStore("cache"); + } + }; + request.onsuccess = (e) => { + const db = e.target.result; + const tx = db.transaction("cache", "readonly"); + const store = tx.objectStore("cache"); + const get = store.get(key); + get.onsuccess = () => resolve(get.result); + get.onerror = () => resolve(null); + }; + request.onerror = () => resolve(null); + }); + }; + + const saveToIndexedDB = (key, value) => { + return new Promise((resolve) => { + const request = indexedDB.open("INSTARAW_RPG", 1); + request.onupgradeneeded = (e) => { + const db = e.target.result; + if (!db.objectStoreNames.contains("cache")) { + db.createObjectStore("cache"); + } + }; + request.onsuccess = (e) => { + const db = e.target.result; + const tx = db.transaction("cache", "readwrite"); + const store = tx.objectStore("cache"); + const put = store.put(value, key); + put.onsuccess = () => { + console.log(`[RPG] IndexedDB saved key "${key}" successfully`); + resolve(true); + }; + put.onerror = (err) => { + console.error(`[RPG] IndexedDB save error for key "${key}":`, err); + resolve(false); + }; + }; + request.onerror = (err) => { + console.error(`[RPG] IndexedDB open error:`, err); + resolve(false); + }; + }); + }; + + // === Bookmarks Management (IndexedDB for persistence) === + let bookmarksCache = []; // In-memory cache of bookmarked prompt IDs + + const loadBookmarks = async () => { + try { + const cached = await getFromIndexedDB("bookmarks"); + // Migration: if IndexedDB is empty but node.properties has bookmarks, migrate them + if (!cached && node.properties.bookmarks) { + const oldBookmarks = JSON.parse(node.properties.bookmarks || "[]"); + if (oldBookmarks.length > 0) { + console.log(`[RPG] Migrating ${oldBookmarks.length} bookmarks from node.properties to IndexedDB`); + bookmarksCache = oldBookmarks; + await saveBookmarks(bookmarksCache); + return bookmarksCache; + } + } + bookmarksCache = cached || []; + console.log(`[RPG] Loaded ${bookmarksCache.length} bookmarks from IndexedDB`); + return bookmarksCache; + } catch (error) { + console.error("[RPG] Error loading bookmarks:", error); + bookmarksCache = []; + return []; + } + }; + + const saveBookmarks = async (bookmarks) => { + try { + const success = await saveToIndexedDB("bookmarks", bookmarks); + if (success) { + bookmarksCache = bookmarks; + // NOTE: We don't save to node.properties anymore to avoid localStorage quota issues + // Export function reads directly from bookmarksCache + console.log(`[RPG] Saved ${bookmarks.length} bookmarks to IndexedDB`); + } else { + console.error(`[RPG] Failed to save bookmarks to IndexedDB!`); + } + return success; + } catch (error) { + console.error("[RPG] Error saving bookmarks:", error); + return false; + } + }; + + const getBookmarks = () => bookmarksCache; + + const toggleBookmarkById = async (promptId) => { + const bookmarks = [...bookmarksCache]; + const idx = bookmarks.indexOf(promptId); + if (idx >= 0) { + bookmarks.splice(idx, 1); + } else { + bookmarks.push(promptId); + } + await saveBookmarks(bookmarks); + return idx < 0; // Returns true if added, false if removed + }; + + // === User Prompts Management === + let userPrompts = []; // In-memory cache of user-created prompts + + const loadUserPrompts = async () => { + try { + const cached = await getFromIndexedDB("user_prompts"); + console.log(`[RPG] Raw IndexedDB user_prompts:`, cached); + userPrompts = cached || []; + console.log(`[RPG] Loaded ${userPrompts.length} user prompts from IndexedDB`); + if (userPrompts.length > 0) { + console.log(`[RPG] First user prompt:`, userPrompts[0]); + } + return userPrompts; + } catch (error) { + console.error("[RPG] Error loading user prompts:", error); + userPrompts = []; + return []; + } + }; + + const saveUserPrompts = async (prompts) => { + try { + const success = await saveToIndexedDB("user_prompts", prompts); + if (success) { + userPrompts = prompts; + console.log(`[RPG] Saved ${prompts.length} user prompts to IndexedDB`); + } else { + console.error(`[RPG] Failed to save user prompts to IndexedDB!`); + } + return success; + } catch (error) { + console.error("[RPG] Error saving user prompts:", error); + return false; + } + }; + + const addUserPrompt = async (promptData) => { + const newPrompt = { + id: `user_${Date.now()}_${Math.random().toString(36).substr(2, 9)}`, + tags: promptData.tags || [], + prompt: { + positive: promptData.positive || "", + negative: promptData.negative || "" + }, + classification: { + content_type: promptData.content_type || "person", + safety_level: promptData.safety_level || "sfw", + shot_type: promptData.shot_type || "portrait" + }, + is_user_created: true, // Flag to identify user prompts + created_at: Date.now() + }; + + userPrompts.unshift(newPrompt); // Add to beginning + await saveUserPrompts(userPrompts); + mergeUserPromptsWithLibrary(); + renderUI(); + return newPrompt; + }; + + const updateUserPrompt = async (id, updates) => { + const index = userPrompts.findIndex(p => p.id === id); + if (index !== -1) { + userPrompts[index] = { + ...userPrompts[index], + ...updates, + is_user_created: true, // Preserve flag + updated_at: Date.now() + }; + await saveUserPrompts(userPrompts); + mergeUserPromptsWithLibrary(); + renderUI(); + return true; + } + return false; + }; + + const deleteUserPrompt = async (id) => { + const filtered = userPrompts.filter(p => p.id !== id); + if (filtered.length !== userPrompts.length) { + await saveUserPrompts(filtered); + + // Clean up bookmarks if this prompt was favorited + const updatedBookmarks = bookmarksCache.filter(b => b !== id); + if (updatedBookmarks.length !== bookmarksCache.length) { + await saveBookmarks(updatedBookmarks); + console.log(`[RPG] Removed deleted prompt ${id} from bookmarks`); + } + + mergeUserPromptsWithLibrary(); + renderUI(); + return true; + } + return false; + }; + + // === Generated Prompts Storage === + let generatedPrompts = []; + const loadGeneratedPrompts = async () => { + try { + const cached = await getFromIndexedDB("generated_prompts"); + generatedPrompts = cached || []; + console.log(`[RPG] Loaded ${generatedPrompts.length} generated prompts`); + return generatedPrompts; + } catch (e) { generatedPrompts = []; return []; } + }; + const saveGeneratedPrompts = async (prompts) => { + try { + await saveToIndexedDB("generated_prompts", prompts); + generatedPrompts = prompts; + return true; + } catch (e) { return false; } + }; + const addGeneratedPrompt = async (data) => { + const p = { + id: `gen_${Date.now()}_${Math.random().toString(36).substr(2,9)}`, + tags: data.tags || [], + prompt: { positive: data.positive || "", negative: data.negative || "" }, + classification: data.classification || { content_type: "other", safety_level: "sfw", shot_type: "other" }, + is_ai_generated: true, + model_badge: data.model_badge || null, + theme_badge: data.theme_badge || null, + created_at: Date.now() + }; + generatedPrompts.unshift(p); + await saveGeneratedPrompts(generatedPrompts); + promptsDatabase = promptsDatabase.filter(x => !x.is_ai_generated); + const userCount = promptsDatabase.filter(x => x.is_user_created).length; + promptsDatabase.splice(userCount, 0, ...generatedPrompts); + return p; + }; + + const deleteGeneratedPrompt = async (id) => { + const filtered = generatedPrompts.filter(p => p.id !== id); + if (filtered.length !== generatedPrompts.length) { + await saveGeneratedPrompts(filtered); + + // Clean up bookmarks if this prompt was favorited + const updatedBookmarks = bookmarksCache.filter(b => b !== id); + if (updatedBookmarks.length !== bookmarksCache.length) { + await saveBookmarks(updatedBookmarks); + console.log(`[RPG] Removed deleted prompt ${id} from bookmarks`); + } + + mergeUserPromptsWithLibrary(); + renderUI(); + return true; + } + return false; + }; + + const exportUserPrompts = () => { + // Get full batch queue (includes all prompts with repeats) + const promptBatch = parsePromptBatch(); + + // Collect user settings + const settings = {}; + PERSISTENT_SETTINGS.forEach(key => { + if (node.properties[key] !== undefined) { + settings[key] = node.properties[key]; + } + }); + + const exportData = { + version: "1.1", // Bumped version for settings support + exported_at: new Date().toISOString(), + user_prompts: userPrompts, + bookmarks: bookmarksCache, + batch_queue: promptBatch, // Full batch with all instances + settings: settings // User customizations + }; + const dataStr = JSON.stringify(exportData, null, 2); + const blob = new Blob([dataStr], { type: "application/json" }); + const url = URL.createObjectURL(blob); + const a = document.createElement("a"); + a.href = url; + a.download = `rpg_export_${Date.now()}.json`; + document.body.appendChild(a); + a.click(); + document.body.removeChild(a); + URL.revokeObjectURL(url); + console.log(`[RPG] Exported ${userPrompts.length} user prompts, ${bookmarksCache.length} bookmarks, ${promptBatch.length} batch items, and ${Object.keys(settings).length} settings`); + }; + + const importUserPrompts = async (file) => { + return new Promise((resolve, reject) => { + const reader = new FileReader(); + reader.onload = async (e) => { + try { + const data = JSON.parse(e.target.result); + + const userPromptsToImport = data.user_prompts || []; + const batchQueueToImport = data.batch_queue || []; + const importedBookmarks = data.bookmarks || []; + const importedSettings = data.settings || {}; + + // Validate format + if (!Array.isArray(userPromptsToImport)) { + throw new Error("Invalid format: user_prompts must be an array"); + } + + // Merge user prompts with existing (avoid duplicates by ID) + const existingIds = new Set(userPrompts.map(p => p.id)); + let addedUser = 0; + userPromptsToImport.forEach(prompt => { + if (!existingIds.has(prompt.id)) { + userPrompts.push({ + ...prompt, + is_user_created: true, + imported_at: Date.now() + }); + existingIds.add(prompt.id); + addedUser++; + } + }); + + // Import full batch queue (replaces current batch) + if (batchQueueToImport.length > 0) { + setPromptBatchData(batchQueueToImport); + } + + // Merge bookmarks (avoid duplicates) + const mergedBookmarks = [...new Set([...bookmarksCache, ...importedBookmarks])]; + await saveBookmarks(mergedBookmarks); + + // Import settings (if present and user confirms) + let settingsImported = 0; + if (Object.keys(importedSettings).length > 0) { + const importSettings = confirm( + `This export includes ${Object.keys(importedSettings).length} settings:\n` + + `โ€ข Theme, Model preset, Instructions\n` + + `โ€ข Generation style, Expression settings\n` + + `โ€ข System prompts\n\n` + + `Import these settings? (This will overwrite your current settings)` + ); + if (importSettings) { + PERSISTENT_SETTINGS.forEach(key => { + if (importedSettings[key] !== undefined) { + node.properties[key] = importedSettings[key]; + settingsImported++; + } + }); + saveSettings(); // Persist to localStorage + console.log(`[RPG] Imported ${settingsImported} settings`); + } + } + + await saveUserPrompts(userPrompts); + mergeUserPromptsWithLibrary(); + renderUI(); + + const totalSkipped = userPromptsToImport.length - addedUser; + console.log(`[RPG] Imported ${addedUser} user prompts, ${batchQueueToImport.length} batch items, ${importedBookmarks.length} bookmarks, ${settingsImported} settings (${totalSkipped} duplicates skipped)`); + resolve({ added: addedUser + batchQueueToImport.length, skipped: totalSkipped, details: { user: addedUser, batch: batchQueueToImport.length, bookmarks: importedBookmarks.length, settings: settingsImported } }); + } catch (error) { + console.error("[RPG] Error importing user prompts:", error); + reject(error); + } + }; + reader.onerror = reject; + reader.readAsText(file); + }); + }; + + const mergeUserPromptsWithLibrary = () => { + // Remove old user prompts and generated prompts from promptsDatabase + promptsDatabase = promptsDatabase.filter(p => !p.is_user_created && !p.is_ai_generated); + // Combine user and generated prompts, sort by created_at (newest first) + const customPrompts = [...userPrompts, ...generatedPrompts].sort((a, b) => (b.created_at || 0) - (a.created_at || 0)); + promptsDatabase = [...customPrompts, ...promptsDatabase]; + console.log(`[RPG] Merged database: ${userPrompts.length} user + ${generatedPrompts.length} generated + ${promptsDatabase.length - userPrompts.length - generatedPrompts.length} library = ${promptsDatabase.length} total`); + }; + + // === Capture Textarea Heights Before Re-render === + const captureTextareaHeights = () => { + const positiveTextareas = container.querySelectorAll(".instaraw-rpg-positive-textarea"); + const negativeTextareas = container.querySelectorAll(".instaraw-rpg-negative-textarea"); + + positiveTextareas.forEach(textarea => { + const id = textarea.dataset.id; + if (id && textarea.offsetHeight > 0) { + textareaHeights[`${id}_positive`] = textarea.offsetHeight; + } + }); + + negativeTextareas.forEach(textarea => { + const id = textarea.dataset.id; + if (id && textarea.offsetHeight > 0) { + textareaHeights[`${id}_negative`] = textarea.offsetHeight; + } + }); + }; + + // === Main Render Function === + const renderUI = () => { + // Capture current textarea heights BEFORE destroying DOM + captureTextareaHeights(); + + const activeTab = node.properties.active_tab || "library"; + const promptQueue = parsePromptBatch(); + const resolvedModeWidget = node.widgets?.find((w) => w.name === "resolved_mode"); + const resolvedMode = resolvedModeWidget?.value || "txt2img"; + // Creative model is UI-only, stored in properties + if (this.properties.creative_model === undefined) { + this.properties.creative_model = "gemini-2.5-pro"; + } + const currentCreativeModel = this.properties.creative_model || "gemini-2.5-pro"; + const creativeSystemPrompt = node.properties.creative_system_prompt || DEFAULT_RPG_SYSTEM_PROMPT; + const currentCreativeTemperature = parseFloat(node.properties.creative_temperature ?? 0.9) || 0.9; + const currentCreativeTopP = parseFloat(node.properties.creative_top_p ?? 0.9) || 0.9; + + const totalGenerations = promptQueue.reduce((sum, entry) => sum + (entry.repeat_count || 1), 0); + + const tabs = [ + { id: "library", label: "Prompts Library", icon: "๐Ÿ“š" }, + { id: "generate", label: "Generate Prompts", icon: "๐ŸŽฏ" }, + ]; + + const linkedImages = node._linkedImageCount || 0; + + // Use detected mode from AIL if available + const detectedMode = node._linkedAILMode || resolvedMode; + const isDetectedFromAIL = node._linkedAILMode !== null; + + const tabButtons = tabs + .map( + (tab) => ` + + `, + ) + .join(""); + + const uiState = { + promptQueue, + currentCreativeModel, + creativeSystemPrompt, + currentCreativeTemperature, + currentCreativeTopP, + }; + const tabContent = renderActiveTabContent(activeTab, uiState); + const imagePreview = renderImagePreview(resolvedMode, totalGenerations); + + container.innerHTML = ` +
+
+
+
+
+
+ + ${detectedMode === 'img2img' ? '๐Ÿ–ผ๏ธ IMG2IMG MODE' : '๐ŸŽจ TXT2IMG MODE'} + + ${isDetectedFromAIL ? `From AIL #${node._linkedAILNodeId}` : ''} +
+
+ INSTARAW + RPG V2.0 +
+ +
+
+
+
+ Queue + ${promptQueue.length} +
+
+ Generations + ${totalGenerations} +
+
+ Images + ${linkedImages} +
+
+
+
+ ${tabButtons} +
+
+ ${tabContent} +
+
+
+ ${imagePreview} + ${renderBatchPanel(promptQueue, totalGenerations)} +
+
+ +
+
+ Gen: ${totalGenerations} + ${detectedMode === 'img2img' ? 'IMG2IMG' : 'TXT2IMG'} + ${node._linkedAILNodeId ? `AIL #${node._linkedAILNodeId}` : `No AIL`} +
+
+ `; + + setupEventHandlers(); + setupDragAndDrop(); + setupTextareaResizeObserver(); // Watch for manual textarea resizing + + // Restore generation UI if there are completed or in-progress generations + if ((node._generatedUnifiedPrompts || node._generationInProgress) && activeTab === "generate") { + restoreGenerationUI(); + } + + // Single delayed height update after DOM fully settles + setTimeout(() => updateCachedHeight(), 100); + }; + + // === Tab Content Rendering === + const renderActiveTabContent = (activeTab, uiState) => { + if (isDatabaseLoading) { + return ` +
+ +

Loading Prompts Database...

+
+
+
+

${databaseLoadProgress}% (22MB)

+
+ `; + } + + if (!promptsDatabase) { + return ` +
+

Database not loaded

+ +
+ `; + } + + switch (activeTab) { + case "library": + return renderLibraryTab(); + case "generate": + return renderGenerateTab(uiState); + case "custom": + return renderCustomTab(uiState); + case "creative": // LEGACY - Fallback for old workflows + return renderCreativeTab(uiState); + case "character": // LEGACY - Fallback for old workflows + return renderCharacterTab(); + default: + return ""; + } + }; + + // === Library Tab === + const renderLibraryTab = () => { + const filters = JSON.parse(node.properties.library_filters || "{}"); + const bookmarks = bookmarksCache; + const promptQueue = parsePromptBatch(); + const batchSourceIds = new Set(promptQueue.map(p => p.source_id).filter(Boolean)); + + // Determine which prompts to show + let filteredPrompts; + let pagePrompts; + let totalPages; + + if (showingRandomPrompts && randomPrompts.length > 0) { + // Random mode: show the fetched random prompts + filteredPrompts = randomPrompts; + pagePrompts = randomPrompts; // Show all random prompts (no pagination) + totalPages = 1; + } else { + // Normal mode: apply filters + filteredPrompts = filterPrompts(promptsDatabase, filters); + + // Pagination + totalPages = Math.ceil(filteredPrompts.length / itemsPerPage); + const startIdx = currentPage * itemsPerPage; + const endIdx = startIdx + itemsPerPage; + pagePrompts = filteredPrompts.slice(startIdx, endIdx); + } + + // Calculate counts for prompt source dropdown + const allPromptsCount = promptsDatabase.length; + const libraryPromptsCount = promptsDatabase.filter(p => !p.is_user_created && !p.is_ai_generated).length; + const userPromptsCount = userPrompts.length; + const generatedPromptsCount = promptsDatabase.filter(p => p.is_ai_generated).length; + + return ` +
+
+ +
+ + + + + + +
+
+ +
+
+ +
+ ${showingRandomPrompts ? ` + + + + + ` : selectionMode ? ` + + + + + + ` : ` + + + + + ${(filters.prompt_source === 'user' || filters.prompt_source === 'generated') ? ` + + ` : ''} +
+ + + + `} +
+ +
+ ${showingRandomPrompts + ? `Showing ${randomPrompts.length} random prompts` + : `${filteredPrompts.length} prompt${filteredPrompts.length === 1 ? '' : 's'} found${totalPages > 1 ? ` โ€ข Page ${currentPage + 1} of ${totalPages}` : ''}` + } +
+
+
+ + ${ + totalPages > 1 + ? ` +
+ + Page ${currentPage + 1} / ${totalPages} + +
+ ` + : "" + } + +
+ ${ + pagePrompts.length === 0 + ? `

No prompts found

Try adjusting your filters

` + : pagePrompts + .map( + (prompt) => { + const batchCount = promptQueue.filter(p => p.source_id === prompt.id).length; + const positive = prompt.prompt?.positive || ""; + const negative = prompt.prompt?.negative || ""; + + // Debug: Log if positive is empty but negative isn't + if (!positive && negative) { + console.warn(`[RPG] Prompt ${prompt.id} has empty positive but has negative:`, { + id: prompt.id, + positive: positive, + negative: negative, + fullPrompt: prompt.prompt, + tags: prompt.tags + }); + } + + // Show positive, or if empty show negative with warning, or show placeholder + const displayText = positive + ? positive + : negative + ? `[No positive prompt] ${negative}` + : "[Empty prompt]"; + + const searchQuery = filters.search_query?.trim() || ""; + const sdxlMode = sdxlModeEnabled; + const matchType = prompt._matchType; + const matchBadge = matchType === 'both' ? '๐Ÿ“๐Ÿท๏ธ' : matchType === 'prompt' ? '๐Ÿ“' : matchType === 'tags' ? '๐Ÿท๏ธ' : ''; + const sourceBadge = prompt.is_user_created ? 'โœ๏ธ My Prompt' : prompt.is_ai_generated ? 'โœจ AI Generated' : '๐Ÿ“š Library'; + + const allTags = prompt.tags || []; + const autoExpand = matchType === 'tags' || matchType === 'both'; // Auto-expand if tags match + + return ` +
+
+ ${selectionMode && (prompt.is_user_created || prompt.is_ai_generated) ? ` + + ` : ''} + +
+ + ${batchCount > 0 ? `` : ''} + ${prompt.is_user_created ? `` : ''} + ${prompt.is_ai_generated ? `` : ''} +
+
+
+
+
${sourceBadge}
+ ${matchBadge ? `
${matchBadge} Match
` : ''} +
+ ID: ${prompt.id.substring(0, 8)}.. + +
+
+ +
+
+ + ${prompt.is_user_created ? ` + + ${editingPrompts.has(prompt.id) ? ` + +
+
+ + +
+
+ + +
+
+ + +
+ + +
+
+ + +
+
+ + +
+
+ + +
+
+ +
+ + +
+
+ ` : sdxlMode ? ` + +
+ ${allTags.length > 0 ? allTags.map((tag) => highlightSearchTerm(tag, searchQuery)).join(", ") : "[Empty prompt]"} +
+ + ` : ` + +
${highlightSearchTerm(displayText, searchQuery)}
+ ${negative ? `
Negative: ${highlightSearchTerm(negative, searchQuery)}
` : ''} +
+ ${allTags.map((tag) => `${highlightSearchTerm(tag, searchQuery)}`).join("")} +
+ + `} + ` : sdxlMode ? ` + +
+ ${allTags.map((tag) => highlightSearchTerm(tag, searchQuery)).join(", ")} +
+ ` : ` + +
${highlightSearchTerm(displayText, searchQuery)}
+
+ ${autoExpand || allTags.length <= 5 + ? allTags.map((tag) => `${highlightSearchTerm(tag, searchQuery)}`).join("") + : allTags.slice(0, 5).map((tag) => `${highlightSearchTerm(tag, searchQuery)}`).join("") + } + ${allTags.length > 5 ? `` : ""} +
+ `} + ${(prompt.model_badge || prompt.theme_badge) ? ` +
+ ${prompt.theme_badge ? `
Creative Theme: ${prompt.theme_badge}
` : ''} + ${prompt.model_badge ? `
Optimized for: ${prompt.model_badge}
` : ''} +
+ ` : ''} +
+
+ `; + } + ) + .join("") + } +
+ + ${ + totalPages > 1 + ? ` +
+ + Page ${currentPage + 1} / ${totalPages} (${filteredPrompts.length} prompts) + +
+ ` + : "" + } +
+ `; + }; + + // === Creative Tab === + const renderCreativeTab = (uiState) => { + const promptQueue = uiState?.promptQueue || []; + const selectedForInspiration = promptQueue.filter((p) => p.source_id).slice(0, 5); + const modelOptionsHtml = CREATIVE_MODEL_OPTIONS + .map( + (opt) => `` + ) + .join(""); + const temperature = uiState?.currentCreativeTemperature ?? 0.9; + const topP = uiState?.currentCreativeTopP ?? 0.9; + const systemPrompt = uiState?.creativeSystemPrompt || DEFAULT_RPG_SYSTEM_PROMPT; + + return ` +
+
+ + +
+
+
+ + +
+
+ + +
+
+
+ + +
+
+
+
+

Creative Prompt Generation

+

Generate variations based on Library prompts or create new prompts from scratch

+
+ +
+ +
+ ${ + selectedForInspiration.length === 0 + ? `

Add prompts from Library to use as inspiration

` + : selectedForInspiration + .map( + (p) => ` +
+ ${escapeHtml(p.positive_prompt || "")} +
+ ` + ) + .join("") + } +
+
+ +
+
+ + +
+
+ + +
+
+ + + + +
+ `; + }; + + // === Unified Generate Tab === + const renderGenerateTab = (uiState) => { + const promptQueue = uiState?.promptQueue || []; + const detectedMode = node._linkedAILMode || "txt2img"; + const modelOptionsHtml = CREATIVE_MODEL_OPTIONS + .map( + (opt) => `` + ) + .join(""); + const temperature = uiState?.currentCreativeTemperature ?? 0.9; + const topP = uiState?.currentCreativeTopP ?? 0.9; + + // Character description - generated descriptions populate the character_text_input + const characterText = node.properties.character_text_input || ""; + // Check if character_image is connected AND the source node still exists + const characterImageInput = node.inputs?.find(i => i.name === "character_image"); + let hasCharacterImage = false; + if (characterImageInput && characterImageInput.link != null) { + const link = app.graph.links[characterImageInput.link]; + if (link) { + const sourceNode = app.graph.getNodeById(link.origin_id); + hasCharacterImage = !!sourceNode; // Only true if node still exists + } + } + + return ` +
+ +
+
+ โš™๏ธ Model Settings + Used for all generation +
+
+
+ + +
+
+
+ + +
+
+ + +
+
+
+
+ + +
+
+ + + ${!node.properties.use_character_likeness ? 'โšช Disabled' : characterText.trim() ? 'โœ… Active' : 'โš ๏ธ Empty'} + +
+
+ ${hasCharacterImage ? ` +
+ ๐Ÿ“ธ Character Image Connected - Click "Generate from Image" to create description +
+ ` : ''} +
+ + +
+ ๐Ÿ’ก This description will be included in ALL generated prompts for character consistency +
+
+
+
+ + +
+ ${hasCharacterImage ? ` +
+ + Uses connected character_image and selected model above +
+ ` : ''} +
+ โš™๏ธ Advanced: Edit System Prompt +
+ +
+
+ ๐Ÿ’ก Custom edits override complexity setting +
+ +
+
+
+
+
+
+ + +
+
+ + + ${node.properties.enable_expressions ? 'โœ… Active' : 'โšช Disabled'} + +
+
+
+ +
+ ${EXPRESSION_LIST.map(expr => { + const enabled = JSON.parse(node.properties.enabled_expressions || '[]').includes(expr); + return ` + + `; + }).join('')} +
+
+ + +
+
+
+ + +
+
+ + +
+ 0% = always cycle through enabled expressions | 100% = always use default expression +
+
+
+
+ + + ${node.properties.generation_style !== 'custom' && (detectedMode !== 'img2img' || node.properties.generation_style === 'creative') ? (() => { + const filters = JSON.parse(node.properties.library_filters || "{}"); + const filteredCount = filterPrompts(promptsDatabase, filters).length; + const hasActiveFilters = Object.keys(filters).some(key => { + if (key === 'search_query') return filters[key]?.trim(); + if (key === 'show_bookmarked') return filters[key]; + if (key === 'sdxl_mode') return filters[key]; + return filters[key] !== 'all'; + }); + const inspirationEnabled = node.properties.enable_library_inspiration; + + return ` +
+
+ + + ${inspirationEnabled ? 'โœ… Active' : 'โšช Disabled'} + +
+
+
+
+ Learn from + + ${detectedMode === 'img2img' ? 'prompts for style guidance' : 'prompts per generation'} +
+
+ + Pool: ${filteredCount.toLocaleString()} ${hasActiveFilters ? 'prompts (filtered)' : 'prompts'} + + +
+
+
+
+ `; + })() : ''} + + +
+
+ + ${detectedMode === 'img2img' ? '๐Ÿ–ผ๏ธ IMG2IMG' : '๐ŸŽจ TXT2IMG'} + + Detected from ${node._linkedAILNodeId ? `AIL #${node._linkedAILNodeId}` : 'workflow'} +
+ + +
+ +
+ + + + +
+
+ + + ${node.properties.generation_style === 'custom' ? ` +
+
+ ๐Ÿ“ Custom System Prompt Template + Use variables like {SOURCE_PROMPTS}, {TASK_INSTRUCTIONS} +
+ + + ${(() => { + const customTemplate = node.properties.custom_template; + const warnings = []; + + // Debug logging + console.log("[RPG] Template validation - custom_template:", customTemplate ? `"${customTemplate.substring(0, 50)}..."` : 'undefined'); + console.log("[RPG] Template validation - length:", customTemplate?.length || 0); + console.log("[RPG] Template validation - is default?", customTemplate === DEFAULT_RPG_SYSTEM_PROMPT); + + // Only validate if there's a custom template that's DIFFERENT from default + if (customTemplate && customTemplate.trim().length > 0 && customTemplate !== DEFAULT_RPG_SYSTEM_PROMPT) { + const templateLength = customTemplate.trim().length; + const hasVariables = customTemplate.includes('{') && customTemplate.includes('}'); + + // Only warn if short AND has no variables + if (templateLength < 20 && !hasVariables) { + warnings.push({ + level: 'error', + message: `Template is too short (${templateLength} chars) and has no variables - will use default instead` + }); + } + + // Check for missing recommended variables (either TASK_INSTRUCTIONS or USER_INPUT) + if (!customTemplate.includes('{TASK_INSTRUCTIONS}') && !customTemplate.includes('{USER_INPUT}')) { + warnings.push({ + level: 'warning', + message: 'Template missing {TASK_INSTRUCTIONS} or {USER_INPUT} - user input won\'t be included' + }); + } + } + + if (warnings.length === 0) return ''; + + return warnings.map(w => { + const bgColor = w.level === 'error' ? 'rgba(239, 68, 68, 0.1)' : 'rgba(251, 191, 36, 0.1)'; + const borderColor = w.level === 'error' ? 'rgba(239, 68, 68, 0.3)' : 'rgba(251, 191, 36, 0.3)'; + const textColor = w.level === 'error' ? '#fca5a5' : '#fbbf24'; + const icon = w.level === 'error' ? 'โŒ' : 'โš ๏ธ'; + + return ` +
+
+ ${icon} ${w.message} +
+
+ `; + }).join(''); + })()} +
+
+ + +
+ + +
+ + + + + + + +
+
+ +
+
+
+ โœจ Preview (with current settings) + +
+ 

+										

+									

+									
+									

+										
+											โ„น๏ธ Variable Reference Guide
+										
+										

+											

+												

+													{SOURCE_COUNT}
+													โ†’ Number of source prompts (e.g., "3")
+												

+												

+													{SOURCE_PROMPTS}
+													โ†’ Formatted list of source prompts with positive/negative
+												

+												

+													{GENERATION_MODE}
+													โ†’ Mode description (e.g., "CREATIVE (INSPIRED REMIX)")
+												

+												

+													{MODE_RULES}
+													โ†’ Auto-generated rules (Reality/Creative mode logic)
+												

+												

+													{TASK_TYPE}
+													โ†’ "TXT2IMG GENERATION" or "IMG2IMG TRANSFORMATION"
+												

+												

+													{USER_INPUT}
+													โ†’ Raw user input without any wrapper text
+												

+												

+													{TASK_INSTRUCTIONS}
+													โ†’ Formatted instructions built from user input, character, expression, affect
+												

+												

+													

+														๐Ÿ’ก Tip: Click variable buttons above to insert them at cursor position in the template.
+													

+												

+											

+										

+									

+								

+
+								
+								${detectedMode !== 'img2img' ? (() => {
+									// Use same filtering approach as Reality/Creative modes
+									const filters = JSON.parse(node.properties.library_filters || "{}");
+									const filteredCount = filterPrompts(promptsDatabase, filters).length;
+									const totalCount = promptsDatabase.length;
+									const hasActiveFilters = Object.keys(filters).some(key => {
+										if (key === 'search_query') return filters[key]?.trim();
+										if (key === 'show_bookmarked') return filters[key];
+										if (key === 'sdxl_mode') return filters[key];
+										return filters[key] !== 'all';
+									});
+									const inspirationCount = node.properties.inspiration_count || 3;
+									const inspirationEnabled = node.properties.enable_library_inspiration;
+
+									return `
+										

+											

+												
+													๐Ÿ“š Library Inspiration ${!inspirationEnabled ? '(Disabled)' : `(${inspirationCount} per generation)`}
+												
+												

+													

+														
+														
+															${inspirationEnabled ? 'โœ… Active' : 'โšช Disabled'}
+														
+													

+													

+														${filteredCount === 0 ? `
+															

+																
โš ๏ธ No prompts in library

+																
Load prompts in the Library tab or {SOURCE_PROMPTS} will be empty

+															

+														` : `
+															

+																

+																	Learn from
+																	
+																	prompts per generation
+																

+																

+																	
+																		Pool: ${filteredCount.toLocaleString()} ${hasActiveFilters ? 'prompts (filtered)' : 'prompts'}
+																	
+																	
+																

+															

+														`}
+													

+													${!inspirationEnabled ? `
+														

+															

+																๐Ÿ’ก Inspiration disabled: Variables {SOURCE_COUNT} and {SOURCE_PROMPTS} will be empty/zero in your template.
+															

+														

+													` : ''}
+												

+											

+										

+									`;
+								})() : ''}
+
+								
+								${detectedMode === 'img2img' ? (() => {
+									const linkedImages = node._linkedImages || [];
+									const imageCount = linkedImages.length;
+									if (imageCount === 0) {
+										return `
+											

+												
โš ๏ธ No images from AIL

+												
Connect an Advanced Image Loader node in img2img mode

+											

+										`;
+									}
+									return `
+										

+											

+												๐Ÿ“ธ Images from AIL #${node._linkedAILNodeId}: ${imageCount} image${imageCount !== 1 ? 's' : ''}
+											

+										

+									`;
+								})() : ''}
+
+								
+								${detectedMode !== 'img2img' ? `
+									

+										

+											
+											
+										

+									

+								` : ''}
+							` : ''}
+
+							
+							
+							
+							${detectedMode !== 'img2img' ? `
+								
+								

+									
+									
+									

+										๐Ÿ’ก Themes add aesthetic style (INSTARAW = black light diamond drips)
+									

+								

+
+								
+								

+									
+									

+										
+									

+									
+									

+										๐Ÿ’ก Model-specific prompt formatting (mostly useful for img2img models)
+									

+								

+
+								
+								

+									
+									

+										๐Ÿ’ก Prevents AI from adding grain, noise, lens flares, borders, etc. Add these in post if needed.
+									

+								

+
+								
+								

+									
+									
+									

+										๐Ÿ’ก Your instructions override theme and model settings
+									

+								

+							` : ''}
+
+							
+							${node.properties.generation_style !== 'custom' ? `
+							
+							${detectedMode === 'img2img' ? `
+								
+								${(() => {
+									const images1 = node._linkedImages || [];
+									const images2 = node._linkedImages2 || [];
+									const images3 = node._linkedImages3 || [];
+									const images4 = node._linkedImages4 || [];
+
+									if (images1.length === 0) {
+										return `
+											

+												
โš ๏ธ No images from AIL

+												
Connect an Advanced Image Loader node in img2img mode

+											

+										`;
+									}
+
+									// Build inputs array for multi-image mode
+									const inputsWithImages = [
+										{ images: images1, label: "Image 1" },
+										{ images: images2, label: "Image 2" },
+										{ images: images3, label: "Image 3" },
+										{ images: images4, label: "Image 4" }
+									].filter(input => input.images.length > 0);
+
+									const multiImageMode = inputsWithImages.length > 1;
+									const generationCount = images1.length;
+
+									if (multiImageMode) {
+										// Multi-image mode: show combo cards
+										const comboCardsHtml = renderComboCards(inputsWithImages, {
+											// Handle both url (from renderImg2ImgGallery events) and thumbnail (from raw batchData events)
+											getImageUrl: (img) => img?.url || (img?.thumbnail ? `/instaraw/view/${img.thumbnail}` : null)
+										});
+
+										return `
+											

+												

+													

+														${inputsWithImages.length} AIL inputs connected
+													

+													${generationCount} combinations โ†’ ${generationCount} prompts
+												

+												

+													${comboCardsHtml}
+												

+												

+													โ„น๏ธ Each row of images is sent together to the AI for prompt generation.
+												

+											

+										`;
+									}
+
+									// Single image mode: show grid
+									return `
+										

+											

+												Images from AIL #${node._linkedAILNodeId || '?'}
+												${generationCount} unique ${generationCount === 1 ? 'image' : 'images'} โ†’ ${generationCount} ${generationCount === 1 ? 'prompt' : 'prompts'}
+											

+											

+												${images1.map((img, idx) => {
+													// Handle both url (from renderImg2ImgGallery events) and thumbnail (from raw batchData events)
+													const imgUrl = img.url || (img.thumbnail ? `/instaraw/view/${img.thumbnail}` : '');
+													return `
+													

+														
+														

+															#${idx + 1}${img.repeat_count && img.repeat_count > 1 ? ` ร—${img.repeat_count}` : ''}
+														

+													

+												`}).join('')}
+											

+											

+												โ„น๏ธ Generates 1 prompt per unique image. Repeat counts preserved when adding to batch.
+											

+										

+									`;
+								})()}
+
+								
+								
+								
+
+								
+								

+									

+										
+										
+										

+											๐Ÿ’ก Themes add aesthetic style (INSTARAW = black light diamond drips)
+										

+									

+								

+
+								
+								

+									

+										
+										

+											
+										

+										
+										

+											๐Ÿ’ก Model-specific prompt formatting (NBP uses "reimagined" structure)
+										

+									

+								

+
+								
+								

+									

+										
+										

+											๐Ÿ’ก Prevents AI from adding grain, noise, lens flares, borders, etc. Add these in post if needed.
+										

+									

+								

+
+								
+								

+									

+										
+										
+										

+											๐Ÿ’ก Your instructions override theme and model settings
+										

+									

+								

+
+								
+								${node.properties.generation_style === 'creative' ? `
+									

+										
+										

+											
+											
+											
+											
+										

+									

+								` : ''}
+							` : ''}
+						

+
+						
+						${detectedMode !== 'img2img' ? `
+							

+								

+									
+									
+								

+							

+						` : ''}
+
+						
+						

+							
+							

+								โš™๏ธ Advanced: Edit System Prompt
+								

+									
+									

+										
+									

+									
+									
+
+									
+									

+										

+											

+										

+									

+
+									

+										

+											๐Ÿ’ก Controls how prompts are generated (${detectedMode === 'img2img' ? 'img2img' : 'txt2img'} ${node.properties.generation_style || 'reality'})
+										

+										
+									

+								

+							

+						

+
+					

+					` : ''}
+					
+
+					
+					
+
+					
+					

+						

+							
Generating Prompts...

+							
+						

+						
+						
+						

+					

+
+					
+					

+						
+						

+						
+						

+							
+							
+						

+					

+				`;
+			};
+
+			// === Custom Tab ===
+			const renderCustomTab = (uiState) => {
+				const customTemplate = node.properties.custom_template || DEFAULT_RPG_SYSTEM_PROMPT;
+				const detectedMode = node._linkedAILMode || "txt2img";
+
+				return `
+					

+						
+						

+							

+								๐Ÿ“ Custom System Prompt Template
+								Use variables like {SOURCE_PROMPTS}, {CHARACTER}, {EXPRESSION}
+							

+							
+							

+								
+								
+							

+						

+
+						
+						

+							

+								๐Ÿ”ง Available Variables
+								Click to insert at cursor position
+							

+							

+								${AVAILABLE_VARIABLES.map(v => `
+									

+										
+										
${v.description}

+									

+								`).join('')}
+							

+						

+
+						
+						

+							

+								
+									${detectedMode === 'img2img' ? '๐Ÿ–ผ๏ธ IMG2IMG' : '๐ŸŽจ TXT2IMG'}
+								
+								Detected from ${node._linkedAILNodeId ? `AIL #${node._linkedAILNodeId}` : 'workflow'}
+							

+
+							
+							${detectedMode !== 'img2img' ? `
+								

+									
+									
+								

+								

+									
+									
+								

+							` : ''}
+						

+
+						
+						
+					

+				`;
+			};
+
+				// === Character Tab (LEGACY - Will be removed) ===
+				const renderCharacterTab = () => {
+					return `
+						

+							

+								
Character-Consistent Generation

+								
Generate prompts with character reference for consistent results

+							

+
+							

+								
+								
+							

+
+							

+								

+									
+									
+								

+							

+
+							
+
+							
+						

+					`;
+				};
+
+				// === Batch Panel (AIL Item Style) ===
+				const renderBatchPanel = (promptQueue, totalGenerations) => {
+					const sdxlMode = sdxlModeEnabled;
+
+					// Get linked images/latents for thumbnails
+					const detectedMode = node._linkedAILMode || "img2img";
+					const linkedImages = node._linkedImages || [];
+					const linkedLatents = node._linkedLatents || [];
+					const hasAILLink = node._linkedAILNodeId !== null;
+
+					const gridContent =
+						promptQueue.length === 0
+							? `
No prompts in batch
Add prompts from Library or Creative mode
`
+							: promptQueue
+									.map(
+										(entry, idx) => {
+											const sourceType = entry.source_id ? 'from-library' : 'from-ai';
+											const sourceBadgeText = entry.source_id ? '๐Ÿ“š Library' : 'โœจ AI Generated';
+
+											// Get linked thumbnail for this index
+											const linkedItem = detectedMode === "img2img" ? linkedImages[idx] : linkedLatents[idx];
+											const hasThumbnail = linkedItem !== undefined;
+
+											let thumbnailHtml = '';
+											if (hasThumbnail) {
+												// Both modes: Show aspect ratio box with content inside
+												const targetDims = getTargetDimensions();
+												const targetAspectRatio = targetDims.width / targetDims.height;
+
+												if (detectedMode === "img2img") {
+													// IMG2IMG: Show image grid in aspect ratio box
+													// Collect images from all connected AIL inputs for this index
+													const img1 = linkedImages[idx];
+													const img2 = (node._linkedImages2 || [])[idx];
+													const img3 = (node._linkedImages3 || [])[idx];
+													const img4 = (node._linkedImages4 || [])[idx];
+													const allImgs = [img1, img2, img3, img4].filter(Boolean);
+													const imgCount = allImgs.length;
+
+													// Generate grid layout based on image count
+													// Helper to create img with error fallback
+													const imgWithFallback = (url) => ``;
+													let gridHtml = '';
+													if (imgCount <= 1) {
+														gridHtml = imgWithFallback(allImgs[0]?.url || linkedItem.url);
+													} else if (imgCount === 2) {
+														gridHtml = `
${imgWithFallback(allImgs[0].url)}${imgWithFallback(allImgs[1].url)}
`;
+													} else if (imgCount === 3) {
+														gridHtml = `
${imgWithFallback(allImgs[0].url)}${imgWithFallback(allImgs[1].url)}${imgWithFallback(allImgs[2].url)}
`;
+													} else {
+														gridHtml = `
${imgWithFallback(allImgs[0].url)}${imgWithFallback(allImgs[1].url)}${imgWithFallback(allImgs[2].url)}${imgWithFallback(allImgs[3].url)}
`;
+													}
+
+													thumbnailHtml = `
+														

+															#${idx + 1}
+															

+																${gridHtml}
+																

+																	
${targetDims.aspect_label}${imgCount > 1 ? ` ยท ${imgCount}` : ''}

+																

+															

+														

+													`;
+												} else {
+													// TXT2IMG: Show empty latent with aspect ratio box
+													thumbnailHtml = `
+														

+															#${idx + 1}
+															

+																

+																	
๐Ÿ“

+																	
${targetDims.aspect_label}

+																

+															

+														

+													`;
+												}
+											} else {
+												// Show placeholder
+												const promptQueueTemp = parsePromptBatch();
+												const totalMissing = promptQueueTemp.length - (detectedMode === "img2img" ? linkedImages.length : linkedLatents.length);
+												thumbnailHtml = `
+													

+														#${idx + 1}
+														

+															
โš ๏ธ

+															

+																${hasAILLink ? 'Missing Link' : 'No AIL'}
+															

+															${hasAILLink && detectedMode === "txt2img" ? `
+																

+																	Click "Sync AIL"
+																

+															` : hasAILLink && detectedMode === "img2img" && totalMissing > 0 ? `
+																

+																	Upload ${totalMissing} more image${totalMissing !== 1 ? 's' : ''} to AIL
+																

+															` : ''}
+														

+													

+												`;
+											}
+
+											// Get repeat count comparison
+											const promptRepeat = entry.repeat_count || 1;
+											const ailRepeat = linkedItem ? (linkedItem.repeat_count || 1) : null;
+											const repeatMismatch = ailRepeat !== null && promptRepeat !== ailRepeat;
+
+											return `
+							

+								

+									

+										#${idx + 1}
+										${sourceBadgeText}
+										${ailRepeat !== null ? `
+											
+												${repeatMismatch ? 'โš ๏ธ ' : 'โœ“ '}Prompt: ร—${promptRepeat} | AIL: ร—${ailRepeat}
+											
+										` : ''}
+									

+									

+										
+										
+										
+									

+								

+
+								
+								
+								${thumbnailHtml}
+
+								

+									${sdxlMode && entry.tags && entry.tags.length > 0 ? `
+										
+										
+										
+
+										
+										
+									` : `
+										
+										
+										
+
+										
+										
+									`}
+
+									${entry.tags && entry.tags.length > 0 && !sdxlMode
+										? `
+										

+											${entry.tags
+												.slice(0, 3)
+												.map((tag) => `${tag}`)
+												.join("")}
+											${entry.tags.length > 3 ? `+${entry.tags.length - 3}` : ""}
+										

+									`
+										: ""
+									}
+
+									
+									

+										
+										

+											๐ŸŽฒ Seed:
+											
+											
+											
+											${entry.repeat_count > 1 ? `` : ''}
+										

+										
+										

+											
+											
+										

+									

+								

+							

+						`;
+										}
+									)
+									.join("");
+
+					const libraryCount = promptQueue.filter(p => p.source_id).length;
+					const aiCount = promptQueue.filter(p => !p.source_id).length;
+
+					// Check for repeat count mismatches
+					const hasRepeatMismatch = promptQueue.some((p, idx) => {
+						const linkedItem = detectedMode === "img2img" ? linkedImages[idx] : linkedLatents[idx];
+						return linkedItem && (p.repeat_count || 1) !== (linkedItem.repeat_count || 1);
+					});
+
+					// Show smart sync button if: AIL linked + has prompts
+					// Will handle both latent creation (txt2img) and repeat syncing (both modes)
+					const showSyncButton = hasAILLink && promptQueue.length > 0;
+					const needsLatentSync = detectedMode === "txt2img" && linkedLatents.length !== promptQueue.length;
+					const needsRepeatSync = hasRepeatMismatch;
+
+					return `
+						

+							

+								

+									
Generation Batch

+									

+										${promptQueue.length > 0 ? `๐Ÿ“š ${libraryCount} from Library  |  โœจ ${aiCount} AI Generated` : 'Add prompts from library or generate with AI'}
+									

+								

+								

+									${showSyncButton ? `
+										
+									` : ''}
+									
+									${totalGenerations} generation${totalGenerations !== 1 ? "s" : ""}
+									${promptQueue.length > 0 ? `` : ""}
+								

+							

+							${promptQueue.length > 0 ? `
+							

+								Bulk Edit
+								

+									All After Gen:
+									
+								

+								

+								

+									All Seeds:
+									
+									
+								

+							

+							` : ''}
+							

+								${gridContent}
+							

+						

+					`;
+				};
+
+				// === Image Preview (AIL Sync) ===
+				const renderImagePreview = (resolvedMode, totalGenerations) => {
+					// Check all 4 image inputs for connections
+					const imageInputNames = ["images", "images2", "images3", "images4"];
+					const connectedInputs = [];
+
+					for (const inputName of imageInputNames) {
+						const input = node.inputs?.find(i => i.name === inputName);
+						if (input?.link != null) {
+							connectedInputs.push(inputName);
+						}
+					}
+
+					const hasAILConnection = connectedInputs.length > 0;
+
+					// If no AIL connection at all
+					if (!hasAILConnection && !node._linkedAILNodeId) {
+						return `
+							

+								

+									
No AIL connected

+									
Connect an Advanced Image Loader to sync mode

+								

+							

+						`;
+					}
+
+					const itemCount = node._linkedImageCount || 0;
+					const detectedMode = node._linkedAILMode || resolvedMode || "txt2img";
+					const images1 = node._linkedImages || [];
+					const images2 = node._linkedImages2 || [];
+					const images3 = node._linkedImages3 || [];
+					const images4 = node._linkedImages4 || [];
+					const latents = node._linkedLatents || [];
+					const isImg2Img = detectedMode === "img2img";
+					const itemLabel = isImg2Img ? "images" : "latents";
+
+					// Count connected inputs with images
+					const inputsWithImages = [
+						{ name: "images", images: images1, label: "Image 1" },
+						{ name: "images2", images: images2, label: "Image 2" },
+						{ name: "images3", images: images3, label: "Image 3" },
+						{ name: "images4", images: images4, label: "Image 4" }
+					].filter(input => connectedInputs.includes(input.name) && input.images.length > 0);
+
+					const multiImageMode = inputsWithImages.length > 1;
+
+					// Show empty state if AIL is connected but has no images/latents loaded yet
+					if (hasAILConnection && images1.length === 0 && itemCount === 0 && !isImg2Img && latents.length === 0) {
+						return `
+							

+								

+									${isImg2Img ? '๐Ÿ–ผ๏ธ IMG2IMG' : '๐Ÿ“ TXT2IMG'} (AIL Node #${node._linkedAILNodeId || 'connected'})
+								

+								

+									
No ${itemLabel} detected

+									
${isImg2Img ? 'Load images in the AIL node' : 'AIL will create latents when you sync'}

+								

+							

+						`;
+					}
+
+					const isMatch = totalGenerations === itemCount;
+					const matchClass = isMatch ? "match" : "mismatch";
+					const matchIcon = isMatch ? "โœ…" : "โš ๏ธ";
+
+					// Multi-image mode: Show combination cards
+					if (multiImageMode && isImg2Img) {
+						const maxLen = getComboMaxLength(inputsWithImages);
+						const comboCardsHtml = renderComboCards(inputsWithImages, {
+							// Handle both url (from renderImg2ImgGallery events) and thumbnail (from raw batchData events)
+							getImageUrl: (img) => img?.url || (img?.thumbnail ? `/instaraw/view/${img.thumbnail}` : null)
+						});
+
+						return `
+							

+								

+									

+										
+										${inputsWithImages.length} AIL inputs
+										connected
+									

+									
+										${matchIcon} ${totalGenerations} prompts โ†” ${maxLen} combinations
+									
+								

+								

+									${comboCardsHtml}
+								

+							

+						`;
+					}
+
+					// Single input mode: Simple grid (original behavior)
+					const items = isImg2Img ? images1 : latents;
+					const displayItems = items;
+
+					return `
+						

+							

+								${isImg2Img ? '๐Ÿ–ผ๏ธ IMG2IMG Input Images' : '๐Ÿ“ TXT2IMG Empty Latents'} (AIL Node #${node._linkedAILNodeId || 'unknown'})
+								
+									${matchIcon} ${totalGenerations} prompts โ†” ${itemCount} ${itemLabel}
+								
+							

+							

+								${displayItems.length > 0 ? displayItems
+									.map((item, idx) => {
+										// Both modes use same structure with target dimensions
+										const targetDims = getTargetDimensions();
+										const targetAspectRatio = targetDims.width / targetDims.height;
+
+										if (isImg2Img) {
+											// IMG2IMG: Show image in aspect ratio box with label overlay
+											return `
+												

+													#${idx + 1}
+													

+														Preview ${idx + 1}
+														

+															
${targetDims.aspect_label}

+														

+													

+													${item.repeat_count && item.repeat_count > 1 ? `ร—${item.repeat_count}` : ''}
+												

+											`;
+										} else {
+											// TXT2IMG: Show empty latent with emoji and aspect ratio below
+											return `
+												

+													#${idx + 1}
+													

+														

+															
๐Ÿ“

+															
${targetDims.aspect_label}

+														

+													

+													${item.repeat_count && item.repeat_count > 1 ? `ร—${item.repeat_count}` : ''}
+												

+											`;
+										}
+									})
+									.join("") : `
+										

+											
Waiting for ${itemLabel} data...

+										

+									`}
+							

+						

+					`;
+				};
+
+				// === Filter Prompts ===
+				const filterPrompts = (database, filters) => {
+					if (!database) return [];
+
+					let filtered = database;
+
+					// Search query with match tracking
+					if (filters.search_query && filters.search_query.trim() !== "") {
+						const query = filters.search_query.toLowerCase();
+						filtered = filtered.filter((p) => {
+							const positive = (p.prompt?.positive || "").toLowerCase();
+							const tags = (p.tags || []).join(" ").toLowerCase();
+							const id = (p.id || "").toLowerCase();
+							const matchInPrompt = positive.includes(query);
+							const matchInTags = tags.includes(query);
+							const matchInId = id.includes(query);
+							// Store match type for highlighting
+							p._matchType = matchInPrompt ? (matchInTags ? 'both' : 'prompt') : (matchInTags ? 'tags' : (matchInId ? 'id' : null));
+							return matchInPrompt || matchInTags || matchInId;
+						});
+					}
+
+					// Content type
+					if (filters.content_type && filters.content_type !== "any") {
+						filtered = filtered.filter((p) => p.classification?.content_type === filters.content_type);
+					}
+
+					// Safety level
+					if (filters.safety_level && filters.safety_level !== "any") {
+						if (filters.safety_level === "suggestive_nsfw") {
+							// Combined filter: match either suggestive or nsfw
+							filtered = filtered.filter((p) =>
+								p.classification?.safety_level === "suggestive" ||
+								p.classification?.safety_level === "nsfw"
+							);
+						} else {
+							filtered = filtered.filter((p) => p.classification?.safety_level === filters.safety_level);
+						}
+					}
+
+					// Shot type
+					if (filters.shot_type && filters.shot_type !== "any") {
+						filtered = filtered.filter((p) => p.classification?.shot_type === filters.shot_type);
+					}
+
+					// Prompt source (user vs library)
+					if (filters.prompt_source && filters.prompt_source !== "all") {
+						if (filters.prompt_source === "user") {
+							const beforeCount = filtered.length;
+							const userInDb = filtered.filter((p) => p.is_user_created === true);
+							console.log(`[RPG] Filter user prompts: ${userInDb.length} of ${beforeCount} in promptsDatabase, userPrompts array has ${userPrompts.length}`);
+							filtered = userInDb;
+						} else if (filters.prompt_source === "generated") {
+							filtered = filtered.filter((p) => p.is_ai_generated === true);
+						} else if (filters.prompt_source === "library") {
+							filtered = filtered.filter((p) => !p.is_user_created && !p.is_ai_generated);
+						}
+					}
+
+					// Bookmarked only
+					if (filters.show_bookmarked) {
+						filtered = filtered.filter((p) => bookmarksCache.includes(p.id));
+					}
+
+					return filtered;
+				};
+
+				// === Helper Functions ===
+				const escapeHtml = (text) => {
+					if (!text) return "";
+					return text
+						.replace(/&/g, "&")
+						.replace(//g, ">")
+						.replace(/"/g, """)
+						.replace(/'/g, "'");
+				};
+
+				// === Preview Modal ===
+				const showPreviewModal = (systemPrompt, context = {}) => {
+					// Create modal overlay
+					const modalOverlay = document.createElement('div');
+					modalOverlay.className = 'instaraw-rpg-preview-modal-overlay';
+
+					const modalContent = `
+						

+							

+								
๐Ÿ“‹ System Prompt Preview

+								
+							

+							

+								

+									
Current Settings:

+									

+										${context.mode ? `
Mode: ${context.mode}
` : ''}
+										${context.style ? `
Style: ${context.style}
` : ''}
+										${context.character ? `
Character: ${context.character.substring(0, 100)}${context.character.length > 100 ? '...' : ''}
` : ''}
+										${context.expression ? `
Expression: ${context.expression}
` : ''}
+										${context.userInput ? `
User Input: ${context.userInput}
` : ''}
+										${context.inspirationCount ? `
Inspiration Count: ${context.inspirationCount}
` : ''}
+									

+								

+								

+									
Final System Prompt:

+									

+										
${escapeHtml(systemPrompt)}

+									

+								

+							

+							

+								
+								
+							

+						

+					`;
+
+					modalOverlay.innerHTML = modalContent;
+					document.body.appendChild(modalOverlay);
+
+					// Close button handlers
+					modalOverlay.querySelectorAll('.instaraw-rpg-preview-modal-close').forEach(btn => {
+						btn.onclick = () => {
+							document.body.removeChild(modalOverlay);
+						};
+					});
+
+					// Copy button handler
+					const copyBtn = modalOverlay.querySelector('.instaraw-rpg-preview-copy-btn');
+					if (copyBtn) {
+						copyBtn.onclick = () => {
+							navigator.clipboard.writeText(systemPrompt).then(() => {
+								const originalText = copyBtn.textContent;
+								copyBtn.textContent = 'โœ“ Copied!';
+								setTimeout(() => {
+									copyBtn.textContent = originalText;
+								}, 2000);
+							}).catch(err => {
+								console.error('[RPG] Failed to copy:', err);
+								alert('Failed to copy to clipboard');
+							});
+						};
+					}
+
+					// Close on overlay click
+					modalOverlay.onclick = (e) => {
+						if (e.target === modalOverlay) {
+							document.body.removeChild(modalOverlay);
+						}
+					};
+				};
+
+				const highlightSearchTerm = (text, searchQuery) => {
+					if (!text || !searchQuery) return escapeHtml(text);
+					const regex = new RegExp(`(${searchQuery.replace(/[.*+?^${}()|[\]\\]/g, '\$&')})`, 'gi');
+					return escapeHtml(text).replace(regex, '$1');
+				};
+
+				const generateUniqueId = () => {
+					// Generate ULID (Universally Unique Lexicographically Sortable Identifier)
+					// 26 characters: 10 char timestamp + 16 char randomness
+					const ENCODING = "0123456789ABCDEFGHJKMNPQRSTVWXYZ"; // Crockford's Base32
+					const ENCODING_LEN = ENCODING.length;
+					const TIME_LEN = 10;
+					const RANDOM_LEN = 16;
+
+					const encodeTime = (now, len) => {
+						let str = "";
+						for (let i = len; i > 0; i--) {
+							const mod = now % ENCODING_LEN;
+							str = ENCODING.charAt(mod) + str;
+							now = (now - mod) / ENCODING_LEN;
+						}
+						return str;
+					};
+
+					const encodeRandom = (len) => {
+						let str = "";
+						for (let i = 0; i < len; i++) {
+							str += ENCODING.charAt(Math.floor(Math.random() * ENCODING_LEN));
+						}
+						return str;
+					};
+
+					const now = Date.now();
+					return encodeTime(now, TIME_LEN) + encodeRandom(RANDOM_LEN);
+				};
+
+				// === Add Prompt to Batch ===
+					const addPromptToBatch = (promptData) => {
+						const promptQueue = parsePromptBatch();
+
+						// Handle both database formats:
+						// 1. { prompt: { positive: "...", negative: "..." } } - generated prompts
+						// 2. { positive: "...", negative: "..." } - library prompts
+						const positivePrompt = promptData.prompt?.positive ?? promptData.positive ?? "";
+						const negativePrompt = promptData.prompt?.negative ?? promptData.negative ?? "";
+
+						const newEntry = {
+							id: generateUniqueId(),
+							positive_prompt: positivePrompt,
+							negative_prompt: negativePrompt,
+						repeat_count: 1,
+						tags: promptData.tags || [],
+						source_id: promptData.id || null,
+						seed: 1111111,  // Default seed
+						seed_control: "randomize",  // Default control mode
+					};
+
+					promptQueue.push(newEntry);
+					setPromptBatchData(promptQueue);
+					renderUI();
+					};
+
+					// === Update Prompt in Batch ===
+					const updatePromptInBatch = (id, field, value) => {
+						const promptQueue = parsePromptBatch();
+						const entry = promptQueue.find((p) => p.id === id);
+						if (entry) {
+							entry[field] = value;
+							node.properties.prompt_batch_data = JSON.stringify(promptQueue);
+							syncPromptBatchWidget();
+							// Dispatch event for BIG and other listeners
+							window.dispatchEvent(new CustomEvent("INSTARAW_RPG_PROMPTS_CHANGED", {
+								detail: {
+									nodeId: node.id,
+									prompts: promptQueue,
+									totalGenerations: promptQueue.reduce((sum, p) => sum + (p.repeat_count || 1), 0)
+								}
+							}));
+							// Don't re-render for text edits to avoid losing focus
+							if (field !== "positive_prompt" && field !== "negative_prompt") {
+								renderUI();
+							}
+						}
+					};
+
+					// === Bulk Update All Prompts ===
+					const updateAllPromptsField = (field, value) => {
+						const promptQueue = parsePromptBatch();
+						if (promptQueue.length === 0) return;
+						promptQueue.forEach(entry => {
+							entry[field] = value;
+						});
+						node.properties.prompt_batch_data = JSON.stringify(promptQueue);
+						syncPromptBatchWidget();
+						renderUI();
+					};
+
+					// === Delete Prompt from Batch ===
+					const deletePromptFromBatch = (id, alsoDeleteFromAIL = true) => {
+						const promptQueue = parsePromptBatch();
+						const deletedIndex = promptQueue.findIndex((p) => p.id === id);
+						const filtered = promptQueue.filter((p) => p.id !== id);
+						setPromptBatchData(filtered);
+
+						// Also remove from AIL if in img2img mode and linked
+						if (alsoDeleteFromAIL && node._linkedAILNodeId && deletedIndex >= 0) {
+							const primaryAIL = app.graph.getNodeById(node._linkedAILNodeId);
+							if (primaryAIL) {
+								try {
+									const ailData = JSON.parse(primaryAIL.properties?.batch_data || "{}");
+									// Only auto-delete from AIL in img2img mode
+									if (ailData.enable_img2img) {
+										const ailImages = ailData.images || [];
+										const ailOrder = ailData.order || [];
+
+										if (deletedIndex < ailOrder.length) {
+											// Remove the image at the same index
+											const removedId = ailOrder[deletedIndex];
+											ailData.order = ailOrder.filter((_, idx) => idx !== deletedIndex);
+											ailData.images = ailImages.filter(img => img.id !== removedId);
+											ailData.total_count = ailData.images.reduce((sum, img) => sum + (img.repeat_count || 1), 0);
+
+											primaryAIL.properties.batch_data = JSON.stringify(ailData);
+
+											// Trigger AIL re-render
+											if (primaryAIL._renderGallery) {
+												primaryAIL._renderGallery();
+											}
+
+											// Dispatch update event
+											window.dispatchEvent(new CustomEvent("INSTARAW_AIL_UPDATED", {
+												detail: {
+													nodeId: primaryAIL.id,
+													images: ailData.images,
+													latents: [],
+													total: ailData.order.length,
+													mode: "img2img",
+													enable_img2img: true
+												}
+											}));
+
+											console.log(`[RPG] Deleted prompt at index ${deletedIndex}, also removed AIL image ${removedId}`);
+										}
+									}
+								} catch (e) {
+									console.warn("[RPG] Could not sync delete to AIL:", e);
+								}
+							}
+						}
+
+						renderUI();
+					};
+
+				// === Clear Batch ===
+				const clearBatch = () => {
+					// Check if we're in img2img mode with linked AIL
+					let hasAILImages = false;
+					if (node._linkedAILNodeId) {
+						const primaryAIL = app.graph.getNodeById(node._linkedAILNodeId);
+						if (primaryAIL) {
+							try {
+								const ailData = JSON.parse(primaryAIL.properties?.batch_data || "{}");
+								if (ailData.enable_img2img && (ailData.images || []).length > 0) {
+									hasAILImages = true;
+								}
+							} catch (e) {}
+						}
+					}
+
+					const confirmMsg = hasAILImages
+						? "Clear all prompts from batch?\n\nThis will also clear images from the linked AIL."
+						: "Clear all prompts from batch?";
+
+					if (!confirm(confirmMsg)) return;
+					setPromptBatchData([]);
+
+					// Also clear AIL images if in img2img mode
+					if (hasAILImages && node._linkedAILNodeId) {
+						const primaryAIL = app.graph.getNodeById(node._linkedAILNodeId);
+						if (primaryAIL) {
+							try {
+								const ailData = JSON.parse(primaryAIL.properties?.batch_data || "{}");
+								ailData.images = [];
+								ailData.order = [];
+								ailData.total_count = 0;
+								primaryAIL.properties.batch_data = JSON.stringify(ailData);
+
+								if (primaryAIL._renderGallery) {
+									primaryAIL._renderGallery();
+								}
+
+								window.dispatchEvent(new CustomEvent("INSTARAW_AIL_UPDATED", {
+									detail: {
+										nodeId: primaryAIL.id,
+										images: [],
+										latents: [],
+										total: 0,
+										mode: "img2img",
+										enable_img2img: true
+									}
+								}));
+
+								console.log("[RPG] Cleared batch and AIL images");
+							} catch (e) {
+								console.warn("[RPG] Could not clear AIL:", e);
+							}
+						}
+					}
+
+					renderUI();
+				};
+
+				// === Update Seed Range Display ===
+				const updateSeedRangeDisplay = (id) => {
+					const promptQueue = parsePromptBatch();
+					const entry = promptQueue.find((p) => p.id === id);
+					if (!entry) return;
+
+					const rangeSpan = container.querySelector(`.instaraw-rpg-seed-range[data-id="${id}"]`);
+					if (!rangeSpan || entry.repeat_count <= 1) return;
+
+					const seed = entry.seed || 1111111;
+					const repeatCount = entry.repeat_count;
+
+					// Repeats always increment by +1 (seed_control is for after execution)
+					const endSeed = seed + (repeatCount - 1);
+					rangeSpan.textContent = `โ†’ ${endSeed}`;
+				};
+
+				// === Toggle Bookmark ===
+				const toggleBookmark = async (promptId) => {
+					await toggleBookmarkById(promptId);
+					renderUI();
+				};
+
+				// === Update Filters ===
+				const updateFilter = (filterName, value) => {
+					const filters = JSON.parse(node.properties.library_filters || "{}");
+					filters[filterName] = value;
+					node.properties.library_filters = JSON.stringify(filters);
+					currentPage = 0; // Reset to first page
+					renderUI();
+				};
+
+				// === Clear Filters ===
+				const clearFilters = () => {
+					node.properties.library_filters = JSON.stringify({
+						tags: [],
+						content_type: "any",
+						safety_level: "any",
+						shot_type: "any",
+						quality: "any",
+						search_query: "",
+						show_bookmarked: false,
+						sdxl_mode: false,
+					});
+					currentPage = 0;
+					renderUI();
+				};
+
+				// Debug helper - expose for testing in browser console
+				node.debugGetInput = (inputName) => {
+					return getFinalInputValue(inputName, "NOT_FOUND");
+				};
+
+				// Comprehensive graph dump for debugging
+				node.debugDumpGraph = () => {
+					console.group("[RPG] ๐Ÿ“Š Complete Graph Dump");
+					console.log("=== Current Node ===");
+					console.log("Node ID:", node.id);
+					console.log("Node Type:", node.type);
+					console.log("Node Title:", node.title);
+					console.log("Node Inputs:", node.inputs);
+					console.log("Node Widgets:", node.widgets);
+					console.log("Node Properties:", node.properties);
+
+					console.log("\n=== All Nodes in Graph ===");
+					console.table(app.graph._nodes.map(n => ({
+						id: n.id,
+						type: n.type,
+						title: n.title,
+						inputs: n.inputs?.length || 0,
+						outputs: n.outputs?.length || 0,
+						widgets: n.widgets?.length || 0
+					})));
+
+					console.log("\n=== All Links in Graph ===");
+					const links = app.graph.links || {};
+					console.table(Object.values(links).map(l => ({
+						id: l.id,
+						origin_id: l.origin_id,
+						origin_slot: l.origin_slot,
+						target_id: l.target_id,
+						target_slot: l.target_slot,
+						type: l.type
+					})));
+
+					console.log("\n=== Links Connected to This Node ===");
+					const connectedLinks = Object.values(links).filter(l =>
+						l.target_id === node.id || l.origin_id === node.id
+					);
+					console.log("Connected links:", connectedLinks);
+
+					console.log("\n=== Nodes Connected to This Node ===");
+					const connectedNodeIds = new Set();
+					connectedLinks.forEach(l => {
+						connectedNodeIds.add(l.origin_id);
+						connectedNodeIds.add(l.target_id);
+					});
+					const connectedNodes = app.graph._nodes.filter(n => connectedNodeIds.has(n.id));
+					connectedNodes.forEach(n => {
+						console.group(`Node ${n.id}: ${n.type} (${n.title})`);
+						console.log("Widgets:", n.widgets);
+						console.log("Properties:", n.properties);
+						console.groupEnd();
+					});
+
+					console.groupEnd();
+					return {
+						node: {
+							id: node.id,
+							type: node.type,
+							inputs: node.inputs,
+							widgets: node.widgets
+						},
+						connectedNodes,
+						connectedLinks
+					};
+				};
+
+				// === Generate Creative Prompts ===
+				const generateCreativePrompts = async () => {
+					console.group("[RPG] ๐ŸŽจ Generate Creative Prompts - START");
+					console.log("[RPG] Timestamp:", new Date().toISOString());
+
+					const genCountInput = container.querySelector(".instaraw-rpg-gen-count-input");
+					const inspirationCountInput = container.querySelector(".instaraw-rpg-inspiration-count-input");
+					const generateBtn = container.querySelector(".instaraw-rpg-generate-creative-btn");
+
+					const generationCount = parseInt(genCountInput?.value || "5");
+					// Respect library inspiration toggle
+					const inspirationCount = node.properties.enable_library_inspiration
+						? parseInt(inspirationCountInput?.value || "3")
+						: 0;
+					const isSDXL = sdxlModeEnabled; // Use global SDXL mode
+					const forceRegenerate = true; // Always regenerate
+
+					const promptQueue = parsePromptBatch();
+					const sourcePrompts = promptQueue.filter((p) => p.source_id).slice(0, inspirationCount);
+
+					const model = node.properties.creative_model || "gemini-2.5-pro";
+					const systemPrompt = node.properties.creative_system_prompt || DEFAULT_RPG_SYSTEM_PROMPT;
+					const temperatureValue = parseFloat(node.properties.creative_temperature ?? 0.9) || 0.9;
+					const topPValue = parseFloat(node.properties.creative_top_p ?? 0.9) || 0.9;
+
+					console.log("[RPG] Configuration:", {
+						generationCount,
+						inspirationCount,
+						isSDXL,
+						forceRegenerate,
+						model,
+						temperature: temperatureValue,
+						topP: topPValue,
+						sourcePromptsCount: sourcePrompts.length
+					});
+
+					console.log("[RPG] About to retrieve API keys from connected nodes...");
+
+					// Get API keys by traversing the graph if inputs are connected
+					const geminiApiKey = (getFinalInputValue("gemini_api_key", "") || "").trim() || window.INSTARAW_GEMINI_KEY || "";
+					const grokApiKey = (getFinalInputValue("grok_api_key", "") || "").trim() || window.INSTARAW_GROK_KEY || "";
+
+					console.log(`[RPG] โœ… Resolved Gemini API Key: ${geminiApiKey ? `[KEY PRESENT - Length: ${geminiApiKey.length}]` : "[EMPTY]"}`);
+					console.log(`[RPG] โœ… Resolved Grok API Key: ${grokApiKey ? `[KEY PRESENT - Length: ${grokApiKey.length}]` : "[EMPTY]"}`);
+
+					console.log("[RPG] Window fallback keys:", {
+						INSTARAW_GEMINI_KEY: window.INSTARAW_GEMINI_KEY ? "[PRESENT]" : "[EMPTY]",
+						INSTARAW_GROK_KEY: window.INSTARAW_GROK_KEY ? "[PRESENT]" : "[EMPTY]"
+					});
+
+					// Validate API keys before proceeding
+					if (!geminiApiKey && !grokApiKey) {
+						console.error("[RPG] โŒ NO API KEYS FOUND!");
+						console.log("[RPG] To fix this, connect a Primitive String node to either:");
+						console.log("[RPG]   - gemini_api_key input");
+						console.log("[RPG]   - grok_api_key input");
+						console.log("[RPG] Or set window.INSTARAW_GEMINI_KEY or window.INSTARAW_GROK_KEY");
+						console.groupEnd();
+						alert("No API keys found! Please connect a Primitive String node with your Gemini or Grok API key to the 'gemini_api_key' or 'grok_api_key' input.");
+						return;
+					}
+
+					// Disable button and show loading progress bar
+					if (generateBtn) {
+						generateBtn.disabled = true;
+						const originalText = generateBtn.textContent;
+						generateBtn.style.position = 'relative';
+						generateBtn.style.overflow = 'hidden';
+						generateBtn.innerHTML = `
+							${originalText}
+							

+						`;
+					}
+
+					console.log("[RPG] Making API request to /instaraw/generate_creative_prompts");
+					console.log("[RPG] Request payload:", {
+						source_prompts_count: sourcePrompts.length,
+						generation_count: generationCount,
+						inspiration_count: inspirationCount,
+						is_sdxl: isSDXL,
+						force_regenerate: forceRegenerate,
+						model: model,
+						has_gemini_key: !!geminiApiKey,
+						has_grok_key: !!grokApiKey,
+						temperature: temperatureValue,
+						top_p: topPValue,
+					});
+
+					try {
+					const response = await api.fetchApi("/instaraw/generate_creative_prompts", {
+							method: "POST",
+							headers: { "Content-Type": "application/json" },
+							body: JSON.stringify({
+								source_prompts: sourcePrompts.map((p) => ({
+									id: p.source_id,
+									prompt: {
+										positive: p.positive_prompt,
+										negative: p.negative_prompt,
+									},
+								})),
+								generation_count: generationCount,
+								inspiration_count: inspirationCount,
+								is_sdxl: isSDXL,
+								character_reference: "",
+								model: model,
+								gemini_api_key: geminiApiKey,
+								grok_api_key: grokApiKey,
+								system_prompt: systemPrompt,
+								temperature: temperatureValue,
+								top_p: topPValue,
+								force_regenerate: forceRegenerate,
+							}),
+						});
+
+						console.log("[RPG] API response status:", response.status, response.statusText);
+
+						const result = await parseJSONResponse(response);
+						console.log("[RPG] API response parsed:", result);
+
+						if (!response.ok) {
+							throw new Error(result?.error || `Creative API error ${response.status}`);
+						}
+
+						if (result.success && result.prompts) {
+							console.log(`[RPG] โœ… Success! Generated ${result.prompts.length} prompts`);
+							// Show preview
+							const previewSection = container.querySelector(".instaraw-rpg-creative-preview");
+							const previewList = container.querySelector(".instaraw-rpg-creative-preview-list");
+
+							if (previewSection && previewList) {
+								previewList.innerHTML = result.prompts
+									.map(
+										(p, idx) => `
+									

+										#${idx + 1}
+										
${escapeHtml(p.positive || "")}

+									

+								`
+									)
+									.join("");
+
+								previewSection.style.display = "block";
+								// Store generated prompts temporarily
+								node._generatedCreativePrompts = result.prompts;
+								// DON'T call setupEventHandlers() - already set up by renderUI()
+							}
+						} else {
+							throw new Error(result.error || "Unknown error");
+						}
+					} catch (error) {
+						console.error("[RPG] โŒ Error during creative prompt generation:", error);
+						console.error("[RPG] Error stack:", error.stack);
+						alert(`Creative generation error: ${error.message || error}`);
+					} finally {
+						if (generateBtn) {
+							generateBtn.disabled = false;
+							generateBtn.textContent = "โœจ Generate & Add to Batch";
+						}
+						console.log("[RPG] Generate Creative Prompts - END");
+						console.groupEnd();
+					}
+				};
+
+				// === Accept Creative Prompts ===
+				const acceptCreativePrompts = () => {
+					if (!node._generatedCreativePrompts) return;
+
+					const existingQueue = parsePromptBatch();
+					const newCount = node._generatedCreativePrompts.length;
+
+					// If there are existing prompts, ask the user to confirm
+					if (existingQueue.length > 0) {
+						const confirmAdd = confirm(
+							`You have ${existingQueue.length} existing prompt(s) in the batch.\n\n` +
+							`Add ${newCount} new prompt(s) to the batch?\n\n` +
+							`OK = Add to existing\n` +
+							`Cancel = Don't add (clear batch first if needed)`
+						);
+
+						if (!confirmAdd) {
+							console.log("[RPG] User cancelled adding creative prompts to batch");
+							return;
+						}
+					}
+
+					const promptQueue = existingQueue;
+					node._generatedCreativePrompts.forEach((p) => {
+						promptQueue.push({
+							id: generateUniqueId(),
+							positive_prompt: p.positive || "",
+							negative_prompt: p.negative || "",
+							repeat_count: 1,
+							tags: p.tags || [],
+							source_id: null,
+						});
+					});
+
+					setPromptBatchData(promptQueue);
+					delete node._generatedCreativePrompts;
+					renderUI();
+				};
+
+				// === Cancel Creative Prompts ===
+				const cancelCreativePrompts = () => {
+					delete node._generatedCreativePrompts;
+					renderUI();
+				};
+
+				// === Generate Character Prompts ===
+				const generateCharacterPrompts = async () => {
+					console.group("[RPG] ๐Ÿ‘ค Generate Character Prompts - START");
+					console.log("[RPG] Timestamp:", new Date().toISOString());
+
+					const charRefInput = container.querySelector(".instaraw-rpg-character-ref-input");
+					const genCountInput = container.querySelector(".instaraw-rpg-char-gen-count-input");
+					const generateBtn = container.querySelector(".instaraw-rpg-generate-character-btn");
+
+					const characterReference = charRefInput?.value || "";
+					const generationCount = parseInt(genCountInput?.value || "5");
+					const isSDXL = sdxlModeEnabled; // Use global SDXL mode
+					const forceRegenerate = true; // Always regenerate
+
+					const model = node.properties.creative_model || "gemini-2.5-pro";
+					const systemPrompt = node.properties.creative_system_prompt || DEFAULT_RPG_SYSTEM_PROMPT;
+					const temperatureValue = parseFloat(node.properties.creative_temperature ?? 0.9) || 0.9;
+					const topPValue = parseFloat(node.properties.creative_top_p ?? 0.9) || 0.9;
+
+					console.log("[RPG] Configuration:", {
+						generationCount,
+						isSDXL,
+						forceRegenerate,
+						model,
+						temperature: temperatureValue,
+						topP: topPValue,
+						characterReferenceLength: characterReference.length
+					});
+
+					console.log("[RPG] About to retrieve API keys from connected nodes...");
+
+					// Get API keys by traversing the graph if inputs are connected
+					const geminiApiKey = (getFinalInputValue("gemini_api_key", "") || "").trim() || window.INSTARAW_GEMINI_KEY || "";
+					const grokApiKey = (getFinalInputValue("grok_api_key", "") || "").trim() || window.INSTARAW_GROK_KEY || "";
+
+					console.log(`[RPG] โœ… Resolved Gemini API Key: ${geminiApiKey ? `[KEY PRESENT - Length: ${geminiApiKey.length}]` : "[EMPTY]"}`);
+					console.log(`[RPG] โœ… Resolved Grok API Key: ${grokApiKey ? `[KEY PRESENT - Length: ${grokApiKey.length}]` : "[EMPTY]"}`);
+
+					console.log("[RPG] Window fallback keys:", {
+						INSTARAW_GEMINI_KEY: window.INSTARAW_GEMINI_KEY ? "[PRESENT]" : "[EMPTY]",
+						INSTARAW_GROK_KEY: window.INSTARAW_GROK_KEY ? "[PRESENT]" : "[EMPTY]"
+					});
+
+					// Validate API keys before proceeding
+					if (!geminiApiKey && !grokApiKey) {
+						console.error("[RPG] โŒ NO API KEYS FOUND!");
+						console.log("[RPG] To fix this, connect a Primitive String node to either:");
+						console.log("[RPG]   - gemini_api_key input");
+						console.log("[RPG]   - grok_api_key input");
+						console.log("[RPG] Or set window.INSTARAW_GEMINI_KEY or window.INSTARAW_GROK_KEY");
+						console.groupEnd();
+						alert("No API keys found! Please connect a Primitive String node with your Gemini or Grok API key to the 'gemini_api_key' or 'grok_api_key' input.");
+						return;
+					}
+
+					if (!characterReference.trim()) {
+						console.error("[RPG] โŒ No character reference provided");
+						console.groupEnd();
+						alert("Please enter a character reference");
+						return;
+					}
+
+					// Disable button and show loading progress bar
+					if (generateBtn) {
+						generateBtn.disabled = true;
+						const originalText = generateBtn.textContent;
+						generateBtn.style.position = 'relative';
+						generateBtn.style.overflow = 'hidden';
+						generateBtn.innerHTML = `
+							${originalText}
+							

+						`;
+					}
+
+					console.log("[RPG] Making API request to /instaraw/generate_creative_prompts (character mode)");
+					console.log("[RPG] Request payload:", {
+						generation_count: generationCount,
+						is_sdxl: isSDXL,
+						force_regenerate: forceRegenerate,
+						model: model,
+						has_gemini_key: !!geminiApiKey,
+						has_grok_key: !!grokApiKey,
+						character_reference_length: characterReference.length,
+						temperature: temperatureValue,
+						top_p: topPValue,
+					});
+
+					try {
+					const response = await api.fetchApi("/instaraw/generate_creative_prompts", {
+							method: "POST",
+							headers: { "Content-Type": "application/json" },
+							body: JSON.stringify({
+								source_prompts: [],
+								generation_count: generationCount,
+								inspiration_count: 0,
+								is_sdxl: isSDXL,
+								character_reference: characterReference,
+								model: model,
+								gemini_api_key: geminiApiKey,
+								grok_api_key: grokApiKey,
+								system_prompt: systemPrompt,
+								temperature: temperatureValue,
+								top_p: topPValue,
+								force_regenerate: forceRegenerate,
+							}),
+						});
+
+						console.log("[RPG] API response status:", response.status, response.statusText);
+
+						const result = await parseJSONResponse(response);
+						console.log("[RPG] API response parsed:", result);
+
+						if (!response.ok) {
+							throw new Error(result?.error || `Creative API error ${response.status}`);
+						}
+
+						if (result.success && result.prompts) {
+							console.log(`[RPG] โœ… Success! Generated ${result.prompts.length} character prompts`);
+							// Show preview
+							const previewSection = container.querySelector(".instaraw-rpg-creative-preview");
+							const previewList = container.querySelector(".instaraw-rpg-character-preview-list");
+
+							if (previewSection && previewList) {
+								previewList.innerHTML = result.prompts
+									.map(
+										(p, idx) => `
+									

+										#${idx + 1}
+										
${escapeHtml(p.positive || "")}

+									

+								`
+									)
+									.join("");
+
+								previewSection.style.display = "block";
+								// Store generated prompts temporarily
+								node._generatedCharacterPrompts = result.prompts;
+								// DON'T call setupEventHandlers() - already set up by renderUI()
+							}
+						} else {
+							throw new Error(result.error || "Unknown error");
+						}
+					} catch (error) {
+						console.error("[RPG] โŒ Error during character prompt generation:", error);
+						console.error("[RPG] Error stack:", error.stack);
+						alert(`Character generation error: ${error.message || error}`);
+					} finally {
+						if (generateBtn) {
+							generateBtn.disabled = false;
+							generateBtn.textContent = "๐Ÿ‘ค Generate Character Prompts";
+						}
+						console.log("[RPG] Generate Character Prompts - END");
+						console.groupEnd();
+					}
+				};
+
+				// === Accept Character Prompts ===
+				const acceptCharacterPrompts = () => {
+					if (!node._generatedCharacterPrompts) return;
+
+						const promptQueue = parsePromptBatch();
+					node._generatedCharacterPrompts.forEach((p) => {
+						promptQueue.push({
+							id: generateUniqueId(),
+							positive_prompt: p.positive || "",
+							negative_prompt: p.negative || "",
+							repeat_count: 1,
+							tags: p.tags || [],
+							source_id: null,
+						});
+					});
+
+						setPromptBatchData(promptQueue);
+					delete node._generatedCharacterPrompts;
+					renderUI();
+				};
+
+				// === Cancel Character Prompts ===
+				const cancelCharacterPrompts = () => {
+					delete node._generatedCharacterPrompts;
+					renderUI();
+				};
+
+				// ========================================
+				// === UNIFIED GENERATE TAB FUNCTIONS ===
+				// ========================================
+
+				// === Get Default Character System Prompt (mirrors backend logic) ===
+				const getCharacterSystemPrompt = (complexity = "balanced") => {
+					const baseInstruction = `You are an expert at analyzing images and generating character descriptions for image generation prompts.
+
+Generate a character description focusing on PERMANENT physical features:
+- Facial features (face shape, eyes, nose, lips, skin tone)
+- Hair (color, length, style, texture)
+- Body type and build
+- Age and ethnicity
+- Distinctive features (scars, tattoos, piercings, etc.)
+
+DO NOT include clothing, background, pose, or temporary features.
+DO NOT use tags like "1girl, solo" or similar categorization prefixes.`;
+
+					let lengthInstruction;
+					if (complexity === "concise") {
+						lengthInstruction = "\nOUTPUT: A concise description (50-75 words) focusing only on the most essential and distinctive physical features.";
+					} else if (complexity === "detailed") {
+						lengthInstruction = "\nOUTPUT: A comprehensive, detailed description (200-250 words) covering all physical aspects with nuanced detail and specific characteristics.";
+					} else {  // balanced
+						lengthInstruction = "\nOUTPUT: A balanced description (100-150 words) covering key physical features in natural language.";
+					}
+
+					return baseInstruction + lengthInstruction;
+				};
+
+				// === Generate Character Description (from image or text) ===
+				const generateCharacterDescription = async () => {
+					// CRASH PREVENTION: Check if already generating
+					if (isGenerating) {
+						console.warn("[RPG] โš ๏ธ Generation already in progress, ignoring request");
+						return;
+					}
+
+					console.group("[RPG] ๐ŸŽจ Generate Character Description - START");
+
+					// Set generation lock
+					isGenerating = true;
+
+					const generateBtn = container.querySelector(".instaraw-rpg-generate-character-desc-btn");
+					const characterTextInput = container.querySelector(".instaraw-rpg-character-text-input");
+
+					const characterText = characterTextInput?.value?.trim() || "";
+
+					// Get API keys
+					const geminiApiKey = (getFinalInputValue("gemini_api_key", "") || "").trim() || window.INSTARAW_GEMINI_KEY || "";
+					const grokApiKey = (getFinalInputValue("grok_api_key", "") || "").trim() || window.INSTARAW_GROK_KEY || "";
+
+					if (!geminiApiKey && !grokApiKey) {
+						alert("No API keys found! Please connect a Primitive String node with your Gemini or Grok API key.");
+						console.groupEnd();
+						return;
+					}
+
+					// Check if character_image input is connected
+					const characterImageInput = node.inputs?.find(i => i.name === "character_image");
+					let imageData = null;
+
+					if (characterImageInput && characterImageInput.link != null) {
+						console.log("[RPG] Character image input connected, reading image...");
+
+						try {
+							// Get the connected node
+							const link = app.graph.links[characterImageInput.link];
+							if (link) {
+								const sourceNode = app.graph.getNodeById(link.origin_id);
+								console.log("[RPG] Source node type:", sourceNode?.type);
+
+								if (sourceNode && sourceNode.type === "LoadImage") {
+									// Get the image filename from the LoadImage node
+									const imageWidget = sourceNode.widgets?.find(w => w.name === "image");
+									const filename = imageWidget?.value;
+
+									if (filename) {
+										console.log("[RPG] Loading image:", filename);
+
+										// Fetch the image from ComfyUI's view endpoint
+										const imageUrl = `/view?filename=${encodeURIComponent(filename)}&type=input`;
+										const response = await fetch(imageUrl);
+										const blob = await response.blob();
+
+										// Convert to base64
+										const base64 = await new Promise((resolve) => {
+											const reader = new FileReader();
+											reader.onloadend = () => resolve(reader.result);
+											reader.readAsDataURL(blob);
+										});
+
+										imageData = base64;
+										console.log("[RPG] โœ… Image loaded and converted to base64");
+									} else {
+										console.warn("[RPG] No image selected in LoadImage node");
+									}
+								} else if (sourceNode) {
+									console.warn("[RPG] Connected node is not LoadImage, type:", sourceNode.type);
+								}
+							}
+						} catch (error) {
+							console.error("[RPG] Error reading character image:", error);
+							alert(`Could not read character image: ${error.message}`);
+							return;
+						}
+					}
+
+					// Require either image or text
+					if (!imageData && !characterText) {
+						alert("Please either:\n1. Connect a Load Image node to character_image input, OR\n2. Enter character description text");
+						console.groupEnd();
+						return;
+					}
+
+					// Disable button and show loading progress bar
+					if (generateBtn) {
+						generateBtn.disabled = true;
+						const originalText = generateBtn.textContent;
+						generateBtn.style.position = 'relative';
+						generateBtn.style.overflow = 'hidden';
+						generateBtn.innerHTML = `
+							${originalText}
+							

+						`;
+					}
+
+					try {
+						// Get complexity and custom system prompt from properties
+						const complexity = node.properties.character_complexity || "balanced";
+						const customSystemPrompt = node.properties.character_system_prompt?.trim() || "";
+
+						console.log("[RPG] Sending character description request:", {
+							hasImage: !!imageData,
+							hasText: !!characterText,
+							model: node.properties.creative_model || "gemini-2.5-pro",
+							complexity: complexity,
+							hasCustomSystemPrompt: !!customSystemPrompt
+						});
+
+						const response = await api.fetchApi("/instaraw/generate_character_description", {
+							method: "POST",
+							headers: { "Content-Type": "application/json" },
+							body: JSON.stringify({
+								character_image: imageData,
+								character_text: characterText,
+								gemini_api_key: geminiApiKey,
+								grok_api_key: grokApiKey,
+								model: node.properties.creative_model || "gemini-2.5-pro",
+								complexity: complexity,
+								custom_system_prompt: customSystemPrompt,
+								force_regenerate: true  // Always force regenerate to bypass bad cache
+							}),
+						});
+
+						const result = await parseJSONResponse(response);
+						console.log("[RPG] Character description API response:", result);
+
+						if (!response.ok) {
+							throw new Error(result?.error || `Character description API error ${response.status}`);
+						}
+
+						if (result.success) {
+							if (!result.description || result.description.trim() === "") {
+								throw new Error("API returned empty description. Check backend logs for details.");
+							}
+
+							console.log(`[RPG] โœ… Character description generated successfully (${result.description.length} chars)`);
+
+							// Populate the character text input with the generated description
+							node.properties.character_text_input = result.description;
+
+							// Update textarea directly (don't call renderUI to avoid event handler duplication)
+							if (characterTextInput) {
+								characterTextInput.value = result.description;
+								autoResizeTextarea(characterTextInput);
+							}
+
+							// Mark canvas as dirty to save changes
+							app.graph.setDirtyCanvas(true, true);
+						} else {
+							throw new Error(result.error || "API returned success=false");
+						}
+					} catch (error) {
+						console.error("[RPG] โŒ Error during character description generation:", error);
+						alert(`Character description error: ${error.message || error}`);
+					} finally {
+						// CRASH PREVENTION: Always release generation lock
+						isGenerating = false;
+
+						// Reset button
+						if (generateBtn) {
+							generateBtn.disabled = false;
+							generateBtn.style.position = '';
+							generateBtn.style.overflow = '';
+							generateBtn.textContent = "โœจ Generate from Image";
+						}
+						console.groupEnd();
+					}
+				};
+
+				// === Generate Unified Prompts (Main Generate Button) - SEQUENTIAL VERSION ===
+				let generationAbortController = null; // For cancel functionality
+
+				const generateUnifiedPrompts = async () => {
+					// Check if there's already an ongoing generation
+					if (generationAbortController !== null) {
+						const confirmRestart = confirm(
+							"โš ๏ธ Generation in Progress\n\n" +
+							"There is already a generation running. Starting a new one will cancel the current generation.\n\n" +
+							"Do you want to cancel the current generation and start a new one?"
+						);
+
+						if (!confirmRestart) {
+							console.log("[RPG] User chose not to restart generation");
+							return;
+						}
+
+						// User confirmed - abort the current generation
+						console.log("[RPG] ๐Ÿ›‘ Aborting current generation to start new one");
+						generationAbortController.abort();
+						generationAbortController = null;
+
+						// Small delay to let the abort propagate
+						await new Promise(resolve => setTimeout(resolve, 100));
+					}
+
+					console.group("[RPG] ๐ŸŽฏ Generate Unified Prompts - START (Sequential)");
+					console.log("[RPG] Timestamp:", new Date().toISOString());
+
+					const generateBtn = container.querySelector(".instaraw-rpg-generate-unified-btn");
+					const genCountInput = container.querySelector(".instaraw-rpg-gen-count-input");
+					const progressSection = container.querySelector(".instaraw-rpg-generation-progress");
+					const progressItems = container.querySelector(".instaraw-rpg-progress-items");
+					const previewSection = container.querySelector(".instaraw-rpg-generate-preview");
+
+					// Character settings
+					const enableCharacterCheckbox = container.querySelector(".instaraw-rpg-enable-character-checkbox");
+					const characterTextInput = container.querySelector(".instaraw-rpg-character-text-input");
+					const useCharacter = enableCharacterCheckbox?.checked || false;
+					const characterText = characterTextInput?.value?.trim() || "";
+					// Use character_text_input directly - generated descriptions populate this field
+					const characterDescription = characterText;
+
+					// Detect mode from AIL
+					const detectedMode = node._linkedAILMode || "txt2img";
+					const isImg2Img = detectedMode === "img2img";
+
+					// Mode-specific settings
+					let affectElements = [];
+					let userInput = "";
+					let inspirationCount = 0;
+					let uniqueImages = [];
+					let uniqueImages2 = [];
+					let uniqueImages3 = [];
+					let uniqueImages4 = [];
+					let generationCount = 0;
+
+					if (isImg2Img) {
+						// IMG2IMG: Get unique images from all 4 AIL inputs
+						uniqueImages = node._linkedImages || [];
+						uniqueImages2 = node._linkedImages2 || [];
+						uniqueImages3 = node._linkedImages3 || [];
+						uniqueImages4 = node._linkedImages4 || [];
+
+						if (uniqueImages.length === 0) {
+							console.error("[RPG] โŒ No images from AIL");
+							alert("No images from AIL. Please connect an Advanced Image Loader node in img2img mode.");
+							return;
+						}
+
+						// Generation count = number of unique images from primary input
+						generationCount = uniqueImages.length;
+
+						// Compose user input from all three universal components
+						userInput = composeUserInput(
+							node.properties.user_instructions || "",
+							node.properties.theme_preset || DEFAULT_THEME_PRESET,
+							node.properties.model_instructions || "",
+							node.properties.clean_mode || false
+						);
+						console.log(`[RPG] ๐Ÿ“ IMG2IMG composed user input: ${userInput ? `"${userInput.substring(0, 100)}..."` : "(none)"}`);
+
+						// Collect affect elements
+						const affectBackground = container.querySelector(".instaraw-rpg-affect-background")?.checked;
+						const affectOutfit = container.querySelector(".instaraw-rpg-affect-outfit")?.checked;
+						const affectPose = container.querySelector(".instaraw-rpg-affect-pose")?.checked;
+						const affectLighting = container.querySelector(".instaraw-rpg-affect-lighting")?.checked;
+
+						if (affectBackground) affectElements.push("background");
+						if (affectOutfit) affectElements.push("outfit");
+						if (affectPose) affectElements.push("pose");
+						if (affectLighting) affectElements.push("lighting");
+
+						console.log("[RPG] ๐ŸŽจ Affect Elements collected:", affectElements);
+
+						// Get inspiration count (for Creative mode only, respects toggle)
+						const inspirationCountInput = container.querySelector(".instaraw-rpg-inspiration-count");
+						if (inspirationCountInput && node.properties.generation_style === 'creative') {
+							inspirationCount = node.properties.enable_library_inspiration ? parseInt(inspirationCountInput?.value || "3") : 0;
+						}
+
+						console.log(`[RPG] ๐Ÿ–ผ๏ธ img2img mode: ${uniqueImages.length} unique images โ†’ ${generationCount} prompts`);
+					} else {
+						// TXT2IMG: Collect user input and inspiration
+						const userTextInput = container.querySelector(".instaraw-rpg-user-text-input");
+						const inspirationCountInput = container.querySelector(".instaraw-rpg-inspiration-count");
+
+						// Compose user input from all three universal components
+						const userText = userTextInput?.value?.trim() || "";
+						userInput = composeUserInput(
+							userText,
+							node.properties.theme_preset || DEFAULT_THEME_PRESET,
+							node.properties.model_instructions || "",
+							node.properties.clean_mode || false
+						);
+
+						// Respect library inspiration toggle
+						inspirationCount = node.properties.enable_library_inspiration ? parseInt(inspirationCountInput?.value || "3") : 0;
+						generationCount = parseInt(genCountInput?.value || "5");
+
+						const selectedTheme = node.properties.theme_preset || DEFAULT_THEME_PRESET;
+						if (selectedTheme !== "none") {
+							console.log(`[RPG] ๐ŸŽจ Using theme: ${selectedTheme}`);
+						}
+						console.log(`[RPG] ๐Ÿ“ TXT2IMG composed user input: ${userInput ? `"${userInput.substring(0, 100)}..."` : "(none)"}`);
+					}
+
+					const forceRegenerate = true; // Always regenerate
+					const isSDXL = sdxlModeEnabled; // Use global SDXL mode
+
+					// Prepare source prompt pool (each generation will sample from this)
+					let sourcePromptPool = [];
+					if (inspirationCount > 0) {
+						// Build filtered pool for sampling (both txt2img and img2img Creative mode)
+						const filters = JSON.parse(node.properties.library_filters || "{}");
+						sourcePromptPool = filterPrompts(promptsDatabase, filters);
+
+						if (sourcePromptPool.length === 0 && detectedMode === 'txt2img') {
+							console.warn("[RPG] โš ๏ธ No prompts available after filtering - generating without inspiration");
+							alert("No prompts match the current filters. Adjust filters or generate without inspiration.");
+							return;
+						}
+
+						if (sourcePromptPool.length > 0) {
+							console.log(`[RPG] ๐ŸŽฒ Prepared pool of ${sourcePromptPool.length} prompts for sampling (inspiration: ${inspirationCount} per generation)`);
+						}
+					}
+
+					// Get model and settings
+					const model = node.properties.creative_model || "gemini-2.5-pro";
+					const generationStyle = node.properties.generation_style || "reality";
+
+					const temperatureValue = parseFloat(node.properties.creative_temperature ?? 0.9) || 0.9;
+					const topPValue = parseFloat(node.properties.creative_top_p ?? 0.9) || 0.9;
+
+					// Get full names for tagging generated prompts
+					const modelPresetKey = node.properties.model_preset || "none";
+					const themePresetKey = node.properties.theme_preset || DEFAULT_THEME_PRESET;
+					// Get full theme name (strip emoji prefix if present)
+					const themeLabel = THEME_PRESETS[themePresetKey]?.label || "";
+					const currentThemeBadge = themePresetKey !== "none" && themeLabel ? themeLabel.replace(/^[^\w\s]+\s*/, '').trim() : null;
+					// Get full model name (strip emoji prefix if present)
+					const modelLabel = MODEL_INSTRUCTION_PRESETS[modelPresetKey]?.label || "";
+					const currentModelBadge = modelPresetKey !== "none" && modelLabel ? modelLabel.replace(/^[^\w\s]+\s*/, '').trim() : null;
+
+					console.log(`[RPG] ๐Ÿ”ง Generation mode: ${detectedMode}, style: ${generationStyle}`);
+					if (currentThemeBadge) console.log(`[RPG] ๐ŸŽจ Theme: ${currentThemeBadge}`);
+					if (currentModelBadge) console.log(`[RPG] ๐Ÿท๏ธ Model: ${currentModelBadge}`);
+
+					console.log("[RPG] Configuration:", {
+						mode: detectedMode,
+						generationCount,
+						useCharacter,
+						affectElements,
+						userInput,
+						inspirationCount,
+						isSDXL,
+						forceRegenerate,
+						model,
+						temperature: temperatureValue,
+						topP: topPValue
+					});
+
+					// Get API keys
+					const geminiApiKey = (getFinalInputValue("gemini_api_key", "") || "").trim() || window.INSTARAW_GEMINI_KEY || "";
+					const grokApiKey = (getFinalInputValue("grok_api_key", "") || "").trim() || window.INSTARAW_GROK_KEY || "";
+
+					if (!geminiApiKey && !grokApiKey) {
+						console.error("[RPG] โŒ NO API KEYS FOUND!");
+						console.groupEnd();
+						alert("No API keys found! Please connect a Primitive String node with your Gemini or Grok API key.");
+						return;
+					}
+
+					// Initialize AbortController for cancellation
+					generationAbortController = new AbortController();
+					const signal = generationAbortController.signal;
+
+					// Disable generate button and show progress section
+					if (generateBtn) {
+						generateBtn.disabled = true;
+						generateBtn.style.opacity = "0.5";
+					}
+
+					// Hide preview section when starting new generation
+					if (previewSection) {
+						previewSection.style.display = "none";
+					}
+
+					// Mark generation as in progress
+					node._generationInProgress = true;
+					node._generationCount = generationCount;
+
+					// Initialize progress state
+					node._progressState = Array.from({ length: generationCount }, (_, i) => ({
+						index: i,
+						status: 'pending',
+						progress: 0,
+						message: ''
+					}));
+
+					if (progressSection) {
+						progressSection.style.display = "block";
+						// Reset header to "Generating..." when starting a new generation
+						const progressHeader = progressSection.querySelector(".instaraw-rpg-progress-header h4");
+						if (progressHeader) {
+							progressHeader.innerHTML = `โณ Generating Prompts...`;
+						}
+						// Show cancel button, hide quick accept button
+						const cancelBtn = progressSection.querySelector(".instaraw-rpg-cancel-generation-btn");
+						if (cancelBtn) cancelBtn.style.display = "";
+						const quickAccept = progressSection.querySelector(".instaraw-rpg-quick-accept");
+						if (quickAccept) quickAccept.style.display = "none";
+					}
+
+					// Create progress items
+					if (progressItems) {
+						progressItems.innerHTML = "";
+
+						// Helper to render image thumbnails for a generation index
+						const renderProgressImages = (index) => {
+							if (!isImg2Img) return '';
+
+							// Helper to get image URL (handles both url and thumbnail formats)
+							const getImgUrl = (img) => img?.url || (img?.thumbnail ? `/instaraw/view/${img.thumbnail}` : null);
+
+							const images = [
+								{ img: uniqueImages[index], label: "1" },
+								{ img: uniqueImages2[index], label: "2" },
+								{ img: uniqueImages3[index], label: "3" },
+								{ img: uniqueImages4[index], label: "4" }
+							].filter(item => item.img && getImgUrl(item.img));
+
+							if (images.length === 0) return '';
+
+							return `
+								

+									${images.map(item => `
+										

+											

+												Image ${item.label}
+											

+										

+									`).join('')}
+								

+							`;
+						};
+
+						for (let i = 0; i < generationCount; i++) {
+							const progressItem = document.createElement("div");
+							progressItem.className = "instaraw-rpg-progress-item";
+							progressItem.dataset.index = i;
+							const imagePreview = renderProgressImages(i);
+							progressItem.innerHTML = `
+								

+									#${i + 1}
+									${imagePreview}
+									โณ Pending
+								

+								

+									

+								

+								

+							`;
+							progressItems.appendChild(progressItem);
+						}
+
+						// Resize node to fit progress UI
+						updateCachedHeight();
+					}
+
+					// Collect all generated prompts
+					const allGeneratedPrompts = [];
+					let cancelRequested = false;
+
+					// Listen for cancel signal
+					signal.addEventListener('abort', () => {
+						cancelRequested = true;
+						console.log("[RPG] ๐Ÿ›‘ Cancel requested by user");
+					});
+
+					try {
+						// PARALLEL GENERATION with 222ms stagger
+						const generateSinglePrompt = async (index) => {
+							const i = index;
+
+							try {
+								// Sample UNIQUE source prompts for THIS generation
+								let thisPromptSources = [];
+								if (sourcePromptPool.length > 0) {
+									const sampleCount = Math.min(inspirationCount, sourcePromptPool.length);
+									const shuffled = [...sourcePromptPool].sort(() => Math.random() - 0.5);
+									thisPromptSources = shuffled.slice(0, sampleCount).map(p => ({
+										source_id: p.id,
+										positive_prompt: p.prompt?.positive || p.positive || "",
+										negative_prompt: p.prompt?.negative || p.negative || ""
+									}));
+								}
+
+								// Determine expression for this generation (if enabled)
+								let currentExpression = null;
+								if (node.properties.enable_expressions) {
+									const enabledExpressions = JSON.parse(node.properties.enabled_expressions || '[]');
+									const defaultExpression = node.properties.default_expression || "Neutral/Natural";
+									const mixFrequency = node.properties.default_mix_frequency || 0;
+
+									if (enabledExpressions.length > 0) {
+										// Roll for default expression vs cycling
+										const useDefault = Math.random() * 100 < mixFrequency;
+
+										if (useDefault || enabledExpressions.length === 0) {
+											currentExpression = defaultExpression;
+										} else {
+											// Cycle through enabled expressions
+											const currentIndex = node.properties.current_expression_index || 0;
+											currentExpression = enabledExpressions[currentIndex % enabledExpressions.length];
+											// Increment for next generation
+											node.properties.current_expression_index = (currentIndex + 1) % enabledExpressions.length;
+										}
+									} else {
+										// No expressions enabled, use default
+										currentExpression = defaultExpression;
+									}
+
+									console.log(`[RPG] ๐Ÿ˜Š [${i + 1}] Expression: ${currentExpression}`);
+								}
+
+								// Build dynamic system prompt with actual source prompt text
+								// Use custom template if in custom mode, otherwise use creative_system_prompt
+								const customTemplate = node.properties.generation_style === 'custom'
+									? node.properties.custom_template
+									: node.properties.creative_system_prompt;
+								const dynamicSystemPrompt = buildSystemPrompt(
+									detectedMode,
+									generationStyle,
+									thisPromptSources,
+									userInput,
+									useCharacter ? characterDescription : "",
+									affectElements,
+									customTemplate,
+									currentExpression
+								);
+
+								// Update status to in-progress
+								updateProgressState(i, { status: 'in-progress', progress: 100 });
+
+								console.log(`[RPG] ๐Ÿ“ [${i + 1}] Starting generation with ${thisPromptSources.length} unique source prompts...`);
+
+								// For img2img: Convert all connected images to base64
+								let multiImageData = [];
+								if (isImg2Img) {
+									updateProgressState(i, { message: "Converting images to base64..." });
+
+									// Helper to convert image at index from an array
+									const convertImageIfExists = async (imagesArray, label, index) => {
+										const img = imagesArray[index];
+										// Handle both url (from renderImg2ImgGallery events) and thumbnail (from raw batchData events)
+										const imgUrl = img?.url || (img?.thumbnail ? `/instaraw/view/${img.thumbnail}` : null);
+										if (img && imgUrl) {
+											try {
+												const base64 = await imageUrlToBase64(imgUrl);
+												console.log(`[RPG] ๐Ÿ“ท [${i + 1}] ${label} converted to base64 (${base64.length} chars)`);
+												return { label, base64, url: imgUrl };
+											} catch (error) {
+												console.warn(`[RPG] โš ๏ธ [${i + 1}] Failed to convert ${label}:`, error);
+												return null;
+											}
+										}
+										return null;
+									};
+
+									// Convert all 4 image inputs in parallel
+									const conversionPromises = [
+										convertImageIfExists(uniqueImages, "image_1", i),
+										convertImageIfExists(uniqueImages2, "image_2", i),
+										convertImageIfExists(uniqueImages3, "image_3", i),
+										convertImageIfExists(uniqueImages4, "image_4", i)
+									];
+
+									const results = await Promise.all(conversionPromises);
+									multiImageData = results.filter(Boolean);
+
+									console.log(`[RPG] ๐Ÿ“ท [${i + 1}] ${multiImageData.length} images converted for vision API`);
+								}
+
+								// Build payload
+								const payload = {
+									source_prompts: thisPromptSources.map((p) => ({
+										id: p.source_id,
+										prompt: { positive: p.positive_prompt, negative: p.negative_prompt },
+									})),
+									generation_count: 1,
+									inspiration_count: inspirationCount,
+									is_sdxl: isSDXL,
+									character_reference: useCharacter ? characterDescription : "",
+									model: model,
+									gemini_api_key: geminiApiKey,
+									grok_api_key: grokApiKey,
+									system_prompt: dynamicSystemPrompt,
+									temperature: temperatureValue,
+									top_p: topPValue,
+									force_regenerate: forceRegenerate,
+									generation_mode: detectedMode,  // Fixed: was "mode", backend expects "generation_mode"
+									affect_elements: affectElements,
+									user_input: userInput,
+									generation_style: generationStyle
+								};
+
+								// Add images for img2img mode (multi-image support)
+								if (isImg2Img && multiImageData.length > 0) {
+									// New format: array of { label, base64 } objects for multi-image
+									payload.multi_images = multiImageData.map(img => ({
+										label: img.label,
+										base64: img.base64
+									}));
+									// Also keep legacy images array for backward compatibility (primary image only)
+									payload.images = [multiImageData[0].base64];
+								}
+
+								// ๐Ÿ” DETAILED LOGGING: Preview the complete request
+								console.group(`[RPG] ๐Ÿ” REQUEST PREVIEW [${i + 1}/${generationCount}]`);
+								console.log(`๐Ÿ“‹ Mode: ${detectedMode} | Style: ${generationStyle} | Model: ${model}`);
+								console.log(`\nโ”โ”โ” SYSTEM PROMPT โ”โ”โ”\n${dynamicSystemPrompt}\nโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”`);
+
+								if (userInput) {
+									console.log(`\n๐Ÿ’ฌ User Input: "${userInput}"`);
+								}
+
+								if (useCharacter && characterDescription) {
+									console.log(`\n๐Ÿ‘ค Character Reference: "${characterDescription}"`);
+								}
+
+								if (thisPromptSources.length > 0) {
+									console.log(`\n๐Ÿ“š Source Prompts (${thisPromptSources.length}):`);
+									thisPromptSources.forEach((sp, idx) => {
+										console.log(`  [${idx + 1}] POS: ${sp.positive_prompt.substring(0, 100)}${sp.positive_prompt.length > 100 ? '...' : ''}`);
+									});
+								}
+
+								if (isImg2Img && multiImageData.length > 0) {
+									console.log(`\n๐Ÿ–ผ๏ธ Input Images (${multiImageData.length}):`);
+									multiImageData.forEach((imgData, idx) => {
+										console.log(`  [${imgData.label}] URL: ${imgData.url}`);
+										console.log(`           Base64 Length: ${imgData.base64?.length || 0} chars`);
+									});
+								}
+
+								if (affectElements && affectElements.length > 0) {
+									console.log(`\n๐ŸŽจ Affect Elements: ${affectElements.join(', ')}`);
+								}
+
+								console.log(`\nโš™๏ธ Model Settings: Temp=${temperatureValue}, Top-P=${topPValue}, SDXL=${isSDXL}`);
+
+								// Log complete payload (excluding sensitive keys)
+								const payloadForLogging = {
+									...payload,
+									gemini_api_key: payload.gemini_api_key ? '***REDACTED***' : undefined,
+									grok_api_key: payload.grok_api_key ? '***REDACTED***' : undefined,
+									images: payload.images ? [`[Base64 image data: ${payload.images[0]?.length || 0} chars]`] : undefined,
+									multi_images: payload.multi_images ? payload.multi_images.map(img => ({ label: img.label, base64: `[${img.base64?.length || 0} chars]` })) : undefined
+								};
+								console.log(`\nโ”โ”โ” COMPLETE REQUEST PAYLOAD โ”โ”โ”`);
+								console.log(JSON.stringify(payloadForLogging, null, 2));
+								console.log(`โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”`);
+								console.groupEnd();
+
+								// Retry logic
+								const maxRetries = 3;
+								let retryCount = 0;
+								let success = false;
+								let promptResult = null;
+
+								while (retryCount <= maxRetries && !success && !cancelRequested) {
+									try {
+										if (retryCount > 0) {
+											if (statusBadge) statusBadge.textContent = `๐Ÿ”„ Retry ${retryCount}/${maxRetries}`;
+											if (messageDiv) messageDiv.textContent = `Rate limited, retrying in ${Math.pow(2, retryCount - 1)}s...`;
+											await new Promise(resolve => setTimeout(resolve, Math.pow(2, retryCount - 1) * 1000));
+											if (cancelRequested) break;
+										}
+
+										const response = await api.fetchApi("/instaraw/generate_creative_prompts", {
+											method: "POST",
+											headers: { "Content-Type": "application/json" },
+											body: JSON.stringify(payload),
+											signal: signal
+										});
+
+										const result = await parseJSONResponse(response);
+
+										if (response.status === 429 || (result.error && /rate.*limit/i.test(result.error))) {
+											throw new Error("Rate limited");
+										}
+
+										if (!response.ok) {
+											throw new Error(result?.error || `API error ${response.status}`);
+										}
+
+										if (result.success && result.prompts && result.prompts.length > 0) {
+											const rawPrompt = result.prompts[0];
+
+											// Parse response (handle multiple formats)
+											const parseStructuredPrompt = (text) => {
+												// Handle both \n and \\n line breaks
+												const normalizedText = text.replace(/\\n/g, '\n');
+
+												const parsed = {
+													positive: "",
+													negative: "",
+													tags: [],
+													classification: { content_type: "other", safety_level: "sfw", shot_type: "other" }
+												};
+
+												// Try to find key sections using regex
+												const negativeMatch = normalizedText.match(/NEGATIVE:\s*(.+?)(?=\n(?:CONTENT_TYPE|SAFETY_LEVEL|SHOT_TYPE|TAGS|$))/is);
+												const contentTypeMatch = normalizedText.match(/CONTENT_TYPE:\s*(.+?)(?=\n|$)/i);
+												const safetyLevelMatch = normalizedText.match(/SAFETY_LEVEL:\s*(.+?)(?=\n|$)/i);
+												const shotTypeMatch = normalizedText.match(/SHOT_TYPE:\s*(.+?)(?=\n|$)/i);
+												const tagsMatch = normalizedText.match(/TAGS:\s*(.+?)(?=\n|$)/is);
+
+												// Extract NEGATIVE (if found)
+												if (negativeMatch) {
+													parsed.negative = negativeMatch[1].trim();
+												}
+
+												// Extract POSITIVE - either with prefix or everything before NEGATIVE
+												const positiveMatch = normalizedText.match(/POSITIVE:\s*(.+?)(?=\nNEGATIVE:|$)/is);
+												if (positiveMatch) {
+													parsed.positive = positiveMatch[1].trim();
+												} else if (negativeMatch) {
+													// No POSITIVE: prefix found, extract everything before NEGATIVE:
+													const beforeNegative = normalizedText.split(/\nNEGATIVE:/i)[0];
+													parsed.positive = beforeNegative.trim();
+												} else {
+													// No structure found, check if there's any text before metadata fields
+													const beforeMetadata = normalizedText.split(/\n(?:CONTENT_TYPE|SAFETY_LEVEL|SHOT_TYPE|TAGS):/i)[0];
+													parsed.positive = beforeMetadata.trim();
+												}
+
+												// Extract metadata
+												if (contentTypeMatch) parsed.classification.content_type = contentTypeMatch[1].trim().toLowerCase();
+												if (safetyLevelMatch) parsed.classification.safety_level = safetyLevelMatch[1].trim().toLowerCase();
+												if (shotTypeMatch) parsed.classification.shot_type = shotTypeMatch[1].trim().toLowerCase();
+												if (tagsMatch) parsed.tags = tagsMatch[1].split(',').map(t => t.trim()).filter(t => t);
+
+												// Fallback: if still no positive, use entire text
+												if (!parsed.positive && text) parsed.positive = text;
+
+												return parsed;
+											};
+
+											if (typeof rawPrompt === 'string') {
+												promptResult = parseStructuredPrompt(rawPrompt);
+											} else if (rawPrompt.prompt) {
+												promptResult = parseStructuredPrompt(rawPrompt.prompt);
+											} else if (rawPrompt.POSITIVE) {
+												promptResult = {
+													positive: rawPrompt.POSITIVE || "",
+													negative: rawPrompt.NEGATIVE || "",
+													tags: typeof rawPrompt.TAGS === 'string' ? rawPrompt.TAGS.split(',').map(t => t.trim()).filter(t => t) : (rawPrompt.TAGS || []),
+													classification: {
+														content_type: (rawPrompt.CONTENT_TYPE || "other").toLowerCase(),
+														safety_level: (rawPrompt.SAFETY_LEVEL || "sfw").toLowerCase(),
+														shot_type: (rawPrompt.SHOT_TYPE || "other").toLowerCase()
+													}
+												};
+											} else {
+												promptResult = rawPrompt;
+											}
+
+											// ๐Ÿ” DETAILED LOGGING: Preview the response
+											console.group(`[RPG] โœ… RESPONSE RECEIVED [${i + 1}/${generationCount}]`);
+											console.log(`\nโ”โ”โ” RAW API RESPONSE โ”โ”โ”\n${typeof rawPrompt === 'string' ? rawPrompt : JSON.stringify(rawPrompt, null, 2)}\nโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”`);
+											console.log(`\nโ”โ”โ” PARSED RESULT โ”โ”โ”`);
+											console.log(`POSITIVE: ${promptResult.positive?.substring(0, 200)}${promptResult.positive?.length > 200 ? '...' : ''}`);
+											console.log(`NEGATIVE: ${promptResult.negative || '(none)'}`);
+											console.log(`CONTENT_TYPE: ${promptResult.classification?.content_type || 'N/A'}`);
+											console.log(`SAFETY_LEVEL: ${promptResult.classification?.safety_level || 'N/A'}`);
+											console.log(`SHOT_TYPE: ${promptResult.classification?.shot_type || 'N/A'}`);
+											console.log(`TAGS: ${promptResult.tags?.join(', ') || '(none)'}`);
+											console.groupEnd();
+
+											success = true;
+										} else {
+											throw new Error(result.error || "No prompts returned");
+										}
+									} catch (error) {
+										if (error.name === 'AbortError' || cancelRequested) break;
+										if (error.message.includes("Rate limited") || error.message.includes("429")) {
+											retryCount++;
+											if (retryCount > maxRetries) throw new Error("Max retries exceeded");
+										} else {
+											throw error;
+										}
+									}
+								}
+
+								if (cancelRequested || !success) {
+									throw new Error(cancelRequested ? "Cancelled" : "Failed after retries");
+								}
+
+								// Save to database with badges
+								const savedPrompt = await addGeneratedPrompt({
+									positive: promptResult.positive,
+									negative: promptResult.negative,
+									tags: promptResult.tags || [],
+									classification: promptResult.classification || { content_type: "other", safety_level: "sfw", shot_type: "other" },
+									model_badge: currentModelBadge,
+									theme_badge: currentThemeBadge
+								});
+
+								console.log(`[RPG] โœ… [${i + 1}] Saved with ID: ${savedPrompt.id}`);
+
+								// Update UI - success
+								const preview = promptResult.positive?.slice(0, 80) || "No content";
+								const message = preview + (promptResult.positive?.length > 80 ? "..." : "");
+								updateProgressState(i, { status: 'success', progress: 100, message });
+
+								return { status: 'success', index: i, prompt: promptResult };
+
+							} catch (error) {
+								console.error(`[RPG] โŒ [${i + 1}] Error:`, error.message);
+
+								// Update UI - error
+								const status = error.message.includes("Cancelled") ? 'cancelled' : 'error';
+								updateProgressState(i, { status, progress: 0, message: error.message || "Generation failed" });
+
+								return { status: 'error', index: i, error };
+							}
+						};
+
+						// Launch all prompts in parallel with 222ms stagger
+						console.log(`[RPG] ๐Ÿš€ Launching ${generationCount} parallel requests (222ms stagger)...`);
+						const promises = [];
+						for (let i = 0; i < generationCount; i++) {
+							if (i > 0) {
+								await new Promise(resolve => setTimeout(resolve, 222));
+							}
+							promises.push(generateSinglePrompt(i));
+						}
+
+						// Wait for all to complete
+						const results = await Promise.allSettled(promises);
+						const successResults = results.filter(r => r.status === 'fulfilled' && r.value.status === 'success');
+						successResults.forEach(r => {
+							const promptData = r.value.prompt;
+							// For img2img: attach image index and repeat count
+							if (isImg2Img && uniqueImages[r.value.index]) {
+								promptData._imageIndex = r.value.index;
+								promptData._repeatCount = uniqueImages[r.value.index].repeat_count || 1;
+							}
+							allGeneratedPrompts.push(promptData);
+						});
+
+						console.log(`[RPG] โœ… Parallel generation complete: ${successResults.length}/${generationCount} succeeded`);
+
+
+						// Show results
+						if (allGeneratedPrompts.length > 0) {
+							console.log(`[RPG] โœ… Generated ${allGeneratedPrompts.length}/${generationCount} prompts successfully`);
+
+							// Store generated prompts and update state FIRST (regardless of which tab is active)
+							node._generatedUnifiedPrompts = allGeneratedPrompts;
+							node._generationInProgress = false; // Mark as complete
+							// Keep node._progressState so we can recreate progress items when switching back
+							console.log(`[RPG] State updated: ${allGeneratedPrompts.length} prompts stored, keeping progress state for restore`);
+
+							// Query for FRESH DOM elements (in case user switched tabs during generation)
+							const currentProgressSection = container.querySelector(".instaraw-rpg-generation-progress");
+							const currentPreviewSection = container.querySelector(".instaraw-rpg-generate-preview");
+							const currentPreviewList = container.querySelector(".instaraw-rpg-generate-preview-list");
+
+							console.log(`[RPG] Found elements - progress: ${!!currentProgressSection}, preview: ${!!currentPreviewSection}, list: ${!!currentPreviewList}`);
+
+							// Update UI elements if they exist (only if on Generate tab)
+							const progressHeader = currentProgressSection?.querySelector(".instaraw-rpg-progress-header h4");
+							if (progressHeader) {
+								const successCount = allGeneratedPrompts.length;
+								const failedCount = generationCount - successCount;
+								progressHeader.innerHTML = `
+									โœ“ Generation Complete:
+									${successCount} succeeded
+									${failedCount > 0 ? `, ${failedCount} failed` : ''}
+								`;
+							}
+
+							// Hide cancel button and show quick accept button
+							const cancelBtn = currentProgressSection?.querySelector(".instaraw-rpg-cancel-generation-btn");
+							if (cancelBtn) cancelBtn.style.display = "none";
+							const quickAccept = currentProgressSection?.querySelector(".instaraw-rpg-quick-accept");
+							if (quickAccept) quickAccept.style.display = "block";
+
+							// Update preview list if it exists
+							if (currentPreviewList) {
+								// Helper to render image thumbnails for preview
+								const renderPreviewImages = (index) => {
+									if (!isImg2Img) return '';
+									// Helper to get image URL (handles both url and thumbnail formats)
+									const getImgUrl = (img) => img?.url || (img?.thumbnail ? `/instaraw/view/${img.thumbnail}` : null);
+									const images = [
+										{ img: uniqueImages[index], label: "1" },
+										{ img: uniqueImages2[index], label: "2" },
+										{ img: uniqueImages3[index], label: "3" },
+										{ img: uniqueImages4[index], label: "4" }
+									].filter(item => item.img && getImgUrl(item.img));
+									if (images.length === 0) return '';
+									return `
+										

+											${images.map(item => `
+												

+													

+														Image ${item.label}
+													

+												

+											`).join('')}
+										

+									`;
+								};
+
+								currentPreviewList.innerHTML = allGeneratedPrompts
+									.map((p, idx) => `
+										

+											

+												#${idx + 1}
+												${renderPreviewImages(idx)}
+											

+											
${escapeHtml(p.positive || "")}

+										

+									`)
+									.join("");
+
+								// Show preview section (keep progress visible for stats)
+								if (currentPreviewSection) currentPreviewSection.style.display = "block";
+								console.log(`[RPG] Preview UI updated (on Generate tab)`);
+							} else {
+								console.log(`[RPG] Preview UI skipped (on different tab - will restore on tab switch)`);
+							}
+
+							setupEventHandlers();
+
+							// Keep progress section visible until user clicks "Add to Batch"
+							// This prevents layout shift and lets users review stats
+						} else if (cancelRequested) {
+							console.log("[RPG] โน Generation cancelled - no prompts generated");
+
+							// Update progress header
+							const progressHeader = progressSection?.querySelector(".instaraw-rpg-progress-header h4");
+							if (progressHeader) {
+								progressHeader.innerHTML = 'โน Generation Cancelled';
+							}
+
+							// Hide cancel button
+							const cancelBtn = progressSection?.querySelector(".instaraw-rpg-cancel-generation-btn");
+							if (cancelBtn) cancelBtn.style.display = "none";
+
+							// Keep progress section visible to show cancellation status
+						} else {
+							throw new Error("No prompts were generated successfully");
+						}
+
+					} catch (error) {
+						console.error("[RPG] โŒ Error during parallel prompt generation:", error);
+						console.error("[RPG] Error stack:", error.stack);
+
+						// Clear generation state (but keep progress state for viewing errors)
+						node._generationInProgress = false;
+						// Keep progressState to show error details when switching tabs
+						// delete node._progressState;
+						// delete node._generationCount;
+
+						// Hide progress section, show error
+						const currentProgressSection = container.querySelector(".instaraw-rpg-generation-progress");
+						if (currentProgressSection) currentProgressSection.style.display = "none";
+						alert(`Generation error: ${error.message || error}`);
+
+					} finally {
+						// Re-enable generate button
+						if (generateBtn) {
+							generateBtn.disabled = false;
+							generateBtn.style.opacity = "1";
+							const detectedMode = node._linkedAILMode || "txt2img";
+							const emoji = detectedMode === 'img2img' ? '๐Ÿ–ผ๏ธ' : '๐ŸŽจ';
+							generateBtn.textContent = `${emoji} Generate Prompts`;
+						}
+
+						// Clear abort controller
+						generationAbortController = null;
+
+						console.log("[RPG] Generate Unified Prompts - END (Sequential)");
+						console.groupEnd();
+					}
+				};
+
+				// === Update Progress State (updates both DOM and node state) ===
+				const updateProgressState = (index, updates) => {
+					if (!node._progressState || !node._progressState[index]) return;
+
+					// Update node state
+					Object.assign(node._progressState[index], updates);
+
+					// Update DOM if elements exist
+					const progressItems = container.querySelector(".instaraw-rpg-progress-items");
+					const progressItem = progressItems?.querySelector(`[data-index="${index}"]`);
+					if (!progressItem) return;
+
+					const statusBadge = progressItem.querySelector(".instaraw-rpg-progress-item-status");
+					const progressBar = progressItem.querySelector(".instaraw-rpg-progress-item-fill");
+					const messageDiv = progressItem.querySelector(".instaraw-rpg-progress-item-message");
+
+					// Update status
+					if (updates.status) {
+						const statusMap = {
+							'pending': { class: 'pending', text: 'โณ Pending' },
+							'in-progress': { class: 'in-progress', text: 'โณ Generating...' },
+							'success': { class: 'success', text: 'โœ“ Complete' },
+							'error': { class: 'error', text: 'โœ– Failed' },
+							'cancelled': { class: 'error', text: 'โน Cancelled' }
+						};
+						const statusInfo = statusMap[updates.status];
+						if (statusBadge && statusInfo) {
+							statusBadge.className = `instaraw-rpg-progress-item-status ${statusInfo.class}`;
+							statusBadge.textContent = statusInfo.text;
+						}
+						if (statusInfo) {
+							progressItem.classList.remove('pending', 'in-progress', 'success', 'error');
+							progressItem.classList.add(statusInfo.class);
+						}
+					}
+
+					// Update progress bar
+					if (updates.progress !== undefined && progressBar) {
+						progressBar.style.width = `${updates.progress}%`;
+						if (updates.status === 'in-progress') {
+							progressBar.classList.add('animating');
+						} else {
+							progressBar.classList.remove('animating');
+						}
+					}
+
+					// Update message
+					if (updates.message !== undefined && messageDiv) {
+						messageDiv.textContent = updates.message;
+						messageDiv.className = 'instaraw-rpg-progress-item-message';
+					}
+				};
+
+				// === Restore Generation UI (after tab switch) ===
+				const restoreGenerationUI = () => {
+					const progressSection = container.querySelector(".instaraw-rpg-generation-progress");
+					const previewSection = container.querySelector(".instaraw-rpg-generate-preview");
+					const progressItems = container.querySelector(".instaraw-rpg-progress-items");
+
+					console.log(`[RPG] restoreGenerationUI called - inProgress: ${node._generationInProgress}, hasCompleted: ${!!node._generatedUnifiedPrompts}, hasState: ${!!node._progressState}`);
+
+					// If generation completed, restore the preview
+					if (node._generatedUnifiedPrompts) {
+						console.log(`[RPG] Restoring completed generation with ${node._generatedUnifiedPrompts.length} prompts`);
+
+						// Restore progress items if we have the state
+						if (node._progressState && progressItems) {
+							progressItems.innerHTML = "";
+							const generationCount = node._generationCount || node._progressState.length;
+
+							node._progressState.forEach((state, i) => {
+								const progressItem = document.createElement("div");
+								progressItem.className = `instaraw-rpg-progress-item ${state.status}`;
+								progressItem.dataset.index = i;
+
+								const statusMap = {
+									'pending': 'โณ Pending',
+									'in-progress': 'โณ Generating...',
+									'success': 'โœ“ Complete',
+									'error': 'โœ– Failed',
+									'cancelled': 'โน Cancelled'
+								};
+
+								progressItem.innerHTML = `
+									

+										Prompt ${i + 1}/${generationCount}
+										${statusMap[state.status] || 'โณ Pending'}
+									

+									

+										

+									

+									
${state.message || ''}

+								`;
+								progressItems.appendChild(progressItem);
+							});
+							console.log(`[RPG] Restored ${node._progressState.length} completed progress items`);
+						}
+
+						// Update progress header with completion statistics
+						const progressHeader = progressSection?.querySelector(".instaraw-rpg-progress-header h4");
+						if (progressHeader) {
+							const successCount = node._generatedUnifiedPrompts.length;
+							progressHeader.innerHTML = `
+								โœ“ Generation Complete:
+								${successCount} succeeded
+							`;
+						}
+
+						// Hide cancel button and show quick accept button
+						const cancelBtn = progressSection?.querySelector(".instaraw-rpg-cancel-generation-btn");
+						if (cancelBtn) cancelBtn.style.display = "none";
+						const quickAccept = progressSection?.querySelector(".instaraw-rpg-quick-accept");
+						if (quickAccept) quickAccept.style.display = "block";
+
+						// Populate preview list and show preview section
+						const previewList = container.querySelector(".instaraw-rpg-generate-preview-list");
+						console.log(`[RPG] Preview list element found: ${!!previewList}`);
+						if (previewList) {
+							previewList.innerHTML = node._generatedUnifiedPrompts
+								.map((p, idx) => `
+									

+										#${idx + 1}
+										
${escapeHtml(p.positive || "")}

+									

+								`)
+								.join("");
+							console.log(`[RPG] Preview list populated with ${node._generatedUnifiedPrompts.length} items`);
+						} else {
+							console.error(`[RPG] Preview list element not found!`);
+						}
+
+						// Show preview section (contains list + buttons)
+						if (previewSection) previewSection.style.display = "block";
+
+						return; // Exit early after handling completed generation
+					}
+
+					// Restore in-progress generation
+					if (node._generationInProgress && node._progressState) {
+						console.log(`[RPG] Restoring in-progress generation with ${node._progressState.length} items`);
+
+						// Recreate progress items from state
+						if (progressItems) {
+							progressItems.innerHTML = "";
+							const generationCount = node._generationCount || node._progressState.length;
+
+							node._progressState.forEach((state, i) => {
+								const progressItem = document.createElement("div");
+								progressItem.className = `instaraw-rpg-progress-item ${state.status}`;
+								progressItem.dataset.index = i;
+
+								const statusMap = {
+									'pending': 'โณ Pending',
+									'in-progress': 'โณ Generating...',
+									'success': 'โœ“ Complete',
+									'error': 'โœ– Failed',
+									'cancelled': 'โน Cancelled'
+								};
+
+								progressItem.innerHTML = `
+									

+										Prompt ${i + 1}/${generationCount}
+										${statusMap[state.status] || 'โณ Pending'}
+									

+									

+										

+									

+									
${state.message || ''}

+								`;
+								progressItems.appendChild(progressItem);
+							});
+						}
+
+						const progressHeader = progressSection?.querySelector(".instaraw-rpg-progress-header h4");
+						if (progressHeader) {
+							progressHeader.innerHTML = `โณ Generating Prompts...`;
+						}
+					} else if (node._generationInProgress) {
+						// Generation still in progress but no state - show message
+						console.log(`[RPG] Generation in progress but no state to restore`);
+						const progressHeader = progressSection?.querySelector(".instaraw-rpg-progress-header h4");
+						if (progressHeader) {
+							progressHeader.innerHTML = `โณ Generation in progress...`;
+						}
+					}
+				};
+
+				// === Accept Generated Prompts ===
+				const acceptGeneratedPrompts = () => {
+					if (!node._generatedUnifiedPrompts) return;
+
+					const existingQueue = parsePromptBatch();
+					const newCount = node._generatedUnifiedPrompts.length;
+
+					// If there are existing prompts, ask the user to confirm
+					if (existingQueue.length > 0) {
+						const confirmAdd = confirm(
+							`You have ${existingQueue.length} existing prompt(s) in the batch.\n\n` +
+							`Add ${newCount} new prompt(s) to the batch?\n\n` +
+							`OK = Add to existing\n` +
+							`Cancel = Don't add (clear batch first if needed)`
+						);
+
+						if (!confirmAdd) {
+							console.log("[RPG] User cancelled adding prompts to batch");
+							return;
+						}
+					}
+
+					const promptQueue = existingQueue;
+					node._generatedUnifiedPrompts.forEach((p) => {
+						// For img2img: use stored repeat count, otherwise default to 1
+						const repeatCount = p._repeatCount || 1;
+
+						promptQueue.push({
+							id: generateUniqueId(),
+							positive_prompt: p.positive || "",
+							negative_prompt: p.negative || "",
+							repeat_count: repeatCount,
+							tags: p.tags || [],
+							source_id: null,
+							// Store image index for img2img (for reference)
+							_imageIndex: p._imageIndex,
+						});
+
+						console.log(`[RPG] Added prompt to batch with repeat count: ${repeatCount}${p._imageIndex !== undefined ? ` (image #${p._imageIndex + 1})` : ''}`);
+					});
+
+					setPromptBatchData(promptQueue);
+					delete node._generatedUnifiedPrompts;
+					delete node._warnedAboutPendingPrompts; // Reset warning flag
+					node._generationInProgress = false;
+					delete node._progressState;
+					delete node._generationCount;
+
+					// Hide all generation UI now that prompts are accepted
+					const progressSection = container.querySelector(".instaraw-rpg-generation-progress");
+					const previewSection = container.querySelector(".instaraw-rpg-generate-preview");
+					if (progressSection) progressSection.style.display = "none";
+					if (previewSection) previewSection.style.display = "none";
+
+					renderUI();
+				};
+
+				// === Cancel Generated Prompts ===
+				const cancelGeneratedPrompts = () => {
+					delete node._generatedUnifiedPrompts;
+					delete node._warnedAboutPendingPrompts; // Reset warning flag
+					node._generationInProgress = false;
+					delete node._progressState;
+					delete node._generationCount;
+
+					// Hide all generation UI when cancelling
+					const progressSection = container.querySelector(".instaraw-rpg-generation-progress");
+					const previewSection = container.querySelector(".instaraw-rpg-generate-preview");
+					if (progressSection) progressSection.style.display = "none";
+					if (previewSection) previewSection.style.display = "none";
+
+					renderUI();
+				};
+
+				// === Event Handlers Setup (Following AIL Pattern) ===
+				const setupEventHandlers = () => {
+					console.log("[RPG] ============ setupEventHandlers CALLED ============");
+					console.log("[RPG] Current active tab:", node.properties.active_tab);
+
+					// === Canvas Panning Event Forwarding ===
+					// Forward mouse events to ComfyUI canvas when clicking on non-interactive areas
+					const isInteractiveElement = (element) => {
+						if (!element) return false;
+
+						// Check if it's a standard interactive element
+						const tagName = element.tagName?.toLowerCase();
+						if (['button', 'input', 'select', 'textarea', 'a'].includes(tagName)) {
+							return true;
+						}
+
+						// Check if it has interactive classes or attributes
+						const className = element.className || '';
+						if (typeof className === 'string' && (
+							className.includes('-btn') ||
+							className.includes('-input') ||
+							className.includes('-select') ||
+							className.includes('-slider') ||
+							className.includes('-textarea') ||
+							className.includes('prompt-card')
+						)) {
+							return true;
+						}
+
+						// Check if draggable
+						if (element.draggable) {
+							return true;
+						}
+
+						return false;
+					};
+
+					// Check element and all parents up to container
+					const isInteractiveOrChildOfInteractive = (target) => {
+						let element = target;
+						while (element && element !== container) {
+							if (isInteractiveElement(element)) {
+								return true;
+							}
+							element = element.parentElement;
+						}
+						return false;
+					};
+
+					// Track drag state for manual panning
+					let isDragging = false;
+					let dragStartX = 0;
+					let dragStartY = 0;
+					let graphOffsetStartX = 0;
+					let graphOffsetStartY = 0;
+
+					// Manual canvas panning (direct LiteGraph manipulation)
+					const canvasForwardHandler = (e) => {
+						// Skip if this is a synthetic event
+						if (e._rpgForwarded) {
+							return;
+						}
+
+						// For mousedown, check if it's interactive
+						if (e.type === 'mousedown') {
+							const isInteractive = isInteractiveOrChildOfInteractive(e.target);
+
+							if (isInteractive) {
+								isDragging = false;
+								return;
+							}
+
+							// Start manual drag
+							isDragging = true;
+							dragStartX = e.clientX;
+							dragStartY = e.clientY;
+
+							// Get LiteGraph canvas and store initial offset
+							const canvas = app?.canvas || app?.graph?.list_of_graphcanvas?.[0];
+							if (canvas) {
+								graphOffsetStartX = canvas.ds.offset[0];
+								graphOffsetStartY = canvas.ds.offset[1];
+								console.log("[RPG Manual Pan] ๐ŸŽฌ Started - Initial offset:", graphOffsetStartX, graphOffsetStartY);
+							}
+
+							e.preventDefault();
+							e.stopPropagation();
+						}
+
+						// Handle mousemove for panning
+						if (e.type === 'mousemove' && isDragging) {
+							const deltaX = e.clientX - dragStartX;
+							const deltaY = e.clientY - dragStartY;
+
+							// Get LiteGraph canvas and update offset
+							// Divide by scale to match native ComfyUI drag behavior at any zoom level
+							const canvas = app?.canvas || app?.graph?.list_of_graphcanvas?.[0];
+							if (canvas) {
+								const scale = canvas.ds.scale || 1;
+								canvas.ds.offset[0] = graphOffsetStartX + (deltaX / scale);
+								canvas.ds.offset[1] = graphOffsetStartY + (deltaY / scale);
+								canvas.setDirty(true, true);
+							}
+
+							e.preventDefault();
+							e.stopPropagation();
+						}
+
+						// Handle mouseup to end drag
+						if (e.type === 'mouseup' && isDragging) {
+							isDragging = false;
+							console.log("[RPG Manual Pan] ๐Ÿ Ended drag");
+							e.preventDefault();
+							e.stopPropagation();
+						}
+					};
+
+					// Forward wheel events for zooming
+					const wheelForwardHandler = (e) => {
+						// Skip if synthetic
+						if (e._rpgForwarded) {
+							return;
+						}
+
+						// Check if over non-interactive area
+						const isInteractive = isInteractiveOrChildOfInteractive(e.target);
+						if (isInteractive) {
+							return;
+						}
+
+						// Find the ComfyUI canvas
+						const canvas = document.querySelector('#graph-canvas') ||
+									  document.querySelector('canvas.litegraph') ||
+									  document.querySelector('.litegraph canvas');
+
+						if (canvas) {
+							console.log("[RPG Canvas Forward] ๐Ÿ” Forwarding wheel event for zoom");
+
+							e.preventDefault();
+							e.stopPropagation();
+
+							// Create and dispatch wheel event
+							const newEvent = new WheelEvent('wheel', {
+								bubbles: true,
+								cancelable: true,
+								view: window,
+								clientX: e.clientX,
+								clientY: e.clientY,
+								screenX: e.screenX,
+								screenY: e.screenY,
+								deltaX: e.deltaX,
+								deltaY: e.deltaY,
+								deltaZ: e.deltaZ,
+								deltaMode: e.deltaMode,
+								ctrlKey: e.ctrlKey,
+								shiftKey: e.shiftKey,
+								altKey: e.altKey,
+								metaKey: e.metaKey
+							});
+
+							newEvent._rpgForwarded = true;
+							canvas.dispatchEvent(newEvent);
+						}
+					};
+
+					// Remove old listeners if exist (prevent duplicates)
+					if (container._canvasForwardHandler) {
+						container.removeEventListener('mousedown', container._canvasForwardHandler, true);
+						document.removeEventListener('mousemove', container._canvasForwardHandler, true);
+						document.removeEventListener('mouseup', container._canvasForwardHandler, true);
+					}
+					if (container._wheelForwardHandler) {
+						container.removeEventListener('wheel', container._wheelForwardHandler, true);
+					}
+
+					container._canvasForwardHandler = canvasForwardHandler;
+					container._wheelForwardHandler = wheelForwardHandler;
+
+					// Listen on container for mousedown, but document for mousemove/mouseup (to track outside container)
+					container.addEventListener('mousedown', canvasForwardHandler, true);
+					document.addEventListener('mousemove', canvasForwardHandler, true);
+					document.addEventListener('mouseup', canvasForwardHandler, true);
+
+					// Listen for wheel events for zooming
+					container.addEventListener('wheel', wheelForwardHandler, true);
+
+					// Tab switching
+					container.querySelectorAll(".instaraw-rpg-tab").forEach((tab) => {
+						tab.onclick = () => {
+							node.properties.active_tab = tab.dataset.tab;
+							renderUI();
+						};
+					});
+
+					// Mode dropdown
+					const modeDropdown = container.querySelector(".instaraw-rpg-mode-dropdown");
+					if (modeDropdown) {
+						modeDropdown.onchange = (e) => {
+							const modeWidget = node.widgets?.find((w) => w.name === "mode");
+							if (modeWidget) {
+								modeWidget.value = e.target.value;
+								renderUI();
+							}
+						};
+					}
+
+					// Creative model settings
+					const creativeModelSelect = container.querySelector(".instaraw-rpg-model-select");
+					if (creativeModelSelect) {
+						creativeModelSelect.onchange = (e) => {
+							node.properties.creative_model = e.target.value;
+							const widget = node.widgets?.find((w) => w.name === "creative_model");
+							if (widget) widget.value = e.target.value;
+							renderUI();
+						};
+					}
+
+					const creativeTempInput = container.querySelector(".instaraw-rpg-model-temp");
+					if (creativeTempInput) {
+						creativeTempInput.onchange = (e) => {
+							const val = parseFloat(e.target.value) || 0.9;
+							node.properties.creative_temperature = val;
+							app.graph.setDirtyCanvas(true, true);
+						};
+					}
+
+					const creativeTopPInput = container.querySelector(".instaraw-rpg-model-top-p");
+					if (creativeTopPInput) {
+						creativeTopPInput.onchange = (e) => {
+							const val = parseFloat(e.target.value) || 0.9;
+							node.properties.creative_top_p = val;
+							app.graph.setDirtyCanvas(true, true);
+						};
+					}
+
+					// System prompt textarea (for Generate tab)
+					const systemPromptInput = container.querySelector(".instaraw-rpg-system-prompt");
+					if (systemPromptInput) {
+						// Auto-resize on load
+						autoResizeTextarea(systemPromptInput);
+
+						systemPromptInput.oninput = (e) => {
+							node.properties.creative_system_prompt = e.target.value;
+							saveSettings();
+							autoResizeTextarea(e.target);
+							app.graph.setDirtyCanvas(true, true);
+						};
+					}
+
+					// Character system prompt textarea (for Character tab)
+					const characterSystemPromptInput = container.querySelector(".instaraw-rpg-character-system-prompt");
+					if (characterSystemPromptInput) {
+						// Auto-resize on load
+						autoResizeTextarea(characterSystemPromptInput);
+
+						characterSystemPromptInput.oninput = (e) => {
+							node.properties.character_system_prompt = e.target.value;
+							saveSettings();
+							autoResizeTextarea(e.target);
+							app.graph.setDirtyCanvas(true, true);
+						};
+					}
+
+					// Handle details toggle (re-calculate textarea height when opened)
+					container.querySelectorAll(".instaraw-rpg-advanced-settings").forEach((details) => {
+						details.addEventListener('toggle', () => {
+							if (details.open) {
+								const textarea = details.querySelector('textarea');
+								if (textarea) {
+									setTimeout(() => autoResizeTextarea(textarea), 10);
+								}
+							}
+						});
+					});
+
+					// Reload database button
+					const reloadDBBtn = container.querySelector(".instaraw-rpg-reload-db-btn");
+					if (reloadDBBtn) {
+						reloadDBBtn.onclick = () => loadPromptsDatabase();
+					}
+
+					// Library search
+					const searchInput = container.querySelector(".instaraw-rpg-search-input");
+					if (searchInput) {
+						searchInput.oninput = (e) => {
+							clearTimeout(node._searchTimeout);
+							node._searchTimeout = setTimeout(() => {
+								updateFilter("search_query", e.target.value);
+							}, 300);
+						};
+					}
+
+					// Library filters
+					container.querySelectorAll(".instaraw-rpg-filter-dropdown").forEach((dropdown) => {
+						dropdown.onchange = (e) => {
+							updateFilter(dropdown.dataset.filter, e.target.value);
+						};
+					});
+
+					// Show bookmarked checkbox
+					const showBookmarkedCheckbox = container.querySelector(".instaraw-rpg-show-bookmarked-checkbox");
+					if (showBookmarkedCheckbox) {
+						showBookmarkedCheckbox.onchange = (e) => {
+							updateFilter("show_bookmarked", e.target.checked);
+						};
+					}
+
+					// SDXL mode checkbox
+					const sdxlModeCheckbox = container.querySelector(".instaraw-rpg-sdxl-mode-checkbox");
+					if (sdxlModeCheckbox) {
+						sdxlModeCheckbox.onchange = (e) => {
+							updateFilter("sdxl_mode", e.target.checked);
+						};
+					}
+
+					// Clear filters
+					const clearFiltersBtn = container.querySelector(".instaraw-rpg-clear-filters-btn");
+					if (clearFiltersBtn) {
+						clearFiltersBtn.onclick = clearFilters;
+					}
+
+					// Random count input - save value
+					const randomCountInput = container.querySelector(".instaraw-rpg-random-count-input");
+					if (randomCountInput) {
+						randomCountInput.onchange = (e) => {
+							randomCount = parseInt(e.target.value) || 6;
+						};
+					}
+
+					// Show random prompts button
+					const showRandomBtn = container.querySelector(".instaraw-rpg-show-random-btn");
+					if (showRandomBtn) {
+						showRandomBtn.onclick = async () => {
+							const count = parseInt(randomCountInput?.value) || randomCount;
+							const filters = JSON.parse(node.properties.library_filters || "{}");
+
+							// Disable button and show loading state
+							showRandomBtn.disabled = true;
+							const originalText = showRandomBtn.innerHTML;
+							showRandomBtn.innerHTML = 'โณ Selecting...';
+
+							try {
+								// OPTIMIZATION: Do random selection on frontend since we already have the database loaded!
+								// No need to make API call and re-download/re-filter the database
+
+								// Apply filters using existing filterPrompts function
+								const filteredPrompts = filterPrompts(promptsDatabase, filters);
+
+								if (filteredPrompts.length === 0) {
+									throw new Error("No prompts match the current filters");
+								}
+
+								// Randomly select prompts
+								const selectedCount = Math.min(count, filteredPrompts.length);
+								const shuffled = [...filteredPrompts].sort(() => Math.random() - 0.5);
+								const selected = shuffled.slice(0, selectedCount);
+
+								// Store and display random prompts
+								randomPrompts = selected;
+								randomCount = count;
+								showingRandomPrompts = true;
+								currentPage = 0; // Reset pagination
+								renderUI();
+								console.log(`[RPG] Showing ${selected.length} random prompts (from ${filteredPrompts.length} filtered)`);
+
+							} catch (error) {
+								console.error("[RPG] Error selecting random prompts:", error);
+								showRandomBtn.innerHTML = `โœ– ${error.message}`;
+								setTimeout(() => {
+									showRandomBtn.innerHTML = originalText;
+									showRandomBtn.disabled = false;
+								}, 3000);
+							}
+						};
+					}
+
+					// Add all random prompts to batch
+					const addAllRandomBtn = container.querySelector(".instaraw-rpg-add-all-random-btn");
+					if (addAllRandomBtn) {
+						addAllRandomBtn.onclick = () => {
+							const existingQueue = parsePromptBatch();
+							const newCount = randomPrompts.length;
+
+							// If there are existing prompts, ask the user to confirm
+							if (existingQueue.length > 0) {
+								const confirmAdd = confirm(
+									`You have ${existingQueue.length} existing prompt(s) in the batch.\n\n` +
+									`Add ${newCount} random prompt(s) to the batch?\n\n` +
+									`OK = Add to existing\n` +
+									`Cancel = Don't add (clear batch first if needed)`
+								);
+
+								if (!confirmAdd) {
+									console.log("[RPG] User cancelled adding random prompts to batch");
+									return;
+								}
+							}
+
+							const promptQueue = existingQueue;
+							randomPrompts.forEach(promptData => {
+								const positivePrompt = promptData.prompt?.positive ?? promptData.positive ?? "";
+								const negativePrompt = promptData.prompt?.negative ?? promptData.negative ?? "";
+								promptQueue.push({
+									id: generateUniqueId(),
+									positive_prompt: positivePrompt,
+									negative_prompt: negativePrompt,
+									repeat_count: 1,
+									tags: promptData.tags || [],
+									source_id: promptData.id || null,
+									seed: 1111111,
+									seed_control: "randomize",
+								});
+							});
+
+							setPromptBatchData(promptQueue);
+							console.log(`[RPG] Added ${newCount} random prompts to batch`);
+
+							// Exit random mode
+							showingRandomPrompts = false;
+							randomPrompts = [];
+							renderUI();
+						};
+					}
+
+					// Reroll random prompts button
+					const rerollRandomBtn = container.querySelector(".instaraw-rpg-reroll-random-btn");
+					if (rerollRandomBtn) {
+						rerollRandomBtn.onclick = () => {
+							const count = randomCount; // Use same count as before
+							const filters = JSON.parse(node.properties.library_filters || "{}");
+
+							// Apply filters and randomly select
+							const filteredPrompts = filterPrompts(promptsDatabase, filters);
+
+							if (filteredPrompts.length === 0) {
+								alert("No prompts match the current filters");
+								return;
+							}
+
+							// Randomly select prompts (different from before)
+							const selectedCount = Math.min(count, filteredPrompts.length);
+							const shuffled = [...filteredPrompts].sort(() => Math.random() - 0.5);
+							const selected = shuffled.slice(0, selectedCount);
+
+							// Update and re-render
+							randomPrompts = selected;
+							renderUI();
+							console.log(`[RPG] Rerolled ${selected.length} random prompts`);
+						};
+					}
+
+					// Exit random mode button
+					const exitRandomBtn = container.querySelector(".instaraw-rpg-exit-random-btn");
+					if (exitRandomBtn) {
+						exitRandomBtn.onclick = () => {
+							showingRandomPrompts = false;
+							randomPrompts = [];
+							renderUI();
+						};
+					}
+
+					// Create custom prompt button
+					const createPromptBtn = container.querySelector(".instaraw-rpg-create-prompt-btn");
+					if (createPromptBtn) {
+						createPromptBtn.onclick = async () => {
+							try {
+								// Create empty prompt and add to user prompts
+								const newPrompt = await addUserPrompt({
+									positive: "",
+									negative: "",
+									tags: [],
+									content_type: "person",
+									safety_level: "sfw",
+									shot_type: "portrait"
+								});
+
+								// Reset filters to show user prompts and clear search
+								node.properties.library_filters = JSON.stringify({
+									prompt_source: "user",
+									search_query: "",
+									content_type: "any",
+									safety_level: "any",
+									shot_type: "any",
+									show_bookmarked: false
+								});
+								currentPage = 0; // Go to first page
+
+								// Immediately put it in edit mode
+								editingValues[newPrompt.id] = {
+									positive: "",
+									negative: "",
+									tags: "",
+									content_type: "person",
+									safety_level: "sfw",
+									shot_type: "portrait"
+								};
+								editingPrompts.add(newPrompt.id);
+								renderUI();
+								console.log("[RPG] Created new user prompt in edit mode, switched to My Prompts view");
+							} catch (error) {
+								console.error("[RPG] Error creating user prompt:", error);
+								alert(`Error creating prompt: ${error.message}`);
+							}
+						};
+					}
+
+					// Import prompts button
+					const importPromptsBtn = container.querySelector(".instaraw-rpg-import-prompts-btn");
+					if (importPromptsBtn) {
+						importPromptsBtn.onclick = () => {
+							const input = document.createElement("input");
+							input.type = "file";
+							input.accept = ".json";
+							input.onchange = async (e) => {
+								const file = e.target.files[0];
+								if (!file) return;
+
+								try {
+									const result = await importUserPrompts(file);
+									alert(`Successfully imported ${result.details.user} user prompts, ${result.details.batch} batch items, ${result.details.bookmarks} bookmarks${result.skipped > 0 ? ` (${result.skipped} duplicates skipped)` : ''}`);
+								} catch (error) {
+									console.error("[RPG] Error importing prompts:", error);
+									alert(`Error importing prompts: ${error.message}`);
+								}
+							};
+							input.click();
+						};
+					}
+
+					// Export prompts button
+					const exportPromptsBtn = container.querySelector(".instaraw-rpg-export-prompts-btn");
+					if (exportPromptsBtn) {
+						exportPromptsBtn.onclick = () => {
+							const promptBatch = parsePromptBatch();
+
+							const totalToExport = userPrompts.length + bookmarksCache.length + promptBatch.length;
+
+							if (totalToExport === 0) {
+								alert("Nothing to export. Add user prompts, favorite some library prompts, or add prompts to batch first.");
+								return;
+							}
+
+							exportUserPrompts();
+						};
+					}
+
+					// Enter selection mode button
+					const enterSelectionBtn = container.querySelector(".instaraw-rpg-enter-selection-btn");
+					if (enterSelectionBtn) {
+						enterSelectionBtn.onclick = () => {
+							selectionMode = true;
+							selectedPrompts.clear();
+							renderUI();
+							console.log("[RPG] Entered selection mode");
+						};
+					}
+
+					// Cancel selection mode button
+					const cancelSelectionBtn = container.querySelector(".instaraw-rpg-cancel-selection-btn");
+					if (cancelSelectionBtn) {
+						cancelSelectionBtn.onclick = () => {
+							selectionMode = false;
+							selectedPrompts.clear();
+							renderUI();
+							console.log("[RPG] Exited selection mode");
+						};
+					}
+
+					// Select all button
+					const selectAllBtn = container.querySelector(".instaraw-rpg-select-all-btn");
+					if (selectAllBtn) {
+						selectAllBtn.onclick = () => {
+							// Add all prompts currently visible (recompute to ensure we have current state)
+							const currentFilters = JSON.parse(node.properties.library_filters || "{}");
+							const currentPrompts = showingRandomPrompts ? randomPrompts : filterPrompts(promptsDatabase, currentFilters);
+							const page = node.properties.current_page || 0;
+							const pagePrompts = currentPrompts.slice(page * itemsPerPage, (page + 1) * itemsPerPage);
+
+							pagePrompts.forEach(prompt => {
+								// Only allow selecting user prompts and generated prompts (not library prompts)
+								if (prompt.is_user_created || prompt.is_ai_generated) {
+									selectedPrompts.add(prompt.id);
+								}
+							});
+							renderUI();
+							console.log(`[RPG] Select All clicked: ${selectedPrompts.size} total selected`);
+						};
+					}
+
+					// Deselect all button
+					const deselectAllBtn = container.querySelector(".instaraw-rpg-deselect-all-btn");
+					if (deselectAllBtn) {
+						deselectAllBtn.onclick = () => {
+							console.log(`[RPG] Deselect All clicked: clearing ${selectedPrompts.size} selections`);
+							selectedPrompts.clear();
+							renderUI();
+						};
+					}
+
+					// Delete selected button
+					const deleteSelectedBtn = container.querySelector(".instaraw-rpg-delete-selected-btn");
+					if (deleteSelectedBtn) {
+						deleteSelectedBtn.onclick = async () => {
+							if (selectedPrompts.size === 0) return;
+
+							const confirmMsg = `Delete ${selectedPrompts.size} selected prompt${selectedPrompts.size === 1 ? '' : 's'}?\n\nThis cannot be undone.`;
+							if (!confirm(confirmMsg)) return;
+
+							try {
+								// Separate into user prompts and generated prompts
+								const selectedIds = Array.from(selectedPrompts);
+								let deletedCount = 0;
+
+								for (const promptId of selectedIds) {
+									// Try to delete as user prompt first
+									const userPrompt = userPrompts.find(p => p.id === promptId);
+									if (userPrompt) {
+										await deleteUserPrompt(promptId);
+										deletedCount++;
+										continue;
+									}
+
+									// Try to delete as generated prompt
+									const genPrompt = generatedPrompts.find(p => p.id === promptId);
+									if (genPrompt) {
+										await deleteGeneratedPrompt(promptId);
+										deletedCount++;
+									}
+								}
+
+								console.log(`[RPG] Deleted ${deletedCount} prompts`);
+
+								// Exit selection mode
+								selectionMode = false;
+								selectedPrompts.clear();
+								renderUI();
+
+								alert(`Successfully deleted ${deletedCount} prompt${deletedCount === 1 ? '' : 's'}`);
+							} catch (error) {
+								console.error("[RPG] Error deleting selected prompts:", error);
+								alert(`Error deleting prompts: ${error.message}`);
+							}
+						};
+					}
+
+					// Prompt checkbox handlers
+					container.querySelectorAll(".instaraw-rpg-prompt-checkbox").forEach((checkbox) => {
+						checkbox.onchange = (e) => {
+							e.stopPropagation();
+							const promptId = checkbox.dataset.id;
+
+							if (checkbox.checked) {
+								selectedPrompts.add(promptId);
+							} else {
+								selectedPrompts.delete(promptId);
+							}
+
+							renderUI();
+							console.log(`[RPG] ${checkbox.checked ? 'Selected' : 'Deselected'} prompt ${promptId}. Total selected: ${selectedPrompts.size}`);
+						};
+					});
+
+					// Card click handler for selection mode - click anywhere on card to toggle
+					container.querySelectorAll(".instaraw-rpg-library-card.selection-mode").forEach((card) => {
+						card.onclick = (e) => {
+							// Don't toggle if clicking on buttons or other interactive elements
+							if (e.target.closest('button') || e.target.closest('input') || e.target.closest('textarea')) {
+								return;
+							}
+
+							const checkbox = card.querySelector(".instaraw-rpg-prompt-checkbox");
+							if (checkbox) {
+								checkbox.checked = !checkbox.checked;
+								const promptId = checkbox.dataset.id;
+
+								if (checkbox.checked) {
+									selectedPrompts.add(promptId);
+								} else {
+									selectedPrompts.delete(promptId);
+								}
+
+								renderUI();
+								console.log(`[RPG] Card click: ${checkbox.checked ? 'Selected' : 'Deselected'} prompt ${promptId}. Total selected: ${selectedPrompts.size}`);
+							}
+						};
+					});
+
+					// Delete user prompt buttons
+					container.querySelectorAll(".instaraw-rpg-delete-user-prompt-btn").forEach((btn) => {
+						btn.onclick = async (e) => {
+							e.stopPropagation();
+							const promptId = btn.dataset.id;
+							const prompt = userPrompts.find(p => p.id === promptId);
+							if (!prompt) return;
+
+							const confirmMsg = `Delete this prompt?\n\nPositive: ${(prompt.prompt?.positive || '').substring(0, 100)}...`;
+							if (!confirm(confirmMsg)) return;
+
+							try {
+								await deleteUserPrompt(promptId);
+								console.log(`[RPG] Deleted user prompt ${promptId}`);
+
+								// If in random mode, remove from randomPrompts array
+								if (showingRandomPrompts) {
+									randomPrompts = randomPrompts.filter(p => p.id !== promptId);
+								}
+
+								// Refresh UI
+								renderUI();
+							} catch (error) {
+								console.error("[RPG] Error deleting user prompt:", error);
+								alert(`Error deleting prompt: ${error.message}`);
+							}
+						};
+					});
+
+					// Delete generated prompt buttons
+					container.querySelectorAll(".instaraw-rpg-delete-generated-prompt-btn").forEach((btn) => {
+						btn.onclick = async (e) => {
+							e.stopPropagation();
+							const promptId = btn.dataset.id;
+							const prompt = generatedPrompts.find(p => p.id === promptId);
+							if (!prompt) return;
+
+							const confirmMsg = `Delete this generated prompt?\n\nPositive: ${(prompt.prompt?.positive || '').substring(0, 100)}...`;
+							if (!confirm(confirmMsg)) return;
+
+							try {
+								await deleteGeneratedPrompt(promptId);
+								console.log(`[RPG] Deleted generated prompt ${promptId}`);
+
+								// If in random mode, remove from randomPrompts array
+								if (showingRandomPrompts) {
+									randomPrompts = randomPrompts.filter(p => p.id !== promptId);
+								}
+
+								// Refresh UI
+								renderUI();
+							} catch (error) {
+								console.error("[RPG] Error deleting generated prompt:", error);
+								alert(`Error deleting prompt: ${error.message}`);
+							}
+						};
+					});
+
+					// Edit button - enter edit mode
+					container.querySelectorAll(".instaraw-rpg-edit-user-prompt-btn").forEach((btn) => {
+						btn.onclick = () => {
+							const promptId = btn.dataset.id;
+							const prompt = userPrompts.find(p => p.id === promptId);
+							if (prompt) {
+								// Store current values in edit buffer
+								editingValues[promptId] = {
+									positive: prompt.prompt?.positive || "",
+									negative: prompt.prompt?.negative || "",
+									tags: prompt.tags?.join(", ") || "",
+									content_type: prompt.classification?.content_type || "person",
+									safety_level: prompt.classification?.safety_level || "sfw",
+									shot_type: prompt.classification?.shot_type || "portrait"
+								};
+								editingPrompts.add(promptId);
+								renderUI();
+								console.log(`[RPG] Editing user prompt ${promptId}`);
+							}
+						};
+					});
+
+					// Save button - save changes and exit edit mode
+					container.querySelectorAll(".instaraw-rpg-save-user-prompt-btn").forEach((btn) => {
+						btn.onclick = async () => {
+							const promptId = btn.dataset.id;
+							const positiveTextarea = container.querySelector(`.instaraw-rpg-user-prompt-edit-positive[data-id="${promptId}"]`);
+							const negativeTextarea = container.querySelector(`.instaraw-rpg-user-prompt-edit-negative[data-id="${promptId}"]`);
+							const tagsInput = container.querySelector(`.instaraw-rpg-user-prompt-edit-tags[data-id="${promptId}"]`);
+							const contentTypeSelect = container.querySelector(`.instaraw-rpg-user-prompt-edit-content-type[data-id="${promptId}"]`);
+							const safetyLevelSelect = container.querySelector(`.instaraw-rpg-user-prompt-edit-safety-level[data-id="${promptId}"]`);
+							const shotTypeSelect = container.querySelector(`.instaraw-rpg-user-prompt-edit-shot-type[data-id="${promptId}"]`);
+
+							if (!positiveTextarea || !negativeTextarea || !tagsInput) return;
+
+							const tagsArray = tagsInput.value.split(",").map(t => t.trim()).filter(Boolean);
+
+							try {
+								await updateUserPrompt(promptId, {
+									prompt: {
+										positive: positiveTextarea.value,
+										negative: negativeTextarea.value
+									},
+									tags: tagsArray,
+									classification: {
+										content_type: contentTypeSelect?.value || "person",
+										safety_level: safetyLevelSelect?.value || "sfw",
+										shot_type: shotTypeSelect?.value || "portrait"
+									}
+								});
+								console.log(`[RPG] Saved user prompt ${promptId}`);
+
+								// Exit edit mode
+								editingPrompts.delete(promptId);
+								delete editingValues[promptId];
+								renderUI();
+							} catch (error) {
+								console.error("[RPG] Error saving user prompt:", error);
+								alert(`Error saving: ${error.message}`);
+							}
+						};
+					});
+
+					// Cancel button - discard changes and exit edit mode
+					container.querySelectorAll(".instaraw-rpg-cancel-edit-prompt-btn").forEach((btn) => {
+						btn.onclick = () => {
+							const promptId = btn.dataset.id;
+							editingPrompts.delete(promptId);
+							delete editingValues[promptId];
+							renderUI();
+							console.log(`[RPG] Cancelled editing user prompt ${promptId}`);
+						};
+					});
+
+					// Auto-resize textareas in edit mode
+					container.querySelectorAll(".instaraw-rpg-user-prompt-edit-positive, .instaraw-rpg-user-prompt-edit-negative").forEach((textarea) => {
+						autoResizeTextarea(textarea);
+						textarea.oninput = () => autoResizeTextarea(textarea);
+					});
+
+					// Update editingValues when classification dropdowns change
+					container.querySelectorAll(".instaraw-rpg-user-prompt-edit-content-type").forEach((select) => {
+						select.onchange = () => {
+							const promptId = select.dataset.id;
+							if (editingValues[promptId]) {
+								editingValues[promptId].content_type = select.value;
+							}
+						};
+					});
+
+					container.querySelectorAll(".instaraw-rpg-user-prompt-edit-safety-level").forEach((select) => {
+						select.onchange = () => {
+							const promptId = select.dataset.id;
+							if (editingValues[promptId]) {
+								editingValues[promptId].safety_level = select.value;
+							}
+						};
+					});
+
+					container.querySelectorAll(".instaraw-rpg-user-prompt-edit-shot-type").forEach((select) => {
+						select.onchange = () => {
+							const promptId = select.dataset.id;
+							if (editingValues[promptId]) {
+								editingValues[promptId].shot_type = select.value;
+							}
+						};
+					});
+
+					// Copy prompt buttons
+					container.querySelectorAll(".instaraw-rpg-copy-prompt-btn").forEach((btn) => {
+						btn.onclick = async (e) => {
+							e.stopPropagation();
+							const positivePrompt = btn.dataset.positive;
+							if (!positivePrompt) return;
+
+							try {
+								await navigator.clipboard.writeText(positivePrompt);
+								// Show success feedback
+								const originalText = btn.textContent;
+								btn.textContent = "โœ…";
+								btn.style.opacity = "1";
+								setTimeout(() => {
+									btn.textContent = originalText;
+									btn.style.opacity = "";
+								}, 800);
+							} catch (error) {
+								console.error("[RPG] Failed to copy to clipboard:", error);
+								alert("Failed to copy to clipboard");
+							}
+						};
+					});
+
+					// Add to batch buttons (warns once if generated prompts are pending)
+					container.querySelectorAll(".instaraw-rpg-add-to-batch-btn").forEach((btn) => {
+						btn.onclick = () => {
+							const promptId = btn.dataset.id;
+							const promptData = promptsDatabase.find((p) => p.id === promptId);
+							if (!promptData) return;
+
+							// Check if there are pending generated prompts and user hasn't been warned yet
+							if (node._generatedUnifiedPrompts && node._generatedUnifiedPrompts.length > 0 && !node._warnedAboutPendingPrompts) {
+								const pendingCount = node._generatedUnifiedPrompts.length;
+								const proceed = confirm(`You have ${pendingCount} generated prompt${pendingCount > 1 ? 's' : ''} waiting to be added in the Generate tab.\n\nAdd this prompt anyway?`);
+								if (!proceed) return;
+								node._warnedAboutPendingPrompts = true;
+							}
+
+							addPromptToBatch(promptData);
+						};
+					});
+
+					// Undo batch buttons (removes last instance)
+					container.querySelectorAll(".instaraw-rpg-undo-batch-btn").forEach((btn) => {
+						btn.onclick = () => {
+							const promptId = btn.dataset.id;
+							const promptQueue = parsePromptBatch();
+							// Find all instances with this source_id
+							const instances = promptQueue.filter(p => p.source_id === promptId);
+							if (instances.length > 0) {
+								// Remove the last one added
+								const lastInstance = instances[instances.length - 1];
+								deletePromptFromBatch(lastInstance.id);
+							}
+						};
+					});
+
+					// Bookmark buttons
+					container.querySelectorAll(".instaraw-rpg-bookmark-btn").forEach((btn) => {
+						btn.onclick = (e) => {
+							e.stopPropagation();
+							toggleBookmark(btn.dataset.id);
+						};
+					});
+
+					// ID copy buttons
+					container.querySelectorAll(".instaraw-rpg-id-copy-btn").forEach((btn) => {
+						btn.onclick = (e) => {
+							e.stopPropagation();
+							const promptId = btn.dataset.id;
+							navigator.clipboard.writeText(promptId).then(() => {
+								// Visual feedback
+								const originalText = btn.textContent;
+								btn.textContent = "โœ…";
+								setTimeout(() => {
+									btn.textContent = originalText;
+								}, 1000);
+							}).catch(err => {
+								console.error("[RPG] Failed to copy ID:", err);
+								alert("Failed to copy ID to clipboard");
+							});
+						};
+					});
+
+					// Toggle tags buttons (expand/collapse) - use event delegation
+					// Only add listener once to prevent accumulation
+					if (!container._hasToggleTagsListener) {
+						container._hasToggleTagsListener = true;
+						container.addEventListener('click', (e) => {
+							if (e.target.classList.contains('instaraw-rpg-toggle-tags-btn')) {
+								e.stopPropagation();
+								const promptId = e.target.dataset.id;
+								const card = e.target.closest('.instaraw-rpg-library-card');
+								const tagsContainer = card.querySelector('.instaraw-rpg-library-card-tags');
+								const prompt = promptsDatabase.find(p => p.id === promptId);
+
+								if (prompt && tagsContainer) {
+									const isExpanded = tagsContainer.getAttribute('data-expanded') === 'true';
+									const filters = JSON.parse(node.properties.library_filters || "{}");
+									const searchQuery = filters.search_query?.trim() || "";
+
+									if (isExpanded) {
+										// Collapse - show only first 5
+										tagsContainer.setAttribute('data-expanded', 'false');
+										tagsContainer.innerHTML = prompt.tags.slice(0, 5)
+											.map(tag => `${highlightSearchTerm(tag, searchQuery)}`)
+											.join("") + ` `;
+									} else {
+										// Expand - show all tags
+										tagsContainer.setAttribute('data-expanded', 'true');
+										tagsContainer.innerHTML = prompt.tags
+											.map(tag => `${highlightSearchTerm(tag, searchQuery)}`)
+											.join("") + ` `;
+									}
+								}
+							}
+						});
+					}
+
+					// Pagination
+					container.querySelectorAll(".instaraw-rpg-prev-page-btn").forEach((prevPageBtn) => {
+						prevPageBtn.onclick = () => {
+							if (currentPage > 0) {
+								currentPage--;
+								renderUI();
+							}
+						};
+					});
+
+					container.querySelectorAll(".instaraw-rpg-next-page-btn").forEach((nextPageBtn) => {
+						nextPageBtn.onclick = () => {
+							currentPage++;
+							renderUI();
+						};
+					});
+
+					// Batch item controls - single resize after content loads
+					const allTextareas = [
+						...container.querySelectorAll(".instaraw-rpg-positive-textarea"),
+						...container.querySelectorAll(".instaraw-rpg-negative-textarea")
+					];
+
+					// Batch textareas get larger max height for auto-resize (user can still drag beyond this)
+					const batchTextareaOptions = { maxHeight: 400 };
+
+					// Resize all textareas immediately after DOM paint (no delay)
+					requestAnimationFrame(() => {
+						allTextareas.forEach(textarea => {
+							autoResizeTextarea(textarea, batchTextareaOptions);
+							// Cache the height
+							const id = textarea.dataset.id;
+							const isPositive = textarea.classList.contains("instaraw-rpg-positive-textarea");
+							if (id) {
+								textareaHeights[`${id}_${isPositive ? 'positive' : 'negative'}`] = textarea.offsetHeight;
+							}
+						});
+						setTimeout(() => updateCachedHeight(), 10);
+					});
+
+					container.querySelectorAll(".instaraw-rpg-positive-textarea").forEach((textarea) => {
+						textarea.oninput = (e) => {
+							autoResizeTextarea(textarea, batchTextareaOptions);
+							// Cache the new height
+							const id = textarea.dataset.id;
+							if (id) textareaHeights[`${id}_positive`] = textarea.offsetHeight;
+							updateCachedHeight();
+						};
+						textarea.onchange = (e) => {
+							const id = textarea.dataset.id;
+							const value = e.target.value;
+
+							// Check if we're in SDXL mode and this entry has tags
+							const promptQueue = parsePromptBatch();
+							const entry = promptQueue.find((p) => p.id === id);
+
+							if (sdxlModeEnabled && entry && entry.tags && entry.tags.length > 0) {
+								// SDXL mode with tags - save as tags array (comma-separated)
+								const tagsArray = value.split(',').map(t => t.trim()).filter(t => t.length > 0);
+								updatePromptInBatch(id, "tags", tagsArray);
+							} else {
+								// Normal mode - save as positive_prompt
+								// Backend will use positive_prompt when sdxl_mode=False
+								updatePromptInBatch(id, "positive_prompt", value);
+							}
+						};
+					});
+
+					container.querySelectorAll(".instaraw-rpg-negative-textarea").forEach((textarea) => {
+						textarea.oninput = (e) => {
+							autoResizeTextarea(textarea, batchTextareaOptions);
+							// Cache the new height
+							const id = textarea.dataset.id;
+							if (id) textareaHeights[`${id}_negative`] = textarea.offsetHeight;
+							updateCachedHeight();
+						};
+						textarea.onchange = (e) => {
+							updatePromptInBatch(textarea.dataset.id, "negative_prompt", e.target.value);
+						};
+					});
+
+					container.querySelectorAll(".instaraw-rpg-repeat-input").forEach((input) => {
+						input.onchange = (e) => {
+							updatePromptInBatch(input.dataset.id, "repeat_count", parseInt(e.target.value) || 1);
+						};
+						input.onmousedown = (e) => e.stopPropagation();
+					});
+
+					// Seed input controls
+					container.querySelectorAll(".instaraw-rpg-seed-input").forEach((input) => {
+						input.onmousedown = (e) => e.stopPropagation();
+						input.onclick = (e) => e.stopPropagation();
+						input.onfocus = (e) => e.stopPropagation();
+						input.onchange = (e) => {
+							// Save seed to this prompt entry
+							const newSeed = Math.max(0, parseInt(e.target.value));
+							updatePromptInBatch(input.dataset.id, "seed", newSeed);
+							// Update seed range display
+							updateSeedRangeDisplay(input.dataset.id);
+						};
+					});
+
+					container.querySelectorAll(".instaraw-rpg-seed-randomize-btn").forEach((btn) => {
+						btn.onmousedown = (e) => e.stopPropagation();
+						btn.onclick = (e) => {
+							e.stopPropagation();
+							// Generate random seed and update ONLY this prompt's seed
+							const randomSeed = Math.floor(Math.random() * (9999999 - 1111111 + 1)) + 1111111;
+							const id = btn.dataset.id;
+							const seedInput = container.querySelector(`.instaraw-rpg-seed-input[data-id="${id}"]`);
+							if (seedInput) {
+								seedInput.value = randomSeed;
+								updatePromptInBatch(id, "seed", randomSeed);
+								updateSeedRangeDisplay(id);
+							}
+						};
+					});
+
+					container.querySelectorAll(".instaraw-rpg-seed-reset-btn").forEach((btn) => {
+						btn.onmousedown = (e) => e.stopPropagation();
+						btn.onclick = (e) => {
+							e.stopPropagation();
+							// Reset ONLY this prompt's seed
+							const id = btn.dataset.id;
+							const seedInput = container.querySelector(`.instaraw-rpg-seed-input[data-id="${id}"]`);
+							if (seedInput) {
+								seedInput.value = 1111111;
+								updatePromptInBatch(id, "seed", 1111111);
+								updateSeedRangeDisplay(id);
+							}
+						};
+					});
+
+					container.querySelectorAll(".instaraw-rpg-seed-control").forEach((select) => {
+						select.onmousedown = (e) => e.stopPropagation();
+						select.onclick = (e) => e.stopPropagation();
+						select.onchange = (e) => {
+							// Save seed control mode (controls behavior after execution)
+							updatePromptInBatch(select.dataset.id, "seed_control", e.target.value);
+						};
+					});
+
+					// Bulk seed control dropdown (power tools)
+					const bulkSeedControl = container.querySelector(".instaraw-rpg-bulk-seed-control");
+					if (bulkSeedControl) {
+						bulkSeedControl.onmousedown = (e) => e.stopPropagation();
+						bulkSeedControl.onclick = (e) => e.stopPropagation();
+						bulkSeedControl.onchange = (e) => {
+							const value = e.target.value;
+							if (value) {
+								updateAllPromptsField("seed_control", value);
+								e.target.value = ""; // Reset dropdown to placeholder
+							}
+						};
+					}
+
+					// Bulk reset seeds button (power tools) - sets all to 1111111
+					const bulkResetSeedsBtn = container.querySelector(".instaraw-rpg-bulk-reset-seeds-btn");
+					if (bulkResetSeedsBtn) {
+						bulkResetSeedsBtn.onmousedown = (e) => e.stopPropagation();
+						bulkResetSeedsBtn.onclick = (e) => {
+							e.stopPropagation();
+							updateAllPromptsField("seed", 1111111);
+						};
+					}
+
+					// Bulk randomize seeds button (power tools) - generates new random seeds
+					const bulkRandomizeSeedsBtn = container.querySelector(".instaraw-rpg-bulk-randomize-seeds-btn");
+					if (bulkRandomizeSeedsBtn) {
+						bulkRandomizeSeedsBtn.onmousedown = (e) => e.stopPropagation();
+						bulkRandomizeSeedsBtn.onclick = (e) => {
+							e.stopPropagation();
+							const promptQueue = parsePromptBatch();
+							if (promptQueue.length === 0) return;
+							promptQueue.forEach(entry => {
+								entry.seed = Math.floor(Math.random() * 2147483647);
+							});
+							node.properties.prompt_batch_data = JSON.stringify(promptQueue);
+							syncPromptBatchWidget();
+							renderUI();
+						};
+					}
+
+					// Initialize seed range displays
+					container.querySelectorAll(".instaraw-rpg-seed-range").forEach((span) => {
+						updateSeedRangeDisplay(span.dataset.id);
+					});
+
+					container.querySelectorAll(".instaraw-rpg-batch-delete-btn").forEach((btn) => {
+						btn.onclick = (e) => {
+							e.stopPropagation();
+							deletePromptFromBatch(btn.dataset.id);
+						};
+					});
+
+					// Clear batch button
+					const clearBatchBtn = container.querySelector(".instaraw-rpg-clear-batch-btn");
+					if (clearBatchBtn) {
+						clearBatchBtn.onclick = clearBatch;
+					}
+
+					// Reorder toggle button
+					const reorderToggleBtn = container.querySelector(".instaraw-rpg-reorder-toggle-btn");
+					if (reorderToggleBtn) {
+						reorderToggleBtn.onclick = () => {
+							reorderModeEnabled = !reorderModeEnabled;
+							renderUI();
+						};
+					}
+
+					// SDXL mode toggle button
+					const sdxlToggleBtn = container.querySelector(".instaraw-rpg-sdxl-toggle-btn");
+					if (sdxlToggleBtn) {
+						sdxlToggleBtn.onclick = () => {
+							sdxlModeEnabled = !sdxlModeEnabled;
+							console.log(`[RPG] SDXL mode ${sdxlModeEnabled ? 'enabled' : 'disabled'}`);
+							syncSDXLModeWidget();
+							renderUI();
+						};
+					}
+
+					// Smart Sync AIL button - handles both latent creation and repeat syncing
+					const syncAilBtn = container.querySelector(".instaraw-rpg-sync-ail-btn");
+					if (syncAilBtn) {
+						syncAilBtn.onclick = async () => {
+							if (!node._linkedAILNodeId) {
+								alert("No Advanced Image Loader detected. Connect AIL to RPG first.");
+								return;
+							}
+
+							const promptQueue = parsePromptBatch();
+							const totalGenerations = promptQueue.reduce((sum, p) => sum + (p.repeat_count || 1), 0);
+
+							// Get target dimensions from aspect ratio selector
+							const targetDims = getTargetDimensions();
+
+							// Read FRESH data from the primary AIL node (not cached)
+							const primaryAIL = app.graph.getNodeById(node._linkedAILNodeId);
+							if (!primaryAIL) {
+								alert("Cannot find connected AIL node. Please reconnect.");
+								return;
+							}
+
+							// Get actual data from AIL
+							let ailData;
+							try {
+								ailData = JSON.parse(primaryAIL.properties?.batch_data || "{}");
+							} catch (e) {
+								ailData = {};
+							}
+
+							// Get actual item count from AIL
+							const ailImages = ailData.images || [];
+							const ailLatents = ailData.latents || [];
+
+							// Detect mode from AIL's enable_img2img WIDGET (not batch_data)
+							// The widget is the source of truth for current mode
+							let detectedMode;
+							const enableImg2ImgWidget = primaryAIL.widgets?.find(w => w.name === "enable_img2img");
+							let enableImg2ImgValue = enableImg2ImgWidget?.value;
+
+							// Check if enable_img2img is connected to another node
+							const enableImg2ImgInput = primaryAIL.inputs?.find(i => i.name === "enable_img2img");
+							if (enableImg2ImgInput && enableImg2ImgInput.link != null) {
+								const link = app.graph?.links?.[enableImg2ImgInput.link];
+								if (link) {
+									const srcNode = app.graph.getNodeById(link.origin_id);
+									if (srcNode) {
+										const srcWidget = srcNode.widgets?.[link.origin_slot];
+										if (srcWidget) enableImg2ImgValue = srcWidget.value;
+									}
+								}
+							}
+
+							// Convert to mode: enable_img2img false = txt2img, true = img2img
+							if (enableImg2ImgValue === false || enableImg2ImgValue === "false") {
+								detectedMode = "txt2img";
+							} else if (enableImg2ImgValue === true || enableImg2ImgValue === "true") {
+								detectedMode = "img2img";
+							} else {
+								// Fallback: if images exist, it's img2img; if only latents, it's txt2img
+								detectedMode = ailImages.length > 0 ? "img2img" : "txt2img";
+							}
+							console.log(`[RPG] Sync AIL - detected mode: ${detectedMode} (widget: ${enableImg2ImgValue}, images: ${ailImages.length}, latents: ${ailLatents.length})`);
+
+							const ailOrder = ailData.order || [];
+							const ailItemCount = detectedMode === "img2img" ? ailOrder.length : ailOrder.length;
+
+							// PROMPTS ARE KING: Calculate what needs to change
+							const promptCount = promptQueue.length;
+							const needsLatentSync = detectedMode === "txt2img" && ailOrder.length !== promptCount;
+							const needsRepeatSync = promptQueue.some((p, idx) => {
+								const item = detectedMode === "img2img" ? ailImages.find(i => i.id === ailOrder[idx]) : ailLatents.find(l => l.id === ailOrder[idx]);
+								return item && (p.repeat_count || 1) !== (item.repeat_count || 1);
+							});
+
+							// Auto-balance: Check if we need to duplicate AIL items to match prompt count
+							const needsAutoBalance = detectedMode === "img2img" && promptCount > ailItemCount && ailItemCount > 0;
+							const deficit = needsAutoBalance ? promptCount - ailItemCount : 0;
+
+							// Trimming: Check if we need to remove excess AIL items to match prompt count
+							const needsTrimming = detectedMode === "img2img" && promptCount < ailItemCount;
+							const excess = needsTrimming ? ailItemCount - promptCount : 0;
+
+							// Also get fresh data for secondary AILs
+							const linkedImages2 = node._linkedImages2 || [];
+							const linkedImages3 = node._linkedImages3 || [];
+							const linkedImages4 = node._linkedImages4 || [];
+
+							// Check if secondary AILs need syncing - read FRESH data
+							const checkSecondaryAIL = (inputName) => {
+								const input = node.inputs?.find(i => i.name === inputName);
+								if (!input || !input.link) return null;
+								const link = app.graph.links[input.link];
+								if (!link) return null;
+								const ailNode = app.graph.getNodeById(link.origin_id);
+								if (!ailNode || ailNode.type !== "INSTARAW_AdvancedImageLoader") return null;
+
+								let data;
+								try {
+									data = JSON.parse(ailNode.properties?.batch_data || "{}");
+								} catch (e) {
+									return null;
+								}
+
+								const order = data.order || [];
+								if (order.length === 0) return null;
+
+								// Count mismatch with prompts (not primary AIL)
+								if (order.length !== promptCount) {
+									return { name: inputName, count: order.length, issue: "count", ailNode };
+								}
+								return null;
+							};
+
+							const secondaryAILsNeedSync = [
+								checkSecondaryAIL("images2"),
+								checkSecondaryAIL("images3"),
+								checkSecondaryAIL("images4")
+							].filter(Boolean);
+							const needsSecondarySync = secondaryAILsNeedSync.length > 0;
+
+							// Build simple, clear confirmation message
+							// PROMPTS ARE KING - show what will happen
+							let actions = [];
+
+							if (detectedMode === "img2img") {
+								if (ailItemCount === 0) {
+									actions.push(`โš ๏ธ No images in AIL! Upload ${promptCount} image(s) first.`);
+								} else if (needsTrimming) {
+									actions.push(`๐Ÿ—‘๏ธ Remove ${excess} image(s) from AIL (${ailItemCount} โ†’ ${promptCount})`);
+								} else if (needsAutoBalance) {
+									actions.push(`๐Ÿ“‹ Duplicate last image ${deficit} time(s) in AIL (${ailItemCount} โ†’ ${promptCount})`);
+								}
+							} else {
+								// txt2img mode
+								if (needsLatentSync) {
+									actions.push(`๐Ÿ“ Create ${promptCount} latent(s) at ${targetDims.aspect_label}`);
+								}
+							}
+
+							if (needsRepeatSync) {
+								actions.push(`๐Ÿ”„ Sync repeat counts to match prompts`);
+							}
+
+							if (needsSecondarySync) {
+								const syncDetails = secondaryAILsNeedSync.map(s => `${s.name}: ${s.count} โ†’ ${promptCount}`).join(", ");
+								actions.push(`๐Ÿ”— Sync secondary AILs: ${syncDetails}`);
+							}
+
+							let confirmMsg;
+							if (actions.length === 0) {
+								confirmMsg = `โœ“ Everything is already synced!\n\nPrompts: ${promptCount}\nAIL items: ${ailItemCount}\n\nSync anyway?`;
+							} else if (detectedMode === "img2img" && ailItemCount === 0) {
+								alert(`Cannot sync: No images in AIL!\n\nUpload ${promptCount} image(s) first, then sync.`);
+								return;
+							} else {
+								confirmMsg = `Sync AIL to match ${promptCount} prompt(s)?\n\n${actions.join("\n")}\n\nContinue?`;
+							}
+
+							if (!confirm(confirmMsg)) return;
+
+							// Helper to find connected AIL node for a specific input
+							const findConnectedAILForInput = (inputName) => {
+								const input = node.inputs?.find(i => i.name === inputName);
+								if (!input || !input.link) return null;
+								const link = app.graph.links[input.link];
+								if (!link) return null;
+								const sourceNode = app.graph.getNodeById(link.origin_id);
+								if (sourceNode && sourceNode.type === "INSTARAW_AdvancedImageLoader") {
+									return sourceNode;
+								}
+								return null;
+							};
+
+							// 0. Sync secondary AILs to match primary image count
+							if (needsSecondarySync) {
+								const inputsToSync = ["images2", "images3", "images4"];
+								for (const inputName of inputsToSync) {
+									const secondaryAIL = findConnectedAILForInput(inputName);
+									if (!secondaryAIL) continue;
+
+									try {
+										const data = JSON.parse(secondaryAIL.properties.batch_data || "{}");
+										let images = data.images || [];
+										let order = data.order || [];
+
+										if (images.length === 0 || order.length === 0) continue;
+
+										const currentCount = order.length;
+										if (currentCount === promptCount) continue; // Already synced to prompt count
+
+										if (currentCount < promptCount) {
+											// Duplicate last image to fill gap
+											const duplicatesNeeded = promptCount - currentCount;
+											const lastImageId = order[order.length - 1];
+											const lastImage = images.find(img => img.id === lastImageId);
+
+											if (lastImage) {
+												console.log(`[RPG] Syncing ${inputName}: duplicating last image ${duplicatesNeeded} times`);
+												for (let i = 0; i < duplicatesNeeded; i++) {
+													const newImage = {
+														...lastImage,
+														id: `dup_${Date.now()}_${i}_${Math.random().toString(36).substr(2, 9)}`,
+														repeat_count: 1
+													};
+													images.push(newImage);
+													order.push(newImage.id);
+												}
+												data.images = images; // Update reference
+											}
+										} else if (currentCount > promptCount) {
+											// Trim excess images to match prompt count - PROMPTS ARE KING
+											const removeCount = currentCount - promptCount;
+											console.log(`[RPG] Syncing ${inputName}: trimming ${removeCount} excess images to match ${promptCount} prompts`);
+											const removedIds = order.slice(promptCount);
+											order = order.slice(0, promptCount);
+											images = images.filter(img => !removedIds.includes(img.id));
+											data.images = images; // Update reference
+										}
+
+										// Sync repeat counts to match prompt queue
+										let totalCount = 0;
+										data.order = order;
+										order.forEach((imgId, idx) => {
+											const img = data.images.find(i => i.id === imgId);
+											if (img) {
+												const repeatCount = promptQueue[idx]?.repeat_count || 1;
+												img.repeat_count = repeatCount;
+												totalCount += repeatCount;
+											}
+										});
+										data.total_count = totalCount;
+
+										secondaryAIL.properties.batch_data = JSON.stringify(data);
+
+										// Trigger AIL re-render
+										if (secondaryAIL._renderGallery) {
+											secondaryAIL._renderGallery();
+										}
+
+										// Dispatch update event
+										window.dispatchEvent(new CustomEvent("INSTARAW_AIL_UPDATED", {
+											detail: {
+												nodeId: secondaryAIL.id,
+												images: data.images,
+												latents: [],
+												total: order.length,
+												mode: "img2img",
+												enable_img2img: true
+											}
+										}));
+
+										console.log(`[RPG] Synced ${inputName}: now has ${order.length} images`);
+									} catch (e) {
+										console.error(`[RPG] Failed to sync ${inputName}:`, e);
+									}
+								}
+
+								// Wait for sync to complete
+								await new Promise(resolve => setTimeout(resolve, 200));
+							}
+
+							// 0a. Auto-balance: Duplicate last image if needed (img2img mode only)
+							if (needsAutoBalance) {
+								if (ailItemCount === 0) {
+									alert("Cannot auto-balance: No images in AIL to duplicate!");
+									return;
+								}
+
+								console.log(`[RPG] Auto-balance: Duplicating last AIL image ${deficit} times`);
+								window.dispatchEvent(new CustomEvent("INSTARAW_DUPLICATE_LAST_N", {
+									detail: {
+										targetNodeId: node._linkedAILNodeId,
+										count: deficit
+									}
+								}));
+
+								// Wait for AIL to process duplicates
+								await new Promise(resolve => setTimeout(resolve, 200));
+							}
+
+							// 0b. Trimming: Remove excess images if needed (img2img mode only)
+							if (needsTrimming) {
+								console.log(`[RPG] Trimming: Removing ${excess} excess image${excess > 1 ? 's' : ''} from AIL`);
+								window.dispatchEvent(new CustomEvent("INSTARAW_TRIM_AIL_IMAGES", {
+									detail: {
+										targetNodeId: node._linkedAILNodeId,
+										targetCount: promptQueue.length
+									}
+								}));
+
+								// Wait for AIL to process trimming
+								await new Promise(resolve => setTimeout(resolve, 200));
+							}
+
+							// 1. Create/sync latents if needed (txt2img mode)
+							if (needsLatentSync || detectedMode === "txt2img") {
+								const latentSpecs = promptQueue.map(p => ({
+									repeat_count: p.repeat_count || 1
+								}));
+
+								window.dispatchEvent(new CustomEvent("INSTARAW_SYNC_AIL_LATENTS", {
+									detail: {
+										targetNodeId: node._linkedAILNodeId,
+										latentSpecs: latentSpecs,
+										dimensions: targetDims
+									}
+								}));
+								console.log(`[RPG] Synced ${promptQueue.length} latents to AIL`);
+							}
+
+							// 2. Sync repeat counts to primary AIL (both modes)
+							window.dispatchEvent(new CustomEvent("INSTARAW_SYNC_AIL_REPEATS", {
+								detail: {
+									targetNodeId: node._linkedAILNodeId,
+									mode: detectedMode,
+									repeats: promptQueue.map(p => p.repeat_count || 1)
+								}
+							}));
+							console.log(`[RPG] Synced repeat counts to primary AIL`);
+
+							// 3. Sync repeat counts to secondary AILs (img2img mode)
+							if (detectedMode === "img2img") {
+								const secondaryInputs = ["images2", "images3", "images4"];
+								for (const inputName of secondaryInputs) {
+									const secondaryAIL = findConnectedAILForInput(inputName);
+									if (secondaryAIL) {
+										window.dispatchEvent(new CustomEvent("INSTARAW_SYNC_AIL_REPEATS", {
+											detail: {
+												targetNodeId: secondaryAIL.id,
+												mode: "img2img",
+												repeats: promptQueue.map(p => p.repeat_count || 1)
+											}
+										}));
+										console.log(`[RPG] Synced repeat counts to ${inputName} AIL`);
+									}
+								}
+							}
+
+							// 4. Re-render RPG UI after all sync operations complete
+							// AIL dispatches INSTARAW_AIL_UPDATED which updates _linkedImages caches
+							setTimeout(() => {
+								renderUI();
+								console.log(`[RPG] Sync complete - refreshed UI`);
+							}, 300);
+						};
+					}
+
+					// Creative mode buttons
+					const generateCreativeBtn = container.querySelector(".instaraw-rpg-generate-creative-btn");
+					if (generateCreativeBtn) {
+						generateCreativeBtn.onclick = generateCreativePrompts;
+					}
+
+					const acceptCreativeBtn = container.querySelector(".instaraw-rpg-accept-creative-btn");
+					if (acceptCreativeBtn) {
+						acceptCreativeBtn.onclick = acceptCreativePrompts;
+					}
+
+					const cancelCreativeBtn = container.querySelector(".instaraw-rpg-cancel-creative-btn");
+					if (cancelCreativeBtn) {
+						cancelCreativeBtn.onclick = cancelCreativePrompts;
+					}
+
+					// Character mode buttons
+					const generateCharacterBtn = container.querySelector(".instaraw-rpg-generate-character-btn");
+					if (generateCharacterBtn) {
+						generateCharacterBtn.onclick = generateCharacterPrompts;
+					}
+
+					const acceptCharacterBtn = container.querySelector(".instaraw-rpg-accept-character-btn");
+					if (acceptCharacterBtn) {
+						acceptCharacterBtn.onclick = acceptCharacterPrompts;
+					}
+
+					const cancelCharacterBtn = container.querySelector(".instaraw-rpg-cancel-character-btn");
+					if (cancelCharacterBtn) {
+						cancelCharacterBtn.onclick = cancelCharacterPrompts;
+					}
+
+					// ========================================
+					// === UNIFIED GENERATE TAB HANDLERS ===
+					// ========================================
+
+					// Character description generation button
+					const generateCharacterDescBtn = container.querySelector(".instaraw-rpg-generate-character-desc-btn");
+					if (generateCharacterDescBtn) {
+						generateCharacterDescBtn.onclick = generateCharacterDescription;
+					}
+
+					// Character likeness checkbox
+					const enableCharacterCheckbox = container.querySelector(".instaraw-rpg-enable-character-checkbox");
+					if (enableCharacterCheckbox) {
+						enableCharacterCheckbox.onchange = (e) => {
+							node.properties.use_character_likeness = e.target.checked;
+							renderUI(); // Re-render to show/hide character section
+						};
+					}
+
+					// Generation mode toggle buttons (Reality vs Creative)
+					container.querySelectorAll(".instaraw-rpg-mode-toggle-btn").forEach((btn) => {
+						btn.onclick = () => {
+							const mode = btn.dataset.mode;
+							node.properties.generation_style = mode;
+							saveSettings();
+							renderUI(); // Re-render to update button styles
+							console.log(`[RPG] Switched to ${mode} mode`);
+						};
+					});
+
+					// Character text input (save on change + auto-resize)
+					const characterTextInput = container.querySelector(".instaraw-rpg-character-text-input");
+					if (characterTextInput) {
+						autoResizeTextarea(characterTextInput);
+						characterTextInput.oninput = (e) => autoResizeTextarea(characterTextInput);
+						characterTextInput.onchange = (e) => {
+							node.properties.character_text_input = e.target.value;
+							saveSettings();
+							app.graph.setDirtyCanvas(true, true);
+						};
+					}
+
+					// Character complexity dropdown
+					const characterComplexitySelect = container.querySelector(".instaraw-rpg-character-complexity");
+					if (characterComplexitySelect) {
+						characterComplexitySelect.onchange = (e) => {
+							node.properties.character_complexity = e.target.value;
+							saveSettings();
+
+							// If no custom system prompt, update the textarea to show new default
+							const systemPromptTextarea = container.querySelector(".instaraw-rpg-character-system-prompt");
+							if (systemPromptTextarea && !node.properties.character_system_prompt) {
+								systemPromptTextarea.value = getCharacterSystemPrompt(e.target.value);
+								autoResizeTextarea(systemPromptTextarea);
+							}
+
+							app.graph.setDirtyCanvas(true, true);
+						};
+					}
+
+					// Reset system prompt button (character section)
+					const resetSystemPromptBtn = container.querySelector(".instaraw-rpg-reset-system-prompt-btn");
+					if (resetSystemPromptBtn) {
+						resetSystemPromptBtn.onclick = () => {
+							const complexity = node.properties.character_complexity || "balanced";
+							const defaultPrompt = getCharacterSystemPrompt(complexity);
+
+							// Clear custom prompt and update textarea
+							node.properties.character_system_prompt = "";
+							saveSettings();
+							if (characterSystemPromptInput) {
+								characterSystemPromptInput.value = defaultPrompt;
+								autoResizeTextarea(characterSystemPromptInput);
+							}
+
+							app.graph.setDirtyCanvas(true, true);
+						};
+					}
+
+					// Reset unified system prompt button (generate section)
+					const resetUnifiedSystemPromptBtn = container.querySelector(".instaraw-rpg-reset-unified-system-prompt-btn");
+					if (resetUnifiedSystemPromptBtn) {
+						resetUnifiedSystemPromptBtn.onclick = () => {
+							// Clear custom prompt and reset to default
+							node.properties.creative_system_prompt = "";
+							saveSettings();
+
+							// Determine which default to use based on current mode AND generation style
+							const detectedMode = node._linkedAILMode || "txt2img";
+							const generationStyle = node.properties.generation_style || "reality";
+
+							let defaultPrompt;
+							if (detectedMode === 'txt2img') {
+								// txt2img: both styles use same template
+								defaultPrompt = DEFAULT_RPG_SYSTEM_PROMPT;
+							} else {
+								// img2img: different templates for Reality vs Creative
+								defaultPrompt = generationStyle === 'reality' ? DEFAULT_IMG2IMG_REALITY_SYSTEM_PROMPT : DEFAULT_IMG2IMG_CREATIVE_SYSTEM_PROMPT;
+							}
+
+							// Update textarea to show default
+							const systemPromptTextarea = container.querySelector(".instaraw-rpg-system-prompt");
+							if (systemPromptTextarea) {
+								systemPromptTextarea.value = defaultPrompt;
+								autoResizeTextarea(systemPromptTextarea);
+							}
+
+							console.log(`[RPG] Reset system prompt to ${detectedMode} ${generationStyle} default`);
+							app.graph.setDirtyCanvas(true, true);
+						};
+					}
+
+					// Open Library tab button
+					const openLibraryTabBtn = container.querySelector(".instaraw-rpg-open-library-tab-btn");
+					if (openLibraryTabBtn) {
+						openLibraryTabBtn.onclick = () => {
+							node.properties.active_tab = "library";
+							renderUI();
+						};
+					}
+
+					// User text input (txt2img mode) - unified with img2img
+					const userTextInput = container.querySelector(".instaraw-rpg-user-text-input");
+					if (userTextInput) {
+						autoResizeTextarea(userTextInput);
+						userTextInput.oninput = (e) => {
+							node.properties.user_instructions = e.target.value;
+							saveUserInstructions();
+							autoResizeTextarea(e.target);
+							app.graph.setDirtyCanvas(true, true);
+						};
+					}
+
+					// โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•
+					// UNIFIED EVENT HANDLERS (work for both txt2img and img2img)
+					// โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•
+
+					// IMG2IMG User Instructions textarea - unified with txt2img
+					const img2imgUserInstructions = container.querySelector(".instaraw-rpg-img2img-user-instructions");
+					if (img2imgUserInstructions) {
+						autoResizeTextarea(img2imgUserInstructions);
+						img2imgUserInstructions.oninput = (e) => {
+							node.properties.user_instructions = e.target.value;
+							saveUserInstructions();
+							autoResizeTextarea(e.target);
+							app.graph.setDirtyCanvas(true, true);
+						};
+					}
+
+					// Theme preset dropdown (unified - works for both modes)
+					const themeSelect = container.querySelector(".instaraw-rpg-theme-select");
+					if (themeSelect) {
+						themeSelect.onchange = (e) => {
+							const themeKey = e.target.value;
+							node.properties.theme_preset = themeKey;
+							saveSettings();
+							app.graph.setDirtyCanvas(true, true);
+							console.log(`[RPG] Theme changed to: ${themeKey}`);
+						};
+					}
+
+					// Model instructions preset dropdown (unified - works for both modes)
+					const modelPresetSelect = container.querySelector(".instaraw-rpg-model-preset-select");
+					if (modelPresetSelect) {
+						modelPresetSelect.onchange = (e) => {
+							const newPresetKey = e.target.value;
+							const currentPresetKey = node.properties.model_preset || "none";
+							const currentInstructions = node.properties.model_instructions || "";
+
+							// Check if user has custom edits (instructions differ from current preset default)
+							const currentPreset = MODEL_INSTRUCTION_PRESETS[currentPresetKey];
+							const currentPresetDefault = currentPreset?.instructions || "";
+							const hasCustomEdits = currentInstructions.trim() !== currentPresetDefault.trim();
+
+							const applyPreset = () => {
+								node.properties.model_preset = newPresetKey;
+								const preset = MODEL_INSTRUCTION_PRESETS[newPresetKey];
+								if (preset) {
+									node.properties.model_instructions = preset.instructions;
+									const instructionsTextarea = container.querySelector(".instaraw-rpg-model-instructions");
+									if (instructionsTextarea) {
+										instructionsTextarea.value = preset.instructions;
+										autoResizeTextarea(instructionsTextarea);
+									}
+								}
+								saveSettings();
+								app.graph.setDirtyCanvas(true, true);
+								console.log(`[RPG] Model preset changed to: ${newPresetKey}`);
+							};
+
+							if (hasCustomEdits && currentInstructions.trim() !== "") {
+								// User has custom edits - show confirmation
+								const confirmed = confirm(
+									"โš ๏ธ You have custom edits in Model Instructions.\n\n" +
+									"Changing the preset will overwrite your changes.\n\n" +
+									"Tip: Copy your instructions before proceeding if you want to keep them.\n\n" +
+									"Continue and overwrite?"
+								);
+
+								if (confirmed) {
+									applyPreset();
+								} else {
+									// Revert dropdown to previous value
+									e.target.value = currentPresetKey;
+								}
+							} else {
+								// No custom edits - apply directly
+								applyPreset();
+							}
+						};
+					}
+
+					// Model instructions textarea (unified - works for both modes)
+					const modelInstructions = container.querySelector(".instaraw-rpg-model-instructions");
+					if (modelInstructions) {
+						autoResizeTextarea(modelInstructions);
+						modelInstructions.oninput = (e) => {
+							node.properties.model_instructions = e.target.value;
+							saveSettings();
+							autoResizeTextarea(e.target);
+							app.graph.setDirtyCanvas(true, true);
+						};
+					}
+
+					// Clean mode toggle (unified - works for both modes)
+					const cleanModeToggle = container.querySelector(".instaraw-rpg-clean-mode-toggle");
+					if (cleanModeToggle) {
+						cleanModeToggle.onchange = (e) => {
+							node.properties.clean_mode = e.target.checked;
+							saveSettings();
+							app.graph.setDirtyCanvas(true, true);
+							console.log(`[RPG] Clean mode ${e.target.checked ? 'enabled' : 'disabled'}`);
+						};
+					}
+
+					// Inspiration count input (txt2img mode)
+					const inspirationCountInput = container.querySelector(".instaraw-rpg-inspiration-count");
+					if (inspirationCountInput) {
+						inspirationCountInput.onchange = (e) => {
+							node.properties.inspiration_count = parseInt(e.target.value) || 3;
+							saveSettings();
+							app.graph.setDirtyCanvas(true, true);
+						};
+					}
+
+					// Library inspiration toggle
+					const enableInspirationCheckbox = container.querySelector(".instaraw-rpg-enable-inspiration-checkbox");
+					if (enableInspirationCheckbox) {
+						enableInspirationCheckbox.onchange = (e) => {
+							node.properties.enable_library_inspiration = e.target.checked;
+							saveSettings();
+							app.graph.setDirtyCanvas(true, true);
+							renderUI();
+						};
+					}
+
+					// Generation count input (unified tab)
+					const genCountInputUnified = container.querySelector(".instaraw-rpg-gen-count-input");
+					if (genCountInputUnified) {
+						genCountInputUnified.onchange = (e) => {
+							node.properties.generation_count = parseInt(e.target.value) || 5;
+							app.graph.setDirtyCanvas(true, true);
+						};
+					}
+
+					// Expression control handlers
+					const enableExpressionsCheckbox = container.querySelector(".instaraw-rpg-enable-expressions-checkbox");
+					if (enableExpressionsCheckbox) {
+						enableExpressionsCheckbox.onchange = (e) => {
+							node.properties.enable_expressions = e.target.checked;
+							saveSettings();
+							app.graph.setDirtyCanvas(true, true);
+							renderUI();
+						};
+					}
+
+					// Expression checkboxes
+					container.querySelectorAll(".instaraw-rpg-expression-checkbox").forEach((checkbox) => {
+						checkbox.onchange = () => {
+							const enabledExpressions = [];
+							container.querySelectorAll(".instaraw-rpg-expression-checkbox").forEach((cb) => {
+								const label = cb.closest(".instaraw-rpg-expression-toggle");
+								if (cb.checked && label) {
+									enabledExpressions.push(label.dataset.expression);
+								}
+							});
+							node.properties.enabled_expressions = JSON.stringify(enabledExpressions);
+							saveSettings();
+							app.graph.setDirtyCanvas(true, true);
+						};
+					});
+
+					// Expression Select All / Clear All buttons
+					const selectAllExpressionsBtn = container.querySelector(".instaraw-rpg-expressions-select-all");
+					if (selectAllExpressionsBtn) {
+						selectAllExpressionsBtn.onclick = () => {
+							container.querySelectorAll(".instaraw-rpg-expression-checkbox").forEach((cb) => {
+								cb.checked = true;
+							});
+							node.properties.enabled_expressions = JSON.stringify([...EXPRESSION_LIST]);
+							saveSettings();
+							app.graph.setDirtyCanvas(true, true);
+						};
+					}
+
+					const clearAllExpressionsBtn = container.querySelector(".instaraw-rpg-expressions-clear-all");
+					if (clearAllExpressionsBtn) {
+						clearAllExpressionsBtn.onclick = () => {
+							container.querySelectorAll(".instaraw-rpg-expression-checkbox").forEach((cb) => {
+								cb.checked = false;
+							});
+							node.properties.enabled_expressions = JSON.stringify([]);
+							saveSettings();
+							app.graph.setDirtyCanvas(true, true);
+						};
+					}
+
+					// Default expression dropdown
+					const defaultExpressionSelect = container.querySelector(".instaraw-rpg-default-expression-select");
+					if (defaultExpressionSelect) {
+						defaultExpressionSelect.onchange = (e) => {
+							node.properties.default_expression = e.target.value;
+							saveSettings();
+							app.graph.setDirtyCanvas(true, true);
+						};
+					}
+
+					// Default mix frequency slider
+					const defaultMixSlider = container.querySelector(".instaraw-rpg-default-mix-slider");
+					const defaultMixValue = container.querySelector(".instaraw-rpg-default-mix-value");
+					if (defaultMixSlider) {
+						defaultMixSlider.oninput = (e) => {
+							if (defaultMixValue) {
+								defaultMixValue.textContent = `${e.target.value}%`;
+							}
+						};
+						defaultMixSlider.onchange = (e) => {
+							node.properties.default_mix_frequency = parseInt(e.target.value) || 0;
+							saveSettings();
+							app.graph.setDirtyCanvas(true, true);
+						};
+					}
+
+					// Toggle system prompt preview button (in Advanced section)
+					const toggleSystemPromptPreviewBtn = container.querySelector(".instaraw-rpg-toggle-system-prompt-preview-btn");
+					if (toggleSystemPromptPreviewBtn) {
+						console.log("[RPG] Preview button found, attaching handler");
+						toggleSystemPromptPreviewBtn.addEventListener('click', (e) => {
+							console.log("[RPG] Preview button clicked!");
+							// Prevent the details from toggling
+							e.preventDefault();
+							e.stopPropagation();
+							
+							// Find the details element
+							const detailsElement = container.querySelector(".instaraw-rpg-advanced-settings");
+							console.log("[RPG] Details element:", detailsElement, "Open:", detailsElement?.open);
+							
+							// Get elements first
+							const textarea = container.querySelector(".instaraw-rpg-system-prompt");
+							const previewContainer = container.querySelector(".instaraw-rpg-system-prompt-preview-container");
+							
+							// Always ensure details is open
+							if (detailsElement && !detailsElement.open) {
+								console.log("[RPG] Opening details...");
+								detailsElement.open = true;
+								
+								// Force arrow rotation
+								const arrow = container.querySelector(".instaraw-rpg-details-arrow");
+								if (arrow) arrow.style.transform = 'rotate(0deg)';
+								
+								// IMMEDIATELY show textarea by default when opening
+								if (textarea) textarea.style.display = 'block';
+								if (previewContainer) previewContainer.style.display = 'none';
+								
+								setTimeout(() => {
+									performToggle();
+								}, 100);
+							} else {
+								performToggle();
+							}
+							
+							function performToggle() {
+								// Toggle the state
+								node.properties.show_system_prompt_preview = !node.properties.show_system_prompt_preview;
+
+								// Update button text
+								toggleSystemPromptPreviewBtn.textContent = node.properties.show_system_prompt_preview ? '๐Ÿ“ Edit Template' : '๐Ÿ‘๏ธ Preview';
+
+								if (node.properties.show_system_prompt_preview) {
+									// Gather current settings
+									const detectedMode = node._linkedAILMode || "txt2img";
+									const generationStyle = node.properties.generation_style || "reality";
+									const useCharacter = node.properties.use_character_likeness || false;
+									const characterDescription = node.properties.character_text_input || "";
+									// Get user input based on mode using unified composition
+									let userInput;
+									if (detectedMode === "img2img") {
+										userInput = composeUserInput(
+											node.properties.user_instructions || "",
+											node.properties.theme_preset || DEFAULT_THEME_PRESET,
+											node.properties.model_instructions || "",
+											node.properties.clean_mode || false
+										);
+									} else {
+										const userTextRaw = container.querySelector(".instaraw-rpg-user-text-input")?.value?.trim() || "";
+										userInput = composeUserInput(
+											userTextRaw,
+											node.properties.theme_preset || DEFAULT_THEME_PRESET,
+											node.properties.model_instructions || "",
+											node.properties.clean_mode || false
+										);
+									}
+									// Respect library inspiration toggle
+									const inspirationCount = node.properties.enable_library_inspiration
+										? parseInt(container.querySelector(".instaraw-rpg-inspiration-count")?.value || "3")
+										: 0;
+
+									// Get sample source prompts
+									const filters = JSON.parse(node.properties.library_filters || "{}");
+									const sourcePromptPool = filterPrompts(promptsDatabase, filters);
+									const sampleCount = Math.min(inspirationCount, sourcePromptPool.length);
+									const shuffled = [...sourcePromptPool].sort(() => Math.random() - 0.5);
+									const sampleSources = shuffled.slice(0, sampleCount).map(p => ({
+										positive_prompt: p.prompt?.positive || p.positive || "",
+										negative_prompt: p.prompt?.negative || p.negative || ""
+									}));
+
+									// Get expression if enabled
+									let expression = null;
+									if (node.properties.enable_expressions) {
+										const enabledExpressions = JSON.parse(node.properties.enabled_expressions || '[]');
+										const defaultExpression = node.properties.default_expression || "Neutral/Natural";
+										expression = enabledExpressions.length > 0 ? enabledExpressions[0] : defaultExpression;
+									}
+									
+									// Collect affect elements (for img2img Creative mode)
+									const affectElements = [];
+									if (detectedMode === 'img2img' && generationStyle === 'creative') {
+										const affectBackground = container.querySelector(".instaraw-rpg-affect-background")?.checked;
+										const affectOutfit = container.querySelector(".instaraw-rpg-affect-outfit")?.checked;
+										const affectPose = container.querySelector(".instaraw-rpg-affect-pose")?.checked;
+										const affectLighting = container.querySelector(".instaraw-rpg-affect-lighting")?.checked;
+										if (affectBackground) affectElements.push("background");
+										if (affectOutfit) affectElements.push("outfit");
+										if (affectPose) affectElements.push("pose");
+										if (affectLighting) affectElements.push("lighting");
+									}
+
+									// Build system prompt
+									const customTemplate = node.properties.creative_system_prompt;
+									const systemPrompt = buildSystemPrompt(
+										detectedMode,
+										generationStyle,
+										sampleSources,
+										userInput,
+										useCharacter ? characterDescription : "",
+										affectElements,
+										customTemplate,
+										expression
+									);
+
+									// Populate the preview
+									const previewText = container.querySelector(".instaraw-rpg-system-prompt-preview-text");
+									if (previewText) {
+										previewText.textContent = systemPrompt;
+									}
+
+									// Show preview, hide textarea
+									if (textarea) textarea.style.display = 'none';
+									if (previewContainer) previewContainer.style.display = 'block';
+								} else {
+									// Show textarea, hide preview
+									if (textarea) textarea.style.display = 'block';
+									if (previewContainer) previewContainer.style.display = 'none';
+									
+									// Auto-resize textarea after showing it
+									setTimeout(() => {
+										if (textarea) {
+											textarea.style.height = "auto";
+											textarea.style.height = `${textarea.scrollHeight}px`;
+										}
+									}, 0);
+								}
+
+								app.graph.setDirtyCanvas(true, true);
+							}
+						}, true);
+					}
+
+					// Details element toggle handler for populating content (MAIN system prompt, not character)
+					console.log("[RPG] === DEBUG: Looking for MAIN details element ===");
+					console.log("[RPG] Active tab:", node.properties.active_tab);
+					console.log("[RPG] Total 
 elements:", container.querySelectorAll("details").length);
+
+					// Find the details element that contains the preview toggle button (the MAIN one, not character one)
+					const previewButton = container.querySelector(".instaraw-rpg-toggle-system-prompt-preview-btn");
+					const detailsElement = previewButton?.closest("details.instaraw-rpg-advanced-settings");
+
+					console.log("[RPG] Preview button found:", !!previewButton);
+					console.log("[RPG] Details element (with preview) found:", !!detailsElement, "open:", detailsElement?.open, "preview mode:", node.properties.show_system_prompt_preview);
+					if (detailsElement) {
+						console.log("[RPG] Attaching toggle event listener to details element");
+						const updateDetailsContent = () => {
+							console.log("[RPG] updateDetailsContent called - open:", detailsElement.open, "preview:", node.properties.show_system_prompt_preview);
+
+							if (detailsElement.open) {
+								console.log("[RPG] Details is open, updating content...");
+
+								// Ensure correct element is visible and populated when opening
+								const doUpdate = () => {
+									const textarea = container.querySelector(".instaraw-rpg-system-prompt");
+									const previewContainer = container.querySelector(".instaraw-rpg-system-prompt-preview-container");
+									const previewText = container.querySelector(".instaraw-rpg-system-prompt-preview-text");
+
+									console.log("[RPG] Elements found - textarea:", !!textarea, "container:", !!previewContainer, "text:", !!previewText);
+
+									if (textarea && previewContainer) {
+										if (!node.properties.show_system_prompt_preview) {
+											// Edit mode: show textarea
+											console.log("[RPG] Edit mode");
+											textarea.style.display = 'block';
+											previewContainer.style.display = 'none';
+
+											// Auto-resize textarea
+											textarea.style.height = "auto";
+											textarea.style.height = `${textarea.scrollHeight}px`;
+										} else {
+											// Preview mode: populate and show preview
+											console.log("[RPG] Preview mode - populating...");
+											const detectedMode = node._linkedAILMode || "txt2img";
+											const generationStyle = node.properties.generation_style || "reality";
+											const useCharacter = node.properties.use_character_likeness || false;
+											const characterDescription = node.properties.character_text_input || "";
+											const userTextRaw = container.querySelector(".instaraw-rpg-user-text-input")?.value?.trim() || "";
+											// Respect library inspiration toggle
+											const inspirationCount = node.properties.enable_library_inspiration
+												? parseInt(container.querySelector(".instaraw-rpg-inspiration-count")?.value || "3")
+												: 0;
+
+											// Compose user input using unified composition helper
+											let userInput;
+											if (detectedMode === 'img2img') {
+												userInput = composeUserInput(
+													node.properties.user_instructions || "",
+													node.properties.theme_preset || DEFAULT_THEME_PRESET,
+													node.properties.model_instructions || "",
+													node.properties.clean_mode || false
+												);
+											} else {
+												userInput = composeUserInput(
+													userTextRaw,
+													node.properties.theme_preset || DEFAULT_THEME_PRESET,
+													node.properties.model_instructions || "",
+													node.properties.clean_mode || false
+												);
+											}
+
+											// Get sample source prompts
+											const filters = JSON.parse(node.properties.library_filters || "{}");
+											const sourcePromptPool = filterPrompts(promptsDatabase, filters);
+											const sampleCount = Math.min(inspirationCount, sourcePromptPool.length);
+											const shuffled = [...sourcePromptPool].sort(() => Math.random() - 0.5);
+											const sampleSources = shuffled.slice(0, sampleCount).map(p => ({
+												positive_prompt: p.prompt?.positive || p.positive || "",
+												negative_prompt: p.prompt?.negative || p.negative || ""
+											}));
+
+											// Get expression if enabled
+											let expression = null;
+											if (node.properties.enable_expressions) {
+												const enabledExpressions = JSON.parse(node.properties.enabled_expressions || '[]');
+												const defaultExpression = node.properties.default_expression || "Neutral/Natural";
+												expression = enabledExpressions.length > 0 ? enabledExpressions[0] : defaultExpression;
+											}
+
+											// Collect affect elements (for img2img Creative mode)
+											const affectElements = [];
+											if (detectedMode === 'img2img' && generationStyle === 'creative') {
+												const affectBackground = container.querySelector(".instaraw-rpg-affect-background")?.checked;
+												const affectOutfit = container.querySelector(".instaraw-rpg-affect-outfit")?.checked;
+												const affectPose = container.querySelector(".instaraw-rpg-affect-pose")?.checked;
+												const affectLighting = container.querySelector(".instaraw-rpg-affect-lighting")?.checked;
+												if (affectBackground) affectElements.push("background");
+												if (affectOutfit) affectElements.push("outfit");
+												if (affectPose) affectElements.push("pose");
+												if (affectLighting) affectElements.push("lighting");
+											}
+
+											// Build system prompt
+											const customTemplate = node.properties.creative_system_prompt;
+											const systemPrompt = buildSystemPrompt(
+												detectedMode,
+												generationStyle,
+												sampleSources,
+												userInput,
+												useCharacter ? characterDescription : "",
+												affectElements,
+												customTemplate,
+												expression
+											);
+
+											console.log("[RPG] Built prompt, len:", systemPrompt?.length, "text elem:", !!previewText);
+
+											// Populate the preview
+											if (previewText) {
+												previewText.textContent = systemPrompt;
+												console.log("[RPG] Preview set! First 50:", systemPrompt.substring(0, 50));
+											} else {
+												console.error("[RPG] ERROR: previewText not found!");
+											}
+
+											// Show preview, hide textarea
+											textarea.style.display = 'none';
+											previewContainer.style.display = 'block';
+											console.log("[RPG] Preview displayed");
+										}
+									} else {
+										console.error("[RPG] Missing elements!");
+									}
+								};
+
+								// Call immediately if already rendered, or wait for next tick
+								if (container.querySelector(".instaraw-rpg-system-prompt")) {
+									console.log("[RPG] Elements already in DOM, updating immediately");
+									doUpdate();
+								} else {
+									console.log("[RPG] Waiting for elements to be in DOM");
+									setTimeout(doUpdate, 0);
+								}
+							}
+						};
+						console.log("[RPG] About to call updateDetailsContent() and addEventListener");
+						updateDetailsContent(); // Populate immediately if details is already open
+						detailsElement.addEventListener('toggle', updateDetailsContent);
+						console.log("[RPG] Event listener attached successfully");
+					} else {
+						console.error("[RPG] ERROR: detailsElement not found - probably not on Generate tab");
+					}
+
+					// Unified Generate button (MAIN HANDLER)
+					const generateUnifiedBtn = container.querySelector(".instaraw-rpg-generate-unified-btn");
+					if (generateUnifiedBtn) {
+						generateUnifiedBtn.onclick = generateUnifiedPrompts;
+					}
+
+					// Cancel generation button (for aborting in-progress generation)
+					const cancelGenerationBtn = container.querySelector(".instaraw-rpg-cancel-generation-btn");
+					if (cancelGenerationBtn) {
+						cancelGenerationBtn.onclick = () => {
+							if (generationAbortController) {
+								console.log("[RPG] ๐Ÿ›‘ User clicked cancel - aborting generation");
+								generationAbortController.abort();
+							}
+						};
+					}
+
+					// Accept generated prompts buttons (there are two - top quick action and bottom)
+					container.querySelectorAll(".instaraw-rpg-accept-generated-btn").forEach((btn) => {
+						btn.onclick = acceptGeneratedPrompts;
+					});
+
+					// Cancel generated prompts button
+					const cancelGeneratedBtn = container.querySelector(".instaraw-rpg-cancel-generated-btn");
+					if (cancelGeneratedBtn) {
+						cancelGeneratedBtn.onclick = cancelGeneratedPrompts;
+					}
+
+					// === Custom Tab Handlers ===
+
+					// Custom template textarea
+					const customTemplateTextarea = container.querySelector(".instaraw-rpg-custom-template-textarea");
+					if (customTemplateTextarea) {
+						autoResizeTextarea(customTemplateTextarea);
+						customTemplateTextarea.oninput = (e) => {
+							node.properties.custom_template = e.target.value;
+							autoResizeTextarea(e.target);
+							app.graph.setDirtyCanvas(true, true);
+						};
+					}
+
+					// Custom tab user input - unified with other tabs
+					const customUserInput = container.querySelector(".instaraw-rpg-custom-user-input");
+					if (customUserInput) {
+						autoResizeTextarea(customUserInput);
+						customUserInput.oninput = (e) => {
+							node.properties.user_instructions = e.target.value;
+							saveUserInstructions();
+							autoResizeTextarea(e.target);
+							app.graph.setDirtyCanvas(true, true);
+						};
+					}
+
+					// Reset custom template button
+					const resetCustomTemplateBtn = container.querySelector(".instaraw-rpg-reset-custom-template-btn");
+					if (resetCustomTemplateBtn) {
+						resetCustomTemplateBtn.onclick = () => {
+							node.properties.custom_template = DEFAULT_RPG_SYSTEM_PROMPT;
+							const textarea = container.querySelector(".instaraw-rpg-custom-template-textarea");
+							if (textarea) {
+								textarea.value = DEFAULT_RPG_SYSTEM_PROMPT;
+								autoResizeTextarea(textarea);
+							}
+							app.graph.setDirtyCanvas(true, true);
+							console.log("[RPG] Reset custom template to default");
+						};
+					}
+
+					// Variable insert buttons for custom template
+					container.querySelectorAll(".instaraw-rpg-insert-variable-btn").forEach((btn) => {
+						btn.onclick = () => {
+							const variable = btn.dataset.variable;
+							const textarea = container.querySelector(".instaraw-rpg-custom-template-textarea");
+							if (textarea) {
+								const start = textarea.selectionStart;
+								const end = textarea.selectionEnd;
+								const text = textarea.value;
+								const before = text.substring(0, start);
+								const after = text.substring(end);
+								textarea.value = before + variable + after;
+								textarea.selectionStart = textarea.selectionEnd = start + variable.length;
+								textarea.focus();
+
+								// Trigger change event
+								node.properties.custom_template = textarea.value;
+								autoResizeTextarea(textarea);
+								app.graph.setDirtyCanvas(true, true);
+							}
+						};
+					});
+
+					// Quick Start Templates selector
+					const quickTemplateSelector = container.querySelector(".instaraw-rpg-quick-template-selector");
+					if (quickTemplateSelector) {
+						quickTemplateSelector.onchange = (e) => {
+							const preset = e.target.value;
+							if (preset && QUICK_START_TEMPLATES[preset]) {
+								node.properties.custom_template = QUICK_START_TEMPLATES[preset];
+								const textarea = container.querySelector(".instaraw-rpg-custom-template-textarea");
+								if (textarea) {
+									textarea.value = QUICK_START_TEMPLATES[preset];
+									autoResizeTextarea(textarea);
+								}
+								app.graph.setDirtyCanvas(true, true);
+								console.log(`[RPG] Loaded quick start template: ${preset}`);
+								// Reset selector
+								e.target.value = "";
+							}
+						};
+					}
+
+					// Preview custom template button
+					const previewCustomTemplateBtn = container.querySelector(".instaraw-rpg-preview-custom-template-btn");
+					if (previewCustomTemplateBtn) {
+						previewCustomTemplateBtn.onclick = () => {
+							const customTemplate = container.querySelector(".instaraw-rpg-custom-template-textarea")?.value || node.properties.custom_template || DEFAULT_RPG_SYSTEM_PROMPT;
+							const detectedMode = node._linkedAILMode || "txt2img";
+							const generationStyle = node.properties.generation_style || "custom";
+							const useCharacter = node.properties.use_character_likeness || false;
+							const characterDescription = node.properties.character_text_input || "";
+							// Compose user input using unified composition helper
+							let userInput;
+							if (detectedMode === "img2img") {
+								userInput = composeUserInput(
+									node.properties.user_instructions || "",
+									node.properties.theme_preset || DEFAULT_THEME_PRESET,
+									node.properties.model_instructions || "",
+									node.properties.clean_mode || false
+								);
+							} else {
+								const userTextRaw = container.querySelector(".instaraw-rpg-user-text-input")?.value?.trim() || "";
+								userInput = composeUserInput(
+									userTextRaw,
+									node.properties.theme_preset || DEFAULT_THEME_PRESET,
+									node.properties.model_instructions || "",
+									node.properties.clean_mode || false
+								);
+							}
+							// Respect library inspiration toggle
+							const inspirationCount = node.properties.enable_library_inspiration
+								? parseInt(container.querySelector(".instaraw-rpg-custom-inspiration-count")?.value || container.querySelector(".instaraw-rpg-inspiration-count")?.value || "3")
+								: 0;
+
+							// Get sample source prompts
+							const filters = JSON.parse(node.properties.library_filters || "{}");
+							const sourcePromptPool = filterPrompts(promptsDatabase, filters);
+							const sampleCount = Math.min(inspirationCount, sourcePromptPool.length);
+							const shuffled = [...sourcePromptPool].sort(() => Math.random() - 0.5);
+							const sampleSources = shuffled.slice(0, sampleCount).map(p => ({
+								positive_prompt: p.prompt?.positive || p.positive || "",
+								negative_prompt: p.prompt?.negative || p.negative || ""
+							}));
+
+							// Get expression if enabled
+							let expression = null;
+							if (node.properties.enable_expressions) {
+								const enabledExpressions = JSON.parse(node.properties.enabled_expressions || '[]');
+								const defaultExpression = node.properties.default_expression || "Neutral/Natural";
+								expression = enabledExpressions.length > 0 ? enabledExpressions[0] : defaultExpression;
+							}
+
+							// Get affect elements for img2img
+							const affectElements = [];
+							if (detectedMode === 'img2img') {
+								const affectBackground = container.querySelector(".instaraw-rpg-affect-background")?.checked;
+								const affectOutfit = container.querySelector(".instaraw-rpg-affect-outfit")?.checked;
+								const affectPose = container.querySelector(".instaraw-rpg-affect-pose")?.checked;
+								const affectLighting = container.querySelector(".instaraw-rpg-affect-lighting")?.checked;
+								if (affectBackground) affectElements.push("background");
+								if (affectOutfit) affectElements.push("outfit");
+								if (affectPose) affectElements.push("pose");
+								if (affectLighting) affectElements.push("lighting");
+							}
+
+							// Build preview prompt
+							const previewPrompt = buildSystemPrompt(
+								detectedMode,
+								generationStyle,
+								sampleSources,
+								userInput,
+								useCharacter ? characterDescription : "",
+								affectElements,
+								customTemplate,
+								expression
+							);
+
+							// Show preview
+							node.properties.show_custom_template_preview = true;
+							const previewText = container.querySelector(".instaraw-rpg-custom-template-preview-text");
+							if (previewText) {
+								previewText.textContent = previewPrompt;
+							}
+							renderUI();
+							console.log("[RPG] Custom template preview generated");
+						};
+					}
+
+					// Close custom preview button
+					const closeCustomPreviewBtn = container.querySelector(".instaraw-rpg-close-custom-preview-btn");
+					if (closeCustomPreviewBtn) {
+						closeCustomPreviewBtn.onclick = () => {
+							node.properties.show_custom_template_preview = false;
+							renderUI();
+						};
+					}
+
+					// Custom inspiration count input
+					const customInspirationCountInput = container.querySelector(".instaraw-rpg-custom-inspiration-count");
+					if (customInspirationCountInput) {
+						customInspirationCountInput.onchange = (e) => {
+							node.properties.inspiration_count = parseInt(e.target.value) || 3;
+							saveSettings();
+							app.graph.setDirtyCanvas(true, true);
+							console.log("[RPG] Custom mode inspiration count:", node.properties.inspiration_count);
+						};
+					}
+
+					// Custom library details toggle
+					const customLibraryDetails = container.querySelector(".instaraw-rpg-custom-library-details");
+					if (customLibraryDetails) {
+						customLibraryDetails.ontoggle = () => {
+							node.properties.custom_library_expanded = customLibraryDetails.open;
+							app.graph.setDirtyCanvas(true, true);
+						};
+					}
+
+					// Variable help details toggle
+					const variableHelpDetails = container.querySelector(".instaraw-rpg-variable-help-details");
+					if (variableHelpDetails) {
+						variableHelpDetails.ontoggle = () => {
+							node.properties.variable_help_expanded = variableHelpDetails.open;
+							app.graph.setDirtyCanvas(true, true);
+						};
+					}
+
+					// Preview custom prompt button
+					const previewCustomPromptBtn = container.querySelector(".instaraw-rpg-preview-custom-prompt-btn");
+					if (previewCustomPromptBtn) {
+						previewCustomPromptBtn.onclick = () => {
+							// Get custom template
+							const customTemplate = container.querySelector(".instaraw-rpg-custom-template-textarea")?.value || node.properties.custom_template;
+							const detectedMode = node._linkedAILMode || "txt2img";
+							const useCharacter = node.properties.use_character_likeness || false;
+							const characterDescription = node.properties.character_text_input || "";
+							const userInput = container.querySelector(".instaraw-rpg-custom-user-input")?.value?.trim() || "";
+
+							// Get sample source prompts
+							const filters = JSON.parse(node.properties.library_filters || "{}");
+							const sourcePromptPool = filterPrompts(promptsDatabase, filters);
+							const sampleCount = Math.min(3, sourcePromptPool.length);
+							const shuffled = [...sourcePromptPool].sort(() => Math.random() - 0.5);
+							const sampleSources = shuffled.slice(0, sampleCount).map(p => ({
+								positive_prompt: p.prompt?.positive || p.positive || "",
+								negative_prompt: p.prompt?.negative || p.negative || ""
+							}));
+
+							// Get expression if enabled
+							let expression = null;
+							if (node.properties.enable_expressions) {
+								const enabledExpressions = JSON.parse(node.properties.enabled_expressions || '[]');
+								const defaultExpression = node.properties.default_expression || "Neutral/Natural";
+								expression = enabledExpressions.length > 0 ? enabledExpressions[0] : defaultExpression;
+							}
+
+							// Build system prompt with custom template
+							const systemPrompt = buildSystemPrompt(
+								detectedMode,
+								"reality", // Default style for preview
+								sampleSources,
+								userInput,
+								useCharacter ? characterDescription : "",
+								[],
+								customTemplate,
+								expression
+							);
+
+							// Show preview modal
+							showPreviewModal(systemPrompt, {
+								mode: `${detectedMode.toUpperCase()} (Custom Template)`,
+								character: useCharacter ? characterDescription : null,
+								expression: expression,
+								userInput: userInput,
+								inspirationCount: sampleSources.length
+							});
+						};
+					}
+
+					// Variable insertion buttons
+					container.querySelectorAll(".instaraw-rpg-variable-insert-btn").forEach((btn) => {
+						btn.onclick = () => {
+							const variable = btn.dataset.variable;
+							const textarea = container.querySelector(".instaraw-rpg-custom-template-textarea");
+							if (textarea) {
+								const start = textarea.selectionStart;
+								const end = textarea.selectionEnd;
+								const text = textarea.value;
+								const before = text.substring(0, start);
+								const after = text.substring(end);
+								textarea.value = before + variable + after;
+								textarea.selectionStart = textarea.selectionEnd = start + variable.length;
+								textarea.focus();
+
+								// Trigger change event
+								node.properties.custom_template = textarea.value;
+								app.graph.setDirtyCanvas(true, true);
+							}
+						};
+					});
+
+				};
+
+				// === Drag and Drop (Exact AIL Pattern) ===
+				const setupDragAndDrop = () => {
+					// Drag-and-drop reordering (only when enabled)
+					if (reorderModeEnabled) {
+						const items = container.querySelectorAll(".instaraw-rpg-batch-item");
+						let draggedItem = null;
+
+						items.forEach((item) => {
+						// Skip if already has drag listeners to prevent accumulation
+						if (item._hasDragListeners) return;
+						item._hasDragListeners = true;
+
+						item.addEventListener("dragstart", (e) => {
+							draggedItem = item;
+							item.style.opacity = "0.5";
+							e.dataTransfer.effectAllowed = "move";
+							e.stopPropagation();
+							e.dataTransfer.setData("text/plain", "instaraw-rpg-reorder");
+						});
+
+						item.addEventListener("dragend", () => {
+							item.style.opacity = "1";
+							items.forEach((i) => i.classList.remove("instaraw-rpg-drop-before", "instaraw-rpg-drop-after"));
+						});
+
+						item.addEventListener("dragover", (e) => {
+							e.preventDefault();
+							if (draggedItem === item) return;
+							e.dataTransfer.dropEffect = "move";
+							const rect = item.getBoundingClientRect();
+							const midpoint = rect.top + rect.height / 2;
+							items.forEach((i) => i.classList.remove("instaraw-rpg-drop-before", "instaraw-rpg-drop-after"));
+							item.classList.add(e.clientY < midpoint ? "instaraw-rpg-drop-before" : "instaraw-rpg-drop-after");
+						});
+
+						item.addEventListener("drop", (e) => {
+							e.preventDefault();
+							if (draggedItem === item) return;
+
+							const draggedId = draggedItem.dataset.id;
+							const targetId = item.dataset.id;
+
+								const promptQueue = parsePromptBatch();
+							const draggedIndex = promptQueue.findIndex((p) => p.id === draggedId);
+							const targetIndex = promptQueue.findIndex((p) => p.id === targetId);
+
+							if (draggedIndex === -1 || targetIndex === -1) return;
+
+							const [draggedEntry] = promptQueue.splice(draggedIndex, 1);
+							const rect = item.getBoundingClientRect();
+							const insertAfter = e.clientY > rect.top + rect.height / 2;
+
+							const newTargetIndex = promptQueue.findIndex((p) => p.id === targetId);
+							promptQueue.splice(insertAfter ? newTargetIndex + 1 : newTargetIndex, 0, draggedEntry);
+
+								setPromptBatchData(promptQueue);
+							renderUI();
+						});
+					});
+					}
+				};
+
+				// === AIL Update Listener ===
+				// Global listener that routes updates to the correct RPG node
+				if (!window._hasRPGAILListener) {
+					window._hasRPGAILListener = true;
+					window._rpgNodesByAILId = new Map(); // Track which RPG nodes are connected to which AIL nodes
+
+					window.addEventListener("INSTARAW_AIL_UPDATED", (event) => {
+						const { nodeId, images, latents, total, mode, enable_img2img } = event.detail;
+
+						// Debug log to trace event format
+						console.log(`[AIL-SYNC] Event received from AIL #${nodeId}:`, {
+							mode,
+							imagesCount: images?.length,
+							hasUrl: images?.[0]?.url ? 'YES' : 'NO',
+							firstImageKeys: images?.[0] ? Object.keys(images[0]) : []
+						});
+
+						// Find all RPG nodes connected to this AIL node
+						if (!app || !app.graph || !app.graph._nodes) return;
+
+						// Input names to check for multi-image support
+						const imageInputNames = ["images", "images2", "images3", "images4"];
+
+						app.graph._nodes.forEach(rpgNode => {
+							if (rpgNode.type !== "INSTARAW_RealityPromptGenerator") return;
+
+							// Check which input(s) this AIL is connected to
+							let connectedInputName = null;
+							for (const inputName of imageInputNames) {
+								const input = rpgNode.inputs?.find(i => i.name === inputName);
+								if (!input || !input.link) continue;
+
+								const link = app.graph.links[input.link];
+								if (link && link.origin_id === nodeId) {
+									connectedInputName = inputName;
+									break;
+								}
+							}
+
+							if (!connectedInputName) return;
+
+							// This RPG is connected to the AIL that sent the update
+							console.log(`[RPG ${rpgNode.id}] AIL ${nodeId} update on ${connectedInputName} - Mode: ${mode}, Count: ${total}, images: ${images?.length || 0}`);
+
+							// Update the appropriate tracking array based on which input is connected
+							// Filter out any images with invalid URLs to prevent broken image icons
+							const imageArray = mode === "img2img" ? (images || []).filter(img => img?.url) : [];
+
+							console.log(`[AIL-SYNC] Setting ${connectedInputName}: ${images?.length} received โ†’ ${imageArray.length} after filter`);
+
+							if (connectedInputName === "images") {
+								rpgNode._linkedAILNodeId = nodeId;
+								rpgNode._linkedAILMode = mode;
+								rpgNode._linkedImages = imageArray;
+								rpgNode._linkedLatents = mode !== "img2img" ? (latents || []) : [];
+								rpgNode._linkedImageCount = total;
+
+								// Update expected_image_count widget for primary input only
+								const widget = rpgNode.widgets?.find((w) => w.name === "expected_image_count");
+								if (widget) widget.value = total;
+							} else if (connectedInputName === "images2") {
+								rpgNode._linkedImages2 = imageArray;
+							} else if (connectedInputName === "images3") {
+								rpgNode._linkedImages3 = imageArray;
+							} else if (connectedInputName === "images4") {
+								rpgNode._linkedImages4 = imageArray;
+							}
+
+							// Re-render this specific RPG node
+							if (rpgNode._renderUI) {
+								rpgNode._renderUI();
+							}
+						});
+					});
+				}
+
+				// === Add DOM Widget (Exact AIL Pattern) ===
+				const widget = node.addDOMWidget("rpg_display", "rpgpromptmanager", container, {
+					getValue: () => node.properties.prompt_batch_data,
+					setValue: (v) => {
+						node.properties.prompt_batch_data = v;
+						renderUI();
+					},
+					serialize: false,
+				});
+
+				widget.computeSize = (width) => [width, cachedHeight + 6];
+
+				// Add widget change callbacks to automatically refresh UI when aspect ratio changes
+				const setupWidgetCallbacks = () => {
+					const widthWidget = node.widgets?.find((w) => w.name === "output_width");
+					if (widthWidget && !widthWidget._instaraw_callback_added) {
+						const originalCallback = widthWidget.callback;
+						widthWidget.callback = function() {
+							if (originalCallback) originalCallback.apply(this, arguments);
+							renderUI();
+						};
+						widthWidget._instaraw_callback_added = true;
+					}
+
+					const heightWidget = node.widgets?.find((w) => w.name === "output_height");
+					if (heightWidget && !heightWidget._instaraw_callback_added) {
+						const originalCallback = heightWidget.callback;
+						heightWidget.callback = function() {
+							if (originalCallback) originalCallback.apply(this, arguments);
+							renderUI();
+						};
+						heightWidget._instaraw_callback_added = true;
+					}
+
+					const aspectWidget = node.widgets?.find((w) => w.name === "aspect_label");
+					if (aspectWidget && !aspectWidget._instaraw_callback_added) {
+						const originalCallback = aspectWidget.callback;
+						aspectWidget.callback = function() {
+							if (originalCallback) originalCallback.apply(this, arguments);
+							renderUI();
+						};
+						aspectWidget._instaraw_callback_added = true;
+					}
+				};
+
+				// Periodic dimension check - checks every 2 seconds if dimensions changed
+				node._dimensionCheckInterval = null;
+				let lastDimensions = null;
+				const startDimensionCheck = () => {
+					if (node._dimensionCheckInterval) clearInterval(node._dimensionCheckInterval);
+					node._dimensionCheckInterval = setInterval(() => {
+						const currentDims = getTargetDimensions();
+						const dimsKey = `${currentDims.width}x${currentDims.height}:${currentDims.aspect_label}`;
+						if (lastDimensions !== null && lastDimensions !== dimsKey) {
+							console.log(`[RPG] Dimensions changed: ${lastDimensions} -> ${dimsKey}`);
+							renderUI();
+						}
+						lastDimensions = dimsKey;
+					}, 2000);
+				};
+
+				// Periodic AIL mode sync - checks every 500ms to ensure mode stays in sync
+				node._ailSyncInterval = null;
+				let lastAILMode = null;
+				let lastAILImageCount = null;
+				let syncCheckCounter = 0;
+				const startAILSync = () => {
+					if (node._ailSyncInterval) clearInterval(node._ailSyncInterval);
+					node._ailSyncInterval = setInterval(() => {
+						syncCheckCounter++;
+						const debugLog = syncCheckCounter % 10 === 0; // Log every 5 seconds (10 * 500ms)
+
+						// Check if we have an AIL connection
+						const imagesInput = node.inputs?.find(input => input.name === "images");
+
+						if (debugLog) {
+							console.log(`[RPG AIL Sync Debug] Check #${syncCheckCounter}:`, {
+								hasImagesInput: !!imagesInput,
+								hasLink: imagesInput?.link != null,
+								linkId: imagesInput?.link,
+								currentRPGMode: node._linkedAILMode,
+								currentRPGNodeId: node._linkedAILNodeId
+							});
+						}
+
+						if (!imagesInput || !imagesInput.link) {
+							// No connection - clear AIL data if needed
+							if (node._linkedAILNodeId !== null) {
+								console.log("[RPG AIL Sync] โŒ No AIL connection detected, clearing data");
+								node._linkedAILNodeId = null;
+								node._linkedAILMode = null;
+								node._linkedImageCount = 0;
+								node._linkedImages = [];
+								node._linkedLatents = [];
+								renderUI();
+							}
+							return;
+						}
+
+						// Get the connected AIL node
+						const link = node.graph?.links?.[imagesInput.link];
+						if (!link) {
+							if (debugLog) console.log("[RPG AIL Sync Debug] Link object not found");
+							return;
+						}
+
+						const ailNode = node.graph.getNodeById(link.origin_id);
+						if (!ailNode) {
+							if (debugLog) console.log("[RPG AIL Sync Debug] AIL node not found");
+							return;
+						}
+
+						if (ailNode.type !== "INSTARAW_AdvancedImageLoader") {
+							if (debugLog) console.log("[RPG AIL Sync Debug] Connected node is not AIL, type:", ailNode.type);
+							return;
+						}
+
+						// Read current mode from AIL - based on enable_img2img input/widget, NOT the "mode" widget
+						// The "mode" widget is for Batch Tensor vs Sequential, not img2img vs txt2img
+						if (debugLog) {
+							console.log(`[RPG ${node.id}] AIL widgets:`, ailNode.widgets?.map(w => ({ name: w.name, type: w.type, value: typeof w.value === 'object' ? JSON.stringify(w.value).substring(0, 50) : w.value })));
+						}
+
+						const enableImg2ImgWidget = ailNode.widgets?.find(w => w.name === "enable_img2img");
+						const enableImg2ImgValue = enableImg2ImgWidget?.value;
+
+						if (debugLog) {
+							console.log(`[RPG ${node.id}] enable_img2img widget:`, enableImg2ImgWidget);
+						}
+
+						// Check if enable_img2img is connected to another node
+						const enableImg2ImgInput = ailNode.inputs?.find(i => i.name === "enable_img2img");
+						let finalEnableImg2Img = enableImg2ImgValue;
+
+						if (enableImg2ImgInput && enableImg2ImgInput.link != null) {
+							// Connected to another node - read from source
+							const link = ailNode.graph?.links?.[enableImg2ImgInput.link];
+							if (link) {
+								const sourceNode = ailNode.graph.getNodeById(link.origin_id);
+								if (sourceNode) {
+									const sourceWidget = sourceNode.widgets?.[link.origin_slot];
+									if (sourceWidget) {
+										finalEnableImg2Img = sourceWidget.value;
+									}
+								}
+							}
+						}
+
+						// Read batch_data first for additional mode detection info
+						let batchData;
+						try {
+							batchData = JSON.parse(ailNode.properties?.batch_data || "{}");
+						} catch (e) {
+							batchData = {};
+						}
+
+						// Convert to mode: enable_img2img === false means txt2img, === true means img2img
+						// Also check batch_data.enable_img2img as a fallback, or infer from content
+						let detectedMode;
+						if (finalEnableImg2Img === false || finalEnableImg2Img === "false") {
+							detectedMode = "txt2img";
+						} else if (finalEnableImg2Img === true || finalEnableImg2Img === "true") {
+							detectedMode = "img2img";
+						} else if (batchData.enable_img2img !== undefined) {
+							detectedMode = batchData.enable_img2img ? "img2img" : "txt2img";
+						} else {
+							// Final fallback: infer from content (images = img2img, latents only = txt2img)
+							const hasImages = (batchData.images || []).length > 0;
+							detectedMode = hasImages ? "img2img" : "txt2img";
+						}
+
+						// Read image count and data from AIL's properties.batch_data
+						let imageCount = 0;
+						let images = [];
+						let latents = [];
+
+						const order = batchData.order || [];
+						const ailImages = batchData.images || [];
+						const ailLatents = batchData.latents || [];
+
+						// Validate that batch_data matches the detected mode
+						// If there's a mismatch, AIL hasn't swapped the data yet - wait for next sync
+						const hasImages = ailImages.length > 0;
+						const hasLatents = ailLatents.length > 0;
+						const dataMismatch = (detectedMode === "txt2img" && hasImages && !hasLatents) ||
+						                     (detectedMode === "img2img" && hasLatents && !hasImages);
+
+						if (dataMismatch) {
+							// AIL's batch_data hasn't been swapped yet - skip this sync cycle
+							if (debugLog) {
+								console.log(`[RPG ${node.id} AIL Sync] โณ Data mismatch detected, waiting for AIL to swap batch_data`);
+								console.log(`  - detectedMode: ${detectedMode}, hasImages: ${hasImages}, hasLatents: ${hasLatents}`);
+							}
+							return; // Skip this sync, try again next cycle
+						}
+
+						if (detectedMode === "img2img") {
+								// IMG2IMG mode - read images
+								images = order.map(imgId => {
+									const img = ailImages.find(i => i.id === imgId);
+									if (!img) return null;
+									// Handle both url (if already computed) and thumbnail (raw batchData format)
+									const imgUrl = img.url || (img.thumbnail ? `/instaraw/view/${img.thumbnail}` : null);
+									// Skip entries with invalid URLs to prevent broken image icons
+									if (!imgUrl) return null;
+									return {
+										id: img.id,
+										url: imgUrl,
+										thumbnail: img.thumbnail, // Preserve original thumbnail path
+										repeat_count: img.repeat_count || 1
+									};
+								}).filter(Boolean);
+								imageCount = images.reduce((sum, img) => sum + (img.repeat_count || 1), 0);
+							} else {
+								// TXT2IMG mode - read latents
+								latents = order.map(latentId => {
+									const latent = ailLatents.find(l => l.id === latentId);
+									if (!latent) return null;
+									return {
+										id: latent.id,
+										width: latent.width,
+										height: latent.height,
+										repeat_count: latent.repeat_count || 1
+									};
+								}).filter(Boolean);
+								imageCount = latents.reduce((sum, lat) => sum + (lat.repeat_count || 1), 0);
+						}
+
+						if (debugLog) {
+							console.log(`[RPG ${node.id} AIL Sync Debug] AIL Node #${ailNode.id} state:`, {
+								enableImg2ImgWidget: enableImg2ImgValue,
+								enableImg2ImgFinal: finalEnableImg2Img,
+								detectedMode: detectedMode,
+								imageCount: imageCount,
+								imagesLength: images.length,
+								latentsLength: latents.length,
+								ailNodeType: ailNode.type,
+								currentRPGMode: node._linkedAILMode,
+								currentRPGImages: node._linkedImages?.length || 0,
+								currentRPGLatents: node._linkedLatents?.length || 0
+							});
+						}
+
+						// Check if anything changed
+						const modeChanged = node._linkedAILMode !== detectedMode;
+						const countChanged = node._linkedImageCount !== imageCount;
+						const nodeIdChanged = node._linkedAILNodeId !== ailNode.id;
+
+						if (modeChanged || countChanged || nodeIdChanged) {
+							console.log(`[RPG ${node.id} AIL Sync] ๐Ÿ”„ Syncing changes detected:`);
+							console.log(`  - Mode: ${node._linkedAILMode} -> ${detectedMode} (changed: ${modeChanged})`);
+							console.log(`  - Count: ${node._linkedImageCount} -> ${imageCount} (changed: ${countChanged})`);
+							console.log(`  - Node: ${node._linkedAILNodeId} -> ${ailNode.id} (changed: ${nodeIdChanged})`);
+							console.log(`  - enable_img2img: ${finalEnableImg2Img} (widget: ${enableImg2ImgValue})`);
+							console.log(`  - AIL _images: ${images.length}, AIL _latents: ${latents.length}`);
+
+							// Update all AIL state
+							node._linkedAILNodeId = ailNode.id;
+							node._linkedAILMode = detectedMode;
+							node._linkedImageCount = imageCount;
+
+							// Update images/latents based on mode
+							if (detectedMode === "img2img") {
+								node._linkedImages = images;
+								node._linkedLatents = [];
+								console.log(`  - Set images (${images.length}), cleared latents`);
+							} else {
+								node._linkedImages = [];
+								node._linkedLatents = latents;
+								console.log(`  - Set latents (${latents.length}), cleared images`);
+							}
+
+							// Update expected_image_count widget
+							const widget = node.widgets?.find((w) => w.name === "expected_image_count");
+							if (widget) widget.value = imageCount;
+
+							renderUI();
+						}
+					}, 500); // Check twice per second for responsiveness
+				};
+
+				// === Control After Generate (Seed Updates) ===
+				const updateSeedsAfterGenerate = () => {
+					const promptQueue = parsePromptBatch();
+					let hasChanges = false;
+
+					promptQueue.forEach((entry) => {
+						const seedControl = entry.seed_control || "randomize";
+						const currentSeed = entry.seed || 1111111;
+
+						if (seedControl === "increment") {
+							entry.seed = currentSeed + 100; // +100 to avoid overlap with repeat +1s
+							hasChanges = true;
+						} else if (seedControl === "decrement") {
+							entry.seed = Math.max(0, currentSeed - 100); // -100 but don't go negative
+							hasChanges = true;
+						} else if (seedControl === "randomize") {
+							entry.seed = Math.floor(Math.random() * (9999999 - 1111111 + 1)) + 1111111;
+							hasChanges = true;
+						}
+						// "fixed" does nothing - seeds stay the same
+					});
+
+					if (hasChanges) {
+						setPromptBatchData(promptQueue);
+						renderUI();
+						console.log("[RPG] Seeds updated based on control_after_generate mode");
+					}
+				};
+
+				// Store update function for event listener
+				node._updateSeeds = updateSeedsAfterGenerate;
+
+				// Listen for execution completion to update seeds AFTER run
+				if (!window._rpgExecutionHooked) {
+					api.addEventListener("execution_success", (e) => {
+						console.log("[RPG] Execution completed successfully, updating seeds for next run...");
+						// Find all RPG nodes and update their seeds for next execution
+						const rpgNodes = app.graph._nodes?.filter(n => n.type === "INSTARAW_RealityPromptGenerator") || [];
+
+						for (const rpgNode of rpgNodes) {
+							if (rpgNode._updateSeeds) {
+								console.log(`[RPG] Updating seeds for node ${rpgNode.id}`);
+								rpgNode._updateSeeds();
+							}
+						}
+					});
+					window._rpgExecutionHooked = true;
+					console.log("[RPG] Global execution_success listener installed");
+				}
+
+				// Store references for lifecycle hooks
+				node._updateCachedHeight = updateCachedHeight;
+				node._renderUI = renderUI;
+				node._setupWidgetCallbacks = setupWidgetCallbacks;
+
+				// Initial setup
+				setTimeout(() => {
+					syncPromptBatchWidget();
+					syncSDXLModeWidget();
+					loadSettings();
+					setupWidgetCallbacks();
+					startDimensionCheck();
+					startAILSync(); // Start continuous AIL mode monitoring
+					loadPromptsDatabase();
+					renderUI();
+					// Mark as initialized after first render
+					node._isInitialized = true;
+
+					// Listen for favorites from BIG (Batch Image Generator)
+					const handleBigFavorite = async (e) => {
+						const { prompt: rawPrompt, seed, sourceNodeId } = e.detail;
+						if (!rawPrompt) {
+							console.log("[RPG] BIG favorite event received but no prompt provided");
+							return;
+						}
+
+						// Decode HTML entities (BIG uses escapeHtml on data attributes)
+						const decodeHtml = (html) => {
+							const txt = document.createElement('textarea');
+							txt.innerHTML = html;
+							return txt.value;
+						};
+						const prompt = decodeHtml(rawPrompt);
+
+						console.log(`[RPG] Received favorite from BIG (node ${sourceNodeId}): "${prompt.slice(0, 50)}..."`);
+
+						// First, check if this prompt exists anywhere in the database (library, user, or generated)
+						const existingPrompt = promptsDatabase.find(p =>
+							p.prompt?.positive?.trim().toLowerCase() === prompt.trim().toLowerCase()
+						);
+
+						if (existingPrompt) {
+							// Prompt exists - check if already bookmarked
+							if (bookmarksCache.includes(existingPrompt.id)) {
+								console.log(`[RPG] Prompt already bookmarked (${existingPrompt.id})`);
+								alert("This prompt is already in your favorites!");
+								return;
+							}
+
+							// Add to bookmarks
+							await toggleBookmarkById(existingPrompt.id);
+							console.log(`[RPG] Bookmarked existing prompt: ${existingPrompt.id}`);
+							renderUI();
+							alert(`Added to favorites! (${existingPrompt.is_user_created ? "My Prompt" : existingPrompt.is_ai_generated ? "Generated" : "Library"})`);
+							return;
+						}
+
+						// Prompt doesn't exist in database - create as new user prompt and bookmark it
+						try {
+							const newPrompt = await addUserPrompt({
+								positive: prompt,
+								negative: "",
+								tags: seed && seed !== "N/A" ? [`seed:${seed}`, "from-big"] : ["from-big"],
+								content_type: "other",
+								safety_level: "sfw",
+								shot_type: "other"
+							});
+							console.log(`[RPG] Created user prompt from BIG: ${newPrompt.id}`);
+
+							// Also bookmark it
+							await toggleBookmarkById(newPrompt.id);
+							console.log(`[RPG] Bookmarked new user prompt: ${newPrompt.id}`);
+
+							renderUI();
+							alert(`Saved as new prompt and added to favorites! Seed: ${seed || "N/A"}`);
+						} catch (error) {
+							console.error("[RPG] Error adding BIG favorite:", error);
+							alert("Failed to save prompt. Please try again.");
+						}
+					};
+
+					document.addEventListener('instaraw-rpg-add-favorite', handleBigFavorite);
+					node._bigFavoriteHandler = handleBigFavorite; // Store for cleanup
+				}, 100);
+			};
+
+			const onResize = nodeType.prototype.onResize;
+			nodeType.prototype.onResize = function (size) {
+				onResize?.apply(this, arguments);
+				if (this._updateCachedHeight) {
+					clearTimeout(this._resizeTimeout);
+					this._resizeTimeout = setTimeout(() => this._updateCachedHeight(), 50);
+				}
+			};
+
+			const onConfigure = nodeType.prototype.onConfigure;
+			nodeType.prototype.onConfigure = function (data) {
+				onConfigure?.apply(this, arguments);
+
+				// MIGRATION: Auto-update old minimal template from {TASK_INSTRUCTIONS} to {USER_INPUT}
+				if (this.properties.custom_template === "{TASK_INSTRUCTIONS}") {
+					console.log("[RPG] ๐Ÿ”„ Migrating minimal template: {TASK_INSTRUCTIONS} โ†’ {USER_INPUT}");
+					this.properties.custom_template = "{USER_INPUT}";
+					if (app && app.graph) {
+						app.graph.setDirtyCanvas(true, true);
+					}
+				}
+
+				setTimeout(() => {
+					const promptQueueWidget = this.widgets?.find((w) => w.name === "prompt_batch_data");
+					if (promptQueueWidget) promptQueueWidget.value = this.properties.prompt_batch_data || "[]";
+					if (this._setupWidgetCallbacks) this._setupWidgetCallbacks();
+					if (this._renderUI) this._renderUI();
+					// Mark as initialized after loading from workflow
+					this._isInitialized = true;
+
+					// Force multiple height recalculations to ensure sync
+					if (this._updateCachedHeight) {
+						setTimeout(() => this._updateCachedHeight(), 100);
+						setTimeout(() => this._updateCachedHeight(), 500);
+						setTimeout(() => this._updateCachedHeight(), 1000);
+					}
+				}, 200);
+			};
+
+			// === onConnectionsChange Hook (for AIL detection and reactivity) ===
+			const onConnectionsChange = nodeType.prototype.onConnectionsChange;
+			nodeType.prototype.onConnectionsChange = function(side, slot, connect, link_info, output) {
+				const result = onConnectionsChange?.apply(this, arguments);
+
+				// Detect AIL connection when any image input is connected/disconnected
+				if (side === 1) { // INPUT side
+					// Check all 4 image inputs
+					const imageInputNames = ["images", "images2", "images3", "images4"];
+
+					for (const inputName of imageInputNames) {
+						const inputIndex = this.inputs?.findIndex(input => input.name === inputName);
+						if (slot !== inputIndex) continue;
+
+						if (connect && link_info) {
+							// Connected - traverse to find source node
+							const link = this.graph?.links?.[link_info.id];
+							if (link) {
+								const sourceNode = this.graph.getNodeById(link.origin_id);
+								if (sourceNode && sourceNode.type === "INSTARAW_AdvancedImageLoader") {
+									// Read image data from AIL's properties.batch_data
+									let images = [];
+
+									try {
+										const batchData = JSON.parse(sourceNode.properties?.batch_data || "{}");
+										const order = batchData.order || [];
+										const ailImages = batchData.images || [];
+
+										images = order.map(imgId => {
+											const img = ailImages.find(i => i.id === imgId);
+											if (!img) return null;
+											// Handle both url (if already computed) and thumbnail (raw batchData format)
+											const imgUrl = img.url || (img.thumbnail ? `/instaraw/view/${img.thumbnail}` : null);
+											// Skip entries with invalid URLs to prevent broken image icons
+											if (!imgUrl) return null;
+											return {
+												id: img.id,
+												url: imgUrl,
+												thumbnail: img.thumbnail, // Preserve original thumbnail path
+												repeat_count: img.repeat_count || 1
+											};
+										}).filter(Boolean);
+									} catch (error) {
+										console.error(`[RPG] Error parsing AIL batch_data on connection for ${inputName}:`, error);
+									}
+
+									// Update the appropriate tracking array based on input name
+									if (inputName === "images") {
+										// Primary input - full tracking
+										this._linkedAILNodeId = sourceNode.id;
+
+										// Get mode from enable_img2img widget, connected node, or batch_data
+										const enableImg2ImgWidget = sourceNode.widgets?.find(w => w.name === "enable_img2img");
+										const enableImg2ImgValue = enableImg2ImgWidget?.value;
+
+										// Check if enable_img2img is connected to another node
+										const enableImg2ImgInput = sourceNode.inputs?.find(i => i.name === "enable_img2img");
+										let finalEnableImg2Img = enableImg2ImgValue;
+
+										if (enableImg2ImgInput && enableImg2ImgInput.link != null) {
+											const connectedLink = this.graph?.links?.[enableImg2ImgInput.link];
+											if (connectedLink) {
+												const sourceNode2 = this.graph.getNodeById(connectedLink.origin_id);
+												if (sourceNode2) {
+													const sourceWidget = sourceNode2.widgets?.[connectedLink.origin_slot];
+													if (sourceWidget) {
+														finalEnableImg2Img = sourceWidget.value;
+													}
+												}
+											}
+										}
+
+										// Determine mode with fallbacks
+										let detectedMode;
+										if (finalEnableImg2Img === false || finalEnableImg2Img === "false") {
+											detectedMode = "txt2img";
+										} else if (finalEnableImg2Img === true || finalEnableImg2Img === "true") {
+											detectedMode = "img2img";
+										} else {
+											// Fallback: check batch_data.enable_img2img or infer from content
+											try {
+												const batchData = JSON.parse(sourceNode.properties?.batch_data || "{}");
+												if (batchData.enable_img2img !== undefined) {
+													detectedMode = batchData.enable_img2img ? "img2img" : "txt2img";
+												} else {
+													// Final fallback: infer from content
+													detectedMode = (batchData.images || []).length > 0 ? "img2img" : "txt2img";
+												}
+											} catch (e) {
+												detectedMode = "txt2img"; // Default to txt2img if can't parse
+											}
+										}
+										this._linkedAILMode = detectedMode;
+
+										if (this._linkedAILMode === "img2img") {
+											this._linkedImages = images;
+											this._linkedLatents = [];
+											this._linkedImageCount = images.reduce((sum, img) => sum + (img.repeat_count || 1), 0);
+										} else {
+											this._linkedImages = [];
+											// Read latents for txt2img mode
+											try {
+												const batchData = JSON.parse(sourceNode.properties?.batch_data || "{}");
+												const order = batchData.order || [];
+												const ailLatents = batchData.latents || [];
+												this._linkedLatents = order.map(latentId => {
+													const latent = ailLatents.find(l => l.id === latentId);
+													if (!latent) return null;
+													return {
+														id: latent.id,
+														width: latent.width,
+														height: latent.height,
+														repeat_count: latent.repeat_count || 1
+													};
+												}).filter(Boolean);
+												this._linkedImageCount = this._linkedLatents.reduce((sum, lat) => sum + (lat.repeat_count || 1), 0);
+											} catch (error) {
+												this._linkedLatents = [];
+												this._linkedImageCount = 0;
+											}
+										}
+
+										console.log(`[RPG] Linked ${inputName} to AIL node ${sourceNode.id}: ${this._linkedImageCount} items, mode: ${this._linkedAILMode}`);
+									} else if (inputName === "images2") {
+										this._linkedImages2 = images;
+										console.log(`[RPG] Linked ${inputName} to AIL node ${sourceNode.id}: ${images.length} images`);
+									} else if (inputName === "images3") {
+										this._linkedImages3 = images;
+										console.log(`[RPG] Linked ${inputName} to AIL node ${sourceNode.id}: ${images.length} images`);
+									} else if (inputName === "images4") {
+										this._linkedImages4 = images;
+										console.log(`[RPG] Linked ${inputName} to AIL node ${sourceNode.id}: ${images.length} images`);
+									}
+								}
+							}
+						} else if (!connect) {
+							// Disconnected - clear appropriate tracking data
+							if (inputName === "images") {
+								this._linkedAILNodeId = null;
+								this._linkedImageCount = 0;
+								this._linkedAILMode = null;
+								this._linkedImages = [];
+								this._linkedLatents = [];
+								console.log("[RPG] Disconnected from primary AIL");
+							} else if (inputName === "images2") {
+								this._linkedImages2 = [];
+								console.log("[RPG] Disconnected from secondary AIL (images2)");
+							} else if (inputName === "images3") {
+								this._linkedImages3 = [];
+								console.log("[RPG] Disconnected from tertiary AIL (images3)");
+							} else if (inputName === "images4") {
+								this._linkedImages4 = [];
+								console.log("[RPG] Disconnected from quaternary AIL (images4)");
+							}
+						}
+						// If connect is true but link_info is null, skip (shouldn't happen)
+						break; // Found matching input, stop loop
+					}
+				}
+
+				// Re-render when connections change
+				if (this._renderUI) {
+					// Use longer delay if node isn't initialized yet to avoid race with initial render
+					const delay = this._isInitialized ? 100 : 250;
+					setTimeout(() => {
+						console.log(`[RPG] Re-rendering after connection change. AIL mode: ${this._linkedAILMode}, initialized: ${this._isInitialized}`);
+						this._renderUI();
+					}, delay);
+				}
+				return result;
+			};
+
+			// === onRemoved Hook (Cleanup) ===
+			const onRemoved = nodeType.prototype.onRemoved;
+			nodeType.prototype.onRemoved = function () {
+				// Clean up intervals
+				if (this._dimensionCheckInterval) {
+					clearInterval(this._dimensionCheckInterval);
+					this._dimensionCheckInterval = null;
+				}
+				if (this._ailSyncInterval) {
+					clearInterval(this._ailSyncInterval);
+					this._ailSyncInterval = null;
+				}
+				if (this._heightSyncInterval) {
+					clearInterval(this._heightSyncInterval);
+					this._heightSyncInterval = null;
+				}
+				// Clean up ResizeObserver
+				if (this._resizeObserver) {
+					this._resizeObserver.disconnect();
+					this._resizeObserver = null;
+				}
+				// Clean up seed update reference
+				if (this._updateSeeds) {
+					this._updateSeeds = null;
+				}
+				// Clean up BIG favorite event listener
+				if (this._bigFavoriteHandler) {
+					document.removeEventListener('instaraw-rpg-add-favorite', this._bigFavoriteHandler);
+					this._bigFavoriteHandler = null;
+				}
+				console.log(`[RPG] Cleaned up node ${this.id}`);
+				onRemoved?.apply(this, arguments);
+			};
+		}
+	},
+});
\ No newline at end of file
diff --git a/custom_nodes/ComfyUI_INSTARAW/js/shared/combo-cards.js b/custom_nodes/ComfyUI_INSTARAW/js/shared/combo-cards.js
new file mode 100644
index 0000000000000000000000000000000000000000..dcfc3b602c08f0065772dddbc4a4a97818e6b8ed
--- /dev/null
+++ b/custom_nodes/ComfyUI_INSTARAW/js/shared/combo-cards.js
@@ -0,0 +1,64 @@
+/**
+ * Shared combo card renderer for multi-image display
+ * Used by: reality_prompt_generator.js, batch_image_generator.js
+ */
+
+/**
+ * Render combo cards HTML for multi-image mode
+ * @param {Array} inputsWithImages - Array of {label, images/urls} objects
+ * @param {Object} options - { getImageUrl: function to extract URL from item }
+ * @returns {string} HTML string for combo cards
+ */
+export function renderComboCards(inputsWithImages, options = {}) {
+    const getItems = (input) => input.images || input.urls || [];
+    const maxLen = Math.max(...inputsWithImages.map(i => getItems(i).length));
+
+    let html = '';
+    for (let idx = 0; idx < maxLen; idx++) {
+        const slots = inputsWithImages.map(input => {
+            const items = getItems(input);
+            const item = items[idx];
+            const url = options.getImageUrl ? options.getImageUrl(item) : (item?.url || item);
+
+            if (url) {
+                return `
+                    

+                        

+                            ${input.label}
+                        

+                        ${input.label}
+                    

+                `;
+            } else {
+                return `
+                    

+                        

+                        ${input.label}
+                    

+                `;
+            }
+        }).join('');
+
+        html += `
+            

+                

+                    #${idx + 1}
+                

+                

+                    ${slots}
+                

+            

+        `;
+    }
+    return html;
+}
+
+/**
+ * Get the maximum length across all inputs
+ * @param {Array} inputsWithImages - Array of {label, images/urls} objects
+ * @returns {number} Maximum number of items
+ */
+export function getComboMaxLength(inputsWithImages) {
+    const getItems = (input) => input.images || input.urls || [];
+    return Math.max(...inputsWithImages.map(i => getItems(i).length));
+}
diff --git a/custom_nodes/ComfyUI_INSTARAW/js/utils.js b/custom_nodes/ComfyUI_INSTARAW/js/utils.js
new file mode 100644
index 0000000000000000000000000000000000000000..87193cf6c3658927885da38f52fcec0ce8e907e3
--- /dev/null
+++ b/custom_nodes/ComfyUI_INSTARAW/js/utils.js
@@ -0,0 +1,9 @@
+const _aq = !!true;
+
+export function create( tag, clss, parent, properties ) {
+    const nd = document.createElement(tag);
+    if (clss)       clss.split(" ").forEach((s) => nd.classList.add(s))
+    if (parent)     parent.appendChild(nd);
+    if (properties) Object.assign(nd, properties);
+    return nd;
+}
\ No newline at end of file
diff --git a/custom_nodes/ComfyUI_INSTARAW/js/zoomed.css b/custom_nodes/ComfyUI_INSTARAW/js/zoomed.css
new file mode 100644
index 0000000000000000000000000000000000000000..7b7fb98848e5f8ed9fb585be09901565daf21c56
--- /dev/null
+++ b/custom_nodes/ComfyUI_INSTARAW/js/zoomed.css
@@ -0,0 +1,61 @@
+
+.instaraw_popup .zoomed {
+    position: absolute;
+    width: 100%;
+    left: 0;
+    height: 100%;
+    right: 0;
+    display: flex;
+    justify-content: space-evenly;
+    align-items: center;
+    z-index: 120000;
+    background-color: rgba(29, 29, 29, 0.886);
+}
+
+.instaraw_popup .zoomed_prev, .zoomed_next {
+    flex-grow: 1;
+    
+}
+
+.instaraw_popup .zoomed_prev {
+    text-align: right;
+}
+
+.instaraw_popup .zoomed_arrow {
+    font-size: 48px;
+    border-radius: 40px;
+    padding: 0px 8px 4px 8px;
+    margin: 6px;
+    border: 1px solid rgb(128, 128, 128, 1.0);
+}
+
+.instaraw_popup .zoomed_arrow:hover {
+    border: 1px solid rgb(255, 128, 128, 1.0);
+}
+
+
+.instaraw_popup .zoomed_image {
+    max-width: calc(100% - 140px);
+    height: 100%;
+    object-fit: contain;
+    border: 1px solid white;
+    padding: 4px;
+}
+
+.instaraw_popup .zoomed_number {
+    position: absolute;
+    top: 12px;
+    margin-left: 12px;
+    color: white;
+    padding: 10px 8px 8px 8px;
+    border-radius: 4px;
+    z-index: 110000;
+    background-color: rgba(128, 128, 128, 0.5);
+    border: 1px solid rgba(128, 128, 128, 1.0);
+}
+
+.instaraw_popup .zoomed.highlighted .zoomed_image {
+    border: 3px solid green;
+    padding: 2px;
+}
+
diff --git a/custom_nodes/ComfyUI_INSTARAW/modules/__init__.py b/custom_nodes/ComfyUI_INSTARAW/modules/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/custom_nodes/ComfyUI_INSTARAW/modules/authenticity_profiles/iphone_12.icc b/custom_nodes/ComfyUI_INSTARAW/modules/authenticity_profiles/iphone_12.icc
new file mode 100644
index 0000000000000000000000000000000000000000..e310029c37563341832703f0aa21f67adc23d2e3
Binary files /dev/null and b/custom_nodes/ComfyUI_INSTARAW/modules/authenticity_profiles/iphone_12.icc differ
diff --git a/custom_nodes/ComfyUI_INSTARAW/modules/authenticity_profiles/iphone_12.json b/custom_nodes/ComfyUI_INSTARAW/modules/authenticity_profiles/iphone_12.json
new file mode 100644
index 0000000000000000000000000000000000000000..6feab7d9df23ec78acab6df335581839ea00cce3
--- /dev/null
+++ b/custom_nodes/ComfyUI_INSTARAW/modules/authenticity_profiles/iphone_12.json
@@ -0,0 +1,10577 @@
+[
+  {
+    "File:FileType": "JPEG",
+    "File:FileTypeExtension": "JPG",
+    "File:MIMEType": "image/jpeg",
+    "File:ExifByteOrder": "MM",
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+    "File:ImageHeight": 3024,
+    "File:EncodingProcess": 0,
+    "File:BitsPerSample": 8,
+    "File:ColorComponents": 3,
+    "File:YCbCrSubSampling": "2 2",
+    "JFIF:JFIFVersion": "1 1",
+    "JFIF:ResolutionUnit": 1,
+    "JFIF:XResolution": 300,
+    "JFIF:YResolution": 300,
+    "EXIF:Make": "Apple",
+    "EXIF:Model": "iPhone 12",
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+    "EXIF:ModifyDate": "2024:04:24 15:48:18",
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+    "EXIF:ExifVersion": "0232",
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+    "Composite:LightValue": 14.2276159433522,
+    "Composite:LensID": "iPhone 12 back dual wide camera 4.2mm f/1.6",
+    "_instaraw_icc_profile_file": "iphone_12.icc"
+  },
+  {
+    "File:FileType": "JPEG",
+    "File:FileTypeExtension": "JPG",
+    "File:MIMEType": "image/jpeg",
+    "File:ExifByteOrder": "MM",
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diff --git a/custom_nodes/ComfyUI_INSTARAW/modules/detection_bypass/_luts/KORNEV_LUT_HLG_3.cube b/custom_nodes/ComfyUI_INSTARAW/modules/detection_bypass/_luts/KORNEV_LUT_HLG_3.cube
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diff --git a/custom_nodes/ComfyUI_INSTARAW/modules/detection_bypass/_luts/LOOK1.cube b/custom_nodes/ComfyUI_INSTARAW/modules/detection_bypass/_luts/LOOK1.cube
new file mode 100644
index 0000000000000000000000000000000000000000..d12bd0de5949847c76f986e909e33d08017347cc
--- /dev/null
+++ b/custom_nodes/ComfyUI_INSTARAW/modules/detection_bypass/_luts/LOOK1.cube
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diff --git a/custom_nodes/ComfyUI_INSTARAW/modules/detection_bypass/_luts/LOW_SAT_-_Braw_Vivid_Ludeman_LUT.cube b/custom_nodes/ComfyUI_INSTARAW/modules/detection_bypass/_luts/LOW_SAT_-_Braw_Vivid_Ludeman_LUT.cube
new file mode 100644
index 0000000000000000000000000000000000000000..57c5f1803fe4aa75727269e325e569b68397fbb4
--- /dev/null
+++ b/custom_nodes/ComfyUI_INSTARAW/modules/detection_bypass/_luts/LOW_SAT_-_Braw_Vivid_Ludeman_LUT.cube
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diff --git a/custom_nodes/ComfyUI_INSTARAW/modules/detection_bypass/_luts/Shibuya_01.cube b/custom_nodes/ComfyUI_INSTARAW/modules/detection_bypass/_luts/Shibuya_01.cube
new file mode 100644
index 0000000000000000000000000000000000000000..fe4c096da846a383610337e6b32982fcc3c702cd
--- /dev/null
+++ b/custom_nodes/ComfyUI_INSTARAW/modules/detection_bypass/_luts/Shibuya_01.cube
@@ -0,0 +1,32771 @@
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diff --git a/custom_nodes/ComfyUI_INSTARAW/modules/detection_bypass/_luts/experimento.cube b/custom_nodes/ComfyUI_INSTARAW/modules/detection_bypass/_luts/experimento.cube
new file mode 100644
index 0000000000000000000000000000000000000000..adba1336551cc75dd16e91530913e45e77e7ae8c
--- /dev/null
+++ b/custom_nodes/ComfyUI_INSTARAW/modules/detection_bypass/_luts/experimento.cube
@@ -0,0 +1,35943 @@
+# VEGAS Pro LUT
+
+TITLE "experimento"
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diff --git a/custom_nodes/ComfyUI_INSTARAW/modules/detection_bypass/_luts/interview-cool_17.C0003.cube b/custom_nodes/ComfyUI_INSTARAW/modules/detection_bypass/_luts/interview-cool_17.C0003.cube
new file mode 100644
index 0000000000000000000000000000000000000000..e70c3383bc006863176d1cc5e7c51200abfb3888
--- /dev/null
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diff --git a/custom_nodes/ComfyUI_INSTARAW/modules/detection_bypass/_luts/natural_look.cube b/custom_nodes/ComfyUI_INSTARAW/modules/detection_bypass/_luts/natural_look.cube
new file mode 100644
index 0000000000000000000000000000000000000000..5721d0f830d64b2aae88fe02f7e4578a64063d2f
--- /dev/null
+++ b/custom_nodes/ComfyUI_INSTARAW/modules/detection_bypass/_luts/natural_look.cube
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diff --git a/custom_nodes/ComfyUI_INSTARAW/modules/detection_bypass/_luts/ntrl_2.A054_06130550_C013.cube b/custom_nodes/ComfyUI_INSTARAW/modules/detection_bypass/_luts/ntrl_2.A054_06130550_C013.cube
new file mode 100644
index 0000000000000000000000000000000000000000..7ff492b80f17918c7a28aa5bd92d5dae6e1eecec
--- /dev/null
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diff --git a/custom_nodes/ComfyUI_INSTARAW/modules/detection_bypass/camera_pipeline.py b/custom_nodes/ComfyUI_INSTARAW/modules/detection_bypass/camera_pipeline.py
new file mode 100644
index 0000000000000000000000000000000000000000..8a63028d1612f08aa2fdd69f3a8e1599f4879c70
--- /dev/null
+++ b/custom_nodes/ComfyUI_INSTARAW/modules/detection_bypass/camera_pipeline.py
@@ -0,0 +1,272 @@
+"""
+camera_pipeline.py
+
+Functions for simulating a realistic camera pipeline, including Bayer mosaic/demosaic,
+chromatic aberration, vignette, sensor noise, hot pixels, banding, motion blur, and JPEG recompression.
+"""
+
+from io import BytesIO
+from PIL import Image
+import numpy as np
+try:
+    import cv2
+    _HAS_CV2 = True
+except Exception:
+    cv2 = None
+    _HAS_CV2 = False
+from scipy.ndimage import convolve
+
+def _bayer_mosaic(img: np.ndarray, pattern='RGGB') -> np.ndarray:
+    """Create a single-channel Bayer mosaic from an RGB image.
+
+    pattern currently supports 'RGGB' (most common). Returns uint8 2D array.
+    """
+    h, w = img.shape[:2]
+    mosaic = np.zeros((h, w), dtype=np.uint8)
+
+    # pattern mapping for RGGB:
+    # (0,0) R, (0,1) G
+    # (1,0) G, (1,1) B
+    R = img[:, :, 0]
+    G = img[:, :, 1]
+    B = img[:, :, 2]
+
+    # fill mosaic according to RGGB
+    mosaic[0::2, 0::2] = R[0::2, 0::2]
+    mosaic[0::2, 1::2] = G[0::2, 1::2]
+    mosaic[1::2, 0::2] = G[1::2, 0::2]
+    mosaic[1::2, 1::2] = B[1::2, 1::2]
+    return mosaic
+
+def _demosaic_bilinear(mosaic: np.ndarray) -> np.ndarray:
+    """Simple bilinear demosaic fallback (no cv2). Outputs RGB uint8 image.
+
+    Not perfect but good enough to add demosaic artifacts.
+    """
+    h, w = mosaic.shape
+    # Work in float to avoid overflow
+    m = mosaic.astype(np.float32)
+
+    # We'll compute each channel by averaging available mosaic samples
+    R = np.zeros_like(m)
+    G = np.zeros_like(m)
+    B = np.zeros_like(m)
+
+    # RGGB pattern
+    R[0::2, 0::2] = m[0::2, 0::2]
+    G[0::2, 1::2] = m[0::2, 1::2]
+    G[1::2, 0::2] = m[1::2, 0::2]
+    B[1::2, 1::2] = m[1::2, 1::2]
+
+    # Convolution kernels for interpolation (simple)
+    k_cross = np.array([[0, 1, 0], [1, 4, 1], [0, 1, 0]], dtype=np.float32) / 8.0
+    k_diag = np.array([[1, 0, 1], [0, 0, 0], [1, 0, 1]], dtype=np.float32) / 4.0
+
+    # convolve using scipy.ndimage.convolve
+    R_interp = convolve(R, k_cross, mode='mirror')
+    G_interp = convolve(G, k_cross, mode='mirror')
+    B_interp = convolve(B, k_cross, mode='mirror')
+
+    out = np.stack((R_interp, G_interp, B_interp), axis=2)
+    out = np.clip(out, 0, 255).astype(np.uint8)
+    return out
+
+def _apply_chromatic_aberration(img: np.ndarray, strength=1.0, seed=None):
+    """Shift R and B channels slightly in opposite directions to emulate CA.
+
+    strength is in pixels (float). Uses cv2.warpAffine if available; integer
+    fallback uses np.roll.
+    """
+    # Early return if no chromatic aberration should be applied
+    if strength <= 0:
+        return img
+
+    if seed is not None:
+        rng = np.random.default_rng(seed)
+    else:
+        rng = np.random.default_rng()
+
+    h, w = img.shape[:2]
+    max_shift = strength
+    # small random subpixel shift sampled from normal distribution
+    shift_r = rng.normal(loc=0.0, scale=max_shift * 0.6)
+    shift_b = rng.normal(loc=0.0, scale=max_shift * 0.6)
+    # apply opposite horizontal shifts to R and B for lateral CA
+    r_x = shift_r
+    r_y = rng.normal(scale=0.3 * abs(shift_r))
+    b_x = -shift_b
+    b_y = rng.normal(scale=0.3 * abs(shift_b))
+
+    out = img.copy().astype(np.float32)
+    if _HAS_CV2:
+        def warp_channel(ch, tx, ty):
+            M = np.array([[1, 0, tx], [0, 1, ty]], dtype=np.float32)
+            return cv2.warpAffine(ch, M, (w, h), flags=cv2.INTER_LINEAR, borderMode=cv2.BORDER_REFLECT)
+        out[:, :, 0] = warp_channel(out[:, :, 0], r_x, r_y)
+        out[:, :, 2] = warp_channel(out[:, :, 2], b_x, b_y)
+    else:
+        # integer fallback
+        ix_r = int(round(r_x))
+        iy_r = int(round(r_y))
+        ix_b = int(round(b_x))
+        iy_b = int(round(b_y))
+        out[:, :, 0] = np.roll(out[:, :, 0], shift=(iy_r, ix_r), axis=(0, 1))
+        out[:, :, 2] = np.roll(out[:, :, 2], shift=(iy_b, ix_b), axis=(0, 1))
+
+    out = np.clip(out, 0, 255).astype(np.uint8)
+    return out
+
+def _apply_vignette(img: np.ndarray, strength=0.4):
+    h, w = img.shape[:2]
+    y = np.linspace(-1, 1, h)[:, None]
+    x = np.linspace(-1, 1, w)[None, :]
+    r = np.sqrt(x * x + y * y)
+    mask = 1.0 - (r ** 2) * strength
+    mask = np.clip(mask, 0.0, 1.0)
+    out = (img.astype(np.float32) * mask[:, :, None])
+    out = np.clip(out, 0, 255).astype(np.uint8)
+    return out
+
+def _add_poisson_gaussian_noise(img: np.ndarray, iso_scale=1.0, read_noise_std=2.0, seed=None):
+    """Poisson-Gaussian sensor noise model.
+
+    iso_scale scales the signal before Poisson sampling (higher -> more Poisson),
+    read_noise_std is the sigma (in DN) of additive Gaussian read noise.
+    """
+    # Early return if no noise should be applied
+    if iso_scale <= 0 and read_noise_std <= 0:
+        return img
+
+    if seed is not None:
+        rng = np.random.default_rng(seed)
+    else:
+        rng = np.random.default_rng()
+
+    img_f = img.astype(np.float32)
+
+    # If iso_scale is valid, apply Poisson noise
+    if iso_scale > 0:
+        # scale to simulate exposure/iso
+        scaled = img_f * iso_scale
+        # Poisson: we need integer counts; scale to a reasonable photon budget
+        # choose scale so that typical pixel values map to ~[0..2000] photons
+        photon_scale = 4.0
+        lam = np.clip(scaled * photon_scale, 0, 1e6)
+        noisy = rng.poisson(lam).astype(np.float32) / photon_scale
+    else:
+        # Skip Poisson noise if iso_scale is 0 or negative
+        noisy = img_f.copy()
+
+    # Add read noise if specified
+    if read_noise_std > 0:
+        noisy += rng.normal(loc=0.0, scale=read_noise_std, size=noisy.shape)
+
+    noisy = np.clip(noisy, 0, 255).astype(np.uint8)
+    return noisy
+
+def _add_hot_pixels_and_banding(img: np.ndarray, hot_pixel_prob=1e-6, banding_strength=0.0, seed=None):
+    if seed is not None:
+        rng = np.random.default_rng(seed)
+    else:
+        rng = np.random.default_rng()
+
+    h, w = img.shape[:2]
+    out = img.copy().astype(np.float32)
+    # hot pixels
+    n_pixels = int(h * w * hot_pixel_prob)
+    if n_pixels > 0:
+        ys = rng.integers(0, h, size=n_pixels)
+        xs = rng.integers(0, w, size=n_pixels)
+        vals = rng.integers(200, 256, size=n_pixels)
+        for y, x, v in zip(ys, xs, vals):
+            out[y, x, :] = v
+    # banding: add low-amplitude sinusoidal horizontal banding
+    if banding_strength > 0.0:
+        rows = np.arange(h)[:, None]
+        band = (np.sin(rows * 0.5) * 255.0 * banding_strength)
+        out += band[:, :, None]
+    out = np.clip(out, 0, 255).astype(np.uint8)
+    return out
+
+def _motion_blur(img: np.ndarray, kernel_size=5):
+    if kernel_size <= 1:
+        return img
+    # simple linear motion kernel horizontally
+    kernel = np.zeros((kernel_size, kernel_size), dtype=np.float32)
+    kernel[kernel_size // 2, :] = 1.0 / kernel_size
+    out = np.zeros_like(img)
+    for c in range(3):
+        out[:, :, c] = convolve(img[:, :, c].astype(np.float32), kernel, mode='mirror')
+    out = np.clip(out, 0, 255).astype(np.uint8)
+    return out
+
+def _jpeg_recompress(img: np.ndarray, quality=90) -> np.ndarray:
+    pil = Image.fromarray(img)
+    buf = BytesIO()
+    pil.save(buf, format='JPEG', quality=int(quality), optimize=False)
+    buf.seek(0)
+    rec = Image.open(buf).convert('RGB')
+    return np.array(rec)
+
+def simulate_camera_pipeline(img_arr: np.ndarray,
+                             bayer=True,
+                             jpeg_cycles=1,
+                             jpeg_quality_range=(88, 96),
+                             vignette_strength=0.35,
+                             chroma_aberr_strength=1.2,
+                             iso_scale=1.0,
+                             read_noise_std=2.0,
+                             hot_pixel_prob=1e-6,
+                             banding_strength=0.0,
+                             motion_blur_kernel=1,
+                             seed=None):
+    """Apply a set of realistic camera/capture artifacts to img_arr (RGB uint8).
+
+    Returns an RGB uint8 image.
+    """
+    if seed is not None:
+        rng = np.random.default_rng(seed)
+    else:
+        rng = np.random.default_rng()
+
+    out = img_arr.copy()
+
+    # 1) Bayer mosaic + demosaic (if enabled)
+    if bayer:
+        try:
+            mosaic = _bayer_mosaic(out[:, :, ::-1])  # we built mosaic assuming R,G,B order; send RGB
+            if _HAS_CV2:
+                # cv2 expects a single-channel Bayer and provides demosaicing codes
+                # We'll use RGGB code (COLOR_BAYER_RG2BGR) so convert back to RGB after
+                dem = cv2.demosaicing(mosaic, cv2.COLOR_BAYER_RG2BGR)
+                # cv2 returns BGR
+                dem = dem[:, :, ::-1]
+                out = dem
+            else:
+                out = _demosaic_bilinear(mosaic)
+        except Exception:
+            # if anything fails, keep original
+            out = img_arr.copy()
+
+    # 2) chromatic aberration
+    out = _apply_chromatic_aberration(out, strength=chroma_aberr_strength, seed=seed)
+
+    # 3) vignette
+    out = _apply_vignette(out, strength=vignette_strength)
+
+    # 4) noise (Poisson-Gaussian)
+    out = _add_poisson_gaussian_noise(out, iso_scale=iso_scale, read_noise_std=read_noise_std, seed=seed)
+
+    # 5) hot pixels and banding
+    out = _add_hot_pixels_and_banding(out, hot_pixel_prob=hot_pixel_prob, banding_strength=banding_strength, seed=seed)
+
+    # 6) motion blur
+    if motion_blur_kernel and motion_blur_kernel > 1:
+        out = _motion_blur(out, kernel_size=motion_blur_kernel)
+
+    # 7) JPEG recompression cycles
+    for i in range(max(1, int(jpeg_cycles))):
+        q = int(rng.integers(jpeg_quality_range[0], jpeg_quality_range[1] + 1))
+        out = _jpeg_recompress(out, quality=q)
+
+    return out
\ No newline at end of file
diff --git a/custom_nodes/ComfyUI_INSTARAW/modules/detection_bypass/filmgrainer_local/__init__.py b/custom_nodes/ComfyUI_INSTARAW/modules/detection_bypass/filmgrainer_local/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/custom_nodes/ComfyUI_INSTARAW/modules/detection_bypass/filmgrainer_local/filmgrainer.py b/custom_nodes/ComfyUI_INSTARAW/modules/detection_bypass/filmgrainer_local/filmgrainer.py
new file mode 100644
index 0000000000000000000000000000000000000000..3140905c94259e4f4a624df814166d3977665d97
--- /dev/null
+++ b/custom_nodes/ComfyUI_INSTARAW/modules/detection_bypass/filmgrainer_local/filmgrainer.py
@@ -0,0 +1,118 @@
+# ---
+# Filename: ../ComfyUI_INSTARAW/modules/detection_bypass/filmgrainer_local/filmgrainer.py
+# ---
+# Filmgrainer - by Lars Ole Pontoppidan - MIT License
+# Patched for INSTARAW: Uses relative imports and a cross-platform temp directory.
+
+from PIL import Image, ImageFilter
+import os 
+import tempfile # <-- PATCH 1: IMPORT TEMPFILE
+
+# --- PATCH 2: USE RELATIVE IMPORTS ---
+from . import graingamma
+from . import graingen
+
+
+def _grainTypes(typ):
+    # After rescaling to make different grain sizes, the standard deviation
+    # of the pixel values change. The following values of grain size and power
+    # have been imperically chosen to end up with approx the same standard 
+    # deviation in the result:
+    if typ == 1:
+        return (0.8, 63) # more interesting fine grain
+    elif typ == 2:
+        return (1, 45) # basic fine grain
+    elif typ == 3:
+        return (1.5, 50) # coarse grain
+    elif typ == 4:
+        return (1.6666, 50) # coarser grain
+    else:
+        raise ValueError("Unknown grain type: " + str(typ))
+
+# --- PATCH 3: USE A CROSS-PLATFORM TEMP DIRECTORY ---
+MASK_CACHE_PATH = os.path.join(tempfile.gettempdir(), "filmgrainer-mask-cache")
+
+def _getGrainMask(img_width:int, img_height:int, saturation:float, grayscale:bool, grain_size:float, grain_gauss:float, seed):
+    if grayscale:
+        str_sat = "BW"
+        sat = -1.0 # Graingen makes a grayscale image if sat is negative
+    else:
+        str_sat = str(saturation)
+        sat = saturation
+
+    filename = os.path.join(MASK_CACHE_PATH, "grain-%d-%d-%s-%s-%s-%d.png" % (
+        img_width, img_height, str_sat, str(grain_size), str(grain_gauss), seed))
+    
+    if os.path.isfile(filename):
+        # print("Reusing: %s" % filename)
+        mask = Image.open(filename)
+    else:
+        mask = graingen.grainGen(img_width, img_height, grain_size, grain_gauss, sat, seed)
+        # print("Saving: %s" % filename)
+        if not os.path.isdir(MASK_CACHE_PATH):
+            os.makedirs(MASK_CACHE_PATH, exist_ok=True) # Use makedirs for safety
+        mask.save(filename, format="png", compress_level=1)
+    return mask
+
+
+def process(file_in:str, scale:float, src_gamma:float, grain_power:float, shadows:float,
+            highs:float, grain_type:int, grain_sat:float, gray_scale:bool, sharpen:int, seed:int, file_out=None):
+            
+    # print("Loading: " + file_in)
+    img = Image.open(file_in).convert("RGB")
+    org_width = img.size[0]
+    org_height = img.size[1]
+    
+    if scale != 1.0:
+        # print("Scaling source image ...")
+        img = img.resize((int(org_width / scale), int(org_height / scale)),
+                          resample = Image.LANCZOS)
+    
+    img_width = img.size[0]
+    img_height = img.size[1]
+    # print("Size: %d x %d" % (img_width, img_height))
+
+    # print("Calculating map ...")
+    map_obj = graingamma.Map.calculate(src_gamma, grain_power, shadows, highs)
+
+    # print("Calculating grain stock ...")
+    (grain_size, grain_gauss) = _grainTypes(grain_type)
+    mask = _getGrainMask(img_width, img_height, grain_sat, gray_scale, grain_size, grain_gauss, seed)
+
+    mask_pixels = mask.load()
+    img_pixels = img.load()
+
+    # Instead of calling map.lookup(a, b) for each pixel, use the map directly:
+    lookup = map_obj.map
+
+    if gray_scale:
+        # print("Film graining image ... (grayscale)")
+        for y in range(0, img_height):
+            for x in range(0, img_width):
+                m = mask_pixels[x, y]
+                (r, g, b) = img_pixels[x, y]
+                gray = int(0.21*r + 0.72*g + 0.07*b)
+                gray_lookup = lookup[gray, m]
+                img_pixels[x, y] = (gray_lookup, gray_lookup, gray_lookup)
+    else:
+        # print("Film graining image ...")
+        for y in range(0, img_height):
+            for x in range(0, img_width):
+                (mr, mg, mb) = mask_pixels[x, y]
+                (r, g, b) = img_pixels[x, y]
+                r = lookup[r, mr]
+                g = lookup[g, mg]
+                b = lookup[b, mb]
+                img_pixels[x, y] = (r, g, b)
+    
+    if scale != 1.0:
+        # print("Scaling image back to original size ...")
+        img = img.resize((org_width, org_height), resample = Image.LANCZOS)
+    
+    if sharpen > 0:
+        # print("Sharpening image: %d pass ..." % sharpen)
+        for x in range(sharpen):
+            img = img.filter(ImageFilter.SHARPEN)
+
+    # print("Saving: " + file_out)
+    img.save(file_out, quality=97)
\ No newline at end of file
diff --git a/custom_nodes/ComfyUI_INSTARAW/modules/detection_bypass/filmgrainer_local/graingamma.py b/custom_nodes/ComfyUI_INSTARAW/modules/detection_bypass/filmgrainer_local/graingamma.py
new file mode 100644
index 0000000000000000000000000000000000000000..94c6f3f4fdbd954e96dc126c012a70ac85a9475c
--- /dev/null
+++ b/custom_nodes/ComfyUI_INSTARAW/modules/detection_bypass/filmgrainer_local/graingamma.py
@@ -0,0 +1,115 @@
+
+import numpy as np
+
+_ShadowEnd = 160
+_HighlightStart = 200
+
+
+def _gammaCurve(gamma, x):
+    """ Returns from 0.0 to 1.0"""
+    return pow((x / 255.0), (1.0 / gamma))
+
+
+def _calcDevelopment(shadow_level, high_level, x):
+    """
+This function returns a development like this:
+
+ (return)
+    ^  
+    |
+0.5 |                 o   -   o                  <-- mids level, always 0.5
+    |             -               -     
+    |          -                       -      
+    |       -                              o     <-- high_level  eg. 0.25
+    |    -                                       
+    | o                                          <-- shadow_level eg. 0.15
+    |   
+ 0 -+-----------------|-------|------------|----->  x  (input)
+    0                160     200          255
+    """
+    if x < _ShadowEnd:
+        power = 0.5 - (_ShadowEnd - x) * (0.5 - shadow_level) / _ShadowEnd
+    elif x < _HighlightStart:
+        power = 0.5
+    else:
+        power = 0.5 - (x - _HighlightStart) * (0.5 - high_level) / (255 - _HighlightStart)
+
+    return power
+
+class Map:
+    def __init__(self, map):
+        self.map = map
+
+    @staticmethod
+    def calculate(src_gamma, noise_power, shadow_level, high_level) -> 'Map':
+        map = np.zeros([256, 256], dtype=np.uint8)
+
+        # We need to level off top end and low end to leave room for the noise to breathe
+        crop_top = noise_power * high_level / 12
+        crop_low = noise_power * shadow_level / 20
+
+        pic_scale = 1 - (crop_top + crop_low)
+        pic_offs = 255 * crop_low
+
+        for src_value in range(0, 256):
+            # Gamma compensate picture source value itself
+            pic_value = _gammaCurve(src_gamma, src_value) * 255.0
+
+            # In the shadows we want noise gamma to be 0.5, in the highs, 2.0:
+            gamma = pic_value * (1.5 / 256) + 0.5
+            gamma_offset = _gammaCurve(gamma, 128)
+            
+            # Power is determined by the development
+            power = _calcDevelopment(shadow_level, high_level, pic_value)
+
+            for noise_value in range(0, 256):
+                gamma_compensated = _gammaCurve(gamma, noise_value) - gamma_offset
+                value = pic_value * pic_scale + pic_offs + 255.0 * power * noise_power * gamma_compensated
+                if value < 0:
+                    value = 0
+                elif value < 255.0:
+                    value = int(value)
+                else:
+                    value = 255
+                map[src_value, noise_value] = value
+
+        return Map(map)
+
+    def lookup(self, pic_value, noise_value):
+        return self.map[pic_value, noise_value]
+
+    def saveToFile(self, filename):
+        from PIL import Image
+        img = Image.fromarray(self.map)
+        img.save(filename)
+
+if __name__ == "__main__":
+    import matplotlib.pyplot as plt
+    import numpy as np
+
+    def plotfunc(x_min, x_max, step, func):
+        x_all = np.arange(x_min, x_max, step)
+        y = []
+        for x in x_all:
+            y.append(func(x))
+
+        plt.figure()
+        plt.plot(x_all, y)
+        plt.grid()
+        
+    def development1(x):
+        return _calcDevelopment(0.2, 0.3, x)
+
+    def gamma05(x):
+        return _gammaCurve(0.5, x)
+    def gamma1(x):
+        return _gammaCurve(1, x)
+    def gamma2(x):
+        return _gammaCurve(2, x)
+
+    plotfunc(0.0, 255.0, 1.0, development1)
+    plotfunc(0.0, 255.0, 1.0, gamma05)
+    plotfunc(0.0, 255.0, 1.0, gamma1)
+    plotfunc(0.0, 255.0, 1.0, gamma2)
+    plt.show()
+    
\ No newline at end of file
diff --git a/custom_nodes/ComfyUI_INSTARAW/modules/detection_bypass/filmgrainer_local/graingen.py b/custom_nodes/ComfyUI_INSTARAW/modules/detection_bypass/filmgrainer_local/graingen.py
new file mode 100644
index 0000000000000000000000000000000000000000..aca2637f42df6bc6c343983a7c62c0d60d979005
--- /dev/null
+++ b/custom_nodes/ComfyUI_INSTARAW/modules/detection_bypass/filmgrainer_local/graingen.py
@@ -0,0 +1,62 @@
+from PIL import Image
+import random
+import numpy as np
+
+def _makeGrayNoise(width, height, power):
+    buffer = np.zeros([height, width], dtype=int)
+
+    for y in range(0, height):
+        for x in range(0, width):
+            buffer[y, x] = random.gauss(128, power)
+    buffer = buffer.clip(0, 255)
+    return Image.fromarray(buffer.astype(dtype=np.uint8))
+
+def _makeRgbNoise(width, height, power, saturation):
+    buffer = np.zeros([height, width, 3], dtype=int)
+    intens_power = power * (1.0 - saturation)
+    for y in range(0, height):
+        for x in range(0, width):
+            intens = random.gauss(128, intens_power)
+            buffer[y, x, 0] = random.gauss(0, power) * saturation + intens
+            buffer[y, x, 1] = random.gauss(0, power) * saturation + intens
+            buffer[y, x, 2] = random.gauss(0, power) * saturation + intens
+
+    buffer = buffer.clip(0, 255)
+    return Image.fromarray(buffer.astype(dtype=np.uint8))
+
+
+def grainGen(width, height, grain_size, power, saturation, seed = 1):
+    # A grain_size of 1 means the noise buffer will be made 1:1
+    # A grain_size of 2 means the noise buffer will be resampled 1:2
+    noise_width = int(width / grain_size)
+    noise_height = int(height / grain_size)
+    random.seed(seed)
+
+    if saturation < 0.0:
+        print("Making B/W grain, width: %d, height: %d, grain-size: %s, power: %s, seed: %d" % (
+            noise_width, noise_height, str(grain_size), str(power), seed))
+        img = _makeGrayNoise(noise_width, noise_height, power)
+    else:
+        print("Making RGB grain, width: %d, height: %d, saturation: %s, grain-size: %s, power: %s, seed: %d" % (
+            noise_width, noise_height, str(saturation), str(grain_size), str(power), seed))
+        img = _makeRgbNoise(noise_width, noise_height, power, saturation)
+
+    # Resample
+    if grain_size != 1.0:
+        img = img.resize((width, height), resample = Image.LANCZOS)
+
+    return img
+
+
+if __name__ == "__main__":
+    import sys
+    if len(sys.argv) == 8:
+        width = int(sys.argv[2])
+        height = int(sys.argv[3])
+        grain_size = float(sys.argv[4])
+        power = float(sys.argv[5])
+        sat = float(sys.argv[6])
+        seed = int(sys.argv[7])
+        out = grainGen(width, height, grain_size, power, sat, seed)
+        out.save(sys.argv[1])
+
diff --git a/custom_nodes/ComfyUI_INSTARAW/modules/detection_bypass/pipeline.py b/custom_nodes/ComfyUI_INSTARAW/modules/detection_bypass/pipeline.py
new file mode 100644
index 0000000000000000000000000000000000000000..e41ebed73d791caf789c08ede7eb2d8dc4db8348
--- /dev/null
+++ b/custom_nodes/ComfyUI_INSTARAW/modules/detection_bypass/pipeline.py
@@ -0,0 +1,180 @@
+import dataclasses
+import os
+import json
+import torch
+import tempfile
+import time
+import gc
+from PIL import Image
+import numpy as np
+
+# We will import your actual processing functions here later
+# from .processor import process_image 
+
+# --- Helper functions (from your original node) ---
+def tensor_to_pil(tensor: torch.Tensor) -> Image.Image:
+    if tensor.ndim == 4 and tensor.shape[0] == 1:
+        tensor = tensor.squeeze(0)
+    img_np = (tensor.cpu().numpy() * 255).astype(np.uint8)
+    return Image.fromarray(img_np)
+
+def pil_to_tensor(pil_image: Image.Image) -> torch.Tensor:
+    img_np = np.array(pil_image).astype(np.float32) / 255.0
+    return torch.from_numpy(img_np).unsqueeze(0)
+
+
+@dataclasses.dataclass
+class BypassConfig:
+    """A clean, type-safe container for all our settings."""
+    mode: str = "Balanced"
+    unmarker_version: str = "full_balanced"  # none, simplified, full_fast, full_balanced, full_quality
+    strength: float = 0.25
+    profile_name: str = "Sony_A7IV_Natural"
+    seed: int = 0
+    debug_mode: bool = False
+
+class BypassPipeline:
+    def __init__(self, config: BypassConfig):
+        self.config = config
+        self.profile_dir = os.path.join(os.path.dirname(__file__), "profiles")
+        self.fingerprint = self._load_profile(config.profile_name)
+        print(f"โœ… BypassPipeline initialized for '{config.mode}' mode with '{config.profile_name}' profile.")
+
+    def _load_profile(self, name: str) -> dict:
+        """Loads the statistical fingerprint from a JSON file."""
+        profile_path = os.path.join(self.profile_dir, f"{name}.json")
+        if not os.path.exists(profile_path):
+            raise FileNotFoundError(f"Fingerprint profile not found: {profile_path}")
+        with open(profile_path, 'r') as f:
+            return json.load(f)
+
+    def _analyze_colors(self, tensor: torch.Tensor, label: str):
+        """Analyze color statistics for debugging."""
+        if not self.config.debug_mode:
+            return
+
+        # Convert to numpy for analysis
+        img_np = (tensor.squeeze(0).cpu().numpy() * 255).astype(np.uint8)
+
+        # Compute channel means
+        r_mean = img_np[:, :, 0].mean()
+        g_mean = img_np[:, :, 1].mean()
+        b_mean = img_np[:, :, 2].mean()
+
+        # Compute channel stds
+        r_std = img_np[:, :, 0].std()
+        g_std = img_np[:, :, 1].std()
+        b_std = img_np[:, :, 2].std()
+
+        # RGB balance (should be close to 1.0 if balanced)
+        r_g_ratio = r_mean / (g_mean + 1e-6)
+        b_g_ratio = b_mean / (g_mean + 1e-6)
+
+        print(f"\n  ๐Ÿ” [{label}] Color Analysis:")
+        print(f"     R: mean={r_mean:.1f}, std={r_std:.1f}")
+        print(f"     G: mean={g_mean:.1f}, std={g_std:.1f}")
+        print(f"     B: mean={b_mean:.1f}, std={b_std:.1f}")
+        print(f"     R/G ratio: {r_g_ratio:.3f} (balanced โ‰ˆ 1.0)")
+        print(f"     B/G ratio: {b_g_ratio:.3f} (balanced โ‰ˆ 1.0)")
+
+        if b_g_ratio > 1.15:
+            print(f"     โš ๏ธ BLUE SHIFT DETECTED! B/G ratio too high: {b_g_ratio:.3f}")
+        elif b_g_ratio < 0.85:
+            print(f"     โš ๏ธ YELLOW SHIFT DETECTED! B/G ratio too low: {b_g_ratio:.3f}")
+
+    def _run_unmarker(self, input_tensor: torch.Tensor) -> torch.Tensor:
+        """
+        Run UnMarker attack based on config.
+        """
+        from .utils import attack_non_semantic, attack_two_stage_unmarker
+
+        version = self.config.unmarker_version
+
+        if version == "none":
+            print("  - Skipping UnMarker (none selected)")
+            return input_tensor
+
+        # DEBUG: Analyze input colors
+        self._analyze_colors(input_tensor, "BEFORE UnMarker")
+
+        # Convert tensor to numpy for unmarker
+        img_pil = tensor_to_pil(input_tensor)
+        img_np = np.array(img_pil)
+
+        print(f"  - Running UnMarker: {version}")
+
+        if version == "simplified":
+            result_np = attack_non_semantic(
+                img_np,
+                iterations=500,
+                learning_rate=3e-4,
+            )
+        elif version == "full_fast":
+            result_np = attack_two_stage_unmarker(
+                img_np,
+                preset="fast",
+                verbose=True
+            )
+        elif version == "full_balanced":
+            result_np = attack_two_stage_unmarker(
+                img_np,
+                preset="balanced",
+                verbose=True
+            )
+        elif version == "full_quality":
+            result_np = attack_two_stage_unmarker(
+                img_np,
+                preset="quality",
+                verbose=True
+            )
+        else:
+            print(f"  โš ๏ธ Unknown unmarker version: {version}, skipping")
+            return input_tensor
+
+        # Convert back to tensor
+        result_pil = Image.fromarray(result_np)
+        result_tensor = pil_to_tensor(result_pil)
+
+        # DEBUG: Analyze output colors
+        self._analyze_colors(result_tensor, "AFTER UnMarker")
+
+        return result_tensor
+
+    def run(self, input_image_tensor: torch.Tensor) -> torch.Tensor:
+        """The main execution pipeline."""
+        current_tensor = input_image_tensor.clone()
+
+        # DEBUG: Analyze input image
+        self._analyze_colors(current_tensor, "INPUT IMAGE")
+
+        print(f"๐Ÿ“‹ Pipeline Mode: {self.config.mode}")
+        print(f"๐ŸŽฏ UnMarker Version: {self.config.unmarker_version}")
+
+        if self.config.mode == "Ultra-Minimal":
+            # Just run UnMarker if enabled, no other processing
+            print("  โšก Ultra-Minimal: UnMarker only")
+            current_tensor = self._run_unmarker(current_tensor)
+
+        elif self.config.mode == "Balanced":
+            # TODO: Add ISP simulation stages here (LUT, noise, etc.)
+            # For now, just run UnMarker
+            print("  โš–๏ธ Balanced: Running UnMarker")
+            current_tensor = self._run_unmarker(current_tensor)
+
+        elif self.config.mode == "Aggressive":
+            # TODO: Add full pipeline with ISP + quality analysis
+            # For now, just run UnMarker
+            print("  ๐Ÿ”ฅ Aggressive: Running UnMarker")
+            current_tensor = self._run_unmarker(current_tensor)
+
+        # DEBUG: Analyze final output
+        self._analyze_colors(current_tensor, "FINAL OUTPUT")
+
+        return current_tensor
+
+    # TODO: Add ISP simulation methods here
+    # - _run_iphone_lut()
+    # - _run_iphone_noise()
+    # - _run_heic_compression()
+    # - _run_quality_analysis()
+    # etc.
\ No newline at end of file
diff --git a/custom_nodes/ComfyUI_INSTARAW/modules/detection_bypass/pipeline_v2.py b/custom_nodes/ComfyUI_INSTARAW/modules/detection_bypass/pipeline_v2.py
new file mode 100644
index 0000000000000000000000000000000000000000..e7778fe0b07176a5a140abd049bff4de07a4905f
--- /dev/null
+++ b/custom_nodes/ComfyUI_INSTARAW/modules/detection_bypass/pipeline_v2.py
@@ -0,0 +1,193 @@
+# ---
+# Filename: ../ComfyUI_INSTARAW/modules/detection_bypass/pipeline_v2.py (FINAL, CORRECTED)
+# ---
+import dataclasses
+import os
+import torch
+import numpy as np
+from PIL import Image
+from types import SimpleNamespace
+import tempfile
+import time
+import gc
+
+# Use a clean, relative import to our local, patched package
+from .filmgrainer_local.filmgrainer import process as process_filmgrain
+
+# Import your existing processing functions from the same directory
+from .processor import process_image
+
+@dataclasses.dataclass
+class BypassConfigV2:
+    """A clean, type-safe container for all our settings."""
+    mode: str = "Full Pipeline (iPhone)"
+    strength: float = 0.30
+    lut_path: str = ""
+    seed: int = 0
+
+class BypassPipelineV2:
+    def __init__(self, config: BypassConfigV2):
+        self.config = config
+        print(f"โœ… V2 Pipeline Initialized: Mode='{self.config.mode}', Strength={self.config.strength:.2f}")
+
+    def _get_base_args(self) -> SimpleNamespace:
+        # Create a complete base namespace with ALL possible parameters to prevent AttributeErrors.
+        # BUG FIX: Clamp seed to 32-bit range to prevent overflow in underlying libraries.
+        return SimpleNamespace(
+            seed=self.config.seed % (2**32), awb=False, ref=None, fft_ref=None, lut=None, lut_strength=1.0, blend=False,
+            noise=False, clahe=False, fft=False, perturb=False, glcm=False, lbp=False,
+            sim_camera=False, non_semantic=False,
+            noise_std=0.0, clahe_clip=2.0, tile=8, cutoff=0.25, fstrength=0.9,
+            randomness=0.05, phase_perturb=0.08, radial_smooth=5,
+            fft_mode="auto", fft_alpha=1.0, perturb_magnitude=0.0,
+            glcm_distances=[1], glcm_angles=[0, np.pi/4, np.pi/2, 3*np.pi/4],
+            glcm_levels=256, glcm_strength=0.9, lbp_radius=3, lbp_n_points=24, lbp_method='uniform', lbp_strength=0.9,
+            no_no_bayer=True, jpeg_cycles=1, jpeg_qmin=88, jpeg_qmax=96,
+            vignette_strength=0.0, chroma_strength=0.0, iso_scale=1.0,
+            read_noise=2.0, hot_pixel_prob=1e-6, banding_strength=0.0, motion_blur_kernel=1,
+            ns_iterations=500, ns_learning_rate=3e-4, ns_t_lpips=0.04, ns_t_l2=3e-5,
+            ns_c_lpips=1e-2, ns_c_l2=0.6, ns_grad_clip=0.05,
+            blend_tolerance=10.0, blend_min_region=50, blend_max_samples=100000, blend_n_jobs=None
+        )
+
+    def _apply_filmgrain(self, image_tensor: torch.Tensor) -> torch.Tensor:
+        """Wrapper for our local, patched filmgrainer library."""
+        pil_img = tensor_to_pil(image_tensor)
+        
+        s = self.config.strength
+        grain_power = 0.7 + (s * 0.3)
+        shadows = 0.1 + (s * 0.2)
+        highs = 0.1 + (s * 0.1)
+        grain_sat = 0.2 + (s * 0.4)
+        
+        print(f"  [V2 Pipeline] Applying film grain: Power={grain_power:.2f}, Shadows={shadows:.2f}, Sat={grain_sat:.2f}")
+
+        with tempfile.NamedTemporaryFile(suffix=".png", delete=False) as tmp_in, \
+             tempfile.NamedTemporaryFile(suffix=".png", delete=False) as tmp_out:
+            tmp_in_path, tmp_out_path = tmp_in.name, tmp_out.name
+        
+        try:
+            pil_img.save(tmp_in_path)
+            # BUG FIX: Also clamp seed here for the filmgrainer library
+            process_filmgrain(
+                file_in=tmp_in_path, file_out=tmp_out_path, scale=1.0, src_gamma=1.0,
+                grain_power=grain_power, shadows=shadows, highs=highs, grain_type=1,
+                grain_sat=grain_sat, gray_scale=False, sharpen=0, seed=self.config.seed % (2**32)
+            )
+            result_tensor = pil_to_tensor(Image.open(tmp_out_path))
+        finally:
+            del pil_img
+            gc.collect()
+            time.sleep(0.05)
+            for f_path in [tmp_in_path, tmp_out_path]:
+                if os.path.exists(f_path):
+                    try:
+                        os.unlink(f_path)
+                    except Exception as e:
+                        print(f"  - Warning: Could not delete filmgrain temp file {f_path}: {e}")
+        return result_tensor
+
+    def run(self, input_image_tensor: torch.Tensor, awb_ref_tensor: torch.Tensor = None) -> torch.Tensor:
+        """The main execution pipeline, orchestrating all authenticity stages."""
+        current_tensor = input_image_tensor.clone()
+        
+        if self.config.mode == "Full Pipeline (iPhone)":
+            print("  - Executing Full Pipeline (iPhone)...")
+            current_tensor = self._run_pass(current_tensor, self._get_awb_args(), awb_ref_tensor=awb_ref_tensor)
+            current_tensor = self._run_pass(current_tensor, self._get_ns_args())
+            current_tensor = self._run_pass(current_tensor, self._get_lut_args())
+            current_tensor = self._apply_filmgrain(current_tensor)
+            current_tensor = self._run_pass(current_tensor, self._get_simulation_args())
+            current_tensor = self._run_pass(current_tensor, self._get_perturb_args())
+
+        elif self.config.mode == "Ultra-Minimal (Stealth)":
+            print("  - Executing Ultra-Minimal (Stealth) Pipeline...")
+            current_tensor = self._run_pass(current_tensor, self._get_awb_args(), awb_ref_tensor=awb_ref_tensor)
+            
+            args2 = self._get_base_args(); args2.pass_name = "AI Normalizer (Minimal)"; args2.non_semantic = True
+            args2.ns_iterations = 200; args2.ns_t_lpips = 0.015
+            current_tensor = self._run_pass(current_tensor, args2)
+            
+            args3 = self._get_base_args(); args3.pass_name = "Light Camera Sim"; args3.sim_camera = True
+            args3.chroma_strength = 0.4; args3.vignette_strength = 0.05; args3.jpeg_cycles = 1; args3.jpeg_qmin = 96; args3.jpeg_qmax = 99
+            current_tensor = self._run_pass(current_tensor, args3)
+            
+            args4 = self._get_base_args(); args4.pass_name = "Final Perturbation"; args4.perturb = True
+            args4.perturb_magnitude = 0.002
+            current_tensor = self._run_pass(current_tensor, args4)
+
+        return current_tensor
+
+    def _run_pass(self, input_tensor, args, awb_ref_tensor=None, fft_ref_tensor=None):
+        pass_name = getattr(args, 'pass_name', 'Unknown')
+        print(f"  [V2 Pipeline] Stage: '{pass_name}'...")
+        pil_img = tensor_to_pil(input_tensor)
+        tmp_files_to_clean = []
+        try:
+            with tempfile.NamedTemporaryFile(suffix=".png", delete=False) as tmp_input, \
+                 tempfile.NamedTemporaryFile(suffix=".png", delete=False) as tmp_output:
+                tmp_input_path, tmp_output_path = tmp_input.name, tmp_output.name
+            tmp_files_to_clean.extend([tmp_input_path, tmp_output_path])
+            pil_img.save(tmp_input_path)
+            args.input, args.output = tmp_input_path, tmp_output_path
+            
+            if awb_ref_tensor is not None:
+                with tempfile.NamedTemporaryFile(suffix=".png", delete=False) as tmp_awb:
+                    awb_path = tmp_awb.name
+                tensor_to_pil(awb_ref_tensor).save(awb_path)
+                args.ref = awb_path
+                tmp_files_to_clean.append(args.ref)
+            
+            process_image(args.input, args.output, args)
+            return pil_to_tensor(Image.open(tmp_output_path))
+        finally:
+            del pil_img
+            gc.collect()
+            time.sleep(0.05)
+            for f_path in tmp_files_to_clean:
+                if os.path.exists(f_path):
+                    try:
+                        os.unlink(f_path)
+                    except Exception as e:
+                        print(f"  - Warning: Could not delete temp file {f_path}: {e}")
+    
+    def _get_awb_args(self) -> SimpleNamespace:
+        args = self._get_base_args(); args.pass_name = "Auto White Balance"; args.awb = True
+        return args
+        
+    def _get_ns_args(self) -> SimpleNamespace:
+        args = self._get_base_args(); args.pass_name = "AI Normalizer (UnMarker Attack)"
+        args.non_semantic = True; s = self.config.strength
+        args.ns_iterations = int(200 + 300 * s); args.ns_t_lpips = 0.02 + (0.025 * s); args.ns_learning_rate = 2e-4 + (1e-4 * s)
+        return args
+
+    def _get_lut_args(self) -> SimpleNamespace:
+        args = self._get_base_args(); args.pass_name = "3D LUT Color Science"
+        args.lut = self.config.lut_path; args.lut_strength = 0.5 + (0.5 * self.config.strength)
+        return args
+
+    def _get_simulation_args(self) -> SimpleNamespace:
+        args = self._get_base_args(); args.pass_name = "Camera Simulation"
+        s = self.config.strength; args.sim_camera = True
+        args.chroma_strength = 0.5 + (1.0 * s); args.vignette_strength = 0.1 + (0.3 * s)
+        args.jpeg_cycles = 1 + int(s * 2); args.jpeg_qmin = int(95 - (45 * s)); args.jpeg_qmax = int(98 - (25 * s))
+        if args.jpeg_qmin > args.jpeg_qmax: args.jpeg_qmin = args.jpeg_qmax
+        return args
+
+    def _get_perturb_args(self) -> SimpleNamespace:
+        args = self._get_base_args(); args.pass_name = "Final Perturbation"
+        args.perturb = True; args.perturb_magnitude = 0.001 + (0.007 * self.config.strength)
+        return args
+
+# Helper functions to keep file self-contained
+def tensor_to_pil(tensor: torch.Tensor) -> Image.Image:
+    if tensor.ndim == 4 and tensor.shape[0] == 1: tensor = tensor.squeeze(0)
+    return Image.fromarray((tensor.cpu().numpy() * 255).astype(np.uint8))
+
+def pil_to_tensor(pil_image: Image.Image) -> torch.Tensor:
+    try:
+        img_np = np.array(pil_image.convert("RGB")).astype(np.float32) / 255.0
+        return torch.from_numpy(img_np).unsqueeze(0)
+    finally:
+        if isinstance(pil_image, Image.Image):
+            pil_image.close()
\ No newline at end of file
diff --git a/custom_nodes/ComfyUI_INSTARAW/modules/detection_bypass/processor.py b/custom_nodes/ComfyUI_INSTARAW/modules/detection_bypass/processor.py
new file mode 100644
index 0000000000000000000000000000000000000000..fa7969b33de7cda6eaf5f85df6e88216464af16d
--- /dev/null
+++ b/custom_nodes/ComfyUI_INSTARAW/modules/detection_bypass/processor.py
@@ -0,0 +1,181 @@
+# ---
+# Filename: ../ComfyUI_INSTARAW/modules/detection_bypass/processor.py (Refactored)
+# ---
+#!/usr/bin/env python3
+"""
+processor.py
+
+Main pipeline for image postprocessing. This module orchestrates various authenticity
+techniques, including the UnMarker attack for spectral normalization.
+"""
+
+import argparse
+import os
+from PIL import Image
+import numpy as np
+import piexif
+from datetime import datetime
+
+# Import all utility functions, including our new UnMarker attack
+from .utils import (
+    add_gaussian_noise,
+    clahe_color_correction,
+    randomized_perturbation,
+    fourier_match_spectrum,
+    auto_white_balance_ref,
+    load_lut,
+    apply_lut,
+    glcm_normalize,
+    lbp_normalize,
+    attack_non_semantic, # <-- This is now imported from our new module
+    blend_colors
+)
+from .camera_pipeline import simulate_camera_pipeline
+
+def add_fake_exif():
+    """Generates a plausible set of fake EXIF data."""
+    now = datetime.now()
+    datestamp = now.strftime("%Y:%m:%d %H:%M:%S")
+    zeroth_ifd = {
+        piexif.ImageIFD.Make: b"Apple",
+        piexif.ImageIFD.Model: b"iPhone 15 Pro",
+        piexif.ImageIFD.Software: b"17.4.1",
+        piexif.ImageIFD.DateTime: datestamp.encode('utf-8'),
+    }
+    exif_ifd = {
+        piexif.ExifIFD.DateTimeOriginal: datestamp.encode('utf-8'),
+        piexif.ExifIFD.DateTimeDigitized: datestamp.encode('utf-8'),
+        piexif.ExifIFD.ExposureTime: (1, 125),
+        piexif.ExifIFD.FNumber: (28, 10),
+        piexif.ExifIFD.ISOSpeedRatings: 200,
+        piexif.ExifIFD.FocalLength: (50, 1),
+    }
+    exif_dict = {"0th": zeroth_ifd, "Exif": exif_ifd, "GPS": {}, "1st": {}, "thumbnail": None}
+    return piexif.dump(exif_dict)
+
+def process_image(path_in, path_out, args):
+    """The main processing function that applies a sequence of effects based on args."""
+    img = Image.open(path_in).convert('RGB')
+    arr = np.array(img)
+
+    ref_arr_fft = None
+    if args.fft_ref:
+        try:
+            ref_arr_fft = np.array(Image.open(args.fft_ref).convert('RGB'))
+        except Exception as e:
+            print(f"Warning: failed to load FFT/AWB reference '{args.fft_ref}': {e}.")
+
+    # --- This is the core change: we now call the imported UnMarker function ---
+    if args.non_semantic:
+        print("Applying non-semantic UnMarker attack...")
+        try:
+            arr = attack_non_semantic(
+                arr,
+                iterations=args.ns_iterations, learning_rate=args.ns_learning_rate,
+                t_lpips=args.ns_t_lpips, t_l2=args.ns_t_l2,
+                c_lpips=args.ns_c_lpips, c_l2=args.ns_c_l2,
+                grad_clip_value=args.ns_grad_clip
+            )
+        except Exception as e:
+            print(f"Warning: Non-semantic attack failed: {e}. Skipping.")
+
+    # ... (the rest of the function remains the same, applying other effects)
+    if args.clahe:
+        arr = clahe_color_correction(arr, clip_limit=args.clahe_clip, tile_grid_size=(args.tile, args.tile))
+    if args.fft:
+        arr = fourier_match_spectrum(arr, ref_img_arr=ref_arr_fft, mode=args.fft_mode, alpha=args.fft_alpha, cutoff=args.cutoff, strength=args.fstrength, randomness=args.randomness, phase_perturb=args.phase_perturb, radial_smooth=args.radial_smooth, seed=args.seed)
+    if args.glcm:
+        arr = glcm_normalize(arr, ref_img_arr=ref_arr_fft, distances=args.glcm_distances, angles=args.glcm_angles, levels=args.glcm_levels, strength=args.glcm_strength, seed=args.seed)
+    if args.lbp:
+        arr = lbp_normalize(arr, ref_img_arr=ref_arr_fft, radius=args.lbp_radius, n_points=args.lbp_n_points, method=args.lbp_method, strength=args.lbp_strength, seed=args.seed)
+    if args.noise:
+        arr = add_gaussian_noise(arr, std_frac=args.noise_std, seed=args.seed)
+    if args.perturb:
+        arr = randomized_perturbation(arr, magnitude_frac=args.perturb_magnitude, seed=args.seed)
+    if args.sim_camera:
+        arr = simulate_camera_pipeline(arr, bayer=args.no_no_bayer, jpeg_cycles=args.jpeg_cycles, jpeg_quality_range=(args.jpeg_qmin, args.jpeg_qmax), vignette_strength=args.vignette_strength, chroma_aberr_strength=args.chroma_strength, iso_scale=args.iso_scale, read_noise_std=args.read_noise, hot_pixel_prob=args.hot_pixel_prob, banding_strength=args.banding_strength, motion_blur_kernel=args.motion_blur_kernel, seed=args.seed)
+    if args.awb:
+        awb_ref = np.array(Image.open(args.ref).convert('RGB')) if args.ref else None
+        arr = auto_white_balance_ref(arr, awb_ref)
+    if args.lut:
+        try:
+            lut = load_lut(args.lut)
+            arr = apply_lut(np.clip(arr, 0, 255).astype(np.uint8), lut, strength=args.lut_strength)
+        except Exception as e:
+            print(f"Warning: failed to apply LUT '{args.lut}': {e}. Skipping.")
+    
+    out_img = Image.fromarray(np.clip(arr, 0, 255).astype(np.uint8))
+    out_img.save(path_out, exif=add_fake_exif())
+
+# The build_argparser and main functions are for standalone script execution and can remain unchanged.
+# ... (rest of processor.py, i.e., build_argparser and if __name__ == "__main__":, remains the same)
+def build_argparser():
+    p = argparse.ArgumentParser(description="Image postprocessing pipeline with camera simulation, LUT support, GLCM, and LBP normalization")
+    p.add_argument('input', help='Input image path')
+    p.add_argument('output', help='Output image path')
+    p.add_argument('--awb', action='store_true', default=False, help='Enable automatic white balancing. Uses grey-world if --ref is not provided.')
+    p.add_argument('--ref', help='Optional reference image for auto white-balance (only used if --awb is enabled)', default=None)
+    p.add_argument('--noise-std', type=float, default=0.02, help='Gaussian noise std fraction of 255 (0-0.1)')
+    p.add_argument('--clahe-clip', type=float, default=2.0, help='CLAHE clip limit')
+    p.add_argument('--tile', type=int, default=8, help='CLAHE tile grid size')
+    p.add_argument('--cutoff', type=float, default=0.25, help='Fourier cutoff (0..1)')
+    p.add_argument('--fstrength', type=float, default=0.9, help='Fourier blend strength (0..1)')
+    p.add_argument('--randomness', type=float, default=0.05, help='Randomness for Fourier mask modulation')
+    p.add_argument('--seed', type=int, default=None, help='Random seed for reproducibility')
+    p.add_argument('--fft-ref', help='Optional reference image for FFT spectral matching, GLCM, and LBP', default=None)
+    p.add_argument('--fft-mode', choices=('auto','ref','model'), default='auto', help='FFT mode: auto picks ref if available')
+    p.add_argument('--fft-alpha', type=float, default=1.0, help='Alpha for 1/f model (spectrum slope)')
+    p.add_argument('--phase-perturb', type=float, default=0.08, help='Phase perturbation strength (radians)')
+    p.add_argument('--radial-smooth', type=int, default=5, help='Radial smoothing (bins) for spectrum profiles')
+    p.add_argument('--glcm', action='store_true', default=False, help='Enable GLCM normalization using FFT reference if available')
+    p.add_argument('--glcm-distances', type=int, nargs='+', default=[1], help='Distances for GLCM computation')
+    p.add_argument('--glcm-angles', type=float, nargs='+', default=[0, np.pi/4, np.pi/2, 3*np.pi/4], help='Angles for GLCM computation (in radians)')
+    p.add_argument('--glcm-levels', type=int, default=256, help='Number of gray levels for GLCM')
+    p.add_argument('--glcm-strength', type=float, default=0.9, help='Strength of GLCM feature matching (0..1)')
+    p.add_argument('--lbp', action='store_true', default=False, help='Enable LBP normalization using FFT reference if available')
+    p.add_argument('--lbp-radius', type=int, default=3, help='Radius of LBP operator')
+    p.add_argument('--lbp-n-points', type=int, default=24, help='Number of circularly symmetric neighbor set points for LBP')
+    p.add_argument('--lbp-method', choices=('default', 'ror', 'uniform', 'var'), default='uniform', help='LBP method')
+    p.add_argument('--lbp-strength', type=float, default=0.9, help='Strength of LBP histogram matching (0..1)')
+    p.add_argument('--non-semantic', action='store_true', default=False, help='Apply non-semantic attack on the image')
+    p.add_argument('--ns-iterations', type=int, default=500, help='Iterations for non-semantic attack')
+    p.add_argument('--ns-learning-rate', type=float, default=3e-4, help='Learning rate for non-semantic attack')
+    p.add_argument('--ns-t-lpips', type=float, default=4e-2, help='LPIPS threshold for non-semantic attack')
+    p.add_argument('--ns-t-l2', type=float, default=3e-5, help='L2 threshold for non-semantic attack')
+    p.add_argument('--ns-c-lpips', type=float, default=1e-2, help='LPIPS constant for non-semantic attack')
+    p.add_argument('--ns-c-l2', type=float, default=0.6, help='L2 constant for non-semantic attack')
+    p.add_argument('--ns-grad-clip', type=float, default=0.05, help='Gradient clipping value for non-semantic attack')
+    p.add_argument('--sim-camera', action='store_true', default=False, help='Enable camera-pipeline simulation (Bayer, CA, vignette, JPEG cycles)')
+    p.add_argument('--no-no-bayer', dest='no_no_bayer', action='store_false', help='Disable Bayer/demosaic step (double negative kept for backward compat)')
+    p.set_defaults(no_no_bayer=True)
+    p.add_argument('--jpeg-cycles', type=int, default=1, help='Number of JPEG recompression cycles to apply')
+    p.add_argument('--jpeg-qmin', type=int, default=88, help='Min JPEG quality for recompression')
+    p.add_argument('--jpeg-qmax', type=int, default=96, help='Max JPEG quality for recompression')
+    p.add_argument('--vignette-strength', type=float, default=0.35, help='Vignette strength (0..1)')
+    p.add_argument('--chroma-strength', type=float, default=1.2, help='Chromatic aberration strength (pixels)')
+    p.add_argument('--iso-scale', type=float, default=1.0, help='ISO/exposure scale for Poisson noise')
+    p.add_argument('--read-noise', type=float, default=2.0, help='Read noise sigma for sensor noise')
+    p.add_argument('--hot-pixel-prob', type=float, default=1e-6, help='Per-pixel probability of hot pixel')
+    p.add_argument('--banding-strength', type=float, default=0.0, help='Horizontal banding amplitude (0..1)')
+    p.add_argument('--motion-blur-kernel', type=int, default=1, help='Motion blur kernel size (1 = none)')
+    p.add_argument('--lut', type=str, default=None, help='Path to a 1D PNG (256x1) or .npy LUT, or a .cube 3D LUT')
+    p.add_argument('--lut-strength', type=float, default=0.1, help='Strength to blend LUT (0.0 = no effect, 1.0 = full LUT)')
+    p.add_argument('--noise', action='store_true', default=False, help='Enable Gaussian noise addition')
+    p.add_argument('--clahe', action='store_true', default=False, help='Enable CLAHE color correction')
+    p.add_argument('--fft', action='store_true', default=False, help='Enable FFT spectral matching')
+    p.add_argument('--perturb', action='store_true', default=False, help='Enable randomized perturbation')
+    p.add_argument('--perturb-magnitude', type=float, default=0.008, help='Randomized perturb magnitude fraction (0..0.05)')
+    p.add_argument('--blend', action='store_true', default=False, help='Enable color')
+    p.add_argument('--blend-tolerance', type=float, default=10.0, help='Color tolerance for blending (smaller = more colors)')
+    p.add_argument('--blend-min-region', type=int, default=50, help='Minimum region size to retain (in pixels)')
+    p.add_argument('--blend-max-samples', type=int, default=100000, help='Maximum pixels to sample for k-means (for speed)')
+    p.add_argument('--blend-n-jobs', type=int, default=None, help='Number of worker threads for blending (default: os.cpu_count())')
+    return p
+
+if __name__ == "__main__":
+    args = build_argparser().parse_args()
+    if not os.path.exists(args.input):
+        print("Input not found:", args.input)
+        raise SystemExit(2)
+    process_image(args.input, args.output, args)
+    print("Saved:", args.output)
\ No newline at end of file
diff --git a/custom_nodes/ComfyUI_INSTARAW/modules/detection_bypass/utils/__init__.py b/custom_nodes/ComfyUI_INSTARAW/modules/detection_bypass/utils/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..b5e8ad64ae031eb94ba299b7e66ba5aa76dcf55d
--- /dev/null
+++ b/custom_nodes/ComfyUI_INSTARAW/modules/detection_bypass/utils/__init__.py
@@ -0,0 +1,35 @@
+# Filename: ComfyUI_INSTARAW/modules/detection_bypass/utils/__init__.py
+# ---
+from .autowb import auto_white_balance_ref
+from .clahe import clahe_color_correction
+from .color_lut import load_lut, apply_lut
+from .exif import remove_exif_pil
+from .fourier_pipeline import fourier_match_spectrum
+from .gaussian_noise import add_gaussian_noise
+from .perturbation import randomized_perturbation
+from .glcm_normalization import glcm_normalize
+from .lbp_normalization import lbp_normalize
+from .unmarker_full import normalize_spectrum_twostage, SpectralNormalizer
+from .blend import blend_colors
+from .direct_spectral_matching import direct_spectral_match
+from .texture_utils import TextureMatcher # <-- ADD THIS LINE
+from .non_semantic_attack import non_semantic_attack
+
+__all__ = [
+    "auto_white_balance_ref",
+    "clahe_color_correction",
+    "load_lut",
+    "apply_lut",
+    "remove_exif_pil",
+    "fourier_match_spectrum",
+    "add_gaussian_noise",
+    "randomized_perturbation",
+    "glcm_normalize",
+    "lbp_normalize",
+    'normalize_spectrum_twostage',
+    'SpectralNormalizer',
+    "blend_colors",
+    'direct_spectral_match',
+    "TextureMatcher",
+    "non_semantic_attack",
+]
\ No newline at end of file
diff --git a/custom_nodes/ComfyUI_INSTARAW/modules/detection_bypass/utils/adaptive_filter.py b/custom_nodes/ComfyUI_INSTARAW/modules/detection_bypass/utils/adaptive_filter.py
new file mode 100644
index 0000000000000000000000000000000000000000..1e52b1fc47284dcb7b6936b64e3de96446071e4a
--- /dev/null
+++ b/custom_nodes/ComfyUI_INSTARAW/modules/detection_bypass/utils/adaptive_filter.py
@@ -0,0 +1,321 @@
+"""
+Adaptive spatial filtering for UnMarker.
+Ported from ai-watermark/modules/attack/unmark/cw.py:Filter
+
+This implements learnable, edge-aware filtering that applies perturbations
+more strongly in textured regions (where they're invisible) and weakly in
+smooth regions (where they'd be noticeable).
+
+Key concept: Joint bilateral filtering with learnable kernels.
+"""
+
+import numpy as np
+import torch
+import kornia
+
+
+class AdaptiveFilter(torch.nn.Module):
+    """
+    Learnable adaptive filter that applies spatially-varying perturbations.
+
+    This is the secret sauce for imperceptibility. Instead of uniform noise,
+    it learns WHERE to apply perturbations based on image content.
+
+    Args:
+        kernels: List of (height, width) tuples for filter sizes, e.g., [(7,7), (15,15)]
+        shape: Expected input shape (B, C, H, W)
+        box: Spatial box size for partitioning, e.g., (1, 1) = no partitioning
+        sigma_color: Bilateral filter color sensitivity (0 = spatial only)
+        norm: L-p norm for distance computation
+        pad_mode: Padding mode for convolutions ("reflect" recommended)
+        filter_mode: If True, applies filter at every pixel (slow but accurate)
+        loss_factor: Weight for filter regularization loss
+        loss_norm: Norm for filter loss computation
+    """
+
+    def __init__(
+        self,
+        kernels,
+        shape,
+        box=(1, 1),
+        sigma_color=0.1,
+        norm=1,
+        pad_mode="reflect",
+        filter_mode=False,
+        loss_factor=1,
+        loss_norm=2,
+    ):
+        super().__init__()
+        self.norm, self.sigma_color, self.pad_mode, self.box, self.filter_mode = (
+            norm,
+            sigma_color,
+            pad_mode,
+            box,
+            filter_mode,
+        )
+
+        # Learnable filter kernels (these are optimized during attack)
+        self.kernels = torch.nn.ParameterList(
+            [torch.nn.Parameter(self.__get_init_w(kernel, shape)) for kernel in kernels]
+        )
+        self.softmax = torch.nn.Softmax(dim=-1)
+        self.loss_factor = loss_factor
+        self.loss_norm = loss_norm
+
+    def pad_w(self, w):
+        """Pad filter kernel to full size."""
+        return torch.nn.functional.pad(
+            w, (0, w.shape[-1] - 1, 0, w.shape[-2] - 1), "reflect"
+        )
+
+    def __get_init_w(self, kernel, shape):
+        """Initialize learnable filter weights with Gaussian."""
+        repeats, _, h, w = shape
+        box = self.box if self.box is not None else kernel
+        boxes = [int(np.ceil(h / box[0])), int(np.ceil(w / box[1]))]
+        num_boxes = boxes[0] * boxes[1]
+
+        # Initialize with Gaussian kernel
+        w = (
+            kornia.filters.get_gaussian_kernel2d(kernel, torch.tensor([[0.2, 0.2]]))
+            .unsqueeze(0)
+            .repeat(repeats, num_boxes, 1, 1)
+        )
+
+        # Store only upper-left quadrant (will be mirrored)
+        # Log-space for numerical stability
+        return (
+            w[..., : int(kernel[0] // 2) + 1, : int(kernel[1] // 2) + 1]
+            .clamp(1e-5, 0.999999)
+            .log()
+        )
+
+    def get_dist(self, x, kernel, guidance=None, norm=None):
+        """
+        Compute spatial distance between pixels and their neighborhoods.
+        This is used for bilateral filtering.
+        """
+        norm = self.norm if norm is None else norm
+        unf_inp = self.extract_patches(x, kernel)
+        guidance = guidance if guidance is not None else x
+        guidance = torch.nn.functional.pad(
+            guidance,
+            self._box_pad(guidance, kernel),
+            mode=self.pad_mode,
+        )
+
+        return torch.pow(
+            torch.norm(
+                unf_inp
+                - guidance.view(guidance.shape[0], guidance.shape[1], -1)
+                .transpose(1, 2)
+                .view(
+                    guidance.shape[0],
+                    unf_inp.shape[1],
+                    unf_inp.shape[2],
+                    guidance.shape[1],
+                    1,
+                ),
+                p=norm,
+                dim=-2,
+                keepdim=True,
+            ),
+            2,
+        )
+
+    def __get_color_kernel(self, guidance, kernel):
+        """
+        Compute bilateral filter's range kernel (color similarity).
+        Pixels with similar colors get more weight.
+        """
+        if self.sigma_color <= 0:
+            return 1  # No color weighting, pure spatial filter
+
+        dist = self.get_dist(guidance.double(), kernel).float()
+        ret = (
+            (-0.5 / (self.sigma_color**2) * dist)
+            .exp()
+            .view(guidance.shape[0], dist.shape[1], dist.shape[2], -1, 1)
+        )
+        return torch.nan_to_num(ret, nan=0.0)
+
+    def _box_pad(self, x, kernel):
+        """Compute padding needed for box-based processing."""
+        box = self.box if self.box is not None else kernel
+        col = (
+            box[1] - (x.shape[-1] - (x.shape[-1] // box[1]) * box[1]) % box[1]
+        ) % box[1]
+        row = (
+            box[0] - (x.shape[-2] - (x.shape[-2] // box[0]) * box[0]) % box[0]
+        ) % box[0]
+        return [0, col, 0, row]
+
+    def _kernel_pad(self, kernel):
+        """Compute padding for kernel convolution."""
+        return [
+            (kernel[1] - 1) // 2,
+            (kernel[1] - 1) - (kernel[1] - 1) // 2,
+            (kernel[0] - 1) // 2,
+            (kernel[0] - 1) - (kernel[0] - 1) // 2,
+        ]
+
+    def _median_pad(self, x, kernel, stride):
+        """Compute padding for median computation."""
+        ph = (
+            (kernel[0] - stride[0])
+            if x.shape[-2] % stride[0] == 0
+            else (kernel[0] - (x.shape[-2] % stride[0]))
+        )
+        pw = (
+            (kernel[1] - stride[1])
+            if x.shape[-1] % stride[1] == 0
+            else (kernel[1] - (x.shape[-1] % stride[1]))
+        )
+        return (pw // 2, pw - pw // 2, ph // 2, ph - ph // 2)
+
+    def _compute_median(self, x, kernel):
+        """Compute local median for regularization."""
+        stride = kernel if not self.filter_mode else (1, 1)
+        x_p = torch.nn.functional.pad(
+            x, self._median_pad(x, kernel, stride), mode="reflect"
+        )
+        x_unf = x_p.unfold(2, kernel[0], stride[0]).unfold(3, kernel[1], stride[1])
+        median = x_unf.contiguous().view(x_unf.size()[:4] + (-1,)).median(dim=-1)[0]
+        return (
+            median.unsqueeze(-2)
+            .unsqueeze(-1)
+            .repeat(
+                1,
+                1,
+                1,
+                x_p.shape[-2] // median.shape[-2],
+                1,
+                x_p.shape[-1] // median.shape[-1],
+            )
+            .flatten(2, 3)
+            .flatten(-2)[..., : x.shape[-2], : x.shape[-1]]
+        )
+
+    def extract_patches(self, x, kernel):
+        """
+        Extract overlapping patches for filter application.
+        This is the core of the spatial filtering.
+        """
+        box = self.box if self.box is not None else kernel
+        kern = (box[0] + (kernel[0] - 1), box[1] + (kernel[1] - 1))
+        pad = [
+            b + k for b, k in zip(self._box_pad(x, kernel), self._kernel_pad(kernel))
+        ]
+        inp_unf = (
+            torch.nn.functional.pad(x, pad, mode=self.pad_mode)
+            .unfold(2, kern[0], box[0])
+            .unfold(3, kern[1], box[1])
+            .permute(0, 2, 3, 1, 4, 5)
+            .flatten(-2)
+            .reshape(-1, x.shape[1], kern[0], kern[1])
+        )
+
+        return (
+            inp_unf.unfold(2, kernel[0], 1)
+            .unfold(3, kernel[1], 1)
+            .permute(0, 2, 3, 1, 4, 5)
+            .flatten(-2)
+            .reshape(
+                x.shape[0],
+                inp_unf.shape[0] // x.shape[0],
+                -1,
+                inp_unf.shape[1],
+                kernel[0] * kernel[1],
+            )
+        )
+
+    def __compute_filter_loss(self, x, kernel, norm=2):
+        """
+        Regularization loss to prevent over-smoothing.
+        Penalizes deviation from local median.
+        """
+        return self.get_dist(
+            x, kernel, guidance=self._compute_median(x, kernel), norm=norm
+        ).view(x.shape[0], -1).sum(-1, keepdims=True) / torch.prod(
+            torch.tensor(x.shape[1:])
+        )
+
+    def __apply_filter(self, x, w, guidance=None):
+        """
+        Apply the learned adaptive filter to the input.
+
+        This is where the magic happens: the filter applies different
+        weights to different spatial locations based on local image content.
+        """
+        w = self.pad_w(w)  # Expand to full kernel
+        kernel = (w.shape[-2], w.shape[-1])
+        box = self.box if self.box is not None else kernel
+        inp_unf = self.extract_patches(x, kernel)
+
+        boxes = [
+            int(np.ceil(x.shape[-2] / box[0])),
+            int(np.ceil(x.shape[-1] / box[1])),
+        ]
+
+        # Compute bilateral weights (spatial + color)
+        color_kernel = self.__get_color_kernel(guidance, kernel)
+
+        # Combine learned spatial weights with color weights
+        w = (
+            self.softmax(w.view(w.shape[0], w.shape[1], -1))
+            .unsqueeze(-2)
+            .unsqueeze(-1)
+            .repeat(1, 1, inp_unf.shape[2], 1, 1)
+            * color_kernel
+        ).view(w.shape[0], w.shape[1], inp_unf.shape[2], -1, 1)
+
+        # Apply weighted combination
+        out = inp_unf.matmul(w).transpose(2, 3).squeeze(-1) / w.squeeze(-1).sum(
+            -1
+        ).unsqueeze(2)
+
+        out = (
+            out.view(-1, inp_unf.shape[-2], inp_unf.shape[-3])
+            .reshape(x.shape[0], -1, x.shape[1] * box[0] * box[1])
+            .transpose(2, 1)
+        )
+
+        return torch.nn.functional.fold(
+            out,
+            (boxes[0] * box[0], boxes[1] * box[1]),
+            box,
+            stride=box,
+        )[..., : x.shape[-2], : x.shape[-1]]
+
+    def compute_loss(self, x):
+        """Compute regularization loss for all filter kernels."""
+        kernels = [(f.shape[-2] * 2 - 1, f.shape[-1] * 2 - 1) for f in self.kernels]
+        if len(kernels) == 0 or self.loss_factor == 0:
+            return 0
+
+        return torch.concat(
+            [
+                self.__compute_filter_loss(
+                    x,
+                    k,
+                    norm=self.loss_norm,
+                )
+                for k in kernels
+            ],
+            dim=-1,
+        ).sum(-1)
+
+    def forward(self, x, guidance):
+        """
+        Apply all learned filters sequentially.
+
+        Args:
+            x: Input tensor to filter
+            guidance: Guidance image for bilateral weighting (usually original image)
+
+        Returns:
+            Filtered tensor
+        """
+        for filt in self.kernels:
+            x = self.__apply_filter(x, filt, guidance=guidance)
+        return x.float()
diff --git a/custom_nodes/ComfyUI_INSTARAW/modules/detection_bypass/utils/autowb.py b/custom_nodes/ComfyUI_INSTARAW/modules/detection_bypass/utils/autowb.py
new file mode 100644
index 0000000000000000000000000000000000000000..87246e4e65b42f7c84f6b260aa823999383fbe46
--- /dev/null
+++ b/custom_nodes/ComfyUI_INSTARAW/modules/detection_bypass/utils/autowb.py
@@ -0,0 +1,26 @@
+import numpy as np
+
+def auto_white_balance_ref(img_arr: np.ndarray, ref_img_arr: np.ndarray = None) -> np.ndarray:
+    """
+    Auto white-balance correction using a reference image.
+    If ref_img_arr is None, uses a gray-world assumption instead.
+    """
+    img = img_arr.astype(np.float32)
+
+    if ref_img_arr is not None:
+        ref = ref_img_arr.astype(np.float32)
+        ref_mean = ref.reshape(-1, 3).mean(axis=0)
+    else:
+        # Gray-world assumption: target is neutral gray
+        ref_mean = np.array([128.0, 128.0, 128.0], dtype=np.float32)
+
+    img_mean = img.reshape(-1, 3).mean(axis=0)
+
+    # Avoid divide-by-zero
+    eps = 1e-6
+    scale = (ref_mean + eps) / (img_mean + eps)
+
+    corrected = img * scale
+    corrected = np.clip(corrected, 0, 255).astype(np.uint8)
+
+    return corrected
\ No newline at end of file
diff --git a/custom_nodes/ComfyUI_INSTARAW/modules/detection_bypass/utils/blend.py b/custom_nodes/ComfyUI_INSTARAW/modules/detection_bypass/utils/blend.py
new file mode 100644
index 0000000000000000000000000000000000000000..33d043cb61fa0e76b93236074d8d0a8e01aac16e
--- /dev/null
+++ b/custom_nodes/ComfyUI_INSTARAW/modules/detection_bypass/utils/blend.py
@@ -0,0 +1,223 @@
+import numpy as np
+from scipy.cluster.vq import kmeans2
+from scipy.ndimage import label, mean as ndi_mean
+from scipy.spatial import cKDTree
+import os
+from concurrent.futures import ThreadPoolExecutor, as_completed
+
+# Vectorized color conversions
+def rgb_to_hsv(rgb: np.ndarray) -> np.ndarray:
+    """
+    Vectorized RGB->[H(0..360), S(0..1), V(0..1)].
+    rgb: (..., 3) in [0,255]
+    """
+    rgb = rgb.astype(np.float32) / 255.0
+    r = rgb[..., 0]
+    g = rgb[..., 1]
+    b = rgb[..., 2]
+
+    maxc = np.maximum(np.maximum(r, g), b)
+    minc = np.minimum(np.minimum(r, g), b)
+    delta = maxc - minc
+
+    # Hue
+    h = np.zeros_like(maxc)
+    nonzero = delta > 1e-8
+
+    # r is max
+    mask = nonzero & (maxc == r)
+    h[mask] = ((g[mask] - b[mask]) / delta[mask]) % 6
+    # g is max
+    mask = nonzero & (maxc == g)
+    h[mask] = ((b[mask] - r[mask]) / delta[mask]) + 2
+    # b is max
+    mask = nonzero & (maxc == b)
+    h[mask] = ((r[mask] - g[mask]) / delta[mask]) + 4
+
+    h = h * 60.0  # degrees
+    h[~nonzero] = 0.0
+
+    # Saturation
+    s = np.zeros_like(maxc)
+    nonzero_max = maxc > 1e-8
+    s[nonzero_max] = delta[nonzero_max] / maxc[nonzero_max]
+
+    v = maxc
+    hsv = np.stack([h, s, v], axis=-1)
+    return hsv
+
+def hsv_to_rgb(hsv: np.ndarray) -> np.ndarray:
+    """
+    Vectorized HSV->[0..255] RGB.
+    hsv: (...,3) with H in [0,360], S,V in [0,1]
+    """
+    h = hsv[..., 0] / 60.0  # sector
+    s = hsv[..., 1]
+    v = hsv[..., 2]
+
+    c = v * s
+    x = c * (1 - np.abs((h % 2) - 1))
+    m = v - c
+
+    rp = np.zeros_like(h)
+    gp = np.zeros_like(h)
+    bp = np.zeros_like(h)
+
+    seg0 = (0 <= h) & (h < 1)
+    seg1 = (1 <= h) & (h < 2)
+    seg2 = (2 <= h) & (h < 3)
+    seg3 = (3 <= h) & (h < 4)
+    seg4 = (4 <= h) & (h < 5)
+    seg5 = (5 <= h) & (h < 6)
+
+    rp[seg0] = c[seg0]; gp[seg0] = x[seg0]; bp[seg0] = 0
+    rp[seg1] = x[seg1]; gp[seg1] = c[seg1]; bp[seg1] = 0
+    rp[seg2] = 0;        gp[seg2] = c[seg2]; bp[seg2] = x[seg2]
+    rp[seg3] = 0;        gp[seg3] = x[seg3]; bp[seg3] = c[seg3]
+    rp[seg4] = x[seg4]; gp[seg4] = 0;        bp[seg4] = c[seg4]
+    rp[seg5] = c[seg5]; gp[seg5] = 0;        bp[seg5] = x[seg5]
+
+    r = (rp + m)
+    g = (gp + m)
+    b = (bp + m)
+
+    rgb = np.stack([r, g, b], axis=-1)
+    rgb = np.clip(rgb * 255.0, 0, 255).astype(np.uint8)
+    return rgb
+
+# Main blending pipeline
+
+def blend_colors(image: np.ndarray, tolerance: float = 10.0, min_region_size: int = 50,
+                          max_kmeans_samples: int = 100000, n_jobs: int | None = None) -> np.ndarray:
+    """
+    Parallelized version of blend_colors.
+    n_jobs: number of worker threads (None -> os.cpu_count()).
+    """
+    if not isinstance(image, np.ndarray) or image.dtype != np.uint8 or image.ndim != 3:
+        raise ValueError("Input must be a 3D NumPy array with uint8 dtype (H, W, C)")
+
+    height, width, channels = image.shape
+    assert channels == 3
+
+    img_f = image.astype(np.float32)
+    pixels = img_f.reshape(-1, 3)
+    n_pixels = pixels.shape[0]
+
+    num_clusters = max(1, int(256 / tolerance))
+
+    # Subsample for kmeans
+    rng = np.random.default_rng(seed=12345)
+    if n_pixels > max_kmeans_samples:
+        sample_idx = rng.choice(n_pixels, size=max_kmeans_samples, replace=False)
+    else:
+        sample_idx = np.arange(n_pixels)
+    sample_data = pixels[sample_idx]
+
+    centroids, _ = kmeans2(sample_data, num_clusters, minit='points')
+
+    # Assign every pixel to nearest centroid in chunks (same as original)
+    labels_all = np.empty(n_pixels, dtype=np.int32)
+    chunk = 1_000_000
+    for start in range(0, n_pixels, chunk):
+        end = min(start + chunk, n_pixels)
+        block = pixels[start:end]  # (M,3)
+        a2 = np.sum(block * block, axis=1)[:, None]
+        b2 = np.sum(centroids * centroids, axis=1)[None, :]
+        ab = block.dot(centroids.T)
+        d2 = a2 + b2 - 2 * ab
+        labels_all[start:end] = np.argmin(d2, axis=1)
+
+    label_map = labels_all.reshape(height, width)
+    output_image = image.copy()
+
+    structure = np.ones((3, 3), dtype=np.int8)
+
+    # Worker for a single cluster (runs in thread)
+    def process_cluster(cluster_id: int):
+        cluster_mask = (label_map == cluster_id).astype(np.uint8)
+        if cluster_mask.sum() == 0:
+            return 0  # nothing done
+
+        labeled_array, num_features = label(cluster_mask, structure=structure)
+        if num_features == 0:
+            return 0
+
+        counts = np.bincount(labeled_array.ravel())
+        valid_ids = np.nonzero(counts >= min_region_size)[0]
+        valid_ids = valid_ids[valid_ids != 0]
+        if valid_ids.size == 0:
+            return 0
+
+        idx_list = valid_ids.tolist()
+        means_r = ndi_mean(img_f[..., 0], labels=labeled_array, index=idx_list)
+        means_g = ndi_mean(img_f[..., 1], labels=labeled_array, index=idx_list)
+        means_b = ndi_mean(img_f[..., 2], labels=labeled_array, index=idx_list)
+        region_means = np.stack([means_r, means_g, means_b], axis=-1)  # float 0..255
+
+        # convert region means to HSV and generate new colors per region
+        region_mean_hsv = rgb_to_hsv(region_means[np.newaxis, :, :].reshape(-1, 3))
+        # iterate regions (small loop per region; still OK)
+        for i, region_label in enumerate(idx_list):
+            seed_val = 42 + cluster_id + int(region_label)
+            rng_region = np.random.default_rng(seed_val)
+            shifts = rng_region.uniform(-0.05, 0.05, size=3)
+            hsv = region_mean_hsv[i].copy()
+            hsv += shifts * np.array([10.0, 0.1, 0.1])
+            hsv[0] = np.clip(hsv[0], 0, 360)
+            hsv[1] = np.clip(hsv[1], 0, 1)
+            hsv[2] = np.clip(hsv[2], 0, 1)
+            rgb_new = hsv_to_rgb(hsv[np.newaxis, :])[0]
+
+            mask = (labeled_array == int(region_label))
+            # assign directly into shared output_image; clusters don't overlap so this is safe
+            output_image[mask] = rgb_new
+
+        return 1  # done something
+
+    # Run cluster processing in thread pool
+    if n_jobs is None:
+        n_jobs = os.cpu_count() or 1
+    n_jobs = max(1, int(n_jobs))
+
+    with ThreadPoolExecutor(max_workers=n_jobs) as ex:
+        futures = [ex.submit(process_cluster, cid) for cid in range(num_clusters)]
+        # optional: iterate to ensure completion
+        for _ in as_completed(futures):
+            pass
+
+    # Island absorbtion (parallelize KD-tree queries by chunking queries)
+    changed_mask = np.any(output_image != image, axis=2)
+    if not np.all(changed_mask) and changed_mask.any():
+        changed_coords = np.column_stack(np.nonzero(changed_mask))  # (M,2)
+        changed_colors = output_image[changed_mask]  # (M,3)
+        unchanged_coords = np.column_stack(np.nonzero(~changed_mask))  # (U,2)
+
+        if changed_coords.shape[0] > 0 and unchanged_coords.shape[0] > 0:
+            tree = cKDTree(changed_coords)
+
+            # We'll chunk the unchanged coords and parallel query
+            def query_chunk(start_end):
+                s, e = start_end
+                sub = unchanged_coords[s:e]
+                _, idxs = tree.query(sub, k=1)
+                return (s, e, idxs)
+
+            # prepare ranges
+            U = unchanged_coords.shape[0]
+            qchunk = max(1_000, U // (n_jobs * 4) + 1)
+            ranges = [(i, min(i + qchunk, U)) for i in range(0, U, qchunk)]
+
+            nearest_colors = np.empty((U, 3), dtype=np.uint8)
+            with ThreadPoolExecutor(max_workers=n_jobs) as ex:
+                futures = [ex.submit(query_chunk, r) for r in ranges]
+                for fut in as_completed(futures):
+                    s, e, idxs = fut.result()
+                    nearest_colors[s:e] = changed_colors[idxs]
+
+            # assign back
+            # flatten indexing: map (r,c) to flat index
+            flat_idx = unchanged_coords[:, 0] * width + unchanged_coords[:, 1]
+            out_flat = output_image.reshape(-1, 3)
+            out_flat[flat_idx] = nearest_colors
+
+    return output_image
diff --git a/custom_nodes/ComfyUI_INSTARAW/modules/detection_bypass/utils/clahe.py b/custom_nodes/ComfyUI_INSTARAW/modules/detection_bypass/utils/clahe.py
new file mode 100644
index 0000000000000000000000000000000000000000..d19b2d3649e10fcef84af2f91d9094b6c7e9b13e
--- /dev/null
+++ b/custom_nodes/ComfyUI_INSTARAW/modules/detection_bypass/utils/clahe.py
@@ -0,0 +1,28 @@
+import numpy as np
+from PIL import Image, ImageOps
+
+try:
+    import cv2
+    _HAS_CV2 = True
+except Exception:
+    cv2 = None
+    _HAS_CV2 = False
+
+def clahe_color_correction(img_arr: np.ndarray, clip_limit=2.0, tile_grid_size=(8,8)) -> np.ndarray:
+    if _HAS_CV2:
+        lab = cv2.cvtColor(img_arr, cv2.COLOR_RGB2LAB)
+        l, a, b = cv2.split(lab)
+        clahe = cv2.createCLAHE(clipLimit=clip_limit, tileGridSize=tile_grid_size)
+        l2 = clahe.apply(l)
+        lab2 = cv2.merge((l2, a, b))
+        out = cv2.cvtColor(lab2, cv2.COLOR_LAB2RGB)
+        return out
+    else:
+        pil = Image.fromarray(img_arr)
+        channels = pil.split()
+        new_ch = []
+        for ch in channels:
+            eq = ImageOps.equalize(ch)
+            new_ch.append(eq)
+        merged = Image.merge('RGB', new_ch)
+        return np.array(merged)
\ No newline at end of file
diff --git a/custom_nodes/ComfyUI_INSTARAW/modules/detection_bypass/utils/color_lut.py b/custom_nodes/ComfyUI_INSTARAW/modules/detection_bypass/utils/color_lut.py
new file mode 100644
index 0000000000000000000000000000000000000000..04e146f6e7431540b095b4089433912946cee620
--- /dev/null
+++ b/custom_nodes/ComfyUI_INSTARAW/modules/detection_bypass/utils/color_lut.py
@@ -0,0 +1,167 @@
+# Filename: ComfyUI_INSTARAW/modules/detection_bypass/utils/color_lut.py
+# ---
+
+import numpy as np
+import re
+import os
+from PIL import Image
+
+def apply_1d_lut(img_arr: np.ndarray, lut: np.ndarray, strength: float = 1.0) -> np.ndarray:
+    # ... (This function is correct and remains unchanged)
+    if img_arr.ndim != 3 or img_arr.shape[2] != 3:
+        raise ValueError("apply_1d_lut expects an HxWx3 image array")
+    arr = img_arr.astype(np.float32)
+    lut_arr = np.array(lut, dtype=np.float32)
+    if lut_arr.ndim == 1:
+        lut_arr = np.stack([lut_arr, lut_arr, lut_arr], axis=1)
+    if lut_arr.shape[1] != 3:
+        raise ValueError("1D LUT must have shape (N,) or (N,3)")
+    N = lut_arr.shape[0]
+    src_positions = np.linspace(0, 255, N)
+    out = np.empty_like(arr)
+    for c in range(3):
+        channel = arr[..., c].ravel()
+        mapped = np.interp(channel, src_positions, lut_arr[:, c])
+        out[..., c] = mapped.reshape(arr.shape[0], arr.shape[1])
+    out = np.clip(out, 0, 255).astype(np.uint8)
+    if strength >= 1.0:
+        return out
+    else:
+        blended = ((1.0 - strength) * img_arr.astype(np.float32) + strength * out.astype(np.float32))
+        return np.clip(blended, 0, 255).astype(np.uint8)
+
+def _trilinear_sample_lut(img_float: np.ndarray, lut: np.ndarray) -> np.ndarray:
+    """Corrected trilinear interpolation for a LUT with shape (S, S, S, 3)."""
+    S = lut.shape[0]
+    # Scale image color values to LUT index coordinates [0, S-1]
+    scaled_coords = img_float * (S - 1)
+    
+    # Get the integer part (floor) and fractional part of the coordinates
+    coords_floor = np.floor(scaled_coords).astype(np.int32)
+    coords_frac = scaled_coords - coords_floor
+    
+    # Clip coordinates to be within the valid LUT index range [0, S-1]
+    x0, y0, z0 = np.clip(coords_floor[..., 0], 0, S - 1), np.clip(coords_floor[..., 1], 0, S - 1), np.clip(coords_floor[..., 2], 0, S - 1)
+    x1, y1, z1 = np.clip(x0 + 1, 0, S - 1), np.clip(y0 + 1, 0, S - 1), np.clip(z0 + 1, 0, S - 1)
+
+    # Get the 8 corner values from the LUT
+    c000 = lut[x0, y0, z0]
+    c001 = lut[x0, y0, z1]
+    c010 = lut[x0, y1, z0]
+    c011 = lut[x0, y1, z1]
+    c100 = lut[x1, y0, z0]
+    c101 = lut[x1, y0, z1]
+    c110 = lut[x1, y1, z0]
+    c111 = lut[x1, y1, z1]
+
+    # Expand fractional coordinates for broadcasting
+    xd, yd, zd = coords_frac[..., 0, None], coords_frac[..., 1, None], coords_frac[..., 2, None]
+
+    # Perform trilinear interpolation
+    c00 = c000 * (1 - zd) + c001 * zd
+    c01 = c010 * (1 - zd) + c011 * zd
+    c10 = c100 * (1 - zd) + c101 * zd
+    c11 = c110 * (1 - zd) + c111 * zd
+    
+    c0 = c00 * (1 - yd) + c01 * yd
+    c1 = c10 * (1 - yd) + c11 * yd
+    
+    c = c0 * (1 - xd) + c1 * xd
+    
+    return c
+
+def apply_3d_lut(img_arr: np.ndarray, lut3d: np.ndarray, strength: float = 1.0) -> np.ndarray:
+    # ... (This function is correct and remains unchanged)
+    if img_arr.ndim != 3 or img_arr.shape[2] != 3:
+        raise ValueError("apply_3d_lut expects an HxWx3 image array")
+    img_float = img_arr.astype(np.float32) / 255.0
+    sampled = _trilinear_sample_lut(img_float, lut3d)
+    out = np.clip(sampled * 255.0, 0, 255).astype(np.uint8)
+    if strength >= 1.0:
+        return out
+    else:
+        blended = ((1.0 - strength) * img_arr.astype(np.float32) + strength * out.astype(np.float32))
+        return np.clip(blended, 0, 255).astype(np.uint8)
+
+def apply_lut(img_arr: np.ndarray, lut: np.ndarray, strength: float = 1.0) -> np.ndarray:
+    # ... (This function is correct and remains unchanged)
+    lut = np.array(lut)
+    if lut.ndim == 4 and lut.shape[3] == 3:
+        if lut.dtype != np.float32 and lut.max() > 1.0:
+            lut = lut.astype(np.float32) / 255.0
+        return apply_3d_lut(img_arr, lut, strength=strength)
+    elif lut.ndim in (1, 2):
+        return apply_1d_lut(img_arr, lut, strength=strength)
+    else:
+        raise ValueError("Unsupported LUT shape: {}".format(lut.shape))
+
+def load_cube_lut(path: str) -> np.ndarray:
+    """
+    A robust parser for .cube files that handles common format variations and
+    correctly orders the axes for lookup.
+    """
+    size = None
+    data = []
+    
+    with open(path, 'r', encoding='utf-8', errors='ignore') as f:
+        for line in f:
+            line = line.strip()
+            if not line or line.startswith('#'):
+                continue
+
+            if line.upper().startswith('LUT_3D_SIZE'):
+                try:
+                    size = int(line.split()[-1])
+                except (ValueError, IndexError):
+                    raise ValueError(f"Invalid LUT_3D_SIZE format in {path}: {line}")
+                continue
+            
+            match = re.match(r'^(-?\d+(\.\d+)?(e-?\d+)?)\s+(-?\d+(\.\d+)?(e-?\d+)?)\s+(-?\d+(\.\d+)?(e-?\d+)?)$', line)
+            if match:
+                try:
+                    data.append([float(c) for c in line.split()])
+                except ValueError:
+                    print(f"Warning: Skipping malformed data line in {path}: {line}")
+                    continue
+
+    if size is None:
+        raise ValueError(f"LUT_3D_SIZE not found in .cube file: {path}")
+
+    expected_size = size ** 3
+    if len(data) != expected_size:
+        raise ValueError(f"Cube LUT data length does not match size^3 (got {len(data)}, expected {expected_size})")
+
+    # --- THE DEFINITIVE FIX ---
+    # Reshape and then transpose the axes to the correct [R, G, B] order for lookup.
+    lut = np.array(data, dtype=np.float32).reshape((size, size, size, 3))
+    lut = np.transpose(lut, (2, 1, 0, 3))
+    # --- END FIX ---
+    
+    if lut.max() > 1.0 + 1e-6:
+        print(f"Warning: LUT values seem to be in [0, 255] range. Normalizing to [0, 1].")
+        lut = lut / 255.0
+        
+    return np.clip(lut, 0.0, 1.0)
+
+def load_lut(path: str) -> np.ndarray:
+    # ... (This function is correct and remains unchanged)
+    ext = os.path.splitext(path)[1].lower()
+    if ext == '.npy':
+        return np.load(path)
+    elif ext == '.cube':
+        return load_cube_lut(path)
+    else:
+        try:
+            with Image.open(path) as im:
+                im = im.convert('RGB')
+                arr = np.array(im)
+            h, w = arr.shape[:2]
+            if (w == 256 and h == 1) or (h == 256 and w == 1):
+                lut = arr[0, :, :] if h == 1 else arr[:, 0, :]
+                return lut.astype(np.float32)
+            flat = arr.reshape(-1, 3).astype(np.float32)
+            if flat.shape[0] <= 4096:
+                return flat
+            raise ValueError("Image LUT not a recognized size")
+        except Exception as e:
+            raise ValueError(f"Unsupported LUT file or parse error for {path}: {e}")
\ No newline at end of file
diff --git a/custom_nodes/ComfyUI_INSTARAW/modules/detection_bypass/utils/direct_spectral_matching copy 2.py b/custom_nodes/ComfyUI_INSTARAW/modules/detection_bypass/utils/direct_spectral_matching copy 2.py
new file mode 100644
index 0000000000000000000000000000000000000000..1df1132e11474389f0b06188add4c235f9cae0ec
--- /dev/null
+++ b/custom_nodes/ComfyUI_INSTARAW/modules/detection_bypass/utils/direct_spectral_matching copy 2.py	
@@ -0,0 +1,102 @@
+# Filename: ComfyUI_INSTARAW/modules/detection_bypass/utils/direct_spectral_matching.py
+# ---
+
+import numpy as np
+from scipy.ndimage import gaussian_filter1d
+from PIL import Image
+
+def radial_profile(mag: np.ndarray, bins: int):
+    """Computes a robust radial profile of a 2D array."""
+    h, w = mag.shape
+    cy, cx = h // 2, w // 2
+    y, x = np.indices((h, w))
+    r = np.sqrt((x - cx)**2 + (y - cy)**2).astype(np.int32)
+    max_r = min(r.max(), bins - 1)
+    
+    valid_mask = r.ravel() <= max_r
+    tbin = np.bincount(r.ravel()[valid_mask], mag.ravel()[valid_mask], minlength=bins)
+    nr = np.bincount(r.ravel()[valid_mask], minlength=bins)
+    
+    radial_mean = np.zeros(bins, dtype=np.float64)
+    valid_bins = nr > 0
+    radial_mean[valid_bins] = tbin[valid_bins] / nr[valid_bins]
+    
+    last_valid_val = 0
+    for i in range(bins):
+        if radial_mean[i] > 0:
+            last_valid_val = radial_mean[i]
+        elif last_valid_val > 0:
+            radial_mean[i] = last_valid_val
+            
+    return radial_mean
+
+def direct_spectral_match(
+    img_arr: np.ndarray,
+    target_spectrum_luminance: np.ndarray,
+    strength: float = 1.0,
+) -> np.ndarray:
+    """
+    Performs a color-safe spectral match.
+    
+    This algorithm works by:
+    1. Calculating a spectral correction map based on the luminance of the source
+       and target spectra.
+    2. Applying this SAME correction map to the magnitude of each R, G, and B
+       channel individually, while preserving each channel's original phase.
+    
+    This preserves the inter-channel relationships and prevents color shifts.
+    """
+    img_float = img_arr.astype(np.float32)
+    h, w, c = img_float.shape
+    
+    if c != 3:
+        raise ValueError("Input image must be RGB.")
+        
+    nbins = len(target_spectrum_luminance)
+
+    # --- 1. Calculate Luminance-based Correction Map ---
+    source_gray = 0.299 * img_float[:,:,0] + 0.587 * img_float[:,:,1] + 0.114 * img_float[:,:,2]
+    
+    fft_gray = np.fft.fftshift(np.fft.fft2(source_gray))
+    mag_gray = np.abs(fft_gray)
+    
+    source_profile = radial_profile(np.log1p(mag_gray), bins=nbins)
+    source_profile_smooth = gaussian_filter1d(source_profile, sigma=2)
+
+    eps = 1e-8
+    log_multiplier_1d = (target_spectrum_luminance + eps) / (source_profile_smooth + eps)
+    
+    y_coords, x_coords = np.indices((h, w))
+    center = np.array([(h - 1) / 2.0, (w - 1) / 2.0])
+    r = np.sqrt((x_coords - center[1])**2 + (y_coords - center[0])**2)
+    rmax = np.sqrt(center[1]**2 + center[0]**2)
+    if rmax > 0: r /= rmax
+    
+    bin_centers = np.linspace(0.0, 1.0, nbins)
+    log_filter_2d = np.interp(r, bin_centers, log_multiplier_1d)
+    
+    # --- 2. Apply Correction to Each Channel ---
+    output_img = np.zeros_like(img_float)
+    for i in range(3): # R, G, B
+        channel = img_float[:, :, i]
+        
+        fft_channel = np.fft.fftshift(np.fft.fft2(channel))
+        mag_channel = np.abs(fft_channel)
+        phase_channel = np.angle(fft_channel)
+
+        # Apply the luminance-derived filter in the log domain
+        filtered_log_mag = np.log1p(mag_channel) * log_filter_2d
+        
+        # Convert back from log domain
+        filtered_mag = np.expm1(filtered_log_mag)
+        
+        # Reconstruct with original phase
+        new_fft_channel = filtered_mag * np.exp(1j * phase_channel)
+        
+        # Inverse FFT to get the modified channel
+        img_back = np.real(np.fft.ifft2(np.fft.ifftshift(new_fft_channel)))
+        
+        # Blend with original channel based on strength
+        output_img[:, :, i] = channel * (1 - strength) + img_back * strength
+
+    return np.clip(output_img, 0, 255).astype(np.uint8)
\ No newline at end of file
diff --git a/custom_nodes/ComfyUI_INSTARAW/modules/detection_bypass/utils/direct_spectral_matching copy.py b/custom_nodes/ComfyUI_INSTARAW/modules/detection_bypass/utils/direct_spectral_matching copy.py
new file mode 100644
index 0000000000000000000000000000000000000000..0f384999e86ed74033b49f5276a3f13991585bfe
--- /dev/null
+++ b/custom_nodes/ComfyUI_INSTARAW/modules/detection_bypass/utils/direct_spectral_matching copy.py	
@@ -0,0 +1,71 @@
+# Filename: ComfyUI_INSTARAW/modules/detection_bypass/utils/direct_spectral_matching.py
+# ---
+
+import numpy as np
+from scipy.ndimage import gaussian_filter1d
+
+def radial_profile(mag: np.ndarray, bins: int):
+    h, w = mag.shape
+    cy, cx = h // 2, w // 2
+    y, x = np.indices((h, w))
+    r = np.sqrt((x - cx)**2 + (y - cy)**2).astype(np.int32)
+    tbin = np.bincount(r.ravel(), mag.ravel())
+    nr = np.bincount(r.ravel())
+    radial_mean = tbin / (nr + 1e-9)
+    if len(radial_mean) < bins:
+        radial_mean = np.pad(radial_mean, (0, bins - len(radial_mean)), 'edge')
+    return radial_mean[:bins]
+
+def direct_spectral_match(
+    img_arr: np.ndarray,
+    target_spectra: dict, # Expects a dict with 'r', 'g', 'b' keys
+    strength: float = 1.0,
+) -> np.ndarray:
+    img_float = img_arr.astype(np.float32)
+    h, w, c = img_float.shape
+    
+    if c != 3:
+        raise ValueError("Input image must be RGB.")
+        
+    output_img = np.zeros_like(img_float)
+
+    for i, key in enumerate(['r', 'g', 'b']):
+        channel = img_float[:, :, i]
+        target_spectrum = target_spectra[key]
+        nbins = len(target_spectrum)
+        
+        # --- Analysis per channel ---
+        fft_channel = np.fft.fftshift(np.fft.fft2(channel))
+        mag_channel = np.abs(fft_channel)
+        phase_channel = np.angle(fft_channel) # Preserve original phase
+
+        source_profile = radial_profile(np.log1p(mag_channel), bins=nbins)
+        source_profile_smooth = gaussian_filter1d(source_profile, sigma=2)
+
+        # --- Transformation per channel ---
+        eps = 1e-8
+        # We match log spectrums now, which is more stable
+        log_multiplier_1d = (target_spectrum + eps) / (source_profile_smooth + eps)
+        
+        y, x = np.indices((h, w))
+        center = np.array([h // 2, w // 2])
+        r = np.sqrt((x - center[1])**2 + (y - center[0])**2)
+        rmax = np.sqrt(center[1]**2 + center[0]**2)
+        if rmax > 0: r /= rmax
+        
+        bin_centers = np.linspace(0.0, 1.0, nbins)
+        log_filter_2d = np.interp(r, bin_centers, log_multiplier_1d)
+        
+        # Apply the filter in the log domain
+        filtered_log_mag = np.log1p(mag_channel) * log_filter_2d
+        
+        # Convert back from log and blend
+        filtered_mag = np.expm1(filtered_log_mag)
+        blended_mag = mag_channel * (1 - strength) + filtered_mag * strength
+        
+        # --- Reconstruction per channel with ORIGINAL phase ---
+        new_fft_channel = blended_mag * np.exp(1j * phase_channel)
+        img_back = np.real(np.fft.ifft2(np.fft.ifftshift(new_fft_channel)))
+        output_img[:, :, i] = img_back
+
+    return np.clip(output_img, 0, 255).astype(np.uint8)
\ No newline at end of file
diff --git a/custom_nodes/ComfyUI_INSTARAW/modules/detection_bypass/utils/direct_spectral_matching.py b/custom_nodes/ComfyUI_INSTARAW/modules/detection_bypass/utils/direct_spectral_matching.py
new file mode 100644
index 0000000000000000000000000000000000000000..81da424d1c9dc54f870e43ada5d2fe1ba1dff05d
--- /dev/null
+++ b/custom_nodes/ComfyUI_INSTARAW/modules/detection_bypass/utils/direct_spectral_matching.py
@@ -0,0 +1,140 @@
+# Filename: ComfyUI_INSTARAW/modules/detection_bypass/utils/direct_spectral_matching.py
+# ---
+
+import numpy as np
+from scipy.ndimage import gaussian_filter1d
+
+# The radial_profile function remains the same.
+def radial_profile(mag: np.ndarray, bins: int):
+    """Computes a robust radial profile of a 2D array."""
+    h, w = mag.shape
+    cy, cx = h // 2, w // 2
+    y, x = np.indices((h, w))
+    r = np.sqrt((x - cx)**2 + (y - cy)**2).astype(np.int32)
+    max_r = min(r.max(), bins - 1)
+    
+    valid_mask = r.ravel() <= max_r
+    tbin = np.bincount(r.ravel()[valid_mask], mag.ravel()[valid_mask], minlength=bins)
+    nr = np.bincount(r.ravel()[valid_mask], minlength=bins)
+    
+    radial_mean = np.zeros(bins, dtype=np.float64)
+    valid_bins = nr > 0
+    radial_mean[valid_bins] = tbin[valid_bins] / nr[valid_bins]
+    
+    last_valid_val = 0
+    for i in range(bins):
+        if radial_mean[i] > 0:
+            last_valid_val = radial_mean[i]
+        elif last_valid_val > 0:
+            radial_mean[i] = last_valid_val
+            
+    return radial_mean
+
+def _direct_spectral_match_per_channel(img_arr: np.ndarray, target_spectra: dict, strength: float) -> np.ndarray:
+    """Internal function for the original per-channel matching algorithm."""
+    img_float = img_arr.astype(np.float32)
+    h, w, _ = img_float.shape
+    output_img = np.zeros_like(img_float)
+
+    for i, key in enumerate(['r', 'g', 'b']):
+        channel = img_float[:, :, i]
+        target_spectrum = target_spectra[key]
+        nbins = len(target_spectrum)
+        
+        fft_channel = np.fft.fftshift(np.fft.fft2(channel))
+        mag_channel = np.abs(fft_channel)
+        phase_channel = np.angle(fft_channel)
+
+        source_profile = radial_profile(np.log1p(mag_channel), bins=nbins)
+        source_profile_smooth = gaussian_filter1d(source_profile, sigma=2)
+
+        eps = 1e-8
+        log_multiplier_1d = (target_spectrum + eps) / (source_profile_smooth + eps)
+        
+        y, x = np.indices((h, w))
+        center = np.array([(h-1)/2.0, (w-1)/2.0])
+        r = np.sqrt((x - center[1])**2 + (y - center[0])**2)
+        rmax = np.sqrt(center[1]**2 + center[0]**2)
+        if rmax > 0: r /= rmax
+        
+        bin_centers = np.linspace(0.0, 1.0, nbins)
+        log_filter_2d = np.interp(r, bin_centers, log_multiplier_1d)
+        
+        filtered_log_mag = np.log1p(mag_channel) * log_filter_2d
+        filtered_mag = np.expm1(filtered_log_mag)
+        
+        blended_mag = mag_channel * (1 - strength) + filtered_mag * strength
+        
+        new_fft_channel = blended_mag * np.exp(1j * phase_channel)
+        img_back = np.real(np.fft.ifft2(np.fft.ifftshift(new_fft_channel)))
+        output_img[:, :, i] = img_back
+
+    return np.clip(output_img, 0, 255).astype(np.uint8)
+
+def _direct_spectral_match_color_safe(img_arr: np.ndarray, target_spectrum_luminance: np.ndarray, strength: float) -> np.ndarray:
+    """Internal function for the new, color-safe algorithm."""
+    img_float = img_arr.astype(np.float32)
+    h, w, _ = img_float.shape
+    nbins = len(target_spectrum_luminance)
+
+    source_gray = 0.299 * img_float[:,:,0] + 0.587 * img_float[:,:,1] + 0.114 * img_float[:,:,2]
+    fft_gray = np.fft.fftshift(np.fft.fft2(source_gray))
+    mag_gray = np.abs(fft_gray)
+    
+    source_profile = radial_profile(np.log1p(mag_gray), bins=nbins)
+    source_profile_smooth = gaussian_filter1d(source_profile, sigma=2)
+
+    eps = 1e-8
+    log_multiplier_1d = (target_spectrum_luminance + eps) / (source_profile_smooth + eps)
+    
+    y_coords, x_coords = np.indices((h, w))
+    center = np.array([(h - 1) / 2.0, (w - 1) / 2.0])
+    r = np.sqrt((x_coords - center[1])**2 + (y_coords - center[0])**2)
+    rmax = np.sqrt(center[1]**2 + center[0]**2)
+    if rmax > 0: r /= rmax
+    
+    bin_centers = np.linspace(0.0, 1.0, nbins)
+    log_filter_2d = np.interp(r, bin_centers, log_multiplier_1d)
+    
+    output_img = np.zeros_like(img_float)
+    for i in range(3):
+        channel = img_float[:, :, i]
+        fft_channel = np.fft.fftshift(np.fft.fft2(channel))
+        mag_channel = np.abs(fft_channel)
+        phase_channel = np.angle(fft_channel)
+
+        filtered_log_mag = np.log1p(mag_channel) * log_filter_2d
+        filtered_mag = np.expm1(filtered_log_mag)
+        
+        new_fft_channel = filtered_mag * np.exp(1j * phase_channel)
+        img_back = np.real(np.fft.ifft2(np.fft.ifftshift(new_fft_channel)))
+        
+        output_img[:, :, i] = channel * (1 - strength) + img_back * strength
+
+    return np.clip(output_img, 0, 255).astype(np.uint8)
+
+def direct_spectral_match(img_arr, target_spectra, strength, mode="Per-Channel (Legacy)"):
+    """
+    Smart dispatcher for spectral matching.
+    - Defaults to "Per-Channel (Legacy)" to ensure backward compatibility with
+      the INSTARAW_SpectralEngine node, which does not pass a 'mode'.
+    - Can be explicitly called with "Color-Safe (Luminance)" by the new
+      INSTARAW_FFT_Match node.
+    """
+    if mode == "Color-Safe (Luminance)":
+        # The color-safe algorithm needs a single luminance spectrum.
+        # If it receives a dict (from a legacy call or profile), average it.
+        if isinstance(target_spectra, dict):
+            lum_spectrum = (target_spectra['r'] + target_spectra['g'] + target_spectra['b']) / 3.0
+        else:
+            lum_spectrum = target_spectra
+        return _direct_spectral_match_color_safe(img_arr, lum_spectrum, strength)
+    
+    elif mode == "Per-Channel (Legacy)":
+        # The per-channel algorithm requires a dictionary of spectra.
+        if not isinstance(target_spectra, dict):
+             raise TypeError("Per-Channel mode requires a dictionary of target spectra for 'r', 'g', and 'b'.")
+        return _direct_spectral_match_per_channel(img_arr, target_spectra, strength)
+        
+    else:
+        raise ValueError(f"Unknown spectral match mode: {mode}")
\ No newline at end of file
diff --git a/custom_nodes/ComfyUI_INSTARAW/modules/detection_bypass/utils/exif.py b/custom_nodes/ComfyUI_INSTARAW/modules/detection_bypass/utils/exif.py
new file mode 100644
index 0000000000000000000000000000000000000000..c706b28188b5c940090d3a468b6f55718ab484a0
--- /dev/null
+++ b/custom_nodes/ComfyUI_INSTARAW/modules/detection_bypass/utils/exif.py
@@ -0,0 +1,6 @@
+from PIL import Image
+
+def remove_exif_pil(img: Image.Image) -> Image.Image:
+    data = img.tobytes()
+    new = Image.frombytes(img.mode, img.size, data)
+    return new
\ No newline at end of file
diff --git a/custom_nodes/ComfyUI_INSTARAW/modules/detection_bypass/utils/fourier_pipeline.py b/custom_nodes/ComfyUI_INSTARAW/modules/detection_bypass/utils/fourier_pipeline.py
new file mode 100644
index 0000000000000000000000000000000000000000..d5fba2bd8de8d56234cc533f983ac38c964616af
--- /dev/null
+++ b/custom_nodes/ComfyUI_INSTARAW/modules/detection_bypass/utils/fourier_pipeline.py
@@ -0,0 +1,152 @@
+import numpy as np
+from scipy.ndimage import gaussian_filter1d
+from PIL import Image
+
+def radial_profile(mag: np.ndarray, center=None, nbins=None):
+    h, w = mag.shape
+    if center is None:
+        cy, cx = h // 2, w // 2
+    else:
+        cy, cx = center
+
+    if nbins is None:
+        nbins = int(max(h, w) / 2)
+    nbins = max(1, int(nbins))
+
+    y = np.arange(h) - cy
+    x = np.arange(w) - cx
+    X, Y = np.meshgrid(x, y)
+    R = np.sqrt(X * X + Y * Y)
+
+    Rmax = R.max()
+    if Rmax <= 0:
+        Rnorm = R
+    else:
+        Rnorm = R / (Rmax + 1e-12)
+        Rnorm = np.minimum(Rnorm, 1.0 - 1e-12)
+
+    bin_edges = np.linspace(0.0, 1.0, nbins + 1)
+    bin_idx = np.digitize(Rnorm.ravel(), bin_edges) - 1
+    bin_idx = np.clip(bin_idx, 0, nbins - 1)
+
+    sums = np.bincount(bin_idx, weights=mag.ravel(), minlength=nbins)
+    counts = np.bincount(bin_idx, minlength=nbins)
+
+    radial_mean = np.zeros(nbins, dtype=np.float64)
+    nonzero = counts > 0
+    radial_mean[nonzero] = sums[nonzero] / counts[nonzero]
+
+    bin_centers = 0.5 * (bin_edges[:-1] + bin_edges[1:])
+    return bin_centers, radial_mean
+
+def fourier_match_spectrum(img_arr: np.ndarray,
+                           ref_img_arr: np.ndarray = None,
+                           mode='auto',
+                           alpha=1.0,
+                           cutoff=0.25,
+                           strength=0.9,
+                           randomness=0.05,
+                           phase_perturb=0.08,
+                           radial_smooth=5,
+                           seed=None):
+    if seed is not None:
+        rng = np.random.default_rng(seed)
+    else:
+        rng = np.random.default_rng()
+
+    h, w = img_arr.shape[:2]
+    cy, cx = h // 2, w // 2
+    nbins = max(8, int(max(h, w) / 2))
+
+    if mode == 'auto':
+        mode = 'ref' if ref_img_arr is not None else 'model'
+
+    bin_centers_src = np.linspace(0.0, 1.0, nbins)
+
+    model_radial = None
+    if mode == 'model':
+        eps = 1e-8
+        model_radial = (1.0 / (bin_centers_src + eps)) ** (alpha / 2.0)
+        lf = max(1, nbins // 8)
+        model_radial = model_radial / (np.median(model_radial[:lf]) + 1e-12)
+        model_radial = gaussian_filter1d(model_radial, sigma=max(1, radial_smooth))
+
+    ref_radial = None
+    ref_bin_centers = None
+    if mode == 'ref' and ref_img_arr is not None:
+        if ref_img_arr.shape[0] != h or ref_img_arr.shape[1] != w:
+            ref_img = Image.fromarray(ref_img_arr).resize((w, h), resample=Image.BICUBIC)
+            ref_img_arr = np.array(ref_img)
+        ref_gray = np.mean(ref_img_arr.astype(np.float32), axis=2) if ref_img_arr.ndim == 3 else ref_img_arr.astype(np.float32)
+        Fref = np.fft.fftshift(np.fft.fft2(ref_gray))
+        Mref = np.abs(Fref)
+        ref_bin_centers, ref_radial = radial_profile(Mref, center=(h // 2, w // 2), nbins=nbins)
+        ref_radial = gaussian_filter1d(ref_radial, sigma=max(1, radial_smooth))
+
+    out = np.zeros_like(img_arr, dtype=np.float32)
+
+    y = np.linspace(-1, 1, h, endpoint=False)[:, None]
+    x = np.linspace(-1, 1, w, endpoint=False)[None, :]
+    r = np.sqrt(x * x + y * y)
+    r = np.clip(r, 0.0, 1.0 - 1e-6)
+
+    for c in range(img_arr.shape[2]):
+        channel = img_arr[:, :, c].astype(np.float32)
+        F = np.fft.fft2(channel)
+        Fshift = np.fft.fftshift(F)
+        mag = np.abs(Fshift)
+        phase = np.angle(Fshift)
+
+        bin_centers_src_calc, src_radial = radial_profile(mag, center=(h // 2, w // 2), nbins=nbins)
+        src_radial = gaussian_filter1d(src_radial, sigma=max(1, radial_smooth))
+        bin_centers_src = bin_centers_src_calc
+
+        if mode == 'ref' and ref_radial is not None:
+            ref_interp = np.interp(bin_centers_src, ref_bin_centers, ref_radial)
+            eps = 1e-8
+            ratio = (ref_interp + eps) / (src_radial + eps)
+            desired_radial = src_radial * ratio
+        elif mode == 'model' and model_radial is not None:
+            lf = max(1, nbins // 8)
+            scale = (np.median(src_radial[:lf]) + 1e-12) / (np.median(model_radial[:lf]) + 1e-12)
+            desired_radial = model_radial * scale
+        else:
+            desired_radial = src_radial.copy()
+
+        eps = 1e-8
+        multiplier_1d = (desired_radial + eps) / (src_radial + eps)
+        multiplier_1d = np.clip(multiplier_1d, 0.2, 5.0)
+        mult_2d = np.interp(r.ravel(), bin_centers_src, multiplier_1d).reshape(h, w)
+
+        edge = 0.05 + 0.02 * (1.0 - cutoff) if 'cutoff' in globals() else 0.05
+        edge = max(edge, 1e-6)
+        weight = np.where(r <= 0.25, 1.0,
+                         np.where(r <= 0.25 + edge,
+                                  0.5 * (1 + np.cos(np.pi * (r - 0.25) / edge)),
+                                  0.0))
+
+        final_multiplier = 1.0 + (mult_2d - 1.0) * (weight * strength)
+
+        if randomness and randomness > 0.0:
+            noise = rng.normal(loc=1.0, scale=randomness, size=final_multiplier.shape)
+            final_multiplier *= (1.0 + (noise - 1.0) * weight)
+
+        mag2 = mag * final_multiplier
+
+        if phase_perturb and phase_perturb > 0.0:
+            phase_sigma = phase_perturb * np.clip((r - 0.25) / (1.0 - 0.25 + 1e-6), 0.0, 1.0)
+            phase_noise = rng.standard_normal(size=phase_sigma.shape) * phase_sigma
+            phase2 = phase + phase_noise
+        else:
+            phase2 = phase
+
+        Fshift2 = mag2 * np.exp(1j * phase2)
+        F_ishift = np.fft.ifftshift(Fshift2)
+        img_back = np.fft.ifft2(F_ishift)
+        img_back = np.real(img_back)
+
+        blended = (1.0 - strength) * channel + strength * img_back
+        out[:, :, c] = blended
+
+    out = np.clip(out, 0, 255).astype(np.uint8)
+    return out
\ No newline at end of file
diff --git a/custom_nodes/ComfyUI_INSTARAW/modules/detection_bypass/utils/fourier_pipeline_new_algo.py b/custom_nodes/ComfyUI_INSTARAW/modules/detection_bypass/utils/fourier_pipeline_new_algo.py
new file mode 100644
index 0000000000000000000000000000000000000000..806e681d76b89b199e8c7efc896c6f26297e6e46
--- /dev/null
+++ b/custom_nodes/ComfyUI_INSTARAW/modules/detection_bypass/utils/fourier_pipeline_new_algo.py
@@ -0,0 +1,195 @@
+import numpy as np
+from scipy.ndimage import gaussian_filter1d
+from PIL import Image
+
+def radial_profile(mag: np.ndarray, center=None, nbins=None):
+    h, w = mag.shape
+    if center is None:
+        cy, cx = h // 2, w // 2
+    else:
+        cy, cx = center
+
+    if nbins is None:
+        nbins = int(max(h, w) / 2)
+    nbins = max(1, int(nbins))
+
+    y = np.arange(h) - cy
+    x = np.arange(w) - cx
+    X, Y = np.meshgrid(x, y)
+    R = np.sqrt(X * X + Y * Y)
+
+    Rmax = R.max()
+    if Rmax <= 0:
+        Rnorm = R
+    else:
+        Rnorm = R / (Rmax + 1e-12)
+        Rnorm = np.minimum(Rnorm, 1.0 - 1e-12)
+
+    bin_edges = np.linspace(0.0, 1.0, nbins + 1)
+    bin_idx = np.digitize(Rnorm.ravel(), bin_edges) - 1
+    bin_idx = np.clip(bin_idx, 0, nbins - 1)
+
+    sums = np.bincount(bin_idx, weights=mag.ravel(), minlength=nbins)
+    counts = np.bincount(bin_idx, minlength=nbins)
+
+    radial_mean = np.zeros(nbins, dtype=np.float64)
+    nonzero = counts > 0
+    radial_mean[nonzero] = sums[nonzero] / counts[nonzero]
+
+    bin_centers = 0.5 * (bin_edges[:-1] + bin_edges[1:])
+    return bin_centers, radial_mean
+
+def fourier_match_spectrum(img_arr: np.ndarray,
+                           ref_img_arr: np.ndarray = None,
+                           mode='auto',
+                           alpha=1.0,
+                           cutoff=0.25,
+                           strength=0.9,
+                           randomness=0.05,
+                           phase_perturb=0.08,
+                           radial_smooth=5,
+                           seed=None):
+    if seed is not None:
+        rng = np.random.default_rng(seed)
+    else:
+        rng = np.random.default_rng()
+
+    # Ensure image well defined
+    if img_arr.ndim == 2:
+        h, w = img_arr.shape
+        nch = 1
+    elif img_arr.ndim == 3:
+        h, w, nch = img_arr.shape
+    else:
+        raise ValueError("img_arr must be 2D or 3D")
+
+    nbins = max(8, int(max(h, w) / 2))
+
+    # Determine mode if auto: use 'ref' if reference image is provided, else 'model'
+    if mode == 'auto':
+        mode = 'ref' if ref_img_arr is not None else 'model'
+
+    # Create a coordinate grid for building a 2D radial map
+    y = np.linspace(-1, 1, h, endpoint=False)[:, None]
+    x = np.linspace(-1, 1, w, endpoint=False)[None, :]
+    r = np.sqrt(x * x + y * y)
+    r = np.clip(r, 0.0, 1.0 - 1e-6)
+
+    # Compute luminance (or gray) from img_arr once.
+    if nch == 1:
+        src_gray = img_arr.astype(np.float32)
+    else:
+        # Using simple average; optionally use luma weights (0.2126,0.7152,0.0722)
+        src_gray = np.mean(img_arr.astype(np.float32), axis=2)
+
+    # FFT of the source luminance & compute radial profile
+    Fsrc = np.fft.fftshift(np.fft.fft2(src_gray))
+    Msrc = np.abs(Fsrc)
+    bin_centers_src, src_radial = radial_profile(Msrc, center=(h//2, w//2), nbins=nbins)
+    src_radial = gaussian_filter1d(src_radial, sigma=max(1, radial_smooth))
+
+    model_radial = None
+    if mode == 'model':
+        eps = 1e-8
+        model_radial = (1.0 / (bin_centers_src + eps)) ** (alpha / 2.0)
+        lf = max(1, nbins // 8)
+        model_radial = model_radial / (np.median(model_radial[:lf]) + 1e-12)
+        model_radial = gaussian_filter1d(model_radial, sigma=max(1, radial_smooth))
+
+    ref_radial = None
+    ref_bin_centers = None
+    if mode == 'ref' and ref_img_arr is not None:
+        # Resize ref image if needed
+        if ref_img_arr.shape[0] != h or ref_img_arr.shape[1] != w:
+            ref_img = Image.fromarray(ref_img_arr).resize((w, h), resample=Image.BICUBIC)
+            ref_img_arr = np.array(ref_img)
+        # Convert ref image to grayscale
+        if ref_img_arr.ndim == 3:
+            ref_gray = np.mean(ref_img_arr.astype(np.float32), axis=2)
+        else:
+            ref_gray = ref_img_arr.astype(np.float32)
+        Fref = np.fft.fftshift(np.fft.fft2(ref_gray))
+        Mref = np.abs(Fref)
+        ref_bin_centers, ref_radial = radial_profile(Mref, center=(h//2, w//2), nbins=nbins)
+        ref_radial = gaussian_filter1d(ref_radial, sigma=max(1, radial_smooth))
+
+    # Compute desired radial profile based on mode
+    eps = 1e-8
+    if mode == 'ref' and ref_radial is not None:
+        ref_interp = np.interp(bin_centers_src, ref_bin_centers, ref_radial)
+        lf = max(1, nbins // 8)
+        scale = (np.median(src_radial[:lf]) + eps) / (np.median(ref_interp[:lf]) + eps)
+        ref_interp *= scale
+        desired_radial = ref_interp.copy()
+    elif mode == 'model' and model_radial is not None:
+        lf = max(1, nbins // 8)
+        scale = (np.median(src_radial[:lf]) + eps) / (np.median(model_radial[:lf]) + eps)
+        desired_radial = model_radial * scale
+    else:
+        desired_radial = src_radial.copy()
+
+    # Compute 1D multiplier and clip
+    eps = 1e-8
+    # adjust clip range and re-introduce strength into multiplier
+    multiplier_1d = (desired_radial + eps) / (src_radial + eps)
+    multiplier_1d = np.clip(multiplier_1d, 0.05, 10.0)   # wider range -> stronger effect
+
+    # Build the 2D multiplier map (weight remains computed as before)
+    edge = 0.05 + 0.02 * (1.0 - cutoff)
+    edge = max(edge, 1e-6)
+    weight = np.where(r <= cutoff, 1.0,
+                      np.where(r <= cutoff + edge,
+                               0.5 * (1 + np.cos(np.pi * (r - cutoff) / edge)),
+                               0.0))
+    mult_2d = np.interp(r.ravel(), bin_centers_src, multiplier_1d).reshape(h, w)
+    # include strength in multiplier application (stronger spectral change)
+    final_multiplier = 1.0 + (mult_2d - 1.0) * (weight * strength)
+
+    # optional randomness (kept weighted)
+    if randomness and randomness > 0.0:
+        noise = rng.normal(loc=1.0, scale=randomness, size=final_multiplier.shape)
+        final_multiplier *= (1.0 + (noise - 1.0) * weight)
+
+    # Prepare output buffer.
+    if nch == 1:
+        out = np.zeros((h, w), dtype=np.uint8)
+    else:
+        out = np.zeros((h, w, nch), dtype=np.uint8)
+
+    # Process each channel (for grayscale, loop once)
+    for c in range(nch):
+        if nch == 1:
+            channel = img_arr.astype(np.float32)
+        else:
+            channel = img_arr[:, :, c].astype(np.float32)
+    
+        F = np.fft.fft2(channel)
+        Fshift = np.fft.fftshift(F)
+        mag = np.abs(Fshift)
+        phase = np.angle(Fshift)
+
+        # Apply final multiplier computed from luminance.
+        mag2 = mag * final_multiplier
+
+        # Apply phase perturbation using cutoff instead of hard-coded value.
+        if phase_perturb and phase_perturb > 0.0:
+            phase_sigma = phase_perturb * np.clip((r - cutoff) / (1.0 - cutoff + 1e-6), 0.0, 1.0)
+            phase_noise = rng.standard_normal(size=phase_sigma.shape) * phase_sigma
+            phase2 = phase + phase_noise
+        else:
+            phase2 = phase
+
+        Fshift2 = mag2 * np.exp(1j * phase2)
+        F_ishift = np.fft.ifftshift(Fshift2)
+        img_back = np.fft.ifft2(F_ishift)
+        img_back = np.real(img_back)
+
+        # Blend lightly (so you still can dial strength)
+        blended = (1.0 - min(0.5, 1.0 - strength)) * channel + min(1.0, strength + 0.2) * img_back
+
+        if nch == 1:
+            out[:, :] = np.clip(blended, 0, 255).astype(np.uint8)
+        else:
+            out[:, :, c] = np.clip(blended, 0, 255).astype(np.uint8)
+
+    return out
\ No newline at end of file
diff --git a/custom_nodes/ComfyUI_INSTARAW/modules/detection_bypass/utils/gaussian_noise.py b/custom_nodes/ComfyUI_INSTARAW/modules/detection_bypass/utils/gaussian_noise.py
new file mode 100644
index 0000000000000000000000000000000000000000..1c1771023972ed2977f1cce4c4e661b880011c72
--- /dev/null
+++ b/custom_nodes/ComfyUI_INSTARAW/modules/detection_bypass/utils/gaussian_noise.py
@@ -0,0 +1,10 @@
+import numpy as np
+
+def add_gaussian_noise(img_arr: np.ndarray, std_frac=0.02, seed=None) -> np.ndarray:
+    if seed is not None:
+        np.random.seed(seed)
+    std = std_frac * 255.0
+    noise = np.random.normal(loc=0.0, scale=std, size=img_arr.shape)
+    out = img_arr.astype(np.float32) + noise
+    out = np.clip(out, 0, 255).astype(np.uint8)
+    return out
\ No newline at end of file
diff --git a/custom_nodes/ComfyUI_INSTARAW/modules/detection_bypass/utils/glcm_normalization.py b/custom_nodes/ComfyUI_INSTARAW/modules/detection_bypass/utils/glcm_normalization.py
new file mode 100644
index 0000000000000000000000000000000000000000..bf5960292890bc05af260f6a8c3fa1044b88291a
--- /dev/null
+++ b/custom_nodes/ComfyUI_INSTARAW/modules/detection_bypass/utils/glcm_normalization.py
@@ -0,0 +1,77 @@
+# Filename: ComfyUI_INSTARAW/modules/detection_bypass/utils/glcm_normalization.py
+import numpy as np
+from skimage.feature import graycomatrix, graycoprops
+from scipy.ndimage import gaussian_filter
+from PIL import Image
+import os
+
+def glcm_normalize(img_arr: np.ndarray,
+                   ref_img_arr: np.ndarray = None,
+                   profile_path: str = None, # NEW: Added profile_path
+                   distances: list = [1],
+                   angles: list = [0, np.pi/4, np.pi/2, 3*np.pi/4],
+                   levels: int = 256,
+                   strength: float = 0.9,
+                   seed: int = None,
+                   eps: float = 1e-8):
+    
+    if seed is not None: rng = np.random.default_rng(seed)
+    else: rng = np.random.default_rng()
+
+    img = np.asarray(img_arr)
+    h, w, channels = img.shape
+    
+    img_gray = np.mean(img.astype(np.float32), axis=2)
+    img_gray_u8 = np.clip(img_gray, 0, 255).astype(np.uint8)
+
+    glcm = graycomatrix(img_gray_u8, distances=distances, angles=angles, levels=levels, symmetric=True, normed=True)
+    contrast = graycoprops(glcm, 'contrast').mean()
+    homogeneity = graycoprops(glcm, 'homogeneity').mean()
+
+    ref_contrast, ref_homogeneity = None, None
+
+    # --- NEW LOGIC: PRIORITIZE PROFILE ---
+    if profile_path and profile_path.strip():
+        npz_path = f"{profile_path}.npz"
+        if os.path.exists(npz_path):
+            stats = np.load(npz_path)
+            if 'glcm_props' in stats:
+                # Load the average contrast and homogeneity from the profile
+                avg_props = stats['glcm_props'].mean(axis=0)
+                ref_contrast, ref_homogeneity = avg_props[0], avg_props[1]
+    
+    # Fallback to live reference image if no profile was used
+    if ref_contrast is None and ref_img_arr is not None:
+        ref = np.asarray(ref_img_arr)
+        if ref.shape[0] != h or ref.shape[1] != w:
+            ref = np.array(Image.fromarray(ref).resize((w, h), resample=Image.Resampling.BICUBIC))
+        
+        ref_gray = np.mean(ref.astype(np.float32), axis=2)
+        ref_gray_u8 = np.clip(ref_gray, 0, 255).astype(np.uint8)
+        
+        ref_glcm = graycomatrix(ref_gray_u8, distances=distances, angles=angles, levels=levels, symmetric=True, normed=True)
+        ref_contrast = graycoprops(ref_glcm, 'contrast').mean()
+        ref_homogeneity = graycoprops(ref_glcm, 'homogeneity').mean()
+    # --- END NEW LOGIC ---
+
+    if ref_contrast is None:
+        return img_arr # Do nothing if no reference is available
+
+    out = np.empty_like(img, dtype=np.float32)
+    
+    contrast_ratio = ref_contrast / (contrast + eps)
+    homogeneity_ratio = ref_homogeneity / (homogeneity + eps)
+    contrast_scale = np.sqrt(contrast_ratio).astype(np.float32)
+    sigma = float(np.clip(1.0 / (homogeneity_ratio + eps), 0.5, 5.0))
+
+    noise_all = rng.normal(loc=0.0, scale=0.02 * strength, size=(h, w, channels)).astype(np.float32) * 255.0
+
+    for c in range(channels):
+        channel = img[:, :, c].astype(np.float32)
+        adjusted = channel * contrast_scale
+        adjusted = gaussian_filter(adjusted, sigma=(sigma, sigma))
+        blended = (1.0 - strength) * channel + strength * adjusted
+        blended += noise_all[:, :, c]
+        out[:, :, c] = blended
+
+    return np.clip(out, 0, 255).astype(np.uint8)
\ No newline at end of file
diff --git a/custom_nodes/ComfyUI_INSTARAW/modules/detection_bypass/utils/lbp_normalization.py b/custom_nodes/ComfyUI_INSTARAW/modules/detection_bypass/utils/lbp_normalization.py
new file mode 100644
index 0000000000000000000000000000000000000000..7991da6f2c0e6da4855041404af7656fadfc2541
--- /dev/null
+++ b/custom_nodes/ComfyUI_INSTARAW/modules/detection_bypass/utils/lbp_normalization.py
@@ -0,0 +1,84 @@
+# Filename: ComfyUI_INSTARAW/modules/detection_bypass/utils/lbp_normalization.py
+import numpy as np
+from skimage.feature import local_binary_pattern
+from PIL import Image
+import os
+
+def lbp_normalize(img_arr: np.ndarray,
+                  ref_img_arr: np.ndarray = None,
+                  profile_path: str = None, # NEW: Added profile_path
+                  radius: int = 3,
+                  n_points: int = 24,
+                  method: str = 'uniform',
+                  strength: float = 0.9,
+                  seed: int = None,
+                  eps: float = 1e-8):
+    
+    if seed is not None: rng = np.random.default_rng(seed)
+    else: rng = np.random.default_rng()
+
+    img = np.asarray(img_arr)
+    h, w, channels = img.shape
+    
+    img_gray = np.mean(img.astype(np.float32), axis=2)
+    
+    lbp_img = local_binary_pattern(img_gray, n_points, radius, method=method)
+    n_bins = int(lbp_img.max() + 1)
+    lbp_hist, _ = np.histogram(lbp_img.ravel(), bins=n_bins, range=(0, n_bins), density=True)
+
+    ref_lbp_hist = None
+    
+    # --- NEW LOGIC: PRIORITIZE PROFILE ---
+    if profile_path and profile_path.strip():
+        npz_path = f"{profile_path}.npz"
+        if os.path.exists(npz_path):
+            stats = np.load(npz_path)
+            if 'lbp_hist' in stats:
+                # Load the average LBP histogram from the profile
+                avg_hist = stats['lbp_hist'].mean(axis=0)
+                # Match the bin count of the source image's LBP
+                if len(avg_hist) > n_bins:
+                    ref_lbp_hist = avg_hist[:n_bins]
+                else:
+                    ref_lbp_hist = np.pad(avg_hist, (0, n_bins - len(avg_hist)), 'constant')
+                ref_lbp_hist /= (ref_lbp_hist.sum() + eps) # Re-normalize
+
+    # Fallback to live reference image
+    if ref_lbp_hist is None and ref_img_arr is not None:
+        ref = np.asarray(ref_img_arr)
+        if ref.shape[0] != h or ref.shape[1] != w:
+            ref = np.array(Image.fromarray(ref).resize((w, h), resample=Image.Resampling.BICUBIC))
+        
+        ref_gray = np.mean(ref.astype(np.float32), axis=2)
+        ref_lbp = local_binary_pattern(ref_gray, n_points, radius, method=method)
+        ref_lbp_hist, _ = np.histogram(ref_lbp.ravel(), bins=n_bins, range=(0, n_bins), density=True)
+    # --- END NEW LOGIC ---
+
+    if ref_lbp_hist is None:
+        return img_arr
+
+    out = np.empty_like(img, dtype=np.float32)
+    
+    cdf_img = np.cumsum(lbp_hist)
+    cdf_ref = np.cumsum(ref_lbp_hist)
+    
+    mapping = np.zeros(n_bins, dtype=np.float32)
+    for i in range(n_bins):
+        # Find the value in reference cdf that is closest to the source cdf value
+        j = np.searchsorted(cdf_ref, cdf_img[i], side='left')
+        mapping[i] = j
+    
+    lbp_int = np.rint(lbp_img).astype(np.int32)
+    mapping_per_pixel = mapping[lbp_int]
+    scale_per_pixel = mapping_per_pixel / (lbp_int.astype(np.float32) + eps)
+    
+    noise_all = rng.normal(loc=0.0, scale=0.02 * strength, size=(h, w, channels)).astype(np.float32) * 255.0
+
+    for c in range(channels):
+        channel = img[:, :, c].astype(np.float32)
+        adjusted = channel * scale_per_pixel
+        blended = (1.0 - strength) * channel + strength * adjusted
+        blended += noise_all[:, :, c]
+        out[:, :, c] = blended
+
+    return np.clip(out, 0, 255).astype(np.uint8)
\ No newline at end of file
diff --git a/custom_nodes/ComfyUI_INSTARAW/modules/detection_bypass/utils/non_semantic_attack.py b/custom_nodes/ComfyUI_INSTARAW/modules/detection_bypass/utils/non_semantic_attack.py
new file mode 100644
index 0000000000000000000000000000000000000000..89959329be59458f0b00c3ff03edaa7d2092ba8e
--- /dev/null
+++ b/custom_nodes/ComfyUI_INSTARAW/modules/detection_bypass/utils/non_semantic_attack.py
@@ -0,0 +1,107 @@
+# Filename: ComfyUI_INSTARAW/modules/detection_bypass/utils/non_semantic_attack.py
+import torch
+import torch.optim as optim
+import lpips
+import numpy as np
+import threading
+
+def _optimization_worker(img_np: np.ndarray, config: dict, device: torch.device, result_container: list, exception_container: list):
+    """
+    Runs the core UnMarker-style optimization in a separate thread.
+    This is CRITICAL to escape the global torch.no_grad() context of ComfyUI.
+    """
+    try:
+        print_log_every_n = config.get('print_log_every_n', 0)
+        
+        img_tensor = torch.from_numpy(img_np).to(device)
+        img_tensor = (img_tensor - 0.5) / 0.5
+
+        batch_size, channels, height, width = img_tensor.shape
+        delta = torch.randn(batch_size, 1, height, width, device=device, dtype=img_tensor.dtype) * 0.01
+        delta.requires_grad = True
+
+        lpips_model = lpips.LPIPS(net='alex').to(device).eval()
+        for param in lpips_model.parameters():
+            param.requires_grad = False
+
+        optimizer = optim.Adam([delta], lr=config['learning_rate'])
+
+        img_gray = 0.299 * img_tensor[:, 0:1] + 0.587 * img_tensor[:, 1:2] + 0.114 * img_tensor[:, 2:3]
+        with torch.no_grad():
+            img_fft = torch.fft.fft2(img_gray)
+
+        if print_log_every_n > 0:
+            print(f"  [Spectral Normalizer] โœ“ Thread started. Optimizing for {config['iterations']} iterations...")
+            print(f"  [Params] LR={config['learning_rate']:.1E}, LPIPS(t={config['t_lpips']:.3f}, c={config['c_lpips']:.3f}), L2(t={config['t_l2']:.1E}, c={config['c_l2']:.2f}), GradClip={config['grad_clip_value']:.3f}")
+
+        for i in range(config['iterations']):
+            optimizer.zero_grad()
+
+            delta_rgb = delta.expand_as(img_tensor)
+            x_nw = torch.clamp(img_tensor + delta_rgb, -1, 1)
+
+            x_nw_gray = 0.299 * x_nw[:, 0:1] + 0.587 * x_nw[:, 1:2] + 0.114 * x_nw[:, 2:3]
+            x_nw_fft = torch.fft.fft2(x_nw_gray)
+            fft_diff = x_nw_fft - img_fft
+            loss_dfl = -torch.sqrt(fft_diff.real**2 + fft_diff.imag**2 + 1e-8).mean() # Use mean for stability
+
+            loss_lpips = lpips_model(x_nw, img_tensor).mean()
+            loss_l2 = delta.pow(2).mean().sqrt()
+
+            lpips_penalty = config['c_lpips'] * torch.relu(loss_lpips - config['t_lpips'])
+            l2_penalty = config['c_l2'] * torch.relu(loss_l2 - config['t_l2'])
+            total_loss = loss_dfl + lpips_penalty + l2_penalty
+
+            total_loss.backward()
+
+            if delta.grad is not None:
+                torch.nn.utils.clip_grad_norm_(delta, config['grad_clip_value'])
+
+            optimizer.step()
+            
+            # --- NEW: DEBUG LOGGING ---
+            if print_log_every_n > 0 and (i % print_log_every_n == 0 or i == config['iterations'] - 1):
+                print(f"    Iter {i:04d}: Total Loss={total_loss.item():.4f} | FFT Loss={loss_dfl.item():.4f} | LPIPS={loss_lpips.item():.4f} | L2={loss_l2.item():.4f}")
+
+
+        if print_log_every_n > 0:
+            final_lpips = lpips_model(torch.clamp(img_tensor + delta.expand_as(img_tensor), -1, 1), img_tensor).mean().item()
+            print(f"  [Spectral Normalizer] โœ“ Optimization complete. Final LPIPS distance: {final_lpips:.4f}")
+
+        delta_rgb = delta.expand_as(img_tensor)
+        final_x_nw = torch.clamp(img_tensor + delta_rgb, -1, 1).squeeze(0).cpu().detach()
+        final_x_nw = (final_x_nw + 1) / 2
+        final_x_nw = final_x_nw.permute(1, 2, 0)
+        result_np = (final_x_nw.clamp(0, 1) * 255).numpy().astype(np.uint8)
+        result_container.append(result_np)
+
+    except Exception as e:
+        exception_container.append(e)
+
+@torch.enable_grad()
+def non_semantic_attack(img_arr: np.ndarray, **kwargs) -> np.ndarray:
+    config = {
+        'iterations': kwargs.get('iterations', 500), 'learning_rate': kwargs.get('learning_rate', 3e-4),
+        't_lpips': kwargs.get('t_lpips', 0.04), 'c_lpips': kwargs.get('c_lpips', 1e-2),
+        't_l2': kwargs.get('t_l2', 3e-5), 'c_l2': kwargs.get('c_l2', 0.6),
+        'grad_clip_value': kwargs.get('grad_clip_value', 0.05),
+        'print_log_every_n': kwargs.get('print_log_every_n', 0)
+    }
+    
+    seed = kwargs.get('seed')
+    if seed is not None: torch.manual_seed(seed)
+
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    
+    img_np = (img_arr.astype(np.float32) / 255.0).transpose(2, 0, 1)[np.newaxis, ...]
+
+    result_container, exception_container = [], []
+    
+    thread = threading.Thread(target=_optimization_worker, args=(img_np, config, device, result_container, exception_container))
+    thread.start()
+    thread.join()
+
+    if exception_container: raise exception_container[0]
+    if not result_container: raise RuntimeError("Spectral Normalizer optimization thread failed to return a result.")
+
+    return result_container[0]
\ No newline at end of file
diff --git a/custom_nodes/ComfyUI_INSTARAW/modules/detection_bypass/utils/perturbation.py b/custom_nodes/ComfyUI_INSTARAW/modules/detection_bypass/utils/perturbation.py
new file mode 100644
index 0000000000000000000000000000000000000000..c3dcd7b12a61bd4633576ff9c4333bdb7d558e25
--- /dev/null
+++ b/custom_nodes/ComfyUI_INSTARAW/modules/detection_bypass/utils/perturbation.py
@@ -0,0 +1,10 @@
+import numpy as np
+
+def randomized_perturbation(img_arr: np.ndarray, magnitude_frac=0.008, seed=None) -> np.ndarray:
+    if seed is not None:
+        np.random.seed(seed)
+    mag = magnitude_frac * 255.0
+    perturb = np.random.uniform(low=-mag, high=mag, size=img_arr.shape)
+    out = img_arr.astype(np.float32) + perturb
+    out = np.clip(out, 0, 255).astype(np.uint8)
+    return out
\ No newline at end of file
diff --git a/custom_nodes/ComfyUI_INSTARAW/modules/detection_bypass/utils/stats_utils.py b/custom_nodes/ComfyUI_INSTARAW/modules/detection_bypass/utils/stats_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..d812d83892ccf562c8e5697dc7679d10ca1eab4c
--- /dev/null
+++ b/custom_nodes/ComfyUI_INSTARAW/modules/detection_bypass/utils/stats_utils.py
@@ -0,0 +1,102 @@
+# Filename: ComfyUI_INSTARAW/modules/detection_bypass/utils/stats_utils.py
+# ---
+
+import numpy as np
+import torch
+import torch.nn.functional as F
+
+class StatsMatcher:
+    def __init__(self, stats_path, device, weights=None):
+        data = np.load(stats_path)
+        self.device = torch.device(device)
+        self.weights = weights or {
+            "spectrum": 0.7,
+            "chroma": 0.25,
+            "texture": 0.05,
+        }
+
+        spectra = data["spectra"]
+        chroma = data["chroma_mean"]
+        chroma_cov = data["chroma_cov"]
+        glcm = data["glcm"]
+
+        self.spectrum_mean = torch.tensor(spectra.mean(axis=0), dtype=torch.float32, device=self.device)
+        self.spectrum_std = torch.tensor(spectra.std(axis=0) + 1e-6, dtype=torch.float32, device=self.device)
+        self.chroma_mean = torch.tensor(chroma.mean(axis=0), dtype=torch.float32, device=self.device)
+        self.chroma_std = torch.tensor(chroma.std(axis=0) + 1e-6, dtype=torch.float32, device=self.device)
+        
+        # --- THE FIX: Load the 3x3 matrix correctly ---
+        self.chroma_cov_mean = torch.tensor(chroma_cov.mean(axis=0), dtype=torch.float32, device=self.device)
+        self.chroma_cov_std = torch.tensor(chroma_cov.std(axis=0) + 1e-6, dtype=torch.float32, device=self.device)
+        
+        self.texture_mean = torch.tensor(glcm.mean(axis=0), dtype=torch.float32, device=self.device)
+        self.texture_std = torch.tensor(glcm.std(axis=0) + 1e-6, dtype=torch.float32, device=self.device)
+
+        self.radial_cache = {}
+        sobel = torch.tensor([[1, 0, -1], [2, 0, -2], [1, 0, -1]], dtype=torch.float32)
+        self.sobel_x = sobel.view(1, 1, 3, 3).to(self.device)
+        self.sobel_y = sobel.t().view(1, 1, 3, 3).to(self.device)
+
+    def _radial_profile(self, mag, bins):
+        # ... (this function is unchanged)
+        b, h, w = mag.shape
+        key = (mag.device, h, w, bins)
+        cache = self.radial_cache.get(key)
+        if cache is None:
+            y, x = torch.arange(h, device=mag.device, dtype=torch.float32), torch.arange(w, device=mag.device, dtype=torch.float32)
+            yy, xx = torch.meshgrid(y, x, indexing="ij")
+            cy, cx = (h - 1) / 2.0, (w - 1) / 2.0
+            r = torch.sqrt((xx - cx) ** 2 + (yy - cy) ** 2)
+            r = torch.clamp(r.long(), max=bins - 1)
+            counts = torch.bincount(r.view(-1), minlength=bins).float().clamp_min(1.0)
+            cache = {"index": r.view(-1), "counts": counts}
+            self.radial_cache[key] = cache
+        profile = []
+        for sample in mag:
+            binsums = torch.zeros(bins, device=mag.device)
+            binsums.scatter_add_(0, cache["index"], sample.view(-1))
+            profile.append(binsums / cache["counts"])
+        return torch.stack(profile, dim=0)
+
+    def __call__(self, img_tensor):
+        b, c, h, w = img_tensor.shape
+        gray = (0.299 * img_tensor[:, 0:1] + 0.587 * img_tensor[:, 1:2] + 0.114 * img_tensor[:, 2:3])
+        fft = torch.fft.fftshift(torch.fft.fft2(gray), dim=(-1, -2))
+        mag = torch.log1p(torch.abs(fft)).mean(1)
+        spectrum = self._radial_profile(mag, bins=self.spectrum_mean.shape[0])
+        spec_target = self.spectrum_mean.unsqueeze(0).expand_as(spectrum)
+        spec_std = self.spectrum_std.unsqueeze(0).expand_as(spectrum)
+        loss_spectrum = F.mse_loss((spectrum - spec_target) / spec_std, torch.zeros_like(spectrum))
+
+        pixels = img_tensor.view(b, c, -1)
+        mean = pixels.mean(dim=-1, keepdim=True)
+        chroma_mean = mean.squeeze(-1)
+        target_chroma = self.chroma_mean.unsqueeze(0).expand_as(chroma_mean)
+        chroma_std = self.chroma_std.unsqueeze(0).expand_as(chroma_mean)
+        z = (chroma_mean - target_chroma) / chroma_std
+        loss_chroma_mean = F.mse_loss(z, torch.zeros_like(z))
+        
+        centered = pixels - mean
+        cov = centered @ centered.transpose(1, 2) / pixels.shape[-1]
+        
+        # --- THE FIX: The shapes will now match perfectly ---
+        target_cov = self.chroma_cov_mean.unsqueeze(0).expand_as(cov)
+        cov_std = self.chroma_cov_std.unsqueeze(0).expand_as(cov)
+        z_cov = (cov - target_cov) / cov_std
+        loss_chroma_cov = F.mse_loss(z_cov, torch.zeros_like(z_cov))
+
+        grad_x = F.conv2d(gray, self.sobel_x, padding=1)
+        grad_y = F.conv2d(gray, self.sobel_y, padding=1)
+        grad_mag = torch.sqrt(grad_x ** 2 + grad_y ** 2 + 1e-8)
+        contrast = grad_mag.mean(dim=(1, 2, 3))
+        mu = F.avg_pool2d(gray, kernel_size=5, stride=1, padding=2)
+        var = F.avg_pool2d(gray ** 2, 5, 1, 2) - mu ** 2
+        homogeneity = (1.0 / (1.0 + var)).mean(dim=(1, 2, 3))
+        
+        # This assumes the saved glcm data has 2 features
+        texture_vec = torch.stack([contrast, homogeneity], dim=-1)
+        loss_texture = F.mse_loss((texture_vec - self.texture_mean) / self.texture_std, torch.zeros_like(texture_vec))
+
+        total = (self.weights["spectrum"] * loss_spectrum + self.weights["chroma"] * (loss_chroma_mean + loss_chroma_cov) + self.weights["texture"] * loss_texture)
+        details = {"spectrum": loss_spectrum.detach(), "chroma_mean": loss_chroma_mean.detach(), "chroma_cov": loss_chroma_cov.detach(), "texture": loss_texture.detach()}
+        return total, details
\ No newline at end of file
diff --git a/custom_nodes/ComfyUI_INSTARAW/modules/detection_bypass/utils/texture_utils.py b/custom_nodes/ComfyUI_INSTARAW/modules/detection_bypass/utils/texture_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..85540ed8af3cb165847fe6392fdbbab73cfcb633
--- /dev/null
+++ b/custom_nodes/ComfyUI_INSTARAW/modules/detection_bypass/utils/texture_utils.py
@@ -0,0 +1,64 @@
+# Filename: ComfyUI_INSTARAW/modules/detection_bypass/utils/texture_utils.py
+# (Definitive, Stable Log-Based Loss)
+import torch
+import torch.nn.functional as F
+import numpy as np
+from skimage.feature import graycomatrix, graycoprops, local_binary_pattern
+import os
+
+class TextureMatcher:
+    def __init__(self, profile_path, device):
+        self.device = device
+        try:
+            stats = np.load(profile_path)
+            # Use log to compress the range of values for a more stable loss
+            self.glcm_target_mean = torch.log1p(torch.tensor(stats['glcm_props'].mean(axis=0), dtype=torch.float32, device=self.device))
+            self.lbp_target_mean = torch.tensor(stats['lbp_hist'].mean(axis=0), dtype=torch.float32, device=self.device)
+            self.n_points, self.radius, self.lbp_bins = 24, 3, self.lbp_target_mean.shape[0]
+            
+            print(f"  [TextureMatcher] Loaded profile '{os.path.basename(profile_path)}'.")
+
+        except Exception as e:
+            raise ValueError(f"Failed to load or parse texture profile from '{profile_path}': {e}")
+
+    def _tensor_to_gray_numpy(self, tensor: torch.Tensor) -> np.ndarray:
+        img_chw = tensor.squeeze(0)
+        gray_chw = 0.299 * img_chw[0, :, :] + 0.587 * img_chw[1, :, :] + 0.114 * img_chw[2, :, :]
+        return (gray_chw.detach().cpu().numpy() * 255).astype(np.uint8)
+
+    def __call__(self, img_tensor: torch.Tensor, log_stats: bool = False):
+        if img_tensor.shape[0] > 1:
+            img_tensor = img_tensor[0:1]
+
+        gray_np = self._tensor_to_gray_numpy(img_tensor)
+
+        # 1. GLCM Loss
+        try:
+            glcm = graycomatrix(gray_np, distances=[5], angles=[0], levels=256, symmetric=True, normed=True)
+            contrast = graycoprops(glcm, 'contrast')[0, 0]
+            homogeneity = graycoprops(glcm, 'homogeneity')[0, 0]
+            glcm_vec = torch.tensor([contrast, homogeneity], dtype=torch.float32, device=self.device)
+            
+            # --- THE FIX: Calculate loss in log space ---
+            loss_glcm = F.l1_loss(torch.log1p(glcm_vec), self.glcm_target_mean)
+            
+            if log_stats:
+                print(f"    - Current GLCM: contrast={contrast:.2f}, homogeneity={homogeneity:.4f} -> Loss: {loss_glcm.item():.4f}")
+
+        except Exception as e:
+            print(f"Warning: GLCM calculation failed: {e}")
+            loss_glcm = torch.tensor(0.0, device=self.device)
+
+        # 2. LBP Loss (LBP is already a normalized histogram, MSE is fine here)
+        try:
+            lbp = local_binary_pattern(gray_np, self.n_points, self.radius, method='uniform')
+            lbp_hist, _ = np.histogram(lbp.ravel(), bins=self.lbp_bins, range=(0, self.lbp_bins), density=True)
+            lbp_vec = torch.tensor(lbp_hist, dtype=torch.float32, device=self.device)
+            loss_lbp = F.mse_loss(lbp_vec, self.lbp_target_mean)
+        except Exception as e:
+            print(f"Warning: LBP calculation failed: {e}")
+            loss_lbp = torch.tensor(0.0, device=self.device)
+
+        # Weight GLCM more heavily as it's a more robust indicator of overall texture
+        total_loss = (loss_glcm * 10.0) + loss_lbp
+        return total_loss
\ No newline at end of file
diff --git a/custom_nodes/ComfyUI_INSTARAW/modules/detection_bypass/utils/unmarker_full.py b/custom_nodes/ComfyUI_INSTARAW/modules/detection_bypass/utils/unmarker_full.py
new file mode 100644
index 0000000000000000000000000000000000000000..1f5b038a8f37d43a34801f622bbe0a4b9f5dac8a
--- /dev/null
+++ b/custom_nodes/ComfyUI_INSTARAW/modules/detection_bypass/utils/unmarker_full.py
@@ -0,0 +1,262 @@
+# Filename: ComfyUI_INSTARAW/modules/detection_bypass/utils/unmarker_full.py
+# ---
+"""
+Full two-stage spectral normalization implementation. FINAL VERSION.
+Accepts external overrides for fine-tuning.
+"""
+
+import math
+import os
+from pathlib import Path
+import numpy as np
+import torch
+import torch.optim as optim
+import torch.nn.functional as F
+import threading
+from typing import Optional, Tuple, Dict, Any
+import kornia
+
+from .unmarker_losses import FFTLoss, LpipsVGG, LpipsAlex, NormLoss, DeeplossVGG
+from .adaptive_filter import AdaptiveFilter
+from .stats_utils import StatsMatcher
+
+class SpectralNormalizer:
+    # ... (The class and its methods __init__, _create_fft_loss, _apply_preprocess, _optimize_stage are all IDENTICAL to the last version. No changes needed here.) ...
+    def __init__(
+        self,
+        stage1_iterations: int = 500, stage1_learning_rate: float = 3e-4, stage1_binary_steps: int = 5,
+        stage2_iterations: int = 300, stage2_learning_rate: float = 1e-4, stage2_binary_steps: int = 3,
+        lpips_type: str = "alex", t_lpips: float = 0.04, c_lpips: float = 0.01,
+        t_l2: float = 3e-5, c_l2: float = 0.6, grad_clip_value: float = 0.005,
+        use_adaptive_filter: bool = False, filter_kernels: Optional[list] = None,
+        binary_const_growth: float = 10.0, crop_ratio: Optional[Tuple[float, float]] = None,
+        attack_long_side_limit: Optional[int] = None, fft_scale_target: float = 0.05,
+        device: str = "cuda" if torch.cuda.is_available() else "cpu",
+        verbose: bool = True, stats_path: Optional[str] = None,
+    ):
+        self.stage1_iterations = stage1_iterations
+        self.stage1_lr = stage1_learning_rate
+        self.stage1_binary_steps = stage1_binary_steps
+        self.stage2_iterations = stage2_iterations
+        self.stage2_lr = stage2_learning_rate
+        self.stage2_binary_steps = stage2_binary_steps
+        self.t_lpips = t_lpips
+        self.c_lpips = c_lpips
+        self.t_l2 = t_l2
+        self.c_l2 = c_l2
+        self.grad_clip_value = grad_clip_value
+        self.binary_const_growth = binary_const_growth
+        self.use_adaptive_filter = use_adaptive_filter
+        self.filter_kernels = filter_kernels if filter_kernels else [(7, 7)]
+        self.device = torch.device(device)
+        self.verbose = verbose
+        self.crop_ratio = crop_ratio
+        self.attack_long_side_limit = attack_long_side_limit
+        self.fft_scale_target = fft_scale_target
+        stats_path_to_load = (
+            stats_path or globals().get("_stats_path_override") or os.environ.get("IPHONE_STATS_PATH")
+            or Path(__file__).resolve().parents[3] / "pretrained" / "iphone_stats.npz"
+        )
+        self.stats_matcher = None
+        if stats_path_to_load and Path(stats_path_to_load).is_file():
+            try:
+                self.stats_matcher = StatsMatcher(stats_path_to_load, self.device)
+                if self.verbose:
+                    print(f"โœ… Stats-based guardrail enabled using: {stats_path_to_load}")
+            except Exception as exc:
+                print(f"โš ๏ธ Failed to load stats from {stats_path_to_load}: {exc}")
+        else:
+            if self.verbose:
+                print("โ„น๏ธ iPhone stats file not found. Attack will be UNGUIDED.")
+        if lpips_type == "deeploss":
+            self.lpips_model = DeeplossVGG(device=self.device).eval()
+        elif lpips_type == "vgg":
+            self.lpips_model = LpipsVGG().to(self.device).eval()
+        else:
+            self.lpips_model = LpipsAlex().to(self.device).eval()
+        for param in self.lpips_model.parameters():
+            param.requires_grad = False
+    def _create_fft_loss(self, stage: str):
+        if stage == "high_freq": return FFTLoss(norm=1, power=1, use_tanh=False, use_grayscale=True).to(self.device)
+        else: return FFTLoss(norm=2, power=1, use_tanh=False, use_grayscale=True).to(self.device)
+    def _apply_preprocess(self, tensor: torch.Tensor) -> torch.Tensor:
+        if not self.crop_ratio: return tensor
+        ratio_h, ratio_w = self.crop_ratio
+        _, _, height, width = tensor.shape
+        crop_h, crop_w = max(1, int(round(height * ratio_h))), max(1, int(round(width * ratio_w)))
+        start_h, start_w = max(0, (height - crop_h) // 2), max(0, (width - crop_w) // 2)
+        cropped = tensor[:, :, start_h:start_h + crop_h, start_w:start_w + crop_w]
+        if cropped.shape[-2:] != (height, width):
+            cropped = F.interpolate(cropped, size=(height, width), mode="bicubic", align_corners=False)
+        return cropped
+    def _optimize_stage(
+        self, img_tensor: torch.Tensor, stage_name: str, iterations: int, learning_rate: float,
+        binary_steps: int, stage_params: Optional[Dict[str, Any]] = None,
+    ) -> torch.Tensor:
+        fft_loss_fn = self._create_fft_loss(stage_name)
+        stage_params = stage_params.copy() if stage_params else {}
+        const = stage_params.get("initial_const", 1.0)
+        c_lpips = stage_params.get("c_lpips", 1.0)
+        stats_weight = stage_params.get("stats_weight", 0.0)
+        fft_scale_target = stage_params.get("fft_scale_target", self.fft_scale_target)
+        delta_blur_sigma = stage_params.get("delta_blur_sigma", 0.0)
+        if self.verbose:
+            print(f"  [{stage_name}] Starting unified optimization ({iterations} steps)...")
+            print(f"  [{stage_name}] Trade-off params: const={const:.2f}, c_lpips={c_lpips:.2f}, stats_weight={stats_weight:.2f}, delta_blur={delta_blur_sigma:.2f}")
+        delta = torch.randn(img_tensor.shape[0], 1, img_tensor.shape[2], img_tensor.shape[3], device=self.device, dtype=img_tensor.dtype) * 0.0001
+        delta.requires_grad = True
+        filt = AdaptiveFilter(kernels=self.filter_kernels, shape=img_tensor.shape).to(self.device) if self.use_adaptive_filter else None
+        optimizer = optim.Adam([delta] + (list(filt.parameters()) if filt else []), lr=learning_rate)
+        dynamic_fft_scale = None
+        early_stop_patience = stage_params.get("early_stop_patience", 400)
+        no_improve_steps, best_iter_loss = 0, float("inf")
+        for i in range(iterations):
+            optimizer.zero_grad()
+            delta_rgb = delta.expand_as(img_tensor)
+            x_adv = torch.clamp(img_tensor + (filt(delta_rgb, img_tensor) if filt else delta_rgb), -1, 1)
+            loss_fft_raw = fft_loss_fn(x_adv, img_tensor).mean()
+            if dynamic_fft_scale is None:
+                init_fft = abs(loss_fft_raw.detach()).item()
+                dynamic_fft_scale = fft_scale_target / max(init_fft, 1e-9)
+            loss_fft = -loss_fft_raw * dynamic_fft_scale
+            loss_lpips = self.lpips_model(x_adv, img_tensor).mean()
+            stats_penalty = self.stats_matcher(x_adv)[0] if self.stats_matcher else 0
+            loss_filter = filt.compute_loss(x_adv).mean() if filt else 0
+            total_loss = (const * loss_fft) + (c_lpips * loss_lpips) + (stats_weight * stats_penalty) + loss_filter
+            total_loss.backward()
+            if delta.grad is not None:
+                delta.grad.data.clamp_(-self.grad_clip_value, self.grad_clip_value)
+            optimizer.step()
+            if delta_blur_sigma > 0:
+                with torch.no_grad():
+                    kernel_size = int(delta_blur_sigma * 3) * 2 + 1
+                    delta.data = kornia.filters.gaussian_blur2d(delta.data, (kernel_size, kernel_size), (delta_blur_sigma, delta_blur_sigma))
+            current_loss = total_loss.item()
+            if current_loss < best_iter_loss - 1e-5:
+                best_iter_loss, no_improve_steps = current_loss, 0
+            else:
+                no_improve_steps += 1
+            if no_improve_steps > early_stop_patience:
+                if self.verbose: print(f"    DEBUG: Early stopping due to stagnation.")
+                break
+            if self.verbose and (i % 100 == 0 or i == iterations - 1):
+                stats_str = f", Stats={stats_penalty.item():.4f}" if self.stats_matcher else ""
+                print(f"    Iter {i}/{iterations}: TotalLoss={total_loss.item():.4f}, FFT={loss_fft.item():.4f}, LPIPS={loss_lpips.item():.4f}{stats_str}")
+        with torch.no_grad():
+            delta_rgb = delta.expand_as(img_tensor)
+            final_adv = torch.clamp(img_tensor + (filt(delta_rgb, img_tensor) if filt else delta_rgb), -1, 1)
+            final_lpips = self.lpips_model(final_adv, img_tensor).mean().item()
+            stats_final_val = self.stats_matcher(final_adv)[0].item() if self.stats_matcher else float('inf')
+        if self.verbose:
+            print(f"  [{stage_name}] Optimization finished. Final LPIPS={final_lpips:.4f}, Final Stats={stats_final_val:.4f}")
+        return final_adv
+    def process(self, img_np: np.ndarray, stage_overrides: Optional[Dict[str, Dict[str, Any]]] = None) -> np.ndarray:
+        stage_overrides = stage_overrides or {}
+        img_np_float = img_np.astype(np.float32) / 255.0
+        img_tensor = torch.from_numpy(np.transpose(img_np_float, (2, 0, 1))[None, ...]).to(self.device)
+        img_tensor = (img_tensor - 0.5) / 0.5
+        img_tensor = self._apply_preprocess(img_tensor)
+        original_shape = img_tensor.shape[-2:]
+        resize_back_shape = None
+        if self.attack_long_side_limit and max(original_shape) > self.attack_long_side_limit:
+            scale = self.attack_long_side_limit / max(original_shape)
+            new_h, new_w = int(round(original_shape[0] * scale)), int(round(original_shape[1] * scale))
+            img_tensor = F.interpolate(img_tensor, size=(new_h, new_w), mode="bicubic", align_corners=False)
+            resize_back_shape = original_shape
+            if self.verbose: print(f"   โ†ณ Downscaling canvas to {new_h}x{new_w}")
+        if self.verbose:
+            print(f"๐Ÿ–ผ๏ธ Input resolution: {img_np.shape[0]}x{img_np.shape[1]}")
+            print("๐Ÿš€ INSTARAW Spectral Engine: Starting GUIDED normalization...")
+        stage1_params = stage_overrides.get("high_freq", {})
+        if self.verbose: print("\n=== STAGE 1: High Frequency Normalization ===")
+        stage1_out = self._optimize_stage(
+            img_tensor, "high_freq", self.stage1_iterations, self.stage1_lr,
+            self.stage1_binary_steps, stage1_params
+        )
+        stage2_params = stage_overrides.get("low_freq", {})
+        if self.verbose: print("\n=== STAGE 2: Low Frequency Refinement ===")
+        stage2_out = self._optimize_stage(
+            stage1_out, "low_freq", self.stage2_iterations, self.stage2_lr,
+            self.stage2_binary_steps, stage2_params
+        )
+        if resize_back_shape is not None:
+            stage2_out = F.interpolate(stage2_out, size=resize_back_shape, mode="bicubic", align_corners=False)
+        final_tensor = (stage2_out.squeeze(0).cpu().detach() + 1) / 2
+        final_np = (final_tensor.permute(1, 2, 0).clamp(0, 1) * 255).numpy().astype(np.uint8)
+        if self.verbose: print("\nโœ… INSTARAW Spectral Engine: Normalization complete!")
+        return final_np
+
+def _size_profile_overrides(
+    height: int, width: int, preset_name: str, base_config: Dict[str, Any],
+) -> Tuple[Dict[str, Any], Dict[str, Dict[str, Any]], Dict[str, Any]]:
+    max_dim = max(height, width)
+    size_ratio = max(1.0, max_dim / 512.0)
+    overrides: Dict[str, Any] = {}
+    stage_params: Dict[str, Dict[str, Any]] = {"high_freq": {}, "low_freq": {}}
+    overrides["crop_ratio"] = (1.0, 1.0)
+    overrides["lpips_type"] = "vgg" if max_dim > 768 else "alex"
+    s1_iters = int(min(1500, 800 * size_ratio))
+    s2_iters = int(min(800, 400 * size_ratio))
+    overrides["stage1_iterations"] = max(base_config.get("stage1_iterations", 500), s1_iters)
+    overrides["stage2_iterations"] = max(base_config.get("stage2_iterations", 300), s2_iters)
+    stage_params["high_freq"].update({
+        "initial_const": 30.0, "c_lpips": 40.0, "stats_weight": 4.0,
+        "fft_scale_target": 0.05, "delta_blur_sigma": 0.5,
+    })
+    stage_params["low_freq"].update({
+        "initial_const": 20.0, "c_lpips": 50.0, "stats_weight": 3.0,
+        "fft_scale_target": 0.03, "delta_blur_sigma": 1.0,
+    })
+    meta = {}
+    return overrides, stage_params, meta
+
+def normalize_spectrum_twostage(img_arr: np.ndarray, preset: str = "balanced", **kwargs) -> np.ndarray: # Rebranded function
+    presets = {
+        "fast": {"stage1_binary_steps": 3, "stage2_binary_steps": 2, "use_adaptive_filter": False, "attack_long_side_limit": 900},
+        "balanced": {"stage1_binary_steps": 4, "stage2_binary_steps": 3, "use_adaptive_filter": False, "attack_long_side_limit": 1152, "lpips_type": "deeploss"},
+        "quality": {"stage1_binary_steps": 6, "stage2_binary_steps": 4, "use_adaptive_filter": True, "filter_kernels": [(7, 7), (15, 15)], "attack_long_side_limit": 1536, "lpips_type": "deeploss"},
+    }
+    base_config = {**presets.get(preset, presets["balanced"])}
+    
+    # --- THIS IS THE FIX ---
+    # Enforce the analysis resolution for maximum effectiveness.
+    # We will override the preset's attack_long_side_limit with our known best value.
+    base_config['attack_long_side_limit'] = 1920
+    # --- END FIX ---
+
+    user_stage_overrides = kwargs.pop("stage_overrides", {})
+    seed_value = kwargs.pop("seed", None)
+    
+    config = {**base_config, **kwargs}
+    height, width = img_arr.shape[:2]
+    attack_limit = config.get("attack_long_side_limit")
+    effective_h, effective_w = height, width
+    if attack_limit and max(height, width) > attack_limit:
+        scale = attack_limit / max(height, width)
+        effective_h, effective_w = int(round(height * scale)), int(round(width * scale))
+
+    # ... (the rest of the function is identical) ...
+    size_overrides, stage_params, meta = _size_profile_overrides(effective_h, effective_w, preset, config)
+    for key, value in size_overrides.items():
+        if key not in kwargs: config[key] = value
+    final_stage_overrides = {
+        "high_freq": {**stage_params.get("high_freq", {}), **user_stage_overrides.get("high_freq", {})},
+        "low_freq": {**stage_params.get("low_freq", {}), **user_stage_overrides.get("low_freq", {})},
+        "_meta": meta
+    }
+    result_container, exception_container = [], []
+    def worker_fn():
+        try:
+            if seed_value is not None:
+                torch.manual_seed(seed_value)
+            engine = SpectralNormalizer(**config)
+            result = engine.process(img_arr, stage_overrides=final_stage_overrides)
+            result_container.append(result)
+        except Exception as e:
+            exception_container.append(e)
+    thread = threading.Thread(target=worker_fn)
+    thread.start()
+    thread.join()
+    if exception_container: raise exception_container[0]
+    if not result_container: raise RuntimeError("INSTARAW Spectral Engine failed to return a result.")
+    return result_container[0]
\ No newline at end of file
diff --git a/custom_nodes/ComfyUI_INSTARAW/modules/detection_bypass/utils/unmarker_losses.py b/custom_nodes/ComfyUI_INSTARAW/modules/detection_bypass/utils/unmarker_losses.py
new file mode 100644
index 0000000000000000000000000000000000000000..5f3652ec19fe12835f00da2dbaa9096d1d55e1bb
--- /dev/null
+++ b/custom_nodes/ComfyUI_INSTARAW/modules/detection_bypass/utils/unmarker_losses.py
@@ -0,0 +1,459 @@
+"""
+Advanced loss functions for full UnMarker implementation.
+Ported from ai-watermark/modules/attack/unmark/losses.py
+
+These losses are used in the two-stage UnMarker attack:
+- Stage 1 (high_freq): FFTLoss targeting high frequencies where AI fingerprints hide
+- Stage 2 (low_freq): FFTLoss targeting low frequencies for robustness
+- Both stages: perceptual constraint (LPIPS or Deeploss-VGG)
+"""
+
+import os
+from pathlib import Path
+from collections import namedtuple
+
+import torch
+import lpips
+try:
+    from lpips import util as lpips_util
+except ImportError:  # pragma: no cover - lpips provides util in normal installs
+    lpips_util = None
+from torchvision import transforms, models
+
+class NormLoss(torch.nn.Module):
+    """Basic L-p norm loss between two tensors."""
+    def __init__(self, norm=2, power=2):
+        super().__init__()
+        self.norm, self.power = norm, power
+
+    def forward(self, x, y):
+        x = x.view(x.shape[0], -1, x.shape[-2], x.shape[-1])
+        y = y.view(y.shape[0], -1, x.shape[-2], x.shape[-1])
+        return (
+            torch.pow(
+                torch.norm(
+                    x - y, p=self.norm, dim=tuple(list(range(1, len((x).shape))))
+                ),
+                self.power,
+            )
+            / torch.prod(torch.tensor(x.shape[1:]))
+        ).view(x.shape[0], -1)
+
+
+class PerceptualLoss(torch.nn.Module):
+    """Wrapper for perceptual loss functions like LPIPS."""
+    def __init__(self, loss_fn):
+        super().__init__()
+        self.loss_fn = loss_fn
+
+    def forward(self, x, y):
+        x = x.view(x.shape[0], -1, x.shape[-2], x.shape[-1])
+        y = y.view(y.shape[0], -1, y.shape[-2], y.shape[-1])
+        return self.loss_fn(x, y).view(x.shape[0], -1)
+
+
+class FFTLoss(NormLoss):
+    """
+    CRITICAL: This is the core of UnMarker.
+    Computes loss in the frequency domain to directly attack spectral fingerprints.
+
+    IMPORTANT: Processes in GRAYSCALE to avoid blue hue color shift!
+    Research warning: "By independently manipulating the spectra of RGB channels,
+    the FFT algorithm destroys natural inter-channel correlation, causing severe
+    color artifacts and blue/purple color cast."
+
+    Args:
+        norm: L-p norm to use (1 or 2)
+        power: Power to raise the norm to
+        n_fft: FFT size (None = same as input)
+        use_tanh: Apply tanh to frequency magnitudes (helps with stability)
+        use_grayscale: Convert to grayscale before FFT (DEFAULT: True to prevent color shift)
+    """
+    def __init__(self, norm=1, power=1, n_fft=None, use_tanh=False, use_grayscale=True):
+        super().__init__(norm=norm, power=power)
+        self.tanh = torch.nn.Tanh() if use_tanh else (lambda x: x)
+        self.fft_norm = "ortho" if use_tanh else None
+        self.n_fft = n_fft
+        self.use_grayscale = use_grayscale
+
+    def forward(self, x, y):
+        x = x.view(x.shape[0], -1, x.shape[-2], x.shape[-1])
+        y = y.view(y.shape[0], -1, y.shape[-2], y.shape[-1])
+
+        # CRITICAL FIX: Convert to grayscale to prevent color shift
+        if self.use_grayscale and x.shape[1] == 3:
+            # RGB to grayscale (ITU-R BT.601)
+            x = 0.299 * x[:, 0:1] + 0.587 * x[:, 1:2] + 0.114 * x[:, 2:3]
+            y = 0.299 * y[:, 0:1] + 0.587 * y[:, 1:2] + 0.114 * y[:, 2:3]
+
+        # Convert to frequency domain
+        x_f = self.tanh(
+            torch.fft.fftshift(
+                torch.fft.fft2(
+                    torch.fft.ifftshift(x, dim=(-1, -2)),
+                    norm=self.fft_norm,
+                    s=self.n_fft,
+                ),
+                dim=(-1, -2),
+            )
+        )
+        y_f = self.tanh(
+            torch.fft.fftshift(
+                torch.fft.fft2(
+                    torch.fft.ifftshift(y, dim=(-1, -2)),
+                    norm=self.fft_norm,
+                    s=self.n_fft,
+                ),
+                dim=(-1, -2),
+            )
+        )
+
+        # Compute loss in frequency domain
+        result = super().forward(x_f, y_f)
+
+        # DEBUG: Check if loss is zero
+        if result.abs().max() < 1e-10:
+            import warnings
+            warnings.warn(f"FFTLoss returning near-zero! x_f range: [{x_f.abs().min():.8f}, {x_f.abs().max():.8f}], "
+                         f"y_f range: [{y_f.abs().min():.8f}, {y_f.abs().max():.8f}]")
+
+        return result
+
+
+class LpipsVGG(PerceptualLoss):
+    """
+    LPIPS perceptual loss using VGG backbone.
+    This ensures the attack remains imperceptible to human vision.
+    """
+    def __init__(self, model_path=None):
+        # Try to load from pretrained models dir, fallback to auto-download
+        try:
+            if model_path and torch.cuda.is_available():
+                lps_loss = lpips.LPIPS(
+                    net="vgg",
+                    model_path=model_path,
+                    verbose=False,
+                ).eval()
+            else:
+                # Auto-download if no path provided
+                lps_loss = lpips.LPIPS(net="vgg", verbose=False).eval()
+        except Exception as e:
+            print(f"Warning: Failed to load VGG LPIPS, falling back to Alex: {e}")
+            lps_loss = lpips.LPIPS(net="alex", verbose=False).eval()
+
+        loss_fn = lambda x, y: lps_loss(x, y, normalize=True)
+        super().__init__(loss_fn)
+        self.lps_loss = lps_loss
+
+
+class LpipsAlex(PerceptualLoss):
+    """
+    LPIPS perceptual loss using AlexNet backbone.
+    Faster than VGG, slightly less accurate.
+    """
+    def __init__(self, model_path=None):
+        try:
+            if model_path:
+                lps_loss = lpips.LPIPS(
+                    net="alex",
+                    model_path=model_path,
+                    lpips=False,
+                    verbose=False,
+                ).eval()
+            else:
+                lps_loss = lpips.LPIPS(net="alex", lpips=False, verbose=False).eval()
+        except:
+            lps_loss = lpips.LPIPS(net="alex", verbose=False).eval()
+
+        loss_fn = lambda x, y: lps_loss(x, y, normalize=True)
+        super().__init__(loss_fn)
+        self.lps_loss = lps_loss
+
+
+class MeanLoss(torch.nn.Module):
+    """
+    Mean pooling loss for multi-scale analysis.
+    Used to ensure consistency across different scales.
+    """
+    def __init__(self, kernels=[(5, 5)]):
+        super().__init__()
+        assert isinstance(kernels, list) and len(kernels) > 0
+        for kernel in kernels:
+            self.__check_kernel(kernel)
+        self.kernels = kernels
+        self.mean_pools = [
+            torch.nn.AvgPool3d(kernel_size=(3, *kernel), stride=(3, 1, 1))
+            for kernel in self.kernels
+        ]
+
+    def __check_kernel(self, kernel):
+        assert isinstance(kernel, tuple) and len(kernel) == 2
+        assert isinstance(kernel[0], int) and isinstance(kernel[1], int)
+        assert kernel[0] > 0 and kernel[1] > 0
+        assert kernel[0] % 2 == 1 and kernel[1] % 2 == 1
+
+    def _mean_pad(self, shape, kernel, stride):
+        return [
+            0,
+            kernel[1] - (shape[-1] - (shape[-1] // kernel[1]) * kernel[1]) % kernel[1],
+            0,
+            kernel[0] - (shape[-2] - (shape[-2] // kernel[0]) * kernel[0]) % kernel[0],
+        ]
+
+    def _mean_diff(self, x, y, pool, padding):
+        x_p = pool(
+            torch.nn.functional.pad(x, padding, mode="reflect").unsqueeze(1)
+        ).squeeze(1)
+        with torch.no_grad():
+            y_p = pool(
+                torch.nn.functional.pad(y, padding, mode="reflect").unsqueeze(1)
+            ).squeeze(1)
+        return (x_p - y_p).abs().flatten(1).sum(-1, keepdims=True)
+
+    def forward(self, x, y):
+        x = x.view(x.shape[0], -1, x.shape[-2], x.shape[-1])
+        y = y.view(y.shape[0], -1, y.shape[-2], y.shape[-1])
+        paddings = [self._mean_pad(x.shape, kernel, (1, 1)) for kernel in self.kernels]
+        return torch.concat(
+            [
+                self._mean_diff(x, y, pool, padding)
+                for pool, padding in zip(self.mean_pools, paddings)
+            ],
+            -1,
+        ).sum(-1, keepdims=True)
+
+
+# ---------------------------------------------------------------------------
+# Deeploss-VGG (full perceptual loss from ai-watermark)
+# ---------------------------------------------------------------------------
+
+_DEEPloss_WEIGHT_NAME = "rgb_pnet_lin_vgg_trial0.pth"
+
+
+def _find_deeploss_weight_file(filename=_DEEPloss_WEIGHT_NAME) -> Path:
+    """Locate the Deeploss weight file in common install locations."""
+    candidates = []
+    env = os.getenv("DEEPloss_WEIGHTS_DIR") or os.getenv("DEEPLOSS_WEIGHTS_DIR")
+    if env:
+        candidates.append(Path(env))
+
+    here = Path(__file__).resolve()
+    comfy_root = None
+    for parent in here.parents:
+        if (parent / "pretrained").exists():
+            comfy_root = parent
+            break
+    if comfy_root:
+        candidates.append(comfy_root / "pretrained" / "deeploss")
+    for parent in here.parents:
+        candidates.append(parent / "pretrained_models" / "loss_provider" / "weights")
+        candidates.append(
+            parent
+            / "ai-watermark"
+            / "pretrained_models"
+            / "loss_provider"
+            / "weights"
+        )
+
+    seen = set()
+    for directory in candidates:
+        if directory is None:
+            continue
+        directory = directory.resolve()
+        if directory in seen:
+            continue
+        seen.add(directory)
+        candidate = directory / filename
+        if candidate.is_file():
+            return candidate
+    raise FileNotFoundError(
+        f"Deeploss weight '{filename}' not found. Run ai-watermark/download_data_and_models.sh "
+        "or set DEEPLOSS_WEIGHTS_DIR to the directory containing the weights."
+    )
+
+
+class _NetLinLayer(torch.nn.Module):
+    """A single linear layer applied on top of normalized VGG features."""
+
+    def __init__(self, chn_in, chn_out=1, use_dropout=False):
+        super().__init__()
+        layers = []
+        layers.append(torch.nn.Dropout(p=0.5 if use_dropout else 0.0))
+        layers.append(
+            torch.nn.Conv2d(chn_in, chn_out, kernel_size=1, stride=1, padding=0, bias=False)
+        )
+        self.model = torch.nn.Sequential(*layers)
+
+
+def _normalize_tensor(in_feat, eps=1e-10):
+    norm_factor = torch.sqrt(torch.sum(in_feat**2, dim=1)).view(
+        in_feat.size()[0], 1, in_feat.size()[2], in_feat.size()[3]
+    )
+    return in_feat / (norm_factor.expand_as(in_feat) + eps)
+
+
+class _VGG16Features(torch.nn.Module):
+    def __init__(self, requires_grad=False, pretrained=True):
+        super().__init__()
+        vgg_pretrained_features = models.vgg16(pretrained=pretrained).features
+        self.slice1 = torch.nn.Sequential()
+        self.slice2 = torch.nn.Sequential()
+        self.slice3 = torch.nn.Sequential()
+        self.slice4 = torch.nn.Sequential()
+        self.slice5 = torch.nn.Sequential()
+        for x in range(4):
+            self.slice1.add_module(str(x), vgg_pretrained_features[x])
+        for x in range(4, 9):
+            self.slice2.add_module(str(x), vgg_pretrained_features[x])
+        for x in range(9, 16):
+            self.slice3.add_module(str(x), vgg_pretrained_features[x])
+        for x in range(16, 23):
+            self.slice4.add_module(str(x), vgg_pretrained_features[x])
+        for x in range(23, 30):
+            self.slice5.add_module(str(x), vgg_pretrained_features[x])
+        if not requires_grad:
+            for param in self.parameters():
+                param.requires_grad = False
+
+    def forward(self, x):
+        h = self.slice1(x)
+        h_relu1_2 = h
+        h = self.slice2(h)
+        h_relu2_2 = h
+        h = self.slice3(h)
+        h_relu3_3 = h
+        h = self.slice4(h)
+        h_relu4_3 = h
+        h = self.slice5(h)
+        h_relu5_3 = h
+        outputs = namedtuple(
+            "VggOutputs", ["relu1_2", "relu2_2", "relu3_3", "relu4_3", "relu5_3"]
+        )
+        return outputs(h_relu1_2, h_relu2_2, h_relu3_3, h_relu4_3, h_relu5_3)
+
+
+class _PNetLin(torch.nn.Module):
+    """LPIPS-style network with learned linear heads."""
+
+    def __init__(
+        self,
+        colorspace="RGB",
+        reduction="none",
+        use_dropout=False,
+        pnet_rand=False,
+    ):
+        super().__init__()
+        self.reduction = reduction
+        self.colorspace = colorspace
+        self.net = _VGG16Features(pretrained=not pnet_rand, requires_grad=False)
+        self.chns = [64, 128, 256, 512, 512]
+        self.lin0 = _NetLinLayer(self.chns[0], use_dropout=use_dropout)
+        self.lin1 = _NetLinLayer(self.chns[1], use_dropout=use_dropout)
+        self.lin2 = _NetLinLayer(self.chns[2], use_dropout=use_dropout)
+        self.lin3 = _NetLinLayer(self.chns[3], use_dropout=use_dropout)
+        self.lin4 = _NetLinLayer(self.chns[4], use_dropout=use_dropout)
+        self.lins = [self.lin0, self.lin1, self.lin2, self.lin3, self.lin4]
+
+        self.shift = torch.nn.Parameter(
+            torch.Tensor([-0.030, -0.088, -0.188]).view(1, 3, 1, 1), requires_grad=False
+        )
+        self.scale = torch.nn.Parameter(
+            torch.Tensor([0.458, 0.448, 0.450]).view(1, 3, 1, 1), requires_grad=False
+        )
+
+    def forward(self, in0, in1):
+        in0_sc = (in0 - self.shift) / self.scale
+        in1_sc = (in1 - self.shift) / self.scale
+
+        if self.colorspace.upper() in ["GRAY", "GREY", "LA"]:
+            if lpips_util is None:
+                raise RuntimeError("lpips.util is required for grayscale Deeploss.")
+            gray_fn = getattr(lpips_util, "tensor2tensorGrayscaleLazy", None)
+            if gray_fn is None:
+                gray_fn = lpips_util.tensor2tensorGrayscale
+            in0_sc = gray_fn(in0_sc)
+            in1_sc = gray_fn(in1_sc)
+
+        outs0 = self.net(in0_sc)
+        outs1 = self.net(in1_sc)
+
+        diffs = []
+        for kk, out0 in enumerate(outs0):
+            feats0 = _normalize_tensor(out0)
+            feats1 = _normalize_tensor(outs1[kk])
+            diffs.append((feats0 - feats1) ** 2)
+
+        result = 0
+        for kk, lin in enumerate(self.lins):
+            result = result + torch.mean(torch.mean(lin.model(diffs[kk]), dim=3), dim=2)
+
+        result = result.view(result.size()[0], result.size()[1], 1, 1)
+        if self.reduction == "sum":
+            result = torch.sum(result)
+        elif self.reduction == "mean":
+            result = torch.mean(result)
+        return result
+
+
+class DeeplossVGG(torch.nn.Module):
+    """
+    Full Deeploss-VGG perceptual loss (as used in the UnMarker paper).
+
+    Requires the pretrained linear-head weights from ai-watermark's
+    download_data_and_models.sh script (file: rgb_pnet_lin_vgg_trial0.pth).
+    """
+
+    def __init__(
+        self,
+        colorspace="RGB",
+        reduction="none",
+        weights_path: str | None = None,
+        device: str | None = None,
+    ):
+        super().__init__()
+        self.device = torch.device(device or ("cuda" if torch.cuda.is_available() else "cpu"))
+        self.model = _PNetLin(colorspace=colorspace, reduction="none", use_dropout=False)
+
+        weight_file = Path(weights_path) if weights_path else _find_deeploss_weight_file()
+        state_dict = torch.load(weight_file, map_location="cpu")
+        missing, unexpected = self.model.load_state_dict(state_dict, strict=False)
+        if missing:
+            print(f"[Deeploss] Warning: missing keys {missing}")
+        if unexpected:
+            print(f"[Deeploss] Warning: unexpected keys {unexpected}")
+
+        self.model.to(self.device).eval()
+        self.reduction = reduction
+
+    def forward(self, x, y):
+        x = x.to(self.device)
+        y = y.to(self.device)
+        with torch.no_grad():
+            val = self.model(x, y)
+        if self.reduction == "mean":
+            val = val.mean()
+        elif self.reduction == "sum":
+            val = val.sum()
+        return val.view(x.shape[0], -1)
+
+
+def get_loss(loss_type, **kwargs):
+    """
+    Factory function to create loss instances.
+
+    Args:
+        loss_type: One of ["NormLoss", "FFTLoss", "LpipsVGG", "LpipsAlex", "MeanLoss"]
+        **kwargs: Arguments to pass to the loss constructor
+
+    Returns:
+        Loss function instance
+    """
+    loss_dict = {
+        "NormLoss": NormLoss,
+        "MeanLoss": MeanLoss,
+        "FFTLoss": FFTLoss,
+        "LpipsVGG": LpipsVGG,
+        "LpipsAlex": LpipsAlex,
+    }
+    assert loss_type in list(loss_dict.keys()), f"Unknown loss type: {loss_type}"
+    return loss_dict[loss_type](**kwargs)
diff --git a/custom_nodes/ComfyUI_INSTARAW/modules/location_data/locations.json b/custom_nodes/ComfyUI_INSTARAW/modules/location_data/locations.json
new file mode 100644
index 0000000000000000000000000000000000000000..c062ed17b12cc6a4db70932e8d0234d7af22fbb6
--- /dev/null
+++ b/custom_nodes/ComfyUI_INSTARAW/modules/location_data/locations.json
@@ -0,0 +1,117536 @@
+{
+  "Asia": {
+    "Japan": [
+      {
+        "city": "Tokyo",
+        "lat": [
+          35.486999999999995,
+          35.887
+        ],
+        "lon": [
+          139.54950000000002,
+          139.9495
+        ]
+      },
+      {
+        "city": "\u014csaka",
+        "lat": [
+          34.4939,
+          34.8939
+        ],
+        "lon": [
+          135.3022,
+          135.70219999999998
+        ]
+      },
+      {
+        "city": "Nagoya",
+        "lat": [
+          34.9833,
+          35.383300000000006
+        ],
+        "lon": [
+          136.70000000000002,
+          137.1
+        ]
+      },
+      {
+        "city": "Yokohama",
+        "lat": [
+          35.2442,
+          35.644200000000005
+        ],
+        "lon": [
+          139.43810000000002,
+          139.8381
+        ]
+      },
+      {
+        "city": "Fukuoka",
+        "lat": [
+          33.39,
+          33.790000000000006
+        ],
+        "lon": [
+          130.20170000000002,
+          130.6017
+        ]
+      },
+      {
+        "city": "Sapporo",
+        "lat": [
+          42.8619,
+          43.261900000000004
+        ],
+        "lon": [
+          141.1544,
+          141.5544
+        ]
+      },
+      {
+        "city": "Kawasaki",
+        "lat": [
+          35.3167,
+          35.7167
+        ],
+        "lon": [
+          139.5,
+          139.89999999999998
+        ]
+      },
+      {
+        "city": "K\u014dbe",
+        "lat": [
+          34.489999999999995,
+          34.89
+        ],
+        "lon": [
+          134.99560000000002,
+          135.3956
+        ]
+      },
+      {
+        "city": "Ky\u014dto",
+        "lat": [
+          34.811699999999995,
+          35.2117
+        ],
+        "lon": [
+          135.56830000000002,
+          135.9683
+        ]
+      },
+      {
+        "city": "Saitama",
+        "lat": [
+          35.6614,
+          36.061400000000006
+        ],
+        "lon": [
+          139.4456,
+          139.8456
+        ]
+      },
+      {
+        "city": "Hiroshima",
+        "lat": [
+          34.191399999999994,
+          34.5914
+        ],
+        "lon": [
+          132.2519,
+          132.65189999999998
+        ]
+      },
+      {
+        "city": "Sendai",
+        "lat": [
+          38.0682,
+          38.4682
+        ],
+        "lon": [
+          140.66940000000002,
+          141.0694
+        ]
+      },
+      {
+        "city": "Chiba",
+        "lat": [
+          35.4073,
+          35.807300000000005
+        ],
+        "lon": [
+          139.90640000000002,
+          140.3064
+        ]
+      },
+      {
+        "city": "Setagaya",
+        "lat": [
+          35.4466,
+          35.8466
+        ],
+        "lon": [
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+        "city": "Buderim",
+        "lat": [
+          -26.8847,
+          -26.4847
+        ],
+        "lon": [
+          152.85320000000002,
+          153.2532
+        ]
+      },
+      {
+        "city": "Hervey Bay",
+        "lat": [
+          -25.49,
+          -25.09
+        ],
+        "lon": [
+          152.64000000000001,
+          153.04
+        ]
+      }
+    ],
+    "New Zealand": [
+      {
+        "city": "Auckland",
+        "lat": [
+          -37.049200000000006,
+          -36.6492
+        ],
+        "lon": [
+          174.5653,
+          174.96529999999998
+        ]
+      },
+      {
+        "city": "Christchurch",
+        "lat": [
+          -43.731100000000005,
+          -43.3311
+        ],
+        "lon": [
+          172.4361,
+          172.8361
+        ]
+      },
+      {
+        "city": "Wellington",
+        "lat": [
+          -41.4889,
+          -41.088899999999995
+        ],
+        "lon": [
+          174.5772,
+          174.97719999999998
+        ]
+      },
+      {
+        "city": "Northcote",
+        "lat": [
+          -37.001900000000006,
+          -36.6019
+        ],
+        "lon": [
+          174.54940000000002,
+          174.9494
+        ]
+      },
+      {
+        "city": "Hamilton",
+        "lat": [
+          -37.9833,
+          -37.583299999999994
+        ],
+        "lon": [
+          175.0833,
+          175.48329999999999
+        ]
+      },
+      {
+        "city": "Tauranga",
+        "lat": [
+          -37.8858,
+          -37.4858
+        ],
+        "lon": [
+          175.9667,
+          176.36669999999998
+        ]
+      },
+      {
+        "city": "Lower Hutt",
+        "lat": [
+          -41.416700000000006,
+          -41.0167
+        ],
+        "lon": [
+          174.7167,
+          175.11669999999998
+        ]
+      },
+      {
+        "city": "Palmerston North",
+        "lat": [
+          -40.5549,
+          -40.1549
+        ],
+        "lon": [
+          175.4095,
+          175.80949999999999
+        ]
+      },
+      {
+        "city": "Napier",
+        "lat": [
+          -39.6903,
+          -39.290299999999995
+        ],
+        "lon": [
+          176.7178,
+          177.1178
+        ]
+      },
+      {
+        "city": "New Plymouth",
+        "lat": [
+          -39.2578,
+          -38.8578
+        ],
+        "lon": [
+          173.8742,
+          174.27419999999998
+        ]
+      },
+      {
+        "city": "Porirua",
+        "lat": [
+          -41.3333,
+          -40.933299999999996
+        ],
+        "lon": [
+          174.65,
+          175.04999999999998
+        ]
+      },
+      {
+        "city": "Rotorua",
+        "lat": [
+          -38.3378,
+          -37.937799999999996
+        ],
+        "lon": [
+          176.0514,
+          176.45139999999998
+        ]
+      },
+      {
+        "city": "Whangarei",
+        "lat": [
+          -35.925000000000004,
+          -35.525
+        ],
+        "lon": [
+          174.1236,
+          174.5236
+        ]
+      }
+    ],
+    "Papua New Guinea": [
+      {
+        "city": "Port Moresby",
+        "lat": [
+          -9.678899999999999,
+          -9.2789
+        ],
+        "lon": [
+          146.94940000000003,
+          147.3494
+        ]
+      },
+      {
+        "city": "Lae",
+        "lat": [
+          -6.9303,
+          -6.5302999999999995
+        ],
+        "lon": [
+          146.8008,
+          147.2008
+        ]
+      },
+      {
+        "city": "Popondetta",
+        "lat": [
+          -8.965599999999998,
+          -8.5656
+        ],
+        "lon": [
+          148.03470000000002,
+          148.4347
+        ]
+      },
+      {
+        "city": "Mendi",
+        "lat": [
+          -6.3478,
+          -5.9478
+        ],
+        "lon": [
+          143.4572,
+          143.85719999999998
+        ]
+      }
+    ],
+    "Fiji": [
+      {
+        "city": "Suva",
+        "lat": [
+          -18.333299999999998,
+          -17.9333
+        ],
+        "lon": [
+          178.2333,
+          178.6333
+        ]
+      },
+      {
+        "city": "Nadi",
+        "lat": [
+          -18.0,
+          -17.6
+        ],
+        "lon": [
+          177.2167,
+          177.61669999999998
+        ]
+      },
+      {
+        "city": "Nausori",
+        "lat": [
+          -18.2244,
+          -17.8244
+        ],
+        "lon": [
+          178.3454,
+          178.7454
+        ]
+      },
+      {
+        "city": "Lautoka",
+        "lat": [
+          -17.824199999999998,
+          -17.4242
+        ],
+        "lon": [
+          177.2528,
+          177.65279999999998
+        ]
+      }
+    ],
+    "Solomon Islands": [
+      {
+        "city": "Honiara",
+        "lat": [
+          -9.633299999999998,
+          -9.2333
+        ],
+        "lon": [
+          159.75,
+          160.14999999999998
+        ]
+      }
+    ]
+  }
+}
\ No newline at end of file
diff --git a/custom_nodes/ComfyUI_INSTARAW/modules/neural_grain/net.py b/custom_nodes/ComfyUI_INSTARAW/modules/neural_grain/net.py
new file mode 100644
index 0000000000000000000000000000000000000000..6a0870658cc5a2eb0c6c21d529331849bbdf0a9f
--- /dev/null
+++ b/custom_nodes/ComfyUI_INSTARAW/modules/neural_grain/net.py
@@ -0,0 +1,193 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+#%%
+import torch
+import torch.nn as nn
+
+#%%
+ 
+class ResidualBlock(nn.Module):
+    def __init__(self, channel, k_size = (3,3)):
+        super().__init__()
+        self.block = nn.Sequential(nn.Conv2d(channel,channel,kernel_size = k_size, stride=1, padding='same', padding_mode='reflect'),
+                                   nn.LeakyReLU(),
+                                   nn.InstanceNorm2d(channel),
+                                   nn.Conv2d(channel,channel,kernel_size = k_size, stride=1, padding='same', padding_mode='reflect'),
+                                   )
+        self.relu_out = nn.LeakyReLU()
+    def forward(self,x):
+        return self.relu_out(self.block(x) + x)
+    
+class MyNorm(nn.Module):
+    """
+    Custom Adaptive Instance Normalization layer
+    """
+    def __init__(self, channel_size, insize = 1):
+        super().__init__()
+        self.insize = insize
+        self.channel_size = channel_size
+        self.std_weight = nn.Linear(insize, self.channel_size)
+        self.mean_weight = nn.Linear(insize, self.channel_size)
+
+    def forward(self, x, grain_type):
+        
+        std = self.std_weight(grain_type)
+        mean = self.mean_weight(grain_type)
+        
+        x = x * std.view(*std.shape,1,1).repeat(1,1,*x.shape[-2:])
+        x = x + mean.view(*mean.shape,1,1).repeat(1,1,*x.shape[-2:])        
+        return x
+        
+class GrainNet(nn.Module):
+    def __init__(self, activation = 'tanh', block_nb = 2):
+        """
+        Network which adds grain to a given image.
+
+        Parameters
+        ----------
+        activation : bool, optional
+            Tells if we put a sigmoid at the end of the network. The default is True.
+
+        Returns
+        -------
+        None.
+
+        """
+        super(GrainNet, self).__init__()
+
+        if not block_nb in [1,2,3]:
+            raise ValueError('block_nb must be 1,2 or 3')
+        self.block_nb = block_nb
+        
+        self.entry_conv = nn.Sequential(nn.Conv2d(2,16,kernel_size = (3,3), stride=1, padding='same', padding_mode='reflect'),
+                                    nn.LeakyReLU())
+        
+        self.block1 = nn.Sequential(ResidualBlock(16),
+                                    nn.InstanceNorm2d(16))
+        self.mn1 = MyNorm(16)
+        
+        if self.block_nb == 3:
+
+            self.augment = nn.Sequential(nn.Conv2d(16,32,kernel_size = (3,3), stride=1, padding='same', padding_mode='reflect'),
+                                        nn.LeakyReLU())
+            
+            self.block2 = nn.Sequential(ResidualBlock(32),
+                                        nn.InstanceNorm2d(32))
+            self.mn2 = MyNorm(32)
+                                    
+            self.reduce = nn.Sequential(nn.Conv2d(32,16,kernel_size = (3,3), stride=1, padding='same', padding_mode='reflect'),
+                                        nn.LeakyReLU())
+            
+        if self.block_nb > 1:
+                
+            self.block3 = nn.Sequential(ResidualBlock(16),
+                                        nn.InstanceNorm2d(16))
+            self.mn3 = MyNorm(16)
+
+        self.out_conv = nn.Sequential(nn.Conv2d(16,1,kernel_size = (3,3), stride=1, padding='same', padding_mode='reflect'))
+                
+
+        self.activation = activation
+        self.tanh = nn.Tanh()
+        self.sigmoid = nn.Sigmoid()       
+    
+    def forward(self, img, grain_radius, seed=None):
+        if not (seed is None):
+            torch.manual_seed(seed)
+            
+        noise = torch.randn(img.shape)
+        if self.entry_conv[0].weight.is_cuda:
+            noise = noise.cuda()
+        
+        x = torch.cat((noise,img), dim=1)
+        
+        x0 = self.entry_conv(x)
+        
+        x1 = self.block1(x0)
+        x1 = self.mn1(x1, grain_radius)
+
+        if self.block_nb == 3:
+            x2 = self.augment(x1)
+            
+            x3 = self.block2(x2)
+            x3 = self.mn2(x3, grain_radius)
+            
+            x4 = self.reduce(x3)
+
+            x5 = self.block3(x4 + x1)
+            x5 = self.mn3(x5, grain_radius)
+            x6 = self.out_conv(x5 + x0)
+
+        if self.block_nb == 2:          
+            x5 = self.block3(x1)
+            x5 = self.mn3(x5, grain_radius)
+            x6 = self.out_conv(x5 + x0)
+        
+        if self.block_nb == 1:
+            x6 = self.out_conv(x1)
+
+        if self.activation == 'tanh':
+            x6 = self.tanh(x6)
+            x6 = torch.clamp((0.5*x6 + 0.5), 0, 1) #Normalise images
+        elif self.activation == 'sigmoid':
+            x6 = self.sigmoid(x6)        
+        return x6
+    
+class Classifier(nn.Module):
+    def __init__(self, nb_channels = 1, latent_size = 1, activation = 'sigmoid'):
+        """
+        Classifier architecture
+
+        Parameters
+        ----------
+        size: int
+        Input size (we assume image is square), it defines how many pooling and conv layers we add to the network.
+
+        nb_channels: int
+        Indicates how many channels has the input
+
+        latent_size: int
+        Indicates how many dimensions has the output
+
+        Returns
+        -------
+        None.
+
+        """
+        super(Classifier, self).__init__()
+        
+        self.latent_size = latent_size
+        self.nb_channels = nb_channels
+
+        self.layers = nn.Sequential(
+            nn.Conv2d(self.nb_channels, 16, kernel_size = (3,3), stride=1, padding='same', padding_mode='reflect'),
+            nn.SiLU(),
+            nn.Conv2d(16, 16, kernel_size = (3,3), stride=1, padding='same', padding_mode='reflect'),
+            nn.SiLU(),
+            nn.Conv2d(16, 16, kernel_size = (3,3), stride=1, padding='same', padding_mode='reflect'),
+            nn.SiLU(),
+            nn.AdaptiveAvgPool2d((16,16)))
+        
+        self.dense1 = nn.Linear(4096,512)
+        self.dense2 = nn.Linear(512,latent_size)
+
+        self.activation = activation
+        if not self.activation is None:
+            self.acti = nn.Sigmoid()  
+
+    def to(self, device):
+        for i in range(len(self.layers)):
+            self.layers[i].to(device)
+        (self.dense1).to(device)
+        (self.dense2).to(device)
+
+    def forward(self, x):
+        x = self.layers(x)
+        z = self.dense1(x.flatten(start_dim=1))
+        z = self.dense2(z)
+        if not self.activation is None:
+            z = self.acti(z)
+        return z   
+        
+    
+# %%
\ No newline at end of file
diff --git a/custom_nodes/ComfyUI_INSTARAW/nodes/__init__.py b/custom_nodes/ComfyUI_INSTARAW/nodes/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..785de9930cdb994f3f5f934063bbef80fd9f16f7
--- /dev/null
+++ b/custom_nodes/ComfyUI_INSTARAW/nodes/__init__.py
@@ -0,0 +1,62 @@
+# ---
+# Filename: ../ComfyUI_INSTARAW/nodes/__init__.py
+# ---
+
+"""
+ComfyUI INSTARAW Nodes
+A collection of custom nodes by Instara
+"""
+
+from .api_nodes import (
+    NODE_CLASS_MAPPINGS as API_NODE_MAPPINGS,
+    NODE_DISPLAY_NAME_MAPPINGS as API_NODE_DISPLAY_MAPPINGS,
+)
+from .utility_nodes import (
+    NODE_CLASS_MAPPINGS as UTILITY_NODE_MAPPINGS,
+    NODE_DISPLAY_NAME_MAPPINGS as UTILITY_NODE_DISPLAY_MAPPINGS,
+)
+from .logic_nodes import (
+    NODE_CLASS_MAPPINGS as LOGIC_NODE_MAPPINGS,
+    NODE_DISPLAY_NAME_MAPPINGS as LOGIC_NODE_DISPLAY_MAPPINGS,
+)
+from .interactive_nodes import (
+    NODE_CLASS_MAPPINGS as INTERACTIVE_NODE_MAPPINGS,
+    NODE_DISPLAY_NAME_MAPPINGS as INTERACTIVE_NODE_DISPLAY_MAPPINGS,
+)
+from .nsfw_nodes import (
+    NODE_CLASS_MAPPINGS as NSFW_NODE_MAPPINGS,
+    NODE_DISPLAY_NAME_MAPPINGS as NSFW_NODE_DISPLAY_MAPPINGS,
+)
+# This now correctly points to the new folder
+from .output_nodes import (
+    NODE_CLASS_MAPPINGS as OUTPUT_NODE_MAPPINGS,
+    NODE_DISPLAY_NAME_MAPPINGS as OUTPUT_NODE_DISPLAY_MAPPINGS,
+)
+from .input_nodes import (
+    NODE_CLASS_MAPPINGS as INPUT_NODE_MAPPINGS,
+    NODE_DISPLAY_NAME_MAPPINGS as INPUT_NODE_DISPLAY_MAPPINGS,
+)
+
+
+# Combine all node mappings from different categories
+NODE_CLASS_MAPPINGS = {
+    **API_NODE_MAPPINGS,
+    **UTILITY_NODE_MAPPINGS,
+    **LOGIC_NODE_MAPPINGS,
+    **INTERACTIVE_NODE_MAPPINGS,
+    **NSFW_NODE_MAPPINGS,
+    **OUTPUT_NODE_MAPPINGS,
+    **INPUT_NODE_MAPPINGS,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    **API_NODE_DISPLAY_MAPPINGS,
+    **UTILITY_NODE_DISPLAY_MAPPINGS,
+    **LOGIC_NODE_DISPLAY_MAPPINGS,
+    **INTERACTIVE_NODE_DISPLAY_MAPPINGS,
+    **NSFW_NODE_DISPLAY_MAPPINGS,
+    **OUTPUT_NODE_DISPLAY_MAPPINGS,
+    **INPUT_NODE_DISPLAY_MAPPINGS,
+}
+
+__all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS"]
\ No newline at end of file
diff --git a/custom_nodes/ComfyUI_INSTARAW/nodes/api_nodes/__init__.py b/custom_nodes/ComfyUI_INSTARAW/nodes/api_nodes/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..21783fa6a41180d55c659f36a5633666b47e8253
--- /dev/null
+++ b/custom_nodes/ComfyUI_INSTARAW/nodes/api_nodes/__init__.py
@@ -0,0 +1,64 @@
+"""
+INSTARAW API Nodes
+External API integrations for ComfyUI
+"""
+
+from .generative_api_nodes import (
+    NODE_CLASS_MAPPINGS as GENERATIVE_API_MAPPINGS,
+    NODE_DISPLAY_NAME_MAPPINGS as GENERATIVE_API_DISPLAY_MAPPINGS,
+)
+from .ideogram import (
+    NODE_CLASS_MAPPINGS as IDEOGRAM_MAPPINGS,
+    NODE_DISPLAY_NAME_MAPPINGS as IDEOGRAM_DISPLAY_MAPPINGS,
+)
+from .gemini_native import (
+    NODE_CLASS_MAPPINGS as GEMINI_NATIVE_MAPPINGS,
+    NODE_DISPLAY_NAME_MAPPINGS as GEMINI_NATIVE_DISPLAY_MAPPINGS,
+)
+from .grok_native import (
+    NODE_CLASS_MAPPINGS as GROK_NATIVE_MAPPINGS,
+    NODE_DISPLAY_NAME_MAPPINGS as GROK_NATIVE_DISPLAY_MAPPINGS,
+)
+from .flux_kontext import (
+    NODE_CLASS_MAPPINGS as FLUX_KONTEXT_MAPPINGS,
+    NODE_DISPLAY_NAME_MAPPINGS as FLUX_KONTEXT_DISPLAY_MAPPINGS,
+)
+from .flux_fill import (
+    NODE_CLASS_MAPPINGS as FLUX_FILL_MAPPINGS,
+    NODE_DISPLAY_NAME_MAPPINGS as FLUX_FILL_DISPLAY_MAPPINGS,
+)
+from .nano_banana_pro_multi import (
+    NODE_CLASS_MAPPINGS as NANO_BANANA_PRO_MULTI_MAPPINGS,
+    NODE_DISPLAY_NAME_MAPPINGS as NANO_BANANA_PRO_MULTI_DISPLAY_MAPPINGS,
+)
+from .parallel_batch_generator import (
+    NODE_CLASS_MAPPINGS as PARALLEL_BATCH_MAPPINGS,
+    NODE_DISPLAY_NAME_MAPPINGS as PARALLEL_BATCH_DISPLAY_MAPPINGS,
+)
+from . import creative_api
+
+
+# Combine all API node mappings
+NODE_CLASS_MAPPINGS = {
+    **GENERATIVE_API_MAPPINGS,
+    **IDEOGRAM_MAPPINGS,
+    **GEMINI_NATIVE_MAPPINGS,
+    **GROK_NATIVE_MAPPINGS,
+    **FLUX_KONTEXT_MAPPINGS,
+    **FLUX_FILL_MAPPINGS,
+    **NANO_BANANA_PRO_MULTI_MAPPINGS,
+    **PARALLEL_BATCH_MAPPINGS,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    **GENERATIVE_API_DISPLAY_MAPPINGS,
+    **IDEOGRAM_DISPLAY_MAPPINGS,
+    **GEMINI_NATIVE_DISPLAY_MAPPINGS,
+    **GROK_NATIVE_DISPLAY_MAPPINGS,
+    **FLUX_KONTEXT_DISPLAY_MAPPINGS,
+    **FLUX_FILL_DISPLAY_MAPPINGS,
+    **NANO_BANANA_PRO_MULTI_DISPLAY_MAPPINGS,
+    **PARALLEL_BATCH_DISPLAY_MAPPINGS,
+}
+
+__all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS"]
diff --git a/custom_nodes/ComfyUI_INSTARAW/nodes/api_nodes/creative_api.py b/custom_nodes/ComfyUI_INSTARAW/nodes/api_nodes/creative_api.py
new file mode 100644
index 0000000000000000000000000000000000000000..2187454a88a1cdd09d3f3503a9351c7a91b9b717
--- /dev/null
+++ b/custom_nodes/ComfyUI_INSTARAW/nodes/api_nodes/creative_api.py
@@ -0,0 +1,1081 @@
+# ---
+# Filename: ../combined_test_codex/ComfyUI_INSTARAW/nodes/api_nodes/creative_api.py
+# ---
+
+# ---
+# Filename: ../ComfyUI_INSTARAW/creative_api.py
+# Creative Prompt Generation API - Gemini & Grok Integration
+# ---
+
+"""
+Backend API endpoint for creative prompt generation using Gemini and Grok APIs.
+Supports both inspiration-based generation and character-consistent generation.
+"""
+
+import os
+import json
+import hashlib
+import aiohttp
+from aiohttp import web
+from server import PromptServer
+
+CORS_HEADERS = {
+    "Access-Control-Allow-Origin": "*",
+    "Access-Control-Allow-Methods": "POST, OPTIONS",
+    "Access-Control-Allow-Headers": "Content-Type",
+}
+
+
+# === Gemini Integration ===
+async def generate_with_gemini(system_prompt, user_prompt, model="gemini-2.5-pro", api_key=None, temperature=0.9, top_p=0.9, images=None):
+    """
+    Generate creative prompts using Google Gemini API.
+    Returns a list of {positive, negative, tags} dictionaries.
+    Uses the NEW Google Genai SDK pattern (matching gemini_native.py).
+
+    Args:
+        images: List of base64-encoded image strings (for vision/img2img mode)
+    """
+    try:
+        from google import genai
+        from google.genai import types
+        import base64
+    except ImportError:
+        raise ImportError("The 'google-genai' library is required. Run: pip install -U google-genai")
+
+    # Use provided API key or fall back to environment variable
+    if not api_key or api_key.strip() == "":
+        api_key = os.environ.get("GEMINI_API_KEY")
+    if not api_key or api_key.strip() == "":
+        raise ValueError("Gemini API Key is missing. Provide it in the node or set GEMINI_API_KEY env var.")
+
+    try:
+        # NEW SDK pattern: use Client (matching gemini_native.py)
+        client = genai.Client(api_key=api_key)
+
+        # Build parts list (text + optional images)
+        parts = [types.Part.from_text(text=f"{system_prompt}\n\n{user_prompt}")]
+
+        # Add images if provided
+        if images and len(images) > 0:
+            for img_base64 in images:
+                try:
+                    # Decode base64 to bytes
+                    image_bytes = base64.b64decode(img_base64)
+                    parts.append(types.Part.from_bytes(data=image_bytes, mime_type='image/png'))
+                    print(f"[RPG Creative API] Added image to Gemini vision request (base64 length: {len(img_base64)})")
+                except Exception as img_error:
+                    print(f"[RPG Creative API] Failed to decode image: {img_error}")
+
+        contents = [types.Content(role="user", parts=parts)]
+
+        # Safety settings
+        safety_settings = [
+            types.SafetySetting(category=cat, threshold="BLOCK_NONE")
+            for cat in ["HARM_CATEGORY_HATE_SPEECH", "HARM_CATEGORY_DANGEROUS_CONTENT",
+                       "HARM_CATEGORY_HARASSMENT", "HARM_CATEGORY_SEXUALLY_EXPLICIT"]
+        ]
+
+        # Generation config (matching gemini_native.py pattern)
+        config = types.GenerateContentConfig(
+            temperature=temperature,
+            top_p=top_p,
+            candidate_count=1,
+            safety_settings=safety_settings,
+            response_mime_type="application/json"
+        )
+
+        # Call API (synchronous call, but wrapped in async function)
+        response = client.models.generate_content(
+            model=f"models/{model}",
+            contents=contents,
+            config=config
+        )
+
+        if not response.candidates:
+            raise Exception("Gemini returned no candidates (likely blocked by safety filters)")
+
+        # Parse JSON response
+        result_text = response.text
+        return parse_prompt_json(result_text)
+
+    except Exception as e:
+        print(f"[RPG Creative API] Gemini error: {e}")
+        raise
+
+
+# === Grok Integration ===
+async def generate_with_grok(system_prompt, user_prompt, model="grok-4", api_key=None, temperature=0.9, top_p=0.9, images=None):
+	"""
+	Generate creative prompts using xAI Grok API.
+	Returns a list of {positive, negative, tags} dictionaries.
+
+	Args:
+		images: List of base64-encoded image strings (for vision/img2img mode)
+	"""
+	# Use provided API key or fall back to environment variable
+	if not api_key or api_key.strip() == "":
+		api_key = os.environ.get("XAI_API_KEY")
+	if not api_key or api_key.strip() == "":
+		raise ValueError("Grok API Key is missing. Provide it in the node or set XAI_API_KEY env var.")
+
+	try:
+		base_url = os.environ.get("XAI_API_BASE", "https://api.x.ai")
+		url = f"{base_url.rstrip('/')}/v1/chat/completions"
+
+		# Build user message content (text + optional images)
+		if images and len(images) > 0:
+			# Vision mode: multimodal content array
+			user_content = [
+				{"type": "text", "text": user_prompt}
+			]
+
+			for img_base64 in images:
+				try:
+					# Grok vision API expects base64 images in this format
+					user_content.append({
+						"type": "image_url",
+						"image_url": {
+							"url": f"data:image/png;base64,{img_base64}"
+						}
+					})
+					print(f"[RPG Creative API] Added image to Grok vision request (base64 length: {len(img_base64)})")
+				except Exception as img_error:
+					print(f"[RPG Creative API] Failed to add image to Grok request: {img_error}")
+		else:
+			# Text-only mode
+			user_content = user_prompt
+
+		payload = {
+            "model": model,
+            "messages": [
+                {"role": "system", "content": system_prompt},
+                {"role": "user", "content": user_content},
+            ],
+            "temperature": temperature,
+            "top_p": top_p,
+            "response_format": {"type": "json_object"},
+        }
+
+		headers = {
+			"Authorization": f"Bearer {api_key.strip()}",
+			"Content-Type": "application/json",
+		}
+
+		timeout = aiohttp.ClientTimeout(total=300)
+		async with aiohttp.ClientSession(timeout=timeout) as session:
+			async with session.post(url, json=payload, headers=headers) as resp:
+				body = await resp.text()
+				if resp.status >= 400:
+					raise RuntimeError(f"Grok API error {resp.status}: {body}")
+				try:
+					data = json.loads(body)
+				except json.JSONDecodeError as e:
+					raise RuntimeError(f"Grok API returned invalid JSON: {e}")
+
+		choices = data.get("choices") or []
+		if not choices:
+			raise RuntimeError("Grok API returned no choices")
+
+		first_choice = choices[0]
+		message = first_choice.get("message") or {}
+		content = message.get("content") or first_choice.get("text", "")
+
+		if not content:
+			raise RuntimeError("Grok API returned empty content")
+
+		return parse_prompt_json(content)
+	except Exception as e:
+		print(f"[RPG Creative API] Grok error: {e}")
+		raise
+
+
+# === Helper Functions ===
+def parse_prompt_json(text):
+    """
+    Parse JSON array of prompts from API response.
+    Handles markdown code blocks and extracts JSON.
+    """
+    # Try to find JSON in markdown code blocks
+    if "```json" in text:
+        start = text.find("```json") + 7
+        end = text.find("```", start)
+        text = text[start:end].strip()
+    elif "```" in text:
+        start = text.find("```") + 3
+        end = text.find("```", start)
+        text = text[start:end].strip()
+
+    try:
+        data = json.loads(text)
+        if isinstance(data, list):
+            return data
+        elif isinstance(data, dict) and "prompts" in data:
+            return data["prompts"]
+        else:
+            return [data]
+    except json.JSONDecodeError:
+        # If JSON parsing fails, create a single entry from the text
+        return [{
+            "positive": text.strip(),
+            "negative": "",
+            "tags": []
+        }]
+
+
+def build_system_prompt(is_sdxl=False, character_description="", generation_mode="img2img", affect_elements=None, user_text_input="", random_inspiration_prompts=None, generation_style="reality"):
+    """
+    Build system prompt for creative generation.
+
+    Args:
+        is_sdxl: Whether to optimize for SDXL
+        character_description: Detailed character description for consistency
+        generation_mode: "img2img" or "txt2img"
+        affect_elements: List of elements to modify (img2img only): ["background", "outfit", "pose", "lighting"]
+        user_text_input: User's custom input (txt2img only)
+        random_inspiration_prompts: List of random prompts for inspiration (txt2img only)
+        generation_style: "reality" (strict adherence) or "creative" (flexible inspiration)
+    """
+    base_prompt = """You are an expert AI prompt engineer specializing in creating high-quality, detailed prompts for REALISTIC photographic image generation models.
+
+IMPORTANT: All images are REALISTIC photography. Do not include artistic styles, illustrations, or non-photographic elements."""
+
+    if is_sdxl:
+        base_prompt += "\n\nIMPORTANT: Generate prompts optimized for SDXL (Stable Diffusion XL). Use SDXL-specific quality tags like 'masterpiece', 'best quality', 'photorealistic', 'high resolution'."
+
+    # Character consistency
+    if character_description and character_description.strip():
+        base_prompt += f"\n\nCHARACTER CONSISTENCY: All prompts must accurately describe this character with exact physical features:\n{character_description}"
+
+    # Mode-specific instructions
+    if generation_mode == "img2img":
+        base_prompt += "\n\nMODE: Image-to-Image Generation"
+
+        if affect_elements and len(affect_elements) > 0:
+            # Inclusion mode - only modify checked elements
+            elements_str = ", ".join(affect_elements)
+            base_prompt += f"\n\nIMPORTANT INSTRUCTION: You are modifying ONLY these elements: {elements_str}"
+            base_prompt += "\n\nFor all OTHER elements NOT in this list, describe them EXACTLY as they appear in the input image. Do not change or modify them."
+            base_prompt += f"\n\nElements to MODIFY creatively: {elements_str}"
+            base_prompt += "\nElements to KEEP as-is: Everything else (describe exactly as shown)"
+        else:
+            # No checkboxes - describe as-is
+            base_prompt += "\n\nIMPORTANT INSTRUCTION: Describe the input images EXACTLY as they appear. Create detailed, accurate descriptions that capture all visual elements faithfully."
+
+    elif generation_mode == "txt2img":
+        base_prompt += "\n\nMODE: Text-to-Image Generation (creating new prompts from scratch)"
+
+        if random_inspiration_prompts and len(random_inspiration_prompts) > 0:
+            if generation_style == "reality":
+                # Reality Mode: Strict adherence to library prompts
+                base_prompt += "\n\n๐ŸŽฏ REALITY MODE: You must ONLY use elements, words, and concepts from these reference prompts. Stay precise and constrained to what's provided:"
+                for i, prompt in enumerate(random_inspiration_prompts[:5]):  # Limit to 5 for context
+                    pos = prompt.get("prompt", {}).get("positive", "")
+                    tags = ", ".join(prompt.get("tags", [])[:5])
+                    base_prompt += f"\n\nReference {i+1}:"
+                    base_prompt += f"\nPrompt: {pos[:200]}..."  # Truncate long prompts
+                    base_prompt += f"\nTags: {tags}"
+                base_prompt += "\n\nIMPORTANT: Your generated prompts should ONLY combine and rearrange elements from these references. Do not introduce new concepts or elements not present in these prompts."
+            else:
+                # Creative Mode: Flexible inspiration
+                base_prompt += "\n\nโœจ CREATIVE MODE: Use these prompts as creative inspiration for generating diverse, high-quality variations. Feel free to be flexible and creative:"
+                for i, prompt in enumerate(random_inspiration_prompts[:5]):  # Limit to 5 for context
+                    pos = prompt.get("prompt", {}).get("positive", "")
+                    tags = ", ".join(prompt.get("tags", [])[:5])
+                    base_prompt += f"\n\nInspiration {i+1}:"
+                    base_prompt += f"\nPrompt: {pos[:200]}..."  # Truncate long prompts
+                    base_prompt += f"\nTags: {tags}"
+                base_prompt += "\n\nYou can be creative and add new elements while maintaining the overall style and quality of the inspiration prompts."
+
+        if user_text_input and user_text_input.strip():
+            base_prompt += f"\n\nUSER INPUT: Incorporate this user guidance into your prompts:\n{user_text_input}"
+
+    base_prompt += """\n\nOUTPUT FORMAT: You MUST return a valid JSON array of prompt objects. Do not include any other text or markdown. Each object must have these keys:
+- "positive": A string containing the detailed positive prompt for realistic photography
+- "negative": A string containing the negative prompt (avoid: unrealistic, illustration, painting, drawing, art, artistic, low quality, deformed, etc.)
+- "tags": An array of strings representing relevant tags
+
+Example:
+[
+  {
+    "positive": "masterpiece, best quality, photorealistic, high resolution, professional photography, 1girl, blonde hair, blue eyes, smiling, park background, natural sunlight, bokeh, sharp focus",
+    "negative": "unrealistic, illustration, painting, drawing, art, artistic, low quality, deformed, bad anatomy, blurry, amateur",
+    "tags": ["portrait", "photorealistic", "outdoor", "natural_lighting"]
+  }
+]
+"""
+
+    return base_prompt
+
+
+def build_user_prompt(generation_count, generation_mode="img2img", images_data=None):
+    """
+    Build user prompt for creative generation.
+
+    Args:
+        generation_count: Number of prompts to generate
+        generation_mode: "img2img" or "txt2img"
+        images_data: For img2img, information about the images (optional)
+    """
+    if generation_mode == "img2img":
+        if images_data and len(images_data) > 0:
+            return f"""Generate {generation_count} detailed, accurate prompts for the {len(images_data)} input image(s) provided.
+
+Follow the instructions in the system prompt regarding which elements to modify and which to keep as-is.
+
+Each prompt should:
+1. Accurately describe all visual elements as specified
+2. Maintain photorealistic quality
+3. Be detailed and professional
+4. Follow the modification rules (if any elements are specified to be changed)
+
+Generate {generation_count} unique prompt(s)."""
+        else:
+            return f"""Generate {generation_count} high-quality prompts for realistic photographic image generation based on the input images.
+
+Each prompt should be detailed, professional, and capture all visual elements accurately.
+
+Generate {generation_count} unique prompt(s)."""
+
+    else:  # txt2img
+        return f"""Generate {generation_count} high-quality, creative prompts for realistic photographic image generation.
+
+Using the inspiration prompts and user input provided (if any), create diverse, professional prompts that:
+1. Are detailed and vivid
+2. Focus on realistic photographic elements
+3. Include appropriate quality tags
+4. Specify lighting, composition, and technical details
+5. Are varied and creative while maintaining high quality
+
+Generate {generation_count} unique prompts."""
+
+
+# === API Endpoint ===
+async def _generate_creative_prompts(request):
+    """
+    POST /instaraw/generate_creative_prompts
+
+    Body:
+    {
+        // Legacy parameters (still supported)
+        "source_prompts": [{id, prompt: {positive, negative}}, ...],
+        "inspiration_count": 3,
+        "character_reference": "",
+
+        // New unified parameters
+        "generation_count": 5,
+        "is_sdxl": false,
+        "model": "gemini-2.5-pro",
+        "gemini_api_key": "",
+        "grok_api_key": "",
+        "temperature": 0.9,
+        "top_p": 0.9,
+        "force_regenerate": false,
+
+        // Character likeness (NEW)
+        "character_description": "Detailed character description...",
+        "use_character_likeness": true,
+
+        // Generation mode (NEW)
+        "generation_mode": "img2img",  // or "txt2img"
+
+        // img2img parameters (NEW)
+        "images": ["base64_image_data..."],  // Images for vision models
+        "affect_elements": ["background", "outfit"],  // Which elements to modify
+
+        // txt2img parameters (NEW)
+        "random_inspiration_prompts": [{prompt, tags}, ...],  // Random library prompts
+        "user_text_input": "Custom user guidance..."
+    }
+
+    Returns:
+    {
+        "success": true,
+        "prompts": [{positive, negative, tags}, ...]
+    }
+    """
+    try:
+        data = await request.json()
+
+        # Core parameters
+        generation_count = int(data.get("generation_count", 5))
+        is_sdxl = bool(data.get("is_sdxl", False))
+        model = data.get("model", "gemini-2.5-pro")
+        gemini_api_key = data.get("gemini_api_key", "")
+        grok_api_key = data.get("grok_api_key", "")
+        temperature = float(data.get("temperature", 0.9))
+        top_p = float(data.get("top_p", 0.9))
+        temperature = max(0.0, min(2.0, temperature))
+        top_p = max(0.0, min(1.0, top_p))
+        force_regenerate = bool(data.get("force_regenerate", False))
+
+        # NEW: Character likeness
+        character_description = data.get("character_description", "")
+        use_character_likeness = bool(data.get("use_character_likeness", False))
+        if not use_character_likeness:
+            character_description = ""
+
+        # NEW: Generation mode
+        generation_mode = data.get("generation_mode", "img2img")
+
+        # NEW: img2img parameters
+        images = data.get("images", [])
+        multi_images = data.get("multi_images", [])  # New format: [{label, base64}, ...]
+        affect_elements = data.get("affect_elements", [])
+
+        # If multi_images provided, convert to images array for backward compatibility
+        # Also build image labels description for the prompt
+        image_labels_context = ""
+        if multi_images and len(multi_images) > 0:
+            images = [img.get("base64") for img in multi_images if img.get("base64")]
+            labels = [img.get("label", f"image_{i+1}") for i, img in enumerate(multi_images)]
+            if len(labels) > 1:
+                image_labels_context = f"\n\nYou are provided with {len(labels)} reference images: {', '.join(labels)}. Consider all images when generating the prompt."
+            print(f"[RPG Creative API] Multi-image mode: {len(multi_images)} images with labels: {labels}")
+
+        # NEW: txt2img parameters
+        random_inspiration_prompts = data.get("random_inspiration_prompts", [])
+        user_text_input = data.get("user_text_input", "")
+
+        # NEW: Generation style (Reality vs Creative)
+        generation_style = data.get("generation_style", "reality")
+
+        # Legacy support: convert old parameters to new format
+        source_prompts = data.get("source_prompts", [])
+        inspiration_count = int(data.get("inspiration_count", 0))
+        character_reference = data.get("character_reference", "")
+
+        if character_reference and not character_description:
+            character_description = character_reference
+            use_character_likeness = True
+
+        if source_prompts and len(source_prompts) > 0 and not random_inspiration_prompts:
+            random_inspiration_prompts = source_prompts[:inspiration_count] if inspiration_count > 0 else []
+
+        # Custom system prompt override
+        custom_system_prompt = (data.get("system_prompt") or "").strip()
+
+        # Build prompts
+        if custom_system_prompt:
+            system_prompt = custom_system_prompt
+        else:
+            system_prompt = build_system_prompt(
+                is_sdxl=is_sdxl,
+                character_description=character_description,
+                generation_mode=generation_mode,
+                affect_elements=affect_elements,
+                user_text_input=user_text_input,
+                random_inspiration_prompts=random_inspiration_prompts,
+                generation_style=generation_style
+            )
+
+        user_prompt = build_user_prompt(
+            generation_count=generation_count,
+            generation_mode=generation_mode,
+            images_data=images
+        )
+
+        # Append multi-image context if available
+        if image_labels_context:
+            user_prompt = user_prompt + image_labels_context
+
+        # Check cache (skip if force_regenerate is True)
+        cache_key = hashlib.sha256(
+            f"{system_prompt}_{user_prompt}_{model}_{temperature}_{top_p}".encode("utf-8")
+        ).hexdigest()
+        cache_dir = os.path.join(os.path.dirname(__file__), "..", "..", "cache")
+        os.makedirs(cache_dir, exist_ok=True)
+        cache_file = os.path.join(cache_dir, f"{cache_key}_creative.json")
+
+        if not force_regenerate and os.path.exists(cache_file):
+            print(f"[RPG Creative API] Using cached result: {cache_key[:8]}")
+            with open(cache_file, 'r', encoding='utf-8') as f:
+                prompts = json.load(f)
+                return web.json_response({"success": True, "prompts": prompts}, headers=CORS_HEADERS)
+
+        if force_regenerate:
+            print(f"[RPG Creative API] Force regenerate enabled - bypassing cache for {cache_key[:8]}")
+
+        # Generate with appropriate API
+        print(f"[RPG Creative API] Generating {generation_count} prompts - Mode: {generation_mode}, Model: {model}")
+        if use_character_likeness:
+            print(f"[RPG Creative API] Using character likeness (description length: {len(character_description)} chars)")
+        if generation_mode == "img2img" and affect_elements:
+            print(f"[RPG Creative API] Affecting elements: {affect_elements}")
+        if generation_mode == "txt2img":
+            print(f"[RPG Creative API] Inspiration prompts: {len(random_inspiration_prompts)}, User input: {len(user_text_input)} chars")
+        if images and len(images) > 0:
+            if len(images) > 1:
+                print(f"[RPG Creative API] ๐Ÿ–ผ๏ธ Multi-image vision mode: {len(images)} images for combined prompt generation")
+            else:
+                print(f"[RPG Creative API] ๐Ÿ–ผ๏ธ Processing {len(images)} image(s) for vision mode - First image base64 length: {len(images[0])} chars")
+
+        if model.startswith("gemini"):
+            prompts = await generate_with_gemini(system_prompt, user_prompt, model, gemini_api_key, temperature, top_p, images=images)
+        elif model.startswith("grok"):
+            prompts = await generate_with_grok(system_prompt, user_prompt, model, grok_api_key, temperature, top_p, images=images)
+        else:
+            raise ValueError(f"Unsupported model: {model}")
+
+        # Cache result
+        with open(cache_file, 'w', encoding='utf-8') as f:
+            json.dump(prompts, f, indent=2)
+
+        print(f"[RPG Creative API] Generated {len(prompts)} prompts with {model}")
+        return web.json_response({"success": True, "prompts": prompts}, headers=CORS_HEADERS)
+
+    except Exception as e:
+        print(f"[RPG Creative API] Error: {e}")
+        import traceback
+        traceback.print_exc()
+        return web.json_response({
+            "success": False,
+            "error": str(e)
+        }, status=500, headers=CORS_HEADERS)
+
+
+@PromptServer.instance.routes.post("/instaraw/generate_creative_prompts")
+async def generate_creative_prompts_endpoint(request):
+    return await _generate_creative_prompts(request)
+
+
+@PromptServer.instance.routes.post("/instaraw/generate_creative_prompts/")
+async def generate_creative_prompts_endpoint_slash(request):
+    return await _generate_creative_prompts(request)
+
+
+@PromptServer.instance.routes.options("/instaraw/generate_creative_prompts")
+@PromptServer.instance.routes.options("/instaraw/generate_creative_prompts/")
+async def generate_creative_prompts_options(request):
+    return web.Response(headers=CORS_HEADERS)
+
+
+# === Character Description Generation ===
+# In-memory cache for character descriptions
+CHARACTER_DESCRIPTION_CACHE = {}
+
+def get_character_system_prompt(complexity="balanced"):
+    """
+    Generate system prompt based on complexity level.
+    Complexity levels: concise (50-75 words), balanced (100-150 words), detailed (200-250 words)
+    """
+    base_instruction = """You are an expert at analyzing images and generating character descriptions for image generation prompts.
+
+Generate a character description focusing on PERMANENT physical features:
+- Facial features (face shape, eyes, nose, lips, skin tone)
+- Hair (color, length, style, texture)
+- Body type and build
+- Age and ethnicity
+- Distinctive features (scars, tattoos, piercings, etc.)
+
+DO NOT include clothing, background, pose, or temporary features.
+DO NOT use tags like "1girl, solo" or similar categorization prefixes."""
+
+    if complexity == "concise":
+        length_instruction = "\nOUTPUT: A concise description (50-75 words) focusing only on the most essential and distinctive physical features."
+    elif complexity == "detailed":
+        length_instruction = "\nOUTPUT: A comprehensive, detailed description (200-250 words) covering all physical aspects with nuanced detail and specific characteristics."
+    else:  # balanced
+        length_instruction = "\nOUTPUT: A balanced description (100-150 words) covering key physical features in natural language."
+
+    return base_instruction + length_instruction
+
+
+async def generate_character_description_with_gemini(user_prompt, model="gemini-2.5-pro", api_key=None, character_image=None, complexity="balanced", custom_system_prompt=None, temperature=0.7, top_p=0.9):
+    """
+    Generate plain text character description using Gemini.
+    Returns raw text, not JSON.
+    Uses the NEW Google Genai SDK pattern (matching gemini_native.py)
+    """
+    try:
+        from google import genai
+        from google.genai import types
+        import base64
+    except ImportError:
+        raise ImportError("The 'google-genai' library is required. Run: pip install -U google-genai")
+
+    if not api_key or api_key.strip() == "":
+        api_key = os.environ.get("GEMINI_API_KEY")
+    if not api_key or api_key.strip() == "":
+        raise ValueError("Gemini API Key is missing")
+
+    try:
+        # NEW SDK pattern: use Client instead of configure()
+        client = genai.Client(api_key=api_key)
+
+        # Use custom system prompt if provided, otherwise generate based on complexity
+        if custom_system_prompt:
+            system_instruction = custom_system_prompt
+        else:
+            system_instruction = get_character_system_prompt(complexity)
+
+        # Build parts list (text + optional image)
+        parts = [types.Part.from_text(text=f"{system_instruction}\n\n{user_prompt}")]
+
+        # Add image if provided (matching gemini_native.py pattern)
+        if character_image:
+            # Extract base64 data (remove data URL prefix if present)
+            if character_image.startswith("data:"):
+                base64_data = character_image.split(",", 1)[1]
+            else:
+                base64_data = character_image
+
+            # Decode base64 to bytes
+            image_bytes = base64.b64decode(base64_data)
+            parts.append(types.Part.from_bytes(data=image_bytes, mime_type='image/png'))
+
+        contents = [types.Content(role="user", parts=parts)]
+
+        # Safety settings
+        safety_settings = [
+            types.SafetySetting(category=cat, threshold="BLOCK_NONE")
+            for cat in ["HARM_CATEGORY_HATE_SPEECH", "HARM_CATEGORY_DANGEROUS_CONTENT",
+                       "HARM_CATEGORY_HARASSMENT", "HARM_CATEGORY_SEXUALLY_EXPLICIT"]
+        ]
+
+        # Generation config with thinking support
+        # Gemini 3.0 uses thinking_level (HIGH/LOW), Gemini 2.5 uses thinking_budget
+        if model == "gemini-3-pro-preview":
+            # Gemini 3.0 format
+            thinking_config = types.ThinkingConfig(thinking_level="HIGH")
+        elif model == "gemini-2.5-pro":
+            # Gemini 2.5 format (unlimited thinking)
+            thinking_config = types.ThinkingConfig(thinking_budget=-1)
+        else:
+            # No thinking for other models
+            thinking_config = None
+
+        config_params = {
+            "temperature": temperature,
+            "top_p": top_p,
+            "candidate_count": 1,
+            "safety_settings": safety_settings
+        }
+
+        # Only add thinking_config if it's not None
+        if thinking_config:
+            config_params["thinking_config"] = thinking_config
+
+        config = types.GenerateContentConfig(**config_params)
+
+        # NEW SDK pattern: use client.models.generate_content()
+        response = client.models.generate_content(
+            model=f"models/{model}",
+            contents=contents,
+            config=config
+        )
+
+        if not response.candidates:
+            raise Exception("Gemini returned no candidates (likely blocked by safety filters)")
+
+        # Return raw text, not JSON
+        return response.text.strip()
+
+    except Exception as e:
+        print(f"[RPG Character API] Gemini error: {e}")
+        raise
+
+
+async def generate_character_description_with_grok(user_prompt, model="grok-4-fast-reasoning", api_key=None, character_image=None, complexity="balanced", custom_system_prompt=None, temperature=0.7, top_p=0.9):
+    """
+    Generate plain text character description using Grok.
+    Supports vision like grok_native.py
+    Returns raw text, not JSON.
+    """
+    if not api_key or api_key.strip() == "":
+        api_key = os.environ.get("XAI_API_KEY")
+    if not api_key or api_key.strip() == "":
+        raise ValueError("Grok API Key is missing")
+
+    try:
+        base_url = os.environ.get("XAI_API_BASE", "https://api.x.ai").rstrip("/")
+        url = f"{base_url}/v1/chat/completions"
+
+        # Use custom system prompt if provided, otherwise generate based on complexity
+        if custom_system_prompt:
+            system_instruction = custom_system_prompt
+        else:
+            system_instruction = get_character_system_prompt(complexity)
+
+        # Build messages array (like grok_native.py)
+        messages = [
+            {"role": "system", "content": system_instruction}
+        ]
+
+        # Build user content (text + image if provided)
+        user_content = [
+            {"type": "text", "text": user_prompt}
+        ]
+
+        # Add image if provided (same format as grok_native.py line 127-132)
+        if character_image:
+            # character_image is already base64 from JavaScript
+            # Extract just the base64 part (remove "data:image/png;base64," prefix if present)
+            if character_image.startswith("data:"):
+                base64_data = character_image.split(",", 1)[1]
+            else:
+                base64_data = character_image
+
+            user_content.append({
+                "type": "image_url",
+                "image_url": {
+                    "url": f"data:image/png;base64,{base64_data}",
+                    "detail": "high"
+                }
+            })
+
+        messages.append({"role": "user", "content": user_content})
+
+        payload = {
+            "model": model,
+            "messages": messages,
+            "temperature": temperature,
+            "top_p": top_p,
+        }
+
+        headers = {
+            "Authorization": f"Bearer {api_key}",
+            "Content-Type": "application/json",
+        }
+
+        timeout = aiohttp.ClientTimeout(total=300)
+        async with aiohttp.ClientSession(timeout=timeout) as session:
+            async with session.post(url, json=payload, headers=headers) as resp:
+                body = await resp.text()
+                if resp.status >= 400:
+                    raise RuntimeError(f"Grok API error {resp.status}: {body}")
+                try:
+                    data = json.loads(body)
+                except json.JSONDecodeError as e:
+                    raise RuntimeError(f"Grok API returned invalid JSON: {e}")
+
+        choices = data.get("choices") or []
+        if not choices:
+            raise RuntimeError("Grok API returned no choices")
+
+        first_choice = choices[0]
+        message = first_choice.get("message") or {}
+        content = message.get("content")
+
+        # Handle different response formats
+        if isinstance(content, list):
+            text = "".join(
+                part.get("text", "") if isinstance(part, dict) else str(part)
+                for part in content
+            ).strip()
+            if text:
+                return text
+        elif isinstance(content, str) and content.strip():
+            return content.strip()
+
+        # Fallback
+        legacy = first_choice.get("text")
+        if legacy:
+            return legacy.strip()
+
+        raise RuntimeError("Unable to extract text from Grok response")
+
+    except Exception as e:
+        print(f"[RPG Character API] Grok error: {e}")
+        raise
+
+
+def build_character_description_prompt():
+    """Build system prompt for character description generation."""
+    return """You are an expert at analyzing images and generating detailed character descriptions for image generation prompts.
+
+Your task is to generate a comprehensive, detailed character description that can be used consistently across multiple image generation prompts.
+
+IMPORTANT: Focus on PERMANENT, CONSISTENT physical features that define the character:
+- Facial features (face shape, eyes, nose, lips, skin tone)
+- Hair (color, length, style, texture)
+- Body type and build
+- Age and ethnicity
+- Distinctive features (scars, tattoos, piercings, etc.)
+
+DO NOT include:
+- Clothing or outfit (this varies per image)
+- Background or setting
+- Pose or action
+- Temporary features (makeup, accessories)
+
+OUTPUT FORMAT: Return a single detailed paragraph (100-150 words) describing the character's permanent physical features. This description will be inserted into other prompts to ensure character consistency.
+
+Example output:
+"A young woman in her mid-20s with an athletic build and olive skin tone. She has striking almond-shaped green eyes, high cheekbones, and full lips. Her dark brown hair falls in natural waves to shoulder length with subtle copper highlights. She has a defined jawline, straight nose, and arched eyebrows. Her features suggest Mediterranean heritage. She stands approximately 5'7" with a toned, balanced physique and confident posture."
+"""
+
+async def _generate_character_description(request):
+    """
+    POST /instaraw/generate_character_description
+
+    Body:
+    {
+        "character_image": "base64_string_or_null",
+        "character_text": "manual_description_or_null",
+        "model": "gemini-2.5-pro",
+        "temperature": 0.7,
+        "top_p": 0.9,
+        "gemini_api_key": "",
+        "grok_api_key": "",
+        "force_regenerate": false
+    }
+
+    Returns:
+    {
+        "success": true,
+        "description": "Detailed character description...",
+        "cached": false,
+        "cache_key": "hash_of_inputs"
+    }
+    """
+    try:
+        data = await request.json()
+
+        character_image = data.get("character_image")
+        character_text = data.get("character_text")
+        model = data.get("model", "gemini-2.5-pro")
+        temperature = float(data.get("temperature", 0.7))
+        top_p = float(data.get("top_p", 0.9))
+        gemini_api_key = data.get("gemini_api_key", "")
+        grok_api_key = data.get("grok_api_key", "")
+        force_regenerate = bool(data.get("force_regenerate", False))
+        complexity = data.get("complexity", "balanced")
+        custom_system_prompt = data.get("custom_system_prompt", "")
+
+        # Validation
+        if not character_image and not character_text:
+            return web.json_response({
+                "success": False,
+                "error": "Either character_image or character_text must be provided"
+            }, status=400, headers=CORS_HEADERS)
+
+        # Generate cache key (include complexity and custom prompt in key)
+        cache_input = character_image if character_image else character_text
+        cache_key = hashlib.sha256(f"{cache_input}_{model}_{complexity}_{custom_system_prompt}".encode("utf-8")).hexdigest()
+
+        # Check cache
+        if not force_regenerate and cache_key in CHARACTER_DESCRIPTION_CACHE:
+            print(f"[RPG Character API] Using cached character description: {cache_key[:8]}")
+            return web.json_response({
+                "success": True,
+                "description": CHARACTER_DESCRIPTION_CACHE[cache_key],
+                "cached": True,
+                "cache_key": cache_key
+            }, headers=CORS_HEADERS)
+
+        # Build user prompt
+        if character_image:
+            user_prompt = "Analyze this image and generate a detailed character description following the instructions."
+        else:
+            user_prompt = f"Enhance this character description into a detailed, structured format suitable for image generation:\n\n{character_text}"
+
+        # Generate description using dedicated function
+        print(f"[RPG Character API] Generating character description with {model} (complexity: {complexity})...")
+
+        if model.startswith("gemini"):
+            description = await generate_character_description_with_gemini(
+                user_prompt,
+                model=model,
+                api_key=gemini_api_key,
+                character_image=character_image,
+                complexity=complexity,
+                custom_system_prompt=custom_system_prompt if custom_system_prompt else None,
+                temperature=temperature,
+                top_p=top_p
+            )
+        elif model.startswith("grok"):
+            description = await generate_character_description_with_grok(
+                user_prompt,
+                model=model,
+                api_key=grok_api_key,
+                character_image=character_image,
+                complexity=complexity,
+                custom_system_prompt=custom_system_prompt if custom_system_prompt else None,
+                temperature=temperature,
+                top_p=top_p
+            )
+        else:
+            raise ValueError(f"Unsupported model: {model}")
+
+        if not description or description.strip() == "":
+            raise ValueError("Generated description is empty. API may have failed.")
+
+        # Cache the description
+        CHARACTER_DESCRIPTION_CACHE[cache_key] = description
+
+        print(f"[RPG Character API] โœ… Generated character description ({len(description)} chars)")
+        return web.json_response({
+            "success": True,
+            "description": description,
+            "cached": False,
+            "cache_key": cache_key
+        }, headers=CORS_HEADERS)
+
+    except Exception as e:
+        print(f"[RPG Character API] Error: {e}")
+        return web.json_response({
+            "success": False,
+            "error": str(e)
+        }, status=500, headers=CORS_HEADERS)
+
+
+# === Random Prompt Selection ===
+PROMPTS_DB_CACHE = None
+PROMPTS_DB_URL = "https://instara.s3.us-east-1.amazonaws.com/prompts.db.json"
+
+async def load_prompts_database():
+    """Load and cache the prompts database from remote URL."""
+    global PROMPTS_DB_CACHE
+
+    if PROMPTS_DB_CACHE is not None:
+        return PROMPTS_DB_CACHE
+
+    print("[RPG Prompts API] Loading prompts database from remote URL...")
+
+    try:
+        timeout = aiohttp.ClientTimeout(total=60)
+        async with aiohttp.ClientSession(timeout=timeout) as session:
+            async with session.get(PROMPTS_DB_URL) as resp:
+                if resp.status >= 400:
+                    raise RuntimeError(f"Failed to load prompts database: HTTP {resp.status}")
+
+                data = await resp.json()
+                PROMPTS_DB_CACHE = data
+                print(f"[RPG Prompts API] Loaded {len(data)} prompts from database")
+                return data
+
+    except Exception as e:
+        print(f"[RPG Prompts API] Error loading database: {e}")
+        raise
+
+
+async def _get_random_prompts(request):
+    """
+    POST /instaraw/get_random_prompts
+
+    Body:
+    {
+        "count": 5,
+        "filters": {
+            "content_type": "person",  // optional
+            "safety_level": "sfw",     // optional
+            "shot_type": "portrait"    // optional
+        }
+    }
+
+    Returns:
+    {
+        "success": true,
+        "prompts": [
+            {
+                "id": "...",
+                "tags": ["..."],
+                "prompt": {
+                    "positive": "...",
+                    "negative": "..."
+                },
+                "classification": {...}
+            }
+        ]
+    }
+    """
+    try:
+        import random
+
+        data = await request.json()
+        count = int(data.get("count", 5))
+        filters = data.get("filters", {})
+
+        # Load database
+        prompts_db = await load_prompts_database()
+
+        # Apply filters
+        filtered_prompts = prompts_db
+
+        if "content_type" in filters and filters["content_type"]:
+            content_type = filters["content_type"]
+            filtered_prompts = [
+                p for p in filtered_prompts
+                if p.get("classification", {}).get("content_type") == content_type
+            ]
+
+        if "safety_level" in filters and filters["safety_level"]:
+            safety_level = filters["safety_level"]
+            filtered_prompts = [
+                p for p in filtered_prompts
+                if p.get("classification", {}).get("safety_level") == safety_level
+            ]
+
+        if "shot_type" in filters and filters["shot_type"]:
+            shot_type = filters["shot_type"]
+            filtered_prompts = [
+                p for p in filtered_prompts
+                if p.get("classification", {}).get("shot_type") == shot_type
+            ]
+
+        # Random selection
+        if len(filtered_prompts) == 0:
+            return web.json_response({
+                "success": False,
+                "error": "No prompts match the specified filters"
+            }, status=400, headers=CORS_HEADERS)
+
+        selected_count = min(count, len(filtered_prompts))
+        selected_prompts = random.sample(filtered_prompts, selected_count)
+
+        print(f"[RPG Prompts API] Selected {selected_count} random prompts from {len(filtered_prompts)} filtered")
+
+        return web.json_response({
+            "success": True,
+            "prompts": selected_prompts
+        }, headers=CORS_HEADERS)
+
+    except Exception as e:
+        print(f"[RPG Prompts API] Error: {e}")
+        return web.json_response({
+            "success": False,
+            "error": str(e)
+        }, status=500, headers=CORS_HEADERS)
+
+
+# === Register New Endpoints ===
+@PromptServer.instance.routes.post("/instaraw/generate_character_description")
+async def generate_character_description_endpoint(request):
+    return await _generate_character_description(request)
+
+
+@PromptServer.instance.routes.post("/instaraw/generate_character_description/")
+async def generate_character_description_endpoint_slash(request):
+    return await _generate_character_description(request)
+
+
+@PromptServer.instance.routes.options("/instaraw/generate_character_description")
+@PromptServer.instance.routes.options("/instaraw/generate_character_description/")
+async def generate_character_description_options(request):
+    return web.Response(headers=CORS_HEADERS)
+
+
+@PromptServer.instance.routes.post("/instaraw/get_random_prompts")
+async def get_random_prompts_endpoint(request):
+    return await _get_random_prompts(request)
+
+
+@PromptServer.instance.routes.post("/instaraw/get_random_prompts/")
+async def get_random_prompts_endpoint_slash(request):
+    return await _get_random_prompts(request)
+
+
+@PromptServer.instance.routes.options("/instaraw/get_random_prompts")
+@PromptServer.instance.routes.options("/instaraw/get_random_prompts/")
+async def get_random_prompts_options(request):
+    return web.Response(headers=CORS_HEADERS)
+
+
+print("[RPG Creative API] Endpoint registered: POST /instaraw/generate_creative_prompts")
+print("[RPG Character API] Endpoint registered: POST /instaraw/generate_character_description")
+print("[RPG Prompts API] Endpoint registered: POST /instaraw/get_random_prompts")
+
+# Add these lines to the end of the file
+NODE_CLASS_MAPPINGS = {}
+NODE_DISPLAY_NAME_MAPPINGS = {}
\ No newline at end of file
diff --git a/custom_nodes/ComfyUI_INSTARAW/nodes/api_nodes/flux_fill.py b/custom_nodes/ComfyUI_INSTARAW/nodes/api_nodes/flux_fill.py
new file mode 100644
index 0000000000000000000000000000000000000000..f9f3db6f5ba252844ceec36ab6ff1fe38b0359b5
--- /dev/null
+++ b/custom_nodes/ComfyUI_INSTARAW/nodes/api_nodes/flux_fill.py
@@ -0,0 +1,241 @@
+# ---
+# ComfyUI INSTARAW - FLUX Fill API Node (fal.ai)
+# Inpainting with FLUX.1 [dev] Fill model
+# Copyright ยฉ 2025 Instara. All rights reserved.
+# PROPRIETARY SOFTWARE - ALL RIGHTS RESERVED
+# ---
+
+import requests
+import base64
+import io
+import time
+import os
+import hashlib
+import numpy as np
+import torch
+from PIL import Image
+
+
+class INSTARAW_FluxFill:
+    """
+    FLUX.1 [dev] Fill inpainting node using fal.ai API.
+    Takes an image and mask, fills the masked area with generated content.
+    Perfect for NSFW fix workflows where you need to add clothing to masked regions.
+    """
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "api_key": ("STRING", {"forceInput": True, "tooltip": "fal.ai API key"}),
+                "image": ("IMAGE", {"tooltip": "Input image to inpaint"}),
+                "mask": ("MASK", {"tooltip": "Mask indicating area to fill (white = fill area)"}),
+                "prompt": ("STRING", {
+                    "multiline": True,
+                    "default": "wearing casual clothing, natural fabric, well-fitted",
+                    "tooltip": "What to generate in the masked area"
+                }),
+            },
+            "optional": {
+                "seed": ("INT", {"default": -1, "min": -1, "max": 2147483647, "tooltip": "-1 for random seed"}),
+                "num_inference_steps": ("INT", {"default": 28, "min": 1, "max": 50, "tooltip": "Number of denoising steps"}),
+                "guidance_scale": ("FLOAT", {"default": 30.0, "min": 1.0, "max": 50.0, "step": 0.5, "tooltip": "How closely to follow the prompt"}),
+                "paste_back": ("BOOLEAN", {"default": True, "tooltip": "Paste original image onto non-inpainted areas"}),
+                "resize_to_original": ("BOOLEAN", {"default": True, "tooltip": "Resize output back to original dimensions"}),
+                "enable_safety_checker": ("BOOLEAN", {"default": False, "tooltip": "Enable fal.ai safety filter"}),
+            },
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    RETURN_NAMES = ("filled_image",)
+    FUNCTION = "fill"
+    CATEGORY = "INSTARAW/API"
+
+    def image_to_base64(self, image_tensor, max_size_mb=7):
+        """Convert image tensor to base64 data URI with compression."""
+        if image_tensor is None:
+            return None
+
+        image_np = image_tensor.cpu().numpy()
+        # Handle both [B, H, W, C] and [H, W, C] tensor formats
+        if image_np.ndim == 4:
+            image_np = image_np[0]  # Remove batch dimension
+        if image_np.max() <= 1.0:
+            image_np = (image_np * 255).astype(np.uint8)
+
+        image_pil = Image.fromarray(image_np).convert("RGB")
+
+        quality_levels = [95, 90, 85, 80, 75]
+        max_bytes = max_size_mb * 1024 * 1024
+
+        buffer = io.BytesIO()
+        for quality in quality_levels:
+            buffer.seek(0)
+            buffer.truncate(0)
+            image_pil.save(buffer, format="JPEG", quality=quality)
+
+            if buffer.tell() <= max_bytes:
+                base64_str = base64.b64encode(buffer.getvalue()).decode()
+                return f"data:image/jpeg;base64,{base64_str}"
+
+        raise Exception(f"Image too large even after compression to quality {quality_levels[-1]}")
+
+    def mask_to_base64(self, mask_tensor, max_size_mb=7):
+        """Convert mask tensor to base64 data URI (grayscale)."""
+        if mask_tensor is None:
+            return None
+
+        # Handle different mask dimensions
+        mask_np = mask_tensor.cpu().numpy()
+        if mask_np.ndim == 4:
+            mask_np = mask_np[0, 0]  # BCHW -> HW
+        elif mask_np.ndim == 3:
+            mask_np = mask_np[0]  # BHW -> HW
+
+        if mask_np.max() <= 1.0:
+            mask_np = (mask_np * 255).astype(np.uint8)
+        else:
+            mask_np = mask_np.astype(np.uint8)
+
+        mask_pil = Image.fromarray(mask_np, mode="L")
+
+        quality_levels = [95, 90, 85, 80, 75]
+        max_bytes = max_size_mb * 1024 * 1024
+
+        buffer = io.BytesIO()
+        for quality in quality_levels:
+            buffer.seek(0)
+            buffer.truncate(0)
+            mask_pil.save(buffer, format="JPEG", quality=quality)
+
+            if buffer.tell() <= max_bytes:
+                base64_str = base64.b64encode(buffer.getvalue()).decode()
+                return f"data:image/jpeg;base64,{base64_str}"
+
+        raise Exception(f"Mask too large even after compression")
+
+    def submit_to_fal(self, api_key, payload):
+        """Submit request to fal.ai FLUX Fill endpoint."""
+        url = "https://fal.run/fal-ai/flux-lora-fill"
+        headers = {
+            "Authorization": f"Key {api_key}",
+            "Content-Type": "application/json",
+        }
+
+        print(f"๐Ÿš€ FLUX Fill: Submitting request to fal.ai...")
+        response = requests.post(url, json=payload, headers=headers, timeout=300)
+
+        if not response.ok:
+            error_text = response.text
+            try:
+                error_data = response.json()
+                if "detail" in error_data:
+                    error_text = str(error_data["detail"])
+            except:
+                pass
+            raise Exception(f"fal.ai API error ({response.status_code}): {error_text}")
+
+        result = response.json()
+        print("๐Ÿ“ฆ FLUX Fill: Response received from fal.ai")
+
+        if "images" in result and len(result["images"]) > 0:
+            return result["images"][0]["url"]
+
+        raise Exception(f"FLUX Fill: No image in response. Full response: {result}")
+
+    def fill(
+        self,
+        api_key,
+        image,
+        mask,
+        prompt,
+        seed=-1,
+        num_inference_steps=28,
+        guidance_scale=30.0,
+        paste_back=True,
+        resize_to_original=True,
+        enable_safety_checker=False,
+    ):
+        # Get original dimensions for cache key
+        _, orig_h, orig_w, _ = image.shape
+
+        # Build cache key
+        hasher = hashlib.sha256()
+        hasher.update(image.cpu().numpy().tobytes())
+        hasher.update(mask.cpu().numpy().tobytes())
+        hasher.update(prompt.encode("utf-8"))
+        hasher.update(str(seed).encode("utf-8"))
+        hasher.update(str(num_inference_steps).encode("utf-8"))
+        hasher.update(str(guidance_scale).encode("utf-8"))
+        hasher.update(str(paste_back).encode("utf-8"))
+        hasher.update(str(resize_to_original).encode("utf-8"))
+
+        cache_dir = os.path.join(os.path.dirname(__file__), "..", "..", "cache")
+        os.makedirs(cache_dir, exist_ok=True)
+        cache_filepath = os.path.join(cache_dir, f"{hasher.hexdigest()}_flux_fill.png")
+
+        # Check cache
+        if os.path.exists(cache_filepath):
+            print(f"โœ… FLUX Fill: Cache hit! Loading from {cache_filepath}")
+            cached_image = Image.open(cache_filepath).convert("RGB")
+            cached_np = np.array(cached_image).astype(np.float32) / 255.0
+            return (torch.from_numpy(cached_np).unsqueeze(0),)
+
+        print("๐Ÿ’จ FLUX Fill: Cache miss. Proceeding with API call...")
+
+        # Convert image and mask to base64
+        image_b64 = self.image_to_base64(image)
+        mask_b64 = self.mask_to_base64(mask)
+
+        # Build payload
+        payload = {
+            "prompt": prompt,
+            "image_url": image_b64,
+            "mask_url": mask_b64,
+            "num_inference_steps": num_inference_steps,
+            "guidance_scale": guidance_scale,
+            "paste_back": paste_back,
+            "resize_to_original": resize_to_original,
+            "enable_safety_checker": enable_safety_checker,
+            "output_format": "jpeg",
+            "num_images": 1,
+        }
+
+        # Add seed if specified
+        if seed >= 0:
+            payload["seed"] = seed
+
+        # Submit request
+        image_url = self.submit_to_fal(api_key, payload)
+
+        # Download result
+        print(f"โฌ‡๏ธ FLUX Fill: Downloading result...")
+        image_response = requests.get(image_url, timeout=60)
+        image_response.raise_for_status()
+
+        result_pil = Image.open(io.BytesIO(image_response.content)).convert("RGB")
+
+        # Save to cache
+        print(f"๐Ÿ’พ FLUX Fill: Saving to cache: {cache_filepath}")
+        result_pil.save(cache_filepath, "PNG")
+
+        # Convert to tensor
+        result_np = np.array(result_pil).astype(np.float32) / 255.0
+        result_tensor = torch.from_numpy(result_np).unsqueeze(0)
+
+        print(f"โœ… FLUX Fill: Complete! Output size: {result_pil.size}")
+
+        return (result_tensor,)
+
+
+# =================================================================================
+# NODE REGISTRATION
+# =================================================================================
+
+NODE_CLASS_MAPPINGS = {
+    "INSTARAW_FluxFill": INSTARAW_FluxFill,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "INSTARAW_FluxFill": "๐ŸŽจ INSTARAW FLUX Fill (Inpaint)",
+}
diff --git a/custom_nodes/ComfyUI_INSTARAW/nodes/api_nodes/flux_kontext.py b/custom_nodes/ComfyUI_INSTARAW/nodes/api_nodes/flux_kontext.py
new file mode 100644
index 0000000000000000000000000000000000000000..5290169f37d687537536e8fe00095958ba4f1e61
--- /dev/null
+++ b/custom_nodes/ComfyUI_INSTARAW/nodes/api_nodes/flux_kontext.py
@@ -0,0 +1,200 @@
+# ---
+# ComfyUI INSTARAW - Flux Kontext LoRA API Node
+# Part of the INSTARAW custom nodes collection by Instara
+#
+# Copyright ยฉ 2025 Instara. All rights reserved.
+# PROPRIETARY SOFTWARE - ALL RIGHTS RESERVED
+# ---
+
+import requests
+import base64
+import io
+import json
+import hashlib
+import os
+from PIL import Image
+import numpy as np
+import torch
+
+# =================================================================================
+# BASE CLASS FOR FAL.AI SYNC REQUESTS
+# =================================================================================
+
+class FalAIBase:
+    def __init__(self):
+        self.api_key = None
+
+    def set_api_key(self, api_key):
+        if not api_key or not api_key.strip():
+            raise ValueError("fal.ai API key is missing or empty.")
+        self.api_key = api_key
+
+    def _log_and_update_hash(self, hasher, key, value):
+        if value is None: return
+        byte_value = b""
+        if isinstance(value, str): byte_value = value.encode("utf-8")
+        elif isinstance(value, (int, float, bool)): byte_value = str(value).encode("utf-8")
+        elif isinstance(value, torch.Tensor): byte_value = value.cpu().numpy().tobytes()
+        else: return
+        hasher.update(byte_value)
+
+    def image_to_base64(self, image_tensor, max_size_mb=10):
+        """Converts a tensor to a base64 data URI and compresses it to stay under API limits."""
+        if image_tensor is None: return None
+
+        image_np = image_tensor.cpu().numpy()
+        # Handle both [B, H, W, C] and [H, W, C] tensor formats
+        if image_np.ndim == 4:
+            image_np = image_np[0]  # Remove batch dimension
+        if image_np.max() <= 1.0: image_np = (image_np * 255).astype(np.uint8)
+        
+        image_pil = Image.fromarray(image_np).convert("RGB")
+        
+        quality_levels = [95, 90, 85, 80, 75]
+        max_bytes = max_size_mb * 1024 * 1024
+
+        buffer = io.BytesIO()
+        for quality in quality_levels:
+            buffer.seek(0)
+            buffer.truncate(0)
+            image_pil.save(buffer, format="JPEG", quality=quality)
+            
+            if buffer.tell() <= max_bytes:
+                print(f"โœ… Image compressed to JPEG (quality={quality}) to fit API limits. Size: {buffer.tell() / 1024:.2f} KB")
+                base64_str = base64.b64encode(buffer.getvalue()).decode()
+                return f"data:image/jpeg;base64,{base64_str}"
+
+        final_size_kb = buffer.tell() / 1024
+        raise Exception(f"Image is too large for API. After compressing to lowest quality JPEG, size is {final_size_kb:.2f} KB (limit is {max_size_mb * 1024:.2f} KB).")
+
+    def _submit_fal_sync(self, endpoint, payload):
+        """Submits a synchronous request to a fal.ai endpoint."""
+        url = f"https://fal.run/{endpoint}"
+        headers = {
+            "Authorization": f"Key {self.api_key}",
+            "Content-Type": "application/json",
+            "Accept": "application/json",
+        }
+        print(f"๐Ÿš€ Submitting SYNC request to fal.ai: {url}")
+        
+        try:
+            response = requests.post(url, json=payload, headers=headers, timeout=600) # 10 minute timeout
+            response.raise_for_status() # Raises HTTPError for bad responses (4xx or 5xx)
+            
+            result = response.json()
+            print("๐Ÿ“ฆ fal.ai sync response received.")
+            
+            if "images" in result and len(result["images"]) > 0 and "url" in result["images"][0]:
+                return result["images"][0]["url"]
+            else:
+                raise Exception(f"API response did not contain a valid image URL. Full response: {json.dumps(result, indent=2)}")
+
+        except requests.exceptions.HTTPError as e:
+            error_message = f"API request failed: {e.response.status_code} - {e.response.text}"
+            print(f"โŒ {error_message}")
+            raise Exception(error_message) from e
+        except Exception as e:
+            print(f"โŒ An unexpected error occurred: {e}")
+            raise
+
+# =================================================================================
+# FLUX KONTEXT LORA API NODE
+# =================================================================================
+
+class INSTARAW_FluxKontextLoraAPI(FalAIBase):
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "api_key": ("STRING", {"forceInput": True}),
+                "image": ("IMAGE",),
+                "prompt": ("STRING", {"multiline": True, "default": "make the woman's breasts larger and her hips wider"}),
+                "lora_url": ("STRING", {"default": "https://huggingface.co/BAZILEVS-BASED/kontext_big_breasts_and_butts/resolve/main/kontext_big_breasts_and_butts.safetensors"}),
+                "lora_scale": ("FLOAT", {"default": 1.0, "min": -5.0, "max": 5.0, "step": 0.05}),
+                "steps": ("INT", {"default": 30, "min": 1, "max": 100}),
+                "guidance_scale": ("FLOAT", {"default": 2.5, "min": 0.0, "max": 20.0, "step": 0.1}),
+                "seed": ("INT", {"default": -1, "min": -1, "max": 0xffffffffffffffff, "control_after_generate": "randomize"}),
+                "enable_safety_checker": ("BOOLEAN", {"default": False}),
+            }
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "execute"
+    CATEGORY = "INSTARAW/API"
+
+    def execute(self, api_key, image, prompt, lora_url, lora_scale, steps, guidance_scale, seed, enable_safety_checker):
+        # --- Caching Logic ---
+        cache_dir = os.path.join(os.path.dirname(__file__), "..", "..", "cache")
+        os.makedirs(cache_dir, exist_ok=True)
+        
+        hasher = hashlib.sha256()
+        all_args = locals()
+        for key in sorted(all_args.keys()):
+            if key not in ['self', 'api_key']: # Don't include self or api_key in hash
+                self._log_and_update_hash(hasher, key, all_args[key])
+        
+        cache_filepath = os.path.join(cache_dir, f"{hasher.hexdigest()}_flux_kontext.png")
+
+        if os.path.exists(cache_filepath):
+            print(f"โœ… Flux Kontext API Cache Hit! Loading image from {cache_filepath}")
+            pil_image = Image.open(cache_filepath).convert("RGB")
+            img_np = np.array(pil_image).astype(np.float32) / 255.0
+            return (torch.from_numpy(img_np).unsqueeze(0),)
+
+        print("๐Ÿ’จ Flux Kontext API Cache Miss. Proceeding with API call...")
+
+        # --- API Call Logic ---
+        self.set_api_key(api_key)
+        
+        # Convert image to base64 data URI
+        base64_image = self.image_to_base64(image)
+
+        # Build the payload
+        payload = {
+            "image_url": base64_image,
+            "prompt": prompt,
+            "num_inference_steps": steps,
+            "guidance_scale": guidance_scale,
+            "enable_safety_checker": enable_safety_checker,
+            "loras": [
+                {
+                    "path": lora_url,
+                    "scale": lora_scale
+                }
+            ],
+            # These are fixed for this specific use case but could be exposed as inputs
+            "num_images": 1,
+            "output_format": "png",
+            "resolution_mode": "match_input", 
+        }
+        if seed >= 0:
+            payload["seed"] = seed
+
+        # Submit the request and get the result image URL
+        image_url = self._submit_fal_sync("fal-ai/flux-kontext-lora", payload)
+
+        if not image_url:
+            raise Exception("API did not return a valid image URL.")
+
+        # Download the resulting image
+        print(f"โœ… Image generated. Downloading from: {image_url}")
+        image_response = requests.get(image_url, timeout=300)
+        image_response.raise_for_status()
+
+        # Convert back to tensor and save to cache
+        pil_image = Image.open(io.BytesIO(image_response.content)).convert("RGB")
+        pil_image.save(cache_filepath, "PNG")
+        
+        img_np = np.array(pil_image).astype(np.float32) / 255.0
+        return (torch.from_numpy(img_np).unsqueeze(0),)
+
+# =================================================================================
+# NODE REGISTRATION
+# =================================================================================
+
+NODE_CLASS_MAPPINGS = {
+    "INSTARAW_FluxKontextLoraAPI": INSTARAW_FluxKontextLoraAPI,
+}
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "INSTARAW_FluxKontextLoraAPI": "๐Ÿ‘ INSTARAW Flux Kontext LoRA API",
+}
\ No newline at end of file
diff --git a/custom_nodes/ComfyUI_INSTARAW/nodes/api_nodes/gemini_native.py b/custom_nodes/ComfyUI_INSTARAW/nodes/api_nodes/gemini_native.py
new file mode 100644
index 0000000000000000000000000000000000000000..e96016a8dd3ec840ad002d7f5017acf8cd4bccd4
--- /dev/null
+++ b/custom_nodes/ComfyUI_INSTARAW/nodes/api_nodes/gemini_native.py
@@ -0,0 +1,116 @@
+# --- Filename: ../ComfyUI_INSTARAW/nodes/api_nodes/gemini_native.py (FINAL CORRECTED VERSION) ---
+
+import os
+import hashlib
+import torch
+import numpy as np
+from PIL import Image
+import io
+
+_k = set([1, 2])
+
+class INSTARAW_GeminiNative:
+    """
+    A native, stable, and fully-featured Google Gemini node for INSTARAW.
+    This version supports up to 4 separate image inputs for a user-friendly workflow.
+    """
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        # We now attempt the import here to define inputs. If it fails, we show an error.
+        try:
+            import google.genai
+            import google.genai.types as types
+            return {
+                "required": {
+                    "api_key": ("STRING", {"multiline": False, "default": ""}),
+                    "prompt": ("STRING", {"multiline": True, "default": "Describe the image."}),
+                    "model": (["gemini-2.5-pro", "gemini-flash-latest"], {"default": "gemini-2.5-pro"}),
+                    "seed": ("INT", {"default": 1111111, "min": 0, "max": 0xffffffffffffffff, "control_after_generate": "randomize"}),
+                    "temperature": ("FLOAT", {"default": 0.111, "min": 0.0, "max": 2.0, "step": 0.001}),
+                    "enable_thinking": ("BOOLEAN", {"default": True, "label_on": "Thinking Enabled", "label_off": "Thinking Disabled"}),
+                    "safety_level": (["Block None", "Block Few (High Only)", "Block Some (Medium+)", "Block Most (Low+)"], {"default": "Block None"}),
+                },
+                "optional": { "image_1": ("IMAGE",), "image_2": ("IMAGE",), "image_3": ("IMAGE",), "image_4": ("IMAGE",) }
+            }
+        except ImportError:
+            return {
+                "required": {
+                    "error": ("STRING", {
+                        "default": "google-genai library failed to import. Please run 'pip install -U google-genai' and restart.",
+                        "multiline": True
+                    })
+                }
+            }
+
+    RETURN_TYPES = ("STRING",)
+    FUNCTION = "generate_content"
+    CATEGORY = "INSTARAW/API"
+
+    def generate_content(self, api_key, prompt, model, seed, temperature, enable_thinking, safety_level, 
+                         image_1=None, image_2=None, image_3=None, image_4=None):
+        
+        # --- THE DEFINITIVE FIX: Perform the import check HERE, at runtime. ---
+        try:
+            from google import genai
+            from google.genai import types
+            from google.genai import errors
+            from google.api_core import exceptions as core_exceptions
+        except ImportError:
+            # This is now the only place the error is raised, ensuring it's a real-time failure.
+            raise ImportError("The 'google-genai' library is required. Please run 'pip install -U google-genai' to ensure you have the latest version.")
+        
+        if not api_key or api_key.strip() == "":
+            api_key = os.environ.get("GEMINI_API_KEY")
+            if not api_key or api_key.strip() == "":
+                raise ValueError("Gemini API Key is missing. Provide it in the node or set GEMINI_API_KEY env var.")
+
+        provided_images = [img for img in [image_1, image_2, image_3, image_4] if img is not None]
+
+        cache_dir = os.path.join(os.path.dirname(__file__), "..", "..", "cache")
+        os.makedirs(cache_dir, exist_ok=True)
+        hasher = hashlib.sha256()
+        hasher.update(str(seed).encode('utf-8')); hasher.update(prompt.encode('utf-8')); hasher.update(model.encode('utf-8'))
+        hasher.update(f"{temperature:.3f}".encode('utf-8')); hasher.update(str(enable_thinking).encode('utf-8')); hasher.update(safety_level.encode('utf-8'))
+        for image_tensor in provided_images: hasher.update(image_tensor.cpu().numpy().tobytes())
+        cache_key = hasher.hexdigest()
+        cache_filepath = os.path.join(cache_dir, f"{cache_key}_gemini_native.txt")
+
+        if os.path.exists(cache_filepath):
+            with open(cache_filepath, 'r', encoding='utf-8') as f: return (f.read(),)
+
+        try:
+            client = genai.Client(api_key=api_key)
+            parts = [types.Part.from_text(text=prompt)]
+            for image_tensor in provided_images:
+                pil_image = Image.fromarray((image_tensor.squeeze(0).cpu().numpy() * 255).astype(np.uint8))
+                buffer = io.BytesIO(); pil_image.save(buffer, format="PNG")
+                parts.append(types.Part.from_bytes(data=buffer.getvalue(), mime_type='image/png'))
+
+            contents = [types.Content(role="user", parts=parts)]
+            thinking_budget = -1 if model == "gemini-2.5-pro" or enable_thinking else 0
+            
+            safety_map = { "Block None": "BLOCK_NONE", "Block Few (High Only)": "BLOCK_ONLY_HIGH", "Block Some (Medium+)": "BLOCK_MEDIUM_AND_ABOVE", "Block Most (Low+)": "BLOCK_LOW_AND_ABOVE" }
+            safety_threshold = safety_map.get(safety_level, "BLOCK_MEDIUM_AND_ABOVE")
+            safety_settings = [types.SafetySetting(category=cat, threshold=safety_threshold) for cat in ["HARM_CATEGORY_HATE_SPEECH", "HARM_CATEGORY_DANGEROUS_CONTENT", "HARM_CATEGORY_HARASSMENT", "HARM_CATEGORY_SEXUALLY_EXPLICIT"]]
+
+            config = types.GenerateContentConfig(temperature=temperature, candidate_count=1, thinking_config=types.ThinkingConfig(thinking_budget=thinking_budget), safety_settings=safety_settings)
+            
+            response = client.models.generate_content(model=f"models/{model}", contents=contents, config=config)
+            
+            if not response.candidates:
+                raise Exception(f"Prompt blocked by safety filters. Reason: {response.prompt_feedback.block_reason.name}" if hasattr(response, 'prompt_feedback') and response.prompt_feedback.block_reason else "API returned no candidates (likely blocked by safety filters).")
+
+            result_text = response.text
+            with open(cache_filepath, 'w', encoding='utf-8') as f: f.write(result_text)
+            return (result_text,)
+
+        except core_exceptions.ResourceExhausted as e:
+            raise Exception("INSTARAW Gemini Error: Rate limit exceeded (429). Enable billing on your Google AI Studio account or wait and try again.") from e
+        except errors.ServerError as e:
+            raise Exception("INSTARAW Gemini Error: Model overloaded or unavailable (503). Enable billing for a higher quota or try again later.") from e
+        except Exception as e:
+            raise e
+
+NODE_CLASS_MAPPINGS = { "INSTARAW_GeminiNative": INSTARAW_GeminiNative }
+NODE_DISPLAY_NAME_MAPPINGS = { "INSTARAW_GeminiNative": "๐Ÿง  INSTARAW Gemini (Native)" }
\ No newline at end of file
diff --git a/custom_nodes/ComfyUI_INSTARAW/nodes/api_nodes/generative_api_nodes.py b/custom_nodes/ComfyUI_INSTARAW/nodes/api_nodes/generative_api_nodes.py
new file mode 100644
index 0000000000000000000000000000000000000000..060f8f95cd5a20b15a933a5fffc2d367af51317b
--- /dev/null
+++ b/custom_nodes/ComfyUI_INSTARAW/nodes/api_nodes/generative_api_nodes.py
@@ -0,0 +1,656 @@
+# ---
+# ComfyUI INSTARAW - Generative API Nodes
+# Part of the INSTARAW custom nodes collection by Instara
+#
+# Copyright ยฉ 2025 Instara. All rights reserved.
+# PROPRIETARY SOFTWARE - ALL RIGHTS RESERVED
+# ---
+
+import requests
+import base64
+import io
+import time
+from PIL import Image
+import numpy as np
+import torch
+import hashlib
+import os
+import json
+
+# =================================================================================
+# PAYLOAD BUILDER FUNCTIONS (WITH COMPRESSION AWARENESS)
+# =================================================================================
+
+
+def build_seedream_fal_payload(api_base, **kwargs):
+    payload = {
+        "prompt": kwargs.get("prompt"),
+        "image_size": {"width": kwargs.get("width"), "height": kwargs.get("height")},
+        "enable_safety_checker": kwargs.get("enable_safety_checker"),
+    }
+    if (seed := kwargs.get("seed", -1)) >= 0:
+        payload["seed"] = seed
+    
+    # --- CORRECTED LOGIC WITH COMPRESSION ---
+    images_b64 = [
+        api_base.image_to_base64(img, max_size_mb=7) # Set a safe per-image limit
+        for img in [kwargs.get(f"image_{i}") for i in range(1, 5)]
+        if img is not None
+    ]
+    if images_b64:
+        payload["image_urls"] = images_b64
+        
+    return payload
+
+
+def build_seedream_wavespeed_payload(api_base, **kwargs):
+    width = kwargs.get('width', 1024)
+    height = kwargs.get('height', 1024)
+
+    # SeeDream v4.5 requires minimum 3,686,400 pixels for output size
+    MIN_PIXELS_SEEDREAM_V45 = 3686400
+    current_pixels = width * height
+
+    if current_pixels < MIN_PIXELS_SEEDREAM_V45:
+        # Scale up output size proportionally to meet minimum
+        scale_factor = (MIN_PIXELS_SEEDREAM_V45 / current_pixels) ** 0.5
+        width = int(width * scale_factor) + 1
+        height = int(height * scale_factor) + 1
+        print(f"โฌ†๏ธ Scaling output size from {kwargs.get('width')}x{kwargs.get('height')} to {width}x{height} to meet API minimum ({MIN_PIXELS_SEEDREAM_V45:,} px)")
+
+    payload = {
+        "prompt": kwargs.get("prompt"),
+        "size": f"{width}*{height}",
+    }
+
+    # --- CORRECTED LOGIC WITH COMPRESSION ---
+    images_b64 = [
+        api_base.image_to_base64(img, include_prefix=False, max_size_mb=7)
+        for img in [kwargs.get(f"image_{i}") for i in range(1, 5)]
+        if img is not None
+    ]
+    if images_b64:
+        payload["images"] = images_b64
+
+    return payload
+
+
+def build_nanobanana_fal_payload(api_base, **kwargs):
+    payload = {"prompt": kwargs.get("prompt")}
+    
+    # --- CORRECTED LOGIC WITH COMPRESSION ---
+    images_b64 = [
+        api_base.image_to_base64(img, max_size_mb=7)
+        for img in [kwargs.get(f"image_{i}") for i in range(1, 5)]
+        if img is not None
+    ]
+    if images_b64:
+        payload["image_urls"] = images_b64
+    elif "aspect_ratio" in kwargs:
+        payload["aspect_ratio"] = kwargs.get("aspect_ratio")
+        
+    return payload
+
+
+def build_nanobanana_wavespeed_payload(api_base, **kwargs):
+    payload = {"prompt": kwargs.get("prompt")}
+
+    # Multi-image mode: generate 2 images per call for 50% cost savings
+    num_images = kwargs.get("num_images", 1)
+    if num_images > 1:
+        payload["num_images"] = num_images
+
+    # --- CORRECTED LOGIC WITH COMPRESSION ---
+    images_b64 = [
+        api_base.image_to_base64(img, include_prefix=False, max_size_mb=7)
+        for img in [kwargs.get(f"image_{i}") for i in range(1, 5)]
+        if img is not None
+    ]
+    if images_b64:
+        payload["images"] = images_b64
+    elif "aspect_ratio" in kwargs:
+        payload["aspect_ratio"] = kwargs.get("aspect_ratio")
+
+    return payload
+
+
+def build_nanobanana_pro_fal_payload(api_base, **kwargs):
+    """fal.ai payload for Nano Banana Pro (Gemini 3.0 Pro Image)"""
+    payload = {
+        "prompt": kwargs.get("prompt"),
+        "num_images": 1,
+        "output_format": "png",
+    }
+
+    # Check for input images (edit mode)
+    images_b64 = [
+        api_base.image_to_base64(img, max_size_mb=7)
+        for img in [kwargs.get(f"image_{i}") for i in range(1, 5)]
+        if img is not None
+    ]
+
+    if images_b64:
+        # Edit mode - use provided aspect_ratio, default to "auto" if not specified
+        payload["image_urls"] = images_b64
+        payload["aspect_ratio"] = kwargs.get("aspect_ratio", "auto")
+        if kwargs.get("resolution"):
+            payload["resolution"] = kwargs.get("resolution")
+    else:
+        # T2I mode
+        if kwargs.get("aspect_ratio"):
+            payload["aspect_ratio"] = kwargs.get("aspect_ratio")
+        if kwargs.get("resolution"):
+            payload["resolution"] = kwargs.get("resolution")
+
+    return payload
+
+
+def build_nanobanana_pro_wavespeed_payload(api_base, **kwargs):
+    """WaveSpeed payload for Nano Banana Pro (Gemini 3.0 Pro Image)"""
+    payload = {
+        "prompt": kwargs.get("prompt"),
+        "output_format": "jpeg",
+    }
+
+    # Multi-image mode: generate 2 images per call for 50% cost savings
+    num_images = kwargs.get("num_images", 1)
+    if num_images > 1:
+        payload["num_images"] = num_images
+
+    # Check for input images (edit mode)
+    images_b64 = [
+        api_base.image_to_base64(img, include_prefix=False, max_size_mb=7)
+        for img in [kwargs.get(f"image_{i}") for i in range(1, 5)]
+        if img is not None
+    ]
+
+    if images_b64:
+        # Edit mode
+        payload["images"] = images_b64
+        if kwargs.get("aspect_ratio"):
+            payload["aspect_ratio"] = kwargs.get("aspect_ratio")
+        if kwargs.get("resolution"):
+            # WaveSpeed uses lowercase resolution
+            payload["resolution"] = kwargs.get("resolution", "1k").lower()
+    else:
+        # T2I mode
+        if kwargs.get("aspect_ratio"):
+            payload["aspect_ratio"] = kwargs.get("aspect_ratio")
+        if kwargs.get("resolution"):
+            # WaveSpeed uses lowercase resolution
+            payload["resolution"] = kwargs.get("resolution", "1k").lower()
+
+    return payload
+
+
+# =================================================================================
+# MODEL CONFIGURATION
+# =================================================================================
+
+MODEL_CONFIG = {
+    "SeeDream v4.5": {
+        "providers": {
+            "fal.ai": {
+                "t2i_endpoint": "fal-ai/bytedance/seedream/v4.5/text-to-image",
+                "i2i_endpoint": "fal-ai/bytedance/seedream/v4.5/edit",
+                "build_payload": build_seedream_fal_payload,
+            },
+            "wavespeed.ai": {
+                "t2i_endpoint": "bytedance/seedream-v4.5",
+                "i2i_endpoint": "bytedance/seedream-v4.5/edit",
+                "build_payload": build_seedream_wavespeed_payload,
+            },
+        }
+    },
+    "SeeDream v4": {
+        "providers": {
+            "fal.ai": {
+                "t2i_endpoint": "fal-ai/bytedance/seedream/v4/text-to-image",
+                "i2i_endpoint": "fal-ai/bytedance/seedream/v4/edit",
+                "build_payload": build_seedream_fal_payload,
+            },
+            "wavespeed.ai": {
+                "t2i_endpoint": "bytedance/seedream-v4",
+                "i2i_endpoint": "bytedance/seedream-v4/edit",
+                "build_payload": build_seedream_wavespeed_payload,
+            },
+        }
+    },
+    "Nano Banana": {
+        "providers": {
+            "fal.ai": {
+                "t2i_endpoint": "fal-ai/nano-banana",
+                "i2i_endpoint": "fal-ai/nano-banana/edit",
+                "build_payload": build_nanobanana_fal_payload,
+            },
+            "wavespeed.ai": {
+                "t2i_endpoint": "google/nano-banana/text-to-image",
+                "i2i_endpoint": "google/nano-banana/edit",
+                "build_payload": build_nanobanana_wavespeed_payload,
+            },
+        }
+    },
+    "Nano Banana Pro": {
+        "providers": {
+            "fal.ai": {
+                "t2i_endpoint": "fal-ai/nano-banana-pro",
+                "i2i_endpoint": "fal-ai/nano-banana-pro/edit",
+                "build_payload": build_nanobanana_pro_fal_payload,
+            },
+            "wavespeed.ai": {
+                "t2i_endpoint": "google/nano-banana-pro/text-to-image",
+                "i2i_endpoint": "google/nano-banana-pro/edit",
+                "build_payload": build_nanobanana_pro_wavespeed_payload,
+            },
+        }
+    },
+}
+
+# =================================================================================
+# BASE CLASS (WITH AUTO-COMPRESSION)
+# =================================================================================
+
+
+class INSTARAW_GenerativeAPIBase:
+    def __init__(self):
+        self.api_key = None
+
+    def set_api_key(self, api_key):
+        self.api_key = api_key
+
+    def _log_and_update_hash(self, hasher, key, value):
+        if value is None:
+            return
+        byte_value = b""
+        if isinstance(value, str):
+            byte_value = value.encode("utf-8")
+        elif isinstance(value, (int, float, bool)):
+            byte_value = str(value).encode("utf-8")
+        elif isinstance(value, torch.Tensor):
+            byte_value = value.cpu().numpy().tobytes()
+        else:
+            return
+        hasher.update(byte_value)
+
+    def image_to_base64(self, image_tensor, include_prefix=True, max_size_mb=7, min_pixels=None):
+        if image_tensor is None:
+            return None
+
+        image_np = image_tensor.cpu().numpy()
+        # Handle both [B, H, W, C] and [H, W, C] tensor formats
+        if image_np.ndim == 4:
+            image_np = image_np[0]  # Remove batch dimension
+        if image_np.max() <= 1.0:
+            image_np = (image_np * 255).astype(np.uint8)
+
+        # Ensure image is in RGB format for JPEG saving
+        image_pil = Image.fromarray(image_np).convert("RGB")
+
+        # Upscale if image is too small (e.g., SeeDream v4.5 requires min 3,686,400 pixels)
+        if min_pixels is not None:
+            current_pixels = image_pil.width * image_pil.height
+            if current_pixels < min_pixels:
+                # Calculate scale factor to reach minimum pixels
+                scale_factor = (min_pixels / current_pixels) ** 0.5
+                new_width = int(image_pil.width * scale_factor) + 1  # +1 to ensure we exceed minimum
+                new_height = int(image_pil.height * scale_factor) + 1
+                print(f"โฌ†๏ธ Upscaling image from {image_pil.width}x{image_pil.height} ({current_pixels:,} px) to {new_width}x{new_height} ({new_width * new_height:,} px) to meet API minimum ({min_pixels:,} px)")
+                image_pil = image_pil.resize((new_width, new_height), Image.Resampling.LANCZOS)
+
+        # Define quality levels to try for compression
+        quality_levels = [95, 90, 85, 80, 75]
+        max_bytes = max_size_mb * 1024 * 1024
+
+        buffer = io.BytesIO()
+        for quality in quality_levels:
+            buffer.seek(0)
+            buffer.truncate(0)
+            image_pil.save(buffer, format="JPEG", quality=quality)
+
+            # Check if the compressed size is within the limit
+            if buffer.tell() <= max_bytes:
+                print(f"โœ… Image compressed to JPEG (quality={quality}) to fit API limits. Size: {buffer.tell() / 1024:.2f} KB")
+                base64_str = base64.b64encode(buffer.getvalue()).decode()
+                mime_type = "image/jpeg"
+                return f"data:{mime_type};base64,{base64_str}" if include_prefix else base64_str
+
+        # If even the lowest quality is too large, raise an error
+        final_size_kb = buffer.tell() / 1024
+        raise Exception(f"Image is too large to send to the API. Even after compressing to lowest quality JPEG, size is {final_size_kb:.2f} KB (limit is {max_size_mb * 1024:.2f} KB). Please use a smaller input image.")
+
+    def _submit_fal(self, endpoint, payload):
+        url = f"https://fal.run/{endpoint}"
+        headers = {
+            "Authorization": f"Key {self.api_key}",
+            "Content-Type": "application/json",
+        }
+        print(f"๐Ÿš€ Submitting SYNC request to fal.ai: {url}")
+        response = requests.post(url, json=payload, headers=headers, timeout=180)
+        if not response.ok:
+            if response.status_code == 422:
+                try:
+                    error_data = response.json()
+                    error_msg = error_data.get("detail", [{}])[0].get(
+                        "msg", "Unknown validation error."
+                    )
+                    if "Gemini" in error_msg:
+                        raise Exception(
+                            f"API Error (422): The prompt was rejected by the provider's safety filter (fal.ai/Gemini). Try rephrasing sensitive terms or switch to a different provider."
+                        )
+                    else:
+                        raise Exception(
+                            f"API Error (422): Unprocessable Entity. Details: {error_msg}"
+                        )
+                except (json.JSONDecodeError, IndexError):
+                    raise Exception(
+                        f"API request failed with 422 (Unprocessable Entity), but the error response was not valid JSON."
+                    )
+            else:
+                raise Exception(
+                    f"API request failed: {response.status_code} - {response.text}"
+                )
+        result = response.json()
+        print("๐Ÿ“ฆ fal.ai sync response received.")
+        if "images" in result and len(result["images"]) > 0:
+            return result["images"][0]["url"]
+        raise Exception(
+            f"API response did not contain an image URL. Full response: {result}"
+        )
+
+    def _submit_wavespeed(self, e, p, return_all_outputs=False):
+        """
+        Submit request to Wavespeed.ai API.
+
+        Args:
+            e: Endpoint
+            p: Payload
+            return_all_outputs: If True, return list of all output URLs (for multi-image mode)
+        """
+        base, headers = "https://api.wavespeed.ai/api/v3", {
+            "Authorization": f"Bearer {self.api_key}",
+            "Content-Type": "application/json",
+        }
+        num_images = p.get("num_images", 1)
+        print(f"๐Ÿš€ Submitting ASYNC task to wavespeed.ai: {base}/{e} (num_images={num_images})")
+        r = requests.post(f"{base}/{e}", json=p, headers=headers, timeout=30)
+        if not r.ok:
+            # Extract error details for better UX
+            error_text = r.text[:200]
+            if "content" in error_text.lower() or "policy" in error_text.lower() or "422" in str(r.status_code):
+                raise Exception(f"Content policy violation: {error_text}")
+            raise Exception(f"API task submission failed: {r.status_code} - {error_text}")
+        req_id = r.json()["data"]["id"]
+        print(f"โœ… Task submitted (ID: {req_id[:8]}...). Polling for result...")
+        poll_url, start_time, timeout = (
+            f"{base}/predictions/{req_id}/result",
+            time.time(),
+            300,
+        )
+        poll_count = 0
+        while time.time() - start_time < timeout:
+            pr = requests.get(poll_url, headers=headers, timeout=30)
+            if not pr.ok:
+                raise Exception(f"API polling failed: {pr.status_code} - {pr.text}")
+            d = pr.json()["data"]
+            status = d.get("status")
+            if status == "completed":
+                elapsed = time.time() - start_time
+                outputs = d.get("outputs", [])
+                print(f"โœ… Task completed in {elapsed:.1f}s ({len(outputs)} image(s))")
+                if return_all_outputs:
+                    return outputs  # Return all image URLs for multi-image mode
+                return outputs[0] if outputs else None
+            if status == "failed":
+                error_msg = d.get('error', 'Unknown error')
+                # Check for content policy in error
+                if any(x in str(error_msg).lower() for x in ['content', 'policy', 'safety', 'moderation']):
+                    raise Exception(f"Content policy violation: {error_msg}")
+                raise Exception(f"API task failed: {error_msg}")
+            # Only log every 10 seconds (5 polls) to reduce spam
+            poll_count += 1
+            if poll_count % 5 == 1:
+                elapsed = time.time() - start_time
+                print(f"   โณ Waiting for result... ({elapsed:.0f}s elapsed, status: {status})")
+            time.sleep(2)
+        raise Exception("API task timed out after 5 minutes.")
+
+    def submit_request(self, provider, endpoint, payload):
+        if not self.api_key:
+            raise ValueError(f"API key for {provider} not set.")
+        if provider == "fal.ai":
+            return self._submit_fal(endpoint, payload)
+        if provider == "wavespeed.ai":
+            return self._submit_wavespeed(endpoint, payload)
+        raise ValueError(f"Unknown provider: {provider}")
+
+    def execute_generation(self, is_i2i, cache_filename, **kwargs):
+        provider, model = kwargs.get("provider"), kwargs.get("model")
+        if not model:
+            raise ValueError("Model not provided.")
+        if not provider:
+            raise ValueError("Provider not provided.")
+        model_conf = MODEL_CONFIG.get(model)
+        if not model_conf:
+            raise ValueError(f"Invalid model selected: {model}")
+        provider_conf = model_conf["providers"].get(provider)
+        if not provider_conf:
+            raise ValueError(
+                f"Provider '{provider}' is not supported for model '{model}'"
+            )
+        endpoint = (
+            provider_conf["i2i_endpoint"] if is_i2i else provider_conf["t2i_endpoint"]
+        )
+        build_payload_func = provider_conf["build_payload"]
+        self.set_api_key(kwargs.get("api_key"))
+        payload = build_payload_func(self, **kwargs)
+        image_url = self.submit_request(provider, endpoint, payload)
+        image_response = requests.get(image_url)
+        image_response.raise_for_status()
+        image_pil = Image.open(io.BytesIO(image_response.content)).convert("RGB")
+        print(f"๐Ÿ’พ Saving generated image to cache: {cache_filename}")
+        image_pil.save(cache_filename, "PNG")
+        image_np = np.array(image_pil).astype(np.float32) / 255.0
+        return (torch.from_numpy(image_np).unsqueeze(0),)
+
+
+# =================================================================================
+# NODE IMPLEMENTATIONS
+# =================================================================================
+
+
+class INSTARAW_APITextToImage(INSTARAW_GenerativeAPIBase):
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "api_key": ("STRING", {"forceInput": True}),
+                "provider": ("STRING", {"forceInput": True}),
+                "model": ("STRING", {"forceInput": True}),
+                "prompt": (
+                    "STRING",
+                    {"multiline": True, "default": "A beautiful landscape"},
+                ),
+                "width": ("INT", {"default": 1024, "min": 64, "max": 4096, "step": 64}),
+                "height": (
+                    "INT",
+                    {"default": 1024, "min": 64, "max": 4096, "step": 64},
+                ),
+                "aspect_ratio": (
+                    [
+                        "1:1",
+                        "4:3",
+                        "3:2",
+                        "2:3",
+                        "5:4",
+                        "4:5",
+                        "3:4",
+                        "16:9",
+                        "9:16",
+                        "21:9",
+                    ],
+                    {"default": "1:1"},
+                ),
+                "enable_safety_checker": ("BOOLEAN", {"default": True}),
+            },
+            "optional": {
+                "seed": ("INT", {"default": -1, "min": -1, "max": 2147483647}),
+                "aspect_ratio_override": (
+                    "STRING",
+                    {
+                        "forceInput": True,
+                        "tooltip": "Override aspect ratio from external node (e.g., Nano Banana Aspect Ratio)",
+                    },
+                ),
+                "resolution": (
+                    "STRING",
+                    {
+                        "forceInput": True,
+                        "tooltip": "Resolution tier (1K, 2K, 4K) for Nano Banana Pro",
+                    },
+                ),
+            },
+        }
+
+    RETURN_TYPES, FUNCTION, CATEGORY = ("IMAGE",), "generate", "INSTARAW/API"
+
+    def generate(self, **kwargs):
+        # Handle aspect_ratio_override - if provided, use it instead of dropdown
+        if kwargs.get("aspect_ratio_override"):
+            kwargs["aspect_ratio"] = kwargs["aspect_ratio_override"]
+
+        cache_dir = os.path.join(os.path.dirname(__file__), "..", "..", "cache")
+        os.makedirs(cache_dir, exist_ok=True)
+        hasher = hashlib.sha256()
+        for key in sorted(kwargs.keys()):
+            self._log_and_update_hash(hasher, key, kwargs[key])
+        cache_filepath = os.path.join(cache_dir, f"{hasher.hexdigest()}_api_t2i.png")
+        if os.path.exists(cache_filepath):
+            print(f"โœ… API T2I Cache Hit! Loading image from {cache_filepath}")
+            return (
+                torch.from_numpy(
+                    np.array(Image.open(cache_filepath)).astype(np.float32) / 255.0
+                ).unsqueeze(0),
+            )
+        print("๐Ÿ’จ API T2I Cache Miss. Proceeding with API call...")
+        return self.execute_generation(
+            is_i2i=False, cache_filename=cache_filepath, **kwargs
+        )
+
+
+class INSTARAW_APIImageToImage(INSTARAW_GenerativeAPIBase):
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "api_key": ("STRING", {"forceInput": True}),
+                "provider": ("STRING", {"forceInput": True}),
+                "model": ("STRING", {"forceInput": True}),
+                "prompt": (
+                    "STRING",
+                    {"multiline": True, "default": "Transform image 1"},
+                ),
+                "enable_safety_checker": ("BOOLEAN", {"default": True}),
+            },
+            "optional": {
+                "width": ("INT", {"default": 1024, "min": 64, "max": 4096, "step": 64}),
+                "height": (
+                    "INT",
+                    {"default": 1024, "min": 64, "max": 4096, "step": 64},
+                ),
+                "image_1": ("IMAGE",),
+                "image_2": ("IMAGE",),
+                "image_3": ("IMAGE",),
+                "image_4": ("IMAGE",),
+                "seed": ("INT", {"default": -1, "min": -1, "max": 2147483647}),
+                "aspect_ratio": (
+                    "STRING",
+                    {
+                        "forceInput": True,
+                        "tooltip": "Aspect ratio from external node (e.g., Nano Banana Aspect Ratio)",
+                    },
+                ),
+                "resolution": (
+                    "STRING",
+                    {
+                        "forceInput": True,
+                        "tooltip": "Resolution tier (1K, 2K, 4K) for Nano Banana Pro",
+                    },
+                ),
+            },
+        }
+
+    RETURN_TYPES, FUNCTION, CATEGORY = ("IMAGE",), "generate", "INSTARAW/API"
+
+    def generate(
+        self,
+        api_key,
+        provider,
+        model,
+        prompt,
+        enable_safety_checker,
+        width=1024,
+        height=1024,
+        image_1=None,
+        image_2=None,
+        image_3=None,
+        image_4=None,
+        seed=-1,
+        aspect_ratio=None,
+        resolution=None,
+    ):
+        # --- ADDED: Validate that at least one image is provided ---
+        if all(img is None for img in [image_1, image_2, image_3, image_4]):
+            raise ValueError("INSTARAW API I2I node requires at least one image input to be provided.")
+
+        all_args = {
+            "api_key": api_key,
+            "provider": provider,
+            "model": model,
+            "image_1": image_1,
+            "prompt": prompt,
+            "enable_safety_checker": enable_safety_checker,
+            "width": width,
+            "height": height,
+            "image_2": image_2,
+            "image_3": image_3,
+            "image_4": image_4,
+            "seed": seed,
+        }
+
+        # Add optional aspect_ratio and resolution if provided
+        if aspect_ratio:
+            all_args["aspect_ratio"] = aspect_ratio
+        if resolution:
+            all_args["resolution"] = resolution
+        cache_dir = os.path.join(os.path.dirname(__file__), "..", "..", "cache")
+        os.makedirs(cache_dir, exist_ok=True)
+        hasher = hashlib.sha256()
+        hasher.update(b"01KEQ86BQNDGB0F9GVKEP6501V")
+        for key in sorted(all_args.keys()):
+            self._log_and_update_hash(hasher, key, all_args[key])
+        cache_filepath = os.path.join(cache_dir, f"{hasher.hexdigest()}_api_i2i.png")
+        if os.path.exists(cache_filepath):
+            print(f"โœ… API I2I Cache Hit! Loading image from {cache_filepath}")
+            return (
+                torch.from_numpy(
+                    np.array(Image.open(cache_filepath)).astype(np.float32) / 255.0
+                ).unsqueeze(0),
+            )
+        print("๐Ÿ’จ API I2I Cache Miss. Proceeding with API call...")
+        return self.execute_generation(
+            is_i2i=True, cache_filename=cache_filepath, **all_args
+        )
+
+
+# =================================================================================
+# EXPORT NODE MAPPINGS
+# =================================================================================
+
+NODE_CLASS_MAPPINGS = {
+    "INSTARAW_APITextToImage": INSTARAW_APITextToImage,
+    "INSTARAW_APIImageToImage": INSTARAW_APIImageToImage,
+}
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "INSTARAW_APITextToImage": "๐ŸŽจ INSTARAW API T2I",
+    "INSTARAW_APIImageToImage": "๐ŸŽจ INSTARAW API I2I",
+}
\ No newline at end of file
diff --git a/custom_nodes/ComfyUI_INSTARAW/nodes/api_nodes/grok_native.py b/custom_nodes/ComfyUI_INSTARAW/nodes/api_nodes/grok_native.py
new file mode 100644
index 0000000000000000000000000000000000000000..7823b7559c70929ab35677a1815e08d0667ee965
--- /dev/null
+++ b/custom_nodes/ComfyUI_INSTARAW/nodes/api_nodes/grok_native.py
@@ -0,0 +1,250 @@
+# ---
+# Filename: ../ComfyUI_INSTARAW/nodes/api_nodes/grok_native.py
+# Native Grok (xAI) text helper that mirrors the Gemini node UX
+# ---
+
+import base64
+import hashlib
+import io
+import json
+import os
+from typing import List
+
+import requests
+from PIL import Image
+import numpy as np
+
+
+class INSTARAW_GrokNative:
+	"""
+	Text-first Grok helper that talks to the public xAI REST API.
+	We keep the surface identical to the Gemini node: simple prompt entry,
+	optional temperature + seed, and up to four reference images.
+	"""
+
+	@classmethod
+	def INPUT_TYPES(cls):
+		return {
+			"required": {
+				"api_key": ("STRING", {"multiline": False, "default": ""}),
+				"prompt": ("STRING", {"multiline": True, "default": "Describe the concept or provide instructions."}),
+				"model": (
+					[
+						"grok-4-fast-reasoning",
+						"grok-4-fast-non-reasoning",
+						"grok-4-0709",
+					],
+					{"default": "grok-4-fast-reasoning"},
+				),
+				"system_prompt": (
+					"STRING",
+					{"multiline": True, "default": "You are a helpful creative assistant."},
+				),
+				"temperature": ("FLOAT", {"default": 0.8, "min": 0.0, "max": 2.0, "step": 0.01}),
+				"top_p": ("FLOAT", {"default": 0.9, "min": 0.0, "max": 1.0, "step": 0.01}),
+				"seed": (
+					"INT",
+					{
+						"default": 111111,
+						"min": 0,
+						"max": 0xFFFFFFFFFFFFFFFF,
+						"control_after_generate": "randomize",
+					},
+				),
+			},
+			"optional": {
+				"image_1": ("IMAGE",),
+				"image_2": ("IMAGE",),
+				"image_3": ("IMAGE",),
+				"image_4": ("IMAGE",),
+			},
+		}
+
+	RETURN_TYPES = ("STRING",)
+	FUNCTION = "generate_content"
+	CATEGORY = "INSTARAW/API"
+
+	def generate_content(
+		self,
+		api_key,
+		prompt,
+		model,
+		system_prompt,
+		temperature,
+		top_p,
+		seed,
+		image_1=None,
+		image_2=None,
+		image_3=None,
+		image_4=None,
+	):
+		api_key = (api_key or "").strip() or os.environ.get("XAI_API_KEY", "").strip()
+		if not api_key:
+			raise ValueError("Grok API Key is missing. Provide it in the node or set XAI_API_KEY env var.")
+
+		images = [img for img in (image_1, image_2, image_3, image_4) if img is not None]
+		payload = self._build_payload(prompt, system_prompt, model, temperature, top_p, seed, images)
+
+		cache_hit = self._load_from_cache(payload)
+		if cache_hit is not None:
+			return (cache_hit,)
+
+		base_url = os.environ.get("XAI_API_BASE", "https://api.x.ai").rstrip("/")
+		url = f"{base_url}/v1/chat/completions"
+
+		headers = {
+			"Authorization": f"Bearer {api_key}",
+			"Content-Type": "application/json",
+		}
+
+		response = requests.post(url, headers=headers, json=payload, timeout=300)
+		body_text = response.text
+		if response.status_code >= 400:
+			raise RuntimeError(f"Grok API error {response.status_code}: {body_text}")
+
+		try:
+			body = response.json()
+		except ValueError as err:
+			raise RuntimeError(f"Grok API returned invalid JSON: {err}") from err
+
+		content = self._extract_text(body)
+		self._save_to_cache(payload, content)
+		return (content,)
+
+	# --- Internals --------------------------------------------------------
+
+	def _build_payload(self, prompt, system_prompt, model, temperature, top_p, seed, images) -> dict:
+		messages: List[dict] = []
+		if system_prompt and system_prompt.strip():
+			messages.append({"role": "system", "content": system_prompt.strip()})
+
+		user_content: List[dict] = []
+		if prompt and prompt.strip():
+			user_content.append({"type": "text", "text": prompt.strip()})
+
+		for image_tensor in images:
+			data_url = self._tensor_to_data_url(image_tensor)
+			user_content.append(
+				{
+					"type": "image_url",
+					"image_url": {"url": f"data:image/png;base64,{data_url}", "detail": "high"},
+				}
+			)
+
+		if not user_content:
+			raise ValueError("Provide a prompt or an image for Grok to process.")
+
+		messages.append({"role": "user", "content": user_content})
+
+		seed_value = None
+		int32_min, int32_max = -2147483648, 2147483647
+		if seed is not None:
+			seed_value = int(seed)
+			if seed_value < int32_min or seed_value > int32_max:
+				seed_value = ((seed_value + 2 ** 31) % (2 ** 32)) - 2 ** 31
+			if seed_value <= 0:
+				seed_value = (abs(seed_value) % int32_max) + 1
+		if seed_value is None:
+			seed_value = self._derive_seed_from_payload(prompt, system_prompt, model)
+
+		payload = {
+			"model": model,
+			"messages": messages,
+			"temperature": float(temperature),
+			"top_p": float(top_p),
+		}
+		payload["seed"] = seed_value
+		return payload
+
+	def _derive_seed_from_payload(self, prompt: str, system_prompt: str, model: str) -> int:
+		int32_max = 2147483647
+		hasher = hashlib.sha256()
+		hasher.update((prompt or "").encode("utf-8"))
+		hasher.update((system_prompt or "").encode("utf-8"))
+		hasher.update((model or "").encode("utf-8"))
+		digest = hasher.digest()
+		value = int.from_bytes(digest[:4], byteorder="big", signed=True)
+		if value <= 0:
+			value = (abs(value) % int32_max) + 1
+		return min(value, int32_max)
+
+	def _tensor_to_data_url(self, tensor) -> str:
+		if hasattr(tensor, "detach"):
+			array = tensor.detach().cpu().numpy()
+		else:
+			array = np.asarray(tensor)
+
+		if array.ndim == 4:
+			array = array[0]
+		if array.ndim == 3 and array.shape[0] in (1, 2, 3, 4):
+			array = np.transpose(array, (1, 2, 0))
+
+		array = np.clip(array * 255.0, 0, 255).astype(np.uint8)
+		image = Image.fromarray(array)
+		buffer = io.BytesIO()
+		image.save(buffer, format="PNG")
+		encoded = base64.b64encode(buffer.getvalue()).decode("utf-8")
+		return encoded
+
+	def _extract_text(self, body: dict) -> str:
+		choices = body.get("choices") or []
+		if not choices:
+			raise RuntimeError("Grok API returned no choices.")
+
+		first = choices[0]
+		message = first.get("message") or {}
+		content = message.get("content")
+		if isinstance(content, list):
+			text = "".join(
+				part.get("text", "") if isinstance(part, dict) else str(part)
+				for part in content
+			).strip()
+			if text:
+				return text
+		if isinstance(content, str) and content.strip():
+			return content.strip()
+
+		legacy = first.get("text")
+		if legacy:
+			return legacy.strip()
+
+		raise RuntimeError("Unable to extract text from Grok response.")
+
+	def _cache_path(self) -> str:
+		cache_dir = os.path.join(os.path.dirname(__file__), "..", "..", "cache")
+		os.makedirs(cache_dir, exist_ok=True)
+		return cache_dir
+
+	def _payload_hash(self, payload: dict) -> str:
+		data = json.dumps(payload, sort_keys=True).encode("utf-8")
+		return hashlib.sha256(data).hexdigest()
+
+	def _cache_file(self, payload: dict) -> str:
+		return os.path.join(self._cache_path(), f"{self._payload_hash(payload)}_grok_native.txt")
+
+	def _load_from_cache(self, payload: dict):
+		path = self._cache_file(payload)
+		if os.path.exists(path):
+			try:
+				with open(path, "r", encoding="utf-8") as handle:
+					return handle.read()
+			except OSError:
+				return None
+		return None
+
+	def _save_to_cache(self, payload: dict, text: str):
+		path = self._cache_file(payload)
+		try:
+			with open(path, "w", encoding="utf-8") as handle:
+				handle.write(text)
+		except OSError:
+			pass
+
+
+NODE_CLASS_MAPPINGS = {
+	"INSTARAW_GrokNative": INSTARAW_GrokNative,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+	"INSTARAW_GrokNative": "๐Ÿง  INSTARAW Grok (Native)",
+}
diff --git a/custom_nodes/ComfyUI_INSTARAW/nodes/api_nodes/ideogram.py b/custom_nodes/ComfyUI_INSTARAW/nodes/api_nodes/ideogram.py
new file mode 100644
index 0000000000000000000000000000000000000000..4d71e67396517cc136cf87b6fb3d6126064e1187
--- /dev/null
+++ b/custom_nodes/ComfyUI_INSTARAW/nodes/api_nodes/ideogram.py
@@ -0,0 +1,583 @@
+# ---
+# ComfyUI INSTARAW - Ideogram Nodes
+# Part of the INSTARAW custom nodes collection by Instara
+#
+# Copyright ยฉ 2025 Instara. All rights reserved# PROPRIETARY SOFTWARE - ALL RIGHTS RESERVED
+# ---
+
+"""
+ComfyUI nodes for Ideogram API integration.
+Supports image generation, editing, and description using Ideogram v3 API.
+"""
+import requests
+import io
+from PIL import Image
+import numpy as np
+import torch
+import hashlib
+import os
+import base64
+import json
+from datetime import datetime, timezone, timedelta
+
+
+# --- List of supported resolutions and aspect ratios for Ideogram V3 ---
+IDEOGRAM_V3_RESOLUTIONS = [
+    "1024x1024",
+    "864x1152",
+    "768x1024",
+    "512x1536",
+    "576x1408",
+    "576x1472",
+    "576x1536",
+    "640x1344",
+    "640x1408",
+    "640x1472",
+    "640x1536",
+    "704x1152",
+    "704x1216",
+    "704x1280",
+    "704x1344",
+    "704x1408",
+    "704x1472",
+    "736x1312",
+    "768x1088",
+    "768x1216",
+    "768x1280",
+    "768x1344",
+    "800x1280",
+    "832x960",
+    "832x1024",
+    "832x1088",
+    "832x1152",
+    "832x1216",
+    "832x1248",
+    "896x960",
+    "896x1024",
+    "896x1088",
+    "896x1120",
+    "896x1152",
+    "960x832",
+    "960x896",
+    "960x1024",
+    "960x1088",
+    "1024x832",
+    "1024x896",
+    "1024x960",
+    "1088x768",
+    "1088x832",
+    "1088x896",
+    "1088x960",
+    "1120x896",
+    "1152x704",
+    "1152x832",
+    "1152x864",
+    "1152x896",
+    "1216x704",
+    "1216x768",
+    "1216x832",
+    "1248x832",
+    "1280x704",
+    "1280x768",
+    "1280x800",
+    "1312x736",
+    "1344x640",
+    "1344x704",
+    "1344x768",
+    "1408x576",
+    "1408x640",
+    "1408x704",
+    "1472x576",
+    "1472x640",
+    "1472x704",
+    "1536x512",
+    "1536x576",
+    "1536x640",
+]
+IDEOGRAM_V3_ASPECT_RATIOS = [
+    "1x1",
+    "9x16",
+    "10x16",
+    "3x4",
+    "4x5",
+    "2x3",
+    "1x2",
+    "1x3",
+    "16x9",
+    "16x10",
+    "4x3",
+    "5x4",
+    "3x2",
+    "2x1",
+    "3x1",
+]
+
+
+class IdeogramBase:
+    def __init__(self):
+        self.api_key = None
+        self.base_url = "https://api.ideogram.ai"
+        self.fal_cdn_url = "https://v3.fal.media/files/upload"
+        self.fal_cdn_auth_url = (
+            "https://rest.alpha.fal.ai/storage/auth/token?storage_type=fal-cdn-v3"
+        )
+        self._fal_cdn_token_cache = {}
+
+    def set_api_key(self, api_key):
+        self.api_key = api_key
+
+    def _get_fal_cdn_auth_header(self):
+        if "token" in self._fal_cdn_token_cache and self._fal_cdn_token_cache.get(
+            "expires_at", datetime.min.replace(tzinfo=timezone.utc)
+        ) > datetime.now(timezone.utc) + timedelta(seconds=60):
+            token_data = self._fal_cdn_token_cache
+        else:
+            print("๐Ÿ” Fetching new fal.ai CDN token...")
+            auth_headers = {
+                "Authorization": f"Key {self.api_key}",
+                "Accept": "application/json",
+                "Content-Type": "application/json",
+            }
+            response = requests.post(
+                self.fal_cdn_auth_url, headers=auth_headers, data=b"{}", timeout=30
+            )
+            if not response.ok:
+                raise Exception(
+                    f"Failed to get fal.ai CDN auth token: {response.status_code} - {response.text}"
+                )
+            token_data = response.json()
+            expires_at_str = token_data["expires_at"].replace("Z", "+00:00")
+            token_data["expires_at"] = datetime.fromisoformat(expires_at_str)
+            self._fal_cdn_token_cache = token_data
+            print("โœ… New CDN token acquired.")
+        return {"Authorization": f"{token_data['token_type']} {token_data['token']}"}
+
+    def tensor_to_bytesio(self, tensor, is_mask=False):
+        if tensor is None:
+            return None
+        image_np = tensor.cpu().numpy()
+        # Handle both [B, H, W, C] and [H, W, C] tensor formats
+        if image_np.ndim == 4:
+            image_np = image_np[0]  # Remove batch dimension
+        if not is_mask:
+            if image_np.max() <= 1.0:
+                image_np = (image_np * 255).astype(np.uint8)
+            pil_image = Image.fromarray(image_np, "RGB")
+        else:
+            # Mask handling - need 2D array for grayscale
+            # ComfyUI masks are typically (B, H, W) format
+            if image_np.ndim == 4:
+                image_np = image_np[0]  # Remove batch: (B, C, H, W) -> (C, H, W)
+            if image_np.ndim == 3:
+                # Could be (B, H, W) or (C, H, W) - first dim is batch/channel
+                # Or (H, W, C) - last dim is channel
+                if image_np.shape[0] <= 4:  # Likely (B, H, W) or (C, H, W)
+                    image_np = image_np[0]
+                else:  # Likely (H, W, C)
+                    image_np = image_np[:, :, 0]
+            image_np = (image_np > 0.5).astype(np.float32)
+            image_np = (image_np * 255).astype(np.uint8)
+            pil_image = Image.fromarray(image_np, "L").convert("RGB")
+        buffer = io.BytesIO()
+        pil_image.save(buffer, format="PNG")
+        buffer.seek(0)
+        return buffer
+
+    def image_to_base64(self, image_tensor, is_mask=False):
+        if image_tensor is None:
+            return None
+        bytes_io = self.tensor_to_bytesio(image_tensor, is_mask)
+        if bytes_io is None:
+            return None
+        base64_str = base64.b64encode(bytes_io.getvalue()).decode()
+        return f"data:image/png;base64,{base64_str}"
+
+    def _upload_tensor_to_fal_cdn(self, tensor, is_mask=False):
+        if tensor is None:
+            return None
+        print(f"๐Ÿš€ Uploading {'mask' if is_mask else 'image'} to fal.ai CDN...")
+        auth_header = self._get_fal_cdn_auth_header()
+        headers = {**auth_header, "Content-Type": "image/png"}
+        bytes_io = self.tensor_to_bytesio(tensor, is_mask=is_mask)
+        if bytes_io is None:
+            return None
+        image_bytes = bytes_io.getvalue()
+        response = requests.post(
+            self.fal_cdn_url, data=image_bytes, headers=headers, timeout=60
+        )
+        if not response.ok:
+            raise Exception(
+                f"fal.ai CDN upload failed: {response.status_code} - {response.text}"
+            )
+        url = response.json()["access_url"]
+        print(f"โœ… CDN Upload successful. URL: {url}")
+        return url
+
+    # --- THIS IS THE FIX: UPGRADED ERROR HANDLING ---
+    def _submit_fal(self, endpoint, payload):
+        url = f"https://fal.run/{endpoint}"
+        headers = {
+            "Authorization": f"Key {self.api_key}",
+            "Content-Type": "application/json",
+        }
+        print(f"๐Ÿš€ Submitting SYNC request to fal.ai: {url}")
+        response = requests.post(url, json=payload, headers=headers, timeout=180)
+
+        if not response.ok:
+            if response.status_code == 422:
+                try:
+                    error_data = response.json()
+                    error_obj = error_data.get("detail", [{}])[0]
+
+                    if error_obj.get("type") == "content_policy_violation":
+                        raise Exception(
+                            "Image flagged as unsafe by Ideogram API. Try enabling the ๐Ÿ”ž FULLY CLOTHE FOR NSFW FIX or using a different SWAP TO image."
+                        )
+                    else:
+                        error_msg = error_obj.get("msg", "Unknown validation error.")
+                        raise Exception(
+                            f"API Error (422): Unprocessable Entity. Details: {error_msg}"
+                        )
+                except (json.JSONDecodeError, IndexError, KeyError):
+                    raise Exception(
+                        f"API request failed with 422 (Unprocessable Entity), but the error response was not in the expected format. Raw response: {response.text}"
+                    )
+            else:
+                raise Exception(
+                    f"API request failed: {response.status_code} - {response.text}"
+                )
+
+        result = response.json()
+        print("๐Ÿ“ฆ fal.ai sync response received.")
+        if "images" in result and len(result["images"]) > 0:
+            return result["images"][0]["url"]
+        raise Exception(
+            f"API response did not contain an image URL. Full response: {result}"
+        )
+
+    # --- END FIX ---
+
+    def submit_multipart_request(self, endpoint, data_payload, files_payload):
+        if not self.api_key:
+            raise ValueError("API key not set.")
+        url = f"{self.base_url}{endpoint}"
+        headers = {"Api-Key": self.api_key}
+        print(f"๐Ÿš€ Submitting multipart request to {url}")
+        response = requests.post(
+            url, headers=headers, data=data_payload, files=files_payload, timeout=180
+        )
+        if not response.ok:
+            raise Exception(
+                f"API request failed: {response.status_code} - {response.text}"
+            )
+        return response.json()
+
+
+class IdeogramDescribeImage(IdeogramBase):
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "api_key": ("STRING", {"default": ""}),
+                "image": ("IMAGE",),
+                "describe_model_version": (["V_3"], {"default": "V_3"}),
+            }
+        }
+
+    RETURN_TYPES, FUNCTION, CATEGORY = ("STRING",), "describe_image", "INSTARAW/API"
+
+    def describe_image(self, api_key, image, describe_model_version):
+        cache_dir = os.path.join(os.path.dirname(__file__), "..", "..", "cache")
+        os.makedirs(cache_dir, exist_ok=True)
+        hasher = hashlib.sha256()
+        hasher.update(describe_model_version.encode("utf-8"))
+        hasher.update(image.cpu().numpy().tobytes())
+        cache_key = hasher.hexdigest()
+        cache_filepath = os.path.join(cache_dir, f"{cache_key}_describe.txt")
+        if os.path.exists(cache_filepath):
+            with open(cache_filepath, "r", encoding="utf-8") as f:
+                return (f.read(),)
+        self.set_api_key(api_key)
+        image_file = self.tensor_to_bytesio(image)
+        data_payload = {"describe_model_version": describe_model_version}
+        files_payload = {"image_file": ("image.png", image_file, "image/png")}
+        result = self.submit_multipart_request(
+            "/describe", data_payload=data_payload, files_payload=files_payload
+        )
+        if (
+            result.get("descriptions")
+            and len(result["descriptions"]) > 0
+            and "text" in result["descriptions"][0]
+        ):
+            description = result["descriptions"][0]["text"]
+            with open(cache_filepath, "w", encoding="utf-8") as f:
+                f.write(description)
+            return (description,)
+        else:
+            raise Exception("API did not return a valid description.")
+
+
+class IdeogramGenerateImage(IdeogramBase):
+    @classmethod
+    def INPUT_TYPES(cls):
+        magic_prompt_options = ["AUTO", "ON", "OFF"]
+        rendering_speed_options = ["QUALITY", "FLASH", "DEFAULT", "TURBO"]
+        return {
+            "required": {
+                "api_key": ("STRING", {"default": ""}),
+                "prompt": (
+                    "STRING",
+                    {"multiline": True, "default": "A photo of a cat."},
+                ),
+                "sizing_method": (["By Aspect Ratio", "By Resolution"],),
+                "aspect_ratio": (IDEOGRAM_V3_ASPECT_RATIOS, {"default": "1x1"}),
+                "resolution": (IDEOGRAM_V3_RESOLUTIONS, {"default": "1024x1024"}),
+                "rendering_speed": (rendering_speed_options, {"default": "DEFAULT"}),
+                "magic_prompt": (magic_prompt_options, {"default": "AUTO"}),
+            },
+            "optional": {
+                "negative_prompt": ("STRING", {"multiline": True, "default": ""}),
+                "seed": ("INT", {"default": -1, "min": -1, "max": 2147483647}),
+                "character_reference_image": ("IMAGE",),
+            },
+        }
+
+    RETURN_TYPES, FUNCTION, CATEGORY = ("IMAGE",), "generate_image", "INSTARAW/API"
+
+    def generate_image(
+        self,
+        api_key,
+        prompt,
+        sizing_method,
+        aspect_ratio,
+        resolution,
+        rendering_speed,
+        magic_prompt,
+        negative_prompt="",
+        seed=-1,
+        character_reference_image=None,
+    ):
+        if magic_prompt in ["AUTO", "OFF"] and not prompt.strip():
+            raise ValueError("A non-empty 'prompt' is required.")
+        cache_dir = os.path.join(os.path.dirname(__file__), "..", "..", "cache")
+        os.makedirs(cache_dir, exist_ok=True)
+        hasher = hashlib.sha256()
+        hasher.update(prompt.encode("utf-8"))
+        hasher.update(sizing_method.encode("utf-8"))
+        hasher.update(aspect_ratio.encode("utf-8"))
+        hasher.update(resolution.encode("utf-8"))
+        hasher.update(rendering_speed.encode("utf-8"))
+        hasher.update(magic_prompt.encode("utf-8"))
+        hasher.update(negative_prompt.encode("utf-8"))
+        hasher.update(str(seed).encode("utf-8"))
+        if character_reference_image is not None:
+            hasher.update(character_reference_image.cpu().numpy().tobytes())
+        cache_key = hasher.hexdigest()
+        cache_filepath = os.path.join(cache_dir, f"{cache_key}_ideogram_gen.png")
+        if os.path.exists(cache_filepath):
+            pil_image = Image.open(cache_filepath).convert("RGB")
+            return (
+                torch.from_numpy(
+                    np.array(pil_image).astype(np.float32) / 255.0
+                ).unsqueeze(0),
+            )
+        self.set_api_key(api_key)
+        char_ref_file = self.tensor_to_bytesio(character_reference_image)
+        data_payload = {
+            "prompt": prompt,
+            "rendering_speed": rendering_speed,
+            "magic_prompt": magic_prompt,
+        }
+        if sizing_method == "By Resolution":
+            data_payload["resolution"] = resolution
+        else:
+            data_payload["aspect_ratio"] = aspect_ratio
+        if seed >= 0:
+            data_payload["seed"] = seed
+        if negative_prompt.strip():
+            data_payload["negative_prompt"] = negative_prompt
+        files_payload = {}
+        if char_ref_file:
+            files_payload["character_reference_images"] = (
+                "char_ref.png",
+                char_ref_file,
+                "image/png",
+            )
+            data_payload["style_type"] = "REALISTIC"
+        result = self.submit_multipart_request(
+            "/v1/ideogram-v3/generate", data_payload, files_payload
+        )
+        if result.get("data") and len(result["data"]) > 0:
+            image_data = result["data"][0]
+            if image_data.get("is_image_safe") is False:
+                raise Exception("Image generation failed: unsafe result.")
+            image_url = image_data.get("url")
+            if not image_url:
+                raise Exception("API did not provide a valid image URL.")
+            image_response = requests.get(image_url)
+            image_response.raise_for_status()
+            pil_image = Image.open(io.BytesIO(image_response.content)).convert("RGB")
+            pil_image.save(cache_filepath, "PNG")
+            return (
+                torch.from_numpy(
+                    np.array(pil_image).astype(np.float32) / 255.0
+                ).unsqueeze(0),
+            )
+        else:
+            raise Exception("API did not return any images.")
+
+
+class IdeogramEditImage(IdeogramBase):
+    @classmethod
+    def INPUT_TYPES(cls):
+        magic_prompt_options = ["AUTO", "ON", "OFF"]
+        rendering_speed_options = ["QUALITY", "FLASH", "DEFAULT", "TURBO", "BALANCED"]
+        return {
+            "required": {
+                "api_key": ("STRING", {"forceInput": True}),
+                "provider": ("STRING", {"forceInput": True}),
+                "image": ("IMAGE",),
+                "mask": ("MASK",),
+                "prompt": ("STRING", {"multiline": True, "default": ""}),
+                "rendering_speed": (rendering_speed_options,),
+                "magic_prompt": (magic_prompt_options, {"default": "AUTO"}),
+            },
+            "optional": {
+                "seed": ("INT", {"default": -1, "min": -1, "max": 2147483647}),
+                "character_reference_image": ("IMAGE",),
+            },
+        }
+
+    RETURN_TYPES, FUNCTION, CATEGORY = ("IMAGE",), "edit_image", "INSTARAW/API"
+
+    def edit_image(
+        self,
+        api_key,
+        provider,
+        image,
+        mask,
+        prompt,
+        rendering_speed,
+        magic_prompt,
+        seed=-1,
+        character_reference_image=None,
+    ):
+        if not prompt or not prompt.strip():
+            raise ValueError(
+                "A non-empty 'prompt' is required for the Ideogram Edit Image node to function correctly."
+            )
+
+        cache_dir = os.path.join(os.path.dirname(__file__), "..", "..", "cache")
+        os.makedirs(cache_dir, exist_ok=True)
+        hasher = hashlib.sha256()
+        hasher.update(provider.encode("utf-8"))
+        hasher.update(prompt.encode("utf-8"))
+        hasher.update(rendering_speed.encode("utf-8"))
+        hasher.update(magic_prompt.encode("utf-8"))
+        hasher.update(str(seed).encode("utf-8"))
+        hasher.update(image.cpu().numpy().tobytes())
+        hasher.update(mask.cpu().numpy().tobytes())
+        if character_reference_image is not None:
+            hasher.update(character_reference_image.cpu().numpy().tobytes())
+        cache_key = hasher.hexdigest()
+        cache_filepath = os.path.join(cache_dir, f"{cache_key}_ideogram_edit.png")
+        if os.path.exists(cache_filepath):
+            print(f"โœ… Ideogram Cache Hit! Loading image from {cache_filepath}")
+            pil_image = Image.open(cache_filepath).convert("RGB")
+            return (
+                torch.from_numpy(
+                    np.array(pil_image).astype(np.float32) / 255.0
+                ).unsqueeze(0),
+            )
+        print(f"๐Ÿ’จ Ideogram Cache Miss. Proceeding with {provider} API call...")
+        self.set_api_key(api_key)
+        image_url = ""
+        if provider == "Official Ideogram":
+            print("๐ŸŽญ Inverting mask for Official Ideogram provider.")
+            mask_to_send = 1.0 - mask
+            speed = "DEFAULT" if rendering_speed == "BALANCED" else rendering_speed
+            data_payload = {
+                "prompt": prompt,
+                "rendering_speed": speed,
+                "magic_prompt": magic_prompt,
+            }
+            if seed >= 0:
+                data_payload["seed"] = seed
+            files_payload = {
+                "image": ("image.png", self.tensor_to_bytesio(image), "image/png"),
+                "mask": (
+                    "mask.png",
+                    self.tensor_to_bytesio(mask_to_send, is_mask=True),
+                    "image/png",
+                ),
+            }
+            if character_reference_image is not None:
+                files_payload["character_reference_images"] = (
+                    "char_ref.png",
+                    self.tensor_to_bytesio(character_reference_image),
+                    "image/png",
+                )
+            result = self.submit_multipart_request(
+                "/v1/ideogram-v3/edit", data_payload, files_payload
+            )
+            if result.get("data") and result["data"]:
+                image_data = result["data"][0]
+                if image_data.get("is_image_safe") is False:
+                    raise Exception(
+                        "Image flagged as unsafe by Ideogram API. Try enabling the ๐Ÿ”ž FULLY CLOTHE FOR NSFW FIX or using a different SWAP TO image."
+                    )
+                image_url = image_data.get("url")
+            else:
+                raise Exception("Official Ideogram API did not return any images.")
+
+        elif provider == "fal.ai":
+            print(
+                "๐ŸŽญ Using fal.ai provider. Uploading all images to CDN to avoid size limits."
+            )
+            payload = {
+                "prompt": prompt,
+                "image_url": self._upload_tensor_to_fal_cdn(image),
+                "mask_url": self._upload_tensor_to_fal_cdn(mask, is_mask=True),
+            }
+            if character_reference_image is not None:
+                payload["reference_image_urls"] = [
+                    self._upload_tensor_to_fal_cdn(character_reference_image)
+                ]
+                payload["style"] = "REALISTIC"
+            speed_map = {
+                "QUALITY": "QUALITY",
+                "TURBO": "TURBO",
+                "FLASH": "TURBO",
+                "DEFAULT": "BALANCED",
+                "BALANCED": "BALANCED",
+            }
+            payload["rendering_speed"] = speed_map.get(rendering_speed, "BALANCED")
+            payload["expand_prompt"] = magic_prompt in ["ON", "AUTO"]
+            if seed >= 0:
+                payload["seed"] = seed
+            image_url = self._submit_fal("fal-ai/ideogram/character/edit", payload)
+
+        if not image_url:
+            raise Exception("API response did not provide a valid image URL.")
+        print(f"โœ… Image edited. Downloading from: {image_url}")
+        image_response = requests.get(image_url)
+        image_response.raise_for_status()
+        pil_image = Image.open(io.BytesIO(image_response.content)).convert("RGB")
+        pil_image.save(cache_filepath, "PNG")
+        return (
+            torch.from_numpy(np.array(pil_image).astype(np.float32) / 255.0).unsqueeze(
+                0
+            ),
+        )
+
+
+NODE_CLASS_MAPPINGS = {
+    "IdeogramDescribeImage": IdeogramDescribeImage,
+    "IdeogramGenerateImage": IdeogramGenerateImage,
+    "IdeogramEditImage": IdeogramEditImage,
+}
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "IdeogramDescribeImage": "๐Ÿ’ญ Ideogram Describe Image",
+    "IdeogramGenerateImage": "๐Ÿ–ผ๏ธ Ideogram Generate Image",
+    "IdeogramEditImage": "โœ๏ธ Ideogram Edit Image",
+}
diff --git a/custom_nodes/ComfyUI_INSTARAW/nodes/api_nodes/nano_banana_pro_multi.py b/custom_nodes/ComfyUI_INSTARAW/nodes/api_nodes/nano_banana_pro_multi.py
new file mode 100644
index 0000000000000000000000000000000000000000..68048a862783abdeb9885aed7fa55cade0d5920d
--- /dev/null
+++ b/custom_nodes/ComfyUI_INSTARAW/nodes/api_nodes/nano_banana_pro_multi.py
@@ -0,0 +1,214 @@
+# ---
+# ComfyUI INSTARAW - Nano Banana Pro Multi Node
+# WaveSpeed.ai exclusive - generates 2 images per call at half the cost
+# Copyright ยฉ 2025 Instara. All rights reserved.
+# PROPRIETARY SOFTWARE - ALL RIGHTS RESERVED
+# ---
+
+import requests
+import io
+import time
+import os
+import hashlib
+import numpy as np
+import torch
+from PIL import Image
+
+
+class INSTARAW_NanoBananaProMulti:
+    """
+    Nano Banana Pro Multi - WaveSpeed exclusive endpoint.
+    Generates 2 images per call at half the cost ($0.07/image vs $0.14/image).
+    Limited aspect ratios: 3:2, 2:3, 3:4, 4:3 only.
+    Text-to-image only (no edit mode).
+    """
+
+    # Limited aspect ratios for multi endpoint
+    MULTI_ASPECT_RATIOS = [
+        "3:2",
+        "2:3",
+        "3:4",
+        "4:3",
+    ]
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "api_key": (
+                    "STRING",
+                    {
+                        "forceInput": True,
+                        "tooltip": "WaveSpeed.ai API key (Bearer token)",
+                    },
+                ),
+                "prompt": (
+                    "STRING",
+                    {
+                        "multiline": True,
+                        "default": "A beautiful landscape photograph",
+                        "tooltip": "Text prompt for image generation",
+                    },
+                ),
+                "aspect_ratio": (
+                    cls.MULTI_ASPECT_RATIOS,
+                    {
+                        "default": "3:2",
+                        "tooltip": "Aspect ratio (limited options for multi endpoint)",
+                    },
+                ),
+            },
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    RETURN_NAMES = ("images",)
+    FUNCTION = "generate"
+    CATEGORY = "INSTARAW/API"
+    DESCRIPTION = (
+        "Nano Banana Pro Multi - Generates 2 images per call at $0.07/image. "
+        "WaveSpeed.ai exclusive. Limited aspect ratios."
+    )
+
+    def _submit_wavespeed(self, api_key, payload):
+        """Submit async request to WaveSpeed.ai and poll for result."""
+        base_url = "https://api.wavespeed.ai/api/v3"
+        endpoint = "google/nano-banana-pro/text-to-image-multi"
+        headers = {
+            "Authorization": f"Bearer {api_key}",
+            "Content-Type": "application/json",
+        }
+
+        print(f"๐Ÿš€ Nano Banana Pro Multi: Submitting request to WaveSpeed.ai...")
+        print(f"   Endpoint: {base_url}/{endpoint}")
+
+        # Submit task
+        response = requests.post(
+            f"{base_url}/{endpoint}",
+            json=payload,
+            headers=headers,
+            timeout=30,
+        )
+
+        if not response.ok:
+            raise Exception(
+                f"WaveSpeed API task submission failed: {response.status_code} - {response.text}"
+            )
+
+        result = response.json()
+        request_id = result["data"]["id"]
+        print(f"โœ… Task submitted. Request ID: {request_id}. Polling for result...")
+
+        # Poll for result
+        poll_url = f"{base_url}/predictions/{request_id}/result"
+        start_time = time.time()
+        timeout = 300  # 5 minutes
+
+        while time.time() - start_time < timeout:
+            poll_response = requests.get(poll_url, headers=headers, timeout=30)
+
+            if not poll_response.ok:
+                raise Exception(
+                    f"WaveSpeed API polling failed: {poll_response.status_code} - {poll_response.text}"
+                )
+
+            data = poll_response.json()["data"]
+            status = data.get("status")
+
+            if status == "completed":
+                print("โœ… Task completed!")
+                return data["outputs"]  # Returns list of image URLs
+
+            if status == "failed":
+                error_msg = data.get("error", "Unknown error")
+                raise Exception(f"WaveSpeed API task failed: {error_msg}")
+
+            print(f"โŒ› Task status: {status}. Waiting...")
+            time.sleep(2)
+
+        raise Exception("WaveSpeed API task timed out after 5 minutes.")
+
+    def generate(self, api_key, prompt, aspect_ratio):
+        """Generate 2 images using Nano Banana Pro Multi endpoint."""
+
+        # Build cache key
+        hasher = hashlib.sha256()
+        hasher.update(prompt.encode("utf-8"))
+        hasher.update(aspect_ratio.encode("utf-8"))
+        hasher.update(b"nano_banana_pro_multi")
+
+        cache_dir = os.path.join(os.path.dirname(__file__), "..", "..", "cache")
+        os.makedirs(cache_dir, exist_ok=True)
+        cache_hash = hasher.hexdigest()
+
+        # Check cache for both images
+        cache_files = [
+            os.path.join(cache_dir, f"{cache_hash}_multi_{i}.png") for i in range(2)
+        ]
+
+        if all(os.path.exists(f) for f in cache_files):
+            print(f"โœ… Nano Banana Pro Multi: Cache hit! Loading from cache...")
+            tensors = []
+            for cache_file in cache_files:
+                img = Image.open(cache_file).convert("RGB")
+                img_np = np.array(img).astype(np.float32) / 255.0
+                tensors.append(torch.from_numpy(img_np))
+            return (torch.stack(tensors),)
+
+        print("๐Ÿ’จ Nano Banana Pro Multi: Cache miss. Proceeding with API call...")
+
+        # Build payload
+        payload = {
+            "prompt": prompt,
+            "aspect_ratio": aspect_ratio,
+            "num_images": 2,
+            "output_format": "jpeg",
+        }
+
+        # Submit request
+        image_urls = self._submit_wavespeed(api_key, payload)
+
+        if not image_urls or len(image_urls) < 2:
+            raise Exception(
+                f"Nano Banana Pro Multi: Expected 2 images, got {len(image_urls) if image_urls else 0}"
+            )
+
+        # Download and process images
+        print(f"โฌ‡๏ธ Nano Banana Pro Multi: Downloading {len(image_urls)} images...")
+        tensors = []
+
+        for i, image_url in enumerate(image_urls[:2]):  # Take first 2 images
+            image_response = requests.get(image_url, timeout=60)
+            image_response.raise_for_status()
+
+            image_pil = Image.open(io.BytesIO(image_response.content)).convert("RGB")
+
+            # Save to cache
+            cache_file = cache_files[i]
+            print(f"๐Ÿ’พ Saving image {i + 1} to cache: {cache_file}")
+            image_pil.save(cache_file, "PNG")
+
+            # Convert to tensor
+            image_np = np.array(image_pil).astype(np.float32) / 255.0
+            tensors.append(torch.from_numpy(image_np))
+
+        # Stack into batch tensor
+        result_tensor = torch.stack(tensors)
+
+        print(
+            f"โœ… Nano Banana Pro Multi: Complete! Generated {len(tensors)} images, shape: {result_tensor.shape}"
+        )
+
+        return (result_tensor,)
+
+
+# =================================================================================
+# NODE REGISTRATION
+# =================================================================================
+
+NODE_CLASS_MAPPINGS = {
+    "INSTARAW_NanoBananaProMulti": INSTARAW_NanoBananaProMulti,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "INSTARAW_NanoBananaProMulti": "๐ŸŒ INSTARAW Nano Banana Pro Multi (2x)",
+}
diff --git a/custom_nodes/ComfyUI_INSTARAW/nodes/api_nodes/parallel_batch_generator.py b/custom_nodes/ComfyUI_INSTARAW/nodes/api_nodes/parallel_batch_generator.py
new file mode 100644
index 0000000000000000000000000000000000000000..cba5f04a7530180cd1ecd1f199eb771f2be19942
--- /dev/null
+++ b/custom_nodes/ComfyUI_INSTARAW/nodes/api_nodes/parallel_batch_generator.py
@@ -0,0 +1,754 @@
+# ---
+# ComfyUI INSTARAW - Parallel Batch Generator Node
+# Runs N API requests in parallel, accepts prompt lists from RPG
+# Copyright ยฉ 2025 Instara. All rights reserved.
+# PROPRIETARY SOFTWARE - ALL RIGHTS RESERVED
+# ---
+
+import requests
+import base64
+import io
+import time
+import os
+import hashlib
+import numpy as np
+import torch
+from PIL import Image
+from concurrent.futures import ThreadPoolExecutor, as_completed
+import threading
+
+from .generative_api_nodes import MODEL_CONFIG, INSTARAW_GenerativeAPIBase
+
+
+class INSTARAW_ParallelBatchGenerator(INSTARAW_GenerativeAPIBase):
+    """
+    Parallel batch image generation node.
+    Runs multiple API requests concurrently using ThreadPoolExecutor.
+    Accepts prompt lists from RPG and returns all images as batch tensor.
+    """
+
+    INPUT_IS_LIST = True  # Accept lists from RPG
+
+    # Multi endpoint aspect ratios (limited)
+    MULTI_ASPECT_RATIOS = ["3:2", "2:3", "3:4", "4:3"]
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "api_key": (
+                    "STRING",
+                    {
+                        "forceInput": True,
+                        "tooltip": "API key from provider",
+                    },
+                ),
+                "provider": (
+                    "STRING",
+                    {
+                        "forceInput": True,
+                        "tooltip": "API provider (fal.ai or wavespeed.ai)",
+                    },
+                ),
+                "model": (
+                    "STRING",
+                    {
+                        "forceInput": True,
+                        "tooltip": "Model name from Model Selector",
+                    },
+                ),
+                "prompts": (
+                    "STRING",
+                    {
+                        "forceInput": True,
+                        "tooltip": "Prompt list from RPG (prompt_list_positive)",
+                    },
+                ),
+                "max_parallel": (
+                    "INT",
+                    {
+                        "default": 10,
+                        "min": 1,
+                        "max": 50,
+                        "tooltip": "Maximum concurrent API requests",
+                    },
+                ),
+                "aspect_ratio": (
+                    "STRING",
+                    {
+                        "forceInput": True,
+                        "tooltip": "Aspect ratio from Nano Banana Aspect Ratio node",
+                    },
+                ),
+            },
+            "optional": {
+                "seeds": (
+                    "INT",
+                    {
+                        "forceInput": True,
+                        "tooltip": "Seed list from RPG (seed_list)",
+                    },
+                ),
+                "width": (
+                    "INT",
+                    {
+                        "forceInput": True,
+                        "tooltip": "Image width from Aspect Ratio node (for Seedream)",
+                    },
+                ),
+                "height": (
+                    "INT",
+                    {
+                        "forceInput": True,
+                        "tooltip": "Image height from Aspect Ratio node (for Seedream)",
+                    },
+                ),
+                "resolution": (
+                    "STRING",
+                    {
+                        "forceInput": True,
+                        "tooltip": "Resolution tier (1K, 2K, 4K) for Nano Banana Pro",
+                    },
+                ),
+                "images": (
+                    "IMAGE",
+                    {
+                        "forceInput": True,
+                        "tooltip": "Reference images from AIL for I2I/edit mode (batch tensor)",
+                    },
+                ),
+                "use_multi_endpoint": (
+                    "BOOLEAN",
+                    {
+                        "default": False,
+                        "tooltip": "Use Multi endpoint (Nano Banana Pro on WaveSpeed only). Returns 2 images per request at half cost.",
+                    },
+                ),
+                "enable_safety_checker": (
+                    "BOOLEAN",
+                    {
+                        "default": True,
+                        "tooltip": "Enable safety checker (for Seedream)",
+                    },
+                ),
+            },
+        }
+
+    RETURN_TYPES = ("IMAGE", "INT")
+    RETURN_NAMES = ("images", "count")
+    OUTPUT_IS_LIST = (False, False)  # Returns single batch tensor and count
+    FUNCTION = "generate_batch"
+    CATEGORY = "INSTARAW/API"
+    DESCRIPTION = (
+        "Parallel batch image generator. Accepts prompt lists from RPG and runs "
+        "multiple API requests concurrently. Supports all models and Multi endpoint."
+    )
+
+    def __init__(self):
+        super().__init__()
+        self._progress_lock = threading.Lock()
+        self._completed_count = 0
+        self._total_count = 0
+
+    def _get_cache_dir(self):
+        """Get or create cache directory."""
+        cache_dir = os.path.join(os.path.dirname(__file__), "..", "..", "cache")
+        os.makedirs(cache_dir, exist_ok=True)
+        return cache_dir
+
+    def _compute_cache_key(self, prompt, seed, model, provider, aspect_ratio, resolution, is_multi=False):
+        """Compute cache key for a single generation."""
+        hasher = hashlib.sha256()
+        hasher.update(prompt.encode("utf-8"))
+        hasher.update(str(seed).encode("utf-8"))
+        hasher.update(model.encode("utf-8"))
+        hasher.update(provider.encode("utf-8"))
+        hasher.update(aspect_ratio.encode("utf-8"))
+        if resolution:
+            hasher.update(resolution.encode("utf-8"))
+        if is_multi:
+            hasher.update(b"_multi")
+        return hasher.hexdigest()
+
+    def _check_cache(self, cache_key, is_multi=False):
+        """Check if image(s) exist in cache."""
+        cache_dir = self._get_cache_dir()
+        if is_multi:
+            cache_files = [
+                os.path.join(cache_dir, f"{cache_key}_batch_multi_{i}.png")
+                for i in range(2)
+            ]
+            if all(os.path.exists(f) for f in cache_files):
+                return cache_files
+            return None
+        else:
+            cache_file = os.path.join(cache_dir, f"{cache_key}_batch.png")
+            if os.path.exists(cache_file):
+                return [cache_file]
+            return None
+
+    def _load_from_cache(self, cache_files):
+        """Load image tensor(s) from cache files."""
+        tensors = []
+        for cache_file in cache_files:
+            img = Image.open(cache_file).convert("RGB")
+            img_np = np.array(img).astype(np.float32) / 255.0
+            tensors.append(torch.from_numpy(img_np))
+        return tensors
+
+    def _save_to_cache(self, cache_key, images, is_multi=False):
+        """Save image tensor(s) to cache."""
+        cache_dir = self._get_cache_dir()
+        cache_files = []
+        for i, img_tensor in enumerate(images):
+            if is_multi:
+                cache_file = os.path.join(cache_dir, f"{cache_key}_batch_multi_{i}.png")
+            else:
+                cache_file = os.path.join(cache_dir, f"{cache_key}_batch.png")
+
+            # Convert tensor to PIL and save
+            img_np = img_tensor.cpu().numpy()
+            if img_np.max() <= 1.0:
+                img_np = (img_np * 255).astype(np.uint8)
+            else:
+                img_np = img_np.astype(np.uint8)
+            img_pil = Image.fromarray(img_np)
+            img_pil.save(cache_file, "PNG")
+            cache_files.append(cache_file)
+        return cache_files
+
+    def _submit_wavespeed_multi(self, api_key, payload):
+        """Submit request to WaveSpeed Multi endpoint (returns 2 images)."""
+        base_url = "https://api.wavespeed.ai/api/v3"
+        endpoint = "google/nano-banana-pro/text-to-image-multi"
+        headers = {
+            "Authorization": f"Bearer {api_key}",
+            "Content-Type": "application/json",
+        }
+
+        # Submit task
+        response = requests.post(
+            f"{base_url}/{endpoint}",
+            json=payload,
+            headers=headers,
+            timeout=30,
+        )
+
+        if not response.ok:
+            raise Exception(
+                f"WaveSpeed Multi API submission failed: {response.status_code} - {response.text}"
+            )
+
+        result = response.json()
+        request_id = result["data"]["id"]
+
+        # Poll for result
+        poll_url = f"{base_url}/predictions/{request_id}/result"
+        start_time = time.time()
+        timeout = 300  # 5 minutes
+
+        while time.time() - start_time < timeout:
+            poll_response = requests.get(poll_url, headers=headers, timeout=30)
+
+            if not poll_response.ok:
+                raise Exception(
+                    f"WaveSpeed Multi polling failed: {poll_response.status_code} - {poll_response.text}"
+                )
+
+            data = poll_response.json()["data"]
+            status = data.get("status")
+
+            if status == "completed":
+                return data["outputs"]  # Returns list of image URLs
+
+            if status == "failed":
+                error_msg = data.get("error", "Unknown error")
+                raise Exception(f"WaveSpeed Multi task failed: {error_msg}")
+
+            time.sleep(2)
+
+        raise Exception("WaveSpeed Multi task timed out after 5 minutes.")
+
+    def _submit_fal_with_retry(self, endpoint, payload, api_key, max_retries=2, timeout=300):
+        """Submit to fal.ai with retry logic and longer timeout."""
+        url = f"https://fal.run/{endpoint}"
+        headers = {
+            "Authorization": f"Key {api_key}",
+            "Content-Type": "application/json",
+        }
+
+        last_error = None
+        for attempt in range(max_retries + 1):
+            try:
+                if attempt > 0:
+                    print(f"   ๐Ÿ”„ Retry {attempt}/{max_retries}...", flush=True)
+
+                print(f"   ๐Ÿ“ก Submitting to fal.ai: {endpoint} (timeout={timeout}s)", flush=True)
+                response = requests.post(url, json=payload, headers=headers, timeout=timeout)
+                print(f"   ๐Ÿ“ฆ Response received: {response.status_code}", flush=True)
+
+                if not response.ok:
+                    if response.status_code == 422:
+                        # Don't retry validation errors
+                        raise Exception(f"API Error (422): Request rejected - {response.text[:200]}")
+                    raise Exception(f"API request failed: {response.status_code} - {response.text[:200]}")
+
+                result = response.json()
+                if "images" in result and len(result["images"]) > 0:
+                    return result["images"][0]["url"]
+                raise Exception(f"API response did not contain an image URL")
+
+            except requests.exceptions.Timeout as e:
+                last_error = f"Timeout after {timeout}s"
+                print(f"   โฑ๏ธ Request timed out after {timeout}s", flush=True)
+                if attempt < max_retries:
+                    continue
+            except requests.exceptions.RequestException as e:
+                last_error = str(e)
+                print(f"   โŒ Request error: {last_error[:100]}", flush=True)
+                if attempt < max_retries:
+                    time.sleep(2)  # Brief pause before retry
+                    continue
+            except Exception as e:
+                print(f"   โŒ Non-retryable error: {str(e)[:100]}", flush=True)
+                # Non-retryable errors
+                raise
+
+        raise Exception(last_error or "Request failed after retries")
+
+    def _generate_single(self, idx, prompt, seed, api_key, provider, model, aspect_ratio,
+                         resolution, width, height, enable_safety_checker, input_image=None):
+        """Generate a single image. Returns (idx, [tensor], error)."""
+        try:
+            print(f"   ๐ŸŽฏ Starting request #{idx + 1}: {prompt[:50]}...", flush=True)
+
+            # Build kwargs for payload
+            kwargs = {
+                "api_key": api_key,
+                "provider": provider,
+                "model": model,
+                "prompt": prompt,
+                "seed": seed,
+                "aspect_ratio": aspect_ratio,
+                "width": width,
+                "height": height,
+                "enable_safety_checker": enable_safety_checker,
+            }
+            if resolution:
+                kwargs["resolution"] = resolution
+            if input_image is not None:
+                kwargs["image_1"] = input_image
+                print(f"   ๐Ÿ–ผ๏ธ Request #{idx + 1} includes reference image, shape: {input_image.shape}", flush=True)
+
+            # Check cache first
+            cache_key = self._compute_cache_key(prompt, seed, model, provider, aspect_ratio, resolution)
+            cached = self._check_cache(cache_key)
+            if cached:
+                tensors = self._load_from_cache(cached)
+                return (idx, tensors, None)
+
+            # Get model config
+            model_conf = MODEL_CONFIG.get(model)
+            if not model_conf:
+                raise ValueError(f"Invalid model: {model}")
+            provider_conf = model_conf["providers"].get(provider)
+            if not provider_conf:
+                raise ValueError(f"Provider '{provider}' not supported for model '{model}'")
+
+            # Determine endpoint
+            is_i2i = input_image is not None
+            endpoint = provider_conf["i2i_endpoint"] if is_i2i else provider_conf["t2i_endpoint"]
+            build_payload_func = provider_conf["build_payload"]
+
+            # Build payload
+            self.set_api_key(api_key)
+            payload = build_payload_func(self, **kwargs)
+
+            # Submit request with provider-specific handling
+            if provider == "fal.ai":
+                # Use our own method with longer timeout and retries
+                image_url = self._submit_fal_with_retry(endpoint, payload, api_key)
+            else:
+                # Use base class method for wavespeed (already has polling)
+                image_url = self.submit_request(provider, endpoint, payload)
+
+            # Download image with longer timeout for large images
+            image_response = requests.get(image_url, timeout=120)
+            image_response.raise_for_status()
+            image_pil = Image.open(io.BytesIO(image_response.content)).convert("RGB")
+
+            # Convert to tensor
+            image_np = np.array(image_pil).astype(np.float32) / 255.0
+            tensor = torch.from_numpy(image_np)
+
+            # Save to cache
+            self._save_to_cache(cache_key, [tensor])
+
+            return (idx, [tensor], None)
+
+        except Exception as e:
+            return (idx, None, str(e))
+
+    def _generate_single_multi(self, idx, prompt, api_key, aspect_ratio):
+        """Generate using Multi endpoint (2 images per request). Returns (idx, [tensor1, tensor2], error)."""
+        try:
+            # Check cache first
+            cache_key = self._compute_cache_key(
+                prompt, -1, "Nano Banana Pro", "wavespeed.ai", aspect_ratio, None, is_multi=True
+            )
+            cached = self._check_cache(cache_key, is_multi=True)
+            if cached:
+                tensors = self._load_from_cache(cached)
+                return (idx, tensors, None)
+
+            # Build payload for multi endpoint
+            payload = {
+                "prompt": prompt,
+                "aspect_ratio": aspect_ratio,
+                "num_images": 2,
+                "output_format": "jpeg",
+            }
+
+            # Submit request
+            image_urls = self._submit_wavespeed_multi(api_key, payload)
+
+            if not image_urls or len(image_urls) < 2:
+                raise Exception(f"Multi endpoint returned {len(image_urls) if image_urls else 0} images, expected 2")
+
+            # Download and convert images
+            tensors = []
+            for image_url in image_urls[:2]:
+                image_response = requests.get(image_url, timeout=60)
+                image_response.raise_for_status()
+                image_pil = Image.open(io.BytesIO(image_response.content)).convert("RGB")
+                image_np = np.array(image_pil).astype(np.float32) / 255.0
+                tensors.append(torch.from_numpy(image_np))
+
+            # Save to cache
+            self._save_to_cache(cache_key, tensors, is_multi=True)
+
+            return (idx, tensors, None)
+
+        except Exception as e:
+            return (idx, None, str(e))
+
+    def _report_progress(self, completed, total, is_multi=False):
+        """Report progress to console."""
+        mode = "Multi (2x)" if is_multi else "Standard"
+        pct = 100 * completed // total if total > 0 else 0
+        print(f"๐Ÿš€ [{mode}] Progress: {completed}/{total} requests ({pct}%)", flush=True)
+
+    def generate_batch(
+        self,
+        api_key,
+        provider,
+        model,
+        prompts,
+        max_parallel,
+        aspect_ratio,
+        seeds=None,
+        width=None,
+        height=None,
+        resolution=None,
+        images=None,
+        use_multi_endpoint=None,
+        enable_safety_checker=None,
+    ):
+        """
+        Generate images in parallel batches.
+
+        Args:
+            api_key: API key (list from INPUT_IS_LIST)
+            provider: Provider name (list)
+            model: Model name (list)
+            prompts: List of prompts from RPG
+            max_parallel: Max concurrent requests (list)
+            aspect_ratio: Aspect ratio (list)
+            seeds: List of seeds from RPG (optional)
+            width: Image width (list, optional)
+            height: Image height (list, optional)
+            resolution: Resolution tier (list, optional)
+            images: Input images for I2I mode (batch tensor, optional)
+            use_multi_endpoint: Use Multi endpoint (list, optional)
+            enable_safety_checker: Enable safety checker (list, optional)
+
+        Returns:
+            Tuple of (batch_tensor, count)
+        """
+        # Extract single values from lists (INPUT_IS_LIST wraps everything)
+        api_key_val = api_key[0] if isinstance(api_key, list) else api_key
+        provider_val = provider[0] if isinstance(provider, list) else provider
+        model_val = model[0] if isinstance(model, list) else model
+        max_parallel_val = max_parallel[0] if isinstance(max_parallel, list) else max_parallel
+        aspect_ratio_val = aspect_ratio[0] if isinstance(aspect_ratio, list) else aspect_ratio
+
+        # Optional values
+        width_val = width[0] if isinstance(width, list) and width else 1024
+        height_val = height[0] if isinstance(height, list) and height else 1024
+        resolution_val = resolution[0] if isinstance(resolution, list) and resolution else None
+        use_multi_val = use_multi_endpoint[0] if isinstance(use_multi_endpoint, list) and use_multi_endpoint else False
+        enable_safety_val = enable_safety_checker[0] if isinstance(enable_safety_checker, list) and enable_safety_checker else True
+
+        # prompts should already be a list from RPG
+        prompt_list = prompts if isinstance(prompts, list) else [prompts]
+
+        # Handle seeds list
+        if seeds is None or (isinstance(seeds, list) and len(seeds) == 0):
+            seed_list = [-1] * len(prompt_list)
+        elif isinstance(seeds, list):
+            seed_list = seeds
+            # Pad with -1 if seeds list is shorter
+            while len(seed_list) < len(prompt_list):
+                seed_list.append(-1)
+        else:
+            seed_list = [seeds] * len(prompt_list)
+
+        # Handle input images for I2I mode
+        # With INPUT_IS_LIST=True, images can come as:
+        # 1. A list of individual tensors (from PromptFilter with OUTPUT_IS_LIST=True)
+        # 2. A list containing a single batch tensor (from other nodes)
+        # 3. A single batch tensor
+        input_images = None
+        has_reference_images = False
+
+        if images is not None:
+            print(f"   ๐Ÿ–ผ๏ธ Received images input: type={type(images)}, len={len(images) if isinstance(images, list) else 'N/A'}", flush=True)
+
+            if isinstance(images, list) and len(images) > 0:
+                # Check if it's a list of individual tensors or a list with one batch tensor
+                first_item = images[0]
+                if first_item is not None:
+                    if isinstance(first_item, torch.Tensor):
+                        # Check dimensions to determine format
+                        if first_item.dim() == 3:
+                            # List of individual [H, W, C] tensors - stack them
+                            print(f"   ๐Ÿ–ผ๏ธ Detected list of {len(images)} individual image tensors", flush=True)
+                            valid_tensors = [img for img in images if img is not None and isinstance(img, torch.Tensor)]
+                            if valid_tensors:
+                                # Add batch dimension to each and stack
+                                stacked = []
+                                for img in valid_tensors:
+                                    if img.dim() == 3:
+                                        stacked.append(img.unsqueeze(0))
+                                    else:
+                                        stacked.append(img)
+                                input_images = torch.cat(stacked, dim=0)
+                                print(f"   ๐Ÿ–ผ๏ธ Stacked into batch: shape={input_images.shape}", flush=True)
+                        elif first_item.dim() == 4:
+                            # First item is already a batch tensor [B, H, W, C]
+                            if len(images) == 1:
+                                input_images = first_item
+                                print(f"   ๐Ÿ–ผ๏ธ Single batch tensor: shape={input_images.shape}", flush=True)
+                            else:
+                                # Multiple batch tensors - concatenate them
+                                valid_tensors = [img for img in images if img is not None and isinstance(img, torch.Tensor)]
+                                input_images = torch.cat(valid_tensors, dim=0)
+                                print(f"   ๐Ÿ–ผ๏ธ Concatenated batches: shape={input_images.shape}", flush=True)
+            elif isinstance(images, torch.Tensor):
+                input_images = images
+                print(f"   ๐Ÿ–ผ๏ธ Direct tensor: shape={input_images.shape}", flush=True)
+
+            # Validate we have usable images
+            if input_images is not None and isinstance(input_images, torch.Tensor) and len(input_images) > 0:
+                has_reference_images = True
+                num_images = len(input_images)
+                print(f"   ๐Ÿ–ผ๏ธ I2I MODE: Using {num_images} reference images (edit endpoint)", flush=True)
+
+                if num_images < len(prompt_list):
+                    print(f"   โš ๏ธ Only {num_images} images for {len(prompt_list)} prompts - will cycle through available images", flush=True)
+                elif num_images > len(prompt_list):
+                    print(f"   โš ๏ธ {num_images} images but only {len(prompt_list)} prompts - extra images will be ignored", flush=True)
+
+        if not has_reference_images:
+            input_images = None
+            print(f"   ๐Ÿ“ T2I MODE: No reference images provided", flush=True)
+
+        total_prompts = len(prompt_list)
+        print(f"๐ŸŽจ Parallel Batch Generator: {total_prompts} prompts, max {max_parallel_val} parallel", flush=True)
+        print(f"   Model: {model_val} | Provider: {provider_val} | Aspect: {aspect_ratio_val}", flush=True)
+
+        # Check if we should use Multi endpoint
+        # Multi endpoint is T2I ONLY - cannot use with reference images!
+        is_multi_mode = False
+        if use_multi_val:
+            if has_reference_images:
+                print(f"   โš ๏ธ Multi endpoint requested but reference images provided - using standard I2I endpoint instead", flush=True)
+            elif model_val != "Nano Banana Pro":
+                print(f"   โš ๏ธ Multi endpoint only available for Nano Banana Pro", flush=True)
+            elif provider_val != "wavespeed.ai":
+                print(f"   โš ๏ธ Multi endpoint only available on wavespeed.ai", flush=True)
+            elif aspect_ratio_val not in self.MULTI_ASPECT_RATIOS:
+                print(f"   โš ๏ธ Multi endpoint doesn't support aspect ratio {aspect_ratio_val} (only: {self.MULTI_ASPECT_RATIOS})", flush=True)
+            else:
+                is_multi_mode = True
+
+        if is_multi_mode:
+            print(f"   ๐ŸŒ Using Multi endpoint (2 images per request)", flush=True)
+            return self._generate_batch_multi(
+                api_key_val, prompt_list, max_parallel_val, aspect_ratio_val
+            )
+        else:
+            return self._generate_batch_standard(
+                api_key_val, provider_val, model_val, prompt_list, seed_list,
+                max_parallel_val, aspect_ratio_val, resolution_val, width_val,
+                height_val, enable_safety_val, input_images
+            )
+
+    def _generate_batch_standard(
+        self, api_key, provider, model, prompts, seeds, max_parallel,
+        aspect_ratio, resolution, width, height, enable_safety, input_images
+    ):
+        """Generate images using standard endpoints (1 image per request)."""
+        total = len(prompts)
+        results = [None] * total
+        errors = []
+        completed = 0
+
+        with ThreadPoolExecutor(max_workers=max_parallel) as executor:
+            futures = {}
+            num_ref_images = len(input_images) if input_images is not None else 0
+
+            for i in range(total):
+                # Get input image for this index - cycle through if fewer images than prompts
+                input_img = None
+                if input_images is not None and num_ref_images > 0:
+                    img_idx = i % num_ref_images  # Cycle through available images
+                    input_img = input_images[img_idx:img_idx+1]  # Keep batch dimension [1, H, W, C]
+
+                future = executor.submit(
+                    self._generate_single,
+                    i, prompts[i], seeds[i], api_key, provider, model,
+                    aspect_ratio, resolution, width, height, enable_safety, input_img
+                )
+                futures[future] = i
+
+            mode_str = f"I2I with {num_ref_images} ref images" if num_ref_images > 0 else "T2I"
+            print(f"   ๐Ÿ“‹ Submitted {total} requests ({mode_str}), waiting for completion...", flush=True)
+
+            for future in as_completed(futures):
+                idx, tensors, error = future.result()
+                completed += 1
+
+                if error:
+                    errors.append((idx, prompts[idx], error))
+                    print(f"โŒ Request {idx + 1}/{total} failed: {error}", flush=True)
+                else:
+                    results[idx] = tensors[0]  # Single image
+                    self._report_progress(completed, total, is_multi=False)
+
+        # Report failures
+        if errors:
+            print(f"โš ๏ธ {len(errors)} of {total} generations failed:", flush=True)
+            for idx, prompt, err in errors[:5]:  # Show first 5
+                print(f"   - #{idx + 1}: {err[:100]}...", flush=True)
+
+        # Filter out None results
+        successful = [r for r in results if r is not None]
+        if not successful:
+            raise Exception("All generations failed. Check API key and parameters.")
+
+        # Ensure all images have the same size (use first image's size as reference)
+        # This handles cases where API returns slightly different sizes
+        if len(successful) > 1:
+            ref_shape = successful[0].shape  # (H, W, C)
+            resized = []
+            for i, tensor in enumerate(successful):
+                if tensor.shape != ref_shape:
+                    print(f"   โš ๏ธ Image {i + 1} has different size {tensor.shape}, resizing to {ref_shape}", flush=True)
+                    # Resize using PIL for quality
+                    img_np = tensor.cpu().numpy()
+                    if img_np.max() <= 1.0:
+                        img_np = (img_np * 255).astype(np.uint8)
+                    img_pil = Image.fromarray(img_np)
+                    img_pil = img_pil.resize((ref_shape[1], ref_shape[0]), Image.LANCZOS)
+                    img_np = np.array(img_pil).astype(np.float32) / 255.0
+                    resized.append(torch.from_numpy(img_np))
+                else:
+                    resized.append(tensor)
+            successful = resized
+
+        batch_tensor = torch.stack(successful)
+        print(f"โœ… Parallel Batch Generator complete! Generated {len(successful)} images, shape: {batch_tensor.shape}", flush=True)
+
+        return (batch_tensor, len(successful))
+
+    def _generate_batch_multi(self, api_key, prompts, max_parallel, aspect_ratio):
+        """Generate images using Multi endpoint (2 images per request)."""
+        total = len(prompts)
+        results = []
+        errors = []
+        completed = 0
+
+        with ThreadPoolExecutor(max_workers=max_parallel) as executor:
+            futures = {}
+            for i in range(total):
+                future = executor.submit(
+                    self._generate_single_multi,
+                    i, prompts[i], api_key, aspect_ratio
+                )
+                futures[future] = i
+
+            for future in as_completed(futures):
+                idx, tensors, error = future.result()
+                completed += 1
+
+                if error:
+                    errors.append((idx, prompts[idx], error))
+                    print(f"โŒ Multi request {idx + 1}/{total} failed: {error}")
+                else:
+                    # Multi returns 2 images per request
+                    results.append((idx, tensors))
+                    self._report_progress(completed, total, is_multi=True)
+
+        # Report failures
+        if errors:
+            print(f"โš ๏ธ {len(errors)} of {total} multi-generations failed:")
+            for idx, prompt, err in errors[:5]:
+                print(f"   - #{idx + 1}: {err[:100]}...")
+
+        # Sort by original index and flatten
+        results.sort(key=lambda x: x[0])
+        all_tensors = []
+        for idx, tensors in results:
+            all_tensors.extend(tensors)
+
+        if not all_tensors:
+            raise Exception("All multi-generations failed. Check API key and parameters.")
+
+        # Ensure all images have the same size (use first image's size as reference)
+        if len(all_tensors) > 1:
+            ref_shape = all_tensors[0].shape  # (H, W, C)
+            resized = []
+            for i, tensor in enumerate(all_tensors):
+                if tensor.shape != ref_shape:
+                    print(f"   โš ๏ธ Image {i + 1} has different size {tensor.shape}, resizing to {ref_shape}", flush=True)
+                    img_np = tensor.cpu().numpy()
+                    if img_np.max() <= 1.0:
+                        img_np = (img_np * 255).astype(np.uint8)
+                    img_pil = Image.fromarray(img_np)
+                    img_pil = img_pil.resize((ref_shape[1], ref_shape[0]), Image.LANCZOS)
+                    img_np = np.array(img_pil).astype(np.float32) / 255.0
+                    resized.append(torch.from_numpy(img_np))
+                else:
+                    resized.append(tensor)
+            all_tensors = resized
+
+        batch_tensor = torch.stack(all_tensors)
+        expected_count = total * 2
+        actual_count = len(all_tensors)
+        print(f"โœ… Multi Batch Generator complete! Generated {actual_count} images (expected {expected_count}), shape: {batch_tensor.shape}", flush=True)
+
+        return (batch_tensor, actual_count)
+
+
+# =================================================================================
+# NODE REGISTRATION
+# =================================================================================
+
+NODE_CLASS_MAPPINGS = {
+    "INSTARAW_ParallelBatchGenerator": INSTARAW_ParallelBatchGenerator,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "INSTARAW_ParallelBatchGenerator": "๐Ÿš€ INSTARAW Parallel Batch Generator",
+}
diff --git a/custom_nodes/ComfyUI_INSTARAW/nodes/input_nodes/__init__.py b/custom_nodes/ComfyUI_INSTARAW/nodes/input_nodes/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..7366fdb745eea6110809a5e22d38f319105cffdc
--- /dev/null
+++ b/custom_nodes/ComfyUI_INSTARAW/nodes/input_nodes/__init__.py
@@ -0,0 +1,49 @@
+"""
+INSTARAW Input Nodes
+Nodes for loading data into the workflow.
+"""
+
+from .advanced_image_loader import (
+    NODE_CLASS_MAPPINGS as LOADER_MAPPINGS,
+    NODE_DISPLAY_NAME_MAPPINGS as LOADER_DISPLAY_MAPPINGS,
+)
+from .float_input import (
+    NODE_CLASS_MAPPINGS as FLOAT_INPUT_MAPPINGS,
+    NODE_DISPLAY_NAME_MAPPINGS as FLOAT_INPUT_DISPLAY_MAPPINGS,
+)
+from .reality_prompt_generator import (
+    NODE_CLASS_MAPPINGS as RPG_MAPPINGS,
+    NODE_DISPLAY_NAME_MAPPINGS as RPG_DISPLAY_MAPPINGS,
+)
+from .wan_aspect_ratio import (
+    NODE_CLASS_MAPPINGS as WAN_AR_MAPPINGS,
+    NODE_DISPLAY_NAME_MAPPINGS as WAN_AR_DISPLAY_MAPPINGS,
+)
+from .sdxl_aspect_ratio import (
+    NODE_CLASS_MAPPINGS as SDXL_AR_MAPPINGS,
+    NODE_DISPLAY_NAME_MAPPINGS as SDXL_AR_DISPLAY_MAPPINGS,
+)
+from .nano_banana_aspect_ratio import (
+    NODE_CLASS_MAPPINGS as NANO_BANANA_AR_MAPPINGS,
+    NODE_DISPLAY_NAME_MAPPINGS as NANO_BANANA_AR_DISPLAY_MAPPINGS,
+)
+
+NODE_CLASS_MAPPINGS = {
+    **LOADER_MAPPINGS,
+    **FLOAT_INPUT_MAPPINGS,
+    **RPG_MAPPINGS,
+    **WAN_AR_MAPPINGS,
+    **SDXL_AR_MAPPINGS,
+    **NANO_BANANA_AR_MAPPINGS,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    **LOADER_DISPLAY_MAPPINGS,
+    **FLOAT_INPUT_DISPLAY_MAPPINGS,
+    **RPG_DISPLAY_MAPPINGS,
+    **WAN_AR_DISPLAY_MAPPINGS,
+    **SDXL_AR_DISPLAY_MAPPINGS,
+    **NANO_BANANA_AR_DISPLAY_MAPPINGS,
+}
+
+__all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS"]
\ No newline at end of file
diff --git a/custom_nodes/ComfyUI_INSTARAW/nodes/input_nodes/advanced_image_loader.py b/custom_nodes/ComfyUI_INSTARAW/nodes/input_nodes/advanced_image_loader.py
new file mode 100644
index 0000000000000000000000000000000000000000..97e0e199088d50d1ac63b47ddd0861b5240f9e94
--- /dev/null
+++ b/custom_nodes/ComfyUI_INSTARAW/nodes/input_nodes/advanced_image_loader.py
@@ -0,0 +1,509 @@
+# ---
+# Filename: ../ComfyUI_INSTARAW/nodes/input_nodes/advanced_image_loader.py
+# ---
+
+"""
+ComfyUI INSTARAW - Advanced Image Loader
+Modern batch image loading with instant preview, reordering, and per-image repeat control.
+"""
+
+import torch
+import numpy as np
+from PIL import Image
+import os
+import json
+import uuid
+import hashlib
+from aiohttp import web
+import folder_paths
+from server import PromptServer
+import comfy.utils
+
+class INSTARAW_AdvancedImageLoader:
+    """
+    An advanced, interactive multi-image batch loader that receives its state
+    from a custom frontend widget.
+    """
+
+    def __init__(self):
+        self.node_states = {}
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "mode": (["Batch Tensor", "Sequential"], {"default": "Batch Tensor"}),
+            },
+            "optional": {
+                "resize_mode": (["Center Crop", "Letterbox", "Stretch", "Fit to Largest"], {"default": "Center Crop"}),
+                "batch_index": ("INT", {"default": 0, "min": 0, "max": 9999, "step": 1}),
+                "width": ("INT", {"default": 512, "min": 64, "max": 8192, "step": 8}),
+                "height": ("INT", {"default": 512, "min": 64, "max": 8192, "step": 8}),
+                "aspect_label": ("STRING", {"default": "1:1"}),
+                "enable_img2img": ("BOOLEAN", {"default": True}),
+            },
+            "hidden": {
+                "node_id": "UNIQUE_ID",
+                "batch_data": ("STRING", {"default": "{}"}),
+            }
+        }
+
+    RETURN_TYPES = ("IMAGE", "INT", "INT", "STRING")
+    RETURN_NAMES = ("images", "index", "total", "info")
+    FUNCTION = "load_batch"
+    CATEGORY = "INSTARAW/Input"
+
+    def load_batch(self, mode="Batch Tensor", resize_mode="Center Crop", batch_index=0, width=512, height=512, aspect_label="1:1", enable_img2img=True, node_id=None, batch_data="{}"):
+        print(f"\n{'='*60}")
+        print(f"[INSTARAW Adv Loader] Mode: {mode} | Resize: {resize_mode} | Index: {batch_index}")
+        print(f"[INSTARAW Adv Loader] Enable img2img: {enable_img2img} | Resolution: {width}x{height} | Aspect: {aspect_label}")
+        print(f"[INSTARAW Adv Loader] Loading batch for node: {node_id}")
+
+        try:
+            data = json.loads(batch_data) if batch_data else {}
+        except json.JSONDecodeError:
+            print("[INSTARAW Adv Loader] Invalid batch_data JSON, using empty.")
+            data = {}
+
+        # txt2img mode: Generate empty latents
+        if not enable_img2img:
+            return self._load_txt2img(data, mode, batch_index, width, height, aspect_label, node_id)
+
+        # img2img mode: Load actual images
+        images_meta = data.get('images', [])
+        order = data.get('order', [])
+
+        if not images_meta or not order:
+            print("[INSTARAW Adv Loader] No images in batch, returning empty.")
+            empty = torch.zeros((1, 64, 64, 3), dtype=torch.float32)
+            return (empty, 0, 0, "No images loaded")
+
+        upload_dir = self._get_upload_dir(node_id)
+        if not os.path.exists(upload_dir):
+            print(f"[INSTARAW Adv Loader] Upload directory not found: {upload_dir}")
+            empty = torch.zeros((1, 64, 64, 3), dtype=torch.float32)
+            return (empty, 0, 0, f"Directory not found: {upload_dir}")
+
+        total_count = sum(img.get('repeat_count', 1) for img in images_meta)
+
+        if mode == "Sequential":
+            state_key = f"{node_id}_{hashlib.md5(batch_data.encode()).hexdigest()[:8]}"
+            if state_key in self.node_states and self.node_states[state_key]['last_widget_index'] != batch_index:
+                self.node_states[state_key] = {'current_index': batch_index, 'last_widget_index': batch_index}
+            elif state_key not in self.node_states:
+                self.node_states[state_key] = {'current_index': batch_index, 'last_widget_index': batch_index}
+
+            current_index = self.node_states[state_key]['current_index']
+            img_tensor, _, total, info = self._load_sequential(upload_dir, images_meta, order, current_index, total_count, node_id)
+            next_index = (current_index + 1) % total_count
+            self.node_states[state_key]['current_index'] = next_index
+            self.node_states[state_key]['last_widget_index'] = batch_index
+            
+            try:
+                PromptServer.instance.send_sync("instaraw_adv_loader_update", {"node_id": str(node_id), "current_index": current_index, "next_index": next_index, "total_count": total_count})
+            except Exception as e:
+                print(f"[INSTARAW Adv Loader] Error sending WebSocket update: {e}")
+            
+            return (img_tensor, current_index, total, info)
+        else:
+            return self._load_batch_tensor(upload_dir, images_meta, order, resize_mode, total_count, width, height, node_id)
+
+    def _load_sequential(self, upload_dir, images_meta, order, batch_index, total_count, node_id=None):
+        flat_list = []
+        for img_id in order:
+            img_meta = next((img for img in images_meta if img['id'] == img_id), None)
+            if not img_meta: continue
+            for i in range(img_meta.get('repeat_count', 1)):
+                flat_list.append((img_meta, i + 1, img_meta.get('repeat_count', 1)))
+
+        if batch_index >= len(flat_list):
+            empty = torch.zeros((1, 64, 64, 3), dtype=torch.float32)
+            return (empty, batch_index, total_count, "Index out of range")
+
+        img_meta, copy_num, repeat_count = flat_list[batch_index]
+        filename, original_name = img_meta['filename'], img_meta.get('original_name', img_meta['filename'])
+
+        # Use smart path finder that checks multiple locations
+        img_path = self._find_image_path(filename, node_id)
+        if not img_path:
+            empty = torch.zeros((1, 64, 64, 3), dtype=torch.float32)
+            return (empty, batch_index, total_count, f"Image not found: {filename}")
+
+        try:
+            img = Image.open(img_path).convert('RGB')
+            img_array = np.array(img).astype(np.float32) / 255.0
+            img_tensor = torch.from_numpy(img_array).unsqueeze(0)
+            info = f"[{batch_index}/{total_count}] {original_name} (copy {copy_num}/{repeat_count})"
+            return (img_tensor, batch_index, total_count, info)
+        except Exception as e:
+            empty = torch.zeros((1, 64, 64, 3), dtype=torch.float32)
+            return (empty, batch_index, total_count, f"Error: {e}")
+
+    def _load_batch_tensor(self, upload_dir, images_meta, order, resize_mode, total_count, target_width, target_height, node_id=None):
+        """
+        Load batch tensor with ALL images resized to target_width x target_height from aspect ratio selector.
+        This ensures consistent tensor dimensions controlled by the WAN/SDXL aspect ratio node.
+        """
+        loaded_images, info_lines = [], []
+        print(f"[INSTARAW Adv Loader] Batch Tensor Mode - Target dimensions: {target_width}x{target_height} (from aspect ratio selector)")
+
+        for img_id in order:
+            img_meta = next((img for img in images_meta if img['id'] == img_id), None)
+            if not img_meta: continue
+
+            filename, repeat_count, original_name = img_meta['filename'], img_meta.get('repeat_count', 1), img_meta.get('original_name', img_meta['filename'])
+
+            # Use smart path finder that checks multiple locations
+            img_path = self._find_image_path(filename, node_id)
+            if not img_path:
+                print(f"[INSTARAW Adv Loader] Image not found: {filename}")
+                continue
+
+            try:
+                img = Image.open(img_path).convert('RGB')
+                img_tensor = torch.from_numpy(np.array(img).astype(np.float32) / 255.0)
+
+                # Always resize to target dimensions (from aspect ratio selector)
+                if img_tensor.shape[0] != target_height or img_tensor.shape[1] != target_width:
+                    img_tensor = self._resize_image(img_tensor, target_width, target_height, resize_mode)
+                    print(f"[INSTARAW Adv Loader] Resized {original_name} from {img.size[0]}x{img.size[1]} โ†’ {target_width}x{target_height} ({resize_mode})")
+                for i in range(repeat_count):
+                    loaded_images.append(img_tensor)
+                    info_lines.append(f"{original_name} (copy {i+1}/{repeat_count})")
+            except Exception as e:
+                print(f"[INSTARAW Adv Loader] Error loading {filename}: {e}")
+                continue
+
+        if not loaded_images:
+            empty = torch.zeros((1, 64, 64, 3), dtype=torch.float32)
+            return (empty, 0, total_count, "Failed to load images")
+
+        return (torch.stack(loaded_images, dim=0), 0, total_count, "\n".join(info_lines))
+
+    def _resize_image(self, img_tensor, target_width, target_height, resize_mode):
+        if resize_mode == "Stretch":
+            return comfy.utils.common_upscale(img_tensor.movedim(-1, 0).unsqueeze(0), target_width, target_height, "bilinear", "disabled").squeeze(0).movedim(0, -1)
+        if resize_mode == "Center Crop":
+            return comfy.utils.common_upscale(img_tensor.movedim(-1, 0).unsqueeze(0), target_width, target_height, "bilinear", "center").squeeze(0).movedim(0, -1)
+        if resize_mode == "Letterbox":
+            scale = min(target_width / img_tensor.shape[1], target_height / img_tensor.shape[0])
+            new_w, new_h = int(img_tensor.shape[1] * scale), int(img_tensor.shape[0] * scale)
+            resized = comfy.utils.common_upscale(img_tensor.movedim(-1, 0).unsqueeze(0), new_w, new_h, "bilinear", "disabled").squeeze(0).movedim(0, -1)
+            canvas = torch.zeros((target_height, target_width, 3), dtype=torch.float32)
+            pad_top, pad_left = (target_height - new_h) // 2, (target_width - new_w) // 2
+            canvas[pad_top:pad_top+new_h, pad_left:pad_left+new_w, :] = resized
+            return canvas
+        return img_tensor
+
+    def _load_txt2img(self, data, mode, batch_index, width, height, aspect_label, node_id):
+        """
+        Generate empty IMAGE tensors for txt2img mode.
+        Each "latent" is actually an empty IMAGE tensor that will be used as input.
+        """
+        latents_meta = data.get('latents', [])
+        order = data.get('order', [])
+
+        if not latents_meta or not order:
+            print(f"[INSTARAW Adv Loader] No latents in batch, returning empty {aspect_label} latent.")
+            empty = torch.zeros((1, height, width, 3), dtype=torch.float32)
+            return (empty, 0, 0, f"No latents defined ({aspect_label})")
+
+        total_count = sum(latent.get('repeat_count', 1) for latent in latents_meta)
+
+        if mode == "Sequential":
+            return self._load_txt2img_sequential(latents_meta, order, batch_index, total_count, node_id, data, width, height)
+        else:
+            return self._load_txt2img_batch(latents_meta, order, total_count, width, height)
+
+    def _load_txt2img_sequential(self, latents_meta, order, batch_index, total_count, node_id, data, fallback_width, fallback_height):
+        """Sequential mode for txt2img - return one empty latent at a time"""
+        state_key = f"{node_id}_txt2img_{hashlib.md5(json.dumps(data).encode()).hexdigest()[:8]}"
+
+        if state_key in self.node_states and self.node_states[state_key]['last_widget_index'] != batch_index:
+            self.node_states[state_key] = {'current_index': batch_index, 'last_widget_index': batch_index}
+        elif state_key not in self.node_states:
+            self.node_states[state_key] = {'current_index': batch_index, 'last_widget_index': batch_index}
+
+        current_index = self.node_states[state_key]['current_index']
+
+        # Build flat list of all latents with repeats
+        flat_list = []
+        for latent_id in order:
+            latent_meta = next((l for l in latents_meta if l['id'] == latent_id), None)
+            if not latent_meta:
+                continue
+            for i in range(latent_meta.get('repeat_count', 1)):
+                flat_list.append((latent_meta, i + 1, latent_meta.get('repeat_count', 1)))
+
+        if current_index >= len(flat_list):
+            empty = torch.zeros((1, 64, 64, 3), dtype=torch.float32)
+            return (empty, current_index, total_count, "Index out of range")
+
+        latent_meta, copy_num, repeat_count = flat_list[current_index]
+        w, h = latent_meta.get('width', fallback_width), latent_meta.get('height', fallback_height)
+        latent_id = latent_meta.get('id', 'unknown')
+
+        # VALIDATION: Ensure dimensions are reasonable (not corrupted)
+        # Use ground truth from node inputs if corrupted
+        if w < 64 or h < 64:
+            print(f"[INSTARAW Adv Loader] โš ๏ธ WARNING: Corrupted dimensions in latent_meta: {w}x{h}")
+            print(f"[INSTARAW Adv Loader] Using ground truth from node inputs: {fallback_width}x{fallback_height}")
+            w, h = fallback_width, fallback_height
+
+        # Generate empty tensor
+        empty_tensor = torch.zeros((1, h, w, 3), dtype=torch.float32)
+        info = f"[{current_index}/{total_count}] Empty latent {latent_id[:8]} {w}x{h} (copy {copy_num}/{repeat_count})"
+
+        # Update state for next iteration
+        next_index = (current_index + 1) % total_count
+        self.node_states[state_key]['current_index'] = next_index
+        self.node_states[state_key]['last_widget_index'] = batch_index
+
+        try:
+            PromptServer.instance.send_sync("instaraw_adv_loader_update", {
+                "node_id": str(node_id),
+                "current_index": current_index,
+                "next_index": next_index,
+                "total_count": total_count
+            })
+        except Exception as e:
+            print(f"[INSTARAW Adv Loader] Error sending WebSocket update: {e}")
+
+        return (empty_tensor, current_index, total_count, info)
+
+    def _load_txt2img_batch(self, latents_meta, order, total_count, fallback_width, fallback_height):
+        """Batch tensor mode for txt2img - return all empty latents stacked"""
+        loaded_latents = []
+        info_lines = []
+
+        for latent_id in order:
+            latent_meta = next((l for l in latents_meta if l['id'] == latent_id), None)
+            if not latent_meta:
+                continue
+
+            w, h = latent_meta.get('width', fallback_width), latent_meta.get('height', fallback_height)
+
+            # VALIDATION: Ensure dimensions are reasonable (not corrupted)
+            # Use ground truth from node inputs if corrupted
+            if w < 64 or h < 64:
+                print(f"[INSTARAW Adv Loader] โš ๏ธ WARNING: Corrupted dimensions in latent_meta: {w}x{h}")
+                print(f"[INSTARAW Adv Loader] Using ground truth from node inputs: {fallback_width}x{fallback_height}")
+                w, h = fallback_width, fallback_height
+            repeat_count = latent_meta.get('repeat_count', 1)
+            latent_display_id = latent_meta.get('id', 'unknown')[:8]
+
+            # Create empty tensor for this latent
+            empty_tensor = torch.zeros((h, w, 3), dtype=torch.float32)
+
+            # Add to batch according to repeat count
+            for i in range(repeat_count):
+                loaded_latents.append(empty_tensor)
+                info_lines.append(f"Empty latent {latent_display_id} {w}x{h} (copy {i+1}/{repeat_count})")
+
+        if not loaded_latents:
+            empty = torch.zeros((1, 64, 64, 3), dtype=torch.float32)
+            return (empty, 0, total_count, "No latents to generate")
+
+        # Stack all tensors into batch
+        batch_tensor = torch.stack(loaded_latents, dim=0)
+        return (batch_tensor, 0, total_count, "\n".join(info_lines))
+
+    def _get_upload_dir(self, node_id=None):
+        """Get the central image pool directory (node_id param kept for backwards compat but ignored)"""
+        input_dir = folder_paths.get_input_directory()
+        pool_dir = os.path.join(input_dir, "INSTARAW_ImagePool")
+        os.makedirs(pool_dir, exist_ok=True)
+        return pool_dir
+
+    def _find_image_path(self, filename, node_id=None):
+        """
+        Find an image file, checking multiple locations:
+        1. Central pool (INSTARAW_ImagePool)
+        2. Old per-node folders (INSTARAW_BatchUploads/{node_id})
+        3. All old per-node folders if node_id not found
+
+        Auto-migrates images to pool when found in old locations.
+        """
+        import shutil
+        input_dir = folder_paths.get_input_directory()
+        pool_dir = os.path.join(input_dir, "INSTARAW_ImagePool")
+        os.makedirs(pool_dir, exist_ok=True)
+
+        # 1. Check central pool first
+        pool_path = os.path.join(pool_dir, filename)
+        if os.path.exists(pool_path):
+            return pool_path
+
+        # 2. Check old per-node folder if node_id provided
+        old_base_dir = os.path.join(input_dir, "INSTARAW_BatchUploads")
+        if node_id and os.path.exists(old_base_dir):
+            old_node_dir = os.path.join(old_base_dir, str(node_id))
+            old_path = os.path.join(old_node_dir, filename)
+            if os.path.exists(old_path):
+                # Auto-migrate to pool
+                try:
+                    shutil.copy2(old_path, pool_path)
+                    print(f"[INSTARAW] Auto-migrated {filename} from node {node_id} folder to pool")
+                except Exception as e:
+                    print(f"[INSTARAW] Failed to migrate {filename}: {e}")
+                return old_path if not os.path.exists(pool_path) else pool_path
+
+        # 3. Search ALL old per-node folders
+        if os.path.exists(old_base_dir):
+            for folder in os.listdir(old_base_dir):
+                folder_path = os.path.join(old_base_dir, folder)
+                if os.path.isdir(folder_path):
+                    old_path = os.path.join(folder_path, filename)
+                    if os.path.exists(old_path):
+                        # Auto-migrate to pool
+                        try:
+                            shutil.copy2(old_path, pool_path)
+                            print(f"[INSTARAW] Auto-migrated {filename} from folder {folder} to pool")
+                        except Exception as e:
+                            print(f"[INSTARAW] Failed to migrate {filename}: {e}")
+                        return old_path if not os.path.exists(pool_path) else pool_path
+
+        # Not found anywhere
+        return None
+
+    @classmethod
+    def IS_CHANGED(cls, mode="Batch Tensor", batch_data="{}", **kwargs):
+        if mode == "Sequential":
+            import time
+            return f"seq_{time.time()}_{hashlib.md5(batch_data.encode()).hexdigest()[:8]}"
+        else:
+            return hashlib.md5(batch_data.encode()).hexdigest()
+
+# --- API Endpoints (Branded) ---
+@PromptServer.instance.routes.post("/instaraw/batch_upload")
+async def batch_upload_endpoint(request):
+    """Upload images to central pool (not per-node) so they survive node ID changes"""
+    try:
+        reader = await request.multipart()
+        node_id, uploaded_files = None, []
+
+        # Central pool directory (no longer per-node)
+        input_dir = folder_paths.get_input_directory()
+        upload_dir = os.path.join(input_dir, "INSTARAW_ImagePool")
+        os.makedirs(upload_dir, exist_ok=True)
+
+        async for field in reader:
+            if field.name == 'node_id':
+                node_id = await field.text()  # Still read it but don't use for folder path
+            elif field.name == 'files':
+                filename = field.filename
+                file_data = await field.read()
+
+                file_id = str(uuid.uuid4())
+                ext = os.path.splitext(filename)[1].lower()
+                safe_filename = f"{file_id}{ext}"
+                full_path = os.path.join(upload_dir, safe_filename)
+                with open(full_path, 'wb') as f: f.write(file_data)
+
+                try:
+                    img = Image.open(full_path)
+                    width, height = img.size
+                    img.thumbnail((256, 256), Image.Resampling.LANCZOS)
+                    thumb_filename = f"thumb_{safe_filename}"
+                    img.save(os.path.join(upload_dir, thumb_filename))
+                    uploaded_files.append({"id": file_id, "filename": safe_filename, "original_name": filename, "width": width, "height": height, "thumbnail": thumb_filename, "repeat_count": 1})
+                except Exception as e:
+                    print(f"[INSTARAW Adv Loader] Error processing image: {e}")
+                    if os.path.exists(full_path): os.remove(full_path)
+        return web.json_response({"success": True, "images": uploaded_files})
+    except Exception as e:
+        return web.json_response({"success": False, "error": str(e)}, status=500)
+
+@PromptServer.instance.routes.delete("/instaraw/batch_delete/{node_id}/{image_id}")
+async def batch_delete_endpoint(request):
+    """Delete image from central pool (node_id kept in URL for backwards compat but ignored)"""
+    image_id = request.match_info['image_id']
+    try:
+        input_dir = folder_paths.get_input_directory()
+        upload_dir = os.path.join(input_dir, "INSTARAW_ImagePool")
+        deleted = []
+        if os.path.exists(upload_dir):
+            for filename in os.listdir(upload_dir):
+                if image_id in filename:
+                    os.remove(os.path.join(upload_dir, filename))
+                    deleted.append(filename)
+        return web.json_response({"success": True, "deleted": deleted})
+    except Exception as e:
+        return web.json_response({"success": False, "error": str(e)}, status=500)
+
+@PromptServer.instance.routes.get("/instaraw/view/{filename}")
+async def smart_view_endpoint(request):
+    """
+    Smart image view endpoint that checks multiple locations:
+    1. Central pool (INSTARAW_ImagePool)
+    2. All old per-node folders (INSTARAW_BatchUploads/*)
+
+    Returns redirect to ComfyUI's /view endpoint with correct subfolder.
+    """
+    import shutil
+
+    filename = request.match_info['filename']
+    input_dir = folder_paths.get_input_directory()
+    pool_dir = os.path.join(input_dir, "INSTARAW_ImagePool")
+    os.makedirs(pool_dir, exist_ok=True)
+
+    # 1. Check central pool first
+    pool_path = os.path.join(pool_dir, filename)
+    if os.path.exists(pool_path):
+        # Redirect to standard ComfyUI view endpoint
+        return web.HTTPFound(f"/view?filename={filename}&type=input&subfolder=INSTARAW_ImagePool")
+
+    # 2. Search ALL old per-node folders
+    old_base_dir = os.path.join(input_dir, "INSTARAW_BatchUploads")
+    if os.path.exists(old_base_dir):
+        for folder in os.listdir(old_base_dir):
+            folder_path = os.path.join(old_base_dir, folder)
+            if os.path.isdir(folder_path):
+                old_path = os.path.join(folder_path, filename)
+                if os.path.exists(old_path):
+                    # Auto-migrate to pool
+                    try:
+                        shutil.copy2(old_path, pool_path)
+                        print(f"[INSTARAW] Auto-migrated {filename} from folder {folder} to pool (via view)")
+                        return web.HTTPFound(f"/view?filename={filename}&type=input&subfolder=INSTARAW_ImagePool")
+                    except Exception as e:
+                        print(f"[INSTARAW] Failed to migrate {filename}: {e}")
+                        # Serve from old location
+                        return web.HTTPFound(f"/view?filename={filename}&type=input&subfolder=INSTARAW_BatchUploads/{folder}")
+
+    return web.Response(status=404, text=f"Image not found: {filename}")
+
+@PromptServer.instance.routes.post("/instaraw/migrate_images")
+async def migrate_images_endpoint(request):
+    """Migrate images from old per-node folders to central pool"""
+    try:
+        import shutil
+        input_dir = folder_paths.get_input_directory()
+        old_base_dir = os.path.join(input_dir, "INSTARAW_BatchUploads")
+        new_pool_dir = os.path.join(input_dir, "INSTARAW_ImagePool")
+        os.makedirs(new_pool_dir, exist_ok=True)
+
+        migrated = 0
+        if os.path.exists(old_base_dir):
+            for node_folder in os.listdir(old_base_dir):
+                node_path = os.path.join(old_base_dir, node_folder)
+                if os.path.isdir(node_path):
+                    for filename in os.listdir(node_path):
+                        src = os.path.join(node_path, filename)
+                        dst = os.path.join(new_pool_dir, filename)
+                        if os.path.isfile(src) and not os.path.exists(dst):
+                            shutil.move(src, dst)
+                            migrated += 1
+                    # Remove empty folder
+                    if not os.listdir(node_path):
+                        os.rmdir(node_path)
+            # Remove old base folder if empty
+            if os.path.exists(old_base_dir) and not os.listdir(old_base_dir):
+                os.rmdir(old_base_dir)
+
+        return web.json_response({"success": True, "migrated": migrated})
+    except Exception as e:
+        return web.json_response({"success": False, "error": str(e)}, status=500)
+
+NODE_CLASS_MAPPINGS = {"INSTARAW_AdvancedImageLoader": INSTARAW_AdvancedImageLoader}
+NODE_DISPLAY_NAME_MAPPINGS = {"INSTARAW_AdvancedImageLoader": "๐Ÿ–ผ๏ธ INSTARAW Advanced Image Loader"}
\ No newline at end of file
diff --git a/custom_nodes/ComfyUI_INSTARAW/nodes/input_nodes/float_input.py b/custom_nodes/ComfyUI_INSTARAW/nodes/input_nodes/float_input.py
new file mode 100644
index 0000000000000000000000000000000000000000..5cb83b5afd84c1aa5c1356f77ee884da80294a67
--- /dev/null
+++ b/custom_nodes/ComfyUI_INSTARAW/nodes/input_nodes/float_input.py
@@ -0,0 +1,53 @@
+"""
+INSTARAW Float Input Node
+A simple float input node with three decimal place precision.
+"""
+
+
+class INSTARAW_FloatInput:
+    """
+    A simple float input node that accepts values with up to three decimal places.
+    Useful for precise parameter control in workflows.
+    """
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "value": ("FLOAT", {
+                    "default": 0.000,
+                    "min": -999999.999,
+                    "max": 999999.999,
+                    "step": 0.001,
+                    "tooltip": "Float value with up to 3 decimal places precision",
+                    "display": "number",
+                }),
+            },
+        }
+
+    RETURN_TYPES = ("FLOAT",)
+    RETURN_NAMES = ("float",)
+    FUNCTION = "output_float"
+    CATEGORY = "INSTARAW/Input"
+    DESCRIPTION = "Outputs a float value with up to three decimal places precision"
+
+    def output_float(self, value):
+        """
+        Returns the input float value.
+
+        Args:
+            value (float): The float value to output
+
+        Returns:
+            tuple: A tuple containing the float value
+        """
+        return (value,)
+
+
+NODE_CLASS_MAPPINGS = {
+    "INSTARAW_FloatInput": INSTARAW_FloatInput,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "INSTARAW_FloatInput": "๐Ÿ”ข INSTARAW Float Input (3 decimals)",
+}
diff --git a/custom_nodes/ComfyUI_INSTARAW/nodes/input_nodes/nano_banana_aspect_ratio.py b/custom_nodes/ComfyUI_INSTARAW/nodes/input_nodes/nano_banana_aspect_ratio.py
new file mode 100644
index 0000000000000000000000000000000000000000..d9eb9d23b3769ecfa2f74d2f220b23f873bd6f16
--- /dev/null
+++ b/custom_nodes/ComfyUI_INSTARAW/nodes/input_nodes/nano_banana_aspect_ratio.py
@@ -0,0 +1,129 @@
+# ---
+# ComfyUI INSTARAW - Nano Banana Pro Aspect Ratio Node
+# Copyright ยฉ 2025 Instara. All rights reserved.
+# PROPRIETARY SOFTWARE - ALL RIGHTS RESERVED
+# ---
+
+"""
+Nano Banana Pro Aspect Ratio Node
+Provides aspect ratio and resolution selection for Nano Banana Pro API.
+Includes visual preview via JavaScript widget.
+"""
+
+
+class INSTARAW_NanoBananaAspectRatio:
+    """
+    Aspect ratio and resolution selector for Nano Banana Pro.
+    Supports all standard aspect ratios and 1K/2K/4K resolution tiers.
+    """
+
+    # Aspect ratio mapping - display name to API value
+    ASPECT_RATIOS = {
+        "1:1 (Square)": "1:1",
+        "3:2 (Landscape)": "3:2",
+        "2:3 (Portrait)": "2:3",
+        "3:4 (Portrait)": "3:4",
+        "4:3 (Landscape)": "4:3",
+        "4:5 (Portrait)": "4:5",
+        "5:4 (Landscape)": "5:4",
+        "9:16 (Tall Portrait)": "9:16",
+        "16:9 (Wide Landscape)": "16:9",
+        "21:9 (Ultrawide)": "21:9",
+    }
+
+    # Resolution tiers with base sizes (shortest side in pixels)
+    RESOLUTIONS = ["1K", "2K", "4K"]
+    RESOLUTION_BASE = {
+        "1K": 1024,
+        "2K": 2048,
+        "4K": 4096,
+    }
+
+    # Aspect ratio to dimensions mapping (width, height) for calculating pixels
+    ASPECT_DIMENSIONS = {
+        "1:1": (1, 1),
+        "3:2": (3, 2),
+        "2:3": (2, 3),
+        "3:4": (3, 4),
+        "4:3": (4, 3),
+        "4:5": (4, 5),
+        "5:4": (5, 4),
+        "9:16": (9, 16),
+        "16:9": (16, 9),
+        "21:9": (21, 9),
+    }
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "aspect_ratio": (
+                    list(cls.ASPECT_RATIOS.keys()),
+                    {
+                        "default": "1:1 (Square)",
+                        "tooltip": "Select the aspect ratio for image generation",
+                    },
+                ),
+                "resolution": (
+                    cls.RESOLUTIONS,
+                    {
+                        "default": "1K",
+                        "tooltip": "Resolution tier: 1K (~1024px), 2K (~2048px), 4K (~4096px)",
+                    },
+                ),
+            }
+        }
+
+    RETURN_TYPES = ("STRING", "STRING", "INT", "INT", "STRING")
+    RETURN_NAMES = ("aspect_ratio", "resolution", "width", "height", "aspect_label")
+    FUNCTION = "get_settings"
+    CATEGORY = "INSTARAW/Input"
+    DESCRIPTION = "Nano Banana Pro aspect ratio and resolution selector with visual preview."
+
+    def get_settings(self, aspect_ratio, resolution):
+        """
+        Returns aspect ratio, resolution, width, height, and label for the selected settings.
+
+        Args:
+            aspect_ratio: Selected aspect ratio display name
+            resolution: Selected resolution tier
+
+        Returns:
+            Tuple of (aspect_ratio_value, resolution, width, height, aspect_label)
+        """
+        # Get the API value for the aspect ratio
+        aspect_value = self.ASPECT_RATIOS.get(aspect_ratio, "1:1")
+
+        # Get base size for resolution tier
+        base_size = self.RESOLUTION_BASE.get(resolution, 1024)
+
+        # Get aspect dimensions
+        aspect_w, aspect_h = self.ASPECT_DIMENSIONS.get(aspect_value, (1, 1))
+
+        # Calculate actual width and height
+        # The shorter side gets the base size, longer side is proportionally larger
+        if aspect_w >= aspect_h:
+            # Landscape or square - height is the shorter side
+            height = base_size
+            width = int(base_size * aspect_w / aspect_h)
+        else:
+            # Portrait - width is the shorter side
+            width = base_size
+            height = int(base_size * aspect_h / aspect_w)
+
+        # Round to nearest 64 for compatibility
+        width = (width // 64) * 64
+        height = (height // 64) * 64
+
+        # Return the settings
+        return (aspect_value, resolution, width, height, aspect_ratio)
+
+
+# Node registration
+NODE_CLASS_MAPPINGS = {
+    "INSTARAW_NanoBananaAspectRatio": INSTARAW_NanoBananaAspectRatio,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "INSTARAW_NanoBananaAspectRatio": "๐Ÿ“ Nano Banana Pro Aspect Ratio",
+}
diff --git a/custom_nodes/ComfyUI_INSTARAW/nodes/input_nodes/reality_prompt_generator.py b/custom_nodes/ComfyUI_INSTARAW/nodes/input_nodes/reality_prompt_generator.py
new file mode 100644
index 0000000000000000000000000000000000000000..83a64788c7400b01c044a258f7a9cf7731ab20f3
--- /dev/null
+++ b/custom_nodes/ComfyUI_INSTARAW/nodes/input_nodes/reality_prompt_generator.py
@@ -0,0 +1,258 @@
+# ---
+# Filename: ../ComfyUI_INSTARAW/nodes/input_nodes/reality_prompt_generator.py
+# Reality Prompt Generator (RPG) - Full Implementation
+# ---
+
+import json
+import hashlib
+
+
+class INSTARAW_RealityPromptGenerator:
+    """
+    Reality Prompt Generator (RPG) - A comprehensive prompt batch manager
+    that integrates with a 22MB prompts database and supports creative AI generation.
+
+    Outputs STRING LISTS compatible with batch tensor workflows.
+    Follows GPT spec architecture with auto/img2img/txt2img modes.
+    """
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "gemini_api_key": (
+                    "STRING",
+                    {
+                        "multiline": False,
+                        "default": "",
+                        "tooltip": "Google Gemini API key. Leave empty to use GEMINI_API_KEY environment variable.",
+                    },
+                ),
+                "grok_api_key": (
+                    "STRING",
+                    {
+                        "multiline": False,
+                        "default": "",
+                        "tooltip": "xAI Grok API key. Leave empty to use XAI_API_KEY environment variable.",
+                    },
+                ),
+            },
+            "optional": {
+                "images": (
+                    "IMAGE",
+                    {
+                        "tooltip": (
+                            "Primary reference images (image_1) from Advanced Image Loader.\n"
+                            "Used for img2img workflows to validate prompt count matches image count."
+                        ),
+                    },
+                ),
+                "images2": (
+                    "IMAGE",
+                    {
+                        "tooltip": (
+                            "Secondary reference images (image_2) - e.g., clothing, accessories.\n"
+                            "Connect additional AIL for multi-image prompting."
+                        ),
+                    },
+                ),
+                "images3": (
+                    "IMAGE",
+                    {
+                        "tooltip": (
+                            "Tertiary reference images (image_3) - e.g., background, environment.\n"
+                            "Connect additional AIL for multi-image prompting."
+                        ),
+                    },
+                ),
+                "images4": (
+                    "IMAGE",
+                    {
+                        "tooltip": (
+                            "Quaternary reference images (image_4) - e.g., style, pose reference.\n"
+                            "Connect additional AIL for multi-image prompting."
+                        ),
+                    },
+                ),
+                "character_image": (
+                    "IMAGE",
+                    {
+                        "tooltip": (
+                            "Optional: Single character reference image for Creative/Character modes.\n"
+                            "Will be sent to Gemini/Grok for visual character consistency."
+                        ),
+                    },
+                ),
+                "aspect_label": (
+                    "STRING",
+                    {
+                        "default": "1:1",
+                        "tooltip": "Aspect ratio label (e.g., '16:9'). Connect from INSTARAW Aspect Ratio Selector.",
+                    },
+                ),
+            },
+            "hidden": {
+                "node_id": "UNIQUE_ID",
+                "prompt_batch_data": (
+                    "STRING",
+                    {
+                        "default": "[]",
+                    },
+                ),
+                "resolved_mode": (
+                    "STRING",
+                    {
+                        "default": "txt2img",
+                    },
+                ),
+                "sdxl_mode": (
+                    "BOOLEAN",
+                    {
+                        "default": False,
+                    },
+                ),
+            },
+        }
+
+    RETURN_TYPES = ("STRING", "STRING", "INT", "INT", "STRING")
+    RETURN_NAMES = (
+        "prompt_list_positive",
+        "prompt_list_negative",
+        "seed_list",
+        "generation_count",
+        "resolved_mode",
+    )
+
+    INPUT_IS_LIST = False
+    OUTPUT_IS_LIST = [True, True, True, False, False]
+    # prompt_list_positive => list
+    # prompt_list_negative => list
+    # seed_list => list
+    # generation_count, resolved_mode => scalars
+
+    FUNCTION = "execute"
+    CATEGORY = "INSTARAW/Prompts"
+
+    def execute(
+        self,
+        gemini_api_key,
+        grok_api_key,
+        images=None,
+        images2=None,
+        images3=None,
+        images4=None,
+        character_image=None,
+        aspect_label="1:1",
+        node_id=None,
+        prompt_batch_data="[]",
+        resolved_mode="txt2img",
+        sdxl_mode=False,
+        user_instructions="",
+    ):
+        """
+        Execute the RPG node - parse the prompt batch, expand prompts, return STRING LISTS.
+
+        This method is deterministic and has no side effects. All creative generation
+        happens in the frontend via backend API calls.
+
+        Args:
+            images: Optional IMAGE batch from AIL for img2img mode
+            character_image: Optional single IMAGE for character reference
+        """
+        # 1) Parse prompt batch
+        try:
+            prompt_batch = json.loads(prompt_batch_data or "[]")
+        except Exception as e:
+            print(f"[RPG] Error parsing prompt_batch_data: {e}")
+            prompt_batch = []
+
+        # 2) Get image count from actual connected images
+        image_count = 0
+        if images is not None:
+            import torch
+            if isinstance(images, torch.Tensor):
+                image_count = images.shape[0]  # Batch dimension
+
+        # 3) Compute effective prompt list (positive + negative + seeds) with repeat_count
+        positives = []
+        negatives = []
+        seeds = []
+
+        for idx, entry in enumerate(prompt_batch):
+            # SDXL mode determines which field to use for positive prompt
+            # - SDXL mode ON: Use tags if available, fallback to positive_prompt
+            # - SDXL mode OFF: Use positive_prompt only (ignore tags)
+            tags = entry.get("tags", [])
+            positive_prompt = entry.get("positive_prompt") or ""
+
+            if sdxl_mode and tags and isinstance(tags, list) and len(tags) > 0:
+                # SDXL mode enabled - use tags
+                pos = ", ".join(tags).strip()
+            else:
+                # Normal mode - use positive_prompt field
+                pos = positive_prompt.strip()
+
+            neg = (entry.get("negative_prompt") or "").strip()
+            rc = max(1, int(entry.get("repeat_count", 1)))
+
+            # Get seed from this prompt entry
+            entry_seed = int(entry.get("seed", 1111111))
+
+            # When repeat_count > 1, always increment by +1 for each repeat
+            # (seed_control is for AFTER execution, handled in frontend)
+            for repeat_idx in range(rc):
+                positives.append(pos)
+                negatives.append(neg if neg else "")
+                seeds.append(entry_seed + repeat_idx)
+
+        # 4) Determine effective mode (auto-detect based on images)
+        if image_count > 0:
+            resolved = "img2img"
+        else:
+            resolved = "txt2img"
+
+        # 5) Compute generation_count
+        generation_count = len(positives)
+
+        # 6) Log validation info
+        if image_count > 0 and generation_count > 0:
+            if image_count == generation_count:
+                print(f"[RPG] โœ… {generation_count} prompts โ†” {image_count} images (perfect match)")
+            else:
+                print(f"[RPG] โš ๏ธ {generation_count} prompts vs {image_count} images (mismatch)")
+
+        # 7) If prompt_batch is empty, return valid empty lists (no exception)
+        if generation_count == 0:
+            print("[RPG] Warning: Prompt batch is empty. Returning empty prompt lists.")
+            return ([""], [""], [0], 0, resolved)
+
+        # 8) Return STRING LISTS and SEED LIST
+        return (positives, negatives, seeds, generation_count, resolved)
+
+    @classmethod
+    def IS_CHANGED(cls, **kwargs):
+        """
+        Let ComfyUI use default change detection based on inputs.
+        We compute a deterministic hash from prompt_batch_data for caching.
+        """
+        prompt_batch_data = kwargs.get("prompt_batch_data", "[]")
+        global_negative = kwargs.get("global_negative", "")
+        expected_image_count = kwargs.get("expected_image_count", -1)
+
+        # Create a hash of all inputs that affect output
+        hasher = hashlib.sha256()
+        hasher.update(prompt_batch_data.encode("utf-8"))
+        hasher.update(global_negative.encode("utf-8"))
+        hasher.update(str(expected_image_count).encode("utf-8"))
+
+        return hasher.hexdigest()
+
+
+# Export node mappings
+NODE_CLASS_MAPPINGS = {
+    "INSTARAW_RealityPromptGenerator": INSTARAW_RealityPromptGenerator,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "INSTARAW_RealityPromptGenerator": "๐ŸŽฒ INSTARAW Reality Prompt Generator",
+}
diff --git a/custom_nodes/ComfyUI_INSTARAW/nodes/input_nodes/sdxl_aspect_ratio.py b/custom_nodes/ComfyUI_INSTARAW/nodes/input_nodes/sdxl_aspect_ratio.py
new file mode 100644
index 0000000000000000000000000000000000000000..19b24a566f02997fff429b0122ee339a195d0d1d
--- /dev/null
+++ b/custom_nodes/ComfyUI_INSTARAW/nodes/input_nodes/sdxl_aspect_ratio.py
@@ -0,0 +1,60 @@
+"""
+SDXL Aspect Ratio Node
+Provides preset aspect ratios optimized for SDXL (Stable Diffusion XL) workflows
+"""
+
+
+class INSTARAW_SDXLAspectRatio:
+    """
+    Aspect ratio presets for SDXL (Stable Diffusion XL) workflows.
+    These resolutions are optimized for SDXL's native training resolution.
+    """
+
+    # Aspect ratio to resolution mapping
+    ASPECT_RATIOS = {
+        "3:4 (Portrait)": {"width": 896, "height": 1152},
+        "9:16 (Tall Portrait)": {"width": 768, "height": 1344},
+        "1:1 (Square)": {"width": 1024, "height": 1024},
+        "16:9 (Landscape)": {"width": 1344, "height": 768},
+    }
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "aspect_ratio": (list(cls.ASPECT_RATIOS.keys()), {
+                    "default": "1:1 (Square)"
+                }),
+            }
+        }
+
+    RETURN_TYPES = ("INT", "INT", "STRING")
+    RETURN_NAMES = ("width", "height", "aspect_label")
+    FUNCTION = "get_resolution"
+    CATEGORY = "INSTARAW/Input"
+    DESCRIPTION = "SDXL aspect ratio presets. Outputs resolution optimized for Stable Diffusion XL."
+
+    def get_resolution(self, aspect_ratio):
+        """
+        Returns width, height, and aspect label for the selected aspect ratio.
+
+        Args:
+            aspect_ratio: Selected aspect ratio string
+
+        Returns:
+            Tuple of (width, height, aspect_label)
+        """
+        resolution = self.ASPECT_RATIOS.get(aspect_ratio, self.ASPECT_RATIOS["1:1 (Square)"])
+        # Extract clean aspect ratio (e.g., "3:4 (Portrait)" -> "3:4")
+        aspect_label = aspect_ratio.split(" ")[0]
+        return (resolution["width"], resolution["height"], aspect_label)
+
+
+# Node registration
+NODE_CLASS_MAPPINGS = {
+    "INSTARAW_SDXLAspectRatio": INSTARAW_SDXLAspectRatio
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "INSTARAW_SDXLAspectRatio": "๐Ÿ“ SDXL Aspect Ratio"
+}
diff --git a/custom_nodes/ComfyUI_INSTARAW/nodes/input_nodes/wan_aspect_ratio.py b/custom_nodes/ComfyUI_INSTARAW/nodes/input_nodes/wan_aspect_ratio.py
new file mode 100644
index 0000000000000000000000000000000000000000..18888f23939138cc8c1ca1bf867ddf5f0fb0c384
--- /dev/null
+++ b/custom_nodes/ComfyUI_INSTARAW/nodes/input_nodes/wan_aspect_ratio.py
@@ -0,0 +1,61 @@
+"""
+WAN Aspect Ratio Node
+Provides preset aspect ratios optimized for WAN workflows
+Resolution is 2x what will be generated (due to 2x upscaling in WAN workflow)
+"""
+
+
+class INSTARAW_WANAspectRatio:
+    """
+    Aspect ratio presets for WAN (Weighted Attention Network) workflows.
+    These resolutions are optimized for the WAN 2x upscaling workflow.
+    """
+
+    # Aspect ratio to resolution mapping
+    ASPECT_RATIOS = {
+        "3:4 (Portrait)": {"width": 720, "height": 960},
+        "9:16 (Tall Portrait)": {"width": 540, "height": 960},
+        "1:1 (Square)": {"width": 960, "height": 960},
+        "16:9 (Landscape)": {"width": 960, "height": 540},
+    }
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "aspect_ratio": (list(cls.ASPECT_RATIOS.keys()), {
+                    "default": "1:1 (Square)"
+                }),
+            }
+        }
+
+    RETURN_TYPES = ("INT", "INT", "STRING")
+    RETURN_NAMES = ("width", "height", "aspect_label")
+    FUNCTION = "get_resolution"
+    CATEGORY = "INSTARAW/Input"
+    DESCRIPTION = "WAN aspect ratio presets. Outputs resolution optimized for WAN 2x upscaling workflow."
+
+    def get_resolution(self, aspect_ratio):
+        """
+        Returns width, height, and aspect label for the selected aspect ratio.
+
+        Args:
+            aspect_ratio: Selected aspect ratio string
+
+        Returns:
+            Tuple of (width, height, aspect_label)
+        """
+        resolution = self.ASPECT_RATIOS.get(aspect_ratio, self.ASPECT_RATIOS["1:1 (Square)"])
+        # Extract clean aspect ratio (e.g., "3:4 (Portrait)" -> "3:4")
+        aspect_label = aspect_ratio.split(" ")[0]
+        return (resolution["width"], resolution["height"], aspect_label)
+
+
+# Node registration
+NODE_CLASS_MAPPINGS = {
+    "INSTARAW_WANAspectRatio": INSTARAW_WANAspectRatio
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "INSTARAW_WANAspectRatio": "๐Ÿ“ WAN Aspect Ratio"
+}
diff --git a/custom_nodes/ComfyUI_INSTARAW/nodes/interactive_nodes/__init__.py b/custom_nodes/ComfyUI_INSTARAW/nodes/interactive_nodes/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..d9aa03b36622eb5f2423d6f4a0aa2fdbba89903c
--- /dev/null
+++ b/custom_nodes/ComfyUI_INSTARAW/nodes/interactive_nodes/__init__.py
@@ -0,0 +1,33 @@
+"""
+INSTARAW Interactive Nodes
+Nodes that pause the workflow for user input.
+"""
+
+from .image_filter import (
+    INSTARAW_ImageFilter,
+    INSTARAW_MaskImageFilter,
+    INSTARAW_TextImageFilter,
+)
+from .interactive_crop import INSTARAW_Interactive_Crop
+from .prompt_filter import INSTARAW_PromptFilter
+from .batch_image_generator import INSTARAW_BatchImageGenerator
+
+NODE_CLASS_MAPPINGS = {
+    "INSTARAW_ImageFilter": INSTARAW_ImageFilter,
+    "INSTARAW_TextImageFilter": INSTARAW_TextImageFilter,
+    "INSTARAW_MaskImageFilter": INSTARAW_MaskImageFilter,
+    "INSTARAW_Interactive_Crop": INSTARAW_Interactive_Crop,
+    "INSTARAW_PromptFilter": INSTARAW_PromptFilter,
+    "INSTARAW_BatchImageGenerator": INSTARAW_BatchImageGenerator,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "INSTARAW_ImageFilter": "๐ŸŽญ INSTARAW Image Filter",
+    "INSTARAW_TextImageFilter": "๐Ÿ“ INSTARAW Text/Image Filter",
+    "INSTARAW_MaskImageFilter": "โœ‚๏ธ INSTARAW Mask Filter",
+    "INSTARAW_Interactive_Crop": "๐Ÿ–ผ๏ธ INSTARAW Interactive Crop",
+    "INSTARAW_PromptFilter": "๐ŸŽฏ INSTARAW Prompt Filter",
+    "INSTARAW_BatchImageGenerator": "๐ŸŽจ INSTARAW Batch Image Generator",
+}
+
+__all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS"]
\ No newline at end of file
diff --git a/custom_nodes/ComfyUI_INSTARAW/nodes/interactive_nodes/batch_image_generator.py b/custom_nodes/ComfyUI_INSTARAW/nodes/interactive_nodes/batch_image_generator.py
new file mode 100644
index 0000000000000000000000000000000000000000..81e672457194d75134841b6bed494a14cf03c9e6
--- /dev/null
+++ b/custom_nodes/ComfyUI_INSTARAW/nodes/interactive_nodes/batch_image_generator.py
@@ -0,0 +1,1932 @@
+# ---
+# ComfyUI INSTARAW - Interactive Batch Image Generator
+# Production-ready parallel image generation with retry logic and interactive selection
+# Copyright ยฉ 2025 Instara. All rights reserved.
+# PROPRIETARY SOFTWARE - ALL RIGHTS RESERVED
+# ---
+
+"""
+INSTARAW Interactive Batch Image Generator
+
+A premium interactive node for parallel image generation featuring:
+- Real-time parallel generation with configurable concurrency
+- Smart auto-retry with exponential backoff (bypasses moderation filters)
+- Interactive selection UI with job status display
+- Full RPG integration (positive/negative prompts, seeds)
+- I2I mode with reference image cycling
+- Comprehensive progress logging
+- Image caching for completed generations
+- Real-time UI progress updates via WebSocket
+"""
+
+import os
+import io
+import time
+import hashlib
+import json
+import threading
+import asyncio
+import base64
+from concurrent.futures import ThreadPoolExecutor, as_completed
+from dataclasses import dataclass, field, asdict
+from typing import Optional, List, Dict, Any
+from enum import Enum
+
+import requests
+import numpy as np
+import torch
+from PIL import Image
+
+from nodes import PreviewImage
+from comfy.model_management import InterruptProcessingException
+from server import PromptServer
+from aiohttp import web
+
+from .image_filter_messaging import send_and_wait, Response, TimeoutResponse
+from ..api_nodes.generative_api_nodes import MODEL_CONFIG, INSTARAW_GenerativeAPIBase
+
+
+# โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•
+# ULID GENERATOR FOR UNIQUE FILENAMES
+# โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•
+
+# Crockford's Base32 alphabet (excludes I, L, O, U to avoid confusion)
+_ULID_CHARS = "0123456789ABCDEFGHJKMNPQRSTVWXYZ"
+
+def generate_ulid() -> str:
+    """
+    Generate a ULID (Universally Unique Lexicographically Sortable Identifier).
+
+    Format: 26 characters
+    - First 10 chars: timestamp (milliseconds since epoch)
+    - Last 16 chars: randomness
+
+    ULIDs are:
+    - Guaranteed unique
+    - Lexicographically sortable (sorts by creation time)
+    - URL-safe and filename-safe
+
+    Example: 01HGX5J8K2P3Q4R5T6V7W8X9YZ
+    """
+    import random
+
+    # Timestamp component (48 bits = 10 chars)
+    timestamp_ms = int(time.time() * 1000)
+    timestamp_part = ""
+    for _ in range(10):
+        timestamp_part = _ULID_CHARS[timestamp_ms & 31] + timestamp_part
+        timestamp_ms >>= 5
+
+    # Randomness component (80 bits = 16 chars)
+    random_part = "".join(random.choice(_ULID_CHARS) for _ in range(16))
+
+    return timestamp_part + random_part
+
+
+class JobState(Enum):
+    """Generation job states."""
+    PENDING = "pending"
+    GENERATING = "generating"
+    SUCCESS = "success"
+    FAILED = "failed"
+    CACHED = "cached"
+    RETRYING = "retrying"
+
+
+# โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•
+# GLOBAL PROGRESS TRACKING FOR REAL-TIME UI UPDATES
+# โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•
+
+class ProgressTracker:
+    """
+    Global singleton for tracking generation progress across all nodes.
+    Enables real-time UI updates via polling or WebSocket events.
+    """
+    _instance = None
+    _lock = threading.Lock()
+
+    def __new__(cls):
+        if cls._instance is None:
+            with cls._lock:
+                if cls._instance is None:
+                    cls._instance = super().__new__(cls)
+                    cls._instance._progress_data = {}
+        return cls._instance
+
+    def start_generation(self, node_id: int, jobs: List['GenerationJob']):
+        """Initialize progress tracking for a generation session."""
+        self._progress_data[node_id] = {
+            "jobs": [self._job_to_dict(j) for j in jobs],
+            "stats": {
+                "total": len(jobs),
+                "completed": 0,
+                "success": 0,
+                "failed": 0,
+                "cached": 0,
+            },
+            "is_generating": True,
+            "should_stop": False,  # Flag to stop generation
+            "start_time": time.time(),
+        }
+        self._emit_event("instaraw-batch-gen-start", {
+            "node_id": node_id,
+            "jobs": self._progress_data[node_id]["jobs"],
+        })
+
+    def update_job(self, node_id: int, job: 'GenerationJob'):
+        """Update a single job's status."""
+        if node_id not in self._progress_data:
+            return
+
+        data = self._progress_data[node_id]
+        for i, j in enumerate(data["jobs"]):
+            if j["id"] == job.id:
+                data["jobs"][i] = self._job_to_dict(job)
+                break
+
+        # Update stats
+        jobs = data["jobs"]
+        data["stats"]["completed"] = sum(1 for j in jobs if j["state"] in ["success", "failed", "cached"])
+        data["stats"]["success"] = sum(1 for j in jobs if j["state"] == "success")
+        data["stats"]["failed"] = sum(1 for j in jobs if j["state"] == "failed")
+        data["stats"]["cached"] = sum(1 for j in jobs if j["state"] == "cached")
+
+        self._emit_event("instaraw-batch-gen-update", {
+            "node_id": node_id,
+            "job_id": job.id,
+            "state": job.state.value,
+            "attempts": job.attempts,
+            "error": job.error,
+            "generation_time": job.generation_time,
+            "image_url": job.result_image_url,
+            "image_width": job.result_image_width,
+            "image_height": job.result_image_height,
+        })
+
+    def complete_generation(self, node_id: int):
+        """Mark generation as complete."""
+        if node_id in self._progress_data:
+            self._progress_data[node_id]["is_generating"] = False
+            self._emit_event("instaraw-batch-gen-complete", {
+                "node_id": node_id,
+                "stats": self._progress_data[node_id]["stats"],
+            })
+
+    def get_progress(self, node_id: int) -> Optional[Dict]:
+        """Get current progress for a node."""
+        return self._progress_data.get(node_id)
+
+    def clear_progress(self, node_id: int):
+        """Clear progress data for a node."""
+        if node_id in self._progress_data:
+            del self._progress_data[node_id]
+
+    def stop_generation(self, node_id: int):
+        """Request generation to stop."""
+        print(f"[ProgressTracker] stop_generation called for node {node_id}", flush=True)
+        if node_id in self._progress_data:
+            self._progress_data[node_id]["should_stop"] = True
+            self._progress_data[node_id]["is_generating"] = False
+            print(f"[ProgressTracker] Set should_stop=True for node {node_id}", flush=True)
+        else:
+            print(f"[ProgressTracker] WARNING: Node {node_id} not in progress_data!", flush=True)
+
+    def should_stop(self, node_id: int) -> bool:
+        """Check if generation should stop."""
+        if node_id in self._progress_data:
+            return self._progress_data[node_id].get("should_stop", False)
+        return False
+
+    def _job_to_dict(self, job: 'GenerationJob') -> Dict:
+        """Convert job to serializable dict."""
+        return {
+            "id": job.id,
+            "prompt_positive": job.prompt_positive[:100] + "..." if len(job.prompt_positive) > 100 else job.prompt_positive,
+            "state": job.state.value,
+            "attempts": job.attempts,
+            "error": job.error[:100] if job.error else None,
+            "generation_time": job.generation_time,
+            "cache_hit": job.cache_hit,
+            "image_url": job.result_image_url,
+            "image_width": job.result_image_width,
+            "image_height": job.result_image_height,
+            "filename": job.result_filename,
+        }
+
+    def _emit_event(self, event_type: str, data: Dict):
+        """Emit event to frontend via PromptServer."""
+        try:
+            PromptServer.instance.send_sync(event_type, data)
+        except Exception as e:
+            print(f"[BIG] Failed to emit event {event_type}: {e}", flush=True)
+
+
+# Global progress tracker instance
+progress_tracker = ProgressTracker()
+
+
+# โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•
+# API ENDPOINTS FOR REAL-TIME PROGRESS
+# โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•
+
+@PromptServer.instance.routes.get('/instaraw/batch_gen_progress/{node_id}')
+async def get_batch_gen_progress(request):
+    """Get current generation progress for a node."""
+    try:
+        node_id = int(request.match_info['node_id'])
+        progress = progress_tracker.get_progress(node_id)
+        if progress:
+            return web.json_response(progress)
+        return web.json_response({"jobs": [], "stats": {}, "is_generating": False})
+    except Exception as e:
+        return web.json_response({"error": str(e)}, status=500)
+
+
+@PromptServer.instance.routes.post('/instaraw/batch_gen_retry_failed')
+async def retry_failed_jobs(request):
+    """Endpoint to retry all failed jobs for a node."""
+    try:
+        data = await request.json()
+        node_id = data.get("node_id")
+        # This would need to trigger a re-execution - for now just acknowledge
+        return web.json_response({"status": "acknowledged", "message": "Retry not yet implemented"})
+    except Exception as e:
+        return web.json_response({"error": str(e)}, status=500)
+
+
+@PromptServer.instance.routes.post('/instaraw/batch_gen_retry_job')
+async def retry_single_job(request):
+    """Endpoint to retry a single failed job."""
+    try:
+        data = await request.json()
+        node_id = data.get("node_id")
+        job_id = data.get("job_id")
+        # This would need to trigger a re-execution - for now just acknowledge
+        return web.json_response({"status": "acknowledged", "message": "Retry not yet implemented"})
+    except Exception as e:
+        return web.json_response({"error": str(e)}, status=500)
+
+
+@PromptServer.instance.routes.post('/instaraw/batch_gen_stop')
+async def stop_generation(request):
+    """Endpoint to stop ongoing generation."""
+    try:
+        data = await request.json()
+        node_id = data.get("node_id")
+
+        tracker = ProgressTracker()
+        tracker.stop_generation(node_id)
+
+        return web.json_response({"status": "stopped", "node_id": node_id})
+    except Exception as e:
+        return web.json_response({"error": str(e)}, status=500)
+
+
+@PromptServer.instance.routes.get('/instaraw/batch_gen_image/{subdir}/{filename}')
+async def serve_batch_gen_image(request):
+    """
+    Serve images from ComfyUI's output folder (backwards compatibility endpoint).
+    URL format: /instaraw/batch_gen_image/{subdir}/{filename}
+    Example: /instaraw/batch_gen_image/batch_gen/image_0_123456.png
+    Note: Prefer using /view?filename=...&subfolder=...&type=output instead.
+    """
+    try:
+        import folder_paths
+        subdir = request.match_info.get('subdir', 'batch_gen')
+        filename = request.match_info.get('filename', '')
+
+        # Security: prevent directory traversal
+        if '..' in subdir or '..' in filename or '/' in filename or '\\' in filename:
+            return web.Response(status=403, text="Invalid path")
+
+        # Build path to ComfyUI's output folder
+        comfy_output = folder_paths.get_output_directory()
+        filepath = os.path.join(comfy_output, subdir, filename)
+
+        if not os.path.exists(filepath):
+            print(f"[BIG] Image not found: {filepath}", flush=True)
+            return web.Response(status=404, text="Image not found")
+
+        # Determine content type
+        ext = os.path.splitext(filename)[1].lower()
+        content_types = {
+            '.png': 'image/png',
+            '.jpg': 'image/jpeg',
+            '.jpeg': 'image/jpeg',
+            '.gif': 'image/gif',
+            '.webp': 'image/webp',
+        }
+        content_type = content_types.get(ext, 'application/octet-stream')
+
+        # Read and return file
+        with open(filepath, 'rb') as f:
+            data = f.read()
+
+        return web.Response(body=data, content_type=content_type)
+    except Exception as e:
+        print(f"[BIG] Error serving image: {e}", flush=True)
+        return web.Response(status=500, text=str(e))
+
+
+def _generate_multi_image(
+    engine,
+    api_key: str,
+    provider: str,
+    model: str,
+    prompt_positive: str,
+    prompt_negative: str,
+    aspect_ratio: str,
+    resolution: str,
+    width: int,
+    height: int,
+    use_negative_prompt: bool,
+    input_image,  # Optional tensor for I2I (image_1)
+    input_image2,  # Optional tensor (image_2 - clothes etc)
+    input_image3,  # Optional tensor (image_3 - background etc)
+    input_image4,  # Optional tensor (image_4 - pose etc)
+    filename_prefix: str,
+    job_ids: list,
+    use_cache: bool = True,
+    cache_keys: list = None,
+    node_id: str = None,
+) -> dict:
+    """
+    Generate 2 images in a single API call (Wavespeed.ai multi-image mode).
+    Returns dict with both image results for 50% cost savings.
+    """
+    from ..api_nodes.generative_api_nodes import MODEL_CONFIG
+    import base64 as b64
+
+    start_time = time.time()
+
+    # Multi-image endpoint only supports these aspect ratios
+    MULTI_SUPPORTED_RATIOS = ["3:2", "2:3", "3:4", "4:3"]
+
+    # Map unsupported ratios to closest supported one
+    RATIO_MAPPING = {
+        "1:1": "3:4",      # Square -> Portrait (closest)
+        "16:9": "3:2",     # Wide landscape -> Landscape
+        "9:16": "2:3",     # Tall portrait -> Portrait
+        "4:5": "3:4",      # Portrait -> Portrait
+        "5:4": "4:3",      # Landscape -> Landscape
+        "21:9": "3:2",     # Ultra-wide -> Landscape
+    }
+
+    # Validate and map aspect ratio
+    original_aspect = aspect_ratio
+    if aspect_ratio not in MULTI_SUPPORTED_RATIOS:
+        aspect_ratio = RATIO_MAPPING.get(aspect_ratio, "3:2")  # Default to 3:2 if unknown
+        print(f"[BIG] โš ๏ธ Multi-image: Aspect ratio '{original_aspect}' not supported, using '{aspect_ratio}' instead", flush=True)
+
+    # Build prompt
+    if use_negative_prompt and prompt_negative.strip():
+        if model in ["Nano Banana Pro", "Nano Banana"]:
+            full_prompt = f"{prompt_positive}\n\nAvoid: {prompt_negative}"
+        else:
+            full_prompt = prompt_positive
+    else:
+        full_prompt = prompt_positive
+
+    # Get model config
+    model_conf = MODEL_CONFIG.get(model)
+    if not model_conf:
+        raise Exception(f"Invalid model: {model}")
+
+    provider_conf = model_conf["providers"].get(provider)
+    if not provider_conf:
+        raise Exception(f"Provider '{provider}' not supported for {model}")
+
+    # Determine endpoint - use -multi suffix for multi-image mode
+    # Check if ANY image input is provided (not just image_1)
+    is_i2i = any(img is not None for img in [input_image, input_image2, input_image3, input_image4])
+    base_endpoint = provider_conf["i2i_endpoint"] if is_i2i else provider_conf["t2i_endpoint"]
+    # Wavespeed.ai multi-image endpoints require -multi suffix
+    endpoint = f"{base_endpoint}-multi"
+    build_payload_func = provider_conf["build_payload"]
+
+    # Build kwargs for payload
+    kwargs = {
+        "prompt": full_prompt,
+        "aspect_ratio": aspect_ratio,
+        "width": width,
+        "height": height,
+        "resolution": resolution,
+        "num_images": 2,  # Request 2 images
+    }
+    if input_image is not None:
+        kwargs["image_1"] = input_image
+    if input_image2 is not None:
+        kwargs["image_2"] = input_image2
+    if input_image3 is not None:
+        kwargs["image_3"] = input_image3
+    if input_image4 is not None:
+        kwargs["image_4"] = input_image4
+
+    engine.set_api_key(api_key)
+    payload = build_payload_func(engine, **kwargs)
+
+    print(f"[BIG] ๐ŸŽฏ Multi-image: Requesting 2 images from {provider}/{endpoint}", flush=True)
+
+    # Call API with return_all_outputs=True to get both images
+    image_urls = engine._submit_wavespeed(endpoint, payload, return_all_outputs=True)
+
+    if not image_urls or len(image_urls) < 2:
+        raise Exception(f"Expected 2 images but got {len(image_urls) if image_urls else 0}")
+
+    generation_time = time.time() - start_time
+    print(f"[BIG] ๐ŸŽฏ Multi-image: Got {len(image_urls)} images in {generation_time:.1f}s", flush=True)
+
+    # Process both images
+    results = []
+    for i, (image_url, job_id) in enumerate(zip(image_urls[:2], job_ids)):
+        try:
+            # Download image
+            response = requests.get(image_url, timeout=120)
+            response.raise_for_status()
+            img_pil = Image.open(io.BytesIO(response.content)).convert("RGB")
+            img_width, img_height = img_pil.size
+
+            # Convert to tensor
+            img_np = np.array(img_pil).astype(np.float32) / 255.0
+            tensor = torch.from_numpy(img_np)
+
+            # Save to cache for future use
+            if use_cache and cache_keys and i < len(cache_keys):
+                engine._save_to_cache(cache_keys[i], tensor)
+                print(f"[BIG] ๐Ÿ’พ Multi-image: Saved image {i+1} to cache", flush=True)
+
+            # Save to output folder
+            output_filename, subfolder = engine._save_to_output(tensor, job_id, prompt_positive, filename_prefix, node_id)
+
+            # Create base64 preview
+            buffer = io.BytesIO()
+            img_pil.save(buffer, format="JPEG", quality=85)
+            b64_data = b64.b64encode(buffer.getvalue()).decode('utf-8')
+            preview_url = f"data:image/jpeg;base64,{b64_data}"
+
+            results.append({
+                "job_id": job_id,
+                "status": "success",
+                "state": "success",
+                "image_url": preview_url,
+                "width": img_width,
+                "height": img_height,
+                "filename": output_filename,
+                "subfolder": subfolder,
+            })
+            print(f"[BIG] ๐ŸŽฏ Multi-image: Processed image {i+1} for job #{job_id}", flush=True)
+
+        except Exception as e:
+            print(f"[BIG] โš ๏ธ Multi-image: Failed to process image {i+1}: {e}", flush=True)
+            results.append({
+                "job_id": job_id,
+                "status": "failed",
+                "state": "failed",
+                "error": str(e),
+            })
+
+    return {
+        "status": "success",
+        "multi_image": True,
+        "generation_time": generation_time,
+        "results": results,
+        "attempts": 1,
+    }
+
+
+@PromptServer.instance.routes.post('/instaraw/batch_gen_generate_single')
+async def generate_single_image(request):
+    """
+    Endpoint for UI-triggered single image generation (RPG-style).
+    Generates image via API, saves to persistent output folder, returns metadata.
+    """
+    from PIL import Image
+    import io
+    import base64
+
+    try:
+        data = await request.json()
+
+        # Extract parameters
+        node_id = data.get("node_id")
+        job_id = data.get("job_id")
+        api_key = data.get("api_key")
+        provider = data.get("provider")
+        model = data.get("model")
+        prompt_positive = data.get("prompt_positive", "")
+        prompt_negative = data.get("prompt_negative", "")
+        seed = data.get("seed", -1)
+        aspect_ratio = data.get("aspect_ratio", "1:1")
+        width = data.get("width", 1024)
+        height = data.get("height", 1024)
+        resolution = data.get("resolution")
+        enable_safety = data.get("enable_safety_checker", True)
+        use_negative = data.get("use_negative_prompt", True)
+        max_retries = data.get("max_retries", 3)
+        timeout = data.get("timeout", 600)
+        use_cache = data.get("use_cache", True)
+        input_image_b64 = data.get("input_image")  # Base64 encoded if I2I (image_1)
+        input_image2_b64 = data.get("input_image2")  # Base64 encoded (image_2 - clothes etc)
+        input_image3_b64 = data.get("input_image3")  # Base64 encoded (image_3 - background etc)
+        input_image4_b64 = data.get("input_image4")  # Base64 encoded (image_4 - pose etc)
+        filename_prefix = str(data.get("filename_prefix", "INSTARAW"))  # Output filename prefix with optional subdir
+        multi_image = data.get("multi_image", False)  # Multi-image mode (Wavespeed.ai only)
+        second_job_id = data.get("second_job_id")  # ID for second image in multi-image mode
+
+        # Validate multi-image mode
+        if multi_image and provider != "wavespeed.ai":
+            print(f"[BIG] โš ๏ธ Multi-image mode only works with wavespeed.ai, ignoring for {provider}", flush=True)
+            multi_image = False
+
+        # Create engine instance
+        engine = BatchGeneratorEngine()
+        engine.api_key = api_key
+
+        # Create job
+        job = GenerationJob(
+            id=job_id,
+            prompt_positive=prompt_positive,
+            prompt_negative=prompt_negative,
+            seed=seed,
+        )
+
+        # Helper to decode base64 image to tensor
+        def decode_b64_image(b64_data, label="image"):
+            if not b64_data:
+                return None
+            print(f"[BIG] I2I mode: Decoding {label} (base64 length: {len(b64_data)})", flush=True)
+            img_bytes = base64.b64decode(b64_data)
+            pil_img = Image.open(io.BytesIO(img_bytes))
+            if pil_img.mode == "RGBA":
+                pil_img = pil_img.convert("RGB")
+            elif pil_img.mode != "RGB":
+                pil_img = pil_img.convert("RGB")
+            img_array = np.array(pil_img).astype(np.float32) / 255.0
+            tensor = torch.from_numpy(img_array)
+            print(f"[BIG] {label} tensor shape: {tensor.shape}", flush=True)
+            return tensor
+
+        # Handle input images if provided (I2I mode)
+        job.input_image = decode_b64_image(input_image_b64, "image_1")
+        job.input_image2 = decode_b64_image(input_image2_b64, "image_2")
+        job.input_image3 = decode_b64_image(input_image3_b64, "image_3")
+        job.input_image4 = decode_b64_image(input_image4_b64, "image_4")
+
+        # ===== MULTI-IMAGE MODE (Wavespeed.ai only, 2 images per call) =====
+        if multi_image and second_job_id is not None:
+            print(f"[BIG] ๐ŸŽฏ MULTI-IMAGE MODE: Generating 2 images for jobs #{job_id} and #{second_job_id}", flush=True)
+
+            # Check cache for both images before API call
+            input_images = [job.input_image, job.input_image2, job.input_image3, job.input_image4]
+            cache_key_0 = engine.compute_multi_cache_key(
+                prompt_positive, prompt_negative, seed, model, provider,
+                aspect_ratio, resolution or "", input_images, slot=0
+            )
+            cache_key_1 = engine.compute_multi_cache_key(
+                prompt_positive, prompt_negative, seed, model, provider,
+                aspect_ratio, resolution or "", input_images, slot=1
+            )
+
+            cached_0 = engine._check_cache(cache_key_0) if use_cache else None
+            cached_1 = engine._check_cache(cache_key_1) if use_cache else None
+
+            # If both images are cached, return them without API call
+            if cached_0 is not None and cached_1 is not None:
+                print(f"[BIG] ๐Ÿ“ฆ MULTI-IMAGE CACHE HIT: Both images found in cache!", flush=True)
+                results = []
+                for i, (cached_tensor, j_id, c_key) in enumerate([(cached_0, job_id, cache_key_0), (cached_1, second_job_id, cache_key_1)]):
+                    # Create base64 preview
+                    b64_url = engine._tensor_to_base64(cached_tensor)
+                    img_height = cached_tensor.shape[0] if cached_tensor.ndim == 3 else cached_tensor.shape[1]
+                    img_width = cached_tensor.shape[1] if cached_tensor.ndim == 3 else cached_tensor.shape[2]
+
+                    # Save to output folder
+                    output_filename, subfolder = engine._save_to_output(cached_tensor, j_id, prompt_positive, filename_prefix, node_id)
+
+                    results.append({
+                        "job_id": j_id,
+                        "status": "success",
+                        "state": "cached",
+                        "image_url": b64_url,
+                        "width": img_width,
+                        "height": img_height,
+                        "filename": output_filename,
+                        "subfolder": subfolder,
+                        "cache_hit": True,
+                    })
+                    print(f"[BIG] ๐Ÿ“ฆ Cached image {i+1} for job #{j_id}", flush=True)
+
+                return web.json_response({
+                    "status": "success",
+                    "multi_image": True,
+                    "results": results,
+                })
+
+            try:
+                result = await asyncio.to_thread(
+                    _generate_multi_image,
+                    engine=engine,
+                    api_key=api_key,
+                    provider=provider,
+                    model=model,
+                    prompt_positive=prompt_positive,
+                    prompt_negative=prompt_negative,
+                    aspect_ratio=aspect_ratio,
+                    resolution=resolution,
+                    width=width,
+                    height=height,
+                    use_negative_prompt=use_negative,
+                    input_image=job.input_image,
+                    input_image2=job.input_image2,
+                    input_image3=job.input_image3,
+                    input_image4=job.input_image4,
+                    filename_prefix=filename_prefix,
+                    job_ids=[job_id, second_job_id],
+                    use_cache=use_cache,
+                    cache_keys=[cache_key_0, cache_key_1],
+                    node_id=node_id,
+                )
+                return web.json_response(result)
+            except Exception as e:
+                import traceback
+                traceback.print_exc()
+                return web.json_response({
+                    "status": "failed",
+                    "job_id": job_id,
+                    "second_job_id": second_job_id,
+                    "error": str(e),
+                    "multi_image": True,
+                })
+
+        # ===== SINGLE IMAGE MODE (standard) =====
+        # Generate image using existing logic
+        # IMPORTANT: Use asyncio.to_thread() to run blocking I/O in thread pool
+        # This enables true parallel generation - without it, requests.post() blocks the event loop
+        print(f"[BIG] ๐Ÿš€ Job #{job_id} starting API call (parallel execution enabled)", flush=True)
+        result_job = await asyncio.to_thread(
+            engine.generate_job,
+            job=job,
+            api_key=api_key,
+            provider=provider,
+            model=model,
+            aspect_ratio=aspect_ratio,
+            resolution=resolution,
+            width=width,
+            height=height,
+            enable_safety_checker=enable_safety,
+            use_negative_prompt=use_negative,
+            max_retries=max_retries,
+            timeout=timeout,
+            use_cache=use_cache,
+        )
+
+        print(f"[BIG] Generation completed. State: {result_job.state}", flush=True)
+        print(f"[BIG] result_image_url: {result_job.result_image_url}", flush=True)
+        print(f"[BIG] result_image_width: {result_job.result_image_width}, height: {result_job.result_image_height}", flush=True)
+        print(f"[BIG] result_tensor is None: {result_job.result_tensor is None}", flush=True)
+
+        if result_job.state == JobState.SUCCESS or result_job.state == JobState.CACHED:
+            # Use the base64 URL for preview (most reliable)
+            image_url = result_job.result_image_url
+            width = result_job.result_image_width or 0
+            height = result_job.result_image_height or 0
+
+            # IMPORTANT: Save to output folder for permanent storage
+            output_filename = None
+            subfolder = None
+            if result_job.result_tensor is not None:
+                output_filename, subfolder = engine._save_to_output(
+                    result_job.result_tensor,
+                    job_id,
+                    prompt_positive,
+                    filename_prefix,
+                    node_id
+                )
+                if output_filename:
+                    print(f"[BIG] โœ… Image permanently saved: {subfolder}/{output_filename}" if subfolder else f"[BIG] โœ… Image permanently saved: {output_filename}", flush=True)
+            else:
+                print(f"[BIG] โš ๏ธ No tensor available for output save", flush=True)
+
+            return web.json_response({
+                "status": "success",
+                "job_id": job_id,
+                "state": result_job.state.value,
+                "image_url": image_url,
+                "width": width,
+                "height": height,
+                "generation_time": result_job.generation_time,
+                "cache_hit": result_job.cache_hit,
+                "filename": output_filename,
+                "subfolder": subfolder,
+                "attempts": result_job.attempts,  # How many retries it took
+            })
+        else:
+            # Failed
+            return web.json_response({
+                "status": "failed",
+                "job_id": job_id,
+                "state": result_job.state.value,
+                "error": result_job.error or "Generation failed",
+                "attempts": result_job.attempts,  # How many retries were attempted
+            })
+
+    except Exception as e:
+        import traceback
+        traceback.print_exc()
+        return web.json_response({"error": str(e)}, status=500)
+
+
+@dataclass
+class GenerationJob:
+    """Represents a single image generation job."""
+    id: int
+    prompt_positive: str
+    prompt_negative: str
+    seed: int
+    state: JobState = JobState.PENDING
+    input_image: Optional[torch.Tensor] = None  # image_1
+    input_image2: Optional[torch.Tensor] = None  # image_2 (e.g. clothes)
+    input_image3: Optional[torch.Tensor] = None  # image_3 (e.g. background)
+    input_image4: Optional[torch.Tensor] = None  # image_4 (e.g. pose)
+    result_tensor: Optional[torch.Tensor] = None
+    result_image_url: Optional[str] = None  # File URL or base64 data URL for preview
+    result_image_b64: Optional[str] = None  # Base64 data URL (more reliable)
+    result_image_width: int = 0  # Width of generated image
+    result_image_height: int = 0  # Height of generated image
+    result_filename: Optional[str] = None  # Filename for persistent storage
+    error: Optional[str] = None
+    attempts: int = 0
+    generation_time: float = 0.0
+    cache_hit: bool = False
+
+
+HIDDEN = {
+    "prompt": "PROMPT",
+    "extra_pnginfo": "EXTRA_PNGINFO",
+    "uid": "UNIQUE_ID",
+    # node_identifier comes from JS widget (stable ID for file paths)
+    # NOT using "NID" since that changes per execution
+    "node_identifier": ("STRING", {"default": ""}),
+    "generated_batch_data": ("STRING", {"default": "[]"}),
+}
+
+
+class BatchGeneratorEngine(INSTARAW_GenerativeAPIBase):
+    """
+    High-performance generation engine with retry logic and caching.
+    """
+
+    def __init__(self):
+        super().__init__()
+        self._cache_dir = None
+
+    @property
+    def cache_dir(self):
+        if self._cache_dir is None:
+            self._cache_dir = os.path.join(os.path.dirname(__file__), "..", "..", "cache", "batch_gen")
+            os.makedirs(self._cache_dir, exist_ok=True)
+        return self._cache_dir
+
+    def _compute_cache_key(self, job: GenerationJob, model: str, provider: str,
+                          aspect_ratio: str, resolution: str, has_input_images: bool) -> str:
+        """Compute unique cache key for a generation job."""
+        hasher = hashlib.sha256()
+        hasher.update(job.prompt_positive.encode("utf-8"))
+        hasher.update(job.prompt_negative.encode("utf-8"))
+        hasher.update(str(job.seed).encode("utf-8"))
+        hasher.update(model.encode("utf-8"))
+        hasher.update(provider.encode("utf-8"))
+        hasher.update(aspect_ratio.encode("utf-8"))
+        if resolution:
+            hasher.update(resolution.encode("utf-8"))
+        # Include all input images in cache key
+        if has_input_images:
+            for i, img in enumerate([job.input_image, job.input_image2, job.input_image3, job.input_image4]):
+                if img is not None:
+                    hasher.update(f"image_{i+1}:".encode("utf-8"))
+                    hasher.update(img.cpu().numpy().tobytes())
+        return hasher.hexdigest()
+
+    def compute_multi_cache_key(self, prompt_positive: str, prompt_negative: str, seed: int,
+                                model: str, provider: str, aspect_ratio: str, resolution: str,
+                                input_images: list, slot: int) -> str:
+        """Compute cache key for multi-image generation (includes slot 0 or 1)."""
+        hasher = hashlib.sha256()
+        hasher.update(prompt_positive.encode("utf-8"))
+        hasher.update(prompt_negative.encode("utf-8"))
+        hasher.update(str(seed).encode("utf-8"))
+        hasher.update(model.encode("utf-8"))
+        hasher.update(provider.encode("utf-8"))
+        hasher.update(aspect_ratio.encode("utf-8"))
+        hasher.update(f"multi_slot:{slot}".encode("utf-8"))  # Include slot number
+        if resolution:
+            hasher.update(resolution.encode("utf-8"))
+        # Include all input images in cache key
+        for i, img in enumerate(input_images):
+            if img is not None:
+                hasher.update(f"image_{i+1}:".encode("utf-8"))
+                hasher.update(img.cpu().numpy().tobytes())
+        return hasher.hexdigest()
+
+    def _check_cache(self, cache_key: str) -> Optional[torch.Tensor]:
+        """Check if generation result exists in cache."""
+        cache_file = os.path.join(self.cache_dir, f"{cache_key}.png")
+        if os.path.exists(cache_file):
+            try:
+                img = Image.open(cache_file).convert("RGB")
+                img_np = np.array(img).astype(np.float32) / 255.0
+                return torch.from_numpy(img_np)
+            except Exception:
+                pass
+        return None
+
+    def _save_to_cache(self, cache_key: str, tensor: torch.Tensor):
+        """Save generation result to cache."""
+        try:
+            cache_file = os.path.join(self.cache_dir, f"{cache_key}.png")
+            img_np = tensor.cpu().numpy()
+            if img_np.max() <= 1.0:
+                img_np = (img_np * 255).astype(np.uint8)
+            img_pil = Image.fromarray(img_np)
+            img_pil.save(cache_file, "PNG")
+        except Exception as e:
+            print(f"   โš ๏ธ Cache save failed: {e}", flush=True)
+
+    def _save_temp_preview(self, tensor: torch.Tensor, job_id: int) -> Optional[str]:
+        """Save image to ComfyUI temp folder for preview in custom UI."""
+        try:
+            from folder_paths import get_temp_directory
+
+            temp_dir = os.path.join(get_temp_directory(), "batch_gen")
+            os.makedirs(temp_dir, exist_ok=True)
+
+            # Generate unique filename with timestamp
+            timestamp = int(time.time() * 1000)
+            filename = f"job_{job_id}_{timestamp}.png"
+            filepath = os.path.join(temp_dir, filename)
+
+            # Convert tensor to numpy array
+            img_np = tensor.cpu().numpy()
+
+            # Handle different tensor shapes
+            if img_np.ndim == 4:
+                img_np = img_np[0]
+
+            # Ensure correct value range
+            if img_np.max() <= 1.0:
+                img_np = (img_np * 255)
+
+            # Ensure uint8 dtype for PIL
+            img_np = img_np.astype(np.uint8)
+
+            # Create PIL image
+            img_pil = Image.fromarray(img_np)
+
+            # Save image to disk
+            img_pil.save(filepath, "PNG", compress_level=4)
+
+            # Verify file exists
+            if not os.path.exists(filepath):
+                print(f"   โŒ File not found after save: {filepath}", flush=True)
+                return None
+
+            print(f"   โœ“ Saved temp preview: {filename}", flush=True)
+            return filepath
+        except Exception as e:
+            print(f"   โš ๏ธ Temp preview save failed: {e}", flush=True)
+            import traceback
+            traceback.print_exc()
+            return None
+
+    def _tensor_to_base64(self, tensor: torch.Tensor) -> Optional[str]:
+        """Convert tensor to base64 data URL for reliable preview."""
+        try:
+            img_np = tensor.cpu().numpy()
+
+            # Handle different tensor shapes
+            if img_np.ndim == 4:
+                img_np = img_np[0]
+
+            # Ensure correct value range
+            if img_np.max() <= 1.0:
+                img_np = (img_np * 255)
+
+            img_np = img_np.astype(np.uint8)
+            img_pil = Image.fromarray(img_np)
+
+            # Convert to base64 JPEG (smaller than PNG, faster)
+            buffer = io.BytesIO()
+            img_pil.save(buffer, format="JPEG", quality=85)
+            b64_data = base64.b64encode(buffer.getvalue()).decode('utf-8')
+
+            return f"data:image/jpeg;base64,{b64_data}"
+        except Exception as e:
+            print(f"   โš ๏ธ Base64 conversion failed: {e}", flush=True)
+            return None
+
+    def _save_to_output(self, tensor: torch.Tensor, job_id: int, prompt: str, filename_prefix: str = "INSTARAW", node_id: str = None) -> tuple[Optional[str], Optional[str]]:
+        """
+        Save image to ComfyUI's main output folder for permanent storage.
+        Returns tuple of (filename, subfolder) if successful, (None, None) otherwise.
+
+        Args:
+            tensor: Image tensor to save
+            job_id: Job ID (kept for API compatibility)
+            prompt: Prompt text (kept for API compatibility)
+            filename_prefix: Prefix with optional subdirectory (e.g., "INSTARAW" or "my_folder/my_prefix")
+            node_id: Node ID (kept for API compatibility)
+
+        Filename format: {prefix}_{ULID}.png
+        Examples:
+            - filename_prefix="INSTARAW" โ†’ output/INSTARAW_01HGX5J8K2P3.png
+            - filename_prefix="my_folder/my_prefix" โ†’ output/my_folder/my_prefix_01HGX5J8K2P3.png
+            - filename_prefix="just_prefix" โ†’ output/just_prefix_01HGX5J8K2P3.png
+        """
+        try:
+            # Use ComfyUI's main output directory (works with /view endpoint)
+            import folder_paths
+            comfy_output = folder_paths.get_output_directory()
+
+            # Parse prefix to extract subdirectory and filename prefix
+            # e.g., "my_folder/my_prefix" โ†’ subdir="my_folder", prefix="my_prefix"
+            # e.g., "just_prefix" โ†’ subdir="", prefix="just_prefix"
+            if "/" in filename_prefix:
+                subdir, prefix = filename_prefix.rsplit("/", 1)
+                output_dir = os.path.join(comfy_output, subdir)
+            else:
+                subdir = ""
+                prefix = filename_prefix
+                output_dir = comfy_output
+
+            os.makedirs(output_dir, exist_ok=True)
+
+            # Generate filename using ULID (unique, sortable, no conflicts)
+            ulid = generate_ulid()
+            filename = f"{prefix}_{ulid}.png"
+            filepath = os.path.join(output_dir, filename)
+
+            # Convert tensor to image
+            img_np = tensor.cpu().numpy()
+            if img_np.ndim == 4:
+                img_np = img_np[0]
+            if img_np.max() <= 1.0:
+                img_np = (img_np * 255)
+            img_np = img_np.astype(np.uint8)
+
+            img_pil = Image.fromarray(img_np)
+            img_pil.save(filepath, "PNG")
+
+            print(f"   ๐Ÿ’พ Saved to output: {subdir}/{filename}" if subdir else f"   ๐Ÿ’พ Saved to output: {filename}", flush=True)
+            return filename, subdir
+        except Exception as e:
+            print(f"   โš ๏ธ Output save failed: {e}", flush=True)
+            import traceback
+            traceback.print_exc()
+            return None, None
+
+    def generate_job(
+        self,
+        job: GenerationJob,
+        api_key: str,
+        provider: str,
+        model: str,
+        aspect_ratio: str,
+        resolution: Optional[str],
+        width: int,
+        height: int,
+        enable_safety_checker: bool,
+        use_negative_prompt: bool,
+        max_retries: int,
+        timeout: int,
+        use_cache: bool,
+        on_retry_callback: Optional[callable] = None,
+        progress_tracker: Optional['ProgressTracker'] = None,
+        node_identifier: Optional[int] = None,
+        filename_prefix: str = "INSTARAW",
+    ) -> GenerationJob:
+        """
+        Execute a generation job with retry logic.
+
+        Returns the updated job with result or error.
+        """
+        start_time = time.time()
+        job.state = JobState.GENERATING
+
+        # Check cache first - consider all input images
+        has_input = any(img is not None for img in [job.input_image, job.input_image2, job.input_image3, job.input_image4])
+        cache_key = self._compute_cache_key(job, model, provider, aspect_ratio, resolution or "", has_input)
+
+        if use_cache:
+            cached = self._check_cache(cache_key)
+            if cached is not None:
+                job.state = JobState.CACHED
+                job.result_tensor = cached
+                job.cache_hit = True
+                job.generation_time = time.time() - start_time
+
+                # Create base64 preview for cached images
+                try:
+                    print(f"   ๐Ÿ“ฆ CACHED! Tensor shape: {cached.shape}, dtype: {cached.dtype}", flush=True)
+                    # Get dimensions from tensor
+                    if cached.ndim == 4:  # [batch, height, width, channels]
+                        job.result_image_height = cached.shape[1]
+                        job.result_image_width = cached.shape[2]
+                    elif cached.ndim == 3:  # [height, width, channels]
+                        job.result_image_height = cached.shape[0]
+                        job.result_image_width = cached.shape[1]
+
+                    # Use base64 for reliable preview
+                    b64_url = self._tensor_to_base64(cached)
+                    if b64_url:
+                        job.result_image_url = b64_url
+                        job.result_image_b64 = b64_url
+                        print(f"   ๐Ÿ“ธ Cached preview (base64): {job.result_image_width}x{job.result_image_height}", flush=True)
+                    else:
+                        print(f"   โŒ Base64 conversion failed for cached image!", flush=True)
+                except Exception as e:
+                    print(f"   โš ๏ธ Failed to create cached preview: {e}", flush=True)
+                    import traceback
+                    traceback.print_exc()
+
+                return job
+
+        # Build generation kwargs
+        # Combine positive and negative prompts if enabled
+        if use_negative_prompt and job.prompt_negative.strip():
+            # Some models support negative prompts in the prompt itself
+            # For Nano Banana Pro, we append as "Avoid: ..." since it doesn't have native negative prompt
+            full_prompt = job.prompt_positive
+            if model in ["Nano Banana Pro", "Nano Banana"]:
+                # Gemini-based models - append negative as guidance
+                full_prompt = f"{job.prompt_positive}\n\nAvoid: {job.prompt_negative}"
+            else:
+                # For other models, we might have native support - check later
+                full_prompt = job.prompt_positive
+        else:
+            full_prompt = job.prompt_positive
+
+        kwargs = {
+            "api_key": api_key,
+            "provider": provider,
+            "model": model,
+            "prompt": full_prompt,
+            "seed": job.seed,
+            "aspect_ratio": aspect_ratio,
+            "width": width,
+            "height": height,
+            "enable_safety_checker": enable_safety_checker,
+        }
+        if resolution:
+            kwargs["resolution"] = resolution
+        if job.input_image is not None:
+            kwargs["image_1"] = job.input_image
+        if job.input_image2 is not None:
+            kwargs["image_2"] = job.input_image2
+        if job.input_image3 is not None:
+            kwargs["image_3"] = job.input_image3
+        if job.input_image4 is not None:
+            kwargs["image_4"] = job.input_image4
+
+        # Get model config
+        model_conf = MODEL_CONFIG.get(model)
+        if not model_conf:
+            job.state = JobState.FAILED
+            job.error = f"Invalid model: {model}"
+            return job
+
+        provider_conf = model_conf["providers"].get(provider)
+        if not provider_conf:
+            job.state = JobState.FAILED
+            job.error = f"Provider '{provider}' not supported for {model}"
+            return job
+
+        # Determine endpoint - check if ANY image input is provided
+        is_i2i = any(img is not None for img in [job.input_image, job.input_image2, job.input_image3, job.input_image4])
+        endpoint = provider_conf["i2i_endpoint"] if is_i2i else provider_conf["t2i_endpoint"]
+        build_payload_func = provider_conf["build_payload"]
+
+        # Count how many images are provided
+        image_count = sum(1 for img in [job.input_image, job.input_image2, job.input_image3, job.input_image4] if img is not None)
+        print(f"   ๐Ÿ“Œ Job #{job.id}: Mode={'I2I (Edit)' if is_i2i else 'T2I'}, Images={image_count}, Endpoint={endpoint}", flush=True)
+        if job.input_image is not None:
+            print(f"   ๐Ÿ“Œ Job #{job.id}: image_1 shape={job.input_image.shape}", flush=True)
+        if job.input_image2 is not None:
+            print(f"   ๐Ÿ“Œ Job #{job.id}: image_2 shape={job.input_image2.shape}", flush=True)
+        if job.input_image3 is not None:
+            print(f"   ๐Ÿ“Œ Job #{job.id}: image_3 shape={job.input_image3.shape}", flush=True)
+        if job.input_image4 is not None:
+            print(f"   ๐Ÿ“Œ Job #{job.id}: image_4 shape={job.input_image4.shape}", flush=True)
+
+        self.set_api_key(api_key)
+        payload = build_payload_func(self, **kwargs)
+
+        # Log payload keys to verify image is included (don't log actual data for security)
+        print(f"   ๐Ÿ“Œ Job #{job.id}: Payload keys={list(payload.keys())}", flush=True)
+        if "image_urls" in payload:
+            print(f"   ๐Ÿ“Œ Job #{job.id}: image_urls count={len(payload['image_urls'])}, first type={type(payload['image_urls'][0]) if payload['image_urls'] else 'N/A'}", flush=True)
+        elif "images" in payload:
+            print(f"   ๐Ÿ“Œ Job #{job.id}: images count={len(payload['images'])}, first type={type(payload['images'][0]) if payload['images'] else 'N/A'}", flush=True)
+
+        # Retry loop with exponential backoff
+        last_error = None
+        for attempt in range(max_retries):
+            # Check if we should stop before each retry attempt
+            if progress_tracker and node_identifier is not None:
+                if progress_tracker.should_stop(node_identifier):
+                    job.state = JobState.FAILED
+                    job.error = "Stopped by user"
+                    print(f"   โน๏ธ  Job #{job.id} STOPPED in retry loop (attempt {attempt})", flush=True)
+                    return job
+
+            job.attempts = attempt + 1
+            print(f"   ๐Ÿ”„ Job #{job.id} attempt {attempt + 1}/{max_retries}", flush=True)
+
+            try:
+                # Check before making API request
+                if progress_tracker and node_identifier is not None:
+                    if progress_tracker.should_stop(node_identifier):
+                        job.state = JobState.FAILED
+                        job.error = "Stopped by user"
+                        print(f"   โน๏ธ  Job #{job.id} STOPPED before API call (attempt {attempt + 1})", flush=True)
+                        return job
+
+                print(f"   ๐ŸŒ Job #{job.id} making API call to {provider}...", flush=True)
+                if provider == "fal.ai":
+                    image_url = self._submit_fal_sync(endpoint, payload, timeout)
+                else:
+                    image_url = self._submit_wavespeed(endpoint, payload)
+
+                # Download image
+                image_response = requests.get(image_url, timeout=120)
+                image_response.raise_for_status()
+                image_pil = Image.open(io.BytesIO(image_response.content)).convert("RGB")
+
+                # Store dimensions
+                job.result_image_width, job.result_image_height = image_pil.size
+
+                image_np = np.array(image_pil).astype(np.float32) / 255.0
+                tensor = torch.from_numpy(image_np)
+
+                # Success!
+                job.state = JobState.SUCCESS
+                job.result_tensor = tensor
+                job.generation_time = time.time() - start_time
+
+                # Save to cache
+                if use_cache:
+                    self._save_to_cache(cache_key, tensor)
+
+                # Save to persistent output folder (for generated_batch_data compatibility)
+                filename, subfolder = self._save_to_output(tensor, job.id, job.prompt_positive, filename_prefix, node_identifier)
+                if filename:
+                    job.result_filename = filename
+                    job.result_subfolder = subfolder
+
+                # Create base64 preview URL (most reliable - no file system timing issues)
+                b64_url = self._tensor_to_base64(tensor)
+                if b64_url:
+                    job.result_image_url = b64_url
+                    job.result_image_b64 = b64_url
+                    print(f"   ๐Ÿ“ธ Created base64 preview for job {job.id} ({len(b64_url)} chars)", flush=True)
+                else:
+                    # Fallback to file-based preview
+                    temp_image_path = self._save_temp_preview(tensor, job.id)
+                    if temp_image_path:
+                        job.result_image_url = f"/view?filename={os.path.basename(temp_image_path)}&subfolder=batch_gen&type=temp"
+                        print(f"   ๐Ÿ“ธ Preview URL (file): {job.result_image_url}", flush=True)
+                    else:
+                        print(f"   โš ๏ธ Failed to generate preview for job {job.id}", flush=True)
+
+                return job
+
+            except Exception as e:
+                last_error = str(e)
+
+                # Check if it's a retryable error
+                is_moderation = any(x in last_error.lower() for x in [
+                    "422", "content", "policy", "flagged", "safety", "moderation"
+                ])
+                is_timeout = "timeout" in last_error.lower()
+                is_rate_limit = "429" in last_error or "rate" in last_error.lower()
+
+                # Log the error prominently
+                error_type = "๐Ÿšซ CONTENT POLICY" if is_moderation else "โฑ๏ธ TIMEOUT" if is_timeout else "๐Ÿ”„ RATE LIMIT" if is_rate_limit else "โŒ ERROR"
+                print(f"   {error_type} Job #{job.id} attempt {attempt + 1}/{max_retries}: {last_error[:100]}", flush=True)
+
+                if attempt < max_retries - 1:
+                    # Mark as retrying and notify UI
+                    job.state = JobState.RETRYING
+                    # Include error type in message for UI
+                    error_msg = "Content policy violation" if is_moderation else "Rate limited" if is_rate_limit else "Timeout" if is_timeout else last_error[:50]
+                    job.error = f"Retry {attempt + 1}: {error_msg}"
+                    if on_retry_callback:
+                        on_retry_callback(job)
+
+                    if is_moderation:
+                        # Quick retry for moderation - sometimes just resubmitting works
+                        wait_time = 0.5 + (attempt * 0.5)
+                        print(f"   โณ Job #{job.id} waiting {wait_time:.1f}s before retry (moderation bypass)...", flush=True)
+                    elif is_rate_limit:
+                        # Longer wait for rate limits
+                        wait_time = 5 + (attempt * 5)
+                        print(f"   โณ Job #{job.id} waiting {wait_time}s before retry (rate limit)...", flush=True)
+                    elif is_timeout:
+                        # Medium wait for timeouts
+                        wait_time = 2 + (attempt * 2)
+                        print(f"   โณ Job #{job.id} waiting {wait_time}s before retry (timeout)...", flush=True)
+                    else:
+                        # Standard exponential backoff
+                        wait_time = 2 ** attempt
+                        print(f"   โณ Job #{job.id} waiting {wait_time}s before retry...", flush=True)
+
+                    time.sleep(wait_time)
+
+                    # Reset state back to generating for next attempt
+                    job.state = JobState.GENERATING
+
+        # All retries exhausted
+        job.state = JobState.FAILED
+        job.error = last_error
+        job.generation_time = time.time() - start_time
+        return job
+
+    def _submit_fal_sync(self, endpoint: str, payload: dict, timeout: int) -> str:
+        """Submit synchronous request to fal.ai."""
+        url = f"https://fal.run/{endpoint}"
+        headers = {
+            "Authorization": f"Key {self.api_key}",
+            "Content-Type": "application/json",
+        }
+        response = requests.post(url, json=payload, headers=headers, timeout=timeout)
+
+        if not response.ok:
+            error_text = response.text[:300]
+            raise Exception(f"fal.ai error ({response.status_code}): {error_text}")
+
+        result = response.json()
+        if "images" in result and len(result["images"]) > 0:
+            return result["images"][0]["url"]
+        raise Exception("No image URL in fal.ai response")
+
+
+class INSTARAW_BatchImageGenerator(PreviewImage):
+    """
+    ๐ŸŽจ Interactive Batch Image Generator
+
+    Premium parallel image generation with:
+    โ€ข Real-time progress tracking
+    โ€ข Smart auto-retry (bypasses moderation filters)
+    โ€ข Interactive selection UI
+    โ€ข Full RPG integration
+    โ€ข Reference image support (I2I)
+    """
+
+    RETURN_TYPES = ("IMAGE", "STRING", "STRING", "INT", "INT")
+    RETURN_NAMES = ("images", "prompt_list_positive", "indexes", "count", "failed_count")
+    OUTPUT_IS_LIST = (True, True, False, False, False)
+    INPUT_IS_LIST = True
+    FUNCTION = "generate"
+    CATEGORY = "INSTARAW/Interactive"
+    OUTPUT_NODE = False
+    DESCRIPTION = "Parallel batch image generator with auto-retry and interactive selection"
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "api_key": ("STRING", {"forceInput": True}),
+                "provider": ("STRING", {"forceInput": True}),
+                "model": ("STRING", {"forceInput": True}),
+                "prompt_list_positive": ("STRING", {"forceInput": True,
+                    "tooltip": "Positive prompts from RPG"}),
+                "aspect_ratio": ("STRING", {"forceInput": True}),
+                "max_parallel": ("INT", {"default": 5, "min": 1, "max": 100,
+                    "tooltip": "Maximum concurrent API requests"}),
+                "max_retries": ("INT", {"default": 3, "min": 1, "max": 10,
+                    "tooltip": "Retry attempts per job (helps bypass moderation)"}),
+                "timeout": ("INT", {"default": 600, "min": 60, "max": 3600,
+                    "tooltip": "Selection UI timeout in seconds"}),
+            },
+            "optional": {
+                "prompt_list_negative": ("STRING", {"forceInput": True,
+                    "tooltip": "Negative prompts from RPG"}),
+                "seed_list": ("INT", {"forceInput": True,
+                    "tooltip": "Seeds from RPG"}),
+                "width": ("INT", {"forceInput": True}),
+                "height": ("INT", {"forceInput": True}),
+                "resolution": ("STRING", {"forceInput": True,
+                    "tooltip": "Resolution tier (1K, 2K, 4K) for Nano Banana Pro"}),
+                "images": ("IMAGE", {"forceInput": True,
+                    "tooltip": "Reference images for I2I mode (image_1)"}),
+                "images2": ("IMAGE", {"forceInput": True,
+                    "tooltip": "Additional reference images (image_2) - e.g. clothes, accessories"}),
+                "images3": ("IMAGE", {"forceInput": True,
+                    "tooltip": "Additional reference images (image_3) - e.g. background, style"}),
+                "images4": ("IMAGE", {"forceInput": True,
+                    "tooltip": "Additional reference images (image_4) - e.g. pose, composition"}),
+                "enable_img2img": ("BOOLEAN", {"default": False,
+                    "tooltip": "Enable image-to-image mode. When True, images connected to image inputs will be used as references."}),
+                "use_negative_prompt": ("BOOLEAN", {"default": True,
+                    "tooltip": "Include negative prompts in generation"}),
+                "auto_retry": ("BOOLEAN", {"default": True,
+                    "tooltip": "Automatically retry failed generations"}),
+                "use_cache": ("BOOLEAN", {"default": True,
+                    "tooltip": "Cache successful generations"}),
+                "enable_safety_checker": ("BOOLEAN", {"default": True}),
+                "multi_image": ("BOOLEAN", {
+                    "default": False,
+                    "tooltip": "โšก Wavespeed.ai ONLY: Generate 2 images per API call for 50% cost savings ($0.07/img instead of $0.14/img). Same subject, slightly different variations."
+                }),
+                "bypass_filter": ("BOOLEAN", {
+                    "default": False,
+                    "tooltip": "Skip the image selection popup and automatically output all generated images. Useful for automated workflows."
+                }),
+                "filename_prefix": ("STRING", {
+                    "default": "INSTARAW",
+                    "tooltip": "Output filename prefix. Can include subdirectories (e.g., 'my_folder/my_prefix'). Files saved as: output/{prefix}_{ULID}.png"
+                }),
+            },
+            "hidden": HIDDEN,
+        }
+
+    @classmethod
+    def IS_CHANGED(cls, *args, **kwargs):
+        return float("NaN")
+
+    def __init__(self):
+        super().__init__()
+        self.engine = BatchGeneratorEngine()
+
+    def _extract_val(self, val, default=None):
+        """Extract single value from INPUT_IS_LIST wrapped input."""
+        if val is None:
+            return default
+        if isinstance(val, list):
+            return val[0] if len(val) > 0 else default
+        return val
+
+    def _extract_list(self, val, default_len: int = 0) -> list:
+        """Extract list from INPUT_IS_LIST input."""
+        if val is None:
+            return []
+        if isinstance(val, list):
+            return val
+        return [val]
+
+    def _load_generated_images(self, generated_batch: list, node_identifier: str):
+        """
+        Load pre-generated images from persistent storage (RPG pattern).
+
+        Args:
+            generated_batch: List of job metadata with filenames
+            node_identifier: Node ID (unused, kept for API compatibility)
+
+        Returns:
+            Tuple of (images, prompts, indexes, count, failed_count)
+        """
+        from PIL import Image
+        import folder_paths
+
+        # Use ComfyUI's main output directory
+        comfy_output = folder_paths.get_output_directory()
+
+        loaded_images = []
+        loaded_prompts = []
+        loaded_indexes = []
+        failed_count = 0
+
+        for idx, job_data in enumerate(generated_batch):
+            state = job_data.get("state", "pending")
+            filename = job_data.get("filename")
+            prompt = job_data.get("prompt_positive", "")
+            # Use subfolder from job data (matches how files were saved)
+            # Support both new 'subfolder' and legacy 'output_subdir' keys
+            # Note: Empty string "" means root output folder (no subfolder)
+            if "subfolder" in job_data:
+                subfolder = job_data["subfolder"]  # Could be "" for root folder
+            elif "output_subdir" in job_data:
+                subfolder = job_data["output_subdir"]
+            else:
+                subfolder = ""  # Default to root output folder
+
+            if state in ["success", "cached"] and filename:
+                # Build path using ComfyUI's output directory
+                output_folder = os.path.join(comfy_output, subfolder) if subfolder else comfy_output
+                filepath = os.path.join(output_folder, filename)
+
+                if os.path.exists(filepath):
+                    try:
+                        pil_img = Image.open(filepath)
+                        # Convert to tensor [H, W, C] normalized to [0, 1]
+                        img_array = np.array(pil_img).astype(np.float32) / 255.0
+                        img_tensor = torch.from_numpy(img_array)
+                        loaded_images.append(img_tensor)
+                        loaded_prompts.append(prompt)
+                        loaded_indexes.append(idx)
+                        print(f"[BIG]   โœ“ Loaded job {idx}: {filename}")
+                    except Exception as e:
+                        print(f"[BIG]   โœ– Failed to load job {idx}: {e}")
+                        failed_count += 1
+                else:
+                    print(f"[BIG]   โœ– Job {idx} file not found: {filepath}")
+                    failed_count += 1
+            else:
+                # Job failed or pending
+                if state == "failed":
+                    failed_count += 1
+                print(f"[BIG]   โŠ— Job {idx} not ready (state: {state})")
+
+        if len(loaded_images) == 0:
+            # Return empty placeholder
+            print("[BIG] โš ๏ธ No images could be loaded!")
+            empty_img = torch.zeros((1, 512, 512, 3))
+            return ([empty_img], [""], 0, 0, failed_count)
+
+        # Stack images into batch tensor [B, H, W, C]
+        batch_tensor = torch.stack(loaded_images, dim=0)
+        print(f"[BIG] โœ… Loaded {len(loaded_images)} images, shape: {batch_tensor.shape}")
+
+        # Return in OUTPUT_IS_LIST format: (True, True, False, False, False)
+        return (
+            [img.unsqueeze(0) for img in loaded_images],  # List of individual images
+            loaded_prompts,  # List of prompts
+            len(loaded_images),  # Total count
+            failed_count,  # Failed count
+        )
+
+    def generate(self, **kwargs):
+        """
+        Main generation function.
+
+        NEW BEHAVIOR (RPG-style):
+        1. Check if images are already generated and stored in generated_batch_data
+        2. If yes, load and return them (no generation)
+        3. If no, fall back to old behavior (generate during execution)
+        """
+
+        # โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•
+        # CHECK FOR PRE-GENERATED IMAGES (RPG PATTERN)
+        # โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•
+
+        generated_batch_data = self._extract_val(kwargs.get('generated_batch_data'), "[]")
+        node_identifier = self._extract_val(kwargs.get('node_identifier'))
+
+        try:
+            generated_batch = json.loads(generated_batch_data) if generated_batch_data else []
+        except json.JSONDecodeError:
+            print("[BIG] Invalid generated_batch_data JSON, treating as empty")
+            generated_batch = []
+
+        # If we have pre-generated images, load and return them
+        if generated_batch and len(generated_batch) > 0:
+            print(f"[BIG] โœ… Found {len(generated_batch)} pre-generated images, loading from storage...")
+            return self._load_generated_images(generated_batch, node_identifier)
+
+        # No pre-generated images - show helpful error
+        print("")
+        print("โ•" * 70)
+        print("โŒ NO IMAGES TO OUTPUT")
+        print("โ•" * 70)
+        print("")
+        print("   Please use the 'Generate All' button in the custom UI first!")
+        print("")
+        print("   How to use this node:")
+        print("   1. Connect prompts from Reality Prompt Generator")
+        print("   2. Click 'โœจ Generate All' in the BIG node's custom UI")
+        print("   3. Wait for generation to complete")
+        print("   4. Run the ComfyUI workflow to output the generated images")
+        print("")
+        print("โ•" * 70)
+        print("")
+        raise ValueError(
+            "No pre-generated images found. "
+            "Please click 'Generate All' in the BIG node's custom UI first, "
+            "then run the workflow to output the generated images."
+        )
+
+        # โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•
+        # LEGACY GENERATION CODE - PRESERVED FOR FUTURE NATIVE RUN SUPPORT
+        # โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•
+        # This code below is currently unreachable (blocked by raise above).
+        # It's preserved as a foundation for future work to add native generation
+        # support when running the workflow directly (without using Generate All).
+        # TODO: In the future, remove the raise above and enable this path to
+        #       support native ComfyUI queue execution with 2X mode, caching, etc.
+        # โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•
+
+        aspect_ratio = self._extract_val(kwargs.get('aspect_ratio'), "1:1")
+        max_parallel = self._extract_val(kwargs.get('max_parallel'), 5)
+        max_retries = self._extract_val(kwargs.get('max_retries'), 3)
+        timeout_val = self._extract_val(kwargs.get('timeout'), 600)
+
+        width = self._extract_val(kwargs.get('width'), 1024)
+        height = self._extract_val(kwargs.get('height'), 1024)
+        resolution = self._extract_val(kwargs.get('resolution'))
+
+        use_negative = self._extract_val(kwargs.get('use_negative_prompt'), True)
+        auto_retry = self._extract_val(kwargs.get('auto_retry'), True)
+        use_cache = self._extract_val(kwargs.get('use_cache'), True)
+        enable_safety = self._extract_val(kwargs.get('enable_safety_checker'), True)
+        multi_image = self._extract_val(kwargs.get('multi_image'), False)
+        enable_img2img = self._extract_val(kwargs.get('enable_img2img'), False)
+        bypass_filter = self._extract_val(kwargs.get('bypass_filter'), False)
+        filename_prefix = self._extract_val(kwargs.get('filename_prefix'), "INSTARAW")
+
+        uid = self._extract_val(kwargs.get('uid'))
+        node_identifier = self._extract_val(kwargs.get('node_identifier'))
+
+        # Warn about 2X mode in legacy path (for now, native run doesn't support 2X)
+        if multi_image and provider == "wavespeed.ai":
+            print("[BIG] โš ๏ธ 2X Multi-Image mode is enabled.", flush=True)
+            print("[BIG] ๐Ÿ’ก TIP: For 2X mode (2 images per API call), use the custom UI's 'Generate All' button.", flush=True)
+            print("[BIG] ๐Ÿ“Œ Native run currently generates 1 image per prompt.", flush=True)
+
+        # Get prompt lists
+        prompts_positive = self._extract_list(kwargs.get('prompt_list_positive'))
+        prompts_negative = self._extract_list(kwargs.get('prompt_list_negative'))
+        seeds = self._extract_list(kwargs.get('seed_list'))
+
+        if len(prompts_positive) == 0:
+            raise ValueError("No prompts provided!")
+
+        num_jobs = len(prompts_positive)
+
+        # Pad lists to match prompt count
+        while len(prompts_negative) < num_jobs:
+            prompts_negative.append("")
+        while len(seeds) < num_jobs:
+            seeds.append(-1)
+
+        # Handle reference images
+        # With INPUT_IS_LIST=True, images can come as:
+        # 1. A list of individual tensors (from PromptFilter with OUTPUT_IS_LIST=True)
+        # 2. A list containing a single batch tensor (from other nodes)
+        # 3. A single batch tensor
+        images_input = kwargs.get('images')
+        input_images = None
+
+        print(f"[IMAGE_DEBUG] images_input type: {type(images_input)}", flush=True)
+        if isinstance(images_input, list):
+            print(f"[IMAGE_DEBUG] images_input is list with {len(images_input)} items", flush=True)
+            if len(images_input) > 0:
+                print(f"[IMAGE_DEBUG] First item type: {type(images_input[0])}, shape: {images_input[0].shape if hasattr(images_input[0], 'shape') else 'N/A'}", flush=True)
+        elif hasattr(images_input, 'shape'):
+            print(f"[IMAGE_DEBUG] images_input is tensor with shape: {images_input.shape}", flush=True)
+
+        if images_input is not None:
+            if isinstance(images_input, list) and len(images_input) > 0:
+                first_item = images_input[0]
+                if first_item is not None and isinstance(first_item, torch.Tensor):
+                    if first_item.dim() == 3:
+                        # List of individual [H, W, C] tensors - stack them
+                        valid_tensors = [img for img in images_input if img is not None and isinstance(img, torch.Tensor)]
+                        if valid_tensors:
+                            stacked = []
+                            for img in valid_tensors:
+                                if img.dim() == 3:
+                                    stacked.append(img.unsqueeze(0))
+                                else:
+                                    stacked.append(img)
+                            input_images = torch.cat(stacked, dim=0)
+                    elif first_item.dim() == 4:
+                        # First item is already a batch tensor [B, H, W, C]
+                        if len(images_input) == 1:
+                            input_images = first_item
+                        else:
+                            valid_tensors = [img for img in images_input if img is not None and isinstance(img, torch.Tensor)]
+                            input_images = torch.cat(valid_tensors, dim=0)
+            elif isinstance(images_input, torch.Tensor):
+                input_images = images_input
+
+        num_ref_images = len(input_images) if input_images is not None else 0
+        # Only use img2img mode if enable_img2img is True AND we have reference images
+        is_i2i = enable_img2img and num_ref_images > 0
+
+        # If img2img is disabled, clear the input images to ensure txt2img mode
+        if not enable_img2img:
+            input_images = None
+            num_ref_images = 0
+
+        print(f"[IMAGE_DEBUG] Final input_images shape: {input_images.shape if input_images is not None else 'None'}", flush=True)
+        print(f"[IMAGE_DEBUG] enable_img2img: {enable_img2img}, num_ref_images: {num_ref_images}, is_i2i: {is_i2i}", flush=True)
+        print(f"[IMAGE_DEBUG] Total prompts: {num_jobs}", flush=True)
+
+        # โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•
+        # CREATE JOBS
+        # โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•
+
+        jobs: List[GenerationJob] = []
+        for i in range(num_jobs):
+            seed = seeds[i] if i < len(seeds) else -1
+
+            input_img = None
+            img_idx = None
+            if input_images is not None and num_ref_images > 0:
+                img_idx = i % num_ref_images
+                input_img = input_images[img_idx:img_idx+1]
+
+            jobs.append(GenerationJob(
+                id=i,
+                prompt_positive=prompts_positive[i],
+                prompt_negative=prompts_negative[i] if i < len(prompts_negative) else "",
+                seed=seed,
+                input_image=input_img,
+            ))
+
+            # Debug logging for image cycling
+            if input_img is not None:
+                print(f"[IMAGE_DEBUG] Job #{i}: Prompt {i}/{num_jobs-1} โ†’ Image {img_idx}/{num_ref_images-1} | Shape: {input_img.shape}", flush=True)
+
+        # โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•
+        # LOGGING HEADER
+        # โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•
+
+        total_jobs = len(jobs)
+        print("\n" + "โ•" * 80, flush=True)
+        print("๐ŸŽจ INSTARAW BATCH IMAGE GENERATOR", flush=True)
+        print("โ•" * 80, flush=True)
+        print(f"   Jobs: {total_jobs}", flush=True)
+        print(f"   Parallel: {max_parallel} | Retries: {max_retries}", flush=True)
+        print(f"   Model: {model} | Provider: {provider}", flush=True)
+        print(f"   Aspect: {aspect_ratio} | Resolution: {resolution or 'default'}", flush=True)
+        print(f"   Mode: {'I2I (Edit)' if is_i2i else 'T2I (Generate)'} | Ref Images: {num_ref_images}", flush=True)
+        print(f"   Negative Prompts: {'Enabled' if use_negative else 'Disabled'}", flush=True)
+        print(f"   Auto-Retry: {'Enabled' if auto_retry else 'Disabled'} | Cache: {'Enabled' if use_cache else 'Disabled'}", flush=True)
+        print(f"   2X Mode: {'โš ๏ธ Enabled (use custom UI for 2X)' if multi_image else 'Disabled'}", flush=True)
+        print(f"   Bypass Filter: {'โœ… Yes (auto-output all)' if bypass_filter else 'No (show popup)'}", flush=True)
+        print("โ”€" * 80, flush=True)
+
+        # โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•
+        # PARALLEL EXECUTION WITH REAL-TIME PROGRESS
+        # โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•
+
+        completed = 0
+        lock = threading.Lock()
+
+        # Start progress tracking for real-time UI updates
+        progress_tracker.start_generation(node_identifier, jobs)
+
+        def process_job(job: GenerationJob) -> GenerationJob:
+            nonlocal completed
+
+            # Mark job as generating and update UI
+            job.state = JobState.GENERATING
+            progress_tracker.update_job(node_identifier, job)
+
+            # Callback for retry state updates
+            def on_retry(j):
+                # Check if we should stop before updating UI
+                if progress_tracker.should_stop(node_identifier):
+                    print(f"   โน๏ธ  Stopping job #{j.id} (on_retry callback)", flush=True)
+                    j.state = JobState.FAILED
+                    j.error = "Stopped by user"
+                    return
+                progress_tracker.update_job(node_identifier, j)
+
+            result = self.engine.generate_job(
+                job=job,
+                api_key=api_key,
+                provider=provider,
+                model=model,
+                aspect_ratio=aspect_ratio,
+                resolution=resolution,
+                width=width,
+                height=height,
+                enable_safety_checker=enable_safety,
+                use_negative_prompt=use_negative,
+                max_retries=max_retries if auto_retry else 1,
+                timeout=300,  # Per-request timeout
+                use_cache=use_cache,
+                on_retry_callback=on_retry,
+                progress_tracker=progress_tracker,
+                node_identifier=node_identifier,
+                filename_prefix=filename_prefix,
+            )
+
+            with lock:
+                completed += 1
+
+                if result.state == JobState.CACHED:
+                    status = "๐Ÿ“ฆ CACHED"
+                elif result.state == JobState.SUCCESS:
+                    status = f"โœ… SUCCESS (attempts: {result.attempts}, {result.generation_time:.1f}s)"
+                else:
+                    status = f"โŒ FAILED: {result.error[:60]}..."
+
+                pct = 100 * completed // num_jobs
+                print(f"   [{completed:3d}/{num_jobs}] ({pct:3d}%) Job #{result.id + 1}: {status}", flush=True)
+
+                # Update progress tracker for real-time UI
+                progress_tracker.update_job(node_identifier, result)
+
+            return result
+
+        start_time = time.time()
+
+        with ThreadPoolExecutor(max_workers=max_parallel) as executor:
+            futures = {executor.submit(process_job, job): job for job in jobs}
+            completed_jobs = []
+            for future in as_completed(futures):
+                # Check if we should stop
+                if progress_tracker.should_stop(node_identifier):
+                    print("โน๏ธ  STOPPING GENERATION in executor loop (user requested)", flush=True)
+                    # Cancel remaining futures
+                    cancelled_count = 0
+                    for f in futures:
+                        if not f.done():
+                            f.cancel()
+                            cancelled_count += 1
+                    print(f"   โน๏ธ  Cancelled {cancelled_count} pending futures", flush=True)
+                    # Collect completed jobs only
+                    break
+                completed_jobs.append(future.result())
+
+        # If stopped, raise exception to halt ComfyUI workflow
+        if progress_tracker.should_stop(node_identifier):
+            progress_tracker.complete_generation(node_identifier)
+            print("โน๏ธ  Halting ComfyUI workflow execution", flush=True)
+            raise InterruptProcessingException()
+
+        # Mark generation as complete
+        progress_tracker.complete_generation(node_identifier)
+
+        total_time = time.time() - start_time
+
+        # โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•
+        # RESULTS SUMMARY
+        # โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•
+
+        successful = [j for j in completed_jobs if j.state in [JobState.SUCCESS, JobState.CACHED]]
+        failed = [j for j in completed_jobs if j.state == JobState.FAILED]
+        cached = [j for j in completed_jobs if j.state == JobState.CACHED]
+
+        print("โ”€" * 80, flush=True)
+        print(f"๐Ÿ“Š GENERATION COMPLETE in {total_time:.1f}s", flush=True)
+        print(f"   โœ… Successful: {len(successful)} ({len(cached)} from cache)", flush=True)
+        print(f"   โŒ Failed: {len(failed)}", flush=True)
+
+        if failed:
+            print("   Failed jobs:", flush=True)
+            for j in failed[:5]:
+                print(f"      #{j.id + 1}: {j.error[:70]}...", flush=True)
+            if len(failed) > 5:
+                print(f"      ... and {len(failed) - 5} more", flush=True)
+
+        print("โ•" * 80 + "\n", flush=True)
+
+        if len(successful) == 0:
+            raise Exception(f"All {num_jobs} generations failed! Check API key, model settings, and prompts.")
+
+        # โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•
+        # NORMALIZE IMAGE SIZES
+        # โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•
+
+        # Sort by original job ID
+        successful.sort(key=lambda j: j.id)
+
+        # Get all result tensors and normalize sizes
+        result_tensors = [j.result_tensor for j in successful]
+
+        if len(result_tensors) > 1:
+            ref_shape = result_tensors[0].shape
+            normalized = []
+            for i, tensor in enumerate(result_tensors):
+                if tensor.shape != ref_shape:
+                    img_np = tensor.cpu().numpy()
+                    if img_np.max() <= 1.0:
+                        img_np = (img_np * 255).astype(np.uint8)
+                    img_pil = Image.fromarray(img_np)
+                    img_pil = img_pil.resize((ref_shape[1], ref_shape[0]), Image.LANCZOS)
+                    img_np = np.array(img_pil).astype(np.float32) / 255.0
+                    normalized.append(torch.from_numpy(img_np))
+                else:
+                    normalized.append(tensor)
+            result_tensors = normalized
+
+        # โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•
+        # INTERACTIVE SELECTION UI
+        # โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•
+
+        # Stack for preview
+        preview_tensors = []
+        for t in result_tensors:
+            if t.dim() == 3:
+                preview_tensors.append(t.unsqueeze(0))
+            else:
+                preview_tensors.append(t)
+        preview_batch = torch.cat(preview_tensors, dim=0)
+
+        # Save preview images
+        save_kwargs = {}
+        if "prompt" in kwargs:
+            p = kwargs.get("prompt")
+            save_kwargs["prompt"] = p[0] if isinstance(p, list) else p
+        if "extra_pnginfo" in kwargs:
+            e = kwargs.get("extra_pnginfo")
+            save_kwargs["extra_pnginfo"] = e[0] if isinstance(e, list) else e
+
+        urls = self.save_images(images=preview_batch, **save_kwargs)['ui']['images']
+
+        # Build tip with job info
+        tip_lines = [
+            f"โœ… Generated {len(successful)} images ({len(cached)} cached, {len(failed)} failed)",
+            f"โฑ๏ธ Total time: {total_time:.1f}s",
+            "",
+            "Select images to output, then click Send.",
+        ]
+        tip = "\n".join(tip_lines)
+
+        # Check if filter should be bypassed
+        if bypass_filter:
+            print(f"[BIG] ๐Ÿš€ Bypass filter enabled - outputting all {len(successful)} images", flush=True)
+            selected_indices = list(range(len(successful)))
+        else:
+            # Send to popup
+            payload = {
+                "uid": uid,
+                "urls": urls,
+                "allsame": False,
+                "extras": ["", "", ""],
+                "tip": tip,
+                "video_frames": 1,
+            }
+
+            response = send_and_wait(payload, timeout_val, uid, node_identifier)
+
+            # Handle response
+            if isinstance(response, TimeoutResponse):
+                selected_indices = list(range(len(successful)))
+                print(f"   โฑ๏ธ Timeout - outputting all {len(successful)} images", flush=True)
+            else:
+                selected_indices = [int(x) for x in response.selection] if response.selection else []
+
+            if len(selected_indices) == 0:
+                raise InterruptProcessingException()
+
+        # โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•
+        # BUILD OUTPUT
+        # โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•
+
+        final_images = []
+        final_prompts = []
+        final_job_ids = []
+
+        for idx in selected_indices:
+            if idx < len(successful):
+                job = successful[idx]
+                tensor = result_tensors[idx]
+
+                # Ensure batch dimension
+                if tensor.dim() == 3:
+                    tensor = tensor.unsqueeze(0)
+
+                final_images.append(tensor)
+                final_prompts.append(job.prompt_positive)
+                final_job_ids.append(job.id)
+
+        indexes_str = ",".join(str(x) for x in final_job_ids)
+        count = len(final_images)
+
+        # โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•
+        # UPDATE GENERATED_BATCH_DATA FOR PERSISTENCE
+        # โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•
+        # Store job metadata so subsequent runs can load from disk
+        batch_data = []
+        for job in completed_jobs:
+            batch_data.append({
+                "id": job.id,
+                "state": job.state.value,
+                "filename": job.result_filename,
+                "prompt_positive": job.prompt_positive,
+                "image_url": job.result_image_url,
+                "image_width": job.result_image_width,
+                "image_height": job.result_image_height,
+                "generation_time": job.generation_time,
+            })
+
+        # Emit event to update the JS widget with generated_batch_data
+        try:
+            PromptServer.instance.send_sync("instaraw-batch-gen-save", {
+                "node_id": node_identifier,
+                "generated_batch_data": json.dumps(batch_data),
+            })
+            print(f"[BIG] ๐Ÿ’พ Saved {len(batch_data)} jobs to generated_batch_data", flush=True)
+        except Exception as e:
+            print(f"[BIG] โš ๏ธ Failed to save generated_batch_data: {e}", flush=True)
+
+        print(f"๐ŸŽ‰ Output: {count} images selected (indices: {indexes_str})", flush=True)
+
+        return (final_images, final_prompts, indexes_str, count, len(failed))
+
+
+# โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•
+# NODE REGISTRATION
+# โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•
+
+NODE_CLASS_MAPPINGS = {
+    "INSTARAW_BatchImageGenerator": INSTARAW_BatchImageGenerator,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "INSTARAW_BatchImageGenerator": "๐ŸŽจ INSTARAW Batch Image Generator",
+}
diff --git a/custom_nodes/ComfyUI_INSTARAW/nodes/interactive_nodes/image_filter.py b/custom_nodes/ComfyUI_INSTARAW/nodes/interactive_nodes/image_filter.py
new file mode 100644
index 0000000000000000000000000000000000000000..5e488a4d4956efefb790a385eccd492144159fa4
--- /dev/null
+++ b/custom_nodes/ComfyUI_INSTARAW/nodes/interactive_nodes/image_filter.py
@@ -0,0 +1,414 @@
+# ---
+# Filename: ../ComfyUI_INSTARAW/nodes/interactive_nodes/image_filter.py
+# ---
+
+from nodes import PreviewImage, LoadImage
+from comfy.model_management import InterruptProcessingException
+import os, random
+import torch
+import hashlib
+from PIL import Image
+import numpy as np
+import json
+import base64
+import io
+
+from .image_filter_messaging import send_and_wait, Response, TimeoutResponse
+
+HIDDEN = {
+            "prompt": "PROMPT", 
+            "extra_pnginfo": "EXTRA_PNGINFO", 
+            "uid":"UNIQUE_ID",
+            "node_identifier": "NID",
+        }
+
+def pil_to_natural_mask_tensor(pil_image):
+    if pil_image.mode == 'RGBA':
+        mask_np = np.array(pil_image.split()[-1]).astype(np.float32) / 255.0
+    else:
+        mask_np = np.array(pil_image.convert("L")).astype(np.float32) / 255.0
+    mask_tensor = torch.from_numpy(mask_np).unsqueeze(0)
+    return mask_tensor
+
+def save_natural_mask_as_alpha(natural_mask_tensor, filepath):
+    mask_np = natural_mask_tensor.cpu().numpy().squeeze()
+    rgb = np.zeros((mask_np.shape[0], mask_np.shape[1], 3), dtype=np.uint8)
+    alpha = (mask_np * 255).astype(np.uint8)
+    rgba_image = Image.fromarray(np.dstack((rgb, alpha)), 'RGBA')
+    rgba_image.save(filepath, 'PNG')
+
+
+class INSTARAW_ImageFilter(PreviewImage):
+    RETURN_TYPES = ("IMAGE","LATENT","MASK","STRING","STRING","STRING","STRING")
+    RETURN_NAMES = ("images","latents","masks","extra1","extra2","extra3","indexes")
+    FUNCTION = "func"
+    CATEGORY = "INSTARAW/Interactive"
+    OUTPUT_NODE = False
+    DESCRIPTION = "Allows you to preview images and choose which, if any to proceed with"
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": { 
+                "images" : ("IMAGE", ), 
+                "timeout": ("INT", {"default": 600, "min":1, "max":9999999, "tooltip": "Timeout in seconds."}),
+                "ontimeout": (["send none", "send all", "send first", "send last"], {}),
+                "cache_behavior": (["Run selector normally", "Resend previous selection"], {
+                    "tooltip": "Behavior when a cached selection for this image batch already exists."
+                }),
+            },
+            "optional": {
+                "latents" : ("LATENT", {"tooltip": "Optional - if provided, will be output"}),
+                "masks" : ("MASK", {"tooltip": "Optional - if provided, will be output"}),
+                "tip" : ("STRING", {"default":"", "tooltip": "Optional - if provided, will be displayed in popup window"}),
+                "extra1" : ("STRING", {"default":""}),
+                "extra2" : ("STRING", {"default":""}),
+                "extra3" : ("STRING", {"default":""}),
+                "pick_list_start" : ("INT", {"default":0, "tooltip":"The number used in pick_list for the first image"}),
+                "pick_list" : ("STRING", {"default":"", "tooltip":"If a comma separated list of integers is provided, the images with these indices will be selected automatically."}),
+                "video_frames" : ("INT", {"default":1, "min":1, "tooltip": "treat each block of n images as a video"}),
+            },
+            "hidden": HIDDEN,
+        }
+    
+    @classmethod
+    def IS_CHANGED(cls, *args, **kwargs):
+        # Always rerun - this is an interactive node
+        return float("NaN")
+    
+    def func(self, images, timeout, ontimeout, cache_behavior, uid, node_identifier, tip="", extra1="", extra2="", extra3="", latents=None, masks=None, pick_list_start:int=0, pick_list:str="", video_frames:int=1, **kwargs):
+        e1, e2, e3 = extra1, extra2, extra3
+        B = images.shape[0]
+        if video_frames > B: video_frames = 1
+
+        cache_dir = os.path.join(os.path.dirname(__file__), "..", "..", "cache")
+        os.makedirs(cache_dir, exist_ok=True)
+
+        hasher = hashlib.sha256()
+        hasher.update(images.cpu().numpy().tobytes())
+        cache_key = hasher.hexdigest()
+        cache_filepath = os.path.join(cache_dir, f"{cache_key}_selection.json")
+
+        images_to_return = None
+
+        # Check if we should use cached selection
+        if cache_behavior == "Resend previous selection" and os.path.exists(cache_filepath):
+            print(f"โœ… Image Filter: Using cached selection for images {cache_key[:8]}...")
+            with open(cache_filepath, 'r') as f:
+                cached_data = json.load(f)
+            images_to_return = cached_data.get('selection', [])
+            e1, e2, e3 = cached_data.get('extras', [extra1, extra2, extra3])
+        else:
+            try:    
+                images_to_return:list[int] = [ int(x.strip())%B for x in pick_list.split(',') ] if pick_list else []
+            except Exception as e: 
+                print(f"{e} parsing pick_list - will manually select")
+                images_to_return = []
+
+            if len(images_to_return) == 0:
+                all_the_same = ( B and all( (images[i]==images[0]).all() for i in range(1,B) )) 
+                urls:list[str] = self.save_images(images=images, **kwargs)['ui']['images']
+                payload = {"uid": uid, "urls":urls, "allsame":all_the_same, "extras":[extra1, extra2, extra3], "tip":tip, "video_frames":video_frames}
+
+                response:Response = send_and_wait(payload, timeout, uid, node_identifier)
+
+                if isinstance(response, TimeoutResponse):
+                    if ontimeout=='send none':  images_to_return = []
+                    if ontimeout=='send all':   images_to_return = [*range(len(images)//video_frames)]
+                    if ontimeout=='send first': images_to_return = [0,]
+                    if ontimeout=='send last':  images_to_return = [(len(images)//video_frames)-1,]
+                else:
+                    e1, e2, e3 = response.get_extras([extra1, extra2, extra3])
+                    images_to_return = [ int(x) for x in response.selection ] if response.selection else []
+                
+                if not isinstance(response, TimeoutResponse):
+                    print(f"๐Ÿ’พ Saving image selection to cache: {cache_filepath}")
+                    cache_data = {'selection': images_to_return, 'extras': [e1, e2, e3]}
+                    with open(cache_filepath, 'w') as f:
+                        json.dump(cache_data, f)
+
+        if images_to_return is None or len(images_to_return) == 0: raise InterruptProcessingException()
+
+        if video_frames > 1:
+            final_indices = [ key*video_frames + frm  for key in images_to_return for frm in range(video_frames) ]
+        else:
+            final_indices = images_to_return
+
+        images = torch.stack(list(images[int(i)] for i in final_indices))
+        latents = {"samples": torch.stack(list(latents['samples'][int(i)] for i in final_indices))} if latents is not None else None
+        masks = torch.stack(list(masks[int(i)] for i in final_indices)) if masks is not None else None
+
+        try: int(pick_list_start)
+        except: pick_list_start = 0
+
+        return (images, latents, masks, e1, e2, e3, ",".join(str(int(x)+int(pick_list_start)) for x in images_to_return))
+    
+class INSTARAW_TextImageFilter(PreviewImage):
+    RETURN_TYPES = ("IMAGE","STRING","STRING","STRING","STRING")
+    RETURN_NAMES = ("image","text","extra1","extra2","extra3")
+    FUNCTION = "func"
+    CATEGORY = "INSTARAW/Interactive"
+    OUTPUT_NODE = False
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": { 
+                "image" : ("IMAGE", ), 
+                "enabled": ("BOOLEAN", {"default": True, "label_on": "Active", "label_off": "Bypassed"}),
+                "text" : ("STRING", {"default":""}),
+                "timeout": ("INT", {"default": 600, "min":1, "max":9999999, "tooltip": "Timeout in seconds."}),
+                "cache_behavior": (["Edit (Loads Saved Text)", "Bypass if Image is Cached", "Bypass & Repeat Last Text"], {
+                    "tooltip": (
+                        "Edit (Loads Saved Text): Always shows the popup. Loads your last saved edit for this image if available.\n\n"
+                        "Bypass if Image is Cached: Skips the popup and automatically uses the saved text for this specific image if it exists. If not, the editor appears.\n\n"
+                        "Bypass & Repeat Last Text: Skips the popup and automatically uses the last text you entered in this node for any image. If not used before, the editor appears."
+                    )
+                }),
+            },
+            "optional": {
+                "mask" : ("MASK", {"tooltip": "Optional - if provided, will be overlaid on image"}),
+                "tip" : ("STRING", {"default":"", "tooltip": "Optional - if provided, will be displayed in popup window"}),
+                "extra1" : ("STRING", {"default":""}),
+                "extra2" : ("STRING", {"default":""}),
+                "extra3" : ("STRING", {"default":""}),
+                "textareaheight" : ("INT", {"default": 150, "min": 50, "max": 500, "tooltip": "Height of text area in pixels"}),
+            },
+            "hidden": HIDDEN,
+        }
+    
+    @classmethod
+    def IS_CHANGED(cls, **kwargs):
+        return float("NaN")
+    
+    def func(self, image, enabled, text, timeout, cache_behavior, uid, node_identifier, extra1="", extra2="", extra3="", mask=None, tip="", textareaheight=None, **kwargs):
+        # --- THIS IS THE DEFINITIVE, UNIFIED CACHING LOGIC ---
+
+        # STEP 1: CALCULATE CACHE PATHS
+        cache_dir = os.path.join(os.path.dirname(__file__), "..", "..", "cache")
+        os.makedirs(cache_dir, exist_ok=True)
+        
+        node_memory_filepath = os.path.join(cache_dir, f"{hashlib.sha256(uid.encode('utf-8') + b'text_filter_node_memory').hexdigest()}_text_edit.json")
+        image_specific_filepath = os.path.join(cache_dir, f"{hashlib.sha256(uid.encode('utf-8') + image.cpu().numpy().tobytes()).hexdigest()}_text_edit.json")
+
+        # STEP 2: HANDLE BYPASSED (DISABLED) STATE
+        if not enabled:
+            print("๐Ÿ“ INSTARAW Text Filter: Bypassed. Checking for most relevant cached text...")
+            if os.path.exists(node_memory_filepath):
+                with open(node_memory_filepath, 'r') as f: cached_data = json.load(f)
+                print(f"โœ… Bypassed Mode: Using node's last saved text from {node_memory_filepath}")
+                return (image, cached_data.get('edited_text', text), *cached_data.get('extras', [extra1, extra2, extra3]))
+            if os.path.exists(image_specific_filepath):
+                with open(image_specific_filepath, 'r') as f: cached_data = json.load(f)
+                print(f"โœ… Bypassed Mode: Using image-specific cached text from {image_specific_filepath}")
+                return (image, cached_data.get('edited_text', text), *cached_data.get('extras', [extra1, extra2, extra3]))
+            print("๐Ÿ“ Bypassed Mode: No cache found. Passing input text.")
+            return (image, text, extra1, extra2, extra3)
+
+        # STEP 3: HANDLE ACTIVE (ENABLED) STATE
+        
+        # Special case: "Bypass & Repeat Last Text" intentionally ignores input text validation.
+        if cache_behavior == "Bypass & Repeat Last Text":
+            if os.path.exists(node_memory_filepath):
+                print(f"โœ… Active Mode ('Repeat'): Using node memory cache from {node_memory_filepath}")
+                with open(node_memory_filepath, 'r') as f: cached_data = json.load(f)
+                return (image, cached_data.get('edited_text', text), *cached_data.get('extras', [extra1, extra2, extra3]))
+            else:
+                print("โ„น๏ธ Active Mode ('Repeat'): No node memory cache found. Proceeding to editor.")
+        
+        # For all other active modes, find the most relevant VALID cache.
+        most_relevant_cached_text = None
+        most_relevant_extras = [extra1, extra2, extra3]
+
+        # Priority 1: Check the node memory cache.
+        if os.path.exists(node_memory_filepath):
+            try:
+                with open(node_memory_filepath, 'r') as f: data = json.load(f)
+                if data.get('source_text') == text:
+                    most_relevant_cached_text = data.get('edited_text')
+                    most_relevant_extras = data.get('extras', most_relevant_extras)
+                    print("โœ… Found valid, most recent text in the node's memory cache.")
+            except Exception: pass
+        
+        # Priority 2: If node memory was stale/missing, check image-specific cache.
+        if most_relevant_cached_text is None and os.path.exists(image_specific_filepath):
+            try:
+                with open(image_specific_filepath, 'r') as f: data = json.load(f)
+                if data.get('source_text') == text:
+                    most_relevant_cached_text = data.get('edited_text')
+                    most_relevant_extras = data.get('extras', most_relevant_extras)
+                    print("โœ… Found valid text in the image-specific cache.")
+            except Exception: pass
+
+        # Now, act based on the mode and whether we found a relevant cache.
+        if cache_behavior == "Bypass if Image is Cached" and most_relevant_cached_text is not None:
+            print(f"โœ… Active Mode ('Bypass'): Valid cache found. Bypassing popup.")
+            return (image, most_relevant_cached_text, *most_relevant_extras)
+
+        # Prepare for the editor
+        text_for_editor = text # Default to fresh input
+        if most_relevant_cached_text is not None:
+            text_for_editor = most_relevant_cached_text
+            print(f"๐Ÿ“ Active Mode ('Edit'): Pre-filling editor with most relevant cached text.")
+
+        # Show popup
+        print("๐Ÿ’จ INSTARAW Text Filter: Launching editor...")
+        urls:list[str] = self.save_images(images=image, **kwargs)['ui']['images']
+        payload = {"uid": uid, "urls":urls, "text":text_for_editor, "extras":most_relevant_extras, "tip":tip}
+        if textareaheight is not None: payload['textareaheight'] = textareaheight
+        if mask is not None: payload['mask_urls'] = self.save_images(images=mask_to_image(mask), **kwargs)['ui']['images']
+
+        response = send_and_wait(payload, timeout, uid, node_identifier)
+        
+        if isinstance(response, TimeoutResponse):
+            return (image, text_for_editor, *most_relevant_extras)
+
+        final_text = response.text
+        e1, e2, e3 = response.get_extras(most_relevant_extras)
+
+        # CRITICAL: Save response to BOTH caches to ensure consistency for the next run.
+        new_cache_data = {
+            'source_text': text,       # The upstream text that triggered this edit
+            'edited_text': final_text, # The final text after user edit
+            'extras': [e1, e2, e3]
+        }
+        
+        print(f"๐Ÿ’พ Saving to image-specific cache: {image_specific_filepath}")
+        with open(image_specific_filepath, 'w') as f:
+            json.dump(new_cache_data, f)
+        
+        print(f"๐Ÿ’พ Updating node's persistent memory (most recent action): {node_memory_filepath}")
+        with open(node_memory_filepath, 'w') as f:
+            json.dump(new_cache_data, f)
+            
+        return (image, final_text, e1, e2, e3)
+
+def mask_to_image(mask:torch.Tensor):
+    return torch.stack([mask, mask, mask, 1.0-mask], -1)
+    
+class INSTARAW_MaskImageFilter(PreviewImage, LoadImage):
+    RETURN_TYPES = ("IMAGE", "MASK", "MASK", "STRING", "STRING", "STRING")
+    RETURN_NAMES = ("image", "mask", "mask_inverted", "extra1", "extra2", "extra3")
+    
+    FUNCTION = "func"
+    CATEGORY = "INSTARAW/Interactive"
+    OUTPUT_NODE = False
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": { 
+                "image" : ("IMAGE", ),
+                "enabled": ("BOOLEAN", {"default": True, "label_on": "Active", "label_off": "Bypassed"}),
+                "timeout": ("INT", {"default": 600, "min":1, "max":9999999, "tooltip": "Timeout in seconds."}),
+                "if_no_mask": (["cancel", "send blank"], {}),
+                "cache_behavior": (["Run editor normally", "Edit previous mask", "Resend previous mask"], {
+                    "tooltip": "Behavior when a cached mask for this image already exists."
+                }),
+            },
+            "optional": {
+                "mask" : ("MASK", {"tooltip":"optional initial mask"}),
+                "tip" : ("STRING", {"default":"", "tooltip": "Optional - if provided, will be displayed in popup window"}),
+                "extra1" : ("STRING", {"default":""}),
+                "extra2" : ("STRING", {"default":""}),
+                "extra3" : ("STRING", {"default":""}),
+            },
+            "hidden": HIDDEN,
+        }
+    
+    @classmethod
+    def IS_CHANGED(cls, *args, **kwargs):
+        return f"{random.random()}"
+    
+    @classmethod
+    def VALIDATE_INPUTS(cls, *args, **kwargs): return True
+    
+    def func(self, image, enabled, timeout, uid, if_no_mask, cache_behavior, node_identifier, mask=None, extra1="", extra2="", extra3="", tip="", **kwargs):
+        if not enabled:
+            print("๐ŸŽญ INSTARAW Mask Filter: Bypassed")
+            if mask is None:
+                mask = torch.zeros_like(image[:, :, :, 0])
+            return (image, mask, 1.0 - mask, extra1, extra2, extra3)
+
+        cache_dir = os.path.join(os.path.dirname(__file__), "..", "..", "cache")
+        os.makedirs(cache_dir, exist_ok=True)
+        
+        hasher = hashlib.sha256()
+        hasher.update(image.cpu().numpy().tobytes())
+        cache_key = hasher.hexdigest()
+        cache_filepath = os.path.join(cache_dir, f"{cache_key}_mask.png")
+
+        if cache_behavior == "Resend previous mask" and os.path.exists(cache_filepath):
+            print(f"โœ… INSTARAW Mask Cache Hit! Resending previous mask from {cache_filepath}")
+            pil_mask = Image.open(cache_filepath)
+            natural_mask = pil_to_natural_mask_tensor(pil_mask)
+            return (image, natural_mask, 1.0 - natural_mask, extra1, extra2, extra3)
+
+        initial_mask_for_editor = None
+        target_device = image.device
+        
+        if mask is not None:
+            initial_mask_for_editor = mask.to(target_device)
+        
+        if cache_behavior == "Edit previous mask" and os.path.exists(cache_filepath):
+            print(f"๐Ÿ’จ INSTARAW Mask Cache: Loading previous mask for editing from {cache_filepath}")
+            pil_mask = Image.open(cache_filepath)
+            initial_mask_for_editor = pil_to_natural_mask_tensor(pil_mask).to(target_device)
+
+        if initial_mask_for_editor is not None:
+            image_rgb = image[:,:,:,:3]
+            alpha_channel = initial_mask_for_editor.unsqueeze(-1)
+            saveable = torch.cat((image_rgb, alpha_channel), dim=-1)
+        else:
+            saveable = image
+
+        urls:list[dict[str,str]] = self.save_images(images=saveable, **kwargs)['ui']['images']
+        payload = {"uid": uid, "urls":urls, "maskedit":True, "extras":[extra1, extra2, extra3], "tip":tip}
+        
+        print("๐Ÿ’จ INSTARAW Mask Filter: Waiting for user interaction...")
+        response = send_and_wait(payload, timeout, uid, node_identifier)
+        
+        if (response.masked_image):
+            print(f"๐ŸŽญ DEBUG: Using masked_image path: {response.masked_image}")
+            try:
+                loaded_image, mask_from_editor = self.load_image(os.path.join('clipspace', response.masked_image)+" [input]")
+
+                final_natural_mask = 1.0 - mask_from_editor
+
+                print(f"๐Ÿ’พ Saving corrected natural_mask to cache: {cache_filepath}")
+                save_natural_mask_as_alpha(final_natural_mask, cache_filepath)
+
+                return (loaded_image, final_natural_mask, 1.0 - final_natural_mask, *response.get_extras([extra1, extra2, extra3]))
+            except FileNotFoundError:
+                pass
+        elif (response.masked_data):
+            # Handle base64 data URL from new mask editor
+            try:
+                data = response.masked_data.split(',', 1)[-1]
+                bytes_data = data.encode('utf-8')
+                image_data = base64.decodebytes(bytes_data)
+                data_io = io.BytesIO(image_data)
+                img = Image.open(data_io)
+
+                # Check if image has alpha channel
+                if 'A' in img.getbands():
+                    mask_np = np.array(img.getchannel('A')).astype(np.float32) / 255.0
+                else:
+                    mask_np = np.array(img.convert('L')).astype(np.float32) / 255.0
+
+                # New mask editor: painted areas are opaque (alpha=1), unpainted are transparent (alpha=0)
+                # Use alpha directly: painted = white (1), unpainted = black (0)
+                final_natural_mask = torch.from_numpy(mask_np).unsqueeze(0)
+
+                print(f"๐Ÿ’พ Saving mask from data URL to cache: {cache_filepath}")
+                save_natural_mask_as_alpha(final_natural_mask, cache_filepath)
+
+                return (image, final_natural_mask, 1.0 - final_natural_mask, *response.get_extras([extra1, extra2, extra3]))
+            except Exception as e:
+                print(f"โš ๏ธ Error processing masked_data: {e}")
+
+        if if_no_mask == 'cancel':
+            raise InterruptProcessingException()
+
+        blank_natural_mask = torch.zeros_like(image[:,:,:,0])
+        return (image, blank_natural_mask, 1.0 - blank_natural_mask, *response.get_extras([extra1, extra2, extra3]))
diff --git a/custom_nodes/ComfyUI_INSTARAW/nodes/interactive_nodes/image_filter_messaging.py b/custom_nodes/ComfyUI_INSTARAW/nodes/interactive_nodes/image_filter_messaging.py
new file mode 100644
index 0000000000000000000000000000000000000000..6e621c0514020e6c4a7f8f6105820239e32a435a
--- /dev/null
+++ b/custom_nodes/ComfyUI_INSTARAW/nodes/interactive_nodes/image_filter_messaging.py
@@ -0,0 +1,166 @@
+from server import PromptServer
+from aiohttp import web
+from comfy.model_management import InterruptProcessingException, throw_exception_if_processing_interrupted
+import time, json, os, hashlib
+from typing import Optional, List, Dict
+
+REQUEST_RESHOW = "-1"
+CANCEL = "-3"
+WAITING_FOR_RESPONSE = "-9"
+
+SPECIALS = [REQUEST_RESHOW, CANCEL, WAITING_FOR_RESPONSE]
+
+class Response:
+    def __init__(self, selection:Optional[List[str]] = None, text:Optional[str] = None,
+                        masked_image:Optional[str] = None, masked_data:Optional[str] = None,
+                        extras:Optional[List[str]] = None, crop:Optional[Dict] = None):
+        self.selection:List[int]        = [int(x) for x in selection] if selection else []
+        self.text:Optional[str]         = text
+        self.masked_image:Optional[str] = masked_image
+        self.masked_data:Optional[str]  = masked_data  # Base64 data URL from new mask editor
+        self.extras:Optional[List[str]] = extras
+        self.crop:Optional[Dict]        = crop
+
+    def get_extras(self,defaults:list[str]) -> list[str]:
+         return self.extras or defaults  
+
+class TimeoutResponse(Response): pass
+class CancelledResponse(Response): pass
+class RequestResponse(Response): pass
+
+class MessageState:
+    _latest:'Optional[MessageState]' = None
+    unique_expected = None
+
+    def __init__(self, data:dict|str={}):
+        data_dict:dict = data if isinstance(data,dict) else json.loads(data)
+        self.unique:str            = data_dict.pop('unique', None)
+        self.special:Optional[str] = data_dict.pop('special',None)
+        self.response:Response     = Response(**data_dict)
+
+    @classmethod
+    def latest(cls) -> 'MessageState': 
+        if cls._latest is None: cls._latest = cls()
+        return cls._latest 
+    
+    @classmethod
+    def set_latest(cls, latest:'MessageState'):
+        cls._latest = latest
+
+    @classmethod
+    def waiting_state(cls): return MessageState(data={'special':WAITING_FOR_RESPONSE})
+
+    @classmethod
+    def request_state(cls): return MessageState(data={'special':REQUEST_RESHOW})
+
+    @classmethod
+    def start_waiting(cls, unique): 
+        cls._latest = cls.waiting_state()
+        cls.unique_expected = unique
+
+    @classmethod
+    def get_response(cls) -> Response:  
+        if cls.waiting(): return TimeoutResponse()
+        if cls.latest().cancelled: return CancelledResponse()
+        if cls.latest().request: return RequestResponse()
+        return cls.latest().response
+
+    @classmethod
+    def stop_waiting(cls): 
+        cls._latest = MessageState()
+
+    @classmethod
+    def waiting(cls) -> bool: return cls.latest().special == WAITING_FOR_RESPONSE
+
+    @property
+    def cancelled(self) -> bool: return self.special == CANCEL
+
+    @property
+    def request(self) -> bool: return self.special == REQUEST_RESHOW
+
+    @property
+    def real(self) -> bool: return self.special is None
+
+
+@PromptServer.instance.routes.post('/instaraw/interactive_message')
+async def cg_image_filter_message(request):
+    post     = await request.post()
+    response = post.get("response")
+    message  = MessageState(response)
+
+    if str(MessageState.unique_expected)==str(message.unique):
+        if (MessageState.waiting()):
+            MessageState.set_latest(message)
+        else:
+            print(f"Ignoring response {response} because not waiting for one")
+    else:
+        print(f"Ignoring mismatched response {response}")
+
+    return web.json_response({})
+
+# --- NEW API ENDPOINT FOR INSTANT CACHE CLEARING ---
+@PromptServer.instance.routes.post('/instaraw/clear_text_filter_cache')
+async def clear_text_filter_cache(request):
+    try:
+        # The UID from the request isn't strictly needed anymore, but we can keep it for logging.
+        data = await request.json()
+        uid = data.get('uid')
+        print(f"๐Ÿงน INSTARAW API: Received request to clear text filter cache from node UID {uid}.")
+
+        cache_dir = os.path.join(os.path.dirname(__file__), "..", "..", "cache")
+        if not os.path.isdir(cache_dir):
+            print("๐Ÿงน INSTARAW API: Cache directory does not exist. Nothing to clear.")
+            return web.json_response({"status": "success", "cleared_count": 0, "message": "Cache directory not found."})
+
+        cleared_count = 0
+        files_cleared = []
+        
+        # Iterate over all files in the cache directory
+        for filename in os.listdir(cache_dir):
+            # If a file is a text filter cache file, delete it
+            if filename.endswith("_text_edit.json"):
+                file_path = os.path.join(cache_dir, filename)
+                try:
+                    os.remove(file_path)
+                    files_cleared.append(filename)
+                    cleared_count += 1
+                except OSError as e:
+                    print(f"โš ๏ธ INSTARAW API: Error clearing cache file {file_path}: {e}")
+                    # We continue even if one file fails to delete
+        
+        print(f"โœ… INSTARAW API: Successfully cleared {cleared_count} text filter cache file(s).")
+        return web.json_response({"status": "success", "cleared_count": cleared_count, "files": files_cleared})
+
+    except Exception as e:
+        print(f"โŒ INSTARAW API: An unexpected error occurred while clearing cache: {e}")
+        return web.json_response({"status": "error", "message": str(e)}, status=500)
+
+
+def wait_for_response(secs, uid, unique) -> Response:
+    MessageState.start_waiting(unique)
+    try:
+        end_time = time.monotonic() + secs
+        # Give a 2 second grace period before checking for interrupts
+        # This allows the mask editor to fully initialize
+        grace_period_end = time.monotonic() + 2.0
+
+        while(time.monotonic() < end_time and MessageState.waiting()):
+            # Only check for interrupts after grace period
+            if time.monotonic() > grace_period_end:
+                throw_exception_if_processing_interrupted()
+            PromptServer.instance.send_sync("instaraw-interactive-images", {"tick": int(end_time - time.monotonic()), "uid": uid, "unique":unique})
+            time.sleep(0.5)
+        if MessageState.waiting():
+            PromptServer.instance.send_sync("instaraw-interactive-images", {"timeout": True, "uid": uid, "unique":unique})
+        return MessageState.get_response()
+    finally: MessageState.stop_waiting()
+    
+def send_and_wait(payload, timeout, uid, unique) -> Response:
+    payload['uid'] = uid
+    payload['unique'] = unique
+
+    while True:
+        PromptServer.instance.send_sync("instaraw-interactive-images", payload)
+        r = wait_for_response(timeout, uid, unique)
+        if isinstance(r,CancelledResponse): raise InterruptProcessingException()
+        if (not isinstance(r, RequestResponse)): return r
\ No newline at end of file
diff --git a/custom_nodes/ComfyUI_INSTARAW/nodes/interactive_nodes/interactive_crop.py b/custom_nodes/ComfyUI_INSTARAW/nodes/interactive_nodes/interactive_crop.py
new file mode 100644
index 0000000000000000000000000000000000000000..bed367b29b133d6b80f2eafe9b84c65eeb12dde3
--- /dev/null
+++ b/custom_nodes/ComfyUI_INSTARAW/nodes/interactive_nodes/interactive_crop.py
@@ -0,0 +1,219 @@
+# ---
+# ComfyUI INSTARAW - Interactive Crop Node (Final Corrected Version)
+# Copyright ยฉ 2025 Instara. All rights reserved.
+# PROPRIETARY SOFTWARE - ALL RIGHTS RESERVED
+# ---
+
+import torch
+import json
+import os
+import hashlib
+from comfy.model_management import InterruptProcessingException
+from nodes import PreviewImage
+from .image_filter_messaging import send_and_wait, Response, TimeoutResponse
+
+ASPECT_RATIOS = {
+    "None (free)": None,
+    "896x1200 (Nano Banana 1K)": (896, 1200),  # Exact Nano Banana Pro 1K output
+    "3:4 (Portrait)": (3, 4),
+    "4:3 (Landscape)": (4, 3),
+    "2:3 (Portrait)": (2, 3),
+    "3:2 (Landscape)": (3, 2),
+    "9:16 (Tall)": (9, 16),
+    "16:9 (Wide)": (16, 9),
+    "1:1 (Square)": (1, 1),
+    "4:5 (Portrait)": (4, 5),
+    "5:4 (Landscape)": (5, 4),
+}
+
+
+class INSTARAW_Interactive_Crop(PreviewImage):
+    """
+    An interactive node that displays an image and allows a user to define a crop region
+    by drawing, moving, and resizing a rectangle. Supports locked aspect ratios.
+    """
+
+    RETURN_TYPES = ("IMAGE", "MASK", "STRING",)
+    RETURN_NAMES = ("cropped_image", "cropped_mask", "crop_data_json",)
+    FUNCTION = "crop_interactively"
+    CATEGORY = "INSTARAW/Interactive"
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "image": ("IMAGE",),
+                "timeout": ("INT", {"default": 600, "min": 1, "max": 999999}),
+                "cache_behavior": (["Run editor normally", "Edit previous crop", "Resend previous crop"], {
+                    "tooltip": "Behavior when a cached crop for this image already exists."
+                }),
+                "bypass": ("BOOLEAN", {
+                    "default": False,
+                    "tooltip": "When enabled, skips interactive UI and applies proposed_crop_json directly (or full image if not provided)."
+                }),
+                "lock_aspect_ratio": (
+                    list(ASPECT_RATIOS.keys()),
+                    {
+                        "default": "3:4 (Portrait)",
+                        "tooltip": "Lock crop box to specific aspect ratio. Set to 'None (free)' for free-form cropping."
+                    }
+                ),
+            },
+            "optional": {
+                "mask": ("MASK",),
+                "proposed_crop_json": ("STRING", {"forceInput": True}),
+            },
+            "hidden": {
+                "prompt": "PROMPT",
+                "extra_pnginfo": "EXTRA_PNGINFO",
+                "uid": "UNIQUE_ID",
+                "node_identifier": "NID",
+            },
+        }
+
+    @classmethod
+    def IS_CHANGED(cls, *args, **kwargs):
+        return float("NaN")
+
+    def crop_interactively(self, **kwargs):
+        image = kwargs.get('image')
+        timeout = kwargs.get('timeout')
+        cache_behavior = kwargs.get('cache_behavior', 'Run editor normally')
+        bypass = kwargs.get('bypass', False)
+        uid = kwargs.get('uid')
+        node_identifier = kwargs.get('node_identifier')
+        mask = kwargs.get('mask')
+        proposed_crop_json = kwargs.get('proposed_crop_json')
+
+        if image is None or timeout is None or uid is None or node_identifier is None:
+            raise ValueError("INSTARAW_Interactive_Crop is missing required inputs. Check connections.")
+
+        img_height = image.shape[1]
+        img_width = image.shape[2]
+
+        # BYPASS MODE: Skip interactive UI entirely
+        if bypass:
+            if proposed_crop_json and proposed_crop_json.strip():
+                try:
+                    crop_data = json.loads(proposed_crop_json)
+                    print(f"โญ๏ธ INSTARAW Crop BYPASS: Applying proposed crop directly: {crop_data}")
+                    return self._apply_crop(image, mask, crop_data)
+                except json.JSONDecodeError:
+                    print("โš ๏ธ INSTARAW Crop BYPASS: Invalid proposed_crop_json, returning full image.")
+            else:
+                print("โญ๏ธ INSTARAW Crop BYPASS: No proposed crop, returning full image.")
+
+            # No valid proposed crop, return full image
+            full_mask = torch.ones(1, img_height, img_width) if mask is None else mask
+            crop_data = {"x": 0, "y": 0, "width": img_width, "height": img_height}
+            return (image, full_mask, json.dumps(crop_data))
+
+        # Setup cache
+        cache_dir = os.path.join(os.path.dirname(__file__), "..", "..", "cache")
+        os.makedirs(cache_dir, exist_ok=True)
+
+        hasher = hashlib.sha256()
+        hasher.update(image.cpu().numpy().tobytes())
+        cache_key = hasher.hexdigest()
+        cache_filepath = os.path.join(cache_dir, f"{cache_key}_crop.json")
+
+        # Check for cached crop - "Resend previous crop" bypasses editor entirely
+        if cache_behavior == "Resend previous crop" and os.path.exists(cache_filepath):
+            print(f"โœ… INSTARAW Crop Cache Hit! Resending previous crop from {cache_filepath}")
+            with open(cache_filepath, 'r') as f:
+                crop_data = json.load(f)
+            return self._apply_crop(image, mask, crop_data)
+
+        save_kwargs = {}
+        if "prompt" in kwargs: save_kwargs["prompt"] = kwargs.get("prompt")
+        if "extra_pnginfo" in kwargs: save_kwargs["extra_pnginfo"] = kwargs.get("extra_pnginfo")
+
+        urls = self.save_images(images=image, **save_kwargs)['ui']['images']
+
+        # Get lock_aspect_ratio setting
+        lock_aspect_ratio = kwargs.get('lock_aspect_ratio', 'None (free)')
+        aspect_ratio_tuple = ASPECT_RATIOS.get(lock_aspect_ratio)
+
+        payload = {
+            "uid": uid,
+            "node_identifier": node_identifier,
+            "urls": urls,
+            "interactive_crop": True,
+        }
+
+        # Pass aspect ratio to frontend if locked
+        if aspect_ratio_tuple is not None:
+            payload["lock_aspect_ratio"] = {
+                "width": aspect_ratio_tuple[0],
+                "height": aspect_ratio_tuple[1],
+                "label": lock_aspect_ratio
+            }
+
+        # Determine proposed crop: from cache (Edit mode) or from input
+        if cache_behavior == "Edit previous crop" and os.path.exists(cache_filepath):
+            print(f"๐Ÿ’จ INSTARAW Crop Cache: Loading previous crop for editing from {cache_filepath}")
+            with open(cache_filepath, 'r') as f:
+                payload["proposed_crop"] = json.load(f)
+        elif proposed_crop_json and proposed_crop_json.strip():
+            try:
+                payload["proposed_crop"] = json.loads(proposed_crop_json)
+            except json.JSONDecodeError:
+                print("โš ๏ธ INSTARAW Interactive Crop: Invalid proposed_crop_json. Ignoring.")
+
+        print("๐Ÿ’จ INSTARAW Interactive Crop: Waiting for user to define crop area...")
+        response = send_and_wait(payload, timeout, uid, node_identifier)
+
+        if isinstance(response, TimeoutResponse):
+            print("โฐ INSTARAW Interactive Crop: Timed out. Passing through original image.")
+            full_mask = torch.ones_like(image[:, :, :, 0]) if mask is None else mask
+            crop_data = {"x": 0, "y": 0, "width": image.shape[2], "height": image.shape[1], "status": "timeout"}
+            return (image, full_mask, json.dumps(crop_data))
+
+        crop_data = response.crop if hasattr(response, 'crop') else None
+
+        if not crop_data:
+            raise InterruptProcessingException("User cancelled the crop operation.")
+
+        print(f"โœ… INSTARAW Interactive Crop: Received crop data: {crop_data}")
+
+        # Save to cache
+        print(f"๐Ÿ’พ Saving crop data to cache: {cache_filepath}")
+        with open(cache_filepath, 'w') as f:
+            json.dump(crop_data, f)
+
+        return self._apply_crop(image, mask, crop_data)
+
+    def _apply_crop(self, image, mask, crop_data):
+        """Apply crop to image and mask, return results."""
+        img_height = image.shape[1]
+        img_width = image.shape[2]
+
+        x = int(crop_data.get("x", 0))
+        y = int(crop_data.get("y", 0))
+        width = int(crop_data.get("width", img_width))
+        height = int(crop_data.get("height", img_height))
+
+        # Clamp crop to image bounds
+        x = max(0, min(x, img_width - 1))
+        y = max(0, min(y, img_height - 1))
+        width = max(1, min(width, img_width - x))
+        height = max(1, min(height, img_height - y))
+
+        if x != crop_data.get("x") or y != crop_data.get("y") or width != crop_data.get("width") or height != crop_data.get("height"):
+            print(f"โš ๏ธ INSTARAW Crop: Adjusted crop to fit image bounds: x={x}, y={y}, w={width}, h={height}")
+
+        cropped_image = image[:, y:y+height, x:x+width, :]
+
+        cropped_mask = None
+        if mask is not None:
+            mask_to_crop = mask
+            if mask.dim() == 4:
+                mask_to_crop = mask.squeeze(1)
+
+            cropped_mask = mask_to_crop[:, y:y+height, x:x+width]
+        else:
+            cropped_mask = torch.ones_like(cropped_image[:, :, :, 0])
+
+        # Return adjusted crop data
+        adjusted_crop_data = {"x": x, "y": y, "width": width, "height": height}
+        return (cropped_image, cropped_mask, json.dumps(adjusted_crop_data))
\ No newline at end of file
diff --git a/custom_nodes/ComfyUI_INSTARAW/nodes/interactive_nodes/prompt_filter.py b/custom_nodes/ComfyUI_INSTARAW/nodes/interactive_nodes/prompt_filter.py
new file mode 100644
index 0000000000000000000000000000000000000000..b1a51642354eb14100e2269562a092d25e64e74d
--- /dev/null
+++ b/custom_nodes/ComfyUI_INSTARAW/nodes/interactive_nodes/prompt_filter.py
@@ -0,0 +1,201 @@
+# ---
+# Filename: ../ComfyUI_INSTARAW/nodes/interactive_nodes/prompt_filter.py
+# ---
+
+"""
+INSTARAW Prompt Filter Node
+Allows filtering a list of images along with their synchronized prompt lists.
+Works with RPG outputs where everything is a list.
+"""
+
+from nodes import PreviewImage
+from comfy.model_management import InterruptProcessingException
+import os
+import torch
+import hashlib
+import json
+
+from .image_filter_messaging import send_and_wait, Response, TimeoutResponse
+
+HIDDEN = {
+    "prompt": "PROMPT",
+    "extra_pnginfo": "EXTRA_PNGINFO",
+    "uid": "UNIQUE_ID",
+    "node_identifier": "NID",
+}
+
+
+class INSTARAW_PromptFilter(PreviewImage):
+    """
+    Filter a list of images along with synchronized positive/negative prompt lists.
+
+    Takes inputs from RPG node (image list + prompt lists) and allows user to select
+    which images to keep. All parallel lists are filtered using the same indices.
+    """
+
+    RETURN_TYPES = ("IMAGE", "STRING", "STRING", "INT", "STRING", "INT")
+    RETURN_NAMES = ("images", "prompt_list_positive", "prompt_list_negative", "seed_list", "indexes", "count")
+    OUTPUT_IS_LIST = (True, True, True, True, False, False)
+    INPUT_IS_LIST = True
+    FUNCTION = "func"
+    CATEGORY = "INSTARAW/Interactive"
+    OUTPUT_NODE = False
+    DESCRIPTION = "Filter images and their associated prompts/seeds together"
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "images": ("IMAGE",),
+                "prompt_list_positive": ("STRING", {"forceInput": True}),
+                "prompt_list_negative": ("STRING", {"forceInput": True}),
+                "timeout": ("INT", {"default": 600, "min": 1, "max": 9999999, "tooltip": "Timeout in seconds."}),
+                "ontimeout": (["send none", "send all", "send first", "send last"], {}),
+                "cache_behavior": (["Run selector normally", "Resend previous selection"], {
+                    "tooltip": "Behavior when a cached selection for this image batch already exists."
+                }),
+            },
+            "optional": {
+                "seed_list": ("INT", {"forceInput": True}),
+                "tip": ("STRING", {"default": "", "tooltip": "Optional tip text displayed in popup window"}),
+            },
+            "hidden": HIDDEN,
+        }
+
+    @classmethod
+    def IS_CHANGED(cls, *args, **kwargs):
+        return float("NaN")
+
+    def func(self, **kwargs):
+        # Extract all inputs from kwargs - this handles INPUT_IS_LIST properly
+        images = kwargs.get('images', [])
+        prompt_list_positive = kwargs.get('prompt_list_positive', [])
+        prompt_list_negative = kwargs.get('prompt_list_negative', [])
+        seed_list = kwargs.get('seed_list')
+        timeout = kwargs.get('timeout', [600])
+        ontimeout = kwargs.get('ontimeout', ['send none'])
+        cache_behavior = kwargs.get('cache_behavior', ['Run selector normally'])
+        tip = kwargs.get('tip', [''])
+        uid = kwargs.get('uid')
+        node_identifier = kwargs.get('node_identifier')
+
+        # When INPUT_IS_LIST=True, all inputs come as lists - extract scalar values
+        timeout_val = timeout[0] if isinstance(timeout, list) else timeout
+        ontimeout_val = ontimeout[0] if isinstance(ontimeout, list) else ontimeout
+        cache_behavior_val = cache_behavior[0] if isinstance(cache_behavior, list) else cache_behavior
+        uid_val = uid[0] if isinstance(uid, list) else uid
+        node_identifier_val = node_identifier[0] if isinstance(node_identifier, list) else node_identifier
+        tip_val = (tip[0] if isinstance(tip, list) and tip else tip) or ""
+
+        # Build save_kwargs for image saving
+        save_kwargs = {}
+        if "prompt" in kwargs:
+            p = kwargs.get("prompt")
+            save_kwargs["prompt"] = p[0] if isinstance(p, list) else p
+        if "extra_pnginfo" in kwargs:
+            e = kwargs.get("extra_pnginfo")
+            save_kwargs["extra_pnginfo"] = e[0] if isinstance(e, list) else e
+
+        # images is a list of tensors, each (1, H, W, C) or (H, W, C)
+        image_list = list(images) if isinstance(images, (list, tuple)) else [images]
+        pos_list = list(prompt_list_positive) if isinstance(prompt_list_positive, (list, tuple)) else [prompt_list_positive]
+        neg_list = list(prompt_list_negative) if isinstance(prompt_list_negative, (list, tuple)) else [prompt_list_negative]
+        seeds = list(seed_list) if seed_list is not None and isinstance(seed_list, (list, tuple)) else (
+            [seed_list[0]] if seed_list is not None else None
+        )
+
+        B = len(image_list)
+
+        if B == 0:
+            raise InterruptProcessingException()
+
+        # Ensure all images have batch dimension and stack into batch
+        normalized_images = []
+        for img in image_list:
+            if img.dim() == 3:  # (H, W, C)
+                img = img.unsqueeze(0)  # (1, H, W, C)
+            normalized_images.append(img)
+
+        # Stack into single batch tensor for preview
+        batch_tensor = torch.cat(normalized_images, dim=0)  # (B, H, W, C)
+
+        # Pad lists if needed
+        if len(pos_list) < B:
+            pos_list = pos_list + [""] * (B - len(pos_list))
+        if len(neg_list) < B:
+            neg_list = neg_list + [""] * (B - len(neg_list))
+        if seeds is None:
+            seeds = [0] * B
+        elif len(seeds) < B:
+            seeds = seeds + [0] * (B - len(seeds))
+
+        # Setup cache
+        cache_dir = os.path.join(os.path.dirname(__file__), "..", "..", "cache")
+        os.makedirs(cache_dir, exist_ok=True)
+
+        hasher = hashlib.sha256()
+        hasher.update(batch_tensor.cpu().numpy().tobytes())
+        cache_key = hasher.hexdigest()
+        cache_filepath = os.path.join(cache_dir, f"{cache_key}_prompt_filter_selection.json")
+
+        images_to_return = None
+
+        # Check if we should use cached selection
+        if cache_behavior_val == "Resend previous selection" and os.path.exists(cache_filepath):
+            print(f"[PromptFilter] Using cached selection for images {cache_key[:8]}...")
+            with open(cache_filepath, 'r') as f:
+                cached_data = json.load(f)
+            images_to_return = cached_data.get('selection', [])
+        else:
+            # Show interactive popup
+            all_the_same = (B > 1 and all((batch_tensor[i] == batch_tensor[0]).all() for i in range(1, B)))
+            urls = self.save_images(images=batch_tensor, **save_kwargs)['ui']['images']
+            payload = {
+                "uid": uid_val,
+                "urls": urls,
+                "allsame": all_the_same,
+                "extras": ["", "", ""],
+                "tip": tip_val,
+                "video_frames": 1,
+            }
+
+            response: Response = send_and_wait(payload, timeout_val, uid_val, node_identifier_val)
+
+            if isinstance(response, TimeoutResponse):
+                if ontimeout_val == 'send none':
+                    images_to_return = []
+                elif ontimeout_val == 'send all':
+                    images_to_return = list(range(B))
+                elif ontimeout_val == 'send first':
+                    images_to_return = [0]
+                elif ontimeout_val == 'send last':
+                    images_to_return = [B - 1]
+            else:
+                images_to_return = [int(x) for x in response.selection] if response.selection else []
+
+            # Cache the selection
+            if not isinstance(response, TimeoutResponse):
+                print(f"[PromptFilter] Saving selection to cache: {cache_filepath}")
+                cache_data = {'selection': images_to_return}
+                with open(cache_filepath, 'w') as f:
+                    json.dump(cache_data, f)
+
+        # Check if no images selected
+        if images_to_return is None or len(images_to_return) == 0:
+            raise InterruptProcessingException()
+
+        # Filter all parallel lists using same indices - output as LISTS
+        final_indices = images_to_return
+
+        # Output images as list of individual tensors
+        filtered_images = [normalized_images[int(i)] for i in final_indices]
+        filtered_pos = [pos_list[int(i)] for i in final_indices]
+        filtered_neg = [neg_list[int(i)] for i in final_indices]
+        filtered_seeds = [seeds[int(i)] for i in final_indices]
+
+        indexes_str = ",".join(str(int(x)) for x in final_indices)
+        count = len(final_indices)
+
+        print(f"[PromptFilter] Selected {count} items at indices: {indexes_str}")
+
+        return (filtered_images, filtered_pos, filtered_neg, filtered_seeds, indexes_str, count)
diff --git a/custom_nodes/ComfyUI_INSTARAW/nodes/logic_nodes/__init__.py b/custom_nodes/ComfyUI_INSTARAW/nodes/logic_nodes/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..4046e936e87ddc38bda09ac6d653d5d59494f64e
--- /dev/null
+++ b/custom_nodes/ComfyUI_INSTARAW/nodes/logic_nodes/__init__.py
@@ -0,0 +1,28 @@
+"""
+INSTARAW Logic Nodes
+Nodes for controlling workflow execution and logic.
+"""
+
+from .logic_nodes import (
+    NODE_CLASS_MAPPINGS as LOGIC_MAPPINGS,
+    NODE_DISPLAY_NAME_MAPPINGS as LOGIC_DISPLAY_MAPPINGS,
+)
+
+# Import from our new virtual nodes file
+from .virtual_nodes import (
+    NODE_CLASS_MAPPINGS as VIRTUAL_LOGIC_MAPPINGS,
+    NODE_DISPLAY_NAME_MAPPINGS as VIRTUAL_LOGIC_DISPLAY_MAPPINGS,
+)
+
+# Combine all logic node mappings
+NODE_CLASS_MAPPINGS = {
+    **LOGIC_MAPPINGS,
+    **VIRTUAL_LOGIC_MAPPINGS,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    **LOGIC_DISPLAY_MAPPINGS,
+    **VIRTUAL_LOGIC_DISPLAY_MAPPINGS,
+}
+
+__all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS"]
\ No newline at end of file
diff --git a/custom_nodes/ComfyUI_INSTARAW/nodes/logic_nodes/logic_nodes.py b/custom_nodes/ComfyUI_INSTARAW/nodes/logic_nodes/logic_nodes.py
new file mode 100644
index 0000000000000000000000000000000000000000..6f672f24f326187f20385f122148bf20428ab7a6
--- /dev/null
+++ b/custom_nodes/ComfyUI_INSTARAW/nodes/logic_nodes/logic_nodes.py
@@ -0,0 +1,245 @@
+# ---
+# Filename: ../ComfyUI_INSTARAW/nodes/logic_nodes/logic_nodes.py
+# ---
+
+# ---
+# ComfyUI INSTARAW - Logic Nodes
+# Part of the INSTARAW custom nodes collection by Instara
+#
+# Copyright ยฉ 2025 Instara. All rights reserved.
+# PROPRIETARY SOFTWARE - ALL RIGHTS RESERVED
+# ---
+
+"""
+Type-specific logic nodes for creating reliable, conditional workflows.
+This approach avoids client-side JavaScript for maximum stability.
+"""
+
+_ag = abs(-1)
+
+# --- Boolean Logic Node ---
+class INSTARAW_BooleanLogic:
+    """
+    Performs logical operations (AND, OR, XOR, NOT) on boolean inputs.
+    Perfect for creating complex conditions to control switches.
+    """
+
+    OPERATIONS = ["AND", "OR", "XOR", "NOT A"]
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "boolean_a": ("BOOLEAN", {"forceInput": True}),
+                "operation": (cls.OPERATIONS, {"default": "AND"}),
+            },
+            "optional": {
+                "boolean_b": ("BOOLEAN", {"forceInput": True, "default": True}),
+            },
+        }
+
+    RETURN_TYPES = ("BOOLEAN",)
+    RETURN_NAMES = ("result",)
+    FUNCTION = "operate"
+    CATEGORY = "INSTARAW/Logic"
+
+    def operate(self, boolean_a, operation, boolean_b=True):
+        result = False
+        if operation == "AND":
+            result = boolean_a and boolean_b
+        elif operation == "OR":
+            result = boolean_a or boolean_b
+        elif operation == "XOR":
+            result = boolean_a != boolean_b
+        elif operation == "NOT A":
+            result = not boolean_a
+
+        return (result,)
+
+
+# --- Image To Boolean Node ---
+class INSTARAW_ImageToBoolean:
+    """
+    Outputs a boolean value based on the presence of an image input.
+    True if an image is connected and not None, otherwise False.
+    Useful for controlling switches based on whether an image exists in the workflow path.
+    """
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "optional": {
+                "image": ("IMAGE",),
+            }
+        }
+
+    RETURN_TYPES = ("BOOLEAN",)
+    RETURN_NAMES = ("boolean",)
+    FUNCTION = "check_image"
+    CATEGORY = "INSTARAW/Logic"
+
+    def check_image(self, image=None):
+        is_present = image is not None
+        return (is_present,)
+
+
+# A simple base class to avoid repeating the switch logic.
+class INSTARAW_SwitchBase:
+    FUNCTION = "switch"
+    CATEGORY = "INSTARAW/Logic"
+
+    def switch(self, boolean=False, input_true=None, input_false=None):
+        """
+        Selects which input to pass through. If boolean is False or None (not connected/bypassed),
+        it defaults to the input_false path.
+        """
+        if boolean:
+            return (input_true,)
+        else:
+            return (input_false,)
+
+
+# --- Type-Specific Switches (UPDATED) ---
+
+
+class INSTARAW_ImageSwitch(INSTARAW_SwitchBase):
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "optional": {
+                "boolean": ("BOOLEAN", {}),
+                "input_true": ("IMAGE",),
+                "input_false": ("IMAGE",),
+            },
+        }
+    RETURN_TYPES = ("IMAGE",); RETURN_NAMES = ("output",)
+
+
+class INSTARAW_MaskSwitch(INSTARAW_SwitchBase):
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "optional": {
+                "boolean": ("BOOLEAN", {}),
+                "input_true": ("MASK",),
+                "input_false": ("MASK",),
+            },
+        }
+    RETURN_TYPES = ("MASK",); RETURN_NAMES = ("output",)
+
+
+class INSTARAW_LatentSwitch(INSTARAW_SwitchBase):
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "optional": {
+                "boolean": ("BOOLEAN", {}),
+                "input_true": ("LATENT",),
+                "input_false": ("LATENT",),
+            },
+        }
+    RETURN_TYPES = ("LATENT",); RETURN_NAMES = ("output",)
+
+
+class INSTARAW_ConditioningSwitch(INSTARAW_SwitchBase):
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "optional": {
+                "boolean": ("BOOLEAN", {}),
+                "input_true": ("CONDITIONING",),
+                "input_false": ("CONDITIONING",),
+            },
+        }
+    RETURN_TYPES = ("CONDITIONING",); RETURN_NAMES = ("output",)
+
+
+class INSTARAW_IntSwitch(INSTARAW_SwitchBase):
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "optional": {
+                "boolean": ("BOOLEAN", {}),
+                "input_true": ("INT", {}),
+                "input_false": ("INT", {}),
+            },
+        }
+    RETURN_TYPES = ("INT",); RETURN_NAMES = ("output",)
+
+
+class INSTARAW_FloatSwitch(INSTARAW_SwitchBase):
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "optional": {
+                "boolean": ("BOOLEAN", {}),
+                "input_true": ("FLOAT", {}),
+                "input_false": ("FLOAT", {}),
+            },
+        }
+    RETURN_TYPES = ("FLOAT",); RETURN_NAMES = ("output",)
+
+
+class INSTARAW_StringSwitch(INSTARAW_SwitchBase):
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "optional": {
+                "boolean": ("BOOLEAN", {}),
+                "input_true": ("STRING", {}),
+                "input_false": ("STRING", {}),
+            },
+        }
+    RETURN_TYPES = ("STRING",); RETURN_NAMES = ("output",)
+
+class INSTARAW_AnySwitch(INSTARAW_SwitchBase):
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "optional": {
+                "boolean": ("BOOLEAN", {}),
+                "input_true": ("*",),
+                "input_false": ("*",),
+            },
+        }
+    RETURN_TYPES = ("*",); RETURN_NAMES = ("output",)
+
+class INSTARAW_InvertBoolean:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {"required": {"boolean": ("BOOLEAN", {"forceInput": True})}}
+    RETURN_TYPES = ("BOOLEAN",); FUNCTION = "invert"; CATEGORY = "INSTARAW/Logic"
+    def invert(self, boolean): return (not boolean,)
+
+
+# =================================================================================
+# EXPORT NODE MAPPINGS
+# =================================================================================
+
+NODE_CLASS_MAPPINGS = {
+    "INSTARAW_BooleanLogic": INSTARAW_BooleanLogic,
+    "INSTARAW_ImageToBoolean": INSTARAW_ImageToBoolean,
+    "INSTARAW_ImageSwitch": INSTARAW_ImageSwitch,
+    "INSTARAW_MaskSwitch": INSTARAW_MaskSwitch,
+    "INSTARAW_LatentSwitch": INSTARAW_LatentSwitch,
+    "INSTARAW_ConditioningSwitch": INSTARAW_ConditioningSwitch,
+    "INSTARAW_IntSwitch": INSTARAW_IntSwitch,
+    "INSTARAW_FloatSwitch": INSTARAW_FloatSwitch,
+    "INSTARAW_StringSwitch": INSTARAW_StringSwitch,
+    "INSTARAW_AnySwitch": INSTARAW_AnySwitch,
+    "INSTARAW_InvertBoolean": INSTARAW_InvertBoolean,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "INSTARAW_BooleanLogic": "๐Ÿ”€ INSTARAW Boolean Logic",
+    "INSTARAW_ImageToBoolean": "๐Ÿ”€ INSTARAW Image to Boolean",
+    "INSTARAW_ImageSwitch": "๐Ÿ”€ INSTARAW Switch (Image)",
+    "INSTARAW_MaskSwitch": "๐Ÿ”€ INSTARAW Switch (Mask)",
+    "INSTARAW_LatentSwitch": "๐Ÿ”€ INSTARAW Switch (Latent)",
+    "INSTARAW_ConditioningSwitch": "๐Ÿ”€ INSTARAW Switch (Conditioning)",
+    "INSTARAW_IntSwitch": "๐Ÿ”€ INSTARAW Switch (Int)",
+    "INSTARAW_FloatSwitch": "๐Ÿ”€ INSTARAW Switch (Float)",
+    "INSTARAW_StringSwitch": "๐Ÿ”€ INSTARAW Switch (String)",
+    "INSTARAW_AnySwitch": "๐Ÿ”€ INSTARAW Switch (Any)",
+    "INSTARAW_InvertBoolean": "๐Ÿ” INSTARAW Invert Boolean",
+}
\ No newline at end of file
diff --git a/custom_nodes/ComfyUI_INSTARAW/nodes/logic_nodes/virtual_nodes.py b/custom_nodes/ComfyUI_INSTARAW/nodes/logic_nodes/virtual_nodes.py
new file mode 100644
index 0000000000000000000000000000000000000000..485477c51496ed72a6db37964220f3b1cf21c180
--- /dev/null
+++ b/custom_nodes/ComfyUI_INSTARAW/nodes/logic_nodes/virtual_nodes.py
@@ -0,0 +1,129 @@
+# --- Filename: ../ComfyUI_INSTARAW/nodes/logic_nodes/virtual_nodes.py (UPDATED) ---
+import json
+from comfy.comfy_types.node_typing import IO
+import torch
+
+
+class INSTARAW_BooleanBypass:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "boolean": ("BOOLEAN", {}),
+                "invert_input": (
+                    "BOOLEAN",
+                    {"default": False, "label_on": "INVERTED", "label_off": "NORMAL"},
+                ),
+            },
+            "optional": {
+                "input_1": ("*",),
+                "input_2": ("*",),
+                "input_3": ("*",),
+                "input_4": ("*",),
+            },
+        }
+
+    OUTPUT_NODE = False
+    RETURN_TYPES = (
+        "*",
+        "*",
+        "*",
+        "*",
+    )
+    RETURN_NAMES = (
+        "output_1",
+        "output_2",
+        "output_3",
+        "output_4",
+    )
+    FUNCTION = "passthrough"
+    CATEGORY = "INSTARAW/Logic"
+
+    def passthrough(
+        self,
+        boolean,
+        invert_input,
+        input_1=None,
+        input_2=None,
+        input_3=None,
+        input_4=None,
+    ):
+        return (
+            input_1,
+            input_2,
+            input_3,
+            input_4,
+        )
+
+# --- NEW NODE CLASS ---
+class INSTARAW_GroupBypassToBoolean:
+    """
+    Outputs a boolean reflecting its own bypass state.
+    Place this node inside a group to use the group's bypass as a boolean controller.
+    """
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                # This widget will be controlled by the JS. It's 'required'
+                # to ensure it's always present for the JS to find.
+                "is_active": ("BOOLEAN", {"default": True}),
+            }
+        }
+
+    RETURN_TYPES = ("BOOLEAN",)
+    RETURN_NAMES = ("boolean",)
+    FUNCTION = "get_status"
+    CATEGORY = "INSTARAW/Logic"
+
+    def get_status(self, is_active):
+        # This function simply returns the value of the widget,
+        # which is dynamically set by the frontend JavaScript.
+        return (is_active,)
+# --- END NEW NODE CLASS ---
+
+
+class INSTARAW_PreviewAnyAndPassthrough:
+    @classmethod
+    def INPUT_TYPES(cls):
+        # We only define the input socket. The UI will be generated automatically by the return message.
+        return {
+            "required": {"source": (IO.ANY, {})},
+        }
+
+    RETURN_TYPES = (IO.ANY,)
+    RETURN_NAMES = ("source_out",)
+    FUNCTION = "preview_and_pass"
+    CATEGORY = "INSTARAW/Logic"
+    OUTPUT_NODE = True  # CRITICAL for passthrough functionality
+
+    def preview_and_pass(self, source=None):
+        value = "None"
+        if isinstance(source, torch.Tensor):
+            value = f"Tensor with shape: {source.shape}\n(dtype: {source.dtype}, device: {source.device})"
+        elif isinstance(source, str):
+            value = source
+        elif isinstance(source, (int, float, bool)):
+            value = str(source)
+        elif source is not None:
+            try:
+                value = json.dumps(source, indent=4)
+            except Exception:
+                try:
+                    value = str(source)
+                except Exception:
+                    value = "source exists, but could not be serialized."
+
+        return {"ui": {"text": [value]}, "result": (source,)}
+
+
+NODE_CLASS_MAPPINGS = {
+    "INSTARAW_BooleanBypass": INSTARAW_BooleanBypass,
+    "INSTARAW_PreviewAnyAndPassthrough": INSTARAW_PreviewAnyAndPassthrough,
+    "INSTARAW_GroupBypassToBoolean": INSTARAW_GroupBypassToBoolean, # Add new node here
+}
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "INSTARAW_BooleanBypass": "๐Ÿ”€ INSTARAW Boolean Bypass",
+    "INSTARAW_PreviewAnyAndPassthrough": "๐Ÿ‘๏ธ INSTARAW Preview Any & Passthrough",
+    "INSTARAW_GroupBypassToBoolean": "๐Ÿ”€ INSTARAW Group Bypass Detector", # And here
+}
\ No newline at end of file
diff --git a/custom_nodes/ComfyUI_INSTARAW/nodes/nsfw_nodes/__init__.py b/custom_nodes/ComfyUI_INSTARAW/nodes/nsfw_nodes/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..b8e575f5949991c7d7254e64c9f568b0422e89b5
--- /dev/null
+++ b/custom_nodes/ComfyUI_INSTARAW/nodes/nsfw_nodes/__init__.py
@@ -0,0 +1,23 @@
+# ---
+# ComfyUI INSTARAW - NSFW Nodes Init
+# ---
+
+from .nsfw_detector import NODE_CLASS_MAPPINGS as DETECTOR_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS as DETECTOR_DISPLAY_MAPPINGS
+from .propose_crop import NODE_CLASS_MAPPINGS as PROPOSER_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS as PROPOSER_DISPLAY_MAPPINGS
+from .restore_crop import NODE_CLASS_MAPPINGS as RESTORE_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS as RESTORE_DISPLAY_MAPPINGS
+
+# Combine mappings
+NODE_CLASS_MAPPINGS = {
+    **DETECTOR_MAPPINGS,
+    **PROPOSER_MAPPINGS,
+    **RESTORE_MAPPINGS,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    **DETECTOR_DISPLAY_MAPPINGS,
+    **PROPOSER_DISPLAY_MAPPINGS,
+    **RESTORE_DISPLAY_MAPPINGS,
+}
+
+
+__all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS"]
\ No newline at end of file
diff --git a/custom_nodes/ComfyUI_INSTARAW/nodes/nsfw_nodes/nsfw_core.py b/custom_nodes/ComfyUI_INSTARAW/nodes/nsfw_nodes/nsfw_core.py
new file mode 100644
index 0000000000000000000000000000000000000000..289ce164f111fe5d497c9ea9c56fedb62bdc6f1c
--- /dev/null
+++ b/custom_nodes/ComfyUI_INSTARAW/nodes/nsfw_nodes/nsfw_core.py
@@ -0,0 +1,75 @@
+# ---
+# ComfyUI INSTARAW - NSFW Core Logic (v4.0 - Correct, Working Version)
+# This version perfectly mimics the original Nudenet.py's preprocessing to ensure identical results.
+# Copyright ยฉ 2025 Instara. All rights reserved.
+# PROPRIETARY SOFTWARE - ALL RIGHTS RESERVED
+# ---
+
+import cv2
+import numpy as np
+import math
+
+def preprocess_image(image_np_float: np.ndarray, target_size: int = 320):
+    if image_np_float.dtype != np.float32:
+        image_np_float = image_np_float.astype(np.float32)
+
+    img_channel_swapped = cv2.cvtColor(image_np_float, cv2.COLOR_RGB2BGR)
+
+    img_height, img_width = img_channel_swapped.shape[:2]
+    aspect = img_width / img_height
+
+    if img_height > img_width:
+        new_height = target_size
+        new_width = int(round(target_size * aspect))
+    else:
+        new_width = target_size
+        new_height = int(round(target_size / aspect))
+
+    resize_factor = math.sqrt((img_width**2 + img_height**2) / (new_width**2 + new_height**2))
+    
+    img_resized = cv2.resize(img_channel_swapped, (new_width, new_height))
+
+    pad_x = target_size - new_width
+    pad_y = target_size - new_height
+    pad_top, pad_bottom = pad_y // 2, pad_y - (pad_y // 2)
+    pad_left, pad_right = pad_x // 2, pad_x - (pad_x // 2)
+
+    img_padded = cv2.copyMakeBorder(
+        img_resized, pad_top, pad_bottom, pad_left, pad_right,
+        cv2.BORDER_CONSTANT, value=[0, 0, 0]
+    )
+    
+    img_final = cv2.resize(img_padded, (target_size, target_size))
+
+    image_data = img_final.astype(np.float32)
+    image_data = np.transpose(image_data, (2, 0, 1))
+    image_data = np.expand_dims(image_data, axis=0)
+    
+    return image_data, resize_factor, pad_left, pad_top
+
+def postprocess_detections(output, resize_factor, pad_left, pad_top, min_score):
+    outputs = np.transpose(np.squeeze(output[0]))
+    rows = outputs.shape[0]
+    boxes, scores, class_ids = [], [], []
+
+    for i in range(rows):
+        classes_scores = outputs[i][4:]
+        max_score = np.amax(classes_scores)
+        if max_score >= min_score:
+            class_id = np.argmax(classes_scores)
+            x, y, w, h = outputs[i][0], outputs[i][1], outputs[i][2], outputs[i][3]
+            
+            left = (x - w * 0.5 - pad_left) * resize_factor
+            top = (y - h * 0.5 - pad_top) * resize_factor
+            width = w * resize_factor
+            height = h * resize_factor
+            
+            class_ids.append(class_id)
+            scores.append(max_score)
+            boxes.append([left, top, width, height])
+
+    indices = cv2.dnn.NMSBoxes(boxes, scores, min_score, 0.45)
+    
+    if len(indices) == 0: return []
+
+    return [{"id": int(class_ids[i]), "score": round(float(scores[i]), 2), "box": [int(round(coord)) for coord in boxes[i]],} for i in indices]
\ No newline at end of file
diff --git a/custom_nodes/ComfyUI_INSTARAW/nodes/nsfw_nodes/nsfw_detector.py b/custom_nodes/ComfyUI_INSTARAW/nodes/nsfw_nodes/nsfw_detector.py
new file mode 100644
index 0000000000000000000000000000000000000000..6f161e2e8f0992020fd92f659e1a0f29640c321b
--- /dev/null
+++ b/custom_nodes/ComfyUI_INSTARAW/nodes/nsfw_nodes/nsfw_detector.py
@@ -0,0 +1,134 @@
+# ---
+# ComfyUI INSTARAW - NSFW Detector Node (v11.0 - UI Polish)
+# Copyright ยฉ 2025 Instara. All rights reserved.
+# PROPRIETARY SOFTWARE - ALL RIGHTS RESERVED
+# ---
+
+import torch
+import numpy as np
+import json
+import onnxruntime
+import os
+import requests
+from tqdm import tqdm
+from .nsfw_core import preprocess_image, postprocess_detections
+import folder_paths
+from typing_extensions import TypedDict
+
+# (Helper functions setup_nsfw_models_folder and download_model_if_missing remain the same)
+def setup_nsfw_models_folder():
+    model_folder = "nsfw_models"
+    if model_folder not in folder_paths.folder_names_and_paths:
+        nsfw_model_path = os.path.join(folder_paths.models_dir, model_folder)
+        os.makedirs(nsfw_model_path, exist_ok=True)
+        folder_paths.add_model_folder_path(model_folder, nsfw_model_path)
+setup_nsfw_models_folder()
+def download_model_if_missing(model_name="nudenet.onnx"):
+    model_folder = "nsfw_models"
+    model_path = folder_paths.get_full_path(model_folder, model_name)
+    if model_path and os.path.exists(model_path): return
+    print(f"๐Ÿ’จ INSTARAW NSFW: Model '{model_name}' not found. Downloading...")
+    target_dir = os.path.join(folder_paths.models_dir, model_folder)
+    os.makedirs(target_dir, exist_ok=True)
+    model_path = os.path.join(target_dir, model_name)
+    url = "https://d2xl8ijk56kv4u.cloudfront.net/models/nudenet.onnx"
+    try:
+        response = requests.get(url, stream=True)
+        response.raise_for_status()
+        total_size = int(response.headers.get('content-length', 0))
+        with open(model_path, 'wb') as f, tqdm(desc=model_name, total=total_size, unit='iB', unit_scale=True, unit_divisor=1024) as bar:
+            for data in response.iter_content(chunk_size=1024): f.write(data); bar.update(len(data))
+        print(f"โœ… INSTARAW NSFW: Model downloaded to {model_path}")
+    except Exception as e:
+        print(f"โŒ INSTARAW NSFW: Failed to download model. Error: {e}")
+        if os.path.exists(model_path): os.remove(model_path)
+        raise e
+
+class INSTARAW_NSFW_ModelLoader:
+    class ModelLoader(TypedDict):
+        session: onnxruntime.InferenceSession; input_width: int; input_name: str
+    @classmethod
+    def INPUT_TYPES(cls):
+        try: download_model_if_missing()
+        except Exception: pass
+        return {"required": {"model_name": (folder_paths.get_filename_list("nsfw_models"),)}}
+    RETURN_TYPES = ("NSFW_MODEL",); FUNCTION = "load_model"; CATEGORY = "INSTARAW/NSFW"
+    def load_model(self, model_name):
+        model_path = folder_paths.get_full_path("nsfw_models", model_name)
+        if not model_path or not os.path.exists(model_path):
+            download_model_if_missing(model_name)
+            model_path = folder_paths.get_full_path("nsfw_models", model_name)
+            if not model_path: raise FileNotFoundError(f"NSFW model '{model_name}' not found.")
+        try: session = onnxruntime.InferenceSession(model_path, providers=['CUDAExecutionProvider', 'CPUExecutionProvider'])
+        except Exception: session = onnxruntime.InferenceSession(model_path, providers=['CPUExecutionProvider'])
+        model_inputs = session.get_inputs()
+        return (self.ModelLoader(session=session, input_width=model_inputs[0].shape[2], input_name=model_inputs[0].name),)
+
+class INSTARAW_CensorLabels:
+    # --- THIS IS THE REBRANDING LOGIC ---
+    # Technical names required by the model
+    ID_MAP = {0: "FEMALE_GENITALIA_COVERED", 1: "FACE_FEMALE", 2: "BUTTOCKS_EXPOSED", 3: "FEMALE_BREAST_EXPOSED", 4: "FEMALE_GENITALIA_EXPOSED", 5: "MALE_BREAST_EXPOSED", 6: "ANUS_EXPOSED", 7: "FEET_EXPOSED", 8: "BELLY_COVERED", 9: "FEET_COVERED", 10: "ARMPITS_COVERED", 11: "ARMPITS_EXPOSED", 12: "FACE_MALE", 13: "BELLY_EXPOSED", 14: "MALE_GENITALIA_EXPOSED", 15: "ANUS_COVERED", 16: "FEMALE_BREAST_COVERED", 17: "BUTTOCKS_COVERED"}
+    LABEL_TO_ID = {v: k for k, v in ID_MAP.items()}
+
+    # New, UI-friendly display names
+    DISPLAY_NAMES = {
+        "ANUS_COVERED": "Anus (Covered)", "ANUS_EXPOSED": "Anus (Exposed)",
+        "ARMPITS_COVERED": "Armpits (Covered)", "ARMPITS_EXPOSED": "Armpits (Exposed)",
+        "BELLY_COVERED": "Belly (Covered)", "BELLY_EXPOSED": "Belly (Exposed)",
+        "BUTTOCKS_COVERED": "Buttocks (Covered)", "BUTTOCKS_EXPOSED": "Buttocks (Exposed)",
+        "FACE_FEMALE": "Face (Female)", "FACE_MALE": "Face (Male)",
+        "FEET_COVERED": "Feet (Covered)", "FEET_EXPOSED": "Feet (Exposed)",
+        "FEMALE_BREAST_COVERED": "Female Breast (Covered)", "FEMALE_BREAST_EXPOSED": "Female Breast (Exposed)",
+        "FEMALE_GENITALIA_COVERED": "Female Genitalia (Covered)", "FEMALE_GENITALIA_EXPOSED": "Female Genitalia (Exposed)",
+        "MALE_BREAST_EXPOSED": "Male Breast (Exposed)", "MALE_GENITALIA_EXPOSED": "Male Genitalia (Exposed)",
+    }
+    # Reverse mapping to get the technical name from the display name
+    NAME_TO_LABEL = {v: k for k, v in DISPLAY_NAMES.items()}
+    
+    @classmethod
+    def INPUT_TYPES(cls):
+        # Use the sorted display names for a clean UI
+        sorted_display_names = sorted(cls.DISPLAY_NAMES.values())
+        return {"required": { name: ("BOOLEAN", {"default": False}) for name in sorted_display_names}}
+
+    RETURN_TYPES = ("CENSOR_LABELS",); FUNCTION = "get_censor_labels"; CATEGORY = "INSTARAW/NSFW"
+    
+    def get_censor_labels(self, **kwargs):
+        # Logic is now: Censor if the toggle is ON (True)
+        censor_ids = []
+        for display_name, should_censor in kwargs.items():
+            if should_censor:
+                technical_label = self.NAME_TO_LABEL[display_name]
+                censor_ids.append(self.LABEL_TO_ID[technical_label])
+        return (censor_ids,)
+
+class INSTARAW_NSFW_Detector:
+    CLASS_MAP = INSTARAW_CensorLabels.ID_MAP
+    @classmethod
+    def INPUT_TYPES(cls): return { "required": { "image": ("IMAGE",), "nsfw_model": ("NSFW_MODEL",), "censor_labels": ("CENSOR_LABELS",), "min_score": ("FLOAT", {"default": 0.2, "min": 0.0, "max": 1.0, "step": 0.01}), } }
+    RETURN_TYPES = ("STRING", "MASK", "STRING",); RETURN_NAMES = ("censored_detections_json", "detection_mask", "all_detections_json",); FUNCTION = "detect"; CATEGORY = "INSTARAW/NSFW"
+    
+    def detect(self, image, nsfw_model, censor_labels, min_score):
+        image_np_float = image[0].cpu().numpy()
+        preprocessed, factor, pad_l, pad_t = preprocess_image(image_np_float, nsfw_model["input_width"])
+        outputs = nsfw_model["session"].run(None, {nsfw_model["input_name"]: preprocessed})
+        all_detections = postprocess_detections(outputs, factor, pad_l, pad_t, min_score)
+        
+        all_detections_labeled = [{'label': self.CLASS_MAP.get(d.get("id"), "UNKNOWN"), **d} for d in all_detections]
+        
+        # Censor a detection if its ID IS in the censor_labels list.
+        censored_detections = [d for d in all_detections_labeled if d.get('id') in censor_labels]
+        
+        _b, h, w, _c = image.shape
+        mask_tensor = torch.zeros((h, w), dtype=torch.float32, device=image.device)
+        for d in censored_detections:
+            box = d["box"]; x, y, w_box, h_box = box[0], box[1], box[2], box[3]
+            x1, y1 = max(0, x), max(0, y)
+            x2, y2 = min(int(w), x + w_box), min(int(h), y + h_box)
+            mask_tensor[y1:y2, x1:x2] = 1.0
+        mask_tensor = mask_tensor.unsqueeze(0)
+        
+        return (json.dumps(censored_detections, indent=2), mask_tensor, json.dumps(all_detections_labeled, indent=2),)
+
+NODE_CLASS_MAPPINGS = { "INSTARAW_NSFW_ModelLoader": INSTARAW_NSFW_ModelLoader, "INSTARAW_CensorLabels": INSTARAW_CensorLabels, "INSTARAW_NSFW_Detector": INSTARAW_NSFW_Detector, }
+NODE_DISPLAY_NAME_MAPPINGS = { "INSTARAW_NSFW_ModelLoader": "๐Ÿ’ฟ INSTARAW NSFW Model Loader", "INSTARAW_CensorLabels": "โœ… INSTARAW Censor Labels", "INSTARAW_NSFW_Detector": "โš ๏ธ INSTARAW NSFW Detector", }
\ No newline at end of file
diff --git a/custom_nodes/ComfyUI_INSTARAW/nodes/nsfw_nodes/propose_crop.py b/custom_nodes/ComfyUI_INSTARAW/nodes/nsfw_nodes/propose_crop.py
new file mode 100644
index 0000000000000000000000000000000000000000..8b7852c65f7dfc1bac22a3c06808dcd28506e61f
--- /dev/null
+++ b/custom_nodes/ComfyUI_INSTARAW/nodes/nsfw_nodes/propose_crop.py
@@ -0,0 +1,86 @@
+# ---
+# ComfyUI INSTARAW - Propose Crop from Detections Node
+# Copyright ยฉ 2025 Instara. All rights reserved.
+# PROPRIETARY SOFTWARE - ALL RIGHTS RESERVED
+# ---
+
+import json
+import torch
+
+class INSTARAW_CropBox_From_BoundingBoxes:
+    """
+    Calculates a "safe" crop area that excludes detected NSFW bounding boxes.
+    It takes JSON-formatted bounding box data and an image (for dimensions)
+    and outputs a JSON-formatted crop proposal.
+    """
+    
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "image": ("IMAGE",),
+                "bounding_boxes_json": ("STRING", {"forceInput": True, "multiline": True}),
+                "crop_strategy": (["Keep Upper Body", "Keep Top Half"], {"default": "Keep Upper Body"}),
+                "padding": ("INT", {"default": 20, "min": 0, "max": 1024, "step": 1, "tooltip": "Safety margin in pixels to add above the highest detected box."}),
+            }
+        }
+
+    RETURN_TYPES = ("STRING",)
+    RETURN_NAMES = ("proposed_crop_json",)
+    FUNCTION = "propose_crop"
+    CATEGORY = "INSTARAW/NSFW"
+
+    def propose_crop(self, image, bounding_boxes_json, crop_strategy, padding):
+        _batch, img_height, img_width, _channels = image.shape
+        
+        try:
+            detections = json.loads(bounding_boxes_json)
+        except (json.JSONDecodeError, TypeError):
+            detections = []
+
+        # If no NSFW regions are detected, propose a full-image crop.
+        if not detections:
+            full_crop = {"x": 0, "y": 0, "width": img_width, "height": img_height, "status": "no_detections"}
+            return (json.dumps(full_crop),)
+            
+        # Find the highest point (minimum y-coordinate) of all detected boxes.
+        # The box format is [x, y, width, height].
+        min_y = min(d['box'][1] for d in detections if 'box' in d and len(d['box']) == 4)
+        
+        # Calculate the final crop height based on the strategy.
+        crop_height = 0
+        if crop_strategy == "Keep Upper Body":
+            # The crop ends 'padding' pixels above the highest detection.
+            crop_height = max(0, min_y - padding)
+        elif crop_strategy == "Keep Top Half":
+            crop_height = img_height // 2
+            
+        # Ensure the crop height is not zero or negative.
+        if crop_height <= 0:
+            print("โš ๏ธ INSTARAW Propose Crop: Calculated crop height is zero or less. Defaulting to top half.")
+            crop_height = img_height // 2
+        
+        # The crop always starts from the top-left corner.
+        proposed_crop = {
+            "x": 0,
+            "y": 0,
+            "width": img_width,
+            "height": int(crop_height),
+            "status": "proposal_generated"
+        }
+        
+        return (json.dumps(proposed_crop),)
+
+# --- ADD THIS CODE TO THE END OF THE FILE ---
+
+# =================================================================================
+# NODE REGISTRATION
+# =================================================================================
+
+NODE_CLASS_MAPPINGS = {
+    "INSTARAW_CropBox_From_BoundingBoxes": INSTARAW_CropBox_From_BoundingBoxes,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "INSTARAW_CropBox_From_BoundingBoxes": "โœ‚๏ธ INSTARAW Propose Crop from Detections",
+}
\ No newline at end of file
diff --git a/custom_nodes/ComfyUI_INSTARAW/nodes/nsfw_nodes/restore_crop.py b/custom_nodes/ComfyUI_INSTARAW/nodes/nsfw_nodes/restore_crop.py
new file mode 100644
index 0000000000000000000000000000000000000000..c049345011332b004ee8f97ff3c95caf68598a72
--- /dev/null
+++ b/custom_nodes/ComfyUI_INSTARAW/nodes/nsfw_nodes/restore_crop.py
@@ -0,0 +1,95 @@
+# ---
+# ComfyUI INSTARAW - Restore Crop to Original Node
+# Copyright ยฉ 2025 Instara. All rights reserved.
+# PROPRIETARY SOFTWARE - ALL RIGHTS RESERVED
+# ---
+
+import json
+import torch
+
+
+class INSTARAW_RestoreCropToOriginal:
+    """
+    Restores a processed cropped image back into the original full image.
+    Used in NSFW fix workflows where you crop to a safe area, process it
+    (e.g., faceswap), then fuse it back into the original.
+    """
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "original_image": ("IMAGE", {"tooltip": "The original full-size image"}),
+                "cropped_image": ("IMAGE", {"tooltip": "The processed cropped image to restore"}),
+                "crop_data_json": ("STRING", {"forceInput": True, "tooltip": "JSON with x, y, width, height from the crop node"}),
+            },
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    RETURN_NAMES = ("restored_image",)
+    FUNCTION = "restore_crop"
+    CATEGORY = "INSTARAW/NSFW"
+
+    def restore_crop(self, original_image, cropped_image, crop_data_json):
+        # Parse crop data
+        try:
+            crop_data = json.loads(crop_data_json)
+        except (json.JSONDecodeError, TypeError) as e:
+            raise ValueError(f"Invalid crop_data_json: {e}")
+
+        x = int(crop_data.get("x", 0))
+        y = int(crop_data.get("y", 0))
+        width = int(crop_data.get("width", 0))
+        height = int(crop_data.get("height", 0))
+
+        # Validate crop data
+        if width <= 0 or height <= 0:
+            print("โš ๏ธ INSTARAW Restore Crop: Invalid crop dimensions. Returning original image.")
+            return (original_image,)
+
+        # Get dimensions
+        batch_size, orig_h, orig_w, channels = original_image.shape
+        _, crop_h, crop_w, _ = cropped_image.shape
+
+        # Check if cropped image matches expected dimensions
+        if crop_w != width or crop_h != height:
+            print(f"โš ๏ธ INSTARAW Restore Crop: Cropped image size ({crop_w}x{crop_h}) doesn't match crop data ({width}x{height}). Resizing...")
+            # Resize cropped image to match expected dimensions
+            # Permute to BCHW for interpolate, then back to BHWC
+            cropped_permuted = cropped_image.permute(0, 3, 1, 2)
+            resized = torch.nn.functional.interpolate(
+                cropped_permuted,
+                size=(height, width),
+                mode="bilinear",
+                align_corners=False
+            )
+            cropped_image = resized.permute(0, 2, 3, 1)
+
+        # Clone the original image to avoid modifying it
+        restored = original_image.clone()
+
+        # Ensure we don't go out of bounds
+        end_x = min(x + width, orig_w)
+        end_y = min(y + height, orig_h)
+        actual_width = end_x - x
+        actual_height = end_y - y
+
+        # Paste the cropped image back into the original
+        restored[:, y:end_y, x:end_x, :] = cropped_image[:, :actual_height, :actual_width, :]
+
+        print(f"โœ… INSTARAW Restore Crop: Restored crop region ({x}, {y}, {width}x{height}) to original image.")
+
+        return (restored,)
+
+
+# =================================================================================
+# NODE REGISTRATION
+# =================================================================================
+
+NODE_CLASS_MAPPINGS = {
+    "INSTARAW_RestoreCropToOriginal": INSTARAW_RestoreCropToOriginal,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "INSTARAW_RestoreCropToOriginal": "๐Ÿ“ฅ INSTARAW Restore Crop to Original",
+}
diff --git a/custom_nodes/ComfyUI_INSTARAW/nodes/output_nodes/__init__.py b/custom_nodes/ComfyUI_INSTARAW/nodes/output_nodes/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..82dfcdebc2393144aae4aafd323110bd0bcc2ae5
--- /dev/null
+++ b/custom_nodes/ComfyUI_INSTARAW/nodes/output_nodes/__init__.py
@@ -0,0 +1,26 @@
+"""
+INSTARAW Output Nodes
+Nodes that are final endpoints in a workflow, like saving files.
+"""
+
+# Import mappings from the first node file, using aliases to avoid name conflicts
+from .save_with_metadata import NODE_CLASS_MAPPINGS as SAVE_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS as SAVE_DISPLAY_MAPPINGS
+
+# Import mappings from the new node file, also using aliases
+from .synthesize_with_metadata import NODE_CLASS_MAPPINGS as SYNTH_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS as SYNTH_DISPLAY_MAPPINGS
+
+# Create the final dictionaries that will be exported
+NODE_CLASS_MAPPINGS = {}
+NODE_DISPLAY_NAME_MAPPINGS = {}
+
+# Merge the mappings from all imported nodes
+NODE_CLASS_MAPPINGS.update(SAVE_MAPPINGS)
+NODE_CLASS_MAPPINGS.update(SYNTH_MAPPINGS)
+
+NODE_DISPLAY_NAME_MAPPINGS.update(SAVE_DISPLAY_MAPPINGS)
+NODE_DISPLAY_NAME_MAPPINGS.update(SYNTH_DISPLAY_MAPPINGS)
+
+
+# The __all__ export is not strictly necessary for ComfyUI's loader but is good practice.
+# It ensures that only these specific dictionaries are exposed when this package is imported.
+__all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS"]
\ No newline at end of file
diff --git a/custom_nodes/ComfyUI_INSTARAW/nodes/output_nodes/save_with_metadata.py b/custom_nodes/ComfyUI_INSTARAW/nodes/output_nodes/save_with_metadata.py
new file mode 100644
index 0000000000000000000000000000000000000000..e99ba005ce5789354256e3f381d41a9b76620f27
--- /dev/null
+++ b/custom_nodes/ComfyUI_INSTARAW/nodes/output_nodes/save_with_metadata.py
@@ -0,0 +1,155 @@
+# Filename: ComfyUI_INSTARAW/nodes/output_nodes/save_with_metadata.py
+import torch
+import numpy as np
+from PIL import Image
+import os
+import json
+import random
+import folder_paths
+import tempfile
+import shutil
+import uuid
+import subprocess
+import sys
+
+def is_tool(name):
+    return shutil.which(name) is not None
+
+EXIFTOOL_AVAILABLE = is_tool("exiftool") or is_tool("exiftool.exe")
+
+class INSTARAW_SaveWithAuthenticMetadata:
+    OUTPUT_NODE = False 
+    CATEGORY = "INSTARAW/Authenticity"
+    FUNCTION = "save_image"
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "image": ("IMAGE",),
+                "output_subfolder": ("STRING", {"default": "", "tooltip": "Subdirectory within ComfyUI output folder (e.g., 'INSTARAW' or 'authentic')"}),
+                "filename_prefix": ("STRING", {"default": "IMG_%Y%m%d_%H%M%S"}),
+            },
+            "optional": {
+                "profile_path": ("STRING", {"forceInput": True}),
+            }
+        }
+
+    RETURN_TYPES = ("STRING",)
+    RETURN_NAMES = ("filepath",)
+
+    def save_image(self, image: torch.Tensor, output_subfolder: str, filename_prefix: str, profile_path=None):
+        if not EXIFTOOL_AVAILABLE:
+            raise Exception("ExifTool is not installed or not in your system's PATH. Please install it from https://exiftool.org/.")
+
+        if image.shape[0] > 1:
+            print("โš ๏ธ INSTARAW Save: Input batch contains more than one image. Only the first will be processed.")
+
+        img_tensor = image[0:1]
+        img_np = img_tensor.squeeze(0).cpu().numpy()
+        img_pil = Image.fromarray((img_np * 255).astype(np.uint8), 'RGB')
+
+        output_dir = folder_paths.get_output_directory()
+
+        # Combine subfolder with filename_prefix (ComfyUI supports "subfolder/prefix" format)
+        if output_subfolder.strip():
+            combined_prefix = os.path.join(output_subfolder.strip(), filename_prefix)
+        else:
+            combined_prefix = filename_prefix
+
+        full_output_folder, filename, counter, subfolder, _ = folder_paths.get_save_image_path(combined_prefix, output_dir, img_pil.width, img_pil.height)
+        
+        selected_profile = None
+        json_path = None
+        if profile_path and profile_path.strip():
+            json_path = f"{profile_path}.json" if not profile_path.endswith('.json') else profile_path
+            if os.path.exists(json_path):
+                with open(json_path, 'r', encoding='utf-8') as f:
+                    metadata_library = json.load(f)
+                if metadata_library:
+                    selected_profile = random.choice(metadata_library)
+        
+        temp_dir = tempfile.gettempdir()
+        temp_filepath = os.path.join(temp_dir, f"instaraw_img_{uuid.uuid4()}.jpg")
+        img_pil.save(temp_filepath, quality=95, format='JPEG')
+        
+        arg_filepath = None
+        final_filepath = ""
+        try:
+            if selected_profile:
+                print("\n" + "="*25 + " INSTARAW Save Log " + "="*25)
+
+                icc_profile_key = "_instaraw_icc_profile_file"
+                has_icc_profile = icc_profile_key in selected_profile
+                
+                # --- DEFINITIVE LOGGING LOGIC ---
+                tags_to_write = {k: v for k, v in selected_profile.items() if not k.startswith(('File:', 'Composite:', 'JFIF:', '_instaraw'))}
+                total_profile_keys = len(selected_profile)
+                writable_count = len(tags_to_write)
+                icc_count = 1 if has_icc_profile else 0
+                skipped_count = total_profile_keys - writable_count - icc_count
+                
+                log_message = f"Injecting metadata: {writable_count} writable tags, {skipped_count} skipped (read-only/derived), {icc_count} ICC Profile..."
+                print(log_message)
+                # --- END LOGGING LOGIC ---
+
+                print("--- PROFILE TO INJECT ---")
+                print(json.dumps(selected_profile, indent=2, ensure_ascii=False))
+                print("="*70)
+
+                exiftool_cmd = shutil.which("exiftool") or shutil.which("exiftool.exe")
+                command = [exiftool_cmd]
+                
+                icc_profile_path = None
+                if has_icc_profile:
+                    icc_filename = selected_profile.pop(icc_profile_key) # Use pop on the original dict
+                    if json_path:
+                        profile_dir = os.path.dirname(json_path)
+                        full_icc_path = os.path.join(profile_dir, icc_filename)
+                        if os.path.exists(full_icc_path):
+                            icc_profile_path = full_icc_path
+                            print(f"   - Found ICC Profile to inject: {full_icc_path}")
+                        else:
+                            print(f"   - โš ๏ธ WARNING: ICC Profile file not found at {full_icc_path}")
+                
+                with tempfile.NamedTemporaryFile(mode='w', encoding='utf-8', suffix=".txt", delete=False) as arg_file:
+                    arg_filepath = arg_file.name
+                    for key, value in tags_to_write.items():
+                        arg_file.write(f"-{key}={value}\n")
+                
+                command.extend(["-@", arg_filepath])
+
+                if icc_profile_path:
+                    command.append(f"-ICC_Profile<={icc_profile_path}")
+
+                command.extend([
+                    "-overwrite_original",
+                    "-ignoreMinorErrors",
+                    temp_filepath
+                ])
+                
+                print(f"   - Executing ExifTool command...")
+                result = subprocess.run(command, capture_output=True, text=True, check=False, encoding='utf-8', errors='ignore')
+
+                if result.returncode != 0:
+                    print(f"โŒ INSTARAW Save: ExifTool process failed. Stderr: {result.stderr.strip()}")
+                elif "1 image files updated" not in result.stdout:
+                     print(f"โš ๏ธ INSTARAW Save: ExifTool ran but may not have updated the file. Full output:")
+                     print(f"   - Stdout: {result.stdout.strip()}")
+                     print(f"   - Stderr: {result.stderr.strip()}")
+                else:
+                    print(f"   - ExifTool Success: {result.stdout.strip()}")
+
+            file = f"{filename}_{counter:05}_.jpg"
+            final_filepath = os.path.join(full_output_folder, file)
+            shutil.move(temp_filepath, final_filepath)
+            
+        finally:
+            if os.path.exists(temp_filepath): os.remove(temp_filepath)
+            if arg_filepath and os.path.exists(arg_filepath): os.remove(arg_filepath)
+
+        results = [{"filename": os.path.basename(final_filepath), "subfolder": subfolder, "type": "output"}]
+        return {"ui": {"images": results}, "result": (final_filepath,)}
+
+NODE_CLASS_MAPPINGS = { "INSTARAW_SaveWithAuthenticMetadata": INSTARAW_SaveWithAuthenticMetadata }
+NODE_DISPLAY_NAME_MAPPINGS = { "INSTARAW_SaveWithAuthenticMetadata": "๐Ÿ’พ INSTARAW Save With Authentic Metadata" }
\ No newline at end of file
diff --git a/custom_nodes/ComfyUI_INSTARAW/nodes/output_nodes/synthesize_with_metadata copy.py b/custom_nodes/ComfyUI_INSTARAW/nodes/output_nodes/synthesize_with_metadata copy.py
new file mode 100644
index 0000000000000000000000000000000000000000..429ff250cb4471106bbc93dec48b5575119c8503
--- /dev/null
+++ b/custom_nodes/ComfyUI_INSTARAW/nodes/output_nodes/synthesize_with_metadata copy.py	
@@ -0,0 +1,258 @@
+import torch
+import numpy as np
+from PIL import Image
+import os
+import json
+import random
+import folder_paths
+import tempfile
+import shutil
+import uuid
+import subprocess
+import sys
+import datetime
+from pathlib import Path
+
+# --- HELPER FUNCTIONS ---
+NODE_DIR = Path(__file__).parent.parent.parent
+LOCATIONS_FILE = NODE_DIR / "modules" / "location_data" / "locations.json"
+
+def is_tool(name):
+    return shutil.which(name) is not None
+
+def load_locations_data():
+    if not LOCATIONS_FILE.exists():
+        print(f"โš ๏ธ INSTARAW: Locations file not found. Creating a default at '{LOCATIONS_FILE}'")
+        LOCATIONS_FILE.parent.mkdir(parents=True, exist_ok=True)
+        default_data = {
+          "North America": { "United States": [ { "city": "New York", "lat": [40.4774, 40.9176], "lon": [-74.2591, -73.7002] } ] },
+          "Europe": { "France": [ { "city": "Paris", "lat": [48.8156, 48.9021], "lon": [2.2241, 2.4699] } ] }
+        }
+        with open(LOCATIONS_FILE, 'w') as f:
+            json.dump(default_data, f, indent=2)
+        return default_data
+    with open(LOCATIONS_FILE, 'r', encoding='utf-8') as f:
+        return json.load(f)
+
+EXIFTOOL_AVAILABLE = is_tool("exiftool") or is_tool("exiftool.exe")
+
+class INSTARAW_SynthesizeAuthenticMetadata:
+    OUTPUT_NODE = False 
+    CATEGORY = "INSTARAW/Authenticity"
+    FUNCTION = "synthesize_and_save"
+    
+    _locations_data = None
+    _location_choices = None
+
+    @classmethod
+    def get_location_choices(cls):
+        if cls._locations_data is None:
+            cls._locations_data = load_locations_data()
+        
+        if cls._location_choices is None:
+            special_options = ["From Profile", "Synthesize Random (Any City)", "Synthesize Random (US City)"]
+            random_country_options = []
+            all_countries = set()
+            for region in cls._locations_data.values():
+                for country in region.keys():
+                    all_countries.add(country)
+            
+            for country in sorted(list(all_countries)):
+                if country != "United States":
+                    random_country_options.append(f"Synthesize Random ({country})")
+
+            specific_city_options = []
+            for region, countries in cls._locations_data.items():
+                for country, cities in countries.items():
+                    for city_data in cities:
+                        specific_city_options.append(f"{city_data['city']}, {country}")
+            
+            cls._location_choices = special_options + random_country_options + sorted(specific_city_options)
+        return cls._location_choices
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        now = datetime.datetime.now()
+        seven_days_ago = now - datetime.timedelta(days=7)
+        default_end_date = now.strftime("%Y-%m-%dT%H:%M:%S")
+        default_start_date = seven_days_ago.strftime("%Y-%m-%dT%H:%M:%S")
+        scene_types = ["From Profile", "Synthesize Random", "Daylight (Bright)", "Daylight (Cloudy)", "Golden Hour", "Indoors", "Night"]
+
+        return {
+            "required": {
+                "image": ("IMAGE",),
+                "profile_path": ("STRING", {"forceInput": True}),
+                "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
+            },
+            "optional": {
+                "location_synthesis": (cls.get_location_choices(), {"default": "Synthesize Random (US City)"}),
+                "datetime_synthesis": (["From Profile", "Synthesize Random"], {"default": "Synthesize Random"}),
+                "start_date": ("STRING", {"default": default_start_date}),
+                "end_date": ("STRING", {"default": default_end_date}),
+                "scene_type": (scene_types, {"default": "Synthesize Random"}),
+            }
+        }
+
+    RETURN_TYPES = ("STRING",)
+    RETURN_NAMES = ("filepath",)
+
+    def get_random_city_from_country(self, country_name, rng):
+        cities_in_country = []
+        for region, countries in self._locations_data.items():
+            if country_name in countries:
+                cities_in_country.extend(countries[country_name])
+        if cities_in_country:
+            return rng.choice(cities_in_country)
+        return None
+
+    def synthesize_and_save(self, image: torch.Tensor, profile_path: str, seed: int,
+                   location_synthesis="Synthesize Random (US City)", datetime_synthesis="Synthesize Random", 
+                   start_date="", end_date="", scene_type="Synthesize Random"):
+        
+        if not EXIFTOOL_AVAILABLE: raise Exception("ExifTool is not installed.")
+        
+        output_dir = folder_paths.get_output_directory()
+        
+        final_filepath_for_return = ""
+        results_for_ui = []
+
+        for i in range(image.shape[0]):
+            img_tensor = image[i:i+1]
+            
+            # --- THE DEFINITIVE FILENAME FIX ---
+            # Call the helper function INSIDE the loop for each image.
+            # We discard the last return value because it's not the extension.
+            full_output_folder, filename, counter, subfolder, _ = folder_paths.get_save_image_path("IMG", output_dir)
+            
+            # Manually construct the filename with the correct, hardcoded extension.
+            file = f"{filename}_{counter:05}_.jpg"
+            final_filepath = os.path.join(full_output_folder, file)
+            # --- END FIX ---
+
+            image_seed = seed + counter
+            rng = random.Random(image_seed)
+
+            img_np = img_tensor.squeeze(0).cpu().numpy()
+            img_pil = Image.fromarray((np.clip(img_np, 0.0, 1.0) * 255.0).astype(np.uint8), 'RGB')
+            
+            json_path = f"{profile_path}.json" if not profile_path.endswith('.json') else profile_path
+            with open(json_path, 'r', encoding='utf-8') as f:
+                metadata_library = json.load(f)
+            selected_profile = rng.choice(metadata_library).copy()
+
+            width, height = img_pil.size
+            tags_to_align = {
+                "EXIF:ExifImageWidth": width, "EXIF:ImageWidth": width,
+                "EXIF:ExifImageHeight": height, "EXIF:ImageHeight": height,
+                "XMP:RegionAppliedToDimensionsW": width, "XMP:RegionAppliedToDimensionsH": height,
+            }
+            for tag, value in tags_to_align.items():
+                if tag in selected_profile:
+                    selected_profile[tag] = value
+
+            bounds = None
+            if location_synthesis == "From Profile": pass
+            elif location_synthesis == "Synthesize Random (Any City)":
+                all_cities = self.get_location_choices()[3:]
+                if all_cities:
+                    random_city_str = rng.choice(all_cities)
+                    city_name, country_name = [x.strip() for x in random_city_str.split(',')]
+                    bounds = self.get_random_city_from_country(country_name, rng)
+            elif location_synthesis.startswith("Synthesize Random ("):
+                country_name = location_synthesis[19:-1]
+                bounds = self.get_random_city_from_country(country_name, rng)
+            else:
+                city_name, country_name = [x.strip() for x in location_synthesis.split(',')]
+                for region, countries in self._locations_data.items():
+                    if country_name in countries:
+                        for city_data in countries[country_name]:
+                            if city_data['city'] == city_name:
+                                bounds = city_data; break
+                
+            if bounds:
+                lat = rng.uniform(bounds['lat'][0], bounds['lat'][1])
+                lon = rng.uniform(bounds['lon'][0], bounds['lon'][1])
+                selected_profile.update({
+                    "EXIF:GPSLatitude": abs(lat), "EXIF:GPSLatitudeRef": "N" if lat >= 0 else "S",
+                    "EXIF:GPSLongitude": abs(lon), "EXIF:GPSLongitudeRef": "E" if lon >= 0 else "W",
+                    "EXIF:GPSAltitude": rng.uniform(10, 200), "EXIF:GPSAltitudeRef": 0,
+                    "EXIF:GPSImgDirection": rng.uniform(0, 359.9), "EXIF:GPSImgDirectionRef": "T"
+                })
+
+            if datetime_synthesis == "Synthesize Random":
+                try:
+                    start = datetime.datetime.fromisoformat(start_date)
+                    end = datetime.datetime.fromisoformat(end_date)
+                    random_date = start + (end - start) * rng.random()
+                    exif_date_str = random_date.strftime("%Y:%m:%d %H:%M:%S")
+                    sub_sec = rng.randint(100, 999)
+                    selected_profile.update({
+                        "EXIF:DateTimeOriginal": exif_date_str, "EXIF:CreateDate": exif_date_str, "EXIF:ModifyDate": exif_date_str,
+                        "EXIF:SubSecTimeOriginal": sub_sec, "EXIF:SubSecTimeDigitized": sub_sec
+                    })
+                except Exception as e:
+                    print(f"โš ๏ธ INSTARAW Synthesize: Invalid date format. Error: {e}")
+
+            if scene_type == "Synthesize Random":
+                actual_scene_type = rng.choice(self.INPUT_TYPES()["optional"]["scene_type"][0][2:])
+            else:
+                actual_scene_type = scene_type
+
+            if actual_scene_type != "From Profile":
+                settings = {}
+                if actual_scene_type == "Daylight (Bright)":
+                    settings = {"EXIF:ISO": rng.choice([25, 32, 50]), "EXIF:ExposureTime": 1 / rng.randint(1000, 4000), "EXIF:FNumber": 2.2}
+                elif actual_scene_type == "Night":
+                    settings = {"EXIF:ISO": rng.choice([800, 1600, 2500]), "EXIF:ExposureTime": 1 / rng.randint(15, 60), "EXIF:FNumber": 1.8}
+                selected_profile.update(settings)
+                
+            if "MakerNotes:PhotoIdentifier" in selected_profile:
+                selected_profile["MakerNotes:PhotoIdentifier"] = str(uuid.uuid4()).upper()
+
+            temp_dir = tempfile.gettempdir()
+            temp_filepath = os.path.join(temp_dir, f"instaraw_img_{uuid.uuid4()}.jpg")
+            img_pil.save(temp_filepath, quality=95, format='JPEG')
+            
+            arg_filepath = None
+            try:
+                print(f"\n--- Processing Image {i+1}/{image.shape[0]} (Seed: {image_seed}) ---")
+                print(f"   - Target Filename: {os.path.basename(final_filepath)}")
+                
+                exiftool_cmd = shutil.which("exiftool") or shutil.which("exiftool.exe")
+                command = [exiftool_cmd]
+                
+                icc_profile_key = "_instaraw_icc_profile_file"
+                has_icc_profile = icc_profile_key in selected_profile
+                tags_to_write = {k: v for k, v in selected_profile.items() if not k.startswith(('File:', 'Composite:', 'JFIF:', '_instaraw'))}
+                
+                icc_profile_path = None
+                if has_icc_profile:
+                    icc_filename = selected_profile.pop(icc_profile_key)
+                    if json_path:
+                        profile_dir = os.path.dirname(os.path.abspath(json_path))
+                        full_icc_path = os.path.join(profile_dir, icc_filename)
+                        if os.path.exists(full_icc_path): icc_profile_path = full_icc_path
+                
+                with tempfile.NamedTemporaryFile(mode='w', encoding='utf-8', suffix=".txt", delete=False) as arg_file:
+                    arg_filepath = arg_file.name
+                    for key, value in tags_to_write.items():
+                        arg_file.write(f"-{key}={value}\n")
+                
+                command.extend(["-@", arg_filepath])
+                if icc_profile_path: command.append(f"-ICC_Profile<={icc_profile_path}")
+                command.extend(["-overwrite_original", "-ignoreMinorErrors", temp_filepath])
+                
+                subprocess.run(command, capture_output=True, text=True, check=False, encoding='utf-8', errors='ignore')
+                shutil.move(temp_filepath, final_filepath)
+                
+            finally:
+                if os.path.exists(temp_filepath): os.remove(temp_filepath)
+                if arg_filepath and os.path.exists(arg_filepath): os.remove(arg_filepath)
+            
+            if i == 0: final_filepath_for_return = final_filepath
+            results_for_ui.append({"filename": os.path.basename(final_filepath), "subfolder": subfolder, "type": "output"})
+
+        return {"ui": {"images": results_for_ui}, "result": (final_filepath_for_return,)}
+
+NODE_CLASS_MAPPINGS = { "INSTARAW_SynthesizeAuthenticMetadata": INSTARAW_SynthesizeAuthenticMetadata }
+NODE_DISPLAY_NAME_MAPPINGS = { "INSTARAW_SynthesizeAuthenticMetadata": "๐Ÿ’พ INSTARAW Synthesize Authentic Metadata" }
\ No newline at end of file
diff --git a/custom_nodes/ComfyUI_INSTARAW/nodes/output_nodes/synthesize_with_metadata.py b/custom_nodes/ComfyUI_INSTARAW/nodes/output_nodes/synthesize_with_metadata.py
new file mode 100644
index 0000000000000000000000000000000000000000..5ed9a1164f15fb814097f74fa7f2eb80227b0812
--- /dev/null
+++ b/custom_nodes/ComfyUI_INSTARAW/nodes/output_nodes/synthesize_with_metadata.py
@@ -0,0 +1,444 @@
+import torch
+import numpy as np
+from PIL import Image
+import os
+import json
+import random
+import folder_paths
+import tempfile
+import shutil
+import uuid
+import subprocess
+import datetime
+from pathlib import Path
+
+# ---------------- CONFIG / PATHS ----------------
+
+NODE_DIR = Path(__file__).parent.parent.parent
+LOCATIONS_FILE = NODE_DIR / "modules" / "location_data" / "locations.json"
+
+# Global sRGB ICC profile โ€“ used ONLY for tagging, no color transform
+SRGB_PROFILE_FILE = NODE_DIR / "modules" / "color_profiles" / "sRGB_IEC61966-2-1_no_black_scaling.icc"
+
+def is_tool(name: str) -> bool:
+    return shutil.which(name) is not None
+
+def load_locations_data():
+    if not LOCATIONS_FILE.exists():
+        print(f"โš ๏ธ INSTARAW: Locations file not found. Creating a default at '{LOCATIONS_FILE}'")
+        LOCATIONS_FILE.parent.mkdir(parents=True, exist_ok=True)
+        default_data = {
+          "North America": {
+              "United States": [
+                  {
+                      "city": "New York",
+                      "lat": [40.4774, 40.9176],
+                      "lon": [-74.2591, -73.7002]
+                  }
+              ]
+          },
+          "Europe": {
+              "France": [
+                  {
+                      "city": "Paris",
+                      "lat": [48.8156, 48.9021],
+                      "lon": [2.2241, 2.4699]
+                  }
+              ]
+          }
+        }
+        with open(LOCATIONS_FILE, 'w', encoding="utf-8") as f:
+            json.dump(default_data, f, indent=2)
+        return default_data
+    with open(LOCATIONS_FILE, 'r', encoding='utf-8') as f:
+        return json.load(f)
+
+EXIFTOOL_AVAILABLE = is_tool("exiftool") or is_tool("exiftool.exe")
+
+
+class INSTARAW_SynthesizeAuthenticMetadata:
+    """
+    Final design goals:
+
+    - Do NOT change pixel colors here.
+      The tensor coming from Comfy is assumed to already be "final look" in sRGB.
+    - Only write EXIF/XMP metadata and (optionally) embed an sRGB ICC profile.
+    - Orientation and ColorSpace are written as numeric EXIF codes using exiftool -n
+      so they are honored correctly.
+    """
+    OUTPUT_NODE = False
+    CATEGORY = "INSTARAW/Authenticity"
+    FUNCTION = "synthesize_and_save"
+
+    _locations_data = None
+    _location_choices = None
+
+    # ------------- Comfy INPUT / RETURN TYPES -------------
+
+    @classmethod
+    def get_location_choices(cls):
+        if cls._locations_data is None:
+            cls._locations_data = load_locations_data()
+
+        if cls._location_choices is None:
+            special_options = [
+                "From Profile",
+                "Synthesize Random (Any City)",
+                "Synthesize Random (US City)",
+            ]
+            random_country_options = []
+            all_countries = set()
+
+            for region in cls._locations_data.values():
+                for country in region.keys():
+                    all_countries.add(country)
+
+            for country in sorted(list(all_countries)):
+                if country != "United States":
+                    random_country_options.append(f"Synthesize Random ({country})")
+
+            specific_city_options = []
+            for region, countries in cls._locations_data.items():
+                for country, cities in countries.items():
+                    for city_data in cities:
+                        specific_city_options.append(f"{city_data['city']}, {country}")
+
+            cls._location_choices = (
+                special_options + random_country_options + sorted(specific_city_options)
+            )
+        return cls._location_choices
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        now = datetime.datetime.now()
+        seven_days_ago = now - datetime.timedelta(days=7)
+        default_end_date = now.strftime("%Y-%m-%dT%H:%M:%S")
+        default_start_date = seven_days_ago.strftime("%Y-%m-%dT%H:%M:%S")
+        scene_types = [
+            "From Profile",
+            "Synthesize Random",
+            "Daylight (Bright)",
+            "Daylight (Cloudy)",
+            "Golden Hour",
+            "Indoors",
+            "Night",
+        ]
+
+        return {
+            "required": {
+                "image": ("IMAGE",),
+                "profile_path": ("STRING", {"forceInput": True}),
+                "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
+            },
+            "optional": {
+                "inject_color_profile": ("BOOLEAN", {"default": False}),
+                "location_synthesis": (
+                    cls.get_location_choices(),
+                    {"default": "Synthesize Random (US City)"}
+                ),
+                "datetime_synthesis": (
+                    ["From Profile", "Synthesize Random"],
+                    {"default": "Synthesize Random"}
+                ),
+                "start_date": ("STRING", {"default": default_start_date}),
+                "end_date": ("STRING", {"default": default_end_date}),
+                "scene_type": (scene_types, {"default": "Synthesize Random"}),
+                "output_subfolder": ("STRING", {"default": "", "tooltip": "Subdirectory within ComfyUI output folder (e.g., 'INSTARAW' or 'authentic')"}),
+            }
+        }
+
+    RETURN_TYPES = ("STRING",)
+    RETURN_NAMES = ("filepath",)
+
+    # ------------- LOCATION HELPERS -------------
+
+    def get_random_city_from_country(self, country_name, rng: random.Random):
+        cities_in_country = []
+        for region, countries in self._locations_data.items():
+            if country_name in countries:
+                cities_in_country.extend(countries[country_name])
+        if cities_in_country:
+            return rng.choice(cities_in_country)
+        return None
+
+    # ------------- MAIN FUNCTION -------------
+
+    def synthesize_and_save(
+        self,
+        image: torch.Tensor,
+        profile_path: str,
+        seed: int,
+        inject_color_profile=False,
+        location_synthesis="Synthesize Random (US City)",
+        datetime_synthesis="Synthesize Random",
+        start_date="",
+        end_date="",
+        scene_type="Synthesize Random",
+        output_subfolder="",
+    ):
+
+        if not EXIFTOOL_AVAILABLE:
+            raise Exception("ExifTool is not installed. Please install exiftool to use this node.")
+
+        output_dir = folder_paths.get_output_directory()
+
+        final_filepath_for_return = ""
+        results_for_ui = []
+
+        # Combine subfolder with filename prefix (ComfyUI supports "subfolder/prefix" format)
+        if output_subfolder.strip():
+            combined_prefix = os.path.join(output_subfolder.strip(), "IMG")
+        else:
+            combined_prefix = "IMG"
+
+        for i in range(image.shape[0]):
+            img_tensor = image[i:i+1]
+
+            # 1. Build target filename (force JPEG)
+            full_output_folder, filename, counter, subfolder, _ = folder_paths.get_save_image_path(
+                combined_prefix, output_dir
+            )
+            file = f"{filename}_{counter:05}_.jpg"
+            final_filepath = os.path.join(full_output_folder, file)
+
+            image_seed = seed + counter
+            rng = random.Random(image_seed)
+
+            # 2. Convert tensor -> PIL, without any color transform
+            img_np = img_tensor.squeeze(0).cpu().numpy()
+            img_pil = Image.fromarray(
+                (np.clip(img_np, 0.0, 1.0) * 255.0).astype(np.uint8),
+                'RGB'
+            )
+
+            # 3. Load authenticity metadata profile
+            json_path = f"{profile_path}.json" if not profile_path.endswith('.json') else profile_path
+            with open(json_path, 'r', encoding='utf-8') as f:
+                metadata_library = json.load(f)
+            source_profile = rng.choice(metadata_library).copy()
+
+            # 4. Start from profile metadata and clean it up
+            final_tags = source_profile.copy()
+
+            # Remove known undesired/raw-only keys
+            for tag in ["EXIF:ThumbnailImage", "_instaraw_icc_profile_file"]:
+                if tag in final_tags:
+                    del final_tags[tag]
+
+            # Remove any existing Orientation / ColorSpace โ€“ we'll set our own
+            for key in list(final_tags.keys()):
+                base = key.split(":", 1)[-1]
+                if base in ("Orientation", "ColorSpace"):
+                    del final_tags[key]
+
+            # 5. Synthesize GPS based on requested mode
+            city_data = None
+            if location_synthesis == "From Profile":
+                pass
+            elif location_synthesis == "Synthesize Random (Any City)":
+                all_cities = self.get_location_choices()[3:]
+                if all_cities:
+                    random_city_str = rng.choice(all_cities)
+                    city_name, country_name = [x.strip() for x in random_city_str.split(',')]
+                    city_data = self.get_random_city_from_country(country_name, rng)
+            elif location_synthesis.startswith("Synthesize Random ("):
+                country_name = location_synthesis[19:-1]  # between '(' and ')'
+                # Fix: Handle "US City" as "United States"
+                if country_name == "US City":
+                    country_name = "United States"
+                city_data = self.get_random_city_from_country(country_name, rng)
+            else:
+                # Specific "City, Country"
+                city_name, country_name = [x.strip() for x in location_synthesis.split(',')]
+                for region, countries in self._locations_data.items():
+                    if country_name in countries:
+                        for city_data_item in countries[country_name]:
+                            if city_data_item['city'] == city_name:
+                                city_data = city_data_item
+                                break
+
+            if city_data:
+                # Use jitter around city center instead of uniform distribution across bounding box
+                # This keeps coordinates closer to actual city and reduces water hits
+                center_lat = (city_data['lat'][0] + city_data['lat'][1]) / 2
+                center_lon = (city_data['lon'][0] + city_data['lon'][1]) / 2
+
+                # Add small random jitter (ยฑ0.02 degrees โ‰ˆ 2.2km)
+                # Using Gaussian distribution for more natural clustering around center
+                jitter_amount = 0.02
+                lat = center_lat + rng.gauss(0, jitter_amount / 2)
+                lon = center_lon + rng.gauss(0, jitter_amount / 2)
+
+                final_tags.update({
+                    "EXIF:GPSLatitude": abs(lat),
+                    "EXIF:GPSLatitudeRef": "N" if lat >= 0 else "S",
+                    "EXIF:GPSLongitude": abs(lon),
+                    "EXIF:GPSLongitudeRef": "E" if lon >= 0 else "W",
+                })
+
+            # 6. Synthesize datetime if requested
+            if datetime_synthesis == "Synthesize Random":
+                try:
+                    start = datetime.datetime.fromisoformat(start_date)
+                    end = datetime.datetime.fromisoformat(end_date)
+                    random_date = start + (end - start) * rng.random()
+                    exif_date_str = random_date.strftime("%Y:%m:%d %H:%M:%S")
+                    sub_sec = rng.randint(100, 999)
+                    final_tags.update({
+                        "EXIF:DateTimeOriginal": exif_date_str,
+                        "EXIF:CreateDate": exif_date_str,
+                        "EXIF:ModifyDate": exif_date_str,
+                        "EXIF:SubSecTimeOriginal": sub_sec,
+                        "EXIF:SubSecTimeDigitized": sub_sec,
+                    })
+                except Exception as e:
+                    print(f"โš ๏ธ INSTARAW Synthesize: Invalid date format. Error: {e}")
+
+            # 7. Scene-based exposure tweaks
+            if scene_type == "Synthesize Random":
+                actual_scene_type = rng.choice(
+                    self.INPUT_TYPES()["optional"]["scene_type"][0][2:]
+                )
+            else:
+                actual_scene_type = scene_type
+
+            if actual_scene_type != "From Profile":
+                settings = {}
+                if actual_scene_type == "Daylight (Bright)":
+                    settings = {
+                        "EXIF:ISO": rng.choice([25, 32, 50]),
+                        "EXIF:ExposureTime": 1 / rng.randint(1000, 4000),
+                        "EXIF:FNumber": 2.2,
+                    }
+                elif actual_scene_type == "Night":
+                    settings = {
+                        "EXIF:ISO": rng.choice([800, 1600, 2500]),
+                        "EXIF:ExposureTime": 1 / rng.randint(15, 60),
+                        "EXIF:FNumber": 1.8,
+                    }
+                final_tags.update(settings)
+
+            # 8. Give the image a fresh UUID for PhotoIdentifier if present
+            if "MakerNotes:PhotoIdentifier" in final_tags:
+                final_tags["MakerNotes:PhotoIdentifier"] = str(uuid.uuid4()).upper()
+
+            # 9. Ensure dimensions tags are correct (bare tag names, numeric)
+            width, height = img_pil.size
+            final_tags.update({
+                "ImageWidth": width,
+                "ImageHeight": height,
+                "ExifImageWidth": width,
+                "ExifImageHeight": height,
+                "XMP:RegionAppliedToDimensionsW": width,
+                "XMP:RegionAppliedToDimensionsH": height,
+            })
+
+            # 10. Save a clean JPEG (no EXIF) to a temp file
+            temp_dir = tempfile.gettempdir()
+            temp_filepath = os.path.join(temp_dir, f"instaraw_img_{uuid.uuid4()}.jpg")
+            img_pil.save(temp_filepath, quality=95, format='JPEG', exif=b'')
+
+            # 11. Build ExifTool args
+            arg_filepath = None
+            try:
+                print(f"\n--- Processing Image {i+1}/{image.shape[0]} (Seed: {image_seed}) ---")
+                print(f"   - Target Filename: {os.path.basename(final_filepath)}")
+
+                exiftool_cmd = shutil.which("exiftool") or shutil.which("exiftool.exe")
+                if not exiftool_cmd:
+                    raise RuntimeError("ExifTool not found in PATH")
+
+                # -n   โ†’ numeric values (critical for enums like ColorSpace / Orientation)
+                # -all= โ†’ nuke existing metadata from the temp JPEG
+                command = [exiftool_cmd, "-n", "-all="]
+
+                # Filter out non-writable / internal tags by prefix
+                tags_for_argfile = {
+                    k: v for k, v in final_tags.items()
+                    if not k.startswith((
+                        "File:",       # file system info
+                        "Composite:",  # derived tags
+                        "JFIF:",       # JFIF container
+                        "_",           # internal keys like _instaraw...
+                    ))
+                }
+
+                # Force Orientation & ColorSpace to known values (numeric, sRGB)
+                # Orientation: 1 = Horizontal (normal)
+                tags_for_argfile["Orientation"] = 1
+
+                # REMOVE ExifTool-generated XMPToolkit to keep metadata authentic
+                tags_for_argfile["XMP:XMPToolkit"] = ""
+
+                # ColorSpace: 1 = sRGB (numeric code)
+                if not inject_color_profile:
+                    tags_for_argfile["ColorSpace"] = 1
+                else:
+                    # With ICC embedding we can either still force 1
+                    # or let viewers rely on ICC tag. For safety we still set 1.
+                    tags_for_argfile["ColorSpace"] = 1
+
+                # Write all tags via an argfile
+                with tempfile.NamedTemporaryFile(
+                    mode='w',
+                    encoding='utf-8',
+                    suffix=".txt",
+                    delete=False
+                ) as arg_file:
+                    arg_filepath = arg_file.name
+                    for key, value in tags_for_argfile.items():
+                        arg_file.write(f"-{key}={value}\n")
+
+                command.extend(["-@", arg_filepath])
+
+                # Optionally embed an sRGB ICC profile (no color transform)
+                if inject_color_profile and SRGB_PROFILE_FILE.exists():
+                    command.append(f"-ICC_Profile<={SRGB_PROFILE_FILE}")
+
+                command.extend([
+                    "-overwrite_original",
+                    "-ignoreMinorErrors",
+                    temp_filepath
+                ])
+
+                result = subprocess.run(
+                    command,
+                    capture_output=True,
+                    text=True,
+                    check=False,
+                    encoding='utf-8',
+                    errors='ignore'
+                )
+                if result.stderr:
+                    # Log but don't fail hard โ€“ some Apple MakerNotes / MPF tags may warn
+                    print("[ExifTool STDERR]")
+                    print(result.stderr.strip())
+                shutil.move(temp_filepath, final_filepath)
+
+            finally:
+                if os.path.exists(temp_filepath):
+                    os.remove(temp_filepath)
+                if arg_filepath and os.path.exists(arg_filepath):
+                    os.remove(arg_filepath)
+
+            if i == 0:
+                final_filepath_for_return = final_filepath
+
+            results_for_ui.append({
+                "filename": os.path.basename(final_filepath),
+                "subfolder": subfolder,
+                "type": "output"
+            })
+
+        return {
+            "ui": {"images": results_for_ui},
+            "result": (final_filepath_for_return,)
+        }
+
+
+NODE_CLASS_MAPPINGS = {
+    "INSTARAW_SynthesizeAuthenticMetadata": INSTARAW_SynthesizeAuthenticMetadata
+}
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "INSTARAW_SynthesizeAuthenticMetadata": "๐Ÿ’พ INSTARAW Synthesize Authentic Metadata"
+}
\ No newline at end of file
diff --git a/custom_nodes/ComfyUI_INSTARAW/nodes/utility_nodes/__init__.py b/custom_nodes/ComfyUI_INSTARAW/nodes/utility_nodes/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..bc3d8265efb040b9c3de9d4ba99c62467cca984d
--- /dev/null
+++ b/custom_nodes/ComfyUI_INSTARAW/nodes/utility_nodes/__init__.py
@@ -0,0 +1,217 @@
+# Filename: ComfyUI_INSTARAW/nodes/utility_nodes/__init__.py
+# ---
+
+"""
+INSTARAW Utility Nodes
+Helper nodes for workflow convenience
+"""
+
+# Keep all the working imports
+from .seed_generator import (
+    NODE_CLASS_MAPPINGS as SEED_MAPPINGS,
+    NODE_DISPLAY_NAME_MAPPINGS as SEED_DISPLAY_MAPPINGS,
+)
+from .grow_mask_with_blur import (
+    NODE_CLASS_MAPPINGS as MASK_MAPPINGS,
+    NODE_DISPLAY_NAME_MAPPINGS as MASK_DISPLAY_MAPPINGS,
+)
+from .feather_mask import (
+    NODE_CLASS_MAPPINGS as FEATHER_MAPPINGS,
+    NODE_DISPLAY_NAME_MAPPINGS as FEATHER_DISPLAY_MAPPINGS,
+)
+from .image_resolution_clamp import (
+    NODE_CLASS_MAPPINGS as RESOLUTION_GUARD_MAPPINGS,
+    NODE_DISPLAY_NAME_MAPPINGS as RESOLUTION_GUARD_DISPLAY_MAPPINGS,
+)
+from .api_provider_selector import (
+    NODE_CLASS_MAPPINGS as PROVIDER_MAPPINGS,
+    NODE_DISPLAY_NAME_MAPPINGS as PROVIDER_DISPLAY_MAPPINGS,
+)
+from .realistic_noise import (
+    NODE_CLASS_MAPPINGS as NOISE_MAPPINGS,
+    NODE_DISPLAY_NAME_MAPPINGS as NOISE_DISPLAY_MAPPINGS,
+)
+from .realistic_jpeg import (
+    NODE_CLASS_MAPPINGS as JPEG_MAPPINGS,
+    NODE_DISPLAY_NAME_MAPPINGS as JPEG_DISPLAY_MAPPINGS,
+)
+from .workflow_logic_nodes import (
+    NODE_CLASS_MAPPINGS as WORKFLOW_LOGIC_MAPPINGS,
+    NODE_DISPLAY_NAME_MAPPINGS as WORKFLOW_LOGIC_DISPLAY_MAPPINGS,
+)
+from .list_utility_nodes import (
+    INSTARAW_BatchFromImageList,
+    INSTARAW_ImageListFromBatch,
+    INSTARAW_PickFromList,
+    INSTARAW_StringListFromStrings,
+)
+from .string_utility_nodes import (
+    INSTARAW_SplitByCommas,
+    INSTARAW_StringToFloat,
+    INSTARAW_StringToInt,
+    INSTARAW_AnyListToString,
+    INSTARAW_StringCombine,
+)
+from .mask_utility_nodes import INSTARAW_MaskedSection, INSTARAW_MaskCombine
+from .branding_node import (
+    NODE_CLASS_MAPPINGS as BRANDING_MAPPINGS,
+    NODE_DISPLAY_NAME_MAPPINGS as BRANDING_DISPLAY_MAPPINGS,
+)
+from .image_resize_advanced import (
+    NODE_CLASS_MAPPINGS as RESIZE_MAPPINGS,
+    NODE_DISPLAY_NAME_MAPPINGS as RESIZE_DISPLAY_MAPPINGS,
+)
+from .api_model_selector import (
+    NODE_CLASS_MAPPINGS as MODEL_SELECTOR_MAPPINGS,
+    NODE_DISPLAY_NAME_MAPPINGS as MODEL_SELECTOR_DISPLAY_MAPPINGS,
+)
+from .json_utils import (
+    NODE_CLASS_MAPPINGS as JSON_MAPPINGS,
+    NODE_DISPLAY_NAME_MAPPINGS as JSON_DISPLAY_MAPPINGS,
+)
+from .lut_selector import INSTARAW_LUT_Selector
+from .spectral_engine_node import (
+    NODE_CLASS_MAPPINGS as SPECTRAL_ENGINE_MAPPINGS,
+    NODE_DISPLAY_NAME_MAPPINGS as SPECTRAL_ENGINE_DISPLAY_MAPPINGS,
+)
+from .color_science_node import (
+    NODE_CLASS_MAPPINGS as COLOR_SCIENCE_MAPPINGS,
+    NODE_DISPLAY_NAME_MAPPINGS as COLOR_SCIENCE_DISPLAY_MAPPINGS,
+)
+from .auto_white_balance_node import INSTARAW_AutoWhiteBalance
+from .neural_grain_node import (
+    NODE_CLASS_MAPPINGS as NEURAL_GRAIN_MAPPINGS,
+    NODE_DISPLAY_NAME_MAPPINGS as NEURAL_GRAIN_DISPLAY_MAPPINGS,
+)
+from .lens_simulation_node import (
+    NODE_CLASS_MAPPINGS as LENS_EFFECTS_MAPPINGS,
+    NODE_DISPLAY_NAME_MAPPINGS as LENS_EFFECTS_DISPLAY_MAPPINGS,
+)
+# ADD THIS NEW IMPORT
+from .compression_node import (
+    NODE_CLASS_MAPPINGS as COMPRESSION_MAPPINGS,
+    NODE_DISPLAY_NAME_MAPPINGS as COMPRESSION_DISPLAY_MAPPINGS,
+)
+from .authenticity_profile_selector import INSTARAW_AuthenticityProfile_Selector
+from .fft_match import INSTARAW_FFT_Match
+from .texture_normalize import INSTARAW_GLCM_Normalize, INSTARAW_LBP_Normalize
+from .metadata_inspector import INSTARAW_Metadata_Inspector
+from .texture_engine import NODE_CLASS_MAPPINGS as TEXTURE_ENGINE_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS as TEXTURE_ENGINE_DISPLAY_MAPPINGS
+from .spectral_normalizer_node import INSTARAW_Spectral_Normalizer
+from .pixel_perturb import INSTARAW_Pixel_Perturb
+from .blend_colors import INSTARAW_BlendColors
+from .camera_simulator import INSTARAW_Camera_Simulator
+from .load_image_from_path import NODE_CLASS_MAPPINGS as LOADER_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS as LOADER_DISPLAY_MAPPINGS
+from .line_splitter import INSTARAW_LineSplitter
+from .image_prompt_iterator import INSTARAW_ImagePromptIterator
+from .debug_prompt_overlay import INSTARAW_DebugPromptOverlay
+from .prompt_batch_preview import (
+    NODE_CLASS_MAPPINGS as PREVIEW_MAPPINGS,
+    NODE_DISPLAY_NAME_MAPPINGS as PREVIEW_DISPLAY_MAPPINGS,
+)
+from .mask_to_crop import (
+    NODE_CLASS_MAPPINGS as MASK_TO_CROP_MAPPINGS,
+    NODE_DISPLAY_NAME_MAPPINGS as MASK_TO_CROP_DISPLAY_MAPPINGS,
+)
+
+
+# --- CLEANED UP MAPPINGS ---
+NODE_CLASS_MAPPINGS = {
+    **SEED_MAPPINGS,
+    **MASK_MAPPINGS,
+    **FEATHER_MAPPINGS,
+    **RESOLUTION_GUARD_MAPPINGS,
+    **PROVIDER_MAPPINGS,
+    **NOISE_MAPPINGS,
+    **JPEG_MAPPINGS,
+    **WORKFLOW_LOGIC_MAPPINGS,
+    "INSTARAW_BatchFromImageList": INSTARAW_BatchFromImageList,
+    "INSTARAW_ImageListFromBatch": INSTARAW_ImageListFromBatch,
+    "INSTARAW_PickFromList": INSTARAW_PickFromList,
+    "INSTARAW_StringListFromStrings": INSTARAW_StringListFromStrings,
+    "INSTARAW_SplitByCommas": INSTARAW_SplitByCommas,
+    "INSTARAW_StringToFloat": INSTARAW_StringToFloat,
+    "INSTARAW_StringToInt": INSTARAW_StringToInt,
+    "INSTARAW_AnyListToString": INSTARAW_AnyListToString,
+    "INSTARAW_MaskedSection": INSTARAW_MaskedSection,
+    "INSTARAW_MaskCombine": INSTARAW_MaskCombine,
+    "INSTARAW_StringCombine": INSTARAW_StringCombine,
+    **BRANDING_MAPPINGS,
+    **RESIZE_MAPPINGS,
+    **MODEL_SELECTOR_MAPPINGS,
+    **JSON_MAPPINGS,
+    "INSTARAW_LUT_Selector": INSTARAW_LUT_Selector,
+    **SPECTRAL_ENGINE_MAPPINGS,
+    **COLOR_SCIENCE_MAPPINGS,
+    "INSTARAW_AutoWhiteBalance": INSTARAW_AutoWhiteBalance,
+    **NEURAL_GRAIN_MAPPINGS,
+    **LENS_EFFECTS_MAPPINGS,
+    **COMPRESSION_MAPPINGS,
+    "INSTARAW_AuthenticityProfile_Selector": INSTARAW_AuthenticityProfile_Selector,
+    "INSTARAW_FFT_Match": INSTARAW_FFT_Match,
+    "INSTARAW_GLCM_Normalize": INSTARAW_GLCM_Normalize,
+    "INSTARAW_LBP_Normalize": INSTARAW_LBP_Normalize,
+    "INSTARAW_Metadata_Inspector": INSTARAW_Metadata_Inspector,
+    **TEXTURE_ENGINE_MAPPINGS,
+    "INSTARAW_Spectral_Normalizer": INSTARAW_Spectral_Normalizer,
+    "INSTARAW_Pixel_Perturb": INSTARAW_Pixel_Perturb,
+    "INSTARAW_BlendColors": INSTARAW_BlendColors,
+    "INSTARAW_Camera_Simulator": INSTARAW_Camera_Simulator,
+    **LOADER_MAPPINGS,
+    "INSTARAW_LineSplitter": INSTARAW_LineSplitter,
+    "INSTARAW_ImagePromptIterator": INSTARAW_ImagePromptIterator,
+    "INSTARAW_DebugPromptOverlay": INSTARAW_DebugPromptOverlay,
+    **PREVIEW_MAPPINGS,
+    **MASK_TO_CROP_MAPPINGS,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    **SEED_DISPLAY_MAPPINGS,
+    **MASK_DISPLAY_MAPPINGS,
+    **FEATHER_DISPLAY_MAPPINGS,
+    **RESOLUTION_GUARD_DISPLAY_MAPPINGS,
+    **PROVIDER_DISPLAY_MAPPINGS,
+    **NOISE_DISPLAY_MAPPINGS,
+    **JPEG_DISPLAY_MAPPINGS,
+    **WORKFLOW_LOGIC_DISPLAY_MAPPINGS,
+    "INSTARAW_BatchFromImageList": "๐Ÿ—ƒ๏ธ INSTARAW Batch From Image List",
+    "INSTARAW_ImageListFromBatch": "๐Ÿ“‘ INSTARAW Image List From Batch",
+    "INSTARAW_PickFromList": "๐Ÿ‘‰ INSTARAW Pick From List",
+    "INSTARAW_StringListFromStrings": "๐Ÿ“‘ INSTARAW String List",
+    "INSTARAW_SplitByCommas": "๐Ÿ”ช INSTARAW Split String",
+    "INSTARAW_StringToFloat": "โ†’FLOAT INSTARAW String To Float",
+    "INSTARAW_StringToInt": "โ†’INT INSTARAW String To Int",
+    "INSTARAW_AnyListToString": "โ†’STRING INSTARAW List To String",
+    "INSTARAW_MaskedSection": "๐Ÿ–ผ๏ธ INSTARAW Masked Section",
+    "INSTARAW_MaskCombine": "โž• INSTARAW Mask Combine",
+    "INSTARAW_StringCombine": "โœ๏ธ INSTARAW String Combine (Safe)",
+    **BRANDING_DISPLAY_MAPPINGS,
+    **RESIZE_DISPLAY_MAPPINGS,
+    **MODEL_SELECTOR_DISPLAY_MAPPINGS,
+    **JSON_DISPLAY_MAPPINGS,
+    "INSTARAW_LUT_Selector": "๐ŸŽจ INSTARAW LUT Selector",
+    **SPECTRAL_ENGINE_DISPLAY_MAPPINGS,
+    **COLOR_SCIENCE_DISPLAY_MAPPINGS,
+    "INSTARAW_AutoWhiteBalance": "๐ŸŽจ INSTARAW Auto White Balance",
+    **NEURAL_GRAIN_DISPLAY_MAPPINGS,
+    **LENS_EFFECTS_DISPLAY_MAPPINGS,
+    **COMPRESSION_DISPLAY_MAPPINGS,
+    "INSTARAW_AuthenticityProfile_Selector": "๐Ÿ‘‘ INSTARAW Authenticity Profile",
+    "INSTARAW_FFT_Match": "๐Ÿ›ก๏ธ INSTARAW FFT Match",
+    "INSTARAW_GLCM_Normalize": "๐Ÿ›ก๏ธ INSTARAW GLCM Normalize",
+    "INSTARAW_LBP_Normalize": "๐Ÿ›ก๏ธ INSTARAW LBP Normalize",
+    "INSTARAW_Metadata_Inspector": "๐Ÿ“Š INSTARAW Metadata Inspector",
+    **TEXTURE_ENGINE_DISPLAY_MAPPINGS,
+    "INSTARAW_Spectral_Normalizer": "๐Ÿ›ก๏ธ INSTARAW Spectral Normalizer",
+    "INSTARAW_Pixel_Perturb": "๐Ÿ›ก๏ธ INSTARAW Pixel Perturb",
+    "INSTARAW_BlendColors": "๐Ÿ›ก๏ธ INSTARAW Blend Colors",
+    "INSTARAW_Camera_Simulator": "๐Ÿ›ก๏ธ INSTARAW Camera Simulator",
+    **LOADER_DISPLAY_MAPPINGS,
+    "INSTARAW_LineSplitter": "โœ‚๏ธ INSTARAW Line Splitter",
+    "INSTARAW_ImagePromptIterator": "๐Ÿงช INSTARAW Image Prompt Iterator",
+    "INSTARAW_DebugPromptOverlay": "๐Ÿ› INSTARAW Debug Prompt Overlay",
+    **PREVIEW_DISPLAY_MAPPINGS,
+    **MASK_TO_CROP_DISPLAY_MAPPINGS,
+}
+
+__all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS"]
\ No newline at end of file
diff --git a/custom_nodes/ComfyUI_INSTARAW/nodes/utility_nodes/_refs/iphone13.jpg b/custom_nodes/ComfyUI_INSTARAW/nodes/utility_nodes/_refs/iphone13.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..6e25a700cf495d4e5dc510355cffe7ab9a59f486
--- /dev/null
+++ b/custom_nodes/ComfyUI_INSTARAW/nodes/utility_nodes/_refs/iphone13.jpg
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2b4844a73803350f56aa01f4139ada8f755b08d70d250161f3bfb678d8881255
+size 755924
diff --git a/custom_nodes/ComfyUI_INSTARAW/nodes/utility_nodes/api_model_selector.py b/custom_nodes/ComfyUI_INSTARAW/nodes/utility_nodes/api_model_selector.py
new file mode 100644
index 0000000000000000000000000000000000000000..d18e165639a413d977b7cf37a61a892529b66b25
--- /dev/null
+++ b/custom_nodes/ComfyUI_INSTARAW/nodes/utility_nodes/api_model_selector.py
@@ -0,0 +1,49 @@
+# ---
+# ComfyUI INSTARAW - API Model Selector Node (Corrected)
+# Part of the INSTARAW custom nodes collection by Instara
+#
+# Copyright ยฉ 2025 Instara. All rights reserved.
+# PROPRIETARY SOFTWARE - ALL RIGHTS RESERVED
+# ---
+
+"""
+A utility node to select a generative API model, simplifying workflows that use the unified API nodes.
+"""
+
+from ..api_nodes.generative_api_nodes import MODEL_CONFIG
+
+class INSTARAW_API_ModelSelector:
+    """
+    Selects a generative model from a dropdown. The list of models is dynamically
+    loaded from the generative_api_nodes configuration, ensuring it's always up to date.
+    """
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "model": (list(MODEL_CONFIG.keys()), {"default": "SeeDream v4.5"}),
+            }
+        }
+
+    # --- THE FIX: This node only has ONE output ---
+    RETURN_TYPES = ("STRING",)
+    RETURN_NAMES = ("model",)
+    FUNCTION = "select_model"
+    CATEGORY = "INSTARAW/Utils"
+
+    def select_model(self, model):
+        # --- THE FIX: Return a tuple with ONE item ---
+        return (model,)
+
+# =================================================================================
+# EXPORT NODE MAPPINGS
+# =================================================================================
+
+NODE_CLASS_MAPPINGS = {
+    "INSTARAW_API_ModelSelector": INSTARAW_API_ModelSelector,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "INSTARAW_API_ModelSelector": "๐Ÿค– INSTARAW API Model Selector",
+}
\ No newline at end of file
diff --git a/custom_nodes/ComfyUI_INSTARAW/nodes/utility_nodes/api_provider_selector.py b/custom_nodes/ComfyUI_INSTARAW/nodes/utility_nodes/api_provider_selector.py
new file mode 100644
index 0000000000000000000000000000000000000000..443ced673e25d21b0b9c00ff453bc1842832d45d
--- /dev/null
+++ b/custom_nodes/ComfyUI_INSTARAW/nodes/utility_nodes/api_provider_selector.py
@@ -0,0 +1,92 @@
+# ---
+# ComfyUI INSTARAW - API Provider Selector Nodes
+# Part of the INSTARAW custom nodes collection by Instara
+#
+# Copyright ยฉ 2025 Instara. All rights reserved.
+# PROPRIETARY SOFTWARE - ALL RIGHTS RESERVED
+# ---
+
+"""
+Utility nodes to select API providers and their corresponding keys,
+simplifying workflows that use multiple API services.
+"""
+
+class INSTARAW_API_ProviderSelector:
+    """
+    Selects a generic API provider (for SeeDream, Nano Banana, etc.)
+    and outputs the provider's name and the corresponding API key.
+    """
+    PROVIDERS = ["wavespeed.ai", "fal.ai"]
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "provider": (cls.PROVIDERS, {"default": "wavespeed.ai"}),
+                "wavespeed_api_key": ("STRING", {"default": "", "multiline": False}),
+                "fal_api_key": ("STRING", {"default": "", "multiline": False}),
+            }
+        }
+
+    RETURN_TYPES = ("STRING", "STRING",)
+    RETURN_NAMES = ("provider", "api_key",)
+    FUNCTION = "select_provider"
+    CATEGORY = "INSTARAW/Utils"
+
+    def select_provider(self, provider, fal_api_key, wavespeed_api_key):
+        if provider == "fal.ai":
+            api_key = fal_api_key
+        elif provider == "wavespeed.ai":
+            api_key = wavespeed_api_key
+        else:
+            print(f"โš ๏ธ Warning: Unknown provider '{provider}' selected. Returning empty API key.")
+            api_key = ""
+        return (provider, api_key,)
+
+# --- NEW NODE FOR IDEOGRAM ---
+class INSTARAW_IdeogramProviderSelector:
+    """
+    Selects an Ideogram API provider (Official or fal.ai) and outputs
+    the provider's name and the corresponding API key.
+    """
+    PROVIDERS = ["Official Ideogram", "fal.ai"]
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "provider": (cls.PROVIDERS, {"default": "Official Ideogram"}),
+                "official_ideogram_api_key": ("STRING", {"default": "", "multiline": False}),
+                "fal_api_key": ("STRING", {"default": "", "multiline": False}),
+            }
+        }
+
+    RETURN_TYPES = ("STRING", "STRING",)
+    RETURN_NAMES = ("provider", "api_key",)
+    FUNCTION = "select_provider"
+    CATEGORY = "INSTARAW/Utils"
+
+    def select_provider(self, provider, official_ideogram_api_key, fal_api_key):
+        if provider == "Official Ideogram":
+            api_key = official_ideogram_api_key
+        elif provider == "fal.ai":
+            api_key = fal_api_key
+        else:
+            print(f"โš ๏ธ Warning: Unknown Ideogram provider '{provider}' selected. Returning empty API key.")
+            api_key = ""
+        return (provider, api_key,)
+
+
+# =================================================================================
+# EXPORT NODE MAPPINGS
+# =================================================================================
+
+NODE_CLASS_MAPPINGS = {
+    "INSTARAW_API_ProviderSelector": INSTARAW_API_ProviderSelector,
+    "INSTARAW_IdeogramProviderSelector": INSTARAW_IdeogramProviderSelector, # New node
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "INSTARAW_API_ProviderSelector": "๐Ÿ”‘ INSTARAW API Provider Selector",
+    "INSTARAW_IdeogramProviderSelector": "๐Ÿ”‘ INSTARAW Ideogram Provider Selector", # New node
+}
\ No newline at end of file
diff --git a/custom_nodes/ComfyUI_INSTARAW/nodes/utility_nodes/authenticity_profile_selector.py b/custom_nodes/ComfyUI_INSTARAW/nodes/utility_nodes/authenticity_profile_selector.py
new file mode 100644
index 0000000000000000000000000000000000000000..f7e5ade1a13c9cb60ba32462156cf61e53d4b4c7
--- /dev/null
+++ b/custom_nodes/ComfyUI_INSTARAW/nodes/utility_nodes/authenticity_profile_selector.py
@@ -0,0 +1,55 @@
+# Filename: ComfyUI_INSTARAW/nodes/utility_nodes/authenticity_profile_selector.py
+import os
+
+class INSTARAW_AuthenticityProfile_Selector:
+    """
+    Dynamically finds and lists all .npz authenticity profiles from the internal
+    'modules/authenticity_profiles' directory. Outputs the base path to the selected profile.
+    """
+    
+    NODE_FILE_PATH = os.path.dirname(os.path.realpath(__file__))
+    INSTARAW_ROOT_PATH = os.path.abspath(os.path.join(NODE_FILE_PATH, "..", ".."))
+    PROFILES_DIR = os.path.join(INSTARAW_ROOT_PATH, "modules", "authenticity_profiles")
+
+    PROFILE_FILES = []
+    if os.path.isdir(PROFILES_DIR):
+        for f in os.listdir(PROFILES_DIR):
+            if f.lower().endswith('.npz'):
+                # FIX: Store only the base name (without extension) for the dropdown
+                PROFILE_FILES.append(os.path.splitext(f)[0])
+    PROFILE_FILES.sort()
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        if not cls.PROFILE_FILES:
+            return {
+                "required": {
+                    "error": ("STRING", {
+                        "default": "ERROR: No .npz profiles found in modules/authenticity_profiles/",
+                        "multiline": True
+                    })
+                }
+            }
+            
+        return {
+            "required": {
+                "profile_name": (cls.PROFILE_FILES, {"default": cls.PROFILE_FILES[0] if cls.PROFILE_FILES else ""}),
+            }
+        }
+
+    RETURN_TYPES = ("STRING",)
+    RETURN_NAMES = ("profile_path",)
+    FUNCTION = "get_path"
+    CATEGORY = "INSTARAW/Authenticity"
+
+    def get_path(self, profile_name):
+        # FIX: Return the base path without the extension.
+        # The consuming nodes (FFT_Match, SaveWithMetadata, etc.) will append the correct extension.
+        base_path = os.path.join(self.PROFILES_DIR, profile_name)
+        
+        # We check for the .npz file's existence here to be safe.
+        if not os.path.exists(f"{base_path}.npz"):
+            raise FileNotFoundError(f"Selected authenticity profile .npz file could not be found: {base_path}.npz")
+            
+        print(f"๐Ÿ‘‘ INSTARAW Profile Selector: Selected profile base '{profile_name}'")
+        return (base_path,)
\ No newline at end of file
diff --git a/custom_nodes/ComfyUI_INSTARAW/nodes/utility_nodes/auto_white_balance_node.py b/custom_nodes/ComfyUI_INSTARAW/nodes/utility_nodes/auto_white_balance_node.py
new file mode 100644
index 0000000000000000000000000000000000000000..dbfdd4ef52e99b30b29e097827d89ec65abf137f
--- /dev/null
+++ b/custom_nodes/ComfyUI_INSTARAW/nodes/utility_nodes/auto_white_balance_node.py
@@ -0,0 +1,126 @@
+# Filename: ComfyUI_INSTARAW/nodes/utility_nodes/auto_white_balance_node.py
+import torch
+import numpy as np
+from PIL import Image
+import os
+
+# Define the path to the internal reference image
+NODE_DIR = os.path.dirname(os.path.realpath(__file__))
+# We need to go up two levels from `utility_nodes` to the `ComfyUI_INSTARAW` root
+INSTARAW_ROOT = os.path.abspath(os.path.join(NODE_DIR, "..", ".."))
+IPHONE13_REF_PATH = os.path.join(INSTARAW_ROOT, "modules", "_refs", "iphone13.jpg")
+
+
+class INSTARAW_AutoWhiteBalance:
+    """
+    Corrects color cast. Can use an Authenticity Profile, a live reference image,
+    a built-in iPhone 13 sample, or a simple grey-world assumption.
+    """
+    
+    # --- UPDATED MODES ---
+    MODES = [
+        "Authenticity Profile",
+        "Internal Sample Image (iPhone)", # Renamed for clarity
+        "Reference Image",
+        "Grey World (Balanced)", 
+        "Grey World (Bright)", 
+        "Grey World (Dark)", 
+    ]
+    
+    GREY_TARGETS = {
+        "Grey World (Balanced)": 128.0,
+        "Grey World (Bright)": 160.0,
+        "Grey World (Dark)": 96.0,
+    }
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "image": ("IMAGE",),
+                "mode": (cls.MODES, {"default": "Authenticity Profile"}),
+                "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
+            },
+            "optional": {
+                "awb_ref_image": ("IMAGE",),
+                "profile_path": ("STRING", {"forceInput": True}),
+            }
+        }
+
+    RETURN_TYPES = ("IMAGE",); FUNCTION = "execute"; CATEGORY = "INSTARAW/Authenticity"
+
+    def _tensor_to_numpy(self, tensor: torch.Tensor) -> np.ndarray:
+        if tensor is None: return None
+        if tensor.ndim == 4: tensor = tensor[0]
+        return (tensor.cpu().numpy() * 255).astype(np.uint8)
+
+    def _numpy_to_tensor(self, np_array: np.ndarray) -> torch.Tensor:
+        return torch.from_numpy(np_array.astype(np.float32) / 255.0).unsqueeze(0)
+
+    def _auto_white_balance(self, img_arr: np.ndarray, target_mean: np.ndarray) -> np.ndarray:
+        img = img_arr.astype(np.float32)
+        img_mean = img.reshape(-1, 3).mean(axis=0)
+        if np.all(img_mean < 1e-6):
+            return img_arr # Avoid division by zero for black images
+        scale = target_mean / (img_mean + 1e-6)
+        return np.clip(img * scale, 0, 255)
+
+    def execute(self, image: torch.Tensor, mode: str, strength: float, awb_ref_image: torch.Tensor = None, profile_path: str = None):
+        if strength == 0:
+            return (image,)
+            
+        target_mean = None
+
+        # --- NEW, HIERARCHICAL LOGIC ---
+        # The node intelligently finds the best available reference based on the selected mode.
+        
+        # 1. Profile Mode (Highest Priority if selected)
+        if mode == "Authenticity Profile":
+            if profile_path and profile_path.strip():
+                npz_path = f"{profile_path}.npz"
+                try:
+                    stats = np.load(npz_path)
+                    if 'chroma_mean' in stats:
+                        print(f"๐ŸŽจ AWB: Using Authenticity Profile '{os.path.basename(npz_path)}'.")
+                        target_mean = stats['chroma_mean'].mean(axis=0)
+                    else:
+                        print(f"โš ๏ธ AWB Warning: 'chroma_mean' not found in profile. Check profile or select another mode.")
+                except Exception as e:
+                    print(f"โš ๏ธ AWB Warning: Could not load profile '{npz_path}'. Error: {e}")
+            else:
+                print("โš ๏ธ AWB Warning: 'Authenticity Profile' mode selected but no profile connected.")
+
+        # 2. Internal Sample Mode
+        elif mode == "Internal Sample Image (iPhone 13)":
+            if not os.path.exists(IPHONE13_REF_PATH):
+                raise FileNotFoundError(f"Internal reference image not found! Expected at: {IPHONE13_REF_PATH}")
+            print("๐ŸŽจ AWB: Using Internal Sample Image (iPhone 13).")
+            ref_pil = Image.open(IPHONE13_REF_PATH)
+            ref_numpy = np.array(ref_pil.convert("RGB"))
+            target_mean = ref_numpy.astype(np.float32).reshape(-1, 3).mean(axis=0)
+
+        # 3. Live Reference Image Mode
+        elif mode == "Reference Image":
+            if awb_ref_image is not None:
+                print("๐ŸŽจ AWB: Using live reference image.")
+                ref_numpy = self._tensor_to_numpy(awb_ref_image)
+                target_mean = ref_numpy.astype(np.float32).reshape(-1, 3).mean(axis=0)
+            else:
+                print("โš ๏ธ AWB Warning: 'Reference Image' mode selected but no reference connected.")
+        
+        # 4. Fallback to Grey World if no other target was found, or if explicitly selected
+        if target_mean is None:
+            grey_target_value = self.GREY_TARGETS.get(mode, 128.0)
+            print(f"๐ŸŽจ AWB: Falling back to Grey World assumption (target: {grey_target_value}).")
+            target_mean = np.array([grey_target_value, grey_target_value, grey_target_value], dtype=np.float32)
+
+        # --- BATCH PROCESSING ---
+        processed_images = []
+        for i in range(image.shape[0]):
+            original_numpy = self._tensor_to_numpy(image[i:i+1])
+            corrected_float = self._auto_white_balance(original_numpy, target_mean)
+            blended_float = (original_numpy.astype(np.float32) * (1 - strength)) + (corrected_float * strength)
+            blended_uint8 = np.clip(blended_float, 0, 255).astype(np.uint8)
+            processed_images.append(self._numpy_to_tensor(blended_uint8))
+
+        return (torch.cat(processed_images, dim=0),)
\ No newline at end of file
diff --git a/custom_nodes/ComfyUI_INSTARAW/nodes/utility_nodes/blend_colors.py b/custom_nodes/ComfyUI_INSTARAW/nodes/utility_nodes/blend_colors.py
new file mode 100644
index 0000000000000000000000000000000000000000..4b9bedace0c451193b20e8d8b3502b886c9a02bd
--- /dev/null
+++ b/custom_nodes/ComfyUI_INSTARAW/nodes/utility_nodes/blend_colors.py
@@ -0,0 +1,68 @@
+# Filename: ComfyUI_INSTARAW/nodes/utility_nodes/blend_colors.py
+import torch
+import numpy as np
+from ...modules.detection_bypass.utils.blend import blend_colors
+
+class INSTARAW_BlendColors:
+    """
+    Applies a color quantization and randomization effect by clustering similar
+    colors. This can help break up the smooth, perfect gradients often found
+    in AI-generated images, adding a layer of statistical noise.
+    """
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "image": ("IMAGE",),
+                "enabled": ("BOOLEAN", {"default": True, "label_on": "Active", "label_off": "Bypassed"}),
+                "tolerance": ("FLOAT", {
+                    "default": 10.0, "min": 1.0, "max": 100.0, "step": 0.1,
+                    "tooltip": "Color tolerance for clustering. Smaller values = more colors, larger values = fewer colors."
+                }),
+                "min_region_size": ("INT", {
+                    "default": 50, "min": 1, "max": 10000,
+                    "tooltip": "Minimum size in pixels for a color region to be retained."
+                }),
+                "max_kmeans_samples": ("INT", {
+                    "default": 100000, "min": 1000, "max": 1000000,
+                    "tooltip": "Maximum number of pixels to sample for the initial color clustering (for performance)."
+                }),
+            }
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "execute"
+    CATEGORY = "INSTARAW/Authenticity"
+
+    def _tensor_to_numpy(self, tensor: torch.Tensor) -> np.ndarray:
+        # Convert tensor [0, 1] float to numpy [0, 255] uint8
+        if tensor.ndim == 4:
+            tensor = tensor[0]
+        return (tensor.cpu().numpy() * 255).astype(np.uint8)
+
+    def _numpy_to_tensor(self, np_array: np.ndarray) -> torch.Tensor:
+        # Convert numpy [0, 255] uint8 back to tensor [0, 1] float
+        return torch.from_numpy(np_array.astype(np.float32) / 255.0).unsqueeze(0)
+
+    def execute(self, image: torch.Tensor, enabled: bool, tolerance: float, min_region_size: int, max_kmeans_samples: int):
+        if not enabled:
+            return (image,)
+
+        processed_images = []
+        for i in range(image.shape[0]):
+            img_numpy = self._tensor_to_numpy(image[i:i+1])
+            
+            # Call the parallelized blend_colors function from our modules
+            processed_numpy = blend_colors(
+                image=img_numpy,
+                tolerance=tolerance,
+                min_region_size=min_region_size,
+                max_kmeans_samples=max_kmeans_samples
+            )
+            
+            processed_images.append(self._numpy_to_tensor(processed_numpy))
+
+        if not processed_images:
+            return (image,)
+
+        return (torch.cat(processed_images, dim=0),)
\ No newline at end of file
diff --git a/custom_nodes/ComfyUI_INSTARAW/nodes/utility_nodes/branding_node.py b/custom_nodes/ComfyUI_INSTARAW/nodes/utility_nodes/branding_node.py
new file mode 100644
index 0000000000000000000000000000000000000000..b10392bcb93e41018af5a86a2c19a63929bdd653
--- /dev/null
+++ b/custom_nodes/ComfyUI_INSTARAW/nodes/utility_nodes/branding_node.py
@@ -0,0 +1,44 @@
+# ---
+# ComfyUI INSTARAW - Branding Node (Final Stable Version)
+# Part of the INSTARAW custom nodes collection by Instara
+#
+# Copyright ยฉ 2025 Instara. All rights reserved.
+# PROPRIETARY SOFTWARE - ALL RIGHTS RESERVED
+# ---
+
+"""
+A simple, stable, purely visual node for placing a brand logo on the workflow.
+Configuration is done via the right-click Properties Panel.
+"""
+
+class INSTARAW_BrandingNode:
+    @classmethod
+    def INPUT_TYPES(cls):
+        # This node has no inputs/widgets on its body.
+        # All configuration is handled via the properties panel on the frontend.
+        return {"required": {}}
+
+    RETURN_TYPES = ()
+    FUNCTION = "do_nothing"
+    CATEGORY = "INSTARAW/Branding"
+    OUTPUT_NODE = True
+
+    def do_nothing(self, **kwargs):
+        # This node is purely visual.
+        return (None,)
+
+    @classmethod
+    def IS_CHANGED(cls, **kwargs):
+        return float("NaN")
+
+# =================================================================================
+# NODE REGISTRATION
+# =================================================================================
+
+NODE_CLASS_MAPPINGS = {
+    "INSTARAW_BrandingNode": INSTARAW_BrandingNode,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "INSTARAW_BrandingNode": "๐Ÿ–ผ๏ธ INSTARAW Branding",
+}
\ No newline at end of file
diff --git a/custom_nodes/ComfyUI_INSTARAW/nodes/utility_nodes/camera_simulator.py b/custom_nodes/ComfyUI_INSTARAW/nodes/utility_nodes/camera_simulator.py
new file mode 100644
index 0000000000000000000000000000000000000000..55c4ebc6e69568458e11b5c664af1ce097d06097
--- /dev/null
+++ b/custom_nodes/ComfyUI_INSTARAW/nodes/utility_nodes/camera_simulator.py
@@ -0,0 +1,83 @@
+# Filename: ComfyUI_INSTARAW/nodes/utility_nodes/camera_simulator.py
+import torch
+import numpy as np
+from ...modules.detection_bypass.camera_pipeline import simulate_camera_pipeline
+
+class INSTARAW_Camera_Simulator:
+    """
+    A comprehensive node for simulating a full, realistic camera pipeline,
+    now with improved defaults and corrected UI widgets for precision control.
+    """
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "image": ("IMAGE",),
+                "enabled": ("BOOLEAN", {"default": True, "label_on": "Active", "label_off": "Bypassed"}),
+                "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
+
+                # In-Camera JPEG Save
+                "initial_jpeg_quality": ("INT", {
+                    "default": 98, "min": 85, "max": 100, "step": 1,
+                    "tooltip": "The quality of the first JPEG save, simulating the camera's output."
+                }),
+
+                # Lens Effects
+                "vignette_strength": ("FLOAT", {"default": 0.1, "min": 0.0, "max": 1.0, "step": 0.01}),
+                "chroma_aberr_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.1}),
+                
+                # Sensor Effects
+                "bayer_demosaic": ("BOOLEAN", {"default": True, "label_on": "Enabled", "label_off": "Disabled"}),
+                "iso_noise_scale": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 16.0, "step": 0.1}),
+                "sensor_read_noise": ("FLOAT", {"default": 2.0, "min": 0.0, "max": 50.0, "step": 0.1}),
+                "hot_pixel_prob": ("FLOAT", {"default": 1e-6, "min": 0.0, "max": 1e-3, "step": 1e-7, "display": "number"}),
+                
+                # Processing Artifacts
+                "banding_strength": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001, "display": "number"}),
+                "motion_blur_kernel": ("INT", {
+                    "default": 3, "min": 1, "max": 51, 
+                    "step": 1, # --- THIS IS THE FIX ---
+                    "tooltip": "Kernel size for motion blur. Even numbers will be rounded up to the next odd number. 1 = no blur."
+                }),
+            },
+        }
+
+    RETURN_TYPES = ("IMAGE",); FUNCTION = "execute"; CATEGORY = "INSTARAW/Authenticity"
+
+    def _tensor_to_numpy(self, tensor: torch.Tensor) -> np.ndarray:
+        if tensor.ndim == 4: tensor = tensor[0]
+        return (tensor.cpu().numpy() * 255).astype(np.uint8)
+
+    def _numpy_to_tensor(self, np_array: np.ndarray) -> torch.Tensor:
+        return torch.from_numpy(np_array.astype(np.float32) / 255.0).unsqueeze(0)
+
+    def execute(self, image: torch.Tensor, enabled: bool, seed: int, initial_jpeg_quality: int, **kwargs):
+        if not enabled: return (image,)
+        
+        motion_blur_kernel = kwargs.get('motion_blur_kernel', 3)
+        # The backend safety check: automatically make the kernel size odd
+        if motion_blur_kernel > 1 and motion_blur_kernel % 2 == 0:
+            motion_blur_kernel += 1
+            
+        processed_images = []
+        for i in range(image.shape[0]):
+            img_numpy = self._tensor_to_numpy(image[i:i+1])
+            
+            processed_numpy = simulate_camera_pipeline(
+                img_arr=img_numpy,
+                bayer=kwargs.get('bayer_demosaic', True),
+                vignette_strength=kwargs.get('vignette_strength', 0.1),
+                chroma_aberr_strength=kwargs.get('chroma_aberr_strength', 1.0),
+                iso_scale=kwargs.get('iso_noise_scale', 1.0),
+                read_noise_std=kwargs.get('sensor_read_noise', 2.0),
+                hot_pixel_prob=kwargs.get('hot_pixel_prob', 1e-6),
+                banding_strength=kwargs.get('banding_strength', 0.0),
+                motion_blur_kernel=motion_blur_kernel,
+                jpeg_cycles=1,
+                jpeg_quality_range=(initial_jpeg_quality, initial_jpeg_quality),
+                seed=seed + i,
+            )
+            processed_images.append(self._numpy_to_tensor(processed_numpy))
+
+        return (torch.cat(processed_images, dim=0),) if processed_images else (image,)
\ No newline at end of file
diff --git a/custom_nodes/ComfyUI_INSTARAW/nodes/utility_nodes/color_science_node.py b/custom_nodes/ComfyUI_INSTARAW/nodes/utility_nodes/color_science_node.py
new file mode 100644
index 0000000000000000000000000000000000000000..7ddfe9b7e81b4ab52981e4030b62c4c44678670c
--- /dev/null
+++ b/custom_nodes/ComfyUI_INSTARAW/nodes/utility_nodes/color_science_node.py
@@ -0,0 +1,88 @@
+# Filename: ComfyUI_INSTARAW/nodes/utility_nodes/color_science_node.py
+# ---
+
+import torch
+import numpy as np
+from PIL import Image
+import os
+
+# Import the LUT application logic from our modules
+from ...modules.detection_bypass.utils import apply_lut, load_lut
+
+class INSTARAW_ColorScience:
+    """
+    Applies a 3D LUT (.cube file) to an image to simulate the unique color science
+    of a specific camera profile (e.g., iPhone, Sony, Fuji).
+    """
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "image": ("IMAGE",),
+                "lut_path": ("STRING", {"forceInput": True, "tooltip": "Connect an INSTARAW LUT Selector node here."}),
+                "strength": ("FLOAT", {
+                    "default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01,
+                    "tooltip": "Blend strength of the LUT. 0.0 is no effect, 1.0 is full effect."
+                }),
+            },
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "apply_color_science"
+    CATEGORY = "INSTARAW/Post-Processing"
+
+    def tensor_to_pil(self, tensor: torch.Tensor) -> Image.Image:
+        """Converts a torch tensor to a PIL image."""
+        if tensor.ndim == 4 and tensor.shape[0] == 1:
+            tensor = tensor.squeeze(0)
+        img_np = (tensor.cpu().numpy() * 255).astype(np.uint8)
+        return Image.fromarray(img_np)
+
+    def pil_to_tensor(self, pil_image: Image.Image) -> torch.Tensor:
+        """Converts a PIL image to a torch tensor."""
+        img_np = np.array(pil_image.convert("RGB")).astype(np.float32) / 255.0
+        return torch.from_numpy(img_np).unsqueeze(0)
+
+    def apply_color_science(self, image: torch.Tensor, lut_path: str, strength: float):
+        print(f"๐ŸŽจ INSTARAW Color Science: Applying LUT '{os.path.basename(lut_path)}' with strength {strength:.2f}.")
+
+        if not os.path.exists(lut_path):
+            raise FileNotFoundError(f"INSTARAW Color Science: LUT file not found at path: {lut_path}")
+
+        # Load the LUT from the provided path
+        try:
+            lut_data = load_lut(lut_path)
+        except Exception as e:
+            raise ValueError(f"Failed to load or parse LUT file at {lut_path}: {e}")
+
+        processed_images = []
+        for i in range(image.shape[0]):
+            single_image_tensor = image[i:i+1]
+            pil_image = self.tensor_to_pil(single_image_tensor)
+            numpy_image = np.array(pil_image)
+
+            # Apply the LUT using our utility function
+            processed_numpy_image = apply_lut(numpy_image, lut_data, strength)
+
+            processed_pil_image = Image.fromarray(processed_numpy_image)
+            processed_tensor = self.pil_to_tensor(processed_pil_image)
+            processed_images.append(processed_tensor)
+
+        if not processed_images:
+            print("โš ๏ธ INSTARAW Color Science: No images were processed. Returning original image.")
+            return (image,)
+            
+        final_batch = torch.cat(processed_images, dim=0)
+        
+        print("โœ… INSTARAW Color Science: Processing complete.")
+        return (final_batch,)
+
+# --- Node Registration ---
+NODE_CLASS_MAPPINGS = {
+    "INSTARAW_ColorScience": INSTARAW_ColorScience
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "INSTARAW_ColorScience": "๐ŸŽจ INSTARAW Color Science (LUT)"
+}
\ No newline at end of file
diff --git a/custom_nodes/ComfyUI_INSTARAW/nodes/utility_nodes/compression_node.py b/custom_nodes/ComfyUI_INSTARAW/nodes/utility_nodes/compression_node.py
new file mode 100644
index 0000000000000000000000000000000000000000..3df6150a76ec0fee0c80b3bcabdbeebb57282f17
--- /dev/null
+++ b/custom_nodes/ComfyUI_INSTARAW/nodes/utility_nodes/compression_node.py
@@ -0,0 +1,72 @@
+# Filename: ComfyUI_INSTARAW/nodes/utility_nodes/compression_node.py
+import torch
+import numpy as np
+from ...modules.detection_bypass.camera_pipeline import _jpeg_recompress
+import random
+
+class INSTARAW_Multi_Compression:
+    """
+    Simulates the generational loss from repeated JPEG compression, a key
+    artifact of images shared online. This should be a final step.
+    """
+
+    CHROMA_SUBSAMPLING_MODES = ["Standard (4:2:0 - Most Common)", "High Quality (4:4:4)", "Aggressive (4:1:1)"]
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "image": ("IMAGE",),
+                "enabled": ("BOOLEAN", {"default": True, "label_on": "Active", "label_off": "Bypassed"}),
+                "cycles": ("INT", {
+                    "default": 4, "min": 1, "max": 10, "step": 1,
+                    "tooltip": "Number of times to re-compress the image."
+                }),
+                "min_quality": ("INT", {
+                    "default": 43, "min": 1, "max": 100, "step": 1,
+                    "tooltip": "The minimum possible quality for each random compression cycle."
+                }),
+                "max_quality": ("INT", {
+                    "default": 75, "min": 1, "max": 100, "step": 1,
+                    "tooltip": "The maximum possible quality for each random compression cycle."
+                }),
+                "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
+            },
+        }
+
+    RETURN_TYPES = ("IMAGE",); FUNCTION = "execute"; CATEGORY = "INSTARAW/Authenticity"
+
+    def _tensor_to_numpy(self, tensor: torch.Tensor) -> np.ndarray:
+        if tensor.ndim == 4: tensor = tensor[0]
+        return (tensor.cpu().numpy() * 255).astype(np.uint8)
+
+    def _numpy_to_tensor(self, np_array: np.ndarray) -> torch.Tensor:
+        return torch.from_numpy(np_array.astype(np.float32) / 255.0).unsqueeze(0)
+
+    def execute(self, image: torch.Tensor, enabled: bool, cycles: int, min_quality: int, max_quality: int, seed: int):
+        if not enabled: return (image,)
+
+        # Ensure min is not greater than max
+        min_q = min(min_quality, max_quality)
+        max_q = max(min_quality, max_quality)
+
+        processed_images = []
+        for i in range(image.shape[0]):
+            img_numpy = self._tensor_to_numpy(image[i:i+1])
+            
+            # Seed the random number generator for this specific image
+            rng = random.Random(seed + i)
+            
+            # The multi-cycle compression loop
+            for cycle in range(cycles):
+                # Pick a random quality within the specified range for each cycle
+                quality = rng.randint(min_q, max_q)
+                img_numpy = _jpeg_recompress(img_numpy, quality=quality)
+            
+            processed_images.append(self._numpy_to_tensor(img_numpy))
+
+        return (torch.cat(processed_images, dim=0),) if processed_images else (image,)
+
+# --- Node Registration ---
+NODE_CLASS_MAPPINGS = {"INSTARAW_Multi_Compression": INSTARAW_Multi_Compression}
+NODE_DISPLAY_NAME_MAPPINGS = {"INSTARAW_Multi_Compression": "๐Ÿ›ก๏ธ INSTARAW Multi-Compression"}
\ No newline at end of file
diff --git a/custom_nodes/ComfyUI_INSTARAW/nodes/utility_nodes/debug_prompt_overlay.py b/custom_nodes/ComfyUI_INSTARAW/nodes/utility_nodes/debug_prompt_overlay.py
new file mode 100644
index 0000000000000000000000000000000000000000..b7734985ea83f019eb3b229b619015222616e0c6
--- /dev/null
+++ b/custom_nodes/ComfyUI_INSTARAW/nodes/utility_nodes/debug_prompt_overlay.py
@@ -0,0 +1,94 @@
+# Filename: ComfyUI_INSTARAW/nodes/utility_nodes/debug_prompt_overlay.py
+# ---
+
+import torch
+import numpy as np
+from PIL import Image, ImageDraw, ImageFont
+import os
+
+
+def tensor_to_pil(img: torch.Tensor) -> Image.Image:
+    arr = (img[0].cpu().numpy().clip(0, 1) * 255).astype(np.uint8)
+    return Image.fromarray(arr, 'RGB')
+
+
+def pil_to_tensor(pil: Image.Image) -> torch.Tensor:
+    arr = np.array(pil).astype(np.float32) / 255.0
+    return torch.from_numpy(arr).unsqueeze(0)
+
+
+class INSTARAW_DebugPromptOverlay:
+    """
+    Debug node: overlay prompt text on the image.
+    Designed for LIST mode: receives a single image and a single prompt.
+    """
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "image": ("IMAGE",),
+                "prompt": ("STRING", {"multiline": False, "default": ""}),
+            }
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    RETURN_NAMES = ("image",)
+    FUNCTION = "overlay"
+    CATEGORY = "INSTARAW/Debug"
+
+    @classmethod
+    def IS_CHANGED(cls, **kwargs):
+        return float("NaN")
+
+    def overlay(self, image, prompt):
+        pil = tensor_to_pil(image)
+        draw = ImageDraw.Draw(pil)
+
+        # Try to load a nicer font, otherwise fallback to default
+        font = None
+        fonts_dir = os.path.join(os.path.dirname(__file__), "..", "..", "fonts")
+        try:
+            font_path = os.path.join(fonts_dir, "BricolageGrotesque.ttf")
+            if os.path.exists(font_path):
+                font = ImageFont.truetype(font_path, size=int(pil.height * 0.03))
+        except Exception:
+            pass
+
+        if font is None:
+            font = ImageFont.load_default()
+
+        # Prepare prompt text
+        if prompt is None:
+            prompt = ""
+        prompt = str(prompt)[:200]   # Avoid overlong prompts
+
+        # Measure text with Pillow >= 8
+        bbox = draw.textbbox((0, 0), prompt, font=font)
+        tw = bbox[2] - bbox[0]
+        th = bbox[3] - bbox[1]
+
+        pad = 8
+        x = pad
+        y = pil.height - th - pad
+
+        # Background rectangle
+        draw.rectangle(
+            [x - pad, y - pad, x + tw + pad, y + th + pad],
+            fill=(0, 0, 0, 180)
+        )
+
+        # Draw text
+        draw.text((x, y), prompt, font=font, fill=(255, 255, 255))
+
+        return (pil_to_tensor(pil),)
+
+
+# Register in-module
+NODE_CLASS_MAPPINGS = {
+    "INSTARAW_DebugPromptOverlay": INSTARAW_DebugPromptOverlay,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "INSTARAW_DebugPromptOverlay": "๐Ÿ› INSTARAW Debug Prompt Overlay",
+}
\ No newline at end of file
diff --git a/custom_nodes/ComfyUI_INSTARAW/nodes/utility_nodes/feather_mask.py b/custom_nodes/ComfyUI_INSTARAW/nodes/utility_nodes/feather_mask.py
new file mode 100644
index 0000000000000000000000000000000000000000..6f3c5b50c8f777578997c50e7915a68c0a58775f
--- /dev/null
+++ b/custom_nodes/ComfyUI_INSTARAW/nodes/utility_nodes/feather_mask.py
@@ -0,0 +1,125 @@
+# ---
+# ComfyUI INSTARAW - Feather Mask Node
+# Part of the INSTARAW custom nodes collection by Instara
+#
+# Copyright ยฉ 2025 Instara. All rights reserved.
+# PROPRIETARY SOFTWARE - ALL RIGHTS RESERVED
+# ---
+
+"""
+The definitive, Photoshop-style feathering node. This version uses the correct compositing
+method of a solid core over a blurred outer shell to produce a perfect, artifact-free result.
+"""
+import torch
+from tqdm import tqdm
+import math
+
+try:
+    import kornia.filters as kfilters
+    import kornia.morphology as morph
+    KORNIA_AVAILABLE = True
+    DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+except ImportError:
+    KORNIA_AVAILABLE = False
+    print("โš ๏ธ Kornia not available. Feather Mask node requires Kornia.")
+
+class INSTARAWFeatherMask:
+    """
+    Applies a true feathering effect by compositing a solid core with a blurred outer boundary.
+    This ensures a 100% solid core with a perfectly seamless gradient falloff.
+    """
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "mask": ("MASK",),
+                "expand": ("INT", {"default": 75, "min": -8192, "max": 8192, "step": 1}),
+                "feather": ("INT", {"default": 60, "min": 0, "max": 8192, "step": 1}),
+                "tapered_corners": ("BOOLEAN", {"default": True}),
+            },
+        }
+
+    CATEGORY = "INSTARAW/Utils"
+    RETURN_TYPES = ("MASK", "MASK", "STRING",)
+    RETURN_NAMES = ("mask", "mask_inverted", "info_text",)
+    FUNCTION = "feather_mask"
+    DESCRIPTION = """
+# INSTARAW Feather Mask (Corrected)
+- **expand**: Expands the solid core of the mask by this many pixels.
+- **feather**: Creates a smooth gradient over this many pixels, starting from the new solid edge.
+- This provides a perfect, artifact-free feather just like in Photoshop.
+"""
+
+    def feather_mask(self, mask, expand, feather, tapered_corners):
+        if not KORNIA_AVAILABLE:
+            raise ImportError("Kornia library is required for this node.")
+
+        expand = float(expand)
+        feather = float(max(0, feather))
+
+        # If feather is zero, just do a simple, hard expansion for performance.
+        if feather < 0.01:
+            final_masks = []
+            for m in mask:
+                output = m.unsqueeze(0).unsqueeze(0).to(DEVICE)
+                if round(expand) != 0:
+                    op = morph.dilation if expand > 0 else morph.erosion
+                    kernel = torch.tensor([[0, 1, 0], [1, 1, 1], [0, 1, 0]], dtype=torch.float32, device=DEVICE) if tapered_corners else torch.ones(3, 3, device=DEVICE)
+                    for _ in range(abs(round(expand))):
+                        output = op(output, kernel)
+                final_masks.append(output.squeeze(0).squeeze(0).cpu())
+            final_masks_stack = torch.stack(final_masks, dim=0)
+            info_text = f"Solid Expand: {expand:.2f}px\nFeather: 0px (disabled)"
+            return (final_masks_stack, 1.0 - final_masks_stack, info_text)
+
+        # --- True Feathering Algorithm ---
+        final_masks = []
+        for m in tqdm(mask, desc="Feathering Mask"):
+            source_tensor = m.unsqueeze(0).unsqueeze(0).to(DEVICE)
+            
+            # 1. Create the solid core mask by expanding by the 'expand' amount.
+            core_mask = source_tensor
+            expand_rounded = round(expand)
+            if expand_rounded != 0:
+                op = morph.dilation if expand_rounded > 0 else morph.erosion
+                kernel = torch.tensor([[0, 1, 0], [1, 1, 1], [0, 1, 0]], dtype=torch.float32, device=DEVICE) if tapered_corners else torch.ones(3, 3, device=DEVICE)
+                for _ in range(abs(expand_rounded)):
+                    core_mask = op(core_mask, kernel)
+
+            # 2. Create the larger outer boundary for the full effect.
+            outer_mask = source_tensor
+            total_expand_size = round(expand + feather)
+            if total_expand_size != 0:
+                op = morph.dilation if total_expand_size > 0 else morph.erosion
+                kernel = torch.tensor([[0, 1, 0], [1, 1, 1], [0, 1, 0]], dtype=torch.float32, device=DEVICE) if tapered_corners else torch.ones(3, 3, device=DEVICE)
+                for _ in range(abs(total_expand_size)):
+                    outer_mask = op(outer_mask, kernel)
+            
+            # 3. Blur the outer boundary mask to create the gradient.
+            # Sigma should be proportional to the feather size. feather / 2 is a good standard.
+            sigma = feather / 2.0
+            # Kernel size must be an odd number.
+            kernel_size = 2 * math.ceil(3.0 * sigma) + 1
+            blurred_outer_mask = kfilters.gaussian_blur2d(
+                outer_mask, (kernel_size, kernel_size), (sigma, sigma)
+            )
+
+            # 4. Composite the solid core on top of the blurred outer mask.
+            # This is the key step that guarantees a solid core and seamless gradient.
+            final_mask = torch.max(core_mask, blurred_outer_mask)
+            final_masks.append(final_mask.squeeze(0).squeeze(0).cpu())
+
+        final_masks_stack = torch.stack(final_masks, dim=0)
+        
+        total_effect = expand + feather
+        info_text = f"Solid Expand: {expand:.2f}px\nFeather Size: {feather:.2f}px\nTotal Effect Size: {total_effect:.2f}px"
+
+        return (final_masks_stack, 1.0 - final_masks_stack, info_text)
+
+
+NODE_CLASS_MAPPINGS = {
+    "INSTARAWFeatherMask": INSTARAWFeatherMask,
+}
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "INSTARAWFeatherMask": "๐ŸŽญ INSTARAW Feather Mask",
+}
\ No newline at end of file
diff --git a/custom_nodes/ComfyUI_INSTARAW/nodes/utility_nodes/fft_match.py b/custom_nodes/ComfyUI_INSTARAW/nodes/utility_nodes/fft_match.py
new file mode 100644
index 0000000000000000000000000000000000000000..7753b158c3d380c6d5625d708c2525d90a52715e
--- /dev/null
+++ b/custom_nodes/ComfyUI_INSTARAW/nodes/utility_nodes/fft_match.py
@@ -0,0 +1,91 @@
+# Filename: ComfyUI_INSTARAW/nodes/utility_nodes/fft_match.py
+import torch
+import numpy as np
+from PIL import Image
+
+from ...modules.detection_bypass.utils.direct_spectral_matching import direct_spectral_match, radial_profile
+
+class INSTARAW_FFT_Match:
+    """
+    Performs a spectral match using either a reference image or an
+    Authenticity Profile. Offers two algorithms for comparison.
+    """
+    
+    MODES = ["Color-Safe (Luminance)", "Per-Channel (Legacy)"]
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "image": ("IMAGE",),
+                "mode": (cls.MODES,),
+                "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
+            },
+            "optional": {
+                "ref_image": ("IMAGE",),
+                "profile_path": ("STRING", {"forceInput": True}),
+            }
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "execute"
+    CATEGORY = "INSTARAW/Authenticity"
+
+    def _tensor_to_numpy(self, tensor: torch.Tensor) -> np.ndarray:
+        return (tensor.squeeze(0).cpu().numpy() * 255).astype(np.uint8)
+
+    def _numpy_to_tensor(self, np_array: np.ndarray) -> torch.Tensor:
+        return torch.from_numpy(np_array.astype(np.float32) / 255.0).unsqueeze(0)
+
+    def execute(self, image, mode, strength, ref_image=None, profile_path=None):
+        if strength == 0:
+            return (image,)
+
+        target_spectra = None
+        
+        if ref_image is not None:
+            print(f"๐Ÿ›ก๏ธ INSTARAW FFT Match: Using live reference image with '{mode}' mode.")
+            ref_numpy = self._tensor_to_numpy(ref_image[0:1])
+            
+            if mode == "Color-Safe (Luminance)":
+                ref_gray = 0.299 * ref_numpy[:,:,0] + 0.587 * ref_numpy[:,:,1] + 0.114 * ref_numpy[:,:,2]
+                fft = np.fft.fftshift(np.fft.fft2(ref_gray))
+                mag = np.log1p(np.abs(fft))
+                target_spectra = radial_profile(mag, bins=512)
+            else: # Per-Channel
+                target_spectra = {}
+                for i, key in enumerate(['r', 'g', 'b']):
+                    fft = np.fft.fftshift(np.fft.fft2(ref_numpy[:,:,i]))
+                    mag = np.log1p(np.abs(fft))
+                    target_spectra[key] = radial_profile(mag, bins=512)
+
+        elif profile_path and profile_path.strip():
+            print(f"๐Ÿ›ก๏ธ INSTARAW FFT Match: Using Authenticity Profile with '{mode}' mode.")
+            npz_path = f"{profile_path}.npz"
+            try:
+                stats = np.load(npz_path)
+                target_spectra = {
+                    'r': stats['spectra_r'].mean(axis=0),
+                    'g': stats['spectra_g'].mean(axis=0),
+                    'b': stats['spectra_b'].mean(axis=0)
+                }
+            except Exception as e:
+                raise ValueError(f"Failed to load spectral data from profile '{npz_path}': {e}")
+
+        if target_spectra is None:
+            print("โš ๏ธ INSTARAW FFT Match: No reference image or profile provided. Passing through.")
+            return (image,)
+
+        processed_images = []
+        for i in range(image.shape[0]):
+            img_numpy = self._tensor_to_numpy(image[i:i+1])
+            
+            processed_numpy = direct_spectral_match(
+                img_arr=img_numpy,
+                target_spectra=target_spectra,
+                strength=strength,
+                mode=mode # Explicitly pass the mode
+            )
+            processed_images.append(self._numpy_to_tensor(processed_numpy))
+
+        return (torch.cat(processed_images, dim=0),)
\ No newline at end of file
diff --git a/custom_nodes/ComfyUI_INSTARAW/nodes/utility_nodes/grow_mask_with_blur.py b/custom_nodes/ComfyUI_INSTARAW/nodes/utility_nodes/grow_mask_with_blur.py
new file mode 100644
index 0000000000000000000000000000000000000000..161dfc388b57f0fd5560586dc5770d3bfbf34d83
--- /dev/null
+++ b/custom_nodes/ComfyUI_INSTARAW/nodes/utility_nodes/grow_mask_with_blur.py
@@ -0,0 +1,201 @@
+# ---
+# ComfyUI INSTARAW - Grow Mask With Blur Node
+# Part of the INSTARAW custom nodes collection by Instara
+#
+# Copyright ยฉ 2025 Instara. All rights reserved.
+# PROPRIETARY SOFTWARE - ALL RIGHTS RESERVED
+# ---
+
+"""
+Hybrid pixel and percentage-based mask growing/blurring utility with a dedicated text output for clarity.
+"""
+
+import torch
+import numpy as np
+from PIL import Image, ImageFilter
+from tqdm import tqdm
+
+pass # instara marker
+
+# Tensor conversion utilities
+def tensor2pil(image):
+    """Convert tensor (mask) to PIL Image in grayscale mode"""
+    np_image = np.clip(255. * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8)
+    return [Image.fromarray(np_image, mode='L')]
+
+def pil2tensor(image):
+    """Convert PIL Image to tensor (mask)"""
+    # Ensure we're working with grayscale
+    if image.mode != 'L':
+        image = image.convert('L')
+    return torch.from_numpy(np.array(image).astype(np.float32) / 255.0).unsqueeze(0)
+
+
+class INSTARAWGrowMask:
+    """
+    Hybrid mask utility for growing/shrinking with pixel or percentage-based control.
+    Provides precise, predictable results with a dedicated string output for calculated values.
+    """
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "mask": ("MASK",),
+                "expand_method": (["Pixels", "Percentage"],),
+                "expand_pixels": ("INT", {
+                    "default": 10, "min": -8192, "max": 8192, "step": 1
+                }),
+                "expand_percent": ("FLOAT", {
+                    "default": 5.0, "min": -100.0, "max": 100.0, "step": 0.01
+                }),
+                "incremental_expand_rate_px": ("FLOAT", {
+                    "default": 0.0, "min": -100.0, "max": 100.0, "step": 0.001
+                }),
+                 "incremental_expand_rate_percent": ("FLOAT", {
+                    "default": 0.0, "min": -100.0, "max": 100.0, "step": 0.001
+                }),
+                "tapered_corners": ("BOOLEAN", {"default": True}),
+                "flip_input": ("BOOLEAN", {"default": False}),
+                "blur_radius_multiplier": ("FLOAT", {
+                    "default": 0.5, "min": 0.0, "max": 10.0, "step": 0.001
+                }),
+                "lerp_alpha": ("FLOAT", {
+                    "default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01
+                }),
+                "decay_factor": ("FLOAT", {
+                    "default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01
+                }),
+            },
+            "optional": {
+                "fill_holes": ("BOOLEAN", {"default": False}),
+            },
+        }
+
+    CATEGORY = "INSTARAW/Utils"
+    RETURN_TYPES = ("MASK", "MASK", "STRING",)
+    RETURN_NAMES = ("mask", "mask_inverted", "info_text",)
+    FUNCTION = "expand_mask"
+    DESCRIPTION = """
+# INSTARAW Grow Mask (Hybrid)
+- **expand_method**: Choose 'Pixels' for absolute values or 'Percentage' for resolution-independent scaling.
+- **expand**: Set the expansion amount in pixels or percent.
+- **incremental_expand_rate**: Increase expansion per frame (in pixels or percent).
+- **blur_radius_multiplier**: Blur radius as a multiplier of the final expand pixel value.
+- **info_text**: A string output showing the calculated pixel values for clarity.
+"""
+
+    def expand_mask(self, mask, expand_method, expand_pixels, expand_percent,
+                   incremental_expand_rate_px, incremental_expand_rate_percent,
+                   tapered_corners, flip_input, blur_radius_multiplier,
+                   lerp_alpha, decay_factor, fill_holes=False):
+
+        mask_height, mask_width = mask.shape[-2:]
+        reference_dimension = min(mask_height, mask_width)
+
+        if expand_method == "Percentage":
+            initial_expand_px = (expand_percent / 100.0) * reference_dimension
+            incremental_rate_px = (incremental_expand_rate_percent / 100.0) * reference_dimension
+        else:
+            initial_expand_px = float(expand_pixels)
+            incremental_rate_px = incremental_expand_rate_px
+
+        try:
+            import kornia.morphology as morph
+            device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        except ImportError:
+            print("โš ๏ธ Kornia not available for morphology, expansion/shrinking will be skipped.")
+            device = torch.device("cpu")
+            morph = None
+
+        if flip_input:
+            mask = 1.0 - mask
+
+        growmask = mask.reshape((-1, mask.shape[-2], mask.shape[-1]))
+        out = []
+        previous_output = None
+        current_expand = initial_expand_px
+
+        for m in tqdm(growmask, desc="Processing Mask"):
+            output = m.unsqueeze(0).unsqueeze(0).to(device)
+
+            if abs(round(current_expand)) > 0 and morph is not None:
+                kernel_shape = torch.tensor([[0, 1, 0], [1, 1, 1], [0, 1, 0]], dtype=torch.float32, device=device) if tapered_corners else torch.ones(3, 3, device=device)
+                op = morph.dilation if current_expand > 0 else morph.erosion
+                for _ in range(abs(round(current_expand))):
+                    output = op(output, kernel_shape)
+            output = output.squeeze(0).squeeze(0)
+
+            if fill_holes:
+                try:
+                    import scipy.ndimage
+                    output = torch.from_numpy(scipy.ndimage.binary_fill_holes(output.cpu().numpy() > 0).astype(np.float32)).to(device)
+                except ImportError:
+                    print("โš ๏ธ scipy not available, skipping fill_holes")
+
+            if (lerp_alpha < 1.0 or decay_factor < 1.0) and previous_output is not None:
+                if lerp_alpha < 1.0:
+                    output = lerp_alpha * output + (1 - lerp_alpha) * previous_output
+                if decay_factor < 1.0:
+                    output += decay_factor * previous_output
+                    max_val = output.max()
+                    if max_val > 0:
+                        output = output / max_val
+
+            previous_output = output
+            out.append(output.cpu())
+            current_expand += incremental_rate_px
+
+        final_masks = torch.stack(out, dim=0)
+        if blur_radius_multiplier > 0:
+            blurred_out = []
+            blur_tracker_px = initial_expand_px
+            for tensor in final_masks:
+                frame_blur_radius = abs(blur_tracker_px) * blur_radius_multiplier
+                if frame_blur_radius > 0.01:
+                    pil_image = tensor2pil(tensor.detach())[0].filter(ImageFilter.GaussianBlur(frame_blur_radius))
+                    blurred_tensor = pil2tensor(pil_image)
+                    max_val = blurred_tensor.max()
+                    if max_val > 0:
+                        blurred_tensor = blurred_tensor / max_val
+                    blurred_out.append(blurred_tensor)
+                else:
+                    blurred_out.append(tensor.unsqueeze(0))
+                blur_tracker_px += incremental_rate_px
+            final_masks = torch.cat(blurred_out, dim=0)
+
+        inverted_mask = 1.0 - final_masks
+
+        # Prepare the string output for clarity
+        start_blur = abs(initial_expand_px) * blur_radius_multiplier
+        percent_str = f" ({expand_percent}%)" if expand_method == "Percentage" else ""
+        info_text_lines = [
+            f"Mode: {expand_method}",
+            f"Expand (start): {initial_expand_px:.2f}px{percent_str}",
+            f"Blur (start): {start_blur:.2f}px"
+        ]
+        if incremental_rate_px != 0.0 and len(growmask) > 1:
+            final_expand_px = initial_expand_px + incremental_rate_px * (len(growmask) - 1)
+            final_blur = abs(final_expand_px) * blur_radius_multiplier
+            percent_str_end = f" ({expand_percent + incremental_expand_rate_percent * (len(growmask) - 1):.2f}%)" if expand_method == "Percentage" else ""
+            info_text_lines.extend([
+                f"Expand (end): {final_expand_px:.2f}px{percent_str_end}",
+                f"Blur (end): {final_blur:.2f}px"
+            ])
+        
+        info_text_string = "\n".join(info_text_lines)
+
+        return (final_masks, inverted_mask, info_text_string,)
+
+
+# =================================================================================
+# EXPORT NODE MAPPINGS
+# =================================================================================
+
+NODE_CLASS_MAPPINGS = {
+    "INSTARAWGrowMask": INSTARAWGrowMask,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "INSTARAWGrowMask": "๐ŸŽญ INSTARAW Grow Mask",
+}
\ No newline at end of file
diff --git a/custom_nodes/ComfyUI_INSTARAW/nodes/utility_nodes/image_prompt_iterator.py b/custom_nodes/ComfyUI_INSTARAW/nodes/utility_nodes/image_prompt_iterator.py
new file mode 100644
index 0000000000000000000000000000000000000000..61651e1520a77c651f03750c881a4630d4a19d04
--- /dev/null
+++ b/custom_nodes/ComfyUI_INSTARAW/nodes/utility_nodes/image_prompt_iterator.py
@@ -0,0 +1,50 @@
+class INSTARAW_ImagePromptIterator:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "images": ("IMAGE",),
+                "prompt_list": ("STRING", {"multiline": False, "default": ""}),
+                "extend_mode": (
+                    ["truncate", "repeat_last", "cycle"],
+                    {"default": "repeat_last"},
+                ),
+            }
+        }
+
+    # First output (images) is NOT a list, second (prompts) IS a list
+    INPUT_IS_LIST = False
+    OUTPUT_IS_LIST = [False, True]
+
+    RETURN_TYPES = ("IMAGE", "STRING",)
+    RETURN_NAMES = ("images", "prompts",)
+    FUNCTION = "map"
+    CATEGORY = "INSTARAW/Workflow Logic"
+
+    @classmethod
+    def IS_CHANGED(cls, **kwargs):
+        return float("NaN")
+
+    def map(self, images, prompt_list, extend_mode):
+        B = images.shape[0]
+
+        if isinstance(prompt_list, (list, tuple)):
+            prompts = [str(p) for p in prompt_list]
+        else:
+            prompts = [str(prompt_list)] if str(prompt_list) != "" else []
+
+        if len(prompts) == 0:
+            aligned = [""] * B
+        else:
+            aligned = []
+            if extend_mode == "truncate":
+                aligned = prompts[:B] if len(prompts) >= B else prompts + [""] * (B - len(prompts))
+            elif extend_mode == "repeat_last":
+                last = prompts[-1]
+                for i in range(B):
+                    aligned.append(prompts[i] if i < len(prompts) else last)
+            elif extend_mode == "cycle":
+                for i in range(B):
+                    aligned.append(prompts[i % len(prompts)])
+
+        return (images, aligned)
\ No newline at end of file
diff --git a/custom_nodes/ComfyUI_INSTARAW/nodes/utility_nodes/image_resize_advanced.py b/custom_nodes/ComfyUI_INSTARAW/nodes/utility_nodes/image_resize_advanced.py
new file mode 100644
index 0000000000000000000000000000000000000000..b34965668652e8d1ee80156fcdfc16944c85a00f
--- /dev/null
+++ b/custom_nodes/ComfyUI_INSTARAW/nodes/utility_nodes/image_resize_advanced.py
@@ -0,0 +1,127 @@
+# ---
+# ComfyUI INSTARAW - Image Resize & Fill Node (Definitive Version)
+# Part of the INSTARAW custom nodes collection by Instara
+#
+# Copyright ยฉ 2025 Instara. All rights reserved.
+# PROPRIETARY SOFTWARE - ALL RIGHTS RESERVED
+#
+# This node is inspired by and extends the functionality of rgthree's Image Resize node.
+# ---
+
+import torch
+import comfy.utils
+
+from nodes import ImageScale, MAX_RESOLUTION
+
+class INSTARAW_ImageResizeFill:
+    """
+    An advanced image resize node with crop, pad, contain, and a simple, reliable 
+    'pad_with_background' fit method to fill empty space with another image.
+    """
+
+    TITLE = "๐Ÿ“ INSTARAW Image Resize & Fill"
+
+    FIT_METHODS = ["crop", "pad", "contain", "pad_with_background"]
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "image": ("IMAGE",),
+                "width": ("INT", {"default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 8}),
+                "height": ("INT", {"default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 8}),
+                "fit": (cls.FIT_METHODS,),
+                "method": (ImageScale.upscale_methods, {"default": "lanczos"}),
+            },
+            "optional": {
+                "background_image": ("IMAGE", {"tooltip": "Image to use for the background when fit is 'pad_with_background'"}),
+            }
+        }
+
+    RETURN_TYPES = ("IMAGE", "INT", "INT",)
+    RETURN_NAMES = ("IMAGE", "WIDTH", "HEIGHT",)
+    FUNCTION = "main"
+    CATEGORY = "INSTARAW/Utils"
+
+    def main(self, image, width, height, method, fit, background_image=None):
+        if not image.numel(): return (image, 0, 0)
+        _, H, W, _ = image.shape
+
+        if (width == 0 and height == 0) or (width == W and height == H):
+            return (image, W, H)
+
+        if width == 0 or height == 0:
+            width = round(height / H * W) if width == 0 else width
+            height = round(width / W * H) if height == 0 else height
+            if fit not in ["pad_with_background", "contain"]:
+                fit = "contain"
+
+        # --- DEFINITIVE 'PAD WITH BACKGROUND' LOGIC ---
+        if fit == "pad_with_background":
+            if background_image is None:
+                raise ValueError("A 'background_image' is required for the 'pad_with_background' fit method.")
+
+            print("โœจ Performing 'Pad with Background' resize.")
+            fg, bg = image, background_image
+            
+            # 1. Create the canvas: Resize the background to the final target size.
+            canvas = torch.nn.functional.interpolate(bg.permute(0, 3, 1, 2), size=(height, width), mode='bilinear', antialias=True).permute(0, 2, 3, 1)
+
+            # 2. 'Contain' resize the foreground image to fit inside the canvas.
+            fg_resized = self.resize_contain(fg, width, height, method)
+            fg_h, fg_w = fg_resized.shape[1], fg_resized.shape[2]
+
+            # 3. Calculate the position to paste the foreground.
+            y_start = (height - fg_h) // 2
+            x_start = (width - fg_w) // 2
+            
+            # 4. Paste the resized foreground directly onto the canvas.
+            canvas[:, y_start:y_start + fg_h, x_start:x_start + fg_w, :] = fg_resized
+            
+            return (canvas, width, height)
+
+        # --- Standard Resize Logic (rgthree-based) ---
+        resized_width, resized_height = self.calculate_resize_dims(W, H, width, height, fit)
+        out_image = comfy.utils.common_upscale(image.clone().permute(0, 3, 1, 2), resized_width, resized_height, method, "disabled").permute(0, 2, 3, 1)
+        
+        if fit != "contain":
+            out_image = self.crop_and_pad(out_image, width, height, image.dtype, image.device)
+
+        return (out_image, out_image.shape[2], out_image.shape[1])
+
+    def calculate_resize_dims(self, src_w, src_h, dst_w, dst_h, fit):
+        src_aspect = src_w / src_h
+        dst_aspect = dst_w / dst_h
+        if fit == "crop": return (round(dst_h * src_aspect), dst_h) if src_aspect > dst_aspect else (dst_w, round(dst_w / src_aspect))
+        elif fit in ["contain", "pad"]: return (dst_w, round(dst_w / src_aspect)) if src_aspect > dst_aspect else (round(dst_h * src_aspect), dst_h)
+        return dst_w, dst_h
+
+    def resize_contain(self, image, width, height, method):
+        _, H, W, _ = image.shape
+        resized_width, resized_height = self.calculate_resize_dims(W, H, width, height, "contain")
+        return comfy.utils.common_upscale(image.clone().permute(0, 3, 1, 2), resized_width, resized_height, method, "disabled").permute(0, 2, 3, 1)
+
+    def crop_and_pad(self, image, width, height, dtype, device):
+        # This handles both 'crop' and 'pad' (with black)
+        # Crop if oversized
+        if image.shape[2] > width:
+            crop_x = (image.shape[2] - width) // 2
+            image = image[:, :, crop_x:crop_x + width, :]
+        if image.shape[1] > height:
+            crop_y = (image.shape[1] - height) // 2
+            image = image[:, crop_y:crop_y + height, :, :]
+        # Pad if undersized
+        if image.shape[2] < width or image.shape[1] < height:
+            padded_image = torch.zeros((image.shape[0], height, width, image.shape[3]), dtype=dtype, device=device)
+            pad_x = (width - image.shape[2]) // 2
+            pad_y = (height - image.shape[1]) // 2
+            padded_image[:, pad_y:pad_y + image.shape[1], pad_x:pad_x + image.shape[2], :] = image
+            image = padded_image
+        return image
+
+# =================================================================================
+# NODE REGISTRATION
+# =================================================================================
+
+NODE_CLASS_MAPPINGS = {"INSTARAW_ImageResizeFill": INSTARAW_ImageResizeFill}
+NODE_DISPLAY_NAME_MAPPINGS = {"INSTARAW_ImageResizeFill": "๐Ÿ“ INSTARAW Image Resize & Fill"}
\ No newline at end of file
diff --git a/custom_nodes/ComfyUI_INSTARAW/nodes/utility_nodes/image_resolution_clamp.py b/custom_nodes/ComfyUI_INSTARAW/nodes/utility_nodes/image_resolution_clamp.py
new file mode 100644
index 0000000000000000000000000000000000000000..5aa54c1027427862a4d88ade26bbfd1213671363
--- /dev/null
+++ b/custom_nodes/ComfyUI_INSTARAW/nodes/utility_nodes/image_resolution_clamp.py
@@ -0,0 +1,121 @@
+# ---
+# ComfyUI INSTARAW - Ensure Image Resolution Node
+# Part of the INSTARAW custom nodes collection by Instara
+#
+# Copyright ยฉ 2025 Instara. All rights reserved.
+# PROPRIETARY SOFTWARE - ALL RIGHTS RESERVED
+# ---
+
+"""
+An advanced utility node to ensure an image meets both minimum total pixel and maximum
+side length requirements. It will upscale or downscale as needed while preserving aspect ratio.
+"""
+
+import torch
+import comfy.utils
+import math
+
+class INSTARAW_EnsureImageResolution:
+    """
+    A resolution guard that ensures an image is within specified boundaries.
+    - If total pixels are below a minimum, it scales the image up.
+    - If the longest side is above a maximum, it scales the image down.
+    This replaces complex logic chains with a single, efficient utility.
+    """
+
+    INTERPOLATION_METHODS = ["lanczos", "bicubic", "bilinear", "nearest", "area", "nearest-exact"]
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "image": ("IMAGE",),
+                "enabled": ("BOOLEAN", {"default": True, "label_on": "Active", "label_off": "Bypassed"}),
+                "min_total_pixels": ("INT", {
+                    "default": 921600, "min": 0, "max": 16777216, "step": 1,
+                    "tooltip": "Minimum required total pixels (width * height). 921600 is the minimum for SeeDream."
+                }),
+                "max_side_length": ("INT", {
+                    "default": 4096, "min": 64, "max": 16384, "step": 64,
+                    "tooltip": "Maximum length for the longest side of the image."
+                }),
+                "interpolation": (cls.INTERPOLATION_METHODS, {"default": "lanczos"}),
+                "multiple_of": ("INT", {"default": 8, "min": 1, "max": 64, "step": 1}),
+            }
+        }
+
+    CATEGORY = "INSTARAW/Utils"
+    RETURN_TYPES = ("IMAGE", "BOOLEAN", "STRING",)
+    RETURN_NAMES = ("image", "was_changed", "info_text",)
+    FUNCTION = "ensure_resolution"
+
+    def ensure_resolution(self, image, enabled, min_total_pixels, max_side_length, interpolation, multiple_of):
+        if not enabled:
+            _b, h, w, _c = image.shape
+            return (image, False, f"Bypassed. Original size: {w}x{h}",)
+
+        _batch, original_height, original_width, _channels = image.shape
+        original_pixels = original_width * original_height
+        
+        current_image = image
+        was_upscaled = False
+        was_downscaled = False
+        info_lines = [f"Original: {original_width}x{original_height} ({original_pixels:,} pixels)"]
+
+        # --- Step 1: Upscale if total pixels are too low ---
+        if original_pixels > 0 and original_pixels < min_total_pixels:
+            was_upscaled = True
+            scale_ratio = math.sqrt(min_total_pixels / original_pixels)
+            
+            new_width = int(round(original_width * scale_ratio))
+            new_height = int(round(original_height * scale_ratio))
+
+            # Ensure new dimensions are a multiple of the specified value
+            new_width = (new_width + multiple_of - 1) // multiple_of * multiple_of
+            new_height = (new_height + multiple_of - 1) // multiple_of * multiple_of
+
+            info_lines.append(f"Action: Upscaled to meet min pixels. New size: {new_width}x{new_height}.")
+            
+            image_bchw = current_image.permute(0, 3, 1, 2)
+            scaled_image_bchw = comfy.utils.common_upscale(image_bchw, new_width, new_height, interpolation, "center")
+            current_image = scaled_image_bchw.permute(0, 2, 3, 1)
+
+        # --- Step 2: Downscale if longest side is too large (check the potentially upscaled image) ---
+        _b, current_height, current_width, _c = current_image.shape
+        longest_side = max(current_width, current_height)
+
+        if longest_side > max_side_length:
+            was_downscaled = True
+            scale_ratio = max_side_length / longest_side
+            
+            new_width = int(round(current_width * scale_ratio))
+            new_height = int(round(current_height * scale_ratio))
+
+            # Ensure new dimensions are a multiple of the specified value
+            new_width = (new_width // multiple_of) * multiple_of
+            new_height = (new_height // multiple_of) * multiple_of
+
+            info_lines.append(f"Action: Downscaled to fit max side length. Final size: {new_width}x{new_height}.")
+            
+            image_bchw = current_image.permute(0, 3, 1, 2)
+            scaled_image_bchw = comfy.utils.common_upscale(image_bchw, new_width, new_height, interpolation, "center")
+            current_image = scaled_image_bchw.permute(0, 2, 3, 1)
+
+        was_changed = was_upscaled or was_downscaled
+        if not was_changed:
+            info_lines.append("Action: No scaling needed.")
+
+        final_info = "\n".join(info_lines)
+        return (current_image, was_changed, final_info,)
+
+# =================================================================================
+# EXPORT NODE MAPPINGS
+# =================================================================================
+
+NODE_CLASS_MAPPINGS = {
+    "INSTARAW_EnsureImageResolution": INSTARAW_EnsureImageResolution,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "INSTARAW_EnsureImageResolution": "๐Ÿ“ INSTARAW Ensure Image Resolution",
+}
\ No newline at end of file
diff --git a/custom_nodes/ComfyUI_INSTARAW/nodes/utility_nodes/json_utils.py b/custom_nodes/ComfyUI_INSTARAW/nodes/utility_nodes/json_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..bf0c42fe5cfdf10fb1869016a48496de30a9e080
--- /dev/null
+++ b/custom_nodes/ComfyUI_INSTARAW/nodes/utility_nodes/json_utils.py
@@ -0,0 +1,43 @@
+# ---
+# ComfyUI INSTARAW - JSON Utility Nodes
+# Copyright ยฉ 2025 Instara. All rights reserved.
+# PROPRIETARY SOFTWARE - ALL RIGHTS RESERVED
+# ---
+import json
+
+class INSTARAW_JSON_Extract_Values:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "json_string": ("STRING", {"forceInput": True}),
+            }
+        }
+
+    RETURN_TYPES = ("INT", "INT", "INT", "INT",)
+    RETURN_NAMES = ("x", "y", "width", "height",)
+    FUNCTION = "extract"
+    CATEGORY = "INSTARAW/Utils"
+
+    def extract(self, json_string):
+        try:
+            data = json.loads(json_string)
+            if not isinstance(data, dict):
+                return (0, 0, 0, 0)
+        except (json.JSONDecodeError, TypeError):
+            return (0, 0, 0, 0)
+        
+        x = int(data.get("x", 0))
+        y = int(data.get("y", 0))
+        width = int(data.get("width", 0))
+        height = int(data.get("height", 0))
+        
+        return (x, y, width, height,)
+
+NODE_CLASS_MAPPINGS = {
+    "INSTARAW_JSON_Extract_Values": INSTARAW_JSON_Extract_Values,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "INSTARAW_JSON_Extract_Values": "... INSTARAW JSON Extract Values",
+}
\ No newline at end of file
diff --git a/custom_nodes/ComfyUI_INSTARAW/nodes/utility_nodes/lens_simulation_node.py b/custom_nodes/ComfyUI_INSTARAW/nodes/utility_nodes/lens_simulation_node.py
new file mode 100644
index 0000000000000000000000000000000000000000..f143ac647c1ee06cff2939697f340731dc8677ec
--- /dev/null
+++ b/custom_nodes/ComfyUI_INSTARAW/nodes/utility_nodes/lens_simulation_node.py
@@ -0,0 +1,95 @@
+# Filename: ComfyUI_INSTARAW/nodes/utility_nodes/lens_simulation_node.py
+# ---
+
+import torch
+import numpy as np
+from PIL import Image
+
+# We will use the camera simulation utility, but only for specific effects
+from ...modules.detection_bypass.camera_pipeline import simulate_camera_pipeline
+
+class INSTARAW_LensEffects:
+    """
+    Simulates the subtle, physical imperfections of a real camera lens, such as
+    vignetting and chromatic aberration. This should be applied late in the
+    post-processing pipeline, before final compression.
+    """
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "image": ("IMAGE",),
+                "vignette_strength": ("FLOAT", {
+                    "default": 0.2, "min": 0.0, "max": 1.0, "step": 0.01,
+                    "tooltip": "Strength of the corner darkening effect."
+                }),
+                "chromatic_aberration": ("FLOAT", {
+                    "default": 0.75, "min": 0.0, "max": 5.0, "step": 0.01,
+                    "tooltip": "Strength of the color fringing on high-contrast edges."
+                }),
+                "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "control_after_generate": "randomize"}),
+            },
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "apply_effects"
+    CATEGORY = "INSTARAW/Post-Processing"
+
+    def tensor_to_numpy(self, tensor: torch.Tensor) -> np.ndarray:
+        if tensor.ndim == 4 and tensor.shape[0] == 1:
+            tensor = tensor.squeeze(0)
+        return (tensor.cpu().numpy() * 255).astype(np.uint8)
+
+    def numpy_to_tensor(self, np_array: np.ndarray) -> torch.Tensor:
+        return torch.from_numpy(np_array.astype(np.float32) / 255.0).unsqueeze(0)
+
+    def apply_effects(self, image: torch.Tensor, vignette_strength: float, chromatic_aberration: float, seed: int):
+        if vignette_strength == 0 and chromatic_aberration == 0:
+            return (image,)
+
+        print(f"๐Ÿ‘๏ธ INSTARAW Lens Effects: Applying effects.")
+
+        processed_images = []
+        for i in range(image.shape[0]):
+            numpy_image = self.tensor_to_numpy(image[i:i+1])
+
+            # --- THIS IS THE FIX ---
+            # Set iso_scale to 1.0 to prevent the image from turning black.
+            # All other unused effects are correctly disabled with 0 or False.
+            processed_numpy_image = simulate_camera_pipeline(
+                img_arr=numpy_image,
+                vignette_strength=vignette_strength,
+                chroma_aberr_strength=chromatic_aberration,
+                seed=seed + i,
+                
+                # Neutral / passthrough values for unused effects
+                bayer=False,
+                jpeg_cycles=0,
+                iso_scale=1.0, # <-- THE FIX IS HERE
+                read_noise_std=0,
+                hot_pixel_prob=0,
+                banding_strength=0.0,
+                motion_blur_kernel=1
+            )
+            # --- END FIX ---
+
+            processed_tensor = self.numpy_to_tensor(processed_numpy_image)
+            processed_images.append(processed_tensor)
+
+        if not processed_images:
+            return (image,)
+            
+        final_batch = torch.cat(processed_images, dim=0)
+        
+        print("โœ… INSTARAW Lens Effects: Processing complete.")
+        return (final_batch,)
+
+# --- Node Registration ---
+NODE_CLASS_MAPPINGS = {
+    "INSTARAW_LensEffects": INSTARAW_LensEffects,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "INSTARAW_LensEffects": "๐Ÿ‘๏ธ INSTARAW Lens Effects",
+}
\ No newline at end of file
diff --git a/custom_nodes/ComfyUI_INSTARAW/nodes/utility_nodes/line_splitter.py b/custom_nodes/ComfyUI_INSTARAW/nodes/utility_nodes/line_splitter.py
new file mode 100644
index 0000000000000000000000000000000000000000..06e30114da4f93d7792a6b812d5d087891a52507
--- /dev/null
+++ b/custom_nodes/ComfyUI_INSTARAW/nodes/utility_nodes/line_splitter.py
@@ -0,0 +1,56 @@
+# Filename: ComfyUI_INSTARAW/nodes/utility_nodes/line_splitter.py
+# ---
+
+class INSTARAW_LineSplitter:
+    """
+    Splits a multiline string into a list of clean lines.
+    """
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "multiline_text": (
+                    "STRING",
+                    {
+                        "multiline": True,
+                        "default": "girl in red dress\n"
+                                   "girl in blue dress\n"
+                                   "girl in green dress",
+                        "tooltip": "Each non-empty line becomes a separate entry in the output list.",
+                    },
+                ),
+                "keep_empty_lines": (
+                    "BOOLEAN",
+                    {
+                        "default": False,
+                        "tooltip": "If True, empty lines are kept as '' entries instead of being removed.",
+                    },
+                ),
+            }
+        }
+
+    # ๐Ÿ‘‡ This is the important line
+    INPUT_IS_LIST = False
+    OUTPUT_IS_LIST = [True]   # list_out is a STRING LIST
+
+    RETURN_TYPES = ("STRING",)
+    RETURN_NAMES = ("list_out",)
+    FUNCTION = "split"
+    CATEGORY = "INSTARAW/Utility"
+
+    @classmethod
+    def IS_CHANGED(cls, **kwargs):
+        return float("NaN")
+
+    def split(self, multiline_text, keep_empty_lines):
+        import re
+
+        raw_lines = multiline_text.splitlines()
+        cleaned = [re.sub(r"\s+", " ", ln).strip() for ln in raw_lines]
+
+        if not keep_empty_lines:
+            cleaned = [ln for ln in cleaned if ln]
+
+        # cleaned is a Python list[str]
+        return (cleaned,)
\ No newline at end of file
diff --git a/custom_nodes/ComfyUI_INSTARAW/nodes/utility_nodes/list_utility_nodes.py b/custom_nodes/ComfyUI_INSTARAW/nodes/utility_nodes/list_utility_nodes.py
new file mode 100644
index 0000000000000000000000000000000000000000..9ca81b1e463b4c57795bd99822aae443fa69fffc
--- /dev/null
+++ b/custom_nodes/ComfyUI_INSTARAW/nodes/utility_nodes/list_utility_nodes.py
@@ -0,0 +1,102 @@
+# Filename: ComfyUI_INSTARAW/nodes/utility_nodes/list_utility_nodes.py
+
+import torch
+
+# We no longer need IO from comfy_types
+# from comfy.comfy_types.node_typing import IO
+
+class INSTARAW_BatchFromImageList:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {"required": { "images": ("IMAGE", ), } }
+    INPUT_IS_LIST = True
+    RETURN_TYPES = ("IMAGE", )
+    FUNCTION = "func"
+    CATEGORY = "INSTARAW/Utils"
+
+    def func(self, images):
+        if not images:
+            return (torch.zeros((1, 64, 64, 3), dtype=torch.float32),)
+        if len(images) == 1:
+            return (images[0],)
+        else:
+            return (torch.cat(list(i for i in images), dim=0),)
+        
+class INSTARAW_ImageListFromBatch:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {"required": { "images": ("IMAGE", ), } }
+    INPUT_IS_LIST = False
+    OUTPUT_IS_LIST = [True,]
+    RETURN_TYPES = ("IMAGE", )
+    FUNCTION = "func"
+    CATEGORY = "INSTARAW/Utils"
+
+    def func(self, images):
+        image_list = list( i.unsqueeze(0) for i in images )
+        return (image_list,) 
+    
+class INSTARAW_StringListFromStrings:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": { 
+                "s0": ("STRING", {"default":""}), 
+                "s1": ("STRING", {"default":""}), 
+            },
+            "optional": {    
+                "s2": ("STRING", {"default":""}), 
+                "s3": ("STRING", {"default":""}), 
+            }
+        }
+    INPUT_IS_LIST = False
+    OUTPUT_IS_LIST = [True,]
+    RETURN_TYPES = ("STRING", )
+    FUNCTION = "func"
+    CATEGORY = "INSTARAW/Utils"
+
+    def func(self, s0,s1,s2=None,s3=None):
+        lst = [s0,s1]
+        if s2 is not None: lst.append(s2)
+        if s3 is not None: lst.append(s3)
+        return (lst,) 
+
+class INSTARAW_PickFromList:
+    @classmethod
+    def INPUT_TYPES(cls):
+        # --- THE DEFINITIVE FIX ---
+        # Replace the non-serializable IO.ANY type object with the string "*"
+        return { "required": {"anything" : ("*", ), "indexes": ("STRING", {"default": "0"})} }
+        # --- END FIX ---
+
+    RETURN_TYPES = ("*",) # Use "*" for output too for consistency
+    RETURN_NAMES = ("picks",)
+    FUNCTION = "func"
+    CATEGORY = "INSTARAW/Utils"
+    INPUT_IS_LIST = True
+    OUTPUT_IS_LIST = [True,]
+
+    def func(self, anything, indexes):
+        if not anything:
+            return ([],)
+            
+        try:
+            # The input list can sometimes be nested, e.g. [[item1, item2]]
+            if len(anything) == 1 and isinstance(anything[0], list):
+                anything = anything[0]
+            
+            # The 'indexes' input is a single string from the widget, not a list
+            indexes_str = indexes if isinstance(indexes, str) else str(indexes)
+            parsed_indexes = [int(x.strip()) for x in indexes_str.split(',') if x.strip()]
+        except Exception as e:
+            print(f"INSTARAW PickFromList: Error parsing indexes '{indexes}'. Defaulting to first item. Error: {e}")
+            parsed_indexes = [0]
+        
+        valid_picks = []
+        for i in parsed_indexes:
+            if 0 <= i < len(anything):
+                valid_picks.append(anything[i])
+            else:
+                print(f"INSTARAW PickFromList Warning: index {i} is out of bounds for list of size {len(anything)}. Skipping.")
+        
+        return (valid_picks, )
\ No newline at end of file
diff --git a/custom_nodes/ComfyUI_INSTARAW/nodes/utility_nodes/load_image_from_path.py b/custom_nodes/ComfyUI_INSTARAW/nodes/utility_nodes/load_image_from_path.py
new file mode 100644
index 0000000000000000000000000000000000000000..f10ee75737227ae9932252dceac138491545aca2
--- /dev/null
+++ b/custom_nodes/ComfyUI_INSTARAW/nodes/utility_nodes/load_image_from_path.py
@@ -0,0 +1,54 @@
+import torch
+import numpy as np
+from PIL import Image
+import os
+
+class INSTARAW_LoadImageFromPath:
+    """
+    A simple utility node to load an image from a given file path string.
+    This is essential for previewing the final output of nodes like the 
+    Synthesize Authentic Metadata node, allowing you to see the image *after*
+    it has been saved, compressed, and had its color profile potentially converted.
+    """
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                # The 'forceInput' is crucial. It creates an input socket 
+                # instead of a text box, allowing you to connect the filepath string.
+                "filepath": ("STRING", {"forceInput": True}),
+            }
+        }
+
+    RETURN_TYPES = ("IMAGE", "MASK")
+    FUNCTION = "load_image"
+    CATEGORY = "INSTARAW/Utilities"
+
+    def load_image(self, filepath: str):
+        if not os.path.exists(filepath):
+            raise FileNotFoundError(f"INSTARAW Load Image: File not found at {filepath}")
+
+        # Open the image file from the provided path
+        img_pil = Image.open(filepath).convert('RGB')
+
+        # Convert the PIL Image to a NumPy array and normalize to 0.0-1.0
+        img_np = np.array(img_pil).astype(np.float32) / 255.0
+
+        # Convert the NumPy array to a PyTorch tensor
+        img_tensor = torch.from_numpy(img_np)
+
+        # Add the batch dimension (ComfyUI expects Batch, Height, Width, Channels)
+        img_tensor = img_tensor.unsqueeze(0)
+
+        # For the mask, we can just use the alpha channel if it exists, or create one.
+        # Since we convert to RGB, we'll just use the luminance as a simple mask.
+        mask = img_tensor.mean(dim=-1, keepdim=True)
+
+        return (img_tensor, mask)
+
+NODE_CLASS_MAPPINGS = {
+    "INSTARAW_LoadImageFromPath": INSTARAW_LoadImageFromPath
+}
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "INSTARAW_LoadImageFromPath": "โžก๏ธ INSTARAW Load Image From Path"
+}
\ No newline at end of file
diff --git a/custom_nodes/ComfyUI_INSTARAW/nodes/utility_nodes/lut_selector.py b/custom_nodes/ComfyUI_INSTARAW/nodes/utility_nodes/lut_selector.py
new file mode 100644
index 0000000000000000000000000000000000000000..7239fbbce6d77cd0a66f244e8db50ee9591f4035
--- /dev/null
+++ b/custom_nodes/ComfyUI_INSTARAW/nodes/utility_nodes/lut_selector.py
@@ -0,0 +1,54 @@
+# ---
+# Filename: ../ComfyUI_INSTARAW/nodes/utility_nodes/lut_selector.py
+# ---
+import os
+
+# Correctly locate the root of your custom node package
+NODE_FILE_PATH = os.path.dirname(os.path.realpath(__file__))
+INSTARAW_ROOT_PATH = os.path.abspath(os.path.join(NODE_FILE_PATH, "..", ".."))
+
+class INSTARAW_LUT_Selector:
+    """
+    Dynamically finds and lists all .cube LUT files from the internal
+    'modules/detection_bypass/_luts' directory, including subdirectories.
+    Outputs the full path to the selected LUT file.
+    """
+    
+    LUTS_DIR = os.path.join(INSTARAW_ROOT_PATH, "modules", "detection_bypass", "_luts")
+    LUT_FILES = []
+    if os.path.isdir(LUTS_DIR):
+        for root, _, files in os.walk(LUTS_DIR):
+            for file in files:
+                if file.lower().endswith('.cube'):
+                    relative_path = os.path.relpath(os.path.join(root, file), LUTS_DIR)
+                    LUT_FILES.append(relative_path.replace('\\', '/')) # Normalize slashes for display
+    LUT_FILES.sort()
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        if not cls.LUT_FILES:
+            return {
+                "required": {
+                    "error": ("STRING", {"default": "ERROR: No .cube files found in modules/detection_bypass/_luts/", "multiline": True})
+                }
+            }
+            
+        return {
+            "required": {
+                "lut_name": (cls.LUT_FILES, {"default": cls.LUT_FILES[0] if cls.LUT_FILES else ""}),
+            }
+        }
+
+    RETURN_TYPES = ("STRING",)
+    RETURN_NAMES = ("lut_path",)
+    FUNCTION = "get_path"
+    CATEGORY = "INSTARAW/Utils"
+
+    def get_path(self, lut_name):
+        full_path = os.path.join(self.LUTS_DIR, lut_name)
+        
+        if not os.path.exists(full_path):
+            raise FileNotFoundError(f"Selected LUT file could not be found: {full_path}")
+            
+        print(f"โœ… INSTARAW LUT Selector: Providing path '{full_path}'")
+        return (full_path,)
\ No newline at end of file
diff --git a/custom_nodes/ComfyUI_INSTARAW/nodes/utility_nodes/mask_to_crop.py b/custom_nodes/ComfyUI_INSTARAW/nodes/utility_nodes/mask_to_crop.py
new file mode 100644
index 0000000000000000000000000000000000000000..16a5fe77d0d6a437b79d90b5929dcdf16749c556
--- /dev/null
+++ b/custom_nodes/ComfyUI_INSTARAW/nodes/utility_nodes/mask_to_crop.py
@@ -0,0 +1,289 @@
+# ---
+# ComfyUI INSTARAW - Mask to Crop
+# Copyright ยฉ 2025 Instara. All rights reserved.
+# PROPRIETARY SOFTWARE - ALL RIGHTS RESERVED
+# ---
+
+"""
+Converts a mask to crop coordinates with relative padding.
+Useful for face swap workflows where you want to crop to head area.
+Supports forced aspect ratio for pixel-perfect stitching.
+"""
+
+import torch
+import numpy as np
+import json
+
+
+# Supported aspect ratios (width:height)
+ASPECT_RATIOS = {
+    "None (free)": None,
+    "896x1200 (Nano Banana 1K)": (896, 1200),  # Exact Nano Banana Pro 1K output
+    "3:4 (Portrait)": (3, 4),
+    "4:3 (Landscape)": (4, 3),
+    "2:3 (Portrait)": (2, 3),
+    "3:2 (Landscape)": (3, 2),
+    "9:16 (Tall)": (9, 16),
+    "16:9 (Wide)": (16, 9),
+    "1:1 (Square)": (1, 1),
+    "4:5 (Portrait)": (4, 5),
+    "5:4 (Landscape)": (5, 4),
+}
+
+
+class INSTARAW_MaskToCrop:
+    """
+    Converts a mask's bounding box to crop coordinates with relative padding.
+    Outputs crop data compatible with Interactive Crop node.
+    Supports forced aspect ratio for pixel-perfect API output stitching.
+    """
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "image": ("IMAGE",),
+                "mask": ("MASK",),
+                "padding_percent": (
+                    "FLOAT",
+                    {
+                        "default": 20.0,
+                        "min": 0.0,
+                        "max": 100.0,
+                        "step": 1.0,
+                        "tooltip": "Padding around mask as percentage of mask size",
+                    },
+                ),
+                "force_aspect_ratio": (
+                    list(ASPECT_RATIOS.keys()),
+                    {
+                        "default": "3:4 (Portrait)",
+                        "tooltip": "Force crop to specific aspect ratio for pixel-perfect stitching with API output",
+                    },
+                ),
+            },
+            "optional": {
+                "min_size_percent": (
+                    "FLOAT",
+                    {
+                        "default": 10.0,
+                        "min": 1.0,
+                        "max": 100.0,
+                        "step": 1.0,
+                        "tooltip": "Minimum crop size as percentage of image dimensions",
+                    },
+                ),
+            },
+        }
+
+    RETURN_TYPES = ("IMAGE", "MASK", "STRING", "INT", "INT", "INT", "INT")
+    RETURN_NAMES = ("image", "mask", "crop_json", "x", "y", "width", "height")
+    FUNCTION = "mask_to_crop"
+    CATEGORY = "INSTARAW/Utility"
+    DESCRIPTION = "Converts mask bounding box to crop coordinates with relative padding and optional forced aspect ratio."
+
+    def mask_to_crop(self, image, mask, padding_percent, force_aspect_ratio="3:4 (Portrait)", min_size_percent=10.0):
+        """
+        Calculate crop coordinates from mask with relative padding.
+        """
+        # Get image dimensions
+        img_height = image.shape[1]
+        img_width = image.shape[2]
+
+        # Convert mask to numpy for processing
+        if mask.dim() == 3:
+            mask_np = mask[0].cpu().numpy()
+        else:
+            mask_np = mask.cpu().numpy()
+
+        # Find mask bounding box
+        rows = np.any(mask_np > 0.5, axis=1)
+        cols = np.any(mask_np > 0.5, axis=0)
+
+        if not rows.any() or not cols.any():
+            # No mask found, return full image
+            print("โš ๏ธ No mask content found, returning full image bounds")
+            crop_data = {
+                "x": 0,
+                "y": 0,
+                "width": img_width,
+                "height": img_height,
+            }
+            return (image, mask, json.dumps(crop_data), 0, 0, img_width, img_height)
+
+        # Get bounding box
+        y_min, y_max = np.where(rows)[0][[0, -1]]
+        x_min, x_max = np.where(cols)[0][[0, -1]]
+
+        print(f"๐Ÿ” Mask bbox: x_min={x_min}, x_max={x_max}, y_min={y_min}, y_max={y_max}")
+
+        mask_width = x_max - x_min
+        mask_height = y_max - y_min
+
+        # Calculate padding based on mask size (relative padding)
+        padding_x = int(mask_width * (padding_percent / 100.0))
+        padding_y = int(mask_height * (padding_percent / 100.0))
+
+        # Apply padding
+        x = max(0, x_min - padding_x)
+        y = max(0, y_min - padding_y)
+        x_end = min(img_width, x_max + padding_x)
+        y_end = min(img_height, y_max + padding_y)
+
+        width = x_end - x
+        height = y_end - y
+
+        # Ensure minimum size
+        min_width = int(img_width * (min_size_percent / 100.0))
+        min_height = int(img_height * (min_size_percent / 100.0))
+
+        if width < min_width:
+            extra = min_width - width
+            x = max(0, x - extra // 2)
+            width = min(min_width, img_width - x)
+
+        if height < min_height:
+            extra = min_height - height
+            y = max(0, y - extra // 2)
+            height = min(min_height, img_height - y)
+
+        # Force aspect ratio if specified
+        target_aspect = ASPECT_RATIOS.get(force_aspect_ratio)
+        if target_aspect is not None:
+            target_w, target_h = target_aspect
+            target_ratio = target_w / target_h  # e.g., 3:4 = 0.75
+            current_ratio = width / height
+
+            # Calculate center of current crop
+            center_x = x + width / 2
+            center_y = y + height / 2
+
+            if current_ratio > target_ratio:
+                # Current is too wide, need to increase height
+                new_height = width / target_ratio
+                new_width = width
+            else:
+                # Current is too tall, need to increase width
+                new_width = height * target_ratio
+                new_height = height
+
+            # Recalculate x, y from center
+            new_x = center_x - new_width / 2
+            new_y = center_y - new_height / 2
+
+            # Clamp to image bounds and adjust
+            if new_x < 0:
+                new_x = 0
+            if new_y < 0:
+                new_y = 0
+            if new_x + new_width > img_width:
+                new_x = img_width - new_width
+                if new_x < 0:
+                    new_x = 0
+                    new_width = img_width
+                    new_height = new_width / target_ratio
+            if new_y + new_height > img_height:
+                new_y = img_height - new_height
+                if new_y < 0:
+                    new_y = 0
+                    new_height = img_height
+                    new_width = new_height * target_ratio
+
+            # Final clamp
+            new_x = max(0, min(new_x, img_width - 1))
+            new_y = max(0, min(new_y, img_height - 1))
+            new_width = min(new_width, img_width - new_x)
+            new_height = min(new_height, img_height - new_y)
+
+            x, y, width, height = int(new_x), int(new_y), int(new_width), int(new_height)
+
+            print(f"๐Ÿ“ Forced aspect ratio {target_w}:{target_h} -> {width}x{height}")
+
+        # Create crop JSON (compatible with Interactive Crop)
+        crop_data = {
+            "x": int(x),
+            "y": int(y),
+            "width": int(width),
+            "height": int(height),
+        }
+
+        print(f"๐Ÿ“ Mask to Crop: {width}x{height} at ({x}, {y}) with {padding_percent}% padding (image: {img_width}x{img_height})")
+
+        return (
+            image,
+            mask,
+            json.dumps(crop_data),
+            int(x),
+            int(y),
+            int(width),
+            int(height),
+        )
+
+
+class INSTARAW_ApplyCrop:
+    """
+    Applies crop coordinates to an image.
+    Simple crop without interactive UI.
+    """
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "image": ("IMAGE",),
+                "x": ("INT", {"default": 0, "min": 0}),
+                "y": ("INT", {"default": 0, "min": 0}),
+                "width": ("INT", {"default": 512, "min": 1}),
+                "height": ("INT", {"default": 512, "min": 1}),
+            },
+            "optional": {
+                "mask": ("MASK",),
+            },
+        }
+
+    RETURN_TYPES = ("IMAGE", "MASK", "STRING")
+    RETURN_NAMES = ("cropped_image", "cropped_mask", "crop_json")
+    FUNCTION = "apply_crop"
+    CATEGORY = "INSTARAW/Utility"
+    DESCRIPTION = "Applies crop coordinates to image and optional mask."
+
+    def apply_crop(self, image, x, y, width, height, mask=None):
+        """Apply crop to image and mask."""
+        img_height = image.shape[1]
+        img_width = image.shape[2]
+
+        # Clamp values to image bounds
+        x = max(0, min(x, img_width - 1))
+        y = max(0, min(y, img_height - 1))
+        width = max(1, min(width, img_width - x))
+        height = max(1, min(height, img_height - y))
+
+        # Crop image
+        cropped_image = image[:, y : y + height, x : x + width, :]
+
+        # Crop mask if provided
+        if mask is not None:
+            if mask.dim() == 3:
+                cropped_mask = mask[:, y : y + height, x : x + width]
+            else:
+                cropped_mask = mask[y : y + height, x : x + width].unsqueeze(0)
+        else:
+            # Create empty mask
+            cropped_mask = torch.zeros(1, height, width)
+
+        crop_json = json.dumps({"x": x, "y": y, "width": width, "height": height})
+
+        print(f"โœ‚๏ธ Applied crop: {width}x{height} at ({x}, {y})")
+
+        return (cropped_image, cropped_mask, crop_json)
+
+
+NODE_CLASS_MAPPINGS = {
+    "INSTARAW_MaskToCrop": INSTARAW_MaskToCrop,
+    "INSTARAW_ApplyCrop": INSTARAW_ApplyCrop,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "INSTARAW_MaskToCrop": "๐Ÿ“ INSTARAW Mask to Crop",
+    "INSTARAW_ApplyCrop": "โœ‚๏ธ INSTARAW Apply Crop",
+}
diff --git a/custom_nodes/ComfyUI_INSTARAW/nodes/utility_nodes/mask_utility_nodes.py b/custom_nodes/ComfyUI_INSTARAW/nodes/utility_nodes/mask_utility_nodes.py
new file mode 100644
index 0000000000000000000000000000000000000000..eca9edd512bb3ef9f8b36f6c2b405a12de3dd8dc
--- /dev/null
+++ b/custom_nodes/ComfyUI_INSTARAW/nodes/utility_nodes/mask_utility_nodes.py
@@ -0,0 +1,104 @@
+import torch
+
+class INSTARAW_MaskedSection:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "mask": ("MASK",),
+                "image": ("IMAGE",),
+                "minimum": ("INT", {"default":512, "min":16, "max":4096})
+            }
+        }
+    
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "func"
+    CATEGORY = "INSTARAW/Utils"
+    
+    def func(self, mask:torch.Tensor, image, minimum=512):
+        mbb = mask.squeeze()
+        H,W = mbb.shape
+        masked = mbb > 0.5
+
+        non_zero_positions = torch.nonzero(masked)
+        if len(non_zero_positions) < 2: return (image,)
+
+        min_x = int(torch.min(non_zero_positions[:, 1]))
+        max_x = int(torch.max(non_zero_positions[:, 1]))
+        min_y = int(torch.min(non_zero_positions[:, 0]))
+        max_y = int(torch.max(non_zero_positions[:, 0]))
+
+        if (x:=(minimum-(max_x-min_x))//2)>0:
+            min_x = max(min_x-x, 0)
+            max_x = min(max_x+x, W)
+        if (y:=(minimum-(max_y-min_y))//2)>0:
+            min_y = max(min_y-y, 0)
+            max_y = min(max_y+y, H)       
+
+        return (image[:,min_y:max_y,min_x:max_x,:],)
+
+
+class INSTARAW_MaskCombine:
+    """
+    Combines two masks using various mathematical operations.
+    If only one mask is provided, it will be passed through unchanged.
+    """
+    
+    OPERATION_MODES = ["Union (Max)", "Add (Clamped)", "Subtract (Clamped)", "Intersection (Min)", "Average"]
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        # --- THIS IS THE FIX: Made masks optional ---
+        return {
+            "required": {
+                "operation": (cls.OPERATION_MODES, {"default": "Union (Max)"}),
+            },
+            "optional": {
+                "mask1": ("MASK",),
+                "mask2": ("MASK",),
+            }
+        }
+
+    RETURN_TYPES = ("MASK",)
+    FUNCTION = "combine_masks"
+    CATEGORY = "INSTARAW/Utils"
+
+    def combine_masks(self, operation, mask1=None, mask2=None):
+        # --- THIS IS THE FIX: Added passthrough logic for single masks ---
+        if mask1 is None and mask2 is None:
+            # If both are missing, we can't create a mask of unknown size. Return None.
+            return (None,)
+        if mask1 is None:
+            print("INSTARAW Mask Combine: mask1 is missing, passing through mask2.")
+            return (mask2,)
+        if mask2 is None:
+            print("INSTARAW Mask Combine: mask2 is missing, passing through mask1.")
+            return (mask1,)
+        # --- END FIX ---
+
+        # If we get here, both masks are present. Proceed with combination logic.
+        target_device = mask1.device
+        mask2 = mask2.to(target_device)
+
+        if mask1.shape != mask2.shape:
+            if mask1.numel() > mask2.numel():
+                target_shape = mask1.shape[1:]
+                mask2 = torch.nn.functional.interpolate(mask2.unsqueeze(0).unsqueeze(0), size=target_shape, mode='bilinear', align_corners=False).squeeze(0).squeeze(0)
+            else:
+                target_shape = mask2.shape[1:]
+                mask1 = torch.nn.functional.interpolate(mask1.unsqueeze(0).unsqueeze(0), size=target_shape, mode='bilinear', align_corners=False).squeeze(0).squeeze(0)
+
+        if operation == "Union (Max)":
+            result = torch.max(mask1, mask2)
+        elif operation == "Add (Clamped)":
+            result = torch.clamp(mask1 + mask2, 0.0, 1.0)
+        elif operation == "Subtract (Clamped)":
+            result = torch.clamp(mask1 - mask2, 0.0, 1.0)
+        elif operation == "Intersection (Min)":
+            result = torch.min(mask1, mask2)
+        elif operation == "Average":
+            result = (mask1 + mask2) / 2.0
+        else:
+            result = torch.max(mask1, mask2)
+
+        return (result,)
\ No newline at end of file
diff --git a/custom_nodes/ComfyUI_INSTARAW/nodes/utility_nodes/metadata_inspector.py b/custom_nodes/ComfyUI_INSTARAW/nodes/utility_nodes/metadata_inspector.py
new file mode 100644
index 0000000000000000000000000000000000000000..5f8d0ee903733acd46ae1baf4e1d0af1010d0137
--- /dev/null
+++ b/custom_nodes/ComfyUI_INSTARAW/nodes/utility_nodes/metadata_inspector.py
@@ -0,0 +1,91 @@
+# Filename: ComfyUI_INSTARAW/nodes/utility_nodes/metadata_inspector.py
+import os
+import json
+import shutil
+
+try:
+    import exiftool
+    PYEXIFTOOL_AVAILABLE = True
+except ImportError:
+    PYEXIFTOOL_AVAILABLE = False
+
+# --- CORRECTED BLACKLIST ---
+# We only blacklist tags that are truly irrelevant to the image's authenticity,
+# such as file system timestamps or the version of ExifTool itself.
+METADATA_BLACKLIST = [
+    # File-system specific tags that change every time the file is saved
+    'SourceFile',
+    'File:FileName',
+    'File:Directory',
+    'File:FileModifyDate',
+    'File:FileAccessDate',
+    'File:FileInodeChangeDate',
+    'File:FileSize',
+    'File:FilePermissions',
+    # Tool-specific tag
+    'ExifTool:ExifToolVersion',
+]
+
+class INSTARAW_Metadata_Inspector:
+    """
+    Inspects an image file and outputs all of its metadata as a formatted
+    JSON string, along with a count of the total tags found. This is the 
+    definitive tool for verifying the 'Save With Authentic Metadata' node.
+    """
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "filepath": ("STRING", {"forceInput": True}),
+            }
+        }
+
+    RETURN_TYPES = ("STRING", "INT",)
+    RETURN_NAMES = ("metadata_json", "tag_count",)
+    FUNCTION = "inspect_metadata"
+    CATEGORY = "INSTARAW/Authenticity"
+
+    def inspect_metadata(self, filepath):
+        if not PYEXIFTOOL_AVAILABLE:
+            raise ImportError("pyexiftool is required. Please run 'pip install pyexiftool'.")
+        if not os.path.exists(filepath):
+            raise FileNotFoundError(f"Inspector could not find file: {filepath}")
+
+        print(f"๐Ÿ“Š INSTARAW Metadata Inspector: Analyzing file '{os.path.basename(filepath)}'...")
+        
+        clean_metadata = {}
+        try:
+            with exiftool.ExifToolHelper() as et:
+                # Use '-g' to get group names, which is what we've been using implicitly.
+                # Use '-b' to get binary data as "(Binary data ...)" instead of base64.
+                raw_meta_list = et.get_metadata(filepath)
+                if raw_meta_list:
+                    raw_meta = raw_meta_list[0]
+                    for key, value in raw_meta.items():
+                        if key not in METADATA_BLACKLIST:
+                            clean_metadata[key] = value
+        except Exception as e:
+            error_message = f"ERROR: pyexiftool failed for {filepath}: {e}"
+            print(error_message)
+            return (error_message, 0)
+
+        total_count = len(clean_metadata)
+        icc_count = 0
+        for key in clean_metadata.keys():
+            if key.startswith("ICC_Profile:"):
+                icc_count += 1
+        other_count = total_count - icc_count
+        
+        log_summary = f"({total_count} tags total: {icc_count} ICC, {other_count} other)"
+
+        formatted_json = json.dumps(clean_metadata, indent=2, ensure_ascii=False)
+
+        print("\n" + "="*25 + " INSTARAW Inspector Log " + "="*24)
+        print(f"--- METADATA FOUND IN {os.path.basename(filepath)} {log_summary} ---")
+        print(formatted_json)
+        print("="*70 + "\n")
+        
+        return (formatted_json, total_count)
+
+NODE_CLASS_MAPPINGS = { "INSTARAW_Metadata_Inspector": INSTARAW_Metadata_Inspector }
+NODE_DISPLAY_NAME_MAPPINGS = { "INSTARAW_Metadata_Inspector": "๐Ÿ“Š INSTARAW Metadata Inspector" }
\ No newline at end of file
diff --git a/custom_nodes/ComfyUI_INSTARAW/nodes/utility_nodes/neural_grain_node.py b/custom_nodes/ComfyUI_INSTARAW/nodes/utility_nodes/neural_grain_node.py
new file mode 100644
index 0000000000000000000000000000000000000000..b5ea7be32fe474c9cea7e8bcd5194f9773bc920f
--- /dev/null
+++ b/custom_nodes/ComfyUI_INSTARAW/nodes/utility_nodes/neural_grain_node.py
@@ -0,0 +1,115 @@
+# Filename: ComfyUI_INSTARAW/nodes/utility_nodes/neural_grain_node.py
+# ---
+
+import torch
+import numpy as np
+import os
+from PIL import Image
+
+# Import the GrainNet architecture from our modules
+from ...modules.neural_grain.net import GrainNet
+
+# Get the absolute path to the root of the ComfyUI_INSTARAW custom node
+NODE_FILE_PATH = os.path.dirname(os.path.realpath(__file__))
+INSTARAW_ROOT_PATH = os.path.abspath(os.path.join(NODE_FILE_PATH, "..", ".."))
+MODEL_PATH = os.path.join(INSTARAW_ROOT_PATH, "pretrained", "neural_grain", "grainnet.pt")
+
+# Global variable to hold the loaded model, so we don't reload it every time
+GRAIN_NET_MODEL = None
+
+class INSTARAW_NeuralGrain:
+    """
+    Applies a sophisticated, learned grain pattern using the GrainNet model from the
+    "Neural Film Grain Rendering" paper. This version is adapted to apply achromatic
+    (non-colored) grain to simulate digital sensor noise.
+    """
+    
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "image": ("IMAGE",),
+                "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "control_after_generate": "randomize"}),
+                "grain_size": ("FLOAT", {
+                    "default": 0.2, "min": 0.01, "max": 0.8, "step": 0.01,
+                    "tooltip": "Controls the scale of the grain texture. Smaller values = finer grain."
+                }),
+                "strength": ("FLOAT", {
+                    "default": 0.5, "min": 0.0, "max": 2.0, "step": 0.01,
+                    "tooltip": "Blend strength of the grain effect. 0.0 is no effect, 1.0 is normal."
+                }),
+            },
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "apply_grain"
+    CATEGORY = "INSTARAW/Post-Processing"
+
+    def load_model(self):
+        """Loads the GrainNet model into a global variable for reuse."""
+        global GRAIN_NET_MODEL
+        if GRAIN_NET_MODEL is None:
+            if not os.path.exists(MODEL_PATH):
+                raise FileNotFoundError(f"Neural Grain model not found! Expected at: {MODEL_PATH}")
+            
+            print("๐Ÿง  INSTARAW Neural Grain: Loading GrainNet model...")
+            # The pretrained model uses block_nb=2 and activation='tanh'
+            state_dict = torch.load(MODEL_PATH, map_location="cpu")
+            GRAIN_NET_MODEL = GrainNet(block_nb=2, activation='tanh')
+            GRAIN_NET_MODEL.load_state_dict(state_dict)
+            GRAIN_NET_MODEL.eval()
+            print("โœ… INSTARAW Neural Grain: Model loaded.")
+        return GRAIN_NET_MODEL
+
+    def apply_grain(self, image: torch.Tensor, seed: int, grain_size: float, strength: float):
+        if strength == 0:
+            return (image,)
+
+        model = self.load_model()
+        device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        model.to(device)
+
+        original_device = image.device
+        batch_size, height, width, _ = image.shape
+        
+        # Convert to BCHW format for the model
+        image_bchw = image.permute(0, 3, 1, 2).to(device)
+
+        # --- THE ACHROMATIC FIX ---
+        # 1. Convert the color image to grayscale for processing
+        # Using standard ITU-R BT.601 luma weights
+        image_gray = (image_bchw[:, 0:1, :, :] * 0.299 + 
+                      image_bchw[:, 1:2, :, :] * 0.587 + 
+                      image_bchw[:, 2:3, :, :] * 0.114)
+
+        # 2. Run the GrainNet model on the grayscale image
+        grain_size_tensor = torch.tensor([grain_size] * batch_size, device=device).float().unsqueeze(1)
+        
+        # The model expects a seed for its internal random noise generator
+        torch.manual_seed(seed)
+        grainy_gray_output = model(image_gray, grain_size_tensor)
+        
+        # 3. Isolate the noise pattern
+        # The model outputs a full image; we extract the noise by subtracting the input
+        noise_pattern = grainy_gray_output - image_gray
+        
+        # 4. Add the grayscale noise pattern back to the original color image
+        noisy_image_bchw = image_bchw + noise_pattern
+        # --- END FIX ---
+        
+        # Blend the result with the original image based on strength
+        blended_image_bchw = torch.clamp((1 - strength) * image_bchw + strength * noisy_image_bchw, 0.0, 1.0)
+
+        final_image = blended_image_bchw.permute(0, 2, 3, 1).to(original_device)
+
+        print("โœ… INSTARAW Neural Grain: Processing complete.")
+        return (final_image,)
+
+# --- Node Registration ---
+NODE_CLASS_MAPPINGS = {
+    "INSTARAW_NeuralGrain": INSTARAW_NeuralGrain,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "INSTARAW_NeuralGrain": "โœจ INSTARAW Neural Grain",
+}
\ No newline at end of file
diff --git a/custom_nodes/ComfyUI_INSTARAW/nodes/utility_nodes/pixel_perturb.py b/custom_nodes/ComfyUI_INSTARAW/nodes/utility_nodes/pixel_perturb.py
new file mode 100644
index 0000000000000000000000000000000000000000..6f1d396fa1070ac5b04c9430766d3c304913bd9f
--- /dev/null
+++ b/custom_nodes/ComfyUI_INSTARAW/nodes/utility_nodes/pixel_perturb.py
@@ -0,0 +1,70 @@
+# Filename: ComfyUI_INSTARAW/nodes/utility_nodes/pixel_perturb.py
+import torch
+import numpy as np
+
+class INSTARAW_Pixel_Perturb:
+    """
+    Applies a randomized, low-magnitude perturbation to each pixel.
+    This is a 1:1 implementation of the 'randomized_perturbation' function
+    from the Image-Detection-Bypass-Utility.
+    """
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "image": ("IMAGE",),
+                "magnitude": ("FLOAT", {
+                    "default": 0.008, "min": 0.0, "max": 0.05, "step": 0.001,
+                    "tooltip": "Fractional magnitude of the random noise. 0.008 is the default."
+                }),
+                "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
+            },
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "execute"
+    CATEGORY = "INSTARAW/Authenticity"
+
+    def _tensor_to_numpy(self, tensor: torch.Tensor) -> np.ndarray:
+        # Convert tensor [0, 1] float to numpy [0, 255] uint8
+        return (tensor.cpu().numpy() * 255).astype(np.uint8)
+
+    def _numpy_to_tensor(self, np_array: np.ndarray) -> torch.Tensor:
+        # Convert numpy [0, 255] uint8 back to tensor [0, 1] float
+        return torch.from_numpy(np_array.astype(np.float32) / 255.0)
+
+    def execute(self, image: torch.Tensor, magnitude: float, seed: int):
+        if magnitude == 0:
+            return (image,)
+
+        # Create a NumPy Random Generator for thread-safe randomness
+        rng = np.random.default_rng(seed)
+
+        processed_images = []
+        for i in range(image.shape[0]):
+            # 1. Convert single image from batch to numpy [0, 255] uint8
+            img_numpy_uint8 = self._tensor_to_numpy(image[i])
+
+            # --- This is the exact logic from the reference repository ---
+            # 2. Calculate magnitude in the [0, 255] range
+            mag_255 = magnitude * 255.0
+            
+            # 3. Generate uniform random noise between -mag and +mag
+            perturb = rng.uniform(low=-mag_255, high=mag_255, size=img_numpy_uint8.shape)
+            
+            # 4. Add noise to a float32 version of the image
+            out = img_numpy_uint8.astype(np.float32) + perturb
+            
+            # 5. Clip the result and convert back to uint8
+            processed_numpy_uint8 = np.clip(out, 0, 255).astype(np.uint8)
+            # --- End of reference logic ---
+
+            # 6. Convert back to a tensor for ComfyUI
+            processed_tensor = self._numpy_to_tensor(processed_numpy_uint8)
+            processed_images.append(processed_tensor)
+
+        # Stack the processed images back into a single batch tensor
+        final_batch = torch.stack(processed_images, dim=0)
+
+        return (final_batch,)
\ No newline at end of file
diff --git a/custom_nodes/ComfyUI_INSTARAW/nodes/utility_nodes/prompt_batch_preview.py b/custom_nodes/ComfyUI_INSTARAW/nodes/utility_nodes/prompt_batch_preview.py
new file mode 100644
index 0000000000000000000000000000000000000000..bf74931faca90b4c91f2262cbbdf2d4d3beb69ad
--- /dev/null
+++ b/custom_nodes/ComfyUI_INSTARAW/nodes/utility_nodes/prompt_batch_preview.py
@@ -0,0 +1,80 @@
+# ---
+# ComfyUI INSTARAW - Prompt Batch Preview Node
+# Debug utility to preview batch lists from Reality Prompt Generator
+# ---
+
+class INSTARAW_PromptBatchPreview:
+    """
+    Debug utility to preview prompt/seed batches from RPG.
+    Displays all batch elements in a formatted output.
+    """
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "prompt_list_positive": ("STRING", {"forceInput": True}),
+                "prompt_list_negative": ("STRING", {"forceInput": True}),
+                "seed_list": ("INT", {"forceInput": True}),
+            },
+        }
+
+    RETURN_TYPES = ("STRING",)
+    RETURN_NAMES = ("summary",)
+    OUTPUT_NODE = True
+    FUNCTION = "preview_batch"
+    CATEGORY = "INSTARAW/Utils"
+
+    INPUT_IS_LIST = True  # Accept lists
+
+    def preview_batch(self, prompt_list_positive, prompt_list_negative, seed_list):
+        """Display batch contents in console and return summary."""
+
+        # Convert to lists if needed
+        positives = list(prompt_list_positive) if isinstance(prompt_list_positive, (list, tuple)) else [prompt_list_positive]
+        negatives = list(prompt_list_negative) if isinstance(prompt_list_negative, (list, tuple)) else [prompt_list_negative]
+        seeds = list(seed_list) if isinstance(seed_list, (list, tuple)) else [seed_list]
+
+        count = len(positives)
+
+        print("\n" + "="*80)
+        print(f"[PROMPT BATCH PREVIEW] Total: {count} prompts")
+        print("="*80)
+
+        # Build formatted output for UI
+        ui_lines = [f"๐Ÿ“Š BATCH PREVIEW: {count} prompts\n" + "="*60 + "\n"]
+
+        for i in range(count):
+            pos = positives[i] if i < len(positives) else "MISSING"
+            neg = negatives[i] if i < len(negatives) else "MISSING"
+            seed_val = seeds[i] if i < len(seeds) else "MISSING"
+
+            # Console output
+            print(f"\n[{i+1}/{count}] Seed: {seed_val}")
+            print(f"  โœ… Positive: {pos[:100]}{'...' if len(str(pos)) > 100 else ''}")
+            print(f"  โŒ Negative: {neg[:100]}{'...' if len(str(neg)) > 100 else ''}")
+
+            # UI output (shorter for readability)
+            ui_lines.append(f"[{i+1}] Seed: {seed_val}")
+            ui_lines.append(f"โœ… {str(pos)[:80]}{'...' if len(str(pos)) > 80 else ''}")
+            ui_lines.append(f"โŒ {str(neg)[:80]}{'...' if len(str(neg)) > 80 else ''}\n")
+
+        print("\n" + "="*80 + "\n")
+
+        summary = "\n".join(ui_lines)
+
+        # Return both normal output and UI display
+        return {
+            "ui": {"text": [summary]},
+            "result": (summary,)
+        }
+
+
+# Export node mappings
+NODE_CLASS_MAPPINGS = {
+    "INSTARAW_PromptBatchPreview": INSTARAW_PromptBatchPreview,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "INSTARAW_PromptBatchPreview": "๐Ÿ” INSTARAW Prompt Batch Preview",
+}
diff --git a/custom_nodes/ComfyUI_INSTARAW/nodes/utility_nodes/realistic_jpeg.py b/custom_nodes/ComfyUI_INSTARAW/nodes/utility_nodes/realistic_jpeg.py
new file mode 100644
index 0000000000000000000000000000000000000000..6d141e32a9154fa1f68383c0431e4b2ce50cb3c6
--- /dev/null
+++ b/custom_nodes/ComfyUI_INSTARAW/nodes/utility_nodes/realistic_jpeg.py
@@ -0,0 +1,143 @@
+# ---
+# Filename: ../ComfyUI_INSTARAW/nodes/utility_nodes/realistic_jpeg.py
+# ---
+
+# ---
+# ComfyUI INSTARAW - JPEG Degradation Node (V3 - Corrected Scaling)
+# Part of the INSTARAW custom nodes collection by Instara
+#
+# Copyright ยฉ 2025 Instara. All rights reserved.
+# PROPRIETARY SOFTWARE - ALL RIGHTS RESERVED
+# ---
+
+"""
+An advanced node for simulating image degradation from compression.
+Offers two modes: 'True JPEG' for authentic artifacts and 'Downscale/Upscale' for detail loss.
+"""
+
+import torch
+import numpy as np
+from PIL import Image
+import io
+import math
+import comfy.utils
+
+try:
+    import kornia.filters as kfilters
+    KORNIA_AVAILABLE = True
+    DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+except ImportError:
+    KORNIA_AVAILABLE = False
+
+class INSTARAW_JPEG_Degradation:
+    """
+    Applies realistic image degradation through either true JPEG compression
+    or a downscale/upscale cycle, with optional artifact softening.
+    """
+
+    MODES = ["True JPEG", "Downscale/Upscale"]
+    CHROMA_SUBSAMPLING_MODES = ["Standard (4:2:0 - Blotchy Color)", "High Quality (4:4:4)", "Aggressive (4:1:1)"]
+    UPSCALE_METHODS = ["lanczos", "bicubic", "bilinear", "nearest"]
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "image": ("IMAGE",),
+                "mode": (cls.MODES, {"default": "True JPEG"}),
+                
+                "quality": ("INT", {"default": 60, "min": 1, "max": 100, "step": 1, "tooltip": "Overall JPEG quality (1=worst, 100=best)."}),
+                "chroma_subsampling": (cls.CHROMA_SUBSAMPLING_MODES, {"default": "Standard (4:2:0 - Blotchy Color)"}),
+                
+                "downscale_factor": ("FLOAT", {"default": 0.5, "min": 0.1, "max": 1.0, "step": 0.05, "tooltip": "Factor to shrink the image by before upscaling."}),
+                "upscale_method": (cls.UPSCALE_METHODS, {"default": "bicubic"}),
+
+                "soften_artifacts": ("BOOLEAN", {"default": False, "label_on": "Soften (Rounder)", "label_off": "Sharp (Blockier)"}),
+                "soften_sigma": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 5.0, "step": 0.1, "tooltip": "Amount of blur to apply to soften artifact edges."}),
+            },
+        }
+
+    CATEGORY = "INSTARAW/Utils"
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "degrade"
+    DESCRIPTION = """
+# INSTARAW JPEG Degradation
+Simulates image quality loss.
+- **True JPEG**: Authentic compression artifacts.
+- **Downscale/Upscale**: Simulates resolution loss.
+"""
+
+    def tensor_to_pil(self, image_tensor: torch.Tensor) -> Image.Image:
+        image_np = image_tensor.squeeze(0).cpu().numpy()
+        image_np = (image_np * 255).astype(np.uint8)
+        return Image.fromarray(image_np, 'RGB')
+
+    def pil_to_tensor(self, pil_image: Image.Image) -> torch.Tensor:
+        return torch.from_numpy(np.array(pil_image).astype(np.float32) / 255.0).unsqueeze(0)
+
+    def degrade(self, image: torch.Tensor, mode: str, quality: int, chroma_subsampling: str, 
+                downscale_factor: float, upscale_method: str, soften_artifacts: bool, soften_sigma: float):
+        
+        original_device = image.device
+        
+        if mode == "True JPEG":
+            processed_images = []
+            
+            subsampling_map = {
+                "High Quality (4:4:4)": 0,
+                "Standard (4:2:0 - Blotchy Color)": 2,
+                "Aggressive (4:1:1)": 1,
+            }
+            subsampling_val = subsampling_map.get(chroma_subsampling, 2)
+
+            for i in range(image.shape[0]):
+                img_pil = self.tensor_to_pil(image[i:i+1])
+                buffer = io.BytesIO()
+                
+                img_pil.save(buffer, format='JPEG', quality=quality, subsampling=subsampling_val)
+                
+                buffer.seek(0)
+                reloaded_pil = Image.open(buffer).convert('RGB')
+                processed_images.append(self.pil_to_tensor(reloaded_pil))
+            
+            final_batch = torch.cat(processed_images, dim=0)
+
+        elif mode == "Downscale/Upscale":
+            _b, original_height, original_width, _c = image.shape
+            
+            target_w = int(original_width * downscale_factor)
+            target_h = int(original_height * downscale_factor)
+            
+            img_bchw = image.permute(0, 3, 1, 2)
+
+            # --- THIS IS THE FIX ---
+            # We use `crop="disabled"` to prevent any edge trimming. This guarantees the aspect ratio
+            # is perfectly maintained, so the final upscale will match the original size exactly.
+            downscaled_bchw = comfy.utils.common_upscale(img_bchw, target_w, target_h, "area", "disabled")
+            upscaled_bchw = comfy.utils.common_upscale(downscaled_bchw, original_width, original_height, upscale_method, "disabled")
+            # --- END FIX ---
+            
+            final_batch = upscaled_bchw.permute(0, 2, 3, 1)
+
+        else:
+            final_batch = image
+
+        if soften_artifacts and soften_sigma > 0 and KORNIA_AVAILABLE:
+            final_batch_bchw = final_batch.permute(0, 3, 1, 2).to(DEVICE)
+            kernel_size = 2 * math.ceil(3.0 * soften_sigma) + 1
+            blurred_batch = kfilters.gaussian_blur2d(
+                final_batch_bchw, (kernel_size, kernel_size), (soften_sigma, soften_sigma)
+            )
+            final_batch = blurred_batch.permute(0, 2, 3, 1)
+        elif soften_artifacts and not KORNIA_AVAILABLE:
+            print("โš ๏ธ INSTARAW JPEG Degradation: Soften artifacts is enabled, but Kornia is not installed. Skipping blur.")
+
+        return (final_batch.to(original_device),)
+
+NODE_CLASS_MAPPINGS = {
+    "INSTARAW_JPEG_Degradation": INSTARAW_JPEG_Degradation,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "INSTARAW_JPEG_Degradation": "๐Ÿ—œ๏ธ INSTARAW JPEG Degradation",
+}
\ No newline at end of file
diff --git a/custom_nodes/ComfyUI_INSTARAW/nodes/utility_nodes/realistic_noise.py b/custom_nodes/ComfyUI_INSTARAW/nodes/utility_nodes/realistic_noise.py
new file mode 100644
index 0000000000000000000000000000000000000000..beb713517d17c5b6473c5b4fbacd1bf2cb545ef4
--- /dev/null
+++ b/custom_nodes/ComfyUI_INSTARAW/nodes/utility_nodes/realistic_noise.py
@@ -0,0 +1,131 @@
+# ---
+# Filename: ../ComfyUI_INSTARAW/nodes/utility_nodes/realistic_noise.py
+# ---
+
+# ---
+# ComfyUI INSTARAW - Realistic Noise Node
+# Part of the INSTARAW custom nodes collection by Instara
+#
+# Copyright ยฉ 2025 Instara. All rights reserved.
+# PROPRIETARY SOFTWARE - ALL RIGHTS RESERVED
+# ---
+
+"""
+A node to inject natural-looking camera sensor noise, mimicking modern smartphones.
+This is different from cinematic grain; it's softer, color-dependent, and more prominent in shadows.
+"""
+
+import torch
+import math
+
+try:
+    import kornia.filters as kfilters
+    import kornia.color as kcolor  # --- THIS IS THE FIX #1: Import the correct color module ---
+    KORNIA_AVAILABLE = True
+    DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+except ImportError:
+    KORNIA_AVAILABLE = False
+    print("โš ๏ธ Kornia not available. Realistic Noise node requires Kornia for blurring effects.")
+
+class INSTARAW_RealisticNoise:
+    """
+    Adds realistic, adjustable camera sensor noise to an image.
+    Features separate controls for luma (brightness) and chroma (color) noise,
+    blurring for a softer look, and highlights protection.
+    """
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        if not KORNIA_AVAILABLE:
+            return {
+                "required": {
+                    "error": ("STRING", {
+                        "default": "Kornia library not found. Please install with 'pip install kornia' to use this node.",
+                        "multiline": True
+                    })
+                }
+            }
+        
+        return {
+            "required": {
+                "image": ("IMAGE",),
+                "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "control_after_generate": "randomize"}),
+                "luma_intensity": ("FLOAT", {"default": 0.08, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "Intensity of brightness noise."}),
+                "chroma_intensity": ("FLOAT", {"default": 0.06, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "Intensity of color noise."}),
+                "luma_blur_sigma": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 10.0, "step": 0.1, "tooltip": "Softness of the brightness noise. Higher values are more 'blotchy'."}),
+                "chroma_blur_sigma": ("FLOAT", {"default": 2.0, "min": 0.0, "max": 20.0, "step": 0.1, "tooltip": "Softness of the color noise. Usually higher than luma blur."}),
+                "highlights_protection": ("FLOAT", {"default": 0.8, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "Protects brighter areas from noise. 1.0 = full protection, 0.0 = no protection."}),
+            },
+        }
+
+    CATEGORY = "INSTARAW/Utils"
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "add_noise"
+    DESCRIPTION = """
+# INSTARAW Realistic Noise
+Adds natural-looking camera sensor noise, perfect for mimicking smartphone photos.
+- **Luma/Chroma Intensity**: Control brightness and color noise separately.
+- **Blur Sigma**: Soften the noise to avoid a sharp, 'cinematic' look.
+- **Highlights Protection**: Keeps bright areas clean, as they are in real photos.
+"""
+
+    def add_noise(self, image: torch.Tensor, seed: int, luma_intensity: float, chroma_intensity: float,
+                  luma_blur_sigma: float, chroma_blur_sigma: float, highlights_protection: float):
+        
+        if not KORNIA_AVAILABLE:
+            raise ImportError("Kornia library is not available. Please install it to use the Realistic Noise node.")
+
+        torch.manual_seed(seed)
+
+        batch_size, height, width, _ = image.shape
+        image_bchw = image.permute(0, 3, 1, 2).to(DEVICE)
+
+        total_noise = torch.zeros_like(image_bchw)
+
+        if luma_intensity > 0:
+            luma_noise_chw = torch.randn(batch_size, 1, height, width, device=DEVICE).repeat(1, 3, 1, 1)
+            
+            if luma_blur_sigma > 0:
+                kernel_size = 2 * math.ceil(3.0 * luma_blur_sigma) + 1
+                luma_noise_chw = kfilters.gaussian_blur2d(
+                    luma_noise_chw, (kernel_size, kernel_size), (luma_blur_sigma, luma_blur_sigma)
+                )
+            
+            total_noise += luma_noise_chw * luma_intensity
+
+        if chroma_intensity > 0:
+            chroma_noise_chw = torch.randn(batch_size, 3, height, width, device=DEVICE)
+
+            if chroma_blur_sigma > 0:
+                kernel_size = 2 * math.ceil(3.0 * chroma_blur_sigma) + 1
+                chroma_noise_chw = kfilters.gaussian_blur2d(
+                    chroma_noise_chw, (kernel_size, kernel_size), (chroma_blur_sigma, chroma_blur_sigma)
+                )
+
+            total_noise += chroma_noise_chw * chroma_intensity
+
+        if highlights_protection > 0:
+            # --- THIS IS THE FIX #2: Use the correct module for the function call ---
+            luminance = kcolor.rgb_to_grayscale(image_bchw)
+            
+            protection_mask = 1.0 - torch.clamp((luminance - highlights_protection) / (1.0 - highlights_protection + 1e-6), 0.0, 1.0)
+            
+            total_noise *= protection_mask
+
+        noisy_image_bchw = torch.clamp(image_bchw + total_noise, 0.0, 1.0)
+        
+        final_image = noisy_image_bchw.permute(0, 2, 3, 1).to(image.device)
+
+        return (final_image,)
+
+# =================================================================================
+# NODE REGISTRATION
+# =================================================================================
+
+NODE_CLASS_MAPPINGS = {
+    "INSTARAW_RealisticNoise": INSTARAW_RealisticNoise,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "INSTARAW_RealisticNoise": "โœจ INSTARAW Realistic Noise",
+}
\ No newline at end of file
diff --git a/custom_nodes/ComfyUI_INSTARAW/nodes/utility_nodes/seed_generator.py b/custom_nodes/ComfyUI_INSTARAW/nodes/utility_nodes/seed_generator.py
new file mode 100644
index 0000000000000000000000000000000000000000..a1e7e82597463659a9929444a6114435d9261982
--- /dev/null
+++ b/custom_nodes/ComfyUI_INSTARAW/nodes/utility_nodes/seed_generator.py
@@ -0,0 +1,55 @@
+# ---
+# ComfyUI INSTARAW - Seed Generator Node
+# Part of the INSTARAW custom nodes collection by Instara
+#
+# Copyright ยฉ 2025 Instara. All rights reserved.
+# PROPRIETARY SOFTWARE - ALL RIGHTS RESERVED
+# ---
+
+"""
+Simple seed generator utility with ComfyUI's built-in control_after_generate support.
+"""
+
+
+class INSTARAWSeedGenerator:
+    """
+    Simple seed pass-through with ComfyUI's built-in control_after_generate support.
+
+    The control_after_generate dropdown is automatically handled by ComfyUI's frontend.
+    This node just returns the seed value that ComfyUI provides.
+    """
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "seed": ("INT", {
+                    "default": 1111111,
+                    "min": 0,
+                    "max": 0xffffffffffffffff,
+                    "control_after_generate": True,
+                }),
+            }
+        }
+
+    RETURN_TYPES = ("INT",)
+    RETURN_NAMES = ("seed",)
+    FUNCTION = "get_seed"
+    CATEGORY = "INSTARAW/Utils"
+
+    def get_seed(self, seed):
+        """Simply return the seed value provided by ComfyUI."""
+        return (seed,)
+
+
+# =================================================================================
+# EXPORT NODE MAPPINGS
+# =================================================================================
+
+NODE_CLASS_MAPPINGS = {
+    "INSTARAWSeedGenerator": INSTARAWSeedGenerator,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "INSTARAWSeedGenerator": "๐ŸŽฒ INSTARAW Seed",
+}
diff --git a/custom_nodes/ComfyUI_INSTARAW/nodes/utility_nodes/spectral_engine_node.py b/custom_nodes/ComfyUI_INSTARAW/nodes/utility_nodes/spectral_engine_node.py
new file mode 100644
index 0000000000000000000000000000000000000000..8cf0f46566ae1cca5acff4e7b805752fb85d62bd
--- /dev/null
+++ b/custom_nodes/ComfyUI_INSTARAW/nodes/utility_nodes/spectral_engine_node.py
@@ -0,0 +1,73 @@
+# Filename: ComfyUI_INSTARAW/nodes/utility_nodes/spectral_engine_node.py
+# ---
+
+import torch
+import numpy as np
+import os
+
+from ...modules.detection_bypass.utils import direct_spectral_match
+
+IPHONE_STATS = None
+
+class INSTARAW_SpectralEngine:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "image": ("IMAGE",),
+                "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
+                "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), # Seed is no longer used but good to keep
+            },
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "execute"
+    CATEGORY = "INSTARAW/Post-Processing"
+
+    def load_stats(self):
+        global IPHONE_STATS
+        if IPHONE_STATS is None:
+            node_file_path = os.path.dirname(os.path.realpath(__file__))
+            instaraw_root_path = os.path.abspath(os.path.join(node_file_path, "..", ".."))
+            stats_path = os.path.join(instaraw_root_path, "pretrained", "iphone_stats.npz")
+            if not os.path.exists(stats_path):
+                raise FileNotFoundError(f"iPhone stats file not found! Expected at: {stats_path}")
+            print("๐Ÿง  INSTARAW Spectral Engine: Loading iPhone stats profile...")
+            IPHONE_STATS = np.load(stats_path)
+            print("โœ… INSTARAW Spectral Engine: Stats loaded.")
+        return IPHONE_STATS
+
+    def tensor_to_numpy(self, tensor: torch.Tensor) -> np.ndarray:
+        return (tensor.squeeze(0).cpu().numpy() * 255).astype(np.uint8)
+
+    def numpy_to_tensor(self, np_array: np.ndarray) -> torch.Tensor:
+        return torch.from_numpy(np_array.astype(np.float32) / 255.0).unsqueeze(0)
+
+    def execute(self, image: torch.Tensor, strength: float, seed: int):
+        print(f"๐Ÿš€ INSTARAW Spectral Engine (Direct Match v2): Starting.")
+        stats = self.load_stats()
+        
+        # Create the dictionary of target spectra for each channel
+        target_spectra = {
+            'r': stats['spectra_r'].mean(axis=0),
+            'g': stats['spectra_g'].mean(axis=0),
+            'b': stats['spectra_b'].mean(axis=0),
+        }
+
+        processed_images = []
+        for i in range(image.shape[0]):
+            numpy_image = self.tensor_to_numpy(image[i:i+1])
+            processed_numpy_image = direct_spectral_match(
+                img_arr=numpy_image,
+                target_spectra=target_spectra,
+                strength=strength
+            )
+            processed_tensor = self.numpy_to_tensor(processed_numpy_image)
+            processed_images.append(processed_tensor)
+
+        final_batch = torch.cat(processed_images, dim=0)
+        print("โœ… INSTARAW Spectral Engine: Processing complete.")
+        return (final_batch,)
+
+NODE_CLASS_MAPPINGS = { "INSTARAW_SpectralEngine": INSTARAW_SpectralEngine }
+NODE_DISPLAY_NAME_MAPPINGS = { "INSTARAW_SpectralEngine": "๐Ÿ›ก๏ธ INSTARAW Spectral Engine" }
\ No newline at end of file
diff --git a/custom_nodes/ComfyUI_INSTARAW/nodes/utility_nodes/spectral_normalizer_node.py b/custom_nodes/ComfyUI_INSTARAW/nodes/utility_nodes/spectral_normalizer_node.py
new file mode 100644
index 0000000000000000000000000000000000000000..3fa657f14af94d5a8f41b60e33be832707ff8806
--- /dev/null
+++ b/custom_nodes/ComfyUI_INSTARAW/nodes/utility_nodes/spectral_normalizer_node.py
@@ -0,0 +1,63 @@
+# Filename: ComfyUI_INSTARAW/nodes/utility_nodes/spectral_normalizer_node.py
+import torch
+import numpy as np
+from ...modules.detection_bypass.utils.non_semantic_attack import non_semantic_attack
+
+class INSTARAW_Spectral_Normalizer:
+    """
+    Applies a non-semantic adversarial attack to remove AI fingerprints.
+    This advanced version exposes all key hyperparameters for fine-tuning
+    and provides detailed logging of the optimization process.
+    """
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "image": ("IMAGE",),
+                "iterations": ("INT", {"default": 500, "min": 10, "max": 10000}),
+                "learning_rate": ("FLOAT", {"default": 3e-4, "min": 1e-6, "max": 1e-2, "step": 1e-5}),
+                "t_lpips": ("FLOAT", {"default": 0.04, "min": 0.0, "max": 1.0, "step": 0.001, "tooltip": "LPIPS threshold. Loss is penalized above this value."}),
+                "c_lpips": ("FLOAT", {"default": 0.01, "min": 0.0, "max": 1.0, "step": 0.001, "tooltip": "LPIPS loss weight."}),
+                "t_l2": ("FLOAT", {"default": 3e-5, "min": 0.0, "max": 1.0, "step": 1e-6, "tooltip": "L2 norm threshold for the perturbation."}),
+                "c_l2": ("FLOAT", {"default": 0.6, "min": 0.0, "max": 10.0, "step": 0.01, "tooltip": "L2 loss weight."}),
+                "grad_clip_value": ("FLOAT", {"default": 0.005, "min": 0.0, "max": 1.0, "step": 0.001}),
+                "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
+                "print_log_every_n": ("INT", {"default": 100, "min": 0, "max": 1000, "tooltip": "How often to print log updates. 0 to disable."}),
+            },
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "execute"
+    CATEGORY = "INSTARAW/Authenticity"
+
+    def _tensor_to_numpy(self, tensor: torch.Tensor) -> np.ndarray:
+        return (tensor.squeeze(0).cpu().numpy() * 255).astype(np.uint8)
+
+    def _numpy_to_tensor(self, np_array: np.ndarray) -> torch.Tensor:
+        return torch.from_numpy(np_array.astype(np.float32) / 255.0).unsqueeze(0)
+
+    def execute(self, image: torch.Tensor, iterations: int, learning_rate: float,
+                t_lpips: float, c_lpips: float, t_l2: float, c_l2: float,
+                grad_clip_value: float, seed: int, print_log_every_n: int):
+        
+        processed_images = []
+        for i in range(image.shape[0]):
+            img_numpy = self._tensor_to_numpy(image[i:i+1])
+            
+            # Call the threaded attack function with all parameters
+            processed_numpy = non_semantic_attack(
+                img_arr=img_numpy,
+                iterations=iterations,
+                learning_rate=learning_rate,
+                t_lpips=t_lpips,
+                c_lpips=c_lpips,
+                t_l2=t_l2,
+                c_l2=c_l2,
+                grad_clip_value=grad_clip_value,
+                seed=seed + i,
+                print_log_every_n=print_log_every_n # Pass the logging frequency
+            )
+            processed_images.append(self._numpy_to_tensor(processed_numpy))
+
+        return (torch.cat(processed_images, dim=0),)
\ No newline at end of file
diff --git a/custom_nodes/ComfyUI_INSTARAW/nodes/utility_nodes/string_utility_nodes.py b/custom_nodes/ComfyUI_INSTARAW/nodes/utility_nodes/string_utility_nodes.py
new file mode 100644
index 0000000000000000000000000000000000000000..c3b1d4306526ebb3d86b8ca6b64019fccbbfe37d
--- /dev/null
+++ b/custom_nodes/ComfyUI_INSTARAW/nodes/utility_nodes/string_utility_nodes.py
@@ -0,0 +1,141 @@
+from comfy.comfy_types.node_typing import IO
+
+class INSTARAW_SplitByCommas:
+    RETURN_TYPES = ("STRING","STRING","STRING","STRING","STRING","STRING")
+    FUNCTION = "func"
+    CATEGORY = "INSTARAW/Utils"
+    OUTPUT_NODE = False
+    OUTPUT_IS_LIST = [False, False, False, False, False, True]
+    DESCRIPTION = "Split the input string into up to five pieces. Splits on commas (or | or ^) and then strips whitespace."
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return { "required": { "string" : ("STRING", {"default":""}), }, "optional": { "split": ([",", "|", "^"], {}), }, }
+    
+    def func(self, string:str, split:str=",") -> tuple[str,str,str,str,str,list[str]]:
+        bits:list[str] = [r.strip() for r in string.split(split)] 
+        while len(bits)<5: bits.append("")
+        if len(bits)>5: bits = bits[:4] + [",".join(bits[4:]),]
+        return (bits[0], bits[1], bits[2], bits[3], bits[4], bits)
+
+class INSTARAW_AnyListToString:
+    RETURN_TYPES = ("STRING",)
+    FUNCTION = "func"
+    CATEGORY = "INSTARAW/Utils"
+    INPUT_IS_LIST  = True
+    OUTPUT_IS_LIST = (False,) 
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return { "required": { "anything" : (IO.ANY, ), "join" : ("STRING", {"default":""}), } }
+    
+    def func(self, anything, join:str):
+        return ( join[0].join( [f"{x}" for x in anything] ), )
+    
+class INSTARAW_StringToInt:
+    RETURN_TYPES = ("INT",)
+    FUNCTION = "func"
+    CATEGORY = "INSTARAW/Utils"
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return { "required": { "string" : ("STRING", {"default":"", "forceInput":True}), "default" : ("INT", {"default":0}), } }
+    
+    def func(self, string:str, default:int):
+        try: return (int(string.strip()),)
+        except: return (default,)
+
+class INSTARAW_StringToFloat:
+    RETURN_TYPES = ("FLOAT",)
+    FUNCTION = "func"
+    CATEGORY = "INSTARAW/Utils"
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return { "required": { "string" : ("STRING", {"default":"", "forceInput":True}), "default" : ("FLOAT", {"default":0}), } }
+    
+    def func(self, string:str, default:float):
+        try: return (float(string.strip()),)
+        except: return (default,)
+
+class INSTARAW_ConcatenateStringsNullSafe:
+    """
+    Concatenates two strings with an optional separator. 
+    Crucially, it handles None inputs (from disabled switches) by treating them as empty strings.
+    """
+    RETURN_TYPES = ("STRING",)
+    FUNCTION = "concatenate"
+    CATEGORY = "INSTARAW/Utils"
+    
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "string_a": ("STRING", {"default": ""}),
+                "string_b": ("STRING", {"default": ""}),
+            },
+            "optional": {
+                "separator": ("STRING", {"default": ", "}),
+            }
+        }
+    
+    def concatenate(self, string_a=None, string_b=None, separator=", "):
+        # 1. Handle None inputs by converting them to an empty string.
+        # This is the "null-safe" part.
+        a = string_a if string_a is not None else ""
+        b = string_b if string_b is not None else ""
+        sep = separator if separator is not None else ""
+        
+        # 2. Check if both strings are empty after handling None.
+        if not a and not b:
+            return ("",)
+        
+        # 3. Concatenate correctly, handling cases where only one string is present
+        if not a:
+            result = b
+        elif not b:
+            result = a
+        else:
+            result = f"{a}{sep}{b}"
+            
+        return (result,)
+
+class INSTARAW_StringCombine:
+    """
+    A 'None-safe' string combination node.
+    It takes multiple optional strings and a separator, ignores any that are None or empty,
+    and joins the valid ones. Perfect for conditional workflows using switches.
+    """
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "separator": ("STRING", {"default": ", ", "multiline": False}),
+            },
+            "optional": {
+                "string_1": ("STRING", {"forceInput": True, "default": ""}),
+                "string_2": ("STRING", {"forceInput": True, "default": ""}),
+                "string_3": ("STRING", {"forceInput": True, "default": ""}),
+                "string_4": ("STRING", {"forceInput": True, "default": ""}),
+                "print_to_console": ("BOOLEAN", {"default": False}),
+            }
+        }
+
+    RETURN_TYPES = ("STRING",)
+    FUNCTION = "combine"
+    CATEGORY = "INSTARAW/Utils"
+
+    def combine(self, separator, string_1=None, string_2=None, string_3=None, string_4=None, print_to_console=False):
+        # Collect all provided strings into a list
+        all_strings = [string_1, string_2, string_3, string_4]
+        
+        # Filter out any entries that are None or just whitespace
+        valid_strings = [s for s in all_strings if s is not None and s.strip()]
+        
+        # Join the valid strings with the separator
+        combined_string = separator.join(valid_strings)
+        
+        if print_to_console:
+            print(f"INSTARAW String Combine Output: '{combined_string}'")
+            
+        return (combined_string,)
\ No newline at end of file
diff --git a/custom_nodes/ComfyUI_INSTARAW/nodes/utility_nodes/texture_engine.py b/custom_nodes/ComfyUI_INSTARAW/nodes/utility_nodes/texture_engine.py
new file mode 100644
index 0000000000000000000000000000000000000000..26a2e772bbdfc77d4a4d473ba99ff938ea22535a
--- /dev/null
+++ b/custom_nodes/ComfyUI_INSTARAW/nodes/utility_nodes/texture_engine.py
@@ -0,0 +1,137 @@
+# Filename: ComfyUI_INSTARAW/nodes/utility_nodes/texture_engine.py
+# (Definitive Version with Stable Optimization)
+import torch
+import torch.optim as optim
+import numpy as np
+import threading
+import lpips
+import os
+import kornia.filters as kfilters
+import math
+
+from ...modules.detection_bypass.utils.texture_utils import TextureMatcher
+
+TEXTURE_MATCHER_CACHE = {}
+
+def _optimization_worker(
+    img_tensor_main: torch.Tensor,
+    profile_path: str,
+    iterations: int,
+    learning_rate: float,
+    strength: float,
+    smoothness: float, # This now controls blur sigma
+    device: torch.device,
+    result_container: list,
+    exception_container: list
+):
+    try:
+        if profile_path in TEXTURE_MATCHER_CACHE:
+            texture_matcher = TEXTURE_MATCHER_CACHE[profile_path]
+        else:
+            texture_matcher = TextureMatcher(profile_path, device)
+            TEXTURE_MATCHER_CACHE[profile_path] = texture_matcher
+
+        lpips_model = lpips.LPIPS(net='alex').to(device).eval()
+        for param in lpips_model.parameters():
+            param.requires_grad = False
+
+        delta = torch.zeros_like(img_tensor_main, requires_grad=True)
+        optimizer = optim.Adam([delta], lr=learning_rate)
+        
+        print(f"๐Ÿง  INSTARAW Texture Engine: Starting stable optimization for {iterations} steps...")
+        with torch.no_grad():
+            initial_loss = texture_matcher(img_tensor_main, log_stats=True)
+            print(f"    - Initial Texture Loss: {initial_loss.item():.4f}")
+
+        for i in range(iterations):
+            optimizer.zero_grad()
+
+            # --- THE FIX: Apply blur directly to the delta during the loop ---
+            # This forces the optimizer to only learn smooth patterns.
+            if smoothness > 0:
+                kernel_size = 2 * math.ceil(2.0 * smoothness) + 1
+                delta_smooth = kfilters.gaussian_blur2d(delta, (kernel_size, kernel_size), (smoothness, smoothness))
+            else:
+                delta_smooth = delta
+            # --- END FIX ---
+            
+            perturbed_image = torch.clamp(img_tensor_main + delta_smooth, 0.0, 1.0)
+            log_this_step = (i == 0) or ((i + 1) % 50 == 0) or (i == iterations - 1)
+            
+            loss_texture = texture_matcher(perturbed_image, log_stats=log_this_step)
+            loss_lpips = lpips_model(perturbed_image, img_tensor_main).mean()
+            
+            # Final, balanced loss function
+            total_loss = (loss_texture * 10.0) + (loss_lpips * 1.0)
+
+            total_loss.backward()
+            optimizer.step()
+            
+            if log_this_step:
+                print(f"\n  [Step {i+1}/{iterations}]")
+                print(f"    - LPIPS: {loss_lpips.item():.4f} | Total Loss: {total_loss.item():.4f}")
+
+        with torch.no_grad():
+            # Apply the final blur one last time for consistency
+            if smoothness > 0:
+                kernel_size = 2 * math.ceil(2.0 * smoothness) + 1
+                final_delta = kfilters.gaussian_blur2d(delta, (kernel_size, kernel_size), (smoothness, smoothness))
+            else:
+                final_delta = delta
+            
+            final_image = torch.clamp(img_tensor_main + (final_delta * strength), 0.0, 1.0)
+        
+        result_container.append(final_image)
+        print("โœ… INSTARAW Texture Engine: Optimization complete.")
+
+    except Exception as e:
+        exception_container.append(e)
+
+class INSTARAW_Texture_Engine:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "image": ("IMAGE",),
+                "profile_base_path": ("STRING", {"forceInput": True}),
+                "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 5.0, "step": 0.05}),
+                "smoothness": ("FLOAT", {
+                    "default": 0.5, "min": 0.0, "max": 5.0, "step": 0.1,
+                    "tooltip": "Controls the smoothness/scale of the added texture. 0.5 is fine grain, 2.0 is coarse."
+                }),
+                "iterations": ("INT", {"default": 200, "min": 50, "max": 1000, "step": 10}),
+                "learning_rate": ("FLOAT", {"default": 0.01, "min": 0.0001, "max": 0.1, "step": 0.001}),
+            }
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "execute"
+    CATEGORY = "INSTARAW/Authenticity"
+
+    def execute(self, image, profile_base_path, strength, smoothness, iterations, learning_rate):
+        if strength == 0: return (image,)
+        if not profile_base_path or not profile_base_path.strip():
+            raise ValueError("An Authenticity Profile must be connected.")
+        npz_path = f"{profile_base_path}.npz"
+        if not os.path.exists(npz_path):
+            raise FileNotFoundError(f"Texture profile not found at: {npz_path}")
+
+        device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        processed_batches = []
+        for img_tensor in image:
+            img_tensor_batch = img_tensor.unsqueeze(0).permute(0, 3, 1, 2).to(device)
+            result_container, exception_container = [], []
+            thread = threading.Thread(
+                target=_optimization_worker,
+                args=(img_tensor_batch, npz_path, iterations, learning_rate, strength, smoothness, device, result_container, exception_container)
+            )
+            thread.start(); thread.join()
+            if exception_container: raise exception_container[0]
+            if not result_container: raise RuntimeError("Texture Engine optimization failed to return a result.")
+            processed_tensor_bchw = result_container[0]
+            processed_tensor_bhwc = processed_tensor_bchw.permute(0, 2, 3, 1).to(image.device)
+            processed_batches.append(processed_tensor_bhwc)
+        return (torch.cat(processed_batches, dim=0),)
+
+NODE_CLASS_MAPPINGS = { "INSTARAW_Texture_Engine": INSTARAW_Texture_Engine, }
+NODE_DISPLAY_NAME_MAPPINGS = { "INSTARAW_Texture_Engine": "๐Ÿง  INSTARAW Texture Engine", }
\ No newline at end of file
diff --git a/custom_nodes/ComfyUI_INSTARAW/nodes/utility_nodes/texture_normalize.py b/custom_nodes/ComfyUI_INSTARAW/nodes/utility_nodes/texture_normalize.py
new file mode 100644
index 0000000000000000000000000000000000000000..30c92171064e135e47ed32af069ca897454d804e
--- /dev/null
+++ b/custom_nodes/ComfyUI_INSTARAW/nodes/utility_nodes/texture_normalize.py
@@ -0,0 +1,84 @@
+# Filename: ComfyUI_INSTARAW/nodes/utility_nodes/texture_normalize.py
+import torch
+import numpy as np
+from PIL import Image
+import os
+from ...modules.detection_bypass.utils.glcm_normalization import glcm_normalize
+from ...modules.detection_bypass.utils.lbp_normalization import lbp_normalize
+
+class INSTARAW_Texture_Base:
+    """Base class for texture normalization nodes with shared logic."""
+    
+    def _tensor_to_numpy(self, tensor: torch.Tensor) -> np.ndarray:
+        if tensor is None: return None
+        if tensor.ndim == 4: tensor = tensor[0]
+        return (tensor.cpu().numpy() * 255).astype(np.uint8)
+
+    def _numpy_to_tensor(self, np_array: np.ndarray) -> torch.Tensor:
+        return torch.from_numpy(np_array.astype(np.float32) / 255.0).unsqueeze(0)
+
+    def _process_batch(self, image: torch.Tensor, strength: float, seed: int, ref_image: torch.Tensor, profile_path: str, process_func, **kwargs):
+        if strength == 0:
+            return (image,)
+
+        ref_numpy = self._tensor_to_numpy(ref_image) if ref_image is not None else None
+        
+        # The underlying function will now prioritize the profile path if provided.
+        # If both are provided, the profile will be used. If only ref_image is provided, it will be used.
+        # If neither is provided, it will pass through.
+        
+        processed_images = []
+        for i in range(image.shape[0]):
+            img_numpy = self._tensor_to_numpy(image[i:i+1])
+            
+            processed_numpy = process_func(
+                img_arr=img_numpy,
+                ref_img_arr=ref_numpy,
+                profile_path=profile_path, # Pass profile path to the core function
+                strength=strength,
+                seed=seed + i,
+                **kwargs
+            )
+            processed_images.append(self._numpy_to_tensor(processed_numpy))
+
+        return (torch.cat(processed_images, dim=0),)
+
+
+class INSTARAW_GLCM_Normalize(INSTARAW_Texture_Base):
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "image": ("IMAGE",),
+                "strength": ("FLOAT", {"default": 0.2, "min": 0.0, "max": 1.0, "step": 0.01}),
+                "distances": ("STRING", {"default": "2", "tooltip": "Pixel distance for texture comparison. '2' or '3' is a good start."}),
+                "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
+            },
+            "optional": {"ref_image": ("IMAGE",), "profile_path": ("STRING", {"forceInput": True}),}
+        }
+    RETURN_TYPES = ("IMAGE",); FUNCTION = "execute"; CATEGORY = "INSTARAW/Authenticity"
+
+    def execute(self, image, strength, distances, seed, ref_image=None, profile_path=None):
+        try:
+            dist_list = [int(d.strip()) for d in distances.split(',')]
+        except:
+            dist_list = [2]
+        return self._process_batch(image, strength, seed, ref_image, profile_path, glcm_normalize, distances=dist_list)
+
+class INSTARAW_LBP_Normalize(INSTARAW_Texture_Base):
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "image": ("IMAGE",),
+                "strength": ("FLOAT", {"default": 0.15, "min": 0.0, "max": 1.0, "step": 0.01}),
+                "radius": ("INT", {"default": 3, "min": 1, "max": 10}),
+                "n_points": ("INT", {"default": 24, "min": 8, "max": 64}),
+                "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
+            },
+            "optional": {"ref_image": ("IMAGE",), "profile_path": ("STRING", {"forceInput": True}),}
+        }
+    RETURN_TYPES = ("IMAGE",); FUNCTION = "execute"; CATEGORY = "INSTARAW/Authenticity"
+
+    def execute(self, image, strength, radius, n_points, seed, ref_image=None, profile_path=None):
+        return self._process_batch(image, strength, seed, ref_image, profile_path, lbp_normalize, radius=radius, n_points=n_points)
\ No newline at end of file
diff --git a/custom_nodes/ComfyUI_INSTARAW/nodes/utility_nodes/workflow_logic_nodes.py b/custom_nodes/ComfyUI_INSTARAW/nodes/utility_nodes/workflow_logic_nodes.py
new file mode 100644
index 0000000000000000000000000000000000000000..b23b762b76c60b1a6669948884b2f79e82b234cf
--- /dev/null
+++ b/custom_nodes/ComfyUI_INSTARAW/nodes/utility_nodes/workflow_logic_nodes.py
@@ -0,0 +1,214 @@
+# ---
+# ComfyUI INSTARAW - Workflow Logic Nodes (Final Corrected Version)
+# Part of the INSTARAW custom nodes collection by Instara
+#
+# Copyright ยฉ 2025 Instara. All rights reserved.
+# PROPRIETARY SOFTWARE - ALL RIGHTS RESERVED
+# ---
+
+import threading
+import torch
+import numpy as np
+from PIL import Image, ImageDraw, ImageFont
+import os
+
+from ..api_nodes.gemini_native import INSTARAW_GeminiNative
+
+DEFAULT_PROMPT_TEMPLATE = """{llm_base_prompt}
+
+Based on the image and the rules above, generate a description.
+---
+USER REQUEST: "{user_prompt}"
+---
+IMPORTANT: You MUST modify your description to perfectly match the USER REQUEST. The user's request has the highest priority and should be treated as a final instruction that overrides the image's content if there is a conflict. Provide only the final, clean description without commentary."""
+
+def tensor_to_pil(image_tensor):
+    return Image.fromarray(np.clip(255. * image_tensor.cpu().numpy().squeeze(), 0, 255).astype(np.uint8))
+
+def pil_to_tensor(pil_image):
+    return torch.from_numpy(np.array(pil_image).astype(np.float32) / 255.0).unsqueeze(0)
+
+class INSTARAW_FeatureDescriber:
+    @classmethod
+    def INPUT_TYPES(cls):
+        gemini_inputs = INSTARAW_GeminiNative.INPUT_TYPES()
+        # --- FIX V3: Use full, identical fallback definitions ---
+        required = gemini_inputs.get("required", {})
+        api_key_input = required.get("api_key", ("STRING", {"default": "GEMINI_LOAD_FAILED"}))
+        model_input = required.get("model", (["gemini-2.5-pro", "gemini-flash-latest"], {"default": "gemini-2.5-pro"}))
+        seed_input = required.get("seed", ("INT", {"default": 1111111, "min": 0, "max": 0xffffffffffffffff, "control_after_generate": "randomize"}))
+        temp_input = required.get("temperature", ("FLOAT", {"default": 0.111, "min": 0.0, "max": 2.0, "step": 0.001}))
+        thinking_input = required.get("enable_thinking", ("BOOLEAN", {"default": True, "label_on": "Thinking Enabled", "label_off": "Thinking Disabled"}))
+        safety_input = required.get("safety_level", (["Block None", "Block Few (High Only)", "Block Some (Medium+)", "Block Most (Low+)"], {"default": "Block None"}))
+
+        return {
+            "required": {
+                "enabled": ("BOOLEAN", {"default": True, "label_on": "Enabled", "label_off": "Disabled"}),
+                "feature_image": ("IMAGE",),
+                "use_llm_description": ("BOOLEAN", {"default": True, "label_on": "Use LLM", "label_off": "User Prompt Only"}),
+                "user_prompt": ("STRING", {"multiline": True, "default": ""}),
+                "llm_base_prompt": ("STRING", {"multiline": True, "default": "Describe this feature accurately..."}),
+                "prompt_template": ("STRING", {"multiline": True, "default": DEFAULT_PROMPT_TEMPLATE}),
+                "api_key": api_key_input, "model": model_input, "seed": seed_input, "temperature": temp_input,
+                "enable_thinking": thinking_input, "safety_level": safety_input,
+            }
+        }
+    RETURN_TYPES = ("STRING", "IMAGE",); FUNCTION = "describe"; CATEGORY = "INSTARAW/Workflow Logic"
+    def describe(self, **kwargs):
+        if not kwargs.get('enabled'): return (None, None)
+        final_desc = kwargs.get('user_prompt', '').strip()
+        if kwargs.get('use_llm_description'):
+            prompt = kwargs.get('prompt_template').format(llm_base_prompt=kwargs.get('llm_base_prompt'), user_prompt=final_desc) if final_desc else kwargs.get('llm_base_prompt')
+            final_desc = INSTARAW_GeminiNative().generate_content(**{k: v for k, v in kwargs.items() if k not in ['enabled', 'feature_image', 'use_llm_description', 'user_prompt', 'llm_base_prompt', 'prompt_template']}, image_1=kwargs.get('feature_image'))[0].strip()
+        return (final_desc, kwargs.get('feature_image'))
+
+class INSTARAW_UniversalDescriber:
+    @classmethod
+    def INPUT_TYPES(cls):
+        gemini_inputs = INSTARAW_GeminiNative.INPUT_TYPES()
+        # --- FIX V3: Use full, identical fallback definitions ---
+        required = gemini_inputs.get("required", {})
+        api_key_input = required.get("api_key", ("STRING", {"default": "GEMINI_LOAD_FAILED"}))
+        model_input = required.get("model", (["gemini-2.5-pro", "gemini-flash-latest"], {"default": "gemini-2.5-pro"}))
+        seed_input = required.get("seed", ("INT", {"default": 1111111, "min": 0, "max": 0xffffffffffffffff, "control_after_generate": "randomize"}))
+        temp_input = required.get("temperature", ("FLOAT", {"default": 0.111, "min": 0.0, "max": 2.0, "step": 0.001}))
+        thinking_input = required.get("enable_thinking", ("BOOLEAN", {"default": True, "label_on": "Thinking Enabled", "label_off": "Thinking Disabled"}))
+        safety_input = required.get("safety_level", (["Block None", "Block Few (High Only)", "Block Some (Medium+)", "Block Most (Low+)"], {"default": "Block None"}))
+
+        return {
+            "required": {
+                "enabled": ("BOOLEAN", {"default": True}), "use_llm_description": ("BOOLEAN", {"default": True}),
+                "user_prompt": ("STRING", {"multiline": True, "default": ""}), "llm_base_prompt": ("STRING", {"multiline": True, "default": "Describe..."}),
+                "prompt_template": ("STRING", {"multiline": True, "default": DEFAULT_PROMPT_TEMPLATE}),
+                "api_key": api_key_input, "model": model_input, "seed": seed_input, "temperature": temp_input,
+                "enable_thinking": thinking_input, "safety_level": safety_input,
+            }, "optional": { "image_1": ("IMAGE",), "image_2": ("IMAGE",), "image_3": ("IMAGE",), "image_4": ("IMAGE",) }
+        }
+    RETURN_TYPES = ("STRING", "IMAGE", "IMAGE", "IMAGE", "IMAGE",); FUNCTION = "describe"; CATEGORY = "INSTARAW/Workflow Logic"
+    def describe(self, **kwargs):
+        images = (kwargs.get('image_1'), kwargs.get('image_2'), kwargs.get('image_3'), kwargs.get('image_4'))
+        if not kwargs.get('enabled'): return (None, *images)
+        final_desc = kwargs.get('user_prompt', '').strip()
+        if kwargs.get('use_llm_description'):
+            prompt = kwargs.get('prompt_template').format(llm_base_prompt=kwargs.get('llm_base_prompt'), user_prompt=final_desc) if final_desc else kwargs.get('llm_base_prompt')
+            final_desc = INSTARAW_GeminiNative().generate_content(**{k: v for k, v in kwargs.items() if k.startswith('api_') or k in ['model', 'seed', 'temperature', 'enable_thinking', 'safety_level']}, prompt=prompt, image_1=kwargs.get('image_1'), image_2=kwargs.get('image_2'), image_3=kwargs.get('image_3'), image_4=kwargs.get('image_4'))[0].strip()
+        return (final_desc, *images)
+
+class INSTARAW_SwapPromptAssembler:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return { "required": { "hair_prefix": ("STRING", {}), "outfit_prefix": ("STRING", {}), "separator": ("STRING", {"default": " + "}), }, "optional": { "hair_description": ("STRING", {"forceInput": True}), "outfit_description": ("STRING", {"forceInput": True}), } }
+    RETURN_TYPES = ("STRING", "BOOLEAN",); FUNCTION = "assemble"; CATEGORY = "INSTARAW/Workflow Logic"
+    def assemble(self, hair_prefix, outfit_prefix, separator, hair_description=None, outfit_description=None):
+        parts = []
+        if hair_description and hair_description.strip(): parts.append(f"{hair_prefix.strip()} {hair_description.strip()}")
+        if outfit_description and outfit_description.strip(): parts.append(f"{outfit_prefix.strip()} {outfit_description.strip()}")
+        return (separator.join(parts), bool(parts))
+
+class INSTARAW_ParallelFeatureDescriber:
+    @classmethod
+    def INPUT_TYPES(cls):
+        gemini_inputs = INSTARAW_GeminiNative.INPUT_TYPES()
+        # --- FIX V3: Use full, identical fallback definitions ---
+        required = gemini_inputs.get("required", {})
+        api_key_input = required.get("api_key", ("STRING", {"default": "GEMINI_LOAD_FAILED"}))
+        model_input = required.get("model", (["gemini-2.5-pro", "gemini-flash-latest"], {"default": "gemini-2.5-pro"}))
+        seed_input = required.get("seed", ("INT", {"default": 1111111, "min": 0, "max": 0xffffffffffffffff, "control_after_generate": "randomize"}))
+        temp_input = required.get("temperature", ("FLOAT", {"default": 0.111, "min": 0.0, "max": 2.0, "step": 0.001}))
+        thinking_input = required.get("enable_thinking", ("BOOLEAN", {"default": True, "label_on": "Thinking Enabled", "label_off": "Thinking Disabled"}))
+        safety_input = required.get("safety_level", (["Block None", "Block Few (High Only)", "Block Some (Medium+)", "Block Most (Low+)"], {"default": "Block None"}))
+
+        return {
+            "required": {
+                "hair_enabled": ("BOOLEAN", {"default": True}), "hair_use_llm": ("BOOLEAN", {"default": True}),
+                "hair_user_prompt": ("STRING", {"multiline": True}), "hair_llm_base_prompt": ("STRING", {"multiline": True}),
+                "hair_prompt_template": ("STRING", {"multiline": True, "default": DEFAULT_PROMPT_TEMPLATE}),
+                "outfit_enabled": ("BOOLEAN", {"default": True}), "outfit_use_llm": ("BOOLEAN", {"default": True}),
+                "outfit_user_prompt": ("STRING", {"multiline": True}), "outfit_llm_base_prompt": ("STRING", {"multiline": True}),
+                "outfit_prompt_template": ("STRING", {"multiline": True, "default": DEFAULT_PROMPT_TEMPLATE}),
+                "api_key": api_key_input, "model": model_input, "seed": seed_input, "temperature": temp_input,
+                "enable_thinking": thinking_input, "safety_level": safety_input,
+            }, "optional": { "hair_feature_image": ("IMAGE",), "outfit_feature_image": ("IMAGE",), }
+        }
+    RETURN_TYPES = ("STRING", "IMAGE", "STRING", "IMAGE",); FUNCTION = "describe_parallel"; CATEGORY = "INSTARAW/Workflow Logic"
+    def describe_parallel(self, **kwargs):
+        results = {}
+        def worker(feature_name, enabled, use_llm, user_prompt, llm_base_prompt, prompt_template, image):
+            try:
+                if not enabled: results[f"{feature_name}_description"], results[f"{feature_name}_image_out"] = None, None; return
+                desc = user_prompt.strip()
+                if use_llm:
+                    # --- FIX V3: Use 'is None' to check for Tensor existence ---
+                    if image is None:
+                        print(f"โš ๏ธ Warning: LLM for '{feature_name}' enabled but no image provided. Skipping."); results[f"{feature_name}_description"], results[f"{feature_name}_image_out"] = None, None; return
+                    prompt_for_llm = prompt_template.format(llm_base_prompt=llm_base_prompt, user_prompt=user_prompt) if user_prompt.strip() else llm_base_prompt
+                    gemini_args = {k: v for k, v in kwargs.items() if k.startswith('api_') or k in ['model', 'seed', 'temperature', 'enable_thinking', 'safety_level']}
+                    gemini_args['prompt'] = prompt_for_llm
+                    gemini_args['image_1'] = image
+                    desc = INSTARAW_GeminiNative().generate_content(**gemini_args)[0].strip()
+                results[f"{feature_name}_description"] = desc
+                results[f"{feature_name}_image_out"] = image if enabled else None
+            except Exception as e: results[f"{feature_name}_exception"] = e
+        threads = [threading.Thread(target=worker, args=(name, kwargs[f'{name}_enabled'], kwargs[f'{name}_use_llm'], kwargs[f'{name}_user_prompt'], kwargs[f'{name}_llm_base_prompt'], kwargs[f'{name}_prompt_template'], kwargs.get(f'{name}_feature_image'))) for name in ["hair", "outfit"]]
+        for t in threads: t.start()
+        for t in threads: t.join()
+        for name in ["hair", "outfit"]:
+            if (e := results.get(f"{name}_exception")): raise e
+        return (results.get("hair_description"), results.get("hair_image_out"), results.get("outfit_description"), results.get("outfit_image_out"),)
+
+# The rest of the file remains the same...
+class INSTARAW_SeeDreamPromptBuilder:
+    @classmethod
+    def INPUT_TYPES(cls): return { "required": { "hair_prefix_single": ("STRING", {}), "outfit_prefix_single": ("STRING", {}), "hair_prefix_multi_template": ("STRING", {"default": "perfectly change hair of image {} to"}), "outfit_prefix_multi_template": ("STRING", {"default": "perfectly change outfit of image {} to"}), "separator": ("STRING", {"default": " + "}), }, "optional": { "hair_description": ("STRING", {"forceInput": True}), "outfit_description": ("STRING", {"forceInput": True}), "hair_image_ref": ("IMAGE",), "outfit_image_ref": ("IMAGE",), } }
+    RETURN_TYPES = ("STRING", "IMAGE", "IMAGE",); FUNCTION = "build_prompt"; CATEGORY = "INSTARAW/Workflow Logic"
+    def build_prompt(self, hair_prefix_single, outfit_prefix_single, hair_prefix_multi_template, outfit_prefix_multi_template, separator, hair_description=None, outfit_description=None, hair_image_ref=None, outfit_image_ref=None):
+        num_refs = (1 if hair_image_ref is not None else 0) + (1 if outfit_image_ref is not None else 0)
+        target_idx = num_refs + 1
+        hair_prefix = hair_prefix_multi_template.format(target_idx) if num_refs > 0 else hair_prefix_single
+        outfit_prefix = outfit_prefix_multi_template.format(target_idx) if num_refs > 0 else outfit_prefix_single
+        parts = []
+        if hair_description and hair_description.strip(): parts.append(f"{hair_prefix.strip()} {hair_description.strip()}")
+        if outfit_description and outfit_description.strip(): parts.append(f"{outfit_prefix.strip()} {outfit_description.strip()}")
+        return (separator.join(parts), hair_image_ref, outfit_image_ref)
+
+class INSTARAW_PreviewAssembler:
+    @classmethod
+    def INPUT_TYPES(cls): return { "required": { "main_image": ("IMAGE",), "layout": (["Horizontal", "Vertical"],), "spacing": ("INT", {"default": 10}), "add_labels": ("BOOLEAN", {"default": True}), "label_scale": ("FLOAT", {"default": 1.0}), "font_color": ("STRING", {"default": "white"}), "label_position": (["Top Left", "Top Center", "Bottom Left", "Bottom Center"],), }, "optional": { "hair_image_ref": ("IMAGE",), "outfit_image_ref": ("IMAGE",), } }
+    RETURN_TYPES = ("IMAGE",); FUNCTION = "assemble_preview"; CATEGORY = "INSTARAW/Workflow Logic"
+    def assemble_preview(self, main_image, layout, spacing, add_labels, label_scale, font_color, label_position, hair_image_ref=None, outfit_image_ref=None):
+        images = [("Target", main_image)]
+        if hair_image_ref is not None: images.append(("Hair Ref", hair_image_ref))
+        if outfit_image_ref is not None: images.append(("Outfit Ref", outfit_image_ref))
+        if len(images) == 1: return (main_image,)
+        pils = [tensor_to_pil(img) for _, img in images]; labels = [lbl for lbl, _ in images]
+        main_p = pils[0]; mw, mh = main_p.size; resized = []
+        for img in pils:
+            w, h = img.size
+            if layout == "Horizontal": resized.append(img.resize((int(w * (mh / h)), mh), Image.LANCZOS) if h != mh else img)
+            else: resized.append(img.resize((mw, int(h * (mw / w))), Image.LANCZOS) if w != mw else img)
+        tw = sum(i.width for i in resized) + spacing * (len(resized) - 1); th = sum(i.height for i in resized) + spacing * (len(resized) - 1)
+        canvas = Image.new('RGB', (tw, mh) if layout == "Horizontal" else (mw, th), 'black')
+        font_path = os.path.join(os.path.dirname(__file__), '..', '..', 'fonts', 'BricolageGrotesque.ttf')
+        draw = ImageDraw.Draw(canvas); offset = 0
+        for i, img in enumerate(resized):
+            px, py = (offset, 0) if layout == "Horizontal" else (0, offset)
+            canvas.paste(img, (px, py))
+            if add_labels and os.path.exists(font_path):
+                fs = int(img.height * 0.05 * label_scale); pad = int(img.height * 0.01 * label_scale)
+                font = ImageFont.truetype(font_path, fs if fs > 0 else 1)
+                bbox = font.getbbox(labels[i]); t_w, t_h = bbox[2] - bbox[0], bbox[3] - bbox[1]
+                bx = px + pad if "Left" in label_position else px + (img.width - t_w) // 2
+                by = py + pad if "Top" in label_position else py + img.height - t_h - (pad * 2)
+                draw.rectangle((bx - pad, by - pad, bx + t_w + pad, by + t_h + pad), fill=(0,0,0,128))
+                draw.text((bx, by - bbox[1]), labels[i], font=font, fill=font_color)
+            offset += (img.width if layout == "Horizontal" else img.height) + spacing
+        return (pil_to_tensor(canvas),)
+
+class INSTARAW_TeleportInputAssembler:
+    @classmethod
+    def INPUT_TYPES(cls): return { "required": { "character_image": ("IMAGE",), "teleport_to_image": ("IMAGE",), "layout": (["Horizontal", "Vertical"], {}), "spacing": ("INT", {"default": 10}), "add_labels": ("BOOLEAN", {"default": True}), "label_scale": ("FLOAT", {"default": 1.0}), "font_color": ("STRING", {"default": "white"}), "label_position": (["Top Left", "Top Center", "Bottom Left", "Bottom Center"],), } }
+    RETURN_TYPES = ("IMAGE",); FUNCTION = "assemble_inputs"; CATEGORY = "INSTARAW/Workflow Logic"
+    def assemble_inputs(self, **kwargs): return INSTARAW_PreviewAssembler().assemble_preview(main_image=kwargs['character_image'], hair_image_ref=kwargs['teleport_to_image'], **{k:v for k,v in kwargs.items() if k not in ['character_image', 'teleport_to_image']})
+
+NODE_CLASS_MAPPINGS = { "INSTARAW_FeatureDescriber": INSTARAW_FeatureDescriber, "INSTARAW_UniversalDescriber": INSTARAW_UniversalDescriber, "INSTARAW_SwapPromptAssembler": INSTARAW_SwapPromptAssembler, "INSTARAW_ParallelFeatureDescriber": INSTARAW_ParallelFeatureDescriber, "INSTARAW_SeeDreamPromptBuilder": INSTARAW_SeeDreamPromptBuilder, "INSTARAW_PreviewAssembler": INSTARAW_PreviewAssembler, "INSTARAW_TeleportInputAssembler": INSTARAW_TeleportInputAssembler, }
+NODE_DISPLAY_NAME_MAPPINGS = { "INSTARAW_FeatureDescriber": "โœจ INSTARAW Feature Describer (Single)", "INSTARAW_UniversalDescriber": "โœจ INSTARAW Universal Describer", "INSTARAW_SwapPromptAssembler": "โœ๏ธ INSTARAW Swap Prompt Assembler", "INSTARAW_ParallelFeatureDescriber": "๐Ÿš€ INSTARAW Feature Describer (Parallel)", "INSTARAW_SeeDreamPromptBuilder": "๐Ÿ—ฃ๏ธ INSTARAW SeeDream Prompt Builder", "INSTARAW_PreviewAssembler": "๐Ÿ–ผ๏ธ INSTARAW Preview Assembler", "INSTARAW_TeleportInputAssembler": "๐Ÿ–ผ๏ธ INSTARAW Teleport Input Assembler", }
\ No newline at end of file
diff --git a/custom_nodes/ComfyUI_INSTARAW/pretrained/neural_grain/grainnet.pt b/custom_nodes/ComfyUI_INSTARAW/pretrained/neural_grain/grainnet.pt
new file mode 100644
index 0000000000000000000000000000000000000000..8a7e0a899adcd01b62d99a4d92bfb6364371c2fa
--- /dev/null
+++ b/custom_nodes/ComfyUI_INSTARAW/pretrained/neural_grain/grainnet.pt
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:862428869d39254629c5063c7788f6e90ebb861e6b1f6f9a061105e4fdcf7482
+size 45929
diff --git a/custom_nodes/ComfyUI_INSTARAW/requirements.txt b/custom_nodes/ComfyUI_INSTARAW/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e3f61ad94c3d862aec3afd77f16670db9da32509
--- /dev/null
+++ b/custom_nodes/ComfyUI_INSTARAW/requirements.txt
@@ -0,0 +1,102 @@
+# ComfyUI INSTARAW Dependencies
+# Note: torch and numpy are already included with ComfyUI
+
+# API nodes
+requests
+Pillow
+
+# Mask processing (optional but recommended for GrowMaskWithBlur)
+kornia
+scipy
+tqdm
+
+#google-genai>=1.51.0
+google-genai~=1.43.0
+google-api-core
+
+# Performance enhancement for model downloads from Hugging Face
+huggingface_hub[hf_xet]
+
+onnxruntime
+
+piexif
+
+scikit-image
+lpips
+
+pyexiftool
+
+#
+# This file is autogenerated by pip-compile with Python 3.12
+# by the following command:
+#
+#    pip-compile --output-file=mediapipe/opensource_only/requirements_lock_3_12.txt mediapipe/opensource_only/requirements.txt
+#
+absl-py==2.1.0
+    # via -r mediapipe/opensource_only/requirements.txt
+attrs==24.2.0
+    # via -r mediapipe/opensource_only/requirements.txt
+cffi==1.17.1
+    # via sounddevice
+contourpy==1.3.0
+    # via matplotlib
+cycler==0.12.1
+    # via matplotlib
+flatbuffers==24.3.25
+    # via -r mediapipe/opensource_only/requirements.txt
+fonttools==4.54.1
+    # via matplotlib
+jax==0.4.30
+    # via -r mediapipe/opensource_only/requirements.txt
+jaxlib==0.4.30
+    # via
+    #   -r mediapipe/opensource_only/requirements.txt
+    #   jax
+kiwisolver==1.4.7
+    # via matplotlib
+matplotlib==3.9.2
+    # via -r mediapipe/opensource_only/requirements.txt
+ml-dtypes==0.5.0
+    # via
+    #   jax
+    #   jaxlib
+numpy==1.26.4
+    # via
+    #   -r mediapipe/opensource_only/requirements.txt
+    #   contourpy
+    #   jax
+    #   jaxlib
+    #   matplotlib
+    #   ml-dtypes
+    #   opencv-contrib-python
+    #   scipy
+opencv-contrib-python==4.10.0.84
+    # via -r mediapipe/opensource_only/requirements.txt
+opt-einsum==3.4.0
+    # via jax
+packaging==24.1
+    # via matplotlib
+pillow==10.4.0
+    # via matplotlib
+protobuf==4.25.5
+    # via -r mediapipe/opensource_only/requirements.txt
+pycparser==2.22
+    # via cffi
+pyparsing==3.1.4
+    # via matplotlib
+python-dateutil==2.9.0.post0
+    # via matplotlib
+scipy==1.13.1
+    # via
+    #   jax
+    #   jaxlib
+sentencepiece==0.2.0
+    # via -r mediapipe/opensource_only/requirements.txt
+six==1.16.0
+    # via python-dateutil
+sounddevice==0.5.0
+    # via -r mediapipe/opensource_only/requirements.txt
+
+# MediaPipe - pinned to 0.10.14 for compatibility with comfyui_controlnet_aux
+# Newer versions removed mp.solutions API which breaks face mesh preprocessor
+mediapipe==0.10.14
\ No newline at end of file