add app files

app.py

@@ -0,0 +1,1064 @@
+import gradio as gr
+import numpy as np
+import random
+import torch
+import spaces
+import base64
+from io import BytesIO
+from typing import Iterable
+from PIL import Image, ImageDraw
+from diffusers import FlowMatchEulerDiscreteScheduler
+from qwenimage.pipeline_qwenimage_edit_plus import QwenImageEditPlusPipeline
+from qwenimage.transformer_qwenimage import QwenImageTransformer2DModel
+from qwenimage.qwen_fa3_processor import QwenDoubleStreamAttnProcessorFA3
+
+from gradio.themes import Soft
+from gradio.themes.utils import colors, fonts, sizes
+
+colors.purple = colors.Color(
+    name="purple",
+    c50="#FAF5FF",
+    c100="#F3E8FF",
+    c200="#E9D5FF",
+    c300="#DAB2FF",
+    c400="#C084FC",
+    c500="#A855F7",
+    c600="#9333EA",
+    c700="#7E22CE",
+    c800="#6B21A8",
+    c900="#581C87",
+    c950="#3B0764",
+)
+
+
+class PurpleTheme(Soft):
+    def __init__(
+        self,
+        *,
+        primary_hue: colors.Color | str = colors.gray,
+        secondary_hue: colors.Color | str = colors.purple,
+        neutral_hue: colors.Color | str = colors.slate,
+        text_size: sizes.Size | str = sizes.text_lg,
+        font: fonts.Font | str | Iterable[fonts.Font | str] = (
+            fonts.GoogleFont("Outfit"), "Arial", "sans-serif",
+        ),
+        font_mono: fonts.Font | str | Iterable[fonts.Font | str] = (
+            fonts.GoogleFont("IBM Plex Mono"), "ui-monospace", "monospace",
+        ),
+    ):
+        super().__init__(
+            primary_hue=primary_hue,
+            secondary_hue=secondary_hue,
+            neutral_hue=neutral_hue,
+            text_size=text_size,
+            font=font,
+            font_mono=font_mono,
+        )
+        super().set(
+            background_fill_primary="*primary_50",
+            background_fill_primary_dark="*primary_900",
+            body_background_fill="linear-gradient(135deg, *primary_200, *primary_100)",
+            body_background_fill_dark="linear-gradient(135deg, *primary_900, *primary_800)",
+            button_primary_text_color="white",
+            button_primary_text_color_hover="white",
+            button_primary_background_fill="linear-gradient(90deg, *secondary_500, *secondary_600)",
+            button_primary_background_fill_hover="linear-gradient(90deg, *secondary_600, *secondary_700)",
+            button_primary_background_fill_dark="linear-gradient(90deg, *secondary_600, *secondary_700)",
+            button_primary_background_fill_hover_dark="linear-gradient(90deg, *secondary_500, *secondary_600)",
+            button_secondary_text_color="black",
+            button_secondary_text_color_hover="white",
+            button_secondary_background_fill="linear-gradient(90deg, *primary_300, *primary_300)",
+            button_secondary_background_fill_hover="linear-gradient(90deg, *primary_400, *primary_400)",
+            button_secondary_background_fill_dark="linear-gradient(90deg, *primary_500, *primary_600)",
+            button_secondary_background_fill_hover_dark="linear-gradient(90deg, *primary_500, *primary_500)",
+            slider_color="*secondary_500",
+            slider_color_dark="*secondary_600",
+            block_title_text_weight="600",
+            block_border_width="3px",
+            block_shadow="*shadow_drop_lg",
+            button_primary_shadow="*shadow_drop_lg",
+            button_large_padding="11px",
+            color_accent_soft="*primary_100",
+            block_label_background_fill="*primary_200",
+        )
+
+
+purple_theme = PurpleTheme()
+
+MAX_SEED = np.iinfo(np.int32).max
+
+dtype = torch.bfloat16
+device = "cuda" if torch.cuda.is_available() else "cpu"
+pipe = QwenImageEditPlusPipeline.from_pretrained(
+    "Qwen/Qwen-Image-Edit-2509",
+    transformer=QwenImageTransformer2DModel.from_pretrained(
+        "prithivMLmods/Qwen-Image-Edit-Rapid-AIO-V4",
+        torch_dtype=dtype,
+        device_map="cuda",
+    ),
+    torch_dtype=dtype,
+).to(device)
+try:
+    pipe.transformer.set_attn_processor(QwenDoubleStreamAttnProcessorFA3())
+    print("Flash Attention 3 Processor set successfully.")
+except Exception as e:
+    print(f"Warning: Could not set FA3 processor: {e}")
+
+ADAPTER_SPECS = {
+    "Object-Remover": {
+        "repo": "prithivMLmods/QIE-2509-Object-Remover-Bbox",
+        "weights": "QIE-2509-Object-Remover-Bbox-5000.safetensors",
+        "adapter_name": "object-remover",
+    },
+}
+loaded = False
+
+DEFAULT_PROMPT = "Remove the red highlighted object from the scene"
+
+
+def b64_to_pil(b64_str: str) -> Image.Image | None:
+    """Helper to decode base64 string from JS into a PIL Image"""
+    if not b64_str or not b64_str.startswith("data:image"):
+        return None
+    try:
+        _, data = b64_str.split(',', 1)
+        image_data = base64.b64decode(data)
+        return Image.open(BytesIO(image_data)).convert("RGB")
+    except Exception as e:
+        print(f"Error decoding image: {e}")
+        return None
+
+
+def burn_boxes_onto_image(pil_image: Image.Image, boxes_json_str: str) -> Image.Image:
+    """Burn red outline-only rectangles onto the image (no fill)."""
+    import json
+    if not pil_image:
+        return pil_image
+    try:
+        boxes = json.loads(boxes_json_str) if boxes_json_str and boxes_json_str.strip() else []
+    except Exception:
+        boxes = []
+    if not boxes:
+        return pil_image
+
+    img = pil_image.copy().convert("RGB")
+    w, h = img.size
+    draw = ImageDraw.Draw(img)
+    bw = max(3, w // 250)
+
+    for b in boxes:
+        x1 = int(b["x1"] * w)
+        y1 = int(b["y1"] * h)
+        x2 = int(b["x2"] * w)
+        y2 = int(b["y2"] * h)
+        lx, rx = min(x1, x2), max(x1, x2)
+        ty, by_ = min(y1, y2), max(y1, y2)
+        # Red outline only — no fill
+        draw.rectangle([lx, ty, rx, by_], outline=(255, 0, 0), width=bw)
+
+    return img
+
+
+@spaces.GPU
+def infer_object_removal(
+    b64_str: str,
+    boxes_json: str,
+    prompt: str,
+    seed: int = 0,
+    randomize_seed: bool = True,
+    guidance_scale: float = 1.0,
+    num_inference_steps: int = 4,
+    height: int = 1024,
+    width: int = 1024,
+):
+    global loaded
+    progress = gr.Progress(track_tqdm=True)
+
+    if not loaded:
+        pipe.load_lora_weights(
+            ADAPTER_SPECS["Object-Remover"]["repo"],
+            weight_name=ADAPTER_SPECS["Object-Remover"]["weights"],
+            adapter_name=ADAPTER_SPECS["Object-Remover"]["adapter_name"],
+        )
+        pipe.set_adapters(
+            [ADAPTER_SPECS["Object-Remover"]["adapter_name"]], adapter_weights=[1.0]
+        )
+        loaded = True
+
+    if not prompt or prompt.strip() == "":
+        prompt = DEFAULT_PROMPT
+    print(f"Prompt: {prompt}")
+    print(f"Boxes JSON received: '{boxes_json}'")
+
+    source_image = b64_to_pil(b64_str)
+    if source_image is None:
+        raise gr.Error("Please upload an image first using the Bbox editor area.")
+
+    import json
+    try:
+        boxes = json.loads(boxes_json) if boxes_json and boxes_json.strip() else []
+    except Exception as e:
+        print(f"JSON parse error: {e}")
+        boxes = []
+
+    if not boxes:
+        raise gr.Error("Please draw at least one bounding box on the image.")
+
+    progress(0.3, desc="Burning red boxes onto image...")
+    marked = burn_boxes_onto_image(source_image, boxes_json)
+
+    progress(0.5, desc="Running object removal inference...")
+
+    if randomize_seed:
+        seed = random.randint(0, MAX_SEED)
+    generator = torch.Generator(device=device).manual_seed(seed)
+
+    result = pipe(
+        image=[marked],
+        prompt=prompt,
+        height=height if height != 0 else None,
+        width=width if width != 0 else None,
+        num_inference_steps=num_inference_steps,
+        generator=generator,
+        guidance_scale=guidance_scale,
+        num_images_per_prompt=1,
+    ).images[0]
+
+    return result, seed, marked
+
+
+def update_dimensions_on_upload(b64_str: str):
+    image = b64_to_pil(b64_str)
+    if image is None:
+        return 1024, 1024
+    original_width, original_height = image.size
+    if original_width > original_height:
+        new_width = 1024
+        aspect_ratio = original_height / original_width
+        new_height = int(new_width * aspect_ratio)
+    else:
+        new_height = 1024
+        aspect_ratio = original_width / original_height
+        new_width = int(new_height * aspect_ratio)
+    new_width = (new_width // 8) * 8
+    new_height = (new_height // 8) * 8
+    return new_width, new_height
+
+
+css = r"""
+@import url('https://fonts.googleapis.com/css2?family=Outfit:wght@400;500;600;700&family=IBM+Plex+Mono:wght@400;500;600&display=swap');
+body,.gradio-container{background-color:#FAF5FF!important;background-image:linear-gradient(#E9D5FF 1px,transparent 1px),linear-gradient(90deg,#E9D5FF 1px,transparent 1px)!important;background-size:40px 40px!important;font-family:'Outfit',sans-serif!important}
+.dark body,.dark .gradio-container{background-color:#1a1a1a!important;background-image:linear-gradient(rgba(168,85,247,.1) 1px,transparent 1px),linear-gradient(90deg,rgba(168,85,247,.1) 1px,transparent 1px)!important;background-size:40px 40px!important}
+#col-container{margin:0 auto;max-width:1200px}
+#main-title{text-align:center!important;padding:1rem 0 .5rem 0}
+#main-title h1{font-size:2.4em!important;font-weight:700!important;background:linear-gradient(135deg,#A855F7 0%,#C084FC 50%,#9333EA 100%);background-size:200% 200%;-webkit-background-clip:text;-webkit-text-fill-color:transparent;background-clip:text;animation:gradient-shift 4s ease infinite;letter-spacing:-.02em}
+@keyframes gradient-shift{0%,100%{background-position:0% 50%}50%{background-position:100% 50%}}
+#subtitle{text-align:center!important;margin-bottom:1.5rem}
+#subtitle p{margin:0 auto;color:#666;font-size:1rem;text-align:center!important}
+#subtitle a{color:#A855F7!important;text-decoration:none;font-weight:500}
+#subtitle a:hover{text-decoration:underline}
+.gradio-group{background:rgba(255,255,255,.9)!important;border:2px solid #E9D5FF!important;border-radius:12px!important;box-shadow:0 4px 24px rgba(168,85,247,.08)!important;backdrop-filter:blur(10px);transition:all .3s ease}
+.gradio-group:hover{box-shadow:0 8px 32px rgba(168,85,247,.12)!important;border-color:#C084FC!important}
+.dark .gradio-group{background:rgba(30,30,30,.9)!important;border-color:rgba(168,85,247,.3)!important}
+.primary{border-radius:8px!important;font-weight:600!important;letter-spacing:.02em!important;transition:all .3s ease!important}
+.primary:hover{transform:translateY(-2px)!important}
+.gradio-textbox textarea{font-family:'IBM Plex Mono',monospace!important;font-size:.95rem!important;line-height:1.7!important;background:rgba(255,255,255,.95)!important;border:1px solid #E9D5FF!important;border-radius:8px!important}
+.gradio-accordion{border-radius:10px!important;border:1px solid #E9D5FF!important}
+.gradio-accordion>.label-wrap{background:rgba(168,85,247,.03)!important;border-radius:10px!important}
+footer{display:none!important}
+@keyframes fadeIn{from{opacity:0;transform:translateY(10px)}to{opacity:1;transform:translateY(0)}}
+.gradio-row{animation:fadeIn .4s ease-out}
+label{font-weight:600!important;color:#333!important}
+.dark label{color:#eee!important}
+.gradio-slider input[type="range"]{accent-color:#A855F7!important}
+::-webkit-scrollbar{width:8px;height:8px}
+::-webkit-scrollbar-track{background:rgba(168,85,247,.05);border-radius:4px}
+::-webkit-scrollbar-thumb{background:linear-gradient(135deg,#A855F7,#C084FC);border-radius:4px}
+::-webkit-scrollbar-thumb:hover{background:linear-gradient(135deg,#9333EA,#A855F7)}
+
+#bbox-draw-wrap{position:relative;border:2px dashed #C084FC;border-radius:12px;overflow:hidden;background:#1a1a1a;min-height:420px;transition: border-color 0.2s ease;}
+#bbox-draw-wrap:hover{border-color:#A855F7}
+#bbox-draw-canvas{cursor:crosshair;display:block;margin:0 auto}
+.bbox-hint{background:rgba(168,85,247,.08);border:1px solid #E9D5FF;border-radius:8px;padding:10px 16px;margin:8px 0;font-size:.9rem;color:#6B21A8}
+.dark .bbox-hint{background:rgba(168,85,247,.15);border-color:rgba(168,85,247,.3);color:#C084FC}
+
+/* Custom Uiverse.io Upload Prompt Component styling */
+.upload-container {
+    position: absolute;
+    top: 50%; left: 50%;
+    transform: translate(-50%, -50%);
+    z-index: 20;
+    height: 300px;
+    width: 300px;
+    border-radius: 10px;
+    box-shadow: 4px 4px 30px rgba(168, 85, 247, 0.2);
+    display: flex;
+    flex-direction: column;
+    align-items: center;
+    justify-content: space-between;
+    padding: 10px;
+    gap: 5px;
+    background-color: rgba(250, 245, 255, 0.95);
+    backdrop-filter: blur(8px);
+}
+.dark .upload-container {
+    background-color: rgba(30, 30, 30, 0.95);
+    box-shadow: 4px 4px 30px rgba(0, 0, 0, 0.5);
+}
+
+.upload-header {
+    flex: 1;
+    width: 100%;
+    border: 2px dashed #A855F7;
+    border-radius: 10px;
+    display: flex;
+    align-items: center;
+    justify-content: center;
+    flex-direction: column;
+    cursor: pointer;
+    transition: background-color 0.2s;
+    color: #A855F7;
+}
+.upload-header:hover {
+    background-color: rgba(168, 85, 247, 0.05);
+}
+.dark .upload-header:hover {
+    background-color: rgba(168, 85, 247, 0.15);
+}
+
+.upload-header svg {
+    height: 100px;
+}
+
+.upload-header p {
+    text-align: center;
+    color: #6B21A8;
+    font-family: 'Outfit', sans-serif;
+    font-weight: 600;
+    margin-top: 10px;
+}
+.dark .upload-header p {
+    color: #DAB2FF;
+}
+
+.upload-footer {
+    background-color: rgba(168, 85, 247, 0.08);
+    width: 100%;
+    height: 40px;
+    padding: 8px;
+    border-radius: 10px;
+    display: flex;
+    align-items: center;
+    justify-content: flex-end;
+    color: #6B21A8;
+    border: none;
+    box-sizing: border-box;
+}
+.dark .upload-footer {
+    background-color: rgba(168, 85, 247, 0.15);
+    color: #DAB2FF;
+}
+
+.upload-footer svg {
+    height: 130%;
+    fill: #A855F7;
+    background-color: rgba(255, 255, 255, 0.5);
+    border-radius: 50%;
+    padding: 2px;
+    cursor: pointer;
+    box-shadow: 0 2px 10px rgba(168, 85, 247, 0.2);
+    transition: transform 0.2s;
+}
+.dark .upload-footer svg {
+    background-color: rgba(0, 0, 0, 0.3);
+}
+.upload-footer svg:hover {
+    transform: scale(1.1);
+}
+
+.upload-footer p {
+    flex: 1;
+    text-align: center;
+    font-family: 'Outfit', sans-serif;
+    font-size: 0.9rem;
+    margin: 0;
+}
+
+.bbox-toolbar-section{
+    display:flex;
+    gap:8px;
+    flex-wrap:wrap;
+    justify-content:center;
+    align-items:center;
+    padding:12px 16px;
+    margin-top:10px;
+    background:rgba(255,255,255,.92);
+    border:2px solid #E9D5FF;
+    border-radius:10px;
+    box-shadow:0 2px 12px rgba(168,85,247,.08);
+}
+.dark .bbox-toolbar-section{
+    background:rgba(30,30,30,.9);
+    border-color:rgba(168,85,247,.3);
+}
+.bbox-toolbar-section .toolbar-label{
+    font-family:'Outfit',sans-serif;
+    font-weight:600;
+    font-size:13px;
+    color:#6B21A8;
+    margin-right:6px;
+    user-select:none;
+}
+.dark .bbox-toolbar-section .toolbar-label{color:#C084FC}
+.bbox-toolbar-section .toolbar-divider{
+    width:1px;
+    height:28px;
+    background:#E9D5FF;
+    margin:0 4px;
+}
+.dark .bbox-toolbar-section .toolbar-divider{background:rgba(168,85,247,.3)}
+.bbox-toolbar-section button{
+    color:#fff;
+    border:none;
+    padding:7px 15px;
+    border-radius:7px;
+    cursor:pointer;
+    font-family:'Outfit',sans-serif;
+    font-weight:600;
+    font-size:13px;
+    box-shadow:0 2px 5px rgba(0,0,0,.15);
+    transition:background .2s,transform .15s,box-shadow .2s;
+}
+.bbox-toolbar-section button:hover{transform:translateY(-1px);box-shadow:0 4px 10px rgba(0,0,0,.2)}
+.bbox-toolbar-section button:active{transform:translateY(0)}
+.bbox-tb-draw{background:#9333EA}
+.bbox-tb-draw:hover{background:#A855F7}
+.bbox-tb-draw.active{background:#22c55e;box-shadow:0 0 8px rgba(34,197,94,.5)}
+.bbox-tb-select{background:#6366f1}
+.bbox-tb-select:hover{background:#818cf8}
+.bbox-tb-select.active{background:#22c55e;box-shadow:0 0 8px rgba(34,197,94,.5)}
+.bbox-tb-del{background:#dc2626}
+.bbox-tb-del:hover{background:#ef4444}
+.bbox-tb-undo{background:#7E22CE}
+.bbox-tb-undo:hover{background:#9333EA}
+.bbox-tb-clear{background:#be123c}
+.bbox-tb-clear:hover{background:#e11d48}
+.bbox-tb-change{background:#4b5563}
+.bbox-tb-change:hover{background:#6b7280}
+
+#bbox-status{position:absolute;top:10px;left:10px;background:rgba(0,0,0,.75);color:#00ff88;padding:5px 10px;border-radius:6px;font-family:'IBM Plex Mono',monospace;font-size:11px;z-index:10;display:none;pointer-events:none}
+#bbox-count{position:absolute;top:10px;right:10px;background:rgba(147,51,234,.85);color:#fff;padding:4px 10px;border-radius:6px;font-family:'IBM Plex Mono',monospace;font-size:11px;z-index:10;display:none}
+
+#bbox-debug-count{
+    text-align:center;
+    padding:6px 12px;
+    margin-top:6px;
+    font-family:'IBM Plex Mono',monospace;
+    font-size:12px;
+    color:#6B21A8;
+    background:rgba(168,85,247,.06);
+    border:1px dashed #C084FC;
+    border-radius:6px;
+}
+.dark #bbox-debug-count{color:#C084FC;background:rgba(168,85,247,.12)}
+
+.hidden-input {
+    display: none !important;
+}
+"""
+
+bbox_drawer_js = r"""
+() => {
+    function initCanvasBbox() {
+        if (window.__bboxInitDone) return;
+
+        const canvas     = document.getElementById('bbox-draw-canvas');
+        const wrap       = document.getElementById('bbox-draw-wrap');
+        const status     = document.getElementById('bbox-status');
+        const badge      = document.getElementById('bbox-count');
+        const debugCount = document.getElementById('bbox-debug-count');
+
+        const btnDraw    = document.getElementById('tb-draw');
+        const btnSelect  = document.getElementById('tb-select');
+        const btnDel     = document.getElementById('tb-del');
+        const btnUndo    = document.getElementById('tb-undo');
+        const btnClear   = document.getElementById('tb-clear');
+        const btnChange  = document.getElementById('tb-change-img');
+        
+        const uploadPrompt = document.getElementById('upload-prompt');
+        const uploadHeader = document.getElementById('upload-header');
+        const fileInput    = document.getElementById('custom-file-input');
+
+        if (!canvas || !wrap || !debugCount || !btnDraw || !fileInput) {
+            console.log('[BBox] waiting for DOM...');
+            setTimeout(initCanvasBbox, 250);
+            return;
+        }
+
+        window.__bboxInitDone = true;
+        console.log('[BBox] canvas init OK');
+        const ctx = canvas.getContext('2d');
+
+        let boxes = [];
+        window.__bboxBoxes = boxes;
+
+        let baseImg = null;
+        let dispW = 512, dispH = 400;
+        let selectedIdx = -1;
+        let mode = 'draw';
+
+        let dragging  = false;
+        let dragType  = null;
+        let dragStart = {x:0, y:0};
+        let dragOrig  = null;
+        const HANDLE  = 7;
+        const RED_STROKE = 'rgba(255,0,0,0.95)';
+        const RED_STROKE_WIDTH = 3;
+        const SEL_STROKE = 'rgba(0,120,255,0.95)';
+
+        function n2px(b) { return {x1:b.x1*dispW, y1:b.y1*dispH, x2:b.x2*dispW, y2:b.y2*dispH}; }
+        function px2n(x1,y1,x2,y2) {
+            return {
+                x1: Math.min(x1,x2)/dispW, y1: Math.min(y1,y2)/dispH,
+                x2: Math.max(x1,x2)/dispW, y2: Math.max(y1,y2)/dispH
+            };
+        }
+        function clamp01(v){return Math.max(0,Math.min(1,v));}
+        function fitSize(nw, nh) {
+            const mw = wrap.clientWidth || 512, mh = 500;
+            const r = Math.min(mw/nw, mh/nh, 1);
+            dispW = Math.round(nw*r); dispH = Math.round(nh*r);
+            canvas.width  = dispW; canvas.height = dispH;
+            canvas.style.width  = dispW+'px';
+            canvas.style.height = dispH+'px';
+        }
+        function canvasXY(e) {
+            const r  = canvas.getBoundingClientRect();
+            const cx = e.touches ? e.touches[0].clientX : e.clientX;
+            const cy = e.touches ? e.touches[0].clientY : e.clientY;
+            return {x: Math.max(0,Math.min(dispW, cx-r.left)),
+                    y: Math.max(0,Math.min(dispH, cy-r.top))};
+        }
+
+        function syncToGradio() {
+            window.__bboxBoxes = boxes;
+            const jsonStr = JSON.stringify(boxes);
+
+            if (debugCount) {
+                debugCount.textContent = boxes.length > 0
+                    ? '\u2705 ' + boxes.length + ' box' + (boxes.length > 1 ? 'es' : '') +
+                      ' ready  |  JSON: ' + jsonStr.substring(0,80) +
+                      (jsonStr.length > 80 ? '\u2026' : '')
+                    : '\u2B1C No boxes drawn yet';
+            }
+
+            const container = document.getElementById('boxes-json-input');
+            if (!container) return;
+            const targets = [
+                ...container.querySelectorAll('textarea'),
+                ...container.querySelectorAll('input:not([type="file"])')
+            ];
+            targets.forEach(el => {
+                const proto = el.tagName === 'TEXTAREA' ? HTMLTextAreaElement.prototype : HTMLInputElement.prototype;
+                const ns = Object.getOwnPropertyDescriptor(proto, 'value');
+                if (ns && ns.set) {
+                    ns.set.call(el, jsonStr);
+                    el.dispatchEvent(new Event('input',  {bubbles:true, composed:true}));
+                    el.dispatchEvent(new Event('change', {bubbles:true, composed:true}));
+                }
+            });
+        }
+
+        function syncImageToGradio(dataUrl) {
+            const container = document.getElementById('hidden-image-b64');
+            if (!container) return;
+            const targets = [
+                ...container.querySelectorAll('textarea'),
+                ...container.querySelectorAll('input')
+            ];
+            targets.forEach(el => {
+                const proto = el.tagName === 'TEXTAREA' ? HTMLTextAreaElement.prototype : HTMLInputElement.prototype;
+                const ns = Object.getOwnPropertyDescriptor(proto, 'value');
+                if (ns && ns.set) {
+                    ns.set.call(el, dataUrl);
+                    el.dispatchEvent(new Event('input', {bubbles:true, composed:true}));
+                    el.dispatchEvent(new Event('change', {bubbles:true, composed:true}));
+                }
+            });
+        }
+
+        function redraw(tempRect) {
+            ctx.clearRect(0,0,dispW,dispH);
+            if (!baseImg) { 
+                ctx.fillStyle='#1a1a1a'; ctx.fillRect(0,0,dispW,dispH);
+                updateBadge(); return; 
+            }
+            ctx.drawImage(baseImg, 0, 0, dispW, dispH);
+
+            boxes.forEach((b,i) => {
+                const p = n2px(b);
+                const lx=p.x1, ty=p.y1, w=p.x2-p.x1, h=p.y2-p.y1;
+
+                /* RED OUTLINE ONLY — no fill */
+                if (i === selectedIdx) {
+                    ctx.strokeStyle = SEL_STROKE;
+                    ctx.lineWidth = RED_STROKE_WIDTH + 1;
+                    ctx.setLineDash([6,3]);
+                } else {
+                    ctx.strokeStyle = RED_STROKE;
+                    ctx.lineWidth = RED_STROKE_WIDTH;
+                    ctx.setLineDash([]);
+                }
+                ctx.strokeRect(lx, ty, w, h);
+                ctx.setLineDash([]);
+
+                /* label tag */
+                ctx.fillStyle = i===selectedIdx ? 'rgba(0,120,255,0.85)' : 'rgba(255,0,0,0.85)';
+                ctx.font = 'bold 11px IBM Plex Mono,monospace';
+                ctx.textAlign = 'left'; ctx.textBaseline = 'top';
+                const label = '#'+(i+1);
+                const tw = ctx.measureText(label).width;
+                ctx.fillRect(lx, ty-16, tw+6, 16);
+                ctx.fillStyle = '#fff';
+                ctx.fillText(label, lx+3, ty-14);
+
+                if (i === selectedIdx) drawHandles(p);
+            });
+
+            /* temp drawing rect — outline only */
+            if (tempRect) {
+                const rx = Math.min(tempRect.x1,tempRect.x2);
+                const ry = Math.min(tempRect.y1,tempRect.y2);
+                const rw = Math.abs(tempRect.x2-tempRect.x1);
+                const rh = Math.abs(tempRect.y2-tempRect.y1);
+                ctx.strokeStyle = RED_STROKE;
+                ctx.lineWidth = RED_STROKE_WIDTH;
+                ctx.setLineDash([6,3]);
+                ctx.strokeRect(rx, ry, rw, rh);
+                ctx.setLineDash([]);
+            }
+            updateBadge();
+        }
+
+        function drawHandles(p) {
+            const pts = handlePoints(p);
+            ctx.fillStyle = 'rgba(0,120,255,0.9)';
+            ctx.strokeStyle = '#fff'; ctx.lineWidth = 1.5;
+            for (const k in pts) {
+                const h = pts[k];
+                ctx.fillRect(h.x-HANDLE, h.y-HANDLE, HANDLE*2, HANDLE*2);
+                ctx.strokeRect(h.x-HANDLE, h.y-HANDLE, HANDLE*2, HANDLE*2);
+            }
+        }
+
+        function handlePoints(p) {
+            const mx = (p.x1+p.x2)/2, my = (p.y1+p.y2)/2;
+            return {
+                tl:{x:p.x1,y:p.y1}, tc:{x:mx,y:p.y1}, tr:{x:p.x2,y:p.y1},
+                ml:{x:p.x1,y:my},                       mr:{x:p.x2,y:my},
+                bl:{x:p.x1,y:p.y2}, bc:{x:mx,y:p.y2}, br:{x:p.x2,y:p.y2}
+            };
+        }
+
+        function hitHandle(px, py, boxIdx) {
+            if (boxIdx < 0) return null;
+            const p = n2px(boxes[boxIdx]);
+            const pts = handlePoints(p);
+            for (const k in pts) {
+                if (Math.abs(px-pts[k].x) <= HANDLE+2 && Math.abs(py-pts[k].y) <= HANDLE+2) return k;
+            }
+            return null;
+        }
+
+        function hitBox(px, py) {
+            for (let i = boxes.length-1; i >= 0; i--) {
+                const p = n2px(boxes[i]);
+                if (px >= p.x1 && px <= p.x2 && py >= p.y1 && py <= p.y2) return i;
+            }
+            return -1;
+        }
+
+        function updateBadge() {
+            if (boxes.length > 0) {
+                badge.style.display = 'block';
+                badge.textContent = boxes.length + ' box' + (boxes.length>1?'es':'');
+            } else {
+                badge.style.display = 'none';
+            }
+        }
+
+        function setMode(m) {
+            mode = m;
+            btnDraw.classList.toggle('active', m==='draw');
+            btnSelect.classList.toggle('active', m==='select');
+            canvas.style.cursor = m==='draw' ? 'crosshair' : 'default';
+            if (m==='draw') selectedIdx = -1;
+            redraw();
+        }
+
+        function showStatus(txt) {
+            status.textContent = txt; status.style.display = 'block';
+        }
+        function hideStatus() { status.style.display = 'none'; }
+
+        function onDown(e) {
+            if (!baseImg) return;
+            e.preventDefault();
+            const {x, y} = canvasXY(e);
+
+            if (mode === 'draw') {
+                dragging = true; dragType = 'new';
+                dragStart = {x, y};
+                selectedIdx = -1;
+            } else {
+                if (selectedIdx >= 0) {
+                    const h = hitHandle(x, y, selectedIdx);
+                    if (h) {
+                        dragging = true; dragType = h;
+                        dragStart = {x, y};
+                        dragOrig = {...boxes[selectedIdx]};
+                        showStatus('Resizing box #'+(selectedIdx+1));
+                        return;
+                    }
+                }
+                const hi = hitBox(x, y);
+                if (hi >= 0) {
+                    selectedIdx = hi;
+                    const h2 = hitHandle(x, y, selectedIdx);
+                    if (h2) {
+                        dragging = true; dragType = h2;
+                        dragStart = {x, y};
+                        dragOrig = {...boxes[selectedIdx]};
+                        showStatus('Resizing box #'+(selectedIdx+1));
+                        redraw(); return;
+                    }
+                    dragging = true; dragType = 'move';
+                    dragStart = {x, y};
+                    dragOrig = {...boxes[selectedIdx]};
+                    showStatus('Moving box #'+(selectedIdx+1));
+                } else {
+                    selectedIdx = -1;
+                    hideStatus();
+                }
+                redraw();
+            }
+        }
+
+        function onMove(e) {
+            if (!baseImg) return;
+            e.preventDefault();
+            const {x, y} = canvasXY(e);
+
+            if (!dragging) {
+                if (mode === 'select') {
+                    if (selectedIdx >= 0 && hitHandle(x,y,selectedIdx)) {
+                        const h = hitHandle(x,y,selectedIdx);
+                        const curs = {tl:'nwse-resize',tr:'nesw-resize',bl:'nesw-resize',br:'nwse-resize',
+                                      tc:'ns-resize',bc:'ns-resize',ml:'ew-resize',mr:'ew-resize'};
+                        canvas.style.cursor = curs[h] || 'move';
+                    } else if (hitBox(x,y) >= 0) {
+                        canvas.style.cursor = 'move';
+                    } else {
+                        canvas.style.cursor = 'default';
+                    }
+                }
+                return;
+            }
+
+            if (dragType === 'new') {
+                redraw({x1:dragStart.x, y1:dragStart.y, x2:x, y2:y});
+                showStatus(Math.abs(x-dragStart.x).toFixed(0)+'\u00d7'+Math.abs(y-dragStart.y).toFixed(0)+' px');
+                return;
+            }
+
+            const dx = (x - dragStart.x) / dispW;
+            const dy = (y - dragStart.y) / dispH;
+            const b  = boxes[selectedIdx];
+            const o  = dragOrig;
+
+            if (dragType === 'move') {
+                const bw = o.x2-o.x1, bh = o.y2-o.y1;
+                let nx1 = o.x1+dx, ny1 = o.y1+dy;
+                nx1 = clamp01(nx1); ny1 = clamp01(ny1);
+                if (nx1+bw > 1) nx1 = 1-bw;
+                if (ny1+bh > 1) ny1 = 1-bh;
+                b.x1=nx1; b.y1=ny1; b.x2=nx1+bw; b.y2=ny1+bh;
+            } else {
+                const t = dragType;
+                if (t.includes('l')) b.x1 = clamp01(o.x1 + dx);
+                if (t.includes('r')) b.x2 = clamp01(o.x2 + dx);
+                if (t.includes('t')) b.y1 = clamp01(o.y1 + dy);
+                if (t.includes('b')) b.y2 = clamp01(o.y2 + dy);
+                if (Math.abs(b.x2-b.x1) < 0.01) { b.x1=o.x1; b.x2=o.x2; }
+                if (Math.abs(b.y2-b.y1) < 0.01) { b.y1=o.y1; b.y2=o.y2; }
+                if (b.x1 > b.x2) { const t2=b.x1; b.x1=b.x2; b.x2=t2; }
+                if (b.y1 > b.y2) { const t2=b.y1; b.y1=b.y2; b.y2=t2; }
+            }
+            redraw();
+        }
+
+        function onUp(e) {
+            if (!dragging) return;
+            if (e) e.preventDefault();
+            dragging = false;
+
+            if (dragType === 'new') {
+                const pt = e ? canvasXY(e) : {x:dragStart.x, y:dragStart.y};
+                if (Math.abs(pt.x-dragStart.x) > 4 && Math.abs(pt.y-dragStart.y) > 4) {
+                    const nb = px2n(dragStart.x, dragStart.y, pt.x, pt.y);
+                    boxes.push(nb);
+                    window.__bboxBoxes = boxes;
+                    selectedIdx = boxes.length - 1;
+                    console.log('[BBox] created box #'+boxes.length, nb);
+                    showStatus('Box #'+boxes.length+' created');
+                } else { hideStatus(); }
+            } else {
+                showStatus('Box #'+(selectedIdx+1)+' updated');
+            }
+            dragType = null; dragOrig = null;
+            syncToGradio();
+            redraw();
+        }
+
+        canvas.addEventListener('mousedown',  onDown);
+        canvas.addEventListener('mousemove',  onMove);
+        canvas.addEventListener('mouseup',    onUp);
+        canvas.addEventListener('mouseleave', (e)=>{if(dragging)onUp(e);});
+        canvas.addEventListener('touchstart', onDown, {passive:false});
+        canvas.addEventListener('touchmove',  onMove, {passive:false});
+        canvas.addEventListener('touchend',   onUp,   {passive:false});
+        canvas.addEventListener('touchcancel',(e)=>{e.preventDefault();dragging=false;redraw();},{passive:false});
+
+        // --- File Upload Logic ---
+        function processFile(file) {
+            if (!file || !file.type.startsWith('image/')) return;
+            const reader = new FileReader();
+            reader.onload = (event) => {
+                const dataUrl = event.target.result;
+                const img = new window.Image();
+                img.crossOrigin = 'anonymous';
+                img.onload = () => {
+                    baseImg = img;
+                    boxes.length = 0;
+                    window.__bboxBoxes = boxes;
+                    selectedIdx = -1;
+                    fitSize(img.naturalWidth, img.naturalHeight);
+                    syncToGradio(); redraw(); hideStatus();
+                    uploadPrompt.style.display = 'none';
+                    syncImageToGradio(dataUrl);
+                };
+                img.src = dataUrl;
+            };
+            reader.readAsDataURL(file);
+        }
+
+        uploadHeader.addEventListener('click', () => fileInput.click());
+        btnChange.addEventListener('click', () => fileInput.click());
+        
+        fileInput.addEventListener('change', (e) => {
+            processFile(e.target.files[0]);
+            e.target.value = ''; // Reset input to allow re-upload of same file
+        });
+
+        wrap.addEventListener('dragover', (e) => {
+            e.preventDefault();
+            wrap.style.borderColor = '#A855F7';
+            wrap.style.boxShadow = '0 0 15px rgba(168,85,247,0.3)';
+        });
+        wrap.addEventListener('dragleave', (e) => {
+            e.preventDefault();
+            wrap.style.borderColor = '';
+            wrap.style.boxShadow = '';
+        });
+        wrap.addEventListener('drop', (e) => {
+            e.preventDefault();
+            wrap.style.borderColor = '';
+            wrap.style.boxShadow = '';
+            if (e.dataTransfer.files.length) {
+                processFile(e.dataTransfer.files[0]);
+            }
+        });
+
+        // --- Toolbar Logic ---
+        btnDraw.addEventListener('click',   ()=>setMode('draw'));
+        btnSelect.addEventListener('click', ()=>setMode('select'));
+
+        btnDel.addEventListener('click', () => {
+            if (selectedIdx >= 0 && selectedIdx < boxes.length) {
+                const removed = selectedIdx + 1;
+                boxes.splice(selectedIdx, 1);
+                window.__bboxBoxes = boxes;
+                selectedIdx = -1;
+                syncToGradio(); redraw();
+                showStatus('Box #'+removed+' deleted');
+            } else {
+                showStatus('No box selected');
+            }
+        });
+
+        btnUndo.addEventListener('click', () => {
+            if (boxes.length > 0) {
+                boxes.pop();
+                window.__bboxBoxes = boxes;
+                selectedIdx = -1;
+                syncToGradio(); redraw();
+                showStatus('Last box removed');
+            }
+        });
+
+        btnClear.addEventListener('click', () => {
+            boxes.length = 0;
+            window.__bboxBoxes = boxes;
+            selectedIdx = -1;
+            syncToGradio(); redraw(); hideStatus();
+        });
+
+        new ResizeObserver(() => {
+            if (baseImg) { fitSize(baseImg.naturalWidth, baseImg.naturalHeight); redraw(); }
+        }).observe(wrap);
+
+        setMode('draw');
+        fitSize(512,400); redraw();
+        syncToGradio();
+    }
+
+    initCanvasBbox();
+}
+"""
+
+
+with gr.Blocks() as demo:
+    gr.Markdown("# **QIE-Object-Remover-Bbox**", elem_id="main-title")
+    gr.Markdown(
+        "Perform diverse image edits using a specialized [LoRA](https://huggingface.co/prithivMLmods/QIE-2509-Object-Remover-Bbox). "
+        "Upload an image directly into the bounding box editor area below, draw red bounding boxes over the objects you want to remove, and click Remove Object. "
+        "Multiple boxes supported. Select, move, resize or delete individual boxes. Open on [GitHub](https://github.com/PRITHIVSAKTHIUR/QIE-Object-Remover-Bbox)",
+        elem_id="subtitle",
+    )
+
+    with gr.Row():
+        with gr.Column(scale=1):
+            
+            hidden_image_b64 = gr.Textbox(
+                elem_id="hidden-image-b64", 
+                elem_classes="hidden-input", 
+                container=False
+            )
+
+            #gr.Markdown("### **Bbox Edit Controller**")
+
+            gr.HTML(
+                """
+                <div id="bbox-draw-wrap">
+                    <div id="upload-prompt" class="upload-container">
+                      <div class="upload-header" id="upload-header">
+                        <svg viewBox="0 0 24 24" fill="none" xmlns="http://www.w3.org/2000/svg">
+                            <path d="M7 10V9C7 6.23858 9.23858 4 12 4C14.7614 4 17 6.23858 17 9V10C19.2091 10 21 11.7909 21 14C21 15.4806 20.1956 16.8084 19 17.5M7 10C4.79086 10 3 11.7909 3 14C3 15.4806 3.8044 16.8084 5 17.5M7 10C7.43285 10 7.84965 10.0688 8.24006 10.1959M12 12V21M12 12L15 15M12 12L9 15" stroke="currentColor" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round"></path>
+                        </svg>
+                        <p>Browse File to upload!</p>
+                      </div>
+                      <div class="upload-footer">
+                        <svg fill="currentColor" viewBox="0 0 32 32" xmlns="http://www.w3.org/2000/svg">
+                            <path d="M15.331 6H8.5v20h15V14.154h-8.169z"></path>
+                            <path d="M18.153 6h-.009v5.342H23.5v-.002z"></path>
+                        </svg>
+                        <p>Not selected file</p>
+                        <svg viewBox="0 0 24 24" fill="none" xmlns="http://www.w3.org/2000/svg">
+                            <path d="M5.16565 10.1534C5.07629 8.99181 5.99473 8 7.15975 8H16.8402C18.0053 8 18.9237 8.9918 18.8344 10.1534L18.142 19.1534C18.0619 20.1954 17.193 21 16.1479 21H7.85206C6.80699 21 5.93811 20.1954 5.85795 19.1534L5.16565 10.1534Z" stroke="currentColor" stroke-width="2"></path>
+                            <path d="M19.5 5H4.5" stroke="currentColor" stroke-width="2" stroke-linecap="round"></path>
+                            <path d="M10 3C10 2.44772 10.4477 2 11 2H13C13.5523 2 14 2.44772 14 3V5H10V3Z" stroke="currentColor" stroke-width="2"></path>
+                        </svg>
+                      </div>
+                      <input id="custom-file-input" type="file" accept="image/*" style="display:none;" />
+                    </div>
+                    
+                    <canvas id="bbox-draw-canvas" width="512" height="400"></canvas>
+                    <div id="bbox-status"></div>
+                    <div id="bbox-count"></div>
+                </div>
+                """
+            )
+
+            gr.HTML(
+                """
+                <div class="bbox-toolbar-section">
+                    <span class="toolbar-label">🛠 Tools:</span>
+                    <button id="tb-draw"   class="bbox-tb-draw active"  title="Draw new boxes">✏️ Draw</button>
+                    <button id="tb-select" class="bbox-tb-select"       title="Select / move / resize">🔲 Select</button>
+                    <div class="toolbar-divider"></div>
+                    <span class="toolbar-label">Actions:</span>
+                    <button id="tb-del"    class="bbox-tb-del"          title="Delete selected box">✕ Delete</button>
+                    <button id="tb-undo"   class="bbox-tb-undo"         title="Remove last box">↩ Undo</button>
+                    <button id="tb-clear"  class="bbox-tb-clear"        title="Remove all boxes">🗑 Clear All</button>
+                    <div class="toolbar-divider"></div>
+                    <button id="tb-change-img" class="bbox-tb-change"   title="Upload a different image">📸 Change Image</button>
+                </div>
+                """
+            )
+
+            gr.HTML('<div id="bbox-debug-count">\u2B1C No boxes drawn yet</div>')
+
+            boxes_json = gr.Textbox(
+                value="[]",
+                elem_id="boxes-json-input",
+                elem_classes="hidden-input",
+                container=False
+            )
+            
+            gr.HTML(
+                '<div class="bbox-hint">'
+                "<b>Draw mode:</b> Click & drag to create red rectangles. "
+                "<b>Select mode:</b> Click a box to select it \u2192 drag to <b>move</b>, "
+                "drag handles to <b>resize</b>. Use <b>Delete Selected</b> to remove one box."
+                "</div>"
+            )
+            
+            prompt = gr.Textbox(
+                label="Prompt",
+                value=DEFAULT_PROMPT,
+                lines=1,
+                info="Edit the prompt if needed",
+            )
+
+            run_btn = gr.Button("\U0001F5D1\uFE0F Remove Object", variant="primary", size="lg")
+
+        with gr.Column(scale=1):
+            result = gr.Image(label="Output Image", height=475, format="png")
+            preview = gr.Image(label="Input Sent to Model (with red boxes)", height=415)
+
+            with gr.Accordion("Advanced Settings", open=False, visible=False):
+                seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0)
+                randomize_seed = gr.Checkbox(label="Randomize Seed", value=True)
+                with gr.Row():
+                    guidance_scale = gr.Slider(label="Guidance Scale", minimum=1.0, maximum=10.0, step=0.1, value=1.0)
+                    num_inference_steps = gr.Slider(label="Inference Steps", minimum=1, maximum=20, step=1, value=4)
+                with gr.Row():
+                    height_slider = gr.Slider(label="Height", minimum=256, maximum=2048, step=8, value=1024)
+                    width_slider = gr.Slider(label="Width", minimum=256, maximum=2048, step=8, value=1024)
+
+    demo.load(fn=None, js=bbox_drawer_js)
+
+    run_btn.click(
+        fn=infer_object_removal,
+        inputs=[hidden_image_b64, boxes_json, prompt, seed, randomize_seed,
+                guidance_scale, num_inference_steps, height_slider, width_slider],
+        outputs=[result, seed, preview],
+        js="""(b64, bj, p, s, rs, gs, nis, h, w) => {
+            const boxes = window.__bboxBoxes || [];
+            const json  = JSON.stringify(boxes);
+            console.log('[BBox] submitting', boxes.length, 'boxes:', json);
+            return [b64, json, p, s, rs, gs, nis, h, w];
+        }""",
+    )
+
+    hidden_image_b64.change(
+        fn=update_dimensions_on_upload,
+        inputs=[hidden_image_b64],
+        outputs=[width_slider, height_slider],
+    )
+
+if __name__ == "__main__":
+    demo.launch(
+        css=css, theme=purple_theme,
+        mcp_server=True, ssr_mode=False, show_error=True
+    )

pre-requirements.txt

@@ -0,0 +1 @@
+pip>=26.0.0

qwenimage/pipeline_qwenimage_edit_plus.py

@@ -0,0 +1,891 @@
+# Copyright 2025 Qwen-Image Team and The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import inspect
+import math
+from typing import Any, Callable, Dict, List, Optional, Union
+
+import numpy as np
+import torch
+from transformers import Qwen2_5_VLForConditionalGeneration, Qwen2Tokenizer, Qwen2VLProcessor
+
+from diffusers.image_processor import PipelineImageInput, VaeImageProcessor
+from diffusers.loaders import QwenImageLoraLoaderMixin
+from diffusers.models import AutoencoderKLQwenImage, QwenImageTransformer2DModel
+from diffusers.schedulers import FlowMatchEulerDiscreteScheduler
+from diffusers.utils import is_torch_xla_available, logging, replace_example_docstring
+from diffusers.utils.torch_utils import randn_tensor
+from diffusers.pipelines.pipeline_utils import DiffusionPipeline
+from diffusers.pipelines.qwenimage.pipeline_output import QwenImagePipelineOutput
+
+
+if is_torch_xla_available():
+    import torch_xla.core.xla_model as xm
+
+    XLA_AVAILABLE = True
+else:
+    XLA_AVAILABLE = False
+
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+EXAMPLE_DOC_STRING = """
+    Examples:
+        ```py
+        >>> import torch
+        >>> from PIL import Image
+        >>> from diffusers import QwenImageEditPlusPipeline
+        >>> from diffusers.utils import load_image
+
+        >>> pipe = QwenImageEditPlusPipeline.from_pretrained("Qwen/Qwen-Image-Edit-2509", torch_dtype=torch.bfloat16)
+        >>> pipe.to("cuda")
+        >>> image = load_image(
+        ...     "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/yarn-art-pikachu.png"
+        ... ).convert("RGB")
+        >>> prompt = (
+        ...     "Make Pikachu hold a sign that says 'Qwen Edit is awesome', yarn art style, detailed, vibrant colors"
+        ... )
+        >>> # Depending on the variant being used, the pipeline call will slightly vary.
+        >>> # Refer to the pipeline documentation for more details.
+        >>> image = pipe(image, prompt, num_inference_steps=50).images[0]
+        >>> image.save("qwenimage_edit_plus.png")
+        ```
+"""
+
+CONDITION_IMAGE_SIZE = 384 * 384
+VAE_IMAGE_SIZE = 1024 * 1024
+
+
+# Copied from diffusers.pipelines.qwenimage.pipeline_qwenimage.calculate_shift
+def calculate_shift(
+    image_seq_len,
+    base_seq_len: int = 256,
+    max_seq_len: int = 4096,
+    base_shift: float = 0.5,
+    max_shift: float = 1.15,
+):
+    m = (max_shift - base_shift) / (max_seq_len - base_seq_len)
+    b = base_shift - m * base_seq_len
+    mu = image_seq_len * m + b
+    return mu
+
+
+# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.retrieve_timesteps
+def retrieve_timesteps(
+    scheduler,
+    num_inference_steps: Optional[int] = None,
+    device: Optional[Union[str, torch.device]] = None,
+    timesteps: Optional[List[int]] = None,
+    sigmas: Optional[List[float]] = None,
+    **kwargs,
+):
+    r"""
+    Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles
+    custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`.
+
+    Args:
+        scheduler (`SchedulerMixin`):
+            The scheduler to get timesteps from.
+        num_inference_steps (`int`):
+            The number of diffusion steps used when generating samples with a pre-trained model. If used, `timesteps`
+            must be `None`.
+        device (`str` or `torch.device`, *optional*):
+            The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.
+        timesteps (`List[int]`, *optional*):
+            Custom timesteps used to override the timestep spacing strategy of the scheduler. If `timesteps` is passed,
+            `num_inference_steps` and `sigmas` must be `None`.
+        sigmas (`List[float]`, *optional*):
+            Custom sigmas used to override the timestep spacing strategy of the scheduler. If `sigmas` is passed,
+            `num_inference_steps` and `timesteps` must be `None`.
+
+    Returns:
+        `Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the
+        second element is the number of inference steps.
+    """
+    if timesteps is not None and sigmas is not None:
+        raise ValueError("Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values")
+    if timesteps is not None:
+        accepts_timesteps = "timesteps" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())
+        if not accepts_timesteps:
+            raise ValueError(
+                f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
+                f" timestep schedules. Please check whether you are using the correct scheduler."
+            )
+        scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs)
+        timesteps = scheduler.timesteps
+        num_inference_steps = len(timesteps)
+    elif sigmas is not None:
+        accept_sigmas = "sigmas" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())
+        if not accept_sigmas:
+            raise ValueError(
+                f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
+                f" sigmas schedules. Please check whether you are using the correct scheduler."
+            )
+        scheduler.set_timesteps(sigmas=sigmas, device=device, **kwargs)
+        timesteps = scheduler.timesteps
+        num_inference_steps = len(timesteps)
+    else:
+        scheduler.set_timesteps(num_inference_steps, device=device, **kwargs)
+        timesteps = scheduler.timesteps
+    return timesteps, num_inference_steps
+
+
+# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.retrieve_latents
+def retrieve_latents(
+    encoder_output: torch.Tensor, generator: Optional[torch.Generator] = None, sample_mode: str = "sample"
+):
+    if hasattr(encoder_output, "latent_dist") and sample_mode == "sample":
+        return encoder_output.latent_dist.sample(generator)
+    elif hasattr(encoder_output, "latent_dist") and sample_mode == "argmax":
+        return encoder_output.latent_dist.mode()
+    elif hasattr(encoder_output, "latents"):
+        return encoder_output.latents
+    else:
+        raise AttributeError("Could not access latents of provided encoder_output")
+
+
+def calculate_dimensions(target_area, ratio):
+    width = math.sqrt(target_area * ratio)
+    height = width / ratio
+
+    width = round(width / 32) * 32
+    height = round(height / 32) * 32
+
+    return width, height
+
+
+class QwenImageEditPlusPipeline(DiffusionPipeline, QwenImageLoraLoaderMixin):
+    r"""
+    The Qwen-Image-Edit pipeline for image editing.
+
+    Args:
+        transformer ([`QwenImageTransformer2DModel`]):
+            Conditional Transformer (MMDiT) architecture to denoise the encoded image latents.
+        scheduler ([`FlowMatchEulerDiscreteScheduler`]):
+            A scheduler to be used in combination with `transformer` to denoise the encoded image latents.
+        vae ([`AutoencoderKL`]):
+            Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
+        text_encoder ([`Qwen2.5-VL-7B-Instruct`]):
+            [Qwen2.5-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct), specifically the
+            [Qwen2.5-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct) variant.
+        tokenizer (`QwenTokenizer`):
+            Tokenizer of class
+            [CLIPTokenizer](https://huggingface.co/docs/transformers/en/model_doc/clip#transformers.CLIPTokenizer).
+    """
+
+    model_cpu_offload_seq = "text_encoder->transformer->vae"
+    _callback_tensor_inputs = ["latents", "prompt_embeds"]
+
+    def __init__(
+        self,
+        scheduler: FlowMatchEulerDiscreteScheduler,
+        vae: AutoencoderKLQwenImage,
+        text_encoder: Qwen2_5_VLForConditionalGeneration,
+        tokenizer: Qwen2Tokenizer,
+        processor: Qwen2VLProcessor,
+        transformer: QwenImageTransformer2DModel,
+    ):
+        super().__init__()
+
+        self.register_modules(
+            vae=vae,
+            text_encoder=text_encoder,
+            tokenizer=tokenizer,
+            processor=processor,
+            transformer=transformer,
+            scheduler=scheduler,
+        )
+        self.vae_scale_factor = 2 ** len(self.vae.temperal_downsample) if getattr(self, "vae", None) else 8
+        self.latent_channels = self.vae.config.z_dim if getattr(self, "vae", None) else 16
+        # QwenImage latents are turned into 2x2 patches and packed. This means the latent width and height has to be divisible
+        # by the patch size. So the vae scale factor is multiplied by the patch size to account for this
+        self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor * 2)
+        self.tokenizer_max_length = 1024
+
+        self.prompt_template_encode = "<|im_start|>system\nDescribe the key features of the input image (color, shape, size, texture, objects, background), then explain how the user's text instruction should alter or modify the image. Generate a new image that meets the user's requirements while maintaining consistency with the original input where appropriate.<|im_end|>\n<|im_start|>user\n{}<|im_end|>\n<|im_start|>assistant\n"
+        self.prompt_template_encode_start_idx = 64
+        self.default_sample_size = 128
+
+    # Copied from diffusers.pipelines.qwenimage.pipeline_qwenimage.QwenImagePipeline._extract_masked_hidden
+    def _extract_masked_hidden(self, hidden_states: torch.Tensor, mask: torch.Tensor):
+        bool_mask = mask.bool()
+        valid_lengths = bool_mask.sum(dim=1)
+        selected = hidden_states[bool_mask]
+        split_result = torch.split(selected, valid_lengths.tolist(), dim=0)
+
+        return split_result
+
+    def _get_qwen_prompt_embeds(
+        self,
+        prompt: Union[str, List[str]] = None,
+        image: Optional[torch.Tensor] = None,
+        device: Optional[torch.device] = None,
+        dtype: Optional[torch.dtype] = None,
+    ):
+        device = device or self._execution_device
+        dtype = dtype or self.text_encoder.dtype
+
+        prompt = [prompt] if isinstance(prompt, str) else prompt
+        img_prompt_template = "Picture {}: <|vision_start|><|image_pad|><|vision_end|>"
+        if isinstance(image, list):
+            base_img_prompt = ""
+            for i, img in enumerate(image):
+                base_img_prompt += img_prompt_template.format(i + 1)
+        elif image is not None:
+            base_img_prompt = img_prompt_template.format(1)
+        else:
+            base_img_prompt = ""
+
+        template = self.prompt_template_encode
+
+        drop_idx = self.prompt_template_encode_start_idx
+        txt = [template.format(base_img_prompt + e) for e in prompt]
+
+        model_inputs = self.processor(
+            text=txt,
+            images=image,
+            padding=True,
+            return_tensors="pt",
+        ).to(device)
+
+        outputs = self.text_encoder(
+            input_ids=model_inputs.input_ids,
+            attention_mask=model_inputs.attention_mask,
+            pixel_values=model_inputs.pixel_values,
+            image_grid_thw=model_inputs.image_grid_thw,
+            output_hidden_states=True,
+        )
+
+        hidden_states = outputs.hidden_states[-1]
+        split_hidden_states = self._extract_masked_hidden(hidden_states, model_inputs.attention_mask)
+        split_hidden_states = [e[drop_idx:] for e in split_hidden_states]
+        attn_mask_list = [torch.ones(e.size(0), dtype=torch.long, device=e.device) for e in split_hidden_states]
+        max_seq_len = max([e.size(0) for e in split_hidden_states])
+        prompt_embeds = torch.stack(
+            [torch.cat([u, u.new_zeros(max_seq_len - u.size(0), u.size(1))]) for u in split_hidden_states]
+        )
+        encoder_attention_mask = torch.stack(
+            [torch.cat([u, u.new_zeros(max_seq_len - u.size(0))]) for u in attn_mask_list]
+        )
+
+        prompt_embeds = prompt_embeds.to(dtype=dtype, device=device)
+
+        return prompt_embeds, encoder_attention_mask
+
+    # Copied from diffusers.pipelines.qwenimage.pipeline_qwenimage_edit.QwenImageEditPipeline.encode_prompt
+    def encode_prompt(
+        self,
+        prompt: Union[str, List[str]],
+        image: Optional[torch.Tensor] = None,
+        device: Optional[torch.device] = None,
+        num_images_per_prompt: int = 1,
+        prompt_embeds: Optional[torch.Tensor] = None,
+        prompt_embeds_mask: Optional[torch.Tensor] = None,
+        max_sequence_length: int = 1024,
+    ):
+        r"""
+
+        Args:
+            prompt (`str` or `List[str]`, *optional*):
+                prompt to be encoded
+            image (`torch.Tensor`, *optional*):
+                image to be encoded
+            device: (`torch.device`):
+                torch device
+            num_images_per_prompt (`int`):
+                number of images that should be generated per prompt
+            prompt_embeds (`torch.Tensor`, *optional*):
+                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
+                provided, text embeddings will be generated from `prompt` input argument.
+        """
+        device = device or self._execution_device
+
+        prompt = [prompt] if isinstance(prompt, str) else prompt
+        batch_size = len(prompt) if prompt_embeds is None else prompt_embeds.shape[0]
+
+        if prompt_embeds is None:
+            prompt_embeds, prompt_embeds_mask = self._get_qwen_prompt_embeds(prompt, image, device)
+
+        _, seq_len, _ = prompt_embeds.shape
+        prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
+        prompt_embeds = prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
+        prompt_embeds_mask = prompt_embeds_mask.repeat(1, num_images_per_prompt, 1)
+        prompt_embeds_mask = prompt_embeds_mask.view(batch_size * num_images_per_prompt, seq_len)
+
+        return prompt_embeds, prompt_embeds_mask
+
+    # Copied from diffusers.pipelines.qwenimage.pipeline_qwenimage_edit.QwenImageEditPipeline.check_inputs
+    def check_inputs(
+        self,
+        prompt,
+        height,
+        width,
+        negative_prompt=None,
+        prompt_embeds=None,
+        negative_prompt_embeds=None,
+        prompt_embeds_mask=None,
+        negative_prompt_embeds_mask=None,
+        callback_on_step_end_tensor_inputs=None,
+        max_sequence_length=None,
+    ):
+        if height % (self.vae_scale_factor * 2) != 0 or width % (self.vae_scale_factor * 2) != 0:
+            logger.warning(
+                f"`height` and `width` have to be divisible by {self.vae_scale_factor * 2} but are {height} and {width}. Dimensions will be resized accordingly"
+            )
+
+        if callback_on_step_end_tensor_inputs is not None and not all(
+            k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs
+        ):
+            raise ValueError(
+                f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}"
+            )
+
+        if prompt is not None and prompt_embeds is not None:
+            raise ValueError(
+                f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
+                " only forward one of the two."
+            )
+        elif prompt is None and prompt_embeds is None:
+            raise ValueError(
+                "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
+            )
+        elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
+            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
+
+        if negative_prompt is not None and negative_prompt_embeds is not None:
+            raise ValueError(
+                f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
+                f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
+            )
+
+        if prompt_embeds is not None and prompt_embeds_mask is None:
+            raise ValueError(
+                "If `prompt_embeds` are provided, `prompt_embeds_mask` also have to be passed. Make sure to generate `prompt_embeds_mask` from the same text encoder that was used to generate `prompt_embeds`."
+            )
+        if negative_prompt_embeds is not None and negative_prompt_embeds_mask is None:
+            raise ValueError(
+                "If `negative_prompt_embeds` are provided, `negative_prompt_embeds_mask` also have to be passed. Make sure to generate `negative_prompt_embeds_mask` from the same text encoder that was used to generate `negative_prompt_embeds`."
+            )
+
+        if max_sequence_length is not None and max_sequence_length > 1024:
+            raise ValueError(f"`max_sequence_length` cannot be greater than 1024 but is {max_sequence_length}")
+
+    @staticmethod
+    # Copied from diffusers.pipelines.qwenimage.pipeline_qwenimage.QwenImagePipeline._pack_latents
+    def _pack_latents(latents, batch_size, num_channels_latents, height, width):
+        latents = latents.view(batch_size, num_channels_latents, height // 2, 2, width // 2, 2)
+        latents = latents.permute(0, 2, 4, 1, 3, 5)
+        latents = latents.reshape(batch_size, (height // 2) * (width // 2), num_channels_latents * 4)
+
+        return latents
+
+    @staticmethod
+    # Copied from diffusers.pipelines.qwenimage.pipeline_qwenimage.QwenImagePipeline._unpack_latents
+    def _unpack_latents(latents, height, width, vae_scale_factor):
+        batch_size, num_patches, channels = latents.shape
+
+        # VAE applies 8x compression on images but we must also account for packing which requires
+        # latent height and width to be divisible by 2.
+        height = 2 * (int(height) // (vae_scale_factor * 2))
+        width = 2 * (int(width) // (vae_scale_factor * 2))
+
+        latents = latents.view(batch_size, height // 2, width // 2, channels // 4, 2, 2)
+        latents = latents.permute(0, 3, 1, 4, 2, 5)
+
+        latents = latents.reshape(batch_size, channels // (2 * 2), 1, height, width)
+
+        return latents
+
+    # Copied from diffusers.pipelines.qwenimage.pipeline_qwenimage_edit.QwenImageEditPipeline._encode_vae_image
+    def _encode_vae_image(self, image: torch.Tensor, generator: torch.Generator):
+        if isinstance(generator, list):
+            image_latents = [
+                retrieve_latents(self.vae.encode(image[i : i + 1]), generator=generator[i], sample_mode="argmax")
+                for i in range(image.shape[0])
+            ]
+            image_latents = torch.cat(image_latents, dim=0)
+        else:
+            image_latents = retrieve_latents(self.vae.encode(image), generator=generator, sample_mode="argmax")
+        latents_mean = (
+            torch.tensor(self.vae.config.latents_mean)
+            .view(1, self.latent_channels, 1, 1, 1)
+            .to(image_latents.device, image_latents.dtype)
+        )
+        latents_std = (
+            torch.tensor(self.vae.config.latents_std)
+            .view(1, self.latent_channels, 1, 1, 1)
+            .to(image_latents.device, image_latents.dtype)
+        )
+        image_latents = (image_latents - latents_mean) / latents_std
+
+        return image_latents
+
+    def prepare_latents(
+        self,
+        images,
+        batch_size,
+        num_channels_latents,
+        height,
+        width,
+        dtype,
+        device,
+        generator,
+        latents=None,
+    ):
+        # VAE applies 8x compression on images but we must also account for packing which requires
+        # latent height and width to be divisible by 2.
+        height = 2 * (int(height) // (self.vae_scale_factor * 2))
+        width = 2 * (int(width) // (self.vae_scale_factor * 2))
+
+        shape = (batch_size, 1, num_channels_latents, height, width)
+
+        image_latents = None
+        if images is not None:
+            if not isinstance(images, list):
+                images = [images]
+            all_image_latents = []
+            for image in images:
+                image = image.to(device=device, dtype=dtype)
+                if image.shape[1] != self.latent_channels:
+                    image_latents = self._encode_vae_image(image=image, generator=generator)
+                else:
+                    image_latents = image
+                if batch_size > image_latents.shape[0] and batch_size % image_latents.shape[0] == 0:
+                    # expand init_latents for batch_size
+                    additional_image_per_prompt = batch_size // image_latents.shape[0]
+                    image_latents = torch.cat([image_latents] * additional_image_per_prompt, dim=0)
+                elif batch_size > image_latents.shape[0] and batch_size % image_latents.shape[0] != 0:
+                    raise ValueError(
+                        f"Cannot duplicate `image` of batch size {image_latents.shape[0]} to {batch_size} text prompts."
+                    )
+                else:
+                    image_latents = torch.cat([image_latents], dim=0)
+
+                image_latent_height, image_latent_width = image_latents.shape[3:]
+                image_latents = self._pack_latents(
+                    image_latents, batch_size, num_channels_latents, image_latent_height, image_latent_width
+                )
+                all_image_latents.append(image_latents)
+            image_latents = torch.cat(all_image_latents, dim=1)
+
+        if isinstance(generator, list) and len(generator) != batch_size:
+            raise ValueError(
+                f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
+                f" size of {batch_size}. Make sure the batch size matches the length of the generators."
+            )
+        if latents is None:
+            latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
+            latents = self._pack_latents(latents, batch_size, num_channels_latents, height, width)
+        else:
+            latents = latents.to(device=device, dtype=dtype)
+
+        return latents, image_latents
+
+    @property
+    def guidance_scale(self):
+        return self._guidance_scale
+
+    @property
+    def attention_kwargs(self):
+        return self._attention_kwargs
+
+    @property
+    def num_timesteps(self):
+        return self._num_timesteps
+
+    @property
+    def current_timestep(self):
+        return self._current_timestep
+
+    @property
+    def interrupt(self):
+        return self._interrupt
+
+    @torch.no_grad()
+    @replace_example_docstring(EXAMPLE_DOC_STRING)
+    def __call__(
+        self,
+        image: Optional[PipelineImageInput] = None,
+        prompt: Union[str, List[str]] = None,
+        negative_prompt: Union[str, List[str]] = None,
+        true_cfg_scale: float = 4.0,
+        height: Optional[int] = None,
+        width: Optional[int] = None,
+        num_inference_steps: int = 50,
+        sigmas: Optional[List[float]] = None,
+        guidance_scale: Optional[float] = None,
+        num_images_per_prompt: int = 1,
+        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
+        latents: Optional[torch.Tensor] = None,
+        prompt_embeds: Optional[torch.Tensor] = None,
+        prompt_embeds_mask: Optional[torch.Tensor] = None,
+        negative_prompt_embeds: Optional[torch.Tensor] = None,
+        negative_prompt_embeds_mask: Optional[torch.Tensor] = None,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        attention_kwargs: Optional[Dict[str, Any]] = None,
+        callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
+        callback_on_step_end_tensor_inputs: List[str] = ["latents"],
+        max_sequence_length: int = 512,
+    ):
+        r"""
+        Function invoked when calling the pipeline for generation.
+
+        Args:
+            image (`torch.Tensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.Tensor]`, `List[PIL.Image.Image]`, or `List[np.ndarray]`):
+                `Image`, numpy array or tensor representing an image batch to be used as the starting point. For both
+                numpy array and pytorch tensor, the expected value range is between `[0, 1]` If it's a tensor or a list
+                or tensors, the expected shape should be `(B, C, H, W)` or `(C, H, W)`. If it is a numpy array or a
+                list of arrays, the expected shape should be `(B, H, W, C)` or `(H, W, C)` It can also accept image
+                latents as `image`, but if passing latents directly it is not encoded again.
+            prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
+                instead.
+            negative_prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts not to guide the image generation. If not defined, one has to pass
+                `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `true_cfg_scale` is
+                not greater than `1`).
+            true_cfg_scale (`float`, *optional*, defaults to 1.0):
+                true_cfg_scale (`float`, *optional*, defaults to 1.0): Guidance scale as defined in [Classifier-Free
+                Diffusion Guidance](https://huggingface.co/papers/2207.12598). `true_cfg_scale` is defined as `w` of
+                equation 2. of [Imagen Paper](https://huggingface.co/papers/2205.11487). Classifier-free guidance is
+                enabled by setting `true_cfg_scale > 1` and a provided `negative_prompt`. Higher guidance scale
+                encourages to generate images that are closely linked to the text `prompt`, usually at the expense of
+                lower image quality.
+            height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
+                The height in pixels of the generated image. This is set to 1024 by default for the best results.
+            width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
+                The width in pixels of the generated image. This is set to 1024 by default for the best results.
+            num_inference_steps (`int`, *optional*, defaults to 50):
+                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
+                expense of slower inference.
+            sigmas (`List[float]`, *optional*):
+                Custom sigmas to use for the denoising process with schedulers which support a `sigmas` argument in
+                their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is passed
+                will be used.
+            guidance_scale (`float`, *optional*, defaults to None):
+                A guidance scale value for guidance distilled models. Unlike the traditional classifier-free guidance
+                where the guidance scale is applied during inference through noise prediction rescaling, guidance
+                distilled models take the guidance scale directly as an input parameter during forward pass. Guidance
+                scale is enabled by setting `guidance_scale > 1`. Higher guidance scale encourages to generate images
+                that are closely linked to the text `prompt`, usually at the expense of lower image quality. This
+                parameter in the pipeline is there to support future guidance-distilled models when they come up. It is
+                ignored when not using guidance distilled models. To enable traditional classifier-free guidance,
+                please pass `true_cfg_scale > 1.0` and `negative_prompt` (even an empty negative prompt like " " should
+                enable classifier-free guidance computations).
+            num_images_per_prompt (`int`, *optional*, defaults to 1):
+                The number of images to generate per prompt.
+            generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
+                One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
+                to make generation deterministic.
+            latents (`torch.Tensor`, *optional*):
+                Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
+                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
+                tensor will be generated by sampling using the supplied random `generator`.
+            prompt_embeds (`torch.Tensor`, *optional*):
+                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
+                provided, text embeddings will be generated from `prompt` input argument.
+            negative_prompt_embeds (`torch.Tensor`, *optional*):
+                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
+                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
+                argument.
+            output_type (`str`, *optional*, defaults to `"pil"`):
+                The output format of the generate image. Choose between
+                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~pipelines.qwenimage.QwenImagePipelineOutput`] instead of a plain tuple.
+            attention_kwargs (`dict`, *optional*):
+                A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
+                `self.processor` in
+                [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
+            callback_on_step_end (`Callable`, *optional*):
+                A function that calls at the end of each denoising steps during the inference. The function is called
+                with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,
+                callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by
+                `callback_on_step_end_tensor_inputs`.
+            callback_on_step_end_tensor_inputs (`List`, *optional*):
+                The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
+                will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
+                `._callback_tensor_inputs` attribute of your pipeline class.
+            max_sequence_length (`int` defaults to 512): Maximum sequence length to use with the `prompt`.
+
+        Examples:
+
+        Returns:
+            [`~pipelines.qwenimage.QwenImagePipelineOutput`] or `tuple`:
+            [`~pipelines.qwenimage.QwenImagePipelineOutput`] if `return_dict` is True, otherwise a `tuple`. When
+            returning a tuple, the first element is a list with the generated images.
+        """
+        image_size = image[-1].size if isinstance(image, list) else image.size
+        calculated_width, calculated_height = calculate_dimensions(1024 * 1024, image_size[0] / image_size[1])
+        height = height or calculated_height
+        width = width or calculated_width
+
+        multiple_of = self.vae_scale_factor * 2
+        width = width // multiple_of * multiple_of
+        height = height // multiple_of * multiple_of
+
+        # 1. Check inputs. Raise error if not correct
+        self.check_inputs(
+            prompt,
+            height,
+            width,
+            negative_prompt=negative_prompt,
+            prompt_embeds=prompt_embeds,
+            negative_prompt_embeds=negative_prompt_embeds,
+            prompt_embeds_mask=prompt_embeds_mask,
+            negative_prompt_embeds_mask=negative_prompt_embeds_mask,
+            callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs,
+            max_sequence_length=max_sequence_length,
+        )
+
+        self._guidance_scale = guidance_scale
+        self._attention_kwargs = attention_kwargs
+        self._current_timestep = None
+        self._interrupt = False
+
+        # 2. Define call parameters
+        if prompt is not None and isinstance(prompt, str):
+            batch_size = 1
+        elif prompt is not None and isinstance(prompt, list):
+            batch_size = len(prompt)
+        else:
+            batch_size = prompt_embeds.shape[0]
+
+        device = self._execution_device
+        # 3. Preprocess image
+        if image is not None and not (isinstance(image, torch.Tensor) and image.size(1) == self.latent_channels):
+            if not isinstance(image, list):
+                image = [image]
+            condition_image_sizes = []
+            condition_images = []
+            vae_image_sizes = []
+            vae_images = []
+            for img in image:
+                image_width, image_height = img.size
+                condition_width, condition_height = calculate_dimensions(
+                    CONDITION_IMAGE_SIZE, image_width / image_height
+                )
+                vae_width, vae_height = calculate_dimensions(VAE_IMAGE_SIZE, image_width / image_height)
+                condition_image_sizes.append((condition_width, condition_height))
+                vae_image_sizes.append((vae_width, vae_height))
+                condition_images.append(self.image_processor.resize(img, condition_height, condition_width))
+                vae_images.append(self.image_processor.preprocess(img, vae_height, vae_width).unsqueeze(2))
+
+        has_neg_prompt = negative_prompt is not None or (
+            negative_prompt_embeds is not None and negative_prompt_embeds_mask is not None
+        )
+
+        if true_cfg_scale > 1 and not has_neg_prompt:
+            logger.warning(
+                f"true_cfg_scale is passed as {true_cfg_scale}, but classifier-free guidance is not enabled since no negative_prompt is provided."
+            )
+        elif true_cfg_scale <= 1 and has_neg_prompt:
+            logger.warning(
+                " negative_prompt is passed but classifier-free guidance is not enabled since true_cfg_scale <= 1"
+            )
+
+        do_true_cfg = true_cfg_scale > 1 and has_neg_prompt
+        prompt_embeds, prompt_embeds_mask = self.encode_prompt(
+            image=condition_images,
+            prompt=prompt,
+            prompt_embeds=prompt_embeds,
+            prompt_embeds_mask=prompt_embeds_mask,
+            device=device,
+            num_images_per_prompt=num_images_per_prompt,
+            max_sequence_length=max_sequence_length,
+        )
+        if do_true_cfg:
+            negative_prompt_embeds, negative_prompt_embeds_mask = self.encode_prompt(
+                image=condition_images,
+                prompt=negative_prompt,
+                prompt_embeds=negative_prompt_embeds,
+                prompt_embeds_mask=negative_prompt_embeds_mask,
+                device=device,
+                num_images_per_prompt=num_images_per_prompt,
+                max_sequence_length=max_sequence_length,
+            )
+
+        # 4. Prepare latent variables
+        num_channels_latents = self.transformer.config.in_channels // 4
+        latents, image_latents = self.prepare_latents(
+            vae_images,
+            batch_size * num_images_per_prompt,
+            num_channels_latents,
+            height,
+            width,
+            prompt_embeds.dtype,
+            device,
+            generator,
+            latents,
+        )
+        img_shapes = [
+            [
+                (1, height // self.vae_scale_factor // 2, width // self.vae_scale_factor // 2),
+                *[
+                    (1, vae_height // self.vae_scale_factor // 2, vae_width // self.vae_scale_factor // 2)
+                    for vae_width, vae_height in vae_image_sizes
+                ],
+            ]
+        ] * batch_size
+
+        # 5. Prepare timesteps
+        sigmas = np.linspace(1.0, 1 / num_inference_steps, num_inference_steps) if sigmas is None else sigmas
+        image_seq_len = latents.shape[1]
+        mu = calculate_shift(
+            image_seq_len,
+            self.scheduler.config.get("base_image_seq_len", 256),
+            self.scheduler.config.get("max_image_seq_len", 4096),
+            self.scheduler.config.get("base_shift", 0.5),
+            self.scheduler.config.get("max_shift", 1.15),
+        )
+        timesteps, num_inference_steps = retrieve_timesteps(
+            self.scheduler,
+            num_inference_steps,
+            device,
+            sigmas=sigmas,
+            mu=mu,
+        )
+        num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
+        self._num_timesteps = len(timesteps)
+
+        # handle guidance
+        if self.transformer.config.guidance_embeds and guidance_scale is None:
+            raise ValueError("guidance_scale is required for guidance-distilled model.")
+        elif self.transformer.config.guidance_embeds:
+            guidance = torch.full([1], guidance_scale, device=device, dtype=torch.float32)
+            guidance = guidance.expand(latents.shape[0])
+        elif not self.transformer.config.guidance_embeds and guidance_scale is not None:
+            logger.warning(
+                f"guidance_scale is passed as {guidance_scale}, but ignored since the model is not guidance-distilled."
+            )
+            guidance = None
+        elif not self.transformer.config.guidance_embeds and guidance_scale is None:
+            guidance = None
+
+        if self.attention_kwargs is None:
+            self._attention_kwargs = {}
+
+        txt_seq_lens = prompt_embeds_mask.sum(dim=1).tolist() if prompt_embeds_mask is not None else None
+        
+        image_rotary_emb = self.transformer.pos_embed(img_shapes, txt_seq_lens, device=latents.device)
+        if do_true_cfg:
+            negative_txt_seq_lens = (
+                negative_prompt_embeds_mask.sum(dim=1).tolist()
+                if negative_prompt_embeds_mask is not None
+                else None
+            )
+            uncond_image_rotary_emb = self.transformer.pos_embed(
+                img_shapes, negative_txt_seq_lens, device=latents.device
+            )
+        else:
+            uncond_image_rotary_emb = None
+
+        # 6. Denoising loop
+        self.scheduler.set_begin_index(0)
+        with self.progress_bar(total=num_inference_steps) as progress_bar:
+            for i, t in enumerate(timesteps):
+                if self.interrupt:
+                    continue
+
+                self._current_timestep = t
+
+                latent_model_input = latents
+                if image_latents is not None:
+                    latent_model_input = torch.cat([latents, image_latents], dim=1)
+
+                # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
+                timestep = t.expand(latents.shape[0]).to(latents.dtype)
+                with self.transformer.cache_context("cond"):
+                    noise_pred = self.transformer(
+                        hidden_states=latent_model_input,
+                        timestep=timestep / 1000,
+                        guidance=guidance,
+                        encoder_hidden_states_mask=prompt_embeds_mask,
+                        encoder_hidden_states=prompt_embeds,
+                        image_rotary_emb=image_rotary_emb,
+                        attention_kwargs=self.attention_kwargs,
+                        return_dict=False,
+                    )[0]
+                    noise_pred = noise_pred[:, : latents.size(1)]
+
+                if do_true_cfg:
+                    with self.transformer.cache_context("uncond"):
+                        neg_noise_pred = self.transformer(
+                            hidden_states=latent_model_input,
+                            timestep=timestep / 1000,
+                            guidance=guidance,
+                            encoder_hidden_states_mask=negative_prompt_embeds_mask,
+                            encoder_hidden_states=negative_prompt_embeds,
+                            image_rotary_emb=uncond_image_rotary_emb,
+                            attention_kwargs=self.attention_kwargs,
+                            return_dict=False,
+                        )[0]
+                    neg_noise_pred = neg_noise_pred[:, : latents.size(1)]
+                    comb_pred = neg_noise_pred + true_cfg_scale * (noise_pred - neg_noise_pred)
+
+                    cond_norm = torch.norm(noise_pred, dim=-1, keepdim=True)
+                    noise_norm = torch.norm(comb_pred, dim=-1, keepdim=True)
+                    noise_pred = comb_pred * (cond_norm / noise_norm)
+
+                # compute the previous noisy sample x_t -> x_t-1
+                latents_dtype = latents.dtype
+                latents = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0]
+
+                if latents.dtype != latents_dtype:
+                    if torch.backends.mps.is_available():
+                        # some platforms (eg. apple mps) misbehave due to a pytorch bug: https://github.com/pytorch/pytorch/pull/99272
+                        latents = latents.to(latents_dtype)
+
+                if callback_on_step_end is not None:
+                    callback_kwargs = {}
+                    for k in callback_on_step_end_tensor_inputs:
+                        callback_kwargs[k] = locals()[k]
+                    callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)
+
+                    latents = callback_outputs.pop("latents", latents)
+                    prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
+
+                # call the callback, if provided
+                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
+                    progress_bar.update()
+
+                if XLA_AVAILABLE:
+                    xm.mark_step()
+
+        self._current_timestep = None
+        if output_type == "latent":
+            image = latents
+        else:
+            latents = self._unpack_latents(latents, height, width, self.vae_scale_factor)
+            latents = latents.to(self.vae.dtype)
+            latents_mean = (
+                torch.tensor(self.vae.config.latents_mean)
+                .view(1, self.vae.config.z_dim, 1, 1, 1)
+                .to(latents.device, latents.dtype)
+            )
+            latents_std = 1.0 / torch.tensor(self.vae.config.latents_std).view(1, self.vae.config.z_dim, 1, 1, 1).to(
+                latents.device, latents.dtype
+            )
+            latents = latents / latents_std + latents_mean
+            image = self.vae.decode(latents, return_dict=False)[0][:, :, 0]
+            image = self.image_processor.postprocess(image, output_type=output_type)
+
+        # Offload all models
+        self.maybe_free_model_hooks()
+
+        if not return_dict:
+            return (image,)
+
+        return QwenImagePipelineOutput(images=image)

qwenimage/qwen_fa3_processor.py

@@ -0,0 +1,142 @@
+"""
+Paired with a good language model. Thanks!
+"""
+
+import torch
+from typing import Optional, Tuple
+from diffusers.models.transformers.transformer_qwenimage import apply_rotary_emb_qwen
+
+try:
+    from kernels import get_kernel
+    _k = get_kernel("kernels-community/vllm-flash-attn3")
+    _flash_attn_func = _k.flash_attn_func
+except Exception as e:
+    _flash_attn_func = None
+    _kernels_err = e
+
+
+def _ensure_fa3_available():
+    if _flash_attn_func is None:
+        raise ImportError(
+            "FlashAttention-3 via Hugging Face `kernels` is required. "
+            "Tried `get_kernel('kernels-community/vllm-flash-attn3')` and failed with:\n"
+            f"{_kernels_err}"
+        )
+
+@torch.library.custom_op("flash::flash_attn_func", mutates_args=())
+def flash_attn_func(
+    q: torch.Tensor, k: torch.Tensor, v: torch.Tensor, causal: bool = False
+) -> torch.Tensor:
+    outputs, lse = _flash_attn_func(q, k, v, causal=causal)
+    return outputs
+
+@flash_attn_func.register_fake
+def _(q, k, v, **kwargs):
+    # two outputs:
+    # 1. output: (batch, seq_len, num_heads, head_dim)
+    # 2. softmax_lse: (batch, num_heads, seq_len) with dtype=torch.float32
+    meta_q = torch.empty_like(q).contiguous()
+    return meta_q #, q.new_empty((q.size(0), q.size(2), q.size(1)), dtype=torch.float32)
+
+
+class QwenDoubleStreamAttnProcessorFA3:
+    """
+    FA3-based attention processor for Qwen double-stream architecture.
+    Computes joint attention over concatenated [text, image] streams using vLLM FlashAttention-3
+    accessed via Hugging Face `kernels`.
+
+    Notes / limitations:
+    - General attention masks are not supported here (FA3 path). `is_causal=False` and no arbitrary mask.
+    - Optional windowed attention / sink tokens / softcap can be plumbed through if you use those features.
+    - Expects an available `apply_rotary_emb_qwen` in scope (same as your non-FA3 processor).
+    """
+
+    _attention_backend = "fa3"  # for parity with your other processors, not used internally
+
+    def __init__(self):
+        _ensure_fa3_available()
+
+    @torch.no_grad()
+    def __call__(
+        self,
+        attn,  # Attention module with to_q/to_k/to_v/add_*_proj, norms, to_out, to_add_out, and .heads
+        hidden_states: torch.FloatTensor,                 # (B, S_img, D_model)  image stream
+        encoder_hidden_states: torch.FloatTensor = None,  # (B, S_txt, D_model)  text stream
+        encoder_hidden_states_mask: torch.FloatTensor = None,  # unused in FA3 path
+        attention_mask: Optional[torch.FloatTensor] = None,    # unused in FA3 path
+        image_rotary_emb: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,  # (img_freqs, txt_freqs)
+    ) -> Tuple[torch.FloatTensor, torch.FloatTensor]:
+        if encoder_hidden_states is None:
+            raise ValueError("QwenDoubleStreamAttnProcessorFA3 requires encoder_hidden_states (text stream).")
+        if attention_mask is not None:
+            # FA3 kernel path here does not consume arbitrary masks; fail fast to avoid silent correctness issues.
+            raise NotImplementedError("attention_mask is not supported in this FA3 implementation.")
+
+        _ensure_fa3_available()
+
+        B, S_img, _ = hidden_states.shape
+        S_txt = encoder_hidden_states.shape[1]
+
+        # ---- QKV projections (image/sample stream) ----
+        img_q = attn.to_q(hidden_states)   # (B, S_img, D)
+        img_k = attn.to_k(hidden_states)
+        img_v = attn.to_v(hidden_states)
+
+        # ---- QKV projections (text/context stream) ----
+        txt_q = attn.add_q_proj(encoder_hidden_states)  # (B, S_txt, D)
+        txt_k = attn.add_k_proj(encoder_hidden_states)
+        txt_v = attn.add_v_proj(encoder_hidden_states)
+
+        # ---- Reshape to (B, S, H, D_h) ----
+        H = attn.heads
+        img_q = img_q.unflatten(-1, (H, -1))
+        img_k = img_k.unflatten(-1, (H, -1))
+        img_v = img_v.unflatten(-1, (H, -1))
+
+        txt_q = txt_q.unflatten(-1, (H, -1))
+        txt_k = txt_k.unflatten(-1, (H, -1))
+        txt_v = txt_v.unflatten(-1, (H, -1))
+
+        # ---- Q/K normalization (per your module contract) ----
+        if getattr(attn, "norm_q", None) is not None:
+            img_q = attn.norm_q(img_q)
+        if getattr(attn, "norm_k", None) is not None:
+            img_k = attn.norm_k(img_k)
+        if getattr(attn, "norm_added_q", None) is not None:
+            txt_q = attn.norm_added_q(txt_q)
+        if getattr(attn, "norm_added_k", None) is not None:
+            txt_k = attn.norm_added_k(txt_k)
+
+        # ---- RoPE (Qwen variant) ----
+        if image_rotary_emb is not None:
+            img_freqs, txt_freqs = image_rotary_emb
+            # expects tensors shaped (B, S, H, D_h)
+            img_q = apply_rotary_emb_qwen(img_q, img_freqs, use_real=False)
+            img_k = apply_rotary_emb_qwen(img_k, img_freqs, use_real=False)
+            txt_q = apply_rotary_emb_qwen(txt_q, txt_freqs, use_real=False)
+            txt_k = apply_rotary_emb_qwen(txt_k, txt_freqs, use_real=False)
+
+        # ---- Joint attention over [text, image] along sequence axis ----
+        # Shapes: (B, S_total, H, D_h)
+        q = torch.cat([txt_q, img_q], dim=1)
+        k = torch.cat([txt_k, img_k], dim=1)
+        v = torch.cat([txt_v, img_v], dim=1)
+
+        # FlashAttention-3 path expects (B, S, H, D_h) and returns (out, softmax_lse)
+        out = flash_attn_func(q, k, v, causal=False)  # out: (B, S_total, H, D_h)
+
+        # ---- Back to (B, S, D_model) ----
+        out = out.flatten(2, 3).to(q.dtype)
+
+        # Split back to text / image segments
+        txt_attn_out = out[:, :S_txt, :]
+        img_attn_out = out[:, S_txt:, :]
+
+        # ---- Output projections ----
+        img_attn_out = attn.to_out[0](img_attn_out)
+        if len(attn.to_out) > 1:
+            img_attn_out = attn.to_out[1](img_attn_out)  # dropout if present
+
+        txt_attn_out = attn.to_add_out(txt_attn_out)
+
+        return img_attn_out, txt_attn_out

qwenimage/transformer_qwenimage.py

@@ -0,0 +1,642 @@
+# Copyright 2025 Qwen-Image Team, The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import functools
+import math
+from typing import Any, Dict, List, Optional, Tuple, Union
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from diffusers.configuration_utils import ConfigMixin, register_to_config
+from diffusers.loaders import FromOriginalModelMixin, PeftAdapterMixin
+from diffusers.utils import USE_PEFT_BACKEND, logging, scale_lora_layers, unscale_lora_layers
+from diffusers.utils.torch_utils import maybe_allow_in_graph
+from diffusers.models.attention import FeedForward, AttentionMixin
+from diffusers.models.attention_dispatch import dispatch_attention_fn
+from diffusers.models.attention_processor import Attention
+from diffusers.models.cache_utils import CacheMixin
+from diffusers.models.embeddings import TimestepEmbedding, Timesteps
+from diffusers.models.modeling_outputs import Transformer2DModelOutput
+from diffusers.models.modeling_utils import ModelMixin
+from diffusers.models.normalization import AdaLayerNormContinuous, RMSNorm
+
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+def get_timestep_embedding(
+    timesteps: torch.Tensor,
+    embedding_dim: int,
+    flip_sin_to_cos: bool = False,
+    downscale_freq_shift: float = 1,
+    scale: float = 1,
+    max_period: int = 10000,
+) -> torch.Tensor:
+    """
+    This matches the implementation in Denoising Diffusion Probabilistic Models: Create sinusoidal timestep embeddings.
+
+    Args
+        timesteps (torch.Tensor):
+            a 1-D Tensor of N indices, one per batch element. These may be fractional.
+        embedding_dim (int):
+            the dimension of the output.
+        flip_sin_to_cos (bool):
+            Whether the embedding order should be `cos, sin` (if True) or `sin, cos` (if False)
+        downscale_freq_shift (float):
+            Controls the delta between frequencies between dimensions
+        scale (float):
+            Scaling factor applied to the embeddings.
+        max_period (int):
+            Controls the maximum frequency of the embeddings
+    Returns
+        torch.Tensor: an [N x dim] Tensor of positional embeddings.
+    """
+    assert len(timesteps.shape) == 1, "Timesteps should be a 1d-array"
+
+    half_dim = embedding_dim // 2
+    exponent = -math.log(max_period) * torch.arange(
+        start=0, end=half_dim, dtype=torch.float32, device=timesteps.device
+    )
+    exponent = exponent / (half_dim - downscale_freq_shift)
+
+    emb = torch.exp(exponent).to(timesteps.dtype)
+    emb = timesteps[:, None].float() * emb[None, :]
+
+    # scale embeddings
+    emb = scale * emb
+
+    # concat sine and cosine embeddings
+    emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=-1)
+
+    # flip sine and cosine embeddings
+    if flip_sin_to_cos:
+        emb = torch.cat([emb[:, half_dim:], emb[:, :half_dim]], dim=-1)
+
+    # zero pad
+    if embedding_dim % 2 == 1:
+        emb = torch.nn.functional.pad(emb, (0, 1, 0, 0))
+    return emb
+
+
+def apply_rotary_emb_qwen(
+    x: torch.Tensor,
+    freqs_cis: Union[torch.Tensor, Tuple[torch.Tensor]],
+    use_real: bool = True,
+    use_real_unbind_dim: int = -1,
+) -> Tuple[torch.Tensor, torch.Tensor]:
+    """
+    Apply rotary embeddings to input tensors using the given frequency tensor. This function applies rotary embeddings
+    to the given query or key 'x' tensors using the provided frequency tensor 'freqs_cis'. The input tensors are
+    reshaped as complex numbers, and the frequency tensor is reshaped for broadcasting compatibility. The resulting
+    tensors contain rotary embeddings and are returned as real tensors.
+
+    Args:
+        x (`torch.Tensor`):
+            Query or key tensor to apply rotary embeddings. [B, S, H, D] xk (torch.Tensor): Key tensor to apply
+        freqs_cis (`Tuple[torch.Tensor]`): Precomputed frequency tensor for complex exponentials. ([S, D], [S, D],)
+
+    Returns:
+        Tuple[torch.Tensor, torch.Tensor]: Tuple of modified query tensor and key tensor with rotary embeddings.
+    """
+    if use_real:
+        cos, sin = freqs_cis  # [S, D]
+        cos = cos[None, None]
+        sin = sin[None, None]
+        cos, sin = cos.to(x.device), sin.to(x.device)
+
+        if use_real_unbind_dim == -1:
+            # Used for flux, cogvideox, hunyuan-dit
+            x_real, x_imag = x.reshape(*x.shape[:-1], -1, 2).unbind(-1)  # [B, S, H, D//2]
+            x_rotated = torch.stack([-x_imag, x_real], dim=-1).flatten(3)
+        elif use_real_unbind_dim == -2:
+            # Used for Stable Audio, OmniGen, CogView4 and Cosmos
+            x_real, x_imag = x.reshape(*x.shape[:-1], 2, -1).unbind(-2)  # [B, S, H, D//2]
+            x_rotated = torch.cat([-x_imag, x_real], dim=-1)
+        else:
+            raise ValueError(f"`use_real_unbind_dim={use_real_unbind_dim}` but should be -1 or -2.")
+
+        out = (x.float() * cos + x_rotated.float() * sin).to(x.dtype)
+
+        return out
+    else:
+        x_rotated = torch.view_as_complex(x.float().reshape(*x.shape[:-1], -1, 2))
+        freqs_cis = freqs_cis.unsqueeze(1)
+        x_out = torch.view_as_real(x_rotated * freqs_cis).flatten(3)
+
+        return x_out.type_as(x)
+
+
+class QwenTimestepProjEmbeddings(nn.Module):
+    def __init__(self, embedding_dim):
+        super().__init__()
+
+        self.time_proj = Timesteps(num_channels=256, flip_sin_to_cos=True, downscale_freq_shift=0, scale=1000)
+        self.timestep_embedder = TimestepEmbedding(in_channels=256, time_embed_dim=embedding_dim)
+
+    def forward(self, timestep, hidden_states):
+        timesteps_proj = self.time_proj(timestep)
+        timesteps_emb = self.timestep_embedder(timesteps_proj.to(dtype=hidden_states.dtype))  # (N, D)
+
+        conditioning = timesteps_emb
+
+        return conditioning
+
+
+class QwenEmbedRope(nn.Module):
+    def __init__(self, theta: int, axes_dim: List[int], scale_rope=False):
+        super().__init__()
+        self.theta = theta
+        self.axes_dim = axes_dim
+        pos_index = torch.arange(4096)
+        neg_index = torch.arange(4096).flip(0) * -1 - 1
+        self.pos_freqs = torch.cat(
+            [
+                self.rope_params(pos_index, self.axes_dim[0], self.theta),
+                self.rope_params(pos_index, self.axes_dim[1], self.theta),
+                self.rope_params(pos_index, self.axes_dim[2], self.theta),
+            ],
+            dim=1,
+        )
+        self.neg_freqs = torch.cat(
+            [
+                self.rope_params(neg_index, self.axes_dim[0], self.theta),
+                self.rope_params(neg_index, self.axes_dim[1], self.theta),
+                self.rope_params(neg_index, self.axes_dim[2], self.theta),
+            ],
+            dim=1,
+        )
+        self.rope_cache = {}
+
+        # DO NOT USING REGISTER BUFFER HERE, IT WILL CAUSE COMPLEX NUMBERS LOSE ITS IMAGINARY PART
+        self.scale_rope = scale_rope
+
+    def rope_params(self, index, dim, theta=10000):
+        """
+        Args:
+            index: [0, 1, 2, 3] 1D Tensor representing the position index of the token
+        """
+        assert dim % 2 == 0
+        freqs = torch.outer(index, 1.0 / torch.pow(theta, torch.arange(0, dim, 2).to(torch.float32).div(dim)))
+        freqs = torch.polar(torch.ones_like(freqs), freqs)
+        return freqs
+
+    def forward(self, video_fhw, txt_seq_lens, device):
+        """
+        Args: video_fhw: [frame, height, width] a list of 3 integers representing the shape of the video Args:
+        txt_length: [bs] a list of 1 integers representing the length of the text
+        """
+        if self.pos_freqs.device != device:
+            self.pos_freqs = self.pos_freqs.to(device)
+            self.neg_freqs = self.neg_freqs.to(device)
+
+        if isinstance(video_fhw, list):
+            video_fhw = video_fhw[0]
+        if not isinstance(video_fhw, list):
+            video_fhw = [video_fhw]
+
+        vid_freqs = []
+        max_vid_index = 0
+        for idx, fhw in enumerate(video_fhw):
+            frame, height, width = fhw
+            rope_key = f"{idx}_{height}_{width}"
+
+            if not torch.compiler.is_compiling():
+                if rope_key not in self.rope_cache:
+                    self.rope_cache[rope_key] = self._compute_video_freqs(frame, height, width, idx)
+                video_freq = self.rope_cache[rope_key]
+            else:
+                video_freq = self._compute_video_freqs(frame, height, width, idx)
+            video_freq = video_freq.to(device)
+            vid_freqs.append(video_freq)
+
+            if self.scale_rope:
+                max_vid_index = max(height // 2, width // 2, max_vid_index)
+            else:
+                max_vid_index = max(height, width, max_vid_index)
+
+        max_len = max(txt_seq_lens)
+        txt_freqs = self.pos_freqs[max_vid_index : max_vid_index + max_len, ...]
+        vid_freqs = torch.cat(vid_freqs, dim=0)
+
+        return vid_freqs, txt_freqs
+
+    @functools.lru_cache(maxsize=None)
+    def _compute_video_freqs(self, frame, height, width, idx=0):
+        seq_lens = frame * height * width
+        freqs_pos = self.pos_freqs.split([x // 2 for x in self.axes_dim], dim=1)
+        freqs_neg = self.neg_freqs.split([x // 2 for x in self.axes_dim], dim=1)
+
+        freqs_frame = freqs_pos[0][idx : idx + frame].view(frame, 1, 1, -1).expand(frame, height, width, -1)
+        if self.scale_rope:
+            freqs_height = torch.cat([freqs_neg[1][-(height - height // 2) :], freqs_pos[1][: height // 2]], dim=0)
+            freqs_height = freqs_height.view(1, height, 1, -1).expand(frame, height, width, -1)
+            freqs_width = torch.cat([freqs_neg[2][-(width - width // 2) :], freqs_pos[2][: width // 2]], dim=0)
+            freqs_width = freqs_width.view(1, 1, width, -1).expand(frame, height, width, -1)
+        else:
+            freqs_height = freqs_pos[1][:height].view(1, height, 1, -1).expand(frame, height, width, -1)
+            freqs_width = freqs_pos[2][:width].view(1, 1, width, -1).expand(frame, height, width, -1)
+
+        freqs = torch.cat([freqs_frame, freqs_height, freqs_width], dim=-1).reshape(seq_lens, -1)
+        return freqs.clone().contiguous()
+
+
+class QwenDoubleStreamAttnProcessor2_0:
+    """
+    Attention processor for Qwen double-stream architecture, matching DoubleStreamLayerMegatron logic. This processor
+    implements joint attention computation where text and image streams are processed together.
+    """
+
+    _attention_backend = None
+
+    def __init__(self):
+        if not hasattr(F, "scaled_dot_product_attention"):
+            raise ImportError(
+                "QwenDoubleStreamAttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0."
+            )
+
+    def __call__(
+        self,
+        attn: Attention,
+        hidden_states: torch.FloatTensor,  # Image stream
+        encoder_hidden_states: torch.FloatTensor = None,  # Text stream
+        encoder_hidden_states_mask: torch.FloatTensor = None,
+        attention_mask: Optional[torch.FloatTensor] = None,
+        image_rotary_emb: Optional[torch.Tensor] = None,
+    ) -> torch.FloatTensor:
+        if encoder_hidden_states is None:
+            raise ValueError("QwenDoubleStreamAttnProcessor2_0 requires encoder_hidden_states (text stream)")
+
+        seq_txt = encoder_hidden_states.shape[1]
+
+        # Compute QKV for image stream (sample projections)
+        img_query = attn.to_q(hidden_states)
+        img_key = attn.to_k(hidden_states)
+        img_value = attn.to_v(hidden_states)
+
+        # Compute QKV for text stream (context projections)
+        txt_query = attn.add_q_proj(encoder_hidden_states)
+        txt_key = attn.add_k_proj(encoder_hidden_states)
+        txt_value = attn.add_v_proj(encoder_hidden_states)
+
+        # Reshape for multi-head attention
+        img_query = img_query.unflatten(-1, (attn.heads, -1))
+        img_key = img_key.unflatten(-1, (attn.heads, -1))
+        img_value = img_value.unflatten(-1, (attn.heads, -1))
+
+        txt_query = txt_query.unflatten(-1, (attn.heads, -1))
+        txt_key = txt_key.unflatten(-1, (attn.heads, -1))
+        txt_value = txt_value.unflatten(-1, (attn.heads, -1))
+
+        # Apply QK normalization
+        if attn.norm_q is not None:
+            img_query = attn.norm_q(img_query)
+        if attn.norm_k is not None:
+            img_key = attn.norm_k(img_key)
+        if attn.norm_added_q is not None:
+            txt_query = attn.norm_added_q(txt_query)
+        if attn.norm_added_k is not None:
+            txt_key = attn.norm_added_k(txt_key)
+
+        # Apply RoPE
+        if image_rotary_emb is not None:
+            img_freqs, txt_freqs = image_rotary_emb
+            img_query = apply_rotary_emb_qwen(img_query, img_freqs, use_real=False)
+            img_key = apply_rotary_emb_qwen(img_key, img_freqs, use_real=False)
+            txt_query = apply_rotary_emb_qwen(txt_query, txt_freqs, use_real=False)
+            txt_key = apply_rotary_emb_qwen(txt_key, txt_freqs, use_real=False)
+
+        # Concatenate for joint attention
+        # Order: [text, image]
+        joint_query = torch.cat([txt_query, img_query], dim=1)
+        joint_key = torch.cat([txt_key, img_key], dim=1)
+        joint_value = torch.cat([txt_value, img_value], dim=1)
+
+        # Compute joint attention
+        joint_hidden_states = dispatch_attention_fn(
+            joint_query,
+            joint_key,
+            joint_value,
+            attn_mask=attention_mask,
+            dropout_p=0.0,
+            is_causal=False,
+            backend=self._attention_backend,
+        )
+
+        # Reshape back
+        joint_hidden_states = joint_hidden_states.flatten(2, 3)
+        joint_hidden_states = joint_hidden_states.to(joint_query.dtype)
+
+        # Split attention outputs back
+        txt_attn_output = joint_hidden_states[:, :seq_txt, :]  # Text part
+        img_attn_output = joint_hidden_states[:, seq_txt:, :]  # Image part
+
+        # Apply output projections
+        img_attn_output = attn.to_out[0](img_attn_output)
+        if len(attn.to_out) > 1:
+            img_attn_output = attn.to_out[1](img_attn_output)  # dropout
+
+        txt_attn_output = attn.to_add_out(txt_attn_output)
+
+        return img_attn_output, txt_attn_output
+
+
+@maybe_allow_in_graph
+class QwenImageTransformerBlock(nn.Module):
+    def __init__(
+        self, dim: int, num_attention_heads: int, attention_head_dim: int, qk_norm: str = "rms_norm", eps: float = 1e-6
+    ):
+        super().__init__()
+
+        self.dim = dim
+        self.num_attention_heads = num_attention_heads
+        self.attention_head_dim = attention_head_dim
+
+        # Image processing modules
+        self.img_mod = nn.Sequential(
+            nn.SiLU(),
+            nn.Linear(dim, 6 * dim, bias=True),  # For scale, shift, gate for norm1 and norm2
+        )
+        self.img_norm1 = nn.LayerNorm(dim, elementwise_affine=False, eps=eps)
+        self.attn = Attention(
+            query_dim=dim,
+            cross_attention_dim=None,  # Enable cross attention for joint computation
+            added_kv_proj_dim=dim,  # Enable added KV projections for text stream
+            dim_head=attention_head_dim,
+            heads=num_attention_heads,
+            out_dim=dim,
+            context_pre_only=False,
+            bias=True,
+            processor=QwenDoubleStreamAttnProcessor2_0(),
+            qk_norm=qk_norm,
+            eps=eps,
+        )
+        self.img_norm2 = nn.LayerNorm(dim, elementwise_affine=False, eps=eps)
+        self.img_mlp = FeedForward(dim=dim, dim_out=dim, activation_fn="gelu-approximate")
+
+        # Text processing modules
+        self.txt_mod = nn.Sequential(
+            nn.SiLU(),
+            nn.Linear(dim, 6 * dim, bias=True),  # For scale, shift, gate for norm1 and norm2
+        )
+        self.txt_norm1 = nn.LayerNorm(dim, elementwise_affine=False, eps=eps)
+        # Text doesn't need separate attention - it's handled by img_attn joint computation
+        self.txt_norm2 = nn.LayerNorm(dim, elementwise_affine=False, eps=eps)
+        self.txt_mlp = FeedForward(dim=dim, dim_out=dim, activation_fn="gelu-approximate")
+
+    def _modulate(self, x, mod_params):
+        """Apply modulation to input tensor"""
+        shift, scale, gate = mod_params.chunk(3, dim=-1)
+        return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1), gate.unsqueeze(1)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        encoder_hidden_states: torch.Tensor,
+        encoder_hidden_states_mask: torch.Tensor,
+        temb: torch.Tensor,
+        image_rotary_emb: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
+        joint_attention_kwargs: Optional[Dict[str, Any]] = None,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        # Get modulation parameters for both streams
+        img_mod_params = self.img_mod(temb)  # [B, 6*dim]
+        txt_mod_params = self.txt_mod(temb)  # [B, 6*dim]
+
+        # Split modulation parameters for norm1 and norm2
+        img_mod1, img_mod2 = img_mod_params.chunk(2, dim=-1)  # Each [B, 3*dim]
+        txt_mod1, txt_mod2 = txt_mod_params.chunk(2, dim=-1)  # Each [B, 3*dim]
+
+        # Process image stream - norm1 + modulation
+        img_normed = self.img_norm1(hidden_states)
+        img_modulated, img_gate1 = self._modulate(img_normed, img_mod1)
+
+        # Process text stream - norm1 + modulation
+        txt_normed = self.txt_norm1(encoder_hidden_states)
+        txt_modulated, txt_gate1 = self._modulate(txt_normed, txt_mod1)
+
+        # Use QwenAttnProcessor2_0 for joint attention computation
+        # This directly implements the DoubleStreamLayerMegatron logic:
+        # 1. Computes QKV for both streams
+        # 2. Applies QK normalization and RoPE
+        # 3. Concatenates and runs joint attention
+        # 4. Splits results back to separate streams
+        joint_attention_kwargs = joint_attention_kwargs or {}
+        attn_output = self.attn(
+            hidden_states=img_modulated,  # Image stream (will be processed as "sample")
+            encoder_hidden_states=txt_modulated,  # Text stream (will be processed as "context")
+            encoder_hidden_states_mask=encoder_hidden_states_mask,
+            image_rotary_emb=image_rotary_emb,
+            **joint_attention_kwargs,
+        )
+
+        # QwenAttnProcessor2_0 returns (img_output, txt_output) when encoder_hidden_states is provided
+        img_attn_output, txt_attn_output = attn_output
+
+        # Apply attention gates and add residual (like in Megatron)
+        hidden_states = hidden_states + img_gate1 * img_attn_output
+        encoder_hidden_states = encoder_hidden_states + txt_gate1 * txt_attn_output
+
+        # Process image stream - norm2 + MLP
+        img_normed2 = self.img_norm2(hidden_states)
+        img_modulated2, img_gate2 = self._modulate(img_normed2, img_mod2)
+        img_mlp_output = self.img_mlp(img_modulated2)
+        hidden_states = hidden_states + img_gate2 * img_mlp_output
+
+        # Process text stream - norm2 + MLP
+        txt_normed2 = self.txt_norm2(encoder_hidden_states)
+        txt_modulated2, txt_gate2 = self._modulate(txt_normed2, txt_mod2)
+        txt_mlp_output = self.txt_mlp(txt_modulated2)
+        encoder_hidden_states = encoder_hidden_states + txt_gate2 * txt_mlp_output
+
+        # Clip to prevent overflow for fp16
+        if encoder_hidden_states.dtype == torch.float16:
+            encoder_hidden_states = encoder_hidden_states.clip(-65504, 65504)
+        if hidden_states.dtype == torch.float16:
+            hidden_states = hidden_states.clip(-65504, 65504)
+
+        return encoder_hidden_states, hidden_states
+
+
+class QwenImageTransformer2DModel(ModelMixin, ConfigMixin, PeftAdapterMixin, FromOriginalModelMixin, CacheMixin, AttentionMixin):
+    """
+    The Transformer model introduced in Qwen.
+
+    Args:
+        patch_size (`int`, defaults to `2`):
+            Patch size to turn the input data into small patches.
+        in_channels (`int`, defaults to `64`):
+            The number of channels in the input.
+        out_channels (`int`, *optional*, defaults to `None`):
+            The number of channels in the output. If not specified, it defaults to `in_channels`.
+        num_layers (`int`, defaults to `60`):
+            The number of layers of dual stream DiT blocks to use.
+        attention_head_dim (`int`, defaults to `128`):
+            The number of dimensions to use for each attention head.
+        num_attention_heads (`int`, defaults to `24`):
+            The number of attention heads to use.
+        joint_attention_dim (`int`, defaults to `3584`):
+            The number of dimensions to use for the joint attention (embedding/channel dimension of
+            `encoder_hidden_states`).
+        guidance_embeds (`bool`, defaults to `False`):
+            Whether to use guidance embeddings for guidance-distilled variant of the model.
+        axes_dims_rope (`Tuple[int]`, defaults to `(16, 56, 56)`):
+            The dimensions to use for the rotary positional embeddings.
+    """
+
+    _supports_gradient_checkpointing = True
+    _no_split_modules = ["QwenImageTransformerBlock"]
+    _skip_layerwise_casting_patterns = ["pos_embed", "norm"]
+    _repeated_blocks = ["QwenImageTransformerBlock"]
+
+    @register_to_config
+    def __init__(
+        self,
+        patch_size: int = 2,
+        in_channels: int = 64,
+        out_channels: Optional[int] = 16,
+        num_layers: int = 60,
+        attention_head_dim: int = 128,
+        num_attention_heads: int = 24,
+        joint_attention_dim: int = 3584,
+        guidance_embeds: bool = False,  # TODO: this should probably be removed
+        axes_dims_rope: Tuple[int, int, int] = (16, 56, 56),
+    ):
+        super().__init__()
+        self.out_channels = out_channels or in_channels
+        self.inner_dim = num_attention_heads * attention_head_dim
+
+        self.pos_embed = QwenEmbedRope(theta=10000, axes_dim=list(axes_dims_rope), scale_rope=True)
+
+        self.time_text_embed = QwenTimestepProjEmbeddings(embedding_dim=self.inner_dim)
+
+        self.txt_norm = RMSNorm(joint_attention_dim, eps=1e-6)
+
+        self.img_in = nn.Linear(in_channels, self.inner_dim)
+        self.txt_in = nn.Linear(joint_attention_dim, self.inner_dim)
+
+        self.transformer_blocks = nn.ModuleList(
+            [
+                QwenImageTransformerBlock(
+                    dim=self.inner_dim,
+                    num_attention_heads=num_attention_heads,
+                    attention_head_dim=attention_head_dim,
+                )
+                for _ in range(num_layers)
+            ]
+        )
+
+        self.norm_out = AdaLayerNormContinuous(self.inner_dim, self.inner_dim, elementwise_affine=False, eps=1e-6)
+        self.proj_out = nn.Linear(self.inner_dim, patch_size * patch_size * self.out_channels, bias=True)
+
+        self.gradient_checkpointing = False
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        encoder_hidden_states: torch.Tensor = None,
+        encoder_hidden_states_mask: torch.Tensor = None,
+        timestep: torch.LongTensor = None,
+        image_rotary_emb: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
+        guidance: torch.Tensor = None,  # TODO: this should probably be removed
+        attention_kwargs: Optional[Dict[str, Any]] = None,
+        return_dict: bool = True,
+    ) -> Union[torch.Tensor, Transformer2DModelOutput]:
+        """
+        The [`QwenTransformer2DModel`] forward method.
+
+        Args:
+            hidden_states (`torch.Tensor` of shape `(batch_size, image_sequence_length, in_channels)`):
+                Input `hidden_states`.
+            encoder_hidden_states (`torch.Tensor` of shape `(batch_size, text_sequence_length, joint_attention_dim)`):
+                Conditional embeddings (embeddings computed from the input conditions such as prompts) to use.
+            encoder_hidden_states_mask (`torch.Tensor` of shape `(batch_size, text_sequence_length)`):
+                Mask of the input conditions.
+            timestep ( `torch.LongTensor`):
+                Used to indicate denoising step.
+            attention_kwargs (`dict`, *optional*):
+                A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
+                `self.processor` in
+                [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~models.transformer_2d.Transformer2DModelOutput`] instead of a plain
+                tuple.
+
+        Returns:
+            If `return_dict` is True, an [`~models.transformer_2d.Transformer2DModelOutput`] is returned, otherwise a
+            `tuple` where the first element is the sample tensor.
+        """
+        if attention_kwargs is not None:
+            attention_kwargs = attention_kwargs.copy()
+            lora_scale = attention_kwargs.pop("scale", 1.0)
+        else:
+            lora_scale = 1.0
+
+        if USE_PEFT_BACKEND:
+            # weight the lora layers by setting `lora_scale` for each PEFT layer
+            scale_lora_layers(self, lora_scale)
+        else:
+            if attention_kwargs is not None and attention_kwargs.get("scale", None) is not None:
+                logger.warning(
+                    "Passing `scale` via `joint_attention_kwargs` when not using the PEFT backend is ineffective."
+                )
+
+        hidden_states = self.img_in(hidden_states)
+
+        timestep = timestep.to(hidden_states.dtype)
+        encoder_hidden_states = self.txt_norm(encoder_hidden_states)
+        encoder_hidden_states = self.txt_in(encoder_hidden_states)
+
+        if guidance is not None:
+            guidance = guidance.to(hidden_states.dtype) * 1000
+
+        temb = (
+            self.time_text_embed(timestep, hidden_states)
+            if guidance is None
+            else self.time_text_embed(timestep, guidance, hidden_states)
+        )
+
+        for index_block, block in enumerate(self.transformer_blocks):
+            if torch.is_grad_enabled() and self.gradient_checkpointing:
+                encoder_hidden_states, hidden_states = self._gradient_checkpointing_func(
+                    block,
+                    hidden_states,
+                    encoder_hidden_states,
+                    encoder_hidden_states_mask,
+                    temb,
+                    image_rotary_emb,
+                )
+
+            else:
+                encoder_hidden_states, hidden_states = block(
+                    hidden_states=hidden_states,
+                    encoder_hidden_states=encoder_hidden_states,
+                    encoder_hidden_states_mask=encoder_hidden_states_mask,
+                    temb=temb,
+                    image_rotary_emb=image_rotary_emb,
+                    joint_attention_kwargs=attention_kwargs,
+                )
+
+        # Use only the image part (hidden_states) from the dual-stream blocks
+        hidden_states = self.norm_out(hidden_states, temb)
+        output = self.proj_out(hidden_states)
+
+        if USE_PEFT_BACKEND:
+            # remove `lora_scale` from each PEFT layer
+            unscale_lora_layers(self, lora_scale)
+
+        if not return_dict:
+            return (output,)
+
+        return Transformer2DModelOutput(sample=output)

requirements.txt

@@ -0,0 +1,14 @@
+git+https://github.com/huggingface/accelerate.git
+git+https://github.com/huggingface/diffusers.git
+git+https://github.com/huggingface/peft.git
+transformers==4.57.3
+huggingface_hub
+sentencepiece
+torchvision
+kernels
+spaces
+hf_xet
+gradio #gradio@6
+torch==2.8.0
+numpy
+av