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This custom node helps to conveniently enhance images through Sampler, Upscaler, Mask, and more.\nNOTE:You should install [a/ComfyUI-Impact-Pack](https://github.com/ltdrdata/ComfyUI-Impact-Pack). Many optimizations are built upon the foundation of ComfyUI-Impact-Pack.", "downloads": 0, "github_stars": 12, "icon": "", "id": "comfyui-hiforce-plugin", "latest_version": { "version": "nightly" }, "license": "{}", "name": "Comfyui HiFORCE Plugin", "publisher": { "createdAt": "2025-03-07T03:01:54.745558Z", "description": "", "id": "admin-11338bd3-f081-43cf-b3f9-295c829826f7", "logo": "", "members": [], "name": "Unclaimed", "source_code_repo": "", "status": "PublisherStatusActive", "support": "", "website": "" }, "rating": 0, "repository": "https://github.com/hiforce/comfyui-hiforce-plugin", "search_ranking": 5, "status": "NodeStatusActive", "status_detail": "", "supported_accelerators": null, "supported_comfyui_frontend_version": "", "supported_comfyui_version": "", "supported_os": null, "tags": [] }, { "author": "", "banner_url": "", "category": "", "created_at": "2026-07-12T00:05:44.580217Z", "description": "Focused local llama-server lifecycle, text, vision, and structured generation nodes for ComfyUI", "downloads": 168, "github_stars": 3, "icon": "", "id": "comfyui-llamacpp", "latest_version": { "changelog": "# ComfyUI llama.cpp Suite 0.3.0\n\nVersion 0.3.0 is the accepted baseline of the broad runtime, protocol, node, frontend, compatibility, packaging, and documentation refactor.\n\n## Highlights\n\n- Safe ownership and lifecycle management for locally launched `llama-server` processes, including deterministic stop escalation and native ComfyUI unload integration.\n- Current llama.cpp router support with explicit model load/unload, model discovery, tokenization, model information, and structured-output helpers.\n- Reliable streaming generation with cancellation, deadlines, partial-output metadata, UTF-8 handling, and bounded redacted server logs.\n- 17 nodes covering server launch and attachment, text and vision generation, prompt templates, runtime release, token count, model information, and structured output.\n- Compatibility coverage for released 0.2.1 node IDs, sockets, defaults, outputs, and saved workflows.\n- Python 3.10 through 3.14 Linux CI, Python 3.13 Windows CI, locked dependencies, Registry metadata, package validation, and built wheel/sdist artifacts.\n\n## Install from source\n\nClone the stable `master` branch into `ComfyUI/custom_nodes`:\n\n```bash\ngit clone https://github.com/Setmaster/comfyui-llamacpp.git\ncd comfyui-llamacpp\npython -m pip install -r requirements.txt\n```\n\nExisting source installations can update with:\n\n```bash\ngit switch master\ngit pull --ff-only origin master\npython -m pip install -r requirements.txt\n```\n\nThe node pack controls a separate local `llama-server` executable. 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"support": "", "website": "" }, "rating": 0, "repository": "https://github.com/JanisBe/ComfyUI_FileGallery", "search_ranking": 5, "status": "NodeStatusActive", "status_detail": "", "supported_accelerators": [], "supported_comfyui_frontend_version": "", "supported_comfyui_version": "", "supported_os": [], "tags": [] }, { "author": "", "banner_url": "", "category": "", "created_at": "2026-06-01T17:37:07.765694Z", "description": "Run local ComfyUI T2I/I2I/T2V/I2V workflows from Telegram via @FilexaAIBot, using your PC resources for image and video generation.", "downloads": 65, "github_stars": 3, "icon": "https://raw.githubusercontent.com/Teutonick/Filexa2ComfyUI/main/web/filexa.png", "id": "filexa2comfyui", "latest_version": { "changelog": "- Excluded README demo GIFs from the Comfy Registry package to keep published installs lean while\n leaving GitHub documentation media unchanged.", "createdAt": "2026-06-04T09:36:51.168709Z", "dependencies": [], "deprecated": false, "downloadUrl": "", 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Independent" ], "tags": [] }, { "author": "", "banner_url": "https://raw.githubusercontent.com/no8d/ComfyUI-NO8D-controls/main/docs/images/no8d-banner-registry.jpg", "category": "", "created_at": "2026-07-08T09:26:11.901686Z", "description": "NO8D custom nodes for LoRA stacking, prompt generation, image loading, generation and masking, style selection, image comparison and composition, and dataset saving in ComfyUI.", "downloads": 699, "github_stars": 130, "icon": "https://raw.githubusercontent.com/no8d/ComfyUI-NO8D-controls/main/docs/images/no8d-icon.png", "id": "no8d-controls", "latest_version": { "changelog": "# Changelog\n\nAll notable changes to ComfyUI-NO8D-controls will be documented in this file.\n\nThe project follows [Semantic Versioning](https://semver.org/).\n\n## [Unreleased]\n\n## [1.4.1] - 2026-08-03\n\n### Fixed\n\n- Kept A/B right-click image actions working when ComfyUI exposes graph coordinates through array-like point objects.\n- Kept localized combo labels separate from the canonical values saved in workflows and sent to node backends.\n\n## [1.4.0] - 2026-08-02\n\n### Added\n\n- Added `NO8D-Match Krea2 Reference Latents` to align Ostris Krea2 reference-latent grids with a target latent.\n\n### Changed\n\n- Updated `NO8D-Generate` reference routing, local inpaint/outpaint construction, canvas interaction, and downstream output handling.\n- Updated `NO8D-Load-images` selection behavior so a loaded image remains selected while browsing, deleting, or opening originals.\n\n### Fixed\n\n- Prevented disabled Krea2 Edit LoRAs from disabling native Krea2 reference conditioning or misrouting Flux2 Klein edits.\n- Kept Krea2 outpaint references aligned to the local edit canvas and preserved the complete source image when changing canvas ratios.\n\n## [1.3.2] - 2026-08-01\n\n### Changed\n\n- Updated native Ollama requests to disable thinking and pass the visual-analysis JSON schema through Ollama's `format` field.\n\n### Fixed\n\n- Added detailed diagnostics when Ollama returns an empty final response, including the model, stop reason, evaluated token count, and thinking-output length.\n\n## [1.3.1] - 2026-07-29\n\n### Added\n\n- Added optional automatic downstream output, manual current-image output, bounded image history, and reset controls to `NO8D-Generate`.\n\n### Changed\n\n- Reworked the `NO8D-Generate` canvas tools, mask property controls, feather preview, source transforms, and edit-state handling.\n- Updated both bundled example workflows for the latest node controls and graph layout.\n\n### Fixed\n\n- Corrected Krea2 Identity Edit inpaint and outpaint reference construction to prevent unchanged content reconstruction and duplicate structures around feathered boundaries.\n\n## [1.3.0] - 2026-07-28\n\n### Added\n\n- Added editable canvas ratios, source-image transforms, native outpainting, and Flux2 Klein/Krea2 Identity Edit reference paths to `NO8D-Generate`.\n- Added the `NO8D-Remove Krea2 Reference Latents` conditioning utility.\n- Added separate `image_a` and `image_b` outputs to `NO8D-A/B preview`.\n- Added original, 1K, 2K, and 4K longest-edge sizing to `NO8D Image Title`.\n- Added a no-style-description mode and locked-style conflict correction to `NO8D-Prompt`.\n\n### Changed\n\n- Delegated `NO8D-Empty latent` creation to ComfyUI's native `EmptyLatentImage` and migrated manual dimensions to short/long-side controls.\n- Delegated UNET loading in `NO8D-LoRA stack` to ComfyUI's native `UNETLoader`; new LoRA entries now start at strength `1.0` with a `0\u20132` range.\n- Updated the bundled example workflows for the new node controls and Prompt Libraries guide entry.\n\n### Fixed\n\n- Kept the clicked A/B side selected across ComfyUI's native right-click handling so image actions target the correct image.\n- Removed duplicate color controls from the image composition nodes.\n\n## [1.1.4] - 2026-07-19\n\n### Changed\n\n- Documented the `NO8D-Krea2 Style Selector` arrow, page, home, and end keyboard shortcuts in both English and Chinese README files.\n\n## [1.1.3] - 2026-07-19\n\n### Changed\n\n- Added light and full Patreon project-support links before and after the node overview in both English and Chinese documentation.\n\n## [1.1.2] - 2026-07-19\n\n### Changed\n\n- Made `NO8D-Load-images` automatically select the first image in each newly imported file-picker, drag-and-drop, or clipboard batch.\n\n## [1.1.1] - 2026-07-19\n\n### Changed\n\n- Moved the `NO8D-Krea2 Style Selector` section to the beginning of the node overview in both English and Chinese documentation.\n\n## [1.1.0] - 2026-07-19\n\n### Added\n\n- Added `NO8D-Krea2 Style Selector` with 285 visual style presets grouped into four categories, localized display names, keyboard and mouse paging, and canonical English prompt output.\n- Added `NO8D Image Grid` with autogrow image inputs, horizontal, vertical, and grid layouts, crop alignment, spacing, and background controls.\n- Added `NO8D Image Title` with per-image batch titles, outside, inside, and center positions, bar opacity and height, typography, color, and alignment controls.\n\n### Changed\n\n- Unified NO8D custom-node controls around a shared blue-accent UI theme and expanded English and Chinese node-title and widget localization.\n\n## [1.0.11] - 2026-07-17\n\n### Changed\n\n- Replaced the bundled example with the latest default NO8D-controls workflow while removing local save paths and private LoRA references from the public file.\n\n## [1.0.10] - 2026-07-17\n\n### Changed\n\n- Moved the fixed-prompt field from `NO8D-Prompt` to `NO8D-Prompt-view`, where it is prepended whenever text is emitted; connected legacy workflows migrate the saved value automatically.\n- Expanded the default weight range for new `NO8D-LoRA stack` entries from `-1..1` to `-2..2`.\n\n### Fixed\n\n- Prevented `NO8D-Prompt-view` from duplicating a fixed prefix across auto, edit, and Send flows while keeping cache signatures and visible editor text consistent.\n- Kept localized Prompt combo options compatible with mutable and function-backed ComfyUI option sources, including bilingual rule, length, and output-language values.\n\n## [1.0.9] - 2026-07-15\n\n### Fixed\n\n- Removed the A/B preview's intermediate composite canvas so full-resolution images draw directly onto the node canvas, preventing black previews with large source images.\n\n## [1.0.8] - 2026-07-15\n\n### Changed\n\n- Changed `NO8D-Load-images` to output only explicitly selected thumbnails: selecting all outputs all images, while selecting none returns an empty list; removed the per-image eye enable/disable control.\n\n### Fixed\n\n- Removed the A/B preview's 1024-pixel source downsampling so the comparison canvas always renders from the full-resolution images returned by ComfyUI.\n\n## [1.0.7] - 2026-07-14\n\n### Added\n\n- Added an `image_a` output and an auto-output switch to `NO8D-A/B preview`; disabling the switch blocks only the connected downstream branch while keeping comparison previews active.\n- Added original image-dimension badges for both sides of `NO8D-A/B preview`, with automatic spacing for the list page badge.\n- Added clipboard image paste support to `NO8D-Load-images`; focus the preview and press `Ctrl+V` to append copied images through ComfyUI's standard upload flow.\n- Added a persistent eye badge to every `NO8D-Load-images` thumbnail so individual images can be excluded from node output without removing them.\n- Changed `NO8D-Load-images` thumbnail double-click from queueing a single-image run to opening the original image through ComfyUI's native `/view` endpoint.\n\n### Fixed\n\n- Allowed active Generate brush, eraser, and lasso strokes to continue beyond the image and node bounds through ComfyUI's captured custom-widget drag, while still requiring strokes to start inside the image; off-canvas geometry is clipped when calculating feather size so distant pointer movement cannot inflate the visible feather region.\n- Kept the A/B preview auto-output switch above the flexible comparison canvas even when ComfyUI creates the native switch widget after the custom preview widget.\n- Moved A/B image dimensions into a dedicated footer so resolution labels never cover comparison image content.\n- Made every Generate execution publish its own ComfyUI-native internal `PreviewImage`, restricted canvas updates to execution events whose `display_node` is the Generate node, and removed downstream preview traversal so resized, cropped, comparison, or multi-output images cannot randomly replace the mask canvas; pointer and overlay mapping now use the fixed mask-base dimensions.\n- Restricted Generate inpaint activation to actual canvas mask content; selecting a brush, lasso, or eraser alone no longer uploads an empty mask or changes normal generation into inpainting.\n- Kept the original base image fixed for the lifetime of a Generate mask session: execution results now update only the visible preview and no longer become the next inpaint base, allowing repeated seeds to produce candidates from identical inputs and fixed seeds to remain deterministic/cacheable.\n- Split Generate mask visualization from execution strength: the canvas retains a clear 100% core/50% feather-ring diagnostic overlay, while the uploaded sampling/compositing mask now uses a 32-level linear 1-to-0 falloff; core extraction uses a 0.99 native threshold so `VAEEncodeForInpaint` does not clear the feather region.\n- Strengthened native inpainting without switching to an empty latent by blending the regular VAE-encoded image with a core-cleared `VAEEncodeForInpaint` latent; original-latent retention now scales smoothly from 100% at denoise 0 to 30% at denoise 1, while the gradient execution mask remains authoritative for sampling and compositing.\n- Made Generate numeric fields select their complete value when clicked or focused, including seed, sampling parameters, brush size, feather, and mask opacity editors.\n- Updated Generate mask editor presets to 30/60/90% feather and 20/40/60% preview opacity while retaining the full manual slider ranges.\n- Grouped connected brush and eraser strokes into independent painted regions and based feather width on each region's short side, so repeatedly painted large areas no longer retain a feather ring limited to one brush radius; outer-mask construction now expands painted strokes and contracts erased strokes to preserve the 50% transition on both operations.\n- Based lasso feather width on the selection's own short-side size instead of the unrelated brush size, making 100% feather extend outward by half of the lasso's short side.\n- Kept Generate feather geometry anchored to the original stroke path: brush/eraser feathering now changes only stroke width, and lasso feathering expands only its boundary instead of scaling the entire shape around its bounding-box center.\n- Changed Generate mask feathering from a gradual falloff to a uniform 50% mask-strength ring around the 100% core, keeping canvas preview and the uploaded execution mask identical.\n- Replaced the nine fixed Prompt style presets with four evidence-driven umbrella categories\u2014realistic photography, anime illustration, hand-drawn art, and digital art\u2014so the model selects and describes the best-supported subtype from the image or text, with legacy workflow migration.\n- Constrained automatic style inference to the same four umbrella categories and added a final specificity audit so vague style labels cannot substitute for an evidence-based subtype.\n- Added a mandatory image-output contract for camera elevation/view direction, camera-to-subject front/side/rear relationship, viewer-relative subject placement and depth, lens perspective, and multi-subject left/right and front/back arrangement.\n- Extended the camera-to-subject output contract to every text expansion and non-photographic style, using an intentionally designed virtual viewpoint when no reference image is available.\n- Added a single-request structured visual-analysis envelope for natural prompts, requiring normalized subject placement, evidence-based camera elevation, lens perspective, and multi-subject depth layout before the model writes the final paragraph; the node strips the worksheet and outputs only the prompt.\n- Versioned the prompt pipeline in ComfyUI's change signature so rule updates invalidate previously cached node results.\n- Expanded structured composition analysis from a single subject anchor to key-part anchors, dominant axis, visible extent, frame occupancy, and per-secondary-subject coordinates, preventing diagonal/full-body layouts from collapsing into false centered medium shots.\n- Prevented visual reverse prompting from guessing named character identities from costume colors alone.\n- Upscaled small vision inputs to a 768-pixel long edge and capped image-analysis temperature at 0.1 for more stable spatial evidence extraction.\n- Replaced model-authored camera prose with deterministic natural-language rendering from structured key anchors, visible body extent, camera direction, lens class, frame occupancy, and secondary-subject coordinates, so contradictory labels such as full-body `medium shot` or diagonal `centered` framing cannot survive downstream.\n- Added separate Text model and Vision model selectors to the Prompt API manager. Existing `*-Thinking` text-model configurations migrate image prompting to an available sibling `*-Instruct` model while preserving the selected text model.\n- Stopped retrying an identical request after a read timeout, avoiding two consecutive 120-second waits for slow Thinking-model responses.\n- Resized oversized inpaint canvas images and soft masks to the incoming latent's native image dimensions with ComfyUI `ImageScale`, `MaskToImage`, and `ImageToMask` nodes before VAE encoding, preventing 2x decode VAEs from making inpaint sampling operate on four times the intended image area.\n- Kept manually entered `NO8D-Prompt-view` text when running the workflow with an empty upstream value, including when ComfyUI's execution callback temporarily supplies an empty array before the actual display result.\n- Defined explicit `NO8D-Prompt-view` display/output rules: non-empty upstream text always refreshes the editor, empty upstream preserves the editor, auto only controls normal downstream output, and Send always emits the editor text.\n- Preserved detected camera elevation, viewing direction, lens perspective, camera roll, and perspective strength during image reverse prompting instead of normalizing unusual low-angle or ultra-wide references to conventional framing.\n- Removed upward-angle prompt anchoring by replacing low-angle-heavy examples with a mutually exclusive, evidence-based downward/level/upward classification and a final contradiction check.\n- Added explicit camera-to-subject azimuth, optical-axis direction, subject orientation, and viewer-relative frame placement analysis for both human and non-human subjects, with safeguards against left/right coordinate confusion.\n- Aligned prompt length caps with open-source Krea 2 ranges: 256 output tokens for Standard and 512 for Detailed, while placing essential subject and spatial relationships first.\n- Made automatic shot-scale inference reproduce the connected image's observed framing while keeping manual shot-scale overrides independent from the other camera geometry properties.\n- Made `NO8D-Generate` enter inpaint mode only while its serialized mask is active, preventing cleared or legacy mask filenames from silently triggering another inpaint pass.\n- Persisted editable mask strokes and inversion state so saved workflows restore the same visible mask instead of retaining hidden server-side mask files.\n- Invalidated stale mask/base-image uploads after clear, preview replacement, newer edits, or node removal so older asynchronous work cannot overwrite current canvas state.\n- Reused identical Generate base/mask uploads through content-addressed filenames without deleting assets that queued prompts or saved workflows may still reference.\n- Made inpaint activation automatic only while the canvas retains mask content; selecting a mask tool alone keeps normal generation active.\n- Preserved grayscale feathering through the native `VAEEncode` and `SetLatentNoiseMask` path so `DifferentialDiffusion` receives the soft mask instead of a rounded binary mask.\n- Removed the Generate seed-control queue-hook override and delegated randomize/fixed timing entirely to ComfyUI's native `control_after_generate` widget.\n- Separated Prompt-view's displayed upstream result from its serialized manual draft so an execution callback no longer causes one unnecessary downstream cache miss.\n- Added a minimal internal packed-RGB normalizer so Krea2/Wan VAE 12-channel decoded output reaches native ComfyUI preview/compositing as a standard RGB `IMAGE`.\n- Resolved Generate-owned preview events directly through ComfyUI display-node IDs and nested subgraph paths.\n\n## [1.0.6] - 2026-07-10\n\n### Fixed\n\n- Matched custom DOM widget bypass/never/ghost visuals to ComfyUI's native node-mode opacity and bypass color rules.\n- Synchronized bypass styling for `NO8D-Generate`, `NO8D-Load-images`, `NO8D-LoRA stack`, `NO8D-Prompt`, `NO8D-Prompt-view`, and `NO8D save` custom controls.\n- Marked `NO8D-Generate` internal decode adapter nodes as deprecated/internal and hid them from frontend node registration/search.\n- Reduced persistent LoRA stack backend memory retention by avoiding long-lived loaded-LoRA state caches on the node instance.\n\n## [1.0.5] - 2026-07-10\n\n### Fixed\n\n- Kept `NO8D-Generate` as an output-capable node so it can still run standalone with its own preview for mask painting.\n- Skipped the internal `PreviewImage` expansion only when the image output already reaches a downstream preview/save node, avoiding duplicate preview work while preserving standalone preview behavior.\n- Avoided retaining unconnected `NO8D-Generate` image/latent/mask outputs, reducing GPU tensor retention when only the built-in canvas preview is needed.\n- Split the normal decode adapter path so non-inpaint generations no longer allocate an unused full-size mask tensor.\n- Reduced Generate and A/B preview display-cache size to lower browser GPU/shared-memory pressure.\n- Kept the `NO8D-Generate` canvas preview synchronized by accepting preview images emitted from downstream preview/output nodes.\n\n## [1.0.3] - 2026-07-10\n\n### Fixed\n\n- Fixed `NO8D-Prompt-view` clearing manually edited prompt text when an auto-output run received an empty upstream text value.\n- Made prompt view UI refresh use the actual displayed/output text instead of always mirroring the upstream input.\n\n## [1.0.2] - 2026-07-10\n\n### Fixed\n\n- Reduced frontend preview memory retention in `NO8D-Generate` by releasing stale image, canvas, mask overlay, editor, and timer resources when previews are replaced or nodes are removed.\n- Reduced persistent mask preview memory by keeping only display-sized overlay canvases while preserving full-resolution mask export.\n- Added cleanup for `NO8D-A/B preview` images, native preview references, and render caches when nodes are removed.\n\n## [1.0.1] - 2026-07-09\n\n### Fixed\n\n- Fixed `NO8D-Prompt-view` returning an empty string when the visible prompt text was used as downstream output in auto mode.\n\n## [1.0.0] - 2026-07-08\n\n### Added\n\n- Initial public GitHub release preparation.\n- `NO8D-LoRA stack` for ordered LoRA loading and weight control.\n- `NO8D-Generate` for native KSampler generation and canvas mask editing.\n- `NO8D-A/B preview` for session-based image comparison.\n- Chinese and English interface text.", "createdAt": "2026-08-02T16:17:13.635911Z", "dependencies": [ "json-repair>=0.61,<1", "openpyxl>=3.1,<4" ], "deprecated": false, "downloadUrl": "", "id": "42693f2a-cbe4-4a80-830f-00b25c6d378f", "node_id": "no8d-controls", "status": "NodeVersionStatusActive", "supported_accelerators": [], "supported_comfyui_frontend_version": "", "supported_comfyui_version": "", "supported_os": [ "OS Independent" ], "tags": [], "tags_admin": [], "version": "1.4.1" }, "license": "{\"file\": \"LICENSE\"}", "name": "NO8D-controls", "publisher": { "createdAt": "2026-07-08T09:09:10.618018Z", "description": "", "id": "no8day", "logo": "", "members": [], "name": "NO8D", "source_code_repo": "", "status": "PublisherStatusActive", "support": "", "website": "" }, "rating": 0, "repository": "https://github.com/no8d/ComfyUI-NO8D-controls", "search_ranking": 5, "status": "NodeStatusActive", "status_detail": "", "supported_accelerators": [], "supported_comfyui_frontend_version": "", "supported_comfyui_version": "", "supported_os": [ "OS Independent" ], "tags": [] }, { "author": "", "banner_url": "", "category": "", "created_at": "2024-12-27T14:08:05.971135Z", "description": "GLHF is a ComfyUI node that facilitates seamless interaction with the GLHF chat API. 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Will download and use pixi package manager. Automatic skeleton extraction for ComfyUI using UniRig (SIGGRAPH 2025)", "downloads": 1957, "github_stars": 415, "icon": "https://raw.githubusercontent.com/PozzettiAndrea/ComfyUI-UniRig/main/assets/icon.png", "id": "comfyui-unirig", "latest_version": { "version": "nightly" }, "license": "{\"file\": \"LICENSE\"}", "name": "UniRig", "publisher": { "createdAt": "2025-11-11T14:34:59.714795Z", "description": "", "id": "pznodes", "logo": "", "members": [], "name": "pozzettiandrea", "source_code_repo": "", "status": "PublisherStatusActive", "support": "", "website": "" }, "rating": 0, "repository": "https://github.com/PozzettiAndrea/ComfyUI-UniRig", "search_ranking": 5, "status": "NodeStatusActive", "status_detail": "", "supported_accelerators": [], "supported_comfyui_frontend_version": "", "supported_comfyui_version": "", "supported_os": [], "tags": [] }, { "author": "", "banner_url": "", "category": "", "created_at": "2025-10-04T01:33:50.12724Z", "description": "ROCM Optimized Nodes for ComfyUI - High-performance VAE decode and sampling nodes specifically tuned for AMD GPUs with ROCm support, particularly targeting gfx1151 architecture", "downloads": 9809, "github_stars": 37, "icon": "https://raw.githubusercontent.com/iGavroche/rocm-ninodes/refs/heads/main/icon.jpg", "id": "rocm-ninodes", "latest_version": { "changelog": "# Changelog\n\n## [2.3.1] - 2025-08-02\n\n### Fixed\n- **`'NestedTensor' object has no attribute 'reshape'` crash on LTXAV runs** (`rocm_nodes/core/sampler.py`): `ROCmSamplerCustomAdvanced` unconditionally re-unpacked the `x0` output whenever samples were nested. On multimodal runs (LTX audio-video) the sampler already hands the callback a `NestedTensor`, so the extra `unpack_latents()` called `.reshape()` on it and crashed after sampling completed. Now mirrors stock: unpack only when samples are nested but `x0` is not, then apply `process_latent_out`.\n\n- **Incorrect memory estimate in video VAE decode** (`rocm_nodes.py`): `ROCmOptimizedVAEDecode`'s low-VRAM estimate multiplied a fixed 4-dimensional shape, undercounting 5D video latents. Now multiplies every sample dimension before applying the decode expansion factor.\n\n## [2.3.0] - 2025-07-20\n\n### Added\n- **ROCm 7.14 support** (`rocm_nodes/utils/architecture.py`): Version detection (`detect_rocm_version`, `is_rocm_7_14_plus`) enables `allow_fp16_accumulation` on gfx1151 for ROCm 7.14+ (was blocked on older ROCm due to numerical drift). New `rocm_version` and `is_rocm_7_14_plus` keys in architecture info dict.\n\n- **HIP OOM mitigation for APU temporal tiling** (`rocm_nodes/core/vae.py:727`, `rocm_nodes/utils/memory.py`): Added `discard_between_chunks()` \u2014 synchronized memory discard (sync+garbage collect+empty cache) between temporal VAE tiles. Added `del chunk_latent` immediately after decode to free input tensor backing pages. Together these prevent the HIP caching allocator from fragmenting on gfx1151 unified memory.\n\n- **gfx1151-specific temporal chunk cap** (`rocm_nodes/constants.py`): `GFX1151_TEMPORAL_CHUNK_SIZE = 8` halves per-chunk memory pressure on APUs. Automatically caps user setting on gfx1151.\n\n- **`TORCH_BLAS_PREFER_HIPBLASLT` advisory** (`architecture.py`): Logs a startup hint when the env var is not set on RDNA3/RDNA3.5 (AMD-recommended for LLM performance with PyTorch < 2.14).\n\n- **TheRock build system + expandable segments diagnostics** (`utils/diagnostics.py`): Detects `/opt/rocm/core`, reports `expandable_segments` and `TORCH_BLAS_PREFER_HIPBLASLT` status.\n\n### Changed\n- **Naming consistency**: All node class names renamed from `ROCM*` prefix to `ROCm*` (e.g. `ROCMOptimizedVAEDecode` \u2192 `ROCmVAEDecode`). Old `NODE_CLASS_MAPPINGS` keys preserved as backward-compatible aliases \u2014 existing workflows continue to load. Updated `NODE_DISPLAY_NAME_MAPPINGS` to use consistent `ROCm` display prefix.\n\n- **Registry configs updated** (`pyproject.toml`, `comfyui_manager.json`, `package.json`): Node lists updated to canonical names. Version bumped to `2.3.0` in all 8 locations.\n\n- **Legacy deprecation**: `rocm_nodes.py` (2663-line monolithic file) deprecated with warning. The refactored `rocm_nodes/` package is the canonical source going forward.\n\n### Fixed\n- **No dynamic chunk sizing** (removed from `vae.py:720`): The OOM-mitigation strategy of dynamically reducing `c_end` mid-loop created undecoded latent frame gaps between tiles, causing visual ghosting/echo. Removed in favor of the safer approach: smaller default chunk size + aggressive `discard_between_chunks()`.\n\n## [2.2.9] - 2025-06-28\n\n### Fixed\n- **Visible seams + extra frames in temporal VAE tiling** (`rocm_nodes/core/vae.py:673`): The overlap-blend step was blending each new chunk against the *truncated tail* of the previous chunk (`result_parts[-1]`) instead of the *cumulative* result tensor. After chunk 1 was shortened by `[1:]` to drop the first decoded frame, the next blend used that 24/32-frame tail as its reference, so `blend_frames` alternated 32 \u2192 24 \u2192 32 \u2192 24 across chunks. This left a visible seam every two chunks in the back half of long videos and appended 8 extra frames per 2 chunks (1393 frames instead of 1201 for a 50 s LTX job). Blending is now done against the cumulative `result` tensor with `torch.cat` (which returns a fresh tensor), so the count is exact and seams disappear.\n\n## [2.2.6] - 2025-06-15\n\n### Fixed\n- **ROCm sampler 2x slowdown on LTX 2.3 video** (`architecture.py:194`, `vae.py:272`): Removed `torch.backends.cuda.matmul.allow_tf32 = False` which unconditionally disabled TF32-accelerated GEMMs on AMD GPUs. This caused all matmuls to fall back to full fp32, hitting DiT-based models (LTX Video) hardest due to their matmul-heavy transformer architecture. TF32 is now left at its ROCm default (`True`), restoring stock performance.\n- **Inconsistent backend settings**: VAE decode also duplicated the same `allow_tf32 = False` inline; now aligned to not set it.\n\n## [2.0.10] - 2025-03-XX\n\n### Changed\n- **ROCm VAE Decode: Video chunking removed**: Video is always decoded in a single pass (no chunking).\n - Ensures correct behavior for all temporal/causal VAEs (WAN, LTX, and similar models).\n - Chunking broke the decoding chain and caused artifacts; full-video decode matches ComfyUI native behavior.\n- **Removed parameters**: `video_chunk_size` and `memory_optimization_enabled` removed from the ROCm VAE Decode node (they only controlled chunking).\n\n## [2.0.9] - 2025-01-XX\n\n### Fixed\n- **LoRA Loader API Compatibility**: Fixed `load_lora() missing 1 required positional argument: 'to_load'` error\n - ComfyUI's `load_lora()` function now requires a `to_load` parameter specifying which components to load\n - Added automatic detection of model/clip presence to build `to_load` dictionary\n - Defaults to loading both model and clip if neither is explicitly provided\n - Fixes LoRA loading for all ComfyUI versions with the updated API\n\n### Changed\n- **LoRA Loader Output Messages**: Removed Unicode emojis from LoRA loader output\n - Replaced with ASCII prefixes: `[LOADING]`, `[INFO]`, `[SUCCESS]`, `[ERROR]`, `[WARNING]`\n - Improves Windows console compatibility and matches project style\n\n## [2.0.8] - 2025-01-XX\n\n### Changed\n- **GGUF Loader UI Simplification**: Removed dtype selector from GGUF loader node\n - GGUF models are already quantized (Q8_0, Q4_0, etc.), so dtype selector was unnecessary\n - Simplified UI - only requires model file selection\n - Dtype is automatically set to fp32 (optimal for ROCm/gfx1151)\n\n### Added\n- **ROCm Optimizations for GGUF Loading**: Added comprehensive ROCm-specific optimizations\n - Automatic ROCm backend configuration (TF32 disabled, FP16 accumulation enabled)\n - gfx1151 architecture detection with automatic fp32 precision\n - Memory cleanup after loading large GGUF models using `simple_memory_cleanup()`\n - Non-blocking tensor transfers throughout dequantization pipeline\n - Optimized device transfers in `cast_bias_weight()`, `get_weight()`, and `dequantize_tensor()`\n\n### Technical Details\n- All `.to(device)` calls now use `non_blocking=True` for better GPU utilization\n- ROCm backend settings configured before model loading for optimal performance\n- Memory cleanup integrated to prevent fragmentation after large model loads\n- Default dtype set to fp32 for gfx1151 architecture (better stability than fp16)\n\n## [2.0.7] - 2025-01-XX\n\n### Added\n- **Complete GGUF Dequantization Support**: Implemented full on-the-fly dequantization for GGUF models\n - Added dequantization functions for Q8_0, Q4_0, Q4_1, Q5_0, Q5_1, and BF16 quantization types\n - Implemented `get_weight()` method that dequantizes tensors before PyTorch operations\n - Implemented `cast_bias_weight()` method for proper dtype/device casting with dequantization\n - Added `forward_comfy_cast_weights()` that routes to dequantization path for all ROCmGGMLTensor objects\n - Based on City96's ComfyUI-GGUF implementation, optimized for ROCm/gfx1151\n\n### Fixed\n- **GGUF Model Runtime Error**: Fixed \"Multiple dispatch failed for torch.nn.linear\" error\n - Issue occurred because ROCmGGMLTensor (custom tensor subclass) was being passed directly to PyTorch operations\n - Now all ROCmGGMLTensor objects (quantized and non-quantized) are properly converted to regular torch.Tensor before operations\n - Ensures F32/F16 tensors wrapped in ROCmGGMLTensor are also converted, not just quantized tensors\n - Fixes model loading and sampling for GGUF models (e.g., flux1-dev-Q8_0.gguf)\n\n### Technical Details\n- Added dequantization utility functions: `dequantize_tensor()`, `dequantize()`, and block-level dequantization functions\n- Implemented lazy dequantization: tensors stay quantized in memory until needed during forward passes\n- Added LoRA patch support in `get_weight()` for compatibility with ComfyUI's LoRA system\n- Added VRAM estimation support via `ggml_save_to_state_dict()` for memory planning\n- All ROCmGGMLTensor objects are now properly handled, ensuring PyTorch operations work correctly\n\n## [2.0.6] - 2025-01-XX\n\n### Fixed\n- **Diffusion Model Loader File Listing**: Fixed issue where no files were showing in the file selector\n - Now checks multiple folder names: `diffusion_models`, `unet`, and `unet_gguf`\n - Combines ComfyUI's default file list with custom scanning for maximum compatibility\n - Supports all model formats: `.safetensors`, `.gguf`, `.ckpt`, `.pt`, `.pth`, `.bin`, `.onnx`\n- **PyTorch 2.0+ GGUF Loading**: Fixed `weights_only` error when loading GGUF files\n - PyTorch 2.0+ changed default `torch.load(weights_only=True)` which breaks GGUF files\n - Temporarily patches `torch.load` to set `weights_only=False` for GGUF files only\n - Restores original `torch.load` after loading to maintain security for other file types\n- **Unicode Emoji Removal**: Removed all Unicode emojis from output messages\n - Replaced with ASCII alternatives: `[INFO]`, `[WARNING]`, `[ERROR]`, `[SUCCESS]`, `[CONFIG]`, `[LOADING]`, `[GPU]`, `[MEMORY]`\n - Improves Windows console compatibility and avoids encoding errors\n\n### Technical Details\n- Enhanced `_get_model_files()` to check multiple folder names and combine sources\n- Added GGUF detection and PyTorch 2.0+ compatibility workaround\n- All print statements now use ASCII-only prefixes for cross-platform compatibility\n\n## [2.0.5] - 2025-01-XX\n\n### Fixed\n- **Windows Console Encoding Error**: Fixed UnicodeEncodeError in install.py on Windows\n - Windows console (cp1252 encoding) cannot display emoji characters\n - Added `safe_print()` function with automatic emoji fallbacks for Windows\n - Emojis are replaced with ASCII alternatives: `[OK]`, `[ERROR]`, `[WARNING]`\n - Issue reported by u/x5nder on Reddit - thank you for the bug report!\n\n### Technical Details\n- Detects Windows console encoding (cp1252, cp850, cp437) and replaces emojis proactively\n- Falls back to emoji replacement if UnicodeEncodeError occurs during printing\n- All print statements in install.py now use safe_print() for cross-platform compatibility\n\n## [2.0.4] - 2025-01-XX\n\n### Fixed\n- **Diffusion Model Loader File Format Support**: Fixed ROCm Diffusion Model loader to accept multiple file formats\n - Previously only accepted `.safetensors` files\n - Now supports `.safetensors`, `.gguf`, `.ckpt`, `.pt`, `.pth`, `.bin`, and `.onnx` formats\n - Custom file scanner replaces ComfyUI's default filter that was too restrictive\n - Issue reported by u/x5nder on Reddit - thank you for the bug report!\n\n### Technical Details\n- Added `_get_model_files()` method that scans `diffusion_models` folder for all supported extensions\n- Falls back to ComfyUI's default file list if custom scanning fails\n- Updated documentation to reflect multi-format support\n\n## [2.0.3] - 2025-01-XX\n\n### Fixed\n- **KSampler Progress Reporting**: Fixed missing progress display in both UI and terminal\n - Progress bar now always shows in ComfyUI UI for both basic and advanced KSampler nodes\n - Terminal now displays step-by-step progress with detailed timing information\n - Fixed issue where `disable_pbar` parameter was preventing callback execution\n - Progress updates every step in terminal, every 0.3 seconds in UI\n - Preview images now appear every 5 steps for image workflows\n\n### Improved\n- **Progress Feedback**: Enhanced progress reporting for better user experience\n - Real-time step-by-step terminal output with elapsed time, remaining time, and average time per step\n - UI progress bar updates smoothly with preview images\n - Progress visible for both `ROCMOptimizedKSampler` and `ROCMOptimizedKSamplerAdvanced` nodes\n - Added `flush=True` to print statements for immediate terminal output\n\n### Technical Details\n- Always create `comfy.utils.ProgressBar` instance for UI feedback\n- Always pass `disable_pbar=False` to `comfy.sample.sample()` to ensure callbacks are called\n- Enhanced callback function reports to both UI (via ProgressBar) and terminal (via print)\n- Progress bar updates throttled to 0.3 seconds to prevent UI lag\n- Preview generation limited to every 5 steps for image workflows to reduce overhead\n\n## [2.0.2] - 2025-01-XX\n\n### Fixed\n- **Video Stuttering in VAE Decode**: Removed chunk overlaps that caused stuttering in video processing\n - Removed frame overlap logic that was causing duplicate frames and stuttering\n - Videos are now processed in exact, non-overlapping chunks\n - Increased default `video_chunk_size` from 8 to 81 frames to handle most videos without chunking\n - Increased maximum `video_chunk_size` from 32 to 200 frames for larger videos\n - Videos with 81 or fewer frames are now processed in a single pass without chunking\n\n### Improved\n- **Video Processing Performance**: Better default settings for video workflows\n - Default chunk size now handles typical video lengths (81 frames) without chunking\n - Eliminated frame duplication artifacts from overlap processing\n - Cleaner video output without stuttering or frame boundary issues\n\n### Technical Details\n- Chunk processing now uses exact frame boundaries without any overlap\n- Removed `overlap_frames` variable and all overlap-related cropping logic\n- Default `video_chunk_size` of 81 allows most videos to be processed without chunking\n- Maximum `video_chunk_size` increased to 200 for very long videos\n\n## [2.0.1] - 2025-11-01\n\n### Fixed\n- **WAN VAE Video Jitter**: Fixed jitter/repeating frames issue in ROCm VAE Decode for WAN models\n - Added reliable WAN VAE detection using `isinstance()` check with imported WAN VAE classes\n - Added fallback detection using `latent_channels` (16 for WAN 2.1, 48 for WAN 2.2)\n - Disabled chunking for WAN VAEs to preserve causal decoding chain with feature caching\n - Matched native ComfyUI behavior exactly for WAN VAE video processing (full video decode)\n - Fixed detection logic to use correct file (`rocm_nodes/core/vae.py` instead of `rocm_nodes.py`)\n\n### Improved\n- **ROCm Backend Optimizations**: Applied ROCm backend settings (TF32 disabled, FP16 accumulation enabled) to video processing\n - Backend optimizations now apply before video processing instead of only for image processing\n - Added async memory cleanup for WAN VAE videos (non-blocking, doesn't interfere with causal decoding)\n - Added gfx1151 architecture detection and logging for WAN VAE processing\n\n### Technical Details\n- WAN VAEs use causal decoding with feature caching - chunking breaks the cache and causes jitter on first frames\n- Native ComfyUI processes WAN VAE videos in full (no chunking) - ROCm node now matches this behavior\n- Detection uses multiple methods for reliability: `isinstance()` check, `latent_channels` check, and `temporal_compression_decode()` verification\n- ROCm backend optimizations are global settings that improve performance without affecting causal decoding\n\n## v2.0.0\n- Samplers reset to stock behavior for correctness; added ROCm opt-in knobs:\n - optimize_for_video: disables preview/progress for multi-frame latents\n - precision_mode: auto|fp32|bf16 with ROCm bf16 safety guard (no forced cast yet)\n - compatibility_mode: forces pure stock behavior\n- Advanced sampler made 1:1 with ComfyUI KSampler (Advanced) and categorized under \"ROCm Ninodes/Sampling\".\n- Import structure stabilized; fallback to legacy `rocm_nodes.py` retained.\n- Added SDPA preference via PyTorch native path (no CUDA-only toggles).\n- Version bumped to 2.0.0 across `__init__.py`, `rocm_nodes/__init__.py`, `pyproject.toml`, `package.json`.\n- Added workflow update helper script `scripts/update_workflows.py` to migrate node names.\n- Docs updated to reflect v2 changes and toggles.\n\nAll notable changes to this project will be documented in this file.\n\nThe format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),\nand this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).\n\n## [1.0.30] - 2025-10-29\n\n### Changed\n- General stability and performance improvements across ROCm-optimized nodes\n\n### Fixed\n- Minor edge cases in node parameter handling and execution flow\n\n### Meta\n- Synchronized version numbers across `pyproject.toml`, `comfyui_manager.json`, and `package.json`\n\n## [1.0.29] - 2025-01-22\n\n### Fixed\n- Video chunk processing boundary artifacts\n - Added 2-frame temporal overlap to prevent darker frames at chunk boundaries\n - Improved chunk concatenation logic with proper overlap cropping\n - Fixed tensor size mismatch errors during concatenation\n - Better handling of 5D video tensor formats\n\n### Improved\n- ROCM VAE Decode chunk processing for WAN video\n - Automatic overlap detection and handling\n - Cleaner boundary transitions between video chunks\n - Reduced visual artifacts in chunked video processing\n\n## [1.0.28] - 2025-10-20\n\n### Fixed\n- Text-to-Video CPU RAM spikes and disk I/O in ROCMOptimizedKSamplerAdvanced\n - Noise tensors now created on the same device as latents (GPU), not CPU\n - Ensured latents/noise stay on GPU to prevent host paging\n- VAE decode moving tensors to CPU (output_device)\n - All decode outputs remain on the active GPU device for end-to-end GPU execution\n- Performance regression from conservative defaults\n - Restored tile_size=768, batch_optimization=True, video_chunk_size=8\n - Only chunk videos when T > 20 frames to avoid unnecessary overhead\n\n### Improved\n- Quantized model compatibility without sacrificing speed\n - Less aggressive detection; optimizations remain unless explicitly using compatibility_mode\n - Preserved dtype for quantized models while keeping GPU residency\n- Reduced GPU idle time during video workflows by eliminating CPU transfers and minimizing sync points\n\n### Documentation\n- Updated README with quantization support details and GPU residency fixes\n- Clarified recommended defaults for WAN 2.2 text-to-video\n\n### Notes\n- This release focuses on keeping tensors on the GPU to avoid CPU RAM usage and heavy disk activity during text-to-video on WAN 2.2.\n\n### Fixed\n- **Mature ROCm Driver Support**: Optimized for mature ROCm drivers and libraries\n - **Essential Environment Variables**: Added `TORCH_ROCM_AOTRITON_ENABLE_EXPERIMENTAL=1` for mature drivers\n - **Gentle Memory Management**: Replaced aggressive memory management with gentle cleanup for better performance\n - **Less Aggressive Settings**: Updated memory allocation settings (256MB chunks, 0.8 threshold) for mature drivers\n - **Removed Overly Complex Functions**: Eliminated force_memory_cleanup, emergency_memory_reset, and other performance-penalizing functions\n - **Clean Starting Script**: Simplified run_comfy.ps1 with essential ROCm variables only\n\n### Improved\n- **Performance**: Removed performance penalties from overly aggressive memory management\n - **Gentle Memory Cleanup**: Single-pass cache clearing instead of multiple aggressive cycles\n - **Better Compatibility**: Works optimally with mature ROCm drivers and libraries\n - **Simplified Codebase**: Removed complex memory management that was causing slowdowns\n - **Cleaner Architecture**: Focused on essential optimizations without performance penalties\n\n### Technical Details\n- **Environment Variables**: `TORCH_ROCM_AOTRITON_ENABLE_EXPERIMENTAL=1` for mature driver support\n- **Memory Management**: Gentle single-pass cleanup instead of aggressive multi-pass cycles\n- **Starting Script**: Clean PowerShell script with essential ROCm variables\n- **Code Cleanup**: Removed unnecessary development files and test workflows\n- **Node Count**: Maintained 9 optimized nodes with gentle memory management\n\n### Removed\n- **Development Files**: Removed test workflows, documentation, and backup files created during development\n- **Aggressive Memory Functions**: Removed force_memory_cleanup, emergency_memory_reset, nuclear_memory_reset\n- **Performance Penalties**: Eliminated functions that were causing 50%+ performance overhead\n- **Complex Memory Management**: Simplified to gentle cleanup for mature ROCm drivers\n\n## [1.0.26] - 2025-01-19\n\n### Fixed\n- **ROCm KSampler Fresh Start**: Complete rebuild with minimal, clean approach\n - **Simplified Memory Management**: Removed all ineffective memory defragmentation functions\n - **Environment Variables**: Optimized settings in run_comfy.ps1 (max_split_size_mb:128, garbage_collection_threshold:0.7)\n - **Removed Dead Code**: Deleted ROCMMemorySafeKSampler, ROCMMemoryDefragmenter, ROCMEmergencyMemoryReset\n - **Minimal ROCm Optimizations**: Simple memory cleanup and monitoring only\n - **Vanilla ComfyUI Path**: Uses standard ComfyUI sampling with minimal overhead\n\n### Technical Details\n- **Environment Variables**: Optimized for gfx1151 unified memory (removed expandable_segments)\n- **Memory Management**: Simple torch.cuda.empty_cache() + gc.collect() only\n- **Node Count**: Reduced from 12 to 9 nodes (removed 3 memory management nodes)\n- **Code Reduction**: ~70% reduction in complexity\n- **ROCm Settings**: Documented optimal settings in ROCM_SETTINGS.md\n\n### Removed\n- **ROCMMemorySafeKSampler**: Replaced with simplified ROCMOptimizedKSampler\n- **ROCMMemoryDefragmenter**: Removed ineffective defragmentation approach\n- **ROCMEmergencyMemoryReset**: Removed complex memory reset logic\n- **Complex Memory Functions**: Removed force_memory_defragmentation, emergency_memory_reset_nuclear, etc.\n\n## [1.0.25] - 2025-01-19\n\n### Fixed\n- **CRITICAL: Environment Variables Not Applied**: Fixed root cause of OOM errors\n - **Environment Variables in main.py**: Added ROCm memory settings to ComfyUI startup BEFORE PyTorch import\n - **Diagnostic Logging**: Added comprehensive logging to verify environment variable application\n - **Memory Pattern Establishment**: Pre-allocates 40GB block to establish good memory pattern\n - **ComfyUI Execution Hooks**: Hooks into model loading and weight patching for memory management\n - **Weight Patching OOM Fix**: Added memory cleanup before/after weight patching (where OOM occurs)\n\n### Technical Details\n- **Environment Variables**: Now set in `main.py` at lines 24-25 BEFORE any PyTorch imports\n- **Memory Pre-allocation**: 40GB block allocated and freed to establish contiguous memory pattern\n- **Execution Hooks**: Hooks into `load_models_gpu` and `patch_weight_to_device` for cleanup\n- **Diagnostic Output**: Comprehensive logging shows environment variable status and memory state\n\n### Removed\n- **Excessive Memory Management**: Removed functions causing 50%+ performance overhead\n - Removed `nuclear_memory_reset()` - 20 seconds of wasted cache clearing\n - Removed `create_memory_pool()` - didn't help fragmentation\n - Removed `patch_pytorch_memory_management()` - caused slowdown on every memory query\n - Removed `setup_memory_environment()` - redundant and ineffective\n - Removed `defragment_memory()` - didn't work without proper env vars\n\n### Improved\n- **Performance**: Sampling now 50%+ faster without nuclear reset overhead\n- **Memory Management**: Aggressive 3x cache clearing with synchronization for better fragmentation control\n- **Compatibility**: Works correctly with PowerShell launch script settings\n- **Fragmentation Control**: Reduced max_split_size_mb from 512MB to 256MB for better memory management\n\n### Technical Details\n- Environment variables now managed exclusively by `run_comfy.ps1` with aggressive settings\n- Memory management: 3x cache clearing with synchronization before all operations\n- Optimized for AMD Radeon 8060S (gfx1151) with 107.87 GB unified memory\n- **CRITICAL**: Requires ComfyUI restart to apply new environment variables\n\n## [1.0.24] - 2025-01-19\n\n### Fixed\n- **Critical Memory Fragmentation**: Fixed persistent OOM errors with nuclear memory management\n - **Early Environment Setup**: Memory environment variables now set BEFORE PyTorch import\n - **Nuclear Memory Reset**: Added `nuclear_memory_reset()` with 20x cache clearing\n - **Memory Pool Creation**: Pre-allocates memory blocks to prevent fragmentation\n - **Enhanced PyTorch Patching**: Patched `torch.cuda.memory_reserved` for better cleanup\n - **Lower Memory Thresholds**: Reduced cleanup thresholds to 50% allocated, 60% reserved\n\n### Improved\n- **Memory Management**: Enhanced memory management for critical operations\n - **Nuclear Reset Before Sampling**: All samplers now perform nuclear reset before operations\n - **Memory Pool System**: Creates memory pools to prevent fragmentation\n - **Better Error Prevention**: More aggressive memory management prevents allocation failures\n - **Memory Synchronization**: Enhanced synchronization between cache clears and garbage collection\n\n### Technical Details\n- **Environment Variables**: Set `PYTORCH_CUDA_ALLOC_CONF` and `PYTORCH_HIP_ALLOC_CONF` before PyTorch import\n- **Memory Pool**: Pre-allocates 20% of total memory in 4 blocks to prevent fragmentation\n- **Nuclear Reset**: 20x cache clearing with memory pool recreation\n- **PyTorch Patching**: Patched `memory_allocated` and `memory_reserved` for automatic cleanup\n- **Critical Operations**: Nuclear reset before all sampling operations\n\n## [1.0.23] - 2025-01-19\n\n### Added\n- **ROCMEmergencyMemoryReset**: New emergency memory reset node for critical situations\n - **Three Reset Levels**: Aggressive, Emergency, and Nuclear memory reset options\n - **Memory Status Reporting**: Detailed before/after memory status and improvement metrics\n - **Smart Recommendations**: Context-aware recommendations based on memory freed\n - **Nuclear Reset**: Most aggressive 15x cache clearing for extreme situations\n\n### Fixed\n- **Critical Memory Fragmentation**: Fixed persistent OOM errors due to memory fragmentation\n - **Early Environment Setup**: Memory environment variables now set at module import time\n - **PyTorch Memory Patching**: Patched PyTorch's memory management for aggressive cleanup\n - **Emergency Memory Reset**: Added `emergency_memory_reset()` with 10x cache clearing\n - **ComfyUI Memory Patching**: Patched ComfyUI's memory management for aggressive cleanup\n - **Lower Fragmentation Threshold**: Reduced fragmentation detection threshold from 10% to 5%\n - **Enhanced Defragmentation**: Increased cache clearing from 3x to 5x iterations\n\n### Improved\n- **Memory Management**: Enhanced memory management for critical operations\n - **Emergency Reset Before Sampling**: All samplers now perform emergency reset before operations\n - **PyTorch Memory Patching**: Patched `torch.cuda.empty_cache` and `torch.cuda.memory_allocated`\n - **Module-Level Patching**: ComfyUI memory management patched at import time\n - **Better Error Prevention**: More aggressive memory management prevents allocation failures\n - **Memory Synchronization**: Enhanced synchronization between cache clears and garbage collection\n\n### Technical Details\n- **Environment Variables**: Set `PYTORCH_CUDA_ALLOC_CONF` and `PYTORCH_HIP_ALLOC_CONF` at import\n- **Memory Patching**: PyTorch and ComfyUI memory management patched for aggressive cleanup\n- **Emergency Reset**: 10x cache clearing with garbage collection for critical operations\n- **Nuclear Reset**: 15x cache clearing for extreme memory situations\n- **Fragmentation Detection**: Lowered threshold to 5% for earlier intervention\n- **Critical Operations**: Emergency reset before all sampling operations\n\n## [1.0.22] - 2025-01-19\n\n### Added\n- **ROCMMemorySafeKSampler**: New memory-safe KSampler specifically designed to prevent OOM errors\n - **Memory Safety Levels**: Conservative, balanced, and aggressive memory management modes\n - **Progressive Parameter Reduction**: Automatically reduces steps, CFG, and denoise when memory is low\n - **Emergency Memory Cleanup**: 5x cache clearing with synchronization for critical memory situations\n - **Memory Safety Checks**: Pre-flight validation before operations to prevent OOM errors\n - **Ultra-Conservative Fallback**: Last-resort sampling with minimal parameters when all else fails\n\n### Fixed\n- **HIP Out of Memory Errors**: Comprehensive OOM prevention system\n - **Emergency Memory Cleanup**: `emergency_memory_cleanup()` function with 5x cache clearing and synchronization\n - **Memory Safety Validation**: `check_memory_safety()` function to validate available memory before operations\n - **Critical Memory Thresholds**: 2GB free memory triggers emergency cleanup, 4GB triggers aggressive cleanup\n - **Enhanced Fallback Mechanisms**: Multiple levels of fallback with increasingly conservative parameters\n - **Memory Error Detection**: Automatic detection of memory-related errors and appropriate response\n\n### Improved\n- **Memory Management**: Enhanced OOM prevention and memory monitoring\n - **Conservative Memory Fractions**: Reduced maximum memory fraction to 70% for better safety\n - **Memory Monitoring**: Added memory safety checks before VAE decode operations\n - **Progressive Error Recovery**: Three-tier fallback system (standard \u2192 conservative \u2192 ultra-conservative)\n - **Better Error Handling**: Enhanced detection and handling of memory-related errors\n - **Video Workflow Optimization**: Improved memory management for WAN VAE video processing\n\n### Technical Details\n- **Emergency Cleanup**: 5x `torch.cuda.empty_cache()` with `torch.cuda.synchronize()` between calls\n- **Memory Safety**: Pre-operation validation with configurable memory requirements\n- **Fallback Strategy**: Standard \u2192 Conservative \u2192 Ultra-Conservative parameter reduction\n- **Error Detection**: Pattern matching for \"out of memory\" and \"oom\" error messages\n- **Memory Fraction**: Conservative 70% maximum memory allocation for stability\n\n## [1.0.21] - 2024-12-19\n\n### Fixed\n- **Memory Calculation Accuracy**: Fixed critical memory calculation errors\n - **Corrected free memory calculation**: Now uses `reserved_memory` instead of `allocated_memory`\n - **Added reserved memory monitoring**: Shows both allocated and reserved memory for accurate picture\n - **Dynamic memory fraction calculation**: Adapts to actual available memory (60-85% for regular, 55-80% for video)\n - **Consistent memory thresholds**: 4GB for regular KSampler, 3GB for video workflows\n - **Improved memory cleanup**: Aggressive cleanup with multiple cache clears and garbage collection\n\n### Improved\n- **Memory Management**: Enhanced OOM prevention and memory monitoring\n - Added `get_gpu_memory_info()` helper function for accurate memory reporting\n - Added `aggressive_memory_cleanup()` helper function for consistent cleanup\n - Better error handling for memory operations\n - More accurate memory reporting in debug output\n\n## [1.0.20] - 2024-12-19\n\n### Improved\n- **ROCM KSampler Advanced Performance**: Optimized for video workflows\n - Added detailed progress indicators to reduce perceived idle time\n - Optimized memory management for video processing\n - Added video-specific progress feedback and status messages\n - Improved fallback handling with better error reporting\n - Enhanced completion feedback for better user experience\n\n### Fixed\n- **Idle Time Issues**: Reduced apparent idle time in video workflows\n - Added progress indicators during parameter preparation\n - Better feedback during noise preparation and sampling\n - Clearer status messages throughout the sampling process\n\n## [1.0.19] - 2024-12-19\n\n### Fixed\n- **Repository Cleanup**: Resolved merge conflicts and cleaned up repository state\n - Fixed unmerged commits issue that was preventing git operations\n - Removed all merge conflict markers from source files\n - Repository now in clean, publishable state\n\n## [1.0.18] - 2024-12-19\n\n### Fixed\n- **Critical VAE Decode Bug**: Fixed tensor dimension mismatch in tiled decoding\n - Corrected `out_channels` parameter from `vae.latent_channels` (16) to 3 (RGB)\n - Resolves \"The size of tensor a (16) must match the size of tensor b (3)\" error\n - Fixes VAE decode failures when processing images with tiled decoding\n\n## [1.0.13] - 2024-10-10\n\n### Fixed\n- **Critical WAN Video Workflow Errors**: Fixed all three major errors preventing WAN video generation\n- **AttributeError**: Fixed `'dict' object has no attribute 'shape'` in VAE decode\n- **IndexError**: Fixed `tuple index out of range` in WAN VAE memory calculation\n- **ValueError**: Fixed `Expected numpy array with ndim 3 but got 4` in video output format\n- **Video Tensor Format**: Proper 5D\u21924D tensor conversion for ComfyUI compatibility\n- **Memory Management**: Corrected WAN VAE memory calculation for 5D tensors\n\n### Added\n- **Comprehensive Test Suite**: 9 test cases covering all error scenarios\n- **Error Prevention Tests**: Automated testing for AttributeError, IndexError, ValueError\n- **Performance Benchmarks**: Decode timing tests for various tensor sizes\n- **Debug Data Collection**: Timestamped debug data for optimization analysis\n- **Video Processing Tests**: Chunked video processing validation\n- **Memory Calculation Tests**: Edge case testing for various tensor shapes\n\n### Improved\n- **WAN Video Support**: Full end-to-end WAN video generation working\n- **ROCm Compatibility**: Better AMD GPU optimization for gfx1151 architecture\n- **Error Handling**: Robust error recovery and fallback mechanisms\n- **Code Quality**: Cleaned up unused files and improved documentation\n\n### Technical Details\n- **VAE Input Format**: Corrected tensor format from 4D to 5D for WAN VAE\n- **Output Format**: Proper 5D\u21924D conversion for ComfyUI video save\n- **Memory Calculation**: Fixed WAN VAE memory calculation for 5D tensors\n- **Test Coverage**: 100% error scenario coverage with automated testing\n\n## [1.2.0] - 2024-12-19\n\n### Added\n- **WindowsPaginationDiagnostic**: New diagnostic node for Windows pagination error 1455\n- **Windows-specific memory management**: Automatic detection and fixes for Windows memory issues\n- **Comprehensive Windows troubleshooting**: 6 different methods to fix pagination errors\n- **PowerShell and Batch scripts**: Ready-to-use scripts for automatic fixes\n- **Real-time memory monitoring**: Live memory status and recommendations\n- **psutil dependency**: Added for advanced memory diagnostics\n\n### Fixed\n- **Windows pagination error 1455**: \"Le fichier de pagination est insuffisant pour terminer cette op\u00e9ration\"\n- **Memory allocation issues on Windows**: Better environment variable management\n- **Process priority optimization**: High priority for better memory management\n- **Aggressive garbage collection**: More frequent cleanup on Windows systems\n\n### Enhanced\n- **Automatic environment variable setup**: PYTORCH_CUDA_ALLOC_CONF, PYTORCH_HIP_ALLOC_CONF\n- **Memory availability checks**: Pre-flight checks before operations\n- **Windows-specific optimizations**: Platform detection and targeted fixes\n- **Error handling**: Better fallback mechanisms for Windows users\n- **Documentation**: Comprehensive Windows troubleshooting guide\n\n### Technical Details\n- Added platform detection and Windows-specific code paths\n- Enhanced memory management with psutil integration\n- Automatic garbage collection threshold adjustment\n- Process priority optimization for better memory handling\n- Comprehensive error detection and user guidance\n\n## [1.1.0] - 2024-12-18\n\n### Fixed\n- **Critical memory allocation bug**: Reduced attention memory modifier from 3x to 1.5x for AMD GPUs\n- **HIP memory management**: Added PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True\n- **Flash attention issues**: Disabled flash attention for AMD GPUs (causes memory issues)\n- **Fallback error handling**: Fixed 'NoneType' object has no attribute 'shape' error\n\n### Performance Improvements\n- **Image Generation (Flux)**: 1024x1024 generation: 500s \u2192 110s (78% improvement!)\n- **i2v Generation (WAN 2.2)**: 320x320px, 2s: 163s \u2192 139s (15% improvement!)\n- **Higher resolutions**: Successfully handles up to 480x720px i2v generation\n\n## [1.0.0] - 2024-12-19\n\n### Added\n- **ROCMOptimizedVAEDecode**: Main optimized VAE decode node for gfx1151 architecture\n- **ROCMOptimizedVAEDecodeTiled**: Advanced tiled VAE decode with temporal support\n- **ROCMOptimizedKSampler**: Optimized KSampler with ROCm-specific optimizations\n- **ROCMOptimizedKSamplerAdvanced**: Advanced KSampler with extended control options\n- **ROCMVAEPerformanceMonitor**: VAE performance analysis and optimization recommendations\n- **ROCMSamplerPerformanceMonitor**: Sampler performance analysis and recommendations\n- Comprehensive documentation and example workflows\n- MIT License and proper package structure\n\n### Features\n- ROCm 6.4+ optimizations for AMD GPUs\n- gfx1151 architecture-specific tuning\n- Automatic precision selection (fp32 for optimal ROCm performance)\n- Memory management optimizations for AMD GPUs\n- Attention mechanism optimizations\n- Performance monitoring and logging\n- Conservative batching strategies for better VRAM usage\n\n### Performance Improvements\n- VAE Decode: 15-25% faster, 20-30% better VRAM usage\n- Sampling: 10-20% faster with better memory management\n- Overall Workflow: 20-40% faster end-to-end generation\n- Memory Efficiency: 25-35% better VRAM usage overall\n- Reduced OOM errors with better memory management\n\n### Technical Details\n- Optimized tile sizes (768-1024) for gfx1151 memory bandwidth\n- Disabled TF32, enabled fp16 accumulation for AMD GPUs\n- Smart memory clearing and fraction setting\n- Optimized attention mechanisms for ROCm\n- Conservative batching for 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