From 66669b2ded7d8f362fdf64bb1c77a8df0f684e2f Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Fri, 8 May 2026 17:32:14 -0700 Subject: [PATCH 1/7] I don't think there was any because nobody complained. (#13807) --- comfy/utils.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/comfy/utils.py b/comfy/utils.py index 7b7faad3a..91e1ba3d3 100644 --- a/comfy/utils.py +++ b/comfy/utils.py @@ -1390,7 +1390,7 @@ def convert_old_quants(state_dict, model_prefix="", metadata={}): k_out = "{}.weight_scale".format(layer) if layer is not None: - layer_conf = {"format": "float8_e4m3fn"} # TODO: check if anyone did some non e4m3fn scaled checkpoints + layer_conf = {"format": "float8_e4m3fn"} if full_precision_matrix_mult: layer_conf["full_precision_matrix_mult"] = full_precision_matrix_mult layers[layer] = layer_conf From 4e823431cc8291deced4fc2dcf3967be2549e4c0 Mon Sep 17 00:00:00 2001 From: Matt Miller Date: Fri, 8 May 2026 19:14:23 -0700 Subject: [PATCH 2/7] Add cloud-runtime experiment node-schema endpoints to spec (#13806) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * Add cloud-runtime experiment node-schema endpoints to spec Replace the GET operations at /api/experiment/nodes and /api/experiment/nodes/{id} with getNodeInfoSchema and getNodeByID — the optimized, ETag-tagged object_info schema endpoints the cloud frontend depends on for the workflow editor. Each operation is tagged x-runtime: [cloud] and uses the runtime-only tag for cloud-side codegen exclusion. Response headers document the ETag and Cache-Control validators; 304 Not Modified is declared for RFC 7232 conditional GETs. Remove the now-unused CloudNodeList schema to keep Spectral clean. Co-authored-by: Matt Miller * spec: document If-None-Match header on conditional GET endpoints Both `getNodeInfoSchema` and `getNodeByID` advertise `ETag` response headers and a `304 Not Modified` response, but the spec didn't declare the `If-None-Match` request header that triggers conditional validation. Adding it as an optional header parameter on both ops so client codegen exposes the conditional-GET pattern. --- openapi.yaml | 106 +++++++++++++++++++++++++-------------------------- 1 file changed, 51 insertions(+), 55 deletions(-) diff --git a/openapi.yaml b/openapi.yaml index 4216c1a6c..d4c9e67ca 100644 --- a/openapi.yaml +++ b/openapi.yaml @@ -74,6 +74,8 @@ tags: description: Cloud workflow management and versioning (cloud-only) - name: task description: Background task management (cloud-only) + - name: runtime-only + description: Operations served exclusively by the cloud runtime with no local equivalent paths: # --------------------------------------------------------------------------- @@ -2573,35 +2575,38 @@ paths: # --------------------------------------------------------------------------- /api/experiment/nodes: get: - operationId: listCloudNodes - tags: [node] - summary: List installed custom nodes - description: "[cloud-only] Returns the list of custom node packages installed in the cloud runtime." + operationId: getNodeInfoSchema + tags: [runtime-only] + summary: Get pre-rendered node info schema + description: "[cloud-only] Returns the static ComfyUI object_info schema, identical for every caller, rendered once at startup with empty model/user-file context. Served by a raw HTTP handler that writes pre-rendered bytes with ETag + Cache-Control validators for RFC 7232 conditional GETs." x-runtime: [cloud] parameters: - - name: limit - in: query + - name: If-None-Match + in: header + required: false schema: - type: integer - description: Maximum number of results - - name: offset - in: query - schema: - type: integer - description: Pagination offset + type: string + description: Entity tag previously returned by this endpoint. When present and matching, the server returns 304 Not Modified. responses: "200": - description: Custom node list + description: Node info schema + headers: + ETag: + schema: + type: string + description: Entity tag for conditional request validation + Cache-Control: + schema: + type: string + description: Cache directives for the response content: application/json: schema: - $ref: "#/components/schemas/CloudNodeList" - "401": - description: Unauthorized - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" + type: object + additionalProperties: + $ref: "#/components/schemas/NodeInfo" + "304": + description: Not Modified — returned when the client sends a matching If-None-Match header post: operationId: installCloudNode tags: [node] @@ -2651,10 +2656,10 @@ paths: /api/experiment/nodes/{id}: get: - operationId: getCloudNode - tags: [node] - summary: Get details of an installed custom node - description: "[cloud-only] Returns details about a specific installed custom node package." + operationId: getNodeByID + tags: [runtime-only] + summary: Get a single node definition by ID + description: "[cloud-only] Returns one node's definition from the pre-indexed object_info schema. Served by a raw HTTP handler that writes pre-rendered bytes with ETag + Cache-Control validators for RFC 7232 conditional GETs." x-runtime: [cloud] parameters: - name: id @@ -2662,26 +2667,33 @@ paths: required: true schema: type: string - description: Custom node package ID + description: Node class identifier + - name: If-None-Match + in: header + required: false + schema: + type: string + description: Entity tag previously returned by this endpoint. When present and matching, the server returns 304 Not Modified. responses: "200": - description: Node detail + description: Single node definition + headers: + ETag: + schema: + type: string + description: Entity tag for conditional request validation + Cache-Control: + schema: + type: string + description: Cache directives for the response content: application/json: schema: - $ref: "#/components/schemas/CloudNode" - "401": - description: Unauthorized - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" + $ref: "#/components/schemas/NodeInfo" + "304": + description: Not Modified — returned when the client sends a matching If-None-Match header "404": - description: Not found - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" + description: Node not found delete: operationId: uninstallCloudNode tags: [node] @@ -7100,22 +7112,6 @@ components: enabled: type: boolean - CloudNodeList: - type: object - x-runtime: [cloud] - description: "[cloud-only] Paginated list of installed custom node packages." - required: - - nodes - properties: - nodes: - type: array - items: - $ref: "#/components/schemas/CloudNode" - total: - type: integer - has_more: - type: boolean - HubLabel: type: object x-runtime: [cloud] From 8b08bfdcbe2b4cd8f4426bd1111aaf17b118e33d Mon Sep 17 00:00:00 2001 From: lin-bot23 Date: Sat, 9 May 2026 12:26:13 +0900 Subject: [PATCH 3/7] Add description field to blueprint subgraphs (#13797) * Add description field to all blueprint subgraphs Sets the 'description' field on every subgraph blueprint node, which will show on the node preview and tooltip. Covers all 51 blueprint files under blueprints/. * Update blueprint descriptions with researched model info * Refine blueprint descriptions with researched model specs from docs Updates subgraph descriptions across all 51 blueprints with accurate model details drawn from ComfyUI docs, including: - Flux.1 Dev: 12B open-weights, Pro-level quality - Flux.2 Klein 4B: fastest Flux, distilled architecture - Qwen-Image: 20B MMDiT, multilingual text rendering - Z-Image-Turbo: distilled 6B DiT, sub-second inference - LTX-2/2.3: 19B DiT audio-video foundation model - Wan2.2: open-source, 14B/1.3B variants - ACE-Step 1.5: ~1s full-song generation - GPU shader nodes consistently labeled as fragment shaders * Strip marketing fluff and license info from descriptions * Fix Canny to Video (LTX 2.0) description * Remove 'local-' prefix from subgraph names * Preserve UTF-8 encoding in JSON files (ensure_ascii=False) * Apply review suggestions from alexisrolland - Rename 'Image to Model (Hunyuan3d 2.1)' -> 'Image to 3D Model (Hunyuan3d 2.1)' - Rename 'Image Upscale(Z-image-Turbo)' -> 'Image Upscale (Z-image-Turbo)' - Rename 'Video Inpaint(Wan2.1 VACE)' -> 'Video Inpaint (Wan 2.1 VACE)' - Use 'Black Forest Labs' branding in Flux descriptions - Use 'Google's Gemini' with possessive in captioning nodes - Normalize 'Wan 2.2' and 'Wan 2.1' spacing in descriptions * fix: revert Color Adjustment.json to preserve original GLSL shader content Only adds the 'description' field without modifying the shader code (which contained Unicode escape \\u2192 that should be preserved). * Apply CodeRabbit review suggestions - Color Adjustment: include vibrance in description - Image Blur: expand to Gaussian/Box/Radial modes - Flux.2 Klein 4B: narrow to image edit only (no T2I) - NetaYume Lumina: correct model base (Neta Lumina, not Lumina-Next) --------- Co-authored-by: linmoumou Co-authored-by: Daxiong (Lin) --- blueprints/Brightness and Contrast.json | 5 +++-- blueprints/Canny to Image (Z-Image-Turbo).json | 7 ++++--- blueprints/Canny to Video (LTX 2.0).json | 7 ++++--- blueprints/Chromatic Aberration.json | 5 +++-- blueprints/Color Adjustment.json | 3 ++- blueprints/Color Balance.json | 3 ++- blueprints/Color Curves.json | 3 ++- blueprints/Crop Images 2x2.json | 3 ++- blueprints/Crop Images 3x3.json | 3 ++- blueprints/Depth to Image (Z-Image-Turbo).json | 6 ++++-- blueprints/Depth to Video (ltx 2.0).json | 6 ++++-- blueprints/Edge-Preserving Blur.json | 5 +++-- blueprints/Film Grain.json | 5 +++-- blueprints/First-Last-Frame to Video (LTX-2.3).json | 3 ++- blueprints/Glow.json | 5 +++-- blueprints/Hue and Saturation.json | 5 +++-- blueprints/Image Blur.json | 3 ++- blueprints/Image Captioning (gemini).json | 3 ++- blueprints/Image Channels.json | 5 +++-- blueprints/Image Edit (FireRed Image Edit 1.1).json | 3 ++- blueprints/Image Edit (Flux.2 Klein 4B).json | 8 +++++--- blueprints/Image Edit (LongCat Image Edit).json | 3 ++- blueprints/Image Edit (Qwen 2511).json | 7 ++++--- blueprints/Image Inpainting (Flux.1 Fill Dev).json | 5 +++-- blueprints/Image Inpainting (Qwen-image).json | 6 ++++-- blueprints/Image Levels.json | 5 +++-- blueprints/Image Outpainting (Qwen-Image).json | 9 ++++++--- blueprints/Image Upscale(Z-image-Turbo).json | 5 +++-- blueprints/Image to Depth Map (Lotus).json | 7 ++++--- blueprints/Image to Layers(Qwen-Image-Layered).json | 3 ++- blueprints/Image to Model (Hunyuan3d 2.1).json | 5 +++-- blueprints/Image to Video (LTX-2.3).json | 3 ++- blueprints/Image to Video (Wan 2.2).json | 5 +++-- blueprints/Pose to Image (Z-Image-Turbo).json | 7 ++++--- blueprints/Pose to Video (LTX 2.0).json | 3 ++- blueprints/Prompt Enhance.json | 5 +++-- blueprints/Sharpen.json | 5 +++-- blueprints/Text to Audio (ACE-Step 1.5).json | 7 ++++--- blueprints/Text to Image (Flux.1 Dev).json | 5 +++-- blueprints/Text to Image (Flux.1 Krea Dev).json | 5 +++-- blueprints/Text to Image (NetaYume Lumina).json | 8 +++++--- blueprints/Text to Image (Qwen-Image 2512).json | 3 ++- blueprints/Text to Image (Qwen-Image).json | 3 ++- blueprints/Text to Image (Z-Image-Turbo).json | 7 ++++--- blueprints/Text to Video (LTX-2.3).json | 3 ++- blueprints/Text to Video (Wan 2.2).json | 5 +++-- blueprints/Unsharp Mask.json | 5 +++-- blueprints/Video Captioning (Gemini).json | 3 ++- blueprints/Video Inpaint(Wan2.1 VACE).json | 5 +++-- blueprints/Video Stitch.json | 5 +++-- blueprints/Video Upscale(GAN x4).json | 5 +++-- 51 files changed, 153 insertions(+), 95 deletions(-) diff --git a/blueprints/Brightness and Contrast.json b/blueprints/Brightness and Contrast.json index 90bfe999d..78fc52f29 100644 --- a/blueprints/Brightness and Contrast.json +++ b/blueprints/Brightness and Contrast.json @@ -431,9 +431,10 @@ "extra": { "workflowRendererVersion": "LG" }, - "category": "Image Tools/Color adjust" + "category": "Image Tools/Color adjust", + "description": "Adjusts image brightness and contrast using a real-time GPU fragment shader." } ] }, "extra": {} -} +} \ No newline at end of file diff --git a/blueprints/Canny to Image (Z-Image-Turbo).json b/blueprints/Canny to Image (Z-Image-Turbo).json index ff9717308..14deb64cc 100644 --- a/blueprints/Canny to Image (Z-Image-Turbo).json +++ b/blueprints/Canny to Image (Z-Image-Turbo).json @@ -162,7 +162,7 @@ }, "revision": 0, "config": {}, - "name": "local-Canny to Image (Z-Image-Turbo)", + "name": "Canny to Image (Z-Image-Turbo)", "inputNode": { "id": -10, "bounding": [ @@ -1553,7 +1553,8 @@ "VHS_MetadataImage": true, "VHS_KeepIntermediate": true }, - "category": "Image generation and editing/Canny to image" + "category": "Image generation and editing/Canny to image", + "description": "Generates an image from a Canny edge map using Z-Image-Turbo, with text conditioning." } ] }, @@ -1574,4 +1575,4 @@ } }, "version": 0.4 -} +} \ No newline at end of file diff --git a/blueprints/Canny to Video (LTX 2.0).json b/blueprints/Canny to Video (LTX 2.0).json index fae8321b9..a9682c8a4 100644 --- a/blueprints/Canny to Video (LTX 2.0).json +++ b/blueprints/Canny to Video (LTX 2.0).json @@ -192,7 +192,7 @@ }, "revision": 0, "config": {}, - "name": "local-Canny to Video (LTX 2.0)", + "name": "Canny to Video (LTX 2.0)", "inputNode": { "id": -10, "bounding": [ @@ -3600,7 +3600,8 @@ "extra": { "workflowRendererVersion": "LG" }, - "category": "Video generation and editing/Canny to video" + "category": "Video generation and editing/Canny to video", + "description": "Generates video from Canny edge maps using LTX-2, with optional synchronized audio." } ] }, @@ -3616,4 +3617,4 @@ } }, "version": 0.4 -} +} \ No newline at end of file diff --git a/blueprints/Chromatic Aberration.json b/blueprints/Chromatic Aberration.json index ae8037b1b..893fb1190 100644 --- a/blueprints/Chromatic Aberration.json +++ b/blueprints/Chromatic Aberration.json @@ -377,8 +377,9 @@ "extra": { "workflowRendererVersion": "LG" }, - "category": "Image Tools/Color adjust" + "category": "Image Tools/Color adjust", + "description": "Adds lens-style chromatic aberration (color fringing) using a real-time GPU fragment shader." } ] } -} +} \ No newline at end of file diff --git a/blueprints/Color Adjustment.json b/blueprints/Color Adjustment.json index 622bf28af..5abbf8baa 100644 --- a/blueprints/Color Adjustment.json +++ b/blueprints/Color Adjustment.json @@ -596,7 +596,8 @@ "extra": { "workflowRendererVersion": "LG" }, - "category": "Image Tools/Color adjust" + "category": "Image Tools/Color adjust", + "description": "Adjusts saturation, temperature, tint, and vibrance using a real-time GPU fragment shader." } ] } diff --git a/blueprints/Color Balance.json b/blueprints/Color Balance.json index 21d6319ed..d921eab37 100644 --- a/blueprints/Color Balance.json +++ b/blueprints/Color Balance.json @@ -1129,7 +1129,8 @@ "extra": { "workflowRendererVersion": "LG" }, - "category": "Image Tools/Color adjust" + "category": "Image Tools/Color adjust", + "description": "Balances colors across shadows, midtones, and highlights using a real-time GPU fragment shader." } ] } diff --git a/blueprints/Color Curves.json b/blueprints/Color Curves.json index 1461cf396..b9bfb7029 100644 --- a/blueprints/Color Curves.json +++ b/blueprints/Color Curves.json @@ -608,7 +608,8 @@ "extra": { "workflowRendererVersion": "LG" }, - "category": "Image Tools/Color adjust" + "category": "Image Tools/Color adjust", + "description": "Fine-tunes tone and color with per-channel curve adjustments using a real-time GPU fragment shader." } ] } diff --git a/blueprints/Crop Images 2x2.json b/blueprints/Crop Images 2x2.json index 2aa42cfc3..99b89b608 100644 --- a/blueprints/Crop Images 2x2.json +++ b/blueprints/Crop Images 2x2.json @@ -1609,7 +1609,8 @@ } ], "extra": {}, - "category": "Image Tools/Crop" + "category": "Image Tools/Crop", + "description": "Splits an image into a 2×2 grid of four equal tiles." } ] }, diff --git a/blueprints/Crop Images 3x3.json b/blueprints/Crop Images 3x3.json index 3a3615ac8..6ac636da4 100644 --- a/blueprints/Crop Images 3x3.json +++ b/blueprints/Crop Images 3x3.json @@ -2946,7 +2946,8 @@ } ], "extra": {}, - "category": "Image Tools/Crop" + "category": "Image Tools/Crop", + "description": "Splits an image into a 3×3 grid of nine equal tiles." } ] }, diff --git a/blueprints/Depth to Image (Z-Image-Turbo).json b/blueprints/Depth to Image (Z-Image-Turbo).json index 4f69a8149..fe9ef0f72 100644 --- a/blueprints/Depth to Image (Z-Image-Turbo).json +++ b/blueprints/Depth to Image (Z-Image-Turbo).json @@ -1579,7 +1579,8 @@ "VHS_MetadataImage": true, "VHS_KeepIntermediate": true }, - "category": "Image generation and editing/Depth to image" + "category": "Image generation and editing/Depth to image", + "description": "Generates an image from a depth map using Z-Image-Turbo with text conditioning." }, { "id": "458bdf3c-4b58-421c-af50-c9c663a4d74c", @@ -2461,7 +2462,8 @@ ] }, "workflowRendererVersion": "LG" - } + }, + "description": "Estimates a monocular depth map from an input image using the Lotus depth estimation model." } ] }, diff --git a/blueprints/Depth to Video (ltx 2.0).json b/blueprints/Depth to Video (ltx 2.0).json index f15212520..bb28695a2 100644 --- a/blueprints/Depth to Video (ltx 2.0).json +++ b/blueprints/Depth to Video (ltx 2.0).json @@ -4233,7 +4233,8 @@ "extra": { "workflowRendererVersion": "LG" }, - "category": "Video generation and editing/Depth to video" + "category": "Video generation and editing/Depth to video", + "description": "Generates video from depth maps using LTX-2, with optional synchronized audio." }, { "id": "38b60539-50a7-42f9-a5fe-bdeca26272e2", @@ -5192,7 +5193,8 @@ ], "extra": { "workflowRendererVersion": "LG" - } + }, + "description": "Estimates a monocular depth map from an input image using the Lotus depth estimation model." } ] }, diff --git a/blueprints/Edge-Preserving Blur.json b/blueprints/Edge-Preserving Blur.json index 18012beb1..fbda9f126 100644 --- a/blueprints/Edge-Preserving Blur.json +++ b/blueprints/Edge-Preserving Blur.json @@ -450,9 +450,10 @@ "extra": { "workflowRendererVersion": "LG" }, - "category": "Image Tools/Blur" + "category": "Image Tools/Blur", + "description": "Applies bilateral (edge-preserving) blur to soften images while retaining detail." } ] }, "extra": {} -} +} \ No newline at end of file diff --git a/blueprints/Film Grain.json b/blueprints/Film Grain.json index a680b3ece..3226ea9aa 100644 --- a/blueprints/Film Grain.json +++ b/blueprints/Film Grain.json @@ -580,8 +580,9 @@ "extra": { "workflowRendererVersion": "LG" }, - "category": "Image Tools/Color adjust" + "category": "Image Tools/Color adjust", + "description": "Adds procedural film grain texture for a cinematic look via GPU fragment shader." } ] } -} +} \ No newline at end of file diff --git a/blueprints/First-Last-Frame to Video (LTX-2.3).json b/blueprints/First-Last-Frame to Video (LTX-2.3).json index 8ec9ed61a..f509aefe0 100644 --- a/blueprints/First-Last-Frame to Video (LTX-2.3).json +++ b/blueprints/First-Last-Frame to Video (LTX-2.3).json @@ -3350,7 +3350,8 @@ } ], "extra": {}, - "category": "Video generation and editing/First-Last-Frame to Video" + "category": "Video generation and editing/First-Last-Frame to Video", + "description": "Generates a video interpolating between first and last keyframes using LTX-2.3." } ] }, diff --git a/blueprints/Glow.json b/blueprints/Glow.json index 1dafb2d35..2bbfdee51 100644 --- a/blueprints/Glow.json +++ b/blueprints/Glow.json @@ -575,8 +575,9 @@ "extra": { "workflowRendererVersion": "LG" }, - "category": "Image Tools/Color adjust" + "category": "Image Tools/Color adjust", + "description": "Adds a glow/bloom effect around bright image areas via GPU fragment shader." } ] } -} +} \ No newline at end of file diff --git a/blueprints/Hue and Saturation.json b/blueprints/Hue and Saturation.json index 1a2df8937..cddf0154a 100644 --- a/blueprints/Hue and Saturation.json +++ b/blueprints/Hue and Saturation.json @@ -752,8 +752,9 @@ "extra": { "workflowRendererVersion": "LG" }, - "category": "Image Tools/Color adjust" + "category": "Image Tools/Color adjust", + "description": "Adjusts hue, saturation, and lightness of an image using a real-time GPU fragment shader." } ] } -} +} \ No newline at end of file diff --git a/blueprints/Image Blur.json b/blueprints/Image Blur.json index 3c7a784b0..0ca8d9931 100644 --- a/blueprints/Image Blur.json +++ b/blueprints/Image Blur.json @@ -374,7 +374,8 @@ "extra": { "workflowRendererVersion": "LG" }, - "category": "Image Tools/Blur" + "category": "Image Tools/Blur", + "description": "Applies Gaussian, Box, or Radial blur to soften images and create stylized depth or motion effects." } ] } diff --git a/blueprints/Image Captioning (gemini).json b/blueprints/Image Captioning (gemini).json index 98cfb8999..2fc5d6746 100644 --- a/blueprints/Image Captioning (gemini).json +++ b/blueprints/Image Captioning (gemini).json @@ -310,7 +310,8 @@ "extra": { "workflowRendererVersion": "LG" }, - "category": "Text generation/Image Captioning" + "category": "Text generation/Image Captioning", + "description": "Generates descriptive captions for images using Google's Gemini multimodal LLM." } ] } diff --git a/blueprints/Image Channels.json b/blueprints/Image Channels.json index 9c7b675b2..b6fdff5be 100644 --- a/blueprints/Image Channels.json +++ b/blueprints/Image Channels.json @@ -315,8 +315,9 @@ "extra": { "workflowRendererVersion": "LG" }, - "category": "Image Tools/Color adjust" + "category": "Image Tools/Color adjust", + "description": "Manipulates individual RGBA channels for masking, compositing, and channel effects." } ] } -} +} \ No newline at end of file diff --git a/blueprints/Image Edit (FireRed Image Edit 1.1).json b/blueprints/Image Edit (FireRed Image Edit 1.1).json index c34246ce6..14310353c 100644 --- a/blueprints/Image Edit (FireRed Image Edit 1.1).json +++ b/blueprints/Image Edit (FireRed Image Edit 1.1).json @@ -2138,7 +2138,8 @@ "extra": { "workflowRendererVersion": "LG" }, - "category": "Image generation and editing/Edit image" + "category": "Image generation and editing/Edit image", + "description": "Edits images via text instructions using FireRed Image Edit 1.1, a diffusion-based instruction-following editing model." } ] }, diff --git a/blueprints/Image Edit (Flux.2 Klein 4B).json b/blueprints/Image Edit (Flux.2 Klein 4B).json index 6f2f7dc01..7f6fa7a4b 100644 --- a/blueprints/Image Edit (Flux.2 Klein 4B).json +++ b/blueprints/Image Edit (Flux.2 Klein 4B).json @@ -1472,7 +1472,8 @@ "extra": { "workflowRendererVersion": "LG" }, - "category": "Image generation and editing/Edit image" + "category": "Image generation and editing/Edit image", + "description": "Edits an input image via text instructions using FLUX.2 [klein] 4B." }, { "id": "6007e698-2ebd-4917-84d8-299b35d7b7ab", @@ -1821,7 +1822,8 @@ ], "extra": { "workflowRendererVersion": "LG" - } + }, + "description": "Applies reference image conditioning for style/identity transfer (Flux.2 Klein 4B)." } ] }, @@ -1837,4 +1839,4 @@ } }, "version": 0.4 -} \ No newline at end of file +} diff --git a/blueprints/Image Edit (LongCat Image Edit).json b/blueprints/Image Edit (LongCat Image Edit).json index 5b4eb18f0..de1c155a2 100644 --- a/blueprints/Image Edit (LongCat Image Edit).json +++ b/blueprints/Image Edit (LongCat Image Edit).json @@ -1417,7 +1417,8 @@ } ], "extra": {}, - "category": "Image generation and editing/Edit image" + "category": "Image generation and editing/Edit image", + "description": "Edits images via text instructions using LongCat Image Edit, an instruction-following image editing diffusion model." } ] }, diff --git a/blueprints/Image Edit (Qwen 2511).json b/blueprints/Image Edit (Qwen 2511).json index 582171fa0..1aa7e5765 100644 --- a/blueprints/Image Edit (Qwen 2511).json +++ b/blueprints/Image Edit (Qwen 2511).json @@ -132,7 +132,7 @@ }, "revision": 0, "config": {}, - "name": "local-Image Edit (Qwen 2511)", + "name": "Image Edit (Qwen 2511)", "inputNode": { "id": -10, "bounding": [ @@ -1468,7 +1468,8 @@ "VHS_MetadataImage": true, "VHS_KeepIntermediate": true }, - "category": "Image generation and editing/Edit image" + "category": "Image generation and editing/Edit image", + "description": "Edits images via text instructions using Qwen-Image-Edit-2511 with improved character consistency and integrated LoRA." } ] }, @@ -1489,4 +1490,4 @@ } }, "version": 0.4 -} +} \ No newline at end of file diff --git a/blueprints/Image Inpainting (Flux.1 Fill Dev).json b/blueprints/Image Inpainting (Flux.1 Fill Dev).json index d40d63594..c1326ed3d 100644 --- a/blueprints/Image Inpainting (Flux.1 Fill Dev).json +++ b/blueprints/Image Inpainting (Flux.1 Fill Dev).json @@ -1188,7 +1188,8 @@ "extra": { "workflowRendererVersion": "LG" }, - "category": "Image generation and editing/Inpaint image" + "category": "Image generation and editing/Inpaint image", + "description": "Inpaints masked image regions using Flux.1 fill [dev], Black Forest Labs' inpainting/outpainting model." } ] }, @@ -1202,4 +1203,4 @@ }, "ue_links": [] } -} \ No newline at end of file +} diff --git a/blueprints/Image Inpainting (Qwen-image).json b/blueprints/Image Inpainting (Qwen-image).json index 95b2909fa..a06d57e19 100644 --- a/blueprints/Image Inpainting (Qwen-image).json +++ b/blueprints/Image Inpainting (Qwen-image).json @@ -1548,7 +1548,8 @@ "extra": { "workflowRendererVersion": "LG" }, - "category": "Image generation and editing/Inpaint image" + "category": "Image generation and editing/Inpaint image", + "description": "Inpaints masked regions using Qwen-Image, extending its multilingual text rendering to inpainting tasks." }, { "id": "56a1f603-fbd2-40ed-94ef-c9ecbd96aca8", @@ -1907,7 +1908,8 @@ ], "extra": { "workflowRendererVersion": "LG" - } + }, + "description": "Expands and softens mask edges to reduce visible seams after image processing." } ] }, diff --git a/blueprints/Image Levels.json b/blueprints/Image Levels.json index ef256a1aa..1a1b18932 100644 --- a/blueprints/Image Levels.json +++ b/blueprints/Image Levels.json @@ -742,9 +742,10 @@ "extra": { "workflowRendererVersion": "LG" }, - "category": "Image Tools/Color adjust" + "category": "Image Tools/Color adjust", + "description": "Adjusts black point, white point, and gamma for tonal range control via GPU shader." } ] }, "extra": {} -} +} \ No newline at end of file diff --git a/blueprints/Image Outpainting (Qwen-Image).json b/blueprints/Image Outpainting (Qwen-Image).json index 218fdc775..6c07227c0 100644 --- a/blueprints/Image Outpainting (Qwen-Image).json +++ b/blueprints/Image Outpainting (Qwen-Image).json @@ -1919,7 +1919,8 @@ "extra": { "workflowRendererVersion": "LG" }, - "category": "Image generation and editing/Outpaint image" + "category": "Image generation and editing/Outpaint image", + "description": "Outpaints beyond image boundaries using Qwen-Image's outpainting capabilities." }, { "id": "f93c215e-c393-460e-9534-ed2c3d8a652e", @@ -2278,7 +2279,8 @@ ], "extra": { "workflowRendererVersion": "LG" - } + }, + "description": "Expands and softens mask edges to reduce visible seams after image processing." }, { "id": "2a4b2cc0-db37-4302-a067-da392f38f06b", @@ -2733,7 +2735,8 @@ ], "extra": { "workflowRendererVersion": "LG" - } + }, + "description": "Scales both image and mask together while preserving alignment for editing workflows." } ] }, diff --git a/blueprints/Image Upscale(Z-image-Turbo).json b/blueprints/Image Upscale(Z-image-Turbo).json index 0d2b6e240..bd803a0b1 100644 --- a/blueprints/Image Upscale(Z-image-Turbo).json +++ b/blueprints/Image Upscale(Z-image-Turbo).json @@ -141,7 +141,7 @@ }, "revision": 0, "config": {}, - "name": "local-Image Upscale(Z-image-Turbo)", + "name": "Image Upscale (Z-image-Turbo)", "inputNode": { "id": -10, "bounding": [ @@ -1302,7 +1302,8 @@ "extra": { "workflowRendererVersion": "LG" }, - "category": "Image generation and editing/Enhance" + "category": "Image generation and editing/Enhance", + "description": "Upscales images to higher resolution using Z-Image-Turbo." } ] }, diff --git a/blueprints/Image to Depth Map (Lotus).json b/blueprints/Image to Depth Map (Lotus).json index 089f2cd42..12f10ba5b 100644 --- a/blueprints/Image to Depth Map (Lotus).json +++ b/blueprints/Image to Depth Map (Lotus).json @@ -99,7 +99,7 @@ }, "revision": 0, "config": {}, - "name": "local-Image to Depth Map (Lotus)", + "name": "Image to Depth Map (Lotus)", "inputNode": { "id": -10, "bounding": [ @@ -948,7 +948,8 @@ "extra": { "workflowRendererVersion": "LG" }, - "category": "Image generation and editing/Depth to image" + "category": "Image generation and editing/Depth to image", + "description": "Estimates a monocular depth map from an input image using the Lotus depth estimation model." } ] }, @@ -964,4 +965,4 @@ "workflowRendererVersion": "LG" }, "version": 0.4 -} +} \ No newline at end of file diff --git a/blueprints/Image to Layers(Qwen-Image-Layered).json b/blueprints/Image to Layers(Qwen-Image-Layered).json index 8a525e7a5..7b44f0563 100644 --- a/blueprints/Image to Layers(Qwen-Image-Layered).json +++ b/blueprints/Image to Layers(Qwen-Image-Layered).json @@ -1586,7 +1586,8 @@ "extra": { "workflowRendererVersion": "LG" }, - "category": "Image generation and editing/Image to layers" + "category": "Image generation and editing/Image to layers", + "description": "Decomposes an image into variable-resolution RGBA layers for independent editing using Qwen-Image-Layered." } ] }, diff --git a/blueprints/Image to Model (Hunyuan3d 2.1).json b/blueprints/Image to Model (Hunyuan3d 2.1).json index 4705603a8..ee5552656 100644 --- a/blueprints/Image to Model (Hunyuan3d 2.1).json +++ b/blueprints/Image to Model (Hunyuan3d 2.1).json @@ -72,7 +72,7 @@ }, "revision": 0, "config": {}, - "name": "local-Image to Model (Hunyuan3d 2.1)", + "name": "Image to 3D Model (Hunyuan3d 2.1)", "inputNode": { "id": -10, "bounding": [ @@ -765,7 +765,8 @@ "extra": { "workflowRendererVersion": "LG" }, - "category": "3D/Image to 3D Model" + "category": "3D/Image to 3D Model", + "description": "Generates 3D mesh models from a single input image using Hunyuan3D 2.0/2.1." } ] }, diff --git a/blueprints/Image to Video (LTX-2.3).json b/blueprints/Image to Video (LTX-2.3).json index 86a601130..3db524ea0 100644 --- a/blueprints/Image to Video (LTX-2.3).json +++ b/blueprints/Image to Video (LTX-2.3).json @@ -4223,7 +4223,8 @@ "extra": { "workflowRendererVersion": "Vue-corrected" }, - "category": "Video generation and editing/Image to video" + "category": "Video generation and editing/Image to video", + "description": "Generates video from a single input image using LTX-2.3." } ] }, diff --git a/blueprints/Image to Video (Wan 2.2).json b/blueprints/Image to Video (Wan 2.2).json index a8dafd3c9..3510aad18 100644 --- a/blueprints/Image to Video (Wan 2.2).json +++ b/blueprints/Image to Video (Wan 2.2).json @@ -206,7 +206,7 @@ }, "revision": 0, "config": {}, - "name": "local-Image to Video (Wan 2.2)", + "name": "Image to Video (Wan 2.2)", "inputNode": { "id": -10, "bounding": [ @@ -2027,7 +2027,8 @@ "extra": { "workflowRendererVersion": "LG" }, - "category": "Video generation and editing/Image to video" + "category": "Video generation and editing/Image to video", + "description": "Generates video from an image and text prompt using Wan 2.2, supporting T2V and I2V." } ] }, diff --git a/blueprints/Pose to Image (Z-Image-Turbo).json b/blueprints/Pose to Image (Z-Image-Turbo).json index a55410ba4..5c2749efe 100644 --- a/blueprints/Pose to Image (Z-Image-Turbo).json +++ b/blueprints/Pose to Image (Z-Image-Turbo).json @@ -134,7 +134,7 @@ }, "revision": 0, "config": {}, - "name": "local-Pose to Image (Z-Image-Turbo)", + "name": "Pose to Image (Z-Image-Turbo)", "inputNode": { "id": -10, "bounding": [ @@ -1298,7 +1298,8 @@ "VHS_MetadataImage": true, "VHS_KeepIntermediate": true }, - "category": "Image generation and editing/Pose to image" + "category": "Image generation and editing/Pose to image", + "description": "Generates an image from pose keypoints using Z-Image-Turbo with text conditioning." } ] }, @@ -1319,4 +1320,4 @@ } }, "version": 0.4 -} +} \ No newline at end of file diff --git a/blueprints/Pose to Video (LTX 2.0).json b/blueprints/Pose to Video (LTX 2.0).json index 580900bc0..1ce49351a 100644 --- a/blueprints/Pose to Video (LTX 2.0).json +++ b/blueprints/Pose to Video (LTX 2.0).json @@ -3870,7 +3870,8 @@ "extra": { "workflowRendererVersion": "LG" }, - "category": "Video generation and editing/Pose to video" + "category": "Video generation and editing/Pose to video", + "description": "Generates video from pose reference frames using LTX-2, with optional synchronized audio." } ] }, diff --git a/blueprints/Prompt Enhance.json b/blueprints/Prompt Enhance.json index 5e57548ff..e260b1203 100644 --- a/blueprints/Prompt Enhance.json +++ b/blueprints/Prompt Enhance.json @@ -270,9 +270,10 @@ "extra": { "workflowRendererVersion": "LG" }, - "category": "Text generation/Prompt enhance" + "category": "Text generation/Prompt enhance", + "description": "Expands short text prompts into detailed descriptions using a text generation model for better generation quality." } ] }, "extra": {} -} +} \ No newline at end of file diff --git a/blueprints/Sharpen.json b/blueprints/Sharpen.json index f332400fd..3c4099c6b 100644 --- a/blueprints/Sharpen.json +++ b/blueprints/Sharpen.json @@ -302,8 +302,9 @@ "extra": { "workflowRendererVersion": "LG" }, - "category": "Image Tools/Sharpen" + "category": "Image Tools/Sharpen", + "description": "Sharpens image details using a GPU fragment shader for enhanced clarity." } ] } -} +} \ No newline at end of file diff --git a/blueprints/Text to Audio (ACE-Step 1.5).json b/blueprints/Text to Audio (ACE-Step 1.5).json index 206cf16be..5b8b8626f 100644 --- a/blueprints/Text to Audio (ACE-Step 1.5).json +++ b/blueprints/Text to Audio (ACE-Step 1.5).json @@ -222,7 +222,7 @@ }, "revision": 0, "config": {}, - "name": "local-Text to Audio (ACE-Step 1.5)", + "name": "Text to Audio (ACE-Step 1.5)", "inputNode": { "id": -10, "bounding": [ @@ -1502,7 +1502,8 @@ "extra": { "workflowRendererVersion": "LG" }, - "category": "Audio/Music generation" + "category": "Audio/Music generation", + "description": "Generates audio/music from text prompts using ACE-Step 1.5, a diffusion-based audio generation model." } ] }, @@ -1518,4 +1519,4 @@ } }, "version": 0.4 -} +} \ No newline at end of file diff --git a/blueprints/Text to Image (Flux.1 Dev).json b/blueprints/Text to Image (Flux.1 Dev).json index 04c3cb95a..45f68f508 100644 --- a/blueprints/Text to Image (Flux.1 Dev).json +++ b/blueprints/Text to Image (Flux.1 Dev).json @@ -1029,7 +1029,8 @@ "extra": { "workflowRendererVersion": "LG" }, - "category": "Image generation and editing/Text to image" + "category": "Image generation and editing/Text to image", + "description": "Generates images from text prompts using Flux.1 [dev], Black Forest Labs' 12B diffusion model." } ] }, @@ -1043,4 +1044,4 @@ }, "ue_links": [] } -} \ No newline at end of file +} diff --git a/blueprints/Text to Image (Flux.1 Krea Dev).json b/blueprints/Text to Image (Flux.1 Krea Dev).json index fe4db1cfc..30a78dca1 100644 --- a/blueprints/Text to Image (Flux.1 Krea Dev).json +++ b/blueprints/Text to Image (Flux.1 Krea Dev).json @@ -1023,7 +1023,8 @@ "extra": { "workflowRendererVersion": "LG" }, - "category": "Image generation and editing/Text to image" + "category": "Image generation and editing/Text to image", + "description": "Generates images from text prompts using Flux.1 Krea Dev, a Black Forest Labs × Krea collaboration variant." } ] }, @@ -1037,4 +1038,4 @@ }, "ue_links": [] } -} \ No newline at end of file +} diff --git a/blueprints/Text to Image (NetaYume Lumina).json b/blueprints/Text to Image (NetaYume Lumina).json index 394ad1608..9e11b7a86 100644 --- a/blueprints/Text to Image (NetaYume Lumina).json +++ b/blueprints/Text to Image (NetaYume Lumina).json @@ -1104,7 +1104,8 @@ "extra": { "workflowRendererVersion": "LG" }, - "category": "Image generation and editing/Text to image" + "category": "Image generation and editing/Text to image", + "description": "Generates images from text prompts using NetaYume Lumina, fine-tuned from Neta Lumina for anime-style and illustration generation." }, { "id": "a07fdf06-1bda-4dac-bdbd-63ee8ebca1c9", @@ -1458,11 +1459,12 @@ ], "extra": { "workflowRendererVersion": "LG" - } + }, + "description": "Encodes a negative text prompt via CLIP for classifier-free guidance in anime-style generation (NetaYume Lumina)." } ] }, "extra": { "ue_links": [] } -} \ No newline at end of file +} diff --git a/blueprints/Text to Image (Qwen-Image 2512).json b/blueprints/Text to Image (Qwen-Image 2512).json index f52ea2ef2..09612be8b 100644 --- a/blueprints/Text to Image (Qwen-Image 2512).json +++ b/blueprints/Text to Image (Qwen-Image 2512).json @@ -1941,7 +1941,8 @@ "extra": { "workflowRendererVersion": "Vue-corrected" }, - "category": "Image generation and editing/Text to image" + "category": "Image generation and editing/Text to image", + "description": "Generates images from text prompts using Qwen-Image-2512, with enhanced human realism and finer natural detail over the base version." } ] }, diff --git a/blueprints/Text to Image (Qwen-Image).json b/blueprints/Text to Image (Qwen-Image).json index 70b4b44b3..e78d5a962 100644 --- a/blueprints/Text to Image (Qwen-Image).json +++ b/blueprints/Text to Image (Qwen-Image).json @@ -1873,7 +1873,8 @@ "extra": { "workflowRendererVersion": "LG" }, - "category": "Image generation and editing/Text to image" + "category": "Image generation and editing/Text to image", + "description": "Generates images from text prompts using Qwen-Image, Alibaba's 20B MMDiT model with excellent multilingual text rendering." } ] }, diff --git a/blueprints/Text to Image (Z-Image-Turbo).json b/blueprints/Text to Image (Z-Image-Turbo).json index 6aa80e327..6975151ea 100644 --- a/blueprints/Text to Image (Z-Image-Turbo).json +++ b/blueprints/Text to Image (Z-Image-Turbo).json @@ -149,7 +149,7 @@ }, "revision": 0, "config": {}, - "name": "local-Text to Image (Z-Image-Turbo)", + "name": "Text to Image (Z-Image-Turbo)", "inputNode": { "id": -10, "bounding": [ @@ -1054,7 +1054,8 @@ "extra": { "workflowRendererVersion": "LG" }, - "category": "Image generation and editing/Text to image" + "category": "Image generation and editing/Text to image", + "description": "Generates images from text prompts using Z-Image-Turbo, Alibaba's distilled 6B DiT model." } ] }, @@ -1075,4 +1076,4 @@ } }, "version": 0.4 -} +} \ No newline at end of file diff --git a/blueprints/Text to Video (LTX-2.3).json b/blueprints/Text to Video (LTX-2.3).json index ff9bc6ccf..f44a216dd 100644 --- a/blueprints/Text to Video (LTX-2.3).json +++ b/blueprints/Text to Video (LTX-2.3).json @@ -4286,7 +4286,8 @@ "extra": { "workflowRendererVersion": "Vue-corrected" }, - "category": "Video generation and editing/Text to video" + "category": "Video generation and editing/Text to video", + "description": "Generates video from text prompts using LTX-2.3, Lightricks' video diffusion model." } ] }, diff --git a/blueprints/Text to Video (Wan 2.2).json b/blueprints/Text to Video (Wan 2.2).json index 0ce485b67..a264a490d 100644 --- a/blueprints/Text to Video (Wan 2.2).json +++ b/blueprints/Text to Video (Wan 2.2).json @@ -1572,7 +1572,8 @@ "extra": { "workflowRendererVersion": "LG" }, - "category": "Video generation and editing/Text to video" + "category": "Video generation and editing/Text to video", + "description": "Generates video from text prompts using Wan2.2, Alibaba's diffusion video model." } ] }, @@ -1586,4 +1587,4 @@ "VHS_KeepIntermediate": true }, "version": 0.4 -} +} \ No newline at end of file diff --git a/blueprints/Unsharp Mask.json b/blueprints/Unsharp Mask.json index 137acaa43..79a4c954f 100644 --- a/blueprints/Unsharp Mask.json +++ b/blueprints/Unsharp Mask.json @@ -434,8 +434,9 @@ "extra": { "workflowRendererVersion": "LG" }, - "category": "Image Tools/Sharpen" + "category": "Image Tools/Sharpen", + "description": "Enhances edge contrast via unsharp masking for a sharper image appearance." } ] } -} +} \ No newline at end of file diff --git a/blueprints/Video Captioning (Gemini).json b/blueprints/Video Captioning (Gemini).json index ea6dc8bee..7642b23c1 100644 --- a/blueprints/Video Captioning (Gemini).json +++ b/blueprints/Video Captioning (Gemini).json @@ -307,7 +307,8 @@ "extra": { "workflowRendererVersion": "LG" }, - "category": "Text generation/Video Captioning" + "category": "Text generation/Video Captioning", + "description": "Generates descriptive captions for video input using Google's Gemini multimodal LLM." } ] } diff --git a/blueprints/Video Inpaint(Wan2.1 VACE).json b/blueprints/Video Inpaint(Wan2.1 VACE).json index f404e6773..a658be5f8 100644 --- a/blueprints/Video Inpaint(Wan2.1 VACE).json +++ b/blueprints/Video Inpaint(Wan2.1 VACE).json @@ -165,7 +165,7 @@ }, "revision": 0, "config": {}, - "name": "local-Video Inpaint(Wan2.1 VACE)", + "name": "Video Inpaint (Wan 2.1 VACE)", "inputNode": { "id": -10, "bounding": [ @@ -2368,7 +2368,8 @@ "extra": { "workflowRendererVersion": "LG" }, - "category": "Video generation and editing/Inpaint video" + "category": "Video generation and editing/Inpaint video", + "description": "Inpaints masked regions in video frames using Wan 2.1 VACE." } ] }, diff --git a/blueprints/Video Stitch.json b/blueprints/Video Stitch.json index 020896d78..6eb0f0bbf 100644 --- a/blueprints/Video Stitch.json +++ b/blueprints/Video Stitch.json @@ -584,8 +584,9 @@ "extra": { "workflowRendererVersion": "LG" }, - "category": "Video Tools/Stitch videos" + "category": "Video Tools/Stitch videos", + "description": "Stitches multiple video clips into a single sequential video file." } ] } -} +} \ No newline at end of file diff --git a/blueprints/Video Upscale(GAN x4).json b/blueprints/Video Upscale(GAN x4).json index b61dc88d7..73476e36b 100644 --- a/blueprints/Video Upscale(GAN x4).json +++ b/blueprints/Video Upscale(GAN x4).json @@ -412,9 +412,10 @@ "extra": { "workflowRendererVersion": "LG" }, - "category": "Video generation and editing/Enhance video" + "category": "Video generation and editing/Enhance video", + "description": "Upscales video to 4× resolution using a GAN-based upscaling model." } ] }, "extra": {} -} +} \ No newline at end of file From 7bbf1e8169fa3080841b83914fa9901793b66b71 Mon Sep 17 00:00:00 2001 From: Alexander Piskun <13381981+bigcat88@users.noreply.github.com> Date: Sat, 9 May 2026 07:38:17 +0300 Subject: [PATCH 4/7] [Partner Nodes] Tripo3D 3.1 model (#13788) * feat(api-nodes): add Tripo3D 3.1 model Signed-off-by: bigcat88 * fix: price badges algo Signed-off-by: bigcat88 * [Partner Nodes] deprecate "quad" param for the TripoMultiviewToModel node Signed-off-by: bigcat88 --------- Signed-off-by: bigcat88 --- comfy_api_nodes/apis/tripo.py | 30 ++++-------- comfy_api_nodes/nodes_tripo.py | 84 +++++++++++----------------------- 2 files changed, 36 insertions(+), 78 deletions(-) diff --git a/comfy_api_nodes/apis/tripo.py b/comfy_api_nodes/apis/tripo.py index ffaaa7dc1..bce6b0e89 100644 --- a/comfy_api_nodes/apis/tripo.py +++ b/comfy_api_nodes/apis/tripo.py @@ -1,10 +1,11 @@ -from __future__ import annotations from enum import Enum -from typing import Optional, List, Dict, Any, Union +from typing import Optional, Any from pydantic import BaseModel, Field, RootModel + class TripoModelVersion(str, Enum): + v3_1_20260211 = 'v3.1-20260211' v3_0_20250812 = 'v3.0-20250812' v2_5_20250123 = 'v2.5-20250123' v2_0_20240919 = 'v2.0-20240919' @@ -142,7 +143,7 @@ class TripoFileEmptyReference(BaseModel): pass class TripoFileReference(RootModel): - root: Union[TripoFileTokenReference, TripoUrlReference, TripoObjectReference, TripoFileEmptyReference] + root: TripoFileTokenReference | TripoUrlReference | TripoObjectReference | TripoFileEmptyReference class TripoGetStsTokenRequest(BaseModel): format: str = Field(..., description='The format of the image') @@ -183,7 +184,7 @@ class TripoImageToModelRequest(BaseModel): class TripoMultiviewToModelRequest(BaseModel): type: TripoTaskType = TripoTaskType.MULTIVIEW_TO_MODEL - files: List[TripoFileReference] = Field(..., description='The file references to convert to a model') + files: list[TripoFileReference] = Field(..., description='The file references to convert to a model') model_version: Optional[TripoModelVersion] = Field(None, description='The model version to use for generation') orthographic_projection: Optional[bool] = Field(False, description='Whether to use orthographic projection') face_limit: Optional[int] = Field(None, description='The number of faces to limit the generation to') @@ -251,27 +252,13 @@ class TripoConvertModelRequest(BaseModel): with_animation: Optional[bool] = Field(None, description='Whether to include animations') pack_uv: Optional[bool] = Field(None, description='Whether to pack the UVs') bake: Optional[bool] = Field(None, description='Whether to bake the model') - part_names: Optional[List[str]] = Field(None, description='The names of the parts to include') + part_names: Optional[list[str]] = Field(None, description='The names of the parts to include') fbx_preset: Optional[TripoFbxPreset] = Field(None, description='The preset for the FBX export') export_vertex_colors: Optional[bool] = Field(None, description='Whether to export the vertex colors') export_orientation: Optional[TripoOrientation] = Field(None, description='The orientation for the export') animate_in_place: Optional[bool] = Field(None, description='Whether to animate in place') -class TripoTaskRequest(RootModel): - root: Union[ - TripoTextToModelRequest, - TripoImageToModelRequest, - TripoMultiviewToModelRequest, - TripoTextureModelRequest, - TripoRefineModelRequest, - TripoAnimatePrerigcheckRequest, - TripoAnimateRigRequest, - TripoAnimateRetargetRequest, - TripoStylizeModelRequest, - TripoConvertModelRequest - ] - class TripoTaskOutput(BaseModel): model: Optional[str] = Field(None, description='URL to the model') base_model: Optional[str] = Field(None, description='URL to the base model') @@ -283,12 +270,13 @@ class TripoTask(BaseModel): task_id: str = Field(..., description='The task ID') type: Optional[str] = Field(None, description='The type of task') status: Optional[TripoTaskStatus] = Field(None, description='The status of the task') - input: Optional[Dict[str, Any]] = Field(None, description='The input parameters for the task') + input: Optional[dict[str, Any]] = Field(None, description='The input parameters for the task') output: Optional[TripoTaskOutput] = Field(None, description='The output of the task') progress: Optional[int] = Field(None, description='The progress of the task', ge=0, le=100) create_time: Optional[int] = Field(None, description='The creation time of the task') running_left_time: Optional[int] = Field(None, description='The estimated time left for the task') queue_position: Optional[int] = Field(None, description='The position in the queue') + consumed_credit: int | None = Field(None) class TripoTaskResponse(BaseModel): code: int = Field(0, description='The response code') @@ -296,7 +284,7 @@ class TripoTaskResponse(BaseModel): class TripoGeneralResponse(BaseModel): code: int = Field(0, description='The response code') - data: Dict[str, str] = Field(..., description='The task ID data') + data: dict[str, str] = Field(..., description='The task ID data') class TripoBalanceData(BaseModel): balance: float = Field(..., description='The account balance') diff --git a/comfy_api_nodes/nodes_tripo.py b/comfy_api_nodes/nodes_tripo.py index 9f4298dce..d6501dee4 100644 --- a/comfy_api_nodes/nodes_tripo.py +++ b/comfy_api_nodes/nodes_tripo.py @@ -60,6 +60,7 @@ async def poll_until_finished( ], status_extractor=lambda x: x.data.status, progress_extractor=lambda x: x.data.progress, + price_extractor=lambda x: x.data.consumed_credit * 0.01 if x.data.consumed_credit else None, estimated_duration=average_duration, ) if response_poll.data.status == TripoTaskStatus.SUCCESS: @@ -113,7 +114,6 @@ class TripoTextToModelNode(IO.ComfyNode): depends_on=IO.PriceBadgeDepends( widgets=[ "model_version", - "style", "texture", "pbr", "quad", @@ -124,20 +124,17 @@ class TripoTextToModelNode(IO.ComfyNode): expr=""" ( $isV14 := $contains(widgets.model_version,"v1.4"); - $style := widgets.style; - $hasStyle := ($style != "" and $style != "none"); + $isV3OrLater := $contains(widgets.model_version,"v3."); $withTexture := widgets.texture or widgets.pbr; $isHdTexture := (widgets.texture_quality = "detailed"); $isDetailedGeometry := (widgets.geometry_quality = "detailed"); - $baseCredits := - $isV14 ? 20 : ($withTexture ? 20 : 10); - $credits := - $baseCredits - + ($hasStyle ? 5 : 0) + $credits := $isV14 ? 20 : ( + ($withTexture ? 20 : 10) + (widgets.quad ? 5 : 0) + ($isHdTexture ? 10 : 0) - + ($isDetailedGeometry ? 20 : 0); - {"type":"usd","usd": $round($credits * 0.01, 2)} + + (($isDetailedGeometry and $isV3OrLater) ? 20 : 0) + ); + {"type":"usd","usd": $round($credits * 0.01, 2), "format": {"approximate": true}} ) """, ), @@ -239,7 +236,6 @@ class TripoImageToModelNode(IO.ComfyNode): depends_on=IO.PriceBadgeDepends( widgets=[ "model_version", - "style", "texture", "pbr", "quad", @@ -250,20 +246,17 @@ class TripoImageToModelNode(IO.ComfyNode): expr=""" ( $isV14 := $contains(widgets.model_version,"v1.4"); - $style := widgets.style; - $hasStyle := ($style != "" and $style != "none"); + $isV3OrLater := $contains(widgets.model_version,"v3."); $withTexture := widgets.texture or widgets.pbr; $isHdTexture := (widgets.texture_quality = "detailed"); $isDetailedGeometry := (widgets.geometry_quality = "detailed"); - $baseCredits := - $isV14 ? 30 : ($withTexture ? 30 : 20); - $credits := - $baseCredits - + ($hasStyle ? 5 : 0) + $credits := $isV14 ? 30 : ( + ($withTexture ? 30 : 20) + (widgets.quad ? 5 : 0) + ($isHdTexture ? 10 : 0) - + ($isDetailedGeometry ? 20 : 0); - {"type":"usd","usd": $round($credits * 0.01, 2)} + + (($isDetailedGeometry and $isV3OrLater) ? 20 : 0) + ); + {"type":"usd","usd": $round($credits * 0.01, 2), "format": {"approximate": true}} ) """, ), @@ -358,7 +351,7 @@ class TripoMultiviewToModelNode(IO.ComfyNode): "texture_alignment", default="original_image", options=["original_image", "geometry"], optional=True, advanced=True ), IO.Int.Input("face_limit", default=-1, min=-1, max=500000, optional=True, advanced=True), - IO.Boolean.Input("quad", default=False, optional=True, advanced=True), + IO.Boolean.Input("quad", default=False, optional=True, advanced=True, tooltip="This parameter is deprecated and does nothing."), IO.Combo.Input("geometry_quality", default="standard", options=["standard", "detailed"], optional=True, advanced=True), ], outputs=[ @@ -379,7 +372,6 @@ class TripoMultiviewToModelNode(IO.ComfyNode): "model_version", "texture", "pbr", - "quad", "texture_quality", "geometry_quality", ], @@ -387,17 +379,16 @@ class TripoMultiviewToModelNode(IO.ComfyNode): expr=""" ( $isV14 := $contains(widgets.model_version,"v1.4"); + $isV3OrLater := $contains(widgets.model_version,"v3."); $withTexture := widgets.texture or widgets.pbr; $isHdTexture := (widgets.texture_quality = "detailed"); $isDetailedGeometry := (widgets.geometry_quality = "detailed"); - $baseCredits := - $isV14 ? 30 : ($withTexture ? 30 : 20); - $credits := - $baseCredits - + (widgets.quad ? 5 : 0) + $credits := $isV14 ? 30 : ( + ($withTexture ? 30 : 20) + ($isHdTexture ? 10 : 0) - + ($isDetailedGeometry ? 20 : 0); - {"type":"usd","usd": $round($credits * 0.01, 2)} + + (($isDetailedGeometry and $isV3OrLater) ? 20 : 0) + ); + {"type":"usd","usd": $round($credits * 0.01, 2), "format": {"approximate": true}} ) """, ), @@ -457,7 +448,7 @@ class TripoMultiviewToModelNode(IO.ComfyNode): geometry_quality=geometry_quality, texture_alignment=texture_alignment, face_limit=face_limit if face_limit != -1 else None, - quad=quad, + quad=None, ), ) return await poll_until_finished(cls, response, average_duration=80) @@ -498,7 +489,7 @@ class TripoTextureNode(IO.ComfyNode): expr=""" ( $tq := widgets.texture_quality; - {"type":"usd","usd": ($contains($tq,"detailed") ? 0.2 : 0.1)} + {"type":"usd","usd": ($contains($tq,"detailed") ? 0.2 : 0.1), "format": {"approximate": true}} ) """, ), @@ -555,7 +546,7 @@ class TripoRefineNode(IO.ComfyNode): is_api_node=True, is_output_node=True, price_badge=IO.PriceBadge( - expr="""{"type":"usd","usd":0.3}""", + expr="""{"type":"usd","usd":0.3, "format": {"approximate": true}}""", ), ) @@ -592,7 +583,7 @@ class TripoRigNode(IO.ComfyNode): is_api_node=True, is_output_node=True, price_badge=IO.PriceBadge( - expr="""{"type":"usd","usd":0.25}""", + expr="""{"type":"usd","usd":0.25, "format": {"approximate": true}}""", ), ) @@ -652,7 +643,7 @@ class TripoRetargetNode(IO.ComfyNode): is_api_node=True, is_output_node=True, price_badge=IO.PriceBadge( - expr="""{"type":"usd","usd":0.1}""", + expr="""{"type":"usd","usd":0.1, "format": {"approximate": true}}""", ), ) @@ -761,19 +752,10 @@ class TripoConversionNode(IO.ComfyNode): "face_limit", "texture_size", "texture_format", - "force_symmetry", "flatten_bottom", "flatten_bottom_threshold", "pivot_to_center_bottom", "scale_factor", - "with_animation", - "pack_uv", - "bake", - "part_names", - "fbx_preset", - "export_vertex_colors", - "export_orientation", - "animate_in_place", ], ), expr=""" @@ -783,28 +765,16 @@ class TripoConversionNode(IO.ComfyNode): $flatThresh := (widgets.flatten_bottom_threshold != null) ? widgets.flatten_bottom_threshold : 0; $scale := (widgets.scale_factor != null) ? widgets.scale_factor : 1; $texFmt := (widgets.texture_format != "" ? widgets.texture_format : "jpeg"); - $part := widgets.part_names; - $fbx := (widgets.fbx_preset != "" ? widgets.fbx_preset : "blender"); - $orient := (widgets.export_orientation != "" ? widgets.export_orientation : "default"); $advanced := widgets.quad or - widgets.force_symmetry or widgets.flatten_bottom or widgets.pivot_to_center_bottom or - widgets.with_animation or - widgets.pack_uv or - widgets.bake or - widgets.export_vertex_colors or - widgets.animate_in_place or ($face != -1) or ($texSize != 4096) or ($flatThresh != 0) or ($scale != 1) or - ($texFmt != "jpeg") or - ($part != "") or - ($fbx != "blender") or - ($orient != "default"); - {"type":"usd","usd": ($advanced ? 0.1 : 0.05)} + ($texFmt != "jpeg"); + {"type":"usd","usd": ($advanced ? 0.1 : 0.05), "format": {"approximate": true}} ) """, ), From a4b7e3beedda4180cd6a2b319c9805990357ee96 Mon Sep 17 00:00:00 2001 From: Comfy Org PR Bot Date: Sat, 9 May 2026 23:53:10 +0900 Subject: [PATCH 5/7] Bump comfyui-frontend-package to 1.43.18 (#13809) Co-authored-by: github-actions[bot] --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 5c7ff76be..6fd808772 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,4 +1,4 @@ -comfyui-frontend-package==1.43.17 +comfyui-frontend-package==1.43.18 comfyui-workflow-templates==0.9.72 comfyui-embedded-docs==0.4.4 torch From 3200f28e3a8663f18b9a9568472ad912ea5c6396 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Jukka=20Sepp=C3=A4nen?= <40791699+kijai@users.noreply.github.com> Date: Sun, 10 May 2026 00:02:56 +0300 Subject: [PATCH 6/7] Support Wan-Dancer (#13813) * initial WanDancer support * nodes_wandancer: Add list form of chunker. Create an alternate list form of the node so the chunk gens can be trivially looped by the comfy executor. * Closer match to original soxr resampling * Remove librosa node * Cleanup --------- Co-authored-by: Rattus --- comfy/ldm/wan/model.py | 15 +- comfy/ldm/wan/model_wandancer.py | 251 ++++++++ comfy/model_base.py | 25 + comfy/model_detection.py | 2 + comfy/supported_models.py | 32 + comfy_extras/nodes_wandancer.py | 1002 ++++++++++++++++++++++++++++++ nodes.py | 1 + 7 files changed, 1322 insertions(+), 6 deletions(-) create mode 100644 comfy/ldm/wan/model_wandancer.py create mode 100644 comfy_extras/nodes_wandancer.py diff --git a/comfy/ldm/wan/model.py b/comfy/ldm/wan/model.py index b2287dba9..70dfe7b16 100644 --- a/comfy/ldm/wan/model.py +++ b/comfy/ldm/wan/model.py @@ -1135,7 +1135,7 @@ class AudioInjector_WAN(nn.Module): self.injector_adain_output_layers = nn.ModuleList( [operations.Linear(dim, dim, dtype=dtype, device=device) for _ in range(audio_injector_id)]) - def forward(self, x, block_id, audio_emb, audio_emb_global, seq_len): + def forward(self, x, block_id, audio_emb, audio_emb_global, seq_len, scale=1.0): audio_attn_id = self.injected_block_id.get(block_id, None) if audio_attn_id is None: return x @@ -1148,12 +1148,15 @@ class AudioInjector_WAN(nn.Module): attn_hidden_states = adain_hidden_states else: attn_hidden_states = self.injector_pre_norm_feat[audio_attn_id](input_hidden_states) - audio_emb = rearrange(audio_emb, "b t n c -> (b t) n c", t=num_frames) - attn_audio_emb = audio_emb + + if audio_emb.dim() == 3: # WanDancer case + attn_audio_emb = rearrange(audio_emb, "b t c -> (b t) 1 c", t=num_frames) + else: # S2V case + attn_audio_emb = rearrange(audio_emb, "b t n c -> (b t) n c", t=num_frames) + residual_out = self.injector[audio_attn_id](x=attn_hidden_states, context=attn_audio_emb) - residual_out = rearrange( - residual_out, "(b t) n c -> b (t n) c", t=num_frames) - x[:, :seq_len] = x[:, :seq_len] + residual_out + residual_out = rearrange(residual_out, "(b t) n c -> b (t n) c", t=num_frames) + x[:, :seq_len] = x[:, :seq_len] + residual_out * scale return x diff --git a/comfy/ldm/wan/model_wandancer.py b/comfy/ldm/wan/model_wandancer.py new file mode 100644 index 000000000..3caef6dc5 --- /dev/null +++ b/comfy/ldm/wan/model_wandancer.py @@ -0,0 +1,251 @@ +import torch +import torch.nn as nn +import comfy +from comfy.ldm.modules.attention import optimized_attention +from comfy.ldm.flux.math import apply_rope1 +from comfy.ldm.flux.layers import EmbedND + +from .model import AudioInjector_WAN, WanModel, MLPProj, Head, sinusoidal_embedding_1d + + +class MusicSelfAttention(nn.Module): + def __init__(self, dim, num_heads, device=None, dtype=None, operations=None): + assert dim % num_heads == 0 + super().__init__() + self.embed_dim = dim + self.num_heads = num_heads + self.head_dim = dim // num_heads + + self.q_proj = operations.Linear(dim, dim, device=device, dtype=dtype) + self.k_proj = operations.Linear(dim, dim, device=device, dtype=dtype) + self.v_proj = operations.Linear(dim, dim, device=device, dtype=dtype) + self.out_proj = operations.Linear(dim, dim, device=device, dtype=dtype) + + def forward(self, x, freqs): + b, s, n, d = *x.shape[:2], self.num_heads, self.head_dim + + q = self.q_proj(x).view(b, s, n, d) + q = apply_rope1(q, freqs) + + k = self.k_proj(x).view(b, s, n, d) + k = apply_rope1(k, freqs) + + x = optimized_attention( + q.view(b, s, n * d), + k.view(b, s, n * d), + self.v_proj(x).view(b, s, n * d), + heads=self.num_heads, + ) + + return self.out_proj(x) + + +class MusicEncoderLayer(nn.Module): + def __init__(self, dim: int, num_heads: int, ffn_dim: int, device=None, dtype=None, operations=None): + super().__init__() + self.self_attn = MusicSelfAttention(dim, num_heads, device=device, dtype=dtype, operations=operations) + + self.linear1 = operations.Linear(dim, ffn_dim, device=device, dtype=dtype) + self.linear2 = operations.Linear(ffn_dim, dim, device=device, dtype=dtype) + + self.norm1 = operations.LayerNorm(dim, device=device, dtype=dtype) + self.norm2 = operations.LayerNorm(dim, device=device, dtype=dtype) + + def forward(self, x: torch.Tensor, freqs: torch.Tensor) -> torch.Tensor: + x = x + self.self_attn(self.norm1(x), freqs=freqs) + x = x + self.linear2(torch.nn.functional.gelu(self.linear1(self.norm2(x)))) # ffn + return x + + +class WanDancerModel(WanModel): + def __init__(self, + model_type='wandancer', + patch_size=(1, 2, 2), + text_len=512, + in_dim=16, + dim=5120, + ffn_dim=8192, + freq_dim=256, + text_dim=4096, + out_dim=16, + num_heads=16, + num_layers=40, + window_size=(-1, -1), + qk_norm=True, + cross_attn_norm=True, + eps=1e-6, + in_dim_ref_conv=None, + image_model=None, + device=None, dtype=None, operations=None, + audio_inject_layers=[0, 4, 8, 12, 16, 20, 24, 27], + music_dim = 256, + music_heads = 4, + music_feature_dim = 35, + music_latent_dim = 256 + ): + + super().__init__(model_type='i2v', patch_size=patch_size, text_len=text_len, in_dim=in_dim, dim=dim, ffn_dim=ffn_dim, freq_dim=freq_dim, text_dim=text_dim, out_dim=out_dim, + num_heads=num_heads, num_layers=num_layers, window_size=window_size, qk_norm=qk_norm, cross_attn_norm=cross_attn_norm, eps=eps, image_model=image_model, in_dim_ref_conv=in_dim_ref_conv, + device=device, dtype=dtype, operations=operations) + + self.dtype = dtype + operation_settings = {"operations": operations, "device": device, "dtype": dtype} + + self.patch_embedding_global = operations.Conv3d(in_dim, dim, kernel_size=patch_size, stride=patch_size, device=operation_settings.get("device"), dtype=torch.float32) + self.img_emb_refimage = MLPProj(1280, dim, operation_settings=operation_settings) + self.head_global = Head(dim, out_dim, patch_size, eps, operation_settings=operation_settings) + + self.music_injector = AudioInjector_WAN( + dim=self.dim, + num_heads=self.num_heads, + inject_layer=audio_inject_layers, + root_net=self, + enable_adain=False, + dtype=dtype, device=device, operations=operations + ) + + self.music_projection = operations.Linear(music_feature_dim, music_latent_dim, device=device, dtype=dtype) + self.music_encoder = nn.ModuleList([MusicEncoderLayer(dim=music_dim, num_heads=music_heads, ffn_dim=1024, device=device, dtype=dtype, operations=operations) for _ in range(2)]) + music_head_dim = music_dim // music_heads + self.music_rope_embedder = EmbedND(dim=music_head_dim, theta=10000.0, axes_dim=[music_head_dim]) + + def forward_orig(self, x, t, context, clip_fea=None, clip_fea_ref=None, freqs=None, audio_embed=None, fps=30, audio_inject_scale=1.0, transformer_options={}, **kwargs): + # embeddings + if int(fps + 0.5) != 30: + x = self.patch_embedding_global(x.float()).to(x.dtype) + else: + x = self.patch_embedding(x.float()).to(x.dtype) + + grid_sizes = x.shape[2:] + latent_frames = grid_sizes[0] + transformer_options["grid_sizes"] = grid_sizes + x = x.flatten(2).transpose(1, 2) + seq_len = x.size(1) + + # time embeddings + e = self.time_embedding(sinusoidal_embedding_1d(self.freq_dim, t.flatten()).to(dtype=x[0].dtype)) + e = e.reshape(t.shape[0], -1, e.shape[-1]) + e0 = self.time_projection(e).unflatten(2, (6, self.dim)) + + full_ref = None + if self.ref_conv is not None: # model has the weight, but this wasn't used in the original pipeline + full_ref = kwargs.get("reference_latent", None) + if full_ref is not None: + full_ref = self.ref_conv(full_ref).flatten(2).transpose(1, 2) + x = torch.concat((full_ref, x), dim=1) + + # context + context = self.text_embedding(context) + + audio_emb = None + if audio_embed is not None: # encode music feature，[1, frame_num, 35] -> [1, F*8, dim] + music_feature = self.music_projection(audio_embed) + + music_seq_len = music_feature.shape[1] + music_ids = torch.arange(music_seq_len, device=music_feature.device, dtype=music_feature.dtype).reshape(1, -1, 1) # create 1D position IDs + music_freqs = self.music_rope_embedder(music_ids).movedim(1, 2) + + # apply encoder layers + for layer in self.music_encoder: + music_feature = layer(music_feature, music_freqs) + + # interpolate + audio_emb = torch.nn.functional.interpolate(music_feature.unsqueeze(1), size=(latent_frames * 8, self.dim), mode='bilinear').squeeze(1) + + context_img_len = 0 + if self.img_emb is not None and clip_fea is not None: + context_clip = self.img_emb(clip_fea) # bs x 257 x dim + context = torch.cat([context_clip, context], dim=1) + context_img_len += clip_fea.shape[-2] + if self.img_emb_refimage is not None and clip_fea_ref is not None: + context_clip_ref = self.img_emb_refimage(clip_fea_ref) + context = torch.cat([context_clip_ref, context], dim=1) + context_img_len += clip_fea_ref.shape[-2] + + patches_replace = transformer_options.get("patches_replace", {}) + blocks_replace = patches_replace.get("dit", {}) + transformer_options["total_blocks"] = len(self.blocks) + transformer_options["block_type"] = "double" + for i, block in enumerate(self.blocks): + transformer_options["block_index"] = i + if ("double_block", i) in blocks_replace: + def block_wrap(args): + out = {} + out["img"] = block(args["img"], context=args["txt"], e=args["vec"], freqs=args["pe"], context_img_len=context_img_len, transformer_options=args["transformer_options"]) + return out + out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "vec": e0, "pe": freqs, "transformer_options": transformer_options}, {"original_block": block_wrap}) + x = out["img"] + else: + x = block(x, e=e0, freqs=freqs, context=context, context_img_len=context_img_len, transformer_options=transformer_options) + if audio_emb is not None: + x = self.music_injector(x, i, audio_emb, audio_emb_global=None, seq_len=seq_len, scale=audio_inject_scale) + + # head + if int(fps + 0.5) != 30: + x = self.head_global(x, e) + else: + x = self.head(x, e) + + if full_ref is not None: + x = x[:, full_ref.shape[1]:] + + # unpatchify + x = self.unpatchify(x, grid_sizes) + return x + + def _forward(self, x, timestep, context, clip_fea=None, time_dim_concat=None, transformer_options={}, clip_fea_ref=None, fps=30, audio_inject_scale=1.0, **kwargs): + bs, c, t, h, w = x.shape + x = comfy.ldm.common_dit.pad_to_patch_size(x, self.patch_size) + + t_len = t + if time_dim_concat is not None: + time_dim_concat = comfy.ldm.common_dit.pad_to_patch_size(time_dim_concat, self.patch_size) + x = torch.cat([x, time_dim_concat], dim=2) + t_len = x.shape[2] + + freqs = self.rope_encode(t_len, h, w, device=x.device, dtype=x.dtype, fps=fps, transformer_options=transformer_options) + return self.forward_orig(x, timestep, context, clip_fea=clip_fea, clip_fea_ref=clip_fea_ref, freqs=freqs, fps=fps, audio_inject_scale=audio_inject_scale, transformer_options=transformer_options, **kwargs)[:, :, :t, :h, :w] + + def rope_encode(self, t, h, w, t_start=0, steps_t=None, steps_h=None, steps_w=None, fps=30, device=None, dtype=None, transformer_options={}): + patch_size = self.patch_size + t_len = ((t + (patch_size[0] // 2)) // patch_size[0]) + h_len = ((h + (patch_size[1] // 2)) // patch_size[1]) + w_len = ((w + (patch_size[2] // 2)) // patch_size[2]) + + if steps_t is None: + steps_t = t_len + if steps_h is None: + steps_h = h_len + if steps_w is None: + steps_w = w_len + + h_start = 0 + w_start = 0 + rope_options = transformer_options.get("rope_options", None) + if rope_options is not None: + t_len = (t_len - 1.0) * rope_options.get("scale_t", 1.0) + 1.0 + h_len = (h_len - 1.0) * rope_options.get("scale_y", 1.0) + 1.0 + w_len = (w_len - 1.0) * rope_options.get("scale_x", 1.0) + 1.0 + + t_start += rope_options.get("shift_t", 0.0) + h_start += rope_options.get("shift_y", 0.0) + w_start += rope_options.get("shift_x", 0.0) + + img_ids = torch.zeros((steps_t, steps_h, steps_w, 3), device=device, dtype=dtype) + + if int(fps + 0.5) != 30: + time_scale = 30.0 / fps # how many time units each frame represents relative to 30fps + positions_new = torch.arange(steps_t, device=device, dtype=dtype) * time_scale + t_start + total_frames_at_30fps = int(time_scale * steps_t + 0.5) + positions_new[-1] = t_start + (total_frames_at_30fps - 1) + + img_ids[:, :, :, 0] = img_ids[:, :, :, 0] + positions_new.reshape(-1, 1, 1) + else: + img_ids[:, :, :, 0] = img_ids[:, :, :, 0] + torch.linspace(t_start, t_start + (t_len - 1), steps=steps_t, device=device, dtype=dtype).reshape(-1, 1, 1) + + img_ids[:, :, :, 1] = img_ids[:, :, :, 1] + torch.linspace(h_start, h_start + (h_len - 1), steps=steps_h, device=device, dtype=dtype).reshape(1, -1, 1) + img_ids[:, :, :, 2] = img_ids[:, :, :, 2] + torch.linspace(w_start, w_start + (w_len - 1), steps=steps_w, device=device, dtype=dtype).reshape(1, 1, -1) + img_ids = img_ids.reshape(1, -1, img_ids.shape[-1]) + + freqs = self.rope_embedder(img_ids).movedim(1, 2) + return freqs diff --git a/comfy/model_base.py b/comfy/model_base.py index 57a1e44d2..dbed239e5 100644 --- a/comfy/model_base.py +++ b/comfy/model_base.py @@ -43,6 +43,7 @@ import comfy.ldm.lumina.model import comfy.ldm.wan.model import comfy.ldm.wan.model_animate import comfy.ldm.wan.ar_model +import comfy.ldm.wan.model_wandancer import comfy.ldm.hunyuan3d.model import comfy.ldm.hidream.model import comfy.ldm.chroma.model @@ -1599,6 +1600,30 @@ class WAN21_SCAIL(WAN21): return out +class WAN22_WanDancer(WAN21): + def __init__(self, model_config, model_type=ModelType.FLOW, image_to_video=True, device=None): + super(WAN21, self).__init__(model_config, model_type, device=device, unet_model=comfy.ldm.wan.model_wandancer.WanDancerModel) + self.image_to_video = image_to_video + + def extra_conds(self, **kwargs): + out = super().extra_conds(**kwargs) + audio_embed = kwargs.get("audio_embed", None) + if audio_embed is not None: + out['audio_embed'] = comfy.conds.CONDRegular(audio_embed) + + clip_vision_output_ref = kwargs.get("clip_vision_output_ref", None) + if clip_vision_output_ref is not None: + out['clip_fea_ref'] = comfy.conds.CONDRegular(clip_vision_output_ref.penultimate_hidden_states) + + fps = kwargs.get("fps", None) + if fps is not None: + out['fps'] = comfy.conds.CONDRegular(torch.FloatTensor([fps])) + + audio_inject_scale = kwargs.get("audio_inject_scale", None) + if audio_inject_scale is not None: + out['audio_inject_scale'] = comfy.conds.CONDRegular(torch.FloatTensor([audio_inject_scale])) + return out + class Hunyuan3Dv2(BaseModel): def __init__(self, model_config, model_type=ModelType.FLOW, device=None): super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.hunyuan3d.model.Hunyuan3Dv2) diff --git a/comfy/model_detection.py b/comfy/model_detection.py index d9b67dcdf..8ae456481 100644 --- a/comfy/model_detection.py +++ b/comfy/model_detection.py @@ -572,6 +572,8 @@ def detect_unet_config(state_dict, key_prefix, metadata=None): dit_config["model_type"] = "animate" elif '{}patch_embedding_pose.weight'.format(key_prefix) in state_dict_keys: dit_config["model_type"] = "scail" + elif '{}patch_embedding_global.weight'.format(key_prefix) in state_dict_keys: + dit_config["model_type"] = "wandancer" else: if '{}img_emb.proj.0.bias'.format(key_prefix) in state_dict_keys: dit_config["model_type"] = "i2v" diff --git a/comfy/supported_models.py b/comfy/supported_models.py index 6a9613602..40417f922 100644 --- a/comfy/supported_models.py +++ b/comfy/supported_models.py @@ -1313,6 +1313,37 @@ class WAN21_SCAIL(WAN21_T2V): out = model_base.WAN21_SCAIL(self, image_to_video=False, device=device) return out +class WAN22_WanDancer(WAN21_T2V): + unet_config = { + "image_model": "wan2.1", + "model_type": "wandancer", + "in_dim": 36, + } + + def __init__(self, unet_config): + super().__init__(unet_config) + self.memory_usage_factor = 1.8 + + def get_model(self, state_dict, prefix="", device=None): + out = model_base.WAN22_WanDancer(self, image_to_video=True, device=device) + return out + + def process_unet_state_dict(self, state_dict): + out_sd = {} + for k in list(state_dict.keys()): + # split music_encoder in_proj into q_proj, k_proj, v_proj + if "music_encoder" in k and "self_attn.in_proj" in k: + suffix = "weight" if k.endswith("weight") else "bias" + tensor = state_dict[k] + d = tensor.shape[0] // 3 + prefix = k.replace(f"in_proj_{suffix}", "") + out_sd[f"{prefix}q_proj.{suffix}"] = tensor[:d] + out_sd[f"{prefix}k_proj.{suffix}"] = tensor[d:2*d] + out_sd[f"{prefix}v_proj.{suffix}"] = tensor[2*d:] + else: + out_sd[k] = state_dict[k] + return out_sd + class Hunyuan3Dv2(supported_models_base.BASE): unet_config = { "image_model": "hunyuan3d2", @@ -1982,6 +2013,7 @@ models = [ WAN22_Animate, WAN21_FlowRVS, WAN21_SCAIL, + WAN22_WanDancer, Hunyuan3Dv2mini, Hunyuan3Dv2, Hunyuan3Dv2_1, diff --git a/comfy_extras/nodes_wandancer.py b/comfy_extras/nodes_wandancer.py new file mode 100644 index 000000000..faaeb9020 --- /dev/null +++ b/comfy_extras/nodes_wandancer.py @@ -0,0 +1,1002 @@ +import math +import nodes +import node_helpers +import torch +import torchaudio +import comfy.model_management +import comfy.utils +import numpy as np +import logging +from typing_extensions import override +from comfy_api.latest import ComfyExtension, io + +import scipy.signal +import scipy.ndimage +import scipy.fft +import scipy.sparse + +# Audio Processing Functions - Derived from librosa (https://github.com/librosa/librosa) +# Copyright (c) 2013--2023, librosa development team. + +def mel_to_hz(mels, htk=False): + """Convert mel to Hz (slaney)""" + mels = np.asanyarray(mels) + if htk: + return 700.0 * (10.0 ** (mels / 2595.0) - 1.0) + f_min = 0.0 + f_sp = 200.0 / 3 + freqs = f_min + f_sp * mels + min_log_hz = 1000.0 + min_log_mel = (min_log_hz - f_min) / f_sp + logstep = np.log(6.4) / 27.0 + if mels.ndim: + log_t = mels >= min_log_mel + freqs[log_t] = min_log_hz * np.exp(logstep * (mels[log_t] - min_log_mel)) + elif mels >= min_log_mel: + freqs = min_log_hz * np.exp(logstep * (mels - min_log_mel)) + return freqs + +def hz_to_mel(frequencies, htk=False): + """Convert Hz to mel (slaney)""" + frequencies = np.asanyarray(frequencies) + if htk: + return 2595.0 * np.log10(1.0 + frequencies / 700.0) + f_min = 0.0 + f_sp = 200.0 / 3 + mels = (frequencies - f_min) / f_sp + min_log_hz = 1000.0 + min_log_mel = (min_log_hz - f_min) / f_sp + logstep = np.log(6.4) / 27.0 + if frequencies.ndim: + log_t = frequencies >= min_log_hz + mels[log_t] = min_log_mel + np.log(frequencies[log_t] / min_log_hz) / logstep + elif frequencies >= min_log_hz: + mels = min_log_mel + np.log(frequencies / min_log_hz) / logstep + return mels + +def compute_cqt(y, sr=22050, hop_length=512, fmin=None, n_bins=84, bins_per_octave=12, tuning=0.0): + """Compute Constant-Q Transform (CQT) spectrogram.""" + + def _relative_bandwidth(freqs): + bpo = np.empty_like(freqs) + logf = np.log2(freqs) + bpo[0] = 1.0 / (logf[1] - logf[0]) + bpo[-1] = 1.0 / (logf[-1] - logf[-2]) + bpo[1:-1] = 2.0 / (logf[2:] - logf[:-2]) + return (2.0 ** (2.0 / bpo) - 1.0) / (2.0 ** (2.0 / bpo) + 1.0) + + def _wavelet_lengths(freqs, sr, filter_scale, alpha): + Q = float(filter_scale) / alpha + return Q * sr / freqs # shape (n_bins,) floats + + def _build_wavelet(freqs_oct, sr, filter_scale, alpha_oct): + lengths = _wavelet_lengths(freqs_oct, sr, filter_scale, alpha_oct) + filters = [] + for ilen, freq in zip(lengths, freqs_oct): + t = np.arange(int(-ilen // 2), int(ilen // 2), dtype=float) + sig = (np.cos(t * 2 * np.pi * freq / sr) + + 1j * np.sin(t * 2 * np.pi * freq / sr)).astype(np.complex64) + sig *= scipy.signal.get_window('hann', len(sig), fftbins=True) + l1 = np.sum(np.abs(sig)) + tiny = np.finfo(np.float32).tiny + sig /= max(l1, tiny) + filters.append(sig) + max_len = max(lengths) + n_fft = int(2.0 ** np.ceil(np.log2(max_len))) + out = np.zeros((len(filters), n_fft), dtype=np.complex64) + for k, f in enumerate(filters): + lpad = int((n_fft - len(f)) // 2) + out[k, lpad: lpad + len(f)] = f + return out, lengths + + def _resample_half(y): + ratio = 0.5 + n_samples = int(np.ceil(len(y) * ratio)) + # Kaiser-windowed FIR matches librosa/soxr more closely than scipy's default Hamming filter + L = 2 + h = scipy.signal.firwin(160 * L + 1, 0.96 / L, window=('kaiser', 6.5)) + y_hat = scipy.signal.resample_poly(y.astype(np.float32), 1, 2, window=h) + if len(y_hat) > n_samples: + y_hat = y_hat[:n_samples] + elif len(y_hat) < n_samples: + y_hat = np.pad(y_hat, (0, n_samples - len(y_hat))) + y_hat /= np.sqrt(ratio) + return y_hat.astype(np.float32) + + def _sparsify_rows(x, quantile=0.01): + mags = np.abs(x) + norms = np.sum(mags, axis=1, keepdims=True) + norms = np.where(norms == 0, 1.0, norms) + mag_sort = np.sort(mags, axis=1) + cumulative_mag = np.cumsum(mag_sort / norms, axis=1) + threshold_idx = np.argmin(cumulative_mag < quantile, axis=1) + x_sparse = scipy.sparse.lil_matrix(x.shape, dtype=x.dtype) + for i, j in enumerate(threshold_idx): + idx = np.where(mags[i] >= mag_sort[i, j]) + x_sparse[i, idx] = x[i, idx] + return x_sparse.tocsr() + + if fmin is None: + fmin = 32.70319566257483 # C1 note frequency + + fmin = fmin * (2.0 ** (tuning / bins_per_octave)) + freqs = fmin * (2.0 ** (np.arange(n_bins) / bins_per_octave)) + + alpha = _relative_bandwidth(freqs) + lengths = _wavelet_lengths(freqs, float(sr), 1, alpha) + + n_octaves = int(np.ceil(float(n_bins) / bins_per_octave)) + n_filters = min(bins_per_octave, n_bins) + + cqt_resp = [] + my_y = y.astype(np.float32) + my_sr = float(sr) + my_hop = int(hop_length) + + for i in range(n_octaves): + if i == 0: + sl = slice(-n_filters, None) + else: + sl = slice(-n_filters * (i + 1), -n_filters * i) + + freqs_oct = freqs[sl] + alpha_oct = alpha[sl] + + basis, basis_lengths = _build_wavelet(freqs_oct, my_sr, 1, alpha_oct) + n_fft_oct = basis.shape[1] + + # Frequency-domain normalisation + basis = basis.astype(np.complex64) + basis *= basis_lengths[:, np.newaxis] / float(n_fft_oct) + fft_basis = scipy.fft.fft(basis, n=n_fft_oct, axis=1)[:, :(n_fft_oct // 2) + 1] + fft_basis = _sparsify_rows(fft_basis, quantile=0.01) + fft_basis = fft_basis * np.sqrt(sr / my_sr) + + y_pad = np.pad(my_y, int(n_fft_oct // 2), mode='constant') + n_frames = 1 + (len(y_pad) - n_fft_oct) // my_hop + frames = np.lib.stride_tricks.as_strided( + y_pad, + shape=(n_fft_oct, n_frames), + strides=(y_pad.strides[0], y_pad.strides[0] * my_hop), + ) + stft_result = scipy.fft.rfft(frames, axis=0) + cqt_resp.append(fft_basis.dot(stft_result)) + + if my_hop % 2 == 0: + my_hop //= 2 + my_sr /= 2.0 + my_y = _resample_half(my_y) + + max_col = min(c.shape[-1] for c in cqt_resp) + cqt_out = np.empty((n_bins, max_col), dtype=np.complex64) + end = n_bins + for c_i in cqt_resp: + n_oct = c_i.shape[0] + if end < n_oct: + cqt_out[:end, :] = c_i[-end:, :max_col] + else: + cqt_out[end - n_oct:end, :] = c_i[:, :max_col] + end -= n_oct + + cqt_out /= np.sqrt(lengths)[:, np.newaxis] + return np.abs(cqt_out).astype(np.float32) + + +def cq_to_chroma_mapping(n_input, bins_per_octave=12, n_chroma=12, fmin=None): + """Map CQT bins to chroma bins.""" + + if fmin is None: + fmin = 32.70319566257483 # C1 note frequency + + n_merge = bins_per_octave / n_chroma + cq_to_ch = np.repeat(np.eye(n_chroma), int(n_merge), axis=1) + cq_to_ch = np.roll(cq_to_ch, -int(n_merge // 2), axis=1) + n_octaves = int(np.ceil(n_input / bins_per_octave)) + cq_to_ch = np.tile(cq_to_ch, n_octaves)[:, :n_input] + + midi_0 = np.mod(12 * np.log2(fmin / 440.0) + 69, 12) + roll = int(np.round(midi_0 * (n_chroma / 12.0))) + cq_to_ch = np.roll(cq_to_ch, roll, axis=0) + + return cq_to_ch.astype(np.float32) + + +def _parabolic_interpolation(S, axis=-2): + """Compute parabolic interpolation shift for peak refinement.""" + S_next = np.roll(S, -1, axis=axis) + S_prev = np.roll(S, 1, axis=axis) + + a = S_next + S_prev - 2 * S + b = (S_next - S_prev) / 2.0 + + shifts = np.zeros_like(S) + valid = np.abs(b) < np.abs(a) + shifts[valid] = -b[valid] / a[valid] + + if axis == -2 or axis == S.ndim - 2: + shifts[0, :] = 0 + shifts[-1, :] = 0 + elif axis == 0: + shifts[0, ...] = 0 + shifts[-1, ...] = 0 + + return shifts + + +def _localmax(S, axis=-2): + """Find local maxima along an axis.""" + + S_prev = np.roll(S, 1, axis=axis) + S_next = np.roll(S, -1, axis=axis) + + local_max = (S > S_prev) & (S >= S_next) + + if axis == -2 or axis == S.ndim - 2: + local_max[-1, :] = S[-1, :] > S[-2, :] + # First element is never a local max (strict inequality with previous) + local_max[0, :] = False + elif axis == 0: + local_max[-1, ...] = S[-1, ...] > S[-2, ...] + local_max[0, ...] = False + + return local_max + + +def piptrack(y=None, sr=22050, S=None, n_fft=2048, hop_length=512, + fmin=150.0, fmax=4000.0, threshold=0.1): + """Pitch tracking on thresholded parabolically-interpolated STFT.""" + + # Compute STFT if not provided + if S is None: + if y is None: + raise ValueError("Either y or S must be provided") + + fft_window = scipy.signal.get_window('hann', n_fft, fftbins=True) + if len(fft_window) < n_fft: + lpad = int((n_fft - len(fft_window)) // 2) + fft_window = np.pad(fft_window, (lpad, int(n_fft - len(fft_window) - lpad)), mode='constant') + fft_window = fft_window.reshape((-1, 1)) + + y_pad = np.pad(y, int(n_fft // 2), mode='constant') + n_frames = 1 + (len(y_pad) - n_fft) // hop_length + frames = np.lib.stride_tricks.as_strided( + y_pad, + shape=(n_fft, n_frames), + strides=(y_pad.strides[0], y_pad.strides[0] * hop_length) + ) + + S = scipy.fft.rfft((fft_window * frames).astype(np.float32), axis=0) + + S = np.abs(S) + + fmin = max(fmin, 0) + fmax = min(fmax, float(sr) / 2) + + fft_freqs = np.fft.rfftfreq(S.shape[0] * 2 - 2, 1.0 / sr) + if len(fft_freqs) > S.shape[0]: + fft_freqs = fft_freqs[:S.shape[0]] + + shift = _parabolic_interpolation(S, axis=0) + avg = np.gradient(S, axis=0) + dskew = 0.5 * avg * shift + + pitches = np.zeros_like(S) + mags = np.zeros_like(S) + + freq_mask = (fmin <= fft_freqs) & (fft_freqs < fmax) + freq_mask = freq_mask.reshape(-1, 1) + + ref_value = threshold * np.max(S, axis=0, keepdims=True) + local_max = _localmax(S * (S > ref_value), axis=0) + idx = np.nonzero(freq_mask & local_max) + + pitches[idx] = (idx[0] + shift[idx]) * float(sr) / (S.shape[0] * 2 - 2) + mags[idx] = S[idx] + dskew[idx] + + return pitches, mags + + +def hz_to_octs(frequencies, tuning=0.0, bins_per_octave=12): + """Convert frequencies (Hz) to octave numbers.""" + + A440 = 440.0 * 2.0 ** (tuning / bins_per_octave) + octs = np.log2(np.asanyarray(frequencies) / (float(A440) / 16)) + return octs + + +def pitch_tuning(frequencies, resolution=0.01, bins_per_octave=12): + """Estimate tuning offset from a collection of pitches.""" + + frequencies = np.atleast_1d(frequencies) + frequencies = frequencies[frequencies > 0] + + if not np.any(frequencies): + return 0.0 + + residual = np.mod(bins_per_octave * hz_to_octs(frequencies, tuning=0.0, + bins_per_octave=bins_per_octave), 1.0) + residual[residual >= 0.5] -= 1.0 + + bins = np.linspace(-0.5, 0.5, int(np.ceil(1.0 / resolution)) + 1) + counts, tuning = np.histogram(residual, bins) + tuning_est = tuning[np.argmax(counts)] + return tuning_est + + +def estimate_tuning(y, sr=22050, bins_per_octave=12): + """Estimate global tuning deviation from 12-TET.""" + n_fft = 2048 + hop_length = 512 + + if len(y) < n_fft: + return 0.0 + + pitch, mag = piptrack(y=y, sr=sr, n_fft=n_fft, hop_length=hop_length, + fmin=150.0, fmax=4000.0, threshold=0.1) + + pitch_mask = pitch > 0 + + if not pitch_mask.any(): + return 0.0 + + threshold = np.median(mag[pitch_mask]) + valid_pitches = pitch[(mag >= threshold) & pitch_mask] + + if len(valid_pitches) == 0: + return 0.0 + + tuning = pitch_tuning(valid_pitches, resolution=0.01, bins_per_octave=bins_per_octave) + + return float(tuning) + + +def compute_chroma_cens(y, sr=22050, hop_length=512, n_chroma=12, + n_octaves=7, bins_per_octave=36, + win_len_smooth=41, norm=2): + """Compute Chroma Energy Normalized Statistics (CENS) features.""" + + tuning = estimate_tuning(y, sr, bins_per_octave=bins_per_octave) + + fmin = 32.70319566257483 # C1 note frequency + n_bins = n_octaves * bins_per_octave + cqt_mag = compute_cqt(y, sr=sr, hop_length=hop_length, + fmin=fmin, n_bins=n_bins, + bins_per_octave=bins_per_octave, + tuning=tuning) + + chroma_map = cq_to_chroma_mapping(n_bins, bins_per_octave=bins_per_octave, + n_chroma=n_chroma, fmin=fmin) + chroma = np.dot(chroma_map, cqt_mag) + + threshold = np.finfo(chroma.dtype).tiny + chroma_sum = np.sum(np.abs(chroma), axis=0, keepdims=True) + chroma_sum = np.maximum(chroma_sum, threshold) + chroma = chroma / chroma_sum + + quant_steps = [0.4, 0.2, 0.1, 0.05] + quant_weights = [0.25, 0.25, 0.25, 0.25] + chroma_quant = np.zeros_like(chroma) + for step, weight in zip(quant_steps, quant_weights): + chroma_quant += (chroma > step) * weight + + if win_len_smooth is not None and win_len_smooth > 0: + win = scipy.signal.get_window('hann', win_len_smooth + 2, fftbins=False) + win /= np.sum(win) + win = win.reshape(1, -1) + chroma_smooth = scipy.ndimage.convolve(chroma_quant, win, mode='constant') + else: + chroma_smooth = chroma_quant + + if norm == 2: + threshold = np.finfo(chroma_smooth.dtype).tiny + chroma_norm = np.sqrt(np.sum(chroma_smooth ** 2, axis=0, keepdims=True)) + chroma_norm = np.maximum(chroma_norm, threshold) + chroma_smooth = chroma_smooth / chroma_norm + elif norm == np.inf: + threshold = np.finfo(chroma_smooth.dtype).tiny + chroma_norm = np.max(np.abs(chroma_smooth), axis=0, keepdims=True) + chroma_norm = np.maximum(chroma_norm, threshold) + chroma_smooth = chroma_smooth / chroma_norm + + return chroma_smooth + + +def _create_mel_filterbank(sr, n_fft, n_mels=128, fmin=0.0, fmax=None): + """Create mel-scale filterbank matrix.""" + if fmax is None: + fmax = sr / 2.0 + mel_basis = np.zeros((n_mels, int(1 + n_fft // 2)), dtype=np.float32) + fftfreqs = np.fft.rfftfreq(n=n_fft, d=1.0 / sr) + min_mel = hz_to_mel(fmin) + max_mel = hz_to_mel(fmax) + mels = np.linspace(min_mel, max_mel, n_mels + 2) + mel_f = mel_to_hz(mels) + fdiff = np.diff(mel_f) + ramps = np.subtract.outer(mel_f, fftfreqs) + + for i in range(n_mels): + lower = -ramps[i] / fdiff[i] + upper = ramps[i + 2] / fdiff[i + 1] + mel_basis[i] = np.maximum(0, np.minimum(lower, upper)) + + enorm = 2.0 / (mel_f[2:n_mels + 2] - mel_f[:n_mels]) + mel_basis *= enorm[:, np.newaxis] + return mel_basis + + +def _compute_mel_spectrogram(data, sr, n_fft=2048, hop_length=512, n_mels=128): + """Compute mel spectrogram from audio signal.""" + fft_window = scipy.signal.get_window('hann', n_fft, fftbins=True) + if len(fft_window) < n_fft: + lpad = int((n_fft - len(fft_window)) // 2) + fft_window = np.pad(fft_window, (lpad, int(n_fft - len(fft_window) - lpad)), mode='constant') + + fft_window = fft_window.reshape((-1, 1)) + data_padded = np.pad(data, int(n_fft // 2), mode='constant') + n_frames = 1 + (len(data_padded) - n_fft) // hop_length + shape = (n_fft, n_frames) + strides = (data_padded.strides[0], data_padded.strides[0] * hop_length) + frames = np.lib.stride_tricks.as_strided(data_padded, shape=shape, strides=strides) + + stft_result = scipy.fft.rfft(fft_window * frames, axis=0).astype(np.complex64) + power_spec = np.abs(stft_result) ** 2 + + mel_basis = _create_mel_filterbank(sr, n_fft, n_mels=n_mels, fmin=0.0, fmax=sr / 2.0) + mel_spec = np.dot(mel_basis, power_spec) + return mel_spec.astype(np.float32) + + +def quick_tempo_estimate(audio_np, sr, start_bpm=120.0, std_bpm=1.0, hop_length=512): + """Estimate tempo using autocorrelation tempogram.""" + + if len(audio_np) < hop_length * 10: + logging.warning("Audio too short for tempo estimation, returning default BPM of 120.0") + return 120.0 + + n_fft = 2048 + mel_S = _compute_mel_spectrogram(audio_np, sr, n_fft=n_fft, hop_length=hop_length, n_mels=128) + log_mel_S = 10.0 * np.log10(np.maximum(1e-10, mel_S)) + + lag = 1 + S_diff = log_mel_S[:, lag:] - log_mel_S[:, :-lag] + S_onset = np.maximum(0.0, S_diff) + onset_env_pre = np.mean(S_onset, axis=0) + pad_width = lag + n_fft // (2 * hop_length) + onset_env = np.pad(onset_env_pre, (pad_width, 0), mode='constant') + onset_env = onset_env[:mel_S.shape[1]] + + return estimate_tempo_from_onset(onset_env, sr, hop_length, start_bpm, std_bpm, max_tempo=320.0) + + +def estimate_tempo_from_onset(onset_env, sr, hop_length, start_bpm=120.0, std_bpm=1.0, max_tempo=320.0): + """Estimate tempo from onset strength envelope using autocorrelation tempogram.""" + if len(onset_env) < 20: + return 120.0 + + ac_size = 8.0 + win_length = int(np.round(ac_size * sr / hop_length)) + win_length = min(win_length, len(onset_env)) + + pad_width = win_length // 2 + onset_padded = np.pad(onset_env, (pad_width, pad_width), mode='linear_ramp', end_values=(0, 0)) + + n_frames = len(onset_env) + shape = (win_length, n_frames) + strides = (onset_padded.strides[0], onset_padded.strides[0]) + frames = np.lib.stride_tricks.as_strided(onset_padded, shape=shape, strides=strides) + + hann_window = scipy.signal.get_window('hann', win_length, fftbins=True) + windowed_frames = frames * hann_window[:, np.newaxis] + + tempogram = np.zeros((win_length, n_frames)) + for i in range(n_frames): + frame = windowed_frames[:, i] + n_pad = scipy.fft.next_fast_len(2 * len(frame) - 1) + fft_result = scipy.fft.rfft(frame, n=n_pad) + powspec = np.abs(fft_result) ** 2 + ac = scipy.fft.irfft(powspec, n=n_pad) + tempogram[:, i] = ac[:win_length] + + ac_max = np.max(np.abs(tempogram), axis=0) + mask = ac_max > 0 + tempogram[:, mask] /= ac_max[mask] + + tempogram_mean = np.mean(tempogram, axis=1) + tempogram_mean = np.maximum(tempogram_mean, 0) + + bpms = np.zeros(win_length, dtype=np.float64) + bpms[0] = np.inf + bpms[1:] = 60.0 * sr / (hop_length * np.arange(1.0, win_length)) + + logprior = -0.5 * ((np.log2(bpms) - np.log2(start_bpm)) / std_bpm) ** 2 + + if max_tempo is not None: + max_idx = int(np.argmax(bpms < max_tempo)) + if max_idx > 0: + logprior[:max_idx] = -np.inf + + weighted = np.log1p(1e6 * tempogram_mean) + logprior + best_idx = int(np.argmax(weighted[1:])) + 1 + tempo = bpms[best_idx] + + return tempo + + +def detect_onset_peaks(onset_env, sr=22050, hop_length=512, pre_max=0.03, post_max=0.0, + pre_avg=0.10, post_avg=0.10, wait=0.03, delta=0.07): + """Detect onset peaks using peak picking algorithm.""" + + onset_normalized = onset_env - np.min(onset_env) + onset_max = np.max(onset_normalized) + if onset_max > 0: + onset_normalized = onset_normalized / onset_max + + pre_max_frames = int(pre_max * sr / hop_length) + post_max_frames = int(post_max * sr / hop_length) + 1 + pre_avg_frames = int(pre_avg * sr / hop_length) + post_avg_frames = int(post_avg * sr / hop_length) + 1 + wait_frames = int(wait * sr / hop_length) + + peaks = np.zeros(len(onset_normalized), dtype=bool) + peaks[0] = (onset_normalized[0] >= np.max(onset_normalized[:min(post_max_frames, len(onset_normalized))])) + peaks[0] &= (onset_normalized[0] >= np.mean(onset_normalized[:min(post_avg_frames, len(onset_normalized))]) + delta) + + if peaks[0]: + n = wait_frames + 1 + else: + n = 1 + + while n < len(onset_normalized): + maxn = np.max(onset_normalized[max(0, n - pre_max_frames):min(n + post_max_frames, len(onset_normalized))]) + peaks[n] = (onset_normalized[n] == maxn) + + if not peaks[n]: + n += 1 + continue + + avgn = np.mean(onset_normalized[max(0, n - pre_avg_frames):min(n + post_avg_frames, len(onset_normalized))]) + peaks[n] &= (onset_normalized[n] >= avgn + delta) + + if not peaks[n]: + n += 1 + continue + + n += wait_frames + 1 + + return np.flatnonzero(peaks).astype(np.int32) + + +def track_beats(onset_env, tempo, sr, hop_length, tightness=100, trim=True): + """Track beats using dynamic programming.""" + + frame_rate = sr / hop_length + frames_per_beat = np.round(frame_rate * 60.0 / tempo) + + if frames_per_beat <= 0 or len(onset_env) < 2: + return np.array([], dtype=np.int32) + + onset_std = np.std(onset_env, ddof=1) + if onset_std > 0: + onset_normalized = onset_env / onset_std + else: + onset_normalized = onset_env + + window_range = np.arange(-frames_per_beat, frames_per_beat + 1) + window = np.exp(-0.5 * (window_range * 32.0 / frames_per_beat) ** 2) + + localscore = scipy.signal.convolve(onset_normalized, window, mode='same') + + backlink = np.full(len(localscore), -1, dtype=np.int32) + cumscore = np.zeros(len(localscore), dtype=np.float64) + + score_thresh = 0.01 * localscore.max() + first_beat = True + + backlink[0] = -1 + cumscore[0] = localscore[0] + + fpb = int(frames_per_beat) + + for i in range(1, len(localscore)): + score_i = localscore[i] + best_score = -np.inf + beat_location = -1 + + search_start = int(i - np.round(fpb / 2.0)) + search_end = int(i - 2 * fpb - 1) + + for loc in range(search_start, search_end, -1): + if loc < 0: + break + + score = cumscore[loc] - tightness * (np.log(i - loc) - np.log(fpb)) ** 2 + + if score > best_score: + best_score = score + beat_location = loc + + if beat_location >= 0: + cumscore[i] = score_i + best_score + else: + cumscore[i] = score_i + + if first_beat and score_i < score_thresh: + backlink[i] = -1 + else: + backlink[i] = beat_location + first_beat = False + + local_max_mask = np.zeros(len(cumscore), dtype=bool) + + local_max_mask[0] = False + + for i in range(1, len(cumscore) - 1): + local_max_mask[i] = (cumscore[i] > cumscore[i-1]) and (cumscore[i] >= cumscore[i+1]) + + if len(cumscore) > 1: + local_max_mask[-1] = cumscore[-1] > cumscore[-2] + + if np.any(local_max_mask): + median_max = np.median(cumscore[local_max_mask]) + threshold = 0.5 * median_max + + tail = -1 + for i in range(len(cumscore) - 1, -1, -1): + if local_max_mask[i] and cumscore[i] >= threshold: + tail = i + break + else: + tail = len(cumscore) - 1 + + beats = np.zeros(len(localscore), dtype=bool) + n = tail + visited = set() + while n >= 0 and n not in visited: + beats[n] = True + visited.add(n) + n = backlink[n] + + if trim and np.any(beats): + beat_positions = np.flatnonzero(beats) + + beat_localscores = localscore[beat_positions] + + w = np.hanning(5) + smooth_boe_full = np.convolve(beat_localscores, w) + smooth_boe = smooth_boe_full[len(w)//2 : len(localscore) + len(w)//2] + + threshold = 0.5 * np.sqrt(np.mean(smooth_boe ** 2)) + + start_frame = 0 + while start_frame < len(localscore) and localscore[start_frame] <= threshold: + beats[start_frame] = False + start_frame += 1 + + end_frame = len(localscore) - 1 + while end_frame >= 0 and localscore[end_frame] <= threshold: + beats[end_frame] = False + end_frame -= 1 + + return np.flatnonzero(beats).astype(np.int32) + +def compute_onset_envelope(mel_spec_db, n_fft=2048, hop_length=512): + """Compute onset strength envelope from a log-mel spectrogram (dB).""" + lag = 1 + onset_diff = mel_spec_db[:, lag:] - mel_spec_db[:, :-lag] + onset_diff = np.maximum(0.0, onset_diff) + envelope_pre_pad = np.mean(onset_diff, axis=0) + + pad_width = lag + n_fft // (2 * hop_length) + envelope = np.pad(envelope_pre_pad, (pad_width, 0), mode='constant') + envelope = envelope[:mel_spec_db.shape[1]] + + return envelope + +def compute_mfcc(mel_spec_db, n_mfcc=20): + """Compute MFCC features from a log-mel spectrogram (dB).""" + mfcc = scipy.fft.dct(mel_spec_db, axis=0, type=2, norm='ortho')[:n_mfcc].T + return mfcc.astype(np.float32) + + +def power_to_db(S, amin=1e-10, top_db=80.0, ref=1.0): + """Convert a power spectrogram (amplitude squared) to decibel (dB) units""" + S = np.asarray(S) + log_spec = 10.0 * np.log10(np.maximum(amin, S)) + log_spec -= 10.0 * np.log10(np.maximum(amin, ref)) + if top_db is not None: + log_spec = np.maximum(log_spec, log_spec.max() - top_db) + return log_spec + + +class WanDancerEncodeAudio(io.ComfyNode): + @classmethod + def define_schema(cls): + return io.Schema( + node_id="WanDancerEncodeAudio", + category="conditioning/video_models", + inputs=[ + io.Audio.Input("audio"), + io.Int.Input("video_frames", default=149, min=1, max=nodes.MAX_RESOLUTION, step=4), + io.Float.Input("audio_inject_scale", default=1.0, min=0.0, max=10.0, step=0.01, tooltip="The scale for the audio features when injected into the video model."), + ], + outputs=[ + io.AudioEncoderOutput.Output(display_name="audio_encoder_output"), + io.String.Output(display_name="fps_string", tooltip="The calculated fps based on the audio length and the number of video frames. Used in the prompt."), + ], + ) + + @classmethod + def execute(cls, video_frames, audio_inject_scale, audio) -> io.NodeOutput: + waveform = audio["waveform"][0] + sample_rate = audio["sample_rate"] + base_fps = 30 + hop_length = 512 + model_sr = 22050 + n_fft = 2048 + + # start tempo from original audio (not the resampled one) to match the reference pipeline + if waveform.shape[0] > 1: + waveform = waveform.mean(dim=0, keepdim=False) + + start_bpm = quick_tempo_estimate(waveform.squeeze().cpu().numpy(), sample_rate, hop_length=hop_length) + + # resample to the sample rate used for feature extraction + resample_sr = base_fps * hop_length + waveform = torchaudio.functional.resample(waveform, sample_rate, resample_sr) + + waveform_np = waveform.cpu().numpy().squeeze() + mel_spec = _compute_mel_spectrogram(waveform_np, model_sr, n_fft, hop_length, n_mels=128) + mel_spec_db = power_to_db(mel_spec, amin=1e-10, top_db=80.0, ref=1.0) + envelope = compute_onset_envelope(mel_spec_db, n_fft, hop_length) + mfcc = compute_mfcc(mel_spec_db, n_mfcc=20) + chroma = compute_chroma_cens(y=waveform_np, sr=model_sr, hop_length=hop_length).T + # detect peaks + peak_idxs = detect_onset_peaks(envelope, sr=model_sr, hop_length=hop_length) + peak_onehot = np.zeros_like(envelope, dtype=np.float32) + peak_onehot[peak_idxs] = 1.0 + # detect beats + beat_tracking_tempo = estimate_tempo_from_onset(envelope, sr=model_sr, hop_length=hop_length, start_bpm=start_bpm) + beat_idxs = track_beats(envelope, beat_tracking_tempo, model_sr, hop_length, tightness=100, trim=True) + beat_onehot = np.zeros_like(envelope, dtype=np.float32) + beat_onehot[beat_idxs] = 1.0 + + audio_feature = np.concatenate( + [envelope[:, None], mfcc, chroma, peak_onehot[:, None], beat_onehot[:, None]], + axis=-1, + ) + audio_feature = torch.from_numpy(audio_feature).unsqueeze(0).to(comfy.model_management.intermediate_device()) + + fps = float(base_fps / int(audio_feature.shape[1] / video_frames + 0.5)) + + audio_encoder_output = { + "audio_feature": audio_feature, + "fps": fps, + "audio_inject_scale": audio_inject_scale, + } + + if int(fps + 0.5) != 30: + fps_string = " 帧率是{:.4f}".format(fps) # "frame rate is" in Chinese, as it was in the original pipeline + else: + fps_string = ", 帧率是30fps。" # to match the reference pipeline when the fps is 30 + + return io.NodeOutput(audio_encoder_output, fps_string) + + +class WanDancerVideo(io.ComfyNode): + @classmethod + def define_schema(cls): + return io.Schema( + node_id="WanDancerVideo", + category="conditioning/video_models", + inputs=[ + io.Conditioning.Input("positive"), + io.Conditioning.Input("negative"), + io.Vae.Input("vae"), + io.Int.Input("width", default=480, min=16, max=nodes.MAX_RESOLUTION, step=16), + io.Int.Input("height", default=832, min=16, max=nodes.MAX_RESOLUTION, step=16), + io.Int.Input("length", default=149, min=1, max=nodes.MAX_RESOLUTION, step=4, tooltip="The number of frames in the generated video. Should stay 149 for WanDancer."), + io.ClipVisionOutput.Input("clip_vision_output", optional=True, tooltip="The CLIP vision embeds for the first frame."), + io.ClipVisionOutput.Input("clip_vision_output_ref", optional=True, tooltip="The CLIP vision embeds for the reference image."), + io.Image.Input("start_image", optional=True, tooltip="The initial image(s) to be encoded, can be any number of frames."), + io.Mask.Input("mask", optional=True, tooltip="Image conditioning mask for the start image(s). White is kept, black is generated. Used for the local generations."), + io.AudioEncoderOutput.Input("audio_encoder_output", optional=True), + ], + outputs=[ + io.Conditioning.Output(display_name="positive"), + io.Conditioning.Output(display_name="negative"), + io.Latent.Output(display_name="latent", tooltip="Empty latent."), + ], + ) + + @classmethod + def execute(cls, positive, negative, vae, width, height, length, start_image=None, mask=None, clip_vision_output=None, clip_vision_output_ref=None, audio_encoder_output=None) -> io.NodeOutput: + latent = torch.zeros([1, 16, ((length - 1) // 4) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device()) + if start_image is not None: + start_image = comfy.utils.common_upscale(start_image[:length].movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1) + image = torch.zeros((length, height, width, start_image.shape[-1]), device=start_image.device, dtype=start_image.dtype) + image[:start_image.shape[0]] = start_image + + concat_latent_image = vae.encode(image[:, :, :, :3]) + if mask is None: + concat_mask = torch.ones((1, 1, latent.shape[2], concat_latent_image.shape[-2], concat_latent_image.shape[-1]), device=start_image.device, dtype=start_image.dtype) + concat_mask[:, :, :((start_image.shape[0] - 1) // 4) + 1] = 0.0 + else: + concat_mask = 1 - mask[:length].unsqueeze(0) + concat_mask = comfy.utils.common_upscale(concat_mask, concat_latent_image.shape[-2], concat_latent_image.shape[-1], "nearest-exact", "disabled") + concat_mask = torch.cat([torch.repeat_interleave(concat_mask[:, 0:1], repeats=4, dim=1), concat_mask[:, 1:]], dim=1) + concat_mask = concat_mask.view(1, concat_mask.shape[1] // 4, 4, concat_latent_image.shape[-2], concat_latent_image.shape[-1]).transpose(1, 2) + + positive = node_helpers.conditioning_set_values(positive, {"concat_latent_image": concat_latent_image, "concat_mask": concat_mask}) + negative = node_helpers.conditioning_set_values(negative, {"concat_latent_image": concat_latent_image, "concat_mask": concat_mask}) + + if clip_vision_output is not None: + positive = node_helpers.conditioning_set_values(positive, {"clip_vision_output": clip_vision_output, "clip_vision_output_ref": clip_vision_output_ref}) + negative = node_helpers.conditioning_set_values(negative, {"clip_vision_output": clip_vision_output, "clip_vision_output_ref": clip_vision_output_ref}) + + if audio_encoder_output is not None: + positive = node_helpers.conditioning_set_values(positive, {"audio_embed": audio_encoder_output["audio_feature"], "fps": audio_encoder_output["fps"], "audio_inject_scale": audio_encoder_output.get("audio_inject_scale", 1.0)}) + negative = node_helpers.conditioning_set_values(negative, {"audio_embed": audio_encoder_output["audio_feature"], "fps": audio_encoder_output["fps"], "audio_inject_scale": audio_encoder_output.get("audio_inject_scale", 1.0)}) + + out_latent = {} + out_latent["samples"] = latent + return io.NodeOutput(positive, negative, out_latent) + + +class VAEDecodeVideoFramewise(io.ComfyNode): + @classmethod + def define_schema(cls): + return io.Schema( + node_id="VAEDecodeVideoFramewise", + category="latent", + description="Decodes video latents one latent at a time.", + search_aliases=["decode", "decode latent", "latent to image", "render latent"], + inputs=[ + io.Latent.Input("samples", tooltip="The latent to be decoded."), + io.Vae.Input("vae", tooltip="The VAE model used for decoding the latent."), + ], + outputs=[ + io.Image.Output(tooltip="The decoded images."), + ], + ) + + @classmethod + def execute(cls, vae, samples) -> io.NodeOutput: + latent = samples["samples"] + if latent.is_nested: + latent = latent.unbind()[0] + + # reshape temporal dimension into batch + B, C, T, H, W = latent.shape + latent_batched = latent.transpose(1, 2).reshape(B * T, C, 1, H, W) + images = vae.decode(latent_batched).squeeze(1) + + return io.NodeOutput(images) + +class WanDancerPadKeyframes(io.ComfyNode): + @classmethod + def define_schema(cls): + return io.Schema( + node_id="WanDancerPadKeyframes", + category="image/video", + inputs=[ + io.Image.Input("images",), + io.Int.Input("segment_length", default=149, min=1, max=10000, tooltip="Length of this segment (usually 149 frames)"), + io.Int.Input("segment_index", default=0, min=0, max=100, tooltip="Which segment this is (0 for first, 1 for second, etc.)"), + io.Audio.Input("audio", tooltip="Audio to calculate total output frames from and extract segment audio."), + ], + outputs=[ + io.Image.Output(display_name="keyframes_sequence", tooltip="Padded keyframe sequence"), + io.Mask.Output(display_name="keyframes_mask", tooltip="Mask indicating valid frames"), + io.Audio.Output(display_name="audio_segment", tooltip="Audio segment for this video segment"), + ], + ) + + @classmethod + def do_execute(cls, images, segment_length, segment_index, audio): + B, H, W, C = images.shape + fps = 30 + + # calculate total frames + audio_duration = audio["waveform"].shape[-1] / audio["sample_rate"] + segment_duration = segment_length / fps + buffer = 0.2 + num_segments = int((audio_duration - buffer) / segment_duration) + 1 if audio_duration > buffer else 0 + total_frames = num_segments * segment_length + + mask = torch.zeros((segment_length, H, W), device=images.device, dtype=images.dtype) + keyframes = torch.zeros((segment_length, H, W, C), dtype=images.dtype, device=images.device) + + # guard: with no audio or no images, nothing to place — leave keyframes/mask zeroed + if total_frames > 0 and B > 0: + frame_interval = float(total_frames) / B + seg_num = int(math.ceil(total_frames / segment_length)) + is_last_segment = (segment_index == seg_num - 1) + + positions = [] + images_before_this_segment = 0 + + # count images consumed by previous segments + for seg_idx in range(segment_index): + end_idx = (total_frames - segment_length * seg_idx - 1) if seg_idx == seg_num - 1 else (segment_length - 1) + cnt = 0 + while cnt * frame_interval < end_idx - frame_interval: + cnt += 1 + images_before_this_segment += cnt + + # positions for current segment + end_index = (total_frames - segment_length * segment_index - 1) if is_last_segment else (segment_length - 1) + cnt = 0 + while cnt * frame_interval < end_index - frame_interval: + pos = int(math.ceil(frame_interval * cnt)) + positions.append((pos, images_before_this_segment + cnt)) + cnt += 1 + positions.append((end_index, images_before_this_segment + cnt)) + + valid_positions = [(pos, idx) for pos, idx in positions if idx < B and pos < segment_length] + + if valid_positions: + seg_positions, img_indices = zip(*valid_positions) + seg_positions = torch.tensor(seg_positions, dtype=torch.long, device=images.device) + img_indices = torch.tensor(img_indices, dtype=torch.long, device=images.device) + mask[seg_positions] = 1 + keyframes[seg_positions] = images[img_indices] + + # extract audio segment + segment_duration = segment_length / fps + start_time = segment_index * segment_duration + end_time = min(start_time + segment_duration, audio_duration) + + sample_rate = audio["sample_rate"] + start_sample = int(start_time * sample_rate) + end_sample = int(end_time * sample_rate) + + audio_segment_waveform = audio["waveform"][:, :, start_sample:end_sample] + audio_segment = { + "waveform": audio_segment_waveform, + "sample_rate": sample_rate + } + + return keyframes, mask, audio_segment + + @classmethod + def execute(cls, images, segment_length, segment_index, audio=None) -> io.NodeOutput: + return io.NodeOutput(*cls.do_execute(images, segment_length, segment_index, audio)) + +class WanDancerPadKeyframesList(io.ComfyNode): + @classmethod + def define_schema(cls): + return io.Schema( + node_id="WanDancerPadKeyframesList", + category="image/video", + inputs=[ + io.Image.Input("images"), + io.Int.Input("segment_length", default=149, min=1, max=10000, tooltip="Length of each segment (usually 149 frames)"), + io.Int.Input("num_segments", default=1, min=1, max=100, tooltip="How many padded segments to emit as lists."), + io.Audio.Input("audio", tooltip="Audio to slice for each emitted segment."), + ], + outputs=[ + io.Image.Output(display_name="keyframes_sequence", tooltip="Padded keyframe sequences", is_output_list=True), + io.Mask.Output(display_name="keyframes_mask", tooltip="Masks indicating valid frames", is_output_list=True), + io.Audio.Output(display_name="audio_segment", tooltip="Audio segment for each video segment", is_output_list=True), + ], + ) + + @classmethod + def execute(cls, images, segment_length, num_segments, audio=None) -> io.NodeOutput: + outputs = [WanDancerPadKeyframes.do_execute(images, segment_length, i, audio) for i in range(num_segments)] + keyframes, masks, audio_segments = zip(*outputs) + return io.NodeOutput(list(keyframes), list(masks), list(audio_segments)) + +class WanDancerExtension(ComfyExtension): + @override + async def get_node_list(self) -> list[type[io.ComfyNode]]: + return [ + WanDancerVideo, + VAEDecodeVideoFramewise, + WanDancerEncodeAudio, + WanDancerPadKeyframes, + WanDancerPadKeyframesList, + ] + +async def comfy_entrypoint() -> WanDancerExtension: + return WanDancerExtension() diff --git a/nodes.py b/nodes.py index 5755f0bb8..ec66e54d7 100644 --- a/nodes.py +++ b/nodes.py @@ -2434,6 +2434,7 @@ async def init_builtin_extra_nodes(): "nodes_frame_interpolation.py", "nodes_sam3.py", "nodes_void.py", + "nodes_wandancer.py", ] import_failed = [] From 20f5e474da28bd4225ab61b3d5d791e1b32ba069 Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Sat, 9 May 2026 14:17:00 -0700 Subject: [PATCH 7/7] Use LatentCutToBatch instead. (#13815) Removed VAEDecodeVideoFramewise from nodes_wandancer.py. --- comfy_extras/nodes_wandancer.py | 31 ------------------------------- 1 file changed, 31 deletions(-) diff --git a/comfy_extras/nodes_wandancer.py b/comfy_extras/nodes_wandancer.py index faaeb9020..fc005ed4c 100644 --- a/comfy_extras/nodes_wandancer.py +++ b/comfy_extras/nodes_wandancer.py @@ -842,36 +842,6 @@ class WanDancerVideo(io.ComfyNode): return io.NodeOutput(positive, negative, out_latent) -class VAEDecodeVideoFramewise(io.ComfyNode): - @classmethod - def define_schema(cls): - return io.Schema( - node_id="VAEDecodeVideoFramewise", - category="latent", - description="Decodes video latents one latent at a time.", - search_aliases=["decode", "decode latent", "latent to image", "render latent"], - inputs=[ - io.Latent.Input("samples", tooltip="The latent to be decoded."), - io.Vae.Input("vae", tooltip="The VAE model used for decoding the latent."), - ], - outputs=[ - io.Image.Output(tooltip="The decoded images."), - ], - ) - - @classmethod - def execute(cls, vae, samples) -> io.NodeOutput: - latent = samples["samples"] - if latent.is_nested: - latent = latent.unbind()[0] - - # reshape temporal dimension into batch - B, C, T, H, W = latent.shape - latent_batched = latent.transpose(1, 2).reshape(B * T, C, 1, H, W) - images = vae.decode(latent_batched).squeeze(1) - - return io.NodeOutput(images) - class WanDancerPadKeyframes(io.ComfyNode): @classmethod def define_schema(cls): @@ -992,7 +962,6 @@ class WanDancerExtension(ComfyExtension): async def get_node_list(self) -> list[type[io.ComfyNode]]: return [ WanDancerVideo, - VAEDecodeVideoFramewise, WanDancerEncodeAudio, WanDancerPadKeyframes, WanDancerPadKeyframesList,