diff --git a/.github/workflows/release-stable-all.yml b/.github/workflows/release-stable-all.yml index 8f07a7b1c..d7cf69fe2 100644 --- a/.github/workflows/release-stable-all.yml +++ b/.github/workflows/release-stable-all.yml @@ -20,29 +20,12 @@ jobs: git_tag: ${{ inputs.git_tag }} cache_tag: "cu130" python_minor: "13" - python_patch: "11" + python_patch: "12" rel_name: "nvidia" rel_extra_name: "" test_release: true secrets: inherit - release_nvidia_cu128: - permissions: - contents: "write" - packages: "write" - pull-requests: "read" - name: "Release NVIDIA cu128" - uses: ./.github/workflows/stable-release.yml - with: - git_tag: ${{ inputs.git_tag }} - cache_tag: "cu128" - python_minor: "12" - python_patch: "10" - rel_name: "nvidia" - rel_extra_name: "_cu128" - test_release: true - secrets: inherit - release_nvidia_cu126: permissions: contents: "write" @@ -76,3 +59,20 @@ jobs: rel_extra_name: "" test_release: false secrets: inherit + + release_xpu: + permissions: + contents: "write" + packages: "write" + pull-requests: "read" + name: "Release Intel XPU" + uses: ./.github/workflows/stable-release.yml + with: + git_tag: ${{ inputs.git_tag }} + cache_tag: "xpu" + python_minor: "13" + python_patch: "12" + rel_name: "intel" + rel_extra_name: "" + test_release: true + secrets: inherit diff --git a/README.md b/README.md index 021f88a31..1eeb810de 100644 --- a/README.md +++ b/README.md @@ -61,6 +61,7 @@ See what ComfyUI can do with the [newer template workflows](https://comfy.org/wo ## Features - Nodes/graph/flowchart interface to experiment and create complex Stable Diffusion workflows without needing to code anything. +- NOTE: There are many more models supported than the list below, if you want to see what is supported see our templates list inside ComfyUI. - Image Models - SD1.x, SD2.x ([unCLIP](https://comfyanonymous.github.io/ComfyUI_examples/unclip/)) - [SDXL](https://comfyanonymous.github.io/ComfyUI_examples/sdxl/), [SDXL Turbo](https://comfyanonymous.github.io/ComfyUI_examples/sdturbo/) @@ -136,7 +137,7 @@ ComfyUI follows a weekly release cycle targeting Monday but this regularly chang - Builds a new release using the latest stable core version 3. **[ComfyUI Frontend](https://github.com/Comfy-Org/ComfyUI_frontend)** - - Weekly frontend updates are merged into the core repository + - Every 2+ weeks frontend updates are merged into the core repository - Features are frozen for the upcoming core release - Development continues for the next release cycle @@ -275,7 +276,7 @@ Nvidia users should install stable pytorch using this command: This is the command to install pytorch nightly instead which might have performance improvements. -```pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cu130``` +```pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cu132``` #### Troubleshooting diff --git 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hashf(uvec2 p, uint offset) {\n return float(pcg(hash2d(p) + offset)) / float(0xffffffffu);\n}\n\n// Convert uniform [0,1] to roughly Gaussian distribution\n// Using simple approximation: average of multiple samples\nfloat toGaussian(uvec2 p) {\n float sum = hashf(p, 0u) + hashf(p, 1u) + hashf(p, 2u) + hashf(p, 3u);\n return (sum - 2.0) * 0.7; // Centered, scaled\n}\n\nfloat toGaussian(uvec2 p, uint offset) {\n float sum = hashf(p, offset) + hashf(p, offset + 1u) \n + hashf(p, offset + 2u) + hashf(p, offset + 3u);\n return (sum - 2.0) * 0.7;\n}\n\n// Smooth noise with better interpolation\nfloat smoothNoise(vec2 p) {\n vec2 i = floor(p);\n vec2 f = fract(p);\n \n // Quintic interpolation (less banding than cubic)\n f = f * f * f * (f * (f * 6.0 - 15.0) + 10.0);\n \n uvec2 ui = uvec2(i);\n float a = toGaussian(ui);\n float b = toGaussian(ui + uvec2(1u, 0u));\n float c = toGaussian(ui + uvec2(0u, 1u));\n float d = toGaussian(ui + uvec2(1u, 1u));\n \n return mix(mix(a, b, f.x), mix(c, d, 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sinStep = sin(angleStep);\n\n float negAngle = -float(RADIAL_SAMPLES) * angleStep;\n vec2 rotDir = vec2(\n dir.x * cos(negAngle) - dir.y * sin(negAngle),\n dir.x * sin(negAngle) + dir.y * cos(negAngle)\n );\n\n for (int i = -RADIAL_SAMPLES; i <= RADIAL_SAMPLES; i++) {\n vec2 uv = center + rotDir * dist;\n float w = 1.0 - abs(float(i)) / float(RADIAL_SAMPLES);\n sum += texture(u_image0, uv) * w;\n totalWeight += w;\n\n rotDir = vec2(\n rotDir.x * cosStep - rotDir.y * sinStep,\n rotDir.x * sinStep + rotDir.y * cosStep\n );\n }\n\n fragColor0 = sum / max(totalWeight, 0.001);\n return;\n }\n\n // Separable Gaussian / Box blur\n int samples = int(ceil(radius));\n\n if (samples == 0) {\n fragColor0 = texture(u_image0, v_texCoord);\n return;\n }\n\n // Direction: pass 0 = horizontal, pass 1 = vertical\n vec2 dir = (u_pass == 0) ? vec2(1.0, 0.0) : vec2(0.0, 1.0);\n\n vec4 color = vec4(0.0);\n float totalWeight = 0.0;\n float sigma = radius / 2.0;\n\n for (int i = -samples; i <= samples; i++) {\n vec2 offset = dir * float(i) * texelSize;\n vec4 sample_color = texture(u_image0, v_texCoord + offset);\n\n float weight;\n if (u_int0 == BLUR_GAUSSIAN) {\n weight = gaussian(float(i), sigma);\n } else {\n // BLUR_BOX\n weight = 1.0;\n }\n\n color += sample_color * weight;\n totalWeight += weight;\n }\n\n fragColor0 = color / totalWeight;\n}\n", + "from_input" + ] + } + ], + "groups": [], + "links": [ + { + "id": 10, + "origin_id": 10, + "origin_slot": 0, + "target_id": 1, + "target_slot": 2, + "type": "FLOAT" + }, + { + "id": 11, + "origin_id": 12, + "origin_slot": 1, + "target_id": 1, + "target_slot": 4, + "type": "INT" + }, + { + "id": 9, + "origin_id": -10, + "origin_slot": 0, + "target_id": 1, + "target_slot": 0, + "type": "IMAGE" + }, + { + "id": 3, + "origin_id": 1, + "origin_slot": 0, + "target_id": -20, + "target_slot": 0, + "type": "IMAGE" + } + ], + "extra": { + "workflowRendererVersion": "LG" + }, + "category": "Image Tools/Blur" + } + ] + } +} diff --git a/blueprints/Image Captioning (gemini).json b/blueprints/Image Captioning (gemini).json index 89ebac802..98cfb8999 100644 --- a/blueprints/Image Captioning (gemini).json +++ b/blueprints/Image Captioning (gemini).json @@ -1 +1,317 @@ -{"revision": 0, "last_node_id": 231, "last_link_id": 0, "nodes": [{"id": 231, "type": "e3e78497-720e-45a2-b4fb-c7bfdb80dd11", "pos": [23.13283014087665, 1034.468391137315], "size": [280, 260], "flags": {}, "order": 8, "mode": 0, "inputs": [{"localized_name": "images", "name": "images", "type": "IMAGE", "link": null}, {"name": "prompt", "type": "STRING", "widget": {"name": "prompt"}, "link": null}, {"name": "model", "type": "COMBO", "widget": {"name": "model"}, "link": null}], "outputs": [{"localized_name": "STRING", "name": "STRING", "type": "STRING", "links": []}], "properties": {"proxyWidgets": [["-1", "prompt"], ["-1", "model"], ["1", "seed"]], "cnr_id": "comfy-core", "ver": "0.13.0"}, "widgets_values": ["Describe this image", "gemini-2.5-pro"], 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This is essential for tasks like content creation, design inspiration, and artistic exploration.\n- Profile: As an AI Image Analysis and Description Specialist, you possess extensive knowledge in computer vision, image processing, and natural language generation. You are adept at interpreting visual data and translating it into descriptive text that can guide the creation of new images.\n- Skills: Proficiency in image recognition, feature extraction, descriptive language generation, and understanding of artistic elements such as composition, color, and texture.\n- Goals: To analyze the provided image, generate a comprehensive and detailed description that captures the key visual elements, and ensure this description can effectively serve as a drawing prompt for creating similar images.\n- Constrains: The description must be clear, concise, and specific enough to guide the creation of a similar image. It should avoid ambiguity and focus on the most salient features of the image. The output should only contain the drawing prompt.\n- OutputFormat: A detailed text description of the image, highlighting key visual elements such as objects, colors, composition, and any unique features.\n- Workflow:\n 1. Analyze the image to identify key visual elements including objects, colors, and composition.\n 2. Generate a detailed description that captures the essence of the image, ensuring it is specific and actionable.\n 3. 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This is essential for tasks like content creation, design inspiration, and artistic exploration.\n- Profile: As an AI Image Analysis and Description Specialist, you possess extensive knowledge in computer vision, image processing, and natural language generation. You are adept at interpreting visual data and translating it into descriptive text that can guide the creation of new images.\n- Skills: Proficiency in image recognition, feature extraction, descriptive language generation, and understanding of artistic elements such as composition, color, and texture.\n- Goals: To analyze the provided image, generate a comprehensive and detailed description that captures the key visual elements, and ensure this description can effectively serve as a drawing prompt for creating similar images.\n- Constrains: The description must be clear, concise, and specific enough to guide the creation of a similar image. It should avoid ambiguity and focus on the most salient features of the image. The output should only contain the drawing prompt.\n- OutputFormat: A detailed text description of the image, highlighting key visual elements such as objects, colors, composition, and any unique features.\n- Workflow:\n 1. Analyze the image to identify key visual elements including objects, colors, and composition.\n 2. Generate a detailed description that captures the essence of the image, ensuring it is specific and actionable.\n 3. Refine the description to ensure clarity and conciseness, making it suitable for use as a drawing prompt." + ], + "color": "#432", + "bgcolor": "#653" + } + ], + "groups": [], + "links": [ + { + "id": 1, + "origin_id": -10, + "origin_slot": 0, + "target_id": 1, + "target_slot": 0, + "type": "IMAGE" + }, + { + "id": 6, + "origin_id": 1, + "origin_slot": 0, + "target_id": -20, + "target_slot": 0, + "type": "*" + }, + { + "id": 15, + "origin_id": -10, + "origin_slot": 1, + "target_id": 1, + "target_slot": 4, + "type": "STRING" + }, + { + "id": 16, + "origin_id": -10, + "origin_slot": 2, + "target_id": 1, + "target_slot": 5, + "type": "COMBO" + } + ], + "extra": { + "workflowRendererVersion": "LG" + }, + "category": "Text generation/Image Captioning" + } + ] + } +} diff --git a/blueprints/Image Channels.json b/blueprints/Image Channels.json index cb3488883..9c7b675b2 100644 --- a/blueprints/Image Channels.json +++ b/blueprints/Image Channels.json @@ -1 +1,322 @@ -{"revision": 0, 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"sampler_name", "type": "COMBO", "widget": {"name": "sampler_name"}, "link": null}, {"localized_name": "scheduler", "name": "scheduler", "type": "COMBO", "widget": {"name": "scheduler"}, "link": null}, {"localized_name": "start_at_step", "name": "start_at_step", "type": "INT", "widget": {"name": "start_at_step"}, "link": null}, {"localized_name": "end_at_step", "name": "end_at_step", "type": "INT", "widget": {"name": "end_at_step"}, "link": null}, {"localized_name": "return_with_leftover_noise", "name": "return_with_leftover_noise", "type": "COMBO", "widget": {"name": "return_with_leftover_noise"}, "link": null}], "outputs": [{"localized_name": "LATENT", "name": "LATENT", "type": "LATENT", "links": [175]}], "properties": {"cnr_id": "comfy-core", "ver": "0.3.45", "Node name for S&R": "KSamplerAdvanced", "ue_properties": {"widget_ue_connectable": {}, "version": "7.1", "input_ue_unconnectable": {}}}, "widgets_values": ["disable", 0, "fixed", 4, 1, "euler", "simple", 2, 4, "disable"]}, {"id": 67, "type": "Note", "pos": [510.0000345979581, 819.9999455547611], "size": [390, 88], "flags": {}, "order": 4, "mode": 0, "inputs": [], "outputs": [], "title": "Video Size", "properties": {"ue_properties": {"widget_ue_connectable": {}, "version": "7.1", "input_ue_unconnectable": {}}}, "widgets_values": ["By default, we set the video to a smaller size for users with low VRAM. If you have enough VRAM, you can change the size"], "color": "#222", "bgcolor": "#000"}, {"id": 105, "type": "MarkdownNote", "pos": [-469.9999795985529, 279.9998197772136], "size": [480, 170.65104166666669], "flags": {}, "order": 5, "mode": 0, "inputs": [], "outputs": [], "title": "VRAM Usage", "properties": {"ue_properties": {"version": "7.1", "widget_ue_connectable": {}, "input_ue_unconnectable": {}}}, "widgets_values": ["## GPU:RTX4090D 24GB\n\n| Model | Size |VRAM Usage | 1st Generation | 2nd Generation |\n|---------------------|-------|-----------|---------------|-----------------|\n| fp8_scaled |640*640| 84% | ≈ 536s | ≈ 513s |\n| fp8_scaled + 4steps LoRA | 640*640 | 83% | ≈ 97s | ≈ 71s |"], "color": "#222", "bgcolor": "#000"}, {"id": 66, "type": "MarkdownNote", "pos": [-469.9999795985529, -320.00012452364496], "size": [480, 572.1354166666667], "flags": {}, "order": 6, "mode": 0, "inputs": [], "outputs": [], "title": "Model Links", "properties": {"ue_properties": {"widget_ue_connectable": {}, "version": "7.1", "input_ue_unconnectable": {}}}, "widgets_values": ["[Tutorial](https://docs.comfy.org/tutorials/video/wan/wan2_2\n)\n\n**Diffusion Model**\n- [wan2.2_i2v_high_noise_14B_fp8_scaled.safetensors](https://huggingface.co/Comfy-Org/Wan_2.2_ComfyUI_Repackaged/resolve/main/split_files/diffusion_models/wan2.2_i2v_high_noise_14B_fp8_scaled.safetensors)\n- [wan2.2_i2v_low_noise_14B_fp8_scaled.safetensors](https://huggingface.co/Comfy-Org/Wan_2.2_ComfyUI_Repackaged/resolve/main/split_files/diffusion_models/wan2.2_i2v_low_noise_14B_fp8_scaled.safetensors)\n\n**LoRA**\n- [wan2.2_i2v_lightx2v_4steps_lora_v1_low_noise.safetensors](https://huggingface.co/Comfy-Org/Wan_2.2_ComfyUI_Repackaged/resolve/main/split_files/loras/wan2.2_i2v_lightx2v_4steps_lora_v1_low_noise.safetensors)\n- [wan2.2_i2v_lightx2v_4steps_lora_v1_high_noise.safetensors](https://huggingface.co/Comfy-Org/Wan_2.2_ComfyUI_Repackaged/resolve/main/split_files/loras/wan2.2_i2v_lightx2v_4steps_lora_v1_high_noise.safetensors)\n\n**VAE**\n- [wan_2.1_vae.safetensors](https://huggingface.co/Comfy-Org/Wan_2.2_ComfyUI_Repackaged/resolve/main/split_files/vae/wan_2.1_vae.safetensors)\n\n**Text Encoder** \n- [umt5_xxl_fp8_e4m3fn_scaled.safetensors](https://huggingface.co/Comfy-Org/Wan_2.1_ComfyUI_repackaged/resolve/main/split_files/text_encoders/umt5_xxl_fp8_e4m3fn_scaled.safetensors)\n\n\nFile save location\n\n```\nComfyUI/\n├───📂 models/\n│ ├───📂 diffusion_models/\n│ │ ├─── wan2.2_i2v_low_noise_14B_fp8_scaled.safetensors\n│ │ └─── wan2.2_i2v_high_noise_14B_fp8_scaled.safetensors\n│ ├───📂 loras/\n│ │ ├─── wan2.2_i2v_lightx2v_4steps_lora_v1_low_noise.safetensors\n│ │ └─── wan2.2_i2v_lightx2v_4steps_lora_v1_low_noise.safetensors\n│ ├───📂 text_encoders/\n│ │ └─── umt5_xxl_fp8_e4m3fn_scaled.safetensors \n│ └───📂 vae/\n│ └── wan_2.1_vae.safetensors\n```\n"], "color": "#222", "bgcolor": "#000"}, {"id": 115, "type": "Note", "pos": [29.999978639114225, -470.00010361843204], "size": [360, 88], "flags": {}, "order": 7, "mode": 0, "inputs": [], "outputs": [], "title": "About 4 Steps LoRA", "properties": {"ue_properties": {"widget_ue_connectable": {}, "version": "7.1", "input_ue_unconnectable": {}}}, "widgets_values": ["Using the Wan2.2 Lighting LoRA will result in the loss of video dynamics, but it will reduce the generation time. This template provides two workflows, and you can enable one as needed."], "color": "#222", "bgcolor": "#000"}, {"id": 117, "type": "CreateVideo", "pos": [1030, 650], "size": [270, 78], "flags": {}, "order": 18, "mode": 0, "inputs": [{"localized_name": "images", "name": "images", "type": "IMAGE", "link": 220}, {"localized_name": "audio", "name": "audio", "shape": 7, "type": "AUDIO", "link": null}, {"localized_name": "fps", "name": "fps", "type": "FLOAT", "widget": {"name": "fps"}, "link": null}], "outputs": [{"localized_name": "VIDEO", "name": "VIDEO", "type": "VIDEO", "links": [221]}], "properties": {"cnr_id": "comfy-core", "ver": "0.11.0", "Node name for S&R": "CreateVideo"}, "widgets_values": [16]}, {"id": 87, "type": "VAEDecode", "pos": [1020, 540], "size": [210, 46], "flags": {}, "order": 15, "mode": 0, "inputs": [{"localized_name": "samples", "name": "samples", "type": "LATENT", "link": 175}, {"localized_name": "vae", "name": "vae", "type": "VAE", "link": 176}], "outputs": 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"pos": [ + 510.0000345979581, + 819.9999455547611 + ], + "size": [ + 390, + 88 + ], + "flags": {}, + "order": 4, + "mode": 0, + "inputs": [], + "outputs": [], + "title": "Video Size", + "properties": { + "ue_properties": { + "widget_ue_connectable": {}, + "version": "7.1", + "input_ue_unconnectable": {} + } + }, + "widgets_values": [ + "By default, we set the video to a smaller size for users with low VRAM. If you have enough VRAM, you can change the size" + ], + "color": "#222", + "bgcolor": "#000" + }, + { + "id": 105, + "type": "MarkdownNote", + "pos": [ + -469.9999795985529, + 279.9998197772136 + ], + "size": [ + 480, + 170.65104166666669 + ], + "flags": {}, + "order": 5, + "mode": 0, + "inputs": [], + "outputs": [], + "title": "VRAM Usage", + "properties": { + "ue_properties": { + "version": "7.1", + "widget_ue_connectable": {}, + "input_ue_unconnectable": {} + } + }, + "widgets_values": [ + "## GPU:RTX4090D 24GB\n\n| Model | Size |VRAM Usage | 1st Generation | 2nd Generation |\n|---------------------|-------|-----------|---------------|-----------------|\n| fp8_scaled |640*640| 84% | ≈ 536s | ≈ 513s |\n| fp8_scaled + 4steps LoRA | 640*640 | 83% | ≈ 97s | ≈ 71s |" + ], + "color": "#222", + "bgcolor": "#000" + }, + { + "id": 66, + "type": "MarkdownNote", + "pos": [ + -469.9999795985529, + -320.00012452364496 + ], + "size": [ + 480, + 572.1354166666667 + ], + "flags": {}, + "order": 6, + "mode": 0, + "inputs": [], + "outputs": [], + "title": "Model Links", + "properties": { + "ue_properties": { + "widget_ue_connectable": {}, + "version": "7.1", + "input_ue_unconnectable": {} + } + }, + "widgets_values": [ + "[Tutorial](https://docs.comfy.org/tutorials/video/wan/wan2_2\n)\n\n**Diffusion Model**\n- [wan2.2_i2v_high_noise_14B_fp8_scaled.safetensors](https://huggingface.co/Comfy-Org/Wan_2.2_ComfyUI_Repackaged/resolve/main/split_files/diffusion_models/wan2.2_i2v_high_noise_14B_fp8_scaled.safetensors)\n- [wan2.2_i2v_low_noise_14B_fp8_scaled.safetensors](https://huggingface.co/Comfy-Org/Wan_2.2_ComfyUI_Repackaged/resolve/main/split_files/diffusion_models/wan2.2_i2v_low_noise_14B_fp8_scaled.safetensors)\n\n**LoRA**\n- [wan2.2_i2v_lightx2v_4steps_lora_v1_low_noise.safetensors](https://huggingface.co/Comfy-Org/Wan_2.2_ComfyUI_Repackaged/resolve/main/split_files/loras/wan2.2_i2v_lightx2v_4steps_lora_v1_low_noise.safetensors)\n- [wan2.2_i2v_lightx2v_4steps_lora_v1_high_noise.safetensors](https://huggingface.co/Comfy-Org/Wan_2.2_ComfyUI_Repackaged/resolve/main/split_files/loras/wan2.2_i2v_lightx2v_4steps_lora_v1_high_noise.safetensors)\n\n**VAE**\n- [wan_2.1_vae.safetensors](https://huggingface.co/Comfy-Org/Wan_2.2_ComfyUI_Repackaged/resolve/main/split_files/vae/wan_2.1_vae.safetensors)\n\n**Text Encoder** \n- [umt5_xxl_fp8_e4m3fn_scaled.safetensors](https://huggingface.co/Comfy-Org/Wan_2.1_ComfyUI_repackaged/resolve/main/split_files/text_encoders/umt5_xxl_fp8_e4m3fn_scaled.safetensors)\n\n\nFile save location\n\n```\nComfyUI/\n├───📂 models/\n│ ├───📂 diffusion_models/\n│ │ ├─── wan2.2_i2v_low_noise_14B_fp8_scaled.safetensors\n│ │ └─── wan2.2_i2v_high_noise_14B_fp8_scaled.safetensors\n│ ├───📂 loras/\n│ │ ├─── wan2.2_i2v_lightx2v_4steps_lora_v1_low_noise.safetensors\n│ │ └─── wan2.2_i2v_lightx2v_4steps_lora_v1_low_noise.safetensors\n│ ├───📂 text_encoders/\n│ │ └─── umt5_xxl_fp8_e4m3fn_scaled.safetensors \n│ └───📂 vae/\n│ └── wan_2.1_vae.safetensors\n```\n" + ], + "color": "#222", + "bgcolor": "#000" + }, + { + "id": 115, + "type": "Note", + "pos": [ + 29.999978639114225, + -470.00010361843204 + ], + "size": [ + 360, + 88 + ], + "flags": {}, + "order": 7, + "mode": 0, + "inputs": [], + "outputs": [], + "title": "About 4 Steps LoRA", + "properties": { + "ue_properties": { + "widget_ue_connectable": {}, + "version": "7.1", + "input_ue_unconnectable": {} + } + }, + "widgets_values": [ + "Using the Wan2.2 Lighting LoRA will result in the loss of video dynamics, but it will reduce the generation time. This template provides two workflows, and you can enable one as needed." + ], + "color": "#222", + "bgcolor": "#000" + }, + { + "id": 117, + "type": "CreateVideo", + "pos": [ + 1030, + 650 + ], + "size": [ + 270, + 78 + ], + "flags": {}, + "order": 18, + "mode": 0, + "inputs": [ + { + "localized_name": "images", + "name": "images", + "type": "IMAGE", + "link": 220 + }, + { + "localized_name": "audio", + "name": "audio", + "shape": 7, + "type": "AUDIO", + "link": null + }, + { + "localized_name": "fps", + "name": "fps", + "type": "FLOAT", + "widget": { + "name": "fps" + }, + "link": null + } + ], + "outputs": [ + { + "localized_name": "VIDEO", + "name": "VIDEO", + "type": "VIDEO", + "links": [ + 221 + ] + } + ], + "properties": { + "cnr_id": "comfy-core", + "ver": "0.11.0", + "Node name for S&R": "CreateVideo" + }, + "widgets_values": [ + 16 + ] + }, + { + "id": 87, + "type": "VAEDecode", + "pos": [ + 1020, + 540 + ], + "size": [ + 210, + 46 + ], + "flags": {}, + "order": 15, + "mode": 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This template provides two workflows, and you can enable one as needed."], "color": "#222", "bgcolor": "#000"}, {"id": 62, "type": "MarkdownNote", "pos": [-489.666771800538, -278.666700527147], "size": [479.9869791666667, 542.1354166666667], "flags": {}, "order": 5, "mode": 0, "inputs": [], "outputs": [], "title": "Model Links", "properties": {}, "widgets_values": ["[Tutorial](https://docs.comfy.org/tutorials/video/wan/wan2_2\n) | [教程](https://docs.comfy.org/zh-CN/tutorials/video/wan/wan2_2\n)\n\n**Diffusion Model** \n- [wan2.2_t2v_high_noise_14B_fp8_scaled.safetensors](https://huggingface.co/Comfy-Org/Wan_2.2_ComfyUI_Repackaged/resolve/main/split_files/diffusion_models/wan2.2_t2v_high_noise_14B_fp8_scaled.safetensors)\n- [wan2.2_t2v_low_noise_14B_fp8_scaled.safetensors](https://huggingface.co/Comfy-Org/Wan_2.2_ComfyUI_Repackaged/resolve/main/split_files/diffusion_models/wan2.2_t2v_low_noise_14B_fp8_scaled.safetensors)\n\n**LoRA**\n\n- [wan2.2_t2v_lightx2v_4steps_lora_v1.1_high_noise.safetensors](https://huggingface.co/Comfy-Org/Wan_2.2_ComfyUI_Repackaged/resolve/main/split_files/loras/wan2.2_t2v_lightx2v_4steps_lora_v1.1_high_noise.safetensors)\n- [wan2.2_t2v_lightx2v_4steps_lora_v1.1_low_noise.safetensors](https://huggingface.co/Comfy-Org/Wan_2.2_ComfyUI_Repackaged/resolve/main/split_files/loras/wan2.2_t2v_lightx2v_4steps_lora_v1.1_low_noise.safetensors)\n\n**VAE**\n- [wan_2.1_vae.safetensors](https://huggingface.co/Comfy-Org/Wan_2.2_ComfyUI_Repackaged/resolve/main/split_files/vae/wan_2.1_vae.safetensors)\n\n**Text Encoder** \n- [umt5_xxl_fp8_e4m3fn_scaled.safetensors](https://huggingface.co/Comfy-Org/Wan_2.1_ComfyUI_repackaged/resolve/main/split_files/text_encoders/umt5_xxl_fp8_e4m3fn_scaled.safetensors)\n\n\nFile save location\n\n```\nComfyUI/\n├───📂 models/\n│ ├───📂 diffusion_models/\n│ │ ├─── wan2.2_t2v_low_noise_14B_fp8_scaled.safetensors\n│ │ └─── wan2.2_t2v_high_noise_14B_fp8_scaled.safetensors\n│ ├───📂 loras/\n│ │ ├───wan2.2_t2v_lightx2v_4steps_lora_v1.1_low_noise.safetensors\n│ │ └───wan2.2_t2v_lightx2v_4steps_lora_v1.1_high_noise.safetensors\n│ ├───📂 text_encoders/\n│ │ └─── umt5_xxl_fp8_e4m3fn_scaled.safetensors \n│ └───📂 vae/\n│ └── wan_2.1_vae.safetensors\n```\n"], "color": "#222", "bgcolor": "#000"}, {"id": 87, "type": "VAEDecode", "pos": [1020.3331497597994, 471.3333837135574], "size": [210, 46], "flags": {"collapsed": false}, "order": 14, "mode": 0, "inputs": [{"localized_name": "samples", "name": "samples", "type": "LATENT", "link": 157}, {"localized_name": "vae", "name": "vae", "type": "VAE", "link": 158}], "outputs": [{"localized_name": "IMAGE", "name": "IMAGE", "type": "IMAGE", "slot_index": 0, "links": [195]}], "properties": {"cnr_id": "comfy-core", "ver": "0.3.45", "Node name for S&R": "VAEDecode"}, "widgets_values": []}, {"id": 72, "type": "CLIPTextEncode", "pos": [440.3333139376125, 331.3333305479798], "size": [500, 170], "flags": {}, "order": 6, "mode": 0, "inputs": 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This template provides two workflows, and you can enable one as needed." + ], + "color": "#222", + "bgcolor": "#000" + }, + { + "id": 62, + "type": "MarkdownNote", + "pos": [ + -489.666771800538, + -278.666700527147 + ], + "size": [ + 479.9869791666667, + 542.1354166666667 + ], + "flags": {}, + "order": 5, + "mode": 0, + "inputs": [], + "outputs": [], + "title": "Model Links", + "properties": {}, + "widgets_values": [ + "[Tutorial](https://docs.comfy.org/tutorials/video/wan/wan2_2\n) | [教程](https://docs.comfy.org/zh-CN/tutorials/video/wan/wan2_2\n)\n\n**Diffusion Model** \n- [wan2.2_t2v_high_noise_14B_fp8_scaled.safetensors](https://huggingface.co/Comfy-Org/Wan_2.2_ComfyUI_Repackaged/resolve/main/split_files/diffusion_models/wan2.2_t2v_high_noise_14B_fp8_scaled.safetensors)\n- [wan2.2_t2v_low_noise_14B_fp8_scaled.safetensors](https://huggingface.co/Comfy-Org/Wan_2.2_ComfyUI_Repackaged/resolve/main/split_files/diffusion_models/wan2.2_t2v_low_noise_14B_fp8_scaled.safetensors)\n\n**LoRA**\n\n- [wan2.2_t2v_lightx2v_4steps_lora_v1.1_high_noise.safetensors](https://huggingface.co/Comfy-Org/Wan_2.2_ComfyUI_Repackaged/resolve/main/split_files/loras/wan2.2_t2v_lightx2v_4steps_lora_v1.1_high_noise.safetensors)\n- [wan2.2_t2v_lightx2v_4steps_lora_v1.1_low_noise.safetensors](https://huggingface.co/Comfy-Org/Wan_2.2_ComfyUI_Repackaged/resolve/main/split_files/loras/wan2.2_t2v_lightx2v_4steps_lora_v1.1_low_noise.safetensors)\n\n**VAE**\n- [wan_2.1_vae.safetensors](https://huggingface.co/Comfy-Org/Wan_2.2_ComfyUI_Repackaged/resolve/main/split_files/vae/wan_2.1_vae.safetensors)\n\n**Text Encoder** \n- [umt5_xxl_fp8_e4m3fn_scaled.safetensors](https://huggingface.co/Comfy-Org/Wan_2.1_ComfyUI_repackaged/resolve/main/split_files/text_encoders/umt5_xxl_fp8_e4m3fn_scaled.safetensors)\n\n\nFile save location\n\n```\nComfyUI/\n├───📂 models/\n│ ├───📂 diffusion_models/\n│ │ ├─── wan2.2_t2v_low_noise_14B_fp8_scaled.safetensors\n│ │ └─── wan2.2_t2v_high_noise_14B_fp8_scaled.safetensors\n│ ├───📂 loras/\n│ │ ├───wan2.2_t2v_lightx2v_4steps_lora_v1.1_low_noise.safetensors\n│ │ └───wan2.2_t2v_lightx2v_4steps_lora_v1.1_high_noise.safetensors\n│ ├───📂 text_encoders/\n│ │ └─── umt5_xxl_fp8_e4m3fn_scaled.safetensors \n│ └───📂 vae/\n│ └── wan_2.1_vae.safetensors\n```\n" + ], + "color": "#222", + "bgcolor": "#000" + }, + { + "id": 87, + "type": "VAEDecode", + "pos": [ + 1020.3331497597994, + 471.3333837135574 + ], + "size": [ + 210, + 46 + ], + "flags": { + "collapsed": false + }, + "order": 14, + "mode": 0, + "inputs": [ + { + "localized_name": "samples", + "name": "samples", + "type": "LATENT", + 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null + } + ], + "outputs": [ + { + "localized_name": "IMAGE0", + "name": "IMAGE0", + "type": "IMAGE", + "links": [ + 40 + ] + }, + { + "localized_name": "IMAGE1", + "name": "IMAGE1", + "type": "IMAGE", + "links": null + }, + { + "localized_name": "IMAGE2", + "name": "IMAGE2", + "type": "IMAGE", + "links": null + }, + { + "localized_name": "IMAGE3", + "name": "IMAGE3", + "type": "IMAGE", + "links": null + } + ], + "properties": { + "Node name for S&R": "GLSLShader" + }, + "widgets_values": [ + "#version 300 es\nprecision highp float;\n\nuniform sampler2D u_image0;\nuniform vec2 u_resolution;\nuniform float u_float0; // amount [0.0 - 3.0] typical: 0.5-1.5\nuniform float u_float1; // radius [0.5 - 10.0] blur radius in pixels\nuniform float u_float2; // threshold [0.0 - 0.1] min difference to sharpen\n\nin vec2 v_texCoord;\nlayout(location = 0) out vec4 fragColor0;\n\nfloat gaussian(float x, float sigma) {\n return exp(-(x * x) / (2.0 * sigma * sigma));\n}\n\nfloat getLuminance(vec3 color) {\n return dot(color, vec3(0.2126, 0.7152, 0.0722));\n}\n\nvoid main() {\n vec2 texel = 1.0 / u_resolution;\n float radius = max(u_float1, 0.5);\n float amount = u_float0;\n float threshold = u_float2;\n\n vec4 original = texture(u_image0, v_texCoord);\n\n // Gaussian blur for the \"unsharp\" mask\n int samples = int(ceil(radius));\n float sigma = radius / 2.0;\n\n vec4 blurred = vec4(0.0);\n float totalWeight = 0.0;\n\n for (int x = -samples; x <= samples; x++) {\n for (int y = -samples; y <= samples; y++) {\n vec2 offset = vec2(float(x), float(y)) * texel;\n vec4 sample_color = texture(u_image0, v_texCoord + offset);\n\n float dist = length(vec2(float(x), float(y)));\n float weight = gaussian(dist, sigma);\n blurred += sample_color * weight;\n totalWeight += weight;\n }\n }\n blurred /= totalWeight;\n\n // Unsharp mask = original - blurred\n vec3 mask = original.rgb - blurred.rgb;\n\n // Luminance-based threshold with smooth falloff\n float lumaDelta = abs(getLuminance(original.rgb) - getLuminance(blurred.rgb));\n float thresholdScale = smoothstep(0.0, threshold, lumaDelta);\n mask *= thresholdScale;\n\n // Sharpen: original + mask * amount\n vec3 sharpened = original.rgb + mask * amount;\n\n fragColor0 = vec4(clamp(sharpened, 0.0, 1.0), original.a);\n}\n", + "from_input" + ] + } + ], + "groups": [], + "links": [ + { + "id": 41, + "origin_id": 27, + "origin_slot": 0, + "target_id": 26, + "target_slot": 2, + "type": "FLOAT" + }, + { + "id": 42, + "origin_id": 28, + "origin_slot": 0, + "target_id": 26, + "target_slot": 3, + "type": "FLOAT" + }, + { + "id": 43, + "origin_id": 29, + "origin_slot": 0, + "target_id": 26, + "target_slot": 4, + "type": "FLOAT" + }, + { + "id": 39, + "origin_id": -10, + "origin_slot": 0, + "target_id": 26, + "target_slot": 0, + "type": "IMAGE" + }, + { + "id": 40, + "origin_id": 26, + "origin_slot": 0, + "target_id": -20, + "target_slot": 0, + "type": "IMAGE" + } + ], + "extra": { + "workflowRendererVersion": "LG" + }, + "category": "Image Tools/Sharpen" + } + ] + } +} diff --git a/blueprints/Video Captioning (Gemini).json b/blueprints/Video Captioning (Gemini).json index 1d72718a1..ea6dc8bee 100644 --- a/blueprints/Video Captioning (Gemini).json +++ b/blueprints/Video Captioning (Gemini).json @@ -1 +1,314 @@ -{"revision": 0, "last_node_id": 233, "last_link_id": 0, "nodes": [{"id": 233, "type": "dcf32045-0ee4-4efc-9aca-9f26f3a157be", "pos": [0, 1140], "size": [400, 260], "flags": {}, "order": 7, "mode": 0, "inputs": [{"name": "prompt", "type": "STRING", "widget": {"name": "prompt"}, "link": null}, {"name": "model", "type": "COMBO", "widget": {"name": "model"}, "link": null}, {"name": "video", "type": "VIDEO", "link": null}], "outputs": [{"localized_name": "STRING", "name": "STRING", "type": "STRING", "links": []}], "title": "Video Captioning(Gemini)", "properties": {"proxyWidgets": [["-1", "prompt"], ["-1", "model"], ["1", "seed"]], "cnr_id": "comfy-core", "ver": "0.13.0"}, "widgets_values": ["Describe this video", 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"GeminiNode", "pos": [-6690, 2360], "size": [390, 430], "flags": {}, "order": 0, "mode": 0, "inputs": [{"localized_name": "images", "name": "images", "shape": 7, "type": "IMAGE", "link": null}, {"localized_name": "audio", "name": "audio", "shape": 7, "type": "AUDIO", "link": null}, {"localized_name": "video", "name": "video", "shape": 7, "type": "VIDEO", "link": 17}, {"localized_name": "files", "name": "files", "shape": 7, "type": "GEMINI_INPUT_FILES", "link": null}, {"localized_name": "prompt", "name": "prompt", "type": "STRING", "widget": {"name": "prompt"}, "link": 15}, {"localized_name": "model", "name": "model", "type": "COMBO", "widget": {"name": "model"}, "link": 16}, {"localized_name": "seed", "name": "seed", "type": "INT", "widget": {"name": "seed"}, "link": null}, {"localized_name": "system_prompt", "name": "system_prompt", "shape": 7, "type": "STRING", "widget": {"name": "system_prompt"}, "link": null}], "outputs": [{"localized_name": "STRING", "name": "STRING", "type": "STRING", "links": [6]}], "properties": {"cnr_id": "comfy-core", "ver": "0.5.1", "Node name for S&R": "GeminiNode"}, "widgets_values": ["Describe this video", "gemini-2.5-pro", 511865409297955, "randomize", "- Role: AI Video Analysis and Description Specialist\n- Background: The user requires a prompt that enables AI to analyze videos (including frame sequences, dynamic movements, audio-visual elements) and generate detailed, structured descriptions. These descriptions must be directly usable as video generation prompts to create similar videos, serving core tasks such as video content creation, creative inspiration extraction, and artistic style exploration.\n- Profile: As an AI Video Analysis and Description Specialist, you possess expertise in computer vision, video temporal sequence processing, motion analysis, and multi-modal natural language generation. You excel at interpreting dynamic visual data (frame-by-frame features + continuous motion) and translating it into precise descriptive text that fully guides the creation of new videos with matching style, rhythm, and content.\n- Skills: Proficiency in video frame feature extraction, motion trajectory recognition, temporal rhythm analysis, scene/shot segmentation, color grading detection, camera movement identification (pan/tilt/zoom/dolly), audio-visual element correlation analysis, and descriptive language generation that captures both static visual features and dynamic temporal characteristics. Mastery of artistic elements in video: composition (per frame + dynamic framing), color palette (consistent + transitional), texture (surface details + motion blur), pacing (frame rate, shot duration), and sound style (background music, ambient sound cues).\n- Goals: To analyze the provided video comprehensively, generate a detailed, structured description that captures all key video elements (static visual features + dynamic motion/temporal characteristics + audio-visual style), and ensure this description can directly serve as a high-quality prompt for creating similar videos.\n- Constraints: \n 1. The description must be clear, structured, and specific enough to guide end-to-end video creation (including frame rate, shot duration, camera movement, motion speed, color transitions).\n 2. Avoid ambiguity; focus on the most salient static (per-frame) and dynamic (temporal) features of the video.\n 3. Prioritize video-specific elements: motion trajectory, shot types (close-up/wide shot/etc.), camera movement, frame rate, scene transitions, rhythm/pacing, and temporal color changes.\n 4. The output must only contain the video generation prompt (no extra explanations).\n- OutputFormat: A detailed, hierarchical text description of the video, structured as follows:\n 1. Core Content & Narrative: Brief overview of the video's subject and temporal progression\n 2. Visual Style (Static): Per-frame key elements (objects, colors, composition, lighting, texture)\n 3. Dynamic Elements (Temporal): Motion details (speed, trajectory, direction), camera movement (type, speed, direction), shot duration/frame rate, scene transitions\n 4. Audio-Visual Style: Color grading (consistent/transitional), rhythm/pacing, and implied audio style (if discernible)\n- Workflow:\n 1. Analyze the video to segment shots/scenes, identify frame-by-frame static visual elements (objects, colors, composition) and cross-frame dynamic elements (motion, camera movement, temporal changes).\n 2. Extract video-specific technical features: frame rate, shot duration, scene transition types, motion speed/rhythm.\n 3. Generate a structured, detailed description that captures the essence of the video (static + dynamic + temporal characteristics), ensuring specificity and actionability for video generation.\n 4. Refine the description for clarity, conciseness, and alignment with video generation prompt norms (e.g., including frame rate, camera movement terms, motion speed descriptors)."], "color": "#432", "bgcolor": "#653"}], "groups": [], "links": [{"id": 6, "origin_id": 1, "origin_slot": 0, "target_id": -20, "target_slot": 0, "type": "*"}, {"id": 15, "origin_id": -10, "origin_slot": 0, "target_id": 1, "target_slot": 4, "type": "STRING"}, {"id": 16, "origin_id": -10, "origin_slot": 1, "target_id": 1, "target_slot": 5, "type": "COMBO"}, {"id": 17, "origin_id": -10, "origin_slot": 2, "target_id": 1, "target_slot": 2, "type": "VIDEO"}], "extra": {"workflowRendererVersion": "LG"}, "category": "Text generation/Video Captioning"}]}} +{ + "revision": 0, + "last_node_id": 233, + "last_link_id": 0, + "nodes": [ + { + "id": 233, + "type": "dcf32045-0ee4-4efc-9aca-9f26f3a157be", + "pos": [ + 0, + 1140 + ], + "size": [ + 400, + 260 + ], + "flags": {}, + "order": 7, + "mode": 0, + "inputs": [ + { + "name": "prompt", + "type": "STRING", + "widget": { + "name": "prompt" + }, + "link": null + }, + { + "name": "model", + "type": "COMBO", + "widget": { + "name": "model" + }, + "link": null + }, + { + "name": "video", + "type": "VIDEO", + "link": null + } + ], + "outputs": [ + { + "localized_name": "STRING", + "name": "STRING", + "type": "STRING", + "links": [] + } + ], + "title": "Video Captioning(Gemini)", + "properties": { + "proxyWidgets": [ + [ + "-1", + "prompt" + ], + [ + "-1", + "model" + ], + [ + "1", + "seed" + ] + ], + "cnr_id": "comfy-core", + "ver": "0.13.0" + }, + "widgets_values": [ + "Describe this video", + "gemini-2.5-pro" + ] + } + ], + "links": [], + "version": 0.4, + "definitions": { + "subgraphs": [ + { + "id": "dcf32045-0ee4-4efc-9aca-9f26f3a157be", + "version": 1, + "state": { + "lastGroupId": 1, + "lastNodeId": 16, + "lastLinkId": 17, + "lastRerouteId": 0 + }, + "revision": 0, + "config": {}, + "name": "Video Captioning(Gemini)", + "inputNode": { + "id": -10, + "bounding": [ + -6870, + 2530, + 120, + 100 + ] + }, + "outputNode": { + "id": -20, + "bounding": [ + -6240, + 2530, + 120, + 60 + ] + }, + "inputs": [ + { + "id": "d8cbd7eb-636a-4d7b-8ff6-b22f1755e26c", + "name": "prompt", + "type": "STRING", + "linkIds": [ + 15 + ], + "pos": [ + -6770, + 2550 + ] + }, + { + "id": "b034e26a-d114-4604-aec2-32783e86aa6b", + "name": "model", + "type": "COMBO", + "linkIds": [ + 16 + ], + "pos": [ + -6770, + 2570 + ] + }, + { + "id": "f7363f60-a106-4e06-90af-df5f53355b98", + "name": "video", + "type": "VIDEO", + "linkIds": [ + 17 + ], + "pos": [ + -6770, + 2590 + ] + } + ], + "outputs": [ + { + "id": "e12c6e80-5210-4328-a581-bc8924c53070", + "name": "STRING", + "type": "STRING", + "linkIds": [ + 6 + ], + "localized_name": "STRING", + "pos": [ + -6220, + 2550 + ] + } + ], + "widgets": [], + "nodes": [ + { + "id": 1, + "type": "GeminiNode", + "pos": [ + -6690, + 2360 + ], + "size": [ + 390, + 430 + ], + "flags": {}, + "order": 0, + "mode": 0, + "inputs": [ + { + "localized_name": "images", + "name": "images", + "shape": 7, + "type": "IMAGE", + "link": null + }, + { + "localized_name": "audio", + "name": "audio", + "shape": 7, + "type": "AUDIO", + "link": null + }, + { + "localized_name": "video", + "name": "video", + "shape": 7, + "type": "VIDEO", + "link": 17 + }, + { + "localized_name": "files", + "name": "files", + "shape": 7, + "type": "GEMINI_INPUT_FILES", + "link": null + }, + { + "localized_name": "prompt", + "name": "prompt", + "type": "STRING", + "widget": { + "name": "prompt" + }, + "link": 15 + }, + { + "localized_name": "model", + "name": "model", + "type": "COMBO", + "widget": { + "name": "model" + }, + "link": 16 + }, + { + "localized_name": "seed", + "name": "seed", + "type": "INT", + "widget": { + "name": "seed" + }, + "link": null + }, + { + "localized_name": "system_prompt", + "name": "system_prompt", + "shape": 7, + "type": "STRING", + "widget": { + "name": "system_prompt" + }, + "link": null + } + ], + "outputs": [ + { + "localized_name": "STRING", + "name": "STRING", + "type": "STRING", + "links": [ + 6 + ] + } + ], + "properties": { + "cnr_id": "comfy-core", + "ver": "0.5.1", + "Node name for S&R": "GeminiNode" + }, + "widgets_values": [ + "Describe this video", + "gemini-2.5-pro", + 511865409297955, + "randomize", + "- Role: AI Video Analysis and Description Specialist\n- Background: The user requires a prompt that enables AI to analyze videos (including frame sequences, dynamic movements, audio-visual elements) and generate detailed, structured descriptions. These descriptions must be directly usable as video generation prompts to create similar videos, serving core tasks such as video content creation, creative inspiration extraction, and artistic style exploration.\n- Profile: As an AI Video Analysis and Description Specialist, you possess expertise in computer vision, video temporal sequence processing, motion analysis, and multi-modal natural language generation. You excel at interpreting dynamic visual data (frame-by-frame features + continuous motion) and translating it into precise descriptive text that fully guides the creation of new videos with matching style, rhythm, and content.\n- Skills: Proficiency in video frame feature extraction, motion trajectory recognition, temporal rhythm analysis, scene/shot segmentation, color grading detection, camera movement identification (pan/tilt/zoom/dolly), audio-visual element correlation analysis, and descriptive language generation that captures both static visual features and dynamic temporal characteristics. Mastery of artistic elements in video: composition (per frame + dynamic framing), color palette (consistent + transitional), texture (surface details + motion blur), pacing (frame rate, shot duration), and sound style (background music, ambient sound cues).\n- Goals: To analyze the provided video comprehensively, generate a detailed, structured description that captures all key video elements (static visual features + dynamic motion/temporal characteristics + audio-visual style), and ensure this description can directly serve as a high-quality prompt for creating similar videos.\n- Constraints: \n 1. The description must be clear, structured, and specific enough to guide end-to-end video creation (including frame rate, shot duration, camera movement, motion speed, color transitions).\n 2. Avoid ambiguity; focus on the most salient static (per-frame) and dynamic (temporal) features of the video.\n 3. Prioritize video-specific elements: motion trajectory, shot types (close-up/wide shot/etc.), camera movement, frame rate, scene transitions, rhythm/pacing, and temporal color changes.\n 4. The output must only contain the video generation prompt (no extra explanations).\n- OutputFormat: A detailed, hierarchical text description of the video, structured as follows:\n 1. Core Content & Narrative: Brief overview of the video's subject and temporal progression\n 2. Visual Style (Static): Per-frame key elements (objects, colors, composition, lighting, texture)\n 3. Dynamic Elements (Temporal): Motion details (speed, trajectory, direction), camera movement (type, speed, direction), shot duration/frame rate, scene transitions\n 4. Audio-Visual Style: Color grading (consistent/transitional), rhythm/pacing, and implied audio style (if discernible)\n- Workflow:\n 1. Analyze the video to segment shots/scenes, identify frame-by-frame static visual elements (objects, colors, composition) and cross-frame dynamic elements (motion, camera movement, temporal changes).\n 2. Extract video-specific technical features: frame rate, shot duration, scene transition types, motion speed/rhythm.\n 3. Generate a structured, detailed description that captures the essence of the video (static + dynamic + temporal characteristics), ensuring specificity and actionability for video generation.\n 4. Refine the description for clarity, conciseness, and alignment with video generation prompt norms (e.g., including frame rate, camera movement terms, motion speed descriptors)." + ], + "color": "#432", + "bgcolor": "#653" + } + ], + "groups": [], + "links": [ + { + "id": 6, + "origin_id": 1, + "origin_slot": 0, + "target_id": -20, + "target_slot": 0, + "type": "*" + }, + { + "id": 15, + "origin_id": -10, + "origin_slot": 0, + "target_id": 1, + "target_slot": 4, + "type": "STRING" + }, + { + "id": 16, + "origin_id": -10, + "origin_slot": 1, + "target_id": 1, + "target_slot": 5, + "type": "COMBO" + }, + { + "id": 17, + "origin_id": -10, + "origin_slot": 2, + "target_id": 1, + "target_slot": 2, + "type": "VIDEO" + } + ], + "extra": { + "workflowRendererVersion": "LG" + }, + "category": "Text generation/Video Captioning" + } + ] + } +} diff --git a/blueprints/Video Inpaint(Wan2.1 VACE).json b/blueprints/Video Inpaint(Wan2.1 VACE).json index a7c6db003..f404e6773 100644 --- 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a/comfy/ldm/ace/ace_step15.py b/comfy/ldm/ace/ace_step15.py index 1d7dc59a8..2ca2d26c4 100644 --- a/comfy/ldm/ace/ace_step15.py +++ b/comfy/ldm/ace/ace_step15.py @@ -611,6 +611,7 @@ class AceStepDiTModel(nn.Module): intermediate_size, patch_size, audio_acoustic_hidden_dim, + condition_dim=None, layer_types=None, sliding_window=128, rms_norm_eps=1e-6, @@ -640,7 +641,7 @@ class AceStepDiTModel(nn.Module): self.time_embed = TimestepEmbedding(256, hidden_size, dtype=dtype, device=device, operations=operations) self.time_embed_r = TimestepEmbedding(256, hidden_size, dtype=dtype, device=device, operations=operations) - self.condition_embedder = Linear(hidden_size, hidden_size, dtype=dtype, device=device) + self.condition_embedder = Linear(condition_dim, hidden_size, dtype=dtype, device=device) if layer_types is None: layer_types = ["full_attention"] * num_layers @@ -1035,6 +1036,9 @@ class AceStepConditionGenerationModel(nn.Module): fsq_dim=2048, fsq_levels=[8, 8, 8, 5, 5, 5], fsq_input_num_quantizers=1, + encoder_hidden_size=2048, + encoder_intermediate_size=6144, + encoder_num_heads=16, audio_model=None, dtype=None, device=None, @@ -1054,24 +1058,24 @@ class AceStepConditionGenerationModel(nn.Module): self.decoder = AceStepDiTModel( in_channels, hidden_size, num_dit_layers, num_heads, num_kv_heads, head_dim, - intermediate_size, patch_size, audio_acoustic_hidden_dim, + intermediate_size, patch_size, audio_acoustic_hidden_dim, condition_dim=encoder_hidden_size, layer_types=layer_types, sliding_window=sliding_window, rms_norm_eps=rms_norm_eps, dtype=dtype, device=device, operations=operations ) self.encoder = AceStepConditionEncoder( - text_hidden_dim, timbre_hidden_dim, hidden_size, num_lyric_layers, num_timbre_layers, - num_heads, num_kv_heads, head_dim, intermediate_size, rms_norm_eps, + text_hidden_dim, timbre_hidden_dim, encoder_hidden_size, num_lyric_layers, num_timbre_layers, + encoder_num_heads, num_kv_heads, head_dim, encoder_intermediate_size, rms_norm_eps, dtype=dtype, device=device, operations=operations ) self.tokenizer = AceStepAudioTokenizer( - audio_acoustic_hidden_dim, hidden_size, pool_window_size, fsq_dim=fsq_dim, fsq_levels=fsq_levels, fsq_input_num_quantizers=fsq_input_num_quantizers, num_layers=num_tokenizer_layers, head_dim=head_dim, rms_norm_eps=rms_norm_eps, + audio_acoustic_hidden_dim, encoder_hidden_size, pool_window_size, fsq_dim=fsq_dim, fsq_levels=fsq_levels, fsq_input_num_quantizers=fsq_input_num_quantizers, num_layers=num_tokenizer_layers, head_dim=head_dim, rms_norm_eps=rms_norm_eps, dtype=dtype, device=device, operations=operations ) self.detokenizer = AudioTokenDetokenizer( - hidden_size, pool_window_size, audio_acoustic_hidden_dim, num_layers=2, head_dim=head_dim, + encoder_hidden_size, pool_window_size, audio_acoustic_hidden_dim, num_layers=2, head_dim=head_dim, dtype=dtype, device=device, operations=operations ) - self.null_condition_emb = nn.Parameter(torch.empty(1, 1, hidden_size, dtype=dtype, device=device)) + self.null_condition_emb = nn.Parameter(torch.empty(1, 1, encoder_hidden_size, dtype=dtype, device=device)) def prepare_condition( self, diff --git a/comfy/ldm/models/autoencoder.py b/comfy/ldm/models/autoencoder.py index 4f50810dc..582b44e69 100644 --- a/comfy/ldm/models/autoencoder.py +++ b/comfy/ldm/models/autoencoder.py @@ -155,6 +155,7 @@ class AutoencodingEngineLegacy(AutoencodingEngine): def __init__(self, embed_dim: int, **kwargs): self.max_batch_size = kwargs.pop("max_batch_size", None) ddconfig = kwargs.pop("ddconfig") + decoder_ddconfig = kwargs.pop("decoder_ddconfig", ddconfig) super().__init__( encoder_config={ "target": "comfy.ldm.modules.diffusionmodules.model.Encoder", @@ -162,7 +163,7 @@ class AutoencodingEngineLegacy(AutoencodingEngine): }, decoder_config={ "target": "comfy.ldm.modules.diffusionmodules.model.Decoder", - "params": ddconfig, + "params": decoder_ddconfig, }, **kwargs, ) diff --git a/comfy/ldm/modules/encoders/noise_aug_modules.py b/comfy/ldm/modules/encoders/noise_aug_modules.py index a5d866030..c853e4298 100644 --- a/comfy/ldm/modules/encoders/noise_aug_modules.py +++ b/comfy/ldm/modules/encoders/noise_aug_modules.py @@ -3,12 +3,9 @@ from ..diffusionmodules.openaimodel import Timestep import torch class CLIPEmbeddingNoiseAugmentation(ImageConcatWithNoiseAugmentation): - def __init__(self, *args, clip_stats_path=None, timestep_dim=256, **kwargs): + def __init__(self, *args, timestep_dim=256, **kwargs): super().__init__(*args, **kwargs) - if clip_stats_path is None: - clip_mean, clip_std = torch.zeros(timestep_dim), torch.ones(timestep_dim) - else: - clip_mean, clip_std = torch.load(clip_stats_path, map_location="cpu") + clip_mean, clip_std = torch.zeros(timestep_dim), torch.ones(timestep_dim) self.register_buffer("data_mean", clip_mean[None, :], persistent=False) self.register_buffer("data_std", clip_std[None, :], persistent=False) self.time_embed = Timestep(timestep_dim) diff --git a/comfy/model_detection.py b/comfy/model_detection.py index 1c8ae2325..8bed6828d 100644 --- a/comfy/model_detection.py +++ b/comfy/model_detection.py @@ -696,6 +696,15 @@ def detect_unet_config(state_dict, key_prefix, metadata=None): if '{}encoder.lyric_encoder.layers.0.input_layernorm.weight'.format(key_prefix) in state_dict_keys: dit_config = {} dit_config["audio_model"] = "ace1.5" + head_dim = 128 + dit_config["hidden_size"] = state_dict['{}decoder.layers.0.self_attn_norm.weight'.format(key_prefix)].shape[0] + dit_config["intermediate_size"] = state_dict['{}decoder.layers.0.mlp.gate_proj.weight'.format(key_prefix)].shape[0] + dit_config["num_heads"] = state_dict['{}decoder.layers.0.self_attn.q_proj.weight'.format(key_prefix)].shape[0] // head_dim + + dit_config["encoder_hidden_size"] = state_dict['{}encoder.lyric_encoder.layers.0.input_layernorm.weight'.format(key_prefix)].shape[0] + dit_config["encoder_num_heads"] = state_dict['{}encoder.lyric_encoder.layers.0.self_attn.q_proj.weight'.format(key_prefix)].shape[0] // head_dim + dit_config["encoder_intermediate_size"] = state_dict['{}encoder.lyric_encoder.layers.0.mlp.gate_proj.weight'.format(key_prefix)].shape[0] + dit_config["num_dit_layers"] = count_blocks(state_dict_keys, '{}decoder.layers.'.format(key_prefix) + '{}.') return dit_config if '{}encoder.pan_blocks.1.cv4.conv.weight'.format(key_prefix) in state_dict_keys: # RT-DETR_v4 diff --git a/comfy/sd.py b/comfy/sd.py index 1b08d46df..b177f8c89 100644 --- a/comfy/sd.py +++ b/comfy/sd.py @@ -556,12 +556,19 @@ class VAE: old_memory_used_decode = self.memory_used_decode self.memory_used_decode = lambda shape, dtype: old_memory_used_decode(shape, dtype) * 4.0 + decoder_ch = sd['decoder.conv_in.weight'].shape[0] // ddconfig['ch_mult'][-1] + if decoder_ch != ddconfig['ch']: + decoder_ddconfig = ddconfig.copy() + decoder_ddconfig['ch'] = decoder_ch + else: + decoder_ddconfig = None + if 'post_quant_conv.weight' in sd: - self.first_stage_model = AutoencoderKL(ddconfig=ddconfig, embed_dim=sd['post_quant_conv.weight'].shape[1]) + self.first_stage_model = AutoencoderKL(ddconfig=ddconfig, embed_dim=sd['post_quant_conv.weight'].shape[1], **({"decoder_ddconfig": decoder_ddconfig} if decoder_ddconfig is not None else {})) else: self.first_stage_model = AutoencodingEngine(regularizer_config={'target': "comfy.ldm.models.autoencoder.DiagonalGaussianRegularizer"}, encoder_config={'target': "comfy.ldm.modules.diffusionmodules.model.Encoder", 'params': ddconfig}, - decoder_config={'target': "comfy.ldm.modules.diffusionmodules.model.Decoder", 'params': ddconfig}) + decoder_config={'target': "comfy.ldm.modules.diffusionmodules.model.Decoder", 'params': decoder_ddconfig if decoder_ddconfig is not None else ddconfig}) elif "decoder.layers.1.layers.0.beta" in sd: config = {} param_key = None @@ -1742,6 +1749,8 @@ def load_diffusion_model_state_dict(sd, model_options={}, metadata=None, disable temp_sd = comfy.utils.state_dict_prefix_replace(sd, {diffusion_model_prefix: ""}, filter_keys=True) if len(temp_sd) > 0: sd = temp_sd + if custom_operations is None: + sd, metadata = comfy.utils.convert_old_quants(sd, "", metadata=metadata) parameters = comfy.utils.calculate_parameters(sd) weight_dtype = comfy.utils.weight_dtype(sd) diff --git a/comfy_api_nodes/apis/wan.py b/comfy_api_nodes/apis/wan.py new file mode 100644 index 000000000..44b65e4f6 --- /dev/null +++ b/comfy_api_nodes/apis/wan.py @@ -0,0 +1,226 @@ +from pydantic import BaseModel, Field + + +class Text2ImageInputField(BaseModel): + prompt: str = Field(...) + negative_prompt: str | None = Field(None) + + +class Image2ImageInputField(BaseModel): + prompt: str = Field(...) + negative_prompt: str | None = Field(None) + images: list[str] = Field(..., min_length=1, max_length=2) + + +class Text2VideoInputField(BaseModel): + prompt: str = Field(...) + negative_prompt: str | None = Field(None) + audio_url: str | None = Field(None) + + +class Image2VideoInputField(BaseModel): + prompt: str = Field(...) + negative_prompt: str | None = Field(None) + img_url: str = Field(...) + audio_url: str | None = Field(None) + + +class Reference2VideoInputField(BaseModel): + prompt: str = Field(...) + negative_prompt: str | None = Field(None) + reference_video_urls: list[str] = Field(...) + + +class Txt2ImageParametersField(BaseModel): + size: str = Field(...) + n: int = Field(1, description="Number of images to generate.") # we support only value=1 + seed: int = Field(..., ge=0, le=2147483647) + prompt_extend: bool = Field(True) + watermark: bool = Field(False) + + +class Image2ImageParametersField(BaseModel): + size: str | None = Field(None) + n: int = Field(1, description="Number of images to generate.") # we support only value=1 + seed: int = Field(..., ge=0, le=2147483647) + watermark: bool = Field(False) + + +class Text2VideoParametersField(BaseModel): + size: str = Field(...) + seed: int = Field(..., ge=0, le=2147483647) + duration: int = Field(5, ge=5, le=15) + prompt_extend: bool = Field(True) + watermark: bool = Field(False) + audio: bool = Field(False, description="Whether to generate audio automatically.") + shot_type: str = Field("single") + + +class Image2VideoParametersField(BaseModel): + resolution: str = Field(...) + seed: int = Field(..., ge=0, le=2147483647) + duration: int = Field(5, ge=5, le=15) + prompt_extend: bool = Field(True) + watermark: bool = Field(False) + audio: bool = Field(False, description="Whether to generate audio automatically.") + shot_type: str = Field("single") + + +class Reference2VideoParametersField(BaseModel): + size: str = Field(...) + duration: int = Field(5, ge=5, le=15) + shot_type: str = Field("single") + seed: int = Field(..., ge=0, le=2147483647) + watermark: bool = Field(False) + + +class Text2ImageTaskCreationRequest(BaseModel): + model: str = Field(...) + input: Text2ImageInputField = Field(...) + parameters: Txt2ImageParametersField = Field(...) + + +class Image2ImageTaskCreationRequest(BaseModel): + model: str = Field(...) + input: Image2ImageInputField = Field(...) + parameters: Image2ImageParametersField = Field(...) + + +class Text2VideoTaskCreationRequest(BaseModel): + model: str = Field(...) + input: Text2VideoInputField = Field(...) + parameters: Text2VideoParametersField = Field(...) + + +class Image2VideoTaskCreationRequest(BaseModel): + model: str = Field(...) + input: Image2VideoInputField = Field(...) + parameters: Image2VideoParametersField = Field(...) + + +class Reference2VideoTaskCreationRequest(BaseModel): + model: str = Field(...) + input: Reference2VideoInputField = Field(...) + parameters: Reference2VideoParametersField = Field(...) + + +class Wan27MediaItem(BaseModel): + type: str = Field(...) + url: str = Field(...) + + +class Wan27ReferenceVideoInputField(BaseModel): + prompt: str = Field(...) + negative_prompt: str | None = Field(None) + media: list[Wan27MediaItem] = Field(...) + + +class Wan27ReferenceVideoParametersField(BaseModel): + resolution: str = Field(...) + ratio: str | None = Field(None) + duration: int = Field(5, ge=2, le=10) + watermark: bool = Field(False) + seed: int = Field(..., ge=0, le=2147483647) + + +class Wan27ReferenceVideoTaskCreationRequest(BaseModel): + model: str = Field(...) + input: Wan27ReferenceVideoInputField = Field(...) + parameters: Wan27ReferenceVideoParametersField = Field(...) + + +class Wan27ImageToVideoInputField(BaseModel): + prompt: str | None = Field(None) + negative_prompt: str | None = Field(None) + media: list[Wan27MediaItem] = Field(...) + + +class Wan27ImageToVideoParametersField(BaseModel): + resolution: str = Field(...) + duration: int = Field(5, ge=2, le=15) + prompt_extend: bool = Field(True) + watermark: bool = Field(False) + seed: int = Field(..., ge=0, le=2147483647) + + +class Wan27ImageToVideoTaskCreationRequest(BaseModel): + model: str = Field(...) + input: Wan27ImageToVideoInputField = Field(...) + parameters: Wan27ImageToVideoParametersField = Field(...) + + +class Wan27VideoEditInputField(BaseModel): + prompt: str = Field(...) + media: list[Wan27MediaItem] = Field(...) + + +class Wan27VideoEditParametersField(BaseModel): + resolution: str = Field(...) + ratio: str | None = Field(None) + duration: int = Field(0) + audio_setting: str = Field("auto") + watermark: bool = Field(False) + seed: int = Field(..., ge=0, le=2147483647) + + +class Wan27VideoEditTaskCreationRequest(BaseModel): + model: str = Field(...) + input: Wan27VideoEditInputField = Field(...) + parameters: Wan27VideoEditParametersField = Field(...) + + +class Wan27Text2VideoParametersField(BaseModel): + resolution: str = Field(...) + ratio: str | None = Field(None) + duration: int = Field(5, ge=2, le=15) + prompt_extend: bool = Field(True) + watermark: bool = Field(False) + seed: int = Field(..., ge=0, le=2147483647) + + +class Wan27Text2VideoTaskCreationRequest(BaseModel): + model: str = Field(...) + input: Text2VideoInputField = Field(...) + parameters: Wan27Text2VideoParametersField = Field(...) + + +class TaskCreationOutputField(BaseModel): + task_id: str = Field(...) + task_status: str = Field(...) + + +class TaskCreationResponse(BaseModel): + output: TaskCreationOutputField | None = Field(None) + request_id: str = Field(...) + code: str | None = Field(None, description="Error code for the failed request.") + message: str | None = Field(None, description="Details about the failed request.") + + +class TaskResult(BaseModel): + url: str | None = Field(None) + code: str | None = Field(None) + message: str | None = Field(None) + + +class ImageTaskStatusOutputField(TaskCreationOutputField): + task_id: str = Field(...) + task_status: str = Field(...) + results: list[TaskResult] | None = Field(None) + + +class VideoTaskStatusOutputField(TaskCreationOutputField): + task_id: str = Field(...) + task_status: str = Field(...) + video_url: str | None = Field(None) + code: str | None = Field(None) + message: str | None = Field(None) + + +class ImageTaskStatusResponse(BaseModel): + output: ImageTaskStatusOutputField | None = Field(None) + request_id: str = Field(...) + + +class VideoTaskStatusResponse(BaseModel): + output: VideoTaskStatusOutputField | None = Field(None) + request_id: str = Field(...) diff --git a/comfy_api_nodes/nodes_wan.py b/comfy_api_nodes/nodes_wan.py index e2afe7f9c..d1470894a 100644 --- a/comfy_api_nodes/nodes_wan.py +++ b/comfy_api_nodes/nodes_wan.py @@ -1,9 +1,40 @@ import re -from pydantic import BaseModel, Field from typing_extensions import override from comfy_api.latest import IO, ComfyExtension, Input +from comfy_api_nodes.apis.wan import ( + Image2ImageInputField, + Image2ImageParametersField, + Image2ImageTaskCreationRequest, + Image2VideoInputField, + Image2VideoParametersField, + Image2VideoTaskCreationRequest, + ImageTaskStatusResponse, + Reference2VideoInputField, + Reference2VideoParametersField, + Reference2VideoTaskCreationRequest, + TaskCreationResponse, + Text2ImageInputField, + Text2ImageTaskCreationRequest, + Text2VideoInputField, + Text2VideoParametersField, + Text2VideoTaskCreationRequest, + Txt2ImageParametersField, + VideoTaskStatusResponse, + Wan27ImageToVideoInputField, + Wan27ImageToVideoParametersField, + Wan27ImageToVideoTaskCreationRequest, + Wan27MediaItem, + Wan27ReferenceVideoInputField, + Wan27ReferenceVideoParametersField, + Wan27ReferenceVideoTaskCreationRequest, + Wan27Text2VideoParametersField, + Wan27Text2VideoTaskCreationRequest, + Wan27VideoEditInputField, + Wan27VideoEditParametersField, + Wan27VideoEditTaskCreationRequest, +) from comfy_api_nodes.util import ( ApiEndpoint, audio_to_base64_string, @@ -13,157 +44,14 @@ from comfy_api_nodes.util import ( poll_op, sync_op, tensor_to_base64_string, + upload_audio_to_comfyapi, + upload_image_to_comfyapi, upload_video_to_comfyapi, validate_audio_duration, + validate_string, validate_video_duration, ) - -class Text2ImageInputField(BaseModel): - prompt: str = Field(...) - negative_prompt: str | None = Field(None) - - -class Image2ImageInputField(BaseModel): - prompt: str = Field(...) - negative_prompt: str | None = Field(None) - images: list[str] = Field(..., min_length=1, max_length=2) - - -class Text2VideoInputField(BaseModel): - prompt: str = Field(...) - negative_prompt: str | None = Field(None) - audio_url: str | None = Field(None) - - -class Image2VideoInputField(BaseModel): - prompt: str = Field(...) - negative_prompt: str | None = Field(None) - img_url: str = Field(...) - audio_url: str | None = Field(None) - - -class Reference2VideoInputField(BaseModel): - prompt: str = Field(...) - negative_prompt: str | None = Field(None) - reference_video_urls: list[str] = Field(...) - - -class Txt2ImageParametersField(BaseModel): - size: str = Field(...) - n: int = Field(1, description="Number of images to generate.") # we support only value=1 - seed: int = Field(..., ge=0, le=2147483647) - prompt_extend: bool = Field(True) - watermark: bool = Field(False) - - -class Image2ImageParametersField(BaseModel): - size: str | None = Field(None) - n: int = Field(1, description="Number of images to generate.") # we support only value=1 - seed: int = Field(..., ge=0, le=2147483647) - watermark: bool = Field(False) - - -class Text2VideoParametersField(BaseModel): - size: str = Field(...) - seed: int = Field(..., ge=0, le=2147483647) - duration: int = Field(5, ge=5, le=15) - prompt_extend: bool = Field(True) - watermark: bool = Field(False) - audio: bool = Field(False, description="Whether to generate audio automatically.") - shot_type: str = Field("single") - - -class Image2VideoParametersField(BaseModel): - resolution: str = Field(...) - seed: int = Field(..., ge=0, le=2147483647) - duration: int = Field(5, ge=5, le=15) - prompt_extend: bool = Field(True) - watermark: bool = Field(False) - audio: bool = Field(False, description="Whether to generate audio automatically.") - shot_type: str = Field("single") - - -class Reference2VideoParametersField(BaseModel): - size: str = Field(...) - duration: int = Field(5, ge=5, le=15) - shot_type: str = Field("single") - seed: int = Field(..., ge=0, le=2147483647) - watermark: bool = Field(False) - - -class Text2ImageTaskCreationRequest(BaseModel): - model: str = Field(...) - input: Text2ImageInputField = Field(...) - parameters: Txt2ImageParametersField = Field(...) - - -class Image2ImageTaskCreationRequest(BaseModel): - model: str = Field(...) - input: Image2ImageInputField = Field(...) - parameters: Image2ImageParametersField = Field(...) - - -class Text2VideoTaskCreationRequest(BaseModel): - model: str = Field(...) - input: Text2VideoInputField = Field(...) - parameters: Text2VideoParametersField = Field(...) - - -class Image2VideoTaskCreationRequest(BaseModel): - model: str = Field(...) - input: Image2VideoInputField = Field(...) - parameters: Image2VideoParametersField = Field(...) - - -class Reference2VideoTaskCreationRequest(BaseModel): - model: str = Field(...) - input: Reference2VideoInputField = Field(...) - parameters: Reference2VideoParametersField = Field(...) - - -class TaskCreationOutputField(BaseModel): - task_id: str = Field(...) - task_status: str = Field(...) - - -class TaskCreationResponse(BaseModel): - output: TaskCreationOutputField | None = Field(None) - request_id: str = Field(...) - code: str | None = Field(None, description="Error code for the failed request.") - message: str | None = Field(None, description="Details about the failed request.") - - -class TaskResult(BaseModel): - url: str | None = Field(None) - code: str | None = Field(None) - message: str | None = Field(None) - - -class ImageTaskStatusOutputField(TaskCreationOutputField): - task_id: str = Field(...) - task_status: str = Field(...) - results: list[TaskResult] | None = Field(None) - - -class VideoTaskStatusOutputField(TaskCreationOutputField): - task_id: str = Field(...) - task_status: str = Field(...) - video_url: str | None = Field(None) - code: str | None = Field(None) - message: str | None = Field(None) - - -class ImageTaskStatusResponse(BaseModel): - output: ImageTaskStatusOutputField | None = Field(None) - request_id: str = Field(...) - - -class VideoTaskStatusResponse(BaseModel): - output: VideoTaskStatusOutputField | None = Field(None) - request_id: str = Field(...) - - RES_IN_PARENS = re.compile(r"\((\d+)\s*[x×]\s*(\d+)\)") @@ -179,7 +67,6 @@ class WanTextToImageApi(IO.ComfyNode): IO.Combo.Input( "model", options=["wan2.5-t2i-preview"], - default="wan2.5-t2i-preview", tooltip="Model to use.", ), IO.String.Input( @@ -936,6 +823,829 @@ class WanReferenceVideoApi(IO.ComfyNode): return IO.NodeOutput(await download_url_to_video_output(response.output.video_url)) +class Wan2TextToVideoApi(IO.ComfyNode): + @classmethod + def define_schema(cls): + return IO.Schema( + node_id="Wan2TextToVideoApi", + display_name="Wan 2.7 Text to Video", + category="api node/video/Wan", + description="Generates a video based on a text prompt using the Wan 2.7 model.", + inputs=[ + IO.DynamicCombo.Input( + "model", + options=[ + IO.DynamicCombo.Option( + "wan2.7-t2v", + [ + IO.String.Input( + "prompt", + multiline=True, + default="", + tooltip="Prompt describing the elements and visual features. " + "Supports English and Chinese.", + ), + IO.String.Input( + "negative_prompt", + multiline=True, + default="", + tooltip="Negative prompt describing what to avoid.", + ), + IO.Combo.Input( + "resolution", + options=["720P", "1080P"], + ), + IO.Combo.Input( + "ratio", + options=["16:9", "9:16", "1:1", "4:3", "3:4"], + ), + IO.Int.Input( + "duration", + default=5, + min=2, + max=15, + step=1, + display_mode=IO.NumberDisplay.number, + ), + ], + ), + ], + ), + IO.Audio.Input( + "audio", + optional=True, + tooltip="Audio for driving video generation (e.g., lip sync, beat-matched motion). " + "Duration: 3s-30s. If not provided, the model automatically generates matching " + "background music or sound effects.", + ), + IO.Int.Input( + "seed", + default=0, + min=0, + max=2147483647, + step=1, + display_mode=IO.NumberDisplay.number, + control_after_generate=True, + tooltip="Seed to use for generation.", + ), + IO.Boolean.Input( + "prompt_extend", + default=True, + tooltip="Whether to enhance the prompt with AI assistance.", + advanced=True, + ), + IO.Boolean.Input( + "watermark", + default=False, + tooltip="Whether to add an AI-generated watermark to the result.", + advanced=True, + ), + ], + outputs=[ + IO.Video.Output(), + ], + hidden=[ + IO.Hidden.auth_token_comfy_org, + IO.Hidden.api_key_comfy_org, + IO.Hidden.unique_id, + ], + is_api_node=True, + price_badge=IO.PriceBadge( + depends_on=IO.PriceBadgeDepends(widgets=["model", "model.resolution", "model.duration"]), + expr=""" + ( + $res := $lookup(widgets, "model.resolution"); + $dur := $lookup(widgets, "model.duration"); + $ppsTable := { "720p": 0.1, "1080p": 0.15 }; + $pps := $lookup($ppsTable, $res); + { "type": "usd", "usd": $pps * $dur } + ) + """, + ), + ) + + @classmethod + async def execute( + cls, + model: dict, + seed: int, + prompt_extend: bool, + watermark: bool, + audio: Input.Audio | None = None, + ): + validate_string(model["prompt"], strip_whitespace=False, min_length=1) + audio_url = None + if audio is not None: + validate_audio_duration(audio, 1.5, 60.0) + audio_url = await upload_audio_to_comfyapi( + cls, audio, container_format="mp3", codec_name="libmp3lame", mime_type="audio/mpeg" + ) + initial_response = await sync_op( + cls, + ApiEndpoint( + path="/proxy/wan/api/v1/services/aigc/video-generation/video-synthesis", + method="POST", + ), + response_model=TaskCreationResponse, + data=Wan27Text2VideoTaskCreationRequest( + model=model["model"], + input=Text2VideoInputField( + prompt=model["prompt"], + negative_prompt=model["negative_prompt"] or None, + audio_url=audio_url, + ), + parameters=Wan27Text2VideoParametersField( + resolution=model["resolution"], + ratio=model["ratio"], + duration=model["duration"], + seed=seed, + prompt_extend=prompt_extend, + watermark=watermark, + ), + ), + ) + if not initial_response.output: + raise Exception(f"An unknown error occurred: {initial_response.code} - {initial_response.message}") + response = await poll_op( + cls, + ApiEndpoint(path=f"/proxy/wan/api/v1/tasks/{initial_response.output.task_id}"), + response_model=VideoTaskStatusResponse, + status_extractor=lambda x: x.output.task_status, + poll_interval=7, + ) + return IO.NodeOutput(await download_url_to_video_output(response.output.video_url)) + + +class Wan2ImageToVideoApi(IO.ComfyNode): + @classmethod + def define_schema(cls): + return IO.Schema( + node_id="Wan2ImageToVideoApi", + display_name="Wan 2.7 Image to Video", + category="api node/video/Wan", + description="Generate a video from a first-frame image, with optional last-frame image and audio.", + inputs=[ + IO.DynamicCombo.Input( + "model", + options=[ + IO.DynamicCombo.Option( + "wan2.7-i2v", + [ + IO.String.Input( + "prompt", + multiline=True, + default="", + tooltip="Prompt describing the elements and visual features. " + "Supports English and Chinese.", + ), + IO.String.Input( + "negative_prompt", + multiline=True, + default="", + tooltip="Negative prompt describing what to avoid.", + ), + IO.Combo.Input( + "resolution", + options=["720P", "1080P"], + ), + IO.Int.Input( + "duration", + default=5, + min=2, + max=15, + step=1, + display_mode=IO.NumberDisplay.number, + ), + ], + ), + ], + ), + IO.Image.Input( + "first_frame", + tooltip="First frame image. The output aspect ratio is derived from this image.", + ), + IO.Image.Input( + "last_frame", + optional=True, + tooltip="Last frame image. The model generates a video transitioning from first to last frame.", + ), + IO.Audio.Input( + "audio", + optional=True, + tooltip="Audio for driving video generation (e.g., lip sync, beat-matched motion). " + "Duration: 2s-30s. If not provided, the model automatically generates matching " + "background music or sound effects.", + ), + IO.Int.Input( + "seed", + default=0, + min=0, + max=2147483647, + step=1, + display_mode=IO.NumberDisplay.number, + control_after_generate=True, + tooltip="Seed to use for generation.", + ), + IO.Boolean.Input( + "prompt_extend", + default=True, + tooltip="Whether to enhance the prompt with AI assistance.", + advanced=True, + ), + IO.Boolean.Input( + "watermark", + default=False, + tooltip="Whether to add an AI-generated watermark to the result.", + advanced=True, + ), + ], + outputs=[ + IO.Video.Output(), + ], + hidden=[ + IO.Hidden.auth_token_comfy_org, + IO.Hidden.api_key_comfy_org, + IO.Hidden.unique_id, + ], + is_api_node=True, + price_badge=IO.PriceBadge( + depends_on=IO.PriceBadgeDepends(widgets=["model", "model.resolution", "model.duration"]), + expr=""" + ( + $res := $lookup(widgets, "model.resolution"); + $dur := $lookup(widgets, "model.duration"); + $ppsTable := { "720p": 0.1, "1080p": 0.15 }; + $pps := $lookup($ppsTable, $res); + { "type": "usd", "usd": $pps * $dur } + ) + """, + ), + ) + + @classmethod + async def execute( + cls, + model: dict, + first_frame: Input.Image, + seed: int, + prompt_extend: bool, + watermark: bool, + last_frame: Input.Image | None = None, + audio: Input.Audio | None = None, + ): + media = [ + Wan27MediaItem( + type="first_frame", + url=await upload_image_to_comfyapi(cls, image=first_frame), + ) + ] + if last_frame is not None: + media.append( + Wan27MediaItem( + type="last_frame", + url=await upload_image_to_comfyapi(cls, image=last_frame), + ) + ) + if audio is not None: + validate_audio_duration(audio, 2.0, 30.0) + audio_url = await upload_audio_to_comfyapi( + cls, audio, container_format="mp3", codec_name="libmp3lame", mime_type="audio/mpeg" + ) + media.append(Wan27MediaItem(type="driving_audio", url=audio_url)) + initial_response = await sync_op( + cls, + ApiEndpoint( + path="/proxy/wan/api/v1/services/aigc/video-generation/video-synthesis", + method="POST", + ), + response_model=TaskCreationResponse, + data=Wan27ImageToVideoTaskCreationRequest( + model=model["model"], + input=Wan27ImageToVideoInputField( + prompt=model["prompt"] or None, + negative_prompt=model["negative_prompt"] or None, + media=media, + ), + parameters=Wan27ImageToVideoParametersField( + resolution=model["resolution"], + duration=model["duration"], + seed=seed, + prompt_extend=prompt_extend, + watermark=watermark, + ), + ), + ) + if not initial_response.output: + raise Exception(f"An unknown error occurred: {initial_response.code} - {initial_response.message}") + response = await poll_op( + cls, + ApiEndpoint(path=f"/proxy/wan/api/v1/tasks/{initial_response.output.task_id}"), + response_model=VideoTaskStatusResponse, + status_extractor=lambda x: x.output.task_status, + poll_interval=7, + ) + return IO.NodeOutput(await download_url_to_video_output(response.output.video_url)) + + +class Wan2VideoContinuationApi(IO.ComfyNode): + @classmethod + def define_schema(cls): + return IO.Schema( + node_id="Wan2VideoContinuationApi", + display_name="Wan 2.7 Video Continuation", + category="api node/video/Wan", + description="Continue a video from where it left off, with optional last-frame control.", + inputs=[ + IO.DynamicCombo.Input( + "model", + options=[ + IO.DynamicCombo.Option( + "wan2.7-i2v", + [ + IO.String.Input( + "prompt", + multiline=True, + default="", + tooltip="Prompt describing the elements and visual features. Supports English and Chinese.", + ), + IO.String.Input( + "negative_prompt", + multiline=True, + default="", + tooltip="Negative prompt describing what to avoid.", + ), + IO.Combo.Input( + "resolution", + options=["720P", "1080P"], + ), + IO.Int.Input( + "duration", + default=5, + min=2, + max=15, + step=1, + display_mode=IO.NumberDisplay.number, + tooltip="Total output duration in seconds. The model generates continuation " + "to fill the remaining time after the input clip.", + ), + ], + ), + ], + ), + IO.Video.Input( + "first_clip", + tooltip="Input video to continue from. Duration: 2s-10s. " + "The output aspect ratio is derived from this video.", + ), + IO.Image.Input( + "last_frame", + optional=True, + tooltip="Last frame image. The continuation will transition towards this frame.", + ), + IO.Int.Input( + "seed", + default=0, + min=0, + max=2147483647, + step=1, + display_mode=IO.NumberDisplay.number, + control_after_generate=True, + tooltip="Seed to use for generation.", + ), + IO.Boolean.Input( + "prompt_extend", + default=True, + tooltip="Whether to enhance the prompt with AI assistance.", + advanced=True, + ), + IO.Boolean.Input( + "watermark", + default=False, + tooltip="Whether to add an AI-generated watermark to the result.", + advanced=True, + ), + ], + outputs=[ + IO.Video.Output(), + ], + hidden=[ + IO.Hidden.auth_token_comfy_org, + IO.Hidden.api_key_comfy_org, + IO.Hidden.unique_id, + ], + is_api_node=True, + price_badge=IO.PriceBadge( + depends_on=IO.PriceBadgeDepends(widgets=["model", "model.resolution", "model.duration"]), + expr=""" + ( + $res := $lookup(widgets, "model.resolution"); + $dur := $lookup(widgets, "model.duration"); + $ppsTable := { "720p": 0.1, "1080p": 0.15 }; + $pps := $lookup($ppsTable, $res); + $outputPrice := $pps * $dur; + { + "type": "range_usd", + "min_usd": 2 * $pps + $outputPrice, + "max_usd": 5 * $pps + $outputPrice + } + ) + """, + ), + ) + + @classmethod + async def execute( + cls, + model: dict, + first_clip: Input.Video, + prompt: str = "", + negative_prompt: str = "", + last_frame: Input.Image | None = None, + seed: int = 0, + prompt_extend: bool = True, + watermark: bool = False, + ): + validate_video_duration(first_clip, min_duration=2, max_duration=10) + media = [ + Wan27MediaItem( + type="first_clip", + url=await upload_video_to_comfyapi(cls, first_clip), + ) + ] + if last_frame is not None: + media.append( + Wan27MediaItem( + type="last_frame", + url=await upload_image_to_comfyapi(cls, image=last_frame), + ) + ) + initial_response = await sync_op( + cls, + ApiEndpoint( + path="/proxy/wan/api/v1/services/aigc/video-generation/video-synthesis", + method="POST", + ), + response_model=TaskCreationResponse, + data=Wan27ImageToVideoTaskCreationRequest( + model=model["model"], + input=Wan27ImageToVideoInputField( + prompt=model["prompt"] or None, + negative_prompt=model["negative_prompt"] or None, + media=media, + ), + parameters=Wan27ImageToVideoParametersField( + resolution=model["resolution"], + duration=model["duration"], + seed=seed, + prompt_extend=prompt_extend, + watermark=watermark, + ), + ), + ) + if not initial_response.output: + raise Exception(f"An unknown error occurred: {initial_response.code} - {initial_response.message}") + response = await poll_op( + cls, + ApiEndpoint(path=f"/proxy/wan/api/v1/tasks/{initial_response.output.task_id}"), + response_model=VideoTaskStatusResponse, + status_extractor=lambda x: x.output.task_status, + poll_interval=7, + ) + return IO.NodeOutput(await download_url_to_video_output(response.output.video_url)) + + +class Wan2VideoEditApi(IO.ComfyNode): + @classmethod + def define_schema(cls): + return IO.Schema( + node_id="Wan2VideoEditApi", + display_name="Wan 2.7 Video Edit", + category="api node/video/Wan", + description="Edit a video using text instructions, reference images, or style transfer.", + inputs=[ + IO.DynamicCombo.Input( + "model", + options=[ + IO.DynamicCombo.Option( + "wan2.7-videoedit", + [ + IO.String.Input( + "prompt", + multiline=True, + default="", + tooltip="Editing instructions or style transfer requirements.", + ), + IO.Combo.Input( + "resolution", + options=["720P", "1080P"], + ), + IO.Combo.Input( + "ratio", + options=["16:9", "9:16", "1:1", "4:3", "3:4"], + tooltip="Aspect ratio. If not changed, approximates the input video ratio.", + ), + IO.Combo.Input( + "duration", + options=["auto", "2", "3", "4", "5", "6", "7", "8", "9", "10"], + default="auto", + tooltip="Output duration in seconds. 'auto' matches the input video duration. " + "A specific value truncates from the start of the video.", + ), + IO.Autogrow.Input( + "reference_images", + template=IO.Autogrow.TemplateNames( + IO.Image.Input("reference_image"), + names=[ + "image1", + "image2", + "image3", + "image4", + ], + min=0, + ), + ), + ], + ), + ], + ), + IO.Video.Input( + "video", + tooltip="The video to edit.", + ), + IO.Int.Input( + "seed", + default=0, + min=0, + max=2147483647, + step=1, + display_mode=IO.NumberDisplay.number, + control_after_generate=True, + tooltip="Seed to use for generation.", + ), + IO.Combo.Input( + "audio_setting", + options=["auto", "origin"], + default="auto", + tooltip="'auto': model decides whether to regenerate audio based on the prompt. " + "'origin': preserve the original audio from the input video.", + advanced=True, + ), + IO.Boolean.Input( + "watermark", + default=False, + tooltip="Whether to add an AI-generated watermark to the result.", + advanced=True, + ), + ], + outputs=[ + IO.Video.Output(), + ], + hidden=[ + IO.Hidden.auth_token_comfy_org, + IO.Hidden.api_key_comfy_org, + IO.Hidden.unique_id, + ], + is_api_node=True, + price_badge=IO.PriceBadge( + depends_on=IO.PriceBadgeDepends(widgets=["model", "model.resolution", "model.duration"]), + expr=""" + ( + $res := $lookup(widgets, "model.resolution"); + $dur := $lookup(widgets, "model.duration"); + $ppsTable := { "720p": 0.1, "1080p": 0.15 }; + $pps := $lookup($ppsTable, $res); + { "type": "usd", "usd": $pps, "format": { "suffix": "/second", "note": "(input + output)" } } + ) + """, + ), + ) + + @classmethod + async def execute( + cls, + model: dict, + video: Input.Video, + seed: int, + audio_setting: str, + watermark: bool, + ): + validate_string(model["prompt"], strip_whitespace=False, min_length=1) + validate_video_duration(video, min_duration=2, max_duration=10) + duration = 0 if model["duration"] == "auto" else int(model["duration"]) + media = [Wan27MediaItem(type="video", url=await upload_video_to_comfyapi(cls, video))] + reference_images = model.get("reference_images", {}) + for key in reference_images: + media.append( + Wan27MediaItem( + type="reference_image", url=await upload_image_to_comfyapi(cls, image=reference_images[key]) + ) + ) + initial_response = await sync_op( + cls, + ApiEndpoint( + path="/proxy/wan/api/v1/services/aigc/video-generation/video-synthesis", + method="POST", + ), + response_model=TaskCreationResponse, + data=Wan27VideoEditTaskCreationRequest( + model=model["model"], + input=Wan27VideoEditInputField(prompt=model["prompt"], media=media), + parameters=Wan27VideoEditParametersField( + resolution=model["resolution"], + ratio=model["ratio"], + duration=duration, + audio_setting=audio_setting, + watermark=watermark, + seed=seed, + ), + ), + ) + if not initial_response.output: + raise Exception(f"An unknown error occurred: {initial_response.code} - {initial_response.message}") + response = await poll_op( + cls, + ApiEndpoint(path=f"/proxy/wan/api/v1/tasks/{initial_response.output.task_id}"), + response_model=VideoTaskStatusResponse, + status_extractor=lambda x: x.output.task_status, + poll_interval=7, + ) + return IO.NodeOutput(await download_url_to_video_output(response.output.video_url)) + + +class Wan2ReferenceVideoApi(IO.ComfyNode): + @classmethod + def define_schema(cls): + return IO.Schema( + node_id="Wan2ReferenceVideoApi", + display_name="Wan 2.7 Reference to Video", + category="api node/video/Wan", + description="Generate a video featuring a person or object from reference materials. " + "Supports single-character performances and multi-character interactions.", + inputs=[ + IO.DynamicCombo.Input( + "model", + options=[ + IO.DynamicCombo.Option( + "wan2.7-r2v", + [ + IO.String.Input( + "prompt", + multiline=True, + default="", + tooltip="Prompt describing the video. Use identifiers such as 'character1' and " + "'character2' to refer to the reference characters.", + ), + IO.String.Input( + "negative_prompt", + multiline=True, + default="", + tooltip="Negative prompt describing what to avoid.", + ), + IO.Combo.Input( + "resolution", + options=["720P", "1080P"], + ), + IO.Combo.Input( + "ratio", + options=["16:9", "9:16", "1:1", "4:3", "3:4"], + ), + IO.Int.Input( + "duration", + default=5, + min=2, + max=10, + step=1, + display_mode=IO.NumberDisplay.number, + ), + IO.Autogrow.Input( + "reference_videos", + template=IO.Autogrow.TemplateNames( + IO.Video.Input("reference_video"), + names=["video1", "video2", "video3"], + min=0, + ), + ), + IO.Autogrow.Input( + "reference_images", + template=IO.Autogrow.TemplateNames( + IO.Image.Input("reference_image"), + names=["image1", "image2", "image3", "image4", "image5"], + min=0, + ), + ), + ], + ), + ], + ), + IO.Int.Input( + "seed", + default=0, + min=0, + max=2147483647, + step=1, + display_mode=IO.NumberDisplay.number, + control_after_generate=True, + tooltip="Seed to use for generation.", + ), + IO.Boolean.Input( + "watermark", + default=False, + tooltip="Whether to add an AI-generated watermark to the result.", + advanced=True, + ), + ], + outputs=[ + IO.Video.Output(), + ], + hidden=[ + IO.Hidden.auth_token_comfy_org, + IO.Hidden.api_key_comfy_org, + IO.Hidden.unique_id, + ], + is_api_node=True, + price_badge=IO.PriceBadge( + depends_on=IO.PriceBadgeDepends(widgets=["model", "model.resolution", "model.duration"]), + expr=""" + ( + $res := $lookup(widgets, "model.resolution"); + $dur := $lookup(widgets, "model.duration"); + $ppsTable := { "720p": 0.1, "1080p": 0.15 }; + $pps := $lookup($ppsTable, $res); + $outputPrice := $pps * $dur; + { + "type": "range_usd", + "min_usd": $outputPrice, + "max_usd": 5 * $pps + $outputPrice + } + ) + """, + ), + ) + + @classmethod + async def execute( + cls, + model: dict, + seed: int, + watermark: bool, + ): + validate_string(model["prompt"], strip_whitespace=False, min_length=1) + media = [] + reference_videos = model.get("reference_videos", {}) + for key in reference_videos: + media.append( + Wan27MediaItem(type="reference_video", url=await upload_video_to_comfyapi(cls, reference_videos[key])) + ) + reference_images = model.get("reference_images", {}) + for key in reference_images: + media.append( + Wan27MediaItem( + type="reference_image", + url=await upload_image_to_comfyapi(cls, image=reference_images[key]), + ) + ) + if not media: + raise ValueError("At least one reference video or reference image must be provided.") + if len(media) > 5: + raise ValueError( + f"Too many references ({len(media)}). The maximum total of reference videos and images is 5." + ) + + initial_response = await sync_op( + cls, + ApiEndpoint( + path="/proxy/wan/api/v1/services/aigc/video-generation/video-synthesis", + method="POST", + ), + response_model=TaskCreationResponse, + data=Wan27ReferenceVideoTaskCreationRequest( + model=model["model"], + input=Wan27ReferenceVideoInputField( + prompt=model["prompt"], + negative_prompt=model["negative_prompt"] or None, + media=media, + ), + parameters=Wan27ReferenceVideoParametersField( + resolution=model["resolution"], + ratio=model["ratio"], + duration=model["duration"], + watermark=watermark, + seed=seed, + ), + ), + ) + if not initial_response.output: + raise Exception(f"An unknown error occurred: {initial_response.code} - {initial_response.message}") + response = await poll_op( + cls, + ApiEndpoint(path=f"/proxy/wan/api/v1/tasks/{initial_response.output.task_id}"), + response_model=VideoTaskStatusResponse, + status_extractor=lambda x: x.output.task_status, + poll_interval=7, + ) + return IO.NodeOutput(await download_url_to_video_output(response.output.video_url)) + + class WanApiExtension(ComfyExtension): @override async def get_node_list(self) -> list[type[IO.ComfyNode]]: @@ -945,6 +1655,11 @@ class WanApiExtension(ComfyExtension): WanTextToVideoApi, WanImageToVideoApi, WanReferenceVideoApi, + Wan2TextToVideoApi, + Wan2ImageToVideoApi, + Wan2VideoContinuationApi, + Wan2VideoEditApi, + Wan2ReferenceVideoApi, ] diff --git a/comfy_extras/nodes_ace.py b/comfy_extras/nodes_ace.py index 9cf84ab4d..cbfaf913d 100644 --- a/comfy_extras/nodes_ace.py +++ b/comfy_extras/nodes_ace.py @@ -80,7 +80,7 @@ class EmptyAceStepLatentAudio(io.ComfyNode): @classmethod def execute(cls, seconds, batch_size) -> io.NodeOutput: length = int(seconds * 44100 / 512 / 8) - latent = torch.zeros([batch_size, 8, 16, length], device=comfy.model_management.intermediate_device()) + latent = torch.zeros([batch_size, 8, 16, length], device=comfy.model_management.intermediate_device(), dtype=comfy.model_management.intermediate_dtype()) return io.NodeOutput({"samples": latent, "type": "audio"}) @@ -103,7 +103,7 @@ class EmptyAceStep15LatentAudio(io.ComfyNode): @classmethod def execute(cls, seconds, batch_size) -> io.NodeOutput: length = round((seconds * 48000 / 1920)) - latent = torch.zeros([batch_size, 64, length], device=comfy.model_management.intermediate_device()) + latent = torch.zeros([batch_size, 64, length], device=comfy.model_management.intermediate_device(), dtype=comfy.model_management.intermediate_dtype()) return io.NodeOutput({"samples": latent, "type": "audio"}) class ReferenceAudio(io.ComfyNode): diff --git a/comfy_extras/nodes_curve.py b/comfy_extras/nodes_curve.py index 9016a84f9..9803e8034 100644 --- a/comfy_extras/nodes_curve.py +++ b/comfy_extras/nodes_curve.py @@ -1,5 +1,7 @@ from __future__ import annotations +import numpy as np + from comfy_api.latest import ComfyExtension, io from comfy_api.input import CurveInput from typing_extensions import override @@ -32,10 +34,58 @@ class CurveEditor(io.ComfyNode): return io.NodeOutput(result, ui=ui) if ui else io.NodeOutput(result) +class ImageHistogram(io.ComfyNode): + @classmethod + def define_schema(cls): + return io.Schema( + node_id="ImageHistogram", + display_name="Image Histogram", + category="utils", + inputs=[ + io.Image.Input("image"), + ], + outputs=[ + io.Histogram.Output("rgb"), + io.Histogram.Output("luminance"), + io.Histogram.Output("red"), + io.Histogram.Output("green"), + io.Histogram.Output("blue"), + ], + ) + + @classmethod + def execute(cls, image) -> io.NodeOutput: + img = image[0].cpu().numpy() + img_uint8 = np.clip(img * 255, 0, 255).astype(np.uint8) + + def bincount(data): + return np.bincount(data.ravel(), minlength=256)[:256] + + hist_r = bincount(img_uint8[:, :, 0]) + hist_g = bincount(img_uint8[:, :, 1]) + hist_b = bincount(img_uint8[:, :, 2]) + + # Average of R, G, B histograms (same as Photoshop's RGB composite) + rgb = ((hist_r + hist_g + hist_b) // 3).tolist() + + # ITU-R BT.709-6, Item 3.2 (p.6) — Derivation of luminance signal + # https://www.itu.int/rec/R-REC-BT.709-6-201506-I/en + lum = 0.2126 * img[:, :, 0] + 0.7152 * img[:, :, 1] + 0.0722 * img[:, :, 2] + luminance = bincount(np.clip(lum * 255, 0, 255).astype(np.uint8)).tolist() + + return io.NodeOutput( + rgb, + luminance, + hist_r.tolist(), + hist_g.tolist(), + hist_b.tolist(), + ) + + class CurveExtension(ComfyExtension): @override async def get_node_list(self): - return [CurveEditor] + return [CurveEditor, ImageHistogram] async def comfy_entrypoint(): diff --git a/requirements.txt b/requirements.txt index 6f0659a00..1a8e1ea1c 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,5 +1,5 @@ comfyui-frontend-package==1.42.8 -comfyui-workflow-templates==0.9.39 +comfyui-workflow-templates==0.9.44 comfyui-embedded-docs==0.4.3 torch torchsde diff --git a/server.py b/server.py index 27b14825e..881da8e66 100644 --- a/server.py +++ b/server.py @@ -146,6 +146,10 @@ def is_loopback(host): def create_origin_only_middleware(): @web.middleware async def origin_only_middleware(request: web.Request, handler): + if 'Sec-Fetch-Site' in request.headers: + sec_fetch_site = request.headers['Sec-Fetch-Site'] + if sec_fetch_site == 'cross-site': + return web.Response(status=403) #this code is used to prevent the case where a random website can queue comfy workflows by making a POST to 127.0.0.1 which browsers don't prevent for some dumb reason. #in that case the Host and Origin hostnames won't match #I know the proper fix would be to add a cookie but this should take care of the problem in the meantime