Routes 3D ResNets PyTorch video action-recognition workflows across training, inference, and data preparation.
日本語の概要は準備中です。原文の説明を表示しています。
Use ComfyUI as a modular AI content-creation engine: launch and automate the server, submit API workflows, validate graph JSON, author custom nodes, and configure model paths/backends.
インストールする前に、エージェントに与えられる指示の中身を確認できます。
Use this repo skill when a user asks for help with ComfyUI workflows, automation, server/API usage, custom nodes, model folder configuration, backend/memory flags, or prompt graph debugging.
ComfyUI is a node-graph AI creation engine for image, video, audio, 3D, and model-tooling workflows. Treat it as an application/runtime with a Python launch script, HTTP/websocket APIs, a node registry, JSON prompt graphs, model-folder conventions, optional hosted API nodes, and hardware-sensitive backend behavior.
/prompt.sub-skills/server-api/: launch ComfyUI, choose server-facing CLI flags, queue API prompt JSON over HTTP, monitor websocket progress, fetch history/output files, use app/user/model/assets routes, and troubleshoot server/API failures.sub-skills/workflow-execution/: validate and debug API prompt JSON, distinguish UI workflow exports from executable prompts, reason about graph links, caching, async/lazy execution, blueprints/templates, and inference failure boundaries.sub-skills/custom-nodes/: implement, scaffold, inspect, and troubleshoot custom nodes using classic INPUT_TYPES classes, NODE_CLASS_MAPPINGS, hidden inputs, async validation, public comfy_api APIs, and API-provider nodes.sub-skills/models-config/: configure model folders and extra_model_paths.yaml, understand checkpoint/LoRA/VAE/controlnet categories, choose backend/VRAM flags, diagnose missing models, and reason about supported model families/quantization.python main.py --listen 127.0.0.1 --port 8188./prompt; normal UI workflow JSON contains canvas metadata and must be exported/converted first./prompt, wait on /ws or poll /history/{prompt_id}, then download file outputs with /view using exact history metadata.extra_data or hidden node credentials; never hardcode API keys in workflow JSON or scripts.extra_model_paths.yaml.references/troubleshooting.md covers cross-cutting install/import, launch, prompt, custom-node, model-path, backend, credential, and security failures.references/repo-provenance.md records the source snapshot and evidence paths used to create this skill for future staleness checks.Most reusable scripts are owned by sub-skills because they operate on a specific surface:
sub-skills/server-api/scripts/comfy_api_client.py: validate, queue, wait for history, and download outputs through REST.sub-skills/server-api/scripts/comfy_websocket_monitor.py: queue and monitor websocket progress while ignoring binary preview frames by default.sub-skills/workflow-execution/scripts/validate_prompt_graph.py: structurally validate API prompt JSON without importing ComfyUI or loading models.sub-skills/custom-nodes/scripts/scaffold_custom_node.py: generate a safe starter custom node package.sub-skills/custom-nodes/scripts/inspect_node_definitions.py: statically inspect classic node mappings and common schema mistakes.sub-skills/models-config/scripts/validate_extra_model_paths.py: validate extra_model_paths.yaml shape and referenced directories.まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
Routes 3D ResNets PyTorch video action-recognition workflows across training, inference, and data preparation.
日本語の概要は準備中です。原文の説明を表示しています。
Guide 3DDFA Python inference, geometry rendering, training/evaluation, and optional C++ ONNX workflows for 3D dense face alignment.
日本語の概要は準備中です。原文の説明を表示しています。
Routes 3DDFA_V2 face-alignment setup, still-image demos, video tracking, and ONNX benchmarking workflows.
日本語の概要は準備中です。原文の説明を表示しています。
Operate AB3DMOT 3D multi-object tracking workflows for KITTI and nuScenes data, tracking, evaluation, and visualization.
日本語の概要は準備中です。原文の説明を表示しています。
Use Hugging Face Accelerate for PyTorch training-loop migration, distributed launch/configuration, DeepSpeed/FSDP/TPU backend setup, big-model inference/offload, checkpointing, tracking, and troubleshooting.
日本語の概要は準備中です。原文の説明を表示しています。
Route Acme reinforcement-learning framework tasks across core loops, replay/data, JAX agents, and TensorFlow/Sonnet agents.
日本語の概要は準備中です。原文の説明を表示しています。