ComfyUI Workflows in OpenMontage

Use this skill before calling comfyui_image, comfyui_video, or comfyui_music, and when converting a community ComfyUI workflow into an OpenMontage tool call.

Server Contract

• ComfyUI must be running before the tool can generate. The default server is http://localhost:8188; override it with COMFYUI_SERVER_URL.
• Running separate ComfyUI instances per capability (different GPU, different model set)? COMFYUI_IMAGE_SERVER_URL / COMFYUI_VIDEO_SERVER_URL / COMFYUI_MUSIC_SERVER_URL each override COMFYUI_SERVER_URL for that one tool only. Optional -- a single-server setup needs none of these.
• Health and hardware status come from GET /system_stats.
• Jobs are submitted to POST /prompt, completed outputs are read from GET /history/{prompt_id}, and artifact bytes are downloaded with GET /view.
• Long waits (video, music) prefer ComfyUI's websocket feed for immediate completion/error detection and transparently fall back to REST polling if websocket-client isn't installed. Either way, a timeout is recoverable: pass the error's prompt_id back in as resume_prompt_id to resume waiting on the same job instead of resubmitting it.
• Export workflows with ComfyUI's API-format JSON, not the UI layout format. If a downloaded workflow will not submit, re-export it from ComfyUI with API format enabled.

Partner Nodes are hosted

• gemini_omni_flash, seedance_2.5, and minimax_h3_api in comfyui_video are official ComfyUI Partner Nodes. They call hosted APIs and require network access, a logged-in Comfy account, and prepaid credits.
• Do not describe Partner Nodes as local, offline, or free merely because the graph runs in a local ComfyUI process.
• minimax_h3_local is a separate open-weight path. It requires the official MiniMax H3 workflow exported in API format, its output_node, and the model stack reported by the tool.

Choosing a Workflow

• Use bundled workflows when the requested operation matches and the local machine has the required models and VRAM.
• Use a custom workflow_json or workflow_path when the user needs a community recipe, a lower-VRAM model, a different style family, or custom nodes.
• For 8GB-12GB GPUs, prefer lower-footprint workflows such as Wan 2.1 1.3B, LTXV FP8 or quantized workflows, or Wan 2.2 GGUF/quantized community workflows. The bundled Wan 2.2 14B FP8 video workflows are a 16GB-class path, not a provider-wide floor.
• Do not promise that arbitrary custom workflows will fit a machine. The workflow, quantization, resolution, frame count, and offload settings determine the real resource envelope.

Output Node Contract

• Custom workflows must pass output_node.
• Pick the node that writes the artifact, usually SaveImage, SaveVideo, VHS_VideoCombine, or another terminal saver node.
• Pass the node ID as a string, for example "108". Do not pass the class name.
• If a workflow has multiple savers, choose the final deliverable node, not previews or intermediates.

Templated vs Fixed Nodes

• Identify templated nodes before execution: prompt text, seed, dimensions, frame count, source image, sampler settings, and output filename prefix.
• Fixed nodes are model loaders, VAEs, text encoders, LoRA loaders, schedulers, and graph wiring. Do not mutate those unless the workflow author intended that customization.
• For community workflows, inspect each loader node and note every required model or custom node before running. Missing models should be handled through the tool's structured missing_models payload when available.

Video workflows need a temporal latent

• A video workflow's empty-latent node must be a video latent -- EmptyHunyuanLatentVideo for Wan 2.2 t2v, Wan22ImageToVideoLatent or WanImageToVideo for the image-conditioned variants. The frame count goes in length; batch_size is how many separate clips to generate and stays at 1.
• EmptyLatentImage with batch_size set to the frame count is a trap: it asks for N unrelated images, and the resulting MP4 has the right frame count, duration and codec and passes ffprobe cleanly -- it just strobes. Check the latent node before running any unfamiliar video workflow.

Model and LoRA Setup

• Use ComfyUI Manager or the workflow author's model links when available, and respect model licenses.
• Place models in the folders expected by the loader nodes: diffusion models under ComfyUI/models/diffusion_models/, text encoders under ComfyUI/models/text_encoders/, VAEs under ComfyUI/models/vae/, and LoRAs under ComfyUI/models/loras/.
• For LoRA stacks, use LoraLoader or LoraLoaderModelOnly chains in the workflow. Record each LoRA name plus strength_model and strength_clip when applicable.
• The current ComfyUI tools do not inject LoRAs into arbitrary graphs. To use LoRAs, provide a workflow that already contains the LoRA loader chain and pass model-stack provenance.

Provenance

• For custom workflows, provide workflow_name and workflow_model when known.
• Provide workflow_model_stack for reproducibility when the workflow is not bundled. Include base checkpoint or diffusion model, quantization, text encoder, VAE, LoRAs and strengths, sampler or scheduler, steps, and guidance if the workflow exposes them.
• The tools record the final workflow hash. Treat that hash plus the model stack, seed, dimensions, and prompt as the reproducibility contract.

Failure Handling

• If the server is unavailable, surface the structured setup offer. Starting ComfyUI or setting COMFYUI_SERVER_URL is the first fix.
• If models are missing, read data.missing_models[]; each item should include the file name, role, destination hint, and download URL when OpenMontage knows it.
• If custom nodes are missing, ask the user to install them through ComfyUI Manager or the workflow author's documented install path, then restart ComfyUI.
• If a long render times out locally, check ComfyUI history before retrying from scratch; the server may still have completed the prompt -- or just call again with resume_prompt_id set to the prompt_id from the timeout error.

Music (comfyui_music)

• Bundled default is ACE-Step v1 (3.5B) text-to-audio, built from ComfyUI's native TextEncodeAceStepAudio/EmptyAceStepLatentAudio nodes (core, not a third-party pack) -- unlike ACE-Step 1.5 or other custom node packs, v1's interface is standardized enough to bundle safely.
• prompt maps to the bundled workflow's tags field (style/genre/mood, e.g. "upbeat electronic pop, female vocals"), matching the same "prompt = music description" convention suno_music uses. lyrics is a separate optional field -- leave empty for instrumental, or use [verse]/[chorus]/[bridge] structure tags and [zh]/[ja]/[ko]-style language-code prefixes for non-English lines.
• duration_seconds, steps, cfg, lyrics_strength, and seed are patchable on the bundled workflow. Missing ace_step_v1_3.5b.safetensors surfaces through the same data.missing_models[] contract as image/video.
• Need ACE-Step 1.5, a different node pack, or a non-ACE-Step audio model? Fall back to workflow_json/workflow_path + output_node, exactly like a custom image/video workflow -- in that mode prompt becomes provenance/logging only again and must already be baked into the graph.
• output_node (bundled or custom) should be the node that writes the final audio -- the bundled workflow's is SaveAudioMP3. The client reads artifacts from that node's "audio" output key (parallel to "images" for image/video savers).
• For custom workflows, provide workflow_name/workflow_model/workflow_model_stack for provenance exactly as you would for a custom image/video workflow.