generate-image

Generate, edit, or modify an image using Comfy Cloud based on the user's description: $ARGUMENTS

Follow these steps exactly:

Step 0 — Route partner-API requests directly. If the user named a provider, model, or capability (e.g. "Flux Pro Ultra", "Nano Banana", "Kling", "DALL-E"), do one search_nodes lookup with the named term. If the matching node's category starts with api node/, try partner_generate first — see its tool description for the currently-wired model ids. Pass type: "image" plus the partner's model slug, prompt, and any optional fields (aspect_ratio/seed/medias[]). On success, return the artifact URL(s) and stop — do NOT continue with the workflow steps below. If partner_generate returns "unknown model" or "not yet implemented", or the matching node has no api node/ prefix (OSS model), continue to Step 1.

1. Use search_templates to find a pre-built workflow template that matches the request (e.g. "text to image", "image to video", "style transfer", "inpainting"). If a good template exists, use it as the base workflow instead of building from scratch.

2. If no suitable template was found, use search_models to find an appropriate checkpoint model for the request. Pick the best match based on the user's description (e.g. realistic photo -> realistic checkpoint, anime -> anime checkpoint, SDXL for high quality).

3. If the user provides an input image (for img2img, style transfer, upscaling, etc.), use upload_file to upload it to Comfy Cloud first. Use the returned filename in a LoadImage node.

4. Build a ComfyUI API-format workflow JSON with the appropriate nodes (or use the template workflow). A standard text-to-image workflow uses: CheckpointLoaderSimple, CLIPTextEncode (positive and negative prompts), EmptyLatentImage, KSampler, VAEDecode, SaveImage. For img2img, replace EmptyLatentImage with LoadImage + VAEEncode.

5. Validate the workflow has inputs and outputs before submitting. Confirm the JSON contains:

   • At least one input node carrying the user's intent (CLIPTextEncode for prompts, LoadImage for img2img/style transfer, etc.).
   • At least one output/save node wired to the final image tensor (SaveImage, PreviewImage, or the partner node's own save output).

   API-backed partner nodes (Nano Banana, Gemini, Flux Kontext, etc.) often produce an image tensor but do not include a save node by default — you must add one and wire it to their output. Without it the job runs successfully but produces nothing retrievable, wasting compute. Do not skip this check.

6. Call submit_workflow with the workflow JSON.

7. Poll get_job_status every 3 seconds until the job is completed. Show the user a brief status update while waiting. If the user asks to cancel, use cancel_job with the prompt_id.

8. Call get_output to retrieve the generated image. Pass a short description parameter (e.g. "cat astronaut in space") so the output gets a descriptive name.

9. Display the image to the user:

   • In GUI environments (Claude Desktop): the response includes an ARTIFACT_HTML: block — create an HTML artifact using that exact HTML so the image appears in the side panel. The file is also saved to disk.
   • In CLI/terminal environments (Claude Code, Cursor): the response contains a temporary signed GCS download URL, a suggested save path, and a ready-to-run curl command on its own line. Execute that curl command verbatim via your shell/Bash tool — do not reconstruct it from the raw URL, and do not re-quote, re-encode, or strip any query parameters. The signature lives in the query string (X-Goog-Signature= and related params), and any modification will invalidate the link and the download will fail. After the file is saved, open it with open (macOS), xdg-open (Linux), or start (Windows).

If any step fails, show the error clearly and suggest what might have gone wrong (wrong model, invalid node configuration, etc.).