/batch

description: Parameter sweep generation across multiple values
argument-hint: "prompt, param:range (e.g. a cat, cfg:5-10, sampler:euler,dpmpp_2m)"

/comfy-batch — Parameter Sweep Generation

The user wants to generate multiple images while sweeping across different parameter values to compare results.

Instructions

1. **Parse the arguments.** The argument is: $ARGUMENTS

If no argument was provided, ask the user for a prompt and which parameters to sweep.

Extract:

• **Prompt text**: everything that isn't a parameter range specifier
• **Parameter ranges**: identified by `param_name:values` syntax

2. **Parse parameter range syntax.** Supported formats:

• `param:min-max` — integer range with step 1 (e.g., `cfg:5-10` produces 5, 6, 7, 8, 9, 10)
• `param:min-max:step` — range with explicit step (e.g., `cfg:4-12:2` produces 4, 6, 8, 10, 12)
• `param:val1,val2,val3` — explicit list (e.g., `sampler:euler,dpmpp_2m,dpmpp_sde`)
• `seed:N` — special: generate N different random seeds (e.g., `seed:4` produces 4 random seeds)

3. **Supported sweep parameters:**

• `cfg` — CFG scale (float)
• `steps` — sampling steps (integer)
• `sampler` or `sampler_name` — sampler algorithm name
• `scheduler` — scheduler name
• `seed` — random seed count or explicit seeds
• `denoise` — denoising strength (float, 0.0-1.0)
• `width` — image width in pixels
• `height` — image height in pixels

4. **Calculate total combinations.** Multiply the count of values for each swept parameter. If the total exceeds 20, warn the user:

• Show the total count and estimated time
• Ask for confirmation before proceeding
• Suggest reducing ranges if the count is very high

5. **Check available models.** Call `list_local_models` with `model_type: "checkpoints"` to find a checkpoint. If none are available, follow the model acquisition steps from the gen command.

6. **Enqueue all combinations.** For each parameter combination:

• Call `create_workflow` with template `"txt2img"` and the current parameter set including `positive_prompt`
• Call `enqueue_workflow(action="enqueue")` with the created workflow
• Collect the returned `prompt_id`

Do NOT poll between enqueues. Queue all jobs first, then monitor them together.

7. **Monitor all jobs in background.** After all workflows are enqueued, start a single background task:

   Bash(run_in_background: true):
   node "${CLAUDE_PLUGIN_ROOT}/scripts/monitor-progress.mjs" <prompt_id_1> <prompt_id_2> ... <prompt_id_N>

This monitors all jobs simultaneously via ComfyUI's WebSocket and reports step-by-step progress and completion for each. ComfyUI processes them sequentially from its queue. Continue the conversation while waiting.

**Fallback**: If the script is unavailable, poll `queue` (action:"status") for each prompt_id until done.

8. **Present results.** After all runs complete, show a summary table:

• Each row: parameter values used, status (success/error), output file
• Highlight which combinations succeeded and which failed
• If any failed, briefly note the error

9. **Suggest best result.** Based on which runs completed without errors, note the successful combinations. If all succeeded, suggest the user compare the outputs visually.

Example

User: `/comfy-batch a majestic eagle in flight, cfg:5-9:2, sampler:euler,dpmpp_2m`

Parsed:

• Prompt: "a majestic eagle in flight"
• cfg: [5, 7, 9]
• sampler: ["euler", "dpmpp_2m"]
• Total: 3 x 2 = 6 images

Steps:

• List checkpoints, select one
• Generate 6 workflows with all combinations
• Run each workflow
• Present a 3x2 grid of results

Notes

• Always randomize seeds unless `seed` is explicitly specified in the sweep
• Use sensible defaults for non-swept parameters (1024x1024 for SDXL, 20 steps, cfg 7)
• Run workflows sequentially, not in parallel — ComfyUI processes one at a time anyway
• If a single run fails, continue with the remaining combinations rather than stopping entirely
• For large sweeps, suggest the user start with a smaller subset to test