--- description: Run a multi-step generation recipe (portrait, hires-fix, style-transfer, etc.) argument-hint: "Recipe name and prompt (e.g. 'portrait a woman with red hair')" --- # /comfy-recipe — Multi-Step Generation Recipes The user wants to run a predefined multi-step image generation pipeline. Each recipe chains several workflows together, passing the output of one step as input to the next. ## Instructions 1. **Parse the arguments.** The argument is: $ARGUMENTS If no argument was provided, list the available recipes and ask the user to choose one. Extract: - **Recipe name**: the first word (e.g., `portrait`, `hires-fix`, `product-shot`, `style-transfer`) - **Prompt**: everything after the recipe name 2. **Available recipes:** - `portrait`: generate a portrait and upscale it - `product-shot`: generate a product image with clean background - `hires-fix`: generate at low resolution, then upscale with img2img for more detail - `style-transfer`: apply a style prompt to an existing image via img2img - `morph`: generate two frames and create a smooth morph video between them 3. **Check available models.** Call `list_local_models` with `model_type: "checkpoints"` to find a checkpoint. Also check `model_type: "upscale_models"` for upscale recipes. If models are missing, download appropriate ones before proceeding. 4. **Execute the recipe.** For each step, create the workflow, run it, and chain the output. ### Portrait Recipe - **Step 1: Generate.** `create_workflow` with `txt2img`, prompt from user, 1024x1024, 25 steps, cfg 7 - **Step 2: Upscale.** `create_workflow` with `upscale`, using the output image from Step 1, 2x upscale - Final output: 2048x2048 upscaled portrait ### Product-Shot Recipe - **Step 1: Generate.** `create_workflow` with `txt2img`, prompt from user (append "product photography, clean white background, studio lighting" to the prompt), 1024x1024, 25 steps, cfg 7 - **Step 2: Remove background.** If a background removal node is available, create a workflow to remove the background. Otherwise, skip this step and note it for the user. - Final output: product image (with or without background removal) ### Hires-Fix Recipe - **Step 1: Low-res generation.** `create_workflow` with `txt2img`, prompt from user, 512x768 (or 768x512 for landscape), 20 steps, cfg 7 - **Step 2: Upscale with img2img.** `create_workflow` with `img2img`, using Step 1 output, target 1024x1536 (or 1536x1024), denoise 0.4-0.5, same prompt, 20 steps - Final output: high-resolution image with more detail than direct high-res generation ### Style-Transfer Recipe - **Step 1: Load source image.** Ask the user for a source image path. Use `upload_image (action:"image")` to make it available. - **Step 2: img2img with style.** `create_workflow` with `img2img`, source image from Step 1, style prompt from user, denoise 0.5-0.7 (higher = more stylized, lower = more faithful to original), 25 steps - Final output: source image with the requested style applied ### Morph Recipe - This recipe generates two frames and creates a smooth morph video transitioning between them using WAN 2.2 First-Last-Frame with dual Hi-Lo architecture. - **Requires the `wan-flf-video` and `qwen-image-edit` skills.** Load them before executing. **Frame preparation, anchor frame strategy:** - Identify which frame is the "anchor", meaning the one with the more complex composition (e.g., a person standing vs. a small animal). Generate the anchor first, then use Qwen Edit to create the second frame from it. This preserves proportions and scene consistency. - If the user provides two existing images, skip generation and go straight to Step 3. - **Step 1: Generate anchor frame.** Use Z-Image Turbo (or user's preferred model) to generate the primary frame. Use portrait orientation (832x1472) for standing subjects, landscape (1664x928) for wide scenes. Clear VRAM after. - **Step 2: Edit to create second frame.** Upload the anchor frame with `upload_image (action:"image")`. Use Qwen Image Edit (lightning 4-step) to transform it into the second frame. Prompt should describe the desired change while specifying relative size/position (e.g., "Replace the woman with a small cat sitting at the bottom of the image"). Clear VRAM after. - **Step 3: Generate morph video.** Upload both frames with `upload_image (action:"image")`. Build the WAN 2.2 dual Hi-Lo FLF workflow per the `wan-flf-video` skill: - Two UNETs (Remix NSFW Hi+Lo with built-in lightning, or GGUF Q8 with lightning LoRAs) - `ModelSamplingSD3` shift=5 on both - `ImageResizeKJv2` to 480x720 (portrait) or 832x480 (landscape) - `WanFirstLastFrameToVideo` → dual `KSamplerAdvanced` (Hi: steps 0→2, Lo: steps 2→4, uni_pc/beta) - Optional: Apply morph LoRA (`wan2.2_i2v_magical_morph_{highnoise,lownoise}.safetensors`) to Hi/Lo Common stacks at strength 0.7-1.0 for smooth morphing instead of dissolve - `VHS_VideoCombine` at 16fps, h264-mp4 - **Step 4 (optional): Upscale.** Use `VRAM_Debug` to free VRAM, then `SeedVR2VideoUpscaler` to upscale to 1080p. **Prompt tips for morph videos:** - Describe the motion/transformation, not just start and end states - **AVOID** "magical", "enchanted", "mystical"; they cause literal sparkle effects - **USE** clean motion language: "smoothly transforms", "seamlessly reshapes", "gradually morphs" - Include scale cues when subjects differ in size: "grows into", "expands upward" - Always include a full negative prompt (see `wan-flf-video` skill) **Timing reference** (RTX 4090): Z-Image ~35s → Qwen Edit ~78s → WAN FLF 81 frames ~139s = ~4 minutes total 5. **Show progress.** After each step completes: - Report what was done and whether it succeeded - Show the intermediate image if available - Proceed to the next step 6. **Present the final result.** Show the final output image and a summary of all steps taken, including parameters used at each stage. 7. **Offer tweaks.** Ask the user if they'd like to: - Adjust any step's parameters and re-run from that point - Change the denoise strength (for hires-fix or style-transfer) - Try a different checkpoint or upscale model - Save the chained workflow for future use ## Example User: `/comfy-recipe portrait a cyberpunk woman with neon hair and chrome implants` Steps: - Parse recipe: "portrait", prompt: "a cyberpunk woman with neon hair and chrome implants" - Check checkpoints and upscale models - Step 1: txt2img at 1024x1024 with the prompt - Step 2: upscale the result 2x - Present final 2048x2048 image - Offer to tweak parameters ## Morph Example User: `/comfy-recipe morph a black cat sitting in front of a barn morphs into a woman standing tall` Steps: - Parse recipe: "morph", prompt: "a black cat sitting in front of a barn morphs into a woman standing tall" - Identify anchor frame: the woman (more complex, fills the frame) - Step 1: Z-Image Turbo txt2img with "Full body portrait of a woman standing in front of a rustic barn..." at 832x1472 - Step 2: Qwen Edit with "Replace the woman with a small cat sitting at the bottom of the image, keep the barn background" - Step 3: Clear VRAM, upload both frames, build WAN 2.2 dual Hi-Lo FLF workflow with morph LoRA - Present final morph video - Offer to adjust morph LoRA strength, frame count, or re-run with different seed ## Notes - Each step is a separate `enqueue_workflow(action="enqueue")` call; use a background monitor (`node "${CLAUDE_PLUGIN_ROOT}/scripts/monitor-progress.mjs" ` with `run_in_background: true`) to track completion. If a step fails, report the error and ask if the user wants to retry or skip - For the portrait recipe, if no upscale model is installed, search for and download one (e.g., RealESRGAN_x2plus or 4x-UltraSharp) - The hires-fix denoise value matters. Too high (>0.6) loses the original composition, too low (<0.3) adds little detail. Default to 0.45. - For style-transfer, the user must provide a source image; if they don't, ask for one - Seed is randomized for Step 1 but preserved across subsequent steps for consistency (unless the user requests otherwise) - Always open the final image for the user using the OS-appropriate command (start/open/xdg-open)