comfyui-mcp/packs/z-image-base-img2img/pack.yaml

Local-first, agent-native control plane for ComfyUI — MCP server + autonomous sidebar agent that drives your live graph in natural language on ANY LLM: Claude/ChatGPT/Gemini on your subscription (no API key), free local models via Ollama (fully offline),

name: z-image-base-img2img
display_name: Z-Image Base — Image to Image
family: z-image
kind: img2img
description: >-
  Z-Image Base (Alibaba Tongyi, 6B S3-DiT base model — trainable, finetuning-friendly)
  image-to-image: re-noise and restyle an input image at a chosen denoise strength.
  Uses a Qwen text encoder (not CLIP/T5), GGUF-quantized to run in <8GB VRAM, paired
  with an 8-step distill LoRA. Single-purpose img2img graph sliced from the z-image-base
  monolith (one pipeline, no group toggles), with a resize stage and optional film-grain /
  laplacian-sharpen post-processing.
vram: "<8GB"
workflow: workflow.json
skill: z-image-txt2img
launch_args: []
sources:
  model: https://huggingface.co/Aitrepreneur/FLX
  mirror: https://huggingface.co/Aitrepreneur/FLX
notes:
  - Run the generated installer from your ComfyUI root (folder with custom_nodes/
    and models/).
  - Sliced + un-bypassed from the monolith's "Z-IMAGE IMAGE TO IMAGE".
  - Load an input image into the LoadImage node and set the denoise strength.
  - The z-image VAE is saved as z-image-ae.safetensors (its weights differ from
    Flux's ae.safetensors despite the same size).
post_install:
  - Restart ComfyUI (or ComfyUI-Manager -> Install missing custom nodes for any
    node Python deps).
  - Load workflow.json.