mcpbeat

Z Image Txt2img

artokun/z-image-txt2img

Build Z-Image txt2img workflows — RedCraft checkpoint, Z-Image Turbo/Base LoRAs, ControlNet, and sampler presets

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Install

one command, takes just this skill from the repository 
npx skills add https://github.com/artokun/comfyui-mcp --skill z-image-txt2img

The instruction itself

30 sections, as written by the author

Z-Image Text-to-Image Workflows

> ⚠️ Launch flag: Z-Image does not sample correctly under

> --use-sage-attention (black / garbled output). Launch ComfyUI with

> --use-pytorch-cross-attention for Z-Image. See

> comfyui-launch-flags.

Overview

Z-Image is a 6B-parameter image generation model from Alibaba's Tongyi Lab using a Scalable Single-Stream DiT (S3-DiT) architecture. It uses a Qwen text encoder (not CLIP-L/T5). Its VAE shares the Flux VAE *architecture* (same tensor shapes, so the file is the same 320MB size) but ships different weights — it is NOT byte-identical to Flux's ae.safetensors and must be kept as a separate file (z-image-ae.safetensors) to avoid clobbering the Flux VAE. Two variants:

  • Z-Image Base (and RedCraft finetune) — Full model, supports negative prompts, LoRA training, ControlNet. 10-30 steps.
  • Z-Image Turbo — DMD-distilled, 8-10 steps, no effective negative prompts (CFG baked in).

Models

RedCraft Redzimage DX1 (Installed — Combined Checkpoint)

| Component | Node | Model | Notes |

|-----------|------|-------|-------|

| Checkpoint | CheckpointLoaderSimple | redcraftRedzimageUpdatedJAN30_redzibDX1.safetensors | 17GB, bundles UNET+CLIP+VAE |

RedCraft is a Z-Image Base finetune by the RedCraft team. Designed for faster inference than stock Z-Image Base. Uses CheckpointLoaderSimple since it's a combined checkpoint — no need for separate loaders.

Z-Image Turbo (Separate Components — May Need Download)

| Component | Node | Model | Notes |

|-----------|------|-------|-------|

| UNET | UNETLoader | z_image_turbo_bf16.safetensors | Not currently installed |

| CLIP | CLIPLoader (type=qwen_image) | qwen_3_4b.safetensors | Not currently installed |

| VAE | VAELoader | z-image-ae.safetensors | 320MB. Flux VAE architecture but different weights — NOT the same file as Flux's ae.safetensors. From Comfy-Org/z_image_turbo (split_files/vae/ae.safetensors) |

Z-Image Base (Separate Components — May Need Download)

| Component | Node | Model | Notes |

|-----------|------|-------|-------|

| UNET | UNETLoader | z_image_base_bf16.safetensors | Not currently installed |

| CLIP | CLIPLoader (type=qwen_image) | qwen_3_4b.safetensors | Not currently installed |

| VAE | VAELoader | z-image-ae.safetensors | 320MB. Flux VAE architecture but different weights — NOT the same file as Flux's ae.safetensors |

Conditioning

TextEncodeZImageOmni (Built-in)

For Z-Image separate component loading. Supports reference images via CLIP Vision:

Required Inputs:
  - clip: CLIP
  - prompt: STRING (multiline)
  - auto_resize_images: BOOLEAN (default true)

Optional Inputs:
  - image_encoder: CLIP_VISION (for reference images)
  - vae: VAE
  - image1-3: IMAGE (up to 3 reference images)

Outputs:
  [0] CONDITIONING

CLIPTextEncode (For RedCraft Checkpoint)

When using CheckpointLoaderSimple, standard CLIPTextEncode works since the checkpoint bundles the correct tokenizer:

{
  "class_type": "CLIPTextEncode",
  "inputs": { "clip": ["<checkpoint>", 1], "text": "<prompt>" }
}

Sampler Settings

RedCraft DX1

| Preset | Steps | CFG | Sampler | Scheduler | Notes |

|--------|-------|-----|---------|-----------|-------|

| Distilled Fast | 10 | 1.0 | euler | simple | Quick iteration |

| Standard | 30 | 4.0 | euler | simple | Full quality |

Z-Image Turbo

| Preset | Steps | CFG | Sampler | Scheduler | Notes |

|--------|-------|-----|---------|-----------|-------|

| Author recommended | 14 | 1.0 | res_2s | simple | CopaxTimeless author pick |

| Beauty/fashion | 10 | 1.0 | euler_ancestral | beta | Smooth skin, fashion photography |

| Sharpest | 10 | 1.0 | dpmpp_sde | beta | Sharpest, most natural (560-image test) |

Z-Image Base (Two-Stage)

Stage 1 — Primary generation:

| Parameter | Value |

|-----------|-------|

| Steps | 22 |

| CFG | 4.0 (range 4–7) |

| Sampler | res_2s |

| Scheduler | beta |

| Denoise | 1.0 |

Stage 2 — Detail refinement (optional img2img pass):

| Parameter | Value |

|-----------|-------|

| Steps | 3 |

| CFG | 4.0 |

| Sampler | res_2s |

| Scheduler | normal |

| Denoise | 0.15 |

Negative Prompts

RedCraft / Z-Image Base

Supports negative prompts at CFG > 1.0:

3D, ai generated, semi realistic, illustrated, drawing, comic, digital painting, 3D model, blender, video game screenshot, screenshot, render, high-fidelity, smooth textures, CGI, masterpiece, text, writing, subtitle, watermark, logo, blurry, low quality, jpeg, artifacts, grainy

Z-Image Turbo

Negative prompts are not effective — CFG is baked in via distillation. Use the positive prompt to guide away from unwanted elements instead.

Recommended positive-side avoidance template:

over-smooth skin, plastic skin, doll face, anime, CGI, waxy texture, blurry face, fake pores, exaggerated makeup, over-sharpening, unrealistic symmetry, flat lighting, low detail skin, extra fingers, distorted anatomy

Resolutions

| Aspect | Resolution | Notes |

|--------|-----------|-------|

| Square | 1024x1024 | Standard |

| Square (native) | 1328x1328 | Higher quality at native resolution |

| Portrait 3:4 | 896x1152 | |

| Portrait 5:8 | 832x1216 | |

| Portrait 9:16 | 768x1344 | |

| Landscape 16:9 | 1280x720 | |

Dimensions must be divisible by 16.

LoRA System

ZImageTurbo LoRAs

Located in loras/ZImageTurbo/ with subfolders:

  • style/ — Style LoRAs (e.g., TurboPussyZ_v2.safetensors)
  • concept/ — Concept LoRAs (e.g., body from below.safetensors, ZITnsfwLoRA.safetensors)
  • character/ — Character LoRAs (e.g., NSFW_master_ZIT_000008766.safetensors)
  • action/ — Action LoRAs

Use with Z-Image Turbo base model. Typical LoRA strength: 0.6–1.0.

ZImageBase LoRAs

Located in loras/ZImageBase/ with subfolders:

  • style/ — Style LoRAs (e.g., NSGIRL-Z-Image-LoRA-By-MM744.safetensors)
  • concept/ — Concept LoRAs

Use with Z-Image Base or RedCraft. Typical LoRA strength: 0.6–1.0.

Z-Image-Aesthetic-Base v1

General aesthetic improvement LoRA:

  • File: Z-Image-Aesthetic-Base v1.safetensors (352MB)
  • Settings: euler_ancestral + beta, 30 steps, CFG 4, strength 0.6–1.0

Applying LoRAs

{
  "class_type": "LoraLoader",
  "inputs": {
    "model": ["<checkpoint_or_unet>", 0],
    "clip": ["<checkpoint_or_clip>", 1],
    "lora_name": "ZImageTurbo\\style\\TurboPussyZ_v2.safetensors",
    "strength_model": 0.8,
    "strength_clip": 0.8
  }
}

Note: When using CheckpointLoaderSimple for RedCraft, model output is index 0 and CLIP output is index 1. When stacking multiple LoRAs, chain them sequentially.

ControlNet

ZImageFunControlnet (Built-in)

Experimental built-in node for Z-Image ControlNet. Patches the model with a control signal:

Required Inputs:
  - model: MODEL
  - model_patch: MODEL_PATCH (from ControlNet loader)
  - vae: VAE
  - strength: FLOAT (default 1.0, range -10 to 10)

Optional Inputs:
  - image: IMAGE (reference/control image)
  - inpaint_image: IMAGE
  - mask: MASK

Outputs:
  [0] MODEL (patched)

Z-Image-Turbo-Fun-Controlnet-Union

A unified ControlNet supporting multiple condition types:

  • Canny, HED, Depth, Pose, MLSD
  • Strength: 0.65–0.80 (v2.1 recommended range)
  • Best paired with res_2s, res_5s, or res_2m samplers + beta57 scheduler

Complete Workflow: RedCraft DX1 (Fast, 10-Step)

{
  "1": { "class_type": "CheckpointLoaderSimple", "inputs": { "ckpt_name": "redcraftRedzimageUpdatedJAN30_redzibDX1.safetensors" }},
  "2": { "class_type": "CLIPTextEncode", "inputs": { "clip": ["1", 1], "text": "<positive prompt>" }, "_meta": { "title": "Positive" }},
  "3": { "class_type": "CLIPTextEncode", "inputs": { "clip": ["1", 1], "text": "" }, "_meta": { "title": "Negative" }},
  "4": { "class_type": "EmptyLatentImage", "inputs": { "width": 1024, "height": 1024, "batch_size": 1 }},
  "5": { "class_type": "KSampler", "inputs": {
    "model": ["1", 0],
    "positive": ["2", 0],
    "negative": ["3", 0],
    "latent_image": ["4", 0],
    "seed": 42, "steps": 10, "cfg": 1, "sampler_name": "euler", "scheduler": "simple", "denoise": 1
  }},
  "6": { "class_type": "VAEDecode", "inputs": { "samples": ["5", 0], "vae": ["1", 2] }},
  "7": { "class_type": "SaveImage", "inputs": { "images": ["6", 0], "filename_prefix": "redcraft" }}
}

Complete Workflow: RedCraft DX1 with LoRA Stack

{
  "1": { "class_type": "CheckpointLoaderSimple", "inputs": { "ckpt_name": "redcraftRedzimageUpdatedJAN30_redzibDX1.safetensors" }},
  "2": { "class_type": "LoraLoader", "inputs": {
    "model": ["1", 0], "clip": ["1", 1],
    "lora_name": "Z-Image-Aesthetic-Base v1.safetensors",
    "strength_model": 0.8, "strength_clip": 0.8
  }},
  "3": { "class_type": "LoraLoader", "inputs": {
    "model": ["2", 0], "clip": ["2", 1],
    "lora_name": "ZImageBase\\style\\NSGIRL-Z-Image-LoRA-By-MM744.safetensors",
    "strength_model": 0.7, "strength_clip": 0.7
  }},
  "4": { "class_type": "CLIPTextEncode", "inputs": { "clip": ["3", 1], "text": "<positive prompt>" }},
  "5": { "class_type": "CLIPTextEncode", "inputs": { "clip": ["3", 1], "text": "<negative prompt>" }},
  "6": { "class_type": "EmptyLatentImage", "inputs": { "width": 896, "height": 1152, "batch_size": 1 }},
  "7": { "class_type": "KSampler", "inputs": {
    "model": ["3", 0],
    "positive": ["4", 0],
    "negative": ["5", 0],
    "latent_image": ["6", 0],
    "seed": 42, "steps": 30, "cfg": 4, "sampler_name": "euler", "scheduler": "simple", "denoise": 1
  }},
  "8": { "class_type": "VAEDecode", "inputs": { "samples": ["7", 0], "vae": ["1", 2] }},
  "9": { "class_type": "SaveImage", "inputs": { "images": ["8", 0], "filename_prefix": "redcraft_lora" }}
}

Prompt Style

Natural language descriptions work best (uses Qwen LLM tokenizer, not CLIP):

Good: "Professional headshot of a confident businesswoman in her 30s, natural makeup, soft studio lighting, neutral gray background, sharp focus on eyes, Canon EOS R5"
Bad: "masterpiece, best quality, 1girl, businesswoman, studio"

VRAM Considerations

| Config | VRAM | Notes |

|--------|------|-------|

| RedCraft DX1 checkpoint | ~17GB | Fits comfortably on RTX 4090 |

| Z-Image Turbo separate | ~8GB UNET + CLIP | Very lightweight |

| Z-Image Base separate | ~12GB | |

  • Always clear_vram before switching to Z-Image from another model family
  • RedCraft is one of the most VRAM-efficient quality models available

Tips

  • RedCraft DX1 with 10 steps / CFG 1.0 is surprisingly fast and high quality for quick iteration
  • For maximum sharpness with Turbo LoRAs, use dpmpp_sde + beta scheduler
  • The Z-Image-Aesthetic-Base v1 LoRA at 0.6–0.8 strength noticeably improves output quality across all Z-Image Base variants
  • Z-Image excels at photorealistic human generation — it's the go-to for portrait and fashion photography
  • When switching between Turbo and Base LoRAs, use the matching base model variant

How to use it

Copy the folder

Take artokun/z-image-txt2img from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

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