Individual skill
z-image-txt2img
Build Z-Image txt2img workflows. RedCraft checkpoint, Z-Image Turbo/Base LoRAs, ControlNet, and sampler presets
plugin/skills/z-image-txt2img/SKILL.md
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- agent-skills Repository topic
- ai-agent Repository topic
- claude-code Repository topic
- claude-plugin Repository topic
- comfyui Repository topic
- comfyui-extension Repository topic
- flux Repository topic
- image-generation Repository topic
- mcp Repository topic
- mcp-server Repository topic
- engineering Derived category
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# 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`](../comfyui-launch-flags/SKILL.md).
## 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:
1. Z-Image Base (and RedCraft finetune). Full model, supports negative prompts, LoRA training, ControlNet. 10-30 steps.
2. Z-Image Turbo. DMD-distilled, 8-10 steps, no effective negative prompts (CFG baked in).
