z-image-txt2img

# 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). ## 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, so no separate loaders are needed. ### Z-Image Turbo (Separate Compon...