README.md

metadata

base_model: Tongyi-MAI/Z-Image
base_model_relation: quantized
license: apache-2.0
pipeline_tag: text-to-image
tags:
  - text-to-image
  - diffusion
  - fp8
  - quantized
  - comfyui

Z-Image · FP8 (transformer)

FP8 (float8_e4m3fn) quantization of the Z-Image DiT (ZImageTransformer2DModel, 6.1B) transformer from Tongyi-MAI/Z-Image. Quantized by axteam.

File	Size
Z-Image-transformer-fp8_e4m3fn.safetensors	6.2 GB

What this is

• Only the transformer (DiT) is quantized to float8_e4m3fn (2-D weight tensors; norms/biases kept in BF16). ~half the size of the BF16 transformer.
• VAE and text encoder(s) are NOT included — use the ones from the original Tongyi-MAI/Z-Image.

Requirements

Runtime	ComfyUI (Load Diffusion Model node, float8_e4m3fn single file). Not run here — no ComfyUI on this server.
Also needed	the Qwen3 text encoder (8.0 GB) and VAE (0.17 GB) from the base repo.
Memory	this file is 6.2 GB; add the text encoder(s) and VAE above.

Usage (ComfyUI)

1. Download the FP8 file into ComfyUI's diffusion-model folder:

hf download axteam/Z-Image-FP8 Z-Image-transformer-fp8_e4m3fn.safetensors --local-dir ComfyUI/models/diffusion_models

2. Get the VAE and text encoder(s) from the original Tongyi-MAI/Z-Image repo (or the matching ComfyUI repackaged files) and put them in ComfyUI/models/vae/ and ComfyUI/models/text_encoders/.
3. In your workflow, load this file with the Load Diffusion Model node (UNETLoader) — leave weight_dtype at default (the file is already float8_e4m3fn) — and wire the original VAE / text-encoder loaders as in the base model's reference workflow.
4. Sampler settings (steps, CFG/guidance, resolution) are the same as for the original model.

The FP8 file is a plain cast of the transformer weights (no scaling), the common ComfyUI fp8_e4m3fn convention; norms and biases stay BF16.

License

Quantized derivative of Tongyi-MAI/Z-Image. Original weights belong to the upstream authors; the base model's license applies to this derivative.

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한국어 요약

Tongyi-MAI/Z-Image 의 transformer(DiT)만 FP8(float8_e4m3fn) 로 양자화(2D 가중치만, norm/bias는 BF16). BF16 대비 약 절반 크기. VAE·텍스트인코더는 미포함(원본 사용). ComfyUI models/unet/ 에 넣어 사용. 작성 axteam.

• 요구사항 — ComfyUI(Load Diffusion Model). 파일 6.2 GB + 기반 리포의 텍스트 인코더·VAE. 이 서버에서는 미실행.
• 실행(ComfyUI) — hf download axteam/Z-Image-FP8 <파일> --local-dir ComfyUI/models/diffusion_models 로 받고, VAE·텍스트 인코더는 원본에서 받아 models/vae, models/text_encoders 에 둔 뒤 Load Diffusion Model 노드로 불러옵니다(weight_dtype 은 default). 샘플러 설정은 원본과 같습니다.