Card fixes 2026-11-01: verified corrections, requirements

6da46c9 verified about 7 hours ago

3.16 kB

---
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`](https://huggingface.co/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`](https://huggingface.co/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:

```bash
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`](https://huggingface.co/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`](https://huggingface.co/Tongyi-MAI/Z-Image). Original weights belong to the upstream authors; the base model's license applies to this derivative.
---
## 한국어 요약
[`Tongyi-MAI/Z-Image`](https://huggingface.co/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.

	---
	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`](https://huggingface.co/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`](https://huggingface.co/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:

	```bash
	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`](https://huggingface.co/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`](https://huggingface.co/Tongyi-MAI/Z-Image). Original weights belong to the upstream authors; the base model's license applies to this derivative.

	---
	## 한국어 요약
	[`Tongyi-MAI/Z-Image`](https://huggingface.co/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.