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README.md

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 ---
 license: apache-2.0
+base_model:
+- Tongyi-MAI/Z-Image-Turbo
+pipeline_tag: text-to-image
+library_name: diffusers
+tags:
+- comfyui
+- w4a8
 ---
+
+# Z-Image-Turbo W4A8
+
+W4A8 (4-bit weight, 8-bit activation) quantized weights for
+[Z-Image-Turbo](https://huggingface.co/Tongyi-MAI/Z-Image-Turbo), made for
+[ComfyUI](https://github.com/comfyanonymous/ComfyUI) using ComfyUI's native
+`asym_w4a8_int8` quantized-diffusion format (Comfy Kitchen).
+
+Both the diffusion model and the Qwen3-4B text encoder are quantized, so the
+whole pipeline fits in low VRAM.
+
+## Files
+
+| File | Size | Notes |
+| --- | --- | --- |
+| `z_image_turbo_w4a8.safetensors` | 3.5 GB | Diffusion model, `asym_w4a8_int8`, group_size 16 + ConvRot |
+| `qwen_3_4b_w4a8.safetensors` | 2.8 GB | Qwen3-4B text encoder, `asym_w4a8_int8`, group_size 16 + ConvRot |
+
+Original BF16 sizes: diffusion 12.3 GB, text encoder 8.0 GB.
+
+## Usage (ComfyUI)
+
+Place the files in your ComfyUI `models` directory:
+
+```
+ComfyUI/
+├── models/
+│   ├── diffusion_models/
+│   │   └── z_image_turbo_w4a8.safetensors
+│   ├── text_encoders/
+│   │   └── qwen_3_4b_w4a8.safetensors
+│   └── vae/
+│       └── flux1-vae.safetensors
+```
+
+Then use the standard Z-Image-Turbo text-to-image workflow with a `Load Diffusion
+Model` node pointed at `z_image_turbo_w4a8.safetensors` and a `Load CLIP` node
+pointed at `qwen_3_4b_w4a8.safetensors`.
+
+Both files are detected automatically by ComfyUI (`.comfy_quant` metadata keys);
+no custom nodes are required. The text encoder must be loaded through the
+Qwen3-4B / Z-Image CLIP path (it does not need the pooled output).
+
+## Quality & Speed
+
+Verified on an 8 GB VRAM GPU (RTX 4060 Laptop) at 1024x1024, 8 sampling steps:
+
+| Model | Steps | Sample time |
+| --- | --- | --- |
+| BF16 | 8 | ~14 s |
+| W4A8 (this repo) | 8 | ~7 s |
+| int8_convrot (official) | 8 | ~6 s |
+
+Image quality is visually identical between BF16, W4A8 and the official
+int8_convrot checkpoint.
+
+## Quantization format
+
+Per quantized Linear layer the file stores:
+
+- `<key>.weight` — int8, ConvRot-rotated packed int4 `[N, K/2]`
+- `<key>.weight_s_rel` — fp8 e4m3fn group scale `[N, K/group_size]`
+- `<key>.weight_s_channel` — fp32 channel scale `[N]`
+- `<key>.weight_codebook` — fp32 Lloyd-Max codebook `[16]`
+- `<key>.comfy_quant` — uint8 JSON `{"format": "asym_w4a8_int8", "group_size": 16, "convrot_groupsize": ...}`
+
+1D norms, biases, the embedding table and `cap_embedder.1` are kept in BF16.

qwen_3_4b_w4a8.safetensors

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z_image_turbo_w4a8.safetensors

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