Instructions to use CuTIsolation/Z-Image-Turbo-W4A8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use CuTIsolation/Z-Image-Turbo-W4A8 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("CuTIsolation/Z-Image-Turbo-W4A8", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Z-Image-Turbo W4A8
W4A8 (4-bit weight, 8-bit activation) quantized weights for
Z-Image-Turbo, made for
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/
βββ models/
β βββ diffusion_models/
β β βββ z_image_turbo_w4a8.safetensors
β βββ text_encoders/
β β βββ qwen_3_4b_w4a8.safetensors
β βββ vae/
β βββ 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>.weightβ int8, ConvRot-rotated packed int4[N, K/2]<key>.weight_s_relβ fp8 e4m3fn group scale[N, K/group_size]<key>.weight_s_channelβ fp32 channel scale[N]<key>.weight_codebookβ fp32 Lloyd-Max codebook[16]<key>.comfy_quantβ 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.
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Model tree for CuTIsolation/Z-Image-Turbo-W4A8
Base model
Tongyi-MAI/Z-Image-Turbo