Instructions to use Rin247/Z-Image with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Rin247/Z-Image with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Rin247/Z-Image", 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
Download text_encoder/z_image_fp4-quantization.json from Rin247/Z-Image: direct link, hf CLI and curl.
- Browser
- Download file 157 Bytes
-
https://huggingface.co/Rin247/Z-Image/resolve/main/text_encoder/z_image_fp4-quantization.json
- Command line
-
hf download hf://Rin247/Z-Image/text_encoder/z_image_fp4-quantization.json
-
curl -L -o z_image_fp4-quantization.json https://huggingface.co/Rin247/Z-Image/resolve/main/text_encoder/z_image_fp4-quantization.json
157 Bytes
| { | |
| "format": "FP4", | |
| "scale": "per-group-16", | |
| "per_tensor_relative_error": 0.117567, | |
| "per_tensor_weights_lost": 0.075634, | |
| "matrices_measured": 253 | |
| } |