Text-to-Image
Diffusers
Safetensors
English
ZImagePipeline
quanto
int8
z-image
transformer-quantization
8-bit precision
Instructions to use ixim/Z-Image-INT8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ixim/Z-Image-INT8 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("ixim/Z-Image-INT8", 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

- Xet hash:
- 148e426e4b0388f825c7a39c9d5e0a17dd5b4baef06e1052037e5668b7a164c3
- Size of remote file:
- 1.41 MB
- SHA256:
- a5df3752117c1ac963837b832de06d01f2a80e274fec9ac5da87914a9f0f0a34
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