Text-to-Image
Diffusers
Safetensors
ZImagePipeline
z-image
nunchaku
svdquant
int4
quantization
8-bit precision
Instructions to use lite-infer/z-image-turbo-nunchaku-lite-int4_r32-bnb4-text-encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use lite-infer/z-image-turbo-nunchaku-lite-int4_r32-bnb4-text-encoder with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("lite-infer/z-image-turbo-nunchaku-lite-int4_r32-bnb4-text-encoder", torch_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
File size: 173 Bytes
8fdcb1d | 1 2 3 4 5 6 7 | {
"_class_name": "FlowMatchEulerDiscreteScheduler",
"_diffusers_version": "0.36.0.dev0",
"num_train_timesteps": 1000,
"use_dynamic_shifting": false,
"shift": 3.0
} |