Text-to-Image
Diffusers
Safetensors
MLX
English
ZImagePipeline
apple-silicon
quantized
4-bit precision
Instructions to use Giniiki/Z-Image-Turbo-mlx-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Giniiki/Z-Image-Turbo-mlx-4bit with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Giniiki/Z-Image-Turbo-mlx-4bit", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - MLX
How to use Giniiki/Z-Image-Turbo-mlx-4bit with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir Z-Image-Turbo-mlx-4bit Giniiki/Z-Image-Turbo-mlx-4bit
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Draw Things
- DiffusionBee
- Atomic Chat
File size: 534 Bytes
3356ea2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 | {
"_class_name": "ZImageTransformer2DModel",
"_diffusers_version": "0.36.0.dev0",
"all_f_patch_size": [
1
],
"all_patch_size": [
2
],
"axes_dims": [
32,
48,
48
],
"axes_lens": [
1536,
512,
512
],
"cap_feat_dim": 2560,
"dim": 3840,
"in_channels": 16,
"n_heads": 30,
"n_kv_heads": 30,
"n_layers": 30,
"n_refiner_layers": 2,
"norm_eps": 1e-05,
"qk_norm": true,
"rope_theta": 256.0,
"t_scale": 1000.0,
"quantization": {
"bits": 4,
"group_size": 64
}
}
|