Instructions to use nightknocker/z-image-bfloat16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use nightknocker/z-image-bfloat16 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("nightknocker/z-image-bfloat16", 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
Upload 2 files
Browse files
transformer/config.json
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{
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"_class_name": "ZImageTransformer2DModel",
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"_diffusers_version": "0.36.0.dev0",
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"all_f_patch_size": [
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1
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],
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"all_patch_size": [
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2
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],
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"axes_dims": [
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32,
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48,
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48
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],
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"axes_lens": [
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1536,
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512,
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512
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],
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"cap_feat_dim": 2560,
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"dim": 3840,
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"in_channels": 16,
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"n_heads": 30,
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"n_kv_heads": 30,
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"n_layers": 30,
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"n_refiner_layers": 2,
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"norm_eps": 1e-05,
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"qk_norm": true,
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"rope_theta": 256.0,
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"t_scale": 1000.0
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}
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transformer/diffusion_pytorch_model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:74c2eece5b137963b7a10a4640250c5d5131c6d581ec148c48be72a2f4bc9245
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size 12309874112
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