Instructions to use BiliSakura/NiT-diffusers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use BiliSakura/NiT-diffusers with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("BiliSakura/NiT-diffusers", 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
Delete vae
Browse files- vae/config.json +0 -88
- vae/diffusion_pytorch_model.safetensors +0 -3
vae/config.json
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"_class_name": "AutoencoderDC",
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"_diffusers_version": "0.32.2",
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"attention_head_dim": 32,
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"decoder_act_fns": "silu",
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"decoder_block_out_channels": [
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"decoder_norm_types": "rms_norm",
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"in_channels": 3,
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"latent_channels": 32,
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"scaling_factor": 0.41407,
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"upsample_block_type": "interpolate"
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}
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vae/diffusion_pytorch_model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:dfd991d1b54ffabf22745c5885589d8f2a7bc59930d95d92bd741c4fc64454bb
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size 1249044836
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