Instructions to use krystv/hestyle-diffusion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use krystv/hestyle-diffusion with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("krystv/hestyle-diffusion", 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
Upload unet with huggingface_hub
Browse files- unet/config.json +1 -1
- unet/diffusion_pytorch_model.bin +1 -1
unet/config.json
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"num_class_embeds": null,
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"only_cross_attention": false,
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"out_channels": 4,
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"sample_size":
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"up_block_types": [
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"UpBlock2D",
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"CrossAttnUpBlock2D",
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"num_class_embeds": null,
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"only_cross_attention": false,
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"out_channels": 4,
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"sample_size": 64,
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"up_block_types": [
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"UpBlock2D",
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"CrossAttnUpBlock2D",
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unet/diffusion_pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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size 3438366373
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version https://git-lfs.github.com/spec/v1
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size 3438366373
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