Instructions to use uumlaut/ddpm-vangogh-faces with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use uumlaut/ddpm-vangogh-faces with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("uumlaut/ddpm-vangogh-faces", 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
update model card README.md
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README.md
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## Model description
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This diffusion model is trained with the [
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on the `imagefolder` dataset.
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## Intended uses & limitations
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### Training results
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## Model description
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This diffusion model is trained with the [рџ¤— Diffusers](https://github.com/huggingface/diffusers) library
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on the `imagefolder` dataset.
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## Intended uses & limitations
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### Training results
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рџ“€ [TensorBoard logs](https://huggingface.co/uumlaut/ddpm-vangogh-faces/tensorboard?#scalars)
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