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ncsnpp-church-256

Unconditional Image Generation
Diffusers
PyTorch
ScoreSdeVePipeline
Model card Files Community
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@@ -48,7 +48,7 @@ for more details on how to write your own denoising loop.
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  For more information generally on how to use `diffusers` for inference, please have a look at the [official inference example](_) # <- TODO(PVP) add link
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  ## Samples
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- 1. ![sample_1](https://huggingface.co/google/ncsnpp-church-256/resolve/main/generated_image_0.png)
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- 2. ![sample_2](https://huggingface.co/google/ncsnpp-church-256/resolve/main/generated_image_1.png)
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- 3. ![sample_3](https://huggingface.co/google/ncsnpp-church-256/resolve/main/generated_image_2.png)
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- 4. ![sample_4](https://huggingface.co/google/ncsnpp-church-256/resolve/main/generated_image_3.png)
 
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  For more information generally on how to use `diffusers` for inference, please have a look at the [official inference example](_) # <- TODO(PVP) add link
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  ## Samples
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+ 1. ![sample_1](https://huggingface.co/google/ncsnpp-church-256/resolve/images/main/generated_image_0.png)
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+ 2. ![sample_2](https://huggingface.co/google/ncsnpp-church-256/resolve/images/main/generated_image_1.png)
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+ 3. ![sample_3](https://huggingface.co/google/ncsnpp-church-256/resolve/images/main/generated_image_2.png)
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+ 4. ![sample_4](https://huggingface.co/google/ncsnpp-church-256/resolve/images/main/generated_image_3.png)