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metrics: []
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<!-- This model card has been generated automatically according to the information the training script had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# VirtualPetDiffusion2
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## Model description
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This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library
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on
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## Intended uses & limitations
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#### How to use
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```python
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```
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#### Limitations and bias
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[TODO: provide examples of latent issues and potential remediations]
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## Training data
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### Training hyperparameters
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metrics: []
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# VirtualPetDiffusion2
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## Model description
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This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library
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on a dataset of roughly 8,000 virtual pet thumbnail images.
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## Intended uses & limitations
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This model can be used to generate small (128x128) virtual pet-like thumbnails.
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The pets are generally somewhat abstract.
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#### How to use
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```python
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from diffusers import DiffusionPipeline
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pipeline = DiffusionPipeline.from_pretrained("Qilex/VirtualPetDiffusion2")
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image = pipeline()["sample"][0]
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#this line only works in jupyter
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display(image)
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```
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## Training data
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This model was trained on roughly 8,000 virtual pet thumbnail images (80x80px).
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The data was randomly flipped, rotated, and perspected using torchvision transforms to prevent some of the issues from the first VirtualPetDiffusion.
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### Training hyperparameters
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