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--- |
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language: en |
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license: apache-2.0 |
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library_name: diffusers |
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tags: [] |
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datasets: Qilex/private_guys |
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metrics: [] |
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--- |
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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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The following hyperparameters were used during training: |
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- learning_rate: 0.0001 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- gradient_accumulation_steps: 1 |
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- optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None |
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- lr_scheduler: None |
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- lr_warmup_steps: 500 |
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- ema_inv_gamma: None |
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- ema_inv_gamma: None |
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- ema_inv_gamma: None |
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- mixed_precision: no |
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### Training results |
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📈 [TensorBoard logs](https://huggingface.co/Qilex/VirtualPetDiffusion2/tensorboard?#scalars) |
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