readme: ---
  language: en
  license: apache-2.0
  library_name: diffusers
  tags: []
  datasets: Qilex/private_guys
  metrics: []
  ---
  
  
  # VirtualPetDiffusion2
  
  ## Model description
  
  This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library 
  on a dataset of roughly 8,000 virtual pet thumbnail images.
  
  ## Intended uses & limitations
  
  This model can be used to generate small (128x128) virtual pet-like thumbnails.
  The pets are generally somewhat abstract.
  
  #### How to use
  
  ```python
  from diffusers import DiffusionPipeline
  pipeline = DiffusionPipeline.from_pretrained("Qilex/VirtualPetDiffusion2")
  image = pipeline()["sample"][0]
  #this line only works in jupyter
  display(image)
  ```
  
  ## Training data
  
  This model was trained on roughly 8,000 virtual pet thumbnail images (80x80px).
  The data was randomly flipped, rotated, and perspected using torchvision transforms to prevent some of the issues from the first VirtualPetDiffusion.
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 0.0001
  - train_batch_size: 16
  - eval_batch_size: 16
  - gradient_accumulation_steps: 1
  - optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None
  - lr_scheduler: None
  - lr_warmup_steps: 500
  - ema_inv_gamma: None
  - ema_inv_gamma: None
  - ema_inv_gamma: None
  - mixed_precision: no
  
  ### Training results
  
  📈 [TensorBoard logs](https://huggingface.co/Qilex/VirtualPetDiffusion2/tensorboard?#scalars)
  
  