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---
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)
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