README.md

---
datasets:
- huggan/anime-faces
language:
- en
library_name: diffusers
tags:
- anime
- generative
---

# anime-faces-ddpm
A Denoising Diffusion Probabilistic Model (DDPM) trained to generate anime faces using [this example as a basis.](https://github.com/huggingface/diffusers/tree/main/examples/unconditional_image_generation)

![image/png](https://cdn-uploads.huggingface.co/production/uploads/65e6420da97628ed6ada1cb8/wM8CUMRQRoOV0FucyRpIi.png)

## Model Description

This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library 
on the `huggan/anime-faces` dataset.

## How To Use

```python
from diffusers import DDPMPipeline

checkpoint = "sweetfelinity/anime-faces-ddpm"
pipeline = DDPMPipeline.from_pretrained(checkpoint)
pipeline = pipeline.to("cuda") # or "cpu"

for i in range(10):
    image = pipeline().images[0]
    image.save(str(i + 1) + ".png")
```

## Training Hyperparameters

The following hyperparameters were used during training:
- resolution=64
- train_batch_size=16
- num_epochs=30
- gradient_accumulation_steps=1
- learning_rate=1e-4
- lr_warmup_steps=500
- mixed_precision=fp16
- checkpointing_steps=2000
- save_images_epochs=4
- use_ema
- adam_weight_decay=1e-6
- lr_scheduler=linear
- eval_batch_size=32

## Training Results
See model folder /generated-images for 100 images created by the DDPM.