File size: 1,506 Bytes
3e86d6b c950b46 a651084 c950b46 a651084 c950b46 a651084 c950b46 a651084 c950b46 a651084 c950b46 a651084 c950b46 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 |
---
license: mit
datasets:
- huggan/anime-faces
pipeline_tag: unconditional-image-generation
tags:
- art
---
# ddpm-anime-faces-64
**ddpm-anime-faces-64** is an educational project for introducing the training and sampling processes of DDPM and DDIM models. The model is trained on [huggan/anime-faces](https://huggingface.co/datasets/huggan/anime-faces) dataset.
## Training Arguments
| Argument | Value |
| :-------------------------: | :----: |
| image_size | 64 |
| train_batch_size | 16 |
| eval_batch_size | 16 |
| num_epochs | 50 |
| gradient_accumulation_steps | 1 |
| learning_rate | 1e-4 |
| lr_warmup_steps | 500 |
| mixed_precision | "fp16" |
For training code, please refer to [this link](https://github.com/LittleNyima/code-snippets/blob/master/ddpm-tutorial/ddpm_training.py).
## Inference
This project aims to implement DDPM from scratch, so `DDPMScheduler` is not used. Instead, I use only `UNet2DModel` and implement a simple scheduler myself.
Please refer to `sampling_ddpm.py` and `sampling_ddim.py` for detailed usages.
## References
1. [DDPM Tutorial (Written in Chinese)](https://littlenyima.github.io/posts/13-denoising-diffusion-probabilistic-models/)
2. [DDIM Tutorial (Written in Chinese)](https://littlenyima.github.io/posts/14-denoising-diffusion-implicit-models/)
3. [GitHub Repo](https://github.com/LittleNyima/code-snippets) |