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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 dataset.

TipoOtherVersãoRevision c950b46Bnota

Fonte do modelo

Tipo
Other
Tamanho do arquivo
434 MB
Versão
vRevision c950b46
Atualizado
27 de set. de 2026
Downloads
14
Fonte
Hugging Face

Descrição da fonte

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 dataset.

Training Arguments

ArgumentValue
image_size64
train_batch_size16
eval_batch_size16
num_epochs50
gradient_accumulation_steps1
learning_rate1e-4
lr_warmup_steps500
mixed_precision"fp16"

For training code, please refer to this link.

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)
  2. DDIM Tutorial (Written in Chinese)
  3. GitHub Repo

Fontes

1 fonte
Hugging FacePrimárioVersão c950b469017adfc9597086c3b534ca345175d5a5
huggingface.co/LittleNyima/ddpm-anime-faces-64/tree/c950b469017adfc9597086c3b534ca345175d5a5

Verificado 8 de set.

Artefatos de modelo

1 artefato

diffusion_pytorch_model.safetensors

safetensors · 434 MB · SHA-256 e75355c71e36…f990 · Hugging Face

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Trechos de fonte

2 trechos

--- 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)

Source context: 14 downloads · 4 likes · Pipeline unconditional-image-generation · Library diffusers · Repo LittleNyima/ddpm-anime-faces-64