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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.

TipoOtherVersiónRevision c950b46Bcalidad

Fuente del modelo

Tipo
Other
Tamaño del archivo
434 MB
Versión
vRevision c950b46
Actualizado
27 sept 2026
Descargas
14
Fuente
Hugging Face

Descripción de la fuente

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

Fuentes

1 fuente
Hugging FacePrincipalVersión c950b469017adfc9597086c3b534ca345175d5a5
huggingface.co/LittleNyima/ddpm-anime-faces-64/tree/c950b469017adfc9597086c3b534ca345175d5a5

Verificado 8 sept

Artefactos del modelo

1 artefacto

diffusion_pytorch_model.safetensors

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

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Extractos de fuentes

2 extractos

--- 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