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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.

ArtOtherVersionRevision c950b46BBewertung

Modellquelle

Art
Other
Dateigröße
434 MB
Version
vRevision c950b46
Aktualisiert
27. Sept. 2026
Downloads
14
Quelle
Hugging Face

Quellenbeschreibung

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

Quellen

1 Quelle
Hugging FacePrimärVersion c950b469017adfc9597086c3b534ca345175d5a5
huggingface.co/LittleNyima/ddpm-anime-faces-64/tree/c950b469017adfc9597086c3b534ca345175d5a5

Verifiziert 8. Sept.

Modellartefakte

1 Artefakt

diffusion_pytorch_model.safetensors

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

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Quellenauszüge

2 Auszüge

--- 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