anime-faces-ddpm

A Denoising Diffusion Probabilistic Model (DDPM) trained to generate anime faces using this example as a basis.

image/png

Model Description

This diffusion model is trained with the 🤗 Diffusers library on the huggan/anime-faces dataset.

How To Use

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.

Downloads last month
3
Inference API
Unable to determine this model’s pipeline type. Check the docs .

Dataset used to train sweetfelinity/anime-faces-ddpm