IDDM v1.2.0

What's Changed

• docs: Add the README in weights. by @chairc in #144
• Refactor model samples and trainer by @chairc in #146
• feat: Add Latent Diffusion Models, Support generate 512*512 images and reduce GPU memory usage. by @chairc in #147
• Update some tiny thing. by @chairc in #148
• Update pip and download link by @chairc in #149

Full Changelog: v1.1.9...v1.2.0

Weights

Note: The weight include model, ema_model and optimizer.

Diffusion Models

• celebahq-120-weight.pt: Trained on a dataset of 30,000 people face, and image size is 120 (celebahq-120-weight.pt)
• animate-ganyu-120-weight.pt: Trained on a dataset of 500 animate ganyu face, and image size is 120 (animate-ganyu-120-weight.pt)
• neu-cls-64-weight.pt: Trained on a dataset of 7,226 defect, and image size is 64 (neu-cls-64-weight.pt)
• neu-120-weight.pt: Trained on a dataset of 1,800 defect, and image size is 120 (neu-120-weight.pt)
• cifar-64-weight.pt: Trained on a dataset of 60,000 images, and image size is 64 (cifar10-64-weight.pt)
• animate-face-64-weight.pt: Trained on a dataset of 63,565 animate face, and image size is 64 (animate-face-64-weight.pt)

Autoencoder Models

• neu-autoencoder-512-weight.pt: Trained on a dataset of 1,800 defect, and image size is 512 (neu-autoencoder-512-weight.pt)

Latent Diffusion Models

• neu-64-latent-weight.pt: Trained on a dataset of 1,800 defect, and image size is 64 (neu-64-latent-weight.pt)