IDDM v1.2.1

What's Changed

• docs: Update the VAE name. by @chairc in #150
• feat: Integrate the latent diffusion. by @chairc in #151
• chore: Remove redundant latent diffusion models code. by @chairc in #152
• Update deploy. by @chairc in #153
• chore: Update the pip version[v1.2.1]. by @chairc in #154

Full Changelog: v1.2.0...v1.2.1

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)
• voc-autoencoder-512-weight.pt: Trained on a dataset of 33,240 images, and image size is 512 (voc-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)