IDDM v1.2.3

What's Changed

• feat: add new sample dpm2 by @RachelElizaUK in #159
• feat: Update samples by @chairc in #160
• refactor: Refactor dpm2 and add sample loop fn. by @RachelElizaUK in #161
• feat: Add dpmpp sample. by @RachelElizaUK in #162
• chore: Update the get_activation_function. by @chairc in #163
• feat: Update PR to support multiple functions. by @chairc in #164
• feat(docker): Add docker file running test. by @chairc in #165
• docs: Update next steps. by @chairc in #166
• chore: Update the version[v1.2.3]. by @chairc in #167

Full Changelog: v1.2.2...v1.2.3

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)