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name: image-diffusion description: "Operate 2D image DDPM/DDIM workflows with denoising-diffusion-pytorch Unet, GaussianDiffusion, Dataset, Trainer, FID, and RePaint guidance." disable-model-invocation: true metadata: disco-role: operating license: MIT

Image Diffusion

Use this sub-skill for 2D image diffusion tasks with denoising-diffusion-pytorch (denoising_diffusion_pytorch import), inspected at version 2.3.1.

Route here for

• Building a 2D Unet and GaussianDiffusion for DDPM or DDIM image loss and sampling.
• Adapting the README-style image tensor recipe into a tiny CPU or CUDA smoke check.
• Using the folder Dataset and Trainer for image-folder training.
• Explaining calculate_fid, FIDEvaluation, and save_best_and_latest_only behavior.
• Using the RePaint module for mask + ground-truth image inpainting / resampling guidance.
• Debugging image shape, channel, schedule, sampling, Trainer, FID, or optional flash-attention failures.

Route elsewhere

• 1D sequence APIs (Unet1D, GaussianDiffusion1D, Dataset1D, Trainer1D) -> ../sequence-diffusion/SKILL.md.
• Classifier-free guidance, external classifier guidance, and XMWrapper -> ../conditioning-guidance/SKILL.md.
• Karras, continuous-time, EDM, simple diffusion, learned variance, weighted objective, and other advanced variants -> ../advanced-variants/SKILL.md.

Operating map

1. For import paths, constructor contracts, parameter constraints, and FID/RePaint API notes, read references/api-reference.md.
2. For safe setup, image loss/sample smoke, Trainer setup, DDPM vs DDIM sampling, interpolation, FID, and RePaint workflows, read references/workflows.md.
3. For common failure diagnosis, read references/troubleshooting.md.
4. To verify an installed package without training or downloads, run scripts/smoke_image_diffusion.py from any working directory after installing the package.

Safe defaults

Prefer tiny smoke settings before any real training: dim=8, dim_mults=(1,), channels=1, image_size=8, timesteps=8, sampling_timesteps=4, beta_schedule='sigmoid', flash_attn=False, and random tensors in [0, 1]. These defaults exercise loss and DDIM sampling without training or dataset access.