---

name: "imagen-pytorch" description: "Routes Imagen-Pytorch workflows for image and video diffusion, config-driven training, and CLI usage." disable-model-invocation: true metadata: disco-role: operating license: MIT

imagen-pytorch

Use this repo skill when a task names imagen-pytorch, Imagen, ElucidatedImagen, ImagenTrainer, Unet, Unet3D, imagen config, imagen train, imagen sample, checkpoint loading, T5 conditioning, image inpainting, or video diffusion.

Start here

• Install the published package with pip install imagen-pytorch.

• For practical generation, training, or video workflows, use a CUDA-capable PyTorch runtime. Tiny CPU checks are still useful for import and config validation.

• Minimal import smoke:

  python -c "from imagen_pytorch import Imagen, Unet, ImagenTrainer, ElucidatedImagen, Unet3D; print('imagen_pytorch ok')"
  

• If import or optional dependencies fail, run scripts/check_imagen_pytorch_env.py before deeper debugging.

• Read references/repo-provenance.md before deciding whether this skill matches the current checkout or before refreshing it.

Route map

If the task is mainly about...	Start with
Building Unet, Imagen, ElucidatedImagen, or direct sampling/training tensors	image-generation
ImagenTrainer, EMA, dataloaders, distributed training, save/load, or resume	training-and-checkpointing
imagen config, imagen train, imagen sample, config JSON, or validator logic	configuration-and-cli
Local image folders, Hugging Face rows, URL/image/text collation, T5 text conditioning, or precomputed embeddings	data-and-text-conditioning
Unet3D, text-to-video, temporal downsampling, or image/video inpainting	video-and-inpainting

Public entry points worth knowing

• imagen_pytorch.imagen_pytorch: core image diffusion models.
• imagen_pytorch.elucidated_imagen: Elucidated/Karras-style diffusion variant.
• imagen_pytorch.trainer: ImagenTrainer and checkpoint orchestration.
• imagen_pytorch.configs: Pydantic config objects.
• imagen_pytorch.data: folder and Hugging Face data helpers.
• imagen_pytorch.t5: text tokenization / encoding helpers.
• imagen_pytorch.utils: checkpoint loading helpers.
• imagen_pytorch.imagen_video: Unet3D and video-specific layers.
• Console script: imagen with config, train, and sample commands.

Use references/api-overview.md when you need a quick map from a symbol to its owning module and the correct sub-skill.

When to read the repo-level references

• references/troubleshooting.md for install/import issues, missing optional dependencies, and package-wide runtime pitfalls.
• references/api-overview.md for the verified package surface and routing hints.
• references/repo-routing-metadata.json is for managed router import; do not edit router Markdown by hand.

Practical guidance

• Choose the most specific sub-skill that matches the user's primary task, then follow the route map above for secondary concerns.
• If the task spans multiple areas, start where the user-facing action happens first. For example, a command-line configuration task starts in configuration-and-cli, then follows into training-and-checkpointing or image-generation as needed.
• Do not assume text prompts are free: raw strings can trigger T5 downloads or Hugging Face cache access. Use data-and-text-conditioning if the task needs safe precomputed text embeddings.
• Do not assume CPU smoke proves practical quality. CPU checks can validate import and control flow, but realistic image/video generation is a CUDA-scale task.