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name: image-generation description: "Construct and use imagen-pytorch image diffusion models for text-conditioned, unconditional, super-resolution, ElucidatedImagen, sampling, and tiny smoke workflows." disable-model-invocation: true metadata: disco-role: operating license: MIT

image-generation

Use this sub-skill when a task asks for image-only imagen-pytorch model construction or direct image diffusion calls with Unet, NullUnet, BaseUnet64, SRUnet256, SRUnet1024, Imagen, or ElucidatedImagen.

Use when

• Building text-to-image, unconditional, cascade, or super-resolution-only Imagen models.
• Choosing condition_on_text, cond_drop_prob, cond_scale, timesteps, noise_schedules, pred_objectives, lowres_sample_noise_level, or Karras / ElucidatedImagen sampling hyperparameters.
• Calling Imagen.forward, ElucidatedImagen.forward, .sample(...), return_pil_images, return_all_unet_outputs, start_at_unet_number, stop_at_unet_number, start_image_or_video, or image inpainting arguments.
• Running a safe no-network API smoke with synthetic tensors.

Route elsewhere

• Trainer state, optimizers, dataloaders, EMA, checkpoint save/load, and resume flows: training-and-checkpointing.
• Config classes, JSON config files, and imagen CLI commands: configuration-and-cli.
• Folder datasets, Hugging Face datasets, T5 helpers, text tokenization, and precomputing text embeddings: data-and-text-conditioning.
• Unet3D, video tensor dimensions, temporal conditioning, and video inpainting: video-and-inpainting.

Quick workflow

1. Pick the conditioning mode first:
   • Unconditional: set Imagen(..., condition_on_text=False) / ElucidatedImagen(..., condition_on_text=False) and do not pass texts or text_embeds.
   • Text-conditioned without network surprises: pass precomputed text_embeds (and optional text_masks) whose final dimension matches text_embed_dim.
   • Text strings: expect the package's T5 path to be used; route text preparation details to data-and-text-conditioning.
2. Match cascade length: len(unets) must equal len(image_sizes). The first unet is base generation; every later unet is automatically cast as low-resolution-conditioned.
3. For cascade training, always provide unet_number; train NullUnet stages never.
4. For upscaler-only sampling, pass both start_at_unet_number > 1 and a low-resolution start_image_or_video.
5. Treat CPU as API smoke only. Realistic generation or training is practical CUDA-scale; quality was not proven by the construction smoke.

References and helper

• API reference lists public image API signatures, tensor contracts, defaults, and important assertions.
• Workflows gives task recipes for unconditional smoke, precomputed text embeddings, super-resolution-only branches, ElucidatedImagen, and output modes.
• Troubleshooting maps common assertion messages and runtime symptoms to fixes.
• tiny_image_smoke.py runs a short no-network unconditional API check with optional sampling.