---

name: data-and-text-conditioning description: "Prepare Imagen-Pytorch image folders, Hugging Face dataset items, collators, and T5 or precomputed text conditioning inputs." disable-model-invocation: true metadata: disco-role: operating license: MIT

Data and Text Conditioning

Use this sub-skill for the preprocessing path that turns local image collections or Hugging Face rows into Imagen-ready batches and text-conditioning inputs.

Owns

• Local image-folder validation for unconditional training.
• Dataset(folder, image_size, exts, convert_image_to_type) and get_images_dataloader(...).
• Collator(image_size, url_label, text_label, image_label, name, channels) and its URL/image/text row handling.
• Raw texts, text_embeds, text_masks, and image-channel conversion for Imagen / ImagenTrainer.
• Shape and mode checks for precomputed text conditioning.

Route elsewhere

• Training loop, optimizers, EMA, checkpoints, and train_step: ../training-and-checkpointing/SKILL.md
• Config files and CLI flags: ../configuration-and-cli/SKILL.md
• Image generation and sampling APIs: ../image-generation/SKILL.md
• Video and inpainting sampling APIs: ../video-and-inpainting/SKILL.md

Short workflow

1. Identify the input path: local folder, HF row schema, or precomputed text tensors.
2. Check the folder or tensor metadata with the bundled scripts:
   • scripts/check_image_folder.py
   • scripts/text_embedding_shape_check.py
3. For local folders, use Dataset(...) or get_images_dataloader(...) to produce image-only batches.
4. For HF rows, use Collator(...) to produce image tensors and encoded text tensors.
5. For text conditioning, choose raw texts or precomputed text_embeds / text_masks and verify the shapes before training.
6. Hand the validated batches to the training or sampling sub-skill.

Key contracts

• The local-folder path is image-only.
• The collator path can download URLs, skip failing rows, and return None if every row in a batch fails.
• Trainer dataloaders are consumed by keyword order, so tuple layout must match the configured output names.
• T5 text handling truncates at 256 tokens and uses the configured T5 model name.
• Channel conversion must match the requested channels value and the input image modes.

References

• references/data-formats.md
• references/text-conditioning.md
• references/troubleshooting.md