---

name: text-conditional description: "Router for LlamaGen caption-conditioned training, sampling, prompt batches, and t2i evaluation." metadata: disco-role: operating disable-model-invocation: true license: MIT

Text-Conditional

Use this sub-skill for the text-conditioned LlamaGen path: stage-1 and stage-2 training, prompt sampling, and evaluation of caption-conditioned generations.

Owns

• Stage-1 and stage-2 t2i training.
• COCO and Parti prompt sampling.
• T5 embedder behavior, padding, and cache layout for the text-conditioning path.
• t2i evaluation inputs, outputs, and packaging expectations.
• Checkpoint / precision / --from-fsdp guidance for the text-conditioned path.

Routes out

• Tokenizer training, finetuning, reconstruction, and code/image round trips -> tokenizers
• Precomputed code or T5 feature extraction, OpenImages manifests, and cache prep -> data-preparation
• ImageNet class-conditional generation, serving, and c2i evaluation -> class-conditional
• Remote publishing or upload helpers -> excluded

Best entry points

• Training: scripts/train_t2i_stage1.sh, scripts/train_t2i_stage2.sh
• Sampling: scripts/sample_t2i_coco.sh, scripts/sample_t2i_parti.sh
• Evaluation: scripts/evaluate_t2i.sh
• T5 behavior: references/t5.md
• Prompt / batch layout: references/evaluation.md
• Workflow selection: references/workflows.md

Read before answering

• references/workflows.md
• references/cli-reference.md
• references/t5.md
• references/evaluation.md
• references/troubleshooting.md

Fast routing rules

• If the user asks to create or refresh text features, route to data-preparation.
• If the user asks for VQ, VQGAN, VAE, or Consistency Decoder reconstruction, route to tokenizers.
• If the user asks for ImageNet c2i training, sampling, serving, or c2i evaluation, route to class-conditional.
• If the user asks about t2i only, stay here and use the bundled wrappers.
• Keep --no-left-padding, prompt CSV/TSV formatting, and evaluation folder layout in mind before running a job.