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name: llamagen description: "Router for LlamaGen autoregressive image-generation workflows, including tokenizers, data preparation, class-conditional generation, and text-conditional generation." metadata: disco-role: operating disable-model-invocation: true license: MIT

LlamaGen

Use this skill when the user asks about the LlamaGen repository, its released checkpoints, or its image-generation workflows.

What this skill owns

• Tokenizer family workflows: VQ, VQGAN, Stable Diffusion VAE, and Consistency Decoder training or reconstruction.
• Preprocessing workflows: discrete-code extraction, T5 feature extraction, OpenImages manifests, and dataset cache layout.
• Class-conditional image generation: c2i training, sampling, serving, and c2i evaluation.
• Text-conditional image generation: t2i stage-1 / stage-2 training, prompt sampling, and t2i evaluation.

When to route where

• Tokenizer training, finetuning, reconstruction, or code/image round-trip checks -> sub-skills/tokenizers/
• Code/T5 cache generation, OpenImages manifests, or dataset-layout questions -> sub-skills/data-preparation/
• ImageNet class-conditional generation, serving, packaging, or c2i evaluation -> sub-skills/class-conditional/
• Caption-conditioned training, sampling, or t2i evaluation -> sub-skills/text-conditional/

Before you start

• Read references/repo-provenance.md to confirm the checkout snapshot.
• Read references/troubleshooting.md when the request mentions installs, imports, CUDA, checkpoints, caches, or evaluation dependencies.
• Run scripts/check_env.py when you need a quick import and backend smoke before a larger workflow.

Setup and smoke

• Install the repo baseline with python -m pip install -r requirements.txt.
• Install any workflow-specific extras called out by the nearest sub-skill references before sampling, serving, or evaluation.
• Minimal import smoke: python scripts/check_env.py --repo-root . --skip-cuda.
• CUDA smoke on a GPU host: python scripts/check_env.py --repo-root . --with-serving --with-eval --with-gradio.

Root guidance

This root skill is a router, not a manual. Read the nearest sub-skill for concrete commands, argument conventions, cache layouts, and troubleshooting.

Typical selection cues

• VQ / VQGAN / VAE / Consistency Decoder -> tokenizer sub-skill.
• ImageNet code caches or FLAN-T5 features -> data-preparation sub-skill.
• train_c2i, sample_c2i, serve_c2i, eval c2i -> class-conditional sub-skill.
• train_t2i, sample_t2i, evaluate t2i -> text-conditional sub-skill.

Cross-cutting runtime notes

• The repo is source-driven; future agents should rely on the bundled references and scripts rather than the original checkout layout.
• Core workflows are CUDA-oriented. If torch.cuda.is_available() is false, the training / generation routes are not ready.
• Evaluation paths depend on extra packages such as TensorFlow, CLIP, and clean-fid; use the bundled troubleshooting notes before assuming a workflow is broken.
• app.py is reference-only and should not be treated as a safe import-time runtime helper.

Useful bundled entry points

• scripts/check_env.py for a quick import / CUDA smoke.
• references/repo-routing-metadata.json for router placement metadata.
• references/troubleshooting.md for cross-cutting install and runtime failures.

How to think about requests

1. Identify whether the user is asking about tokenizers, preprocessing, c2i, or t2i.
2. Route to the matching sub-skill.
3. Use the sub-skill references for exact flags, layouts, and failure modes.
4. Fall back to the root troubleshooting notes only for cross-cutting issues.