---

name: class-conditional description: "Router for LlamaGen class-conditional ImageNet generation, training, serving, packaging, and evaluation." metadata: disco-role: operating disable-model-invocation: true license: MIT

class-conditional

Use this sub-skill for LlamaGen class-conditional ImageNet workflows that do not require tokenizer training or text conditioning. The bundled wrappers pin --gpt-type c2i; use the text-conditional sub-skill for t2i.

In scope

• DDP class-conditional training and resume flows.
• FSDP class-conditional training and resume flows.
• Single-process and DDP sampling.
• vLLM serving for class-conditional checkpoints.
• Packaging sample folders into .npz batches for evaluation.
• c2i evaluation against ImageNet reference batches.
• Model-family and checkpoint-format guidance for c2i checkpoints.

Route elsewhere

• Tokenizer training or reconstruction -> tokenizers
• Preprocessing requests, including discrete-code extraction or T5 extraction -> data-preparation
• Text-conditional workflows -> text-conditional
• Publish / upload scripts -> excluded

Start here

• references/workflows.md for workflow selection and checkpoint shape notes.
• references/cli-reference.md for exact wrapper flags and env vars.
• references/serving.md for vLLM path, model-id, and memory notes.
• references/evaluation.md for .npz layout and packaging expectations.
• references/troubleshooting.md for DDP, FSDP, vLLM, and evaluator failures.

Bundled scripts

• scripts/train_c2i.sh
• scripts/train_c2i_fsdp.sh
• scripts/sample_c2i.sh
• scripts/sample_c2i_ddp.sh
• scripts/sample_c2i_ddp_pack_npz.py
• scripts/serve_c2i_vllm.sh
• scripts/evaluate_c2i.sh

Checkpoint and model notes

• DDP training saves checkpoints with model, optimizer, steps, args, and optional ema.
• FSDP resume expects a directory with consolidated.pth, optimizer.*.pth, and resume_step.txt.
• Sampling and serving can read model, module, or state_dict checkpoints; raw FSDP weights need --from-fsdp.
• The vLLM path is wired for the class-conditional fake JSON configs only.

Troubleshooting priorities

• Fix CUDA / NCCL / world-size mismatches first.
• Check checkpoint key shape or FSDP world-size compatibility next.
• Then check vLLM model-id paths, GPU memory sizing, and evaluator .npz inputs.
• Treat app.py as reference-only because it loads checkpoints at import time.