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name: imagenet-training description: "Routes Flag-DiT, Next-DiT, and Next-DiT-MoE ImageNet training and sampling tasks." disable-model-invocation: true metadata: disco-role: operating license: MIT

ImageNet Training

Use this subskill for the benchmark branches that train or sample on ImageNet. It covers the Flag-DiT, Next-DiT, and Next-DiT-MoE directories, including the single-node and Slurm launch scripts.

Include here

• ImageNet folder-layout preparation.
• Flag-DiT, Next-DiT, and Next-DiT-MoE training launches.
• Sampling / evaluation routes in the Next-DiT benchmark branches.
• Stage script editing for the local ImageNet root.
• Distributed launcher selection (torchrun, srun, Slurm wrappers).

Exclude or route elsewhere

• Lumina text-to-image training: use image-training.
• Inference or checkpoint conversion for the Lumina image models: use image-generation.
• Audio/music demos: use audio-music.
• Visual anagrams: use visual-anagrams.

Read first

• references/workflows.md for the benchmark launch patterns.
• references/data-layout.md for the ImageNet directory shape and train_data_root editing.
• references/troubleshooting.md for GPU-count, FlashAttention, and checkpoint issues.
• scripts/check_imagenet_layout.py before a long run if the dataset layout is uncertain.

Fast routing hints

• If the user says ImageNet, FSDP, run_8gpus.sh, slurm, or class_labels, use this subskill.