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name: image-generation description: "Routes Lumina-T2I, Lumina-Next-T2I, mini, compositional, checkpoint-conversion, demo, and img2img/SD3 image-generation tasks." disable-model-invocation: true metadata: disco-role: operating license: MIT

Image Generation

Use this subskill for text-to-image inference and related demo/setup tasks across the Lumina image branches. It covers the lumina and lumina_next console scripts, the demo.py and sample.py entry points, compositional image generation, checkpoint conversion, and the simplified mini/img2img/SD3 inference paths.

Include here

• Lumina-T2I image inference from a checkpoint directory.
• Lumina-Next-T2I image inference, including the console lumina_next infer route.
• Checkpoint conversion between .pth and .safetensors for image models.
• Lumina-Next-T2I-Mini inference, img2img, and SD3 image generation.
• Compositional image generation with multiple captions per region.
• Demo launch and prompt/resolution configuration for image generation.

Exclude or route elsewhere

• Training, finetuning, or DreamBooth-style adaptation: use image-training.
• Audio or music generation: use audio-music.
• Visual anagrams and illusion animation: use visual-anagrams.
• ImageNet benchmark training: use imagenet-training.

Read first

• references/workflows.md for the supported image-generation commands and checkpoint layouts.
• references/troubleshooting.md for the shared image backend and checkpoint failures.
• scripts/check_checkpoints.py before launching inference if the checkpoint tree is uncertain.

Fast routing hints

• If the user says lumina infer or lumina convert, stay in this subskill.
• If the user says lumina_next infer, sample.py, img2img, compositional generation, or SD3, stay in this subskill.
• If the user asks about training, resume, torchrun, or JourneyDB, hand off to image-training.