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name: generation-and-ranking description: "Generate images or text with DALLE-pytorch checkpoints, prime with image tokens, and rank generations with CLIP." metadata: disco-role: operating disable-model-invocation: true license: MIT

Generation and ranking

Use this sub-skill when the user asks to generate images from text, generate text completions, split multiple prompts, inspect generation checkpoint compatibility, prime with an image crop, or use CLIP to rank generated images.

Read first

• references/workflows.md for generation CLI/API recipes, prompt/output layout, cond_scale, image priming, and CLIP ranking.
• references/troubleshooting.md for checkpoint/VAE mismatches, CUDA-only helper behavior, text generation caveats, and output path issues.
• scripts/build_generate_command.py to produce a shell-safe command template for the historical helper surface.
• scripts/tiny_generation_api_smoke.py for a no-checkpoint API smoke.

Typical routes

Request	Action
"Generate images from a trained checkpoint"	Check checkpoint/VAE compatibility, build a command template, and warn that the helper calls CUDA and writes output images.
"Use generate_images in code"	Use the API recipe and tiny smoke; no source checkout is required.
"Generate multiple prompts"	Use pipe-separated prompts in the helper template or loop over tokenized prompts in API code.
"Rank samples with CLIP"	Build/bring a compatible CLIP scorer and use DALLE.generate_images(text, clip=clip) returning (images, scores).
"Text completion with DALL-E"	Use generate_texts, but note source code creates CUDA tensors internally.

Boundary notes

• Creating VAE checkpoints belongs to ../vae-training/SKILL.md.
• Creating/resuming DALL-E checkpoints belongs to ../dalle-training/SKILL.md.
• CUDA/DeepSpeed/Horovod/Docker setup belongs to ../distributed-and-backends/SKILL.md.

Validation checklist

• DALL-E checkpoint has hparams, weights, vae_params, and a compatible vae_class_name.
• Tokenizer choice at generation matches training.
• --taming and VQGAN paths are used only when the checkpoint was trained with VQGAN.
• User approved output directory writes and GPU use.
• top_k, batch_size, and num_images are chosen to fit memory.