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name: dalle-pytorch description: "Operate lucidrains DALLE-pytorch for DALL-E style VAE training, transformer training, generation, CLIP ranking, and backend troubleshooting." metadata: disco-role: operating disable-model-invocation: true license: MIT

DALLE-pytorch operating skill

Use this skill when the task is about the dalle-pytorch / DALLE-pytorch package: DALL-E style text-to-image transformer modeling, discrete VAE codebooks, CLIP ranking, image/text training data, generation checkpoints, sparse attention, DeepSpeed, Horovod, Apex, or the package's training/generation helper surfaces.

First checks

1. Confirm the user wants DALLE-pytorch, not DALL-E 2 or a diffusion package.
2. Check whether they are using a pip install, a local checkout, or only asking for API guidance. The published package exports DiscreteVAE, DALLE, CLIP, OpenAIDiscreteVAE, and VQGanVAE; the historical training/generation helpers were top-level repo scripts, not package console entry points.
3. Run the bundled install/API smoke when behavior matters:

python scripts/check_dalle_pytorch_install.py

Read references/api-reference.md for public signatures and object relationships. Read references/troubleshooting.md before fixing install/import, torch, VAE download, checkpoint, or backend failures.

Route by task

User task	Read next	Why
Train or debug a discrete VAE, choose OpenAI/VQGAN/custom VAE, inspect VAE checkpoint contents, or create a VAE command	sub-skills/vae-training/SKILL.md	Owns DiscreteVAE, VAE checkpoints, image-folder training, VAE alternatives, and VAE-specific failures.
Train/resume the DALL-E transformer, validate image-text folders, use WebDataset shards, choose tokenizer flags, or understand DALL-E checkpoint payloads	sub-skills/dalle-training/SKILL.md	Owns transformer training, data layout, tokenizer choices, checkpoint/resume paths, and data validators.
Generate images/text, split prompts, prime from an image crop, use CLIP ranking, or debug generation checkpoints	sub-skills/generation-and-ranking/SKILL.md	Owns generate_images, generate_texts, CLIP, prompt/output layout, and generation troubleshooting.
Decide on DeepSpeed/Horovod/Apex/Docker/CUDA, sparse attention, --distributed_backend, or optional backend installation	sub-skills/distributed-and-backends/SKILL.md	Owns distributed wrappers, backend availability checks, attention variants, Docker and source-build caveats.

Safe operating boundaries

• Treat full training and generation as GPU/side-effect workflows. The original helpers call .cuda(), write checkpoints or outputs, and may log to W&B. Use bundled command builders and validators first; run long jobs only after the user approves compute, data, checkpoint, and logging side effects.
• Do not instantiate OpenAIDiscreteVAE blindly on modern torch. The source asserts torch <= 1.10; with newer torch the class fails before downloading weights.
• Do not trigger default OpenAI VAE or VQGAN downloads unless the user wants model-cache/network side effects.
• DeepSpeed sparse attention, Apex AMP, Horovod, and Docker GPU builds require machine-specific CUDA/toolchain decisions. Route those to distributed-and-backends instead of installing broad extras.
• When a user has only a pip install, prefer API recipes and bundled validators. When they have a checkout containing the historical scripts, bundled command builders can produce matching commands without requiring this skill to link back to source files.

Quick package smoke

Use this for a fast sanity check:

python scripts/check_dalle_pytorch_install.py --include-cuda

Expected high-level signal:

• package metadata and dalle_pytorch import succeed;
• DiscreteVAE, DALLE, CLIP, tokenizer, and data helper signatures are visible;
• a tiny CPU DiscreteVAE/DALLE/CLIP smoke passes;
• optional CUDA availability is reported without running training.

Key facts to remember

• Distribution name: dalle-pytorch; import root: dalle_pytorch; version baseline: 1.6.6.
• DiscreteVAE can be trained directly or loaded as a checkpoint payload containing hparams and weights.
• DALLE freezes the VAE, predicts text and image tokens, and can generate images autoregressively with optional classifier-free cond_scale.
• Image-text folder training pairs image files and .txt files by matching stem recursively; each text file may contain multiple newline-separated captions.
• WebDataset training expects --wds <image_key>,<caption_key> and an image-text folder argument that points to a tar, shard folder, URL brace pattern, or GCS path.
• CLI helpers are checkout scripts. This skill bundles safe command builders and API smokes instead of copying long CUDA/W&B training scripts.

References

• references/api-reference.md: public signatures, outputs, and model relationships.
• references/troubleshooting.md: cross-cutting install/import/backend/checkpoint issues.
• references/repo-provenance.md: source commit and evidence paths for refresh decisions.
• references/repo-routing-metadata.json: structured router metadata for managed imports.