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generation-and-api

generation-and-api

Use DALLE2-pytorch public generation and model APIs: DALLE2, diffusion prior, decoder, CLIP adapters, inpainting, latent diffusion/VQGAN, checkpoints, tokenizer, and dream CLI.

Independent GitHub snapshot · Automated classification · Original documentation governs

Vvectorspacelab / arex-skillApache-2.0

Documentation de la Skill

GitHub

name: generation-and-api description: "Use DALLE2-pytorch public generation and model APIs: DALLE2, diffusion prior, decoder, CLIP adapters, inpainting, latent diffusion/VQGAN, checkpoints, tokenizer, and dream CLI." disable-model-invocation: true metadata: disco-role: operating license: MIT

generation-and-api

Use this sub-skill when the task is to construct or call DALLE2-pytorch public Python generation APIs, diagnose model/checkpoint generation failures, or use the installed dream console command for a trained DALL-E 2 checkpoint.

Load This When

  • Building DALLE2, DiffusionPriorNetwork, DiffusionPrior, Unet, or Decoder objects from Python.
  • Choosing between OpenAIClipAdapter, OpenClipAdapter, package CLIP, precomputed embeddings, and trained checkpoints.
  • Sampling with a prior, decoder, or chained DALLE2 model, including cond_scale and prior_cond_scale.
  • Debugging decoder inpainting, cascading decoder resolution order, latent diffusion with VQGanVAE, or tokenizer length issues.
  • Checking that the installed dalle2-pytorch package can import or run a tiny CPU-safe forward-loss smoke test.

Route Elsewhere

  • JSON training launchers, config classes, trainer loops, Accelerate/DeepSpeed commands, checkpoint save/resume during training: ../training-and-configs/SKILL.md.
  • WebDataset, EmbeddingReader, shard layouts, experiment trackers, W&B/HuggingFace/S3 save/load configuration: ../data-and-tracking/SKILL.md.

Runtime Contract

  • Public package: pip install dalle2-pytorch.
  • Public import namespace: import dalle2_pytorch; top-level exports include DALLE2, DiffusionPriorNetwork, DiffusionPrior, Unet, Decoder, OpenAIClipAdapter, OpenClipAdapter, DecoderTrainer, DiffusionPriorTrainer, VQGanVAE, and CLIP.
  • Version covered by this skill: dalle2-pytorch 1.15.6.
  • Full text-to-image generation needs trained prior and decoder weights; realistic CLIP/decoder/prior sampling usually needs GPU memory. CPU is suitable for imports, CLI help, config checks, and tiny synthetic forward-loss smoke tests only.

References And Bundled Script

First Checks

python -m pip show dalle2-pytorch
python scripts/check_dalle2_runtime.py --mode imports
python scripts/check_dalle2_runtime.py --mode cli-help

For a CPU-only synthetic smoke test that does not download model weights:

python scripts/check_dalle2_runtime.py --mode tiny-forward

If any check fails, use references/troubleshooting.md before escalating to training/config or data/tracking guidance.

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