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name: min-dalle description: "Use the min(DALL·E) Python package for text-to-image generation, model asset/runtime setup, CLI-style requests, notebook/UI workflows, and Replicate/Cog deployment troubleshooting." disable-model-invocation: true metadata: disco-role: operating license: MIT

min(DALL·E)

Use this repo skill when a task names min-dalle, min_dalle, min(DALL·E), DALL·E Mini/Mega inference in PyTorch, or asks for lightweight text-to-image generation code with MinDalle. This skill is for operating the package as a library or interface; it is not a training, model-conversion, or upstream Flax/JAX maintenance guide.

Start safely

Install the public package and verify importability before running any generation that might download weights:

python -m pip install min-dalle
python - <<'PY'
from min_dalle import MinDalle
print(MinDalle)
PY

For a no-download API/backend inspection, run the bundled helper:

python scripts/inspect_min_dalle_api.py

Read references/troubleshooting.md when installation, imports, PyTorch, Pillow, emoji, or model-cache setup fails. Read references/repo-provenance.md before deciding whether this skill is stale for a newer checkout.

Route by task

• Use sub-skills/text-to-image-generation/SKILL.md to write or debug Python MinDalle generation calls: generate_image, generate_images, stream APIs, tensor/PIL conversion, seeds, grid_size, temperature, top_k, supercondition_factor, seamless mode, is_mega, is_reusable, device, and dtype.
• Use sub-skills/model-assets-and-runtime/SKILL.md to plan or troubleshoot models_root, tokenizer/weight downloads, cache layout, Mega versus Mini constants, CPU/CUDA/dtype choices, tokenizer normalization, and internal tensor shapes without unnecessary downloads.
• Use sub-skills/deployment-and-interfaces/SKILL.md for command-line style requests, the safe CLI template, notebook/Colab progressive display, Tkinter UI behavior, Replicate/Cog predictor inputs, and deployment-specific failures.

Common choices

• Start with device="cpu", dtype=torch.float32, is_mega=False, and grid_size=1 only for the smallest compatibility check; full generation can still be slow and may download model assets.
• Use CUDA for practical Mega generation when available and verified. dtype=torch.float16 can reduce GPU memory; torch.bfloat16 is only appropriate on capable hardware such as Ampere-class CUDA.
• Use is_reusable=True for repeated prompts when memory permits. Use is_reusable=False for one-shot runs under memory pressure.
• Pass grid_size as a keyword argument for tensor batch APIs because the implementation reads kwargs["grid_size"] while reshaping.

Bundled scripts

• scripts/inspect_min_dalle_api.py prints installed distribution/dependency versions, MinDalle signatures, and optional CUDA visibility without constructing a model.
• sub-skills/text-to-image-generation/scripts/generation_request_template.py plans Python API generation calls and requires --run before model construction or downloads.
• sub-skills/model-assets-and-runtime/scripts/tokenizer_smoke.py checks tokenizer normalization with a synthetic vocabulary and no network.
• sub-skills/deployment-and-interfaces/scripts/min_dalle_cli_template.py is a safe replacement for the repo's CLI-style behavior, defaulting to dry run.
• sub-skills/deployment-and-interfaces/scripts/replicate_filename_sanitize.py previews Replicate-style output basenames without Cog or model loading.

Boundaries

This skill intentionally does not bundle model weights, example images, notebooks, or deployment images. Full image generation may require network access to model assets, significant disk space, substantial CPU/GPU memory, and runtime approval. Use the dry-run and inspection helpers before any side-effectful call.