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データ・研究 · SKILL.md

custom-diffusion

custom-diffusion

Route Custom Diffusion workflows for concept data prep, diffusers training and SDXL, sample generation, delta tools, and CustomConcept101 evaluation.

Independent GitHub snapshot · Automated classification · Original documentation governs

スキルの説明

GitHub

name: custom-diffusion description: "Route Custom Diffusion workflows for concept data prep, diffusers training and SDXL, sample generation, delta tools, and CustomConcept101 evaluation." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

Custom Diffusion

Use this repo skill for the diffusers-side Custom Diffusion workflow family:

  • prepare concept data and real-prior bundles
  • train single- and multi-concept models with diffusers or SDXL
  • sample from delta checkpoints
  • extract, compress, and compose delta artifacts
  • evaluate generated samples on CustomConcept101

Start here

  1. Read references/repo-provenance.md to confirm the source baseline.
  2. Read references/workflows.md for the route map.
  3. Run scripts/check_runtime.py --require-cuda to confirm the CUDA/import stack.
  4. Use scripts/validate_concepts.py when a concept-list JSON needs a structural check.
  5. Open the focused sub-skill for the task family you have.

Routes

data-preparation

Use when you need instance images, real-prior image lists, caption files, or concept JSON for training. Start with sub-skills/data-preparation/SKILL.md.

training

Use when you need diffusers training, SDXL training, prior preservation, modifier tokens, or checkpoint output planning. Start with sub-skills/training/SKILL.md.

inference

Use when you need to load a Custom Diffusion delta and generate samples from prompts or prompt files. Start with sub-skills/inference/SKILL.md.

checkpoint-tools

Use when you need delta extraction, compression, or composition. Start with sub-skills/checkpoint-tools/SKILL.md.

benchmarking

Use when you need to score generated images with CustomConcept101 CLIP/DINO evaluation. Start with sub-skills/benchmarking/SKILL.md.

Install and runtime notes

  • Use Python 3.11 or newer.
  • Install a CUDA-enabled PyTorch/torchvision pair before the diffusers workflows.
  • Verified public stack: diffusers==0.21.4, accelerate==0.24.1, transformers==4.31.0, clip-retrieval==2.45.0, pandas, scipy, scikit-learn, tqdm.
  • Optional workflow extras: xformers, bitsandbytes, deepspeed, modelcards.
  • The source training, sampling, compression, composition, and evaluation flows are CUDA-backed; the delta extraction route is the only selected CPU-substitutable workflow.
  • Legacy checkout-based Stable Diffusion scripts are treated as reference-only or excluded in this generated skill.

Minimal smoke check

After installing the stack, run:

python scripts/check_runtime.py --require-cuda

If you are only checking importability, omit --require-cuda.

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