跳到正文
cccskills
免费在 GitHub 公开

custom-diffusion

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

中文简介正在准备中。当前显示原文说明。

查看安装方法

包含的文件(34)

  • SKILL.md2.8 KB
  • references/repo-provenance.md1.5 KB
  • references/repo-routing-metadata.json432 B
  • references/troubleshooting.md2.7 KB
  • references/workflows.md3.4 KB
  • scripts/check_runtime.py2.3 KB
  • scripts/validate_concepts.py2.9 KB
  • sub-skills/benchmarking/references/data-formats.md2.2 KB
  • sub-skills/benchmarking/references/troubleshooting.md826 B
  • sub-skills/benchmarking/references/workflows.md1013 B
  • sub-skills/benchmarking/scripts/validate_benchmark_layout.py2.8 KB
  • sub-skills/benchmarking/SKILL.md1.4 KB
  • sub-skills/checkpoint-tools/references/api-reference.md1.9 KB
  • sub-skills/checkpoint-tools/references/troubleshooting.md919 B
  • sub-skills/checkpoint-tools/references/workflows.md1.1 KB
  • sub-skills/checkpoint-tools/scripts/check_delta_layout.py4.9 KB
  • sub-skills/checkpoint-tools/scripts/compress_delta.py4.2 KB
  • sub-skills/checkpoint-tools/scripts/extract_deltas.py3.7 KB
  • sub-skills/checkpoint-tools/SKILL.md1.3 KB
  • sub-skills/data-preparation/references/data-formats.md1.9 KB
  • sub-skills/data-preparation/references/troubleshooting.md1.0 KB
  • sub-skills/data-preparation/references/workflows.md1.2 KB
  • sub-skills/data-preparation/scripts/validate_regularization_layout.py4.8 KB
  • sub-skills/data-preparation/SKILL.md1.5 KB
  • sub-skills/inference/references/api-reference.md1.9 KB
  • sub-skills/inference/references/troubleshooting.md1.2 KB
  • sub-skills/inference/references/workflows.md1.4 KB
  • sub-skills/inference/SKILL.md1.5 KB
  • sub-skills/training/references/cli-reference.md3.8 KB
  • sub-skills/training/references/data-formats.md1.4 KB
  • sub-skills/training/references/troubleshooting.md1.3 KB
  • sub-skills/training/references/workflows.md1.4 KB
  • sub-skills/training/scripts/validate_training_inputs.py6.6 KB
  • sub-skills/training/SKILL.md1.8 KB

SKILL.md(原文)

安装前可以查看交给 AI 代理的指令内容。

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.

评价

还没有评价。欢迎分享你的使用感受。

评论

无需登录即可发表。欢迎分享使用感受、提问,或说明在哪些环境下无法运行。

还没有评论。来写第一条吧。

提交后会立即公开。请勿填写邮箱、电话等个人信息。URL 最多 2 个。

0 / 1000 字

此表单受 reCAPTCHA 保护,适用 Google 的隐私权政策和服务条款。

同一仓库的技能

Routes 3D ResNets PyTorch video action-recognition workflows across training, inference, and data preparation.

中文简介正在准备中。当前显示原文说明。

VectorSpaceLab/AREX-Skill3392026年9月3日 更新

3ddfa

免费

Guide 3DDFA Python inference, geometry rendering, training/evaluation, and optional C++ ONNX workflows for 3D dense face alignment.

中文简介正在准备中。当前显示原文说明。

VectorSpaceLab/AREX-Skill3392026年9月3日 更新

3ddfa-v2

免费

Routes 3DDFA_V2 face-alignment setup, still-image demos, video tracking, and ONNX benchmarking workflows.

中文简介正在准备中。当前显示原文说明。

VectorSpaceLab/AREX-Skill3392026年9月3日 更新

ab3dmot

免费

Operate AB3DMOT 3D multi-object tracking workflows for KITTI and nuScenes data, tracking, evaluation, and visualization.

中文简介正在准备中。当前显示原文说明。

VectorSpaceLab/AREX-Skill3392026年9月3日 更新

Use Hugging Face Accelerate for PyTorch training-loop migration, distributed launch/configuration, DeepSpeed/FSDP/TPU backend setup, big-model inference/offload, checkpointing, tracking, and troubleshooting.

中文简介正在准备中。当前显示原文说明。

VectorSpaceLab/AREX-Skill3392026年9月3日 更新

acme

免费

Route Acme reinforcement-learning framework tasks across core loops, replay/data, JAX agents, and TensorFlow/Sonnet agents.

中文简介正在准备中。当前显示原文说明。

VectorSpaceLab/AREX-Skill3392026年9月3日 更新

查看 VectorSpaceLab 的全部技能

报告此技能的问题