본문으로 건너뛰기
cccskills
무료GitHub에 공개

denoising-diffusion-pytorch

Use denoising-diffusion-pytorch for PyTorch DDPM/DDIM image diffusion, 1D sequence diffusion, conditioning and guidance, and advanced diffusion variants.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

VectorSpaceLab/AREX-Skill
설치 방법 보기

포함된 파일(27)

  • SKILL.md2.9 KB
  • references/package-overview.md2.4 KB
  • references/repo-provenance.md1.4 KB
  • references/repo-routing-metadata.json540 B
  • references/troubleshooting.md1.8 KB
  • scripts/check_env.py2.8 KB
  • sub-skills/advanced-variants/references/api-reference.md3.7 KB
  • sub-skills/advanced-variants/references/compatibility.md3.5 KB
  • sub-skills/advanced-variants/references/troubleshooting.md2.9 KB
  • sub-skills/advanced-variants/references/workflows.md4.9 KB
  • sub-skills/advanced-variants/scripts/smoke_advanced_variants.py5.0 KB
  • sub-skills/advanced-variants/SKILL.md2.7 KB
  • sub-skills/conditioning-guidance/references/api-reference.md4.3 KB
  • sub-skills/conditioning-guidance/references/troubleshooting.md2.8 KB
  • sub-skills/conditioning-guidance/references/workflows.md3.1 KB
  • sub-skills/conditioning-guidance/scripts/smoke_conditioning_guidance.py4.7 KB
  • sub-skills/conditioning-guidance/SKILL.md2.7 KB
  • sub-skills/image-diffusion/references/api-reference.md5.0 KB
  • sub-skills/image-diffusion/references/troubleshooting.md3.0 KB
  • sub-skills/image-diffusion/references/workflows.md4.5 KB
  • sub-skills/image-diffusion/scripts/smoke_image_diffusion.py4.1 KB
  • sub-skills/image-diffusion/SKILL.md2.4 KB
  • sub-skills/sequence-diffusion/references/api-reference.md3.5 KB
  • sub-skills/sequence-diffusion/references/troubleshooting.md2.2 KB
  • sub-skills/sequence-diffusion/references/workflows.md3.5 KB
  • sub-skills/sequence-diffusion/scripts/smoke_sequence_diffusion.py5.6 KB
  • sub-skills/sequence-diffusion/SKILL.md2.5 KB

SKILL.md(원문)

설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.

denoising-diffusion-pytorch

Use this repo skill when a Researcher needs to operate the public denoising-diffusion-pytorch package (denoising_diffusion_pytorch import) from self-contained guidance. It covers the inspected public API surface at version 2.3.1.

Install and sanity check

python -m pip install denoising-diffusion-pytorch
python - <<'PYCODE'
from denoising_diffusion_pytorch import Unet, GaussianDiffusion, Unet1D, GaussianDiffusion1D
print('denoising_diffusion_pytorch imports ok')
PYCODE

For a no-training environment check, run scripts/check_env.py. It verifies package metadata, public imports, a tiny CPU tensor operation, and optional CUDA visibility.

Route by task

User taskRead
2D image DDPM/DDIM, Unet, GaussianDiffusion, image folder Trainer, FID, RePaint/inpainting, image tensor shape errorssub-skills/image-diffusion/SKILL.md
1D/time-series/sequence tensors, Unet1D, GaussianDiffusion1D, Dataset1D, Trainer1D, channel-first vs channel-last layoutsub-skills/sequence-diffusion/SKILL.md
classifier-free guidance, class labels, cond_scale, CFG++, external classifier cond_fn, XMWrapper multi-candidate losssub-skills/conditioning-guidance/SKILL.md
Karras UNets, video-shaped Karras 3D, continuous-time / v-param / EDM / simple diffusion, learned variance, weighted objective, flash/SDPA compatibilitysub-skills/advanced-variants/SKILL.md

Shared references

Operating rules

  • Use the bundled scripts for smoke tests; do not run long training or FID for routine verification.
  • Keep data tensors normalized to [0, 1] unless auto_normalize=False is explicitly chosen.
  • CPU is the default verification backend; CUDA, flash attention, FID, and Accelerate multi-GPU are optional accelerators unless a user explicitly asks for them.
  • The package has no public CLI entry points. Workflows are Python API workflows.
  • If a task asks to edit this repository rather than use the package, this repo skill can explain APIs, but repository-maintenance policy is outside the selected operating graph.

리뷰

아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.

댓글

로그인 없이 쓸 수 있습니다. 사용 후기, 질문, 작동하지 않은 환경 등을 편하게 남겨 주세요.

아직 댓글이 없습니다. 첫 댓글을 남겨 보세요.

보내면 바로 공개됩니다. 이메일 주소나 전화번호 같은 개인 정보는 쓰지 마세요. URL은 2개까지입니다.

0 / 1,000자

이 양식은 reCAPTCHA로 보호되며 Google 개인정보처리방침 및 서비스 약관이 적용됩니다.

같은 저장소의 스킬

3d-resnets-pytorch

무료

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

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

VectorSpaceLab/AREX-Skill3392026년 9월 3일 업데이트

3ddfa

무료

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

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

VectorSpaceLab/AREX-Skill3392026년 9월 3일 업데이트

3ddfa-v2

무료

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

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

VectorSpaceLab/AREX-Skill3392026년 9월 3일 업데이트

ab3dmot

무료

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

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

VectorSpaceLab/AREX-Skill3392026년 9월 3일 업데이트

accelerate

무료

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.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

VectorSpaceLab/AREX-Skill3392026년 9월 3일 업데이트

acme

무료

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

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

VectorSpaceLab/AREX-Skill3392026년 9월 3일 업데이트

VectorSpaceLab의 스킬 모두 보기

이 스킬의 문제 신고