Routes 3D ResNets PyTorch video action-recognition workflows across training, inference, and data preparation.
The English summary is being prepared. Showing the original description.
Route Custom Diffusion workflows for concept data prep, diffusers training and SDXL, sample generation, delta tools, and CustomConcept101 evaluation.
The English summary is being prepared. Showing the original description.
See how to installReview the instructions the agent will receive before you install.
Use this repo skill for the diffusers-side Custom Diffusion workflow family:
references/repo-provenance.md to confirm the source baseline.references/workflows.md for the route map.scripts/check_runtime.py --require-cuda to confirm the CUDA/import stack.scripts/validate_concepts.py when a concept-list JSON needs a structural check.data-preparationUse when you need instance images, real-prior image lists, caption files, or concept JSON for training.
Start with sub-skills/data-preparation/SKILL.md.
trainingUse when you need diffusers training, SDXL training, prior preservation, modifier tokens, or checkpoint output planning.
Start with sub-skills/training/SKILL.md.
inferenceUse 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-toolsUse when you need delta extraction, compression, or composition.
Start with sub-skills/checkpoint-tools/SKILL.md.
benchmarkingUse when you need to score generated images with CustomConcept101 CLIP/DINO evaluation.
Start with sub-skills/benchmarking/SKILL.md.
diffusers==0.21.4, accelerate==0.24.1, transformers==4.31.0, clip-retrieval==2.45.0, pandas, scipy, scikit-learn, tqdm.xformers, bitsandbytes, deepspeed, modelcards.After installing the stack, run:
python scripts/check_runtime.py --require-cuda
If you are only checking importability, omit --require-cuda.
No reviews yet. Tell others what you thought.
No login needed. Share how it worked for you, ask a question, or note where it did not run.
No comments yet. Be the first to write one.
Routes 3D ResNets PyTorch video action-recognition workflows across training, inference, and data preparation.
The English summary is being prepared. Showing the original description.
Guide 3DDFA Python inference, geometry rendering, training/evaluation, and optional C++ ONNX workflows for 3D dense face alignment.
The English summary is being prepared. Showing the original description.
Routes 3DDFA_V2 face-alignment setup, still-image demos, video tracking, and ONNX benchmarking workflows.
The English summary is being prepared. Showing the original description.
Operate AB3DMOT 3D multi-object tracking workflows for KITTI and nuScenes data, tracking, evaluation, and visualization.
The English summary is being prepared. Showing the original description.
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.
The English summary is being prepared. Showing the original description.
Route Acme reinforcement-learning framework tasks across core loops, replay/data, JAX agents, and TensorFlow/Sonnet agents.
The English summary is being prepared. Showing the original description.