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name: stability-ai-generative-models description: "Use Stability AI's generative-models package for SDXL inference APIs, SVD/SV3D/SV4D video sampling, config-driven training, demos, and watermarking workflows." disable-model-invocation: true metadata: disco-role: operating license: MIT

Generative Models

Use this repo skill when a task involves Stability AI generative-models, the installed sgm package, SDXL inference APIs, Stable Video Diffusion or SV3D/SV4D sampling scripts, config-driven diffusion training, demo apps, or invisible watermark detection.

Route By Task

• Use inference-api for sgm.inference.api, SamplingPipeline, SDXL text-to-image/image-to-image, base+refiner latent handoff, sampler choices, and checkpoint/config mismatch diagnosis.
• Use video-sampling for standalone SVD, SVD-XT, SV3D, SV4D, SV4D2, 8-view video-to-4D, camera-path, input video/frame, and low-VRAM sampling workflows.
• Use training-and-configs for main.py --base, OmegaConf dotlist overrides, instantiate_from_config, DiffusionEngine, AutoencodingEngine, toy/example training configs, and static config validation.
• Use demos-and-watermarking for Streamlit/Gradio demo routing, SDXL Turbo demo behavior, converting demo controls into automated calls, and watermark bit-match interpretation.

Install And Import Context

• The Python distribution is sgm; the public import is sgm; the repository snapshot used for this skill reports version 0.1.0.
• The package metadata does not declare the full runtime dependency set. For real sampling/training, install a PyTorch-compatible environment plus the documented runtime requirements for the selected workflow.
• For static inspection or planning, use the minimal import check below before attempting checkpoint-backed execution.

python -c "import sgm; print(sgm.__version__)"
python scripts/check_environment.py --json

Working Safely

• Do not load checkpoints, start UI servers, download model weights, or launch training until the user confirms runtime cost, credentials, GPU availability, and output paths.
• Treat CUDA/GPU, checkpoint files, Hugging Face access, xformers, rembg, Streamlit/Gradio, and video encoders as workflow-specific prerequisites rather than assumptions.
• Prefer bundled helper scripts for safe inspection: they avoid checkpoint loads and long-running execution.
• Read references/troubleshooting.md for cross-cutting install/import, optional dependency, checkpoint, CUDA, and data/config failures.
• Read references/repo-provenance.md before deciding whether this skill matches a current checkout or should be refreshed.

Bundled Helpers

• scripts/check_environment.py checks required imports, optional dependency visibility, distribution versions, and torch/CUDA facts without loading checkpoints.
• sub-skills/inference-api/scripts/inspect_inference_api.py inspects API signatures, enum values, and model specs.
• sub-skills/video-sampling/scripts/inspect_video_sampling_cli.py statically summarizes video sampling script defaults, configs, checkpoints, inputs, and command templates.
• sub-skills/training-and-configs/scripts/inspect_training_config.py validates and summarizes training config files without starting training.
• sub-skills/demos-and-watermarking/scripts/watermark_match_thresholds.py classifies watermark bit-match counts without image-processing dependencies.

Common Non-Fits

• Use a Diffusers-specific skill for Hugging Face Diffusers pipeline APIs that do not involve sgm or this repository's scripts.
• Use a generic PyTorch Lightning skill for framework-only trainer questions without generative-models configs or classes.
• Use a computer-vision/model-serving skill when the request is about deployment frameworks rather than this package's generation, demo, or training surfaces.