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name: inference-api description: "Use the installed sgm.inference.api Python API for SDXL text-to-image, image-to-image, refiner handoff, sampler choices, model specs, and safe API inspection." disable-model-invocation: true metadata: disco-role: operating license: MIT

Inference API

Use this sub-skill when a task asks for the installed sgm.inference.api Python API: constructing SamplingPipeline, choosing SDXL base/refiner architectures, selecting samplers/discretizations/guiders, using SamplingParams, returning latents, running image_to_image with strength, or inspecting the API safely without loading checkpoints.

Route Here For

• SamplingPipeline construction and the default model_path="checkpoints", config_path="configs/inference", device="cuda", use_fp16=True behavior.
• SDXL text-to-image, image-to-image, and base-to-refiner plans that use SamplingParams and return_latents=True.
• API-level sampler selection among EulerEDMSampler, HeunEDMSampler, EulerAncestralSampler, DPMPP2SAncestralSampler, DPMPP2MSampler, and LinearMultistepSampler.
• Diagnosing checkpoint/config mismatches, CPU-only environments, invalid enum strings, img2img_strength, image tensor shape, and missing optional dependencies.

Start With

• Read references/api-reference.md for exact signatures, enum values, SamplingParams defaults, model specs, and helper behavior.
• Read references/workflows.md for SDXL txt2img, img2img, base+refiner, dry-run inspection, and validation patterns.
• Read references/troubleshooting.md when a pipeline fails to initialize, sample, load a checkpoint, or run in CPU-only conditions.
• Run python scripts/inspect_inference_api.py --json --assert-expected from this sub-skill directory, or call the script by path from any cwd, to inspect importable API facts without loading configs or checkpoints.

Boundaries And Cross-Links

• Use the root generative-models skill for repository-level routing and package context.
• Route standalone SVD, SV3D, SV4D, and video sampling scripts to video-sampling.
• Route training configuration authoring and training YAML design to training-and-configs.
• Route UI demos, Streamlit surfaces, and watermark detection workflows to demos-and-watermarking.
• Keep this sub-skill focused on the installed SDXL inference API and its helpers; do not require future agents to reopen source repository files.