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name: pipelines-and-inference description: "Use Diffusers pipelines for loading, text-to-image, image-to-image, inpainting, control-conditioned inference, batching, callbacks, deterministic seeds, device/dtype/offload choices, and serving-safe invocation patterns." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Pipelines and Inference

Use this sub-skill when the task is about running or wiring Diffusers inference pipelines: loading checkpoints, choosing pipeline classes, passing prompts/images/masks/control images, batching, reproducibility, callbacks, device placement, memory/offload settings, or serving pipelines behind an API.

Route First

• For a new inference script, start with references/pipeline-workflows.md and choose the smallest pipeline family that matches the inputs: text-only, image-to-image, inpainting, or control-conditioned generation.
• For loading, offline/local operation, dtype, device placement, memory, and reproducibility decisions, use references/loading-and-runtime.md.
• For installation, optional dependencies, device/dtype mistakes, local file failures, API misuse, inpainting shape issues, callback errors, and server concurrency problems, use references/troubleshooting.md.
• For a safe environment sanity check, run python scripts/pipeline_env_check.py --help or python scripts/pipeline_env_check.py.
• For a no-download invocation skeleton, run python scripts/pipeline_invocation_template.py --help; add --allow-download only when network/model downloads are intentional.

Boundaries

• Training and fine-tuning recipes belong to training-recipes.
• Scheduler algorithm trade-offs and custom scheduler design belong to schedulers; this sub-skill only shows where to attach or clone a scheduler for inference.
• LoRA, IP-Adapter, textual inversion, and adapter weight mechanics belong to adapters-and-loaders; this sub-skill only routes control/image conditioning inputs at pipeline-call level.
• Modular pipeline block authoring belongs to modular-pipelines.
• Checkpoint conversion, repo maintenance, and CLI conversion utilities belong to conversion-and-maintenance.

Default Inference Checklist

1. Import a concrete AutoPipelineFor... class when task type is known, otherwise use DiffusionPipeline for generic loading.
2. Load with from_pretrained(model_or_path, **kwargs); use local_files_only=True for offline/local-only execution.
3. Choose device and dtype together: CPU uses default/float32; CUDA commonly uses torch.float16 or torch.bfloat16; MPS usually loads then .to("mps") and may need memory-saving attention slicing.
4. Pass workflow-specific inputs by name: prompt, image, mask_image, control_image, negative_prompt, height, width, strength, generator, callback_on_step_end.
5. Use a fresh torch.Generator(device="cpu").manual_seed(seed) for repeatable single-image calls; use one generator per item for batched calls.
6. Treat output as a pipeline output object unless return_dict=False; generated images are usually in result.images.