Port custom model pipeline implementations to Diffusers. Use when migrating custom or non-Diffusers pipeline code into SD.Next repo-local pipeline files such as pipelines/model_<name>.py or pipelines/<model>/pipeline.py while preserving behavior, avoiding new dependencies, and keeping device/attention handling configurable.
Port or add a model to SD.Next using existing Diffusers and custom pipeline patterns. Use when implementing a new model loader, custom pipeline, checkpoint conversion path, or SD.Next model-type integration.
Update wiki markdown docs for syntax correctness, readability, link integrity, heading hierarchy normalization, and code block language tagging. Use when a user asks to clean up markdown formatting and improve clarity while preserving technical meaning.
Maintain and validate SD.Next model reference catalogs in data/reference*.json, including schema consistency, deduplication, link checks, and thumbnail alignment.
Create or edit code that is compliant with Hugging Face diffusers conventions, including models, pipelines, schedulers, tests, docs, and PR preparation targeting diffusers.
Analyze an external model URL (typically Hugging Face) to determine implementation style and estimate SD.Next porting difficulty using the port-model workflow.
Audit SD.Next model integrations end-to-end: loaders, detect/routing, reference catalogs, and pipeline API contracts.
Audit scheduler registrations starting from modules/sd_samplers_diffusers.py and verify class loadability, config validity against scheduler capabilities, and SamplerData correctness.