check-models

name: check-models description: "Audit SD.Next model integrations end-to-end: loaders, detect/routing, reference catalogs, and pipeline API contracts." argument-hint: "Optionally focus on a specific model family, repo id, or one or more audit categories: loader, detect-routing, references, pipeline-contracts"

Check Model Integrations End-To-End

Run a consolidated model-integration audit that combines loader checks, detect/routing checks, reference-catalog checks, and pipeline contract checks.

When To Use

• A new model family was added and needs a completeness audit
• Existing model support appears inconsistent across detection, loading, and UI references
• A custom pipeline was ported and needs contract validation
• You want a pre-PR integration quality gate for model-related changes

Guidance

• Consult .github/instructions/core.instructions.md for relevant core runtime and model integration guidance before proceeding.

Combined Scope

This skill combines four audit surfaces. Run them in this order unless user scope limits categories:

1. Loader consistency (check-loaders equivalent)
2. Detect/routing parity (check-detect-routing equivalent)
3. Reference-catalog integrity (check-reference-catalog equivalent)
4. Pipeline API contract conformance (check-pipeline-contracts equivalent)

Primary Files

• pipelines/model_*.py
• modules/sd_detect.py
• modules/sd_models.py
• modules/modeldata.py
• data/reference.json
• data/reference-cloud.json
• data/reference-quant.json
• data/reference-distilled.json
• data/reference-nunchaku.json
• data/reference-community.json
• models/Reference/

Pipeline files as needed:

• pipelines/<model>/pipeline.py
• pipelines/<model>/model.py

Audit A: Loader Consistency

For each target model loader in pipelines/model_*.py, verify:

• Correct sd_models.path_to_repo(checkpoint_info) and sd_models.hf_auth_check(...) usage
• Load args built with model_quant.get_dit_args(...) where applicable
• No duplicated kwargs (for example duplicate torch_dtype)
• Correct component loading path (generic.load_transformer, generic.load_text_encoder, tokenizer/processor)
• Proper post-load hooks (sd_hijack_te, sd_hijack_vae) where required
• Correct pipe.task_args defaults where needed
• Cleanup and devices.torch_gc(...) present

Flag stale patterns, missing hooks, or conflicting load behavior.

Audit B: Detect/Routing Parity

Verify model family alignment across:

• modules/sd_detect.py detection heuristics
• modules/sd_models.py load dispatch branch
• modules/modeldata.py reverse classification from loaded pipeline class

Checks:

• Family is detectable by name/repo conventions
• Dispatch routes to the intended loader
• Loaded pipeline class is classified back to the same model family
• Branch ordering does not cause broad matches to shadow specific families

Audit C: Reference Catalog Integrity

Verify references for model families intended to appear in model references.

Checks:

• Correct category file placement by type:
  • base -> data/reference.json
  • cloud -> data/reference-cloud.json
  • quant -> data/reference-quant.json
  • distilled -> data/reference-distilled.json
  • nunchaku -> data/reference-nunchaku.json
  • community -> data/reference-community.json
• Required fields present per entry (path, preview, desc when expected)
• Duplicate repo/path collisions across reference files are intentional or flagged
• Preview filename convention is consistent
• Referenced preview file exists in models/Reference/ (or explicitly placeholder if intentional)
• JSON validity for touched reference files

Audit D: Pipeline API Contracts

For custom pipelines (pipelines/<model>/pipeline.py), verify:

• Inherits from diffusers.DiffusionPipeline
• Registers modules correctly
• from_pretrained wiring is coherent with actual artifact layout
• encode_prompt semantics are consistent with tokenizer/text encoder setup
• __call__ supports expected public args for its task and does not expose unsupported generic args
• Batch and negative prompt behavior are coherent
• Output conversion aligns with model output domain (latent vs pixel space)
• output_type and return_dict behavior are consistent

Runtime Validation (Preferred)

When feasible:

• Import-level smoke tests for loaders and pipeline modules
• Lightweight loader construction checks without full heavy generation where possible
• One minimal generation/sampling pass for changed model families

If runtime checks are not feasible, report limitations clearly.

Reporting Format

Return findings by severity:

1. Blocking integration failures
2. Contract mismatches (load/detect/reference/pipeline)
3. Consistency and quality issues
4. Optional improvements

For each finding include:

• model family
• layer (loader, detect-routing, reference, pipeline-contract)
• file location
• mismatch summary
• minimal fix

Also include summary counts:

• loaders checked
• model families checked for detect/routing parity
• reference files checked
• pipeline contracts checked
• runtime checks executed vs skipped

Pass Criteria

A full pass requires all of the following in audited scope:

• loader path is coherent and non-conflicting
• detect/routing/modeldata parity holds
• reference entries are valid, categorized correctly, and have preview files
• custom pipeline contracts are consistent with actual model behavior

If any area is intentionally out of scope, mark as partial pass with explicit exclusions.