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check-models

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Audit SD.Next model integrations end-to-end: loaders, detect/routing, reference catalogs, and pipeline API contracts.

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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-base.json
  • data/reference-cloud.json
  • data/reference-quantized.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-base.json
    • cloud -> data/reference-cloud.json
    • quant -> data/reference-quantized.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.