check-models
Testing & QualityAudit 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.mdfor 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:
- Loader consistency (
check-loadersequivalent) - Detect/routing parity (
check-detect-routingequivalent) - Reference-catalog integrity (
check-reference-catalogequivalent) - Pipeline API contract conformance (
check-pipeline-contractsequivalent)
Primary Files
pipelines/model_*.pymodules/sd_detect.pymodules/sd_models.pymodules/modeldata.pydata/reference-base.jsondata/reference-cloud.jsondata/reference-quantized.jsondata/reference-distilled.jsondata/reference-nunchaku.jsondata/reference-community.jsonmodels/Reference/
Pipeline files as needed:
pipelines/<model>/pipeline.pypipelines/<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)andsd_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_argsdefaults 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.pydetection heuristicsmodules/sd_models.pyload dispatch branchmodules/modeldata.pyreverse 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
- base ->
- Required fields present per entry (
path,preview,descwhen 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_pretrainedwiring is coherent with actual artifact layoutencode_promptsemantics 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_typeandreturn_dictbehavior 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:
- Blocking integration failures
- Contract mismatches (load/detect/reference/pipeline)
- Consistency and quality issues
- 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.