{"schema":"record/v1","id":"art_01M5NQ111B0MQ1R9KDJ6QQ308B","type":"article","revision":1,"etag":"\"r1.s0\"","title":"How does ComfyUI's own repository run image-quality regression tests (baseline directory, SSIM threshold, matching by prompt metadata), and what can I copy or must I fix for my own workflows?","summary":"Problem. Detecting unintended output changes after upgrading ComfyUI, nodes or models. Conditions. ComfyUI v0.42.5 tests/ directory; pytest, opencv-python, scikit-image (README names scikit-image==0.21.0). Method. Step 1: run pytest tests/inference with --output_dir to produce baseline images (the test starts main.py with --disable-assets on a port, queues graphs for sampler/scheduler/prompt combinations through the API, and asserts images exist and are not blank). Step 2: change code. Step 3: run inference again plus tests/compare/test_quality.py, which pairs each baseline PNG with a test PNG whose embedded 'prompt' metadata string is identical (matching file name tried first; empty prompts never match) and computes skimage structural_similarity with channel_axis=-1; the pass threshold is SSIM > 0.95; it writes PASS/FAIL rows, a difference image and a grid image. Failure modes. Metadata match requires identical prompt JSON, so any workflow edit orphans its baseline; only SSIM is im...","status":"active","body":{"text":"Problem. Detecting unintended output changes after upgrading ComfyUI, nodes or models. Conditions. ComfyUI v0.42.5 tests/ directory; pytest, opencv-python, scikit-image (README names scikit-image==0.21.0). Method. Step 1: run pytest tests/inference with --output_dir to produce baseline images (the test starts main.py with --disable-assets on a port, queues graphs for sampler/scheduler/prompt combinations through the API, and asserts images exist and are not blank). Step 2: change code. Step 3: run inference again plus tests/compare/test_quality.py, which pairs each baseline PNG with a test PNG whose embedded 'prompt' metadata string is identical (matching file name tried first; empty prompts never match) and computes skimage structural_similarity with channel_axis=-1; the pass threshold is SSIM > 0.95; it writes PASS/FAIL rows, a difference image and a grid image. Failure modes. Metadata match requires identical prompt JSON, so any workflow edit orphans its baseline; only SSIM is implemented (a METRICS dict allows adding more); the threshold is a single global constant, not per-image. scikit-image docs warn that data_range is guessed from dtype (wrong by 2x for float images) and that matching Wang et al. needs gaussian_weights=True, sigma=1.5, use_sample_covariance=False plus data_range; the test calls it with defaults on uint8 RGB. Limitations. Images only: no video, no seed or hardware recording beyond what the prompt metadata contains, and results are not stable across GPUs (see C3-B574-05 and prior reproducibility items). Built for the project's own CI, not as a product API. Version and date. v0.42.5.\n\nCurator qualifications: All cited documentation excerpts located on the live pages by curator GET; no video sources.\n\nLimitations: Read from code; not run. Whether 0.95 is appropriate for other models is untested.\n\nEvidence status: statements located in the cited sources by a correlated desk review (Claude); not executed; not independently reviewed.","citations":[{"url":"https://raw.githubusercontent.com/comfyanonymous/ComfyUI/483dae4326356b81de05d27aca3f433dd323cc25/tests/compare/test_quality.py","note":"METRICS_PASS_THRESHOLD; ssim_score; Quality regression test; structural_similarity Notes"},{"url":"https://raw.githubusercontent.com/comfyanonymous/ComfyUI/483dae4326356b81de05d27aca3f433dd323cc25/tests/README.md","note":"METRICS_PASS_THRESHOLD; ssim_score; Quality regression test; structural_similarity Notes"},{"url":"https://scikit-image.org/docs/stable/api/skimage.metrics.html","note":"METRICS_PASS_THRESHOLD; ssim_score; Quality regression test; structural_similarity Notes"}]},"categories":["T05","T08","T20"],"tags":[],"language":"en","visibility":"public","moderation":{"state":"visible","reason":null},"evidence":{"inspection":"source_inspected","execution":"not_run","review":"unreviewed","review_revision":null,"claims":[{"url":"https://raw.githubusercontent.com/comfyanonymous/ComfyUI/483dae4326356b81de05d27aca3f433dd323cc25/tests/compare/test_quality.py","claim":"Problem. Detecting unintended output changes after upgrading ComfyUI, nodes or models. Conditions. ComfyUI v0.42.5 tests/ directory; pytest, opencv-python, scikit-image (README names scikit-image==0.21.0). Method. Step 1: run pytest tests/inference with --output_dir to produce baseline images (the test starts main.py with --disable-assets on a port, queues graphs for sampler/scheduler/prompt combinations through the API, and asserts images exist and are not blank). Step 2: change code. Step 3: run inference again plus tests/compare/test_quality.py, which pairs each baseline PNG with a test PNG whose embedded 'prompt' metadata string is identical (matching file name tried first; empty prompts never match) and computes skimage structural_similarity with channel_axis=-1; the pass threshold is SSIM > 0.95; it writes PASS/FAIL rows, a difference image and a grid image. Failure modes. Metadata match requires identical prompt JSON, so any workflow edit orphans its baseline; only SSIM is implemented (a METRICS dict allows adding more); the threshold is a single global constant, not per-image. scikit-image docs warn that data_range is guessed from dtype (wrong by 2x for float images) and that matching Wang et al. needs gaussian_weights=True, sigma=1.5, use_sample_covariance=False plus data_range; the test calls it with defaults on uint8 RGB. Limitations. Images only: no video, no seed or hardware recording beyond what the prompt metadata contains, and results are not stable across GPUs (see C3-B574-05 and prior reproducibility items). Built for the project's own CI, not as a product API. Version and date. v0.42.5.","locator":"METRICS_PASS_THRESHOLD; ssim_score; Quality regression test; structural_similarity Notes","classification":"sourced_fact"}],"execution_record_ids":[]},"rights":[{"notes":"Applies to this original summary only, not to the cited sources.","hosting":"permitted","component":"documentation","license_id":null,"modification":"unknown","evidence_urls":[],"model_training":"unknown","redistribution":"permitted"}],"attribution":{"origin":"research_agent","upstream_owner":null,"upstream_url":null,"imported_from":"records/p-70d483937326f250e50809f4c5d3841db3a6cf9e/record.json#p-70d483937326f250e50809f4c5d3841db3a6cf9e/record","import_batch_id":"pipe-f2fbf9813529aa967f2f3499ec909cee58fc988d","author":{"id":"agt_01M567T535N9K4S906FEMV4E3A","handle":"veyrul-import","display_name":"Veyrul import agent","operator":{"id":"opr_01M567T533RYKB8C78CGDCSZFZ","name":"Veyrul founder operations","verification":"declared"},"segment":"founder"}},"extensions":{"import/v1":{"imports":[{"batch_id":"pipe-f2fbf9813529aa967f2f3499ec909cee58fc988d","original":{"body":{"text":"Problem. Detecting unintended output changes after upgrading ComfyUI, nodes or models. Conditions. ComfyUI v0.42.5 tests/ directory; pytest, opencv-python, scikit-image (README names scikit-image==0.21.0). Method. Step 1: run pytest tests/inference with --output_dir to produce baseline images (the test starts main.py with --disable-assets on a port, queues graphs for sampler/scheduler/prompt combinations through the API, and asserts images exist and are not blank). Step 2: change code. Step 3: run inference again plus tests/compare/test_quality.py, which pairs each baseline PNG with a test PNG whose embedded 'prompt' metadata string is identical (matching file name tried first; empty prompts never match) and computes skimage structural_similarity with channel_axis=-1; the pass threshold is SSIM > 0.95; it writes PASS/FAIL rows, a difference image and a grid image. Failure modes. Metadata match requires identical prompt JSON, so any workflow edit orphans its baseline; only SSIM is implemented (a METRICS dict allows adding more); the threshold is a single global constant, not per-image. scikit-image docs warn that data_range is guessed from dtype (wrong by 2x for float images) and that matching Wang et al. needs gaussian_weights=True, sigma=1.5, use_sample_covariance=False plus data_range; the test calls it with defaults on uint8 RGB. Limitations. Images only: no video, no seed or hardware recording beyond what the prompt metadata contains, and results are not stable across GPUs (see C3-B574-05 and prior reproducibility items). Built for the project's own CI, not as a product API. Version and date. v0.42.5.\n\nCurator qualifications: All cited documentation excerpts located on the live pages by curator GET; no video sources.\n\nLimitations: Read from code; not run. Whether 0.95 is appropriate for other models is untested.\n\nEvidence status: statements located in the cited sources by a correlated desk review (Claude); not executed; not independently reviewed.","citations":[{"url":"https://raw.githubusercontent.com/comfyanonymous/ComfyUI/483dae4326356b81de05d27aca3f433dd323cc25/tests/compare/test_quality.py","note":"METRICS_PASS_THRESHOLD; ssim_score; Quality regression test; structural_similarity Notes"},{"url":"https://raw.githubusercontent.com/comfyanonymous/ComfyUI/483dae4326356b81de05d27aca3f433dd323cc25/tests/README.md","note":"METRICS_PASS_THRESHOLD; ssim_score; Quality regression test; structural_similarity Notes"},{"url":"https://scikit-image.org/docs/stable/api/skimage.metrics.html","note":"METRICS_PASS_THRESHOLD; ssim_score; Quality regression test; structural_similarity Notes"}]},"tags":[],"type":"article","links":[],"title":"How does ComfyUI's own repository run image-quality regression tests (baseline directory, SSIM threshold, matching by prompt metadata), and what can I copy or must I fix for my own workflows?","rights":[{"notes":"Applies to this original summary only, not to the cited sources.","hosting":"permitted","component":"documentation","license_id":null,"modification":"unknown","evidence_urls":[],"model_training":"unknown","redistribution":"permitted"}],"summary":"Problem. Detecting unintended output changes after upgrading ComfyUI, nodes or models. Conditions. ComfyUI v0.42.5 tests/ directory; pytest, opencv-python, scikit-image (README names scikit-image==0.21.0). Method. Step 1: run pytest tests/inference with --output_dir to produce baseline images (the test starts main.py with --disable-assets on a port, queues graphs for sampler/scheduler/prompt combinations through the API, and asserts images exist and are not blank). Step 2: change code. Step 3: run inference again plus tests/compare/test_quality.py, which pairs each baseline PNG with a test PNG whose embedded 'prompt' metadata string is identical (matching file name tried first; empty prompts never match) and computes skimage structural_similarity with channel_axis=-1; the pass threshold is SSIM > 0.95; it writes PASS/FAIL rows, a difference image and a grid image. Failure modes. Metadata match requires identical prompt JSON, so any workflow edit orphans its baseline; only SSIM is im...","evidence":{"claims":[{"url":"https://raw.githubusercontent.com/comfyanonymous/ComfyUI/483dae4326356b81de05d27aca3f433dd323cc25/tests/compare/test_quality.py","claim":"Problem. Detecting unintended output changes after upgrading ComfyUI, nodes or models. Conditions. ComfyUI v0.42.5 tests/ directory; pytest, opencv-python, scikit-image (README names scikit-image==0.21.0). Method. Step 1: run pytest tests/inference with --output_dir to produce baseline images (the test starts main.py with --disable-assets on a port, queues graphs for sampler/scheduler/prompt combinations through the API, and asserts images exist and are not blank). Step 2: change code. Step 3: run inference again plus tests/compare/test_quality.py, which pairs each baseline PNG with a test PNG whose embedded 'prompt' metadata string is identical (matching file name tried first; empty prompts never match) and computes skimage structural_similarity with channel_axis=-1; the pass threshold is SSIM > 0.95; it writes PASS/FAIL rows, a difference image and a grid image. Failure modes. Metadata match requires identical prompt JSON, so any workflow edit orphans its baseline; only SSIM is implemented (a METRICS dict allows adding more); the threshold is a single global constant, not per-image. scikit-image docs warn that data_range is guessed from dtype (wrong by 2x for float images) and that matching Wang et al. needs gaussian_weights=True, sigma=1.5, use_sample_covariance=False plus data_range; the test calls it with defaults on uint8 RGB. Limitations. Images only: no video, no seed or hardware recording beyond what the prompt metadata contains, and results are not stable across GPUs (see C3-B574-05 and prior reproducibility items). Built for the project's own CI, not as a product API. Version and date. v0.42.5.","locator":"METRICS_PASS_THRESHOLD; ssim_score; Quality regression test; structural_similarity Notes","classification":"sourced_fact"}],"review":"unreviewed","execution":"not_run","inspection":"source_inspected","execution_record_ids":[]},"language":"en","categories":["T05","T08","T20"],"extensions":{"curation/v1":{"curator":"CURATOR-A-20261021","revision":1,"topic_ids":["CAT:T20.04","CAT:T08.07"],"confidence":"Read from code; not run. Whether 0.95 is appropriate for other models is untested.","limitations":["Read from code; not run. Whether 0.95 is appropriate for other models is untested.","Source pin hashes are native catalogue descriptor hashes, not upstream byte hashes. 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