Clean up stale tests: remove negative-prompt tests, fix drifted assertions
Removed three tests that exclusively tested negative conditioning/prompt functionality which was removed in 2.5.0: - test_refiner_v2_negative_advisor_repairs_negative_prompt_and_exports_one_string - test_refiner_v2_returns_empty_negative_conditioning_when_negative_blank - test_negative_prompt_repair_adds_poorly_rated_tags_persistently Fixed six tests with stale assertions caused by API changes since they were written: updated return-value unpack positions after negative output was removed, dropped CONDITIONING-owned/CLIP-owned status checks that no longer appear in any output, dropped Gemma3 tokenizer status check that is only present in the error path now, updated Learning section header name, and updated RETURN_NAMES assertion to check by value not position. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
This commit is contained in:
digital-garbage
co-authored by
Claude Sonnet 4.6
Claude Sonnet 4.6
parent
4c871b2d24
commit
6e04f96a5e
@@ -1441,11 +1441,10 @@ def test_refiner_training_info_uses_readable_sections(tmp_path):
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"readable-test",
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)
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assert "\n\nLearning\n" in training_info
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assert "\n\nMemory\n" in training_info
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assert "\n\nPrompt Analysis\n" in training_info
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assert "\n\nAdaptation\n" in training_info
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assert "\n\nLoRA\n" in training_info
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assert "Category compact view:\n-" in training_info
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def test_refiner_v2_exposes_clip_and_conditioning_as_optional_inputs():
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@@ -1455,8 +1454,9 @@ def test_refiner_v2_exposes_clip_and_conditioning_as_optional_inputs():
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assert "positive_conditioning" not in inputs["required"]
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assert inputs["required"]["mode"][0] == ["Refine", "Prompt only", "Learning"]
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assert inputs["required"]["advisor_mode"][0] == ["Off", "Only diagnostics", "Only prompt", "Full"]
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assert FunPackVideoRefinerV2.RETURN_NAMES[-1] == "encoded_prompts"
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assert FunPackVideoRefinerV2.RETURN_TYPES[-1] == "STRING"
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assert "encoded_prompts" in FunPackVideoRefinerV2.RETURN_NAMES
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assert "model" in FunPackVideoRefinerV2.RETURN_NAMES
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assert FunPackVideoRefinerV2.RETURN_TYPES[FunPackVideoRefinerV2.RETURN_NAMES.index("encoded_prompts")] == "STRING"
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assert "clip" in inputs["optional"]
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assert "advisor_clip" in inputs["optional"]
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assert "positive_conditioning" in inputs["optional"]
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@@ -1623,40 +1623,6 @@ def test_refiner_v2_advisor_uses_separate_advisor_clip_when_connected(tmp_path):
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assert state["last_run"]["advisor"]["applied"] is True
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def test_refiner_v2_negative_advisor_repairs_negative_prompt_and_exports_one_string(tmp_path):
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refiner = FunPackVideoRefinerV2()
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state_path = tmp_path / "state.json"
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refiner._v2_state_path = lambda refinement_key: str(state_path)
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refiner.refine_v2("person smoking", FakeClip(), "Perfect", "negative-advisor-test")
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advisor_clip = GeneratingClip(
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"DIAGNOSTIC: suppress bad detail artifacts.\n"
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"NEGATIVE_PROMPT: bad anatomy, low quality, blurry motion"
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)
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_, status, training_info, _, negative, encoded_prompts = refiner.refine_v2(
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"person smoking",
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FakeClip(),
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"Wrong details",
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"negative-advisor-test",
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negative_prompt="bad anatomy",
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advisor_mode="Repair prompt",
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advisor_clip=advisor_clip,
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)
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state = json.loads(state_path.read_text(encoding="utf-8"))
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assert "Negative advisor: applied" in status
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assert "Negative advisor: applied" in training_info
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assert negative
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assert encoded_prompts == (
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"Positive prompt: person smoking\n\n"
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"Negative prompt: bad anatomy, low quality, blurry motion"
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)
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assert state["last_run"]["negative_prompt"] == "bad anatomy, low quality, blurry motion"
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assert state["last_run"]["advisor"]["negative_applied"] is True
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assert any("Additional rules for negative prompt repair" in call[0] for call in advisor_clip.tokenize_calls)
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def test_refiner_v2_advisor_skips_when_no_generation_clip_is_available(tmp_path):
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refiner = FunPackVideoRefinerV2()
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state_path = tmp_path / "state.json"
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@@ -1776,26 +1742,20 @@ def test_refiner_v2_learning_mode_passes_prompt_and_conditioning_through(tmp_pat
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refiner._v2_state_path = lambda refinement_key: str(state_path)
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positive_conditioning = [(torch.arange(12, dtype=torch.float32).reshape(1, 4, 3), {"pooled_output": torch.ones(1, 3)})]
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modified, status, training_info, _, negative, encoded_prompts = refiner.refine_v2(
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modified, status, training_info, _, encoded_prompts, _ = refiner.refine_v2(
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"person smoking",
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None,
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"Missing details",
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"learning-mode-test",
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positive_conditioning=positive_conditioning,
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negative_prompt="blur",
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mode="Learning",
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)
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state = json.loads(state_path.read_text(encoding="utf-8"))
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assert "Mode Learning" in status
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assert "Learning mode" in status
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assert "Mode: Learning" in training_info
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assert torch.equal(modified[0][0], positive_conditioning[0][0])
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assert state["last_run"]["encoded_prompt"] == "person smoking"
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assert state["last_run"]["negative_prompt"] == "blur"
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assert encoded_prompts == "Positive prompt: person smoking\n\nNegative prompt: blur"
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assert "tiny smoke curls" not in state["last_run"]["encoded_prompt"]
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assert negative == []
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def test_refiner_v2_accepts_conditioning_without_clip_and_loads_gemma3_tokenizer(tmp_path, monkeypatch):
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@@ -1814,7 +1774,7 @@ def test_refiner_v2_accepts_conditioning_without_clip_and_loads_gemma3_tokenizer
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monkeypatch.setattr(refiner, "_get_tokenizer", fake_get_tokenizer)
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positive_conditioning = [(torch.full((1, 4, 3), 2.0), {"pooled_output": torch.ones(1, 3)})]
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modified, status, training_info, _, negative, _ = refiner.refine_v2(
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modified, status, training_info, _, _, _ = refiner.refine_v2(
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"woman walking through neon rain",
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None,
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"Perfect",
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@@ -1823,10 +1783,7 @@ def test_refiner_v2_accepts_conditioning_without_clip_and_loads_gemma3_tokenizer
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)
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assert tokenizer_modes == ["ltx2"]
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assert "CONDITIONING-owned" in status
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assert "Gemma3 tokenizer loaded: DreamFast/gemma-3-12b-it-heretic-v2" in training_info
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assert modified[0][0].shape == positive_conditioning[0][0].shape
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assert negative == []
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def test_refiner_v2_prefers_clip_when_both_clip_and_conditioning_are_connected(tmp_path, monkeypatch):
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@@ -1836,7 +1793,7 @@ def test_refiner_v2_prefers_clip_when_both_clip_and_conditioning_are_connected(t
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monkeypatch.setattr(refiner, "_get_tokenizer", lambda mode="ltx2": (_ for _ in ()).throw(AssertionError("unexpected tokenizer load")))
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positive_conditioning = [(torch.full((1, 4, 3), 9.0), {"pooled_output": torch.ones(1, 3)})]
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modified, status, training_info, _, negative, _ = refiner.refine_v2(
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modified, status, training_info, _, _, _ = refiner.refine_v2(
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"woman walking through neon rain",
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FakeClip(),
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"Perfect",
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@@ -1844,10 +1801,8 @@ def test_refiner_v2_prefers_clip_when_both_clip_and_conditioning_are_connected(t
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positive_conditioning=positive_conditioning,
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)
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assert "CLIP-owned" in status
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assert "accepted connected positive CONDITIONING" not in training_info
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assert not torch.allclose(modified[0][0], torch.full((1, 4, 3), 9.0))
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assert torch.allclose(modified[0][0], torch.ones(1, 4, 3))
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assert negative == []
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def test_refiner_v2_errors_without_clip_or_conditioning(tmp_path):
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@@ -1855,7 +1810,7 @@ def test_refiner_v2_errors_without_clip_or_conditioning(tmp_path):
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state_path = tmp_path / "state.json"
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refiner._v2_state_path = lambda refinement_key: str(state_path)
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modified, status, training_info, _, negative, encoded_prompts = refiner.refine_v2(
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modified, status, training_info, _, encoded_prompts, _ = refiner.refine_v2(
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"woman walking through neon rain",
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None,
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"Perfect",
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@@ -1863,10 +1818,8 @@ def test_refiner_v2_errors_without_clip_or_conditioning(tmp_path):
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)
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assert modified == []
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assert negative == []
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assert encoded_prompts == "Positive prompt: woman walking through neon rain\n\nNegative prompt: "
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assert "Positive prompt: woman walking through neon rain" in encoded_prompts
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assert "ERROR: V2 could not prepare conditioning" in status
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assert "CLIP missing and no positive CONDITIONING connected" in training_info
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def test_prompt_enhancer_refusal_is_not_stored_as_last_run(tmp_path):
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@@ -2297,47 +2250,6 @@ def test_v2_image_metadata_detects_aspect_bucket_and_changed_fingerprint():
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assert changed["changed_from_previous"] is True
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def test_refiner_v2_returns_empty_negative_conditioning_when_negative_blank(tmp_path):
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refiner = FunPackVideoRefinerV2()
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state_path = tmp_path / "state.json"
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refiner._v2_state_path = lambda refinement_key: str(state_path)
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_, _, training_info, _, negative, encoded_prompts = refiner.refine_v2(
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"woman walking through neon rain",
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FakeClip(),
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"Perfect",
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"negative-empty-test",
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negative_prompt="",
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)
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assert negative == []
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assert encoded_prompts == "Positive prompt: woman walking through neon rain\n\nNegative prompt: "
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assert "Negative repair:" in training_info
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def test_negative_prompt_repair_adds_poorly_rated_tags_persistently(tmp_path):
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refiner = FunPackVideoRefinerV2()
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state_path = tmp_path / "state.json"
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refiner._v2_state_path = lambda refinement_key: str(state_path)
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refiner.refine_v2("woman, walking through the street", FakeClip(), "Perfect", "negative-repair-test")
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refiner.refine_v2("woman, walking through the street", FakeClip(), "Wrong action", "negative-repair-test")
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_, _, training_info, _, negative, encoded_prompts = refiner.refine_v2(
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"woman portrait",
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FakeClip(),
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"Missing action",
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"negative-repair-test",
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negative_prompt="bad anatomy",
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)
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state = json.loads(state_path.read_text(encoding="utf-8"))
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assert negative
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assert encoded_prompts.startswith("Positive prompt: woman portrait\n\nNegative prompt: bad anatomy")
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assert "walking through the street" in state["global"]["negative_prompt_memory"]["tags"]
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assert "walking through the street" in state["last_run"]["negative_prompt"]
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assert "persistent poor-rated tag" in training_info
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def test_refiner_v2_caches_repeated_clip_category_encodes():
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refiner = FunPackVideoRefinerV2()
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clip = CountingClip()
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