digital-garbageandClaude Opus 5.5
9dcb8b0f63
core.patching.strip now also removes our object patches, so a feature switched off does not keep its patch on a model that goes round again. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
57 lines
1.9 KiB
Python
57 lines
1.9 KiB
Python
"""Video detail on a real H3 forward: picture changes, sound is bit-identical."""
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import pytest
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import torch
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@pytest.fixture(autouse=True)
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def _needs_comfy(comfyui):
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"""Imports comfy."""
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def _load(tiny, patcher=None, **values):
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from modules.sampling.modifiers.nodes import FunPackLoadModifiers
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return FunPackLoadModifiers.execute(
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patcher or tiny.patcher, {"video_detail": {"enabled": True, **values}}).result
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def _run_patched(tiny, patched, **run):
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"""Object patches apply at load; do what ComfyUI's load does."""
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patched.patch_model()
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try:
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return tiny.run(patched, **run)
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finally:
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patched.unpatch_model()
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def test_picture_moves_and_sound_does_not(tiny_h3):
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base = tiny_h3.run()
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patched, status = _load(tiny_h3, amount=1.5)
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assert "video_detail: 1.5x on the picture" in status
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out = _run_patched(tiny_h3, patched)
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assert not torch.allclose(out[0], base[0])
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assert torch.equal(out[1], base[1]), "the sound changed"
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def test_both_directions_move_the_picture(tiny_h3):
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base = tiny_h3.run()[0]
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mags = []
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for amount in (0.5, 1.5):
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patched, _ = _load(tiny_h3, amount=amount)
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mags.append(float((_run_patched(tiny_h3, patched)[0] - base).abs().mean()))
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assert all(m > 0 for m in mags)
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def test_switched_off_on_a_second_pass_the_patch_is_gone(tiny_h3):
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once, _ = _load(tiny_h3, amount=1.5)
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from modules.sampling.modifiers.nodes import FunPackLoadModifiers
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off, _ = FunPackLoadModifiers.execute(once, {"video_detail": {"enabled": False}}).result
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assert not off.object_patches, "a feature switched off kept its patch"
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assert torch.allclose(_run_patched(tiny_h3, off)[0], tiny_h3.run()[0])
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def test_the_norm_is_left_as_it_was(tiny_h3):
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patched, _ = _load(tiny_h3, amount=1.5)
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_run_patched(tiny_h3, patched)
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assert "forward" not in tiny_h3.patcher.model.diffusion_model.final_layer.norm.__dict__
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