digital-garbageandClaude Sonnet 5
37c9c501bc
Compose-mode message and Editor tooltip were stating a false remedy (turn REINS down to passive-capture-only/strength 0 to free blocks) -- REINS claims all 50 blocks the moment it's on at all, strength only gates injection not installation. Corrected to say only fully disabling REINS frees any blocks. _select_best_seed had three silent failure paths (no usable prediction, a scoring exception, zero candidates scored) that burned real GPU compute and reported nothing. Added prints to all three. Added _output_value_fn_sample_count so "not ready yet" messages show the actual banked sample count instead of leaving the user blind on how close they are to the threshold. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
281 lines
12 KiB
Python
281 lines
12 KiB
Python
"""_install_h3_shadow_negative: install/no-op behaviour at the model-patching level.
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Exercised with fake model/diffusion-model stand-ins -- no real H3 weights or GPU needed.
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The per-block math itself (nag_blend, presentation-span detection, ...) is covered in
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tests/test_h3_shadow_negative.py; this file is about whether the installer clones the
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right model, hooks the right blocks, and is honest about not composing with the other
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dit-patch mechanisms.
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"""
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import sys
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from pathlib import Path
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import torch
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sys.path.insert(0, str(Path(__file__).resolve().parents[1]))
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sys.path.insert(0, ".")
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import _comfy_stubs # noqa: E402, F401
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from samplers import FunPackLTXAVSceneChainSampler as S, _format_block_ranges # noqa: E402
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def test_format_block_ranges_compresses_consecutive_runs():
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assert _format_block_ranges([0, 1, 2, 3, 25, 40, 41, 42]) == "0-3, 25, 40-42"
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def test_format_block_ranges_handles_empty_and_single():
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assert _format_block_ranges([]) == ""
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assert _format_block_ranges([7]) == "7"
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def test_format_block_ranges_sorts_and_dedupes():
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assert _format_block_ranges([5, 3, 4, 3]) == "3-5"
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class _FakeDiffusionModel:
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def __init__(self, n_blocks=4):
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self.blocks = [object() for _ in range(n_blocks)]
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self.hidden_size = 16
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class _FakeModel:
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def __init__(self, dm=None):
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self.model_options = {}
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self._dm = dm if dm is not None else _FakeDiffusionModel()
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def clone(self):
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m = _FakeModel(self._dm)
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m.model_options = dict(self.model_options)
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return m
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def get_model_object(self, name):
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if name == "diffusion_model":
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return self._dm
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raise KeyError(name)
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def _negative(tensor=None):
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return [(tensor if tensor is not None else torch.randn(1, 3, 16), {})]
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def test_both_scales_at_one_is_a_true_noop():
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model = _FakeModel()
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node = S()
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out = node._install_h3_shadow_negative(model, _negative(), 1.0, 1.0, 2.5, 0.35, 0.0, 0.6)
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assert out is model
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def test_unparseable_scale_is_a_noop():
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model = _FakeModel()
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node = S()
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out = node._install_h3_shadow_negative(model, _negative(), "nope", 1.0, 2.5, 0.35, 0.0, 0.6)
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assert out is model
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def test_nan_scale_is_a_noop_not_a_silent_corruption(capsys):
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"""NaN fails `video_scale == 1.0`, so that alone would let it through and NAG-blend
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every row to NaN with no warning -- must be caught explicitly, same as the codebase's
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other dit-patch strength guards (av_decouple, q_steering)."""
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model = _FakeModel()
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node = S()
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out = node._install_h3_shadow_negative(model, _negative(), float("nan"), 1.0,
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2.5, 0.35, 0.0, 0.6)
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assert out is model
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assert "NaN or infinite" in capsys.readouterr().out
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def test_inf_tau_is_a_noop():
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model = _FakeModel()
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node = S()
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out = node._install_h3_shadow_negative(model, _negative(), 3.0, 1.0,
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float("inf"), 0.35, 0.0, 0.6)
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assert out is model
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def test_nan_percent_is_a_noop():
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model = _FakeModel()
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node = S()
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out = node._install_h3_shadow_negative(model, _negative(), 3.0, 1.0,
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2.5, 0.35, float("nan"), 0.6)
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assert out is model
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def test_missing_negative_is_a_noop():
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model = _FakeModel()
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node = S()
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out = node._install_h3_shadow_negative(model, [], 3.0, 1.0, 2.5, 0.35, 0.0, 0.6)
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assert out is model
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def test_negative_with_empty_tensor_is_a_noop():
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model = _FakeModel()
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node = S()
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out = node._install_h3_shadow_negative(model, _negative(torch.zeros(1, 0, 16)),
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3.0, 1.0, 2.5, 0.35, 0.0, 0.6)
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assert out is model
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def test_a_model_with_no_double_blocks_is_a_noop():
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class _NoBlocks:
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pass
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model = _FakeModel(dm=_NoBlocks())
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node = S()
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out = node._install_h3_shadow_negative(model, _negative(), 3.0, 1.0, 2.5, 0.35, 0.0, 0.6)
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assert out is model
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def test_enabled_clones_and_hooks_every_block():
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model = _FakeModel(dm=_FakeDiffusionModel(n_blocks=5))
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node = S()
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patched = node._install_h3_shadow_negative(model, _negative(), 3.0, 1.0, 2.5, 0.35, 0.0, 0.6)
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assert patched is not model
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dit = patched.model_options["transformer_options"]["patches_replace"]["dit"]
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assert {k for k in dit if k[0] == "double_block"} == {("double_block", i) for i in range(5)}
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def test_swapped_start_and_end_percent_are_normalized():
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"""A user typing 0.6/0.0 instead of 0.0/0.6 should not silently produce an empty
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(start > end) active window -- the installer should sort them."""
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import h3_shadow_negative as sn
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model = _FakeModel()
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node = S()
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patched = node._install_h3_shadow_negative(model, _negative(), 3.0, 1.0, 2.5, 0.35, 0.6, 0.0)
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hook = patched.model_options["transformer_options"]["patches_replace"]["dit"][("double_block", 0)]
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state = next(c.cell_contents for c, name in
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zip(hook.__closure__, hook.__code__.co_freevars)
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if name == "state" and isinstance(c.cell_contents, sn.ShadowState))
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assert state.start_percent == 0.0 and state.end_percent == 0.6
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def test_supersedes_an_existing_block_patch_rather_than_composing(capsys):
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"""Non-composing by explicit design: it must fully replace whatever dit-patch mechanism
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(REINS/Q-steer/av_decouple/block-repeat/attn_temperature) already hooked a block, and
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say so loudly rather than silently overwrite it."""
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model = _FakeModel(dm=_FakeDiffusionModel(n_blocks=2))
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model.model_options["transformer_options"] = {"patches_replace": {"dit": {
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("double_block", 0): lambda args, extra: {"img": args["img"] * 0.0},
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}}}
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node = S()
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patched = node._install_h3_shadow_negative(model, _negative(), 3.0, 1.0, 2.5, 0.35, 0.0, 0.6)
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out = capsys.readouterr().out
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assert "supersedes" in out
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dit = patched.model_options["transformer_options"]["patches_replace"]["dit"]
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# The old hook must be GONE, not chained through -- calling the new one must not
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# reduce to zero the way the old one did.
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img = torch.ones(3, 4)
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result = dit[("double_block", 0)]({"img": img, "mod_segments": []},
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{"original_block": lambda a: {"img": a["img"]}})
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assert torch.allclose(result["img"], img), "should fall through to original_block, not the old hook"
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def test_a_hooked_block_with_no_mod_segments_falls_through_untouched():
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model = _FakeModel(dm=_FakeDiffusionModel(n_blocks=1))
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node = S()
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patched = node._install_h3_shadow_negative(model, _negative(), 3.0, 1.0, 2.5, 0.35, 0.0, 0.6)
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dit = patched.model_options["transformer_options"]["patches_replace"]["dit"]
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img = torch.ones(3, 4)
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out = dit[("double_block", 0)]({"img": img, "mod_segments": []},
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{"original_block": lambda a: {"img": a["img"] * 2.0}})
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assert torch.allclose(out["img"], img * 2.0)
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# --- compose=True: coexist with, rather than supersede, an already-claimed block ----------
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def test_compose_leaves_an_already_claimed_block_untouched(capsys):
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"""The whole point of compose: REINS' own block keeps its ORIGINAL hook (whatever it
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was), completely unreplaced, so REINS keeps capturing/steering there."""
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model = _FakeModel(dm=_FakeDiffusionModel(n_blocks=3))
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original_reins_hook = lambda args, extra: {"img": args["img"] * 0.0}
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model.model_options["transformer_options"] = {"patches_replace": {"dit": {
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("double_block", 1): original_reins_hook,
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}}}
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node = S()
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patched = node._install_h3_shadow_negative(model, _negative(), 3.0, 1.0, 2.5, 0.35, 0.0, 0.6,
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compose=True)
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dit = patched.model_options["transformer_options"]["patches_replace"]["dit"]
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assert dit[("double_block", 1)] is original_reins_hook, "claimed block must be untouched"
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assert "supersedes" not in capsys.readouterr().out
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def test_compose_still_hooks_every_unclaimed_block():
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model = _FakeModel(dm=_FakeDiffusionModel(n_blocks=3))
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model.model_options["transformer_options"] = {"patches_replace": {"dit": {
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("double_block", 1): lambda args, extra: {"img": args["img"]},
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}}}
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node = S()
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patched = node._install_h3_shadow_negative(model, _negative(), 3.0, 1.0, 2.5, 0.35, 0.0, 0.6,
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compose=True)
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dit = patched.model_options["transformer_options"]["patches_replace"]["dit"]
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import h3_shadow_negative as sn
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for i in (0, 2):
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hook = dit[("double_block", i)]
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state = next((c.cell_contents for c, name in
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zip(hook.__closure__, hook.__code__.co_freevars)
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if name == "state" and isinstance(c.cell_contents, sn.ShadowState)), None)
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assert state is not None, f"block {i} should have been hooked by shadow negative"
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def test_compose_off_still_supersedes_by_default(capsys):
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"""compose defaults to False, so existing behaviour (and existing tests/workflows) is
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unchanged unless the new checkbox is explicitly turned on."""
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model = _FakeModel(dm=_FakeDiffusionModel(n_blocks=2))
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model.model_options["transformer_options"] = {"patches_replace": {"dit": {
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("double_block", 0): lambda args, extra: {"img": args["img"] * 0.0},
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}}}
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node = S()
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patched = node._install_h3_shadow_negative(model, _negative(), 3.0, 1.0, 2.5, 0.35, 0.0, 0.6)
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out = capsys.readouterr().out
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assert "supersedes" in out
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dit = patched.model_options["transformer_options"]["patches_replace"]["dit"]
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assert dit[("double_block", 0)] is not None
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img = torch.ones(3, 4)
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result = dit[("double_block", 0)]({"img": img, "mod_segments": []},
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{"original_block": lambda a: {"img": a["img"]}})
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assert torch.allclose(result["img"], img), "default is still supersede, not compose"
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def test_compose_with_nothing_claimed_hooks_everything_like_the_default():
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model = _FakeModel(dm=_FakeDiffusionModel(n_blocks=4))
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node = S()
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patched = node._install_h3_shadow_negative(model, _negative(), 3.0, 1.0, 2.5, 0.35, 0.0, 0.6,
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compose=True)
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dit = patched.model_options["transformer_options"]["patches_replace"]["dit"]
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assert {k for k in dit if k[0] == "double_block"} == {("double_block", i) for i in range(4)}
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def test_compose_with_every_block_claimed_gets_its_own_clear_no_effect_message(capsys):
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"""REINS claims ALL 50 blocks unconditionally the moment it runs at all (steering OR
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passive-capture-only) -- not just its named steer block, and NOT gated by strength --
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for comfy_aimdo allocation-uniformity reasons, so compose mode is left with nothing at
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all whenever REINS is on in any form. This must read as a standalone "did nothing"
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headline, not a generic N/50 line buried under a full block-index dump, and it must
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give the CORRECT remedy: turning strength to 0 does not free anything (round-2 review
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caught the first version of this message claiming otherwise), only turning REINS fully
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off does."""
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model = _FakeModel(dm=_FakeDiffusionModel(n_blocks=3))
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model.model_options["transformer_options"] = {"patches_replace": {"dit": {
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("double_block", i): (lambda args, extra: {"img": args["img"]}) for i in range(3)
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}}}
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node = S()
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patched = node._install_h3_shadow_negative(model, _negative(), 3.0, 1.0, 2.5, 0.35, 0.0, 0.6,
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compose=True)
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out = capsys.readouterr().out
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assert "NO EFFECT" in out
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assert "aimdo" in out
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assert "strength only gates whether" in out, "must say strength controls injection, not installation"
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assert "fully OFF" in out and "passive-capture" in out, \
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"the only real remedy (disable REINS entirely) must be stated, not 'lower strength'"
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assert "down to passive-capture-only (strength 0)" not in out, \
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"this was the round-2-caught false remedy -- passive-capture ALSO claims all 50 blocks"
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dit = patched.model_options["transformer_options"]["patches_replace"]["dit"]
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assert all(k[0] != "double_block" or dit[k] is not None for k in dit)
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img = torch.ones(3, 4)
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result = dit[("double_block", 0)]({"img": img, "mod_segments": []},
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{"original_block": lambda a: {"img": a["img"] * 2.0}})
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assert torch.allclose(result["img"], img), "original per-block hook must survive untouched"
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if __name__ == "__main__":
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test_both_scales_at_one_is_a_true_noop()
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test_enabled_clones_and_hooks_every_block()
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print("ok (run via pytest for the rest)")
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