digital-garbageandClaude Opus 5
8b680dc4aa
A latent unsharp on H3's 24-channel latent does not translate to picture sharpness. Wrong mechanism, not wrong number; removed rather than tuned. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
211 lines
9.1 KiB
Python
211 lines
9.1 KiB
Python
"""Quality sharpness on a sampler whose loop we do not own.
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The unsharp mask that recovers fine detail lived inside Hybrid Euler 2S and Distilled Flow
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because that is where it was written. It reads only the current x0 prediction, the previous
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one, and the step's sigma — and sigma is an argument of every model call — so it lifts out
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through a denoiser proxy, the same way ALG does, and a stock KSampler can have it too.
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What these tests pin: the window is a fraction of the schedule (not a raw sigma), audio on a
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packed AV latent is never sharpened, and asking for it never costs you the sampler you chose.
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"""
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import sys
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import types
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from pathlib import Path
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import pytest
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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.modules.setdefault("folder_paths", types.SimpleNamespace(models_dir=""))
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import comfy.samplers as _cs # noqa: E402
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import samplers # noqa: E402
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from conditioning import FunPackStudio # noqa: E402
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def _sampler(fn):
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return types.SimpleNamespace(sampler_function=fn, extra_options={}, inpaint_options={})
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class _Model:
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"""A denoiser that returns a fixed prediction per call, and records what it was given."""
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def __init__(self, outputs):
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self._outputs = list(outputs)
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self.calls = []
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def __call__(self, x, sigma, **kwargs):
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self.calls.append(float(sigma.flatten()[0]) if hasattr(sigma, "flatten") else float(sigma))
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return self._outputs[min(len(self.calls) - 1, len(self._outputs) - 1)]
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def _capture(model, sigmas, sharpness=0.5, start_pct=0.5):
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"""Run the wrapped sampler over `sigmas`, returning what the proxy handed back each call."""
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seen = []
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def fn(m, x, sig, extra_args=None, callback=None, disable=None, **options):
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for s in sig[:-1]:
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seen.append(m(x, s.reshape(1)))
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return x
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wrapped = samplers._sharpen_wrap_sampler(_sampler(fn), sharpness, start_pct)
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wrapped.sampler_function(model, torch.zeros(1, 4), sigmas)
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return seen
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# --- the wrapper's contract -----------------------------------------------------------
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def test_off_returns_the_very_same_sampler():
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"""0 must not wrap. A no-op proxy still costs a Python call per model eval, and it would
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also hide a sampler that cannot be wrapped behind a wrapper that appears to work."""
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s = _sampler(lambda *a, **kw: None)
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assert samplers._sharpen_wrap_sampler(s, 0.0, 0.35) is s
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assert samplers._sharpen_wrap_sampler(s, None, 0.35) is s
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def test_unwrappable_sampler_reports_rather_than_pretending():
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"""Not every SAMPLER producer is KSAMPLER-shaped. None tells the caller to say so."""
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assert samplers._sharpen_wrap_sampler(object(), 0.5, 0.35) is None
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def test_wrapping_preserves_the_samplers_options():
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s = types.SimpleNamespace(sampler_function=lambda *a, **kw: None,
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extra_options={"eta": 0.5}, inpaint_options={"foo": 1})
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w = samplers._sharpen_wrap_sampler(s, 0.5, 0.35)
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assert w.extra_options == {"eta": 0.5}
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assert w.inpaint_options == {"foo": 1}
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# --- the window -----------------------------------------------------------------------
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def test_only_the_late_steps_are_sharpened():
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"""start_pct is a fraction of the schedule, converted to the sigma that starts it — the
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same derivation Hybrid Euler 2S uses for high_quality_pct, so the number means the same
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thing on both samplers."""
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preds = [torch.full((1, 4), float(v)) for v in (1.0, 2.0, 4.0, 8.0)]
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sigmas = torch.tensor([1.0, 0.75, 0.5, 0.25, 0.0])
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out = _capture(_Model(preds), sigmas, sharpness=1.0, start_pct=0.5)
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# 4 steps, last 50% = the final 2 -> threshold is sigmas[2] = 0.5.
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assert out[0] is preds[0] # sigma 1.0, early: untouched
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assert out[1] is preds[1] # sigma 0.75, early: untouched
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# sigma 0.5: first sharpened call, prev = 2.0 -> 4 + 1.0 * 0.5 * (4 - 2) = 5
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assert float(out[2][0, 0]) == pytest.approx(5.0)
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def test_first_sharpened_call_has_no_previous_and_passes_through():
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"""The window's opening evaluation has nothing to high-pass against. It must return the
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prediction unchanged rather than treating a missing reference as zero."""
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preds = [torch.full((1, 4), 3.0), torch.full((1, 4), 7.0)]
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out = _capture(_Model(preds), torch.tensor([1.0, 0.5, 0.0]), sharpness=1.0, start_pct=1.0)
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assert float(out[0][0, 0]) == pytest.approx(3.0)
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def test_start_pct_zero_never_fires():
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"""0% is off, and it must be off by never sharpening — not by sharpening at sigma 0."""
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preds = [torch.full((1, 4), float(v)) for v in (1.0, 5.0, 9.0)]
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out = _capture(_Model(preds), torch.tensor([1.0, 0.5, 0.25, 0.0]),
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sharpness=1.0, start_pct=0.0)
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assert [float(o[0, 0]) for o in out] == [1.0, 5.0, 9.0]
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def test_previous_is_the_sharpened_result_so_the_boost_is_self_limiting():
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"""The in-loop samplers set prev_denoised AFTER sharpening, which keeps the high-pass
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measured against an already-sharpened reference instead of compounding every step. The
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proxy matches that, or the same setting drifts between sampler types."""
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preds = [torch.full((1, 4), float(v)) for v in (0.0, 2.0, 4.0, 6.0)]
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out = _capture(_Model(preds), torch.tensor([1.0, 0.9, 0.8, 0.7, 0.0]),
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sharpness=1.0, start_pct=1.0)
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# prev=0 -> 2 + 0.5*(2-0) = 3 ; prev=3 -> 4 + 0.5*(4-3) = 4.5 ; prev=4.5 -> 6 + 0.75 = 6.75
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assert [float(o[0, 0]) for o in out] == pytest.approx([0.0, 3.0, 4.5, 6.75])
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# --- audio protection -----------------------------------------------------------------
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def test_audio_stream_of_a_packed_latent_is_not_sharpened(monkeypatch):
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"""On a packed [video | audio] latent the unsharp is a video-tuned perturbation; letting
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it reach the waveform is how the soundtrack degrades while the picture looks fine."""
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monkeypatch.setattr(
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samplers, "_get_latent_shapes",
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lambda model: [torch.Size([1, 2, 2, 2]), torch.Size([1, 2])], raising=False)
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preds = [torch.full((1, 1, 10), 1.0), torch.full((1, 1, 10), 3.0)]
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seen = []
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def fn(m, x, sig, extra_args=None, callback=None, disable=None, **options):
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for s in sig[:-1]:
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seen.append(m(x, s.reshape(1)))
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return x
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wrapped = samplers._sharpen_wrap_sampler(_sampler(fn), 1.0, 1.0)
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wrapped.sampler_function(_Model(preds), torch.zeros(1, 1, 10),
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torch.tensor([1.0, 0.5, 0.0]))
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assert seen[1].shape == preds[1].shape # the mask must not reshape the result
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assert float(seen[1][0, 0, 0]) == pytest.approx(4.0) # video: 3 + 0.5*(3-1)
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assert float(seen[1][0, 0, 9]) == pytest.approx(3.0) # audio: untouched
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def test_single_stream_latent_sharpens_everything():
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"""No packed layout means no audio to protect — the mask is None and must not be read as
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'mask everything out'."""
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preds = [torch.full((1, 4), 1.0), torch.full((1, 4), 3.0)]
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out = _capture(_Model(preds), torch.tensor([1.0, 0.5, 0.0]), sharpness=1.0, start_pct=1.0)
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assert float(out[1][0, 0]) == pytest.approx(4.0)
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# --- reaching it from the Studio ------------------------------------------------------
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@pytest.fixture
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def comfy_sampler_stub(monkeypatch):
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def calculate_sigmas(model_sampling, scheduler_name, steps):
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return torch.linspace(1.0, 0.0, steps + 1)
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monkeypatch.setattr(_cs, "calculate_sigmas", calculate_sigmas, raising=False)
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monkeypatch.setattr(_cs, "sampler_object",
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lambda name: _sampler(lambda *a, **kw: None), raising=False)
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class _FakeModel:
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def get_model_object(self, name):
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return f"model_sampling::{name}"
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def test_studio_wraps_the_ksampler_when_sharpness_is_set(comfy_sampler_stub):
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sampler, _ = FunPackStudio._build_one_sampler(
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{"type": "KSampler", "ksampler_name": "euler", "ksampler_steps": 8,
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"ksampler_scheduler": "karras", "ksampler_sharpness": 0.3},
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model=_FakeModel(),
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)
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assert sampler.sampler_function.__name__.endswith("_sharpen")
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def test_studio_leaves_the_ksampler_alone_at_the_default(comfy_sampler_stub):
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"""The default is off, and off must be the untouched stock sampler object."""
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sampler, _ = FunPackStudio._build_one_sampler(
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{"type": "KSampler", "ksampler_name": "euler", "ksampler_steps": 8,
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"ksampler_scheduler": "karras"},
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model=_FakeModel(),
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)
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assert not sampler.sampler_function.__name__.endswith("_sharpen")
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def test_studio_keeps_sampling_when_the_sampler_cannot_be_wrapped(monkeypatch, capsys):
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"""Asking for sharpening must never cost you the sampler you picked."""
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monkeypatch.setattr(_cs, "calculate_sigmas",
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lambda ms, name, steps: torch.linspace(1.0, 0.0, steps + 1),
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raising=False)
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monkeypatch.setattr(_cs, "sampler_object", lambda name: "SAMPLER::" + name, raising=False)
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sampler, _ = FunPackStudio._build_one_sampler(
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{"type": "KSampler", "ksampler_name": "euler", "ksampler_steps": 8,
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"ksampler_scheduler": "karras", "ksampler_sharpness": 0.3},
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model=_FakeModel(),
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)
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assert sampler == "SAMPLER::euler"
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assert "no sampler_function to wrap" in capsys.readouterr().out
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