digital-garbageandClaude Sonnet 4.6
6009d79ad4
Adaptive Low-Pass Guidance (arXiv:2506.08456): blurs the i2v anchor frame while sigma is in the near-pure-noise region, then swaps back to the sharp anchor, to stop the model shortcutting to a near-static video that just matches the reference image. Opt-in, off by default, no-op without an i2v anchor. Swaps model.latent_image per step (the same attribute ComfyUI's inpaint compositing already re-pastes every step) instead of monkeypatching comfy internals. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
102 lines
3.9 KiB
Python
102 lines
3.9 KiB
Python
"""Unit tests for the experimental ALG (Adaptive Low-Pass Guidance, arXiv:2506.08456)
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i2v anchor de-staticking helper used by sample_funpack_distilled_flow.
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Covers: frame-0-only blurring of the packed video stream, shape/identity preservation
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of every other region (later video frames + audio), and the no-op fallbacks when there's
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no usable latent_image or the packed layout can't be read.
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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 torch
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sys.path.insert(0, str(Path(__file__).resolve().parents[1]))
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# Minimal comfy stubs so `import samplers` works without a full ComfyUI env.
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for _name in (
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"comfy", "comfy.k_diffusion", "comfy.k_diffusion.sampling",
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"comfy.model_sampling", "comfy.nested_tensor", "comfy.sample",
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"comfy.samplers", "comfy.utils",
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):
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sys.modules.setdefault(_name, types.ModuleType(_name))
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sys.modules["comfy.nested_tensor"].NestedTensor = object
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import samplers # noqa: E402
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B, C, T, H, W = 1, 2, 2, 4, 4
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VIDEO_SHAPE = (B, C, T, H, W)
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AUDIO_SHAPE = (B, 6)
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VIDEO_SIZE = C * T * H * W # 64
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AUDIO_SIZE = 6
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N = VIDEO_SIZE + AUDIO_SIZE
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def _fake_model(shapes=(VIDEO_SHAPE, AUDIO_SHAPE)):
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return types.SimpleNamespace(inner_model=types.SimpleNamespace(conds={
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"positive": [{"model_conds": {"latent_shapes": types.SimpleNamespace(cond=list(shapes))}}],
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}))
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def _packed_latent_image():
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video = torch.arange(VIDEO_SIZE, dtype=torch.float32).reshape(1, 1, VIDEO_SIZE)
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# Make frame 0 a sharp checkerboard so blurring visibly changes it.
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checker = torch.tensor([[(i + j) % 2 for j in range(W)] for i in range(H)], dtype=torch.float32)
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video = video.reshape(B, C, T, H, W)
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video[:, :, 0] = checker
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audio = torch.full((B, 1, AUDIO_SIZE), 5.0)
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return torch.cat([video.reshape(B, 1, VIDEO_SIZE), audio], dim=-1)
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def test_alg_blur_returns_none_without_latent_image():
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model = _fake_model()
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assert samplers._alg_blur_video_frame0(model, None, 2.5) is None
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def test_alg_blur_returns_none_without_latent_shapes():
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model = types.SimpleNamespace(inner_model=types.SimpleNamespace(conds={}))
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latent_image = _packed_latent_image()
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assert samplers._alg_blur_video_frame0(model, latent_image, 2.5) is None
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def test_alg_blur_returns_none_on_layout_mismatch():
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model = _fake_model()
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wrong_size_latent = torch.zeros(1, 1, N + 1)
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assert samplers._alg_blur_video_frame0(model, wrong_size_latent, 2.5) is None
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def test_alg_blur_only_touches_frame_zero():
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model = _fake_model()
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latent_image = _packed_latent_image()
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out = samplers._alg_blur_video_frame0(model, latent_image, 2.5)
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assert out is not None
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assert out.shape == latent_image.shape
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video_out = out[..., :VIDEO_SIZE].reshape(B, C, T, H, W)
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video_in = latent_image[..., :VIDEO_SIZE].reshape(B, C, T, H, W)
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# Frame 0 changed (blurred away from the sharp checkerboard).
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assert not torch.allclose(video_out[:, :, 0], video_in[:, :, 0])
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# Every later video frame is untouched.
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assert torch.equal(video_out[:, :, 1:], video_in[:, :, 1:])
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# Audio region is byte-identical — ALG never touches audio.
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assert torch.equal(out[..., VIDEO_SIZE:], latent_image[..., VIDEO_SIZE:])
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def test_alg_blur_reduces_high_frequency_variance():
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model = _fake_model()
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latent_image = _packed_latent_image()
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out = samplers._alg_blur_video_frame0(model, latent_image, 2.5)
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frame0_in = latent_image[..., :VIDEO_SIZE].reshape(B, C, T, H, W)[:, :, 0]
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frame0_out = out[..., :VIDEO_SIZE].reshape(B, C, T, H, W)[:, :, 0]
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# A checkerboard has maximal local variance; the down+up-sampled copy should not.
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assert float(frame0_out.var()) < float(frame0_in.var())
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def test_alg_blur_is_deterministic():
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model = _fake_model()
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latent_image = _packed_latent_image()
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out1 = samplers._alg_blur_video_frame0(model, latent_image, 2.5)
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out2 = samplers._alg_blur_video_frame0(model, latent_image, 2.5)
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assert torch.equal(out1, out2)
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