olivv-cs-ComfyUI-FunPack/tests/test_second_pass.py

"""second_pass — a general two-pass split on the Chain Sampler, independent of anchor_shift.

anchor_shift built the machinery (stop the schedule, hand the state over, re-enter exactly);
this exposes it on its own so a scene can be finished differently from how it was started.
The invariants that matter here are the ones a split can silently get wrong:

1. BOTH schedules run in full, exactly as written. Pass 1 is not cut short and pass 2 is
   not derived from it — total steps are simply the two added up. (An earlier version cut
   pass 1 at pass 2's first sigma, which is anchor_shift's behaviour and does not belong
   here; it silently shortened the run and distorted the result.)
2. A hand-typed schedule is the one malformed-able input, and both ways it can be wrong
   are silent in the OUTPUT rather than loud at runtime, so they are refused up front.
3. Nothing is ever a silent no-op: every refusal comes back with a reason naming the fix.
4. The i2v anchor stays PINNED for pass 2 — the opposite of anchor_shift, which deletes
   the pin on purpose. Without this pass 2 re-denoises the reference frame.
"""
import sys
import types
from pathlib import Path

import torch

sys.path.insert(0, str(Path(__file__).resolve().parents[1]))

for _name in (
    "comfy", "comfy.k_diffusion", "comfy.k_diffusion.sampling",
    "comfy.model_sampling", "comfy.nested_tensor", "comfy.sample",
    "comfy.samplers", "comfy.utils", "comfy.model_management",
):
    sys.modules.setdefault(_name, types.ModuleType(_name))
sys.modules["comfy.nested_tensor"].NestedTensor = object
# _run_upsampler (detailing.py) moves tensors to the torch device and reads the VAE's
# per-channel statistics; on CPU with vae=None both are no-ops, which is what we want here.
sys.modules["comfy.model_management"].get_torch_device = lambda: torch.device("cpu")
sys.modules["comfy"].model_management = sys.modules["comfy.model_management"]

import samplers  # noqa: E402


# The user's own 9-step schedule.
SCHEDULE = torch.tensor([1.0, 0.955, 0.893, 0.812, 0.715, 0.603, 0.482, 0.241, 0.121, 0.0])


def _node():
    return samplers.FunPackLTXAVSceneChainSampler()


# ── the pass-2 schedule ─────────────────────────────────────────────────────

def _sched(alt):
    return _node()._second_pass_schedule(alt)


def test_a_well_formed_schedule_is_taken_exactly_as_written():
    """Nothing is derived, cut or continued. Pass 1 runs the main schedule in full and pass 2
    runs this one in full, so on the user's 9-step main a 4-step second pass is 13 steps —
    not a re-slicing of the main schedule."""
    alt = torch.tensor([0.812, 0.6, 0.35, 0.15, 0.0])
    out, reason = _sched(alt)
    assert reason is None and out is alt
    assert (SCHEDULE.numel() - 1) + (out.numel() - 1) == 13


def test_an_ascending_schedule_is_refused():
    """Both malformed cases are silent in the OUTPUT rather than loud at runtime, so they
    have to be caught before sampling. An ascending pair walks the trajectory backwards."""
    out, reason = _sched(torch.tensor([0.812, 0.4, 0.55, 0.0]))
    assert out is None and "must descend" in reason


def test_a_schedule_that_stops_short_of_zero_is_refused():
    """Stopping above 0 leaves the clip partially denoised — the noise-artefact symptom."""
    out, reason = _sched(torch.tensor([0.812, 0.5, 0.25]))
    assert out is None and "not 0" in reason


def test_equal_neighbouring_sigmas_are_not_treated_as_ascending():
    out, reason = _sched(torch.tensor([0.812, 0.6, 0.6, 0.0]))
    assert reason is None and out is not None


def test_a_missing_or_degenerate_schedule_is_refused_with_a_reason():
    """second_pass with nothing to run must say so, not silently sample once."""
    for alt in (None, torch.tensor([]), torch.tensor([0.5]), "0.8, 0.0"):
        out, reason = _sched(alt)
        assert out is None and reason


def test_a_schedule_starting_at_zero_is_refused():
    out, reason = _sched(torch.tensor([0.0, 0.0]))
    assert out is None and "nothing for it to denoise" in reason


# ── the pin survives the split ──────────────────────────────────────────────

def test_the_i2v_anchor_is_re_pinned_for_pass_two():
    """_sample_chunk drops noise_mask from what it returns, so without this pass 2 runs
    unpinned and re-denoises the reference frame. anchor_shift wants that; this does not."""
    frames = 6
    clean = torch.arange(frames, dtype=torch.float32).view(1, 1, frames, 1, 1).repeat(1, 4, 1, 2, 2)
    mask = torch.ones_like(clean)
    mask[:, :, :1] = 0.0                       # one pinned anchor frame
    chunk = {"samples": clean, "noise_mask": mask}
    # A "state" where the anchor frame has been polluted (as a rewind re-noise would).
    state = {"samples": torch.full_like(clean, -99.0)}

    out = _node()._restore_pinned_prefix(state, chunk)
    assert out.get("noise_mask") is not None
    # The pinned frame is back to the clean anchor...
    assert torch.equal(out["samples"][:, :, :1], clean[:, :, :1])
    # ...and nothing else was touched.
    assert torch.equal(out["samples"][:, :, 1:], state["samples"][:, :, 1:])


def test_restore_is_a_no_op_on_a_t2v_scene():
    """No pinned prefix = no anchor to protect; the state must come back untouched."""
    clean = torch.zeros(1, 4, 6, 2, 2)
    state = {"samples": torch.full_like(clean, 3.0)}
    for chunk in ({"samples": clean, "noise_mask": torch.ones_like(clean)},
                  {"samples": clean}):
        assert _node()._restore_pinned_prefix(state, chunk) is state


# ── the optional between-pass operations ────────────────────────────────────

class _FakeUpsampler:
    """Stands in for Lightricks' LatentUpsampler: 2x nearest on the spatial axes."""
    def __call__(self, x):
        return x.repeat_interleave(2, dim=-1).repeat_interleave(2, dim=-2)

    def parameters(self):
        yield torch.zeros(1)

    def to(self, *a, **k):
        return self

    def cpu(self):
        return self


def _video(f=4, h=8, w=8, fill=2.0):
    return {"samples": torch.full((1, 4, f, h, w), fill)}


def test_no_operation_is_the_default_and_returns_the_same_object():
    """The whole feature is opt-in: 'none' must not clone, resample or touch anything."""
    lat = _video()
    for op in ("none", None, ""):
        out, note = _node()._second_pass_operate(lat, op, _FakeUpsampler(), None)
        assert out is lat and note is None


def test_sharpen_comes_back_at_the_original_size():
    """sharpen is upsampler -> downscale: the latent pass 2 receives must be the SAME shape,
    or it is silently an upscale and costs four times as much."""
    out, note = _node()._second_pass_operate(_video(h=8, w=8), "sharpen", _FakeUpsampler(), None)
    assert out["samples"].shape == (1, 4, 4, 8, 8)
    assert "sharpen" in note


def test_upscale_2x_keeps_the_larger_latent():
    out, note = _node()._second_pass_operate(_video(h=8, w=8), "upscale_2x", _FakeUpsampler(), None)
    assert out["samples"].shape == (1, 4, 4, 16, 16)
    assert "4x the pixels" in note


def test_a_missing_upsampler_degrades_instead_of_failing_the_render():
    """A whole montage must not die because one optional sharpen could not load its model."""
    lat = _video()
    out, note = _node()._second_pass_operate(lat, "sharpen", None, None)
    assert out is lat and "no latent upsampler" in note


def test_an_upsampler_that_throws_degrades_too():
    class _Boom(_FakeUpsampler):
        def __call__(self, x):
            raise RuntimeError("out of memory")
    lat = _video()
    out, note = _node()._second_pass_operate(lat, "sharpen", _Boom(), None)
    assert out is lat and "upsampler failed" in note


def test_an_unknown_operation_is_reported_not_guessed():
    lat = _video()
    out, note = _node()._second_pass_operate(lat, "enhance", _FakeUpsampler(), None)
    assert out is lat and "unknown operation" in note


def test_the_pin_is_dropped_when_the_operation_changed_the_resolution():
    """upscale_2x leaves the chunk's anchor and mask the wrong size. Rescaling them would be
    inventing an anchor, so the pin is dropped — and the caller reports it."""
    clean = torch.zeros(1, 4, 4, 8, 8)
    mask = torch.ones_like(clean)
    mask[:, :, :1] = 0.0
    chunk = {"samples": clean, "noise_mask": mask}
    upscaled = {"samples": torch.zeros(1, 4, 4, 16, 16)}
    assert _node()._restore_pinned_prefix(upscaled, chunk) is upscaled