File size: 2,650 Bytes
04eb75a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 | from unittest.mock import patch
import pytest
import torch
import torch.nn as nn
from comfy.cli_args import args as cli_args
if not torch.cuda.is_available():
cli_args.cpu = True
import comfy.ldm.seedvr.vae as vae_mod # noqa: E402
from comfy_extras import nodes_seedvr # noqa: E402
_LATENT_CHANNELS = vae_mod.SEEDVR2_LATENT_CHANNELS
def _make_wrapper() -> vae_mod.VideoAutoencoderKLWrapper:
wrapper = vae_mod.VideoAutoencoderKLWrapper.__new__(
vae_mod.VideoAutoencoderKLWrapper
)
nn.Module.__init__(wrapper)
return wrapper
def _fingerprint_decode_(self, z, return_dict=True):
b = int(z.shape[0])
t = int(z.shape[2])
h = int(z.shape[3])
w = int(z.shape[4])
out = torch.empty(b, 3, t, h * 8, w * 8)
for batch_idx in range(b):
out[batch_idx].fill_(float(batch_idx + 1))
return out
def _decode_with_patches(wrapper, z):
with patch.object(vae_mod.VideoAutoencoderKL, "decode_", _fingerprint_decode_):
return wrapper.decode(z)
def test_decode_b2_t3_multi_frame_batch_unchanged():
wrapper = _make_wrapper()
out = _decode_with_patches(wrapper, torch.zeros(2, _LATENT_CHANNELS * 3, 2, 2))
assert tuple(out.shape) == (2, 3, 3, 16, 16)
class _Wrapper(vae_mod.VideoAutoencoderKLWrapper):
def __init__(self):
nn.Module.__init__(self)
self.calls = []
def parameters(self):
return iter([torch.nn.Parameter(torch.zeros(()))])
def _decode_stub(self, latent):
self.calls.append(tuple(latent.shape))
return torch.zeros(latent.shape[0], 3, latent.shape[2], latent.shape[3] * 8, latent.shape[4] * 8)
def test_seedvr2_wrapper_decode_accepts_5d_channel_first_latents_without_preprocessor_state():
wrapper = _Wrapper()
with patch.object(vae_mod.VideoAutoencoderKL, "decode_", _decode_stub):
out = wrapper.decode(torch.zeros(1, _LATENT_CHANNELS, 2, 4, 5))
assert tuple(out.shape) == (1, 3, 2, 32, 40)
assert wrapper.calls == [(1, _LATENT_CHANNELS, 2, 4, 5)]
def test_seedvr2_wrapper_decode_rejects_wrong_rank_latents():
wrapper = _Wrapper()
with pytest.raises(RuntimeError, match=r"latent input must be 4-D collapsed .* or 5-D"):
wrapper.decode(torch.zeros(1, _LATENT_CHANNELS, 4))
def _t_padded(t_in: int) -> int:
if t_in == 1:
return 1
if t_in <= 4:
return 5
if (t_in - 1) % 4 == 0:
return t_in
return t_in + (4 - ((t_in - 1) % 4))
@pytest.mark.parametrize("t_in", [1, 5, 9])
def test_t_padded_matches_cut_videos(t_in):
dummy = torch.zeros(1, t_in, 1, 1, 1)
assert nodes_seedvr.cut_videos(dummy).shape[1] == _t_padded(t_in)
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