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Improve "Align Image to Reference"-node further #2956

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f6d01d8
remove manual 1 pixel shift to the left
pifroggi Jun 21, 2024
8ea4040
align reference to itself to compensate for shifts
pifroggi Jun 21, 2024
d9b97e9
try fixing ruff errors
pifroggi Aug 22, 2024
d2df798
try fixing ruff errors
pifroggi Aug 22, 2024
7a7c565
try fixing furr errors
pifroggi Aug 22, 2024
4ad9b80
try fixing ruff errors
pifroggi Aug 22, 2024
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174 changes: 102 additions & 72 deletions backend/src/nodes/impl/pytorch/rife/IFNet_HDv3_v4_14_align.py
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Original file line number Diff line number Diff line change
Expand Up @@ -144,99 +144,119 @@ def align_images(
blur_strength, # noqa: ANN001
ensemble, # noqa: ANN001
device, # noqa: ANN001
flow2=None, # noqa: ANN001
img1_blurred=None, # noqa: ANN001
):
# optional blur
if blur_strength is not None and blur_strength > 0:
blur = transforms.GaussianBlur(
kernel_size=(5, 5), sigma=(blur_strength, blur_strength)
)
img0_blurred = blur(img0)
img1_blurred = blur(img1)
else:
img0_blurred = img0
img1_blurred = img1

f0 = self.encode(img0_blurred[:, :3])
f1 = self.encode(img1_blurred[:, :3])
flow_list = []
mask_list = []
flow = None
mask = None
block = [self.block0, self.block1, self.block2, self.block3]
for i in range(4):
if flow is None:
flow, mask = block[i](
torch.cat(
(img0_blurred[:, :3], img1_blurred[:, :3], f0, f1, timestep), 1
),
None,
scale=scale_list[i],
)
if ensemble:
f_, m_ = block[i](
def compute_flow(
img0_blurred: np.ndarray, img1_blurred: np.ndarray, timestep: float
) -> None:
f0 = self.encode(img0_blurred[:, :3])
f1 = self.encode(img1_blurred[:, :3])
flow = None
mask = None
block = [self.block0, self.block1, self.block2, self.block3]
for i in range(4):
if flow is None:
flow, mask = block[i](
torch.cat(
(
img1_blurred[:, :3],
img0_blurred[:, :3],
f1,
img1_blurred[:, :3],
f0,
1 - timestep,
f1,
timestep,
),
1,
),
None,
scale=scale_list[i],
)
flow = (flow + torch.cat((f_[:, 2:4], f_[:, :2]), 1)) / 2
mask = (mask + (-m_)) / 2
else:
wf0 = warp(f0, flow[:, :2], device)
wf1 = warp(f1, flow[:, 2:4], device)
fd, m0 = block[i](
torch.cat(
(
img0_blurred[:, :3],
img1_blurred[:, :3],
wf0,
wf1,
timestep,
mask,
),
1,
),
flow,
scale=scale_list[i],
)
if ensemble:
f_, m_ = block[i](
if ensemble:
f_, m_ = block[i](
torch.cat(
(
img1_blurred[:, :3],
img0_blurred[:, :3],
f1,
f0,
1 - timestep,
),
1,
),
None,
scale=scale_list[i],
)
flow = (flow + torch.cat((f_[:, 2:4], f_[:, :2]), 1)) / 2
mask = (mask + (-m_)) / 2
else:
wf0 = warp(f0, flow[:, :2], device)
wf1 = warp(f1, flow[:, 2:4], device)
fd, m0 = block[i](
torch.cat(
(
img1_blurred[:, :3],
img0_blurred[:, :3],
wf1,
img1_blurred[:, :3],
wf0,
1 - timestep,
-mask,
wf1,
timestep,
mask,
),
1,
),
torch.cat((flow[:, 2:4], flow[:, :2]), 1),
flow,
scale=scale_list[i],
)
fd = (fd + torch.cat((f_[:, 2:4], f_[:, :2]), 1)) / 2
mask = (m0 + (-m_)) / 2
else:
mask = m0
flow = flow + fd
mask_list.append(mask)
flow_list.append(flow)
if ensemble:
f_, m_ = block[i](
torch.cat(
(
img1_blurred[:, :3],
img0_blurred[:, :3],
wf1,
wf0,
1 - timestep,
-mask,
),
1,
),
torch.cat((flow[:, 2:4], flow[:, :2]), 1),
scale=scale_list[i],
)
fd = (fd + torch.cat((f_[:, 2:4], f_[:, :2]), 1)) / 2
mask = (m0 + (-m_)) / 2
else:
mask = m0
flow = flow + fd
return flow

# Optional blur
if blur_strength is not None and blur_strength > 0:
blur = transforms.GaussianBlur(
kernel_size=(5, 5), sigma=(blur_strength, blur_strength)
)
img0_blurred = blur(img0)
if img1_blurred is None:
img1_blurred = blur(img1)
else:
img0_blurred = img0
img1_blurred = img1

# align image to reference
flow1 = compute_flow(img0_blurred, img1_blurred, timestep)

# align image to itself
if flow2 is None:
flow2 = compute_flow(img1_blurred, img1_blurred, timestep)

# subtract flow2 from flow1 to compensate for shifts
compensated_flow = flow1 - flow2

# apply warp to original image
aligned_img0 = warp(img0, flow_list[-1][:, :2], device)
# warp image with the compensated flow
aligned_img0 = warp(img0, compensated_flow[:, :2], device)

# add clamp here instead of in warplayer script, as it changes the output there
aligned_img0 = aligned_img0.clamp(min=0.0, max=1.0)
return aligned_img0, flow_list[-1]
aligned_img0 = aligned_img0.clamp(0, 1)
return aligned_img0, compensated_flow, flow2, img1_blurred

def forward(
self,
Expand All @@ -262,9 +282,19 @@ def forward(
else:
timestep = timestep.repeat(1, 1, img0.shape[2], img0.shape[3]) # type: ignore

flow2 = None
img1_blurred = None
for _iteration in range(num_iterations):
aligned_img0, flow = self.align_images(
img0, img1, timestep, scale_list, blur_strength, ensemble, device
aligned_img0, flow, flow2, img1_blurred = self.align_images(
img0,
img1,
timestep,
scale_list,
blur_strength,
ensemble,
device,
flow2,
img1_blurred,
)
img0 = aligned_img0 # use the aligned image as img0 for the next iteration

Expand Down
4 changes: 1 addition & 3 deletions backend/src/packages/chaiNNer_pytorch/pytorch/processing/align_image_to_reference.py
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Original file line number Diff line number Diff line change
Expand Up @@ -105,12 +105,10 @@ def align_images(

source_h, source_w, _ = get_h_w_c(source_img)

# resize, then shift reference left because rife shifts slightly to the right
# resize image to reference
target_img_resized = resize(
target_img, (source_w, source_h), filter=ResizeFilter.LANCZOS
)
target_img_resized = np.roll(target_img_resized, -1, axis=1)
target_img_resized[:, -1] = target_img_resized[:, -2]

# padding because rife can only work with multiples of 32 (changes with precision mode)
pad_h, pad_w = calculate_padding(source_h, source_w, precision_mode)
Expand Down
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