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Update core.py #632

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Update core.py #632

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35 changes: 4 additions & 31 deletions modules/impact/core.py
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Original file line number Diff line number Diff line change
Expand Up @@ -420,38 +420,11 @@ def enhance_detail_for_animatediff(image_frames, model, clip, vae, guide_size, g
# upscale the mask tensor by a factor of 2 using bilinear interpolation
if isinstance(noise_mask, np.ndarray):
noise_mask = torch.from_numpy(noise_mask)
noise_mask = noise_mask.unsqueeze(0) if len(noise_mask.shape) == 2 else noise_mask
upscaled_mask = [torch.nn.functional.interpolate(m.unsqueeze(0).unsqueeze(0), size=(new_h, new_w), mode='bilinear', align_corners=False).squeeze(0) for m in noise_mask]
upscaled_mask = torch.stack(upscaled_mask, dim=0) if len(upscaled_mask) > 1 else upscaled_mask[0]

if len(noise_mask.shape) == 2:
noise_mask = noise_mask.unsqueeze(0)
else: # == 3
noise_mask = noise_mask

upscaled_mask = None

for single_mask in noise_mask:
single_mask = single_mask.unsqueeze(0).unsqueeze(0)
upscaled_single_mask = torch.nn.functional.interpolate(single_mask, size=(new_h, new_w), mode='bilinear', align_corners=False)
upscaled_single_mask = upscaled_single_mask.squeeze(0)

if upscaled_mask is None:
upscaled_mask = upscaled_single_mask
else:
upscaled_mask = torch.cat((upscaled_mask, upscaled_single_mask), dim=0)

latent_frames = None
for image in image_frames:
image = torch.from_numpy(image).unsqueeze(0)

# upscale
upscaled_image = tensor_resize(image, new_w, new_h)

# ksampler
samples = to_latent_image(upscaled_image, vae)['samples']

if latent_frames is None:
latent_frames = samples
else:
latent_frames = torch.concat((latent_frames, samples), dim=0)
latent_frames = torch.cat([to_latent_image(tensor_resize(torch.from_numpy(frame).unsqueeze(0), new_w, new_h)['samples']) for frame in image_frames], dim=0)

cnet_images = None
if control_net_wrapper is not None:
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