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padding fix

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glide-the 2024-10-03 13:25:55 +08:00
parent 824feef38d
commit f0098c0662
1 changed files with 28 additions and 19 deletions
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47
inference/gradio_composite_demo/rife_model.py
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@ -9,7 +9,6 @@ import logging
import skvideo.io import skvideo.io
from rife.RIFE_HDv3 import Model from rife.RIFE_HDv3 import Model
from huggingface_hub import hf_hub_download, snapshot_download from huggingface_hub import hf_hub_download, snapshot_download
logger = logging.getLogger(__name__) logger = logging.getLogger(__name__)
device = "cuda" if torch.cuda.is_available() else "cpu" device = "cuda" if torch.cuda.is_available() else "cpu"
@ -20,8 +19,7 @@ def pad_image(img, scale):
tmp = max(32, int(32 / scale)) tmp = max(32, int(32 / scale))
ph = ((h - 1) // tmp + 1) * tmp ph = ((h - 1) // tmp + 1) * tmp
pw = ((w - 1) // tmp + 1) * tmp pw = ((w - 1) // tmp + 1) * tmp
padding = (0, pw - w, 0, ph - h) padding = (0, pw - w, 0, ph - h)
return F.pad(img, padding), padding return F.pad(img, padding), padding
@ -47,15 +45,15 @@ def ssim_interpolation_rife(model, samples, exp=1, upscale_amount=1, output_devi
for b in range(samples.shape[0]): for b in range(samples.shape[0]):
frame = samples[b : b + 1] frame = samples[b : b + 1]
_, _, h, w = frame.shape _, _, h, w = frame.shape
I0 = samples[b : b + 1] I0 = samples[b : b + 1]
I1 = samples[b + 1 : b + 2] if b + 2 < samples.shape[0] else samples[-1:] I1 = samples[b + 1 : b + 2] if b + 2 < samples.shape[0] else samples[-1:]
I0, padding = pad_image(I0, upscale_amount) I0, padding = pad_image(I0, upscale_amount)
I0 = I0.to(torch.float) I0 = I0.to(torch.float)
I1, _ = pad_image(I1, upscale_amount) I1, _ = pad_image(I1, upscale_amount)
I1 = I1.to(torch.float) I1 = I1.to(torch.float)
# [c, h, w] # [c, h, w]
I0_small = F.interpolate(I0, (32, 32), mode="bilinear", align_corners=False) I0_small = F.interpolate(I0, (32, 32), mode="bilinear", align_corners=False)
I1_small = F.interpolate(I1, (32, 32), mode="bilinear", align_corners=False) I1_small = F.interpolate(I1, (32, 32), mode="bilinear", align_corners=False)
@ -72,15 +70,23 @@ def ssim_interpolation_rife(model, samples, exp=1, upscale_amount=1, output_devi
# print(f'I0 shape:{I0.shape}') # print(f'I0 shape:{I0.shape}')
# print(f'I1 shape:{I1.shape}') # print(f'I1 shape:{I1.shape}')
I1 = make_inference(model, I0, I1, upscale_amount, 1) I1 = make_inference(model, I0, I1, upscale_amount, 1)
# print(f'I0 shape:{I0.shape}') # print(f'I0 shape:{I0.shape}')
# print(f'I1[0] shape:{I1[0].shape}') # print(f'I1[0] shape:{I1[0].shape}')
I1 = I1[0] I1 = I1[0]
# print(f'I1[0] unpadded shape:{I1.shape}') # print(f'I1[0] unpadded shape:{I1.shape}')
I1_small = F.interpolate(I1, (32, 32), mode="bilinear", align_corners=False) I1_small = F.interpolate(I1, (32, 32), mode="bilinear", align_corners=False)
ssim = ssim_matlab(I0_small[:, :3], I1_small[:, :3]) ssim = ssim_matlab(I0_small[:, :3], I1_small[:, :3])
frame = I1[padding[0] :, padding[2] :, : -padding[3], padding[1] :] if padding[3] > 0 and padding[1] >0 :
frame = I1[:, :, : -padding[3],:-padding[1]]
elif padding[3] > 0:
frame = I1[:, :, : -padding[3],:]
elif padding[1] >0:
frame = I1[:, :, :,:-padding[1]]
else:
frame = I1
tmp_output = [] tmp_output = []
if ssim < 0.2: if ssim < 0.2:
@ -95,7 +101,8 @@ def ssim_interpolation_rife(model, samples, exp=1, upscale_amount=1, output_devi
frame = F.interpolate(frame, size=(h, w)) frame = F.interpolate(frame, size=(h, w))
output.append(frame.to(output_device)) output.append(frame.to(output_device))
for i, tmp_frame in enumerate(tmp_output): for i, tmp_frame in enumerate(tmp_output):
# tmp_frame, _ = pad_image(tmp_frame, upscale_amount) # tmp_frame, _ = pad_image(tmp_frame, upscale_amount)
tmp_frame = F.interpolate(tmp_frame, size=(h, w)) tmp_frame = F.interpolate(tmp_frame, size=(h, w))
output.append(tmp_frame.to(output_device)) output.append(tmp_frame.to(output_device))
@ -138,7 +145,9 @@ def rife_inference_with_path(model, video_path):
frame_rgb = frame[..., ::-1] frame_rgb = frame[..., ::-1]
frame_rgb = frame_rgb.copy() frame_rgb = frame_rgb.copy()
tensor = torch.from_numpy(frame_rgb).float().to("cpu", non_blocking=True).float() / 255.0 tensor = torch.from_numpy(frame_rgb).float().to("cpu", non_blocking=True).float() / 255.0
pt_frame_data.append(tensor.permute(2, 0, 1)) # to [c, h, w,] pt_frame_data.append(
tensor.permute(2, 0, 1)
) # to [c, h, w,]
pt_frame = torch.from_numpy(np.stack(pt_frame_data)) pt_frame = torch.from_numpy(np.stack(pt_frame_data))
pt_frame = pt_frame.to(device) pt_frame = pt_frame.to(device)
@ -167,9 +176,9 @@ def rife_inference_with_latents(model, latents):
return torch.stack(rife_results) return torch.stack(rife_results)
if __name__ == "__main__": # if __name__ == "__main__":
snapshot_download(repo_id="AlexWortega/RIFE", local_dir="model_rife") # snapshot_download(repo_id="AlexWortega/RIFE", local_dir="model_rife")
model = load_rife_model("model_rife") # model = load_rife_model("model_rife")
video_path = rife_inference_with_path(model, "/mnt/ceph/develop/jiawei/CogVideo/output/chunk_3710_1.mp4") # video_path = rife_inference_with_path(model, "/mnt/ceph/develop/jiawei/CogVideo/output/20241003_130720.mp4")
print(video_path) # print(video_path)