Spaces:
Running
Running
File size: 4,399 Bytes
1aceaa0 |
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 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 |
import torch
from diffusers.image_processor import VaeImageProcessor
from torch.nn import functional as F
import cv2
import utils
from rife.pytorch_msssim import ssim_matlab
import numpy as np
import logging
import skvideo.io
from rife.RIFE_HDv3 import Model
logger = logging.getLogger(__name__)
device = "cuda" if torch.cuda.is_available() else "cpu"
def pad_image(img, scale):
_, _, h, w = img.shape
tmp = max(32, int(32 / scale))
ph = ((h - 1) // tmp + 1) * tmp
pw = ((w - 1) // tmp + 1) * tmp
padding = (0, 0, pw - w, ph - h)
return F.pad(img, padding)
def make_inference(model, I0, I1, upscale_amount, n):
middle = model.inference(I0, I1, upscale_amount)
if n == 1:
return [middle]
first_half = make_inference(model, I0, middle, upscale_amount, n=n // 2)
second_half = make_inference(model, middle, I1, upscale_amount, n=n // 2)
if n % 2:
return [*first_half, middle, *second_half]
else:
return [*first_half, *second_half]
@torch.inference_mode()
def ssim_interpolation_rife(model, samples, exp=1, upscale_amount=1, output_device="cpu"):
print(f"samples dtype:{samples.dtype}")
print(f"samples shape:{samples.shape}")
output = []
# [f, c, h, w]
for b in range(samples.shape[0]):
frame = samples[b : b + 1]
_, _, h, w = frame.shape
I0 = samples[b : b + 1]
I1 = samples[b + 1 : b + 2] if b + 2 < samples.shape[0] else samples[-1:]
I1 = pad_image(I1, upscale_amount)
# [c, h, w]
I0_small = F.interpolate(I0, (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])
if ssim > 0.996:
I1 = I0
I1 = pad_image(I1, upscale_amount)
I1 = make_inference(model, I0, I1, upscale_amount, 1)
I1_small = F.interpolate(I1[0], (32, 32), mode="bilinear", align_corners=False)
ssim = ssim_matlab(I0_small[:, :3], I1_small[:, :3])
frame = I1[0]
I1 = I1[0]
tmp_output = []
if ssim < 0.2:
for i in range((2**exp) - 1):
tmp_output.append(I0)
else:
tmp_output = make_inference(model, I0, I1, upscale_amount, 2**exp - 1) if exp else []
frame = pad_image(frame, upscale_amount)
tmp_output = [frame] + tmp_output
for i, frame in enumerate(tmp_output):
output.append(frame.to(output_device))
return output
def load_rife_model(model_path):
model = Model()
model.load_model(model_path, -1)
model.eval()
return model
# Create a generator that yields each frame, similar to cv2.VideoCapture
def frame_generator(video_capture):
while True:
ret, frame = video_capture.read()
if not ret:
break
yield frame
video_capture.release()
def rife_inference_with_path(model, video_path):
video_capture = cv2.VideoCapture(video_path)
tot_frame = video_capture.get(cv2.CAP_PROP_FRAME_COUNT)
pt_frame_data = []
pt_frame = skvideo.io.vreader(video_path)
for frame in pt_frame:
pt_frame_data.append(
torch.from_numpy(np.transpose(frame, (2, 0, 1))).to("cpu", non_blocking=True).float() / 255.0
)
pt_frame = torch.from_numpy(np.stack(pt_frame_data))
pt_frame = pt_frame.to(device)
pbar = utils.ProgressBar(tot_frame, desc="RIFE inference")
frames = ssim_interpolation_rife(model, pt_frame)
pt_image = torch.stack([frames[i].squeeze(0) for i in range(len(frames))])
image_np = VaeImageProcessor.pt_to_numpy(pt_image) # (to [49, 512, 480, 3])
image_pil = VaeImageProcessor.numpy_to_pil(image_np)
video_path = utils.save_video(image_pil, fps=16)
if pbar:
pbar.update(1)
return video_path
def rife_inference_with_latents(model, latents):
pbar = utils.ProgressBar(latents.shape[1], desc="RIFE inference")
rife_results = []
latents = latents.to(device)
for i in range(latents.size(0)):
# [f, c, w, h]
latent = latents[i]
frames = ssim_interpolation_rife(model, latent)
pt_image = torch.stack([frames[i].squeeze(0) for i in range(len(frames))]) # (to [f, c, w, h])
rife_results.append(pt_image)
return torch.stack(rife_results)
|