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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)
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