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 from huggingface_hub import hf_hub_download, snapshot_download 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, pw - w, 0, ph - h) return F.pad(img, padding), 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 = [] pbar = utils.ProgressBar(samples.shape[0], desc="RIFE inference") # [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:] I0, padding = pad_image(I0, upscale_amount) I0 = I0.to(torch.float) I1, _ = pad_image(I1, upscale_amount) I1 = I1.to(torch.float) # [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 = samples[b : b + 1] # print(f'upscale_amount:{upscale_amount}') # print(f'ssim:{upscale_amount}') # print(f'I0 shape:{I0.shape}') # print(f'I1 shape:{I1.shape}') I1, padding = pad_image(I1, upscale_amount) # print(f'I0 shape:{I0.shape}') # print(f'I1 shape:{I1.shape}') I1 = make_inference(model, I0, I1, upscale_amount, 1) # print(f'I0 shape:{I0.shape}') # print(f'I1[0] shape:{I1[0].shape}') I1 = I1[0] # print(f'I1[0] unpadded shape:{I1.shape}') I1_small = F.interpolate(I1, (32, 32), mode="bilinear", align_corners=False) ssim = ssim_matlab(I0_small[:, :3], I1_small[:, :3]) 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 = [] 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) # print(f'frame shape:{frame.shape}') frame = F.interpolate(frame, size=(h, w)) output.append(frame.to(output_device)) for i, tmp_frame in enumerate(tmp_output): # tmp_frame, _ = pad_image(tmp_frame, upscale_amount) tmp_frame = F.interpolate(tmp_frame, size=(h, w)) output.append(tmp_frame.to(output_device)) pbar.update(1) 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): # Open the video file video_capture = cv2.VideoCapture(video_path) fps = video_capture.get(cv2.CAP_PROP_FPS) # Get the frames per second tot_frame = int(video_capture.get(cv2.CAP_PROP_FRAME_COUNT)) # Total frames in the video pt_frame_data = [] pt_frame = skvideo.io.vreader(video_path) # Cyclic reading of the video frames while video_capture.isOpened(): ret, frame = video_capture.read() if not ret: break # BGR to RGB frame_rgb = frame[..., ::-1] frame_rgb = frame_rgb.copy() 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 = 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): 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) # if __name__ == "__main__": # snapshot_download(repo_id="AlexWortega/RIFE", local_dir="model_rife") # model = load_rife_model("model_rife") # video_path = rife_inference_with_path(model, "/mnt/ceph/develop/jiawei/CogVideo/output/20241003_130720.mp4") # print(video_path)