diff --git a/inference/gradio_composite_demo/rife_model.py b/inference/gradio_composite_demo/rife_model.py index dbb7d00..901038d 100644 --- a/inference/gradio_composite_demo/rife_model.py +++ b/inference/gradio_composite_demo/rife_model.py @@ -8,8 +8,10 @@ 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" @@ -18,8 +20,9 @@ def pad_image(img, scale): 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) + padding = (0, pw - w, 0, ph - h) + + return F.pad(img, padding), padding def make_inference(model, I0, I1, upscale_amount, n): @@ -36,15 +39,23 @@ def make_inference(model, I0, I1, upscale_amount, n): @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:] - I1 = pad_image(I1, upscale_amount) + + 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) @@ -52,15 +63,25 @@ def ssim_interpolation_rife(model, samples, exp=1, upscale_amount=1, output_devi ssim = ssim_matlab(I0_small[:, :3], I1_small[:, :3]) if ssim > 0.996: - I1 = I0 - I1 = pad_image(I1, upscale_amount) + 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) - 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] + # 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]) + frame = I1[padding[0] :, padding[2] :, : -padding[3], padding[1] :] + tmp_output = [] if ssim < 0.2: for i in range((2**exp) - 1): @@ -69,10 +90,16 @@ def ssim_interpolation_rife(model, samples, exp=1, upscale_amount=1, output_devi 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)) + 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 @@ -94,14 +121,24 @@ def frame_generator(video_capture): def rife_inference_with_path(model, video_path): + # Open the video file video_capture = cv2.VideoCapture(video_path) - tot_frame = video_capture.get(cv2.CAP_PROP_FRAME_COUNT) + 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) - 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 - ) + # 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) @@ -122,8 +159,17 @@ def rife_inference_with_latents(model, latents): 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/chunk_3710_1.mp4") + print(video_path)