| import torch |
| import torch.nn as nn |
| import torch.nn.functional as F |
| import numpy as np |
| from PIL import Image |
|
|
|
|
| def warp(tenInput, tenFlow, device): |
| backwarp_tenGrid = {} |
| k = (str(tenFlow.device), str(tenFlow.size())) |
| if k not in backwarp_tenGrid: |
| tenHorizontal = torch.linspace(-1.0, 1.0, tenFlow.shape[3], device=device).view( |
| 1, 1, 1, tenFlow.shape[3]).expand(tenFlow.shape[0], -1, tenFlow.shape[2], -1) |
| tenVertical = torch.linspace(-1.0, 1.0, tenFlow.shape[2], device=device).view( |
| 1, 1, tenFlow.shape[2], 1).expand(tenFlow.shape[0], -1, -1, tenFlow.shape[3]) |
| backwarp_tenGrid[k] = torch.cat( |
| [tenHorizontal, tenVertical], 1).to(device) |
|
|
| tenFlow = torch.cat([tenFlow[:, 0:1, :, :] / ((tenInput.shape[3] - 1.0) / 2.0), |
| tenFlow[:, 1:2, :, :] / ((tenInput.shape[2] - 1.0) / 2.0)], 1) |
|
|
| g = (backwarp_tenGrid[k] + tenFlow).permute(0, 2, 3, 1) |
| return torch.nn.functional.grid_sample(input=tenInput, grid=g, mode='bilinear', padding_mode='border', align_corners=True) |
|
|
|
|
| def conv(in_planes, out_planes, kernel_size=3, stride=1, padding=1, dilation=1): |
| return nn.Sequential( |
| nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride, |
| padding=padding, dilation=dilation, bias=True), |
| nn.PReLU(out_planes) |
| ) |
|
|
|
|
| class IFBlock(nn.Module): |
| def __init__(self, in_planes, c=64): |
| super(IFBlock, self).__init__() |
| self.conv0 = nn.Sequential(conv(in_planes, c//2, 3, 2, 1), conv(c//2, c, 3, 2, 1),) |
| self.convblock0 = nn.Sequential(conv(c, c), conv(c, c)) |
| self.convblock1 = nn.Sequential(conv(c, c), conv(c, c)) |
| self.convblock2 = nn.Sequential(conv(c, c), conv(c, c)) |
| self.convblock3 = nn.Sequential(conv(c, c), conv(c, c)) |
| self.conv1 = nn.Sequential(nn.ConvTranspose2d(c, c//2, 4, 2, 1), nn.PReLU(c//2), nn.ConvTranspose2d(c//2, 4, 4, 2, 1)) |
| self.conv2 = nn.Sequential(nn.ConvTranspose2d(c, c//2, 4, 2, 1), nn.PReLU(c//2), nn.ConvTranspose2d(c//2, 1, 4, 2, 1)) |
|
|
| def forward(self, x, flow, scale=1): |
| x = F.interpolate(x, scale_factor= 1. / scale, mode="bilinear", align_corners=False, recompute_scale_factor=False) |
| flow = F.interpolate(flow, scale_factor= 1. / scale, mode="bilinear", align_corners=False, recompute_scale_factor=False) * 1. / scale |
| feat = self.conv0(torch.cat((x, flow), 1)) |
| feat = self.convblock0(feat) + feat |
| feat = self.convblock1(feat) + feat |
| feat = self.convblock2(feat) + feat |
| feat = self.convblock3(feat) + feat |
| flow = self.conv1(feat) |
| mask = self.conv2(feat) |
| flow = F.interpolate(flow, scale_factor=scale, mode="bilinear", align_corners=False, recompute_scale_factor=False) * scale |
| mask = F.interpolate(mask, scale_factor=scale, mode="bilinear", align_corners=False, recompute_scale_factor=False) |
| return flow, mask |
|
|
|
|
| class IFNet(nn.Module): |
| def __init__(self, **kwargs): |
| super(IFNet, self).__init__() |
| self.block0 = IFBlock(7+4, c=90) |
| self.block1 = IFBlock(7+4, c=90) |
| self.block2 = IFBlock(7+4, c=90) |
| self.block_tea = IFBlock(10+4, c=90) |
|
|
| def forward(self, x, scale_list=[4, 2, 1], training=False): |
| if training == False: |
| channel = x.shape[1] // 2 |
| img0 = x[:, :channel] |
| img1 = x[:, channel:] |
| flow_list = [] |
| merged = [] |
| mask_list = [] |
| warped_img0 = img0 |
| warped_img1 = img1 |
| flow = (x[:, :4]).detach() * 0 |
| mask = (x[:, :1]).detach() * 0 |
| block = [self.block0, self.block1, self.block2] |
| for i in range(3): |
| f0, m0 = block[i](torch.cat((warped_img0[:, :3], warped_img1[:, :3], mask), 1), flow, scale=scale_list[i]) |
| f1, m1 = block[i](torch.cat((warped_img1[:, :3], warped_img0[:, :3], -mask), 1), torch.cat((flow[:, 2:4], flow[:, :2]), 1), scale=scale_list[i]) |
| flow = flow + (f0 + torch.cat((f1[:, 2:4], f1[:, :2]), 1)) / 2 |
| mask = mask + (m0 + (-m1)) / 2 |
| mask_list.append(mask) |
| flow_list.append(flow) |
| warped_img0 = warp(img0, flow[:, :2], device=x.device) |
| warped_img1 = warp(img1, flow[:, 2:4], device=x.device) |
| merged.append((warped_img0, warped_img1)) |
| ''' |
| c0 = self.contextnet(img0, flow[:, :2]) |
| c1 = self.contextnet(img1, flow[:, 2:4]) |
| tmp = self.unet(img0, img1, warped_img0, warped_img1, mask, flow, c0, c1) |
| res = tmp[:, 1:4] * 2 - 1 |
| ''' |
| for i in range(3): |
| mask_list[i] = torch.sigmoid(mask_list[i]) |
| merged[i] = merged[i][0] * mask_list[i] + merged[i][1] * (1 - mask_list[i]) |
| return flow_list, mask_list[2], merged |
| |
| @staticmethod |
| def state_dict_converter(): |
| return IFNetStateDictConverter() |
|
|
|
|
| class IFNetStateDictConverter: |
| def __init__(self): |
| pass |
|
|
| def from_diffusers(self, state_dict): |
| state_dict_ = {k.replace("module.", ""): v for k, v in state_dict.items()} |
| return state_dict_ |
| |
| def from_civitai(self, state_dict): |
| return self.from_diffusers(state_dict), {"upcast_to_float32": True} |
|
|
|
|
| class RIFEInterpolater: |
| def __init__(self, model, device="cuda"): |
| self.model = model |
| self.device = device |
| |
| self.torch_dtype = torch.float32 |
|
|
| @staticmethod |
| def from_model_manager(model_manager): |
| return RIFEInterpolater(model_manager.fetch_model("rife"), device=model_manager.device) |
|
|
| def process_image(self, image): |
| width, height = image.size |
| if width % 32 != 0 or height % 32 != 0: |
| width = (width + 31) // 32 |
| height = (height + 31) // 32 |
| image = image.resize((width, height)) |
| image = torch.Tensor(np.array(image, dtype=np.float32)[:, :, [2,1,0]] / 255).permute(2, 0, 1) |
| return image |
| |
| def process_images(self, images): |
| images = [self.process_image(image) for image in images] |
| images = torch.stack(images) |
| return images |
| |
| def decode_images(self, images): |
| images = (images[:, [2,1,0]].permute(0, 2, 3, 1) * 255).clip(0, 255).numpy().astype(np.uint8) |
| images = [Image.fromarray(image) for image in images] |
| return images |
| |
| def add_interpolated_images(self, images, interpolated_images): |
| output_images = [] |
| for image, interpolated_image in zip(images, interpolated_images): |
| output_images.append(image) |
| output_images.append(interpolated_image) |
| output_images.append(images[-1]) |
| return output_images |
| |
|
|
| @torch.no_grad() |
| def interpolate_(self, images, scale=1.0): |
| input_tensor = self.process_images(images) |
| input_tensor = torch.cat((input_tensor[:-1], input_tensor[1:]), dim=1) |
| input_tensor = input_tensor.to(device=self.device, dtype=self.torch_dtype) |
| flow, mask, merged = self.model(input_tensor, [4/scale, 2/scale, 1/scale]) |
| output_images = self.decode_images(merged[2].cpu()) |
| if output_images[0].size != images[0].size: |
| output_images = [image.resize(images[0].size) for image in output_images] |
| return output_images |
| |
|
|
| @torch.no_grad() |
| def interpolate(self, images, scale=1.0, batch_size=4, num_iter=1, progress_bar=lambda x:x): |
| |
| processed_images = self.process_images(images) |
|
|
| for iter in range(num_iter): |
| |
| input_tensor = torch.cat((processed_images[:-1], processed_images[1:]), dim=1) |
|
|
| |
| output_tensor = [] |
| for batch_id in progress_bar(range(0, input_tensor.shape[0], batch_size)): |
| batch_id_ = min(batch_id + batch_size, input_tensor.shape[0]) |
| batch_input_tensor = input_tensor[batch_id: batch_id_] |
| batch_input_tensor = batch_input_tensor.to(device=self.device, dtype=self.torch_dtype) |
| flow, mask, merged = self.model(batch_input_tensor, [4/scale, 2/scale, 1/scale]) |
| output_tensor.append(merged[2].cpu()) |
| |
| |
| output_tensor = torch.concat(output_tensor, dim=0).clip(0, 1) |
| processed_images = self.add_interpolated_images(processed_images, output_tensor) |
| processed_images = torch.stack(processed_images) |
|
|
| |
| output_images = self.decode_images(processed_images) |
| if output_images[0].size != images[0].size: |
| output_images = [image.resize(images[0].size) for image in output_images] |
| return output_images |
|
|
|
|
| class RIFESmoother(RIFEInterpolater): |
| def __init__(self, model, device="cuda"): |
| super(RIFESmoother, self).__init__(model, device=device) |
|
|
| @staticmethod |
| def from_model_manager(model_manager): |
| return RIFEInterpolater(model_manager.fetch_model("rife"), device=model_manager.device) |
| |
| def process_tensors(self, input_tensor, scale=1.0, batch_size=4): |
| output_tensor = [] |
| for batch_id in range(0, input_tensor.shape[0], batch_size): |
| batch_id_ = min(batch_id + batch_size, input_tensor.shape[0]) |
| batch_input_tensor = input_tensor[batch_id: batch_id_] |
| batch_input_tensor = batch_input_tensor.to(device=self.device, dtype=self.torch_dtype) |
| flow, mask, merged = self.model(batch_input_tensor, [4/scale, 2/scale, 1/scale]) |
| output_tensor.append(merged[2].cpu()) |
| output_tensor = torch.concat(output_tensor, dim=0) |
| return output_tensor |
|
|
| @torch.no_grad() |
| def __call__(self, rendered_frames, scale=1.0, batch_size=4, num_iter=1, **kwargs): |
| |
| processed_images = self.process_images(rendered_frames) |
|
|
| for iter in range(num_iter): |
| |
| input_tensor = torch.cat((processed_images[:-2], processed_images[2:]), dim=1) |
|
|
| |
| output_tensor = self.process_tensors(input_tensor, scale=scale, batch_size=batch_size) |
| |
| |
| input_tensor = torch.cat((processed_images[1:-1], output_tensor), dim=1) |
| output_tensor = self.process_tensors(input_tensor, scale=scale, batch_size=batch_size) |
|
|
| |
| processed_images[1:-1] = output_tensor |
|
|
| |
| output_images = self.decode_images(processed_images) |
| if output_images[0].size != rendered_frames[0].size: |
| output_images = [image.resize(rendered_frames[0].size) for image in output_images] |
| return output_images |
|
|
