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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):
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
def state_dict_converter(self):
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)
class RIFEInterpolater:
def __init__(self, model, device="cuda"):
self.model = model
self.device = device
# IFNet only does not support float16
self.torch_dtype = torch.float32
@staticmethod
def from_model_manager(model_manager):
return RIFEInterpolater(model_manager.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):
# Preprocess
processed_images = self.process_images(images)
for iter in range(num_iter):
# Input
input_tensor = torch.cat((processed_images[:-1], processed_images[1:]), dim=1)
# Interpolate
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
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)
# To 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 RIFESmoother(model_manager.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):
# Preprocess
processed_images = self.process_images(rendered_frames)
for iter in range(num_iter):
# Input
input_tensor = torch.cat((processed_images[:-2], processed_images[2:]), dim=1)
# Interpolate
output_tensor = self.process_tensors(input_tensor, scale=scale, batch_size=batch_size)
# Blend
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)
# Add to frames
processed_images[1:-1] = output_tensor
# To images
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
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