Spaces:
Runtime error
Runtime error
File size: 15,454 Bytes
e7c3832 191cab2 e7c3832 3c6ed74 9e88758 e7c3832 296dc6e e7c3832 d1d6b7c e7c3832 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 |
from __future__ import annotations
import gradio as gr
import pathlib
import sys
sys.path.insert(0, 'vtoonify')
from util import load_psp_standalone, get_video_crop_parameter, tensor2cv2
import torch
import torch.nn as nn
import numpy as np
import dlib
import cv2
#from model.vtoonify import VToonify
#from model.bisenet.model import BiSeNet
from vtoonify import VToonify
from bisenet_model import BiSeNet
import torch.nn.functional as F
from torchvision import transforms
from encoder_align_all_parallel import align_face
import gc
import huggingface_hub
import os
MODEL_REPO = 'PKUWilliamYang/VToonify'
class Model():
def __init__(self, device):
super().__init__()
self.device = device
self.style_types = {
'cartoon1': ['vtoonify_d_cartoon/vtoonify_s026_d0.5.pt', 26],
'cartoon1-d': ['vtoonify_d_cartoon/vtoonify_s_d.pt', 26],
'cartoon2-d': ['vtoonify_d_cartoon/vtoonify_s_d.pt', 64],
'cartoon3-d': ['vtoonify_d_cartoon/vtoonify_s_d.pt', 153],
'cartoon4': ['vtoonify_d_cartoon/vtoonify_s299_d0.5.pt', 299],
'cartoon4-d': ['vtoonify_d_cartoon/vtoonify_s_d.pt', 299],
'cartoon5-d': ['vtoonify_d_cartoon/vtoonify_s_d.pt', 8],
'comic1-d': ['vtoonify_d_comic/vtoonify_s_d.pt', 28],
'comic2-d': ['vtoonify_d_comic/vtoonify_s_d.pt', 18],
'arcane1': ['vtoonify_d_arcane/vtoonify_s000_d0.5.pt', 0],
'arcane1-d': ['vtoonify_d_arcane/vtoonify_s_d.pt', 0],
'arcane2': ['vtoonify_d_arcane/vtoonify_s077_d0.5.pt', 77],
'arcane2-d': ['vtoonify_d_arcane/vtoonify_s_d.pt', 77],
'caricature1': ['vtoonify_d_caricature/vtoonify_s039_d0.5.pt', 39],
'caricature2': ['vtoonify_d_caricature/vtoonify_s068_d0.5.pt', 68],
'pixar': ['vtoonify_d_pixar/vtoonify_s052_d0.5.pt', 52],
'pixar-d': ['vtoonify_d_pixar/vtoonify_s_d.pt', 52],
'illustration1-d': ['vtoonify_d_illustration/vtoonify_s054_d_c.pt', 54],
'illustration2-d': ['vtoonify_d_illustration/vtoonify_s004_d_c.pt', 4],
'illustration3-d': ['vtoonify_d_illustration/vtoonify_s009_d_c.pt', 9],
'illustration4-d': ['vtoonify_d_illustration/vtoonify_s043_d_c.pt', 43],
'illustration5-d': ['vtoonify_d_illustration/vtoonify_s086_d_c.pt', 86],
}
self.landmarkpredictor = self._create_dlib_landmark_model()
self.parsingpredictor = self._create_parsing_model()
self.pspencoder = self._load_encoder()
self.transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean=[0.5, 0.5, 0.5],std=[0.5,0.5,0.5]),
])
self.vtoonify, self.exstyle = self._load_default_model()
self.color_transfer = False
self.style_name = 'cartoon1'
self.video_limit_cpu = 100
self.video_limit_gpu = 300
@staticmethod
def _create_dlib_landmark_model():
return dlib.shape_predictor(huggingface_hub.hf_hub_download(MODEL_REPO,
'models/shape_predictor_68_face_landmarks.dat'))
def _create_parsing_model(self):
parsingpredictor = BiSeNet(n_classes=19)
parsingpredictor.load_state_dict(torch.load(huggingface_hub.hf_hub_download(MODEL_REPO, 'models/faceparsing.pth'),
map_location=lambda storage, loc: storage))
parsingpredictor.to(self.device).eval()
return parsingpredictor
def _load_encoder(self) -> nn.Module:
style_encoder_path = huggingface_hub.hf_hub_download(MODEL_REPO,'models/encoder.pt')
return load_psp_standalone(style_encoder_path, self.device)
def _load_default_model(self) -> tuple[torch.Tensor, str]:
vtoonify = VToonify(backbone = 'dualstylegan')
vtoonify.load_state_dict(torch.load(huggingface_hub.hf_hub_download(MODEL_REPO,
'models/vtoonify_d_cartoon/vtoonify_s026_d0.5.pt'),
map_location=lambda storage, loc: storage)['g_ema'])
vtoonify.to(self.device)
tmp = np.load(huggingface_hub.hf_hub_download(MODEL_REPO,'models/vtoonify_d_cartoon/exstyle_code.npy'), allow_pickle=True).item()
exstyle = torch.tensor(tmp[list(tmp.keys())[26]]).to(self.device)
with torch.no_grad():
exstyle = vtoonify.zplus2wplus(exstyle)
return vtoonify, exstyle
def load_model(self, style_type: str) -> tuple[torch.Tensor, str]:
if 'illustration' in style_type:
self.color_transfer = True
else:
self.color_transfer = False
if style_type not in self.style_types.keys():
return None, 'Oops, wrong Style Type. Please select a valid model.'
self.style_name = style_type
model_path, ind = self.style_types[style_type]
style_path = os.path.join('models',os.path.dirname(model_path),'exstyle_code.npy')
self.vtoonify.load_state_dict(torch.load(huggingface_hub.hf_hub_download(MODEL_REPO,'models/'+model_path),
map_location=lambda storage, loc: storage)['g_ema'])
tmp = np.load(huggingface_hub.hf_hub_download(MODEL_REPO, style_path), allow_pickle=True).item()
exstyle = torch.tensor(tmp[list(tmp.keys())[ind]]).to(self.device)
with torch.no_grad():
exstyle = self.vtoonify.zplus2wplus(exstyle)
return exstyle, 'Model of %s loaded.'%(style_type)
def detect_and_align(self, frame, top, bottom, left, right, return_para=False):
message = 'Error: no face detected! Please retry or change the photo.'
paras = get_video_crop_parameter(frame, self.landmarkpredictor, [left, right, top, bottom])
instyle = None
h, w, scale = 0, 0, 0
if paras is not None:
h,w,top,bottom,left,right,scale = paras
H, W = int(bottom-top), int(right-left)
# for HR image, we apply gaussian blur to it to avoid over-sharp stylization results
kernel_1d = np.array([[0.125],[0.375],[0.375],[0.125]])
if scale <= 0.75:
frame = cv2.sepFilter2D(frame, -1, kernel_1d, kernel_1d)
if scale <= 0.375:
frame = cv2.sepFilter2D(frame, -1, kernel_1d, kernel_1d)
frame = cv2.resize(frame, (w, h))[top:bottom, left:right]
with torch.no_grad():
I = align_face(frame, self.landmarkpredictor)
if I is not None:
I = self.transform(I).unsqueeze(dim=0).to(self.device)
instyle = self.pspencoder(I)
instyle = self.vtoonify.zplus2wplus(instyle)
message = 'Successfully rescale the frame to (%d, %d)'%(bottom-top, right-left)
else:
frame = np.zeros((256,256,3), np.uint8)
else:
frame = np.zeros((256,256,3), np.uint8)
if return_para:
return frame, instyle, message, w, h, top, bottom, left, right, scale
return frame, instyle, message
#@torch.inference_mode()
def detect_and_align_image(self, image: str, top: int, bottom: int, left: int, right: int
) -> tuple[np.ndarray, torch.Tensor, str]:
if image is None:
return np.zeros((256,256,3), np.uint8), None, 'Error: fail to load empty file.'
frame = cv2.imread(image)
if frame is None:
return np.zeros((256,256,3), np.uint8), None, 'Error: fail to load the image.'
frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
return self.detect_and_align(frame, top, bottom, left, right)
def detect_and_align_video(self, video: str, top: int, bottom: int, left: int, right: int
) -> tuple[np.ndarray, torch.Tensor, str]:
if video is None:
return np.zeros((256,256,3), np.uint8), None, 'Error: fail to load empty file.'
video_cap = cv2.VideoCapture(video)
if video_cap.get(7) == 0:
video_cap.release()
return np.zeros((256,256,3), np.uint8), torch.zeros(1,18,512).to(self.device), 'Error: fail to load the video.'
success, frame = video_cap.read()
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
video_cap.release()
return self.detect_and_align(frame, top, bottom, left, right)
def detect_and_align_full_video(self, video: str, top: int, bottom: int, left: int, right: int) -> tuple[str, torch.Tensor, str]:
message = 'Error: no face detected! Please retry or change the video.'
instyle = None
if video is None:
return 'default.mp4', instyle, 'Error: fail to load empty file.'
video_cap = cv2.VideoCapture(video)
if video_cap.get(7) == 0:
video_cap.release()
return 'default.mp4', instyle, 'Error: fail to load the video.'
num = min(self.video_limit_gpu, int(video_cap.get(7)))
if self.device == 'cpu':
num = min(self.video_limit_cpu, num)
success, frame = video_cap.read()
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
frame, instyle, message, w, h, top, bottom, left, right, scale = self.detect_and_align(frame, top, bottom, left, right, True)
if instyle is None:
return 'default.mp4', instyle, message
fourcc = cv2.VideoWriter_fourcc(*'mp4v')
videoWriter = cv2.VideoWriter('input.mp4', fourcc, video_cap.get(5), (int(right-left), int(bottom-top)))
videoWriter.write(cv2.cvtColor(frame, cv2.COLOR_RGB2BGR))
kernel_1d = np.array([[0.125],[0.375],[0.375],[0.125]])
for i in range(num-1):
success, frame = video_cap.read()
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
if scale <= 0.75:
frame = cv2.sepFilter2D(frame, -1, kernel_1d, kernel_1d)
if scale <= 0.375:
frame = cv2.sepFilter2D(frame, -1, kernel_1d, kernel_1d)
frame = cv2.resize(frame, (w, h))[top:bottom, left:right]
videoWriter.write(cv2.cvtColor(frame, cv2.COLOR_RGB2BGR))
videoWriter.release()
video_cap.release()
return 'input.mp4', instyle, 'Successfully rescale the video to (%d, %d)'%(bottom-top, right-left)
def image_toonify(self, aligned_face: np.ndarray, instyle: torch.Tensor, exstyle: torch.Tensor, style_degree: float, style_type: str) -> tuple[np.ndarray, str]:
#print(style_type + ' ' + self.style_name)
if instyle is None or aligned_face is None:
return np.zeros((256,256,3), np.uint8), 'Opps, something wrong with the input. Please go to Step 2 and Rescale Image/First Frame again.'
if self.style_name != style_type:
exstyle, _ = self.load_model(style_type)
if exstyle is None:
return np.zeros((256,256,3), np.uint8), 'Opps, something wrong with the style type. Please go to Step 1 and load model again.'
with torch.no_grad():
if self.color_transfer:
s_w = exstyle
else:
s_w = instyle.clone()
s_w[:,:7] = exstyle[:,:7]
x = self.transform(aligned_face).unsqueeze(dim=0).to(self.device)
x_p = F.interpolate(self.parsingpredictor(2*(F.interpolate(x, scale_factor=2, mode='bilinear', align_corners=False)))[0],
scale_factor=0.5, recompute_scale_factor=False).detach()
inputs = torch.cat((x, x_p/16.), dim=1)
y_tilde = self.vtoonify(inputs, s_w.repeat(inputs.size(0), 1, 1), d_s = style_degree)
y_tilde = torch.clamp(y_tilde, -1, 1)
print('*** Toonify %dx%d image with style of %s'%(y_tilde.shape[2], y_tilde.shape[3], style_type))
return ((y_tilde[0].cpu().numpy().transpose(1, 2, 0) + 1.0) * 127.5).astype(np.uint8), 'Successfully toonify the image with style of %s'%(self.style_name)
def video_tooniy(self, aligned_video: str, instyle: torch.Tensor, exstyle: torch.Tensor, style_degree: float, style_type: str) -> tuple[str, str]:
#print(style_type + ' ' + self.style_name)
if aligned_video is None:
return 'default.mp4', 'Opps, something wrong with the input. Please go to Step 2 and Rescale Video again.'
video_cap = cv2.VideoCapture(aligned_video)
if instyle is None or aligned_video is None or video_cap.get(7) == 0:
video_cap.release()
return 'default.mp4', 'Opps, something wrong with the input. Please go to Step 2 and Rescale Video again.'
if self.style_name != style_type:
exstyle, _ = self.load_model(style_type)
if exstyle is None:
return 'default.mp4', 'Opps, something wrong with the style type. Please go to Step 1 and load model again.'
num = min(self.video_limit_gpu, int(video_cap.get(7)))
if self.device == 'cpu':
num = min(self.video_limit_cpu, num)
fourcc = cv2.VideoWriter_fourcc(*'mp4v')
videoWriter = cv2.VideoWriter('output.mp4', fourcc,
video_cap.get(5), (int(video_cap.get(3)*4),
int(video_cap.get(4)*4)))
batch_frames = []
if video_cap.get(3) != 0:
if self.device == 'cpu':
batch_size = max(1, int(4 * 256* 256/ video_cap.get(3) / video_cap.get(4)))
else:
batch_size = min(max(1, int(4 * 400 * 360/ video_cap.get(3) / video_cap.get(4))), 4)
else:
batch_size = 1
print('*** Toonify using batch size of %d on %dx%d video of %d frames with style of %s'%(batch_size, int(video_cap.get(3)*4), int(video_cap.get(4)*4), num, style_type))
with torch.no_grad():
if self.color_transfer:
s_w = exstyle
else:
s_w = instyle.clone()
s_w[:,:7] = exstyle[:,:7]
for i in range(num):
success, frame = video_cap.read()
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
batch_frames += [self.transform(frame).unsqueeze(dim=0).to(self.device)]
if len(batch_frames) == batch_size or (i+1) == num:
x = torch.cat(batch_frames, dim=0)
batch_frames = []
with torch.no_grad():
x_p = F.interpolate(self.parsingpredictor(2*(F.interpolate(x, scale_factor=2, mode='bilinear', align_corners=False)))[0],
scale_factor=0.5, recompute_scale_factor=False).detach()
inputs = torch.cat((x, x_p/16.), dim=1)
y_tilde = self.vtoonify(inputs, s_w.repeat(inputs.size(0), 1, 1), style_degree)
y_tilde = torch.clamp(y_tilde, -1, 1)
for k in range(y_tilde.size(0)):
videoWriter.write(tensor2cv2(y_tilde[k].cpu()))
gc.collect()
videoWriter.release()
video_cap.release()
return 'output.mp4', 'Successfully toonify video of %d frames with style of %s'%(num, self.style_name) |