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vtoonify_model.py
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vtoonify_model.py
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# this code is for web demo in colab notebook
from __future__ import annotations
import gradio as gr
import pathlib
import sys
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
import torch.nn.functional as F
from torchvision import transforms
from model.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) -> tuple[np.ndarray, str]:
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 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.'
if exstyle is None:
exstyle = self.exstyle
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'%(y_tilde.shape[2], y_tilde.shape[3]))
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) -> tuple[str, str]:
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 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'%(batch_size, int(video_cap.get(3)*4), int(video_cap.get(4)*4), num))
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)