from __future__ import annotations import numpy as np import gradio as gr import os import pathlib import gc import torch import dlib import cv2 import PIL from tqdm import tqdm import numpy as np import torch.nn.functional as F import torchvision from torchvision import transforms, utils from argparse import Namespace from datasets import augmentations from huggingface_hub import hf_hub_download from scripts.align_all_parallel import align_face from latent_optimization import latent_optimization from utils.inference_utils import save_image, load_image, visualize, get_video_crop_parameter, tensor2cv2, tensor2label, labelcolormap from models.psp import pSp from models.bisenet.model import BiSeNet from models.stylegan2.model import Generator class Model(): def __init__(self, device): super().__init__() self.device = device self.task_name = None self.editing_w = None self.pspex = None self.landmarkpredictor = dlib.shape_predictor(hf_hub_download('PKUWilliamYang/VToonify', 'models/shape_predictor_68_face_landmarks.dat')) self.transform = transforms.Compose([ transforms.ToTensor(), transforms.Normalize(mean=[0.5, 0.5, 0.5],std=[0.5,0.5,0.5]), ]) self.to_tensor = transforms.Compose([ transforms.ToTensor(), transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)), ]) self.maskpredictor = BiSeNet(n_classes=19) self.maskpredictor.load_state_dict(torch.load(hf_hub_download('PKUWilliamYang/VToonify', 'models/faceparsing.pth'), map_location='cpu')) self.maskpredictor.to(self.device).eval() self.parameters = {} self.parameters['inversion'] = {'path':'pretrained_models/styleganex_inversion.pt', 'image_path':'./data/ILip77SbmOE.png'} self.parameters['sr-32'] = {'path':'pretrained_models/styleganex_sr32.pt', 'image_path':'./data/pexels-daniel-xavier-1239291.jpg'} self.parameters['sr'] = {'path':'pretrained_models/styleganex_sr.pt', 'image_path':'./data/pexels-daniel-xavier-1239291.jpg'} self.parameters['sketch2face'] = {'path':'pretrained_models/styleganex_sketch2face.pt', 'image_path':'./data/234_sketch.jpg'} self.parameters['mask2face'] = {'path':'pretrained_models/styleganex_mask2face.pt', 'image_path':'./data/540.jpg'} self.parameters['edit_age'] = {'path':'pretrained_models/styleganex_edit_age.pt', 'image_path':'./data/390.mp4'} self.parameters['edit_hair'] = {'path':'pretrained_models/styleganex_edit_hair.pt', 'image_path':'./data/390.mp4'} self.parameters['toonify_pixar'] = {'path':'pretrained_models/styleganex_toonify_pixar.pt', 'image_path':'./data/pexels-anthony-shkraba-production-8136210.mp4'} self.parameters['toonify_cartoon'] = {'path':'pretrained_models/styleganex_toonify_cartoon.pt', 'image_path':'./data/pexels-anthony-shkraba-production-8136210.mp4'} self.parameters['toonify_arcane'] = {'path':'pretrained_models/styleganex_toonify_arcane.pt', 'image_path':'./data/pexels-anthony-shkraba-production-8136210.mp4'} self.print_log = True self.editing_dicts = torch.load(hf_hub_download('PKUWilliamYang/StyleGANEX', 'direction_dics.pt')) self.generator = Generator(1024, 512, 8) self.model_type = None self.error_info = 'Error: no face detected! \ StyleGANEX uses dlib.get_frontal_face_detector but sometimes it fails to detect a face. \ You can try several times or use other images until a face is detected, \ then switch back to the original image.' def load_model(self, task_name: str) -> None: if task_name == self.task_name: return if self.pspex is not None: del self.pspex torch.cuda.empty_cache() gc.collect() path = self.parameters[task_name]['path'] local_path = hf_hub_download('PKUWilliamYang/StyleGANEX', path) ckpt = torch.load(local_path, map_location='cpu') opts = ckpt['opts'] opts['checkpoint_path'] = local_path opts['device'] = self.device opts = Namespace(**opts) self.pspex = pSp(opts, ckpt).to(self.device).eval() self.pspex.latent_avg = self.pspex.latent_avg.to(self.device) if 'editing_w' in ckpt.keys(): self.editing_w = ckpt['editing_w'].clone().to(self.device) self.task_name = task_name torch.cuda.empty_cache() gc.collect() def load_G_model(self, model_type: str) -> None: if model_type == self.model_type: return torch.cuda.empty_cache() gc.collect() local_path = hf_hub_download('rinong/stylegan-nada-models', model_type+'.pt') self.generator.load_state_dict(torch.load(local_path, map_location='cpu')['g_ema'], strict=False) self.generator.to(self.device).eval() self.model_type = model_type torch.cuda.empty_cache() gc.collect() def tensor2np(self, img): tmp = ((img.cpu().numpy().transpose(1, 2, 0) + 1.0) * 127.5).astype(np.uint8) return tmp def process_sr(self, input_image: str, resize_scale: int, model: str) -> list[np.ndarray]: #false_image = np.zeros((256,256,3), np.uint8) #info = 'Error: no face detected! Please retry or change the photo.' if input_image is None: #return [false_image, false_image], 'Error: fail to load empty file.' raise gr.Error("Error: fail to load empty file.") frame = cv2.imread(input_image) if frame is None: #return [false_image, false_image], 'Error: fail to load the image.' raise gr.Error("Error: fail to load the image.") frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) if model is None or model == 'SR for 32x': task_name = 'sr-32' resize_scale = 32 else: task_name = 'sr' with torch.no_grad(): paras = get_video_crop_parameter(frame, self.landmarkpredictor) if paras is None: #return [false_image, false_image], info raise gr.Error(self.error_info) h,w,top,bottom,left,right,scale = paras H, W = int(bottom-top), int(right-left) frame = cv2.resize(frame, (w, h))[top:bottom, left:right] x1 = PIL.Image.fromarray(np.uint8(frame)) x1 = augmentations.BilinearResize(factors=[resize_scale//4])(x1) x1_up = x1.resize((W, H)) x2_up = align_face(np.array(x1_up), self.landmarkpredictor) if x2_up is None: #return [false_image, false_image], 'Error: no face detected! Please retry or change the photo.' raise gr.Error(self.error_info) x1_up = transforms.ToTensor()(x1_up).unsqueeze(dim=0).to(self.device) * 2 - 1 x2_up = self.transform(x2_up).unsqueeze(dim=0).to(self.device) if self.print_log: print('image loaded') self.load_model(task_name) if self.print_log: print('model %s loaded'%(task_name)) y_hat = torch.clamp(self.pspex(x1=x1_up, x2=x2_up, use_skip=self.pspex.opts.use_skip, resize=False), -1, 1) return [self.tensor2np(x1_up[0]), self.tensor2np(y_hat[0])] def process_s2f(self, input_image: str, seed: int) -> np.ndarray: task_name = 'sketch2face' with torch.no_grad(): x1 = transforms.ToTensor()(PIL.Image.open(input_image)).unsqueeze(0).to(self.device) if x1.shape[2] > 513: x1 = x1[:,:,(x1.shape[2]//2-256)//8*8:(x1.shape[2]//2+256)//8*8] if x1.shape[3] > 513: x1 = x1[:,:,:,(x1.shape[3]//2-256)//8*8:(x1.shape[3]//2+256)//8*8] x1 = x1[:,0:1] # uploaded files will be transformed to 3-channel RGB image! if self.print_log: print('image loaded') self.load_model(task_name) if self.print_log: print('model %s loaded'%(task_name)) self.pspex.train() torch.manual_seed(seed) y_hat = self.pspex(x1=x1, resize=False, latent_mask=[8,9,10,11,12,13,14,15,16,17], use_skip=self.pspex.opts.use_skip, inject_latent= self.pspex.decoder.style(torch.randn(1, 512).to(self.device)).unsqueeze(1).repeat(1,18,1) * 0.7) y_hat = torch.clamp(y_hat, -1, 1) self.pspex.eval() return self.tensor2np(y_hat[0]) def process_m2f(self, input_image: str, input_type: str, seed: int) -> list[np.ndarray]: #false_image = np.zeros((256,256,3), np.uint8) if input_image is None: raise gr.Error('Error: fail to load empty file.' ) #return [false_image, false_image], 'Error: fail to load empty file.' task_name = 'mask2face' with torch.no_grad(): if input_type == 'parsing mask': x1 = PIL.Image.open(input_image).getchannel(0) # uploaded files will be transformed to 3-channel RGB image! x1 = augmentations.ToOneHot(19)(x1) x1 = transforms.ToTensor()(x1).unsqueeze(dim=0).float().to(self.device) #print(x1.shape) else: frame = cv2.imread(input_image) if frame is None: #return [false_image, false_image], 'Error: fail to load the image.' raise gr.Error('Error: fail to load the image.' ) frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) paras = get_video_crop_parameter(frame, self.landmarkpredictor) if paras is None: #return [false_image, false_image], 'Error: no face detected! Please retry or change the photo.' raise gr.Error(self.error_info) h,w,top,bottom,left,right,scale = paras H, W = int(bottom-top), int(right-left) frame = cv2.resize(frame, (w, h))[top:bottom, left:right] # convert face image to segmentation mask x1 = self.to_tensor(frame).unsqueeze(0).to(self.device) # upsample image for precise segmentation x1 = F.interpolate(x1, scale_factor=2, mode='bilinear') x1 = self.maskpredictor(x1)[0] x1 = F.interpolate(x1, scale_factor=0.5).argmax(dim=1) x1 = F.one_hot(x1, num_classes=19).permute(0, 3, 1, 2).float().to(self.device) if x1.shape[2] > 513: x1 = x1[:,:,(x1.shape[2]//2-256)//8*8:(x1.shape[2]//2+256)//8*8] if x1.shape[3] > 513: x1 = x1[:,:,:,(x1.shape[3]//2-256)//8*8:(x1.shape[3]//2+256)//8*8] x1_viz = (tensor2label(x1[0], 19) / 192 * 256).astype(np.uint8) if self.print_log: print('image loaded') self.load_model(task_name) if self.print_log: print('model %s loaded'%(task_name)) self.pspex.train() torch.manual_seed(seed) y_hat = self.pspex(x1=x1, resize=False, latent_mask=[8,9,10,11,12,13,14,15,16,17], use_skip=self.pspex.opts.use_skip, inject_latent= self.pspex.decoder.style(torch.randn(1, 512).to(self.device)).unsqueeze(1).repeat(1,18,1) * 0.7) y_hat = torch.clamp(y_hat, -1, 1) self.pspex.eval() return [x1_viz, self.tensor2np(y_hat[0])] def process_editing(self, input_image: str, scale_factor: float, model_type: str) -> np.ndarray: #false_image = np.zeros((256,256,3), np.uint8) #info = 'Error: no face detected! Please retry or change the photo.' if input_image is None: #return false_image, false_image, 'Error: fail to load empty file.' raise gr.Error('Error: fail to load empty file.') frame = cv2.imread(input_image) if frame is None: #return false_image, false_image, 'Error: fail to load the image.' raise gr.Error('Error: fail to load the image.') frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) if model_type is None or model_type == 'reduce age': task_name = 'edit_age' else: task_name = 'edit_hair' with torch.no_grad(): paras = get_video_crop_parameter(frame, self.landmarkpredictor) if paras is None: #return false_image, false_image, info raise gr.Error(self.error_info) h,w,top,bottom,left,right,scale = paras H, W = int(bottom-top), int(right-left) frame = cv2.resize(frame, (w, h))[top:bottom, left:right] x1 = self.transform(frame).unsqueeze(0).to(self.device) x2 = align_face(frame, self.landmarkpredictor) if x2 is None: #return false_image, 'Error: no face detected! Please retry or change the photo.' raise gr.Error(self.error_info) x2 = self.transform(x2).unsqueeze(dim=0).to(self.device) if self.print_log: print('image loaded') self.load_model(task_name) if self.print_log: print('model %s loaded'%(task_name)) y_hat = self.pspex(x1=x1, x2=x2, use_skip=self.pspex.opts.use_skip, zero_noise=True, resize=False, editing_w= - scale_factor* self.editing_w[0:1]) y_hat = torch.clamp(y_hat, -1, 1) return self.tensor2np(y_hat[0]) def process_vediting(self, input_video: str, scale_factor: float, model_type: str, frame_num: int) -> tuple[list[np.ndarray], str]: #false_image = np.zeros((256,256,3), np.uint8) #info = 'Error: no face detected! Please retry or change the video.' if input_video is None: #return [false_image], 'default.mp4', 'Error: fail to load empty file.' raise gr.Error('Error: fail to load empty file.') video_cap = cv2.VideoCapture(input_video) success, frame = video_cap.read() if success is False: #return [false_image], 'default.mp4', 'Error: fail to load the video.' raise gr.Error('Error: fail to load the video.') frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) if model_type is None or model_type == 'reduce age': task_name = 'edit_age' else: task_name = 'edit_hair' with torch.no_grad(): paras = get_video_crop_parameter(frame, self.landmarkpredictor) if paras is None: #return [false_image], 'default.mp4', info raise gr.Error(self.error_info) h,w,top,bottom,left,right,scale = paras H, W = int(bottom-top), int(right-left) frame = cv2.resize(frame, (w, h))[top:bottom, left:right] x1 = self.transform(frame).unsqueeze(0).to(self.device) x2 = align_face(frame, self.landmarkpredictor) if x2 is None: #return [false_image], 'default.mp4', info raise gr.Error(self.error_info) x2 = self.transform(x2).unsqueeze(dim=0).to(self.device) if self.print_log: print('first frame loaded') self.load_model(task_name) if self.print_log: print('model %s loaded'%(task_name)) fourcc = cv2.VideoWriter_fourcc(*'mp4v') videoWriter = cv2.VideoWriter('output.mp4', fourcc, video_cap.get(5), (4*W, 4*H)) viz_frames = [] for i in range(frame_num): if i > 0: success, frame = video_cap.read() frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) frame = cv2.resize(frame, (w, h))[top:bottom, left:right] x1 = self.transform(frame).unsqueeze(0).to(self.device) y_hat = self.pspex(x1=x1, x2=x2, use_skip=self.pspex.opts.use_skip, zero_noise=True, resize=False, editing_w= - scale_factor * self.editing_w[0:1]) y_hat = torch.clamp(y_hat, -1, 1) videoWriter.write(tensor2cv2(y_hat[0].cpu())) if i < min(frame_num, 4): viz_frames += [self.tensor2np(y_hat[0])] videoWriter.release() return viz_frames, 'output.mp4' def process_toonify(self, input_image: str, style_type: str) -> np.ndarray: #false_image = np.zeros((256,256,3), np.uint8) #info = 'Error: no face detected! Please retry or change the photo.' if input_image is None: raise gr.Error('Error: fail to load empty file.') #return false_image, false_image, 'Error: fail to load empty file.' frame = cv2.imread(input_image) if frame is None: raise gr.Error('Error: fail to load the image.') #return false_image, false_image, 'Error: fail to load the image.' frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) if style_type is None or style_type == 'Pixar': task_name = 'toonify_pixar' elif style_type == 'Cartoon': task_name = 'toonify_cartoon' else: task_name = 'toonify_arcane' with torch.no_grad(): paras = get_video_crop_parameter(frame, self.landmarkpredictor) if paras is None: raise gr.Error(self.error_info) #return false_image, false_image, info h,w,top,bottom,left,right,scale = paras H, W = int(bottom-top), int(right-left) frame = cv2.resize(frame, (w, h))[top:bottom, left:right] x1 = self.transform(frame).unsqueeze(0).to(self.device) x2 = align_face(frame, self.landmarkpredictor) if x2 is None: raise gr.Error(self.error_info) #return false_image, 'Error: no face detected! Please retry or change the photo.' x2 = self.transform(x2).unsqueeze(dim=0).to(self.device) if self.print_log: print('image loaded') self.load_model(task_name) if self.print_log: print('model %s loaded'%(task_name)) y_hat = self.pspex(x1=x1, x2=x2, use_skip=self.pspex.opts.use_skip, zero_noise=True, resize=False) y_hat = torch.clamp(y_hat, -1, 1) return self.tensor2np(y_hat[0]), 'Done!' def process_vtoonify(self, input_video: str, style_type: str, frame_num: int) -> tuple[list[np.ndarray], str]: #false_image = np.zeros((256,256,3), np.uint8) #info = 'Error: no face detected! Please retry or change the video.' if input_video is None: raise gr.Error('Error: fail to load empty file.') #return [false_image], 'default.mp4', 'Error: fail to load empty file.' video_cap = cv2.VideoCapture(input_video) success, frame = video_cap.read() if success is False: raise gr.Error('Error: fail to load the video.') #return [false_image], 'default.mp4', 'Error: fail to load the video.' frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) if style_type is None or style_type == 'Pixar': task_name = 'toonify_pixar' elif style_type == 'Cartoon': task_name = 'toonify_cartoon' else: task_name = 'toonify_arcane' with torch.no_grad(): paras = get_video_crop_parameter(frame, self.landmarkpredictor) if paras is None: raise gr.Error(self.error_info) #return [false_image], 'default.mp4', info h,w,top,bottom,left,right,scale = paras H, W = int(bottom-top), int(right-left) frame = cv2.resize(frame, (w, h))[top:bottom, left:right] x1 = self.transform(frame).unsqueeze(0).to(self.device) x2 = align_face(frame, self.landmarkpredictor) if x2 is None: raise gr.Error(self.error_info) #return [false_image], 'default.mp4', info x2 = self.transform(x2).unsqueeze(dim=0).to(self.device) if self.print_log: print('first frame loaded') self.load_model(task_name) if self.print_log: print('model %s loaded'%(task_name)) fourcc = cv2.VideoWriter_fourcc(*'mp4v') videoWriter = cv2.VideoWriter('output.mp4', fourcc, video_cap.get(5), (4*W, 4*H)) viz_frames = [] for i in range(frame_num): if i > 0: success, frame = video_cap.read() frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) frame = cv2.resize(frame, (w, h))[top:bottom, left:right] x1 = self.transform(frame).unsqueeze(0).to(self.device) y_hat = self.pspex(x1=x1, x2=x2, use_skip=self.pspex.opts.use_skip, zero_noise=True, resize=False) y_hat = torch.clamp(y_hat, -1, 1) videoWriter.write(tensor2cv2(y_hat[0].cpu())) if i < min(frame_num, 4): viz_frames += [self.tensor2np(y_hat[0])] videoWriter.release() return viz_frames, 'output.mp4' def process_inversion(self, input_image: str, optimize: str, input_latent: file-object, editing_options: str, scale_factor: float, seed: int) -> tuple[np.ndarray, np.ndarray]: #false_image = np.zeros((256,256,3), np.uint8) #info = 'Error: no face detected! Please retry or change the photo.' if input_image is None: raise gr.Error('Error: fail to load empty file.') #return false_image, false_image, 'Error: fail to load empty file.' frame = cv2.imread(input_image) if frame is None: raise gr.Error('Error: fail to load the image.') #return false_image, false_image, 'Error: fail to load the image.' frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) task_name = 'inversion' self.load_model(task_name) if self.print_log: print('model %s loaded'%(task_name)) if input_latent is not None: if '.pt' not in input_latent.name: raise gr.Error('Error: the latent format is wrong') #return false_image, false_image, 'Error: the latent format is wrong' latents = torch.load(input_latent.name) if 'wplus' not in latents.keys() or 'f' not in latents.keys(): raise gr.Error('Error: the latent format is wrong') #return false_image, false_image, 'Error: the latent format is wrong' wplus = latents['wplus'].to(self.device) # w+ f = [latents['f'][0].to(self.device)] # f elif optimize == 'Latent optimization': wplus, f, _, _, _ = latent_optimization(frame, self.pspex, self.landmarkpredictor, step=500, device=self.device) else: with torch.no_grad(): paras = get_video_crop_parameter(frame, self.landmarkpredictor) if paras is None: raise gr.Error(self.error_info) #return false_image, false_image, info h,w,top,bottom,left,right,scale = paras H, W = int(bottom-top), int(right-left) frame = cv2.resize(frame, (w, h))[top:bottom, left:right] x1 = self.transform(frame).unsqueeze(0).to(self.device) x2 = align_face(frame, self.landmarkpredictor) if x2 is None: raise gr.Error(self.error_info) #return false_image, false_image, 'Error: no face detected! Please retry or change the photo.' x2 = self.transform(x2).unsqueeze(dim=0).to(self.device) if self.print_log: print('image loaded') wplus = self.pspex.encoder(x2) + self.pspex.latent_avg.unsqueeze(0) _, f = self.pspex.encoder(x1, return_feat=True) with torch.no_grad(): y_hat, _ = self.pspex.decoder([wplus], input_is_latent=True, first_layer_feature=f) y_hat = torch.clamp(y_hat, -1, 1) if 'Style Mixing' in editing_options: torch.manual_seed(seed) wplus[:, 8:] = self.pspex.decoder.style(torch.randn(1, 512).to(self.device)).unsqueeze(1).repeat(1,10,1) * 0.7 y_hat_edit, _ = self.pspex.decoder([wplus], input_is_latent=True, first_layer_feature=f) elif 'Attribute Editing' in editing_options: editing_w = self.editing_dicts[editing_options[19:]].to(self.device) y_hat_edit, _ = self.pspex.decoder([wplus+scale_factor*editing_w], input_is_latent=True, first_layer_feature=f) elif 'Domain Transfer' in editing_options: self.load_G_model(editing_options[17:]) if self.print_log: print('model %s loaded'%(editing_options[17:])) y_hat_edit, _ = self.generator([wplus], input_is_latent=True, first_layer_feature=f) else: y_hat_edit = y_hat y_hat_edit = torch.clamp(y_hat_edit, -1, 1) return self.tensor2np(y_hat[0]), self.tensor2np(y_hat_edit[0])