import os from posixpath import basename import torch import gradio as gr import os import sys import numpy as np from e4e.models.psp import pSp from util import * from huggingface_hub import hf_hub_download import os import sys import tempfile import shutil from argparse import Namespace from pathlib import Path import shutil import dlib import numpy as np import torchvision.transforms as transforms from torchvision import utils from PIL import Image from model.sg2_model import Generator from generate_videos import generate_frames, video_from_interpolations, vid_to_gif model_dir = "models" os.makedirs(model_dir, exist_ok=True) model_repos = {"e4e": ("akhaliq/JoJoGAN_e4e_ffhq_encode", "e4e_ffhq_encode.pt"), "dlib": ("akhaliq/jojogan_dlib", "shape_predictor_68_face_landmarks.dat"), "base": ("akhaliq/jojogan-stylegan2-ffhq-config-f", "stylegan2-ffhq-config-f.pt"), "anime": ("rinong/stylegan-nada-models", "anime.pt"), "joker": ("rinong/stylegan-nada-models", "joker.pt"), # "simpson": ("rinong/stylegan-nada-models", "simpson.pt"), # "ssj": ("rinong/stylegan-nada-models", "ssj.pt"), # "white_walker": ("rinong/stylegan-nada-models", "white_walker.pt"), # "zuckerberg": ("rinong/stylegan-nada-models", "zuckerberg.pt"), # "cubism": ("rinong/stylegan-nada-models", "cubism.pt"), # "disney_princess": ("rinong/stylegan-nada-models", "disney_princess.pt"), # "edvard_munch": ("rinong/stylegan-nada-models", "edvard_munch.pt"), # "van_gogh": ("rinong/stylegan-nada-models", "van_gogh.pt"), # "oil": ("rinong/stylegan-nada-models", "oil.pt"), # "rick_morty": ("rinong/stylegan-nada-models", "rick_morty.pt"), # "botero": ("rinong/stylegan-nada-models", "botero.pt"), # "crochet": ("rinong/stylegan-nada-models", "crochet.pt"), # "modigliani": ("rinong/stylegan-nada-models", "modigliani.pt"), # "shrek": ("rinong/stylegan-nada-models", "shrek.pt"), # "sketch": ("rinong/stylegan-nada-models", "sketch.pt"), # "thanos": ("rinong/stylegan-nada-models", "thanos.pt"), } def get_models(): os.makedirs(model_dir, exist_ok=True) model_paths = {} for model_name, repo_details in model_repos.items(): download_path = hf_hub_download(repo_id=repo_details[0], filename=repo_details[1]) model_paths[model_name] = download_path return model_paths model_paths = get_models() class ImageEditor(object): def __init__(self): self.device = "cuda" if torch.cuda.is_available() else "cpu" latent_size = 512 n_mlp = 8 channel_mult = 2 model_size = 1024 self.generators = {} self.model_list = [name for name in model_paths.keys() if name not in ["e4e", "dlib"]] for model in self.model_list: g_ema = Generator( model_size, latent_size, n_mlp, channel_multiplier=channel_mult ).to(self.device) checkpoint = torch.load(model_paths[model], map_location=self.device) g_ema.load_state_dict(checkpoint['g_ema']) self.generators[model] = g_ema self.experiment_args = {"model_path": model_paths["e4e"]} self.experiment_args["transform"] = transforms.Compose( [ transforms.Resize((256, 256)), transforms.ToTensor(), transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5]), ] ) self.resize_dims = (256, 256) model_path = self.experiment_args["model_path"] ckpt = torch.load(model_path, map_location="cpu") opts = ckpt["opts"] opts["checkpoint_path"] = model_path opts = Namespace(**opts) self.e4e_net = pSp(opts, self.device) self.e4e_net.eval() self.shape_predictor = dlib.shape_predictor( model_paths["dlib"] ) print("setup complete") def get_style_list(self): # style_list = ['all', 'list - enter below'] style_list = [] for key in self.generators: style_list.append(key) return style_list def invert_image(self, input_image): input_image = self.run_alignment(str(input)) input_image = input_image.resize(self.resize_dims) img_transforms = self.experiment_args["transform"] transformed_image = img_transforms(input_image) with torch.no_grad(): images, latents = self.run_on_batch(transformed_image.unsqueeze(0)) result_image, latent = images[0], latents[0] inverted_latent = latent.unsqueeze(0).unsqueeze(1) return inverted_latent def get_generators_for_styles(self, output_styles): return [self.generators[style] for style in output_styles] def edit_image(self, input, output_styles): return self.predict(input, output_styles, False, False, None) def edit_video(self, input, output_styles, with_editing, video_format): return self.predict(input, output_styles, True, with_editing, video_format) def predict( self, input, # Input image path output_styles, # Which output style do you want to use? generate_video, # Generate a video instead of an output image with_editing, # Apply latent space editing to the generated video video_format # Choose gif to display in browser, mp4 for higher-quality downloadable video ): # @title Align image out_dir = Path(tempfile.mkdtemp()) out_path = out_dir / "out.jpg" inverted_latent = self.invert_image(input) generators = self.get_generators_for_styles(output_styles) if not generate_video: with torch.no_grad(): img_list = [] for g_ema in generators: img, _ = g_ema(inverted_latent, input_is_latent=True, truncation=1, randomize_noise=False) img_list.append(img) out_img = torch.cat(img_list, axis=0) utils.save_image(out_img, out_path, nrow=int(np.sqrt(out_img.size(0))), normalize=True, scale_each=True, range=(-1, 1)) return Path(out_path) return self.generate_vid(generators, inverted_latent, out_dir, video_format, with_editing) def generate_vid(self, generators, latent, out_dir, video_format, with_editing): np_latent = latent.squeeze(0).cpu().detach().numpy() args = { 'fps': 24, 'target_latents': None, 'edit_directions': None, 'unedited_frames': 0 if with_editing else 40 * (len(generators) - 1) } args = Namespace(**args) with tempfile.TemporaryDirectory() as dirpath: generate_frames(args, np_latent, generators, dirpath) video_from_interpolations(args.fps, dirpath) gen_path = Path(dirpath) / "out.mp4" out_path = out_dir / f"out.{video_format}" if video_format == 'gif': vid_to_gif(gen_path, out_dir, scale=256, fps=args.fps) else: shutil.copy2(gen_path, out_path) return out_path def run_alignment(self, image_path): aligned_image = align_face(filepath=image_path, predictor=self.shape_predictor) print("Aligned image has shape: {}".format(aligned_image.size)) return aligned_image def run_on_batch(self, inputs): images, latents = self.e4e_net( inputs.to(self.device).float(), randomize_noise=False, return_latents=True ) return images, latents editor = ImageEditor() # def change_component_visibility(component_types, invert_choices): # def visibility_impl(visible): # return [component_types[idx].update(visible=visible ^ invert_choices[idx]) for idx in range(len(component_types))] # return visibility_impl def group_visibility(visible): print("visible: ", visible) return gr.Group.update(visibile=visible) blocks = gr.Blocks() with blocks: gr.Markdown("
StyleGAN-NADA: CLIP-Guided Domain Adaptation of Image Generators | Project Page | Code