import sys import os import torch sys.path.append(".") from gradio_wrapper.gradio_options import GradioTestOptions from models.hyperstyle.utils.model_utils import load_model from models.hyperstyle.utils.common import tensor2im from models.hyperstyle.utils.inference_utils import run_inversion from hyperstyle_global_directions.edit import load_direction_calculator, edit_image from torchvision import transforms import gradio as gr from utils.alignment import align_face import dlib from argparse import Namespace from mapper.styleclip_mapper import StyleCLIPMapper from PIL import Image opts_args = ['--no_fine_mapper'] opts = GradioTestOptions().parse(opts_args) mapper_dict = { 'afro':'./pretrained_models/styleCLIP_mappers/afro_hairstyle.pt', 'bob':'./pretrained_models/styleCLIP_mappers/bob_hairstyle.pt', 'bowl':'./pretrained_models/styleCLIP_mappers/bowl_hairstyle.pt', 'buzz':'./pretrained_models/styleCLIP_mappers/buzz_hairstyle.pt', 'caesar':'./pretrained_models/styleCLIP_mappers/caesar_hairstyle.pt', 'crew':'./pretrained_models/styleCLIP_mappers/crew_hairstyle.pt', 'pixie':'./pretrained_models/styleCLIP_mappers/pixie_hairstyle.pt', 'straight':'./pretrained_models/styleCLIP_mappers/straight_hairstyle.pt', 'undercut':'./pretrained_models/styleCLIP_mappers/undercut_hairstyle.pt', 'wavy':'./pretrained_models/styleCLIP_mappers/wavy_hairstyle.pt' } mapper_descs = { 'afro':'A face with an afro', 'bob':'A face with a bob-cut hairstyle', 'bowl':'A face with a bowl cut hairstyle', 'buzz':'A face with a buzz cut hairstyle', 'caesar':'A face with a caesar cut hairstyle', 'crew':'A face with a crew cut hairstyle', 'pixie':'A face with a pixie cut hairstyle', 'straight':'A face with a straight hair hairstyle', 'undercut':'A face with a undercut hairstyle', 'wavy':'A face with a wavy hair hairstyle', } predictor = dlib.shape_predictor("./pretrained_models/hyperstyle/shape_predictor_68_face_landmarks.lfs.dat") hyperstyle, hyperstyle_args = load_model(opts.hyperstyle_checkpoint_path, update_opts=opts) resize_amount = (256, 256) if hyperstyle_args.resize_outputs else (hyperstyle_args.output_size, hyperstyle_args.output_size) im2tensor_transforms = transforms.Compose([transforms.Resize((256, 256)), transforms.ToTensor(), transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])]) direction_calculator = load_direction_calculator(opts) with gr.Blocks() as demo: with gr.Row() as row: with gr.Column() as inputs: source = gr.Image(label="Image to Map", type='filepath') align = gr.Checkbox(True, label='Align Image') inverter_bools = gr.CheckboxGroup(["Hyperstyle", "E4E"], value=['Hyperstyle'], label='Inverter Choices') n_hyperstyle_iterations = gr.Number(5, label='Number of Iterations For Hyperstyle', precision=0) with gr.Box(): mapper_bool = gr.Checkbox(True, label='Output Mapper Result') with gr.Box() as mapper_opts: mapper_choice = gr.Dropdown(list(mapper_dict.keys()), value='afro', label='What Hairstyle Mapper to Use?') mapper_alpha = gr.Slider(minimum=-0.5, maximum=0.5, value=0.1, step=0.01, label='Strength of Mapper Alpha',) with gr.Box(): gd_bool = gr.Checkbox(False, label='Output Global Direction Result') with gr.Box(visible=False) as gd_opts: neutral_text = gr.Text(value='A face with hair', label='Neutral Text') target_text = gr.Text(value=mapper_descs['afro'], label='Target Text') alpha = gr.Slider(minimum=-10.0, maximum=10.0, value=4.1, step=0.1, label="Alpha for Global Direction") beta = gr.Slider(minimum=0.0, maximum=0.30, value=0.15, step=0.01, label="Beta for Global Direction") submit_button = gr.Button("Edit Image") with gr.Column() as outputs: with gr.Row() as hyperstyle_images: output_hyperstyle_mapper = gr.Image(type='pil', label="Hyperstyle Mapper") output_hyperstyle_gd = gr.Image(type='pil', label="Hyperstyle Global Directions", visible=False) with gr.Row(visible=False) as e4e_images: output_e4e_mapper = gr.Image(type='pil', label="E4E Mapper") output_e4e_gd = gr.Image(type='pil', label="E4E Global Directions", visible=False) def n_iter_change(number): if number < 0: return 0 else: return number def mapper_change(new_mapper): return mapper_descs[new_mapper] def inverter_toggles(bools): e4e_bool = 'E4E' in bools hyperstyle_bool = 'Hyperstyle' in bools return { hyperstyle_images: gr.update(visible=hyperstyle_bool), e4e_images: gr.update(visible=e4e_bool), n_hyperstyle_iterations: gr.update(visible=hyperstyle_bool) } def mapper_toggles(bool): return { mapper_opts: gr.update(visible=bool), output_hyperstyle_mapper: gr.update(visible=bool), output_e4e_mapper: gr.update(visible=bool) } def gd_toggles(bool): return { gd_opts: gr.update(visible=bool), output_hyperstyle_gd: gr.update(visible=bool), output_e4e_gd: gr.update(visible=bool) } n_hyperstyle_iterations.change(n_iter_change, n_hyperstyle_iterations, n_hyperstyle_iterations) mapper_choice.change(mapper_change, mapper_choice, [target_text]) inverter_bools.change(inverter_toggles, inverter_bools, [hyperstyle_images, e4e_images, n_hyperstyle_iterations]) mapper_bool.change(mapper_toggles, mapper_bool, [mapper_opts, output_hyperstyle_mapper, output_e4e_mapper]) gd_bool.change(gd_toggles, gd_bool, [gd_opts, output_hyperstyle_gd, output_e4e_gd]) def map_latent(mapper, inputs, stylespace=False, weight_deltas=None, strength=0.1): w = inputs.cuda() with torch.no_grad(): if stylespace: delta = mapper.mapper(w) w_hat = [c + strength * delta_c for (c, delta_c) in zip(w, delta)] x_hat, _, w_hat = mapper.decoder([w_hat], input_is_latent=True, return_latents=True, randomize_noise=False, truncation=1, input_is_stylespace=True, weights_deltas=weight_deltas) else: delta = mapper.mapper(w) w_hat = w + strength * delta x_hat, w_hat, _ = mapper.decoder([w_hat], input_is_latent=True, return_latents=True, randomize_noise=False, truncation=1, weights_deltas=weight_deltas) result_batch = (x_hat, w_hat) return result_batch def submit( src, align_img, inverter_bools, n_iterations, mapper_bool, mapper_choice, mapper_alpha, gd_bool, neutral_text, target_text, alpha, beta, ): torch.cuda.empty_cache() opts.checkpoint_path = mapper_dict[mapper_choice] ckpt = torch.load(mapper_dict[mapper_choice], map_location='cpu') mapper_args = ckpt['opts'] mapper_args.update(vars(opts)) mapper_args = Namespace(**mapper_args) mapper = StyleCLIPMapper(mapper_args) mapper.eval() mapper.cuda() with torch.no_grad(): output_imgs = [] if align_img: input_img = align_face(src, predictor) else: input_img = Image.open(src).convert('RGB') input_img = im2tensor_transforms(input_img).cuda() if gd_bool: opts.neutral_text = neutral_text opts.target_text = target_text opts.alpha = alpha opts.beta = beta if 'Hyperstyle' in inverter_bools: hyperstyle_batch, hyperstyle_latents, hyperstyle_deltas, _ = run_inversion(input_img.unsqueeze(0), hyperstyle, hyperstyle_args, return_intermediate_results=False) if mapper_bool: mapped_hyperstyle, _ = map_latent(mapper, hyperstyle_latents, stylespace=False, weight_deltas=hyperstyle_deltas, strength=mapper_alpha) mapped_hyperstyle = tensor2im(mapped_hyperstyle[0]) else: mapped_hyperstyle = None if gd_bool: gd_hyperstyle = edit_image(_, hyperstyle_latents[0], hyperstyle.decoder, direction_calculator, opts, hyperstyle_deltas)[0] gd_hyperstyle = tensor2im(gd_hyperstyle) else: gd_hyperstyle = None hyperstyle_output = [mapped_hyperstyle,gd_hyperstyle] else: hyperstyle_output = [None, None] output_imgs.extend(hyperstyle_output) if 'E4E' in inverter_bools: e4e_batch, e4e_latents = hyperstyle.w_invert(input_img.unsqueeze(0)) e4e_deltas = None if mapper_bool: mapped_e4e, _ = map_latent(mapper, e4e_latents, stylespace=False, weight_deltas=e4e_deltas, strength=mapper_alpha) mapped_e4e = tensor2im(mapped_e4e[0]) else: mapped_e4e = None if gd_bool: gd_e4e = edit_image(_, e4e_latents[0], hyperstyle.decoder, direction_calculator, opts, e4e_deltas)[0] gd_e4e = tensor2im(gd_e4e) else: gd_e4e = None e4e_output = [mapped_e4e, gd_e4e] else: e4e_output = [None, None] output_imgs.extend(e4e_output) return output_imgs submit_button.click( submit, [ source, align, inverter_bools, n_hyperstyle_iterations, mapper_bool, mapper_choice, mapper_alpha, gd_bool, neutral_text, target_text, alpha, beta, ], [output_hyperstyle_mapper, output_hyperstyle_gd, output_e4e_mapper, output_e4e_gd] ) demo.launch()