app.py

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()