app.py

import os
import random

import torch
import gradio as gr

from e4e.models.psp import pSp
from util import *
from huggingface_hub import hf_hub_download

import tempfile
from argparse import Namespace
import shutil

import dlib
import numpy as np
import torchvision.transforms as transforms
from torchvision import utils

from model.sg2_model import Generator
from generate_videos import generate_frames, video_from_interpolations, project_code_by_edit_name
from styleclip.styleclip_global import project_code_with_styleclip, style_tensor_to_style_dict

import clip

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"),
               "sc_fs3": ("rinong/stylegan-nada-models", "fs3.npy"),
               "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", "sc_fs3"]]

        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"]
        )

        self.styleclip_fs3 = torch.from_numpy(np.load(model_paths["sc_fs3"])).to(self.device)

        self.clip_model, _ = clip.load("ViT-B/32", device=self.device)

        print("setup complete")

    def get_style_list(self):
        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_image))
        
        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, loop_styles=False):

        if "base" in output_styles:              # always start with base if chosen
            output_styles.insert(0, output_styles.pop(output_styles.index("base")))
        if loop_styles:
            output_styles.append(output_styles[0])

        return [self.generators[style] for style in output_styles]

    def _pack_edits(func):
        def inner(self, 
                  edit_type_choice, 
                  pose_slider, 
                  smile_slider, 
                  gender_slider, 
                  age_slider, 
                  hair_slider, 
                  src_text_styleclip, 
                  tar_text_styleclip, 
                  alpha_styleclip, 
                  beta_styleclip,
                  *args):

            edit_choices = {"edit_type": edit_type_choice,
                            "pose": pose_slider,
                            "smile": smile_slider,
                            "gender": gender_slider,
                            "age": age_slider,
                            "hair_length": hair_slider,
                            "src_text": src_text_styleclip,
                            "tar_text": tar_text_styleclip,
                            "alpha": alpha_styleclip,
                            "beta": beta_styleclip}
                            

            return func(self, *args, edit_choices)

        return inner

    def get_target_latents(self, source_latent, edit_choices, generators):

        np_source_latent = source_latent.squeeze(0).cpu().detach().numpy()

        target_latents = []

        if edit_choices["edit_type"] == "InterFaceGAN":
            for attribute_name in ["pose", "smile", "gender", "age", "hair_length"]:
                strength = edit_choices[attribute_name]
                if strength != 0.0:
                    target_latents.append(project_code_by_edit_name(np_source_latent, attribute_name, strength))

        elif edit_choices["edit_type"] == "StyleCLIP":
            source_s_dict = generators[0].get_s_code(source_latent, input_is_latent=True)
            target_latents.append(project_code_with_styleclip(source_s_dict, 
                                                              edit_choices["src_text"], 
                                                              edit_choices["tar_text"], 
                                                              edit_choices["alpha"], 
                                                              edit_choices["beta"], 
                                                              generators[0],
                                                              self.styleclip_fs3, 
                                                              self.clip_model))
        
        # if edit type is none or if all slides were set to 0
        if not target_latents:
            target_latents = [np_source_latent, ] * max((len(generators) - 1), 1)
        
        return target_latents

    @_pack_edits
    def edit_image(self, input, output_styles, edit_choices):
        return self.predict(input, output_styles, edit_choices=edit_choices)

    @_pack_edits
    def edit_video(self, input, output_styles, loop_styles, edit_choices):
        return self.predict(input, output_styles, generate_video=True, loop_styles=loop_styles, edit_choices=edit_choices)

    def predict(
        self,
        input,                  # Input image path
        output_styles,          # Style checkbox options.
        generate_video = False, # Generate a video instead of an output image
        loop_styles    = False, # Loop back to the initial style
        edit_choices   = None,  # Optional dictionary with edit choice arguments
    ):  

        if edit_choices is None:
            edit_choices = {"edit_type": "None"}

        # @title Align image
        out_dir = tempfile.mkdtemp()
        
        inverted_latent = self.invert_image(input)
        generators = self.get_generators_for_styles(output_styles, loop_styles)

        target_latents = self.get_target_latents(inverted_latent, edit_choices, generators)

        if not generate_video:
            output_paths = []

            with torch.no_grad():
                for g_ema in generators:
                    latent_for_gen = random.choice(target_latents)

                    if edit_choices["edit_type"] == "StyleCLIP":
                        latent_for_gen = style_tensor_to_style_dict(latent_for_gen, g_ema)
                        img, _ = g_ema(latent_for_gen, input_is_s_code=True, input_is_latent=True, truncation=1, randomize_noise=False)
                    else:
                        latent_for_gen = [torch.from_numpy(latent_for_gen).float().to(self.device)]
                        img, _ = g_ema(latent_for_gen, input_is_latent=True, truncation=1, randomize_noise=False)

                    output_path = os.path.join(out_dir, f"out_{len(output_paths)}.jpg")
                    utils.save_image(img, output_path, nrow=1, normalize=True, range=(-1, 1))

                    output_paths.append(output_path)

            return output_paths
        
        return self.generate_vid(generators, inverted_latent, target_latents, out_dir)

    def generate_vid(self, generators, source_latent, target_latents, out_dir):    

        fps = 24

        np_latent = source_latent.squeeze(0).cpu().detach().numpy()

        with tempfile.TemporaryDirectory() as dirpath:

            generate_frames(np_latent, target_latents, generators, dirpath)
            video_from_interpolations(fps, dirpath)
            
            gen_path = os.path.join(dirpath, "out.mp4")
            out_path = os.path.join(out_dir, "out.mp4")

            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("<h1><center>StyleGAN-NADA</center></h1>")
    gr.Markdown(
        "Demo for StyleGAN-NADA: CLIP-Guided Domain Adaptation of Image Generators (SIGGRAPH 2022)."
    )
    gr.Markdown(
        "For more information about the paper and code for training your own models (with examples OR text), see below."
    )


    gr.Markdown("<h4 style='font-size: 110%;margin-top:.5em'>On biases</h4><div>This model relies on StyleGAN and CLIP, both of which are prone to biases such as poor representation of minorities or reinforcement of societal biases, such as gender norms. </div>")
    
    with gr.Row():
        input_img = gr.inputs.Image(type="filepath", label="Input image")

        with gr.Column():    
            style_choice = gr.inputs.CheckboxGroup(choices=editor.get_style_list(), type="value", label="Choose your styles!")

            editing_type_choice = gr.Radio(choices=["None", "InterFaceGAN", "StyleCLIP"], label="Choose latent space editing option. For InterFaceGAN and StyleCLIP, set the options below:")
            
            with gr.Tabs():
                with gr.TabItem("InterFaceGAN Editing Options"):
                    gr.Markdown("Move the sliders to make the chosen attribute stronger (e.g. the person older) or leave at 0 to disable editing.")
                    gr.Markdown("If multiple options are provided, they will be used randomly between images (or sequentially for a video), <u>not</u> together.")
                    gr.Markdown("Please note that some directions may be entangled. For example, hair length adjustments are likely to also modify the perceived gender.")

                    pose_slider   = gr.Slider(label="Pose", minimum=-1, maximum=1, value=0, step=0.05)
                    smile_slider  = gr.Slider(label="Smile", minimum=-1, maximum=1, value=0, step=0.05)
                    gender_slider = gr.Slider(label="Perceived Gender", minimum=-1, maximum=1, value=0, step=0.05)
                    age_slider    = gr.Slider(label="Age", minimum=-1, maximum=1, value=0, step=0.05)
                    hair_slider   = gr.Slider(label="Hair Length", minimum=-1, maximum=1, value=0, step=0.05)

                    ig_edit_choices = [pose_slider, smile_slider, gender_slider, age_slider, hair_slider]

                with gr.TabItem("StyleCLIP Editing Options"):
                    gr.Markdown("Move the sliders to make the chosen attribute stronger (e.g. the person older) or leave at 0 to disable editing.")
                    gr.Markdown("If multiple options are provided, they will be used randomly between images (or sequentially for a video), <u>not</u> together")

                    src_text_styleclip = gr.Textbox(label="Source text")
                    tar_text_styleclip = gr.Textbox(label="Target text")

                    alpha_styleclip    = gr.Slider(label="Edit strength", minimum=-10, maximum=10, value=0, step=0.1)
                    beta_styleclip     = gr.Slider(label="Disentanglement Threshold", minimum=0.08, maximum=0.3, value=0.14, step=0.01)

                    sc_edit_choices = [src_text_styleclip, tar_text_styleclip, alpha_styleclip, beta_styleclip]

    with gr.Tabs():
        with gr.TabItem("Edit Images"):
            with gr.Row():
                with gr.Column():
                    with gr.Row():
                        img_button = gr.Button("Edit Image")
                with gr.Column():
                    img_output = gr.Gallery(label="Output Images")
            
        with gr.TabItem("Create Video"):
            with gr.Row():
                with gr.Column():
                    with gr.Row():
                        vid_button  = gr.Button("Generate Video")
                        loop_styles = gr.inputs.Checkbox(default=True, label="Loop video back to the initial style?")
                    with gr.Row():
                        gr.Markdown("Warning: Videos generation requires the synthesis of hundreds of frames and is expected to take several minutes.")
                        gr.Markdown("To reduce queue times, we significantly reduced the number of video frames. Using more than 3 styles will further reduce the frames per style, leading to quicker transitions. For better control, we reccomend cloning the gradio app, adjusting `num_alphas` in `generate_videos`, and running the code locally.")
                with gr.Column():
                    vid_output = gr.outputs.Video(label="Output Video")

    edit_inputs = [editing_type_choice] + ig_edit_choices + sc_edit_choices
    img_button.click(fn=editor.edit_image, inputs=edit_inputs + [input_img, style_choice], outputs=img_output)
    vid_button.click(fn=editor.edit_video, inputs=edit_inputs + [input_img, style_choice, loop_styles], outputs=vid_output)

    article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2108.00946' target='_blank'>StyleGAN-NADA: CLIP-Guided Domain Adaptation of Image Generators</a> | <a href='https://stylegan-nada.github.io/' target='_blank'>Project Page</a> | <a href='https://github.com/rinongal/StyleGAN-nada' target='_blank'>Code</a></p> <center><img src='https://visitor-badge.glitch.me/badge?page_id=rinong_sgnada' alt='visitor badge'></center>"
    gr.Markdown(article)

blocks.launch(enable_queue=True)