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

import base64

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"),
               "sketch": ("rinong/stylegan-nada-models", "sketch.pt"),
               "joker": ("rinong/stylegan-nada-models", "joker.pt"),
               "pixar": ("rinong/stylegan-nada-models", "pixar.pt"),
               "botero": ("rinong/stylegan-nada-models", "botero.pt"),
               "white_walker": ("rinong/stylegan-nada-models", "white_walker.pt"),
               "zuckerberg": ("rinong/stylegan-nada-models", "zuckerberg.pt"),
               "simpson": ("rinong/stylegan-nada-models", "simpson.pt"),
               "ssj": ("rinong/stylegan-nada-models", "ssj.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"),
               "anime": ("rinong/stylegan-nada-models", "anime.pt"),
               "shrek": ("rinong/stylegan-nada-models", "shrek.pt"),
               "thanos": ("rinong/stylegan-nada-models", "thanos.pt"),
               "ukiyoe": ("rinong/stylegan-nada-models", "ukiyoe.pt"),
               "groot": ("rinong/stylegan-nada-models", "groot.pt"),
               "witcher": ("rinong/stylegan-nada-models", "witcher.pt"),
               "grafitti_on_wall": ("rinong/stylegan-nada-models", "grafitti_on_wall.pt"),
               "modernism": ("rinong/stylegan-nada-models", "modernism.pt"),
               "marble": ("rinong/stylegan-nada-models", "marble.pt"),
               "vintage_comics": ("rinong/stylegan-nada-models", "vintage_comics.pt"),
               "crochet": ("rinong/stylegan-nada-models", "crochet.pt"),
               "modigliani": ("rinong/stylegan-nada-models", "modigliani.pt"),
               "ghibli": ("rinong/stylegan-nada-models", "ghibli.pt"),
               "elf": ("rinong/stylegan-nada-models", "elf.pt"),
               "zombie": ("rinong/stylegan-nada-models", "zombie.pt"),
               "werewolf": ("rinong/stylegan-nada-models", "werewolf.pt"),
               "plastic_puppet": ("rinong/stylegan-nada-models", "plastic_puppet.pt"),
               "mona_lisa": ("rinong/stylegan-nada-models", "mona_lisa.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):
        print("----- invert_image -----")
        print(str(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):

        target_latents = []

        if edit_choices["edit_type"] == "InterFaceGAN":
            np_source_latent = source_latent.squeeze(0).cpu().detach().numpy()

            for attribute_name in ["pose", "smile", "gender", "age", "hair_length"]:
                strength = edit_choices[attribute_name]
                if strength != 0.0:
                    projected_code_np = project_code_by_edit_name(np_source_latent, attribute_name, strength)
                    target_latents.append(torch.from_numpy(projected_code_np).float().to(self.device))

        elif edit_choices["edit_type"] == "StyleCLIP":
            if edit_choices["alpha"] != 0.0:
                source_s_dict = generators[0].get_s_code(source_latent, input_is_latent=True)[0]
                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 sliders were set to 0
        if not target_latents:
            target_latents = [source_latent.squeeze(0), ] * 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
    ):  

        print("----- edit_image input -----")
        print(input)
        print("----- edit_image output_styles -----")
        print(output_styles)
        print("----- edit_image edit_choices -----")
        print(edit_choices)

        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:
                        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)

            print("------ img_output ----\n")
            print(output_paths)
            
            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

        with tempfile.TemporaryDirectory() as dirpath:
            generate_frames(source_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):
        print("----- run_alignment image_path -----")
        print(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()

blocks = gr.Blocks()

with blocks:
    gr.Markdown("<h1><center>StyleGAN-NADA</center></h1>")
    gr.Markdown(
        "<h4 style='font-size: 110%;margin-top:.5em'>Inference demo for StyleGAN-NADA: CLIP-Guided Domain Adaptation of Image Generators (SIGGRAPH 2022).</h4>"
    )
    gr.Markdown(
        "<h4 style='font-size: 110%;margin-top:.5em'>Usage</h4><div>Upload an image of your face, pick your desired output styles, and apply StyleGAN-based editing.</div>"
        "<div>Choose the edit image tab to create static images in all chosen styles. Choose the video tab in order to interpolate between all chosen styles</div><div>(To make it easier on the servers, we've limited video length. If you add too many styles (we recommend no more than 3!), they'll pass in the blink of an eye! 🤗)</div>"
    )
    gr.Markdown(
        "For more information about the paper and code for training your own models (with text or images), please visit our <a href='https://stylegan-nada.github.io/' target='_blank'>project page</a> or the <a href='https://github.com/rinongal/StyleGAN-nada' target='_blank'>official repository</a>."
    )

    gr.Markdown("<h4 style='font-size: 110%;margin-top:.5em'>A note on social impact</h4><div>This model relies on StyleGAN and CLIP, both of which are prone to biases inherited from their training data and their architecture. These may include (but are not limited to) poor representation of minorities or the perpetution of societal biases, such as gender norms. In particular, StyleGAN editing may induce undesired changes in skin tones. Moreover, generative models can, and have been used to create deep fake imagery which may assist in the spread of propaganda. However, <a href='https://github.com/NVlabs/stylegan3-detector' target='_blank'>tools are available</a> for identifying StyleGAN generated imagery, and any 'realistic' results produced by this model should be easily identifiable through such tools.</div>")
    
    with gr.Row():
        with gr.Column():
            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.Row():
        with gr.Column():
            with gr.Tabs():
                with gr.TabItem("Edit Images"):
                    img_button = gr.Button("Edit Image")
                    img_output = gr.Gallery(label="Output Images")
                    
                with gr.TabItem("Create Video"):
                    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():
                        with gr.Column():
                            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 recommend cloning the gradio app, adjusting <b>num_alphas</b> in <b>generate_videos.py</b>, and running the code locally.")
                    vid_output = gr.outputs.Video(label="Output Video")

        with gr.Column():            
            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.")

                    gr.Markdown("For more information about InterFaceGAN, please visit <a href='https://github.com/genforce/interfacegan' target='_blank'>the official repository</a>")

                    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("Choose source and target descriptors, such as 'face with hair' to 'face with curly hair'")
                    gr.Markdown("Editing strength controls the magnitude of change. Disentanglement thresholds limits the number of channels the network can modify, reducing possible leak into other attributes. Setting the threshold too high may lead to no available channels. If you see an error, lower the threshold and try again.")
                    gr.Markdown("For more information about StyleCLIP, please visit <a href='https://github.com/orpatashnik/StyleCLIP' target='_blank'>the official repository</a>")

                    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=1, 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]

    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()


"""
def my_inference_function(image,text):
  #img_dir = tempfile.mkdtemp()
    
  #img_path = os.path.join(img_dir, f"_112.jpeg")
  #img_path ="/tmp/111_112.jpeg"
  #decodedData = base64.b64decode((image))

  #print("----- my_inference_function img_path -----")
  #print(img_path)
    
  # Write Image from Base64 File
  #imgFile = open(img_path, 'wb')
  #imgFile.write(decodedData)
  #imgFile.close()

  #with open(img_path, "wb") as fh:
  #  fh.write(base64.urlsafe_b64decode(image))

  #file = open(img_path, 'rb')
  #encoded_data = file.read()
  #file.close()

  tmp_img_file = tempfile.NamedTemporaryFile(delete=None, suffix='.jpg')
  img_blob = base64.urlsafe_b64decode(image)
  tmp_img_file.write(img_blob)
  tmp_img_file.close()
     
  print("Name of the file is:", tmp_img_file.name)

  img_path = tmp_img_file.name
      
  return editor.predict(img_path,[text],edit_choices={'edit_type': 'None', 'pose': 0, 'smile': 0, 'gender': 0, 'age': 0, 'hair_length': 0, 'src_text': '', 'tar_text': '', 'alpha': 1, 'beta': 0.14})

gradio_interface = gr.Interface(
  fn = my_inference_function, 
  inputs = ["text","text"], 
  outputs = "text"
#  gr.outputs.Image(type="file"),
)
gradio_interface.launch(enable_queue=True)


#gradio_interface = gr.Interface(
#  fn=editor.edit_image,   
#  inputs=["text","text"],   
    
#  outputs=img_output
#)
#gradio_interface.launch()
"""