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 project_code_by_edit_name
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"),
"base": ("akhaliq/jojogan-stylegan2-ffhq-config-f", "stylegan2-ffhq-config-f.pt"),
"sketch": ("rinong/stylegan-nada-models", "sketch.pt"),
"santa": ("mjdolan/stylegan-nada-models", "santa.pt"),
"jesus": ("mjdolan/stylegan-nada-models", "jesus.pt"),
"mariah": ("mjdolan/stylegan-nada-models", "mariah.pt"),
"heat_miser": ("mjdolan/stylegan-nada-models", "heat.pt"),
"claymation": ("mjdolan/stylegan-nada-models", "claymation.pt"),
"elf": ("mjdolan/stylegan-nada-models", "elf.pt"),
"krampus": ("mjdolan/stylegan-nada-models", "krampus.pt"),
"grinch": ("mjdolan/stylegan-nada-models", "grinch.pt"),
"jack_frost": ("mjdolan/stylegan-nada-models", "jack_frost.pt"),
"rudolph": ("mjdolan/stylegan-nada-models", "rudolph.pt"),
"home_alone": ("mjdolan/stylegan-nada-models", "home_alone.pt"),
"puppet":("rinong/stylegan-nada-models", "plastic_puppet.pt"),
"crochet": ("rinong/stylegan-nada-models", "crochet.pt"),
"shrek": ("rinong/stylegan-nada-models", "shrek.pt"),
"pixar": ("rinong/stylegan-nada-models", "pixar.pt")
}
interface_gan_map = {"None": None, "Masculine": ("gender", 1.0), "Feminine": ("gender", -1.0),
"Smiling": ("smile", 1.0),
"Frowning": ("smile", -1.0), "Young": ("age", -1.0), "Old": ("age", 1.0),
"Long Hair": ("hair_length", -1.0), "Short Hair": ("hair_length", 1.0)}
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="cuda:0" if torch.cuda.is_available() else "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.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 get_target_latent(self, source_latent, alter, generators):
np_source_latent = source_latent.squeeze(0).cpu().detach().numpy()
if alter == "None":
return random.choice([source_latent.squeeze(0),] * max((len(generators) - 1), 1))
edit = interface_gan_map[alter]
projected_code_np = project_code_by_edit_name(np_source_latent, edit[0], edit[1])
return torch.from_numpy(projected_code_np).float().to(self.device)
def edit_image(self, input, output_styles, edit_choices):
return self.predict(input, output_styles, edit_choices=edit_choices)
def predict(
self,
input, # Input image path
output_styles, # Style checkbox options.
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)
output_paths = []
with torch.no_grad():
for g_ema in generators:
latent_for_gen = self.get_target_latent(inverted_latent, edit_choices, generators)
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
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()
blocks = gr.Blocks(theme="darkdefault")
with blocks:
gr.Markdown("<h1><center>Holiday Filters (StyleGAN-NADA)</center></h1>")
gr.Markdown(
"<div>Upload an image of your face, pick your desired output styles, pick any modifiers, and apply StyleGAN-based editing.</div>"
)
with gr.Row():
with gr.Column():
input_img = gr.Image(type="filepath", label="Input image")
with gr.Column():
style_choice = gr.CheckboxGroup(choices=editor.get_style_list(), value=editor.get_style_list(), type="value", label="Styles")
alter = gr.Dropdown(
choices=["None", "Masculine", "Feminine", "Smiling", "Frowning", "Young", "Old", "Short Hair",
"Long Hair"], value="None", label="Additional Modifiers")
img_button = gr.Button("Edit Image")
with gr.Row():
img_output = gr.Gallery(label="Output Images")
img_output.style(grid=(3, 3, 4, 4, 6, 6))
img_button.click(fn=editor.edit_image, inputs=[input_img, style_choice, alter], outputs=img_output)
ex = gr.Examples(examples=[['example1.jpg', editor.get_style_list(), "Smiling"], ['example2.jpg', editor.get_style_list(), "Long Hair"]], fn=editor.edit_image, inputs=[input_img, style_choice, alter],
outputs=[img_output], cache_examples=True,
run_on_click=True)
ex.dataset.headers = [""]
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=mjdolan.holiday_stylegan_nada' alt='visitor badge'></center>"
gr.Markdown(article)
blocks.launch(enable_queue=True)