TargetCLIPで、画像のスタイルを転送する

cedro

3年前

1.はじめに

　今まで、ある画像から別の画像へ色やテクスチャを転送する「スタイル転送」という技術がありましたが、今回ご紹介するのはもっと自然なスタイル転送を可能にする「TargetCLIP」と言う技術です。

＊この論文は、2021.10に提出されました。

2.TargetCLIPとは？

　TargetCLIPは、ある画像の概念的な「スタイル」と別の画像の客観的な「コンテンツ」を全く新しい画像に組み合わせる技術です。この技術は、画像を生成するStyleGAN２とCLIPが持っている画像を特徴ベクトルに変換するモジュールを組み合わせることによって実現されています。

　２つの損失関数を使用します。１つ目は、ターゲット画像とソース画像をそれぞれCLIPでエンコードしたベクトルのCOS類似度をできるだけ上げるような関数。２つ目は、ソースの変換前後の画像をそれぞれCLIPでエンコードしたベクトルのCOS類似度をできるだけ上げるような関数です。

　この２つの損失関数を最適化することによって、下記のような結果が得られます。

　では、早速コードを動かしてみましょう。

3.コード

　コードはGoogle Colabで動かす形にしてGithubに上げてありますので、それに沿って説明して行きます。自分で動かしてみたい方は、この「リンク」をクリックし表示されたノートブックの先頭にある「Colab on Web」ボタンをクリックすると動かせます.

　最初に、セットアップをおこないます。まず、画像から逆算してStyleGAN2のベクトルを求めるためのフレームワークe4eをセットアップします。

#@title e4e Setup (may take a few minutes)
import os
os.chdir('/content')
CODE_DIR = 'encoder4editing'

%tensorflow_version 1.x
! pip install torch==1.7.1+cu110 torchvision==0.8.2+cu110 torchaudio==0.7.2 -f https://download.pytorch.org/whl/torch_stable.html 

!git clone https://github.com/omertov/encoder4editing.git $CODE_DIR
!wget https://github.com/ninja-build/ninja/releases/download/v1.8.2/ninja-linux.zip
!sudo unzip ninja-linux.zip -d /usr/local/bin/
!sudo update-alternatives --install /usr/bin/ninja ninja /usr/local/bin/ninja 1 --force
os.chdir(f'./{CODE_DIR}')

from argparse import Namespace
import time
import os
import sys
import numpy as np
from PIL import Image
import torch
import torchvision.transforms as transforms

sys.path.append(".")
sys.path.append("..")

from utils.common import tensor2im
from models.psp import pSp  # we use the pSp framework to load the e4e encoder.

%load_ext autoreload
%autoreload 2


# --- Download e4e model ---
experiment_type = 'ffhq_encode'
def get_download_model_command(file_id, file_name):
    """ Get wget download command for downloading the desired model and save to directory pretrained_models. """
    current_directory = os.getcwd()
    save_path = os.path.join(os.path.dirname(current_directory), CODE_DIR, "pretrained_models")
    if not os.path.exists(save_path):
        os.makedirs(save_path)
    url = r"""wget --load-cookies /tmp/cookies.txt "https://docs.google.com/uc?export=download&confirm=$(wget --quiet --save-cookies /tmp/cookies.txt --keep-session-cookies --no-check-certificate 'https://docs.google.com/uc?export=download&id={FILE_ID}' -O- | sed -rn 's/.*confirm=([0-9A-Za-z_]+).*/\1\n/p')&id={FILE_ID}" -O {SAVE_PATH}/{FILE_NAME} && rm -rf /tmp/cookies.txt""".format(FILE_ID=file_id, FILE_NAME=file_name, SAVE_PATH=save_path)
    return url    

MODEL_PATHS = {
    "ffhq_encode": {"id": "1cUv_reLE6k3604or78EranS7XzuVMWeO", "name": "e4e_ffhq_encode.pt"},
    "cars_encode": {"id": "17faPqBce2m1AQeLCLHUVXaDfxMRU2QcV", "name": "e4e_cars_encode.pt"},
    "horse_encode": {"id": "1TkLLnuX86B_BMo2ocYD0kX9kWh53rUVX", "name": "e4e_horse_encode.pt"},
    "church_encode": {"id": "1-L0ZdnQLwtdy6-A_Ccgq5uNJGTqE7qBa", "name": "e4e_church_encode.pt"}
}

path = MODEL_PATHS[experiment_type]
download_command = get_download_model_command(file_id=path["id"], file_name=path["name"]) 

!wget {download_command}


# --- e4e setup ---
import gdown
gdown.download('https://drive.google.com/u/0/uc?id=1jZnwdPOXhte2gseETvTwRtx8r8TT196J', '/content/encoder4editing/e4e_ffhq_encode.pt', quiet=False)
experiment_type = 'ffhq_encode'

os.chdir('/content/encoder4editing')

EXPERIMENT_ARGS = {
        "model_path": "e4e_ffhq_encode.pt"
    }
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])])
resize_dims = (256, 256)

model_path = EXPERIMENT_ARGS['model_path']
ckpt = torch.load(model_path, map_location='cpu')
opts = ckpt['opts']
# pprint.pprint(opts)  # Display full options used
# update the training options
opts['checkpoint_path'] = model_path
opts= Namespace(**opts)
net = pSp(opts)
net.eval()
net.cuda()
print('Model successfully loaded!')

#@title e4e Setup (may take a few minutes)

import os

os.chdir('/content')

CODE_DIR = 'encoder4editing'

%tensorflow_version 1.x

! pip install torch==1.7.1+cu110 torchvision==0.8.2+cu110 torchaudio==0.7.2 -f https://download.pytorch.org/whl/torch_stable.html

!git clone https://github.com/omertov/encoder4editing.git $CODE_DIR

!wget https://github.com/ninja-build/ninja/releases/download/v1.8.2/ninja-linux.zip

!sudo unzip ninja-linux.zip -d /usr/local/bin/

!sudo update-alternatives --install /usr/bin/ninja ninja /usr/local/bin/ninja 1 --force

os.chdir(f'./{CODE_DIR}')

from argparse import Namespace

import time

import os

import sys

import numpy as np

from PIL import Image

import torch

import torchvision.transforms as transforms

sys.path.append(".")

sys.path.append("..")

from utils.common import tensor2im

from models.psp import pSp # we use the pSp framework to load the e4e encoder.

%load_ext autoreload

%autoreload 2

# --- Download e4e model ---

experiment_type = 'ffhq_encode'

def get_download_model_command(file_id, file_name):

""" Get wget download command for downloading the desired model and save to directory pretrained_models. """

current_directory = os.getcwd()

save_path = os.path.join(os.path.dirname(current_directory), CODE_DIR, "pretrained_models")

if not os.path.exists(save_path):

os.makedirs(save_path)

url = r"""wget --load-cookies /tmp/cookies.txt "https://docs.google.com/uc?export=download&confirm=$(wget --quiet --save-cookies /tmp/cookies.txt --keep-session-cookies --no-check-certificate 'https://docs.google.com/uc?export=download&id={FILE_ID}' -O- | sed -rn 's/.*confirm=([0-9A-Za-z_]+).*/\1\n/p')&id={FILE_ID}" -O {SAVE_PATH}/{FILE_NAME} && rm -rf /tmp/cookies.txt""".format(FILE_ID=file_id, FILE_NAME=file_name, SAVE_PATH=save_path)

return url

MODEL_PATHS = {

"ffhq_encode": {"id": "1cUv_reLE6k3604or78EranS7XzuVMWeO", "name": "e4e_ffhq_encode.pt"},

"cars_encode": {"id": "17faPqBce2m1AQeLCLHUVXaDfxMRU2QcV", "name": "e4e_cars_encode.pt"},

"horse_encode": {"id": "1TkLLnuX86B_BMo2ocYD0kX9kWh53rUVX", "name": "e4e_horse_encode.pt"},

"church_encode": {"id": "1-L0ZdnQLwtdy6-A_Ccgq5uNJGTqE7qBa", "name": "e4e_church_encode.pt"}

}

path = MODEL_PATHS[experiment_type]

download_command = get_download_model_command(file_id=path["id"], file_name=path["name"])

!wget {download_command}

# --- e4e setup ---

import gdown

gdown.download('https://drive.google.com/u/0/uc?id=1jZnwdPOXhte2gseETvTwRtx8r8TT196J', '/content/encoder4editing/e4e_ffhq_encode.pt', quiet=False)

experiment_type = 'ffhq_encode'

os.chdir('/content/encoder4editing')

EXPERIMENT_ARGS = {

"model_path": "e4e_ffhq_encode.pt"

}

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])])

resize_dims = (256, 256)

model_path = EXPERIMENT_ARGS['model_path']

ckpt = torch.load(model_path, map_location='cpu')

opts = ckpt['opts']

# pprint.pprint(opts) # Display full options used

# update the training options

opts['checkpoint_path'] = model_path

opts= Namespace(**opts)

net = pSp(opts)

net.eval()

net.cuda()

print('Model successfully loaded!')

　続いて、TargetCLIP本体をセットアップします。

#@title TargetCLIP Setup
import os
!git clone https://github.com/hila-chefer/TargetCLIP
os.chdir(f'./TargetCLIP')

!pip install ftfy regex tqdm
!pip install git+https://github.com/openai/CLIP.git

from pydrive.auth import GoogleAuth
from pydrive.drive import GoogleDrive
from google.colab import auth
from oauth2client.client import GoogleCredentials

# Authenticate and create the PyDrive client.
auth.authenticate_user()
gauth = GoogleAuth()
gauth.credentials = GoogleCredentials.get_application_default()
drive = GoogleDrive(gauth)

# downloads StyleGAN's weights
ids = ['1EM87UquaoQmk17Q8d5kYIAHqu0dkYqdT']
for file_id in ids:
  downloaded = drive.CreateFile({'id':file_id})
  downloaded.FetchMetadata(fetch_all=True)
  downloaded.GetContentFile(downloaded.metadata['title'])


import argparse
import os
os.chdir(f'../TargetCLIP')
import numpy as np
import torch
import torchvision
from torch import optim
from tqdm import tqdm
import clip

# from criteria.clip_loss import CLIPLoss
from models.stylegan2.model import Generator
import math
import copy


# aux function
def get_latent(args, g_ema):
    mean_latent = g_ema.mean_latent(4096)
    latent_code_init_not_trunc = torch.randn(1, 512).cuda()
    with torch.no_grad():
        # _, latent_code_init = g_ema([latent_code_init_not_trunc], return_latents=True,
        #                             truncation=args.truncation, truncation_latent=mean_latent)
        _, latent_code_init,_ = g_ema([latent_code_init_not_trunc], return_latents=True,
                                    truncation=args.truncation, truncation_latent=mean_latent)

    direction = latent_code_init.detach().clone()
    direction.requires_grad = True
    return direction


def load_model(args):
    g_ema = Generator(args.stylegan_size, 512, 8)
    g_ema.load_state_dict(torch.load(args.ckpt)["g_ema"], strict=False)
    g_ema.eval()
    g_ema = g_ema.cuda()
    return g_ema


def get_lr(t, initial_lr, rampdown=0.75, rampup=0.005):
    lr_ramp = min(1, (1 - t) / rampdown)
    lr_ramp = 0.5 - 0.5 * math.cos(lr_ramp * math.pi)
    lr_ramp = lr_ramp * min(1, t / rampup)

    return initial_lr * lr_ramp

args = {
    "ckpt": "stylegan2-ffhq-config-f.pt",
    "stylegan_size": 1024,
    "lr": 0.1,
    "truncation": 0.7,
    "save_intermediate_image_every": 1,
    "results_dir": "results",
    "dir_name": "results",
    "num_batches": 1,
    "real_images": True,
    "data_path": "train_faces.pt",
}

from argparse import Namespace
a=Namespace(**args)

g_ema = load_model(a)

#@title TargetCLIP Setup

import os

!git clone https://github.com/hila-chefer/TargetCLIP

os.chdir(f'./TargetCLIP')

!pip install ftfy regex tqdm

!pip install git+https://github.com/openai/CLIP.git

from pydrive.auth import GoogleAuth

from pydrive.drive import GoogleDrive

from google.colab import auth

from oauth2client.client import GoogleCredentials

# Authenticate and create the PyDrive client.

auth.authenticate_user()

gauth = GoogleAuth()

gauth.credentials = GoogleCredentials.get_application_default()

drive = GoogleDrive(gauth)

# downloads StyleGAN's weights

ids = ['1EM87UquaoQmk17Q8d5kYIAHqu0dkYqdT']

for file_id in ids:

downloaded = drive.CreateFile({'id':file_id})

downloaded.FetchMetadata(fetch_all=True)

downloaded.GetContentFile(downloaded.metadata['title'])

import argparse

import os

os.chdir(f'../TargetCLIP')

import numpy as np

import torch

import torchvision

from torch import optim

from tqdm import tqdm

import clip

# from criteria.clip_loss import CLIPLoss

from models.stylegan2.model import Generator

import math

import copy

# aux function

def get_latent(args, g_ema):

mean_latent = g_ema.mean_latent(4096)

latent_code_init_not_trunc = torch.randn(1, 512).cuda()

with torch.no_grad():

# _, latent_code_init = g_ema([latent_code_init_not_trunc], return_latents=True,

# truncation=args.truncation, truncation_latent=mean_latent)

_, latent_code_init,_ = g_ema([latent_code_init_not_trunc], return_latents=True,

truncation=args.truncation, truncation_latent=mean_latent)

direction = latent_code_init.detach().clone()

direction.requires_grad = True

return direction

def load_model(args):

g_ema = Generator(args.stylegan_size, 512, 8)

g_ema.load_state_dict(torch.load(args.ckpt)["g_ema"], strict=False)

g_ema.eval()

g_ema = g_ema.cuda()

return g_ema

def get_lr(t, initial_lr, rampdown=0.75, rampup=0.005):

lr_ramp = min(1, (1 - t) / rampdown)

lr_ramp = 0.5 - 0.5 * math.cos(lr_ramp * math.pi)

lr_ramp = lr_ramp * min(1, t / rampup)

return initial_lr * lr_ramp

args = {

"ckpt": "stylegan2-ffhq-config-f.pt",

"stylegan_size": 1024,

"lr": 0.1,

"truncation": 0.7,

"save_intermediate_image_every": 1,

"results_dir": "results",

"dir_name": "results",

"num_batches": 1,

"real_images": True,

"data_path": "train_faces.pt",

}

from argparse import Namespace

a=Namespace(**args)

g_ema = load_model(a)

　サンプル画像を読み込みます。自分の用意した画像を読み込む場合は、TargetCLIP/picフォルダーに画像を保存し、image_nameをそのファイル名に変更してください。ここでは、’./TargetCLIP/pic/01.png’ を指定しています。

#@markdown Upload an image to the encoder4editing folder and set the image_name into the image name
image_name = './TargetCLIP/pic/01.png' #@param {type:"string"}
os.chdir('/content/encoder4editing')
EXPERIMENT_DATA_ARGS = {
    "ffhq_encode": {
        "model_path": "pretrained_models/e4e_ffhq_encode.pt",
        "image_path": image_name
    }
    
}
# Setup required image transformations
EXPERIMENT_ARGS = EXPERIMENT_DATA_ARGS[experiment_type]
if experiment_type == 'cars_encode':
    EXPERIMENT_ARGS['transform'] = transforms.Compose([
            transforms.Resize((192, 256)),
            transforms.ToTensor(),
            transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])])
    resize_dims = (256, 192)
else:
    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])])
    resize_dims = (256, 256)

#@markdown Upload an image to the encoder4editing folder and set the image_name into the image name

image_name = './TargetCLIP/pic/01.png' #@param {type:"string"}

os.chdir('/content/encoder4editing')

EXPERIMENT_DATA_ARGS = {

"ffhq_encode": {

"model_path": "pretrained_models/e4e_ffhq_encode.pt",

"image_path": image_name

}

# Setup required image transformations

EXPERIMENT_ARGS = EXPERIMENT_DATA_ARGS[experiment_type]

if experiment_type == 'cars_encode':

EXPERIMENT_ARGS['transform'] = transforms.Compose([

transforms.Resize((192, 256)),

transforms.ToTensor(),

transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])])

resize_dims = (256, 192)

else:

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])])

resize_dims = (256, 256)

　顔を所定の位置で切り取り水平にする処理（align処理）を行い、その画像を表示します。

#@title Show aligned original image
image_path = EXPERIMENT_DATA_ARGS[experiment_type]["image_path"]
original_image = Image.open(image_path)
original_image = original_image.convert("RGB")
if experiment_type == "ffhq_encode" and 'shape_predictor_68_face_landmarks.dat' not in os.listdir():
    !wget http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2
    !bzip2 -dk shape_predictor_68_face_landmarks.dat.bz2

def run_alignment(image_path):
  import dlib
  from utils.alignment import align_face
  predictor = dlib.shape_predictor("shape_predictor_68_face_landmarks.dat")
  aligned_image = align_face(filepath=image_path, predictor=predictor) 
  print("Aligned image has shape: {}".format(aligned_image.size))
  return aligned_image 

if experiment_type == "ffhq_encode":
  input_image = run_alignment(image_path)
else:
  input_image = original_image

input_image.resize(resize_dims)

#@title Show aligned original image

image_path = EXPERIMENT_DATA_ARGS[experiment_type]["image_path"]

original_image = Image.open(image_path)

original_image = original_image.convert("RGB")

if experiment_type == "ffhq_encode" and 'shape_predictor_68_face_landmarks.dat' not in os.listdir():

!wget http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2

!bzip2 -dk shape_predictor_68_face_landmarks.dat.bz2

def run_alignment(image_path):

import dlib

from utils.alignment import align_face

predictor = dlib.shape_predictor("shape_predictor_68_face_landmarks.dat")

aligned_image = align_face(filepath=image_path, predictor=predictor)

print("Aligned image has shape: {}".format(aligned_image.size))

return aligned_image

if experiment_type == "ffhq_encode":

input_image = run_alignment(image_path)

else:

input_image = original_image

input_image.resize(resize_dims)

　align処理した画像からStyleGAN2のベクトルを逆算で求めます。そして、そのベクトルで生成した画像とオリジナル画像を並べて表示します。右が逆算、左がオリジナルの画像です。

#@title Invert
img_transforms = EXPERIMENT_ARGS['transform']
transformed_image = img_transforms(input_image)

def display_alongside_source_image(result_image, source_image):
    res = np.concatenate([np.array(source_image.resize(resize_dims)),
                          np.array(result_image.resize(resize_dims))], axis=1)
    return Image.fromarray(res)

def run_on_batch(inputs, net):
    images, latents = net(inputs.to("cuda").float(), randomize_noise=False, return_latents=True)
    if experiment_type == 'cars_encode':
        images = images[:, :, 32:224, :]
    return images, latents

with torch.no_grad():
    tic = time.time()
    images, latents = run_on_batch(transformed_image.unsqueeze(0), net)
    result_image, latent = images[0], latents[0]
    toc = time.time()
    print('Inference took {:.4f} seconds.'.format(toc - tic))

# Display inversion:
display_alongside_source_image(tensor2im(result_image), input_image)

#@title Invert

img_transforms = EXPERIMENT_ARGS['transform']

transformed_image = img_transforms(input_image)

def display_alongside_source_image(result_image, source_image):

res = np.concatenate([np.array(source_image.resize(resize_dims)),

np.array(result_image.resize(resize_dims))], axis=1)

return Image.fromarray(res)

def run_on_batch(inputs, net):

images, latents = net(inputs.to("cuda").float(), randomize_noise=False, return_latents=True)

if experiment_type == 'cars_encode':

images = images[:, :, 32:224, :]

return images, latents

with torch.no_grad():

tic = time.time()

images, latents = run_on_batch(transformed_image.unsqueeze(0), net)

result_image, latent = images[0], latents[0]

toc = time.time()

print('Inference took {:.4f} seconds.'.format(toc - tic))

# Display inversion:

display_alongside_source_image(tensor2im(result_image), input_image)

　スタイルを転送するためのターゲットの画像を選択します。’Elsa’, ‘Joker’など9種類のターゲットが用意されています。ここでは、’Elsa’を選択します。

#@title Chose target
os.chdir('/content/encoder4editing/TargetCLIP')

dirs = {
  'Elsa': 'dirs/elsa.npy',
  'Pocahontas': 'dirs/pocahontas.npy',
  'Keanu Reeves': 'dirs/keanu.npy',
  'Trump': 'dirs/trump.npy',
  'Joker': 'dirs/joker.npy',
  'Ariel': 'dirs/ariel.npy',
  'Doc Brown': 'dirs/doc.npy',
  'Beyonce': 'dirs/beyonce.npy',
  'Morgan Freeman': 'dirs/morgan.npy',
}

targets = {
  'Elsa': 'dirs/targets/elsa.jpg',   
  'Pocahontas': 'dirs/targets/pocahontas.jpg',
  'Keanu Reeves': 'dirs/targets/keanu.jpg',
  'Trump': 'dirs/targets/trump.jpg',
  'Joker': 'dirs/targets/joker.jpg',
  'Ariel': 'dirs/targets/ariel.jpeg',
  'Doc Brown': 'dirs/targets/doc_brown.jpg',
  'Beyonce': 'dirs/targets/beyonce.jpg',
  'Morgan Freeman': 'dirs/targets/morgan_freeman.jpg',
}

sources_ids = {
  'Taylor Swift': 67,   
  'Elon Musk': 4,
  'Hillary Clinton': 9,
  'Alfie Allen': 34,
  'Obama': 61
}

target = 'Ariel' #@param ['Trump','Keanu Reeves', 'Elsa', 'Pocahontas', 'Joker', 'Ariel', 'Doc Brown', 'Beyonce', 'Morgan Freeman']
source = latents
dir = torch.from_numpy(np.load(dirs[target]))
#dir = torch.load('dirs/10me.pt')
target_path = targets[target]


# title Show target image
assert(target_path is not None)
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
img = mpimg.imread(target_path)
imgplot = plt.imshow(img)
plt.xticks([])
plt.yticks([])
plt.show()

#@title Chose target

os.chdir('/content/encoder4editing/TargetCLIP')

dirs = {

'Elsa': 'dirs/elsa.npy',

'Pocahontas': 'dirs/pocahontas.npy',

'Keanu Reeves': 'dirs/keanu.npy',

'Trump': 'dirs/trump.npy',

'Joker': 'dirs/joker.npy',

'Ariel': 'dirs/ariel.npy',

'Doc Brown': 'dirs/doc.npy',

'Beyonce': 'dirs/beyonce.npy',

'Morgan Freeman': 'dirs/morgan.npy',

}

targets = {

'Elsa': 'dirs/targets/elsa.jpg',

'Pocahontas': 'dirs/targets/pocahontas.jpg',

'Keanu Reeves': 'dirs/targets/keanu.jpg',

'Trump': 'dirs/targets/trump.jpg',

'Joker': 'dirs/targets/joker.jpg',

'Ariel': 'dirs/targets/ariel.jpeg',

'Doc Brown': 'dirs/targets/doc_brown.jpg',

'Beyonce': 'dirs/targets/beyonce.jpg',

'Morgan Freeman': 'dirs/targets/morgan_freeman.jpg',

}

sources_ids = {

'Taylor Swift': 67,

'Elon Musk': 4,

'Hillary Clinton': 9,

'Alfie Allen': 34,

'Obama': 61

}

target = 'Ariel' #@param ['Trump','Keanu Reeves', 'Elsa', 'Pocahontas', 'Joker', 'Ariel', 'Doc Brown', 'Beyonce', 'Morgan Freeman']

source = latents

dir = torch.from_numpy(np.load(dirs[target]))

#dir = torch.load('dirs/10me.pt')

target_path = targets[target]

# title Show target image

assert(target_path is not None)

import matplotlib.pyplot as plt

import matplotlib.image as mpimg

img = mpimg.imread(target_path)

imgplot = plt.imshow(img)

plt.xticks([])

plt.yticks([])

plt.show()

　　変換係数を設定してスタイルを転送します。そしてその結果を表示します。

#@title Show manipulation on source
from PIL import Image
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
#alpha=1
alpha = 1.2 #@param {type:"slider", min:0, max:2, step:0.1}
dir = dir.cuda()
source = source.cuda()
source_img, _ = g_ema([source], input_is_latent=True, randomize_noise=False)
source_amp, _ = g_ema([source + dir * alpha], input_is_latent=True,
                        randomize_noise=False)

torchvision.utils.save_image(source_img, f"results_orig.png", normalize=True, range=(-1, 1))
torchvision.utils.save_image(source_amp, f"results_manipulated.png", normalize=True, range=(-1, 1))
plt.figure(figsize=(14,7), dpi= 100)
plt.subplot(1,2,1)
plt.imshow(mpimg.imread('results_orig.png'))
plt.title('original')
plt.axis('off')
plt.subplot(1,2,2)
plt.imshow(mpimg.imread('results_manipulated.png'))
plt.title('manipulated')
plt.axis('off')
plt.tight_layout()

#@title Show manipulation on source

from PIL import Image

import matplotlib.pyplot as plt

import matplotlib.image as mpimg

#alpha=1

alpha = 1.2 #@param {type:"slider", min:0, max:2, step:0.1}

dir = dir.cuda()

source = source.cuda()

source_img, _ = g_ema([source], input_is_latent=True, randomize_noise=False)

source_amp, _ = g_ema([source + dir * alpha], input_is_latent=True,

randomize_noise=False)

torchvision.utils.save_image(source_img, f"results_orig.png", normalize=True, range=(-1, 1))

torchvision.utils.save_image(source_amp, f"results_manipulated.png", normalize=True, range=(-1, 1))

plt.figure(figsize=(14,7), dpi= 100)

plt.subplot(1,2,1)

plt.imshow(mpimg.imread('results_orig.png'))

plt.title('original')

plt.axis('off')

plt.subplot(1,2,2)

plt.imshow(mpimg.imread('results_manipulated.png'))

plt.title('manipulated')

plt.axis('off')

plt.tight_layout()

　変換係数を徐々に変化させたときの静止画から動画を作成します。作成した動画は、./TargetCLIP/output.mp4 に保存されます。

#@title Make manipulated movie
import os 
import shutil
from tqdm import trange

# reset images folder
if os.path.isdir('images'):
    shutil.rmtree('images')
os.makedirs('images', exist_ok=True)

# delete output.mp4 
if os.path.exists('./output.mp4'):
   os.remove('./output.mp4')

alpha = 1.2 #@param {type:"slider", min:0, max:2, step:0.1}
dir = dir.cuda()
source = source.cuda()

end = int(alpha * 100)
for i in trange(0, end):
  source_amp, _ = g_ema([source + dir * i/100], input_is_latent=True,
                        randomize_noise=False)
  torchvision.utils.save_image(source_amp, 'images/'+str(i).zfill(4)+'.png', normalize=True, range=(-1, 1))

# make movie from png in images folder
! ffmpeg -r 30 -i images/%4d.png\
               -vcodec libx264 -pix_fmt yuv420p output.mp4

#@title Make manipulated movie

import os

import shutil

from tqdm import trange

# reset images folder

if os.path.isdir('images'):

shutil.rmtree('images')

os.makedirs('images', exist_ok=True)

# delete output.mp4

if os.path.exists('./output.mp4'):

os.remove('./output.mp4')

alpha = 1.2 #@param {type:"slider", min:0, max:2, step:0.1}

dir = dir.cuda()

source = source.cuda()

end = int(alpha * 100)

for i in trange(0, end):

source_amp, _ = g_ema([source + dir * i/100], input_is_latent=True,

randomize_noise=False)

torchvision.utils.save_image(source_amp, 'images/'+str(i).zfill(4)+'.png', normalize=True, range=(-1, 1))

# make movie from png in images folder

! ffmpeg -r 30 -i images/%4d.png\

-vcodec libx264 -pix_fmt yuv420p output.mp4

　作成した動画を再生します。

#@title play movie
from IPython.display import HTML
from base64 import b64encode

mp4 = open('./output.mp4', 'rb').read()
data_url = 'data:video/mp4;base64,' + b64encode(mp4).decode()
HTML(f"""
<video width="70%" height="70%" controls>
      <source src="{data_url}" type="video/mp4">
</video>""")

#@title play movie

from IPython.display import HTML

from base64 import b64encode

mp4 = open('./output.mp4', 'rb').read()

data_url = 'data:video/mp4;base64,' + b64encode(mp4).decode()

HTML(f"""

</video>""")

　どうでしょうか。実写にディズニープリンセスのスタイルが上手く転送されているのが分かると思います。

　では、また。

（オリジナルgithub)　https://github.com/hila-chefer/TargetCLIP