File size: 13,695 Bytes
009c38e
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
#!/usr/bin/env python

from __future__ import annotations

import argparse
import os
import sys
from typing import Callable

import dlib
import gradio as gr
import huggingface_hub
import numpy as np
import PIL.Image
import torch
import torch.nn as nn
import torchvision.transforms as T

if os.environ.get('SYSTEM') == 'spaces':
    os.system("sed -i '10,17d' DualStyleGAN/model/stylegan/op/fused_act.py")
    os.system("sed -i '10,17d' DualStyleGAN/model/stylegan/op/upfirdn2d.py")

sys.path.insert(0, 'DualStyleGAN')

from model.dualstylegan import DualStyleGAN
from model.encoder.align_all_parallel import align_face
from model.encoder.psp import pSp

STYLE_IMAGE_PATHS = {
    'cartoon':
    'https://raw.githubusercontent.com/williamyang1991/DualStyleGAN/main/doc_images/cartoon_overview.jpg',
    'caricature':
    'https://raw.githubusercontent.com/williamyang1991/DualStyleGAN/main/doc_images/caricature_overview.jpg',
    'anime':
    'https://raw.githubusercontent.com/williamyang1991/DualStyleGAN/main/doc_images/anime_overview.jpg',
    'arcane':
    'https://raw.githubusercontent.com/williamyang1991/DualStyleGAN/main/doc_images/Reconstruction_arcane_overview.jpg',
    'comic':
    'https://raw.githubusercontent.com/williamyang1991/DualStyleGAN/main/doc_images/Reconstruction_comic_overview.jpg',
    'pixar':
    'https://raw.githubusercontent.com/williamyang1991/DualStyleGAN/main/doc_images/Reconstruction_pixar_overview.jpg',
    'slamdunk':
    'https://raw.githubusercontent.com/williamyang1991/DualStyleGAN/main/doc_images/Reconstruction_slamdunk_overview.jpg',
}

TOKEN = os.environ['TOKEN']
MODEL_REPO = 'hysts/DualStyleGAN'


def parse_args() -> argparse.Namespace:
    parser = argparse.ArgumentParser()
    parser.add_argument('--device', type=str, default='cpu')
    parser.add_argument('--theme', type=str)
    parser.add_argument('--live', action='store_true')
    parser.add_argument('--share', action='store_true')
    parser.add_argument('--port', type=int)
    parser.add_argument('--disable-queue',
                        dest='enable_queue',
                        action='store_false')
    parser.add_argument('--allow-flagging', type=str, default='never')
    return parser.parse_args()


class App:

    def __init__(self, device: torch.device):
        self.device = device
        self.face_detector = self._create_dlib_landmark_model()
        self.encoder = self._load_encoder()
        self.transform = self._create_transform()

        self.style_types = [
            'cartoon',
            'caricature',
            'anime',
            'arcane',
            'comic',
            'pixar',
            'slamdunk',
        ]
        self.generator_dict = {
            style_type: self._load_generator(style_type)
            for style_type in self.style_types
        }
        self.exstyle_dict = {
            style_type: self._load_exstylecode(style_type)
            for style_type in self.style_types
        }

    @staticmethod
    def _create_dlib_landmark_model():
        path = huggingface_hub.hf_hub_download(
            'hysts/dlib_face_landmark_model',
            'shape_predictor_68_face_landmarks.dat',
            use_auth_token=TOKEN)
        return dlib.shape_predictor(path)

    def _load_encoder(self) -> nn.Module:
        ckpt_path = huggingface_hub.hf_hub_download(MODEL_REPO,
                                                    'models/encoder.pt',
                                                    use_auth_token=TOKEN)
        ckpt = torch.load(ckpt_path, map_location='cpu')
        opts = ckpt['opts']
        opts['device'] = self.device.type
        opts['checkpoint_path'] = ckpt_path
        opts = argparse.Namespace(**opts)
        model = pSp(opts)
        model.to(self.device)
        model.eval()
        return model

    @staticmethod
    def _create_transform() -> Callable:
        transform = T.Compose([
            T.Resize(256),
            T.CenterCrop(256),
            T.ToTensor(),
            T.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5]),
        ])
        return transform

    def _load_generator(self, style_type: str) -> nn.Module:
        model = DualStyleGAN(1024, 512, 8, 2, res_index=6)
        ckpt_path = huggingface_hub.hf_hub_download(
            MODEL_REPO,
            f'models/{style_type}/generator.pt',
            use_auth_token=TOKEN)
        ckpt = torch.load(ckpt_path, map_location='cpu')
        model.load_state_dict(ckpt['g_ema'])
        model.to(self.device)
        model.eval()
        return model

    @staticmethod
    def _load_exstylecode(style_type: str) -> dict[str, np.ndarray]:
        if style_type in ['cartoon', 'caricature', 'anime']:
            filename = 'refined_exstyle_code.npy'
        else:
            filename = 'exstyle_code.npy'
        path = huggingface_hub.hf_hub_download(
            MODEL_REPO,
            f'models/{style_type}/{filename}',
            use_auth_token=TOKEN)
        exstyles = np.load(path, allow_pickle=True).item()
        return exstyles

    def detect_and_align_face(self, image) -> np.ndarray:
        image = align_face(filepath=image.name, predictor=self.face_detector)
        return image

    @staticmethod
    def denormalize(tensor: torch.Tensor) -> torch.Tensor:
        return torch.clamp((tensor + 1) / 2 * 255, 0, 255).to(torch.uint8)

    def postprocess(self, tensor: torch.Tensor) -> np.ndarray:
        tensor = self.denormalize(tensor)
        return tensor.cpu().numpy().transpose(1, 2, 0)

    @torch.inference_mode()
    def reconstruct_face(self,
                         image: np.ndarray) -> tuple[np.ndarray, torch.Tensor]:
        image = PIL.Image.fromarray(image)
        input_data = self.transform(image).unsqueeze(0).to(self.device)
        img_rec, instyle = self.encoder(input_data,
                                        randomize_noise=False,
                                        return_latents=True,
                                        z_plus_latent=True,
                                        return_z_plus_latent=True,
                                        resize=False)
        img_rec = torch.clamp(img_rec.detach(), -1, 1)
        img_rec = self.postprocess(img_rec[0])
        return img_rec, instyle

    @torch.inference_mode()
    def generate(self, style_type: str, style_id: int, structure_weight: float,
                 color_weight: float, structure_only: bool,
                 instyle: torch.Tensor) -> np.ndarray:
        generator = self.generator_dict[style_type]
        exstyles = self.exstyle_dict[style_type]

        style_id = int(style_id)
        stylename = list(exstyles.keys())[style_id]

        latent = torch.tensor(exstyles[stylename]).to(self.device)
        if structure_only:
            latent[0, 7:18] = instyle[0, 7:18]
        exstyle = generator.generator.style(
            latent.reshape(latent.shape[0] * latent.shape[1],
                           latent.shape[2])).reshape(latent.shape)

        img_gen, _ = generator([instyle],
                               exstyle,
                               z_plus_latent=True,
                               truncation=0.7,
                               truncation_latent=0,
                               use_res=True,
                               interp_weights=[structure_weight] * 7 +
                               [color_weight] * 11)
        img_gen = torch.clamp(img_gen.detach(), -1, 1)
        img_gen = self.postprocess(img_gen[0])
        return img_gen


def update_slider(choice: str):
    max_vals = {
        'cartoon': 316,
        'caricature': 198,
        'anime': 173,
        'arcane': 99,
        'comic': 100,
        'pixar': 121,
        'slamdunk': 119,
    }
    return gr.Slider.update(maximum=max_vals[choice] + 1, value=26)


def update_style_image(choice: str):
    style_image_path = STYLE_IMAGE_PATHS[choice]
    text = f'<center><img src="{style_image_path}" alt="style image" width="800" height="400"></center>'
    return gr.Markdown.update(value=text)


def main():
    args = parse_args()
    app = App(device=torch.device(args.device))

    with gr.Blocks(theme=args.theme) as demo:
        gr.Markdown(
            '''<center><h1>Portrait Style Transfer with DualStyleGAN</h1></center>

This is an unofficial demo app for https://github.com/williamyang1991/DualStyleGAN.

<center><img src="https://raw.githubusercontent.com/williamyang1991/DualStyleGAN/main/doc_images/overview.jpg" alt="overview" width="800" height="400"></center>

Related App: https://huggingface.co/spaces/hysts/DualStyleGAN
''')

        with gr.Box():
            gr.Markdown('''## Step 1

- Drop an image containing a near-frontal face to the **Input Image**.
    - If there are multiple faces in the image, hit the Edit button in the upper right corner and crop the input image beforehand.
- Hit the **Detect & Align** button.
- Hit the **Reconstruct Face** button.
    - The final result will be based on this **Reconstructed Face**. So, if the reconstructed image is not satisfactory, you may want to change the input image.
''')
            with gr.Row():
                with gr.Column():
                    with gr.Row():
                        input_image = gr.Image(label='Input Image',
                                               type='file')
                    with gr.Row():
                        detect_button = gr.Button('Detect & Align Face')
                with gr.Column():
                    with gr.Row():
                        face_image = gr.Image(label='Aligned Face',
                                              type='numpy')
                    with gr.Row():
                        reconstruct_button = gr.Button('Reconstruct Face')
                with gr.Column():
                    reconstructed_face = gr.Image(label='Reconstructed Face',
                                                  type='numpy')
                    instyle = gr.Variable()

        with gr.Box():
            gr.Markdown('''## Step 2

- Select **Style Type**.
- Select **Style Image Index** from the image table below.
''')
            with gr.Row():
                with gr.Column():
                    with gr.Column():
                        style_type = gr.Radio(app.style_types,
                                              label='Style Type')
                    with gr.Column():
                        style_index = gr.Slider(0,
                                                317,
                                                value=26,
                                                step=1,
                                                label='Style Image Index',
                                                interactive=True)
                    style_image_path = STYLE_IMAGE_PATHS['cartoon']
                    text = f'<center><img src="{style_image_path}" alt="style image" width="800" height="400"></center>'
                    style_image = gr.Markdown(value=text)

        with gr.Box():
            gr.Markdown('''## Step 3

- Adjust **Structure Weight** and **Color Weight**.
    - These are weights for the style image, so the larger the value, the closer the resulting image will be to the style image.
- Hit the **Generate** button.
''')
            with gr.Row():
                with gr.Column():
                    with gr.Row():
                        structure_weight = gr.Slider(0,
                                                     1,
                                                     value=0.6,
                                                     step=0.1,
                                                     label='Structure Weight')
                    with gr.Row():
                        color_weight = gr.Slider(0,
                                                 1,
                                                 value=1,
                                                 step=0.1,
                                                 label='Color Weight')
                    with gr.Row():
                        structure_only = gr.Checkbox(label='Structure Only')
                    with gr.Row():
                        generate_button = gr.Button('Generate')

                with gr.Column():
                    output_image = gr.Image(label='Output Image')

        gr.Markdown(
            '<center><img src="https://visitor-badge.glitch.me/badge?page_id=gradio-blocks.dualstylegan" alt="visitor badge"/></center>'
        )

        detect_button.click(fn=app.detect_and_align_face,
                            inputs=input_image,
                            outputs=face_image)
        reconstruct_button.click(fn=app.reconstruct_face,
                                 inputs=face_image,
                                 outputs=[reconstructed_face, instyle])
        style_type.change(fn=update_slider,
                          inputs=style_type,
                          outputs=style_index)
        style_type.change(fn=update_style_image,
                          inputs=style_type,
                          outputs=style_image)
        generate_button.click(fn=app.generate,
                              inputs=[
                                  style_type,
                                  style_index,
                                  structure_weight,
                                  color_weight,
                                  structure_only,
                                  instyle,
                              ],
                              outputs=output_image)

    demo.launch(
        enable_queue=args.enable_queue,
        server_port=args.port,
        share=args.share,
    )


if __name__ == '__main__':
    main()