File size: 17,064 Bytes
3a19a1a
210c702
3a19a1a
 
b879b4e
 
3a19a1a
 
 
 
 
 
db3750b
3a19a1a
 
 
 
 
 
 
210c702
fcf0449
 
 
3a19a1a
 
 
 
5479d05
 
fcf0449
1408f30
 
8e301b6
4ec364e
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1408f30
3a19a1a
 
db3750b
 
5479d05
3a19a1a
5479d05
 
 
3a19a1a
5479d05
3a19a1a
5479d05
3a19a1a
 
 
 
 
 
 
 
 
 
 
 
fcf0449
5479d05
 
3a19a1a
 
 
 
a85cdf5
3a19a1a
 
 
 
 
5479d05
3a19a1a
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
ea00796
3a19a1a
 
 
5479d05
3a19a1a
 
fcf0449
 
 
 
3a19a1a
 
 
96b8eee
3a19a1a
 
 
 
 
 
1edbcae
cbac29e
3a19a1a
 
 
 
 
 
 
 
 
 
 
1edbcae
 
 
9cdf7e4
 
210c702
9cdf7e4
 
 
 
1edbcae
 
210c702
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
6104a4e
210c702
 
 
 
83dc64b
210c702
 
 
 
 
 
 
 
 
 
 
 
6104a4e
210c702
 
 
 
 
6f52ac4
fcf0449
 
 
 
 
 
 
 
210c702
 
 
6104a4e
210c702
 
 
 
 
83dc64b
1edbcae
210c702
 
83dc64b
1edbcae
 
 
9cdf7e4
 
 
 
210c702
1edbcae
cbac29e
210c702
 
 
1edbcae
210c702
3a19a1a
1edbcae
9cdf7e4
3a19a1a
210c702
 
3a19a1a
210c702
 
3a19a1a
 
210c702
3a19a1a
fcf0449
 
 
 
 
 
210c702
 
 
 
 
 
 
3a19a1a
6104a4e
210c702
 
 
 
 
 
 
3a19a1a
 
210c702
 
3a19a1a
210c702
 
3a19a1a
210c702
3a19a1a
210c702
3a19a1a
 
 
 
 
 
 
 
720df82
3a19a1a
 
 
 
 
210c702
8e301b6
210c702
 
bb723df
210c702
96b8eee
7a331ca
 
96b8eee
7a331ca
a39b130
8e301b6
 
 
 
 
 
 
 
 
 
fcf0449
 
 
210c702
7a331ca
210c702
96b8eee
210c702
7a331ca
96b8eee
210c702
1edbcae
210c702
 
 
fcf0449
 
210c702
6104a4e
 
 
 
 
210c702
 
 
 
 
 
1edbcae
210c702
 
 
 
 
 
 
 
 
 
83dc64b
 
65a078b
 
83dc64b
 
210c702
 
 
 
83dc64b
 
 
fcf0449
4663a72
 
 
210c702
 
 
 
 
 
3a19a1a
8e301b6
 
3a19a1a
8e301b6
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
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
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)[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 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():
                        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 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)