File size: 11,733 Bytes
f4c3c2b
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto.  Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.

"""Generate images using pretrained network pickle."""

import os
import re
import random
import math
import time
import click
import legacy
from typing import List, Optional

import cv2
import clip
import dnnlib
import numpy as np
import torchvision
from torchvision.transforms import Compose, Resize, CenterCrop, ToTensor, Normalize
import PIL.Image
import matplotlib.pyplot as plt
import torch
from torch import linalg as LA
import torch.nn.functional as F
from torch_utils import misc
from torch_utils import persistence
from torch_utils.ops import conv2d_resample
from torch_utils.ops import upfirdn2d
from torch_utils.ops import bias_act
from torch_utils.ops import fma


def block_forward(self, x, img, ws, shapes, force_fp32=False, fused_modconv=None, **layer_kwargs):
        misc.assert_shape(ws, [None, self.num_conv + self.num_torgb, self.w_dim])
        w_iter = iter(ws.unbind(dim=1))
        dtype = torch.float16 if self.use_fp16 and not force_fp32 else torch.float32
        memory_format = torch.channels_last if self.channels_last and not force_fp32 else torch.contiguous_format
        if fused_modconv is None:
            with misc.suppress_tracer_warnings(): # this value will be treated as a constant
                fused_modconv = (not self.training) and (dtype == torch.float32 or int(x.shape[0]) == 1)

        # Input.
        if self.in_channels == 0:
            x = self.const.to(dtype=dtype, memory_format=memory_format)
            x = x.unsqueeze(0).repeat([ws.shape[0], 1, 1, 1])
        else:
            misc.assert_shape(x, [None, self.in_channels, self.resolution // 2, self.resolution // 2])
            x = x.to(dtype=dtype, memory_format=memory_format)

        # Main layers.
        if self.in_channels == 0:
            x = self.conv1(x, next(w_iter)[...,:shapes[0]], fused_modconv=fused_modconv, **layer_kwargs)
        elif self.architecture == 'resnet':
            y = self.skip(x, gain=np.sqrt(0.5))
            x = self.conv0(x, next(w_iter), fused_modconv=fused_modconv, **layer_kwargs)
            x = self.conv1(x, next(w_iter), fused_modconv=fused_modconv, gain=np.sqrt(0.5), **layer_kwargs)
            x = y.add_(x)
        else:
            x = self.conv0(x, next(w_iter)[...,:shapes[0]], fused_modconv=fused_modconv, **layer_kwargs)
            x = self.conv1(x, next(w_iter)[...,:shapes[1]], fused_modconv=fused_modconv, **layer_kwargs)

        # ToRGB.
        if img is not None:
            misc.assert_shape(img, [None, self.img_channels, self.resolution // 2, self.resolution // 2])
            img = upfirdn2d.upsample2d(img, self.resample_filter)
        if self.is_last or self.architecture == 'skip':
            y = self.torgb(x, next(w_iter)[...,:shapes[2]], fused_modconv=fused_modconv)
            y = y.to(dtype=torch.float32, memory_format=torch.contiguous_format)
            img = img.add_(y) if img is not None else y

        assert x.dtype == dtype
        assert img is None or img.dtype == torch.float32
        return x, img


def unravel_index(index, shape):
    out = []
    for dim in reversed(shape):
        out.append(index % dim)
        index = index // dim
    return tuple(reversed(out))


def num_range(s: str) -> List[int]:
    """
    Accept either a comma separated list of numbers 'a,b,c' or a range 'a-c' and return as a list of ints.
    """

    range_re = re.compile(r'^(\d+)-(\d+)$')
    m = range_re.match(s)
    if m:
        return list(range(int(m.group(1)), int(m.group(2))+1))
    vals = s.split(',')
    return [int(x) for x in vals]


@click.command()
@click.pass_context
@click.option('--network', 'network_pkl', help='Network pickle filename', required=True)
@click.option('--seeds', type=num_range, help='List of random seeds')
@click.option('--trunc', 'truncation_psi', type=float, help='Truncation psi', default=0.7, show_default=True)
@click.option('--class', 'class_idx', type=int, help='Class label (unconditional if not specified)')
@click.option('--noise-mode', help='Noise mode', type=click.Choice(['const', 'random', 'none']), default='const', show_default=True)
@click.option('--projected-w', help='Projection result file', type=str, metavar='FILE')
@click.option('--s_input', help='Projection result file', type=str, metavar='FILE')
@click.option('--outdir', help='Where to save the output images', type=str, required=True, metavar='DIR')
@click.option('--text_prompt', help='Text', type=str, required=True)
@click.option('--change_power', help='Change power', type=int, required=True)
@click.option('--from_video', 'from_video', is_flag=True, help="generate from video")

def generate_images(
    ctx: click.Context,
    network_pkl: str,
    seeds: Optional[List[int]],
    truncation_psi: float,
    noise_mode: str,
    outdir: str,
    class_idx: Optional[int],
    projected_w: Optional[str],
    s_input: Optional[str],
    text_prompt: str,
    change_power: int,
    from_video: bool,
):
    """
    Generate images using pretrained network pickle.

    Examples:
    # Generate curated MetFaces images without truncation (Fig.10 left)
    python generate.py --outdir=out --trunc=1 --seeds=85,265,297,849 \\
        --network=https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/metfaces.pkl

    # Generate uncurated MetFaces images with truncation (Fig.12 upper left)
    python generate.py --outdir=out --trunc=0.7 --seeds=600-605 \\
        --network=https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/metfaces.pkl

    # Generate class conditional CIFAR-10 images (Fig.17 left, Car)
    python generate.py --outdir=out --seeds=0-35 --class=1 \\
        --network=https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/cifar10.pkl

    # Render an image from projected W
    python generate.py --outdir=out --projected_w=projected_w.npz \\
        --network=https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/metfaces.pkl
    """

    print('Loading networks from "%s"...' % network_pkl)
    # Use GPU if available
    if torch.cuda.is_available():
        device = torch.device("cuda")
    else:
        device = torch.device("cpu")

    with dnnlib.util.open_url(network_pkl) as f:
        G = legacy.load_network_pkl(f)['G_ema'].to(device) # type: ignore

    os.makedirs(outdir, exist_ok=True)

    # Synthesize the result of a W projection.
    if projected_w is not None:
        if seeds is not None:
            print ('warn: --seeds is ignored when using --projected-w')
        print(f'Generating images from projected W "{projected_w}"')
        ws = np.load(projected_w)['w']
        ws = torch.tensor(ws, device=device) # pylint: disable=not-callable
        assert ws.shape[1:] == (G.num_ws, G.w_dim)
        for idx, w in enumerate(ws):
            img = G.synthesis(w.unsqueeze(0), noise_mode=noise_mode)
            img = (img.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255).to(torch.uint8)
            img = PIL.Image.fromarray(img[0].cpu().numpy(), 'RGB')
            img.save(f'{outdir}/proj{idx:02d}.png')
        return

    # Labels
    label = torch.zeros([1, G.c_dim], device=device).requires_grad_()
    if G.c_dim != 0:
        if class_idx is None:
            ctx.fail('Must specify class label with --class when using a conditional network')
        label[:, class_idx] = 1
    else:
        if class_idx is not None:
            print ('warn: --class=lbl ignored when running on an unconditional network')

    # Generate images
    for i in G.parameters():
      i.requires_grad = False


    temp_shapes = []
    for res in G.synthesis.block_resolutions:
        block = getattr(G.synthesis, f'b{res}')
        if res == 4:
            temp_shape = (block.conv1.affine.weight.shape[0], block.conv1.affine.weight.shape[0], block.torgb.affine.weight.shape[0])
            block.conv1.affine = torch.nn.Identity()
            block.torgb.affine = torch.nn.Identity()

        else:
            temp_shape = (block.conv0.affine.weight.shape[0], block.conv1.affine.weight.shape[0], block.torgb.affine.weight.shape[0])
            block.conv0.affine = torch.nn.Identity()
            block.conv1.affine = torch.nn.Identity()
            block.torgb.affine = torch.nn.Identity()

        temp_shapes.append(temp_shape)


    if s_input is not None:
        styles = np.load(s_input)['s']
        styles_direction = np.load(f'{outdir}/direction_'+text_prompt.replace(" ", "_")+'.npz')['s']

        styles_direction = torch.tensor(styles_direction, device=device)
        styles = torch.tensor(styles, device=device)

    if from_video and not os.path.isdir(f'{outdir}_video'):
        os.makedirs(f'{outdir}_video')

    with torch.no_grad():
        if from_video:
            name_i = 1000
            for grad_change in np.arange(0, 1, 0.02)*change_power:
                imgs = []
                name_i += 1

                styles += styles_direction*grad_change
                styles_idx = 0
                x = img = None
                for k , res in enumerate(G.synthesis.block_resolutions):
                    block = getattr(G.synthesis, f'b{res}')

                    if res == 4:
                        x, img = block_forward(block, x, img, styles[:, styles_idx:styles_idx+2, :], temp_shapes[k], noise_mode=noise_mode, force_fp32=True)
                        styles_idx += 2
                    else:
                        x, img = block_forward(block, x, img, styles[:, styles_idx:styles_idx+3, :], temp_shapes[k], noise_mode=noise_mode, force_fp32=True)
                        styles_idx += 3

                img = (img.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255)
                imgs.append(img[0].to(torch.uint8).cpu().numpy())

                styles -= styles_direction*grad_change
                img_filepath = '{}_video/{}_{}_{}.jpeg'.format(outdir, text_prompt.replace(" ", "_"), change_power, name_i)
                PIL.Image.fromarray(np.concatenate(imgs, axis=1), 'RGB').save(img_filepath, quality=95)
        else:
            imgs = []
            grad_changes = [0, 0.25*change_power, 0.5*change_power, 0.75*change_power, change_power]

            for grad_change in grad_changes:
                styles += styles_direction*grad_change

                styles_idx = 0
                x = img = None
                for k , res in enumerate(G.synthesis.block_resolutions):
                    block = getattr(G.synthesis, f'b{res}')

                    if res == 4:
                        x, img = block_forward(block, x, img, styles[:, styles_idx:styles_idx+2, :], temp_shapes[k], noise_mode=noise_mode, force_fp32=True)
                        styles_idx += 2
                    else:
                        x, img = block_forward(block, x, img, styles[:, styles_idx:styles_idx+3, :], temp_shapes[k], noise_mode=noise_mode, force_fp32=True)
                        styles_idx += 3

                img = (img.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255)
                imgs.append(img[0].to(torch.uint8).cpu().numpy())

                styles -= styles_direction*grad_change

            img_filepath = f'{outdir}/'+text_prompt.replace(" ", "_")+'_'+str(change_power)+'.jpeg'
            PIL.Image.fromarray(np.concatenate(imgs, axis=1), 'RGB').save(img_filepath, quality=95)



if __name__ == "__main__":
    generate_images()