forked from crowsonkb/v-diffusion-pytorch
-
Notifications
You must be signed in to change notification settings - Fork 3
/
cfg_sample.py
executable file
·175 lines (150 loc) · 7.06 KB
/
cfg_sample.py
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
#!/usr/bin/env python3
"""Classifier-free guidance sampling from a diffusion model."""
import argparse
from functools import partial
from pathlib import Path
from PIL import Image
import torch
from torch import nn
from torch.nn import functional as F
from torchvision import transforms
from torchvision.transforms import functional as TF
from tqdm import trange
from CLIP import clip
from diffusion import get_model, get_models, sampling, utils
MODULE_DIR = Path(__file__).resolve().parent
def parse_prompt(prompt, default_weight=3.):
if prompt.startswith('http://') or prompt.startswith('https://'):
vals = prompt.rsplit(':', 2)
vals = [vals[0] + ':' + vals[1], *vals[2:]]
else:
vals = prompt.rsplit(':', 1)
vals = vals + ['', default_weight][len(vals):]
return vals[0], float(vals[1])
def resize_and_center_crop(image, size):
fac = max(size[0] / image.size[0], size[1] / image.size[1])
image = image.resize((int(fac * image.size[0]), int(fac * image.size[1])), Image.LANCZOS)
return TF.center_crop(image, size[::-1])
def main():
p = argparse.ArgumentParser(description=__doc__,
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
p.add_argument('prompts', type=str, default=[], nargs='*',
help='the text prompts to use')
p.add_argument('--images', type=str, default=[], nargs='*', metavar='IMAGE',
help='the image prompts')
p.add_argument('--batch-size', '-bs', type=int, default=1,
help='the number of images per batch')
p.add_argument('--checkpoint', type=str,
help='the checkpoint to use')
p.add_argument('--device', type=str,
help='the device to use')
p.add_argument('--eta', type=float, default=0.,
help='the amount of noise to add during sampling (0-1)')
p.add_argument('--init', type=str,
help='the init image')
p.add_argument('--method', type=str, default='plms',
choices=['ddpm', 'ddim', 'prk', 'plms', 'pie', 'plms2'],
help='the sampling method to use')
p.add_argument('--model', type=str, default='cc12m_1_cfg', choices=['cc12m_1_cfg'],
help='the model to use')
p.add_argument('-n', type=int, default=1,
help='the number of images to sample')
p.add_argument('--seed', type=int, default=0,
help='the random seed')
p.add_argument('--size', type=int, nargs=2,
help='the output image size')
p.add_argument('--starting-timestep', '-st', type=float, default=0.9,
help='the timestep to start at (used with init images)')
p.add_argument('--steps', type=int, default=50,
help='the number of timesteps')
args = p.parse_args()
if args.device:
device = torch.device(args.device)
else:
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
print('Using device:', device)
model = get_model(args.model)()
_, side_y, side_x = model.shape
if args.size:
side_x, side_y = args.size
checkpoint = args.checkpoint
if not checkpoint:
checkpoint = MODULE_DIR / f'checkpoints/{args.model}.pth'
model.load_state_dict(torch.load(checkpoint, map_location='cpu'))
if device.type == 'cuda':
model = model.half()
model = model.to(device).eval().requires_grad_(False)
clip_model_name = model.clip_model if hasattr(model, 'clip_model') else 'ViT-B/16'
clip_model = clip.load(clip_model_name, jit=False, device=device)[0]
clip_model.eval().requires_grad_(False)
normalize = transforms.Normalize(mean=[0.48145466, 0.4578275, 0.40821073],
std=[0.26862954, 0.26130258, 0.27577711])
if args.init:
init = Image.open(utils.fetch(args.init)).convert('RGB')
init = resize_and_center_crop(init, (side_x, side_y))
init = utils.from_pil_image(init).to(device)[None].repeat([args.n, 1, 1, 1])
zero_embed = torch.zeros([1, clip_model.visual.output_dim], device=device)
target_embeds, weights = [zero_embed], []
for prompt in args.prompts:
txt, weight = parse_prompt(prompt)
target_embeds.append(clip_model.encode_text(clip.tokenize(txt).to(device)).float())
weights.append(weight)
for prompt in args.images:
path, weight = parse_prompt(prompt)
img = Image.open(utils.fetch(path)).convert('RGB')
clip_size = clip_model.visual.input_resolution
img = resize_and_center_crop(img, (clip_size, clip_size))
batch = TF.to_tensor(img)[None].to(device)
embed = F.normalize(clip_model.encode_image(normalize(batch)).float(), dim=-1)
target_embeds.append(embed)
weights.append(weight)
weights = torch.tensor([1 - sum(weights), *weights], device=device)
torch.manual_seed(args.seed)
# PF- Added two values to display during iterations:
print('seed', args.seed)
print('starting_timestep', args.starting_timestep)
def cfg_model_fn(x, t):
n = x.shape[0]
n_conds = len(target_embeds)
x_in = x.repeat([n_conds, 1, 1, 1])
t_in = t.repeat([n_conds])
clip_embed_in = torch.cat([*target_embeds]).repeat_interleave(n, 0)
vs = model(x_in, t_in, clip_embed_in).view([n_conds, n, *x.shape[1:]])
v = vs.mul(weights[:, None, None, None, None]).sum(0)
return v
def run(x, steps):
if args.method == 'ddpm':
return sampling.sample(cfg_model_fn, x, steps, 1., {})
if args.method == 'ddim':
return sampling.sample(cfg_model_fn, x, steps, args.eta, {})
if args.method == 'prk':
return sampling.prk_sample(cfg_model_fn, x, steps, {})
if args.method == 'plms':
return sampling.plms_sample(cfg_model_fn, x, steps, {})
if args.method == 'pie':
return sampling.pie_sample(cfg_model_fn, x, steps, {})
if args.method == 'plms2':
return sampling.plms2_sample(cfg_model_fn, x, steps, {})
assert False
def run_all(n, batch_size):
x = torch.randn([n, 3, side_y, side_x], device=device)
t = torch.linspace(1, 0, args.steps + 1, device=device)[:-1]
steps = utils.get_spliced_ddpm_cosine_schedule(t)
if args.init:
steps = steps[steps < args.starting_timestep]
alpha, sigma = utils.t_to_alpha_sigma(steps[0])
x = init * alpha + x * sigma
for i in trange(0, n, batch_size):
cur_batch_size = min(n - i, batch_size)
outs = run(x[i:i+cur_batch_size], steps)
#PF - Modified to accomodate loop over seeds and starting timesteps
for j, out in enumerate(outs):
str_ts = "{:.3f}".format(args.starting_timestep)
utils.to_pil_image(out).save(f'results/seed_{args.seed}_st_'+str_ts+'.png')
# utils.to_pil_image(out).save(f'out_{i + j:05}.png')
try:
run_all(args.n, args.batch_size)
except KeyboardInterrupt:
pass
if __name__ == '__main__':
main()