-
Notifications
You must be signed in to change notification settings - Fork 27
/
gradio_demo.py
258 lines (230 loc) · 7.86 KB
/
gradio_demo.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
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
import os
import gradio as gr
import open_clip
import torch
import taming.models.vqgan
import ml_collections
import einops
import random
# Model
from libs.muse import MUSE
import utils
import numpy as np
from glob import glob
empty_context = np.load("assets/contexts/empty_context.npy")
def set_seed(seed: int):
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
def d(**kwargs):
"""Helper of creating a config dict."""
return ml_collections.ConfigDict(initial_dictionary=kwargs)
def get_config():
config = ml_collections.ConfigDict()
config.seed = 1234
config.z_shape = (8, 16, 16)
config.autoencoder = d(
config_file='vq-f16-jax.yaml',
)
config.resume_root="assets/ckpts/cc3m-285000.ckpt"
config.adapter_path=None
config.optimizer = d(
name='adamw',
lr=0.0002,
weight_decay=0.03,
betas=(0.99, 0.99),
)
config.lr_scheduler = d(
name='customized',
warmup_steps=5000
)
config.nnet = d(
name='uvit_t2i_vq',
img_size=16,
codebook_size=1024,
in_chans=4,
embed_dim=1152,
depth=28,
num_heads=16,
mlp_ratio=4,
qkv_bias=False,
clip_dim=1280,
num_clip_token=77,
use_checkpoint=True,
skip=True,
d_prj=32,
is_shared=False
)
config.muse = d(
ignore_ind=-1,
smoothing=0.1,
gen_temp=4.5
)
config.sample = d(
sample_steps=36,
n_samples=50,
mini_batch_size=8,
cfg=True,
linear_inc_scale=True,
scale=10.,
path='',
lambdaA=2.0, # Stage I: 2.0; Stage II: TODO
lambdaB=5.0, # Stage I: 5.0; Stage II: TODO
)
return config
def cfg_nnet(x, context, scale=None,lambdaA=None,lambdaB=None):
_cond = nnet_ema(x, context=context)
_cond_w_adapter = nnet_ema(x,context=context,use_adapter=True)
_empty_context = torch.tensor(empty_context, device=device)
_empty_context = einops.repeat(_empty_context, 'L D -> B L D', B=x.size(0))
_uncond = nnet_ema(x, context=_empty_context)
res = _cond + scale * (_cond - _uncond)
if lambdaA is not None:
res = _cond_w_adapter + lambdaA*(_cond_w_adapter - _cond) + lambdaB*(_cond - _uncond)
return res
def unprocess(x):
x.clamp_(0., 1.)
return x
config = get_config()
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
# Load open_clip and vq model
prompt_model,_,_ = open_clip.create_model_and_transforms('ViT-bigG-14', 'laion2b_s39b_b160k')
prompt_model = prompt_model.to(device)
prompt_model.eval()
tokenizer = open_clip.get_tokenizer('ViT-bigG-14')
vq_model = taming.models.vqgan.get_model('vq-f16-jax.yaml')
vq_model.eval()
vq_model.requires_grad_(False)
vq_model.to(device)
## config
muse = MUSE(codebook_size=vq_model.n_embed, device=device, **config.muse)
train_state = utils.initialize_train_state(config, device)
train_state.resume(ckpt_root=config.resume_root)
nnet_ema = train_state.nnet_ema
nnet_ema.eval()
nnet_ema.requires_grad_(False)
nnet_ema.to(device)
style_adapters = glob("style_adapter/*")
style_adapters = [os.path.basename(x).split('.')[0] for x in style_adapters]
# style_ref = {
# "None": None,
# **{x: os.path.join("style_adapter", x, "adapter.pth") for x in style_adapters}
# }
# style_postfix = {
# "None": "",
# **{x: f" in {x.replace('_', ' ')} style" for x in style_adapters}
# }
style_ref = {
"None":None,
"0102":"style_adapter/0102.pth",
"0103":"style_adapter/0103.pth",
"0106":"style_adapter/0106.pth",
"0108":"style_adapter/0108.pth",
"0301":"style_adapter/0301.pth",
"0305":"style_adapter/0305.pth",
}
style_postfix ={
"None":"",
"0102":" in watercolor painting style",
"0103":" in watercolor painting style",
"0106":" in line drawing style",
"0108":" in oil painting style",
"0301":" in 3d rendering style",
"0305":" in kid crayon drawing style",
}
def decode(_batch):
return vq_model.decode_code(_batch)
def process(prompt,num_samples,lambdaA,lambdaB,style,seed,sample_steps,image=None):
config.sample.lambdaA = lambdaA
config.sample.lambdaB = lambdaB
config.sample.sample_steps = sample_steps
print(style)
adapter_path = style_ref[style]
adapter_postfix = style_postfix[style]
print(f"load adapter path: {adapter_path}")
if adapter_path is not None:
nnet_ema.adapter.load_state_dict(torch.load(adapter_path))
else:
config.sample.lambdaA=None
config.sample.lambdaB=None
print("load adapter Done!")
# Encode prompt
prompt = prompt+adapter_postfix
text_tokens = tokenizer(prompt).to(device)
text_embedding = prompt_model.encode_text(text_tokens)
text_embedding = text_embedding.repeat(num_samples, 1, 1) # B 77 1280
print(text_embedding.shape)
print(f"lambdaA: {lambdaA}, lambdaB: {lambdaB}, sample_steps: {sample_steps}")
if seed==-1:
seed = random.randint(0,65535)
config.seed = seed
print(f"seed: {seed}")
set_seed(config.seed)
res = muse.generate(config,num_samples,cfg_nnet,decode,is_eval=True,context=text_embedding)
print(res.shape)
res = (res*255+0.5).clamp_(0,255).permute(0,2,3,1).to('cpu',torch.uint8).numpy()
im = [res[i] for i in range(num_samples)]
return im
block = gr.Blocks()
with block:
with gr.Row():
gr.Markdown("## StyleDrop based on Muse (Inference Only) ")
with gr.Row():
with gr.Column():
prompt = gr.Textbox(label="Prompt")
run_button = gr.Button(label="Run")
num_samples = gr.Slider(label="Images", minimum=1, maximum=12, value=1, step=1)
seed = gr.Slider(label="Seed", minimum=-1, maximum=2147483647, step=1, value=1234)
style = gr.Radio(choices=style_adapters+["None"], type="value",value="None",label="Style")
with gr.Accordion("Advanced options",open=False):
lambdaA = gr.Slider(label="lambdaA", minimum=0.0, maximum=5.0, value=2.0, step=0.01)
lambdaB = gr.Slider(label="lambdaB", minimum=0.0, maximum=10.0, value=5.0, step=0.01)
sample_steps = gr.Slider(label="Sample steps", minimum=1, maximum=50, value=36, step=1)
image=gr.Image(value=None)
with gr.Column():
result_gallery = gr.Gallery(label='Output', show_label=False, elem_id="gallery").style(columns=2, height='auto')
with gr.Row():
examples = [
[ "data/image_01_03.jpg",
"A banana on the table",
1,2.0,5.0,"0103",1234,36,
],
[
"data/image_01_02.jpg",
"A cow",
1,2.0,5.0,"0102",1234,36
],
[
"data/image_01_06.jpg",
"A portrait of tabby cat",
1,2.0,5.0,"0106",1234,36,
],
[
"data/image_01_08.jpg",
"A church in the field",
1,2.0,5.0,"0108",1234,36,
],
[
"data/image_03_05.jpg",
"A Christmas tree",
1,2.0,5.0,"0305",1234,36,
]
]
gr.Examples(examples=examples,
fn=process,
inputs=[
image,
prompt,
num_samples,lambdaA,lambdaB,style,seed,sample_steps,
],
outputs=result_gallery,
cache_examples=os.getenv('SYSTEM') == 'spaces'
)
ips = [prompt,num_samples,lambdaA,lambdaB,style,seed,sample_steps,image]
run_button.click(
fn=process,
inputs=ips,
outputs=[result_gallery]
)
block.launch(share=True,show_error=True)