Spaces:
Runtime error
Runtime error
File size: 21,733 Bytes
49226f5 797142e 134c419 91920f4 ad223ef a5fe30f 49226f5 a5fe30f 797142e 49226f5 797142e 2dfa0cd a5fe30f 2a589fd 12e6773 134c419 d0740f4 49226f5 a5fe30f 49226f5 134c419 a5fe30f e8c43c7 a5fe30f 91920f4 ad223ef 1c5b4b7 ad223ef 76d3903 ad223ef 51507f3 9f475ea ad223ef 49226f5 12e6773 49226f5 a5fe30f 12e6773 a5fe30f 49226f5 a5fe30f 49226f5 a5fe30f 2a589fd a5fe30f 134c419 76d3903 134c419 797142e 5ada03b 797142e 3ec570b a5fe30f 3ec570b 797142e b028a73 797142e 51b6984 797142e 51b6984 5181cd5 49226f5 797142e 49226f5 4eb6ffc 49226f5 797142e 3842624 ad223ef 3842624 797142e 3842624 9dccf54 ea46a22 9dccf54 797142e 3ec570b 51b6984 49226f5 37db064 49226f5 37db064 49226f5 797142e 49226f5 a5fe30f 49226f5 9dccf54 49226f5 797142e 49226f5 76d3903 49226f5 b028a73 852d36a 49226f5 12e6773 1e5047d 49226f5 1e5047d 76d3903 49226f5 134c419 49226f5 76d3903 5ada03b 49226f5 76d3903 49226f5 76d3903 49226f5 76d3903 797142e 3842624 49226f5 8369427 49226f5 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 |
import random
import gradio as gr
from datasets import load_dataset
from PIL import Image
from set import ExpiringMap
# from model import get_sd_small, get_sd_tiny, get_sd_every
from trans_google import google_translator
import replicate
from i18n import i18nTranslator
word_list_dataset = load_dataset("Gustavosta/Stable-Diffusion-Prompts")
word_list = word_list_dataset["train"]['Prompt']
#
# from diffusers import EulerDiscreteScheduler, DDIMScheduler, KDPM2AncestralDiscreteScheduler, \
# UniPCMultistepScheduler, DPMSolverSinglestepScheduler, DEISMultistepScheduler, PNDMScheduler, \
# DPMSolverMultistepScheduler, HeunDiscreteScheduler, EulerAncestralDiscreteScheduler, DDPMScheduler, \
# LMSDiscreteScheduler, KDPM2DiscreteScheduler
# import torch
# import base64
# from io import BytesIO
is_gpu_busy = False
# translator = i18nTranslator()
# translator.init(path='locales')
samplers = [
"EulerDiscrete",
"EulerAncestralDiscrete",
"UniPCMultistep",
"DPMSolverSinglestep",
"DPMSolverMultistep",
"KDPM2Discrete",
"KDPM2AncestralDiscrete",
"DEISMultistep",
"HeunDiscrete",
"PNDM",
"DDPM",
"DDIM",
"LMSDiscrete",
]
re_sampler = [
"DDIM",
"K_EULER",
"DPMSolverMultistep",
"K_EULER_ANCESTRAL",
"PNDM",
"KLMS"
]
rand = random.Random()
translator = google_translator()
# tiny_pipe = get_sd_tiny()
# small_pipe = get_sd_small()
# every_pipe = get_sd_every()
# def get_pipe(width: int, height: int):
# if width == 512 and height == 512:
# return tiny_pipe
# elif width == 256 and height == 256:
# return small_pipe
# else:
# return every_pipe
time_client_map = ExpiringMap()
count_client_map = ExpiringMap()
def infer(prompt: str, negative: str, width: int, height: int, sampler: str,
steps: int, seed: int, scale, request: gr.Request):
client_ip = request.client.host
headers = request.kwargs['headers']
if headers and 'x-forwarded-for' in headers:
x_forwarded_for = headers['x-forwarded-for']
client_ip = x_forwarded_for.split(' ')[0] if x_forwarded_for else ""
print("client_ip", client_ip, text, "\n\n")
if client_ip != '127.0.0.1' and client_ip != 'localhost' and client_ip != '0.0.0.0':
if time_client_map.get(client_ip):
return None, "Too many requests, please try again later."
else:
time_client_map.put(client_ip, 1, 10) # 添加一个过期时间为 10 秒的项
count = count_client_map.get(client_ip)
if count is None:
count = 0
count += 1
if count > 5:
print(client_ip)
print(count)
return None, "Too many requests, please try again later more."
else:
count_client_map.put(client_ip, count, 24 * 60 * 60) # 添加一个过期时间为 24 小时的项
global is_gpu_busy
if seed == 0:
seed = rand.randint(0, 10000)
else:
seed = int(seed)
#
# pipeline = get_pipe(width, height)
#
images = []
# if torch.cuda.is_available():
# generator = torch.Generator(device="cuda").manual_seed(seed)
# else:
# generator = None
# if sampler == "EulerDiscrete":
# pipeline.scheduler = EulerDiscreteScheduler.from_config(pipeline.scheduler.config)
# elif sampler == "EulerAncestralDiscrete":
# pipeline.scheduler = EulerAncestralDiscreteScheduler.from_config(pipeline.scheduler.config)
# elif sampler == "KDPM2Discrete":
# pipeline.scheduler = KDPM2DiscreteScheduler.from_config(pipeline.scheduler.config)
# elif sampler == "KDPM2AncestralDiscrete":
# pipeline.scheduler = KDPM2AncestralDiscreteScheduler.from_config(pipeline.scheduler.config)
# elif sampler == "UniPCMultistep":
# pipeline.scheduler = UniPCMultistepScheduler.from_config(pipeline.scheduler.config)
# elif sampler == "DPMSolverSinglestep":
# pipeline.scheduler = DPMSolverSinglestepScheduler.from_config(pipeline.scheduler.config)
# elif sampler == "DPMSolverMultistep":
# pipeline.scheduler = DPMSolverMultistepScheduler.from_config(pipeline.scheduler.config)
# elif sampler == "HeunDiscrete":
# pipeline.scheduler = HeunDiscreteScheduler.from_config(pipeline.scheduler.config)
# elif sampler == "DEISMultistep":
# pipeline.scheduler = DEISMultistepScheduler.from_config(pipeline.scheduler.config)
# elif sampler == "PNDM":
# pipeline.scheduler = PNDMScheduler.from_config(pipeline.scheduler.config)
# elif sampler == "DDPM":
# pipeline.scheduler = DDPMScheduler.from_config(pipeline.scheduler.config)
# elif sampler == "DDIM":
# pipeline.scheduler = DDIMScheduler.from_config(pipeline.scheduler.config)
# elif sampler == "LMSDiscrete":
# pipeline.scheduler = LMSDiscreteScheduler.from_config(pipeline.scheduler.config)
try:
translate_prompt = translator.translate(prompt, lang_tgt='en')
translate_negative = translator.translate(negative, lang_tgt='en')
except Exception as ex:
print(ex)
translate_prompt = prompt
translate_negative = negative
output = replicate.run(
"stability-ai/stable-diffusion:db21e45d3f7023abc2a46ee38a23973f6dce16bb082a930b0c49861f96d1e5bf",
input={
"prompt": translate_prompt,
"negative_prompt": translate_negative,
"guidance_scale": scale,
"num_inference_steps": steps,
"seed": seed,
"scheduler": sampler,
}
)
# image = pipeline(prompt=translate_prompt,
# negative_prompt=translate_negative,
# guidance_scale=scale,
# num_inference_steps=steps,
# generator=generator,
# height=height,
# width=width).images[0]
# buffered = BytesIO()
# image.save(buffered, format="JPEG")
# img_str = base64.b64encode(buffered.getvalue())
# img_base64 = bytes("data:image/jpeg;base64,", encoding='utf-8') + img_str
images.append(output[0])
return images, ""
css = """
.gradio-container {
font-family: 'IBM Plex Sans', sans-serif;
}
.gr-button {
color: white;
border-color: black;
background: black;
}
input[type='range'] {
accent-color: black;
}
.dark input[type='range'] {
accent-color: #dfdfdf;
}
.container {
max-width: 1130px;
margin: auto;
padding-top: 1.5rem;
}
#prompt-column {
min-height: 500px
}
#gallery {
min-height: 22rem;
margin-bottom: 15px;
margin-left: auto;
margin-right: auto;
border-bottom-right-radius: .5rem !important;
border-bottom-left-radius: .5rem !important;
}
#gallery>div>.h-full {
min-height: 20rem;
}
.details:hover {
text-decoration: underline;
}
.gr-button {
white-space: nowrap;
}
.gr-button:focus {
border-color: rgb(147 197 253 / var(--tw-border-opacity));
outline: none;
box-shadow: var(--tw-ring-offset-shadow), var(--tw-ring-shadow), var(--tw-shadow, 0 0 #0000);
--tw-border-opacity: 1;
--tw-ring-offset-shadow: var(--tw-ring-inset) 0 0 0 var(--tw-ring-offset-width) var(--tw-ring-offset-color);
--tw-ring-shadow: var(--tw-ring-inset) 0 0 0 calc(3px var(--tw-ring-offset-width)) var(--tw-ring-color);
--tw-ring-color: rgb(191 219 254 / var(--tw-ring-opacity));
--tw-ring-opacity: .5;
}
#advanced-btn {
font-size: .7rem !important;
line-height: 19px;
margin-top: 12px;
margin-bottom: 12px;
padding: 2px 8px;
border-radius: 14px !important;
}
#advanced-options {
display: none;
margin-bottom: 20px;
}
.footer {
margin-bottom: 45px;
margin-top: 35px;
text-align: center;
border-bottom: 1px solid #e5e5e5;
}
.footer>p {
font-size: .8rem;
display: inline-block;
padding: 0 10px;
transform: translateY(10px);
background: white;
}
.dark .footer {
border-color: #303030;
}
.dark .footer>p {
background: #0b0f19;
}
.acknowledgments h4{
margin: 1.25em 0 .25em 0;
font-weight: bold;
font-size: 115%;
}
.animate-spin {
animation: spin 1s linear infinite;
}
@keyframes spin {
from {
transform: rotate(0deg);
}
to {
transform: rotate(360deg);
}
}
#share-btn-container {
display: flex; padding-left: 0.5rem !important; padding-right: 0.5rem !important; background-color: #000000; justify-content: center; align-items: center; border-radius: 9999px !important; width: 13rem;
margin-top: 10px;
margin-left: auto;
}
#share-btn {
all: initial; color: #ffffff;font-weight: 600; cursor:pointer; font-family: 'IBM Plex Sans', sans-serif; margin-left: 0.5rem !important; padding-top: 0.25rem !important; padding-bottom: 0.25rem !important;right:0;
}
#share-btn * {
all: unset;
}
#share-btn-container div:nth-child(-n+2){
width: auto !important;
min-height: 0px !important;
}
#share-btn-container .wrap {
display: none !important;
}
.gr-form{
flex: 1 1 50%; border-top-right-radius: 0; border-bottom-right-radius: 0;
}
#prompt-container{
gap: 0;
}
#prompt-text-input, #negative-prompt-text-input{padding: .45rem 0.625rem}
#component-16{border-top-width: 1px!important;margin-top: 1em}
.image_duplication{position: absolute; width: 100px; left: 50px}
.generate-container {display: flex; justify-content: flex-end;}
#generate-btn {background: linear-gradient(to bottom right, #ffedd5, #fdba74)}
"""
block = gr.Blocks(css=css)
# text, negative, width, height, sampler, steps, seed, guidance_scale
# examples = [
# [
# 'A high tech solarpunk utopia in the Amazon rainforest',
# 'low quality',
# 512,
# 512,
# 'ddim',
# 30,
# 0,
# 9
# ],
# [
# 'A pikachu fine dining with a view to the Eiffel Tower',
# 'low quality',
# 512,
# 512,
# 'ddim',
# 30,
# 0,
# 9
# ],
# [
# 'A mecha robot in a favela in expressionist style',
# 'low quality, 3d, photorealistic',
# 512,
# 512,
# 'ddim',
# 30,
# 0,
# 9
# ],
# [
# 'an insect robot preparing a delicious meal',
# 'low quality, illustration',
# 512,
# 512,
# 'ddim',
# 30,
# 0,
# 9
# ],
# [
# "A small cabin on top of a snowy mountain in the style of Disney, artstation",
# 'low quality, ugly',
# 512,
# 512,
# 'ddim',
# 30,
# 0,
# 9
# ],
# ]
examples = list(map(lambda x: [
x,
'low quality',
512,
512,
'DPMSolverMultistep',
30,
0,
9
], word_list))[:500]
with block:
title = "Stable Diffusion 2.1 Demo"
desc = """ small stable diffusion Demo App. <br />
Click <strong>Generate image</strong> Button to generate image. <br />
Also Change params to have a try <br />
more size may cost more time. <br />
It's just a simplified demo, you can use more advanced features optimize image quality <br />"""
tutorial_link = "https://docs.cworld.ai/docs/cworld-ai/quick-start-stable-diffusion"
gr.HTML(
f"""
<div style="text-align: center; margin: 0 auto;">
<a href="https://cworld.ai">
<svg style="margin: 0 auto;" width="155" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 407 100">
<g id="SvgjsG2746"
transform="matrix(0.8454106280193237,0,0,0.8454106280193237,-4.2270531400966185,-4.2270531400966185)"
fill="#111">
<g xmlns="http://www.w3.org/2000/svg">
<g>
<g>
<path d="M50,11c21.5,0,39,17.5,39,39S71.5,89,50,89S11,71.5,11,50S28.5,11,50,11 M50,5C25.1,5,5,25.1,5,50s20.1,45,45,45 s45-20.1,45-45S74.9,5,50,5L50,5z"></path>
</g>
</g>
<path d="M55,75H45v-5c0-2.8,2.2-5,5-5h0c2.8,0,5,2.2,5,5V75z"></path>
<rect x="25" y="35" width="10" height="20"></rect>
<rect x="65" y="35" width="10" height="20"></rect>
</g>
</g>
<g id="SvgjsG2747"
transform="matrix(3.3650250410766605,0,0,3.3650250410766605,93.98098208712985,-3.546415304677616)"
fill="#111">
<path
d="M8.1 17.42 l1.42 1.28 c-0.94 1.04 -2.28 1.5 -3.78 1.5 c-2.84 0 -5.14 -2.18 -5.14 -5.12 s2.3 -5.14 5.14 -5.14 c1.5 0 2.84 0.46 3.78 1.5 l-1.42 1.28 c-0.58 -0.78 -1.42 -1.08 -2.36 -1.08 c-1.7 0 -3.08 1.42 -3.08 3.44 c0 2 1.38 3.44 3.08 3.44 c0.94 0 1.78 -0.3 2.36 -1.1 z M23.42 10.12 l2.06 0 l-3.76 9.88 l-1.26 0 l-2.46 -6.4 l-2.44 6.4 l-1.26 0 l-3.78 -9.88 l2.08 0 l2.34 6.9 l2.06 -6.08 l0.26 -0.82 l1.48 0 l0.28 0.82 l2.06 6.08 z M31.62 11.64 c-1.7 0 -3.08 1.42 -3.08 3.44 c0 2 1.38 3.44 3.08 3.44 s3.08 -1.44 3.08 -3.44 c0 -2.02 -1.38 -3.44 -3.08 -3.44 z M31.62 9.94 c2.84 0 5.14 2.2 5.14 5.14 s-2.3 5.12 -5.14 5.12 s-5.14 -2.18 -5.14 -5.12 s2.3 -5.14 5.14 -5.14 z M44.9 10.24 l-0.44 1.62 c-0.14 -0.08 -0.58 -0.22 -0.94 -0.22 c-1.7 0 -2.5 1.62 -2.5 3.62 l0 4.74 l-2.06 0 l0 -9.88 l2.06 0 l0 1.4 c0.24 -0.92 1.3 -1.58 2.48 -1.58 c0.54 0 1.12 0.14 1.4 0.3 z M48.379999999999995 4.619999999999999 l0 15.38 l-2.08 0 l0 -15.38 l2.08 0 z M50.98 15.08 c0 -2.94 2.1 -5.14 4.94 -5.14 c0.98 0 2.18 0.42 2.84 0.96 l0 -5.9 l2.08 0 l0 15 l-2.08 0 l0 -0.74 c-0.78 0.58 -1.86 0.94 -2.84 0.94 c-2.84 0 -4.94 -2.18 -4.94 -5.12 z M53.06 15.08 c0 2 1.38 3.44 3.06 3.44 c1.12 0 2.12 -0.52 2.64 -1.58 c0.28 -0.54 0.44 -1.18 0.44 -1.86 s-0.16 -1.32 -0.44 -1.88 c-0.52 -1.06 -1.52 -1.56 -2.64 -1.56 c-1.68 0 -3.06 1.42 -3.06 3.44 z M66.46 18.78 c0 0.8 -0.62 1.42 -1.42 1.42 c-0.78 0 -1.4 -0.62 -1.4 -1.42 c0 -0.76 0.62 -1.38 1.4 -1.38 c0.8 0 1.42 0.62 1.42 1.38 z M73.08 9.92 c2.84 0 3.98 1.72 3.98 3.18 l0 6.9 l-2.06 0 l0 -1.08 c-0.72 0.98 -2 1.26 -2.8 1.26 c-2.26 0 -3.74 -1.32 -3.74 -3.08 c0 -2.46 1.84 -3.34 3.74 -3.34 l2.8 0 l0 -0.66 c0 -0.62 -0.24 -1.48 -1.92 -1.48 c-0.94 0 -1.8 0.5 -2.36 1.28 l-1.42 -1.28 c0.94 -1.04 2.28 -1.7 3.78 -1.7 z M75 16.92 l0 -1.48 l-2.52 0 c-1.22 0 -2.08 0.62 -1.94 1.74 c0.12 0.94 0.88 1.32 1.94 1.32 c1.9 0 2.52 -0.9 2.52 -1.58 z M81.9 10.12 l0 9.88 l-2.06 0 l0 -9.88 l2.06 0 z M82 6.5 c0 0.64 -0.5 1.14 -1.14 1.14 c-0.62 0 -1.12 -0.5 -1.12 -1.14 c0 -0.62 0.5 -1.12 1.12 -1.12 c0.64 0 1.14 0.5 1.14 1.12 z"></path>
</g>
</svg>
</a>
<div
style="
display: inline-flex;
align-items: center;
gap: 0.8rem;
font-size: 1.75rem;
"
>
<h1 style="font-weight: 900; margin-bottom: 7px;margin-top:5px">
{title}
</h1>
</div>
<p style="margin-bottom: 10px; font-size: 94%; line-height: 23px;">
{desc}
There is the <a href="{tutorial_link}"> tutorial </a>
</p>
</div>
"""
)
with gr.Group():
with gr.Box():
with gr.Row(elem_id="prompt-container").style(mobile_collapse=False, equal_height=True):
with gr.Column(elem_id="prompt-column"):
text = gr.Textbox(
label="Enter your prompt",
show_label=False,
max_lines=1,
placeholder="Enter your prompt",
elem_id="prompt-text-input",
).style(
border=(True, False, True, True),
rounded=(True, False, False, True),
container=False,
)
negative = gr.Textbox(
label="Enter your negative prompt",
show_label=False,
max_lines=1,
placeholder="Enter a negative prompt",
elem_id="negative-prompt-text-input",
).style(
border=(True, False, True, True),
rounded=(True, False, False, True),
container=False,
)
with gr.Row(elem_id="txt2img_size", scale=4):
width = gr.Slider(minimum=64, maximum=1024, step=8, label="Width", value=512,
elem_id="txt2img_width")
height = gr.Slider(minimum=64, maximum=1024, step=8, label="Height", value=512,
elem_id="txt2img_height")
with gr.Row(elem_id="txt2img_sampler", scale=4):
seed = gr.Number(value=0, label="Seed", elem_id="txt2img_seed")
sampler = gr.Dropdown(
re_sampler, value="DPMSolverMultistep",
multiselect=False,
label="Sampler",
info="sampler select"
)
steps = gr.Slider(minimum=1, maximum=50, step=1, elem_id=f"steps", label="Sampling steps",
value=20)
with gr.Accordion("Advanced settings", open=False):
# gr.Markdown("Advanced settings are temporarily unavailable")
# samples = gr.Slider(label="Images", minimum=1, maximum=4, value=4, step=1)
# steps = gr.Slider(label="Steps", minimum=1, maximum=50, value=45, step=1)
guidance_scale = gr.Slider(
label="Guidance Scale", minimum=0, maximum=40, value=9, step=0.1
)
with gr.Row(elem_id="generate-container", elem_classes="generate-container").style(height="100"):
btn = gr.Button("Generate image", elem_id="generate-btn", elem_classes="generate-btn").style(
margin=False,
rounded=(False, True, True, False),
full_width=False,
)
with gr.Column():
gallery = gr.Gallery(
label="Generated images", show_label=False, elem_id="gallery"
).style()
result = gr.Textbox(label="Run Status")
# with gr.Group(elem_id="container-advanced-btns"):
# # advanced_button = gr.Button("Advanced options", elem_id="advanced-btn")
# with gr.Group(elem_id="share-btn-container"):
# community_icon = gr.HTML(community_icon_html)
# loading_icon = gr.HTML(loading_icon_html)
# share_button = gr.Button("Share to community", elem_id="share-btn")
ex = gr.Examples(examples=examples, fn=infer,
inputs=[text, negative, width, height, sampler, steps, seed, guidance_scale],
outputs=[gallery, result],
examples_per_page=5,
cache_examples=False)
ex.dataset.headers = [""]
negative.submit(infer, inputs=[text, negative, width, height, sampler, steps, seed, guidance_scale],
outputs=[gallery, result], postprocess=False)
text.submit(infer, inputs=[text, negative, width, height, sampler, steps, seed, guidance_scale],
outputs=[gallery, result], postprocess=False)
btn.click(infer, inputs=[text, negative, width, height, sampler, steps, seed, guidance_scale],
outputs=[gallery, result], postprocess=False)
block.queue(concurrency_count=5,
max_size=100).launch(
max_threads=150,
# server_port=6006,
# share=True,
)
|