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import os | |
import re | |
import spaces | |
import random | |
import string | |
import torch | |
import requests | |
import gradio as gr | |
import numpy as np | |
from lxml.html import fromstring | |
from diffusers import AutoPipelineForText2Image | |
#from tempfile import NamedTemporaryFile | |
from pathos.threading import ThreadPool as Pool | |
#model_id = "runwayml/stable-diffusion-v1-5" | |
#model_id = "kandinsky-community/kandinsky-3" | |
model_id = "stabilityai/stable-diffusion-3-medium-diffusers" | |
device = "cuda" if torch.cuda.is_available() else "cpu" | |
if torch.cuda.is_available(): | |
torch.cuda.max_memory_allocated(device=device) | |
pipe = AutoPipelineForText2Image.from_pretrained(model_id, torch_dtype=torch.float16, variant="fp16", use_safetensors=True, token=os.getenv('hf_token')) | |
pipe = pipe.to(device) | |
else: | |
pipe = AutoPipelineForText2Image.from_pretrained(model_id, use_safetensors=True, token=os.getenv('hf_token')) | |
pipe = pipe.to(device) | |
def translate(text,lang): | |
if text == None or lang == None: | |
return "" | |
text = re.sub(f'[{string.punctuation}]', '', re.sub('[\s+]', ' ', text)).lower().strip() | |
lang = re.sub(f'[{string.punctuation}]', '', re.sub('[\s+]', ' ', lang)).lower().strip() | |
if text == "" or lang == "": | |
return "" | |
if len(text) > 38: | |
raise Exception("Translation Error: Too long text!") | |
user_agents = [ | |
'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/126.0.0.0 Safari/537.36', | |
'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/126.0.0.0 Safari/537.36', | |
'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/126.0.0.0 Safari/537.36', | |
'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/16.1 Safari/605.1.15', | |
'Mozilla/5.0 (Macintosh; Intel Mac OS X 13_1) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/16.1 Safari/605.1.15' | |
] | |
padded_chars = re.sub("[(^\-)(\-$)]","",text.replace("","-").replace("- -"," ")).strip() | |
query_text = f'Please translate {padded_chars}, into {lang}' | |
url = f'https://www.google.com/search?q={query_text}' | |
print(url) | |
resp = requests.get( | |
url = url, | |
headers = { | |
'User-Agent': random.choice(user_agents) | |
} | |
) | |
content = resp.content | |
html = fromstring(content) | |
translated = text | |
try: | |
src_lang = html.xpath('//*[@class="source-language"]')[0].text_content().lower().strip() | |
trgt_lang = html.xpath('//*[@class="target-language"]')[0].text_content().lower().strip() | |
src_text = html.xpath('//*[@id="tw-source-text"]/*')[0].text_content().lower().strip() | |
trgt_text = html.xpath('//*[@id="tw-target-text"]/*')[0].text_content().lower().strip() | |
if trgt_lang == lang: | |
translated = trgt_text | |
except: | |
print(f'Translation Warning: Failed To Translate!') | |
ret = re.sub(f'[{string.punctuation}]', '', re.sub('[\s+]', ' ', translated)).lower().strip() | |
print(ret) | |
return ret | |
def generate_random_string(length): | |
characters = string.ascii_letters + string.digits | |
return ''.join(random.choice(characters) for _ in range(length)) | |
def Piper(_do,_dont): | |
return pipe( | |
_do, | |
height=1024, | |
width=2048, | |
negative_prompt=_dont, | |
num_inference_steps=100, | |
guidance_scale=10 | |
) | |
def infer(prompt_en,prompt2_en): | |
name = generate_random_string(12)+".png" | |
if prompt_en == "": | |
_do = 'photographed reasonable situation' | |
else: | |
_do = f'photographed { prompt_en } reasonable situation' | |
if prompt2_en == "": | |
_dont = 'unformed, unproportional, boring, smooth, fictional, blurred, twisted, distorted, divined, human body defects, deformed fingers, ugly fingers, wrong fingers, damaged, signs, prints' | |
else: | |
_dont = f'{prompt2_en} in photographed {prompt_en}, unformed, unproportional, boring, smooth, fictional, blurred, twisted, distorted, divined, human body defects, deformed fingers, ugly fingers, wrong fingers, damaged, signs, prints' | |
image = Piper(_do,_dont).images[0].save(name) | |
return name | |
css=""" | |
input, input::placeholder { | |
text-align: center !important; | |
} | |
*, *::placeholder { | |
direction: rtl !important; | |
font-family: Suez One !important; | |
} | |
h1,h2,h3,h4,h5,h6,span,p,pre { | |
width: 100% !important; | |
text-align: center !important; | |
display: block !important; | |
} | |
footer { | |
display: none !important; | |
} | |
#col-container { | |
margin: 0 auto !important; | |
max-width: 15cm !important; | |
} | |
.image-container { | |
aspect-ratio: 2048 / 1024 !important; | |
} | |
.dropdown-arrow { | |
display: none !important; | |
} | |
*:has(button), button { | |
width: 100% !important; | |
margin: 0 auto !important; | |
} | |
""" | |
js=""" | |
function custom(){ | |
document.querySelector("div#prompt input").setAttribute("maxlength","27"); | |
document.querySelector("div#prompt2 input").setAttribute("maxlength","27"); | |
} | |
""" | |
if torch.cuda.is_available(): | |
power_device = "GPU" | |
else: | |
power_device = "CPU" | |
with gr.Blocks(theme=gr.themes.Soft(),css=css,js=js) as demo: | |
result = [] | |
with gr.Column(elem_id="col-container"): | |
gr.Markdown(f""" | |
# 诪讞讜诇诇 转诪讜谞讜转 {power_device} | |
""") | |
with gr.Row(): | |
prompt = gr.Textbox( | |
elem_id="prompt", | |
placeholder="诪讛 *讻谉* 诇讛讜住讬祝", | |
container=False, | |
rtl=True, | |
max_lines=1 | |
) | |
with gr.Row(): | |
prompt2 = gr.Textbox( | |
elem_id="prompt2", | |
placeholder="诪讛 *诇讗* 诇讛讜住讬祝", | |
container=False, | |
rtl=True, | |
max_lines=1 | |
) | |
with gr.Row(): | |
run_button = gr.Button("讛转讞诇讛",scale=0) | |
with gr.Row(): | |
result.append(gr.Image(interactive=False,elem_classes="image-container", label="Result", show_label=False, type='filepath', show_share_button=False)) | |
result.append(gr.Image(interactive=False,elem_classes="image-container", label="Result", show_label=False, type='filepath', show_share_button=False)) | |
result.append(gr.Image(interactive=False,elem_classes="image-container", label="Result", show_label=False, type='filepath', show_share_button=False)) | |
def _ret(idx,p1,p2): | |
print(f'Starting {idx}: {p1} {p2}') | |
v = infer(p1,p2) | |
print(f'Finished {idx}: {v}') | |
return v | |
def _rets(p1,p2): | |
p1_en = translate(p1,"english") | |
p2_en = translate(p2,"english") | |
ln = len(result) | |
idxs = list(range(ln)) | |
p1s = [p1_en for _ in idxs] | |
p2s = [p2_en for _ in idxs] | |
return list(Pool(ln).imap( _ret, idxs, p1s, p2s )) | |
run_button.click(fn=_rets,inputs=[prompt,prompt2],outputs=result) | |
demo.queue().launch() |