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import gradio as gr | |
#from tempfile import NamedTemporaryFile | |
import numpy as np | |
import spaces | |
import random | |
import string | |
from diffusers import StableDiffusionPipeline as DiffusionPipeline | |
import torch | |
from pathos.multiprocessing import ProcessingPool as ProcessPoolExecutor | |
import requests | |
from lxml.html import fromstring | |
pool = ProcessPoolExecutor(16) | |
pool.__enter__() | |
model_id = "runwayml/stable-diffusion-v1-5" | |
device = "cuda" if torch.cuda.is_available() else "cpu" | |
if torch.cuda.is_available(): | |
torch.cuda.max_memory_allocated(device=device) | |
pipe = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16, variant="fp16", use_safetensors=True) | |
pipe = pipe.to(device) | |
else: | |
pipe = DiffusionPipeline.from_pretrained(model_id, use_safetensors=True) | |
pipe = pipe.to(device) | |
def translate(text,lang): | |
resp = requests.post( | |
url = "https://www.bing.com/ttranslatev3?isVertical=1&&IG=13172331D0494B12ABFA8F4454EEB479&IID=translator.5026", | |
referrer = "https://www.bing.com/translator?to=en", | |
referrerPolicy = "origin-when-cross-origin", | |
data = f"&fromLang=auto-detect&to={lang}}&token=cdkbEXg93_iQE28MFPv9ScrPY_fs2OAw&key=1722124106496&text={text}&tryFetchingGenderDebiasedTranslations=true", | |
method = "POST", | |
mode = "cors", | |
credentials = "include", | |
headers = { | |
"accept": "*/*", | |
"accept-language": "en-US,en;q=0.9,he;q=0.8,ha;q=0.7", | |
"content-type": "application/x-www-form-urlencoded", | |
"priority": "u=1, i", | |
"sec-ch-ua": "\"Not/A)Brand\";v=\"8\", \"Chromium\";v=\"126\", \"Google Chrome\";v=\"126\"", | |
"sec-ch-ua-arch": "\"x86\"", | |
"sec-ch-ua-bitness": "\"64\"", | |
"sec-ch-ua-full-version": "\"126.0.6478.185\"", | |
"sec-ch-ua-full-version-list": "\"Not/A)Brand\";v=\"8.0.0.0\", \"Chromium\";v=\"126.0.6478.185\", \"Google Chrome\";v=\"126.0.6478.185\"", | |
"sec-ch-ua-mobile": "?0", | |
"sec-ch-ua-model": "\"\"", | |
"sec-ch-ua-platform": "\"Windows\"", | |
"sec-ch-ua-platform-version": "\"10.0.0\"", | |
"sec-fetch-dest": "empty", | |
"sec-fetch-mode": "cors", | |
"sec-fetch-site": "same-origin" | |
} | |
) | |
print(resp) | |
jsn = resp.json() | |
print(jsn) | |
translated = jsn[0]["translations"][0]["text"] | |
return translated | |
def generate_random_string(length): | |
characters = string.ascii_letters + string.digits | |
return ''.join(random.choice(characters) for _ in range(length)) | |
def infer(prompt): | |
name = generate_random_string(12)+".png" | |
english_prompt = "Generate the most true and authentic and real and genuine single photograph, for " + translate(prompt,"en") | |
print(f'Final prompt: {english_prompt}') | |
image = pipe(english_prompt).images[0].save(name) | |
return name | |
css=""" | |
#col-container { | |
margin: 0 auto; | |
max-width: 12cm; | |
} | |
#image-container { | |
aspect-ratio: 1 / 1; | |
} | |
""" | |
if torch.cuda.is_available(): | |
power_device = "GPU" | |
else: | |
power_device = "CPU" | |
with gr.Blocks(css=css) as demo: | |
with gr.Column(elem_id="col-container"): | |
gr.Markdown(f""" | |
# Image Generator | |
Currently running on {power_device}. | |
""") | |
with gr.Row(): | |
prompt = gr.Text( | |
label="Prompt", | |
show_label=False, | |
max_lines=1, | |
placeholder="Enter your prompt", | |
container=False, | |
) | |
run_button = gr.Button("Run", scale=0) | |
result = gr.Image(elem_id="image-container", label="Result", show_label=False, type='filepath') | |
run_button.click( | |
fn = infer, | |
inputs = [prompt], | |
outputs = [result] | |
) | |
demo.queue().launch() |