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| import gradio as gr | |
| import os | |
| import re | |
| #from tempfile import NamedTemporaryFile | |
| import numpy as np | |
| import spaces | |
| import random | |
| import string | |
| from diffusers import StableDiffusion3Pipeline | |
| import torch | |
| from pathos.multiprocessing import ProcessingPool as ProcessPoolExecutor | |
| import requests | |
| from lxml.html import fromstring | |
| pool = ProcessPoolExecutor(1000) | |
| pool.__enter__() | |
| #model_id = "runwayml/stable-diffusion-v1-5" | |
| 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 = StableDiffusion3Pipeline.from_pretrained(model_id, torch_dtype=torch.float16, variant="fp16", use_safetensors=True, token=os.getenv('hf_token')) | |
| pipe = pipe.to(device) | |
| else: | |
| pipe = StableDiffusion3Pipeline.from_pretrained(model_id, use_safetensors=True) | |
| pipe = pipe.to(device) | |
| def translate(text,lang): | |
| 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 (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' | |
| ] | |
| url = f'https://www.google.com/search?q=translate to {lang}: {text}' | |
| print(url) | |
| resp = requests.get( | |
| url = url, | |
| headers = { | |
| 'User-Agent': random.choice(user_agents) | |
| } | |
| ) | |
| print(resp) | |
| content = resp.content | |
| html = fromstring(content) | |
| rslt = html.xpath('//pre[@aria-label="Translated text"]/span') | |
| translated = rslt[0].text.strip() | |
| 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 Pipe(english_prompt, height, width): | |
| return Pipe( | |
| english_prompt, | |
| height=192, | |
| width=192, | |
| negative_prompt="", | |
| num_inference_steps=150, | |
| guidance_scale=10 | |
| ) | |
| def infer(prompt): | |
| name = generate_random_string(12)+".png" | |
| english_prompt = f'TRUE {translate(prompt,"english").upper()}:' | |
| print(f'Final prompt: {english_prompt}') | |
| image = Pipe(english_prompt, height, width).images[0].save(name) | |
| return name | |
| css=""" | |
| #col-container { | |
| margin: 0 auto; | |
| max-width: 14cm; | |
| } | |
| #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() |