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 AutoPipelineForText2Image import torch from pathos.multiprocessing import ProcessingPool as ProcessPoolExecutor import requests from lxml.html import fromstring pool = ProcessPoolExecutor(4) pool.__enter__() #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): text = re.sub(f'[{string.punctuation}]', '', re.sub('[\s+]', ' ', text)).lower().strip() lang = re.sub(f'[{string.punctuation}]', '', re.sub('[\s+]', ' ', lang)).lower().strip() 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 = 'https://www.google.com/search' resp = requests.get( url = url, params = {'q': f'{lang} translate {text}'}, headers = { 'User-Agent': random.choice(user_agents) } ) content = resp.content html = fromstring(content) #src = html.xpath('//pre[@data-placeholder="Enter text"]/textarea')[0].text.strip() translated = text try: trgt = html.xpath('//span[@class="target-language"]')[0].text.strip() rslt = html.xpath('//pre[@aria-label="Translated text"]/span')[0].text.strip() if trgt.lower() == lang.lower(): translated = rslt except: raise Exception("Translation Error!") 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)) @spaces.GPU(duration=120) def Piper(_do,_dont): return pipe( _do, height=512, width=768, negative_prompt=_dont, num_inference_steps=40, guidance_scale=10 ) def infer(prompt,prompt2): name = generate_random_string(12)+".png" _do = f'{ translate(prompt,"english") }, hot colors palette, muted colors, dynamic poze, realistic, accurate, matched, realistic details, award winning photograph, soft natural lighting, deep field, high definition, highly detailed, 8k'.lower() _dont = f'{translate(prompt2,"english")}, ugly, deformed, disfigured, poor details, bad anatomy, labels, text, logo' image = Piper(_do,_dont).images[0].save(name) return name css=""" #col-container { margin: 0 auto; max-width: 15cm; } #image-container { aspect-ratio: 3 / 2; } .dropdown-arrow { display: none !important; } """ js=""" function custom(){ document.querySelector("div#prompt input").setAttribute("maxlength","38"); document.querySelector("div#prompt2 input").setAttribute("maxlength","38"); } """ 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: with gr.Column(elem_id="col-container"): gr.Markdown(f""" # Image Generator Currently running on {power_device}. """) with gr.Row(): prompt = gr.Textbox( elem_id="prompt", placeholder="Photo Description", container=False, rtl=True, max_lines=1 ) prompt2 = gr.Textbox( elem_id="prompt2", placeholder="Forbidden Content", container=False, rtl=True, max_lines=1 ) with gr.Row(): run_button = gr.Button("Run") result = gr.Image(elem_id="image-container", label="Result", show_label=False, type='filepath') run_button.click( fn = infer, inputs = [prompt,prompt2], outputs = [result] ) demo.queue().launch()