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| import gradio as gr | |
| #from tempfile import NamedTemporaryFile | |
| import numpy as np | |
| import random | |
| import string | |
| from diffusers import StableDiffusionPipeline as DiffusionPipeline | |
| import torch | |
| from pathos.multiprocessing import ProcessingPool as ProcessPoolExecutor | |
| import requests | |
| from lxml.html.soupparser import fromstring | |
| pool = ProcessPoolExecutor(4) | |
| pool.__enter__() | |
| model_id = "runwayml/stable-diffusion-v1-5" | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| user_agents = [ | |
| 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/109.0.0.0 Safari/537.36' | |
| 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/109.0.0.0 Safari/537.36' | |
| 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/108.0.0.0 Safari/537.36' | |
| 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/108.0.0.0 Safari/537.36' | |
| 'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/108.0.0.0 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' | |
| ] | |
| 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): | |
| html_str = requests.get( | |
| url = "http://translate.google.com", | |
| params = {"sl": "auto", "tl": lang, "op": "translate", "text": text}, | |
| headers = {'User-Agent': random.choice(user_agents)} | |
| ).text | |
| tree = fromstring(html_str) | |
| translated = tree.xpath('//span[@lang="'+lang+'"]/span/span[text()]')[0] | |
| 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" | |
| image = pipe(translate(prompt,"en")).images[0].save(name) | |
| return name | |
| css=""" | |
| #col-container { | |
| margin: 0 auto; | |
| max-width: 520px; | |
| } | |
| """ | |
| 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(label="Result", show_label=False, type='filepath') | |
| run_button.click( | |
| fn = infer, | |
| inputs = [prompt], | |
| outputs = [result] | |
| ) | |
| demo.queue().launch() |