Spaces:
Sleeping
Sleeping
File size: 3,326 Bytes
b4f9b4b 210ed13 b1328e8 62c5b0c 210ed13 76b48d0 b4f9b4b ecc81cb 62c5b0c 76b48d0 62c5b0c 210ed13 62c5b0c 210ed13 62c5b0c 210ed13 3ed5fef 93b8891 5087a64 ecc81cb 3ed5fef b4f9b4b 62c5b0c b4f9b4b 93b8891 b4f9b4b 210ed13 62c5b0c 210ed13 62c5b0c 210ed13 62c5b0c 210ed13 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 |
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 import fromstring
pool = ProcessPoolExecutor(4)
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):
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'
]
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)}
).content
root = fromstring(html_str)
translated = root.xpath(f'//span[@lang="{lang}"]/span/span')[0].text_content().strip()
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 = 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: 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() |