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import spaces
import gradio as gr
#from tempfile import NamedTemporaryFile
import numpy as np
import random
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(100)
#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 he_to_en(prompt):
html_str = requests.get( url = "https://translate.google.com", params = {"sl": "iw", "tl": "en", "op": "translate", "text": prompt} ).text()
tree = fromstring(html_str)
english = tree.xpath('span[lang="en"]/span/span/text()')[0]
return english
def generate_random_string(length):
characters = string.ascii_letters + string.digits
return ''.join(random.choice(characters) for _ in range(length))
@spaces.GPU(25)
def infer(prompt):
name = generate_random_string(12)+".png"
image = pipe(he_to_en(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() |