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import os, torch
import gradio as gr
from diffusers import StableDiffusionPipeline
# get hf user access token as an environment variable
TOKEN_KEY = os.getenv('AUTH_TOKEN')
# choose GPU else fallback to CPU
device = "cuda" if torch.cuda.is_available() else "cpu"
# setup pipeline
if device == "cuda":
pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", revision="fp16", torch_dtype=torch.float16, use_auth_token=TOKEN_KEY)
else:
pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", use_auth_token=TOKEN_KEY)
pipe = pipe.to(device)
# define gradio function
def generate(prompt:str, seed:int, guidance:float, steps:int):
generator = torch.Generator(device).manual_seed(seed)
image = pipe(prompt=prompt, generator=generator, guidance_scale=guidance, num_inference_steps=steps).images[0]
return image
if device == "cuda":
device_name = torch.cuda.get_device_name(0)
print(device_name + " available.")
else:
print("Using CPU.")
# create the gradio UI
# set precision to 0 to round value to nearest int
demo = gr.Interface(
fn=generate,
inputs=[gr.Textbox(placeholder="castle on a mountain"), gr.Number(value=123456, precision=0), gr.Slider(0,10), gr.Number(value=50, precision=0)],
outputs="image",
allow_flagging="never",
)
# allow queueing or incoming requests, max=3
demo.queue(concurrency_count=3)
# launch demo
# demo.launch(share=True) # uncomment if running outside of huggingface spaces
demo.launch()
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