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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() | |