Spaces:
Runtime error
Runtime error
| import os, torch | |
| import gradio as gr | |
| from diffusers import StableDiffusionPipeline | |
| # check if running in google colab environment | |
| def is_google_colab(): | |
| try: | |
| import google.colab | |
| return True | |
| except: | |
| return False | |
| is_colab = is_google_colab() | |
| # 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! Using GPU 🔥") | |
| 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(debug=is_colab, share=is_colab) | |