demo.py

import base64
from io import BytesIO

import gradio as gr
import PIL.Image
import torch
from diffusers import StableDiffusionPipeline, AutoencoderKL, AutoencoderTiny

device = "cuda"   # Linux & Windows
weight_type = torch.float16  # torch.float16 works as well, but pictures seem to be a bit worse

pipe = StableDiffusionPipeline.from_pretrained("IDKiro/sdxs-512-dreamshaper", torch_dtype=weight_type)
pipe.to(torch_device=device, torch_dtype=weight_type)

vae_tiny = AutoencoderTiny.from_pretrained("IDKiro/sdxs-512-dreamshaper", subfolder="vae")
vae_tiny.to(device, dtype=weight_type)

vae_large = AutoencoderKL.from_pretrained("IDKiro/sdxs-512-dreamshaper", subfolder="vae_large")
vae_tiny.to(device, dtype=weight_type)

def pil_image_to_data_url(img, format="PNG"):
    buffered = BytesIO()
    img.save(buffered, format=format)
    img_str = base64.b64encode(buffered.getvalue()).decode()
    return f"data:image/{format.lower()};base64,{img_str}"


def run(
    prompt: str,
    device_type="GPU",
    vae_type=None,
    param_dtype='torch.float16',
) -> PIL.Image.Image:
    if vae_type == "tiny vae":
        pipe.vae = vae_tiny
    elif vae_type == "large vae":
        pipe.vae = vae_large

    if device_type == "CPU":
        device = "cpu" 
        param_dtype = 'torch.float32'
    else:
        device = "cuda"
    
    pipe.to(torch_device=device, torch_dtype=torch.float16 if param_dtype == 'torch.float16' else torch.float32)

    result = pipe(
        prompt=prompt,
        guidance_scale=0.0,
        num_inference_steps=1,
        output_type="pil",
    ).images[0]

    result_url = pil_image_to_data_url(result)

    return (result, result_url)


examples = [
    "A photo of beautiful mountain with realistic sunset and blue lake, highly detailed, masterpiece",
]

with gr.Blocks(css="style.css") as demo:
    gr.Markdown("# SDXS-512-DreamShaper")
    with gr.Group():
        with gr.Row():
            with gr.Column(min_width=685):
                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)
                    
                device_choices = ['GPU','CPU']
                device_type = gr.Radio(device_choices, label='Device',  
                                            value=device_choices[0],
                                            interactive=True,
                                            info='Please choose GPU if you have a GPU.')

                vae_choices = ['tiny vae','large vae']
                vae_type = gr.Radio(vae_choices, label='Image Decoder Type',  
                                            value=vae_choices[0],
                                            interactive=True,
                                            info='To save GPU memory, use tiny vae. For better quality, use large vae.')

                dtype_choices = ['torch.float16','torch.float32']
                param_dtype = gr.Radio(dtype_choices,label='torch.weight_type',  
                                            value=dtype_choices[0],
                                            interactive=True,
                                            info='To save GPU memory, use torch.float16. For better quality, use torch.float32.')
                
                download_output = gr.Button("Download output", elem_id="download_output")

            with gr.Column(min_width=512):
                result = gr.Image(label="Result", height=512, width=512, elem_id="output_image", show_label=False, show_download_button=True)

    gr.Examples(
        examples=examples,
        inputs=prompt,
        outputs=result,
        fn=run
    )

    demo.load(None,None,None)

    inputs = [prompt, device_type, vae_type, param_dtype]
    outputs = [result, download_output]
    prompt.submit(fn=run, inputs=inputs, outputs=outputs)
    run_button.click(fn=run, inputs=inputs, outputs=outputs)

if __name__ == "__main__":
    demo.queue().launch(debug=True)