patchedstabledifftoonnx/pix2pixUI.py

import numpy as np
import gradio as gr

from pipeline_onnx_stable_diffusion_instruct_pix2pix import OnnxStableDiffusionInstructPix2PixPipeline
from diffusers import DDIMScheduler,  EulerAncestralDiscreteScheduler, LMSDiscreteScheduler

def pix2pix(input_img, prompt, guide, iguide, steps, seed):
    if seed == -1:
        generator=None
    else:
        generator=np.random
        generator.seed(seed)
    img = pipe(
        prompt=prompt,
        image=input_img,
        num_inference_steps=steps,
        guidance_scale=guide,
        image_guidance_scale=iguide,
        generator=generator).images[0]
    return img
		
if __name__ == "__main__":	
    model="./model/ip2p-base-fp16-vae_ft_mse-autoslicing"
    pipe = OnnxStableDiffusionInstructPix2PixPipeline.from_pretrained(model, provider="DmlExecutionProvider", safety_checker=None)
    pipe.scheduler=EulerAncestralDiscreteScheduler.from_pretrained(model, subfolder="scheduler")

    demo=gr.Interface(pix2pix, gr.Image(shape=(512,512)), "image")
    title="ONNX Instruct Pix 2 Pix"
    css = "#imgbox img {max-width: 100%  !important; }\n#imgbox div {height: auto;}"
    with gr.Blocks(title=title, css=css) as demo:
        with gr.Row():
            with gr.Column(scale=1):
                seed = gr.Number(value=-1, label="seed", precision=0)
            with gr.Column(scale=14):
                prompt = gr.Textbox(value="", lines=2, label="prompt")
        with gr.Row():
            with gr.Column(scale=1):
                guide = gr.Slider(1.1, 10, value=3, step=0.1, label="Text guidance")
            with gr.Column(scale=1):
                iguide = gr.Slider(1, 10, value=1.1, step=0.1, label="Image guidance")
            with gr.Column(scale=1):
                steps = gr.Slider(10,100, value=30, step=1, label="Steps")
        with gr.Row():
            with gr.Column(scale=1):
                input_img = gr.Image(label="Input Image", type="pil", elem_id="imgbox").style(width=600,height=600)
            with gr.Column(scale=1):
                image_out = gr.Image(value=None, label="Output Image", elem_id="imgbox").style(width=600,height=600)
        gen_btn = gr.Button("Generate", variant="primary", elem_id="gen_button")        
    
        inputs=[input_img, prompt, guide, iguide, steps, seed]
        gen_btn.click(fn=pix2pix, inputs=inputs, outputs=[image_out])
    
    demo.launch()