app.py

from PIL import Image
import gradio as gr
from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, UniPCMultistepScheduler
import torch

controlnet = ControlNetModel.from_pretrained("ioclab/control_v1p_sd15_brightness", torch_dtype=torch.float32, use_safetensors=True)


pipe = StableDiffusionControlNetPipeline.from_pretrained(
    "runwayml/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float32,
)

pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)

# pipe.enable_xformers_memory_efficient_attention()
pipe.enable_model_cpu_offload()


def infer(prompt, negative_prompt, num_inference_steps, conditioning_image):
    # conditioning_image = Image.open(conditioning_image)
    conditioning_image = Image.fromarray(conditioning_image)
    generator = torch.Generator(device="cpu").manual_seed(1500)

    output_image = pipe(
        prompt,
        conditioning_image,
        height=512,
        width=512,
        num_inference_steps=num_inference_steps,
        generator=generator,
        negative_prompt=negative_prompt,
        controlnet_conditioning_scale=1.0,
    ).images[0]

    return output_image

with gr.Blocks() as demo:
    gr.Markdown(
        """
    # ControlNet on Brightness

    This is a demo on ControlNet based on brightness.
    """)

    with gr.Row():
        with gr.Column():
            prompt = gr.Textbox(
                label="Prompt",
            )
            negative_prompt = gr.Textbox(
                label="Negative Prompt",
            )
            num_inference_steps = gr.Slider(
                10, 40, 20,
                step=1,
                label="Steps",
            )
            conditioning_image = gr.Image(
                label="Conditioning Image",
            )
            submit_btn = gr.Button(
                value="Submit",
                variant="primary"
            )
        with gr.Column(min_width=300):
            output = gr.Image(
                label="Result",
            )

    submit_btn.click(
        fn=infer,
        inputs=[
            prompt, negative_prompt, num_inference_steps, conditioning_image
        ],
        outputs=output
    )
    gr.Examples(
        examples=[
            ["a painting of a village in the mountains", "monochrome", "./conditioning_images/conditioning_image_1.jpg"],
            ["three people walking in an alleyway with hats and pants", "monochrome", "./conditioning_images/conditioning_image_2.jpg"],
        ],
        inputs=[
            prompt, negative_prompt, conditioning_image
        ],
    )

demo.launch()