from PIL import Image
import gradio as gr
from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, UniPCMultistepScheduler
import torch
torch.backends.cuda.matmul.allow_tf32 = True

controlnet = ControlNetModel.from_pretrained("ioclab/control_v1p_sd15_brightness", torch_dtype=torch.float16, use_safetensors=True)

pipe = StableDiffusionControlNetPipeline.from_pretrained(
    "runwayml/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float16,
)

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

pipe.enable_xformers_memory_efficient_attention()
pipe.enable_model_cpu_offload()
pipe.enable_attention_slicing()

def infer(prompt, negative_prompt, conditioning_image, num_inference_steps, size, guidance_scale, seed):

    conditioning_image = Image.fromarray(conditioning_image)
    conditioning_image = conditioning_image.convert('L')

    g_cpu = torch.Generator()

    if seed == -1:
        generator = g_cpu.manual_seed(g_cpu.seed())
    else:
        generator = g_cpu.manual_seed(seed)

    output_images = pipe(
        prompt,
        conditioning_image,
        height=size,
        width=size,
        num_inference_steps=num_inference_steps,
        generator=generator,
        negative_prompt=negative_prompt,
        guidance_scale=guidance_scale,
        controlnet_conditioning_scale=1.0,
    ).images

    return output_images

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",
            )
            conditioning_image = gr.Image(
                label="Conditioning Image",
            )
            with gr.Accordion('Advanced options', open=False):
                with gr.Row():
                    num_inference_steps = gr.Slider(
                        10, 40, 20,
                        step=1,
                        label="Steps",
                    )
                    size = gr.Slider(
                        256, 768, 512,
                        step=128,
                        label="Size",
                    )
                with gr.Row():
                    guidance_scale = gr.Slider(
                        label='Guidance Scale',
                        minimum=0.1,
                        maximum=30.0,
                        value=7.0,
                        step=0.1
                    )
                    seed = gr.Slider(
                        label='Seed',
                        value=-1,
                        minimum=-1,
                        maximum=2147483647,
                        step=1,
                        # randomize=True
                    )
            submit_btn = gr.Button(
                value="Submit",
                variant="primary"
            )
        with gr.Column(min_width=300):
            output = gr.Gallery(
                label="Result",
            )

    submit_btn.click(
        fn=infer,
        inputs=[
            prompt, negative_prompt, conditioning_image, num_inference_steps, size, guidance_scale, seed
        ],
        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"],
            ["an anime character, natural skin", "monochrome", "./conditioning_images/conditioning_image_3.jpg"],
            ["white object standing on colorful ground", "monochrome", "./conditioning_images/conditioning_image_4.jpg"],
        ],
        inputs=[
            prompt, negative_prompt, conditioning_image
        ],
    )

demo.launch()