app.py

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

model_id = "andite/pastel-mix"
model_url = "https://huggingface.co/andite/pastel-mix/blob/main/pastelmix-better-vae.ckpt"

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

pipe = StableDiffusionControlNetPipeline.from_from_ckpt(
    model_url,
    controlnet=controlnet,
    torch_dtype=torch.float16,
    safety_checker=None,
)
pipe = pipe.to("cuda")
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=30,
        size=768,
        guidance_scale=7.0,
        seed=1234,
        ill=0.6,
):

    conditioning_image_raw = Image.fromarray(conditioning_image)
    conditioning_image = conditioning_image_raw.convert('L')
    w = conditioning_image.width
    h = conditioning_image.height
    ratio = 768 / max(w, h)
    w = int(w * ratio)
    h = int(h * ratio)
    g_cpu = torch.Generator()

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

    output_image = pipe(
        prompt,
        conditioning_image,
        height=h,
        width=w,
        num_inference_steps=num_inference_steps,
        generator=generator,
        negative_prompt=negative_prompt,
        guidance_scale=guidance_scale,
        controlnet_conditioning_scale=ill,
        
    ).images[0]

    del conditioning_image, conditioning_image_raw

    gc.collect()

    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",
            )
            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
                    )
                with gr.Row():
                    ill = gr.Slider(
                        label='controlnet_ILL_scale',
                        minimum=0,
                        maximum=1,
                        value=0.6,
                        step=0.05
                    )
                    
            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, conditioning_image, num_inference_steps, size, guidance_scale, seed,ill,
        ],
        outputs=output
    )
    gr.Examples(
        examples=[
            ["masterpiece, best quality, High contrast,A bamboo forest, a stream,The rising sun, colorful,", "((nsfw)),(blush),(bare),(worst quality:2, low quality:2),(zombie, sketch, interlocked fingers), greyscale", "./conditioning_images/ty1.jpg","./conditioning_images/tyt1.jpg"],
                        ["masterpiece, best quality, High contrast,A bamboo forest, a stream,The rising sun, colorful,", "((nsfw)),(blush),(bare),(worst quality:2, low quality:2),(zombie, sketch, interlocked fingers), greyscale", "./conditioning_images/ty2.jpg","./conditioning_images/tyt2.jpg"],

                    ],
        inputs=[
            prompt, negative_prompt, conditioning_image
        ],
        outputs=output,
        fn=infer,
        cache_examples=True,
    )
    gr.Markdown(
        """
    * [Dataset](https://huggingface.co/datasets/ioclab/grayscale_image_aesthetic_3M) Note that this was handled extra, and a preview version of the processing is here
      [Anime Dataset](https://huggingface.co/datasets/ioclab/lighttestout)  [Nature Dataset] (https://huggingface.co/datasets/ioclab/light)
    * [Diffusers model](https://huggingface.co/ioclab/connow/tree/main), [Web UI model](https://huggingface.co/ioclab/control_v1u_sd15_illumination_webui)
    * [Training Report](https://huggingface.co/ioclab/control_v1u_sd15_illumination_webui), [Doc(Chinese)](https://aigc.ioclab.com/sd-showcase/light_controlnet.html)
    """)

demo.launch()