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BRIA-2.2-ControlNet-Canny
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#!/usr/bin/env python

import gradio as gr

def create_demo(process):
    with gr.Blocks() as demo:
        gr.Markdown("## BRIA 2.2 ControlNet Canny")
        gr.HTML('''
          <p style="margin-bottom: 10px; font-size: 94%">
            This is a demo for ControlNet Canny that using
            <a href="https://huggingface.co/briaai/BRIA-2.2" target="_blank">BRIA 2.2 text-to-image model</a> as backbone. 
            Trained on licensed data, BRIA 2.2 provide full legal liability coverage for copyright and privacy infringement.
          </p>
        ''')
        with gr.Row():
            with gr.Column():
                input_image = gr.Image(sources=None, type="pil") # None for upload, ctrl+v and webcam
                prompt = gr.Textbox(label="Prompt")
                negative_prompt = gr.Textbox(label="Negative prompt", value="Logo,Watermark,Text,Ugly,Morbid,Extra fingers,Poorly drawn hands,Mutation,Blurry,Extra limbs,Gross proportions,Missing arms,Mutated hands,Long neck,Duplicate,Mutilated,Mutilated hands,Poorly drawn face,Deformed,Bad anatomy,Cloned face,Malformed limbs,Missing legs,Too many fingers")
                num_steps = gr.Slider(label="Number of steps", minimum=25, maximum=100, value=50, step=1)
                controlnet_conditioning_scale = gr.Slider(label="ControlNet conditioning scale", minimum=0.1, maximum=2.0, value=1.0, step=0.05)
                seed = gr.Slider(label="Seed", minimum=0, maximum=2147483647, step=1, randomize=True,)
                run_button = gr.Button(value="Run")
            with gr.Column():
                result_gallery = gr.Gallery(label='Output', show_label=False, elem_id="gallery", columns=[2], height='auto')
        inputs = [input_image, prompt, negative_prompt, num_steps, controlnet_conditioning_scale, seed]

        run_button.click(
            fn=process,
            inputs=inputs,
            outputs=result_gallery,
            api_name="canny",
        )
    return demo


if __name__ == "__main__":
    from model import Model

    model = Model(task_name="Canny")
    demo = create_demo(model.process_canny)
    demo.queue().launch()







################################################################################################################################




# from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL, EulerAncestralDiscreteScheduler
# from diffusers.utils import load_image
# from PIL import Image
# import torch
# import numpy as np
# import cv2
# import gradio as gr
# from torchvision import transforms 

# controlnet = ControlNetModel.from_pretrained(
#     "briaai/BRIA-2.2-ControlNet-Canny",
#     torch_dtype=torch.float16
# ).to('cuda')

# pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
#     "briaai/BRIA-2.2",
#     controlnet=controlnet,
#     torch_dtype=torch.float16,
#     device_map='auto',
#     low_cpu_mem_usage=True,
#     offload_state_dict=True,
# ).to('cuda')
# pipe.scheduler = EulerAncestralDiscreteScheduler(
#     beta_start=0.00085,
#     beta_end=0.012,
#     beta_schedule="scaled_linear",
#     num_train_timesteps=1000,
#     steps_offset=1
# )
# # pipe.enable_freeu(b1=1.1, b2=1.1, s1=0.5, s2=0.7)
# pipe.enable_xformers_memory_efficient_attention()
# pipe.force_zeros_for_empty_prompt = False

# low_threshold = 100
# high_threshold = 200

# def resize_image(image):
#     image = image.convert('RGB')
#     current_size = image.size
#     if current_size[0] > current_size[1]:
#         center_cropped_image = transforms.functional.center_crop(image, (current_size[1], current_size[1]))
#     else:
#         center_cropped_image = transforms.functional.center_crop(image, (current_size[0], current_size[0]))
#     resized_image = transforms.functional.resize(center_cropped_image, (1024, 1024))
#     return resized_image

# def get_canny_filter(image):
    
#     if not isinstance(image, np.ndarray):
#         image = np.array(image) 
        
#     image = cv2.Canny(image, low_threshold, high_threshold)
#     image = image[:, :, None]
#     image = np.concatenate([image, image, image], axis=2)
#     canny_image = Image.fromarray(image)
#     return canny_image

# def process(input_image, prompt, negative_prompt, num_steps, controlnet_conditioning_scale, seed):
#     generator = torch.manual_seed(seed)
    
#     # resize input_image to 1024x1024
#     input_image = resize_image(input_image)
    
#     canny_image = get_canny_filter(input_image)
  
#     images = pipe(
#         prompt, negative_prompt=negative_prompt, image=canny_image, num_inference_steps=num_steps, controlnet_conditioning_scale=float(controlnet_conditioning_scale),
#         generator=generator,
#         ).images

#     return [canny_image,images[0]]
    
# block = gr.Blocks().queue()

# with block:
#     gr.Markdown("## BRIA 2.2 ControlNet Canny")
#     gr.HTML('''
#       <p style="margin-bottom: 10px; font-size: 94%">
#         This is a demo for ControlNet Canny that using
#         <a href="https://huggingface.co/briaai/BRIA-2.2" target="_blank">BRIA 2.2 text-to-image model</a> as backbone. 
#         Trained on licensed data, BRIA 2.2 provide full legal liability coverage for copyright and privacy infringement.
#       </p>
#     ''')
#     with gr.Row():
#         with gr.Column():
#             input_image = gr.Image(sources=None, type="pil") # None for upload, ctrl+v and webcam
#             prompt = gr.Textbox(label="Prompt")
#             negative_prompt = gr.Textbox(label="Negative prompt", value="Logo,Watermark,Text,Ugly,Morbid,Extra fingers,Poorly drawn hands,Mutation,Blurry,Extra limbs,Gross proportions,Missing arms,Mutated hands,Long neck,Duplicate,Mutilated,Mutilated hands,Poorly drawn face,Deformed,Bad anatomy,Cloned face,Malformed limbs,Missing legs,Too many fingers")
#             num_steps = gr.Slider(label="Number of steps", minimum=25, maximum=100, value=50, step=1)
#             controlnet_conditioning_scale = gr.Slider(label="ControlNet conditioning scale", minimum=0.1, maximum=2.0, value=1.0, step=0.05)
#             seed = gr.Slider(label="Seed", minimum=0, maximum=2147483647, step=1, randomize=True,)
#             run_button = gr.Button(value="Run")
            
            
#         with gr.Column():
#             result_gallery = gr.Gallery(label='Output', show_label=False, elem_id="gallery", columns=[2], height='auto')
#     ips = [input_image, prompt, negative_prompt, num_steps, controlnet_conditioning_scale, seed]
#     run_button.click(fn=process, inputs=ips, outputs=[result_gallery])

# block.launch(debug = True)