model.py

from __future__ import annotations

import gc

import numpy as np
from PIL import Image
import torch

from diffusers import (
    ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL, EulerAncestralDiscreteScheduler
)

import cv2
from torchvision import transforms 


CONTROLNET_MODEL_IDS = {
    "Canny": "briaai/BRIA-2.2-ControlNet-Canny",
    "Depth": "briaai/BRIA-2.2-ControlNet-Depth",
    "Recoloring": "briaai/BRIA-2.2-ControlNet-Recoloring",
}


# def download_all_controlnet_weights() -> None:
#     for model_id in CONTROLNET_MODEL_IDS.values():
#         ControlNetModel.from_pretrained(model_id)


class Model:
    def __init__(self, base_model_id: str = "briaai/BRIA-2.2", task_name: str = "Canny"):
        self.device = torch.device("cuda:0")
        self.base_model_id = ""
        self.task_name = ""
        self.pipe = self.load_pipe(base_model_id, task_name)

    def load_pipe(self, base_model_id: str, task_name) -> DiffusionPipeline:
        if (
            base_model_id == self.base_model_id
            and task_name == self.task_name
            and hasattr(self, "pipe")
            and self.pipe is not None
        ):
            return self.pipe
        model_id = CONTROLNET_MODEL_IDS[task_name]
        controlnet = ControlNetModel.from_pretrained(model_id, torch_dtype=torch.float16).to('cuda')
        pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
            base_model_id,
            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

        torch.cuda.empty_cache()
        gc.collect()
        self.base_model_id = base_model_id
        self.task_name = task_name
        print(f'Loaded {base_model_id}...')
        print(f'Loaded {model_id}...')
        return pipe

    # def set_base_model(self, base_model_id: str) -> str:
    #     if not base_model_id or base_model_id == self.base_model_id:
    #         return self.base_model_id
    #     del self.pipe
    #     torch.cuda.empty_cache()
    #     gc.collect()
    #     try:
    #         self.pipe = self.load_pipe(base_model_id, self.task_name)
    #     except Exception:
    #         self.pipe = self.load_pipe(self.base_model_id, self.task_name)
    #     return self.base_model_id

    def load_controlnet_weight(self, task_name: str) -> None:
        print('Entered load_controlnet_weight....')
        # if task_name == self.task_name:
        #     return
        # if self.pipe is not None and hasattr(self.pipe, "controlnet"):
        #     del self.pipe.controlnet
        # torch.cuda.empty_cache()
        # gc.collect()
        # model_id = CONTROLNET_MODEL_IDS[task_name]
        # controlnet = ControlNetModel.from_pretrained(model_id, torch_dtype=torch.float16)
        # print(f'Loaded {model_id}...')
        # controlnet.to(self.device)
        # torch.cuda.empty_cache()
        # gc.collect()
        # self.pipe.controlnet = controlnet
        # self.task_name = task_name

    def get_prompt(self, prompt: str, additional_prompt: str) -> str:
        if not prompt:
            prompt = additional_prompt
        else:
            prompt = f"{prompt}, {additional_prompt}"
        return prompt

    @torch.autocast("cuda")
    def run_pipe(
        self,
        prompt: str,
        negative_prompt: str,
        control_image: Image.Image,
        num_images: int,
        num_steps: int,
        controlnet_conditioning_scale: float,
        seed: int,
    ) -> list[Image.Image]:
        generator = torch.Generator().manual_seed(seed)
        return self.pipe(
            prompt=prompt,
            negative_prompt=negative_prompt,
            controlnet_conditioning_scale=controlnet_conditioning_scale,
            num_images_per_prompt=num_images,
            num_inference_steps=num_steps,
            generator=generator,
            image=control_image,
        ).images

    
    def resize_image(self, 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(self, image):
        low_threshold = 100
        high_threshold = 200
        
        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


    
    @torch.inference_mode()
    def process_canny(
        self,
        image: np.ndarray,
        prompt: str,
        negative_prompt: str,
        # image_resolution: int,
        num_steps: int,
        controlnet_conditioning_scale: float,
        seed: int,
    ) -> list[Image.Image]:

        # resize input_image to 1024x1024
        input_image = self.resize_image(image)
        
        canny_image = self.get_canny_filter(input_image)

        self.load_controlnet_weight("Canny")
        results = self.run_pipe(
            prompt=prompt, negative_prompt=negative_prompt, control_image=canny_image, num_steps=num_steps, controlnet_conditioning_scale=float(controlnet_conditioning_scale), seed=seed, num_images=1,
        )
        print(f'Image is {results[0]}')
        print(prompt)
        print(negative_prompt)
        print(num_steps)
        print(controlnet_conditioning_scale)
        print(seed)
        return [canny_image, results[0]]







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



# from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
# 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)