Create model.py

model.py

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+from __future__ import annotations
+
+import gc
+
+import numpy as np
+import PIL.Image
+import torch
+
+from diffusers import (
+    ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL, EulerAncestralDiscreteScheduler
+)
+
+from cv_utils import resize_image
+from preprocessor import Preprocessor
+from settings import MAX_IMAGE_RESOLUTION, MAX_NUM_IMAGES
+
+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)
+        self.preprocessor = Preprocessor()
+
+    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
+        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:
+        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)
+        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: PIL.Image.Image,
+        num_images: int,
+        num_steps: int,
+        controlnet_conditioning_scale: float,
+        seed: int,
+    ) -> list[PIL.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(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):
+        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[PIL.Image.Image]:
+
+        # resize input_image to 1024x1024
+        input_image = resize_image(image)
+        
+        canny_image = get_canny_filter(input_image)
+
+        self.load_controlnet_weight("Canny")
+        results = self.run_pipe(
+            prompt, negative_prompt=negative_prompt, image=canny_image, num_inference_steps=num_steps, controlnet_conditioning_scale=float(controlnet_conditioning_scale)
+        )
+        return [control_image] + results
+
+
+
+
+
+
+
+----------------------------------------------------------------------------
+
+
+
+# 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)