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| 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 | |
| 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 | |
| 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 | |
| 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, | |
| ) | |
| return [canny_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) |