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| import spaces | |
| 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-Depth", | |
| 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 | |
| from transformers import DPTFeatureExtractor, DPTForDepthEstimation | |
| depth_estimator = DPTForDepthEstimation.from_pretrained("Intel/dpt-hybrid-midas").to("cuda") | |
| feature_extractor = DPTFeatureExtractor.from_pretrained("Intel/dpt-hybrid-midas") | |
| 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_depth_map(image): | |
| image = feature_extractor(images=image, return_tensors="pt").pixel_values.to("cuda") | |
| with torch.no_grad(), torch.autocast("cuda"): | |
| depth_map = depth_estimator(image).predicted_depth | |
| image = transforms.functional.center_crop(image, min(image.shape[-2:])) | |
| depth_map = torch.nn.functional.interpolate( | |
| depth_map.unsqueeze(1), | |
| size=(1024, 1024), | |
| mode="bicubic", | |
| align_corners=False, | |
| ) | |
| depth_min = torch.amin(depth_map, dim=[1, 2, 3], keepdim=True) | |
| depth_max = torch.amax(depth_map, dim=[1, 2, 3], keepdim=True) | |
| depth_map = (depth_map - depth_min) / (depth_max - depth_min) | |
| image = torch.cat([depth_map] * 3, dim=1) | |
| image = image.permute(0, 2, 3, 1).cpu().numpy()[0] | |
| image = Image.fromarray((image * 255.0).clip(0, 255).astype(np.uint8)) | |
| return image | |
| def generate_(prompt, negative_prompt, canny_image, num_steps, controlnet_conditioning_scale, seed): | |
| generator = torch.Generator("cuda").manual_seed(seed) | |
| 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 images | |
| def process(input_image, prompt, negative_prompt, num_steps, controlnet_conditioning_scale, seed): | |
| # resize input_image to 1024x1024 | |
| input_image = resize_image(input_image) | |
| depth_image = get_depth_map(input_image) | |
| images = generate_(prompt, negative_prompt, depth_image, num_steps, controlnet_conditioning_scale, seed) | |
| return [depth_image, images[0]] | |
| block = gr.Blocks().queue() | |
| with block: | |
| gr.Markdown("## BRIA 2.2 ControlNet Depth") | |
| gr.HTML(''' | |
| <p style="margin-bottom: 10px; font-size: 94%"> | |
| This is a demo for ControlNet Depth 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) |