import torch from pipelines.inverted_ve_pipeline import STYLE_DESCRIPTION_DICT, create_image_grid import gradio as gr import os, json import numpy as np from PIL import Image from pipelines.pipeline_controlnet_sd_xl import StableDiffusionXLControlNetPipeline from diffusers import ControlNetModel, AutoencoderKL from transformers import DPTFeatureExtractor, DPTForDepthEstimation from random import randint from utils import init_latent device = 'cuda' if torch.cuda.is_available() else 'cpu' if device == 'cpu': torch_dtype = torch.float32 else: torch_dtype = torch.float16 def memory_efficient(model): try: model.to(device) except Exception as e: print("Error moving model to device:", e) try: model.enable_model_cpu_offload() except AttributeError: print("enable_model_cpu_offload is not supported.") try: model.enable_vae_slicing() except AttributeError: print("enable_vae_slicing is not supported.") if device == 'cuda': try: model.enable_xformers_memory_efficient_attention() except AttributeError: print("enable_xformers_memory_efficient_attention is not supported.") controlnet = ControlNetModel.from_pretrained("diffusers/controlnet-depth-sdxl-1.0", torch_dtype=torch_dtype) vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch_dtype) model_controlnet = StableDiffusionXLControlNetPipeline.from_pretrained( "stabilityai/stable-diffusion-xl-base-1.0", controlnet=controlnet, vae=vae, torch_dtype=torch_dtype ) print("vae") memory_efficient(vae) print("control") memory_efficient(controlnet) print("ControlNet-SDXL") memory_efficient(model_controlnet) depth_estimator = DPTForDepthEstimation.from_pretrained("Intel/dpt-hybrid-midas").to(device) feature_extractor = DPTFeatureExtractor.from_pretrained("Intel/dpt-hybrid-midas") # controlnet_scale, canny thres 1, 2 (2 > 1, 2:1, 3:1) def parse_config(config): with open(config, 'r') as f: config = json.load(f) return config def get_depth_map(image): image = feature_extractor(images=image, return_tensors="pt").pixel_values.to(device) with torch.no_grad(), torch.autocast(device): depth_map = depth_estimator(image).predicted_depth 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 get_depth_edge_array(depth_img_path): depth_image_tmp = Image.fromarray(depth_img_path) # get depth map depth_map = get_depth_map(depth_image_tmp) return depth_map def load_example_controlnet(): folder_path = 'assets/ref' examples = [] for filename in os.listdir(folder_path): if filename.endswith((".png")): image_path = os.path.join(folder_path, filename) image_name = os.path.basename(image_path) style_name = image_name.split('_')[1] config_path = './config/{}.json'.format(style_name) config = parse_config(config_path) inf_object_name = config["inference_info"]["inf_object_list"][0] canny_path = './assets/depth_dir/gundam.png' image_info = [image_path, canny_path, style_name, inf_object_name, 1, 0.5, 50] examples.append(image_info) return examples def controlnet_fn(image_path, depth_image_path, style_name, content_text, output_number, controlnet_scale=0.5, diffusion_step=50): """ :param style_name: 어떤 json 파일 부를거냐 ? :param content_text: 어떤 콘텐츠로 변화를 원하니 ? :param output_number: 몇개 생성할거니 ? :return: """ config_path = './config/{}.json'.format(style_name) config = parse_config(config_path) inf_object = content_text inf_seeds = [randint(0, 10**10) for _ in range(int(output_number))] # inf_seeds = [i for i in range(int(output_number))] activate_layer_indices_list = config['inference_info']['activate_layer_indices_list'] activate_step_indices_list = config['inference_info']['activate_step_indices_list'] ref_seed = config['reference_info']['ref_seeds'][0] attn_map_save_steps = config['inference_info']['attn_map_save_steps'] guidance_scale = config['guidance_scale'] use_inf_negative_prompt = config['inference_info']['use_negative_prompt'] style_name = config["style_name_list"][0] ref_object = config["reference_info"]["ref_object_list"][0] ref_with_style_description = config['reference_info']['with_style_description'] inf_with_style_description = config['inference_info']['with_style_description'] use_shared_attention = config['inference_info']['use_shared_attention'] adain_queries = config['inference_info']['adain_queries'] adain_keys = config['inference_info']['adain_keys'] adain_values = config['inference_info']['adain_values'] use_advanced_sampling = config['inference_info']['use_advanced_sampling'] #get canny edge array depth_image = get_depth_edge_array(depth_image_path) style_description_pos, style_description_neg = STYLE_DESCRIPTION_DICT[style_name][0], \ STYLE_DESCRIPTION_DICT[style_name][1] # Inference with torch.inference_mode(): grid = None if ref_with_style_description: ref_prompt = style_description_pos.replace("{object}", ref_object) else: ref_prompt = ref_object if inf_with_style_description: inf_prompt = style_description_pos.replace("{object}", inf_object) else: inf_prompt = inf_object for activate_layer_indices in activate_layer_indices_list: for activate_step_indices in activate_step_indices_list: str_activate_layer, str_activate_step = model_controlnet.activate_layer( activate_layer_indices=activate_layer_indices, attn_map_save_steps=attn_map_save_steps, activate_step_indices=activate_step_indices, use_shared_attention=use_shared_attention, adain_queries=adain_queries, adain_keys=adain_keys, adain_values=adain_values, ) # ref_latent = model_controlnet.get_init_latent(ref_seed, precomputed_path=None) ref_latent = init_latent(model_controlnet, device_name=device, dtype=torch_dtype, seed=ref_seed) latents = [ref_latent] for inf_seed in inf_seeds: # latents.append(model_controlnet.get_init_latent(inf_seed, precomputed_path=None)) inf_latent = init_latent(model_controlnet, device_name=device, dtype=torch_dtype, seed=inf_seed) latents.append(inf_latent) latents = torch.cat(latents, dim=0) latents.to(device) images = model_controlnet.generated_ve_inference( prompt=ref_prompt, negative_prompt=style_description_neg, guidance_scale=guidance_scale, num_inference_steps=diffusion_step, controlnet_conditioning_scale=controlnet_scale, latents=latents, num_images_per_prompt=len(inf_seeds) + 1, target_prompt=inf_prompt, image=depth_image, use_inf_negative_prompt=use_inf_negative_prompt, use_advanced_sampling=use_advanced_sampling )[0][1:] n_row = 1 n_col = len(inf_seeds) # 원본추가하려면 + 1 # make grid grid = create_image_grid(images, n_row, n_col) torch.cuda.empty_cache() return grid description_md = """ ### We introduce `Visual Style Prompting`, which reflects the style of a reference image to the images generated by a pretrained text-to-image diffusion model without finetuning or optimization (e.g., Figure N). ### 📖 [[Paper](https://arxiv.org/abs/2402.12974)] | ✨ [[Project page](https://curryjung.github.io/VisualStylePrompt)] | ✨ [[Code](https://github.com/naver-ai/Visual-Style-Prompting)] ### 🔥 [[Default ver](https://huggingface.co/spaces/naver-ai/VisualStylePrompting)] --- ### Visual Style Prompting also works on `ControlNet` which specifies the shape of the results by depthmap or keypoints. ### To try out our demo with ControlNet, 1. Upload an `image for depth control`. An off-the-shelf model will produce the depthmap from it. 2. Choose `ControlNet scale` which determines the alignment to the depthmap. 3. Choose a `style reference` from the collection of images below. 4. Enter the `text prompt`. (`Empty text` is okay, but a depthmap description helps.) 5. Choose the `number of outputs`. ### To achieve faster results, we recommend lowering the diffusion steps to 30. ### Enjoy ! 😄 """ iface_controlnet = gr.Interface( fn=controlnet_fn, inputs=[ gr.components.Image(label="Style image"), gr.components.Image(label="Depth image"), gr.components.Textbox(label='Style name', visible=False), gr.components.Textbox(label="Text prompt", placeholder="Enter Text prompt"), gr.components.Textbox(label="Number of outputs", placeholder="Enter Number of outputs"), gr.components.Slider(minimum=0.5, maximum=10, step=0.5, value=0.5, label="Controlnet scale"), gr.components.Slider(minimum=50, maximum=50, step=10, value=50, label="Diffusion steps") ], outputs=gr.components.Image(type="pil"), title="🎨 Visual Style Prompting (w/ ControlNet)", description=description_md, examples=load_example_controlnet(), ) iface_controlnet.launch(debug=True)