import spaces 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_stable_diffusion_xl import StableDiffusionXLPipeline from random import randint from utils import init_latent from transformers import Blip2Processor, Blip2ForConditionalGeneration from diffusers import DDIMScheduler 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.") model = StableDiffusionXLPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch_dtype) print("SDXL") memory_efficient(model) blip_processor = Blip2Processor.from_pretrained("Salesforce/blip2-opt-2.7b") blip_model = Blip2ForConditionalGeneration.from_pretrained("Salesforce/blip2-opt-2.7b", torch_dtype=torch_dtype).to(device) # 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 load_example_style(): 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] image_info = [image_path, style_name, inf_object_name, 1, 50] examples.append(image_info) return examples def blip_inf_prompt(image): inputs = blip_processor(images=image, return_tensors="pt").to(device, torch.float16) generated_ids = blip_model.generate(**inputs) generated_text = blip_processor.batch_decode(generated_ids, skip_special_tokens=True)[0].strip() return generated_text @spaces.GPU def style_fn(image_path, style_name, content_text, output_number=1, diffusion_step=50): user_image_flag = not style_name.strip() # empty if not user_image_flag: real_img = None 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))] 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'] 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'] use_prompt_as_null = False style_name = config["style_name_list"][0] style_description_pos, style_description_neg = STYLE_DESCRIPTION_DICT[style_name][0], \ STYLE_DESCRIPTION_DICT[style_name][1] 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 else: model.scheduler = DDIMScheduler.from_config(model.scheduler.config) origin_real_img = Image.open(image_path).resize((1024, 1024), resample=Image.BICUBIC) real_img = np.array(origin_real_img).astype(np.float32) / 255.0 style_name = 'default' 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))] activate_layer_indices_list = config['inference_info']['activate_layer_indices_list'] activate_step_indices_list = config['inference_info']['activate_step_indices_list'] ref_seed = 0 attn_map_save_steps = config['inference_info']['attn_map_save_steps'] guidance_scale = config['guidance_scale'] use_inf_negative_prompt = False 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 = False use_prompt_as_null = True ref_prompt = blip_inf_prompt(origin_real_img) inf_prompt = inf_object style_description_neg = None # Inference with torch.inference_mode(): grid = None 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.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 = init_latent(model, device_name=device, dtype=torch_dtype, seed=ref_seed) latents = [ref_latent] num_images_per_prompt = len(inf_seeds) + 1 for inf_seed in inf_seeds: # latents.append(model.get_init_latent(inf_seed, precomputed_path=None)) inf_latent = init_latent(model, device_name=device, dtype=torch_dtype, seed=inf_seed) latents.append(inf_latent) latents = torch.cat(latents, dim=0) latents.to(device) images = model( prompt=ref_prompt, negative_prompt=style_description_neg, guidance_scale=guidance_scale, num_inference_steps=diffusion_step, latents=latents, num_images_per_prompt=num_images_per_prompt, target_prompt=inf_prompt, use_inf_negative_prompt=use_inf_negative_prompt, use_advanced_sampling=use_advanced_sampling, use_prompt_as_null=use_prompt_as_null, image=real_img )[0][1:] n_row = 1 n_col = len(inf_seeds) + 1 # 원본추가하려면 + 1 # make grid grid = create_image_grid(images, n_row, n_col, padding=10) 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)] ### 🔥 [[w/ Controlnet ver](https://huggingface.co./spaces/naver-ai/VisualStylePrompting_Controlnet)] --- ### 🔥 To try out our vanilla demo, 1. Choose a `style reference` from the collection of images below. 2. Enter the `text prompt`. 3. Choose the `number of outputs`. ### 👉 To achieve faster results, we recommend lowering the diffusion steps to 30. ### Enjoy ! 😄 """ iface_style = gr.Interface( fn=style_fn, inputs=[ gr.components.Image(label="Style Image", type="filepath"), 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=10, maximum=50, step=10, value=50, label="Diffusion steps") ], outputs=gr.components.Image(label="Generated Image"), title="🎨 Visual Style Prompting (default)", description=description_md, examples=load_example_style(), ) iface_style.launch(debug=True)