# import torch # from diffusers import FluxKontextPipeline, FluxPipeline # from diffusers.utils import load_image # pipe = FluxKontextPipeline.from_pretrained("/data/xcl/Flux-Kontext/model/FLUX-Kontext-dev", torch_dtype=torch.bfloat16) # # pipe.to("cuda") # pipe.enable_model_cpu_offload() # pipe.image_processor # input_image = load_image("/data/xcl/dataSet/RSICD_1/test_png/7.png") # image = pipe( # image=input_image, # # prompt="Make the paper in the image appear wrinkled and crumpled.", # # prompt="Make the handwriting appear messy and wobbly, as if written by a student in a hurry or with uneven hand pressure. Keep the same content and layout.", # # prompt="Make the text in the image look like it was handwritten perfunctorily on paper.", # # prompt="Reduce overall brightness, add mild shadows, and lower contrast slightly while keeping the handwriting readable.", # # prompt="Change the background to look like aged, yellowed paper with slight stains or discoloration.", # # prompt="Add a few natural-looking coffee stains or water rings to the background", # prompt="Replace planes with trucks.", # guidance_scale=2.5, # height=512, # width=512, # ).images[0] # image.save("junshi.png") # import faulthandler # # 在import之后直接添加以下启用代码即可 # faulthandler.enable() # import torch # from diffusers import FluxKontextPipeline # from diffusers.utils import load_image # pipe = FluxKontextPipeline.from_pretrained("/data/xcl/Flux-Kontext/model/FLUX-Kontext-dev", torch_dtype=torch.bfloat16, device_map="balanced") # # pipe = FluxKontextPipeline.from_pretrained("/data/xcl/model/flux-kontext", torch_dtype=torch.bfloat16) # # pipe.to("cuda") # # pipe.enable_model_cpu_offload() # input_image = load_image("/data/xcl/dataSet/images/8.png") # # input_image = input_image.resize((512, 512)) # image = pipe( # image=input_image, # prompt="Replace the ships with airplanes.", # guidance_scale=2.5, # ).images[0] # image.save("8.png") # import os # import json # from PIL import Image # import torch # from diffusers import FluxKontextPipeline # def process_images(): # # 初始化管道 - 使用 FluxKontextPipeline # pipeline = FluxKontextPipeline.from_pretrained( # "/data/xcl/Flux-Kontext/model/FLUX-Kontext-dev", # torch_dtype=torch.bfloat16, # device_map="balanced" # ) # # 启用 CPU offload 以节省显存 # # pipeline.enable_model_cpu_offload() # print("FluxKontextPipeline loaded with automatic device mapping and CPU offload.") # pipeline.set_progress_bar_config(disable=None) # # 路径配置 # input_dir = "/data/xcl/dataSet/images" # output_dir = "/data/xcl/dataSet/images_entity_kontext" # json_file = "/data/xcl/dataSet/junshi_images_entity.json" # # 创建输出目录 # os.makedirs(output_dir, exist_ok=True) # # 加载JSON文件 # with open(json_file, 'r') as f: # prompt_dict = json.load(f) # print(f"Loaded {len(prompt_dict)} image prompts from JSON file.") # # 处理每张图片 # processed_count = 0 # for filename, prompt in prompt_dict.items(): # input_path = os.path.join(input_dir, filename) # output_path = os.path.join(output_dir, filename) # # 检查输入图片是否存在 # if not os.path.exists(input_path): # print(f"Warning: Image {input_path} not found, skipping...") # continue # # 检查输出是否已存在（避免重复处理） # if os.path.exists(output_path): # print(f"Warning: Output {output_path} already exists, skipping...") # continue # try: # # 加载并处理图片 # image = Image.open(input_path).convert("RGB") # width, height = image.size # # 准备输入参数 - 根据新模型的API调整 # inputs = { # "image": image, # "prompt": prompt, # "guidance_scale": 2.5, # 新模型使用 guidance_scale 而不是 true_cfg_scale # "height": height, # 使用输入图像的高度 # "width": width, # 使用输入图像的宽度 # # "generator": torch.manual_seed(0), # 新模型可能不需要这个参数 # # "negative_prompt": " ", # 新模型可能不需要negative_prompt # # "num_inference_steps": 50, # 新模型可能使用默认步数 # } # # 执行图像编辑 # with torch.inference_mode(): # output = pipeline(**inputs) # output_image = output.images[0] # output_image.save(output_path) # # 清理GPU缓存 # if torch.cuda.is_available(): # torch.cuda.empty_cache() # processed_count += 1 # print(f"Processed {filename} -> {output_path}") # except Exception as e: # print(f"Error processing {filename}: {str(e)}") # # 出错时也清理GPU缓存 # if torch.cuda.is_available(): # torch.cuda.empty_cache() # continue # print(f"Processing completed! Successfully processed {processed_count} images.") # if __name__ == "__main__": # process_images() import os import json from PIL import Image import torch from diffusers import FluxKontextPipeline def resize_image_if_needed(image, max_size=1024): """ 如果图片的最长边超过max_size，则按比例调整大小 """ width, height = image.size max_dimension = max(width, height) if max_dimension <= max_size: return image # 计算新的尺寸，保持宽高比 if width > height: new_width = max_size new_height = int(height * (max_size / width)) else: new_height = max_size new_width = int(width * (max_size / height)) # 使用LANCZOS重采样以获得更好的质量 resized_image = image.resize((new_width, new_height), Image.LANCZOS) print(f"Resized image from {width}x{height} to {new_width}x{new_height}") return resized_image def process_images(): # 初始化管道 - 使用 FluxKontextPipeline pipeline = FluxKontextPipeline.from_pretrained( "/data/xcl/Flux-Kontext/model/FLUX-Kontext-dev", torch_dtype=torch.bfloat16, device_map="balanced" ) # 启用 CPU offload 以节省显存 # pipeline.enable_model_cpu_offload() print("FluxKontextPipeline loaded with automatic device mapping and CPU offload.") pipeline.set_progress_bar_config(disable=None) # 路径配置 input_dir = "/data/xcl/dataSet/images" output_dir = "/data/xcl/dataSet/images_entity_background_kontext" json_file = "/data/xcl/dataSet/junshi_images_entity_background.json" # 创建输出目录 os.makedirs(output_dir, exist_ok=True) # 加载JSON文件 with open(json_file, 'r') as f: prompt_dict = json.load(f) print(f"Loaded {len(prompt_dict)} image prompts from JSON file.") # 处理每张图片 processed_count = 0 for filename, prompt in prompt_dict.items(): input_path = os.path.join(input_dir, filename) output_path = os.path.join(output_dir, filename) # 检查输入图片是否存在 if not os.path.exists(input_path): print(f"Warning: Image {input_path} not found, skipping...") continue # 检查输出是否已存在（避免重复处理） if os.path.exists(output_path): print(f"Warning: Output {output_path} already exists, skipping...") continue try: # 加载图片 image = Image.open(input_path).convert("RGB") original_width, original_height = image.size # 检查并调整图片尺寸 image = resize_image_if_needed(image, max_size=1024) new_width, new_height = image.size # 准备输入参数 - 根据新模型的API调整 inputs = { "image": image, "prompt": prompt, "guidance_scale": 2.5, # 新模型使用 guidance_scale 而不是 true_cfg_scale "height": new_height, # 使用调整后的高度 "width": new_width, # 使用调整后的宽度 # "generator": torch.manual_seed(0), # 新模型可能不需要这个参数 # "negative_prompt": " ", # 新模型可能不需要negative_prompt # "num_inference_steps": 50, # 新模型可能使用默认步数 } # 执行图像编辑 with torch.inference_mode(): output = pipeline(**inputs) output_image = output.images[0] output_image.save(output_path) # 清理GPU缓存 if torch.cuda.is_available(): torch.cuda.empty_cache() processed_count += 1 print(f"Processed {filename} (original: {original_width}x{original_height}, processed: {new_width}x{new_height}) -> {output_path}") except Exception as e: print(f"Error processing {filename}: {str(e)}") # 出错时也清理GPU缓存 if torch.cuda.is_available(): torch.cuda.empty_cache() continue print(f"Processing completed! Successfully processed {processed_count} images.") if __name__ == "__main__": process_images()