| # 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() | |