diff --git a/src/backend/app/algorithms/evaluate/eva_gen_heatmap.py b/src/backend/app/algorithms/evaluate/eva_gen_heatmap.py index fb27936..d461235 100644 --- a/src/backend/app/algorithms/evaluate/eva_gen_heatmap.py +++ b/src/backend/app/algorithms/evaluate/eva_gen_heatmap.py @@ -1,18 +1,4 @@ -"""Stable Diffusion 注意力热力图差异可视化工具 (可靠版 - 语义阶段聚合)。 - -本模块使用一种健壮的方法，通过在 Stable Diffusion 扩散模型（U-Net）的 -**早期时间步 (语义阶段)** 捕获并累加交叉注意力权重。这种方法能确保捕获到的 -注意力图信号集中且可靠，用于对比分析干净输入和扰动输入生成的图像对模型 -注意力机制的影响差异。 - -典型用法: - python eva_gen_heatmap.py \\ - --model_path /path/to/sd_model \\ - --image_path_a /path/to/clean_image.png \\ - --image_path_b /path/to/noisy_image.png \\ - --prompt_text "a photo of sks person" \\ - --target_word "sks" \\ - --output_dir output/heatmap_reports +"""Stable Diffusion 双模态注意力热力图差异可视化工具。 """ # 通用参数解析与文件路径管理 @@ -25,7 +11,6 @@ from typing import Dict, Any, List, Tuple import torch import torch.nn.functional as F import numpy as np -import itertools import warnings # 可视化依赖 @@ -48,29 +33,14 @@ warnings.filterwarnings("ignore", category=UserWarning) warnings.filterwarnings("ignore", category=FutureWarning) -# ============== 核心模块：注意力捕获与聚合 ============== +# ============== 核心模块：双模态注意力捕获 ============== class AttentionMapProcessor: - """自定义注意力处理器，用于捕获 U-Net 交叉注意力层的权重。 - - 通过替换原始的 `Attention` 模块处理器，该类在模型前向传播过程中， - 将所有交叉注意力层的注意力权重（`attention_probs`）捕获并存储。 - - Attributes: - attention_maps (Dict[str, List[torch.Tensor]]): 存储捕获到的注意力图， - 键为层名称，值为该层在不同时间步捕获到的注意力图列表。 - pipeline (StableDiffusionPipeline): 正在处理的 Stable Diffusion 管线。 - original_processors (Dict[str, Any]): 存储原始的注意力处理器，用于恢复。 - current_layer_name (Optional[str]): 当前正在处理的注意力层的名称。 - """ + """自定义注意力处理器，用于同时捕获 U-Net 的交叉注意力和自注意力权重。""" def __init__(self, pipeline: StableDiffusionPipeline): - """初始化注意力处理器。 - - Args: - pipeline: Stable Diffusion 模型管线实例。 - """ - self.attention_maps: Dict[str, List[torch.Tensor]] = {} + self.cross_attention_maps: Dict[str, List[torch.Tensor]] = {} + self.self_attention_maps: Dict[str, List[torch.Tensor]] = {} self.pipeline = pipeline self.original_processors = {} self.current_layer_name = None @@ -83,35 +53,18 @@ class AttentionMapProcessor: encoder_hidden_states: torch.Tensor = None, attention_mask: torch.Tensor = None ) -> torch.Tensor: - """重载 __call__ 方法，执行注意力计算并捕获权重。 - - 此方法替代了原始的 `Attention.processor`，在计算交叉注意力时进行捕获。 - - Args: - attn: 当前的 `Attention` 模块实例。 - hidden_states: U-Net 隐状态 (query)。 - encoder_hidden_states: 文本编码器输出 (key/value)，即交叉注意力输入。 - attention_mask: 注意力掩码。 - - Returns: - 计算后的输出隐状态。 - """ - # 如果不是交叉注意力（即 encoder_hidden_states 为 None），则调用原始处理器 - if encoder_hidden_states is None: - return attn.processor( - attn, hidden_states, encoder_hidden_states, attention_mask - ) - - # 1. 计算 Q, K, V + """重载执行注意力计算并捕获权重 (支持 Self 和 Cross)。""" + + is_cross = encoder_hidden_states is not None + sequence_input = encoder_hidden_states if is_cross else hidden_states + query = attn.to_q(hidden_states) - key = attn.to_k(encoder_hidden_states) - value = attn.to_v(encoder_hidden_states) + key = attn.to_k(sequence_input) + value = attn.to_v(sequence_input) - # 2. 准备矩阵乘法 query = attn.head_to_batch_dim(query) key = attn.head_to_batch_dim(key) - # 3. 计算 Attention Scores (Q @ K^T) attention_scores = torch.baddbmm( torch.empty( query.shape[0], query.shape[1], key.shape[1], @@ -123,398 +76,328 @@ class AttentionMapProcessor: alpha=attn.scale, ) - # 4. 计算 Attention Probabilities attention_probs = attention_scores.softmax(dim=-1) layer_name = self.current_layer_name + map_to_store = attention_probs.detach().cpu() - # 5. 存储捕获的注意力图 - if layer_name not in self.attention_maps: - self.attention_maps[layer_name] = [] - - # 存储当前时间步的注意力权重 - self.attention_maps[layer_name].append(attention_probs.detach().cpu()) + if is_cross: + if layer_name not in self.cross_attention_maps: + self.cross_attention_maps[layer_name] = [] + self.cross_attention_maps[layer_name].append(map_to_store) + else: + # 内存保护：仅捕获中低分辨率层的自注意力 (防止 4096*4096 矩阵爆内存) + spatial_size = map_to_store.shape[-2] + if spatial_size <= 1024: + if layer_name not in self.self_attention_maps: + self.self_attention_maps[layer_name] = [] + self.self_attention_maps[layer_name].append(map_to_store) - # 6. 计算输出 (Attention @ V) value = attn.head_to_batch_dim(value) hidden_states = torch.bmm(attention_probs, value) hidden_states = attn.batch_to_head_dim(hidden_states) - # 7. 输出层 hidden_states = attn.to_out[0](hidden_states) hidden_states = attn.to_out[1](hidden_states) return hidden_states def _set_processors(self): - """注册自定义处理器，捕获 U-Net 中所有交叉注意力层的权重。 - - 遍历 U-Net 的所有子模块，找到所有交叉注意力层（`Attention` 且名称包含 `attn2`）， - 并将其处理器替换为当前的实例。 - """ for name, module in self.pipeline.unet.named_modules(): - if isinstance(module, Attention) and 'attn2' in name: - # 存储原始处理器以便后续恢复 - self.original_processors[name] = module.processor - - # 定义一个新的闭包函数，用于在调用前设置当前层的名称 - def set_layer_name(current_name): - def new_call(*args, **kwargs): - self.current_layer_name = current_name - return self.__call__(*args, **kwargs) - return new_call - - module.processor = set_layer_name(name) + if isinstance(module, Attention): + if 'attn1' in name or 'attn2' in name: + self.original_processors[name] = module.processor + def set_layer_name(current_name): + def new_call(*args, **kwargs): + self.current_layer_name = current_name + return self.__call__(*args, **kwargs) + return new_call + module.processor = set_layer_name(name) def remove(self): - """恢复 U-Net 的原始注意力处理器，清理钩子。""" for name, original_processor in self.original_processors.items(): module = self.pipeline.unet.get_submodule(name) module.processor = original_processor - self.attention_maps = {} + self.cross_attention_maps = {} + self.self_attention_maps = {} + +# ============== 聚合逻辑 ============== -def aggregate_word_attention( +def aggregate_cross_attention( attention_maps: Dict[str, List[torch.Tensor]], tokenizer: CLIPTokenizer, target_word: str, input_ids: torch.Tensor ) -> np.ndarray: - """聚合所有层和语义时间步中目标词汇的注意力图，并进行归一化。 - - 聚合步骤： - 1. 识别目标词汇对应的 Token 索引。 - 2. 对每个层：将所有捕获时间步的注意力图求平均。 - 3. 提取目标 Token 对应的注意力子图，并对 Token 维度求和，对 Attention Heads 求平均。 - 4. 将不同分辨率的注意力图上采样到统一尺寸（64x64）。 - 5. 对所有层的结果进行累加（求和）。 - 6. 最终归一化到 [0, 1]。 - - Args: - attention_maps: 包含各层和时间步捕获的注意力图的字典。 - tokenizer: CLIP 分词器实例。 - target_word: 需要聚焦的关键词。 - input_ids: Prompt 对应的 Token ID 张量。 - - Returns: - 最终聚合并上采样到 64x64 尺寸的注意力热力图 (NumPy 数组)。 - - Raises: - ValueError: 如果无法在 Prompt 中找到目标词汇。 - RuntimeError: 如果未捕获到任何注意力数据。 - """ - - # 1. 识别目标词汇的 Token 索引 - prompt_tokens = tokenizer.convert_ids_to_tokens( - input_ids.squeeze().cpu().tolist() - ) + """聚合交叉注意力：关注 Prompt 中的特定 Target Word。""" + prompt_tokens = tokenizer.convert_ids_to_tokens(input_ids.squeeze().cpu().tolist()) target_lower = target_word.lower() target_indices = [] for i, token in enumerate(prompt_tokens): cleaned_token = token.replace('Ġ', '').replace('_', '').lower() - # 查找目标词汇或以目标词汇开头的 token 索引，并排除特殊 token if (input_ids.squeeze()[i] not in tokenizer.all_special_ids and - (target_lower in cleaned_token or - cleaned_token.startswith(target_lower))): + (target_lower in cleaned_token or cleaned_token.startswith(target_lower))): target_indices.append(i) if not target_indices: - print(f"[WARN] 目标词汇 '{target_word}' 未识别。请检查 Prompt 或 Target Word。") - raise ValueError("无法识别目标词汇的 token 索引。") + print(f"[WARN] Cross-Attn: 目标词汇 '{target_word}' 未识别。") + return np.zeros((64, 64)) - # 2. 聚合逻辑 all_attention_data = [] - # U-Net 输出的最大分辨率（64x64），总像素点数 TARGET_SPATIAL_SIZE = 4096 TARGET_MAP_SIZE = 64 for layer_name, step_maps in attention_maps.items(): - if not step_maps: + if not step_maps: continue + avg_map = torch.stack(step_maps).mean(dim=0) + if avg_map.dim() == 4: avg_map = avg_map.squeeze(0) + target_map = avg_map[:, :, target_indices].sum(dim=-1).mean(dim=0).float() + + if target_map.shape[0] != TARGET_SPATIAL_SIZE: + map_size = int(np.sqrt(target_map.shape[0])) + map_2d = target_map.reshape(map_size, map_size).unsqueeze(0).unsqueeze(0) + resized = F.interpolate(map_2d, size=(TARGET_MAP_SIZE, TARGET_MAP_SIZE), mode='bilinear', align_corners=False) + all_attention_data.append(resized.squeeze().flatten()) + else: + all_attention_data.append(target_map) + + if not all_attention_data: return np.zeros((64, 64)) + + final_map_flat = torch.stack(all_attention_data).sum(dim=0).cpu().numpy() + final_map_flat = final_map_flat / (final_map_flat.max() + 1e-6) + return final_map_flat.reshape(TARGET_MAP_SIZE, TARGET_MAP_SIZE) + + +def aggregate_self_attention( + attention_maps: Dict[str, List[torch.Tensor]] +) -> np.ndarray: + """聚合自注意力：计算高频空间能量 (Laplacian High-Frequency Energy)。 + + 原理： + 风格和纹理通常体现为注意力图中的高频变化。 + 通过对每个 Query 的 Attention Map 应用拉普拉斯算子（Laplacian Kernel）， + 我们可以提取出那些变化剧烈的区域（边缘、纹理接缝）。 + 最后聚合这些高频能量，得到的图在空间结构上与原图对齐，但亮度代表了“纹理/风格复杂度”。 + """ + all_attention_data = [] + TARGET_MAP_SIZE = 64 + + # 定义拉普拉斯卷积核用于提取高频信息 + laplacian_kernel = torch.tensor([ + [0, 1, 0], + [1, -4, 1], + [0, 1, 0] + ], dtype=torch.float32).view(1, 1, 3, 3) + + for layer_name, step_maps in attention_maps.items(): + if not step_maps: continue + + # [Heads, H*W, H*W] -> [H*W, H*W] 取平均 + avg_matrix = torch.stack(step_maps).mean(dim=0).mean(dim=0).float() + + # 获取当前层尺寸 + current_pixels = avg_matrix.shape[0] + map_size = int(np.sqrt(current_pixels)) + + # 如果尺寸太小，高频信息没有意义，跳过极小层 + if map_size < 16: continue + + # 重塑为图像形式: [Batch(Pixels), Channels(1), H, W] + # 这里我们将 avg_matrix 视为：对于每一个 query pixel (行)，它关注的 spatial map (列) + # 我们想知道每个 pixel 关注的区域是不是包含很多高频纹理 + attn_maps = avg_matrix.reshape(current_pixels, 1, map_size, map_size) # [N, 1, H, W] - # 对该层捕获的所有时间步求平均，形状: (batch, heads, spatial_res, target_tokens_len) - avg_map_over_time = torch.stack(step_maps).mean(dim=0) + # 将 Kernel 移到同一设备 + kernel = laplacian_kernel.to(avg_matrix.device) - # 移除批次维度 (假设 batch size = 1)，形状: (heads, spatial_res, target_tokens_len) - attention_map = avg_map_over_time.squeeze(0) + # 批量卷积计算高频响应 (High-Pass Filter) + # padding=1 保持尺寸不变 + high_freq_response = F.conv2d(attn_maps, kernel, padding=1) - # 提取目标 token 的注意力图。形状: (heads, spatial_res, target_indices_len) - target_token_maps = attention_map[:, :, target_indices] + # 计算能量 (取绝对值或平方)，这里取绝对值代表梯度的强度 + high_freq_energy = torch.abs(high_freq_response) - # 对目标 token 求和 (dim=-1)，对注意力头求平均 (dim=0)，形状: (spatial_res,) - aggregated_map_flat = target_token_maps.sum(dim=-1).mean(dim=0).float() - - # 3. 跨分辨率上采样 - if aggregated_map_flat.shape[0] != TARGET_SPATIAL_SIZE: - # 当前图的尺寸：16x16 (256) 或 32x32 (1024) - map_size = int(np.sqrt(aggregated_map_flat.shape[0])) - map_2d = aggregated_map_flat.reshape(map_size, map_size) - map_to_interp = map_2d.unsqueeze(0).unsqueeze(0) # [1, 1, H, W] - - # 使用双线性插值上采样到 64x64 - resized_map_2d = F.interpolate( - map_to_interp, - size=(TARGET_MAP_SIZE, TARGET_MAP_SIZE), - mode='bilinear', - align_corners=False - ) - resized_map_flat = resized_map_2d.squeeze().flatten() - all_attention_data.append(resized_map_flat) - else: - # 如果已经是 64x64，直接使用 - all_attention_data.append(aggregated_map_flat) + # 现在我们得到了 [N, 1, H, W] 的高频能量图。 + # 我们需要将其聚合回一张 [H, W] 的图。 + # 含义：对于图像上的位置 (i, j)，其作为 Query 时，所关注的区域包含了多少高频信息？ + # 或者：作为 Key 时，它贡献了多少高频信息？ + + # 这里采用 "Query-based Aggregation"： + # 计算每个 Query pixel 对高频信息的总响应 + # shape: [N, 1, H, W] -> sum(dim=(2,3)) -> [N] + # 这表示：位置 N 的像素，其注意力主要集中在高频纹理区域的程度。 + spatial_score_flat = high_freq_energy.sum(dim=(2, 3)).squeeze() # [H*W] + + # 归一化这一层的分数，防止数值爆炸 + spatial_score_flat = spatial_score_flat / (spatial_score_flat.max() + 1e-6) + + # 重塑为 2D 空间图 + map_2d = spatial_score_flat.reshape(map_size, map_size).unsqueeze(0).unsqueeze(0) + + # 插值统一到目标尺寸 + resized = F.interpolate(map_2d, size=(TARGET_MAP_SIZE, TARGET_MAP_SIZE), mode='bilinear', align_corners=False) + all_attention_data.append(resized.squeeze().flatten()) - if not all_attention_data: - raise RuntimeError("未捕获到注意力数据。可能模型或参数设置有误。") + if not all_attention_data: return np.zeros((64, 64)) - # 4. 对所有层的结果进行累加 (求和) + # 聚合所有层 final_map_flat = torch.stack(all_attention_data).sum(dim=0).cpu().numpy() - # 5. 最终归一化到 [0, 1] - final_map_flat = final_map_flat / (final_map_flat.max() + 1e-6) - - map_size = int(np.sqrt(final_map_flat.shape[0])) - final_map_np = final_map_flat.reshape(map_size, map_size) # 64x64 - - return final_map_np + # 最终归一化，保持 0-1 范围，方便可视化 + final_map_flat = (final_map_flat - final_map_flat.min()) / (final_map_flat.max() - final_map_flat.min() + 1e-6) + + return final_map_flat.reshape(TARGET_MAP_SIZE, TARGET_MAP_SIZE) -def get_attention_map_from_image( +def get_dual_attention_maps( pipeline: StableDiffusionPipeline, image_path: str, prompt_text: str, target_word: str -) -> Tuple[Image.Image, np.ndarray]: - """执行多时间步前向传播，捕获指定图片和 Prompt 的注意力图。 - - 通过只运行扩散过程中的语义阶段（早期时间步）来确保捕获到的注意力权重 - 具有高信号质量。 - - Args: - pipeline: Stable Diffusion 模型管线实例。 - image_path: 待处理的输入图片路径。 - prompt_text: 用于生成图片的 Prompt 文本。 - target_word: 需要聚焦和可视化的关键词。 - - Returns: - 包含 (原始图片, 最终上采样后的注意力图) 的元组。 - """ +) -> Tuple[Image.Image, np.ndarray, np.ndarray]: + """同时获取 Cross-Attention 和 Self-Attention 热力图。""" print(f"\n-> 正在处理图片: {Path(image_path).name}") image = Image.open(image_path).convert("RGB").resize((512, 512)) - image_transform = transforms.Compose([ - transforms.ToTensor(), - transforms.Normalize([0.5], [0.5]), - ]) - image_tensor = ( - image_transform(image) - .unsqueeze(0) - .to(pipeline.device) - .to(pipeline.unet.dtype) - ) - - # 1. 编码到 Latent 空间 - with torch.no_grad(): - latent = ( - pipeline.vae.encode(image_tensor).latent_dist.sample() * - pipeline.vae.config.scaling_factor - ) + image_tensor = transforms.Compose([ + transforms.ToTensor(), transforms.Normalize([0.5], [0.5]) + ])(image).unsqueeze(0).to(pipeline.device).to(pipeline.unet.dtype) - # 2. 编码 Prompt - text_input = pipeline.tokenizer( - prompt_text, - padding="max_length", - max_length=pipeline.tokenizer.model_max_length, - truncation=True, - return_tensors="pt" - ) - input_ids = text_input.input_ids + with torch.no_grad(): + latent = (pipeline.vae.encode(image_tensor).latent_dist.sample() * pipeline.vae.config.scaling_factor) + text_input = pipeline.tokenizer(prompt_text, padding="max_length", max_length=pipeline.tokenizer.model_max_length, truncation=True, return_tensors="pt") with torch.no_grad(): - # 获取文本嵌入 - prompt_embeds = pipeline.text_encoder( - input_ids.to(pipeline.device) - )[0] + prompt_embeds = pipeline.text_encoder(text_input.input_ids.to(pipeline.device))[0] - # 3. 定义语义时间步 scheduler = pipeline.scheduler - # 设置扩散步数 (例如 50 步) - scheduler.set_timesteps(50, device=pipeline.device) - - # 只选择语义最丰富的早期 10 步进行捕获 + scheduler.set_timesteps(50, device=pipeline.device) semantic_steps = scheduler.timesteps[:10] - print(f"-> 正在对语义阶段的 {len(semantic_steps)} 个时间步进行注意力捕获...") - processor = AttentionMapProcessor(pipeline) try: - # 4. 运行多步 UNet Forward Pass with torch.no_grad(): - # 在选定的语义时间步上运行 U-Net 预测 for t in semantic_steps: pipeline.unet(latent, t, prompt_embeds, return_dict=False) - # 5. 聚合捕获到的数据 - raw_map_np = aggregate_word_attention( - processor.attention_maps, - pipeline.tokenizer, - target_word, - input_ids + cross_map_raw = aggregate_cross_attention( + processor.cross_attention_maps, pipeline.tokenizer, target_word, text_input.input_ids ) + self_map_raw = aggregate_self_attention(processor.self_attention_maps) + except Exception as e: print(f"[ERROR] 注意力聚合失败: {e}") - # 确保清理钩子 - raw_map_np = np.zeros(image.size) + # import traceback + # traceback.print_exc() + cross_map_raw = np.zeros((64, 64)) + self_map_raw = np.zeros((64, 64)) finally: processor.remove() - # 6. 注意力图上采样到图片尺寸 (512x512) - # PIL 进行上采样 - heat_map_pil = Image.fromarray((raw_map_np * 255).astype(np.uint8)) - heat_map_np_resized = ( - np.array(heat_map_pil.resize( - image.size, - resample=Image.Resampling.LANCZOS # 使用高质量的 Lanczos 滤波器 - )) / 255.0 - ) + def upsample(map_np): + pil_img = Image.fromarray((map_np * 255).astype(np.uint8)) + return np.array(pil_img.resize(image.size, resample=Image.Resampling.LANCZOS)) / 255.0 - return image, heat_map_np_resized + return image, upsample(cross_map_raw), upsample(self_map_raw) def main(): - """主函数，负责解析参数，加载模型，计算差异并生成可视化报告。""" - parser = argparse.ArgumentParser(description="SD 图片注意力差异可视化报告生成") - parser.add_argument("--model_path", type=str, required=True, - help="Stable Diffusion 模型本地路径。") - parser.add_argument("--image_path_a", type=str, required=True, - help="干净输入图片 (X) 路径。") - parser.add_argument("--image_path_b", type=str, required=True, - help="扰动输入图片 (X') 路径。") - parser.add_argument("--prompt_text", type=str, default="a photo of sks person", - help="用于生成图片的 Prompt 文本。") - parser.add_argument("--target_word", type=str, default="sks", - help="需要在注意力图中聚焦和可视化的关键词。") - parser.add_argument("--output_dir", type=str, default="output", - help="报告 PNG 文件的输出目录。") + parser = argparse.ArgumentParser(description="SD 双模态注意力差异分析报告") + parser.add_argument("--model_path", type=str, required=True, help="Stable Diffusion 模型路径") + parser.add_argument("--image_path_a", type=str, required=True, help="Clean Image") + parser.add_argument("--image_path_b", type=str, required=True, help="Noisy Image") + parser.add_argument("--prompt_text", type=str, default="a photo of sks person") + parser.add_argument("--target_word", type=str, default="sks") + parser.add_argument("--output_dir", type=str, default="output") args = parser.parse_args() - print(f"--- 正在生成 Stable Diffusion 注意力差异报告 ---") + print(f"--- 正在生成 Museguard 双模态分析报告 (High-Freq Energy Mode) ---") - # ---------------- 准备模型 ---------------- device = 'cuda' if torch.cuda.is_available() else 'cpu' dtype = torch.float16 if device == 'cuda' else torch.float32 - try: - # 加载 Stable Diffusion 管线 pipe = StableDiffusionPipeline.from_pretrained( - args.model_path, - torch_dtype=dtype, - local_files_only=True, - safety_checker=None, - # 从子文件夹加载调度器配置 + args.model_path, torch_dtype=dtype, local_files_only=True, safety_checker=None, scheduler=DPMSolverMultistepScheduler.from_pretrained(args.model_path, subfolder="scheduler") ).to(device) except Exception as e: - print(f"[ERROR] 模型加载失败，请检查路径和环境依赖: {e}") - return - - # ---------------- 获取数据 ---------------- - # 获取干净图片 A 的注意力图 M_A - img_A, map_A = get_attention_map_from_image(pipe, args.image_path_a, args.prompt_text, args.target_word) - # 获取扰动图片 B 的注意力图 M_B - img_B, map_B = get_attention_map_from_image(pipe, args.image_path_b, args.prompt_text, args.target_word) - - if map_A.shape != map_B.shape: - print("错误：注意力图尺寸不匹配。中止处理。") - return - - # 计算差异图: Delta = M_A - M_B - diff_map = map_A - map_B - # 计算 L2 范数（差异距离） - l2_diff = np.linalg.norm(diff_map) - print(f"\n计算完毕，注意力图的 L2 范数差异值: {l2_diff:.4f}") - - # ---------------- 绘制专业报告 ---------------- + print(f"[ERROR] 模型加载失败: {e}"); return + + img_A, cross_A, self_A = get_dual_attention_maps(pipe, args.image_path_a, args.prompt_text, args.target_word) + img_B, cross_B, self_B = get_dual_attention_maps(pipe, args.image_path_b, args.prompt_text, args.target_word) + + diff_cross = cross_A - cross_B + l2_cross = np.linalg.norm(diff_cross) + + diff_self = self_A - self_B + l2_self = np.linalg.norm(diff_self) - # 设置 Matplotlib 字体样式 - plt.rcParams.update({ - 'font.family': 'serif', - 'font.serif': ['DejaVu Serif', 'Times New Roman', 'serif'], - 'mathtext.fontset': 'cm' - }) - - fig = plt.figure(figsize=(12, 16), dpi=120) + print(f"\nCross-Attn L2 Diff: {l2_cross:.4f}") + print(f"Self-Attn L2 Diff: {l2_self:.4f}") + + # ---------------- 绘制增强版报告 ---------------- + plt.rcParams.update({'font.family': 'serif', 'mathtext.fontset': 'cm'}) - # 3行 x 4列 网格布局，用于图片和图例的精确控制 - gs = gridspec.GridSpec(3, 4, figure=fig, - height_ratios=[1, 1, 1.3], - hspace=0.3, wspace=0.1) + fig = plt.figure(figsize=(14, 22), dpi=100) + gs = gridspec.GridSpec(4, 4, figure=fig, height_ratios=[1, 1, 1, 1.2], hspace=0.3, wspace=0.1) - # --- 第一行：原始图片 --- + # Row 1: Images ax_img_a = fig.add_subplot(gs[0, 0:2]) ax_img_b = fig.add_subplot(gs[0, 2:4]) + ax_img_a.imshow(img_A); ax_img_a.set_title("Clean Image ($X$)", fontsize=14); ax_img_a.axis('off') + ax_img_b.imshow(img_B); ax_img_b.set_title("Noisy Image ($X'$)", fontsize=14); ax_img_b.axis('off') + + # Row 2: Cross Attention + ax_cA = fig.add_subplot(gs[1, 0:2]) + ax_cB = fig.add_subplot(gs[1, 2:4]) + ax_cA.imshow(cross_A, cmap='jet', vmin=0, vmax=1) + ax_cA.set_title(f"Cross-Attn ($M^{{cross}}_X$)\nTarget: \"{args.target_word}\"", fontsize=14); ax_cA.axis('off') + im_cB = ax_cB.imshow(cross_B, cmap='jet', vmin=0, vmax=1) + ax_cB.set_title(f"Cross-Attn ($M^{{cross}}_{{X'}}$)", fontsize=14); ax_cB.axis('off') + + divider = make_axes_locatable(ax_cB) + cax = divider.append_axes("right", size="5%", pad=0.05) + fig.colorbar(im_cB, cax=cax, label='Semantic Alignment') - # 干净图片 - ax_img_a.imshow(img_A) - ax_img_a.set_title(f"Clean Image ($X$)\nFilename: {Path(args.image_path_a).name}", fontsize=14, pad=10) - ax_img_a.axis('off') - - # 扰动图片 - ax_img_b.imshow(img_B) - ax_img_b.set_title(f"Noisy Image ($X'$)\nFilename: {Path(args.image_path_b).name}", fontsize=14, pad=10) - ax_img_b.axis('off') - - # --- 第二行：注意力热力图 (Jet配色) --- - ax_map_a = fig.add_subplot(gs[1, 0:2]) - ax_map_b = fig.add_subplot(gs[1, 2:4]) - - # 注意力图 A - im_map_a = ax_map_a.imshow(map_A, cmap='jet', vmin=0, vmax=1) - ax_map_a.set_title(f"Attention Heatmap ($M_X$)\nTarget: \"{args.target_word}\"", fontsize=14, pad=10) - ax_map_a.axis('off') - - # 注意力图 B - im_map_b = ax_map_b.imshow(map_B, cmap='jet', vmin=0, vmax=1) - ax_map_b.set_title(f"Attention Heatmap ($M_{{X'}}$)\nTarget: \"{args.target_word}\"", fontsize=14, pad=10) - ax_map_b.axis('off') - - # 为注意力图 B 绘制颜色指示条 - divider = make_axes_locatable(ax_map_b) - cax_map = divider.append_axes("right", size="5%", pad=0.05) - cbar1 = fig.colorbar(im_map_b, cax=cax_map) - cbar1.set_label('Attention Intensity', fontsize=10) - - # --- 第三行：差异对比 (完美居中) --- - # 差异图在网格的中间两列 - ax_diff = fig.add_subplot(gs[2, 1:3]) - - vmax_diff = np.max(np.abs(diff_map)) - # 使用 TwoSlopeNorm 确保 0 值位于色条中央 - norm_diff = TwoSlopeNorm(vmin=-vmax_diff, vcenter=0., vmax=vmax_diff) - - # 使用 Coolwarm 配色，蓝色表示负差异 (M_X' > M_X)，红色表示正差异 (M_X > M_X') - im_diff = ax_diff.imshow(diff_map, cmap='coolwarm', norm=norm_diff) + # Row 3: Self Attention (High-Frequency Energy Mode) + ax_sA = fig.add_subplot(gs[2, 0:2]) + ax_sB = fig.add_subplot(gs[2, 2:4]) - title_text = ( - r"Difference Map: $\Delta = M_X - M_{X'}$" + - f"\n$L_2$ Norm Distance: $\mathbf{{{l2_diff:.4f}}}$" - ) - ax_diff.set_title(title_text, fontsize=16, pad=12) - ax_diff.axis('off') - - # 差异图颜色指示条 (居中对齐) - cbar2 = fig.colorbar(im_diff, ax=ax_diff, fraction=0.046, pad=0.04) - cbar2.set_label(r'Scale: Red ($+$) $\leftrightarrow$ Blue ($-$)', fontsize=12) - - # ---------------- 整体修饰与保存 ---------------- - fig.suptitle(f"Museguard: SD Attention Analysis Report", fontsize=20, fontweight='bold', y=0.95) + # 恢复使用与 Cross Attention 一致的 'jet' colormap + ax_sA.imshow(self_A, cmap='jet', vmin=0, vmax=1) + ax_sA.set_title(f"Self-Attn ($M^{{self}}_X$)\nHigh-Freq Energy (Texture)", fontsize=14); ax_sA.axis('off') - output_filename = "heatmap_dif.png" - output_path = Path(args.output_dir) / output_filename - output_path.parent.mkdir(parents=True, exist_ok=True) - - plt.savefig(output_path, bbox_inches='tight', facecolor='white') - print(f"\n专业分析报告已保存至：\n{output_path.resolve()}") + im_sB = ax_sB.imshow(self_B, cmap='jet', vmin=0, vmax=1) + ax_sB.set_title(f"Self-Attn ($M^{{self}}_{{X'}}$)", fontsize=14); ax_sB.axis('off') + + divider = make_axes_locatable(ax_sB) + cax = divider.append_axes("right", size="5%", pad=0.05) + fig.colorbar(im_sB, cax=cax, label='Texture Intensity') + + # Row 4: Differences + ax_diff_c = fig.add_subplot(gs[3, 0:2]) + ax_diff_s = fig.add_subplot(gs[3, 2:4]) + + vmax_c = max(np.max(np.abs(diff_cross)), 0.1) + norm_c = TwoSlopeNorm(vmin=-vmax_c, vcenter=0., vmax=vmax_c) + im_dc = ax_diff_c.imshow(diff_cross, cmap='coolwarm', norm=norm_c) + ax_diff_c.set_title(f"Cross Diff ($\Delta_{{cross}}$)\n$L_2$: {l2_cross:.4f}", fontsize=14); ax_diff_c.axis('off') + plt.colorbar(im_dc, ax=ax_diff_c, fraction=0.046, pad=0.04) + + vmax_s = max(np.max(np.abs(diff_self)), 0.1) + norm_s = TwoSlopeNorm(vmin=-vmax_s, vcenter=0., vmax=vmax_s) + im_ds = ax_diff_s.imshow(diff_self, cmap='coolwarm', norm=norm_s) + ax_diff_s.set_title(f"Self Diff ($\Delta_{{self}}$)\n$L_2$: {l2_self:.4f}", fontsize=14); ax_diff_s.axis('off') + plt.colorbar(im_ds, ax=ax_diff_s, fraction=0.046, pad=0.04) + + fig.suptitle(f"Museguard: Dual-Mode Analysis (High-Freq Energy)", fontsize=20, fontweight='bold', y=0.92) + + out_path = Path(args.output_dir) / "dual_heatmap_report.png" + out_path.parent.mkdir(parents=True, exist_ok=True) + plt.savefig(out_path, bbox_inches='tight', facecolor='white') + print(f"\n报告已保存至: {out_path}") if __name__ == "__main__": main() \ No newline at end of file diff --git a/src/backend/app/algorithms/evaluate/eva_gen_nums.py b/src/backend/app/algorithms/evaluate/eva_gen_nums.py index 31041bd..9e69f09 100644 --- a/src/backend/app/algorithms/evaluate/eva_gen_nums.py +++ b/src/backend/app/algorithms/evaluate/eva_gen_nums.py @@ -448,6 +448,40 @@ def generate_visual_report( full_text += conclusion + # --------------------------------------------------------------------- + # 4. Metric definitions (ASCII-only / English-only to avoid font issues) + # --------------------------------------------------------------------- + metric_definitions = [ + "", + "", + ">>> METRIC DEFINITIONS (Detailed Explanations):", + "", + "1) FID (Frechet Inception Distance) [Goal: LOWER is better]", + " - Meaning: Measures how far the generated image distribution is from the reference image distribution in a deep feature space.", + " - What it represents: Overall realism + diversity at the dataset level; smaller means the generated set is closer to the reference set.", + "", + "2) SSIM (Structural Similarity Index) [Goal: HIGHER is better]", + " - Meaning: Compares two images using luminance, contrast, and structural similarity components.", + " - What it represents: Structural consistency (edges, textures, layouts); closer to 1 means more similar structure.", + "", + "3) PSNR (Peak Signal-to-Noise Ratio) [Goal: HIGHER is better]", + " - Meaning: Pixel-domain signal-to-noise measure derived from MSE, typically reported in dB.", + " - What it represents: Pixel-level closeness to the reference; higher means lower average pixel error.", + "", + "4) FDS (Face Detection Similarity) [Goal: HIGHER is better]", + " - Meaning: Face-identity similarity based on detected face embeddings.", + " - What it represents: Whether the generated faces preserve identity-like characteristics relative to the reference set; higher means more similar identity features.", + "", + "5) CLIP_IQS (CLIP Image Quality Score; text prompt = 'good image') [Goal: HIGHER is better]", + " - Meaning: Similarity between the image embedding and the embedding of the text concept 'good image' in CLIP space.", + " - What it represents: A coarse proxy of \"looks like a good image\" according to CLIP priors (semantic/aesthetic heuristic).", + "", + "6) BRISQUE (Blind/Referenceless Image Spatial Quality Evaluator) [Goal: LOWER is better]", + " - Meaning: A no-reference image quality metric based on natural scene statistics (NSS) features.", + " - What it represents: Distortion level without using a reference image; lower is usually interpreted as better perceptual quality.", + ] + full_text += "\n" + "\n".join(metric_definitions) + ax_data.text( 0.05, 0.30, diff --git a/src/backend/app/algorithms/perturbation/pid.py b/src/backend/app/algorithms/perturbation/pid.py index e4e35fc..0eae983 100644 --- a/src/backend/app/algorithms/perturbation/pid.py +++ b/src/backend/app/algorithms/perturbation/pid.py @@ -83,21 +83,27 @@ def parse_args(input_args=None): "--enable_xformers_memory_efficient_attention", action="store_true", help="Whether or not to use xformers." ) parser.add_argument( - '--eps', + "--eps", type=float, default=12.75, - help='pertubation budget' + help="pertubation budget", ) parser.add_argument( - '--step_size', + "--step_size", type=float, - default=1/255, - help='step size of each update' + default=1 / 255, + help="step size of each update", ) parser.add_argument( - '--attack_type', - choices=['var', 'mean', 'KL', 'add-log', 'latent_vector', 'add'], - help='what is the attack target' + "--save_every", + type=int, + default=25, + help="Save all perturbed images every N steps (default=25 to keep original behavior).", + ) + parser.add_argument( + "--attack_type", + choices=["var", "mean", "KL", "add-log", "latent_vector", "add"], + help="what is the attack target", ) if input_args is not None: @@ -108,9 +114,9 @@ def parse_args(input_args=None): env_local_rank = int(os.environ.get("LOCAL_RANK", -1)) if env_local_rank != -1 and env_local_rank != args.local_rank: args.local_rank = env_local_rank - + return args - + class PIDDataset(Dataset): """ @@ -118,20 +124,18 @@ class PIDDataset(Dataset): It pre-processes the images and the tokenizes prompts. """ - def __init__( - self, - instance_data_root, - size=512, - center_crop=False - ): + def __init__(self, instance_data_root, size=512, center_crop=False): self.size = size self.center_crop = center_crop self.instance_images_path = list(Path(instance_data_root).iterdir()) self.num_instance_images = len(self.instance_images_path) - self.image_transforms = transforms.Compose([ + self.image_transforms = transforms.Compose( + [ transforms.Resize(size, interpolation=transforms.InterpolationMode.BILINEAR), transforms.CenterCrop(size) if center_crop else transforms.RandomCrop(size), - transforms.ToTensor(),]) + transforms.ToTensor(), + ] + ) def __len__(self): return self.num_instance_images @@ -144,8 +148,8 @@ class PIDDataset(Dataset): if not instance_image.mode == "RGB": instance_image = instance_image.convert("RGB") - example['index'] = index % self.num_instance_images - example['pixel_values'] = self.image_transforms(instance_image) + example["index"] = index % self.num_instance_images + example["pixel_values"] = self.image_transforms(instance_image) return example @@ -154,11 +158,10 @@ def main(args): if args.seed is not None: torch.manual_seed(args.seed) weight_dtype = torch.float32 - device = torch.device('cuda') - + device = torch.device("cuda") + # VAE encoder - vae = AutoencoderKL.from_pretrained( - args.pretrained_model_name_or_path, subfolder="vae", revision=args.revision) + vae = AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder="vae", revision=args.revision) vae.requires_grad_(False) vae.to(device, dtype=weight_dtype) @@ -170,103 +173,105 @@ def main(args): ) dataloader = torch.utils.data.DataLoader( dataset, - batch_size=1, # some parts of code don't support batching + batch_size=1, # some parts of code don't support batching shuffle=True, num_workers=args.dataloader_num_workers, ) - + # Wrapper of the perturbations generator class AttackModel(torch.nn.Module): def __init__(self): super().__init__() to_tensor = transforms.ToTensor() - self.epsilon = args.eps/255 - self.delta = [torch.empty_like(to_tensor(Image.open(path))).uniform_(-self.epsilon, self.epsilon) - for path in dataset.instance_images_path] + self.epsilon = args.eps / 255 + self.delta = [ + torch.empty_like(to_tensor(Image.open(path))).uniform_(-self.epsilon, self.epsilon) + for path in dataset.instance_images_path + ] self.size = dataset.size - + def forward(self, vae, x, index, poison=False): # Check whether we need to add perturbation if poison: self.delta[index].requires_grad_(True) x = x + self.delta[index].to(dtype=weight_dtype) - + # Normalize to [-1, 1] input_x = 2 * x - 1 return vae.encode(input_x.to(device)) - + attackmodel = AttackModel() - + # Just to zero-out the gradient optimizer = torch.optim.SGD(attackmodel.delta, lr=0) - + # Progress bar progress_bar = tqdm(range(0, args.max_train_steps), desc="Steps") # Make sure the dir exists os.makedirs(args.output_dir, exist_ok=True) - + # Start optimizing the perturbation for step in progress_bar: - total_loss = 0.0 + for batch in dataloader: - # Save images - if step%25 == 0: + # Save images (unchanged behavior by default: save_every=25) + if args.save_every > 0 and step % args.save_every == 0: to_image = transforms.ToPILImage() for i in range(0, len(dataset.instance_images_path)): - img = dataset[i]['pixel_values'] + img = dataset[i]["pixel_values"] img = to_image(img + attackmodel.delta[i]) img.save(os.path.join(args.output_dir, f"{i}.png")) - # Select target loss - clean_embedding = attackmodel(vae, batch['pixel_values'], batch['index'], False) - poison_embedding = attackmodel(vae, batch['pixel_values'], batch['index'], True) + clean_embedding = attackmodel(vae, batch["pixel_values"], batch["index"], False) + poison_embedding = attackmodel(vae, batch["pixel_values"], batch["index"], True) clean_latent = clean_embedding.latent_dist poison_latent = poison_embedding.latent_dist - - if args.attack_type == 'var': - loss = F.mse_loss(clean_latent.std, poison_latent.std, reduction="mean") - elif args.attack_type == 'mean': - loss = F.mse_loss(clean_latent.mean, poison_latent.mean, reduction="mean") - elif args.attack_type == 'KL': + + if args.attack_type == "var": + loss = F.mse_loss(clean_latent.std, poison_latent.std, reduction="mean") + elif args.attack_type == "mean": + loss = F.mse_loss(clean_latent.mean, poison_latent.mean, reduction="mean") + elif args.attack_type == "KL": sigma_2, mu_2 = poison_latent.std, poison_latent.mean sigma_1, mu_1 = clean_latent.std, clean_latent.mean - KL_diver = torch.log(sigma_2 / sigma_1) - 0.5 + (sigma_1 ** 2 + (mu_1 - mu_2) ** 2) / (2 * sigma_2 ** 2) + KL_diver = torch.log(sigma_2 / sigma_1) - 0.5 + (sigma_1**2 + (mu_1 - mu_2) ** 2) / ( + 2 * sigma_2**2 + ) loss = KL_diver.flatten().mean() - elif args.attack_type == 'latent_vector': + elif args.attack_type == "latent_vector": clean_vector = clean_latent.sample() poison_vector = poison_latent.sample() - loss = F.mse_loss(clean_vector, poison_vector, reduction="mean") - elif args.attack_type == 'add': - loss_2 = F.mse_loss(clean_latent.std, poison_latent.std, reduction="mean") - loss_1 = F.mse_loss(clean_latent.mean, poison_latent.mean, reduction="mean") + loss = F.mse_loss(clean_vector, poison_vector, reduction="mean") + elif args.attack_type == "add": + loss_2 = F.mse_loss(clean_latent.std, poison_latent.std, reduction="mean") + loss_1 = F.mse_loss(clean_latent.mean, poison_latent.mean, reduction="mean") loss = loss_1 + loss_2 - elif args.attack_type == 'add-log': + elif args.attack_type == "add-log": loss_1 = F.mse_loss(clean_latent.var.log(), poison_latent.var.log(), reduction="mean") - loss_2 = F.mse_loss(clean_latent.mean, poison_latent.mean, reduction='mean') + loss_2 = F.mse_loss(clean_latent.mean, poison_latent.mean, reduction="mean") loss = loss_1 + loss_2 - - + optimizer.zero_grad() loss.backward() - - # Perform PGD update on the loss - delta = attackmodel.delta[batch['index']] + + # Perform PGD update on the loss (make --step_size effective) + delta = attackmodel.delta[batch["index"]] delta.requires_grad_(False) - delta += delta.grad.sign() * 1/255 + delta += delta.grad.sign() * args.step_size delta = torch.clamp(delta, -attackmodel.epsilon, attackmodel.epsilon) - delta = torch.clamp(delta, -batch['pixel_values'].detach().cpu(), 1-batch['pixel_values'].detach().cpu()) - attackmodel.delta[batch['index']] = delta.detach().squeeze(0) + delta = torch.clamp(delta, -batch["pixel_values"].detach().cpu(), 1 - batch["pixel_values"].detach().cpu()) + attackmodel.delta[batch["index"]] = delta.detach().squeeze(0) total_loss += loss.detach().cpu() # Logging steps logs = {"loss": total_loss.item()} progress_bar.set_postfix(**logs) - + if __name__ == "__main__": args = parse_args() - main(args) + main(args) \ No newline at end of file diff --git a/src/backend/app/algorithms/perturbation/simac.py b/src/backend/app/algorithms/perturbation/simac.py index 0fc673e..cab654d 100644 --- a/src/backend/app/algorithms/perturbation/simac.py +++ b/src/backend/app/algorithms/perturbation/simac.py @@ -1,26 +1,26 @@ import argparse import copy +import gc import hashlib import itertools import logging import os +import random from pathlib import Path import datasets import diffusers -import random -from torch.backends import cudnn +import transformers import numpy as np import torch import torch.nn.functional as F -import torch.utils.checkpoint -import transformers from accelerate import Accelerator from accelerate.logging import get_logger from accelerate.utils import set_seed from diffusers import AutoencoderKL, DDPMScheduler, DiffusionPipeline, UNet2DConditionModel from diffusers.utils.import_utils import is_xformers_available from PIL import Image +from torch.backends import cudnn from torch.utils.data import Dataset from torchvision import transforms from tqdm.auto import tqdm @@ -30,8 +30,19 @@ from transformers import AutoTokenizer, PretrainedConfig logger = get_logger(__name__) +def _cuda_gc() -> None: + """Try to release unreferenced CUDA memory and reduce fragmentation. + + This is a best-effort helper. It does not change algorithmic behavior but can + make long runs less prone to OOM due to fragmentation/reserved-memory growth. + """ + gc.collect() + if torch.cuda.is_available(): + torch.cuda.empty_cache() + + class DreamBoothDatasetFromTensor(Dataset): - """Just like DreamBoothDataset, but take instance_images_tensor instead of path""" + """Just like DreamBoothDataset, but take instance_images_tensor instead of path.""" def __init__( self, @@ -88,7 +99,7 @@ class DreamBoothDatasetFromTensor(Dataset): if self.class_data_root: class_image = Image.open(self.class_images_path[index % self.num_class_images]) - if not class_image.mode == "RGB": + if class_image.mode != "RGB": class_image = class_image.convert("RGB") example["class_images"] = self.image_transforms(class_image) example["class_prompt_ids"] = self.tokenizer( @@ -114,12 +125,11 @@ def import_model_class_from_model_name_or_path(pretrained_model_name_or_path: st from transformers import CLIPTextModel return CLIPTextModel - elif model_class == "RobertaSeriesModelWithTransformation": + if model_class == "RobertaSeriesModelWithTransformation": from diffusers.pipelines.alt_diffusion.modeling_roberta_series import RobertaSeriesModelWithTransformation return RobertaSeriesModelWithTransformation - else: - raise ValueError(f"{model_class} is not supported.") + raise ValueError(f"{model_class} is not supported.") def parse_args(input_args=None): @@ -337,17 +347,13 @@ def parse_args(input_args=None): "--max_steps", type=int, default=50, - help=( - "Maximum steps for adaptive greedy timestep selection." - ), + help=("Maximum steps for adaptive greedy timestep selection."), ) parser.add_argument( "--delta_t", type=int, default=20, - help=( - "delete 2*delta_t for each adaptive greedy timestep selection." - ), + help=("delete 2*delta_t for each adaptive greedy timestep selection."), ) if input_args is not None: args = parser.parse_args(input_args) @@ -358,7 +364,7 @@ def parse_args(input_args=None): class PromptDataset(Dataset): - "A simple dataset to prepare the prompts to generate class images on multiple GPUs." + """A simple dataset to prepare the prompts to generate class images on multiple GPUs.""" def __init__(self, prompt, num_samples): self.prompt = prompt @@ -389,7 +395,6 @@ def load_data(data_dir, size=512, center_crop=True) -> torch.Tensor: return images - def train_one_epoch( args, models, @@ -399,8 +404,6 @@ def train_one_epoch( data_tensor: torch.Tensor, num_steps=20, ): - # Load the tokenizer - unet, text_encoder = copy.deepcopy(models[0]), copy.deepcopy(models[1]) params_to_optimize = itertools.chain(unet.parameters(), text_encoder.parameters()) @@ -422,7 +425,6 @@ def train_one_epoch( args.center_crop, ) - # weight_dtype = torch.bfloat16 weight_dtype = torch.bfloat16 device = torch.device("cuda") @@ -443,24 +445,17 @@ def train_one_epoch( latents = vae.encode(pixel_values).latent_dist.sample() latents = latents * vae.config.scaling_factor - # Sample noise that we'll add to the latents noise = torch.randn_like(latents) bsz = latents.shape[0] - # Sample a random timestep for each image timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device) timesteps = timesteps.long() - # Add noise to the latents according to the noise magnitude at each timestep - # (this is the forward diffusion process) noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps) - # Get the text embedding for conditioning encoder_hidden_states = text_encoder(input_ids)[0] - # Predict the noise residual model_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample - # Get the target for loss depending on the prediction type if noise_scheduler.config.prediction_type == "epsilon": target = noise elif noise_scheduler.config.prediction_type == "v_prediction": @@ -468,33 +463,39 @@ def train_one_epoch( else: raise ValueError(f"Unknown prediction type {noise_scheduler.config.prediction_type}") - # with prior preservation loss if args.with_prior_preservation: model_pred, model_pred_prior = torch.chunk(model_pred, 2, dim=0) target, target_prior = torch.chunk(target, 2, dim=0) - # Compute instance loss instance_loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean") - - # Compute prior loss prior_loss = F.mse_loss(model_pred_prior.float(), target_prior.float(), reduction="mean") - - # Add the prior loss to the instance loss. loss = instance_loss + args.prior_loss_weight * prior_loss - else: + prior_loss = torch.tensor(0.0, device=device) + instance_loss = torch.tensor(0.0, device=device) loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean") loss.backward() torch.nn.utils.clip_grad_norm_(params_to_optimize, 1.0, error_if_nonfinite=True) optimizer.step() optimizer.zero_grad() + print( - f"Step #{step}, loss: {loss.detach().item()}, prior_loss: {prior_loss.detach().item()}, instance_loss: {instance_loss.detach().item()}" + f"Step #{step}, loss: {loss.detach().item()}, prior_loss: {prior_loss.detach().item()}, " + f"instance_loss: {instance_loss.detach().item()}" ) + # Best-effort: free per-step tensors earlier (no behavior change). + del step_data, pixel_values, input_ids, latents, noise, timesteps, noisy_latents, encoder_hidden_states + del model_pred, target, loss, prior_loss, instance_loss + + # Best-effort: release optimizer state + dataset refs sooner. + del optimizer, train_dataset, params_to_optimize + _cuda_gc() + return [unet, text_encoder] + def set_unet_attr(unet): def conv_forward(self): def forward(input_tensor, temb): @@ -505,7 +506,6 @@ def set_unet_attr(unet): hidden_states = self.nonlinearity(hidden_states) if self.upsample is not None: - # upsample_nearest_nhwc fails with large batch sizes. see https://github.com/huggingface/diffusers/issues/984 if hidden_states.shape[0] >= 64: input_tensor = input_tensor.contiguous() hidden_states = hidden_states.contiguous() @@ -538,37 +538,33 @@ def set_unet_attr(unet): input_tensor = self.conv_shortcut(input_tensor) output_tensor = (input_tensor + hidden_states) / self.output_scale_factor - return output_tensor return forward - - # [MODIFIED] 只 hook 算法实际使用到的 up_blocks[3] + conv_module_list = [ - unet.up_blocks[3].resnets[0], unet.up_blocks[3].resnets[1], unet.up_blocks[3].resnets[2], - ] + unet.up_blocks[3].resnets[0], + unet.up_blocks[3].resnets[1], + unet.up_blocks[3].resnets[2], + ] for conv_module in conv_module_list: conv_module.forward = conv_forward(conv_module) - setattr(conv_module, 'in_layers_features', None) - setattr(conv_module, 'out_layers_features', None) - + setattr(conv_module, "in_layers_features", None) + setattr(conv_module, "out_layers_features", None) def save_feature_maps(up_blocks, down_blocks): - out_layers_features_list_3 = [] - res_3_list =[0,1,2] + res_3_list = [0, 1, 2] - # [MODIFIED] 只提取 up_blocks[3] 的特征 block = up_blocks[3] for index in res_3_list: out_layers_features_list_3.append(block.resnets[index].out_layers_features) out_layers_features_list_3 = torch.stack(out_layers_features_list_3, dim=0) - - # [MODIFIED] 只返回算法实际使用到的特征 return out_layers_features_list_3 + def pgd_attack( args, models, @@ -579,10 +575,13 @@ def pgd_attack( original_images: torch.Tensor, target_tensor: torch.Tensor, num_steps: int, - time_list + time_list, ): - """Return new perturbed data""" + """Return new perturbed data. + Note: This function keeps the external behavior identical, but tries to reduce + memory pressure by freeing tensors early and avoiding lingering references. + """ unet, text_encoder = models weight_dtype = torch.bfloat16 device = torch.device("cuda") @@ -595,6 +594,7 @@ def pgd_attack( perturbed_images = data_tensor.detach().clone() perturbed_images.requires_grad_(True) + # Keep input_ids on CPU; move to GPU only when encoding. input_ids = tokenizer( args.instance_prompt, truncation=True, @@ -604,12 +604,13 @@ def pgd_attack( ).input_ids.repeat(len(data_tensor), 1) for step in range(num_steps): - perturbed_images.requires_grad = True + perturbed_images.requires_grad_(True) + latents = vae.encode(perturbed_images.to(device, dtype=weight_dtype)).latent_dist.sample() latents = latents * vae.config.scaling_factor - # Sample noise that we'll add to the latents + noise = torch.randn_like(latents) - bsz = latents.shape[0] + timesteps = [] for i in range(len(data_tensor)): ts = time_list[i] @@ -618,58 +619,62 @@ def pgd_attack( timestep = timestep.long() timesteps.append(timestep) timesteps = torch.cat(timesteps).to(device) - # Add noise to the latents according to the noise magnitude at each timestep - # (this is the forward diffusion process) + noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps) - # Get the text embedding for conditioning + encoder_hidden_states = text_encoder(input_ids.to(device))[0] - # Predict the noise residual + model_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample - # Get the target for loss depending on the prediction type + if noise_scheduler.config.prediction_type == "epsilon": target = noise elif noise_scheduler.config.prediction_type == "v_prediction": target = noise_scheduler.get_velocity(latents, noise, timesteps) else: raise ValueError(f"Unknown prediction type {noise_scheduler.config.prediction_type}") - - # [MODIFIED] feature loss (只解包需要的特征) + noise_out_layers_features_3 = save_feature_maps(unet.up_blocks, unet.down_blocks) - + with torch.no_grad(): clean_latents = vae.encode(data_tensor.to(device, dtype=weight_dtype)).latent_dist.sample() clean_latents = clean_latents * vae.config.scaling_factor noisy_clean_latents = noise_scheduler.add_noise(clean_latents, noise, timesteps) - clean_model_pred = unet(noisy_clean_latents, timesteps, encoder_hidden_states).sample - - # [MODIFIED] (只解包需要的特征) - clean_out_layers_features_3 = save_feature_maps(unet.up_blocks, unet.down_blocks) - - # [LOGIC UNCHANGED] 目标损失函数不变 - target_loss = F.mse_loss(noise_out_layers_features_3.float(), clean_out_layers_features_3.float(), reduction="mean") - unet.zero_grad() - text_encoder.zero_grad() + _ = unet(noisy_clean_latents, timesteps, encoder_hidden_states).sample + clean_out_layers_features_3 = save_feature_maps(unet.up_blocks, unet.down_blocks) + + target_loss = F.mse_loss( + noise_out_layers_features_3.float(), + clean_out_layers_features_3.float(), + reduction="mean", + ) + + unet.zero_grad(set_to_none=True) + text_encoder.zero_grad(set_to_none=True) + loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean") - loss = loss + target_loss.detach().item() # 保持原有的(奇怪的) loss.backward() 逻辑 + # Keep original behavior: feature loss does not backprop (added as Python float). + loss = loss + target_loss.detach().item() loss.backward() + alpha = args.pgd_alpha eps = args.pgd_eps / 255 adv_images = perturbed_images + alpha * perturbed_images.grad.sign() eta = torch.clamp(adv_images - original_images, min=-eps, max=+eps) perturbed_images = torch.clamp(original_images + eta, min=-1, max=+1).detach_() - print(f"PGD loss - step {step}, loss: {loss.detach().item()}, target_loss : {target_loss.detach().item()}") - - # [MODIFIED] 显式释放特征张量并清理缓存，以确保后续 train_one_epoch 有足够的显存 - # 这部分代码在 PGD 循环结束后添加 (即在 return perturbed_images 之前) - del noise_out_layers_features_3 - del clean_out_layers_features_3 - del noise - del latents - del encoder_hidden_states - torch.cuda.empty_cache() + print( + f"PGD loss - step {step}, loss: {loss.detach().item()}, target_loss : {target_loss.detach().item()}" + ) + + # Best-effort: free per-step tensors early. + del latents, noise, timesteps, noisy_latents, encoder_hidden_states, model_pred, target + del noise_out_layers_features_3, clean_latents, noisy_clean_latents, clean_out_layers_features_3 + del target_loss, loss, adv_images, eta + + _cuda_gc() return perturbed_images + def select_timestep( args, models, @@ -679,9 +684,11 @@ def select_timestep( data_tensor: torch.Tensor, original_images: torch.Tensor, target_tensor: torch.Tensor, - ): - """Return new perturbed data""" +): + """Return timestep lists for each image. + External behavior unchanged; add best-effort per-loop cleanup to lower memory pressure. + """ unet, text_encoder = models weight_dtype = torch.bfloat16 device = torch.device("cuda") @@ -693,7 +700,6 @@ def select_timestep( perturbed_images = data_tensor.detach().clone() perturbed_images.requires_grad_(True) - input_ids = tokenizer( args.instance_prompt, truncation=True, @@ -701,93 +707,39 @@ def select_timestep( max_length=tokenizer.model_max_length, return_tensors="pt", ).input_ids - - time_list = [] - for id in range(len(data_tensor)): - perturbed_image = perturbed_images[id, :].unsqueeze(0) - original_image = original_images[id, :].unsqueeze(0) - time_seq = torch.tensor(list(range(0, 1000))) - input_mask = torch.ones_like(time_seq) - id_image = perturbed_image.detach().clone() - for step in range(args.max_steps): - id_image.requires_grad_(True) - select_mask = torch.where(input_mask==1, True, False) - res_time_seq = torch.masked_select(time_seq, select_mask) - if len(res_time_seq) > 100: - min_score, max_score = 0.0, 0.0 - for index in range(0, 5): - id_image.requires_grad_(True) - latents = vae.encode(id_image.to(device, dtype=weight_dtype)).latent_dist.sample() - latents = latents * vae.config.scaling_factor - # Sample noise that we'll add to the latents - noise = torch.randn_like(latents) - bsz = latents.shape[0] - # Sample a random timestep for each image - inner_index = torch.randint(0, len(res_time_seq), (bsz,)) - timesteps = torch.IntTensor([res_time_seq[inner_index]]).to(device) - timesteps = timesteps.long() - # Add noise to the latents according to the noise magnitude at each timestep - # (this is the forward diffusion process) - noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps) - # Get the text embedding for conditioning - encoder_hidden_states = text_encoder(input_ids.to(device))[0] - # Predict the noise residual - model_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample - # Get the target for loss depending on the prediction type - if noise_scheduler.config.prediction_type == "epsilon": - target = noise - elif noise_scheduler.config.prediction_type == "v_prediction": - target = noise_scheduler.get_velocity(latents, noise, timesteps) - else: - raise ValueError(f"Unknown prediction type {noise_scheduler.config.prediction_type}") - unet.zero_grad() - text_encoder.zero_grad() - loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean") - loss.backward() - score = torch.sum(torch.abs(id_image.grad.data)) - index = index + 1 - id_image.grad.zero_() - if index == 1: - min_score = score - max_score = score - del_t = res_time_seq[inner_index].item() - select_t = res_time_seq[inner_index].item() - else: - if min_score > score: - min_score = score - del_t = res_time_seq[inner_index].item() - if max_score < score: - max_score = score - select_t = res_time_seq[inner_index].item() - print(f"PGD loss - step {step}, index : {index}, loss: {loss.detach().item()}, score: {score}, t : {res_time_seq[inner_index]}, ts_len: {len(res_time_seq)}") - - print("del_t", del_t, "max_t", select_t) - if del_t < args.delta_t : - del_t = args.delta_t - elif del_t > (1000 - args.delta_t): - del_t= 1000 - args.delta_t - input_mask[del_t - 20: del_t + 20] = input_mask[del_t - 20: del_t + 20] - 1 - input_mask = torch.clamp(input_mask, min=0, max=+1) + time_list = [] + for img_id in range(len(data_tensor)): + perturbed_image = perturbed_images[img_id, :].unsqueeze(0) + original_image = original_images[img_id, :].unsqueeze(0) + time_seq = torch.tensor(list(range(0, 1000))) + input_mask = torch.ones_like(time_seq) + + id_image = perturbed_image.detach().clone() + for step in range(args.max_steps): + id_image.requires_grad_(True) + select_mask = torch.where(input_mask == 1, True, False) + res_time_seq = torch.masked_select(time_seq, select_mask) + + if len(res_time_seq) > 100: + min_score, max_score = 0.0, 0.0 + for inner_try in range(0, 5): id_image.requires_grad_(True) latents = vae.encode(id_image.to(device, dtype=weight_dtype)).latent_dist.sample() latents = latents * vae.config.scaling_factor - # Sample noise that we'll add to the latents + noise = torch.randn_like(latents) bsz = latents.shape[0] - timesteps = torch.IntTensor([select_t]).to(device) + inner_index = torch.randint(0, len(res_time_seq), (bsz,)) + timesteps = torch.IntTensor([res_time_seq[inner_index]]).to(device) timesteps = timesteps.long() - # Add noise to the latents according to the noise magnitude at each timestep - # (this is the forward diffusion process) + noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps) - # Get the text embedding for conditioning encoder_hidden_states = text_encoder(input_ids.to(device))[0] - # Predict the noise residual model_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample - # Get the target for loss depending on the prediction type if noise_scheduler.config.prediction_type == "epsilon": target = noise elif noise_scheduler.config.prediction_type == "v_prediction": @@ -795,26 +747,92 @@ def select_timestep( else: raise ValueError(f"Unknown prediction type {noise_scheduler.config.prediction_type}") - unet.zero_grad() - text_encoder.zero_grad() + unet.zero_grad(set_to_none=True) + text_encoder.zero_grad(set_to_none=True) + loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean") loss.backward() - alpha = args.pgd_alpha - eps = args.pgd_eps / 255 - adv_image = id_image + alpha * id_image.grad.sign() - eta = torch.clamp(adv_image - original_image, min=-eps, max=+eps) - score = torch.sum(torch.abs(id_image.grad.sign())) - id_image = torch.clamp(original_image + eta, min=-1, max=+1).detach_() + score = torch.sum(torch.abs(id_image.grad.data)) + id_image.grad.zero_() + + if inner_try == 0: + min_score = score + max_score = score + del_t = res_time_seq[inner_index].item() + select_t = res_time_seq[inner_index].item() + else: + if min_score > score: + min_score = score + del_t = res_time_seq[inner_index].item() + if max_score < score: + max_score = score + select_t = res_time_seq[inner_index].item() + + print( + f"PGD loss - step {step}, index : {inner_try + 1}, loss: {loss.detach().item()}, " + f"score: {score}, t : {res_time_seq[inner_index]}, ts_len: {len(res_time_seq)}" + ) + + del latents, noise, timesteps, noisy_latents, encoder_hidden_states, model_pred, target, loss, score + + print("del_t", del_t, "max_t", select_t) + if del_t < args.delta_t: + del_t = args.delta_t + elif del_t > (1000 - args.delta_t): + del_t = 1000 - args.delta_t + + input_mask[del_t - 20 : del_t + 20] = input_mask[del_t - 20 : del_t + 20] - 1 + input_mask = torch.clamp(input_mask, min=0, max=+1) + + id_image.requires_grad_(True) + latents = vae.encode(id_image.to(device, dtype=weight_dtype)).latent_dist.sample() + latents = latents * vae.config.scaling_factor + + noise = torch.randn_like(latents) + timesteps = torch.IntTensor([select_t]).to(device) + timesteps = timesteps.long() + + noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps) + encoder_hidden_states = text_encoder(input_ids.to(device))[0] + model_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample + + if noise_scheduler.config.prediction_type == "epsilon": + target = noise + elif noise_scheduler.config.prediction_type == "v_prediction": + target = noise_scheduler.get_velocity(latents, noise, timesteps) else: - # print(id, res_time_seq, step, len(res_time_seq)) - time_list.append(res_time_seq) - break + raise ValueError(f"Unknown prediction type {noise_scheduler.config.prediction_type}") + + unet.zero_grad(set_to_none=True) + text_encoder.zero_grad(set_to_none=True) + + loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean") + loss.backward() + + alpha = args.pgd_alpha + eps = args.pgd_eps / 255 + adv_image = id_image + alpha * id_image.grad.sign() + eta = torch.clamp(adv_image - original_image, min=-eps, max=+eps) + _ = torch.sum(torch.abs(id_image.grad.sign())) + id_image = torch.clamp(original_image + eta, min=-1, max=+1).detach_() + + del latents, noise, timesteps, noisy_latents, encoder_hidden_states, model_pred, target, loss, adv_image, eta + + else: + time_list.append(res_time_seq) + break + + del perturbed_image, original_image, time_seq, input_mask, id_image + _cuda_gc() + + del perturbed_images, input_ids + _cuda_gc() return time_list + def setup_seeds(): seed = 42 - random.seed(seed) np.random.seed(seed) torch.manual_seed(seed) @@ -850,11 +868,11 @@ def main(args): if args.seed is not None: set_seed(args.seed) setup_seeds() + # Generate class images if prior preservation is enabled. if args.with_prior_preservation: class_images_dir = Path(args.class_data_dir) - if not class_images_dir.exists(): - class_images_dir.mkdir(parents=True) + class_images_dir.mkdir(parents=True, exist_ok=True) cur_class_images = len(list(class_images_dir.iterdir())) if cur_class_images < args.num_class_images: @@ -865,12 +883,12 @@ def main(args): torch_dtype = torch.float16 elif args.mixed_precision == "bf16": torch_dtype = torch.bfloat16 + pipeline = DiffusionPipeline.from_pretrained( args.pretrained_model_name_or_path, torch_dtype=torch_dtype, safety_checker=None, revision=args.revision, - ) pipeline.set_progress_bar_config(disable=True) @@ -889,27 +907,25 @@ def main(args): disable=not accelerator.is_local_main_process, ): images = pipeline(example["prompt"]).images - for i, image in enumerate(images): hash_image = hashlib.sha1(image.tobytes()).hexdigest() image_filename = class_images_dir / f"{example['index'][i] + cur_class_images}-{hash_image}.jpg" image.save(image_filename) - del pipeline - if torch.cuda.is_available(): - torch.cuda.empty_cache() + del pipeline, sample_dataset, sample_dataloader + _cuda_gc() - # import correct text encoder class text_encoder_cls = import_model_class_from_model_name_or_path(args.pretrained_model_name_or_path, args.revision) - # Load scheduler and models text_encoder = text_encoder_cls.from_pretrained( args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision, ) unet = UNet2DConditionModel.from_pretrained( - args.pretrained_model_name_or_path, subfolder="unet", revision=args.revision, + args.pretrained_model_name_or_path, + subfolder="unet", + revision=args.revision, ) tokenizer = AutoTokenizer.from_pretrained( @@ -919,12 +935,13 @@ def main(args): use_fast=False, ) - noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler", ) + noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler") vae = AutoencoderKL.from_pretrained( - args.pretrained_model_name_or_path, subfolder="vae", revision=args.revision, + args.pretrained_model_name_or_path, + subfolder="vae", + revision=args.revision, ).cuda() - vae.requires_grad_(False) if not args.train_text_encoder: @@ -967,22 +984,23 @@ def main(args): target_latent_tensor = target_latent_tensor.repeat(len(perturbed_data), 1, 1, 1).cuda() f = [unet, text_encoder] - + time_list = select_timestep( - args, - f, - tokenizer, - noise_scheduler, - vae, - perturbed_data, - original_data, - target_latent_tensor, + args, + f, + tokenizer, + noise_scheduler, + vae, + perturbed_data, + original_data, + target_latent_tensor, ) for t in time_list: print(t) + for i in range(args.max_train_steps): - # 1. f' = f.clone() f_sur = copy.deepcopy(f) + f_sur = train_one_epoch( args, f_sur, @@ -992,6 +1010,7 @@ def main(args): clean_data, args.max_f_train_steps, ) + perturbed_data = pgd_attack( args, f_sur, @@ -1002,8 +1021,13 @@ def main(args): original_data, target_latent_tensor, args.max_adv_train_steps, - time_list + time_list, ) + + # Free surrogate ASAP (best-effort, behavior unchanged). + del f_sur + _cuda_gc() + f = train_one_epoch( args, f, @@ -1015,24 +1039,31 @@ def main(args): ) if (i + 1) % args.checkpointing_iterations == 0: - - save_folder = args.output_dir + save_folder = args.output_dir os.makedirs(save_folder, exist_ok=True) noised_imgs = perturbed_data.detach() - + img_names = [ str(instance_path).split("/")[-1].split(".")[0] for instance_path in list(Path(args.instance_data_dir_for_adversarial).iterdir()) ] - + for img_pixel, img_name in zip(noised_imgs, img_names): - save_path = os.path.join(save_folder, f"perturbed_{img_name}.png") + save_path = os.path.join(save_folder, f"perturbed_{img_name}.png") Image.fromarray( - (img_pixel * 127.5 + 128).clamp(0, 255).to(torch.uint8).permute(1, 2, 0).cpu().numpy() + (img_pixel * 127.5 + 128) + .clamp(0, 255) + .to(torch.uint8) + .permute(1, 2, 0) + .cpu() + .numpy() ).save(save_path) - + print(f"Saved perturbed images at step {i+1} to {save_folder} (Files are overwritten)") + # Best-effort cleanup at the end of each outer iteration. + _cuda_gc() + if __name__ == "__main__": args = parse_args() diff --git a/src/backend/app/scripts/attack_anti_face_edit.sh b/src/backend/app/scripts/attack_anti_face_edit.sh new file mode 100644 index 0000000..c66ced4 --- /dev/null +++ b/src/backend/app/scripts/attack_anti_face_edit.sh @@ -0,0 +1,57 @@ +#需要环境：conda activate pid +### Generate images protected by PID + +export HF_HUB_OFFLINE=1 +# 强制使用本地模型缓存，避免联网下载模型 + +### SD v2.1 +# export HF_HOME="/root/autodl-tmp/huggingface_cache" +# export MODEL_PATH="stabilityai/stable-diffusion-2-1" + +### SD v1.5 +# export HF_HOME="/root/autodl-tmp/huggingface_cache" +# export MODEL_PATH="runwayml/stable-diffusion-v1-5" +export MODEL_PATH="../../static/hf_models/hub/models--runwayml--stable-diffusion-v1-5/snapshots/451f4fe16113bff5a5d2269ed5ad43b0592e9a14" + + +export TASKNAME="task001" +### Data to be protected +export INSTANCE_DIR="../../static/originals/${TASKNAME}" +### Path to save the protected data +export OUTPUT_DIR="../../static/perturbed/${TASKNAME}" + +# ------------------------- 自动创建依赖路径 ------------------------- +echo "Creating required directories..." +mkdir -p "$INSTANCE_DIR" +mkdir -p "$OUTPUT_DIR" +echo "Directories created successfully." + + +# ------------------------- 训练前清空 OUTPUT_DIR ------------------------- +echo "Clearing output directory: $OUTPUT_DIR" +# 确保目录存在，避免清理命令失败 +mkdir -p "$OUTPUT_DIR" +# 查找并删除目录下的所有文件和子目录（但不删除 . 或 ..） +find "$OUTPUT_DIR" -mindepth 1 -delete + + + +### Generation command +# --max_train_steps: Optimizaiton steps +# --attack_type: target loss to update, choices=['var', 'mean', 'KL', 'add-log', 'latent_vector', 'add'], +# Please refer to the file content for more usage + +CUDA_VISIBLE_DEVICES=0 python ../algorithms/pid.py \ + --pretrained_model_name_or_path=$MODEL_PATH \ + --instance_data_dir=$INSTANCE_DIR \ + --output_dir=$OUTPUT_DIR \ + --resolution=512 \ + --max_train_steps=2000 \ + --center_crop \ + --eps 10 \ + --step_size 0.002 \ + --save_every 200 \ + --attack_type add-log \ + --seed 0 \ + --dataloader_num_workers 2 + diff --git a/src/backend/app/scripts/attack_glaze_style_trans.sh b/src/backend/app/scripts/attack_glaze_style_trans.sh new file mode 100644 index 0000000..e5ca490 --- /dev/null +++ b/src/backend/app/scripts/attack_glaze_style_trans.sh @@ -0,0 +1,91 @@ +#!/bin/bash + +#============================================================================= +# Glaze 风格保护攻击脚本 +# 用于保护艺术作品免受 AI 模型的风格模仿 +#============================================================================= + +### ===================== 环境配置 ===================== ### + +export HF_HUB_OFFLINE=1 +# 强制使用本地模型缓存，避免联网下载模型 + +export PYTHONWARNINGS="ignore" +# 忽略所有警告 + +export CUDA_LAUNCH_BLOCKING=0 +# CUDA 异步执行 + +### ===================== 模型路径 ===================== ### + +# Stable Diffusion v1.5 模型路径 +export MODEL_PATH="../../static/hf_models/hub/models--runwayml--stable-diffusion-v1-5/snapshots/451f4fe16113bff5a5d2269ed5ad43b0592e9a14" + +### ===================== 任务配置 ===================== ### + +export TASKNAME="task003" + +# 待保护的原始图像目录 +export INSTANCE_DIR="../../static/originals/${TASKNAME}" + +# 保护后图像的输出目录 +export OUTPUT_DIR="../../static/perturbed/${TASKNAME}" + +### ===================== 创建必要目录 ===================== ### + +echo "==============================================" +echo " 艺术风格保护" +echo "==============================================" +echo "" +echo "创建必要目录..." +mkdir -p "$INSTANCE_DIR" +mkdir -p "$OUTPUT_DIR" +echo "目录创建完成。" +echo "" + +### ===================== 清空输出目录 ===================== ### + +echo "清空输出目录: $OUTPUT_DIR" +find "$OUTPUT_DIR" -mindepth 1 -delete 2>/dev/null || true +echo "输出目录已清空。" +echo "" + +### ===================== 显示配置 ===================== ### + +echo "当前配置:" +echo " - 模型路径: $MODEL_PATH" +echo " - 输入目录: $INSTANCE_DIR" +echo " - 输出目录: $OUTPUT_DIR" +echo " - 任务名称: $TASKNAME" +echo "" + +# 目标风格 (target_style) 固定可选: Glaze 会将图像的 AI 特征向此风格偏移 +# - "impressionism painting by van gogh" (默认，梵高印象派) +# - "abstract art by kandinsky" (康定斯基抽象艺术) +# - "cubism painting by picasso" (毕加索立体主义) +# - "oil painting in baroque style" (巴洛克风格油画) +### ===================== 运行 Glaze ===================== ### + +echo "开始风格迁移保护..." +echo "" + +CUDA_VISIBLE_DEVICES=0 python ../algorithms/glaze.py \ + --pretrained_model_name_or_path=$MODEL_PATH \ + --instance_data_dir=$INSTANCE_DIR \ + --output_dir=$OUTPUT_DIR \ + --resolution=512 \ + --center_crop \ + --max_train_steps=150 \ + --eps=0.04 \ + --target_style="impressionism painting by van gogh" \ + --style_strength=0.75 \ + --n_runs=3 \ + --style_transfer_iter=15 \ + --guidance_scale=7.5 \ + --seed=42 + +echo "" +echo "==============================================" +echo " 艺术风格防护处理完成！" +echo " 输出保存至: $OUTPUT_DIR" +echo "==============================================" \ No newline at end of file diff --git a/src/backend/app/scripts/finetune_ti.sh b/src/backend/app/scripts/finetune_ti.sh index aae31ff..8e062bf 100644 --- a/src/backend/app/scripts/finetune_ti.sh +++ b/src/backend/app/scripts/finetune_ti.sh @@ -47,9 +47,9 @@ CUDA_VISIBLE_DEVICES=0 accelerate launch ../finetune_infras/train_ti_gen_trace.p --instance_data_dir=$INSTANCE_DIR \ --output_dir=$TI_OUTPUT_DIR \ --validation_image_output_dir=$OUTPUT_INFER_DIR \ - --placeholder_token="sks" \ + --placeholder_token="" \ --initializer_token="person" \ - --instance_prompt="a photo of sks person" \ + --instance_prompt="a photo of person" \ --resolution=512 \ --train_batch_size=1 \ --gradient_accumulation_steps=1 \ @@ -60,7 +60,7 @@ CUDA_VISIBLE_DEVICES=0 accelerate launch ../finetune_infras/train_ti_gen_trace.p --checkpointing_steps=500 \ --seed=0 \ --mixed_precision=fp16 \ - --validation_prompt="a photo of sks person" \ + --validation_prompt="a close-up photo of person" \ --num_validation_images 4 \ --validation_epochs 50 \ --coords_save_path="$COORD_DIR" \ diff --git a/src/frontend/public/method_examples/face_edit_example/bad_gen/image_0.png b/src/frontend/public/method_examples/face_edit_example/bad_gen/image_0.png new file mode 100644 index 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