src/backend/app/scripts/attack_anti_customize_gen.sh

@@ -0,0 +1,64 @@
+#需要环境：conda activate simac
+export HF_HUB_OFFLINE=1
+export MODEL_PATH="../../static/hf_models/diffusers/models--stabilityai--stable-diffusion-2-1-base/snapshots/5ede9e4bf3e3fd1cb0ef2f7a3fff13ee514fdf06"
+export TASKNAME="task001"
+
+# ------------------------- Train ASPL on set CLEAN_ADV_DIR -------------------------
+export CLEAN_TRAIN_DIR="../../static/originals/${TASKNAME}" 
+export CLEAN_ADV_DIR="../../static/originals/${TASKNAME}"
+export OUTPUT_DIR="../../static/perturbed/${TASKNAME}"
+export CLASS_DIR="../../static/class/${TASKNAME}"
+
+# ------------------------- 自动创建依赖路径 -------------------------
+echo "Creating required directories..."
+mkdir -p "$CLEAN_TRAIN_DIR"
+mkdir -p "$CLEAN_ADV_DIR"
+mkdir -p "$OUTPUT_DIR"
+mkdir -p "$CLASS_DIR"
+echo "Directories created successfully."
+
+
+# ------------------------- 训练前清空 OUTPUT_DIR -------------------------
+echo "Clearing output directory: $OUTPUT_DIR"
+# 确保目录存在，避免清理命令失败
+# 注意：虽然前面已经创建，但这里保留是为了代码逻辑清晰，也可以删除
+mkdir -p "$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 "$OUTPUT_DIR" -mindepth 1 -delete
+find "$CLASS_DIR" -mindepth 1 -delete
+
+
+
+accelerate launch ../algorithms/simac.py \
+  --pretrained_model_name_or_path=$MODEL_PATH \
+  --enable_xformers_memory_efficient_attention \
+  --instance_data_dir_for_train=$CLEAN_TRAIN_DIR \
+  --instance_data_dir_for_adversarial=$CLEAN_ADV_DIR \
+  --instance_prompt="a photo of <sks> person" \
+  --class_data_dir=$CLASS_DIR \
+  --num_class_images=100 \
+  --class_prompt="a photo of person" \
+  --output_dir=$OUTPUT_DIR \
+  --center_crop \
+  --with_prior_preservation \
+  --prior_loss_weight=1.0 \
+  --resolution=384 \
+  --train_batch_size=1 \
+  --max_train_steps=100 \
+  --max_f_train_steps=3 \
+  --max_adv_train_steps=6 \
+  --checkpointing_iterations=20 \
+  --learning_rate=5e-7 \
+  --pgd_alpha=0.005 \
+  --pgd_eps=10 \
+  --seed=0
+
+# ------------------------- 训练后清空 CLASS_DIR -------------------------
+# 注意：这会在 accelerate launch 成功结束后执行
+echo "Clearing class directory: $CLASS_DIR"
+# 确保目录存在，避免清理命令失败
+mkdir -p "$CLASS_DIR"
+# 查找并删除目录下的所有文件和子目录（但不删除 . 或 ..）
+find "$CLASS_DIR" -mindepth 1 -delete
+
+echo "Script finished."

src/backend/app/scripts/attack_simac.sh

@@ -20,8 +20,12 @@ echo "Directories created successfully."
 
 # ------------------------- 训练前清空 OUTPUT_DIR -------------------------
 echo "Clearing output directory: $OUTPUT_DIR"
+# 确保目录存在，避免清理命令失败
+# 注意：虽然前面已经创建，但这里保留是为了代码逻辑清晰，也可以删除
+mkdir -p "$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 "$OUTPUT_DIR" -mindepth 1 -delete
+find "$CLASS_DIR" -mindepth 1 -delete
 
 
 
@@ -30,9 +34,9 @@ accelerate launch ../algorithms/simac.py \
   --enable_xformers_memory_efficient_attention \
   --instance_data_dir_for_train=$CLEAN_TRAIN_DIR \
   --instance_data_dir_for_adversarial=$CLEAN_ADV_DIR \
-  --instance_prompt="a photo of sks person" \
+  --instance_prompt="a photo of <sks> person" \
   --class_data_dir=$CLASS_DIR \
-  --num_class_images=200 \
+  --num_class_images=100 \
   --class_prompt="a photo of person" \
   --output_dir=$OUTPUT_DIR \
   --center_crop \
@@ -40,13 +44,13 @@ accelerate launch ../algorithms/simac.py \
   --prior_loss_weight=1.0 \
   --resolution=384 \
   --train_batch_size=1 \
-  --max_train_steps=50 \
+  --max_train_steps=60 \
   --max_f_train_steps=3 \
   --max_adv_train_steps=6 \
   --checkpointing_iterations=10 \
-  --learning_rate=5e-7 \
+  --learning_rate=2e-6 \
   --pgd_alpha=0.005 \
-  --pgd_eps=8 \
+  --pgd_eps=10 \
   --seed=0
 
 # ------------------------- 训练后清空 CLASS_DIR -------------------------

src/backend/app/scripts/finetune_db.sh

@@ -1,34 +1,23 @@
-#需要环境：conda activate pid
-### Trianing model
+# 需要环境：conda activate 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"
+# SD v1.5 本地路径
 export MODEL_PATH="../../static/hf_models/hub/models--runwayml--stable-diffusion-v1-5/snapshots/451f4fe16113bff5a5d2269ed5ad43b0592e9a14"
 
-
 export TASKNAME="task001"
-export TYPE="clean" #clean or perturbed
-
+export TYPE="perturbed" # clean or perturbed
 
 if [ "$TYPE" == "clean" ]; then
     export INSTANCE_DIR="../../static/originals/${TASKNAME}"
 else
     export INSTANCE_DIR="../../static/perturbed/${TASKNAME}"
 fi
-export DREAMBOOTH_OUTPUT_DIR="../../static/hf_models/fine_tuned/${TYPE}/${TASKNAME}" 
-export OUTPUT_INFER_DIR="../../static/model_outputs/${TYPE}/${TASKNAME}" 
-export CLASS_DIR="../../static/class/${TASKNAME}"
-export COORD_DIR="../../static/eva_res/position/${TASKNAME}" 
 
+export DREAMBOOTH_OUTPUT_DIR="../../static/hf_models/fine_tuned/${TYPE}/${TASKNAME}"
+export OUTPUT_INFER_DIR="../../static/model_outputs/${TYPE}/${TASKNAME}"
+export CLASS_DIR="../../static/class/${TASKNAME}"
+export COORD_DIR="../../static/eva_res/position/${TASKNAME}"
 
-# ------------------------- 自动创建依赖路径 -------------------------
 echo "Creating required directories..."
 mkdir -p "$INSTANCE_DIR"
 mkdir -p "$DREAMBOOTH_OUTPUT_DIR"
@@ -36,52 +25,43 @@ mkdir -p "$OUTPUT_INFER_DIR"
 mkdir -p "$CLASS_DIR"
 mkdir -p "$COORD_DIR"
 
-
-# ------------------------- 自动清除旧文件 -------------------------
 echo "Clearing output directory: $DREAMBOOTH_OUTPUT_DIR and $OUTPUT_INFER_DIR and $COORD_DIR"
-# 查找并删除目录下的所有文件和子目录（但不删除 . 或 ..）
 find "$DREAMBOOTH_OUTPUT_DIR" -mindepth 1 -delete
 find "$OUTPUT_INFER_DIR" -mindepth 1 -delete
 find "$COORD_DIR" -mindepth 1 -delete
 
-
-# ------------------------- Fine-tune DreamBooth on images  -------------------------
 CUDA_VISIBLE_DEVICES=0 accelerate launch ../finetune_infras/train_db_gen_trace.py \
-  --pretrained_model_name_or_path=$MODEL_PATH  \
+  --pretrained_model_name_or_path=$MODEL_PATH \
   --instance_data_dir=$INSTANCE_DIR \
   --class_data_dir=$CLASS_DIR \
   --output_dir=$DREAMBOOTH_OUTPUT_DIR \
-  --validation_image_output_dir=$OUTPUT_INFER_DIR \
   --with_prior_preservation \
-  --prior_loss_weight=1.0 \
-  --instance_prompt="a photo of sks person" \
-  --class_prompt="a photo of person" \
+  --train_text_encoder \
+  --prior_loss_weight=0.4 \
+  --instance_prompt="a selfie photo of <sks> person" \
+  --class_prompt="a selfie photo of person" \
   --resolution=512 \
   --train_batch_size=1 \
   --gradient_accumulation_steps=1 \
-  --learning_rate=2e-6 \
-  --lr_scheduler="constant" \
-  --lr_warmup_steps=0 \
-  --num_class_images=200 \
-  --max_train_steps=1000 \
-  --checkpointing_steps=500 \
-  --center_crop \
+  --learning_rate=5e-7 \
+  --lr_scheduler="constant_with_warmup" \
+  --lr_warmup_steps=50 \
+  --num_class_images=100 \
+  --max_train_steps=800 \
+  --checkpointing_steps=400 \
   --mixed_precision=bf16 \
   --prior_generation_precision=bf16 \
-  --sample_batch_size=5  \
-  --validation_prompt="a photo of sks person" \
-  --num_validation_images 10 \
-  --validation_steps 500 \
+  --sample_batch_size=5 \
+  --validation_prompt="a selfie photo of <sks> person, head-and-shoulders, face looking at the camera, Eiffel Tower clearly visible behind, outdoor daytime, realistic" \
+  --num_validation_images=5 \
+  --validation_num_inference_steps=120 \
+  --validation_guidance_scale=7.0 \
+  --validation_image_output_dir=$OUTPUT_INFER_DIR \
   --coords_save_path=$COORD_DIR \
   --coords_log_interval=10
 
-
-# ------------------------- 训练后清空 CLASS_DIR -------------------------
-# 注意：这会在 accelerate launch 成功结束后执行
 echo "Clearing class directory: $CLASS_DIR"
-# 确保目录存在，避免清理命令失败
 mkdir -p "$CLASS_DIR"
-# 查找并删除目录下的所有文件和子目录（但不删除 . 或 ..）
 find "$CLASS_DIR" -mindepth 1 -delete
 
 echo "Script finished."