src/backend/app/database/__init__.py

@@ -112,7 +112,9 @@ class DataType(db.Model):
     data_type_code = db.Column(String(50), nullable=False)
     instance_prompt = db.Column(Text, comment='数据集相关的Prompt (Instance Prompt Template, e.g. "a photo of sks person")')
     class_prompt = db.Column(String(255), comment='类别Prompt (e.g. "a photo of person")')
-    placeholder_token = db.Column(String(50), comment='TI Placeholder (e.g. "<sks-concept>")')
+    validation_prompt_prefix_db_lora = db.Column(Text, comment='DreamBooth/LoRA验证生成图Prompt前缀')
+    validation_prompt_prefix_ti = db.Column(Text, comment='Textual Inversion验证生成图Prompt前缀')
+    placeholder_token = db.Column(String(50), comment='TI Placeholder (e.g. "<sks>")')
     initializer_token = db.Column(String(50), comment='TI Initializer (e.g. "person")')
     description = db.Column(Text)
     

src/backend/app/workers/finetune_worker.py

@@ -71,7 +71,9 @@ def run_finetune_task(task_id, finetune_method, train_images_dir,
             # 默认值 (Fallback)
             instance_prompt = "a photo of sks person"
             class_prompt = "a photo of person"
-            placeholder_token = "<sks-concept>"
+            validation_prompt_prefix_db_lora = "((a selfie photo of <sks> person face)), distinct <sks> feature, head and shoulders shot, front view, face looking at camera"
+            validation_prompt_prefix_ti = "a selfie photo of <sks> person"
+            placeholder_token = "<sks>"
             initializer_token = "person"
             
             if data_type:
@@ -79,30 +81,30 @@ def run_finetune_task(task_id, finetune_method, train_images_dir,
                     instance_prompt = data_type.instance_prompt
                 if data_type.class_prompt:
                     class_prompt = data_type.class_prompt
+                if data_type.validation_prompt_prefix_db_lora:
+                    validation_prompt_prefix_db_lora = data_type.validation_prompt_prefix_db_lora
+                if data_type.validation_prompt_prefix_ti:
+                    validation_prompt_prefix_ti = data_type.validation_prompt_prefix_ti
                 if data_type.placeholder_token:
                     placeholder_token = data_type.placeholder_token
                 if data_type.initializer_token:
                     initializer_token = data_type.initializer_token
             
-            logger.info(f"DataType Config - Template: '{instance_prompt}', Class: '{class_prompt}'")
-
-            # 根据微调方法调整 Instance Prompt
+            # 根据微调方法选择对应的 validation_prompt_prefix
             if finetune_method == 'textual_inversion':
-                # TI: 将 'sks' 替换为 placeholder_token
-                instance_prompt_prefix = instance_prompt.replace('sks', placeholder_token)
-            else:
-                # DreamBooth/LoRA: 直接使用模板
-                instance_prompt_prefix = instance_prompt
+                validation_prompt_prefix = validation_prompt_prefix_ti
+            else:  # dreambooth 或 lora
+                validation_prompt_prefix = validation_prompt_prefix_db_lora
+            
+            logger.info(f"DataType Config - Instance: '{instance_prompt}', Class: '{class_prompt}', Validation Prefix ({finetune_method}): '{validation_prompt_prefix}'")
 
-            # 处理 Validation Prompt
+            # 处理 Validation Prompt：使用 validation_prompt_prefix + custom_prompt
             prompt_suffix = finetune.custom_prompt.strip() if finetune.custom_prompt else ""
             
             if prompt_suffix:
-                validation_prompt = f"{instance_prompt_prefix}, {prompt_suffix}"
+                validation_prompt = f"{validation_prompt_prefix}, {prompt_suffix}"
             else:
-                validation_prompt = instance_prompt_prefix
-            
-            instance_prompt = instance_prompt_prefix
+                validation_prompt = validation_prompt_prefix
             
             logger.info(f"Prompts Finalized - Instance: '{instance_prompt}', Class: '{class_prompt}', Validation: '{validation_prompt}'")
             

src/backend/app/workers/heatmap_worker.py

@@ -60,7 +60,7 @@ def run_heatmap_task(task_id, original_image_path, perturbed_image_path,
             logger.info(f"Starting heatmap task {task_id}")
             
             # 从数据库获取提示词（从关联的Perturbation任务获取）
-            prompt_text = "a photo of sks person"  # 默认值
+            prompt_text = "a selfie photo of <sks> person"  # 默认值
             target_word = "person"  # 默认值
             
             # 通过flow_id查找关联的Perturbation任务

src/backend/app/workers/perturbation_worker.py

@@ -109,6 +109,11 @@ def run_perturbation_task(task_id, algorithm_code, epsilon, input_dir, output_di
             
             # 保存扰动图片到数据库
             _save_perturbed_images(task_id, output_dir)
+
+            logs_dir = os.path.join(output_dir, 'logs')
+            if os.path.exists(logs_dir) and os.path.isdir(logs_dir):
+                logger.info(f"Final cleanup of logs directory: {logs_dir}")
+                shutil.rmtree(logs_dir)
             
             # 更新任务状态为完成
             completed_status = TaskStatus.query.filter_by(task_status_code='completed').first()

src/backend/config/algorithm_config.py

@@ -46,12 +46,12 @@ class AlgorithmConfig:
         },
         'picasso': {
             'name': '毕加索立体派',
-            'prompt': 'cubist painting by picasso',
+            'prompt': 'cubism painting by picasso',
             'description': '模仿毕加索的立体主义风格'
         },
         'baroque': {
             'name': '巴洛克风格',
-            'prompt': 'baroque style painting',
+            'prompt': 'oil painting in baroque style',
             'description': '经典巴洛克艺术风格'
         }
     }
@@ -91,8 +91,8 @@ class AlgorithmConfig:
             'default_params': {
                 'pretrained_model_name_or_path': MODELS_DIR['model1'],
                 'enable_xformers_memory_efficient_attention': True,
-                'instance_prompt': 'a photo of sks person',
-                'class_prompt': 'a photo of person',
+                'instance_prompt': 'a selfie photo of <sks> person',
+                'class_prompt': 'a selfie photo of person',
                 'num_class_images': 200,
                 'center_crop': True,
                 'with_prior_preservation': True,
@@ -115,8 +115,8 @@ class AlgorithmConfig:
             'default_params': {
                 'pretrained_model_name_or_path': MODELS_DIR['model1'],
                 'enable_xformers_memory_efficient_attention': True,
-                'instance_prompt': 'a photo of <sks> person',
-                'class_prompt': 'a photo of person',
+                'instance_prompt': 'a selfie photo of <sks> person',
+                'class_prompt': 'a selfie photo of person',
                 'num_class_images': 100,
                 'center_crop': True,
                 'with_prior_preservation': True,
@@ -138,7 +138,7 @@ class AlgorithmConfig:
             'conda_env': CONDA_ENVS['caat'],
             'default_params': {
                 'pretrained_model_name_or_path': MODELS_DIR['model2'],
-                'instance_prompt': 'a photo of a person',
+                'instance_prompt': 'a painting in <sks> style',
                 'resolution': 512,
                 'learning_rate': 1e-5,
                 'lr_warmup_steps': 0,
@@ -155,8 +155,8 @@ class AlgorithmConfig:
             'default_params': {
                 'pretrained_model_name_or_path': MODELS_DIR['model2'],
                 'with_prior_preservation': True,
-                'instance_prompt': 'a photo of a person',
-                'class_prompt': 'person',
+                'instance_prompt': 'a selfie photo of <sks> person',
+                'class_prompt': 'a selfie photo of person',
                 'num_class_images': 200,
                 'resolution': 512,
                 'learning_rate': 1e-5,
@@ -191,7 +191,7 @@ class AlgorithmConfig:
                 'center_crop': True,
                 'max_train_steps': 150,
                 'eps': 0.05,
-                'target_style': 'cubism painting by picasso',
+                'target_style': 'impressionism painting by van gogh',
                 'style_strength': 0.75,
                 'n_runs': 3,
                 'style_transfer_iter': 15,
@@ -206,8 +206,8 @@ class AlgorithmConfig:
             'default_params': {
                 'pretrained_model_name_or_path': MODELS_DIR['model1'],
                 'enable_xformers_memory_efficient_attention': True,
-                'instance_prompt': 'a photo of sks person',
-                'class_prompt': 'a photo of person',
+                'instance_prompt': 'a selfie photo of <sks> person',
+                'class_prompt': 'a selfie photo of persons',
                 'num_class_images': 100,
                 'center_crop': True,
                 'with_prior_preservation': True,
@@ -324,7 +324,7 @@ class AlgorithmConfig:
                 'resolution': 512,
                 'train_batch_size': 1,
                 'gradient_accumulation_steps': 1,
-                'learning_rate': 5e-7,
+                'learning_rate': 1e-6,
                 'lr_scheduler': 'constant_with_warmup',
                 'lr_warmup_steps': 50,
                 'num_class_images': 100,
@@ -334,7 +334,7 @@ class AlgorithmConfig:
                 'mixed_precision': 'bf16',
                 'prior_generation_precision': 'bf16',
                 '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',
+                'validation_prompt': '((a selfie photo of <sks> person face)), distinct <sks> feature, head and shoulders shot, front view, face looking at camera',
                 'num_validation_images': 5,
                 'validation_num_inference_steps': 120,
                 'validation_guidance_scale': 7.0,
@@ -349,8 +349,8 @@ class AlgorithmConfig:
                 'pretrained_model_name_or_path': MODELS_DIR['model2'],
                 'with_prior_preservation': True,
                 'prior_loss_weight': 1.0,
-                'instance_prompt': 'a photo of sks person',
-                'class_prompt': 'a photo of person',
+                '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,
@@ -363,7 +363,7 @@ class AlgorithmConfig:
                 'seed': 0,
                 'mixed_precision': 'fp16',
                 'rank': 4,
-                'validation_prompt': 'a photo of sks person',
+                'validation_prompt': '((a selfie photo of <sks> person face)), distinct <sks> feature, head and shoulders shot, front view, face looking at camera',
                 'num_validation_images': 10,
                 'coords_log_interval': 10
             }
@@ -374,9 +374,9 @@ class AlgorithmConfig:
             'conda_env': CONDA_ENVS['textual_inversion'],
             'default_params': {
                 'pretrained_model_name_or_path': MODELS_DIR['model2'],
-                'placeholder_token': '<sks-concept>',
+                'placeholder_token': '<sks>',
                 'initializer_token': 'person',
-                'instance_prompt': 'a photo of <sks-concept> person',
+                'instance_prompt': 'a selfie photo of <sks> person',
                 'resolution': 512,
                 'train_batch_size': 1,
                 'gradient_accumulation_steps': 1,
@@ -387,7 +387,7 @@ class AlgorithmConfig:
                 'checkpointing_steps': 500,
                 'seed': 0,
                 'mixed_precision': 'fp16',
-                'validation_prompt': 'a close-up photo of <sks-concept> person',
+                'validation_prompt': 'a selfie photo of <sks> person',
                 'num_validation_images': 4,
                 'validation_epochs': 50,
                 'coords_log_interval': 10

src/backend/init_db.py

@@ -93,18 +93,22 @@ def init_database():
         data_types = [
             {
                 'data_type_code': 'face', 
-                'instance_prompt': 'a photo of sks person', 
-                'class_prompt': 'a photo of person',
-                'placeholder_token': '<sks-concept>',
+                'instance_prompt': 'a selfie photo of <sks> person', 
+                'class_prompt': 'a selfie photo of person',
+                'validation_prompt_prefix_db_lora': '((a selfie photo of <sks> person face)), distinct <sks> feature, head and shoulders shot, front view, face looking at camera',
+                'validation_prompt_prefix_ti': 'a selfie photo of <sks> person',
+                'placeholder_token': '<sks>',
                 'initializer_token': 'person',
                 'description': '人脸类型的数据集'
             },
             {
                 'data_type_code': 'art', 
-                'instance_prompt': 'a painting in <sks-style> style', 
+                'instance_prompt': 'a painting in <sks> style', 
                 'class_prompt': 'a painting',
-                'placeholder_token': '<sks-style>',
-                'initializer_token': 'painting',
+                'validation_prompt_prefix_db_lora': '((a painting in <sks> style)), distinct <sks> feature, coherent aesthetic, balanced lighting and color, fine art texture',
+                'validation_prompt_prefix_ti': 'a painting in <sks> style',
+                'placeholder_token': '<sks>',
+                'initializer_token': 'style',
                 'description': '艺术品类型的数据集'
             }
         ]