""" 图像反推（图片反推提示词）节点 - V3 版本 V3 迁移说明： - 继承 VLMNodeBase + io.ComfyNode - INPUT_TYPES → define_schema() - IS_CHANGED → fingerprint_inputs() - def analyze_image(self, ...) → @classmethod execute(cls, ...) """ from comfy.model_management import InterruptProcessingException from comfy_api.latest import io from ..services.vlm import VisionService from ..utils.common import ( format_api_error, format_model_with_thinking, generate_request_id, log_prepare, log_error, TASK_IMAGE_CAPTION, SOURCE_NODE ) from ..services.thinking_control import build_thinking_suppression from .base import VLMNodeBase class ImageCaptionNode(VLMNodeBase, io.ComfyNode): """图像反推节点（V3）""" @classmethod def define_schema(cls): # 动态获取服务列表 service_options = cls.get_vlm_service_options() default_service = service_options[0] if service_options else "智谱" # 获取模板 from ..config_manager import config_manager system_prompts = config_manager.get_system_prompts() vision_prompts = {} active_vision_id = None if system_prompts: vision_prompts = system_prompts.get('vision_prompts', {}) or {} active_vision_id = system_prompts.get('active_prompts', {}).get('vision') prompt_template_options = [] id_to_display_name = {} for key, value in vision_prompts.items(): show_in = value.get('showIn', ["frontend", "node"]) if 'node' not in show_in: continue name = value.get('name', key) category = value.get('category', '') display_name = f"{category}/{name}" if category else name id_to_display_name[key] = display_name prompt_template_options.append(display_name) default_template_name = prompt_template_options[0] if prompt_template_options else "反推-自然语言" if active_vision_id and active_vision_id in id_to_display_name: default_template_name = id_to_display_name[active_vision_id] if not prompt_template_options: prompt_template_options = ["反推-自然语言"] return io.Schema( node_id="ImageCaptionNode", display_name="✨Image Caption (VLM)", category="✨Prompt Assistant", description="Extract text prompt from image using Vision-Language Models", inputs=[ io.Image.Input("image", tooltip="The image to analyze"), io.Combo.Input( "rule", options=prompt_template_options, default=default_template_name, tooltip="Choose a preset rule for analysis" ), io.Boolean.Input( "custom_rule", default=False, label_on="Enable", label_off="Disable" ), io.String.Input( "custom_rule_content", multiline=True, default="", ), io.String.Input( "user_prompt", multiline=True, default="", ), io.Combo.Input( "vlm_service", options=service_options, default=default_service, ), io.Boolean.Input( "ollama_auto_unload", default=True, label_on="Enable", label_off="Disable" ), io.Int.Input( "seed", default=0, min=0, max=0xffffffffffffffff, control_after_generate=True, ), ], outputs=[ io.String.Output("caption_text"), ], hidden=[io.Hidden.unique_id], ) @classmethod def fingerprint_inputs( cls, image=None, rule=None, custom_rule=None, custom_rule_content=None, user_prompt=None, vlm_service=None, ollama_auto_unload=None, seed=None ): """替代 V1 IS_CHANGED""" import hashlib temp_rule_hash = hashlib.md5((custom_rule_content or "").encode('utf-8')).hexdigest() user_hint_hash = hashlib.md5((user_prompt or "").encode('utf-8')).hexdigest() img_hash = cls._compute_image_hash(image) return hash(( img_hash, rule, bool(custom_rule), temp_rule_hash, user_hint_hash, vlm_service, bool(ollama_auto_unload), seed )) @classmethod def execute( cls, image, rule, custom_rule, custom_rule_content, user_prompt, vlm_service, ollama_auto_unload, seed=None ): unique_id = cls.hidden.unique_id request_id = None try: if image is None: raise ValueError("No image provided. Please connect an image to the 'image' input.") system_message = None rule_name = "Custom Rule" if (custom_rule and custom_rule_content) else rule if custom_rule and custom_rule_content: system_message = {"role": "system", "content": custom_rule_content} else: from ..config_manager import config_manager system_prompts = config_manager.get_system_prompts() vision_prompts = system_prompts.get('vision_prompts', {}) if system_prompts else {} template_found = False for key, value in vision_prompts.items(): name = value.get('name', key) category = value.get('category', '') display_name = f"{category}/{name}" if category else name if display_name == rule or value.get('name') == rule or key == rule: system_message = {"role": value.get('role', 'system'), "content": value.get('content', '')} template_found = True break if not template_found or not system_message or not system_message.get('content'): system_message = {"role": "system", "content": "请详细描述这张图片的内容"} rule_name = "Default Rule" service_id, model_name = cls.parse_service_model(vlm_service) if not service_id: raise ValueError(f"Invalid service selection: {vlm_service}") from ..config_manager import config_manager service = config_manager.get_service(service_id) if not service: raise ValueError(f"Service config not found: {vlm_service}") vlm_models = service.get('vlm_models', []) target_model = None if model_name: target_model = next((m for m in vlm_models if m.get('name') == model_name), None) if not target_model: target_model = next((m for m in vlm_models if m.get('is_default')), vlm_models[0] if vlm_models else None) if not target_model: raise ValueError(f"Service {vlm_service} has no available vision models") provider_config = { 'provider': service_id, 'model': target_model.get('name', ''), 'base_url': service.get('base_url', ''), 'api_key': service.get('api_key', ''), 'temperature': target_model.get('temperature', 0.7), 'max_tokens': target_model.get('max_tokens', 1000), 'top_p': target_model.get('top_p', 0.9), } if service.get('type') == 'ollama': provider_config['auto_unload'] = ollama_auto_unload request_id = generate_request_id("vlm", None, unique_id) base64_image = cls._image_to_base64(image) model_full_name = provider_config.get('model') disable_thinking_enabled = service.get('disable_thinking', True) thinking_extra = build_thinking_suppression(service_id, model_full_name) if disable_thinking_enabled else None model_display = format_model_with_thinking(model_full_name, bool(thinking_extra)) service_display_name = service.get('name', service_id) log_prepare(TASK_IMAGE_CAPTION, request_id, SOURCE_NODE, service_display_name, model_display, rule_name) if not provider_config.get('api_key', '') or not provider_config.get('model', ''): raise ValueError(f"Please configure API key and model for {vlm_service}") # 从 system_message 字典中提取纯文本内容 system_text = system_message.get('content', '') if isinstance(system_message, dict) else str(system_message) prompt_to_send = f"{system_text}\n\n{user_prompt}".strip() if user_prompt else system_text result = cls._run_vision_task( VisionService.analyze_image, service_id, image_data=base64_image, prompt_content=prompt_to_send, request_id=request_id, custom_provider=service_id, custom_provider_config=provider_config, source=SOURCE_NODE ) if result and result.get('success'): # V3 重构后返回的键名是 description 而不是 caption data = result.get('data', {}) caption_text = data.get('description', data.get('caption', '')).strip() if not caption_text: error_msg = 'API returned empty result' log_error(TASK_IMAGE_CAPTION, request_id, error_msg, source=SOURCE_NODE) raise RuntimeError(f"Analysis failed: {error_msg}") return io.NodeOutput(caption_text) else: error_msg = result.get('error', 'Unknown error') if result else 'No result returned' if error_msg == "任务被中断": raise InterruptProcessingException() log_error(TASK_IMAGE_CAPTION, request_id, error_msg, source=SOURCE_NODE) raise RuntimeError(f"Analysis failed: {error_msg}") except InterruptProcessingException: raise except Exception as e: error_msg = format_api_error(e, vlm_service) log_error(TASK_IMAGE_CAPTION, request_id, error_msg, source=SOURCE_NODE) raise RuntimeError(f"Analysis error: {error_msg}")