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