299 lines
12 KiB
Python
299 lines
12 KiB
Python
"""
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视频反推（视频理解）节点 - V3 版本
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"""
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import os
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import shutil
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import tempfile
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import hashlib
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from typing import List, Tuple
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from io import BytesIO
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from PIL import Image as PILImage
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import torch
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import numpy as np
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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_VIDEO_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 VideoCaptionNode(VLMNodeBase, io.ComfyNode):
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"""视频反推节点（V3）"""
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@classmethod
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def define_schema(cls):
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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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from ..config_manager import config_manager
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system_prompts = config_manager.get_system_prompts()
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video_prompts = {}
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active_video_id = None
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if system_prompts:
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video_prompts = system_prompts.get('video_prompts', {}) or {}
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active_video_id = system_prompts.get('active_prompts', {}).get('video')
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prompt_template_options = []
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id_to_display_name = {}
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for key, value in video_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 "视频-自然语言"
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if active_video_id and active_video_id in id_to_display_name:
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default_template_name = id_to_display_name[active_video_id]
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if not prompt_template_options:
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prompt_template_options = ["视频-自然语言"]
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return io.Schema(
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node_id="VideoCaptionNode",
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display_name="✨Video Caption (VLM)",
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category="✨Prompt Assistant",
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description="Extract text prompt from video frames using Vision-Language Models",
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inputs=[
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# ComfyUI 的 VIDEO 数据类型比较特殊，可能并不是直接的一个 tensor
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# 很多节点出来的视频类型是类似于 IMAGE batch（一堆图片）
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# 这里我们假设它是 IMAGE 类型，因为 ComfyUI 通常用批量 IMAGE 表示视频
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io.Image.Input("video_frames", tooltip="The video frames to analyze (IMAGE batch)"),
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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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),
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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.Int.Input(
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"frame_count",
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default=4,
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min=1,
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max=16,
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step=1
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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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video_frames=None, rule=None, custom_rule=None, custom_rule_content=None,
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user_prompt=None, frame_count=None, vlm_service=None, ollama_auto_unload=None, seed=None
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):
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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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# 计算视频帧哈希
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video_hash = ""
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if video_frames is not None:
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shape_str = str(video_frames.shape)
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hasher = hashlib.md5(shape_str.encode('utf-8'))
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if video_frames.numel() > 0:
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step = max(1, video_frames.shape[0] // min(4, video_frames.shape[0]))
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sample = video_frames[::step]
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hasher.update(str(int(sample.sum().item())).encode('utf-8'))
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video_hash = hasher.hexdigest()
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return hash((
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video_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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frame_count,
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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 _extract_frames(cls, video_tensor: torch.Tensor, target_count: int) -> List[str]:
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"""按时间均匀抽取视频帧并转为 base64"""
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total_frames = video_tensor.shape[0]
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if total_frames == 0:
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return []
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if target_count >= total_frames:
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indices = list(range(total_frames))
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else:
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indices = np.linspace(0, total_frames - 1, target_count, dtype=int).tolist()
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base64_frames = []
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for idx in indices:
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frame_tensor = video_tensor[idx:idx+1]
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b64 = cls._image_to_base64(frame_tensor, quality=85)
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base64_frames.append(b64)
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return base64_frames
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@classmethod
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def execute(
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cls,
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video_frames, rule, custom_rule, custom_rule_content,
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user_prompt, frame_count, 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 video_frames is None or len(video_frames) == 0:
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raise ValueError("No video frames provided.")
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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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video_prompts = system_prompts.get('video_prompts', {}) if system_prompts else {}
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template_found = False
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for key, value in video_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": "请分析这些视频抽帧图片"}
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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("video", None, unique_id)
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# 抽帧
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base64_images = cls._extract_frames(video_frames, frame_count)
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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_VIDEO_CAPTION, request_id, SOURCE_NODE, service_display_name, model_display, rule_name, {"帧数": len(base64_images)})
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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_images,
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service_id,
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base64_images=base64_images,
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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_VIDEO_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_VIDEO_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_VIDEO_CAPTION, request_id, error_msg, source=SOURCE_NODE)
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raise RuntimeError(f"Analysis error: {error_msg}")
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