621 lines
28 KiB
Python
621 lines
28 KiB
Python
# Copyright (C) 2025 AIDC-AI
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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# http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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Standard Video Generation Pipeline
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Standard workflow for generating short videos from topic or fixed script.
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This is the default pipeline for general-purpose video generation.
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"""
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from datetime import datetime
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from pathlib import Path
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from typing import Optional, Callable, Literal
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from loguru import logger
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import asyncio
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from pixelle_video.pipelines.base import BasePipeline
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from pixelle_video.models.progress import ProgressEvent
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from pixelle_video.models.storyboard import (
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Storyboard,
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StoryboardFrame,
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StoryboardConfig,
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ContentMetadata,
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VideoGenerationResult
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)
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from pixelle_video.utils.content_generators import (
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generate_title,
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generate_narrations_from_topic,
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split_narration_script,
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generate_image_prompts,
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)
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# Parallel limit for RunningHub workflows (Call by sequential if set to 1)
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RUNNING_HUB_PARALLEL_LIMIT = 1
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class StandardPipeline(BasePipeline):
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"""
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Standard video generation pipeline
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Workflow:
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1. Generate/determine title
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2. Generate narrations (from topic or split fixed script)
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3. Generate image prompts for each narration
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4. For each frame:
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- Generate audio (TTS)
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- Generate image
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- Compose frame with template
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- Create video segment
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5. Concatenate all segments
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6. Add BGM (optional)
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Supports two modes:
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- "generate": LLM generates narrations from topic
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- "fixed": Use provided script as-is (each line = one narration)
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"""
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async def __call__(
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self,
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# === Input (Required) ===
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text: str,
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media_width: int, # Required: Media width (from template)
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media_height: int, # Required: Media height (from template)
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# === Processing Mode ===
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mode: Literal["generate", "fixed"] = "generate",
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# === Optional Title ===
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title: Optional[str] = None,
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# === Basic Config ===
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n_scenes: int = 5, # Only used in generate mode; ignored in fixed mode
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# === TTS Parameters (supports both old and new parameter names) ===
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tts_inference_mode: Optional[str] = None, # "local" or "comfyui" (web UI)
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voice_id: Optional[str] = None, # For backward compatibility (deprecated)
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tts_voice: Optional[str] = None, # Voice ID for local mode (web UI)
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tts_workflow: Optional[str] = None,
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tts_speed: float = 1.2,
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ref_audio: Optional[str] = None, # Reference audio for voice cloning
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output_path: Optional[str] = None,
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# === LLM Parameters ===
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min_narration_words: int = 5,
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max_narration_words: int = 20,
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min_image_prompt_words: int = 30,
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max_image_prompt_words: int = 60,
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# === Media Workflow ===
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media_workflow: Optional[str] = None,
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# === Video Parameters ===
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video_fps: int = 30,
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# === Frame Template (determines video size) ===
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frame_template: Optional[str] = None,
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# === Template Custom Parameters ===
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template_params: Optional[dict] = None, # Custom template parameters
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# === Image Style ===
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prompt_prefix: Optional[str] = None,
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# === BGM Parameters ===
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bgm_path: Optional[str] = None,
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bgm_volume: float = 0.2,
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bgm_mode: Literal["once", "loop"] = "loop",
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# === Advanced Options ===
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content_metadata: Optional[ContentMetadata] = None,
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progress_callback: Optional[Callable[[ProgressEvent], None]] = None,
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) -> VideoGenerationResult:
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"""
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Generate short video from text input
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Args:
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text: Text input (required)
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- For generate mode: topic/theme (e.g., "如何提高学习效率")
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- For fixed mode: complete narration script (each line is a narration)
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mode: Processing mode (default "generate")
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- "generate": LLM generates narrations from topic, creates n_scenes
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- "fixed": Use existing script as-is, each line becomes a narration
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Note: In fixed mode, n_scenes is ignored (uses actual line count)
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title: Video title (optional)
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- If provided, use it as the video title
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- If not provided:
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* generate mode → use text as title
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* fixed mode → LLM generates title from script
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n_scenes: Number of storyboard scenes (default 5)
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Only effective in generate mode; ignored in fixed mode
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voice_id: TTS voice ID (default "[Chinese] zh-CN Yunjian")
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tts_workflow: TTS workflow filename (e.g., "tts_edge.json", None = use default)
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tts_speed: TTS speed multiplier (1.0 = normal, 1.2 = 20% faster, default 1.2)
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ref_audio: Reference audio path for voice cloning (optional)
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output_path: Output video path (auto-generated if None)
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min_narration_words: Min narration length (generate mode only)
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max_narration_words: Max narration length (generate mode only)
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min_image_prompt_words: Min image prompt length
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max_image_prompt_words: Max image prompt length
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media_width: Media width (image or video, required)
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media_height: Media height (image or video, required)
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media_workflow: Media workflow filename (image or video, e.g., "image_flux.json", "video_wan.json", None = use default)
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video_fps: Video frame rate (default 30)
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frame_template: HTML template path with size (None = use default "1080x1920/default.html")
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Format: "SIZExSIZE/template.html" (e.g., "1080x1920/default.html", "1920x1080/modern.html")
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Video size is automatically determined from template path
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template_params: Custom template parameters (optional dict)
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e.g., {"accent_color": "#ff0000", "author": "John Doe"}
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prompt_prefix: Image prompt prefix (overrides config.yaml if provided)
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e.g., "anime style, vibrant colors" or "" for no prefix
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bgm_path: BGM path (filename like "default.mp3", custom path, or None)
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bgm_volume: BGM volume 0.0-1.0 (default 0.2)
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bgm_mode: BGM mode "once" or "loop" (default "loop")
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content_metadata: Content metadata (optional, for display)
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progress_callback: Progress callback function(ProgressEvent)
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Returns:
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VideoGenerationResult with video path and metadata
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"""
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# ========== Step 0: Process text and determine title ==========
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logger.info(f"🚀 Starting StandardPipeline in '{mode}' mode")
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logger.info(f" Text length: {len(text)} chars")
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# === Handle TTS parameter compatibility ===
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# Support both old API (voice_id) and new API (tts_inference_mode + tts_voice)
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final_voice_id = None
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final_tts_workflow = tts_workflow
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if tts_inference_mode:
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# New API from web UI
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if tts_inference_mode == "local":
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# Local Edge TTS mode - use tts_voice
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final_voice_id = tts_voice or "zh-CN-YunjianNeural"
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final_tts_workflow = None # Don't use workflow in local mode
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logger.debug(f"TTS Mode: local (voice={final_voice_id})")
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elif tts_inference_mode == "comfyui":
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# ComfyUI workflow mode
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final_voice_id = None # Don't use voice_id in ComfyUI mode
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# tts_workflow already set from parameter
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logger.debug(f"TTS Mode: comfyui (workflow={final_tts_workflow})")
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else:
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# Old API (backward compatibility)
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final_voice_id = voice_id or tts_voice or "zh-CN-YunjianNeural"
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# tts_workflow already set from parameter
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logger.debug(f"TTS Mode: legacy (voice_id={final_voice_id}, workflow={final_tts_workflow})")
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# Determine final title
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if title:
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final_title = title
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logger.info(f" Title: '{title}' (user-specified)")
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else:
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self._report_progress(progress_callback, "generating_title", 0.01)
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if mode == "generate":
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final_title = await generate_title(self.llm, text, strategy="auto")
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logger.info(f" Title: '{final_title}' (auto-generated)")
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else: # fixed
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final_title = await generate_title(self.llm, text, strategy="llm")
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logger.info(f" Title: '{final_title}' (LLM-generated)")
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# ========== Step 0.5: Create isolated task directory ==========
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from pixelle_video.utils.os_util import (
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create_task_output_dir,
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get_task_final_video_path
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)
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task_dir, task_id = create_task_output_dir()
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logger.info(f"📁 Task directory created: {task_dir}")
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logger.info(f" Task ID: {task_id}")
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# Determine final video path
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user_specified_output = None
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if output_path is None:
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output_path = get_task_final_video_path(task_id)
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else:
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user_specified_output = output_path
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output_path = get_task_final_video_path(task_id)
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logger.info(f" Will copy final video to: {user_specified_output}")
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# Create storyboard config
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config = StoryboardConfig(
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task_id=task_id,
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n_storyboard=n_scenes,
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min_narration_words=min_narration_words,
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max_narration_words=max_narration_words,
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min_image_prompt_words=min_image_prompt_words,
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max_image_prompt_words=max_image_prompt_words,
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video_fps=video_fps,
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tts_inference_mode=tts_inference_mode or "local", # TTS inference mode (CRITICAL FIX)
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voice_id=final_voice_id, # Use processed voice_id
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tts_workflow=final_tts_workflow, # Use processed workflow
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tts_speed=tts_speed,
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ref_audio=ref_audio,
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media_width=media_width,
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media_height=media_height,
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media_workflow=media_workflow,
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frame_template=frame_template or "1080x1920/default.html",
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template_params=template_params # Custom template parameters
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)
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# Create storyboard
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storyboard = Storyboard(
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title=final_title,
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config=config,
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content_metadata=content_metadata,
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created_at=datetime.now()
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)
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try:
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# ========== Step 1: Generate/Split narrations ==========
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if mode == "generate":
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self._report_progress(progress_callback, "generating_narrations", 0.05)
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narrations = await generate_narrations_from_topic(
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self.llm,
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topic=text,
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n_scenes=n_scenes,
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min_words=min_narration_words,
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max_words=max_narration_words
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)
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logger.info(f"✅ Generated {len(narrations)} narrations")
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else: # fixed
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self._report_progress(progress_callback, "splitting_script", 0.05)
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narrations = await split_narration_script(text)
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logger.info(f"✅ Split script into {len(narrations)} segments (by lines)")
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logger.info(f" Note: n_scenes={n_scenes} is ignored in fixed mode")
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# ========== Step 2: Check template type and conditionally generate image prompts ==========
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# Detect template type to determine if media generation is needed
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from pathlib import Path
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from pixelle_video.utils.template_util import get_template_type
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template_name = Path(config.frame_template).name
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template_type = get_template_type(template_name)
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template_requires_media = (template_type in ["image", "video"])
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if template_type == "image":
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logger.info(f"📸 Template requires image generation")
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elif template_type == "video":
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logger.info(f"🎬 Template requires video generation")
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else: # static
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logger.info(f"⚡ Static template - skipping media generation pipeline")
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logger.info(f" 💡 Benefits: Faster generation + Lower cost + No ComfyUI dependency")
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# Only generate image prompts if template requires media
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if template_requires_media:
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self._report_progress(progress_callback, "generating_image_prompts", 0.15)
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# Override prompt_prefix if provided
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original_prefix = None
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if prompt_prefix is not None:
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image_config = self.core.config.get("comfyui", {}).get("image", {})
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original_prefix = image_config.get("prompt_prefix")
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image_config["prompt_prefix"] = prompt_prefix
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logger.info(f"Using custom prompt_prefix: '{prompt_prefix}'")
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try:
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# Create progress callback wrapper for image prompt generation
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def image_prompt_progress(completed: int, total: int, message: str):
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batch_progress = completed / total if total > 0 else 0
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overall_progress = 0.15 + (batch_progress * 0.15)
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self._report_progress(
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progress_callback,
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"generating_image_prompts",
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overall_progress,
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extra_info=message
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)
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# Generate base image prompts
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base_image_prompts = await generate_image_prompts(
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self.llm,
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narrations=narrations,
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min_words=min_image_prompt_words,
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max_words=max_image_prompt_words,
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progress_callback=image_prompt_progress
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)
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# Apply prompt prefix
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from pixelle_video.utils.prompt_helper import build_image_prompt
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image_config = self.core.config.get("comfyui", {}).get("image", {})
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prompt_prefix_to_use = prompt_prefix if prompt_prefix is not None else image_config.get("prompt_prefix", "")
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image_prompts = []
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for base_prompt in base_image_prompts:
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final_prompt = build_image_prompt(base_prompt, prompt_prefix_to_use)
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image_prompts.append(final_prompt)
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finally:
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# Restore original prompt_prefix
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if original_prefix is not None:
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image_config["prompt_prefix"] = original_prefix
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logger.info(f"✅ Generated {len(image_prompts)} image prompts")
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else:
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# Static template - skip image prompt generation entirely
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image_prompts = [None] * len(narrations)
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logger.info(f"⚡ Skipped image prompt generation (static template)")
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logger.info(f" 💡 Savings: {len(narrations)} LLM calls + {len(narrations)} media generations")
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# ========== Step 3: Create frames ==========
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for i, (narration, image_prompt) in enumerate(zip(narrations, image_prompts)):
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frame = StoryboardFrame(
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index=i,
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narration=narration,
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image_prompt=image_prompt,
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created_at=datetime.now()
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)
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storyboard.frames.append(frame)
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# ========== Step 4: Process each frame ==========
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# Check if using RunningHub workflows for parallel processing
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# Enable parallel if either TTS or Image uses RunningHub (most time-consuming parts)
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is_runninghub = (
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(config.tts_workflow and config.tts_workflow.startswith("runninghub/")) or
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(config.media_workflow and config.media_workflow.startswith("runninghub/"))
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)
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if is_runninghub and RUNNING_HUB_PARALLEL_LIMIT > 1:
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logger.info(f"🚀 Using parallel processing for RunningHub workflows (max {RUNNING_HUB_PARALLEL_LIMIT} concurrent)")
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logger.info(f" TTS: {'runninghub' if config.tts_workflow and config.tts_workflow.startswith('runninghub/') else 'local'}")
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logger.info(f" Media: {'runninghub' if config.media_workflow and config.media_workflow.startswith('runninghub/') else 'local'}")
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semaphore = asyncio.Semaphore(RUNNING_HUB_PARALLEL_LIMIT)
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completed_count = 0
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async def process_frame_with_semaphore(i: int, frame: StoryboardFrame):
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nonlocal completed_count
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async with semaphore:
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base_progress = 0.2
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frame_range = 0.6
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per_frame_progress = frame_range / len(storyboard.frames)
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# Create frame-specific progress callback
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def frame_progress_callback(event: ProgressEvent):
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overall_progress = base_progress + (per_frame_progress * completed_count) + (per_frame_progress * event.progress)
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if progress_callback:
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adjusted_event = ProgressEvent(
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event_type=event.event_type,
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progress=overall_progress,
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frame_current=i+1,
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frame_total=len(storyboard.frames),
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step=event.step,
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action=event.action
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)
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progress_callback(adjusted_event)
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# Report frame start
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self._report_progress(
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progress_callback,
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"processing_frame",
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base_progress + (per_frame_progress * completed_count),
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frame_current=i+1,
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frame_total=len(storyboard.frames)
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)
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processed_frame = await self.core.frame_processor(
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frame=frame,
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storyboard=storyboard,
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config=config,
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total_frames=len(storyboard.frames),
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progress_callback=frame_progress_callback
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)
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completed_count += 1
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logger.info(f"✅ Frame {i+1} completed ({processed_frame.duration:.2f}s) [{completed_count}/{len(storyboard.frames)}]")
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return i, processed_frame
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# Create all tasks and execute in parallel
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tasks = [process_frame_with_semaphore(i, frame) for i, frame in enumerate(storyboard.frames)]
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results = await asyncio.gather(*tasks)
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# Update frames in order and calculate total duration
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for idx, processed_frame in sorted(results, key=lambda x: x[0]):
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storyboard.frames[idx] = processed_frame
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storyboard.total_duration += processed_frame.duration
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logger.info(f"✅ All frames processed in parallel (total duration: {storyboard.total_duration:.2f}s)")
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else:
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# Serial processing for non-RunningHub workflows
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logger.info("⚙️ Using serial processing (non-RunningHub workflow)")
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for i, frame in enumerate(storyboard.frames):
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base_progress = 0.2
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frame_range = 0.6
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per_frame_progress = frame_range / len(storyboard.frames)
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# Create frame-specific progress callback
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def frame_progress_callback(event: ProgressEvent):
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overall_progress = base_progress + (per_frame_progress * i) + (per_frame_progress * event.progress)
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if progress_callback:
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adjusted_event = ProgressEvent(
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event_type=event.event_type,
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progress=overall_progress,
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frame_current=event.frame_current,
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frame_total=event.frame_total,
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step=event.step,
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action=event.action
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)
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progress_callback(adjusted_event)
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# Report frame start
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self._report_progress(
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progress_callback,
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"processing_frame",
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base_progress + (per_frame_progress * i),
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frame_current=i+1,
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frame_total=len(storyboard.frames)
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)
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processed_frame = await self.core.frame_processor(
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frame=frame,
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storyboard=storyboard,
|
|
config=config,
|
|
total_frames=len(storyboard.frames),
|
|
progress_callback=frame_progress_callback
|
|
)
|
|
storyboard.total_duration += processed_frame.duration
|
|
logger.info(f"✅ Frame {i+1} completed ({processed_frame.duration:.2f}s)")
|
|
|
|
# ========== Step 5: Concatenate videos ==========
|
|
self._report_progress(progress_callback, "concatenating", 0.85)
|
|
segment_paths = [frame.video_segment_path for frame in storyboard.frames]
|
|
|
|
from pixelle_video.services.video import VideoService
|
|
video_service = VideoService()
|
|
|
|
final_video_path = video_service.concat_videos(
|
|
videos=segment_paths,
|
|
output=output_path,
|
|
bgm_path=bgm_path,
|
|
bgm_volume=bgm_volume,
|
|
bgm_mode=bgm_mode
|
|
)
|
|
|
|
storyboard.final_video_path = final_video_path
|
|
storyboard.completed_at = datetime.now()
|
|
|
|
# Copy to user-specified path if provided
|
|
if user_specified_output:
|
|
import shutil
|
|
Path(user_specified_output).parent.mkdir(parents=True, exist_ok=True)
|
|
shutil.copy2(final_video_path, user_specified_output)
|
|
logger.info(f"📹 Final video copied to: {user_specified_output}")
|
|
final_video_path = user_specified_output
|
|
storyboard.final_video_path = user_specified_output
|
|
|
|
logger.success(f"🎬 Video generation completed: {final_video_path}")
|
|
|
|
# ========== Step 6: Create result ==========
|
|
self._report_progress(progress_callback, "completed", 1.0)
|
|
|
|
video_path_obj = Path(final_video_path)
|
|
file_size = video_path_obj.stat().st_size
|
|
|
|
result = VideoGenerationResult(
|
|
video_path=final_video_path,
|
|
storyboard=storyboard,
|
|
duration=storyboard.total_duration,
|
|
file_size=file_size
|
|
)
|
|
|
|
logger.info(f"✅ Generated video: {final_video_path}")
|
|
logger.info(f" Duration: {storyboard.total_duration:.2f}s")
|
|
logger.info(f" Size: {file_size / (1024*1024):.2f} MB")
|
|
logger.info(f" Frames: {len(storyboard.frames)}")
|
|
|
|
# ========== Step 7: Persist metadata and storyboard ==========
|
|
await self._persist_task_data(
|
|
storyboard=storyboard,
|
|
result=result,
|
|
input_params={
|
|
"text": text,
|
|
"mode": mode,
|
|
"title": title,
|
|
"n_scenes": n_scenes,
|
|
"tts_inference_mode": tts_inference_mode,
|
|
"tts_voice": tts_voice,
|
|
"voice_id": voice_id,
|
|
"tts_workflow": tts_workflow,
|
|
"tts_speed": tts_speed,
|
|
"ref_audio": ref_audio,
|
|
"media_workflow": media_workflow,
|
|
"prompt_prefix": prompt_prefix,
|
|
"frame_template": frame_template,
|
|
"template_params": template_params,
|
|
"bgm_path": bgm_path,
|
|
"bgm_volume": bgm_volume,
|
|
"bgm_mode": bgm_mode,
|
|
}
|
|
)
|
|
|
|
return result
|
|
|
|
except Exception as e:
|
|
logger.error(f"❌ Video generation failed: {e}")
|
|
raise
|
|
|
|
async def _persist_task_data(
|
|
self,
|
|
storyboard: Storyboard,
|
|
result: VideoGenerationResult,
|
|
input_params: dict
|
|
):
|
|
"""
|
|
Persist task metadata and storyboard to filesystem
|
|
|
|
Args:
|
|
storyboard: Complete storyboard
|
|
result: Video generation result
|
|
input_params: Input parameters used for generation
|
|
"""
|
|
try:
|
|
task_id = storyboard.config.task_id
|
|
if not task_id:
|
|
logger.warning("No task_id in storyboard, skipping persistence")
|
|
return
|
|
|
|
# Build metadata
|
|
# If user didn't provide a title, use the generated one from storyboard
|
|
input_with_title = input_params.copy()
|
|
if not input_with_title.get("title"):
|
|
input_with_title["title"] = storyboard.title
|
|
|
|
metadata = {
|
|
"task_id": task_id,
|
|
"created_at": storyboard.created_at.isoformat() if storyboard.created_at else None,
|
|
"completed_at": storyboard.completed_at.isoformat() if storyboard.completed_at else None,
|
|
"status": "completed",
|
|
|
|
"input": input_with_title,
|
|
|
|
"result": {
|
|
"video_path": result.video_path,
|
|
"duration": result.duration,
|
|
"file_size": result.file_size,
|
|
"n_frames": len(storyboard.frames)
|
|
},
|
|
|
|
"config": {
|
|
"llm_model": self.core.config.get("llm", {}).get("model", "unknown"),
|
|
"llm_base_url": self.core.config.get("llm", {}).get("base_url", "unknown"),
|
|
"comfyui_url": self.core.config.get("comfyui", {}).get("comfyui_url", "unknown"),
|
|
"runninghub_enabled": bool(self.core.config.get("comfyui", {}).get("runninghub_api_key")),
|
|
}
|
|
}
|
|
|
|
# Save metadata
|
|
await self.core.persistence.save_task_metadata(task_id, metadata)
|
|
logger.info(f"💾 Saved task metadata: {task_id}")
|
|
|
|
# Save storyboard
|
|
await self.core.persistence.save_storyboard(task_id, storyboard)
|
|
logger.info(f"💾 Saved storyboard: {task_id}")
|
|
|
|
except Exception as e:
|
|
logger.error(f"Failed to persist task data: {e}")
|
|
# Don't raise - persistence failure shouldn't break video generation
|
|
|