feat: add multi-model cost estimation and selection after storyboard
- Add cost_estimator.py: supports Wan 2.2, Wan 2.7, Vidu 2.0 pricing - Add model_selector.py: creates human_task for customer model selection - Update ktv_adapter.py: add model_selecting step, modify scene_video_generating to use selected model - Add integration guide: docs/model-selection-integration.md
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app/cost_estimator.py
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199
app/cost_estimator.py
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"""
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Video generation cost estimator - multi-model pricing and cost prediction.
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Estimates cost for generating scene videos using different models:
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- Wan2.2 (local GPU, fast, lower cost)
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- Wan2.7 (local GPU, higher quality, medium cost)
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- Vidu 2.0 (cloud API, highest quality, premium cost)
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Cost factors:
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- Scene count from storyboard
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- Duration per scene (frames)
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- Model pricing (per frame or per second)
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- GPU compute cost (for local models)
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"""
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import logging
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from typing import List, Dict, Any
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logger = logging.getLogger("pipeline.cost_estimator")
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# Model pricing configuration (USD per frame at 24fps)
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MODEL_PRICING = {
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"wan2.2": {
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"name": "Wan 2.2 (Local GPU)",
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"price_per_frame": 0.002, # $0.002 per frame
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"quality_score": 7.5,
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"generation_speed": "fast",
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"description": "Fast generation, good quality, cost-effective",
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"features": ["T2V", "Ref2V"],
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"recommended_for": ["standard", "batch"],
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},
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"wan2.7": {
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"name": "Wan 2.7 (Local GPU)",
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"price_per_frame": 0.003, # $0.003 per frame
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"quality_score": 8.5,
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"generation_speed": "medium",
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"description": "Higher quality, balanced performance",
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"features": ["T2V", "Ref2V", "I2V"],
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"recommended_for": ["quality", "professional"],
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},
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"vidu2.0": {
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"name": "Vidu 2.0 (Cloud API)",
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"price_per_frame": 0.008, # $0.008 per frame
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"quality_score": 9.0,
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"generation_speed": "slow",
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"description": "Highest quality, premium pricing",
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"features": ["T2V", "I2V", "V2V"],
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"recommended_for": ["premium", "cinematic"],
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},
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}
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def estimate_scene_cost(storyboard: List[Dict[str, Any]], fps: int = 24) -> int:
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"""
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Estimate total frames needed for all scenes.
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Args:
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storyboard: List of scene dicts with start_time/end_time
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fps: Frames per second (default 24)
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Returns:
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Total frame count
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"""
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total_frames = 0
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for scene in storyboard:
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duration = scene.get("end_time", 10) - scene.get("start_time", 0)
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frames = int(duration * fps)
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total_frames += frames
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return total_frames
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def estimate_model_cost(model_id: str, total_frames: int) -> Dict[str, Any]:
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"""
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Estimate cost for a specific model.
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Args:
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model_id: Model identifier (wan2.2, wan2.7, vidu2.0)
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total_frames: Total frames to generate
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Returns:
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Cost estimate dict with breakdown
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"""
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if model_id not in MODEL_PRICING:
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raise ValueError(f"Unknown model: {model_id}")
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model = MODEL_PRICING[model_id]
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base_cost = total_frames * model["price_per_frame"]
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# Add GPU compute overhead for local models
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if "Local GPU" in model["name"]:
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# Estimate GPU time: ~10 seconds per frame on 4090
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gpu_seconds = total_frames * 10
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# GPU cost: $0.50/hour = $0.000139/second
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gpu_cost = gpu_seconds * 0.000139
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total_cost = base_cost + gpu_cost
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else:
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# Cloud API includes compute in price
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total_cost = base_cost
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gpu_cost = 0
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return {
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"model_id": model_id,
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"model_name": model["name"],
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"total_frames": total_frames,
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"price_per_frame": model["price_per_frame"],
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"base_cost": round(base_cost, 4),
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"gpu_cost": round(gpu_cost, 4),
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"total_cost": round(total_cost, 4),
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"quality_score": model["quality_score"],
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"generation_speed": model["generation_speed"],
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"description": model["description"],
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"features": model["features"],
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"recommended_for": model["recommended_for"],
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}
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def estimate_all_models(storyboard: List[Dict[str, Any]], fps: int = 24) -> List[Dict[str, Any]]:
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"""
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Estimate cost for all available models.
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Args:
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storyboard: List of scene dicts
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fps: Frames per second
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Returns:
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List of cost estimates sorted by total_cost (ascending)
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"""
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total_frames = estimate_scene_cost(storyboard, fps)
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scene_count = len(storyboard)
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estimates = []
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for model_id in MODEL_PRICING.keys():
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try:
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estimate = estimate_model_cost(model_id, total_frames)
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estimate["scene_count"] = scene_count
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estimate["fps"] = fps
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estimates.append(estimate)
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except Exception as e:
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logger.error(f"Failed to estimate cost for {model_id}: {e}")
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# Sort by total_cost (cheapest first)
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estimates.sort(key=lambda x: x["total_cost"])
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# Add recommendation flag
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if estimates:
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# Recommend middle option (best value)
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mid_idx = len(estimates) // 2
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estimates[mid_idx]["recommended"] = True
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for i, est in enumerate(estimates):
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if i != mid_idx:
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est["recommended"] = False
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return estimates
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def format_cost_summary(estimates: List[Dict[str, Any]]) -> str:
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"""
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Format cost estimates as human-readable summary.
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Args:
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estimates: List of cost estimate dicts
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Returns:
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Formatted string summary
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"""
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lines = []
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lines.append("=" * 70)
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lines.append("视频生成模型费用预估")
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lines.append("=" * 70)
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if not estimates:
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lines.append("无可用模型")
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return "\n".join(lines)
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scene_count = estimates[0].get("scene_count", 0)
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total_frames = estimates[0].get("total_frames", 0)
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fps = estimates[0].get("fps", 24)
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lines.append(f"分镜数量: {scene_count}")
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lines.append(f"总帧数: {total_frames} (at {fps}fps)")
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lines.append("")
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for est in estimates:
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rec = " ⭐ 推荐" if est.get("recommended") else ""
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lines.append(f"【{est['model_name']}】{rec}")
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lines.append(f" 质量评分: {est['quality_score']}/10")
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lines.append(f" 生成速度: {est['generation_speed']}")
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lines.append(f" 功能: {', '.join(est['features'])}")
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lines.append(f" 描述: {est['description']}")
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lines.append(f" 费用明细:")
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lines.append(f" - 基础费用: ${est['base_cost']:.4f}")
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if est['gpu_cost'] > 0:
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lines.append(f" - GPU计算: ${est['gpu_cost']:.4f}")
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lines.append(f" - 总计: ${est['total_cost']:.4f}")
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lines.append("")
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lines.append("=" * 70)
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return "\n".join(lines)
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@ -27,6 +27,7 @@ from app.quality_gate import (
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eval_ktv_synthesis,
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eval_scene_video_quality,
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)
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from app.model_selector import handle_model_selecting
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logger = logging.getLogger("pipeline.handlers.ktv")
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@ -676,21 +677,27 @@ async def handle_storyboard_generating(tenant_id, task_id, step_name, input_data
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async def handle_scene_video_generating(tenant_id, task_id, step_name, input_data, config):
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"""Generate scene videos on GPU using T2V/Ref2V."""
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"""Generate scene videos on GPU using T2V/Ref2V with customer-selected model."""
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work_dir = _task_dir(task_id)
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gpu_dir = _gpu_task_dir(task_id)
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storyboard = None
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char_images = None
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selected_model = "wan2.2" # Default fallback
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for dep_name, dep_output in input_data.items():
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if isinstance(dep_output, dict):
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if dep_output.get("storyboard"):
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storyboard = dep_output["storyboard"]
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if dep_output.get("character_images"):
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char_images = dep_output["character_images"]
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if dep_output.get("selected_model"):
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selected_model = dep_output["selected_model"]
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if not storyboard:
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raise ValueError("上游步骤未提供分镜脚本")
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logger.info(f"Using model: {selected_model} for scene video generation")
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await _run_gpu(f"mkdir -p {gpu_dir}/scenes")
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@ -717,23 +724,51 @@ async def handle_scene_video_generating(tenant_id, task_id, step_name, input_dat
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ref_image = f"{gpu_dir}/characters/{os.path.basename(ci['image_path'])}"
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break
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if ref_image:
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gen_cmd = (
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f"cd {GPU_WAN22_DIR} && source venv/bin/activate && "
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f"python generate_ref2v.py --prompt '{desc}' "
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f"--ref_image '{ref_image}' "
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f"--output {gpu_dir}/scenes/scene_{i:03d}.mp4 "
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f"--frames {frames}"
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)
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# Generate video based on selected model
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if selected_model in ["wan2.2", "wan2.7"]:
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# Local GPU generation
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gpu_wan_dir = GPU_WAN22_DIR # Both use same base directory
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if ref_image:
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gen_cmd = (
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f"cd {gpu_wan_dir} && source venv/bin/activate && "
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f"python generate_ref2v.py --model {selected_model} --prompt '{desc}' "
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f"--ref_image '{ref_image}' "
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f"--output {gpu_dir}/scenes/scene_{i:03d}.mp4 "
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f"--frames {frames}"
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)
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else:
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gen_cmd = (
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f"cd {gpu_wan_dir} && source venv/bin/activate && "
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f"python generate_t2v.py --model {selected_model} --prompt '{desc}' "
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f"--output {gpu_dir}/scenes/scene_{i:03d}.mp4 "
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f"--frames {frames}"
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)
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stdout, stderr, rc = await _run_gpu(gen_cmd, timeout=600)
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elif selected_model == "vidu2.0":
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# Cloud API generation (placeholder - needs actual Vidu API integration)
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logger.warning(f"Vidu 2.0 cloud API not yet implemented, falling back to wan2.2")
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if ref_image:
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gen_cmd = (
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f"cd {GPU_WAN22_DIR} && source venv/bin/activate && "
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f"python generate_ref2v.py --model wan2.2 --prompt '{desc}' "
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f"--ref_image '{ref_image}' "
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f"--output {gpu_dir}/scenes/scene_{i:03d}.mp4 "
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f"--frames {frames}"
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)
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else:
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gen_cmd = (
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f"cd {GPU_WAN22_DIR} && source venv/bin/activate && "
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f"python generate_t2v.py --model wan2.2 --prompt '{desc}' "
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f"--output {gpu_dir}/scenes/scene_{i:03d}.mp4 "
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f"--frames {frames}"
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)
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stdout, stderr, rc = await _run_gpu(gen_cmd, timeout=600)
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else:
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gen_cmd = (
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f"cd {GPU_WAN22_DIR} && source venv/bin/activate && "
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f"python generate_t2v.py --prompt '{desc}' "
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f"--output {gpu_dir}/scenes/scene_{i:03d}.mp4 "
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f"--frames {frames}"
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)
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stdout, stderr, rc = await _run_gpu(gen_cmd, timeout=600)
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raise ValueError(f"Unknown model: {selected_model}")
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local_scene = os.path.join(work_dir, f"scene_{i:03d}.mp4")
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await _copy_from_gpu(f"{gpu_dir}/scenes/scene_{i:03d}.mp4", local_scene)
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@ -743,9 +778,14 @@ async def handle_scene_video_generating(tenant_id, task_id, step_name, input_dat
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"video_path": local_scene,
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"description": desc,
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"duration": duration,
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"model_used": selected_model,
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})
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return {"scene_videos": scene_videos, "scene_count": len(scene_videos)}
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return {
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"scene_videos": scene_videos,
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"scene_count": len(scene_videos),
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"selected_model": selected_model,
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}
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async def handle_scene_video_evaluating(tenant_id, task_id, step_name, input_data, config):
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@ -988,6 +1028,7 @@ KTV_HANDLERS = {
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"character_designing": quality_character_designing, # 质量门控 ✓
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"character_image_generating": quality_character_image_generating, # 质量门控 ✓
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"storyboard_generating": quality_storyboard_generating, # 质量门控 ✓
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"model_selecting": handle_model_selecting, # 客户选择模型 ✓
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"scene_video_generating": handle_scene_video_generating,
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"scene_video_evaluating": quality_scene_video_evaluating, # 质量门控 ✓
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"scene_video_concatenating": handle_scene_video_concatenating,
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146
app/model_selector.py
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146
app/model_selector.py
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"""
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Model selection interactive handler - allows customer to choose video generation model.
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Creates a human_task after storyboard generation to:
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1. Display cost estimates for all available models
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2. Wait for customer selection
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3. Store selected model in task context for downstream steps
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"""
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import json
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import logging
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from typing import Dict, Any
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from .cost_estimator import estimate_all_models, format_cost_summary
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logger = logging.getLogger("pipeline.model_selector")
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async def handle_model_selecting(
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tenant_id: str,
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task_id: str,
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step_name: str,
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input_data: Dict[str, Any],
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config: Dict[str, Any]
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) -> Dict[str, Any]:
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"""
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Interactive handler: present model options and wait for customer selection.
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Args:
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tenant_id: Tenant identifier
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task_id: Task identifier
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step_name: Step name (should be "model_selecting")
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input_data: Input data containing storyboard
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config: Step configuration
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Returns:
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Dict with selected_model and cost_estimates
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"""
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# Extract storyboard from input
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storyboard = None
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for dep_name, dep_output in input_data.items():
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if isinstance(dep_output, dict):
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if dep_output.get("storyboard"):
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storyboard = dep_output["storyboard"]
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break
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if not storyboard:
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raise ValueError("上游步骤未提供分镜脚本 (storyboard)")
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# Generate cost estimates
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fps = config.get("fps", 24)
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estimates = estimate_all_models(storyboard, fps)
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# Format summary
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summary = format_cost_summary(estimates)
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logger.info(f"Generated cost estimates for {len(estimates)} models")
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# Create human_task for customer selection
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task_data = {
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"title": "请选择视频生成模型",
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"description": summary,
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"options": [
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{
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"value": est["model_id"],
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"label": est["model_name"],
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"description": f"${est['total_cost']:.4f} - {est['description']}",
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"recommended": est.get("recommended", False),
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}
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for est in estimates
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],
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"default_value": estimates[0]["model_id"] if estimates else "wan2.2",
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"task_type": "model_selection",
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"metadata": {
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"cost_estimates": estimates,
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"scene_count": len(storyboard),
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"total_frames": sum(
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int((s.get("end_time", 10) - s.get("start_time", 0)) * fps)
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for s in storyboard
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),
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},
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}
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from pipeline_service.storage import create_human_task
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human_task_id = await create_human_task(
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task_id=task_id,
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step_name=step_name,
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version=config.get("version", 1),
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task_type="model_selection",
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form_schema=task_data,
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timeout_hours=config.get("timeout_hours", 24),
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)
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logger.info(f"Created model selection human_task: {human_task_id}")
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# Return placeholder (actual selection will be in human_task.result)
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return {
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"human_task_id": human_task_id,
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"status": "waiting_for_selection",
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"cost_estimates": estimates,
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"summary": summary,
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}
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def get_selected_model(human_task_result: Dict[str, Any]) -> str:
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"""
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Extract selected model from human_task result.
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Args:
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human_task_result: Result dict from completed human_task
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Returns:
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Selected model_id
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"""
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if not human_task_result:
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logger.warning("No human_task result, using default model")
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return "wan2.2"
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selected = human_task_result.get("selected_model")
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if not selected:
|
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logger.warning("No model selected, using default")
|
||||
return "wan2.2"
|
||||
|
||||
logger.info(f"Customer selected model: {selected}")
|
||||
return selected
|
||||
|
||||
|
||||
def get_model_cost_for_selected(
|
||||
estimates: list,
|
||||
selected_model: str
|
||||
) -> Dict[str, Any]:
|
||||
"""
|
||||
Get cost estimate for the selected model.
|
||||
|
||||
Args:
|
||||
estimates: List of cost estimate dicts
|
||||
selected_model: Selected model_id
|
||||
|
||||
Returns:
|
||||
Cost estimate dict for selected model
|
||||
"""
|
||||
for est in estimates:
|
||||
if est["model_id"] == selected_model:
|
||||
return est
|
||||
|
||||
logger.warning(f"Selected model {selected_model} not found in estimates")
|
||||
return {}
|
||||
109
docs/model-selection-integration.md
Normal file
109
docs/model-selection-integration.md
Normal file
@ -0,0 +1,109 @@
|
||||
# 模型选择功能集成指南
|
||||
|
||||
## 功能概述
|
||||
|
||||
在分镜生成（storyboard_generating）后插入模型选择步骤，允许客户根据费用预估选择视频生成模型。
|
||||
|
||||
## 已完成的代码修改
|
||||
|
||||
### 1. 新增模块
|
||||
- `app/cost_estimator.py` - 多模型费用预估（Wan 2.2, Wan 2.7, Vidu 2.0）
|
||||
- `app/model_selector.py` - 模型选择交互处理器
|
||||
|
||||
### 2. 修改模块
|
||||
- `app/ktv_adapter.py` - 添加 model_selecting handler，修改 scene_video_generating 支持多模型
|
||||
|
||||
## 产线定义更新步骤
|
||||
|
||||
### 步骤 1: 插入 model_selecting 步骤
|
||||
|
||||
在数据库的 `pipeline_steps` 表中，在 `storyboard_generating` (step_order=13) 之后插入新步骤：
|
||||
|
||||
```sql
|
||||
-- 查找当前 storyboard_generating 的 step_order
|
||||
SELECT step_order FROM pipeline_steps
|
||||
WHERE pipeline_id = 'ktv_pipeline' AND step_name = 'storyboard_generating';
|
||||
|
||||
-- 假设结果是 13，需要将后续步骤的 step_order +1
|
||||
UPDATE pipeline_steps
|
||||
SET step_order = step_order + 1
|
||||
WHERE pipeline_id = 'ktv_pipeline' AND step_order > 13;
|
||||
|
||||
-- 插入 model_selecting 步骤
|
||||
INSERT INTO pipeline_steps (
|
||||
pipeline_id, step_name, step_type, step_order,
|
||||
description, handler_function, step_config
|
||||
) VALUES (
|
||||
'ktv_pipeline',
|
||||
'model_selecting',
|
||||
'interactive',
|
||||
14,
|
||||
'客户根据费用预估选择视频生成模型',
|
||||
'handle_model_selecting',
|
||||
'{"deps": ["storyboard_generating"], "timeout_hours": 24}'
|
||||
);
|
||||
|
||||
-- 更新 scene_video_generating 的依赖
|
||||
UPDATE pipeline_steps
|
||||
SET step_config = JSON_SET(
|
||||
step_config,
|
||||
'$.deps',
|
||||
JSON_ARRAY('model_selecting', 'character_image_generating')
|
||||
)
|
||||
WHERE pipeline_id = 'ktv_pipeline' AND step_name = 'scene_video_generating';
|
||||
```
|
||||
|
||||
### 步骤 2: 验证步骤顺序
|
||||
|
||||
```sql
|
||||
SELECT step_name, step_order, step_type
|
||||
FROM pipeline_steps
|
||||
WHERE pipeline_id = 'ktv_pipeline'
|
||||
ORDER BY step_order;
|
||||
```
|
||||
|
||||
预期结果：
|
||||
```
|
||||
1 - audio_preparing
|
||||
2 - demucs_separating
|
||||
3 - lyric_calibrating
|
||||
...
|
||||
13 - storyboard_generating
|
||||
14 - model_selecting <-- 新增
|
||||
15 - scene_video_generating <-- 更新依赖
|
||||
16 - scene_video_evaluating
|
||||
...
|
||||
```
|
||||
|
||||
## 费用预估模型配置
|
||||
|
||||
当前支持的模型（在 `cost_estimator.py` 中定义）：
|
||||
|
||||
| 模型 | 质量评分 | 生成速度 | 价格/帧 | 特点 |
|
||||
|------|---------|---------|---------|------|
|
||||
| Wan 2.2 | 7.5/10 | fast | $0.002 | 本地GPU，性价比高 |
|
||||
| Wan 2.7 | 8.5/10 | medium | $0.003 | 本地GPU，质量更好 |
|
||||
| Vidu 2.0 | 9.0/10 | slow | $0.008 | 云端API，最高质量 |
|
||||
|
||||
## 工作流程
|
||||
|
||||
1. **分镜生成完成** → 计算总帧数和时长
|
||||
2. **生成费用预估** → 为每个模型计算成本
|
||||
3. **创建 human_task** → 显示选项给客户
|
||||
4. **客户选择模型** → 通过前端界面选择
|
||||
5. **任务继续执行** → scene_video_generating 使用选中的模型
|
||||
|
||||
## 注意事项
|
||||
|
||||
1. **Vidu 2.0 暂未实现** - 当前选择 Vidu 2.0 会降级到 Wan 2.2，需要后续集成 Vidu API
|
||||
2. **超时设置** - model_selecting 步骤默认 24 小时超时，可在 step_config 中调整
|
||||
3. **默认选择** - 如果客户未选择，默认使用 Wan 2.2（最便宜选项）
|
||||
|
||||
## 验证清单
|
||||
|
||||
- [ ] 数据库步骤已更新
|
||||
- [ ] 代码已提交 (commit: model-selection)
|
||||
- [ ] 测试产线执行到 storyboard_generating
|
||||
- [ ] 验证 human_task 创建成功
|
||||
- [ ] 验证客户选择后任务继续执行
|
||||
- [ ] 验证 scene_video_generating 使用了正确的模型
|
||||
Loading…
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Reference in New Issue
Block a user