Genmo AI视频：文生视频创作引擎

#!/usr/bin/env python3"""Genmo AI视频创作引擎模拟器 - 演示文生视频和图生视频工作流"""import numpy as npfrom dataclasses import dataclass, fieldfrom typing import List, Dict, Optionalfrom enum import Enum class GenerationMode(Enum):    """生成模式"""    TEXT_TO_VIDEO = "text_to_video"    IMAGE_TO_VIDEO = "image_to_video" class SceneComplexity(Enum):    """场景复杂度"""    SIMPLE = "simple"    MEDIUM = "medium"    COMPLEX = "complex" @dataclassclass VideoClip:    """视频片段"""    clip_id: str    mode: GenerationMode    prompt: str    negative_prompt: str = ""    duration: float = 5.0    resolution: str = "720p"    fps: int = 24    frames: List[Dict] = field(default_factory=list)    quality_score: float = 0.0    temporal_consistency: float = 0.0 class GenmoSimulator:    """Genmo AI视频创作引擎模拟器"""        def __init__(self):        self.clips: Dict[str, VideoClip] = {}        self.generation_count = 0        def generate_text_to_video(self, prompt, negative_prompt="",                                duration=5.0, scene_complexity=SceneComplexity.SIMPLE):        """文生视频模式"""        clip_id = f"CLIP-{self.generation_count+1:04d}"        self.generation_count += 1                quality = self._estimate_quality(prompt, scene_complexity, "text")        consistency = self._estimate_consistency(scene_complexity, quality)                clip = VideoClip(            clip_id=clip_id,            mode=GenerationMode.TEXT_TO_VIDEO,            prompt=prompt,            negative_prompt=negative_prompt,            duration=duration,            quality_score=quality,            temporal_consistency=consistency        )                num_frames = int(duration * clip.fps)        clip.frames = self._generate_frames(num_frames, quality, consistency)                self.clips[clip_id] = clip        print(f"[文生视频] {clip_id}: 质量={quality:.2f}, "              f"时序一致性={consistency:.2f}, 帧数={num_frames}")        return clip        def generate_image_to_video(self, image_path, prompt,                                duration=5.0, scene_complexity=SceneComplexity.SIMPLE):        """图生视频模式"""        clip_id = f"CLIP-{self.generation_count+1:04d}"        self.generation_count += 1                quality = self._estimate_quality(prompt, scene_complexity, "image")        consistency = self._estimate_consistency(scene_complexity, quality)        style_retention = self._estimate_style_retention(scene_complexity)                clip = VideoClip(            clip_id=clip_id,            mode=GenerationMode.IMAGE_TO_VIDEO,            prompt=f"[image: {image_path}] {prompt}",            duration=duration,            quality_score=quality,            temporal_consistency=consistency        )                num_frames = int(duration * clip.fps)        clip.frames = self._generate_frames(num_frames, quality, consistency)                self.clips[clip_id] = clip        print(f"[图生视频] {clip_id}: 质量={quality:.2f}, "              f"时序一致性={consistency:.2f}, "              f"风格保留={style_retention:.2f}, 帧数={num_frames}")        return clip        def _estimate_quality(self, prompt, complexity, mode):        """估算生成质量分数 (0-1)"""        base_quality = 0.75 if mode == "text" else 0.80        prompt_factor = min(len(prompt) / 200, 1.0) * 0.15        complexity_penalty = {            SceneComplexity.SIMPLE: 0.0,            SceneComplexity.MEDIUM: -0.10,            SceneComplexity.COMPLEX: -0.20        }.get(complexity, 0)        noise = np.random.uniform(-0.05, 0.05)        return max(0.3, min(0.95, base_quality + prompt_factor + complexity_penalty + noise))        def _estimate_consistency(self, complexity, quality):        """估算时序一致性分数 (0-1)"""        base_consistency = 0.85        complexity_penalty = {            SceneComplexity.SIMPLE: 0.0,            SceneComplexity.MEDIUM: -0.15,            SceneComplexity.COMPLEX: -0.30        }.get(complexity, 0)        quality_bonus = (quality - 0.7) * 0.2        noise = np.random.uniform(-0.03, 0.03)        return max(0.4, min(0.95, base_consistency + complexity_penalty + quality_bonus + noise))        def _estimate_style_retention(self, complexity):        """估算风格保留度 (图生视频模式)"""        base = 0.85        penalty = {            SceneComplexity.SIMPLE: 0.0,            SceneComplexity.MEDIUM: -0.10,            SceneComplexity.COMPLEX: -0.15        }.get(complexity, 0)        return max(0.5, min(0.95, base + penalty + np.random.uniform(-0.03, 0.03)))        def _generate_frames(self, num_frames, quality, consistency):        """生成帧序列（模拟）"""        frames = []        prev_content = np.random.rand(100)        for i in range(num_frames):            consistency_factor = consistency + np.random.uniform(-0.05, 0.05)            content = prev_content * consistency_factor + np.random.rand(100) * (1 - consistency_factor)            frames.append({                "frame_id": i,                "quality": quality + np.random.uniform(-0.02, 0.02),                "content_hash": hash(content.tobytes()) % 10000            })            prev_content = content        return frames        def extend_video(self, clip_id, extra_seconds=3.0, prompt=""):        """视频扩展功能"""        clip = self.clips[clip_id]        original_duration = clip.duration        new_duration = original_duration + extra_seconds        extra_frames = int(extra_seconds * clip.fps)                quality_decay = 0.02 * (extra_seconds / 3.0)        clip.quality_score = max(0.3, clip.quality_score - quality_decay)        clip.temporal_consistency = max(0.4, clip.temporal_consistency - quality_decay * 0.5)                new_frames = self._generate_frames(            extra_frames, clip.quality_score, clip.temporal_consistency        )        clip.frames.extend(new_frames)        clip.duration = new_duration                print(f"[视频扩展] {clip_id}: {original_duration}s -> {new_duration}s "              f"(质量衰减={quality_decay:.3f})")        return clip        def batch_generate(self, prompt, count=5, **kwargs):        """批量生成并筛选最佳结果"""        results = []        for i in range(count):            clip = self.generate_text_to_video(prompt, **kwargs)            results.append(clip)                best = max(results, key=lambda c: c.quality_score * c.temporal_consistency)        print(f"[批量生成] 生成{count}个, 筛选最佳: {best.clip_id} "              f"(综合分={best.quality_score * best.temporal_consistency:.3f})")        return best        def get_stats(self):        """获取生成统计"""        if not self.clips:            return {"total_clips": 0}        return {            "total_clips": len(self.clips),            "avg_quality": round(float(np.mean([c.quality_score for c in self.clips.values()])), 3),            "avg_consistency": round(float(np.mean([c.temporal_consistency for c in self.clips.values()])), 3),            "text_to_video_count": sum(1 for c in self.clips.values() if c.mode == GenerationMode.TEXT_TO_VIDEO),            "image_to_video_count": sum(1 for c in self.clips.values() if c.mode == GenerationMode.IMAGE_TO_VIDEO)        } # 运行模拟if __name__ == "__main__":    genmo = GenmoSimulator()        # 文生视频示例    genmo.generate_text_to_video(        prompt="sunset beach, golden light on waves, palms swaying gently",        negative_prompt="blur, shake, distortion",        duration=5.0,        scene_complexity=SceneComplexity.SIMPLE    )        # 图生视频示例    genmo.generate_image_to_video(        image_path="brand_logo.png",        prompt="logo elements flowing smoothly, soft light changes",        duration=6.0,        scene_complexity=SceneComplexity.SIMPLE    )        # 复杂场景示例    genmo.generate_text_to_video(        prompt="crowded street, people walking fast, cars moving",        duration=5.0,        scene_complexity=SceneComplexity.COMPLEX    )        # 批量生成筛选    genmo.batch_generate(        prompt="forest landscape, leaves rustling, sunlight filtering",        count=5,        scene_complexity=SceneComplexity.MEDIUM    )        # 视频扩展    genmo.extend_video("CLIP-0001", extra_seconds=3.0)        # 统计    stats = genmo.get_stats()    print(f"\n=== Genmo生成统计 ===")    for k, v in stats.items():        print(f"  {k}: {v}")