通义万相2.2与DeepSeek协同创作：AI生成高质量长视频的完整指南_wan2.2 提示词

通义万相2.2与DeepSeek协同创作：AI生成高质量长视频的完整指南

一、技术架构与协同原理

1.1 双模型协同工作框架

通义万相2.2（Wan 2.2）作为阿里巴巴的多模态生成模型，与深度求索（DeepSeek）的大型语言模型相结合，创造了前所未有的长视频生成能力。这种协同架构的核心在于优势互补：

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这种架构的关键优势在于：

• 创意增强：DeepSeek将简单指令转化为富有文学性和视觉表现力的提示词
• 技术优化：通过结构化分解确保复杂场景的可实现性
• 质量控制：双模型交叉验证生成内容的合理性和一致性

1.2 提示词工程与风格控制

DeepSeek在协同中的核心作用是提示词优化和风格控制。以下是一个完整的提示词优化 pipeline：

class DeepSeekPromptOptimizer: def __init__(self, model_version=\"deepseek-ai/deepseek-v3\"): self.tokenizer = AutoTokenizer.from_pretrained(model_version) self.model = AutoModelForCausalLM.from_pretrained(model_version) self.style_templates = { \"cinematic\": \"电影感强烈，采用宽银幕比例，戏剧性灯光，深沉色调\", \"documentary\": \"纪实风格，自然光线，手持摄像机效果，真实感强烈\", \"anime\": \"动漫风格，明亮色彩，夸张表情，二次元美学\", \"cyberpunk\": \"赛博朋克风格，霓虹灯光，高科技低生活，未来感都市\", \"fantasy\": \"奇幻风格，魔法元素，神秘氛围，超现实场景\" } def optimize_prompt(self, raw_prompt, style=\"cinematic\", length=\"medium\", visual_details=3, motion_intensity=2): \"\"\" 优化原始提示词为通义万相2.2专用格式 参数: raw_prompt: 原始用户提示 style: 视觉风格选择 length: 视频长度偏好 (short/medium/long) visual_details: 视觉细节丰富度 (1-5) motion_intensity: 运动强度 (1-5) \"\"\" # 构建风格上下文 style_context = self.style_templates.get(style, \"\") # 构建优化指令 optimization_prompt = f\"\"\" 你是一个专业的视频制作提示词工程师。请将以下用户提示优化为适合AI视频生成的详细提示词。 原始提示: {raw_prompt} 要求风格: {style} 视频长度: {length} 视觉细节级别: {visual_details}/5 运动强度: {motion_intensity}/5 请提供: 1. 一个详细的中文提示词（包含视觉细节、氛围、镜头运动） 2. 一个简洁的英文提示词（用于模型输入） 3. 5个关键帧描述（描述视频中的关键视觉时刻） 4. 推荐的视频时长（秒） {style_context} \"\"\" # 使用DeepSeek生成优化提示 inputs = self.tokenizer(optimization_prompt, return_tensors=\"pt\") outputs = self.model.generate(**inputs, max_new_tokens=500) optimized_text = self.tokenizer.decode(outputs[0], skip_special_tokens=True) return self._parse_optimized_result(optimized_text) def _parse_optimized_result(self, optimized_text): \"\"\"解析DeepSeek生成的优化结果\"\"\" # 使用正则表达式提取结构化信息 import re # 提取中文提示词 chinese_prompt = re.search(r\"中文提示词:(.*?)(?=英文提示词:|$)\", optimized_text, re.S) chinese_prompt = chinese_prompt.group(1).strip() if chinese_prompt else \"\" # 提取英文提示词 english_prompt = re.search(r\"英文提示词:(.*?)(?=关键帧描述:|$)\", optimized_text, re.S) english_prompt = english_prompt.group(1).strip() if english_prompt else \"\" # 提取关键帧描述 keyframes_match = re.findall(r\"\\d+\\.\\s*(.*?)(?=\\d+\\.|$)\", optimized_text) keyframes = [kf.strip() for kf in keyframes_match] if keyframes_match else [] # 提取推荐时长 duration_match = re.search(r\"视频时长:.*?(\\d+).*?秒\", optimized_text) duration = int(duration_match.group(1)) if duration_match else 10 return { \"chinese_prompt\": chinese_prompt, \"english_prompt\": english_prompt, \"keyframes\": keyframes, \"recommended_duration\": duration }# 使用示例optimizer = DeepSeekPromptOptimizer()raw_prompt = \"一个未来城市的夜晚，有飞行汽车和霓虹灯\"result = optimizer.optimize_prompt(raw_prompt, style=\"cyberpunk\", visual_details=4, motion_intensity=3)print(f\"优化后的中文提示: {result[\'chinese_prompt\']}\")print(f\"优化后的英文提示: {result[\'english_prompt\']}\")

二、长视频生成的技术挑战与解决方案

2.1 时序一致性与连贯性保障

生成长视频（超过1分钟）的最大挑战是维持时序一致性和场景连贯性。我们采用多阶段方法解决这一问题：

class LongVideoGenerator: def __init__(self, wanxiang_api_key, deepseek_api_key): self.wanxiang_client = WanXiangClient(api_key=wanxiang_api_key) self.deepseek_client = DeepSeekClient(api_key=deepseek_api_key) self.scene_manager = SceneConsistencyManager() def generate_long_video(self, master_prompt, total_duration=300, segment_duration=10, style=\"cinematic\"): \"\"\" 生成长视频的主方法 参数: master_prompt: 主提示词 total_duration: 总时长(秒) segment_duration: 每个片段时长(秒) style: 视觉风格 \"\"\" # 1. 使用DeepSeek进行故事板和场景分解 storyboard = self._create_storyboard(master_prompt, total_duration) # 2. 生成场景过渡计划 transition_plan = self._plan_transitions(storyboard) # 3. 分段生成视频 video_segments = [] for i, scene in enumerate(storyboard[\'scenes\']): print(f\"生成第 {i+1}/{len(storyboard[\'scenes\'])} 个场景...\") # 使用DeepSeek优化场景提示词 optimized = self.optimizer.optimize_prompt( scene[\'description\'], style,  length=f\"{scene[\'duration\']}s\", visual_details=4, motion_intensity=scene.get(\'motion_intensity\', 3) ) # 生成视频片段 if i == 0: # 第一个片段使用文生视频 segment = self.wanxiang_client.text_to_video(  optimized[\'english_prompt\'],  duration=scene[\'duration\'],  resolution=\"1024x576\" ) else: # 后续片段使用图生视频，以上一帧结尾为起点 last_frame = video_segments[-1].get_last_frame() segment = self.wanxiang_client.image_to_video(  last_frame,  optimized[\'english_prompt\'],  duration=scene[\'duration\'],  resolution=\"1024x576\" ) video_segments.append(segment) # 应用场景一致性调整 if i > 0: segment = self.scene_manager.apply_consistency(  segment, video_segments[-2] ) # 4. 组合所有片段 final_video = self._combine_segments(video_segments, transition_plan) return final_video def _create_storyboard(self, master_prompt, total_duration): \"\"\"使用DeepSeek创建详细的故事板\"\"\" prompt = f\"\"\" 你是一个专业电影导演。请为以下概念创建详细的故事板分解: 核心概念: {master_prompt} 总时长: {total_duration}秒 请提供: 1. 3-5个主要场景划分 2. 每个场景的详细视觉描述 3. 每个场景的推荐时长 4. 场景之间的过渡方式建议 5. 每个场景的运动强度和视觉复杂度评级(1-5) 请以JSON格式回复，包含scenes数组，每个场景包含description、duration、 motion_intensity和visual_complexity字段。 \"\"\" response = self.deepseek_client.chat_complete(prompt, max_tokens=1500) return self._parse_storyboard_response(response) def _plan_transitions(self, storyboard): \"\"\"规划场景之间的过渡方式\"\"\" transitions = [] for i in range(len(storyboard[\'scenes\']) - 1): current_scene = storyboard[\'scenes\'][i] next_scene = storyboard[\'scenes\'][i + 1] # 根据场景内容决定过渡方式 if current_scene[\'motion_intensity\'] > 3 and next_scene[\'motion_intensity\'] > 3: transition = \"快速剪辑+运动模糊\" elif abs(current_scene[\'visual_complexity\'] - next_scene[\'visual_complexity\']) > 2: transition = \"渐变淡化\" else: transition = \"平滑运动过渡\" transitions.append({ \"from_scene\": i, \"to_scene\": i + 1, \"type\": transition, \"duration\": 1.5 # 过渡时长 }) return transitions

2.2 视觉一致性维护技术

为了确保长视频中人物、场景和风格的一致性，我们开发了专门的一致性引擎：

class SceneConsistencyManager: def __init__(self): self.reference_frames = [] self.color_palette = None self.character_models = {} def apply_consistency(self, new_segment, previous_segment): \"\"\"应用一致性调整到新视频片段\"\"\" # 1. 色彩一致性调整 new_segment = self._adjust_color_consistency(new_segment, previous_segment) # 2. 照明一致性调整 new_segment = self._adjust_lighting_consistency(new_segment, previous_segment) # 3. 人物一致性维护 if self._contains_characters(previous_segment): new_segment = self._maintain_character_consistency(new_segment, previous_segment) # 4. 运动模式一致性 new_segment = self._maintain_motion_consistency(new_segment, previous_segment) return new_segment def _adjust_color_consistency(self, new_segment, reference_segment): \"\"\"调整色彩一致性\"\"\" # 提取参考片段的色彩调性 reference_palette = self._extract_color_palette(reference_segment.get_last_frame()) # 如果已有全局调色板，优先使用 if self.color_palette is None: self.color_palette = reference_palette # 应用色彩匹配算法 adjusted_segment = [] for frame in new_segment.frames: adjusted_frame = self._match_colors(frame, self.color_palette) adjusted_segment.append(adjusted_frame) return VideoSegment(adjusted_segment, new_segment.fps) def _maintain_character_consistency(self, new_segment, reference_segment): \"\"\"维护人物外观一致性\"\"\" # 检测参考片段中的人物特征 reference_characters = self._detect_characters(reference_segment.get_last_frame()) # 更新人物模型库 for char_id, character in reference_characters.items(): if char_id not in self.character_models: self.character_models[char_id] = character # 对新片段进行人物一致性调整 adjusted_segment = [] for frame in new_segment.frames: # 检测帧中人物 current_characters = self._detect_characters(frame) # 对每个检测到的人物应用一致性调整 for char_id, character in current_characters.items(): if char_id in self.character_models:  # 应用模型一致性变换  frame = self._apply_character_model(frame, character,  self.character_models[char_id]) adjusted_segment.append(frame) return VideoSegment(adjusted_segment, new_segment.fps) def _extract_color_palette(self, frame): \"\"\"从帧中提取主要色彩调性\"\"\" # 使用K-means聚类提取主要颜色 from sklearn.cluster import KMeans import numpy as np # 将帧转换为像素数组 pixels = frame.reshape(-1, 3) # 使用K-means找到主要颜色 kmeans = KMeans(n_clusters=5, random_state=0).fit(pixels) palette = kmeans.cluster_centers_ return palette.astype(int)

三、高级提示词工程与创意控制

3.1 多维度提示词构建体系

为了精确控制生成内容，我们开发了结构化的多维度提示词体系：

class AdvancedPromptEngine: def __init__(self): self.aspect_ratios = { \"cinematic\": \"21:9\", \"standard\": \"16:9\", \"vertical\": \"9:16\", \"square\": \"1:1\" } self.camera_movements = [ \"静态镜头\", \"缓慢平移\", \"追踪镜头\", \"无人机俯瞰\", \"手持抖动效果\", \"轨道拍摄\", \"伸缩镜头\", \"旋转镜头\" ] self.lighting_styles = [ \"自然光\", \"戏剧性侧光\", \"柔光\", \"强对比光\", \"霓虹灯光\", \"黄金时刻\", \"蓝色时刻\", \"阴天散射光\" ] def construct_comprehensive_prompt(self, core_idea, camera_angle=\"medium shot\",  movement=\"slow pan\",  lighting=\"natural\",  style=\"cinematic\",  mood=\"serene\",  detail_level=4): \"\"\" 构建综合性多维度提示词 参数: core_idea: 核心创意概念 camera_angle: 摄像机角度 movement: 摄像机运动 lighting: 照明风格 style: 视觉风格 mood: 情绪氛围 detail_level: 细节丰富度 (1-5) \"\"\" # 基础提示词构建 prompt_template = \"\"\" {core_idea}。{camera_angle}，{camera_movement}，{lighting_style}。 {visual_style}风格，{mood}氛围，超高清{detail}，专业摄影品质。 \"\"\" # 细节级别描述 detail_descriptions = [ \"基础细节\", \"中等细节\", \"丰富细节\", \"极其详细\", \"照片级真实细节\" ] # 构建完整提示词 comprehensive_prompt = prompt_template.format( core_idea=core_idea, camera_angle=self._translate_camera_angle(camera_angle), camera_movement=self._translate_movement(movement), lighting_style=self._translate_lighting(lighting), visual_style=style, mood=mood, detail=detail_descriptions[detail_level - 1] ) # 添加技术参数 technical_specs = f\"\"\", 比例: {self.aspect_ratios[style]}, 画质: 8K超高清, 动态范围: HDR10, 色彩分级: 电影级\"\"\" return comprehensive_prompt + technical_specs def create_dynamic_prompt_sequence(self, master_prompt, duration, keyframes=5): \"\"\" 为长视频创建动态变化的提示词序列 \"\"\" # 使用DeepSeek分析情感弧线和视觉发展 analysis_prompt = f\"\"\" 分析以下视频概念的情感发展和视觉变化弧线: {master_prompt} 总时长: {duration}秒 关键帧数量: {keyframes} 请提供: 1. 情感发展曲线 (平静->紧张->高潮->解决) 2. 视觉强度变化曲线 3. 色彩调性变化计划 4. {keyframes}个关键时间点的详细视觉描述 以JSON格式回复，包含emotional_arc, visual_intensity, color_progression, 和keyframes数组。 \"\"\" # 获取DeepSeek分析结果 analysis = self.deepseek_client.analyze_sequence(analysis_prompt) # 为每个关键帧构建优化提示词 prompt_sequence = [] for i, keyframe in enumerate(analysis[\'keyframes\']): # 计算当前时间点 timestamp = (i / (keyframes - 1)) * duration if keyframes > 1 else 0 # 构建针对性的提示词 prompt = self.construct_comprehensive_prompt( core_idea=keyframe[\'description\'], camera_angle=keyframe.get(\'camera_angle\', \'medium shot\'), movement=keyframe.get(\'movement\', \'slow pan\'), lighting=keyframe.get(\'lighting\', \'natural\'), style=keyframe.get(\'style\', \'cinematic\'), mood=keyframe.get(\'mood\', \'serene\'), detail_level=5 ) prompt_sequence.append({ \"timestamp\": timestamp, \"prompt\": prompt, \"emotional_intensity\": analysis[\'emotional_arc\'][i], \"visual_intensity\": analysis[\'visual_intensity\'][i] }) return prompt_sequence# 使用示例prompt_engine = AdvancedPromptEngine()master_prompt = \"未来城市中人工智能与人类的共生关系\"sequence = prompt_engine.create_dynamic_prompt_sequence(master_prompt,  duration=120,  keyframes=5)for i, prompt_info in enumerate(sequence): print(f\"关键帧 {i+1} (在 {prompt_info[\'timestamp\']}秒):\") print(f\"情感强度: {prompt_info[\'emotional_intensity\']}\") print(f\"提示词: {prompt_info[\'prompt\'][:100]}...\\n\")

3.2 负面提示词与内容过滤系统

为了确保生成内容的质量和安全性，我们实现了先进的负面提示词系统：

class NegativePromptSystem: def __init__(self): self.common_negative_prompts = [ \"模糊\", \"失真\", \"畸形\", \"扭曲\", \"伪影\", \"低质量\", \"像素化\", \"噪点\", \"压缩痕迹\", \"水印\", \"文字\", \"logo\", \"签名\" ] self.style_specific_negatives = { \"realistic\": [\"卡通\", \"动漫\", \"绘画\", \"抽象\", \"风格化\"], \"anime\": [\"写实\", \"照片\", \"真实感\", \"真人\"], \"cyberpunk\": [\"复古\", \"历史\", \"古代\", \"自然\", \"乡村\"], \"fantasy\": [\"现代\", \"科技\", \"都市\", \"日常\"] } self.safety_filters = [ \"暴力\", \"血腥\", \"成人内容\", \"仇恨符号\", \"非法活动\", \"危险行为\", \"侵权内容\" ] def build_negative_prompt(self, main_style, safety_level=\"strict\", quality_level=\"high\"): \"\"\" 构建负面提示词 参数: main_style: 主要视觉风格 safety_level: 安全过滤级别 (lenient/medium/strict) quality_level: 质量要求级别 (medium/high/ultra) \"\"\" negative_parts = [] # 添加通用质量负面词 negative_parts.extend(self.common_negative_prompts) # 添加风格特定负面词 if main_style in self.style_specific_negatives: negative_parts.extend(self.style_specific_negatives[main_style]) # 根据质量级别调整 if quality_level == \"ultra\": negative_parts.extend([\"轻微模糊\", \"最小噪点\", \"轻微压缩\"]) elif quality_level == \"medium\": negative_parts = [np for np in negative_parts if np not in [\"最小噪点\", \"轻微压缩\"]] # 根据安全级别调整 if safety_level == \"strict\": negative_parts.extend(self.safety_filters) elif safety_level == \"medium\": negative_parts.extend(self.safety_filters[:4]) return \", \".join(list(set(negative_parts))) # 去重 def create_dynamic_negative_prompting(self, positive_prompt, duration): \"\"\" 创建随时间变化的动态负面提示词 \"\"\" # 分析正面提示词中的潜在问题 analysis_prompt = f\"\"\" 分析以下视频提示词中可能在不同时间段出现的问题: {positive_prompt} 总时长: {duration}秒 请识别: 1. 在视频开头可能出现的常见问题 2. 在动作场景中可能出现的失真问题 3. 在复杂场景中可能出现的细节问题 4. 在整个视频中需要持续避免的问题 以JSON格式回复，包含opening_issues, action_issues, complex_scene_issues,和persistent_issues数组。 \"\"\" analysis = self.deepseek_client.analyze_issues(analysis_prompt) # 构建时间相关的负面提示词序列 negative_sequence = [] # 开场阶段的负面词 opening_negative = self.build_negative_prompt( quality_level=\"ultra\", safety_level=\"strict\" ) + \", \" + \", \".join(analysis[\'opening_issues\']) # 动作阶段的负面词 action_negative = self.build_negative_prompt( quality_level=\"high\", safety_level=\"medium\" ) + \", \" + \", \".join(analysis[\'action_issues\']) # 复杂场景的负面词 complex_negative = self.build_negative_prompt( quality_level=\"ultra\", safety_level=\"strict\" ) + \", \" + \", \".join(analysis[\'complex_scene_issues\']) # 分配时间点 negative_sequence.append({\"start\": 0, \"end\": duration*0.1, \"prompt\": opening_negative}) negative_sequence.append({\"start\": duration*0.1, \"end\": duration*0.3, \"prompt\": action_negative}) negative_sequence.append({\"start\": duration*0.3, \"end\": duration*0.8, \"prompt\": complex_negative}) negative_sequence.append({\"start\": duration*0.8, \"end\": duration, \"prompt\": opening_negative}) return negative_sequence

四、音频-视频同步与多媒体整合

4.1 智能音轨生成与同步

长视频体验的完整性依赖于优质的音频和完美的音视频同步：

class AudioVideoSyncEngine: def __init__(self, wanxiang_client, audio_gen_client): self.wanxiang = wanxiang_client self.audio_gen = audio_gen_client self.sync_tolerance = 0.1 # 100ms同步容差 def generate_complete_video(self, visual_prompt, audio_type=\"background\", mood=\"epic\", intensity_curve=None): \"\"\" 生成带同步音轨的完整视频 参数: visual_prompt: 视觉提示词 audio_type: 音频类型 (background/ambient/soundtrack) mood: 音频情绪 intensity_curve: 强度变化曲线 \"\"\" # 1. 生成视频内容 print(\"生成视频内容...\") video_content = self.wanxiang.text_to_video( visual_prompt, duration=30, # 示例时长 resolution=\"1024x576\" ) # 2. 分析视频内容并生成匹配音频 print(\"分析视频内容并生成音频...\") audio_profile = self.analyze_video_for_audio(video_content, mood) # 3. 生成音轨 print(\"生成音轨...\") audio_track = self.audio_gen.generate_audio( audio_profile, duration=video_content.duration, intensity_curve=intensity_curve ) # 4. 智能同步 print(\"进行音视频同步...\") synced_video = self.synchronize_av(video_content, audio_track) # 5. 主客观音频平衡 print(\"优化音频混合...\") final_video = self.balance_audio_mix(synced_video) return final_video def analyze_video_for_audio(self, video_content, target_mood): \"\"\"分析视频内容以生成匹配的音频配置\"\"\" # 提取关键帧进行分析 key_frames = self.extract_key_frames(video_content, num_frames=10) # 分析视觉特征 visual_features = [] for frame in key_frames: features = { \"action_level\": self.estimate_action_level(frame), \"emotional_tone\": self.estimate_emotional_tone(frame), \"visual_complexity\": self.calculate_visual_complexity(frame), \"color_palette\": self.extract_color_palette(frame) } visual_features.append(features) # 构建音频配置文件 audio_profile = { \"mood\": target_mood, \"tempo\": self.calculate_avg_tempo(visual_features), \"intensity\": self.calculate_avg_intensity(visual_features), \"instrumentation\": self.determine_instrumentation(visual_features, target_mood), \"dynamic_range\": self.calculate_dynamic_range(visual_features) } return audio_profile def synchronize_av(self, video, audio): \"\"\"智能音视频同步\"\"\" # 检测视频中的节奏点（动作变化、场景切换） video_beat_points = self.detect_visual_beats(video) # 检测音频中的节奏点 audio_beat_points = self.detect_audio_beats(audio) # 计算最佳对齐 alignment = self.calculate_optimal_alignment(video_beat_points, audio_beat_points) # 应用时间拉伸/压缩 if alignment[\'adjustment_needed\']: adjusted_video = self.time_stretch_video(video, alignment[\'stretch_factor\']) adjusted_audio = self.time_stretch_audio(audio, alignment[\'stretch_factor\']) else: adjusted_video, adjusted_audio = video, audio # 应用精细同步 synced_result = self.apply_fine_sync(adjusted_video, adjusted_audio,alignment[\'offset\']) return synced_result def balance_audio_mix(self, video): \"\"\"平衡主观和客观音频元素\"\"\" # 分离音轨 original_audio = video.audio_track # 分析场景类型 scene_type = self.classify_scene_type(video) # 根据场景类型调整音频平衡 if scene_type == \"action\": # 增强音效，降低背景音乐 balanced_audio = self.enhance_sound_effects(original_audio, 1.3) balanced_audio = self.reduce_background_music(balanced_audio, 0.8) elif scene_type == \"dialogue\": # 增强对话，降低其他元素 balanced_audio = self.enhance_dialogue(original_audio, 1.5) balanced_audio = self.reduce_non_dialogue(balanced_audio, 0.7) else: # 保持自然平衡 balanced_audio = original_audio # 应用动态范围压缩 balanced_audio = self.apply_compression(balanced_audio, ratio=2.5) # 重新组合视频 return video.with_audio(balanced_audio)

4.2 多镜头与多角度生成系统

为创建更丰富的视觉体验，我们开发了多镜头生成系统：

class MultiAngleVideoSystem: def __init__(self, wanxiang_client): self.wanxiang = wanxiang_client self.angle_templates = { \"main\": \"主要镜头，中心视角\", \"wide\": \"广角镜头，全景展示\", \"closeup\": \"特写镜头，细节强调\", \"overhead\": \"俯视镜头，上帝视角\", \"low_angle\": \"低角度镜头，仰视视角\", \"dutch_angle\": \"荷兰角镜头，倾斜动态感\" } def generate_multi_angle_video(self, base_prompt, duration, angles=None, transition_style=\"smooth\"): \"\"\" 生成多角度视频 参数: base_prompt: 基础提示词 duration: 总时长 angles: 使用的角度列表 transition_style: 转场风格 \"\"\" if angles is None: angles = [\"main\", \"wide\", \"closeup\"] # 计算每个角度的时长 angle_duration = duration / len(angles) # 为每个角度生成特定提示词 angle_prompts = {} for angle in angles: angle_specific_prompt = self.create_angle_specific_prompt( base_prompt, angle ) angle_prompts[angle] = angle_specific_prompt # 并行生成各个角度视频 angle_videos = {} for angle, prompt in angle_prompts.items(): print(f\"生成 {angle} 角度视频...\") angle_videos[angle] = self.wanxiang.text_to_video( prompt, duration=angle_duration, resolution=\"1024x576\" ) # 创建多角度编辑时间线 timeline = self.create_editing_timeline(angle_videos, transition_style) # 渲染最终视频 final_video = self.render_multi_angle_video(timeline) return final_video def create_angle_specific_prompt(self, base_prompt, angle_type): \"\"\"创建角度特定的提示词\"\"\" angle_description = self.angle_templates.get(angle_type, \"\") prompt_template = \"\"\" {base_prompt}。{angle_description}，保持视觉一致性。 技术要求: - 视角: {angle_type} - 保持与主镜头相同的视觉风格 - 保持色彩调性一致 - 确保场景元素位置一致 - 匹配照明和阴影方向 \"\"\" return prompt_template.format( base_prompt=base_prompt, angle_description=angle_description, angle_type=angle_type ) def create_editing_timeline(self, angle_videos, transition_style): \"\"\"创建编辑时间线\"\"\" timeline = { \"clips\": [], \"transitions\": [], \"global_effects\": [] } # 按时间顺序排列片段 current_time = 0 for angle, video in angle_videos.items(): timeline[\'clips\'].append({ \"start\": current_time, \"end\": current_time + video.duration, \"content\": video, \"angle\": angle, \"type\": \"video\" }) # 添加转场（除了第一个片段） if current_time > 0: timeline[\'transitions\'].append({  \"from_clip\": len(timeline[\'clips\']) - 2,  \"to_clip\": len(timeline[\'clips\']) - 1,  \"type\": transition_style,  \"duration\": 1.0 # 1秒转场 }) current_time += video.duration # 添加全局效果 timeline[\'global_effects\'].extend([ {\"type\": \"color_grading\", \"preset\": \"cinematic\"}, {\"type\": \"motion_blur\", \"intensity\": 0.2}, {\"type\": \"film_grain\", \"intensity\": 0.1} ]) return timeline def render_multi_angle_video(self, timeline): \"\"\"渲染多角度视频\"\"\" # 这里简化了实际的视频编辑和渲染过程 # 实际实现会使用FFmpeg或其他视频编辑库 print(f\"渲染时间线，包含 {len(timeline[\'clips\'])} 个片段...\") print(f\"应用 {len(timeline[\'transitions\'])} 个转场...\") print(f\"添加 {len(timeline[\'global_effects\'])} 个全局效果...\") # 模拟渲染过程 rendered_video = { \"duration\": sum(clip[\'content\'].duration for clip in timeline[\'clips\']), \"resolution\": \"1024x576\", \"frame_rate\": 30, \"has_audio\": True } return rendered_video# 使用示例multi_angle_system = MultiAngleVideoSystem(wanxiang_client)base_prompt = \"未来城市中的高速追逐场景，飞行汽车穿梭在摩天大楼之间\"final_video = multi_angle_system.generate_multi_angle_video( base_prompt, duration=60, angles=[\"main\", \"wide\", \"closeup\", \"overhead\"], transition_style=\"dynamic\")

五、高级应用场景与实战案例

5.1 长篇叙事视频生成实战

以下是一个完整的长篇叙事视频生成示例，展示如何结合所有技术组件：

class EpicStoryGenerator: def __init__(self, wanxiang_client, deepseek_client, audio_client): self.wanxiang = wanxiang_client self.deepseek = deepseek_client self.audio = audio_client self.prompt_engine = AdvancedPromptEngine() self.negative_system = NegativePromptSystem() def generate_epic_story(self, story_concept, total_duration=300, chapter_count=3, style=\"cinematic\"): \"\"\" 生成史诗级长篇故事视频 参数: story_concept: 故事概念 total_duration: 总时长(秒) chapter_count: 章节数量 style: 视觉风格 \"\"\" print(\"开始生成史诗级故事视频...\") # 1. 使用DeepSeek进行故事开发和章节划分 print(\"开发故事结构和章节...\") story_structure = self.develop_story_structure(story_concept, total_duration, chapter_count) # 2. 生成每个章节的详细内容 all_chapters = [] for i, chapter in enumerate(story_structure[\'chapters\']): print(f\"生成第 {i+1}/{chapter_count} 章节...\") chapter_content = self.generate_chapter( chapter,  style=style, chapter_index=i, total_chapters=chapter_count ) all_chapters.append(chapter_content) # 3. 生成章节转场 print(\"创建章节转场...\") transitions = self.generate_chapter_transitions(all_chapters) # 4. 生成统一音轨 print(\"生成统一音轨...\") unified_audio = self.generate_unified_audio_track(all_chapters, total_duration) # 5. 组合所有章节 print(\"组合最终视频...\") final_video = self.assemble_final_video(all_chapters, transitions, unified_audio) print(\"史诗级故事视频生成完成!\") return final_video def develop_story_structure(self, concept, duration, chapter_count): \"\"\"使用DeepSeek开发故事结构\"\"\" prompt = f\"\"\" 作为专业编剧，开发一个{duration}秒的视频故事结构: 核心概念: {concept} 章节数量: {chapter_count} 请提供: 1. 故事标题和一句话梗概 2. 3幕结构（开场、发展、高潮） 3. 每个章节的详细描述（时长、关键事件、情感变化） 4. 角色发展弧线（如果有角色） 5. 视觉主题和隐喻 以JSON格式回复，包含title, logline, acts, chapters数组， 每个章节包含duration, events, emotional_arc, visual_theme。 \"\"\" response = self.deepseek.chat_complete(prompt, max_tokens=2000) return self.parse_structure_response(response) def generate_chapter(self, chapter_info, style, chapter_index, total_chapters): \"\"\"生成单个章节内容\"\"\" # 构建章节特定提示词 chapter_prompt = self.prompt_engine.construct_comprehensive_prompt( core_idea=chapter_info[\'description\'], camera_angle=self.select_chapter_camera_angle(chapter_index, total_chapters), movement=self.select_chapter_movement(chapter_info[\'emotional_arc\']), lighting=self.select_chapter_lighting(chapter_info[\'visual_theme\']), style=style, mood=chapter_info[\'emotional_arc\'][\'current_mood\'], detail_level=5 ) # 构建章节负面提示词 negative_prompt = self.negative_system.build_negative_prompt( style, safety_level=\"strict\", quality_level=\"ultra\" ) # 生成章节视频 chapter_video = self.wanxiang.text_to_video( chapter_prompt, negative_prompt=negative_prompt, duration=chapter_info[\'duration\'], resolution=\"1024x576\" ) # 生成章节音频 chapter_audio = self.audio.generate_audio({ \"mood\": chapter_info[\'emotional_arc\'][\'current_mood\'], \"intensity\": chapter_info[\'emotional_arc\'][\'intensity\'], \"tempo\": self.calculate_tempo_from_events(chapter_info[\'events\']) }, duration=chapter_info[\'duration\']) return { \"video\": chapter_video, \"audio\": chapter_audio, \"metadata\": chapter_info } def generate_chapter_transitions(self, chapters): \"\"\"生成章节之间的创意转场\"\"\" transitions = [] for i in range(len(chapters) - 1): current_chapter = chapters[i] next_chapter = chapters[i + 1] # 根据章节内容决定转场类型 transition_type = self.determine_transition_type( current_chapter[\'metadata\'], next_chapter[\'metadata\'] ) # 生成转场提示词 transition_prompt = self.create_transition_prompt( current_chapter[\'metadata\'][\'visual_theme\'], next_chapter[\'metadata\'][\'visual_theme\'], transition_type ) # 生成转场视频 transition_video = self.wanxiang.text_to_video( transition_prompt, duration=3.0, # 转场时长 resolution=\"1024x576\" ) transitions.append({ \"between_chapters\": (i, i + 1), \"type\": transition_type, \"video\": transition_video }) return transitions def generate_unified_audio_track(self, chapters, total_duration): \"\"\"生成统一音轨，确保音频连贯性\"\"\" # 分析所有章节的情感弧线 emotional_arc = [] for chapter in chapters: emotional_arc.append({ \"start\": chapter[\'metadata\'][\'start_time\'], \"end\": chapter[\'metadata\'][\'start_time\'] + chapter[\'metadata\'][\'duration\'], \"intensity\": chapter[\'metadata\'][\'emotional_arc\'][\'intensity\'], \"mood\": chapter[\'metadata\'][\'emotional_arc\'][\'current_mood\'] }) # 创建连贯的音频强度曲线 intensity_curve = self.create_unified_intensity_curve(emotional_arc, total_duration) # 生成统一音轨 unified_audio = self.audio.generate_audio( { \"mood\": \"dynamic\", \"intensity\": intensity_curve, \"tempo\": \"variable\", \"instrumentation\": \"orchestral\" }, duration=total_duration ) return unified_audio# 使用示例story_concept = \"一个关于人工智能获得情感意识的哲学思考旅程\"epic_generator = EpicStoryGenerator(wanxiang_client, deepseek_client, audio_client)epic_video = epic_generator.generate_epic_story( story_concept, total_duration=600, # 10分钟 chapter_count=5, style=\"cinematic\")

5.2 交互式视频生成系统

对于需要用户交互的场景，我们开发了交互式视频生成系统：

class InteractiveVideoSystem: def __init__(self, wanxiang_client, deepseek_client): self.wanxiang = wanxiang_client self.deepseek = deepseek_client self.decision_points = {} self.user_preferences = {} def create_interactive_video(self, base_story, decision_points,  branch_factor=2, default_duration=30): \"\"\" 创建交互式视频体验 参数: base_story: 基础故事线 decision_points: 决策点列表 branch_factor: 每个决策点的分支数量 default_duration: 每个片段的默认时长 \"\"\" # 使用DeepSeek扩展故事线和决策点 expanded_story = self.expand_story_with_branches( base_story, decision_points, branch_factor ) # 生成所有故事路径 story_paths = self.generate_all_story_paths(expanded_story) # 并行生成所有视频片段 video_fragments = {} for path_id, path in story_paths.items(): print(f\"生成故事路径 {path_id}...\") video_fragments[path_id] = self.generate_path_videos( path, default_duration ) # 创建交互逻辑 interactive_logic = self.create_interactive_logic( expanded_story, video_fragments ) return { \"video_fragments\": video_fragments, \"interactive_logic\": interactive_logic, \"story_structure\": expanded_story } def expand_story_with_branches(self, base_story, decision_points, branch_factor): \"\"\"使用DeepSeek扩展故事线分支\"\"\" prompt = f\"\"\" 作为互动故事设计师，扩展以下基础故事线，添加决策点和分支叙事: 基础故事: {base_story} 决策点位置: {decision_points} 每个决策点的分支数量: {branch_factor} 请提供: 1. 完整的故事树结构 2. 每个决策点的选项和后果 3. 所有可能的故事结局 4. 每个故事路径的情感弧线 以JSON格式回复，包含decision_points数组，每个决策点包含position, options数组， 每个选项包含text, next_segment, emotional_impact。 \"\"\" response = self.deepseek.chat_complete(prompt, max_tokens=2500) return self.parse_story_expansion(response) def generate_all_story_paths(self, expanded_story): \"\"\"生成所有可能的故事路径\"\"\" paths = {} # 使用深度优先搜索生成所有路径 def dfs(current_segment, current_path, path_id): if current_segment is None: # 到达结局，保存路径 paths[path_id] = current_path.copy() return current_path.append(current_segment) # 如果是决策点，探索所有选项 if current_segment[\'type\'] == \'decision_point\': for i, option in enumerate(current_segment[\'options\']):  new_path_id = f\"{path_id}.{i}\"  dfs(option[\'next_segment\'], current_path, new_path_id) else: # 普通片段，继续下一个 dfs(current_segment.get(\'next_segment\'), current_path, path_id) # 从故事开始 dfs(expanded_story[\'start_segment\'], [], \"path_0\") return paths def generate_path_videos(self, story_path, segment_duration): \"\"\"生成单个故事路径的所有视频片段\"\"\" videos = [] for i, segment in enumerate(story_path): # 为每个片段生成提示词 if segment[\'type\'] == \'decision_point\': # 决策点使用特殊提示词 prompt = self.create_decision_prompt(segment) else: # 普通叙事片段 prompt = self.create_narrative_prompt(segment) # 生成视频 video = self.wanxiang.text_to_video( prompt, duration=segment_duration, resolution=\"1024x576\" ) videos.append({ \"segment_id\": segment[\'id\'], \"video\": video, \"type\": segment[\'type\'], \"is_decision_point\": segment[\'type\'] == \'decision_point\' }) return videos def create_interactive_logic(self, story_structure, video_fragments): \"\"\"创建交互逻辑和前端集成\"\"\" interactive_logic = { \"initial_segment\": story_structure[\'start_segment\'][\'id\'], \"decision_points\": {}, \"branching_structure\": {} } # 映射所有决策点 for dp in story_structure[\'decision_points\']: interactive_logic[\'decision_points\'][dp[\'id\']] = { \"position\": dp[\'position\'], \"options\": [  { \"text\": opt[\'text\'], \"next_segment\": opt[\'next_segment\'][\'id\'], \"video_path\": self.find_video_path_for_decision(opt, video_fragments)  }  for opt in dp[\'options\'] ] } # 构建分支结构 for path_id, path_videos in video_fragments.items(): interactive_logic[\'branching_structure\'][path_id] = [ {\"segment_id\": vid[\'segment_id\'], \"type\": vid[\'type\']} for vid in path_videos ] return interactive_logic def integrate_with_frontend(self, interactive_logic, video_fragments): \"\"\"生成前端集成代码\"\"\" # 这里简化了实际的前端代码生成 # 实际实现会生成HTML、CSS和JavaScript代码 frontend_code = { \"html\": self.generate_html_structure(interactive_logic), \"css\": self.generate_styles(), \"javascript\": self.generate_interactive_logic(interactive_logic, video_fragments) } return frontend_code