Google Veo：高质感文生视频模型

# Veo 视频生成流程简化示意import numpy as np def simulate_veo_pipeline(text_prompt, n_frames=120, resolution=(1920, 1080)):    """模拟 Veo 的文生视频流程"""    print(f"输入提示词: {text_prompt}")    print(f"目标帧数: {n_frames}, 分辨率: {resolution}")        # Step 1: 文本编码    text_embedding = np.random.randn(512)  # 文本特征向量    print(f"文本编码维度: {text_embedding.shape}")        # Step 2: 视频 tokenizer 将视频压缩到潜在空间    latent_shape = (n_frames // 4, resolution[0] // 16, resolution[1] // 16, 16)    print(f"潜在空间形状: {latent_shape}")        # Step 3: 扩散去噪（从噪声逐步生成视频潜在表示）    noise = np.random.randn(*latent_shape)    denoised = noise  # 简化：模拟去噪过程    for step in range(50):        # Transformer 预测噪声并去除        denoised = denoised - 0.02 * np.random.randn(*latent_shape)        # Step 4: 视频解码器还原为像素空间    frames = np.random.randn(n_frames, *resolution, 3)    print(f"输出视频: {frames.shape[0]}帧, {frames.shape[1]}x{frames.shape[2]}")    return frames result = simulate_veo_pipeline("一只金毛犬在夕阳下的海滩上奔跑", n_frames=120)

# Veo 提示词工程示例def create_veo_prompt(subject, action, environment, camera="", style=""):    """构建高质量的 Veo 提示词"""    components = []        # 主体描述    components.append(subject)    # 动作描述    components.append(action)    # 环境描述    components.append(environment)    # 镜头语言（可选）    if camera:        components.append(camera)    # 风格描述（可选）    if style:        components.append(style)        prompt = "，".join(components)    return prompt # 示例：不同复杂度的提示词prompts = [    create_veo_prompt(        subject="一只金毛猎犬",        action="在夕阳下的海滩上奔跑，毛发随风飘动",        environment="金色的夕阳洒在沙滩上，海浪轻轻拍打岸边",        camera="电影级跟拍镜头，镜头与狗保持相同速度",        style="4K超高清，暖色调，胶片质感"    ),    create_veo_prompt(        subject="一杯冒着热气的咖啡",        action="牛奶缓缓倒入咖啡中，形成美丽的漩涡图案",        environment="木质桌面上，背景是模糊的窗户和晨光",        camera="微距特写镜头，缓慢推进",        style="柔和的自然光，浅景深"    )] for i, p in enumerate(prompts, 1):    print(f"提示词 {i}: {p}")    print(f"字数: {len(p)}")    print()

#!/usr/bin/env python3"""Google Veo 视频生成流程模拟演示文生视频的核心步骤：文本编码、潜在空间扩散去噪、视频解码"""import numpy as np class TextEncoder:    """模拟文本编码器（类似CLIP/T5）"""    def __init__(self, embed_dim=512):        self.embed_dim = embed_dim        def encode(self, text):        """将文本编码为特征向量"""        # 实际中使用预训练的CLIP/T5编码器        np.random.seed(hash(text) % (2**32))        return np.random.randn(self.embed_dim) class VideoTokenizer:    """模拟视频tokenizer（将视频压缩到潜在空间）"""    def __init__(self, temporal_compress=4, spatial_compress=16, latent_channels=16):        self.temporal_compress = temporal_compress        self.spatial_compress = spatial_compress        self.latent_channels = latent_channels        def get_latent_shape(self, n_frames, height, width):        """计算潜在空间形状"""        return (            n_frames // self.temporal_compress,            height // self.spatial_compress,            width // self.spatial_compress,            self.latent_channels        ) class DiffusionTransformer:    """模拟Veo的核心：扩散Transformer去噪"""    def __init__(self, n_steps=50, guidance_scale=7.5):        self.n_steps = n_steps        self.guidance_scale = guidance_scale        def denoise(self, noise, text_emb, tokenizer):        """从噪声逐步去噪生成视频潜在表示"""        latents = noise.copy()        for step in range(self.n_steps):            t = 1.0 - step / self.n_steps  # 时间步从1到0            # 模拟Transformer预测噪声            pred_noise = np.random.randn(*latents.shape) * t * 0.1            # Classifier-free guidance            unconditional = pred_noise * 0.5            guided = unconditional + self.guidance_scale * (pred_noise - unconditional)            # 更新潜空间            latents = latents - (1.0 / self.n_steps) * guided        return latents class VideoDecoder:    """模拟视频解码器"""    def decode(self, latents, temporal_compress=4, spatial_compress=16):        """将潜在表示解码为视频帧"""        n_t, h, w, c = latents.shape        n_frames = n_t * temporal_compress        height = h * spatial_compress        width = w * spatial_compress        return np.random.randn(n_frames, height, width, 3) # 完整流程def generate_video(prompt, n_frames=120, width=1920, height=1080):    """模拟Veo完整的文生视频流程"""    print(f"[Veo] 提示词: {prompt}")        # 1. 文本编码    encoder = TextEncoder(embed_dim=512)    text_emb = encoder.encode(prompt)    print(f"[1] 文本编码完成: dim={text_emb.shape[0]}")        # 2. 计算潜在空间形状    tokenizer = VideoTokenizer()    latent_shape = tokenizer.get_latent_shape(n_frames, height, width)    print(f"[2] 潜在空间: {latent_shape} (压缩比: 帧/4, 空间/16)")        # 3. 扩散去噪    model = DiffusionTransformer(n_steps=50, guidance_scale=7.5)    noise = np.random.randn(*latent_shape)    latents = model.denoise(noise, text_emb, tokenizer)    print(f"[3] 扩散去噪完成: 50步, CFG=7.5")        # 4. 视频解码    decoder = VideoDecoder()    frames = decoder.decode(latents)    print(f"[4] 视频解码完成: {frames.shape[0]}帧, {frames.shape[1]}x{frames.shape[2]}")        duration = frames.shape[0] / 24  # 24fps    print(f"\n[输出] {frames.shape[0]}帧 @ 24fps = {duration:.1f}秒")    return frames # 运行示例frames = generate_video("一只金毛犬在夕阳下的海滩上奔跑，电影级跟拍镜头")