Mochi 1文生视频模型体验评测

# Mochi 1时空注意力机制简化示意import numpy as np def spatial_temporal_attention(Q, K, V, T, H, W, d_k):    """    简化的时空注意力计算    Q, K, V: 形状为 (T*H*W, d_k) 的查询、键、值矩阵    T: 帧数, H: 高, W: 宽, d_k: 键维度    """    N = T * H * W  # 总token数    # 计算注意力分数: QK^T / sqrt(d_k)    scores = np.matmul(Q, K.T) / np.sqrt(d_k)    # Softmax归一化    scores_max = np.max(scores, axis=-1, keepdims=True)    exp_scores = np.exp(scores - scores_max)    attn_weights = exp_scores / np.sum(exp_scores, axis=-1, keepdims=True))    # 加权求和    output = np.matmul(attn_weights, V)    return output, attn_weights # 示例: 8帧, 16x16分辨率, 64维T, H, W, d_k = 8, 16, 16, 64N = T * H * W  # 2048 tokensQ = np.random.randn(N, d_k).astype(np.float32)K = np.random.randn(N, d_k).astype(np.float32)V = np.random.randn(N, d_k).astype(np.float32) output, weights = spatial_temporal_attention(Q, K, V, T, H, W, d_k)print(f"输入: {T}帧, {H}x{W}分辨率, {d_k}维")print(f"总token数: {N}")print(f"注意力矩阵大小: {N}x{N} = {N*N:,} 个元素")print(f"输出形状: {output.shape}")

# Mochi 1本地部署模拟import time class Mochi1DeploySimulator:    """模拟Mochi 1部署和视频生成过程"""    def __init__(self, gpu_memory_gb=24, gpu_name="RTX 4090"):        self.gpu_memory_gb = gpu_memory_gb        self.gpu_name = gpu_name        self.model_size_gb = 14  # 模型权重大小        self.min_vram_required = 24  # 最低显存要求        def check_hardware(self):        """检查硬件是否满足要求"""        if self.gpu_memory_gb < self.min_vram_required:            return False, f"显存不足: {self.gpu_memory_gb}GB < {self.min_vram_required}GB"        available = self.gpu_memory_gb - self.model_size_gb        return True, f"{self.gpu_name} ({self.gpu_memory_gb}GB), 可用显存: {available}GB"        def estimate_generation_time(self, resolution, n_frames, n_steps):        """估算生成时间"""        # 简化的时间估算模型        base_time = 0.1  # 每步每帧基础时间(秒)        res_factor = {"480p": 1.0, "720p": 2.25, "1080p": 5.0}        factor = res_factor.get(resolution, 1.0)        total_time = base_time * n_frames * n_steps * factor        return total_time # 模拟部署sim = Mochi1DeploySimulator(gpu_memory_gb=24, gpu_name="RTX 4090")ok, msg = sim.check_hardware()print(f"硬件检查: {'通过' if ok else '失败'} - {msg}") # 估算不同配置的生成时间configs = [    ("480p", 60, 20),   # 短低分辨率    ("480p", 120, 30),  # 标准配置    ("720p", 120, 30),  # 高分辨率    ("1080p", 120, 50), # 超高分辨率]print("\n生成时间估算:")for res, frames, steps in configs:    t = sim.estimate_generation_time(res, frames, steps)    print(f"  {res}, {frames}帧, {steps}步: {t/60:.1f}分钟")

# LoRA微调参数计算def calculate_lora_params(original_params, rank):    """计算LoRA微调的参数量"""    # 原始: W (d x d), 参数量 = d^2    # LoRA: A (d x r) + B (r x d), 参数量 = 2*d*r    d = int(original_params ** 0.5)    lora_params = 2 * d * rank    ratio = lora_params / original_params * 100    return lora_params, ratio # 示例: 一个320x320的注意力层d = 320original = d * d  # 102400print(f"原始参数量: {original:,}")for rank in [4, 8, 16, 32, 64]:    lora_p, ratio = calculate_lora_params(original, rank)    print(f"  rank={rank:2d}: LoRA参数={lora_p:6,}, 占比={ratio:.2f}%") # 整个模型的LoRA微调显存估算total_params_million = 1500  # 1.5B参数模型print(f"\n模型总参数: {total_params_million}M")for rank in [4, 8, 16]:    # 假设对UNet中所有注意力层注入LoRA    n_attention_layers = 24    per_layer_d = 320    total_lora = n_attention_layers * 2 * per_layer_d * rank    # 显存: 参数(FP32) + 梯度(FP32) + 优化器状态(Adam: 2x)    vram_gb = total_lora * 4 * 4 / (1024**3)  # 粗略估算    print(f"  rank={rank}: LoRA参数={total_lora:,}, 估算显存≈{vram_gb:.1f}GB")

#!/usr/bin/env python3"""Mochi 1视频生成核心流程模拟演示扩散模型生成视频的基本原理"""import numpy as np class SimpleVideoDiffusion:    """简化的视频扩散模型模拟"""    def __init__(self, n_frames=8, height=16, width=16, channels=3):        self.n_frames = n_frames        self.height = height        self.width = width        self.channels = channels        self.shape = (n_frames, height, width, channels)        def forward_diffusion(self, clean_video, t, T=1000):        """前向扩散: 在t步向视频添加高斯噪声"""        # 噪声调度: beta从0.0001线性增长到0.02        beta = 0.0001 + (0.02 - 0.0001) * t / T        # alpha = 1 - beta        alpha = 1.0 - beta        # 累积alpha_t = alpha_1 * alpha_2 * ... * alpha_t        alpha_bar = alpha ** t        # 采样噪声        noise = np.random.randn(*self.shape)        # 加噪: x_t = sqrt(alpha_bar) * x_0 + sqrt(1-alpha_bar) * noise        noisy = np.sqrt(alpha_bar) * clean_video + np.sqrt(1 - alpha_bar) * noise        return noisy, noise        def generate_video(self, text_prompt, n_steps=20, cfg_scale=7.5):        """反向去噪生成视频（简化模拟）"""        np.random.seed(42)        # 从纯噪声开始        video = np.random.randn(*self.shape)        print(f"提示词: '{text_prompt}'")        print(f"生成配置: {n_steps}步, CFG={cfg_scale}")        print(f"视频形状: {self.n_frames}帧 x {self.height}x{self.width} x {self.channels}通道")                for step in range(n_steps, 0, -1):            t = step            beta = 0.0001 + (0.02 - 0.0001) * t / 1000            alpha = 1.0 - beta            alpha_bar = alpha ** t            alpha_bar_prev = alpha ** (t - 1)                        # 模拟模型预测噪声（实际中由神经网络完成）            predicted_noise = np.random.randn(*self.shape) * 0.1                        # CFG: 有条件预测 + cfg_scale * (有条件 - 无条件)            # 简化: 直接缩放预测            guided_noise = predicted_noise * cfg_scale                        # 去噪步骤            video = (video - (1 - alpha) / np.sqrt(1 - alpha_bar) * guided_noise) / np.sqrt(alpha)            if t > 1:                video += np.sqrt(beta) * np.random.randn(*self.shape)                        if step % 5 == 0:                noise_level = np.std(video)                print(f"  步骤 {step:3d}/20, 噪声水平: {noise_level:.4f}")                # 裁剪到[0,1]范围        video = np.clip((video - video.min()) / (video.max() - video.min()), 0, 1)        return video # 演示生成model = SimpleVideoDiffusion(n_frames=8, height=16, width=16)result = model.generate_video(    text_prompt="一只橘猫在花园草地上缓慢行走，阳光明媚",    n_steps=20,    cfg_scale=7.5)print(f"\n生成完成! 视频值范围: [{result.min():.3f}, {result.max():.3f}]")print(f"总像素数: {result.size:,}")