458 lines
12 KiB
Markdown
458 lines
12 KiB
Markdown
# SVD与视频扩散架构
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## 概述
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Stable Video Diffusion（SVD）是Stability AI发布的基于扩散模型的视频生成模型，能够生成高质量的短视频序列。SVD采用时序U-Net和3D卷积结合的架构，通过关键帧生成策略实现连贯且高质量的视频生成。
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## 1. Stable Video Diffusion概述
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### 1.1 模型简介
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SVD是基于SD（Stable Diffusion）架构扩展的视频生成模型：
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```
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核心架构：
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- 空间层：继承SD的2D UNet架构
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- 时序层：新增3D卷积和时空注意力
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- 训练策略：先图像后视频的两阶段训练
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```
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### 1.2 模型版本
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| 版本 | 参数量 | 视频长度 | 分辨率 |
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|------|--------|----------|--------|
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| SVD | 1.5B | 25 frames | 576×1024 |
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| SVD-XT | 1.5B | 25-50 frames | 576×1024 |
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| SVD-Ext | 1.5B | 90 frames | 576×1024 |
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## 2. 时序U-Net架构
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### 2.1 时序U-Net结构
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```python
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class TemporalUNet(nn.Module):
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def __init__(self):
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# 编码器（空间）
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self.encoder_blocks = nn.ModuleList([
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SpatialConvBlock(channels[0]),
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SpatialConvBlock(channels[1]),
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SpatialConvBlock(channels[2]),
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SpatialConvBlock(channels[3])
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])
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# 时序块（插入到空间块之间）
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self.temporal_blocks = nn.ModuleList([
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TemporalConvBlock(channels[0]),
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TemporalConvBlock(channels[1]),
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TemporalConvBlock(channels[2])
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])
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# 解码器
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self.decoder_blocks = nn.ModuleList([...])
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```
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### 2.2 时空特征提取
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```python
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class TemporalConvBlock(nn.Module):
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"""
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时序卷积块
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在帧维度上提取时序信息
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"""
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def __init__(self, channels):
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self.temporal_conv = nn.Conv3d(
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in_channels=channels,
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out_channels=channels,
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kernel_size=(3, 1, 1), # 只在时间维度卷积
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padding=(1, 0, 0)
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)
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self.temporal_attn = TemporalAttention(channels)
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def forward(self, x):
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# x: [B, C, F, H, W]
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B, C, F, H, W = x.shape
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# 时序卷积
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x = rearrange(x, 'b c f h w -> (b h w) c f')
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x = self.temporal_conv(x)
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x = rearrange(x, '(b h w) c f -> b c f h w', b=B, h=H, w=W)
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# 时序注意力
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x = x + self.temporal_attn(x)
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return x
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```
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### 2.3 3D卷积与时空注意力结合
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```python
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class SpatioTemporalBlock(nn.Module):
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"""
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时空块：3D卷积 + 时空注意力
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"""
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def __init__(self, in_channels, out_channels):
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# 3D空间-时间卷积
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self.spatial_conv = nn.Conv3d(
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in_channels, out_channels,
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kernel_size=(1, 3, 3),
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padding=(0, 1, 1)
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)
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# 时间维度卷积
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self.temporal_conv = nn.Conv3d(
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out_channels, out_channels,
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kernel_size=(3, 1, 1),
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padding=(1, 0, 0)
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)
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# 时空注意力
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self.spatiotemporal_attn = SpatioTemporalAttention(out_channels)
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def forward(self, x):
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# 空间特征提取
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x = self.spatial_conv(x)
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# 时间特征提取
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x = self.temporal_conv(x)
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# 时空注意力
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x = self.spatiotemporal_attn(x)
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return x
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```
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## 3. 关键帧生成策略
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### 3.1 关键帧定义
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```python
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# 视频生成中的关键帧策略
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# 将视频分为多个segments，每个segment有1个关键帧
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class KeyFrameStrategy:
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def __init__(self, num_frames=25, fps=24):
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self.num_frames = num_frames
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self.fps = fps
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# 关键帧间隔
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self.keyframe_interval = 4 # 每4帧有1个关键帧
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# 在训练时使用关键帧引导
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# 在推理时先生成关键帧，再插值
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```
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### 3.2 两阶段生成
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```python
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def generate_video(prompt, model, num_frames=25):
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"""
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两阶段视频生成
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"""
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# Stage 1: 生成关键帧
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keyframe_indices = [0, 4, 8, 12, 16, 20, 24]
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keyframes = []
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for i, idx in enumerate(keyframe_indices):
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if i == 0:
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# 首帧直接生成
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frame = generate_frame(prompt, first_frame=None)
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else:
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# 以首帧为条件生成关键帧
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frame = generate_frame(prompt, first_frame=keyframes[0])
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keyframes.append(frame)
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# Stage 2: 插值生成中间帧
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all_frames = []
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for i in range(len(keyframes) - 1):
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# 在两个关键帧之间插值
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interp_frames = interpolate(keyframes[i], keyframes[i+1], num=3)
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all_frames.extend(interp_frames)
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return all_frames
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```
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### 3.3 时序一致性保证
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```python
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class TemporalConsistencyLoss(nn.Module):
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"""
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时序一致性损失
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确保相邻帧之间的平滑过渡
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"""
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def forward(self, frames):
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# frames: [B, F, C, H, W]
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# 光流损失
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flow = compute_optical_flow(frames[:, :-1], frames[:, 1:])
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# 重建损失
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reconstructed = warp_frames(frames[:, :-1], flow)
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# 一致性损失
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loss = F.mse_loss(reconstructed, frames[:, 1:])
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return loss
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```
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## 4. 训练策略
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### 4.1 两阶段预训练
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```python
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# Stage 1: 图像预训练（继承SD能力）
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stage1_config = {
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'data': LAION 5B, # 图像数据
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'freeze_spatial': True,
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'train_temporal': True,
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'lr': 1e-4
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}
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# Stage 2: 视频微调
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stage2_config = {
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'data': WebVid-10M, # 视频数据
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'freeze_temporal': False,
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'train_all': True,
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'lr': 5e-5
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}
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```
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### 4.2 视频数据处理
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```python
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# 视频预处理
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class VideoProcessor:
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def __init__(self, num_frames=25, resolution=(576, 1024)):
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self.num_frames = num_frames
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self.resolution = resolution
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def __call__(self, video_path):
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# 1. 加载视频
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frames = load_video(video_path)
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# 2. 采样帧
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frame_indices = np.linspace(0, len(frames)-1, self.num_frames)
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sampled_frames = frames[frame_indices]
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# 3. 调整大小
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resized = [resize(f, self.resolution) for f in sampled_frames]
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# 4. 归一化
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normalized = [normalize(f) for f in resized]
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return torch.stack(normalized) # [F, C, H, W]
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```
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### 4.3 数据集
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| 数据集 | 时长 | 质量 | 用途 |
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|--------|------|------|------|
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| WebVid-10M | 10M | 中等 | 视频预训练 |
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| InternVid-10M | 10M | 高 | 高质量微调 |
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| 自采集数据 | 专有 | 高 | 特定场景 |
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## 5. 模型架构细节
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### 5.1 潜在扩散框架
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SVD继承SD的潜空间扩散框架：
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```python
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class VideoVAE(nn.Module):
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"""
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视频VAE
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将视频压缩到潜空间
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"""
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def __init__(self):
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# 空间编码器（与SD VAE相同）
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self.spatial_encoder = SpatialEncoder()
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# 时间编码器（新增）
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self.temporal_encoder = TemporalEncoder()
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# 潜在空间：[B, F, 4, H/8, W/8]
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def encode(self, video):
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# video: [B, F, C, H, W]
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# 逐帧编码到latent
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latents = []
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for frame in video:
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latent = self.spatial_encoder(frame)
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latents.append(latent)
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# 时间压缩
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temporal_latent = self.temporal_encoder(torch.stack(latents))
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return temporal_latent # [B, F', 4, H/8, W/8]
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def decode(self, latent):
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# latent: [B, F', 4, H/8, W/8]
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# 时间解码
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frames_latent = self.temporal_decoder(latent)
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# 逐帧解码
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frames = [self.spatial_decoder(f) for f in frames_latent]
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return torch.stack(frames) # [B, F, C, H, W]
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```
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### 5.2 时空注意力机制
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```python
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class SpatioTemporalAttention(nn.Module):
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"""
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时空注意力
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同时建模空间和时间关系
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"""
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def __init__(self, dim, num_heads=8):
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self.spatial_attn = nn.MultiheadAttention(dim, num_heads)
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self.temporal_attn = nn.MultiheadAttention(dim, num_heads)
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def forward(self, x):
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# x: [B, C, F, H, W]
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B, C, F, H, W = x.shape
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# 空间注意力（在各帧内）
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x_spatial = rearrange(x, 'b c f h w -> (b f) (h w) c')
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x_spatial = self.spatial_attn(x_spatial, x_spatial, x_spatial)
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x_spatial = rearrange(x_spatial, '(b f) (h w) c -> b c f h w', b=B, f=F)
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# 时间注意力（在各空间位置）
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x_temporal = rearrange(x, 'b c f h w -> (b h w) f c')
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x_temporal = self.temporal_attn(x_temporal, x_temporal, x_temporal)
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x_temporal = rearrange(x_temporal, '(b h w) f c -> b c f h w', b=B, h=H, w=W)
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return x_spatial + x_temporal
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```
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## 6. 推理与采样
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### 6.1 视频采样器
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```python
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class VideoSampler:
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def __init__(self, model, num_frames=25, guidance_scale=7.5):
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self.model = model
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self.num_frames = num_frames
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self.guidance_scale = guidance_scale
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@torch.no_grad()
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def sample(self, prompt, num_steps=25):
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# 初始化latent噪声
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latents = torch.randn(1, 4, self.num_frames//4, 72, 128)
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# 扩散采样
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for t in reversed(range(num_steps)):
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# 条件注入
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context = self.model.encode_prompt(prompt)
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# 去噪
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latents = self.model.denoise(latents, t, context)
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# 解码到视频
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video = self.model.decode(latents)
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return video
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```
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### 6.2 帧率控制
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```python
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# 推理时可调整帧率
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# 训练：25帧，24fps ≈ 1秒视频
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# 推理时可生成更多帧
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configs = {
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'SVD': {'frames': 25, 'fps': 24},
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'SVD-XT': {'frames': 50, 'fps': 24},
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'SVD-Ext': {'frames': 90, 'fps': 24}
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}
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```
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## 7. 质量控制
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### 7.1 评估指标
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| 指标 | 描述 | 测量方式 |
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|------|------|----------|
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| FVD | 视频质量 | Frechet Distance |
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| LPIPS | 感知质量 | 学习感知相似度 |
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| PSNR | 像素质量 | 峰值信噪比 |
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| 运动流畅度 | 时序连贯 | 光流分析 |
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### 7.2 生成质量优化
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```python
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# 分类器自由引导（CFG）增强
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def cfg_forward(model, x_t, t, cond, uncond, scale=7.5):
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cond_out = model(x_t, t, cond)
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uncond_out = model(x_t, t, uncond)
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# 引导
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guided = uncond_out + scale * (cond_out - uncond_out)
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return guided
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# 时序增强采样
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def temporal_enhanced_sampling(model, num_steps=25, temporal_denoise=2):
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for t in reversed(range(num_steps)):
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# 多次时序去噪
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for _ in range(temporal_denoise):
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x_t = model.denoise_temporal(x_t, t)
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x_t = model.denoise(x_t, t)
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```
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## 8. 应用场景
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### 8.1 创意视频生成
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```python
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# 文本到视频
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prompt = "a serene lake at sunset, cinematic quality"
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video = pipe.generate(prompt, num_frames=25)
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# 图像到视频（Image-to-Video）
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init_image = load("starting_frame.png")
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video = pipe.generate(
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prompt="the scene continues with gentle motion",
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image=init_image
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)
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```
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### 8.2 视频编辑
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```python
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# 视频续写
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existing_video = load("video.mp4")
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continuation = pipe.generate(
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prompt="the scene continues",
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init_video=existing_video
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)
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# 视频风格化
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style_prompt = "animated style"
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styled_video = pipe.generate(
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video=existing_video,
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prompt=style_prompt
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)
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```
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## 9. 与其他模型对比
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| 模型 | 厂商 | 特点 | 限制 |
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|------|------|------|------|
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| SVD | Stability AI | 开源、高质量 | 长度有限 |
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| Gen-2 | Runway | 商业、综合 | 闭源 |
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| Pika | Pika Labs | 快速生成 | 质量一般 |
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| Sora | OpenAI | 长视频、强理解 | 闭源、未开放 |
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## 10. 总结
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SVD的核心贡献：
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| 技术 | 作用 |
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|------|------|
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| 时序U-Net | 继承SD空间能力，增加时序建模 |
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| 3D卷积 | 提取时空特征 |
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| 时空注意力 | 全局时序依赖建模 |
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| 关键帧策略 | 保证时序一致性 |
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| 两阶段训练 | 图像到视频的知识迁移 |
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SVD代表了开源视频扩散模型的重要里程碑，为后续的视频生成模型提供了重要的技术参考。 |