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| """
ZYT-World: 实时可控世界模型
关键创新:
1. Plücker适配器编码多摄像头几何
2. 40步→1步蒸馏 (107.7×加速)
3. TinyVAE 19M参数解码 (59.8×加速)
4. 隐式记忆模块保持场景一致性
"""
import torch
import torch.nn as nn
class PluckerAdapter(nn.Module):
"""
Plücker适配器: 编码摄像头几何
每个视角的摄像头参数编码为Plücker射线表示:
- 原点 (相机位置)
- 方向 (光轴方向)
这使得模型能区分不同视角的几何关系
"""
def __init__(self, latent_dim=512):
super().__init__()
self.proj = nn.Linear(6, latent_dim)
def forward(self, plucker_rays):
"""
Args:
plucker_rays: (B, num_views, 6) Plücker射线参数
Returns:
view_embeds: (B, num_views, latent_dim) 视角嵌入
"""
return self.proj(plucker_rays)
class EgoMotionAdaLN(nn.Module):
"""自车运动自适应层归一化: 全局运动控制"""
def __init__(self, latent_dim, num_layers):
super().__init__()
self.norm = nn.LayerNorm(latent_dim)
self.ego_mlp = nn.Sequential(
nn.Linear(6, latent_dim),
nn.SiLU(),
nn.Linear(latent_dim, latent_dim * 2)
)
def forward(self, x, ego_motion):
scale, shift = self.ego_mlp(ego_motion).chunk(2, dim=-1)
return self.norm(x) * (1 + scale) + shift
class TinyVAE(nn.Module):
"""TinyVAE: 19M参数的轻量级VAE解码器"""
def __init__(self, latent_channels=16, out_channels=3):
super().__init__()
self.decoder = nn.Sequential(
nn.ConvTranspose2d(latent_channels, 256, 4, stride=2, padding=1),
nn.GELU(),
nn.ConvTranspose2d(256, 128, 4, stride=2, padding=1),
nn.GELU(),
nn.ConvTranspose2d(128, 64, 4, stride=2, padding=1),
nn.GELU(),
nn.ConvTranspose2d(64, 32, 4, stride=2, padding=1),
nn.GELU(),
nn.Conv2d(32, out_channels, 3, padding=1)
)
def forward(self, x):
return self.decoder(x)
class RigCritic(nn.Module):
"""RigCritic: 联合评估7视角一致性"""
def __init__(self, latent_dim=512, num_views=7):
super().__init__()
self.num_views = num_views
self.critic = nn.Sequential(
nn.Linear(latent_dim * num_views, 1024),
nn.GELU(),
nn.Linear(1024, 512),
nn.GELU(),
nn.Linear(512, 1)
)
def forward(self, view_latents):
"""
Args:
view_latents: (B, num_views, latent_dim)
Returns:
consistency_score: (B, 1)
"""
B = view_latents.shape[0]
flat = view_latents.reshape(B, -1)
return self.critic(flat)
class ImplicitMemory(nn.Module):
"""隐式记忆模块: 跨轨迹场景保持"""
def __init__(self, latent_dim=512, memory_size=256):
super().__init__()
self.memory_size = memory_size
self.memory_key = nn.Parameter(torch.randn(memory_size, latent_dim))
self.memory_value = nn.Parameter(torch.randn(memory_size, latent_dim))
self.query = nn.Linear(latent_dim, latent_dim)
def forward(self, x):
"""
基于位置的记忆检索
Args:
x: (B, latent_dim) 当前场景编码
Returns:
augmented: (B, latent_dim) 增强后的表征
"""
q = self.query(x)
attn = torch.matmul(q, self.memory_key.T)
attn = torch.softmax(attn / (latent_dim ** 0.5), dim=-1)
retrieved = torch.matmul(attn, self.memory_value)
return x + retrieved
class DistillationPipeline:
"""40步教师→1步学生蒸馏"""
def __init__(self):
self.steps = {
"teacher": {
"type": "bidirectional",
"steps": 40,
"arch": "full_diffusion"
},
"stage1": {
"type": "causal_consistency_distillation",
"description": "因果一致性蒸馏",
"goal": "减少步数同时保持因果性"
},
"stage2": {
"type": "self_rollout_distribution_matching",
"description": "自回归分布匹配蒸馏",
"goal": "单步推理质量匹配多步"
},
"stage3": {
"type": "rig_critic_training",
"description": "RigCritic联合评估",
"goal": "7视角联合一致性"
},
"student": {
"type": "one_step",
"steps": 1,
"speedup": "107.7×"
}
}
if __name__ == "__main__":
B, N, D = 4, 7, 512
plucker = PluckerAdapter(D)
rays = torch.randn(B, N, 6)
view_embeds = plucker(rays)
vae = TinyVAE()
latent = torch.randn(B, 16, 32, 32)
decoded = vae(latent)
critic = RigCritic(D, N)
score = critic(view_embeds)
memory = ImplicitMemory(D)
enhanced = memory(view_embeds.mean(dim=1))
print(f"Plücker输出: {view_embeds.shape}")
print(f"TinyVAE输出: {decoded.shape}")
print(f"RigCritic分数: {score.shape}")
print(f"记忆增强: {enhanced.shape}")
print("\n=== ZYT-World关键指标 ===")
print("视角: 7 (4鱼眼+3针孔)")
print("蒸馏: 40步→1步, 107.7×加速")
print("TinyVAE: 19M参数, 59.8×加速")
print("量化: W8A8")
print("30秒rollout: 稳定")
print("跨轨迹重访: 场景一致性保持")
|
