1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
| import torch
import torch.nn as nn
class DiffusionGenerationPipeline(nn.Module):
"""
Cosmos 扩散生成管道
核心架构：
- 文本编码器：T5编码语义提示
- 扩散模型：基于条件的视频生成
- 安全护栏：过滤不当内容
支持的生成模式：
1. 文本→视频：从自然语言描述生成驾驶场景
2. 视频→视频：基于现有视频生成变体
3. 动作→视频：基于车辆动作序列生成
4. 多视角→视频：多摄像头一致性生成
5. 图像→LiDAR：从图像生成LiDAR点云
"""
def __init__(self, model_config: dict):
super().__init__()
self.text_encoder = T5TextEncoder(
model_name='google/t5-v1.1-xxl',
max_length=256
)
self.diffusion_model = CosmosDiffusionModel(
**model_config
)
self.guardrails = SafetyGuardrails()
def forward(self, condition: dict, mode: str = 'text_to_video') -> dict:
"""
Args:
condition: 生成条件（文本/视频/动作/多视角）
mode: 生成模式
Returns:
{'video': tensor, 'metadata': dict}
"""
if mode == 'text_to_video':
text_emb = self.text_encoder(condition['text'])
cond_emb = text_emb
elif mode == 'video_to_video':
text_emb = self.text_encoder(condition.get('text', ''))
video_emb = self.diffusion_model.encode_video(condition['video'])
cond_emb = text_emb + video_emb
else:
cond_emb = self.text_encoder(condition.get('text', ''))
if not self.guardrails.check(cond_emb):
return {'video': None, 'error': 'content_filtered'}
video = self.diffusion_model.sample(
cond_emb,
num_frames=condition.get('num_frames', 16),
height=condition.get('height', 576),
width=condition.get('width', 1024),
)
return {'video': video, 'metadata': {'mode': mode}}
class T5TextEncoder(nn.Module):
"""T5文本编码器"""
def __init__(self, model_name: str, max_length: int = 256):
super().__init__()
from transformers import T5Encoder
self.encoder = T5Encoder.from_pretrained(model_name)
self.max_length = max_length
def forward(self, text: str) -> torch.Tensor:
return torch.randn(1, 4096)
class CosmosDiffusionModel(nn.Module):
"""Cosmos扩散模型"""
def __init__(self, hidden_dim: int = 512, num_steps: int = 1000):
super().__init__()
self.hidden_dim = hidden_dim
self.num_steps = num_steps
self.unet = nn.Sequential(
nn.Conv2d(3, hidden_dim, 3, padding=1),
nn.GroupNorm(8, hidden_dim),
nn.SiLU(),
nn.Conv2d(hidden_dim, hidden_dim, 3, padding=1),
nn.GroupNorm(8, hidden_dim),
nn.SiLU(),
nn.Conv2d(hidden_dim, 3, 3, padding=1)
)
def sample(self, cond_emb, num_frames=16, height=576, width=1024):
"""采样生成视频"""
return torch.randn(num_frames, 3, height, width)
class SafetyGuardrails(nn.Module):
"""安全护栏模块"""
def __init__(self):
super().__init__()
self.classifier = nn.Linear(4096, 2)
def check(self, embedding: torch.Tensor) -> bool:
"""检查内容安全性"""
with torch.no_grad():
score = torch.sigmoid(self.classifier(embedding))
return score[0, 0] > 0.5
|
