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1# Clone the repository
2git clone https://github.com/yourusername/ttv-1b.git
3cd ttv-1b
4
5# Install dependencies
6pip install -r requirements.txt1from train import Trainer
2from video_ttv_1b import create_model
3
4# Create model
5device = 'cuda'
6model = create_model(device)
7
8# Create datasets (replace with your data)
9train_dataset = YourVideoDataset(...)
10val_dataset = YourVideoDataset(...)
11
12# Initialize trainer
13trainer = Trainer(
14 model=model,
15 train_dataset=train_dataset,
16 val_dataset=val_dataset,
17 batch_size=2,
18 gradient_accumulation_steps=8,
19 mixed_precision=True,
20 learning_rate=1e-4,
21 num_epochs=100,
22)
23
24# Start training
25trainer.train()python train.py1from inference import generate_video_from_prompt
2
3# Generate video
4video = generate_video_from_prompt(
5 prompt="A cat playing with a ball of yarn",
6 checkpoint_path="checkpoints/checkpoint_best.pt",
7 output_path="output.mp4",
8 num_steps=50,
9 guidance_scale=7.5,
10)1python inference.py \
2 --prompt "A serene sunset over the ocean" \
3 --checkpoint checkpoints/checkpoint_best.pt \
4 --output generated_video.mp4 \
5 --steps 50 \
6 --guidance 7.5Input: Text Prompt + Random Noise Video
↓
┌─────────────────────────┐
│ Text Encoder (6L) │
│ 768d, 12 heads │
└─────────────────────────┘
↓
┌─────────────────────────┐
│ Text Projection │
│ 768d → 1536d │
└─────────────────────────┘
↓
┌─────────────────────────┐
│ 3D Patch Embedding │
│ (2,16,16) patches │
└─────────────────────────┘
↓
┌─────────────────────────┐
│ 24× DiT Blocks │
│ • 3D Spatio-Temporal │
│ Attention (24 heads)│
│ • Rotary Embeddings │
│ • AdaLN Modulation │
│ • Feed-Forward Net │
└─────────────────────────┘
↓
┌─────────────────────────┐
│ Final Layer + AdaLN │
└─────────────────────────┘
↓
┌─────────────────────────┐
│ Unpatchify to Video │
└─────────────────────────┘
↓
Output: Predicted Noise / Denoised Video1# Example 1: Basic generation
2from inference import VideoGenerator, load_model
3from video_ttv_1b import DDPMScheduler
4
5model = load_model("checkpoints/best.pt")
6scheduler = DDPMScheduler()
7generator = VideoGenerator(model, scheduler)
8
9video = generator.generate(
10 prompt="A beautiful waterfall in a lush forest",
11 num_inference_steps=50,
12)
13
14# Example 2: Batch generation
15from inference import batch_generate
16
17prompts = [
18 "A dog running in a park",
19 "Fireworks in the night sky",
20 "Ocean waves crashing on rocks",
21]
22
23batch_generate(
24 prompts=prompts,
25 checkpoint_path="checkpoints/best.pt",
26 output_dir="./outputs",
27 num_steps=50,
28)| Metric | Value |
|---|---|
| Parameters | 1.0B |
| FLOPs (per frame) | ~250 GFLOPs |
| Inference Time (50 steps, A100) | ~15-20 seconds |
| Training Loss (final) | ~0.05 MSE |
| Video Quality (FVD) | TBD |
1VideoTTV1B(
2 img_size=(256, 256), # Output resolution
3 num_frames=16, # Video length
4 patch_size=(2, 16, 16), # Patch dimensions
5 in_channels=3, # RGB
6 hidden_dim=1536, # Model width
7 depth=24, # Number of layers
8 num_heads=24, # Attention heads
9 mlp_ratio=4.0, # MLP expansion
10 text_dim=768, # Text encoder dim
11 vocab_size=50257, # Vocabulary size
12)1Trainer(
2 batch_size=2,
3 gradient_accumulation_steps=8,
4 learning_rate=1e-4,
5 weight_decay=0.01,
6 num_epochs=100,
7 mixed_precision=True,
8)ttv-1b/
├── video_ttv_1b.py # Model architecture
├── train.py # Training script
├── inference.py # Inference & generation
├── requirements.txt # Dependencies
├── README.md # Documentation
├── checkpoints/ # Model checkpoints
├── data/ # Training data
└── outputs/ # Generated videos1@misc{ttv1b2024,
2 title={TTV-1B: A 1 Billion Parameter Text-to-Video Model},
3 author={Your Name},
4 year={2024},
5 url={https://github.com/yourusername/ttv-1b}
6}