Dc Video Gen Compression Adaptation

---
name: dc-video-gen-compression-adaptation
title: "DC-VideoGen: Efficient Video Diffusion via Deep Compression"
version: 0.0.2
engine: skillxiv-v0.0.2-claude-opus-4.6
license: MIT
url: https://arxiv.org/abs/2509.25182
keywords: [video-generation, diffusion-models, compression, efficiency, autoencoder]
description: "Accelerate video generation by 14.8x through deep compression autoencoder (32x-64x spatial, 4x temporal compression) combined with lightweight adapter-based model adaptation. Use when deploying video diffusion models under compute or latency constraints."
---

# DC-VideoGen: Efficient Video Diffusion via Deep Compression

DC-VideoGen introduces a post-training acceleration framework for video diffusion models combining a deep compression autoencoder with efficient adaptation mechanisms, enabling high-resolution video generation on resource-constrained systems.

## Core Architecture

- **DC-AE-V autoencoder**: Chunk-causal temporal modeling with 32-64x spatial and 4x temporal compression
- **Preservation guarantee**: Reconstruction quality maintained despite aggressive compression
- **AE-Adapt-V mechanism**: Two-stage adaptation via embedding space alignment and LoRA fine-tuning
- **Efficiency gains**: 14.8x speedup with competitive quality metrics

## Implementation Steps

Construct deep compression autoencoder with chunk-causal design:

```python
# Build compression autoencoder with temporal chunking
from dc_videogen import DCAutoencoder, ChunkCausalEncoder

# Define compression strategy
ae = DCAutoencoder(
    spatial_compression=32,    # 32x spatial reduction
    temporal_compression=4,     # 4x temporal reduction
    chunk_size=8,              # temporal chunk for causal modeling
    latent_dim=32
)

# Train on base video dataset
ae.train(
    video_dataset=your_videos,
    reconstruction_loss="l2",
    perceptual_loss_weight=0.1,
    epochs=50,
    batch_size=4
)
```

Adapt pretrained diffusion model to compressed latent space:

```python
# Two-stage adaptation: alignment + LoRA fine-tuning
from dc_videogen import AEAdaptV, LoRAAdapter

adapter = AEAdaptV(
    base_model=pretrained_diffusion_model,
    compression_ae=ae,
    alignment_method="linear_projection"
)

# Stage 1: Embedding space alignment (10 H100 GPU days)
adapter.align_embeddings(
    sample_videos=sample_set,
    lr=1e-4,
    epochs=5
)

# Stage 2: LoRA fine-tuning on full diffusion (trained weights)
lora_config = LoRAAdapter.Config(
    r=8,
    lora_alpha=16,
    target_modules=["to_q", "to_v"],
    lora_dropout=0.05
)

adapter.finetune_lora(
    dataset=training_data,
    config=lora_config,
    epochs=10,
    learning_rate=5e-5
)
```

## Practical Guidance

**When to use DC-VideoGen:**
- Deploying video generation on consumer hardware or edge devices
- Latency-sensitive applications requiring real-time generation
- Production systems with strict compute budgets
- Generating videos at 512x512 or higher resolutions

**When NOT to use:**
- Highest-quality output required (quality-efficiency tradeoff unavoidable)
- Applications requiring extremely high-resolution (2k+) generation
- Workflows already optimized for specific base models
- Tasks with limited video data for adapter training

**Hyperparameters:**
- **Spatial compression (32-64x)**: 32x optimal for 512x512 output; increase to 64x for lower resolutions
- **Temporal compression (4x)**: Preserve 4x for most applications; increase to 8x for static-heavy videos
- **Chunk size (8)**: Standard for causal causality; increase to 16 for longer-form coherence
- **LoRA rank (r=8)**: Increase to 16 for larger divergence from base model; keep at 8 for style transfer
- **Adaptation data**: 100-500 sample videos sufficient for most domains

## Quality-Efficiency Tradeoffs

- **Reconstruction PSNR**: ~28dB maintained despite 128x total compression
- **Speedup factor**: 14.8x typical; up to 20x with post-training optimization
- **Quality degradation**: Minimal on perceptual metrics; users rarely distinguish compressed output

## Compatibility

Works with multiple base diffusion models:
- Wan-2.1 (text-to-video)
- Wan-2.1-I2V (image-to-video)
- Custom trained models using same UNet architecture

## References

Relates to efficient diffusion model inference and video compression literature.