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:

# 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:

# 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=
)


lora_config = LoRAAdapter.Config(
    r=,
    lora_alpha=,
    target_modules=[, ],
    lora_dropout=
)

adapter.finetune_lora(
    dataset=training_data,
    config=lora_config,
    epochs=,
    learning_rate=
)