<2-3 sentences summarising what the project addresses and solves, the target audience, and the benefit.> DC-Gen accelerates diffusion models via post-training adaptation into a deeply compressed latent space. It targets researchers and practitioners, enabling faster inference and high-resolution generation without quality loss, significantly reducing computational costs.

How It Works

DC-Gen transfers pre-trained diffusion models to a compressed latent space using a lightweight Deep Compression Autoencoder (DC-AE). This avoids costly retraining. "Embedding Alignment" transfers the model's knowledge to the new latent space, preserving semantics, with quality recovered via LoRA finetuning. This approach yields substantial speedups and enables high-resolution generation.

Quick Start & Requirements

Setup requires Conda with Python 3.10 and pip install -U -r requirements.txt. Specific hardware (e.g., H100, NVIDIA 5090) is implied. Code and models are pending release due to legal review. Links to DC-AE/DC-AE-Lite setup and demos are provided.

Highlighted Details

• Achieves up to 53x latency reduction and 56x throughput improvement at 4K resolution on H100.
• Enables native high-resolution (4K) image generation, often prohibitive for base models.
• Preserves base model realism and text quality while boosting inference speed.
• Facilitates rapid autoencoder adaptation via Embedding Alignment, bypassing full model retraining.

Maintenance & Community

Associated with dc-ai-projects. No explicit community links or roadmap provided. Active research indicated by multiple arXiv preprints and ICCV 2025 acceptance for DC-AE 1.5.

Licensing & Compatibility

The README does not specify the software license. Users should verify licensing terms before commercial use or integration.

Limitations & Caveats

Code and models are not yet publicly available due to an ongoing legal review, preventing immediate adoption. The effectiveness of quality recovery via Embedding Alignment and LoRA finetuning may vary.