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Dc Ae F32c32 Sana 1.1 Diffusers

Developed by mit-han-lab
DC-AE is a novel autoencoder architecture designed to accelerate high-resolution diffusion models. It maintains reconstruction quality at high spatial compression ratios through residual autoencoding and decoupled high-resolution adaptation techniques.
Downloads 1,127
Release Time : 1/24/2025

Model Overview

DC-AE addresses the issue of degraded reconstruction accuracy in high spatial compression ratio autoencoders, significantly speeding up the training and inference processes of diffusion models while preserving image generation quality.

Model Features

High-compression-ratio reconstruction
Supports spatial compression ratios up to 128x while maintaining high-quality image reconstruction capability
Residual autoencoding
Learns residuals based on spatial-channel transformation features, alleviating optimization challenges in high-compression-ratio autoencoders
Decoupled high-resolution adaptation
Employs a three-stage decoupled training strategy to mitigate generalization penalties in high-compression-ratio autoencoders
Efficient inference
Provides 19.1x faster inference for UViT-H models compared to SD-VAE-f8 autoencoder

Model Capabilities

High-resolution image generation
Image compression and reconstruction
Efficient diffusion model acceleration

Use Cases

Creative content generation
Art creation
Rapid generation of high-quality artistic images
512x512 resolution image generation
Industrial design
Product prototype design
Generates product design concept images based on text descriptions
High-fidelity image output
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