Memory-efficient Diffusion Transformers with Quanto and Diffusers
Over the past few months, we have seen an emergence in the use of Transformer-based diffusion backbones for high-resolution text-to-image (T2I) generation. These models use the transformer architecture as the building block for the diffusion process, instead of the UNet architecture that was prevalent in many of the initial diffusion models. Thanks to the nature of Transformers, these backbones show good scalability, with models ranging from 0.6B to 8B parameters.
As models become larger, memory requirements increase. The problem intensifies because a diffusion pipeline usually consists of several components: a text encoder, a diffusion backbone, and an image decoder. Furthermore, modern diffusion pipelines use multiple text encoders – for example, there are three in the case of Stable Diffusion 3. It takes 18.765 GB of GPU memory to run SD3 inference using FP16 precision.
These high memory requirements can make it difficult to use these models with consumer GPUs, slowing adoption and making experimentation harder. In this post, we show how to improve the memory efficiency of Transformer-based diffusion pipelines by leveraging Quanto's quantization utilities from the Diffusers library.
Over the past few months, we have seen an emergence in the use of Transformer-based diffusion backbones for high-resolution text-to-image (T2I) generation. These models use the transformer architecture as the building block for the diffusion process, instead of the UNet architecture that was prevalent in many of the initial diffusion models. Thanks to the nature of Transformers, these backbones show good scalability, with models ranging from 0.6B to 8B parameters.
As models become larger, memory requirements increase. The problem intensifies because a diffusion pipeline usually consists of several components: a text encoder, a diffusion backbone, and an image decoder. Furthermore, modern diffusion pipelines use multiple text encoders – for example, there are three in the case of Stable Diffusion 3. It takes 18.765 GB of GPU memory to run SD3 inference using FP16 precision.
These high memory requirements can make it difficult to use these models with consumer GPUs, slowing adoption and making experimentation harder. In this post, we show how to improve the memory efficiency of Transformer-based diffusion pipelines by leveraging Quanto's quantization utilities from the Diffusers library.