# Designing Parameter and Compute Efficient Diffusion Transformers using Distillation

arXiv: https://arxiv.org/abs/2502.14226 · HTML: https://mlsanity.com/paper/2502.14226/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Diffusion foundations and sampling, one of 14 directions in Diffusion models.
- Builds on 14 of its 45 references that are in the map.
- First posted on arXiv 20 Feb 2025.

## Placed in

- Diffusion models / [Diffusion foundations and sampling](https://mlsanity.com/diffusion/diffusion-foundations-and-sampling/)

## Builds on (most cited first, 14 of 14)

- [Score-Based Generative Modeling through Stochastic Differential Equations](https://mlsanity.com/paper/2011.13456/) (2020)
- [DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning](https://mlsanity.com/paper/2501.12948/) (2025)
- [Consistency Models](https://mlsanity.com/paper/2303.01469/) (2023)
- [SiT: Exploring Flow and Diffusion-based Generative Models with Scalable Interpolant Transformers](https://mlsanity.com/paper/2401.08740/) (2024)
- [Improved Distribution Matching Distillation for Fast Image Synthesis](https://mlsanity.com/paper/2405.14867/) (2024)
- [Knowledge Distillation in Iterative Generative Models for Improved Sampling Speed](https://mlsanity.com/paper/2101.02388/) (2021)
- [TRACT: Denoising Diffusion Models with Transitive Closure Time-Distillation](https://mlsanity.com/paper/2303.04248/) (2023)
- [Q-DiT: Accurate Post-Training Quantization for Diffusion Transformers](https://mlsanity.com/paper/2406.17343/) (2024)
- [PTQ4DiT: Post-training Quantization for Diffusion Transformers](https://mlsanity.com/paper/2405.16005/) (2024)
- [Progressive Distillation for Fast Sampling of Diffusion Models](https://mlsanity.com/paper/2202.00512/) (2022)
- [Trajectory Consistency Distillation: Improved Latent Consistency Distillation by Semi-Linear Consistency Function with Trajectory Mapping](https://mlsanity.com/paper/2402.19159/) (2024)
- [TinyFusion: Diffusion Transformers Learned Shallow](https://mlsanity.com/paper/2412.01199/) (2024)
- [MobileDiffusion: Instant Text-to-Image Generation on Mobile Devices](https://mlsanity.com/paper/2311.16567/) (2023)
- [Efficient Diffusion Transformer with Step-wise Dynamic Attention Mediators](https://mlsanity.com/paper/2408.05710/) (2024)

## Abstract (arXiv)

Diffusion Transformers (DiTs) with billions of model parameters form the backbone of popular image and video generation models like DALL.E, Stable-Diffusion and SORA. Though these models are necessary in many low-latency applications like Augmented/Virtual Reality, they cannot be deployed on resource-constrained Edge devices (like Apple Vision Pro or Meta Ray-Ban glasses) due to their huge computational complexity. To overcome this, we turn to knowledge distillation and perform a thorough design-space exploration to achieve the best DiT for a given parameter size. In particular, we provide principles for how to choose design knobs such as depth, width, attention heads and distillation setup for a DiT. During the process, a three-way trade-off emerges between model performance, size and speed that is crucial for Edge implementation of diffusion. We also propose two distillation approaches - Teaching Assistant (TA) method and Multi-In-One (MI1) method - to perform feature distillation in the DiT context. Unlike existing solutions, we demonstrate and benchmark the efficacy of our approaches on practical Edge devices such as NVIDIA Jetson Orin Nano.