# TMP: Tree-structured Mixed-policy Pruning for Large-scale Image Generation and Editing

arXiv: https://arxiv.org/abs/2606.27089 · HTML: https://mlsanity.com/paper/2606.27089/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Vision mixture-of-experts, one of 12 directions in Mixture-of-experts.
- Builds on 23 of its 28 references that are in the map.
- First posted on arXiv 25 Jun 2026.

## Placed in

- Mixture-of-experts / [Vision mixture-of-experts](https://mlsanity.com/moe/vision-mixture-of-experts/)

## Builds on (most cited first, 23 of 23)

- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Scaling Rectified Flow Transformers for High-Resolution Image Synthesis](https://mlsanity.com/paper/2403.03206/) (2024)
- [Qwen-Image Technical Report](https://mlsanity.com/paper/2508.02324/) (2025)
- [On-Policy Distillation of Language Models: Learning from Self-Generated Mistakes](https://mlsanity.com/paper/2306.13649/) (2023)
- [Importance Estimation for Neural Network Pruning](https://mlsanity.com/paper/1906.10771/) (2019)
- [Z-Image: An Efficient Image Generation Foundation Model with Single-Stream Diffusion Transformer](https://mlsanity.com/paper/2511.22699/) (2025)
- [Structural Pruning for Diffusion Models](https://mlsanity.com/paper/2305.10924/) (2023)
- [SANA 1.5: Efficient Scaling of Training-Time and Inference-Time Compute in Linear Diffusion Transformer](https://mlsanity.com/paper/2501.18427/) (2025)
- [HunyuanImage 3.0 Technical Report](https://mlsanity.com/paper/2509.23951/) (2025)
- [X-Omni: Reinforcement Learning Makes Discrete Autoregressive Image Generative Models Great Again](https://mlsanity.com/paper/2507.22058/) (2025)
- [OneIG-Bench: Omni-dimensional Nuanced Evaluation for Image Generation](https://mlsanity.com/paper/2506.07977/) (2025)
- [Revisiting Locally Supervised Learning: an Alternative to End-to-end Training](https://mlsanity.com/paper/2101.10832/) (2021)
- [LD-Pruner: Efficient Pruning of Latent Diffusion Models using Task-Agnostic Insights](https://mlsanity.com/paper/2404.11936/) (2024)
- [TinyFusion: Diffusion Transformers Learned Shallow](https://mlsanity.com/paper/2412.01199/) (2024)
- [OBS-Diff: Accurate Pruning For Diffusion Models in One-Shot](https://mlsanity.com/paper/2510.06751/) (2025)
- [MosaicDiff: Training-free Structural Pruning for Diffusion Model Acceleration Reflecting Pretraining Dynamics](https://mlsanity.com/paper/2510.11962/) (2025)
- [HierarchicalPrune: Position-Aware Compression for Large-Scale Diffusion Models](https://mlsanity.com/paper/2508.04663/) (2025)
- [Elastic Diffusion Transformer](https://mlsanity.com/paper/2602.13993/) (2026)
- [Pluggable Pruning with Contiguous Layer Distillation for Diffusion Transformers](https://mlsanity.com/paper/2511.16156/) (2025)
- [FastFLUX: Pruning FLUX with Block-wise Replacement and Sandwich Training](https://mlsanity.com/paper/2506.10035/) (2025)
- [ALTER: All-in-One Layer Pruning and Temporal Expert Routing for Efficient Diffusion Generation](https://mlsanity.com/paper/2505.21817/) (2025)
- [Amber-Image: Efficient Compression of Large-Scale Diffusion Transformers](https://mlsanity.com/paper/2602.17047/) (2026)
- [Diff-ES: Stage-wise Structural Diffusion Pruning via Evolutionary Search](https://mlsanity.com/paper/2603.05105/) (2026)

## Abstract (arXiv)

Modern image generation model rapidly grows their sizes to meet high-fidelity image synthesis. However, they gradually become unaffordable for their enormous parameter consumption and computation budget that lead to massive resources requirement and gpu memory footprint. In this paper, we propose TMP, the first Tree-structured Mixed-policy Pruning framework that generalizes prevalent image tasks (T2I and TI2I) and architectures (Mixture-of-Experts (MoE) and Diffusion transformer (DiT)). It could be applied to the step-distilled models and contribute as the last stage. We perform experiments upon current open-sourced SOTA HunyuanImage-3.0 instruct and a popular efficient model Z-Image turbo. The proposed pruning framework manages to compress HunyuanImage 3.0 from 80B to 20B parameters at 75% reduction ratio, sacrificing limited generation quality. We also optimize to enable the inference of the pruned 20B version of HunyuanImage 3.0 on a single 24GB 4090 GPU by engineering skills. The inference script and model weight have been integrated into the existing HunyuanImage3.0 open-source github and huggingface repository. Besides, we prove the efficacy of TMP by compressing Z-Image turbo from 6B to 4B (33% reduction) with negligible degradation.