--- language: - en tags: - image-generation - diffusion - latent-alignment - model-composition pipeline_tag: text-to-image ---

InstantFusion — A Shared Latent Interface for Image Generators

Composing diffusion models through a shared latent space

GitHub · Project Page · Paper

## Overview InstantFusion connects heterogeneous image generators through a learned shared latent interface. Model-specific Latent AutoEncoders (LAEs) align intermediate states while preserving the information needed for reconstruction and continued denoising. The base generators remain frozen during LAE training. The shared interface supports generation acceleration, multi-preference composition, and cross-model on-policy distillation (OPD).

InstantFusion training and inference overview

## Released checkpoints This repository provides trained LAE interface weights. The base generators must be obtained separately. | Checkpoint | Connected generators | | --- | --- | | [sd3_qwen_lae.bin](sd3_qwen_lae.bin) | SD3 and Qwen-Image | | [flux1_dev_qwen_lae.bin](flux1_dev_qwen_lae.bin) | FLUX.1-dev and Qwen-Image | | [flux1_schnell_qwen_lae.bin](flux1_schnell_qwen_lae.bin) | FLUX.1-schnell and Qwen-Image | | [flux1_sd3_qwen_lae.bin](flux1_sd3_qwen_lae.bin) | FLUX.1, SD3, and Qwen-Image | | [flux2_dev_qwen_lae.bin](flux2_dev_qwen_lae.bin) | FLUX.2-dev and Qwen-Image | | [flux2_klein_qwen_lae.bin](flux2_klein_qwen_lae.bin) | FLUX.2-Klein and Qwen-Image | | [zimage_turbo_qwen_lae.bin](zimage_turbo_qwen_lae.bin) | Z-Image-Turbo and Qwen-Image | ## Visual examples ### Latent reconstruction LAE reconstruction examples for Qwen-Image, FLUX.1, and SD3. Mean SSIM is reported over 100 images for each model.

LAE reconstruction examples and mean SSIM for Qwen-Image, FLUX.1, and SD3

### Cross-model denoising Intermediate states can be transferred through the shared space so that another generator can continue denoising. The examples below show cross-model generation with SD3, FLUX.1, and Qwen-Image.

Cross-model handoff and continued denoising examples

For implementation details, supported inference configurations, and training instructions, visit the [InstantFusion GitHub repository](https://github.com/InternLM/InstantFusion).