---
license: mit
tags:
- diffusion
- transformers
- image-generation
- face-generation
- cvpr2026
- pytorch
---

# MMFace-DiT: A Dual-Stream Diffusion Transformer for High-Fidelity Multimodal Face Generation

[![Conference](https://img.shields.io/badge/CVPR-2026-blue)](https://cvpr.thecvf.com/)
[![Paper](https://img.shields.io/badge/ArXiv-Paper-red)](https://arxiv.org/abs/2603.29029)
[![Project Page](https://img.shields.io/badge/Project_Page-GitHub.io-blue)](https://vcbsl.github.io/MMFace-DiT/)
[![Code](https://img.shields.io/badge/Code-GitHub-black)](https://github.com/Bharath-K3/MMFace-DiT)
[![Dataset](https://img.shields.io/badge/Dataset-HuggingFace-yellow)](https://huggingface.co/datasets/BharathK333/MMFace-DiT-Datasets)
[![Demo](https://img.shields.io/badge/Demo-HuggingFace-orange)](https://huggingface.co/spaces/BharathK333/MMFace-DiT)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

**Authors:** Bharath Krishnamurthy and Ajita Rattani  
**Affiliation:** University of North Texas, Denton, Texas, USA  

_Accepted to IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2026)_

## Abstract
Recent multimodal face generation models address the spatial control limitations of text-to-image diffusion models by augmenting text-based conditioning with spatial priors such as segmentation masks, sketches, or edge maps. However, existing approaches typically append auxiliary control modules or stitch together separate uni-modal networks.

We introduce **MMFace-DiT**, a unified dual-stream diffusion transformer engineered for synergistic multimodal face synthesis. Its core novelty lies in a dual-stream transformer block that processes spatial (mask/sketch) and semantic (text) tokens in parallel, deeply fusing them through a shared **Rotary Position-Embedded (RoPE) Attention** mechanism. Furthermore, a novel **Modality Embedder** enables a single cohesive model to dynamically adapt to varying spatial conditions without retraining. MMFace-DiT achieves a 40% improvement in visual fidelity and prompt alignment over five state-of-the-art multimodal face generation models.

## Repository Contents
This repository contains the trained model checkpoints for MMFace-DiT. The models are provided for both Diffusion and Rectified Flow Matching (Flow) paradigms across different resolutions.

* `dit-unified-flux-vae-256`: Diffusion paradigm model for 256x256 resolution using the unified FLUX VAE (checkpoint-440700).
* `dit-unified-flux-vae-256-rfm`: Rectified Flow Matching (RFM) paradigm model for 256x256 resolution (checkpoint-283517).
* `dit-unified-flux-vae-512-rfm`: Rectified Flow Matching (RFM) paradigm model for 512x512 resolution (checkpoint-44070).
* `VAE`: Standalone VAE weights utilizing the compressed 16-channel FLUX latent space.
* `stable-diffusion-2-1-base`: Base SD 2.1 component structures required for the pipeline (Tokenizers, Text Encoders, Schedulers).

## Usage & Inference
Please refer to our [Official GitHub Project Page](https://vcbsl.github.io/MMFace-DiT/) for complete inference scripts, training code, and setup instructions.

### Example Inference (Flow - Mask Conditioning)
```bash
python sample_flow.py \
  --config_path "configs/flow/config_256_unified_rfm.yml" \
  --weights_path "path/to/downloaded/dit-unified-flux-vae-256-rfm/checkpoint-283517/dit_model_weights_ema.safetensors" \
  --modality "mask" \
  --conditioning_path "path/to/mask.png" \
  --prompt "A stunning young woman with long, wavy blonde hair..." \
  --output_dir "Generated_Samples" \
  --num_samples 4 \
  --guidance_scale 7.5
```

## Citation:
If you find this work helpful for your research, please cite our CVPR paper:

```bibtex
@article{krishnamurthy2026mmface,
  title={MMFace-DiT: A Dual-Stream Diffusion Transformer for High-Fidelity Multimodal Face Generation},
  author={Krishnamurthy, Bharath and Rattani, Ajita},
  journal={arXiv preprint arXiv:2603.29029},
  year={2026}
}
```