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BharathK333/
MMFace-DiT-Models

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

License: 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 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

@inproceedings{krishnamurthy2026mmface,
  title={MMFace-DiT: A Dual-Stream Diffusion Transformer for High-Fidelity Multimodal Face Generation},
  author={Krishnamurthy, Bharath and Rattani, Ajita},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={4580--4589},
  year={2026}
}

@article{krishnamurthy2026mmface_arxiv,
  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}
}

BharathK333/MMFace-DiT-Models