Emu3.5: Native Multimodal Models are World Learners

Emu3.5 Team, BAAI

Project Page | 🤗HF Models | Paper

🔹	Core Concept	Description
🧠	Unified World Modeling	Predicts the next state jointly across vision and language, enabling coherent world modeling and generation.
🧩	End-to-End Pretraining	Trained with a unified next-token prediction objective over interleaved vision–language sequences.
📚	Over 10T+ Multimodal Tokens	Pre-trained on over 10 trillion interleaved tokens from video frames and transcripts, capturing spatiotemporal structure.
🔄	Native Multimodal I/O	Processes and generates interleaved visual–text sequences without modality adapters or task-specific heads.
🎯	RL Post-Training	Large-scale reinforcement learning enhances reasoning, compositionality, and generation quality.
⚡	Discrete Diffusion Adaptation (DiDA)	Converts sequential decoding → bidirectional parallel prediction, achieving ≈20× faster inference without performance loss.
🖼️	Versatile Generation	Excels in long-horizon vision–language generation, any-to-image (X2I) synthesis, and text-rich image creation.
🌐	Generalizable World Modeling	Enables spatiotemporally consistent world exploration, and open-world embodied manipulation across diverse scenarios.
🏆	Performance Benchmark	Matches Gemini 2.5 Flash Image (Nano Banana) on image generation/editing, and outperforms on interleaved generation tasks.

🔥 News

• 2025-11-19 · 🚀 vLLM Offline Inference Released — Meet inference_vllm.py with a new cond/uncond batch scheduler, delivering 4–5× faster end-to-end generation on vLLM 0.11.0 across Emu3.5 tasks. Jump to #Run Inference with vLLM for setup guidance and see PR #47 for full details.
• 2025-11-17 · 🎛️ Gradio Demo (Transformers Backend) — Introduced gradio_demo_image.py and gradio_demo_interleave.py presets for the standard Transformers runtime, providing turnkey T2I/X2I and interleaved generation experiences with streaming output. Try the commands in #Gradio Demo to launch both UIs locally.

Table of Contents

1. Model & Weights
2. Quick Start
3. Gradio Demo
4. Schedule
5. Citation

1. Model & Weights

Model name	HF Weight
Emu3.5	🤗 HF link
Emu3.5-Image	🤗 HF link
Emu3.5-VisionTokenizer	🤗 HF link

Note:

• Emu3.5 supports general-purpose multimodal predictions, including interleaved image-text generation and single-image generation (T2I/X2I) tasks.
• Emu3.5-Image is a model focused on T2I/X2I tasks for best performance on these scenarios.
• Both models are pure next-token predictors without DiDA acceleration (each image may take several minutes to generate).
• ⚡ Stay tuned for DiDA-accelerated weights.

💡 Usage tip:
For interleaved image-text generation, use Emu3.5.
For single-image generation (T2I and X2I), use Emu3.5-Image for the best quality.

2. Quick Start

Environment Setup

# Requires Python 3.12 or higher.
git clone https://github.com/baaivision/Emu3.5
cd Emu3.5
pip install -r requirements/transformers.txt
pip install flash_attn==2.8.3 --no-build-isolation

Configuration

Edit configs/config.py to set:

• Paths: model_path, vq_path
• Task template: task_type in {t2i, x2i, howto, story, explore, vla}
• Input image: use_image (True to provide reference images, controls <|IMAGE|> token); set reference_image in each prompt to specify the image path. For x2i task, we recommand using reference_image as a list containing single/multiple image paths to be compatible with multi-image input.
• Sampling: sampling_params (classifier_free_guidance, temperature, top_k/top_p, etc.)
• Aspect Ratio (for t2i task): aspect_ratio ("4:3", "21:9", "1:1", "auto" etc..)

Run Inference

python inference.py --cfg configs/config.py

Example Configurations by Task

Below are example commands for different tasks. Make sure to set CUDA_VISIBLE_DEVICES according to your available GPUs.

# 🖼️ Text-to-Image (T2I) task
CUDA_VISIBLE_DEVICES=0 python inference.py --cfg configs/example_config_t2i.py

# 🔄 Any-to-Image (X2I) task
CUDA_VISIBLE_DEVICES=0,1 python inference.py --cfg configs/example_config_x2i.py

# 🎯 Visual Guidance task
CUDA_VISIBLE_DEVICES=0,1 python inference.py --cfg configs/example_config_visual_guidance.py

# 📖 Visual Narrative task
CUDA_VISIBLE_DEVICES=0,1 python inference.py --cfg configs/example_config_visual_narrative.py


# After running inference, the model will generate results in protobuf format (.pb files) for each input prompt.

Protobuf outputs are written to outputs/<exp_name>/proto/. For better throughput, we recommend ≥2 GPUs.

Run Inference with vLLM

vLLM Enviroment Setup

1. [Optional Recommendation] Use a new virtual environment for vLLM backend.

conda create -n Emu3p5 python=3.12

2. Install vLLM and apply the patch files.

# Requires Python 3.12 or higher.
# Recommended: CUDA 12.8.
pip install -r requirements/vllm.txt
pip install flash_attn==2.8.3 --no-build-isolation

cd Emu3.5
python src/patch/apply.py

Example Configurations by Task

# 🖼️ Text-to-Image (T2I) task
CUDA_VISIBLE_DEVICES=0,1 python inference_vllm.py --cfg configs/example_config_t2i.py

# 🔄 Any-to-Image (X2I) task
CUDA_VISIBLE_DEVICES=0,1 python inference_vllm.py --cfg configs/example_config_x2i.py

# 🎯 Visual Guidance task
CUDA_VISIBLE_DEVICES=0,1 python inference_vllm.py --cfg configs/example_config_visual_guidance.py

# 📖 Visual Narrative task
CUDA_VISIBLE_DEVICES=0,1 python inference_vllm.py --cfg configs/example_config_visual_narrative.py

Visualize Protobuf Outputs

To visualize generated protobuf files (--video: Generate video visualizations for interleaved output):

python src/utils/vis_proto.py --input <input_proto_path> [--output <output_dir>] [--video]

• --input: supports a single .pb file or a directory; directories are scanned recursively.
• --output: optional; defaults to <input_dir>/results/<file_stem> for files, or <parent_dir_of_input>/results for directories.

Expected output directory layout (example):

results/<pb_name>/
├── 000_question.txt
├── 000_global_cot.txt
├── 001_text.txt
├── 001_00_image.png
├── 001_00_image_cot.txt
├── 002_text.txt
├── 002_00_image.png
├── ...
└── video.mp4              # only when --video is enabled

Each *_text.txt stores decoded segments, *_image.png stores generated frames, and matching *_image_cot.txt keeps image-level chain-of-thought notes when available.

3. Gradio Demo

We provide two Gradio Demos for different application scenarios:

Emu3.5-Image Demo —— Interactive interface optimized for Text-to-Image (T2I) and Any-to-Image (X2I) tasks:

CUDA_VISIBLE_DEVICES=0,1 python gradio_demo_image.py --host 0.0.0.0 --port 7860

Emu3.5-Interleave Demo —— Launch Emu3.5 Interleave Tasks (Visual Guidance and Visual Narrate) Gradio Demo

CUDA_VISIBLE_DEVICES=0,1 python gradio_demo_interleave.py --host 0.0.0.0 --port 7860

Features

• Image Generation: Support Text-to-Image Generation and Multimodal Image Generation
• Interleaved Generation: Support long-sequence creation with alternating image and text generation
• Multiple Aspect Ratios for T2I: 9 preset aspect ratios (4:3, 16:9, 1:1, etc.) plus auto mode
• Chain-of-Thought Display: Automatically parse and format model's internal thinking process
• Real-time Streaming: Stream text and image generation with live updates

4. Schedule

• Inference Code (NTP Version)
• Advanced Image Decoder
• Discrete Diffusion Adaptation (DiDA) Inference & Weights

5. Citation

@misc{cui2025emu35nativemultimodalmodels,
      title={Emu3.5: Native Multimodal Models are World Learners}, 
      author={Yufeng Cui and Honghao Chen and Haoge Deng and Xu Huang and Xinghang Li and Jirong Liu and Yang Liu and Zhuoyan Luo and Jinsheng Wang and Wenxuan Wang and Yueze Wang and Chengyuan Wang and Fan Zhang and Yingli Zhao and Ting Pan and Xianduo Li and Zecheng Hao and Wenxuan Ma and Zhuo Chen and Yulong Ao and Tiejun Huang and Zhongyuan Wang and Xinlong Wang},
      year={2025},
      eprint={2510.26583},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2510.26583}, 
}