Emu3.5

Emu3.5 Team, BAAI

Project Page | 🤗HF Models | Paper | App

🔔 Latest: Emu3.5 Web & Mobile Apps and vLLM offline inference are live — see 🔥 News for details.

🔥 News

• 2025-11-28 · 🌐 Emu3.5 Web & Mobile Apps Live — Official product experience is now available on the web at zh.emu.world (Mainland China) and emu.world (global) 🎉 The new homepage highlights featured cases and a “Get Started” entry, while the workspace and mobile apps bring together creation, inspiration feed, history, profile, and language switch across web, Android APK, and H5. (See more details below.)
• 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

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 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 i