Model reference · open weights
Emu3.5 is an open-weight language model from BAAI. AxForge deploys and operates it for you on dedicated EU-owned hardware — with the licence handled where one is required.
Available as managed deployment — configured and operated for you on dedicated EU hardware, quoted per deployment.
What it is
| Maker | BAAI |
|---|---|
| Type | Language models |
| Task | Omni (any→any) |
| Parameters (lead) | 34.1B |
| Context | 32k tokens |
| Runs with | transformers |
| Released | 2025-10-31 |
| Popularity | 399 downloads / month |
| Licence | Open weights |
About
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.
| 🔹 | 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. |
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.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.| Model name | HF Weight |
|---|---|
| Emu3.5 | 🤗 HF link |
| Emu3.5-Image | 🤗 HF link |
| Emu3.5-VisionTokenizer | 🤗 HF link |
Note:
💡 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.
# 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
Edit configs/config.py to set:
model_path, vq_pathtask_type in {t2i, x2i, howto, story, explore, vla}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_params (classifier_free_guidance, temperature, top_k/top_p, etc.)aspect_ratio ("4:3", "21:9", "1:1", "auto" etc..)python inference.py --cfg configs/config.py
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 iFrom the published model card. Full card on the HuggingFace links in the sidebar.
Using it via the API
Once AxForge deploys emu3-5 for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (emu3-5 below is illustrative; you get the exact model name on deployment.)
$ curl -sS https://api.axforge.ai/v1/chat/completions \
-H "Authorization: Bearer $AXFORGE_API_KEY" \
-H "Content-Type: application/json" \
-d '{"model":"emu3-5","messages":[{"role":"user","content":"Hello"}]}'
Create an account — your API key is available in the console. 5M tokens/month currently included with every new account at launch.