| --- |
| license: apache-2.0 |
| library_name: transformers |
| --- |
| |
| <div align='center'> |
| <h1>Emu3: Next-Token Prediction is All You Need</h1h1> |
| <h3></h3> |
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| [Emu3 Team, BAAI](https://www.baai.ac.cn/english.html) |
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| | [Project Page](https://emu.baai.ac.cn) | [Paper](https://huggingface.co/papers/2409.18869) | [🤗HF Models](https://huggingface.co/collections/BAAI/emu3-66f4e64f70850ff358a2e60f) | [github](https://github.com/baaivision/Emu3) | [Demo](https://huggingface.co/spaces/BAAI/Emu3) | |
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| </div> |
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| <div align='center'> |
| <img src="https://github.com/baaivision/Emu3/blob/main/assets/arch.png?raw=True" class="interpolation-image" alt="arch." height="80%" width="70%" /> |
| </div> |
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| We introduce **Emu3**, a new suite of state-of-the-art multimodal models trained solely with **<i>next-token prediction</i>**! By tokenizing images, text, and videos into a discrete space, we train a single transformer from scratch on a mixture of multimodal sequences. |
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| ### Emu3 excels in both generation and perception |
| **Emu3** outperforms several well-established task-specific models in both generation and perception tasks, surpassing flagship open models such as SDXL, LLaVA-1.6 and OpenSora-1.2, while eliminating the need for diffusion or compositional architectures. |
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| <div align='center'> |
| <img src="https://github.com/baaivision/Emu3/blob/main//assets/comparison.png?raw=True" class="interpolation-image" alt="comparison." height="80%" width="80%" /> |
| </div> |
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| ### Highlights |
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| - **Emu3** is capable of generating high-quality images following the text input, by simply predicting the next vision token. The model naturally supports flexible resolutions and styles. |
| - **Emu3** shows strong vision-language understanding capabilities to see the physical world and provides coherent text responses. Notably, this capability is achieved without depending on a CLIP and a pretrained LLM. |
| - **Emu3** simply generates a video causally by predicting the next token in a video sequence, unlike the video diffusion model as in Sora. With a video in context, Emu3 can also naturally extend the video and predict what will happen next. |
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| ### Quickstart for Autoencoding |
| ```python |
| import os |
| import os.path as osp |
| |
| from PIL import Image |
| import torch |
| from transformers import AutoModel, AutoImageProcessor |
| |
| MODEL_HUB = "BAAI/Emu3-VisionTokenizer" |
| |
| model = AutoModel.from_pretrained(MODEL_HUB, trust_remote_code=True).eval().cuda() |
| processor = AutoImageProcessor.from_pretrained(MODEL_HUB, trust_remote_code=True) |
| |
| # TODO: you need to modify the path here |
| VIDEO_FRAMES_PATH = "YOUR_VIDEO_FRAMES_PATH" |
| |
| video = os.listdir(VIDEO_FRAMES_PATH) |
| video.sort() |
| video = [Image.open(osp.join(VIDEO_FRAMES_PATH, v)) for v in video] |
| |
| images = processor(video, return_tensors="pt")["pixel_values"] |
| images = images.unsqueeze(0).cuda() |
| |
| # image autoencode |
| image = images[:, 0] |
| print(image.shape) |
| with torch.no_grad(): |
| # encode |
| codes = model.encode(image) |
| # decode |
| recon = model.decode(codes) |
| |
| recon = recon.view(-1, *recon.shape[2:]) |
| recon_image = processor.postprocess(recon)["pixel_values"][0] |
| recon_image.save("recon_image.png") |
| |
| # video autoencode |
| images = images.view( |
| -1, |
| model.config.temporal_downsample_factor, |
| *images.shape[2:], |
| ) |
| |
| print(images.shape) |
| with torch.no_grad(): |
| # encode |
| codes = model.encode(images) |
| # decode |
| recon = model.decode(codes) |
| |
| recon = recon.view(-1, *recon.shape[2:]) |
| recon_images = processor.postprocess(recon)["pixel_values"] |
| for idx, im in enumerate(recon_images): |
| im.save(f"recon_video_{idx}.png") |
| |
| ``` |
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