Emu3: Next-Token Prediction is All You Need

We introduce Emu3, a new suite of state-of-the-art multimodal models trained solely with next-token prediction! By tokenizing images, text, and videos into a discrete space, we train a single transformer from scratch on a mixture of multimodal sequences.

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.

Highlights

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.

Quickstart for Autoencoding

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")