Emu3: Next-Token Prediction is All You Need

[Emu3 Team, BAAI](https://www.baai.ac.cn/english.html) | [Project Page](https://emu.baai.ac.cn) | [Paper](https://baai-solution.ks3-cn-beijing.ksyuncs.com/emu3/Emu3-tech-report.pdf?KSSAccessKeyId=AKLTgew6Kdg6RsK92QSfB2KLA&Expires=2591406552&Signature=6BvwfLVqvfww26Bhwvk3mG0FrL8%3D) | [🤗HF Models](https://huggingface.co/collections/BAAI/emu3-66f4e64f70850ff358a2e60f) | [github](https://github.com/baaivision/Emu3) |

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 ```python from PIL import Image from transformers import AutoTokenizer, AutoModel, AutoImageProcessor, AutoModelForCausalLM from transformers.generation.configuration_utils import GenerationConfig from transformers.generation import LogitsProcessorList, PrefixConstrainedLogitsProcessor, UnbatchedClassifierFreeGuidanceLogitsProcessor import torch import sys sys.path.append(PATH_TO_BAAI_Emu3-Gen_MODEL) from processing_emu3 import Emu3Processor # model path EMU_HUB = "BAAI/Emu3-Gen" VQ_HUB = "BAAI/Emu3-VisionTokenizer" # prepare model and processor model = AutoModelForCausalLM.from_pretrained( EMU_HUB, device_map="cuda:0", torch_dtype=torch.bfloat16, attn_implementation="flash_attention_2", trust_remote_code=True, ) tokenizer = AutoTokenizer.from_pretrained(EMU_HUB, trust_remote_code=True) image_processor = AutoImageProcessor.from_pretrained(VQ_HUB, trust_remote_code=True) image_tokenizer = AutoModel.from_pretrained(VQ_HUB, device_map="cuda:0", trust_remote_code=True).eval() processor = Emu3Processor(image_processor, image_tokenizer, tokenizer) # prepare input POSITIVE_PROMPT = " masterpiece, film grained, best quality." NEGATIVE_PROMPT = "lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry." classifier_free_guidance = 3.0 prompt = "a portrait of young girl." prompt += POSITIVE_PROMPT kwargs = dict( mode='G', ratio="1:1", image_area=model.config.image_area, return_tensors="pt", ) pos_inputs = processor(text=prompt, **kwargs) neg_inputs = processor(text=NEGATIVE_PROMPT, **kwargs) # prepare hyper parameters GENERATION_CONFIG = GenerationConfig( use_cache=True, eos_token_id=model.config.eos_token_id, pad_token_id=model.config.pad_token_id, max_new_tokens=40960, do_sample=True, top_k=2048, ) h, w = pos_inputs.image_size[0] constrained_fn = processor.build_prefix_constrained_fn(h, w) logits_processor = LogitsProcessorList([ UnbatchedClassifierFreeGuidanceLogitsProcessor( classifier_free_guidance, model, unconditional_ids=neg_inputs.input_ids.to("cuda:0"), ), PrefixConstrainedLogitsProcessor( constrained_fn , num_beams=1, ), ]) # generate outputs = model.generate( pos_inputs.input_ids.to("cuda:0"), GENERATION_CONFIG, logits_processor=logits_processor ) mm_list = processor.decode(outputs[0]) for idx, im in enumerate(mm_list): if not isinstance(im, Image.Image): continue im.save(f"result_{idx}.png") ```