Aurora EMU2 Emulator

The human ability to easily solve multimodal tasks in context (i.e., with only a few demonstrations or simple instructions), is what current multimodal systems have largely struggled to imitate. In this work, we demonstrate that the task-agnostic in-context learning capabilities of large multimodal models can be significantly enhanced by effective scaling-up. We introduce Emu2, a generative.. Inference (Huggingface Version) Emu2-Gen import cv2 from diffusers import DiffusionPipeline import numpy as np from PIL import Image import requests from transformers import AutoModelForCausalLM, AutoTokenizer import torch # For the first time of using, # you need to download the huggingface repo "BAAI/Emu2-GEN" to local first path = "path to local BAAI/Emu2-GEN" multimodal_encoder.

Inference (Huggingface Version) Single GPU from PIL import Image import requests import torch from transformers import AutoModelForCausalLM, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("BAAI/Emu2") model = AutoModelForCausalLM.from_pretrained( "BAAI/Emu2", torch_dtype=torch.bfloat16, low_cpu_mem_usage= True, trust_remote_code= True).to('cuda'). eval () # `[]` is the image.. 2024.9 We introduce Emu3, a new suite of state-of-the-art multimodal models trained solely with next-token prediction. 🔥🔥🔥; 2024.2 Emu1 and Emu2 are accepted by ICLR 2024 and CVPR 2024 respectively! 🎉; 2023.12 Inference code, model and demo of Emu2 are available. Enjoy the demo.; 2023.12 We have released Emu2, open and largest generative multimodal models that achieve new state of.