Within the rapidly evolving field of generative AI, challenges persist in achieving efficient and high-quality video generation models and the necessity for precise and versatile image editing tools. Traditional methods often involve complex cascades of models or need assistance with over-modification, limiting their efficacy. Meta AI researchers address these challenges head-on by introducing two groundbreaking advancements: Emu Video and Emu Edit.

Current text-to-video generation methods often require deep cascades of models, demanding substantial computational resources. Emu Video, an extension of the foundational Emu model, introduces a factorized approach to streamline the method. It involves generating images conditioned on a text prompt, followed by video generation based on the text and the generated image. The simplicity of this method, requiring only two diffusion models, sets a brand new standard for high-quality video generation, outperforming previous works.

Meanwhile, traditional image editing tools have to be improved to offer users precise control.

Emu Edit, is a multi-task image editing model that redefines instruction-based image manipulation. Leveraging multi-task learning, Emu Edit handles diverse image editing tasks, including region-based and free-form editing, alongside crucial computer vision tasks like detection and segmentation.

Emu Video‘s factorized approach streamlines training and yields impressive results. Generating 512×512 four-second videos at 16 frames per second with just two diffusion models represents a major step forward. Human evaluations consistently favor Emu Video over prior works, highlighting its excellence in each video quality and faithfulness to the text prompt. Moreover, the model’s versatility extends to animating user-provided images, setting recent standards on this domain.

Emu Edit’s architecture is tailored for multi-task learning, demonstrating adaptability across various image editing tasks. The incorporation of learned task embeddings ensures precise control in executing editing instructions. Few-shot adaptation experiments reveal Emu Edit’s swift adaptability to recent tasks, making it advantageous in scenarios with limited labeled examples or computational resources. The benchmark dataset released with Emu Edit allows for rigorous evaluations, positioning it as a model excelling in instruction faithfulness and image quality.

In conclusion, Emu Video and Emu Edit represent a transformative leap in generative AI. These innovations address challenges in text-to-video generation and instruction-based image editing, offering streamlined processes, superior quality, and unprecedented adaptability. The potential applications, from creating fascinating videos to achieving precise image manipulations, underscore the profound impact these advancements could have on creative expression. Whether animating user-provided images or executing intricate image edits, Emu Video and Emu Edit open up exciting possibilities for users to precise themselves with newfound control and creativity.

EMU Video Paper: https://emu-video.metademolab.com/assets/emu_video.pdf

EMU Edit Paper: https://emu-edit.metademolab.com/assets/emu_edit.pdf