Meta's MusicGen: The LLaMA Moment for Music AI
Plus: Google’s Imagen Editor outperforms Stable Diffusion & DALL-E 2
Hello Engineering Leaders and AI Enthusiasts!
Welcome to the 39th edition of The AI Edge newsletter. This edition brings you Meta’s MusicGen, a controllable music generation model that can be prompted by both text and melody.
And a huge shoutout to all our readers out there. We appreciate you! 😊
In today’s edition:
🎵 Meta’s MusicGen: The LLaMA moment for music AI
📸 Google’s Imagen Editor outperforms Stable Diffusion and DALL-E 2
📚 Knowledge Nugget: LLM tuning & dataset Perspectives by
Let’s go!
Meta’s MusicGen: The LLaMA moment for music AI
META released MusicGen, a controllable music generation model for producing high-quality music. MusicGen can be prompted by both text and melody.
The best thing is anyone can try it for free now. It uses a single-stage transformer language model with efficient token interleaving patterns, eliminating the need for multiple models.
MusicGen will generate 12 seconds of audio based on the description provided. You can optionally provide a reference audio from which a broad melody will be extracted. Then the model will try to follow both the description and melody provided. You can also use your own GPU or a Google Colab by following the instructions on their repo.
📃 Paper: https://arxiv.org/abs/2306.05284
💻 GitHub: https://github.com/facebookresearch/audiocraft
🎧 Try it here: https://huggingface.co/spaces/facebook/MusicGen
Why does this matter?
The release of Meta's MusicGen brings exciting developments in controllable music generation and accessibility for creative individuals. By eliminating the need for multiple models, MusicGen simplifies the music generation process.
Google’s Imagen Editor outperforms Stable Diffusion and DALL-E 2
Despite the explosion of breakthroughs in text-to-image AI, text-guided image editing (TGIE) is a convenient solution when recreating visuals would be time-consuming or infeasible. Google has introduced Imagen Editor, a SoTA solution for text-guided image inpainting fine-tuned from Imagen.
It takes three inputs from the user: 1) the image to be edited, 2) a binary mask to specify the edit region, and 3) a text prompt — all three inputs guide the output samples. The model meaningfully incorporates the user’s intent and performs photorealistic edits.
Google also introduced EditBench, a method that gauges the quality of image editing models. It drills down to various types of attributes, objects, and scenes for a more fine-grained understanding of performance.
When evaluated against Stable Diffusion (SD) and DALL-E 2 (DL2), Imagen Editor outperforms them by substantial margins across all EditBench evaluation categories.
Why does this matter?
Imagen Editor and EditBench indicate significant advancements in text-guided image inpainting and evaluation. However, TGIE itself represents a substantial opportunity to improve training of foundational models themselves.
Multimodal models require diverse data to train properly, and TGIE can enable the generation and recombination of high-quality and scalable synthetic data. Perhaps most importantly, this can provide methods to optimize training data distribution along any given axis.
Knowledge Nugget: LLM tuning & dataset perspectives
In this article,
shares his unique take on recent projects that delve into understanding LLM training where several interesting questions finally get answered, such as:Do we need reinforcement learning with human feedback to align LLMs?
How good are LLMs trained on ChatGPT-imitation data?
Should we train LLMs using multiple training epochs?
Not all the questions are answered. But if you are looking for exciting research directions, this article highlights some interesting papers to learn from.
Why does this matter?
In the last couple of months, we have seen a lot of sharing and open-sourcing various kinds of LLMs and datasets, which is significant. From a research perspective, it felt more like a race to be out there first (which is understandable) versus doing principled analyses. However, there has been a rise in studies that delve into understanding LLM training, and the author shares a list of some noteworthy projects as well as his insights on them.
What Else Is Happening
👀 Impressive new AI Eye contact feature by NVIDIA! (Link)
🔊 Microsoft’s Bing Chat can now answer questions in its own voice with added voice search! (Link)
🎨 Stability AI’s new outpainting tool, ‘Clipdrop’ allows changing image ratio. (Link)
✨ The new all-in-one Adobe Express (beta) will upgrade your craft! (Link)
💻 Whisper Web unleashes power of running transformers in a browser, No server required! (Link)
🤖 Metamate, Meta’s internal AI chatbot, will help employees summarize meetings, write code, debug features & much more! (Link)
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That's all for now!
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