If you have been amazed by the extraordinary capabilities of AI text-to-image generators like DALL-E or Midjourney, be ready to be confounded by yet another incredible tool. Google has introduced Google Muse, an Al image generator model that is seemingly way better than current such tools.
This AI-based text-to-image generator can be a veritable goldmine of artistic possibilities, allowing users to tap into the creative genius of a master artist with a mere tap of their fingers. Incidentally, the Muse Al image generator utilizes a pre-trained language model and can comprehend the nuances of language, leading to the generation of high-quality images. Due to its amazing capabilities, Google’s Muse can truly be a game-changer in the realm of AI-generated art, expanding the horizons of what is possible.
In this post, I will explore Google’s Muse in more detail, covering its working and the unique features that put it far ahead of currently available text-to-image generator tools like Midjourney.
Google’ Muse Technical Details
|Model Type||Text-to-Image Generator Model|
|Language Model Used||Pretrained on T5-XXL Large Language Model|
|Sub Models||VQGAN tokenizer|
|Speed||512×512 image in 1.3 seconds on TPUv4|
|Capabilities||Zero Shot & Mask Free Editing|
What Is Google Muse?
After Imagen, Google has come up with yet another ultra-intuitive text-to-image generator model called Muse. Introduced in early January this year, Muse is a text-to-image model that utilizes a technique called masked modeling in discrete token space. It means that Muse has been trained to predict image tokens (parts of an image) based on a text prompt, and it uses discrete tokens instead of pixels to generate images.
Moreover, Muse utilizes a pre-trained language model to understand the nitty-gritty of language, leading to high-quality image generation. Incidentally, Muse can generate images without the need for additional modifications. On top of that, it can also perform tasks such as inpainting (filling in missing parts of an image) and outpainting (adding new parts to an image). Besides, Muse also uses parallel decoding, making it faster and more efficient than other models.
Likewise, Muse is also composed of multiple component models, including the VQGAN tokenizer model, a base masked image model, and a super res transformer model based on T5-XXL embeddings. It utilizes these sub-models to encode and decode texts, predict the token distribution and enhance the quality of low-resolution images.
Google Muse: Features
Per the research paper presented by Google, Muse has much to offer and could be way better than currently used text-to-image models. Here are some notable features that allow Muse to outshine the likes of DALL-E 2 or Midjourney;
- Zero-Shot and Mask-Free Editing – Muse uses a concept called iterative resampling of picture tokens according to the given text prompts. It allows it to make changes to any area of an image based on given text prompts – no need to mask the other areas. Midjourney and DLLE-2, although revolutionary in their own sense, don’t have this ability.
- Faster Image Generation – Muse 3B model can generate 512×512 in a mere 1.3 seconds on TPUv4. No other text-to-image generator tool reaches this speed. In fact, Muse even outperforms the previously fastest model, Stable Diffusion 1.4, which has an image generation speed of around 3.7 seconds. Faster speed enhances efficiency and lowers the computing cost of image generation.
- Needs Fewer Sampling Iterations – Muse doesn’t use diffusion; instead, it utilizes compressed discrete tokens requiring fewer sampling interactions or text prompts. It allows Muse to be more precise, efficient and faster.
- Parallel Encoding – Muse uses parallel encoding than traditional sequential decoding. With this architecture, Muse can produce high-quality images even with a smaller sample size. It’s a 3 billion parameter model trained on 460 million text-to-image pairs from Imagen. Furthermore, it also uses T5xxl – a 4.6 billion parameter LLM model to understand text prompts.
- Better Spatial Understanding – Muse processes complete text prompts and not specific parts; this approach allows Muse better to understand visual concepts like pose and spatial relationship.
How Is Google Muse Better Than Other Text-to-Image Generator Models?
Muse takes a new approach to understanding the text prompts and generating images. Incidentally, Muse is trained on a masked modeling task in discrete token space. Also, it uses a pre-trained language model to extract text embeddings and then predicts randomly masked image tokens. This approach makes Muse more efficient than other models like Imagen and DALL-E 2, which use pixel-space diffusion models.
Similarly, using discrete tokens and fewer sampling iterations enables Muse to generate images faster than other models. Additionally, Muse utilizes parallel instead of sequential decoding, which allows it to be faster and more efficient than traditional autoregressive models such as Parti.
The pre-trained language model is another advantage tied to Muse. It enables Muse to understand the technicalities of language, making it more adept at understanding the underlying context and generating high-fidelity images. Besides, it also allows Muse to understand visual concepts such as objects, their relationships with the surroundings, pose, and cardinality.
Overall, Muse offers a new approach to text-to-image generation, which is more efficient and accurate than the traditional models like DALL-E, Imagen and Parti.
Conclusion: Google Muse
Google’s Muse is indeed a state-of-the-art text-to-image generator model that offers a new approach to image generation. In theory, it does seem to be significantly more efficient and accurate than traditional models like Imagen, DALL-E, and Parti. Imagine the ability to perform inpainting, painting, and mask-free editing with but a few keystrokes and all without the need for additional modifications or inversions. It’s akin to having a personal Al muse to inspire and assist in one’s artistic endeavors. In short, Muse, with its ability to understand fine-grained language and generate high-quality images, has the potential to revolutionize the field of image generation.
The answer is No. Although Muse’s idea and suggested working mechanism is quite promising, Google has only released a research paper. There’s no code, tool or software to demonstrate its abilities practically?
According to the research paper, Muse seems ahead of other text-to-image generator models. Its use of discrete tokens, parallel decoding and the T5XXL language model allows it to be faster, more adept at producing high-quality images and better understand visual arrangements.
Per the technical details given in the research paper, Muse can generate a 512×512 image in 1.3 seconds and a 256×256 image in 0.5 seconds. At this Soper, Muse is ten times faster than Parti 3B and Imagen 3B models and three times faster than Stable Diffusion.
Mask-free editing is a technique that eliminates the use of masks in image editing. Masks isolate a specific part of an image so that changes reflect only on those parts. Mask-free editing allows you to edit the entire image or specific parts without the need first to create a mask. It eliminates the additional step of creating a mask and makes the editing process faster and more efficient.