Recently, there have been significant advancements in creating images from text descriptions and mixing text and pictures to generate recent ones. Nevertheless, one unexplored area is image generation from generalized vision-language inputs (for instance, generating a picture from a scene description involving multiple objects and other people). A team of researchers from Microsoft Research, Recent York University, and the University of Waterloo introduced KOSMOS-G, a model that leverages Multimodal LLMs to tackle this issue.

KOSMOS-G can create detailed images from complex combos of text and multiple pictures, even when it hasn’t seen these examples. It’s the primary model that may generate images in situations where various objects or things are in the images based on an outline. KOSMOS-G may be used instead of CLIP, which opens up recent possibilities for using other techniques like ControlNet and LoRA for various applications.

KOSMOS-G uses a clever approach to generate images from text and pictures. It first starts by training a multimodal LLM (which might understand each text and pictures together), which is then aligned with the CLIP text encoder (which is sweet at understanding text). 

Once we give KOSMOS-G a caption with text and segmented images, it’s trained to create images that match the outline and follow the instructions. It does this by utilizing a pre-trained image decoder and leveraging what it has learned from the pictures to generate accurate pictures in several situations.

KOSMOS-G can generate images based on instructions and input data. It has three stages of coaching. In the primary stage, the model is pre-trained on multimodal corpora. Within the second stage, an AlignerNet is trained to align the output space of KOSMOS-G to U-Net’s input space through CLIP supervision. Within the third stage, KOSMOS-G is fine-tuned through a compositional generation task on curated data. During Stage 1, only the MLLM is trained. In Stage 2, AlignerNet is trained with MLLM frozen. During Stage 3, each AlignerNet and MLLM are jointly trained. The image decoder stays frozen throughout all stages.

KOSMOS-G is actually good at zero-shot image generation across different settings. It might make images that make sense, look good, and be customized in a different way. It might do things like changing the context, adding a selected style, making modifications, and adding extra details to the pictures. KOSMOS-G is the primary model to realize multi-entity VL2I in a zero-shot setting.

KOSMOS-G can easily take the place of CLIP in image generation systems. This opens up exciting recent possibilities for applications that were previously not possible. By constructing on the inspiration of CLIP, KOSMOS-G is predicted to advance the shift from generating images based on text to generating images based on a mix of text and visual information, creating opportunities for a lot of progressive applications.

In summary, KOSMOS-G is a model that may create detailed images from each text and multiple pictures. It uses a novel strategy called “align before instruct” in its training. KOSMOS-G is sweet at making images of individual objects and is the primary to do that with multiple objects. It might also replace CLIP and be used with other techniques like ControlNet and LoRA for brand new applications. In brief, KOSMOS-G is an initial step toward making images like a language in image generation.

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