Why LLMs/LMMs cannot generate designs?
Let's do an experiment by generating designs with Gemini Pro and ChaGPT-4 with vision (Dall-E 3) using the below prompt.
create a WhatsApp promo for my furniture shop Woodlands Furnitures, for an offer of 20%. Give my contact details +1 888 234 5643. Here is my product image to use
Outcomes from Gemini Pro
Outcomes from ChatGPT-4 + Dall-E 3
Outcomes from Sivi
Explore Sivi Gen-2, an advanced generative AI model launching soon.
Observation
Even though the images by Gemini or ChatGPT have pleasing objects and colors, the designs are unusable as the users have no control over what these models generate and there's no way to edit the layers.
In contrast, Sivi utilizes user-added assets and copy to generate relevant, editable designs.
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TL;DR
LLMs and LMMs excel in various tasks. Their capabilities are more aligned with processing and generating textual content rather than creating visually appealing designs.While LLM/LMM can analyze images to a certain extent and answer questions based on them, they lack the aesthetic training and nuanced understanding of design principles required for graphic design tasks.Image generation models have constraints such as distorted text, single-layered images, and no support for vector graphics.Graphic design requires specialized models like Sivi, COLE, and CanvasVAE.Sivi, in particular, generates multi-layered editable designs in multiple languages and supports user-provided assets.
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Exploring the Limitations of Large Language Models in Graphic Design
In generative artificial intelligence, the capabilities of large language models (LLMs) and large multimodal models (LMMs) like ChatGPT from OpenAI and Google’s Gemini have sparked immense curiosity and innovation. However, while these models excel in various natural language processing tasks, they have limitations when it comes to generating designs. Let’s see why LLMs and LMMs cannot generate designs and why we need specialized models like Sivi.
1. Understanding the Role of LLMs or LMMs
Large language models are primarily designed for processing and generating text-based content. They excel in tasks such as language translation, text summarization, and creative writing. LMMs, on the other hand, expand on this capability by incorporating multimodal inputs, including images and text, to generate more contextually relevant text.
2. Limitations of Image Generation Models
While LLMs/LMMs can analyze images to a certain extent, they are not inherently equipped to generate designs. Image generation models like DALL-E are specifically tailored for creating images based on textual prompts. However, these models have their own set of limitations:
They generate monolithic images from prompts, limiting the incorporation of user-provided assets or logos.
Text inputs are often distorted and unreadable, restricting the use of more than a word or line of text.
Generated images typically consist of a single layer, lacking the depth and complexity required for intricate designs.
These models lack support for vector graphics and cannot providing editable outcomes, hindering the flexibility required in graphic design workflows.
When asked to generate designs incorporating the provided product, logo, and copy, DALL-E advises engaging a professional designer or utilizing a graphic design tool.
It explicitly states its inability to analyze images when prompted to extract the dominant color from a given image.
Similarly, Gemini clarifies its capabilities, offering to provide copy, direct users to design resources, or give feedback, but it does not have the capacity to generate designs.
3. Incompatibility with Graphic Design Tasks
Graphic design encompasses a wide array of skills, including aesthetic judgment, typography, color theory, and composition. While LLMs/LMMs can perform multiple tasks admirably, the nuanced understanding of design principles and aesthetic sensibilities required in graphic design goes beyond their capabilities.
Here’s an excerpt from Microsoft COLE.
4. The Need for Specialized Models
To address the limitations of LLMs/LMMs in graphic design, there is a growing need for specialized tools like Sivi, COLE, and CanvasVAE. These platforms are designed with the explicit purpose of facilitating graphic design tasks by leveraging specialized models and datasets. Key features of these tools include:
Specialized datasets and models tailored for graphic design tasks.
Enhanced support for user-provided assets and logos.
Improved text handling capabilities, including readability and flexibility.
Multi-layered image generation to enable more intricate designs.
Support for vector graphics and editable outcomes, empowering designers to fine-tune their creations.
Read this article to know more about the state of the art of generative AI in graphic design.
5. The Role of Sivi in Revolutionizing Graphic Design
Sivi's DGDS 1.6 outperforms models like CanvasVAE or COLE by 8 to 10 times in aesthetic scoring. With its focus on design generation, Sivi offers several advantages:
Generating visually appealing and contextually relevant designs in infinite dimensions.
Utilization of specialized datasets to train models specifically for design tasks.
Introducing content engineering to allow the users to add their own copy and assets.
Adhering to brand guidelines and generating designs in 72+ languages.
Providing designer-friendly customizations with layered designs.
Get ready for the future of design with Sivi Gen-2, an advanced generative AI model!
Conclusion
While LLMs and LMMs have revolutionized many aspects of artificial intelligence and natural language processing, their inherent limitations make them unsuitable for graphic design tasks. The complexities of design generation require specialized tools and algorithms, such as those developed by Sivi, to unlock the full potential of AI in the realm of visual communication. As technology continues to evolve, the fusion of AI and graphic design promises to reshape the creative landscape, offering new possibilities for designers and enthusiasts alike.
Share your thoughts on this graphic design revolution.
