Using The Same Prompt With Different Models

As AI more and more dominates the narrative in know-how and enterprise, most individuals’s understanding of it stays restricted to instruments like ChatGPT. Nonetheless, one quickly advancing space is AI picture era. Chances are you’ll be accustomed to some instruments on this area, however I purpose to look at how completely different picture era fashions reply to the identical immediate.

First, let’s briefly discover how AI picture era works and the mechanical variations between AI textual content and picture era.

How Do Picture Technology Fashions Work?

Fashions like DALL-E are skilled utilizing huge datasets of photos and, in some instances, accompanying textual content descriptions. Throughout coaching, the AI is fed hundreds of thousands of image-text pairs, studying associations between phrases and visible ideas. When given a textual content immediate, the mannequin generates a corresponding picture by synthesizing pixels in alignment with the patterns and visible relationships from its coaching knowledge. Basically, the AI acts like a painter, creating ‘brush strokes’ based mostly on its database of image-text pairs. This course of can result in bias, which we are going to discover additional on this article.

How do textual content era fashions work? In distinction, text-based AI fashions, corresponding to GPT-4, are skilled on intensive textual content knowledge, studying language patterns, grammar, and context. When prompted, they generate textual content by predicting the most probably subsequent phrase or phrase based mostly on the enter and their coaching, primarily ‘guessing’ one of the best subsequent phrases based mostly in your enter.

The important thing distinction between picture and textual content era is that AI should interpret your phrases and visualize the idea you current.

Testing Picture Technology with the Similar Immediate

One pitfall of picture era is that restricted coaching knowledge can result in divergent or biased outputs. As a Bay Space-based contributor, I examined the identical immediate throughout 4 completely different picture turbines: “A picture of 4 pals consuming wine in Napa, CA on a sunny day.”

For this check, I used:

I restricted the check to the ‘first picture’ output from every mannequin, as these accustomed to these instruments know they generate a number of photos per immediate. For Dall-E and Imagen, I accessed the pictures by means of Canva, which has separate apps for each. Right here had been the outcomes:

The outputs tended to converge on comparable imagery. Notably, Midjourney confirmed probably the most divergence among the many 4 outcomes, adopted by Firefly. The outputs from Dall-E and Imagen had been comparatively comparable based mostly on anecdotal observations.

Whereas picture era know-how is advancing quickly, it raises considerations about bias and different potential points. As coaching knowledge expands, these fashions will enhance. Nonetheless, with video era nearing mainstream adoption by means of firms like Runway and Pika, further warning is important when counting on text-to-image and text-to-video outputs to keep away from reinforcing societal biases.