When using generative models, having an image of good quality as input is essential. The composition and lighting of the subject should be taken into account, as well as the subject itself as not every type of product is suitable for the current AI models. While experimentation is key, there are couple important aspects to always keep in mind.
Garbage in, garbage out is a common pitfall when it comes to generative models, so taking the time to create a quality input can save a lot of frustration in the long run.
Remember the old saying "garbage in, garbage out" when it comes to preparing product photographs for AI tools
Results may vary
When using generative models, it's important to keep in mind that even if you use a similar photograph, the generated results can vary due to randomness involved in the process. Additionally, some types of products or subjects may not work well with AI models. The random aspect of it allows you to acheive unique results but it also means that no two generations will be exactly identical.
There's no one-size-fits-all approach to using generative models, and experimentation is necessary to achieve the desired results. To ensure the best outcome, it's recommended to generate multiple iterations using the same input and carefully evaluate each one.
The magic wand
It's crucial to manage your expectations when working with generative models. While AI can produce impressive results, it's not a magic wand that can fix every image flaw. It's important to understand the limitations of the technology and not expect it to produce results beyond its capabilities.
Being aware of potential biases and inaccuracies in the generated output can help avoid mistakes and misinterpretations. It's also important to keep up with advancements and updates in the field to continuously improve results and avoid common pitfalls.
Giving up too early
As trivial as it may sound, it's also important not to give up too early, as sometimes it takes multiple iterations to get the desired results. Persistence and patience are key when working with generative models, as the more you experiment and generate, the better the chances of getting the desired output.
With persistence and continued experimentation, you can achieve amazing results with generative models.