The unpredictability of machine learning proved to be both challenging and promising in my commercial visual work.

Upgrading CGI Quality

To meet client demands for high-quality visuals, I learned that in-painting straight onto high resolution Blender renders, give me the most control.

Before After

Masking in Latent Space

I also learned to use masking in the latent space to introduce elements that were not recognised by the ML model.

An effective strategy for rendering these ‘unlearnable’ elements into photographically-enhanced CGI environments, whilst maintaining their unique design features.

Before After

Overcoming Prompt Engineering Limitations

While the surprising and creative outcomes from text-to-image models can be beneficial, I found pure prompt engineering in text-to-image models restrictive. Leading me more towards image-to-image workflows to cater more effectively to my clients expectations.

Enhancing Workflow Efficiency

There are plenty ways to boost the entire workflow efficiency for stock photography using tools like ComfyUI, SDXL, ControlNet Depth, and IPAdapter, facilitating a quicker turnaround for projects that require less specific elements.

The Struggle with Cloud-Based Models

The dynamic nature of cloud-based models, although beneficial, impacted the reproducibility of my client work. 

This led me to rely on local and open-source software, ensuring a more dependable and consistent workflow.