What is Stable Cascading?

How will Stable Cascading transform visual content generation?

Start with
Stable Diffusion

It’s the foundational approach to creating images from textual descriptions, and that’s been the baseline for everything over the past 2 -3 years.

It works by gradually refining a random noise pattern into a coherent image that matches the input text, effectively "diffusing" the noise into a structured form.

This process allows for the generation of highly detailed and varied images based on textual prompts, this is what makes it such a powerful tool for content generation.

Well, we’re about to welcome a new approach.

What is Stable Cascading?

Stable Cascading is a decoder running the diffusion in stages to generate assets, which is specifically targeted to the creation of visual media.

 It works in a similar way to diffusion, but instead of making a single full image - it creates the content in multiple phases.

This phased process will dramatically improve the speed, quality, flexibility, and efficiency of text-to-image models, but what’s really interesting is the reduction in compute power needed to achieve these comparable results.

We have been trying to do this for the past few months with modular workflows – but this efficiency is now happening in the model phase too!

This approach will allow models to be refined and trained much quicker and with a fraction of the computational effort and deliver a higher volume of results, at a much lower computational requirement.

More consistent control, and much more functionality.

This technique will set new benchmarks in how we generate of digital imagery. We are looking at more individuality across ControlNet’s, LoRAs, and fine-tuning phases. 

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