> For the complete documentation index, see [llms.txt](https://inspireai.gitbook.io/inspire-ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://inspireai.gitbook.io/inspire-ai/technical/adversarial-diffusion-distillation.md). # Adversarial Diffusion Distillation Inspire AI utilizes the latest and greatest in open-source technology. This is what grants us the unparalleled speed and accurate imagery it generates. Adversarial Diffusion Distillation (ADD) is a novel training approach introduced to efficiently sample large-scale foundational image diffusion models in just 1-4 steps while maintaining high image quality. This technique leverages large-scale off-the-shelf image diffusion models as a teacher signal, combined with an adversarial loss to ensure high image fidelity even in the low-step regime, such as one or two sampling steps. The analyses demonstrate that ADD outperforms existing few-step methods, including GANs and Latent Consistency Models, in a single step and reaches the performance of state-of-the-art diffusion models, such as SDXL, in only four steps. ADD represents the first method capable of unlocking single-step, real-time image synthesis with foundation models.

The ADD-student is trained as a denoiser that receives diffused input images xs and outputs samples and optimizes two objectives: a) adversarial loss: the model aims to fool a discriminator which is trained to distinguish the generated samples from real images b) distillation loss: the model is trained to match the denoised targets xˆψ of a frozen DM teacher. Inspire AI's software backbone is in this technology. As it's continually worked and improved upon by the open-source community, we'll continually update our systems to utilize the most advanced technologies possible.