arxiv.org
Scalable Diffusion Models with TransformersWe explore a new class of diffusion models based on the transformer
architecture. We train latent diffusion models of images, replacing the
commonly-used U-Net backbone with a transformer that operates on latent
patches. We analyze the scalability of our Diffusion Transformers (DiTs)
through the lens of forward pass complexity as measured by Gflops. We find that
DiTs with higher Gflops -- through increased transformer depth/width or
increased number of input tokens -- consistently have lower FID. In addition to
possessing good scalability properties, our largest DiT-XL/2 models outperform
all prior diffusion models on the class-conditional ImageNet 512x512 and
256x256 benchmarks, achieving a state-of-the-art FID of 2.27 on the latter.
Scalable Diffusion Models with Transformers are a groundbreaking advancement, revolutionizing natural language processing. The integration of transformers has elevated the capabilities of diffusion models, enabling efficient handling of large datasets with unparalleled accuracy. This innovation opens avenues for diverse applications across industries, from healthcare to finance. Specifically, in e-commerce platforms like Shopify, SEO experts can leverage these models to enhance product recommendations, personalized messaging, and customer engagement strategies. The fusion of scalability and precision in these models empowers businesses to navigate dynamic market landscapes with agility and insight. As we continue to witness advancements in AI, the synergy between diffusion models and transformers promises endless possibilities for transformative solutions.