![]() Reads and writes are O(Nˆ2), compute is also O(Nˆ2) NxN result stored in main memory, NxH reads If we put aside the batch dimension (global multiplier), and use N for the context length and H for the head size (let’s suppose Q, K and V have the same dimensions for the sake of clarity), a breakdown of this operation as executed by PyTorch is as follows: Its formulation is as follows, and looks fairly innocuous: attention = softmax(QKˆT).V įrom a complexity standpoint, three things can be considered here: the compute cost of this operation, its memory footprint, and the I/O (input/output, ie: memory operations) that it entails. This operation is not restricted to Transformers though, and the latent diffusion model on which is based Stable Diffusion uses it inside the core denoising steps, notably to take various forms of guidance into account. ![]() If all three refer to the same tensor, it becomes known as self-attention. This operation typically takes three inputs: the Query, the Key and the Value. It’s very useful for a model to make sense of the connections which can happen between elements of a sequence, which can be sound bites, pixels or words for instance. ![]() The attention operation is at the heart of the Transformer model architecture, which got popular in the last couple of years in the AI space. A few words about memory efficient attention In a previous blog post, we investigated how to make stable diffusion faster using TensorRT at inference time, here we will investigate how to make it even faster, using Memory Efficient Attention from the xformers library. Their quality and expressivity, starting from a user prompt, were an opportunity to improve the PhotoRoomer experience. Diffusion models are a recent take on this, based on iterative steps: a pipeline runs recursive operations starting from a noisy image until it generates the final high-quality image. At PhotoRoom we build photo editing apps, and being able to generate what you have in mind is a superpower. ![]()
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