What would be the preferred way to make an encoder-decoder architecture with Mamba? I tried concatenating embeddings to decoder inputs with no luck. My use case is a diffusion model and the encoder ...

These encoders and multilingual training datasets unveil the real multilingual text-to-image generation experience! Kandinsky 2.0 was trained on a large 1B multilingual set, including samples that we ...

Diffusion models, emerging as powerful deep generative tools, excel in various applications. They operate through a two-steps process: introducing noise into training samples and then employing a ...

We adopted U-net, an encoder-decoder CNN (ED-CNN), which is an emerging deep neural network architecture for medical image segmentation, 16 and evaluated its performance in diffusion lesion volume ...

As with the encoder, the decoder holds this large activation volume in memory while computing two additional convolutions, constant multiplications and additions, layer normalization with reshaped ...

MobileDiffusion consists of three main components: a text encoder, a diffusion network, and an image decoder. The UNet contains a self-attention layer, a cross-attention layer, and a feed-forward ...

2.2. Multi-Scale Encoder-Decoder Self-Attention Mechanism As mentioned earlier, by providing two levels of attention mechanisms (global and local attention), we facilitate a deep neural network ...