How to implement a UNet-based diffusion model for text-to-image generation using PyTorch

Question

With the help of code can i know How to implement a UNet-based diffusion model for text-to-image generation using PyTorch.

Answer 1

You can implement a UNet-based diffusion model for text-to-image generation using PyTorch by conditioning a denoising UNet on text embeddings through cross-attention.

Here is the code snippet below:

In the above code, we are using the following key points:

• A simplified UNet architecture for denoising

• Multihead cross-attention mechanism to condition on text embeddings

• Text embeddings are averaged and expanded to match the spatial dimensions

• Model outputs a denoised image from the noisy latent

Hence, this approach enables effective text-to-image generation by combining diffusion modeling with language-conditioned UNet architectures.