You can develop a Style Injection Model using diffusion-based latent encoding by integrating a style vector into the latent space before each diffusion step to control output appearance.
Here is the code snippet below:

In the above code we are using the following key points:
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StyleInjector module that maps style vectors to latent space.
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Latent manipulation before the diffusion step to inject style dynamically.
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Integration with pre-trained diffusion components (VAE and UNet).
Hence, this approach enables fine-grained style modulation directly within the latent diffusion process for customizable outputs.