Stable Diffusion follows a series of steps to convert text input into visual output. Could you explain the process, particularly the role of the UNet architecture and the cross-attention mechanism? I'm also curious about how the latent space is utilized in this process and what factors you consider critical for reproducibility. I'd love to hear your thoughts and experiences on this!
How does text-to-image matching work in Stable Diffusion?
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Thanks bro, thanks to UNet's encoder-decoder structure and cross-attention mechanism, the text embedding is injected into the latent space and iteratively denoised, which really shapes the reproducibility. In your experience, which prompt engineering or scheduler setting turns out to be the most critical factor?