![]() The model’s generative power, noise sampling, and textual guidance options contribute to the versatility and realism of the inpainting process. It uses the encoder to compress the input image, applies a mask to identify the region to be inpainted, and then uses the decoder to generate content that seamlessly fills in the missing or corrupted part of the image while maintaining visual coherence. Inpainting in Stable Diffusion relies on an autoencoder architecture with strong generative capabilities. This means that textual descriptions can influence the content generated to fill in the masked region, helping to achieve inpainting results that align with desired artistic or contextual intentions.
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