DeepFloyd IF’s base and super-resolution models adopt diffusion models, making use of Markov chain steps to introduce random noise into the data before reversing the process to generate new data samples from the noise. The image-to-image translation can be achieved by resizing the original image to 64 pixels, adding some level of noise via forward diffusion, and denoising the image with a new prompt during the backward diffusion process.
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