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This is a merge of NoobAI Eps 1.1 and Animagine 4.0 that uses a technique inspired from Fisher-weighted averaging. (https://arxiv.org/abs/2111.09832)
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The importance of each parameter is estimated from some notion of curvature of the loss landscape, and then for each corresponding parameter in both models, we simply average with respect to the estimated importance.
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Naively applying the formulas from the paper gave poor results when trying to apply them to diffusion models through ELBO, so I had to innovate a little bit.
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This is a merge of NoobAI Eps 1.1 (https://huggingface.co/Laxhar/noobai-XL-1.0) and Animagine 4.0 (https://huggingface.co/cagliostrolab/animagine-xl-4.0) that uses a technique inspired from Fisher-weighted averaging. (https://arxiv.org/abs/2111.09832)
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The importance of each parameter is estimated from some notion of curvature of the loss landscape, and then for each corresponding parameter in both models, we simply average with respect to the estimated importance.
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Naively applying the formulas from the paper gave poor results when trying to apply them to diffusion models through ELBO, so I had to innovate a little bit.
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