Making Reconstruction FID Predictive of Diffusion Generation FID
It is well known that the reconstruction FID (rFID) of a VAE is poorly correlated with the generation FID (gFID) of a latent diffusion model. We propose interpolated FID (iFID), a simple variant of rFID that exhibits a strong correlation with gFID. Specifically, for each element in the dataset, we retrieve its nearest neighbor (NN) in the latent space and interpolate their latent representations. We then decode the interpolated latent and compute the FID between the decoded samples and the original dataset. Additionally, we refine the claim that rFID correlates poorly with gFID, by showing that rFID correlates with sample quality in the diffusion refinement phase, whereas iFID correlates with sample quality in the diffusion navigation phase. Furthermore, we provide an explanation for why iFID correlates well with gFID, and why reconstruction metrics are negatively correlated with gFID, by connecting to results in the diffusion generalization and hallucination. Empirically, iFID is the first metric to demonstrate a strong correlation with diffusion gFID, achieving Pearson linear and Spearman rank correlations approximately 0.85. The source code is provided in https://github.com/tongdaxu/Making-rFID-Predictive-of-Diffusion-gFID.
69ae838e9893f58272f74202
Tongda Xu
69ae838e9893f58272f74203
Mingwei He
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Shady Abu-Hussein
69ae838e9893f58272f74205
Jose Miguel Hernandez-Lobato
69ae838e9893f58272f74206
Haotian Zhang
69ae838e9893f58272f74207
Kai Zhao
69ae838e9893f58272f74208
Chao Zhou
69ae838e9893f58272f74209
Ya-Qin Zhang
69ae838e9893f58272f7420a
Yan Wang
Making Reconstruction FID Predictive of Diffusion Generation FID
It is well known that the reconstruction FID (rFID) of a VAE is poorly correlated with the generation FID (gFID) of a latent diffusion model. We propose interpolated FID (iFID), a simple variant of rFID that exhibits a strong correlation with gFID. Specifically, for each element in the dataset, we retrieve its nearest neighbor (NN) in the latent space and interpolate their latent representations. We then decode the interpolated latent and compute the FID between the decoded samples and the original dataset. Additionally, we refine the claim that rFID correlates poorly with gFID, by showing that rFID correlates with sample quality in the diffusion refinement phase, whereas iFID correlates with sample quality in the diffusion navigation phase. Furthermore, we provide an explanation for why iFID correlates well with gFID, and why reconstruction metrics are negatively correlated with gFID, by connecting to results in the diffusion generalization and hallucination. Empirically, iFID is the first metric to demonstrate a strong correlation with diffusion gFID, achieving Pearson linear and Spearman rank correlations approximately 0.85. The source code is provided in https://github.com/tongdaxu/Making-rFID-Predictive-of-Diffusion-gFID.
A new metric called interpolated FID is proposed that shows strong correlation with generation FID in diffusion models, addressing the poor correlation issue between reconstruction FID and generation FID.
reconstruction FID generation FID latent diffusion model nearest neighbor latent space interpolated FID diffusion refinement phase diffusion navigation phase diffusion generalization hallucination Pearson linear correlation Spearman rank correlation