Semantic Image Synthesis with Semantically Coupled VQ-Model
- URL: http://arxiv.org/abs/2209.02536v1
- Date: Tue, 6 Sep 2022 14:37:01 GMT
- Title: Semantic Image Synthesis with Semantically Coupled VQ-Model
- Authors: Stephan Alaniz, Thomas Hummel, Zeynep Akata
- Abstract summary: We conditionally synthesize the latent space from a vector quantized model (VQ-model) pre-trained to autoencode images.
We show that our model improves semantic image synthesis using autoregressive models on popular semantic image datasets ADE20k, Cityscapes and COCO-Stuff.
- Score: 42.19799555533789
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Semantic image synthesis enables control over unconditional image generation
by allowing guidance on what is being generated. We conditionally synthesize
the latent space from a vector quantized model (VQ-model) pre-trained to
autoencode images. Instead of training an autoregressive Transformer on
separately learned conditioning latents and image latents, we find that jointly
learning the conditioning and image latents significantly improves the modeling
capabilities of the Transformer model. While our jointly trained VQ-model
achieves a similar reconstruction performance to a vanilla VQ-model for both
semantic and image latents, tying the two modalities at the autoencoding stage
proves to be an important ingredient to improve autoregressive modeling
performance. We show that our model improves semantic image synthesis using
autoregressive models on popular semantic image datasets ADE20k, Cityscapes and
COCO-Stuff.
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