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gptkbp:instanceOf
|
gptkb:model
|
|
gptkbp:affiliation
|
gptkb:Heidelberg_University
|
|
gptkbp:architecture
|
gptkb:convolutional_neural_network
Vector Quantization
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|
gptkbp:category
|
gptkb:artificial_intelligence
gptkb:machine_learning
computer vision
|
|
gptkbp:citation
|
1000+
Esser, P., Rombach, R., & Ommer, B. (2021). Taming Transformers for High-Resolution Image Synthesis. arXiv preprint arXiv:2012.09841.
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gptkbp:component
|
gptkb:transformation
decoder
encoder
GAN discriminator
vector quantizer
|
|
gptkbp:developedBy
|
gptkb:Björn_Ommer
gptkb:Patrick_Esser
gptkb:Robin_Rombach
|
|
gptkbp:feature
|
efficient training
combines VQ-VAE and GAN
discrete latent space
high-resolution synthesis
supports transformer-based modeling
|
|
gptkbp:format
|
gptkb:Tensor
|
|
gptkbp:fullName
|
Vector Quantized Generative Adversarial Network
|
|
https://www.w3.org/2000/01/rdf-schema#label
|
VQGAN
|
|
gptkbp:influenced
|
gptkb:Midjourney
gptkb:Stable_Diffusion
DALL-E mini
|
|
gptkbp:input
|
gptkb:illustrator
|
|
gptkbp:language
|
gptkb:Python
|
|
gptkbp:license
|
gptkb:MIT_License
|
|
gptkbp:notableFor
|
text-to-image synthesis
VQGAN+CLIP art generation
|
|
gptkbp:notablePublication
|
Taming Transformers for High-Resolution Image Synthesis
https://arxiv.org/abs/2012.09841
|
|
gptkbp:openSource
|
yes
|
|
gptkbp:output
|
gptkb:illustrator
|
|
gptkbp:publishedIn
|
2021
|
|
gptkbp:relatedTo
|
gptkb:CLIP
gptkb:GAN
gptkb:VQ-VAE
|
|
gptkbp:repository
|
https://github.com/CompVis/taming-transformers
|
|
gptkbp:trainer
|
gptkb:COCO
gptkb:ImageNet
gptkb:OpenImages
image datasets
|
|
gptkbp:usedFor
|
image generation
image synthesis
|
|
gptkbp:bfsParent
|
gptkb:AI_artists
gptkb:VQGAN+CLIP
|
|
gptkbp:bfsLayer
|
7
|