ModelVerifiedExternal

Geolip SVAE

KindCheckpointPublisher@AbstractPhilAgrade

Model source

Kind: Checkpoint, Text encoder, VAE
File size: 824–904 B
Updated: Sep 14, 2026
Publisher: @AbstractPhil
Parameters: 111M

Source excerpt

Model artifacts

64 artifacts

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Source excerpts

3 excerpts

ReadmeJul 11huggingface.co/AbstractPhil/geolip-SVAE

--- license: apache-2.0 --- # 5/7/2026: Converting to five prototypes I think I fixed the downstream codebook error from missing components or faulty types, so that's likely fixed now. There are two core sister components that will require creating and with that 3 model design concepts. ## SVAE Centrifuge aka "The Shredder" This component's primary purpose is to take in the center-mass of the SVAE and blend it with it's own, then capture using a different decoder than the SVAE was trained on to begin with. A secondary decoder's job is to capture the three-band extrapolation of the system when bias is introduced using CV cayley-menger processing. As redundant as this sounds, the current encoder/decoder paradigm has proven that it can work. **The Johanna Grandmaster Experiment** Johanna's decoder was retrained into grandmaster to denoise, which was a model directly trained to inference using the omega noise battery - 16 types of noise, adversarial, cooperative, and simple combined together to create the decoder of Grandmaster. Grandmaster's decoder is Johanna's decoder, but the decoder itself was conditioned using Fresnel's omega tokens and then trained to reconstruct clean images. Experts: * Fresnel -> Imagenet trained to recon nearly perfect. * Johanna -> 16 types of noise trained to recon nearly perfectly. Both are about 17m params each in their larger prototype stages with d16 as their bottleneck size. Trained no kl_divergence or anything else that would corrupt the internals of the system with invalid pathways. * Clean Image -> Into Fresnel * Sigma Noised Image -> Into Johanna * Capture Fresnel's Omega tokens center-mass while Fresnel reconstructs. * Capture and train masked loss * Maximize recon capacity towards clean and discourage noise Outcome: https://huggingface.co/AbstractPhil/geolip-SVAE/tree/main/v30_grandmaster ![image](https://cdn-uploads.huggingface.co/production/uploads/630cf55b15433862cfc9556f/i4qvzPuFlqkpKcJ6iH9AQ.png) The experiment shows marked denoise improvement in a direct pragmatic visual-sense over time. ![image](https://cdn-uploads.huggingface.co/production/uploads/630cf55b15433862cfc9556f/fpa87CfUDjrnSHPuDLW4j.png) This is a direct visual representative outcome. Johanna saw noisy images with a frozen encoder, Johanna's decoder retrained to denoise images into closer-to-grandmaster image states. The outcome yielde...

NoteJul 11huggingface.co/AbstractPhil/geolip-SVAE

Source context: 0 downloads · 2 likes · Repo AbstractPhil/geolip-SVAE

NoteApr 29huggingface.co/api/models/AbstractPhil/geolip-SVAE

Source context: 0 downloads · 0 likes · Repo AbstractPhil/geolip-SVAE