Official fitted Jacobian lens for JLD and JLD-fast, by Shreshth Saini, Balu Adsumilli, and Alan C. Bovik. JLD measures the perceptual distance between an aligned reference image and a distorted image. Lower scores mean more similar images. It projects early DINOv2 patch features onto directions selected by the encoder's output sensitivity. The lens is fitted from unlabeled images without human quality ratings. This installs the versioned release wheel and records it in uv.lock. The distribution is named jacobian-lens-distance; the Python import remains jld. The package already includes this lens. The encoder weights are downloaded separately through timm on first use. Python 3.10 or newer…
Independent publisher
Shreshth
shreshthsaini
Generative Models, Image/Video Gen, Post-training, RL for Media Gen, Inverse Problems, Computer vision, video processing, ML
Models in Library3
Datasets in Library0
Models on Hugging Face30
Followers3
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Published tasks: Other, Text to image, Text to video.
Reported licenses: mit. Open a record to review its specific license and conditions.
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Models
Diffusers pipelines for Rectified-CFG++, a training-free guidance method for rectified-flow models (NeurIPS 2025). This repo has no weights. It holds pipeline code that you load on top of an existing checkpoint with custompipeline. Classifier-free guidance (CFG) extrapolates from the unconditional velocity toward the conditional one at every ODE step. On rectified-flow models this extrapolation pushes samples off the learned data manifold, and at high guidance scales the images show over-saturated colours and broken structure. Rectified-CFG++ replaces that extrapolation with a predictor-corrector update: 1. Predictor: take a step along the conditional velocity from the current point. 2.…
CachedSearch makes best-of-N seed search cheaper for video diffusion models. It generates every candidate with a training-free cache turned on, scores the cached drafts with a verifier, and regenerates only the winning seed at full compute. The method needs no training, so this repository holds no weights. It holds the official implementation: the cachedsearch Python package and two example scripts, copied unchanged from the GitHub repository at commit cc2a637. 1. Generate every candidate with a training-free cache enabled. 2. Score the cached drafts with the verifier the search uses. 3. Select the seed with the highest draft score. 4. Regenerate only that seed at full compute and return…