Computer Vision and Learning research group at Ludwig Maximilian University of Munich (formerly Computer Vision Group at Heidelberg University)
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CompVis is a research group at Ludwig Maximilian University of Munich, focusing on Computer Vision and Learning. Their GitHub presence features a wide range of public repositories, primarily in Python and Jupyter Notebook, with notable projects like stable-diffusion and latent-diffusion contributing to advancements in image synthesis and diffusion models.
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Public repositories
stable-diffusion
A latent text-to-image diffusion model
latent-diffusion
High-Resolution Image Synthesis with Latent Diffusion Models
taming-transformers
Taming Transformers for High-Resolution Image Synthesis
depth-fm
[AAAI 2025, Oral] DepthFM: Fast Monocular Depth Estimation with Flow Matching
adaptive-style-transfer
source code for the ECCV18 paper A Style-Aware Content Loss for Real-time HD Style Transfer
EDGS
[CVPR 2026] A PyTorch implementation of the paper "EDGS: Eliminating Densification for Efficient Convergence of 3DGS"
vunet
A generative model conditioned on shape and appearance.
geometry-free-view-synthesis
Is a geometric model required to synthesize novel views from a single image?
zigma
A PyTorch implementation of the paper "ZigMa: A DiT-Style Mamba-based Diffusion Model" (ECCV 2024)
metric-learning-divide-and-conquer
Source code for the paper "Divide and Conquer the Embedding Space for Metric Learning", CVPR 2019
fm-boosting
[ECCV 2024, Oral] FMBoost: Boosting Latent Diffusion with Flow Matching
net2net
Network-to-Network Translation with Conditional Invertible Neural Networks
image2video-synthesis-using-cINNs
Implementation of Stochastic Image-to-Video Synthesis using cINNs.
tread
No description provided for this repository.
brushstroke-parameterized-style-transfer
TensorFlow implementation of our CVPR 2021 Paper "Rethinking Style Transfer: From Pixels to Parameterized Brushstrokes".
retrieval-augmented-diffusion-models
Official codebase for the Paper “Retrieval-Augmented Diffusion Models”
attribute-control
Fine-Grained Subject-Specific Attribute Expression Control in T2I Models
cleandift
No description provided for this repository.
LoRAdapter
No description provided for this repository.
imagebart
ImageBART: Bidirectional Context with Multinomial Diffusion for Autoregressive Image Synthesis
discrete-interpolants
The official implementation of "[MASK] is All You Need"
iin
A Disentangling Invertible Interpretation Network
diff2flow
[CVPR 2025] Diff2Flow: Training Flow Matching Models via Diffusion Model Alignment
content-style-disentangled-ST
Content and Style Disentanglement for Artistic Style Transfer [ICCV19]
patch-forcing
[CVPR 2026] Denoising, Fast and Slow: Difficulty-Aware Adaptive Sampling for Image Generation
flow-poke-transformer
No description provided for this repository.
RepTok
[ICLR 2026] Adapting Self-Supervised Representations as a Latent Space for Efficient Generation
invariances
Making Sense of CNNs: Interpreting Deep Representations & Their Invariances with Invertible Neural Networks
interactive-image2video-synthesis
No description provided for this repository.
unsupervised-disentangling
No description provided for this repository.
ipoke
iPOKE: Poking a Still Image for Controlled Stochastic Video Synthesis
SCFlow
[ICCV 2025] SCFlow: Implicitly Learning Style and Content Disentanglement with Flow Models
rayder
No description provided for this repository.
long-term-motion
No description provided for this repository.
distilldift
[WACV 2025] DistillDIFT: Distillation of Diffusion Features for Semantic Correspondence
unsupervised-part-segmentation
Code for GCPR 2020 Oral : "Unsupervised Part Discovery by Unsupervised Disentanglement"
instant-lora-composition
No description provided for this repository.
DisMo
[NeurIPS 2025] DisMo: DIsentangled Motion Representations for Open-World Motion Transfer
maskflow
MaskFlow: Discrete Flows For Flexible and Efficient Long Video Generation
behavior-driven-video-synthesis
No description provided for this repository.
DisCLIP
[AAAI 2025] Does VLM Classification Benefit from LLM Description Semantics?
robust-disentangling
Unsupervised Robust Disentangling of Latent Characteristics for Image Synthesis
content-targeted-style-transfer
Content Transformation Block For Image Style Transfer [CVPR19]
metric-learning-divide-and-conquer-improved
Source code for the paper "Improving Deep Metric Learning byDivide and Conquer"
cuneiform-sign-detection-dataset
Dataset provided with the article "Deep learning for cuneiform sign detection with weak supervision using transliteration alignment". It comprises image references, transliterations and sign annotations of clay tablets from the Neo-Assyrian epoch.
visual-search
Visual search interface
cuneiform-sign-detection-code
Code for the article "Deep learning of cuneiform sign detection with weak supervision using transliteration alignment"
hbugen2018
Towards Learning a Realistic Rendering of Human Behavior
magnify-posture-deviations
Unsupervised Magnification of Posture Deviations Across Subjects
weather-rf
[CVPR 2026] Probabilistic Precipitation Nowcasting with Rectified Flow Transformers
Art-fm
[ICCV 2025] Stochastic Interpolants for Revealing Stylistic Flows across the History of Art
myriad
Landing point for "Envisioning the Future, One Step at a Time"
sparse-guidance
No description provided for this repository.
wast3d
Official project page for the paper "WaSt-3D: Wasserstein-2 Distance for Scene-to-Scene Stylization on 3D Gaussians"
Characterizing_Generalization_in_DML
No description provided for this repository.
AutomaticBehaviorAnalysis_NatureComm
Source Code + Documentation of our Automatic Behavior Analysis Software
cuneiform-sign-detection-webapp
Code for demo web application of the article "Deep learning for cuneiform sign detection with weak supervision using transliteration alignment".
zigma2
No description provided for this repository.
network-fusion
No description provided for this repository.
cliquecnn
Code for our paper "CliqueCNN: Deep Unsupervised Exemplar Learning" https://arxiv.org/abs/1608.08792
deep_unsupervised_posets
Deep Unsupervised Similarity Learning using Partially Ordered Sets (CVPR17)
mask
The official implementation of "[MASK] is All You Need"
Frequently asked questions
What does CompVis build on GitHub?
CompVis builds several notable projects on GitHub, including stable-diffusion, latent-diffusion, and taming-transformers. These repositories focus on advanced techniques in image synthesis and diffusion models, showcasing their research contributions.
Which programming languages does CompVis use?
CompVis primarily utilizes Python and Jupyter Notebook for their projects, along with JavaScript, HTML, Matlab, and CSS. This variety of languages supports their diverse research initiatives in computer vision and learning.
Are CompVis's repositories public?
Yes, CompVis's repositories are public on GitHub. This open access allows the research community and developers to explore and contribute to their significant projects in computer vision and learning.
Is this exposure intended?
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