CompVis - Computer Vision and Learning LMU Munich
Computer Vision and Learning research group at Ludwig Maximilian University of Munich (formerly Computer Vision Group at Heidelberg University)
Germany
Pinned Repositories
adaptive-style-transfer
source code for the ECCV18 paper A Style-Aware Content Loss for Real-time HD Style Transfer
depth-fm
DepthFM: Fast Monocular Depth Estimation with Flow Matching
geometry-free-view-synthesis
Is a geometric model required to synthesize novel views from a single image?
latent-diffusion
High-Resolution Image Synthesis with Latent Diffusion Models
metric-learning-divide-and-conquer
Source code for the paper "Divide and Conquer the Embedding Space for Metric Learning", CVPR 2019
net2net
Network-to-Network Translation with Conditional Invertible Neural Networks
stable-diffusion
A latent text-to-image diffusion model
taming-transformers
Taming Transformers for High-Resolution Image Synthesis
vunet
A generative model conditioned on shape and appearance.
zigma
A PyTorch implementation of the paper "ZigMa: A DiT-Style Mamba-based Diffusion Model"
CompVis - Computer Vision and Learning LMU Munich's Repositories
CompVis/stable-diffusion
A latent text-to-image diffusion model
CompVis/latent-diffusion
High-Resolution Image Synthesis with Latent Diffusion Models
CompVis/taming-transformers
Taming Transformers for High-Resolution Image Synthesis
CompVis/adaptive-style-transfer
source code for the ECCV18 paper A Style-Aware Content Loss for Real-time HD Style Transfer
CompVis/vunet
A generative model conditioned on shape and appearance.
CompVis/geometry-free-view-synthesis
Is a geometric model required to synthesize novel views from a single image?
CompVis/depth-fm
DepthFM: Fast Monocular Depth Estimation with Flow Matching
CompVis/net2net
Network-to-Network Translation with Conditional Invertible Neural Networks
CompVis/zigma
A PyTorch implementation of the paper "ZigMa: A DiT-Style Mamba-based Diffusion Model"
CompVis/image2video-synthesis-using-cINNs
Implementation of Stochastic Image-to-Video Synthesis using cINNs.
CompVis/brushstroke-parameterized-style-transfer
TensorFlow implementation of our CVPR 2021 Paper "Rethinking Style Transfer: From Pixels to Parameterized Brushstrokes".
CompVis/iin
A Disentangling Invertible Interpretation Network
CompVis/imagebart
ImageBART: Bidirectional Context with Multinomial Diffusion for Autoregressive Image Synthesis
CompVis/retrieval-augmented-diffusion-models
Official codebase for the Paper “Retrieval-Augmented Diffusion Models”
CompVis/content-style-disentangled-ST
Content and Style Disentanglement for Artistic Style Transfer [ICCV19]
CompVis/fm-boosting
Boosting Latent Diffusion with Flow Matching
CompVis/attribute-control
Fine-Grained Subject-Specific Attribute Expression Control in T2I Models
CompVis/LoRAdapter
CompVis/interactive-image2video-synthesis
CompVis/invariances
Making Sense of CNNs: Interpreting Deep Representations & Their Invariances with Invertible Neural Networks
CompVis/ipoke
iPOKE: Poking a Still Image for Controlled Stochastic Video Synthesis
CompVis/instant-lora-composition
CompVis/behavior-driven-video-synthesis
CompVis/robust-disentangling
Unsupervised Robust Disentangling of Latent Characteristics for Image Synthesis
CompVis/metric-learning-divide-and-conquer-improved
Source code for the paper "Improving Deep Metric Learning byDivide and Conquer"
CompVis/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.
CompVis/visual-search
Visual search interface
CompVis/cuneiform-sign-detection-code
Code for the article "Deep learning of cuneiform sign detection with weak supervision using transliteration alignment"
CompVis/cuneiform-sign-detection-webapp
Code for demo web application of the article "Deep learning for cuneiform sign detection with weak supervision using transliteration alignment".
CompVis/Characterizing_Generalization_in_DML