kornia – kornia

Kornia is a differentiable computer vision library that provides a rich set of differentiable image processing and geometric vision algorithms. Built on top of PyTorch, Kornia integrates seamlessly into existing AI workflows, allowing you to leverage powerful [batch transformations](), [auto-differentiation]() and [GPU acceleration](). Whether you're working on image transformations, augmentations, or AI-driven image processing, Kornia equips you with the tools you need to bring your ideas to life.

> 📢 Announcement: Kornia is shifting towards end-to-end vision models. We are focusing on integrating state-of-the-art Vision Language Models (VLM) and Vision Language Agents (VLA) to provide comprehensive end-to-end vision solutions.

Key Components

1. Differentiable Image Processing
   

Kornia provides a comprehensive suite of image processing operators, all differentiable and ready to integrate into deep learning pipelines.

• Filters: Gaussian, Sobel, Median, Box Blur, etc.
• Transformations: Affine, Homography, Perspective, etc.
• Enhancements: Histogram Equalization, CLAHE, Gamma Correction, etc.
• Edge Detection: Canny, Laplacian, Sobel, etc.
• ... check our docs for more.

1. Advanced Augmentations
   

Perform powerful data augmentation with Kornia’s built-in functions, ideal for training AI models with complex augmentation pipelines.

• Augmentation Pipeline: AugmentationSequential, PatchSequential, VideoSequential, etc.
• Automatic Augmentation: AutoAugment, RandAugment, TrivialAugment.

1. AI Models
   

Leverage pre-trained AI models optimized for a variety of vision tasks, all within the Kornia ecosystem.

• Face Detection: YuNet
• Feature Matching: LoFTR, LightGlue
• Feature Descriptor: DISK, DeDoDe, SOLD2
• Segmentation: SAM
• Classification: MobileViT, VisionTransformer.

Half-Precision Support

✅ Supported   ⚠️ Partial   ❌ Not supported

Test results (commit 6131e98, 2026-03-21):

See the full precision guide for details.

Sponsorship

Kornia is an open-source project that is developed and maintained by volunteers. Whether you're using it for research or commercial purposes, consider sponsoring or collaborating with us. Your support will help ensure Kornia's growth and ongoing innovation. Reach out to us today and be a part of shaping the future of this exciting initiative!

Installation

<img src="https://img.shields.io/pypi/pyversions/kornia" alt="PyPI python" class="ra0-md-img" loading="lazy" />
<img src="https://img.shields.io/badge/PyTorch_2.0.0+-ee4c2c?logo=pytorch&logoColor=white" alt="pytorch" class="ra0-md-img" loading="lazy" />

From pip

  pip install kornia

  pip install -e .

  # Install pixi (if not already installed)
  curl -fsSL https://pixi.sh/install.sh | bash

  # Install dependencies and set up the development environment
  pixi install

  # Run tests
  pixi run test

  # For CUDA development
  pixi run -e cuda install
  pixi run -e cuda test-cuda

  pip install git+https://github.com/kornia/kornia

Quick Start

Kornia is not just another computer vision library — it's your gateway to effortless Computer Vision and AI.

import numpy as np
import kornia_rs as kr

from kornia.augmentation import AugmentationSequential, RandomAffine, RandomBrightness
from kornia.filters import StableDiffusionDissolving

# Load and prepare your image
img: np.ndarray = kr.read_image_any("img.jpeg")
img = kr.resize(img, (256, 256), interpolation="bilinear")

# alternatively, load image with PIL
# img = Image.open("img.jpeg").resize((256, 256))
# img = np.array(img)

img = np.stack([img] * 2)  # batch images

# Define an augmentation pipeline
augmentation_pipeline = AugmentationSequential(
    RandomAffine((-45., 45.), p=1.),
    RandomBrightness((0.,1.), p=1.)
)

# Leveraging StableDiffusion models
dslv_op = StableDiffusionDissolving()

img = augmentation_pipeline(img)
dslv_op(img, step_number=500)

dslv_op.save("Kornia-enhanced.jpg")

import numpy as np
from kornia.onnx import ONNXSequential
# Chain ONNX models from HuggingFace repo and your own local model together
onnx_seq = ONNXSequential(
    "hf://operators/kornia.geometry.transform.flips.Hflip",
    "hf://models/kornia.models.detection.rtdetr_r18vd_640x640",  # Or you may use "YOUR_OWN_MODEL.onnx"
)
# Prepare some input data
input_data = np.random.randn(1, 3, 384, 512).astype(np.float32)
# Perform inference
outputs = onnx_seq(input_data)
# Print the model outputs
print(outputs)

# Export a new ONNX model that chains up all three models together!
onnx_seq.export("chained_model.onnx")

Multi-framework support

You can now use Kornia with TensorFlow, JAX, and NumPy. See Multi-Framework Support for more details.

import kornia
tf_kornia = kornia.to_tensorflow()

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Call For Contributors

Are you passionate about computer vision, AI, and open-source development? Join us in shaping the future of Kornia! We are actively seeking contributors to help expand and enhance our library, making it even more powerful, accessible, and versatile. Whether you're an experienced developer or just starting, there's a place for you in our community.

Accessible AI Models

We are excited to announce our latest advancement: a new initiative designed to seamlessly integrate lightweight AI models into Kornia.
We aim to run any models as smooth as big models such as StableDiffusion, to support them well in many perspectives.

Priority Focus: VLM/VLA Models

Our primary focus is on integrating Vision Language Models (VLM) and Vision Language Agents (VLA) to enable end-to-end vision solutions. We're actively seeking contributors to help us:

• VLM/VLA Integration (Priority): Implement and integrate state-of-the-art Vision Language Models and Vision Language Agents. This includes models like Qwen2.5-VL, SAM-3, and other cutting-edge VLM/VLA architectures. If you are a researcher working on VLM/VLA models, Kornia is an excellent place for you to promote your model!
• Expand the Model Selection: Import decent models into our library. If you are a researcher, Kornia is an excellent place for you to promote your model!
• Model Optimization: Work on optimizing models to reduce their computational footprint while maintaining accuracy and performance. You may start from offering ONNX support!
• Model Documentation: Create detailed guides and examples to help users get the most out of these models in their projects.

Documentation And Tutorial Optimization

Kornia's foundation lies in its extensive collection of classic computer vision operators, providing robust tools for image processing, feature extraction, and geometric transformations. We continuously seek for contributors to help us improve our documentation and present nice tutorials to our users.

Contributing

We welcome contributions to Kornia! Whether you are fixing bugs, improving documentation, or adding new computer vision features, please check out our Contribution Guidelines to get started with setting up your development environment, running tests, and submitting pull requests.

Cite

If you are using kornia in your research-related documents, it is recommended that you cite the paper. See more in CITATION.

  @inproceedings{eriba2019kornia,
    author    = {E. Riba, D. Mishkin, D. Ponsa, E. Rublee and G. Bradski},
    title     = {Kornia: an Open Source Differentiable Computer Vision Library for PyTorch},
    booktitle = {Winter Conference on Applications of Computer Vision},
    year      = {2020},
    url       = {https://arxiv.org/pdf/1910.02190.pdf}
  }

Contributing

We appreciate all contributions. If you are planning to contribute back bug-fixes, please do so without any further discussion. If you plan to contribute new features, utility functions or extensions, please first open an issue and discuss the feature with us. Please, consider reading the CONTRIBUTING notes. The participation in this open source project is subject to Code of Conduct.

AI Policy

Kornia accepts AI-assisted code but strictly rejects AI-generated contributions where the submitter acts as a proxy. All contributors must be the Sole Responsible Author for every line of code. Please review our AI Policy before submitting pull requests. Key requirements include:

• Proof of Verification: PRs must include local test logs proving execution
• Pre-Discussion: All PRs must be discussed in Discord or via a GitHub issue before implementation
• Library References: Implementations must be based on existing library references (PyTorch, OpenCV, etc.)
• Use Existing Utilities: Use existing kornia utilities instead of reinventing the wheel
• Explain It: You must be able to explain any code you submit

Automated AI reviewers (e.g., GitHub Copilot) will check PRs against these policies. See AI_POLICY.md for complete details.

Community

• Discord: Join our workspace to keep in touch with our core contributors, get latest updates on the industry and be part of our community. JOIN HERE
• GitHub Issues: bug reports, feature requests, install issues, RFCs, thoughts, etc. OPEN
• Forums: discuss implementations, research, etc. GitHub Forums

Made with contrib.rocks.

License

Kornia is released under the Apache 2.0 license. See the LICENSE file for more information.