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.

📢 Direction: Kornia is becoming the reference implementation and executable specification for differentiable computer vision and geometry in the PyTorch ecosystem — explicit conventions, conformance tests, and honest benchmarks over API growth. Read the Roadmap.

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.
2. 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.
3. 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.

The status below comes from the Linux CPU half-precision CI jobs, which run the whole test suite in float16 and in bfloat16 against strict known-failure manifests (cpu_float16.txt, cpu_bfloat16.txt). The known failures column counts the manifest entries per module; the remaining work is tracked in #4153. No CI job covers CUDA or MPS half precision.

✅ No known CPU failures   ⚠️ Runs, with known failures (mostly accuracy; notes name the ops that raise)

Test results:

Pass% = passed ÷ (passed + failed). The CPU rows are the nightly main CI jobs (Linux x86_64, Python 3.11, PyTorch 2.9.1, no --runslow). In the half jobs, Failed is the manifest's entry count: CI reports those tests as strict xfails, and it fails if any of them passes or fails differently. Tests marked xfail in the source are excluded from every row. Reproduce a CPU half row in that environment with KORNIA_TEST_OPTIMIZER= pixi run test-module tests/ --verify-known-failures --known-failure-profile=cpu-float16 (or cpu-bfloat16), and the baseline with pixi run test-f32. pixi run test-half is an unseeded sweep of both dtypes whose counts can drift slightly from the manifests. The CUDA rows have not been re-measured since March 2026 and predate the CPU half-precision fixes merged since then.

See the full precision guide for details.

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!

pip install kornia

Some features (ONNX, Stable Diffusion dissolving) need extra packages; see Optional extras.

pip install -e .

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

# Create the Pixi environment and install development dependencies
pixi install
pixi run -e default 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

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((-45.0, 45.0), p=1.0), RandomBrightness((0.0, 1.0), p=1.0))

# 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")

If kornia is useful to you and you would like to help, contributions of many kinds are welcome: code, bug reports, benchmarks, documentation, questions, answers, and examples. The maintainers have limited time, so we cannot promise that every proposal or pull request will be reviewed or merged.

Kornia's differentiated value is its geometry core: warping and sampling, homographies, cameras, epipolar geometry, rotations and Lie groups, and geometry-consistent augmentation. The highest-impact contributions make that core more trustworthy — see the Roadmap for the full picture. Great entry points:

• Benchmark results from your hardware: run the benchmark suite with --contribute and send the JSON — CUDA numbers from diverse GPUs are especially useful.
• Convention pinning tests and conformance vectors for core geometry operations.
• Corrective error messages: upgrade bare shape assertions into errors that state what was wrong, what was expected, and which convention applies.
• Classical vision in the core domain: camera intrinsic calibration, fiducial markers (ArUco/ChArUco), classical tracking, dense stereo, and Hough transforms. An early design discussion can be useful for work of this size.

See the Roadmap's contributor areas for more project context.

The model zoo is currently frozen for expansion while maintainer bandwidth concentrates on the core. Shipped models (LoFTR, LightGlue, DISK, DeDoDe, SAM, and friends) stay available and maintained, and model work approved before the freeze (Efficient LoFTR, SANDesc) will be completed under its existing scope. New integrations — including VLM/VLA models — require a named maintainer sponsor who accepts ongoing ownership of the integration; a contributor implementation alone cannot reopen the surface. See the Roadmap for the reasoning and the reopen condition.

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.

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}
}

See CONTRIBUTING.md for our social contract, development setup, and technical guidelines. Participation is subject to the Code of Conduct.

• Discord: talk with people who use and develop kornia. Join the server
• GitHub Issues: report bugs and propose concrete changes. Open an issue
• GitHub Discussions: ask questions and discuss implementations, research, and ideas. Join a discussion

Made with contrib.rocks.

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