Kandinsky 2.1

Kandinsky inherits best practices from Dall-E 2 and Latent diffusion, while introducing some new ideas.

Playground

Try the model in real time below.

Depreciated! Please use our selection of newer models.
output image

FEATURES

PixelFlow allows you to use all these features

Unlock the full potential of generative AI with Segmind. Create stunning visuals and innovative designs with total creative control. Take advantage of powerful development tools to automate processes and models, elevating your creative workflow.

Segmented Creation Workflow

Gain greater control by dividing the creative process into distinct steps, refining each phase.

Customized Output

Customize at various stages, from initial generation to final adjustments, ensuring tailored creative outputs.

Layering Different Models

Integrate and utilize multiple models simultaneously, producing complex and polished creative results.

Workflow APIs

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Kandinsky 2.1

Kadinsky is an AI-powered tool that can be used to create abstract art. It works by first generating a random image. Then, it uses a deep learning model to transform the image into an abstract painting. Kadinsky is a powerful tool that can be used to create a variety of abstract art styles, such as expressionism, cubism, and surrealism.

To use Kadinsky, you can follow these steps:

  1. Go to the Segmind website: https://segmind.com/

  2. Click on the "Models" tab and select "Kadinsky".

  3. Click on the "Try it out" button and upload an image that you want to use as a starting point.

  4. Click on the "Generate" button to generate the abstract painting.

Kadinsky is a powerful tool that can be used to create abstract art. It is easy to use and it can be used to achieve impressive results. If you are interested in experimenting with the tool, contact us for customized solutions, large-scale deployment, and research support.

Applications/Use Cases

  1. Try using different starting images to see how they affect the output painting.

  2. Experiment with different settings for the abstract painting model to see how they affect the output painting.

  3. Compare the results of Kadinsky to other abstract art generation algorithms.

F.A.Q.

Frequently Asked Questions

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