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
Deploy Pixelflows as APIs quickly, without server setup, ensuring scalability and efficiency.
Aura Flow
Aura Flow is a flow-based generation model, a powerful approach used in machine learning to explicitly model probability distributions. Unlike some other generative models, which might rely on complex architectures or adversarial training, redefining the possibilities of text-to-image synthesis.
Key Highlights
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Open-Source: Aura Flow is the largest fully open-sourced flow-based model, capable of transforming textual prompts into vivid visual representations.
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Text-to-Image Magic: AuraFlow seamlessly bridges language and pixels. Feed it a descriptive text, and it weaves intricate images—whether serene landscapes, fantastical creatures, or everyday scenes.
Other Popular Models
fooocus
Fooocus enables high-quality image generation effortlessly, combining the best of Stable Diffusion and Midjourney.

faceswap-v2
Take a picture/gif and replace the face in it with a face of your choice. You only need one image of the desired face. No dataset, no training

sdxl-inpaint
This model is capable of generating photo-realistic images given any text input, with the extra capability of inpainting the pictures by using a mask

sd2.1-faceswapper
Take a picture/gif and replace the face in it with a face of your choice. You only need one image of the desired face. No dataset, no training
