5 (Non SD) Open Source Text To Image Model

Sujeet Kumar
5 Min Read

In the ever-evolving landscape of artificial intelligence, the ability to transform text descriptions into vivid, photorealistic images has captivated researchers and enthusiasts alike. While Stable Diffusion-based models have dominated this field, a wave of innovation has given rise to a new generation of open-source Text-to-Image models that diverge from the conventional path.

In this article, we delve into the fascinating realm of Non-Stable Diffusion Open Source Text-to-Image Models, exploring their unique features, benefits, and the potential they hold for revolutionizing the way we bring words to life through visuals.

(Non SD) Open Source Text To Image Model


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Würstchen is a groundbreaking diffusion model designed for text-to-image generation. Its uniqueness lies in its exceptional data compression, a staggering 42x spatial compression, which significantly reduces both training and inference computational costs. It employs a two-stage compression process, using a VQGAN (Stage A) and a Diffusion Autoencoder (Stage B), collectively known as the Decoder, to faithfully reconstruct images from the highly compressed latent space.

Würstchen’s standout feature is its impressive speed and efficiency. It outpaces models like Stable Diffusion XL in image generation while being remarkably memory-efficient. This makes it an excellent choice for those seeking fast image generation without the need for high-end hardware. It’s a game-changer for cost-effective and rapid text-to-image generation.

DeciDiffusion 1.0

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DeciDiffusion 1.0, an open-source text-to-image model, stands out with its innovation. Trained on the LAION-v2 dataset and fine-tuned on LAION-ART, this 820 million parameter model offers promising potential. It builds upon the foundation of Stable Diffusion by introducing the Efficient U-Net, a design innovation by Deci. This upgrade streamlines the model for better computational efficiency without compromising quality. While we lack concrete results, DeciDiffusion 1.0 promises to be a noteworthy alternative in the text-to-image generation landscape.


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IF-I-XL-v1.0, also known as DeepFloyd-IF, is an impressive pixel-based text-to-image model. It excels in producing images with remarkable photorealism while grasping the nuances of language. This model stands out for its remarkable efficiency, surpassing current state-of-the-art models. It boasts an exceptional zero-shot FID-30K score of 6.66 on the COCO dataset. What’s noteworthy is that it’s one of the few models capable of generating flawless text and nearly lifelike hand images. To run this model, you’ll need a minimum of 14 GB of VRAM, making it accessible for various applications.

Karlo v1

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Karlo v1 is an open-source text-to-image model that builds upon OpenAI’s unCLIP architecture. What sets it apart is its enhanced super-resolution capability, allowing it to transform low-resolution images (64px) into high-quality ones (256px). This magic happens in just a few denoising steps.

First, a standard SR module, trained using the DDPM objective, uses a technique called respacing to upscale the image from 64px to 256px in the initial six denoising steps. Then, an additional fine-tuned SR module, trained with a VQ-GAN-style loss, takes the reins, performing the final steps to recover intricate high-frequency details.

In short, Karlo v1 excels in swiftly enhancing image quality, making it a noteworthy alternative in the realm of text-to-image models.

Kandinsky 2.2

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Kandinsky 2.2 is a cutting-edge open-source Text-to-Image model that builds upon its predecessor, Kandinsky 2.1, with impressive enhancements. It introduces the CLIP-ViT-G image encoder, elevating its image generation capabilities and text comprehension, resulting in more aesthetically pleasing and accurate outputs.

What sets Kandinsky 2.2 apart is the incorporation of ControlNet support, which empowers the model to exert precise control over image generation. This feature enhances the model’s ability to produce visually appealing images and opens new avenues for text-guided image manipulation. In a nutshell, Kandinsky 2.2 is a powerful and versatile tool for creating stunning images from text input.

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SK, an ardent writer whose creativity knows no bounds. With a profound love for anime, a fascination for the world of VFX, and an insatiable appetite for innovative storytelling, SK embarks on a journey where art and artificial intelligence converge to bring captivating narratives to life.