Top 10 Open Source AI Image Generators to Use for Free

An open source AI image generator is a tool that uses artificial intelligence to generate images based on the text descriptions provided by the user. It utilizes Natural Language Processing (NLP) to understand the context of the text and then uses Generative Adversarial Networks (GANs), Diffusion, Transformer, or other technologies to create new synthetic images that match the text description.

Being open source means the code, models, and training frameworks are publicly available under licenses that allow people to freely use, change, improve, and redistribute them. This enables collaboration and innovation. Meanwhile, thoughtful governance of these powerful technologies is crucial as they continue rapidly evolving.

No matter whether you are a pro seeking open source code or common users wanting to try AI image generators for free, here's a full list of recent open-source AI image models and algorithms that you can use to create images without any restriction on credits, resolutions, or speed. Additionally, there are tips for enhancing images quality with AI for printing, display, or further editing at the end of the post.

Open Source License

Notice that not all open source licenses are the same. Some, like the MIT License and the Apache License, are very permissive and allow for almost any kind of use, including commercial use and the creation of proprietary software based on the open source code. Others, like the GNU General Public License, require that any derivative works also be released under the same open-source license.

1. PixArt-Alpha

AI art models: PIXART-α

License: GNU Affero General Public License v3.0

Repository: https://github.com/PixArt-alpha/PixArt-alpha

Online demo: https://huggingface.co/spaces/PixArt-alpha/PixArt-alpha

PixArt-Alpha has emerged as a promising new player in the realm of open-source text-to-image diffusion models. This AI-powered tool has quickly gained attention for its ability to generate high-quality, photorealistic images that rival those produced by established models like Stable Diffusion XL and even approach the quality of Midjourney. Users have reported that PixArt-Alpha often outperforms SDXL when given the same prompts, showcasing its impressive capabilities.

One of the most significant advantages of PixArt-Alpha is its remarkably efficient training process. It requires only 10.8% of the training time compared to Stable Diffusion v1.5, which translates to substantial savings in both cost and CO2 emissions. This efficiency doesn't come at the expense of performance, as the model supports high-resolution image synthesis up to 1024px, making it versatile for a wide range of applications.

Being open-source, PixArt-Alpha benefits from community-driven development and customization, giving it an edge over closed models like Midjourney. However, it's worth noting that the model is relatively demanding in terms of hardware resources. The full version requires around 18GB of VRAM, although a 512px variant is available for systems with 12GB VRAM, making it more accessible to users with less powerful hardware.

PixArt-Alpha has demonstrated strong capabilities in accurately following detailed prompts, often surpassing SDXL in this aspect. It performs well across various styles, including fantasy art, 3D rendering, and manga-style images, showcasing its versatility. To make it more user-friendly, automatic installers and step-by-step guides are available for both local and cloud-based setups, catering to users with varying levels of technical expertise.

While PixArt-Alpha shows great promise, it's important to remember that it's still a relatively new model. Its integration into popular frameworks like Automatic1111's Stable Diffusion WebUI is ongoing, which may currently limit its widespread adoption. Nevertheless, PixArt-Alpha represents a significant step forward in open-source AI image generation, offering high-quality results with more efficient training. As it continues to develop and integrate with existing tools, it has the potential to become a preferred choice for many AI artists and developers in the near future.

2. StableStudio

AI art models: SDXL and other Stable Diffusion models

License: MIT License

Repository: https://github.com/Stability-AI/StableStudio

Online demo: None

StableStudio is an open-source iteration of Stability AI's commercial DreamStudio interface, designed for generating images from text prompts using the Stable Diffusion model. Here's a comprehensive review of StableStudio:

StableStudio represents Stability AI's commitment to fostering open-source development in the AI image generation space. By releasing this platform, the company aims to engage the broader developer community and benefit from potential improvements contributed by users. The interface is designed to be flexible and extensible, allowing for local-first development and exploration of a new plugin system.

One of the key advantages of StableStudio is its accessibility. It's a web-based application that allows users to create and edit generated images with relative ease. The platform integrates Stability AI's latest AI models, including the Stable Diffusion XL image generator and the StableLM language model. This integration provides users with a comprehensive toolkit for AI-powered image creation and manipulation.

StableStudio's open-source nature offers several benefits. It allows for community-driven development, which can lead to rapid improvements and innovative features. Users have the freedom to customize the interface to suit their specific needs, and developers can create plugins to extend its functionality. This openness aligns with Stability AI's broader strategy of leveraging open-source initiatives to generate interest in its products and build public trust through transparent and accessible models.

However, StableStudio's release has also raised some questions about Stability AI's business strategy. The coexistence of StableStudio alongside the commercial DreamStudio platform could potentially lead to internal competition. Additionally, reports of financial challenges and dependencies on external collaborations for critical models like Stable Diffusion highlight the complexities in Stability AI's operational landscape.

From a technical standpoint, StableStudio requires Node.js and Yarn for installation, and it can be run locally on a user's machine. This local-first approach provides users with more control over their AI image generation process and can be beneficial for those concerned about privacy or those who prefer to work offline.

While StableStudio offers many of the same features as DreamStudio, there are some key differences. StableStudio has removed DreamStudio-specific branding and Stability-specific account features such as billing and API key management. It also replaces "over-the-wire" API calls with a plugin system, allowing users to easily swap out the back-end and potentially use StableStudio with any compatible AI model.

3. InvokeAI

AI art models: Stable Diffusion models

License: Apache License

Repository: https://github.com/invoke-ai/InvokeAI

Online demo: None

InvokeAI is a powerful and versatile open-source AI image generation tool that has gained significant popularity among professionals, artists, and enthusiasts. Here's a comprehensive review of InvokeAI:

InvokeAI is built as a creative engine for Stable Diffusion models, offering a wide range of features for generating and manipulating visual media. It started as a small command-line interface for running the original Stable Diffusion model, created by Dr. Lincoln Stein, and has since evolved into a full-fledged creative application with strong community support.

One of InvokeAI's key strengths is its user-friendly web interface, which provides a streamlined process for image generation while still offering advanced options for more experienced users. This interface includes features like a unified canvas for image-to-image transformations, inpainting, and outpainting, making it versatile for various creative tasks.

InvokeAI supports multiple platforms, including Windows, Mac, and Linux, and can run on GPU cards with as little as 4 GB of RAM. This accessibility makes it an attractive option for users with different hardware configurations.

The tool offers robust image and model management capabilities. Users can install and manage various models, including ControlNet models and style/subject concepts. It also supports model merging, allowing for the creation of custom models tailored to specific artistic needs.

For enterprises and professional studios, InvokeAI provides enhanced security features. It has been tested and approved by InfoSec teams at major studios and is SOC-2 Compliant (Type 1). This makes it suitable for commercial use, addressing potential legal and compliance risks associated with AI-generated content.

One of the most significant advantages of InvokeAI is its commitment to user ownership and control. Unlike some other AI platforms, InvokeAI ensures that users retain full ownership of their assets, creations, and trained models. This policy extends even after a user stops using the platform, providing long-term security for intellectual property.

InvokeAI also offers flexibility in deployment. While there's a cloud-based version available (Invoke Indie, Premier, and Enterprise), users can also opt for the open-source Community Edition, which can be installed locally on compatible hardware.

The development team behind InvokeAI is actively engaged with the community, continuously improving the software based on user feedback. They maintain a strong presence on platforms like GitHub and Discord, encouraging contributions from developers and creators alike.

4. DALL-E mini

AI art models: DALL-E mini

License: Apache License 2.0

Repository: https://github.com/borisdayma/dalle-mini

Online demo: https://huggingface.co/spaces/dalle-mini/dalle-mini

DALL-E mini, now known as Craiyon, is an open-source AI image generation model that has gained significant attention for its ability to create images from text descriptions. Here's a comprehensive review of DALL-E mini:

DALL-E mini was developed as an attempt to reproduce the impressive results of OpenAI's DALL-E model in an open-source format. Created by a team led by Boris Dayma, the model uses a combination of advanced AI techniques to generate images based on text prompts.

The core of DALL-E mini's architecture is a sequence-to-sequence decoder network based on the BART (Bidirectional and Auto-Regressive Transformer) model. This network takes in text prompts and generates semantically relevant image tokens. These tokens are then converted into actual images using a Vector Quantized Generative Adversarial Network (VQGAN).

One of DALL-E mini's key strengths is its accessibility. Unlike some proprietary models, DALL-E mini is open-source, allowing researchers and developers to study, modify, and improve upon the model. This openness has contributed to its rapid adoption and improvement by the AI community.

The model was trained on approximately 15 million caption-image pairs, which helps it understand the relationship between text descriptions and visual elements. This training allows DALL-E mini to generate a wide variety of images, from realistic scenes to abstract concepts.

In terms of performance, DALL-E mini has shown impressive capabilities, especially considering its open-source nature. While it may not match the quality of more advanced models like DALL-E 2, it can still produce creative and often surprising results. The model is particularly good at understanding and combining multiple concepts within a single prompt.

However, DALL-E mini does have some limitations. The generated images are often lower resolution and may contain artifacts or distortions. Complex prompts can sometimes lead to confused or inaccurate results. Additionally, the model's understanding of certain concepts or details may be limited compared to more advanced AI image generators.

Despite these limitations, DALL-E mini has found widespread use due to its accessibility and the fun, often whimsical results it produces. It has become popular for creating memes, conceptual art, and exploring creative ideas.

For businesses and developers, DALL-E mini offers an opportunity to experiment with AI image generation without the need for expensive proprietary models. It can be used for prototyping ideas, generating concept art, or as a starting point for more refined image creation processes.

5. DeepFloyd IF

AI art models: Multiple neural modules, base and super-resolution models, etc.

License: DeepFloyd IF License Agreement

Repository: https://github.com/deep-floyd/IF

Online demo: None

DeepFloyd IF is a state-of-the-art open-source text-to-image model developed by DeepFloyd Lab at Stability AI. This model stands out for its high degree of photorealism and advanced language understanding capabilities, making it a significant player in the field of AI-generated imagery.

The architecture of DeepFloyd IF is modular, composed of a frozen text encoder and three cascaded pixel diffusion modules. These modules work in stages to progressively enhance the resolution of the generated images. The first stage generates a 64x64 pixel image based on the text prompt. The second stage upscales this image to 256x256 pixels, and the third stage further enhances it to 1024x1024 pixels. This multi-stage approach allows DeepFloyd IF to produce highly detailed and photorealistic images.

One of the key features of DeepFloyd IF is its use of a frozen text encoder based on the T5 transformer. This encoder extracts text embeddings, which are then fed into a UNet architecture enhanced with cross-attention and attention-pooling mechanisms. This design enables the model to achieve a zero-shot FID score of 6.66 on the COCO dataset, outperforming many current state-of-the-art models.

DeepFloyd IF is notable for its efficiency and performance. It has been optimized to run on GPUs with as little as 14 GB of VRAM, making it accessible to a broader range of users. The model is integrated with the Hugging Face diffusers library, which provides tools for optimizing speed and memory usage during the inference process. Users can also fine-tune the model using Dreambooth scripts, allowing for the addition of new concepts with a single GPU and approximately 28 GB of VRAM.

The open-source nature of DeepFloyd IF allows for extensive customization and community-driven development. Users can access the model through platforms like Hugging Face and GitHub, where they can find detailed documentation and examples of how to use the model for various applications, including text-to-image generation, image-to-image generation, and inpainting.

DeepFloyd IF's modular design and advanced capabilities make it a versatile tool for both individual creators and professional studios. Its ability to generate high-resolution, photorealistic images from text prompts opens up numerous possibilities for creative projects, from concept art to detailed visualizations.

6. Openjourney

AI art models: Openjourney, Openjourney v4

License: MIT License

Repository: https://huggingface.co/prompthero/openjourney/tree/main

Online demo: https://prompthero.com/users/sign_in (Sign in and select Openjourney model)

Openjourney is an open-source AI image generation model that aims to replicate the style and capabilities of Midjourney. Here's a comprehensive review of Openjourney:

Openjourney was developed by Muhammadreza Haghiri and is based on the Stable Diffusion v1.5 model. It was fine-tuned using over 124,000 images over 12,400 steps and 4 epochs, resulting in about 32 hours of training time. This extensive training allows Openjourney to generate high-quality images in a style similar to Midjourney.

One of the key advantages of Openjourney is its accessibility. As an open-source model, it's freely available for users to download and use, making it an attractive option for those who want to experiment with AI image generation without the costs associated with some proprietary models. The model has gained significant popularity, with over 4 million total runs and 500,000+ downloads in the last month alone.

Openjourney is designed to be user-friendly and can be easily integrated into existing Stable Diffusion workflows. Users can download the model checkpoint file and set it up with a UI for running Stable Diffusion models, such as AUTOMATIC1111. This flexibility allows both beginners and experienced users to leverage the power of Openjourney.

The model excels at generating a wide range of image styles, from realistic scenes to more abstract or artistic compositions. Users can create images by providing detailed text prompts, and the model will interpret these prompts to generate corresponding visuals.

One interesting feature of Openjourney is that it initially required users to include "mdjrny-v4 style" in their prompts to achieve the Midjourney-like style. However, recent updates have made this unnecessary, streamlining the prompt creation process.

Openjourney has received positive feedback from the AI art community, with a rating of 4.7 out of 5 based on over 23 ratings. This high rating suggests that users are generally satisfied with the quality and versatility of the images it produces.

For developers and researchers, Openjourney offers opportunities for further experimentation and customization. Its open-source nature allows for modifications and improvements, contributing to the ongoing development of AI image generation technologies.

While Openjourney provides impressive results, it's worth noting that it may require some technical knowledge to set up and use effectively. Additionally, as with all AI models, the quality of the output can vary depending on the specificity and clarity of the input prompts.

7. Waifu Diffusion

AI art models: waifu-diffusion (based on Stable Diffusion)

License: CreativeML OpenRAIL License

Repository: https://github.com/harubaru/waifu-diffusion/

Online demo: None

Waifu Diffusion is a powerful text-to-image model that creates impressive anime images based on text descriptions. It has had a significant impact on the anime community and is highly regarded as one of the best AI art tools in the industry. It leverages AI technology to produce a diverse range of images capturing specific traits, scenes, and emotions. The model can also learn from user feedback and fine-tune its generation processes, resulting in more accurate and impressive images. Artists, anime enthusiasts, researchers, or anyone interested in AIGC can explore endless possibilities in this tool.

8. VQGAN+CLIP

AI art algorithms: VQGAN (Vector Quantized Generative Adversarial Networks), CLIP (Contrastive Language-Image Pre-Training)

License: MIT License

Repository: https://github.com/CompVis/taming-transformers; https://github.com/openai/CLIP

Online demo: None

Different from other text-to-image AI models, VQGAN and CLIP are two separate machine learning algorithms. They were combined and published on Google Colab by AI-generated art enthusiasts Ryan Murdock and Katherine Crowson. VQGAN creates images that resemble others, while CLIP determines how well a prompt matches an image. This led to a viral trend of people using the technique to create and share their own impressive artworks on social media platforms.

9. Pixray

AI art algorithms: VQGAN, CLIP

License: MIT License

Repository: https://github.com/pixray/pixray

Online demo: https://replicate.com/pixray/text2image

Pixray is an open-source AI art generation model that uses VQGAN and CLIP algorithms. Here's a comprehensive review of its key features and capabilities:

Pixray is a text-to-image generation model that leverages the VQGAN (Vector Quantized Generative Adversarial Network) and CLIP (Contrastive Language-Image Pre-training) architectures. It was developed as an open-source alternative to proprietary models like DALL-E and Midjourney.

One of the key advantages of Pixray is its ability to generate visually striking and creative images from text prompts. The model can produce a wide range of styles, from photorealistic to abstract and surreal. While the image quality may not quite match the latest state-of-the-art models, Pixray still delivers impressive results.

Pixray is relatively straightforward to use, with a simple command-line interface and well-documented installation process. It can be run locally on a user's machine, which is appealing for those who prefer to maintain control over their data and computing resources.

As an open-source project, Pixray allows for community-driven development and customization. Users can access the source code, experiment with different configurations, and even fine-tune the model for their specific needs. This flexibility is a significant advantage over closed-source models.

Pixray has relatively modest hardware requirements compared to some other AI art generation models. It can run on GPUs with as little as 8GB of VRAM, making it accessible to a wider range of users.

While Pixray is a capable model, it does have some limitations. The generated images can sometimes exhibit artifacts or inconsistencies, and the model may struggle with highly complex or detailed prompts. Additionally, the open-source nature of the project means that it may not receive the same level of ongoing development and support as proprietary models.

Pixray is well-suited for a variety of creative applications, such as concept art, illustration, and experimental art projects. Its open-source nature also makes it an attractive option for developers and researchers who want to explore and build upon the capabilities of text-to-image generation.

10. Kandinsky 2.2

AI art models: Latent Diffusion U-Net

License: Apache License 2.0

Repository: https://github.com/ai-forever/Kandinsky-2

Online demo: https://replicate.com/ai-forever/kandinsky-2

Kandinsky is an open source AI artwork generator evolving for years. The recent v2.2 improves upon Kandinsky 2.1 with a new image encoder, CLIP-ViT-G, and ControlNet support, resulting in more accurate and visually appealing outputs and enabling text-guided image manipulation. Previously, models were trained on low-res images. Now it can create 1024x1024 pixel images and supports more aspect ratios.

How to Enhance AI-Generated Images

Existing open source AI image generators can create compelling images but are often constrained by resolution limits. The maximum of image resolution of most open source AI art creators is 1024px x 1024px or 2048px x 2048px. Moreover, running these AI models also demands powerful, expensive GPU hardware.

To address these limitations, you can use VideoProc Converter AI, an AI-powered image upscaling software that can enlarge and enhance images up to 10K resolution (10000px x 10000px). It leverages the state-of-the-art AI Super Resolution model to add realistic details and textures when enlarging low-res images, photos, and artworks from Midjourney, Stable Diffusion, DALL-E, etc.

Unlike running complex AI image models that put strain on your GPU and CPU, VideoProc Converter AI is optimized to efficiently upscale images with a user-friendly interface. It works even on moderate laptop and desktop hardware for hassle-free high-resolution AI image enhancement.

Step 1. Launch VideoProc Converter AI. Click "Super Resolution" on its main interface. Then drag and drop images generated by AI to it.

Step 2. Select the image type, "Anime" or "Reality". It helps apply a proper upscaling model.

Step 3. Choose the scale, "2x", "3x", or "4x". You can right-click on the image file at the bottom to apply the upscaling settings to the rest. Or go on tweaking separately.

Step 4. Click the "RUN" to export.

FAQs about Open Source AI Image Generators

Are AI-generated images copyright free?

AI-generated artwork is not copyrighted or attributed to a person, but the existing artwork used to train the generator algorithms is often owned or attributed to real human artists and creators. Therefore, the ownership and attribution of the original artwork used to train the AI algorithm must be considered when using AI-generated images.

How to run an open source AI image generator on your computer?

There are multiple ways to run open source AI image generators. For instance, try online interactive demos, incorporate advanced AI capabilities into certain apps via APIs, run the source code with online programming tools like Google Colab, or get Python and Git installed to run a generative AI model file from GitHub or HuggingFace.

Can an open source license be revoked?

No, once an open source license has been applied to a particular version of software or code, it cannot be revoked for that version. However, for future versions, the license can be changed. This means that if you are using open source AI image generator models or algorithms for commercial purposes, it is important to check the latest license status to ensure that you are complying with the terms of the license.

Additional resources:

[1] Stable Diffusion Models: A Beginner's Guide
[2] Revolutionizing Open Source Licenses with AI Restricted MIT License
[3] Who owns AI art? - The Verge