The best open-source image generation model

Image generation models are AI models that can create detailed, realistic images from simple text prompts. Back in 2022, image generation models went mainstream, first with OpenAI’s proprietary DALL-E 2, then with the open-source Stable Diffusion model family a few months later.

Today, options have proliferated. You have multiple foundation models, each with its own versions, variants, and sizes. There are several popular approaches for fine-tuning, tons of tools for complex pipelines, and plenty of optimizations for faster image generation.

This list of open-source image generation models will give you a good starting point in your search for the best model for your project or product.

The best open-source image generation model: FLUX.1

FLUX.1 is a recent model family by Black Forest Labs, a European research lab dedicated to creating new frontier models. The family includes two open-weight options, FLUX.1 [dev] and FLUX.1 [schnell] (“schnell” is the German word for “fast”).

These models outperform proprietary models like DALL-E 3 and Midjourney 6 on benchmarks for quality, prompt adherence, accurate word generation, and more. The [dev] variant is a larger model for higher-quality images, while the [schnell] variant offers faster inference.

✕

What we love about FLUX.1:

• Best-in-class output quality with realistic faces, hands, animals, composition, lighting, and details.

• Accurate typography for rendering both in-image text and text overlays.

• Strict prompt adherence without sacrificing the ability to fill in unspecified details at the model’s discretion.

What to watch out for with FLUX.1:

• While the FLUX.1 [schnell] variant is fully open-source under the Apache 2.0 license, the [dev] variant is open-weight but requires a separate license for commercial use.

• With 12 billion parameters, FLUX.1 is large for an image model, and thus inference is slower than other models on this list, though that can be addressed with performance optimizations.

• As a newer model, FLUX.1 has a smaller ecosystem than Stable Diffusion with fewer examples and less tooling for everything from model optimization to fine-tuning.

Deploy FLUX.1 [dev] and FLUX.1 [schnell] from the Baseten model library, and contact us for guidance on licensing FLUX for commercial use.

Another great model: Stable Diffusion 3

Stable Diffusion 3 by Stability AI is another solid pick for an image generation model. As the third generation of the model family that’s been synonymous with open-source text-to-image for the last two years, Stable Diffusion 3 brings refinement and accuracy alongside new capabilities like typography in an efficient package of as little as 2 billion parameters for Stable Diffusion 3 Medium. It also benefits from the existing ecosystem of performance optimization for previous generations, such as faster inference with TensorRT.

✕

What we love about Stable Diffusion 3:

• High-quality output and a generally neutral default style make Stable Diffusion 3 a great foundation for many projects.

• Unlike previous Stable Diffusion models like SDXL, Stable Diffusion 3 is capable of generating accurate and realistic text.

• After two years as the standard open-source image generation model family, the tooling ecosystem for Stable Diffusion 3 is unmatched.

What to watch out for with Stable Diffusion 3:

• While Stable Diffusion 3 Medium is an open-weight model, it requires a membership or license for commercial use.

• Stable Diffusion 3 Medium has just 2 billion parameters vs 12 billion for FLUX.1, and as a smaller model, it may not be equally capable.

• Stable Diffusion 3 comes in several sizes with different prompt encoding models and quantizations. Ensure you’re using the desired implementation.

You can deploy Stable Diffusion 3 Medium from the Baseten model library, and we’re here to help with licensing for commercial use.

The fastest image generation model: SDXL Lightning

Stable Diffusion inference takes a few seconds because the diffusion stage, where the image is generated, is an iterative process with as many as 50 steps. Various techniques, such as latent consistency models, have been invented to address this.

SDXL Lightning is an adaptation of SDXL by Bytedance that generates high-quality 1024x1024-pixel images in as little as 2 steps. This shortens the end-to-end generation time to just a few hundred milliseconds. While the rapid inference process can lead to worse images, SDXL Lightning exhibits better output quality and prompt adherence than similar models like SDXL Turbo.

✕

What we love about SDXL Lightning:

• Inference is blazing fast, with images generated in <1 second.

• The image quality is high for a few-step model with full 1024x1024-pixel resolution.

• The model is fully open-source and available for commercial use.

What to watch out for with SDXL Lightning:

• The image quality for any few-step model will not be high enough for many use cases.

• Full speed requires going down to 2 UNet steps, but image quality is much higher at 4 or 8 steps.

• Compared to top-choice models like FLUX.1, SDXL Lighting struggles to generate accurate and readable text.

Deploy SDXL Lightning from the model library and see a detailed breakdown of SDXL Lightning vs SDXL Turbo.

The best Midjourney replacement: Playground 2.5

Playground 2.5 Aesthetic 1024x1024 is a model based on Stable Diffusion XL but trained to mirror the style of Midjourney, a popular proprietary image generation model. Playground 2.5 is simple to use—you don’t need to add “trending on artstation” to every prompt—but it lacks some of the stylistic range of more general models like Stable Diffusion due to its training data. You can see examples of Playground vs SDXL output to decide for yourself if the style is what you’re looking for.

✕

What we love about Playground 2.5:

• Images share a consistent aesthetic and are usually detailed and accurate.

• Playground 2.5 is unusually good at interpreting abstract prompts like “the meaning of life” and coming up with something relevant.

• Playground 2.5 has strong prompt adherence for both subjects and backgrounds.

What to watch out for with Playground 2.5:

• Though a high-quality model, the image quality, prompt adherence, and typography capabilities of newer models often exceed Playground 2.5.

• In testing, Playground 2 may produce better images than Playground 2.5 for certain prompts.

• The Playground license allows for commercial use but has a monthly active users cutoff and other limitations.

Deploy Playground from the Baseten model library to generate Midjourney-style images!

Common image generation questions

How do open-source models compare to DALL-E 3?

Today, open-source image generation models match or beat the image quality of closed-source models like DALL-E 3. And by going open source, you get better control, more customization options, and better reliability with dedicated deployments.

How can I get more control over model output?

With open-source models, you get a great deal of control over the image generation process at every step of the process: model selection, model prompting, and most importantly, by combining multiple models.

For model selection, you can pick any open-source foundation model as a starting point. There are tons of community fine-tunes of Stable Diffusion and related models for all sorts of tasks, and if nothing matches what you’re looking for you can always fine-tune the model yourself.

For the prompt, it’s important to experiment with your model directly. Each model interprets prompts differently. Different levels of detail and description, as well as techniques like negative prompting, work best on different models. And of course, you can always set a fixed seed and control various model parameters for maximum control.

If you’re looking to build something multi-step, like a model that generates art on top of a logo or QR code, one great option is ControlNet. With ControlNet, you can use any open-source model and have it start the generation with a mask of a provided image. This allows a level of direction that would be impossible with prompting alone.

How do I build complex image generation pipelines?

For even more advanced multi-step image generation tasks, you can use ComfyUI. ComfyUI is an open-source toolkit for building workflows around multiple image generation models to get an incredible degree of control over the final output. With Baseten, you can deploy your ComfyUI projects behind an API endpoint to build applications on top of your advanced image generation pipelines.

Deploy the best open-source image generation models

Deploy the best model for your project from Baseten’s model library:

• The best open source image generation model: FLUX.1 [dev] and FLUX.1 [schnell]

• Another great image model: Stable Diffusion 3

• The fastest image generation model: SDXL Lightning

• The best Midjourney replacement: Playground

For generating images on top of logos, QR codes, or other masks, check out ControlNet. And read about deploying image pipelines with ComfyUI to build more complex image generation workflows.