Maintainer: PixArt-alpha

Total Score


Last updated 5/19/2024


Model LinkView on HuggingFace
API SpecView on HuggingFace
Github LinkNo Github link provided
Paper LinkNo paper link provided

Get summaries of the top AI models delivered straight to your inbox:

Model overview

The PixArt-XL-2-1024-MS is a diffusion-transformer-based text-to-image generative model developed by PixArt-alpha. It can directly generate 1024px images from text prompts within a single sampling process, using a fixed, pretrained T5 text encoder and a VAE latent feature encoder.

The model is similar to other transformer latent diffusion models like stable-diffusion-xl-refiner-1.0 and pixart-xl-2, which also leverage transformer architectures for text-to-image generation. However, the PixArt-XL-2-1024-MS is specifically optimized for generating high-resolution 1024px images in a single pass.

Model inputs and outputs


  • Text prompts: The model can generate images directly from natural language text descriptions.


  • 1024px images: The model outputs visually impressive, high-resolution 1024x1024 pixel images based on the input text prompts.


The PixArt-XL-2-1024-MS model excels at generating detailed, photorealistic images from a wide range of text descriptions. It can create realistic scenes, objects, and characters with a high level of visual fidelity. The model's ability to produce 1024px images in a single step sets it apart from other text-to-image models that may require multiple stages or lower-resolution outputs.

What can I use it for?

The PixArt-XL-2-1024-MS model can be a powerful tool for a variety of applications, including:

  • Art and design: Generating unique, high-quality images for use in art, illustration, graphic design, and other creative fields.
  • Education and training: Creating visual aids and educational materials to complement lesson plans or research.
  • Entertainment and media: Producing images for use in video games, films, animations, and other media.
  • Research and development: Exploring the capabilities and limitations of advanced text-to-image generative models.

The model's maintainers provide access to the model through a Hugging Face demo, a GitHub project page, and a free trial on Google Colab, making it readily available for a wide range of users and applications.

Things to try

One interesting aspect of the PixArt-XL-2-1024-MS model is its ability to generate highly detailed and photorealistic images. Try experimenting with specific, descriptive prompts that challenge the model's capabilities, such as:

  • "A futuristic city skyline at night, with neon-lit skyscrapers and flying cars in the background"
  • "A close-up portrait of a dragon, with intricate scales and glowing eyes"
  • "A serene landscape of a snow-capped mountain range, with a crystal-clear lake in the foreground"

By pushing the boundaries of the model's abilities, you can uncover its strengths, limitations, and unique qualities, ultimately gaining a deeper understanding of its potential applications and the field of text-to-image generation as a whole.

This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!

Related Models




Total Score


The PixArt-alpha is a diffusion-transformer-based text-to-image generative model developed by the PixArt-alpha team. It can directly generate 1024px images from text prompts within a single sampling process, as described in the PixArt-alpha paper on arXiv. The model is similar to other text-to-image models like PixArt-XL-2-1024-MS, PixArt-Sigma, pixart-xl-2, and pixart-lcm-xl-2, all of which are based on the PixArt-alpha architecture. Model inputs and outputs Inputs Text prompts:** The model takes in natural language text prompts as input, which it then uses to generate corresponding images. Outputs 1024px images:** The model outputs high-resolution 1024px images that are generated based on the input text prompts. Capabilities The PixArt-alpha model is capable of generating a wide variety of photorealistic images from text prompts, with performance comparable or even better than existing state-of-the-art models according to user preference evaluations. It is particularly efficient, with a significantly lower training cost and environmental impact compared to larger models like RAPHAEL. What can I use it for? The PixArt-alpha model is intended for research purposes only, and can be used for tasks such as generation of artworks, use in educational or creative tools, research on generative models, and understanding the limitations and biases of such models. While the model has impressive capabilities, it is not suitable for generating factual or true representations of people or events, as it was not trained for this purpose. Things to try One key highlight of the PixArt-alpha model is its training efficiency, which is significantly better than larger models. Researchers and developers can explore ways to further improve the model's performance and efficiency, potentially by incorporating advancements like the SA-Solver diffusion sampler mentioned in the model description.

Read more

Updated Invalid Date




Total Score


The stable-diffusion-xl-base-1.0 model is a text-to-image generative AI model developed by Stability AI. It is a Latent Diffusion Model that uses two fixed, pretrained text encoders (OpenCLIP-ViT/G and CLIP-ViT/L). The model is an ensemble of experts pipeline, where the base model generates latents that are then further processed by a specialized refinement model. Alternatively, the base model can be used on its own to generate latents, which can then be processed using a high-resolution model and the SDEdit technique for image-to-image generation. Similar models include the stable-diffusion-xl-refiner-1.0 and stable-diffusion-xl-refiner-0.9 models, which serve as the refinement modules for the base stable-diffusion-xl-base-1.0 model. Model inputs and outputs Inputs Text prompt**: A natural language description of the desired image to generate. Outputs Generated image**: An image generated from the input text prompt. Capabilities The stable-diffusion-xl-base-1.0 model can generate a wide variety of images based on text prompts, ranging from photorealistic scenes to more abstract and stylized imagery. The model performs particularly well on tasks like generating artworks, fantasy scenes, and conceptual designs. However, it struggles with more complex tasks involving compositionality, such as rendering an image of a red cube on top of a blue sphere. What can I use it for? The stable-diffusion-xl-base-1.0 model is intended for research purposes, such as: Generation of artworks and use in design and other artistic processes. Applications in educational or creative tools. Research on generative models and their limitations and biases. Safe deployment of models with the potential to generate harmful content. For commercial use, Stability AI provides a membership program, as detailed on their website. Things to try One interesting aspect of the stable-diffusion-xl-base-1.0 model is its ability to generate high-quality images with relatively few inference steps. By using the specialized refinement model or the SDEdit technique, users can achieve impressive results with a more efficient inference process. Additionally, the model's performance can be further optimized by utilizing techniques like CPU offloading or torch.compile, as mentioned in the provided documentation.

Read more

Updated Invalid Date

AI model preview image



Total Score


Stable Diffusion is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input. Developed by Stability AI, it is an impressive AI model that can create stunning visuals from simple text prompts. The model has several versions, with each newer version being trained for longer and producing higher-quality images than the previous ones. The main advantage of Stable Diffusion is its ability to generate highly detailed and realistic images from a wide range of textual descriptions. This makes it a powerful tool for creative applications, allowing users to visualize their ideas and concepts in a photorealistic way. The model has been trained on a large and diverse dataset, enabling it to handle a broad spectrum of subjects and styles. Model inputs and outputs Inputs Prompt**: The text prompt that describes the desired image. This can be a simple description or a more detailed, creative prompt. Seed**: An optional random seed value to control the randomness of the image generation process. Width and Height**: The desired dimensions of the generated image, which must be multiples of 64. Scheduler**: The algorithm used to generate the image, with options like DPMSolverMultistep. Num Outputs**: The number of images to generate (up to 4). Guidance Scale**: The scale for classifier-free guidance, which controls the trade-off between image quality and faithfulness to the input prompt. Negative Prompt**: Text that specifies things the model should avoid including in the generated image. Num Inference Steps**: The number of denoising steps to perform during the image generation process. Outputs Array of image URLs**: The generated images are returned as an array of URLs pointing to the created images. Capabilities Stable Diffusion is capable of generating a wide variety of photorealistic images from text prompts. It can create images of people, animals, landscapes, architecture, and more, with a high level of detail and accuracy. The model is particularly skilled at rendering complex scenes and capturing the essence of the input prompt. One of the key strengths of Stable Diffusion is its ability to handle diverse prompts, from simple descriptions to more creative and imaginative ideas. The model can generate images of fantastical creatures, surreal landscapes, and even abstract concepts with impressive results. What can I use it for? Stable Diffusion can be used for a variety of creative applications, such as: Visualizing ideas and concepts for art, design, or storytelling Generating images for use in marketing, advertising, or social media Aiding in the development of games, movies, or other visual media Exploring and experimenting with new ideas and artistic styles The model's versatility and high-quality output make it a valuable tool for anyone looking to bring their ideas to life through visual art. By combining the power of AI with human creativity, Stable Diffusion opens up new possibilities for visual expression and innovation. Things to try One interesting aspect of Stable Diffusion is its ability to generate images with a high level of detail and realism. Users can experiment with prompts that combine specific elements, such as "a steam-powered robot exploring a lush, alien jungle," to see how the model handles complex and imaginative scenes. Additionally, the model's support for different image sizes and resolutions allows users to explore the limits of its capabilities. By generating images at various scales, users can see how the model handles the level of detail and complexity required for different use cases, such as high-resolution artwork or smaller social media graphics. Overall, Stable Diffusion is a powerful and versatile AI model that offers endless possibilities for creative expression and exploration. By experimenting with different prompts, settings, and output formats, users can unlock the full potential of this cutting-edge text-to-image technology.

Read more

Updated Invalid Date




Total Score


The PixArt-Sigma is a text-to-image AI model developed by PixArt-alpha. While the platform did not provide a detailed description of this model, we can infer that it is likely a variant or extension of the pixart-xl-2 model, which is described as a transformer-based text-to-image diffusion system trained on text embeddings from T5. Model inputs and outputs The PixArt-Sigma model takes text prompts as input and generates corresponding images as output. The specific details of the input and output formats are not provided, but we can expect the model to follow common conventions for text-to-image AI models. Inputs Text prompts that describe the desired image Outputs Generated images that match the input text prompts Capabilities The PixArt-Sigma model is capable of generating images from text prompts, which can be a powerful tool for various applications. By leveraging the model's ability to translate language into visual representations, users can create custom images for a wide range of purposes, such as illustrations, concept art, product designs, and more. What can I use it for? The PixArt-Sigma model can be useful for PixArt-alpha's own projects or for those working on similar text-to-image tasks. It could be integrated into creative workflows, content creation pipelines, or even used to generate images for marketing and advertising purposes. Things to try Experimenting with different text prompts and exploring the model's capabilities in generating diverse and visually appealing images can be a good starting point. Users may also want to compare the PixArt-Sigma model's performance to other similar text-to-image models, such as DGSpitzer-Art-Diffusion, sd-webui-models, or pixart-xl-2, to better understand its strengths and limitations.

Read more

Updated Invalid Date