Skip to content

Text-to-Image

The MaaS-DALL-E model generates images based on user-provided text prompts. MaaS-DALL-E 3 is available to the public via a REST API.

The following models are now available for purchase:

  • MaaS-DALL-E 3
  • MaaS-DALL-E 2（On demand）
  • MaaS-Flux-1-schnell

MaaS-Flux-1-schnell

  • High-Quality Image Generation with Outstanding Detail

Capable of generating high-definition images with rich details, whether it’s the physical structure of objects or the expression of spirit, all can be accurately represented. For example, when generating portraits, one can clearly see the subject's gaze, skin texture, etc., making the image more lifelike.

  • Diversity of High-Quality Image Generation Functions

It can cater to various styles and creative needs, whether it's realistic, abstract, or any other specific style. Users can obtain the desired effects by adjusting the prompts. This provides more possibilities for users’ creative expressions, meeting various user needs for different style images.

  • Enhanced User Experience

The model can accurately understand and execute the human text input by users, and the generated images match the elements, styles, and overall quality of the text description. This allows users to directly control the generation of images through text, reducing situations where the model’s understanding leads to unexpected results.

  • Speed Optimization

This model is a leader in the field. For users, whether they need to generate images in a short time or require integration into specific workflows, it significantly enhances the user experience. For instance, in the rapid processing of MaaS-Flux-1-schnell, it can quickly generate concept images for initial design scenarios.

MaaS-DALL-E 3

  • Powerful Image Detail and Variation Recognition

    Capable of comprehending subtle nuances and intricacies better, thus more accurately transforming user ideas into precise images. For instance, it resolves issues from previous versions, such as the inability to write text, and improves handling of elements like hand drawings and textual content.

  • High-Quality Image Generation

    Accurately reflects the content of the prompts, effectively presenting every detail in the prompts, such as translucent textures, complex scenes, and textual displays.

  • Enhanced Understanding of Context and Long Prompts

    Improves comprehension of the text context, better handling longer prompts, and more thoroughly and accurately understanding users' complex requirements to generate corresponding images.

MaaS-DALL-E 2

  • High-Quality Image Generation

    The generated images excel in realism and detail, offering higher resolution and more authentic image quality.

  • Multimodal Generation Capability

    Able to create various forms of images based on text descriptions, including objects, scenes, abstract concepts, and more. For example, it can generate complex scenes such as "an astronaut riding a horse."

  • Concept Combination and Innovation

    Capable of blending different concepts, attributes, and styles to produce images, demonstrating a certain level of creativity and imagination. For instance, it can generate unique scenes in a specific style.

  • Image Editing and Expansion

    Can perform realistic edits on existing images, adding or removing elements while taking into account shadows, reflections, and textures. Additionally, it can extend the original canvas of an image to create new compositions.

  • Image Variant Generation

    Capable of taking an image and creating various inspired variants, maintaining the relationship between elements, with each variant appearing very natural.

  • Zero-Shot Learning

    Supports zero-shot learning, allowing it to generate images that match text descriptions without prior training data, providing greater flexibility and applicability in fields like personalized customization.

  • Based on Deep Learning Technology

    Utilizes deep learning models such as diffusion models and Transformer architectures. Trained on extensive data, it can comprehend text semantics and translate them into corresponding image representations.

  • Tightly Integrated with Natural Language

    Relies on natural language processing, learning from large-scale paired text and image data to understand the relationship between them, thereby accurately generating images based on text prompts.