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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-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.