Image Generation - MyTokenGate

1. Introduction to Image Generation Models

The platform provides two main usage methods for image generation models: one is generating images directly based on prompt input, and the other is generating image variants based on existing images combined with prompt input.

• Creating Images Based on Text Prompts: When using text-to-image models, carefully designing the input prompt is crucial for generating high-quality images. Below are some tips:

  • Specific Descriptions: Provide as much detail as possible about the image you want to generate.
  • Emotion and Atmosphere: Include descriptions of the emotion or atmosphere, such as “warm,” “mysterious,” or “vibrant.”
  • Style Specification: If you have a preference for a particular artistic style, explicitly mention it in the prompt.
  • Avoid Vague Terms: Try to avoid using overly abstract or vague terms, such as “beautiful” or “nice.”
  • Use Negative Prompts: If you want to exclude certain elements, use negative prompts.
  • Step-by-Step Inputs: For complex scenes, try breaking the prompt into steps.
  • Experiment: Different wording can yield different results. Experiment with various phrasing.
  • Leverage Model-Specific Features: Some models offer specific features like controlling resolution or style intensity.

By following these methods, you can effectively enhance the quality of images generated using text-to-image models. However, since different models may have unique characteristics and preferences, practical usage may require adjustments based on the specific model’s features and feedback.

Here are some example prompts:

A futuristic eco-friendly skyscraper in central Tokyo. The building incorporates lush vertical gardens on every floor, with cascading plants and trees lining glass terraces. Solar panels and wind turbines are integrated into the structure’s design, reflecting a sustainable future. The Tokyo Tower is visible in the background, contrasting the modern eco-architecture with traditional city landmarks.

An elegant snow leopard perched on a cliff in the Himalayan mountains, surrounded by swirling snow. The animal’s fur is intricately detailed with distinctive patterns and a thick winter coat. The scene captures the majesty and isolation of the leopard’s habitat, with mist and mountain peaks fading into the background.

• Generating Image Variants Based on Existing Images Some image generation models support creating image variants based on existing images. In this case, it is still necessary to input an appropriate prompt to achieve the desired effect. Refer to the prompt crafting tips above for guidance.

2. Experience the Feature

You can explore the image generation feature via Image Generation or use the API Documentation to make API calls.

• Key Parameter Descriptions:
  • image_size: Controls the resolution of the generated image.
  • num_inference_steps: Controls the number of steps for image generation.
  • batch_size: Number of images to generate at once. Default: 1, Max: 4.
  • negative_prompt: Specify elements you do not want to appear in the image.
  • seed: Set to a fixed value for consistent images across multiple runs.

3. Supported Models

Currently supported image generation models:

• Text-to-Image Series: black-forest-labs Series: black-forest-labs/FLUX.1-dev black-forest-labs/FLUX.1-schnell black-forest-labs/FLUX.1-Kontext-dev black-forest-labs/FLUX.1-Kontext-max black-forest-labs/FLUX.1-Kontext-pro black-forest-labs/FLUX-1.1-pro black-forest-labs/FLUX-1.1-pro-Ultra

Models