WAN 2.5 TEXT TO IMAGE

Wan 2.5 Text to Image is a powerful text-to-image generation model provided by fal.ai, designed to transform detailed text prompts into high-quality images. Built for inference and available for commercial use, this model allows users to specify elaborate scene descriptions and obtain photorealistic, cinematic outputs. Users interact with the model by inputting a required prompt—up to 2000 characters—which guides the image creation process. The model is multilingual at the input level, supporting both Chinese and English prompts.

Users have substantial control over the image generation process through several configurable parameters. You can specify the output image’s size either by selecting from preset aspect ratios (such as square, landscape_16_9, or portrait_4_3) or by entering explicit width and height values, with constraints ensuring that the aspect ratio remains between 1:4 and 4:1 and that pixel totals fall between 768×768 and 1440×1440. For more creative flexibility and reproducibility, a random seed can be set. If not provided, the seed is randomly chosen, ensuring unique outputs each time by default.

In addition to the positive prompt, the model features a 'negative prompt' setting. This allows users to specify undesirable attributes (up to 500 characters), actively avoiding unwanted elements such as low resolution or defects in the generated image. The user can also select how many images to produce per prompt—between one and four—enabling rapid iteration or generation of image sets in a single session.

Wan 2.5 integrates an optional prompt expansion capability powered by a large language model (LLM). When enabled, brief user prompts are rewritten and expanded to achieve improved output quality, particularly for shorter descriptions. This feature may increase processing time but generally enhances the richness and relevance of the generated image. Another critical parameter is the safety checker, which, when active, helps screen outputs for potentially unsafe or inappropriate content, offering an additional layer of operational assurance.

Image generation results are returned in both image and JSON output modalities, supporting flexible downstream integration and analysis. Images can be easily previewed or downloaded via the provided interface.

An example input illustrates the model's ability to interpret complex, descriptive prompts: "A lone samurai standing on the edge of a cliff at twilight..." The output demonstrates the model's capacity for delivering hyper-realistic, cinematic imagery with dramatic contrasts and deep atmospheric qualities. This highlights the model's strength in producing visually compelling, highly detailed scenes from narrative-style inputs.

Overall, Wan 2.5 Text to Image empowers users to turn intricate textual descriptions into striking images through a precise and customizable workflow. The model is suitable for commercial contexts and creative projects where fine control over image quality, content, and scene style is required. Key considerations include the need to write detailed prompts toward optimal results, make use of negative prompting to refine outputs, and leverage prompt expansion and safety controls as needed for the project’s goals. All configuration settings and operational guidelines are accessible through the model's API, allowing seamless integration and rapid prototyping for developers and creators alike.