VIDU

Vidu Reference-to-Image is an advanced image generation model developed by fal.ai that enables the creation of new images by intelligently combining up to three reference images with a detailed text prompt. Unlike standard single-reference workflows, Vidu’s architecture processes multiple reference images simultaneously, maintaining character or object consistency throughout various scene variations described in the prompt. This unique capability addresses the challenge faced by creators who require visual continuity across different generations without resorting to manual compositing or style transfer techniques.

The model accepts input in the form of up to three image URLs and a natural language prompt of up to 1,500 characters. This allows users to describe the desired scene and subject behavior or appearance with significant detail. Aspect ratio selection is natively supported, offering direct output in 16:9, 9:16, or 1:1 formats without the need for post-generation cropping. Output images are provided in common formats including PNG, WebP, and JPG, making them suitable for a wide range of creative and commercial applications.

Ideal use cases for Vidu Reference-to-Image, as documented, include character design, product visualization, and brand asset creation. Character design workflows benefit from the model’s subject appearance consistency, enabling artists to create multiple scenes featuring the same character. Product visualization tasks can leverage the multi-reference capability to depict products or branded objects across diverse scenarios while retaining brand-defining traits. Those involved in brand asset creation can ensure brand consistency across visual materials, avoiding the variations and manual corrections often required with single-input systems.

Performance-wise, Vidu is built for scalable workflows and commercial use via a fal partnership. It supports up to three reference images per request, outperforming typical single-image models in maintaining consistency and saving significant manual editing time. The model is described as efficient for multi-step creative applications, trading a degree of speed for the added benefit of multi-reference processing. Deterministic output is supported through the optional use of a seed parameter, allowing users to reproduce image generations reliably for iterative design or versioning.

Technical specifications, as included in the documentation, detail the input and output parameters. The request schema requires a prompt (up to 1,500 characters), an array of reference image URLs (maximum of three), a selectable aspect ratio (16:9, 9:16, or 1:1), and an optional seed for randomization control. Generated images include metadata such as file name, content type, file size, width, height, and a direct download URL. The output format flexibility (PNG, WebP, JPG) ensures compatibility with downstream creative, design, and production pipelines.

Vidu’s architecture supports commercial-scale inference and partner integrations, and is available for use through API access, as well as web-based playgrounds for interactive experimentation. Its design is positioned to eliminate the challenges of manual subject compositing and inconsistent results when generating visual content that must feature the same subject or style across multiple scenes.

It is noted that Vidu’s reference-to-image model is distinct from other endpoints, such as the standard Vidu Image to Image variant, which is better suited for single-reference, speed-optimized workflows where cross-image consistency is not essential. In contrast, Vidu’s multi-reference model prioritizes subject continuity and flexible scene composition, supporting more demanding visual design requirements.

Best practices, as reflected in the documentation, include leveraging the extended prompt length to describe scene changes in detail while ensuring the selected reference images clearly depict the subject or object for which consistency is desired. Direct support for common aspect ratios allows for production-ready outputs without post-processing. Further, deterministic seeding enables precise control over generative workflows for use cases that require consistent results across iterations or collaborative review cycles.

Limitations are not explicitly detailed in the documentation, but based on the described trade-offs, users should anticipate that processing multiple reference images may be less rapid than single-image workflows. The model also focuses on maintaining consistency and visual continuity rather than specialized effects such as garment fit simulation, which is offered by other subject-specific tools mentioned for comparison.

In summary, Vidu Reference-to-Image offers a powerful solution for creators, designers, and brand teams who require subjectively coherent image generations across varied scenes. With support for multi-reference processing, flexible text prompts, native aspect ratio handling, reproducible output, and commercial-ready output formats, it streamlines creative workflows previously hampered by manual editing, subject drift, or limited prompt context.