VIDU

Vidu is a reference-to-image AI generator developed by fal.ai, specifically designed to create images that maintain consistent subject appearance across different scenes and contexts. The model accepts up to three reference images and a detailed text prompt, combining them to produce a new output image that preserves the key characteristics of the referenced subject or object. Unlike traditional single-image workflows that often require manual compositing or style transfer to achieve visual consistency, Vidu’s unique multi-reference approach automates subject consistency, making it particularly valuable for creative professionals and brands requiring iteration and control over character or product imagery.

Functionality and Key Capabilities

• Multi-Reference Consistency: Vidu can simultaneously process up to three reference images, combining their visual features effectively. This allows for reliable subject appearance retention across generated outputs, even as the context or scene changes.
• Prompt-Driven Scene Composition: Users can provide a text prompt of up to 1,500 characters, offering extensive detail and scene description. This long-form prompting enables intricate control over the scene’s elements and modifications while still grounding the result in the visual identity provided by the reference images.
• Flexible Output Options: The model supports multiple native aspect ratios (16:9, 9:16, 1:1), allowing users to select the best format for their needs without additional post-processing or cropping. Output images can be returned in PNG, WebP, or JPG formats.
• Deterministic Generation: Output can be made reproducible with the ability to set a custom seed parameter, which is especially important for iterative design workflows where consistent results over multiple runs are desired.
• Performance at Scale: Vidu is positioned for commercial-scale creative deployment, trading raw speed for the advanced feature of multi-reference processing. This can save significant manual effort when character or product consistency is required across many generated assets.

Ideal Use Cases and Target Users Based on the documentation, Vidu is primarily targeted at workflows where visual continuity and brand or character consistency are crucial. Documented examples include:

• Character Design Iteration: Enabling artists and creators to visualize the same character in multiple poses, environments, or scenes without losing distinctive features.
• Product Visualization: Supporting brands in generating product shots or variations that maintain consistent visual branding and product identity from multiple reference angles or photos.
• Brand Asset Creation: Providing marketers and designers with the tools to generate a wide range of consistent branded imagery for campaigns, social media, or advertising. These capabilities make Vidu especially suitable for professionals in creative industries, advertising, gaming, and e-commerce.

Technical Details

• Input Modalities: Multiple reference image URLs (up to three), plus a detailed natural language text prompt (maximum 1,500 characters).
• Output Modalities: Single generated image per request, delivered in PNG, WebP, or JPG formats. Each output includes image metadata such as file type, dimensions, and direct URL.
• Aspect Ratios: Native support for 16:9, 9:16, or 1:1, eliminating the need for manual cropping or resizing after generation.
• Seed Parameter: Allows for setting a random seed, which helps generate reproducible outputs for iterative workflows.
• API and Playground: The model is accessible via an API, with playground and documentation for developers and designers to experiment and integrate into their pipelines.

Quality and Performance

• Vidu is designed to deliver consistency in subject appearance that single-image models or manual compositing typically struggle with, especially across multiple generated scenes. The multi-reference architecture maintains the distinct look and visual integrity of characters or products, even as scenes and prompts are changed.
• The model is optimized for creative use cases that demand iterative control and flexibility, supporting a broad range of visual styles and scene variations as described by detailed prompts.
• Vidu is not positioned as the lowest-latency or fastest model, as it prioritizes the multi-reference consistency feature over speed. This tradeoff is intended for users who value subject accuracy across outputs more highly than raw throughput.

Limitations and Considerations

• The model processes a maximum of three reference images per generation. Users needing more than three input references may need to select the most representative or combine images externally prior to use.
• High consistency is achieved by processing multiple references, but effectiveness depends on the clarity and alignment of the input images. Poor-quality or mismatched reference images may reduce the steady appearance of the subject in generated outputs.
• Only the documented aspect ratios are natively supported (16:9, 9:16, 1:1), so any other required format would need to be cropped externally.
• While the prompt can be up to 1,500 characters, scene details that lack visual counterparts in the provided references may not transfer to the output as intended if not well aligned with the input imagery.

Best Practices

• Use clear, high-resolution reference images that accurately represent the target subject or product.
• When subject consistency is critical across multiple assets, use all three reference image slots for maximum coverage.
• Provide a detailed, descriptive text prompt within the character limit for precise scene control.
• Utilize the seed parameter for iterative workflows that require reproducible results.

In summary, Vidu is a specialized image-text-to-image model offering exceptional subject consistency through multi-reference processing. It is ideal for character design, product visualization, and any workflow demanding visual continuity, and is accessible through multiple commercial endpoints and flexible API integration.