MINIMAX
Minimax Subject Reference model focuses on preserving the main subject in the image while adapting the style or background. It ensures the subject stays accurate and consistent across generations.
Official Partner
Avg Run Time: 30.000s
Model Slug: minimax-subject-reference
Playground
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Output
Example Result
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API & SDK
Create a Prediction
Send a POST request to create a new prediction. This will return a prediction ID that you'll use to check the result. The request should include your model inputs and API key.
Get Prediction Result
Poll the prediction endpoint with the prediction ID until the result is ready. The API uses long-polling, so you'll need to repeatedly check until you receive a success status.
Readme
Overview
minimax-subject-reference — Image-to-Image AI Model
Developed by Minimax as part of the minimax family, minimax-subject-reference is an advanced image-to-image AI model that preserves the main subject from your input image with exceptional accuracy while transforming styles, backgrounds, or scenes. This subject reference capability solves a core challenge in AI image editing: maintaining consistent identity and details across generations, ideal for creators seeking reliable image-to-image AI model outputs without losing key elements. Whether adapting a portrait to a fantasy setting or swapping e-commerce product backgrounds, minimax-subject-reference ensures the primary subject remains photorealistic and true-to-form.
Powered by Minimax's image-01 architecture, which supports both text-to-image and image-to-image with dedicated subject reference for people, this model delivers fine-grained details in diverse aspect ratios and resolutions. Developers and designers turn to minimax-subject-reference API for scalable, consistent edits in workflows like AI photo editing for e-commerce.
Technical Specifications
What Sets minimax-subject-reference Apart
minimax-subject-reference stands out in the competitive landscape of Minimax image-to-image tools through its specialized subject reference feature, which locks in the main subject's identity—especially for people—across style or background changes. This enables seamless repurposing of personal photos into varied artistic or commercial visuals without retraining or multiple iterations.
Unlike generic image editors, it integrates directly with Minimax's high-quality image-01 model, producing fine-grained details in custom aspect ratios and resolutions tailored for professional use. Users gain flexibility for diverse outputs, from square product shots to wide landscapes, streamlining edit images with AI pipelines.
- Subject Reference Precision: Locks facial features, poses, and details from input images, even in dynamic transformations—perfect for consistent character visuals in marketing or design.
- Fine-Grained Detail Retention: Maintains photorealistic textures and nuances post-edit, outperforming basic models in complex scene adaptations.
- Custom Resolution Support: Handles varied aspect ratios with high-fidelity outputs, supporting professional-grade image-to-image workflows without quality loss.
Technical specs include support for JPG, JPEG, PNG inputs (up to 20MB), with outputs in flexible resolutions like 1080p-equivalent detail when paired with related Minimax tools.
Key Considerations
- The quality of the reference image directly impacts subject preservation; use clear, high-resolution images for best results
- Prompts should be concise and descriptive, focusing on the desired style or background changes while avoiding ambiguity about the subject
- Overly complex prompts or low-quality reference images may reduce output fidelity or introduce artifacts
- There is a trade-off between strict subject preservation and creative adaptation; adjusting prompt strictness can help balance these factors
- Iterative refinement (re-generating with adjusted prompts or reference images) often yields optimal results
- For best speed and quality, use recommended image resolutions and formats
Tips & Tricks
How to Use minimax-subject-reference on Eachlabs
Access minimax-subject-reference seamlessly through Eachlabs Playground for instant testing, API for production-scale integrations, or SDK for custom apps. Upload a reference image (JPG/PNG, under 20MB) alongside a text prompt describing style or background changes, select resolution/aspect ratio, and generate high-fidelity image-to-image outputs with preserved subjects. Eachlabs delivers fast processing and consistent quality for all workflows.
---Capabilities
- Preserves the main subject’s appearance and identity across diverse style or background transformations
- Delivers high prompt accuracy and photorealistic detail in generated images
- Supports both text-to-image and image-to-image workflows
- Adaptable to a wide range of visual styles and compositions
- Maintains visual balance and consistency, even with complex backgrounds or artistic effects
- Enables creative storytelling, design, and content generation with subject continuity
What Can I Use It For?
Use Cases for minimax-subject-reference
For content creators: Upload a portrait and prompt style shifts like "transform this person's outfit to cyberpunk streetwear under neon lights," retaining exact facial identity for personalized avatars or social media graphics. This subject reference ensures lifelike consistency, saving hours on manual edits.
For marketers in e-commerce: Developers building an AI image editor API can input product photos with prompts such as "place this sneaker on a urban rooftop at sunset, keep branding intact," generating lifestyle composites while preserving product details perfectly. It accelerates campaign visuals without photography costs.
For designers prototyping concepts: Feed character sketches into minimax-subject-reference with "adapt this figure to a medieval fantasy background with armor," maintaining pose and proportions for rapid iterations in game art or branding. The model's precision reduces revisions, boosting workflow efficiency.
For developers integrating AI photo editing: Use the API to batch-process user uploads, swapping environments while anchoring subjects—ideal for apps handling personalized visuals like virtual try-ons or automated headshots.
Things to Be Aware Of
- Some users have reported that the model may struggle with highly complex backgrounds or when the reference image is low quality
- Experimental features, such as prompt optimization, can be toggled for more or less creative control
- Performance is generally robust, but large or high-resolution images may require more computational resources
- Consistency across multiple generations is a noted strength, but extreme style changes can sometimes introduce minor artifacts
- Positive feedback centers on the model’s ability to maintain subject identity and deliver visually appealing results
- Common concerns include occasional loss of fine detail in the subject or background blending issues in edge cases
Limitations
- The model’s effectiveness is reduced with poor-quality or ambiguous reference images
- May not perform optimally in scenarios requiring extreme stylistic transformation or highly abstract backgrounds
- Limited transparency regarding underlying architecture and parameter count may restrict advanced customization or integration for some technical users
Pricing
Pricing Detail
This model runs at a cost of $0.010 per execution.
Pricing Type: Fixed
The cost remains the same regardless of which model you use or how long it runs. There are no variables affecting the price. It is a set, fixed amount per run, as the name suggests. This makes budgeting simple and predictable because you pay the same fee every time you execute the model.
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