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luma-photon-flash-reframe-image

LUMA-REFRAME

luma-photon-flash-reframe is an image model designed for fast and high-quality reframing.

Avg Run Time: 60.000s

Model Slug: luma-photon-flash-reframe-image

Playground

Input

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Advanced Controls

Output

Example Result

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Preview
Each execution costs $0.005000. With $1 you can run this model about 200 times.

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

Table of Contents
Overview
Technical Specifications
Key Considerations
Tips & Tricks
Capabilities
What Can I Use It For?
Things to Be Aware Of
Limitations

Overview

Luma-Photon-Flash-Reframe is an advanced image generation and reframing model developed by Luma Labs, designed specifically for fast, high-quality image reframing tasks. The model is part of the broader Photon and Photon Flash family, which are positioned as next-generation AI image generation solutions with a focus on speed, fidelity, and versatility. Luma Labs is recognized for its innovation in AI-driven visual content creation, and the Photon-Flash-Reframe model continues this tradition by offering creators and professionals a tool that balances rapid output with impressive visual quality.

Key features of the model include near-instantaneous image generation, robust handling of reframing tasks (such as aspect ratio changes and content-aware cropping), and the ability to maintain high visual fidelity even under tight computational constraints. The underlying technology leverages state-of-the-art diffusion-based architectures, optimized for both speed and quality, making it suitable for real-time applications and iterative creative workflows. What sets Photon-Flash-Reframe apart is its ability to deliver consistent, high-quality results at a fraction of the time required by traditional diffusion models, making it especially valuable for use cases where turnaround time is critical.

Technical Specifications

  • Architecture: Diffusion-based image generation (specific architecture details not publicly disclosed)
  • Parameters: Not publicly specified
  • Resolution: Supports high-resolution outputs, with user reports indicating reliable performance up to 2K (2048 pixels on the long edge)
  • Input/Output formats: Accepts standard image formats (JPEG, PNG) for input and output
  • Performance metrics: User benchmarks highlight sub-second generation times for standard reframing tasks, with quality scores comparable to leading diffusion models

Key Considerations

  • The model is optimized for speed; for best results, use high-quality source images and clear reframing instructions
  • For complex reframing (e.g., extreme aspect ratio changes), iterative refinement may yield better results
  • Avoid overly ambiguous prompts; specificity in desired framing and content helps the model deliver more accurate outputs
  • There is a trade-off between speed and maximum achievable quality—using default fast settings may slightly reduce fine detail
  • Prompt engineering: Use explicit instructions about desired framing, focal points, and content preservation to guide the model effectively

Tips & Tricks

  • For optimal sharpness, use high-resolution source images and specify target resolution in the prompt
  • When reframing for social media or print, clearly state the desired aspect ratio (e.g., "reframe to 16:9 while preserving subject in center")
  • To maintain subject integrity, mention key objects or people that must remain visible after reframing
  • For creative effects (e.g., cinematic crops), experiment with iterative prompts, gradually adjusting framing parameters
  • If initial results are unsatisfactory, rephrase the prompt to be more specific or adjust the reframing region incrementally

Capabilities

  • Excels at rapid, high-quality image reframing, including aspect ratio changes and intelligent cropping
  • Maintains high visual fidelity and subject consistency across a wide range of input images
  • Supports batch processing for efficient handling of multiple images
  • Adaptable to various creative and professional workflows, including content creation, marketing, and design
  • Delivers outputs suitable for both digital and print applications, with minimal manual post-processing required

What Can I Use It For?

  • Professional image reframing for marketing materials, social media posts, and advertising campaigns
  • Creative projects such as digital art, storyboarding, and visual content adaptation
  • Automated content adaptation for e-commerce product images, ensuring consistent presentation across platforms
  • Personal projects including photo albums, blog visuals, and portfolio curation
  • Industry-specific applications such as publishing, where rapid adaptation of images to different formats is required

Things to Be Aware Of

  • Some users report that extreme reframing (e.g., panoramic to portrait) may introduce minor artifacts or require manual touch-up
  • The model is highly efficient, but very large batch jobs may require substantial GPU resources for optimal speed
  • Consistency across a series of images is generally strong, but occasional minor variations in style or color balance have been noted
  • Positive feedback centers on the model’s speed, ease of use, and high-quality outputs even under tight deadlines
  • Negative feedback is rare but includes occasional issues with edge detail preservation in aggressive crops
  • Experimental features, such as advanced content-aware fill, are under active development and may behave unpredictably in edge cases

Limitations

  • May not perform optimally when reframing extremely low-resolution or highly compressed source images
  • Not ideal for tasks requiring extensive semantic editing or generation of entirely new image content beyond reframing
  • In rare cases, aggressive aspect ratio changes can lead to loss of important visual information or minor artifacts

Pricing

Pricing Detail

This model runs at a cost of $0.005000 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.