FLUX

flux-trained — Image-to-Image AI Model

flux-trained, powered by Black Forest Labs' FLUX PuLID architecture based on FLUX-dev, revolutionizes image-to-image AI with pure and lightning-fast ID customization through contrastive alignment, enabling precise identity preservation in edits without quality loss. Developed as part of the flux family, this model excels at transforming input images while maintaining facial details, styles, and compositions, solving common issues in AI photo editing like identity drift or inconsistent outputs. Ideal for developers seeking a Black Forest Labs image-to-image solution, flux-trained supports high-resolution edits up to 4 megapixels, making it perfect for professional workflows in e-commerce and content creation.

What Sets flux-trained Apart

Unlike generic image-to-image models, flux-trained leverages FLUX-dev's contrastive alignment for superior ID fidelity, distilling pure identity customization that outperforms standard fine-tuning methods in speed and accuracy. This enables users to lock in subject identities across edits with minimal steps, ideal for AI image editor API integrations requiring consistent character rendering.

• Lightning ID Customization: Achieves sub-second inference for ID-preserving edits via 4-step distillation, inheriting FLUX's advanced understanding of lighting and textures—users can customize identities 10x faster than traditional LoRAs while running on consumer GPUs (~13GB VRAM).
• Multi-Reference Support: Handles up to 4 input images for style transfer and character consistency, preserving geometry and details in high-res outputs—perfect for generating product variants or multi-scene narratives without hallucinations.
• 4MP Resolution Editing: Edits at up to 4 megapixels with coherent spatial relationships and readable text, supporting any aspect ratio—outshines smaller models in detail retention for image to image AI model applications like bulk catalog generation.

Technical specs include latent flow matching for efficient processing, input resolutions from 64x64, and output in standard image formats, with average times under 1 second on optimized hardware.

Image customization with identity preservation

Integrated with FLUX.1-dev text-to-image model

High identity similarity maintenance

Background, lighting, and style consistency

Advanced editing options

How to Use flux-trained on Eachlabs

Access flux-trained through Eachlabs' Playground for instant testing with text prompts, input images (up to 4 references), and settings like resolution or steps; integrate via API or SDK by providing base image, customization prompt, and ID lock parameters for 4MP outputs in seconds. Eachlabs delivers production-ready, high-fidelity edits optimized for image-to-image AI model scale.

Identity Customization: Seamless integration of specific identities into generated images.

Attribute Modification: Ability to alter attributes such as age, expression, and hairstyle through text prompts.

High-Quality Output: Generation of images with high resolution and fidelity.

Use Cases for flux-trained

For creators building personalized avatars, flux-trained takes a source portrait and applies "transform this face onto a cyberpunk character in neon-lit streets, maintain exact eye shape and smile" to deliver photorealistic results with perfect ID lock—streamlining avatar pipelines for gaming apps.

Marketers using AI photo editing for e-commerce can input product shots with references: feed a shoe image plus style refs for "place on urban sidewalk with dynamic shadows, match brand lighting"—generating A/B test variants at scale without studio reshoots.

Developers integrating flux-trained API for apps edit user uploads with multi-references, ensuring consistent branding across scenes, like adapting a logo to various backgrounds while preserving text legibility in 100+ languages.

Designers handling automated image editing API workflows use its pose guidance and hex color matching to refine mockups, such as "adjust fabric texture on this dress to silk sheen, hex #C0C0C0, keep model pose intact"—accelerating iterative design cycles.

• Combine Text Prompts with Visual Inputs
  Use creative and descriptive text prompts alongside input images to generate dynamic and unique outputs. For example:
  • "A futuristic version of [input identity] in a cyberpunk city"
  • "A hand-drawn sketch of [input identity] in an ancient warrior's armor."
• Style Transformation
  Explore various artistic styles, such as watercolor, oil painting, or surrealism, while preserving the subject’s identity.
  • Example: "An oil painting of [input identity] in the style of the Renaissance."
• Attribute Modification
  Modify age, expression, or other features using text prompts.
  • Example: "Make [input identity] look 20 years older with a happy expression."
• Background Customization
  Experiment with generating outputs in different settings.
  • Example: "[Input identity] standing on a tropical beach during sunset."
• High-Quality Outputs for Printing
  Use input images with resolutions of 512x512 or higher and set parameters for ultra-high-quality outputs suitable for printing or showcasing.
• Focus on Lighting and Composition
  Add lighting effects or scene descriptions for dramatic results.
  • Example: "A cinematic portrait of [input identity] under a spotlight in a dark theater."
• Multiple Identity Blends
  Try blending multiple identities or input features to create hybrid outputs.
  • Example: "A fusion of [input identity 1] and [input identity 2] as a superhero."
• Creative Use Cases
  Push the boundaries of the model’s capabilities by combining functionality with other AI tools.
  • Example: Use outputs as input for video editing, digital animations, or augmented reality applications.

Experiment with different timestep values

Test various styles and compositions

Try different lighting and background combinations

Compare stylized and realistic outputs

Experiment with portrait and full-body photos

Create images in various artistic styles

Identity Fidelity: While improved, the model may still struggle with accurately replicating certain identities, particularly male faces.
Output Format: PNG, WEBP