Kontext fine-tunes

Which models are the fastest?

All Kontext fine-tunes run on the same underlying black-forest-labs/flux.1-kontext architecture, so performance is fairly consistent.
Lighter LoRAs like fofr/kontext-fix-jpeg-compression and maikocode/ascii-style process quickly because they make minimal structural changes to the input image.

Which models offer the best balance of cost and quality?

If you want refined photo edits without heavy compute, fofr/kontext-make-person-real and fofr/kontext-fix-jpeg-compression hit a good middle ground.
They enhance realism and clean up compression artifacts while keeping detail and skin tone consistent.

What works best for stylistic or creative transformations?

For aesthetic or artistic edits, try:

• maikocode/ascii-style — converts photos into stylized ASCII art.
• fofr/kontext-ps1 — gives any photo a nostalgic PS1/PS2 video game look.
• fofr/kontext-old-and-damaged — applies a vintage, weathered texture for aged-photo effects.
  These are great for concept art, retro visuals, or creative moodboards.

What works best for realism or photo repair?

For realistic results and cleanup tasks:

• fofr/kontext-make-person-real removes the glossy “AI skin” look for more natural portraits.
• fofr/kontext-fix-jpeg-compression repairs blocky, low-quality artifacts.
• fofr/kontext-long-exposure-for-water simulates a smooth, natural long-exposure look on water scenes.

What’s the difference between Kontext fine-tunes and the base FLUX Kontext model?

Fine-tunes are LoRA-based adapters built on top of the black-forest-labs/flux.1-kontext image-editing model.
They don’t change the core model — instead, they inject specialized styles or effects. You can mix or disable them easily, making them modular and lightweight.

What kinds of outputs can I expect from these models?

These models generate edited or restyled images while preserving structure and composition.
Outputs vary by fine-tune: some (like fofr/kontext-ps1) change the aesthetic entirely, while others (like fofr/kontext-fix-jpeg-compression) focus on subtle improvements.

How can I create my own Kontext fine-tune?

You can use replicate/fast-flux-kontext-trainer to train your own LoRA.
Upload your dataset, set parameters like steps and learning rate, and fine-tune on top of FLUX Kontext. The result is a lightweight LoRA file that you can apply to any image-editing workflow.

Can I use these models for commercial work?

Many Kontext fine-tunes are released under permissive terms, but some are experimental or non-commercial.
Always check the License section on each model’s page before using outputs in products or commercial content.

How do I use or run these models?

Upload an image and describe your desired change in plain language.
For example:

• “Make this look like a PS1 screenshot” → fofr/kontext-ps1
• “Smooth the water as if it were long exposure” → fofr/kontext-long-exposure-for-water
• “Fix compression artifacts in this photo” → fofr/kontext-fix-jpeg-compression

What should I know before running a job in this collection?

• Kontext fine-tunes rely on black-forest-labs/flux.1-kontext as the base — your input should be clear and well-lit for best results.
• LoRAs can be stacked or combined, but combining too many may cause visual noise.
• Use low creativity or strength settings for subtle edits and higher ones for stylized effects.

Any other collection-specific tips or considerations?

• For game or art projects, fofr/portraits_dialogues creates expressive RPG-style character portraits.
• fofr/kontext-realearth can transform satellite images into realistic aerial photography, great for maps or 3D scenes.
• Keep prompts specific (“old, torn film photo from 1960s”) to help the fine-tune guide style more effectively.
• You can mix a realism LoRA (like fofr/kontext-make-person-real) with a stylized one (like fofr/kontext-ps1) for unique hybrid looks.