Flux Kontext Dev Lora by BLACK-FOREST-LABS
black-forest-labs/flux-kontext-dev-loraFast FLUX.1 Kontext [dev] endpoint with LoRA support for rapid image editing using pre-trained adapters for brand and style. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.
Set up the model and run your first generation.
Ctrl ⏎Examples
Runs published with this model. Load one into the playground and change whatever you like.
Restore this old photograph with high fidelity and realism. Remove scratches, dust, stains, creases, and noise while preserving the original facial features, expressions, and historical authenticity. Improve clarity, sharpness, and contrast gently without over-smoothing. Correct faded tones and restore natural skin color while maintaining the original lighting and atmosphere. Do not alter clothing, pose, or background. Keep the photo realistic, natural, and true to the original era.Subtle and historically accurate colorization, realistic skin tones, natural fabric colors, no oversaturation, no modern color palette.
Providers
Where this model can run, priced for your account. Auto routing picks the top row and moves down it if a provider fails.
| Provider | Price | Speed | Completed |
|---|---|---|---|
| Atlas CloudLowest price | $0.030 /gen | — | — |
| Replicate | $0.032 /gen | — | — |
| fal.ai | — | — | — |
| OpenRouter | — | — | — |
Speed and reliability appear once this platform has enough recent runs on a provider.
Questions
- $0.030 per generation on ElliSekiz — the provider's own price, billed 1:1 from a prepaid USD balance with no markup and no subscription. Failed generations are free.
- Atlas Cloud, Replicate, fal.ai, OpenRouter. Requests route to the cheapest available provider automatically, or you can pin one per request or per model.
- POST to https://ellisekiz.ai/api/v1/generations with "model": "black-forest-labs-flux-kontext-dev-lora" and your prompt, authenticated with an ElliSekiz API token. The API tab on this page has the full request for this model, including its own parameters.
- image, guidance_scale, num_images, num_inference_steps, output_format, seed, size — each one is in the playground form on this page and in the API example.