Move your team from Leonardo AI to Flux.
Each person gets a short interview, then a personalized step-by-step guide showing exactly where their saved work, prompts, and projects land in Flux — and the few things that need a manual step, walked through. You send one invite; nobody becomes the help desk, and nothing in Leonardo AI is changed or cancelled. Want to see where you’re paying for both first? Run the free audit.
Both platforms provide outpainting and inpainting capabilities for extending or locally editing existing images. Flux delivers these through FLUX Tools task-specific API endpoints: outpainting extends any image beyond its borders without a text prompt (the model infers content from existing edges), and object removal fills erased areas using coherent reconstruction. Leonardo.Ai's Canvas Editor is a browser-based tool supporting inpainting (mask a region, regenerate it) and outpainting (extend canvas borders) controlled via the init_strength parameter. To move from Flux API-based outpainting/removal to Leonardo.Ai: upload your source image to the Canvas Editor in the web app, or use the image-to-image guidance API endpoint with inpainting mask parameters. For API automation on Leonardo.Ai, use the inpainting endpoint with an init_image and mask. Moving from Leonardo.Ai Canvas Editor to Flux: call the FLUX Tools outpainting endpoint with the source image URL, or the object removal endpoint with a mask specification.
- Warning: Leonardo.Ai Canvas Editor is documented as a legacy feature — long-term support is uncertain, and newer Leonardo workflows may supersede it.
- Warning: Leonardo.Ai Canvas Editor caps image dimensions at 1536×1536 px (dimensions must be divisible by 8); Flux Tools does not publish the same hard dimension caps, giving more flexibility for large-format work.
- Warning: Flux outpainting does not require a text prompt — it infers continuation automatically. Leonardo.Ai inpainting uses a prompt to guide region regeneration; omitting or changing the prompt changes what fills the masked area.
- Warning: Batch sizes on Leonardo.Ai Canvas inpainting drop at higher resolutions (max 8 at 512×512, max 2 above 1024×1024), which limits throughput for bulk editing jobs compared to Flux's API.
Both products support LoRA-based fine-tuning to encode a specific style, character, or product look. On Flux, fine-tuning rights are tied to Open Weights license tiers — you download model weights and train in your own infrastructure (GPU servers, cloud VMs). On Leonardo.Ai, fine-tuning is done through the platform as 'Elements' — upload a dataset of 10–50 images, set an instance_prompt trigger word, and call the Train Custom Element API endpoint. To move a trained style from Flux to Leonardo.Ai: export representative generated outputs from your Flux fine-tune as a dataset (~10–50 images), then use those as the training set for a new Leonardo Element with Flux Dev as the base model (the current default). Apply the trained Element at generation time via the userElements API parameter. Moving the reverse (Leonardo Element to Flux self-hosted fine-tune) requires exporting generated samples and retraining Flux weights locally — Leonardo does not export LoRA weight files.
- Warning: Leonardo.Ai Free plan allows zero Element trainings per month; Apprentice allows 1/month, Artisan 5/month, Maestro 20/month — this is a hard constraint if switching from Flux where training frequency is limited only by your own compute budget.
- Warning: Flux fine-tuning is entirely self-hosted and requires GPU infrastructure; Leonardo.Ai handles compute on its cloud, eliminating infra overhead but removing direct weight control.
- Warning: Leonardo Elements cannot export trained LoRA weight files — you cannot port a Leonardo-trained LoRA to run on a self-hosted Flux setup directly.
- Warning: Flux Open Weights require a commercial license (Builder tier minimum); using the weights without a license for production output is a terms-of-service violation.
Both platforms expose REST APIs for programmatic image generation, but their billing and authentication models differ. Flux (Black Forest Labs) is pure pay-as-you-go with no subscriptions or seat fees — authenticate with an API key, call docs.bfl.ai endpoints, and pay per image based on model tier and output dimensions. Leonardo.Ai REST API lives at cloud.leonardo.ai/api/rest/v1/ and uses a Bearer token (Production API key); API credits are purchased separately from web-app subscriptions in non-expiring dollar amounts. To migrate from Flux to Leonardo.Ai API: replace BFL endpoint URLs with Leonardo.Ai equivalents, swap API key header format, remap model names to Leonardo model IDs, and restructure image dimension/seed parameters per Leonardo's schema. If you were using Flux's MCP connector (from Claude or Cursor), Leonardo.Ai also documents an MCP Server integration. To migrate from Leonardo.Ai to Flux: set up a BFL account at bfl.ai, generate an API key, and call the appropriate FLUX model endpoint (FLUX.1.1 Pro maps to Leonardo Phoenix quality-wise). Default concurrency on Leonardo.Ai is 10 parallel jobs (raisable via Custom API Plan); Flux does not publish a default concurrency limit.
- Warning: Leonardo.Ai Production API access must be purchased as a standalone plan — a web-app subscription (Apprentice/Artisan/Maestro) does NOT grant API access, and API credits do not expire.
- Warning: Flux has no free API tier; Black Forest Labs charges per-image from the first call. Leonardo.Ai's web-app free tokens cannot be used via the API.
- Warning: Leonardo.Ai webhooks (firing image_generation.complete events) require HTTPS endpoints and allowlisting of six specific Leonardo IP addresses — factor this into server setup when migrating automated pipelines from Flux's simpler polling approach.
- Flux's MCP connector integrates natively into Claude and Cursor workflows; Leonardo.Ai's MCP integration is documented but less mature — verify it meets your toolchain requirements before committing.
Both platforms allow steering generation using one or more reference images alongside a text prompt, though the mechanism differs. Flux uses in-context editing: FLUX.2 accepts up to 10 reference images in a single API call (passed as URLs or base64 in the request body); the model preserves character identity, lighting, or brand style across outputs automatically. Leonardo.Ai uses explicit ControlNet modes via the preprocessorId parameter — choose from 12 types (Style Reference ID 67, Character Reference ID 133, Depth to Image, Pose to Image, etc.) with strengthType controlling influence level. To migrate from Flux to Leonardo.Ai: identify the type of consistency being enforced (character → Character Reference, style → Style Reference, composition → Depth or Pose), then map your Flux reference images to the equivalent ControlNet preprocessorId. Leonardo.Ai supports up to 4 reference images simultaneously on Apprentice+ plans (up to 6 on Omni-class models). Moving from Leonardo.Ai to Flux: pass all ControlNet reference images as the images array in the FLUX.2 API call and describe the desired consistency in the text prompt.
- Warning: Flux accepts up to 10 reference images in one call; Leonardo.Ai caps at 4 simultaneously (Apprentice+ plan) or 6 with Omni-class models — Free plan is limited to 1.
- Warning: Leonardo.Ai ControlNet model compatibility varies: Character Reference (ID 133) and Style Reference (ID 67) only work with a subset of models (most SDXL models); switching models may break the guidance type.
- Warning: Flux does not expose explicit ControlNet mode types — reference image influence is holistic. If you need fine-grained control (e.g. pose-only without style bleeding), Leonardo.Ai's explicit preprocessorId gives more surgical control.
Both platforms accept text prompts and return generated images, but their access models differ. On Flux (Black Forest Labs), generation is purely pay-as-you-go via the REST API — choose the model tier (FLUX.2 Max for top quality, FLUX.2 Klein for speed) and pay per image. On Leonardo.Ai, free users receive 150 tokens/day via the web app, while production API access requires a separate standalone API plan paid in non-expiring credits. To move from Flux to Leonardo.Ai: port prompts as-is (both use natural-language text descriptions), select an equivalent model (Leonardo Phoenix 1.0 or FLUX model hosted on Leonardo), set dimensions and batch size, and call the /generations endpoint. To move from Leonardo.Ai to Flux: replace Leonardo API calls with BFL REST API calls (docs.bfl.ai), choose FLUX.1.1 Pro or FLUX.2 as the equivalent to Phoenix, and update image dimension parameters — Flux supports aspect ratios up to 4MP on Pro Ultra. Note that Leonardo.Ai's web-app subscription tokens do not carry over to the API.
- Warning: Leonardo.Ai's Production API credits are sold separately from web-app subscriptions — a paid Artisan or Maestro plan does NOT include API access; you must purchase an additional API plan.
- Warning: Flux does not have a free tier for API use; all API calls are billed pay-as-you-go. Leonardo.Ai offers 150 free tokens/day on its web app, but those are inaccessible from the API.
- Warning: FLUX.1.1 Pro Ultra supports up to 4MP output; Leonardo.Ai Phoenix standard web outputs are typically lower resolution and the Universal Upscaler must be invoked separately to reach comparable sizes.