FLUX.2 [dev] LoRA
FLUX.2 [dev] with LoRA support brings personalized, studio-quality text-to-image generation by letting you inject custom styles, characters, and brand identities through stackable adapter modules.
Black Forest Labs 
WaveSpeed Text-to-image generation with stackable LoRA adapters
FLUX.2 [dev] LoRA is a text-to-image model published by Black Forest Labs, built on a 32-billion parameter diffusion transformer architecture. It extends the FLUX.2 [dev] base model with Low-Rank Adaptation (LoRA) support, enabling users to inject custom styles, characters, or brand identities into image outputs without retraining the full model. The model uses a Mistral Small 3.1 text encoder for prompt processing and runs on WaveSpeedAI's infrastructure with no cold starts. It was made available in November 2025.
The model supports stacking up to four LoRA adapters simultaneously in a single generation request, with independently adjustable strength per adapter. This makes it well-suited for brand-consistent marketing, character-consistent content creation, product visualization, and design iteration workflows. Custom LoRAs can be trained on as few as 15 to 30 images, lowering the barrier for teams that need fine-grained visual control. The model also supports batch generation of one to four images per request, useful for producing consistent campaign sets or A/B variants.
What FLUX.2 [dev] LoRA supports
LoRA Adapter Stacking
Supports combining up to 4 LoRA adapters in a single generation request, each with independently adjustable strength for mixing styles, characters, or brand elements.
Text-to-Image Generation
Generates images from text prompts using a 32-billion parameter diffusion transformer, with accurate placement of elements and intended style from descriptions.
Image URL Input
Accepts arrays of image URLs as input references, enabling source-image-guided generation for consistent visual outputs.
Seed Control
Accepts a seed value as input to make generation reproducible, allowing the same prompt and settings to produce identical outputs across runs.
Batch Generation
Generates between 1 and 4 images per request using the same LoRA stack, supporting consistent campaign sets or side-by-side variant comparisons.
Text Rendering
Produces legible typography within generated images, making it suitable for infographics, UI mockups, and marketing materials.
No Cold Start Inference
Runs on WaveSpeedAI's infrastructure with no cold starts, providing consistent response times for production and high-throughput use cases.
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Get Started FreeCommon questions about FLUX.2 [dev] LoRA
What is the context window for FLUX.2 [dev] LoRA?
The model has a context window of 10,000 tokens, as specified in its metadata.
How many LoRA adapters can I use at once?
You can stack up to 4 LoRA adapters in a single generation request, each with independently adjustable strength values.
How many images does the model require to train a custom LoRA?
Custom LoRAs can be trained on as few as 15 to 30 images, making fine-tuning accessible without large datasets.
When was FLUX.2 [dev] LoRA trained?
The model's training date is listed as November 2025.
What input types does the model accept?
The model accepts numeric parameters (such as image dimensions or step counts), arrays of image URLs for reference inputs, LoRA adapter configurations, and a seed value for reproducible generation.
Who publishes and hosts this model?
The model is published by Black Forest Labs and hosted on WaveSpeedAI's infrastructure, which provides no-cold-start inference.
Parameters & options
If you want to edit an existing image, provide the URL(s) or variables
Up to 3 LoRAs.
A specific value that is used to guide the 'randomness' of the generation.
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