Text-to-Image Generation
Generates images from natural language prompts using a 12-billion parameter rectified flow transformer. Supports detailed prompt descriptions to control composition, style, and subject matter.
Blackforestlabs
FLUX.1 [dev] LoRA
FLUX.1 [dev] LoRA is an image generation model built on FLUX.1 [dev], a 12-billion parameter rectified flow transformer developed by Black Forest Labs and released in August 2024. It extends the base FLUX.1 [dev] model with LoRA (Low-Rank Adaptation) support, allowing users to load pre-trained style and character adapters to shape the visual output without retraining the underlying model. The model is served through WaveSpeed AI's inference platform, which provides a REST API with no cold starts and consistent availability. It supports both text-to-image and image-to-image workflows, with output resolutions ranging from 256×256 up to 1536×1536 pixels. This model is well suited for developers and creators who need stylistically flexible image generation at scale. By swapping LoRA adapters — such as community options like Flux-Super-Realism-LoRA or yarn_art_Flux_LoRA — users can shift between hyper-realistic photography, painterly aesthetics, and character-driven art within the same base model. A prompt enhancer input is also available to refine natural language prompts before generation. Common use cases include product visualization, character design, creative exploration, and content production workflows.
Unknown
10,000 context
N/A output
Text-to-Image Generation
LoRA Style Adapters
Image-to-Image Transformation
Flexible Output Sizing
Prompt Enhancement
Seed Control
Model Overview
High-signal model metadata in a structured two-column overview table.
Provider
The entity that provides this model.
Blackforestlabs
Input Context Window
The number of tokens supported by the input context window.
10,000
tokens
Maximum Output Tokens
The number of tokens that can be generated by the model in a single request.
N/A
tokens
Open Source
Whether the model's code is available for public use.
No
Release Date
When the model was first released.
Unknown
Knowledge Cut-off Date
When the model's knowledge was last updated.
Unknown
API Providers
The providers that offer this model. This is not an exhaustive list.
Hugging Face
Modalities
Types of data this model can process.
Image
Text
What is FLUX.1 [dev] LoRA
A fuller summary of positioning, capabilities, and source-specific details for FLUX.1 [dev] LoRA.
FLUX.1 [dev] LoRA is an image generation model built on FLUX.1 [dev], a 12-billion parameter rectified flow transformer developed by Black Forest Labs and released in August 2024. It extends the base FLUX.1 [dev] model with LoRA (Low-Rank Adaptation) support, allowing users to load pre-trained style and character adapters to shape the visual output without retraining the underlying model. The model is served through WaveSpeed AI's inference platform, which provides a REST API with no cold starts and consistent availability. It supports both text-to-image and image-to-image workflows, with output resolutions ranging from 256×256 up to 1536×1536 pixels.
This model is well suited for developers and creators who need stylistically flexible image generation at scale. By swapping LoRA adapters — such as community options like Flux-Super-Realism-LoRA or yarn_art_Flux_LoRA — users can shift between hyper-realistic photography, painterly aesthetics, and character-driven art within the same base model. A prompt enhancer input is also available to refine natural language prompts before generation. Common use cases include product visualization, character design, creative exploration, and content production workflows.
Capabilities
What FLUX.1 [dev] LoRA supports
LoRA Style Adapters
Loads one or more pre-trained LoRA adapters to apply specific artistic styles, characters, or aesthetics without retraining the base model. Compatible with community LoRAs such as Flux-Super-Realism-LoRA and yarn_art_Flux_LoRA.
Image-to-Image Transformation
Accepts an input image URL alongside a strength parameter to guide how much the output deviates from the source image. Useful for style transfer and iterative visual refinement.
Flexible Output Sizing
Supports output resolutions from 256×256 up to 1536×1536 pixels with multiple aspect ratio presets selectable via a dropdown input.
Prompt Enhancement
Includes an optional prompt enhancer that automatically refines natural language prompts before passing them to the model to improve generation quality.
Seed Control
Accepts a numeric seed input to make image generation reproducible, allowing the same prompt and settings to produce consistent outputs across runs.
REST API Access
Served via WaveSpeed AI's REST API with no cold starts, enabling integration into production workflows without managing model infrastructure.
Pricing for FLUX.1 [dev] LoRA
Primary API pricing shown in the same “quick compare” spirit as the reference page.
Input tokens
N/A
Per million tokens
Output tokens
N/A
Per million tokens
API Access & Providers
Places where this model is available, based on the synced detail-page metadata.
Hugging Face
Configuration & Parameters
The configurable options currently documented for this model.
Mask Image
Image URLWhite areas indicate where to generate new pixels; black areas preserve the original image.
Strength
NumberStrength of the reference image
Size
SelectInference Steps
NumberGuidance Scale
NumberSeed
SeedSupported Request Parameters
Parameters currently listed by OpenRouter or the local catalog for this model.
Resources & Documentation
Official model cards, release notes, docs, and other references synced from the source page.
Community discussion
What people think about FLUX.1 [dev] LoRA
FLUX.1 [dev] LoRA discussions are most active in r/StableDiffusion. Top Reddit threads cluster around benchmark and model-comparison threads. The strongest match in this snapshot has 7 upvotes and 16 comments.
I have trained my Lora with both flux1- d and chroma HD. same exact data set and captions. my flux Lora is amazing and I love the results but my chroma HD Lora drifts in likeness very easily. anyone else have this experience? any tips for getting my chroma Lora on point?
Hi all!
I recently trained a LoRA on Flux for texture replication (wood, marble, leather, etc.) and I’m getting great results at 1024x1024. Now I would like to push the resolution up to around **20 k × 20 k**.
So far I’ve tried using **Ultimate SD Upscale (USDU) in ComfyUI** on a patch-based workflow. It stitches the large tiles without visible seams, but the final image looks blurry and loses detail when I zoom in.
* **Has anyone found a better approach for ultra-high-res textures?**
* Or, if you’ve had success with USDU, what parameters worked for you?
Any pointers would be hugely appreciated, thanks!
FAQ
Common questions about FLUX.1 [dev] LoRA
What is the context window for FLUX.1 [dev] LoRA?
The model has a context window of 10,000 tokens, which applies to the text prompt input used to guide image generation.
When was this model trained?
The underlying FLUX.1 [dev] base model was trained as of August 2024, which is the training date reflected in the metadata.
How do LoRA adapters work with this model?
Users pass one or more LoRA adapter references via the loras input field. The model applies these adapters at inference time to shift the visual style or introduce specific characters or aesthetics without modifying the base model weights.
What input types does this model accept?
The model accepts image URLs (for image-to-image workflows), LoRA adapter references, numeric parameters such as strength and seed, and a select input for aspect ratio or output size presets.
Is there a cold start delay when using this model?
According to WaveSpeed AI's platform documentation, this model is hosted with no cold starts, meaning it is consistently available without initialization delays between requests.
What output resolutions are supported?
The model supports output image sizes ranging from 256×256 up to 1536×1536 pixels, with multiple aspect ratio presets available through the select input.
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