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.
Blackforestlabs
FLUX.2 [dev] LoRA
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.
Unknown
10,000 context
N/A output
LoRA Adapter Stacking
Text-to-Image Generation
Image URL Input
Seed Control
Batch Generation
Text Rendering
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
Code
What is FLUX.2 [dev] LoRA
A fuller summary of positioning, capabilities, and source-specific details for FLUX.2 [dev] LoRA.
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.
Capabilities
What FLUX.2 [dev] LoRA supports
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.
Pricing for FLUX.2 [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.
Width
NumberHeight
NumberSource Images
Image URL ArrayIf you want to edit an existing image, provide the URL(s) or variables
LoRAs
LoRAUp to 3 LoRAs.
Seed
SeedA specific value that is used to guide the 'randomness' of the generation.
Supported 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.2 [dev] LoRA
FLUX.2 [dev] LoRA discussions are most active in r/StableDiffusion, r/comfyui. The strongest match in this snapshot has 1 upvotes and 12 comments.
I just rented a Runpod and was following ai-toolkit video for training a Flux 2 dev LoRA, had 50 images, training on a 6000 pro.
The problem: at about 1000 steps, the samples look completely degraded mess.
At 1250 complete corruption.
Any idea what's going on?
Here's the config.
job: "extension"
config:
name: "RPB"
process:
- type: "diffusion_trainer"
training_folder: "/app/ai-toolkit/output"
sqlite_db_path: "./aitk_db.db"
device: "cuda"
trigger_word: null
performance_log_every: 10
network:
type: "lora"
linear: 32
linear_alpha: 32
conv: 16
conv_alpha: 16
lokr_full_rank: true
lokr_factor: -1
network_kwargs:
ignore_if_contains: []
save:
dtype: "bf16"
save_every: 250
max_step_saves_to_keep: 4
save_format: "diffusers"
push_to_hub: false
datasets:
- folder_path: "/app/ai-toolkit/datasets/b"
mask_path: null
mask_min_value: 0.1
default_caption: ""
caption_ext: "txt"
caption_dropout_rate: 0.05
cache_latents_to_disk: false
is_reg: false
network_weight: 1
resolution:
- 512
- 768
- 1024
controls: []
shrink_video_to_frames: true
num_frames: 1
flip_x: false
flip_y: false
num_repeats: 1
control_path_1: null
control_path_2: null
control_path_3: null
train:
batch_size: 1
bypass_guidance_embedding: false
steps: 5000
gradient_accumulation: 1
train_unet: true
train_text_encoder: false
gradient_checkpointing: true
noise_scheduler: "flowmatch"
optimizer: "adamw8bit"
timestep_type: "weighted"
content_or_style: "balanced"
optimizer_params:
weight_decay: 0.0001
unload_text_encoder: false
cache_text_embeddings: true
lr: 0.0001
ema_config:
use_ema: false
ema_decay: 0.99
skip_first_sample: false
force_first_sample: false
disable_sampling: false
dtype: "bf16"
diff_output_preservation: false
diff_output_preservation_multiplier: 1
diff_output_preservation_class: "person"
switch_boundary_every: 1
loss_type: "mse"
logging:
log_every: 1
use_ui_logger: true
model:
name_or_path: "black-forest-labs/FLUX.2-dev"
quantize: true
qtype: "qfloat8"
quantize_te: true
qtype_te: "qfloat8"
arch: "flux2"
low_vram: true
model_kwargs:
match_target_res: true
layer_offloading: false
layer_offloading_text_encoder_percent: 1
layer_offloading_transformer_percent: 1
sample:
sampler: "flowmatch"
sample_every: 250
width: 1024
height: 1024
neg: ""
seed: 42
walk_seed: true
guidance_scale: 4
sample_steps: 30
num_frames: 1
fps: 1
meta:
name: "[name]"
version: "1.0"
I am using Flux 2 K 9 distilled model from past 2 days and what I found is its great with consistency of faces as long as you only change clothing or minor things in the image but as soon as you try to change pose or extend img (like from closeup to full body shot), the consistency of the face takes a hit and likeness falls.
I have used various seeds and batches, but it's hit or miss (mostly miss). I have used the default comfyUI WF and other WF as well,l but the same problem. When using Klein 9 base the skin becomes plastic (old Flux problem).
I wanted to know are you're getting the same consistency in the faces or if it's the same issue? What will you suggest to get maximum likeness even when changing pose or clothing?
WF link - [https://www.runninghub.ai/post/2012104741957931009?inviteCode=rh-v1152](https://www.runninghub.ai/post/2012104741957931009?inviteCode=rh-v1152)
My rig - 3070Ti 8GB+32GB
Additional- does Flux 2 Dev lora work on this or I need to wait for loras that will appear in Civitai?
I am using Flux 2 K 9 distilled model from past 2 days and what I found is its great with consistency of faces as long as you only change clothing or minor things in the image but as soon as you try to change pose or extend img (like from closeup to full body shot), the consistency of the face takes a hit and likeness falls.
I have used various seeds and batches, but it's hit or miss (mostly miss). I have used the default comfyUI WF and other WF as well,l but the same problem. When using Klein 9 base the skin becomes plastic (old Flux problem).
I wanted to know are you're getting the same consistency in the faces or if it's the same issue? What will you suggest to get maximum likeness even when changing pose or clothing?
WF link - [https://www.runninghub.ai/post/2012104741957931009?inviteCode=rh-v1152](https://www.runninghub.ai/post/2012104741957931009?inviteCode=rh-v1152)
My rig - 3070Ti 8GB+32GB
Additional- does Flux 2 Dev lora work on this or I need to wait for loras that will appear in Civitai?
FAQ
Common 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.
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