SliderEdit: Continuous Image Editing with Fine-Grained Instruction Control

Paper | Project Page

We introduce SliderEdit, a framework for continuous image editing with fine-grained, interpretable instruction control. Given a multi-part edit instruction, SliderEdit disentangles the individual instructions and exposes each as a globally trained slider, allowing smooth adjustment of its strength.

⚙️Setup

1. Clone the repository

   git clone git@github.com:ArmanZarei/SliderEdit.git
   cd SliderEdit

2. Create and activate the conda environment

   conda env create -f environment.yml
   conda activate slideredit
   pip install -e .

🚀Quick Start

First, load the SliderEdit pipeline:

from slideredit.pipelines import SliderEditFluxKontextPipeline

pipe = SliderEditFluxKontextPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-Kontext-dev",
    torch_dtype=torch.bfloat16
).to("cuda")

GSTLoRA

GSTLoRA is designed for single-instruction editing scenarios or when a single slider is sufficient to control the overall edit intensity.

pipe.load_gstlora("PATH_TO_CKPT")

output_image = pipe(
    image=IMAGE_TO_BE_EDITED,
    prompt="EDIT PROMPT",
    generator=torch.Generator().manual_seed(SEED)
    slider_alpha=STRENGH_VALUE,
).images[0]

The parameter slider_alpha controls edit strength. Negative values increase intensity, while positive values suppress the effect. We recommend initially sweeping values in the range [-1, 1].

STLoRA

STLoRA is designed for multi-instruction editing prompts, providing independent control over the strength of each individual instruction.

pipe.load_stlora("PATH_TO_CKPT")

output_image = pipe(
    image=IMAGE_TO_BE_EDITED,
    prompt="Edit_Instruction_1 and Edit_Instruct_2 and ...",
    generator=torch.Generator().manual_seed(SEED)
    subprompts_list=["Edit_Instruction_1", "Edit_Instruct_2", ...],
    slider_alpha_list=[Strength_Value_1, Strength_Value_2, ...],
).images[0]

Parameters:

• subprompts_list: Individual instructions (sub-prompts) from the original edit prompt
• slider_alpha_list: Corresponding intensity values for each instruction (we recommend initially sweeping values in the range: [-1, 1])

See sample_inference.ipynb for complete inference examples.

🔬Training SliderEdit

GSTLoRA

The training script for GSTLoRA is available at train_gstlora_flux_kontext.py. An example configuration using an open-source image editing dataset is provided in train_gstlora_flux_kontext.yaml.

To launch training:

python training/train_gstlora_flux_kontext.py --config="training/configs/train_gstlora_flux_kontext.yaml"

View training progress and slider visualizations in this W&B report.

STLoRA

Training STLoRA with our proposed PPS loss requires multi-instruction edit prompts. (If you instead wish to use the SPPS loss, the same dataset as in the GSTLoRA setting can be reused)

Below, we provide a simple small-scale example illustrating how to construct such a dataset and perform training. In this example, we focus on human face editing by manually combining a set of predefined single-instruction edits to form multi-instruction prompts.

For more details, please refer to the training script train_stlora_flux_kontext.py and the example configuration file train_stlora_pps_flux_kontext.yaml.

First, download the sample human faces dataset:

mkdir -p datasets
cd datasets
gdown 183-Jubsu2rFiQmgpBYjFpAxGSwqn2XOM
unzip slideredit_faces_dataset.zip
cd ..

Then, launch training:

python training/train_stlora_flux_kontext.py --config="training/configs/train_stlora_pps_flux_kontext.yaml"

View training progress and slider visualizations in this W&B report.

Inference

Below are example checkpoints from models trained using the above configurations:

mkdir -p checkpoints
gdown 1YHrHhSeKovEPGpFgFbv0iPL67YRgg6rG -O checkpoints/ # GSTLoRA iter500
gdown 1PdORTgzFzfGGbNAoPQb0T5su3t82xErY -O checkpoints/ # STLoRA iter1200

Example results:

• GSTLoRA

  
  "Make her hair curly"

• STLoRA

  
  "make the person fat and make the person laugh"

See sample_inference.ipynb for more details.

BibTeX

@article{zarei2025slideredit,
  title={SliderEdit: Continuous Image Editing with Fine-Grained Instruction Control},
  author={Zarei, Arman and Basu, Samyadeep and Pournemat, Mobina and Nag, Sayan and Rossi, Ryan and Feizi, Soheil},
  journal={arXiv preprint arXiv:2511.09715},
  year={2025}
}