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DiffSynth-Studio/
FLUX.1-Kontext-dev-lora-SuperOutpainting

Name: FLUX.1-Kontext-dev-lora-SuperOutpainting
Availability: InStock

safetensors

apache-2.0

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High-Resolution Inpainting - Kontext Image Editing LoRA

Model Introduction

This LoRA model is trained based on the Kontext model and DiffSynth-Studio. After loading this model, you can input the instruction Outpaint the image. to perform image outpainting.

Model Results

	Example 1	Example 2	Example 3
Prompt	Outpaint the image.	Outpaint the image.	Outpaint the image.
Input
Outpainted Image

	Example 4	Example 5	Example 6
Prompt	Outpaint the image. A chicken-headed man in suspenders is playing basketball.	Outpaint the image. A man in suspenders is playing basketball.	Outpaint the image. A chicken-headed man in suspenders is playing basketball with a white background.
Input
Outpainted Image

Usage Instructions

This model is built upon the DiffSynth-Studio framework. Please install it first:

git clone https://github.com/modelscope/DiffSynth-Studio.git  
cd DiffSynth-Studio
pip install -e .

python

import torch
from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig
from PIL import Image
from modelscope import snapshot_download

```python
snapshot_download("DiffSynth-Studio/FLUX.1-Kontext-dev-lora-SuperOutpainting", cache_dir="./models")
pipe = FluxImagePipeline.from_pretrained(
    torch_dtype=torch.bfloat16,
    device="cuda",
    model_configs=[
        ModelConfig(model_id="black-forest-labs/FLUX.1-Kontext-dev", origin_file_pattern="flux1-kontext-dev.safetensors"),
        ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors"),
        ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder_2/"),
        ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="ae.safetensors"),
    ],
)
pipe.load_lora(pipe.dit, "models/DiffSynth-Studio/FLUX.1-Kontext-dev-lora-SuperOutpainting/model.safetensors", alpha=1)

image = Image.open("your_image.jpg")
image = pipe(
    prompt="Outpaint the image.",
    kontext_images=image,
    embedded_guidance=2.5,
    seed=0,
)
image.save("output.jpg")