---
title: Flux-Kontext-InScene
canonical_url: "https://www.modelscope.cn/models/AI-ModelScope/Flux-Kontext-InScene"
md_url: "https://www.modelscope.cn/models/AI-ModelScope/Flux-Kontext-InScene.md"
repository: AI-ModelScope/Flux-Kontext-InScene
last_updated: 2026-06-16
license: apache-2.0
pipeline_tag: image-to-image
tasks:
  - image-to-image
base_model:
  - black-forest-labs/FLUX.1-Kontext-dev
base_model_relation: adapter
parameters: 171.8M
tensor_type:
  - BF16
library_name:
  - lora
  - diffusers
  - safetensors
  - pytorch
frameworks:
  - pytorch
supports_inference: img2img
downloads: 214
stars: 2
tags:
  - image
  - editing
  - lora
  - diffusers
---

# Flux-Kontext-InScene

> Flux-Kontext-InScene - AI-ModelScope 在 ModelScope 开源的模型。InScene: Flux.1-Kontext.dev LoRA

AI-ModelScope/Flux-Kontext-InScene 是 ModelScope 魔搭社区上的 171.8M 参数image-to-image模型，采用 apache-2.0 许可，基于 black-forest-labs/FLUX.1-Kontext-dev 构建，并支持在线推理（img2img）。

- **Repository**: AI-ModelScope/Flux-Kontext-InScene
- **License**: apache-2.0
- **Tasks**: image-to-image
- **Parameters**: 171.8M
- **Base model**: black-forest-labs/FLUX.1-Kontext-dev
- **Online inference**: img2img
- **Tags**: image, editing, lora, diffusers
- **Downloads**: 214
- **Stars**: 2
- **Last updated**: 2026-06-16

Source: https://www.modelscope.cn/models/AI-ModelScope/Flux-Kontext-InScene

---

# InScene: Flux.1-Kontext.dev LoRA

## Model Description

**InScene** is a LoRA for Flux.Kontext.dev that's designed to generate images that maintain scene consistency with a source image. It is trained on top of Flux.1-Kontext.dev.

The primary use case is to generate variations of a shot while keeping the background and overall environment, characters, and styles the same:
![samples.png](samples.png)

## How to Use

To get the best results, start your prompt with the phrase:

`Make a shot in the same scene of `

And describe your new image.

For example:
`Make a shot in the same scene of the car up very close to the camera with the driver smiling manically.`


### Strengths & Weaknesses

The model excels at:
- Generating realistic shots that are consistent with the original scene.
- Handling most common photographic and artistic styles.

The model may struggle with:
- Action-oriented prompts (e.g., "punching", "running").
- Uncommon or highly abstract styles.

## Training Data

The `InScene` LoRA was trained on 394 image pairs. This dataset was created by extracting and enriching frames from the WebVid dataset.

You can find the public dataset used for training here:
[https://huggingface.co/datasets/peteromallet/InScene-Dataset](https://huggingface.co/datasets/peteromallet/InScene-Dataset)