---
title: LoRA-Encoder-FLUX.1-Dev
canonical_url: "https://www.modelscope.cn/models/DiffSynth-Studio/LoRA-Encoder-FLUX.1-Dev"
md_url: "https://www.modelscope.cn/models/DiffSynth-Studio/LoRA-Encoder-FLUX.1-Dev.md"
repository: DiffSynth-Studio/LoRA-Encoder-FLUX.1-Dev
chinese_name: "LoRA 编码器 - FLUX.1-Dev"
last_updated: 2025-07-21
license: "Apache License 2.0"
pipeline_tag: text-to-image-synthesis
tasks:
  - text-to-image-synthesis
parameters: 701.1M
tensor_type:
  - BF16
library_name:
  - pytorch
  - safetensors
frameworks:
  - Pytorch
downloads: 301
stars: 0
---

# LoRA-Encoder-FLUX.1-Dev

> LoRA-Encoder-FLUX.1-Dev - DiffSynth-Studio 在 ModelScope 开源的模型。LoRA 编码器（FLUX.1-Dev）

DiffSynth-Studio/LoRA-Encoder-FLUX.1-Dev 是 ModelScope 魔搭社区上的 701.1M 参数text-to-image-synthesis模型，采用 Apache License 2.0 许可。

- **Repository**: DiffSynth-Studio/LoRA-Encoder-FLUX.1-Dev
- **License**: Apache License 2.0
- **Tasks**: text-to-image-synthesis
- **Parameters**: 701.1M
- **Downloads**: 301
- **Stars**: 0
- **Last updated**: 2025-07-21

Source: https://www.modelscope.cn/models/DiffSynth-Studio/LoRA-Encoder-FLUX.1-Dev

---

# LoRA 编码器（FLUX.1-Dev）

本模型可以将 FLUX 模型的 LoRA 模型编码为 Embedding 向量，激发出 LoRA 模型的能力。

以 LoRA 模型 [VoidOc/F.1_动物森友会LoRA](https://www.modelscope.cn/models/VoidOc/flux_animal_forest1) 为例，LoRA 编码器有以下几种使用方法。

## 使用方法1：LoRA 用途推断

给定一个 LoRA 模型，在没有任何额外信息的条件下，使用空提示词可以直接激发 LoRA 模型的能力，进而推断出 LoRA 的用途。

提示词：`""`

|不使用 LoRA 编码器|使用 LoRA 编码器|
|-|-|
|![](./assets/image_1_origin.jpg)|![](./assets/image_1.jpg)|

## 使用方法2：免触发词激发 LoRA 能力

无需填写触发词，即可自动激发 LoRA 的能力。

提示词：`"a car"`

|不使用 LoRA 编码器|使用 LoRA 编码器|
|-|-|
|![](./assets/image_2_origin.jpg)|![](./assets/image_2.jpg)|

## 使用方法3：LoRA 强度控制

我们预留了一个额外的参数 `scale`，控制 LoRA 对模型生成图像的影响大小。

在下面的例子中，提示词为“a cat”，当 `scale=1` 时，LoRA 强度为最大，画面中生成了动物森友会中的角色和一只猫；当 `scale=0.5` 时，LoRA 强度被减弱，画面中生成了动物森友会中的猫猫角色。`scale` 的最优数值与 LoRA 模型本身有关，我们建议在角色 LoRA 上使用较大的数值，在风格 LoRA 上使用较小的数值。

提示词：`"a cat"`

|`scale=1`|`scale=0.5`|
|-|-|
|![](./assets/image_3.jpg)|![](./assets/image_3_scale.jpg)|

## 推理代码

```
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


pipe = FluxImagePipeline.from_pretrained(
    torch_dtype=torch.bfloat16,
    device="cuda",
    model_configs=[
        ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="flux1-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"),
        ModelConfig(model_id="DiffSynth-Studio/LoRA-Encoder-FLUX.1-Dev", origin_file_pattern="model.safetensors"),
    ],
)
pipe.enable_lora_magic()

lora = ModelConfig(model_id="VoidOc/flux_animal_forest1", origin_file_pattern="20.safetensors")
pipe.load_lora(pipe.dit, lora, hotload=True) # Use `pipe.clear_lora()` to drop the loaded LoRA.

# Empty prompt can automatically activate LoRA capabilities.
image = pipe(prompt="", seed=0, lora_encoder_inputs=lora)
image.save("image_1.jpg")

image = pipe(prompt="", seed=0)
image.save("image_1_origin.jpg")

# Prompt without trigger words can also activate LoRA capabilities.
image = pipe(prompt="a car", seed=0, lora_encoder_inputs=lora)
image.save("image_2.jpg")

image = pipe(prompt="a car", seed=0,)
image.save("image_2_origin.jpg")

# Adjust the activation intensity through the scale parameter.
image = pipe(prompt="a cat", seed=0, lora_encoder_inputs=lora, lora_encoder_scale=1.0)
image.save("image_3.jpg")

image = pipe(prompt="a cat", seed=0, lora_encoder_inputs=lora, lora_encoder_scale=0.5)
image.save("image_3_scale.jpg")
```