---
title: FLUX.1-dev-ControlNet-Union-Pro
canonical_url: "https://www.modelscope.cn/models/AI-ModelScope/FLUX.1-dev-ControlNet-Union-Pro"
md_url: "https://www.modelscope.cn/models/AI-ModelScope/FLUX.1-dev-ControlNet-Union-Pro.md"
repository: AI-ModelScope/FLUX.1-dev-ControlNet-Union-Pro
last_updated: 2024-09-05
license: other
pipeline_tag: text-to-image-synthesis
tasks:
  - text-to-image-synthesis
base_model:
  - black-forest-labs/FLUX.1-dev
base_model_relation: finetune
parameters: 3.3B
tensor_type:
  - BF16
library_name:
  - pytorch
  - lora
  - diffusers
  - safetensors
frameworks:
  - pytorch
language:
  - en
downloads: 2777
stars: 2
tags:
  - Text-to-Image
  - ControlNet
  - Diffusers
  - Flux.1-dev
  - image-generation
  - "Stable Diffusion"
---

# FLUX.1-dev-ControlNet-Union-Pro

> FLUX.1-dev-ControlNet-Union-Pro - AI-ModelScope 在 ModelScope 开源的模型。FLUX.1-dev-ControlNet-Union-Pro

AI-ModelScope/FLUX.1-dev-ControlNet-Union-Pro 是 ModelScope 魔搭社区上的 3.3B 参数text-to-image-synthesis模型，采用 other 许可，基于 black-forest-labs/FLUX.1-dev 构建。

- **Repository**: AI-ModelScope/FLUX.1-dev-ControlNet-Union-Pro
- **License**: other
- **Tasks**: text-to-image-synthesis
- **Parameters**: 3.3B
- **Base model**: black-forest-labs/FLUX.1-dev
- **Tags**: Text-to-Image, ControlNet, Diffusers, Flux.1-dev, image-generation, Stable Diffusion
- **Downloads**: 2777
- **Stars**: 2
- **Last updated**: 2024-09-05

Source: https://www.modelscope.cn/models/AI-ModelScope/FLUX.1-dev-ControlNet-Union-Pro

---

# FLUX.1-dev-ControlNet-Union-Pro

This repository contains a unified ControlNet for FLUX.1-dev model jointly released by researchers from [InstantX Team](https://huggingface.co/InstantX) and [Shakker Labs](https://huggingface.co/Shakker-Labs).

<div class="container">
  <img src="./assets/poster.png" width="1024"/>
</div>


# Model Cards
- This checkpoint is a Pro version of [FLUX.1-dev-Controlnet-Union](https://huggingface.co/InstantX/FLUX.1-dev-Controlnet-Union) trained with more steps and datasets.
- This model supports 7 control modes, including canny (0), tile (1), depth (2), blur (3), pose (4), gray (5), low quality (6).
- The recommended controlnet_conditioning_scale is 0.3-0.8.
- This model can be jointly used with other ControlNets.


# Showcases

<div class="container">
  <img src="./assets/teaser1.png" width="1024"/>
  <img src="./assets/teaser2.png" width="1024"/>
  <img src="./assets/teaser3.png" width="1024"/>
</div>


# Inference
Please install `diffusers` from [the source](https://github.com/huggingface/diffusers), as [the PR](https://github.com/huggingface/diffusers/pull/9175) has not been included in currently released version yet.

# Multi-Controls Inference
```python
import torch
from diffusers.utils import load_image

from diffusers import FluxControlNetPipeline, FluxControlNetModel
from diffusers.models import FluxMultiControlNetModel

base_model = 'black-forest-labs/FLUX.1-dev'
controlnet_model_union = 'Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro'

controlnet_union = FluxControlNetModel.from_pretrained(controlnet_model_union, torch_dtype=torch.bfloat16)
controlnet = FluxMultiControlNetModel([controlnet_union]) # we always recommend loading via FluxMultiControlNetModel

pipe = FluxControlNetPipeline.from_pretrained(base_model, controlnet=controlnet, torch_dtype=torch.bfloat16)
pipe.to("cuda")

prompt = 'A bohemian-style female travel blogger with sun-kissed skin and messy beach waves.'
control_image_depth = load_image("https://huggingface.co/Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro/resolve/main/assets/depth.jpg")
control_mode_depth = 2

control_image_canny = load_image("https://huggingface.co/Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro/resolve/main/assets/canny.jpg")
control_mode_canny = 0

width, height = control_image_depth.size

image = pipe(
    prompt, 
    control_image=[control_image_depth, control_image_canny],
    control_mode=[control_mode_depth, control_mode_canny],
    width=width,
    height=height,
    controlnet_conditioning_scale=[0.2, 0.4],
    num_inference_steps=24, 
    guidance_scale=3.5,
    generator=torch.manual_seed(42),
).images[0]
```

We also support loading multiple ControlNets as before, you can load as
```python
from diffusers import FluxControlNetModel
from diffusers.models import FluxMultiControlNetModel

controlnet_model_union = 'Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro'
controlnet_union = FluxControlNetModel.from_pretrained(controlnet_model_union, torch_dtype=torch.bfloat16)

controlnet_model_depth = 'Shakker-Labs/FLUX.1-dev-Controlnet-Depth'
controlnet_depth = FluxControlNetModel.from_pretrained(controlnet_model_depth, torch_dtype=torch.bfloat16)

controlnet = FluxMultiControlNetModel([controlnet_union, controlnet_depth])

# set mode to None for other ControlNets
control_mode=[2, None]
```

# Resources
- [InstantX/FLUX.1-dev-Controlnet-Canny](https://huggingface.co/InstantX/FLUX.1-dev-Controlnet-Canny)
- [Shakker-Labs/FLUX.1-dev-ControlNet-Depth](https://huggingface.co/Shakker-Labs/FLUX.1-dev-ControlNet-Depth)
- [Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro](https://huggingface.co/Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro)

# Acknowledgements
This project is trained by [InstantX Team](https://huggingface.co/InstantX) and sponsored by [Shakker AI](https://www.shakker.ai/). The original idea is inspired by [xinsir/controlnet-union-sdxl-1.0](https://huggingface.co/xinsir/controlnet-union-sdxl-1.0). All copyright reserved.