README.md

---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md

language:
  - en
library_name: diffusers
pipeline_tag: text-to-image

tags:
- Text-to-Image
- ControlNet
- Diffusers
- Flux.1-dev
- image-generation
- Stable Diffusion
base_model: black-forest-labs/FLUX.1-dev
---

# FLUX.1-dev-ControlNet-Union-Pro-2.0

This repository contains an unified ControlNet for FLUX.1-dev model released by [Shakker Labs](https://huggingface.co/Shakker-Labs). We provide an [online demo](https://huggingface.co/spaces/Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro-2.0). A FP8 quantized version provided by community can be found in [ABDALLALSWAITI/FLUX.1-dev-ControlNet-Union-Pro-2.0-fp8](https://huggingface.co/ABDALLALSWAITI/FLUX.1-dev-ControlNet-Union-Pro-2.0-fp8).

# Keynotes
In comparison with [Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro](https://huggingface.co/Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro),
- Remove mode embedding, has smaller model size.
- Improve on canny and pose, better control and aesthetics.
- Add support for soft edge. Remove support for tile.

# Model Cards
- This ControlNet consists of 6 double blocks and 0 single block. Mode embedding is removed.
- We train the model from scratch for 300k steps using a dataset of 20M high-quality general and human images. We train at 512x512 resolution in BFloat16, batch size = 128, learning rate = 2e-5, the guidance is uniformly sampled from [1, 7]. We set the text drop ratio to 0.20.
- This model supports multiple control modes, including canny, soft edge, depth, pose, gray. You can use it just as a normal ControlNet.
- This model can be jointly used with other ControlNets.

# Showcases

<table>
  <tr>
    <td><img src="./images/canny.png" alt="canny" style="height:100%"></td>
  </tr>
  <tr>
    <td><img src="./images/softedge.png" alt="softedge" style="height:100%"></td>
  </tr>
  <tr>
    <td><img src="./images/pose.png" alt="pose" style="height:100%"></td>
  </tr>
  <tr>
    <td><img src="./images/depth.png" alt="depth" style="height:100%"></td>
  </tr>
  <tr>
    <td><img src="./images/gray.png" alt="gray" style="height:100%"></td>
  </tr>
</table>
 
# Inference
```python
import torch
from diffusers.utils import load_image
from diffusers import FluxControlNetPipeline, FluxControlNetModel

base_model = 'black-forest-labs/FLUX.1-dev'
controlnet_model_union = 'Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro-2.0'

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

# replace with other conds
control_image = load_image("./conds/canny.png")
width, height = control_image.size

prompt = "A young girl stands gracefully at the edge of a serene beach, her long, flowing hair gently tousled by the sea breeze. She wears a soft, pastel-colored dress that complements the tranquil blues and greens of the coastal scenery. The golden hues of the setting sun cast a warm glow on her face, highlighting her serene expression. The background features a vast, azure ocean with gentle waves lapping at the shore, surrounded by distant cliffs and a clear, cloudless sky. The composition emphasizes the girl's serene presence amidst the natural beauty, with a balanced blend of warm and cool tones."

image = pipe(
    prompt, 
    control_image=control_image,
    width=width,
    height=height,
    controlnet_conditioning_scale=0.7,
    control_guidance_end=0.8,
    num_inference_steps=30, 
    guidance_scale=3.5,
    generator=torch.Generator(device="cuda").manual_seed(42),
).images[0]
```

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

# https://github.com/huggingface/diffusers/pull/11350
# You can directly import from diffusers by install the laster version from source
# from diffusers import FluxControlNetPipeline, FluxControlNetModel

# use local files for this moment
from pipeline_flux_controlnet import FluxControlNetPipeline
from controlnet_flux import FluxControlNetModel

base_model = 'black-forest-labs/FLUX.1-dev'
controlnet_model_union = 'Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro-2.0'

controlnet = FluxControlNetModel.from_pretrained(controlnet_model_union, torch_dtype=torch.bfloat16)
pipe = FluxControlNetPipeline.from_pretrained(base_model, controlnet=[controlnet], torch_dtype=torch.bfloat16) # use [] to enable multi-CNs
pipe.to("cuda")

# replace with other conds
control_image = load_image("./conds/canny.png")
width, height = control_image.size

prompt = "A young girl stands gracefully at the edge of a serene beach, her long, flowing hair gently tousled by the sea breeze. She wears a soft, pastel-colored dress that complements the tranquil blues and greens of the coastal scenery. The golden hues of the setting sun cast a warm glow on her face, highlighting her serene expression. The background features a vast, azure ocean with gentle waves lapping at the shore, surrounded by distant cliffs and a clear, cloudless sky. The composition emphasizes the girl's serene presence amidst the natural beauty, with a balanced blend of warm and cool tones."

image = pipe(
    prompt, 
    control_image=[control_image, control_image], # try with different conds such as canny&depth, pose&depth
    width=width,
    height=height,
    controlnet_conditioning_scale=[0.35, 0.35],
    control_guidance_end=[0.8, 0.8],
    num_inference_steps=30, 
    guidance_scale=3.5,
    generator=torch.Generator(device="cuda").manual_seed(42),
).images[0]
```

# Recommended Parameters
You can adjust controlnet_conditioning_scale and control_guidance_end for stronger control and better detail preservation. For better stability, we highly suggest to use detailed prompt, for some cases, multi-conditions help.
- Canny: use cv2.Canny, controlnet_conditioning_scale=0.7, control_guidance_end=0.8.
- Soft Edge: use [AnylineDetector](https://github.com/huggingface/controlnet_aux), controlnet_conditioning_scale=0.7, control_guidance_end=0.8.
- Depth: use [depth-anything](https://github.com/DepthAnything/Depth-Anything-V2), controlnet_conditioning_scale=0.8, control_guidance_end=0.8.
- Pose: use [DWPose](https://github.com/IDEA-Research/DWPose/tree/onnx), controlnet_conditioning_scale=0.9, control_guidance_end=0.65.
- Gray: use cv2.cvtColor, controlnet_conditioning_scale=0.9, control_guidance_end=0.8.

# Resources
- [InstantX/FLUX.1-dev-IP-Adapter](https://huggingface.co/InstantX/FLUX.1-dev-IP-Adapter)
- [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 model is developed by [Shakker Labs](https://huggingface.co/Shakker-Labs). The original idea is inspired by [xinsir/controlnet-union-sdxl-1.0](https://huggingface.co/xinsir/controlnet-union-sdxl-1.0). All copyright reserved.

# Citation
If you find this project useful in your research, please cite us via
```
@misc{flux-cn-union-pro-2,
    author = {Shakker-Labs},
    title = {https://huggingface.co/Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro-2.0},
    year = {2025},
}
}
```