sun

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


# FLUX.1-dev Controlnet



<img src="./images/image_union.png" width = "1000" />


## Release

- [2024/08/20] 🔥 Release the first beta version.
Until the next Diffusers pypi release,
please install Diffusers from source and use [this PR](https://github.com/huggingface/diffusers/pull/9175) to be able to use. 
Before merging into the official main branch of diffusers, you can use this [diffusers_flux](https://github.com/instantX-research/diffusers_flux).

- [2024/08/14] Release the alpha version.





## Checkpoint

The training of union controlnet requires a significant amount of computational power. 
The current release is the first beta version checkpoint that maybe not been fully trained. 
The fully trainedbeta version is in the training process. 
We have conducted ablation studies that have demonstrated the validity of the code. 
The open-source release of the first beta version is solely to facilitate the rapid growth of the open-source community and the Flux ecosystem; 
it is common to encounter bad cases (please accept my apologies). 
It is worth noting that we have found that even a fully trained Union model may not perform as well as specialized models, such as pose control. 
However, as training progresses, the performance of the Union model will continue to approach that of specialized models.


## Control Mode

| Control Mode | Description | Current Model Validity |
|:------------:|:-----------:|:-----------:|
|0|canny|🟢high|
|1|tile|🟢high|
|2|depth|🟢high|
|3|blur|🟢high|
|4|pose|🟢high|
|5|gray|🔴low|
|6|lq|🟢high|




# Demo
```python
import torch
from diffusers.utils import load_image
from diffusers.pipelines.flux.pipeline_flux_controlnet import FluxControlNetPipeline
from diffusers.models.controlnet_flux import FluxControlNetModel

# load
base_model = 'black-forest-labs/FLUX.1-dev'
controlnet_model = 'InstantX/FLUX.1-dev-Controlnet-Union'
controlnet = FluxControlNetModel.from_pretrained(controlnet_model, torch_dtype=torch.bfloat16)
pipe = FluxControlNetPipeline.from_pretrained(base_model, controlnet=controlnet, torch_dtype=torch.bfloat16)
pipe.to("cuda")

# image cfg
width, height = 1024, 1024
controlnet_conditioning_scale = 0.5
seed = 6666

# canny
control_image = load_image("https://huggingface.co/InstantX/FLUX.1-dev-Controlnet-Union-alpha/resolve/main/images/canny.jpg")
prompt = "A girl in city, 25 years old, cool, futuristic."
control_mode = 0

# tile
control_image = load_image("https://huggingface.co/InstantX/FLUX.1-dev-Controlnet-Union-alpha/resolve/main/images/tile.jpg")
prompt = "A girl, 25 years old."
control_mode = 1

# depth
control_image = load_image("https://huggingface.co/InstantX/FLUX.1-dev-Controlnet-Union-alpha/resolve/main/images/depth.jpg")
prompt = "A girl in city, 25 years old, cool, futuristic."
control_mode = 2

# blur
control_image = load_image("https://huggingface.co/InstantX/FLUX.1-dev-Controlnet-Union-alpha/resolve/main/images/blur.jpg")
prompt = "A girl, 25 years old."
control_mode = 3

# pose
control_image = load_image("https://huggingface.co/InstantX/FLUX.1-dev-Controlnet-Union-alpha/resolve/main/images/pose.jpg")
prompt = "A girl in city, 25 years old, cool, futuristic."
control_mode = 4

# gray
control_image = load_image("https://huggingface.co/InstantX/FLUX.1-dev-Controlnet-Union-alpha/resolve/main/images/gray.jpg")
prompt = "A girl, 25 years old."
control_mode = 5

# low quality
control_image = load_image("https://huggingface.co/InstantX/FLUX.1-dev-Controlnet-Union-alpha/resolve/main/images/lq.jpg")
prompt = "A girl in city"
control_mode = 6

# go go go
image = pipe(
    prompt, 
    control_image=control_image,
    control_mode=control_mode,
    width=width,
    height=height,
    controlnet_conditioning_scale=controlnet_conditioning_scale,
    num_inference_steps=28, 
    guidance_scale=3.5,
    generator=torch.manual_seed(seed),
).images[0]
image.save("image.jpg")
```



# Acknowledgements

Thank you, [zzzzzero](https://github.com/zzzzzero), for pointing out the bug in the model.