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---
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-Depth
This repository contains a Depth ControlNet for FLUX.1-dev model jointly trained 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
- The model consists of 4 FluxTransformerBlock and 1 FluxSingleTransformerBlock.
- This checkpoint is trained on both real and generated image datasets, with 16\*A800 for 70K steps. The batch size 16\*4=64 with resolution=1024. The learning rate is set to 5e-6. We use [Depth-Anything-V2](https://github.com/DepthAnything/Depth-Anything-V2) to extract depth maps.
- The recommended controlnet_conditioning_scale is 0.3-0.7.
# Showcases
<div class="container">
<img src="./assets/teaser.png" width="1024"/>
</div>
# Inference
```python
import torch
from diffusers.utils import load_image
from diffusers import FluxControlNetPipeline, FluxControlNetModel
controlnet_model = "black-forest-labs/FLUX.1-dev"
base_model = "Shakker-Labs/FLUX.1-dev-ControlNet-Depth"
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")
control_image = load_image("https://huggingface.co/Shakker-Labs/FLUX.1-dev-ControlNet-Depth/resolve/main/assets/cond1.png")
prompt = "an old man with white hair"
image = pipe(prompt,
control_image=control_image,
controlnet_conditioning_scale=0.5,
width=control_image.size[0],
height=control_image.size[1],
num_inference_steps=24,
guidance_scale=3.5,
).images[0]
```
For multi-ControlNets support, please refer to [Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro](https://huggingface.co/Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro).
# 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 sponsored and released by [Shakker AI](https://www.shakker.ai/). All copyright reserved.
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