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---
base_model:
- black-forest-labs/FLUX.1-dev
library_name: diffusers
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
pipeline_tag: image-to-image
tags:
- ControlNet
---
# ⚡ Flux.1-dev: Depth ControlNet ⚡
This is [Flux.1-dev](https://huggingface.co/black-forest-labs/FLUX.1-dev) ControlNet for Depth map developped by Jasper research team.
<p align="center">
<img style="width:700px;" src="examples/showcase.jpg">
</p>
# How to use
This model can be used directly with the `diffusers` library
```python
import torch
from diffusers.utils import load_image
from diffusers import FluxControlNetModel
from diffusers.pipelines import FluxControlNetPipeline
# Load pipeline
controlnet = FluxControlNetModel.from_pretrained(
"jasperai/Flux.1-dev-Controlnet-Depth",
torch_dtype=torch.bfloat16
)
pipe = FluxControlNetPipeline.from_pretrained(
"black-forest-labs/FLUX.1-dev",
controlnet=controlnet,
torch_dtype=torch.bfloat16
)
# Load a control image
control_image = load_image(
"https://huggingface.co/jasperai/Flux.1-dev-Controlnet-Depth/resolve/main/examples/depth.jpg"
)
prompt = "a statue of a gnome in a field of purple tulips"
image = pipe(
prompt,
control_image=control_image,
controlnet_conditioning_scale=0.6,
num_inference_steps=28,
guidance_scale=3.5,
height=control_image.size[1],
width=control_image.size[0]
).images[0]
image
```
<p align="center">
<img style="width:500px;" src="examples/output.jpg">
</p>
💡 Note: You can compute the conditioning map using for instance the `MidasDetector` from the `controlnet_aux` library
```python
from controlnet_aux import MidasDetector
from diffusers.utils import load_image
midas = MidasDetector.from_pretrained("lllyasviel/Annotators")
# Load an image
im = load_image(
"https://huggingface.co/jasperai/jasperai/Flux.1-dev-Controlnet-Depth/resolve/main/examples/output.jpg"
)
surface = midas(im)
```
# Training
This model was trained with depth maps computed with [Clipdrop's depth estimator model](https://clipdrop.co/apis/docs/portrait-depth-estimation) as well as open-souce depth estimation models such as Midas or Leres.
# Licence
The licence under the Flux.1-dev model applies to this model. |