File size: 2,434 Bytes
8311637 5c3d006 8311637 96c7f05 ee620e4 8311637 689d647 ac0c36e 689d647 431cdce 689d647 431cdce 689d647 d6fecf3 689d647 cec6b93 689d647 ac0c36e 689d647 5c3d006 689d647 92ca95d 3b084d2 689d647 ee620e4 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 |
---
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
license: cc-by-nc-4.0
---
# ⚡ 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
This model is released under the the Creative Commons BY-NC license.
Note that if used with Flux.1-dev, the model under the Flux.1-dev model also applies to this model. |