Instructions to use jasperai/Flux.1-dev-Controlnet-Depth with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use jasperai/Flux.1-dev-Controlnet-Depth with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import load_image # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("jasperai/Flux.1-dev-Controlnet-Depth", dtype=torch.bfloat16, device_map="cuda") prompt = "Turn this cat into a dog" input_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png") image = pipe(image=input_image, prompt=prompt).images[0] - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -60,6 +60,22 @@ image
|
|
| 60 |
<img style="width:500px;" src="examples/output.jpg">
|
| 61 |
</p>
|
| 62 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 63 |
# Training
|
| 64 |
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.
|
| 65 |
|
|
|
|
| 60 |
<img style="width:500px;" src="examples/output.jpg">
|
| 61 |
</p>
|
| 62 |
|
| 63 |
+
💡 Note: You can compute the conditioning map using for instance the `MidasDetector` from the `controlnet_aux` library
|
| 64 |
+
|
| 65 |
+
```python
|
| 66 |
+
from controlnet_aux import MidasDetector
|
| 67 |
+
from diffusers.utils import load_image
|
| 68 |
+
|
| 69 |
+
midas = MidasDetector.from_pretrained("lllyasviel/Annotators")
|
| 70 |
+
|
| 71 |
+
# Load an image
|
| 72 |
+
im = load_image(
|
| 73 |
+
"https://huggingface.co/jasperai/jasperai/Flux.1-dev-Controlnet-Depth/resolve/main/examples/output.jpg"
|
| 74 |
+
)
|
| 75 |
+
|
| 76 |
+
surface = midas(im)
|
| 77 |
+
```
|
| 78 |
+
|
| 79 |
# Training
|
| 80 |
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.
|
| 81 |
|