Create README.md

README.md

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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.