--- base_model: - black-forest-labs/FLUX.1-dev library_name: diffusers license: cc-by-nc-4.0 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.

# 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 ) pipe.to("cuda") # 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 ```

💡 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.