jasperai
/

Flux.1-dev-Controlnet-Upscaler

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 ---
 library_name: diffusers
 ---
-# Model Card for Model ID
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-This is the model card of a 🧨 diffusers model that has been pushed on the Hub. This model card has been automatically generated.
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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
+- super-resolution
+- upscaler
 ---
+# ⚡ 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-Upscaler",
+  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-Upscaler/resolve/main/examples/depth.jpg"
+)
+w, h = control_image.size
+# Upscale x4
+control_image = control_image.resize((w * 4, h * 4))
+image = pipe(
+    "",
+    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 a synthetic complex data degradation scheme taking as input a *real-life* image and artificially degrading it by combining several degradations such as amongst other image noising (Gaussian, Poisson), image blurring and JPEG compression. In a similar spirit as [1]
+[1] Wang, Xintao, et al. "Real-esrgan: Training real-world blind super-resolution with pure synthetic data." Proceedings of the IEEE/CVF international conference on computer vision. 2021.
+# Licence
+The licence under the Flux.1-dev model applies to this model.