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@@ -11,9 +11,12 @@ tags:
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  inference: false
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  ---
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- # SDXL-controlnet: Depth
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- These are controlnet weights trained on stabilityai/stable-diffusion-xl-base-1.0 with depth conditioning. You can find some example images in the following.
 
 
 
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  ![images_0)](./zoe-depth-example.png)
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@@ -82,7 +85,7 @@ def colorize(value, vmin=None, vmax=None, cmap='gray_r', invalid_val=-99, invali
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  def get_zoe_depth_map(image):
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  with torch.autocast("cuda", enabled=True):
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  depth = model_zoe_n.infer_pil(image)
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- depth = colorize(depth, cmap="gray_r", gamma_corrected=True)
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  return depth
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  ```
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  inference: false
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  ---
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+ # SDXL-controlnet: Zoe-Depth
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+ These are ControlNet weights trained on stabilityai/stable-diffusion-xl-base-1.0 with zoe depth conditioning. [Zoe-depth](https://github.com/isl-org/ZoeDepth) is an open-source
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+ SOTA depth estimation model which produces high-quality depth maps, which are better suited for conditioning.
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+
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+ You can find some example images in the following.
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  ![images_0)](./zoe-depth-example.png)
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  def get_zoe_depth_map(image):
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  with torch.autocast("cuda", enabled=True):
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  depth = model_zoe_n.infer_pil(image)
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+ depth = colorize(depth, cmap="gray_r")
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  return depth
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  ```
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