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•
a123370
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Parent(s):
12daec9
upload
Browse files- app.py +3 -3
- pipeline_genpercept.py +1 -5
app.py
CHANGED
@@ -79,8 +79,8 @@ def process_image(
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show_progress_bar=False,
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)
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depth_pred = pipe_out.
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depth_colored = pipe_out.
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depth_16bit = (depth_pred * 65535.0).astype(np.uint16)
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np.save(path_out_fp32, depth_pred)
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@@ -266,7 +266,7 @@ def main():
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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dtype = torch.
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vae = AutoencoderKL.from_pretrained("guangkaixu/GenPercept", subfolder='vae').to(dtype)
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unet_depth_v1 = UNet2DConditionModel.from_pretrained('guangkaixu/GenPercept', subfolder="unet_depth_v1").to(dtype)
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show_progress_bar=False,
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)
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+
depth_pred = pipe_out.pred_np
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depth_colored = pipe_out.pred_colored
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depth_16bit = (depth_pred * 65535.0).astype(np.uint16)
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np.save(path_out_fp32, depth_pred)
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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dtype = torch.float32
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vae = AutoencoderKL.from_pretrained("guangkaixu/GenPercept", subfolder='vae').to(dtype)
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unet_depth_v1 = UNet2DConditionModel.from_pretrained('guangkaixu/GenPercept', subfolder="unet_depth_v1").to(dtype)
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pipeline_genpercept.py
CHANGED
@@ -148,14 +148,10 @@ class GenPerceptPipeline(DiffusionPipeline):
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# Normalize rgb values
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rgb = np.transpose(image, (2, 0, 1)) # [H, W, rgb] -> [rgb, H, W]
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rgb_norm = rgb / 255.0 * 2.0 - 1.0
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rgb_norm = torch.from_numpy(rgb_norm).to(self.dtype)
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rgb_norm = rgb_norm[None].to(device)
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assert rgb_norm.min() >= -1.0 and rgb_norm.max() <= 1.0
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bs_imgs = 1
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print('rgb_norm :', rgb_norm.dtype)
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print('unet :', self.unet.dtype)
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print('vae :', self.vae.dtype)
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# ----------------- Predicting depth -----------------
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# Normalize rgb values
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rgb = np.transpose(image, (2, 0, 1)) # [H, W, rgb] -> [rgb, H, W]
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rgb_norm = rgb / 255.0 * 2.0 - 1.0
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rgb_norm = torch.from_numpy(rgb_norm).to(self.unet.dtype)
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rgb_norm = rgb_norm[None].to(device)
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assert rgb_norm.min() >= -1.0 and rgb_norm.max() <= 1.0
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bs_imgs = 1
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# ----------------- Predicting depth -----------------
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