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modified: src/flux/math.py

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  1. src/flux/__pycache__/__init__.cpython-310.pyc +0 -0
  2. src/flux/__pycache__/_version.cpython-310.pyc +0 -0
  3. src/flux/__pycache__/math.cpython-310.pyc +0 -0
  4. src/flux/__pycache__/model.cpython-310.pyc +0 -0
  5. src/flux/__pycache__/sampling.cpython-310.pyc +0 -0
  6. src/flux/__pycache__/util.cpython-310.pyc +0 -0
  7. src/flux/math.py +2 -2
  8. src/flux/modules/__pycache__/autoencoder.cpython-310.pyc +0 -0
  9. src/flux/modules/__pycache__/conditioner.cpython-310.pyc +0 -0
  10. src/flux/modules/__pycache__/layers.cpython-310.pyc +0 -0
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src/flux/math.py CHANGED
@@ -145,8 +145,8 @@ def scaled_dot_product_attention(query, key, value, txt_shape, img_shape, cur_st
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  mask_img[mask_img >= thre] = 1
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  mask_img[mask_img < thre] = 0
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  #save_image(mask_img.unsqueeze(0), save_path)
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- print(f"[DEBUG] mask_img (after thresholding) unique values: {mask_img.unique().tolist()}")
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- print(f"[DEBUG] mask_img (after thresholding) values:\n{mask_img}")
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  mask_tensor = torch.zeros_like(mask_img) # Set mask_tensor as a zero tensor
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  if cur_step > 3:
 
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  mask_img[mask_img >= thre] = 1
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  mask_img[mask_img < thre] = 0
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  #save_image(mask_img.unsqueeze(0), save_path)
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+ #print(f"[DEBUG] mask_img (after thresholding) unique values: {mask_img.unique().tolist()}")
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+ #print(f"[DEBUG] mask_img (after thresholding) values:\n{mask_img}")
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  mask_tensor = torch.zeros_like(mask_img) # Set mask_tensor as a zero tensor
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  if cur_step > 3:
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src/flux/modules/__pycache__/conditioner.cpython-310.pyc CHANGED
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src/flux/modules/__pycache__/layers.cpython-310.pyc CHANGED
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