mirror of
https://github.com/Stability-AI/stablediffusion.git
synced 2024-12-22 23:55:00 +00:00
Reformatting
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parent
a825f77092
commit
462a9d3298
15 changed files with 47 additions and 72 deletions
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@ -472,7 +472,6 @@ class DDIMSampler(object):
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use_original_steps=False,
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use_original_steps=False,
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callback=None,
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callback=None,
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):
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):
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timesteps = (
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timesteps = (
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np.arange(self.ddpm_num_timesteps)
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np.arange(self.ddpm_num_timesteps)
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if use_original_steps
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if use_original_steps
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@ -1358,7 +1358,6 @@ class LatentDiffusion(DDPM):
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start_T=None,
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start_T=None,
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log_every_t=None,
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log_every_t=None,
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):
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):
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if not log_every_t:
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if not log_every_t:
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log_every_t = self.log_every_t
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log_every_t = self.log_every_t
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device = self.betas.device
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device = self.betas.device
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@ -519,7 +519,6 @@ def degradation_bsrgan(img, sf=4, lq_patchsize=72, isp_model=None):
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)
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)
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for i in shuffle_order:
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for i in shuffle_order:
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if i == 0:
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if i == 0:
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img = add_blur(img, sf=sf)
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img = add_blur(img, sf=sf)
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@ -623,7 +622,6 @@ def degradation_bsrgan_variant(image, sf=4, isp_model=None):
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)
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)
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for i in shuffle_order:
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for i in shuffle_order:
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if i == 0:
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if i == 0:
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image = add_blur(image, sf=sf)
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image = add_blur(image, sf=sf)
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@ -520,7 +520,6 @@ def degradation_bsrgan(img, sf=4, lq_patchsize=72, isp_model=None):
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)
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)
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for i in shuffle_order:
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for i in shuffle_order:
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if i == 0:
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if i == 0:
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img = add_blur(img, sf=sf)
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img = add_blur(img, sf=sf)
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@ -624,7 +623,6 @@ def degradation_bsrgan_variant(image, sf=4, isp_model=None, up=False):
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)
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)
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for i in shuffle_order:
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for i in shuffle_order:
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if i == 0:
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if i == 0:
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image = add_blur(image, sf=sf)
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image = add_blur(image, sf=sf)
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@ -271,22 +271,18 @@ def read_img(path):
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def uint2single(img):
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def uint2single(img):
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return np.float32(img / 255.0)
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return np.float32(img / 255.0)
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def single2uint(img):
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def single2uint(img):
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return np.uint8((img.clip(0, 1) * 255.0).round())
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return np.uint8((img.clip(0, 1) * 255.0).round())
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def uint162single(img):
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def uint162single(img):
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return np.float32(img / 65535.0)
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return np.float32(img / 65535.0)
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def single2uint16(img):
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def single2uint16(img):
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return np.uint16((img.clip(0, 1) * 65535.0).round())
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return np.uint16((img.clip(0, 1) * 65535.0).round())
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@ -586,18 +582,14 @@ def rgb2ycbcr(img, only_y=True):
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if only_y:
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if only_y:
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rlt = np.dot(img, [65.481, 128.553, 24.966]) / 255.0 + 16.0
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rlt = np.dot(img, [65.481, 128.553, 24.966]) / 255.0 + 16.0
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else:
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else:
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rlt = (
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rlt = np.matmul(
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np.matmul(
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img,
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img,
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[
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[
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[65.481, -37.797, 112.0],
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[65.481, -37.797, 112.0],
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[128.553, -74.203, -93.786],
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[128.553, -74.203, -93.786],
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[24.966, 112.0, -18.214],
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[24.966, 112.0, -18.214],
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],
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],
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)
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) / 255.0 + [16, 128, 128]
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/ 255.0
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+ [16, 128, 128]
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)
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if in_img_type == np.uint8:
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if in_img_type == np.uint8:
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rlt = rlt.round()
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rlt = rlt.round()
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else:
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else:
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@ -616,18 +608,14 @@ def ycbcr2rgb(img):
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if in_img_type != np.uint8:
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if in_img_type != np.uint8:
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img *= 255.0
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img *= 255.0
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# convert
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# convert
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rlt = (
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rlt = np.matmul(
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np.matmul(
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img,
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img,
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[
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[
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[0.00456621, 0.00456621, 0.00456621],
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[0.00456621, 0.00456621, 0.00456621],
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[0, -0.00153632, 0.00791071],
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[0, -0.00153632, 0.00791071],
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[0.00625893, -0.00318811, 0],
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[0.00625893, -0.00318811, 0],
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],
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],
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)
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) * 255.0 + [-222.921, 135.576, -276.836]
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* 255.0
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+ [-222.921, 135.576, -276.836]
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)
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if in_img_type == np.uint8:
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if in_img_type == np.uint8:
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rlt = rlt.round()
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rlt = rlt.round()
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else:
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else:
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@ -650,18 +638,14 @@ def bgr2ycbcr(img, only_y=True):
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if only_y:
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if only_y:
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rlt = np.dot(img, [24.966, 128.553, 65.481]) / 255.0 + 16.0
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rlt = np.dot(img, [24.966, 128.553, 65.481]) / 255.0 + 16.0
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else:
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else:
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rlt = (
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rlt = np.matmul(
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np.matmul(
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img,
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img,
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[
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[
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[24.966, 112.0, -18.214],
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[24.966, 112.0, -18.214],
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[128.553, -74.203, -93.786],
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[128.553, -74.203, -93.786],
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[65.481, -37.797, 112.0],
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[65.481, -37.797, 112.0],
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],
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],
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)
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) / 255.0 + [16, 128, 128]
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/ 255.0
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+ [16, 128, 128]
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)
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if in_img_type == np.uint8:
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if in_img_type == np.uint8:
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rlt = rlt.round()
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rlt = rlt.round()
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else:
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else:
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@ -36,7 +36,6 @@ class T2ISampler(BaseSampler):
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clip_stat_path: str,
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clip_stat_path: str,
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sampling_type: str = "default",
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sampling_type: str = "default",
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):
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):
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model = cls(
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model = cls(
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root_dir=root_dir,
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root_dir=root_dir,
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sampling_type=sampling_type,
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sampling_type=sampling_type,
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@ -33,7 +33,6 @@ class DPT(BaseModel):
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channels_last=False,
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channels_last=False,
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use_bn=False,
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use_bn=False,
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):
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):
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super(DPT, self).__init__()
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super(DPT, self).__init__()
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self.channels_last = channels_last
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self.channels_last = channels_last
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@ -17,7 +17,6 @@ def read_pfm(path):
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tuple: (data, scale)
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tuple: (data, scale)
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"""
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"""
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with open(path, "rb") as file:
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with open(path, "rb") as file:
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color = None
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color = None
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width = None
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width = None
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height = None
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height = None
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@ -16,7 +16,7 @@ gradio==3.13.2
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kornia==0.6
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kornia==0.6
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invisible-watermark>=0.1.5
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invisible-watermark>=0.1.5
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streamlit-drawable-canvas==0.8.0
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streamlit-drawable-canvas==0.8.0
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black==21.9b0
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black==23.3.0
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isort==5.9.3
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isort==5.9.3
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flake8==4.0.1
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flake8==4.0.1
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click==8.0.3
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click==8.0.3
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