diff --git a/ldm/models/diffusion/ddim.py b/ldm/models/diffusion/ddim.py
index 786dcf4..a0eb522 100644
--- a/ldm/models/diffusion/ddim.py
+++ b/ldm/models/diffusion/ddim.py
@@ -360,7 +360,7 @@ class DDIMSampler(object):
             raise NotImplementedError()
 
         # direction pointing to x_t
-        dir_xt = (1.0 - a_prev - sigma_t ** 2).sqrt() * e_t
+        dir_xt = (1.0 - a_prev - sigma_t**2).sqrt() * e_t
         noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature
         if noise_dropout > 0.0:
             noise = torch.nn.functional.dropout(noise, p=noise_dropout)
@@ -472,7 +472,6 @@ class DDIMSampler(object):
         use_original_steps=False,
         callback=None,
     ):
-
         timesteps = (
             np.arange(self.ddpm_num_timesteps)
             if use_original_steps
diff --git a/ldm/models/diffusion/ddpm.py b/ldm/models/diffusion/ddpm.py
index 6162192..75ad0e1 100644
--- a/ldm/models/diffusion/ddpm.py
+++ b/ldm/models/diffusion/ddpm.py
@@ -242,7 +242,7 @@ class DDPM(pl.LightningModule):
         )
 
         if self.parameterization == "eps":
-            lvlb_weights = self.betas ** 2 / (
+            lvlb_weights = self.betas**2 / (
                 2
                 * self.posterior_variance
                 * to_torch(alphas)
@@ -256,7 +256,7 @@ class DDPM(pl.LightningModule):
             )
         elif self.parameterization == "v":
             lvlb_weights = torch.ones_like(
-                self.betas ** 2
+                self.betas**2
                 / (
                     2
                     * self.posterior_variance
@@ -1358,7 +1358,6 @@ class LatentDiffusion(DDPM):
         start_T=None,
         log_every_t=None,
     ):
-
         if not log_every_t:
             log_every_t = self.log_every_t
         device = self.betas.device
diff --git a/ldm/models/diffusion/plms.py b/ldm/models/diffusion/plms.py
index cb6267f..f82e829 100644
--- a/ldm/models/diffusion/plms.py
+++ b/ldm/models/diffusion/plms.py
@@ -339,7 +339,7 @@ class PLMSSampler(object):
             if dynamic_threshold is not None:
                 pred_x0 = norm_thresholding(pred_x0, dynamic_threshold)
             # direction pointing to x_t
-            dir_xt = (1.0 - a_prev - sigma_t ** 2).sqrt() * e_t
+            dir_xt = (1.0 - a_prev - sigma_t**2).sqrt() * e_t
             noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature
             if noise_dropout > 0.0:
                 noise = torch.nn.functional.dropout(noise, p=noise_dropout)
diff --git a/ldm/modules/attention.py b/ldm/modules/attention.py
index faf00f4..21bf1e1 100644
--- a/ldm/modules/attention.py
+++ b/ldm/modules/attention.py
@@ -144,7 +144,7 @@ class CrossAttention(nn.Module):
         inner_dim = dim_head * heads
         context_dim = default(context_dim, query_dim)
 
-        self.scale = dim_head ** -0.5
+        self.scale = dim_head**-0.5
         self.heads = heads
 
         self.to_q = nn.Linear(query_dim, inner_dim, bias=False)
diff --git a/ldm/modules/diffusionmodules/openaimodel.py b/ldm/modules/diffusionmodules/openaimodel.py
index 21253eb..44e1f2d 100644
--- a/ldm/modules/diffusionmodules/openaimodel.py
+++ b/ldm/modules/diffusionmodules/openaimodel.py
@@ -43,7 +43,7 @@ class AttentionPool2d(nn.Module):
     ):
         super().__init__()
         self.positional_embedding = nn.Parameter(
-            th.randn(embed_dim, spacial_dim ** 2 + 1) / embed_dim ** 0.5
+            th.randn(embed_dim, spacial_dim**2 + 1) / embed_dim**0.5
         )
         self.qkv_proj = conv_nd(1, embed_dim, 3 * embed_dim, 1)
         self.c_proj = conv_nd(1, embed_dim, output_dim or embed_dim, 1)
@@ -354,7 +354,7 @@ def count_flops_attn(model, _x, y):
     # We perform two matmuls with the same number of ops.
     # The first computes the weight matrix, the second computes
     # the combination of the value vectors.
-    matmul_ops = 2 * b * (num_spatial ** 2) * c
+    matmul_ops = 2 * b * (num_spatial**2) * c
     model.total_ops += th.DoubleTensor([matmul_ops])
 
 
diff --git a/ldm/modules/diffusionmodules/util.py b/ldm/modules/diffusionmodules/util.py
index f7cc16f..2ace948 100644
--- a/ldm/modules/diffusionmodules/util.py
+++ b/ldm/modules/diffusionmodules/util.py
@@ -25,7 +25,7 @@ def make_beta_schedule(
     if schedule == "linear":
         betas = (
             torch.linspace(
-                linear_start ** 0.5, linear_end ** 0.5, n_timestep, dtype=torch.float64
+                linear_start**0.5, linear_end**0.5, n_timestep, dtype=torch.float64
             )
             ** 2
         )
diff --git a/ldm/modules/image_degradation/bsrgan.py b/ldm/modules/image_degradation/bsrgan.py
index 346ff9b..3b48bea 100644
--- a/ldm/modules/image_degradation/bsrgan.py
+++ b/ldm/modules/image_degradation/bsrgan.py
@@ -403,7 +403,7 @@ def add_Gaussian_noise(img, noise_level1=2, noise_level2=25):
         U = orth(np.random.rand(3, 3))
         conv = np.dot(np.dot(np.transpose(U), D), U)
         img = img + np.random.multivariate_normal(
-            [0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]
+            [0, 0, 0], np.abs(L**2 * conv), img.shape[:2]
         ).astype(np.float32)
     img = np.clip(img, 0.0, 1.0)
     return img
@@ -427,7 +427,7 @@ def add_speckle_noise(img, noise_level1=2, noise_level2=25):
         U = orth(np.random.rand(3, 3))
         conv = np.dot(np.dot(np.transpose(U), D), U)
         img += img * np.random.multivariate_normal(
-            [0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]
+            [0, 0, 0], np.abs(L**2 * conv), img.shape[:2]
         ).astype(np.float32)
     img = np.clip(img, 0.0, 1.0)
     return img
@@ -519,7 +519,6 @@ def degradation_bsrgan(img, sf=4, lq_patchsize=72, isp_model=None):
         )
 
     for i in shuffle_order:
-
         if i == 0:
             img = add_blur(img, sf=sf)
 
@@ -623,7 +622,6 @@ def degradation_bsrgan_variant(image, sf=4, isp_model=None):
         )
 
     for i in shuffle_order:
-
         if i == 0:
             image = add_blur(image, sf=sf)
 
diff --git a/ldm/modules/image_degradation/bsrgan_light.py b/ldm/modules/image_degradation/bsrgan_light.py
index 03020db..8995bb0 100644
--- a/ldm/modules/image_degradation/bsrgan_light.py
+++ b/ldm/modules/image_degradation/bsrgan_light.py
@@ -404,7 +404,7 @@ def add_Gaussian_noise(img, noise_level1=2, noise_level2=25):
         U = orth(np.random.rand(3, 3))
         conv = np.dot(np.dot(np.transpose(U), D), U)
         img = img + np.random.multivariate_normal(
-            [0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]
+            [0, 0, 0], np.abs(L**2 * conv), img.shape[:2]
         ).astype(np.float32)
     img = np.clip(img, 0.0, 1.0)
     return img
@@ -428,7 +428,7 @@ def add_speckle_noise(img, noise_level1=2, noise_level2=25):
         U = orth(np.random.rand(3, 3))
         conv = np.dot(np.dot(np.transpose(U), D), U)
         img += img * np.random.multivariate_normal(
-            [0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]
+            [0, 0, 0], np.abs(L**2 * conv), img.shape[:2]
         ).astype(np.float32)
     img = np.clip(img, 0.0, 1.0)
     return img
@@ -520,7 +520,6 @@ def degradation_bsrgan(img, sf=4, lq_patchsize=72, isp_model=None):
         )
 
     for i in shuffle_order:
-
         if i == 0:
             img = add_blur(img, sf=sf)
 
@@ -624,7 +623,6 @@ def degradation_bsrgan_variant(image, sf=4, isp_model=None, up=False):
         )
 
     for i in shuffle_order:
-
         if i == 0:
             image = add_blur(image, sf=sf)
 
diff --git a/ldm/modules/image_degradation/utils_image.py b/ldm/modules/image_degradation/utils_image.py
index 2f95aa1..c933af5 100644
--- a/ldm/modules/image_degradation/utils_image.py
+++ b/ldm/modules/image_degradation/utils_image.py
@@ -271,22 +271,18 @@ def read_img(path):
 
 
 def uint2single(img):
-
     return np.float32(img / 255.0)
 
 
 def single2uint(img):
-
     return np.uint8((img.clip(0, 1) * 255.0).round())
 
 
 def uint162single(img):
-
     return np.float32(img / 65535.0)
 
 
 def single2uint16(img):
-
     return np.uint16((img.clip(0, 1) * 65535.0).round())
 
 
@@ -586,18 +582,14 @@ def rgb2ycbcr(img, only_y=True):
     if only_y:
         rlt = np.dot(img, [65.481, 128.553, 24.966]) / 255.0 + 16.0
     else:
-        rlt = (
-            np.matmul(
-                img,
-                [
-                    [65.481, -37.797, 112.0],
-                    [128.553, -74.203, -93.786],
-                    [24.966, 112.0, -18.214],
-                ],
-            )
-            / 255.0
-            + [16, 128, 128]
-        )
+        rlt = np.matmul(
+            img,
+            [
+                [65.481, -37.797, 112.0],
+                [128.553, -74.203, -93.786],
+                [24.966, 112.0, -18.214],
+            ],
+        ) / 255.0 + [16, 128, 128]
     if in_img_type == np.uint8:
         rlt = rlt.round()
     else:
@@ -616,18 +608,14 @@ def ycbcr2rgb(img):
     if in_img_type != np.uint8:
         img *= 255.0
     # convert
-    rlt = (
-        np.matmul(
-            img,
-            [
-                [0.00456621, 0.00456621, 0.00456621],
-                [0, -0.00153632, 0.00791071],
-                [0.00625893, -0.00318811, 0],
-            ],
-        )
-        * 255.0
-        + [-222.921, 135.576, -276.836]
-    )
+    rlt = np.matmul(
+        img,
+        [
+            [0.00456621, 0.00456621, 0.00456621],
+            [0, -0.00153632, 0.00791071],
+            [0.00625893, -0.00318811, 0],
+        ],
+    ) * 255.0 + [-222.921, 135.576, -276.836]
     if in_img_type == np.uint8:
         rlt = rlt.round()
     else:
@@ -650,18 +638,14 @@ def bgr2ycbcr(img, only_y=True):
     if only_y:
         rlt = np.dot(img, [24.966, 128.553, 65.481]) / 255.0 + 16.0
     else:
-        rlt = (
-            np.matmul(
-                img,
-                [
-                    [24.966, 112.0, -18.214],
-                    [128.553, -74.203, -93.786],
-                    [65.481, -37.797, 112.0],
-                ],
-            )
-            / 255.0
-            + [16, 128, 128]
-        )
+        rlt = np.matmul(
+            img,
+            [
+                [24.966, 112.0, -18.214],
+                [128.553, -74.203, -93.786],
+                [65.481, -37.797, 112.0],
+            ],
+        ) / 255.0 + [16, 128, 128]
     if in_img_type == np.uint8:
         rlt = rlt.round()
     else:
@@ -752,11 +736,11 @@ def ssim(img1, img2):
 
     mu1 = cv2.filter2D(img1, -1, window)[5:-5, 5:-5]  # valid
     mu2 = cv2.filter2D(img2, -1, window)[5:-5, 5:-5]
-    mu1_sq = mu1 ** 2
-    mu2_sq = mu2 ** 2
+    mu1_sq = mu1**2
+    mu2_sq = mu2**2
     mu1_mu2 = mu1 * mu2
-    sigma1_sq = cv2.filter2D(img1 ** 2, -1, window)[5:-5, 5:-5] - mu1_sq
-    sigma2_sq = cv2.filter2D(img2 ** 2, -1, window)[5:-5, 5:-5] - mu2_sq
+    sigma1_sq = cv2.filter2D(img1**2, -1, window)[5:-5, 5:-5] - mu1_sq
+    sigma2_sq = cv2.filter2D(img2**2, -1, window)[5:-5, 5:-5] - mu2_sq
     sigma12 = cv2.filter2D(img1 * img2, -1, window)[5:-5, 5:-5] - mu1_mu2
 
     ssim_map = ((2 * mu1_mu2 + C1) * (2 * sigma12 + C2)) / (
@@ -775,8 +759,8 @@ def ssim(img1, img2):
 # matlab 'imresize' function, now only support 'bicubic'
 def cubic(x):
     absx = torch.abs(x)
-    absx2 = absx ** 2
-    absx3 = absx ** 3
+    absx2 = absx**2
+    absx3 = absx**3
     return (1.5 * absx3 - 2.5 * absx2 + 1) * ((absx <= 1).type_as(absx)) + (
         -0.5 * absx3 + 2.5 * absx2 - 4 * absx + 2
     ) * (((absx > 1) * (absx <= 2)).type_as(absx))
diff --git a/ldm/modules/karlo/kakao/models/clip.py b/ldm/modules/karlo/kakao/models/clip.py
index 7b8d030..8cebead 100644
--- a/ldm/modules/karlo/kakao/models/clip.py
+++ b/ldm/modules/karlo/kakao/models/clip.py
@@ -106,7 +106,7 @@ class CustomizedCLIP(CLIP):
             )
             vision_patch_size = None
             assert (
-                output_width ** 2 + 1
+                output_width**2 + 1
                 == state_dict["visual.attnpool.positional_embedding"].shape[0]
             )
             image_resolution = output_width * 32
diff --git a/ldm/modules/karlo/kakao/modules/diffusion/gaussian_diffusion.py b/ldm/modules/karlo/kakao/modules/diffusion/gaussian_diffusion.py
index 7fc5167..6a111aa 100644
--- a/ldm/modules/karlo/kakao/modules/diffusion/gaussian_diffusion.py
+++ b/ldm/modules/karlo/kakao/modules/diffusion/gaussian_diffusion.py
@@ -26,8 +26,8 @@ def get_beta_schedule(beta_schedule, *, beta_start, beta_end, num_diffusion_time
     if beta_schedule == "quad":
         betas = (
             np.linspace(
-                beta_start ** 0.5,
-                beta_end ** 0.5,
+                beta_start**0.5,
+                beta_end**0.5,
                 num_diffusion_timesteps,
                 dtype=np.float64,
             )
@@ -681,7 +681,7 @@ class GaussianDiffusion(th.nn.Module):
         noise = th.randn_like(x)
         mean_pred = (
             out["pred_xstart"] * th.sqrt(alpha_bar_prev)
-            + th.sqrt(1 - alpha_bar_prev - sigma ** 2) * eps
+            + th.sqrt(1 - alpha_bar_prev - sigma**2) * eps
         )
         nonzero_mask = (
             (t != 0).float().view(-1, *([1] * (len(x.shape) - 1)))
diff --git a/ldm/modules/karlo/kakao/sampler.py b/ldm/modules/karlo/kakao/sampler.py
index f5453bd..f9355be 100644
--- a/ldm/modules/karlo/kakao/sampler.py
+++ b/ldm/modules/karlo/kakao/sampler.py
@@ -36,7 +36,6 @@ class T2ISampler(BaseSampler):
         clip_stat_path: str,
         sampling_type: str = "default",
     ):
-
         model = cls(
             root_dir=root_dir,
             sampling_type=sampling_type,
diff --git a/ldm/modules/midas/midas/dpt_depth.py b/ldm/modules/midas/midas/dpt_depth.py
index 526db6b..d877bfd 100644
--- a/ldm/modules/midas/midas/dpt_depth.py
+++ b/ldm/modules/midas/midas/dpt_depth.py
@@ -33,7 +33,6 @@ class DPT(BaseModel):
         channels_last=False,
         use_bn=False,
     ):
-
         super(DPT, self).__init__()
 
         self.channels_last = channels_last
diff --git a/ldm/modules/midas/utils.py b/ldm/modules/midas/utils.py
index d327b48..47956b7 100644
--- a/ldm/modules/midas/utils.py
+++ b/ldm/modules/midas/utils.py
@@ -17,7 +17,6 @@ def read_pfm(path):
         tuple: (data, scale)
     """
     with open(path, "rb") as file:
-
         color = None
         width = None
         height = None
diff --git a/requirements.txt b/requirements.txt
index 7073dfd..eae4d18 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -16,7 +16,7 @@ gradio==3.13.2
 kornia==0.6
 invisible-watermark>=0.1.5
 streamlit-drawable-canvas==0.8.0
-black==21.9b0
+black==23.3.0
 isort==5.9.3
 flake8==4.0.1
 click==8.0.3