[pre-commit.ci] pre-commit autoupdate (#5572)

* [pre-commit.ci] pre-commit autoupdate

updates:
- [github.com/PyCQA/autoflake: v2.2.1 → v2.3.1](https://github.com/PyCQA/autoflake/compare/v2.2.1...v2.3.1)
- [github.com/pycqa/isort: 5.12.0 → 5.13.2](https://github.com/pycqa/isort/compare/5.12.0...5.13.2)
- [github.com/psf/black-pre-commit-mirror: 23.9.1 → 24.4.2](https://github.com/psf/black-pre-commit-mirror/compare/23.9.1...24.4.2)
- [github.com/pre-commit/mirrors-clang-format: v13.0.1 → v18.1.7](https://github.com/pre-commit/mirrors-clang-format/compare/v13.0.1...v18.1.7)
- [github.com/pre-commit/pre-commit-hooks: v4.3.0 → v4.6.0](https://github.com/pre-commit/pre-commit-hooks/compare/v4.3.0...v4.6.0)

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

---------

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

examples/images/diffusion/ldm/models/diffusion/ddpm.py

@@ -229,9 +229,7 @@ class DDPM(pl.LightningModule):
         )
 
         if self.parameterization == "eps":
-            lvlb_weights = self.betas**2 / (
-                2 * self.posterior_variance * to_torch(alphas) * (1 - self.alphas_cumprod)
-            )
+            lvlb_weights = self.betas**2 / (2 * self.posterior_variance * to_torch(alphas) * (1 - self.alphas_cumprod))
         elif self.parameterization == "x0":
             lvlb_weights = 0.5 * np.sqrt(torch.Tensor(alphas_cumprod)) / (2.0 * 1 - torch.Tensor(alphas_cumprod))
         elif self.parameterization == "v":
@@ -1186,9 +1184,11 @@ class LatentDiffusion(DDPM):
         if cond is not None:
             if isinstance(cond, dict):
                 cond = {
-                    key: cond[key][:batch_size]
-                    if not isinstance(cond[key], list)
-                    else list(map(lambda x: x[:batch_size], cond[key]))
+                    key: (
+                        cond[key][:batch_size]
+                        if not isinstance(cond[key], list)
+                        else list(map(lambda x: x[:batch_size], cond[key]))
+                    )
                     for key in cond
                 }
             else:
@@ -1321,9 +1321,11 @@ class LatentDiffusion(DDPM):
         if cond is not None:
             if isinstance(cond, dict):
                 cond = {
-                    key: cond[key][:batch_size]
-                    if not isinstance(cond[key], list)
-                    else list(map(lambda x: x[:batch_size], cond[key]))
+                    key: (
+                        cond[key][:batch_size]
+                        if not isinstance(cond[key], list)
+                        else list(map(lambda x: x[:batch_size], cond[key]))
+                    )
                     for key in cond
                 }
             else:

examples/images/diffusion/ldm/models/diffusion/dpm_solver/sampler.py

@@ -1,4 +1,5 @@
 """SAMPLING ONLY."""
+
 import torch
 
 from .dpm_solver import DPM_Solver, NoiseScheduleVP, model_wrapper