autoformat

imaginairy/modules/diffusion/ddpm.py

@@ -11,8 +11,8 @@ from functools import partial
 import numpy as np
 import pytorch_lightning as pl
 import torch
-from torch import nn
 from einops import rearrange
+from torch import nn
 from torchvision.utils import make_grid
 from tqdm import tqdm
 
@@ -348,16 +348,10 @@ class LatentDiffusion(DDPM):
             model = instantiate_from_config(config)
             self.cond_stage_model = model
 
-    def _get_denoise_row_from_list(
-        self, samples, desc=""
-    ):
+    def _get_denoise_row_from_list(self, samples, desc=""):
         denoise_row = []
         for zd in tqdm(samples, desc=desc):
-            denoise_row.append(
-                self.decode_first_stage(
-                    zd.to(self.device)
-                )
-            )
+            denoise_row.append(self.decode_first_stage(zd.to(self.device)))
         n_imgs_per_row = len(denoise_row)
         denoise_row = torch.stack(denoise_row)  # n_log_step, n_row, C, H, W
         denoise_grid = rearrange(denoise_row, "n b c h w -> b n c h w")

imaginairy/modules/find_noise.py

@@ -15,6 +15,7 @@ from torch import autocast
 from imaginairy.utils import get_device, pillow_img_to_torch_image
 from imaginairy.vendored import k_diffusion as K
 
+
 def pil_img_to_latent(model, img, batch_size=1, half=True):
     # init_image = pil_img_to_torch(img, half=half).to(device)
     init_image = pillow_img_to_torch_image(img).to(get_device())

imaginairy/samplers/base.py

@@ -3,7 +3,6 @@ from torch import nn
 
 from imaginairy.utils import get_device
 
-
 SAMPLER_TYPE_OPTIONS = [
     "plms",
     "ddim",