1 files changed, 11 insertions, 12 deletions
diff --git a/modules/sd_samplers_kdiffusion.py b/modules/sd_samplers_kdiffusion.py
index eb98e599..e9e41818 100644
--- a/modules/sd_samplers_kdiffusion.py
+++ b/modules/sd_samplers_kdiffusion.py
@@ -1,7 +1,6 @@
 from collections import deque
 import torch
 import inspect
-import einops
 import k_diffusion.sampling
 from modules import prompt_parser, devices, sd_samplers_common
 
@@ -87,17 +86,17 @@ class CFGDenoiser(torch.nn.Module):
         conds_list, tensor = prompt_parser.reconstruct_multicond_batch(cond, self.step)
         uncond = prompt_parser.reconstruct_cond_batch(uncond, self.step)
 
-        assert not is_edit_model or all([len(conds) == 1 for conds in conds_list]), "AND is not supported for InstructPix2Pix checkpoint (unless using Image CFG scale = 1.0)"
+        assert not is_edit_model or all(len(conds) == 1 for conds in conds_list), "AND is not supported for InstructPix2Pix checkpoint (unless using Image CFG scale = 1.0)"
 
         batch_size = len(conds_list)
         repeats = [len(conds_list[i]) for i in range(batch_size)]
 
         if shared.sd_model.model.conditioning_key == "crossattn-adm":
             image_uncond = torch.zeros_like(image_cond)
-            make_condition_dict = lambda c_crossattn, c_adm: {"c_crossattn": c_crossattn, "c_adm": c_adm} 
+            make_condition_dict = lambda c_crossattn, c_adm: {"c_crossattn": c_crossattn, "c_adm": c_adm}
         else:
             image_uncond = image_cond
-            make_condition_dict = lambda c_crossattn, c_concat: {"c_crossattn": c_crossattn, "c_concat": [c_concat]} 
+            make_condition_dict = lambda c_crossattn, c_concat: {"c_crossattn": c_crossattn, "c_concat": [c_concat]}
 
         if not is_edit_model:
             x_in = torch.cat([torch.stack([x[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [x])
@@ -198,7 +197,7 @@ class TorchHijack:
         if hasattr(torch, item):
             return getattr(torch, item)
 
-        raise AttributeError("'{}' object has no attribute '{}'".format(type(self).__name__, item))
+        raise AttributeError(f"'{type(self).__name__}' object has no attribute '{item}'")
 
     def randn_like(self, x):
         if self.sampler_noises:
@@ -317,7 +316,7 @@ class KDiffusionSampler:
 
         sigma_sched = sigmas[steps - t_enc - 1:]
         xi = x + noise * sigma_sched[0]
-        
+
         extra_params_kwargs = self.initialize(p)
         parameters = inspect.signature(self.func).parameters
 
@@ -340,9 +339,9 @@ class KDiffusionSampler:
         self.model_wrap_cfg.init_latent = x
         self.last_latent = x
         extra_args={
-            'cond': conditioning, 
-            'image_cond': image_conditioning, 
-            'uncond': unconditional_conditioning, 
+            'cond': conditioning,
+            'image_cond': image_conditioning,
+            'uncond': unconditional_conditioning,
             'cond_scale': p.cfg_scale,
             's_min_uncond': self.s_min_uncond
         }
@@ -375,9 +374,9 @@ class KDiffusionSampler:
 
         self.last_latent = x
         samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args={
-            'cond': conditioning, 
-            'image_cond': image_conditioning, 
-            'uncond': unconditional_conditioning, 
+            'cond': conditioning,
+            'image_cond': image_conditioning,
+            'uncond': unconditional_conditioning,
             'cond_scale': p.cfg_scale,
             's_min_uncond': self.s_min_uncond
         }, disable=False, callback=self.callback_state, **extra_params_kwargs))