lint

backend/sampling/condition.py

@@ -2,8 +2,6 @@ import math
 
 import torch
 
-from modules import shared
-
 
 def repeat_to_batch_size(tensor, batch_size):
     if tensor.shape[0] > batch_size:

backend/sampling/sampling_function.py

@@ -113,11 +113,6 @@ def can_concat_cond(c1, c2):
 
 
 def cond_cat(c_list):
-    c_crossattn = []
-    c_concat = []
-    c_adm = []
-    crossattn_max_len = 0
-
     temp = {}
     for x in c_list:
         for k in x:
@@ -392,8 +387,6 @@ def sampling_prepare(unet: "UnetPatcher", x: torch.Tensor):
     for cnet in unet.list_controlnets():
         cnet.pre_run(real_model, percent_to_timestep_function)
 
-    return
-
 
 def sampling_cleanup(unet: "UnetPatcher"):
     if unet.has_online_lora():