Update gligen/ldm/models/diffusion/plms.py

gligen/ldm/models/diffusion/plms.py

@@ -3,7 +3,7 @@ import numpy as np
 from tqdm import tqdm
 from functools import partial
 from copy import deepcopy
-from diffusers import AutoencoderKL, LMSDiscreteScheduler
+
 from ldm.modules.diffusionmodules.util import make_ddim_sampling_parameters, make_ddim_timesteps, noise_like
 import math
 from ldm.models.diffusion.loss import  caculate_loss_att_fixed_cnt, caculate_loss_self_att, caculate_loss_LoCo,caculate_loss_LAC, caculate_loss_LoCo_V2
@@ -82,11 +82,6 @@ class PLMSSampler(object):
         if self.alpha_generator_func != None:
             alphas = self.alpha_generator_func(len(time_range))
 
-        # 新加的scheduler
-        noise_scheduler = LMSDiscreteScheduler(beta_start=0.00085, beta_end=0.012,
-                                            beta_schedule="scaled_linear", num_train_timesteps=1000)
-        noise_scheduler.set_timesteps(50)
-
         for i, step in enumerate(time_range):
 
             # set alpha and restore first conv layer 
@@ -109,7 +104,7 @@ class PLMSSampler(object):
             if loss_type !=None and loss_type!='standard':
                 if input['object_position'] != []:
 
-                    time_factor = noise_scheduler.sigmas[i] ** 2
+
                     x = self.update_loss_LoCo( input,i, index, ts,  time_factor = time_factor)      
            
                     input["x"] = x
@@ -122,7 +117,6 @@ class PLMSSampler(object):
         return img
      
 
-
     def update_loss_LoCo(self, input,index1, index, ts, time_factor, type_loss='self_accross'):
         
         # loss_scale = 30