Update src/loss.py

src/loss.py

@@ -81,7 +81,7 @@ class LossScheduler:
 	def save(A,path):B,C,D,E=A.timesteps,A.model.wx,A.model.we,A.model.scale_factor;torch.save((B,C,D,E),path)
 	def set_timesteps(A,num_inference_steps,device='cuda'):B=device;A.xT=_A;A.e_prev=[];A.t_prev=-1;A.model=A.model.to(B);A.timesteps=A.timesteps.to(B)
 	def scale_model_input(A,sample,*B,**C):return sample
-    def step(self,model_output,timestep,sample,*D,**E):
+	def step(self,model_output,timestep,sample,*D,**E):
 		A=self;B=A.timesteps.tolist().index(timestep);assert A.t_prev==-1 or B==A.t_prev+1
 		if A.t_prev==-1:A.xT=sample
 		A.e_prev.append(model_output);C=A.model(B,A.xT,A.e_prev)
@@ -119,7 +119,7 @@ class SchedulerWrapper:
 		global count
 		D=sample;E=model_output;A=timestep; 
 		if timestep==B.timesteps[0]: print("resetting"); image_count+=1;count=0;
-        if timestep==1: print("starting"); image_count+=1;count=0;
+		if timestep==1: print("starting"); image_count+=1;count=0;
 		if False:
 			C=B.scheduler.step(E,A,D,**F);A=A.tolist();
 			if A not in B.catch_x:B.catch_x[A]=[];B.catch_e[A]=[];B.catch_x_[A]=[]