diff --git a/ldm/generate.py b/ldm/generate.py
index d268b909db..292329d24c 100644
--- a/ldm/generate.py
+++ b/ldm/generate.py
@@ -34,23 +34,7 @@ from ldm.dream.image_util import InitImageResizer
 from ldm.dream.devices import choose_torch_device, choose_precision
 from ldm.dream.conditioning import get_uc_and_c
 
-def fix_func(orig):
-    if hasattr(torch.backends, 'mps') and torch.backends.mps.is_available():
-        def new_func(*args, **kw):
-            device = kw.get("device", "mps")
-            kw["device"]="cpu"
-            return orig(*args, **kw).to(device)
-        return new_func
-    return orig
 
-torch.rand = fix_func(torch.rand)
-torch.rand_like = fix_func(torch.rand_like)
-torch.randn = fix_func(torch.randn)
-torch.randn_like = fix_func(torch.randn_like)
-torch.randint = fix_func(torch.randint)
-torch.randint_like = fix_func(torch.randint_like)
-torch.bernoulli = fix_func(torch.bernoulli)
-torch.multinomial = fix_func(torch.multinomial)
 
 def fix_func(orig):
     if hasattr(torch.backends, 'mps') and torch.backends.mps.is_available():
@@ -70,23 +54,7 @@ torch.randint_like = fix_func(torch.randint_like)
 torch.bernoulli = fix_func(torch.bernoulli)
 torch.multinomial = fix_func(torch.multinomial)
 
-def fix_func(orig):
-    if hasattr(torch.backends, 'mps') and torch.backends.mps.is_available():
-        def new_func(*args, **kw):
-            device = kw.get("device", "mps")
-            kw["device"]="cpu"
-            return orig(*args, **kw).to(device)
-        return new_func
-    return orig
 
-torch.rand = fix_func(torch.rand)
-torch.rand_like = fix_func(torch.rand_like)
-torch.randn = fix_func(torch.randn)
-torch.randn_like = fix_func(torch.randn_like)
-torch.randint = fix_func(torch.randint)
-torch.randint_like = fix_func(torch.randint_like)
-torch.bernoulli = fix_func(torch.bernoulli)
-torch.multinomial = fix_func(torch.multinomial)
 
 """Simplified text to image API for stable diffusion/latent diffusion
 
diff --git a/ldm/models/diffusion/sampler.py b/ldm/models/diffusion/sampler.py
index a31ec96d6f..3056cbb6b8 100644
--- a/ldm/models/diffusion/sampler.py
+++ b/ldm/models/diffusion/sampler.py
@@ -39,6 +39,7 @@ class Sampler(object):
             ddim_eta=0.0,
             verbose=False,
     ):
+        self.total_steps = ddim_num_steps
         self.ddim_timesteps = make_ddim_timesteps(
             ddim_discr_method=ddim_discretize,
             num_ddim_timesteps=ddim_num_steps,
@@ -211,6 +212,7 @@ class Sampler(object):
             if ddim_use_original_steps
             else np.flip(timesteps)
         )
+
         total_steps=steps
 
         iterator = tqdm(
@@ -305,7 +307,7 @@ class Sampler(object):
 
         time_range = np.flip(timesteps)
         total_steps = timesteps.shape[0]
-        print(f'>> Running {self.__class__.__name__} Sampling with {total_steps} timesteps')
+        print(f'>> Running {self.__class__.__name__} sampling starting at step {self.total_steps - t_start} of {self.total_steps} ({total_steps} new sampling steps)')
 
         iterator = tqdm(time_range, desc='Decoding image', total=total_steps)
         x_dec    = x_latent
@@ -351,11 +353,10 @@ class Sampler(object):
         return x_dec
 
     def get_initial_image(self,x_T,shape,timesteps=None):
-        x = torch.randn(shape, device=self.device)
         if x_T is None:
-            return x
+            return torch.randn(shape, device=self.device)
         else:
-            return x_T + x
+            return x_T
     
     def p_sample(
             self,