diff --git a/comfy/sample.py b/comfy/sample.py
index 981781b5..84eefcb7 100644
--- a/comfy/sample.py
+++ b/comfy/sample.py
@@ -2,30 +2,25 @@ import torch
 import comfy.model_management
 
 
-def prepare_noise(latent, seed):
-    """creates random noise given a LATENT and a seed"""
-    latent_image = latent["samples"]
-    batch_index = 0
-    if "batch_index" in latent:
-        batch_index = latent["batch_index"]
-
+def prepare_noise(latent_image, seed, skip=0):
+    """
+    creates random noise given a latent image and a seed.
+    optional arg skip can be used to skip and discard x number of noise generations for a given seed
+    """
     generator = torch.manual_seed(seed)
-    for i in range(batch_index):
+    for _ in range(skip):
         noise = torch.randn([1] + list(latent_image.size())[1:], dtype=latent_image.dtype, layout=latent_image.layout, generator=generator, device="cpu")
     noise = torch.randn(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, generator=generator, device="cpu")
     return noise
 
-def create_mask(latent, noise):
-    """creates a mask for a given LATENT and noise"""
-    noise_mask = None
+def prepare_mask(noise_mask, noise):
+    """ensures noise mask is of proper dimensions"""
     device = comfy.model_management.get_torch_device()
-    if "noise_mask" in latent:
-        noise_mask = latent['noise_mask']
-        noise_mask = torch.nn.functional.interpolate(noise_mask[None,None,], size=(noise.shape[2], noise.shape[3]), mode="bilinear")
-        noise_mask = noise_mask.round()
-        noise_mask = torch.cat([noise_mask] * noise.shape[1], dim=1)
-        noise_mask = torch.cat([noise_mask] * noise.shape[0])
-        noise_mask = noise_mask.to(device)
+    noise_mask = torch.nn.functional.interpolate(noise_mask[None,None,], size=(noise.shape[2], noise.shape[3]), mode="bilinear")
+    noise_mask = noise_mask.round()
+    noise_mask = torch.cat([noise_mask] * noise.shape[1], dim=1)
+    noise_mask = torch.cat([noise_mask] * noise.shape[0])
+    noise_mask = noise_mask.to(device)
     return noise_mask
 
 def broadcast_cond(cond, noise):
@@ -40,22 +35,20 @@ def broadcast_cond(cond, noise):
         copy += [[t] + p[1:]]
     return copy
 
-def load_c_nets(positive, negative):
-    """loads control nets in positive and negative conditioning"""
-    def get_models(cond):
-        models = []
-        for c in cond:
-            if 'control' in c[1]:
-                models += [c[1]['control']]
-            if 'gligen' in c[1]:
-                models += [c[1]['gligen'][1]]
-        return models
-
-    return get_models(positive) + get_models(negative)
+def get_models_from_cond(cond, model_type):
+    models = []
+    for c in cond:
+        if model_type in c[1]:
+            models += [c[1][model_type]]
+    return models
 
 def load_additional_models(positive, negative):
     """loads additional models in positive and negative conditioning"""
-    models = load_c_nets(positive, negative)
+    models = []
+    models += get_models_from_cond(positive, "control")
+    models += get_models_from_cond(negative, "control")
+    models += get_models_from_cond(positive, "gligen")
+    models += get_models_from_cond(negative, "gligen")
     comfy.model_management.load_controlnet_gpu(models)
     return models
 
diff --git a/nodes.py b/nodes.py
index b8c6d350..f9bedc97 100644
--- a/nodes.py
+++ b/nodes.py
@@ -747,9 +747,12 @@ def common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive,
     if disable_noise:
         noise = torch.zeros(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, device="cpu")
     else:
-        noise = comfy.sample.prepare_noise(latent, seed)
+        skip = latent["batch_index"] if "batch_index" in latent else 0
+        noise = comfy.sample.prepare_noise(latent_image, seed, skip)
 
-    noise_mask = comfy.sample.create_mask(latent, noise)
+    noise_mask = None
+    if "noise_mask" in latent:
+        noise_mask = comfy.sample.prepare_mask(latent["noise_mask"], noise)
 
     real_model = None
     comfy.model_management.load_model_gpu(model)