diff --git a/__init__.py b/__init__.py
index 3b809fac..0eca469b 100644
--- a/__init__.py
+++ b/__init__.py
@@ -88,6 +88,7 @@ def get_selection_preview(self):
         bpy.types.Scene.dream_textures_history_selection_preview = bpy.props.StringProperty(name="", default="", get=get_selection_preview, set=lambda _, __: None)
         bpy.types.Scene.dream_textures_progress = bpy.props.IntProperty(name="", default=0, min=0, max=0)
         bpy.types.Scene.dream_textures_info = bpy.props.StringProperty(name="Info")
+        bpy.types.Scene.dream_textures_last_execution_time = bpy.props.StringProperty(name="Last Execution Time", default="")
 
         bpy.types.Scene.dream_textures_viewport_enabled = BoolProperty(name="Viewport Enabled", default=False)
         bpy.types.Scene.dream_textures_render_properties_enabled = BoolProperty(default=False)
diff --git a/generator_process/actions/control_net.py b/generator_process/actions/control_net.py
index b9174fb6..39b0e54f 100644
--- a/generator_process/actions/control_net.py
+++ b/generator_process/actions/control_net.py
@@ -353,6 +353,14 @@ def __call__(
                     num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
                     with self.progress_bar(total=num_inference_steps) as progress_bar:
                         for i, t in enumerate(timesteps):
+                            # NOTE: Modified to support disabling CFG
+                            if do_classifier_free_guidance and (i / len(timesteps)) >= kwargs['cfg_end']:
+                                do_classifier_free_guidance = False
+                                prompt_embeds = prompt_embeds[prompt_embeds.size(0) // 2:]
+                                image = [i[i.size(0) // 2:] for i in image]
+                                if mask is not None:
+                                    mask = mask[mask.size(0) // 2:]
+                                    masked_image_latents = masked_image_latents[masked_image_latents.size(0) // 2:]
                             # expand the latents if we are doing classifier free guidance
                             latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
                             latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
@@ -522,7 +530,8 @@ def __call__(
                     return_dict=True,
                     callback=None,
                     callback_steps=1,
-                    step_preview_mode=step_preview_mode
+                    step_preview_mode=step_preview_mode,
+                    cfg_end=optimizations.cfg_end
                 )
         case Pipeline.STABILITY_SDK:
             import stability_sdk
diff --git a/generator_process/actions/depth_to_image.py b/generator_process/actions/depth_to_image.py
index 53bc8899..306b3550 100644
--- a/generator_process/actions/depth_to_image.py
+++ b/generator_process/actions/depth_to_image.py
@@ -277,6 +277,11 @@ def __call__(
                     num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
                     with self.progress_bar(total=num_inference_steps) as progress_bar:
                         for i, t in enumerate(timesteps):
+                            # NOTE: Modified to support disabling CFG
+                            if do_classifier_free_guidance and (i / len(timesteps)) >= kwargs['cfg_end']:
+                                do_classifier_free_guidance = False
+                                text_embeddings = text_embeddings[text_embeddings.size(0) // 2:]
+                                depth = depth[depth.size(0) // 2:]
                             # expand the latents if we are doing classifier free guidance
                             latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
 
@@ -385,7 +390,8 @@ def __call__(
                     return_dict=True,
                     callback=None,
                     callback_steps=1,
-                    step_preview_mode=step_preview_mode
+                    step_preview_mode=step_preview_mode,
+                    cfg_end=optimizations.cfg_end
                 )
         case Pipeline.STABILITY_SDK:
             import stability_sdk
diff --git a/generator_process/actions/image_to_image.py b/generator_process/actions/image_to_image.py
index fed2923e..a2f15c7a 100644
--- a/generator_process/actions/image_to_image.py
+++ b/generator_process/actions/image_to_image.py
@@ -105,6 +105,10 @@ def __call__(
                     num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
                     with self.progress_bar(total=num_inference_steps) as progress_bar:
                         for i, t in enumerate(timesteps):
+                            # NOTE: Modified to support disabling CFG
+                            if do_classifier_free_guidance and (i / len(timesteps)) >= kwargs['cfg_end']:
+                                do_classifier_free_guidance = False
+                                text_embeddings = text_embeddings[text_embeddings.size(0) // 2:]
                             # expand the latents if we are doing classifier free guidance
                             latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
                             latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
@@ -131,6 +135,8 @@ def __call__(
                     # TODO: Add UI to enable this
                     # 10. Run safety checker
                     # image, has_nsfw_concept = self.run_safety_checker(image, device, text_embeddings.dtype)
+                    
+                    image = self.image_processor.postprocess(image, output_type=output_type)
 
                     # Offload last model to CPU
                     if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
@@ -139,7 +145,7 @@ def __call__(
                     # NOTE: Modified to yield the decoded image as a numpy array.
                     yield ImageGenerationResult(
                         [np.asarray(ImageOps.flip(image).convert('RGBA'), dtype=np.float32) / 255.
-                            for i, image in enumerate(self.numpy_to_pil(image))],
+                            for i, image in enumerate(image)],
                         [gen.initial_seed() for gen in generator] if isinstance(generator, list) else [generator.initial_seed()],
                         num_inference_steps,
                         True
@@ -199,7 +205,8 @@ def __call__(
                     return_dict=True,
                     callback=None,
                     callback_steps=1,
-                    step_preview_mode=step_preview_mode
+                    step_preview_mode=step_preview_mode,
+                    cfg_end=optimizations.cfg_end
                 )
         case Pipeline.STABILITY_SDK:
             import stability_sdk.client
diff --git a/generator_process/actions/inpaint.py b/generator_process/actions/inpaint.py
index eac812d1..758a7356 100644
--- a/generator_process/actions/inpaint.py
+++ b/generator_process/actions/inpaint.py
@@ -143,6 +143,12 @@ def __call__(
                     num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
                     with self.progress_bar(total=num_inference_steps) as progress_bar:
                         for i, t in enumerate(timesteps):
+                            # NOTE: Modified to support disabling CFG
+                            if do_classifier_free_guidance and (i / len(timesteps)) >= kwargs['cfg_end']:
+                                do_classifier_free_guidance = False
+                                text_embeddings = text_embeddings[text_embeddings.size(0) // 2:]
+                                mask = mask[mask.size(0) // 2:]
+                                masked_image_latents = masked_image_latents[masked_image_latents.size(0) // 2:]
                             # expand the latents if we are doing classifier free guidance
                             latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
 
@@ -248,7 +254,8 @@ def __call__(
                     return_dict=True,
                     callback=None,
                     callback_steps=1,
-                    step_preview_mode=step_preview_mode
+                    step_preview_mode=step_preview_mode,
+                    cfg_end=optimizations.cfg_end
                 )
         case Pipeline.STABILITY_SDK:
             import stability_sdk.client
diff --git a/generator_process/actions/prompt_to_image.py b/generator_process/actions/prompt_to_image.py
index 14d124cd..8a9e845d 100644
--- a/generator_process/actions/prompt_to_image.py
+++ b/generator_process/actions/prompt_to_image.py
@@ -160,6 +160,7 @@ class Optimizations:
     vae_tiling: str = "off"
     vae_tile_size: int = 512
     vae_tile_blend: int = 64
+    cfg_end: float = 1.0
 
     cpu_only: bool = False
 
@@ -541,6 +542,11 @@ def __call__(
 
                     # 7. Denoising loop
                     for i, t in enumerate(self.progress_bar(timesteps)):
+                        # NOTE: Modified to support disabling CFG
+                        if do_classifier_free_guidance and (i / len(timesteps)) >= kwargs['cfg_end']:
+                            do_classifier_free_guidance = False
+                            text_embeddings = text_embeddings[text_embeddings.size(0) // 2:]
+
                         # expand the latents if we are doing classifier free guidance
                         latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
                         latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
@@ -621,7 +627,8 @@ def __call__(
                     return_dict=True,
                     callback=None,
                     callback_steps=1,
-                    step_preview_mode=step_preview_mode
+                    step_preview_mode=step_preview_mode,
+                    cfg_end=optimizations.cfg_end
                 )
         case Pipeline.STABILITY_SDK:
             import stability_sdk.client
diff --git a/operators/dream_texture.py b/operators/dream_texture.py
index de6a23ae..324e4fe5 100644
--- a/operators/dream_texture.py
+++ b/operators/dream_texture.py
@@ -10,6 +10,7 @@
 from ..generator_process import Generator
 from ..generator_process.actions.prompt_to_image import ImageGenerationResult, Pipeline
 from ..generator_process.actions.huggingface_hub import ModelType
+import time
 
 def bpy_image(name, width, height, pixels, existing_image):
     if existing_image is not None and (existing_image.size[0] != width or existing_image.size[1] != height):
@@ -89,8 +90,10 @@ def step_progress_update(self, context):
         scene.dream_textures_info = "Starting..."
 
         last_data_block = None
+        execution_start = time.time()
         def step_callback(_, step_image: ImageGenerationResult):
             nonlocal last_data_block
+            scene.dream_textures_last_execution_time = f"{time.time() - execution_start:.2f} seconds"
             if step_image.final:
                 return
             scene.dream_textures_progress = step_image.step
diff --git a/property_groups/dream_prompt.py b/property_groups/dream_prompt.py
index a58426d5..60166d1f 100644
--- a/property_groups/dream_prompt.py
+++ b/property_groups/dream_prompt.py
@@ -186,6 +186,8 @@ def optimization(optim, property=None, **kwargs):
                 property = BoolProperty
             case int():
                 property = IntProperty
+            case float():
+                property = FloatProperty
             case _:
                 raise TypeError(f"{optim} cannot infer optimization property from {type(kwargs['default'])}")
     attributes[f"optimizations_{optim}"] = property(**kwargs)
@@ -221,6 +223,7 @@ def optimization(optim, property=None, **kwargs):
 ), default=0, name="VAE Tiling", description="Decodes generated images in tiled regions to reduce memory usage in exchange for longer decode time and less accurate colors.\nCan allow for generating larger images that would otherwise run out of memory on the final step")
 optimization("vae_tile_size", min=1, name="VAE Tile Size", description="Width and height measurement of tiles. Smaller sizes are more likely to cause inaccurate colors and other undesired artifacts")
 optimization("vae_tile_blend", min=0, name="VAE Tile Blend", description="Minimum amount of how much each edge of a tile will intersect its adjacent tile")
+optimization("cfg_end", name="CFG End", min=0, max=1, description="The percentage of steps to complete before disabling classifier-free guidance")
 optimization("cpu_only", name="CPU Only", description="Disables GPU acceleration and is extremely slow")
 
 def map_structure_token_items(value):
diff --git a/ui/panels/dream_texture.py b/ui/panels/dream_texture.py
index 295be252..25ed24ab 100644
--- a/ui/panels/dream_texture.py
+++ b/ui/panels/dream_texture.py
@@ -308,6 +308,7 @@ def optimization(prop):
             optimization("half_precision")
             optimization("channels_last_memory_format")
             optimization("batch_size")
+            optimization("cfg_end")
     yield SpeedOptimizationPanel
 
     class MemoryOptimizationPanel(sub_panel):
@@ -378,6 +379,12 @@ def draw(self, context):
                 row.operator(CancelGenerator.bl_idname, icon="CANCEL", text="")
             row.operator(ReleaseGenerator.bl_idname, icon="X", text="")
 
+            if context.scene.dream_textures_last_execution_time != "":
+                r = layout.row()
+                r.scale_x = 0.5
+                r.scale_y = 0.5
+                r.label(text=context.scene.dream_textures_last_execution_time, icon="SORTTIME")
+
             # Validation
             try:
                 prompt.validate(context)