diff --git a/sampling.py b/sampling.py
index f33c8e2..9970804 100644
--- a/sampling.py
+++ b/sampling.py
@@ -429,13 +429,14 @@ class PlotParameters:
                     "cols_value": (["none", "time", "seed", "steps", "denoise", "sampler", "scheduler", "guidance", "max_shift", "base_shift"], ),
                     "cols_num": ("INT", {"default": -1, "min": -1, "max": 1024 }),
                     "add_prompt": (["false", "true", "excerpt"], ),
+                    "add_params": (["false", "true", "changes only"], {"default": "true"}),
                 }}
 
     RETURN_TYPES = ("IMAGE", )
     FUNCTION = "execute"
     CATEGORY = "essentials/sampling"
 
-    def execute(self, images, params, order_by, cols_value, cols_num, add_prompt):
+    def execute(self, images, params, order_by, cols_value, cols_num, add_prompt, add_params):
         from PIL import Image, ImageDraw, ImageFont
         import math
         import textwrap
@@ -443,15 +444,17 @@ class PlotParameters:
         if images.shape[0] != len(params):
             raise ValueError("Number of images and number of parameters do not match.")
 
+        _params = params.copy()
+
         if order_by != "none":
-            sorted_params = sorted(params, key=lambda x: x[order_by])
-            indices = [params.index(item) for item in sorted_params]
+            sorted_params = sorted(_params, key=lambda x: x[order_by])
+            indices = [_params.index(item) for item in sorted_params]
             images = images[torch.tensor(indices)]
-            params = sorted_params
+            _params = sorted_params
 
         if cols_value != "none" and cols_num > -1:
             groups = {}
-            for p in params:
+            for p in _params:
                 value = p[cols_value]
                 if value not in groups:
                     groups[value] = []
@@ -463,9 +466,9 @@ class PlotParameters:
             for g in zip(*groups):
                 sorted_params.extend(g)
             
-            indices = [params.index(item) for item in sorted_params]
+            indices = [_params.index(item) for item in sorted_params]
             images = images[torch.tensor(indices)]
-            params = sorted_params
+            _params = sorted_params
         elif cols_num == 0:
             cols_num = int(math.sqrt(images.shape[0]))
             cols_num = max(1, min(cols_num, 1024))
@@ -478,22 +481,44 @@ class PlotParameters:
         line_height = font.getmask('Q').getbbox()[3] + font.getmetrics()[1] + text_padding*2
         char_width = font.getbbox('M')[2]+1 # using monospace font
 
-        for (image, param) in zip(images, params):
+        if add_params == "changes only":
+            value_tracker = {}
+            for p in _params:
+                for key, value in p.items():
+                    if key != "time":
+                        if key not in value_tracker:
+                            value_tracker[key] = set()
+                        value_tracker[key].add(value)            
+            changing_keys = {key for key, values in value_tracker.items() if len(values) > 1 or key == "prompt"}
+
+            result = []
+            for p in _params:
+                changing_params = {key: value for key, value in p.items() if key in changing_keys}
+                result.append(changing_params)
+            
+            _params = result
+
+        for (image, param) in zip(images, _params):
             image = image.permute(2, 0, 1)
 
-            text = f"time: {param['time']:.2f}s, seed: {param['seed']}, steps: {param['steps']}, size: {param['width']}×{param['height']}\ndenoise: {param['denoise']}, sampler: {param['sampler']}, sched: {param['scheduler']}\nguidance: {param['guidance']}, max/base shift: {param['max_shift']}/{param['base_shift']}"
-            lines = text.split("\n")
-            text_height = line_height * len(lines)
-            text_image = Image.new('RGB', (width, text_height), color=(0, 0, 0))
+            if add_params != "false":
+                if add_params == "changes only":
+                    text = "\n".join([f"{key}: {value}" for key, value in param.items() if key != "prompt"])
+                else:
+                    text = f"time: {param['time']:.2f}s, seed: {param['seed']}, steps: {param['steps']}, size: {param['width']}×{param['height']}\ndenoise: {param['denoise']}, sampler: {param['sampler']}, sched: {param['scheduler']}\nguidance: {param['guidance']}, max/base shift: {param['max_shift']}/{param['base_shift']}"
 
-            for i, line in enumerate(lines):
-                draw = ImageDraw.Draw(text_image)
-                draw.text((text_padding, i * line_height + text_padding), line, font=font, fill=(255, 255, 255))
-            
-            text_image = T.ToTensor()(text_image).to(image.device)
-            image = torch.cat([image, text_image], 1)
+                lines = text.split("\n")
+                text_height = line_height * len(lines)
+                text_image = Image.new('RGB', (width, text_height), color=(0, 0, 0))
 
-            if param['prompt'] and add_prompt != "false":
+                for i, line in enumerate(lines):
+                    draw = ImageDraw.Draw(text_image)
+                    draw.text((text_padding, i * line_height + text_padding), line, font=font, fill=(255, 255, 255))
+                
+                text_image = T.ToTensor()(text_image).to(image.device)
+                image = torch.cat([image, text_image], 1)
+
+            if 'prompt' in param and param['prompt'] and add_prompt != "false":
                 prompt = param['prompt']
                 if add_prompt == "excerpt":
                     prompt = " ".join(param['prompt'].split()[:64])
@@ -516,7 +541,7 @@ class PlotParameters:
             out_image.append(image)
         
         # ensure all images have the same height
-        if add_prompt != "false":
+        if add_prompt != "false" or add_params == "changes only":
             max_height = max([image.shape[1] for image in out_image])
             out_image = [F.pad(image, (0, 0, 0, max_height - image.shape[1])) for image in out_image]