diff --git a/ui_trt.py b/ui_trt.py
index 0dd72f5..b7df969 100644
--- a/ui_trt.py
+++ b/ui_trt.py
@@ -1,3 +1,4 @@
+import asyncio
 import html
 import os
 
@@ -13,26 +14,162 @@
 from modules.ui_components import FormRow
 
 
-def export_unet_to_onnx(filename, opset):
-    if not filename:
-        modelname = shared.sd_model.sd_checkpoint_info.model_name + ".onnx"
-        filename = os.path.join(paths_internal.models_path, "Unet-onnx", modelname)
-
-    export_onnx.export_current_unet_to_onnx(filename, opset)
-
-    return f'Saved as {filename}', ''
-
-
-def get_trt_filename(filename, onnx_filename):
+def export_unet_to_onnx(filename, opset, batch_run, batch_directory):
+    print(f'Starting Conversion to .onnx')
+    
+    # Check if 'Unet-onnx' directory exists and create it if not
+    unet_onnx_path = os.path.join(paths_internal.models_path, "Unet-onnx")
+    os.makedirs(unet_onnx_path, exist_ok=True)
+    
+    # Use default folder if batch_directory is empty
+    if not batch_directory:
+        batch_directory = os.path.join(paths_internal.models_path, "Stable-diffusion")
+    # Batch mode
+    if batch_run:
+        print(f"--Batch Models mode--")
+        
+        onnx_files = os.listdir(os.path.join(paths_internal.models_path, "Unet-onnx"))
+        onnx_files_to_process = list()
+        # Take all files if destination folder is empty
+        if not onnx_files:
+            print(f"Unet-onnx is empty, adding all .safetensors and .ckpt files from {batch_directory}\n")  # Debug line
+            onnx_files_to_process = [file for file in os.listdir(batch_directory) if file.endswith(".safetensors") or file.endswith(".ckpt")]
+        else:
+            for batch_file in os.listdir(batch_directory):
+                add_flag = True
+                for onnx_file in onnx_files:
+                    if batch_file.split('.')[0] == onnx_file.split('.')[0]:
+                        add_flag = False
+                if add_flag and (batch_file.endswith(".safetensors") or batch_file.endswith(".ckpt")):
+                    onnx_files_to_process.append(batch_file)
+        print(f"Files to process:\n{onnx_files_to_process}\n")
+        # Exit if no files to process
+        if not onnx_files_to_process:
+            print(f'No files to convert...\nPlease uncheck the "Run Batch" checkbox or use a folder containing models.')
+            return f'No files to convert...\nPlease uncheck the "Run Batch" checkbox or use a folder containing models.', ''
+        # Process files
+        for i, file in enumerate(onnx_files_to_process):
+            print(f"Converting model file: {file}")  # Debug line
+            modelname = os.path.splitext(file)[0] + ".onnx"
+            onnx_filename = os.path.join(unet_onnx_path, modelname)
+            print(f"Target ONNX filename: {onnx_filename}\n")  # Debug line
+            
+            export_onnx.export_current_unet_to_onnx(onnx_filename, opset)
+            
+        # Ending message
+        print(f'Batch conversion completed for files in {batch_directory}')
+        return f'Batch conversion completed for files in {batch_directory}', ''
+    # Single mode
+    else:
+        print(f"--Single Model mode--")
+        if not filename:
+            modelname = shared.sd_model.sd_checkpoint_info.model_name + ".onnx"
+            filename = os.path.join(unet_onnx_path, modelname)
+        else:
+            modelname = os.path.splitext(filename)[0] + ".onnx"
+            filename = os.path.join(unet_onnx_path, modelname)
+        print(f"Target ONNX filename: {filename}\n")  # Debug line
+        
+        export_onnx.export_current_unet_to_onnx(filename, opset)
+
+        # Ending message
+        print(f'Done! Model saved as {filename}')
+        return f'Done! Model saved as {filename}', ''
+
+
+def convert_onnx_to_trt(filename, onnx_filename, add_shape_to_filename, batch_run, batch_directory, *args):
+    assert not cmd_opts.disable_extension_access, "Won't run the command to create TensorRT file because extension access is disabled (use --enable-insecure-extension-access)"
+    print(f'Starting Conversion to .trt')
+    
+    # Check if 'Unet-trt' directory exists and create it if not
+    unet_onnx_path = os.path.join(paths_internal.models_path, "Unet-trt")
+    os.makedirs(unet_onnx_path, exist_ok=True)
+    
+    # Use default folder if batch_directory is empty
+    if not batch_directory:
+        batch_directory = os.path.join(paths_internal.models_path, "Unet-onnx")
+    # Batch mode
+    if batch_run:
+        print(f"--Batch Models mode--")
+        
+        trt_files = os.listdir(os.path.join(paths_internal.models_path, "Unet-trt"))
+        trt_files_to_process = list()
+        # Take all files if destination folder is empty
+        if not trt_files:
+            print(f"Unet-trt is empty, adding all .onnx files from {batch_directory}\n")  # Debug line
+            trt_files_to_process = [file for file in os.listdir(batch_directory) if file.endswith(".onnx")]
+        else:
+            for batch_file in os.listdir(batch_directory):
+                add_flag = True
+                for trt_file in trt_files:
+                    size = len(trt_file.split('_'))
+                    trt_file_shape = ""
+                    for tokens in trt_file.split('_'):
+                        if size > 1:
+                            trt_file_shape += tokens
+                            if size > 2:
+                                trt_file_shape += '_'
+                        size -= 1
+                    if batch_file.split('.')[0] == trt_file.split('.')[0] or batch_file.split('.')[0] == trt_file_shape:
+                        add_flag = False
+                if add_flag and batch_file.endswith(".onnx"):
+                    trt_files_to_process.append(batch_file)
+        print(f"Files to process:\n{trt_files_to_process}\n")
+        # Exit if no files to process
+        if not trt_files_to_process:
+            print(f'No files to convert...\nPlease uncheck the "Run Batch" checkbox or use a folder containing models.')
+            return f'No files to convert...\nPlease uncheck the "Run Batch" checkbox or use a folder containing models.', ''
+        # Process files
+        for file in trt_files_to_process:
+            onnx_file = os.path.join(batch_directory, file)
+            print(f"Converting ONNX file: {onnx_file}")  # Debug line
+            modelname = os.path.splitext(file)[0] + ".trt"
+            filename = os.path.join(paths_internal.models_path, "Unet-trt", modelname)
+
+            trt_filename = get_trt_filename(filename, onnx_file, batch_run, add_shape_to_filename, *args)
+            print(f"Target TRT filename: {trt_filename}\n")  # Debug line
+            command = export_trt.get_trt_command(trt_filename, onnx_file, *args)
+            
+            launch.run(command, live=True)
+            
+        # Ending message
+        print(f'Batch conversion completed for files in {batch_directory}')
+        return f'Batch conversion completed for files in {batch_directory}', ''
+    # Single mode
+    else:
+        print(f"--Single Model mode--")
+        trt_filename = get_trt_filename(filename, onnx_filename, add_shape_to_filename, *args)
+        print(f"Target TRT filename: {trt_filename}\n")  # Debug line
+        command = export_trt.get_trt_command(trt_filename, onnx_filename, *args)
+        
+        launch.run(command, live=True)
+
+        # Ending message
+        print(f'Done! Model saved as {trt_filename}')
+        return f'Done! Model saved as {trt_filename}', ''
+
+
+def get_trt_filename(filename, onnx_filename, batch_run=False, add_shape_to_filename=False, *args):
+    modelname = os.path.splitext(os.path.basename(onnx_filename))[0];
+    #print("Shape args: ", args) # args:  (1, 1, 75, 750, 512, 768, 512, 960, True, '')
+    #({0}min_bs, {1}max_bs, {2}min_token_count, {3}max_token_count, {4}min_width, {5}max_width, {6}min_height, {7}max_height, {8}use_fp16, {9}trt_extra_args)
+    if(add_shape_to_filename):
+        if len(args) == 9:
+            modelname += f'_{args[0]}x{args[4]}x{args[6]}' + ".trt"
+        if len(args) == 10:
+            modelname += f'_{args[1]}x{args[5]}x{args[7]}' + ".trt"
+    else:
+        modelname += ".trt"
+    if batch_run:
+        return os.path.join(paths_internal.models_path, "Unet-trt", modelname)
     if filename:
         return filename
-
-    modelname = os.path.splitext(os.path.basename(onnx_filename))[0] + ".trt"
     return os.path.join(paths_internal.models_path, "Unet-trt", modelname)
 
 
-def get_trt_command(filename, onnx_filename, *args):
-    filename = get_trt_filename(filename, onnx_filename)
+def get_trt_command(filename, onnx_filename, add_shape_to_filename, *args):
+    filename = get_trt_filename(filename, onnx_filename, False, add_shape_to_filename, *args)
+    
     command = export_trt.get_trt_command(filename, onnx_filename, *args)
 
     env_command = f"""
@@ -59,16 +196,41 @@ def get_trt_command(filename, onnx_filename, *args):
 </p>
 """
 
-
-def convert_onnx_to_trt(filename, onnx_filename, *args):
-    assert not cmd_opts.disable_extension_access, "won't run the command to create TensorRT file because extension access is dsabled (use --enable-insecure-extension-access)"
-
-    filename = get_trt_filename(filename, onnx_filename)
-    command = export_trt.get_trt_command(filename, onnx_filename, *args)
-
-    launch.run(command, live=True)
-
-    return f'Saved as {filename}', ''
+batch_sizes = {
+     1: 92160, 
+     2: 129024,
+     3: 159744,
+     4: 184320,
+     5: 184320,
+     6: 221184,
+     7: 229376,
+     8: 229376,
+     9: 276480,
+    10: 286720,
+    11: 281600
+}
+
+async def calculate_and_check_constraints(max_width, max_height, max_batch_size):
+    B = max_batch_size * 2
+    unknown = 4
+    H = max_height / 8
+    W = max_width / 8
+
+    calculated_value = int(B * unknown * H * W)
+    
+    color = "#00FF00"; #green 
+    batch_size = batch_sizes[max_batch_size] if max_batch_size in batch_sizes else batch_sizes[11]
+    if(calculated_value > batch_size):
+        color="#FF0000" #red
+    value = f"{calculated_value} / {batch_size}"
+    
+    return gr.Label.update(value=value, color=color), ""
+
+
+def on_slider_change(max_width, max_height, max_bs):
+    loop = asyncio.get_event_loop()
+    calculated_value, _ = loop.run_until_complete(calculate_and_check_constraints(max_width, max_height, max_bs))
+    return calculated_value, ""
 
 
 def on_ui_tabs():
@@ -82,11 +244,18 @@ def on_ui_tabs():
                         onnx_filename = gr.Textbox(label='Filename', value="", elem_id="onnx_filename", info="Leave empty to use the same name as model and put results into models/Unet-onnx directory")
                         onnx_opset = gr.Number(label='ONNX opset version', precision=0, value=17, info="Leave this alone unless you know what you are doing")
 
+                        batch_run_onnx = gr.Checkbox(label='Run Batch', value=False)
+                        batch_directory_onnx = gr.Textbox(label='Directory', value="", info="Input directory containing models. Leave empty to use the default 'models/Stable-diffusion' folder as source.")
+
                         button_export_unet = gr.Button(value="Convert Unet to ONNX", variant='primary', elem_id="onnx_export_unet")
 
                     with gr.Tab(label="Convert ONNX to TensorRT"):
                         trt_source_filename = gr.Textbox(label='Onnx model filename', value="", elem_id="trt_source_filename")
                         trt_filename = gr.Textbox(label='Output filename', value="", elem_id="trt_filename", info="Leave empty to use the same name as onnx and put results into models/Unet-trt directory")
+                        add_shape_to_filename = gr.Checkbox(label='Add Shape to end of filename', value=False)
+                        
+                        with gr.Column(elem_id="trt_calculated_value"):
+                            calculated_value_label = gr.Label(elem_id="calculated_value", value="32768 / 92160", label="Current Value / Limit NOTE: move sliders slowly to make sure they update properly", show_label=True, color="#00FF00")
 
                         with gr.Column(elem_id="trt_width"):
                             min_width = gr.Slider(minimum=64, maximum=2048, step=64, label="Minimum width", value=512, elem_id="trt_min_width")
@@ -103,36 +272,55 @@ def on_ui_tabs():
                         with gr.Column(elem_id="trt_token_count"):
                             min_token_count = gr.Slider(minimum=75, maximum=750, step=75, label="Minimum prompt token count", value=75, elem_id="trt_min_token_count")
                             max_token_count = gr.Slider(minimum=75, maximum=750, step=75, label="Maximum prompt token count", value=75, elem_id="trt_max_token_count")
-
+                                                
                         trt_extra_args = gr.Textbox(label='Extra arguments', value="", elem_id="trt_extra_args", info="Extra arguments for trtexec command in plain text form")
 
                         with FormRow(elem_classes="checkboxes-row", variant="compact"):
                             use_fp16 = gr.Checkbox(label='Use half floats', value=True, elem_id="trt_fp16")
 
+                        batch_run_trt = gr.Checkbox(label='Run Batch', value=False)
+                        batch_directory_trt = gr.Textbox(label='Directory', value="", info="Input directory containing models. Leave empty to use the default 'models/Unet-onnx' folder as source.")
+
                         button_export_trt = gr.Button(value="Convert ONNX to TensorRT", variant='primary', elem_id="trt_convert_from_onnx")
                         button_show_trt_command = gr.Button(value="Show command for conversion", variant='secondary', elem_id="trt_convert_from_onnx")
 
             with gr.Column(variant='panel'):
                 trt_result = gr.Label(elem_id="trt_result", value="", show_label=False)
                 trt_info = gr.HTML(elem_id="trt_info", value="")
-
+                calculated_value_label
+        max_width.change(
+        on_slider_change,
+            inputs=[max_width, max_height, max_bs],
+            outputs=[calculated_value_label, trt_info],
+        )
+        max_height.change(
+        on_slider_change,
+            inputs=[max_width, max_height, max_bs],
+            outputs=[calculated_value_label, trt_info],
+        )
+        max_bs.change(
+        on_slider_change,
+            inputs=[max_width, max_height, max_bs],
+            outputs=[calculated_value_label, trt_info],
+        )
+        
         button_export_unet.click(
             wrap_gradio_gpu_call(export_unet_to_onnx, extra_outputs=["Conversion failed"]),
-            inputs=[onnx_filename, onnx_opset],
+            inputs=[onnx_filename, onnx_opset, batch_run_onnx, batch_directory_onnx],
             outputs=[trt_result, trt_info],
         )
 
         button_export_trt.click(
             wrap_gradio_gpu_call(convert_onnx_to_trt, extra_outputs=[""]),
-            inputs=[trt_filename, trt_source_filename, min_bs, max_bs, min_token_count, max_token_count, min_width, max_width, min_height, max_height, use_fp16, trt_extra_args],
+            inputs=[trt_filename, trt_source_filename, add_shape_to_filename, batch_run_trt, batch_directory_trt, min_bs, max_bs, min_token_count, max_token_count, min_width, max_width, min_height, max_height, use_fp16, trt_extra_args],
             outputs=[trt_result, trt_info],
         )
 
         button_show_trt_command.click(
             wrap_gradio_gpu_call(get_trt_command, extra_outputs=[""]),
-            inputs=[trt_filename, trt_source_filename, min_bs, max_bs, min_token_count, max_token_count, min_width, max_width, min_height, max_height, use_fp16, trt_extra_args],
+            inputs=[trt_filename, trt_source_filename, add_shape_to_filename, min_bs, max_bs, min_token_count, max_token_count, min_width, max_width, min_height, max_height, use_fp16, trt_extra_args],
             outputs=[trt_result, trt_info],
         )
 
     return [(trt_interface, "TensorRT", "tensorrt")]
-
+    
\ No newline at end of file