diff --git a/drag_bench_evaluation/run_drag_diffusion.py b/drag_bench_evaluation/run_drag_diffusion.py
index 6d9f26b..a7a11d6 100644
--- a/drag_bench_evaluation/run_drag_diffusion.py
+++ b/drag_bench_evaluation/run_drag_diffusion.py
@@ -155,7 +155,7 @@ def run_drag(source_image,
 
     # feature shape: [1280,16,16], [1280,32,32], [640,64,64], [320,64,64]
     # update according to the given supervision
-    updated_init_code = drag_diffusion_update(model, init_code,
+    updated_init_code, opt_seq = drag_diffusion_update(model, init_code,
         None, t, handle_points, target_points, mask, args)
 
     # hijack the attention module
diff --git a/drag_ui.py b/drag_ui.py
index 3edd9fa..9b1a43e 100755
--- a/drag_ui.py
+++ b/drag_ui.py
@@ -61,6 +61,7 @@
             prompt = gr.Textbox(label="Prompt")
             lora_path = gr.Textbox(value="./lora_tmp", label="LoRA path")
             lora_status_bar = gr.Textbox(label="display LoRA training status")
+            results_path = gr.Textbox(value="./results", label="Folder for results")
 
         # algorithm specific parameters
         with gr.Tab("Drag Config"):
@@ -79,6 +80,11 @@
                 latent_lr = gr.Number(value=0.01, label="latent lr")
                 start_step = gr.Number(value=0, label="start_step", precision=0, visible=False)
                 start_layer = gr.Number(value=10, label="start_layer", precision=0, visible=False)
+                save_optimization_seq_rgb = gr.Checkbox(
+                    value=False, 
+                    label="Save Opt Seq",
+                    info="Each step of optimization latent is saved in png file. Will take more time."
+                )
 
         with gr.Tab("Base Model Config"):
             with gr.Row():
@@ -136,6 +142,7 @@
         with gr.Row():
             pos_prompt_gen = gr.Textbox(label="Positive Prompt")
             neg_prompt_gen = gr.Textbox(label="Negative Prompt")
+            results_path_gen = gr.Textbox(value="./results", label="Folder for results")
 
         with gr.Tab("Generation Config"):
             with gr.Row():
@@ -218,6 +225,11 @@
                 latent_lr_gen = gr.Number(value=0.01, label="latent lr")
                 start_step_gen = gr.Number(value=0, label="start_step", precision=0, visible=False)
                 start_layer_gen = gr.Number(value=10, label="start_layer", precision=0, visible=False)
+                save_optimization_seq_rgb_gen = gr.Checkbox(
+                    value=False, 
+                    label="Save Opt Seq",
+                    info="Each step of optimization latent is saved in png file. Will take more time."
+                )
 
     # event definition
     # event for dragging user-input real image
@@ -265,6 +277,8 @@
         lora_path,
         start_step,
         start_layer,
+        results_path,
+        save_optimization_seq_rgb,
         ],
         [output_image]
     )
@@ -343,6 +357,8 @@
         b2_gen,
         s1_gen,
         s2_gen,
+        results_path_gen,
+        save_optimization_seq_rgb_gen,
         ],
         [output_image_gen]
     )
diff --git a/utils/drag_utils.py b/utils/drag_utils.py
index ff4f9e4..e8f836f 100755
--- a/utils/drag_utils.py
+++ b/utils/drag_utils.py
@@ -101,6 +101,7 @@ def drag_diffusion_update(model,
     # prepare optimizable init_code and optimizer
     init_code.requires_grad_(True)
     optimizer = torch.optim.Adam([init_code], lr=args.lr)
+    opt_seq = [init_code.detach().clone()]
 
     # prepare for point tracking and background regularization
     handle_points_init = copy.deepcopy(handle_points)
@@ -157,8 +158,9 @@ def drag_diffusion_update(model,
         scaler.step(optimizer)
         scaler.update()
         optimizer.zero_grad()
+        opt_seq.append(init_code.detach().clone())
 
-    return init_code
+    return init_code, opt_seq
 
 def drag_diffusion_update_gen(model,
                               init_code,
@@ -218,6 +220,7 @@ def drag_diffusion_update_gen(model,
 
     # prepare amp scaler for mixed-precision training
     scaler = torch.cuda.amp.GradScaler()
+    opt_seq = [init_code.detach().clone()]
     for step_idx in range(args.n_pix_step):
         with torch.autocast(device_type='cuda', dtype=torch.float16):
             if args.guidance_scale > 1.:
@@ -281,6 +284,7 @@ def drag_diffusion_update_gen(model,
         scaler.step(optimizer)
         scaler.update()
         optimizer.zero_grad()
+        opt_seq.append(init_code.detach().clone())
 
-    return init_code
+    return init_code, opt_seq
 
diff --git a/utils/ui_utils.py b/utils/ui_utils.py
index 6f1f09a..bdbb187 100755
--- a/utils/ui_utils.py
+++ b/utils/ui_utils.py
@@ -185,7 +185,8 @@ def run_drag(source_image,
              lora_path,
              start_step,
              start_layer,
-             save_dir="./results"
+             save_dir="./results",
+             save_seq=True,
     ):
     # initialize model
     device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
@@ -288,7 +289,7 @@ def run_drag(source_image,
     text_embeddings = text_embeddings.float()
     model.unet = model.unet.float()
 
-    updated_init_code = drag_diffusion_update(
+    updated_init_code, opt_seq = drag_diffusion_update(
         model,
         init_code,
         text_embeddings,
@@ -345,6 +346,27 @@ def run_drag(source_image,
     save_prefix = datetime.datetime.now().strftime("%Y-%m-%d-%H%M-%S")
     save_image(save_result, os.path.join(save_dir, save_prefix + '.png'))
 
+    if save_seq:
+        os.mkdir(os.path.join(save_dir, save_prefix))
+        # save list of latents in pt file
+        torch.save(opt_seq, os.path.join(save_dir, save_prefix, 'opt_seq.pt'))
+        for i in range(0, len(opt_seq), 2):
+            # denoise latents and save
+            latents = torch.cat([opt_seq[i].half(), opt_seq[i+1].half() if i+1 < len(opt_seq) else opt_seq[i].half()], dim=0)
+            gen_image_seq = model(
+                args.prompt,
+                encoder_hidden_states=torch.cat([text_embeddings]*2, dim=0),
+                batch_size=2,
+                latents=latents,
+                guidance_scale=args.guidance_scale,
+                num_inference_steps=args.n_inference_step,
+                num_actual_inference_steps=args.n_actual_inference_step
+            )
+            gen_image_seq = F.interpolate(gen_image_seq, (full_h, full_w), mode='bilinear')
+            save_image(gen_image_seq[0].unsqueeze(dim=0), os.path.join(save_dir, save_prefix, f'iter_{i}.png'))
+            if i+1 < len(opt_seq):
+                save_image(gen_image_seq[1].unsqueeze(dim=0), os.path.join(save_dir, save_prefix, f'iter_{i+1}.png'))
+
     out_image = gen_image.cpu().permute(0, 2, 3, 1).numpy()[0]
     out_image = (out_image * 255).astype(np.uint8)
     return out_image
@@ -464,7 +486,9 @@ def run_drag_gen(
     b2,
     s1,
     s2,
-    save_dir="./results"):
+    save_dir="./results",
+    save_seq=True,
+    ):
     # initialize model
     device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
     model = DragPipeline.from_pretrained(model_path, torch_dtype=torch.float16)
@@ -572,7 +596,7 @@ def run_drag_gen(
     init_code = init_code.to(torch.float32)
     text_embeddings = text_embeddings.to(torch.float32)
     model.unet = model.unet.to(torch.float32)
-    updated_init_code = drag_diffusion_update_gen(model, init_code,
+    updated_init_code, opt_seq = drag_diffusion_update_gen(model, init_code,
         text_embeddings, t, handle_points, target_points, mask, args)
     updated_init_code = updated_init_code.to(torch.float16)
     text_embeddings = text_embeddings.to(torch.float16)
@@ -619,6 +643,27 @@ def run_drag_gen(
     save_prefix = datetime.datetime.now().strftime("%Y-%m-%d-%H%M-%S")
     save_image(save_result, os.path.join(save_dir, save_prefix + '.png'))
 
+    if save_seq:
+        os.mkdir(os.path.join(save_dir, save_prefix))
+        # save list of latents in pt file
+        torch.save(opt_seq, os.path.join(save_dir, save_prefix, 'opt_seq.pt'))
+        for i in range(0, len(opt_seq), 2):
+            # denoise latents and save
+            latents = torch.cat([opt_seq[i].half(), opt_seq[i+1].half() if i+1 < len(opt_seq) else opt_seq[i].half()], dim=0)
+            gen_image_seq = model(
+                args.prompt,
+                encoder_hidden_states=torch.cat([text_embeddings]*2, dim=0),
+                batch_size=2,
+                latents=latents,
+                guidance_scale=args.guidance_scale,
+                num_inference_steps=args.n_inference_step,
+                num_actual_inference_steps=args.n_actual_inference_step
+            )
+            gen_image_seq = F.interpolate(gen_image_seq, (full_h, full_w), mode='bilinear')
+            save_image(gen_image_seq[0].unsqueeze(dim=0), os.path.join(save_dir, save_prefix, f'iter_{i}.png'))
+            if i+1 < len(opt_seq):
+                save_image(gen_image_seq[1].unsqueeze(dim=0), os.path.join(save_dir, save_prefix, f'iter_{i+1}.png'))
+
     out_image = gen_image.cpu().permute(0, 2, 3, 1).numpy()[0]
     out_image = (out_image * 255).astype(np.uint8)
     return out_image