diff --git a/lumina_minimal_inference.py b/lumina_minimal_inference.py
index 4f915179..31362c00 100644
--- a/lumina_minimal_inference.py
+++ b/lumina_minimal_inference.py
@@ -257,6 +257,11 @@ def setup_parser() -> argparse.ArgumentParser:
         help="LoRA weights, each argument is a `path;multiplier` (semi-colon separated)",
     )
     parser.add_argument("--merge_lora_weights", action="store_true", help="Merge LoRA weights to model")
+    parser.add_argument(
+        "--interactive",
+        action="store_true",
+        help="Enable interactive mode for generating multiple images / 対話モードで複数の画像を生成する",
+    )
     return parser
 
 
@@ -294,9 +299,7 @@ if __name__ == "__main__":
             multiplier = 1.0
 
         weights_sd = load_file(weights_file)
-        lora_model, _ = lora_lumina.create_network_from_weights(
-            multiplier, None, ae, [gemma2], model, weights_sd, True
-        )
+        lora_model, _ = lora_lumina.create_network_from_weights(multiplier, None, ae, [gemma2], model, weights_sd, True)
 
         if args.merge_lora_weights:
             lora_model.merge_to([gemma2], model, weights_sd)
@@ -304,25 +307,109 @@ if __name__ == "__main__":
             lora_model.apply_to([gemma2], model)
             info = lora_model.load_state_dict(weights_sd, strict=True)
             logger.info(f"Loaded LoRA weights from {weights_file}: {info}")
+            lora_model.to(device)
+            lora_model.set_multiplier(multiplier)
             lora_model.eval()
 
         lora_models.append(lora_model)
 
-    generate_image(
-        model,
-        gemma2,
-        ae,
-        args.prompt,
-        args.system_prompt,
-        args.seed,
-        args.image_width,
-        args.image_height,
-        args.steps,
-        args.guidance_scale,
-        args.negative_prompt,
-        args,
-        args.cfg_trunc_ratio,
-        args.renorm_cfg,
-    )
+    if not args.interactive:
+        generate_image(
+            model,
+            gemma2,
+            ae,
+            args.prompt,
+            args.system_prompt,
+            args.seed,
+            args.image_width,
+            args.image_height,
+            args.steps,
+            args.guidance_scale,
+            args.negative_prompt,
+            args,
+            args.cfg_trunc_ratio,
+            args.renorm_cfg,
+        )
+    else:
+        # Interactive mode loop
+        image_width = args.image_width
+        image_height = args.image_height
+        steps = args.steps
+        guidance_scale = args.guidance_scale
+        cfg_trunc_ratio = args.cfg_trunc_ratio
+        renorm_cfg = args.renorm_cfg
+
+        print("Entering interactive mode.")
+        while True:
+            print(
+                "\nEnter prompt (or 'exit'). Options: --w <int> --h <int> --s <int> --d <int> --g <float> --n <str> --ctr <float> --rcfg <float> --m <m1,m2...>"
+            )
+            user_input = input()
+            if user_input.lower() == "exit":
+                break
+            if not user_input:
+                continue
+
+            # Parse options
+            options = user_input.split("--")
+            prompt = options[0].strip()
+
+            # Set defaults for each generation
+            seed = None  # New random seed each time unless specified
+            negative_prompt = args.negative_prompt  # Reset to default
+
+            for opt in options[1:]:
+                try:
+                    opt = opt.strip()
+                    if not opt:
+                        continue
+
+                    key, value = (opt.split(None, 1) + [""])[:2]
+
+                    if key == "w":
+                        image_width = int(value)
+                    elif key == "h":
+                        image_height = int(value)
+                    elif key == "s":
+                        steps = int(value)
+                    elif key == "d":
+                        seed = int(value)
+                    elif key == "g":
+                        guidance_scale = float(value)
+                    elif key == "n":
+                        negative_prompt = value if value != "-" else ""
+                    elif key == "ctr":
+                        cfg_trunc_ratio = float(value)
+                    elif key == "rcfg":
+                        renorm_cfg = float(value)
+                    elif key == "m":
+                        multipliers = value.split(",")
+                        if len(multipliers) != len(lora_models):
+                            logger.error(f"Invalid number of multipliers, expected {len(lora_models)}")
+                            continue
+                        for i, lora_model in enumerate(lora_models):
+                            lora_model.set_multiplier(float(multipliers[i].strip()))
+                    else:
+                        logger.warning(f"Unknown option: --{key}")
+
+                except (ValueError, IndexError) as e:
+                    logger.error(f"Invalid value for option --{key}: '{value}'. Error: {e}")
+
+            generate_image(
+                model,
+                gemma2,
+                ae,
+                prompt,
+                args.system_prompt,
+                seed,
+                image_width,
+                image_height,
+                steps,
+                guidance_scale,
+                negative_prompt,
+                args,
+                cfg_trunc_ratio,
+                renorm_cfg,
+            )
 
     logger.info("Done.")