diff --git a/README.md b/README.md
index ccc83e6e..c0d50a5a 100644
--- a/README.md
+++ b/README.md
@@ -39,6 +39,8 @@ The trained LoRA model can be used with ComfyUI.
 
 The inference script is also available. The script is `flux_minimal_inference.py`. See `--help` for options. 
 
+Aug 12: `--interactive` option is now working.
+
 ```
 python flux_minimal_inference.py --ckpt flux1-dev.sft --clip_l sd3/clip_l.safetensors --t5xxl sd3/t5xxl_fp16.safetensors --ae ae.sft --dtype bf16 --prompt "a cat holding a sign that says hello world" --out path/to/output/dir --seed 1 --flux_dtype fp8 --offload --lora lora-flux-name.safetensors;1.0
 ```
diff --git a/flux_minimal_inference.py b/flux_minimal_inference.py
index f3affca8..b09f6380 100644
--- a/flux_minimal_inference.py
+++ b/flux_minimal_inference.py
@@ -5,7 +5,7 @@ import datetime
 import math
 import os
 import random
-from typing import Callable, Optional, Tuple
+from typing import Callable, List, Optional, Tuple
 import einops
 import numpy as np
 
@@ -121,6 +121,9 @@ def generate_image(
     steps: Optional[int],
     guidance: float,
 ):
+    seed = seed if seed is not None else random.randint(0, 2**32 - 1)
+    logger.info(f"Seed: {seed}")
+
     # make first noise with packed shape
     # original: b,16,2*h//16,2*w//16, packed: b,h//16*w//16,16*2*2
     packed_latent_height, packed_latent_width = math.ceil(image_height / 16), math.ceil(image_width / 16)
@@ -183,9 +186,7 @@ def generate_image(
         steps = 4 if is_schnell else 50
 
     img_ids = img_ids.to(device)
-    x = do_sample(
-        accelerator, model, noise, img_ids, l_pooled, t5_out, txt_ids, steps, guidance_scale, is_schnell, device, flux_dtype
-    )
+    x = do_sample(accelerator, model, noise, img_ids, l_pooled, t5_out, txt_ids, steps, guidance, is_schnell, device, flux_dtype)
     if args.offload:
         model = model.cpu()
     # del model
@@ -255,6 +256,7 @@ if __name__ == "__main__":
         default=[],
         help="LoRA weights, only supports networks.lora_flux, 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("--width", type=int, default=target_width)
     parser.add_argument("--height", type=int, default=target_height)
     parser.add_argument("--interactive", action="store_true")
@@ -341,6 +343,7 @@ if __name__ == "__main__":
         ae = accelerator.prepare(ae)
 
     # LoRA
+    lora_models: List[lora_flux.LoRANetwork] = []
     for weights_file in args.lora_weights:
         if ";" in weights_file:
             weights_file, multiplier = weights_file.split(";")
@@ -351,7 +354,16 @@ if __name__ == "__main__":
         lora_model, weights_sd = lora_flux.create_network_from_weights(
             multiplier, weights_file, ae, [clip_l, t5xxl], model, None, True
         )
-        lora_model.merge_to([clip_l, t5xxl], model, weights_sd)
+        if args.merge_lora_weights:
+            lora_model.merge_to([clip_l, t5xxl], model, weights_sd)
+        else:
+            lora_model.apply_to([clip_l, t5xxl], model)
+            info = lora_model.load_state_dict(weights_sd, strict=True)
+            logger.info(f"Loaded LoRA weights from {weights_file}: {info}")
+            lora_model.eval()
+            lora_model.to(device)
+
+        lora_models.append(lora_model)
 
     if not args.interactive:
         generate_image(model, clip_l, t5xxl, ae, args.prompt, args.seed, args.width, args.height, args.steps, args.guidance)
@@ -363,7 +375,9 @@ if __name__ == "__main__":
         guidance = args.guidance
 
         while True:
-            print("Enter prompt (empty to exit). Options: --w <width> --h <height> --s <steps> --d <seed> --g <guidance>")
+            print(
+                "Enter prompt (empty to exit). Options: --w <width> --h <height> --s <steps> --d <seed> --g <guidance> --m <multipliers for LoRA>"
+            )
             prompt = input()
             if prompt == "":
                 break
@@ -384,6 +398,13 @@ if __name__ == "__main__":
                     seed = int(opt[1:].strip())
                 elif opt.startswith("g"):
                     guidance = float(opt[1:].strip())
+                elif opt.startswith("m"):
+                    mutipliers = opt[1:].strip().split(",")
+                    if len(mutipliers) != 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(mutipliers[i]))
 
             generate_image(model, clip_l, t5xxl, ae, prompt, seed, width, height, steps, guidance)