From f77668ff53ca7de35e08c54f4fdfa4e52a98e72e Mon Sep 17 00:00:00 2001
From: aimfordeb <133010426+aimfordeb@users.noreply.github.com>
Date: Fri, 23 Jan 2026 22:55:10 +0200
Subject: [PATCH] Update funpack.py

---
 funpack.py | 39 ++++++++++++++++++++-------------------
 1 file changed, 20 insertions(+), 19 deletions(-)

diff --git a/funpack.py b/funpack.py
index bcd7b8be..30280355 100644
--- a/funpack.py
+++ b/funpack.py
@@ -774,29 +774,29 @@ class FunPackStoryWriter:
                         {"role": "system", "content": "Analyze the given array of sequences and the list of LoRAs and decide which LoRAs out of user's list are recommended to use for each sequence, based on the content of those sequences and the names of LoRAs. Output only list of recommended LoRAs in the format:\nSequence 1: lora name, lora name\nSequence 2: lora name, lora name\nSequence N: lora name, lora name"},
                         {"role": "user", "content": lora_list}
                     ]
-                for seq_idx, seq_output in enumerate(outputs):
-                    seq_output = outputs[seq_idx]
-                    lora_messages.append({"role": "user", "content": f"This is sequence ID {seq_idx + 1}: {seq_output}"})
+                    for seq_idx, seq_output in enumerate(outputs):
+                        seq_output = outputs[seq_idx]
+                        lora_messages.append({"role": "user", "content": f"This is sequence ID {seq_idx + 1}: {seq_output}"})
 
                 # Generate recommended LoRAs
-                    llm_tokens = llm_tokenizer.apply_chat_template(
-                        lora_messages, add_generation_prompt=True, return_tensors="pt", tokenize=True
-                    ).to(llm_model_device)
+                llm_tokens = llm_tokenizer.apply_chat_template(
+                      lora_messages, add_generation_prompt=True, return_tensors="pt", tokenize=True
+                ).to(llm_model_device)
 
-                    print("[FunPackStoryWriter] Generating LoRA recommendations...")
-                    with torch.no_grad():
-                        generated_ids = llm_model.generate(
-                            input_ids=llm_tokens,
-                            do_sample=True,
-                            top_p=top_p, top_k=top_k, temperature=temperature,
-                            max_new_tokens=max_new_tokens,
-                            repetition_penalty=repetition_penalty,
-                            pad_token_id=llm_tokenizer.pad_token_id,
-                            eos_token_id=llm_tokenizer.eos_token_id,
-                        )
+                print("[FunPackStoryWriter] Generating LoRA recommendations...")
+                with torch.no_grad():
+                    generated_ids = llm_model.generate(
+                        input_ids=llm_tokens,
+                        do_sample=True,
+                        top_p=top_p, top_k=top_k, temperature=temperature,
+                        max_new_tokens=max_new_tokens,
+                        repetition_penalty=repetition_penalty,
+                        pad_token_id=llm_tokenizer.pad_token_id,
+                        eos_token_id=llm_tokenizer.eos_token_id,
+                    )
 
-                    recommended_loras = llm_tokenizer.decode(generated_ids[0][llm_tokens.shape[1]:], skip_special_tokens=True).strip()
-                    print(f"[FunPackStoryWriter] LLM recommended to use this list of LoRAs: {recommended_loras}")
+                recommended_loras = llm_tokenizer.decode(generated_ids[0][llm_tokens.shape[1]:], skip_special_tokens=True).strip()
+                print(f"[FunPackStoryWriter] LLM recommended to use this list of LoRAs: {recommended_loras}")
             
             return tuple(outputs)
 
@@ -1444,5 +1444,6 @@ NODE_DISPLAY_NAME_MAPPINGS = {
 
 
 
+