From 1b125fe2331e884297aa724ffc971e82e137afc2 Mon Sep 17 00:00:00 2001
From: Chris Malone <chrismrulz@hotmail.com>
Date: Mon, 24 Mar 2025 23:18:02 +1100
Subject: [PATCH] merge latest vram fixes

---
 gradio_server.py | 10 ++++++----
 1 file changed, 6 insertions(+), 4 deletions(-)

diff --git a/gradio_server.py b/gradio_server.py
index 2c71042..9671334 100644
--- a/gradio_server.py
+++ b/gradio_server.py
@@ -854,6 +854,8 @@ def load_models(i2v):
     kwargs = { "extraModelsToQuantize": None}
     if profile == 2 or profile == 4:
         kwargs["budgets"] = { "transformer" : 100 if preload  == 0 else preload, "text_encoder" : 100, "*" : 1000 }
+        if profile == 4:
+            kwargs["partialPinning"] = True
     elif profile == 3:
         kwargs["budgets"] = { "*" : "70%" }
     offloadobj = offload.profile(pipe, profile_no= profile, compile = compile, quantizeTransformer = quantizeTransformer, loras = "transformer", **kwargs)  
@@ -864,7 +866,7 @@ wan_model, offloadobj, transformer = load_models(use_image2video)
 if check_loras:
     setup_loras(use_image2video, transformer,  get_lora_dir(use_image2video), "", None)
     exit()
-
+del transformer
 gen_in_progress = False
 
 def get_auto_attention():
@@ -1080,10 +1082,10 @@ def generate_video(
     file_model_needed = model_needed(image2video)
     if(server_config.get("reload_model",1) == 2):
         if file_model_needed !=  model_filename or reload_needed:
+            del wan_model
             if offloadobj is not None:
                 offloadobj.release()
-                offloadobj = None
-            wan_model = None
+            del offloadobj
             gc.collect()
             print(f"Loading model {get_model_name(file_model_needed)}...")
             wan_model, offloadobj, trans = load_models(image2video)
@@ -1194,7 +1196,7 @@ def generate_video(
         if len(list_mult_choices_nums ) < len(loras_choices):
             list_mult_choices_nums  += [1.0] * ( len(loras_choices) - len(list_mult_choices_nums ) )
         loras_selected = [ lora for i, lora in enumerate(loras) if str(i) in loras_choices]
-        pinnedLora = False !=5 #False # # # 
+        pinnedLora = False #profile !=5 #False # # #
         offload.load_loras_into_model(trans, loras_selected, list_mult_choices_nums, activate_all_loras=True, preprocess_sd=preprocess_loras, pinnedLora=pinnedLora, split_linear_modules_map = None) 
         errors = trans._loras_errors
         if len(errors) > 0: