add extra GPU reservation buffer slider 0-10s and unique guest IP identification

pipeline_manager.py

@@ -60,7 +60,7 @@ def get_inference_duration(
     guidance_scale, guidance_scale_2, current_seed, scheduler_name, flow_shift, 
     frame_multiplier, quality, duration_seconds, safe_mode=False, lora_groups=None, 
     custom_lora_url="", custom_lora_scale=1.0, enable_prompt_relay=False,
-    relay_prompt_schedule="", noise_temperature=1.0, *args, **kwargs
+    relay_prompt_schedule="", noise_temperature=1.0, extra_gpu_buffer=0, *args, **kwargs
 ):
     width, height = resized_image.size
     # Non-linear 3D attention memory & sequence scaling for Wan 2.2 frame count
@@ -78,7 +78,13 @@ def get_inference_duration(
         gen_time = gen_time * 2.0
 
     overhead = 2.0 if num_frames <= 33 else (3.0 if num_frames <= 65 else 5.0)
-    total_time = overhead + gen_time
+
+    # Automatically add +3 seconds overhead if custom LoRA is requested
+    cached_l = str(kwargs.get("cached_lora") or "").strip()
+    if (custom_lora_url and str(custom_lora_url).strip()) or (cached_l and cached_l != "(None / Disable)"):
+        overhead += 3.0
+
+    total_time = overhead + gen_time + float(extra_gpu_buffer or 0)
     if safe_mode:
         total_time = total_time * 1.25
 
@@ -90,7 +96,7 @@ def run_inference(
     guidance_scale, guidance_scale_2, current_seed, scheduler_name, flow_shift, 
     frame_multiplier, quality, duration_seconds, safe_mode=False, lora_groups=None, 
     custom_lora_url="", custom_lora_scale=1.0, enable_prompt_relay=False,
-    relay_prompt_schedule="", noise_temperature=1.0, progress=gr.Progress(track_tqdm=True)
+    relay_prompt_schedule="", noise_temperature=1.0, extra_gpu_buffer=0, progress=gr.Progress(track_tqdm=True)
 ):
     scheduler_class = config.SCHEDULER_MAP.get(scheduler_name)
     if scheduler_class.__name__ != pipe.scheduler.config._class_name or flow_shift != pipe.scheduler.config.get("flow_shift", "shift"):
@@ -207,26 +213,46 @@ def generate_video(
     vip_rife_enhance_face=False,
     vip_password="",
     cached_lora="",
+    extra_gpu_buffer=0,
     request: gr.Request = None,
     progress=gr.Progress(track_tqdm=True)
 ):
     if input_image is None:
         raise gr.Error("Please upload an input image.")
 
-    hf_user = "Anonymous / Guests"
+    hf_user = "Guest"
+    user_ip = "Unknown"
     if request is not None:
         try:
-            if hasattr(request, "username") and request.username:
-                hf_user = request.username
-            elif hasattr(request, "headers") and request.headers:
-                hf_user = (
+            if hasattr(request, "headers") and request.headers:
+                user_ip = (
+                    request.headers.get("x-forwarded-for") or 
+                    request.headers.get("x-real-ip") or 
+                    request.headers.get("cf-connecting-ip") or 
+                    getattr(getattr(request, "client", None), "host", "Unknown")
+                )
+                if "," in user_ip:
+                    user_ip = user_ip.split(",")[0].strip()
+
+                hf_name = (
                     request.headers.get("x-hf-user-name") or 
                     request.headers.get("x-hf-user") or 
                     request.headers.get("x-username") or 
-                    "Anonymous / Guests"
+                    getattr(request, "username", None)
                 )
-        except Exception:
-            pass
+                if hf_name and str(hf_name).strip():
+                    hf_user = str(hf_name).strip()
+                elif user_ip and user_ip != "Unknown":
+                    hf_user = f"Guest ({user_ip})"
+                else:
+                    hf_user = "Guest (Anonymous)"
+            elif hasattr(request, "username") and request.username:
+                hf_user = request.username
+        except Exception as err:
+            print(f"User identification notice: {err}")
+
+    if hf_user == "Guest" and user_ip != "Unknown":
+        hf_user = f"Guest ({user_ip})"
 
     active_custom_lora = str(custom_lora_url or "").strip()
     if not active_custom_lora and cached_lora and str(cached_lora).strip() != "(None / Disable)":
@@ -259,7 +285,7 @@ def generate_video(
         guidance_scale, guidance_scale_2, current_seed, scheduler, flow_shift,
         frame_multiplier, quality, duration_seconds, safe_mode, None,
         active_custom_lora, custom_lora_scale, enable_prompt_relay,
-        relay_prompt_schedule, noise_temperature, progress
+        relay_prompt_schedule, noise_temperature, extra_gpu_buffer, progress, cached_lora=active_custom_lora
     )
 
     raw_frames_np, task_n, gpu_time = run_inference(
@@ -267,7 +293,7 @@ def generate_video(
         guidance_scale, guidance_scale_2, current_seed, scheduler, flow_shift,
         frame_multiplier, quality, duration_seconds, safe_mode, None,
         active_custom_lora, custom_lora_scale, enable_prompt_relay,
-        relay_prompt_schedule, noise_temperature, progress
+        relay_prompt_schedule, noise_temperature, extra_gpu_buffer, progress
     )
     
     print(f"GPU complete: {task_n}. Release GPU lock and now processing post-processing on CPU...")

ui.py

@@ -83,7 +83,8 @@ def create_ui():
             vip['vip_rife_upscale_checkbox'],
             vip['vip_rife_enhance_face_checkbox'],
             vip['vip_master_password_input'],
-            c1['cached_lora_dropdown']
+            c1['cached_lora_dropdown'],
+            c1['extra_gpu_buffer_slider']
         ]
         
         c1['generate_button'].click(

ui_modules/column_inputs.py

@@ -120,6 +120,14 @@ def render_column_inputs():
                 value=1.0,
                 info="Strength of the custom LoRA effect"
             )
+            extra_gpu_buffer_slider = gr.Slider(
+                label="⚡ Extra GPU Reservation Buffer (Seconds)",
+                minimum=0,
+                maximum=10,
+                step=1,
+                value=3,
+                info="Manually add 0-10 extra seconds to ZeroGPU time reservation for LoRA fusion & heavy processing"
+            )
             with gr.Row():
                 download_lora_btn = gr.Button("📥 Pre-Download LoRA to Cache (CPU)", variant="secondary")
             download_status_box = gr.HTML()
@@ -159,6 +167,7 @@ def render_column_inputs():
         "cached_lora_dropdown": cached_lora_dropdown,
         "custom_lora_url_input": custom_lora_url_input,
         "custom_lora_scale_input": custom_lora_scale_input,
+        "extra_gpu_buffer_slider": extra_gpu_buffer_slider,
         "safe_mode_checkbox": safe_mode_checkbox,
         "generate_button": generate_button
     }