Significantly reduce thread abuse for faster model moving

This will move all major gradio calls into the main thread rather than random gradio threads.
This ensures that all torch.module.to() are performed in main thread to completely possible avoid GPU fragments.
In my test now model moving is 0.7 ~ 1.2 seconds faster, which means all 6GB/8GB VRAM users will get 0.7 ~ 1.2 seconds faster per image on SDXL.

modules/shared_state.py

@@ -2,6 +2,8 @@ import datetime
 import logging
 import threading
 import time
+import traceback
+import torch
 
 from modules import errors, shared, devices
 from typing import Optional
@@ -134,6 +136,7 @@ class State:
 
         devices.torch_gc()
 
+    @torch.inference_mode()
     def set_current_image(self):
         """if enough sampling steps have been made after the last call to this, sets self.current_image from self.current_latent, and modifies self.id_live_preview accordingly"""
         if not shared.parallel_processing_allowed:
@@ -142,6 +145,7 @@ class State:
         if self.sampling_step - self.current_image_sampling_step >= shared.opts.show_progress_every_n_steps and shared.opts.live_previews_enable and shared.opts.show_progress_every_n_steps != -1:
             self.do_set_current_image()
 
+    @torch.inference_mode()
     def do_set_current_image(self):
         if self.current_latent is None:
             return
@@ -156,11 +160,14 @@ class State:
 
             self.current_image_sampling_step = self.sampling_step
 
-        except Exception:
+        except Exception as e:
+            # traceback.print_exc()
+            # print(e)
             # when switching models during genration, VAE would be on CPU, so creating an image will fail.
             # we silently ignore this error
             errors.record_exception()
 
+    @torch.inference_mode()
     def assign_current_image(self, image):
         self.current_image = image
         self.id_live_preview += 1