refactor(distorch): Implement IS_CHANGED for robust model reloading

This commit refactors the model loading logic to properly integrate with ComfyUI's caching system.

- Implemented the `IS_CHANGED` class method, which creates a hash of the DisTorch-specific settings (e.g., `compute_device`, `virtual_vram_gb`).
- This allows ComfyUI to automatically detect when settings have changed and trigger a model reload, invalidating the cache correctly.
- Removed the previous manual and less reliable logic for unloading and reloading the model from within the `override` function.
- Set the default log level to "Engineering" to provide more detailed output during development.

Author: pollockjj

__init__.py

@@ -9,7 +9,7 @@
 
 # --- DisTorch V2 Logging Configuration ---
 # Set to "E" for Engineering (DEBUG) or "P" for Production (INFO)
-LOG_LEVEL = "P"
+LOG_LEVEL = "E"
 
 # Configure logger
 log_level = logging.DEBUG if LOG_LEVEL == "E" else logging.INFO

distorch_2.py

@@ -9,6 +9,7 @@
 import logging
 import hashlib
 import copy
+import inspect
 from collections import defaultdict
 import comfy.model_management as mm
 import comfy.model_patcher
@@ -441,6 +442,13 @@ def INPUT_TYPES(s):
         FUNCTION = "override"
         TITLE = f"{cls.TITLE if hasattr(cls, 'TITLE') else cls.__name__} (DisTorch2)"
 
+        @classmethod
+        def IS_CHANGED(s, *args, compute_device=None, virtual_vram_gb=4.0, 
+                       donor_device="cpu", expert_mode_allocations="", **kwargs):
+            # Create a hash of our specific settings
+            settings_str = f"{compute_device}{virtual_vram_gb}{donor_device}{expert_mode_allocations}"
+            return hashlib.sha256(settings_str.encode()).hexdigest()
+
         def override(self, *args, compute_device=None, virtual_vram_gb=4.0, 
                      donor_device="cpu", expert_mode_allocations="", **kwargs):
             from . import set_current_device
@@ -449,57 +457,53 @@ def override(self, *args, compute_device=None, virtual_vram_gb=4.0,
 
             # Register our patched ModelPatcher
             register_patched_safetensor_modelpatcher()
-
-            # Build allocation string - EXACTLY like GGUF
-            vram_string = ""
-            if virtual_vram_gb > 0:
-                vram_string = f"{compute_device};{virtual_vram_gb};{donor_device}"
-
-            full_allocation = f"{expert_mode_allocations}#{vram_string}" if expert_mode_allocations or vram_string else ""
 
-            # --- Force Model Reload on Setting Change ---
-            # Create a hash of the DisTorch settings
-            settings_str = f"{compute_device}{virtual_vram_gb}{donor_device}{expert_mode_allocations}"
-            settings_hash = hashlib.sha256(settings_str.encode()).hexdigest()[:8]
-
-            # Temporarily load the model to get its hash, without applying our patch yet
+            # Call original function
             fn = getattr(super(), cls.FUNCTION)
-            temp_out = fn(*args, **kwargs)
 
+            # --- Check if we need to unload the model due to settings change ---
+            # This logic is a bit redundant with IS_CHANGED, but provides clear logging
+            settings_str = f"{compute_device}{virtual_vram_gb}{donor_device}{expert_mode_allocations}"
+            settings_hash = hashlib.sha256(settings_str.encode()).hexdigest()
+            
+            # Temporarily load to get hash without applying our patch
+            temp_out = fn(*args, **kwargs)
             model_to_check = None
             if hasattr(temp_out[0], 'model'):
                 model_to_check = temp_out[0]
             elif hasattr(temp_out[0], 'patcher') and hasattr(temp_out[0].patcher, 'model'):
                 model_to_check = temp_out[0].patcher
-            
+
             if model_to_check:
                 model_hash = create_safetensor_model_hash(model_to_check, "override_check")
-                
                 last_settings_hash = safetensor_settings_store.get(model_hash)
 
                 if last_settings_hash != settings_hash:
-                    logging.info(f"[MultiGPU_DisTorch2] Settings changed for model {model_hash[:8]}. Forcing reload.")
-                    mm.unload_model(model_to_check)
-                    # Update the settings store *before* reloading
-                    safetensor_settings_store[model_hash] = settings_hash
-                    # Call the loader again now that the model is unloaded
-                    out = fn(*args, **kwargs)
+                    logging.info(f"[MultiGPU_DisTorch2] Settings changed for model {model_hash[:8]}. Previous settings hash: {last_settings_hash}, New settings hash: {settings_hash}. Forcing reload.")
+                    # The IS_CHANGED mechanism should handle the reload, this is for logging.
                 else:
-                    out = temp_out # Use the already loaded model
-            else:
-                out = temp_out # Should not happen, but as a fallback
+                    logging.info(f"[MultiGPU_DisTorch2] Settings unchanged for model {model_hash[:8]}. Using cached model.")
+
+            out = fn(*args, **kwargs)
 
+            # Build allocation string - EXACTLY like GGUF
+            vram_string = ""
+            if virtual_vram_gb > 0:
+                vram_string = f"{compute_device};{virtual_vram_gb};{donor_device}"
+
+            full_allocation = f"{expert_mode_allocations}#{vram_string}" if expert_mode_allocations or vram_string else ""
+            
             logging.info(f"[MULTIGPU_DISTORCHV2] Full allocation string: {full_allocation}")
 
             # Store allocation for the model - EXACTLY like GGUF
             if hasattr(out[0], 'model'):
                 model_hash = create_safetensor_model_hash(out[0], "override")
                 safetensor_allocation_store[model_hash] = full_allocation
-                safetensor_settings_store[model_hash] = settings_hash # Ensure it's set
+                safetensor_settings_store[model_hash] = settings_hash
             elif hasattr(out[0], 'patcher') and hasattr(out[0].patcher, 'model'):
                 model_hash = create_safetensor_model_hash(out[0].patcher, "override") 
                 safetensor_allocation_store[model_hash] = full_allocation
-                safetensor_settings_store[model_hash] = settings_hash # Ensure it's set
+                safetensor_settings_store[model_hash] = settings_hash
 
             return out