fix: add memory precheck before VAE decode to prevent crash

comfy/model_management.py

@@ -1093,6 +1093,69 @@ def sync_stream(device, stream):
         return
     current_stream(device).wait_stream(stream)
 
+def use_tiled_vae_decode(memory_needed, device=None):
+    try:
+        if device is None:
+            device = get_torch_device()
+
+        # If running everything on CPU, no GPU memory check needed
+        if cpu_state == CPUState.CPU or args.cpu_vae:
+            return False
+
+        inference_memory = minimum_inference_memory()
+        memory_required = max(inference_memory, memory_needed + extra_reserved_memory())
+        
+        gpu_free = get_free_memory(device)
+        cpu_free = psutil.virtual_memory().available
+        
+        # Check if GPU have enough space for full decode (with reserves)?
+        if gpu_free >= memory_required:
+            return False
+        
+        # With --gpu-only, models can't offload to CPU (offload device = GPU)
+        if args.gpu_only:
+            return True
+        
+        # Calculate memory_to_free 
+        memory_to_free = memory_required - gpu_free
+        
+        # Calculate how much we can offload from currently loaded models - only count models whose offload_device is CPU
+        # With --highvram, UNet has offload_device=GPU so it CAN'T be offloaded.
+        loaded_model_memory  = 0
+        cpu_offloadable_memory  = 0
+        
+        for loaded_model in current_loaded_models:
+            if loaded_model.device == device:
+                model_size = loaded_model.model_loaded_memory()
+                loaded_model_memory  += model_size
+                if hasattr(loaded_model.model, 'offload_device'):
+                    offload_dev = loaded_model.model.offload_device
+                    if is_device_cpu(offload_dev):
+                        cpu_offloadable_memory  += model_size
+                else:
+                    cpu_offloadable_memory  += model_size
+        
+        # Check is there enough to offload (to CPU)?
+        if cpu_offloadable_memory  < memory_to_free:
+            return True  # Can't offload enough, must tile
+        
+        # Check if CPU can receive the offload (which prevents 0xC0000005 crash)
+        # Smart Memory ON (default) - partial offload: only memory_to_free bytes move to CPU
+        # Smart Memory OFF (--disable-smart-memory) - full offload: ALL models get fully unloaded
+        if DISABLE_SMART_MEMORY:
+            if cpu_free < loaded_model_memory:
+                return True
+            else:
+                return False
+        else:
+            # With smart memory, only partial offload (memory_to_free bytes) moves to CPU
+            if cpu_free < memory_to_free:
+                return True
+        return False
+        
+    except Exception as e:
+        return True
+
 def cast_to(weight, dtype=None, device=None, non_blocking=False, copy=False, stream=None):
     if device is None or weight.device == device:
         if not copy:

comfy/sd.py

@@ -894,29 +894,51 @@ class VAE:
         do_tile = False
         if self.latent_dim == 2 and samples_in.ndim == 5:
             samples_in = samples_in[:, :, 0]
-        try:
-            memory_used = self.memory_used_decode(samples_in.shape, self.vae_dtype)
-            model_management.load_models_gpu([self.patcher], memory_required=memory_used, force_full_load=self.disable_offload)
-            free_memory = model_management.get_free_memory(self.device)
-            batch_number = int(free_memory / memory_used)
-            batch_number = max(1, batch_number)
-
-            for x in range(0, samples_in.shape[0], batch_number):
-                samples = samples_in[x:x+batch_number].to(self.vae_dtype).to(self.device)
-                out = self.process_output(self.first_stage_model.decode(samples, **vae_options).to(self.output_device).float())
-                if pixel_samples is None:
-                    pixel_samples = torch.empty((samples_in.shape[0],) + tuple(out.shape[1:]), device=self.output_device)
-                pixel_samples[x:x+batch_number] = out
-        except model_management.OOM_EXCEPTION:
-            logging.warning("Warning: Ran out of memory when regular VAE decoding, retrying with tiled VAE decoding.")
-            #NOTE: We don't know what tensors were allocated to stack variables at the time of the
-            #exception and the exception itself refs them all until we get out of this except block.
-            #So we just set a flag for tiler fallback so that tensor gc can happen once the
-            #exception is fully off the books.
+        
+        memory_used = self.memory_used_decode(samples_in.shape, self.vae_dtype)
+        
+        # Memory check: switch to tiled decode if GPU can't fit full decode
+        # and models can't offload to CPU, preventing 0xC0000005 crash
+        if model_management.use_tiled_vae_decode(memory_used, self.device):
+            logging.warning("[VAE DECODE] Insufficient memory for regular VAE decoding, switching to tiled VAE decoding.")
             do_tile = True
+        
+        if not do_tile:
+            try:
+                model_management.load_models_gpu([self.patcher], memory_required=memory_used, force_full_load=self.disable_offload)                
+                free_memory = model_management.get_free_memory(self.device)
+                batch_number = int(free_memory / memory_used)
+                batch_number = max(1, batch_number)
+
+                for x in range(0, samples_in.shape[0], batch_number):
+                    samples = samples_in[x:x+batch_number].to(self.vae_dtype).to(self.device)
+                    out = self.process_output(self.first_stage_model.decode(samples, **vae_options).to(self.output_device).float())
+                    if pixel_samples is None:
+                        pixel_samples = torch.empty((samples_in.shape[0],) + tuple(out.shape[1:]), device=self.output_device)
+                    pixel_samples[x:x+batch_number] = out
+                    
+            except model_management.OOM_EXCEPTION:
+                logging.warning("[VAE DECODE] OOM! Ran out of memory during regular VAE decoding, retrying with tiled VAE decoding.")
+                #NOTE: We don't know what tensors were allocated to stack variables at the time of the
+                #exception and the exception itself refs them all until we get out of this except block.
+                #So we just set a flag for tiler fallback so that tensor gc can happen once the
+                #exception is fully off the books.
 
         if do_tile:
             dims = samples_in.ndim - 2
+            if dims == 1:
+                tile_shape = (1, samples_in.shape[1], 128)  # 1D tile estimate
+            elif dims == 2:
+                tile_shape = (1, samples_in.shape[1], 64, 64)  # 2D tile: 64x64
+            else:
+                tile = 256 // self.spacial_compression_decode()
+                tile_shape = (1, samples_in.shape[1], 8, tile, tile)  # 3D tile estimate
+
+            # Calculate tile memory
+            tile_memory = self.memory_used_decode(tile_shape, self.vae_dtype)
+            
+            model_management.load_models_gpu([self.patcher], memory_required=tile_memory, force_full_load=self.disable_offload)
+ 
             if dims == 1 or self.extra_1d_channel is not None:
                 pixel_samples = self.decode_tiled_1d(samples_in)
             elif dims == 2: