diff --git a/comfy/model_base.py b/comfy/model_base.py
index 49efd700b6fb..7939bbc7b2e5 100644
--- a/comfy/model_base.py
+++ b/comfy/model_base.py
@@ -62,6 +62,7 @@
 from typing import TYPE_CHECKING
 if TYPE_CHECKING:
     from comfy.model_patcher import ModelPatcher
+from comfy.model_management import get_free_memory
 
 class ModelType(Enum):
     EPS = 1
@@ -305,8 +306,15 @@ def load_model_weights(self, sd, unet_prefix=""):
             if k.startswith(unet_prefix):
                 to_load[k[len(unet_prefix):]] = sd.pop(k)
 
+        free_cpu_memory = get_free_memory(torch.device("cpu"))
+        logging.debug(f"load model weights start, free cpu memory size {free_cpu_memory/(1024*1024*1024)} GB")
+        logging.debug(f"model destination device {next(self.diffusion_model.parameters()).device}")
         to_load = self.model_config.process_unet_state_dict(to_load)
-        m, u = self.diffusion_model.load_state_dict(to_load, strict=False)
+        logging.debug(f"load model {self.model_config} weights process end")
+        # replace tensor with mmap tensor by assign
+        m, u = self.diffusion_model.load_state_dict(to_load, strict=False, assign=True)
+        free_cpu_memory = get_free_memory(torch.device("cpu"))
+        logging.debug(f"load model {self.model_config} weights end, free cpu memory size {free_cpu_memory/(1024*1024*1024)} GB")
         if len(m) > 0:
             logging.warning("unet missing: {}".format(m))
 
diff --git a/comfy/model_management.py b/comfy/model_management.py
index e5de4a5b5238..fa8fb76ec1ff 100644
--- a/comfy/model_management.py
+++ b/comfy/model_management.py
@@ -27,6 +27,17 @@
 import gc
 import os
 
+from functools import lru_cache
+
+@lru_cache(maxsize=1)
+def get_mmap_mem_threshold_gb():
+    mmap_mem_threshold_gb = int(os.environ.get("MMAP_MEM_THRESHOLD_GB", "0"))
+    logging.debug(f"MMAP_MEM_THRESHOLD_GB: {mmap_mem_threshold_gb}")
+    return mmap_mem_threshold_gb
+
+def get_free_disk():
+    return psutil.disk_usage("/").free
+
 class VRAMState(Enum):
     DISABLED = 0    #No vram present: no need to move models to vram
     NO_VRAM = 1     #Very low vram: enable all the options to save vram
@@ -535,16 +546,50 @@ def should_reload_model(self, force_patch_weights=False):
         return False
 
     def model_unload(self, memory_to_free=None, unpatch_weights=True):
-        if memory_to_free is not None:
-            if memory_to_free < self.model.loaded_size():
-                freed = self.model.partially_unload(self.model.offload_device, memory_to_free)
-                if freed >= memory_to_free:
-                    return False
-        self.model.detach(unpatch_weights)
-        self.model_finalizer.detach()
-        self.model_finalizer = None
-        self.real_model = None
-        return True
+        model_loaded_size = self.model.loaded_size()
+        if memory_to_free is None:
+            # free the full model
+            memory_to_free = model_loaded_size
+
+        logging.debug(f"model_unload: {self.model.model.__class__.__name__}")
+        logging.debug(f"memory_to_free: {memory_to_free/(1024*1024*1024)} GB")
+        logging.debug(f"unpatch_weights: {unpatch_weights}")
+        logging.debug(f"loaded_size: {model_loaded_size/(1024*1024*1024)} GB")
+        logging.debug(f"offload_device: {self.model.offload_device}")
+
+        available_memory = get_free_memory(self.model.offload_device)
+        logging.debug(f"before unload, available_memory of offload device {self.model.offload_device}: {available_memory/(1024*1024*1024)} GB")
+
+        mmap_mem_threshold = get_mmap_mem_threshold_gb() * 1024 * 1024 * 1024  # this is reserved memory for other system usage
+        if min(memory_to_free, model_loaded_size) > available_memory - mmap_mem_threshold or memory_to_free < model_loaded_size:
+            partially_unload = True
+        else:
+            partially_unload = False
+
+        if partially_unload:
+            logging.debug("Do partially unload")
+            freed = self.model.partially_unload(self.model.offload_device, memory_to_free)
+            logging.debug(f"partially_unload freed vram: {freed/(1024*1024*1024)} GB")
+            if freed < memory_to_free:
+                logging.warning(f"Partially unload not enough memory, freed {freed/(1024*1024*1024)} GB, memory_to_free {memory_to_free/(1024*1024*1024)} GB")
+            if freed == model_loaded_size:
+                partially_unload = False
+        else:
+            logging.debug("Do full unload")
+            self.model.detach(unpatch_weights)
+            logging.debug("Do full unload done")
+            self.model_finalizer.detach()
+            self.model_finalizer = None
+            self.real_model = None
+
+        available_memory = get_free_memory(self.model.offload_device)
+        logging.debug(f"after unload, available_memory of offload device {self.model.offload_device}: {available_memory/(1024*1024*1024)} GB")
+
+        if partially_unload:
+            return False
+        else:
+            return True
+
 
     def model_use_more_vram(self, extra_memory, force_patch_weights=False):
         return self.model.partially_load(self.device, extra_memory, force_patch_weights=force_patch_weights)
@@ -593,6 +638,7 @@ def minimum_inference_memory():
     return (1024 * 1024 * 1024) * 0.8 + extra_reserved_memory()
 
 def free_memory(memory_required, device, keep_loaded=[]):
+    logging.debug("start to free mem")
     cleanup_models_gc()
     unloaded_model = []
     can_unload = []
@@ -630,6 +676,7 @@ def free_memory(memory_required, device, keep_loaded=[]):
     return unloaded_models
 
 def load_models_gpu(models, memory_required=0, force_patch_weights=False, minimum_memory_required=None, force_full_load=False):
+    logging.debug(f"start to load models")
     cleanup_models_gc()
     global vram_state
 
@@ -651,6 +698,7 @@ def load_models_gpu(models, memory_required=0, force_patch_weights=False, minimu
     models_to_load = []
 
     for x in models:
+        logging.debug(f"start loading model to vram: {x.model.__class__.__name__}")
         loaded_model = LoadedModel(x)
         try:
             loaded_model_index = current_loaded_models.index(loaded_model)
diff --git a/comfy/model_patcher.py b/comfy/model_patcher.py
index f6b80a40f1f9..90a9ea7b88ec 100644
--- a/comfy/model_patcher.py
+++ b/comfy/model_patcher.py
@@ -27,6 +27,10 @@
 from typing import Callable, Optional
 
 import torch
+import os
+import tempfile
+import weakref
+import gc
 
 import comfy.float
 import comfy.hooks
@@ -37,6 +41,87 @@
 from comfy.comfy_types import UnetWrapperFunction
 from comfy.quant_ops import QuantizedTensor
 from comfy.patcher_extension import CallbacksMP, PatcherInjection, WrappersMP
+from comfy.model_management import get_free_memory, get_mmap_mem_threshold_gb, get_free_disk
+
+def need_mmap() -> bool:
+    free_cpu_mem = get_free_memory(torch.device("cpu"))
+    mmap_mem_threshold_gb = get_mmap_mem_threshold_gb()
+    if free_cpu_mem < mmap_mem_threshold_gb * 1024 * 1024 * 1024:
+        logging.debug(f"Enabling mmap, current free cpu memory {free_cpu_mem/(1024*1024*1024)} GB < {mmap_mem_threshold_gb} GB")
+        return True
+    return False
+
+def to_mmap(t: torch.Tensor, filename: Optional[str] = None) -> torch.Tensor:
+    """
+    Convert a tensor to a memory-mapped CPU tensor using PyTorch's native mmap support.
+    """
+    # Create temporary file
+    if filename is None:
+        temp_file = tempfile.mkstemp(suffix='.pt', prefix='comfy_mmap_')[1]
+    else:
+        temp_file = filename
+    
+    # Save tensor to file
+    cpu_tensor = t.cpu()
+    torch.save(cpu_tensor, temp_file)
+    
+    # If we created a CPU copy from other device, delete it to free memory
+    if not t.device.type == 'cpu':
+        del cpu_tensor
+        gc.collect()
+    
+    # Load with mmap - this doesn't load all data into RAM
+    mmap_tensor = torch.load(temp_file, map_location='cpu', mmap=True, weights_only=False)
+    
+    # Register cleanup callback - will be called when tensor is garbage collected
+    def _cleanup():
+        try:
+            if os.path.exists(temp_file):
+                os.remove(temp_file)
+                logging.debug(f"Cleaned up mmap file: {temp_file}")
+        except Exception:
+            pass
+    
+    weakref.finalize(mmap_tensor, _cleanup)
+
+    return mmap_tensor
+                
+def model_to_mmap(model: torch.nn.Module):
+    """Convert all parameters and buffers to memory-mapped tensors
+    
+    This function mimics PyTorch's Module.to() behavior but converts
+    tensors to memory-mapped format instead, using _apply() method.
+    
+    Reference: https://github.com/pytorch/pytorch/blob/0fabc3ba44823f257e70ce397d989c8de5e362c1/torch/nn/modules/module.py#L1244
+    
+    Note: For Parameters, we modify .data in-place because 
+    MemoryMappedTensor cannot be wrapped in torch.nn.Parameter.
+    For buffers, _apply() will automatically update the reference.
+    
+    Args:
+        model: PyTorch module to convert
+        
+    Returns:
+        The same model with all tensors converted to memory-mapped format
+    """
+    free_cpu_mem = get_free_memory(torch.device("cpu"))
+    logging.debug(f"Converting model {model.__class__.__name__} to mmap, current free cpu memory: {free_cpu_mem/(1024*1024*1024)} GB")
+    
+    def convert_fn(t):
+        if isinstance(t, QuantizedTensor):
+            logging.debug(f"QuantizedTensor detected, mmap skipped, tensor meta info: size {t.size()}, dtype {t.dtype}, device {t.device}, is_contiguous {t.is_contiguous()}")
+            return t
+        elif isinstance(t, torch.nn.Parameter):
+            new_tensor = to_mmap(t.detach())
+            return torch.nn.Parameter(new_tensor, requires_grad=t.requires_grad)
+        elif isinstance(t, torch.Tensor):
+            return to_mmap(t)
+        return t
+    
+    new_model = model._apply(convert_fn)
+    free_cpu_mem = get_free_memory(torch.device("cpu"))
+    logging.debug(f"Model {model.__class__.__name__} converted to mmap, current free cpu memory: {free_cpu_mem/(1024*1024*1024)} GB")
+    return new_model
 
 
 def string_to_seed(data):
@@ -506,6 +591,7 @@ def get_model_object(self, name: str) -> torch.nn.Module:
                 return comfy.utils.get_attr(self.model, name)
 
     def model_patches_to(self, device):
+        # TODO(sf): to mmap
         to = self.model_options["transformer_options"]
         if "patches" in to:
             patches = to["patches"]
@@ -855,9 +941,15 @@ def unpatch_model(self, device_to=None, unpatch_weights=True):
             self.model.current_weight_patches_uuid = None
             self.backup.clear()
 
+                
             if device_to is not None:
-                self.model.to(device_to)
+                if need_mmap():
+                    # offload to mmap
+                    model_to_mmap(self.model)
+                else:
+                    self.model.to(device_to)
                 self.model.device = device_to
+            
             self.model.model_loaded_weight_memory = 0
             self.model.model_offload_buffer_memory = 0
 
@@ -916,7 +1008,14 @@ def partially_unload(self, device_to, memory_to_free=0, force_patch_weights=Fals
                     bias_key = "{}.bias".format(n)
                     if move_weight:
                         cast_weight = self.force_cast_weights
-                        m.to(device_to)
+                        if need_mmap():
+                            if get_free_disk() < module_mem:
+                                logging.warning(f"Not enough disk space to offload {n} to mmap, current free disk space {get_free_disk()/(1024*1024*1024)} GB < {module_mem/(1024*1024*1024)} GB")
+                                break
+                            # offload to mmap
+                            model_to_mmap(m)
+                        else:
+                            m.to(device_to)
                         module_mem += move_weight_functions(m, device_to)
                         if lowvram_possible:
                             if weight_key in self.patches:
diff --git a/comfy/sd.py b/comfy/sd.py
index 5a7221620290..28bb1810c349 100644
--- a/comfy/sd.py
+++ b/comfy/sd.py
@@ -1516,6 +1516,7 @@ def load_diffusion_model_state_dict(sd, model_options={}, metadata=None):
                     logging.warning("{} {}".format(diffusers_keys[k], k))
 
     offload_device = model_management.unet_offload_device()
+    logging.debug(f"loader load model to offload device: {offload_device}")
     unet_weight_dtype = list(model_config.supported_inference_dtypes)
     if model_config.quant_config is not None:
         weight_dtype = None
diff --git a/comfy/utils.py b/comfy/utils.py
index ffa98c9b1b81..84467b3bed50 100644
--- a/comfy/utils.py
+++ b/comfy/utils.py
@@ -61,6 +61,8 @@ def load_torch_file(ckpt, safe_load=False, device=None, return_metadata=False):
     metadata = None
     if ckpt.lower().endswith(".safetensors") or ckpt.lower().endswith(".sft"):
         try:
+            if not DISABLE_MMAP:
+                logging.debug(f"load_torch_file of safetensors into mmap True")
             with safetensors.safe_open(ckpt, framework="pt", device=device.type) as f:
                 sd = {}
                 for k in f.keys():
@@ -81,6 +83,7 @@ def load_torch_file(ckpt, safe_load=False, device=None, return_metadata=False):
     else:
         torch_args = {}
         if MMAP_TORCH_FILES:
+            logging.debug(f"load_torch_file of torch state dict into mmap True")
             torch_args["mmap"] = True
 
         if safe_load or ALWAYS_SAFE_LOAD:
diff --git a/tests/inference/test_model_mmap.py b/tests/inference/test_model_mmap.py
new file mode 100644
index 000000000000..a7bff3bfc5cb
--- /dev/null
+++ b/tests/inference/test_model_mmap.py
@@ -0,0 +1,287 @@
+import pytest
+import torch
+import torch.nn as nn
+import psutil
+import os
+import gc
+import tempfile
+import sys
+
+# Ensure the project root is on the Python path (so `import comfy` works when running tests from this folder)
+sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..', '..')))
+
+from comfy.model_patcher import model_to_mmap, to_mmap
+
+
+class LargeModel(nn.Module):
+    """A simple model with large parameters for testing memory mapping"""
+    
+    def __init__(self, size_gb=10):
+        super().__init__()
+        # Calculate number of float32 elements needed for target size
+        # 1 GB = 1024^3 bytes, float32 = 4 bytes
+        bytes_per_gb = 1024 * 1024 * 1024
+        elements_per_gb = bytes_per_gb // 4  # float32 is 4 bytes
+        total_elements = int(size_gb * elements_per_gb)
+        
+        # Create a large linear layer
+        # Split into multiple layers to avoid single tensor size limits
+        self.layers = nn.ModuleList()
+        elements_per_layer = 500 * 1024 * 1024  # 500M elements per layer (~2GB)
+        num_layers = (total_elements + elements_per_layer - 1) // elements_per_layer
+        
+        for i in range(num_layers):
+            if i == num_layers - 1:
+                # Last layer gets the remaining elements
+                remaining = total_elements - (i * elements_per_layer)
+                in_features = int(remaining ** 0.5)
+                out_features = (remaining + in_features - 1) // in_features
+            else:
+                in_features = int(elements_per_layer ** 0.5)
+                out_features = (elements_per_layer + in_features - 1) // in_features
+            
+            # Create layer without bias to control size precisely
+            self.layers.append(nn.Linear(in_features, out_features, bias=False))
+    
+    def forward(self, x):
+        for layer in self.layers:
+            x = layer(x)
+        return x
+
+
+def get_process_memory_gb():
+    """Get current process memory usage in GB"""
+    process = psutil.Process(os.getpid())
+    mem_info = process.memory_info()
+    return mem_info.rss / (1024 ** 3)  # Convert to GB
+
+
+def get_model_size_gb(model):
+    """Calculate model size in GB"""
+    total_size = 0
+    for param in model.parameters():
+        total_size += param.nelement() * param.element_size()
+    for buffer in model.buffers():
+        total_size += buffer.nelement() * buffer.element_size()
+    return total_size / (1024 ** 3)
+
+
+def test_model_to_mmap_memory_efficiency():
+    """Test that model_to_mmap reduces memory usage for a 10GB model to less than 1GB
+    
+    The typical use case is:
+    1. Load a large model on CUDA
+    2. Convert to mmap to offload from GPU to disk-backed memory
+    3. This frees GPU memory and reduces CPU RAM usage
+    """
+    
+    # Check if CUDA is available
+    if not torch.cuda.is_available():
+        pytest.skip("CUDA is not available, skipping test")
+    
+    # Force garbage collection before starting
+    gc.collect()
+    torch.cuda.empty_cache()
+    
+    # Record initial memory
+    initial_cpu_memory = get_process_memory_gb()
+    initial_gpu_memory = torch.cuda.memory_allocated() / (1024 ** 3)
+    print(f"\nInitial CPU memory: {initial_cpu_memory:.2f} GB")
+    print(f"Initial GPU memory: {initial_gpu_memory:.2f} GB")
+    
+    # Create a 10GB model
+    print("Creating 10GB model...")
+    model = LargeModel(size_gb=10)
+    
+    # Verify model size
+    model_size = get_model_size_gb(model)
+    print(f"Model size: {model_size:.2f} GB")
+    assert model_size >= 9.5, f"Model size {model_size:.2f} GB is less than expected 10 GB"
+    
+    # Move model to CUDA
+    print("Moving model to CUDA...")
+    model = model.cuda()
+    torch.cuda.synchronize()
+    
+    # Memory after moving to CUDA
+    cpu_after_cuda = get_process_memory_gb()
+    gpu_after_cuda = torch.cuda.memory_allocated() / (1024 ** 3)
+    print(f"CPU memory after moving to CUDA: {cpu_after_cuda:.2f} GB")
+    print(f"GPU memory after moving to CUDA: {gpu_after_cuda:.2f} GB")
+    
+    # Convert to mmap (this should move model from GPU to disk-backed memory)
+    # Note: model_to_mmap modifies the model in-place via _apply()
+    # so model and model_mmap will be the same object
+    print("Converting model to mmap...")
+    model_mmap = model_to_mmap(model)
+    
+    # Verify that model and model_mmap are the same object (in-place modification)
+    assert model is model_mmap, "model_to_mmap should modify the model in-place"
+    
+    # Force garbage collection and clear CUDA cache
+    # The original CUDA tensors should be automatically freed when replaced
+    gc.collect()
+    torch.cuda.empty_cache()
+    torch.cuda.synchronize()
+    
+    # Memory after mmap conversion
+    cpu_after_mmap = get_process_memory_gb()
+    gpu_after_mmap = torch.cuda.memory_allocated() / (1024 ** 3)
+    print(f"CPU memory after mmap: {cpu_after_mmap:.2f} GB")
+    print(f"GPU memory after mmap: {gpu_after_mmap:.2f} GB")
+    
+    # Calculate memory changes from CUDA state (the baseline we're converting from)
+    cpu_increase = cpu_after_mmap - cpu_after_cuda
+    gpu_decrease = gpu_after_cuda - gpu_after_mmap  # Should be positive (freed)
+    print(f"\nCPU memory increase from CUDA: {cpu_increase:.2f} GB")
+    print(f"GPU memory freed: {gpu_decrease:.2f} GB")
+    
+    # Verify that CPU memory usage increase is less than 1GB
+    # The mmap should use disk-backed storage, keeping CPU RAM usage low
+    # We use 1.5 GB threshold to account for overhead
+    assert cpu_increase < 1.5, (
+        f"CPU memory increase after mmap ({cpu_increase:.2f} GB) should be less than 1.5 GB. "
+        f"CUDA state: {cpu_after_cuda:.2f} GB, After mmap: {cpu_after_mmap:.2f} GB"
+    )
+    
+    # Verify that GPU memory has been freed
+    # We expect at least 9 GB to be freed (original 10GB model with some tolerance)
+    assert gpu_decrease > 9.0, (
+        f"GPU memory should be freed after mmap. "
+        f"Freed: {gpu_decrease:.2f} GB (from {gpu_after_cuda:.2f} to {gpu_after_mmap:.2f} GB), expected > 9 GB"
+    )
+    
+    # Verify the model is still functional (basic sanity check)
+    assert model_mmap is not None
+    assert len(list(model_mmap.parameters())) > 0
+    
+    print(f"\n✓ Test passed!")
+    print(f"  CPU memory increase: {cpu_increase:.2f} GB < 1.5 GB")
+    print(f"  GPU memory freed: {gpu_decrease:.2f} GB > 9.0 GB")
+    print(f"  Model successfully offloaded from GPU to disk-backed memory")
+    
+    # Cleanup (model and model_mmap are the same object)
+    del model, model_mmap
+    gc.collect()
+    torch.cuda.empty_cache()
+
+
+def test_to_mmap_cuda_cycle():
+    """Test CUDA -> mmap -> CUDA cycle
+    
+    This test verifies:
+    1. CUDA tensor can be converted to mmap tensor
+    2. CPU memory increase is minimal when using mmap (< 0.1 GB)
+    3. GPU memory is freed when converting to mmap
+    4. mmap tensor can be moved back to CUDA
+    5. Data remains consistent throughout the cycle
+    6. mmap file is automatically cleaned up via garbage collection
+    """
+    
+    # Check if CUDA is available
+    if not torch.cuda.is_available():
+        pytest.skip("CUDA is not available, skipping test")
+    
+    # Force garbage collection
+    gc.collect()
+    torch.cuda.empty_cache()
+    
+    print("\nTest: CUDA -> mmap -> CUDA cycle")
+    
+    # Record initial CPU memory
+    initial_cpu_memory = get_process_memory_gb()
+    print(f"Initial CPU memory: {initial_cpu_memory:.2f} GB")
+    
+    # Step 1: Create a CUDA tensor
+    print("\n1. Creating CUDA tensor...")
+    original_data = torch.randn(5000, 5000).cuda()
+    original_sum = original_data.sum().item()
+    print(f"   Shape: {original_data.shape}")
+    print(f"   Device: {original_data.device}")
+    print(f"   Sum: {original_sum:.2f}")
+    
+    # Record GPU and CPU memory after CUDA allocation
+    cpu_after_cuda = get_process_memory_gb()
+    gpu_before_mmap = torch.cuda.memory_allocated() / (1024 ** 3)
+    print(f"   GPU memory: {gpu_before_mmap:.2f} GB")
+    print(f"   CPU memory: {cpu_after_cuda:.2f} GB")
+    
+    # Step 2: Convert to mmap tensor
+    print("\n2. Converting to mmap tensor...")
+    mmap_tensor = to_mmap(original_data)
+    del original_data
+    gc.collect()
+    torch.cuda.empty_cache()
+    
+    print(f"   Device: {mmap_tensor.device}")
+    print(f"   Sum: {mmap_tensor.sum().item():.2f}")
+    
+    # Verify GPU memory is freed
+    gpu_after_mmap = torch.cuda.memory_allocated() / (1024 ** 3)
+    cpu_after_mmap = get_process_memory_gb()
+    print(f"   GPU memory freed: {gpu_before_mmap - gpu_after_mmap:.2f} GB")
+    print(f"   CPU memory: {cpu_after_mmap:.2f} GB")
+    
+    # Verify GPU memory is freed
+    assert gpu_after_mmap < 0.1, f"GPU memory should be freed, but {gpu_after_mmap:.2f} GB still allocated"
+    
+    # Verify CPU memory increase is minimal (should be close to 0 due to mmap)
+    cpu_increase = cpu_after_mmap - cpu_after_cuda
+    print(f"   CPU memory increase: {cpu_increase:.2f} GB")
+    assert cpu_increase < 0.1, f"CPU memory should increase minimally, but increased by {cpu_increase:.2f} GB"
+    
+    # Get the temp file path (we'll check if it gets cleaned up)
+    # The file should exist at this point
+    temp_files_before = len([f for f in os.listdir(tempfile.gettempdir()) if f.startswith('comfy_mmap_')])
+    print(f"   Temp mmap files exist: {temp_files_before}")
+    
+    # Step 3: Move back to CUDA
+    print("\n3. Moving back to CUDA...")
+    cuda_tensor = mmap_tensor.to('cuda')
+    torch.cuda.synchronize()
+    
+    print(f"   Device: {cuda_tensor.device}")
+    final_sum = cuda_tensor.sum().item()
+    print(f"   Sum: {final_sum:.2f}")
+    
+    # Verify GPU memory is used again
+    gpu_after_cuda = torch.cuda.memory_allocated() / (1024 ** 3)
+    print(f"   GPU memory: {gpu_after_cuda:.2f} GB")
+    
+    # Step 4: Verify data consistency
+    print("\n4. Verifying data consistency...")
+    sum_diff = abs(original_sum - final_sum)
+    print(f"   Original sum: {original_sum:.2f}")
+    print(f"   Final sum: {final_sum:.2f}")
+    print(f"   Difference: {sum_diff:.6f}")
+    assert sum_diff < 0.01, f"Data should be consistent, but difference is {sum_diff:.6f}"
+    
+    # Step 5: Verify file cleanup (delayed until garbage collection)
+    print("\n5. Verifying file cleanup...")
+    # Delete the mmap tensor reference to trigger garbage collection
+    del mmap_tensor
+    gc.collect()
+    import time
+    time.sleep(0.1)  # Give OS time to clean up
+    temp_files_after = len([f for f in os.listdir(tempfile.gettempdir()) if f.startswith('comfy_mmap_')])
+    print(f"   Temp mmap files after GC: {temp_files_after}")
+    # File should be cleaned up after garbage collection
+    assert temp_files_after <= temp_files_before, "mmap file should be cleaned up after garbage collection"
+    
+    print("\n✓ Test passed!")
+    print("  CUDA -> mmap -> CUDA cycle works correctly")
+    print(f"  CPU memory increase: {cpu_increase:.2f} GB < 0.1 GB (mmap efficiency)")
+    print("  Data consistency maintained")
+    print("  File cleanup successful (via garbage collection)")
+    
+    # Cleanup
+    del cuda_tensor  # mmap_tensor already deleted in Step 5
+    gc.collect()
+    torch.cuda.empty_cache()
+
+
+if __name__ == "__main__":
+    # Run the tests directly
+    test_model_to_mmap_memory_efficiency()
+    test_to_mmap_cuda_cycle()
+