DiffSynth-Studio 2.0 major update

diffsynth/core/vram/disk_map.py

@@ -0,0 +1,93 @@
+from safetensors import safe_open
+import torch, os
+
+
+class SafetensorsCompatibleTensor:
+    def __init__(self, tensor):
+        self.tensor = tensor
+    
+    def get_shape(self):
+        return list(self.tensor.shape)
+
+
+class SafetensorsCompatibleBinaryLoader:
+    def __init__(self, path, device):
+        print("Detected non-safetensors files, which may cause slower loading. It's recommended to convert it to a safetensors file.")
+        self.state_dict = torch.load(path, weights_only=True, map_location=device)
+        
+    def keys(self):
+        return self.state_dict.keys()
+    
+    def get_tensor(self, name):
+        return self.state_dict[name]
+    
+    def get_slice(self, name):
+        return SafetensorsCompatibleTensor(self.state_dict[name])
+
+
+class DiskMap:
+
+    def __init__(self, path, device, torch_dtype=None, state_dict_converter=None, buffer_size=10**9):
+        self.path = path if isinstance(path, list) else [path]
+        self.device = device
+        self.torch_dtype = torch_dtype
+        if os.environ.get('DIFFSYNTH_DISK_MAP_BUFFER_SIZE') is not None:
+            self.buffer_size = int(os.environ.get('DIFFSYNTH_DISK_MAP_BUFFER_SIZE'))
+        else:
+            self.buffer_size = buffer_size
+        self.files = []
+        self.flush_files()
+        self.name_map = {}
+        for file_id, file in enumerate(self.files):
+            for name in file.keys():
+                self.name_map[name] = file_id
+        self.rename_dict = self.fetch_rename_dict(state_dict_converter)
+        
+    def flush_files(self):
+        if len(self.files) == 0:
+            for path in self.path:
+                if path.endswith(".safetensors"):
+                    self.files.append(safe_open(path, framework="pt", device=str(self.device)))
+                else:
+                    self.files.append(SafetensorsCompatibleBinaryLoader(path, device=self.device))
+        else:
+            for i, path in enumerate(self.path):
+                if path.endswith(".safetensors"):
+                    self.files[i] = safe_open(path, framework="pt", device=str(self.device))
+        self.num_params = 0
+
+    def __getitem__(self, name):
+        if self.rename_dict is not None: name = self.rename_dict[name]
+        file_id = self.name_map[name]
+        param = self.files[file_id].get_tensor(name)
+        if self.torch_dtype is not None and isinstance(param, torch.Tensor):
+            param = param.to(self.torch_dtype)
+        if isinstance(param, torch.Tensor) and param.device == "cpu":
+            param = param.clone()
+        if isinstance(param, torch.Tensor):
+            self.num_params += param.numel()
+        if self.num_params > self.buffer_size:
+            self.flush_files()
+        return param
+
+    def fetch_rename_dict(self, state_dict_converter):
+        if state_dict_converter is None:
+            return None
+        state_dict = {}
+        for file in self.files:
+            for name in file.keys():
+                state_dict[name] = name
+        state_dict = state_dict_converter(state_dict)
+        return state_dict
+    
+    def __iter__(self):
+        if self.rename_dict is not None:
+            return self.rename_dict.__iter__()
+        else:
+            return self.name_map.__iter__()
+    
+    def __contains__(self, x):
+        if self.rename_dict is not None:
+            return x in self.rename_dict
+        else:
+            return x in self.name_map