update version

Z-Image and Z-Image-i2L

diffsynth/core/loader/config.py

@@ -1,5 +1,5 @@
 import torch, glob, os
-from typing import Optional, Union
+from typing import Optional, Union, Dict
 from dataclasses import dataclass
 from modelscope import snapshot_download
 from huggingface_hub import snapshot_download as hf_snapshot_download
@@ -23,6 +23,7 @@ class ModelConfig:
     computation_device: Optional[Union[str, torch.device]] = None
     computation_dtype: Optional[torch.dtype] = None
     clear_parameters: bool = False
+    state_dict: Dict[str, torch.Tensor] = None
     
     def check_input(self):
         if self.path is None and self.model_id is None:

diffsynth/core/loader/file.py

@@ -2,16 +2,25 @@ from safetensors import safe_open
 import torch, hashlib
 
 
-def load_state_dict(file_path, torch_dtype=None, device="cpu"):
+def load_state_dict(file_path, torch_dtype=None, device="cpu", pin_memory=False, verbose=0):
     if isinstance(file_path, list):
         state_dict = {}
         for file_path_ in file_path:
-            state_dict.update(load_state_dict(file_path_, torch_dtype, device))
+            state_dict.update(load_state_dict(file_path_, torch_dtype, device, pin_memory=pin_memory, verbose=verbose))
-        return state_dict
-    if file_path.endswith(".safetensors"):
-        return load_state_dict_from_safetensors(file_path, torch_dtype=torch_dtype, device=device)
     else:
-        return load_state_dict_from_bin(file_path, torch_dtype=torch_dtype, device=device)
+        if verbose >= 1:
+            print(f"Loading file [started]: {file_path}")
+        if file_path.endswith(".safetensors"):
+            state_dict = load_state_dict_from_safetensors(file_path, torch_dtype=torch_dtype, device=device)
+        else:
+            state_dict = load_state_dict_from_bin(file_path, torch_dtype=torch_dtype, device=device)
+        # If load state dict in CPU memory, `pin_memory=True` will make `model.to("cuda")` faster.
+        if pin_memory:
+            for i in state_dict:
+                state_dict[i] = state_dict[i].pin_memory()
+        if verbose >= 1:
+            print(f"Loading file [done]: {file_path}")
+    return state_dict
 
 
 def load_state_dict_from_safetensors(file_path, torch_dtype=None, device="cpu"):

diffsynth/core/loader/model.py

@@ -5,7 +5,7 @@ from .file import load_state_dict
 import torch
 
 
-def load_model(model_class, path, config=None, torch_dtype=torch.bfloat16, device="cpu", state_dict_converter=None, use_disk_map=False, module_map=None, vram_config=None, vram_limit=None):
+def load_model(model_class, path, config=None, torch_dtype=torch.bfloat16, device="cpu", state_dict_converter=None, use_disk_map=False, module_map=None, vram_config=None, vram_limit=None, state_dict=None):
     config = {} if config is None else config
     # Why do we use `skip_model_initialization`?
     # It skips the random initialization of model parameters,
@@ -20,7 +20,7 @@ def load_model(model_class, path, config=None, torch_dtype=torch.bfloat16, devic
         dtypes = [vram_config["offload_dtype"], vram_config["onload_dtype"], vram_config["preparing_dtype"], vram_config["computation_dtype"]]
         dtype = [d for d in dtypes if d != "disk"][0]
         if vram_config["offload_device"] != "disk":
-            state_dict = DiskMap(path, device, torch_dtype=dtype)
+            if state_dict is None: state_dict = DiskMap(path, device, torch_dtype=dtype)
             if state_dict_converter is not None:
                 state_dict = state_dict_converter(state_dict)
             else:
@@ -35,7 +35,9 @@ def load_model(model_class, path, config=None, torch_dtype=torch.bfloat16, devic
         # Sometimes a model file contains multiple models,
         # and DiskMap can load only the parameters of a single model,
         # avoiding the need to load all parameters in the file.
-        if use_disk_map:
+        if state_dict is not None:
+            pass
+        elif use_disk_map:
             state_dict = DiskMap(path, device, torch_dtype=torch_dtype)
         else:
             state_dict = load_state_dict(path, torch_dtype, device)

diffsynth/diffusion/base_pipeline.py

@@ -296,6 +296,7 @@ class BasePipeline(torch.nn.Module):
                 vram_config=vram_config,
                 vram_limit=vram_limit,
                 clear_parameters=model_config.clear_parameters,
+                state_dict=model_config.state_dict,
             )
         return model_pool
     

diffsynth/models/model_loader.py

@@ -29,7 +29,7 @@ class ModelPool:
             module_map = None
         return module_map
     
-    def load_model_file(self, config, path, vram_config, vram_limit=None):
+    def load_model_file(self, config, path, vram_config, vram_limit=None, state_dict=None):
         model_class = self.import_model_class(config["model_class"])
         model_config = config.get("extra_kwargs", {})
         if "state_dict_converter" in config:
@@ -43,6 +43,7 @@ class ModelPool:
             state_dict_converter,
             use_disk_map=True,
             vram_config=vram_config, module_map=module_map, vram_limit=vram_limit,
+            state_dict=state_dict,
         )
         return model
     
@@ -59,7 +60,7 @@ class ModelPool:
         }
         return vram_config
     
-    def auto_load_model(self, path, vram_config=None, vram_limit=None, clear_parameters=False):
+    def auto_load_model(self, path, vram_config=None, vram_limit=None, clear_parameters=False, state_dict=None):
         print(f"Loading models from: {json.dumps(path, indent=4)}")
         if vram_config is None:
             vram_config = self.default_vram_config()
@@ -67,7 +68,7 @@ class ModelPool:
         loaded = False
         for config in MODEL_CONFIGS:
             if config["model_hash"] == model_hash:
-                model = self.load_model_file(config, path, vram_config, vram_limit=vram_limit)
+                model = self.load_model_file(config, path, vram_config, vram_limit=vram_limit, state_dict=state_dict)
                 if clear_parameters: self.clear_parameters(model)
                 self.model.append(model)
                 model_name = config["model_name"]

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "diffsynth"
-version = "2.0.3"
+version = "2.0.4"
 description = "Enjoy the magic of Diffusion models!"
 authors = [{name = "ModelScope Team"}]
 license = {text = "Apache-2.0"}