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Merge branch 'main' into ltx-2

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This commit is contained in:
Zhongjie Duan
2026-02-03 13:06:44 +08:00
committed by GitHub
parent 49bc84f78e a781138413
commit 25c3a3d3e2
34 changed files with 2132 additions and 37 deletions
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7
diffsynth/core/loader/config.py
View File

@@ -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
@@ -23,13 +23,14 @@ 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:
raise ValueError(f"""No valid model files. Please use `ModelConfig(path="xxx")` or `ModelConfig(model_id="xxx/yyy", origin_file_pattern="zzz")`. `skip_download=True` only supports the first one.""")
def parse_original_file_pattern(self):
if self.origin_file_pattern is None or self.origin_file_pattern == "":
if self.origin_file_pattern in [None, "", "./"]:
return "*"
elif self.origin_file_pattern.endswith("/"):
return self.origin_file_pattern + "*"
@@ -98,7 +99,7 @@ class ModelConfig:
if self.require_downloading():
self.download()
if self.path is None:
if self.origin_file_pattern is None or self.origin_file_pattern == "":
if self.origin_file_pattern in [None, "", "./"]:
self.path = os.path.join(self.local_model_path, self.model_id)
else:
self.path = glob.glob(os.path.join(self.local_model_path, self.model_id, self.origin_file_pattern))

21
diffsynth/core/loader/file.py
View File

@@ -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))
return state_dict
if file_path.endswith(".safetensors"):
return load_state_dict_from_safetensors(file_path, torch_dtype=torch_dtype, device=device)
state_dict.update(load_state_dict(file_path_, torch_dtype, device, pin_memory=pin_memory, verbose=verbose))
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"):

8
diffsynth/core/loader/model.py
View File

@@ -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)