diff options
| -rw-r--r-- | extensions-builtin/Lora/networks.py | 5 | ||||
| -rw-r--r-- | modules/sd_disable_initialization.py | 63 | ||||
| -rw-r--r-- | modules/sd_models.py | 22 |
3 files changed, 77 insertions, 13 deletions
diff --git a/extensions-builtin/Lora/networks.py b/extensions-builtin/Lora/networks.py index 9fca36b6..96f935b2 100644 --- a/extensions-builtin/Lora/networks.py +++ b/extensions-builtin/Lora/networks.py @@ -304,7 +304,10 @@ def network_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn wanted_names = tuple((x.name, x.te_multiplier, x.unet_multiplier, x.dyn_dim) for x in loaded_networks)
weights_backup = getattr(self, "network_weights_backup", None)
- if weights_backup is None:
+ if weights_backup is None and wanted_names != ():
+ if current_names != ():
+ raise RuntimeError("no backup weights found and current weights are not unchanged")
+
if isinstance(self, torch.nn.MultiheadAttention):
weights_backup = (self.in_proj_weight.to(devices.cpu, copy=True), self.out_proj.weight.to(devices.cpu, copy=True))
else:
diff --git a/modules/sd_disable_initialization.py b/modules/sd_disable_initialization.py index 695c5736..8863107a 100644 --- a/modules/sd_disable_initialization.py +++ b/modules/sd_disable_initialization.py @@ -155,10 +155,16 @@ class LoadStateDictOnMeta(ReplaceHelper): ```
"""
- def __init__(self, state_dict, device):
+ def __init__(self, state_dict, device, weight_dtype_conversion=None):
super().__init__()
self.state_dict = state_dict
self.device = device
+ self.weight_dtype_conversion = weight_dtype_conversion or {}
+ self.default_dtype = self.weight_dtype_conversion.get('')
+
+ def get_weight_dtype(self, key):
+ key_first_term, _ = key.split('.', 1)
+ return self.weight_dtype_conversion.get(key_first_term, self.default_dtype)
def __enter__(self):
if shared.cmd_opts.disable_model_loading_ram_optimization:
@@ -167,23 +173,60 @@ class LoadStateDictOnMeta(ReplaceHelper): sd = self.state_dict
device = self.device
- def load_from_state_dict(original, self, state_dict, prefix, *args, **kwargs):
- params = [(name, param) for name, param in self._parameters.items() if param is not None and param.is_meta]
+ def load_from_state_dict(original, module, state_dict, prefix, *args, **kwargs):
+ used_param_keys = []
- for name, param in params:
- if param.is_meta:
- self._parameters[name] = torch.nn.parameter.Parameter(torch.zeros_like(param, device=device), requires_grad=param.requires_grad)
+ for name, param in module._parameters.items():
+ if param is None:
+ continue
- original(self, state_dict, prefix, *args, **kwargs)
+ key = prefix + name
+ sd_param = sd.pop(key, None)
+ if sd_param is not None:
+ state_dict[key] = sd_param.to(dtype=self.get_weight_dtype(key))
+ used_param_keys.append(key)
- for name, _ in params:
+ if param.is_meta:
+ dtype = sd_param.dtype if sd_param is not None else param.dtype
+ module._parameters[name] = torch.nn.parameter.Parameter(torch.zeros_like(param, device=device, dtype=dtype), requires_grad=param.requires_grad)
+
+ for name in module._buffers:
key = prefix + name
- if key in sd:
- del sd[key]
+ sd_param = sd.pop(key, None)
+ if sd_param is not None:
+ state_dict[key] = sd_param
+ used_param_keys.append(key)
+
+ original(module, state_dict, prefix, *args, **kwargs)
+
+ for key in used_param_keys:
+ state_dict.pop(key, None)
+
+ def load_state_dict(original, module, state_dict, strict=True):
+ """torch makes a lot of copies of the dictionary with weights, so just deleting entries from state_dict does not help
+ because the same values are stored in multiple copies of the dict. The trick used here is to give torch a dict with
+ all weights on meta device, i.e. deleted, and then it doesn't matter how many copies torch makes.
+
+ In _load_from_state_dict, the correct weight will be obtained from a single dict with the right weights (sd).
+
+ The dangerous thing about this is if _load_from_state_dict is not called, (if some exotic module overloads
+ the function and does not call the original) the state dict will just fail to load because weights
+ would be on the meta device.
+ """
+
+ if state_dict == sd:
+ state_dict = {k: v.to(device="meta", dtype=v.dtype) for k, v in state_dict.items()}
+
+ original(module, state_dict, strict=strict)
+
+ module_load_state_dict = self.replace(torch.nn.Module, 'load_state_dict', lambda *args, **kwargs: load_state_dict(module_load_state_dict, *args, **kwargs))
+ module_load_from_state_dict = self.replace(torch.nn.Module, '_load_from_state_dict', lambda *args, **kwargs: load_from_state_dict(module_load_from_state_dict, *args, **kwargs))
linear_load_from_state_dict = self.replace(torch.nn.Linear, '_load_from_state_dict', lambda *args, **kwargs: load_from_state_dict(linear_load_from_state_dict, *args, **kwargs))
conv2d_load_from_state_dict = self.replace(torch.nn.Conv2d, '_load_from_state_dict', lambda *args, **kwargs: load_from_state_dict(conv2d_load_from_state_dict, *args, **kwargs))
mha_load_from_state_dict = self.replace(torch.nn.MultiheadAttention, '_load_from_state_dict', lambda *args, **kwargs: load_from_state_dict(mha_load_from_state_dict, *args, **kwargs))
+ layer_norm_load_from_state_dict = self.replace(torch.nn.LayerNorm, '_load_from_state_dict', lambda *args, **kwargs: load_from_state_dict(layer_norm_load_from_state_dict, *args, **kwargs))
+ group_norm_load_from_state_dict = self.replace(torch.nn.GroupNorm, '_load_from_state_dict', lambda *args, **kwargs: load_from_state_dict(group_norm_load_from_state_dict, *args, **kwargs))
def __exit__(self, exc_type, exc_val, exc_tb):
self.restore()
diff --git a/modules/sd_models.py b/modules/sd_models.py index f6fbdcd6..685585b1 100644 --- a/modules/sd_models.py +++ b/modules/sd_models.py @@ -343,7 +343,10 @@ def load_model_weights(model, checkpoint_info: CheckpointInfo, state_dict, timer model.to(memory_format=torch.channels_last)
timer.record("apply channels_last")
- if not shared.cmd_opts.no_half:
+ if shared.cmd_opts.no_half:
+ model.float()
+ timer.record("apply float()")
+ else:
vae = model.first_stage_model
depth_model = getattr(model, 'depth_model', None)
@@ -518,6 +521,13 @@ def send_model_to_cpu(m): devices.torch_gc()
+def model_target_device():
+ if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
+ return devices.cpu
+ else:
+ return devices.device
+
+
def send_model_to_device(m):
if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
lowvram.setup_for_low_vram(m, shared.cmd_opts.medvram)
@@ -579,7 +589,15 @@ def load_model(checkpoint_info=None, already_loaded_state_dict=None): timer.record("create model")
- with sd_disable_initialization.LoadStateDictOnMeta(state_dict, devices.cpu):
+ if shared.cmd_opts.no_half:
+ weight_dtype_conversion = None
+ else:
+ weight_dtype_conversion = {
+ 'first_stage_model': None,
+ '': torch.float16,
+ }
+
+ with sd_disable_initialization.LoadStateDictOnMeta(state_dict, device=model_target_device(), weight_dtype_conversion=weight_dtype_conversion):
load_model_weights(sd_model, checkpoint_info, state_dict, timer)
timer.record("load weights from state dict")
|