diff --git a/extensions-builtin/Lora/networks.py b/extensions-builtin/Lora/networks.py index 9fca36b6a53..96f935b236f 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 695c5736269..8863107ae6f 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 @@ def __enter__(self): 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 f6fbdcd60ab..685585b1cbc 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")