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fix for #3086 failing to load any previous hypernet

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discus0434 2022-10-19 16:31:12 +00:00
parent c664b231a8
commit 2ce52d32e4
1 changed files with 28 additions and 32 deletions
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52
modules/hypernetworks/hypernetwork.py
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@ -24,11 +24,10 @@ class HypernetworkModule(torch.nn.Module):
def __init__(self, dim, state_dict=None, layer_structure=None, add_layer_norm=False): def __init__(self, dim, state_dict=None, layer_structure=None, add_layer_norm=False):
super().__init__() super().__init__()
if layer_structure is not None:
assert layer_structure is not None, "layer_structure mut not be None"
assert layer_structure[0] == 1, "Multiplier Sequence should start with size 1!" assert layer_structure[0] == 1, "Multiplier Sequence should start with size 1!"
assert layer_structure[-1] == 1, "Multiplier Sequence should end with size 1!" assert layer_structure[-1] == 1, "Multiplier Sequence should end with size 1!"
else:
layer_structure = parse_layer_structure(dim, state_dict)
linears = [] linears = []
for i in range(len(layer_structure) - 1): for i in range(len(layer_structure) - 1):
@ -39,10 +38,8 @@ class HypernetworkModule(torch.nn.Module):
self.linear = torch.nn.Sequential(*linears) self.linear = torch.nn.Sequential(*linears)
if state_dict is not None: if state_dict is not None:
try: self.fix_old_state_dict(state_dict)
self.load_state_dict(state_dict) self.load_state_dict(state_dict)
except RuntimeError:
self.try_load_previous(state_dict)
else: else:
for layer in self.linear: for layer in self.linear:
layer.weight.data.normal_(mean=0.0, std=0.01) layer.weight.data.normal_(mean=0.0, std=0.01)
@ -50,12 +47,21 @@ class HypernetworkModule(torch.nn.Module):
self.to(devices.device) self.to(devices.device)
def try_load_previous(self, state_dict): def fix_old_state_dict(self, state_dict):
states = self.state_dict() changes = {
states['linear.0.bias'].copy_(state_dict['linear1.bias']) 'linear1.bias': 'linear.0.bias',
states['linear.0.weight'].copy_(state_dict['linear1.weight']) 'linear1.weight': 'linear.0.weight',
states['linear.1.bias'].copy_(state_dict['linear2.bias']) 'linear2.bias': 'linear.1.bias',
states['linear.1.weight'].copy_(state_dict['linear2.weight']) 'linear2.weight': 'linear.1.weight',
}
for fr, to in changes.items():
x = state_dict.get(fr, None)
if x is None:
continue
del state_dict[fr]
state_dict[to] = x
def forward(self, x): def forward(self, x):
return x + self.linear(x) * self.multiplier return x + self.linear(x) * self.multiplier
@ -71,18 +77,6 @@ def apply_strength(value=None):
HypernetworkModule.multiplier = value if value is not None else shared.opts.sd_hypernetwork_strength HypernetworkModule.multiplier = value if value is not None else shared.opts.sd_hypernetwork_strength
def parse_layer_structure(dim, state_dict):
i = 0
layer_structure = [1]
while (key := "linear.{}.weight".format(i)) in state_dict:
weight = state_dict[key]
layer_structure.append(len(weight) // dim)
i += 1
return layer_structure
class Hypernetwork: class Hypernetwork:
filename = None filename = None
name = None name = None
@ -135,17 +129,18 @@ class Hypernetwork:
state_dict = torch.load(filename, map_location='cpu') state_dict = torch.load(filename, map_location='cpu')
self.layer_structure = state_dict.get('layer_structure', [1, 2, 1])
self.add_layer_norm = state_dict.get('is_layer_norm', False)
for size, sd in state_dict.items(): for size, sd in state_dict.items():
if type(size) == int: if type(size) == int:
self.layers[size] = ( self.layers[size] = (
HypernetworkModule(size, sd[0], state_dict["layer_structure"], state_dict["is_layer_norm"]), HypernetworkModule(size, sd[0], self.layer_structure, self.add_layer_norm),
HypernetworkModule(size, sd[1], state_dict["layer_structure"], state_dict["is_layer_norm"]), HypernetworkModule(size, sd[1], self.layer_structure, self.add_layer_norm),
) )
self.name = state_dict.get('name', self.name) self.name = state_dict.get('name', self.name)
self.step = state_dict.get('step', 0) self.step = state_dict.get('step', 0)
self.layer_structure = state_dict.get('layer_structure', None)
self.add_layer_norm = state_dict.get('is_layer_norm', False)
self.sd_checkpoint = state_dict.get('sd_checkpoint', None) self.sd_checkpoint = state_dict.get('sd_checkpoint', None)
self.sd_checkpoint_name = state_dict.get('sd_checkpoint_name', None) self.sd_checkpoint_name = state_dict.get('sd_checkpoint_name', None)
@ -244,6 +239,7 @@ def stack_conds(conds):
return torch.stack(conds) return torch.stack(conds)
def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log_directory, training_width, training_height, steps, create_image_every, save_hypernetwork_every, template_file, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height): def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log_directory, training_width, training_height, steps, create_image_every, save_hypernetwork_every, template_file, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
assert hypernetwork_name, 'hypernetwork not selected' assert hypernetwork_name, 'hypernetwork not selected'