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change hypernets to use sha256 hashes

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AUTOMATIC
2023-01-14 10:25:21 +03:00
parent a95f135308
commit f9ac3352cb
4 changed files with 26 additions and 19 deletions
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40
modules/hypernetworks/hypernetwork.py
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@@ -12,7 +12,7 @@ import torch
import tqdm
from einops import rearrange, repeat
from ldm.util import default
from modules import devices, processing, sd_models, shared, sd_samplers
from modules import devices, processing, sd_models, shared, sd_samplers, hashes
from modules.textual_inversion import textual_inversion, logging
from modules.textual_inversion.learn_schedule import LearnRateScheduler
from torch import einsum
@@ -225,7 +225,7 @@ class Hypernetwork:
torch.save(state_dict, filename)
if shared.opts.save_optimizer_state and self.optimizer_state_dict:
optimizer_saved_dict['hash'] = sd_models.model_hash(filename)
optimizer_saved_dict['hash'] = self.shorthash()
optimizer_saved_dict['optimizer_state_dict'] = self.optimizer_state_dict
torch.save(optimizer_saved_dict, filename + '.optim')
@@ -237,32 +237,33 @@ class Hypernetwork:
state_dict = torch.load(filename, map_location='cpu')
self.layer_structure = state_dict.get('layer_structure', [1, 2, 1])
print(self.layer_structure)
optional_info = state_dict.get('optional_info', None)
if optional_info is not None:
print(f"INFO:\n {optional_info}\n")
self.optional_info = optional_info
self.optional_info = state_dict.get('optional_info', None)
self.activation_func = state_dict.get('activation_func', None)
print(f"Activation function is {self.activation_func}")
self.weight_init = state_dict.get('weight_initialization', 'Normal')
print(f"Weight initialization is {self.weight_init}")
self.add_layer_norm = state_dict.get('is_layer_norm', False)
print(f"Layer norm is set to {self.add_layer_norm}")
self.dropout_structure = state_dict.get('dropout_structure', None)
self.use_dropout = True if self.dropout_structure is not None and any(self.dropout_structure) else state_dict.get('use_dropout', False)
print(f"Dropout usage is set to {self.use_dropout}" )
self.activate_output = state_dict.get('activate_output', True)
print(f"Activate last layer is set to {self.activate_output}")
self.last_layer_dropout = state_dict.get('last_layer_dropout', False)
# Dropout structure should have same length as layer structure, Every digits should be in [0,1), and last digit must be 0.
if self.dropout_structure is None:
print("Using previous dropout structure")
self.dropout_structure = parse_dropout_structure(self.layer_structure, self.use_dropout, self.last_layer_dropout)
print(f"Dropout structure is set to {self.dropout_structure}")
optimizer_saved_dict = torch.load(self.filename + '.optim', map_location = 'cpu') if os.path.exists(self.filename + '.optim') else {}
if shared.opts.print_hypernet_extra:
if self.optional_info is not None:
print(f" INFO:\n {self.optional_info}\n")
if sd_models.model_hash(filename) == optimizer_saved_dict.get('hash', None):
print(f" Layer structure: {self.layer_structure}")
print(f" Activation function: {self.activation_func}")
print(f" Weight initialization: {self.weight_init}")
print(f" Layer norm: {self.add_layer_norm}")
print(f" Dropout usage: {self.use_dropout}" )
print(f" Activate last layer: {self.activate_output}")
print(f" Dropout structure: {self.dropout_structure}")
optimizer_saved_dict = torch.load(self.filename + '.optim', map_location='cpu') if os.path.exists(self.filename + '.optim') else {}
if self.shorthash() == optimizer_saved_dict.get('hash', None):
self.optimizer_state_dict = optimizer_saved_dict.get('optimizer_state_dict', None)
else:
self.optimizer_state_dict = None
@@ -289,6 +290,11 @@ class Hypernetwork:
self.sd_checkpoint_name = state_dict.get('sd_checkpoint_name', None)
self.eval()
def shorthash(self):
sha256 = hashes.sha256(self.filename, f'hypernet/{self.name}')
return sha256[0:10]
def list_hypernetworks(path):
res = {}
@@ -296,7 +302,7 @@ def list_hypernetworks(path):
name = os.path.splitext(os.path.basename(filename))[0]
# Prevent a hypothetical "None.pt" from being listed.
if name != "None":
res[name + f"({sd_models.model_hash(filename)})"] = filename
res[name] = filename
return res