From 2f4c91894d4c0a055c1069b2fda0e4da8fcda188 Mon Sep 17 00:00:00 2001 From: guaneec Date: Wed, 26 Oct 2022 12:10:30 +0800 Subject: Remove activation from final layer of HNs --- modules/hypernetworks/hypernetwork.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) (limited to 'modules/hypernetworks') diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index d647ea55..54346b64 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -41,8 +41,8 @@ class HypernetworkModule(torch.nn.Module): # Add a fully-connected layer linears.append(torch.nn.Linear(int(dim * layer_structure[i]), int(dim * layer_structure[i+1]))) - # Add an activation func - if activation_func == "linear" or activation_func is None: + # Add an activation func except last layer + if activation_func == "linear" or activation_func is None or i >= len(layer_structure) - 3: pass elif activation_func in self.activation_dict: linears.append(self.activation_dict[activation_func]()) @@ -53,7 +53,7 @@ class HypernetworkModule(torch.nn.Module): if add_layer_norm: linears.append(torch.nn.LayerNorm(int(dim * layer_structure[i+1]))) - # Add dropout expect last layer + # Add dropout except last layer if use_dropout and i < len(layer_structure) - 3: linears.append(torch.nn.Dropout(p=0.3)) -- cgit v1.2.1 From c702d4d0df21790199d199818f25c449213ffe0f Mon Sep 17 00:00:00 2001 From: guaneec Date: Wed, 26 Oct 2022 13:43:04 +0800 Subject: Fix off-by-one --- modules/hypernetworks/hypernetwork.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) (limited to 'modules/hypernetworks') diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index 54346b64..3ce85bb5 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -42,7 +42,7 @@ class HypernetworkModule(torch.nn.Module): linears.append(torch.nn.Linear(int(dim * layer_structure[i]), int(dim * layer_structure[i+1]))) # Add an activation func except last layer - if activation_func == "linear" or activation_func is None or i >= len(layer_structure) - 3: + if activation_func == "linear" or activation_func is None or i >= len(layer_structure) - 2: pass elif activation_func in self.activation_dict: linears.append(self.activation_dict[activation_func]()) @@ -54,7 +54,7 @@ class HypernetworkModule(torch.nn.Module): linears.append(torch.nn.LayerNorm(int(dim * layer_structure[i+1]))) # Add dropout except last layer - if use_dropout and i < len(layer_structure) - 3: + if use_dropout and i < len(layer_structure) - 2: linears.append(torch.nn.Dropout(p=0.3)) self.linear = torch.nn.Sequential(*linears) -- cgit v1.2.1 From 877d94f97ca5491d8779440769b191e0dcd32c8e Mon Sep 17 00:00:00 2001 From: guaneec Date: Wed, 26 Oct 2022 14:50:58 +0800 Subject: Back compatibility --- modules/hypernetworks/hypernetwork.py | 17 ++++++++++------- 1 file changed, 10 insertions(+), 7 deletions(-) (limited to 'modules/hypernetworks') diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index 3ce85bb5..dd317085 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -28,7 +28,7 @@ class HypernetworkModule(torch.nn.Module): "swish": torch.nn.Hardswish, } - def __init__(self, dim, state_dict=None, layer_structure=None, activation_func=None, add_layer_norm=False, use_dropout=False): + def __init__(self, dim, state_dict=None, layer_structure=None, activation_func=None, add_layer_norm=False, use_dropout=False, activate_output=False): super().__init__() assert layer_structure is not None, "layer_structure must not be None" @@ -42,7 +42,7 @@ class HypernetworkModule(torch.nn.Module): linears.append(torch.nn.Linear(int(dim * layer_structure[i]), int(dim * layer_structure[i+1]))) # Add an activation func except last layer - if activation_func == "linear" or activation_func is None or i >= len(layer_structure) - 2: + if activation_func == "linear" or activation_func is None or (i >= len(layer_structure) - 2 and not activate_output): pass elif activation_func in self.activation_dict: linears.append(self.activation_dict[activation_func]()) @@ -105,7 +105,7 @@ class Hypernetwork: filename = None name = None - def __init__(self, name=None, enable_sizes=None, layer_structure=None, activation_func=None, add_layer_norm=False, use_dropout=False): + def __init__(self, name=None, enable_sizes=None, layer_structure=None, activation_func=None, add_layer_norm=False, use_dropout=False, activate_output=False): self.filename = None self.name = name self.layers = {} @@ -116,11 +116,12 @@ class Hypernetwork: self.activation_func = activation_func self.add_layer_norm = add_layer_norm self.use_dropout = use_dropout + self.activate_output = activate_output for size in enable_sizes or []: self.layers[size] = ( - HypernetworkModule(size, None, self.layer_structure, self.activation_func, self.add_layer_norm, self.use_dropout), - HypernetworkModule(size, None, self.layer_structure, self.activation_func, self.add_layer_norm, self.use_dropout), + HypernetworkModule(size, None, self.layer_structure, self.activation_func, self.add_layer_norm, self.use_dropout, self.activate_output), + HypernetworkModule(size, None, self.layer_structure, self.activation_func, self.add_layer_norm, self.use_dropout, self.activate_output), ) def weights(self): @@ -147,6 +148,7 @@ class Hypernetwork: state_dict['use_dropout'] = self.use_dropout state_dict['sd_checkpoint'] = self.sd_checkpoint state_dict['sd_checkpoint_name'] = self.sd_checkpoint_name + state_dict['activate_output'] = self.activate_output torch.save(state_dict, filename) @@ -161,12 +163,13 @@ class Hypernetwork: self.activation_func = state_dict.get('activation_func', None) self.add_layer_norm = state_dict.get('is_layer_norm', False) self.use_dropout = state_dict.get('use_dropout', False) + self.activate_output = state_dict.get('activate_output', True) for size, sd in state_dict.items(): if type(size) == int: self.layers[size] = ( - HypernetworkModule(size, sd[0], self.layer_structure, self.activation_func, self.add_layer_norm, self.use_dropout), - HypernetworkModule(size, sd[1], self.layer_structure, self.activation_func, self.add_layer_norm, self.use_dropout), + HypernetworkModule(size, sd[0], self.layer_structure, self.activation_func, self.add_layer_norm, self.use_dropout, self.activate_output), + HypernetworkModule(size, sd[1], self.layer_structure, self.activation_func, self.add_layer_norm, self.use_dropout, self.activate_output), ) self.name = state_dict.get('name', self.name) -- cgit v1.2.1 From 91bb35b1e6842b30ce7553009c8ecea3643de8d2 Mon Sep 17 00:00:00 2001 From: guaneec Date: Wed, 26 Oct 2022 15:00:03 +0800 Subject: Merge fix --- modules/hypernetworks/hypernetwork.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) (limited to 'modules/hypernetworks') diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index eab8b32f..bd171793 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -190,7 +190,7 @@ class Hypernetwork: 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.use_dropout = state_dict.get('use_dropout', False + self.use_dropout = 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) -- cgit v1.2.1 From b6a8bb123bd519736306417399f6441e504f1e8b Mon Sep 17 00:00:00 2001 From: guaneec Date: Wed, 26 Oct 2022 15:15:19 +0800 Subject: Fix merge --- modules/hypernetworks/hypernetwork.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) (limited to 'modules/hypernetworks') diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index bd171793..2997cead 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -60,7 +60,7 @@ class HypernetworkModule(torch.nn.Module): linears.append(torch.nn.LayerNorm(int(dim * layer_structure[i+1]))) # Add dropout except last layer - if use_dropout and i < len(layer_structure) - 2: + if use_dropout and i < len(layer_structure) - 3: linears.append(torch.nn.Dropout(p=0.3)) self.linear = torch.nn.Sequential(*linears) @@ -126,7 +126,7 @@ class Hypernetwork: filename = None name = None - def __init__(self, name=None, enable_sizes=None, layer_structure=None, activation_func=None, weight_init=None, add_layer_norm=False, use_dropout=False, activate_output=False) + def __init__(self, name=None, enable_sizes=None, layer_structure=None, activation_func=None, weight_init=None, add_layer_norm=False, use_dropout=False, activate_output=False): self.filename = None self.name = name self.layers = {} -- cgit v1.2.1 From 85fcccc105aa50f1d78de559233eaa9f384608b5 Mon Sep 17 00:00:00 2001 From: AngelBottomless <35677394+aria1th@users.noreply.github.com> Date: Wed, 26 Oct 2022 22:24:33 +0900 Subject: Squashed commit of fixing dropout silently fix dropouts for future hypernetworks add kwargs for Hypernetwork class hypernet UI for gradio input add recommended options remove as options revert adding options in ui --- modules/hypernetworks/hypernetwork.py | 25 +++++++++++++++++-------- 1 file changed, 17 insertions(+), 8 deletions(-) (limited to 'modules/hypernetworks') diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index 2997cead..dd921153 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -34,7 +34,8 @@ class HypernetworkModule(torch.nn.Module): } activation_dict.update({cls_name.lower(): cls_obj for cls_name, cls_obj in inspect.getmembers(torch.nn.modules.activation) if inspect.isclass(cls_obj) and cls_obj.__module__ == 'torch.nn.modules.activation'}) - def __init__(self, dim, state_dict=None, layer_structure=None, activation_func=None, weight_init='Normal', add_layer_norm=False, use_dropout=False, activate_output=False): + def __init__(self, dim, state_dict=None, layer_structure=None, activation_func=None, weight_init='Normal', + add_layer_norm=False, use_dropout=False, activate_output=False, **kwargs): super().__init__() assert layer_structure is not None, "layer_structure must not be None" @@ -60,7 +61,7 @@ class HypernetworkModule(torch.nn.Module): linears.append(torch.nn.LayerNorm(int(dim * layer_structure[i+1]))) # Add dropout except last layer - if use_dropout and i < len(layer_structure) - 3: + if 'last_layer_dropout' in kwargs and kwargs['last_layer_dropout'] and use_dropout and i < len(layer_structure) - 2: linears.append(torch.nn.Dropout(p=0.3)) self.linear = torch.nn.Sequential(*linears) @@ -126,7 +127,7 @@ class Hypernetwork: filename = None name = None - def __init__(self, name=None, enable_sizes=None, layer_structure=None, activation_func=None, weight_init=None, add_layer_norm=False, use_dropout=False, activate_output=False): + def __init__(self, name=None, enable_sizes=None, layer_structure=None, activation_func=None, weight_init=None, add_layer_norm=False, use_dropout=False, activate_output=False, **kwargs): self.filename = None self.name = name self.layers = {} @@ -139,11 +140,14 @@ class Hypernetwork: self.add_layer_norm = add_layer_norm self.use_dropout = use_dropout self.activate_output = activate_output + self.last_layer_dropout = kwargs['last_layer_dropout'] if 'last_layer_dropout' in kwargs else True for size in enable_sizes or []: self.layers[size] = ( - HypernetworkModule(size, None, self.layer_structure, self.activation_func, self.weight_init, self.add_layer_norm, self.use_dropout, self.activate_output), - HypernetworkModule(size, None, self.layer_structure, self.activation_func, self.weight_init, self.add_layer_norm, self.use_dropout, self.activate_output), + HypernetworkModule(size, None, self.layer_structure, self.activation_func, self.weight_init, + self.add_layer_norm, self.use_dropout, self.activate_output, last_layer_dropout=self.last_layer_dropout), + HypernetworkModule(size, None, self.layer_structure, self.activation_func, self.weight_init, + self.add_layer_norm, self.use_dropout, self.activate_output, last_layer_dropout=self.last_layer_dropout), ) def weights(self): @@ -172,7 +176,8 @@ class Hypernetwork: state_dict['sd_checkpoint'] = self.sd_checkpoint state_dict['sd_checkpoint_name'] = self.sd_checkpoint_name state_dict['activate_output'] = self.activate_output - + state_dict['last_layer_dropout'] = self.last_layer_dropout + torch.save(state_dict, filename) def load(self, filename): @@ -193,12 +198,16 @@ class Hypernetwork: self.use_dropout = 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) for size, sd in state_dict.items(): if type(size) == int: self.layers[size] = ( - HypernetworkModule(size, sd[0], self.layer_structure, self.activation_func, self.weight_init, self.add_layer_norm, self.use_dropout, self.activate_output), - HypernetworkModule(size, sd[1], self.layer_structure, self.activation_func, self.weight_init, self.add_layer_norm, self.use_dropout, self.activate_output), + HypernetworkModule(size, sd[0], self.layer_structure, self.activation_func, self.weight_init, + self.add_layer_norm, self.use_dropout, self.activate_output, last_layer_dropout=self.last_layer_dropout), + HypernetworkModule(size, sd[1], self.layer_structure, self.activation_func, self.weight_init, + self.add_layer_norm, self.use_dropout, self.activate_output, last_layer_dropout=self.last_layer_dropout), ) self.name = state_dict.get('name', self.name) -- cgit v1.2.1 From cc56df996e95c2c82295ab7b9928da2544791220 Mon Sep 17 00:00:00 2001 From: guaneec Date: Wed, 26 Oct 2022 23:51:51 +0800 Subject: Fix dropout logic --- modules/hypernetworks/hypernetwork.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) (limited to 'modules/hypernetworks') diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index dd921153..b17598fe 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -35,7 +35,7 @@ class HypernetworkModule(torch.nn.Module): activation_dict.update({cls_name.lower(): cls_obj for cls_name, cls_obj in inspect.getmembers(torch.nn.modules.activation) if inspect.isclass(cls_obj) and cls_obj.__module__ == 'torch.nn.modules.activation'}) def __init__(self, dim, state_dict=None, layer_structure=None, activation_func=None, weight_init='Normal', - add_layer_norm=False, use_dropout=False, activate_output=False, **kwargs): + add_layer_norm=False, use_dropout=False, activate_output=False, last_layer_dropout=True): super().__init__() assert layer_structure is not None, "layer_structure must not be None" @@ -61,7 +61,7 @@ class HypernetworkModule(torch.nn.Module): linears.append(torch.nn.LayerNorm(int(dim * layer_structure[i+1]))) # Add dropout except last layer - if 'last_layer_dropout' in kwargs and kwargs['last_layer_dropout'] and use_dropout and i < len(layer_structure) - 2: + if use_dropout and (i < len(layer_structure) - 3 or last_layer_dropout and i < len(layer_structure) - 2): linears.append(torch.nn.Dropout(p=0.3)) self.linear = torch.nn.Sequential(*linears) -- cgit v1.2.1 From 029d7c75436558f1e884bb127caed73caaecb83a Mon Sep 17 00:00:00 2001 From: AngelBottomless <35677394+aria1th@users.noreply.github.com> Date: Thu, 27 Oct 2022 14:44:53 +0900 Subject: Revert unresolved changes in Bias initialization it should be zeros_ or parameterized in future properly. --- modules/hypernetworks/hypernetwork.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) (limited to 'modules/hypernetworks') diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index b17598fe..25427a37 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -75,7 +75,7 @@ class HypernetworkModule(torch.nn.Module): w, b = layer.weight.data, layer.bias.data if weight_init == "Normal" or type(layer) == torch.nn.LayerNorm: normal_(w, mean=0.0, std=0.01) - normal_(b, mean=0.0, std=0.005) + normal_(b, mean=0.0, std=0) elif weight_init == 'XavierUniform': xavier_uniform_(w) zeros_(b) -- cgit v1.2.1 From 20194fd9752a280306fb66b57b258609b0918c46 Mon Sep 17 00:00:00 2001 From: AngelBottomless <35677394+aria1th@users.noreply.github.com> Date: Sat, 29 Oct 2022 16:56:42 +0900 Subject: We have duplicate linear now --- modules/hypernetworks/ui.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) (limited to 'modules/hypernetworks') diff --git a/modules/hypernetworks/ui.py b/modules/hypernetworks/ui.py index aad09ffc..c2d4b51c 100644 --- a/modules/hypernetworks/ui.py +++ b/modules/hypernetworks/ui.py @@ -9,7 +9,7 @@ from modules import devices, sd_hijack, shared from modules.hypernetworks import hypernetwork not_available = ["hardswish", "multiheadattention"] -keys = ["linear"] + list(x for x in hypernetwork.HypernetworkModule.activation_dict.keys() if x not in not_available) +keys = list(x for x in hypernetwork.HypernetworkModule.activation_dict.keys() if x not in not_available) def create_hypernetwork(name, enable_sizes, overwrite_old, layer_structure=None, activation_func=None, weight_init=None, add_layer_norm=False, use_dropout=False): # Remove illegal characters from name. -- cgit v1.2.1 From 9d96d7d0a0aa0a966a9aefd24342345eb65952ed Mon Sep 17 00:00:00 2001 From: aria1th <35677394+aria1th@users.noreply.github.com> Date: Sun, 30 Oct 2022 20:39:04 +0900 Subject: resolve conflicts --- modules/hypernetworks/hypernetwork.py | 44 ++++++++++++++++++++++++++++++----- 1 file changed, 38 insertions(+), 6 deletions(-) (limited to 'modules/hypernetworks') diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index a11e01d6..8f74cdea 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -21,6 +21,7 @@ from torch.nn.init import normal_, xavier_normal_, xavier_uniform_, kaiming_norm from collections import defaultdict, deque from statistics import stdev, mean +optimizer_dict = {optim_name : cls_obj for optim_name, cls_obj in inspect.getmembers(torch.optim, inspect.isclass) if optim_name != "Optimizer"} class HypernetworkModule(torch.nn.Module): multiplier = 1.0 @@ -139,6 +140,8 @@ class Hypernetwork: self.weight_init = weight_init self.add_layer_norm = add_layer_norm self.use_dropout = use_dropout + self.optimizer_name = None + self.optimizer_state_dict = None for size in enable_sizes or []: self.layers[size] = ( @@ -171,6 +174,10 @@ class Hypernetwork: state_dict['use_dropout'] = self.use_dropout state_dict['sd_checkpoint'] = self.sd_checkpoint state_dict['sd_checkpoint_name'] = self.sd_checkpoint_name + if self.optimizer_name is not None: + state_dict['optimizer_name'] = self.optimizer_name + if self.optimizer_state_dict: + state_dict['optimizer_state_dict'] = self.optimizer_state_dict torch.save(state_dict, filename) @@ -190,7 +197,14 @@ class Hypernetwork: self.add_layer_norm = state_dict.get('is_layer_norm', False) print(f"Layer norm is set to {self.add_layer_norm}") self.use_dropout = state_dict.get('use_dropout', False) - print(f"Dropout usage is set to {self.use_dropout}" ) + print(f"Dropout usage is set to {self.use_dropout}") + self.optimizer_name = state_dict.get('optimizer_name', 'AdamW') + print(f"Optimizer name is {self.optimizer_name}") + self.optimizer_state_dict = state_dict.get('optimizer_state_dict', None) + if self.optimizer_state_dict: + print("Loaded existing optimizer from checkpoint") + else: + print("No saved optimizer exists in checkpoint") for size, sd in state_dict.items(): if type(size) == int: @@ -392,8 +406,19 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log weights = hypernetwork.weights() for weight in weights: weight.requires_grad = True - # if optimizer == "AdamW": or else Adam / AdamW / SGD, etc... - optimizer = torch.optim.AdamW(weights, lr=scheduler.learn_rate) + # Here we use optimizer from saved HN, or we can specify as UI option. + if (optimizer_name := hypernetwork.optimizer_name) in optimizer_dict: + optimizer = optimizer_dict[hypernetwork.optimizer_name](params=weights, lr=scheduler.learn_rate) + else: + print(f"Optimizer type {optimizer_name} is not defined!") + optimizer = torch.optim.AdamW(params=weights, lr=scheduler.learn_rate) + optimizer_name = 'AdamW' + if hypernetwork.optimizer_state_dict: # This line must be changed if Optimizer type can be different from saved optimizer. + try: + optimizer.load_state_dict(hypernetwork.optimizer_state_dict) + except RuntimeError as e: + print("Cannot resume from saved optimizer!") + print(e) steps_without_grad = 0 @@ -455,8 +480,11 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log # Before saving, change name to match current checkpoint. hypernetwork_name_every = f'{hypernetwork_name}-{steps_done}' last_saved_file = os.path.join(hypernetwork_dir, f'{hypernetwork_name_every}.pt') + hypernetwork.optimizer_name = optimizer_name + if shared.opts.save_optimizer_state: + hypernetwork.optimizer_state_dict = optimizer.state_dict() save_hypernetwork(hypernetwork, checkpoint, hypernetwork_name, last_saved_file) - + hypernetwork.optimizer_state_dict = None # dereference it after saving, to save memory. textual_inversion.write_loss(log_directory, "hypernetwork_loss.csv", hypernetwork.step, len(ds), { "loss": f"{previous_mean_loss:.7f}", "learn_rate": scheduler.learn_rate @@ -514,14 +542,18 @@ Last saved hypernetwork: {html.escape(last_saved_file)}
Last saved image: {html.escape(last_saved_image)}

""" - report_statistics(loss_dict) filename = os.path.join(shared.cmd_opts.hypernetwork_dir, f'{hypernetwork_name}.pt') + hypernetwork.optimizer_name = optimizer_name + if shared.opts.save_optimizer_state: + hypernetwork.optimizer_state_dict = optimizer.state_dict() save_hypernetwork(hypernetwork, checkpoint, hypernetwork_name, filename) - + del optimizer + hypernetwork.optimizer_state_dict = None # dereference it after saving, to save memory. return hypernetwork, filename + def save_hypernetwork(hypernetwork, checkpoint, hypernetwork_name, filename): old_hypernetwork_name = hypernetwork.name old_sd_checkpoint = hypernetwork.sd_checkpoint if hasattr(hypernetwork, "sd_checkpoint") else None -- cgit v1.2.1 From 0b143c1163a96b193a4e8512be9c5831c661a50d Mon Sep 17 00:00:00 2001 From: aria1th <35677394+aria1th@users.noreply.github.com> Date: Thu, 3 Nov 2022 14:30:53 +0900 Subject: Separate .optim file from model --- modules/hypernetworks/hypernetwork.py | 12 ++++++++---- 1 file changed, 8 insertions(+), 4 deletions(-) (limited to 'modules/hypernetworks') diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index 8f74cdea..63c25de8 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -161,6 +161,7 @@ class Hypernetwork: def save(self, filename): state_dict = {} + optimizer_saved_dict = {} for k, v in self.layers.items(): state_dict[k] = (v[0].state_dict(), v[1].state_dict()) @@ -175,9 +176,10 @@ class Hypernetwork: state_dict['sd_checkpoint'] = self.sd_checkpoint state_dict['sd_checkpoint_name'] = self.sd_checkpoint_name if self.optimizer_name is not None: - state_dict['optimizer_name'] = self.optimizer_name + optimizer_saved_dict['optimizer_name'] = self.optimizer_name if self.optimizer_state_dict: - state_dict['optimizer_state_dict'] = self.optimizer_state_dict + optimizer_saved_dict['optimizer_state_dict'] = self.optimizer_state_dict + torch.save(optimizer_saved_dict, filename + '.optim') torch.save(state_dict, filename) @@ -198,9 +200,11 @@ class Hypernetwork: print(f"Layer norm is set to {self.add_layer_norm}") self.use_dropout = state_dict.get('use_dropout', False) print(f"Dropout usage is set to {self.use_dropout}") - self.optimizer_name = state_dict.get('optimizer_name', 'AdamW') + + optimizer_saved_dict = torch.load(self.filename + '.optim', map_location = 'cpu') if os.path.exists(self.filename + '.optim') else {} + self.optimizer_name = optimizer_saved_dict.get('optimizer_name', 'AdamW') print(f"Optimizer name is {self.optimizer_name}") - self.optimizer_state_dict = state_dict.get('optimizer_state_dict', None) + self.optimizer_state_dict = optimizer_saved_dict.get('optimizer_state_dict', None) if self.optimizer_state_dict: print("Loaded existing optimizer from checkpoint") else: -- cgit v1.2.1 From 1764ac3c8bc482bd575987850e96630d9115e51a Mon Sep 17 00:00:00 2001 From: aria1th <35677394+aria1th@users.noreply.github.com> Date: Thu, 3 Nov 2022 14:49:26 +0900 Subject: use hash to check valid optim --- modules/hypernetworks/hypernetwork.py | 13 +++++++++---- 1 file changed, 9 insertions(+), 4 deletions(-) (limited to 'modules/hypernetworks') diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index 63c25de8..4230b8cf 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -177,12 +177,13 @@ class Hypernetwork: state_dict['sd_checkpoint_name'] = self.sd_checkpoint_name if self.optimizer_name is not None: optimizer_saved_dict['optimizer_name'] = self.optimizer_name + + torch.save(state_dict, filename) if self.optimizer_state_dict: + optimizer_saved_dict['hash'] = sd_models.model_hash(filename) optimizer_saved_dict['optimizer_state_dict'] = self.optimizer_state_dict torch.save(optimizer_saved_dict, filename + '.optim') - torch.save(state_dict, filename) - def load(self, filename): self.filename = filename if self.name is None: @@ -204,7 +205,10 @@ class Hypernetwork: optimizer_saved_dict = torch.load(self.filename + '.optim', map_location = 'cpu') if os.path.exists(self.filename + '.optim') else {} self.optimizer_name = optimizer_saved_dict.get('optimizer_name', 'AdamW') print(f"Optimizer name is {self.optimizer_name}") - self.optimizer_state_dict = optimizer_saved_dict.get('optimizer_state_dict', None) + if sd_models.model_hash(filename) == optimizer_saved_dict.get('hash', None): + self.optimizer_state_dict = optimizer_saved_dict.get('optimizer_state_dict', None) + else: + self.optimizer_state_dict = None if self.optimizer_state_dict: print("Loaded existing optimizer from checkpoint") else: @@ -229,7 +233,7 @@ 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] = filename + res[name + f"({sd_models.model_hash(filename)})"] = filename return res @@ -375,6 +379,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log else: hypernetwork_dir = None + hypernetwork_name = hypernetwork_name.rsplit('(', 1)[0] if create_image_every > 0: images_dir = os.path.join(log_directory, "images") os.makedirs(images_dir, exist_ok=True) -- cgit v1.2.1 From 0abb39f461baa343ae7c23abffb261e57c3168d4 Mon Sep 17 00:00:00 2001 From: aria1th <35677394+aria1th@users.noreply.github.com> Date: Fri, 4 Nov 2022 15:47:19 +0900 Subject: resolve conflict - first revert --- modules/hypernetworks/hypernetwork.py | 123 ++++++++++++++-------------------- 1 file changed, 52 insertions(+), 71 deletions(-) (limited to 'modules/hypernetworks') diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index 4230b8cf..674fcedd 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -21,7 +21,6 @@ from torch.nn.init import normal_, xavier_normal_, xavier_uniform_, kaiming_norm from collections import defaultdict, deque from statistics import stdev, mean -optimizer_dict = {optim_name : cls_obj for optim_name, cls_obj in inspect.getmembers(torch.optim, inspect.isclass) if optim_name != "Optimizer"} class HypernetworkModule(torch.nn.Module): multiplier = 1.0 @@ -34,9 +33,12 @@ class HypernetworkModule(torch.nn.Module): "tanh": torch.nn.Tanh, "sigmoid": torch.nn.Sigmoid, } - activation_dict.update({cls_name.lower(): cls_obj for cls_name, cls_obj in inspect.getmembers(torch.nn.modules.activation) if inspect.isclass(cls_obj) and cls_obj.__module__ == 'torch.nn.modules.activation'}) + activation_dict.update( + {cls_name.lower(): cls_obj for cls_name, cls_obj in inspect.getmembers(torch.nn.modules.activation) if + inspect.isclass(cls_obj) and cls_obj.__module__ == 'torch.nn.modules.activation'}) - def __init__(self, dim, state_dict=None, layer_structure=None, activation_func=None, weight_init='Normal', add_layer_norm=False, use_dropout=False): + def __init__(self, dim, state_dict=None, layer_structure=None, activation_func=None, weight_init='Normal', + add_layer_norm=False, use_dropout=False): super().__init__() assert layer_structure is not None, "layer_structure must not be None" @@ -47,7 +49,7 @@ class HypernetworkModule(torch.nn.Module): for i in range(len(layer_structure) - 1): # Add a fully-connected layer - linears.append(torch.nn.Linear(int(dim * layer_structure[i]), int(dim * layer_structure[i+1]))) + linears.append(torch.nn.Linear(int(dim * layer_structure[i]), int(dim * layer_structure[i + 1]))) # Add an activation func if activation_func == "linear" or activation_func is None: @@ -59,7 +61,7 @@ class HypernetworkModule(torch.nn.Module): # Add layer normalization if add_layer_norm: - linears.append(torch.nn.LayerNorm(int(dim * layer_structure[i+1]))) + linears.append(torch.nn.LayerNorm(int(dim * layer_structure[i + 1]))) # Add dropout expect last layer if use_dropout and i < len(layer_structure) - 3: @@ -128,7 +130,8 @@ class Hypernetwork: filename = None name = None - def __init__(self, name=None, enable_sizes=None, layer_structure=None, activation_func=None, weight_init=None, add_layer_norm=False, use_dropout=False): + def __init__(self, name=None, enable_sizes=None, layer_structure=None, activation_func=None, weight_init=None, + add_layer_norm=False, use_dropout=False): self.filename = None self.name = name self.layers = {} @@ -140,13 +143,13 @@ class Hypernetwork: self.weight_init = weight_init self.add_layer_norm = add_layer_norm self.use_dropout = use_dropout - self.optimizer_name = None - self.optimizer_state_dict = None for size in enable_sizes or []: self.layers[size] = ( - HypernetworkModule(size, None, self.layer_structure, self.activation_func, self.weight_init, self.add_layer_norm, self.use_dropout), - HypernetworkModule(size, None, self.layer_structure, self.activation_func, self.weight_init, self.add_layer_norm, self.use_dropout), + HypernetworkModule(size, None, self.layer_structure, self.activation_func, self.weight_init, + self.add_layer_norm, self.use_dropout), + HypernetworkModule(size, None, self.layer_structure, self.activation_func, self.weight_init, + self.add_layer_norm, self.use_dropout), ) def weights(self): @@ -161,7 +164,6 @@ class Hypernetwork: def save(self, filename): state_dict = {} - optimizer_saved_dict = {} for k, v in self.layers.items(): state_dict[k] = (v[0].state_dict(), v[1].state_dict()) @@ -175,14 +177,8 @@ class Hypernetwork: state_dict['use_dropout'] = self.use_dropout state_dict['sd_checkpoint'] = self.sd_checkpoint state_dict['sd_checkpoint_name'] = self.sd_checkpoint_name - if self.optimizer_name is not None: - optimizer_saved_dict['optimizer_name'] = self.optimizer_name torch.save(state_dict, filename) - if self.optimizer_state_dict: - optimizer_saved_dict['hash'] = sd_models.model_hash(filename) - optimizer_saved_dict['optimizer_state_dict'] = self.optimizer_state_dict - torch.save(optimizer_saved_dict, filename + '.optim') def load(self, filename): self.filename = filename @@ -202,23 +198,13 @@ class Hypernetwork: self.use_dropout = state_dict.get('use_dropout', False) print(f"Dropout usage is set to {self.use_dropout}") - optimizer_saved_dict = torch.load(self.filename + '.optim', map_location = 'cpu') if os.path.exists(self.filename + '.optim') else {} - self.optimizer_name = optimizer_saved_dict.get('optimizer_name', 'AdamW') - print(f"Optimizer name is {self.optimizer_name}") - if sd_models.model_hash(filename) == optimizer_saved_dict.get('hash', None): - self.optimizer_state_dict = optimizer_saved_dict.get('optimizer_state_dict', None) - else: - self.optimizer_state_dict = None - if self.optimizer_state_dict: - print("Loaded existing optimizer from checkpoint") - else: - print("No saved optimizer exists in checkpoint") - for size, sd in state_dict.items(): if type(size) == int: self.layers[size] = ( - HypernetworkModule(size, sd[0], self.layer_structure, self.activation_func, self.weight_init, self.add_layer_norm, self.use_dropout), - HypernetworkModule(size, sd[1], self.layer_structure, self.activation_func, self.weight_init, self.add_layer_norm, self.use_dropout), + HypernetworkModule(size, sd[0], self.layer_structure, self.activation_func, self.weight_init, + self.add_layer_norm, self.use_dropout), + HypernetworkModule(size, sd[1], self.layer_structure, self.activation_func, self.weight_init, + self.add_layer_norm, self.use_dropout), ) self.name = state_dict.get('name', self.name) @@ -233,7 +219,7 @@ 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 @@ -330,7 +316,7 @@ def statistics(data): std = 0 else: std = stdev(data) - total_information = f"loss:{mean(data):.3f}" + u"\u00B1" + f"({std/ (len(data) ** 0.5):.3f})" + total_information = f"loss:{mean(data):.3f}" + u"\u00B1" + f"({std / (len(data) ** 0.5):.3f})" recent_data = data[-32:] if len(recent_data) < 2: std = 0 @@ -340,7 +326,7 @@ def statistics(data): return total_information, recent_information -def report_statistics(loss_info:dict): +def report_statistics(loss_info: dict): keys = sorted(loss_info.keys(), key=lambda x: sum(loss_info[x]) / len(loss_info[x])) for key in keys: try: @@ -352,14 +338,18 @@ def report_statistics(loss_info:dict): print(e) - -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): # images allows training previews to have infotext. Importing it at the top causes a circular import problem. from modules import images save_hypernetwork_every = save_hypernetwork_every or 0 create_image_every = create_image_every or 0 - textual_inversion.validate_train_inputs(hypernetwork_name, learn_rate, batch_size, data_root, template_file, steps, save_hypernetwork_every, create_image_every, log_directory, name="hypernetwork") + textual_inversion.validate_train_inputs(hypernetwork_name, learn_rate, batch_size, data_root, template_file, steps, + save_hypernetwork_every, create_image_every, log_directory, + name="hypernetwork") path = shared.hypernetworks.get(hypernetwork_name, None) shared.loaded_hypernetwork = Hypernetwork() @@ -379,7 +369,6 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log else: hypernetwork_dir = None - hypernetwork_name = hypernetwork_name.rsplit('(', 1)[0] if create_image_every > 0: images_dir = os.path.join(log_directory, "images") os.makedirs(images_dir, exist_ok=True) @@ -395,39 +384,34 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log return hypernetwork, filename scheduler = LearnRateScheduler(learn_rate, steps, ititial_step) - + # dataset loading may take a while, so input validations and early returns should be done before this shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..." with torch.autocast("cuda"): - ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=hypernetwork_name, model=shared.sd_model, device=devices.device, template_file=template_file, include_cond=True, batch_size=batch_size) + ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, + height=training_height, + repeats=shared.opts.training_image_repeats_per_epoch, + placeholder_token=hypernetwork_name, + model=shared.sd_model, device=devices.device, + template_file=template_file, include_cond=True, + batch_size=batch_size) if unload: shared.sd_model.cond_stage_model.to(devices.cpu) shared.sd_model.first_stage_model.to(devices.cpu) size = len(ds.indexes) - loss_dict = defaultdict(lambda : deque(maxlen = 1024)) + loss_dict = defaultdict(lambda: deque(maxlen=1024)) losses = torch.zeros((size,)) previous_mean_losses = [0] previous_mean_loss = 0 print("Mean loss of {} elements".format(size)) - + weights = hypernetwork.weights() for weight in weights: weight.requires_grad = True - # Here we use optimizer from saved HN, or we can specify as UI option. - if (optimizer_name := hypernetwork.optimizer_name) in optimizer_dict: - optimizer = optimizer_dict[hypernetwork.optimizer_name](params=weights, lr=scheduler.learn_rate) - else: - print(f"Optimizer type {optimizer_name} is not defined!") - optimizer = torch.optim.AdamW(params=weights, lr=scheduler.learn_rate) - optimizer_name = 'AdamW' - if hypernetwork.optimizer_state_dict: # This line must be changed if Optimizer type can be different from saved optimizer. - try: - optimizer.load_state_dict(hypernetwork.optimizer_state_dict) - except RuntimeError as e: - print("Cannot resume from saved optimizer!") - print(e) + # if optimizer == "AdamW": or else Adam / AdamW / SGD, etc... + optimizer = torch.optim.AdamW(weights, lr=scheduler.learn_rate) steps_without_grad = 0 @@ -441,7 +425,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log if len(loss_dict) > 0: previous_mean_losses = [i[-1] for i in loss_dict.values()] previous_mean_loss = mean(previous_mean_losses) - + scheduler.apply(optimizer, hypernetwork.step) if scheduler.finished: break @@ -460,7 +444,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log losses[hypernetwork.step % losses.shape[0]] = loss.item() for entry in entries: loss_dict[entry.filename].append(loss.item()) - + optimizer.zero_grad() weights[0].grad = None loss.backward() @@ -475,9 +459,9 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log steps_done = hypernetwork.step + 1 - if torch.isnan(losses[hypernetwork.step % losses.shape[0]]): + if torch.isnan(losses[hypernetwork.step % losses.shape[0]]): raise RuntimeError("Loss diverged.") - + if len(previous_mean_losses) > 1: std = stdev(previous_mean_losses) else: @@ -489,11 +473,8 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log # Before saving, change name to match current checkpoint. hypernetwork_name_every = f'{hypernetwork_name}-{steps_done}' last_saved_file = os.path.join(hypernetwork_dir, f'{hypernetwork_name_every}.pt') - hypernetwork.optimizer_name = optimizer_name - if shared.opts.save_optimizer_state: - hypernetwork.optimizer_state_dict = optimizer.state_dict() save_hypernetwork(hypernetwork, checkpoint, hypernetwork_name, last_saved_file) - hypernetwork.optimizer_state_dict = None # dereference it after saving, to save memory. + textual_inversion.write_loss(log_directory, "hypernetwork_loss.csv", hypernetwork.step, len(ds), { "loss": f"{previous_mean_loss:.7f}", "learn_rate": scheduler.learn_rate @@ -529,7 +510,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log preview_text = p.prompt processed = processing.process_images(p) - image = processed.images[0] if len(processed.images)>0 else None + image = processed.images[0] if len(processed.images) > 0 else None if unload: shared.sd_model.cond_stage_model.to(devices.cpu) @@ -537,7 +518,10 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log if image is not None: shared.state.current_image = image - last_saved_image, last_text_info = images.save_image(image, images_dir, "", p.seed, p.prompt, shared.opts.samples_format, processed.infotexts[0], p=p, forced_filename=forced_filename, save_to_dirs=False) + last_saved_image, last_text_info = images.save_image(image, images_dir, "", p.seed, p.prompt, + shared.opts.samples_format, processed.infotexts[0], + p=p, forced_filename=forced_filename, + save_to_dirs=False) last_saved_image += f", prompt: {preview_text}" shared.state.job_no = hypernetwork.step @@ -551,15 +535,12 @@ Last saved hypernetwork: {html.escape(last_saved_file)}
Last saved image: {html.escape(last_saved_image)}

""" + report_statistics(loss_dict) filename = os.path.join(shared.cmd_opts.hypernetwork_dir, f'{hypernetwork_name}.pt') - hypernetwork.optimizer_name = optimizer_name - if shared.opts.save_optimizer_state: - hypernetwork.optimizer_state_dict = optimizer.state_dict() save_hypernetwork(hypernetwork, checkpoint, hypernetwork_name, filename) - del optimizer - hypernetwork.optimizer_state_dict = None # dereference it after saving, to save memory. + return hypernetwork, filename @@ -576,4 +557,4 @@ def save_hypernetwork(hypernetwork, checkpoint, hypernetwork_name, filename): hypernetwork.sd_checkpoint = old_sd_checkpoint hypernetwork.sd_checkpoint_name = old_sd_checkpoint_name hypernetwork.name = old_hypernetwork_name - raise + raise \ No newline at end of file -- cgit v1.2.1 From 0d07cbfa15d34294a4fa22d74359cdd6fe2f799c Mon Sep 17 00:00:00 2001 From: AngelBottomless <35677394+aria1th@users.noreply.github.com> Date: Fri, 4 Nov 2022 15:50:54 +0900 Subject: I blame code autocomplete --- modules/hypernetworks/hypernetwork.py | 76 +++++++++++++---------------------- 1 file changed, 27 insertions(+), 49 deletions(-) (limited to 'modules/hypernetworks') diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index 674fcedd..a11e01d6 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -33,12 +33,9 @@ class HypernetworkModule(torch.nn.Module): "tanh": torch.nn.Tanh, "sigmoid": torch.nn.Sigmoid, } - activation_dict.update( - {cls_name.lower(): cls_obj for cls_name, cls_obj in inspect.getmembers(torch.nn.modules.activation) if - inspect.isclass(cls_obj) and cls_obj.__module__ == 'torch.nn.modules.activation'}) + activation_dict.update({cls_name.lower(): cls_obj for cls_name, cls_obj in inspect.getmembers(torch.nn.modules.activation) if inspect.isclass(cls_obj) and cls_obj.__module__ == 'torch.nn.modules.activation'}) - def __init__(self, dim, state_dict=None, layer_structure=None, activation_func=None, weight_init='Normal', - add_layer_norm=False, use_dropout=False): + def __init__(self, dim, state_dict=None, layer_structure=None, activation_func=None, weight_init='Normal', add_layer_norm=False, use_dropout=False): super().__init__() assert layer_structure is not None, "layer_structure must not be None" @@ -49,7 +46,7 @@ class HypernetworkModule(torch.nn.Module): for i in range(len(layer_structure) - 1): # Add a fully-connected layer - linears.append(torch.nn.Linear(int(dim * layer_structure[i]), int(dim * layer_structure[i + 1]))) + linears.append(torch.nn.Linear(int(dim * layer_structure[i]), int(dim * layer_structure[i+1]))) # Add an activation func if activation_func == "linear" or activation_func is None: @@ -61,7 +58,7 @@ class HypernetworkModule(torch.nn.Module): # Add layer normalization if add_layer_norm: - linears.append(torch.nn.LayerNorm(int(dim * layer_structure[i + 1]))) + linears.append(torch.nn.LayerNorm(int(dim * layer_structure[i+1]))) # Add dropout expect last layer if use_dropout and i < len(layer_structure) - 3: @@ -130,8 +127,7 @@ class Hypernetwork: filename = None name = None - def __init__(self, name=None, enable_sizes=None, layer_structure=None, activation_func=None, weight_init=None, - add_layer_norm=False, use_dropout=False): + def __init__(self, name=None, enable_sizes=None, layer_structure=None, activation_func=None, weight_init=None, add_layer_norm=False, use_dropout=False): self.filename = None self.name = name self.layers = {} @@ -146,10 +142,8 @@ class Hypernetwork: for size in enable_sizes or []: self.layers[size] = ( - HypernetworkModule(size, None, self.layer_structure, self.activation_func, self.weight_init, - self.add_layer_norm, self.use_dropout), - HypernetworkModule(size, None, self.layer_structure, self.activation_func, self.weight_init, - self.add_layer_norm, self.use_dropout), + HypernetworkModule(size, None, self.layer_structure, self.activation_func, self.weight_init, self.add_layer_norm, self.use_dropout), + HypernetworkModule(size, None, self.layer_structure, self.activation_func, self.weight_init, self.add_layer_norm, self.use_dropout), ) def weights(self): @@ -196,15 +190,13 @@ class Hypernetwork: self.add_layer_norm = state_dict.get('is_layer_norm', False) print(f"Layer norm is set to {self.add_layer_norm}") self.use_dropout = state_dict.get('use_dropout', False) - print(f"Dropout usage is set to {self.use_dropout}") + print(f"Dropout usage is set to {self.use_dropout}" ) for size, sd in state_dict.items(): if type(size) == int: self.layers[size] = ( - HypernetworkModule(size, sd[0], self.layer_structure, self.activation_func, self.weight_init, - self.add_layer_norm, self.use_dropout), - HypernetworkModule(size, sd[1], self.layer_structure, self.activation_func, self.weight_init, - self.add_layer_norm, self.use_dropout), + HypernetworkModule(size, sd[0], self.layer_structure, self.activation_func, self.weight_init, self.add_layer_norm, self.use_dropout), + HypernetworkModule(size, sd[1], self.layer_structure, self.activation_func, self.weight_init, self.add_layer_norm, self.use_dropout), ) self.name = state_dict.get('name', self.name) @@ -316,7 +308,7 @@ def statistics(data): std = 0 else: std = stdev(data) - total_information = f"loss:{mean(data):.3f}" + u"\u00B1" + f"({std / (len(data) ** 0.5):.3f})" + total_information = f"loss:{mean(data):.3f}" + u"\u00B1" + f"({std/ (len(data) ** 0.5):.3f})" recent_data = data[-32:] if len(recent_data) < 2: std = 0 @@ -326,7 +318,7 @@ def statistics(data): return total_information, recent_information -def report_statistics(loss_info: dict): +def report_statistics(loss_info:dict): keys = sorted(loss_info.keys(), key=lambda x: sum(loss_info[x]) / len(loss_info[x])) for key in keys: try: @@ -338,18 +330,14 @@ def report_statistics(loss_info: dict): print(e) -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): # images allows training previews to have infotext. Importing it at the top causes a circular import problem. from modules import images save_hypernetwork_every = save_hypernetwork_every or 0 create_image_every = create_image_every or 0 - textual_inversion.validate_train_inputs(hypernetwork_name, learn_rate, batch_size, data_root, template_file, steps, - save_hypernetwork_every, create_image_every, log_directory, - name="hypernetwork") + textual_inversion.validate_train_inputs(hypernetwork_name, learn_rate, batch_size, data_root, template_file, steps, save_hypernetwork_every, create_image_every, log_directory, name="hypernetwork") path = shared.hypernetworks.get(hypernetwork_name, None) shared.loaded_hypernetwork = Hypernetwork() @@ -384,29 +372,23 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log return hypernetwork, filename scheduler = LearnRateScheduler(learn_rate, steps, ititial_step) - + # dataset loading may take a while, so input validations and early returns should be done before this shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..." with torch.autocast("cuda"): - ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, - height=training_height, - repeats=shared.opts.training_image_repeats_per_epoch, - placeholder_token=hypernetwork_name, - model=shared.sd_model, device=devices.device, - template_file=template_file, include_cond=True, - batch_size=batch_size) + ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=hypernetwork_name, model=shared.sd_model, device=devices.device, template_file=template_file, include_cond=True, batch_size=batch_size) if unload: shared.sd_model.cond_stage_model.to(devices.cpu) shared.sd_model.first_stage_model.to(devices.cpu) size = len(ds.indexes) - loss_dict = defaultdict(lambda: deque(maxlen=1024)) + loss_dict = defaultdict(lambda : deque(maxlen = 1024)) losses = torch.zeros((size,)) previous_mean_losses = [0] previous_mean_loss = 0 print("Mean loss of {} elements".format(size)) - + weights = hypernetwork.weights() for weight in weights: weight.requires_grad = True @@ -425,7 +407,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log if len(loss_dict) > 0: previous_mean_losses = [i[-1] for i in loss_dict.values()] previous_mean_loss = mean(previous_mean_losses) - + scheduler.apply(optimizer, hypernetwork.step) if scheduler.finished: break @@ -444,7 +426,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log losses[hypernetwork.step % losses.shape[0]] = loss.item() for entry in entries: loss_dict[entry.filename].append(loss.item()) - + optimizer.zero_grad() weights[0].grad = None loss.backward() @@ -459,9 +441,9 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log steps_done = hypernetwork.step + 1 - if torch.isnan(losses[hypernetwork.step % losses.shape[0]]): + if torch.isnan(losses[hypernetwork.step % losses.shape[0]]): raise RuntimeError("Loss diverged.") - + if len(previous_mean_losses) > 1: std = stdev(previous_mean_losses) else: @@ -510,7 +492,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log preview_text = p.prompt processed = processing.process_images(p) - image = processed.images[0] if len(processed.images) > 0 else None + image = processed.images[0] if len(processed.images)>0 else None if unload: shared.sd_model.cond_stage_model.to(devices.cpu) @@ -518,10 +500,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log if image is not None: shared.state.current_image = image - last_saved_image, last_text_info = images.save_image(image, images_dir, "", p.seed, p.prompt, - shared.opts.samples_format, processed.infotexts[0], - p=p, forced_filename=forced_filename, - save_to_dirs=False) + last_saved_image, last_text_info = images.save_image(image, images_dir, "", p.seed, p.prompt, shared.opts.samples_format, processed.infotexts[0], p=p, forced_filename=forced_filename, save_to_dirs=False) last_saved_image += f", prompt: {preview_text}" shared.state.job_no = hypernetwork.step @@ -535,7 +514,7 @@ Last saved hypernetwork: {html.escape(last_saved_file)}
Last saved image: {html.escape(last_saved_image)}

""" - + report_statistics(loss_dict) filename = os.path.join(shared.cmd_opts.hypernetwork_dir, f'{hypernetwork_name}.pt') @@ -543,7 +522,6 @@ Last saved image: {html.escape(last_saved_image)}
return hypernetwork, filename - def save_hypernetwork(hypernetwork, checkpoint, hypernetwork_name, filename): old_hypernetwork_name = hypernetwork.name old_sd_checkpoint = hypernetwork.sd_checkpoint if hasattr(hypernetwork, "sd_checkpoint") else None @@ -557,4 +535,4 @@ def save_hypernetwork(hypernetwork, checkpoint, hypernetwork_name, filename): hypernetwork.sd_checkpoint = old_sd_checkpoint hypernetwork.sd_checkpoint_name = old_sd_checkpoint_name hypernetwork.name = old_hypernetwork_name - raise \ No newline at end of file + raise -- cgit v1.2.1 From 283249d2390f0f3a1c8a55d5d9aa551e3e9b2f9c Mon Sep 17 00:00:00 2001 From: aria1th <35677394+aria1th@users.noreply.github.com> Date: Fri, 4 Nov 2022 15:57:17 +0900 Subject: apply --- modules/hypernetworks/hypernetwork.py | 54 +++++++++++++++++++++++++++++++---- 1 file changed, 49 insertions(+), 5 deletions(-) (limited to 'modules/hypernetworks') diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index 6e1a10cf..de8688a9 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -22,6 +22,8 @@ from collections import defaultdict, deque from statistics import stdev, mean +optimizer_dict = {optim_name : cls_obj for optim_name, cls_obj in inspect.getmembers(torch.optim, inspect.isclass) if optim_name != "Optimizer"} + class HypernetworkModule(torch.nn.Module): multiplier = 1.0 activation_dict = { @@ -142,6 +144,8 @@ class Hypernetwork: self.use_dropout = use_dropout self.activate_output = activate_output self.last_layer_dropout = kwargs['last_layer_dropout'] if 'last_layer_dropout' in kwargs else True + self.optimizer_name = None + self.optimizer_state_dict = None for size in enable_sizes or []: self.layers[size] = ( @@ -163,6 +167,7 @@ class Hypernetwork: def save(self, filename): state_dict = {} + optimizer_saved_dict = {} for k, v in self.layers.items(): state_dict[k] = (v[0].state_dict(), v[1].state_dict()) @@ -178,8 +183,15 @@ class Hypernetwork: state_dict['sd_checkpoint_name'] = self.sd_checkpoint_name state_dict['activate_output'] = self.activate_output state_dict['last_layer_dropout'] = self.last_layer_dropout - + + if self.optimizer_name is not None: + optimizer_saved_dict['optimizer_name'] = self.optimizer_name + torch.save(state_dict, filename) + if self.optimizer_state_dict: + optimizer_saved_dict['hash'] = sd_models.model_hash(filename) + optimizer_saved_dict['optimizer_state_dict'] = self.optimizer_state_dict + torch.save(optimizer_saved_dict, filename + '.optim') def load(self, filename): self.filename = filename @@ -202,6 +214,18 @@ class Hypernetwork: print(f"Activate last layer is set to {self.activate_output}") self.last_layer_dropout = state_dict.get('last_layer_dropout', False) + optimizer_saved_dict = torch.load(self.filename + '.optim', map_location = 'cpu') if os.path.exists(self.filename + '.optim') else {} + self.optimizer_name = optimizer_saved_dict.get('optimizer_name', 'AdamW') + print(f"Optimizer name is {self.optimizer_name}") + if sd_models.model_hash(filename) == optimizer_saved_dict.get('hash', None): + self.optimizer_state_dict = optimizer_saved_dict.get('optimizer_state_dict', None) + else: + self.optimizer_state_dict = None + if self.optimizer_state_dict: + print("Loaded existing optimizer from checkpoint") + else: + print("No saved optimizer exists in checkpoint") + for size, sd in state_dict.items(): if type(size) == int: self.layers[size] = ( @@ -223,7 +247,7 @@ 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] = filename + res[name + f"({sd_models.model_hash(filename)})"] = filename return res @@ -369,6 +393,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log else: hypernetwork_dir = None + hypernetwork_name = hypernetwork_name.rsplit('(', 1)[0] if create_image_every > 0: images_dir = os.path.join(log_directory, "images") os.makedirs(images_dir, exist_ok=True) @@ -404,8 +429,19 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log weights = hypernetwork.weights() for weight in weights: weight.requires_grad = True - # if optimizer == "AdamW": or else Adam / AdamW / SGD, etc... - optimizer = torch.optim.AdamW(weights, lr=scheduler.learn_rate) + # Here we use optimizer from saved HN, or we can specify as UI option. + if (optimizer_name := hypernetwork.optimizer_name) in optimizer_dict: + optimizer = optimizer_dict[hypernetwork.optimizer_name](params=weights, lr=scheduler.learn_rate) + else: + print(f"Optimizer type {optimizer_name} is not defined!") + optimizer = torch.optim.AdamW(params=weights, lr=scheduler.learn_rate) + optimizer_name = 'AdamW' + if hypernetwork.optimizer_state_dict: # This line must be changed if Optimizer type can be different from saved optimizer. + try: + optimizer.load_state_dict(hypernetwork.optimizer_state_dict) + except RuntimeError as e: + print("Cannot resume from saved optimizer!") + print(e) steps_without_grad = 0 @@ -467,7 +503,11 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log # Before saving, change name to match current checkpoint. hypernetwork_name_every = f'{hypernetwork_name}-{steps_done}' last_saved_file = os.path.join(hypernetwork_dir, f'{hypernetwork_name_every}.pt') + hypernetwork.optimizer_name = optimizer_name + if shared.opts.save_optimizer_state: + hypernetwork.optimizer_state_dict = optimizer.state_dict() save_hypernetwork(hypernetwork, checkpoint, hypernetwork_name, last_saved_file) + hypernetwork.optimizer_state_dict = None # dereference it after saving, to save memory. textual_inversion.write_loss(log_directory, "hypernetwork_loss.csv", hypernetwork.step, len(ds), { "loss": f"{previous_mean_loss:.7f}", @@ -530,8 +570,12 @@ Last saved image: {html.escape(last_saved_image)}
report_statistics(loss_dict) filename = os.path.join(shared.cmd_opts.hypernetwork_dir, f'{hypernetwork_name}.pt') + hypernetwork.optimizer_name = optimizer_name + if shared.opts.save_optimizer_state: + hypernetwork.optimizer_state_dict = optimizer.state_dict() save_hypernetwork(hypernetwork, checkpoint, hypernetwork_name, filename) - + del optimizer + hypernetwork.optimizer_state_dict = None # dereference it after saving, to save memory. return hypernetwork, filename def save_hypernetwork(hypernetwork, checkpoint, hypernetwork_name, filename): -- cgit v1.2.1 From f5d394214d6ee74a682d0a1016bcbebc4b43c13a Mon Sep 17 00:00:00 2001 From: aria1th <35677394+aria1th@users.noreply.github.com> Date: Fri, 4 Nov 2022 16:04:03 +0900 Subject: split before declaring file name --- modules/hypernetworks/hypernetwork.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) (limited to 'modules/hypernetworks') diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index de8688a9..9b6a3e62 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -382,6 +382,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log shared.state.textinfo = "Initializing hypernetwork training..." shared.state.job_count = steps + hypernetwork_name = hypernetwork_name.rsplit('(', 1)[0] filename = os.path.join(shared.cmd_opts.hypernetwork_dir, f'{hypernetwork_name}.pt') log_directory = os.path.join(log_directory, datetime.datetime.now().strftime("%Y-%m-%d"), hypernetwork_name) @@ -393,7 +394,6 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log else: hypernetwork_dir = None - hypernetwork_name = hypernetwork_name.rsplit('(', 1)[0] if create_image_every > 0: images_dir = os.path.join(log_directory, "images") os.makedirs(images_dir, exist_ok=True) -- cgit v1.2.1 From 1ca0bcd3a7003dd2c1324de7d97fd2a6fc5ddc53 Mon Sep 17 00:00:00 2001 From: aria1th <35677394+aria1th@users.noreply.github.com> Date: Fri, 4 Nov 2022 16:09:19 +0900 Subject: only save if option is enabled --- modules/hypernetworks/hypernetwork.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) (limited to 'modules/hypernetworks') diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index 9b6a3e62..b1f308e2 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -188,7 +188,7 @@ class Hypernetwork: optimizer_saved_dict['optimizer_name'] = self.optimizer_name torch.save(state_dict, filename) - if self.optimizer_state_dict: + if shared.opts.save_optimizer_state and self.optimizer_state_dict: optimizer_saved_dict['hash'] = sd_models.model_hash(filename) optimizer_saved_dict['optimizer_state_dict'] = self.optimizer_state_dict torch.save(optimizer_saved_dict, filename + '.optim') -- cgit v1.2.1 From 39541d7725bc42f456a604b07c50aba503a5a09a Mon Sep 17 00:00:00 2001 From: Fampai <> Date: Fri, 4 Nov 2022 04:50:22 -0400 Subject: Fixes race condition in training when VAE is unloaded set_current_image can attempt to use the VAE when it is unloaded to the CPU while training --- modules/hypernetworks/hypernetwork.py | 4 ++++ 1 file changed, 4 insertions(+) (limited to 'modules/hypernetworks') diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index 6e1a10cf..fcb96059 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -390,7 +390,10 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log with torch.autocast("cuda"): ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=hypernetwork_name, model=shared.sd_model, device=devices.device, template_file=template_file, include_cond=True, batch_size=batch_size) + old_parallel_processing_allowed = shared.parallel_processing_allowed + if unload: + shared.parallel_processing_allowed = False shared.sd_model.cond_stage_model.to(devices.cpu) shared.sd_model.first_stage_model.to(devices.cpu) @@ -531,6 +534,7 @@ Last saved image: {html.escape(last_saved_image)}
filename = os.path.join(shared.cmd_opts.hypernetwork_dir, f'{hypernetwork_name}.pt') save_hypernetwork(hypernetwork, checkpoint, hypernetwork_name, filename) + shared.parallel_processing_allowed = old_parallel_processing_allowed return hypernetwork, filename -- cgit v1.2.1 From fd62727893f9face287b0a9620251afaa38a627d Mon Sep 17 00:00:00 2001 From: Isaac Poulton Date: Fri, 4 Nov 2022 18:34:35 +0700 Subject: Sort hypernetworks --- modules/hypernetworks/hypernetwork.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) (limited to 'modules/hypernetworks') diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index 6e1a10cf..f1f04a70 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -224,7 +224,7 @@ def list_hypernetworks(path): # Prevent a hypothetical "None.pt" from being listed. if name != "None": res[name] = filename - return res + return dict(sorted(res.items())) def load_hypernetwork(filename): -- cgit v1.2.1 From 08feb4c364e8b2aed929fd7d22dfa21a93d78b2c Mon Sep 17 00:00:00 2001 From: Isaac Poulton Date: Fri, 4 Nov 2022 20:53:11 +0700 Subject: Sort straight out of the glob --- modules/hypernetworks/hypernetwork.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) (limited to 'modules/hypernetworks') diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index f1f04a70..a441ab10 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -219,12 +219,12 @@ class Hypernetwork: def list_hypernetworks(path): res = {} - for filename in glob.iglob(os.path.join(path, '**/*.pt'), recursive=True): + for filename in sorted(glob.iglob(os.path.join(path, '**/*.pt'), recursive=True)): name = os.path.splitext(os.path.basename(filename))[0] # Prevent a hypothetical "None.pt" from being listed. if name != "None": res[name] = filename - return dict(sorted(res.items())) + return res def load_hypernetwork(filename): -- cgit v1.2.1 From 62e3d71aa778928d63cab81d9d8cde33e55bebb3 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Sat, 5 Nov 2022 17:09:42 +0300 Subject: rework the code to not use the walrus operator because colab's 3.7 does not support it --- modules/hypernetworks/hypernetwork.py | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) (limited to 'modules/hypernetworks') diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index 5ceed6ee..7f182712 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -429,13 +429,16 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log weights = hypernetwork.weights() for weight in weights: weight.requires_grad = True + # Here we use optimizer from saved HN, or we can specify as UI option. - if (optimizer_name := hypernetwork.optimizer_name) in optimizer_dict: + if hypernetwork.optimizer_name in optimizer_dict: optimizer = optimizer_dict[hypernetwork.optimizer_name](params=weights, lr=scheduler.learn_rate) + optimizer_name = hypernetwork.optimizer_name else: - print(f"Optimizer type {optimizer_name} is not defined!") + print(f"Optimizer type {hypernetwork.optimizer_name} is not defined!") optimizer = torch.optim.AdamW(params=weights, lr=scheduler.learn_rate) optimizer_name = 'AdamW' + if hypernetwork.optimizer_state_dict: # This line must be changed if Optimizer type can be different from saved optimizer. try: optimizer.load_state_dict(hypernetwork.optimizer_state_dict) -- cgit v1.2.1 From cdc8020d13c5eef099c609b0a911ccf3568afc0d Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Sat, 19 Nov 2022 12:01:51 +0300 Subject: change StableDiffusionProcessing to internally use sampler name instead of sampler index --- modules/hypernetworks/hypernetwork.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) (limited to 'modules/hypernetworks') diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index 7f182712..fbb87dd1 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -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 +from modules import devices, processing, sd_models, shared, sd_samplers from modules.textual_inversion import textual_inversion from modules.textual_inversion.learn_schedule import LearnRateScheduler from torch import einsum @@ -535,7 +535,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log p.prompt = preview_prompt p.negative_prompt = preview_negative_prompt p.steps = preview_steps - p.sampler_index = preview_sampler_index + p.sampler_name = sd_samplers.samplers[preview_sampler_index].name p.cfg_scale = preview_cfg_scale p.seed = preview_seed p.width = preview_width -- cgit v1.2.1 From bd68e35de3b7cf7547ed97d8bdf60147402133cc Mon Sep 17 00:00:00 2001 From: flamelaw Date: Sun, 20 Nov 2022 12:35:26 +0900 Subject: Gradient accumulation, autocast fix, new latent sampling method, etc --- modules/hypernetworks/hypernetwork.py | 269 ++++++++++++++++++---------------- 1 file changed, 146 insertions(+), 123 deletions(-) (limited to 'modules/hypernetworks') diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index fbb87dd1..3d3301b0 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -367,13 +367,13 @@ def report_statistics(loss_info:dict): -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, gradient_step, data_root, log_directory, training_width, training_height, steps, shuffle_tags, tag_drop_out, latent_sampling_method, 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): # images allows training previews to have infotext. Importing it at the top causes a circular import problem. from modules import images save_hypernetwork_every = save_hypernetwork_every or 0 create_image_every = create_image_every or 0 - textual_inversion.validate_train_inputs(hypernetwork_name, learn_rate, batch_size, data_root, template_file, steps, save_hypernetwork_every, create_image_every, log_directory, name="hypernetwork") + textual_inversion.validate_train_inputs(hypernetwork_name, learn_rate, batch_size, gradient_step, data_root, template_file, steps, save_hypernetwork_every, create_image_every, log_directory, name="hypernetwork") path = shared.hypernetworks.get(hypernetwork_name, None) shared.loaded_hypernetwork = Hypernetwork() @@ -403,28 +403,24 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log hypernetwork = shared.loaded_hypernetwork checkpoint = sd_models.select_checkpoint() - ititial_step = hypernetwork.step or 0 - if ititial_step >= steps: + initial_step = hypernetwork.step or 0 + if initial_step >= steps: shared.state.textinfo = f"Model has already been trained beyond specified max steps" return hypernetwork, filename - scheduler = LearnRateScheduler(learn_rate, steps, ititial_step) - + scheduler = LearnRateScheduler(learn_rate, steps, initial_step) + # dataset loading may take a while, so input validations and early returns should be done before this shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..." - with torch.autocast("cuda"): - ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=hypernetwork_name, model=shared.sd_model, device=devices.device, template_file=template_file, include_cond=True, batch_size=batch_size) + + pin_memory = shared.opts.pin_memory + + ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=hypernetwork_name, model=shared.sd_model, cond_model=shared.sd_model.cond_stage_model, device=devices.device, template_file=template_file, include_cond=True, batch_size=batch_size, gradient_step=gradient_step, shuffle_tags=shuffle_tags, tag_drop_out=tag_drop_out, latent_sampling_method=latent_sampling_method) + dl = modules.textual_inversion.dataset.PersonalizedDataLoader(ds, batch_size=ds.batch_size, pin_memory=pin_memory) if unload: shared.sd_model.cond_stage_model.to(devices.cpu) shared.sd_model.first_stage_model.to(devices.cpu) - - size = len(ds.indexes) - loss_dict = defaultdict(lambda : deque(maxlen = 1024)) - losses = torch.zeros((size,)) - previous_mean_losses = [0] - previous_mean_loss = 0 - print("Mean loss of {} elements".format(size)) weights = hypernetwork.weights() for weight in weights: @@ -436,8 +432,8 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log optimizer_name = hypernetwork.optimizer_name else: print(f"Optimizer type {hypernetwork.optimizer_name} is not defined!") - optimizer = torch.optim.AdamW(params=weights, lr=scheduler.learn_rate) - optimizer_name = 'AdamW' + optimizer = torch.optim.AdamW(params=weights, lr=scheduler.learn_rate) + optimizer_name = 'AdamW' if hypernetwork.optimizer_state_dict: # This line must be changed if Optimizer type can be different from saved optimizer. try: @@ -446,131 +442,155 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log print("Cannot resume from saved optimizer!") print(e) + scaler = torch.cuda.amp.GradScaler() + + batch_size = ds.batch_size + gradient_step = ds.gradient_step + # n steps = batch_size * gradient_step * n image processed + steps_per_epoch = len(ds) // batch_size // gradient_step + max_steps_per_epoch = len(ds) // batch_size - (len(ds) // batch_size) % gradient_step + loss_step = 0 + _loss_step = 0 #internal + # size = len(ds.indexes) + # loss_dict = defaultdict(lambda : deque(maxlen = 1024)) + # losses = torch.zeros((size,)) + # previous_mean_losses = [0] + # previous_mean_loss = 0 + # print("Mean loss of {} elements".format(size)) + steps_without_grad = 0 last_saved_file = "" last_saved_image = "" forced_filename = "" - pbar = tqdm.tqdm(enumerate(ds), total=steps - ititial_step) - for i, entries in pbar: - hypernetwork.step = i + ititial_step - if len(loss_dict) > 0: - previous_mean_losses = [i[-1] for i in loss_dict.values()] - previous_mean_loss = mean(previous_mean_losses) - - scheduler.apply(optimizer, hypernetwork.step) - if scheduler.finished: - break - - if shared.state.interrupted: - break - - with torch.autocast("cuda"): - c = stack_conds([entry.cond for entry in entries]).to(devices.device) - # c = torch.vstack([entry.cond for entry in entries]).to(devices.device) - x = torch.stack([entry.latent for entry in entries]).to(devices.device) - loss = shared.sd_model(x, c)[0] - del x - del c - - losses[hypernetwork.step % losses.shape[0]] = loss.item() - for entry in entries: - loss_dict[entry.filename].append(loss.item()) + pbar = tqdm.tqdm(total=steps - initial_step) + try: + for i in range((steps-initial_step) * gradient_step): + if scheduler.finished: + break + if shared.state.interrupted: + break + for j, batch in enumerate(dl): + # works as a drop_last=True for gradient accumulation + if j == max_steps_per_epoch: + break + scheduler.apply(optimizer, hypernetwork.step) + if scheduler.finished: + break + if shared.state.interrupted: + break + + with torch.autocast("cuda"): + x = batch.latent_sample.to(devices.device, non_blocking=pin_memory) + if tag_drop_out != 0 or shuffle_tags: + shared.sd_model.cond_stage_model.to(devices.device) + c = shared.sd_model.cond_stage_model(batch.cond_text).to(devices.device, non_blocking=pin_memory) + shared.sd_model.cond_stage_model.to(devices.cpu) + else: + c = stack_conds(batch.cond).to(devices.device, non_blocking=pin_memory) + loss = shared.sd_model(x, c)[0] / gradient_step + del x + del c + + _loss_step += loss.item() + scaler.scale(loss).backward() + # go back until we reach gradient accumulation steps + if (j + 1) % gradient_step != 0: + continue + # print(f"grad:{weights[0].grad.detach().cpu().abs().mean().item():.7f}") + # scaler.unscale_(optimizer) + # print(f"grad:{weights[0].grad.detach().cpu().abs().mean().item():.15f}") + # torch.nn.utils.clip_grad_norm_(weights, max_norm=1.0) + # print(f"grad:{weights[0].grad.detach().cpu().abs().mean().item():.15f}") + scaler.step(optimizer) + scaler.update() + hypernetwork.step += 1 + pbar.update() + optimizer.zero_grad(set_to_none=True) + loss_step = _loss_step + _loss_step = 0 + + steps_done = hypernetwork.step + 1 - optimizer.zero_grad() - weights[0].grad = None - loss.backward() - - if weights[0].grad is None: - steps_without_grad += 1 - else: - steps_without_grad = 0 - assert steps_without_grad < 10, 'no gradient found for the trained weight after backward() for 10 steps in a row; this is a bug; training cannot continue' - - optimizer.step() - - steps_done = hypernetwork.step + 1 - - if torch.isnan(losses[hypernetwork.step % losses.shape[0]]): - raise RuntimeError("Loss diverged.") - - if len(previous_mean_losses) > 1: - std = stdev(previous_mean_losses) - else: - std = 0 - dataset_loss_info = f"dataset loss:{mean(previous_mean_losses):.3f}" + u"\u00B1" + f"({std / (len(previous_mean_losses) ** 0.5):.3f})" - pbar.set_description(dataset_loss_info) - - if hypernetwork_dir is not None and steps_done % save_hypernetwork_every == 0: - # Before saving, change name to match current checkpoint. - hypernetwork_name_every = f'{hypernetwork_name}-{steps_done}' - last_saved_file = os.path.join(hypernetwork_dir, f'{hypernetwork_name_every}.pt') - hypernetwork.optimizer_name = optimizer_name - if shared.opts.save_optimizer_state: - hypernetwork.optimizer_state_dict = optimizer.state_dict() - save_hypernetwork(hypernetwork, checkpoint, hypernetwork_name, last_saved_file) - hypernetwork.optimizer_state_dict = None # dereference it after saving, to save memory. - - textual_inversion.write_loss(log_directory, "hypernetwork_loss.csv", hypernetwork.step, len(ds), { - "loss": f"{previous_mean_loss:.7f}", - "learn_rate": scheduler.learn_rate - }) - - if images_dir is not None and steps_done % create_image_every == 0: - forced_filename = f'{hypernetwork_name}-{steps_done}' - last_saved_image = os.path.join(images_dir, forced_filename) - - optimizer.zero_grad() - shared.sd_model.cond_stage_model.to(devices.device) - shared.sd_model.first_stage_model.to(devices.device) - - p = processing.StableDiffusionProcessingTxt2Img( - sd_model=shared.sd_model, - do_not_save_grid=True, - do_not_save_samples=True, - ) - - if preview_from_txt2img: - p.prompt = preview_prompt - p.negative_prompt = preview_negative_prompt - p.steps = preview_steps - p.sampler_name = sd_samplers.samplers[preview_sampler_index].name - p.cfg_scale = preview_cfg_scale - p.seed = preview_seed - p.width = preview_width - p.height = preview_height - else: - p.prompt = entries[0].cond_text - p.steps = 20 + epoch_num = hypernetwork.step // steps_per_epoch + epoch_step = hypernetwork.step % steps_per_epoch + + pbar.set_description(f"[Epoch {epoch_num}: {epoch_step+1}/{steps_per_epoch}]loss: {loss_step:.7f}") + if hypernetwork_dir is not None and steps_done % save_hypernetwork_every == 0: + # Before saving, change name to match current checkpoint. + hypernetwork_name_every = f'{hypernetwork_name}-{steps_done}' + last_saved_file = os.path.join(hypernetwork_dir, f'{hypernetwork_name_every}.pt') + hypernetwork.optimizer_name = optimizer_name + if shared.opts.save_optimizer_state: + hypernetwork.optimizer_state_dict = optimizer.state_dict() + save_hypernetwork(hypernetwork, checkpoint, hypernetwork_name, last_saved_file) + hypernetwork.optimizer_state_dict = None # dereference it after saving, to save memory. + + textual_inversion.write_loss(log_directory, "hypernetwork_loss.csv", hypernetwork.step, steps_per_epoch, { + "loss": f"{loss_step:.7f}", + "learn_rate": scheduler.learn_rate + }) + + if images_dir is not None and steps_done % create_image_every == 0: + forced_filename = f'{hypernetwork_name}-{steps_done}' + last_saved_image = os.path.join(images_dir, forced_filename) + + shared.sd_model.cond_stage_model.to(devices.device) + shared.sd_model.first_stage_model.to(devices.device) + + p = processing.StableDiffusionProcessingTxt2Img( + sd_model=shared.sd_model, + do_not_save_grid=True, + do_not_save_samples=True, + ) + + if preview_from_txt2img: + p.prompt = preview_prompt + p.negative_prompt = preview_negative_prompt + p.steps = preview_steps + p.sampler_name = sd_samplers.samplers[preview_sampler_index].name + p.cfg_scale = preview_cfg_scale + p.seed = preview_seed + p.width = preview_width + p.height = preview_height + else: + p.prompt = batch.cond_text[0] + p.steps = 20 + p.width = training_width + p.height = training_height - preview_text = p.prompt + preview_text = p.prompt - processed = processing.process_images(p) - image = processed.images[0] if len(processed.images)>0 else None + processed = processing.process_images(p) + image = processed.images[0] if len(processed.images) > 0 else None - if unload: - shared.sd_model.cond_stage_model.to(devices.cpu) - shared.sd_model.first_stage_model.to(devices.cpu) + if unload: + shared.sd_model.cond_stage_model.to(devices.cpu) + shared.sd_model.first_stage_model.to(devices.cpu) - if image is not None: - shared.state.current_image = image - last_saved_image, last_text_info = images.save_image(image, images_dir, "", p.seed, p.prompt, shared.opts.samples_format, processed.infotexts[0], p=p, forced_filename=forced_filename, save_to_dirs=False) - last_saved_image += f", prompt: {preview_text}" + if image is not None: + shared.state.current_image = image + last_saved_image, last_text_info = images.save_image(image, images_dir, "", p.seed, p.prompt, shared.opts.samples_format, processed.infotexts[0], p=p, forced_filename=forced_filename, save_to_dirs=False) + last_saved_image += f", prompt: {preview_text}" - shared.state.job_no = hypernetwork.step + shared.state.job_no = hypernetwork.step - shared.state.textinfo = f""" + shared.state.textinfo = f"""

-Loss: {previous_mean_loss:.7f}
+Loss: {loss_step:.7f}
Step: {hypernetwork.step}
-Last prompt: {html.escape(entries[0].cond_text)}
+Last prompt: {html.escape(batch.cond_text[0])}
Last saved hypernetwork: {html.escape(last_saved_file)}
Last saved image: {html.escape(last_saved_image)}

""" - - report_statistics(loss_dict) + except Exception: + print(traceback.format_exc(), file=sys.stderr) + finally: + pbar.leave = False + pbar.close() + #report_statistics(loss_dict) filename = os.path.join(shared.cmd_opts.hypernetwork_dir, f'{hypernetwork_name}.pt') hypernetwork.optimizer_name = optimizer_name @@ -579,6 +599,9 @@ Last saved image: {html.escape(last_saved_image)}
save_hypernetwork(hypernetwork, checkpoint, hypernetwork_name, filename) del optimizer hypernetwork.optimizer_state_dict = None # dereference it after saving, to save memory. + shared.sd_model.cond_stage_model.to(devices.device) + shared.sd_model.first_stage_model.to(devices.device) + return hypernetwork, filename def save_hypernetwork(hypernetwork, checkpoint, hypernetwork_name, filename): -- cgit v1.2.1 From 5b57f61ba47f8b11d19a5b46e7fb5a52458abae5 Mon Sep 17 00:00:00 2001 From: flamelaw Date: Mon, 21 Nov 2022 10:15:46 +0900 Subject: fix pin_memory with different latent sampling method --- modules/hypernetworks/hypernetwork.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) (limited to 'modules/hypernetworks') diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index 3d3301b0..0128419b 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -416,7 +416,10 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, gradient_step, pin_memory = shared.opts.pin_memory ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=hypernetwork_name, model=shared.sd_model, cond_model=shared.sd_model.cond_stage_model, device=devices.device, template_file=template_file, include_cond=True, batch_size=batch_size, gradient_step=gradient_step, shuffle_tags=shuffle_tags, tag_drop_out=tag_drop_out, latent_sampling_method=latent_sampling_method) - dl = modules.textual_inversion.dataset.PersonalizedDataLoader(ds, batch_size=ds.batch_size, pin_memory=pin_memory) + + latent_sampling_method = ds.latent_sampling_method + + dl = modules.textual_inversion.dataset.PersonalizedDataLoader(ds, latent_sampling_method=latent_sampling_method, batch_size=ds.batch_size, pin_memory=pin_memory) if unload: shared.sd_model.cond_stage_model.to(devices.cpu) -- cgit v1.2.1 From 89d8ecff09b426ddc89eb5b432825f8f4c218051 Mon Sep 17 00:00:00 2001 From: flamelaw Date: Wed, 23 Nov 2022 02:49:01 +0900 Subject: small fixes --- modules/hypernetworks/hypernetwork.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) (limited to 'modules/hypernetworks') diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index 0128419b..4541af18 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -435,8 +435,8 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, gradient_step, optimizer_name = hypernetwork.optimizer_name else: print(f"Optimizer type {hypernetwork.optimizer_name} is not defined!") - optimizer = torch.optim.AdamW(params=weights, lr=scheduler.learn_rate) - optimizer_name = 'AdamW' + optimizer = torch.optim.AdamW(params=weights, lr=scheduler.learn_rate) + optimizer_name = 'AdamW' if hypernetwork.optimizer_state_dict: # This line must be changed if Optimizer type can be different from saved optimizer. try: @@ -582,7 +582,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, gradient_step, shared.state.textinfo = f"""

Loss: {loss_step:.7f}
-Step: {hypernetwork.step}
+Step: {steps_done}
Last prompt: {html.escape(batch.cond_text[0])}
Last saved hypernetwork: {html.escape(last_saved_file)}
Last saved image: {html.escape(last_saved_image)}
-- cgit v1.2.1 From d2c97fc3fe5857d6fba9ad1695ed3ac6ec455ca9 Mon Sep 17 00:00:00 2001 From: flamelaw Date: Wed, 23 Nov 2022 20:00:00 +0900 Subject: fix dropout, implement train/eval mode --- modules/hypernetworks/hypernetwork.py | 24 ++++++++++++++++++------ 1 file changed, 18 insertions(+), 6 deletions(-) (limited to 'modules/hypernetworks') diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index 4541af18..9388959f 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -154,16 +154,28 @@ class Hypernetwork: HypernetworkModule(size, None, self.layer_structure, self.activation_func, self.weight_init, self.add_layer_norm, self.use_dropout, self.activate_output, last_layer_dropout=self.last_layer_dropout), ) + self.eval_mode() def weights(self): res = [] + for k, layers in self.layers.items(): + for layer in layers: + res += layer.parameters() + return res + def train_mode(self): for k, layers in self.layers.items(): for layer in layers: layer.train() - res += layer.trainables() + for param in layer.parameters(): + param.requires_grad = True - return res + def eval_mode(self): + for k, layers in self.layers.items(): + for layer in layers: + layer.eval() + for param in layer.parameters(): + param.requires_grad = False def save(self, filename): state_dict = {} @@ -426,8 +438,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, gradient_step, shared.sd_model.first_stage_model.to(devices.cpu) weights = hypernetwork.weights() - for weight in weights: - weight.requires_grad = True + hypernetwork.train_mode() # Here we use optimizer from saved HN, or we can specify as UI option. if hypernetwork.optimizer_name in optimizer_dict: @@ -538,7 +549,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, gradient_step, if images_dir is not None and steps_done % create_image_every == 0: forced_filename = f'{hypernetwork_name}-{steps_done}' last_saved_image = os.path.join(images_dir, forced_filename) - + hypernetwork.eval_mode() shared.sd_model.cond_stage_model.to(devices.device) shared.sd_model.first_stage_model.to(devices.device) @@ -571,7 +582,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, gradient_step, if unload: shared.sd_model.cond_stage_model.to(devices.cpu) shared.sd_model.first_stage_model.to(devices.cpu) - + hypernetwork.train_mode() if image is not None: shared.state.current_image = image last_saved_image, last_text_info = images.save_image(image, images_dir, "", p.seed, p.prompt, shared.opts.samples_format, processed.infotexts[0], p=p, forced_filename=forced_filename, save_to_dirs=False) @@ -593,6 +604,7 @@ Last saved image: {html.escape(last_saved_image)}
finally: pbar.leave = False pbar.close() + hypernetwork.eval_mode() #report_statistics(loss_dict) filename = os.path.join(shared.cmd_opts.hypernetwork_dir, f'{hypernetwork_name}.pt') -- cgit v1.2.1 From 1bd57cc9791e2e742f72a3d74d589f2c289e8e92 Mon Sep 17 00:00:00 2001 From: flamelaw Date: Wed, 23 Nov 2022 20:21:52 +0900 Subject: last_layer_dropout default to False --- modules/hypernetworks/hypernetwork.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) (limited to 'modules/hypernetworks') diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index 9388959f..8466887f 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -38,7 +38,7 @@ class HypernetworkModule(torch.nn.Module): activation_dict.update({cls_name.lower(): cls_obj for cls_name, cls_obj in inspect.getmembers(torch.nn.modules.activation) if inspect.isclass(cls_obj) and cls_obj.__module__ == 'torch.nn.modules.activation'}) def __init__(self, dim, state_dict=None, layer_structure=None, activation_func=None, weight_init='Normal', - add_layer_norm=False, use_dropout=False, activate_output=False, last_layer_dropout=True): + add_layer_norm=False, use_dropout=False, activate_output=False, last_layer_dropout=False): super().__init__() assert layer_structure is not None, "layer_structure must not be None" -- cgit v1.2.1 From 4d5f1691dda971ec7b461dd880426300fd54ccee Mon Sep 17 00:00:00 2001 From: brkirch Date: Mon, 28 Nov 2022 21:36:35 -0500 Subject: Use devices.autocast instead of torch.autocast --- modules/hypernetworks/hypernetwork.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) (limited to 'modules/hypernetworks') diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index 8466887f..eb5ae372 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -495,7 +495,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, gradient_step, if shared.state.interrupted: break - with torch.autocast("cuda"): + with devices.autocast(): x = batch.latent_sample.to(devices.device, non_blocking=pin_memory) if tag_drop_out != 0 or shuffle_tags: shared.sd_model.cond_stage_model.to(devices.device) -- cgit v1.2.1 From 3bf5591efe9a9f219c6088be322a87adc4f48f95 Mon Sep 17 00:00:00 2001 From: Yuval Aboulafia Date: Sat, 24 Dec 2022 21:35:29 +0200 Subject: fix F541 f-string without any placeholders --- modules/hypernetworks/hypernetwork.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) (limited to 'modules/hypernetworks') diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index c406ffb3..9d3034ae 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -277,7 +277,7 @@ def load_hypernetwork(filename): print(traceback.format_exc(), file=sys.stderr) else: if shared.loaded_hypernetwork is not None: - print(f"Unloading hypernetwork") + print("Unloading hypernetwork") shared.loaded_hypernetwork = None @@ -417,7 +417,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, gradient_step, initial_step = hypernetwork.step or 0 if initial_step >= steps: - shared.state.textinfo = f"Model has already been trained beyond specified max steps" + shared.state.textinfo = "Model has already been trained beyond specified max steps" return hypernetwork, filename scheduler = LearnRateScheduler(learn_rate, steps, initial_step) -- cgit v1.2.1 From 5f1dfbbc959855fd90ba80c0c76301d2063772fa Mon Sep 17 00:00:00 2001 From: Vladimir Mandic Date: Sat, 24 Dec 2022 18:02:22 -0500 Subject: implement train api --- modules/hypernetworks/hypernetwork.py | 26 ++++++++++++++++++++++++++ modules/hypernetworks/ui.py | 31 ++++--------------------------- 2 files changed, 30 insertions(+), 27 deletions(-) (limited to 'modules/hypernetworks') diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index c406ffb3..3182ff03 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -378,6 +378,32 @@ def report_statistics(loss_info:dict): print(e) +def create_hypernetwork(name, enable_sizes, overwrite_old, layer_structure=None, activation_func=None, weight_init=None, add_layer_norm=False, use_dropout=False): + # Remove illegal characters from name. + name = "".join( x for x in name if (x.isalnum() or x in "._- ")) + + fn = os.path.join(shared.cmd_opts.hypernetwork_dir, f"{name}.pt") + if not overwrite_old: + assert not os.path.exists(fn), f"file {fn} already exists" + + if type(layer_structure) == str: + layer_structure = [float(x.strip()) for x in layer_structure.split(",")] + + hypernet = modules.hypernetworks.hypernetwork.Hypernetwork( + name=name, + enable_sizes=[int(x) for x in enable_sizes], + layer_structure=layer_structure, + activation_func=activation_func, + weight_init=weight_init, + add_layer_norm=add_layer_norm, + use_dropout=use_dropout, + ) + hypernet.save(fn) + + shared.reload_hypernetworks() + + return fn + def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, gradient_step, data_root, log_directory, training_width, training_height, steps, shuffle_tags, tag_drop_out, latent_sampling_method, 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): # images allows training previews to have infotext. Importing it at the top causes a circular import problem. diff --git a/modules/hypernetworks/ui.py b/modules/hypernetworks/ui.py index c2d4b51c..e7f9e593 100644 --- a/modules/hypernetworks/ui.py +++ b/modules/hypernetworks/ui.py @@ -3,39 +3,16 @@ import os import re import gradio as gr -import modules.textual_inversion.preprocess -import modules.textual_inversion.textual_inversion +import modules.hypernetworks.hypernetwork from modules import devices, sd_hijack, shared -from modules.hypernetworks import hypernetwork not_available = ["hardswish", "multiheadattention"] -keys = list(x for x in hypernetwork.HypernetworkModule.activation_dict.keys() if x not in not_available) +keys = list(x for x in modules.hypernetworks.hypernetwork.HypernetworkModule.activation_dict.keys() if x not in not_available) def create_hypernetwork(name, enable_sizes, overwrite_old, layer_structure=None, activation_func=None, weight_init=None, add_layer_norm=False, use_dropout=False): - # Remove illegal characters from name. - name = "".join( x for x in name if (x.isalnum() or x in "._- ")) + filename = modules.hypernetworks.hypernetwork.create_hypernetwork(name, enable_sizes, overwrite_old, layer_structure, activation_func, weight_init, add_layer_norm, use_dropout) - fn = os.path.join(shared.cmd_opts.hypernetwork_dir, f"{name}.pt") - if not overwrite_old: - assert not os.path.exists(fn), f"file {fn} already exists" - - if type(layer_structure) == str: - layer_structure = [float(x.strip()) for x in layer_structure.split(",")] - - hypernet = modules.hypernetworks.hypernetwork.Hypernetwork( - name=name, - enable_sizes=[int(x) for x in enable_sizes], - layer_structure=layer_structure, - activation_func=activation_func, - weight_init=weight_init, - add_layer_norm=add_layer_norm, - use_dropout=use_dropout, - ) - hypernet.save(fn) - - shared.reload_hypernetworks() - - return gr.Dropdown.update(choices=sorted([x for x in shared.hypernetworks.keys()])), f"Created: {fn}", "" + return gr.Dropdown.update(choices=sorted([x for x in shared.hypernetworks.keys()])), f"Created: {filename}", "" def train_hypernetwork(*args): -- cgit v1.2.1 From 192ddc04d6de0d780f73aa5fbaa8c66cd4642e1c Mon Sep 17 00:00:00 2001 From: Vladimir Mandic Date: Tue, 3 Jan 2023 10:34:51 -0500 Subject: add job info to modules --- modules/hypernetworks/hypernetwork.py | 1 + 1 file changed, 1 insertion(+) (limited to 'modules/hypernetworks') diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index 109e8078..450fecac 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -417,6 +417,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, gradient_step, shared.loaded_hypernetwork = Hypernetwork() shared.loaded_hypernetwork.load(path) + shared.state.job = "train-hypernetwork" shared.state.textinfo = "Initializing hypernetwork training..." shared.state.job_count = steps -- cgit v1.2.1