784 lines
35 KiB
Python
784 lines
35 KiB
Python
import datetime
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import glob
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import html
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import os
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import inspect
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from contextlib import closing
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import modules.textual_inversion.dataset
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import torch
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import tqdm
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from einops import rearrange, repeat
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from ldm.util import default
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from modules import devices, processing, sd_models, shared, sd_samplers, hashes, sd_hijack_checkpoint, errors
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from modules.textual_inversion import textual_inversion, logging
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from modules.textual_inversion.learn_schedule import LearnRateScheduler
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from torch import einsum
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from torch.nn.init import normal_, xavier_normal_, xavier_uniform_, kaiming_normal_, kaiming_uniform_, zeros_
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from collections import deque
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from statistics import stdev, mean
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optimizer_dict = {optim_name : cls_obj for optim_name, cls_obj in inspect.getmembers(torch.optim, inspect.isclass) if optim_name != "Optimizer"}
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class HypernetworkModule(torch.nn.Module):
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activation_dict = {
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"linear": torch.nn.Identity,
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"relu": torch.nn.ReLU,
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"leakyrelu": torch.nn.LeakyReLU,
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"elu": torch.nn.ELU,
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"swish": torch.nn.Hardswish,
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"tanh": torch.nn.Tanh,
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"sigmoid": torch.nn.Sigmoid,
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}
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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'})
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def __init__(self, dim, state_dict=None, layer_structure=None, activation_func=None, weight_init='Normal',
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add_layer_norm=False, activate_output=False, dropout_structure=None):
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super().__init__()
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self.multiplier = 1.0
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assert layer_structure is not None, "layer_structure must not be None"
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assert layer_structure[0] == 1, "Multiplier Sequence should start with size 1!"
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assert layer_structure[-1] == 1, "Multiplier Sequence should end with size 1!"
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linears = []
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for i in range(len(layer_structure) - 1):
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# Add a fully-connected layer
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linears.append(torch.nn.Linear(int(dim * layer_structure[i]), int(dim * layer_structure[i+1])))
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# Add an activation func except last layer
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if activation_func == "linear" or activation_func is None or (i >= len(layer_structure) - 2 and not activate_output):
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pass
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elif activation_func in self.activation_dict:
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linears.append(self.activation_dict[activation_func]())
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else:
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raise RuntimeError(f'hypernetwork uses an unsupported activation function: {activation_func}')
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# Add layer normalization
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if add_layer_norm:
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linears.append(torch.nn.LayerNorm(int(dim * layer_structure[i+1])))
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# Everything should be now parsed into dropout structure, and applied here.
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# Since we only have dropouts after layers, dropout structure should start with 0 and end with 0.
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if dropout_structure is not None and dropout_structure[i+1] > 0:
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assert 0 < dropout_structure[i+1] < 1, "Dropout probability should be 0 or float between 0 and 1!"
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linears.append(torch.nn.Dropout(p=dropout_structure[i+1]))
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# Code explanation : [1, 2, 1] -> dropout is missing when last_layer_dropout is false. [1, 2, 2, 1] -> [0, 0.3, 0, 0], when its True, [0, 0.3, 0.3, 0].
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self.linear = torch.nn.Sequential(*linears)
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if state_dict is not None:
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self.fix_old_state_dict(state_dict)
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self.load_state_dict(state_dict)
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else:
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for layer in self.linear:
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if type(layer) == torch.nn.Linear or type(layer) == torch.nn.LayerNorm:
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w, b = layer.weight.data, layer.bias.data
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if weight_init == "Normal" or type(layer) == torch.nn.LayerNorm:
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normal_(w, mean=0.0, std=0.01)
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normal_(b, mean=0.0, std=0)
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elif weight_init == 'XavierUniform':
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xavier_uniform_(w)
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zeros_(b)
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elif weight_init == 'XavierNormal':
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xavier_normal_(w)
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zeros_(b)
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elif weight_init == 'KaimingUniform':
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kaiming_uniform_(w, nonlinearity='leaky_relu' if 'leakyrelu' == activation_func else 'relu')
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zeros_(b)
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elif weight_init == 'KaimingNormal':
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kaiming_normal_(w, nonlinearity='leaky_relu' if 'leakyrelu' == activation_func else 'relu')
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zeros_(b)
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else:
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raise KeyError(f"Key {weight_init} is not defined as initialization!")
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self.to(devices.device)
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def fix_old_state_dict(self, state_dict):
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changes = {
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'linear1.bias': 'linear.0.bias',
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'linear1.weight': 'linear.0.weight',
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'linear2.bias': 'linear.1.bias',
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'linear2.weight': 'linear.1.weight',
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}
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for fr, to in changes.items():
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x = state_dict.get(fr, None)
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if x is None:
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continue
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del state_dict[fr]
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state_dict[to] = x
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def forward(self, x):
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return x + self.linear(x) * (self.multiplier if not self.training else 1)
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def trainables(self):
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layer_structure = []
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for layer in self.linear:
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if type(layer) == torch.nn.Linear or type(layer) == torch.nn.LayerNorm:
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layer_structure += [layer.weight, layer.bias]
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return layer_structure
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#param layer_structure : sequence used for length, use_dropout : controlling boolean, last_layer_dropout : for compatibility check.
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def parse_dropout_structure(layer_structure, use_dropout, last_layer_dropout):
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if layer_structure is None:
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layer_structure = [1, 2, 1]
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if not use_dropout:
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return [0] * len(layer_structure)
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dropout_values = [0]
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dropout_values.extend([0.3] * (len(layer_structure) - 3))
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if last_layer_dropout:
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dropout_values.append(0.3)
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else:
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dropout_values.append(0)
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dropout_values.append(0)
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return dropout_values
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class Hypernetwork:
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filename = None
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name = None
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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):
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self.filename = None
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self.name = name
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self.layers = {}
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self.step = 0
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self.sd_checkpoint = None
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self.sd_checkpoint_name = None
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self.layer_structure = layer_structure
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self.activation_func = activation_func
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self.weight_init = weight_init
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self.add_layer_norm = add_layer_norm
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self.use_dropout = use_dropout
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self.activate_output = activate_output
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self.last_layer_dropout = kwargs.get('last_layer_dropout', True)
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self.dropout_structure = kwargs.get('dropout_structure', None)
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if self.dropout_structure is None:
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self.dropout_structure = parse_dropout_structure(self.layer_structure, self.use_dropout, self.last_layer_dropout)
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self.optimizer_name = None
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self.optimizer_state_dict = None
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self.optional_info = None
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for size in enable_sizes or []:
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self.layers[size] = (
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HypernetworkModule(size, None, self.layer_structure, self.activation_func, self.weight_init,
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self.add_layer_norm, self.activate_output, dropout_structure=self.dropout_structure),
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HypernetworkModule(size, None, self.layer_structure, self.activation_func, self.weight_init,
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self.add_layer_norm, self.activate_output, dropout_structure=self.dropout_structure),
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)
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self.eval()
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def weights(self):
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res = []
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for layers in self.layers.values():
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for layer in layers:
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res += layer.parameters()
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return res
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def train(self, mode=True):
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for layers in self.layers.values():
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for layer in layers:
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layer.train(mode=mode)
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for param in layer.parameters():
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param.requires_grad = mode
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def to(self, device):
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for layers in self.layers.values():
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for layer in layers:
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layer.to(device)
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return self
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def set_multiplier(self, multiplier):
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for layers in self.layers.values():
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for layer in layers:
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layer.multiplier = multiplier
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return self
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def eval(self):
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for layers in self.layers.values():
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for layer in layers:
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layer.eval()
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for param in layer.parameters():
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param.requires_grad = False
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def save(self, filename):
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state_dict = {}
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optimizer_saved_dict = {}
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for k, v in self.layers.items():
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state_dict[k] = (v[0].state_dict(), v[1].state_dict())
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state_dict['step'] = self.step
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state_dict['name'] = self.name
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state_dict['layer_structure'] = self.layer_structure
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state_dict['activation_func'] = self.activation_func
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state_dict['is_layer_norm'] = self.add_layer_norm
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state_dict['weight_initialization'] = self.weight_init
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state_dict['sd_checkpoint'] = self.sd_checkpoint
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state_dict['sd_checkpoint_name'] = self.sd_checkpoint_name
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state_dict['activate_output'] = self.activate_output
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state_dict['use_dropout'] = self.use_dropout
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state_dict['dropout_structure'] = self.dropout_structure
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state_dict['last_layer_dropout'] = (self.dropout_structure[-2] != 0) if self.dropout_structure is not None else self.last_layer_dropout
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state_dict['optional_info'] = self.optional_info if self.optional_info else None
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if self.optimizer_name is not None:
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optimizer_saved_dict['optimizer_name'] = self.optimizer_name
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torch.save(state_dict, filename)
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if shared.opts.save_optimizer_state and self.optimizer_state_dict:
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optimizer_saved_dict['hash'] = self.shorthash()
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optimizer_saved_dict['optimizer_state_dict'] = self.optimizer_state_dict
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torch.save(optimizer_saved_dict, filename + '.optim')
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def load(self, filename):
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self.filename = filename
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if self.name is None:
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self.name = os.path.splitext(os.path.basename(filename))[0]
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state_dict = torch.load(filename, map_location='cpu')
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self.layer_structure = state_dict.get('layer_structure', [1, 2, 1])
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self.optional_info = state_dict.get('optional_info', None)
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self.activation_func = state_dict.get('activation_func', None)
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self.weight_init = state_dict.get('weight_initialization', 'Normal')
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self.add_layer_norm = state_dict.get('is_layer_norm', False)
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self.dropout_structure = state_dict.get('dropout_structure', None)
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self.use_dropout = True if self.dropout_structure is not None and any(self.dropout_structure) else state_dict.get('use_dropout', False)
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self.activate_output = state_dict.get('activate_output', True)
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self.last_layer_dropout = state_dict.get('last_layer_dropout', False)
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# Dropout structure should have same length as layer structure, Every digits should be in [0,1), and last digit must be 0.
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if self.dropout_structure is None:
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self.dropout_structure = parse_dropout_structure(self.layer_structure, self.use_dropout, self.last_layer_dropout)
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if shared.opts.print_hypernet_extra:
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if self.optional_info is not None:
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print(f" INFO:\n {self.optional_info}\n")
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print(f" Layer structure: {self.layer_structure}")
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print(f" Activation function: {self.activation_func}")
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print(f" Weight initialization: {self.weight_init}")
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print(f" Layer norm: {self.add_layer_norm}")
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print(f" Dropout usage: {self.use_dropout}" )
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print(f" Activate last layer: {self.activate_output}")
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print(f" Dropout structure: {self.dropout_structure}")
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optimizer_saved_dict = torch.load(self.filename + '.optim', map_location='cpu') if os.path.exists(self.filename + '.optim') else {}
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if self.shorthash() == optimizer_saved_dict.get('hash', None):
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self.optimizer_state_dict = optimizer_saved_dict.get('optimizer_state_dict', None)
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else:
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self.optimizer_state_dict = None
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if self.optimizer_state_dict:
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self.optimizer_name = optimizer_saved_dict.get('optimizer_name', 'AdamW')
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if shared.opts.print_hypernet_extra:
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print("Loaded existing optimizer from checkpoint")
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print(f"Optimizer name is {self.optimizer_name}")
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else:
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self.optimizer_name = "AdamW"
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if shared.opts.print_hypernet_extra:
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print("No saved optimizer exists in checkpoint")
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for size, sd in state_dict.items():
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if type(size) == int:
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self.layers[size] = (
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HypernetworkModule(size, sd[0], self.layer_structure, self.activation_func, self.weight_init,
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self.add_layer_norm, self.activate_output, self.dropout_structure),
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HypernetworkModule(size, sd[1], self.layer_structure, self.activation_func, self.weight_init,
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self.add_layer_norm, self.activate_output, self.dropout_structure),
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)
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self.name = state_dict.get('name', self.name)
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self.step = state_dict.get('step', 0)
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self.sd_checkpoint = state_dict.get('sd_checkpoint', None)
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self.sd_checkpoint_name = state_dict.get('sd_checkpoint_name', None)
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self.eval()
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def shorthash(self):
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sha256 = hashes.sha256(self.filename, f'hypernet/{self.name}')
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return sha256[0:10] if sha256 else None
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def list_hypernetworks(path):
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res = {}
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for filename in sorted(glob.iglob(os.path.join(path, '**/*.pt'), recursive=True), key=str.lower):
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name = os.path.splitext(os.path.basename(filename))[0]
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# Prevent a hypothetical "None.pt" from being listed.
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if name != "None":
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res[name] = filename
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return res
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def load_hypernetwork(name):
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path = shared.hypernetworks.get(name, None)
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if path is None:
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return None
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try:
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hypernetwork = Hypernetwork()
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hypernetwork.load(path)
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return hypernetwork
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except Exception:
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errors.report(f"Error loading hypernetwork {path}", exc_info=True)
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return None
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def load_hypernetworks(names, multipliers=None):
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already_loaded = {}
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for hypernetwork in shared.loaded_hypernetworks:
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if hypernetwork.name in names:
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already_loaded[hypernetwork.name] = hypernetwork
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shared.loaded_hypernetworks.clear()
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for i, name in enumerate(names):
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hypernetwork = already_loaded.get(name, None)
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if hypernetwork is None:
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hypernetwork = load_hypernetwork(name)
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if hypernetwork is None:
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continue
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hypernetwork.set_multiplier(multipliers[i] if multipliers else 1.0)
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shared.loaded_hypernetworks.append(hypernetwork)
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def apply_single_hypernetwork(hypernetwork, context_k, context_v, layer=None):
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hypernetwork_layers = (hypernetwork.layers if hypernetwork is not None else {}).get(context_k.shape[2], None)
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if hypernetwork_layers is None:
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return context_k, context_v
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if layer is not None:
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layer.hyper_k = hypernetwork_layers[0]
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layer.hyper_v = hypernetwork_layers[1]
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context_k = devices.cond_cast_unet(hypernetwork_layers[0](devices.cond_cast_float(context_k)))
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context_v = devices.cond_cast_unet(hypernetwork_layers[1](devices.cond_cast_float(context_v)))
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return context_k, context_v
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def apply_hypernetworks(hypernetworks, context, layer=None):
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context_k = context
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context_v = context
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for hypernetwork in hypernetworks:
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context_k, context_v = apply_single_hypernetwork(hypernetwork, context_k, context_v, layer)
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return context_k, context_v
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def attention_CrossAttention_forward(self, x, context=None, mask=None, **kwargs):
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h = self.heads
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q = self.to_q(x)
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context = default(context, x)
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context_k, context_v = apply_hypernetworks(shared.loaded_hypernetworks, context, self)
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k = self.to_k(context_k)
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v = self.to_v(context_v)
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q, k, v = (rearrange(t, 'b n (h d) -> (b h) n d', h=h) for t in (q, k, v))
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sim = einsum('b i d, b j d -> b i j', q, k) * self.scale
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if mask is not None:
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mask = rearrange(mask, 'b ... -> b (...)')
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max_neg_value = -torch.finfo(sim.dtype).max
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mask = repeat(mask, 'b j -> (b h) () j', h=h)
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sim.masked_fill_(~mask, max_neg_value)
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# attention, what we cannot get enough of
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attn = sim.softmax(dim=-1)
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out = einsum('b i j, b j d -> b i d', attn, v)
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out = rearrange(out, '(b h) n d -> b n (h d)', h=h)
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return self.to_out(out)
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def stack_conds(conds):
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if len(conds) == 1:
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return torch.stack(conds)
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# same as in reconstruct_multicond_batch
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token_count = max([x.shape[0] for x in conds])
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for i in range(len(conds)):
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if conds[i].shape[0] != token_count:
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last_vector = conds[i][-1:]
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last_vector_repeated = last_vector.repeat([token_count - conds[i].shape[0], 1])
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conds[i] = torch.vstack([conds[i], last_vector_repeated])
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return torch.stack(conds)
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def statistics(data):
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if len(data) < 2:
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std = 0
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else:
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std = stdev(data)
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total_information = f"loss:{mean(data):.3f}" + u"\u00B1" + f"({std/ (len(data) ** 0.5):.3f})"
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recent_data = data[-32:]
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if len(recent_data) < 2:
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std = 0
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else:
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std = stdev(recent_data)
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recent_information = f"recent 32 loss:{mean(recent_data):.3f}" + u"\u00B1" + f"({std / (len(recent_data) ** 0.5):.3f})"
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return total_information, recent_information
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def create_hypernetwork(name, enable_sizes, overwrite_old, layer_structure=None, activation_func=None, weight_init=None, add_layer_norm=False, use_dropout=False, dropout_structure=None):
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# Remove illegal characters from name.
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name = "".join( x for x in name if (x.isalnum() or x in "._- "))
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assert name, "Name cannot be empty!"
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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(",")]
|
|
|
|
if use_dropout and dropout_structure and type(dropout_structure) == str:
|
|
dropout_structure = [float(x.strip()) for x in dropout_structure.split(",")]
|
|
else:
|
|
dropout_structure = [0] * len(layer_structure)
|
|
|
|
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,
|
|
dropout_structure=dropout_structure
|
|
)
|
|
hypernet.save(fn)
|
|
|
|
shared.reload_hypernetworks()
|
|
|
|
|
|
def train_hypernetwork(id_task, hypernetwork_name, learn_rate, batch_size, gradient_step, data_root, log_directory, training_width, training_height, varsize, steps, clip_grad_mode, clip_grad_value, shuffle_tags, tag_drop_out, latent_sampling_method, use_weight, create_image_every, save_hypernetwork_every, template_filename, 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
|
|
template_file = textual_inversion.textual_inversion_templates.get(template_filename, None)
|
|
textual_inversion.validate_train_inputs(hypernetwork_name, learn_rate, batch_size, gradient_step, data_root, template_file, template_filename, steps, save_hypernetwork_every, create_image_every, log_directory, name="hypernetwork")
|
|
template_file = template_file.path
|
|
|
|
path = shared.hypernetworks.get(hypernetwork_name, None)
|
|
hypernetwork = Hypernetwork()
|
|
hypernetwork.load(path)
|
|
shared.loaded_hypernetworks = [hypernetwork]
|
|
|
|
shared.state.job = "train-hypernetwork"
|
|
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)
|
|
unload = shared.opts.unload_models_when_training
|
|
|
|
if save_hypernetwork_every > 0:
|
|
hypernetwork_dir = os.path.join(log_directory, "hypernetworks")
|
|
os.makedirs(hypernetwork_dir, exist_ok=True)
|
|
else:
|
|
hypernetwork_dir = None
|
|
|
|
if create_image_every > 0:
|
|
images_dir = os.path.join(log_directory, "images")
|
|
os.makedirs(images_dir, exist_ok=True)
|
|
else:
|
|
images_dir = None
|
|
|
|
checkpoint = sd_models.select_checkpoint()
|
|
|
|
initial_step = hypernetwork.step or 0
|
|
if initial_step >= steps:
|
|
shared.state.textinfo = "Model has already been trained beyond specified max steps"
|
|
return hypernetwork, filename
|
|
|
|
scheduler = LearnRateScheduler(learn_rate, steps, initial_step)
|
|
|
|
clip_grad = torch.nn.utils.clip_grad_value_ if clip_grad_mode == "value" else torch.nn.utils.clip_grad_norm_ if clip_grad_mode == "norm" else None
|
|
if clip_grad:
|
|
clip_grad_sched = LearnRateScheduler(clip_grad_value, steps, initial_step, verbose=False)
|
|
|
|
if shared.opts.training_enable_tensorboard:
|
|
tensorboard_writer = textual_inversion.tensorboard_setup(log_directory)
|
|
|
|
# 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)}..."
|
|
|
|
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, varsize=varsize, use_weight=use_weight)
|
|
|
|
if shared.opts.save_training_settings_to_txt:
|
|
saved_params = dict(
|
|
model_name=checkpoint.model_name, model_hash=checkpoint.shorthash, num_of_dataset_images=len(ds),
|
|
**{field: getattr(hypernetwork, field) for field in ['layer_structure', 'activation_func', 'weight_init', 'add_layer_norm', 'use_dropout', ]}
|
|
)
|
|
logging.save_settings_to_file(log_directory, {**saved_params, **locals()})
|
|
|
|
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)
|
|
|
|
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)
|
|
|
|
weights = hypernetwork.weights()
|
|
hypernetwork.train()
|
|
|
|
# Here we use optimizer from saved HN, or we can specify as UI option.
|
|
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 {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)
|
|
except RuntimeError as e:
|
|
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))
|
|
loss_logging = deque(maxlen=len(ds) * 3) # this should be configurable parameter, this is 3 * epoch(dataset size)
|
|
# 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 = "<none>"
|
|
last_saved_image = "<none>"
|
|
forced_filename = "<none>"
|
|
|
|
pbar = tqdm.tqdm(total=steps - initial_step)
|
|
try:
|
|
sd_hijack_checkpoint.add()
|
|
|
|
for _ 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
|
|
|
|
if clip_grad:
|
|
clip_grad_sched.step(hypernetwork.step)
|
|
|
|
with devices.autocast():
|
|
x = batch.latent_sample.to(devices.device, non_blocking=pin_memory)
|
|
if use_weight:
|
|
w = batch.weight.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)
|
|
if use_weight:
|
|
loss = shared.sd_model.weighted_forward(x, c, w)[0] / gradient_step
|
|
del w
|
|
else:
|
|
loss = shared.sd_model.forward(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
|
|
loss_logging.append(_loss_step)
|
|
if clip_grad:
|
|
clip_grad(weights, clip_grad_sched.learn_rate)
|
|
|
|
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
|
|
|
|
epoch_num = hypernetwork.step // steps_per_epoch
|
|
epoch_step = hypernetwork.step % steps_per_epoch
|
|
|
|
description = f"Training hypernetwork [Epoch {epoch_num}: {epoch_step+1}/{steps_per_epoch}]loss: {loss_step:.7f}"
|
|
pbar.set_description(description)
|
|
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.
|
|
|
|
|
|
|
|
if shared.opts.training_enable_tensorboard:
|
|
epoch_num = hypernetwork.step // len(ds)
|
|
epoch_step = hypernetwork.step - (epoch_num * len(ds)) + 1
|
|
mean_loss = sum(loss_logging) / len(loss_logging)
|
|
textual_inversion.tensorboard_add(tensorboard_writer, loss=mean_loss, global_step=hypernetwork.step, step=epoch_step, learn_rate=scheduler.learn_rate, epoch_num=epoch_num)
|
|
|
|
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)
|
|
hypernetwork.eval()
|
|
rng_state = torch.get_rng_state()
|
|
cuda_rng_state = None
|
|
if torch.cuda.is_available():
|
|
cuda_rng_state = torch.cuda.get_rng_state_all()
|
|
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,
|
|
)
|
|
|
|
p.disable_extra_networks = 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
|
|
|
|
with closing(p):
|
|
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)
|
|
torch.set_rng_state(rng_state)
|
|
if torch.cuda.is_available():
|
|
torch.cuda.set_rng_state_all(cuda_rng_state)
|
|
hypernetwork.train()
|
|
if image is not None:
|
|
shared.state.assign_current_image(image)
|
|
if shared.opts.training_enable_tensorboard and shared.opts.training_tensorboard_save_images:
|
|
textual_inversion.tensorboard_add_image(tensorboard_writer,
|
|
f"Validation at epoch {epoch_num}", image,
|
|
hypernetwork.step)
|
|
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.textinfo = f"""
|
|
<p>
|
|
Loss: {loss_step:.7f}<br/>
|
|
Step: {steps_done}<br/>
|
|
Last prompt: {html.escape(batch.cond_text[0])}<br/>
|
|
Last saved hypernetwork: {html.escape(last_saved_file)}<br/>
|
|
Last saved image: {html.escape(last_saved_image)}<br/>
|
|
</p>
|
|
"""
|
|
except Exception:
|
|
errors.report("Exception in training hypernetwork", exc_info=True)
|
|
finally:
|
|
pbar.leave = False
|
|
pbar.close()
|
|
hypernetwork.eval()
|
|
sd_hijack_checkpoint.remove()
|
|
|
|
|
|
|
|
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.
|
|
shared.sd_model.cond_stage_model.to(devices.device)
|
|
shared.sd_model.first_stage_model.to(devices.device)
|
|
shared.parallel_processing_allowed = old_parallel_processing_allowed
|
|
|
|
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
|
|
old_sd_checkpoint_name = hypernetwork.sd_checkpoint_name if hasattr(hypernetwork, "sd_checkpoint_name") else None
|
|
try:
|
|
hypernetwork.sd_checkpoint = checkpoint.shorthash
|
|
hypernetwork.sd_checkpoint_name = checkpoint.model_name
|
|
hypernetwork.name = hypernetwork_name
|
|
hypernetwork.save(filename)
|
|
except:
|
|
hypernetwork.sd_checkpoint = old_sd_checkpoint
|
|
hypernetwork.sd_checkpoint_name = old_sd_checkpoint_name
|
|
hypernetwork.name = old_hypernetwork_name
|
|
raise
|