48dbf99e84
Someone had 6000 images in their dataset, and it was shown as 0, which was confusing. This will allow tracking real time dataset-average loss for registered objects.
476 lines
18 KiB
Python
476 lines
18 KiB
Python
import csv
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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 sys
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import traceback
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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
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from modules.textual_inversion import textual_inversion
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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 statistics import stdev, mean
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class HypernetworkModule(torch.nn.Module):
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multiplier = 1.0
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activation_dict = {
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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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}
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def __init__(self, dim, state_dict=None, layer_structure=None, activation_func=None, add_layer_norm=False, use_dropout=False):
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super().__init__()
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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
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if activation_func == "linear" or activation_func is None:
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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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# Add dropout expect last layer
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if use_dropout and i < len(layer_structure) - 3:
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linears.append(torch.nn.Dropout(p=0.3))
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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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layer.weight.data.normal_(mean=0.0, std=0.01)
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layer.bias.data.zero_()
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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
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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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def apply_strength(value=None):
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HypernetworkModule.multiplier = value if value is not None else shared.opts.sd_hypernetwork_strength
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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, add_layer_norm=False, use_dropout=False):
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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.add_layer_norm = add_layer_norm
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self.use_dropout = use_dropout
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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.add_layer_norm, self.use_dropout),
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HypernetworkModule(size, None, self.layer_structure, self.activation_func, self.add_layer_norm, self.use_dropout),
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)
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def weights(self):
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res = []
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for k, layers in self.layers.items():
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for layer in layers:
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layer.train()
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res += layer.trainables()
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return res
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def save(self, filename):
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state_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['use_dropout'] = self.use_dropout
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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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torch.save(state_dict, filename)
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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.activation_func = state_dict.get('activation_func', None)
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self.add_layer_norm = state_dict.get('is_layer_norm', False)
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self.use_dropout = state_dict.get('use_dropout', False)
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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.add_layer_norm, self.use_dropout),
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HypernetworkModule(size, sd[1], self.layer_structure, self.activation_func, self.add_layer_norm, self.use_dropout),
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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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def list_hypernetworks(path):
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res = {}
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for filename in glob.iglob(os.path.join(path, '**/*.pt'), recursive=True):
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name = os.path.splitext(os.path.basename(filename))[0]
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res[name] = filename
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return res
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def load_hypernetwork(filename):
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path = shared.hypernetworks.get(filename, None)
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if path is not None:
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print(f"Loading hypernetwork {filename}")
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try:
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shared.loaded_hypernetwork = Hypernetwork()
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shared.loaded_hypernetwork.load(path)
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except Exception:
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print(f"Error loading hypernetwork {path}", file=sys.stderr)
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print(traceback.format_exc(), file=sys.stderr)
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else:
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if shared.loaded_hypernetwork is not None:
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print(f"Unloading hypernetwork")
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shared.loaded_hypernetwork = None
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def find_closest_hypernetwork_name(search: str):
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if not search:
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return None
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search = search.lower()
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applicable = [name for name in shared.hypernetworks if search in name.lower()]
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if not applicable:
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return None
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applicable = sorted(applicable, key=lambda name: len(name))
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return applicable[0]
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def apply_hypernetwork(hypernetwork, context, layer=None):
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hypernetwork_layers = (hypernetwork.layers if hypernetwork is not None else {}).get(context.shape[2], None)
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if hypernetwork_layers is None:
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return context, context
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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 = hypernetwork_layers[0](context)
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context_v = hypernetwork_layers[1](context)
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return context_k, context_v
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def attention_CrossAttention_forward(self, x, context=None, mask=None):
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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_hypernetwork(shared.loaded_hypernetwork, 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 = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (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 log_statistics(loss_info:dict, key, value):
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if key not in loss_info:
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loss_info[key] = [value]
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else:
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loss_info[key].append(value)
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if len(loss_info) > 1024:
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loss_info.pop(0)
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def statistics(data):
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total_information = f"loss:{mean(data):.3f}"+u"\u00B1"+f"({stdev(data)/ (len(data)**0.5):.3f})"
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recent_data = data[-32:]
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recent_information = f"recent 32 loss:{mean(recent_data):.3f}"+u"\u00B1"+f"({stdev(recent_data)/ (len(recent_data)**0.5):.3f})"
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return total_information, recent_information
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def report_statistics(loss_info:dict):
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keys = sorted(loss_info.keys(), key=lambda x: sum(loss_info[x]) / len(loss_info[x]))
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for key in keys:
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info, recent = statistics(loss_info[key])
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print("Loss statistics for file " + key)
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print(info)
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print(recent)
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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):
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# images allows training previews to have infotext. Importing it at the top causes a circular import problem.
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from modules import images
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assert hypernetwork_name, 'hypernetwork not selected'
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path = shared.hypernetworks.get(hypernetwork_name, None)
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shared.loaded_hypernetwork = Hypernetwork()
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shared.loaded_hypernetwork.load(path)
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shared.state.textinfo = "Initializing hypernetwork training..."
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shared.state.job_count = steps
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filename = os.path.join(shared.cmd_opts.hypernetwork_dir, f'{hypernetwork_name}.pt')
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log_directory = os.path.join(log_directory, datetime.datetime.now().strftime("%Y-%m-%d"), hypernetwork_name)
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unload = shared.opts.unload_models_when_training
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if save_hypernetwork_every > 0:
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hypernetwork_dir = os.path.join(log_directory, "hypernetworks")
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os.makedirs(hypernetwork_dir, exist_ok=True)
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else:
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hypernetwork_dir = None
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if create_image_every > 0:
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images_dir = os.path.join(log_directory, "images")
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os.makedirs(images_dir, exist_ok=True)
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else:
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images_dir = None
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shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..."
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with torch.autocast("cuda"):
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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)
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if unload:
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shared.sd_model.cond_stage_model.to(devices.cpu)
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shared.sd_model.first_stage_model.to(devices.cpu)
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hypernetwork = shared.loaded_hypernetwork
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weights = hypernetwork.weights()
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for weight in weights:
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weight.requires_grad = True
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size = len(ds.indexes)
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loss_dict = {}
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losses = torch.zeros((size,))
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previous_mean_loss = 0
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print("Mean loss of {} elements".format(size))
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last_saved_file = "<none>"
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last_saved_image = "<none>"
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forced_filename = "<none>"
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ititial_step = hypernetwork.step or 0
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if ititial_step > steps:
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return hypernetwork, filename
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scheduler = LearnRateScheduler(learn_rate, steps, ititial_step)
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# if optimizer == "AdamW": or else Adam / AdamW / SGD, etc...
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optimizer = torch.optim.AdamW(weights, lr=scheduler.learn_rate)
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steps_without_grad = 0
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pbar = tqdm.tqdm(enumerate(ds), total=steps - ititial_step)
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for i, entries in pbar:
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hypernetwork.step = i + ititial_step
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if len(loss_dict) > 0:
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previous_mean_loss = sum(i[-1] for i in loss_dict.values()) / len(loss_dict)
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scheduler.apply(optimizer, hypernetwork.step)
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if scheduler.finished:
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break
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if shared.state.interrupted:
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break
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with torch.autocast("cuda"):
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c = stack_conds([entry.cond for entry in entries]).to(devices.device)
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# c = torch.vstack([entry.cond for entry in entries]).to(devices.device)
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x = torch.stack([entry.latent for entry in entries]).to(devices.device)
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loss = shared.sd_model(x, c)[0]
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del x
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del c
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losses[hypernetwork.step % losses.shape[0]] = loss.item()
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for entry in entries:
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log_statistics(loss_dict, entry.filename, loss.item())
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optimizer.zero_grad()
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weights[0].grad = None
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loss.backward()
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if weights[0].grad is None:
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steps_without_grad += 1
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else:
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steps_without_grad = 0
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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'
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optimizer.step()
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if torch.isnan(losses[hypernetwork.step % losses.shape[0]]):
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raise RuntimeError("Loss diverged.")
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pbar.set_description(f"dataset loss: {previous_mean_loss:.7f}")
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if hypernetwork.step > 0 and hypernetwork_dir is not None and hypernetwork.step % save_hypernetwork_every == 0:
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# Before saving, change name to match current checkpoint.
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hypernetwork.name = f'{hypernetwork_name}-{hypernetwork.step}'
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last_saved_file = os.path.join(hypernetwork_dir, f'{hypernetwork.name}.pt')
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hypernetwork.save(last_saved_file)
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textual_inversion.write_loss(log_directory, "hypernetwork_loss.csv", hypernetwork.step, len(ds), {
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"loss": f"{previous_mean_loss:.7f}",
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"learn_rate": scheduler.learn_rate
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})
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if hypernetwork.step > 0 and images_dir is not None and hypernetwork.step % create_image_every == 0:
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forced_filename = f'{hypernetwork_name}-{hypernetwork.step}'
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last_saved_image = os.path.join(images_dir, forced_filename)
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optimizer.zero_grad()
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shared.sd_model.cond_stage_model.to(devices.device)
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shared.sd_model.first_stage_model.to(devices.device)
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p = processing.StableDiffusionProcessingTxt2Img(
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sd_model=shared.sd_model,
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do_not_save_grid=True,
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do_not_save_samples=True,
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)
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if preview_from_txt2img:
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p.prompt = preview_prompt
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p.negative_prompt = preview_negative_prompt
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p.steps = preview_steps
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p.sampler_index = preview_sampler_index
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p.cfg_scale = preview_cfg_scale
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p.seed = preview_seed
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p.width = preview_width
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p.height = preview_height
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else:
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p.prompt = entries[0].cond_text
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p.steps = 20
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preview_text = p.prompt
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processed = processing.process_images(p)
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image = processed.images[0] if len(processed.images)>0 else None
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if unload:
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shared.sd_model.cond_stage_model.to(devices.cpu)
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shared.sd_model.first_stage_model.to(devices.cpu)
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if image is not None:
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shared.state.current_image = image
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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)
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last_saved_image += f", prompt: {preview_text}"
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shared.state.job_no = hypernetwork.step
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shared.state.textinfo = f"""
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<p>
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Loss: {previous_mean_loss:.7f}<br/>
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Step: {hypernetwork.step}<br/>
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Last prompt: {html.escape(entries[0].cond_text)}<br/>
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Last saved hypernetwork: {html.escape(last_saved_file)}<br/>
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Last saved image: {html.escape(last_saved_image)}<br/>
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</p>
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"""
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report_statistics(loss_dict)
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checkpoint = sd_models.select_checkpoint()
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hypernetwork.sd_checkpoint = checkpoint.hash
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hypernetwork.sd_checkpoint_name = checkpoint.model_name
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# Before saving for the last time, change name back to the base name (as opposed to the save_hypernetwork_every step-suffixed naming convention).
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hypernetwork.name = hypernetwork_name
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filename = os.path.join(shared.cmd_opts.hypernetwork_dir, f'{hypernetwork.name}.pt')
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hypernetwork.save(filename)
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return hypernetwork, filename
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