mirror of
https://github.com/AUTOMATIC1111/stable-diffusion-webui.git
synced 2024-12-21 07:30:02 +08:00
476 lines
18 KiB
Python
476 lines
18 KiB
Python
import csv
|
|
import datetime
|
|
import glob
|
|
import html
|
|
import os
|
|
import sys
|
|
import traceback
|
|
|
|
import modules.textual_inversion.dataset
|
|
import torch
|
|
import tqdm
|
|
from einops import rearrange, repeat
|
|
from ldm.util import default
|
|
from modules import devices, processing, sd_models, shared
|
|
from modules.textual_inversion import textual_inversion
|
|
from modules.textual_inversion.learn_schedule import LearnRateScheduler
|
|
from torch import einsum
|
|
|
|
from statistics import stdev, mean
|
|
|
|
class HypernetworkModule(torch.nn.Module):
|
|
multiplier = 1.0
|
|
activation_dict = {
|
|
"relu": torch.nn.ReLU,
|
|
"leakyrelu": torch.nn.LeakyReLU,
|
|
"elu": torch.nn.ELU,
|
|
"swish": torch.nn.Hardswish,
|
|
}
|
|
|
|
def __init__(self, dim, state_dict=None, layer_structure=None, activation_func=None, add_layer_norm=False, use_dropout=False):
|
|
super().__init__()
|
|
|
|
assert layer_structure is not None, "layer_structure must not be None"
|
|
assert layer_structure[0] == 1, "Multiplier Sequence should start with size 1!"
|
|
assert layer_structure[-1] == 1, "Multiplier Sequence should end with size 1!"
|
|
|
|
linears = []
|
|
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])))
|
|
|
|
# Add an activation func
|
|
if activation_func == "linear" or activation_func is None:
|
|
pass
|
|
elif activation_func in self.activation_dict:
|
|
linears.append(self.activation_dict[activation_func]())
|
|
else:
|
|
raise RuntimeError(f'hypernetwork uses an unsupported activation function: {activation_func}')
|
|
|
|
# Add layer normalization
|
|
if add_layer_norm:
|
|
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:
|
|
linears.append(torch.nn.Dropout(p=0.3))
|
|
|
|
self.linear = torch.nn.Sequential(*linears)
|
|
|
|
if state_dict is not None:
|
|
self.fix_old_state_dict(state_dict)
|
|
self.load_state_dict(state_dict)
|
|
else:
|
|
for layer in self.linear:
|
|
if type(layer) == torch.nn.Linear or type(layer) == torch.nn.LayerNorm:
|
|
layer.weight.data.normal_(mean=0.0, std=0.01)
|
|
layer.bias.data.zero_()
|
|
|
|
self.to(devices.device)
|
|
|
|
def fix_old_state_dict(self, state_dict):
|
|
changes = {
|
|
'linear1.bias': 'linear.0.bias',
|
|
'linear1.weight': 'linear.0.weight',
|
|
'linear2.bias': 'linear.1.bias',
|
|
'linear2.weight': 'linear.1.weight',
|
|
}
|
|
|
|
for fr, to in changes.items():
|
|
x = state_dict.get(fr, None)
|
|
if x is None:
|
|
continue
|
|
|
|
del state_dict[fr]
|
|
state_dict[to] = x
|
|
|
|
def forward(self, x):
|
|
return x + self.linear(x) * self.multiplier
|
|
|
|
def trainables(self):
|
|
layer_structure = []
|
|
for layer in self.linear:
|
|
if type(layer) == torch.nn.Linear or type(layer) == torch.nn.LayerNorm:
|
|
layer_structure += [layer.weight, layer.bias]
|
|
return layer_structure
|
|
|
|
|
|
def apply_strength(value=None):
|
|
HypernetworkModule.multiplier = value if value is not None else shared.opts.sd_hypernetwork_strength
|
|
|
|
|
|
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):
|
|
self.filename = None
|
|
self.name = name
|
|
self.layers = {}
|
|
self.step = 0
|
|
self.sd_checkpoint = None
|
|
self.sd_checkpoint_name = None
|
|
self.layer_structure = layer_structure
|
|
self.activation_func = activation_func
|
|
self.add_layer_norm = add_layer_norm
|
|
self.use_dropout = use_dropout
|
|
|
|
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),
|
|
)
|
|
|
|
def weights(self):
|
|
res = []
|
|
|
|
for k, layers in self.layers.items():
|
|
for layer in layers:
|
|
layer.train()
|
|
res += layer.trainables()
|
|
|
|
return res
|
|
|
|
def save(self, filename):
|
|
state_dict = {}
|
|
|
|
for k, v in self.layers.items():
|
|
state_dict[k] = (v[0].state_dict(), v[1].state_dict())
|
|
|
|
state_dict['step'] = self.step
|
|
state_dict['name'] = self.name
|
|
state_dict['layer_structure'] = self.layer_structure
|
|
state_dict['activation_func'] = self.activation_func
|
|
state_dict['is_layer_norm'] = self.add_layer_norm
|
|
state_dict['use_dropout'] = self.use_dropout
|
|
state_dict['sd_checkpoint'] = self.sd_checkpoint
|
|
state_dict['sd_checkpoint_name'] = self.sd_checkpoint_name
|
|
|
|
torch.save(state_dict, filename)
|
|
|
|
def load(self, filename):
|
|
self.filename = filename
|
|
if self.name is None:
|
|
self.name = os.path.splitext(os.path.basename(filename))[0]
|
|
|
|
state_dict = torch.load(filename, map_location='cpu')
|
|
|
|
self.layer_structure = state_dict.get('layer_structure', [1, 2, 1])
|
|
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)
|
|
|
|
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),
|
|
)
|
|
|
|
self.name = state_dict.get('name', self.name)
|
|
self.step = state_dict.get('step', 0)
|
|
self.sd_checkpoint = state_dict.get('sd_checkpoint', None)
|
|
self.sd_checkpoint_name = state_dict.get('sd_checkpoint_name', None)
|
|
|
|
|
|
def list_hypernetworks(path):
|
|
res = {}
|
|
for filename in glob.iglob(os.path.join(path, '**/*.pt'), recursive=True):
|
|
name = os.path.splitext(os.path.basename(filename))[0]
|
|
res[name] = filename
|
|
return res
|
|
|
|
|
|
def load_hypernetwork(filename):
|
|
path = shared.hypernetworks.get(filename, None)
|
|
if path is not None:
|
|
print(f"Loading hypernetwork {filename}")
|
|
try:
|
|
shared.loaded_hypernetwork = Hypernetwork()
|
|
shared.loaded_hypernetwork.load(path)
|
|
|
|
except Exception:
|
|
print(f"Error loading hypernetwork {path}", file=sys.stderr)
|
|
print(traceback.format_exc(), file=sys.stderr)
|
|
else:
|
|
if shared.loaded_hypernetwork is not None:
|
|
print(f"Unloading hypernetwork")
|
|
|
|
shared.loaded_hypernetwork = None
|
|
|
|
|
|
def find_closest_hypernetwork_name(search: str):
|
|
if not search:
|
|
return None
|
|
search = search.lower()
|
|
applicable = [name for name in shared.hypernetworks if search in name.lower()]
|
|
if not applicable:
|
|
return None
|
|
applicable = sorted(applicable, key=lambda name: len(name))
|
|
return applicable[0]
|
|
|
|
|
|
def apply_hypernetwork(hypernetwork, context, layer=None):
|
|
hypernetwork_layers = (hypernetwork.layers if hypernetwork is not None else {}).get(context.shape[2], None)
|
|
|
|
if hypernetwork_layers is None:
|
|
return context, context
|
|
|
|
if layer is not None:
|
|
layer.hyper_k = hypernetwork_layers[0]
|
|
layer.hyper_v = hypernetwork_layers[1]
|
|
|
|
context_k = hypernetwork_layers[0](context)
|
|
context_v = hypernetwork_layers[1](context)
|
|
return context_k, context_v
|
|
|
|
|
|
def attention_CrossAttention_forward(self, x, context=None, mask=None):
|
|
h = self.heads
|
|
|
|
q = self.to_q(x)
|
|
context = default(context, x)
|
|
|
|
context_k, context_v = apply_hypernetwork(shared.loaded_hypernetwork, context, self)
|
|
k = self.to_k(context_k)
|
|
v = self.to_v(context_v)
|
|
|
|
q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
|
|
|
|
sim = einsum('b i d, b j d -> b i j', q, k) * self.scale
|
|
|
|
if mask is not None:
|
|
mask = rearrange(mask, 'b ... -> b (...)')
|
|
max_neg_value = -torch.finfo(sim.dtype).max
|
|
mask = repeat(mask, 'b j -> (b h) () j', h=h)
|
|
sim.masked_fill_(~mask, max_neg_value)
|
|
|
|
# attention, what we cannot get enough of
|
|
attn = sim.softmax(dim=-1)
|
|
|
|
out = einsum('b i j, b j d -> b i d', attn, v)
|
|
out = rearrange(out, '(b h) n d -> b n (h d)', h=h)
|
|
return self.to_out(out)
|
|
|
|
|
|
def stack_conds(conds):
|
|
if len(conds) == 1:
|
|
return torch.stack(conds)
|
|
|
|
# same as in reconstruct_multicond_batch
|
|
token_count = max([x.shape[0] for x in conds])
|
|
for i in range(len(conds)):
|
|
if conds[i].shape[0] != token_count:
|
|
last_vector = conds[i][-1:]
|
|
last_vector_repeated = last_vector.repeat([token_count - conds[i].shape[0], 1])
|
|
conds[i] = torch.vstack([conds[i], last_vector_repeated])
|
|
|
|
return torch.stack(conds)
|
|
|
|
|
|
def log_statistics(loss_info:dict, key, value):
|
|
if key not in loss_info:
|
|
loss_info[key] = [value]
|
|
else:
|
|
loss_info[key].append(value)
|
|
if len(loss_info) > 1024:
|
|
loss_info.pop(0)
|
|
|
|
|
|
def statistics(data):
|
|
total_information = f"loss:{mean(data):.3f}"+u"\u00B1"+f"({stdev(data)/ (len(data)**0.5):.3f})"
|
|
recent_data = data[-32:]
|
|
recent_information = f"recent 32 loss:{mean(recent_data):.3f}"+u"\u00B1"+f"({stdev(recent_data)/ (len(recent_data)**0.5):.3f})"
|
|
return total_information, recent_information
|
|
|
|
|
|
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:
|
|
info, recent = statistics(loss_info[key])
|
|
print("Loss statistics for file " + key)
|
|
print(info)
|
|
print(recent)
|
|
|
|
|
|
|
|
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
|
|
|
|
assert hypernetwork_name, 'hypernetwork not selected'
|
|
|
|
path = shared.hypernetworks.get(hypernetwork_name, None)
|
|
shared.loaded_hypernetwork = Hypernetwork()
|
|
shared.loaded_hypernetwork.load(path)
|
|
|
|
shared.state.textinfo = "Initializing hypernetwork training..."
|
|
shared.state.job_count = steps
|
|
|
|
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
|
|
|
|
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)
|
|
if unload:
|
|
shared.sd_model.cond_stage_model.to(devices.cpu)
|
|
shared.sd_model.first_stage_model.to(devices.cpu)
|
|
|
|
hypernetwork = shared.loaded_hypernetwork
|
|
weights = hypernetwork.weights()
|
|
for weight in weights:
|
|
weight.requires_grad = True
|
|
|
|
size = len(ds.indexes)
|
|
loss_dict = {}
|
|
losses = torch.zeros((size,))
|
|
previous_mean_loss = 0
|
|
print("Mean loss of {} elements".format(size))
|
|
|
|
last_saved_file = "<none>"
|
|
last_saved_image = "<none>"
|
|
forced_filename = "<none>"
|
|
|
|
ititial_step = hypernetwork.step or 0
|
|
if ititial_step > steps:
|
|
return hypernetwork, filename
|
|
|
|
scheduler = LearnRateScheduler(learn_rate, steps, ititial_step)
|
|
# if optimizer == "AdamW": or else Adam / AdamW / SGD, etc...
|
|
optimizer = torch.optim.AdamW(weights, lr=scheduler.learn_rate)
|
|
|
|
steps_without_grad = 0
|
|
|
|
pbar = tqdm.tqdm(enumerate(ds), total=steps - ititial_step)
|
|
for i, entries in pbar:
|
|
hypernetwork.step = i + ititial_step
|
|
if loss_dict and i % size == 0:
|
|
previous_mean_loss = sum(i[-1] for i in loss_dict.values()) / len(loss_dict)
|
|
|
|
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:
|
|
log_statistics(loss_dict, entry.filename, loss.item())
|
|
|
|
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()
|
|
|
|
if torch.isnan(losses[hypernetwork.step % losses.shape[0]]):
|
|
raise RuntimeError("Loss diverged.")
|
|
pbar.set_description(f"dataset loss: {previous_mean_loss:.7f}")
|
|
|
|
if hypernetwork.step > 0 and hypernetwork_dir is not None and hypernetwork.step % save_hypernetwork_every == 0:
|
|
# Before saving, change name to match current checkpoint.
|
|
hypernetwork.name = f'{hypernetwork_name}-{hypernetwork.step}'
|
|
last_saved_file = os.path.join(hypernetwork_dir, f'{hypernetwork.name}.pt')
|
|
hypernetwork.save(last_saved_file)
|
|
|
|
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 hypernetwork.step > 0 and images_dir is not None and hypernetwork.step % create_image_every == 0:
|
|
forced_filename = f'{hypernetwork_name}-{hypernetwork.step}'
|
|
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_index = preview_sampler_index
|
|
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
|
|
|
|
preview_text = p.prompt
|
|
|
|
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 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)
|
|
last_saved_image += f", prompt: {preview_text}"
|
|
|
|
shared.state.job_no = hypernetwork.step
|
|
|
|
shared.state.textinfo = f"""
|
|
<p>
|
|
Loss: {previous_mean_loss:.7f}<br/>
|
|
Step: {hypernetwork.step}<br/>
|
|
Last prompt: {html.escape(entries[0].cond_text)}<br/>
|
|
Last saved hypernetwork: {html.escape(last_saved_file)}<br/>
|
|
Last saved image: {html.escape(last_saved_image)}<br/>
|
|
</p>
|
|
"""
|
|
|
|
report_statistics(loss_dict)
|
|
checkpoint = sd_models.select_checkpoint()
|
|
|
|
hypernetwork.sd_checkpoint = checkpoint.hash
|
|
hypernetwork.sd_checkpoint_name = checkpoint.model_name
|
|
# Before saving for the last time, change name back to the base name (as opposed to the save_hypernetwork_every step-suffixed naming convention).
|
|
hypernetwork.name = hypernetwork_name
|
|
filename = os.path.join(shared.cmd_opts.hypernetwork_dir, f'{hypernetwork.name}.pt')
|
|
hypernetwork.save(filename)
|
|
|
|
return hypernetwork, filename
|