hypernetwork training mk1

This commit is contained in:
AUTOMATIC 2022-10-07 23:22:22 +03:00
parent f7c787eb7c
commit 12c4d5c6b5
12 changed files with 414 additions and 107 deletions

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@ -1,88 +0,0 @@
import glob
import os
import sys
import traceback
import torch
from ldm.util import default
from modules import devices, shared
import torch
from torch import einsum
from einops import rearrange, repeat
class HypernetworkModule(torch.nn.Module):
def __init__(self, dim, state_dict):
super().__init__()
self.linear1 = torch.nn.Linear(dim, dim * 2)
self.linear2 = torch.nn.Linear(dim * 2, dim)
self.load_state_dict(state_dict, strict=True)
self.to(devices.device)
def forward(self, x):
return x + (self.linear2(self.linear1(x)))
class Hypernetwork:
filename = None
name = None
def __init__(self, filename):
self.filename = filename
self.name = os.path.splitext(os.path.basename(filename))[0]
self.layers = {}
state_dict = torch.load(filename, map_location='cpu')
for size, sd in state_dict.items():
self.layers[size] = (HypernetworkModule(size, sd[0]), HypernetworkModule(size, sd[1]))
def load_hypernetworks(path):
res = {}
for filename in glob.iglob(path + '**/*.pt', recursive=True):
try:
hn = Hypernetwork(filename)
res[hn.name] = hn
except Exception:
print(f"Error loading hypernetwork {filename}", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
return res
def attention_CrossAttention_forward(self, x, context=None, mask=None):
h = self.heads
q = self.to_q(x)
context = default(context, x)
hypernetwork = shared.selected_hypernetwork()
hypernetwork_layers = (hypernetwork.layers if hypernetwork is not None else {}).get(context.shape[2], None)
if hypernetwork_layers is not None:
k = self.to_k(hypernetwork_layers[0](context))
v = self.to_v(hypernetwork_layers[1](context))
else:
k = self.to_k(context)
v = self.to_v(context)
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)

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@ -0,0 +1,267 @@
import datetime
import glob
import html
import os
import sys
import traceback
import tqdm
import torch
from ldm.util import default
from modules import devices, shared, processing, sd_models
import torch
from torch import einsum
from einops import rearrange, repeat
import modules.textual_inversion.dataset
class HypernetworkModule(torch.nn.Module):
def __init__(self, dim, state_dict=None):
super().__init__()
self.linear1 = torch.nn.Linear(dim, dim * 2)
self.linear2 = torch.nn.Linear(dim * 2, dim)
if state_dict is not None:
self.load_state_dict(state_dict, strict=True)
else:
self.linear1.weight.data.fill_(0.0001)
self.linear1.bias.data.fill_(0.0001)
self.linear2.weight.data.fill_(0.0001)
self.linear2.bias.data.fill_(0.0001)
self.to(devices.device)
def forward(self, x):
return x + (self.linear2(self.linear1(x)))
class Hypernetwork:
filename = None
name = None
def __init__(self, name=None):
self.filename = None
self.name = name
self.layers = {}
self.step = 0
self.sd_checkpoint = None
self.sd_checkpoint_name = None
for size in [320, 640, 768, 1280]:
self.layers[size] = (HypernetworkModule(size), HypernetworkModule(size))
def weights(self):
res = []
for k, layers in self.layers.items():
for layer in layers:
layer.train()
res += [layer.linear1.weight, layer.linear1.bias, layer.linear2.weight, layer.linear2.bias]
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['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')
for size, sd in state_dict.items():
if type(size) == int:
self.layers[size] = (HypernetworkModule(size, sd[0]), HypernetworkModule(size, sd[1]))
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 load_hypernetworks(path):
res = {}
for filename in glob.iglob(path + '**/*.pt', recursive=True):
try:
hn = Hypernetwork()
hn.load(filename)
res[hn.name] = hn
except Exception:
print(f"Error loading hypernetwork {filename}", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
return res
def attention_CrossAttention_forward(self, x, context=None, mask=None):
h = self.heads
q = self.to_q(x)
context = default(context, x)
hypernetwork_layers = (shared.hypernetwork.layers if shared.hypernetwork is not None else {}).get(context.shape[2], None)
if hypernetwork_layers is not None:
hypernetwork_k, hypernetwork_v = hypernetwork_layers
self.hypernetwork_k = hypernetwork_k
self.hypernetwork_v = hypernetwork_v
context_k = hypernetwork_k(context)
context_v = hypernetwork_v(context)
else:
context_k = context
context_v = context
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 train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory, steps, create_image_every, save_hypernetwork_every, template_file, preview_image_prompt):
assert hypernetwork_name, 'embedding not selected'
shared.hypernetwork = shared.hypernetworks[hypernetwork_name]
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)
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
cond_model = shared.sd_model.cond_stage_model
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, size=512, placeholder_token=hypernetwork_name, model=shared.sd_model, device=devices.device, template_file=template_file)
hypernetwork = shared.hypernetworks[hypernetwork_name]
weights = hypernetwork.weights()
for weight in weights:
weight.requires_grad = True
optimizer = torch.optim.AdamW(weights, lr=learn_rate)
losses = torch.zeros((32,))
last_saved_file = "<none>"
last_saved_image = "<none>"
ititial_step = hypernetwork.step or 0
if ititial_step > steps:
return hypernetwork, filename
pbar = tqdm.tqdm(enumerate(ds), total=steps-ititial_step)
for i, (x, text) in pbar:
hypernetwork.step = i + ititial_step
if hypernetwork.step > steps:
break
if shared.state.interrupted:
break
with torch.autocast("cuda"):
c = cond_model([text])
x = x.to(devices.device)
loss = shared.sd_model(x.unsqueeze(0), c)[0]
del x
losses[hypernetwork.step % losses.shape[0]] = loss.item()
optimizer.zero_grad()
loss.backward()
optimizer.step()
pbar.set_description(f"loss: {losses.mean():.7f}")
if hypernetwork.step > 0 and hypernetwork_dir is not None and hypernetwork.step % save_hypernetwork_every == 0:
last_saved_file = os.path.join(hypernetwork_dir, f'{hypernetwork_name}-{hypernetwork.step}.pt')
hypernetwork.save(last_saved_file)
if hypernetwork.step > 0 and images_dir is not None and hypernetwork.step % create_image_every == 0:
last_saved_image = os.path.join(images_dir, f'{hypernetwork_name}-{hypernetwork.step}.png')
preview_text = text if preview_image_prompt == "" else preview_image_prompt
p = processing.StableDiffusionProcessingTxt2Img(
sd_model=shared.sd_model,
prompt=preview_text,
steps=20,
do_not_save_grid=True,
do_not_save_samples=True,
)
processed = processing.process_images(p)
image = processed.images[0]
shared.state.current_image = image
image.save(last_saved_image)
last_saved_image += f", prompt: {preview_text}"
shared.state.job_no = hypernetwork.step
shared.state.textinfo = f"""
<p>
Loss: {losses.mean():.7f}<br/>
Step: {hypernetwork.step}<br/>
Last prompt: {html.escape(text)}<br/>
Last saved embedding: {html.escape(last_saved_file)}<br/>
Last saved image: {html.escape(last_saved_image)}<br/>
</p>
"""
checkpoint = sd_models.select_checkpoint()
hypernetwork.sd_checkpoint = checkpoint.hash
hypernetwork.sd_checkpoint_name = checkpoint.model_name
hypernetwork.save(filename)
return hypernetwork, filename

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@ -0,0 +1,43 @@
import html
import os
import gradio as gr
import modules.textual_inversion.textual_inversion
import modules.textual_inversion.preprocess
from modules import sd_hijack, shared
def create_hypernetwork(name):
fn = os.path.join(shared.cmd_opts.hypernetwork_dir, f"{name}.pt")
assert not os.path.exists(fn), f"file {fn} already exists"
hypernetwork = modules.hypernetwork.hypernetwork.Hypernetwork(name=name)
hypernetwork.save(fn)
shared.reload_hypernetworks()
shared.hypernetwork = shared.hypernetworks.get(shared.opts.sd_hypernetwork, None)
return gr.Dropdown.update(choices=sorted([x for x in shared.hypernetworks.keys()])), f"Created: {fn}", ""
def train_hypernetwork(*args):
initial_hypernetwork = shared.hypernetwork
try:
sd_hijack.undo_optimizations()
hypernetwork, filename = modules.hypernetwork.hypernetwork.train_hypernetwork(*args)
res = f"""
Training {'interrupted' if shared.state.interrupted else 'finished'} at {hypernetwork.step} steps.
Hypernetwork saved to {html.escape(filename)}
"""
return res, ""
except Exception:
raise
finally:
shared.hypernetwork = initial_hypernetwork
sd_hijack.apply_optimizations()

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@ -8,7 +8,7 @@ from torch import einsum
from torch.nn.functional import silu from torch.nn.functional import silu
import modules.textual_inversion.textual_inversion import modules.textual_inversion.textual_inversion
from modules import prompt_parser, devices, sd_hijack_optimizations, shared, hypernetwork from modules import prompt_parser, devices, sd_hijack_optimizations, shared
from modules.shared import opts, device, cmd_opts from modules.shared import opts, device, cmd_opts
import ldm.modules.attention import ldm.modules.attention
@ -32,6 +32,8 @@ def apply_optimizations():
def undo_optimizations(): def undo_optimizations():
from modules.hypernetwork import hypernetwork
ldm.modules.attention.CrossAttention.forward = hypernetwork.attention_CrossAttention_forward ldm.modules.attention.CrossAttention.forward = hypernetwork.attention_CrossAttention_forward
ldm.modules.diffusionmodules.model.nonlinearity = diffusionmodules_model_nonlinearity ldm.modules.diffusionmodules.model.nonlinearity = diffusionmodules_model_nonlinearity
ldm.modules.diffusionmodules.model.AttnBlock.forward = diffusionmodules_model_AttnBlock_forward ldm.modules.diffusionmodules.model.AttnBlock.forward = diffusionmodules_model_AttnBlock_forward

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@ -45,8 +45,7 @@ def split_cross_attention_forward(self, x, context=None, mask=None):
q_in = self.to_q(x) q_in = self.to_q(x)
context = default(context, x) context = default(context, x)
hypernetwork = shared.selected_hypernetwork() hypernetwork_layers = (shared.hypernetwork.layers if shared.hypernetwork is not None else {}).get(context.shape[2], None)
hypernetwork_layers = (hypernetwork.layers if hypernetwork is not None else {}).get(context.shape[2], None)
if hypernetwork_layers is not None: if hypernetwork_layers is not None:
k_in = self.to_k(hypernetwork_layers[0](context)) k_in = self.to_k(hypernetwork_layers[0](context))

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@ -13,7 +13,7 @@ import modules.memmon
import modules.sd_models import modules.sd_models
import modules.styles import modules.styles
import modules.devices as devices import modules.devices as devices
from modules import sd_samplers, hypernetwork from modules import sd_samplers
from modules.paths import models_path, script_path, sd_path from modules.paths import models_path, script_path, sd_path
sd_model_file = os.path.join(script_path, 'model.ckpt') sd_model_file = os.path.join(script_path, 'model.ckpt')
@ -28,6 +28,7 @@ parser.add_argument("--no-half", action='store_true', help="do not switch the mo
parser.add_argument("--no-progressbar-hiding", action='store_true', help="do not hide progressbar in gradio UI (we hide it because it slows down ML if you have hardware acceleration in browser)") parser.add_argument("--no-progressbar-hiding", action='store_true', help="do not hide progressbar in gradio UI (we hide it because it slows down ML if you have hardware acceleration in browser)")
parser.add_argument("--max-batch-count", type=int, default=16, help="maximum batch count value for the UI") parser.add_argument("--max-batch-count", type=int, default=16, help="maximum batch count value for the UI")
parser.add_argument("--embeddings-dir", type=str, default=os.path.join(script_path, 'embeddings'), help="embeddings directory for textual inversion (default: embeddings)") parser.add_argument("--embeddings-dir", type=str, default=os.path.join(script_path, 'embeddings'), help="embeddings directory for textual inversion (default: embeddings)")
parser.add_argument("--hypernetwork-dir", type=str, default=os.path.join(models_path, 'hypernetworks'), help="hypernetwork directory")
parser.add_argument("--allow-code", action='store_true', help="allow custom script execution from webui") parser.add_argument("--allow-code", action='store_true', help="allow custom script execution from webui")
parser.add_argument("--medvram", action='store_true', help="enable stable diffusion model optimizations for sacrificing a little speed for low VRM usage") parser.add_argument("--medvram", action='store_true', help="enable stable diffusion model optimizations for sacrificing a little speed for low VRM usage")
parser.add_argument("--lowvram", action='store_true', help="enable stable diffusion model optimizations for sacrificing a lot of speed for very low VRM usage") parser.add_argument("--lowvram", action='store_true', help="enable stable diffusion model optimizations for sacrificing a lot of speed for very low VRM usage")
@ -76,11 +77,15 @@ parallel_processing_allowed = not cmd_opts.lowvram and not cmd_opts.medvram
config_filename = cmd_opts.ui_settings_file config_filename = cmd_opts.ui_settings_file
hypernetworks = hypernetwork.load_hypernetworks(os.path.join(models_path, 'hypernetworks'))
def reload_hypernetworks():
from modules.hypernetwork import hypernetwork
hypernetworks.clear()
hypernetworks.update(hypernetwork.load_hypernetworks(cmd_opts.hypernetwork_dir))
def selected_hypernetwork(): hypernetworks = {}
return hypernetworks.get(opts.sd_hypernetwork, None) hypernetwork = None
class State: class State:

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@ -22,7 +22,6 @@ def preprocess(*args):
def train_embedding(*args): def train_embedding(*args):
try: try:
sd_hijack.undo_optimizations() sd_hijack.undo_optimizations()

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@ -37,6 +37,7 @@ import modules.generation_parameters_copypaste
from modules import prompt_parser from modules import prompt_parser
from modules.images import save_image from modules.images import save_image
import modules.textual_inversion.ui import modules.textual_inversion.ui
import modules.hypernetwork.ui
# this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the bowser will not show any UI # this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the bowser will not show any UI
mimetypes.init() mimetypes.init()
@ -965,6 +966,18 @@ def create_ui(wrap_gradio_gpu_call):
with gr.Column(): with gr.Column():
create_embedding = gr.Button(value="Create", variant='primary') create_embedding = gr.Button(value="Create", variant='primary')
with gr.Group():
gr.HTML(value="<p style='margin-bottom: 0.7em'>Create a new hypernetwork</p>")
new_hypernetwork_name = gr.Textbox(label="Name")
with gr.Row():
with gr.Column(scale=3):
gr.HTML(value="")
with gr.Column():
create_hypernetwork = gr.Button(value="Create", variant='primary')
with gr.Group(): with gr.Group():
gr.HTML(value="<p style='margin-bottom: 0.7em'>Preprocess images</p>") gr.HTML(value="<p style='margin-bottom: 0.7em'>Preprocess images</p>")
@ -986,6 +999,7 @@ def create_ui(wrap_gradio_gpu_call):
with gr.Group(): with gr.Group():
gr.HTML(value="<p style='margin-bottom: 0.7em'>Train an embedding; must specify a directory with a set of 512x512 images</p>") gr.HTML(value="<p style='margin-bottom: 0.7em'>Train an embedding; must specify a directory with a set of 512x512 images</p>")
train_embedding_name = gr.Dropdown(label='Embedding', choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys())) train_embedding_name = gr.Dropdown(label='Embedding', choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys()))
train_hypernetwork_name = gr.Dropdown(label='Hypernetwork', choices=[x for x in shared.hypernetworks.keys()])
learn_rate = gr.Number(label='Learning rate', value=5.0e-03) learn_rate = gr.Number(label='Learning rate', value=5.0e-03)
dataset_directory = gr.Textbox(label='Dataset directory', placeholder="Path to directory with input images") dataset_directory = gr.Textbox(label='Dataset directory', placeholder="Path to directory with input images")
log_directory = gr.Textbox(label='Log directory', placeholder="Path to directory where to write outputs", value="textual_inversion") log_directory = gr.Textbox(label='Log directory', placeholder="Path to directory where to write outputs", value="textual_inversion")
@ -993,15 +1007,12 @@ def create_ui(wrap_gradio_gpu_call):
steps = gr.Number(label='Max steps', value=100000, precision=0) steps = gr.Number(label='Max steps', value=100000, precision=0)
create_image_every = gr.Number(label='Save an image to log directory every N steps, 0 to disable', value=500, precision=0) create_image_every = gr.Number(label='Save an image to log directory every N steps, 0 to disable', value=500, precision=0)
save_embedding_every = gr.Number(label='Save a copy of embedding to log directory every N steps, 0 to disable', value=500, precision=0) save_embedding_every = gr.Number(label='Save a copy of embedding to log directory every N steps, 0 to disable', value=500, precision=0)
preview_image_prompt = gr.Textbox(label='Preview prompt', value="")
with gr.Row():
with gr.Column(scale=2):
gr.HTML(value="")
with gr.Column():
with gr.Row(): with gr.Row():
interrupt_training = gr.Button(value="Interrupt") interrupt_training = gr.Button(value="Interrupt")
train_embedding = gr.Button(value="Train", variant='primary') train_hypernetwork = gr.Button(value="Train Hypernetwork", variant='primary')
train_embedding = gr.Button(value="Train Embedding", variant='primary')
with gr.Column(): with gr.Column():
progressbar = gr.HTML(elem_id="ti_progressbar") progressbar = gr.HTML(elem_id="ti_progressbar")
@ -1027,6 +1038,18 @@ def create_ui(wrap_gradio_gpu_call):
] ]
) )
create_hypernetwork.click(
fn=modules.hypernetwork.ui.create_hypernetwork,
inputs=[
new_hypernetwork_name,
],
outputs=[
train_hypernetwork_name,
ti_output,
ti_outcome,
]
)
run_preprocess.click( run_preprocess.click(
fn=wrap_gradio_gpu_call(modules.textual_inversion.ui.preprocess, extra_outputs=[gr.update()]), fn=wrap_gradio_gpu_call(modules.textual_inversion.ui.preprocess, extra_outputs=[gr.update()]),
_js="start_training_textual_inversion", _js="start_training_textual_inversion",
@ -1062,12 +1085,33 @@ def create_ui(wrap_gradio_gpu_call):
] ]
) )
train_hypernetwork.click(
fn=wrap_gradio_gpu_call(modules.hypernetwork.ui.train_hypernetwork, extra_outputs=[gr.update()]),
_js="start_training_textual_inversion",
inputs=[
train_hypernetwork_name,
learn_rate,
dataset_directory,
log_directory,
steps,
create_image_every,
save_embedding_every,
template_file,
preview_image_prompt,
],
outputs=[
ti_output,
ti_outcome,
]
)
interrupt_training.click( interrupt_training.click(
fn=lambda: shared.state.interrupt(), fn=lambda: shared.state.interrupt(),
inputs=[], inputs=[],
outputs=[], outputs=[],
) )
def create_setting_component(key): def create_setting_component(key):
def fun(): def fun():
return opts.data[key] if key in opts.data else opts.data_labels[key].default return opts.data[key] if key in opts.data else opts.data_labels[key].default

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@ -78,8 +78,7 @@ def apply_checkpoint(p, x, xs):
def apply_hypernetwork(p, x, xs): def apply_hypernetwork(p, x, xs):
hn = shared.hypernetworks.get(x, None) shared.hypernetwork = shared.hypernetworks.get(x, None)
opts.data["sd_hypernetwork"] = hn.name if hn is not None else 'None'
def format_value_add_label(p, opt, x): def format_value_add_label(p, opt, x):
@ -199,7 +198,7 @@ class Script(scripts.Script):
modules.processing.fix_seed(p) modules.processing.fix_seed(p)
p.batch_size = 1 p.batch_size = 1
initial_hn = opts.sd_hypernetwork initial_hn = shared.hypernetwork
def process_axis(opt, vals): def process_axis(opt, vals):
if opt.label == 'Nothing': if opt.label == 'Nothing':
@ -308,6 +307,6 @@ class Script(scripts.Script):
# restore checkpoint in case it was changed by axes # restore checkpoint in case it was changed by axes
modules.sd_models.reload_model_weights(shared.sd_model) modules.sd_models.reload_model_weights(shared.sd_model)
opts.data["sd_hypernetwork"] = initial_hn shared.hypernetwork = initial_hn
return processed return processed

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@ -0,0 +1,27 @@
a photo of a [filewords]
a rendering of a [filewords]
a cropped photo of the [filewords]
the photo of a [filewords]
a photo of a clean [filewords]
a photo of a dirty [filewords]
a dark photo of the [filewords]
a photo of my [filewords]
a photo of the cool [filewords]
a close-up photo of a [filewords]
a bright photo of the [filewords]
a cropped photo of a [filewords]
a photo of the [filewords]
a good photo of the [filewords]
a photo of one [filewords]
a close-up photo of the [filewords]
a rendition of the [filewords]
a photo of the clean [filewords]
a rendition of a [filewords]
a photo of a nice [filewords]
a good photo of a [filewords]
a photo of the nice [filewords]
a photo of the small [filewords]
a photo of the weird [filewords]
a photo of the large [filewords]
a photo of a cool [filewords]
a photo of a small [filewords]

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@ -0,0 +1 @@
picture

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@ -74,6 +74,15 @@ def wrap_gradio_gpu_call(func, extra_outputs=None):
return modules.ui.wrap_gradio_call(f, extra_outputs=extra_outputs) return modules.ui.wrap_gradio_call(f, extra_outputs=extra_outputs)
def set_hypernetwork():
shared.hypernetwork = shared.hypernetworks.get(shared.opts.sd_hypernetwork, None)
shared.reload_hypernetworks()
shared.opts.onchange("sd_hypernetwork", set_hypernetwork)
set_hypernetwork()
modules.scripts.load_scripts(os.path.join(script_path, "scripts")) modules.scripts.load_scripts(os.path.join(script_path, "scripts"))
shared.sd_model = modules.sd_models.load_model() shared.sd_model = modules.sd_models.load_model()