mirror of
https://github.com/vladmandic/automatic
synced 2026-09-20 01:31:13 +02:00
Vladimir Mandic
GitHub
@@ -32,3 +32,4 @@ notification.mp3
|
||||
/extensions
|
||||
/test/stdout.txt
|
||||
/test/stderr.txt
|
||||
/cache.json
|
||||
|
||||
+16
-9
@@ -143,14 +143,6 @@ function confirm_clear_prompt(prompt, negative_prompt) {
|
||||
|
||||
|
||||
opts = {}
|
||||
function apply_settings(jsdata){
|
||||
console.log(jsdata)
|
||||
|
||||
opts = JSON.parse(jsdata)
|
||||
|
||||
return jsdata
|
||||
}
|
||||
|
||||
onUiUpdate(function(){
|
||||
if(Object.keys(opts).length != 0) return;
|
||||
|
||||
@@ -160,7 +152,7 @@ onUiUpdate(function(){
|
||||
textarea = json_elem.querySelector('textarea')
|
||||
jsdata = textarea.value
|
||||
opts = JSON.parse(jsdata)
|
||||
|
||||
executeCallbacks(optionsChangedCallbacks);
|
||||
|
||||
Object.defineProperty(textarea, 'value', {
|
||||
set: function(newValue) {
|
||||
@@ -171,6 +163,8 @@ onUiUpdate(function(){
|
||||
if (oldValue != newValue) {
|
||||
opts = JSON.parse(textarea.value)
|
||||
}
|
||||
|
||||
executeCallbacks(optionsChangedCallbacks);
|
||||
},
|
||||
get: function() {
|
||||
var valueProp = Object.getOwnPropertyDescriptor(HTMLTextAreaElement.prototype, 'value');
|
||||
@@ -201,6 +195,19 @@ onUiUpdate(function(){
|
||||
}
|
||||
})
|
||||
|
||||
|
||||
onOptionsChanged(function(){
|
||||
elem = gradioApp().getElementById('sd_checkpoint_hash')
|
||||
sd_checkpoint_hash = opts.sd_checkpoint_hash || ""
|
||||
shorthash = sd_checkpoint_hash.substr(0,10)
|
||||
|
||||
if(elem && elem.textContent != shorthash){
|
||||
elem.textContent = shorthash
|
||||
elem.title = sd_checkpoint_hash
|
||||
elem.href = "https://google.com/search?q=" + sd_checkpoint_hash
|
||||
}
|
||||
})
|
||||
|
||||
let txt2img_textarea, img2img_textarea = undefined;
|
||||
let wait_time = 800
|
||||
let token_timeout;
|
||||
|
||||
+1
-1
@@ -371,7 +371,7 @@ class Api:
|
||||
return upscalers
|
||||
|
||||
def get_sd_models(self):
|
||||
return [{"title":x.title, "model_name":x.model_name, "hash":x.hash, "filename": x.filename, "config": find_checkpoint_config(x)} for x in checkpoints_list.values()]
|
||||
return [{"title": x.title, "model_name": x.model_name, "hash": x.shorthash, "sha256": x.sha256, "filename": x.filename, "config": find_checkpoint_config(x)} for x in checkpoints_list.values()]
|
||||
|
||||
def get_hypernetworks(self):
|
||||
return [{"name": name, "path": shared.hypernetworks[name]} for name in shared.hypernetworks]
|
||||
|
||||
@@ -224,7 +224,8 @@ class UpscalerItem(BaseModel):
|
||||
class SDModelItem(BaseModel):
|
||||
title: str = Field(title="Title")
|
||||
model_name: str = Field(title="Model Name")
|
||||
hash: str = Field(title="Hash")
|
||||
hash: Optional[str] = Field(title="Short hash")
|
||||
sha256: Optional[str] = Field(title="sha256 hash")
|
||||
filename: str = Field(title="Filename")
|
||||
config: str = Field(title="Config file")
|
||||
|
||||
|
||||
@@ -0,0 +1,84 @@
|
||||
import hashlib
|
||||
import json
|
||||
import os.path
|
||||
|
||||
import filelock
|
||||
|
||||
|
||||
cache_filename = "cache.json"
|
||||
cache_data = None
|
||||
|
||||
|
||||
def dump_cache():
|
||||
with filelock.FileLock(cache_filename+".lock"):
|
||||
with open(cache_filename, "w", encoding="utf8") as file:
|
||||
json.dump(cache_data, file, indent=4)
|
||||
|
||||
|
||||
def cache(subsection):
|
||||
global cache_data
|
||||
|
||||
if cache_data is None:
|
||||
with filelock.FileLock(cache_filename+".lock"):
|
||||
if not os.path.isfile(cache_filename):
|
||||
cache_data = {}
|
||||
else:
|
||||
with open(cache_filename, "r", encoding="utf8") as file:
|
||||
cache_data = json.load(file)
|
||||
|
||||
s = cache_data.get(subsection, {})
|
||||
cache_data[subsection] = s
|
||||
|
||||
return s
|
||||
|
||||
|
||||
def calculate_sha256(filename):
|
||||
hash_sha256 = hashlib.sha256()
|
||||
|
||||
with open(filename, "rb") as f:
|
||||
for chunk in iter(lambda: f.read(4096), b""):
|
||||
hash_sha256.update(chunk)
|
||||
|
||||
return hash_sha256.hexdigest()
|
||||
|
||||
|
||||
def sha256_from_cache(filename, title):
|
||||
hashes = cache("hashes")
|
||||
ondisk_mtime = os.path.getmtime(filename)
|
||||
|
||||
if title not in hashes:
|
||||
return None
|
||||
|
||||
cached_sha256 = hashes[title].get("sha256", None)
|
||||
cached_mtime = hashes[title].get("mtime", 0)
|
||||
|
||||
if ondisk_mtime > cached_mtime or cached_sha256 is None:
|
||||
return None
|
||||
|
||||
return cached_sha256
|
||||
|
||||
|
||||
def sha256(filename, title):
|
||||
hashes = cache("hashes")
|
||||
|
||||
sha256_value = sha256_from_cache(filename, title)
|
||||
if sha256_value is not None:
|
||||
return sha256_value
|
||||
|
||||
print(f"Calculating sha256 for {filename}: ", end='')
|
||||
sha256_value = calculate_sha256(filename)
|
||||
print(f"{sha256_value}")
|
||||
|
||||
hashes[title] = {
|
||||
"mtime": os.path.getmtime(filename),
|
||||
"sha256": sha256_value,
|
||||
}
|
||||
|
||||
dump_cache()
|
||||
|
||||
return sha256_value
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
@@ -12,7 +12,7 @@ import torch
|
||||
import tqdm
|
||||
from einops import rearrange, repeat
|
||||
from ldm.util import default
|
||||
from modules import devices, processing, sd_models, shared, sd_samplers
|
||||
from modules import devices, processing, sd_models, shared, sd_samplers, hashes
|
||||
from modules.textual_inversion import textual_inversion, logging
|
||||
from modules.textual_inversion.learn_schedule import LearnRateScheduler
|
||||
from torch import einsum
|
||||
@@ -225,7 +225,7 @@ class Hypernetwork:
|
||||
|
||||
torch.save(state_dict, filename)
|
||||
if shared.opts.save_optimizer_state and self.optimizer_state_dict:
|
||||
optimizer_saved_dict['hash'] = sd_models.model_hash(filename)
|
||||
optimizer_saved_dict['hash'] = self.shorthash()
|
||||
optimizer_saved_dict['optimizer_state_dict'] = self.optimizer_state_dict
|
||||
torch.save(optimizer_saved_dict, filename + '.optim')
|
||||
|
||||
@@ -237,32 +237,33 @@ class Hypernetwork:
|
||||
state_dict = torch.load(filename, map_location='cpu')
|
||||
|
||||
self.layer_structure = state_dict.get('layer_structure', [1, 2, 1])
|
||||
print(self.layer_structure)
|
||||
optional_info = state_dict.get('optional_info', None)
|
||||
if optional_info is not None:
|
||||
print(f"INFO:\n {optional_info}\n")
|
||||
self.optional_info = optional_info
|
||||
self.optional_info = state_dict.get('optional_info', None)
|
||||
self.activation_func = state_dict.get('activation_func', None)
|
||||
print(f"Activation function is {self.activation_func}")
|
||||
self.weight_init = state_dict.get('weight_initialization', 'Normal')
|
||||
print(f"Weight initialization is {self.weight_init}")
|
||||
self.add_layer_norm = state_dict.get('is_layer_norm', False)
|
||||
print(f"Layer norm is set to {self.add_layer_norm}")
|
||||
self.dropout_structure = state_dict.get('dropout_structure', None)
|
||||
self.use_dropout = True if self.dropout_structure is not None and any(self.dropout_structure) else state_dict.get('use_dropout', False)
|
||||
print(f"Dropout usage is set to {self.use_dropout}" )
|
||||
self.activate_output = state_dict.get('activate_output', True)
|
||||
print(f"Activate last layer is set to {self.activate_output}")
|
||||
self.last_layer_dropout = state_dict.get('last_layer_dropout', False)
|
||||
# Dropout structure should have same length as layer structure, Every digits should be in [0,1), and last digit must be 0.
|
||||
if self.dropout_structure is None:
|
||||
print("Using previous dropout structure")
|
||||
self.dropout_structure = parse_dropout_structure(self.layer_structure, self.use_dropout, self.last_layer_dropout)
|
||||
print(f"Dropout structure is set to {self.dropout_structure}")
|
||||
|
||||
optimizer_saved_dict = torch.load(self.filename + '.optim', map_location = 'cpu') if os.path.exists(self.filename + '.optim') else {}
|
||||
if shared.opts.print_hypernet_extra:
|
||||
if self.optional_info is not None:
|
||||
print(f" INFO:\n {self.optional_info}\n")
|
||||
|
||||
if sd_models.model_hash(filename) == optimizer_saved_dict.get('hash', None):
|
||||
print(f" Layer structure: {self.layer_structure}")
|
||||
print(f" Activation function: {self.activation_func}")
|
||||
print(f" Weight initialization: {self.weight_init}")
|
||||
print(f" Layer norm: {self.add_layer_norm}")
|
||||
print(f" Dropout usage: {self.use_dropout}" )
|
||||
print(f" Activate last layer: {self.activate_output}")
|
||||
print(f" Dropout structure: {self.dropout_structure}")
|
||||
|
||||
optimizer_saved_dict = torch.load(self.filename + '.optim', map_location='cpu') if os.path.exists(self.filename + '.optim') else {}
|
||||
|
||||
if self.shorthash() == optimizer_saved_dict.get('hash', None):
|
||||
self.optimizer_state_dict = optimizer_saved_dict.get('optimizer_state_dict', None)
|
||||
else:
|
||||
self.optimizer_state_dict = None
|
||||
@@ -289,6 +290,11 @@ class Hypernetwork:
|
||||
self.sd_checkpoint_name = state_dict.get('sd_checkpoint_name', None)
|
||||
self.eval()
|
||||
|
||||
def shorthash(self):
|
||||
sha256 = hashes.sha256(self.filename, f'hypernet/{self.name}')
|
||||
|
||||
return sha256[0:10]
|
||||
|
||||
|
||||
def list_hypernetworks(path):
|
||||
res = {}
|
||||
@@ -296,7 +302,7 @@ def list_hypernetworks(path):
|
||||
name = os.path.splitext(os.path.basename(filename))[0]
|
||||
# Prevent a hypothetical "None.pt" from being listed.
|
||||
if name != "None":
|
||||
res[name + f"({sd_models.model_hash(filename)})"] = filename
|
||||
res[name] = filename
|
||||
return res
|
||||
|
||||
|
||||
@@ -509,7 +515,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, gradient_step,
|
||||
|
||||
if shared.opts.save_training_settings_to_txt:
|
||||
saved_params = dict(
|
||||
model_name=checkpoint.model_name, model_hash=checkpoint.hash, num_of_dataset_images=len(ds),
|
||||
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()})
|
||||
@@ -737,7 +743,7 @@ def save_hypernetwork(hypernetwork, checkpoint, hypernetwork_name, filename):
|
||||
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.hash
|
||||
hypernetwork.sd_checkpoint = checkpoint.shorthash
|
||||
hypernetwork.sd_checkpoint_name = checkpoint.model_name
|
||||
hypernetwork.name = hypernetwork_name
|
||||
hypernetwork.save(filename)
|
||||
|
||||
@@ -437,7 +437,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iter
|
||||
"Model hash": getattr(p, 'sd_model_hash', None if not opts.add_model_hash_to_info or not shared.sd_model.sd_model_hash else shared.sd_model.sd_model_hash),
|
||||
"Model": (None if not opts.add_model_name_to_info or not shared.sd_model.sd_checkpoint_info.model_name else shared.sd_model.sd_checkpoint_info.model_name.replace(',', '').replace(':', '')),
|
||||
"Hypernet": (None if shared.loaded_hypernetwork is None else shared.loaded_hypernetwork.name),
|
||||
"Hypernet hash": (None if shared.loaded_hypernetwork is None else sd_models.model_hash(shared.loaded_hypernetwork.filename)),
|
||||
"Hypernet hash": (None if shared.loaded_hypernetwork is None else shared.loaded_hypernetwork.shorthash()),
|
||||
"Hypernet strength": (None if shared.loaded_hypernetwork is None or shared.opts.sd_hypernetwork_strength >= 1 else shared.opts.sd_hypernetwork_strength),
|
||||
"Batch size": (None if p.batch_size < 2 else p.batch_size),
|
||||
"Batch pos": (None if p.batch_size < 2 else position_in_batch),
|
||||
|
||||
+78
-43
@@ -14,17 +14,58 @@ import ldm.modules.midas as midas
|
||||
|
||||
from ldm.util import instantiate_from_config
|
||||
|
||||
from modules import shared, modelloader, devices, script_callbacks, sd_vae, sd_disable_initialization, errors
|
||||
from modules import shared, modelloader, devices, script_callbacks, sd_vae, sd_disable_initialization, errors, hashes
|
||||
from modules.paths import models_path
|
||||
from modules.sd_hijack_inpainting import do_inpainting_hijack, should_hijack_inpainting
|
||||
|
||||
model_dir = "Stable-diffusion"
|
||||
model_path = os.path.abspath(os.path.join(models_path, model_dir))
|
||||
|
||||
CheckpointInfo = namedtuple("CheckpointInfo", ['filename', 'title', 'hash', 'model_name'])
|
||||
checkpoints_list = {}
|
||||
checkpoint_alisases = {}
|
||||
checkpoints_loaded = collections.OrderedDict()
|
||||
|
||||
|
||||
class CheckpointInfo:
|
||||
def __init__(self, filename):
|
||||
self.filename = filename
|
||||
abspath = os.path.abspath(filename)
|
||||
|
||||
if shared.cmd_opts.ckpt_dir is not None and abspath.startswith(shared.cmd_opts.ckpt_dir):
|
||||
name = abspath.replace(shared.cmd_opts.ckpt_dir, '')
|
||||
elif abspath.startswith(model_path):
|
||||
name = abspath.replace(model_path, '')
|
||||
else:
|
||||
name = os.path.basename(filename)
|
||||
|
||||
if name.startswith("\\") or name.startswith("/"):
|
||||
name = name[1:]
|
||||
|
||||
self.title = name
|
||||
self.model_name = os.path.splitext(name.replace("/", "_").replace("\\", "_"))[0]
|
||||
self.hash = model_hash(filename)
|
||||
|
||||
self.sha256 = hashes.sha256_from_cache(self.filename, "checkpoint/" + self.title)
|
||||
self.shorthash = self.sha256[0:10] if self.sha256 else None
|
||||
|
||||
self.ids = [self.hash, self.model_name, self.title, f'{name} [{self.hash}]'] + ([self.shorthash, self.sha256] if self.shorthash else [])
|
||||
|
||||
def register(self):
|
||||
checkpoints_list[self.title] = self
|
||||
for id in self.ids:
|
||||
checkpoint_alisases[id] = self
|
||||
|
||||
def calculate_shorthash(self):
|
||||
self.sha256 = hashes.sha256(self.filename, "checkpoint/" + self.title)
|
||||
self.shorthash = self.sha256[0:10]
|
||||
|
||||
if self.shorthash not in self.ids:
|
||||
self.ids += [self.shorthash, self.sha256]
|
||||
self.register()
|
||||
|
||||
return self.shorthash
|
||||
|
||||
|
||||
try:
|
||||
# this silences the annoying "Some weights of the model checkpoint were not used when initializing..." message at start.
|
||||
|
||||
@@ -43,10 +84,14 @@ def setup_model():
|
||||
enable_midas_autodownload()
|
||||
|
||||
|
||||
def checkpoint_tiles():
|
||||
convert = lambda name: int(name) if name.isdigit() else name.lower()
|
||||
alphanumeric_key = lambda key: [convert(c) for c in re.split('([0-9]+)', key)]
|
||||
return sorted([x.title for x in checkpoints_list.values()], key = alphanumeric_key)
|
||||
def checkpoint_tiles():
|
||||
def convert(name):
|
||||
return int(name) if name.isdigit() else name.lower()
|
||||
|
||||
def alphanumeric_key(key):
|
||||
return [convert(c) for c in re.split('([0-9]+)', key)]
|
||||
|
||||
return sorted([x.title for x in checkpoints_list.values()], key=alphanumeric_key)
|
||||
|
||||
|
||||
def find_checkpoint_config(info):
|
||||
@@ -62,48 +107,38 @@ def find_checkpoint_config(info):
|
||||
|
||||
def list_models():
|
||||
checkpoints_list.clear()
|
||||
checkpoint_alisases.clear()
|
||||
model_list = modelloader.load_models(model_path=model_path, command_path=shared.cmd_opts.ckpt_dir, ext_filter=[".ckpt", ".safetensors"], ext_blacklist=[".vae.safetensors"])
|
||||
|
||||
def modeltitle(path, shorthash):
|
||||
abspath = os.path.abspath(path)
|
||||
|
||||
if shared.cmd_opts.ckpt_dir is not None and abspath.startswith(shared.cmd_opts.ckpt_dir):
|
||||
name = abspath.replace(shared.cmd_opts.ckpt_dir, '')
|
||||
elif abspath.startswith(model_path):
|
||||
name = abspath.replace(model_path, '')
|
||||
else:
|
||||
name = os.path.basename(path)
|
||||
|
||||
if name.startswith("\\") or name.startswith("/"):
|
||||
name = name[1:]
|
||||
|
||||
shortname = os.path.splitext(name.replace("/", "_").replace("\\", "_"))[0]
|
||||
|
||||
return f'{name} [{shorthash}]', shortname
|
||||
|
||||
cmd_ckpt = shared.cmd_opts.ckpt
|
||||
if os.path.exists(cmd_ckpt):
|
||||
h = model_hash(cmd_ckpt)
|
||||
title, short_model_name = modeltitle(cmd_ckpt, h)
|
||||
checkpoints_list[title] = CheckpointInfo(cmd_ckpt, title, h, short_model_name)
|
||||
shared.opts.data['sd_model_checkpoint'] = title
|
||||
checkpoint_info = CheckpointInfo(cmd_ckpt)
|
||||
checkpoint_info.register()
|
||||
|
||||
shared.opts.data['sd_model_checkpoint'] = checkpoint_info.title
|
||||
elif cmd_ckpt is not None and cmd_ckpt != shared.default_sd_model_file:
|
||||
print(f"Checkpoint in --ckpt argument not found (Possible it was moved to {model_path}: {cmd_ckpt}", file=sys.stderr)
|
||||
|
||||
for filename in model_list:
|
||||
h = model_hash(filename)
|
||||
title, short_model_name = modeltitle(filename, h)
|
||||
|
||||
checkpoints_list[title] = CheckpointInfo(filename, title, h, short_model_name)
|
||||
checkpoint_info = CheckpointInfo(filename)
|
||||
checkpoint_info.register()
|
||||
|
||||
|
||||
def get_closet_checkpoint_match(searchString):
|
||||
applicable = sorted([info for info in checkpoints_list.values() if searchString in info.title], key = lambda x:len(x.title))
|
||||
if len(applicable) > 0:
|
||||
return applicable[0]
|
||||
def get_closet_checkpoint_match(search_string):
|
||||
checkpoint_info = checkpoint_alisases.get(search_string, None)
|
||||
if checkpoint_info is not None:
|
||||
return checkpoint_info
|
||||
|
||||
found = sorted([info for info in checkpoints_list.values() if search_string in info.title], key=lambda x: len(x.title))
|
||||
if found:
|
||||
return found[0]
|
||||
|
||||
return None
|
||||
|
||||
|
||||
def model_hash(filename):
|
||||
"""old hash that only looks at a small part of the file and is prone to collisions"""
|
||||
|
||||
try:
|
||||
with open(filename, "rb") as file:
|
||||
import hashlib
|
||||
@@ -119,7 +154,7 @@ def model_hash(filename):
|
||||
def select_checkpoint():
|
||||
model_checkpoint = shared.opts.sd_model_checkpoint
|
||||
|
||||
checkpoint_info = checkpoints_list.get(model_checkpoint, None)
|
||||
checkpoint_info = checkpoint_alisases.get(model_checkpoint, None)
|
||||
if checkpoint_info is not None:
|
||||
return checkpoint_info
|
||||
|
||||
@@ -189,9 +224,8 @@ def read_state_dict(checkpoint_file, print_global_state=False, map_location=None
|
||||
return sd
|
||||
|
||||
|
||||
def load_model_weights(model, checkpoint_info, vae_file="auto"):
|
||||
checkpoint_file = checkpoint_info.filename
|
||||
sd_model_hash = checkpoint_info.hash
|
||||
def load_model_weights(model, checkpoint_info: CheckpointInfo, vae_file="auto"):
|
||||
sd_model_hash = checkpoint_info.calculate_shorthash()
|
||||
|
||||
cache_enabled = shared.opts.sd_checkpoint_cache > 0
|
||||
|
||||
@@ -201,9 +235,9 @@ def load_model_weights(model, checkpoint_info, vae_file="auto"):
|
||||
model.load_state_dict(checkpoints_loaded[checkpoint_info])
|
||||
else:
|
||||
# load from file
|
||||
print(f"Loading weights [{sd_model_hash}] from {checkpoint_file}")
|
||||
print(f"Loading weights [{sd_model_hash}] from {checkpoint_info.filename}")
|
||||
|
||||
sd = read_state_dict(checkpoint_file)
|
||||
sd = read_state_dict(checkpoint_info.filename)
|
||||
model.load_state_dict(sd, strict=False)
|
||||
del sd
|
||||
|
||||
@@ -235,14 +269,15 @@ def load_model_weights(model, checkpoint_info, vae_file="auto"):
|
||||
checkpoints_loaded.popitem(last=False) # LRU
|
||||
|
||||
model.sd_model_hash = sd_model_hash
|
||||
model.sd_model_checkpoint = checkpoint_file
|
||||
model.sd_model_checkpoint = checkpoint_info.filename
|
||||
model.sd_checkpoint_info = checkpoint_info
|
||||
shared.opts.data["sd_checkpoint_hash"] = checkpoint_info.sha256
|
||||
|
||||
model.logvar = model.logvar.to(devices.device) # fix for training
|
||||
|
||||
sd_vae.delete_base_vae()
|
||||
sd_vae.clear_loaded_vae()
|
||||
vae_file = sd_vae.resolve_vae(checkpoint_file, vae_file=vae_file)
|
||||
vae_file = sd_vae.resolve_vae(checkpoint_info.filename, vae_file=vae_file)
|
||||
sd_vae.load_vae(model, vae_file)
|
||||
|
||||
|
||||
|
||||
+10
-5
@@ -138,7 +138,7 @@ def samples_to_image_grid(samples, approximation=None):
|
||||
def store_latent(decoded):
|
||||
state.current_latent = decoded
|
||||
|
||||
if opts.show_progress_every_n_steps > 0 and shared.state.sampling_step % opts.show_progress_every_n_steps == 0:
|
||||
if opts.live_previews_enable and opts.show_progress_every_n_steps > 0 and shared.state.sampling_step % opts.show_progress_every_n_steps == 0:
|
||||
if not shared.parallel_processing_allowed:
|
||||
shared.state.current_image = sample_to_image(decoded)
|
||||
|
||||
@@ -243,7 +243,7 @@ class VanillaStableDiffusionSampler:
|
||||
self.nmask = p.nmask if hasattr(p, 'nmask') else None
|
||||
|
||||
def adjust_steps_if_invalid(self, p, num_steps):
|
||||
if (self.config.name == 'DDIM' and p.ddim_discretize == 'uniform') or (self.config.name == 'PLMS'):
|
||||
if (self.config.name == 'DDIM' and p.ddim_discretize == 'uniform') or (self.config.name == 'PLMS'):
|
||||
valid_step = 999 / (1000 // num_steps)
|
||||
if valid_step == floor(valid_step):
|
||||
return int(valid_step) + 1
|
||||
@@ -266,8 +266,7 @@ class VanillaStableDiffusionSampler:
|
||||
if image_conditioning is not None:
|
||||
conditioning = {"c_concat": [image_conditioning], "c_crossattn": [conditioning]}
|
||||
unconditional_conditioning = {"c_concat": [image_conditioning], "c_crossattn": [unconditional_conditioning]}
|
||||
|
||||
|
||||
|
||||
samples = self.launch_sampling(t_enc + 1, lambda: self.sampler.decode(x1, conditioning, t_enc, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning))
|
||||
|
||||
return samples
|
||||
@@ -352,6 +351,11 @@ class CFGDenoiser(torch.nn.Module):
|
||||
|
||||
x_out[-uncond.shape[0]:] = self.inner_model(x_in[-uncond.shape[0]:], sigma_in[-uncond.shape[0]:], cond={"c_crossattn": [uncond], "c_concat": [image_cond_in[-uncond.shape[0]:]]})
|
||||
|
||||
if opts.live_preview_content == "Prompt":
|
||||
store_latent(x_out[0:uncond.shape[0]])
|
||||
elif opts.live_preview_content == "Negative prompt":
|
||||
store_latent(x_out[-uncond.shape[0]:])
|
||||
|
||||
denoised = self.combine_denoised(x_out, conds_list, uncond, cond_scale)
|
||||
|
||||
if self.mask is not None:
|
||||
@@ -423,7 +427,8 @@ class KDiffusionSampler:
|
||||
def callback_state(self, d):
|
||||
step = d['i']
|
||||
latent = d["denoised"]
|
||||
store_latent(latent)
|
||||
if opts.live_preview_content == "Combined":
|
||||
store_latent(latent)
|
||||
self.last_latent = latent
|
||||
|
||||
if self.stop_at is not None and step > self.stop_at:
|
||||
|
||||
+12
-5
@@ -182,7 +182,7 @@ class State:
|
||||
self.interrupted = True
|
||||
|
||||
def nextjob(self):
|
||||
if opts.show_progress_every_n_steps == -1:
|
||||
if opts.live_previews_enable and opts.show_progress_every_n_steps == -1:
|
||||
self.do_set_current_image()
|
||||
|
||||
self.job_no += 1
|
||||
@@ -230,7 +230,7 @@ class State:
|
||||
if not parallel_processing_allowed:
|
||||
return
|
||||
|
||||
if self.sampling_step - self.current_image_sampling_step >= opts.show_progress_every_n_steps and opts.show_progress_every_n_steps > 0:
|
||||
if self.sampling_step - self.current_image_sampling_step >= opts.show_progress_every_n_steps and opts.live_previews_enable:
|
||||
self.do_set_current_image()
|
||||
|
||||
def do_set_current_image(self):
|
||||
@@ -367,6 +367,7 @@ options_templates.update(options_section(('system', "System"), {
|
||||
"memmon_poll_rate": OptionInfo(8, "VRAM usage polls per second during generation. Set to 0 to disable.", gr.Slider, {"minimum": 0, "maximum": 40, "step": 1}),
|
||||
"samples_log_stdout": OptionInfo(False, "Always print all generation info to standard output"),
|
||||
"multiple_tqdm": OptionInfo(True, "Add a second progress bar to the console that shows progress for an entire job."),
|
||||
"print_hypernet_extra": OptionInfo(False, "Print extra hypernetwork information to console."),
|
||||
}))
|
||||
|
||||
options_templates.update(options_section(('training', "Training"), {
|
||||
@@ -428,13 +429,11 @@ options_templates.update(options_section(('interrogate', "Interrogate Options"),
|
||||
|
||||
options_templates.update(options_section(('ui', "User interface"), {
|
||||
"show_progressbar": OptionInfo(True, "Show progressbar"),
|
||||
"show_progress_every_n_steps": OptionInfo(0, "Show image creation progress every N sampling steps. Set to 0 to disable. Set to -1 to show after completion of batch.", gr.Slider, {"minimum": -1, "maximum": 32, "step": 1}),
|
||||
"show_progress_type": OptionInfo("Full", "Image creation progress preview mode", gr.Radio, {"choices": ["Full", "Approx NN", "Approx cheap"]}),
|
||||
"show_progress_grid": OptionInfo(True, "Show previews of all images generated in a batch as a grid"),
|
||||
"return_grid": OptionInfo(True, "Show grid in results for web"),
|
||||
"do_not_show_images": OptionInfo(False, "Do not show any images in results for web"),
|
||||
"add_model_hash_to_info": OptionInfo(True, "Add model hash to generation information"),
|
||||
"add_model_name_to_info": OptionInfo(False, "Add model name to generation information"),
|
||||
"add_model_name_to_info": OptionInfo(True, "Add model name to generation information"),
|
||||
"disable_weights_auto_swap": OptionInfo(False, "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint."),
|
||||
"send_seed": OptionInfo(True, "Send seed when sending prompt or image to other interface"),
|
||||
"send_size": OptionInfo(True, "Send size when sending prompt or image to another interface"),
|
||||
@@ -449,6 +448,13 @@ options_templates.update(options_section(('ui', "User interface"), {
|
||||
'localization': OptionInfo("None", "Localization (requires restart)", gr.Dropdown, lambda: {"choices": ["None"] + list(localization.localizations.keys())}, refresh=lambda: localization.list_localizations(cmd_opts.localizations_dir)),
|
||||
}))
|
||||
|
||||
options_templates.update(options_section(('ui', "Live previews"), {
|
||||
"live_previews_enable": OptionInfo(True, "Show live previews of the created image"),
|
||||
"show_progress_every_n_steps": OptionInfo(10, "Show new live preview image every N sampling steps. Set to -1 to show after completion of batch.", gr.Slider, {"minimum": -1, "maximum": 32, "step": 1}),
|
||||
"show_progress_type": OptionInfo("Approx NN", "Image creation progress preview mode", gr.Radio, {"choices": ["Full", "Approx NN", "Approx cheap"]}),
|
||||
"live_preview_content": OptionInfo("Prompt", "Live preview subject", gr.Radio, {"choices": ["Combined", "Prompt", "Negative prompt"]}),
|
||||
}))
|
||||
|
||||
options_templates.update(options_section(('sampler-params', "Sampler parameters"), {
|
||||
"hide_samplers": OptionInfo([], "Hide samplers in user interface (requires restart)", gr.CheckboxGroup, lambda: {"choices": [x.name for x in list_samplers()]}),
|
||||
"eta_ddim": OptionInfo(0.0, "eta (noise multiplier) for DDIM", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
|
||||
@@ -463,6 +469,7 @@ options_templates.update(options_section(('sampler-params', "Sampler parameters"
|
||||
|
||||
options_templates.update(options_section((None, "Hidden options"), {
|
||||
"disabled_extensions": OptionInfo([], "Disable those extensions"),
|
||||
"sd_checkpoint_hash": OptionInfo("", "SHA256 hash of the current checkpoint"),
|
||||
}))
|
||||
|
||||
options_templates.update()
|
||||
|
||||
@@ -407,7 +407,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_
|
||||
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=embedding_name, model=shared.sd_model, cond_model=shared.sd_model.cond_stage_model, device=devices.device, template_file=template_file, 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)
|
||||
|
||||
if shared.opts.save_training_settings_to_txt:
|
||||
save_settings_to_file(log_directory, {**dict(model_name=checkpoint.model_name, model_hash=checkpoint.hash, num_of_dataset_images=len(ds), num_vectors_per_token=len(embedding.vec)), **locals()})
|
||||
save_settings_to_file(log_directory, {**dict(model_name=checkpoint.model_name, model_hash=checkpoint.shorthash, num_of_dataset_images=len(ds), num_vectors_per_token=len(embedding.vec)), **locals()})
|
||||
|
||||
latent_sampling_method = ds.latent_sampling_method
|
||||
|
||||
@@ -584,7 +584,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_
|
||||
|
||||
checkpoint = sd_models.select_checkpoint()
|
||||
footer_left = checkpoint.model_name
|
||||
footer_mid = '[{}]'.format(checkpoint.hash)
|
||||
footer_mid = '[{}]'.format(checkpoint.shorthash)
|
||||
footer_right = '{}v {}s'.format(vectorSize, steps_done)
|
||||
|
||||
captioned_image = caption_image_overlay(image, title, footer_left, footer_mid, footer_right)
|
||||
@@ -626,7 +626,7 @@ def save_embedding(embedding, optimizer, checkpoint, embedding_name, filename, r
|
||||
old_sd_checkpoint_name = embedding.sd_checkpoint_name if hasattr(embedding, "sd_checkpoint_name") else None
|
||||
old_cached_checksum = embedding.cached_checksum if hasattr(embedding, "cached_checksum") else None
|
||||
try:
|
||||
embedding.sd_checkpoint = checkpoint.hash
|
||||
embedding.sd_checkpoint = checkpoint.shorthash
|
||||
embedding.sd_checkpoint_name = checkpoint.model_name
|
||||
if remove_cached_checksum:
|
||||
embedding.cached_checksum = None
|
||||
|
||||
@@ -1841,4 +1841,6 @@ xformers: {xformers_version}
|
||||
gradio: {gr.__version__}
|
||||
•
|
||||
commit: <a href="https://github.com/AUTOMATIC1111/stable-diffusion-webui/commit/{commit}">{short_commit}</a>
|
||||
•
|
||||
checkpoint: <a id="sd_checkpoint_hash">N/A</a>
|
||||
"""
|
||||
|
||||
@@ -52,7 +52,7 @@ def check_progress_call(id_part):
|
||||
image = gr.update(visible=False)
|
||||
preview_visibility = gr.update(visible=False)
|
||||
|
||||
if opts.show_progress_every_n_steps != 0:
|
||||
if opts.live_previews_enable:
|
||||
shared.state.set_current_image()
|
||||
image = shared.state.current_image
|
||||
|
||||
|
||||
@@ -14,6 +14,7 @@ function get_uiCurrentTabContent() {
|
||||
|
||||
uiUpdateCallbacks = []
|
||||
uiTabChangeCallbacks = []
|
||||
optionsChangedCallbacks = []
|
||||
let uiCurrentTab = null
|
||||
|
||||
function onUiUpdate(callback){
|
||||
@@ -22,6 +23,9 @@ function onUiUpdate(callback){
|
||||
function onUiTabChange(callback){
|
||||
uiTabChangeCallbacks.push(callback)
|
||||
}
|
||||
function onOptionsChanged(callback){
|
||||
optionsChangedCallbacks.push(callback)
|
||||
}
|
||||
|
||||
function runCallback(x, m){
|
||||
try {
|
||||
|
||||
@@ -146,11 +146,7 @@ class Script(scripts.Script):
|
||||
else:
|
||||
args = {"prompt": line}
|
||||
|
||||
n_iter = args.get("n_iter", 1)
|
||||
if n_iter != 1:
|
||||
job_count += n_iter
|
||||
else:
|
||||
job_count += 1
|
||||
job_count += args.get("n_iter", p.n_iter)
|
||||
|
||||
jobs.append(args)
|
||||
|
||||
|
||||
@@ -78,6 +78,8 @@ def initialize():
|
||||
print("Stable diffusion model failed to load, exiting", file=sys.stderr)
|
||||
exit(1)
|
||||
|
||||
shared.opts.data["sd_model_checkpoint"] = shared.sd_model.sd_checkpoint_info.title
|
||||
|
||||
shared.opts.onchange("sd_model_checkpoint", wrap_queued_call(lambda: modules.sd_models.reload_model_weights()))
|
||||
shared.opts.onchange("sd_vae", wrap_queued_call(lambda: modules.sd_vae.reload_vae_weights()), call=False)
|
||||
shared.opts.onchange("sd_vae_as_default", wrap_queued_call(lambda: modules.sd_vae.reload_vae_weights()), call=False)
|
||||
|
||||
Reference in New Issue
Block a user