747 lines
27 KiB
Python
747 lines
27 KiB
Python
import collections
|
|
import os.path
|
|
import sys
|
|
import gc
|
|
import threading
|
|
|
|
import torch
|
|
import re
|
|
import safetensors.torch
|
|
from omegaconf import OmegaConf
|
|
from os import mkdir
|
|
from urllib import request
|
|
import ldm.modules.midas as midas
|
|
|
|
from ldm.util import instantiate_from_config
|
|
|
|
from modules import paths, shared, modelloader, devices, script_callbacks, sd_vae, sd_disable_initialization, errors, hashes, sd_models_config, sd_unet, sd_models_xl, cache
|
|
from modules.timer import Timer
|
|
import tomesd
|
|
|
|
model_dir = "Stable-diffusion"
|
|
model_path = os.path.abspath(os.path.join(paths.models_path, model_dir))
|
|
|
|
checkpoints_list = {}
|
|
checkpoint_aliases = {}
|
|
checkpoint_alisases = checkpoint_aliases # for compatibility with old name
|
|
checkpoints_loaded = collections.OrderedDict()
|
|
|
|
|
|
class CheckpointInfo:
|
|
def __init__(self, filename):
|
|
self.filename = filename
|
|
abspath = os.path.abspath(filename)
|
|
|
|
self.is_safetensors = os.path.splitext(filename)[1].lower() == ".safetensors"
|
|
|
|
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:]
|
|
|
|
def read_metadata():
|
|
metadata = read_metadata_from_safetensors(filename)
|
|
self.modelspec_thumbnail = metadata.pop('modelspec.thumbnail', None)
|
|
|
|
return metadata
|
|
|
|
self.metadata = {}
|
|
if self.is_safetensors:
|
|
try:
|
|
self.metadata = cache.cached_data_for_file('safetensors-metadata', "checkpoint/" + name, filename, read_metadata)
|
|
except Exception as e:
|
|
errors.display(e, f"reading metadata for {filename}")
|
|
|
|
self.name = name
|
|
self.name_for_extra = os.path.splitext(os.path.basename(filename))[0]
|
|
self.model_name = os.path.splitext(name.replace("/", "_").replace("\\", "_"))[0]
|
|
self.hash = model_hash(filename)
|
|
|
|
self.sha256 = hashes.sha256_from_cache(self.filename, f"checkpoint/{name}")
|
|
self.shorthash = self.sha256[0:10] if self.sha256 else None
|
|
|
|
self.title = name if self.shorthash is None else f'{name} [{self.shorthash}]'
|
|
self.short_title = self.name_for_extra if self.shorthash is None else f'{self.name_for_extra} [{self.shorthash}]'
|
|
|
|
self.ids = [self.hash, self.model_name, self.title, name, self.name_for_extra, f'{name} [{self.hash}]'] + ([self.shorthash, self.sha256, f'{self.name} [{self.shorthash}]'] if self.shorthash else [])
|
|
|
|
def register(self):
|
|
checkpoints_list[self.title] = self
|
|
for id in self.ids:
|
|
checkpoint_aliases[id] = self
|
|
|
|
def calculate_shorthash(self):
|
|
self.sha256 = hashes.sha256(self.filename, f"checkpoint/{self.name}")
|
|
if self.sha256 is None:
|
|
return
|
|
|
|
self.shorthash = self.sha256[0:10]
|
|
|
|
if self.shorthash not in self.ids:
|
|
self.ids += [self.shorthash, self.sha256, f'{self.name} [{self.shorthash}]']
|
|
|
|
checkpoints_list.pop(self.title, None)
|
|
self.title = f'{self.name} [{self.shorthash}]'
|
|
self.short_title = f'{self.name_for_extra} [{self.shorthash}]'
|
|
self.register()
|
|
|
|
return self.shorthash
|
|
|
|
|
|
try:
|
|
# this silences the annoying "Some weights of the model checkpoint were not used when initializing..." message at start.
|
|
from transformers import logging, CLIPModel # noqa: F401
|
|
|
|
logging.set_verbosity_error()
|
|
except Exception:
|
|
pass
|
|
|
|
|
|
def setup_model():
|
|
os.makedirs(model_path, exist_ok=True)
|
|
|
|
enable_midas_autodownload()
|
|
|
|
|
|
def checkpoint_tiles(use_short=False):
|
|
return [x.short_title if use_short else x.title for x in checkpoints_list.values()]
|
|
|
|
|
|
def list_models():
|
|
checkpoints_list.clear()
|
|
checkpoint_aliases.clear()
|
|
|
|
cmd_ckpt = shared.cmd_opts.ckpt
|
|
if shared.cmd_opts.no_download_sd_model or cmd_ckpt != shared.sd_model_file or os.path.exists(cmd_ckpt):
|
|
model_url = None
|
|
else:
|
|
model_url = "https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.safetensors"
|
|
|
|
model_list = modelloader.load_models(model_path=model_path, model_url=model_url, command_path=shared.cmd_opts.ckpt_dir, ext_filter=[".ckpt", ".safetensors"], download_name="v1-5-pruned-emaonly.safetensors", ext_blacklist=[".vae.ckpt", ".vae.safetensors"])
|
|
|
|
if os.path.exists(cmd_ckpt):
|
|
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:
|
|
checkpoint_info = CheckpointInfo(filename)
|
|
checkpoint_info.register()
|
|
|
|
|
|
re_strip_checksum = re.compile(r"\s*\[[^]]+]\s*$")
|
|
|
|
|
|
def get_closet_checkpoint_match(search_string):
|
|
checkpoint_info = checkpoint_aliases.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]
|
|
|
|
search_string_without_checksum = re.sub(re_strip_checksum, '', search_string)
|
|
found = sorted([info for info in checkpoints_list.values() if search_string_without_checksum 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
|
|
m = hashlib.sha256()
|
|
|
|
file.seek(0x100000)
|
|
m.update(file.read(0x10000))
|
|
return m.hexdigest()[0:8]
|
|
except FileNotFoundError:
|
|
return 'NOFILE'
|
|
|
|
|
|
def select_checkpoint():
|
|
"""Raises `FileNotFoundError` if no checkpoints are found."""
|
|
model_checkpoint = shared.opts.sd_model_checkpoint
|
|
|
|
checkpoint_info = checkpoint_aliases.get(model_checkpoint, None)
|
|
if checkpoint_info is not None:
|
|
return checkpoint_info
|
|
|
|
if len(checkpoints_list) == 0:
|
|
error_message = "No checkpoints found. When searching for checkpoints, looked at:"
|
|
if shared.cmd_opts.ckpt is not None:
|
|
error_message += f"\n - file {os.path.abspath(shared.cmd_opts.ckpt)}"
|
|
error_message += f"\n - directory {model_path}"
|
|
if shared.cmd_opts.ckpt_dir is not None:
|
|
error_message += f"\n - directory {os.path.abspath(shared.cmd_opts.ckpt_dir)}"
|
|
error_message += "Can't run without a checkpoint. Find and place a .ckpt or .safetensors file into any of those locations."
|
|
raise FileNotFoundError(error_message)
|
|
|
|
checkpoint_info = next(iter(checkpoints_list.values()))
|
|
if model_checkpoint is not None:
|
|
print(f"Checkpoint {model_checkpoint} not found; loading fallback {checkpoint_info.title}", file=sys.stderr)
|
|
|
|
return checkpoint_info
|
|
|
|
|
|
checkpoint_dict_replacements = {
|
|
'cond_stage_model.transformer.embeddings.': 'cond_stage_model.transformer.text_model.embeddings.',
|
|
'cond_stage_model.transformer.encoder.': 'cond_stage_model.transformer.text_model.encoder.',
|
|
'cond_stage_model.transformer.final_layer_norm.': 'cond_stage_model.transformer.text_model.final_layer_norm.',
|
|
}
|
|
|
|
|
|
def transform_checkpoint_dict_key(k):
|
|
for text, replacement in checkpoint_dict_replacements.items():
|
|
if k.startswith(text):
|
|
k = replacement + k[len(text):]
|
|
|
|
return k
|
|
|
|
|
|
def get_state_dict_from_checkpoint(pl_sd):
|
|
pl_sd = pl_sd.pop("state_dict", pl_sd)
|
|
pl_sd.pop("state_dict", None)
|
|
|
|
sd = {}
|
|
for k, v in pl_sd.items():
|
|
new_key = transform_checkpoint_dict_key(k)
|
|
|
|
if new_key is not None:
|
|
sd[new_key] = v
|
|
|
|
pl_sd.clear()
|
|
pl_sd.update(sd)
|
|
|
|
return pl_sd
|
|
|
|
|
|
def read_metadata_from_safetensors(filename):
|
|
import json
|
|
|
|
with open(filename, mode="rb") as file:
|
|
metadata_len = file.read(8)
|
|
metadata_len = int.from_bytes(metadata_len, "little")
|
|
json_start = file.read(2)
|
|
|
|
assert metadata_len > 2 and json_start in (b'{"', b"{'"), f"{filename} is not a safetensors file"
|
|
json_data = json_start + file.read(metadata_len-2)
|
|
json_obj = json.loads(json_data)
|
|
|
|
res = {}
|
|
for k, v in json_obj.get("__metadata__", {}).items():
|
|
res[k] = v
|
|
if isinstance(v, str) and v[0:1] == '{':
|
|
try:
|
|
res[k] = json.loads(v)
|
|
except Exception:
|
|
pass
|
|
|
|
return res
|
|
|
|
|
|
def read_state_dict(checkpoint_file, print_global_state=False, map_location=None):
|
|
_, extension = os.path.splitext(checkpoint_file)
|
|
if extension.lower() == ".safetensors":
|
|
device = map_location or shared.weight_load_location or devices.get_optimal_device_name()
|
|
|
|
if not shared.opts.disable_mmap_load_safetensors:
|
|
pl_sd = safetensors.torch.load_file(checkpoint_file, device=device)
|
|
else:
|
|
pl_sd = safetensors.torch.load(open(checkpoint_file, 'rb').read())
|
|
pl_sd = {k: v.to(device) for k, v in pl_sd.items()}
|
|
else:
|
|
pl_sd = torch.load(checkpoint_file, map_location=map_location or shared.weight_load_location)
|
|
|
|
if print_global_state and "global_step" in pl_sd:
|
|
print(f"Global Step: {pl_sd['global_step']}")
|
|
|
|
sd = get_state_dict_from_checkpoint(pl_sd)
|
|
return sd
|
|
|
|
|
|
def get_checkpoint_state_dict(checkpoint_info: CheckpointInfo, timer):
|
|
sd_model_hash = checkpoint_info.calculate_shorthash()
|
|
timer.record("calculate hash")
|
|
|
|
if checkpoint_info in checkpoints_loaded:
|
|
# use checkpoint cache
|
|
print(f"Loading weights [{sd_model_hash}] from cache")
|
|
return checkpoints_loaded[checkpoint_info]
|
|
|
|
print(f"Loading weights [{sd_model_hash}] from {checkpoint_info.filename}")
|
|
res = read_state_dict(checkpoint_info.filename)
|
|
timer.record("load weights from disk")
|
|
|
|
return res
|
|
|
|
|
|
def load_model_weights(model, checkpoint_info: CheckpointInfo, state_dict, timer):
|
|
sd_model_hash = checkpoint_info.calculate_shorthash()
|
|
timer.record("calculate hash")
|
|
|
|
shared.opts.data["sd_model_checkpoint"] = checkpoint_info.title
|
|
|
|
if state_dict is None:
|
|
state_dict = get_checkpoint_state_dict(checkpoint_info, timer)
|
|
|
|
model.is_sdxl = hasattr(model, 'conditioner')
|
|
model.is_sd2 = not model.is_sdxl and hasattr(model.cond_stage_model, 'model')
|
|
model.is_sd1 = not model.is_sdxl and not model.is_sd2
|
|
|
|
if model.is_sdxl:
|
|
sd_models_xl.extend_sdxl(model)
|
|
|
|
model.load_state_dict(state_dict, strict=False)
|
|
timer.record("apply weights to model")
|
|
|
|
if shared.opts.sd_checkpoint_cache > 0:
|
|
# cache newly loaded model
|
|
checkpoints_loaded[checkpoint_info] = state_dict
|
|
|
|
del state_dict
|
|
|
|
if shared.cmd_opts.opt_channelslast:
|
|
model.to(memory_format=torch.channels_last)
|
|
timer.record("apply channels_last")
|
|
|
|
if not shared.cmd_opts.no_half:
|
|
vae = model.first_stage_model
|
|
depth_model = getattr(model, 'depth_model', None)
|
|
|
|
# with --no-half-vae, remove VAE from model when doing half() to prevent its weights from being converted to float16
|
|
if shared.cmd_opts.no_half_vae:
|
|
model.first_stage_model = None
|
|
# with --upcast-sampling, don't convert the depth model weights to float16
|
|
if shared.cmd_opts.upcast_sampling and depth_model:
|
|
model.depth_model = None
|
|
|
|
model.half()
|
|
model.first_stage_model = vae
|
|
if depth_model:
|
|
model.depth_model = depth_model
|
|
|
|
timer.record("apply half()")
|
|
|
|
devices.dtype_unet = torch.float16 if model.is_sdxl and not shared.cmd_opts.no_half else model.model.diffusion_model.dtype
|
|
devices.unet_needs_upcast = shared.cmd_opts.upcast_sampling and devices.dtype == torch.float16 and devices.dtype_unet == torch.float16
|
|
|
|
model.first_stage_model.to(devices.dtype_vae)
|
|
timer.record("apply dtype to VAE")
|
|
|
|
# clean up cache if limit is reached
|
|
while len(checkpoints_loaded) > shared.opts.sd_checkpoint_cache:
|
|
checkpoints_loaded.popitem(last=False)
|
|
|
|
model.sd_model_hash = sd_model_hash
|
|
model.sd_model_checkpoint = checkpoint_info.filename
|
|
model.sd_checkpoint_info = checkpoint_info
|
|
shared.opts.data["sd_checkpoint_hash"] = checkpoint_info.sha256
|
|
|
|
if hasattr(model, 'logvar'):
|
|
model.logvar = model.logvar.to(devices.device) # fix for training
|
|
|
|
sd_vae.delete_base_vae()
|
|
sd_vae.clear_loaded_vae()
|
|
vae_file, vae_source = sd_vae.resolve_vae(checkpoint_info.filename)
|
|
sd_vae.load_vae(model, vae_file, vae_source)
|
|
timer.record("load VAE")
|
|
|
|
|
|
def enable_midas_autodownload():
|
|
"""
|
|
Gives the ldm.modules.midas.api.load_model function automatic downloading.
|
|
|
|
When the 512-depth-ema model, and other future models like it, is loaded,
|
|
it calls midas.api.load_model to load the associated midas depth model.
|
|
This function applies a wrapper to download the model to the correct
|
|
location automatically.
|
|
"""
|
|
|
|
midas_path = os.path.join(paths.models_path, 'midas')
|
|
|
|
# stable-diffusion-stability-ai hard-codes the midas model path to
|
|
# a location that differs from where other scripts using this model look.
|
|
# HACK: Overriding the path here.
|
|
for k, v in midas.api.ISL_PATHS.items():
|
|
file_name = os.path.basename(v)
|
|
midas.api.ISL_PATHS[k] = os.path.join(midas_path, file_name)
|
|
|
|
midas_urls = {
|
|
"dpt_large": "https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt",
|
|
"dpt_hybrid": "https://github.com/intel-isl/DPT/releases/download/1_0/dpt_hybrid-midas-501f0c75.pt",
|
|
"midas_v21": "https://github.com/AlexeyAB/MiDaS/releases/download/midas_dpt/midas_v21-f6b98070.pt",
|
|
"midas_v21_small": "https://github.com/AlexeyAB/MiDaS/releases/download/midas_dpt/midas_v21_small-70d6b9c8.pt",
|
|
}
|
|
|
|
midas.api.load_model_inner = midas.api.load_model
|
|
|
|
def load_model_wrapper(model_type):
|
|
path = midas.api.ISL_PATHS[model_type]
|
|
if not os.path.exists(path):
|
|
if not os.path.exists(midas_path):
|
|
mkdir(midas_path)
|
|
|
|
print(f"Downloading midas model weights for {model_type} to {path}")
|
|
request.urlretrieve(midas_urls[model_type], path)
|
|
print(f"{model_type} downloaded")
|
|
|
|
return midas.api.load_model_inner(model_type)
|
|
|
|
midas.api.load_model = load_model_wrapper
|
|
|
|
|
|
def repair_config(sd_config):
|
|
|
|
if not hasattr(sd_config.model.params, "use_ema"):
|
|
sd_config.model.params.use_ema = False
|
|
|
|
if hasattr(sd_config.model.params, 'unet_config'):
|
|
if shared.cmd_opts.no_half:
|
|
sd_config.model.params.unet_config.params.use_fp16 = False
|
|
elif shared.cmd_opts.upcast_sampling:
|
|
sd_config.model.params.unet_config.params.use_fp16 = True
|
|
|
|
if getattr(sd_config.model.params.first_stage_config.params.ddconfig, "attn_type", None) == "vanilla-xformers" and not shared.xformers_available:
|
|
sd_config.model.params.first_stage_config.params.ddconfig.attn_type = "vanilla"
|
|
|
|
# For UnCLIP-L, override the hardcoded karlo directory
|
|
if hasattr(sd_config.model.params, "noise_aug_config") and hasattr(sd_config.model.params.noise_aug_config.params, "clip_stats_path"):
|
|
karlo_path = os.path.join(paths.models_path, 'karlo')
|
|
sd_config.model.params.noise_aug_config.params.clip_stats_path = sd_config.model.params.noise_aug_config.params.clip_stats_path.replace("checkpoints/karlo_models", karlo_path)
|
|
|
|
|
|
sd1_clip_weight = 'cond_stage_model.transformer.text_model.embeddings.token_embedding.weight'
|
|
sd2_clip_weight = 'cond_stage_model.model.transformer.resblocks.0.attn.in_proj_weight'
|
|
sdxl_clip_weight = 'conditioner.embedders.1.model.ln_final.weight'
|
|
sdxl_refiner_clip_weight = 'conditioner.embedders.0.model.ln_final.weight'
|
|
|
|
|
|
class SdModelData:
|
|
def __init__(self):
|
|
self.sd_model = None
|
|
self.loaded_sd_models = []
|
|
self.was_loaded_at_least_once = False
|
|
self.lock = threading.Lock()
|
|
|
|
def get_sd_model(self):
|
|
if self.was_loaded_at_least_once:
|
|
return self.sd_model
|
|
|
|
if self.sd_model is None:
|
|
with self.lock:
|
|
if self.sd_model is not None or self.was_loaded_at_least_once:
|
|
return self.sd_model
|
|
|
|
try:
|
|
load_model()
|
|
|
|
except Exception as e:
|
|
errors.display(e, "loading stable diffusion model", full_traceback=True)
|
|
print("", file=sys.stderr)
|
|
print("Stable diffusion model failed to load", file=sys.stderr)
|
|
self.sd_model = None
|
|
|
|
return self.sd_model
|
|
|
|
def set_sd_model(self, v):
|
|
self.sd_model = v
|
|
|
|
try:
|
|
self.loaded_sd_models.remove(v)
|
|
except ValueError:
|
|
pass
|
|
|
|
if v is not None:
|
|
self.loaded_sd_models.insert(0, v)
|
|
|
|
|
|
model_data = SdModelData()
|
|
|
|
|
|
def get_empty_cond(sd_model):
|
|
from modules import extra_networks, processing
|
|
|
|
p = processing.StableDiffusionProcessingTxt2Img()
|
|
extra_networks.activate(p, {})
|
|
|
|
if hasattr(sd_model, 'conditioner'):
|
|
d = sd_model.get_learned_conditioning([""])
|
|
return d['crossattn']
|
|
else:
|
|
return sd_model.cond_stage_model([""])
|
|
|
|
|
|
def send_model_to_cpu(m):
|
|
from modules import lowvram
|
|
|
|
if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
|
|
lowvram.send_everything_to_cpu()
|
|
else:
|
|
m.to(devices.cpu)
|
|
|
|
devices.torch_gc()
|
|
|
|
|
|
def send_model_to_device(m):
|
|
from modules import lowvram
|
|
|
|
if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
|
|
lowvram.setup_for_low_vram(m, shared.cmd_opts.medvram)
|
|
else:
|
|
m.to(shared.device)
|
|
|
|
|
|
def send_model_to_trash(m):
|
|
m.to(device="meta")
|
|
devices.torch_gc()
|
|
|
|
|
|
def load_model(checkpoint_info=None, already_loaded_state_dict=None):
|
|
from modules import sd_hijack
|
|
checkpoint_info = checkpoint_info or select_checkpoint()
|
|
|
|
timer = Timer()
|
|
|
|
if model_data.sd_model:
|
|
send_model_to_trash(model_data.sd_model)
|
|
model_data.sd_model = None
|
|
devices.torch_gc()
|
|
|
|
timer.record("unload existing model")
|
|
|
|
if already_loaded_state_dict is not None:
|
|
state_dict = already_loaded_state_dict
|
|
else:
|
|
state_dict = get_checkpoint_state_dict(checkpoint_info, timer)
|
|
|
|
checkpoint_config = sd_models_config.find_checkpoint_config(state_dict, checkpoint_info)
|
|
clip_is_included_into_sd = any(x for x in [sd1_clip_weight, sd2_clip_weight, sdxl_clip_weight, sdxl_refiner_clip_weight] if x in state_dict)
|
|
|
|
timer.record("find config")
|
|
|
|
sd_config = OmegaConf.load(checkpoint_config)
|
|
repair_config(sd_config)
|
|
|
|
timer.record("load config")
|
|
|
|
print(f"Creating model from config: {checkpoint_config}")
|
|
|
|
sd_model = None
|
|
try:
|
|
with sd_disable_initialization.DisableInitialization(disable_clip=clip_is_included_into_sd or shared.cmd_opts.do_not_download_clip):
|
|
with sd_disable_initialization.InitializeOnMeta():
|
|
sd_model = instantiate_from_config(sd_config.model)
|
|
|
|
except Exception as e:
|
|
errors.display(e, "creating model quickly", full_traceback=True)
|
|
|
|
if sd_model is None:
|
|
print('Failed to create model quickly; will retry using slow method.', file=sys.stderr)
|
|
|
|
with sd_disable_initialization.InitializeOnMeta():
|
|
sd_model = instantiate_from_config(sd_config.model)
|
|
|
|
sd_model.used_config = checkpoint_config
|
|
|
|
timer.record("create model")
|
|
|
|
with sd_disable_initialization.LoadStateDictOnMeta(state_dict, devices.cpu):
|
|
load_model_weights(sd_model, checkpoint_info, state_dict, timer)
|
|
timer.record("load weights from state dict")
|
|
|
|
send_model_to_device(sd_model)
|
|
timer.record("move model to device")
|
|
|
|
sd_hijack.model_hijack.hijack(sd_model)
|
|
|
|
timer.record("hijack")
|
|
|
|
sd_model.eval()
|
|
model_data.set_sd_model(sd_model)
|
|
model_data.was_loaded_at_least_once = True
|
|
|
|
sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings(force_reload=True) # Reload embeddings after model load as they may or may not fit the model
|
|
|
|
timer.record("load textual inversion embeddings")
|
|
|
|
script_callbacks.model_loaded_callback(sd_model)
|
|
|
|
timer.record("scripts callbacks")
|
|
|
|
with devices.autocast(), torch.no_grad():
|
|
sd_model.cond_stage_model_empty_prompt = get_empty_cond(sd_model)
|
|
|
|
timer.record("calculate empty prompt")
|
|
|
|
print(f"Model loaded in {timer.summary()}.")
|
|
|
|
return sd_model
|
|
|
|
|
|
def reuse_model_from_already_loaded(sd_model, checkpoint_info, timer):
|
|
"""
|
|
Checks if the desired checkpoint from checkpoint_info is not already loaded in model_data.loaded_sd_models.
|
|
If it is loaded, returns that (moving it to GPU if necessary, and moving the currently loadded model to CPU if necessary).
|
|
If not, returns the model that can be used to load weights from checkpoint_info's file.
|
|
If no such model exists, returns None.
|
|
Additionaly deletes loaded models that are over the limit set in settings (sd_checkpoints_limit).
|
|
"""
|
|
|
|
already_loaded = None
|
|
for i in reversed(range(len(model_data.loaded_sd_models))):
|
|
loaded_model = model_data.loaded_sd_models[i]
|
|
if loaded_model.sd_checkpoint_info.filename == checkpoint_info.filename:
|
|
already_loaded = loaded_model
|
|
continue
|
|
|
|
if len(model_data.loaded_sd_models) > shared.opts.sd_checkpoints_limit > 0:
|
|
print(f"Unloading model {len(model_data.loaded_sd_models)} over the limit of {shared.opts.sd_checkpoints_limit}: {loaded_model.sd_checkpoint_info.title}")
|
|
model_data.loaded_sd_models.pop()
|
|
send_model_to_trash(loaded_model)
|
|
timer.record("send model to trash")
|
|
|
|
if shared.opts.sd_checkpoints_keep_in_cpu:
|
|
send_model_to_cpu(sd_model)
|
|
timer.record("send model to cpu")
|
|
|
|
if already_loaded is not None:
|
|
send_model_to_device(already_loaded)
|
|
timer.record("send model to device")
|
|
|
|
model_data.set_sd_model(already_loaded)
|
|
print(f"Using already loaded model {already_loaded.sd_checkpoint_info.title}: done in {timer.summary()}")
|
|
return model_data.sd_model
|
|
elif shared.opts.sd_checkpoints_limit > 1 and len(model_data.loaded_sd_models) < shared.opts.sd_checkpoints_limit:
|
|
print(f"Loading model {checkpoint_info.title} ({len(model_data.loaded_sd_models) + 1} out of {shared.opts.sd_checkpoints_limit})")
|
|
|
|
model_data.sd_model = None
|
|
load_model(checkpoint_info)
|
|
return model_data.sd_model
|
|
elif len(model_data.loaded_sd_models) > 0:
|
|
sd_model = model_data.loaded_sd_models.pop()
|
|
model_data.sd_model = sd_model
|
|
|
|
print(f"Reusing loaded model {sd_model.sd_checkpoint_info.title} to load {checkpoint_info.title}")
|
|
return sd_model
|
|
else:
|
|
return None
|
|
|
|
|
|
def reload_model_weights(sd_model=None, info=None):
|
|
from modules import devices, sd_hijack
|
|
checkpoint_info = info or select_checkpoint()
|
|
|
|
timer = Timer()
|
|
|
|
if not sd_model:
|
|
sd_model = model_data.sd_model
|
|
|
|
if sd_model is None: # previous model load failed
|
|
current_checkpoint_info = None
|
|
else:
|
|
current_checkpoint_info = sd_model.sd_checkpoint_info
|
|
if sd_model.sd_model_checkpoint == checkpoint_info.filename:
|
|
return sd_model
|
|
|
|
sd_model = reuse_model_from_already_loaded(sd_model, checkpoint_info, timer)
|
|
if sd_model is not None and sd_model.sd_checkpoint_info.filename == checkpoint_info.filename:
|
|
return sd_model
|
|
|
|
if sd_model is not None:
|
|
sd_unet.apply_unet("None")
|
|
send_model_to_cpu(sd_model)
|
|
sd_hijack.model_hijack.undo_hijack(sd_model)
|
|
|
|
state_dict = get_checkpoint_state_dict(checkpoint_info, timer)
|
|
|
|
checkpoint_config = sd_models_config.find_checkpoint_config(state_dict, checkpoint_info)
|
|
|
|
timer.record("find config")
|
|
|
|
if sd_model is None or checkpoint_config != sd_model.used_config:
|
|
if sd_model is not None:
|
|
send_model_to_trash(sd_model)
|
|
|
|
load_model(checkpoint_info, already_loaded_state_dict=state_dict)
|
|
return model_data.sd_model
|
|
|
|
try:
|
|
load_model_weights(sd_model, checkpoint_info, state_dict, timer)
|
|
except Exception:
|
|
print("Failed to load checkpoint, restoring previous")
|
|
load_model_weights(sd_model, current_checkpoint_info, None, timer)
|
|
raise
|
|
finally:
|
|
sd_hijack.model_hijack.hijack(sd_model)
|
|
timer.record("hijack")
|
|
|
|
script_callbacks.model_loaded_callback(sd_model)
|
|
timer.record("script callbacks")
|
|
|
|
if not shared.cmd_opts.lowvram and not shared.cmd_opts.medvram:
|
|
sd_model.to(devices.device)
|
|
timer.record("move model to device")
|
|
|
|
print(f"Weights loaded in {timer.summary()}.")
|
|
|
|
model_data.set_sd_model(sd_model)
|
|
|
|
return sd_model
|
|
|
|
|
|
def unload_model_weights(sd_model=None, info=None):
|
|
from modules import devices, sd_hijack
|
|
timer = Timer()
|
|
|
|
if model_data.sd_model:
|
|
model_data.sd_model.to(devices.cpu)
|
|
sd_hijack.model_hijack.undo_hijack(model_data.sd_model)
|
|
model_data.sd_model = None
|
|
sd_model = None
|
|
gc.collect()
|
|
devices.torch_gc()
|
|
|
|
print(f"Unloaded weights {timer.summary()}.")
|
|
|
|
return sd_model
|
|
|
|
|
|
def apply_token_merging(sd_model, token_merging_ratio):
|
|
"""
|
|
Applies speed and memory optimizations from tomesd.
|
|
"""
|
|
|
|
current_token_merging_ratio = getattr(sd_model, 'applied_token_merged_ratio', 0)
|
|
|
|
if current_token_merging_ratio == token_merging_ratio:
|
|
return
|
|
|
|
if current_token_merging_ratio > 0:
|
|
tomesd.remove_patch(sd_model)
|
|
|
|
if token_merging_ratio > 0:
|
|
tomesd.apply_patch(
|
|
sd_model,
|
|
ratio=token_merging_ratio,
|
|
use_rand=False, # can cause issues with some samplers
|
|
merge_attn=True,
|
|
merge_crossattn=False,
|
|
merge_mlp=False
|
|
)
|
|
|
|
sd_model.applied_token_merged_ratio = token_merging_ratio
|