384 lines
14 KiB
Python
384 lines
14 KiB
Python
import collections
|
|
import os.path
|
|
import sys
|
|
import gc
|
|
from collections import namedtuple
|
|
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 shared, modelloader, devices, script_callbacks, sd_vae
|
|
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 = {}
|
|
checkpoints_loaded = collections.OrderedDict()
|
|
|
|
try:
|
|
# this silences the annoying "Some weights of the model checkpoint were not used when initializing..." message at start.
|
|
|
|
from transformers import logging, CLIPModel
|
|
|
|
logging.set_verbosity_error()
|
|
except Exception:
|
|
pass
|
|
|
|
|
|
def setup_model():
|
|
if not os.path.exists(model_path):
|
|
os.makedirs(model_path)
|
|
|
|
list_models()
|
|
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 find_checkpoint_config(info):
|
|
config = os.path.splitext(info.filename)[0] + ".yaml"
|
|
if os.path.exists(config):
|
|
return config
|
|
|
|
return shared.cmd_opts.config
|
|
|
|
|
|
def list_models():
|
|
checkpoints_list.clear()
|
|
model_list = modelloader.load_models(model_path=model_path, command_path=shared.cmd_opts.ckpt_dir, ext_filter=[".ckpt", ".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
|
|
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)
|
|
|
|
|
|
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]
|
|
return None
|
|
|
|
|
|
def model_hash(filename):
|
|
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():
|
|
model_checkpoint = shared.opts.sd_model_checkpoint
|
|
|
|
checkpoint_info = checkpoints_list.get(model_checkpoint, None)
|
|
if checkpoint_info is not None:
|
|
return checkpoint_info
|
|
|
|
if len(checkpoints_list) == 0:
|
|
print("No checkpoints found. When searching for checkpoints, looked at:", file=sys.stderr)
|
|
if shared.cmd_opts.ckpt is not None:
|
|
print(f" - file {os.path.abspath(shared.cmd_opts.ckpt)}", file=sys.stderr)
|
|
print(f" - directory {model_path}", file=sys.stderr)
|
|
if shared.cmd_opts.ckpt_dir is not None:
|
|
print(f" - directory {os.path.abspath(shared.cmd_opts.ckpt_dir)}", file=sys.stderr)
|
|
print("Can't run without a checkpoint. Find and place a .ckpt file into any of those locations. The program will exit.", file=sys.stderr)
|
|
exit(1)
|
|
|
|
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
|
|
|
|
|
|
chckpoint_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 chckpoint_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_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
|
|
if device is None:
|
|
device = devices.get_cuda_device_string() if torch.cuda.is_available() else "cpu"
|
|
pl_sd = safetensors.torch.load_file(checkpoint_file, device=device)
|
|
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 load_model_weights(model, checkpoint_info, vae_file="auto"):
|
|
checkpoint_file = checkpoint_info.filename
|
|
sd_model_hash = checkpoint_info.hash
|
|
|
|
cache_enabled = shared.opts.sd_checkpoint_cache > 0
|
|
|
|
if cache_enabled and checkpoint_info in checkpoints_loaded:
|
|
# use checkpoint cache
|
|
print(f"Loading weights [{sd_model_hash}] from cache")
|
|
model.load_state_dict(checkpoints_loaded[checkpoint_info])
|
|
else:
|
|
# load from file
|
|
print(f"Loading weights [{sd_model_hash}] from {checkpoint_file}")
|
|
|
|
sd = read_state_dict(checkpoint_file)
|
|
model.load_state_dict(sd, strict=False)
|
|
del sd
|
|
|
|
if cache_enabled:
|
|
# cache newly loaded model
|
|
checkpoints_loaded[checkpoint_info] = model.state_dict().copy()
|
|
|
|
if shared.cmd_opts.opt_channelslast:
|
|
model.to(memory_format=torch.channels_last)
|
|
|
|
if not shared.cmd_opts.no_half:
|
|
vae = model.first_stage_model
|
|
|
|
# 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
|
|
|
|
model.half()
|
|
model.first_stage_model = vae
|
|
|
|
devices.dtype = torch.float32 if shared.cmd_opts.no_half else torch.float16
|
|
devices.dtype_vae = torch.float32 if shared.cmd_opts.no_half or shared.cmd_opts.no_half_vae else torch.float16
|
|
|
|
model.first_stage_model.to(devices.dtype_vae)
|
|
|
|
# clean up cache if limit is reached
|
|
if cache_enabled:
|
|
while len(checkpoints_loaded) > shared.opts.sd_checkpoint_cache + 1: # we need to count the current model
|
|
checkpoints_loaded.popitem(last=False) # LRU
|
|
|
|
model.sd_model_hash = sd_model_hash
|
|
model.sd_model_checkpoint = checkpoint_file
|
|
model.sd_checkpoint_info = checkpoint_info
|
|
|
|
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)
|
|
sd_vae.load_vae(model, vae_file)
|
|
|
|
|
|
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(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 load_model(checkpoint_info=None):
|
|
from modules import lowvram, sd_hijack
|
|
checkpoint_info = checkpoint_info or select_checkpoint()
|
|
checkpoint_config = find_checkpoint_config(checkpoint_info)
|
|
|
|
if checkpoint_config != shared.cmd_opts.config:
|
|
print(f"Loading config from: {checkpoint_config}")
|
|
|
|
if shared.sd_model:
|
|
sd_hijack.model_hijack.undo_hijack(shared.sd_model)
|
|
shared.sd_model = None
|
|
gc.collect()
|
|
devices.torch_gc()
|
|
|
|
sd_config = OmegaConf.load(checkpoint_config)
|
|
|
|
if should_hijack_inpainting(checkpoint_info):
|
|
# Hardcoded config for now...
|
|
sd_config.model.target = "ldm.models.diffusion.ddpm.LatentInpaintDiffusion"
|
|
sd_config.model.params.conditioning_key = "hybrid"
|
|
sd_config.model.params.unet_config.params.in_channels = 9
|
|
sd_config.model.params.finetune_keys = None
|
|
|
|
if not hasattr(sd_config.model.params, "use_ema"):
|
|
sd_config.model.params.use_ema = False
|
|
|
|
do_inpainting_hijack()
|
|
|
|
if shared.cmd_opts.no_half:
|
|
sd_config.model.params.unet_config.params.use_fp16 = False
|
|
|
|
sd_model = instantiate_from_config(sd_config.model)
|
|
|
|
load_model_weights(sd_model, checkpoint_info)
|
|
|
|
if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
|
|
lowvram.setup_for_low_vram(sd_model, shared.cmd_opts.medvram)
|
|
else:
|
|
sd_model.to(shared.device)
|
|
|
|
sd_hijack.model_hijack.hijack(sd_model)
|
|
|
|
sd_model.eval()
|
|
shared.sd_model = sd_model
|
|
|
|
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
|
|
|
|
script_callbacks.model_loaded_callback(sd_model)
|
|
|
|
print("Model loaded.")
|
|
|
|
return sd_model
|
|
|
|
|
|
def reload_model_weights(sd_model=None, info=None):
|
|
from modules import lowvram, devices, sd_hijack
|
|
checkpoint_info = info or select_checkpoint()
|
|
|
|
if not sd_model:
|
|
sd_model = shared.sd_model
|
|
|
|
current_checkpoint_info = sd_model.sd_checkpoint_info
|
|
checkpoint_config = find_checkpoint_config(current_checkpoint_info)
|
|
|
|
if sd_model.sd_model_checkpoint == checkpoint_info.filename:
|
|
return
|
|
|
|
if checkpoint_config != find_checkpoint_config(checkpoint_info) or should_hijack_inpainting(checkpoint_info) != should_hijack_inpainting(sd_model.sd_checkpoint_info):
|
|
del sd_model
|
|
checkpoints_loaded.clear()
|
|
load_model(checkpoint_info)
|
|
return shared.sd_model
|
|
|
|
if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
|
|
lowvram.send_everything_to_cpu()
|
|
else:
|
|
sd_model.to(devices.cpu)
|
|
|
|
sd_hijack.model_hijack.undo_hijack(sd_model)
|
|
|
|
try:
|
|
load_model_weights(sd_model, checkpoint_info)
|
|
except Exception as e:
|
|
print("Failed to load checkpoint, restoring previous")
|
|
load_model_weights(sd_model, current_checkpoint_info)
|
|
raise
|
|
finally:
|
|
sd_hijack.model_hijack.hijack(sd_model)
|
|
script_callbacks.model_loaded_callback(sd_model)
|
|
|
|
if not shared.cmd_opts.lowvram and not shared.cmd_opts.medvram:
|
|
sd_model.to(devices.device)
|
|
|
|
print("Weights loaded.")
|
|
|
|
return sd_model
|