stable-diffusion-webui/modules/sd_models.py

422 lines
15 KiB
Python
Raw Normal View History

import collections
import os.path
import sys
import gc
import time
from collections import namedtuple
import torch
import re
2022-11-27 04:46:40 -07:00
import safetensors.torch
from omegaconf import OmegaConf
2022-12-08 17:14:35 -07:00
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, sd_disable_initialization, errors
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.
2022-10-16 09:53:56 -06:00
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()
2022-12-08 17:14:35 -07:00
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):
if info is None:
return shared.cmd_opts.config
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"], 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
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)
2022-09-28 15:30:09 -06:00
def get_closet_checkpoint_match(searchString):
2022-09-29 12:08:03 -06:00
applicable = sorted([info for info in checkpoints_list.values() if searchString in info.title], key = lambda x:len(x.title))
if len(applicable) > 0:
2022-09-28 15:30:09 -06:00
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
2022-10-19 03:45:30 -06:00
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:
2022-11-02 05:41:29 -06:00
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()
2022-11-02 05:41:29 -06:00
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)
2022-11-02 05:41:29 -06:00
# 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
2022-10-31 03:27:27 -06:00
checkpoints_loaded.popitem(last=False) # LRU
model.sd_model_hash = sd_model_hash
2022-10-08 13:12:24 -06:00
model.sd_model_checkpoint = checkpoint_file
model.sd_checkpoint_info = checkpoint_info
2023-01-01 14:38:09 -07:00
model.logvar = model.logvar.to(devices.device) # fix for training
2022-11-12 21:11:14 -07:00
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)
2022-12-08 17:14:35 -07:00
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
class Timer:
def __init__(self):
self.start = time.time()
def elapsed(self):
end = time.time()
res = end - self.start
self.start = end
return res
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()
2022-11-26 11:28:44 -07:00
if shared.cmd_opts.no_half:
sd_config.model.params.unet_config.params.use_fp16 = False
timer = Timer()
sd_model = None
try:
with sd_disable_initialization.DisableInitialization():
sd_model = instantiate_from_config(sd_config.model)
except Exception as e:
pass
if sd_model is None:
print('Failed to create model quickly; will retry using slow method.', file=sys.stderr)
sd_model = instantiate_from_config(sd_config.model)
elapsed_create = timer.elapsed()
load_model_weights(sd_model, checkpoint_info)
elapsed_load_weights = timer.elapsed()
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)
elapsed_the_rest = timer.elapsed()
print(f"Model loaded in {elapsed_create + elapsed_load_weights + elapsed_the_rest:.1f}s ({elapsed_create:.1f}s create model, {elapsed_load_weights:.1f}s load weights).")
2022-12-31 09:27:02 -07:00
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
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
checkpoint_config = find_checkpoint_config(current_checkpoint_info)
if current_checkpoint_info is None or 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)
timer = Timer()
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)
elapsed = timer.elapsed()
print(f"Weights loaded in {elapsed:.1f}s.")
return sd_model