stable-diffusion-webui/modules/sd_models.py

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import collections
import os.path
import sys
import gc
from collections import namedtuple
import torch
from safetensors.torch import load_file, save_file
import re
from omegaconf import OmegaConf
from ldm.util import instantiate_from_config
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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', 'config', 'exttype'])
checkpoints_list = {}
checkpoints_loaded = collections.OrderedDict()
checkpoint_types = {'.ckpt':'pickle','.safetensors':'safetensors'}
try:
# this silences the annoying "Some weights of the model checkpoint were not used when initializing..." message at start.
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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()
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 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, ext = os.path.splitext(name.replace("/", "_").replace("\\", "_"))
return f'{name} [{checkpoint_types[ext]}] [{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.cmd_opts.config, '')
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)
basename, ext = os.path.splitext(filename)
config = basename + ".yaml"
if not os.path.exists(config):
config = shared.cmd_opts.config
checkpoints_list[title] = CheckpointInfo(filename, title, h, short_model_name, config, ext)
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def get_closet_checkpoint_match(searchString):
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applicable = sorted([info for info in checkpoints_list.values() if searchString in info.title], key = lambda x:len(x.title))
if len(applicable) > 0:
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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(f"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(f"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 torch_load(model_filename, model_info, map_override=None):
map_override=shared.weight_load_location if not map_override else map_override
if(checkpoint_types[model_info.exttype] == 'safetensors'):
# safely load weights
# TODO: safetensors supports zero copy fast load to gpu, see issue #684
return load_file(model_filename, device=map_override)
else:
return torch.load(model_filename, map_location=map_override)
def torch_save(model, output_filename):
basename, exttype = os.path.splitext(output_filename)
if(checkpoint_types[exttype] == 'safetensors'):
# [===== >] Reticulating brines...
save_file(model, output_filename, metadata={"format": "pt"})
else:
torch.save(model, output_filename)
def get_state_dict_from_checkpoint(pl_sd):
if "state_dict" in pl_sd:
pl_sd = pl_sd["state_dict"]
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
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pl_sd.clear()
pl_sd.update(sd)
return pl_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}")
pl_sd = torch_load(checkpoint_file, checkpoint_info)
if "global_step" in pl_sd:
print(f"Global Step: {pl_sd['global_step']}")
sd = get_state_dict_from_checkpoint(pl_sd)
del pl_sd
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:
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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()
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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)
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# 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
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checkpoints_loaded.popitem(last=False) # LRU
model.sd_model_hash = sd_model_hash
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model.sd_model_checkpoint = checkpoint_file
model.sd_checkpoint_info = checkpoint_info
vae_file = sd_vae.resolve_vae(checkpoint_file, vae_file=vae_file)
sd_vae.load_vae(model, vae_file)
def load_model(checkpoint_info=None):
from modules import lowvram, sd_hijack
checkpoint_info = checkpoint_info or select_checkpoint()
if checkpoint_info.config != shared.cmd_opts.config:
print(f"Loading config from: {checkpoint_info.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_info.config)
if should_hijack_inpainting(checkpoint_info):
# Hardcoded config for now...
sd_config.model.target = "ldm.models.diffusion.ddpm.LatentInpaintDiffusion"
sd_config.model.params.use_ema = False
sd_config.model.params.conditioning_key = "hybrid"
sd_config.model.params.unet_config.params.in_channels = 9
# Create a "fake" config with a different name so that we know to unload it when switching models.
checkpoint_info = checkpoint_info._replace(config=checkpoint_info.config.replace(".yaml", "-inpainting.yaml"))
do_inpainting_hijack()
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
script_callbacks.model_loaded_callback(sd_model)
print(f"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()
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if not sd_model:
sd_model = shared.sd_model
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if sd_model.sd_model_checkpoint == checkpoint_info.filename:
return
if sd_model.sd_checkpoint_info.config != checkpoint_info.config 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)
load_model_weights(sd_model, checkpoint_info)
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(f"Weights loaded.")
return sd_model