stable-diffusion-webui/modules/sd_models.py
d8ahazard 11875f5863 Use model loader with stable-diffusion too.
Hook the model loader into the SD_models file.
Add default url/download if checkpoint is not found.
Add matching stablediffusion-models-path argument.
Add message that --ckpt-dir will be removed in the future, but have it pipe to stablediffusion-models-path for now.
Update help strings for models-path args so they're more or less uniform.
Move sd_model "setup" call to webUI with the others.
Ensure "cleanup_models" method moves existing models to the new locations, including SD, and that we aren't deleting folders that still have stuff in them.
2022-09-27 11:01:13 -05:00

162 lines
5.0 KiB
Python

import glob
import os.path
import sys
from collections import namedtuple
import torch
from omegaconf import OmegaConf
from ldm.util import instantiate_from_config
from modules import shared, modelloader
from modules.paths import models_path
model_dir = "Stable-diffusion"
model_path = os.path.join(models_path, model_dir)
model_name = "sd-v1-4.ckpt"
model_url = "https://drive.yerf.org/wl/?id=EBfTrmcCCUAGaQBXVIj5lJmEhjoP1tgl&mode=grid&download=1"
CheckpointInfo = namedtuple("CheckpointInfo", ['filename', 'title', 'hash'])
checkpoints_list = {}
try:
# this silences the annoying "Some weights of the model checkpoint were not used when initializing..." message at start.
from transformers import logging
logging.set_verbosity_error()
except Exception:
pass
def modeltitle(path, h):
abspath = os.path.abspath(path)
if abspath.startswith(model_dir):
name = abspath.replace(model_dir, '')
else:
name = os.path.basename(path)
if name.startswith("\\") or name.startswith("/"):
name = name[1:]
return f'{name} [{h}]'
def setup_model(dirname):
global model_path
global model_name
global model_url
if not os.path.exists(model_path):
os.makedirs(model_path)
checkpoints_list.clear()
model_list = modelloader.load_models(model_path, model_url, dirname, model_name, ext_filter=".ckpt")
cmd_ckpt = shared.cmd_opts.ckpt
if os.path.exists(cmd_ckpt):
h = model_hash(cmd_ckpt)
title = modeltitle(cmd_ckpt, h)
checkpoints_list[title] = CheckpointInfo(cmd_ckpt, title, h)
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 = modeltitle(filename, h)
checkpoints_list[title] = CheckpointInfo(filename, title, h)
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)
print(f" - file {os.path.abspath(shared.cmd_opts.ckpt)}", file=sys.stderr)
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
def load_model_weights(model, checkpoint_file, sd_model_hash):
print(f"Loading weights [{sd_model_hash}] from {checkpoint_file}")
pl_sd = torch.load(checkpoint_file, map_location="cpu")
if "global_step" in pl_sd:
print(f"Global Step: {pl_sd['global_step']}")
sd = pl_sd["state_dict"]
model.load_state_dict(sd, strict=False)
if shared.cmd_opts.opt_channelslast:
model.to(memory_format=torch.channels_last)
if not shared.cmd_opts.no_half:
model.half()
model.sd_model_hash = sd_model_hash
model.sd_model_checkpint = checkpoint_file
def load_model():
from modules import lowvram, sd_hijack
checkpoint_info = select_checkpoint()
sd_config = OmegaConf.load(shared.cmd_opts.config)
sd_model = instantiate_from_config(sd_config.model)
load_model_weights(sd_model, checkpoint_info.filename, checkpoint_info.hash)
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()
print(f"Model loaded.")
return sd_model
def reload_model_weights(sd_model, info=None):
from modules import lowvram, devices
checkpoint_info = info or select_checkpoint()
if sd_model.sd_model_checkpint == checkpoint_info.filename:
return
if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
lowvram.send_everything_to_cpu()
else:
sd_model.to(devices.cpu)
load_model_weights(sd_model, checkpoint_info.filename, checkpoint_info.hash)
if not shared.cmd_opts.lowvram and not shared.cmd_opts.medvram:
sd_model.to(devices.device)
print(f"Weights loaded.")
return sd_model