mirror of
https://github.com/AUTOMATIC1111/stable-diffusion-webui.git
synced 2024-06-07 21:20:49 +00:00
154 lines
4.9 KiB
Python
154 lines
4.9 KiB
Python
import glob
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import os.path
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import sys
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from collections import namedtuple
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import torch
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from omegaconf import OmegaConf
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from ldm.util import instantiate_from_config
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from modules import shared
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CheckpointInfo = namedtuple("CheckpointInfo", ['filename', 'title', 'hash', 'model_name'])
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checkpoints_list = {}
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try:
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# 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
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logging.set_verbosity_error()
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except Exception:
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pass
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def list_models():
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checkpoints_list.clear()
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model_dir = os.path.abspath(shared.cmd_opts.ckpt_dir)
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def modeltitle(path, h):
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abspath = os.path.abspath(path)
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if abspath.startswith(model_dir):
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name = abspath.replace(model_dir, '')
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else:
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name = os.path.basename(path)
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if name.startswith("\\") or name.startswith("/"):
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name = name[1:]
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return f'{name} [{h}]'
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cmd_ckpt = shared.cmd_opts.ckpt
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if os.path.exists(cmd_ckpt):
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h = model_hash(cmd_ckpt)
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title = modeltitle(cmd_ckpt, h)
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model_name = title.rsplit(".",1)[0] # remove extension if present
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checkpoints_list[title] = CheckpointInfo(cmd_ckpt, title, h, model_name)
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elif cmd_ckpt is not None and cmd_ckpt != shared.default_sd_model_file:
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print(f"Checkpoint in --ckpt argument not found: {cmd_ckpt}", file=sys.stderr)
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if os.path.exists(model_dir):
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for filename in glob.glob(model_dir + '/**/*.ckpt', recursive=True):
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h = model_hash(filename)
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title = modeltitle(filename, h)
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model_name = title.rsplit(".",1)[0] # remove extension if present
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checkpoints_list[title] = CheckpointInfo(filename, title, h, model_name)
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def model_hash(filename):
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try:
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with open(filename, "rb") as file:
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import hashlib
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m = hashlib.sha256()
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file.seek(0x100000)
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m.update(file.read(0x10000))
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return m.hexdigest()[0:8]
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except FileNotFoundError:
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return 'NOFILE'
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def select_checkpoint():
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model_checkpoint = shared.opts.sd_model_checkpoint
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checkpoint_info = checkpoints_list.get(model_checkpoint, None)
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if checkpoint_info is not None:
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return checkpoint_info
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if len(checkpoints_list) == 0:
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print(f"No checkpoints found. When searching for checkpoints, looked at:", file=sys.stderr)
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print(f" - file {os.path.abspath(shared.cmd_opts.ckpt)}", file=sys.stderr)
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print(f" - directory {os.path.abspath(shared.cmd_opts.ckpt_dir)}", file=sys.stderr)
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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)
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exit(1)
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checkpoint_info = next(iter(checkpoints_list.values()))
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if model_checkpoint is not None:
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print(f"Checkpoint {model_checkpoint} not found; loading fallback {checkpoint_info.title}", file=sys.stderr)
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return checkpoint_info
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def load_model_weights(model, checkpoint_file, sd_model_hash):
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print(f"Loading weights [{sd_model_hash}] from {checkpoint_file}")
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pl_sd = torch.load(checkpoint_file, map_location="cpu")
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if "global_step" in pl_sd:
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print(f"Global Step: {pl_sd['global_step']}")
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sd = pl_sd["state_dict"]
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model.load_state_dict(sd, strict=False)
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if shared.cmd_opts.opt_channelslast:
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model.to(memory_format=torch.channels_last)
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if not shared.cmd_opts.no_half:
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model.half()
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model.sd_model_hash = sd_model_hash
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model.sd_model_checkpint = checkpoint_file
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def load_model():
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from modules import lowvram, sd_hijack
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checkpoint_info = select_checkpoint()
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sd_config = OmegaConf.load(shared.cmd_opts.config)
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sd_model = instantiate_from_config(sd_config.model)
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load_model_weights(sd_model, checkpoint_info.filename, checkpoint_info.hash)
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if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
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lowvram.setup_for_low_vram(sd_model, shared.cmd_opts.medvram)
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else:
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sd_model.to(shared.device)
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sd_hijack.model_hijack.hijack(sd_model)
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sd_model.eval()
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print(f"Model loaded.")
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return sd_model
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def reload_model_weights(sd_model, info=None):
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from modules import lowvram, devices
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checkpoint_info = info or select_checkpoint()
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if sd_model.sd_model_checkpint == checkpoint_info.filename:
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return
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if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
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lowvram.send_everything_to_cpu()
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else:
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sd_model.to(devices.cpu)
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load_model_weights(sd_model, checkpoint_info.filename, checkpoint_info.hash)
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if not shared.cmd_opts.lowvram and not shared.cmd_opts.medvram:
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sd_model.to(devices.device)
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print(f"Weights loaded.")
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return sd_model
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