366 lines
13 KiB
Python
366 lines
13 KiB
Python
import collections
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import os.path
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import sys
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import gc
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from collections import namedtuple
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import torch
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import re
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import safetensors.torch
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from omegaconf import OmegaConf
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from os import mkdir
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from urllib import request
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import ldm.modules.midas as midas
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from ldm.util import instantiate_from_config
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from modules import shared, modelloader, devices, script_callbacks, sd_vae
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from modules.paths import models_path
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from modules.sd_hijack_inpainting import do_inpainting_hijack, should_hijack_inpainting
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model_dir = "Stable-diffusion"
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model_path = os.path.abspath(os.path.join(models_path, model_dir))
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CheckpointInfo = namedtuple("CheckpointInfo", ['filename', 'title', 'hash', 'model_name', 'config'])
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checkpoints_list = {}
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checkpoints_loaded = collections.OrderedDict()
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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, CLIPModel
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logging.set_verbosity_error()
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except Exception:
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pass
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def setup_model():
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if not os.path.exists(model_path):
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os.makedirs(model_path)
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list_models()
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enable_midas_autodownload()
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def checkpoint_tiles():
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convert = lambda name: int(name) if name.isdigit() else name.lower()
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alphanumeric_key = lambda key: [convert(c) for c in re.split('([0-9]+)', key)]
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return sorted([x.title for x in checkpoints_list.values()], key = alphanumeric_key)
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def list_models():
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checkpoints_list.clear()
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model_list = modelloader.load_models(model_path=model_path, command_path=shared.cmd_opts.ckpt_dir, ext_filter=[".ckpt", ".safetensors"])
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def modeltitle(path, shorthash):
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abspath = os.path.abspath(path)
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if shared.cmd_opts.ckpt_dir is not None and abspath.startswith(shared.cmd_opts.ckpt_dir):
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name = abspath.replace(shared.cmd_opts.ckpt_dir, '')
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elif abspath.startswith(model_path):
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name = abspath.replace(model_path, '')
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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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shortname = os.path.splitext(name.replace("/", "_").replace("\\", "_"))[0]
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return f'{name} [{shorthash}]', shortname
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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, short_model_name = modeltitle(cmd_ckpt, h)
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checkpoints_list[title] = CheckpointInfo(cmd_ckpt, title, h, short_model_name, shared.cmd_opts.config)
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shared.opts.data['sd_model_checkpoint'] = title
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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 (Possible it was moved to {model_path}: {cmd_ckpt}", file=sys.stderr)
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for filename in model_list:
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h = model_hash(filename)
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title, short_model_name = modeltitle(filename, h)
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basename, _ = os.path.splitext(filename)
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config = basename + ".yaml"
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if not os.path.exists(config):
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config = shared.cmd_opts.config
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checkpoints_list[title] = CheckpointInfo(filename, title, h, short_model_name, config)
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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))
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if len(applicable) > 0:
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return applicable[0]
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return None
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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("No checkpoints found. When searching for checkpoints, looked at:", file=sys.stderr)
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if shared.cmd_opts.ckpt is not None:
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print(f" - file {os.path.abspath(shared.cmd_opts.ckpt)}", file=sys.stderr)
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print(f" - directory {model_path}", file=sys.stderr)
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if shared.cmd_opts.ckpt_dir is not None:
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print(f" - directory {os.path.abspath(shared.cmd_opts.ckpt_dir)}", file=sys.stderr)
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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)
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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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chckpoint_dict_replacements = {
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'cond_stage_model.transformer.embeddings.': 'cond_stage_model.transformer.text_model.embeddings.',
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'cond_stage_model.transformer.encoder.': 'cond_stage_model.transformer.text_model.encoder.',
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'cond_stage_model.transformer.final_layer_norm.': 'cond_stage_model.transformer.text_model.final_layer_norm.',
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}
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def transform_checkpoint_dict_key(k):
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for text, replacement in chckpoint_dict_replacements.items():
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if k.startswith(text):
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k = replacement + k[len(text):]
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return k
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def get_state_dict_from_checkpoint(pl_sd):
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pl_sd = pl_sd.pop("state_dict", pl_sd)
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pl_sd.pop("state_dict", None)
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sd = {}
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for k, v in pl_sd.items():
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new_key = transform_checkpoint_dict_key(k)
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if new_key is not None:
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sd[new_key] = v
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pl_sd.clear()
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pl_sd.update(sd)
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return pl_sd
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def read_state_dict(checkpoint_file, print_global_state=False, map_location=None):
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_, extension = os.path.splitext(checkpoint_file)
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if extension.lower() == ".safetensors":
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pl_sd = safetensors.torch.load_file(checkpoint_file, device=map_location or shared.weight_load_location)
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else:
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pl_sd = torch.load(checkpoint_file, map_location=map_location or shared.weight_load_location)
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if print_global_state and "global_step" in pl_sd:
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print(f"Global Step: {pl_sd['global_step']}")
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sd = get_state_dict_from_checkpoint(pl_sd)
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return sd
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def load_model_weights(model, checkpoint_info, vae_file="auto"):
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checkpoint_file = checkpoint_info.filename
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sd_model_hash = checkpoint_info.hash
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cache_enabled = shared.opts.sd_checkpoint_cache > 0
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if cache_enabled and checkpoint_info in checkpoints_loaded:
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# use checkpoint cache
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print(f"Loading weights [{sd_model_hash}] from cache")
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model.load_state_dict(checkpoints_loaded[checkpoint_info])
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else:
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# load from file
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print(f"Loading weights [{sd_model_hash}] from {checkpoint_file}")
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sd = read_state_dict(checkpoint_file)
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model.load_state_dict(sd, strict=False)
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del sd
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if cache_enabled:
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# cache newly loaded model
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checkpoints_loaded[checkpoint_info] = model.state_dict().copy()
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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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vae = model.first_stage_model
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# with --no-half-vae, remove VAE from model when doing half() to prevent its weights from being converted to float16
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if shared.cmd_opts.no_half_vae:
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model.first_stage_model = None
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model.half()
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model.first_stage_model = vae
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devices.dtype = torch.float32 if shared.cmd_opts.no_half else torch.float16
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devices.dtype_vae = torch.float32 if shared.cmd_opts.no_half or shared.cmd_opts.no_half_vae else torch.float16
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model.first_stage_model.to(devices.dtype_vae)
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# clean up cache if limit is reached
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if cache_enabled:
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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
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model.sd_model_hash = sd_model_hash
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model.sd_model_checkpoint = checkpoint_file
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model.sd_checkpoint_info = checkpoint_info
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sd_vae.delete_base_vae()
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sd_vae.clear_loaded_vae()
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vae_file = sd_vae.resolve_vae(checkpoint_file, vae_file=vae_file)
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sd_vae.load_vae(model, vae_file)
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def enable_midas_autodownload():
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"""
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Gives the ldm.modules.midas.api.load_model function automatic downloading.
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When the 512-depth-ema model, and other future models like it, is loaded,
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it calls midas.api.load_model to load the associated midas depth model.
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This function applies a wrapper to download the model to the correct
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location automatically.
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"""
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midas_path = os.path.join(models_path, 'midas')
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# stable-diffusion-stability-ai hard-codes the midas model path to
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# a location that differs from where other scripts using this model look.
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# HACK: Overriding the path here.
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for k, v in midas.api.ISL_PATHS.items():
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file_name = os.path.basename(v)
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midas.api.ISL_PATHS[k] = os.path.join(midas_path, file_name)
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midas_urls = {
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"dpt_large": "https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt",
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"dpt_hybrid": "https://github.com/intel-isl/DPT/releases/download/1_0/dpt_hybrid-midas-501f0c75.pt",
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"midas_v21": "https://github.com/AlexeyAB/MiDaS/releases/download/midas_dpt/midas_v21-f6b98070.pt",
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"midas_v21_small": "https://github.com/AlexeyAB/MiDaS/releases/download/midas_dpt/midas_v21_small-70d6b9c8.pt",
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}
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midas.api.load_model_inner = midas.api.load_model
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def load_model_wrapper(model_type):
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path = midas.api.ISL_PATHS[model_type]
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if not os.path.exists(path):
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if not os.path.exists(midas_path):
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mkdir(midas_path)
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print(f"Downloading midas model weights for {model_type} to {path}")
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request.urlretrieve(midas_urls[model_type], path)
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print(f"{model_type} downloaded")
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return midas.api.load_model_inner(model_type)
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midas.api.load_model = load_model_wrapper
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def load_model(checkpoint_info=None):
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from modules import lowvram, sd_hijack
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checkpoint_info = checkpoint_info or select_checkpoint()
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if checkpoint_info.config != shared.cmd_opts.config:
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print(f"Loading config from: {checkpoint_info.config}")
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if shared.sd_model:
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sd_hijack.model_hijack.undo_hijack(shared.sd_model)
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shared.sd_model = None
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gc.collect()
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devices.torch_gc()
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sd_config = OmegaConf.load(checkpoint_info.config)
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if should_hijack_inpainting(checkpoint_info):
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# Hardcoded config for now...
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sd_config.model.target = "ldm.models.diffusion.ddpm.LatentInpaintDiffusion"
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sd_config.model.params.conditioning_key = "hybrid"
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sd_config.model.params.unet_config.params.in_channels = 9
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sd_config.model.params.finetune_keys = None
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# Create a "fake" config with a different name so that we know to unload it when switching models.
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checkpoint_info = checkpoint_info._replace(config=checkpoint_info.config.replace(".yaml", "-inpainting.yaml"))
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if not hasattr(sd_config.model.params, "use_ema"):
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sd_config.model.params.use_ema = False
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do_inpainting_hijack()
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if shared.cmd_opts.no_half:
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sd_config.model.params.unet_config.params.use_fp16 = False
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sd_model = instantiate_from_config(sd_config.model)
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load_model_weights(sd_model, checkpoint_info)
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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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shared.sd_model = sd_model
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script_callbacks.model_loaded_callback(sd_model)
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print("Model loaded.")
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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
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return sd_model
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def reload_model_weights(sd_model=None, info=None):
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from modules import lowvram, devices, sd_hijack
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checkpoint_info = info or select_checkpoint()
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if not sd_model:
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sd_model = shared.sd_model
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if sd_model.sd_model_checkpoint == checkpoint_info.filename:
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return
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if sd_model.sd_checkpoint_info.config != checkpoint_info.config or should_hijack_inpainting(checkpoint_info) != should_hijack_inpainting(sd_model.sd_checkpoint_info):
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del sd_model
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checkpoints_loaded.clear()
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load_model(checkpoint_info)
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return shared.sd_model
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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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sd_hijack.model_hijack.undo_hijack(sd_model)
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load_model_weights(sd_model, checkpoint_info)
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sd_hijack.model_hijack.hijack(sd_model)
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script_callbacks.model_loaded_callback(sd_model)
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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("Weights loaded.")
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return sd_model
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