import collections import os.path import sys import gc from collections import namedtuple import torch import re import safetensors.torch from omegaconf import OmegaConf 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 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. 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() 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"]) 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) def get_closet_checkpoint_match(searchString): applicable = sorted([info for info in checkpoints_list.values() if searchString in info.title], key = lambda x:len(x.title)) if len(applicable) > 0: 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 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: 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() 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) # 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 checkpoints_loaded.popitem(last=False) # LRU model.sd_model_hash = sd_model_hash model.sd_model_checkpoint = checkpoint_file model.sd_checkpoint_info = checkpoint_info model.logvar = model.logvar.to(devices.device) # fix for training 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) 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 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() if shared.cmd_opts.no_half: sd_config.model.params.unet_config.params.use_fp16 = False 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 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) print("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() 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) 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) print("Weights loaded.") return sd_model