import collections import os.path import sys import gc import threading 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 paths, shared, modelloader, devices, script_callbacks, sd_vae, sd_disable_initialization, errors, hashes, sd_models_config, sd_unet, sd_models_xl, cache, extra_networks, processing, lowvram, sd_hijack from modules.timer import Timer import tomesd model_dir = "Stable-diffusion" model_path = os.path.abspath(os.path.join(paths.models_path, model_dir)) checkpoints_list = {} checkpoint_aliases = {} checkpoint_alisases = checkpoint_aliases # for compatibility with old name checkpoints_loaded = collections.OrderedDict() def replace_key(d, key, new_key, value): keys = list(d.keys()) d[new_key] = value if key not in keys: return d index = keys.index(key) keys[index] = new_key new_d = {k: d[k] for k in keys} d.clear() d.update(new_d) return d class CheckpointInfo: def __init__(self, filename): self.filename = filename abspath = os.path.abspath(filename) self.is_safetensors = os.path.splitext(filename)[1].lower() == ".safetensors" 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(filename) if name.startswith("\\") or name.startswith("/"): name = name[1:] def read_metadata(): metadata = read_metadata_from_safetensors(filename) self.modelspec_thumbnail = metadata.pop('modelspec.thumbnail', None) return metadata self.metadata = {} if self.is_safetensors: try: self.metadata = cache.cached_data_for_file('safetensors-metadata', "checkpoint/" + name, filename, read_metadata) except Exception as e: errors.display(e, f"reading metadata for {filename}") self.name = name self.name_for_extra = os.path.splitext(os.path.basename(filename))[0] self.model_name = os.path.splitext(name.replace("/", "_").replace("\\", "_"))[0] self.hash = model_hash(filename) self.sha256 = hashes.sha256_from_cache(self.filename, f"checkpoint/{name}") self.shorthash = self.sha256[0:10] if self.sha256 else None self.title = name if self.shorthash is None else f'{name} [{self.shorthash}]' self.short_title = self.name_for_extra if self.shorthash is None else f'{self.name_for_extra} [{self.shorthash}]' self.ids = [self.hash, self.model_name, self.title, name, self.name_for_extra, f'{name} [{self.hash}]'] if self.shorthash: self.ids += [self.shorthash, self.sha256, f'{self.name} [{self.shorthash}]', f'{self.name_for_extra} [{self.shorthash}]'] def register(self): checkpoints_list[self.title] = self for id in self.ids: checkpoint_aliases[id] = self def calculate_shorthash(self): self.sha256 = hashes.sha256(self.filename, f"checkpoint/{self.name}") if self.sha256 is None: return shorthash = self.sha256[0:10] if self.shorthash == self.sha256[0:10]: return self.shorthash self.shorthash = shorthash if self.shorthash not in self.ids: self.ids += [self.shorthash, self.sha256, f'{self.name} [{self.shorthash}]', f'{self.name_for_extra} [{self.shorthash}]'] old_title = self.title self.title = f'{self.name} [{self.shorthash}]' self.short_title = f'{self.name_for_extra} [{self.shorthash}]' replace_key(checkpoints_list, old_title, self.title, self) self.register() return self.shorthash try: # this silences the annoying "Some weights of the model checkpoint were not used when initializing..." message at start. from transformers import logging, CLIPModel # noqa: F401 logging.set_verbosity_error() except Exception: pass def setup_model(): os.makedirs(model_path, exist_ok=True) enable_midas_autodownload() def checkpoint_tiles(use_short=False): return [x.short_title if use_short else x.title for x in checkpoints_list.values()] def list_models(): checkpoints_list.clear() checkpoint_aliases.clear() cmd_ckpt = shared.cmd_opts.ckpt if shared.cmd_opts.no_download_sd_model or cmd_ckpt != shared.sd_model_file or os.path.exists(cmd_ckpt): model_url = None else: model_url = "https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.safetensors" model_list = modelloader.load_models(model_path=model_path, model_url=model_url, command_path=shared.cmd_opts.ckpt_dir, ext_filter=[".ckpt", ".safetensors"], download_name="v1-5-pruned-emaonly.safetensors", ext_blacklist=[".vae.ckpt", ".vae.safetensors"]) if os.path.exists(cmd_ckpt): checkpoint_info = CheckpointInfo(cmd_ckpt) checkpoint_info.register() shared.opts.data['sd_model_checkpoint'] = checkpoint_info.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: checkpoint_info = CheckpointInfo(filename) checkpoint_info.register() re_strip_checksum = re.compile(r"\s*\[[^]]+]\s*$") def get_closet_checkpoint_match(search_string): if not search_string: return None checkpoint_info = checkpoint_aliases.get(search_string, None) if checkpoint_info is not None: return checkpoint_info found = sorted([info for info in checkpoints_list.values() if search_string in info.title], key=lambda x: len(x.title)) if found: return found[0] search_string_without_checksum = re.sub(re_strip_checksum, '', search_string) found = sorted([info for info in checkpoints_list.values() if search_string_without_checksum in info.title], key=lambda x: len(x.title)) if found: return found[0] return None def model_hash(filename): """old hash that only looks at a small part of the file and is prone to collisions""" 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(): """Raises `FileNotFoundError` if no checkpoints are found.""" model_checkpoint = shared.opts.sd_model_checkpoint checkpoint_info = checkpoint_aliases.get(model_checkpoint, None) if checkpoint_info is not None: return checkpoint_info if len(checkpoints_list) == 0: error_message = "No checkpoints found. When searching for checkpoints, looked at:" if shared.cmd_opts.ckpt is not None: error_message += f"\n - file {os.path.abspath(shared.cmd_opts.ckpt)}" error_message += f"\n - directory {model_path}" if shared.cmd_opts.ckpt_dir is not None: error_message += f"\n - directory {os.path.abspath(shared.cmd_opts.ckpt_dir)}" error_message += "Can't run without a checkpoint. Find and place a .ckpt or .safetensors file into any of those locations." raise FileNotFoundError(error_message) 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 checkpoint_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 checkpoint_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_metadata_from_safetensors(filename): import json with open(filename, mode="rb") as file: metadata_len = file.read(8) metadata_len = int.from_bytes(metadata_len, "little") json_start = file.read(2) assert metadata_len > 2 and json_start in (b'{"', b"{'"), f"{filename} is not a safetensors file" json_data = json_start + file.read(metadata_len-2) json_obj = json.loads(json_data) res = {} for k, v in json_obj.get("__metadata__", {}).items(): res[k] = v if isinstance(v, str) and v[0:1] == '{': try: res[k] = json.loads(v) except Exception: pass return res 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 or devices.get_optimal_device_name() if not shared.opts.disable_mmap_load_safetensors: pl_sd = safetensors.torch.load_file(checkpoint_file, device=device) else: pl_sd = safetensors.torch.load(open(checkpoint_file, 'rb').read()) pl_sd = {k: v.to(device) for k, v in pl_sd.items()} 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 get_checkpoint_state_dict(checkpoint_info: CheckpointInfo, timer): sd_model_hash = checkpoint_info.calculate_shorthash() timer.record("calculate hash") if checkpoint_info in checkpoints_loaded: # use checkpoint cache print(f"Loading weights [{sd_model_hash}] from cache") return checkpoints_loaded[checkpoint_info] print(f"Loading weights [{sd_model_hash}] from {checkpoint_info.filename}") res = read_state_dict(checkpoint_info.filename) timer.record("load weights from disk") return res class SkipWritingToConfig: """This context manager prevents load_model_weights from writing checkpoint name to the config when it loads weight.""" skip = False previous = None def __enter__(self): self.previous = SkipWritingToConfig.skip SkipWritingToConfig.skip = True return self def __exit__(self, exc_type, exc_value, exc_traceback): SkipWritingToConfig.skip = self.previous def load_model_weights(model, checkpoint_info: CheckpointInfo, state_dict, timer): sd_model_hash = checkpoint_info.calculate_shorthash() timer.record("calculate hash") if not SkipWritingToConfig.skip: shared.opts.data["sd_model_checkpoint"] = checkpoint_info.title if state_dict is None: state_dict = get_checkpoint_state_dict(checkpoint_info, timer) model.is_sdxl = hasattr(model, 'conditioner') model.is_sd2 = not model.is_sdxl and hasattr(model.cond_stage_model, 'model') model.is_sd1 = not model.is_sdxl and not model.is_sd2 if model.is_sdxl: sd_models_xl.extend_sdxl(model) model.load_state_dict(state_dict, strict=False) timer.record("apply weights to model") if shared.opts.sd_checkpoint_cache > 0: # cache newly loaded model checkpoints_loaded[checkpoint_info] = state_dict del state_dict if shared.cmd_opts.opt_channelslast: model.to(memory_format=torch.channels_last) timer.record("apply channels_last") if shared.cmd_opts.no_half: model.float() devices.dtype_unet = torch.float32 timer.record("apply float()") else: vae = model.first_stage_model depth_model = getattr(model, 'depth_model', None) # 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 # with --upcast-sampling, don't convert the depth model weights to float16 if shared.cmd_opts.upcast_sampling and depth_model: model.depth_model = None model.half() model.first_stage_model = vae if depth_model: model.depth_model = depth_model devices.dtype_unet = torch.float16 timer.record("apply half()") devices.unet_needs_upcast = shared.cmd_opts.upcast_sampling and devices.dtype == torch.float16 and devices.dtype_unet == torch.float16 model.first_stage_model.to(devices.dtype_vae) timer.record("apply dtype to VAE") # clean up cache if limit is reached while len(checkpoints_loaded) > shared.opts.sd_checkpoint_cache: checkpoints_loaded.popitem(last=False) model.sd_model_hash = sd_model_hash model.sd_model_checkpoint = checkpoint_info.filename model.sd_checkpoint_info = checkpoint_info shared.opts.data["sd_checkpoint_hash"] = checkpoint_info.sha256 if hasattr(model, 'logvar'): model.logvar = model.logvar.to(devices.device) # fix for training sd_vae.delete_base_vae() sd_vae.clear_loaded_vae() vae_file, vae_source = sd_vae.resolve_vae(checkpoint_info.filename).tuple() sd_vae.load_vae(model, vae_file, vae_source) timer.record("load VAE") 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(paths.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 repair_config(sd_config): if not hasattr(sd_config.model.params, "use_ema"): sd_config.model.params.use_ema = False if hasattr(sd_config.model.params, 'unet_config'): if shared.cmd_opts.no_half: sd_config.model.params.unet_config.params.use_fp16 = False elif shared.cmd_opts.upcast_sampling: sd_config.model.params.unet_config.params.use_fp16 = True if getattr(sd_config.model.params.first_stage_config.params.ddconfig, "attn_type", None) == "vanilla-xformers" and not shared.xformers_available: sd_config.model.params.first_stage_config.params.ddconfig.attn_type = "vanilla" # For UnCLIP-L, override the hardcoded karlo directory if hasattr(sd_config.model.params, "noise_aug_config") and hasattr(sd_config.model.params.noise_aug_config.params, "clip_stats_path"): karlo_path = os.path.join(paths.models_path, 'karlo') sd_config.model.params.noise_aug_config.params.clip_stats_path = sd_config.model.params.noise_aug_config.params.clip_stats_path.replace("checkpoints/karlo_models", karlo_path) sd1_clip_weight = 'cond_stage_model.transformer.text_model.embeddings.token_embedding.weight' sd2_clip_weight = 'cond_stage_model.model.transformer.resblocks.0.attn.in_proj_weight' sdxl_clip_weight = 'conditioner.embedders.1.model.ln_final.weight' sdxl_refiner_clip_weight = 'conditioner.embedders.0.model.ln_final.weight' class SdModelData: def __init__(self): self.sd_model = None self.loaded_sd_models = [] self.was_loaded_at_least_once = False self.lock = threading.Lock() def get_sd_model(self): if self.was_loaded_at_least_once: return self.sd_model if self.sd_model is None: with self.lock: if self.sd_model is not None or self.was_loaded_at_least_once: return self.sd_model try: load_model() except Exception as e: errors.display(e, "loading stable diffusion model", full_traceback=True) print("", file=sys.stderr) print("Stable diffusion model failed to load", file=sys.stderr) self.sd_model = None return self.sd_model def set_sd_model(self, v, already_loaded=False): self.sd_model = v if already_loaded: sd_vae.base_vae = getattr(v, "base_vae", None) sd_vae.loaded_vae_file = getattr(v, "loaded_vae_file", None) sd_vae.checkpoint_info = v.sd_checkpoint_info try: self.loaded_sd_models.remove(v) except ValueError: pass if v is not None: self.loaded_sd_models.insert(0, v) model_data = SdModelData() def get_empty_cond(sd_model): p = processing.StableDiffusionProcessingTxt2Img() extra_networks.activate(p, {}) if hasattr(sd_model, 'conditioner'): d = sd_model.get_learned_conditioning([""]) return d['crossattn'] else: return sd_model.cond_stage_model([""]) def send_model_to_cpu(m): if m.lowvram: lowvram.send_everything_to_cpu() else: m.to(devices.cpu) devices.torch_gc() def model_target_device(m): if lowvram.is_needed(m): return devices.cpu else: return devices.device def send_model_to_device(m): lowvram.apply(m) if not m.lowvram: m.to(shared.device) def send_model_to_trash(m): m.to(device="meta") devices.torch_gc() def load_model(checkpoint_info=None, already_loaded_state_dict=None): from modules import sd_hijack checkpoint_info = checkpoint_info or select_checkpoint() timer = Timer() if model_data.sd_model: send_model_to_trash(model_data.sd_model) model_data.sd_model = None devices.torch_gc() timer.record("unload existing model") if already_loaded_state_dict is not None: state_dict = already_loaded_state_dict else: state_dict = get_checkpoint_state_dict(checkpoint_info, timer) checkpoint_config = sd_models_config.find_checkpoint_config(state_dict, checkpoint_info) clip_is_included_into_sd = any(x for x in [sd1_clip_weight, sd2_clip_weight, sdxl_clip_weight, sdxl_refiner_clip_weight] if x in state_dict) timer.record("find config") sd_config = OmegaConf.load(checkpoint_config) repair_config(sd_config) timer.record("load config") print(f"Creating model from config: {checkpoint_config}") sd_model = None try: with sd_disable_initialization.DisableInitialization(disable_clip=clip_is_included_into_sd or shared.cmd_opts.do_not_download_clip): with sd_disable_initialization.InitializeOnMeta(): sd_model = instantiate_from_config(sd_config.model) except Exception as e: errors.display(e, "creating model quickly", full_traceback=True) if sd_model is None: print('Failed to create model quickly; will retry using slow method.', file=sys.stderr) with sd_disable_initialization.InitializeOnMeta(): sd_model = instantiate_from_config(sd_config.model) sd_model.used_config = checkpoint_config timer.record("create model") if shared.cmd_opts.no_half: weight_dtype_conversion = None else: weight_dtype_conversion = { 'first_stage_model': None, '': torch.float16, } with sd_disable_initialization.LoadStateDictOnMeta(state_dict, device=model_target_device(sd_model), weight_dtype_conversion=weight_dtype_conversion): load_model_weights(sd_model, checkpoint_info, state_dict, timer) timer.record("load weights from state dict") send_model_to_device(sd_model) timer.record("move model to device") sd_hijack.model_hijack.hijack(sd_model) timer.record("hijack") sd_model.eval() model_data.set_sd_model(sd_model) model_data.was_loaded_at_least_once = True 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 timer.record("load textual inversion embeddings") script_callbacks.model_loaded_callback(sd_model) timer.record("scripts callbacks") with devices.autocast(), torch.no_grad(): sd_model.cond_stage_model_empty_prompt = get_empty_cond(sd_model) timer.record("calculate empty prompt") print(f"Model loaded in {timer.summary()}.") return sd_model def reuse_model_from_already_loaded(sd_model, checkpoint_info, timer): """ Checks if the desired checkpoint from checkpoint_info is not already loaded in model_data.loaded_sd_models. If it is loaded, returns that (moving it to GPU if necessary, and moving the currently loadded model to CPU if necessary). If not, returns the model that can be used to load weights from checkpoint_info's file. If no such model exists, returns None. Additionaly deletes loaded models that are over the limit set in settings (sd_checkpoints_limit). """ already_loaded = None for i in reversed(range(len(model_data.loaded_sd_models))): loaded_model = model_data.loaded_sd_models[i] if loaded_model.sd_checkpoint_info.filename == checkpoint_info.filename: already_loaded = loaded_model continue if len(model_data.loaded_sd_models) > shared.opts.sd_checkpoints_limit > 0: print(f"Unloading model {len(model_data.loaded_sd_models)} over the limit of {shared.opts.sd_checkpoints_limit}: {loaded_model.sd_checkpoint_info.title}") model_data.loaded_sd_models.pop() send_model_to_trash(loaded_model) timer.record("send model to trash") if shared.opts.sd_checkpoints_keep_in_cpu: send_model_to_cpu(sd_model) timer.record("send model to cpu") if already_loaded is not None: send_model_to_device(already_loaded) timer.record("send model to device") model_data.set_sd_model(already_loaded, already_loaded=True) if not SkipWritingToConfig.skip: shared.opts.data["sd_model_checkpoint"] = already_loaded.sd_checkpoint_info.title shared.opts.data["sd_checkpoint_hash"] = already_loaded.sd_checkpoint_info.sha256 print(f"Using already loaded model {already_loaded.sd_checkpoint_info.title}: done in {timer.summary()}") sd_vae.reload_vae_weights(already_loaded) return model_data.sd_model elif shared.opts.sd_checkpoints_limit > 1 and len(model_data.loaded_sd_models) < shared.opts.sd_checkpoints_limit: print(f"Loading model {checkpoint_info.title} ({len(model_data.loaded_sd_models) + 1} out of {shared.opts.sd_checkpoints_limit})") model_data.sd_model = None load_model(checkpoint_info) return model_data.sd_model elif len(model_data.loaded_sd_models) > 0: sd_model = model_data.loaded_sd_models.pop() model_data.sd_model = sd_model sd_vae.base_vae = getattr(sd_model, "base_vae", None) sd_vae.loaded_vae_file = getattr(sd_model, "loaded_vae_file", None) sd_vae.checkpoint_info = sd_model.sd_checkpoint_info print(f"Reusing loaded model {sd_model.sd_checkpoint_info.title} to load {checkpoint_info.title}") return sd_model else: return None def reload_model_weights(sd_model=None, info=None): checkpoint_info = info or select_checkpoint() timer = Timer() if not sd_model: sd_model = model_data.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 sd_model sd_model = reuse_model_from_already_loaded(sd_model, checkpoint_info, timer) if sd_model is not None and sd_model.sd_checkpoint_info.filename == checkpoint_info.filename: return sd_model if sd_model is not None: sd_unet.apply_unet("None") send_model_to_cpu(sd_model) sd_hijack.model_hijack.undo_hijack(sd_model) state_dict = get_checkpoint_state_dict(checkpoint_info, timer) checkpoint_config = sd_models_config.find_checkpoint_config(state_dict, checkpoint_info) timer.record("find config") if sd_model is None or checkpoint_config != sd_model.used_config: if sd_model is not None: send_model_to_trash(sd_model) load_model(checkpoint_info, already_loaded_state_dict=state_dict) return model_data.sd_model try: load_model_weights(sd_model, checkpoint_info, state_dict, timer) except Exception: print("Failed to load checkpoint, restoring previous") load_model_weights(sd_model, current_checkpoint_info, None, timer) raise finally: sd_hijack.model_hijack.hijack(sd_model) timer.record("hijack") script_callbacks.model_loaded_callback(sd_model) timer.record("script callbacks") if not sd_model.lowvram: sd_model.to(devices.device) timer.record("move model to device") print(f"Weights loaded in {timer.summary()}.") model_data.set_sd_model(sd_model) sd_unet.apply_unet() return sd_model def unload_model_weights(sd_model=None, info=None): timer = Timer() if model_data.sd_model: model_data.sd_model.to(devices.cpu) sd_hijack.model_hijack.undo_hijack(model_data.sd_model) model_data.sd_model = None sd_model = None gc.collect() devices.torch_gc() print(f"Unloaded weights {timer.summary()}.") return sd_model def apply_token_merging(sd_model, token_merging_ratio): """ Applies speed and memory optimizations from tomesd. """ current_token_merging_ratio = getattr(sd_model, 'applied_token_merged_ratio', 0) if current_token_merging_ratio == token_merging_ratio: return if current_token_merging_ratio > 0: tomesd.remove_patch(sd_model) if token_merging_ratio > 0: tomesd.apply_patch( sd_model, ratio=token_merging_ratio, use_rand=False, # can cause issues with some samplers merge_attn=True, merge_crossattn=False, merge_mlp=False ) sd_model.applied_token_merged_ratio = token_merging_ratio