import gc from threading import Lock from warnings import filterwarnings import torch from DeepCache import DeepCacheSDHelper from diffusers.models import AutoencoderKL from .config import Config from .upscaler import RealESRGAN __import__("diffusers").logging.set_verbosity_error() filterwarnings("ignore", category=FutureWarning, module="torch") filterwarnings("ignore", category=FutureWarning, module="diffusers") class Loader: _instance = None _lock = Lock() def __new__(cls): with cls._lock: if cls._instance is None: cls._instance = super().__new__(cls) cls._instance.pipe = None cls._instance.model = None cls._instance.refiner = None cls._instance.upscaler_2x = None cls._instance.upscaler_4x = None return cls._instance def _flush(self): gc.collect() torch.cuda.empty_cache() torch.cuda.ipc_collect() torch.cuda.reset_max_memory_allocated() torch.cuda.reset_peak_memory_stats() torch.cuda.synchronize() def _should_unload_pipeline(self, model=""): if self.pipe is None: return False if self.model.lower() != model.lower(): return True return False def _unload(self, model): to_unload = [] if self._should_unload_pipeline(model): to_unload.append("model") to_unload.append("pipe") for component in to_unload: delattr(self, component) self._flush() for component in to_unload: setattr(self, component, None) def _load_pipeline(self, kind, model, tqdm, **kwargs): pipeline = Config.PIPELINES[kind] if self.pipe is None: try: print(f"Loading {model}...") self.model = model if model.lower() in Config.MODEL_CHECKPOINTS.keys(): self.pipe = pipeline.from_single_file( f"https://huggingface.co/{model}/{Config.MODEL_CHECKPOINTS[model.lower()]}", **kwargs, ).to("cuda") else: self.pipe = pipeline.from_pretrained(model, **kwargs).to("cuda") if self.refiner is not None: self.refiner.vae = self.pipe.vae self.refiner.scheduler = self.pipe.scheduler self.refiner.tokenizer_2 = self.pipe.tokenizer_2 self.refiner.text_encoder_2 = self.pipe.text_encoder_2 except Exception as e: print(f"Error loading {model}: {e}") self.model = None self.pipe = None return if not isinstance(self.pipe, pipeline): self.pipe = pipeline.from_pipe(self.pipe).to("cuda") if self.pipe is not None: self.pipe.set_progress_bar_config(disable=not tqdm) def _load_refiner(self, refiner, tqdm, **kwargs): if refiner and self.refiner is None: model = Config.REFINER_MODEL pipeline = Config.PIPELINES["img2img"] try: print(f"Loading {model}...") self.refiner = pipeline.from_pretrained(model, **kwargs).to("cuda") except Exception as e: print(f"Error loading {model}: {e}") self.refiner = None return if self.refiner is not None: self.refiner.set_progress_bar_config(disable=not tqdm) def _load_upscaler(self, scale=1): if scale == 2 and self.upscaler_2x is None: try: print("Loading 2x upscaler...") self.upscaler_2x = RealESRGAN(2, "cuda") self.upscaler_2x.load_weights() except Exception as e: print(f"Error loading 2x upscaler: {e}") self.upscaler_2x = None if scale == 4 and self.upscaler_4x is None: try: print("Loading 4x upscaler...") self.upscaler_4x = RealESRGAN(4, "cuda") self.upscaler_4x.load_weights() except Exception as e: print(f"Error loading 4x upscaler: {e}") self.upscaler_4x = None def _load_deepcache(self, interval=1): pipe_has_deepcache = hasattr(self.pipe, "deepcache") if pipe_has_deepcache and self.pipe.deepcache.params["cache_interval"] == interval: return if pipe_has_deepcache: self.pipe.deepcache.disable() else: self.pipe.deepcache = DeepCacheSDHelper(pipe=self.pipe) self.pipe.deepcache.set_params(cache_interval=interval) self.pipe.deepcache.enable() if self.refiner is not None: refiner_has_deepcache = hasattr(self.refiner, "deepcache") if refiner_has_deepcache and self.refiner.deepcache.params["cache_interval"] == interval: return if refiner_has_deepcache: self.refiner.deepcache.disable() else: self.refiner.deepcache = DeepCacheSDHelper(pipe=self.refiner) self.refiner.deepcache.set_params(cache_interval=interval) self.refiner.deepcache.enable() def load(self, kind, model, scheduler, deepcache, scale, karras, refiner, tqdm): model_lower = model.lower() scheduler_kwargs = { "beta_start": 0.00085, "beta_end": 0.012, "beta_schedule": "scaled_linear", "timestep_spacing": "leading", "steps_offset": 1, } if scheduler not in ["DDIM", "Euler a"]: scheduler_kwargs["use_karras_sigmas"] = karras # https://github.com/huggingface/diffusers/blob/8a3f0c1/scripts/convert_original_stable_diffusion_to_diffusers.py#L939 if scheduler == "DDIM": scheduler_kwargs["clip_sample"] = False scheduler_kwargs["set_alpha_to_one"] = False if model_lower not in Config.MODEL_CHECKPOINTS.keys(): variant = "fp16" else: variant = None dtype = torch.float16 pipe_kwargs = { "variant": variant, "torch_dtype": dtype, "add_watermarker": False, "scheduler": Config.SCHEDULERS[scheduler](**scheduler_kwargs), "vae": AutoencoderKL.from_pretrained(Config.VAE_MODEL, torch_dtype=dtype), } self._unload(model) self._load_pipeline(kind, model, tqdm, **pipe_kwargs) # error loading model if self.pipe is None: return same_scheduler = isinstance(self.pipe.scheduler, Config.SCHEDULERS[scheduler]) same_karras = ( not hasattr(self.pipe.scheduler.config, "use_karras_sigmas") or self.pipe.scheduler.config.use_karras_sigmas == karras ) # same model, different scheduler if self.model.lower() == model_lower: if not same_scheduler: print(f"Switching to {scheduler}...") if not same_karras: print(f"{'Enabling' if karras else 'Disabling'} Karras sigmas...") if not same_scheduler or not same_karras: self.pipe.scheduler = Config.SCHEDULERS[scheduler](**scheduler_kwargs) if self.refiner is not None: self.refiner.scheduler = self.pipe.scheduler # https://huggingface.co/stabilityai/stable-diffusion-xl-refiner-1.0/blob/main/model_index.json refiner_kwargs = { "variant": "fp16", "torch_dtype": dtype, "add_watermarker": False, "requires_aesthetics_score": True, "force_zeros_for_empty_prompt": False, "vae": self.pipe.vae, "scheduler": self.pipe.scheduler, "tokenizer_2": self.pipe.tokenizer_2, "text_encoder_2": self.pipe.text_encoder_2, } self._load_refiner(refiner, tqdm, **refiner_kwargs) self._load_upscaler(scale) self._load_deepcache(deepcache)