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import gc
from threading import Lock
import torch
from DeepCache import DeepCacheSDHelper
from diffusers.models import AutoencoderKL
from .config import Config
from .logger import Logger
from .upscaler import RealESRGAN
from .utils import clear_cuda_cache, timer
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 = None
cls._instance.log = Logger("Loader")
return cls._instance
def _should_unload_refiner(self, refiner=False):
if self.refiner is None:
return False
if not refiner:
return True
return False
def _should_unload_upscaler(self, scale=1):
if self.upscaler is not None and self.upscaler.scale != scale:
return True
return False
def _should_unload_deepcache(self, interval=1):
has_deepcache = hasattr(self.pipe, "deepcache")
if has_deepcache and interval == 1:
return True
if has_deepcache and self.pipe.deepcache.params["cache_interval"] != interval:
return True
return False
def _should_unload_pipeline(self, model=""):
if self.pipe is None:
return False
if self.model and self.model.lower() != model.lower():
return True
return False
def _unload_refiner(self):
if self.refiner is not None:
with timer("Unloading refiner"):
self.refiner.to("cpu", silence_dtype_warnings=True)
def _unload_upscaler(self):
if self.upscaler is not None:
with timer(f"Unloading {self.upscaler.scale}x upscaler"):
self.upscaler.to("cpu")
def _unload_deepcache(self):
if self.pipe.deepcache is not None:
self.log.info("Disabling DeepCache")
self.pipe.deepcache.disable()
delattr(self.pipe, "deepcache")
if self.refiner is not None:
if hasattr(self.refiner, "deepcache"):
self.refiner.deepcache.disable()
delattr(self.refiner, "deepcache")
def _unload_pipeline(self):
if self.pipe is not None:
with timer(f"Unloading {self.model}"):
self.pipe.to("cpu", silence_dtype_warnings=True)
if self.refiner is not None:
self.refiner.vae = None
self.refiner.scheduler = None
self.refiner.tokenizer_2 = None
self.refiner.text_encoder_2 = None
def _unload(self, model, refiner, deepcache, scale):
to_unload = []
if self._should_unload_deepcache(deepcache): # remove deepcache first
self._unload_deepcache()
if self._should_unload_refiner(refiner):
self._unload_refiner()
to_unload.append("refiner")
if self._should_unload_upscaler(scale):
self._unload_upscaler()
to_unload.append("upscaler")
if self._should_unload_pipeline(model):
self._unload_pipeline()
to_unload.append("model")
to_unload.append("pipe")
# Flush cache and run garbage collector
clear_cuda_cache()
for component in to_unload:
setattr(self, component, None)
gc.collect()
def _should_load_refiner(self, refiner=False):
if self.refiner is None and refiner:
return True
return False
def _should_load_upscaler(self, scale=1):
if self.upscaler is None and scale > 1:
return True
return False
def _should_load_deepcache(self, interval=1):
has_deepcache = hasattr(self.pipe, "deepcache")
if not has_deepcache and interval != 1:
return True
if has_deepcache and self.pipe.deepcache.params["cache_interval"] != interval:
return True
return False
def _should_load_pipeline(self):
if self.pipe is None:
return True
return False
def _load_refiner(self, refiner, progress, **kwargs):
if self._should_load_refiner(refiner):
model = Config.REFINER_MODEL
pipeline = Config.PIPELINES["img2img"]
try:
with timer(f"Loading {model}"):
self.refiner = pipeline.from_pretrained(model, **kwargs).to("cuda")
except Exception as e:
self.log.error(f"Error loading {model}: {e}")
self.refiner = None
return
if self.refiner is not None:
self.refiner.set_progress_bar_config(disable=progress is not None)
def _load_upscaler(self, scale=1):
if self._should_load_upscaler(scale):
try:
with timer(f"Loading {scale}x upscaler"):
self.upscaler = RealESRGAN(scale, device=self.pipe.device)
self.upscaler.load_weights()
except Exception as e:
self.log.error(f"Error loading {scale}x upscaler: {e}")
self.upscaler = None
def _load_deepcache(self, interval=1):
if self._should_load_deepcache(interval):
self.log.info("Enabling DeepCache")
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:
self.refiner.deepcache = DeepCacheSDHelper(pipe=self.refiner)
self.refiner.deepcache.set_params(cache_interval=interval)
self.refiner.deepcache.enable()
def _load_pipeline(self, kind, model, progress, **kwargs):
pipeline = Config.PIPELINES[kind]
if self._should_load_pipeline():
try:
with timer(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
self.refiner.to(self.pipe.device)
except Exception as e:
self.log.error(f"Error loading {model}: {e}")
self.model = None
self.pipe = None
self.refiner = 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=progress is not None)
def load(self, kind, model, scheduler, deepcache, scale, karras, refiner, progress):
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, refiner, deepcache, scale)
self._load_pipeline(kind, model, progress, **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:
self.log.info(f"Enabling {scheduler}")
if not same_karras:
self.log.info(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
if self._should_load_refiner(refiner):
# 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, progress, **refiner_kwargs) # load refiner before deepcache
if self._should_load_deepcache(deepcache):
self._load_deepcache(deepcache)
if self._should_load_upscaler(scale):
self._load_upscaler(scale)
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