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# import gc
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 cuda_collect, timer
class Loader:
def __init__(self):
self.model = ""
self.refiner = None
self.pipeline = None
self.upscaler = None
self.log = Logger("Loader")
def should_unload_refiner(self, use_refiner=False):
return self.refiner is not None and not use_refiner
def should_unload_upscaler(self, scale=1):
return self.upscaler is not None and self.upscaler.scale != scale
def should_unload_deepcache(self, interval=1):
has_deepcache = hasattr(self.pipeline, "deepcache")
if has_deepcache and interval == 1:
return True
if has_deepcache and self.pipeline.deepcache.params["cache_interval"] != interval:
return True
return False
def should_unload_pipeline(self, model=""):
return self.pipeline is not None and self.model.lower() != model.lower()
def should_load_refiner(self, use_refiner=False):
return self.refiner is None and use_refiner
def should_load_upscaler(self, scale=1):
return self.upscaler is None and scale > 1
def should_load_deepcache(self, interval=1):
has_deepcache = hasattr(self.pipeline, "deepcache")
if not has_deepcache and interval != 1:
return True
if has_deepcache and self.pipeline.deepcache.params["cache_interval"] != interval:
return True
return False
def should_load_pipeline(self):
return self.pipeline is None
def unload(self, model, use_refiner, deepcache_interval, scale):
needs_gc = False
if self.should_unload_deepcache(deepcache_interval):
self.log.info("Disabling DeepCache")
self.pipeline.deepcache.disable()
delattr(self.pipeline, "deepcache")
if self.refiner:
self.refiner.deepcache.disable()
delattr(self.refiner, "deepcache")
if self.should_unload_refiner(use_refiner):
with timer("Unloading refiner"):
self.refiner.to("cpu", silence_dtype_warnings=True)
self.refiner = None
needs_gc = True
if self.should_unload_upscaler(scale):
with timer(f"Unloading {self.upscaler.scale}x upscaler"):
self.upscaler.to("cpu")
self.upscaler = None
needs_gc = True
if self.should_unload_pipeline(model):
with timer(f"Unloading {self.model}"):
self.pipeline.to("cpu", silence_dtype_warnings=True)
if self.refiner:
self.refiner.vae = None
self.refiner.scheduler = None
self.refiner.tokenizer_2 = None
self.refiner.text_encoder_2 = None
self.pipeline = None
self.model = None
needs_gc = True
if needs_gc:
cuda_collect()
# gc.collect()
def load_refiner(self, refiner_kwargs={}, progress=None):
model = Config.REFINER_MODEL
try:
with timer(f"Loading {model}"):
Pipeline = Config.PIPELINES["img2img"]
self.refiner = Pipeline.from_pretrained(model, **refiner_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.pipeline.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.pipeline.deepcache = DeepCacheSDHelper(pipe=self.pipeline)
self.pipeline.deepcache.set_params(cache_interval=interval)
self.pipeline.deepcache.enable()
if self.refiner:
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_interval, scale, use_karras, use_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"] = use_karras
if scheduler == "DDIM":
scheduler_kwargs["clip_sample"] = False
scheduler_kwargs["set_alpha_to_one"] = False
if model.lower() not in Config.SINGLE_FILE_MODELS:
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, use_refiner, deepcache_interval, scale)
Pipeline = Config.PIPELINES[kind]
Scheduler = Config.SCHEDULERS[scheduler]
try:
with timer(f"Loading {model}"):
self.model = model
if model.lower() in Config.SINGLE_FILE_MODELS:
checkpoint = Config.HF_REPOS[model][0]
self.pipeline = Pipeline.from_single_file(
f"https://huggingface.co/{model}/{checkpoint}",
**pipe_kwargs,
).to("cuda")
else:
self.pipeline = Pipeline.from_pretrained(model, **pipe_kwargs).to("cuda")
except Exception as e:
self.log.error(f"Error loading {model}: {e}")
self.model = None
self.pipeline = None
return
if not isinstance(self.pipeline, Pipeline):
self.pipeline = Pipeline.from_pipe(self.pipeline).to("cuda")
if self.pipeline is not None:
self.pipeline.set_progress_bar_config(disable=progress is not None)
# Check and update scheduler if necessary
same_scheduler = isinstance(self.pipeline.scheduler, Scheduler)
same_karras = (
not hasattr(self.pipeline.scheduler.config, "use_karras_sigmas")
or self.pipeline.scheduler.config.use_karras_sigmas == use_karras
)
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 use_karras else 'Disabling'} Karras sigmas")
if not same_scheduler or not same_karras:
self.pipeline.scheduler = Scheduler(**scheduler_kwargs)
if self.refiner is not None:
self.refiner.scheduler = self.pipeline.scheduler
if self.should_load_refiner(use_refiner):
refiner_kwargs = {
"variant": "fp16",
"torch_dtype": dtype,
"add_watermarker": False,
"requires_aesthetics_score": True,
"force_zeros_for_empty_prompt": False,
"vae": self.pipeline.vae,
"scheduler": self.pipeline.scheduler,
"tokenizer_2": self.pipeline.tokenizer_2,
"text_encoder_2": self.pipeline.text_encoder_2,
}
self.load_refiner(refiner_kwargs, progress)
if self.should_load_deepcache(deepcache_interval):
self.load_deepcache(deepcache_interval)
if self.should_load_upscaler(scale):
self.load_upscaler(scale)
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