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import os
from modules import modelloader, errors
from modules.shared import cmd_opts, opts
from modules.upscaler import Upscaler, UpscalerData
from modules.upscaler_utils import upscale_with_model
from modules_forge.utils import prepare_free_memory
class UpscalerDAT(Upscaler):
def __init__(self, user_path):
self.name = "DAT"
self.user_path = user_path
self.scalers = []
super().__init__()
for file in self.find_models(ext_filter=[".pt", ".pth"]):
name = modelloader.friendly_name(file)
scaler_data = UpscalerData(name, file, upscaler=self, scale=None)
self.scalers.append(scaler_data)
for model in get_dat_models(self):
if model.name in opts.dat_enabled_models:
self.scalers.append(model)
def do_upscale(self, img, path):
prepare_free_memory()
try:
info = self.load_model(path)
except Exception:
errors.report(f"Unable to load DAT model {path}", exc_info=True)
return img
model_descriptor = modelloader.load_spandrel_model(
info.local_data_path,
device=self.device,
prefer_half=(not cmd_opts.no_half and not cmd_opts.upcast_sampling),
expected_architecture="DAT",
)
return upscale_with_model(
model_descriptor,
img,
tile_size=opts.DAT_tile,
tile_overlap=opts.DAT_tile_overlap,
)
def load_model(self, path):
for scaler in self.scalers:
if scaler.data_path == path:
if scaler.local_data_path.startswith("http"):
scaler.local_data_path = modelloader.load_file_from_url(
scaler.data_path,
model_dir=self.model_download_path,
hash_prefix=scaler.sha256,
)
if os.path.getsize(scaler.local_data_path) < 200:
# Re-download if the file is too small, probably an LFS pointer
scaler.local_data_path = modelloader.load_file_from_url(
scaler.data_path,
model_dir=self.model_download_path,
hash_prefix=scaler.sha256,
re_download=True,
)
if not os.path.exists(scaler.local_data_path):
raise FileNotFoundError(f"DAT data missing: {scaler.local_data_path}")
return scaler
raise ValueError(f"Unable to find model info: {path}")
def get_dat_models(scaler):
return [
UpscalerData(
name="DAT x2",
path="https://huggingface.co/w-e-w/DAT/resolve/main/experiments/pretrained_models/DAT/DAT_x2.pth",
scale=2,
upscaler=scaler,
sha256='7760aa96e4ee77e29d4f89c3a4486200042e019461fdb8aa286f49aa00b89b51',
),
UpscalerData(
name="DAT x3",
path="https://huggingface.co/w-e-w/DAT/resolve/main/experiments/pretrained_models/DAT/DAT_x3.pth",
scale=3,
upscaler=scaler,
sha256='581973e02c06f90d4eb90acf743ec9604f56f3c2c6f9e1e2c2b38ded1f80d197',
),
UpscalerData(
name="DAT x4",
path="https://huggingface.co/w-e-w/DAT/resolve/main/experiments/pretrained_models/DAT/DAT_x4.pth",
scale=4,
upscaler=scaler,
sha256='391a6ce69899dff5ea3214557e9d585608254579217169faf3d4c353caff049e',
),
]
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