|
|
import os |
|
|
|
|
|
from modules import modelloader, errors |
|
|
from modules.shared import cmd_opts, opts, hf_endpoint |
|
|
from modules.upscaler import Upscaler, UpscalerData |
|
|
from modules.upscaler_utils import upscale_with_model |
|
|
|
|
|
|
|
|
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", ".safetensors"]): |
|
|
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): |
|
|
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: |
|
|
|
|
|
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=f"{hf_endpoint}/w-e-w/DAT/resolve/main/experiments/pretrained_models/DAT/DAT_x2.pth", |
|
|
scale=2, |
|
|
upscaler=scaler, |
|
|
sha256='7760aa96e4ee77e29d4f89c3a4486200042e019461fdb8aa286f49aa00b89b51', |
|
|
), |
|
|
UpscalerData( |
|
|
name="DAT x3", |
|
|
path=f"{hf_endpoint}/w-e-w/DAT/resolve/main/experiments/pretrained_models/DAT/DAT_x3.pth", |
|
|
scale=3, |
|
|
upscaler=scaler, |
|
|
sha256='581973e02c06f90d4eb90acf743ec9604f56f3c2c6f9e1e2c2b38ded1f80d197', |
|
|
), |
|
|
UpscalerData( |
|
|
name="DAT x4", |
|
|
path=f"{hf_endpoint}/w-e-w/DAT/resolve/main/experiments/pretrained_models/DAT/DAT_x4.pth", |
|
|
scale=4, |
|
|
upscaler=scaler, |
|
|
sha256='391a6ce69899dff5ea3214557e9d585608254579217169faf3d4c353caff049e', |
|
|
), |
|
|
] |
|
|
|
