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import logging
import sys
import torch
from PIL import Image
from modules import devices, modelloader, script_callbacks, shared, upscaler_utils
from modules.upscaler import Upscaler, UpscalerData
SWINIR_MODEL_URL = "https://github.com/JingyunLiang/SwinIR/releases/download/v0.0/003_realSR_BSRGAN_DFOWMFC_s64w8_SwinIR-L_x4_GAN.pth"
logger = logging.getLogger(__name__)
class UpscalerSwinIR(Upscaler):
def __init__(self, dirname):
self._cached_model = None # keep the model when SWIN_torch_compile is on to prevent re-compile every runs
self._cached_model_config = None # to clear '_cached_model' when changing model (v1/v2) or settings
self.name = "SwinIR"
self.model_url = SWINIR_MODEL_URL
self.model_name = "SwinIR 4x"
self.user_path = dirname
super().__init__()
scalers = []
model_files = self.find_models(ext_filter=[".pt", ".pth"])
for model in model_files:
if model.startswith("http"):
name = self.model_name
else:
name = modelloader.friendly_name(model)
model_data = UpscalerData(name, model, self)
scalers.append(model_data)
self.scalers = scalers
def do_upscale(self, img: Image.Image, model_file: str) -> Image.Image:
current_config = (model_file, shared.opts.SWIN_tile)
if self._cached_model_config == current_config:
model = self._cached_model
else:
try:
model = self.load_model(model_file)
except Exception as e:
print(f"Failed loading SwinIR model {model_file}: {e}", file=sys.stderr)
return img
self._cached_model = model
self._cached_model_config = current_config
img = upscaler_utils.upscale_2(
img,
model,
tile_size=shared.opts.SWIN_tile,
tile_overlap=shared.opts.SWIN_tile_overlap,
scale=model.scale,
desc="SwinIR",
)
devices.torch_gc()
return img
def load_model(self, path, scale=4):
if path.startswith("http"):
filename = modelloader.load_file_from_url(
url=path,
model_dir=self.model_download_path,
file_name=f"{self.model_name.replace(' ', '_')}.pth",
)
else:
filename = path
model_descriptor = modelloader.load_spandrel_model(
filename,
device=self._get_device(),
prefer_half=(devices.dtype == torch.float16),
expected_architecture="SwinIR",
)
if getattr(shared.opts, 'SWIN_torch_compile', False):
try:
model_descriptor.model.compile()
except Exception:
logger.warning("Failed to compile SwinIR model, fallback to JIT", exc_info=True)
return model_descriptor
def _get_device(self):
return devices.get_device_for('swinir')
def on_ui_settings():
import gradio as gr
shared.opts.add_option("SWIN_tile", shared.OptionInfo(192, "Tile size for all SwinIR.", gr.Slider, {"minimum": 16, "maximum": 512, "step": 16}, section=('upscaling', "Upscaling")))
shared.opts.add_option("SWIN_tile_overlap", shared.OptionInfo(8, "Tile overlap, in pixels for SwinIR. Low values = visible seam.", gr.Slider, {"minimum": 0, "maximum": 48, "step": 1}, section=('upscaling', "Upscaling")))
shared.opts.add_option("SWIN_torch_compile", shared.OptionInfo(False, "Use torch.compile to accelerate SwinIR.", gr.Checkbox, {"interactive": True}, section=('upscaling', "Upscaling")).info("Takes longer on first run"))
script_callbacks.on_ui_settings(on_ui_settings)
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