mirror of
https://github.com/vladmandic/automatic
synced 2026-09-06 13:00:44 +02:00
CalamitousFelicitousness
76aa949a26
Vendor sharpfin library (Apache 2.0) and add centralized wrapper module (images_sharpfin.py) replacing torchvision tensor/PIL conversion and resize operations throughout the codebase. - Add modules/sharpfin/ vendored library with MKS2021, Lanczos3, Mitchell, Catmull-Rom kernels and optional Triton sparse acceleration - Add modules/images_sharpfin.py wrapper with to_tensor(), to_pil(), pil_to_tensor(), normalize(), resize(), resize_tensor() - Add resize_quality and resize_linearize_srgb settings - Add MKS2021 and Lanczos3 upscaler entries - Replace torchvision.transforms.functional imports across 18 files - to_pil() auto-detects HWC/BHWC layout, adds .round() before uint8 - Sparse Triton path falls back to dense GPU on compilation failure - Mixed-axis resize splits into two single-axis scale() calls - Masks and non-sRGB data always use linearize=False
104 lines
4.7 KiB
Python
104 lines
4.7 KiB
Python
from PIL import Image
|
|
from modules.upscaler import Upscaler, UpscalerData
|
|
from modules.shared import log
|
|
|
|
|
|
class UpscalerNone(Upscaler):
|
|
def __init__(self, dirname=None): # pylint: disable=unused-argument
|
|
super().__init__(False)
|
|
self.name = "None"
|
|
self.scalers = [UpscalerData("None", None, self)]
|
|
|
|
def load_model(self, path):
|
|
pass
|
|
|
|
def do_upscale(self, img, selected_model=None):
|
|
return img
|
|
|
|
|
|
class UpscalerResize(Upscaler):
|
|
def __init__(self, dirname=None): # pylint: disable=unused-argument
|
|
super().__init__(False)
|
|
self.name = "Resize"
|
|
self.scalers = [
|
|
UpscalerData("Resize Nearest", None, self),
|
|
UpscalerData("Resize Lanczos", None, self),
|
|
UpscalerData("Resize Bicubic", None, self),
|
|
UpscalerData("Resize Bilinear", None, self),
|
|
UpscalerData("Resize Hamming", None, self),
|
|
UpscalerData("Resize Box", None, self),
|
|
UpscalerData("Resize Sharpfin MKS2021", None, self),
|
|
UpscalerData("Resize Sharpfin Lanczos3", None, self),
|
|
]
|
|
|
|
def do_upscale(self, img: Image, selected_model=None):
|
|
if selected_model is None:
|
|
return img
|
|
elif selected_model == "Resize Nearest":
|
|
return img.resize((int(img.width * self.scale), int(img.height * self.scale)), resample=Image.Resampling.NEAREST)
|
|
elif selected_model == "Resize Lanczos":
|
|
return img.resize((int(img.width * self.scale), int(img.height * self.scale)), resample=Image.Resampling.LANCZOS)
|
|
elif selected_model == "Resize Bicubic":
|
|
return img.resize((int(img.width * self.scale), int(img.height * self.scale)), resample=Image.Resampling.BICUBIC)
|
|
elif selected_model == "Resize Bilinear":
|
|
return img.resize((int(img.width * self.scale), int(img.height * self.scale)), resample=Image.Resampling.BILINEAR)
|
|
elif selected_model == "Resize Hamming":
|
|
return img.resize((int(img.width * self.scale), int(img.height * self.scale)), resample=Image.Resampling.HAMMING)
|
|
elif selected_model == "Resize Box":
|
|
return img.resize((int(img.width * self.scale), int(img.height * self.scale)), resample=Image.Resampling.BOX)
|
|
elif selected_model == "Resize Sharpfin MKS2021":
|
|
from modules import images_sharpfin
|
|
return images_sharpfin.resize(img, (int(img.width * self.scale), int(img.height * self.scale)), kernel="Sharpfin MKS2021")
|
|
elif selected_model == "Resize Sharpfin Lanczos3":
|
|
from modules import images_sharpfin
|
|
return images_sharpfin.resize(img, (int(img.width * self.scale), int(img.height * self.scale)), kernel="Sharpfin Lanczos3")
|
|
else:
|
|
return img
|
|
|
|
|
|
def load_model(self, _):
|
|
pass
|
|
|
|
|
|
class UpscalerLatent(Upscaler):
|
|
def __init__(self, dirname=None): # pylint: disable=unused-argument
|
|
super().__init__(False)
|
|
self.name = "Latent"
|
|
self.scalers = [
|
|
UpscalerData("Latent Nearest", None, self),
|
|
UpscalerData("Latent Nearest exact", None, self),
|
|
UpscalerData("Latent Area", None, self),
|
|
UpscalerData("Latent Bilinear", None, self),
|
|
UpscalerData("Latent Bicubic", None, self),
|
|
UpscalerData("Latent Bilinear antialias", None, self),
|
|
UpscalerData("Latent Bicubic antialias", None, self),
|
|
]
|
|
|
|
def do_upscale(self, img: Image, selected_model=None):
|
|
import torch
|
|
import torch.nn.functional as F
|
|
if isinstance(img, torch.Tensor) and (len(img.shape) == 4):
|
|
_batch, _channel, h, w = img.shape
|
|
else:
|
|
log.error(f"Upscale: type=latent image={img.shape if isinstance(img, torch.Tensor) else img} type={type(img)} if not supported")
|
|
return img
|
|
h, w = int((8 * h * self.scale) // 8), int((8 * w * self.scale) // 8)
|
|
mode, antialias = '', ''
|
|
if selected_model == "Latent Nearest":
|
|
mode, antialias = 'nearest', False
|
|
elif selected_model == "Latent Nearest exact":
|
|
mode, antialias = 'nearest-exact', False
|
|
elif selected_model == "Latent Area":
|
|
mode, antialias = 'area', False
|
|
elif selected_model == "Latent Bilinear":
|
|
mode, antialias = 'bilinear', False
|
|
elif selected_model == "Latent Bicubic":
|
|
mode, antialias = 'bicubic', False
|
|
elif selected_model == "Latent Bilinear antialias":
|
|
mode, antialias = 'bilinear', True
|
|
elif selected_model == "Latent Bicubic antialias":
|
|
mode, antialias = 'bicubic', True
|
|
else:
|
|
raise log.error(f"Upscale: type=latent model={selected_model} unknown")
|
|
return F.interpolate(img, size=(h, w), mode=mode, antialias=antialias)
|