refactor: integrate sharpfin for high-quality image resize

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

scripts/ctrlx/media.py

@@ -1,6 +1,6 @@
 import numpy as np
 import torch
-import torchvision.transforms.functional as vF
+from modules import images_sharpfin
 import PIL
 
 
@@ -13,7 +13,7 @@ def preprocess(image, processor, **kwargs):
     elif isinstance(image, np.ndarray):
         image = PIL.Image.fromarray(image)
     elif isinstance(image, torch.Tensor):
-        image = vF.to_pil_image(image)
+        image = images_sharpfin.to_pil(image)
     else:
         raise TypeError(f"Image must be of type PIL.Image, np.ndarray, or torch.Tensor, got {type(image)} instead.")