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update sharpfin usage 

Signed-off-by: vladmandic <mandic00@live.com>
pull/4668/head
vladmandic 2026-02-11 09:28:05 +01:00
parent dc8ecb0a64
commit 3ae9909b2a
4 changed files with 84 additions and 243 deletions
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11
CHANGELOG.md
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@ -1,12 +1,14 @@
# Change Log for SD.Next
## Update for 2026-02-09
## Update for 2026-02-11
- **Upscalers**
- **Image manipulation**
- use high-quality [sharpfin](https://github.com/drhead/Sharpfin) accelerated library
when available (cuda-only), thanks @CalamitousFelicitousness
- add support for [spandrel](https://github.com/chaiNNer-org/spandrel)
upscaling engine with suport for new upscaling model families
**upscaling** engine with suport for new upscaling model families
- add two new ai upscalers: *RealPLKSR NomosWebPhoto* and *RealPLKSR AnimeSharpV2*
- add two new interpolation methods: *HQX* and *ICB*
- add two new **interpolation** methods: *HQX* and *ICB*
- **Features**
- pipelines: add **ZImageInpaint**, thanks @CalamitousFelicitousness
- add `--remote` command line flag that reduces client/server chatter and improves link stability
@ -18,6 +20,7 @@
- ui: **themes** add *CTD-NT64Light* and *CTD-NT64Dark*, thanks @resonantsky
- ui: **gallery** add option to auto-refresh gallery, thanks @awsr
- **Internal**
- refactor: to/from image/tensor logic, thanks @CalamitousFelicitousness
- refactor: switch to `pyproject.toml` for tool configs
- refactor: reorganize `cli` scripts
- refactor: move tests to dedicated `/test/`

4
modules/images_resize.py
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@ -136,7 +136,7 @@ def resize_image(resize_mode: int, im: Union[Image.Image, torch.Tensor], width:
return res
im = verify_image(im)
if not isinstance(im, Image.Image):
shared.log.error(f'Image resize: image={type(im)} invalid type')
shared.log.error(f'Resize image: image={type(im)} invalid type')
return im
if (resize_mode == 0) or ((im.width == width) and (im.height == height)) or (width == 0 and height == 0): # none
res = im.copy()
@ -158,5 +158,5 @@ def resize_image(resize_mode: int, im: Union[Image.Image, torch.Tensor], width:
t1 = time.time()
fn = f'{sys._getframe(2).f_code.co_name}:{sys._getframe(1).f_code.co_name}' # pylint: disable=protected-access
if im.width != width or im.height != height:
shared.log.debug(f'Image resize: source={im.width}:{im.height} target={width}:{height} mode="{shared.resize_modes[resize_mode]}" upscaler="{upscaler_name}" type={output_type} time={t1-t0:.2f} fn={fn}') # pylint: disable=protected-access
shared.log.debug(f'Resize image: source={im.width}:{im.height} target={width}:{height} mode="{shared.resize_modes[resize_mode]}" upscaler="{upscaler_name}" type={output_type} time={t1-t0:.2f} fn={fn}') # pylint: disable=protected-access
return np.array(res) if output_type == 'np' else res

122
modules/images_sharpfin.py
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@ -7,20 +7,21 @@ and Triton GPU acceleration when available.
Non-CUDA devices fall back to PIL/torch.nn.functional automatically.
"""
import sys
import torch
import numpy as np
from PIL import Image
from installer import log
_sharpfin_checked = False
_sharpfin_ok = False
_triton_ok = False
def _check():
def check_sharpfin():
global _sharpfin_checked, _sharpfin_ok, _triton_ok # pylint: disable=global-statement
if not _sharpfin_checked:
# DEBUG: no try/except — let import errors propagate
from modules.sharpfin.functional import scale # pylint: disable=unused-import
_sharpfin_ok = True
try:
@ -39,7 +40,7 @@ KERNEL_MAP = {
}
def _resolve_kernel(kernel=None):
def get_kernel(kernel=None):
"""Resolve kernel name to ResizeKernel enum. Returns None for PIL fallback."""
if kernel is not None:
name = kernel
@ -52,7 +53,7 @@ def _resolve_kernel(kernel=None):
return getattr(ResizeKernel, KERNEL_MAP[name])
def _resolve_linearize(linearize=None, is_mask=False):
def get_linearize(linearize=None, is_mask=False):
"""Determine sRGB linearization setting."""
if is_mask:
return False
@ -62,19 +63,17 @@ def _resolve_linearize(linearize=None, is_mask=False):
return shared.opts.resize_linearize_srgb
def _should_use_sharpfin(device=None):
def allow_sharpfin(device=None):
"""Determine if sharpfin should be used based on device."""
if device is None:
from modules import devices
device = devices.device
# Sharpfin is optimized for CUDA with Triton
# For other devices (CPU, MPS, OpenVINO), use torch/PIL optimized kernels
# Sharpfin is optimized for CUDA with Triton, for other devices (CPU, MPS, OpenVINO), use torch/PIL optimized kernels
return hasattr(device, 'type') and device.type == 'cuda'
def resize(image, target_size, *, kernel=None, linearize=None, device=None, dtype=None):
"""Resize PIL.Image or torch.Tensor, returning same type.
Args:
image: PIL.Image or torch.Tensor [B,C,H,W] / [C,H,W]
target_size: (width, height) for PIL, (H, W) for tensor
@ -83,9 +82,9 @@ def resize(image, target_size, *, kernel=None, linearize=None, device=None, dtyp
device: Override compute device
dtype: Override compute dtype
"""
_check()
check_sharpfin()
if isinstance(image, Image.Image):
return _resize_pil(image, target_size, kernel=kernel, linearize=linearize, device=device, dtype=dtype)
return resize_pil(image, target_size, kernel=kernel, linearize=linearize, device=device, dtype=dtype)
elif isinstance(image, torch.Tensor):
return resize_tensor(image, target_size, kernel=kernel, linearize=linearize if linearize is not None else False)
return image
@ -132,22 +131,31 @@ def _scale_pil(scale_fn, tensor, out_res, rk, dev, dt, do_linear, src_h, src_w,
return scale_fn(intermediate, (h, w), resize_kernel=rk, device=dev, dtype=dt, do_srgb_conversion=do_linear, use_sparse=False)
def _resize_pil(image, target_size, *, kernel=None, linearize=None, device=None, dtype=None):
def resize_pil(image: Image.Image, target_size: tuple[int, int], *, kernel=None, linearize=None, device=None, dtype=None):
"""Resize a PIL Image via sharpfin, falling back to PIL on error."""
fn = f'{sys._getframe(2).f_code.co_name}:{sys._getframe(1).f_code.co_name}' # pylint: disable=protected-access
w, h = target_size
if image.width == w and image.height == h:
is_mask = image.mode == 'L'
if (image.width == w) and (image.height == h):
log.debug(f'Resize image: skip={w}x{h} fn={fn}')
return image
from modules import devices
dev = device if device is not None else devices.device
if not _should_use_sharpfin(dev):
if not allow_sharpfin(dev):
log.debug(f'Resize image: method=PIL source={image.width}x{image.height} target={w}x{h} device={dev} fn={fn}')
return image.resize((w, h), resample=Image.Resampling.LANCZOS)
is_mask = image.mode == 'L'
rk = _resolve_kernel(kernel)
rk = get_kernel(kernel)
if rk is None:
log.debug(f'Resize image: method=PI source={image.width}x{image.height} target={w}x{h} kernel=None fn={fn}')
return image.resize((w, h), resample=Image.Resampling.LANCZOS)
from modules.sharpfin.functional import scale
dt = dtype if dtype is not None else torch.float16
do_linear = _resolve_linearize(linearize, is_mask=is_mask)
dt = dtype or torch.float16
do_linear = get_linearize(linearize, is_mask=is_mask)
log.debug(f'Resize image: method=sharpfin source={image.width}x{image.height} target={w}x{h} kernel={rk} device={dev} linearize={do_linear} fn={fn}')
tensor = to_tensor(image)
if tensor.dim() == 3:
tensor = tensor.unsqueeze(0)
@ -160,7 +168,7 @@ def _resize_pil(image, target_size, *, kernel=None, linearize=None, device=None,
return to_pil(result)
def resize_tensor(tensor, target_size, *, kernel=None, linearize=False):
def resize_tensor(tensor: torch.Tensor, target_size: tuple[int, int], *, kernel=None, linearize=False):
"""Resize tensor [B,C,H,W] or [C,H,W] -> Tensor. For in-pipeline tensor resizes.
Args:
@ -169,20 +177,24 @@ def resize_tensor(tensor, target_size, *, kernel=None, linearize=False):
kernel: Override kernel name
linearize: sRGB linearization (default False for latent/mask data)
"""
_check()
fn = f'{sys._getframe(2).f_code.co_name}:{sys._getframe(1).f_code.co_name}' # pylint: disable=protected-access
check_sharpfin()
from modules import devices
dev = devices.device
if not _should_use_sharpfin(dev):
mode = 'bilinear' if target_size[0] * target_size[1] > tensor.shape[-2] * tensor.shape[-1] else 'area'
if not allow_sharpfin(dev):
mode = 'bilinear' if (target_size[0] * target_size[1]) > (tensor.shape[-2] * tensor.shape[-1]) else 'area'
log.debug(f'Resize tensor: method=torch mode={mode} shape={tensor.shape} target={target_size} fn={fn}')
inp = tensor if tensor.dim() == 4 else tensor.unsqueeze(0)
result = torch.nn.functional.interpolate(inp, size=target_size, mode=mode, antialias=True)
return result.squeeze(0) if tensor.dim() == 3 else result
rk = _resolve_kernel(kernel)
rk = get_kernel(kernel)
if rk is None:
mode = 'bilinear' if target_size[0] * target_size[1] > tensor.shape[-2] * tensor.shape[-1] else 'area'
mode = 'bilinear' if (target_size[0] * target_size[1]) > (tensor.shape[-2] * tensor.shape[-1]) else 'area'
log.debug(f'Resize tensor: method=torch mode={mode} shape={tensor.shape} target={target_size} kernel=None fn={fn}')
inp = tensor if tensor.dim() == 4 else tensor.unsqueeze(0)
result = torch.nn.functional.interpolate(inp, size=target_size, mode=mode, antialias=True)
return result.squeeze(0) if tensor.dim() == 3 else result
from modules.sharpfin.functional import scale
dt = torch.float16
squeezed = False
@ -195,8 +207,10 @@ def resize_tensor(tensor, target_size, *, kernel=None, linearize=False):
both_up = (th >= src_h and tw >= src_w)
if both_down or both_up:
use_sparse = _triton_ok and dev.type == 'cuda' and rk.value == 'magic_kernel_sharp_2021' and both_down
log.debug(f'Resize tensor: method=sharpfin shape={tensor.shape} target={target_size} direction={both_up}:{both_down} kernel={rk} sparse={use_sparse} fn={fn}')
result = scale(tensor, target_size, resize_kernel=rk, device=dev, dtype=dt, do_srgb_conversion=linearize, use_sparse=use_sparse)
else:
log.debug(f'Resize tensor: method=sharpfin shape={tensor.shape} target={target_size} direction={both_up}:{both_down} kernel={rk} sparse=False fn={fn}')
if th > src_h:
intermediate = scale(tensor, (th, src_w), resize_kernel=rk, device=dev, dtype=dt, do_srgb_conversion=linearize, use_sparse=False)
result = scale(intermediate, (th, tw), resize_kernel=rk, device=dev, dtype=dt, do_srgb_conversion=linearize, use_sparse=False)
@ -208,42 +222,56 @@ def resize_tensor(tensor, target_size, *, kernel=None, linearize=False):
return result
def to_tensor(image):
def to_tensor(image: Image.Image | np.ndarray):
"""PIL Image -> float32 CHW tensor [0,1]. Pure torch, no torchvision."""
# fn = f'{sys._getframe(2).f_code.co_name}:{sys._getframe(1).f_code.co_name}' # pylint: disable=protected-access
if not isinstance(image, Image.Image):
raise TypeError(f"Expected PIL Image, got {type(image)}")
pic = np.array(image, copy=True)
pic = np.array(image, copy=True)
elif isinstance(image, np.ndarray):
pic = image.copy()
else:
raise TypeError(f"Expected PIL Image or np.ndarray, got {type(image)}")
if pic.ndim == 2:
pic = pic[:, :, np.newaxis]
tensor = torch.from_numpy(pic.transpose((2, 0, 1))).contiguous()
# log.debug(f'Convert: source={type(image)} target={tensor.shape} fn={fn}')
if tensor.dtype == torch.uint8:
return tensor.to(torch.float32).div_(255.0)
return tensor.to(torch.float32)
def to_pil(tensor):
def to_pil(tensor: torch.Tensor | np.ndarray):
"""Float CHW/HWC or BCHW/BHWC tensor [0,1] -> PIL Image. Pure torch, no torchvision."""
if not isinstance(tensor, torch.Tensor):
raise TypeError(f"Expected torch.Tensor, got {type(tensor)}")
tensor = tensor.detach().cpu()
if tensor.dim() == 4:
if tensor.shape[-1] in (1, 3, 4) and tensor.shape[-1] < tensor.shape[-2]: # BHWC
tensor = tensor.permute(0, 3, 1, 2)
tensor = tensor[0]
elif tensor.dim() == 3:
if tensor.shape[-1] in (1, 3, 4) and tensor.shape[-1] < tensor.shape[-2] and tensor.shape[-1] < tensor.shape[-3]: # HWC
tensor = tensor.permute(2, 0, 1)
if tensor.dtype != torch.uint8:
tensor = (tensor.clamp(0, 1) * 255).round().to(torch.uint8)
ndarr = tensor.permute(1, 2, 0).numpy()
if ndarr.shape[2] == 1:
ndarr = ndarr[:, :, 0]
mode = 'L'
elif ndarr.shape[2] == 3:
mode = 'RGB'
if isinstance(tensor, torch.Tensor):
tensor = tensor.detach().cpu()
elif isinstance(tensor, np.ndarray):
tensor = torch.from_numpy(tensor)
else:
mode = 'RGBA'
return Image.fromarray(ndarr, mode=mode)
raise TypeError(f"Expected torch.Tensor, got {type(tensor)}")
try:
if tensor.dim() == 4:
if tensor.shape[-1] in (1, 3, 4) and tensor.shape[-1] < tensor.shape[-2]: # BHWC
tensor = tensor.permute(0, 3, 1, 2)
tensor = tensor[0]
elif tensor.dim() == 3:
if tensor.shape[-1] in (1, 3, 4) and tensor.shape[-1] < tensor.shape[-2] and tensor.shape[-1] < tensor.shape[-3]: # HWC
tensor = tensor.permute(2, 0, 1)
if tensor.dtype != torch.uint8:
tensor = (tensor.clamp(0, 1) * 255).round().to(torch.uint8)
ndarr = tensor.permute(1, 2, 0).numpy()
if ndarr.shape[2] == 1:
ndarr = ndarr[:, :, 0]
mode = 'L'
elif ndarr.shape[2] == 3:
mode = 'RGB'
else:
mode = 'RGBA'
image = Image.fromarray(ndarr, mode=mode)
except Exception as e:
image = Image.new('RGB', (tensor.shape[-1], tensor.shape[-2]), color=(152, 32, 48))
fn = f'{sys._getframe(2).f_code.co_name}:{sys._getframe(1).f_code.co_name}' # pylint: disable=protected-access
log.error(f'Convert: source={type(tensor)} target={image} fn={fn} {e}')
return image
def pil_to_tensor(image):

190
modules/sharpfin/LICENSE
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@ -1,190 +0,0 @@
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