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https://github.com/vladmandic/automatic
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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
50 lines
1.2 KiB
Python
50 lines
1.2 KiB
Python
"""Sharpfin utility types and color space conversion functions.
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Vendored from https://github.com/drhead/sharpfin (Apache 2.0)
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"""
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from enum import Enum
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import torch
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def srgb_to_linear(image: torch.Tensor) -> torch.Tensor:
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return torch.where(
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image <= 0.04045,
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image / 12.92,
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# Clamping is for protection against NaNs during backwards passes.
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((torch.clamp(image, min=0.04045) + 0.055) / 1.055) ** 2.4
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)
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def linear_to_srgb(image: torch.Tensor) -> torch.Tensor:
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return torch.where(
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image <= 0.0031308,
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image * 12.92,
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torch.clamp(1.055 * image ** (1 / 2.4) - 0.055, min=0.0, max=1.0)
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)
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class ResizeKernel(Enum):
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NEAREST = "nearest"
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BILINEAR = "bilinear"
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CATMULL_ROM = "catmull-rom"
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MITCHELL = "mitchell"
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B_SPLINE = "b-spline"
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LANCZOS2 = "lanczos2"
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LANCZOS3 = "lanczos3"
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MAGIC_KERNEL = "magic_kernel"
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MAGIC_KERNEL_SHARP_2013 = "magic_kernel_sharp_2013"
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MAGIC_KERNEL_SHARP_2021 = "magic_kernel_sharp_2021"
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class SharpenKernel(Enum):
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SHARP_2013 = "sharp_2013"
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SHARP_2021 = "sharp_2021"
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class QuantHandling(Enum):
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TRUNCATE = "truncate"
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ROUND = "round"
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STOCHASTIC_ROUND = "stochastic_round"
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BAYER = "bayer"
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