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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
22 lines
567 B
Python
22 lines
567 B
Python
import numpy as np
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import torch
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from modules import images_sharpfin
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import PIL
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JPEG_QUALITY = 95
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def preprocess(image, processor, **kwargs):
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if isinstance(image, PIL.Image.Image):
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pass
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elif isinstance(image, np.ndarray):
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image = PIL.Image.fromarray(image)
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elif isinstance(image, torch.Tensor):
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image = images_sharpfin.to_pil(image)
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else:
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raise TypeError(f"Image must be of type PIL.Image, np.ndarray, or torch.Tensor, got {type(image)} instead.")
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image = processor.preprocess(image, **kwargs)
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return image
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