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refactor: integrate sharpfin for high-quality image resize

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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
This commit is contained in:
CalamitousFelicitousness
2026-02-10 00:13:35 +00:00
committed by vladmandic
parent 2c4d0751d9
commit 76aa949a26
30 changed files with 2878 additions and 78 deletions
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modules/processing_diffusers.py
+4 -5
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@@ -3,9 +3,8 @@ import os
import time
import numpy as np
import torch
import torchvision.transforms.functional as TF
from PIL import Image
from modules import shared, devices, processing, sd_models, errors, sd_hijack_hypertile, processing_vae, sd_models_compile, timer, modelstats, extra_networks, attention
from modules import shared, devices, processing, sd_models, errors, sd_hijack_hypertile, processing_vae, sd_models_compile, timer, modelstats, extra_networks, attention, images_sharpfin
from modules.processing_helpers import resize_hires, calculate_base_steps, calculate_hires_steps, calculate_refiner_steps, save_intermediate, update_sampler, is_txt2img, is_refiner_enabled, get_job_name
from modules.processing_args import set_pipeline_args
from modules.onnx_impl import preprocess_pipeline as preprocess_onnx_pipeline, check_parameters_changed as olive_check_parameters_changed
@@ -270,9 +269,9 @@ def process_hires(p: processing.StableDiffusionProcessing, output):
sd_hijack_hypertile.hypertile_set(p, hr=True)
elif torch.is_tensor(output.images) and output.images.shape[-1] == 3: # nhwc
if output.images.dim() == 3:
output.images = TF.to_pil_image(output.images.permute(2,0,1))
output.images = images_sharpfin.to_pil(output.images)
elif output.images.dim() == 4:
output.images = [TF.to_pil_image(output.images[i].permute(2,0,1)) for i in range(output.images.shape[0])]
output.images = [images_sharpfin.to_pil(output.images[i]) for i in range(output.images.shape[0])]
strength = p.hr_denoising_strength if p.hr_denoising_strength > 0 else p.denoising_strength
if (p.hr_upscaler is not None) and (p.hr_upscaler.lower().startswith('latent') or p.hr_force) and strength > 0:
@@ -572,7 +571,7 @@ def process_diffusers(p: processing.StableDiffusionProcessing):
if hasattr(shared.sd_model, 'unet') and hasattr(shared.sd_model.unet, 'config') and hasattr(shared.sd_model.unet.config, 'in_channels') and shared.sd_model.unet.config.in_channels == 9 and not is_control:
shared.sd_model = sd_models.set_diffuser_pipe(shared.sd_model, sd_models.DiffusersTaskType.INPAINTING) # force pipeline
if len(getattr(p, 'init_images', [])) == 0:
p.init_images = [TF.to_pil_image(torch.rand((3, getattr(p, 'height', 512), getattr(p, 'width', 512))))]
p.init_images = [images_sharpfin.to_pil(torch.rand((3, getattr(p, 'height', 512), getattr(p, 'width', 512))))]
if not p.prompts:
p.prompts = p.all_prompts[p.iteration * p.batch_size:(p.iteration+1) * p.batch_size]
if not p.negative_prompts: