nodes/lquesada-ComfyUI-Inpaint-CropAndStitch
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Dramatically improve resize sampling with Pillow

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PyTorch image resampling is inherently bad.
Use Pillow instead to vastly improve it.

More info: https://zuru.tech/blog/the-dangers-behind-image-resizing#qualitative-results
This commit is contained in:
catboxanon
2024-10-26 11:40:38 -04:00
committed by GitHub
parent d39c305423
commit 301f20b56b
1 changed files with 11 additions and 12 deletions
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inpaint_cropandstitch.py
+11 -12
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@@ -3,17 +3,16 @@ import math
import nodes
import numpy as np
import torch
import torchvision.transforms.functional as F
from PIL import Image
from scipy.ndimage import gaussian_filter, grey_dilation, binary_fill_holes, binary_closing
def rescale(samples, width, height, algorithm):
if algorithm == "nearest":
return torch.nn.functional.interpolate(samples, size=(height, width), mode="nearest")
elif algorithm == "bilinear":
return torch.nn.functional.interpolate(samples, size=(height, width), mode="bilinear")
elif algorithm == "bicubic":
return torch.nn.functional.interpolate(samples, size=(height, width), mode="bicubic")
elif algorithm == "bislerp":
return comfy.utils.bislerp(samples, width, height)
def rescale(samples, width, height, algorithm: str):
if algorithm == "bislerp": # convert for compatibility with old workflows
algorithm = "bicubic"
algorithm = getattr(Image, algorithm.upper()) # i.e. Image.BICUBIC
samples_pil: Image.Image = F.to_pil_image(samples[0].cpu()).resize((width, height), algorithm)
samples = F.to_tensor(samples_pil).unsqueeze(0)
return samples
class InpaintCrop:
@@ -38,7 +37,7 @@ class InpaintCrop:
"blur_mask_pixels": ("FLOAT", {"default": 16.0, "min": 0.0, "max": 64.0, "step": 0.1}),
"invert_mask": ("BOOLEAN", {"default": False}),
"blend_pixels": ("FLOAT", {"default": 16.0, "min": 0.0, "max": 32.0, "step": 0.1}),
"rescale_algorithm": (["nearest", "bilinear", "bicubic", "bislerp"], {"default": "bicubic"}),
"rescale_algorithm": (["nearest", "bilinear", "bicubic", "bislerp", "lanczos", "box", "hamming"], {"default": "bicubic"}),
"mode": (["ranged size", "forced size", "free size"], {"default": "ranged size"}),
"force_width": ("INT", {"default": 1024, "min": 0, "max": nodes.MAX_RESOLUTION, "step": 1}), # force
"force_height": ("INT", {"default": 1024, "min": 0, "max": nodes.MAX_RESOLUTION, "step": 1}), # force
@@ -497,7 +496,7 @@ class InpaintStitch:
"required": {
"stitch": ("STITCH",),
"inpainted_image": ("IMAGE",),
"rescale_algorithm": (["nearest", "bilinear", "bicubic", "bislerp"], {"default": "bislerp"}),
"rescale_algorithm": (["nearest", "bilinear", "bicubic", "bislerp", "lanczos", "box", "hamming"], {"default": "bislerp"}),
}
}
@@ -773,7 +772,7 @@ class InpaintResize:
"required": {
"image": ("IMAGE",),
"mask": ("MASK",),
"rescale_algorithm": (["nearest", "bilinear", "bicubic", "bislerp"], {"default": "bicubic"}),
"rescale_algorithm": (["nearest", "bilinear", "bicubic", "bislerp", "lanczos", "box", "hamming"], {"default": "bicubic"}),
"mode": (["ensure minimum size", "factor"], {"default": "ensure minimum size"}),
"min_width": ("INT", {"default": 1024, "min": 0, "max": nodes.MAX_RESOLUTION, "step": 1}), # ranged
"min_height": ("INT", {"default": 1024, "min": 0, "max": nodes.MAX_RESOLUTION, "step": 1}), # ranged