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author | AUTOMATIC1111 <16777216c@gmail.com> | 2022-11-06 08:27:54 +0000 |
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committer | GitHub <noreply@github.com> | 2022-11-06 08:27:54 +0000 |
commit | 07d1bd426722b4c53b38ff682c5aab53177d8530 (patch) | |
tree | 4fdad803a4536cec2bd3e622c5f4cfb980f04550 /modules/upscaler.py | |
parent | 3f3d14afd5abd07d3843370dc1c28be299dbdbab (diff) | |
parent | 6e4de5b4422dfc0d45063b2c8c78b19f00321615 (diff) | |
download | stable-diffusion-webui-gfx803-07d1bd426722b4c53b38ff682c5aab53177d8530.tar.gz stable-diffusion-webui-gfx803-07d1bd426722b4c53b38ff682c5aab53177d8530.tar.bz2 stable-diffusion-webui-gfx803-07d1bd426722b4c53b38ff682c5aab53177d8530.zip |
Merge branch 'master' into roy.add_simple_interrogate_api
Diffstat (limited to 'modules/upscaler.py')
-rw-r--r-- | modules/upscaler.py | 25 |
1 files changed, 24 insertions, 1 deletions
diff --git a/modules/upscaler.py b/modules/upscaler.py index 6ab2fb40..c4e6e6bd 100644 --- a/modules/upscaler.py +++ b/modules/upscaler.py @@ -10,6 +10,7 @@ import modules.shared from modules import modelloader, shared LANCZOS = (Image.Resampling.LANCZOS if hasattr(Image, 'Resampling') else Image.LANCZOS) +NEAREST = (Image.Resampling.NEAREST if hasattr(Image, 'Resampling') else Image.NEAREST) from modules.paths import models_path @@ -56,10 +57,18 @@ class Upscaler: self.scale = scale dest_w = img.width * scale dest_h = img.height * scale + for i in range(3): + shape = (img.width, img.height) + + img = self.do_upscale(img, selected_model) + + if shape == (img.width, img.height): + break + if img.width >= dest_w and img.height >= dest_h: break - img = self.do_upscale(img, selected_model) + if img.width != dest_w or img.height != dest_h: img = img.resize((int(dest_w), int(dest_h)), resample=LANCZOS) @@ -120,3 +129,17 @@ class UpscalerLanczos(Upscaler): self.name = "Lanczos" self.scalers = [UpscalerData("Lanczos", None, self)] + +class UpscalerNearest(Upscaler): + scalers = [] + + def do_upscale(self, img, selected_model=None): + return img.resize((int(img.width * self.scale), int(img.height * self.scale)), resample=NEAREST) + + def load_model(self, _): + pass + + def __init__(self, dirname=None): + super().__init__(False) + self.name = "Nearest" + self.scalers = [UpscalerData("Nearest", None, self)]
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