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Add custom nodes, Civitai loras (LFS), and vast.ai setup script
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Browse Source 
Includes 30 custom nodes committed directly, 7 Civitai-exclusive
loras stored via Git LFS, and a setup script that installs all
dependencies and downloads HuggingFace-hosted models on vast.ai.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
jaidaken
2026-02-09 00:55:26 +00:00
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ultimate-upscale-for-automatic1111
Copyright (C) 2023 Mirzam
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see <https://www.gnu.org/licenses/>.
Also add information on how to contact you by electronic and paper mail.
If the program does terminal interaction, make it output a short
notice like this when it starts in an interactive mode:
<program> Copyright (C) 2023 Mirzam
This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
This is free software, and you are welcome to redistribute it
under certain conditions; type `show c' for details.
The hypothetical commands `show w' and `show c' should show the appropriate
parts of the General Public License. Of course, your program's commands
might be different; for a GUI interface, you would use an "about box".
You should also get your employer (if you work as a programmer) or school,
if any, to sign a "copyright disclaimer" for the program, if necessary.
For more information on this, and how to apply and follow the GNU GPL, see
<https://www.gnu.org/licenses/>.
The GNU General Public License does not permit incorporating your program
into proprietary programs. If your program is a subroutine library, you
may consider it more useful to permit linking proprietary applications with
the library. If this is what you want to do, use the GNU Lesser General
Public License instead of this License. But first, please read
<https://www.gnu.org/licenses/why-not-lgpl.html>.

119
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# Ultimate SD Upscale extension for [AUTOMATIC1111 Stable Diffusion web UI](https://github.com/AUTOMATIC1111/stable-diffusion-webui)
Now you have the opportunity to use a large denoise (0.3-0.5) and not spawn many artifacts. Works on any video card, since you can use a 512x512 tile size and the image will converge.
News channel: https://t.me/usdunews
# Instructions
All instructions can be found on the project's [wiki](https://github.com/Coyote-A/ultimate-upscale-for-automatic1111/wiki).
# Refs
https://github.com/ssitu/ComfyUI_UltimateSDUpscale - Implementation for ComfyUI
# Examples
More on [wiki page](https://github.com/Coyote-A/ultimate-upscale-for-automatic1111/wiki/Examples)
<details>
<summary>E1</summary>
Original image
![Original](https://i.imgur.com/J8mRYOD.png)
2k upscaled. **Tile size**: 512, **Padding**: 32, **Mask blur**: 16, **Denoise**: 0.4
![2k upscale](https://i.imgur.com/0aKua4r.png)
</details>
<details>
<summary>E2</summary>
Original image
![Original](https://i.imgur.com/aALNI2w.png)
2k upscaled. **Tile size**: 768, **Padding**: 55, **Mask blur**: 20, **Denoise**: 0.35
![2k upscale](https://i.imgur.com/B5PHz0J.png)
4k upscaled. **Tile size**: 768, **Padding**: 55, **Mask blur**: 20, **Denoise**: 0.35
![4k upscale](https://i.imgur.com/tIUQ7TJ.jpg)
</details>
<details>
<summary>E3</summary>
Original image
![Original](https://i.imgur.com/AGtszA8.png)
4k upscaled. **Tile size**: 768, **Padding**: 55, **Mask blur**: 20, **Denoise**: 0.4
![4k upscale](https://i.imgur.com/LCYLfCs.jpg)
</details>
# API Usage
```javascript
{
"script_name" : "ultimate sd upscale",
"script_args" : [
null, // _ (not used)
512, // tile_width
512, // tile_height
8, // mask_blur
32, // padding
64, // seams_fix_width
0.35, // seams_fix_denoise
32, // seams_fix_padding
0, // upscaler_index
true, // save_upscaled_image a.k.a Upscaled
0, // redraw_mode
false, // save_seams_fix_image a.k.a Seams fix
8, // seams_fix_mask_blur
0, // seams_fix_type
0, // target_size_type
2048, // custom_width
2048, // custom_height
2 // custom_scale
]
}
```
upscaler_index
| Value | |
|:-------------:| -----:|
| 0 | None |
| 1 | Lanczos |
| 2 | Nearest |
| 3 | ESRGAN_4x |
| 4 | LDSR |
| 5 | R-ESRGAN_4x+ |
| 6 | R-ESRGAN 4x+ Anime6B |
| 7 | ScuNET GAN |
| 8 | ScuNET PSNR |
| 9 | SwinIR 4x |
redraw_mode
| Value | |
|:-------------:| -----:|
| 0 | Linear |
| 1 | Chess |
| 2 | None |
seams_fix_mask_blur
| Value | |
|:-------------:| -----:|
| 0 | None |
| 1 | BAND_PASS |
| 2 | HALF_TILE |
| 3 | HALF_TILE_PLUS_INTERSECTIONS |
seams_fix_type
| Value | |
|:-------------:| -----:|
| 0 | None |
| 1 | Band pass |
| 2 | Half tile offset pass |
| 3 | Half tile offset pass + intersections |
seams_fix_type
| Value | |
|:-------------:| -----:|
| 0 | From img2img2 settings |
| 1 | Custom size |
| 2 | Scale from image size |

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import math
import gradio as gr
from PIL import Image, ImageDraw, ImageOps
from modules import processing, shared, images, devices, scripts
from modules.processing import StableDiffusionProcessing
from modules.processing import Processed
from modules.shared import opts, state
from enum import Enum
elem_id_prefix = "ultimateupscale"
class USDUMode(Enum):
LINEAR = 0
CHESS = 1
NONE = 2
class USDUSFMode(Enum):
NONE = 0
BAND_PASS = 1
HALF_TILE = 2
HALF_TILE_PLUS_INTERSECTIONS = 3
class USDUpscaler():
def __init__(self, p, image, upscaler_index:int, save_redraw, save_seams_fix, tile_width, tile_height) -> None:
self.p:StableDiffusionProcessing = p
self.image:Image = image
self.scale_factor = math.ceil(max(p.width, p.height) / max(image.width, image.height))
self.upscaler = shared.sd_upscalers[upscaler_index]
self.redraw = USDURedraw()
self.redraw.save = save_redraw
self.redraw.tile_width = tile_width if tile_width > 0 else tile_height
self.redraw.tile_height = tile_height if tile_height > 0 else tile_width
self.seams_fix = USDUSeamsFix()
self.seams_fix.save = save_seams_fix
self.seams_fix.tile_width = tile_width if tile_width > 0 else tile_height
self.seams_fix.tile_height = tile_height if tile_height > 0 else tile_width
self.initial_info = None
self.rows = math.ceil(self.p.height / self.redraw.tile_height)
self.cols = math.ceil(self.p.width / self.redraw.tile_width)
def get_factor(self, num):
# Its just return, don't need elif
if num == 1:
return 2
if num % 4 == 0:
return 4
if num % 3 == 0:
return 3
if num % 2 == 0:
return 2
return 0
def get_factors(self):
scales = []
current_scale = 1
current_scale_factor = self.get_factor(self.scale_factor)
while current_scale_factor == 0:
self.scale_factor += 1
current_scale_factor = self.get_factor(self.scale_factor)
while current_scale < self.scale_factor:
current_scale_factor = self.get_factor(self.scale_factor // current_scale)
scales.append(current_scale_factor)
current_scale = current_scale * current_scale_factor
if current_scale_factor == 0:
break
self.scales = enumerate(scales)
def upscale(self):
# Log info
print(f"Canva size: {self.p.width}x{self.p.height}")
print(f"Image size: {self.image.width}x{self.image.height}")
print(f"Scale factor: {self.scale_factor}")
# Check upscaler is not empty
if self.upscaler.name == "None":
self.image = self.image.resize((self.p.width, self.p.height), resample=Image.LANCZOS)
return
# Get list with scale factors
self.get_factors()
# Upscaling image over all factors
for index, value in self.scales:
print(f"Upscaling iteration {index+1} with scale factor {value}")
self.image = self.upscaler.scaler.upscale(self.image, value, self.upscaler.data_path)
# Resize image to set values
self.image = self.image.resize((self.p.width, self.p.height), resample=Image.LANCZOS)
def setup_redraw(self, redraw_mode, padding, mask_blur):
self.redraw.mode = USDUMode(redraw_mode)
self.redraw.enabled = self.redraw.mode != USDUMode.NONE
self.redraw.padding = padding
self.p.mask_blur = mask_blur
def setup_seams_fix(self, padding, denoise, mask_blur, width, mode):
self.seams_fix.padding = padding
self.seams_fix.denoise = denoise
self.seams_fix.mask_blur = mask_blur
self.seams_fix.width = width
self.seams_fix.mode = USDUSFMode(mode)
self.seams_fix.enabled = self.seams_fix.mode != USDUSFMode.NONE
def save_image(self):
if type(self.p.prompt) != list:
images.save_image(self.image, self.p.outpath_samples, "", self.p.seed, self.p.prompt, opts.samples_format, info=self.initial_info, p=self.p)
else:
images.save_image(self.image, self.p.outpath_samples, "", self.p.seed, self.p.prompt[0], opts.samples_format, info=self.initial_info, p=self.p)
def calc_jobs_count(self):
redraw_job_count = (self.rows * self.cols) if self.redraw.enabled else 0
seams_job_count = 0
if self.seams_fix.mode == USDUSFMode.BAND_PASS:
seams_job_count = self.rows + self.cols - 2
elif self.seams_fix.mode == USDUSFMode.HALF_TILE:
seams_job_count = self.rows * (self.cols - 1) + (self.rows - 1) * self.cols
elif self.seams_fix.mode == USDUSFMode.HALF_TILE_PLUS_INTERSECTIONS:
seams_job_count = self.rows * (self.cols - 1) + (self.rows - 1) * self.cols + (self.rows - 1) * (self.cols - 1)
state.job_count = redraw_job_count + seams_job_count
def print_info(self):
print(f"Tile size: {self.redraw.tile_width}x{self.redraw.tile_height}")
print(f"Tiles amount: {self.rows * self.cols}")
print(f"Grid: {self.rows}x{self.cols}")
print(f"Redraw enabled: {self.redraw.enabled}")
print(f"Seams fix mode: {self.seams_fix.mode.name}")
def add_extra_info(self):
self.p.extra_generation_params["Ultimate SD upscale upscaler"] = self.upscaler.name
self.p.extra_generation_params["Ultimate SD upscale tile_width"] = self.redraw.tile_width
self.p.extra_generation_params["Ultimate SD upscale tile_height"] = self.redraw.tile_height
self.p.extra_generation_params["Ultimate SD upscale mask_blur"] = self.p.mask_blur
self.p.extra_generation_params["Ultimate SD upscale padding"] = self.redraw.padding
def process(self):
state.begin()
self.calc_jobs_count()
self.result_images = []
if self.redraw.enabled:
self.image = self.redraw.start(self.p, self.image, self.rows, self.cols)
self.initial_info = self.redraw.initial_info
self.result_images.append(self.image)
if self.redraw.save:
self.save_image()
if self.seams_fix.enabled:
self.image = self.seams_fix.start(self.p, self.image, self.rows, self.cols)
self.initial_info = self.seams_fix.initial_info
self.result_images.append(self.image)
if self.seams_fix.save:
self.save_image()
state.end()
class USDURedraw():
def init_draw(self, p, width, height):
p.inpaint_full_res = True
p.inpaint_full_res_padding = self.padding
p.width = math.ceil((self.tile_width+self.padding) / 64) * 64
p.height = math.ceil((self.tile_height+self.padding) / 64) * 64
mask = Image.new("L", (width, height), "black")
draw = ImageDraw.Draw(mask)
return mask, draw
def calc_rectangle(self, xi, yi):
x1 = xi * self.tile_width
y1 = yi * self.tile_height
x2 = xi * self.tile_width + self.tile_width
y2 = yi * self.tile_height + self.tile_height
return x1, y1, x2, y2
def linear_process(self, p, image, rows, cols):
mask, draw = self.init_draw(p, image.width, image.height)
for yi in range(rows):
for xi in range(cols):
if state.interrupted:
break
draw.rectangle(self.calc_rectangle(xi, yi), fill="white")
p.init_images = [image]
p.image_mask = mask
processed = processing.process_images(p)
draw.rectangle(self.calc_rectangle(xi, yi), fill="black")
if (len(processed.images) > 0):
image = processed.images[0]
p.width = image.width
p.height = image.height
self.initial_info = processed.infotext(p, 0)
return image
def chess_process(self, p, image, rows, cols):
mask, draw = self.init_draw(p, image.width, image.height)
tiles = []
# calc tiles colors
for yi in range(rows):
for xi in range(cols):
if state.interrupted:
break
if xi == 0:
tiles.append([])
color = xi % 2 == 0
if yi > 0 and yi % 2 != 0:
color = not color
tiles[yi].append(color)
for yi in range(len(tiles)):
for xi in range(len(tiles[yi])):
if state.interrupted:
break
if not tiles[yi][xi]:
tiles[yi][xi] = not tiles[yi][xi]
continue
tiles[yi][xi] = not tiles[yi][xi]
draw.rectangle(self.calc_rectangle(xi, yi), fill="white")
p.init_images = [image]
p.image_mask = mask
processed = processing.process_images(p)
draw.rectangle(self.calc_rectangle(xi, yi), fill="black")
if (len(processed.images) > 0):
image = processed.images[0]
for yi in range(len(tiles)):
for xi in range(len(tiles[yi])):
if state.interrupted:
break
if not tiles[yi][xi]:
continue
draw.rectangle(self.calc_rectangle(xi, yi), fill="white")
p.init_images = [image]
p.image_mask = mask
processed = processing.process_images(p)
draw.rectangle(self.calc_rectangle(xi, yi), fill="black")
if (len(processed.images) > 0):
image = processed.images[0]
p.width = image.width
p.height = image.height
self.initial_info = processed.infotext(p, 0)
return image
def start(self, p, image, rows, cols):
self.initial_info = None
if self.mode == USDUMode.LINEAR:
return self.linear_process(p, image, rows, cols)
if self.mode == USDUMode.CHESS:
return self.chess_process(p, image, rows, cols)
class USDUSeamsFix():
def init_draw(self, p):
self.initial_info = None
p.width = math.ceil((self.tile_width+self.padding) / 64) * 64
p.height = math.ceil((self.tile_height+self.padding) / 64) * 64
def half_tile_process(self, p, image, rows, cols):
self.init_draw(p)
processed = None
gradient = Image.linear_gradient("L")
row_gradient = Image.new("L", (self.tile_width, self.tile_height), "black")
row_gradient.paste(gradient.resize(
(self.tile_width, self.tile_height//2), resample=Image.BICUBIC), (0, 0))
row_gradient.paste(gradient.rotate(180).resize(
(self.tile_width, self.tile_height//2), resample=Image.BICUBIC),
(0, self.tile_height//2))
col_gradient = Image.new("L", (self.tile_width, self.tile_height), "black")
col_gradient.paste(gradient.rotate(90).resize(
(self.tile_width//2, self.tile_height), resample=Image.BICUBIC), (0, 0))
col_gradient.paste(gradient.rotate(270).resize(
(self.tile_width//2, self.tile_height), resample=Image.BICUBIC), (self.tile_width//2, 0))
p.denoising_strength = self.denoise
p.mask_blur = self.mask_blur
for yi in range(rows-1):
for xi in range(cols):
if state.interrupted:
break
p.width = self.tile_width
p.height = self.tile_height
p.inpaint_full_res = True
p.inpaint_full_res_padding = self.padding
mask = Image.new("L", (image.width, image.height), "black")
mask.paste(row_gradient, (xi*self.tile_width, yi*self.tile_height + self.tile_height//2))
p.init_images = [image]
p.image_mask = mask
processed = processing.process_images(p)
if (len(processed.images) > 0):
image = processed.images[0]
for yi in range(rows):
for xi in range(cols-1):
if state.interrupted:
break
p.width = self.tile_width
p.height = self.tile_height
p.inpaint_full_res = True
p.inpaint_full_res_padding = self.padding
mask = Image.new("L", (image.width, image.height), "black")
mask.paste(col_gradient, (xi*self.tile_width+self.tile_width//2, yi*self.tile_height))
p.init_images = [image]
p.image_mask = mask
processed = processing.process_images(p)
if (len(processed.images) > 0):
image = processed.images[0]
p.width = image.width
p.height = image.height
if processed is not None:
self.initial_info = processed.infotext(p, 0)
return image
def half_tile_process_corners(self, p, image, rows, cols):
fixed_image = self.half_tile_process(p, image, rows, cols)
processed = None
self.init_draw(p)
gradient = Image.radial_gradient("L").resize(
(self.tile_width, self.tile_height), resample=Image.BICUBIC)
gradient = ImageOps.invert(gradient)
p.denoising_strength = self.denoise
#p.mask_blur = 0
p.mask_blur = self.mask_blur
for yi in range(rows-1):
for xi in range(cols-1):
if state.interrupted:
break
p.width = self.tile_width
p.height = self.tile_height
p.inpaint_full_res = True
p.inpaint_full_res_padding = 0
mask = Image.new("L", (fixed_image.width, fixed_image.height), "black")
mask.paste(gradient, (xi*self.tile_width + self.tile_width//2,
yi*self.tile_height + self.tile_height//2))
p.init_images = [fixed_image]
p.image_mask = mask
processed = processing.process_images(p)
if (len(processed.images) > 0):
fixed_image = processed.images[0]
p.width = fixed_image.width
p.height = fixed_image.height
if processed is not None:
self.initial_info = processed.infotext(p, 0)
return fixed_image
def band_pass_process(self, p, image, cols, rows):
self.init_draw(p)
processed = None
p.denoising_strength = self.denoise
p.mask_blur = 0
gradient = Image.linear_gradient("L")
mirror_gradient = Image.new("L", (256, 256), "black")
mirror_gradient.paste(gradient.resize((256, 128), resample=Image.BICUBIC), (0, 0))
mirror_gradient.paste(gradient.rotate(180).resize((256, 128), resample=Image.BICUBIC), (0, 128))
row_gradient = mirror_gradient.resize((image.width, self.width), resample=Image.BICUBIC)
col_gradient = mirror_gradient.rotate(90).resize((self.width, image.height), resample=Image.BICUBIC)
for xi in range(1, rows):
if state.interrupted:
break
p.width = self.width + self.padding * 2
p.height = image.height
p.inpaint_full_res = True
p.inpaint_full_res_padding = self.padding
mask = Image.new("L", (image.width, image.height), "black")
mask.paste(col_gradient, (xi * self.tile_width - self.width // 2, 0))
p.init_images = [image]
p.image_mask = mask
processed = processing.process_images(p)
if (len(processed.images) > 0):
image = processed.images[0]
for yi in range(1, cols):
if state.interrupted:
break
p.width = image.width
p.height = self.width + self.padding * 2
p.inpaint_full_res = True
p.inpaint_full_res_padding = self.padding
mask = Image.new("L", (image.width, image.height), "black")
mask.paste(row_gradient, (0, yi * self.tile_height - self.width // 2))
p.init_images = [image]
p.image_mask = mask
processed = processing.process_images(p)
if (len(processed.images) > 0):
image = processed.images[0]
p.width = image.width
p.height = image.height
if processed is not None:
self.initial_info = processed.infotext(p, 0)
return image
def start(self, p, image, rows, cols):
if USDUSFMode(self.mode) == USDUSFMode.BAND_PASS:
return self.band_pass_process(p, image, rows, cols)
elif USDUSFMode(self.mode) == USDUSFMode.HALF_TILE:
return self.half_tile_process(p, image, rows, cols)
elif USDUSFMode(self.mode) == USDUSFMode.HALF_TILE_PLUS_INTERSECTIONS:
return self.half_tile_process_corners(p, image, rows, cols)
else:
return image
class Script(scripts.Script):
def title(self):
return "Ultimate SD upscale"
def show(self, is_img2img):
return is_img2img
def ui(self, is_img2img):
target_size_types = [
"From img2img2 settings",
"Custom size",
"Scale from image size"
]
seams_fix_types = [
"None",
"Band pass",
"Half tile offset pass",
"Half tile offset pass + intersections"
]
redrow_modes = [
"Linear",
"Chess",
"None"
]
info = gr.HTML(
"<p style=\"margin-bottom:0.75em\">Will upscale the image depending on the selected target size type</p>")
with gr.Row():
target_size_type = gr.Dropdown(label="Target size type", elem_id=f"{elem_id_prefix}_target_size_type", choices=[k for k in target_size_types], type="index",
value=next(iter(target_size_types)))
custom_width = gr.Slider(label='Custom width', elem_id=f"{elem_id_prefix}_custom_width", minimum=64, maximum=8192, step=64, value=2048, visible=False, interactive=True)
custom_height = gr.Slider(label='Custom height', elem_id=f"{elem_id_prefix}_custom_height", minimum=64, maximum=8192, step=64, value=2048, visible=False, interactive=True)
custom_scale = gr.Slider(label='Scale', elem_id=f"{elem_id_prefix}_custom_scale", minimum=1, maximum=16, step=0.01, value=2, visible=False, interactive=True)
gr.HTML("<p style=\"margin-bottom:0.75em\">Redraw options:</p>")
with gr.Row():
upscaler_index = gr.Radio(label='Upscaler', elem_id=f"{elem_id_prefix}_upscaler_index", choices=[x.name for x in shared.sd_upscalers],
value=shared.sd_upscalers[0].name, type="index")
with gr.Row():
redraw_mode = gr.Dropdown(label="Type", elem_id=f"{elem_id_prefix}_redraw_mode", choices=[k for k in redrow_modes], type="index", value=next(iter(redrow_modes)))
tile_width = gr.Slider(elem_id=f"{elem_id_prefix}_tile_width", minimum=0, maximum=2048, step=64, label='Tile width', value=512)
tile_height = gr.Slider(elem_id=f"{elem_id_prefix}_tile_height", minimum=0, maximum=2048, step=64, label='Tile height', value=0)
mask_blur = gr.Slider(elem_id=f"{elem_id_prefix}_mask_blur", label='Mask blur', minimum=0, maximum=64, step=1, value=8)
padding = gr.Slider(elem_id=f"{elem_id_prefix}_padding", label='Padding', minimum=0, maximum=512, step=1, value=32)
gr.HTML("<p style=\"margin-bottom:0.75em\">Seams fix:</p>")
with gr.Row():
seams_fix_type = gr.Dropdown(label="Type", elem_id=f"{elem_id_prefix}_seams_fix_type", choices=[k for k in seams_fix_types], type="index", value=next(iter(seams_fix_types)))
seams_fix_denoise = gr.Slider(label='Denoise', elem_id=f"{elem_id_prefix}_seams_fix_denoise", minimum=0, maximum=1, step=0.01, value=0.35, visible=False, interactive=True)
seams_fix_width = gr.Slider(label='Width', elem_id=f"{elem_id_prefix}_seams_fix_width", minimum=0, maximum=128, step=1, value=64, visible=False, interactive=True)
seams_fix_mask_blur = gr.Slider(label='Mask blur', elem_id=f"{elem_id_prefix}_seams_fix_mask_blur", minimum=0, maximum=64, step=1, value=4, visible=False, interactive=True)
seams_fix_padding = gr.Slider(label='Padding', elem_id=f"{elem_id_prefix}_seams_fix_padding", minimum=0, maximum=128, step=1, value=16, visible=False, interactive=True)
gr.HTML("<p style=\"margin-bottom:0.75em\">Save options:</p>")
with gr.Row():
save_upscaled_image = gr.Checkbox(label="Upscaled", elem_id=f"{elem_id_prefix}_save_upscaled_image", value=True)
save_seams_fix_image = gr.Checkbox(label="Seams fix", elem_id=f"{elem_id_prefix}_save_seams_fix_image", value=False)
def select_fix_type(fix_index):
all_visible = fix_index != 0
mask_blur_visible = fix_index == 2 or fix_index == 3
width_visible = fix_index == 1
return [gr.update(visible=all_visible),
gr.update(visible=width_visible),
gr.update(visible=mask_blur_visible),
gr.update(visible=all_visible)]
seams_fix_type.change(
fn=select_fix_type,
inputs=seams_fix_type,
outputs=[seams_fix_denoise, seams_fix_width, seams_fix_mask_blur, seams_fix_padding]
)
def select_scale_type(scale_index):
is_custom_size = scale_index == 1
is_custom_scale = scale_index == 2
return [gr.update(visible=is_custom_size),
gr.update(visible=is_custom_size),
gr.update(visible=is_custom_scale),
]
target_size_type.change(
fn=select_scale_type,
inputs=target_size_type,
outputs=[custom_width, custom_height, custom_scale]
)
def init_field(scale_name):
try:
scale_index = target_size_types.index(scale_name)
custom_width.visible = custom_height.visible = scale_index == 1
custom_scale.visible = scale_index == 2
except:
pass
target_size_type.init_field = init_field
return [info, tile_width, tile_height, mask_blur, padding, seams_fix_width, seams_fix_denoise, seams_fix_padding,
upscaler_index, save_upscaled_image, redraw_mode, save_seams_fix_image, seams_fix_mask_blur,
seams_fix_type, target_size_type, custom_width, custom_height, custom_scale]
def run(self, p, _, tile_width, tile_height, mask_blur, padding, seams_fix_width, seams_fix_denoise, seams_fix_padding,
upscaler_index, save_upscaled_image, redraw_mode, save_seams_fix_image, seams_fix_mask_blur,
seams_fix_type, target_size_type, custom_width, custom_height, custom_scale):
# Init
processing.fix_seed(p)
devices.torch_gc()
p.do_not_save_grid = True
p.do_not_save_samples = True
p.inpaint_full_res = False
p.inpainting_fill = 1
p.n_iter = 1
p.batch_size = 1
seed = p.seed
# Init image
init_img = p.init_images[0]
if init_img == None:
return Processed(p, [], seed, "Empty image")
init_img = images.flatten(init_img, opts.img2img_background_color)
#override size
if target_size_type == 1:
p.width = custom_width
p.height = custom_height
if target_size_type == 2:
p.width = math.ceil((init_img.width * custom_scale) / 64) * 64
p.height = math.ceil((init_img.height * custom_scale) / 64) * 64
# Upscaling
upscaler = USDUpscaler(p, init_img, upscaler_index, save_upscaled_image, save_seams_fix_image, tile_width, tile_height)
upscaler.upscale()
# Drawing
upscaler.setup_redraw(redraw_mode, padding, mask_blur)
upscaler.setup_seams_fix(seams_fix_padding, seams_fix_denoise, seams_fix_mask_blur, seams_fix_width, seams_fix_type)
upscaler.print_info()
upscaler.add_extra_info()
upscaler.process()
result_images = upscaler.result_images
return Processed(p, result_images, seed, upscaler.initial_info if upscaler.initial_info is not None else "")