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import math
import re
import gradio as gr
import modules.scripts as scripts
from modules import devices, images, processing, shared
from modules.processing import Processed
from modules.shared import opts, state
from PIL import Image
class SDUpscale(scripts.Script):
def title(self):
return "SD Upscale"
def show(self, is_img2img):
return is_img2img
def ui(self, is_img2img):
gr.HTML(
"""<p align="center">Upscale the image by the selected <b>Scale Factor</b>;
use the <b>Width</b> and <b>Height</b> to set the tile size;
use the <b>Batch size</b> to process multiple tiles at once</p>"""
)
with gr.Row():
upscaler_index = gr.Dropdown(
label="Upscaler",
choices=[x.name for x in shared.sd_upscalers],
value=shared.sd_upscalers[0].name,
type="index",
elem_id=self.elem_id("upscaler_index"),
)
scale_factor = gr.Slider(
label="Scale Factor",
value=2.0,
minimum=1.0,
maximum=8.0,
step=0.05,
elem_id=self.elem_id("scale_factor"),
)
with gr.Row():
overlap = gr.Slider(
label="Tile Overlap",
value=64,
minimum=0,
maximum=256,
step=16,
elem_id=self.elem_id("overlap"),
)
override = gr.Checkbox(
label="Save to Extras folder instead",
value=False,
elem_id=self.elem_id("override"),
)
return [overlap, upscaler_index, scale_factor, override]
def run(self, p, overlap, upscaler_index, scale_factor, override):
if isinstance(upscaler_index, str):
upscaler = next(
(x for x in shared.sd_upscalers if x.name == upscaler_index),
None,
)
assert upscaler is not None
else:
assert isinstance(upscaler_index, int)
upscaler = shared.sd_upscalers[upscaler_index]
processing.fix_seed(p)
p.extra_generation_params["SD Upscale - Overlap"] = overlap
p.extra_generation_params["SD Upscale - Upscaler"] = upscaler.name
initial_info = None
seed = p.seed
init_img = p.init_images[0]
init_img = images.flatten(init_img, opts.img2img_background_color)
if upscaler.name != "None":
img = upscaler.scaler.upscale(init_img, scale_factor, upscaler.data_path)
else:
img = init_img
devices.torch_gc()
grid = images.split_grid(img, tile_w=p.width, tile_h=p.height, overlap=overlap)
batch_size = p.batch_size
upscale_count = p.n_iter
p.n_iter = 1
p.do_not_save_grid = True
p.do_not_save_samples = True
work = []
for _, _, row in grid.tiles:
for tiledata in row:
work.append(tiledata[2])
batch_count = math.ceil(len(work) / batch_size)
state.job_count = batch_count * upscale_count
print(
f"""
[SD Upscale]
- Processing {len(grid.tiles[0][2])}x{len(grid.tiles)} tiles
- totaling {len(work)} images at a batch size of {batch_size}
- resulting in {state.job_count} iterations
"""
)
result_images = []
for n in range(upscale_count):
start_seed = seed + n
p.seed = start_seed
work_results = []
for i in range(batch_count):
p.batch_size = batch_size
p.init_images = work[i * batch_size : (i + 1) * batch_size]
state.job = f"Batch {i + 1 + n * batch_count} out of {state.job_count}"
processed = processing.process_images(p)
if initial_info is None:
initial_info = processed.info
p.seed = processed.seed + 1
work_results += processed.images
image_index = 0
for _, _, row in grid.tiles:
for tiledata in row:
tiledata[2] = (
work_results[image_index]
if image_index < len(work_results)
else Image.new("RGB", (p.width, p.height))
)
image_index += 1
combined_image = images.combine_grid(grid)
result_images.append(combined_image)
if opts.samples_save:
if override:
images.save_image(
combined_image,
path=opts.outdir_samples or opts.outdir_extras_samples,
basename="",
extension=opts.samples_format,
info=initial_info,
short_filename=True,
no_prompt=True,
grid=False,
pnginfo_section_name="extras",
existing_info=None,
forced_filename=None,
suffix="",
)
else:
images.save_image(
combined_image,
p.outpath_samples,
"",
start_seed,
p.prompt,
opts.samples_format,
info=initial_info,
p=p,
)
new_w, new_h = img.size
pattern = r"Size: (\d+)x(\d+)"
initial_info = re.sub(pattern, f"Size: {new_w}x{new_h}", initial_info)
return Processed(p, result_images, seed, initial_info)
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