mirror of
https://github.com/vladmandic/sdnext.git
synced 2026-01-27 15:02:48 +03:00
96 lines
4.0 KiB
Python
96 lines
4.0 KiB
Python
import math
|
|
import gradio as gr
|
|
from PIL import Image
|
|
import modules.scripts as scripts
|
|
from modules import processing, shared, images, devices
|
|
from modules.processing import Processed
|
|
from modules.shared import opts, state, log
|
|
|
|
|
|
class Script(scripts.Script):
|
|
def title(self):
|
|
return "SD Upscale"
|
|
|
|
def show(self, is_img2img):
|
|
return is_img2img
|
|
|
|
def ui(self, is_img2img):
|
|
with gr.Row():
|
|
info = gr.HTML("<span>  SD Upscale</span><br>")
|
|
with gr.Row():
|
|
overlap = gr.Slider(minimum=0, maximum=256, step=16, label='Tile overlap', value=64, elem_id=self.elem_id("overlap"))
|
|
scale_factor = gr.Slider(minimum=1.0, maximum=4.0, step=0.05, label='Scale Factor', value=2.0, elem_id=self.elem_id("scale_factor"))
|
|
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"))
|
|
return [info, overlap, upscaler_index, scale_factor]
|
|
|
|
def run(self, p, _, overlap, upscaler_index, scale_factor): # pylint: disable=arguments-differ
|
|
init_img = None
|
|
if hasattr(p, 'init_images') and p.init_images is not None:
|
|
init_img = p.init_images[0]
|
|
elif hasattr(p.task_args, 'image') and p.task_args['image'] is not None:
|
|
init_img = p.task_args['image'][0]
|
|
|
|
if init_img is None:
|
|
return None
|
|
init_img = images.flatten(init_img, opts.img2img_background_color)
|
|
|
|
if isinstance(upscaler_index, str):
|
|
upscaler_index = [x.name.lower() for x in shared.sd_upscalers].index(upscaler_index.lower())
|
|
processing.fix_seed(p)
|
|
upscaler = shared.sd_upscalers[upscaler_index]
|
|
p.extra_generation_params["SD upscale overlap"] = overlap
|
|
p.extra_generation_params["SD upscale upscaler"] = upscaler.name
|
|
initial_info = None
|
|
seed = p.seed
|
|
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=init_img.width, tile_h=init_img.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 _y, _h, 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
|
|
log.info(f"SD upscale: images={len(work)} tiles={len(grid.tiles)} batches={state.job_count}")
|
|
|
|
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"upscale batch {i+1+n*batch_count}/{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 _y, _h, 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:
|
|
images.save_image(combined_image, p.outpath_samples, "", start_seed, p.prompt, opts.samples_format, info=initial_info, p=p)
|
|
|
|
processed = Processed(p, result_images, seed, initial_info)
|
|
log.info(f"SD upscale: images={result_images}")
|
|
return processed
|