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import gradio as gr
import numpy as np
from modules import codeformer_model, scripts_postprocessing
from modules.ui_components import InputAccordion
from PIL import Image
class CodeFormerPostprocessing(scripts_postprocessing.ScriptPostprocessing):
name = "CodeFormer"
order = 3000
def ui(self):
with InputAccordion(False, label="CodeFormer") as enable:
with gr.Row():
codeformer_visibility = gr.Slider(
label="Visibility",
value=1.0,
minimum=0.0,
maximum=1.0,
step=0.05,
elem_id="extras_codeformer_visibility",
)
codeformer_weight = gr.Slider(
label="Weight (0 = maximum effect, 1 = minimum effect)",
value=0,
minimum=0.0,
maximum=1.0,
step=0.05,
elem_id="extras_codeformer_weight",
)
return {
"enable": enable,
"codeformer_visibility": codeformer_visibility,
"codeformer_weight": codeformer_weight,
}
def process(
self,
pp: scripts_postprocessing.PostprocessedImage,
enable,
codeformer_visibility,
codeformer_weight,
):
if not enable or codeformer_visibility < 0.05:
return
restored_img = codeformer_model.codeformer.restore(
np.array(pp.image, dtype=np.uint8), w=codeformer_weight
)
res = Image.fromarray(restored_img)
if codeformer_visibility < 1.0:
res = Image.blend(pp.image, res, codeformer_visibility)
pp.image = res
pp.info["CodeFormer Visibility"] = round(codeformer_visibility, 2)
pp.info["CodeFormer Weight"] = round(codeformer_weight, 2)
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