33 lines
1.1 KiB
Python
33 lines
1.1 KiB
Python
from PIL import Image
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import numpy as np
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from modules import scripts_postprocessing, gfpgan_model, ui_components
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import gradio as gr
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class ScriptPostprocessingGfpGan(scripts_postprocessing.ScriptPostprocessing):
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name = "GFPGAN"
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order = 2000
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def ui(self):
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with ui_components.InputAccordion(False, label="GFPGAN") as enable:
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gfpgan_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="Visibility", value=1.0, elem_id="extras_gfpgan_visibility")
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return {
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"enable": enable,
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"gfpgan_visibility": gfpgan_visibility,
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}
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def process(self, pp: scripts_postprocessing.PostprocessedImage, enable, gfpgan_visibility):
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if gfpgan_visibility == 0 or not enable:
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return
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restored_img = gfpgan_model.gfpgan_fix_faces(np.array(pp.image.convert("RGB"), dtype=np.uint8))
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res = Image.fromarray(restored_img)
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if gfpgan_visibility < 1.0:
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res = Image.blend(pp.image, res, gfpgan_visibility)
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pp.image = res
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pp.info["GFPGAN visibility"] = round(gfpgan_visibility, 3)
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