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