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="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: res = Image.blend(pp.image, res, gfpgan_visibility) pp.image = res pp.info["GFPGAN visibility"] = round(gfpgan_visibility, 3)