| 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, 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) | |