| 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) |
|
|
