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			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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| 
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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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| 
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| 
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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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| 
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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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| 
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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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| 
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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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| 
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|         restored_img = gfpgan_model.gfpgan_fix_faces(np.array(pp.image, dtype=np.uint8))
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|         res = Image.fromarray(restored_img)
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| 
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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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| 
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|         pp.image = res
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|         pp.info["GFPGAN visibility"] = round(gfpgan_visibility, 3)
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