219 lines
8.4 KiB
Python
219 lines
8.4 KiB
Python
import gradio as gr
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import modules.scripts as scripts
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from modules.upscaler import Upscaler, UpscalerData
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from modules import scripts, shared, images, scripts_postprocessing
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from modules.processing import (
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StableDiffusionProcessing,
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StableDiffusionProcessingImg2Img,
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)
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from modules.shared import cmd_opts, opts, state
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from PIL import Image
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import glob
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from modules.face_restoration import FaceRestoration
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from scripts.roop_logging import logger
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from scripts.swapper import UpscaleOptions, swap_face, ImageResult
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from scripts.roop_version import version_flag
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import os
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def get_models():
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models_path = os.path.join(scripts.basedir(), "models/roop/*")
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models = glob.glob(models_path)
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models = [x for x in models if x.endswith(".onnx") or x.endswith(".pth")]
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return models
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class FaceSwapScript(scripts.Script):
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def title(self):
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return f"NSFW-Roop"
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def show(self, is_img2img):
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return scripts.AlwaysVisible
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def ui(self, is_img2img):
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with gr.Accordion(f"NSFW-Roop {version_flag}", open=False):
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with gr.Column():
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img = gr.inputs.Image(type="pil")
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enable = gr.Checkbox(False, placeholder="enable", label="Enable")
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source_faces_index = gr.Textbox(
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value="0",
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placeholder="Which face(s) to use as source (comma separated)",
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label="Comma separated face number(s) from swap-source image (above)",
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)
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faces_index = gr.Textbox(
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value="0",
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placeholder="Which face to swap (comma separated)",
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label="Comma separated face number(s) for target image (result)",
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)
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with gr.Row():
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face_restorer_name = gr.Radio(
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label="Restore Face",
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choices=["None"] + [x.name() for x in shared.face_restorers],
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value=shared.face_restorers[0].name(),
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type="value",
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)
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face_restorer_visibility = gr.Slider(
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0, 1, 1, step=0.1, label="Restore visibility"
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)
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restore_first = gr.Checkbox(
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True,
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placeholder="Restore face, then Upscale",
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label="1. Restore face -> 2. Upscale (-Uncheck- if you want vice versa)",
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visible=is_img2img,
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)
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upscaler_name = gr.inputs.Dropdown(
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choices=[upscaler.name for upscaler in shared.sd_upscalers],
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label="Upscaler",
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)
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upscaler_scale = gr.Slider(1, 8, 1, step=0.1, label="Upscaler scale")
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upscaler_visibility = gr.Slider(
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0, 1, 1, step=0.1, label="Upscaler visibility (if scale = 1)"
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)
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models = get_models()
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if len(models) == 0:
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logger.warning(
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"You should at least have one model in models directory, please read the doc here : https://github.com/s0md3v/sd-webui-roop-nsfw/"
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)
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model = gr.inputs.Dropdown(
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choices=models,
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label="Model not found, please download one and reload automatic 1111",
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)
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else:
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model = gr.inputs.Dropdown(
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choices=models, label="Model", default=models[0]
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)
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swap_in_source = gr.Checkbox(
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False,
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placeholder="Swap face in source image",
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label="Swap in source image",
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visible=is_img2img,
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)
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swap_in_generated = gr.Checkbox(
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True,
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placeholder="Swap face in generated image",
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label="Swap in generated image",
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visible=is_img2img,
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)
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return [
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img,
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enable,
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source_faces_index,
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faces_index,
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model,
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face_restorer_name,
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face_restorer_visibility,
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restore_first,
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upscaler_name,
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upscaler_scale,
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upscaler_visibility,
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swap_in_source,
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swap_in_generated,
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]
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@property
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def upscaler(self) -> UpscalerData:
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for upscaler in shared.sd_upscalers:
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if upscaler.name == self.upscaler_name:
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return upscaler
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return None
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@property
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def face_restorer(self) -> FaceRestoration:
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for face_restorer in shared.face_restorers:
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if face_restorer.name() == self.face_restorer_name:
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return face_restorer
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return None
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@property
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def upscale_options(self) -> UpscaleOptions:
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return UpscaleOptions(
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do_restore_first = self.restore_first,
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scale=self.upscaler_scale,
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upscaler=self.upscaler,
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face_restorer=self.face_restorer,
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upscale_visibility=self.upscaler_visibility,
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restorer_visibility=self.face_restorer_visibility,
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)
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def process(
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self,
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p: StableDiffusionProcessing,
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img,
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enable,
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source_faces_index,
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faces_index,
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model,
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face_restorer_name,
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face_restorer_visibility,
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restore_first,
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upscaler_name,
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upscaler_scale,
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upscaler_visibility,
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swap_in_source,
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swap_in_generated,
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):
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self.source = img
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self.face_restorer_name = face_restorer_name
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self.upscaler_scale = upscaler_scale
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self.upscaler_visibility = upscaler_visibility
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self.face_restorer_visibility = face_restorer_visibility
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self.enable = enable
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self.restore_first = restore_first
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self.upscaler_name = upscaler_name
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self.swap_in_generated = swap_in_generated
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self.model = model
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self.source_faces_index = [
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int(x) for x in source_faces_index.strip(",").split(",") if x.isnumeric()
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]
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self.faces_index = [
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int(x) for x in faces_index.strip(",").split(",") if x.isnumeric()
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]
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if len(self.source_faces_index) == 0:
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self.source_faces_index = [0]
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if len(self.faces_index) == 0:
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self.faces_index = [0]
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if self.enable:
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if self.source is not None:
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if isinstance(p, StableDiffusionProcessingImg2Img) and swap_in_source:
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logger.info(f"NSFW-Roop is enabled, face index %s", self.faces_index)
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for i in range(len(p.init_images)):
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logger.info(f"Swap in source %s", i)
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result = swap_face(
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self.source,
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p.init_images[i],
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source_faces_index=self.source_faces_index,
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faces_index=self.faces_index,
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model=self.model,
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upscale_options=self.upscale_options,
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)
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p.init_images[i] = result.image()
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else:
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logger.error(f"Please provide a source face")
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def postprocess_batch(self, p, *args, **kwargs):
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if self.enable:
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images = kwargs["images"]
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def postprocess_image(self, p, script_pp: scripts.PostprocessImageArgs, *args):
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if self.enable and self.swap_in_generated:
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if self.source is not None:
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image: Image.Image = script_pp.image
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result: ImageResult = swap_face(
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self.source,
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image,
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source_faces_index=self.source_faces_index,
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faces_index=self.faces_index,
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model=self.model,
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upscale_options=self.upscale_options,
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)
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pp = scripts_postprocessing.PostprocessedImage(result.image())
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pp.info = {}
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p.extra_generation_params.update(pp.info)
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script_pp.image = pp.image
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