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283 lines
11 KiB
Python
283 lines
11 KiB
Python
from typing import List
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from scripts.faceswaplab_ui.faceswaplab_inpainting_ui import face_inpainting_ui
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from scripts.faceswaplab_utils.face_checkpoints_utils import get_face_checkpoints
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import gradio as gr
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from modules.shared import opts
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from modules import shared
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def faceswap_unit_advanced_options(
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is_img2img: bool, unit_num: int = 1, id_prefix: str = "faceswaplab_"
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) -> List[gr.components.Component]:
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with gr.Accordion(f"Post-Processing & Advanced Mask Options", open=False):
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gr.Markdown("""Post-processing and mask settings for unit faces""")
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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=lambda: opts.data.get(
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"faceswaplab_default_upscaled_swapper_face_restorer",
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"None",
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),
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type="value",
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elem_id=f"{id_prefix}_face{unit_num}_face_restorer",
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)
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with gr.Column():
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face_restorer_visibility = gr.Slider(
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0,
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1,
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value=lambda: opts.data.get(
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"faceswaplab_default_upscaled_swapper_face_restorer_visibility",
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1.0,
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),
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step=0.001,
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label="Restore visibility",
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elem_id=f"{id_prefix}_face{unit_num}_face_restorer_visibility",
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)
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codeformer_weight = gr.Slider(
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0,
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1,
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value=lambda: opts.data.get(
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"faceswaplab_default_upscaled_swapper_face_restorer_weight", 1.0
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),
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step=0.001,
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label="codeformer weight",
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elem_id=f"{id_prefix}_face{unit_num}_face_restorer_weight",
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)
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upscaler_name = gr.Dropdown(
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choices=[upscaler.name for upscaler in shared.sd_upscalers],
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value=lambda: opts.data.get(
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"faceswaplab_default_upscaled_swapper_upscaler", ""
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),
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label="Upscaler",
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elem_id=f"{id_prefix}_face{unit_num}_upscaler",
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)
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improved_mask = gr.Checkbox(
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lambda: opts.data.get(
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"faceswaplab_default_upscaled_swapper_improved_mask", False
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),
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interactive=True,
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label="Use improved segmented mask (use pastenet to mask only the face)",
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elem_id=f"{id_prefix}_face{unit_num}_improved_mask",
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)
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color_corrections = gr.Checkbox(
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lambda: opts.data.get(
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"faceswaplab_default_upscaled_swapper_fixcolor", False
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),
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interactive=True,
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label="Use color corrections",
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elem_id=f"{id_prefix}_face{unit_num}_color_corrections",
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)
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sharpen_face = gr.Checkbox(
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lambda: opts.data.get(
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"faceswaplab_default_upscaled_swapper_sharpen", False
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),
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interactive=True,
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label="sharpen face",
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elem_id=f"{id_prefix}_face{unit_num}_sharpen_face",
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)
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erosion_factor = gr.Slider(
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0.0,
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10.0,
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lambda: opts.data.get("faceswaplab_default_upscaled_swapper_erosion", 1.0),
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step=0.01,
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label="Upscaled swapper mask erosion factor, 1 = default behaviour.",
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elem_id=f"{id_prefix}_face{unit_num}_erosion_factor",
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)
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return [
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face_restorer_name,
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face_restorer_visibility,
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codeformer_weight,
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upscaler_name,
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improved_mask,
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color_corrections,
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sharpen_face,
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erosion_factor,
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]
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def faceswap_unit_ui(
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is_img2img: bool, unit_num: int = 1, id_prefix: str = "faceswaplab"
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) -> List[gr.components.Component]:
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with gr.Tab(f"Face {unit_num}"):
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with gr.Column():
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gr.Markdown(
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"""Reference is an image. First face will be extracted.
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First face of batches sources will be extracted and used as input (or blended if blend is activated)."""
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)
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with gr.Row():
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img = gr.components.Image(
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type="pil",
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label="Reference",
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elem_id=f"{id_prefix}_face{unit_num}_reference_image",
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)
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batch_files = gr.components.File(
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type="file",
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file_count="multiple",
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label="Batch Sources Images",
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optional=True,
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elem_id=f"{id_prefix}_face{unit_num}_batch_source_face_files",
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)
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gr.Markdown(
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"""Face checkpoint built with the checkpoint builder in tools. Will overwrite reference image."""
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)
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with gr.Row():
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face = gr.Dropdown(
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choices=get_face_checkpoints(),
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label="Face Checkpoint (precedence over reference face)",
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elem_id=f"{id_prefix}_face{unit_num}_face_checkpoint",
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)
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refresh = gr.Button(
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value="↻",
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variant="tool",
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elem_id=f"{id_prefix}_face{unit_num}_refresh_checkpoints",
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)
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def refresh_fn(selected: str) -> None:
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return gr.Dropdown.update(
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value=selected, choices=get_face_checkpoints()
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)
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refresh.click(fn=refresh_fn, inputs=face, outputs=face)
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with gr.Row():
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enable = gr.Checkbox(
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False,
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placeholder="enable",
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label="Enable",
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elem_id=f"{id_prefix}_face{unit_num}_enable",
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)
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blend_faces = gr.Checkbox(
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True,
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placeholder="Blend Faces",
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label="Blend Faces ((Source|Checkpoint)+References = 1)",
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elem_id=f"{id_prefix}_face{unit_num}_blend_faces",
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interactive=True,
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)
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gr.Markdown(
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"""Select the face to be swapped, you can sort by size or use the same gender as the desired face:"""
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)
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with gr.Row():
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same_gender = gr.Checkbox(
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False,
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placeholder="Same Gender",
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label="Same Gender",
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elem_id=f"{id_prefix}_face{unit_num}_same_gender",
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)
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sort_by_size = gr.Checkbox(
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False,
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placeholder="Sort by size",
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label="Sort by size (larger>smaller)",
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elem_id=f"{id_prefix}_face{unit_num}_sort_by_size",
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)
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target_faces_index = gr.Textbox(
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value=f"{unit_num-1}",
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placeholder="Which face to swap (comma separated), start from 0 (by gender if same_gender is enabled)",
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label="Target face : Comma separated face number(s)",
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elem_id=f"{id_prefix}_face{unit_num}_target_faces_index",
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)
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gr.Markdown(
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"""The following will only affect reference face image (and is not affected by sort by size) :"""
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)
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reference_faces_index = gr.Number(
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value=0,
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precision=0,
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minimum=0,
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placeholder="Which face to get from reference image start from 0",
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label="Reference source face : start from 0",
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elem_id=f"{id_prefix}_face{unit_num}_reference_face_index",
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)
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gr.Markdown(
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"""Configure swapping. Swapping can occure before img2img, after or both :""",
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visible=is_img2img,
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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 (blended face)",
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visible=is_img2img,
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elem_id=f"{id_prefix}_face{unit_num}_swap_in_source",
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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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elem_id=f"{id_prefix}_face{unit_num}_swap_in_generated",
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)
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with gr.Accordion("Similarity", open=False):
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gr.Markdown("""Discard images with low similarity or no faces :""")
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with gr.Row():
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check_similarity = gr.Checkbox(
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False,
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placeholder="discard",
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label="Check similarity",
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elem_id=f"{id_prefix}_face{unit_num}_check_similarity",
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)
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compute_similarity = gr.Checkbox(
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False,
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label="Compute similarity",
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elem_id=f"{id_prefix}_face{unit_num}_compute_similarity",
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)
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min_sim = gr.Slider(
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0,
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1,
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0,
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step=0.01,
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label="Min similarity",
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elem_id=f"{id_prefix}_face{unit_num}_min_similarity",
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)
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min_ref_sim = gr.Slider(
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0,
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1,
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0,
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step=0.01,
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label="Min reference similarity",
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elem_id=f"{id_prefix}_face{unit_num}_min_ref_similarity",
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)
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pre_inpainting = face_inpainting_ui(
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name="Pre-Inpainting (Before swapping)",
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id_prefix=f"{id_prefix}_face{unit_num}_preinpainting",
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description="Pre-inpainting sends face to inpainting before swapping",
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)
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options = faceswap_unit_advanced_options(is_img2img, unit_num, id_prefix)
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post_inpainting = face_inpainting_ui(
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name="Post-Inpainting (After swapping)",
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id_prefix=f"{id_prefix}_face{unit_num}_postinpainting",
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description="Post-inpainting sends face to inpainting after swapping",
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)
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gradio_components: List[gr.components.Component] = (
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[
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img,
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face,
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batch_files,
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blend_faces,
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enable,
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same_gender,
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sort_by_size,
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check_similarity,
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compute_similarity,
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min_sim,
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min_ref_sim,
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target_faces_index,
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reference_faces_index,
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swap_in_source,
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swap_in_generated,
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]
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+ pre_inpainting
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+ options
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+ post_inpainting
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)
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# If changed, you need to change FaceSwapUnitSettings accordingly
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# ORDER of parameters is IMPORTANT. It should match the result of FaceSwapUnitSettings
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return gradio_components
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