157 lines
5.4 KiB
Python
157 lines
5.4 KiB
Python
from PIL import Image
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import numpy as np
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from fastapi import FastAPI
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from modules.api import api
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from scripts.faceswaplab_api.faceswaplab_api_types import (
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FaceSwapResponse,
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)
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from scripts.faceswaplab_globals import VERSION_FLAG
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import gradio as gr
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from typing import Dict, List, Optional, Union
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from scripts.faceswaplab_swapping import swapper
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from scripts.faceswaplab_ui.faceswaplab_unit_settings import FaceSwapUnitSettings
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from scripts.faceswaplab_utils.imgutils import (
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base64_to_pil,
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)
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from scripts.faceswaplab_utils.models_utils import get_current_model
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from modules.shared import opts
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from scripts.faceswaplab_postprocessing.postprocessing import enhance_image
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from scripts.faceswaplab_postprocessing.postprocessing_options import (
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PostProcessingOptions,
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)
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from scripts.faceswaplab_api import faceswaplab_api_types
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from scripts.faceswaplab_postprocessing.postprocessing_options import InpaintingWhen
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def encode_to_base64(image: Union[str, Image.Image, np.ndarray]) -> str: # type: ignore
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"""
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Encode an image to a base64 string.
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The image can be a file path (str), a PIL Image, or a NumPy array.
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Args:
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image (Union[str, Image.Image, np.ndarray]): The image to encode.
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Returns:
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str: The base64-encoded image if successful, otherwise an empty string.
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"""
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if isinstance(image, str):
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return image
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elif isinstance(image, Image.Image):
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return api.encode_pil_to_base64(image)
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elif isinstance(image, np.ndarray):
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return encode_np_to_base64(image)
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else:
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return ""
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def encode_np_to_base64(image: np.ndarray) -> str: # type: ignore
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"""
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Encode a NumPy array to a base64 string.
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The array is first converted to a PIL Image, then encoded.
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Args:
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image (np.ndarray): The NumPy array to encode.
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Returns:
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str: The base64-encoded image.
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"""
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pil = Image.fromarray(image)
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return api.encode_pil_to_base64(pil)
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def get_postprocessing_options(
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options: faceswaplab_api_types.PostProcessingOptions,
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) -> PostProcessingOptions:
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pp_options = PostProcessingOptions(
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face_restorer_name=options.face_restorer_name,
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restorer_visibility=options.restorer_visibility,
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codeformer_weight=options.codeformer_weight,
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upscaler_name=options.upscaler_name,
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scale=options.scale,
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upscale_visibility=options.upscaler_visibility,
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inpainting_denoising_strengh=options.inpainting_denoising_strengh,
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inpainting_prompt=options.inpainting_prompt,
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inpainting_negative_prompt=options.inpainting_negative_prompt,
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inpainting_steps=options.inpainting_steps,
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inpainting_sampler=options.inpainting_sampler,
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inpainting_when=options.inpainting_when,
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inpainting_model=options.inpainting_model,
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)
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assert isinstance(
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pp_options.inpainting_when, InpaintingWhen
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), "Value is not a valid InpaintingWhen enum"
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return pp_options
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def get_faceswap_units_settings(
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api_units: List[faceswaplab_api_types.FaceSwapUnit],
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) -> List[FaceSwapUnitSettings]:
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units = []
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for u in api_units:
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units.append(
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FaceSwapUnitSettings(
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source_img=base64_to_pil(u.source_img),
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source_face=u.source_face,
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_batch_files=u.get_batch_images(),
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blend_faces=u.blend_faces,
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enable=True,
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same_gender=u.same_gender,
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sort_by_size=u.sort_by_size,
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check_similarity=u.check_similarity,
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_compute_similarity=u.compute_similarity,
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min_ref_sim=u.min_ref_sim,
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min_sim=u.min_sim,
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_faces_index=",".join([str(i) for i in (u.faces_index)]),
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reference_face_index=u.reference_face_index,
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swap_in_generated=True,
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swap_in_source=False,
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)
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)
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return units
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def faceswaplab_api(_: gr.Blocks, app: FastAPI) -> None:
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@app.get(
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"/faceswaplab/version",
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tags=["faceswaplab"],
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description="Get faceswaplab version",
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)
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async def version() -> Dict[str, str]:
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return {"version": VERSION_FLAG}
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# use post as we consider the method non idempotent (which is debatable)
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@app.post(
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"/faceswaplab/swap_face",
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tags=["faceswaplab"],
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description="Swap a face in an image using units",
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)
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async def swap_face(
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request: faceswaplab_api_types.FaceSwapRequest,
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) -> faceswaplab_api_types.FaceSwapResponse:
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units: List[FaceSwapUnitSettings] = []
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src_image: Optional[Image.Image] = base64_to_pil(request.image)
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response = FaceSwapResponse(images=[], infos=[])
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if request.postprocessing:
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pp_options = get_postprocessing_options(request.postprocessing)
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if src_image is not None:
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units = get_faceswap_units_settings(request.units)
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swapped_images = swapper.process_images_units(
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get_current_model(),
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images=[(src_image, None)],
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units=units,
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upscaled_swapper=opts.data.get("faceswaplab_upscaled_swapper", False),
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)
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for img, info in swapped_images:
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if pp_options:
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img = enhance_image(img, pp_options)
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response.images.append(encode_to_base64(img))
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response.infos.append(info)
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return response
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