add pre-commit hooks configuration
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Tran Xen
+108
-42
@@ -6,89 +6,155 @@ import base64, io
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from io import BytesIO
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from typing import List, Tuple, Optional
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class InpaintingWhen(Enum):
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NEVER = "Never"
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BEFORE_UPSCALING = "Before Upscaling/all"
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BEFORE_RESTORE_FACE = "After Upscaling/Before Restore Face"
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AFTER_ALL = "After All"
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class FaceSwapUnit(BaseModel) :
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class FaceSwapUnit(BaseModel):
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# The image given in reference
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source_img: str = Field(description='base64 reference image', examples=["data:image/jpeg;base64,/9j/4AAQSkZJRgABAQECWAJYAAD...."], default=None)
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source_img: str = Field(
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description="base64 reference image",
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examples=["data:image/jpeg;base64,/9j/4AAQSkZJRgABAQECWAJYAAD...."],
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default=None,
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)
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# The checkpoint file
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source_face : str = Field(description='face checkpoint (from models/faceswaplab/faces)',examples=["my_face.pkl"], default=None)
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source_face: str = Field(
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description="face checkpoint (from models/faceswaplab/faces)",
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examples=["my_face.pkl"],
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default=None,
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)
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# base64 batch source images
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batch_images: Tuple[str] = Field(description='list of base64 batch source images',examples=["data:image/jpeg;base64,/9j/4AAQSkZJRgABAQECWAJYAAD....", "data:image/jpeg;base64,/9j/4AAQSkZJRgABAQECWAJYAAD...."], default=None)
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batch_images: Tuple[str] = Field(
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description="list of base64 batch source images",
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examples=[
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"data:image/jpeg;base64,/9j/4AAQSkZJRgABAQECWAJYAAD....",
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"data:image/jpeg;base64,/9j/4AAQSkZJRgABAQECWAJYAAD....",
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],
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default=None,
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)
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# Will blend faces if True
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blend_faces: bool = Field(description='Will blend faces if True', default=True)
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blend_faces: bool = Field(description="Will blend faces if True", default=True)
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# Use same gender filtering
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same_gender: bool = Field(description='Use same gender filtering', default=True)
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same_gender: bool = Field(description="Use same gender filtering", default=True)
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# If True, discard images with low similarity
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check_similarity : bool = Field(description='If True, discard images with low similarity', default=False)
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check_similarity: bool = Field(
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description="If True, discard images with low similarity", default=False
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)
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# if True will compute similarity and add it to the image info
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compute_similarity : bool = Field(description='If True will compute similarity and add it to the image info', default=False)
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compute_similarity: bool = Field(
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description="If True will compute similarity and add it to the image info",
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default=False,
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)
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# Minimum similarity against the used face (reference, batch or checkpoint)
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min_sim: float = Field(description='Minimum similarity against the used face (reference, batch or checkpoint)', default=0.0)
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min_sim: float = Field(
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description="Minimum similarity against the used face (reference, batch or checkpoint)",
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default=0.0,
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)
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# Minimum similarity against the reference (reference or checkpoint if checkpoint is given)
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min_ref_sim: float = Field(description='Minimum similarity against the reference (reference or checkpoint if checkpoint is given)', default=0.0)
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min_ref_sim: float = Field(
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description="Minimum similarity against the reference (reference or checkpoint if checkpoint is given)",
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default=0.0,
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)
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# The face index to use for swapping
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faces_index: Tuple[int] = Field(description='The face index to use for swapping, list of face numbers starting from 0', default=(0,))
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faces_index: Tuple[int] = Field(
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description="The face index to use for swapping, list of face numbers starting from 0",
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default=(0,),
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)
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class PostProcessingOptions (BaseModel):
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face_restorer_name: str = Field(description='face restorer name', default=None)
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restorer_visibility: float = Field(description='face restorer visibility', default=1, le=1, ge=0)
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codeformer_weight: float = Field(description='face restorer codeformer weight', default=1, le=1, ge=0)
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class PostProcessingOptions(BaseModel):
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face_restorer_name: str = Field(description="face restorer name", default=None)
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restorer_visibility: float = Field(
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description="face restorer visibility", default=1, le=1, ge=0
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)
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codeformer_weight: float = Field(
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description="face restorer codeformer weight", default=1, le=1, ge=0
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)
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upscaler_name: str = Field(description='upscaler name', default=None)
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scale: float = Field(description='upscaling scale', default=1, le=10, ge=0)
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upscale_visibility: float = Field(description='upscaler visibility', default=1, le=1, ge=0)
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inpainting_denoising_strengh : float = Field(description='Inpainting denoising strenght', default=0, lt=1, ge=0)
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inpainting_prompt : str = Field(description='Inpainting denoising strenght',examples=["Portrait of a [gender]"], default="Portrait of a [gender]")
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inpainting_negative_prompt : str = Field(description='Inpainting denoising strenght',examples=["Deformed, blurry, bad anatomy, disfigured, poorly drawn face, mutation"], default="")
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inpainting_steps : int = Field(description='Inpainting steps',examples=["Portrait of a [gender]"], ge=1, le=150, default=20)
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inpainting_sampler : str = Field(description='Inpainting sampler',examples=["Euler"], default="Euler")
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inpainting_when : InpaintingWhen = Field(description='When inpainting happens', examples=[e.value for e in InpaintingWhen.__members__.values()], default=InpaintingWhen.NEVER)
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upscaler_name: str = Field(description="upscaler name", default=None)
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scale: float = Field(description="upscaling scale", default=1, le=10, ge=0)
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upscale_visibility: float = Field(
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description="upscaler visibility", default=1, le=1, ge=0
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)
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inpainting_denoising_strengh: float = Field(
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description="Inpainting denoising strenght", default=0, lt=1, ge=0
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)
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inpainting_prompt: str = Field(
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description="Inpainting denoising strenght",
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examples=["Portrait of a [gender]"],
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default="Portrait of a [gender]",
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)
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inpainting_negative_prompt: str = Field(
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description="Inpainting denoising strenght",
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examples=[
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"Deformed, blurry, bad anatomy, disfigured, poorly drawn face, mutation"
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],
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default="",
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)
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inpainting_steps: int = Field(
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description="Inpainting steps",
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examples=["Portrait of a [gender]"],
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ge=1,
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le=150,
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default=20,
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)
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inpainting_sampler: str = Field(
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description="Inpainting sampler", examples=["Euler"], default="Euler"
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)
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inpainting_when: InpaintingWhen = Field(
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description="When inpainting happens",
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examples=[e.value for e in InpaintingWhen.__members__.values()],
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default=InpaintingWhen.NEVER,
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)
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class FaceSwapRequest(BaseModel) :
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image : str = Field(description='base64 reference image', examples=["data:image/jpeg;base64,/9j/4AAQSkZJRgABAQECWAJYAAD...."], default=None)
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units : List[FaceSwapUnit]
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postprocessing : PostProcessingOptions
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class FaceSwapRequest(BaseModel):
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image: str = Field(
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description="base64 reference image",
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examples=["data:image/jpeg;base64,/9j/4AAQSkZJRgABAQECWAJYAAD...."],
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default=None,
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)
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units: List[FaceSwapUnit]
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postprocessing: PostProcessingOptions
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class FaceSwapResponse(BaseModel) :
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images : List[str] = Field(description='base64 swapped image',default=None)
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infos : List[str]
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class FaceSwapResponse(BaseModel):
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images: List[str] = Field(description="base64 swapped image", default=None)
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infos: List[str]
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@property
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def pil_images(self) :
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def pil_images(self):
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return [base64_to_pil(img) for img in self.images]
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def pil_to_base64(img):
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if isinstance(img, str):
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img = Image.open(img)
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buffer = BytesIO()
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img.save(buffer, format='PNG')
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img.save(buffer, format="PNG")
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img_data = buffer.getvalue()
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base64_data = base64.b64encode(img_data)
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return base64_data.decode('utf-8')
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return base64_data.decode("utf-8")
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def base64_to_pil(base64str : Optional[str]) -> Optional[Image.Image] :
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if base64str is None :
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def base64_to_pil(base64str: Optional[str]) -> Optional[Image.Image]:
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if base64str is None:
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return None
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if 'base64,' in base64str: # check if the base64 string has a data URL scheme
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base64_data = base64str.split('base64,')[-1]
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if "base64," in base64str: # check if the base64 string has a data URL scheme
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base64_data = base64str.split("base64,")[-1]
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img_bytes = base64.b64decode(base64_data)
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else:
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# if no data URL scheme, just decode
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img_bytes = base64.b64decode(base64str)
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return Image.open(io.BytesIO(img_bytes))
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return Image.open(io.BytesIO(img_bytes))
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@@ -1,39 +1,45 @@
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import requests
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from PIL import Image
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from client_utils import FaceSwapRequest, FaceSwapUnit, PostProcessingOptions, FaceSwapResponse, pil_to_base64
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from client_utils import (
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FaceSwapRequest,
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FaceSwapUnit,
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PostProcessingOptions,
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FaceSwapResponse,
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pil_to_base64,
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)
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address = 'http://127.0.0.1:7860'
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address = "http://127.0.0.1:7860"
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# First face unit :
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unit1 = FaceSwapUnit(
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source_img=pil_to_base64("../../references/man.png"), # The face you want to use
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faces_index=(0,) # Replace first face
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source_img=pil_to_base64("../../references/man.png"), # The face you want to use
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faces_index=(0,), # Replace first face
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)
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# Second face unit :
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unit2 = FaceSwapUnit(
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source_img=pil_to_base64("../../references/woman.png"), # The face you want to use
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source_img=pil_to_base64("../../references/woman.png"), # The face you want to use
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same_gender=True,
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faces_index=(0,) # Replace first woman since same gender is on
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faces_index=(0,), # Replace first woman since same gender is on
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)
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# Post-processing config :
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pp = PostProcessingOptions(
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face_restorer_name="CodeFormer",
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codeformer_weight=0.5,
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restorer_visibility= 1)
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face_restorer_name="CodeFormer", codeformer_weight=0.5, restorer_visibility=1
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)
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# Prepare the request
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request = FaceSwapRequest (
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image = pil_to_base64("test_image.png"),
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units= [unit1, unit2],
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postprocessing=pp
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# Prepare the request
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request = FaceSwapRequest(
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image=pil_to_base64("test_image.png"), units=[unit1, unit2], postprocessing=pp
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)
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result = requests.post(url=f'{address}/faceswaplab/swap_face', data=request.json(), headers={"Content-Type": "application/json; charset=utf-8"})
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result = requests.post(
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url=f"{address}/faceswaplab/swap_face",
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data=request.json(),
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headers={"Content-Type": "application/json; charset=utf-8"},
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)
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response = FaceSwapResponse.parse_obj(result.json())
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for img, info in zip(response.pil_images, response.infos):
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img.show(title = info)
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img.show(title=info)
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