CalvinBackup/sd-webui-faceswaplab
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add pre-commit hooks configuration

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This commit is contained in:
Tran Xen
2023-07-28 18:25:28 +02:00
parent 8577d0186d
commit 5d4a29ff1e
33 changed files with 1674 additions and 820 deletions
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scripts/faceswaplab_api/faceswaplab_api.py
+48 -26
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@@ -4,14 +4,22 @@ from fastapi import FastAPI, Body
from fastapi.exceptions import HTTPException
from modules.api.models import *
from modules.api import api
from scripts.faceswaplab_api.faceswaplab_api_types import FaceSwapUnit, FaceSwapRequest, FaceSwapResponse
from scripts.faceswaplab_api.faceswaplab_api_types import (
FaceSwapUnit,
FaceSwapRequest,
FaceSwapResponse,
)
from scripts.faceswaplab_globals import VERSION_FLAG
import gradio as gr
from typing import List, Optional
from scripts.faceswaplab_swapping import swapper
from scripts.faceswaplab_utils.faceswaplab_logging import save_img_debug
from scripts.faceswaplab_ui.faceswaplab_unit_settings import FaceSwapUnitSettings
from scripts.faceswaplab_utils.imgutils import (pil_to_cv2,check_against_nsfw, base64_to_pil)
from scripts.faceswaplab_utils.imgutils import (
pil_to_cv2,
check_against_nsfw,
base64_to_pil,
)
from scripts.faceswaplab_utils.models_utils import get_current_model
from modules.shared import opts
@@ -26,45 +34,59 @@ def encode_to_base64(image):
else:
return ""
def encode_np_to_base64(image):
pil = Image.fromarray(image)
return api.encode_pil_to_base64(pil)
def faceswaplab_api(_: gr.Blocks, app: FastAPI):
@app.get("/faceswaplab/version", tags=["faceswaplab"], description="Get faceswaplab version")
@app.get(
"/faceswaplab/version",
tags=["faceswaplab"],
description="Get faceswaplab version",
)
async def version():
return {"version": VERSION_FLAG}
# use post as we consider the method non idempotent (which is debatable)
@app.post("/faceswaplab/swap_face", tags=["faceswaplab"], description="Swap a face in an image using units")
async def swap_face(request : FaceSwapRequest) -> FaceSwapResponse:
units : List[FaceSwapUnitSettings]= []
src_image : Optional[Image.Image] = base64_to_pil(request.image)
response = FaceSwapResponse(images = [], infos=[])
if src_image is not None :
@app.post(
"/faceswaplab/swap_face",
tags=["faceswaplab"],
description="Swap a face in an image using units",
)
async def swap_face(request: FaceSwapRequest) -> FaceSwapResponse:
units: List[FaceSwapUnitSettings] = []
src_image: Optional[Image.Image] = base64_to_pil(request.image)
response = FaceSwapResponse(images=[], infos=[])
if src_image is not None:
for u in request.units:
units.append(
FaceSwapUnitSettings(source_img=base64_to_pil(u.source_img),
source_face = u.source_face,
_batch_files = u.get_batch_images(),
blend_faces= u.blend_faces,
enable = True,
same_gender = u.same_gender,
check_similarity=u.check_similarity,
_compute_similarity=u.compute_similarity,
min_ref_sim= u.min_ref_sim,
min_sim= u.min_sim,
_faces_index = ",".join([str(i) for i in (u.faces_index)]),
swap_in_generated=True,
swap_in_source=False
)
FaceSwapUnitSettings(
source_img=base64_to_pil(u.source_img),
source_face=u.source_face,
_batch_files=u.get_batch_images(),
blend_faces=u.blend_faces,
enable=True,
same_gender=u.same_gender,
check_similarity=u.check_similarity,
_compute_similarity=u.compute_similarity,
min_ref_sim=u.min_ref_sim,
min_sim=u.min_sim,
_faces_index=",".join([str(i) for i in (u.faces_index)]),
swap_in_generated=True,
swap_in_source=False,
)
)
swapped_images = swapper.process_images_units(get_current_model(), images=[(src_image,None)], units=units, upscaled_swapper=opts.data.get("faceswaplab_upscaled_swapper", False))
swapped_images = swapper.process_images_units(
get_current_model(),
images=[(src_image, None)],
units=units,
upscaled_swapper=opts.data.get("faceswaplab_upscaled_swapper", False),
)
for img, info in swapped_images:
response.images.append(encode_to_base64(img))
response.infos.append(info)
return response
scripts/faceswaplab_api/faceswaplab_api_types.py
+106 -38
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@@ -5,69 +5,137 @@ import dill as pickle
import gradio as gr
from insightface.app.common import Face
from PIL import Image
from scripts.faceswaplab_utils.imgutils import (pil_to_cv2,check_against_nsfw, base64_to_pil)
from scripts.faceswaplab_utils.imgutils import (
pil_to_cv2,
check_against_nsfw,
base64_to_pil,
)
from scripts.faceswaplab_utils.faceswaplab_logging import logger
from pydantic import BaseModel, Field
from scripts.faceswaplab_postprocessing.postprocessing_options import InpaintingWhen
class FaceSwapUnit(BaseModel) :
class FaceSwapUnit(BaseModel):
# The image given in reference
source_img: str = Field(description='base64 reference image', examples=["data:image/jpeg;base64,/9j/4AAQSkZJRgABAQECWAJYAAD...."], default=None)
source_img: str = Field(
description="base64 reference image",
examples=["data:image/jpeg;base64,/9j/4AAQSkZJRgABAQECWAJYAAD...."],
default=None,
)
# The checkpoint file
source_face : str = Field(description='face checkpoint (from models/faceswaplab/faces)',examples=["my_face.pkl"], default=None)
source_face: str = Field(
description="face checkpoint (from models/faceswaplab/faces)",
examples=["my_face.pkl"],
default=None,
)
# base64 batch source images
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)
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,
)
# Will blend faces if True
blend_faces: bool = Field(description='Will blend faces if True', default=True)
blend_faces: bool = Field(description="Will blend faces if True", default=True)
# Use same gender filtering
same_gender: bool = Field(description='Use same gender filtering', default=True)
same_gender: bool = Field(description="Use same gender filtering", default=True)
# If True, discard images with low similarity
check_similarity : bool = Field(description='If True, discard images with low similarity', default=False)
check_similarity: bool = Field(
description="If True, discard images with low similarity", default=False
)
# if True will compute similarity and add it to the image info
compute_similarity : bool = Field(description='If True will compute similarity and add it to the image info', default=False)
compute_similarity: bool = Field(
description="If True will compute similarity and add it to the image info",
default=False,
)
# Minimum similarity against the used face (reference, batch or checkpoint)
min_sim: float = Field(description='Minimum similarity against the used face (reference, batch or checkpoint)', default=0.0)
min_sim: float = Field(
description="Minimum similarity against the used face (reference, batch or checkpoint)",
default=0.0,
)
# Minimum similarity against the reference (reference or checkpoint if checkpoint is given)
min_ref_sim: float = Field(description='Minimum similarity against the reference (reference or checkpoint if checkpoint is given)', default=0.0)
min_ref_sim: float = Field(
description="Minimum similarity against the reference (reference or checkpoint if checkpoint is given)",
default=0.0,
)
# The face index to use for swapping
faces_index: Tuple[int] = Field(description='The face index to use for swapping, list of face numbers starting from 0', default=(0,))
faces_index: Tuple[int] = Field(
description="The face index to use for swapping, list of face numbers starting from 0",
default=(0,),
)
def get_batch_images(self) -> List[Image.Image] :
def get_batch_images(self) -> List[Image.Image]:
images = []
if self.batch_images :
for img in self.batch_images :
if self.batch_images:
for img in self.batch_images:
images.append(base64_to_pil(img))
return images
class PostProcessingOptions (BaseModel):
face_restorer_name: str = Field(description='face restorer name', default=None)
restorer_visibility: float = Field(description='face restorer visibility', default=1, le=1, ge=0)
codeformer_weight: float = Field(description='face restorer codeformer weight', default=1, le=1, ge=0)
upscaler_name: str = Field(description='upscaler name', default=None)
scale: float = Field(description='upscaling scale', default=1, le=10, ge=0)
upscale_visibility: float = Field(description='upscaler visibility', default=1, le=1, ge=0)
inpainting_denoising_strengh : float = Field(description='Inpainting denoising strenght', default=0, lt=1, ge=0)
inpainting_prompt : str = Field(description='Inpainting denoising strenght',examples=["Portrait of a [gender]"], default="Portrait of a [gender]")
inpainting_negative_prompt : str = Field(description='Inpainting denoising strenght',examples=["Deformed, blurry, bad anatomy, disfigured, poorly drawn face, mutation"], default="")
inpainting_steps : int = Field(description='Inpainting steps',examples=["Portrait of a [gender]"], ge=1, le=150, default=20)
inpainting_sampler : str = Field(description='Inpainting sampler',examples=["Euler"], default="Euler")
inpainting_when : InpaintingWhen = Field(description='When inpainting happens', examples=[e.value for e in InpaintingWhen.__members__.values()], default=InpaintingWhen.NEVER)
class PostProcessingOptions(BaseModel):
face_restorer_name: str = Field(description="face restorer name", default=None)
restorer_visibility: float = Field(
description="face restorer visibility", default=1, le=1, ge=0
)
codeformer_weight: float = Field(
description="face restorer codeformer weight", default=1, le=1, ge=0
)
upscaler_name: str = Field(description="upscaler name", default=None)
scale: float = Field(description="upscaling scale", default=1, le=10, ge=0)
upscale_visibility: float = Field(
description="upscaler visibility", default=1, le=1, ge=0
)
inpainting_denoising_strengh: float = Field(
description="Inpainting denoising strenght", default=0, lt=1, ge=0
)
inpainting_prompt: str = Field(
description="Inpainting denoising strenght",
examples=["Portrait of a [gender]"],
default="Portrait of a [gender]",
)
inpainting_negative_prompt: str = Field(
description="Inpainting denoising strenght",
examples=[
"Deformed, blurry, bad anatomy, disfigured, poorly drawn face, mutation"
],
default="",
)
inpainting_steps: int = Field(
description="Inpainting steps",
examples=["Portrait of a [gender]"],
ge=1,
le=150,
default=20,
)
inpainting_sampler: str = Field(
description="Inpainting sampler", examples=["Euler"], default="Euler"
)
inpainting_when: InpaintingWhen = Field(
description="When inpainting happens",
examples=[e.value for e in InpaintingWhen.__members__.values()],
default=InpaintingWhen.NEVER,
)
class FaceSwapRequest(BaseModel) :
image : str = Field(description='base64 reference image', examples=["data:image/jpeg;base64,/9j/4AAQSkZJRgABAQECWAJYAAD...."], default=None)
units : List[FaceSwapUnit]
postprocessing : PostProcessingOptions
class FaceSwapRequest(BaseModel):
image: str = Field(
description="base64 reference image",
examples=["data:image/jpeg;base64,/9j/4AAQSkZJRgABAQECWAJYAAD...."],
default=None,
)
units: List[FaceSwapUnit]
postprocessing: PostProcessingOptions
class FaceSwapResponse(BaseModel) :
images : List[str] = Field(description='base64 swapped image',default=None)
infos : List[str]
class FaceSwapResponse(BaseModel):
images: List[str] = Field(description="base64 swapped image", default=None)
infos: List[str]