Fix huge problem in multiple face swapping. Start working on api
Tran Xen
parent
51f284748d
commit
5bf91b8b49
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@ -1,39 +0,0 @@
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from PIL import Image
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import numpy as np
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from fastapi import FastAPI, Body
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from fastapi.exceptions import HTTPException
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from modules.api.models import *
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from modules.api import api
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from scripts.roop_globals import VERSION_FLAG
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import gradio as gr
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def encode_to_base64(image):
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if type(image) is str:
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return image
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elif type(image) is Image.Image:
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return api.encode_pil_to_base64(image)
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elif type(image) is 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):
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pil = Image.fromarray(image)
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return api.encode_pil_to_base64(pil)
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def roop_api(_: gr.Blocks, app: FastAPI):
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@app.get("/roop/version")
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async def version():
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return {"version": VERSION_FLAG}
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@app.get("/roop/swap_face")
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async def swap_face():
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return {"version": VERSION_FLAG}
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try:
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import modules.script_callbacks as script_callbacks
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script_callbacks.on_app_started(roop_api)
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except:
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pass
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@ -1,5 +1,6 @@
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import importlib
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from scripts.roop_utils.models_utils import get_models, get_face_checkpoints
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from scripts.roop_api import roop_api
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from scripts.roop_utils.models_utils import get_current_model, get_face_checkpoints
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from scripts import (roop_globals, roop_logging, faceswap_settings, faceswap_tab, faceswap_unit_ui)
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from scripts.roop_swapping import swapper
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@ -9,6 +10,7 @@ from scripts.roop_postprocessing import upscaling
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from pprint import pprint
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import numpy as np
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import logging
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from copy import deepcopy
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#Reload all the modules when using "apply and restart"
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#This is mainly done for development purposes
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@ -20,6 +22,7 @@ importlib.reload(upscaling)
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importlib.reload(faceswap_settings)
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importlib.reload(models_utils)
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importlib.reload(faceswap_unit_ui)
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importlib.reload(roop_api)
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import base64
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import io
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@ -41,7 +44,6 @@ from modules.shared import opts
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from PIL import Image
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from scripts.roop_utils.imgutils import (pil_to_cv2,convert_to_sd)
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from scripts.roop_logging import logger, save_img_debug
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from scripts.roop_globals import VERSION_FLAG
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from scripts.roop_postprocessing.postprocessing_options import PostProcessingOptions
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@ -55,6 +57,12 @@ EXTENSION_PATH=os.path.join("extensions","sd-webui-roop")
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# Register the tab, done here to prevent it from being added twice
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script_callbacks.on_ui_tabs(faceswap_tab.on_ui_tabs)
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try:
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import modules.script_callbacks as script_callbacks
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script_callbacks.on_app_started(roop_api.roop_api)
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except:
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pass
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class FaceSwapScript(scripts.Script):
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@ -79,22 +87,18 @@ class FaceSwapScript(scripts.Script):
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"""Return True if any unit is enabled and the state is not interupted"""
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return any([u.enable for u in self.units]) and not shared.state.interrupted
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@property
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def model(self) -> str :
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model = opts.data.get("roop_model", None)
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if model is None :
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models = get_models()
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model = models[0] if len(models) else None
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logger.info("Try to use model : %s", model)
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if not os.path.isfile(model):
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logger.error("The model %s cannot be found or loaded", model)
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raise FileNotFoundError("No faceswap model found. Please add it to the roop directory.")
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return model
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@property
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def keep_original_images(self) :
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return opts.data.get("roop_keep_original", False)
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@property
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def swap_in_generated_units(self) :
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return [u for u in self.units if u.swap_in_generated and u.enable]
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@property
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def swap_in_source_units(self) :
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return [u for u in self.units if u.swap_in_source and u.enable]
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def title(self):
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return f"roop"
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@ -155,138 +159,53 @@ class FaceSwapScript(scripts.Script):
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#If is instance of img2img, we check if face swapping in source is required.
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if isinstance(p, StableDiffusionProcessingImg2Img):
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if self.enabled:
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init_images = p.init_images
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for i, unit in enumerate(self.units):
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if unit.enable and unit.swap_in_source :
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blend_config = unit.blend_faces # store blend config
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unit.blend_faces = True # force blending
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(init_images, result_infos) = self.process_images_unit(unit, init_images, upscaled_swapper=self.upscaled_swapper_in_source)
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logger.info(f"unit {i+1}> processed init images: {len(init_images)}, {len(result_infos)}")
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unit.blend_faces = blend_config #restore blend config
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init_images : List[Tuple[Image.Image, str]] = [(img,None) for img in p.init_images]
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new_inits = swapper.process_images_units(get_current_model(), self.swap_in_source_units,images=init_images, upscaled_swapper=self.upscaled_swapper_in_source,force_blend=True)
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logger.info(f"processed init images: {len(init_images)}")
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p.init_images = [img[0] for img in new_inits]
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p.init_images = init_images
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def process_image_unit(self, unit : FaceSwapUnitSettings, image: Image.Image, info = None, upscaled_swapper = False) -> List:
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"""Process one image and return a List of (image, info) (one if blended, many if not).
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Args:
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unit : the current unit
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image : the image where to apply swapping
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info : The info
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Returns:
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List of tuple of (image, info) where image is the image where swapping has been applied and info is the image info with similarity infos.
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"""
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results = []
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if unit.enable :
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if convert_to_sd(image) :
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return [(image, info)]
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if not unit.blend_faces :
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src_faces = unit.faces
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logger.info(f"will generate {len(src_faces)} images")
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else :
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logger.info("blend all faces together")
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src_faces = [unit.blended_faces]
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assert(not np.array_equal(unit.reference_face.embedding,src_faces[0].embedding) if len(unit.faces)>1 else True), "Reference face cannot be the same as blended"
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for i,src_face in enumerate(src_faces):
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logger.info(f"Process face {i}")
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if unit.reference_face is not None :
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reference_face = unit.reference_face
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else :
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logger.info("Use source face as reference face")
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reference_face = src_face
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save_img_debug(image, "Before swap")
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result: swapper.ImageResult = swapper.swap_face(
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reference_face,
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src_face,
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image,
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faces_index=unit.faces_index,
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model=self.model,
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same_gender=unit.same_gender,
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upscaled_swapper=upscaled_swapper,
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compute_similarity=unit.compute_similarity
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)
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save_img_debug(result.image, "After swap")
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if result.image is None :
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logger.error("Result image is None")
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if (not unit.check_similarity) or result.similarity and all([result.similarity.values()!=0]+[x >= unit.min_sim for x in result.similarity.values()]) and all([result.ref_similarity.values()!=0]+[x >= unit.min_ref_sim for x in result.ref_similarity.values()]):
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results.append((result.image, f"{info}, similarity = {result.similarity}, ref_similarity = {result.ref_similarity}"))
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else:
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logger.warning(
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f"skip, similarity to low, sim = {result.similarity} (target {unit.min_sim}) ref sim = {result.ref_similarity} (target = {unit.min_ref_sim})"
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)
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return results
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def process_images_unit(self, unit : FaceSwapUnitSettings, images : List[Image.Image], infos = None, upscaled_swapper = False) -> Tuple[List[Image.Image], List[str]] :
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if unit.enable :
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result_images : List[Image.Image] = []
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result_infos : List[str]= []
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if not infos :
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# this allows the use of zip afterwards if no infos are present
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# we make sure infos size is the same as images size
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infos = [None] * len(images)
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for i, (img, info) in enumerate(zip(images, infos)):
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swapped_images = self.process_image_unit(unit, img, info, upscaled_swapper)
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for result_image, result_info in swapped_images :
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result_images.append(result_image)
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result_infos.append(result_info)
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logger.info(f"{len(result_images)} images processed")
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return (result_images, result_infos)
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return (images, infos)
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def postprocess(self, p : StableDiffusionProcessing, processed: Processed, *args):
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if self.enabled :
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# Get the original images without the grid
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orig_images = processed.images[processed.index_of_first_image:]
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orig_infotexts = processed.infotexts[processed.index_of_first_image:]
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orig_images : List[Image.Image] = processed.images[processed.index_of_first_image:]
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orig_infotexts : List[str] = processed.infotexts[processed.index_of_first_image:]
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keep_original = self.keep_original_images
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# These are were images and infos of swapped images will be stored
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images = []
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infotexts = []
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if (len(self.swap_in_generated_units))>0 :
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for i,(img,info) in enumerate(zip(orig_images, orig_infotexts)):
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batch_index = i%p.batch_size
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swapped_images = swapper.process_images_units(get_current_model(), self.swap_in_generated_units, images=[(img,info)], upscaled_swapper=self.upscaled_swapper_in_generated)
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logger.info(f"{len(swapped_images)} images swapped")
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for swp_img, new_info in swapped_images :
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img = swp_img # Will only swap the last image in the batch in next units (FIXME : hard to fix properly but not really critical)
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for i,(img,info) in enumerate(zip(orig_images, orig_infotexts)):
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if any([u.enable for u in self.units]):
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for unit_i, unit in enumerate(self.units):
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#convert image position to batch index
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#this should work (not completely confident)
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batch_index = i%p.batch_size
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if unit.enable :
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if unit.swap_in_generated :
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img_to_swap = img
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swapped_images = self.process_image_unit(image=img_to_swap, unit=unit, info=info, upscaled_swapper=self.upscaled_swapper_in_generated)
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logger.info(f"{len(swapped_images)} images swapped")
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for swp_img, new_info in swapped_images :
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logger.info(f"unit {unit_i+1}> processed")
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if swp_img is not None :
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if swp_img is not None :
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save_img_debug(swp_img,"Before apply mask")
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swp_img = imgutils.apply_mask(swp_img, p, batch_index)
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save_img_debug(swp_img,"After apply mask")
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save_img_debug(swp_img,"Before apply mask")
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swp_img = imgutils.apply_mask(swp_img, p, batch_index)
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save_img_debug(swp_img,"After apply mask")
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try :
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if self.postprocess_options is not None:
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swp_img = enhance_image(swp_img, self.postprocess_options)
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except Exception as e:
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logger.error("Failed to upscale : %s", e)
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try :
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if self.postprocess_options is not None:
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swp_img = enhance_image(swp_img, self.postprocess_options)
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except Exception as e:
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logger.error("Failed to upscale : %s", e)
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logger.info("Add swp image to processed")
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images.append(swp_img)
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infotexts.append(new_info)
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if p.outpath_samples and opts.samples_save :
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save_image(swp_img, p.outpath_samples, "", p.all_seeds[batch_index], p.all_prompts[batch_index], opts.samples_format,info=new_info, p=p, suffix="-swapped")
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else :
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logger.error("swp image is None")
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elif unit.swap_in_source and not self.keep_original_images :
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# if images were swapped in source, but we don't keep original
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# no images will be showned unless we add it as a swap image :
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images.append(img)
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infotexts.append(new_info)
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logger.info("Add swp image to processed")
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images.append(swp_img)
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infotexts.append(new_info)
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if p.outpath_samples and opts.samples_save :
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save_image(swp_img, p.outpath_samples, "", p.all_seeds[batch_index], p.all_prompts[batch_index], opts.samples_format,info=new_info, p=p, suffix="-swapped")
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else :
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logger.error("swp image is None")
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else :
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keep_original=True
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# Generate grid :
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@ -299,7 +218,7 @@ class FaceSwapScript(scripts.Script):
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grid.info["parameters"] = text
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images.insert(0, grid)
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if self.keep_original_images:
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if keep_original:
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# If we want to keep original images, we add all existing (including grid this time)
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images += processed.images
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infotexts += processed.infotexts
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@ -18,7 +18,7 @@ class FaceSwapUnitSettings:
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# The checkpoint file
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source_face : str
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# The batch source images
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_batch_files: gr.components.File
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_batch_files: Union[gr.components.File,List[Image.Image]]
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# Will blend faces if True
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blend_faces: bool
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# Enable this unit
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@ -118,7 +118,11 @@ class FaceSwapUnitSettings:
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if self.batch_files is not None and not hasattr(self,"_faces") :
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self._faces = [self.reference_face] if self.reference_face is not None else []
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for file in self.batch_files :
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img = Image.open(file.name)
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if isinstance(file, Image.Image) :
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img = file
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else :
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img = Image.open(file.name)
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face = swapper.get_or_default(swapper.get_faces(pil_to_cv2(img)), 0, None)
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if face is not None :
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self._faces.append(face)
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@ -0,0 +1,68 @@
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from PIL import Image
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import numpy as np
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from fastapi import FastAPI, Body
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from fastapi.exceptions import HTTPException
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from modules.api.models import *
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from modules.api import api
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from scripts.roop_api.roop_api_types import FaceSwapUnit, FaceSwapRequest
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from scripts.roop_globals import VERSION_FLAG
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import gradio as gr
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from typing import Dict, List, Set, Tuple, Union, Optional
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import json
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from scripts.roop_swapping import swapper
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from scripts.faceswap_unit_settings import FaceSwapUnitSettings
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from scripts.roop_utils.imgutils import (pil_to_cv2,convert_to_sd, base64_to_pil)
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from scripts.roop_utils.models_utils import get_current_model
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def encode_to_base64(image):
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if type(image) is str:
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return image
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elif type(image) is Image.Image:
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return api.encode_pil_to_base64(image)
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elif type(image) is 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):
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pil = Image.fromarray(image)
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return api.encode_pil_to_base64(pil)
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def roop_api(_: gr.Blocks, app: FastAPI):
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@app.get("/roop/version")
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async def version():
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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("/roop/swap_face")
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async def swap_face(request : FaceSwapRequest) -> List[FaceSwapUnit]:
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units : List[FaceSwapUnitSettings]= []
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src_image = base64_to_pil(request.image)
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for u in request.units:
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units.append(
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FaceSwapUnitSettings(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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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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swap_in_generated=True,
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swap_in_source=False
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)
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)
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# result_images = []
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# for unit_i, unit in enumerate(units):
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# swapped_images = swapper.process_image_unit(get_current_model(), image=, unit=unit, info=info, upscaled_swapper=self.upscaled_swapper_in_generated)
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return request.units
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@ -0,0 +1,72 @@
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from scripts.roop_swapping import swapper
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import numpy as np
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import base64
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import io
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from dataclasses import dataclass, fields
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from typing import Dict, List, Set, Tuple, Union, Optional
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import dill as pickle
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import gradio as gr
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from insightface.app.common import Face
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from PIL import Image
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from scripts.roop_utils.imgutils import (pil_to_cv2,convert_to_sd, base64_to_pil)
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from scripts.roop_logging import logger
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from pydantic import BaseModel, Field
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from scripts.roop_postprocessing.postprocessing_options import InpaintingWhen
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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)
|
||||
# The checkpoint file
|
||||
source_face : str = Field(description='face checkpoint (from models/roop/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)
|
||||
|
||||
# Will blend faces if 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)
|
||||
|
||||
# If True, discard images with low similarity
|
||||
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)
|
||||
|
||||
# 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)
|
||||
# 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)
|
||||
|
||||
# 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,))
|
||||
|
||||
def get_batch_images(self) -> List[Image.Image] :
|
||||
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=0.02, 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)
|
||||
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.BEFORE_UPSCALING)
|
||||
|
||||
|
||||
class FaceSwapRequest(BaseModel) :
|
||||
image : str = Field(description='base64 reference image', examples=["data:image/jpeg;base64,/9j/4AAQSkZJRgABAQECWAJYAAD...."], default=None)
|
||||
units : List[FaceSwapUnit]
|
||||
postprocessing : PostProcessingOptions
|
||||
|
|
@ -1,46 +0,0 @@
|
|||
from scripts.roop_swapping import swapper
|
||||
import numpy as np
|
||||
import base64
|
||||
import io
|
||||
from dataclasses import dataclass, fields
|
||||
from typing import Dict, List, Set, Tuple, Union, Optional
|
||||
import dill as pickle
|
||||
import gradio as gr
|
||||
from insightface.app.common import Face
|
||||
from PIL import Image
|
||||
from scripts.roop_utils.imgutils import (pil_to_cv2,convert_to_sd)
|
||||
from scripts.roop_logging import logger
|
||||
|
||||
|
||||
@dataclass
|
||||
class FaceSwapUnitDTO :
|
||||
# The image given in reference
|
||||
source_img: str
|
||||
# The checkpoint file
|
||||
source_face : str
|
||||
# base64 batch source images
|
||||
batch_files: List[str]
|
||||
|
||||
# Will blend faces if True
|
||||
blend_faces: bool
|
||||
# Enable this unit
|
||||
enable: bool
|
||||
# Use same gender filtering
|
||||
same_gender: bool
|
||||
|
||||
# If True, discard images with low similarity
|
||||
check_similarity : bool
|
||||
# if True will compute similarity and add it to the image info
|
||||
compute_similarity :bool
|
||||
|
||||
# Minimum similarity against the used face (reference, batch or checkpoint)
|
||||
min_sim: float
|
||||
# Minimum similarity against the reference (reference or checkpoint if checkpoint is given)
|
||||
min_ref_sim: float
|
||||
# The face index to use for swapping
|
||||
faces_index: int
|
||||
|
||||
# Swap in the source image in img2img (before processing)
|
||||
swap_in_source: bool
|
||||
# Swap in the generated image in img2img (always on for txt2img)
|
||||
swap_in_generated: bool
|
||||
|
|
@ -3,7 +3,7 @@ import os
|
|||
|
||||
MODELS_DIR = os.path.abspath(os.path.join("models","roop"))
|
||||
ANALYZER_DIR = os.path.abspath(os.path.join(MODELS_DIR, "analysers"))
|
||||
VERSION_FLAG = "v0.3.0"
|
||||
VERSION_FLAG = "v1.0.0"
|
||||
EXTENSION_PATH=os.path.join("extensions","sd-webui-roop")
|
||||
SD_CONVERT_SCORE = 0.7
|
||||
|
||||
|
|
|
|||
|
|
@ -13,11 +13,13 @@ from PIL import Image
|
|||
from sklearn.metrics.pairwise import cosine_similarity
|
||||
|
||||
from scripts.roop_swapping import upscaled_inswapper
|
||||
from scripts.roop_utils.imgutils import cv2_to_pil, pil_to_cv2
|
||||
from scripts.roop_logging import logger
|
||||
from scripts.roop_utils.imgutils import cv2_to_pil, pil_to_cv2, convert_to_sd
|
||||
from scripts.roop_logging import logger, save_img_debug
|
||||
from scripts import roop_globals
|
||||
from modules.shared import opts
|
||||
from functools import lru_cache
|
||||
from scripts.faceswap_unit_settings import FaceSwapUnitSettings
|
||||
|
||||
providers = ["CPUExecutionProvider"]
|
||||
|
||||
|
||||
|
|
@ -335,4 +337,82 @@ def swap_face(
|
|||
except Exception as e :
|
||||
logger.error("Conversion failed %s", e)
|
||||
raise e
|
||||
return return_result
|
||||
return return_result
|
||||
|
||||
|
||||
def process_image_unit(model, unit : FaceSwapUnitSettings, image: Image.Image, info = None, upscaled_swapper = False, force_blend = False) -> List:
|
||||
"""Process one image and return a List of (image, info) (one if blended, many if not).
|
||||
|
||||
Args:
|
||||
unit : the current unit
|
||||
image : the image where to apply swapping
|
||||
info : The info
|
||||
|
||||
Returns:
|
||||
List of tuple of (image, info) where image is the image where swapping has been applied and info is the image info with similarity infos.
|
||||
"""
|
||||
|
||||
results = []
|
||||
if unit.enable :
|
||||
if convert_to_sd(image) :
|
||||
return [(image, info)]
|
||||
if not unit.blend_faces and not force_blend :
|
||||
src_faces = unit.faces
|
||||
logger.info(f"will generate {len(src_faces)} images")
|
||||
else :
|
||||
logger.info("blend all faces together")
|
||||
src_faces = [unit.blended_faces]
|
||||
assert(not np.array_equal(unit.reference_face.embedding,src_faces[0].embedding) if len(unit.faces)>1 else True), "Reference face cannot be the same as blended"
|
||||
|
||||
|
||||
for i,src_face in enumerate(src_faces):
|
||||
logger.info(f"Process face {i}")
|
||||
if unit.reference_face is not None :
|
||||
reference_face = unit.reference_face
|
||||
else :
|
||||
logger.info("Use source face as reference face")
|
||||
reference_face = src_face
|
||||
|
||||
save_img_debug(image, "Before swap")
|
||||
result: ImageResult = swap_face(
|
||||
reference_face,
|
||||
src_face,
|
||||
image,
|
||||
faces_index=unit.faces_index,
|
||||
model=model,
|
||||
same_gender=unit.same_gender,
|
||||
upscaled_swapper=upscaled_swapper,
|
||||
compute_similarity=unit.compute_similarity
|
||||
)
|
||||
save_img_debug(result.image, "After swap")
|
||||
|
||||
if result.image is None :
|
||||
logger.error("Result image is None")
|
||||
if (not unit.check_similarity) or result.similarity and all([result.similarity.values()!=0]+[x >= unit.min_sim for x in result.similarity.values()]) and all([result.ref_similarity.values()!=0]+[x >= unit.min_ref_sim for x in result.ref_similarity.values()]):
|
||||
results.append((result.image, f"{info}, similarity = {result.similarity}, ref_similarity = {result.ref_similarity}"))
|
||||
else:
|
||||
logger.warning(
|
||||
f"skip, similarity to low, sim = {result.similarity} (target {unit.min_sim}) ref sim = {result.ref_similarity} (target = {unit.min_ref_sim})"
|
||||
)
|
||||
logger.debug("process_image_unit : Unit produced %s results", len(results))
|
||||
return results
|
||||
|
||||
def process_images_units(model, units : List[FaceSwapUnitSettings], images: List[Tuple[Image.Image, str]], upscaled_swapper = False, force_blend = False) -> List:
|
||||
if len(units) == 0 :
|
||||
logger.info("Finished processing image, return %s images", len(images))
|
||||
return None
|
||||
|
||||
logger.debug("%s more units", len(units))
|
||||
|
||||
processed_images = []
|
||||
for i,(image, info) in enumerate(images) :
|
||||
logger.debug("Processing image %s", i)
|
||||
swapped = process_image_unit(model,units[0],image, info, upscaled_swapper, force_blend)
|
||||
logger.debug("Image %s -> %s images", i, len(swapped))
|
||||
nexts = process_images_units(model,units[1:],swapped, upscaled_swapper,force_blend)
|
||||
if nexts :
|
||||
processed_images.extend(nexts)
|
||||
else :
|
||||
processed_images.extend(swapped)
|
||||
|
||||
return processed_images
|
||||
|
|
@ -1,3 +1,5 @@
|
|||
import io
|
||||
from typing import Optional
|
||||
from PIL import Image, ImageChops, ImageOps,ImageFilter
|
||||
import cv2
|
||||
import numpy as np
|
||||
|
|
@ -6,7 +8,7 @@ import torch
|
|||
from ifnude import detect
|
||||
from scripts.roop_globals import SD_CONVERT_SCORE
|
||||
from modules import processing
|
||||
|
||||
import base64
|
||||
|
||||
def convert_to_sd(img):
|
||||
shapes = []
|
||||
|
|
@ -168,4 +170,15 @@ def prepare_mask(
|
|||
#FIXME : Properly fix blur
|
||||
# if getattr(p, "mask_blur", 0) > 0:
|
||||
# mask = mask.filter(ImageFilter.GaussianBlur(p.mask_blur))
|
||||
return mask
|
||||
return mask
|
||||
|
||||
def base64_to_pil(base64str : Optional[str]) :
|
||||
if base64str is None :
|
||||
return None
|
||||
if 'base64,' in base64str: # check if the base64 string has a data URL scheme
|
||||
base64_data = base64str.split('base64,')[-1]
|
||||
img_bytes = base64.b64decode(base64_data)
|
||||
else:
|
||||
# if no data URL scheme, just decode
|
||||
img_bytes = base64.b64decode(base64str)
|
||||
return Image.open(io.BytesIO(img_bytes))
|
||||
|
|
@ -4,6 +4,8 @@ import os
|
|||
import modules.scripts as scripts
|
||||
from modules import scripts
|
||||
from scripts.roop_globals import EXTENSION_PATH
|
||||
from modules.shared import opts
|
||||
from scripts.roop_logging import logger
|
||||
|
||||
def get_models():
|
||||
"""
|
||||
|
|
@ -27,6 +29,17 @@ def get_models():
|
|||
|
||||
return models
|
||||
|
||||
def get_current_model() -> str :
|
||||
model = opts.data.get("roop_model", None)
|
||||
if model is None :
|
||||
models = get_models()
|
||||
model = models[0] if len(models) else None
|
||||
logger.info("Try to use model : %s", model)
|
||||
if not os.path.isfile(model):
|
||||
logger.error("The model %s cannot be found or loaded", model)
|
||||
raise FileNotFoundError("No faceswap model found. Please add it to the roop directory.")
|
||||
return model
|
||||
|
||||
def get_face_checkpoints():
|
||||
"""
|
||||
Retrieve a list of face checkpoint paths.
|
||||
|
|
|
|||
Loading…
Reference in New Issue