Upload 14 files

.gitattributes

@@ -33,3 +33,8 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+data/bruce.png filter=lfs diff=lfs merge=lfs -text
+data/hp-3faces.jpeg filter=lfs diff=lfs merge=lfs -text
+data/src.jpg filter=lfs diff=lfs merge=lfs -text
+data/swap_src.jpeg filter=lfs diff=lfs merge=lfs -text
+data/target.jpg filter=lfs diff=lfs merge=lfs -text

.gitignore

@@ -0,0 +1,9 @@
+venv/*
+.vscode
+.idea
+*.jpg
+*.log
+.DS_Store
+checkpoints
+CodeFormer
+__pycache__

Dockerfile

@@ -0,0 +1,19 @@
+FROM python:3.9.9
+
+RUN apt-get update
+
+RUN apt-get install ffmpeg libsm6 libxext6  -y
+
+
+RUN apt-get install -y git git-lfs
+
+WORKDIR /app
+
+COPY  . /app/
+
+
+RUN git lfs install
+
+RUN pip3 install -r requirements.txt
+
+CMD ["python3","app.py","-H","0.0.0.0","-p","5000"]

LICENSE

@@ -0,0 +1,674 @@
+                    GNU GENERAL PUBLIC LICENSE
+                       Version 3, 29 June 2007
+
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+version or of any later version published by the Free Software
+Foundation.  If the Program does not specify a version number of the
+GNU General Public License, you may choose any version ever published
+by the Free Software Foundation.
+
+  If the Program specifies that a proxy can decide which future
+versions of the GNU General Public License can be used, that proxy's
+public statement of acceptance of a version permanently authorizes you
+to choose that version for the Program.
+
+  Later license versions may give you additional or different
+permissions.  However, no additional obligations are imposed on any
+author or copyright holder as a result of your choosing to follow a
+later version.
+
+  15. Disclaimer of Warranty.
+
+  THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY
+APPLICABLE LAW.  EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT
+HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY
+OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO,
+THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
+PURPOSE.  THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM
+IS WITH YOU.  SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF
+ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
+
+  16. Limitation of Liability.
+
+  IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
+WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
+THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
+GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
+USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
+DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
+PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
+EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
+SUCH DAMAGES.
+
+  17. Interpretation of Sections 15 and 16.
+
+  If the disclaimer of warranty and limitation of liability provided
+above cannot be given local legal effect according to their terms,
+reviewing courts shall apply local law that most closely approximates
+an absolute waiver of all civil liability in connection with the
+Program, unless a warranty or assumption of liability accompanies a
+copy of the Program in return for a fee.
+
+                     END OF TERMS AND CONDITIONS
+
+            How to Apply These Terms to Your New Programs
+
+  If you develop a new program, and you want it to be of the greatest
+possible use to the public, the best way to achieve this is to make it
+free software which everyone can redistribute and change under these terms.
+
+  To do so, attach the following notices to the program.  It is safest
+to attach them to the start of each source file to most effectively
+state the exclusion of warranty; and each file should have at least
+the "copyright" line and a pointer to where the full notice is found.
+
+    <one line to give the program's name and a brief idea of what it does.>
+    Copyright (C) <year>  <name of author>
+
+    This program is free software: you can redistribute it and/or modify
+    it under the terms of the GNU General Public License as published by
+    the Free Software Foundation, either version 3 of the License, or
+    (at your option) any later version.
+
+    This program is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+    GNU General Public License for more details.
+
+    You should have received a copy of the GNU General Public License
+    along with this program.  If not, see <https://www.gnu.org/licenses/>.
+
+Also add information on how to contact you by electronic and paper mail.
+
+  If the program does terminal interaction, make it output a short
+notice like this when it starts in an interactive mode:
+
+    <program>  Copyright (C) <year>  <name of author>
+    This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
+    This is free software, and you are welcome to redistribute it
+    under certain conditions; type `show c' for details.
+
+The hypothetical commands `show w' and `show c' should show the appropriate
+parts of the General Public License.  Of course, your program's commands
+might be different; for a GUI interface, you would use an "about box".
+
+  You should also get your employer (if you work as a programmer) or school,
+if any, to sign a "copyright disclaimer" for the program, if necessary.
+For more information on this, and how to apply and follow the GNU GPL, see
+<https://www.gnu.org/licenses/>.
+
+  The GNU General Public License does not permit incorporating your program
+into proprietary programs.  If your program is a subroutine library, you
+may consider it more useful to permit linking proprietary applications with
+the library.  If this is what you want to do, use the GNU Lesser General
+Public License instead of this License.  But first, please read
+<https://www.gnu.org/licenses/why-not-lgpl.html>.

README.md

@@ -1,10 +1,89 @@
----
-title: API
-emoji: 🐨
-colorFrom: purple
-colorTo: yellow
-sdk: docker
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# inswapper-flask-api
+
+Python Flask API for Face Swapper and Restoration
+powered by [insightface](https://github.com/deepinsight/insightface).
+
+## Installation
+
+### Clone this repository
+
+```bash
+git clone https://github.com/ashleykleynhans/inswapper-flask-api.git
+cd inswapper-flask-api
+```
+
+### Install the required Python dependencies
+
+#### Linux and Mac
+
+```
+python3 -m venv venv
+source venv/bin/activate
+pip3 install -r requirements.txt
+```
+
+#### Windows
+
+```
+python3 -m venv venv
+venv\Scripts\activate
+pip3 install -r requirements.txt
+```
+
+## Download Checkpoints
+
+You will need to download the [face swap model](
+https://huggingface.co/ashleykleynhans/inswapper/resolve/main/inswapper_128.onnx) and
+save it under `./checkpoints`.
+
+```bash
+mkdir checkpoints
+wget -O ./checkpoints/inswapper_128.onnx https://huggingface.co/ashleykleynhans/inswapper/resolve/main/inswapper_128.onnx
+```
+
+## Face Restoration
+
+In order to obtain better results, it is highly recommended to enable
+a face restoration model, which will improve image quality.
+This application uses [CodeFormer](https://github.com/sczhou/CodeFormer)
+for face restoration.
+
+The required models will be downloaded automatically the first time
+the face swap API is called.
+
+You will require [Git Large File Storage](
+https://docs.github.com/en/repositories/working-with-files/managing-large-files/installing-git-large-file-storage)
+to be installed on your system before you can run the following command.
+
+```bash
+git lfs install
+git clone https://huggingface.co/spaces/sczhou/CodeFormer
+```
+
+## Examples
+
+Refer to the [examples](./examples) provided for getting started
+with making calls to the API.
+
+## Benchmarks
+
+These benchmarks are for a source image with a resolution of 960x1280
+and a target image with a resolution of 1200x750, upscaled by 1 and
+with CodeFormer Face Restoration enabled.
+
+| System                                      | Time Taken    |
+|---------------------------------------------|---------------|
+| macOS Ventura 13.4.1 on Apple M1 Max        | 68.6 seconds  |
+| Ubuntu 22.04 LTS on t3a.xlarge AWS instance | 248.1 seconds |
+| Ubuntu 22.04 LTS on an A5000 RunPod GPU pod | 14.2 seconds  |
+| Windows 10                                  | 103.9 seconds |
+
+Get a [RunPod](https://runpod.io?ref=w18gds2n) account.
+
+## Acknowledgements
+
+1. Thanks [insightface.ai](https://insightface.ai/) for releasing their powerful swap model that made this possible.
+2. This codebase is built on top of [inswapper](https://github.com/haofanwang/inswapper) and [CodeFormer](
+   https://huggingface.co/spaces/sczhou/CodeFormer).
+3. [inswapper](https://github.com/haofanwang/inswapper) is built on the top of [sd-webui-roop](
+   https://github.com/s0md3v/sd-webui-roop) and [CodeFormer](https://huggingface.co/spaces/sczhou/CodeFormer).

app.py

@@ -0,0 +1,559 @@
+#!/usr/bin/env python3
+import json
+import sys
+import os
+import io
+import argparse
+import uuid
+import base64
+import logging
+import time
+import copy
+import cv2
+import insightface
+import numpy as np
+from typing import List, Union
+from PIL import Image
+from restoration import *
+from flask import Flask, request, jsonify, make_response
+from waitress import serve
+
+LOG_LEVEL = logging.DEBUG
+TMP_PATH = '/tmp/inswapper'
+script_dir = os.path.dirname(os.path.abspath(__file__))
+log_path = ''
+
+# Mac does not have permission to /var/log for example
+if sys.platform == 'linux':
+    log_path = '/var/log/'
+
+logging.basicConfig(
+    filename=f'{log_path}inswapper.log',
+    format='%(asctime)s : %(levelname)s : %(message)s',
+    level=LOG_LEVEL
+)
+
+logging.getLogger().addHandler(logging.StreamHandler(sys.stdout))
+
+
+def process_request(request_obj):
+    try:
+        logging.debug('Swapping face')
+        face_swap_timer = Timer()
+        result_image = face_swap(request_obj['source_image'], request_obj['target_image'])
+        face_swap_time = face_swap_timer.get_elapsed_time()
+        logging.info(f'Time taken to swap face: {face_swap_time} seconds')
+
+        response = {
+            'status': 'ok',
+            'image': result_image
+        }
+    except Exception as e:
+        logging.error(e)
+        response = {
+            'status': 'error',
+            'msg': 'Face swap failed',
+            'detail': str(e)
+        }
+
+    return response
+
+
+class Timer:
+    def __init__(self):
+        self.start = time.time()
+
+    def restart(self):
+        self.start = time.time()
+
+    def get_elapsed_time(self):
+        end = time.time()
+        return round(end - self.start, 1)
+
+
+def get_args():
+    parser = argparse.ArgumentParser(
+        description='Inswapper REST API'
+    )
+
+    parser.add_argument(
+        '-p', '--port',
+        help='Port to listen on',
+        type=int,
+        default=8090
+    )
+
+    parser.add_argument(
+        '-H', '--host',
+        help='Host to bind to',
+        default='0.0.0.0'
+    )
+
+    return parser.parse_args()
+
+
+def determine_file_extension(image_data):
+    try:
+        if image_data.startswith('/9j/'):
+            image_extension = '.jpg'
+        elif image_data.startswith('iVBORw0Kg'):
+            image_extension = '.png'
+        else:
+            # Default to png if we can't figure out the extension
+            image_extension = '.png'
+    except Exception as e:
+        image_extension = '.png'
+
+    return image_extension
+
+
+def write_base64_to_disk(file_b64: str, file_path: str):
+    with open(file_path, 'wb') as file:
+        file.write(base64.b64decode(file_b64))
+
+
+def get_face_swap_model(model_path: str):
+    model = insightface.model_zoo.get_model(model_path)
+    return model
+
+
+def get_face_analyser(model_path: str,
+                      det_size=(320, 320)):
+    face_analyser = insightface.app.FaceAnalysis(name="buffalo_l", root="./checkpoints")
+    face_analyser.prepare(ctx_id=0, det_size=det_size)
+    return face_analyser
+
+
+def get_one_face(face_analyser,
+                 frame:np.ndarray):
+    face = face_analyser.get(frame)
+    try:
+        return min(face, key=lambda x: x.bbox[0])
+    except ValueError:
+        return None
+
+
+def get_many_faces(face_analyser,
+                   frame:np.ndarray):
+    """
+    get faces from left to right by order
+    """
+    try:
+        face = face_analyser.get(frame)
+        return sorted(face, key=lambda x: x.bbox[0])
+    except IndexError:
+        return None
+
+
+def swap_face(face_swapper,
+              source_faces,
+              target_faces,
+              source_index,
+              target_index,
+              temp_frame):
+    """
+    paste source_face on target image
+    """
+    source_face = source_faces[source_index]
+    target_face = target_faces[target_index]
+
+    return face_swapper.get(temp_frame, target_face, source_face, paste_back=True)
+
+
+def process(source_img: Union[Image.Image, List],
+            target_img: Image.Image,
+            source_indexes: str,
+            target_indexes: str,
+            model: str):
+
+    # load face_analyser
+    face_analyser = get_face_analyser(model)
+
+    # load face_swapper
+    model_path = os.path.join(os.path.abspath(os.path.dirname(__file__)), model)
+    face_swapper = get_face_swap_model(model_path)
+
+    # read target image
+    target_img = cv2.cvtColor(np.array(target_img), cv2.COLOR_RGB2BGR)
+
+    # detect faces that will be replaced in target_img
+    target_faces = get_many_faces(face_analyser, target_img)
+    num_target_faces = len(target_faces)
+    num_source_images = len(source_img)
+
+    if target_faces is not None:
+        temp_frame = copy.deepcopy(target_img)
+        if isinstance(source_img, list) and num_source_images == num_target_faces:
+            logging.debug('Replacing the faces in the target image from left to right by order')
+            for i in range(num_target_faces):
+                source_faces = get_many_faces(face_analyser, cv2.cvtColor(np.array(source_img[i]), cv2.COLOR_RGB2BGR))
+                source_index = i
+                target_index = i
+
+                if source_faces is None:
+                    raise Exception('No source faces found!')
+
+                temp_frame = swap_face(
+                    face_swapper,
+                    source_faces,
+                    target_faces,
+                    source_index,
+                    target_index,
+                    temp_frame
+                )
+        elif num_source_images == 1:
+            # detect source faces that will be replaced into the target image
+            source_faces = get_many_faces(face_analyser, cv2.cvtColor(np.array(source_img[0]), cv2.COLOR_RGB2BGR))
+            num_source_faces = len(source_faces)
+            logging.debug(f'Source faces: {num_source_faces}')
+            logging.debug(f'Target faces: {num_target_faces}')
+
+            if source_faces is None:
+                raise Exception('No source faces found!')
+
+            if target_indexes == "-1":
+                if num_source_faces == 1:
+                    logging.debug('Replacing all faces in target image with the same face from the source image')
+                    num_iterations = num_target_faces
+                elif num_source_faces < num_target_faces:
+                    logging.debug('There are less faces in the source image than the target image, replacing as many as we can')
+                    num_iterations = num_source_faces
+                elif num_target_faces < num_source_faces:
+                    logging.debug('There are less faces in the target image than the source image, replacing as many as we can')
+                    num_iterations = num_target_faces
+                else:
+                    logging.debug('Replacing all faces in the target image with the faces from the source image')
+                    num_iterations = num_target_faces
+
+                for i in range(num_iterations):
+                    source_index = 0 if num_source_faces == 1 else i
+                    target_index = i
+
+                    temp_frame = swap_face(
+                        face_swapper,
+                        source_faces,
+                        target_faces,
+                        source_index,
+                        target_index,
+                        temp_frame
+                    )
+            elif source_indexes == '-1' and target_indexes == '-1':
+                logging.debug('Replacing specific face(s) in the target image with the face from the source image')
+                target_indexes = target_indexes.split(',')
+                source_index = 0
+
+                for target_index in target_indexes:
+                    target_index = int(target_index)
+
+                    temp_frame = swap_face(
+                        face_swapper,
+                        source_faces,
+                        target_faces,
+                        source_index,
+                        target_index,
+                        temp_frame
+                    )
+            else:
+                logging.debug('Replacing specific face(s) in the target image with specific face(s) from the source image')
+
+                if source_indexes == "-1":
+                    source_indexes = ','.join(map(lambda x: str(x), range(num_source_faces)))
+
+                if target_indexes == "-1":
+                    target_indexes = ','.join(map(lambda x: str(x), range(num_target_faces)))
+
+                source_indexes = source_indexes.split(',')
+                target_indexes = target_indexes.split(',')
+                num_source_faces_to_swap = len(source_indexes)
+                num_target_faces_to_swap = len(target_indexes)
+
+                if num_source_faces_to_swap > num_source_faces:
+                    raise Exception('Number of source indexes is greater than the number of faces in the source image')
+
+                if num_target_faces_to_swap > num_target_faces:
+                    raise Exception('Number of target indexes is greater than the number of faces in the target image')
+
+                if num_source_faces_to_swap > num_target_faces_to_swap:
+                    num_iterations = num_source_faces_to_swap
+                else:
+                    num_iterations = num_target_faces_to_swap
+
+                if num_source_faces_to_swap == num_target_faces_to_swap:
+                    for index in range(num_iterations):
+                        source_index = int(source_indexes[index])
+                        target_index = int(target_indexes[index])
+
+                        if source_index > num_source_faces-1:
+                            raise ValueError(f'Source index {source_index} is higher than the number of faces in the source image')
+
+                        if target_index > num_target_faces-1:
+                            raise ValueError(f'Target index {target_index} is higher than the number of faces in the target image')
+
+                        temp_frame = swap_face(
+                            face_swapper,
+                            source_faces,
+                            target_faces,
+                            source_index,
+                            target_index,
+                            temp_frame
+                        )
+        else:
+            logging.error('Unsupported face configuration')
+            raise Exception('Unsupported face configuration')
+        result = temp_frame
+    else:
+        logging.error('No target faces found')
+        raise Exception('No target faces found!')
+
+    result_image = Image.fromarray(cv2.cvtColor(result, cv2.COLOR_BGR2RGB))
+    return result_image
+
+
+def face_swap(src_img_path,
+              target_img_path,
+              source_indexes,
+              target_indexes,
+              background_enhance,
+              face_restore,
+              face_upsample,
+              upscale,
+              codeformer_fidelity,
+              output_format):
+
+    source_img_paths = src_img_path.split(';')
+    source_img = [Image.open(img_path) for img_path in source_img_paths]
+    target_img = Image.open(target_img_path)
+
+    # download from https://huggingface.co/ashleykleynhans/inswapper/tree/main
+    model = os.path.join(script_dir, 'checkpoints/inswapper_128.onnx')
+    logging.debug(f'Face swap model: {model}')
+
+    try:
+        logging.debug('Performing face swap')
+        result_image = process(
+            source_img,
+            target_img,
+            source_indexes,
+            target_indexes,
+            model
+        )
+        logging.debug('Face swap complete')
+    except Exception as e:
+        raise
+
+    # make sure the ckpts downloaded successfully
+    check_ckpts()
+
+    if face_restore:
+        # https://huggingface.co/spaces/sczhou/CodeFormer
+        logging.debug('Setting upsampler to RealESRGAN_x2plus')
+        upsampler = set_realesrgan()
+
+        if torch.cuda.is_available():
+            torch_device = 'cuda'
+        else:
+            torch_device = 'cpu'
+
+        logging.debug(f'Torch device: {torch_device.upper()}')
+        device = torch.device(torch_device)
+
+        codeformer_net = ARCH_REGISTRY.get('CodeFormer')(
+            dim_embd=512,
+            codebook_size=1024,
+            n_head=8,
+            n_layers=9,
+            connect_list=['32', '64', '128', '256'],
+        ).to(device)
+
+        ckpt_path = os.path.join(script_dir, 'CodeFormer/CodeFormer/weights/CodeFormer/codeformer.pth')
+        logging.debug(f'Loading CodeFormer model: {ckpt_path}')
+        checkpoint = torch.load(ckpt_path)['params_ema']
+        codeformer_net.load_state_dict(checkpoint)
+        codeformer_net.eval()
+        result_image = cv2.cvtColor(np.array(result_image), cv2.COLOR_RGB2BGR)
+        logging.debug('Performing face restoration using CodeFormer')
+
+        try:
+            result_image = face_restoration(
+                result_image,
+                background_enhance,
+                face_upsample,
+                upscale,
+                codeformer_fidelity,
+                upsampler,
+                codeformer_net,
+                device
+            )
+        except Exception as e:
+            raise
+
+        logging.debug('CodeFormer face restoration completed successfully')
+        result_image = Image.fromarray(result_image)
+
+    output_buffer = io.BytesIO()
+    result_image.save(output_buffer, format=output_format)
+    image_data = output_buffer.getvalue()
+
+    return base64.b64encode(image_data).decode('utf-8')
+
+
+app = Flask(__name__)
+
+
+@app.errorhandler(400)
+def not_found(error):
+    return make_response(jsonify(
+        {
+            'status': 'error',
+            'msg': f'Bad Request',
+            'detail': str(error)
+        }
+    ), 400)
+
+
+@app.errorhandler(404)
+def not_found(error):
+    return make_response(jsonify(
+        {
+            'status': 'error',
+            'msg': f'{request.url} not found',
+            'detail': str(error)
+        }
+    ), 404)
+
+
+@app.errorhandler(500)
+def internal_server_error(error):
+    return make_response(jsonify(
+        {
+            'status': 'error',
+            'msg': 'Internal Server Error',
+            'detail': str(error)
+        }
+    ), 500)
+
+
+@app.route('/', methods=['GET'])
+def ping():
+    return make_response(jsonify(
+        {
+            'status': 'ok'
+        }
+    ), 200)
+
+
+@app.route('/faceswap', methods=['POST'])
+def face_swap_api():
+    total_timer = Timer()
+    logging.debug('Received face swap API request')
+    payload = request.get_json()
+
+    if not os.path.exists(TMP_PATH):
+        logging.debug(f'Creating temporary directory: {TMP_PATH}')
+        os.makedirs(TMP_PATH)
+
+    unique_id = uuid.uuid4()
+    source_image_data = payload['source_image']
+    target_image_data = payload['target_image']
+
+    # Decode the source image data
+    source_image = base64.b64decode(source_image_data)
+    source_file_extension = determine_file_extension(source_image_data)
+    source_image_path = f'{TMP_PATH}/source_{unique_id}{source_file_extension}'
+
+    # Save the source image to disk
+    with open(source_image_path, 'wb') as source_file:
+        source_file.write(source_image)
+
+    # Decode the target image data
+    target_image = base64.b64decode(target_image_data)
+    target_file_extension = determine_file_extension(target_image_data)
+    target_image_path = f'{TMP_PATH}/target_{unique_id}{target_file_extension}'
+
+    # Save the target image to disk
+    with open(target_image_path, 'wb') as target_file:
+        target_file.write(target_image)
+
+    # Set defaults if they are not specified in the payload
+    if 'source_indexes' not in payload:
+        payload['source_indexes'] = '-1'
+
+    if 'target_indexes' not in payload:
+        payload['target_indexes'] = '-1'
+
+    if 'background_enhance' not in payload:
+        payload['background_enhance'] = True
+
+    if 'face_restore' not in payload:
+        payload['face_restore'] = True
+
+    if 'face_upsample' not in payload:
+        payload['face_upsample'] = True
+
+    if 'upscale' not in payload:
+        payload['upscale'] = 1
+
+    if 'codeformer_fidelity' not in payload:
+        payload['codeformer_fidelity'] = 0.5
+
+    if 'output_format' not in payload:
+        payload['output_format'] = 'JPEG'
+
+    try:
+        logging.debug(f'Source indexes: {payload["source_indexes"]}')
+        logging.debug(f'Target indexes: {payload["target_indexes"]}')
+        logging.debug(f'Background enhance: {payload["background_enhance"]}')
+        logging.debug(f'Face Restoration: {payload["face_restore"]}')
+        logging.debug(f'Face Upsampling: {payload["face_upsample"]}')
+        logging.debug(f'Upscale: {payload["upscale"]}')
+        logging.debug(f'Codeformer Fidelity: {payload["codeformer_fidelity"]}')
+        logging.debug(f'Output Format: {payload["output_format"]}')
+
+        result_image = face_swap(
+            source_image_path,
+            target_image_path,
+            payload['source_indexes'],
+            payload['target_indexes'],
+            payload['background_enhance'],
+            payload['face_restore'],
+            payload['face_upsample'],
+            payload['upscale'],
+            payload['codeformer_fidelity'],
+            payload['output_format']
+        )
+
+        status_code = 200
+
+        response = {
+            'status': 'ok',
+            'image': result_image
+        }
+    except Exception as e:
+        logging.error(e)
+
+        response = {
+            'status': 'error',
+            'msg': 'Face swap failed',
+            'detail': str(e)
+        }
+
+        status_code = 500
+
+    # Clean up temporary images
+    os.remove(source_image_path)
+    os.remove(target_image_path)
+
+    total_time = total_timer.get_elapsed_time()
+    logging.info(f'Total time taken for face swap API call {total_time} seconds')
+
+    return make_response(jsonify(response), status_code)
+
+
+if __name__ == '__main__':
+    args = get_args()
+
+    serve(
+        app,
+        host=args.host,
+        port=args.port
+    )

examples/face_swap.py

@@ -0,0 +1,68 @@
+#!/usr/bin/env python3
+import io
+import json
+import uuid
+import requests
+import base64
+from PIL import Image
+
+URL = 'http://127.0.0.1:5000/faceswap'
+
+SOURCE_IMAGE = '../data/src.jpg'
+TARGET_IMAGE = '../data/target.jpg'
+SOURCE_INDEXES = '-1'
+TARGET_INDEXES = '-1'
+BACKGROUND_ENHANCE = True
+FACE_RESTORE = True
+FACE_UPSAMPLE = True
+UPSCALE = 1
+CODEFORMER_FIDELITY = 0.5
+OUTPUT_FORMAT = 'JPEG'
+
+
+def encode_image_to_base64(image_path):
+    with open(image_path, 'rb') as image_file:
+        return base64.b64encode(image_file.read()).decode('utf-8')
+
+
+def save_result_image(resp_json):
+    img = Image.open(io.BytesIO(base64.b64decode(resp_json['output']['image'])))
+    output_file = f'{uuid.uuid4()}.jpg'
+
+    with open(output_file, 'wb') as f:
+        print(f'Saving image: {output_file}')
+        img.save(f, format='JPEG')
+
+
+if __name__ == '__main__':
+    payload = {
+        "source_image": encode_image_to_base64(SOURCE_IMAGE),
+        "target_image": encode_image_to_base64(TARGET_IMAGE),
+        "source_indexes": SOURCE_INDEXES,
+        "target_indexes": TARGET_INDEXES,
+        "background_enhance": BACKGROUND_ENHANCE,
+        "face_restore": FACE_RESTORE,
+        "face_upsample": FACE_UPSAMPLE,
+        "upscale": UPSCALE,
+        "codeformer_fidelity": CODEFORMER_FIDELITY,
+        "output_format": OUTPUT_FORMAT
+    }
+
+    r = requests.post(
+        URL,
+        json=payload
+    )
+
+    print(f'HTTP status code: {r.status_code}')
+
+    resp_json = r.json()
+
+    if r.status_code == 200:
+        img = Image.open(io.BytesIO(base64.b64decode(resp_json['image'])))
+        output_file = f'{uuid.uuid4()}.jpg'
+
+        with open(output_file, 'wb') as f:
+            print(f'Saving image: {output_file}')
+            img.save(f, format='JPEG')
+    else:
+        print(json.dumps(resp_json, indent=4, default=str))

requirements.txt

@@ -0,0 +1,21 @@
+Pillow
+PyYAML
+addict
+flask
+future
+insightface>=0.7.3
+lmdb
+lpips
+numpy
+onnxruntime
+opencv-python
+pillow>=9.5.0
+protobuf>=4.23.2
+scikit-image
+scipy
+tb-nightly
+torch
+torchvision
+tqdm
+yapf
+waitress

restoration.py

@@ -0,0 +1,160 @@
+import sys
+sys.path.append('./CodeFormer/CodeFormer')
+
+import os
+import cv2
+import torch
+import torch.nn.functional as F
+from torchvision.transforms.functional import normalize
+
+from basicsr.utils import imwrite, img2tensor, tensor2img
+from basicsr.utils.download_util import load_file_from_url
+from facelib.utils.face_restoration_helper import FaceRestoreHelper
+from facelib.utils.misc import is_gray
+from basicsr.archs.rrdbnet_arch import RRDBNet
+from basicsr.utils.realesrgan_utils import RealESRGANer
+from basicsr.utils.registry import ARCH_REGISTRY
+
+
+def check_ckpts():
+    pretrain_model_url = {
+        'codeformer': 'https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/codeformer.pth',
+        'detection': 'https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/detection_Resnet50_Final.pth',
+        'parsing': 'https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/parsing_parsenet.pth',
+        'realesrgan': 'https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/RealESRGAN_x2plus.pth'
+    }
+    # download weights
+    if not os.path.exists('CodeFormer/CodeFormer/weights/CodeFormer/codeformer.pth'):
+        load_file_from_url(url=pretrain_model_url['codeformer'], model_dir='CodeFormer/CodeFormer/weights/CodeFormer', progress=True, file_name=None)
+    if not os.path.exists('CodeFormer/CodeFormer/weights/facelib/detection_Resnet50_Final.pth'):
+        load_file_from_url(url=pretrain_model_url['detection'], model_dir='CodeFormer/CodeFormer/weights/facelib', progress=True, file_name=None)
+    if not os.path.exists('CodeFormer/CodeFormer/weights/facelib/parsing_parsenet.pth'):
+        load_file_from_url(url=pretrain_model_url['parsing'], model_dir='CodeFormer/CodeFormer/weights/facelib', progress=True, file_name=None)
+    if not os.path.exists('CodeFormer/CodeFormer/weights/realesrgan/RealESRGAN_x2plus.pth'):
+        load_file_from_url(url=pretrain_model_url['realesrgan'], model_dir='CodeFormer/CodeFormer/weights/realesrgan', progress=True, file_name=None)
+    
+    
+# set enhancer with RealESRGAN
+def set_realesrgan():
+    half = True if torch.cuda.is_available() else False
+    model = RRDBNet(
+        num_in_ch=3,
+        num_out_ch=3,
+        num_feat=64,
+        num_block=23,
+        num_grow_ch=32,
+        scale=2,
+    )
+    upsampler = RealESRGANer(
+        scale=2,
+        model_path="CodeFormer/CodeFormer/weights/realesrgan/RealESRGAN_x2plus.pth",
+        model=model,
+        tile=400,
+        tile_pad=40,
+        pre_pad=0,
+        half=half,
+    )
+    return upsampler
+
+
+def face_restoration(img, background_enhance, face_upsample, upscale, codeformer_fidelity, upsampler, codeformer_net, device):
+    """Run a single prediction on the model"""
+    try: # global try
+        # take the default setting for the demo
+        has_aligned = False
+        only_center_face = False
+        draw_box = False
+        detection_model = "retinaface_resnet50"
+
+        background_enhance = background_enhance if background_enhance is not None else True
+        face_upsample = face_upsample if face_upsample is not None else True
+        upscale = upscale if (upscale is not None and upscale > 0) else 2
+
+        upscale = int(upscale) # convert type to int
+        if upscale > 4: # avoid memory exceeded due to too large upscale
+            upscale = 4 
+        if upscale > 2 and max(img.shape[:2])>1000: # avoid memory exceeded due to too large img resolution
+            upscale = 2 
+        if max(img.shape[:2]) > 1500: # avoid memory exceeded due to too large img resolution
+            upscale = 1
+            background_enhance = False
+            face_upsample = False
+
+        face_helper = FaceRestoreHelper(
+            upscale,
+            face_size=512,
+            crop_ratio=(1, 1),
+            det_model=detection_model,
+            save_ext="png",
+            use_parse=True,
+            device=device,
+        )
+        bg_upsampler = upsampler if background_enhance else None
+        face_upsampler = upsampler if face_upsample else None
+
+        if has_aligned:
+            # the input faces are already cropped and aligned
+            img = cv2.resize(img, (512, 512), interpolation=cv2.INTER_LINEAR)
+            face_helper.is_gray = is_gray(img, threshold=5)
+            face_helper.cropped_faces = [img]
+        else:
+            face_helper.read_image(img)
+            # get face landmarks for each face
+            num_det_faces = face_helper.get_face_landmarks_5(
+            only_center_face=only_center_face, resize=640, eye_dist_threshold=5
+            )
+            # align and warp each face
+            face_helper.align_warp_face()
+
+        # face restoration for each cropped face
+        for idx, cropped_face in enumerate(face_helper.cropped_faces):
+            # prepare data
+            cropped_face_t = img2tensor(
+                cropped_face / 255.0, bgr2rgb=True, float32=True
+            )
+            normalize(cropped_face_t, (0.5, 0.5, 0.5), (0.5, 0.5, 0.5), inplace=True)
+            cropped_face_t = cropped_face_t.unsqueeze(0).to(device)
+
+            try:
+                with torch.no_grad():
+                    output = codeformer_net(
+                        cropped_face_t, w=codeformer_fidelity, adain=True
+                    )[0]
+                    restored_face = tensor2img(output, rgb2bgr=True, min_max=(-1, 1))
+                del output
+                torch.cuda.empty_cache()
+            except RuntimeError as error:
+                print(f"Failed inference for CodeFormer: {error}")
+                restored_face = tensor2img(
+                    cropped_face_t, rgb2bgr=True, min_max=(-1, 1)
+                )
+
+            restored_face = restored_face.astype("uint8")
+            face_helper.add_restored_face(restored_face)
+
+        # paste_back
+        if not has_aligned:
+            # upsample the background
+            if bg_upsampler is not None:
+                # Now only support RealESRGAN for upsampling background
+                bg_img = bg_upsampler.enhance(img, outscale=upscale)[0]
+            else:
+                bg_img = None
+            face_helper.get_inverse_affine(None)
+            # paste each restored face to the input image
+            if face_upsample and face_upsampler is not None:
+                restored_img = face_helper.paste_faces_to_input_image(
+                    upsample_img=bg_img,
+                    draw_box=draw_box,
+                    face_upsampler=face_upsampler,
+                )
+            else:
+                restored_img = face_helper.paste_faces_to_input_image(
+                    upsample_img=bg_img, draw_box=draw_box
+                )
+
+        restored_img = cv2.cvtColor(restored_img, cv2.COLOR_BGR2RGB)
+        return restored_img
+    except Exception as error:
+        print('Global exception', error)
+        return None, None