diff --git a/roop-unleashed-main/.flake8 b/roop-unleashed-main/.flake8
deleted file mode 100644
index 43a1b76932b6cb62486ec7e925caf1853693a403..0000000000000000000000000000000000000000
--- a/roop-unleashed-main/.flake8
+++ /dev/null
@@ -1,3 +0,0 @@
-[flake8]
-select = E3, E4, F
-per-file-ignores = roop/core.py:E402
\ No newline at end of file
diff --git a/roop-unleashed-main/.github/ISSUE_TEMPLATE/bug_report.md b/roop-unleashed-main/.github/ISSUE_TEMPLATE/bug_report.md
deleted file mode 100644
index e8e22cd1eeec326f617f42dd87739c2b0a201ecb..0000000000000000000000000000000000000000
--- a/roop-unleashed-main/.github/ISSUE_TEMPLATE/bug_report.md
+++ /dev/null
@@ -1,37 +0,0 @@
----
-name: Bug report
-about: Create a report to help us improve
-title: ''
-labels: ''
-assignees: ''
-
----
-
-**Describe the bug**
-A clear and concise description of what the bug is.
-
-**To Reproduce**
-Steps to reproduce the behavior:
-1. Go to '...'
-2. Click on '....'
-3. Scroll down to '....'
-4. See error
-
-**Details**
-What OS are you using?
-- [ ] Linux
-- [ ] Linux in WSL
-- [ ] Windows
-- [ ] Mac
-
-Are you using a GPU?
-- [ ] No. CPU FTW
-- [ ] NVIDIA
-- [ ] AMD
-- [ ] Intel
-- [ ] Mac
-
-**Which version of roop unleashed are you using?**
-
-**Screenshots**
-If applicable, add screenshots to help explain your problem.
diff --git a/roop-unleashed-main/.github/workflows/stale.yml b/roop-unleashed-main/.github/workflows/stale.yml
deleted file mode 100644
index 87169171c24c7a2f27c88f7a1d00b654afad90d3..0000000000000000000000000000000000000000
--- a/roop-unleashed-main/.github/workflows/stale.yml
+++ /dev/null
@@ -1,29 +0,0 @@
-# This workflow warns and then closes issues and PRs that have had no activity for a specified amount of time.
-#
-# You can adjust the behavior by modifying this file.
-# For more information, see:
-# https://github.com/actions/stale
-name: Mark stale issues and pull requests
-
-on:
- schedule:
- - cron: '32 0 * * *'
-
-jobs:
- stale:
-
- runs-on: ubuntu-latest
- permissions:
- issues: write
- pull-requests: write
-
- steps:
- - uses: actions/stale@v5
- with:
- repo-token: ${{ secrets.GITHUB_TOKEN }}
- stale-issue-message: 'This issue is stale because it has been open 30 days with no activity. Remove stale label or comment or this will be closed in 5 days.'
- stale-pr-message: 'This PR is stale because it has been open 45 days with no activity. Remove stale label or comment or this will be closed in 10 days.'
- close-issue-message: 'This issue was closed because it has been stalled for 5 days with no activity.'
- days-before-stale: 30
- days-before-close: 5
- days-before-pr-close: -1
diff --git a/roop-unleashed-main/.gitignore b/roop-unleashed-main/.gitignore
deleted file mode 100644
index de72980338a5aed2296b06c24fb4e1bb0be7751b..0000000000000000000000000000000000000000
--- a/roop-unleashed-main/.gitignore
+++ /dev/null
@@ -1,15 +0,0 @@
-.vs
-.idea
-models
-temp
-__pycache__
-*.pth
-/start.bat
-/env
-.vscode
-output
-temp
-config.yaml
-run.bat
-venv
-start.sh
\ No newline at end of file
diff --git a/roop-unleashed-main/Dockerfile b/roop-unleashed-main/Dockerfile
deleted file mode 100644
index 1fef507110d4bba6658c0d26af3f29388c032a0d..0000000000000000000000000000000000000000
--- a/roop-unleashed-main/Dockerfile
+++ /dev/null
@@ -1,18 +0,0 @@
-FROM python:3.11
-
-# making app folder
-WORKDIR /app
-
-# copying files
-COPY . .
-
-# installing requirements
-RUN apt-get update
-RUN apt-get install ffmpeg -y
-RUN pip install --upgrade pip
-RUN pip install -r ./requirements.txt
-
-# launching gradio app
-ENV GRADIO_SERVER_NAME="0.0.0.0"
-EXPOSE 7860
-ENTRYPOINT python ./run.py
\ No newline at end of file
diff --git a/roop-unleashed-main/LICENSE b/roop-unleashed-main/LICENSE
deleted file mode 100644
index 0ad25db4bd1d86c452db3f9602ccdbe172438f52..0000000000000000000000000000000000000000
--- a/roop-unleashed-main/LICENSE
+++ /dev/null
@@ -1,661 +0,0 @@
- GNU AFFERO GENERAL PUBLIC LICENSE
- Version 3, 19 November 2007
-
- Copyright (C) 2007 Free Software Foundation, Inc.
- Everyone is permitted to copy and distribute verbatim copies
- of this license document, but changing it is not allowed.
-
- Preamble
-
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-software and other kinds of works, specifically designed to ensure
-cooperation with the community in the case of network server software.
-
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-to take away your freedom to share and change the works. By contrast,
-our General Public Licenses are intended to guarantee your freedom to
-share and change all versions of a program--to make sure it remains free
-software for all its users.
-
- When we speak of free software, we are referring to freedom, not
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-the scope of its coverage, prohibits the exercise of, or is
-conditioned on the non-exercise of one or more of the rights that are
-specifically granted under this License. You may not convey a covered
-work if you are a party to an arrangement with a third party that is
-in the business of distributing software, under which you make payment
-to the third party based on the extent of your activity of conveying
-the work, and under which the third party grants, to any of the
-parties who would receive the covered work from you, a discriminatory
-patent license (a) in connection with copies of the covered work
-conveyed by you (or copies made from those copies), or (b) primarily
-for and in connection with specific products or compilations that
-contain the covered work, unless you entered into that arrangement,
-or that patent license was granted, prior to 28 March 2007.
-
- Nothing in this License shall be construed as excluding or limiting
-any implied license or other defenses to infringement that may
-otherwise be available to you under applicable patent law.
-
- 12. No Surrender of Others' Freedom.
-
- If conditions are imposed on you (whether by court order, agreement or
-otherwise) that contradict the conditions of this License, they do not
-excuse you from the conditions of this License. If you cannot convey a
-covered work so as to satisfy simultaneously your obligations under this
-License and any other pertinent obligations, then as a consequence you may
-not convey it at all. For example, if you agree to terms that obligate you
-to collect a royalty for further conveying from those to whom you convey
-the Program, the only way you could satisfy both those terms and this
-License would be to refrain entirely from conveying the Program.
-
- 13. Remote Network Interaction; Use with the GNU General Public License.
-
- Notwithstanding any other provision of this License, if you modify the
-Program, your modified version must prominently offer all users
-interacting with it remotely through a computer network (if your version
-supports such interaction) an opportunity to receive the Corresponding
-Source of your version by providing access to the Corresponding Source
-from a network server at no charge, through some standard or customary
-means of facilitating copying of software. This Corresponding Source
-shall include the Corresponding Source for any work covered by version 3
-of the GNU General Public License that is incorporated pursuant to the
-following paragraph.
-
- Notwithstanding any other provision of this License, you have
-permission to link or combine any covered work with a work licensed
-under version 3 of the GNU General Public License into a single
-combined work, and to convey the resulting work. The terms of this
-License will continue to apply to the part which is the covered work,
-but the work with which it is combined will remain governed by version
-3 of the GNU General Public License.
-
- 14. Revised Versions of this License.
-
- The Free Software Foundation may publish revised and/or new versions of
-the GNU Affero General Public License from time to time. Such new versions
-will be similar in spirit to the present version, but may differ in detail to
-address new problems or concerns.
-
- Each version is given a distinguishing version number. If the
-Program specifies that a certain numbered version of the GNU Affero General
-Public License "or any later version" applies to it, you have the
-option of following the terms and conditions either of that numbered
-version or of any later version published by the Free Software
-Foundation. If the Program does not specify a version number of the
-GNU Affero 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 Affero 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.
-
-
- Copyright (C)
-
- This program is free software: you can redistribute it and/or modify
- it under the terms of the GNU Affero 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 Affero General Public License for more details.
-
- You should have received a copy of the GNU Affero General Public License
- along with this program. If not, see .
-
-Also add information on how to contact you by electronic and paper mail.
-
- If your software can interact with users remotely through a computer
-network, you should also make sure that it provides a way for users to
-get its source. For example, if your program is a web application, its
-interface could display a "Source" link that leads users to an archive
-of the code. There are many ways you could offer source, and different
-solutions will be better for different programs; see section 13 for the
-specific requirements.
-
- 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 AGPL, see
-.
diff --git a/roop-unleashed-main/README.md b/roop-unleashed-main/README.md
deleted file mode 100644
index 0773a4bbc6fda74e2a063d1282a9c190658f9d5b..0000000000000000000000000000000000000000
--- a/roop-unleashed-main/README.md
+++ /dev/null
@@ -1,253 +0,0 @@
-# roop-unleashed
-
-[Changelog](#changelog) โข [Usage](#usage) โข [Wiki](https://github.com/C0untFloyd/roop-unleashed/wiki)
-
-
-Uncensored Deepfakes for images and videos without training and an easy-to-use GUI.
-
-
-
-
-### Features
-
-- Platform-independant Browser GUI
-- Selection of multiple input/output faces in one go
-- Many different swapping modes, first detected, face selections, by gender
-- Batch processing of images/videos
-- Masking of face occluders using text prompts or automatically
-- Optional Face Upscaler/Restoration using different enhancers
-- Preview swapping from different video frames
-- Live Fake Cam using your webcam
-- Extras Tab for cutting videos etc.
-- Settings - storing configuration for next session
-- Theme Support
-
-and lots more...
-
-
-## Disclaimer
-
-This project is for technical and academic use only.
-Users of this software are expected to use this software responsibly while abiding the local law. If a face of a real person is being used, users are suggested to get consent from the concerned person and clearly mention that it is a deepfake when posting content online. Developers of this software will not be responsible for actions of end-users.
-**Please do not apply it to illegal and unethical scenarios.**
-
-In the event of violation of the legal and ethical requirements of the user's country or region, this code repository is exempt from liability
-
-### Installation
-
-Please refer to the [wiki](https://github.com/C0untFloyd/roop-unleashed/wiki).
-
-#### macOS Installation
-Simply run the following command. It will check and install all dependencies if necessary.
-
-`/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/PJF16/roop-unleashed/master/installer/macOSinstaller.sh)"`
-
-
-
-### Usage
-
-- Windows: run the `windows_run.bat` from the Installer.
-- Linux: `python run.py`
-- macOS: `sh runMacOS.sh`
-- Dockerfile:
- ```shell
- docker build -t roop-unleashed . && docker run -t \
- -p 7860:7860 \
- -v ./config.yaml:/app/config.yaml \
- -v ./models:/app/models \
- -v ./temp:/app/temp \
- -v ./output:/app/output \
- roop-unleashed
- ```
-
-
- 
-
-
-
-Additional commandline arguments are currently unsupported and settings should be done via the UI.
-
-> Note: When you run this program for the first time, it will download some models roughly ~2Gb in size.
-
-
-
-
-### Changelog
-
-**31.12.2024** v4.4.0 Hotfix
-
-Bugfix: Updated Colab to use present Cuda Drivers
-Bugfix: Live-Cam not working because of new face swapper
-Set default swapping model back to Insightface
-
-Happy New Year!
-
-
-**30.12.2024** v4.4.0
-
-- Added random face selection mode
-- Added alternative face swapping model with 128px & 256 px output ([ReSwapper](https://github.com/somanchiu/ReSwapper/tree/main))
-- Video repair added to Extras Tab
-- Updated most packages to newer versions. CUDA >= 12.4 now required!
-- Several minor bugfixes and QoL Changes
-
-
-**28.9.2024** v4.3.1
-
-- Bugfix: Several possible memory leaks
-- Added different output modes, e.g. to virtual cam stream
-- New swapping mode "All input faces"
-- Average total fps displayed and setting for autorun
-
-
-**16.9.2024** v4.2.8
-
-- Bugfix: Starting roop-unleashed without NVIDIA gpu but cuda option enabled
-- Bugfix: Target Faces couldn't be moved left/right
-- Bugfix: Enhancement and upscaling working again in virtual cam
-- Corrupt videos caught when adding to target files, displaying warning msg
-- Source Files Component cleared after face detection to release temp files
-- Added masking and mouth restore options to virtual cam
-
-
-**9.9.2024** v4.2.3
-
-- Hotfix for gradio pydantic issue with fastapi
-- Upgraded to Gradio 4.43 hoping it will fix remaining issues
-- Added new action when no face detected -> use last swapped
-- Specified image format for image controls - opening new tabs on preview images possible again!
-- Hardcoded image output format for livecam to jpeg - might be faster than previous webp
-- Chain events to be only executed if previous was a success
-
-
-**5.9.2024** v4.2.0
-
-- Added ability to move input & target faces order
-- New CLI Arguments override settings
-- Small UI changes to faceswapping tab
-- Added mask option and code for restoration of original mouth area
-- Updated gradio to v4.42.0
-- Added CLI Arguments --server_share and --cuda_device_id
-- Added webp image support
-
-
-**15.07.2024** v4.1.1
-
-- Bugfix: Post-processing after swapping
-
-
-**14.07.2024** v4.1.0
-
-- Added subsample upscaling to increase swap resolution
-- Upgraded gradio
-
-
-**12.05.2024** v4.0.0
-
-- Bugfix: Unnecessary init every frame in live-cam
-- Bugfix: Installer downloading insightface package each run
-- Added xseg masking to live-cam
-- Added realesrganx2 to frame processors
-- Upgraded some requirements
-- Added subtypes and different model support to frame processors
-- Allow frame processors to change resolutions of videos
-- Different OpenCV Cap for MacOS Virtual Cam
-- Added complete frame processing to extras tab
-- Colorize, upscale and misc filters added
-
-
-**22.04.2024** v3.9.0
-
-- Bugfix: Face detection bounding box corrupt values at weird angles
-- Rewrote mask previewing to work with every model
-- Switching mask engines toggles text interactivity
-- Clearing target files, resets face selection dropdown
-- Massive rewrite of swapping architecture, needed for xseg implementation
-- Added DFL Xseg Support for partial face occlusion
-- Face masking only runs when there is a face detected
-- Removed unnecessary toggle checkbox for text masking
-
-
-**22.03.2024** v3.6.5
-
-- Bugfix: Installer pulling latest update on first installation
-- Bugfix: Regression issue, blurring/erosion missing from face swap
-- Exposed erosion and blur amounts to UI
-- Using same values for manual masking too
-
-
-**20.03.2024** v3.6.3
-
-- Bugfix: Workaround for Gradio Slider Change Bug
-- Bugfix: CSS Styling to fix Gradio Image Height Bug
-- Made face swapping mask offsets resolution independant
-- Show offset mask as overlay
-- Changed layout for masking
-
-
-**18.03.2024** v3.6.0
-
-- Updated to Gradio 4.21.0 - requiring many changes under the hood
-- New manual masking (draw the mask yourself)
-- Extras Tab, streamlined cutting/joining videos
-- Re-added face selection by gender (on-demand loading, default turned off)
-- Removed unnecessary activate live-cam option
-- Added time info to preview frame and changed frame slider event to allow faster changes
-
-
-**10.03.2024** v3.5.5
-
-- Bugfix: Installer Path Env
-- Bugfix: file attributes
-- Video processing checks for presence of ffmpeg and displays warning if not found
-- Removed gender + age detection to speed up processing. Option removed from UI
-- Replaced restoreformer with restoreformer++
-- Live Cam recoded to run separate from virtual cam and without blocking controls
-- Swapping with only 1 target face allows selecting from several input faces
-
-
-
-**08.01.2024** v3.5.0
-
-- Bugfix: wrong access options when creating folders
-- New auto rotation of horizontal faces, fixing bad landmark positions (expanded on )
-- Simple VR Option for stereo Images/Movies, best used in selected face mode
-- Added RestoreFormer Enhancer - https://github.com/wzhouxiff/RestoreFormer
-- Bumped up package versions for onnx/Torch etc.
-
-
-**16.10.2023** v3.3.4
-
-**11.8.2023** v2.7.0
-
-Initial Gradio Version - old TkInter Version now deprecated
-
-- Re-added unified padding to face enhancers
-- Fixed DMDNet for all resolutions
-- Selecting target face now automatically switches swapping mode to selected
-- GPU providers are correctly set using the GUI (needs restart currently)
-- Local output folder can be opened from page
-- Unfinished extras functions disabled for now
-- Installer checks out specific commit, allowing to go back to first install
-- Updated readme for new gradio version
-- Updated Colab
-
-
-# Acknowledgements
-
-Lots of ideas, code or pre-trained models borrowed from the following projects:
-
-https://github.com/deepinsight/insightface
-https://github.com/s0md3v/roop
-https://github.com/AUTOMATIC1111/stable-diffusion-webui
-https://github.com/Hillobar/Rope
-https://github.com/TencentARC/GFPGAN
-https://github.com/kadirnar/codeformer-pip
-https://github.com/csxmli2016/DMDNet
-https://github.com/glucauze/sd-webui-faceswaplab
-https://github.com/ykk648/face_power
-
-
-
-Thanks to all developers!
-
diff --git a/roop-unleashed-main/clip/__init__.py b/roop-unleashed-main/clip/__init__.py
deleted file mode 100644
index dcc5619538c0f7c782508bdbd9587259d805e0d9..0000000000000000000000000000000000000000
--- a/roop-unleashed-main/clip/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-from .clip import *
diff --git a/roop-unleashed-main/clip/bpe_simple_vocab_16e6.txt.gz b/roop-unleashed-main/clip/bpe_simple_vocab_16e6.txt.gz
deleted file mode 100644
index 36a15856e00a06a9fbed8cdd34d2393fea4a3113..0000000000000000000000000000000000000000
--- a/roop-unleashed-main/clip/bpe_simple_vocab_16e6.txt.gz
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:924691ac288e54409236115652ad4aa250f48203de50a9e4722a6ecd48d6804a
-size 1356917
diff --git a/roop-unleashed-main/clip/clip.py b/roop-unleashed-main/clip/clip.py
deleted file mode 100644
index f983b7b35a19634bfc941733ab24d69b132ebeac..0000000000000000000000000000000000000000
--- a/roop-unleashed-main/clip/clip.py
+++ /dev/null
@@ -1,241 +0,0 @@
-import hashlib
-import os
-import urllib
-import warnings
-from typing import Any, Union, List
-
-import torch
-from PIL import Image
-from torchvision.transforms import Compose, Resize, CenterCrop, ToTensor, Normalize
-from tqdm import tqdm
-
-from .model import build_model
-from .simple_tokenizer import SimpleTokenizer as _Tokenizer
-
-try:
- from torchvision.transforms import InterpolationMode
- BICUBIC = InterpolationMode.BICUBIC
-except ImportError:
- BICUBIC = Image.BICUBIC
-
-
-
-__all__ = ["available_models", "load", "tokenize"]
-_tokenizer = _Tokenizer()
-
-_MODELS = {
- "RN50": "https://openaipublic.azureedge.net/clip/models/afeb0e10f9e5a86da6080e35cf09123aca3b358a0c3e3b6c78a7b63bc04b6762/RN50.pt",
- "RN101": "https://openaipublic.azureedge.net/clip/models/8fa8567bab74a42d41c5915025a8e4538c3bdbe8804a470a72f30b0d94fab599/RN101.pt",
- "RN50x4": "https://openaipublic.azureedge.net/clip/models/7e526bd135e493cef0776de27d5f42653e6b4c8bf9e0f653bb11773263205fdd/RN50x4.pt",
- "RN50x16": "https://openaipublic.azureedge.net/clip/models/52378b407f34354e150460fe41077663dd5b39c54cd0bfd2b27167a4a06ec9aa/RN50x16.pt",
- "RN50x64": "https://openaipublic.azureedge.net/clip/models/be1cfb55d75a9666199fb2206c106743da0f6468c9d327f3e0d0a543a9919d9c/RN50x64.pt",
- "ViT-B/32": "https://openaipublic.azureedge.net/clip/models/40d365715913c9da98579312b702a82c18be219cc2a73407c4526f58eba950af/ViT-B-32.pt",
- "ViT-B/16": "https://openaipublic.azureedge.net/clip/models/5806e77cd80f8b59890b7e101eabd078d9fb84e6937f9e85e4ecb61988df416f/ViT-B-16.pt",
- "ViT-L/14": "https://openaipublic.azureedge.net/clip/models/b8cca3fd41ae0c99ba7e8951adf17d267cdb84cd88be6f7c2e0eca1737a03836/ViT-L-14.pt",
- "ViT-L/14@336px": "https://openaipublic.azureedge.net/clip/models/3035c92b350959924f9f00213499208652fc7ea050643e8b385c2dac08641f02/ViT-L-14-336px.pt",
-}
-
-
-def _download(url: str, root: str):
- os.makedirs(root, exist_ok=True)
- filename = os.path.basename(url)
-
- expected_sha256 = url.split("/")[-2]
- download_target = os.path.join(root, filename)
-
- if os.path.exists(download_target) and not os.path.isfile(download_target):
- raise RuntimeError(f"{download_target} exists and is not a regular file")
-
- if os.path.isfile(download_target):
- if hashlib.sha256(open(download_target, "rb").read()).hexdigest() == expected_sha256:
- return download_target
- else:
- warnings.warn(f"{download_target} exists, but the SHA256 checksum does not match; re-downloading the file")
-
- with urllib.request.urlopen(url) as source, open(download_target, "wb") as output:
- with tqdm(total=int(source.info().get("Content-Length")), ncols=80, unit='iB', unit_scale=True, unit_divisor=1024) as loop:
- while True:
- buffer = source.read(8192)
- if not buffer:
- break
-
- output.write(buffer)
- loop.update(len(buffer))
-
- if hashlib.sha256(open(download_target, "rb").read()).hexdigest() != expected_sha256:
- raise RuntimeError("Model has been downloaded but the SHA256 checksum does not not match")
-
- return download_target
-
-
-def _convert_image_to_rgb(image):
- return image.convert("RGB")
-
-
-def _transform(n_px):
- return Compose([
- Resize(n_px, interpolation=BICUBIC),
- CenterCrop(n_px),
- _convert_image_to_rgb,
- ToTensor(),
- Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711)),
- ])
-
-
-def available_models() -> List[str]:
- """Returns the names of available CLIP models"""
- return list(_MODELS.keys())
-
-
-def load(name: str, device: Union[str, torch.device] = "cuda" if torch.cuda.is_available() else "cpu", jit: bool = False, download_root: str = None):
- """Load a CLIP model
-
- Parameters
- ----------
- name : str
- A model name listed by `clip.available_models()`, or the path to a model checkpoint containing the state_dict
-
- device : Union[str, torch.device]
- The device to put the loaded model
-
- jit : bool
- Whether to load the optimized JIT model or more hackable non-JIT model (default).
-
- download_root: str
- path to download the model files; by default, it uses "~/.cache/clip"
-
- Returns
- -------
- model : torch.nn.Module
- The CLIP model
-
- preprocess : Callable[[PIL.Image], torch.Tensor]
- A torchvision transform that converts a PIL image into a tensor that the returned model can take as its input
- """
- if name in _MODELS:
- model_path = _download(_MODELS[name], download_root or os.path.expanduser("~/.cache/clip"))
- elif os.path.isfile(name):
- model_path = name
- else:
- raise RuntimeError(f"Model {name} not found; available models = {available_models()}")
-
- with open(model_path, 'rb') as opened_file:
- try:
- # loading JIT archive
- model = torch.jit.load(opened_file, map_location=device if jit else "cpu").eval()
- state_dict = None
- except RuntimeError:
- # loading saved state dict
- if jit:
- warnings.warn(f"File {model_path} is not a JIT archive. Loading as a state dict instead")
- jit = False
- state_dict = torch.load(opened_file, map_location="cpu")
-
- if not jit:
- model = build_model(state_dict or model.state_dict()).to(device)
- if str(device) == "cpu":
- model.float()
- return model, _transform(model.visual.input_resolution)
-
- # patch the device names
- device_holder = torch.jit.trace(lambda: torch.ones([]).to(torch.device(device)), example_inputs=[])
- device_node = [n for n in device_holder.graph.findAllNodes("prim::Constant") if "Device" in repr(n)][-1]
-
- def _node_get(node: torch._C.Node, key: str):
- """Gets attributes of a node which is polymorphic over return type.
-
- From https://github.com/pytorch/pytorch/pull/82628
- """
- sel = node.kindOf(key)
- return getattr(node, sel)(key)
-
- def patch_device(module):
- try:
- graphs = [module.graph] if hasattr(module, "graph") else []
- except RuntimeError:
- graphs = []
-
- if hasattr(module, "forward1"):
- graphs.append(module.forward1.graph)
-
- for graph in graphs:
- for node in graph.findAllNodes("prim::Constant"):
- if "value" in node.attributeNames() and str(_node_get(node, "value")).startswith("cuda"):
- node.copyAttributes(device_node)
-
- model.apply(patch_device)
- patch_device(model.encode_image)
- patch_device(model.encode_text)
-
- # patch dtype to float32 on CPU
- if str(device) == "cpu":
- float_holder = torch.jit.trace(lambda: torch.ones([]).float(), example_inputs=[])
- float_input = list(float_holder.graph.findNode("aten::to").inputs())[1]
- float_node = float_input.node()
-
- def patch_float(module):
- try:
- graphs = [module.graph] if hasattr(module, "graph") else []
- except RuntimeError:
- graphs = []
-
- if hasattr(module, "forward1"):
- graphs.append(module.forward1.graph)
-
- for graph in graphs:
- for node in graph.findAllNodes("aten::to"):
- inputs = list(node.inputs())
- for i in [1, 2]: # dtype can be the second or third argument to aten::to()
- if _node_get(inputs[i].node(), "value") == 5:
- inputs[i].node().copyAttributes(float_node)
-
- model.apply(patch_float)
- patch_float(model.encode_image)
- patch_float(model.encode_text)
-
- model.float()
-
- return model, _transform(model.input_resolution.item())
-
-
-def tokenize(texts: Union[str, List[str]], context_length: int = 77, truncate: bool = False) -> Union[torch.IntTensor, torch.LongTensor]:
- """
- Returns the tokenized representation of given input string(s)
-
- Parameters
- ----------
- texts : Union[str, List[str]]
- An input string or a list of input strings to tokenize
-
- context_length : int
- The context length to use; all CLIP models use 77 as the context length
-
- truncate: bool
- Whether to truncate the text in case its encoding is longer than the context length
-
- Returns
- -------
- A two-dimensional tensor containing the resulting tokens, shape = [number of input strings, context_length].
- We return LongTensor when torch version is <1.8.0, since older index_select requires indices to be long.
- """
- if isinstance(texts, str):
- texts = [texts]
-
- sot_token = _tokenizer.encoder["<|startoftext|>"]
- eot_token = _tokenizer.encoder["<|endoftext|>"]
- all_tokens = [[sot_token] + _tokenizer.encode(text) + [eot_token] for text in texts]
- #if packaging.version.parse(torch.__version__) < packaging.version.parse("1.8.0"):
- # result = torch.zeros(len(all_tokens), context_length, dtype=torch.long)
- #else:
- result = torch.zeros(len(all_tokens), context_length, dtype=torch.int)
-
- for i, tokens in enumerate(all_tokens):
- if len(tokens) > context_length:
- if truncate:
- tokens = tokens[:context_length]
- tokens[-1] = eot_token
- else:
- raise RuntimeError(f"Input {texts[i]} is too long for context length {context_length}")
- result[i, :len(tokens)] = torch.tensor(tokens)
-
- return result
diff --git a/roop-unleashed-main/clip/clipseg.py b/roop-unleashed-main/clip/clipseg.py
deleted file mode 100644
index 6adc7e4893cbb2bff31eb822dacf96a7c9a87e27..0000000000000000000000000000000000000000
--- a/roop-unleashed-main/clip/clipseg.py
+++ /dev/null
@@ -1,538 +0,0 @@
-import math
-from os.path import basename, dirname, join, isfile
-import torch
-from torch import nn
-from torch.nn import functional as nnf
-from torch.nn.modules.activation import ReLU
-
-
-def get_prompt_list(prompt):
- if prompt == 'plain':
- return ['{}']
- elif prompt == 'fixed':
- return ['a photo of a {}.']
- elif prompt == 'shuffle':
- return ['a photo of a {}.', 'a photograph of a {}.', 'an image of a {}.', '{}.']
- elif prompt == 'shuffle+':
- return ['a photo of a {}.', 'a photograph of a {}.', 'an image of a {}.', '{}.',
- 'a cropped photo of a {}.', 'a good photo of a {}.', 'a photo of one {}.',
- 'a bad photo of a {}.', 'a photo of the {}.']
- else:
- raise ValueError('Invalid value for prompt')
-
-
-def forward_multihead_attention(x, b, with_aff=False, attn_mask=None):
- """
- Simplified version of multihead attention (taken from torch source code but without tons of if clauses).
- The mlp and layer norm come from CLIP.
- x: input.
- b: multihead attention module.
- """
-
- x_ = b.ln_1(x)
- q, k, v = nnf.linear(x_, b.attn.in_proj_weight, b.attn.in_proj_bias).chunk(3, dim=-1)
- tgt_len, bsz, embed_dim = q.size()
-
- head_dim = embed_dim // b.attn.num_heads
- scaling = float(head_dim) ** -0.5
-
- q = q.contiguous().view(tgt_len, bsz * b.attn.num_heads, b.attn.head_dim).transpose(0, 1)
- k = k.contiguous().view(-1, bsz * b.attn.num_heads, b.attn.head_dim).transpose(0, 1)
- v = v.contiguous().view(-1, bsz * b.attn.num_heads, b.attn.head_dim).transpose(0, 1)
-
- q = q * scaling
-
- attn_output_weights = torch.bmm(q, k.transpose(1, 2)) # n_heads * batch_size, tokens^2, tokens^2
- if attn_mask is not None:
-
-
- attn_mask_type, attn_mask = attn_mask
- n_heads = attn_output_weights.size(0) // attn_mask.size(0)
- attn_mask = attn_mask.repeat(n_heads, 1)
-
- if attn_mask_type == 'cls_token':
- # the mask only affects similarities compared to the readout-token.
- attn_output_weights[:, 0, 1:] = attn_output_weights[:, 0, 1:] * attn_mask[None,...]
- # attn_output_weights[:, 0, 0] = 0*attn_output_weights[:, 0, 0]
-
- if attn_mask_type == 'all':
- # print(attn_output_weights.shape, attn_mask[:, None].shape)
- attn_output_weights[:, 1:, 1:] = attn_output_weights[:, 1:, 1:] * attn_mask[:, None]
-
-
- attn_output_weights = torch.softmax(attn_output_weights, dim=-1)
-
- attn_output = torch.bmm(attn_output_weights, v)
- attn_output = attn_output.transpose(0, 1).contiguous().view(tgt_len, bsz, embed_dim)
- attn_output = b.attn.out_proj(attn_output)
-
- x = x + attn_output
- x = x + b.mlp(b.ln_2(x))
-
- if with_aff:
- return x, attn_output_weights
- else:
- return x
-
-
-class CLIPDenseBase(nn.Module):
-
- def __init__(self, version, reduce_cond, reduce_dim, prompt, n_tokens):
- super().__init__()
-
- import clip
-
- # prec = torch.FloatTensor
- self.clip_model, _ = clip.load(version, device='cpu', jit=False)
- self.model = self.clip_model.visual
-
- # if not None, scale conv weights such that we obtain n_tokens.
- self.n_tokens = n_tokens
-
- for p in self.clip_model.parameters():
- p.requires_grad_(False)
-
- # conditional
- if reduce_cond is not None:
- self.reduce_cond = nn.Linear(512, reduce_cond)
- for p in self.reduce_cond.parameters():
- p.requires_grad_(False)
- else:
- self.reduce_cond = None
-
- self.film_mul = nn.Linear(512 if reduce_cond is None else reduce_cond, reduce_dim)
- self.film_add = nn.Linear(512 if reduce_cond is None else reduce_cond, reduce_dim)
-
- self.reduce = nn.Linear(768, reduce_dim)
-
- self.prompt_list = get_prompt_list(prompt)
-
- # precomputed prompts
- import pickle
- if isfile('precomputed_prompt_vectors.pickle'):
- precomp = pickle.load(open('precomputed_prompt_vectors.pickle', 'rb'))
- self.precomputed_prompts = {k: torch.from_numpy(v) for k, v in precomp.items()}
- else:
- self.precomputed_prompts = dict()
-
- def rescaled_pos_emb(self, new_size):
- assert len(new_size) == 2
-
- a = self.model.positional_embedding[1:].T.view(1, 768, *self.token_shape)
- b = nnf.interpolate(a, new_size, mode='bicubic', align_corners=False).squeeze(0).view(768, new_size[0]*new_size[1]).T
- return torch.cat([self.model.positional_embedding[:1], b])
-
- def visual_forward(self, x_inp, extract_layers=(), skip=False, mask=None):
-
-
- with torch.no_grad():
-
- inp_size = x_inp.shape[2:]
-
- if self.n_tokens is not None:
- stride2 = x_inp.shape[2] // self.n_tokens
- conv_weight2 = nnf.interpolate(self.model.conv1.weight, (stride2, stride2), mode='bilinear', align_corners=True)
- x = nnf.conv2d(x_inp, conv_weight2, bias=self.model.conv1.bias, stride=stride2, dilation=self.model.conv1.dilation)
- else:
- x = self.model.conv1(x_inp) # shape = [*, width, grid, grid]
-
- x = x.reshape(x.shape[0], x.shape[1], -1) # shape = [*, width, grid ** 2]
- x = x.permute(0, 2, 1) # shape = [*, grid ** 2, width]
-
- x = torch.cat([self.model.class_embedding.to(x.dtype) + torch.zeros(x.shape[0], 1, x.shape[-1], dtype=x.dtype, device=x.device), x], dim=1) # shape = [*, grid ** 2 + 1, width]
-
- standard_n_tokens = 50 if self.model.conv1.kernel_size[0] == 32 else 197
-
- if x.shape[1] != standard_n_tokens:
- new_shape = int(math.sqrt(x.shape[1]-1))
- x = x + self.rescaled_pos_emb((new_shape, new_shape)).to(x.dtype)[None,:,:]
- else:
- x = x + self.model.positional_embedding.to(x.dtype)
-
- x = self.model.ln_pre(x)
-
- x = x.permute(1, 0, 2) # NLD -> LND
-
- activations, affinities = [], []
- for i, res_block in enumerate(self.model.transformer.resblocks):
-
- if mask is not None:
- mask_layer, mask_type, mask_tensor = mask
- if mask_layer == i or mask_layer == 'all':
- # import ipdb; ipdb.set_trace()
- size = int(math.sqrt(x.shape[0] - 1))
-
- attn_mask = (mask_type, nnf.interpolate(mask_tensor.unsqueeze(1).float(), (size, size)).view(mask_tensor.shape[0], size * size))
-
- else:
- attn_mask = None
- else:
- attn_mask = None
-
- x, aff_per_head = forward_multihead_attention(x, res_block, with_aff=True, attn_mask=attn_mask)
-
- if i in extract_layers:
- affinities += [aff_per_head]
-
- #if self.n_tokens is not None:
- # activations += [nnf.interpolate(x, inp_size, mode='bilinear', align_corners=True)]
- #else:
- activations += [x]
-
- if len(extract_layers) > 0 and i == max(extract_layers) and skip:
- print('early skip')
- break
-
- x = x.permute(1, 0, 2) # LND -> NLD
- x = self.model.ln_post(x[:, 0, :])
-
- if self.model.proj is not None:
- x = x @ self.model.proj
-
- return x, activations, affinities
-
- def sample_prompts(self, words, prompt_list=None):
-
- prompt_list = prompt_list if prompt_list is not None else self.prompt_list
-
- prompt_indices = torch.multinomial(torch.ones(len(prompt_list)), len(words), replacement=True)
- prompts = [prompt_list[i] for i in prompt_indices]
- return [promt.format(w) for promt, w in zip(prompts, words)]
-
- def get_cond_vec(self, conditional, batch_size):
- # compute conditional from a single string
- if conditional is not None and type(conditional) == str:
- cond = self.compute_conditional(conditional)
- cond = cond.repeat(batch_size, 1)
-
- # compute conditional from string list/tuple
- elif conditional is not None and type(conditional) in {list, tuple} and type(conditional[0]) == str:
- assert len(conditional) == batch_size
- cond = self.compute_conditional(conditional)
-
- # use conditional directly
- elif conditional is not None and type(conditional) == torch.Tensor and conditional.ndim == 2:
- cond = conditional
-
- # compute conditional from image
- elif conditional is not None and type(conditional) == torch.Tensor:
- with torch.no_grad():
- cond, _, _ = self.visual_forward(conditional)
- else:
- raise ValueError('invalid conditional')
- return cond
-
- def compute_conditional(self, conditional):
- import clip
-
- dev = next(self.parameters()).device
-
- if type(conditional) in {list, tuple}:
- text_tokens = clip.tokenize(conditional).to(dev)
- cond = self.clip_model.encode_text(text_tokens)
- else:
- if conditional in self.precomputed_prompts:
- cond = self.precomputed_prompts[conditional].float().to(dev)
- else:
- text_tokens = clip.tokenize([conditional]).to(dev)
- cond = self.clip_model.encode_text(text_tokens)[0]
-
- if self.shift_vector is not None:
- return cond + self.shift_vector
- else:
- return cond
-
-
-def clip_load_untrained(version):
- assert version == 'ViT-B/16'
- from clip.model import CLIP
- from clip.clip import _MODELS, _download
- model = torch.jit.load(_download(_MODELS['ViT-B/16'])).eval()
- state_dict = model.state_dict()
-
- vision_width = state_dict["visual.conv1.weight"].shape[0]
- vision_layers = len([k for k in state_dict.keys() if k.startswith("visual.") and k.endswith(".attn.in_proj_weight")])
- vision_patch_size = state_dict["visual.conv1.weight"].shape[-1]
- grid_size = round((state_dict["visual.positional_embedding"].shape[0] - 1) ** 0.5)
- image_resolution = vision_patch_size * grid_size
- embed_dim = state_dict["text_projection"].shape[1]
- context_length = state_dict["positional_embedding"].shape[0]
- vocab_size = state_dict["token_embedding.weight"].shape[0]
- transformer_width = state_dict["ln_final.weight"].shape[0]
- transformer_heads = transformer_width // 64
- transformer_layers = len(set(k.split(".")[2] for k in state_dict if k.startswith(f"transformer.resblocks")))
-
- return CLIP(embed_dim, image_resolution, vision_layers, vision_width, vision_patch_size,
- context_length, vocab_size, transformer_width, transformer_heads, transformer_layers)
-
-
-class CLIPDensePredT(CLIPDenseBase):
-
- def __init__(self, version='ViT-B/32', extract_layers=(3, 6, 9), cond_layer=0, reduce_dim=128, n_heads=4, prompt='fixed',
- extra_blocks=0, reduce_cond=None, fix_shift=False,
- learn_trans_conv_only=False, limit_to_clip_only=False, upsample=False,
- add_calibration=False, rev_activations=False, trans_conv=None, n_tokens=None, complex_trans_conv=False):
-
- super().__init__(version, reduce_cond, reduce_dim, prompt, n_tokens)
- # device = 'cpu'
-
- self.extract_layers = extract_layers
- self.cond_layer = cond_layer
- self.limit_to_clip_only = limit_to_clip_only
- self.process_cond = None
- self.rev_activations = rev_activations
-
- depth = len(extract_layers)
-
- if add_calibration:
- self.calibration_conds = 1
-
- self.upsample_proj = nn.Conv2d(reduce_dim, 1, kernel_size=1) if upsample else None
-
- self.add_activation1 = True
-
- self.version = version
-
- self.token_shape = {'ViT-B/32': (7, 7), 'ViT-B/16': (14, 14)}[version]
-
- if fix_shift:
- # self.shift_vector = nn.Parameter(torch.load(join(dirname(basename(__file__)), 'clip_text_shift_vector.pth')), requires_grad=False)
- self.shift_vector = nn.Parameter(torch.load(join(dirname(basename(__file__)), 'shift_text_to_vis.pth')), requires_grad=False)
- # self.shift_vector = nn.Parameter(-1*torch.load(join(dirname(basename(__file__)), 'shift2.pth')), requires_grad=False)
- else:
- self.shift_vector = None
-
- if trans_conv is None:
- trans_conv_ks = {'ViT-B/32': (32, 32), 'ViT-B/16': (16, 16)}[version]
- else:
- # explicitly define transposed conv kernel size
- trans_conv_ks = (trans_conv, trans_conv)
-
- if not complex_trans_conv:
- self.trans_conv = nn.ConvTranspose2d(reduce_dim, 1, trans_conv_ks, stride=trans_conv_ks)
- else:
- assert trans_conv_ks[0] == trans_conv_ks[1]
-
- tp_kernels = (trans_conv_ks[0] // 4, trans_conv_ks[0] // 4)
-
- self.trans_conv = nn.Sequential(
- nn.Conv2d(reduce_dim, reduce_dim, kernel_size=3, padding=1),
- nn.ReLU(),
- nn.ConvTranspose2d(reduce_dim, reduce_dim // 2, kernel_size=tp_kernels[0], stride=tp_kernels[0]),
- nn.ReLU(),
- nn.ConvTranspose2d(reduce_dim // 2, 1, kernel_size=tp_kernels[1], stride=tp_kernels[1]),
- )
-
-# self.trans_conv = nn.ConvTranspose2d(reduce_dim, 1, trans_conv_ks, stride=trans_conv_ks)
-
- assert len(self.extract_layers) == depth
-
- self.reduces = nn.ModuleList([nn.Linear(768, reduce_dim) for _ in range(depth)])
- self.blocks = nn.ModuleList([nn.TransformerEncoderLayer(d_model=reduce_dim, nhead=n_heads) for _ in range(len(self.extract_layers))])
- self.extra_blocks = nn.ModuleList([nn.TransformerEncoderLayer(d_model=reduce_dim, nhead=n_heads) for _ in range(extra_blocks)])
-
- # refinement and trans conv
-
- if learn_trans_conv_only:
- for p in self.parameters():
- p.requires_grad_(False)
-
- for p in self.trans_conv.parameters():
- p.requires_grad_(True)
-
- self.prompt_list = get_prompt_list(prompt)
-
-
- def forward(self, inp_image, conditional=None, return_features=False, mask=None):
-
- assert type(return_features) == bool
-
- inp_image = inp_image.to(self.model.positional_embedding.device)
-
- if mask is not None:
- raise ValueError('mask not supported')
-
- # x_inp = normalize(inp_image)
- x_inp = inp_image
-
- bs, dev = inp_image.shape[0], x_inp.device
-
- cond = self.get_cond_vec(conditional, bs)
-
- visual_q, activations, _ = self.visual_forward(x_inp, extract_layers=[0] + list(self.extract_layers))
-
- activation1 = activations[0]
- activations = activations[1:]
-
- _activations = activations[::-1] if not self.rev_activations else activations
-
- a = None
- for i, (activation, block, reduce) in enumerate(zip(_activations, self.blocks, self.reduces)):
-
- if a is not None:
- a = reduce(activation) + a
- else:
- a = reduce(activation)
-
- if i == self.cond_layer:
- if self.reduce_cond is not None:
- cond = self.reduce_cond(cond)
-
- a = self.film_mul(cond) * a + self.film_add(cond)
-
- a = block(a)
-
- for block in self.extra_blocks:
- a = a + block(a)
-
- a = a[1:].permute(1, 2, 0) # rm cls token and -> BS, Feats, Tokens
-
- size = int(math.sqrt(a.shape[2]))
-
- a = a.view(bs, a.shape[1], size, size)
-
- a = self.trans_conv(a)
-
- if self.n_tokens is not None:
- a = nnf.interpolate(a, x_inp.shape[2:], mode='bilinear', align_corners=True)
-
- if self.upsample_proj is not None:
- a = self.upsample_proj(a)
- a = nnf.interpolate(a, x_inp.shape[2:], mode='bilinear')
-
- if return_features:
- return a, visual_q, cond, [activation1] + activations
- else:
- return a,
-
-
-
-class CLIPDensePredTMasked(CLIPDensePredT):
-
- def __init__(self, version='ViT-B/32', extract_layers=(3, 6, 9), cond_layer=0, reduce_dim=128, n_heads=4,
- prompt='fixed', extra_blocks=0, reduce_cond=None, fix_shift=False, learn_trans_conv_only=False,
- refine=None, limit_to_clip_only=False, upsample=False, add_calibration=False, n_tokens=None):
-
- super().__init__(version=version, extract_layers=extract_layers, cond_layer=cond_layer, reduce_dim=reduce_dim,
- n_heads=n_heads, prompt=prompt, extra_blocks=extra_blocks, reduce_cond=reduce_cond,
- fix_shift=fix_shift, learn_trans_conv_only=learn_trans_conv_only,
- limit_to_clip_only=limit_to_clip_only, upsample=upsample, add_calibration=add_calibration,
- n_tokens=n_tokens)
-
- def visual_forward_masked(self, img_s, seg_s):
- return super().visual_forward(img_s, mask=('all', 'cls_token', seg_s))
-
- def forward(self, img_q, cond_or_img_s, seg_s=None, return_features=False):
-
- if seg_s is None:
- cond = cond_or_img_s
- else:
- img_s = cond_or_img_s
-
- with torch.no_grad():
- cond, _, _ = self.visual_forward_masked(img_s, seg_s)
-
- return super().forward(img_q, cond, return_features=return_features)
-
-
-
-class CLIPDenseBaseline(CLIPDenseBase):
-
- def __init__(self, version='ViT-B/32', cond_layer=0,
- extract_layer=9, reduce_dim=128, reduce2_dim=None, prompt='fixed',
- reduce_cond=None, limit_to_clip_only=False, n_tokens=None):
-
- super().__init__(version, reduce_cond, reduce_dim, prompt, n_tokens)
- device = 'cpu'
-
- # self.cond_layer = cond_layer
- self.extract_layer = extract_layer
- self.limit_to_clip_only = limit_to_clip_only
- self.shift_vector = None
-
- self.token_shape = {'ViT-B/32': (7, 7), 'ViT-B/16': (14, 14)}[version]
-
- assert reduce2_dim is not None
-
- self.reduce2 = nn.Sequential(
- nn.Linear(reduce_dim, reduce2_dim),
- nn.ReLU(),
- nn.Linear(reduce2_dim, reduce_dim)
- )
-
- trans_conv_ks = {'ViT-B/32': (32, 32), 'ViT-B/16': (16, 16)}[version]
- self.trans_conv = nn.ConvTranspose2d(reduce_dim, 1, trans_conv_ks, stride=trans_conv_ks)
-
-
- def forward(self, inp_image, conditional=None, return_features=False):
-
- inp_image = inp_image.to(self.model.positional_embedding.device)
-
- # x_inp = normalize(inp_image)
- x_inp = inp_image
-
- bs, dev = inp_image.shape[0], x_inp.device
-
- cond = self.get_cond_vec(conditional, bs)
-
- visual_q, activations, affinities = self.visual_forward(x_inp, extract_layers=[self.extract_layer])
-
- a = activations[0]
- a = self.reduce(a)
- a = self.film_mul(cond) * a + self.film_add(cond)
-
- if self.reduce2 is not None:
- a = self.reduce2(a)
-
- # the original model would execute a transformer block here
-
- a = a[1:].permute(1, 2, 0) # rm cls token and -> BS, Feats, Tokens
-
- size = int(math.sqrt(a.shape[2]))
-
- a = a.view(bs, a.shape[1], size, size)
- a = self.trans_conv(a)
-
- if return_features:
- return a, visual_q, cond, activations
- else:
- return a,
-
-
-class CLIPSegMultiLabel(nn.Module):
-
- def __init__(self, model) -> None:
- super().__init__()
-
- from third_party.JoEm.data_loader import get_seen_idx, get_unseen_idx, VOC
-
- self.pascal_classes = VOC
-
- from clip.clipseg import CLIPDensePredT
- from general_utils import load_model
- # self.clipseg = load_model('rd64-vit16-neg0.2-phrasecut', strict=False)
- self.clipseg = load_model(model, strict=False)
-
- self.clipseg.eval()
-
- def forward(self, x):
-
- bs = x.shape[0]
- out = torch.ones(21, bs, 352, 352).to(x.device) * -10
-
- for class_id, class_name in enumerate(self.pascal_classes):
-
- fac = 3 if class_name == 'background' else 1
-
- with torch.no_grad():
- pred = torch.sigmoid(self.clipseg(x, class_name)[0][:,0]) * fac
-
- out[class_id] += pred
-
-
- out = out.permute(1, 0, 2, 3)
-
- return out
-
- # construct output tensor
-
diff --git a/roop-unleashed-main/clip/model.py b/roop-unleashed-main/clip/model.py
deleted file mode 100644
index 232b7792eb97440642547bd462cf128df9243933..0000000000000000000000000000000000000000
--- a/roop-unleashed-main/clip/model.py
+++ /dev/null
@@ -1,436 +0,0 @@
-from collections import OrderedDict
-from typing import Tuple, Union
-
-import numpy as np
-import torch
-import torch.nn.functional as F
-from torch import nn
-
-
-class Bottleneck(nn.Module):
- expansion = 4
-
- def __init__(self, inplanes, planes, stride=1):
- super().__init__()
-
- # all conv layers have stride 1. an avgpool is performed after the second convolution when stride > 1
- self.conv1 = nn.Conv2d(inplanes, planes, 1, bias=False)
- self.bn1 = nn.BatchNorm2d(planes)
- self.relu1 = nn.ReLU(inplace=True)
-
- self.conv2 = nn.Conv2d(planes, planes, 3, padding=1, bias=False)
- self.bn2 = nn.BatchNorm2d(planes)
- self.relu2 = nn.ReLU(inplace=True)
-
- self.avgpool = nn.AvgPool2d(stride) if stride > 1 else nn.Identity()
-
- self.conv3 = nn.Conv2d(planes, planes * self.expansion, 1, bias=False)
- self.bn3 = nn.BatchNorm2d(planes * self.expansion)
- self.relu3 = nn.ReLU(inplace=True)
-
- self.downsample = None
- self.stride = stride
-
- if stride > 1 or inplanes != planes * Bottleneck.expansion:
- # downsampling layer is prepended with an avgpool, and the subsequent convolution has stride 1
- self.downsample = nn.Sequential(OrderedDict([
- ("-1", nn.AvgPool2d(stride)),
- ("0", nn.Conv2d(inplanes, planes * self.expansion, 1, stride=1, bias=False)),
- ("1", nn.BatchNorm2d(planes * self.expansion))
- ]))
-
- def forward(self, x: torch.Tensor):
- identity = x
-
- out = self.relu1(self.bn1(self.conv1(x)))
- out = self.relu2(self.bn2(self.conv2(out)))
- out = self.avgpool(out)
- out = self.bn3(self.conv3(out))
-
- if self.downsample is not None:
- identity = self.downsample(x)
-
- out += identity
- out = self.relu3(out)
- return out
-
-
-class AttentionPool2d(nn.Module):
- def __init__(self, spacial_dim: int, embed_dim: int, num_heads: int, output_dim: int = None):
- super().__init__()
- self.positional_embedding = nn.Parameter(torch.randn(spacial_dim ** 2 + 1, embed_dim) / embed_dim ** 0.5)
- self.k_proj = nn.Linear(embed_dim, embed_dim)
- self.q_proj = nn.Linear(embed_dim, embed_dim)
- self.v_proj = nn.Linear(embed_dim, embed_dim)
- self.c_proj = nn.Linear(embed_dim, output_dim or embed_dim)
- self.num_heads = num_heads
-
- def forward(self, x):
- x = x.flatten(start_dim=2).permute(2, 0, 1) # NCHW -> (HW)NC
- x = torch.cat([x.mean(dim=0, keepdim=True), x], dim=0) # (HW+1)NC
- x = x + self.positional_embedding[:, None, :].to(x.dtype) # (HW+1)NC
- x, _ = F.multi_head_attention_forward(
- query=x[:1], key=x, value=x,
- embed_dim_to_check=x.shape[-1],
- num_heads=self.num_heads,
- q_proj_weight=self.q_proj.weight,
- k_proj_weight=self.k_proj.weight,
- v_proj_weight=self.v_proj.weight,
- in_proj_weight=None,
- in_proj_bias=torch.cat([self.q_proj.bias, self.k_proj.bias, self.v_proj.bias]),
- bias_k=None,
- bias_v=None,
- add_zero_attn=False,
- dropout_p=0,
- out_proj_weight=self.c_proj.weight,
- out_proj_bias=self.c_proj.bias,
- use_separate_proj_weight=True,
- training=self.training,
- need_weights=False
- )
- return x.squeeze(0)
-
-
-class ModifiedResNet(nn.Module):
- """
- A ResNet class that is similar to torchvision's but contains the following changes:
- - There are now 3 "stem" convolutions as opposed to 1, with an average pool instead of a max pool.
- - Performs anti-aliasing strided convolutions, where an avgpool is prepended to convolutions with stride > 1
- - The final pooling layer is a QKV attention instead of an average pool
- """
-
- def __init__(self, layers, output_dim, heads, input_resolution=224, width=64):
- super().__init__()
- self.output_dim = output_dim
- self.input_resolution = input_resolution
-
- # the 3-layer stem
- self.conv1 = nn.Conv2d(3, width // 2, kernel_size=3, stride=2, padding=1, bias=False)
- self.bn1 = nn.BatchNorm2d(width // 2)
- self.relu1 = nn.ReLU(inplace=True)
- self.conv2 = nn.Conv2d(width // 2, width // 2, kernel_size=3, padding=1, bias=False)
- self.bn2 = nn.BatchNorm2d(width // 2)
- self.relu2 = nn.ReLU(inplace=True)
- self.conv3 = nn.Conv2d(width // 2, width, kernel_size=3, padding=1, bias=False)
- self.bn3 = nn.BatchNorm2d(width)
- self.relu3 = nn.ReLU(inplace=True)
- self.avgpool = nn.AvgPool2d(2)
-
- # residual layers
- self._inplanes = width # this is a *mutable* variable used during construction
- self.layer1 = self._make_layer(width, layers[0])
- self.layer2 = self._make_layer(width * 2, layers[1], stride=2)
- self.layer3 = self._make_layer(width * 4, layers[2], stride=2)
- self.layer4 = self._make_layer(width * 8, layers[3], stride=2)
-
- embed_dim = width * 32 # the ResNet feature dimension
- self.attnpool = AttentionPool2d(input_resolution // 32, embed_dim, heads, output_dim)
-
- def _make_layer(self, planes, blocks, stride=1):
- layers = [Bottleneck(self._inplanes, planes, stride)]
-
- self._inplanes = planes * Bottleneck.expansion
- for _ in range(1, blocks):
- layers.append(Bottleneck(self._inplanes, planes))
-
- return nn.Sequential(*layers)
-
- def forward(self, x):
- def stem(x):
- x = self.relu1(self.bn1(self.conv1(x)))
- x = self.relu2(self.bn2(self.conv2(x)))
- x = self.relu3(self.bn3(self.conv3(x)))
- x = self.avgpool(x)
- return x
-
- x = x.type(self.conv1.weight.dtype)
- x = stem(x)
- x = self.layer1(x)
- x = self.layer2(x)
- x = self.layer3(x)
- x = self.layer4(x)
- x = self.attnpool(x)
-
- return x
-
-
-class LayerNorm(nn.LayerNorm):
- """Subclass torch's LayerNorm to handle fp16."""
-
- def forward(self, x: torch.Tensor):
- orig_type = x.dtype
- ret = super().forward(x.type(torch.float32))
- return ret.type(orig_type)
-
-
-class QuickGELU(nn.Module):
- def forward(self, x: torch.Tensor):
- return x * torch.sigmoid(1.702 * x)
-
-
-class ResidualAttentionBlock(nn.Module):
- def __init__(self, d_model: int, n_head: int, attn_mask: torch.Tensor = None):
- super().__init__()
-
- self.attn = nn.MultiheadAttention(d_model, n_head)
- self.ln_1 = LayerNorm(d_model)
- self.mlp = nn.Sequential(OrderedDict([
- ("c_fc", nn.Linear(d_model, d_model * 4)),
- ("gelu", QuickGELU()),
- ("c_proj", nn.Linear(d_model * 4, d_model))
- ]))
- self.ln_2 = LayerNorm(d_model)
- self.attn_mask = attn_mask
-
- def attention(self, x: torch.Tensor):
- self.attn_mask = self.attn_mask.to(dtype=x.dtype, device=x.device) if self.attn_mask is not None else None
- return self.attn(x, x, x, need_weights=False, attn_mask=self.attn_mask)[0]
-
- def forward(self, x: torch.Tensor):
- x = x + self.attention(self.ln_1(x))
- x = x + self.mlp(self.ln_2(x))
- return x
-
-
-class Transformer(nn.Module):
- def __init__(self, width: int, layers: int, heads: int, attn_mask: torch.Tensor = None):
- super().__init__()
- self.width = width
- self.layers = layers
- self.resblocks = nn.Sequential(*[ResidualAttentionBlock(width, heads, attn_mask) for _ in range(layers)])
-
- def forward(self, x: torch.Tensor):
- return self.resblocks(x)
-
-
-class VisionTransformer(nn.Module):
- def __init__(self, input_resolution: int, patch_size: int, width: int, layers: int, heads: int, output_dim: int):
- super().__init__()
- self.input_resolution = input_resolution
- self.output_dim = output_dim
- self.conv1 = nn.Conv2d(in_channels=3, out_channels=width, kernel_size=patch_size, stride=patch_size, bias=False)
-
- scale = width ** -0.5
- self.class_embedding = nn.Parameter(scale * torch.randn(width))
- self.positional_embedding = nn.Parameter(scale * torch.randn((input_resolution // patch_size) ** 2 + 1, width))
- self.ln_pre = LayerNorm(width)
-
- self.transformer = Transformer(width, layers, heads)
-
- self.ln_post = LayerNorm(width)
- self.proj = nn.Parameter(scale * torch.randn(width, output_dim))
-
- def forward(self, x: torch.Tensor):
- x = self.conv1(x) # shape = [*, width, grid, grid]
- x = x.reshape(x.shape[0], x.shape[1], -1) # shape = [*, width, grid ** 2]
- x = x.permute(0, 2, 1) # shape = [*, grid ** 2, width]
- x = torch.cat([self.class_embedding.to(x.dtype) + torch.zeros(x.shape[0], 1, x.shape[-1], dtype=x.dtype, device=x.device), x], dim=1) # shape = [*, grid ** 2 + 1, width]
- x = x + self.positional_embedding.to(x.dtype)
- x = self.ln_pre(x)
-
- x = x.permute(1, 0, 2) # NLD -> LND
- x = self.transformer(x)
- x = x.permute(1, 0, 2) # LND -> NLD
-
- x = self.ln_post(x[:, 0, :])
-
- if self.proj is not None:
- x = x @ self.proj
-
- return x
-
-
-class CLIP(nn.Module):
- def __init__(self,
- embed_dim: int,
- # vision
- image_resolution: int,
- vision_layers: Union[Tuple[int, int, int, int], int],
- vision_width: int,
- vision_patch_size: int,
- # text
- context_length: int,
- vocab_size: int,
- transformer_width: int,
- transformer_heads: int,
- transformer_layers: int
- ):
- super().__init__()
-
- self.context_length = context_length
-
- if isinstance(vision_layers, (tuple, list)):
- vision_heads = vision_width * 32 // 64
- self.visual = ModifiedResNet(
- layers=vision_layers,
- output_dim=embed_dim,
- heads=vision_heads,
- input_resolution=image_resolution,
- width=vision_width
- )
- else:
- vision_heads = vision_width // 64
- self.visual = VisionTransformer(
- input_resolution=image_resolution,
- patch_size=vision_patch_size,
- width=vision_width,
- layers=vision_layers,
- heads=vision_heads,
- output_dim=embed_dim
- )
-
- self.transformer = Transformer(
- width=transformer_width,
- layers=transformer_layers,
- heads=transformer_heads,
- attn_mask=self.build_attention_mask()
- )
-
- self.vocab_size = vocab_size
- self.token_embedding = nn.Embedding(vocab_size, transformer_width)
- self.positional_embedding = nn.Parameter(torch.empty(self.context_length, transformer_width))
- self.ln_final = LayerNorm(transformer_width)
-
- self.text_projection = nn.Parameter(torch.empty(transformer_width, embed_dim))
- self.logit_scale = nn.Parameter(torch.ones([]) * np.log(1 / 0.07))
-
- self.initialize_parameters()
-
- def initialize_parameters(self):
- nn.init.normal_(self.token_embedding.weight, std=0.02)
- nn.init.normal_(self.positional_embedding, std=0.01)
-
- if isinstance(self.visual, ModifiedResNet):
- if self.visual.attnpool is not None:
- std = self.visual.attnpool.c_proj.in_features ** -0.5
- nn.init.normal_(self.visual.attnpool.q_proj.weight, std=std)
- nn.init.normal_(self.visual.attnpool.k_proj.weight, std=std)
- nn.init.normal_(self.visual.attnpool.v_proj.weight, std=std)
- nn.init.normal_(self.visual.attnpool.c_proj.weight, std=std)
-
- for resnet_block in [self.visual.layer1, self.visual.layer2, self.visual.layer3, self.visual.layer4]:
- for name, param in resnet_block.named_parameters():
- if name.endswith("bn3.weight"):
- nn.init.zeros_(param)
-
- proj_std = (self.transformer.width ** -0.5) * ((2 * self.transformer.layers) ** -0.5)
- attn_std = self.transformer.width ** -0.5
- fc_std = (2 * self.transformer.width) ** -0.5
- for block in self.transformer.resblocks:
- nn.init.normal_(block.attn.in_proj_weight, std=attn_std)
- nn.init.normal_(block.attn.out_proj.weight, std=proj_std)
- nn.init.normal_(block.mlp.c_fc.weight, std=fc_std)
- nn.init.normal_(block.mlp.c_proj.weight, std=proj_std)
-
- if self.text_projection is not None:
- nn.init.normal_(self.text_projection, std=self.transformer.width ** -0.5)
-
- def build_attention_mask(self):
- # lazily create causal attention mask, with full attention between the vision tokens
- # pytorch uses additive attention mask; fill with -inf
- mask = torch.empty(self.context_length, self.context_length)
- mask.fill_(float("-inf"))
- mask.triu_(1) # zero out the lower diagonal
- return mask
-
- @property
- def dtype(self):
- return self.visual.conv1.weight.dtype
-
- def encode_image(self, image):
- return self.visual(image.type(self.dtype))
-
- def encode_text(self, text):
- x = self.token_embedding(text).type(self.dtype) # [batch_size, n_ctx, d_model]
-
- x = x + self.positional_embedding.type(self.dtype)
- x = x.permute(1, 0, 2) # NLD -> LND
- x = self.transformer(x)
- x = x.permute(1, 0, 2) # LND -> NLD
- x = self.ln_final(x).type(self.dtype)
-
- # x.shape = [batch_size, n_ctx, transformer.width]
- # take features from the eot embedding (eot_token is the highest number in each sequence)
- x = x[torch.arange(x.shape[0]), text.argmax(dim=-1)] @ self.text_projection
-
- return x
-
- def forward(self, image, text):
- image_features = self.encode_image(image)
- text_features = self.encode_text(text)
-
- # normalized features
- image_features = image_features / image_features.norm(dim=1, keepdim=True)
- text_features = text_features / text_features.norm(dim=1, keepdim=True)
-
- # cosine similarity as logits
- logit_scale = self.logit_scale.exp()
- logits_per_image = logit_scale * image_features @ text_features.t()
- logits_per_text = logits_per_image.t()
-
- # shape = [global_batch_size, global_batch_size]
- return logits_per_image, logits_per_text
-
-
-def convert_weights(model: nn.Module):
- """Convert applicable model parameters to fp16"""
-
- def _convert_weights_to_fp16(l):
- if isinstance(l, (nn.Conv1d, nn.Conv2d, nn.Linear)):
- l.weight.data = l.weight.data.half()
- if l.bias is not None:
- l.bias.data = l.bias.data.half()
-
- if isinstance(l, nn.MultiheadAttention):
- for attr in [*[f"{s}_proj_weight" for s in ["in", "q", "k", "v"]], "in_proj_bias", "bias_k", "bias_v"]:
- tensor = getattr(l, attr)
- if tensor is not None:
- tensor.data = tensor.data.half()
-
- for name in ["text_projection", "proj"]:
- if hasattr(l, name):
- attr = getattr(l, name)
- if attr is not None:
- attr.data = attr.data.half()
-
- model.apply(_convert_weights_to_fp16)
-
-
-def build_model(state_dict: dict):
- vit = "visual.proj" in state_dict
-
- if vit:
- vision_width = state_dict["visual.conv1.weight"].shape[0]
- vision_layers = len([k for k in state_dict.keys() if k.startswith("visual.") and k.endswith(".attn.in_proj_weight")])
- vision_patch_size = state_dict["visual.conv1.weight"].shape[-1]
- grid_size = round((state_dict["visual.positional_embedding"].shape[0] - 1) ** 0.5)
- image_resolution = vision_patch_size * grid_size
- else:
- counts: list = [len(set(k.split(".")[2] for k in state_dict if k.startswith(f"visual.layer{b}"))) for b in [1, 2, 3, 4]]
- vision_layers = tuple(counts)
- vision_width = state_dict["visual.layer1.0.conv1.weight"].shape[0]
- output_width = round((state_dict["visual.attnpool.positional_embedding"].shape[0] - 1) ** 0.5)
- vision_patch_size = None
- assert output_width ** 2 + 1 == state_dict["visual.attnpool.positional_embedding"].shape[0]
- image_resolution = output_width * 32
-
- embed_dim = state_dict["text_projection"].shape[1]
- context_length = state_dict["positional_embedding"].shape[0]
- vocab_size = state_dict["token_embedding.weight"].shape[0]
- transformer_width = state_dict["ln_final.weight"].shape[0]
- transformer_heads = transformer_width // 64
- transformer_layers = len(set(k.split(".")[2] for k in state_dict if k.startswith("transformer.resblocks")))
-
- model = CLIP(
- embed_dim,
- image_resolution, vision_layers, vision_width, vision_patch_size,
- context_length, vocab_size, transformer_width, transformer_heads, transformer_layers
- )
-
- for key in ["input_resolution", "context_length", "vocab_size"]:
- if key in state_dict:
- del state_dict[key]
-
- convert_weights(model)
- model.load_state_dict(state_dict)
- return model.eval()
diff --git a/roop-unleashed-main/clip/simple_tokenizer.py b/roop-unleashed-main/clip/simple_tokenizer.py
deleted file mode 100644
index 0a66286b7d5019c6e221932a813768038f839c91..0000000000000000000000000000000000000000
--- a/roop-unleashed-main/clip/simple_tokenizer.py
+++ /dev/null
@@ -1,132 +0,0 @@
-import gzip
-import html
-import os
-from functools import lru_cache
-
-import ftfy
-import regex as re
-
-
-@lru_cache()
-def default_bpe():
- return os.path.join(os.path.dirname(os.path.abspath(__file__)), "bpe_simple_vocab_16e6.txt.gz")
-
-
-@lru_cache()
-def bytes_to_unicode():
- """
- Returns list of utf-8 byte and a corresponding list of unicode strings.
- The reversible bpe codes work on unicode strings.
- This means you need a large # of unicode characters in your vocab if you want to avoid UNKs.
- When you're at something like a 10B token dataset you end up needing around 5K for decent coverage.
- This is a signficant percentage of your normal, say, 32K bpe vocab.
- To avoid that, we want lookup tables between utf-8 bytes and unicode strings.
- And avoids mapping to whitespace/control characters the bpe code barfs on.
- """
- bs = list(range(ord("!"), ord("~")+1))+list(range(ord("ยก"), ord("ยฌ")+1))+list(range(ord("ยฎ"), ord("รฟ")+1))
- cs = bs[:]
- n = 0
- for b in range(2**8):
- if b not in bs:
- bs.append(b)
- cs.append(2**8+n)
- n += 1
- cs = [chr(n) for n in cs]
- return dict(zip(bs, cs))
-
-
-def get_pairs(word):
- """Return set of symbol pairs in a word.
- Word is represented as tuple of symbols (symbols being variable-length strings).
- """
- pairs = set()
- prev_char = word[0]
- for char in word[1:]:
- pairs.add((prev_char, char))
- prev_char = char
- return pairs
-
-
-def basic_clean(text):
- text = ftfy.fix_text(text)
- text = html.unescape(html.unescape(text))
- return text.strip()
-
-
-def whitespace_clean(text):
- text = re.sub(r'\s+', ' ', text)
- text = text.strip()
- return text
-
-
-class SimpleTokenizer(object):
- def __init__(self, bpe_path: str = default_bpe()):
- self.byte_encoder = bytes_to_unicode()
- self.byte_decoder = {v: k for k, v in self.byte_encoder.items()}
- merges = gzip.open(bpe_path).read().decode("utf-8").split('\n')
- merges = merges[1:49152-256-2+1]
- merges = [tuple(merge.split()) for merge in merges]
- vocab = list(bytes_to_unicode().values())
- vocab = vocab + [v+'' for v in vocab]
- for merge in merges:
- vocab.append(''.join(merge))
- vocab.extend(['<|startoftext|>', '<|endoftext|>'])
- self.encoder = dict(zip(vocab, range(len(vocab))))
- self.decoder = {v: k for k, v in self.encoder.items()}
- self.bpe_ranks = dict(zip(merges, range(len(merges))))
- self.cache = {'<|startoftext|>': '<|startoftext|>', '<|endoftext|>': '<|endoftext|>'}
- self.pat = re.compile(r"""<\|startoftext\|>|<\|endoftext\|>|'s|'t|'re|'ve|'m|'ll|'d|[\p{L}]+|[\p{N}]|[^\s\p{L}\p{N}]+""", re.IGNORECASE)
-
- def bpe(self, token):
- if token in self.cache:
- return self.cache[token]
- word = tuple(token[:-1]) + ( token[-1] + '',)
- pairs = get_pairs(word)
-
- if not pairs:
- return token+''
-
- while True:
- bigram = min(pairs, key = lambda pair: self.bpe_ranks.get(pair, float('inf')))
- if bigram not in self.bpe_ranks:
- break
- first, second = bigram
- new_word = []
- i = 0
- while i < len(word):
- try:
- j = word.index(first, i)
- new_word.extend(word[i:j])
- i = j
- except:
- new_word.extend(word[i:])
- break
-
- if word[i] == first and i < len(word)-1 and word[i+1] == second:
- new_word.append(first+second)
- i += 2
- else:
- new_word.append(word[i])
- i += 1
- new_word = tuple(new_word)
- word = new_word
- if len(word) == 1:
- break
- else:
- pairs = get_pairs(word)
- word = ' '.join(word)
- self.cache[token] = word
- return word
-
- def encode(self, text):
- bpe_tokens = []
- text = whitespace_clean(basic_clean(text)).lower()
- for token in re.findall(self.pat, text):
- token = ''.join(self.byte_encoder[b] for b in token.encode('utf-8'))
- bpe_tokens.extend(self.encoder[bpe_token] for bpe_token in self.bpe(token).split(' '))
- return bpe_tokens
-
- def decode(self, tokens):
- text = ''.join([self.decoder[token] for token in tokens])
- text = bytearray([self.byte_decoder[c] for c in text]).decode('utf-8', errors="replace").replace('', ' ')
- return text
diff --git a/roop-unleashed-main/clip/vitseg.py b/roop-unleashed-main/clip/vitseg.py
deleted file mode 100644
index ed621431ddf930fcfa27b5929999776b96fede63..0000000000000000000000000000000000000000
--- a/roop-unleashed-main/clip/vitseg.py
+++ /dev/null
@@ -1,286 +0,0 @@
-import math
-from posixpath import basename, dirname, join
-# import clip
-from clip.model import convert_weights
-import torch
-import json
-from torch import nn
-from torch.nn import functional as nnf
-from torch.nn.modules import activation
-from torch.nn.modules.activation import ReLU
-from torchvision import transforms
-
-normalize = transforms.Normalize(mean=(0.48145466, 0.4578275, 0.40821073), std=(0.26862954, 0.26130258, 0.27577711))
-
-from torchvision.models import ResNet
-
-
-def process_prompts(conditional, prompt_list, conditional_map):
- # DEPRECATED
-
- # randomly sample a synonym
- words = [conditional_map[int(i)] for i in conditional]
- words = [syns[torch.multinomial(torch.ones(len(syns)), 1, replacement=True).item()] for syns in words]
- words = [w.replace('_', ' ') for w in words]
-
- if prompt_list is not None:
- prompt_indices = torch.multinomial(torch.ones(len(prompt_list)), len(words), replacement=True)
- prompts = [prompt_list[i] for i in prompt_indices]
- else:
- prompts = ['a photo of {}'] * (len(words))
-
- return [promt.format(w) for promt, w in zip(prompts, words)]
-
-
-class VITDenseBase(nn.Module):
-
- def rescaled_pos_emb(self, new_size):
- assert len(new_size) == 2
-
- a = self.model.positional_embedding[1:].T.view(1, 768, *self.token_shape)
- b = nnf.interpolate(a, new_size, mode='bicubic', align_corners=False).squeeze(0).view(768, new_size[0]*new_size[1]).T
- return torch.cat([self.model.positional_embedding[:1], b])
-
- def visual_forward(self, x_inp, extract_layers=(), skip=False, mask=None):
-
- with torch.no_grad():
-
- x_inp = nnf.interpolate(x_inp, (384, 384))
-
- x = self.model.patch_embed(x_inp)
- cls_token = self.model.cls_token.expand(x.shape[0], -1, -1) # stole cls_tokens impl from Phil Wang, thanks
- if self.model.dist_token is None:
- x = torch.cat((cls_token, x), dim=1)
- else:
- x = torch.cat((cls_token, self.model.dist_token.expand(x.shape[0], -1, -1), x), dim=1)
- x = self.model.pos_drop(x + self.model.pos_embed)
-
- activations = []
- for i, block in enumerate(self.model.blocks):
- x = block(x)
-
- if i in extract_layers:
- # permute to be compatible with CLIP
- activations += [x.permute(1,0,2)]
-
- x = self.model.norm(x)
- x = self.model.head(self.model.pre_logits(x[:, 0]))
-
- # again for CLIP compatibility
- # x = x.permute(1, 0, 2)
-
- return x, activations, None
-
- def sample_prompts(self, words, prompt_list=None):
-
- prompt_list = prompt_list if prompt_list is not None else self.prompt_list
-
- prompt_indices = torch.multinomial(torch.ones(len(prompt_list)), len(words), replacement=True)
- prompts = [prompt_list[i] for i in prompt_indices]
- return [promt.format(w) for promt, w in zip(prompts, words)]
-
- def get_cond_vec(self, conditional, batch_size):
- # compute conditional from a single string
- if conditional is not None and type(conditional) == str:
- cond = self.compute_conditional(conditional)
- cond = cond.repeat(batch_size, 1)
-
- # compute conditional from string list/tuple
- elif conditional is not None and type(conditional) in {list, tuple} and type(conditional[0]) == str:
- assert len(conditional) == batch_size
- cond = self.compute_conditional(conditional)
-
- # use conditional directly
- elif conditional is not None and type(conditional) == torch.Tensor and conditional.ndim == 2:
- cond = conditional
-
- # compute conditional from image
- elif conditional is not None and type(conditional) == torch.Tensor:
- with torch.no_grad():
- cond, _, _ = self.visual_forward(conditional)
- else:
- raise ValueError('invalid conditional')
- return cond
-
- def compute_conditional(self, conditional):
- import clip
-
- dev = next(self.parameters()).device
-
- if type(conditional) in {list, tuple}:
- text_tokens = clip.tokenize(conditional).to(dev)
- cond = self.clip_model.encode_text(text_tokens)
- else:
- if conditional in self.precomputed_prompts:
- cond = self.precomputed_prompts[conditional].float().to(dev)
- else:
- text_tokens = clip.tokenize([conditional]).to(dev)
- cond = self.clip_model.encode_text(text_tokens)[0]
-
- return cond
-
-
-class VITDensePredT(VITDenseBase):
-
- def __init__(self, extract_layers=(3, 6, 9), cond_layer=0, reduce_dim=128, n_heads=4, prompt='fixed',
- depth=3, extra_blocks=0, reduce_cond=None, fix_shift=False,
- learn_trans_conv_only=False, refine=None, limit_to_clip_only=False, upsample=False,
- add_calibration=False, process_cond=None, not_pretrained=False):
- super().__init__()
- # device = 'cpu'
-
- self.extract_layers = extract_layers
- self.cond_layer = cond_layer
- self.limit_to_clip_only = limit_to_clip_only
- self.process_cond = None
-
- if add_calibration:
- self.calibration_conds = 1
-
- self.upsample_proj = nn.Conv2d(reduce_dim, 1, kernel_size=1) if upsample else None
-
- self.add_activation1 = True
-
- import timm
- self.model = timm.create_model('vit_base_patch16_384', pretrained=True)
- self.model.head = nn.Linear(768, 512 if reduce_cond is None else reduce_cond)
-
- for p in self.model.parameters():
- p.requires_grad_(False)
-
- import clip
- self.clip_model, _ = clip.load('ViT-B/16', device='cpu', jit=False)
- # del self.clip_model.visual
-
-
- self.token_shape = (14, 14)
-
- # conditional
- if reduce_cond is not None:
- self.reduce_cond = nn.Linear(512, reduce_cond)
- for p in self.reduce_cond.parameters():
- p.requires_grad_(False)
- else:
- self.reduce_cond = None
-
- # self.film = AVAILABLE_BLOCKS['film'](512, 128)
- self.film_mul = nn.Linear(512 if reduce_cond is None else reduce_cond, reduce_dim)
- self.film_add = nn.Linear(512 if reduce_cond is None else reduce_cond, reduce_dim)
-
- # DEPRECATED
- # self.conditional_map = {c['id']: c['synonyms'] for c in json.load(open(cond_map))}
-
- assert len(self.extract_layers) == depth
-
- self.reduces = nn.ModuleList([nn.Linear(768, reduce_dim) for _ in range(depth)])
- self.blocks = nn.ModuleList([nn.TransformerEncoderLayer(d_model=reduce_dim, nhead=n_heads) for _ in range(len(self.extract_layers))])
- self.extra_blocks = nn.ModuleList([nn.TransformerEncoderLayer(d_model=reduce_dim, nhead=n_heads) for _ in range(extra_blocks)])
-
- trans_conv_ks = (16, 16)
- self.trans_conv = nn.ConvTranspose2d(reduce_dim, 1, trans_conv_ks, stride=trans_conv_ks)
-
- # refinement and trans conv
-
- if learn_trans_conv_only:
- for p in self.parameters():
- p.requires_grad_(False)
-
- for p in self.trans_conv.parameters():
- p.requires_grad_(True)
-
- if prompt == 'fixed':
- self.prompt_list = ['a photo of a {}.']
- elif prompt == 'shuffle':
- self.prompt_list = ['a photo of a {}.', 'a photograph of a {}.', 'an image of a {}.', '{}.']
- elif prompt == 'shuffle+':
- self.prompt_list = ['a photo of a {}.', 'a photograph of a {}.', 'an image of a {}.', '{}.',
- 'a cropped photo of a {}.', 'a good photo of a {}.', 'a photo of one {}.',
- 'a bad photo of a {}.', 'a photo of the {}.']
- elif prompt == 'shuffle_clip':
- from models.clip_prompts import imagenet_templates
- self.prompt_list = imagenet_templates
-
- if process_cond is not None:
- if process_cond == 'clamp' or process_cond[0] == 'clamp':
-
- val = process_cond[1] if type(process_cond) in {list, tuple} else 0.2
-
- def clamp_vec(x):
- return torch.clamp(x, -val, val)
-
- self.process_cond = clamp_vec
-
- elif process_cond.endswith('.pth'):
-
- shift = torch.load(process_cond)
- def add_shift(x):
- return x + shift.to(x.device)
-
- self.process_cond = add_shift
-
- import pickle
- precomp = pickle.load(open('precomputed_prompt_vectors.pickle', 'rb'))
- self.precomputed_prompts = {k: torch.from_numpy(v) for k, v in precomp.items()}
-
-
- def forward(self, inp_image, conditional=None, return_features=False, mask=None):
-
- assert type(return_features) == bool
-
- # inp_image = inp_image.to(self.model.positional_embedding.device)
-
- if mask is not None:
- raise ValueError('mask not supported')
-
- # x_inp = normalize(inp_image)
- x_inp = inp_image
-
- bs, dev = inp_image.shape[0], x_inp.device
-
- inp_image_size = inp_image.shape[2:]
-
- cond = self.get_cond_vec(conditional, bs)
-
- visual_q, activations, _ = self.visual_forward(x_inp, extract_layers=[0] + list(self.extract_layers))
-
- activation1 = activations[0]
- activations = activations[1:]
-
- a = None
- for i, (activation, block, reduce) in enumerate(zip(activations[::-1], self.blocks, self.reduces)):
-
- if a is not None:
- a = reduce(activation) + a
- else:
- a = reduce(activation)
-
- if i == self.cond_layer:
- if self.reduce_cond is not None:
- cond = self.reduce_cond(cond)
-
- a = self.film_mul(cond) * a + self.film_add(cond)
-
- a = block(a)
-
- for block in self.extra_blocks:
- a = a + block(a)
-
- a = a[1:].permute(1, 2, 0) # rm cls token and -> BS, Feats, Tokens
-
- size = int(math.sqrt(a.shape[2]))
-
- a = a.view(bs, a.shape[1], size, size)
-
- if self.trans_conv is not None:
- a = self.trans_conv(a)
-
- if self.upsample_proj is not None:
- a = self.upsample_proj(a)
- a = nnf.interpolate(a, x_inp.shape[2:], mode='bilinear')
-
- a = nnf.interpolate(a, inp_image_size)
-
- if return_features:
- return a, visual_q, cond, [activation1] + activations
- else:
- return a,
diff --git a/roop-unleashed-main/config_colab.yaml b/roop-unleashed-main/config_colab.yaml
deleted file mode 100644
index 2c47f3f6f17f35eeb2089e8aba2ff42c80077ba5..0000000000000000000000000000000000000000
--- a/roop-unleashed-main/config_colab.yaml
+++ /dev/null
@@ -1,14 +0,0 @@
-clear_output: true
-force_cpu: false
-max_threads: 3
-memory_limit: 0
-output_image_format: png
-output_template: '{file}_{time}'
-output_video_codec: libx264
-output_video_format: mp4
-provider: cuda
-selected_theme: Default
-server_name: ''
-server_port: 0
-server_share: true
-video_quality: 14
diff --git a/roop-unleashed-main/docs/screenshot.png b/roop-unleashed-main/docs/screenshot.png
deleted file mode 100644
index cc5fd8868554b756c9e5630e7185c9c52bea4cdb..0000000000000000000000000000000000000000
--- a/roop-unleashed-main/docs/screenshot.png
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:a86df433a470c2b123dbcc4b3e93b7ba00f261a862e5a5b8c747764dc5d6c147
-size 3549458
diff --git a/roop-unleashed-main/installer/installer.py b/roop-unleashed-main/installer/installer.py
deleted file mode 100644
index c19769089181ad09ba9e6419ed84c87b838f5975..0000000000000000000000000000000000000000
--- a/roop-unleashed-main/installer/installer.py
+++ /dev/null
@@ -1,87 +0,0 @@
-import argparse
-import glob
-import os
-import shutil
-import site
-import subprocess
-import sys
-
-
-script_dir = os.getcwd()
-
-
-def run_cmd(cmd, capture_output=False, env=None):
- # Run shell commands
- return subprocess.run(cmd, shell=True, capture_output=capture_output, env=env)
-
-
-def check_env():
- # If we have access to conda, we are probably in an environment
- conda_not_exist = run_cmd("conda", capture_output=True).returncode
- if conda_not_exist:
- print("Conda is not installed. Exiting...")
- sys.exit()
-
- # Ensure this is a new environment and not the base environment
- if os.environ["CONDA_DEFAULT_ENV"] == "base":
- print("Create an environment for this project and activate it. Exiting...")
- sys.exit()
-
-
-def install_dependencies():
- global MY_PATH
-
- # Install Git and clone repo
- run_cmd("conda install -y -k git")
- run_cmd("git clone https://github.com/C0untFloyd/roop-unleashed.git")
- os.chdir(MY_PATH)
- run_cmd("git checkout 5bfafdc97a0c47b46ec83e6530a57399aaad75d7")
- # Installs dependencies from requirements.txt
- run_cmd("python -m pip install -r requirements.txt")
-
-
-
-def update_dependencies():
- global MY_PATH
-
- os.chdir(MY_PATH)
- # do a hard reset for to update even if there are local changes
- run_cmd("git fetch --all")
- run_cmd("git reset --hard origin/main")
- run_cmd("git pull")
- # Installs/Updates dependencies from all requirements.txt
- run_cmd("python -m pip install -r requirements.txt")
-
-
-def start_app():
- global MY_PATH
-
- os.chdir(MY_PATH)
- # forward commandline arguments
- sys.argv.pop(0)
- args = ' '.join(sys.argv)
- print("Launching App")
- run_cmd(f'python run.py {args}')
-
-
-if __name__ == "__main__":
- global MY_PATH
-
- MY_PATH = "roop-unleashed"
-
-
- # Verifies we are in a conda environment
- check_env()
-
- # If webui has already been installed, skip and run
- if not os.path.exists(MY_PATH):
- install_dependencies()
- else:
- # moved update from batch to here, because of batch limitations
- updatechoice = input("Check for Updates? [y/n]").lower()
- if updatechoice == "y":
- update_dependencies()
-
- # Run the model with webui
- os.chdir(script_dir)
- start_app()
diff --git a/roop-unleashed-main/installer/macOSinstaller.sh b/roop-unleashed-main/installer/macOSinstaller.sh
deleted file mode 100644
index 90eb3ddd31727c81dbd702cb8327fdbfb06193f0..0000000000000000000000000000000000000000
--- a/roop-unleashed-main/installer/macOSinstaller.sh
+++ /dev/null
@@ -1,73 +0,0 @@
-#!/bin/bash
-
-# This script checks and installs all dependencies which are needed to run roop-unleashed. After that, it clones the repo.
-# Execute this easily with /bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/PJF16/roop-unleashed/master/installer/macOSinstaller.sh)
-
-# Function to check if a command exists
-command_exists() {
- command -v "$1" >/dev/null 2>&1
-}
-
-echo "Starting check and installation process of dependencies for roop-unleashed"
-
-# Check if Homebrew is installed
-if ! command_exists brew; then
- echo "Homebrew is not installed. Starting installation..."
- /bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"
-else
- echo "Homebrew is already installed."
-fi
-
-# Update Homebrew
-echo "Updating Homebrew..."
-brew update
-
-# Check if Python 3.11 is installed
-if brew list --versions python@3.11 >/dev/null; then
- echo "Python 3.11 is already installed."
-else
- echo "Python 3.11 is not installed. Installing it now..."
- brew install python@3.11
-fi
-
-# Check if python-tk@3.11 is installed
-if brew list --versions python-tk@3.11 >/dev/null; then
- echo "python-tk@3.11 is already installed."
-else
- echo "python-tk@3.11 is not installed. Installing it now..."
- brew install python-tk@3.11
-fi
-
-# Check if ffmpeg is installed
-if command_exists ffmpeg; then
- echo "ffmpeg is already installed."
-else
- echo "ffmpeg is not installed. Installing it now..."
- brew install ffmpeg
-fi
-
-# Check if git is installed
-if command_exists git; then
- echo "git is already installed."
-else
- echo "git is not installed. Installing it now..."
- brew install git
-fi
-
-# Clone the repository
-REPO_URL="https://github.com/C0untFloyd/roop-unleashed.git"
-REPO_NAME="roop-unleashed"
-
-echo "Cloning the repository $REPO_URL..."
-git clone $REPO_URL
-
-# Check if the repository was cloned successfully
-if [ -d "$REPO_NAME" ]; then
- echo "Repository cloned successfully. Changing into directory $REPO_NAME..."
- cd "$REPO_NAME"
-else
- echo "Failed to clone the repository."
-fi
-
-echo "Check and installation process completed."
-
diff --git a/roop-unleashed-main/installer/windows_run.bat b/roop-unleashed-main/installer/windows_run.bat
deleted file mode 100644
index cb5f90dece8a3717644e08c159741a9d7baacd15..0000000000000000000000000000000000000000
--- a/roop-unleashed-main/installer/windows_run.bat
+++ /dev/null
@@ -1,95 +0,0 @@
-@echo off
-
-REM No CLI arguments supported anymore
-set COMMANDLINE_ARGS=
-
-cd /D "%~dp0"
-
-echo "%CD%"| findstr /C:" " >nul && echo This script relies on Miniconda which can not be silently installed under a path with spaces. && goto end
-
-set PATH=%PATH%;%SystemRoot%\system32
-
-@rem config
-set INSTALL_DIR=%cd%\installer_files
-set CONDA_ROOT_PREFIX=%cd%\installer_files\conda
-set INSTALL_ENV_DIR=%cd%\installer_files\env
-set MINICONDA_DOWNLOAD_URL=https://repo.anaconda.com/miniconda/Miniconda3-latest-Windows-x86_64.exe
-set FFMPEG_DOWNLOAD_URL=https://github.com/GyanD/codexffmpeg/releases/download/7.1/ffmpeg-7.1-essentials_build.zip
-set INSTALL_FFMPEG_DIR=%cd%\installer_files\ffmpeg
-set INSIGHTFACE_PACKAGE_URL=https://github.com/C0untFloyd/roop-unleashed/releases/download/3.6.6/insightface-0.7.3-cp310-cp310-win_amd64.whl
-set INSIGHTFACE_PACKAGE_PATH=%INSTALL_DIR%\insightface-0.7.3-cp310-cp310-win_amd64.whl
-
-set conda_exists=F
-set ffmpeg_exists=F
-
-@rem figure out whether git and conda needs to be installed
-call "%CONDA_ROOT_PREFIX%\_conda.exe" --version >nul 2>&1
-if "%ERRORLEVEL%" EQU "0" set conda_exists=T
-
-@rem Check if FFmpeg is already in PATH
-where ffmpeg >nul 2>&1
-if "%ERRORLEVEL%" EQU "0" (
- echo FFmpeg is already installed.
- set ffmpeg_exists=T
-)
-
-@rem (if necessary) install git and conda into a contained environment
-
-@rem download conda
-if "%conda_exists%" == "F" (
- echo Downloading Miniconda from %MINICONDA_DOWNLOAD_URL% to %INSTALL_DIR%\miniconda_installer.exe
- mkdir "%INSTALL_DIR%"
- call curl -Lk "%MINICONDA_DOWNLOAD_URL%" > "%INSTALL_DIR%\miniconda_installer.exe" || ( echo. && echo Miniconda failed to download. && goto end )
- echo Installing Miniconda to %CONDA_ROOT_PREFIX%
- start /wait "" "%INSTALL_DIR%\miniconda_installer.exe" /InstallationType=JustMe /NoShortcuts=1 /AddToPath=0 /RegisterPython=0 /NoRegistry=1 /S /D=%CONDA_ROOT_PREFIX%
-
- @rem test the conda binary
- echo Miniconda version:
- call "%CONDA_ROOT_PREFIX%\_conda.exe" --version || ( echo. && echo Miniconda not found. && goto end )
-)
-
-@rem create the installer env
-if not exist "%INSTALL_ENV_DIR%" (
- echo Creating Conda Environment
- call "%CONDA_ROOT_PREFIX%\_conda.exe" create --no-shortcuts -y -k --prefix "%INSTALL_ENV_DIR%" python=3.10 || ( echo. && echo ERROR: Conda environment creation failed. && goto end )
- @rem check if conda environment was actually created
- if not exist "%INSTALL_ENV_DIR%\python.exe" ( echo. && echo ERROR: Conda environment is empty. && goto end )
- @rem activate installer env
- call "%CONDA_ROOT_PREFIX%\condabin\conda.bat" activate "%INSTALL_ENV_DIR%" || ( echo. && echo ERROR: Miniconda hook not found. && goto end )
- @rem Download insightface package
- echo Downloading insightface package from %INSIGHTFACE_PACKAGE_URL% to %INSIGHTFACE_PACKAGE_PATH%
- call curl -Lk "%INSIGHTFACE_PACKAGE_URL%" > "%INSIGHTFACE_PACKAGE_PATH%" || ( echo. && echo ERROR: Insightface package failed to download. && goto end )
- @rem install insightface package using pip
- echo Installing insightface package
- call pip install "%INSIGHTFACE_PACKAGE_PATH%" || ( echo. && echo ERROR: Insightface package installation failed. && goto end )
-)
-
-@rem Download and install FFmpeg if not already installed
-if "%ffmpeg_exists%" == "F" (
- if not exist "%INSTALL_FFMPEG_DIR%" (
- echo Downloading ffmpeg from %FFMPEG_DOWNLOAD_URL% to %INSTALL_DIR%
- call curl -Lk "%FFMPEG_DOWNLOAD_URL%" > "%INSTALL_DIR%\ffmpeg.zip" || ( echo. && echo ffmpeg failed to download. && goto end )
- call powershell -command "Expand-Archive -Force '%INSTALL_DIR%\ffmpeg.zip' '%INSTALL_DIR%\'"
- cd "%INSTALL_DIR%"
- move ffmpeg-* ffmpeg
- setx PATH "%INSTALL_FFMPEG_DIR%\bin\;%PATH%"
- echo To use videos, you need to restart roop after this installation.
- cd ..
- )
-) else (
- echo Skipping FFmpeg installation as it is already available.
-)
-
-@rem setup installer env
-@rem check if conda environment was actually created
-if not exist "%INSTALL_ENV_DIR%\python.exe" ( echo. && echo ERROR: Conda environment is empty. && goto end )
-@rem activate installer env
-call "%CONDA_ROOT_PREFIX%\condabin\conda.bat" activate "%INSTALL_ENV_DIR%" || ( echo. && echo ERROR: Miniconda hook not found. && goto end )
-echo Launching roop unleashed
-call python installer.py %COMMANDLINE_ARGS%
-
-echo.
-echo Done!
-
-:end
-pause
diff --git a/roop-unleashed-main/mypy.ini b/roop-unleashed-main/mypy.ini
deleted file mode 100644
index 64218bc23688632a08c98ec4a0451ed46f8ed5e5..0000000000000000000000000000000000000000
--- a/roop-unleashed-main/mypy.ini
+++ /dev/null
@@ -1,7 +0,0 @@
-[mypy]
-check_untyped_defs = True
-disallow_any_generics = True
-disallow_untyped_calls = True
-disallow_untyped_defs = True
-ignore_missing_imports = True
-strict_optional = False
diff --git a/roop-unleashed-main/requirements.txt b/roop-unleashed-main/requirements.txt
deleted file mode 100644
index 446ee524551444b3be06b7212b447c1f811adf95..0000000000000000000000000000000000000000
--- a/roop-unleashed-main/requirements.txt
+++ /dev/null
@@ -1,19 +0,0 @@
---extra-index-url https://download.pytorch.org/whl/cu124
-numpy==1.26.4
-gradio==5.9.1
-opencv-python-headless==4.10.0.84
-onnx==1.16.1
-insightface==0.7.3
-albucore==0.0.16
-psutil==5.9.6
-torch==2.5.1+cu124; sys_platform != 'darwin'
-torch==2.5.1; sys_platform == 'darwin'
-torchvision==0.20.1+cu124; sys_platform != 'darwin'
-torchvision==0.20.1; sys_platform == 'darwin'
-onnxruntime==1.20.1; sys_platform == 'darwin' and platform_machine != 'arm64'
-onnxruntime-silicon==1.20.1; sys_platform == 'darwin' and platform_machine == 'arm64'
-onnxruntime-gpu==1.20.1; sys_platform != 'darwin'
-tqdm==4.66.4
-ftfy
-regex
-pyvirtualcam
diff --git a/roop-unleashed-main/roop-unleashed.ipynb b/roop-unleashed-main/roop-unleashed.ipynb
deleted file mode 100644
index 0ef9842f23914c6d1de07bc3d695e48466e095ab..0000000000000000000000000000000000000000
--- a/roop-unleashed-main/roop-unleashed.ipynb
+++ /dev/null
@@ -1,166 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "G9BdiCppV6AS"
- },
- "source": [
- "# Colab for roop-unleashed - Gradio version\n",
- "https://github.com/C0untFloyd/roop-unleashed\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "CanIXgLJgaOj"
- },
- "source": [
- "Install CUDA 12.6 on Google Cloud Compute\n",
- "(currently unnecessary because the latest 12.x should be already installed)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "96GE4UgYg3Ej"
- },
- "outputs": [],
- "source": [
- "# don't run this cell if you know that there is at least Cuda 12.4 installed\n",
- "!apt-get -y update\n",
- "!apt-get -y install cuda-toolkit-12-6\n",
- "import os\n",
- "os.environ[\"LD_LIBRARY_PATH\"] += \":\" + \"/usr/local/cuda-12/lib64\"\n",
- "os.environ[\"LD_LIBRARY_PATH\"] += \":\" + \"/usr/local/cuda-12.6/lib64\""
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "0ZYRNb0AWLLW"
- },
- "source": [
- "Installing & preparing requirements"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "t1yPuhdySqCq"
- },
- "outputs": [],
- "source": [
- "!git clone https://github.com/C0untFloyd/roop-unleashed.git\n",
- "%cd roop-unleashed\n",
- "!mv config_colab.yaml config.yaml\n",
- "!pip install -r requirements.txt"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "u_4JQiSlV9Fi"
- },
- "source": [
- "Running roop-unleashed with default config"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "Is6U2huqSzLE"
- },
- "outputs": [],
- "source": [
- "!python run.py"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "UdQ1VHdI8lCf"
- },
- "source": [
- "### Download generated images folder\n",
- "(only needed if you want to zip the generated output)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 17
- },
- "id": "oYjWveAmw10X",
- "outputId": "5b4c3650-f951-434a-c650-5525a8a70c1e"
- },
- "outputs": [
- {
- "data": {
- "application/javascript": "\n async function download(id, filename, size) {\n if (!google.colab.kernel.accessAllowed) {\n return;\n }\n const div = document.createElement('div');\n const label = document.createElement('label');\n label.textContent = `Downloading \"${filename}\": `;\n div.appendChild(label);\n const progress = document.createElement('progress');\n progress.max = size;\n div.appendChild(progress);\n document.body.appendChild(div);\n\n const buffers = [];\n let downloaded = 0;\n\n const channel = await google.colab.kernel.comms.open(id);\n // Send a message to notify the kernel that we're ready.\n channel.send({})\n\n for await (const message of channel.messages) {\n // Send a message to notify the kernel that we're ready.\n channel.send({})\n if (message.buffers) {\n for (const buffer of message.buffers) {\n buffers.push(buffer);\n downloaded += buffer.byteLength;\n progress.value = downloaded;\n }\n }\n }\n const blob = new Blob(buffers, {type: 'application/binary'});\n const a = document.createElement('a');\n a.href = window.URL.createObjectURL(blob);\n a.download = filename;\n div.appendChild(a);\n a.click();\n div.remove();\n }\n ",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "application/javascript": "download(\"download_789eab11-93d2-4880-adf3-6aceee0cc5f9\", \"fake_output.zip.zip\", 80125)",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "import shutil\n",
- "import os\n",
- "from google.colab import files\n",
- "\n",
- "def zip_directory(directory_path, zip_path):\n",
- " shutil.make_archive(zip_path, 'zip', directory_path)\n",
- "\n",
- "# Set the directory path you want to download\n",
- "directory_path = '/content/roop-unleashed/output'\n",
- "\n",
- "# Set the zip file name\n",
- "zip_filename = 'fake_output.zip'\n",
- "\n",
- "# Zip the directory\n",
- "zip_directory(directory_path, zip_filename)\n",
- "\n",
- "# Download the zip file\n",
- "files.download(zip_filename+'.zip')\n"
- ]
- }
- ],
- "metadata": {
- "accelerator": "GPU",
- "colab": {
- "collapsed_sections": [
- "UdQ1VHdI8lCf"
- ],
- "gpuType": "T4",
- "provenance": []
- },
- "kernelspec": {
- "display_name": "Python 3",
- "name": "python3"
- },
- "language_info": {
- "name": "python"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
diff --git a/roop-unleashed-main/roop/FaceSet.py b/roop-unleashed-main/roop/FaceSet.py
deleted file mode 100644
index 9e426219fe4265290883a026fbde2d0513d5d554..0000000000000000000000000000000000000000
--- a/roop-unleashed-main/roop/FaceSet.py
+++ /dev/null
@@ -1,20 +0,0 @@
-import numpy as np
-
-class FaceSet:
- faces = []
- ref_images = []
- embedding_average = 'None'
- embeddings_backup = None
-
- def __init__(self):
- self.faces = []
- self.ref_images = []
- self.embeddings_backup = None
-
- def AverageEmbeddings(self):
- if len(self.faces) > 1 and self.embeddings_backup is None:
- self.embeddings_backup = self.faces[0]['embedding']
- embeddings = [face.embedding for face in self.faces]
-
- self.faces[0]['embedding'] = np.mean(embeddings, axis=0)
- # try median too?
diff --git a/roop-unleashed-main/roop/ProcessEntry.py b/roop-unleashed-main/roop/ProcessEntry.py
deleted file mode 100644
index 2dd53239463a14769954a10f1371d332bd88e05d..0000000000000000000000000000000000000000
--- a/roop-unleashed-main/roop/ProcessEntry.py
+++ /dev/null
@@ -1,7 +0,0 @@
-class ProcessEntry:
- def __init__(self, filename: str, start: int, end: int, fps: float):
- self.filename = filename
- self.finalname = None
- self.startframe = start
- self.endframe = end
- self.fps = fps
\ No newline at end of file
diff --git a/roop-unleashed-main/roop/ProcessMgr.py b/roop-unleashed-main/roop/ProcessMgr.py
deleted file mode 100644
index 9c56cd513a45d1559933307a172bbd47a6196681..0000000000000000000000000000000000000000
--- a/roop-unleashed-main/roop/ProcessMgr.py
+++ /dev/null
@@ -1,911 +0,0 @@
-import os
-import cv2
-import numpy as np
-import psutil
-
-from roop.ProcessOptions import ProcessOptions
-
-from roop.face_util import get_first_face, get_all_faces, rotate_anticlockwise, rotate_clockwise, clamp_cut_values
-from roop.utilities import compute_cosine_distance, get_device, str_to_class, shuffle_array
-import roop.vr_util as vr
-
-from typing import Any, List, Callable
-from roop.typing import Frame, Face
-from concurrent.futures import ThreadPoolExecutor, as_completed
-from threading import Thread, Lock
-from queue import Queue
-from tqdm import tqdm
-from roop.ffmpeg_writer import FFMPEG_VideoWriter
-from roop.StreamWriter import StreamWriter
-import roop.globals
-
-
-
-# Poor man's enum to be able to compare to int
-class eNoFaceAction():
- USE_ORIGINAL_FRAME = 0
- RETRY_ROTATED = 1
- SKIP_FRAME = 2
- SKIP_FRAME_IF_DISSIMILAR = 3,
- USE_LAST_SWAPPED = 4
-
-
-
-def create_queue(temp_frame_paths: List[str]) -> Queue[str]:
- queue: Queue[str] = Queue()
- for frame_path in temp_frame_paths:
- queue.put(frame_path)
- return queue
-
-
-def pick_queue(queue: Queue[str], queue_per_future: int) -> List[str]:
- queues = []
- for _ in range(queue_per_future):
- if not queue.empty():
- queues.append(queue.get())
- return queues
-
-
-
-class ProcessMgr():
- input_face_datas = []
- target_face_datas = []
-
- imagemask = None
-
- processors = []
- options : ProcessOptions = None
-
- num_threads = 1
- current_index = 0
- processing_threads = 1
- buffer_wait_time = 0.1
-
- lock = Lock()
-
- frames_queue = None
- processed_queue = None
-
- videowriter= None
- streamwriter = None
-
- progress_gradio = None
- total_frames = 0
-
- num_frames_no_face = 0
- last_swapped_frame = None
-
- output_to_file = None
- output_to_cam = None
-
-
- plugins = {
- 'faceswap' : 'FaceSwapInsightFace',
- 'mask_clip2seg' : 'Mask_Clip2Seg',
- 'mask_xseg' : 'Mask_XSeg',
- 'codeformer' : 'Enhance_CodeFormer',
- 'gfpgan' : 'Enhance_GFPGAN',
- 'dmdnet' : 'Enhance_DMDNet',
- 'gpen' : 'Enhance_GPEN',
- 'restoreformer++' : 'Enhance_RestoreFormerPPlus',
- 'colorizer' : 'Frame_Colorizer',
- 'filter_generic' : 'Frame_Filter',
- 'removebg' : 'Frame_Masking',
- 'upscale' : 'Frame_Upscale'
- }
-
- def __init__(self, progress):
- if progress is not None:
- self.progress_gradio = progress
-
- def reuseOldProcessor(self, name:str):
- for p in self.processors:
- if p.processorname == name:
- return p
-
- return None
-
-
- def initialize(self, input_faces, target_faces, options):
- self.input_face_datas = input_faces
- self.target_face_datas = target_faces
- self.num_frames_no_face = 0
- self.last_swapped_frame = None
- self.options = options
- devicename = get_device()
-
- roop.globals.g_desired_face_analysis=["landmark_3d_68", "landmark_2d_106","detection","recognition"]
- if options.swap_mode == "all_female" or options.swap_mode == "all_male":
- roop.globals.g_desired_face_analysis.append("genderage")
- elif options.swap_mode == "all_random":
- # don't modify original list
- self.input_face_datas = input_faces.copy()
- shuffle_array(self.input_face_datas)
-
-
- for p in self.processors:
- newp = next((x for x in options.processors.keys() if x == p.processorname), None)
- if newp is None:
- p.Release()
- del p
-
- newprocessors = []
- for key, extoption in options.processors.items():
- p = self.reuseOldProcessor(key)
- if p is None:
- classname = self.plugins[key]
- module = 'roop.processors.' + classname
- p = str_to_class(module, classname)
- if p is not None:
- extoption.update({"devicename": devicename})
- if p.type == "swap":
- if self.options.swap_modelname == "InSwapper 128":
- extoption.update({"modelname": "inswapper_128.onnx"})
- elif self.options.swap_modelname == "ReSwapper 128":
- extoption.update({"modelname": "reswapper_128.onnx"})
- elif self.options.swap_modelname == "ReSwapper 256":
- extoption.update({"modelname": "reswapper_256.onnx"})
-
- p.Initialize(extoption)
- newprocessors.append(p)
- else:
- print(f"Not using {module}")
- self.processors = newprocessors
-
-
-
- if isinstance(self.options.imagemask, dict) and self.options.imagemask.get("layers") and len(self.options.imagemask["layers"]) > 0:
- self.options.imagemask = self.options.imagemask.get("layers")[0]
- # Get rid of alpha
- self.options.imagemask = cv2.cvtColor(self.options.imagemask, cv2.COLOR_RGBA2GRAY)
- if np.any(self.options.imagemask):
- mo = self.input_face_datas[0].faces[0].mask_offsets
- self.options.imagemask = self.blur_area(self.options.imagemask, mo[4], mo[5])
- self.options.imagemask = self.options.imagemask.astype(np.float32) / 255
- self.options.imagemask = cv2.cvtColor(self.options.imagemask, cv2.COLOR_GRAY2RGB)
- else:
- self.options.imagemask = None
-
- self.options.frame_processing = False
- for p in self.processors:
- if p.type.startswith("frame_"):
- self.options.frame_processing = True
-
-
-
-
-
-
- def run_batch(self, source_files, target_files, threads:int = 1):
- progress_bar_format = '{l_bar}{bar}| {n_fmt}/{total_fmt} [{elapsed}<{remaining}, {rate_fmt}{postfix}]'
- self.total_frames = len(source_files)
- self.num_threads = threads
- with tqdm(total=self.total_frames, desc='Processing', unit='frame', dynamic_ncols=True, bar_format=progress_bar_format) as progress:
- with ThreadPoolExecutor(max_workers=threads) as executor:
- futures = []
- queue = create_queue(source_files)
- queue_per_future = max(len(source_files) // threads, 1)
- while not queue.empty():
- future = executor.submit(self.process_frames, source_files, target_files, pick_queue(queue, queue_per_future), lambda: self.update_progress(progress))
- futures.append(future)
- for future in as_completed(futures):
- future.result()
-
-
- def process_frames(self, source_files: List[str], target_files: List[str], current_files, update: Callable[[], None]) -> None:
- for f in current_files:
- if not roop.globals.processing:
- return
-
- # Decode the byte array into an OpenCV image
- temp_frame = cv2.imdecode(np.fromfile(f, dtype=np.uint8), cv2.IMREAD_COLOR)
- if temp_frame is not None:
- if self.options.frame_processing:
- for p in self.processors:
- frame = p.Run(temp_frame)
- resimg = frame
- else:
- resimg = self.process_frame(temp_frame)
- if resimg is not None:
- i = source_files.index(f)
- # Also let numpy write the file to support utf-8/16 filenames
- cv2.imencode(f'.{roop.globals.CFG.output_image_format}',resimg)[1].tofile(target_files[i])
- if update:
- update()
-
-
-
- def read_frames_thread(self, cap, frame_start, frame_end, num_threads):
- num_frame = 0
- total_num = frame_end - frame_start
- if frame_start > 0:
- cap.set(cv2.CAP_PROP_POS_FRAMES,frame_start)
-
- while True and roop.globals.processing:
- ret, frame = cap.read()
- if not ret:
- break
-
- self.frames_queue[num_frame % num_threads].put(frame, block=True)
- num_frame += 1
- if num_frame == total_num:
- break
-
- for i in range(num_threads):
- self.frames_queue[i].put(None)
-
-
-
- def process_videoframes(self, threadindex, progress) -> None:
- while True:
- frame = self.frames_queue[threadindex].get()
- if frame is None:
- self.processing_threads -= 1
- self.processed_queue[threadindex].put((False, None))
- return
- else:
- if self.options.frame_processing:
- for p in self.processors:
- frame = p.Run(frame)
- resimg = frame
- else:
- resimg = self.process_frame(frame)
- self.processed_queue[threadindex].put((True, resimg))
- del frame
- progress()
-
-
- def write_frames_thread(self):
- nextindex = 0
- num_producers = self.num_threads
-
- while True:
- process, frame = self.processed_queue[nextindex % self.num_threads].get()
- nextindex += 1
- if frame is not None:
- if self.output_to_file:
- self.videowriter.write_frame(frame)
- if self.output_to_cam:
- self.streamwriter.WriteToStream(frame)
- del frame
- elif process == False:
- num_producers -= 1
- if num_producers < 1:
- return
-
-
-
- def run_batch_inmem(self, output_method, source_video, target_video, frame_start, frame_end, fps, threads:int = 1):
- if len(self.processors) < 1:
- print("No processor defined!")
- return
-
- cap = cv2.VideoCapture(source_video)
- # frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
- frame_count = (frame_end - frame_start) + 1
- width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
- height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
-
- processed_resolution = None
- for p in self.processors:
- if hasattr(p, 'getProcessedResolution'):
- processed_resolution = p.getProcessedResolution(width, height)
- print(f"Processed resolution: {processed_resolution}")
- if processed_resolution is not None:
- width = processed_resolution[0]
- height = processed_resolution[1]
-
-
- self.total_frames = frame_count
- self.num_threads = threads
-
- self.processing_threads = self.num_threads
- self.frames_queue = []
- self.processed_queue = []
- for _ in range(threads):
- self.frames_queue.append(Queue(1))
- self.processed_queue.append(Queue(1))
-
- self.output_to_file = output_method != "Virtual Camera"
- self.output_to_cam = output_method == "Virtual Camera" or output_method == "Both"
-
- if self.output_to_file:
- self.videowriter = FFMPEG_VideoWriter(target_video, (width, height), fps, codec=roop.globals.video_encoder, crf=roop.globals.video_quality, audiofile=None)
- if self.output_to_cam:
- self.streamwriter = StreamWriter((width, height), int(fps))
-
- readthread = Thread(target=self.read_frames_thread, args=(cap, frame_start, frame_end, threads))
- readthread.start()
-
- writethread = Thread(target=self.write_frames_thread)
- writethread.start()
-
- progress_bar_format = '{l_bar}{bar}| {n_fmt}/{total_fmt} [{elapsed}<{remaining}, {rate_fmt}{postfix}]'
- with tqdm(total=self.total_frames, desc='Processing', unit='frames', dynamic_ncols=True, bar_format=progress_bar_format) as progress:
- with ThreadPoolExecutor(thread_name_prefix='swap_proc', max_workers=self.num_threads) as executor:
- futures = []
-
- for threadindex in range(threads):
- future = executor.submit(self.process_videoframes, threadindex, lambda: self.update_progress(progress))
- futures.append(future)
-
- for future in as_completed(futures):
- future.result()
- # wait for the task to complete
- readthread.join()
- writethread.join()
- cap.release()
- if self.output_to_file:
- self.videowriter.close()
- if self.output_to_cam:
- self.streamwriter.Close()
-
- self.frames_queue.clear()
- self.processed_queue.clear()
-
-
-
-
- def update_progress(self, progress: Any = None) -> None:
- process = psutil.Process(os.getpid())
- memory_usage = process.memory_info().rss / 1024 / 1024 / 1024
- progress.set_postfix({
- 'memory_usage': '{:.2f}'.format(memory_usage).zfill(5) + 'GB',
- 'execution_threads': self.num_threads
- })
- progress.update(1)
- if self.progress_gradio is not None:
- self.progress_gradio((progress.n, self.total_frames), desc='Processing', total=self.total_frames, unit='frames')
-
-
-
- def process_frame(self, frame:Frame):
- if len(self.input_face_datas) < 1 and not self.options.show_face_masking:
- return frame
- temp_frame = frame.copy()
- num_swapped, temp_frame = self.swap_faces(frame, temp_frame)
- if num_swapped > 0:
- if roop.globals.no_face_action == eNoFaceAction.SKIP_FRAME_IF_DISSIMILAR:
- if len(self.input_face_datas) > num_swapped:
- return None
- self.num_frames_no_face = 0
- self.last_swapped_frame = temp_frame.copy()
- return temp_frame
- if roop.globals.no_face_action == eNoFaceAction.USE_LAST_SWAPPED:
- if self.last_swapped_frame is not None and self.num_frames_no_face < self.options.max_num_reuse_frame:
- self.num_frames_no_face += 1
- return self.last_swapped_frame.copy()
- return frame
-
- elif roop.globals.no_face_action == eNoFaceAction.USE_ORIGINAL_FRAME:
- return frame
- if roop.globals.no_face_action == eNoFaceAction.SKIP_FRAME:
- #This only works with in-mem processing, as it simply skips the frame.
- #For 'extract frames' it simply leaves the unprocessed frame unprocessed and it gets used in the final output by ffmpeg.
- #If we could delete that frame here, that'd work but that might cause ffmpeg to fail unless the frames are renamed, and I don't think we have the info on what frame it actually is?????
- #alternatively, it could mark all the necessary frames for deletion, delete them at the end, then rename the remaining frames that might work?
- return None
- else:
- return self.retry_rotated(frame)
-
- def retry_rotated(self, frame):
- copyframe = frame.copy()
- copyframe = rotate_clockwise(copyframe)
- temp_frame = copyframe.copy()
- num_swapped, temp_frame = self.swap_faces(copyframe, temp_frame)
- if num_swapped > 0:
- return rotate_anticlockwise(temp_frame)
-
- copyframe = frame.copy()
- copyframe = rotate_anticlockwise(copyframe)
- temp_frame = copyframe.copy()
- num_swapped, temp_frame = self.swap_faces(copyframe, temp_frame)
- if num_swapped > 0:
- return rotate_clockwise(temp_frame)
- del copyframe
- return frame
-
-
-
- def swap_faces(self, frame, temp_frame):
- num_faces_found = 0
-
- if self.options.swap_mode == "first":
- face = get_first_face(frame)
-
- if face is None:
- return num_faces_found, frame
-
- num_faces_found += 1
- temp_frame = self.process_face(self.options.selected_index, face, temp_frame)
- del face
-
- else:
- faces = get_all_faces(frame)
- if faces is None:
- return num_faces_found, frame
-
- if self.options.swap_mode == "all":
- for face in faces:
- num_faces_found += 1
- temp_frame = self.process_face(self.options.selected_index, face, temp_frame)
-
- elif self.options.swap_mode == "all_input" or self.options.swap_mode == "all_random":
- for i,face in enumerate(faces):
- num_faces_found += 1
- if i < len(self.input_face_datas):
- temp_frame = self.process_face(i, face, temp_frame)
- else:
- break
-
- elif self.options.swap_mode == "selected":
- num_targetfaces = len(self.target_face_datas)
- use_index = num_targetfaces == 1
- for i,tf in enumerate(self.target_face_datas):
- for face in faces:
- if compute_cosine_distance(tf.embedding, face.embedding) <= self.options.face_distance_threshold:
- if i < len(self.input_face_datas):
- if use_index:
- temp_frame = self.process_face(self.options.selected_index, face, temp_frame)
- else:
- temp_frame = self.process_face(i, face, temp_frame)
- num_faces_found += 1
- if not roop.globals.vr_mode and num_faces_found == num_targetfaces:
- break
- elif self.options.swap_mode == "all_female" or self.options.swap_mode == "all_male":
- gender = 'F' if self.options.swap_mode == "all_female" else 'M'
- for face in faces:
- if face.sex == gender:
- num_faces_found += 1
- temp_frame = self.process_face(self.options.selected_index, face, temp_frame)
-
- # might be slower but way more clean to release everything here
- for face in faces:
- del face
- faces.clear()
-
-
-
- if roop.globals.vr_mode and num_faces_found % 2 > 0:
- # stereo image, there has to be an even number of faces
- num_faces_found = 0
- return num_faces_found, frame
- if num_faces_found == 0:
- return num_faces_found, frame
-
- #maskprocessor = next((x for x in self.processors if x.type == 'mask'), None)
-
- if self.options.imagemask is not None and self.options.imagemask.shape == frame.shape:
- temp_frame = self.simple_blend_with_mask(temp_frame, frame, self.options.imagemask)
- return num_faces_found, temp_frame
-
-
- def rotation_action(self, original_face:Face, frame:Frame):
- (height, width) = frame.shape[:2]
-
- bounding_box_width = original_face.bbox[2] - original_face.bbox[0]
- bounding_box_height = original_face.bbox[3] - original_face.bbox[1]
- horizontal_face = bounding_box_width > bounding_box_height
-
- center_x = width // 2.0
- start_x = original_face.bbox[0]
- end_x = original_face.bbox[2]
- bbox_center_x = start_x + (bounding_box_width // 2.0)
-
- # need to leverage the array of landmarks as decribed here:
- # https://github.com/deepinsight/insightface/tree/master/alignment/coordinate_reg
- # basically, we should be able to check for the relative position of eyes and nose
- # then use that to determine which way the face is actually facing when in a horizontal position
- # and use that to determine the correct rotation_action
-
- forehead_x = original_face.landmark_2d_106[72][0]
- chin_x = original_face.landmark_2d_106[0][0]
-
- if horizontal_face:
- if chin_x < forehead_x:
- # this is someone lying down with their face like this (:
- return "rotate_anticlockwise"
- elif forehead_x < chin_x:
- # this is someone lying down with their face like this :)
- return "rotate_clockwise"
- if bbox_center_x >= center_x:
- # this is someone lying down with their face in the right hand side of the frame
- return "rotate_anticlockwise"
- if bbox_center_x < center_x:
- # this is someone lying down with their face in the left hand side of the frame
- return "rotate_clockwise"
-
- return None
-
-
- def auto_rotate_frame(self, original_face, frame:Frame):
- target_face = original_face
- original_frame = frame
-
- rotation_action = self.rotation_action(original_face, frame)
-
- if rotation_action == "rotate_anticlockwise":
- #face is horizontal, rotating frame anti-clockwise and getting face bounding box from rotated frame
- frame = rotate_anticlockwise(frame)
- elif rotation_action == "rotate_clockwise":
- #face is horizontal, rotating frame clockwise and getting face bounding box from rotated frame
- frame = rotate_clockwise(frame)
-
- return target_face, frame, rotation_action
-
-
- def auto_unrotate_frame(self, frame:Frame, rotation_action):
- if rotation_action == "rotate_anticlockwise":
- return rotate_clockwise(frame)
- elif rotation_action == "rotate_clockwise":
- return rotate_anticlockwise(frame)
-
- return frame
-
-
-
- def process_face(self,face_index, target_face:Face, frame:Frame):
- from roop.face_util import align_crop
-
- enhanced_frame = None
- if(len(self.input_face_datas) > 0):
- inputface = self.input_face_datas[face_index].faces[0]
- else:
- inputface = None
-
- rotation_action = None
- if roop.globals.autorotate_faces:
- # check for sideways rotation of face
- rotation_action = self.rotation_action(target_face, frame)
- if rotation_action is not None:
- (startX, startY, endX, endY) = target_face["bbox"].astype("int")
- width = endX - startX
- height = endY - startY
- offs = int(max(width,height) * 0.25)
- rotcutframe,startX, startY, endX, endY = self.cutout(frame, startX - offs, startY - offs, endX + offs, endY + offs)
- if rotation_action == "rotate_anticlockwise":
- rotcutframe = rotate_anticlockwise(rotcutframe)
- elif rotation_action == "rotate_clockwise":
- rotcutframe = rotate_clockwise(rotcutframe)
- # rotate image and re-detect face to correct wonky landmarks
- rotface = get_first_face(rotcutframe)
- if rotface is None:
- rotation_action = None
- else:
- saved_frame = frame.copy()
- frame = rotcutframe
- target_face = rotface
-
-
-
- # if roop.globals.vr_mode:
- # bbox = target_face.bbox
- # [orig_width, orig_height, _] = frame.shape
-
- # # Convert bounding box to ints
- # x1, y1, x2, y2 = map(int, bbox)
-
- # # Determine the center of the bounding box
- # x_center = (x1 + x2) / 2
- # y_center = (y1 + y2) / 2
-
- # # Normalize coordinates to range [-1, 1]
- # x_center_normalized = x_center / (orig_width / 2) - 1
- # y_center_normalized = y_center / (orig_width / 2) - 1
-
- # # Convert normalized coordinates to spherical (theta, phi)
- # theta = x_center_normalized * 180 # Theta ranges from -180 to 180 degrees
- # phi = -y_center_normalized * 90 # Phi ranges from -90 to 90 degrees
-
- # img = vr.GetPerspective(frame, 90, theta, phi, 1280, 1280) # Generate perspective image
-
-
- """ Code ported/adapted from Facefusion which borrowed the idea from Rope:
- Kind of subsampling the cutout and aligned face image and faceswapping slices of it up to
- the desired output resolution. This works around the current resolution limitations without using enhancers.
- """
- model_output_size = self.options.swap_output_size
- subsample_size = max(self.options.subsample_size, model_output_size)
- subsample_total = subsample_size // model_output_size
- aligned_img, M = align_crop(frame, target_face.kps, subsample_size)
-
- fake_frame = aligned_img
- target_face.matrix = M
-
- for p in self.processors:
- if p.type == 'swap':
- swap_result_frames = []
- subsample_frames = self.implode_pixel_boost(aligned_img, model_output_size, subsample_total)
- for sliced_frame in subsample_frames:
- for _ in range(0,self.options.num_swap_steps):
- sliced_frame = self.prepare_crop_frame(sliced_frame)
- sliced_frame = p.Run(inputface, target_face, sliced_frame)
- sliced_frame = self.normalize_swap_frame(sliced_frame)
- swap_result_frames.append(sliced_frame)
- fake_frame = self.explode_pixel_boost(swap_result_frames, model_output_size, subsample_total, subsample_size)
- fake_frame = fake_frame.astype(np.uint8)
- scale_factor = 0.0
- elif p.type == 'mask':
- fake_frame = self.process_mask(p, aligned_img, fake_frame)
- else:
- enhanced_frame, scale_factor = p.Run(self.input_face_datas[face_index], target_face, fake_frame)
-
- upscale = 512
- orig_width = fake_frame.shape[1]
- if orig_width != upscale:
- fake_frame = cv2.resize(fake_frame, (upscale, upscale), cv2.INTER_CUBIC)
- mask_offsets = (0,0,0,0,1,20) if inputface is None else inputface.mask_offsets
-
-
- if enhanced_frame is None:
- scale_factor = int(upscale / orig_width)
- result = self.paste_upscale(fake_frame, fake_frame, target_face.matrix, frame, scale_factor, mask_offsets)
- else:
- result = self.paste_upscale(fake_frame, enhanced_frame, target_face.matrix, frame, scale_factor, mask_offsets)
-
- # Restore mouth before unrotating
- if self.options.restore_original_mouth:
- mouth_cutout, mouth_bb = self.create_mouth_mask(target_face, frame)
- result = self.apply_mouth_area(result, mouth_cutout, mouth_bb)
-
- if rotation_action is not None:
- fake_frame = self.auto_unrotate_frame(result, rotation_action)
- result = self.paste_simple(fake_frame, saved_frame, startX, startY)
-
- return result
-
-
-
-
- def cutout(self, frame:Frame, start_x, start_y, end_x, end_y):
- if start_x < 0:
- start_x = 0
- if start_y < 0:
- start_y = 0
- if end_x > frame.shape[1]:
- end_x = frame.shape[1]
- if end_y > frame.shape[0]:
- end_y = frame.shape[0]
- return frame[start_y:end_y, start_x:end_x], start_x, start_y, end_x, end_y
-
- def paste_simple(self, src:Frame, dest:Frame, start_x, start_y):
- end_x = start_x + src.shape[1]
- end_y = start_y + src.shape[0]
-
- start_x, end_x, start_y, end_y = clamp_cut_values(start_x, end_x, start_y, end_y, dest)
- dest[start_y:end_y, start_x:end_x] = src
- return dest
-
- def simple_blend_with_mask(self, image1, image2, mask):
- # Blend the images
- blended_image = image1.astype(np.float32) * (1.0 - mask) + image2.astype(np.float32) * mask
- return blended_image.astype(np.uint8)
-
-
- def paste_upscale(self, fake_face, upsk_face, M, target_img, scale_factor, mask_offsets):
- M_scale = M * scale_factor
- IM = cv2.invertAffineTransform(M_scale)
-
- face_matte = np.full((target_img.shape[0],target_img.shape[1]), 255, dtype=np.uint8)
- # Generate white square sized as a upsk_face
- img_matte = np.zeros((upsk_face.shape[0],upsk_face.shape[1]), dtype=np.uint8)
-
- w = img_matte.shape[1]
- h = img_matte.shape[0]
-
- top = int(mask_offsets[0] * h)
- bottom = int(h - (mask_offsets[1] * h))
- left = int(mask_offsets[2] * w)
- right = int(w - (mask_offsets[3] * w))
- img_matte[top:bottom,left:right] = 255
-
- # Transform white square back to target_img
- img_matte = cv2.warpAffine(img_matte, IM, (target_img.shape[1], target_img.shape[0]), flags=cv2.INTER_NEAREST, borderValue=0.0)
- ##Blacken the edges of face_matte by 1 pixels (so the mask in not expanded on the image edges)
- img_matte[:1,:] = img_matte[-1:,:] = img_matte[:,:1] = img_matte[:,-1:] = 0
-
- img_matte = self.blur_area(img_matte, mask_offsets[4], mask_offsets[5])
- #Normalize images to float values and reshape
- img_matte = img_matte.astype(np.float32)/255
- face_matte = face_matte.astype(np.float32)/255
- img_matte = np.minimum(face_matte, img_matte)
- if self.options.show_face_area_overlay:
- # Additional steps for green overlay
- green_overlay = np.zeros_like(target_img)
- green_color = [0, 255, 0] # RGB for green
- for i in range(3): # Apply green color where img_matte is not zero
- green_overlay[:, :, i] = np.where(img_matte > 0, green_color[i], 0) ##Transform upcaled face back to target_img
- img_matte = np.reshape(img_matte, [img_matte.shape[0],img_matte.shape[1],1])
- paste_face = cv2.warpAffine(upsk_face, IM, (target_img.shape[1], target_img.shape[0]), borderMode=cv2.BORDER_REPLICATE)
- if upsk_face is not fake_face:
- fake_face = cv2.warpAffine(fake_face, IM, (target_img.shape[1], target_img.shape[0]), borderMode=cv2.BORDER_REPLICATE)
- paste_face = cv2.addWeighted(paste_face, self.options.blend_ratio, fake_face, 1.0 - self.options.blend_ratio, 0)
-
- # Re-assemble image
- paste_face = img_matte * paste_face
- paste_face = paste_face + (1-img_matte) * target_img.astype(np.float32)
- if self.options.show_face_area_overlay:
- # Overlay the green overlay on the final image
- paste_face = cv2.addWeighted(paste_face.astype(np.uint8), 1 - 0.5, green_overlay, 0.5, 0)
- return paste_face.astype(np.uint8)
-
-
- def blur_area(self, img_matte, num_erosion_iterations, blur_amount):
- # Detect the affine transformed white area
- mask_h_inds, mask_w_inds = np.where(img_matte==255)
- # Calculate the size (and diagonal size) of transformed white area width and height boundaries
- mask_h = np.max(mask_h_inds) - np.min(mask_h_inds)
- mask_w = np.max(mask_w_inds) - np.min(mask_w_inds)
- mask_size = int(np.sqrt(mask_h*mask_w))
- # Calculate the kernel size for eroding img_matte by kernel (insightface empirical guess for best size was max(mask_size//10,10))
- # k = max(mask_size//12, 8)
- k = max(mask_size//(blur_amount // 2) , blur_amount // 2)
- kernel = np.ones((k,k),np.uint8)
- img_matte = cv2.erode(img_matte,kernel,iterations = num_erosion_iterations)
- #Calculate the kernel size for blurring img_matte by blur_size (insightface empirical guess for best size was max(mask_size//20, 5))
- # k = max(mask_size//24, 4)
- k = max(mask_size//blur_amount, blur_amount//5)
- kernel_size = (k, k)
- blur_size = tuple(2*i+1 for i in kernel_size)
- return cv2.GaussianBlur(img_matte, blur_size, 0)
-
-
- def prepare_crop_frame(self, swap_frame):
- model_type = 'inswapper'
- model_mean = [0.0, 0.0, 0.0]
- model_standard_deviation = [1.0, 1.0, 1.0]
-
- if model_type == 'ghost':
- swap_frame = swap_frame[:, :, ::-1] / 127.5 - 1
- else:
- swap_frame = swap_frame[:, :, ::-1] / 255.0
- swap_frame = (swap_frame - model_mean) / model_standard_deviation
- swap_frame = swap_frame.transpose(2, 0, 1)
- swap_frame = np.expand_dims(swap_frame, axis = 0).astype(np.float32)
- return swap_frame
-
-
- def normalize_swap_frame(self, swap_frame):
- model_type = 'inswapper'
- swap_frame = swap_frame.transpose(1, 2, 0)
-
- if model_type == 'ghost':
- swap_frame = (swap_frame * 127.5 + 127.5).round()
- else:
- swap_frame = (swap_frame * 255.0).round()
- swap_frame = swap_frame[:, :, ::-1]
- return swap_frame
-
- def implode_pixel_boost(self, aligned_face_frame, model_size, pixel_boost_total : int):
- subsample_frame = aligned_face_frame.reshape(model_size, pixel_boost_total, model_size, pixel_boost_total, 3)
- subsample_frame = subsample_frame.transpose(1, 3, 0, 2, 4).reshape(pixel_boost_total ** 2, model_size, model_size, 3)
- return subsample_frame
-
-
- def explode_pixel_boost(self, subsample_frame, model_size, pixel_boost_total, pixel_boost_size):
- final_frame = np.stack(subsample_frame, axis = 0).reshape(pixel_boost_total, pixel_boost_total, model_size, model_size, 3)
- final_frame = final_frame.transpose(2, 0, 3, 1, 4).reshape(pixel_boost_size, pixel_boost_size, 3)
- return final_frame
-
- def process_mask(self, processor, frame:Frame, target:Frame):
- img_mask = processor.Run(frame, self.options.masking_text)
- img_mask = cv2.resize(img_mask, (target.shape[1], target.shape[0]))
- img_mask = np.reshape(img_mask, [img_mask.shape[0],img_mask.shape[1],1])
-
- if self.options.show_face_masking:
- result = (1 - img_mask) * frame.astype(np.float32)
- return np.uint8(result)
-
-
- target = target.astype(np.float32)
- result = (1-img_mask) * target
- result += img_mask * frame.astype(np.float32)
- return np.uint8(result)
-
-
- # Code for mouth restoration adapted from https://github.com/iVideoGameBoss/iRoopDeepFaceCam
-
- def create_mouth_mask(self, face: Face, frame: Frame):
- mouth_cutout = None
-
- landmarks = face.landmark_2d_106
- if landmarks is not None:
- # Get mouth landmarks (indices 52 to 71 typically represent the outer mouth)
- mouth_points = landmarks[52:71].astype(np.int32)
-
- # Add padding to mouth area
- min_x, min_y = np.min(mouth_points, axis=0)
- max_x, max_y = np.max(mouth_points, axis=0)
- min_x = max(0, min_x - (15*6))
- min_y = max(0, min_y - 22)
- max_x = min(frame.shape[1], max_x + (15*6))
- max_y = min(frame.shape[0], max_y + (90*6))
-
- # Extract the mouth area from the frame using the calculated bounding box
- mouth_cutout = frame[min_y:max_y, min_x:max_x].copy()
-
- return mouth_cutout, (min_x, min_y, max_x, max_y)
-
-
-
- def create_feathered_mask(self, shape, feather_amount=30):
- mask = np.zeros(shape[:2], dtype=np.float32)
- center = (shape[1] // 2, shape[0] // 2)
- cv2.ellipse(mask, center, (shape[1] // 2 - feather_amount, shape[0] // 2 - feather_amount),
- 0, 0, 360, 1, -1)
- mask = cv2.GaussianBlur(mask, (feather_amount*2+1, feather_amount*2+1), 0)
- return mask / np.max(mask)
-
- def apply_mouth_area(self, frame: np.ndarray, mouth_cutout: np.ndarray, mouth_box: tuple) -> np.ndarray:
- min_x, min_y, max_x, max_y = mouth_box
- box_width = max_x - min_x
- box_height = max_y - min_y
-
-
- # Resize the mouth cutout to match the mouth box size
- if mouth_cutout is None or box_width is None or box_height is None:
- return frame
- try:
- resized_mouth_cutout = cv2.resize(mouth_cutout, (box_width, box_height))
-
- # Extract the region of interest (ROI) from the target frame
- roi = frame[min_y:max_y, min_x:max_x]
-
- # Ensure the ROI and resized_mouth_cutout have the same shape
- if roi.shape != resized_mouth_cutout.shape:
- resized_mouth_cutout = cv2.resize(resized_mouth_cutout, (roi.shape[1], roi.shape[0]))
-
- # Apply color transfer from ROI to mouth cutout
- color_corrected_mouth = self.apply_color_transfer(resized_mouth_cutout, roi)
-
- # Create a feathered mask with increased feather amount
- feather_amount = min(30, box_width // 15, box_height // 15)
- mask = self.create_feathered_mask(resized_mouth_cutout.shape, feather_amount)
-
- # Blend the color-corrected mouth cutout with the ROI using the feathered mask
- mask = mask[:,:,np.newaxis] # Add channel dimension to mask
- blended = (color_corrected_mouth * mask + roi * (1 - mask)).astype(np.uint8)
-
- # Place the blended result back into the frame
- frame[min_y:max_y, min_x:max_x] = blended
- except Exception as e:
- print(f'Error {e}')
- pass
-
- return frame
-
- def apply_color_transfer(self, source, target):
- """
- Apply color transfer from target to source image
- """
- source = cv2.cvtColor(source, cv2.COLOR_BGR2LAB).astype("float32")
- target = cv2.cvtColor(target, cv2.COLOR_BGR2LAB).astype("float32")
-
- source_mean, source_std = cv2.meanStdDev(source)
- target_mean, target_std = cv2.meanStdDev(target)
-
- # Reshape mean and std to be broadcastable
- source_mean = source_mean.reshape(1, 1, 3)
- source_std = source_std.reshape(1, 1, 3)
- target_mean = target_mean.reshape(1, 1, 3)
- target_std = target_std.reshape(1, 1, 3)
-
- # Perform the color transfer
- source = (source - source_mean) * (target_std / source_std) + target_mean
- return cv2.cvtColor(np.clip(source, 0, 255).astype("uint8"), cv2.COLOR_LAB2BGR)
-
-
-
- def unload_models():
- pass
-
-
- def release_resources(self):
- for p in self.processors:
- p.Release()
- self.processors.clear()
- if self.videowriter is not None:
- self.videowriter.close()
- if self.streamwriter is not None:
- self.streamwriter.Close()
-
diff --git a/roop-unleashed-main/roop/ProcessOptions.py b/roop-unleashed-main/roop/ProcessOptions.py
deleted file mode 100644
index 4d272efc89d2125688fe99884fcb4d4eb2a3a448..0000000000000000000000000000000000000000
--- a/roop-unleashed-main/roop/ProcessOptions.py
+++ /dev/null
@@ -1,18 +0,0 @@
-class ProcessOptions:
-
- def __init__(self, swap_model, processordefines:dict, face_distance, blend_ratio, swap_mode, selected_index, masking_text, imagemask, num_steps, subsample_size, show_face_area, restore_original_mouth, show_mask=False):
- self.swap_modelname = swap_model
- self.swap_output_size = int(swap_model.split()[-1])
- self.processors = processordefines
- self.face_distance_threshold = face_distance
- self.blend_ratio = blend_ratio
- self.swap_mode = swap_mode
- self.selected_index = selected_index
- self.masking_text = masking_text
- self.imagemask = imagemask
- self.num_swap_steps = num_steps
- self.show_face_area_overlay = show_face_area
- self.show_face_masking = show_mask
- self.subsample_size = subsample_size
- self.restore_original_mouth = restore_original_mouth
- self.max_num_reuse_frame = 15
\ No newline at end of file
diff --git a/roop-unleashed-main/roop/StreamWriter.py b/roop-unleashed-main/roop/StreamWriter.py
deleted file mode 100644
index 5030fa419c6bab703ff2917c4f02c80625ffc1fa..0000000000000000000000000000000000000000
--- a/roop-unleashed-main/roop/StreamWriter.py
+++ /dev/null
@@ -1,60 +0,0 @@
-import threading
-import time
-import pyvirtualcam
-
-
-class StreamWriter():
- FPS = 30
- VCam = None
- Active = False
- THREAD_LOCK_STREAM = threading.Lock()
- time_last_process = None
- timespan_min = 0.0
-
- def __enter__(self):
- return self
-
- def __exit__(self, exc_type, exc_value, traceback):
- self.Close()
-
- def __init__(self, size, fps):
- self.time_last_process = time.perf_counter()
- self.FPS = fps
- self.timespan_min = 1.0 / fps
- print('Detecting virtual cam devices')
- self.VCam = pyvirtualcam.Camera(width=size[0], height=size[1], fps=fps, fmt=pyvirtualcam.PixelFormat.BGR, print_fps=False)
- if self.VCam is None:
- print("No virtual camera found!")
- return
- print(f'Using virtual camera: {self.VCam.device}')
- print(f'Using {self.VCam.native_fmt}')
- self.Active = True
-
-
- def LimitFrames(self):
- while True:
- current_time = time.perf_counter()
- time_passed = current_time - self.time_last_process
- if time_passed >= self.timespan_min:
- break
-
- # First version used a queue and threading. Surprisingly this
- # totally simple, blocking version is 10 times faster!
- def WriteToStream(self, frame):
- if self.VCam is None:
- return
- with self.THREAD_LOCK_STREAM:
- self.LimitFrames()
- self.VCam.send(frame)
- self.time_last_process = time.perf_counter()
-
-
- def Close(self):
- self.Active = False
- if self.VCam is None:
- self.VCam.close()
- self.VCam = None
-
-
-
-
diff --git a/roop-unleashed-main/roop/__init__.py b/roop-unleashed-main/roop/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/roop-unleashed-main/roop/capturer.py b/roop-unleashed-main/roop/capturer.py
deleted file mode 100644
index 1d6567c91aefb1504a3d8c8a857f6e1ab033e59c..0000000000000000000000000000000000000000
--- a/roop-unleashed-main/roop/capturer.py
+++ /dev/null
@@ -1,46 +0,0 @@
-from typing import Optional
-import cv2
-import numpy as np
-
-from roop.typing import Frame
-
-current_video_path = None
-current_frame_total = 0
-current_capture = None
-
-def get_image_frame(filename: str):
- try:
- return cv2.imdecode(np.fromfile(filename, dtype=np.uint8), cv2.IMREAD_COLOR)
- except:
- print(f"Exception reading {filename}")
- return None
-
-
-def get_video_frame(video_path: str, frame_number: int = 0) -> Optional[Frame]:
- global current_video_path, current_capture, current_frame_total
-
- if video_path != current_video_path:
- release_video()
- current_capture = cv2.VideoCapture(video_path)
- current_video_path = video_path
- current_frame_total = current_capture.get(cv2.CAP_PROP_FRAME_COUNT)
-
- current_capture.set(cv2.CAP_PROP_POS_FRAMES, min(current_frame_total, frame_number - 1))
- has_frame, frame = current_capture.read()
- if has_frame:
- return frame
- return None
-
-def release_video():
- global current_capture
-
- if current_capture is not None:
- current_capture.release()
- current_capture = None
-
-
-def get_video_frame_total(video_path: str) -> int:
- capture = cv2.VideoCapture(video_path)
- video_frame_total = int(capture.get(cv2.CAP_PROP_FRAME_COUNT))
- capture.release()
- return video_frame_total
diff --git a/roop-unleashed-main/roop/core.py b/roop-unleashed-main/roop/core.py
deleted file mode 100644
index 251a038ca2f98f2366560d7f082cbaaf4e9a307a..0000000000000000000000000000000000000000
--- a/roop-unleashed-main/roop/core.py
+++ /dev/null
@@ -1,406 +0,0 @@
-#!/usr/bin/env python3
-
-import os
-import sys
-import shutil
-# single thread doubles cuda performance - needs to be set before torch import
-if any(arg.startswith('--execution-provider') for arg in sys.argv):
- os.environ['OMP_NUM_THREADS'] = '1'
-
-import warnings
-from typing import List
-import platform
-import signal
-import torch
-import onnxruntime
-import pathlib
-import argparse
-
-from time import time
-
-import roop.globals
-import roop.metadata
-import roop.utilities as util
-import roop.util_ffmpeg as ffmpeg
-import ui.main as main
-from settings import Settings
-from roop.face_util import extract_face_images
-from roop.ProcessEntry import ProcessEntry
-from roop.ProcessMgr import ProcessMgr
-from roop.ProcessOptions import ProcessOptions
-from roop.capturer import get_video_frame_total, release_video
-
-
-clip_text = None
-
-call_display_ui = None
-
-process_mgr = None
-
-
-if 'ROCMExecutionProvider' in roop.globals.execution_providers:
- del torch
-
-warnings.filterwarnings('ignore', category=FutureWarning, module='insightface')
-warnings.filterwarnings('ignore', category=UserWarning, module='torchvision')
-
-
-def parse_args() -> None:
- signal.signal(signal.SIGINT, lambda signal_number, frame: destroy())
- roop.globals.headless = False
-
- program = argparse.ArgumentParser(formatter_class=lambda prog: argparse.HelpFormatter(prog, max_help_position=100))
- program.add_argument('--server_share', help='Public server', dest='server_share', action='store_true', default=False)
- program.add_argument('--cuda_device_id', help='Index of the cuda gpu to use', dest='cuda_device_id', type=int, default=0)
- roop.globals.startup_args = program.parse_args()
- # Always enable all processors when using GUI
- roop.globals.frame_processors = ['face_swapper', 'face_enhancer']
-
-
-def encode_execution_providers(execution_providers: List[str]) -> List[str]:
- return [execution_provider.replace('ExecutionProvider', '').lower() for execution_provider in execution_providers]
-
-
-def decode_execution_providers(execution_providers: List[str]) -> List[str]:
- list_providers = [provider for provider, encoded_execution_provider in zip(onnxruntime.get_available_providers(), encode_execution_providers(onnxruntime.get_available_providers()))
- if any(execution_provider in encoded_execution_provider for execution_provider in execution_providers)]
-
- try:
- for i in range(len(list_providers)):
- if list_providers[i] == 'CUDAExecutionProvider':
- list_providers[i] = ('CUDAExecutionProvider', {'device_id': roop.globals.cuda_device_id})
- torch.cuda.set_device(roop.globals.cuda_device_id)
- break
- except:
- pass
-
- return list_providers
-
-
-
-def suggest_max_memory() -> int:
- if platform.system().lower() == 'darwin':
- return 4
- return 16
-
-
-def suggest_execution_providers() -> List[str]:
- return encode_execution_providers(onnxruntime.get_available_providers())
-
-
-def suggest_execution_threads() -> int:
- if 'DmlExecutionProvider' in roop.globals.execution_providers:
- return 1
- if 'ROCMExecutionProvider' in roop.globals.execution_providers:
- return 1
- return 8
-
-
-def limit_resources() -> None:
- # limit memory usage
- if roop.globals.max_memory:
- memory = roop.globals.max_memory * 1024 ** 3
- if platform.system().lower() == 'darwin':
- memory = roop.globals.max_memory * 1024 ** 6
- if platform.system().lower() == 'windows':
- import ctypes
- kernel32 = ctypes.windll.kernel32 # type: ignore[attr-defined]
- kernel32.SetProcessWorkingSetSize(-1, ctypes.c_size_t(memory), ctypes.c_size_t(memory))
- else:
- import resource
- resource.setrlimit(resource.RLIMIT_DATA, (memory, memory))
-
-
-
-def release_resources() -> None:
- import gc
- global process_mgr
-
- if process_mgr is not None:
- process_mgr.release_resources()
- process_mgr = None
-
- gc.collect()
- # if 'CUDAExecutionProvider' in roop.globals.execution_providers and torch.cuda.is_available():
- # with torch.cuda.device('cuda'):
- # torch.cuda.empty_cache()
- # torch.cuda.ipc_collect()
-
-
-def pre_check() -> bool:
- if sys.version_info < (3, 9):
- update_status('Python version is not supported - please upgrade to 3.9 or higher.')
- return False
-
- download_directory_path = util.resolve_relative_path('../models')
- util.conditional_download(download_directory_path, ['https://huggingface.co/countfloyd/deepfake/resolve/main/inswapper_128.onnx'])
- util.conditional_download(download_directory_path, ['https://huggingface.co/countfloyd/deepfake/resolve/main/reswapper_128.onnx'])
- util.conditional_download(download_directory_path, ['https://huggingface.co/countfloyd/deepfake/resolve/main/reswapper_256.onnx'])
- util.conditional_download(download_directory_path, ['https://huggingface.co/countfloyd/deepfake/resolve/main/GFPGANv1.4.onnx'])
- util.conditional_download(download_directory_path, ['https://github.com/csxmli2016/DMDNet/releases/download/v1/DMDNet.pth'])
- util.conditional_download(download_directory_path, ['https://huggingface.co/countfloyd/deepfake/resolve/main/GPEN-BFR-512.onnx'])
- util.conditional_download(download_directory_path, ['https://huggingface.co/countfloyd/deepfake/resolve/main/restoreformer_plus_plus.onnx'])
- util.conditional_download(download_directory_path, ['https://huggingface.co/countfloyd/deepfake/resolve/main/xseg.onnx'])
- download_directory_path = util.resolve_relative_path('../models/CLIP')
- util.conditional_download(download_directory_path, ['https://huggingface.co/countfloyd/deepfake/resolve/main/rd64-uni-refined.pth'])
- download_directory_path = util.resolve_relative_path('../models/CodeFormer')
- util.conditional_download(download_directory_path, ['https://huggingface.co/countfloyd/deepfake/resolve/main/CodeFormerv0.1.onnx'])
- download_directory_path = util.resolve_relative_path('../models/Frame')
- util.conditional_download(download_directory_path, ['https://huggingface.co/countfloyd/deepfake/resolve/main/deoldify_artistic.onnx'])
- util.conditional_download(download_directory_path, ['https://huggingface.co/countfloyd/deepfake/resolve/main/deoldify_stable.onnx'])
- util.conditional_download(download_directory_path, ['https://huggingface.co/countfloyd/deepfake/resolve/main/isnet-general-use.onnx'])
- util.conditional_download(download_directory_path, ['https://huggingface.co/countfloyd/deepfake/resolve/main/real_esrgan_x4.onnx'])
- util.conditional_download(download_directory_path, ['https://huggingface.co/countfloyd/deepfake/resolve/main/real_esrgan_x2.onnx'])
- util.conditional_download(download_directory_path, ['https://huggingface.co/countfloyd/deepfake/resolve/main/lsdir_x4.onnx'])
-
- if not shutil.which('ffmpeg'):
- update_status('ffmpeg is not installed.')
- return True
-
-def set_display_ui(function):
- global call_display_ui
-
- call_display_ui = function
-
-
-def update_status(message: str) -> None:
- global call_display_ui
-
- print(message)
- if call_display_ui is not None:
- call_display_ui(message)
-
-
-
-
-def start() -> None:
- if roop.globals.headless:
- print('Headless mode currently unsupported - starting UI!')
- # faces = extract_face_images(roop.globals.source_path, (False, 0))
- # roop.globals.INPUT_FACES.append(faces[roop.globals.source_face_index])
- # faces = extract_face_images(roop.globals.target_path, (False, util.has_image_extension(roop.globals.target_path)))
- # roop.globals.TARGET_FACES.append(faces[roop.globals.target_face_index])
- # if 'face_enhancer' in roop.globals.frame_processors:
- # roop.globals.selected_enhancer = 'GFPGAN'
-
- batch_process_regular(None, False, None)
-
-
-def get_processing_plugins(masking_engine):
- processors = { "faceswap": {}}
- if masking_engine is not None:
- processors.update({masking_engine: {}})
-
- if roop.globals.selected_enhancer == 'GFPGAN':
- processors.update({"gfpgan": {}})
- elif roop.globals.selected_enhancer == 'Codeformer':
- processors.update({"codeformer": {}})
- elif roop.globals.selected_enhancer == 'DMDNet':
- processors.update({"dmdnet": {}})
- elif roop.globals.selected_enhancer == 'GPEN':
- processors.update({"gpen": {}})
- elif roop.globals.selected_enhancer == 'Restoreformer++':
- processors.update({"restoreformer++": {}})
- return processors
-
-
-def live_swap(frame, options):
- global process_mgr
-
- if frame is None:
- return frame
-
- if process_mgr is None:
- process_mgr = ProcessMgr(None)
-
-# if len(roop.globals.INPUT_FACESETS) <= selected_index:
-# selected_index = 0
- process_mgr.initialize(roop.globals.INPUT_FACESETS, roop.globals.TARGET_FACES, options)
- newframe = process_mgr.process_frame(frame)
- if newframe is None:
- return frame
- return newframe
-
-
-def batch_process_regular(swap_model, output_method, files:list[ProcessEntry], masking_engine:str, new_clip_text:str, use_new_method, imagemask, restore_original_mouth, num_swap_steps, progress, selected_index = 0) -> None:
- global clip_text, process_mgr
-
- release_resources()
- limit_resources()
- if process_mgr is None:
- process_mgr = ProcessMgr(progress)
- mask = imagemask["layers"][0] if imagemask is not None else None
- if len(roop.globals.INPUT_FACESETS) <= selected_index:
- selected_index = 0
- options = ProcessOptions(swap_model, get_processing_plugins(masking_engine), roop.globals.distance_threshold, roop.globals.blend_ratio,
- roop.globals.face_swap_mode, selected_index, new_clip_text, mask, num_swap_steps,
- roop.globals.subsample_size, False, restore_original_mouth)
- process_mgr.initialize(roop.globals.INPUT_FACESETS, roop.globals.TARGET_FACES, options)
- batch_process(output_method, files, use_new_method)
- return
-
-def batch_process_with_options(files:list[ProcessEntry], options, progress):
- global clip_text, process_mgr
-
- release_resources()
- limit_resources()
- if process_mgr is None:
- process_mgr = ProcessMgr(progress)
- process_mgr.initialize(roop.globals.INPUT_FACESETS, roop.globals.TARGET_FACES, options)
- roop.globals.keep_frames = False
- roop.globals.wait_after_extraction = False
- roop.globals.skip_audio = False
- batch_process("Files", files, True)
-
-
-
-def batch_process(output_method, files:list[ProcessEntry], use_new_method) -> None:
- global clip_text, process_mgr
-
- roop.globals.processing = True
-
- # limit threads for some providers
- max_threads = suggest_execution_threads()
- if max_threads == 1:
- roop.globals.execution_threads = 1
-
- imagefiles:list[ProcessEntry] = []
- videofiles:list[ProcessEntry] = []
-
- update_status('Sorting videos/images')
-
-
- for index, f in enumerate(files):
- fullname = f.filename
- if util.has_image_extension(fullname):
- destination = util.get_destfilename_from_path(fullname, roop.globals.output_path, f'.{roop.globals.CFG.output_image_format}')
- destination = util.replace_template(destination, index=index)
- pathlib.Path(os.path.dirname(destination)).mkdir(parents=True, exist_ok=True)
- f.finalname = destination
- imagefiles.append(f)
-
- elif util.is_video(fullname) or util.has_extension(fullname, ['gif']):
- destination = util.get_destfilename_from_path(fullname, roop.globals.output_path, f'__temp.{roop.globals.CFG.output_video_format}')
- f.finalname = destination
- videofiles.append(f)
-
-
-
- if(len(imagefiles) > 0):
- update_status('Processing image(s)')
- origimages = []
- fakeimages = []
- for f in imagefiles:
- origimages.append(f.filename)
- fakeimages.append(f.finalname)
-
- process_mgr.run_batch(origimages, fakeimages, roop.globals.execution_threads)
- origimages.clear()
- fakeimages.clear()
-
- if(len(videofiles) > 0):
- for index,v in enumerate(videofiles):
- if not roop.globals.processing:
- end_processing('Processing stopped!')
- return
- fps = v.fps if v.fps > 0 else util.detect_fps(v.filename)
- if v.endframe == 0:
- v.endframe = get_video_frame_total(v.filename)
-
- is_streaming_only = output_method == "Virtual Camera"
- if is_streaming_only == False:
- update_status(f'Creating {os.path.basename(v.finalname)} with {fps} FPS...')
-
- start_processing = time()
- if is_streaming_only == False and roop.globals.keep_frames or not use_new_method:
- util.create_temp(v.filename)
- update_status('Extracting frames...')
- ffmpeg.extract_frames(v.filename,v.startframe,v.endframe, fps)
- if not roop.globals.processing:
- end_processing('Processing stopped!')
- return
-
- temp_frame_paths = util.get_temp_frame_paths(v.filename)
- process_mgr.run_batch(temp_frame_paths, temp_frame_paths, roop.globals.execution_threads)
- if not roop.globals.processing:
- end_processing('Processing stopped!')
- return
- if roop.globals.wait_after_extraction:
- extract_path = os.path.dirname(temp_frame_paths[0])
- util.open_folder(extract_path)
- input("Press any key to continue...")
- print("Resorting frames to create video")
- util.sort_rename_frames(extract_path)
-
- ffmpeg.create_video(v.filename, v.finalname, fps)
- if not roop.globals.keep_frames:
- util.delete_temp_frames(temp_frame_paths[0])
- else:
- if util.has_extension(v.filename, ['gif']):
- skip_audio = True
- else:
- skip_audio = roop.globals.skip_audio
- process_mgr.run_batch_inmem(output_method, v.filename, v.finalname, v.startframe, v.endframe, fps,roop.globals.execution_threads)
-
- if not roop.globals.processing:
- end_processing('Processing stopped!')
- return
-
- video_file_name = v.finalname
- if os.path.isfile(video_file_name):
- destination = ''
- if util.has_extension(v.filename, ['gif']):
- gifname = util.get_destfilename_from_path(v.filename, roop.globals.output_path, '.gif')
- destination = util.replace_template(gifname, index=index)
- pathlib.Path(os.path.dirname(destination)).mkdir(parents=True, exist_ok=True)
-
- update_status('Creating final GIF')
- ffmpeg.create_gif_from_video(video_file_name, destination)
- if os.path.isfile(destination):
- os.remove(video_file_name)
- else:
- skip_audio = roop.globals.skip_audio
- destination = util.replace_template(video_file_name, index=index)
- pathlib.Path(os.path.dirname(destination)).mkdir(parents=True, exist_ok=True)
-
- if not skip_audio:
- ffmpeg.restore_audio(video_file_name, v.filename, v.startframe, v.endframe, destination)
- if os.path.isfile(destination):
- os.remove(video_file_name)
- else:
- shutil.move(video_file_name, destination)
-
- elif is_streaming_only == False:
- update_status(f'Failed processing {os.path.basename(v.finalname)}!')
- elapsed_time = time() - start_processing
- average_fps = (v.endframe - v.startframe) / elapsed_time
- update_status(f'\nProcessing {os.path.basename(destination)} took {elapsed_time:.2f} secs, {average_fps:.2f} frames/s')
- end_processing('Finished')
-
-
-def end_processing(msg:str):
- update_status(msg)
- roop.globals.target_folder_path = None
- release_resources()
-
-
-def destroy() -> None:
- if roop.globals.target_path:
- util.clean_temp(roop.globals.target_path)
- release_resources()
- sys.exit()
-
-
-def run() -> None:
- parse_args()
- if not pre_check():
- return
- roop.globals.CFG = Settings('config.yaml')
- roop.globals.cuda_device_id = roop.globals.startup_args.cuda_device_id
- roop.globals.execution_threads = roop.globals.CFG.max_threads
- roop.globals.video_encoder = roop.globals.CFG.output_video_codec
- roop.globals.video_quality = roop.globals.CFG.video_quality
- roop.globals.max_memory = roop.globals.CFG.memory_limit if roop.globals.CFG.memory_limit > 0 else None
- if roop.globals.startup_args.server_share:
- roop.globals.CFG.server_share = True
- main.run()
diff --git a/roop-unleashed-main/roop/face_util.py b/roop-unleashed-main/roop/face_util.py
deleted file mode 100644
index 0ea858c37fcbf8ab4f304285c3b9f520b6b1e80c..0000000000000000000000000000000000000000
--- a/roop-unleashed-main/roop/face_util.py
+++ /dev/null
@@ -1,338 +0,0 @@
-import threading
-from typing import Any
-import insightface
-
-import roop.globals
-from roop.typing import Frame, Face
-
-import cv2
-import numpy as np
-from skimage import transform as trans
-from roop.capturer import get_video_frame
-from roop.utilities import resolve_relative_path, conditional_thread_semaphore
-
-FACE_ANALYSER = None
-#THREAD_LOCK_ANALYSER = threading.Lock()
-#THREAD_LOCK_SWAPPER = threading.Lock()
-FACE_SWAPPER = None
-
-
-def get_face_analyser() -> Any:
- global FACE_ANALYSER
-
- with conditional_thread_semaphore():
- if FACE_ANALYSER is None or roop.globals.g_current_face_analysis != roop.globals.g_desired_face_analysis:
- model_path = resolve_relative_path('..')
- # removed genderage
- allowed_modules = roop.globals.g_desired_face_analysis
- roop.globals.g_current_face_analysis = roop.globals.g_desired_face_analysis
- if roop.globals.CFG.force_cpu:
- print("Forcing CPU for Face Analysis")
- FACE_ANALYSER = insightface.app.FaceAnalysis(
- name="buffalo_l",
- root=model_path, providers=["CPUExecutionProvider"],allowed_modules=allowed_modules
- )
- else:
- FACE_ANALYSER = insightface.app.FaceAnalysis(
- name="buffalo_l", root=model_path, providers=roop.globals.execution_providers,allowed_modules=allowed_modules
- )
- FACE_ANALYSER.prepare(
- ctx_id=0,
- det_size=(640, 640) if roop.globals.default_det_size else (320, 320),
- )
- return FACE_ANALYSER
-
-
-def get_first_face(frame: Frame) -> Any:
- try:
- faces = get_face_analyser().get(frame)
- return min(faces, key=lambda x: x.bbox[0])
- # return sorted(faces, reverse=True, key=lambda x: (x.bbox[2] - x.bbox[0]) * (x.bbox[3] - x.bbox[1]))[0]
- except:
- return None
-
-
-def get_all_faces(frame: Frame) -> Any:
- try:
- faces = get_face_analyser().get(frame)
- return sorted(faces, key=lambda x: x.bbox[0])
- except:
- return None
-
-
-def extract_face_images(source_filename, video_info, extra_padding=-1.0):
- face_data = []
- source_image = None
-
- if video_info[0]:
- frame = get_video_frame(source_filename, video_info[1])
- if frame is not None:
- source_image = frame
- else:
- return face_data
- else:
- source_image = cv2.imdecode(np.fromfile(source_filename, dtype=np.uint8), cv2.IMREAD_COLOR)
-
- faces = get_all_faces(source_image)
- if faces is None:
- return face_data
-
- i = 0
- for face in faces:
- (startX, startY, endX, endY) = face["bbox"].astype("int")
- startX, endX, startY, endY = clamp_cut_values(startX, endX, startY, endY, source_image)
- if extra_padding > 0.0:
- if source_image.shape[:2] == (512, 512):
- i += 1
- face_data.append([face, source_image])
- continue
-
- found = False
- for i in range(1, 3):
- (startX, startY, endX, endY) = face["bbox"].astype("int")
- startX, endX, startY, endY = clamp_cut_values(startX, endX, startY, endY, source_image)
- cutout_padding = extra_padding
- # top needs extra room for detection
- padding = int((endY - startY) * cutout_padding)
- oldY = startY
- startY -= padding
-
- factor = 0.25 if i == 1 else 0.5
- cutout_padding = factor
- padding = int((endY - oldY) * cutout_padding)
- endY += padding
- padding = int((endX - startX) * cutout_padding)
- startX -= padding
- endX += padding
- startX, endX, startY, endY = clamp_cut_values(
- startX, endX, startY, endY, source_image
- )
- face_temp = source_image[startY:endY, startX:endX]
- face_temp = resize_image_keep_content(face_temp)
- testfaces = get_all_faces(face_temp)
- if testfaces is not None and len(testfaces) > 0:
- i += 1
- face_data.append([testfaces[0], face_temp])
- found = True
- break
-
- if not found:
- print("No face found after resizing, this shouldn't happen!")
- continue
-
- face_temp = source_image[startY:endY, startX:endX]
- if face_temp.size < 1:
- continue
-
- i += 1
- face_data.append([face, face_temp])
- return face_data
-
-
-def clamp_cut_values(startX, endX, startY, endY, image):
- if startX < 0:
- startX = 0
- if endX > image.shape[1]:
- endX = image.shape[1]
- if startY < 0:
- startY = 0
- if endY > image.shape[0]:
- endY = image.shape[0]
- return startX, endX, startY, endY
-
-
-
-def face_offset_top(face: Face, offset):
- face["bbox"][1] += offset
- face["bbox"][3] += offset
- lm106 = face.landmark_2d_106
- add = np.full_like(lm106, [0, offset])
- face["landmark_2d_106"] = lm106 + add
- return face
-
-
-def resize_image_keep_content(image, new_width=512, new_height=512):
- dim = None
- (h, w) = image.shape[:2]
- if h > w:
- r = new_height / float(h)
- dim = (int(w * r), new_height)
- else:
- # Calculate the ratio of the width and construct the dimensions
- r = new_width / float(w)
- dim = (new_width, int(h * r))
- image = cv2.resize(image, dim, interpolation=cv2.INTER_AREA)
- (h, w) = image.shape[:2]
- if h == new_height and w == new_width:
- return image
- resize_img = np.zeros(shape=(new_height, new_width, 3), dtype=image.dtype)
- offs = (new_width - w) if h == new_height else (new_height - h)
- startoffs = int(offs // 2) if offs % 2 == 0 else int(offs // 2) + 1
- offs = int(offs // 2)
-
- if h == new_height:
- resize_img[0:new_height, startoffs : new_width - offs] = image
- else:
- resize_img[startoffs : new_height - offs, 0:new_width] = image
- return resize_img
-
-
-def rotate_image_90(image, rotate=True):
- if rotate:
- return np.rot90(image)
- else:
- return np.rot90(image, 1, (1, 0))
-
-
-def rotate_anticlockwise(frame):
- return rotate_image_90(frame)
-
-
-def rotate_clockwise(frame):
- return rotate_image_90(frame, False)
-
-
-def rotate_image_180(image):
- return np.flip(image, 0)
-
-
-# alignment code from insightface https://github.com/deepinsight/insightface/blob/master/python-package/insightface/utils/face_align.py
-
-arcface_dst = np.array(
- [
- [38.2946, 51.6963],
- [73.5318, 51.5014],
- [56.0252, 71.7366],
- [41.5493, 92.3655],
- [70.7299, 92.2041],
- ],
- dtype=np.float32,
-)
-
-
-""" def estimate_norm(lmk, image_size=112):
- assert lmk.shape == (5, 2)
- if image_size % 112 == 0:
- ratio = float(image_size) / 112.0
- diff_x = 0
- elif image_size % 128 == 0:
- ratio = float(image_size) / 128.0
- diff_x = 8.0 * ratio
- elif image_size % 512 == 0:
- ratio = float(image_size) / 512.0
- diff_x = 32.0 * ratio
-
- dst = arcface_dst * ratio
- dst[:, 0] += diff_x
- tform = trans.SimilarityTransform()
- tform.estimate(lmk, dst)
- M = tform.params[0:2, :]
- return M
- """
-
-def estimate_norm(lmk, image_size=112):
- if image_size%112==0:
- ratio = float(image_size)/112.0
- diff_x = 0
- else:
- ratio = float(image_size)/128.0
- diff_x = 8.0*ratio
- dst = arcface_dst * ratio
- dst[:,0] += diff_x
-
- if image_size == 160:
- dst[:,0] += 0.1
- dst[:,1] += 0.1
- elif image_size == 256:
- dst[:,0] += 0.5
- dst[:,1] += 0.5
- elif image_size == 320:
- dst[:,0] += 0.75
- dst[:,1] += 0.75
- elif image_size == 512:
- dst[:,0] += 1.5
- dst[:,1] += 1.5
-
- tform = trans.SimilarityTransform()
- tform.estimate(lmk, dst)
- M = tform.params[0:2, :]
- return M
-
-
-
-# aligned, M = norm_crop2(f[1], face.kps, 512)
-def align_crop(img, landmark, image_size=112, mode="arcface"):
- M = estimate_norm(landmark, image_size)
- warped = cv2.warpAffine(img, M, (image_size, image_size), borderValue=0.0)
- return warped, M
-
-
-def square_crop(im, S):
- if im.shape[0] > im.shape[1]:
- height = S
- width = int(float(im.shape[1]) / im.shape[0] * S)
- scale = float(S) / im.shape[0]
- else:
- width = S
- height = int(float(im.shape[0]) / im.shape[1] * S)
- scale = float(S) / im.shape[1]
- resized_im = cv2.resize(im, (width, height))
- det_im = np.zeros((S, S, 3), dtype=np.uint8)
- det_im[: resized_im.shape[0], : resized_im.shape[1], :] = resized_im
- return det_im, scale
-
-
-def transform(data, center, output_size, scale, rotation):
- scale_ratio = scale
- rot = float(rotation) * np.pi / 180.0
- # translation = (output_size/2-center[0]*scale_ratio, output_size/2-center[1]*scale_ratio)
- t1 = trans.SimilarityTransform(scale=scale_ratio)
- cx = center[0] * scale_ratio
- cy = center[1] * scale_ratio
- t2 = trans.SimilarityTransform(translation=(-1 * cx, -1 * cy))
- t3 = trans.SimilarityTransform(rotation=rot)
- t4 = trans.SimilarityTransform(translation=(output_size / 2, output_size / 2))
- t = t1 + t2 + t3 + t4
- M = t.params[0:2]
- cropped = cv2.warpAffine(data, M, (output_size, output_size), borderValue=0.0)
- return cropped, M
-
-
-def trans_points2d(pts, M):
- new_pts = np.zeros(shape=pts.shape, dtype=np.float32)
- for i in range(pts.shape[0]):
- pt = pts[i]
- new_pt = np.array([pt[0], pt[1], 1.0], dtype=np.float32)
- new_pt = np.dot(M, new_pt)
- # print('new_pt', new_pt.shape, new_pt)
- new_pts[i] = new_pt[0:2]
-
- return new_pts
-
-
-def trans_points3d(pts, M):
- scale = np.sqrt(M[0][0] * M[0][0] + M[0][1] * M[0][1])
- # print(scale)
- new_pts = np.zeros(shape=pts.shape, dtype=np.float32)
- for i in range(pts.shape[0]):
- pt = pts[i]
- new_pt = np.array([pt[0], pt[1], 1.0], dtype=np.float32)
- new_pt = np.dot(M, new_pt)
- # print('new_pt', new_pt.shape, new_pt)
- new_pts[i][0:2] = new_pt[0:2]
- new_pts[i][2] = pts[i][2] * scale
-
- return new_pts
-
-
-def trans_points(pts, M):
- if pts.shape[1] == 2:
- return trans_points2d(pts, M)
- else:
- return trans_points3d(pts, M)
-
-def create_blank_image(width, height):
- img = np.zeros((height, width, 4), dtype=np.uint8)
- img[:] = [0,0,0,0]
- return img
-
diff --git a/roop-unleashed-main/roop/ffmpeg_writer.py b/roop-unleashed-main/roop/ffmpeg_writer.py
deleted file mode 100644
index 9642efad2de4e2b3463a62d1ee04b5f02402702c..0000000000000000000000000000000000000000
--- a/roop-unleashed-main/roop/ffmpeg_writer.py
+++ /dev/null
@@ -1,218 +0,0 @@
-"""
-FFMPEG_Writer - write set of frames to video file
-
-original from
-https://github.com/Zulko/moviepy/blob/master/moviepy/video/io/ffmpeg_writer.py
-
-removed unnecessary dependencies
-
-The MIT License (MIT)
-
-Copyright (c) 2015 Zulko
-Copyright (c) 2023 Janvarev Vladislav
-"""
-
-import os
-import subprocess as sp
-
-PIPE = -1
-STDOUT = -2
-DEVNULL = -3
-
-FFMPEG_BINARY = "ffmpeg"
-
-class FFMPEG_VideoWriter:
- """ A class for FFMPEG-based video writing.
-
- A class to write videos using ffmpeg. ffmpeg will write in a large
- choice of formats.
-
- Parameters
- -----------
-
- filename
- Any filename like 'video.mp4' etc. but if you want to avoid
- complications it is recommended to use the generic extension
- '.avi' for all your videos.
-
- size
- Size (width,height) of the output video in pixels.
-
- fps
- Frames per second in the output video file.
-
- codec
- FFMPEG codec. It seems that in terms of quality the hierarchy is
- 'rawvideo' = 'png' > 'mpeg4' > 'libx264'
- 'png' manages the same lossless quality as 'rawvideo' but yields
- smaller files. Type ``ffmpeg -codecs`` in a terminal to get a list
- of accepted codecs.
-
- Note for default 'libx264': by default the pixel format yuv420p
- is used. If the video dimensions are not both even (e.g. 720x405)
- another pixel format is used, and this can cause problem in some
- video readers.
-
- audiofile
- Optional: The name of an audio file that will be incorporated
- to the video.
-
- preset
- Sets the time that FFMPEG will take to compress the video. The slower,
- the better the compression rate. Possibilities are: ultrafast,superfast,
- veryfast, faster, fast, medium (default), slow, slower, veryslow,
- placebo.
-
- bitrate
- Only relevant for codecs which accept a bitrate. "5000k" offers
- nice results in general.
-
- """
-
- def __init__(self, filename, size, fps, codec="libx265", crf=14, audiofile=None,
- preset="medium", bitrate=None,
- logfile=None, threads=None, ffmpeg_params=None):
-
- if logfile is None:
- logfile = sp.PIPE
-
- self.filename = filename
- self.codec = codec
- self.ext = self.filename.split(".")[-1]
- w = size[0] - 1 if size[0] % 2 != 0 else size[0]
- h = size[1] - 1 if size[1] % 2 != 0 else size[1]
-
-
- # order is important
- cmd = [
- FFMPEG_BINARY,
- '-hide_banner',
- '-hwaccel', 'auto',
- '-y',
- '-loglevel', 'error' if logfile == sp.PIPE else 'info',
- '-f', 'rawvideo',
- '-vcodec', 'rawvideo',
- '-s', '%dx%d' % (size[0], size[1]),
- #'-pix_fmt', 'rgba' if withmask else 'rgb24',
- '-pix_fmt', 'bgr24',
- '-r', str(fps),
- '-an', '-i', '-'
- ]
-
- if audiofile is not None:
- cmd.extend([
- '-i', audiofile,
- '-acodec', 'copy'
- ])
-
- cmd.extend([
- '-vcodec', codec,
- '-crf', str(crf)
- #'-preset', preset,
- ])
- if ffmpeg_params is not None:
- cmd.extend(ffmpeg_params)
- if bitrate is not None:
- cmd.extend([
- '-b', bitrate
- ])
-
- # scale to a resolution divisible by 2 if not even
- cmd.extend(['-vf', f'scale={w}:{h}' if w != size[0] or h != size[1] else 'colorspace=bt709:iall=bt601-6-625:fast=1'])
-
- if threads is not None:
- cmd.extend(["-threads", str(threads)])
-
- cmd.extend([
- '-pix_fmt', 'yuv420p',
-
- ])
- cmd.extend([
- filename
- ])
-
- test = str(cmd)
- print(test)
-
- popen_params = {"stdout": DEVNULL,
- "stderr": logfile,
- "stdin": sp.PIPE}
-
- # This was added so that no extra unwanted window opens on windows
- # when the child process is created
- if os.name == "nt":
- popen_params["creationflags"] = 0x08000000 # CREATE_NO_WINDOW
-
- self.proc = sp.Popen(cmd, **popen_params)
-
-
- def write_frame(self, img_array):
- """ Writes one frame in the file."""
- try:
- #if PY3:
- self.proc.stdin.write(img_array.tobytes())
- # else:
- # self.proc.stdin.write(img_array.tostring())
- except IOError as err:
- _, ffmpeg_error = self.proc.communicate()
- error = (str(err) + ("\n\nroop unleashed error: FFMPEG encountered "
- "the following error while writing file %s:"
- "\n\n %s" % (self.filename, str(ffmpeg_error))))
-
- if b"Unknown encoder" in ffmpeg_error:
-
- error = error+("\n\nThe video export "
- "failed because FFMPEG didn't find the specified "
- "codec for video encoding (%s). Please install "
- "this codec or change the codec when calling "
- "write_videofile. For instance:\n"
- " >>> clip.write_videofile('myvid.webm', codec='libvpx')")%(self.codec)
-
- elif b"incorrect codec parameters ?" in ffmpeg_error:
-
- error = error+("\n\nThe video export "
- "failed, possibly because the codec specified for "
- "the video (%s) is not compatible with the given "
- "extension (%s). Please specify a valid 'codec' "
- "argument in write_videofile. This would be 'libx264' "
- "or 'mpeg4' for mp4, 'libtheora' for ogv, 'libvpx for webm. "
- "Another possible reason is that the audio codec was not "
- "compatible with the video codec. For instance the video "
- "extensions 'ogv' and 'webm' only allow 'libvorbis' (default) as a"
- "video codec."
- )%(self.codec, self.ext)
-
- elif b"encoder setup failed" in ffmpeg_error:
-
- error = error+("\n\nThe video export "
- "failed, possibly because the bitrate you specified "
- "was too high or too low for the video codec.")
-
- elif b"Invalid encoder type" in ffmpeg_error:
-
- error = error + ("\n\nThe video export failed because the codec "
- "or file extension you provided is not a video")
-
-
- raise IOError(error)
-
- def close(self):
- if self.proc:
- self.proc.stdin.close()
- if self.proc.stderr is not None:
- self.proc.stderr.close()
- self.proc.wait()
-
- self.proc = None
-
- # Support the Context Manager protocol, to ensure that resources are cleaned up.
-
- def __enter__(self):
- return self
-
- def __exit__(self, exc_type, exc_value, traceback):
- self.close()
-
-
-
-
diff --git a/roop-unleashed-main/roop/globals.py b/roop-unleashed-main/roop/globals.py
deleted file mode 100644
index cd241b521b72361dcd31b1d001e5cd218cc72f00..0000000000000000000000000000000000000000
--- a/roop-unleashed-main/roop/globals.py
+++ /dev/null
@@ -1,56 +0,0 @@
-from settings import Settings
-from typing import List
-
-source_path = None
-target_path = None
-output_path = None
-target_folder_path = None
-startup_args = None
-
-cuda_device_id = 0
-frame_processors: List[str] = []
-keep_fps = None
-keep_frames = None
-autorotate_faces = None
-vr_mode = None
-skip_audio = None
-wait_after_extraction = None
-many_faces = None
-use_batch = None
-source_face_index = 0
-target_face_index = 0
-face_position = None
-video_encoder = None
-video_quality = None
-max_memory = None
-execution_providers: List[str] = []
-execution_threads = None
-headless = None
-log_level = 'error'
-selected_enhancer = None
-subsample_size = 128
-face_swap_mode = None
-blend_ratio = 0.5
-distance_threshold = 0.65
-default_det_size = True
-
-no_face_action = 0
-
-processing = False
-
-g_current_face_analysis = None
-g_desired_face_analysis = None
-
-FACE_ENHANCER = None
-
-INPUT_FACESETS = []
-TARGET_FACES = []
-
-
-IMAGE_CHAIN_PROCESSOR = None
-VIDEO_CHAIN_PROCESSOR = None
-BATCH_IMAGE_CHAIN_PROCESSOR = None
-
-CFG: Settings = None
-
-
diff --git a/roop-unleashed-main/roop/metadata.py b/roop-unleashed-main/roop/metadata.py
deleted file mode 100644
index 461cf39403df85dadc32f57562fa97c207ae9919..0000000000000000000000000000000000000000
--- a/roop-unleashed-main/roop/metadata.py
+++ /dev/null
@@ -1,2 +0,0 @@
-name = 'roop unleashed'
-version = '4.4.0'
diff --git a/roop-unleashed-main/roop/processors/Enhance_CodeFormer.py b/roop-unleashed-main/roop/processors/Enhance_CodeFormer.py
deleted file mode 100644
index 323902a9aabbf0bb17689ca8e3600adf246329f7..0000000000000000000000000000000000000000
--- a/roop-unleashed-main/roop/processors/Enhance_CodeFormer.py
+++ /dev/null
@@ -1,71 +0,0 @@
-from typing import Any, List, Callable
-import cv2
-import numpy as np
-import onnxruntime
-import roop.globals
-
-from roop.typing import Face, Frame, FaceSet
-from roop.utilities import resolve_relative_path
-
-class Enhance_CodeFormer():
- model_codeformer = None
-
- plugin_options:dict = None
-
- processorname = 'codeformer'
- type = 'enhance'
-
-
- def Initialize(self, plugin_options:dict):
- if self.plugin_options is not None:
- if self.plugin_options["devicename"] != plugin_options["devicename"]:
- self.Release()
-
- self.plugin_options = plugin_options
- if self.model_codeformer is None:
- # replace Mac mps with cpu for the moment
- self.devicename = self.plugin_options["devicename"].replace('mps', 'cpu')
- model_path = resolve_relative_path('../models/CodeFormer/CodeFormerv0.1.onnx')
- self.model_codeformer = onnxruntime.InferenceSession(model_path, None, providers=roop.globals.execution_providers)
- self.model_inputs = self.model_codeformer.get_inputs()
- model_outputs = self.model_codeformer.get_outputs()
- self.io_binding = self.model_codeformer.io_binding()
- self.io_binding.bind_cpu_input(self.model_inputs[1].name, np.array([0.5]))
- self.io_binding.bind_output(model_outputs[0].name, self.devicename)
-
-
- def Run(self, source_faceset: FaceSet, target_face: Face, temp_frame: Frame) -> Frame:
- input_size = temp_frame.shape[1]
- # preprocess
- temp_frame = cv2.resize(temp_frame, (512, 512), cv2.INTER_CUBIC)
- temp_frame = cv2.cvtColor(temp_frame, cv2.COLOR_BGR2RGB)
- temp_frame = temp_frame.astype('float32') / 255.0
- temp_frame = (temp_frame - 0.5) / 0.5
- temp_frame = np.expand_dims(temp_frame, axis=0).transpose(0, 3, 1, 2)
-
- self.io_binding.bind_cpu_input(self.model_inputs[0].name, temp_frame.astype(np.float32))
- self.model_codeformer.run_with_iobinding(self.io_binding)
- ort_outs = self.io_binding.copy_outputs_to_cpu()
- result = ort_outs[0][0]
- del ort_outs
-
- # post-process
- result = result.transpose((1, 2, 0))
-
- un_min = -1.0
- un_max = 1.0
- result = np.clip(result, un_min, un_max)
- result = (result - un_min) / (un_max - un_min)
-
- result = cv2.cvtColor(result, cv2.COLOR_RGB2BGR)
- result = (result * 255.0).round()
- scale_factor = int(result.shape[1] / input_size)
- return result.astype(np.uint8), scale_factor
-
-
- def Release(self):
- del self.model_codeformer
- self.model_codeformer = None
- del self.io_binding
- self.io_binding = None
-
diff --git a/roop-unleashed-main/roop/processors/Enhance_DMDNet.py b/roop-unleashed-main/roop/processors/Enhance_DMDNet.py
deleted file mode 100644
index 3b6a6bb2d2fdad863dcbf66da8e498555d357a64..0000000000000000000000000000000000000000
--- a/roop-unleashed-main/roop/processors/Enhance_DMDNet.py
+++ /dev/null
@@ -1,898 +0,0 @@
-from typing import Any, List, Callable
-import cv2
-import numpy as np
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import torch.nn.utils.spectral_norm as SpectralNorm
-import threading
-from torchvision.ops import roi_align
-
-from math import sqrt
-
-from torchvision.transforms.functional import normalize
-
-from roop.typing import Face, Frame, FaceSet
-
-
-THREAD_LOCK_DMDNET = threading.Lock()
-
-
-class Enhance_DMDNet():
- plugin_options:dict = None
- model_dmdnet = None
- torchdevice = None
-
- processorname = 'dmdnet'
- type = 'enhance'
-
-
- def Initialize(self, plugin_options:dict):
- if self.plugin_options is not None:
- if self.plugin_options["devicename"] != plugin_options["devicename"]:
- self.Release()
-
- self.plugin_options = plugin_options
- if self.model_dmdnet is None:
- self.model_dmdnet = self.create(self.plugin_options["devicename"])
-
-
- # temp_frame already cropped+aligned, bbox not
- def Run(self, source_faceset: FaceSet, target_face: Face, temp_frame: Frame) -> Frame:
- input_size = temp_frame.shape[1]
-
- result = self.enhance_face(source_faceset, temp_frame, target_face)
- scale_factor = int(result.shape[1] / input_size)
- return result.astype(np.uint8), scale_factor
-
-
- def Release(self):
- self.model_gfpgan = None
-
-
- # https://stackoverflow.com/a/67174339
- def landmarks106_to_68(self, pt106):
- map106to68=[1,10,12,14,16,3,5,7,0,23,21,19,32,30,28,26,17,
- 43,48,49,51,50,
- 102,103,104,105,101,
- 72,73,74,86,78,79,80,85,84,
- 35,41,42,39,37,36,
- 89,95,96,93,91,90,
- 52,64,63,71,67,68,61,58,59,53,56,55,65,66,62,70,69,57,60,54
- ]
-
- pt68 = []
- for i in range(68):
- index = map106to68[i]
- pt68.append(pt106[index])
- return pt68
-
-
-
-
- def check_bbox(self, imgs, boxes):
- boxes = boxes.view(-1, 4, 4)
- colors = [(0, 255, 0), (0, 255, 0), (255, 255, 0), (255, 0, 0)]
- i = 0
- for img, box in zip(imgs, boxes):
- img = (img + 1)/2 * 255
- img2 = img.permute(1, 2, 0).float().cpu().flip(2).numpy().copy()
- for idx, point in enumerate(box):
- cv2.rectangle(img2, (int(point[0]), int(point[1])), (int(point[2]), int(point[3])), color=colors[idx], thickness=2)
- cv2.imwrite('dmdnet_{:02d}.png'.format(i), img2)
- i += 1
-
-
- def trans_points2d(self, pts, M):
- new_pts = np.zeros(shape=pts.shape, dtype=np.float32)
- for i in range(pts.shape[0]):
- pt = pts[i]
- new_pt = np.array([pt[0], pt[1], 1.0], dtype=np.float32)
- new_pt = np.dot(M, new_pt)
- new_pts[i] = new_pt[0:2]
-
- return new_pts
-
-
- def enhance_face(self, ref_faceset: FaceSet, temp_frame, face: Face):
- # preprocess
- start_x, start_y, end_x, end_y = map(int, face['bbox'])
- lm106 = face.landmark_2d_106
- lq_landmarks = np.asarray(self.landmarks106_to_68(lm106))
-
- if temp_frame.shape[0] != 512 or temp_frame.shape[1] != 512:
- # scale to 512x512
- scale_factor = 512 / temp_frame.shape[1]
-
- M = face.matrix * scale_factor
-
- lq_landmarks = self.trans_points2d(lq_landmarks, M)
- temp_frame = cv2.resize(temp_frame, (512,512), interpolation = cv2.INTER_AREA)
-
- if temp_frame.ndim == 2:
- temp_frame = cv2.cvtColor(temp_frame, cv2.COLOR_GRAY2RGB) # GGG
- # else:
- # temp_frame = cv2.cvtColor(temp_frame, cv2.COLOR_BGR2RGB) # RGB
-
- lq = read_img_tensor(temp_frame)
-
- LQLocs = get_component_location(lq_landmarks)
- # self.check_bbox(lq, LQLocs.unsqueeze(0))
-
- # specific, change 1000 to 1 to activate
- if len(ref_faceset.faces) > 1:
- SpecificImgs = []
- SpecificLocs = []
- for i,face in enumerate(ref_faceset.faces):
- lm106 = face.landmark_2d_106
- lq_landmarks = np.asarray(self.landmarks106_to_68(lm106))
- ref_image = ref_faceset.ref_images[i]
- if ref_image.shape[0] != 512 or ref_image.shape[1] != 512:
- # scale to 512x512
- scale_factor = 512 / ref_image.shape[1]
-
- M = face.matrix * scale_factor
-
- lq_landmarks = self.trans_points2d(lq_landmarks, M)
- ref_image = cv2.resize(ref_image, (512,512), interpolation = cv2.INTER_AREA)
-
- if ref_image.ndim == 2:
- temp_frame = cv2.cvtColor(temp_frame, cv2.COLOR_GRAY2RGB) # GGG
- # else:
- # temp_frame = cv2.cvtColor(temp_frame, cv2.COLOR_BGR2RGB) # RGB
-
- ref_tensor = read_img_tensor(ref_image)
- ref_locs = get_component_location(lq_landmarks)
- # self.check_bbox(ref_tensor, ref_locs.unsqueeze(0))
-
- SpecificImgs.append(ref_tensor)
- SpecificLocs.append(ref_locs.unsqueeze(0))
-
- SpecificImgs = torch.cat(SpecificImgs, dim=0)
- SpecificLocs = torch.cat(SpecificLocs, dim=0)
- # check_bbox(SpecificImgs, SpecificLocs)
- SpMem256, SpMem128, SpMem64 = self.model_dmdnet.generate_specific_dictionary(sp_imgs = SpecificImgs.to(self.torchdevice), sp_locs = SpecificLocs)
- SpMem256Para = {}
- SpMem128Para = {}
- SpMem64Para = {}
- for k, v in SpMem256.items():
- SpMem256Para[k] = v
- for k, v in SpMem128.items():
- SpMem128Para[k] = v
- for k, v in SpMem64.items():
- SpMem64Para[k] = v
- else:
- # generic
- SpMem256Para, SpMem128Para, SpMem64Para = None, None, None
-
- with torch.no_grad():
- with THREAD_LOCK_DMDNET:
- try:
- GenericResult, SpecificResult = self.model_dmdnet(lq = lq.to(self.torchdevice), loc = LQLocs.unsqueeze(0), sp_256 = SpMem256Para, sp_128 = SpMem128Para, sp_64 = SpMem64Para)
- except Exception as e:
- print(f'Error {e} there may be something wrong with the detected component locations.')
- return temp_frame
-
- if SpecificResult is not None:
- save_specific = SpecificResult * 0.5 + 0.5
- save_specific = save_specific.squeeze(0).permute(1, 2, 0).flip(2) # RGB->BGR
- save_specific = np.clip(save_specific.float().cpu().numpy(), 0, 1) * 255.0
- temp_frame = save_specific.astype("uint8")
- if False:
- save_generic = GenericResult * 0.5 + 0.5
- save_generic = save_generic.squeeze(0).permute(1, 2, 0).flip(2) # RGB->BGR
- save_generic = np.clip(save_generic.float().cpu().numpy(), 0, 1) * 255.0
- check_lq = lq * 0.5 + 0.5
- check_lq = check_lq.squeeze(0).permute(1, 2, 0).flip(2) # RGB->BGR
- check_lq = np.clip(check_lq.float().cpu().numpy(), 0, 1) * 255.0
- cv2.imwrite('dmdnet_comparison.png', cv2.cvtColor(np.hstack((check_lq, save_generic, save_specific)),cv2.COLOR_RGB2BGR))
- else:
- save_generic = GenericResult * 0.5 + 0.5
- save_generic = save_generic.squeeze(0).permute(1, 2, 0).flip(2) # RGB->BGR
- save_generic = np.clip(save_generic.float().cpu().numpy(), 0, 1) * 255.0
- temp_frame = save_generic.astype("uint8")
- temp_frame = cv2.cvtColor(temp_frame, cv2.COLOR_RGB2BGR) # RGB
- return temp_frame
-
-
-
- def create(self, devicename):
- self.torchdevice = torch.device(devicename)
- model_dmdnet = DMDNet().to(self.torchdevice)
- weights = torch.load('./models/DMDNet.pth')
- model_dmdnet.load_state_dict(weights, strict=True)
-
- model_dmdnet.eval()
- num_params = 0
- for param in model_dmdnet.parameters():
- num_params += param.numel()
- return model_dmdnet
-
- # print('{:>8s} : {}'.format('Using device', device))
- # print('{:>8s} : {:.2f}M'.format('Model params', num_params/1e6))
-
-
-
-def read_img_tensor(Img=None): #rgb -1~1
- Img = Img.transpose((2, 0, 1))/255.0
- Img = torch.from_numpy(Img).float()
- normalize(Img, [0.5,0.5,0.5], [0.5,0.5,0.5], inplace=True)
- ImgTensor = Img.unsqueeze(0)
- return ImgTensor
-
-
-def get_component_location(Landmarks, re_read=False):
- if re_read:
- ReadLandmark = []
- with open(Landmarks,'r') as f:
- for line in f:
- tmp = [float(i) for i in line.split(' ') if i != '\n']
- ReadLandmark.append(tmp)
- ReadLandmark = np.array(ReadLandmark) #
- Landmarks = np.reshape(ReadLandmark, [-1, 2]) # 68*2
- Map_LE_B = list(np.hstack((range(17,22), range(36,42))))
- Map_RE_B = list(np.hstack((range(22,27), range(42,48))))
- Map_LE = list(range(36,42))
- Map_RE = list(range(42,48))
- Map_NO = list(range(29,36))
- Map_MO = list(range(48,68))
-
- Landmarks[Landmarks>504]=504
- Landmarks[Landmarks<8]=8
-
- #left eye
- Mean_LE = np.mean(Landmarks[Map_LE],0)
- L_LE1 = Mean_LE[1] - np.min(Landmarks[Map_LE_B,1])
- L_LE1 = L_LE1 * 1.3
- L_LE2 = L_LE1 / 1.9
- L_LE_xy = L_LE1 + L_LE2
- L_LE_lt = [L_LE_xy/2, L_LE1]
- L_LE_rb = [L_LE_xy/2, L_LE2]
- Location_LE = np.hstack((Mean_LE - L_LE_lt + 1, Mean_LE + L_LE_rb)).astype(int)
-
- #right eye
- Mean_RE = np.mean(Landmarks[Map_RE],0)
- L_RE1 = Mean_RE[1] - np.min(Landmarks[Map_RE_B,1])
- L_RE1 = L_RE1 * 1.3
- L_RE2 = L_RE1 / 1.9
- L_RE_xy = L_RE1 + L_RE2
- L_RE_lt = [L_RE_xy/2, L_RE1]
- L_RE_rb = [L_RE_xy/2, L_RE2]
- Location_RE = np.hstack((Mean_RE - L_RE_lt + 1, Mean_RE + L_RE_rb)).astype(int)
-
- #nose
- Mean_NO = np.mean(Landmarks[Map_NO],0)
- L_NO1 =( np.max([Mean_NO[0] - Landmarks[31][0], Landmarks[35][0] - Mean_NO[0]])) * 1.25
- L_NO2 = (Landmarks[33][1] - Mean_NO[1]) * 1.1
- L_NO_xy = L_NO1 * 2
- L_NO_lt = [L_NO_xy/2, L_NO_xy - L_NO2]
- L_NO_rb = [L_NO_xy/2, L_NO2]
- Location_NO = np.hstack((Mean_NO - L_NO_lt + 1, Mean_NO + L_NO_rb)).astype(int)
-
- #mouth
- Mean_MO = np.mean(Landmarks[Map_MO],0)
- L_MO = np.max((np.max(np.max(Landmarks[Map_MO],0) - np.min(Landmarks[Map_MO],0))/2,16)) * 1.1
- MO_O = Mean_MO - L_MO + 1
- MO_T = Mean_MO + L_MO
- MO_T[MO_T>510]=510
- Location_MO = np.hstack((MO_O, MO_T)).astype(int)
- return torch.cat([torch.FloatTensor(Location_LE).unsqueeze(0), torch.FloatTensor(Location_RE).unsqueeze(0), torch.FloatTensor(Location_NO).unsqueeze(0), torch.FloatTensor(Location_MO).unsqueeze(0)], dim=0)
-
-
-
-
-def calc_mean_std_4D(feat, eps=1e-5):
- # eps is a small value added to the variance to avoid divide-by-zero.
- size = feat.size()
- assert (len(size) == 4)
- N, C = size[:2]
- feat_var = feat.view(N, C, -1).var(dim=2) + eps
- feat_std = feat_var.sqrt().view(N, C, 1, 1)
- feat_mean = feat.view(N, C, -1).mean(dim=2).view(N, C, 1, 1)
- return feat_mean, feat_std
-
-def adaptive_instance_normalization_4D(content_feat, style_feat): # content_feat is ref feature, style is degradate feature
- size = content_feat.size()
- style_mean, style_std = calc_mean_std_4D(style_feat)
- content_mean, content_std = calc_mean_std_4D(content_feat)
- normalized_feat = (content_feat - content_mean.expand(size)) / content_std.expand(size)
- return normalized_feat * style_std.expand(size) + style_mean.expand(size)
-
-
-def convU(in_channels, out_channels,conv_layer, norm_layer, kernel_size=3, stride=1,dilation=1, bias=True):
- return nn.Sequential(
- SpectralNorm(conv_layer(in_channels, out_channels, kernel_size=kernel_size, stride=stride, dilation=dilation, padding=((kernel_size-1)//2)*dilation, bias=bias)),
- nn.LeakyReLU(0.2),
- SpectralNorm(conv_layer(out_channels, out_channels, kernel_size=kernel_size, stride=stride, dilation=dilation, padding=((kernel_size-1)//2)*dilation, bias=bias)),
- )
-
-
-class MSDilateBlock(nn.Module):
- def __init__(self, in_channels,conv_layer=nn.Conv2d, norm_layer=nn.BatchNorm2d, kernel_size=3, dilation=[1,1,1,1], bias=True):
- super(MSDilateBlock, self).__init__()
- self.conv1 = convU(in_channels, in_channels,conv_layer, norm_layer, kernel_size,dilation=dilation[0], bias=bias)
- self.conv2 = convU(in_channels, in_channels,conv_layer, norm_layer, kernel_size,dilation=dilation[1], bias=bias)
- self.conv3 = convU(in_channels, in_channels,conv_layer, norm_layer, kernel_size,dilation=dilation[2], bias=bias)
- self.conv4 = convU(in_channels, in_channels,conv_layer, norm_layer, kernel_size,dilation=dilation[3], bias=bias)
- self.convi = SpectralNorm(conv_layer(in_channels*4, in_channels, kernel_size=kernel_size, stride=1, padding=(kernel_size-1)//2, bias=bias))
- def forward(self, x):
- conv1 = self.conv1(x)
- conv2 = self.conv2(x)
- conv3 = self.conv3(x)
- conv4 = self.conv4(x)
- cat = torch.cat([conv1, conv2, conv3, conv4], 1)
- out = self.convi(cat) + x
- return out
-
-
-class AdaptiveInstanceNorm(nn.Module):
- def __init__(self, in_channel):
- super().__init__()
- self.norm = nn.InstanceNorm2d(in_channel)
-
- def forward(self, input, style):
- style_mean, style_std = calc_mean_std_4D(style)
- out = self.norm(input)
- size = input.size()
- out = style_std.expand(size) * out + style_mean.expand(size)
- return out
-
-class NoiseInjection(nn.Module):
- def __init__(self, channel):
- super().__init__()
- self.weight = nn.Parameter(torch.zeros(1, channel, 1, 1))
- def forward(self, image, noise):
- if noise is None:
- b, c, h, w = image.shape
- noise = image.new_empty(b, 1, h, w).normal_()
- return image + self.weight * noise
-
-class StyledUpBlock(nn.Module):
- def __init__(self, in_channel, out_channel, kernel_size=3, padding=1,upsample=False, noise_inject=False):
- super().__init__()
-
- self.noise_inject = noise_inject
- if upsample:
- self.conv1 = nn.Sequential(
- nn.Upsample(scale_factor=2, mode='bilinear', align_corners=False),
- SpectralNorm(nn.Conv2d(in_channel, out_channel, kernel_size, padding=padding)),
- nn.LeakyReLU(0.2),
- )
- else:
- self.conv1 = nn.Sequential(
- SpectralNorm(nn.Conv2d(in_channel, out_channel, kernel_size, padding=padding)),
- nn.LeakyReLU(0.2),
- SpectralNorm(nn.Conv2d(out_channel, out_channel, kernel_size, padding=padding)),
- )
- self.convup = nn.Sequential(
- nn.Upsample(scale_factor=2, mode='bilinear', align_corners=False),
- SpectralNorm(nn.Conv2d(out_channel, out_channel, kernel_size, padding=padding)),
- nn.LeakyReLU(0.2),
- SpectralNorm(nn.Conv2d(out_channel, out_channel, kernel_size, padding=padding)),
- )
- if self.noise_inject:
- self.noise1 = NoiseInjection(out_channel)
-
- self.lrelu1 = nn.LeakyReLU(0.2)
-
- self.ScaleModel1 = nn.Sequential(
- SpectralNorm(nn.Conv2d(in_channel,out_channel,3, 1, 1)),
- nn.LeakyReLU(0.2),
- SpectralNorm(nn.Conv2d(out_channel, out_channel, 3, 1, 1))
- )
- self.ShiftModel1 = nn.Sequential(
- SpectralNorm(nn.Conv2d(in_channel,out_channel,3, 1, 1)),
- nn.LeakyReLU(0.2),
- SpectralNorm(nn.Conv2d(out_channel, out_channel, 3, 1, 1)),
- )
-
- def forward(self, input, style):
- out = self.conv1(input)
- out = self.lrelu1(out)
- Shift1 = self.ShiftModel1(style)
- Scale1 = self.ScaleModel1(style)
- out = out * Scale1 + Shift1
- if self.noise_inject:
- out = self.noise1(out, noise=None)
- outup = self.convup(out)
- return outup
-
-
-####################################################################
-###############Face Dictionary Generator
-####################################################################
-def AttentionBlock(in_channel):
- return nn.Sequential(
- SpectralNorm(nn.Conv2d(in_channel, in_channel, 3, 1, 1)),
- nn.LeakyReLU(0.2),
- SpectralNorm(nn.Conv2d(in_channel, in_channel, 3, 1, 1)),
- )
-
-class DilateResBlock(nn.Module):
- def __init__(self, dim, dilation=[5,3] ):
- super(DilateResBlock, self).__init__()
- self.Res = nn.Sequential(
- SpectralNorm(nn.Conv2d(dim, dim, 3, 1, ((3-1)//2)*dilation[0], dilation[0])),
- nn.LeakyReLU(0.2),
- SpectralNorm(nn.Conv2d(dim, dim, 3, 1, ((3-1)//2)*dilation[1], dilation[1])),
- )
- def forward(self, x):
- out = x + self.Res(x)
- return out
-
-
-class KeyValue(nn.Module):
- def __init__(self, indim, keydim, valdim):
- super(KeyValue, self).__init__()
- self.Key = nn.Sequential(
- SpectralNorm(nn.Conv2d(indim, keydim, kernel_size=(3,3), padding=(1,1), stride=1)),
- nn.LeakyReLU(0.2),
- SpectralNorm(nn.Conv2d(keydim, keydim, kernel_size=(3,3), padding=(1,1), stride=1)),
- )
- self.Value = nn.Sequential(
- SpectralNorm(nn.Conv2d(indim, valdim, kernel_size=(3,3), padding=(1,1), stride=1)),
- nn.LeakyReLU(0.2),
- SpectralNorm(nn.Conv2d(valdim, valdim, kernel_size=(3,3), padding=(1,1), stride=1)),
- )
- def forward(self, x):
- return self.Key(x), self.Value(x)
-
-class MaskAttention(nn.Module):
- def __init__(self, indim):
- super(MaskAttention, self).__init__()
- self.conv1 = nn.Sequential(
- SpectralNorm(nn.Conv2d(indim, indim//3, kernel_size=(3,3), padding=(1,1), stride=1)),
- nn.LeakyReLU(0.2),
- SpectralNorm(nn.Conv2d(indim//3, indim//3, kernel_size=(3,3), padding=(1,1), stride=1)),
- )
- self.conv2 = nn.Sequential(
- SpectralNorm(nn.Conv2d(indim, indim//3, kernel_size=(3,3), padding=(1,1), stride=1)),
- nn.LeakyReLU(0.2),
- SpectralNorm(nn.Conv2d(indim//3, indim//3, kernel_size=(3,3), padding=(1,1), stride=1)),
- )
- self.conv3 = nn.Sequential(
- SpectralNorm(nn.Conv2d(indim, indim//3, kernel_size=(3,3), padding=(1,1), stride=1)),
- nn.LeakyReLU(0.2),
- SpectralNorm(nn.Conv2d(indim//3, indim//3, kernel_size=(3,3), padding=(1,1), stride=1)),
- )
- self.convCat = nn.Sequential(
- SpectralNorm(nn.Conv2d(indim//3 * 3, indim, kernel_size=(3,3), padding=(1,1), stride=1)),
- nn.LeakyReLU(0.2),
- SpectralNorm(nn.Conv2d(indim, indim, kernel_size=(3,3), padding=(1,1), stride=1)),
- )
- def forward(self, x, y, z):
- c1 = self.conv1(x)
- c2 = self.conv2(y)
- c3 = self.conv3(z)
- return self.convCat(torch.cat([c1,c2,c3], dim=1))
-
-class Query(nn.Module):
- def __init__(self, indim, quedim):
- super(Query, self).__init__()
- self.Query = nn.Sequential(
- SpectralNorm(nn.Conv2d(indim, quedim, kernel_size=(3,3), padding=(1,1), stride=1)),
- nn.LeakyReLU(0.2),
- SpectralNorm(nn.Conv2d(quedim, quedim, kernel_size=(3,3), padding=(1,1), stride=1)),
- )
- def forward(self, x):
- return self.Query(x)
-
-def roi_align_self(input, location, target_size):
- test = (target_size.item(),target_size.item())
- return torch.cat([F.interpolate(input[i:i+1,:,location[i,1]:location[i,3],location[i,0]:location[i,2]],test,mode='bilinear',align_corners=False) for i in range(input.size(0))],0)
-
-class FeatureExtractor(nn.Module):
- def __init__(self, ngf = 64, key_scale = 4):#
- super().__init__()
-
- self.key_scale = 4
- self.part_sizes = np.array([80,80,50,110]) #
- self.feature_sizes = np.array([256,128,64]) #
-
- self.conv1 = nn.Sequential(
- SpectralNorm(nn.Conv2d(3, ngf, 3, 2, 1)),
- nn.LeakyReLU(0.2),
- SpectralNorm(nn.Conv2d(ngf, ngf, 3, 1, 1)),
- )
- self.conv2 = nn.Sequential(
- SpectralNorm(nn.Conv2d(ngf, ngf, 3, 1, 1)),
- nn.LeakyReLU(0.2),
- SpectralNorm(nn.Conv2d(ngf, ngf, 3, 1, 1))
- )
- self.res1 = DilateResBlock(ngf, [5,3])
- self.res2 = DilateResBlock(ngf, [5,3])
-
-
- self.conv3 = nn.Sequential(
- SpectralNorm(nn.Conv2d(ngf, ngf*2, 3, 2, 1)),
- nn.LeakyReLU(0.2),
- SpectralNorm(nn.Conv2d(ngf*2, ngf*2, 3, 1, 1)),
- )
- self.conv4 = nn.Sequential(
- SpectralNorm(nn.Conv2d(ngf*2, ngf*2, 3, 1, 1)),
- nn.LeakyReLU(0.2),
- SpectralNorm(nn.Conv2d(ngf*2, ngf*2, 3, 1, 1))
- )
- self.res3 = DilateResBlock(ngf*2, [3,1])
- self.res4 = DilateResBlock(ngf*2, [3,1])
-
- self.conv5 = nn.Sequential(
- SpectralNorm(nn.Conv2d(ngf*2, ngf*4, 3, 2, 1)),
- nn.LeakyReLU(0.2),
- SpectralNorm(nn.Conv2d(ngf*4, ngf*4, 3, 1, 1)),
- )
- self.conv6 = nn.Sequential(
- SpectralNorm(nn.Conv2d(ngf*4, ngf*4, 3, 1, 1)),
- nn.LeakyReLU(0.2),
- SpectralNorm(nn.Conv2d(ngf*4, ngf*4, 3, 1, 1))
- )
- self.res5 = DilateResBlock(ngf*4, [1,1])
- self.res6 = DilateResBlock(ngf*4, [1,1])
-
- self.LE_256_Q = Query(ngf, ngf // self.key_scale)
- self.RE_256_Q = Query(ngf, ngf // self.key_scale)
- self.MO_256_Q = Query(ngf, ngf // self.key_scale)
- self.LE_128_Q = Query(ngf * 2, ngf * 2 // self.key_scale)
- self.RE_128_Q = Query(ngf * 2, ngf * 2 // self.key_scale)
- self.MO_128_Q = Query(ngf * 2, ngf * 2 // self.key_scale)
- self.LE_64_Q = Query(ngf * 4, ngf * 4 // self.key_scale)
- self.RE_64_Q = Query(ngf * 4, ngf * 4 // self.key_scale)
- self.MO_64_Q = Query(ngf * 4, ngf * 4 // self.key_scale)
-
-
- def forward(self, img, locs):
- le_location = locs[:,0,:].int().cpu().numpy()
- re_location = locs[:,1,:].int().cpu().numpy()
- no_location = locs[:,2,:].int().cpu().numpy()
- mo_location = locs[:,3,:].int().cpu().numpy()
-
-
- f1_0 = self.conv1(img)
- f1_1 = self.res1(f1_0)
- f2_0 = self.conv2(f1_1)
- f2_1 = self.res2(f2_0)
-
- f3_0 = self.conv3(f2_1)
- f3_1 = self.res3(f3_0)
- f4_0 = self.conv4(f3_1)
- f4_1 = self.res4(f4_0)
-
- f5_0 = self.conv5(f4_1)
- f5_1 = self.res5(f5_0)
- f6_0 = self.conv6(f5_1)
- f6_1 = self.res6(f6_0)
-
-
- ####ROI Align
- le_part_256 = roi_align_self(f2_1.clone(), le_location//2, self.part_sizes[0]//2)
- re_part_256 = roi_align_self(f2_1.clone(), re_location//2, self.part_sizes[1]//2)
- mo_part_256 = roi_align_self(f2_1.clone(), mo_location//2, self.part_sizes[3]//2)
-
- le_part_128 = roi_align_self(f4_1.clone(), le_location//4, self.part_sizes[0]//4)
- re_part_128 = roi_align_self(f4_1.clone(), re_location//4, self.part_sizes[1]//4)
- mo_part_128 = roi_align_self(f4_1.clone(), mo_location//4, self.part_sizes[3]//4)
-
- le_part_64 = roi_align_self(f6_1.clone(), le_location//8, self.part_sizes[0]//8)
- re_part_64 = roi_align_self(f6_1.clone(), re_location//8, self.part_sizes[1]//8)
- mo_part_64 = roi_align_self(f6_1.clone(), mo_location//8, self.part_sizes[3]//8)
-
-
- le_256_q = self.LE_256_Q(le_part_256)
- re_256_q = self.RE_256_Q(re_part_256)
- mo_256_q = self.MO_256_Q(mo_part_256)
-
- le_128_q = self.LE_128_Q(le_part_128)
- re_128_q = self.RE_128_Q(re_part_128)
- mo_128_q = self.MO_128_Q(mo_part_128)
-
- le_64_q = self.LE_64_Q(le_part_64)
- re_64_q = self.RE_64_Q(re_part_64)
- mo_64_q = self.MO_64_Q(mo_part_64)
-
- return {'f256': f2_1, 'f128': f4_1, 'f64': f6_1,\
- 'le256': le_part_256, 're256': re_part_256, 'mo256': mo_part_256, \
- 'le128': le_part_128, 're128': re_part_128, 'mo128': mo_part_128, \
- 'le64': le_part_64, 're64': re_part_64, 'mo64': mo_part_64, \
- 'le_256_q': le_256_q, 're_256_q': re_256_q, 'mo_256_q': mo_256_q,\
- 'le_128_q': le_128_q, 're_128_q': re_128_q, 'mo_128_q': mo_128_q,\
- 'le_64_q': le_64_q, 're_64_q': re_64_q, 'mo_64_q': mo_64_q}
-
-
-class DMDNet(nn.Module):
- def __init__(self, ngf = 64, banks_num = 128):
- super().__init__()
- self.part_sizes = np.array([80,80,50,110]) # size for 512
- self.feature_sizes = np.array([256,128,64]) # size for 512
-
- self.banks_num = banks_num
- self.key_scale = 4
-
- self.E_lq = FeatureExtractor(key_scale = self.key_scale)
- self.E_hq = FeatureExtractor(key_scale = self.key_scale)
-
- self.LE_256_KV = KeyValue(ngf, ngf // self.key_scale, ngf)
- self.RE_256_KV = KeyValue(ngf, ngf // self.key_scale, ngf)
- self.MO_256_KV = KeyValue(ngf, ngf // self.key_scale, ngf)
-
- self.LE_128_KV = KeyValue(ngf * 2 , ngf * 2 // self.key_scale, ngf * 2)
- self.RE_128_KV = KeyValue(ngf * 2 , ngf * 2 // self.key_scale, ngf * 2)
- self.MO_128_KV = KeyValue(ngf * 2 , ngf * 2 // self.key_scale, ngf * 2)
-
- self.LE_64_KV = KeyValue(ngf * 4 , ngf * 4 // self.key_scale, ngf * 4)
- self.RE_64_KV = KeyValue(ngf * 4 , ngf * 4 // self.key_scale, ngf * 4)
- self.MO_64_KV = KeyValue(ngf * 4 , ngf * 4 // self.key_scale, ngf * 4)
-
-
- self.LE_256_Attention = AttentionBlock(64)
- self.RE_256_Attention = AttentionBlock(64)
- self.MO_256_Attention = AttentionBlock(64)
-
- self.LE_128_Attention = AttentionBlock(128)
- self.RE_128_Attention = AttentionBlock(128)
- self.MO_128_Attention = AttentionBlock(128)
-
- self.LE_64_Attention = AttentionBlock(256)
- self.RE_64_Attention = AttentionBlock(256)
- self.MO_64_Attention = AttentionBlock(256)
-
- self.LE_256_Mask = MaskAttention(64)
- self.RE_256_Mask = MaskAttention(64)
- self.MO_256_Mask = MaskAttention(64)
-
- self.LE_128_Mask = MaskAttention(128)
- self.RE_128_Mask = MaskAttention(128)
- self.MO_128_Mask = MaskAttention(128)
-
- self.LE_64_Mask = MaskAttention(256)
- self.RE_64_Mask = MaskAttention(256)
- self.MO_64_Mask = MaskAttention(256)
-
- self.MSDilate = MSDilateBlock(ngf*4, dilation = [4,3,2,1])
-
- self.up1 = StyledUpBlock(ngf*4, ngf*2, noise_inject=False) #
- self.up2 = StyledUpBlock(ngf*2, ngf, noise_inject=False) #
- self.up3 = StyledUpBlock(ngf, ngf, noise_inject=False) #
- self.up4 = nn.Sequential(
- SpectralNorm(nn.Conv2d(ngf, ngf, 3, 1, 1)),
- nn.LeakyReLU(0.2),
- UpResBlock(ngf),
- UpResBlock(ngf),
- SpectralNorm(nn.Conv2d(ngf, 3, kernel_size=3, stride=1, padding=1)),
- nn.Tanh()
- )
-
- # define generic memory, revise register_buffer to register_parameter for backward update
- self.register_buffer('le_256_mem_key', torch.randn(128,16,40,40))
- self.register_buffer('re_256_mem_key', torch.randn(128,16,40,40))
- self.register_buffer('mo_256_mem_key', torch.randn(128,16,55,55))
- self.register_buffer('le_256_mem_value', torch.randn(128,64,40,40))
- self.register_buffer('re_256_mem_value', torch.randn(128,64,40,40))
- self.register_buffer('mo_256_mem_value', torch.randn(128,64,55,55))
-
-
- self.register_buffer('le_128_mem_key', torch.randn(128,32,20,20))
- self.register_buffer('re_128_mem_key', torch.randn(128,32,20,20))
- self.register_buffer('mo_128_mem_key', torch.randn(128,32,27,27))
- self.register_buffer('le_128_mem_value', torch.randn(128,128,20,20))
- self.register_buffer('re_128_mem_value', torch.randn(128,128,20,20))
- self.register_buffer('mo_128_mem_value', torch.randn(128,128,27,27))
-
- self.register_buffer('le_64_mem_key', torch.randn(128,64,10,10))
- self.register_buffer('re_64_mem_key', torch.randn(128,64,10,10))
- self.register_buffer('mo_64_mem_key', torch.randn(128,64,13,13))
- self.register_buffer('le_64_mem_value', torch.randn(128,256,10,10))
- self.register_buffer('re_64_mem_value', torch.randn(128,256,10,10))
- self.register_buffer('mo_64_mem_value', torch.randn(128,256,13,13))
-
-
- def readMem(self, k, v, q):
- sim = F.conv2d(q, k)
- score = F.softmax(sim/sqrt(sim.size(1)), dim=1) #B * S * 1 * 1 6*128
- sb,sn,sw,sh = score.size()
- s_m = score.view(sb, -1).unsqueeze(1)#2*1*M
- vb,vn,vw,vh = v.size()
- v_in = v.view(vb, -1).repeat(sb,1,1)#2*M*(c*w*h)
- mem_out = torch.bmm(s_m, v_in).squeeze(1).view(sb, vn, vw,vh)
- max_inds = torch.argmax(score, dim=1).squeeze()
- return mem_out, max_inds
-
-
- def memorize(self, img, locs):
- fs = self.E_hq(img, locs)
- LE256_key, LE256_value = self.LE_256_KV(fs['le256'])
- RE256_key, RE256_value = self.RE_256_KV(fs['re256'])
- MO256_key, MO256_value = self.MO_256_KV(fs['mo256'])
-
- LE128_key, LE128_value = self.LE_128_KV(fs['le128'])
- RE128_key, RE128_value = self.RE_128_KV(fs['re128'])
- MO128_key, MO128_value = self.MO_128_KV(fs['mo128'])
-
- LE64_key, LE64_value = self.LE_64_KV(fs['le64'])
- RE64_key, RE64_value = self.RE_64_KV(fs['re64'])
- MO64_key, MO64_value = self.MO_64_KV(fs['mo64'])
-
- Mem256 = {'LE256Key': LE256_key, 'LE256Value': LE256_value, 'RE256Key': RE256_key, 'RE256Value': RE256_value,'MO256Key': MO256_key, 'MO256Value': MO256_value}
- Mem128 = {'LE128Key': LE128_key, 'LE128Value': LE128_value, 'RE128Key': RE128_key, 'RE128Value': RE128_value,'MO128Key': MO128_key, 'MO128Value': MO128_value}
- Mem64 = {'LE64Key': LE64_key, 'LE64Value': LE64_value, 'RE64Key': RE64_key, 'RE64Value': RE64_value,'MO64Key': MO64_key, 'MO64Value': MO64_value}
-
- FS256 = {'LE256F':fs['le256'], 'RE256F':fs['re256'], 'MO256F':fs['mo256']}
- FS128 = {'LE128F':fs['le128'], 'RE128F':fs['re128'], 'MO128F':fs['mo128']}
- FS64 = {'LE64F':fs['le64'], 'RE64F':fs['re64'], 'MO64F':fs['mo64']}
-
- return Mem256, Mem128, Mem64
-
- def enhancer(self, fs_in, sp_256=None, sp_128=None, sp_64=None):
- le_256_q = fs_in['le_256_q']
- re_256_q = fs_in['re_256_q']
- mo_256_q = fs_in['mo_256_q']
-
- le_128_q = fs_in['le_128_q']
- re_128_q = fs_in['re_128_q']
- mo_128_q = fs_in['mo_128_q']
-
- le_64_q = fs_in['le_64_q']
- re_64_q = fs_in['re_64_q']
- mo_64_q = fs_in['mo_64_q']
-
-
- ####for 256
- le_256_mem_g, le_256_inds = self.readMem(self.le_256_mem_key, self.le_256_mem_value, le_256_q)
- re_256_mem_g, re_256_inds = self.readMem(self.re_256_mem_key, self.re_256_mem_value, re_256_q)
- mo_256_mem_g, mo_256_inds = self.readMem(self.mo_256_mem_key, self.mo_256_mem_value, mo_256_q)
-
- le_128_mem_g, le_128_inds = self.readMem(self.le_128_mem_key, self.le_128_mem_value, le_128_q)
- re_128_mem_g, re_128_inds = self.readMem(self.re_128_mem_key, self.re_128_mem_value, re_128_q)
- mo_128_mem_g, mo_128_inds = self.readMem(self.mo_128_mem_key, self.mo_128_mem_value, mo_128_q)
-
- le_64_mem_g, le_64_inds = self.readMem(self.le_64_mem_key, self.le_64_mem_value, le_64_q)
- re_64_mem_g, re_64_inds = self.readMem(self.re_64_mem_key, self.re_64_mem_value, re_64_q)
- mo_64_mem_g, mo_64_inds = self.readMem(self.mo_64_mem_key, self.mo_64_mem_value, mo_64_q)
-
- if sp_256 is not None and sp_128 is not None and sp_64 is not None:
- le_256_mem_s, _ = self.readMem(sp_256['LE256Key'], sp_256['LE256Value'], le_256_q)
- re_256_mem_s, _ = self.readMem(sp_256['RE256Key'], sp_256['RE256Value'], re_256_q)
- mo_256_mem_s, _ = self.readMem(sp_256['MO256Key'], sp_256['MO256Value'], mo_256_q)
- le_256_mask = self.LE_256_Mask(fs_in['le256'],le_256_mem_s,le_256_mem_g)
- le_256_mem = le_256_mask*le_256_mem_s + (1-le_256_mask)*le_256_mem_g
- re_256_mask = self.RE_256_Mask(fs_in['re256'],re_256_mem_s,re_256_mem_g)
- re_256_mem = re_256_mask*re_256_mem_s + (1-re_256_mask)*re_256_mem_g
- mo_256_mask = self.MO_256_Mask(fs_in['mo256'],mo_256_mem_s,mo_256_mem_g)
- mo_256_mem = mo_256_mask*mo_256_mem_s + (1-mo_256_mask)*mo_256_mem_g
-
- le_128_mem_s, _ = self.readMem(sp_128['LE128Key'], sp_128['LE128Value'], le_128_q)
- re_128_mem_s, _ = self.readMem(sp_128['RE128Key'], sp_128['RE128Value'], re_128_q)
- mo_128_mem_s, _ = self.readMem(sp_128['MO128Key'], sp_128['MO128Value'], mo_128_q)
- le_128_mask = self.LE_128_Mask(fs_in['le128'],le_128_mem_s,le_128_mem_g)
- le_128_mem = le_128_mask*le_128_mem_s + (1-le_128_mask)*le_128_mem_g
- re_128_mask = self.RE_128_Mask(fs_in['re128'],re_128_mem_s,re_128_mem_g)
- re_128_mem = re_128_mask*re_128_mem_s + (1-re_128_mask)*re_128_mem_g
- mo_128_mask = self.MO_128_Mask(fs_in['mo128'],mo_128_mem_s,mo_128_mem_g)
- mo_128_mem = mo_128_mask*mo_128_mem_s + (1-mo_128_mask)*mo_128_mem_g
-
- le_64_mem_s, _ = self.readMem(sp_64['LE64Key'], sp_64['LE64Value'], le_64_q)
- re_64_mem_s, _ = self.readMem(sp_64['RE64Key'], sp_64['RE64Value'], re_64_q)
- mo_64_mem_s, _ = self.readMem(sp_64['MO64Key'], sp_64['MO64Value'], mo_64_q)
- le_64_mask = self.LE_64_Mask(fs_in['le64'],le_64_mem_s,le_64_mem_g)
- le_64_mem = le_64_mask*le_64_mem_s + (1-le_64_mask)*le_64_mem_g
- re_64_mask = self.RE_64_Mask(fs_in['re64'],re_64_mem_s,re_64_mem_g)
- re_64_mem = re_64_mask*re_64_mem_s + (1-re_64_mask)*re_64_mem_g
- mo_64_mask = self.MO_64_Mask(fs_in['mo64'],mo_64_mem_s,mo_64_mem_g)
- mo_64_mem = mo_64_mask*mo_64_mem_s + (1-mo_64_mask)*mo_64_mem_g
- else:
- le_256_mem = le_256_mem_g
- re_256_mem = re_256_mem_g
- mo_256_mem = mo_256_mem_g
- le_128_mem = le_128_mem_g
- re_128_mem = re_128_mem_g
- mo_128_mem = mo_128_mem_g
- le_64_mem = le_64_mem_g
- re_64_mem = re_64_mem_g
- mo_64_mem = mo_64_mem_g
-
- le_256_mem_norm = adaptive_instance_normalization_4D(le_256_mem, fs_in['le256'])
- re_256_mem_norm = adaptive_instance_normalization_4D(re_256_mem, fs_in['re256'])
- mo_256_mem_norm = adaptive_instance_normalization_4D(mo_256_mem, fs_in['mo256'])
-
- ####for 128
- le_128_mem_norm = adaptive_instance_normalization_4D(le_128_mem, fs_in['le128'])
- re_128_mem_norm = adaptive_instance_normalization_4D(re_128_mem, fs_in['re128'])
- mo_128_mem_norm = adaptive_instance_normalization_4D(mo_128_mem, fs_in['mo128'])
-
- ####for 64
- le_64_mem_norm = adaptive_instance_normalization_4D(le_64_mem, fs_in['le64'])
- re_64_mem_norm = adaptive_instance_normalization_4D(re_64_mem, fs_in['re64'])
- mo_64_mem_norm = adaptive_instance_normalization_4D(mo_64_mem, fs_in['mo64'])
-
-
- EnMem256 = {'LE256Norm': le_256_mem_norm, 'RE256Norm': re_256_mem_norm, 'MO256Norm': mo_256_mem_norm}
- EnMem128 = {'LE128Norm': le_128_mem_norm, 'RE128Norm': re_128_mem_norm, 'MO128Norm': mo_128_mem_norm}
- EnMem64 = {'LE64Norm': le_64_mem_norm, 'RE64Norm': re_64_mem_norm, 'MO64Norm': mo_64_mem_norm}
- Ind256 = {'LE': le_256_inds, 'RE': re_256_inds, 'MO': mo_256_inds}
- Ind128 = {'LE': le_128_inds, 'RE': re_128_inds, 'MO': mo_128_inds}
- Ind64 = {'LE': le_64_inds, 'RE': re_64_inds, 'MO': mo_64_inds}
- return EnMem256, EnMem128, EnMem64, Ind256, Ind128, Ind64
-
- def reconstruct(self, fs_in, locs, memstar):
- le_256_mem_norm, re_256_mem_norm, mo_256_mem_norm = memstar[0]['LE256Norm'], memstar[0]['RE256Norm'], memstar[0]['MO256Norm']
- le_128_mem_norm, re_128_mem_norm, mo_128_mem_norm = memstar[1]['LE128Norm'], memstar[1]['RE128Norm'], memstar[1]['MO128Norm']
- le_64_mem_norm, re_64_mem_norm, mo_64_mem_norm = memstar[2]['LE64Norm'], memstar[2]['RE64Norm'], memstar[2]['MO64Norm']
-
- le_256_final = self.LE_256_Attention(le_256_mem_norm - fs_in['le256']) * le_256_mem_norm + fs_in['le256']
- re_256_final = self.RE_256_Attention(re_256_mem_norm - fs_in['re256']) * re_256_mem_norm + fs_in['re256']
- mo_256_final = self.MO_256_Attention(mo_256_mem_norm - fs_in['mo256']) * mo_256_mem_norm + fs_in['mo256']
-
- le_128_final = self.LE_128_Attention(le_128_mem_norm - fs_in['le128']) * le_128_mem_norm + fs_in['le128']
- re_128_final = self.RE_128_Attention(re_128_mem_norm - fs_in['re128']) * re_128_mem_norm + fs_in['re128']
- mo_128_final = self.MO_128_Attention(mo_128_mem_norm - fs_in['mo128']) * mo_128_mem_norm + fs_in['mo128']
-
- le_64_final = self.LE_64_Attention(le_64_mem_norm - fs_in['le64']) * le_64_mem_norm + fs_in['le64']
- re_64_final = self.RE_64_Attention(re_64_mem_norm - fs_in['re64']) * re_64_mem_norm + fs_in['re64']
- mo_64_final = self.MO_64_Attention(mo_64_mem_norm - fs_in['mo64']) * mo_64_mem_norm + fs_in['mo64']
-
-
- le_location = locs[:,0,:]
- re_location = locs[:,1,:]
- mo_location = locs[:,3,:]
-
- # Somehow with latest Torch it doesn't like numpy wrappers anymore
-
- # le_location = le_location.cpu().int().numpy()
- # re_location = re_location.cpu().int().numpy()
- # mo_location = mo_location.cpu().int().numpy()
- le_location = le_location.cpu().int()
- re_location = re_location.cpu().int()
- mo_location = mo_location.cpu().int()
-
- up_in_256 = fs_in['f256'].clone()# * 0
- up_in_128 = fs_in['f128'].clone()# * 0
- up_in_64 = fs_in['f64'].clone()# * 0
-
- for i in range(fs_in['f256'].size(0)):
- up_in_256[i:i+1,:,le_location[i,1]//2:le_location[i,3]//2,le_location[i,0]//2:le_location[i,2]//2] = F.interpolate(le_256_final[i:i+1,:,:,:].clone(), (le_location[i,3]//2-le_location[i,1]//2,le_location[i,2]//2-le_location[i,0]//2),mode='bilinear',align_corners=False)
- up_in_256[i:i+1,:,re_location[i,1]//2:re_location[i,3]//2,re_location[i,0]//2:re_location[i,2]//2] = F.interpolate(re_256_final[i:i+1,:,:,:].clone(), (re_location[i,3]//2-re_location[i,1]//2,re_location[i,2]//2-re_location[i,0]//2),mode='bilinear',align_corners=False)
- up_in_256[i:i+1,:,mo_location[i,1]//2:mo_location[i,3]//2,mo_location[i,0]//2:mo_location[i,2]//2] = F.interpolate(mo_256_final[i:i+1,:,:,:].clone(), (mo_location[i,3]//2-mo_location[i,1]//2,mo_location[i,2]//2-mo_location[i,0]//2),mode='bilinear',align_corners=False)
-
- up_in_128[i:i+1,:,le_location[i,1]//4:le_location[i,3]//4,le_location[i,0]//4:le_location[i,2]//4] = F.interpolate(le_128_final[i:i+1,:,:,:].clone(), (le_location[i,3]//4-le_location[i,1]//4,le_location[i,2]//4-le_location[i,0]//4),mode='bilinear',align_corners=False)
- up_in_128[i:i+1,:,re_location[i,1]//4:re_location[i,3]//4,re_location[i,0]//4:re_location[i,2]//4] = F.interpolate(re_128_final[i:i+1,:,:,:].clone(), (re_location[i,3]//4-re_location[i,1]//4,re_location[i,2]//4-re_location[i,0]//4),mode='bilinear',align_corners=False)
- up_in_128[i:i+1,:,mo_location[i,1]//4:mo_location[i,3]//4,mo_location[i,0]//4:mo_location[i,2]//4] = F.interpolate(mo_128_final[i:i+1,:,:,:].clone(), (mo_location[i,3]//4-mo_location[i,1]//4,mo_location[i,2]//4-mo_location[i,0]//4),mode='bilinear',align_corners=False)
-
- up_in_64[i:i+1,:,le_location[i,1]//8:le_location[i,3]//8,le_location[i,0]//8:le_location[i,2]//8] = F.interpolate(le_64_final[i:i+1,:,:,:].clone(), (le_location[i,3]//8-le_location[i,1]//8,le_location[i,2]//8-le_location[i,0]//8),mode='bilinear',align_corners=False)
- up_in_64[i:i+1,:,re_location[i,1]//8:re_location[i,3]//8,re_location[i,0]//8:re_location[i,2]//8] = F.interpolate(re_64_final[i:i+1,:,:,:].clone(), (re_location[i,3]//8-re_location[i,1]//8,re_location[i,2]//8-re_location[i,0]//8),mode='bilinear',align_corners=False)
- up_in_64[i:i+1,:,mo_location[i,1]//8:mo_location[i,3]//8,mo_location[i,0]//8:mo_location[i,2]//8] = F.interpolate(mo_64_final[i:i+1,:,:,:].clone(), (mo_location[i,3]//8-mo_location[i,1]//8,mo_location[i,2]//8-mo_location[i,0]//8),mode='bilinear',align_corners=False)
-
- ms_in_64 = self.MSDilate(fs_in['f64'].clone())
- fea_up1 = self.up1(ms_in_64, up_in_64)
- fea_up2 = self.up2(fea_up1, up_in_128) #
- fea_up3 = self.up3(fea_up2, up_in_256) #
- output = self.up4(fea_up3) #
- return output
-
- def generate_specific_dictionary(self, sp_imgs=None, sp_locs=None):
- return self.memorize(sp_imgs, sp_locs)
-
- def forward(self, lq=None, loc=None, sp_256 = None, sp_128 = None, sp_64 = None):
- try:
- fs_in = self.E_lq(lq, loc) # low quality images
- except Exception as e:
- print(e)
-
- GeMemNorm256, GeMemNorm128, GeMemNorm64, Ind256, Ind128, Ind64 = self.enhancer(fs_in)
- GeOut = self.reconstruct(fs_in, loc, memstar = [GeMemNorm256, GeMemNorm128, GeMemNorm64])
- if sp_256 is not None and sp_128 is not None and sp_64 is not None:
- GSMemNorm256, GSMemNorm128, GSMemNorm64, _, _, _ = self.enhancer(fs_in, sp_256, sp_128, sp_64)
- GSOut = self.reconstruct(fs_in, loc, memstar = [GSMemNorm256, GSMemNorm128, GSMemNorm64])
- else:
- GSOut = None
- return GeOut, GSOut
-
-class UpResBlock(nn.Module):
- def __init__(self, dim, conv_layer = nn.Conv2d, norm_layer = nn.BatchNorm2d):
- super(UpResBlock, self).__init__()
- self.Model = nn.Sequential(
- SpectralNorm(conv_layer(dim, dim, 3, 1, 1)),
- nn.LeakyReLU(0.2),
- SpectralNorm(conv_layer(dim, dim, 3, 1, 1)),
- )
- def forward(self, x):
- out = x + self.Model(x)
- return out
diff --git a/roop-unleashed-main/roop/processors/Enhance_GFPGAN.py b/roop-unleashed-main/roop/processors/Enhance_GFPGAN.py
deleted file mode 100644
index 0ce3333706fff733e50c3a855ee358d536d69a3e..0000000000000000000000000000000000000000
--- a/roop-unleashed-main/roop/processors/Enhance_GFPGAN.py
+++ /dev/null
@@ -1,73 +0,0 @@
-from typing import Any, List, Callable
-import cv2
-import numpy as np
-import onnxruntime
-import roop.globals
-
-from roop.typing import Face, Frame, FaceSet
-from roop.utilities import resolve_relative_path
-
-class Enhance_GFPGAN():
- plugin_options:dict = None
-
- model_gfpgan = None
- name = None
- devicename = None
-
- processorname = 'gfpgan'
- type = 'enhance'
-
-
- def Initialize(self, plugin_options:dict):
- if self.plugin_options is not None:
- if self.plugin_options["devicename"] != plugin_options["devicename"]:
- self.Release()
-
- self.plugin_options = plugin_options
- if self.model_gfpgan is None:
- model_path = resolve_relative_path('../models/GFPGANv1.4.onnx')
- self.model_gfpgan = onnxruntime.InferenceSession(model_path, None, providers=roop.globals.execution_providers)
- # replace Mac mps with cpu for the moment
- self.devicename = self.plugin_options["devicename"].replace('mps', 'cpu')
-
- self.name = self.model_gfpgan.get_inputs()[0].name
-
- def Run(self, source_faceset: FaceSet, target_face: Face, temp_frame: Frame) -> Frame:
- # preprocess
- input_size = temp_frame.shape[1]
- temp_frame = cv2.resize(temp_frame, (512, 512), cv2.INTER_CUBIC)
-
- temp_frame = cv2.cvtColor(temp_frame, cv2.COLOR_BGR2RGB)
- temp_frame = temp_frame.astype('float32') / 255.0
- temp_frame = (temp_frame - 0.5) / 0.5
- temp_frame = np.expand_dims(temp_frame, axis=0).transpose(0, 3, 1, 2)
-
- io_binding = self.model_gfpgan.io_binding()
- io_binding.bind_cpu_input("input", temp_frame)
- io_binding.bind_output("1288", self.devicename)
- self.model_gfpgan.run_with_iobinding(io_binding)
- ort_outs = io_binding.copy_outputs_to_cpu()
- result = ort_outs[0][0]
-
- # post-process
- result = np.clip(result, -1, 1)
- result = (result + 1) / 2
- result = result.transpose(1, 2, 0) * 255.0
- result = cv2.cvtColor(result, cv2.COLOR_RGB2BGR)
- scale_factor = int(result.shape[1] / input_size)
- return result.astype(np.uint8), scale_factor
-
-
- def Release(self):
- self.model_gfpgan = None
-
-
-
-
-
-
-
-
-
-
-
diff --git a/roop-unleashed-main/roop/processors/Enhance_GPEN.py b/roop-unleashed-main/roop/processors/Enhance_GPEN.py
deleted file mode 100644
index 9821e70534e3bddcd2a932548fd7b9250d85a41a..0000000000000000000000000000000000000000
--- a/roop-unleashed-main/roop/processors/Enhance_GPEN.py
+++ /dev/null
@@ -1,63 +0,0 @@
-from typing import Any, List, Callable
-import cv2
-import numpy as np
-import onnxruntime
-import roop.globals
-
-from roop.typing import Face, Frame, FaceSet
-from roop.utilities import resolve_relative_path
-
-
-class Enhance_GPEN():
- plugin_options:dict = None
-
- model_gpen = None
- name = None
- devicename = None
-
- processorname = 'gpen'
- type = 'enhance'
-
-
- def Initialize(self, plugin_options:dict):
- if self.plugin_options is not None:
- if self.plugin_options["devicename"] != plugin_options["devicename"]:
- self.Release()
-
- self.plugin_options = plugin_options
- if self.model_gpen is None:
- model_path = resolve_relative_path('../models/GPEN-BFR-512.onnx')
- self.model_gpen = onnxruntime.InferenceSession(model_path, None, providers=roop.globals.execution_providers)
- # replace Mac mps with cpu for the moment
- self.devicename = self.plugin_options["devicename"].replace('mps', 'cpu')
-
- self.name = self.model_gpen.get_inputs()[0].name
-
- def Run(self, source_faceset: FaceSet, target_face: Face, temp_frame: Frame) -> Frame:
- # preprocess
- input_size = temp_frame.shape[1]
- temp_frame = cv2.resize(temp_frame, (512, 512), cv2.INTER_CUBIC)
-
- temp_frame = cv2.cvtColor(temp_frame, cv2.COLOR_BGR2RGB)
- temp_frame = temp_frame.astype('float32') / 255.0
- temp_frame = (temp_frame - 0.5) / 0.5
- temp_frame = np.expand_dims(temp_frame, axis=0).transpose(0, 3, 1, 2)
-
- io_binding = self.model_gpen.io_binding()
- io_binding.bind_cpu_input("input", temp_frame)
- io_binding.bind_output("output", self.devicename)
- self.model_gpen.run_with_iobinding(io_binding)
- ort_outs = io_binding.copy_outputs_to_cpu()
- result = ort_outs[0][0]
-
- # post-process
- result = np.clip(result, -1, 1)
- result = (result + 1) / 2
- result = result.transpose(1, 2, 0) * 255.0
- result = cv2.cvtColor(result, cv2.COLOR_RGB2BGR)
- scale_factor = int(result.shape[1] / input_size)
- return result.astype(np.uint8), scale_factor
-
-
- def Release(self):
- self.model_gpen = None
diff --git a/roop-unleashed-main/roop/processors/Enhance_RestoreFormerPPlus.py b/roop-unleashed-main/roop/processors/Enhance_RestoreFormerPPlus.py
deleted file mode 100644
index f8d71034573cf1e63be77a4b9acafc854f189536..0000000000000000000000000000000000000000
--- a/roop-unleashed-main/roop/processors/Enhance_RestoreFormerPPlus.py
+++ /dev/null
@@ -1,64 +0,0 @@
-from typing import Any, List, Callable
-import cv2
-import numpy as np
-import onnxruntime
-import roop.globals
-
-from roop.typing import Face, Frame, FaceSet
-from roop.utilities import resolve_relative_path
-
-class Enhance_RestoreFormerPPlus():
- plugin_options:dict = None
- model_restoreformerpplus = None
- devicename = None
- name = None
-
- processorname = 'restoreformer++'
- type = 'enhance'
-
-
- def Initialize(self, plugin_options:dict):
- if self.plugin_options is not None:
- if self.plugin_options["devicename"] != plugin_options["devicename"]:
- self.Release()
-
- self.plugin_options = plugin_options
- if self.model_restoreformerpplus is None:
- # replace Mac mps with cpu for the moment
- self.devicename = self.plugin_options["devicename"].replace('mps', 'cpu')
- model_path = resolve_relative_path('../models/restoreformer_plus_plus.onnx')
- self.model_restoreformerpplus = onnxruntime.InferenceSession(model_path, None, providers=roop.globals.execution_providers)
- self.model_inputs = self.model_restoreformerpplus.get_inputs()
- model_outputs = self.model_restoreformerpplus.get_outputs()
- self.io_binding = self.model_restoreformerpplus.io_binding()
- self.io_binding.bind_output(model_outputs[0].name, self.devicename)
-
- def Run(self, source_faceset: FaceSet, target_face: Face, temp_frame: Frame) -> Frame:
- # preprocess
- input_size = temp_frame.shape[1]
- temp_frame = cv2.resize(temp_frame, (512, 512), cv2.INTER_CUBIC)
- temp_frame = cv2.cvtColor(temp_frame, cv2.COLOR_BGR2RGB)
- temp_frame = temp_frame.astype('float32') / 255.0
- temp_frame = (temp_frame - 0.5) / 0.5
- temp_frame = np.expand_dims(temp_frame, axis=0).transpose(0, 3, 1, 2)
-
- self.io_binding.bind_cpu_input(self.model_inputs[0].name, temp_frame) # .astype(np.float32)
- self.model_restoreformerpplus.run_with_iobinding(self.io_binding)
- ort_outs = self.io_binding.copy_outputs_to_cpu()
- result = ort_outs[0][0]
- del ort_outs
-
- result = np.clip(result, -1, 1)
- result = (result + 1) / 2
- result = result.transpose(1, 2, 0) * 255.0
- result = cv2.cvtColor(result, cv2.COLOR_RGB2BGR)
- scale_factor = int(result.shape[1] / input_size)
- return result.astype(np.uint8), scale_factor
-
-
- def Release(self):
- del self.model_restoreformerpplus
- self.model_restoreformerpplus = None
- del self.io_binding
- self.io_binding = None
-
diff --git a/roop-unleashed-main/roop/processors/FaceSwapInsightFace.py b/roop-unleashed-main/roop/processors/FaceSwapInsightFace.py
deleted file mode 100644
index 64934ab07e1c3596fe856919eee68c22bf00596e..0000000000000000000000000000000000000000
--- a/roop-unleashed-main/roop/processors/FaceSwapInsightFace.py
+++ /dev/null
@@ -1,60 +0,0 @@
-import roop.globals
-import numpy as np
-import onnx
-import onnxruntime
-
-from roop.typing import Face, Frame
-from roop.utilities import resolve_relative_path
-
-
-
-class FaceSwapInsightFace():
- plugin_options:dict = None
- model_swap_insightface = None
-
- processorname = 'faceswap'
- type = 'swap'
-
-
- def Initialize(self, plugin_options:dict):
- if self.plugin_options is not None:
- if self.plugin_options["devicename"] != plugin_options["devicename"] or self.plugin_options["modelname"] != plugin_options["modelname"]:
- self.Release()
-
- self.plugin_options = plugin_options
- if self.model_swap_insightface is None:
- model_path = resolve_relative_path('../models/' + self.plugin_options["modelname"])
- graph = onnx.load(model_path).graph
- self.emap = onnx.numpy_helper.to_array(graph.initializer[-1])
- self.devicename = self.plugin_options["devicename"].replace('mps', 'cpu')
- self.input_mean = 0.0
- self.input_std = 255.0
- #cuda_options = {"arena_extend_strategy": "kSameAsRequested", 'cudnn_conv_algo_search': 'DEFAULT'}
- sess_options = onnxruntime.SessionOptions()
- sess_options.enable_cpu_mem_arena = False
- self.model_swap_insightface = onnxruntime.InferenceSession(model_path, sess_options, providers=roop.globals.execution_providers)
-
-
-
- def Run(self, source_face: Face, target_face: Face, temp_frame: Frame) -> Frame:
- latent = source_face.normed_embedding.reshape((1,-1))
- latent = np.dot(latent, self.emap)
- latent /= np.linalg.norm(latent)
- io_binding = self.model_swap_insightface.io_binding()
- io_binding.bind_cpu_input("target", temp_frame)
- io_binding.bind_cpu_input("source", latent)
- io_binding.bind_output("output", self.devicename)
- self.model_swap_insightface.run_with_iobinding(io_binding)
- ort_outs = io_binding.copy_outputs_to_cpu()[0]
- return ort_outs[0]
-
-
- def Release(self):
- del self.model_swap_insightface
- self.model_swap_insightface = None
-
-
-
-
-
-
diff --git a/roop-unleashed-main/roop/processors/Frame_Colorizer.py b/roop-unleashed-main/roop/processors/Frame_Colorizer.py
deleted file mode 100644
index 372f81870b6c47f543707e8eefff3a474532b493..0000000000000000000000000000000000000000
--- a/roop-unleashed-main/roop/processors/Frame_Colorizer.py
+++ /dev/null
@@ -1,70 +0,0 @@
-import cv2
-import numpy as np
-import onnxruntime
-import roop.globals
-
-from roop.utilities import resolve_relative_path
-from roop.typing import Frame
-
-class Frame_Colorizer():
- plugin_options:dict = None
- model_colorizer = None
- devicename = None
- prev_type = None
-
- processorname = 'deoldify'
- type = 'frame_colorizer'
-
-
- def Initialize(self, plugin_options:dict):
- if self.plugin_options is not None:
- if self.plugin_options["devicename"] != plugin_options["devicename"]:
- self.Release()
-
- self.plugin_options = plugin_options
- if self.prev_type is not None and self.prev_type != self.plugin_options["subtype"]:
- self.Release()
- self.prev_type = self.plugin_options["subtype"]
- if self.model_colorizer is None:
- # replace Mac mps with cpu for the moment
- self.devicename = self.plugin_options["devicename"].replace('mps', 'cpu')
- if self.prev_type == "deoldify_artistic":
- model_path = resolve_relative_path('../models/Frame/deoldify_artistic.onnx')
- elif self.prev_type == "deoldify_stable":
- model_path = resolve_relative_path('../models/Frame/deoldify_stable.onnx')
-
- onnxruntime.set_default_logger_severity(3)
- self.model_colorizer = onnxruntime.InferenceSession(model_path, None, providers=roop.globals.execution_providers)
- self.model_inputs = self.model_colorizer.get_inputs()
- model_outputs = self.model_colorizer.get_outputs()
- self.io_binding = self.model_colorizer.io_binding()
- self.io_binding.bind_output(model_outputs[0].name, self.devicename)
-
- def Run(self, input_frame: Frame) -> Frame:
- temp_frame = cv2.cvtColor(input_frame, cv2.COLOR_BGR2GRAY)
- temp_frame = cv2.cvtColor(temp_frame, cv2.COLOR_GRAY2RGB)
- temp_frame = cv2.resize(temp_frame, (256, 256))
- temp_frame = temp_frame.transpose((2, 0, 1))
- temp_frame = np.expand_dims(temp_frame, axis=0).astype(np.float32)
- self.io_binding.bind_cpu_input(self.model_inputs[0].name, temp_frame)
- self.model_colorizer.run_with_iobinding(self.io_binding)
- ort_outs = self.io_binding.copy_outputs_to_cpu()
- result = ort_outs[0][0]
- del ort_outs
- colorized_frame = result.transpose(1, 2, 0)
- colorized_frame = cv2.resize(colorized_frame, (input_frame.shape[1], input_frame.shape[0]))
- temp_blue_channel, _, _ = cv2.split(input_frame)
- colorized_frame = cv2.cvtColor(colorized_frame, cv2.COLOR_BGR2RGB).astype(np.uint8)
- colorized_frame = cv2.cvtColor(colorized_frame, cv2.COLOR_BGR2LAB)
- _, color_green_channel, color_red_channel = cv2.split(colorized_frame)
- colorized_frame = cv2.merge((temp_blue_channel, color_green_channel, color_red_channel))
- colorized_frame = cv2.cvtColor(colorized_frame, cv2.COLOR_LAB2BGR)
- return colorized_frame.astype(np.uint8)
-
-
- def Release(self):
- del self.model_colorizer
- self.model_colorizer = None
- del self.io_binding
- self.io_binding = None
-
diff --git a/roop-unleashed-main/roop/processors/Frame_Filter.py b/roop-unleashed-main/roop/processors/Frame_Filter.py
deleted file mode 100644
index b1405c329167a4e7f4f926ade5cf06ab6166466f..0000000000000000000000000000000000000000
--- a/roop-unleashed-main/roop/processors/Frame_Filter.py
+++ /dev/null
@@ -1,105 +0,0 @@
-import cv2
-import numpy as np
-
-from roop.typing import Frame
-
-class Frame_Filter():
- processorname = 'generic_filter'
- type = 'frame_processor'
-
- plugin_options:dict = None
-
- c64_palette = np.array([
- [0, 0, 0],
- [255, 255, 255],
- [0x81, 0x33, 0x38],
- [0x75, 0xce, 0xc8],
- [0x8e, 0x3c, 0x97],
- [0x56, 0xac, 0x4d],
- [0x2e, 0x2c, 0x9b],
- [0xed, 0xf1, 0x71],
- [0x8e, 0x50, 0x29],
- [0x55, 0x38, 0x00],
- [0xc4, 0x6c, 0x71],
- [0x4a, 0x4a, 0x4a],
- [0x7b, 0x7b, 0x7b],
- [0xa9, 0xff, 0x9f],
- [0x70, 0x6d, 0xeb],
- [0xb2, 0xb2, 0xb2]
- ])
-
-
- def RenderC64Screen(self, image):
- # Simply round the color values to the nearest color in the palette
- image = cv2.resize(image,(320,200))
- palette = self.c64_palette / 255.0 # Normalize palette
- img_normalized = image / 255.0 # Normalize image
-
- # Calculate the index in the palette that is closest to each pixel in the image
- indices = np.sqrt(((img_normalized[:, :, None, :] - palette[None, None, :, :]) ** 2).sum(axis=3)).argmin(axis=2)
- # Map the image to the palette colors
- mapped_image = palette[indices]
- return (mapped_image * 255).astype(np.uint8) # Denormalize and return the image
-
-
- def RenderDetailEnhance(self, image):
- return cv2.detailEnhance(image)
-
- def RenderStylize(self, image):
- return cv2.stylization(image)
-
- def RenderPencilSketch(self, image):
- imgray, imout = cv2.pencilSketch(image, sigma_s=60, sigma_r=0.07, shade_factor=0.05)
- return imout
-
- def RenderCartoon(self, image):
- numDownSamples = 2 # number of downscaling steps
- numBilateralFilters = 7 # number of bilateral filtering steps
-
- img_color = image
- for _ in range(numDownSamples):
- img_color = cv2.pyrDown(img_color)
- for _ in range(numBilateralFilters):
- img_color = cv2.bilateralFilter(img_color, 9, 9, 7)
- for _ in range(numDownSamples):
- img_color = cv2.pyrUp(img_color)
- img_gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
- img_blur = cv2.medianBlur(img_gray, 7)
- img_edge = cv2.adaptiveThreshold(img_blur, 255,
- cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, 9, 2)
- img_edge = cv2.cvtColor(img_edge, cv2.COLOR_GRAY2RGB)
- if img_color.shape != image.shape:
- img_color = cv2.resize(img_color, (image.shape[1], image.shape[0]), interpolation=cv2.INTER_LINEAR)
- if img_color.shape != img_edge.shape:
- img_edge = cv2.resize(img_edge, (img_color.shape[1], img_color.shape[0]), interpolation=cv2.INTER_LINEAR)
- return cv2.bitwise_and(img_color, img_edge)
-
-
- def Initialize(self, plugin_options:dict):
- if self.plugin_options is not None:
- if self.plugin_options["devicename"] != plugin_options["devicename"]:
- self.Release()
- self.plugin_options = plugin_options
-
- def Run(self, temp_frame: Frame) -> Frame:
- subtype = self.plugin_options["subtype"]
- if subtype == "stylize":
- return self.RenderStylize(temp_frame).astype(np.uint8)
- if subtype == "detailenhance":
- return self.RenderDetailEnhance(temp_frame).astype(np.uint8)
- if subtype == "pencil":
- return self.RenderPencilSketch(temp_frame).astype(np.uint8)
- if subtype == "cartoon":
- return self.RenderCartoon(temp_frame).astype(np.uint8)
- if subtype == "C64":
- return self.RenderC64Screen(temp_frame).astype(np.uint8)
-
-
- def Release(self):
- pass
-
- def getProcessedResolution(self, width, height):
- if self.plugin_options["subtype"] == "C64":
- return (320,200)
- return None
-
diff --git a/roop-unleashed-main/roop/processors/Frame_Masking.py b/roop-unleashed-main/roop/processors/Frame_Masking.py
deleted file mode 100644
index 2b4e77fec51854fc67c5274193665fd3555c24bb..0000000000000000000000000000000000000000
--- a/roop-unleashed-main/roop/processors/Frame_Masking.py
+++ /dev/null
@@ -1,71 +0,0 @@
-import cv2
-import numpy as np
-import onnxruntime
-import roop.globals
-
-from roop.utilities import resolve_relative_path
-from roop.typing import Frame
-
-class Frame_Masking():
- plugin_options:dict = None
- model_masking = None
- devicename = None
- name = None
-
- processorname = 'removebg'
- type = 'frame_masking'
-
-
- def Initialize(self, plugin_options:dict):
- if self.plugin_options is not None:
- if self.plugin_options["devicename"] != plugin_options["devicename"]:
- self.Release()
-
- self.plugin_options = plugin_options
- if self.model_masking is None:
- # replace Mac mps with cpu for the moment
- self.devicename = self.plugin_options["devicename"]
- self.devicename = self.devicename.replace('mps', 'cpu')
- model_path = resolve_relative_path('../models/Frame/isnet-general-use.onnx')
- self.model_masking = onnxruntime.InferenceSession(model_path, None, providers=roop.globals.execution_providers)
- self.model_inputs = self.model_masking.get_inputs()
- model_outputs = self.model_masking.get_outputs()
- self.io_binding = self.model_masking.io_binding()
- self.io_binding.bind_output(model_outputs[0].name, self.devicename)
-
- def Run(self, temp_frame: Frame) -> Frame:
- # Pre process:Resize, BGR->RGB, float32 cast
- input_image = cv2.resize(temp_frame, (1024, 1024))
- input_image = cv2.cvtColor(input_image, cv2.COLOR_BGR2RGB)
- mean = [0.5, 0.5, 0.5]
- std = [1.0, 1.0, 1.0]
- input_image = (input_image / 255.0 - mean) / std
- input_image = input_image.transpose(2, 0, 1)
- input_image = np.expand_dims(input_image, axis=0)
- input_image = input_image.astype('float32')
-
- self.io_binding.bind_cpu_input(self.model_inputs[0].name, input_image)
- self.model_masking.run_with_iobinding(self.io_binding)
- ort_outs = self.io_binding.copy_outputs_to_cpu()
- result = ort_outs[0][0]
- del ort_outs
- # Post process:squeeze, Sigmoid, Normarize, uint8 cast
- mask = np.squeeze(result[0])
- min_value = np.min(mask)
- max_value = np.max(mask)
- mask = (mask - min_value) / (max_value - min_value)
- #mask = np.where(mask < score_th, 0, 1)
- #mask *= 255
- mask = cv2.resize(mask, (temp_frame.shape[1], temp_frame.shape[0]), interpolation=cv2.INTER_LINEAR)
- mask = np.reshape(mask, [mask.shape[0],mask.shape[1],1])
- result = mask * temp_frame.astype(np.float32)
- return result.astype(np.uint8)
-
-
-
- def Release(self):
- del self.model_masking
- self.model_masking = None
- del self.io_binding
- self.io_binding = None
-
diff --git a/roop-unleashed-main/roop/processors/Frame_Upscale.py b/roop-unleashed-main/roop/processors/Frame_Upscale.py
deleted file mode 100644
index f260767e025f57898cd4305b109a440ca020865a..0000000000000000000000000000000000000000
--- a/roop-unleashed-main/roop/processors/Frame_Upscale.py
+++ /dev/null
@@ -1,129 +0,0 @@
-import cv2
-import numpy as np
-import onnxruntime
-import roop.globals
-
-from roop.utilities import resolve_relative_path, conditional_thread_semaphore
-from roop.typing import Frame
-
-
-class Frame_Upscale():
- plugin_options:dict = None
- model_upscale = None
- devicename = None
- prev_type = None
-
- processorname = 'upscale'
- type = 'frame_enhancer'
-
-
- def Initialize(self, plugin_options:dict):
- if self.plugin_options is not None:
- if self.plugin_options["devicename"] != plugin_options["devicename"]:
- self.Release()
-
- self.plugin_options = plugin_options
- if self.prev_type is not None and self.prev_type != self.plugin_options["subtype"]:
- self.Release()
- self.prev_type = self.plugin_options["subtype"]
- if self.model_upscale is None:
- # replace Mac mps with cpu for the moment
- self.devicename = self.plugin_options["devicename"].replace('mps', 'cpu')
- if self.prev_type == "esrganx4":
- model_path = resolve_relative_path('../models/Frame/real_esrgan_x4.onnx')
- self.scale = 4
- elif self.prev_type == "esrganx2":
- model_path = resolve_relative_path('../models/Frame/real_esrgan_x2.onnx')
- self.scale = 2
- elif self.prev_type == "lsdirx4":
- model_path = resolve_relative_path('../models/Frame/lsdir_x4.onnx')
- self.scale = 4
- onnxruntime.set_default_logger_severity(3)
- self.model_upscale = onnxruntime.InferenceSession(model_path, None, providers=roop.globals.execution_providers)
- self.model_inputs = self.model_upscale.get_inputs()
- model_outputs = self.model_upscale.get_outputs()
- self.io_binding = self.model_upscale.io_binding()
- self.io_binding.bind_output(model_outputs[0].name, self.devicename)
-
- def getProcessedResolution(self, width, height):
- return (width * self.scale, height * self.scale)
-
-# borrowed from facefusion -> https://github.com/facefusion/facefusion
- def prepare_tile_frame(self, tile_frame : Frame) -> Frame:
- tile_frame = np.expand_dims(tile_frame[:, :, ::-1], axis = 0)
- tile_frame = tile_frame.transpose(0, 3, 1, 2)
- tile_frame = tile_frame.astype(np.float32) / 255
- return tile_frame
-
-
- def normalize_tile_frame(self, tile_frame : Frame) -> Frame:
- tile_frame = tile_frame.transpose(0, 2, 3, 1).squeeze(0) * 255
- tile_frame = tile_frame.clip(0, 255).astype(np.uint8)[:, :, ::-1]
- return tile_frame
-
- def create_tile_frames(self, input_frame : Frame, size):
- input_frame = np.pad(input_frame, ((size[1], size[1]), (size[1], size[1]), (0, 0)))
- tile_width = size[0] - 2 * size[2]
- pad_size_bottom = size[2] + tile_width - input_frame.shape[0] % tile_width
- pad_size_right = size[2] + tile_width - input_frame.shape[1] % tile_width
- pad_vision_frame = np.pad(input_frame, ((size[2], pad_size_bottom), (size[2], pad_size_right), (0, 0)))
- pad_height, pad_width = pad_vision_frame.shape[:2]
- row_range = range(size[2], pad_height - size[2], tile_width)
- col_range = range(size[2], pad_width - size[2], tile_width)
- tile_frames = []
-
- for row_frame in row_range:
- top = row_frame - size[2]
- bottom = row_frame + size[2] + tile_width
- for column_vision_frame in col_range:
- left = column_vision_frame - size[2]
- right = column_vision_frame + size[2] + tile_width
- tile_frames.append(pad_vision_frame[top:bottom, left:right, :])
- return tile_frames, pad_width, pad_height
-
-
- def merge_tile_frames(self, tile_frames, temp_width : int, temp_height : int, pad_width : int, pad_height : int, size) -> Frame:
- merge_frame = np.zeros((pad_height, pad_width, 3)).astype(np.uint8)
- tile_width = tile_frames[0].shape[1] - 2 * size[2]
- tiles_per_row = min(pad_width // tile_width, len(tile_frames))
-
- for index, tile_frame in enumerate(tile_frames):
- tile_frame = tile_frame[size[2]:-size[2], size[2]:-size[2]]
- row_index = index // tiles_per_row
- col_index = index % tiles_per_row
- top = row_index * tile_frame.shape[0]
- bottom = top + tile_frame.shape[0]
- left = col_index * tile_frame.shape[1]
- right = left + tile_frame.shape[1]
- merge_frame[top:bottom, left:right, :] = tile_frame
- merge_frame = merge_frame[size[1] : size[1] + temp_height, size[1]: size[1] + temp_width, :]
- return merge_frame
-
-
- def Run(self, temp_frame: Frame) -> Frame:
- size = (128, 8, 2)
- temp_height, temp_width = temp_frame.shape[:2]
- upscale_tile_frames, pad_width, pad_height = self.create_tile_frames(temp_frame, size)
-
- for index, tile_frame in enumerate(upscale_tile_frames):
- tile_frame = self.prepare_tile_frame(tile_frame)
- with conditional_thread_semaphore():
- self.io_binding.bind_cpu_input(self.model_inputs[0].name, tile_frame)
- self.model_upscale.run_with_iobinding(self.io_binding)
- ort_outs = self.io_binding.copy_outputs_to_cpu()
- result = ort_outs[0]
- upscale_tile_frames[index] = self.normalize_tile_frame(result)
- final_frame = self.merge_tile_frames(upscale_tile_frames, temp_width * self.scale
- , temp_height * self.scale
- , pad_width * self.scale, pad_height * self.scale
- , (size[0] * self.scale, size[1] * self.scale, size[2] * self.scale))
- return final_frame.astype(np.uint8)
-
-
-
- def Release(self):
- del self.model_upscale
- self.model_upscale = None
- del self.io_binding
- self.io_binding = None
-
diff --git a/roop-unleashed-main/roop/processors/Mask_Clip2Seg.py b/roop-unleashed-main/roop/processors/Mask_Clip2Seg.py
deleted file mode 100644
index 5df3b3e37ea10eb2440828a08e129d8c62f98086..0000000000000000000000000000000000000000
--- a/roop-unleashed-main/roop/processors/Mask_Clip2Seg.py
+++ /dev/null
@@ -1,94 +0,0 @@
-import cv2
-import numpy as np
-import torch
-import threading
-from torchvision import transforms
-from clip.clipseg import CLIPDensePredT
-import numpy as np
-
-from roop.typing import Frame
-
-THREAD_LOCK_CLIP = threading.Lock()
-
-
-class Mask_Clip2Seg():
- plugin_options:dict = None
- model_clip = None
-
- processorname = 'clip2seg'
- type = 'mask'
-
-
- def Initialize(self, plugin_options:dict):
- if self.plugin_options is not None:
- if self.plugin_options["devicename"] != plugin_options["devicename"]:
- self.Release()
-
- self.plugin_options = plugin_options
- if self.model_clip is None:
- self.model_clip = CLIPDensePredT(version='ViT-B/16', reduce_dim=64, complex_trans_conv=True)
- self.model_clip.eval();
- self.model_clip.load_state_dict(torch.load('models/CLIP/rd64-uni-refined.pth', map_location=torch.device('cpu')), strict=False)
-
- device = torch.device(self.plugin_options["devicename"])
- self.model_clip.to(device)
-
-
- def Run(self, img1, keywords:str) -> Frame:
- if keywords is None or len(keywords) < 1 or img1 is None:
- return img1
-
- source_image_small = cv2.resize(img1, (256,256))
-
- img_mask = np.full((source_image_small.shape[0],source_image_small.shape[1]), 0, dtype=np.float32)
- mask_border = 1
- l = 0
- t = 0
- r = 1
- b = 1
-
- mask_blur = 5
- clip_blur = 5
-
- img_mask = cv2.rectangle(img_mask, (mask_border+int(l), mask_border+int(t)),
- (256 - mask_border-int(r), 256-mask_border-int(b)), (255, 255, 255), -1)
- img_mask = cv2.GaussianBlur(img_mask, (mask_blur*2+1,mask_blur*2+1), 0)
- img_mask /= 255
-
-
- input_image = source_image_small
-
- transform = transforms.Compose([
- transforms.ToTensor(),
- transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
- transforms.Resize((256, 256)),
- ])
- img = transform(input_image).unsqueeze(0)
-
- thresh = 0.5
- prompts = keywords.split(',')
- with THREAD_LOCK_CLIP:
- with torch.no_grad():
- preds = self.model_clip(img.repeat(len(prompts),1,1,1), prompts)[0]
- clip_mask = torch.sigmoid(preds[0][0])
- for i in range(len(prompts)-1):
- clip_mask += torch.sigmoid(preds[i+1][0])
-
- clip_mask = clip_mask.data.cpu().numpy()
- np.clip(clip_mask, 0, 1)
-
- clip_mask[clip_mask>thresh] = 1.0
- clip_mask[clip_mask<=thresh] = 0.0
- kernel = np.ones((5, 5), np.float32)
- clip_mask = cv2.dilate(clip_mask, kernel, iterations=1)
- clip_mask = cv2.GaussianBlur(clip_mask, (clip_blur*2+1,clip_blur*2+1), 0)
-
- img_mask *= clip_mask
- img_mask[img_mask<0.0] = 0.0
- return img_mask
-
-
-
- def Release(self):
- self.model_clip = None
-
diff --git a/roop-unleashed-main/roop/processors/Mask_XSeg.py b/roop-unleashed-main/roop/processors/Mask_XSeg.py
deleted file mode 100644
index 12fab6540354dd2e898ede41eb6f3a53281636a9..0000000000000000000000000000000000000000
--- a/roop-unleashed-main/roop/processors/Mask_XSeg.py
+++ /dev/null
@@ -1,58 +0,0 @@
-import numpy as np
-import cv2
-import onnxruntime
-import roop.globals
-
-from roop.typing import Frame
-from roop.utilities import resolve_relative_path, conditional_thread_semaphore
-
-
-
-class Mask_XSeg():
- plugin_options:dict = None
-
- model_xseg = None
-
- processorname = 'mask_xseg'
- type = 'mask'
-
-
- def Initialize(self, plugin_options:dict):
- if self.plugin_options is not None:
- if self.plugin_options["devicename"] != plugin_options["devicename"]:
- self.Release()
-
- self.plugin_options = plugin_options
- if self.model_xseg is None:
- model_path = resolve_relative_path('../models/xseg.onnx')
- onnxruntime.set_default_logger_severity(3)
- self.model_xseg = onnxruntime.InferenceSession(model_path, None, providers=roop.globals.execution_providers)
- self.model_inputs = self.model_xseg.get_inputs()
- self.model_outputs = self.model_xseg.get_outputs()
-
- # replace Mac mps with cpu for the moment
- self.devicename = self.plugin_options["devicename"].replace('mps', 'cpu')
-
-
- def Run(self, img1, keywords:str) -> Frame:
- temp_frame = cv2.resize(img1, (256, 256), cv2.INTER_CUBIC)
- temp_frame = temp_frame.astype('float32') / 255.0
- temp_frame = temp_frame[None, ...]
- io_binding = self.model_xseg.io_binding()
- io_binding.bind_cpu_input(self.model_inputs[0].name, temp_frame)
- io_binding.bind_output(self.model_outputs[0].name, self.devicename)
- self.model_xseg.run_with_iobinding(io_binding)
- ort_outs = io_binding.copy_outputs_to_cpu()
- result = ort_outs[0][0]
- result = np.clip(result, 0, 1.0)
- result[result < 0.1] = 0
- # invert values to mask areas to keep
- result = 1.0 - result
- return result
-
-
- def Release(self):
- del self.model_xseg
- self.model_xseg = None
-
-
diff --git a/roop-unleashed-main/roop/processors/__init__.py b/roop-unleashed-main/roop/processors/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/roop-unleashed-main/roop/template_parser.py b/roop-unleashed-main/roop/template_parser.py
deleted file mode 100644
index a51113b69830119fc84fd15c2a428321ac1d8010..0000000000000000000000000000000000000000
--- a/roop-unleashed-main/roop/template_parser.py
+++ /dev/null
@@ -1,23 +0,0 @@
-import re
-from datetime import datetime
-
-template_functions = {
- "timestamp": lambda data: str(int(datetime.now().timestamp())),
- "i": lambda data: data.get("index", False),
- "file": lambda data: data.get("file", False),
- "date": lambda data: datetime.now().strftime("%Y-%m-%d"),
- "time": lambda data: datetime.now().strftime("%H-%M-%S"),
-}
-
-
-def parse(text: str, data: dict):
- pattern = r"\{([^}]+)\}"
-
- matches = re.findall(pattern, text)
-
- for match in matches:
- replacement = template_functions[match](data)
- if replacement is not False:
- text = text.replace(f"{{{match}}}", replacement)
-
- return text
diff --git a/roop-unleashed-main/roop/typing.py b/roop-unleashed-main/roop/typing.py
deleted file mode 100644
index 263f1b5b0331332dfab9f682438b364c612cfdf8..0000000000000000000000000000000000000000
--- a/roop-unleashed-main/roop/typing.py
+++ /dev/null
@@ -1,9 +0,0 @@
-from typing import Any
-
-from insightface.app.common import Face
-from roop.FaceSet import FaceSet
-import numpy
-
-Face = Face
-FaceSet = FaceSet
-Frame = numpy.ndarray[Any, Any]
diff --git a/roop-unleashed-main/roop/util_ffmpeg.py b/roop-unleashed-main/roop/util_ffmpeg.py
deleted file mode 100644
index 87012995169d2a6319d1d978333076ba8acbac98..0000000000000000000000000000000000000000
--- a/roop-unleashed-main/roop/util_ffmpeg.py
+++ /dev/null
@@ -1,130 +0,0 @@
-
-import os
-import subprocess
-import roop.globals
-import roop.utilities as util
-
-from typing import List, Any
-
-def run_ffmpeg(args: List[str]) -> bool:
- commands = ['ffmpeg', '-hide_banner', '-hwaccel', 'auto', '-y', '-loglevel', roop.globals.log_level]
- commands.extend(args)
- print ("Running ffmpeg")
- try:
- subprocess.check_output(commands, stderr=subprocess.STDOUT)
- return True
- except Exception as e:
- print("Running ffmpeg failed! Commandline:")
- print (" ".join(commands))
- return False
-
-
-
-def cut_video(original_video: str, cut_video: str, start_frame: int, end_frame: int, reencode: bool):
- fps = util.detect_fps(original_video)
- start_time = start_frame / fps
- num_frames = end_frame - start_frame
-
- if reencode:
- run_ffmpeg(['-ss', format(start_time, ".2f"), '-i', original_video, '-c:v', roop.globals.video_encoder, '-c:a', 'aac', '-frames:v', str(num_frames), cut_video])
- else:
- run_ffmpeg(['-ss', format(start_time, ".2f"), '-i', original_video, '-frames:v', str(num_frames), '-c:v' ,'copy','-c:a' ,'copy', cut_video])
-
-def join_videos(videos: List[str], dest_filename: str, simple: bool):
- if simple:
- txtfilename = util.resolve_relative_path('../temp')
- txtfilename = os.path.join(txtfilename, 'joinvids.txt')
- with open(txtfilename, "w", encoding="utf-8") as f:
- for v in videos:
- v = v.replace('\\', '/')
- f.write(f"file {v}\n")
- commands = ['-f', 'concat', '-safe', '0', '-i', f'{txtfilename}', '-vcodec', 'copy', f'{dest_filename}']
- run_ffmpeg(commands)
-
- else:
- inputs = []
- filter = ''
- for i,v in enumerate(videos):
- inputs.append('-i')
- inputs.append(v)
- filter += f'[{i}:v:0][{i}:a:0]'
- run_ffmpeg([" ".join(inputs), '-filter_complex', f'"{filter}concat=n={len(videos)}:v=1:a=1[outv][outa]"', '-map', '"[outv]"', '-map', '"[outa]"', dest_filename])
-
- # filter += f'[{i}:v:0][{i}:a:0]'
- # run_ffmpeg([" ".join(inputs), '-filter_complex', f'"{filter}concat=n={len(videos)}:v=1:a=1[outv][outa]"', '-map', '"[outv]"', '-map', '"[outa]"', dest_filename])
-
-
-
-def extract_frames(target_path : str, trim_frame_start, trim_frame_end, fps : float) -> bool:
- util.create_temp(target_path)
- temp_directory_path = util.get_temp_directory_path(target_path)
- commands = ['-i', target_path, '-q:v', '1', '-pix_fmt', 'rgb24', ]
- if trim_frame_start is not None and trim_frame_end is not None:
- commands.extend([ '-vf', 'trim=start_frame=' + str(trim_frame_start) + ':end_frame=' + str(trim_frame_end) + ',fps=' + str(fps) ])
- commands.extend(['-vsync', '0', os.path.join(temp_directory_path, '%06d.' + roop.globals.CFG.output_image_format)])
- return run_ffmpeg(commands)
-
-
-def create_video(target_path: str, dest_filename: str, fps: float = 24.0, temp_directory_path: str = None) -> None:
- if temp_directory_path is None:
- temp_directory_path = util.get_temp_directory_path(target_path)
- run_ffmpeg(['-r', str(fps), '-i', os.path.join(temp_directory_path, f'%06d.{roop.globals.CFG.output_image_format}'), '-c:v', roop.globals.video_encoder, '-crf', str(roop.globals.video_quality), '-pix_fmt', 'yuv420p', '-vf', 'colorspace=bt709:iall=bt601-6-625:fast=1', '-y', dest_filename])
- return dest_filename
-
-
-def create_gif_from_video(video_path: str, gif_path):
- from roop.capturer import get_video_frame, release_video
-
- fps = util.detect_fps(video_path)
- frame = get_video_frame(video_path)
- release_video()
-
- scalex = frame.shape[0]
- scaley = frame.shape[1]
-
- if scalex >= scaley:
- scaley = -1
- else:
- scalex = -1
-
- run_ffmpeg(['-i', video_path, '-vf', f'fps={fps},scale={int(scalex)}:{int(scaley)}:flags=lanczos,split[s0][s1];[s0]palettegen[p];[s1][p]paletteuse', '-loop', '0', gif_path])
-
-
-
-def create_video_from_gif(gif_path: str, output_path):
- fps = util.detect_fps(gif_path)
- filter = """scale='trunc(in_w/2)*2':'trunc(in_h/2)*2',format=yuv420p,fps=10"""
- run_ffmpeg(['-i', gif_path, '-vf', f'"{filter}"', '-movflags', '+faststart', '-shortest', output_path])
-
-
-def repair_video(original_video: str, final_video : str):
- run_ffmpeg(['-i', original_video, '-movflags', 'faststart', '-acodec', 'copy', '-vcodec', 'copy', final_video])
-
-
-def restore_audio(intermediate_video: str, original_video: str, trim_frame_start, trim_frame_end, final_video : str) -> None:
- fps = util.detect_fps(original_video)
- commands = [ '-i', intermediate_video ]
- if trim_frame_start is None and trim_frame_end is None:
- commands.extend([ '-c:a', 'copy' ])
- else:
- # if trim_frame_start is not None:
- # start_time = trim_frame_start / fps
- # commands.extend([ '-ss', format(start_time, ".2f")])
- # else:
- # commands.extend([ '-ss', '0' ])
- # if trim_frame_end is not None:
- # end_time = trim_frame_end / fps
- # commands.extend([ '-to', format(end_time, ".2f")])
- # commands.extend([ '-c:a', 'aac' ])
- if trim_frame_start is not None:
- start_time = trim_frame_start / fps
- commands.extend([ '-ss', format(start_time, ".2f")])
- else:
- commands.extend([ '-ss', '0' ])
- if trim_frame_end is not None:
- end_time = trim_frame_end / fps
- commands.extend([ '-to', format(end_time, ".2f")])
- commands.extend([ '-i', original_video, "-c", "copy" ])
-
- commands.extend([ '-map', '0:v:0', '-map', '1:a:0?', '-shortest', final_video ])
- run_ffmpeg(commands)
diff --git a/roop-unleashed-main/roop/utilities.py b/roop-unleashed-main/roop/utilities.py
deleted file mode 100644
index 4d24df931f218c3553bc986abb681f877cdeae1e..0000000000000000000000000000000000000000
--- a/roop-unleashed-main/roop/utilities.py
+++ /dev/null
@@ -1,393 +0,0 @@
-import glob
-import mimetypes
-import os
-import platform
-import shutil
-import ssl
-import subprocess
-import sys
-import urllib
-import torch
-import gradio
-import tempfile
-import cv2
-import zipfile
-import traceback
-import threading
-import threading
-import random
-
-from typing import Union, Any
-from contextlib import nullcontext
-
-from pathlib import Path
-from typing import List, Any
-from tqdm import tqdm
-from scipy.spatial import distance
-
-import roop.template_parser as template_parser
-
-import roop.globals
-
-TEMP_FILE = "temp.mp4"
-TEMP_DIRECTORY = "temp"
-
-THREAD_SEMAPHORE = threading.Semaphore()
-NULL_CONTEXT = nullcontext()
-
-
-# monkey patch ssl for mac
-if platform.system().lower() == "darwin":
- ssl._create_default_https_context = ssl._create_unverified_context
-
-
-# https://github.com/facefusion/facefusion/blob/master/facefusion
-def detect_fps(target_path: str) -> float:
- fps = 24.0
- cap = cv2.VideoCapture(target_path)
- if cap.isOpened():
- fps = cap.get(cv2.CAP_PROP_FPS)
- cap.release()
- return fps
-
-
-# Gradio wants Images in RGB
-def convert_to_gradio(image):
- if image is None:
- return None
- return cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
-
-
-def sort_filenames_ignore_path(filenames):
- """Sorts a list of filenames containing a complete path by their filename,
- while retaining their original path.
-
- Args:
- filenames: A list of filenames containing a complete path.
-
- Returns:
- A sorted list of filenames containing a complete path.
- """
- filename_path_tuples = [
- (os.path.split(filename)[1], filename) for filename in filenames
- ]
- sorted_filename_path_tuples = sorted(filename_path_tuples, key=lambda x: x[0])
- return [
- filename_path_tuple[1] for filename_path_tuple in sorted_filename_path_tuples
- ]
-
-
-def sort_rename_frames(path: str):
- filenames = os.listdir(path)
- filenames.sort()
- for i in range(len(filenames)):
- of = os.path.join(path, filenames[i])
- newidx = i + 1
- new_filename = os.path.join(
- path, f"{newidx:06d}." + roop.globals.CFG.output_image_format
- )
- os.rename(of, new_filename)
-
-
-def get_temp_frame_paths(target_path: str) -> List[str]:
- temp_directory_path = get_temp_directory_path(target_path)
- return glob.glob(
- (
- os.path.join(
- glob.escape(temp_directory_path),
- f"*.{roop.globals.CFG.output_image_format}",
- )
- )
- )
-
-
-def get_temp_directory_path(target_path: str) -> str:
- target_name, _ = os.path.splitext(os.path.basename(target_path))
- target_directory_path = os.path.dirname(target_path)
- return os.path.join(target_directory_path, TEMP_DIRECTORY, target_name)
-
-
-def get_temp_output_path(target_path: str) -> str:
- temp_directory_path = get_temp_directory_path(target_path)
- return os.path.join(temp_directory_path, TEMP_FILE)
-
-
-def normalize_output_path(source_path: str, target_path: str, output_path: str) -> Any:
- if source_path and target_path:
- source_name, _ = os.path.splitext(os.path.basename(source_path))
- target_name, target_extension = os.path.splitext(os.path.basename(target_path))
- if os.path.isdir(output_path):
- return os.path.join(
- output_path, source_name + "-" + target_name + target_extension
- )
- return output_path
-
-
-def get_destfilename_from_path(
- srcfilepath: str, destfilepath: str, extension: str
-) -> str:
- fn, ext = os.path.splitext(os.path.basename(srcfilepath))
- if "." in extension:
- return os.path.join(destfilepath, f"{fn}{extension}")
- return os.path.join(destfilepath, f"{fn}{extension}{ext}")
-
-
-def replace_template(file_path: str, index: int = 0) -> str:
- fn, ext = os.path.splitext(os.path.basename(file_path))
-
- # Remove the "__temp" placeholder that was used as a temporary filename
- fn = fn.replace("__temp", "")
-
- template = roop.globals.CFG.output_template
- replaced_filename = template_parser.parse(
- template, {"index": str(index), "file": fn}
- )
-
- return os.path.join(roop.globals.output_path, f"{replaced_filename}{ext}")
-
-
-def create_temp(target_path: str) -> None:
- temp_directory_path = get_temp_directory_path(target_path)
- Path(temp_directory_path).mkdir(parents=True, exist_ok=True)
-
-
-def move_temp(target_path: str, output_path: str) -> None:
- temp_output_path = get_temp_output_path(target_path)
- if os.path.isfile(temp_output_path):
- if os.path.isfile(output_path):
- os.remove(output_path)
- shutil.move(temp_output_path, output_path)
-
-
-def clean_temp(target_path: str) -> None:
- temp_directory_path = get_temp_directory_path(target_path)
- parent_directory_path = os.path.dirname(temp_directory_path)
- if not roop.globals.keep_frames and os.path.isdir(temp_directory_path):
- shutil.rmtree(temp_directory_path)
- if os.path.exists(parent_directory_path) and not os.listdir(parent_directory_path):
- os.rmdir(parent_directory_path)
-
-
-def delete_temp_frames(filename: str) -> None:
- dir = os.path.dirname(os.path.dirname(filename))
- shutil.rmtree(dir)
-
-
-def has_image_extension(image_path: str) -> bool:
- return image_path.lower().endswith(("png", "jpg", "jpeg", "webp"))
-
-
-def has_extension(filepath: str, extensions: List[str]) -> bool:
- return filepath.lower().endswith(tuple(extensions))
-
-
-def is_image(image_path: str) -> bool:
- if image_path and os.path.isfile(image_path):
- if image_path.endswith(".webp"):
- return True
- mimetype, _ = mimetypes.guess_type(image_path)
- return bool(mimetype and mimetype.startswith("image/"))
- return False
-
-
-def is_video(video_path: str) -> bool:
- if video_path and os.path.isfile(video_path):
- mimetype, _ = mimetypes.guess_type(video_path)
- return bool(mimetype and mimetype.startswith("video/"))
- return False
-
-
-def conditional_download(download_directory_path: str, urls: List[str]) -> None:
- if not os.path.exists(download_directory_path):
- os.makedirs(download_directory_path)
- for url in urls:
- download_file_path = os.path.join(
- download_directory_path, os.path.basename(url)
- )
- if not os.path.exists(download_file_path):
- request = urllib.request.urlopen(url) # type: ignore[attr-defined]
- total = int(request.headers.get("Content-Length", 0))
- with tqdm(
- total=total,
- desc=f"Downloading {url}",
- unit="B",
- unit_scale=True,
- unit_divisor=1024,
- ) as progress:
- urllib.request.urlretrieve(url, download_file_path, reporthook=lambda count, block_size, total_size: progress.update(block_size)) # type: ignore[attr-defined]
-
-
-def get_local_files_from_folder(folder: str) -> List[str]:
- if not os.path.exists(folder) or not os.path.isdir(folder):
- return None
- files = [
- os.path.join(folder, f)
- for f in os.listdir(folder)
- if os.path.isfile(os.path.join(folder, f))
- ]
- return files
-
-
-def resolve_relative_path(path: str) -> str:
- return os.path.abspath(os.path.join(os.path.dirname(__file__), path))
-
-
-def get_device() -> str:
- if len(roop.globals.execution_providers) < 1:
- roop.globals.execution_providers = ["CPUExecutionProvider"]
-
- prov = roop.globals.execution_providers[0]
- if "CoreMLExecutionProvider" in prov:
- return "mps"
- if "CUDAExecutionProvider" in prov or "ROCMExecutionProvider" in prov:
- return "cuda"
- if "OpenVINOExecutionProvider" in prov:
- return "mkl"
- return "cpu"
-
-
-def str_to_class(module_name, class_name) -> Any:
- from importlib import import_module
-
- class_ = None
- try:
- module_ = import_module(module_name)
- try:
- class_ = getattr(module_, class_name)()
- except AttributeError:
- print(f"Class {class_name} does not exist")
- except ImportError:
- print(f"Module {module_name} does not exist")
- return class_
-
-def is_installed(name:str) -> bool:
- return shutil.which(name);
-
-# Taken from https://stackoverflow.com/a/68842705
-def get_platform() -> str:
- if sys.platform == "linux":
- try:
- proc_version = open("/proc/version").read()
- if "Microsoft" in proc_version:
- return "wsl"
- except:
- pass
- return sys.platform
-
-def open_with_default_app(filename:str):
- if filename == None:
- return
- platform = get_platform()
- if platform == "darwin":
- subprocess.call(("open", filename))
- elif platform in ["win64", "win32"]: os.startfile(filename.replace("/", "\\"))
- elif platform == "wsl":
- subprocess.call("cmd.exe /C start".split() + [filename])
- else: # linux variants
- subprocess.call("xdg-open", filename)
-
-
-def prepare_for_batch(target_files) -> str:
- print("Preparing temp files")
- tempfolder = os.path.join(tempfile.gettempdir(), "rooptmp")
- if os.path.exists(tempfolder):
- shutil.rmtree(tempfolder)
- Path(tempfolder).mkdir(parents=True, exist_ok=True)
- for f in target_files:
- newname = os.path.basename(f.name)
- shutil.move(f.name, os.path.join(tempfolder, newname))
- return tempfolder
-
-
-def zip(files, zipname):
- with zipfile.ZipFile(zipname, "w") as zip_file:
- for f in files:
- zip_file.write(f, os.path.basename(f))
-
-
-def unzip(zipfilename: str, target_path: str):
- with zipfile.ZipFile(zipfilename, "r") as zip_file:
- zip_file.extractall(target_path)
-
-
-def mkdir_with_umask(directory):
- oldmask = os.umask(0)
- # mode needs octal
- os.makedirs(directory, mode=0o775, exist_ok=True)
- os.umask(oldmask)
-
-
-def open_folder(path: str):
- platform = get_platform()
- try:
- if platform == "darwin":
- subprocess.call(("open", path))
- elif platform in ["win64", "win32"]:
- open_with_default_app(path)
- elif platform == "wsl":
- subprocess.call("cmd.exe /C start".split() + [path])
- else: # linux variants
- subprocess.Popen(["xdg-open", path])
- except Exception as e:
- traceback.print_exc()
- pass
- # import webbrowser
- # webbrowser.open(url)
-
-
-def create_version_html() -> str:
- python_version = ".".join([str(x) for x in sys.version_info[0:3]])
- versions_html = f"""
-python: {python_version}
-โข
-torch: {getattr(torch, '__long_version__',torch.__version__)}
-โข
-gradio: {gradio.__version__}
-"""
- return versions_html
-
-
-def compute_cosine_distance(emb1, emb2) -> float:
- return distance.cosine(emb1, emb2)
-
-def has_cuda_device():
- return torch.cuda is not None and torch.cuda.is_available()
-
-
-def print_cuda_info():
- try:
- print(f'Number of CUDA devices: {torch.cuda.device_count()} Currently used Id: {torch.cuda.current_device()} Device Name: {torch.cuda.get_device_name(torch.cuda.current_device())}')
- except:
- print('No CUDA device found!')
-
-def clean_dir(path: str):
- contents = os.listdir(path)
- for item in contents:
- item_path = os.path.join(path, item)
- try:
- if os.path.isfile(item_path):
- os.remove(item_path)
- elif os.path.isdir(item_path):
- shutil.rmtree(item_path)
- except Exception as e:
- print(e)
-
-
-def conditional_thread_semaphore() -> Union[Any, Any]:
- if 'DmlExecutionProvider' in roop.globals.execution_providers or 'ROCMExecutionProvider' in roop.globals.execution_providers:
- return THREAD_SEMAPHORE
- return NULL_CONTEXT
-
-def shuffle_array(arr):
- """
- Shuffles the given array in place using the Fisher-Yates shuffle algorithm.
-
- Args:
- arr: The array to be shuffled.
-
- Returns:
- None. The array is shuffled in place.
- """
- for i in range(len(arr) - 1, 0, -1):
- j = random.randint(0, i)
- arr[i], arr[j] = arr[j], arr[i]
diff --git a/roop-unleashed-main/roop/virtualcam.py b/roop-unleashed-main/roop/virtualcam.py
deleted file mode 100644
index b4ad76d6ecf5baea7022f900becf35edb3edbf32..0000000000000000000000000000000000000000
--- a/roop-unleashed-main/roop/virtualcam.py
+++ /dev/null
@@ -1,88 +0,0 @@
-import cv2
-import roop.globals
-import ui.globals
-import pyvirtualcam
-import threading
-import platform
-
-
-cam_active = False
-cam_thread = None
-vcam = None
-
-def virtualcamera(swap_model, streamobs, use_xseg, use_mouthrestore, cam_num,width,height):
- from roop.ProcessOptions import ProcessOptions
- from roop.core import live_swap, get_processing_plugins
-
- global cam_active
-
- #time.sleep(2)
- print('Starting capture')
- cap = cv2.VideoCapture(cam_num, cv2.CAP_DSHOW if platform.system() != 'Darwin' else cv2.CAP_AVFOUNDATION)
- if not cap.isOpened():
- print("Cannot open camera")
- cap.release()
- del cap
- return
-
- pref_width = width
- pref_height = height
- pref_fps_in = 30
- cap.set(cv2.CAP_PROP_FRAME_WIDTH, pref_width)
- cap.set(cv2.CAP_PROP_FRAME_HEIGHT, pref_height)
- cap.set(cv2.CAP_PROP_FPS, pref_fps_in)
- cam_active = True
-
- # native format UYVY
-
- cam = None
- if streamobs:
- print('Detecting virtual cam devices')
- cam = pyvirtualcam.Camera(width=pref_width, height=pref_height, fps=pref_fps_in, fmt=pyvirtualcam.PixelFormat.BGR, print_fps=False)
- if cam:
- print(f'Using virtual camera: {cam.device}')
- print(f'Using {cam.native_fmt}')
- else:
- print(f'Not streaming to virtual camera!')
- subsample_size = roop.globals.subsample_size
-
-
- options = ProcessOptions(swap_model, get_processing_plugins("mask_xseg" if use_xseg else None), roop.globals.distance_threshold, roop.globals.blend_ratio,
- "all", 0, None, None, 1, subsample_size, False, use_mouthrestore)
- while cam_active:
- ret, frame = cap.read()
- if not ret:
- break
-
- if len(roop.globals.INPUT_FACESETS) > 0:
- frame = live_swap(frame, options)
- if cam:
- cam.send(frame)
- cam.sleep_until_next_frame()
- ui.globals.ui_camera_frame = frame
-
- if cam:
- cam.close()
- cap.release()
- print('Camera stopped')
-
-
-
-def start_virtual_cam(swap_model, streamobs, use_xseg, use_mouthrestore, cam_number, resolution):
- global cam_thread, cam_active
-
- if not cam_active:
- width, height = map(int, resolution.split('x'))
- cam_thread = threading.Thread(target=virtualcamera, args=[swap_model, streamobs, use_xseg, use_mouthrestore, cam_number, width, height])
- cam_thread.start()
-
-
-
-def stop_virtual_cam():
- global cam_active, cam_thread
-
- if cam_active:
- cam_active = False
- cam_thread.join()
-
-
diff --git a/roop-unleashed-main/roop/vr_util.py b/roop-unleashed-main/roop/vr_util.py
deleted file mode 100644
index a72845e3c2c3cc89f6567ebfc13bf77d306710ff..0000000000000000000000000000000000000000
--- a/roop-unleashed-main/roop/vr_util.py
+++ /dev/null
@@ -1,57 +0,0 @@
-import cv2
-import numpy as np
-
-# VR Lense Distortion
-# Taken from https://github.com/g0kuvonlange/vrswap
-
-
-def get_perspective(img, FOV, THETA, PHI, height, width):
- #
- # THETA is left/right angle, PHI is up/down angle, both in degree
- #
- [orig_width, orig_height, _] = img.shape
- equ_h = orig_height
- equ_w = orig_width
- equ_cx = (equ_w - 1) / 2.0
- equ_cy = (equ_h - 1) / 2.0
-
- wFOV = FOV
- hFOV = float(height) / width * wFOV
-
- w_len = np.tan(np.radians(wFOV / 2.0))
- h_len = np.tan(np.radians(hFOV / 2.0))
-
- x_map = np.ones([height, width], np.float32)
- y_map = np.tile(np.linspace(-w_len, w_len, width), [height, 1])
- z_map = -np.tile(np.linspace(-h_len, h_len, height), [width, 1]).T
-
- D = np.sqrt(x_map**2 + y_map**2 + z_map**2)
- xyz = np.stack((x_map, y_map, z_map), axis=2) / np.repeat(
- D[:, :, np.newaxis], 3, axis=2
- )
-
- y_axis = np.array([0.0, 1.0, 0.0], np.float32)
- z_axis = np.array([0.0, 0.0, 1.0], np.float32)
- [R1, _] = cv2.Rodrigues(z_axis * np.radians(THETA))
- [R2, _] = cv2.Rodrigues(np.dot(R1, y_axis) * np.radians(-PHI))
-
- xyz = xyz.reshape([height * width, 3]).T
- xyz = np.dot(R1, xyz)
- xyz = np.dot(R2, xyz).T
- lat = np.arcsin(xyz[:, 2])
- lon = np.arctan2(xyz[:, 1], xyz[:, 0])
-
- lon = lon.reshape([height, width]) / np.pi * 180
- lat = -lat.reshape([height, width]) / np.pi * 180
-
- lon = lon / 180 * equ_cx + equ_cx
- lat = lat / 90 * equ_cy + equ_cy
-
- persp = cv2.remap(
- img,
- lon.astype(np.float32),
- lat.astype(np.float32),
- cv2.INTER_CUBIC,
- borderMode=cv2.BORDER_WRAP,
- )
- return persp
diff --git a/roop-unleashed-main/run.py b/roop-unleashed-main/run.py
deleted file mode 100644
index b52e5cc4a8ea9ce5cadd4e7111fb15531f380314..0000000000000000000000000000000000000000
--- a/roop-unleashed-main/run.py
+++ /dev/null
@@ -1,6 +0,0 @@
-#!/usr/bin/env python3
-
-from roop import core
-
-if __name__ == '__main__':
- core.run()
diff --git a/roop-unleashed-main/runMacOS.sh b/roop-unleashed-main/runMacOS.sh
deleted file mode 100644
index c72ac8d76e177d087d5641128553ae1aeae1ae20..0000000000000000000000000000000000000000
--- a/roop-unleashed-main/runMacOS.sh
+++ /dev/null
@@ -1,48 +0,0 @@
-#!/bin/bash
-
-# Check if we are in the correct repository directory
-if [ ! -f "run.py" ]; then
- echo "run.py not found!"
- exit 1
-fi
-
-# Create a hidden Python 3.11 virtual environment in the .venv folder
-VENV_DIR=".venv"
-
-# Check if Python 3.11 is installed
-if ! brew list --versions python@3.11 >/dev/null; then
- echo "Python 3.11 is not installed. Please install it first."
- exit 1
-fi
-
-# Use Python 3.11 to create the virtual environment
-echo "Creating a virtual environment using Python 3.11..."
-python3.11 -m venv $VENV_DIR
-
-# Activate the virtual environment
-echo "Activating the virtual environment..."
-source "$VENV_DIR/bin/activate"
-
-# Check if the activation was successful
-if [ "$VIRTUAL_ENV" != "" ]; then
- echo "Virtual environment activated successfully."
-else
- echo "Failed to activate the virtual environment."
- exit 1
-fi
-
-# Install dependencies from requirements.txt
-if [ -f "requirements.txt" ]; then
- echo "Installing dependencies from requirements.txt..."
- pip install -r requirements.txt
-else
- echo "requirements.txt not found. Skipping dependency installation."
-fi
-
-# Run roop-unleashed. This can take a while - especially at first startup...
-echo "Running the run.py script..."
-python run.py
-
-# Deactivate the virtual environment after execution
-echo "Deactivating the virtual environment..."
-deactivate
\ No newline at end of file
diff --git a/roop-unleashed-main/settings.py b/roop-unleashed-main/settings.py
deleted file mode 100644
index c13de94b7ac4d9d921969281800605077870a5d0..0000000000000000000000000000000000000000
--- a/roop-unleashed-main/settings.py
+++ /dev/null
@@ -1,69 +0,0 @@
-import yaml
-
-class Settings:
- def __init__(self, config_file):
- self.config_file = config_file
- self.load()
-
- def default_get(_, data, name, default):
- value = default
- try:
- value = data.get(name, default)
- except:
- pass
- return value
-
-
- def load(self):
- try:
- with open(self.config_file, 'r') as f:
- data = yaml.load(f, Loader=yaml.FullLoader)
- except:
- data = None
-
- self.selected_theme = self.default_get(data, 'selected_theme', "Default")
- self.server_name = self.default_get(data, 'server_name', "")
- self.server_port = self.default_get(data, 'server_port', 0)
- self.server_share = self.default_get(data, 'server_share', False)
- self.output_image_format = self.default_get(data, 'output_image_format', 'png')
- self.output_video_format = self.default_get(data, 'output_video_format', 'mp4')
- self.output_video_codec = self.default_get(data, 'output_video_codec', 'libx264')
- self.video_quality = self.default_get(data, 'video_quality', 14)
- self.clear_output = self.default_get(data, 'clear_output', True)
- self.max_threads = self.default_get(data, 'max_threads', 2)
- self.memory_limit = self.default_get(data, 'memory_limit', 0)
- self.provider = self.default_get(data, 'provider', 'cuda')
- self.force_cpu = self.default_get(data, 'force_cpu', False)
- self.output_template = self.default_get(data, 'output_template', '{file}_{time}')
- self.use_os_temp_folder = self.default_get(data, 'use_os_temp_folder', False)
- self.output_show_video = self.default_get(data, 'output_show_video', True)
- self.launch_browser = self.default_get(data, 'launch_browser', True)
-
-
-
-
-
- def save(self):
- data = {
- 'selected_theme': self.selected_theme,
- 'server_name': self.server_name,
- 'server_port': self.server_port,
- 'server_share': self.server_share,
- 'output_image_format' : self.output_image_format,
- 'output_video_format' : self.output_video_format,
- 'output_video_codec' : self.output_video_codec,
- 'video_quality' : self.video_quality,
- 'clear_output' : self.clear_output,
- 'max_threads' : self.max_threads,
- 'memory_limit' : self.memory_limit,
- 'provider' : self.provider,
- 'force_cpu' : self.force_cpu,
- 'output_template' : self.output_template,
- 'use_os_temp_folder' : self.use_os_temp_folder,
- 'output_show_video' : self.output_show_video
- }
- with open(self.config_file, 'w') as f:
- yaml.dump(data, f)
-
-
-
diff --git a/roop-unleashed-main/ui/globals.py b/roop-unleashed-main/ui/globals.py
deleted file mode 100644
index dc9495e2ee58f86f6d8a642013d372898e78bcdf..0000000000000000000000000000000000000000
--- a/roop-unleashed-main/ui/globals.py
+++ /dev/null
@@ -1,17 +0,0 @@
-ui_restart_server = False
-
-SELECTION_FACES_DATA = None
-ui_SELECTED_INPUT_FACE_INDEX = 0
-
-ui_selected_enhancer = None
-ui_upscale = None
-ui_blend_ratio = None
-ui_input_thumbs = []
-ui_target_thumbs = []
-ui_camera_frame = None
-ui_selected_swap_model = None
-
-
-
-
-
diff --git a/roop-unleashed-main/ui/main.py b/roop-unleashed-main/ui/main.py
deleted file mode 100644
index 94cab10f1887fc100451b6f18078f4822196ae0d..0000000000000000000000000000000000000000
--- a/roop-unleashed-main/ui/main.py
+++ /dev/null
@@ -1,96 +0,0 @@
-import os
-import time
-import gradio as gr
-import roop.globals
-import roop.metadata
-import roop.utilities as util
-import ui.globals as uii
-
-from ui.tabs.faceswap_tab import faceswap_tab
-from ui.tabs.livecam_tab import livecam_tab
-from ui.tabs.facemgr_tab import facemgr_tab
-from ui.tabs.extras_tab import extras_tab
-from ui.tabs.settings_tab import settings_tab
-
-roop.globals.keep_fps = None
-roop.globals.keep_frames = None
-roop.globals.skip_audio = None
-roop.globals.use_batch = None
-
-
-def prepare_environment():
- roop.globals.output_path = os.path.abspath(os.path.join(os.getcwd(), "output"))
- os.makedirs(roop.globals.output_path, exist_ok=True)
- if not roop.globals.CFG.use_os_temp_folder:
- os.environ["TEMP"] = os.environ["TMP"] = os.path.abspath(os.path.join(os.getcwd(), "temp"))
- os.makedirs(os.environ["TEMP"], exist_ok=True)
- os.environ["GRADIO_TEMP_DIR"] = os.environ["TEMP"]
- os.environ['GRADIO_ANALYTICS_ENABLED'] = '0'
-
-def run():
- from roop.core import decode_execution_providers, set_display_ui
-
- prepare_environment()
-
- set_display_ui(show_msg)
- if roop.globals.CFG.provider == "cuda" and util.has_cuda_device() == False:
- roop.globals.CFG.provider = "cpu"
-
- roop.globals.execution_providers = decode_execution_providers([roop.globals.CFG.provider])
- gputype = util.get_device()
- if gputype == 'cuda':
- util.print_cuda_info()
-
- print(f'Using provider {roop.globals.execution_providers} - Device:{gputype}')
-
- run_server = True
- uii.ui_restart_server = False
- mycss = """
- span {color: var(--block-info-text-color)}
- #fixedheight {
- max-height: 238.4px;
- overflow-y: auto !important;
- }
- .image-container.svelte-1l6wqyv {height: 100%}
-
- """
-
- while run_server:
- server_name = roop.globals.CFG.server_name
- if server_name is None or len(server_name) < 1:
- server_name = None
- server_port = roop.globals.CFG.server_port
- if server_port <= 0:
- server_port = None
- ssl_verify = False if server_name == '0.0.0.0' else True
- with gr.Blocks(title=f'{roop.metadata.name} {roop.metadata.version}', theme=roop.globals.CFG.selected_theme, css=mycss, delete_cache=(60, 86400)) as ui:
- with gr.Row(variant='compact'):
- gr.Markdown(f"### [{roop.metadata.name} {roop.metadata.version}](https://github.com/C0untFloyd/roop-unleashed)")
- gr.HTML(util.create_version_html(), elem_id="versions")
- faceswap_tab()
- livecam_tab()
- facemgr_tab()
- extras_tab()
- settings_tab()
- launch_browser = roop.globals.CFG.launch_browser
-
- uii.ui_restart_server = False
- try:
- ui.queue().launch(inbrowser=launch_browser, server_name=server_name, server_port=server_port, share=roop.globals.CFG.server_share, ssl_verify=ssl_verify, prevent_thread_lock=True, show_error=True)
- except Exception as e:
- print(f'Exception {e} when launching Gradio Server!')
- uii.ui_restart_server = True
- run_server = False
- try:
- while uii.ui_restart_server == False:
- time.sleep(1.0)
-
- except (KeyboardInterrupt, OSError):
- print("Keyboard interruption in main thread... closing server.")
- run_server = False
- ui.close()
-
-
-def show_msg(msg: str):
- gr.Info(msg)
-
diff --git a/roop-unleashed-main/ui/tabs/extras_tab.py b/roop-unleashed-main/ui/tabs/extras_tab.py
deleted file mode 100644
index 7a1eb25c446fc1be59b32a2808d98008d55fbba9..0000000000000000000000000000000000000000
--- a/roop-unleashed-main/ui/tabs/extras_tab.py
+++ /dev/null
@@ -1,245 +0,0 @@
-import os
-import gradio as gr
-import shutil
-import roop.utilities as util
-import roop.util_ffmpeg as ffmpeg
-import roop.globals
-from roop.utilities import clean_dir
-
-frame_filters_map = {
- "Colorize B/W Images (Deoldify Artistic)" : {"colorizer" : {"subtype": "deoldify_artistic"}},
- "Colorize B/W Images (Deoldify Stable)" : {"colorizer" : {"subtype": "deoldify_stable"}},
- "Background remove" : {"removebg" : {"subtype": ""}},
- "Filter Stylize" : {"filter_generic" : {"subtype" : "stylize" }},
- "Filter Detail Enhance" : {"filter_generic" : {"subtype" : "detailenhance" }},
- "Filter Pencil Sketch" : {"filter_generic" : {"subtype" : "pencil" }},
- "Filter Cartoon" : {"filter_generic" : {"subtype" : "cartoon" }},
- "Filter C64" : {"filter_generic" : {"subtype" : "C64" }}
- }
-
-frame_upscalers_map = {
- "ESRGAN x2" : {"upscale" : {"subtype": "esrganx2"}},
- "ESRGAN x4" : {"upscale" : {"subtype": "esrganx4"}},
- "LSDIR x4" : {"upscale" : {"subtype": "lsdirx4"}}
-}
-
-def extras_tab():
- filternames = ["None"]
- for f in frame_filters_map.keys():
- filternames.append(f)
- upscalernames = ["None"]
- for f in frame_upscalers_map.keys():
- upscalernames.append(f)
-
- with gr.Tab("๐ Extras"):
- with gr.Row():
- files_to_process = gr.Files(label='File(s) to process', file_count="multiple", file_types=["image", "video"])
- with gr.Row(variant='panel'):
- with gr.Accordion(label="Video/GIF", open=False):
- with gr.Row(variant='panel'):
- with gr.Column():
- gr.Markdown("""
- # Poor man's video editor
- Re-encoding uses your configuration from the Settings Tab.
- """)
- with gr.Column():
- cut_start_time = gr.Slider(0, 1000000, value=0, label="Start Frame", step=1.0, interactive=True)
- with gr.Column():
- cut_end_time = gr.Slider(1, 1000000, value=1, label="End Frame", step=1.0, interactive=True)
- with gr.Column():
- extras_chk_encode = gr.Checkbox(label='Re-encode videos (necessary for videos with different codecs)', value=False)
- start_cut_video = gr.Button("Cut video")
- start_extract_frames = gr.Button("Extract frames")
- start_join_videos = gr.Button("Join videos")
-
- with gr.Row(variant='panel'):
- with gr.Column():
- gr.Markdown("""
- # Create video/gif from images
- """)
- with gr.Column():
- extras_fps = gr.Slider(minimum=0, maximum=120, value=30, label="Video FPS", step=1.0, interactive=True)
- extras_images_folder = gr.Textbox(show_label=False, placeholder="/content/", interactive=True)
- with gr.Column():
- extras_chk_creategif = gr.Checkbox(label='Create GIF from video', value=False)
- extras_create_video=gr.Button("Create")
- with gr.Row(variant='panel'):
- with gr.Column():
- gr.Markdown("""
- # Create video from gif
- """)
- with gr.Column():
- extras_video_fps = gr.Slider(minimum=0, maximum=120, value=0, label="Video FPS", step=1.0, interactive=True)
- with gr.Column():
- extras_create_video_from_gif=gr.Button("Create")
- with gr.Row(variant='panel'):
- with gr.Column(scale=2):
- gr.Markdown("""
- # Repair video
-
- Uses FFMpeg to fix corrupt videos.
- """)
- with gr.Column():
- extras_repair_video=gr.Button("Repair")
-
-
- with gr.Row(variant='panel'):
- with gr.Accordion(label="Full frame processing", open=True):
- with gr.Row(variant='panel'):
- filterselection = gr.Dropdown(filternames, value="None", label="Colorizer/FilterFX", interactive=True)
- upscalerselection = gr.Dropdown(upscalernames, value="None", label="Enhancer", interactive=True)
- with gr.Row(variant='panel'):
- start_frame_process=gr.Button("Start processing")
-
- with gr.Row():
- gr.Button("๐ Open Output Folder", size='sm').click(fn=lambda: util.open_folder(roop.globals.output_path))
- with gr.Row():
- extra_files_output = gr.Files(label='Resulting output files', file_count="multiple")
-
- start_cut_video.click(fn=on_cut_video, inputs=[files_to_process, cut_start_time, cut_end_time, extras_chk_encode], outputs=[extra_files_output])
- start_extract_frames.click(fn=on_extras_extract_frames, inputs=[files_to_process], outputs=[extra_files_output])
- start_join_videos.click(fn=on_join_videos, inputs=[files_to_process, extras_chk_encode], outputs=[extra_files_output])
- extras_create_video.click(fn=on_extras_create_video, inputs=[files_to_process, extras_images_folder, extras_fps, extras_chk_creategif], outputs=[extra_files_output])
- extras_create_video_from_gif.click(fn=on_extras_create_video_from_gif, inputs=[files_to_process, extras_video_fps], outputs=[extra_files_output])
- extras_repair_video.click(fn=on_extras_repair_video, inputs=[files_to_process], outputs=[extra_files_output])
- start_frame_process.click(fn=on_frame_process, inputs=[files_to_process, filterselection, upscalerselection], outputs=[extra_files_output])
-
-
-def on_cut_video(files, cut_start_frame, cut_end_frame, reencode):
- if files is None:
- return None
-
- resultfiles = []
- for tf in files:
- f = tf.name
- destfile = util.get_destfilename_from_path(f, roop.globals.output_path, '_cut')
- ffmpeg.cut_video(f, destfile, cut_start_frame, cut_end_frame, reencode)
- if os.path.isfile(destfile):
- resultfiles.append(destfile)
- else:
- gr.Error('Cutting video failed!')
- return resultfiles
-
-
-def on_join_videos(files, chk_encode):
- if files is None:
- return None
-
- filenames = []
- for f in files:
- filenames.append(f.name)
- destfile = util.get_destfilename_from_path(filenames[0], roop.globals.output_path, '_join')
- sorted_filenames = util.sort_filenames_ignore_path(filenames)
- ffmpeg.join_videos(sorted_filenames, destfile, not chk_encode)
- resultfiles = []
- if os.path.isfile(destfile):
- resultfiles.append(destfile)
- else:
- gr.Error('Joining videos failed!')
- return resultfiles
-
-def on_extras_create_video_from_gif(files,fps):
- if files is None:
- return None
-
- filenames = []
- resultfiles = []
- for f in files:
- filenames.append(f.name)
-
- destfilename = os.path.join(roop.globals.output_path, "img2video." + roop.globals.CFG.output_video_format)
- ffmpeg.create_video_from_gif(filenames[0], destfilename)
- if os.path.isfile(destfilename):
- resultfiles.append(destfilename)
- return resultfiles
-
-
-def on_extras_repair_video(files):
- if files is None:
- return None
-
- resultfiles = []
- for tf in files:
- f = tf.name
- destfile = util.get_destfilename_from_path(f, roop.globals.output_path, '_repair')
- ffmpeg.repair_video(f, destfile)
- if os.path.isfile(destfile):
- resultfiles.append(destfile)
- else:
- gr.Error('Repairing video failed!')
- return resultfiles
-
-
-
-
-
-def on_extras_create_video(files, images_path,fps, create_gif):
- if images_path is None:
- return None
- resultfiles = []
- if len(files) > 0 and util.is_video(files[0]) and create_gif:
- destfilename = files[0]
- else:
- util.sort_rename_frames(os.path.dirname(images_path))
- destfilename = os.path.join(roop.globals.output_path, "img2video." + roop.globals.CFG.output_video_format)
- ffmpeg.create_video('', destfilename, fps, images_path)
- if os.path.isfile(destfilename):
- resultfiles.append(destfilename)
- else:
- return None
- if create_gif:
- gifname = util.get_destfilename_from_path(destfilename, './output', '.gif')
- ffmpeg.create_gif_from_video(destfilename, gifname)
- if os.path.isfile(destfilename):
- resultfiles.append(gifname)
- return resultfiles
-
-
-def on_extras_extract_frames(files):
- if files is None:
- return None
-
- resultfiles = []
- for tf in files:
- f = tf.name
- resfolder = ffmpeg.extract_frames(f)
- for file in os.listdir(resfolder):
- outfile = os.path.join(resfolder, file)
- if os.path.isfile(outfile):
- resultfiles.append(outfile)
- return resultfiles
-
-
-def on_frame_process(files, filterselection, upscaleselection):
- import pathlib
- from roop.core import batch_process_with_options
- from roop.ProcessEntry import ProcessEntry
- from roop.ProcessOptions import ProcessOptions
- from ui.main import prepare_environment
-
-
- if files is None:
- return None
-
- if roop.globals.CFG.clear_output:
- clean_dir(roop.globals.output_path)
- prepare_environment()
- list_files_process : list[ProcessEntry] = []
-
- for tf in files:
- list_files_process.append(ProcessEntry(tf.name, 0,0, 0))
-
- processoroptions = {}
- filter = next((x for x in frame_filters_map.keys() if x == filterselection), None)
- if filter is not None:
- processoroptions.update(frame_filters_map[filter])
- filter = next((x for x in frame_upscalers_map.keys() if x == upscaleselection), None)
- if filter is not None:
- processoroptions.update(frame_upscalers_map[filter])
- options = ProcessOptions(processoroptions, 0, 0, "all", 0, None, None, 0, 128, False, False)
- batch_process_with_options(list_files_process, options, None)
- outdir = pathlib.Path(roop.globals.output_path)
- outfiles = [str(item) for item in outdir.rglob("*") if item.is_file()]
- return outfiles
-
-
diff --git a/roop-unleashed-main/ui/tabs/facemgr_tab.py b/roop-unleashed-main/ui/tabs/facemgr_tab.py
deleted file mode 100644
index fa3ecc94e9b57ffd891190755f77f5061f171611..0000000000000000000000000000000000000000
--- a/roop-unleashed-main/ui/tabs/facemgr_tab.py
+++ /dev/null
@@ -1,187 +0,0 @@
-import os
-import shutil
-import cv2
-import gradio as gr
-import roop.utilities as util
-import roop.globals
-from roop.face_util import extract_face_images
-from roop.capturer import get_video_frame, get_video_frame_total
-from typing import List, Tuple, Optional
-from roop.typing import Frame, Face, FaceSet
-
-selected_face_index = -1
-thumbs = []
-images = []
-
-
-def facemgr_tab() -> None:
- with gr.Tab("๐จโ๐ฉโ๐งโ๐ฆ Face Management"):
- with gr.Row():
- gr.Markdown("""
- # Create blending facesets
- Add multiple reference images into a faceset file.
- """)
- with gr.Row():
- videoimagefst = gr.Image(label="Cut face from video frame", height=576, interactive=False, visible=True, format="jpeg")
- with gr.Row():
- frame_num_fst = gr.Slider(1, 1, value=1, label="Frame Number", info='0:00:00', step=1.0, interactive=False)
- fb_cutfromframe = gr.Button("Use faces from this frame", variant='secondary', interactive=False)
- with gr.Row():
- fb_facesetfile = gr.Files(label='Faceset', file_count='single', file_types=['.fsz'], interactive=True)
- fb_files = gr.Files(label='Input Files', file_count="multiple", file_types=["image", "video"], interactive=True)
- with gr.Row():
- with gr.Column():
- gr.Button("๐ Open Output Folder", size='sm').click(fn=lambda: util.open_folder(roop.globals.output_path))
- with gr.Column():
- gr.Markdown(' ')
- with gr.Row():
- faces = gr.Gallery(label="Faces in this Faceset", allow_preview=True, preview=True, height=128, object_fit="scale-down")
- with gr.Row():
- fb_remove = gr.Button("Remove selected", variant='secondary')
- fb_update = gr.Button("Create/Update Faceset file", variant='primary')
- fb_clear = gr.Button("Clear all", variant='stop')
-
- fb_facesetfile.change(fn=on_faceset_changed, inputs=[fb_facesetfile], outputs=[faces])
- fb_files.change(fn=on_fb_files_changed, inputs=[fb_files], outputs=[faces, videoimagefst, frame_num_fst, fb_cutfromframe])
- fb_update.click(fn=on_update_clicked, outputs=[fb_facesetfile])
- fb_remove.click(fn=on_remove_clicked, outputs=[faces])
- fb_clear.click(fn=on_clear_clicked, outputs=[faces, fb_files, fb_facesetfile])
- fb_cutfromframe.click(fn=on_cutfromframe_clicked, inputs=[fb_files, frame_num_fst], outputs=[faces])
- frame_num_fst.release(fn=on_frame_num_fst_changed, inputs=[fb_files, frame_num_fst], outputs=[videoimagefst])
- faces.select(fn=on_face_selected)
-
-
-def on_faceset_changed(faceset, progress=gr.Progress()) -> List[Frame]:
- global thumbs, images
-
- if faceset is None:
- return thumbs
-
- thumbs.clear()
- filename = faceset.name
-
- if filename.lower().endswith('fsz'):
- progress(0, desc="Retrieving faces from Faceset File", )
- unzipfolder = os.path.join(os.environ["TEMP"], 'faceset')
- if os.path.isdir(unzipfolder):
- shutil.rmtree(unzipfolder)
- util.mkdir_with_umask(unzipfolder)
- util.unzip(filename, unzipfolder)
- for file in os.listdir(unzipfolder):
- if file.endswith(".png"):
- SELECTION_FACES_DATA = extract_face_images(os.path.join(unzipfolder,file), (False, 0), 0.5)
- if len(SELECTION_FACES_DATA) < 1:
- gr.Warning(f"No face detected in {file}!")
- for f in SELECTION_FACES_DATA:
- image = f[1]
- images.append(image)
- thumbs.append(util.convert_to_gradio(image))
-
- return thumbs
-
-
-def on_fb_files_changed(inputfiles, progress=gr.Progress()) -> Tuple[List[Frame], Optional[gr.Image], Optional[gr.Slider], Optional[gr.Button]]:
- global thumbs, images, total_frames, current_video_fps
-
- if inputfiles is None or len(inputfiles) < 1:
- return thumbs, None, None, None
-
- progress(0, desc="Retrieving faces from images", )
- slider = None
- video_image = None
- cut_button = None
- for f in inputfiles:
- source_path = f.name
- if util.has_image_extension(source_path):
- slider = gr.Slider(interactive=False)
- video_image = gr.Image(interactive=False)
- cut_button = gr.Button(interactive=False)
- roop.globals.source_path = source_path
- SELECTION_FACES_DATA = extract_face_images(roop.globals.source_path, (False, 0), 0.5)
- for f in SELECTION_FACES_DATA:
- image = f[1]
- images.append(image)
- thumbs.append(util.convert_to_gradio(image))
- elif util.is_video(source_path) or source_path.lower().endswith('gif'):
- total_frames = get_video_frame_total(source_path)
- current_video_fps = util.detect_fps(source_path)
- cut_button = gr.Button(interactive=True)
- video_image, slider = display_video_frame(source_path, 1, total_frames)
-
- return thumbs, video_image, slider, cut_button
-
-
-def display_video_frame(filename: str, frame_num: int, total: int=0) -> Tuple[gr.Image, gr.Slider]:
- global current_video_fps
-
- current_frame = get_video_frame(filename, frame_num)
- if current_video_fps == 0:
- current_video_fps = 1
- secs = (frame_num - 1) / current_video_fps
- minutes = secs / 60
- secs = secs % 60
- hours = minutes / 60
- minutes = minutes % 60
- milliseconds = (secs - int(secs)) * 1000
- timeinfo = f"{int(hours):0>2}:{int(minutes):0>2}:{int(secs):0>2}.{int(milliseconds):0>3}"
- if total > 0:
- return gr.Image(value=util.convert_to_gradio(current_frame), interactive=True), gr.Slider(info=timeinfo, minimum=1, maximum=total, interactive=True)
- return gr.Image(value=util.convert_to_gradio(current_frame), interactive=True), gr.Slider(info=timeinfo, interactive=True)
-
-
-def on_face_selected(evt: gr.SelectData) -> None:
- global selected_face_index
-
- if evt is not None:
- selected_face_index = evt.index
-
-def on_frame_num_fst_changed(inputfiles: List[gr.Files], frame_num: int) -> Frame:
- filename = inputfiles[0].name
- video_image, _ = display_video_frame(filename, frame_num, 0)
- return video_image
-
-
-def on_cutfromframe_clicked(inputfiles: List[gr.Files], frame_num: int) -> List[Frame]:
- global thumbs
-
- filename = inputfiles[0].name
- SELECTION_FACES_DATA = extract_face_images(filename, (True, frame_num), 0.5)
- for f in SELECTION_FACES_DATA:
- image = f[1]
- images.append(image)
- thumbs.append(util.convert_to_gradio(image))
- return thumbs
-
-
-def on_remove_clicked() -> List[Frame]:
- global thumbs, images, selected_face_index
-
- if len(thumbs) > selected_face_index:
- f = thumbs.pop(selected_face_index)
- del f
- f = images.pop(selected_face_index)
- del f
- return thumbs
-
-def on_clear_clicked() -> Tuple[List[Frame], None, None]:
- global thumbs, images
-
- thumbs.clear()
- images.clear()
- return thumbs, None, None
-
-
-def on_update_clicked() -> Optional[str]:
- if len(images) < 1:
- gr.Warning(f"No faces to create faceset from!")
- return None
-
- imgnames = []
- for index,img in enumerate(images):
- filename = os.path.join(roop.globals.output_path, f'{index}.png')
- cv2.imwrite(filename, img)
- imgnames.append(filename)
-
- finalzip = os.path.join(roop.globals.output_path, 'faceset.fsz')
- util.zip(imgnames, finalzip)
- return finalzip
diff --git a/roop-unleashed-main/ui/tabs/faceswap_tab.py b/roop-unleashed-main/ui/tabs/faceswap_tab.py
deleted file mode 100644
index a5fdeeaa2b163e4c294e6bb94ca6d175344d7aee..0000000000000000000000000000000000000000
--- a/roop-unleashed-main/ui/tabs/faceswap_tab.py
+++ /dev/null
@@ -1,835 +0,0 @@
-import os
-import shutil
-import pathlib
-import gradio as gr
-import roop.utilities as util
-import roop.globals
-import ui.globals
-from roop.face_util import extract_face_images, create_blank_image
-from roop.capturer import get_video_frame, get_video_frame_total, get_image_frame
-from roop.ProcessEntry import ProcessEntry
-from roop.ProcessOptions import ProcessOptions
-from roop.FaceSet import FaceSet
-from roop.utilities import clean_dir
-
-last_image = None
-
-
-IS_INPUT = True
-SELECTED_FACE_INDEX = 0
-
-SELECTED_INPUT_FACE_INDEX = 0
-SELECTED_TARGET_FACE_INDEX = 0
-
-input_faces = None
-target_faces = None
-face_selection = None
-previewimage = None
-
-selected_preview_index = 0
-
-is_processing = False
-
-list_files_process : list[ProcessEntry] = []
-model_swap_choices = ["InSwapper 128", "ReSwapper 128", "ReSwapper 256"]
-
-no_face_choices = ["Use untouched original frame","Retry rotated", "Skip Frame", "Skip Frame if no similar face", "Use last swapped"]
-swap_choices = ["First found", "All input faces", "All input faces (random)", "All female", "All male", "All faces", "Selected face"]
-
-current_video_fps = 50
-
-manual_masking = False
-
-
-def faceswap_tab():
- global no_face_choices, previewimage
-
- with gr.Tab("๐ญ Face Swap"):
- with gr.Row(variant='panel'):
- with gr.Column(scale=2):
- with gr.Row():
- input_faces = gr.Gallery(label="Input faces gallery", allow_preview=False, preview=False, height=138, columns=64, object_fit="scale-down", interactive=False)
- target_faces = gr.Gallery(label="Target faces gallery", allow_preview=False, preview=False, height=138, columns=64, object_fit="scale-down", interactive=False)
- with gr.Row():
- bt_move_left_input = gr.Button("โฌ
Move left", size='sm')
- bt_move_right_input = gr.Button("โก Move right", size='sm')
- bt_move_left_target = gr.Button("โฌ
Move left", size='sm')
- bt_move_right_target = gr.Button("โก Move right", size='sm')
- with gr.Row():
- bt_remove_selected_input_face = gr.Button("โ Remove selected", size='sm')
- bt_clear_input_faces = gr.Button("๐ฅ Clear all", variant='stop', size='sm')
- bt_remove_selected_target_face = gr.Button("โ Remove selected", size='sm')
- bt_add_local = gr.Button('Add local files from', size='sm')
-
- with gr.Row():
- with gr.Column(scale=2):
- with gr.Accordion(label="Advanced Masking", open=False):
- chk_showmaskoffsets = gr.Checkbox(
- label="Show mask overlay in preview",
- value=False,
- interactive=True,
- )
- chk_restoreoriginalmouth = gr.Checkbox(
- label="Restore original mouth area",
- value=False,
- interactive=True,
- )
- mask_top = gr.Slider(
- 0,
- 1.0,
- value=0,
- label="Offset Face Top",
- step=0.01,
- interactive=True,
- )
- mask_bottom = gr.Slider(
- 0,
- 1.0,
- value=0,
- label="Offset Face Bottom",
- step=0.01,
- interactive=True,
- )
- mask_left = gr.Slider(
- 0,
- 1.0,
- value=0,
- label="Offset Face Left",
- step=0.01,
- interactive=True,
- )
- mask_right = gr.Slider(
- 0,
- 1.0,
- value=0,
- label="Offset Face Right",
- step=0.01,
- interactive=True,
- )
- mask_erosion = gr.Slider(
- 1.0,
- 3.0,
- value=1.0,
- label="Erosion Iterations",
- step=1.00,
- interactive=True,
- )
- mask_blur = gr.Slider(
- 10.0,
- 50.0,
- value=20.0,
- label="Blur size",
- step=1.00,
- interactive=True,
- )
- bt_toggle_masking = gr.Button(
- "Toggle manual masking", variant="secondary", size="sm"
- )
- selected_mask_engine = gr.Dropdown(
- ["None", "Clip2Seg", "DFL XSeg"],
- value="None",
- label="Face masking engine",
- )
- clip_text = gr.Textbox(
- label="List of objects to mask and restore back on fake face",
- value="cup,hands,hair,banana",
- interactive=False,
- )
- bt_preview_mask = gr.Button(
- "๐ฅ Show Mask Preview", variant="secondary"
- )
- with gr.Column(scale=2):
- local_folder = gr.Textbox(show_label=False, placeholder="/content/", interactive=True)
- with gr.Row(variant='panel'):
- bt_srcfiles = gr.Files(label='Source Images or Facesets', file_count="multiple", file_types=["image", ".fsz", ".webp"], elem_id='filelist', height=233)
- bt_destfiles = gr.Files(label='Target File(s)', file_count="multiple", file_types=["image", "video", ".webp"], elem_id='filelist', height=233)
- with gr.Row(variant='panel'):
- ui.globals.ui_selected_swap_model = gr.Dropdown(model_swap_choices, value=model_swap_choices[0], label="Specify Face Swap Model")
- forced_fps = gr.Slider(minimum=0, maximum=120, value=0, label="Video FPS", info='Overrides detected fps if not 0', step=1.0, interactive=True, container=True)
-
- with gr.Column(scale=2):
- previewimage = gr.Image(label="Preview Image", height=576, interactive=False, visible=True, format=get_gradio_output_format())
- maskimage = gr.ImageEditor(label="Manual mask Image", sources=["clipboard"], transforms="", type="numpy",
- brush=gr.Brush(color_mode="fixed", colors=["rgba(255, 255, 255, 1"]), interactive=True, visible=False)
- with gr.Row(variant='panel'):
- fake_preview = gr.Checkbox(label="Face swap frames", value=False)
- bt_refresh_preview = gr.Button("๐ Refresh", variant='secondary', size='sm')
- bt_use_face_from_preview = gr.Button("Use Face from this Frame", variant='primary', size='sm')
- with gr.Row():
- preview_frame_num = gr.Slider(1, 1, value=1, label="Frame Number", info='0:00:00', step=1.0, interactive=True)
- with gr.Row():
- text_frame_clip = gr.Markdown('Processing frame range [0 - 0]')
- set_frame_start = gr.Button("โฌ
Set as Start", size='sm')
- set_frame_end = gr.Button("โก Set as End", size='sm')
- with gr.Row(visible=False) as dynamic_face_selection:
- with gr.Column(scale=2):
- face_selection = gr.Gallery(label="Detected faces", allow_preview=False, preview=False, height=138, object_fit="cover", columns=32)
- with gr.Column():
- bt_faceselect = gr.Button("โ Use selected face", size='sm')
- bt_cancelfaceselect = gr.Button("Done", size='sm')
- with gr.Column():
- gr.Markdown(' ')
-
- with gr.Row(variant='panel'):
- with gr.Column(scale=1):
- selected_face_detection = gr.Dropdown(swap_choices, value="First found", label="Specify face selection for swapping")
- with gr.Column(scale=1):
- num_swap_steps = gr.Slider(1, 5, value=1, step=1.0, label="Number of swapping steps", info="More steps may increase likeness")
- with gr.Column(scale=2):
- ui.globals.ui_selected_enhancer = gr.Dropdown(["None", "Codeformer", "DMDNet", "GFPGAN", "GPEN", "Restoreformer++"], value="None", label="Select post-processing")
-
- with gr.Row(variant='panel'):
- with gr.Column(scale=1):
- max_face_distance = gr.Slider(0.01, 1.0, value=0.65, label="Max Face Similarity Threshold", info="0.0 = identical 1.0 = no similarity")
- with gr.Column(scale=1):
- ui.globals.ui_upscale = gr.Dropdown(["128px", "256px", "512px"], value="128px", label="Subsample upscale to", interactive=True)
- with gr.Column(scale=2):
- ui.globals.ui_blend_ratio = gr.Slider(0.0, 1.0, value=0.65, label="Original/Enhanced image blend ratio", info="Only used with active post-processing")
-
- with gr.Row(variant='panel'):
- with gr.Column(scale=1):
- video_swapping_method = gr.Dropdown(["Extract Frames to media","In-Memory processing"], value="In-Memory processing", label="Select video processing method", interactive=True)
- no_face_action = gr.Dropdown(choices=no_face_choices, value=no_face_choices[0], label="Action on no face detected", interactive=True)
- vr_mode = gr.Checkbox(label="VR Mode", value=False)
- with gr.Column(scale=1):
- with gr.Group():
- autorotate = gr.Checkbox(label="Auto rotate horizontal Faces", value=True)
- roop.globals.skip_audio = gr.Checkbox(label="Skip audio", value=False)
- roop.globals.keep_frames = gr.Checkbox(label="Keep Frames (relevant only when extracting frames)", value=False)
- roop.globals.wait_after_extraction = gr.Checkbox(label="Wait for user key press before creating video ", value=False)
-
- with gr.Row(variant='panel'):
- with gr.Column():
- bt_start = gr.Button("โถ Start", variant='primary')
- with gr.Column():
- bt_stop = gr.Button("โน Stop", variant='secondary', interactive=False)
- gr.Button("๐ Open Output Folder", size='sm').click(fn=lambda: util.open_folder(roop.globals.output_path))
- with gr.Column(scale=2):
- output_method = gr.Dropdown(["File","Virtual Camera", "Both"], value="File", label="Select Output Method", interactive=True)
- with gr.Row(variant='panel'):
- with gr.Column():
- resultfiles = gr.Files(label='Processed File(s)', interactive=False)
- with gr.Column():
- resultimage = gr.Image(type='filepath', label='Final Image', interactive=False )
- resultvideo = gr.Video(label='Final Video', interactive=False, visible=False)
-
- previewinputs = [ui.globals.ui_selected_swap_model, preview_frame_num, bt_destfiles, fake_preview, ui.globals.ui_selected_enhancer, selected_face_detection,
- max_face_distance, ui.globals.ui_blend_ratio, selected_mask_engine, clip_text, no_face_action, vr_mode, autorotate, maskimage, chk_showmaskoffsets, chk_restoreoriginalmouth, num_swap_steps, ui.globals.ui_upscale]
- previewoutputs = [previewimage, maskimage, preview_frame_num]
- input_faces.select(on_select_input_face, None, None).success(fn=on_preview_frame_changed, inputs=previewinputs, outputs=previewoutputs)
-
- bt_move_left_input.click(fn=move_selected_input, inputs=[bt_move_left_input], outputs=[input_faces])
- bt_move_right_input.click(fn=move_selected_input, inputs=[bt_move_right_input], outputs=[input_faces])
- bt_move_left_target.click(fn=move_selected_target, inputs=[bt_move_left_target], outputs=[target_faces])
- bt_move_right_target.click(fn=move_selected_target, inputs=[bt_move_right_target], outputs=[target_faces])
-
- bt_remove_selected_input_face.click(fn=remove_selected_input_face, outputs=[input_faces])
- bt_srcfiles.change(fn=on_srcfile_changed, show_progress='full', inputs=bt_srcfiles, outputs=[dynamic_face_selection, face_selection, input_faces, bt_srcfiles])
-
- mask_top.release(fn=on_mask_top_changed, inputs=[mask_top], show_progress='hidden')
- mask_bottom.release(fn=on_mask_bottom_changed, inputs=[mask_bottom], show_progress='hidden')
- mask_left.release(fn=on_mask_left_changed, inputs=[mask_left], show_progress='hidden')
- mask_right.release(fn=on_mask_right_changed, inputs=[mask_right], show_progress='hidden')
- mask_erosion.release(fn=on_mask_erosion_changed, inputs=[mask_erosion], show_progress='hidden')
- mask_blur.release(fn=on_mask_blur_changed, inputs=[mask_blur], show_progress='hidden')
- selected_mask_engine.change(fn=on_mask_engine_changed, inputs=[selected_mask_engine], outputs=[clip_text], show_progress='hidden')
-
- target_faces.select(on_select_target_face, None, None)
- bt_remove_selected_target_face.click(fn=remove_selected_target_face, outputs=[target_faces])
-
- forced_fps.change(fn=on_fps_changed, inputs=[forced_fps], show_progress='hidden')
- bt_destfiles.change(fn=on_destfiles_changed, inputs=[bt_destfiles], outputs=[preview_frame_num, text_frame_clip], show_progress='hidden').success(fn=on_preview_frame_changed, inputs=previewinputs, outputs=previewoutputs, show_progress='hidden')
- bt_destfiles.select(fn=on_destfiles_selected, outputs=[preview_frame_num, text_frame_clip, forced_fps], show_progress='hidden').success(fn=on_preview_frame_changed, inputs=previewinputs, outputs=previewoutputs, show_progress='hidden')
- bt_destfiles.clear(fn=on_clear_destfiles, outputs=[target_faces, selected_face_detection])
- resultfiles.select(fn=on_resultfiles_selected, inputs=[resultfiles], outputs=[resultimage, resultvideo])
-
- face_selection.select(on_select_face, None, None)
- bt_faceselect.click(fn=on_selected_face, outputs=[input_faces, target_faces, selected_face_detection])
- bt_cancelfaceselect.click(fn=on_end_face_selection, outputs=[dynamic_face_selection, face_selection])
-
- bt_clear_input_faces.click(fn=on_clear_input_faces, outputs=[input_faces])
-
- bt_add_local.click(fn=on_add_local_folder, inputs=[local_folder], outputs=[bt_destfiles])
- bt_preview_mask.click(fn=on_preview_mask, inputs=[preview_frame_num, bt_destfiles, clip_text, selected_mask_engine], outputs=[previewimage])
-
- start_event = bt_start.click(fn=start_swap,
- inputs=[ui.globals.ui_selected_swap_model, output_method, ui.globals.ui_selected_enhancer, selected_face_detection, roop.globals.keep_frames, roop.globals.wait_after_extraction,
- roop.globals.skip_audio, max_face_distance, ui.globals.ui_blend_ratio, selected_mask_engine, clip_text,video_swapping_method, no_face_action, vr_mode, autorotate, chk_restoreoriginalmouth, num_swap_steps, ui.globals.ui_upscale, maskimage],
- outputs=[bt_start, bt_stop, resultfiles], show_progress='full')
- after_swap_event = start_event.success(fn=on_resultfiles_finished, inputs=[resultfiles], outputs=[resultimage, resultvideo])
-
- bt_stop.click(fn=stop_swap, cancels=[start_event, after_swap_event], outputs=[bt_start, bt_stop], queue=False)
-
- bt_refresh_preview.click(fn=on_preview_frame_changed, inputs=previewinputs, outputs=previewoutputs)
- bt_toggle_masking.click(fn=on_toggle_masking, inputs=[previewimage, maskimage], outputs=[previewimage, maskimage])
- fake_preview.change(fn=on_preview_frame_changed, inputs=previewinputs, outputs=previewoutputs)
- preview_frame_num.release(fn=on_preview_frame_changed, inputs=previewinputs, outputs=previewoutputs, show_progress='hidden', )
- bt_use_face_from_preview.click(fn=on_use_face_from_selected, show_progress='full', inputs=[bt_destfiles, preview_frame_num], outputs=[dynamic_face_selection, face_selection, target_faces, selected_face_detection])
- set_frame_start.click(fn=on_set_frame, inputs=[set_frame_start, preview_frame_num], outputs=[text_frame_clip])
- set_frame_end.click(fn=on_set_frame, inputs=[set_frame_end, preview_frame_num], outputs=[text_frame_clip])
-
-
-def on_mask_top_changed(mask_offset):
- set_mask_offset(0, mask_offset)
-
-def on_mask_bottom_changed(mask_offset):
- set_mask_offset(1, mask_offset)
-
-def on_mask_left_changed(mask_offset):
- set_mask_offset(2, mask_offset)
-
-def on_mask_right_changed(mask_offset):
- set_mask_offset(3, mask_offset)
-
-def on_mask_erosion_changed(mask_offset):
- set_mask_offset(4, mask_offset)
-def on_mask_blur_changed(mask_offset):
- set_mask_offset(5, mask_offset)
-
-
-def set_mask_offset(index, mask_offset):
- global SELECTED_INPUT_FACE_INDEX
-
- if len(roop.globals.INPUT_FACESETS) > SELECTED_INPUT_FACE_INDEX:
- offs = roop.globals.INPUT_FACESETS[SELECTED_INPUT_FACE_INDEX].faces[0].mask_offsets
- offs[index] = mask_offset
- if offs[0] + offs[1] > 0.99:
- offs[0] = 0.99
- offs[1] = 0.0
- if offs[2] + offs[3] > 0.99:
- offs[2] = 0.99
- offs[3] = 0.0
- roop.globals.INPUT_FACESETS[SELECTED_INPUT_FACE_INDEX].faces[0].mask_offsets = offs
-
-def on_mask_engine_changed(mask_engine):
- if mask_engine == "Clip2Seg":
- return gr.Textbox(interactive=True)
- return gr.Textbox(interactive=False)
-
-
-def on_add_local_folder(folder):
- files = util.get_local_files_from_folder(folder)
- if files is None:
- gr.Warning("Empty folder or folder not found!")
- return files
-
-
-def on_srcfile_changed(srcfiles, progress=gr.Progress()):
- global SELECTION_FACES_DATA, IS_INPUT, input_faces, face_selection, last_image
-
- IS_INPUT = True
-
- if srcfiles is None or len(srcfiles) < 1:
- return gr.Column(visible=False), None, ui.globals.ui_input_thumbs, None
-
- for f in srcfiles:
- source_path = f.name
- if source_path.lower().endswith('fsz'):
- progress(0, desc="Retrieving faces from Faceset File")
- unzipfolder = os.path.join(os.environ["TEMP"], 'faceset')
- if os.path.isdir(unzipfolder):
- files = os.listdir(unzipfolder)
- for file in files:
- os.remove(os.path.join(unzipfolder, file))
- else:
- os.makedirs(unzipfolder)
- util.mkdir_with_umask(unzipfolder)
- util.unzip(source_path, unzipfolder)
- is_first = True
- face_set = FaceSet()
- for file in os.listdir(unzipfolder):
- if file.endswith(".png"):
- filename = os.path.join(unzipfolder,file)
- progress(0, desc="Extracting faceset")
- SELECTION_FACES_DATA = extract_face_images(filename, (False, 0))
- for f in SELECTION_FACES_DATA:
- face = f[0]
- face.mask_offsets = (0,0,0,0,1,20)
- face_set.faces.append(face)
- if is_first:
- image = util.convert_to_gradio(f[1])
- ui.globals.ui_input_thumbs.append(image)
- is_first = False
- face_set.ref_images.append(get_image_frame(filename))
- if len(face_set.faces) > 0:
- if len(face_set.faces) > 1:
- face_set.AverageEmbeddings()
- roop.globals.INPUT_FACESETS.append(face_set)
-
- elif util.has_image_extension(source_path):
- progress(0, desc="Retrieving faces from image")
- roop.globals.source_path = source_path
- SELECTION_FACES_DATA = extract_face_images(roop.globals.source_path, (False, 0))
- progress(0.5, desc="Retrieving faces from image")
- for f in SELECTION_FACES_DATA:
- face_set = FaceSet()
- face = f[0]
- face.mask_offsets = (0,0,0,0,1,20)
- face_set.faces.append(face)
- image = util.convert_to_gradio(f[1])
- ui.globals.ui_input_thumbs.append(image)
- roop.globals.INPUT_FACESETS.append(face_set)
-
- progress(1.0)
- return gr.Column(visible=False), None, ui.globals.ui_input_thumbs,None
-
-
-def on_select_input_face(evt: gr.SelectData):
- global SELECTED_INPUT_FACE_INDEX
-
- SELECTED_INPUT_FACE_INDEX = evt.index
-
-
-def remove_selected_input_face():
- global SELECTED_INPUT_FACE_INDEX
-
- if len(roop.globals.INPUT_FACESETS) > SELECTED_INPUT_FACE_INDEX:
- f = roop.globals.INPUT_FACESETS.pop(SELECTED_INPUT_FACE_INDEX)
- del f
- if len(ui.globals.ui_input_thumbs) > SELECTED_INPUT_FACE_INDEX:
- f = ui.globals.ui_input_thumbs.pop(SELECTED_INPUT_FACE_INDEX)
- del f
-
- return ui.globals.ui_input_thumbs
-
-def move_selected_input(button_text):
- global SELECTED_INPUT_FACE_INDEX
-
- if button_text == "โฌ
Move left":
- if SELECTED_INPUT_FACE_INDEX <= 0:
- return ui.globals.ui_input_thumbs
- offset = -1
- else:
- if len(ui.globals.ui_input_thumbs) <= SELECTED_INPUT_FACE_INDEX:
- return ui.globals.ui_input_thumbs
- offset = 1
-
- f = roop.globals.INPUT_FACESETS.pop(SELECTED_INPUT_FACE_INDEX)
- roop.globals.INPUT_FACESETS.insert(SELECTED_INPUT_FACE_INDEX + offset, f)
- f = ui.globals.ui_input_thumbs.pop(SELECTED_INPUT_FACE_INDEX)
- ui.globals.ui_input_thumbs.insert(SELECTED_INPUT_FACE_INDEX + offset, f)
- return ui.globals.ui_input_thumbs
-
-
-def move_selected_target(button_text):
- global SELECTED_TARGET_FACE_INDEX
-
- if button_text == "โฌ
Move left":
- if SELECTED_TARGET_FACE_INDEX <= 0:
- return ui.globals.ui_target_thumbs
- offset = -1
- else:
- if len(ui.globals.ui_target_thumbs) <= SELECTED_TARGET_FACE_INDEX:
- return ui.globals.ui_target_thumbs
- offset = 1
-
- f = roop.globals.TARGET_FACES.pop(SELECTED_TARGET_FACE_INDEX)
- roop.globals.TARGET_FACES.insert(SELECTED_TARGET_FACE_INDEX + offset, f)
- f = ui.globals.ui_target_thumbs.pop(SELECTED_TARGET_FACE_INDEX)
- ui.globals.ui_target_thumbs.insert(SELECTED_TARGET_FACE_INDEX + offset, f)
- return ui.globals.ui_target_thumbs
-
-
-
-
-def on_select_target_face(evt: gr.SelectData):
- global SELECTED_TARGET_FACE_INDEX
-
- SELECTED_TARGET_FACE_INDEX = evt.index
-
-def remove_selected_target_face():
- if len(ui.globals.ui_target_thumbs) > SELECTED_TARGET_FACE_INDEX:
- f = roop.globals.TARGET_FACES.pop(SELECTED_TARGET_FACE_INDEX)
- del f
- if len(ui.globals.ui_target_thumbs) > SELECTED_TARGET_FACE_INDEX:
- f = ui.globals.ui_target_thumbs.pop(SELECTED_TARGET_FACE_INDEX)
- del f
- return ui.globals.ui_target_thumbs
-
-
-def on_use_face_from_selected(files, frame_num):
- global IS_INPUT, SELECTION_FACES_DATA
-
- IS_INPUT = False
- thumbs = []
-
- roop.globals.target_path = files[selected_preview_index].name
- if util.is_image(roop.globals.target_path) and not roop.globals.target_path.lower().endswith(('gif')):
- SELECTION_FACES_DATA = extract_face_images(roop.globals.target_path, (False, 0))
- if len(SELECTION_FACES_DATA) > 0:
- for f in SELECTION_FACES_DATA:
- image = util.convert_to_gradio(f[1])
- thumbs.append(image)
- else:
- gr.Info('No faces detected!')
- roop.globals.target_path = None
-
- elif util.is_video(roop.globals.target_path) or roop.globals.target_path.lower().endswith(('gif')):
- selected_frame = frame_num
- SELECTION_FACES_DATA = extract_face_images(roop.globals.target_path, (True, selected_frame))
- if len(SELECTION_FACES_DATA) > 0:
- for f in SELECTION_FACES_DATA:
- image = util.convert_to_gradio(f[1])
- thumbs.append(image)
- else:
- gr.Info('No faces detected!')
- roop.globals.target_path = None
- else:
- gr.Info('Unknown image/video type!')
- roop.globals.target_path = None
-
- if len(thumbs) == 1:
- roop.globals.TARGET_FACES.append(SELECTION_FACES_DATA[0][0])
- ui.globals.ui_target_thumbs.append(thumbs[0])
- return gr.Row(visible=False), None, ui.globals.ui_target_thumbs, gr.Dropdown(value='Selected face')
-
- return gr.Row(visible=True), thumbs, gr.Gallery(visible=True), gr.Dropdown(visible=True)
-
-
-def on_select_face(evt: gr.SelectData): # SelectData is a subclass of EventData
- global SELECTED_FACE_INDEX
- SELECTED_FACE_INDEX = evt.index
-
-
-def on_selected_face():
- global IS_INPUT, SELECTED_FACE_INDEX, SELECTION_FACES_DATA
-
- fd = SELECTION_FACES_DATA[SELECTED_FACE_INDEX]
- image = util.convert_to_gradio(fd[1])
- if IS_INPUT:
- face_set = FaceSet()
- fd[0].mask_offsets = (0,0,0,0,1,20)
- face_set.faces.append(fd[0])
- roop.globals.INPUT_FACESETS.append(face_set)
- ui.globals.ui_input_thumbs.append(image)
- return ui.globals.ui_input_thumbs, gr.Gallery(visible=True), gr.Dropdown(visible=True)
- else:
- roop.globals.TARGET_FACES.append(fd[0])
- ui.globals.ui_target_thumbs.append(image)
- return gr.Gallery(visible=True), ui.globals.ui_target_thumbs, gr.Dropdown(value='Selected face')
-
-# bt_faceselect.click(fn=on_selected_face, outputs=[dynamic_face_selection, face_selection, input_faces, target_faces])
-
-def on_end_face_selection():
- return gr.Column(visible=False), None
-
-
-def on_preview_frame_changed(swap_model, frame_num, files, fake_preview, enhancer, detection, face_distance, blend_ratio,
- selected_mask_engine, clip_text, no_face_action, vr_mode, auto_rotate, maskimage, show_face_area, restore_original_mouth, num_steps, upsample):
- global SELECTED_INPUT_FACE_INDEX, manual_masking, current_video_fps
-
- from roop.core import live_swap, get_processing_plugins
-
- manual_masking = False
- mask_offsets = (0,0,0,0)
- if len(roop.globals.INPUT_FACESETS) > SELECTED_INPUT_FACE_INDEX:
- if not hasattr(roop.globals.INPUT_FACESETS[SELECTED_INPUT_FACE_INDEX].faces[0], 'mask_offsets'):
- roop.globals.INPUT_FACESETS[SELECTED_INPUT_FACE_INDEX].faces[0].mask_offsets = mask_offsets
- mask_offsets = roop.globals.INPUT_FACESETS[SELECTED_INPUT_FACE_INDEX].faces[0].mask_offsets
-
- timeinfo = '0:00:00'
- if files is None or selected_preview_index >= len(files) or frame_num is None:
- return None,None, gr.Slider(info=timeinfo)
-
- filename = files[selected_preview_index].name
- if util.is_video(filename) or filename.lower().endswith('gif'):
- current_frame = get_video_frame(filename, frame_num)
- if current_video_fps == 0:
- current_video_fps = 1
- secs = (frame_num - 1) / current_video_fps
- minutes = secs / 60
- secs = secs % 60
- hours = minutes / 60
- minutes = minutes % 60
- milliseconds = (secs - int(secs)) * 1000
- timeinfo = f"{int(hours):0>2}:{int(minutes):0>2}:{int(secs):0>2}.{int(milliseconds):0>3}"
- else:
- current_frame = get_image_frame(filename)
- if current_frame is None:
- return None, None, gr.Slider(info=timeinfo)
-
- layers = None
- if maskimage is not None:
- layers = maskimage["layers"]
-
- if not fake_preview or len(roop.globals.INPUT_FACESETS) < 1:
- return gr.Image(value=util.convert_to_gradio(current_frame), visible=True), gr.ImageEditor(visible=False), gr.Slider(info=timeinfo)
-
- roop.globals.face_swap_mode = translate_swap_mode(detection)
- roop.globals.selected_enhancer = enhancer
- roop.globals.distance_threshold = face_distance
- roop.globals.blend_ratio = blend_ratio
- roop.globals.no_face_action = index_of_no_face_action(no_face_action)
- roop.globals.vr_mode = vr_mode
- roop.globals.autorotate_faces = auto_rotate
- roop.globals.subsample_size = int(upsample[:3])
-
-
- mask_engine = map_mask_engine(selected_mask_engine, clip_text)
-
- roop.globals.execution_threads = roop.globals.CFG.max_threads
- mask = layers[0] if layers is not None else None
- face_index = SELECTED_INPUT_FACE_INDEX
- if len(roop.globals.INPUT_FACESETS) <= face_index:
- face_index = 0
-
- options = ProcessOptions(swap_model, get_processing_plugins(mask_engine), roop.globals.distance_threshold, roop.globals.blend_ratio,
- roop.globals.face_swap_mode, face_index, clip_text, maskimage, num_steps, roop.globals.subsample_size, show_face_area, restore_original_mouth)
-
- current_frame = live_swap(current_frame, options)
- if current_frame is None:
- return gr.Image(visible=True), None, gr.Slider(info=timeinfo)
- return gr.Image(value=util.convert_to_gradio(current_frame), visible=True), gr.ImageEditor(visible=False), gr.Slider(info=timeinfo)
-
-def map_mask_engine(selected_mask_engine, clip_text):
- if selected_mask_engine == "Clip2Seg":
- mask_engine = "mask_clip2seg"
- if clip_text is None or len(clip_text) < 1:
- mask_engine = None
- elif selected_mask_engine == "DFL XSeg":
- mask_engine = "mask_xseg"
- else:
- mask_engine = None
- return mask_engine
-
-
-def on_toggle_masking(previewimage, mask):
- global manual_masking
-
- manual_masking = not manual_masking
- if manual_masking:
- layers = mask["layers"]
- if len(layers) == 1:
- layers = [create_blank_image(previewimage.shape[1],previewimage.shape[0])]
- return gr.Image(visible=False), gr.ImageEditor(value={"background": previewimage, "layers": layers, "composite": None}, visible=True)
- return gr.Image(visible=True), gr.ImageEditor(visible=False)
-
-def gen_processing_text(start, end):
- return f'Processing frame range [{start} - {end}]'
-
-def on_set_frame(sender:str, frame_num):
- global selected_preview_index, list_files_process
-
- idx = selected_preview_index
- if list_files_process[idx].endframe == 0:
- return gen_processing_text(0,0)
-
- start = list_files_process[idx].startframe
- end = list_files_process[idx].endframe
- if sender.lower().endswith('start'):
- list_files_process[idx].startframe = min(frame_num, end)
- else:
- list_files_process[idx].endframe = max(frame_num, start)
-
- return gen_processing_text(list_files_process[idx].startframe,list_files_process[idx].endframe)
-
-
-def on_preview_mask(frame_num, files, clip_text, mask_engine):
- from roop.core import live_swap, get_processing_plugins
- global is_processing
-
- if is_processing or files is None or selected_preview_index >= len(files) or clip_text is None or frame_num is None:
- return None
-
- filename = files[selected_preview_index].name
- if util.is_video(filename) or filename.lower().endswith('gif'):
- current_frame = get_video_frame(filename, frame_num
- )
- else:
- current_frame = get_image_frame(filename)
- if current_frame is None or mask_engine is None:
- return None
- if mask_engine == "Clip2Seg":
- mask_engine = "mask_clip2seg"
- if clip_text is None or len(clip_text) < 1:
- mask_engine = None
- elif mask_engine == "DFL XSeg":
- mask_engine = "mask_xseg"
- options = ProcessOptions(get_processing_plugins(mask_engine), roop.globals.distance_threshold, roop.globals.blend_ratio,
- "all", 0, clip_text, None, 0, 128, False, False, True)
-
- current_frame = live_swap(current_frame, options)
- return util.convert_to_gradio(current_frame)
-
-
-def on_clear_input_faces():
- ui.globals.ui_input_thumbs.clear()
- roop.globals.INPUT_FACESETS.clear()
- return ui.globals.ui_input_thumbs
-
-def on_clear_destfiles():
- roop.globals.TARGET_FACES.clear()
- ui.globals.ui_target_thumbs.clear()
- return ui.globals.ui_target_thumbs, gr.Dropdown(value="First found")
-
-
-def index_of_no_face_action(dropdown_text):
- global no_face_choices
-
- return no_face_choices.index(dropdown_text)
-
-def translate_swap_mode(dropdown_text):
- if dropdown_text == "Selected face":
- return "selected"
- elif dropdown_text == "First found":
- return "first"
- elif dropdown_text == "All input faces":
- return "all_input"
- elif dropdown_text == "All input faces (random)":
- return "all_random"
- elif dropdown_text == "All female":
- return "all_female"
- elif dropdown_text == "All male":
- return "all_male"
-
- return "all"
-
-
-def start_swap( swap_model, output_method, enhancer, detection, keep_frames, wait_after_extraction, skip_audio, face_distance, blend_ratio,
- selected_mask_engine, clip_text, processing_method, no_face_action, vr_mode, autorotate, restore_original_mouth, num_swap_steps, upsample, imagemask, progress=gr.Progress()):
- from ui.main import prepare_environment
- from roop.core import batch_process_regular
- global is_processing, list_files_process
-
- if list_files_process is None or len(list_files_process) <= 0:
- return gr.Button(variant="primary"), None, None
-
- if roop.globals.CFG.clear_output:
- clean_dir(roop.globals.output_path)
-
- if not util.is_installed("ffmpeg"):
- msg = "ffmpeg is not installed! No video processing possible."
- gr.Warning(msg)
-
- prepare_environment()
-
- roop.globals.selected_enhancer = enhancer
- roop.globals.target_path = None
- roop.globals.distance_threshold = face_distance
- roop.globals.blend_ratio = blend_ratio
- roop.globals.keep_frames = keep_frames
- roop.globals.wait_after_extraction = wait_after_extraction
- roop.globals.skip_audio = skip_audio
- roop.globals.face_swap_mode = translate_swap_mode(detection)
- roop.globals.no_face_action = index_of_no_face_action(no_face_action)
- roop.globals.vr_mode = vr_mode
- roop.globals.autorotate_faces = autorotate
- roop.globals.subsample_size = int(upsample[:3])
- mask_engine = map_mask_engine(selected_mask_engine, clip_text)
-
- if roop.globals.face_swap_mode == 'selected':
- if len(roop.globals.TARGET_FACES) < 1:
- gr.Error('No Target Face selected!')
- return gr.Button(variant="primary"), None, None
-
- is_processing = True
- yield gr.Button(variant="secondary", interactive=False), gr.Button(variant="primary", interactive=True), None
- roop.globals.execution_threads = roop.globals.CFG.max_threads
- roop.globals.video_encoder = roop.globals.CFG.output_video_codec
- roop.globals.video_quality = roop.globals.CFG.video_quality
- roop.globals.max_memory = roop.globals.CFG.memory_limit if roop.globals.CFG.memory_limit > 0 else None
-
- batch_process_regular(swap_model, output_method, list_files_process, mask_engine, clip_text, processing_method == "In-Memory processing", imagemask, restore_original_mouth, num_swap_steps, progress, SELECTED_INPUT_FACE_INDEX)
- is_processing = False
- outdir = pathlib.Path(roop.globals.output_path)
- outfiles = [str(item) for item in outdir.rglob("*") if item.is_file()]
- if len(outfiles) > 0:
- yield gr.Button(variant="primary", interactive=True),gr.Button(variant="secondary", interactive=False),gr.Files(value=outfiles)
- else:
- yield gr.Button(variant="primary", interactive=True),gr.Button(variant="secondary", interactive=False),None
-
-
-def stop_swap():
- roop.globals.processing = False
- gr.Info('Aborting processing - please wait for the remaining threads to be stopped')
- return gr.Button(variant="primary", interactive=True),gr.Button(variant="secondary", interactive=False),None
-
-
-def on_fps_changed(fps):
- global selected_preview_index, list_files_process
-
- if len(list_files_process) < 1 or list_files_process[selected_preview_index].endframe < 1:
- return
- list_files_process[selected_preview_index].fps = fps
-
-
-def on_destfiles_changed(destfiles):
- global selected_preview_index, list_files_process, current_video_fps
-
- if destfiles is None or len(destfiles) < 1:
- list_files_process.clear()
- return gr.Slider(value=1, maximum=1, info='0:00:00'), ''
-
- for f in destfiles:
- list_files_process.append(ProcessEntry(f.name, 0,0, 0))
-
- selected_preview_index = 0
- idx = selected_preview_index
-
- filename = list_files_process[idx].filename
-
- if util.is_video(filename) or filename.lower().endswith('gif'):
- total_frames = get_video_frame_total(filename)
- if total_frames is None or total_frames < 1:
- total_frames = 1
- gr.Warning(f"Corrupted video {filename}, can't detect number of frames!")
- else:
- current_video_fps = util.detect_fps(filename)
- else:
- total_frames = 1
- list_files_process[idx].endframe = total_frames
- if total_frames > 1:
- return gr.Slider(value=1, maximum=total_frames, info='0:00:00'), gen_processing_text(list_files_process[idx].startframe,list_files_process[idx].endframe)
- return gr.Slider(value=1, maximum=total_frames, info='0:00:00'), ''
-
-
-def on_destfiles_selected(evt: gr.SelectData):
- global selected_preview_index, list_files_process, current_video_fps
-
- if evt is not None:
- selected_preview_index = evt.index
- idx = selected_preview_index
- filename = list_files_process[idx].filename
- fps = list_files_process[idx].fps
- if util.is_video(filename) or filename.lower().endswith('gif'):
- total_frames = get_video_frame_total(filename)
- current_video_fps = util.detect_fps(filename)
- if list_files_process[idx].endframe == 0:
- list_files_process[idx].endframe = total_frames
- else:
- total_frames = 1
-
- if total_frames > 1:
- return gr.Slider(value=list_files_process[idx].startframe, maximum=total_frames, info='0:00:00'), gen_processing_text(list_files_process[idx].startframe,list_files_process[idx].endframe), fps
- return gr.Slider(value=1, maximum=total_frames, info='0:00:00'), gen_processing_text(0,0), fps
-
-
-def on_resultfiles_selected(evt: gr.SelectData, files):
- selected_index = evt.index
- filename = files[selected_index].name
- return display_output(filename)
-
-def on_resultfiles_finished(files):
- selected_index = 0
- if files is None or len(files) < 1:
- return None, None
-
- filename = files[selected_index].name
- return display_output(filename)
-
-
-def get_gradio_output_format():
- if roop.globals.CFG.output_image_format == "jpg":
- return "jpeg"
- return roop.globals.CFG.output_image_format
-
-
-def display_output(filename):
- if util.is_video(filename) and roop.globals.CFG.output_show_video:
- return gr.Image(visible=False), gr.Video(visible=True, value=filename)
- else:
- if util.is_video(filename) or filename.lower().endswith('gif'):
- current_frame = get_video_frame(filename)
- else:
- current_frame = get_image_frame(filename)
- return gr.Image(visible=True, value=util.convert_to_gradio(current_frame)), gr.Video(visible=False)
diff --git a/roop-unleashed-main/ui/tabs/livecam_tab.py b/roop-unleashed-main/ui/tabs/livecam_tab.py
deleted file mode 100644
index a9985cc6b6d85b2bbb5b6bc708e356f01bc916b9..0000000000000000000000000000000000000000
--- a/roop-unleashed-main/ui/tabs/livecam_tab.py
+++ /dev/null
@@ -1,57 +0,0 @@
-import gradio as gr
-import roop.globals
-import ui.globals
-
-
-camera_frame = None
-
-def livecam_tab():
- with gr.Tab("๐ฅ Live Cam"):
- with gr.Row(variant='panel'):
- gr.Markdown("""
- This feature will allow you to use your physical webcam and apply the selected faces to the stream.
- You can also forward the stream to a virtual camera, which can be used in video calls or streaming software.
- Supported are: v4l2loopback (linux), OBS Virtual Camera (macOS/Windows) and unitycapture (Windows).
- **Please note:** to change the face or any other settings you need to stop and restart a running live cam.
- """)
-
- with gr.Row(variant='panel'):
- with gr.Column():
- bt_start = gr.Button("โถ Start", variant='primary')
- with gr.Column():
- bt_stop = gr.Button("โน Stop", variant='secondary', interactive=False)
- with gr.Column():
- camera_num = gr.Slider(0, 8, value=0, label="Camera Number", step=1.0, interactive=True)
- cb_obs = gr.Checkbox(label="Forward stream to virtual camera", interactive=True)
- with gr.Column():
- dd_reso = gr.Dropdown(choices=["640x480","1280x720", "1920x1080"], value="1280x720", label="Fake Camera Resolution", interactive=True)
- cb_xseg = gr.Checkbox(label="Use DFL Xseg masking", interactive=True, value=True)
- cb_mouthrestore = gr.Checkbox(label="Restore original mouth area", interactive=True, value=False)
-
- with gr.Row():
- fake_cam_image = gr.Image(label='Fake Camera Output', interactive=False, format="jpeg")
-
- start_event = bt_start.click(fn=start_cam, inputs=[ui.globals.ui_selected_swap_model, cb_obs, cb_xseg, cb_mouthrestore, camera_num, dd_reso, ui.globals.ui_selected_enhancer, ui.globals.ui_blend_ratio, ui.globals.ui_upscale],outputs=[bt_start, bt_stop,fake_cam_image])
- bt_stop.click(fn=stop_swap, cancels=[start_event], outputs=[bt_start, bt_stop], queue=False)
-
-
-def start_cam(swap_model, stream_to_obs, use_xseg, use_mouthrestore, cam, reso, enhancer, blend_ratio, upscale):
- from roop.virtualcam import start_virtual_cam
- from roop.utilities import convert_to_gradio
-
- roop.globals.selected_enhancer = enhancer
- roop.globals.blend_ratio = blend_ratio
- roop.globals.subsample_size = int(upscale[:3])
- start_virtual_cam(swap_model, stream_to_obs, use_xseg, use_mouthrestore, cam, reso)
- while True:
- yield gr.Button(interactive=False), gr.Button(interactive=True), convert_to_gradio(ui.globals.ui_camera_frame)
-
-
-def stop_swap():
- from roop.virtualcam import stop_virtual_cam
- stop_virtual_cam()
- return gr.Button(interactive=True), gr.Button(interactive=False)
-
-
-
-
diff --git a/roop-unleashed-main/ui/tabs/settings_tab.py b/roop-unleashed-main/ui/tabs/settings_tab.py
deleted file mode 100644
index 2cbe02793cb60d5a606743904fe876d8c2ec93b5..0000000000000000000000000000000000000000
--- a/roop-unleashed-main/ui/tabs/settings_tab.py
+++ /dev/null
@@ -1,129 +0,0 @@
-import shutil
-import os
-import gradio as gr
-import roop.globals
-import ui.globals
-from roop.utilities import clean_dir
-
-available_themes = ["Default", "gradio/glass", "gradio/monochrome", "gradio/seafoam", "gradio/soft", "gstaff/xkcd", "freddyaboulton/dracula_revamped", "ysharma/steampunk"]
-image_formats = ['jpg','png', 'webp']
-video_formats = ['avi','mkv', 'mp4', 'webm']
-video_codecs = ['libx264', 'libx265', 'libvpx-vp9', 'h264_nvenc', 'hevc_nvenc']
-providerlist = None
-
-settings_controls = []
-
-def settings_tab():
- from roop.core import suggest_execution_providers
- global providerlist
-
- providerlist = suggest_execution_providers()
- with gr.Tab("โ Settings"):
- with gr.Row():
- with gr.Column():
- themes = gr.Dropdown(available_themes, label="Theme", info="Change needs complete restart", value=roop.globals.CFG.selected_theme)
- with gr.Column():
- settings_controls.append(gr.Checkbox(label="Public Server", value=roop.globals.CFG.server_share, elem_id='server_share', interactive=True))
- settings_controls.append(gr.Checkbox(label='Clear output folder before each run', value=roop.globals.CFG.clear_output, elem_id='clear_output', interactive=True))
- output_template = gr.Textbox(label="Filename Output Template", info="(file extension is added automatically)", lines=1, placeholder='{file}_{time}', value=roop.globals.CFG.output_template)
- with gr.Column():
- input_server_name = gr.Textbox(label="Server Name", lines=1, info="Leave blank to run locally", value=roop.globals.CFG.server_name)
- with gr.Column():
- input_server_port = gr.Number(label="Server Port", precision=0, info="Leave at 0 to use default", value=roop.globals.CFG.server_port)
- with gr.Row():
- with gr.Column():
- settings_controls.append(gr.Dropdown(providerlist, label="Provider", value=roop.globals.CFG.provider, elem_id='provider', interactive=True))
- chk_det_size = gr.Checkbox(label="Use default Det-Size", value=True, elem_id='default_det_size', interactive=True)
- settings_controls.append(gr.Checkbox(label="Force CPU for Face Analyser", value=roop.globals.CFG.force_cpu, elem_id='force_cpu', interactive=True))
- max_threads = gr.Slider(1, 32, value=roop.globals.CFG.max_threads, label="Max. Number of Threads", info='default: 3', step=1.0, interactive=True)
- with gr.Column():
- memory_limit = gr.Slider(0, 128, value=roop.globals.CFG.memory_limit, label="Max. Memory to use (Gb)", info='0 meaning no limit', step=1.0, interactive=True)
- settings_controls.append(gr.Dropdown(image_formats, label="Image Output Format", info='default: png', value=roop.globals.CFG.output_image_format, elem_id='output_image_format', interactive=True))
- with gr.Column():
- settings_controls.append(gr.Dropdown(video_codecs, label="Video Codec", info='default: libx264', value=roop.globals.CFG.output_video_codec, elem_id='output_video_codec', interactive=True))
- settings_controls.append(gr.Dropdown(video_formats, label="Video Output Format", info='default: mp4', value=roop.globals.CFG.output_video_format, elem_id='output_video_format', interactive=True))
- video_quality = gr.Slider(0, 100, value=roop.globals.CFG.video_quality, label="Video Quality (crf)", info='default: 14', step=1.0, interactive=True)
- with gr.Column():
- with gr.Group():
- settings_controls.append(gr.Checkbox(label='Use OS temp folder', value=roop.globals.CFG.use_os_temp_folder, elem_id='use_os_temp_folder', interactive=True))
- settings_controls.append(gr.Checkbox(label='Show video in browser (re-encodes output)', value=roop.globals.CFG.output_show_video, elem_id='output_show_video', interactive=True))
- button_apply_restart = gr.Button("Restart Server", variant='primary')
- button_clean_temp = gr.Button("Clean temp folder")
- button_apply_settings = gr.Button("Apply Settings")
-
- chk_det_size.select(fn=on_option_changed)
-
- # Settings
- for s in settings_controls:
- s.select(fn=on_settings_changed)
- max_threads.input(fn=lambda a,b='max_threads':on_settings_changed_misc(a,b), inputs=[max_threads])
- memory_limit.input(fn=lambda a,b='memory_limit':on_settings_changed_misc(a,b), inputs=[memory_limit])
- video_quality.input(fn=lambda a,b='video_quality':on_settings_changed_misc(a,b), inputs=[video_quality])
-
- # button_clean_temp.click(fn=clean_temp, outputs=[bt_srcfiles, input_faces, target_faces, bt_destfiles])
- button_clean_temp.click(fn=clean_temp)
- button_apply_settings.click(apply_settings, inputs=[themes, input_server_name, input_server_port, output_template])
- button_apply_restart.click(restart)
-
-
-def on_option_changed(evt: gr.SelectData):
- attribname = evt.target.elem_id
- if isinstance(evt.target, gr.Checkbox):
- if hasattr(roop.globals, attribname):
- setattr(roop.globals, attribname, evt.selected)
- return
- elif isinstance(evt.target, gr.Dropdown):
- if hasattr(roop.globals, attribname):
- setattr(roop.globals, attribname, evt.value)
- return
- raise gr.Error(f'Unhandled Setting for {evt.target}')
-
-
-def on_settings_changed_misc(new_val, attribname):
- if hasattr(roop.globals.CFG, attribname):
- setattr(roop.globals.CFG, attribname, new_val)
- else:
- print("Didn't find attrib!")
-
-
-
-def on_settings_changed(evt: gr.SelectData):
- attribname = evt.target.elem_id
- if isinstance(evt.target, gr.Checkbox):
- if hasattr(roop.globals.CFG, attribname):
- setattr(roop.globals.CFG, attribname, evt.selected)
- return
- elif isinstance(evt.target, gr.Dropdown):
- if hasattr(roop.globals.CFG, attribname):
- setattr(roop.globals.CFG, attribname, evt.value)
- return
-
- raise gr.Error(f'Unhandled Setting for {evt.target}')
-
-def clean_temp():
- from ui.main import prepare_environment
-
- ui.globals.ui_input_thumbs.clear()
- roop.globals.INPUT_FACESETS.clear()
- roop.globals.TARGET_FACES.clear()
- ui.globals.ui_target_thumbs = []
- if not roop.globals.CFG.use_os_temp_folder:
- clean_dir(os.environ["TEMP"])
- prepare_environment()
- gr.Info('Temp Files removed')
- return None,None,None,None
-
-
-def apply_settings(themes, input_server_name, input_server_port, output_template):
- from ui.main import show_msg
-
- roop.globals.CFG.selected_theme = themes
- roop.globals.CFG.server_name = input_server_name
- roop.globals.CFG.server_port = input_server_port
- roop.globals.CFG.output_template = output_template
- roop.globals.CFG.save()
- show_msg('Settings saved')
-
-
-def restart():
- ui.globals.ui_restart_server = True