Delete app

app/.flake8

@@ -1,3 +0,0 @@
-[flake8]
-select = E3, E4, F
-per-file-ignores = roop/core.py:E402

app/.github/ISSUE_TEMPLATE/bug_report.md

@@ -1,39 +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 try to use a GPU?
-- [ ] No. I am not using the `---gpu` flag
-- [ ] NVIDIA
-- [ ] AMD
-- [ ] Intel
-- [ ] Mac
-
-**Screenshots**
-If applicable, add screenshots to help explain your problem.
-
-**Sanity Check**
-- [ ] I have the latest code from the github repository
-- [ ] I have followed the installation guide

app/.github/ISSUE_TEMPLATE/suggestion.md

@@ -1,11 +0,0 @@
----
-name: Suggestion
-about: Suggest an idea for this project
-title: ''
-labels: ''
-assignees: ''
-
----
-
-**Describe your suggestion**
-A clear and concise description of what you want to happen.

app/.github/examples/snapshot.mp4

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:6021bee403a32b33b9d0b12916fcc40fee520681910a3faaef4b1f7e66ee386e
-size 2435482

app/.github/workflows/ci.yml

@@ -1,33 +0,0 @@
-name: ci
-
-on: [ push, pull_request ]
-
-jobs:
- lint:
-  runs-on: ubuntu-latest
-  steps:
-  - name: Checkout
-    uses: actions/checkout@v2
-  - name: Set up Python 3.9
-    uses: actions/setup-python@v2
-    with:
-     python-version: 3.9
-  - run: pip install flake8
-  - run: pip install mypy
-  - run: flake8 run.py roop
-  - run: mypy run.py roop
- test:
-  runs-on: ubuntu-latest
-  steps:
-  - name: Checkout
-    uses: actions/checkout@v2
-  - name: Set up ffmpeg
-    uses: FedericoCarboni/setup-ffmpeg@v2
-  - name: Set up Python 3.9
-    uses: actions/setup-python@v2
-    with:
-     python-version: 3.9
-  - run: pip install -r requirements-ci.txt
-  - run: python run.py -s=.github/examples/source.jpg -t=.github/examples/target.mp4 -o=.github/examples/output.mp4
-  - run: ffmpeg -i .github/examples/snapshot.mp4 -i .github/examples/output.mp4 -filter_complex psnr -f null -
-

app/.gitignore

@@ -1,12 +0,0 @@
-.vs
-.idea
-models
-temp
-__pycache__
-*.pth
-/start.bat
-/env
-.vscode
-output
-temp
-config.yaml

app/Dockerfile

@@ -1,7 +0,0 @@
-FROM python:3.11
-WORKDIR /usr/src/app
-RUN apt-get update && apt-get install -y libgl1-mesa-glx
-COPY requirements.txt ./
-RUN pip install --no-cache-dir -r requirements.txt
-COPY . .
-CMD ["python", "run.py"]

app/LICENSE

@@ -1,661 +0,0 @@
-                    GNU AFFERO GENERAL PUBLIC LICENSE
-                       Version 3, 19 November 2007
-
- Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
- Everyone is permitted to copy and distribute verbatim copies
- of this license document, but changing it is not allowed.
-
-                            Preamble
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-
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-conveyed by you (or copies made from those copies), or (b) primarily
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-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
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-shall include the Corresponding Source for any work covered by version 3
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-following paragraph.
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-under version 3 of the GNU General Public License into a single
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-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
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-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
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-to choose that version for the Program.
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-  15. Disclaimer of Warranty.
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-APPLICABLE LAW.  EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT
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-IS WITH YOU.  SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF
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-GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
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-DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
-PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
-EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
-SUCH DAMAGES.
-
-  17. Interpretation of Sections 15 and 16.
-
-  If the disclaimer of warranty and limitation of liability provided
-above cannot be given local legal effect according to their terms,
-reviewing courts shall apply local law that most closely approximates
-an absolute waiver of all civil liability in connection with the
-Program, unless a warranty or assumption of liability accompanies a
-copy of the Program in return for a fee.
-
-                     END OF TERMS AND CONDITIONS
-
-            How to Apply These Terms to Your New Programs
-
-  If you develop a new program, and you want it to be of the greatest
-possible use to the public, the best way to achieve this is to make it
-free software which everyone can redistribute and change under these terms.
-
-  To do so, attach the following notices to the program.  It is safest
-to attach them to the start of each source file to most effectively
-state the exclusion of warranty; and each file should have at least
-the "copyright" line and a pointer to where the full notice is found.
-
-    <one line to give the program's name and a brief idea of what it does.>
-    Copyright (C) <year>  <name of author>
-
-    This program is free software: you can redistribute it and/or modify
-    it under the terms of the GNU 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 <https://www.gnu.org/licenses/>.
-
-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
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-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
-<https://www.gnu.org/licenses/>.

app/README.md

@@ -1,9 +0,0 @@
----
-title: face-swap
-app_file: run.py
-sdk: gradio
-sdk_version: 3.40.1
----
-# roop-unleashed
-
-WIP Version of roop-unleashed using Gradio UI

app/chain_img_processor/__init__.py

@@ -1,4 +0,0 @@
-from .image import ChainImgProcessor, ChainImgPlugin, get_single_image_processor, version
-from .video import ChainVideoProcessor, get_single_video_processor
-from .batchimage import ChainBatchImageProcessor
-from .ffmpeg_writer import FFMPEG_VideoWriter

app/chain_img_processor/batchimage.py

@@ -1,86 +0,0 @@
-from typing import Any, List, Callable
-import psutil
-import os
-from concurrent.futures import ThreadPoolExecutor, as_completed
-from queue import Queue
-from .image import ChainImgProcessor
-from tqdm import tqdm
-import cv2
-
-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 ChainBatchImageProcessor(ChainImgProcessor):
-    chain = None
-    func_params_gen = None
-    num_threads = 1
-
-    def __init__(self):
-        ChainImgProcessor.__init__(self)
-
-
-    def init_with_plugins(self):
-        self.init_plugins(["core"])
-        self.display_init_info()
-
-        init_on_start_arr = self.init_on_start.split(",")
-        for proc_id in init_on_start_arr:
-            self.init_processor(proc_id)
-
-    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.refresh()
-        progress.update(1)
-
-
-    def process_frames(self, source_files: List[str], target_files: List[str], current_files, update: Callable[[], None]) -> None:
-        for f in current_files:
-            temp_frame = cv2.imread(f)
-            if temp_frame is not None:
-                if self.func_params_gen:
-                    params = self.func_params_gen(None, temp_frame)
-                else:
-                    params = {}
-                resimg, _ = self.run_chain(temp_frame, params, self.chain)
-                if resimg is not None:
-                    i = source_files.index(f)
-                    cv2.imwrite(target_files[i], resimg)
-            if update:
-                update()
-
-
-    def run_batch_chain(self, source_files, target_files, threads:int = 1, chain = None, params_frame_gen_func = None):
-        self.chain = chain
-        self.func_params_gen = params_frame_gen_func
-        progress_bar_format = '{l_bar}{bar}| {n_fmt}/{total_fmt} [{elapsed}<{remaining}, {rate_fmt}{postfix}]'
-        total = len(source_files)
-        self.num_threads = threads
-        with tqdm(total=total, 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()
-

app/chain_img_processor/ffmpeg_writer.py

@@ -1,253 +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\nMoviePy 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()
-
-
-
-def ffmpeg_write_image(filename, image, logfile=False):
-    """ Writes an image (HxWx3 or HxWx4 numpy array) to a file, using
-        ffmpeg. """
-
-    if image.dtype != 'uint8':
-          image = image.astype("uint8")
-
-    cmd = [ FFMPEG_BINARY, '-y',
-           '-s', "%dx%d"%(image.shape[:2][::-1]),
-           "-f", 'rawvideo',
-           '-pix_fmt', "rgba" if (image.shape[2] == 4) else "rgb24",
-           '-i','-', filename]
-
-    if logfile:
-        log_file = open(filename + ".log", 'w+')
-    else:
-        log_file = sp.PIPE
-
-    popen_params = {"stdout": DEVNULL,
-                    "stderr": log_file,
-                    "stdin": sp.PIPE}
-
-    if os.name == "nt":
-        popen_params["creationflags"] = 0x08000000
-
-    proc = sp.Popen(cmd, **popen_params)
-    out, err = proc.communicate(image.tostring())
-
-    if proc.returncode:
-        err = "\n".join(["[MoviePy] Running : %s\n" % cmd,
-                         "WARNING: this command returned an error:",
-                         err.decode('utf8')])
-        raise IOError(err)
-
-    del proc
-    

app/chain_img_processor/image.py

@@ -1,176 +0,0 @@
-from jaa import JaaCore
-from roop.utilities import get_device
-
-
-from typing import Any
-
-version = "4.0.0"
-
-class ChainImgProcessor(JaaCore):
-
-    def __init__(self):
-        JaaCore.__init__(self)
-
-        self.processors:dict = {
-        }
-
-        self.processors_objects:dict[str,list[ChainImgPlugin]] = {}
-
-        self.default_chain = ""
-        self.init_on_start = ""
-
-        self.inited_processors = []
-
-        self.is_demo_row_render = False
-
-    def process_plugin_manifest(self, modname, manifest):
-        # adding processors from plugin manifest
-        if "img_processor" in manifest:  # process commands
-            for cmd in manifest["img_processor"].keys():
-                self.processors[cmd] = manifest["img_processor"][cmd]
-
-        return manifest
-
-    def init_with_plugins(self):
-        self.init_plugins(["core"])
-        self.display_init_info()
-
-        #self.init_translator_engine(self.default_translator)
-        init_on_start_arr = self.init_on_start.split(",")
-        for proc_id in init_on_start_arr:
-            self.init_processor(proc_id)
-
-    def run_chain(self, img, params:dict[str,Any] = None, chain:str = None, thread_index:int = 0):
-        if chain is None:
-            chain = self.default_chain
-        if params is None:
-            params = {}
-        params["_thread_index"] = thread_index
-        chain_ar = chain.split(",")
-        # init all not inited processors first
-        for proc_id in chain_ar:
-            if proc_id != "":
-                if not proc_id in self.inited_processors:
-                    self.init_processor(proc_id)
-
-
-
-        # run processing
-        if self.is_demo_row_render:
-            import cv2
-            import numpy as np
-            height, width, channels = img.shape
-            img_blank = np.zeros((height+30, width*(1+len(chain_ar)), 3), dtype=np.uint8)
-            img_blank.fill(255)
-
-            y = 30
-            x = 0
-            img_blank[y:y + height, x:x + width] = img
-
-            # Set the font scale and thickness
-            font_scale = 1
-            thickness = 2
-
-            # Set the font face to a monospace font
-            font_face = cv2.FONT_HERSHEY_SIMPLEX
-
-            cv2.putText(img_blank, "original", (x+4, y-7), font_face, font_scale, (0, 0, 0), thickness)
-
-
-        i = 0
-        for proc_id in chain_ar:
-            i += 1
-            if proc_id != "":
-                #img = self.processors[proc_id][1](self, img, params) # params can be modified inside
-                y = 30
-                img = self.processors_objects[proc_id][thread_index].process(img,params)
-                if self.is_demo_row_render:
-                    x = width*i
-                    img_blank[y:y + height, x:x + width] = img
-                    cv2.putText(img_blank, proc_id, (x + 4, y - 7), font_face, font_scale, (0, 0, 0), thickness)
-
-        if self.is_demo_row_render:
-            return img_blank, params
-
-        return img, params
-
-    # ---------------- init translation stuff ----------------
-    def fill_processors_for_thread_chains(self, threads:int = 1, chain:str = None):
-        if chain is None:
-            chain = self.default_chain
-
-        chain_ar = chain.split(",")
-        # init all not initialized processors first
-        for processor_id in chain_ar:
-            if processor_id != "":
-                if self.processors_objects.get(processor_id) is None:
-                    self.processors_objects[processor_id] = []
-                while len(self.processors_objects[processor_id]) < threads:
-                    self.add_processor_to_list(processor_id)
-
-    def add_processor_to_list(self, processor_id: str):
-        obj = self.processors[processor_id](self)
-        obj.init_plugin()
-        if self.processors_objects.get(processor_id) is None:
-            self.processors_objects[processor_id] = []
-        self.processors_objects[processor_id].append(obj)
-    def init_processor(self, processor_id: str):
-        if processor_id == "": # blank line case
-            return
-
-        if processor_id in self.inited_processors:
-            return
-
-        try:
-            if self.verbose:
-                self.print_blue("TRY: init processor plugin '{0}'...".format(processor_id))
-            self.add_processor_to_list(processor_id)
-            self.inited_processors.append(processor_id)
-            if self.verbose:
-                self.print_blue("SUCCESS: '{0}' initialized!".format(processor_id))
-
-        except Exception as e:
-            self.print_error("Error init processor plugin {0}...".format(processor_id), e)
-
-    # ------------ formatting stuff -------------------
-    def display_init_info(self):
-        if self.verbose:
-            print("ChainImgProcessor v{0}:".format(version))
-            self.format_print_key_list("processors:", self.processors.keys())
-
-    def format_print_key_list(self, key:str, value:list):
-        print(key+": ".join(value))
-
-    def print_error(self,err_txt,e:Exception = None):
-        print(err_txt,"red")
-        # if e != None:
-        #     cprint(e,"red")
-        import traceback
-        traceback.print_exc()
-
-    def print_red(self,txt):
-        print(txt)
-
-    def print_blue(self, txt):
-        print(txt)
-
-class ChainImgPlugin:
-
-    device = 'cpu'
-
-    def __init__(self, core: ChainImgProcessor):
-        self.core = core
-        self.device = get_device()
-
-    def init_plugin(self): # here you can init something. Called once
-        pass
-    def process(self, img, params:dict): # process img. Called multiple
-        return img
-
-_img_processor:ChainImgProcessor = None
-def get_single_image_processor() -> ChainImgProcessor:
-    global _img_processor
-    if _img_processor is None:
-        _img_processor = ChainImgProcessor()
-        _img_processor.init_with_plugins()
-    return _img_processor

app/chain_img_processor/video.py

@@ -1,132 +0,0 @@
-import roop.globals
-
-from threading import Thread
-from chain_img_processor import ChainImgProcessor
-
-class ThreadWithReturnValue(Thread):
-
-    def __init__(self, group=None, target=None, name=None,
-                 args=(), kwargs={}, Verbose=None):
-        Thread.__init__(self, group, target, name, args, kwargs)
-        self._return = None
-
-    def run(self):
-        if self._target is not None:
-            self._return = self._target(*self._args,
-                                        **self._kwargs)
-
-    def join(self, *args):
-        Thread.join(self, *args)
-        return self._return
-
-
-# in beta
-class ChainVideoProcessor(ChainImgProcessor):
-    def __init__(self):
-        ChainImgProcessor.__init__(self)
-
-        self.video_save_codec = "libx264"
-        self.video_save_crf = 14
-
-    def init_with_plugins(self):
-        self.init_plugins(["core","core_video"])
-        self.display_init_info()
-
-        init_on_start_arr = self.init_on_start.split(",")
-        for proc_id in init_on_start_arr:
-            self.init_processor(proc_id)
-
-    def run_video_chain(self, source_video, target_video, fps, threads:int = 1, chain = None, params_frame_gen_func = None, video_audio = None):
-        import cv2
-        from tqdm import tqdm
-        from chain_img_processor.ffmpeg_writer import FFMPEG_VideoWriter # ffmpeg install needed
-
-        cap = cv2.VideoCapture(source_video)
-        # width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
-        # height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
-        frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
-
-        # first frame do manually - because upscale may happen, we need to estimate width/height
-        ret, frame = cap.read()
-        if params_frame_gen_func is not None:
-            params = params_frame_gen_func(self, frame)
-        else:
-            params = {}
-        params["original_frame"] = frame
-        frame_processed, params = self.run_chain(frame,params,chain)
-        height, width, channels = frame_processed.shape
-
-        self.fill_processors_for_thread_chains(threads,chain)
-        #print(self.processors_objects)
-        #import threading
-        #locks:list[threading.Lock] = []
-        locks: list[bool] = []
-        for i in range(threads):
-            #locks.append(threading.Lock())
-            locks.append(False)
-
-        temp = []
-        with FFMPEG_VideoWriter(target_video, (width, height), fps, codec=roop.globals.video_encoder, crf=roop.globals.video_quality, audiofile=video_audio) as output_video_ff:
-            with tqdm(total=frame_count, desc='Processing', unit="frame", dynamic_ncols=True,
-                      bar_format='{l_bar}{bar}| {n_fmt}/{total_fmt} [{elapsed}<{remaining}, {rate_fmt}{postfix}]') as progress:
-
-                # do first frame
-                output_video_ff.write_frame(frame_processed)
-                progress.update(1) #
-                cnt_frames = 0
-
-                # do rest frames
-                while True:
-                    # getting frame
-                    ret, frame = cap.read()
-
-                    if not ret:
-                        break
-                    cnt_frames+=1
-                    thread_ind = cnt_frames % threads
-                    # we are having an array of length %gpu_threads%, running in parallel
-                    # so if array is equal or longer than gpu threads, waiting
-                    #while len(temp) >= threads:
-                    while locks[thread_ind]:
-                        #print('WAIT', thread_ind)
-                        # we are order dependent, so we are forced to wait for first element to finish. When finished removing thread from the list
-                        frame_processed, params = temp.pop(0).join()
-                        locks[params["_thread_index"]] = False
-                        #print('OFF',cnt_frames,locks[params["_thread_index"]],locks)
-                        # writing into output
-                        output_video_ff.write_frame(frame_processed)
-                        # updating the status
-                        progress.update(1)
-
-                    # calc params for frame
-                    if params_frame_gen_func is not None:
-                        params = params_frame_gen_func(self,frame)
-                    else:
-                        params = {}
-
-                    # adding new frame to the list and starting it
-                    locks[thread_ind] = True
-                    #print('ON', cnt_frames, thread_ind, locks)
-                    params["original_frame"] = frame
-                    temp.append(
-                        ThreadWithReturnValue(target=self.run_chain, args=(frame, params, chain, thread_ind)))
-                    temp[-1].start()
-
-                while len(temp) > 0:
-                    # we are order dependent, so we are forced to wait for first element to finish. When finished removing thread from the list
-                    frame_processed, params = temp.pop(0).join()
-                    locks[params["_thread_index"]] = False
-                    # writing into output
-                    output_video_ff.write_frame(frame_processed)
-
-                    progress.update(1)
-
-                #print("FINAL", locks)
-
-_video_processor:ChainVideoProcessor = None
-def get_single_video_processor() -> ChainVideoProcessor:
-    global _video_processor
-    if _video_processor is None:
-        _video_processor = ChainVideoProcessor()
-        _video_processor.init_with_plugins()
-    return _video_processor

app/clip/__init__.py

@@ -1 +0,0 @@
-from .clip import *

app/clip/bpe_simple_vocab_16e6.txt.gz

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:924691ac288e54409236115652ad4aa250f48203de50a9e4722a6ecd48d6804a
-size 1356917

app/clip/clip.py

@@ -1,245 +0,0 @@
-import hashlib
-import os
-import urllib
-import warnings
-from typing import Any, Union, List
-from pkg_resources import packaging
-
-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
-
-
-if packaging.version.parse(torch.__version__) < packaging.version.parse("1.7.1"):
-    warnings.warn("PyTorch version 1.7.1 or higher is recommended")
-
-
-__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

app/clip/clipseg.py

@@ -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
-                    

app/clip/model.py

@@ -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()

app/clip/simple_tokenizer.py

@@ -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("¡"), ord("¬")+1))+list(range(ord("®"), ord("ÿ")+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+'</w>' 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] + '</w>',)
-        pairs = get_pairs(word)
-
-        if not pairs:
-            return token+'</w>'
-
-        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('</w>', ' ')
-        return text

app/clip/vitseg.py

@@ -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,

app/gfpgan/weights/detection_Resnet50_Final.pth

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:6d1de9c2944f2ccddca5f5e010ea5ae64a39845a86311af6fdf30841b0a5a16d
-size 109497761

app/gfpgan/weights/parsing_parsenet.pth

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:3d558d8d0e42c20224f13cf5a29c79eba2d59913419f945545d8cf7b72920de2
-size 85331193

app/installer/installer.py

@@ -1,83 +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():
-    # Install Git and clone repo
-    run_cmd("conda install -y -k git")
-    run_cmd("git clone https://github.com/C0untFloyd/roop-unleashed.git")
-
-    # Install the webui dependencies
-    update_dependencies()
-
-
-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()

app/installer/windows_run.bat

@@ -1,80 +0,0 @@
-@echo off
-REM Please set the following commandline arguments to your prefered settings
-set COMMANDLINE_ARGS=--execution-provider cuda --frame-processor face_swapper face_enhancer --video-encoder libvpx-vp9
-
-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/2023-06-21-git-1bcb8a7338/ffmpeg-2023-06-21-git-1bcb8a7338-essentials_build.zip
-set INSTALL_FFMPEG_DIR=%cd%\installer_files\ffmpeg
-set conda_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 (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 Packages to install: %PACKAGES_TO_INSTALL%
-  call "%CONDA_ROOT_PREFIX%\_conda.exe" create --no-shortcuts -y -k --prefix "%INSTALL_ENV_DIR%" python=3.10 || ( echo. && echo Conda environment creation failed. && goto end )
-)
-
-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 "installer_files"
-	setlocal EnableExtensions EnableDelayedExpansion
-
-	for /f "tokens=*" %%f in ('dir /s /b /ad "ffmpeg*"') do (
-		ren "%%f" "ffmpeg"
-	)
-	endlocal
-	setx PATH "%INSTALL_FFMPEG_DIR%\bin\;%PATH%"
-	echo To use videos, you need to restart roop after this installation. 
-	cd ..
-)
-
-@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 Miniconda hook not found. && goto end )
-
-@rem setup installer env
-echo Launching roop unleashed - please edit windows_run.bat to customize commandline arguments
-call python installer.py %COMMANDLINE_ARGS%
-
-echo.
-echo Done!
-
-:end
-pause
-
-
-

app/jaa.py

@@ -1,355 +0,0 @@
-"""
-Jaa.py Plugin Framework
-Author: Janvarev Vladislav
-
-Jaa.py - minimalistic one-file plugin framework with no dependencies.
-Main functions:
-- run all plugins files from "plugins" folder, base on filename
-- save each plugin options in "options" folder in JSON text files for further editing
-
-- Plugins
-must located in plugins/ folder
-must have "start(core)" function, that returns manifest dict
-manifest must contain keys "name" and "version"
-can contain "default_options"
-- if contain - options will be saved in "options" folder and reload instead next time
-- if contain - "start_with_options(core,manifest)" function will run with manifest with "options" key
-manifest will be processed in "process_plugin_manifest" function if you override it
-
-- Options (for plugins)
-are saved under "options" folder in JSON format
-created at first run plugin with "default_options"
-updated when plugin change "version"
-
-- Example usage:
-class VoiceAssCore(JaaCore): # class must override JaaCore
-    def __init__(self):
-        JaaCore.__init__(self,__file__)
-  ...
-
-main = VoiceAssCore()
-main.init_plugins(["core"]) # 1 param - first plugins to be initialized
-                            # Good if you need some "core" options/plugin to be loaded before others
-                            # not necessary starts with "plugin_" prefix
-
-also can be run like
-
-main.init_plugins()
-
-- Requirements
-Python 3.5+ (due to dict mix in final_options calc), can be relaxed
-"""
-
-import os
-import traceback
-import json
-
-# here we trying to use termcolor to highlight plugin info and errors during load
-try:
-    from termcolor import cprint
-except Exception as e:
-    # not found? making a stub!
-    def cprint(p,color=None):
-        if color == None:
-            print(p)
-        else:
-            print(str(color).upper(),p)
-
-version = "2.2.0"
-
-class JaaCore:
-    verbose = False
-
-    def __init__(self,root_file = __file__):
-        self.jaaPluginPrefix = "plugin_"
-        self.jaaVersion = version
-        self.jaaRootFolder = os.path.dirname(root_file)
-        self.jaaOptionsPath = self.jaaRootFolder+os.path.sep+"plugin_options"
-        self.jaaShowTracebackOnPluginErrors = False
-        if self.verbose:
-            cprint("JAA.PY v{0} class created!".format(version),"blue")
-
-    # ------------- plugins -----------------
-    def init_plugins(self, list_first_plugins = []):
-        self.plugin_manifests = {}
-
-        # 1. run first plugins first!
-        for modname in list_first_plugins:
-            self.init_plugin(modname)
-
-        # 2. run all plugins from plugins folder
-        from os import listdir
-        from os.path import isfile, join
-        pluginpath = self.jaaRootFolder+"/plugins"
-        files = [f for f in listdir(pluginpath) if isfile(join(pluginpath, f))]
-
-        for fil in files:
-            # print fil[:-3]
-            if fil.startswith(self.jaaPluginPrefix) and fil.endswith(".py"):
-                modfile = fil[:-3]
-                self.init_plugin(modfile)
-
-
-
-    def init_plugin(self,modname):
-        # import
-        try:
-            mod = self.import_plugin("plugins."+modname)
-        except Exception as e:
-            self.print_error("JAA PLUGIN ERROR: {0} error on load: {1}".format(modname, str(e)))
-            return False
-
-        # run start function
-        try:
-            res = mod.start(self)
-        except Exception as e:
-            self.print_error("JAA PLUGIN ERROR: {0} error on start: {1}".format(modname, str(e)))
-            return False
-
-        # if plugin has an options
-        if "default_options" in res:
-            try:
-                # saved options try to read
-                saved_options = {}
-                try:
-                    with open(self.jaaOptionsPath+'/'+modname+'.json', 'r', encoding="utf-8") as f:
-                        s = f.read()
-                    saved_options = json.loads(s)
-                    #print("Saved options", saved_options)
-                except Exception as e:
-                    pass
-
-                res["default_options"]["v"] = res["version"]
-
-
-                # only string needs Python 3.5
-                final_options = {**res["default_options"], **saved_options}
-
-                # if no option found or version is differ from mod version
-                if len(saved_options) == 0 or saved_options["v"] != res["version"]:
-                    final_options["v"] = res["version"]
-                    self.save_plugin_options(modname,final_options)
-
-                res["options"] = final_options
-
-                try:
-                    res2 = mod.start_with_options(self,res)
-                    if res2 != None:
-                        res = res2
-                except Exception as e:
-                    self.print_error("JAA PLUGIN ERROR: {0} error on start_with_options processing: {1}".format(modname, str(e)))
-                    return False
-
-            except Exception as e:
-                self.print_error("JAA PLUGIN ERROR: {0} error on options processing: {1}".format(modname, str(e)))
-                return False
-
-
-        # processing plugin manifest
-        try:
-            # set up name and version
-            plugin_name = res["name"]
-            plugin_version = res["version"]
-
-
-            self.process_plugin_manifest(modname,res)
-
-        except Exception as e:
-            print("JAA PLUGIN ERROR: {0} error on process startup options: {1}".format(modname, str(e)))
-            return False
-
-        self.plugin_manifests[modname] = res
-
-        self.on_succ_plugin_start(modname,plugin_name,plugin_version)
-        return True
-
-    def on_succ_plugin_start(self, modname, plugin_name, plugin_version):
-        if self.verbose:
-            cprint("JAA PLUGIN: {1} {2} ({0}) started!".format(modname, plugin_name, plugin_version))
-
-    def print_error(self,p):
-        cprint(p,"red")
-        if self.jaaShowTracebackOnPluginErrors:
-            traceback.print_exc()
-
-    def import_plugin(self, module_name):
-        import sys
-
-        __import__(module_name)
-
-        if module_name in sys.modules:
-            return sys.modules[module_name]
-        return None
-
-    def save_plugin_options(self,modname,options):
-        # check folder exists
-        if not os.path.exists(self.jaaOptionsPath):
-            os.makedirs(self.jaaOptionsPath)
-
-        str_options = json.dumps(options, sort_keys=True, indent=4, ensure_ascii=False)
-        with open(self.jaaOptionsPath+'/'+modname+'.json', 'w', encoding="utf-8") as f:
-            f.write(str_options)
-            f.close()
-
-    # process manifest must be overrided in inherit class
-    def process_plugin_manifest(self,modname,manifest):
-        print("JAA PLUGIN: {0} manifest dummy procession (override 'process_plugin_manifest' function)".format(modname))
-        return
-
-    def plugin_manifest(self,pluginname):
-        if pluginname in self.plugin_manifests:
-            return self.plugin_manifests[pluginname]
-        return {}
-
-    def plugin_options(self,pluginname):
-        manifest = self.plugin_manifest(pluginname)
-        if "options" in manifest:
-            return manifest["options"]
-        return None
-
-    # ------------ gradio stuff --------------
-    def gradio_save(self,pluginname):
-        print("Saving options for {0}!".format(pluginname))
-        self.save_plugin_options(pluginname,self.plugin_options(pluginname))
-
-    def gradio_upd(self, pluginname, option, val):
-        options = self.plugin_options(pluginname)
-
-        # special case
-        if isinstance(options[option], (list, dict)) and isinstance(val, str):
-            import json
-            try:
-                options[option] = json.loads(val)
-            except Exception as e:
-                print(e)
-                pass
-        else:
-            options[option] = val
-        print(option,val,options)
-
-    def gradio_render_settings_interface(self, title:str="Settings manager", required_fields_to_show_plugin:list=["default_options"]):
-        import gradio as gr
-
-        with gr.Blocks() as gr_interface:
-            gr.Markdown("# {0}".format(title))
-            for pluginname in self.plugin_manifests:
-                manifest = self.plugin_manifests[pluginname]
-
-                # calculate if we show plugin
-                is_show_plugin = False
-                if len(required_fields_to_show_plugin) == 0:
-                    is_show_plugin = True
-                else:
-                    for k in required_fields_to_show_plugin:
-                        if manifest.get(k) is not None:
-                            is_show_plugin = True
-
-                if is_show_plugin:
-                    with gr.Tab(pluginname):
-                        gr.Markdown("## {0} v{1}".format(manifest["name"],manifest["version"]))
-                        if manifest.get("description") is not None:
-                            gr.Markdown(manifest.get("description"))
-
-                        if manifest.get("url") is not None:
-                            gr.Markdown("**URL:** [{0}]({0})".format(manifest.get("url")))
-
-
-                        if "options" in manifest:
-                            options = manifest["options"]
-                            if len(options) > 1: # not only v
-                                text_button = gr.Button("Save options".format(pluginname))
-                                #options_int_list = []
-                                for option in options:
-
-                                    #gr.Label(label=option)
-                                    if option != "v":
-                                        val = options[option]
-                                        label = option
-
-                                        if manifest.get("options_label") is not None:
-                                            if manifest.get("options_label").get(option) is not None:
-                                                label = option+": "+manifest.get("options_label").get(option)
-
-
-                                        if isinstance(val, (bool, )):
-                                            gr_elem = gr.Checkbox(value=val,label=label)
-                                        elif isinstance(val, (dict,list)):
-                                            import json
-                                            gr_elem = gr.Textbox(value=json.dumps(val,ensure_ascii=False), label=label)
-                                        else:
-                                            gr_elem = gr.Textbox(value=val, label=label)
-
-                                        def handler(x,pluginname=pluginname,option=option):
-                                            self.gradio_upd(pluginname, option, x)
-
-                                        gr_elem.change(handler, gr_elem, None)
-
-                                def handler_save(pluginname=pluginname):
-                                    self.gradio_save(pluginname)
-
-                                text_button.click(handler_save,inputs=None,outputs=None)
-                        else:
-                            gr.Markdown("_No options for this plugin_")
-
-        return gr_interface
-
-
-def load_options(options_file=None,py_file=None,default_options={}):
-    # 1. calculating options filename
-    if options_file == None:
-        if py_file == None:
-            raise Exception('JAA: Options or PY file is not defined, cant calc options filename')
-        else:
-            options_file = py_file[:-3]+'.json'
-
-    # 2. try to read saved options
-    saved_options = {}
-    try:
-        with open(options_file, 'r', encoding="utf-8") as f:
-            s = f.read()
-        saved_options = json.loads(s)
-        #print("Saved options", saved_options)
-    except Exception as e:
-        pass
-
-    # 3. calculating final options
-
-    # only string needs Python 3.5
-    final_options = {**default_options, **saved_options}
-
-    # 4. calculating hash from def options to check - is file rewrite needed?
-    import hashlib
-    hash = hashlib.md5((json.dumps(default_options, sort_keys=True)).encode('utf-8')).hexdigest()
-
-    # 5. if no option file found or hash was from other default options
-    if len(saved_options) == 0 or not ("hash" in saved_options.keys()) or saved_options["hash"] != hash:
-        final_options["hash"] = hash
-        #self.save_plugin_options(modname,final_options)
-
-        # saving in file
-        str_options = json.dumps(final_options, sort_keys=True, indent=4, ensure_ascii=False)
-        with open(options_file, 'w', encoding="utf-8") as f:
-            f.write(str_options)
-            f.close()
-
-    return final_options
-
-"""
-The MIT License (MIT)
-Copyright (c) 2021 Janvarev Vladislav
-
-Permission is hereby granted, free of charge, to any person obtaining a copy 
-of this software and associated documentation files (the “Software”), to deal 
-in the Software without restriction, including without limitation the rights to use, 
-copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, 
-and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
-
-The above copyright notice and this permission notice shall be included in all copies or 
-substantial portions of the Software.
-
-THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, 
-INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR 
-PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE 
-FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, 
-ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
-"""

app/models/CLIP/rd64-uni-refined.pth

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:a4956f9a7978a75630b08c9d6ec075b7c51cf43b4751b686e3a011d4012ddc9d
-size 4720707

app/models/CodeFormer/codeformer.pth

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:1009e537e0c2a07d4cabce6355f53cb66767cd4b4297ec7a4a64ca4b8a5684b7
-size 376637898

app/models/CodeFormer/facelib/detection_Resnet50_Final.pth

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:6d1de9c2944f2ccddca5f5e010ea5ae64a39845a86311af6fdf30841b0a5a16d
-size 109497761

app/models/CodeFormer/facelib/parsing_parsenet.pth

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