UP

.buddy/docker-build.fixed.yml

@@ -0,0 +1,6 @@
+- pipeline: "docker-build"
+  events:
+  - type: "PUSH"
+    refs:
+    - "refs/heads/main"
+  fail_on_prepare_env_warning: true

.circleci/config.yml

@@ -0,0 +1,37 @@
+# This config was automatically generated from your source code
+# Stacks detected: cicd:github-actions:.github/workflows,deps:python:.
+version: 2.1
+orbs:
+  python: circleci/python@2
+jobs:
+  test-python:
+    # Install dependencies and run tests
+    docker:
+      - image: cimg/python:3.8-node
+    steps:
+      - checkout
+      - python/install-packages
+      - run:
+          name: Run tests
+          command: pytest --junitxml=junit.xml || ((($? == 5)) && echo 'Did not find any tests to run.')
+      - store_test_results:
+          path: junit.xml
+  deploy:
+    # This is an example deploy job, not actually used by the workflow
+    docker:
+      - image: cimg/base:stable
+    steps:
+      # Replace this with steps to deploy to users
+      - run:
+          name: deploy
+          command: '#e.g. ./deploy.sh'
+      - run:
+          name: found github actions config
+          command: ':'
+workflows:
+  build-and-test:
+    jobs:
+      - test-python
+    # - deploy:
+    #     requires:
+    #       - test-python

.flake8

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

.gitattributes

@@ -0,0 +1,6 @@
+.github/examples/snapshot.mp4 filter=lfs diff=lfs merge=lfs -text
+examples/snapshot.mp4 filter=lfs diff=lfs merge=lfs -text
+*.mp4 filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.png filter=lfs diff=lfs merge=lfs -text
+

.gitattributes.bak

@@ -0,0 +1,3 @@
+.github/examples/snapshot.mp4 filter=lfs diff=lfs merge=lfs -text
+examples/snapshot.mp4 filter=lfs diff=lfs merge=lfs -text
+*.mp4 filter=lfs diff=lfs merge=lfs -text

.github/ISSUE_TEMPLATE/bug_report.md

@@ -0,0 +1,37 @@
+---
+name: Bug report
+about: Create a report to help us improve
+title: ''
+labels: ''
+assignees: ''
+
+---
+
+**Describe the bug**
+A clear and concise description of what the bug is.
+
+**To Reproduce**
+Steps to reproduce the behavior:
+1. Go to '...'
+2. Click on '....'
+3. Scroll down to '....'
+4. See error
+
+**Details**
+What OS are you using?
+- [ ] Linux
+- [ ] Linux in WSL
+- [ ] Windows 
+- [ ] Mac
+
+Are you using a GPU?
+- [ ] No. CPU FTW
+- [ ] NVIDIA
+- [ ] AMD
+- [ ] Intel
+- [ ] Mac
+
+**Which version of roop unleashed are you using?**
+
+**Screenshots**
+If applicable, add screenshots to help explain your problem.

.github/workflows/stale.yml

@@ -0,0 +1,29 @@
+# This workflow warns and then closes issues and PRs that have had no activity for a specified amount of time.
+#
+# You can adjust the behavior by modifying this file.
+# For more information, see:
+# https://github.com/actions/stale
+name: Mark stale issues and pull requests
+
+on:
+  schedule:
+  - cron: '32 0 * * *'
+
+jobs:
+  stale:
+
+    runs-on: ubuntu-latest
+    permissions:
+      issues: write
+      pull-requests: write
+
+    steps:
+    - uses: actions/stale@v5
+      with:
+        repo-token: ${{ secrets.GITHUB_TOKEN }}
+        stale-issue-message: 'This issue is stale because it has been open 30 days with no activity. Remove stale label or comment or this will be closed in 5 days.'
+        stale-pr-message: 'This PR is stale because it has been open 45 days with no activity. Remove stale label or comment or this will be closed in 10 days.'
+        close-issue-message: 'This issue was closed because it has been stalled for 5 days with no activity.'
+        days-before-stale: 30
+        days-before-close: 5
+        days-before-pr-close: -1

.gitignore

@@ -0,0 +1,15 @@
+.vs
+.idea
+models
+temp
+__pycache__
+*.pth
+/start.bat
+/env
+.vscode
+output
+temp
+config.yaml
+run.bat
+venv
+start.sh

LICENSE

@@ -0,0 +1,661 @@
+                    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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+
+  Nothing in this License shall be construed as excluding or limiting
+any implied license or other defenses to infringement that may
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+
+  12. No Surrender of Others' Freedom.
+
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+
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+
+  17. Interpretation of Sections 15 and 16.
+
+  If the disclaimer of warranty and limitation of liability provided
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+
+                     END OF TERMS AND CONDITIONS
+
+            How to Apply These Terms to Your New Programs
+
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+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.
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+to attach them to the start of each source file to most effectively
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+
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+    Copyright (C) <year>  <name of author>
+
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+    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
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+
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+
+  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/>.

README.md

@@ -0,0 +1,156 @@
+# roop-unleashed
+
+[Changelog](#changelog) • [Usage](#usage) • [Wiki](https://github.com/C0untFloyd/roop-unleashed/wiki)
+
+
+Uncensored Deepfakes for images and videos without training and an easy-to-use GUI.
+
+
+![Screen](https://github.com/C0untFloyd/roop-unleashed/assets/131583554/6ee6860d-efbe-4337-8c62-a67598863637)
+
+### Features
+
+- Platform-independant Browser GUI
+- Selection of multiple input/output faces in one go
+- Many different swapping modes, first detected, face selections, by gender
+- Batch processing of images/videos
+- Masking of face occluders using text prompts or automatically
+- Optional Face Upscaler/Restoration using different enhancers
+- Preview swapping from different video frames
+- Live Fake Cam using your webcam
+- Extras Tab for cutting videos etc.
+- Settings - storing configuration for next session
+- Theme Support
+
+and lots more...
+
+
+## Disclaimer
+
+This project is for technical and academic use only.
+Users of this software are expected to use this software responsibly while abiding the local law. If a face of a real person is being used, users are suggested to get consent from the concerned person and clearly mention that it is a deepfake when posting content online. Developers of this software will not be responsible for actions of end-users.
+**Please do not apply it to illegal and unethical scenarios.**
+
+In the event of violation of the legal and ethical requirements of the user's country or region, this code repository is exempt from liability
+
+### Installation
+
+Please refer to the [wiki](https://github.com/C0untFloyd/roop-unleashed/wiki).
+
+
+
+
+### Usage
+
+- Windows: run the `windows_run.bat` from the Installer.
+- Linux: `python run.py`
+
+<a target="_blank" href="https://colab.research.google.com/github/C0untFloyd/roop-unleashed/blob/main/roop-unleashed.ipynb">
+  <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
+</a>
+  
+
+Additional commandline arguments are currently unsupported and settings should be done via the UI.
+
+> Note: When you run this program for the first time, it will download some models roughly ~2Gb in size.
+
+
+
+
+### Changelog
+
+**22.04.2024** v3.9.0
+
+- Bugfix: Face detection bounding box corrupt values at weird angles
+- Rewrote mask previewing to work with every model
+- Switching mask engines toggles text interactivity
+- Clearing target files, resets face selection dropdown
+- Massive rewrite of swapping architecture, needed for xseg implementation
+- Added DFL Xseg Support for partial face occlusion
+- Face masking only runs when there is a face detected
+- Removed unnecessary toggle checkbox for text masking
+
+
+**22.03.2024** v3.6.5
+
+- Bugfix: Installer pulling latest update on first installation
+- Bugfix: Regression issue, blurring/erosion missing from face swap
+- Exposed erosion and blur amounts to UI
+- Using same values for manual masking too
+
+
+**20.03.2024** v3.6.3
+
+- Bugfix: Workaround for Gradio Slider Change Bug
+- Bugfix: CSS Styling to fix Gradio Image Height Bug
+- Made face swapping mask offsets resolution independant
+- Show offset mask as overlay
+- Changed layout for masking
+
+
+**18.03.2024** v3.6.0
+
+- Updated to Gradio 4.21.0 - requiring many changes under the hood
+- New manual masking (draw the mask yourself)
+- Extras Tab, streamlined cutting/joining videos
+- Re-added face selection by gender (on-demand loading, default turned off)
+- Removed unnecessary activate live-cam option
+- Added time info to preview frame and changed frame slider event to allow faster changes
+
+
+**10.03.2024** v3.5.5
+
+- Bugfix: Installer Path Env
+- Bugfix: file attributes
+- Video processing checks for presence of ffmpeg and displays warning if not found
+- Removed gender + age detection to speed up processing. Option removed from UI
+- Replaced restoreformer with restoreformer++
+- Live Cam recoded to run separate from virtual cam and without blocking controls
+- Swapping with only 1 target face allows selecting from several input faces
+
+
+
+**08.01.2024** v3.5.0
+
+- Bugfix: wrong access options when creating folders
+- New auto rotation of horizontal faces, fixing bad landmark positions (expanded on ![PR 364](https://github.com/C0untFloyd/roop-unleashed/pull/364))
+- Simple VR Option for stereo Images/Movies, best used in selected face mode
+- Added RestoreFormer Enhancer - https://github.com/wzhouxiff/RestoreFormer
+- Bumped up package versions for onnx/Torch etc.   
+
+
+**16.10.2023** v3.3.4
+
+**11.8.2023** v2.7.0
+
+Initial Gradio Version - old TkInter Version now deprecated
+
+- Re-added unified padding to face enhancers
+- Fixed DMDNet for all resolutions
+- Selecting target face now automatically switches swapping mode to selected
+- GPU providers are correctly set using the GUI (needs restart currently)
+- Local output folder can be opened from page
+- Unfinished extras functions disabled for now
+- Installer checks out specific commit, allowing to go back to first install
+- Updated readme for new gradio version
+- Updated Colab
+
+
+# Acknowledgements
+
+Lots of ideas, code or pre-trained models borrowed from the following projects:
+
+https://github.com/deepinsight/insightface<br />
+https://github.com/s0md3v/roop<br />
+https://github.com/AUTOMATIC1111/stable-diffusion-webui<br /> 
+https://github.com/Hillobar/Rope<br />
+https://github.com/TencentARC/GFPGAN<br />   
+https://github.com/kadirnar/codeformer-pip<br />
+https://github.com/csxmli2016/DMDNet<br />
+https://github.com/glucauze/sd-webui-faceswaplab<br />
+https://github.com/ykk648/face_power<br />
+
+<br />
+<br />
+Thanks to all developers!
+

clip/__init__.py

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

clip/bpe_simple_vocab_16e6.txt.gz

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

clip/clip.py

@@ -0,0 +1,241 @@
+import hashlib
+import os
+import urllib
+import warnings
+from typing import Any, Union, List
+
+import torch
+from PIL import Image
+from torchvision.transforms import Compose, Resize, CenterCrop, ToTensor, Normalize
+from tqdm import tqdm
+
+from .model import build_model
+from .simple_tokenizer import SimpleTokenizer as _Tokenizer
+
+try:
+    from torchvision.transforms import InterpolationMode
+    BICUBIC = InterpolationMode.BICUBIC
+except ImportError:
+    BICUBIC = Image.BICUBIC
+
+
+
+__all__ = ["available_models", "load", "tokenize"]
+_tokenizer = _Tokenizer()
+
+_MODELS = {
+    "RN50": "https://openaipublic.azureedge.net/clip/models/afeb0e10f9e5a86da6080e35cf09123aca3b358a0c3e3b6c78a7b63bc04b6762/RN50.pt",
+    "RN101": "https://openaipublic.azureedge.net/clip/models/8fa8567bab74a42d41c5915025a8e4538c3bdbe8804a470a72f30b0d94fab599/RN101.pt",
+    "RN50x4": "https://openaipublic.azureedge.net/clip/models/7e526bd135e493cef0776de27d5f42653e6b4c8bf9e0f653bb11773263205fdd/RN50x4.pt",
+    "RN50x16": "https://openaipublic.azureedge.net/clip/models/52378b407f34354e150460fe41077663dd5b39c54cd0bfd2b27167a4a06ec9aa/RN50x16.pt",
+    "RN50x64": "https://openaipublic.azureedge.net/clip/models/be1cfb55d75a9666199fb2206c106743da0f6468c9d327f3e0d0a543a9919d9c/RN50x64.pt",
+    "ViT-B/32": "https://openaipublic.azureedge.net/clip/models/40d365715913c9da98579312b702a82c18be219cc2a73407c4526f58eba950af/ViT-B-32.pt",
+    "ViT-B/16": "https://openaipublic.azureedge.net/clip/models/5806e77cd80f8b59890b7e101eabd078d9fb84e6937f9e85e4ecb61988df416f/ViT-B-16.pt",
+    "ViT-L/14": "https://openaipublic.azureedge.net/clip/models/b8cca3fd41ae0c99ba7e8951adf17d267cdb84cd88be6f7c2e0eca1737a03836/ViT-L-14.pt",
+    "ViT-L/14@336px": "https://openaipublic.azureedge.net/clip/models/3035c92b350959924f9f00213499208652fc7ea050643e8b385c2dac08641f02/ViT-L-14-336px.pt",
+}
+
+
+def _download(url: str, root: str):
+    os.makedirs(root, exist_ok=True)
+    filename = os.path.basename(url)
+
+    expected_sha256 = url.split("/")[-2]
+    download_target = os.path.join(root, filename)
+
+    if os.path.exists(download_target) and not os.path.isfile(download_target):
+        raise RuntimeError(f"{download_target} exists and is not a regular file")
+
+    if os.path.isfile(download_target):
+        if hashlib.sha256(open(download_target, "rb").read()).hexdigest() == expected_sha256:
+            return download_target
+        else:
+            warnings.warn(f"{download_target} exists, but the SHA256 checksum does not match; re-downloading the file")
+
+    with urllib.request.urlopen(url) as source, open(download_target, "wb") as output:
+        with tqdm(total=int(source.info().get("Content-Length")), ncols=80, unit='iB', unit_scale=True, unit_divisor=1024) as loop:
+            while True:
+                buffer = source.read(8192)
+                if not buffer:
+                    break
+
+                output.write(buffer)
+                loop.update(len(buffer))
+
+    if hashlib.sha256(open(download_target, "rb").read()).hexdigest() != expected_sha256:
+        raise RuntimeError("Model has been downloaded but the SHA256 checksum does not not match")
+
+    return download_target
+
+
+def _convert_image_to_rgb(image):
+    return image.convert("RGB")
+
+
+def _transform(n_px):
+    return Compose([
+        Resize(n_px, interpolation=BICUBIC),
+        CenterCrop(n_px),
+        _convert_image_to_rgb,
+        ToTensor(),
+        Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711)),
+    ])
+
+
+def available_models() -> List[str]:
+    """Returns the names of available CLIP models"""
+    return list(_MODELS.keys())
+
+
+def load(name: str, device: Union[str, torch.device] = "cuda" if torch.cuda.is_available() else "cpu", jit: bool = False, download_root: str = None):
+    """Load a CLIP model
+
+    Parameters
+    ----------
+    name : str
+        A model name listed by `clip.available_models()`, or the path to a model checkpoint containing the state_dict
+
+    device : Union[str, torch.device]
+        The device to put the loaded model
+
+    jit : bool
+        Whether to load the optimized JIT model or more hackable non-JIT model (default).
+
+    download_root: str
+        path to download the model files; by default, it uses "~/.cache/clip"
+
+    Returns
+    -------
+    model : torch.nn.Module
+        The CLIP model
+
+    preprocess : Callable[[PIL.Image], torch.Tensor]
+        A torchvision transform that converts a PIL image into a tensor that the returned model can take as its input
+    """
+    if name in _MODELS:
+        model_path = _download(_MODELS[name], download_root or os.path.expanduser("~/.cache/clip"))
+    elif os.path.isfile(name):
+        model_path = name
+    else:
+        raise RuntimeError(f"Model {name} not found; available models = {available_models()}")
+
+    with open(model_path, 'rb') as opened_file:
+        try:
+            # loading JIT archive
+            model = torch.jit.load(opened_file, map_location=device if jit else "cpu").eval()
+            state_dict = None
+        except RuntimeError:
+            # loading saved state dict
+            if jit:
+                warnings.warn(f"File {model_path} is not a JIT archive. Loading as a state dict instead")
+                jit = False
+            state_dict = torch.load(opened_file, map_location="cpu")
+
+    if not jit:
+        model = build_model(state_dict or model.state_dict()).to(device)
+        if str(device) == "cpu":
+            model.float()
+        return model, _transform(model.visual.input_resolution)
+
+    # patch the device names
+    device_holder = torch.jit.trace(lambda: torch.ones([]).to(torch.device(device)), example_inputs=[])
+    device_node = [n for n in device_holder.graph.findAllNodes("prim::Constant") if "Device" in repr(n)][-1]
+
+    def _node_get(node: torch._C.Node, key: str):
+        """Gets attributes of a node which is polymorphic over return type.
+        
+        From https://github.com/pytorch/pytorch/pull/82628
+        """
+        sel = node.kindOf(key)
+        return getattr(node, sel)(key)
+
+    def patch_device(module):
+        try:
+            graphs = [module.graph] if hasattr(module, "graph") else []
+        except RuntimeError:
+            graphs = []
+
+        if hasattr(module, "forward1"):
+            graphs.append(module.forward1.graph)
+
+        for graph in graphs:
+            for node in graph.findAllNodes("prim::Constant"):
+                if "value" in node.attributeNames() and str(_node_get(node, "value")).startswith("cuda"):
+                    node.copyAttributes(device_node)
+
+    model.apply(patch_device)
+    patch_device(model.encode_image)
+    patch_device(model.encode_text)
+
+    # patch dtype to float32 on CPU
+    if str(device) == "cpu":
+        float_holder = torch.jit.trace(lambda: torch.ones([]).float(), example_inputs=[])
+        float_input = list(float_holder.graph.findNode("aten::to").inputs())[1]
+        float_node = float_input.node()
+
+        def patch_float(module):
+            try:
+                graphs = [module.graph] if hasattr(module, "graph") else []
+            except RuntimeError:
+                graphs = []
+
+            if hasattr(module, "forward1"):
+                graphs.append(module.forward1.graph)
+
+            for graph in graphs:
+                for node in graph.findAllNodes("aten::to"):
+                    inputs = list(node.inputs())
+                    for i in [1, 2]:  # dtype can be the second or third argument to aten::to()
+                        if _node_get(inputs[i].node(), "value") == 5:
+                            inputs[i].node().copyAttributes(float_node)
+
+        model.apply(patch_float)
+        patch_float(model.encode_image)
+        patch_float(model.encode_text)
+
+        model.float()
+
+    return model, _transform(model.input_resolution.item())
+
+
+def tokenize(texts: Union[str, List[str]], context_length: int = 77, truncate: bool = False) -> Union[torch.IntTensor, torch.LongTensor]:
+    """
+    Returns the tokenized representation of given input string(s)
+
+    Parameters
+    ----------
+    texts : Union[str, List[str]]
+        An input string or a list of input strings to tokenize
+
+    context_length : int
+        The context length to use; all CLIP models use 77 as the context length
+
+    truncate: bool
+        Whether to truncate the text in case its encoding is longer than the context length
+
+    Returns
+    -------
+    A two-dimensional tensor containing the resulting tokens, shape = [number of input strings, context_length].
+    We return LongTensor when torch version is <1.8.0, since older index_select requires indices to be long.
+    """
+    if isinstance(texts, str):
+        texts = [texts]
+
+    sot_token = _tokenizer.encoder["<|startoftext|>"]
+    eot_token = _tokenizer.encoder["<|endoftext|>"]
+    all_tokens = [[sot_token] + _tokenizer.encode(text) + [eot_token] for text in texts]
+    #if packaging.version.parse(torch.__version__) < packaging.version.parse("1.8.0"):
+    #    result = torch.zeros(len(all_tokens), context_length, dtype=torch.long)
+    #else:
+    result = torch.zeros(len(all_tokens), context_length, dtype=torch.int)
+
+    for i, tokens in enumerate(all_tokens):
+        if len(tokens) > context_length:
+            if truncate:
+                tokens = tokens[:context_length]
+                tokens[-1] = eot_token
+            else:
+                raise RuntimeError(f"Input {texts[i]} is too long for context length {context_length}")
+        result[i, :len(tokens)] = torch.tensor(tokens)
+
+    return result

clip/clipseg.py

@@ -0,0 +1,538 @@
+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
+                    

clip/model.py

@@ -0,0 +1,436 @@
+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()

clip/simple_tokenizer.py

@@ -0,0 +1,132 @@
+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

clip/vitseg.py

@@ -0,0 +1,286 @@
+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,

config_colab.yaml

@@ -0,0 +1,14 @@
+clear_output: true
+force_cpu: false
+max_threads: 3
+memory_limit: 0
+output_image_format: png
+output_template: '{file}_{time}'
+output_video_codec: libx264
+output_video_format: mp4
+provider: cuda
+selected_theme: Default
+server_name: ''
+server_port: 0
+server_share: true
+video_quality: 14

installer/installer.py

@@ -0,0 +1,87 @@
+import argparse
+import glob
+import os
+import shutil
+import site
+import subprocess
+import sys
+
+
+script_dir = os.getcwd()
+
+
+def run_cmd(cmd, capture_output=False, env=None):
+    # Run shell commands
+    return subprocess.run(cmd, shell=True, capture_output=capture_output, env=env)
+
+
+def check_env():
+    # If we have access to conda, we are probably in an environment
+    conda_not_exist = run_cmd("conda", capture_output=True).returncode
+    if conda_not_exist:
+        print("Conda is not installed. Exiting...")
+        sys.exit()
+    
+    # Ensure this is a new environment and not the base environment
+    if os.environ["CONDA_DEFAULT_ENV"] == "base":
+        print("Create an environment for this project and activate it. Exiting...")
+        sys.exit()
+
+
+def install_dependencies():
+    global MY_PATH
+
+    # Install Git and clone repo
+    run_cmd("conda install -y -k git")
+    run_cmd("git clone https://github.com/C0untFloyd/roop-unleashed.git")
+    os.chdir(MY_PATH)
+    run_cmd("git checkout ebf163acdb66de17abf408a86a72d00ddf49480c")
+    # Installs dependencies from requirements.txt
+    run_cmd("python -m pip install -r requirements.txt")
+
+
+
+def update_dependencies():
+    global MY_PATH
+    
+    os.chdir(MY_PATH)
+	# do a hard reset for to update even if there are local changes
+    run_cmd("git fetch --all")
+    run_cmd("git reset --hard origin/main")
+    run_cmd("git pull")
+    # Installs/Updates dependencies from all requirements.txt
+    run_cmd("python -m pip install -r requirements.txt")
+
+
+def start_app():
+    global MY_PATH
+    
+    os.chdir(MY_PATH)
+    # forward commandline arguments
+    sys.argv.pop(0)
+    args = ' '.join(sys.argv)
+    print("Launching App")
+    run_cmd(f'python run.py {args}')
+
+
+if __name__ == "__main__":
+    global MY_PATH
+    
+    MY_PATH = "roop-unleashed"
+
+    
+    # Verifies we are in a conda environment
+    check_env()
+
+    # If webui has already been installed, skip and run
+    if not os.path.exists(MY_PATH):
+        install_dependencies()
+    else:
+        # moved update from batch to here, because of batch limitations
+        updatechoice = input("Check for Updates? [y/n]").lower()
+        if updatechoice == "y":
+           update_dependencies()
+
+    # Run the model with webui
+    os.chdir(script_dir)
+    start_app()

installer/windows_run.bat

@@ -0,0 +1,99 @@
+@echo off
+
+REM No CLI arguments supported anymore
+set COMMANDLINE_ARGS=
+
+cd /D "%~dp0"
+
+echo "%CD%"| findstr /C:" " >nul && echo This script relies on Miniconda which can not be silently installed under a path with spaces. && goto end
+
+set PATH=%PATH%;%SystemRoot%\system32
+
+@rem config
+set INSTALL_DIR=%cd%\installer_files
+set CONDA_ROOT_PREFIX=%cd%\installer_files\conda
+set INSTALL_ENV_DIR=%cd%\installer_files\env
+set MINICONDA_DOWNLOAD_URL=https://repo.anaconda.com/miniconda/Miniconda3-latest-Windows-x86_64.exe
+set FFMPEG_DOWNLOAD_URL=https://github.com/GyanD/codexffmpeg/releases/download/2023-06-21-git-1bcb8a7338/ffmpeg-2023-06-21-git-1bcb8a7338-essentials_build.zip
+set INSTALL_FFMPEG_DIR=%cd%\installer_files\ffmpeg
+set INSIGHTFACE_PACKAGE_URL=https://github.com/C0untFloyd/roop-unleashed/releases/download/3.6.6/insightface-0.7.3-cp310-cp310-win_amd64.whl
+set INSIGHTFACE_PACKAGE_PATH=%INSTALL_DIR%\insightface-0.7.3-cp310-cp310-win_amd64.whl
+
+set conda_exists=F
+set ffmpeg_exists=F
+
+@rem figure out whether git and conda needs to be installed
+call "%CONDA_ROOT_PREFIX%\_conda.exe" --version >nul 2>&1
+if "%ERRORLEVEL%" EQU "0" set conda_exists=T
+
+@rem Check if FFmpeg is already in PATH
+where ffmpeg >nul 2>&1
+if "%ERRORLEVEL%" EQU "0" (
+    echo FFmpeg is already installed.
+    set ffmpeg_exists=T
+)
+
+@rem (if necessary) install git and conda into a contained environment
+
+@rem download conda
+if "%conda_exists%" == "F" (
+    echo Downloading Miniconda from %MINICONDA_DOWNLOAD_URL% to %INSTALL_DIR%\miniconda_installer.exe
+    mkdir "%INSTALL_DIR%"
+    call curl -Lk "%MINICONDA_DOWNLOAD_URL%" > "%INSTALL_DIR%\miniconda_installer.exe" || ( echo. && echo Miniconda failed to download. && goto end )
+    echo Installing Miniconda to %CONDA_ROOT_PREFIX%
+    start /wait "" "%INSTALL_DIR%\miniconda_installer.exe" /InstallationType=JustMe /NoShortcuts=1 /AddToPath=0 /RegisterPython=0 /NoRegistry=1 /S /D=%CONDA_ROOT_PREFIX%
+
+    @rem test the conda binary
+    echo Miniconda version:
+    call "%CONDA_ROOT_PREFIX%\_conda.exe" --version || ( echo. && echo Miniconda not found. && goto end )
+)
+
+@rem create the installer env
+if not exist "%INSTALL_ENV_DIR%" (
+    echo Creating Conda Environment
+    call "%CONDA_ROOT_PREFIX%\_conda.exe" create --no-shortcuts -y -k --prefix "%INSTALL_ENV_DIR%" python=3.10 || ( echo. && echo ERROR: Conda environment creation failed. && goto end )
+    @rem check if conda environment was actually created
+    if not exist "%INSTALL_ENV_DIR%\python.exe" ( echo. && echo ERROR: Conda environment is empty. && goto end )
+    @rem activate installer env
+    call "%CONDA_ROOT_PREFIX%\condabin\conda.bat" activate "%INSTALL_ENV_DIR%" || ( echo. && echo ERROR: Miniconda hook not found. && goto end )
+    @rem Download insightface package
+    echo Downloading insightface package from %INSIGHTFACE_PACKAGE_URL% to %INSIGHTFACE_PACKAGE_PATH%
+    call curl -Lk "%INSIGHTFACE_PACKAGE_URL%" > "%INSIGHTFACE_PACKAGE_PATH%" || ( echo. && echo ERROR: Insightface package failed to download. && goto end )
+    @rem install insightface package using pip
+    echo Installing insightface package
+    call pip install "%INSIGHTFACE_PACKAGE_PATH%" || ( echo. && echo ERROR: Insightface package installation failed. && goto end )
+)
+
+@rem Download and install FFmpeg if not already installed
+if "%ffmpeg_exists%" == "F" (
+    if not exist "%INSTALL_FFMPEG_DIR%" (
+        echo Downloading ffmpeg from %FFMPEG_DOWNLOAD_URL% to %INSTALL_DIR%
+        call curl -Lk "%FFMPEG_DOWNLOAD_URL%" > "%INSTALL_DIR%\ffmpeg.zip" || ( echo. && echo ffmpeg failed to download. && goto end )
+        call powershell -command "Expand-Archive -Force '%INSTALL_DIR%\ffmpeg.zip' '%INSTALL_DIR%\'"
+        cd "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 ..
+    )
+) else (
+    echo Skipping FFmpeg installation as it is already available.
+)
+
+@rem setup installer env
+@rem check if conda environment was actually created
+if not exist "%INSTALL_ENV_DIR%\python.exe" ( echo. && echo ERROR: Conda environment is empty. && goto end )
+@rem activate installer env
+call "%CONDA_ROOT_PREFIX%\condabin\conda.bat" activate "%INSTALL_ENV_DIR%" || ( echo. && echo ERROR: Miniconda hook not found. && goto end )
+echo Launching roop unleashed
+call python installer.py %COMMANDLINE_ARGS%
+
+echo.
+echo Done!
+
+:end
+pause

mypy.ini

@@ -0,0 +1,7 @@
+[mypy]
+check_untyped_defs = True
+disallow_any_generics = True
+disallow_untyped_calls = True
+disallow_untyped_defs = True
+ignore_missing_imports = True
+strict_optional = False

requirements.txt

@@ -0,0 +1,19 @@
+--extra-index-url https://download.pytorch.org/whl/cu118
+
+numpy==1.26.4
+gradio==4.32.1
+opencv-python==4.9.0.80
+onnx==1.16.0
+insightface==0.7.3
+psutil==5.9.6
+torch==2.1.2+cu118; sys_platform != 'darwin'
+torch==2.1.2; sys_platform == 'darwin'
+torchvision==0.16.2+cu118; sys_platform != 'darwin'
+torchvision==0.16.2; sys_platform == 'darwin'
+onnxruntime==1.17.1; sys_platform == 'darwin' and platform_machine != 'arm64'
+onnxruntime-silicon==1.16.3; sys_platform == 'darwin' and platform_machine == 'arm64'
+onnxruntime-gpu==1.17.1; sys_platform != 'darwin'
+tqdm==4.66.4
+ftfy
+regex
+pyvirtualcam

roop-unleashed.ipynb

@@ -0,0 +1,208 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "gpuType": "T4",
+      "collapsed_sections": [
+        "UdQ1VHdI8lCf"
+      ]
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    },
+    "accelerator": "GPU"
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Colab for roop-unleashed - Gradio version\n",
+        "https://github.com/C0untFloyd/roop-unleashed\n"
+      ],
+      "metadata": {
+        "id": "G9BdiCppV6AS"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Install CUDA V11.8 on Google Cloud Compute"
+      ],
+      "metadata": {
+        "id": "CanIXgLJgaOj"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!apt-get -y update\n",
+        "!apt-get -y install cuda-toolkit-11-8\n",
+        "import os\n",
+        "os.environ[\"LD_LIBRARY_PATH\"] += \":\" + \"/usr/local/cuda-11/lib64\"\n",
+        "os.environ[\"LD_LIBRARY_PATH\"] += \":\" + \"/usr/local/cuda-11.8/lib64\""
+      ],
+      "metadata": {
+        "id": "96GE4UgYg3Ej"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Installing & preparing requirements"
+      ],
+      "metadata": {
+        "id": "0ZYRNb0AWLLW"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "t1yPuhdySqCq"
+      },
+      "outputs": [],
+      "source": [
+        "!git clone https://github.com/C0untFloyd/roop-unleashed.git\n",
+        "%cd roop-unleashed\n",
+        "!mv config_colab.yaml config.yaml\n",
+        "!pip install pip install -r requirements.txt"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Running roop-unleashed with default config"
+      ],
+      "metadata": {
+        "id": "u_4JQiSlV9Fi"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!python run.py"
+      ],
+      "metadata": {
+        "id": "Is6U2huqSzLE"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Download generated images folder\n",
+        "(only needed if you want to zip the generated output)"
+      ],
+      "metadata": {
+        "id": "UdQ1VHdI8lCf"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import shutil\n",
+        "import os\n",
+        "from google.colab import files\n",
+        "\n",
+        "def zip_directory(directory_path, zip_path):\n",
+        "    shutil.make_archive(zip_path, 'zip', directory_path)\n",
+        "\n",
+        "# Set the directory path you want to download\n",
+        "directory_path = '/content/roop-unleashed/output'\n",
+        "\n",
+        "# Set the zip file name\n",
+        "zip_filename = 'fake_output.zip'\n",
+        "\n",
+        "# Zip the directory\n",
+        "zip_directory(directory_path, zip_filename)\n",
+        "\n",
+        "# Download the zip file\n",
+        "files.download(zip_filename+'.zip')\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 17
+        },
+        "id": "oYjWveAmw10X",
+        "outputId": "5b4c3650-f951-434a-c650-5525a8a70c1e"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.Javascript object>"
+            ],
+            "application/javascript": [
+              "\n",
+              "    async function download(id, filename, size) {\n",
+              "      if (!google.colab.kernel.accessAllowed) {\n",
+              "        return;\n",
+              "      }\n",
+              "      const div = document.createElement('div');\n",
+              "      const label = document.createElement('label');\n",
+              "      label.textContent = `Downloading \"${filename}\": `;\n",
+              "      div.appendChild(label);\n",
+              "      const progress = document.createElement('progress');\n",
+              "      progress.max = size;\n",
+              "      div.appendChild(progress);\n",
+              "      document.body.appendChild(div);\n",
+              "\n",
+              "      const buffers = [];\n",
+              "      let downloaded = 0;\n",
+              "\n",
+              "      const channel = await google.colab.kernel.comms.open(id);\n",
+              "      // Send a message to notify the kernel that we're ready.\n",
+              "      channel.send({})\n",
+              "\n",
+              "      for await (const message of channel.messages) {\n",
+              "        // Send a message to notify the kernel that we're ready.\n",
+              "        channel.send({})\n",
+              "        if (message.buffers) {\n",
+              "          for (const buffer of message.buffers) {\n",
+              "            buffers.push(buffer);\n",
+              "            downloaded += buffer.byteLength;\n",
+              "            progress.value = downloaded;\n",
+              "          }\n",
+              "        }\n",
+              "      }\n",
+              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
+              "      const a = document.createElement('a');\n",
+              "      a.href = window.URL.createObjectURL(blob);\n",
+              "      a.download = filename;\n",
+              "      div.appendChild(a);\n",
+              "      a.click();\n",
+              "      div.remove();\n",
+              "    }\n",
+              "  "
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.Javascript object>"
+            ],
+            "application/javascript": [
+              "download(\"download_789eab11-93d2-4880-adf3-6aceee0cc5f9\", \"fake_output.zip.zip\", 80125)"
+            ]
+          },
+          "metadata": {}
+        }
+      ]
+    }
+  ]
+}

roop/FaceSet.py

@@ -0,0 +1,20 @@
+import numpy as np
+
+class FaceSet:
+    faces = []
+    ref_images = []
+    embedding_average = 'None'
+    embeddings_backup = None
+
+    def __init__(self):
+        self.faces = []
+        self.ref_images = []
+        self.embeddings_backup = None
+
+    def AverageEmbeddings(self):
+        if len(self.faces) > 1 and self.embeddings_backup is None:
+            self.embeddings_backup = self.faces[0]['embedding']
+            embeddings = [face.embedding for face in self.faces]
+
+            self.faces[0]['embedding'] = np.mean(embeddings, axis=0)
+            # try median too?

roop/ProcessEntry.py

@@ -0,0 +1,7 @@
+class ProcessEntry:
+    def __init__(self, filename: str, start: int, end: int, fps: float):
+        self.filename = filename
+        self.finalname = None
+        self.startframe = start
+        self.endframe = end
+        self.fps = fps

roop/ProcessMgr.py

@@ -0,0 +1,702 @@
+import os
+import cv2 
+import numpy as np
+import psutil
+
+from enum import Enum
+from roop.ProcessOptions import ProcessOptions
+
+from roop.face_util import get_first_face, get_all_faces, rotate_image_180, rotate_anticlockwise, rotate_clockwise, clamp_cut_values
+from roop.utilities import compute_cosine_distance, get_device, str_to_class
+import roop.vr_util as vr
+
+from typing import Any, List, Callable
+from roop.typing import Frame, Face
+from concurrent.futures import ThreadPoolExecutor, as_completed
+from threading import Thread, Lock
+from queue import Queue
+from tqdm import tqdm
+from roop.ffmpeg_writer import FFMPEG_VideoWriter
+import roop.globals
+
+
+# Poor man's enum to be able to compare to int
+class eNoFaceAction():
+    USE_ORIGINAL_FRAME = 0
+    RETRY_ROTATED = 1
+    SKIP_FRAME = 2
+    SKIP_FRAME_IF_DISSIMILAR = 3
+
+
+
+def create_queue(temp_frame_paths: List[str]) -> Queue[str]:
+    queue: Queue[str] = Queue()
+    for frame_path in temp_frame_paths:
+        queue.put(frame_path)
+    return queue
+
+
+def pick_queue(queue: Queue[str], queue_per_future: int) -> List[str]:
+    queues = []
+    for _ in range(queue_per_future):
+        if not queue.empty():
+            queues.append(queue.get())
+    return queues
+
+
+class ProcessMgr():
+    input_face_datas = []
+    target_face_datas = []
+
+    imagemask = None
+
+    processors = []
+    options : ProcessOptions = None
+    
+    num_threads = 1
+    current_index = 0
+    processing_threads = 1
+    buffer_wait_time = 0.1
+
+    lock = Lock()
+
+    frames_queue = None
+    processed_queue = None
+
+    videowriter= None
+
+    progress_gradio = None
+    total_frames = 0
+
+    
+
+
+    plugins =  { 
+    'faceswap'          : 'FaceSwapInsightFace',
+    'mask_clip2seg'     : 'Mask_Clip2Seg',
+    'mask_xseg'         : 'Mask_XSeg',
+    'codeformer'        : 'Enhance_CodeFormer',
+    'gfpgan'            : 'Enhance_GFPGAN',
+    'dmdnet'            : 'Enhance_DMDNet',
+    'gpen'              : 'Enhance_GPEN',
+    'restoreformer++'   : 'Enhance_RestoreFormerPPlus',
+    'colorizer'         : 'Frame_Colorizer',
+    'filter_generic'    : 'Frame_Filter',
+    'removebg'          : 'Frame_Masking',
+    'upscale'           : 'Frame_Upscale'
+    }
+
+    def __init__(self, progress):
+        if progress is not None:
+            self.progress_gradio = progress
+
+    def reuseOldProcessor(self, name:str):
+        for p in self.processors:
+            if p.processorname == name:
+                return p
+            
+        return None
+
+
+    def initialize(self, input_faces, target_faces, options):
+        self.input_face_datas = input_faces
+        self.target_face_datas = target_faces
+        self.options = options
+        devicename = get_device()
+
+        roop.globals.g_desired_face_analysis=["landmark_3d_68", "landmark_2d_106","detection","recognition"]
+        if options.swap_mode == "all_female" or options.swap_mode == "all_male":
+            roop.globals.g_desired_face_analysis.append("genderage")
+
+        for p in self.processors:
+            newp = next((x for x in options.processors.keys() if x == p.processorname), None)
+            if newp is None:
+                p.Release()
+                del p
+
+        newprocessors = []
+        for key, extoption in options.processors.items():
+            p = self.reuseOldProcessor(key)
+            if p is None:
+                classname = self.plugins[key]
+                module = 'roop.processors.' + classname
+                p = str_to_class(module, classname)
+            if p is not None:
+                extoption.update({"devicename": devicename})
+                p.Initialize(extoption)
+                newprocessors.append(p)
+            else:
+                print(f"Not using {module}")
+        self.processors = newprocessors
+
+
+
+        if isinstance(self.options.imagemask, dict) and self.options.imagemask.get("layers") and len(self.options.imagemask["layers"]) > 0:
+            self.options.imagemask  = self.options.imagemask.get("layers")[0]
+            # Get rid of alpha
+            self.options.imagemask = cv2.cvtColor(self.options.imagemask, cv2.COLOR_RGBA2GRAY)
+            if np.any(self.options.imagemask):
+                mo = self.input_face_datas[0].faces[0].mask_offsets
+                self.options.imagemask = self.blur_area(self.options.imagemask, mo[4], mo[5])
+                self.options.imagemask = self.options.imagemask.astype(np.float32) / 255
+                self.options.imagemask = cv2.cvtColor(self.options.imagemask, cv2.COLOR_GRAY2RGB)
+            else:
+                self.options.imagemask = None
+
+        self.options.frame_processing = False
+        for p in self.processors:
+            if p.type.startswith("frame_"):
+                self.options.frame_processing = True
+
+            
+ 
+
+
+
+    def run_batch(self, source_files, target_files, threads:int = 1):
+        progress_bar_format = '{l_bar}{bar}| {n_fmt}/{total_fmt} [{elapsed}<{remaining}, {rate_fmt}{postfix}]'
+        self.total_frames = len(source_files)
+        self.num_threads = threads
+        with tqdm(total=self.total_frames, desc='Processing', unit='frame', dynamic_ncols=True, bar_format=progress_bar_format) as progress:
+            with ThreadPoolExecutor(max_workers=threads) as executor:
+                futures = []
+                queue = create_queue(source_files)
+                queue_per_future = max(len(source_files) // threads, 1)
+                while not queue.empty():
+                    future = executor.submit(self.process_frames, source_files, target_files, pick_queue(queue, queue_per_future), lambda: self.update_progress(progress))
+                    futures.append(future)
+                for future in as_completed(futures):
+                    future.result()
+
+
+    def process_frames(self, source_files: List[str], target_files: List[str], current_files, update: Callable[[], None]) -> None:
+        for f in current_files:
+            if not roop.globals.processing:
+                return
+            
+            # Decode the byte array into an OpenCV image
+            temp_frame = cv2.imdecode(np.fromfile(f, dtype=np.uint8), cv2.IMREAD_COLOR)
+            if temp_frame is not None:
+                if self.options.frame_processing:
+                    for p in self.processors:
+                        frame = p.Run(temp_frame)
+                    resimg = frame
+                else:
+                    resimg = self.process_frame(temp_frame)
+                if resimg is not None:
+                    i = source_files.index(f)
+                    cv2.imwrite(target_files[i], resimg)
+            if update:
+                update()
+
+
+
+    def read_frames_thread(self, cap, frame_start, frame_end, num_threads):
+        num_frame = 0
+        total_num = frame_end - frame_start
+        if frame_start > 0:
+            cap.set(cv2.CAP_PROP_POS_FRAMES,frame_start)
+
+        while True and roop.globals.processing:
+            ret, frame = cap.read()
+            if not ret:
+                break
+                
+            self.frames_queue[num_frame % num_threads].put(frame, block=True)
+            num_frame += 1
+            if num_frame == total_num:
+                break
+
+        for i in range(num_threads):
+            self.frames_queue[i].put(None)
+
+
+
+    def process_videoframes(self, threadindex, progress) -> None:
+        while True:
+            frame = self.frames_queue[threadindex].get()
+            if frame is None:
+                self.processing_threads -= 1
+                self.processed_queue[threadindex].put((False, None))
+                return
+            else:
+                if self.options.frame_processing:
+                    for p in self.processors:
+                        frame = p.Run(frame)
+                    resimg = frame
+                else:                            
+                    resimg = self.process_frame(frame)
+                self.processed_queue[threadindex].put((True, resimg))
+                del frame
+                progress()
+
+
+    def write_frames_thread(self):
+        nextindex = 0
+        num_producers = self.num_threads
+        
+        while True:
+            process, frame = self.processed_queue[nextindex % self.num_threads].get()
+            nextindex += 1
+            if frame is not None:
+                self.videowriter.write_frame(frame)
+                del frame
+            elif process == False:
+                num_producers -= 1
+                if num_producers < 1:
+                    return
+            
+
+
+    def run_batch_inmem(self, source_video, target_video, frame_start, frame_end, fps, threads:int = 1, skip_audio=False):
+        cap = cv2.VideoCapture(source_video)
+        # frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
+        frame_count = (frame_end - frame_start) + 1
+        width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+        height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+
+        processed_resolution = None
+        for p in self.processors:
+            if hasattr(p, 'getProcessedResolution'):
+                processed_resolution = p.getProcessedResolution(width, height)
+                print(f"Processed resolution: {processed_resolution}")
+        if processed_resolution is not None:
+            width = processed_resolution[0]
+            height = processed_resolution[1]
+
+
+        self.total_frames = frame_count
+        self.num_threads = threads
+
+        self.processing_threads = self.num_threads
+        self.frames_queue = []
+        self.processed_queue = []
+        for _ in range(threads):
+            self.frames_queue.append(Queue(1))
+            self.processed_queue.append(Queue(1))
+
+        self.videowriter =  FFMPEG_VideoWriter(target_video, (width, height), fps, codec=roop.globals.video_encoder, crf=roop.globals.video_quality, audiofile=None)
+
+        readthread = Thread(target=self.read_frames_thread, args=(cap, frame_start, frame_end, threads))
+        readthread.start()
+
+        writethread = Thread(target=self.write_frames_thread)
+        writethread.start()
+
+        progress_bar_format = '{l_bar}{bar}| {n_fmt}/{total_fmt} [{elapsed}<{remaining}, {rate_fmt}{postfix}]'
+        with tqdm(total=self.total_frames, desc='Processing', unit='frames', dynamic_ncols=True, bar_format=progress_bar_format) as progress:
+            with ThreadPoolExecutor(thread_name_prefix='swap_proc', max_workers=self.num_threads) as executor:
+                futures = []
+                
+                for threadindex in range(threads):
+                    future = executor.submit(self.process_videoframes, threadindex, lambda: self.update_progress(progress))
+                    futures.append(future)
+                
+                for future in as_completed(futures):
+                    future.result()
+        # wait for the task to complete
+        readthread.join()
+        writethread.join()
+        cap.release()
+        self.videowriter.close()
+        self.frames_queue.clear()
+        self.processed_queue.clear()
+
+
+
+
+    def update_progress(self, progress: Any = None) -> None:
+        process = psutil.Process(os.getpid())
+        memory_usage = process.memory_info().rss / 1024 / 1024 / 1024
+        progress.set_postfix({
+            'memory_usage': '{:.2f}'.format(memory_usage).zfill(5) + 'GB',
+            'execution_threads': self.num_threads
+        })
+        progress.update(1)
+        if self.progress_gradio is not None:
+            self.progress_gradio((progress.n, self.total_frames), desc='Processing', total=self.total_frames, unit='frames')
+
+
+# https://github.com/deepinsight/insightface#third-party-re-implementation-of-arcface
+# https://github.com/deepinsight/insightface/blob/master/alignment/coordinate_reg/image_infer.py
+# https://github.com/deepinsight/insightface/issues/1350
+# https://github.com/linghu8812/tensorrt_inference
+
+
+    def process_frame(self, frame:Frame):
+        if len(self.input_face_datas) < 1 and not self.options.show_face_masking:
+            return frame
+        temp_frame = frame.copy()
+        num_swapped, temp_frame = self.swap_faces(frame, temp_frame)
+        if num_swapped > 0:
+            if roop.globals.no_face_action == eNoFaceAction.SKIP_FRAME_IF_DISSIMILAR:
+                if len(self.input_face_datas) > num_swapped:
+                    return None
+            return temp_frame
+        if roop.globals.no_face_action == eNoFaceAction.USE_ORIGINAL_FRAME:
+            return frame
+        if roop.globals.no_face_action == eNoFaceAction.SKIP_FRAME:
+            #This only works with in-mem processing, as it simply skips the frame.
+            #For 'extract frames' it simply leaves the unprocessed frame unprocessed and it gets used in the final output by ffmpeg.
+            #If we could delete that frame here, that'd work but that might cause ffmpeg to fail unless the frames are renamed, and I don't think we have the info on what frame it actually is?????
+            #alternatively, it could mark all the necessary frames for deletion, delete them at the end, then rename the remaining frames that might work?
+            return None
+        else:
+            return self.retry_rotated(frame)
+
+    def retry_rotated(self, frame):
+        copyframe = frame.copy()
+        copyframe = rotate_clockwise(copyframe)
+        temp_frame = copyframe.copy()
+        num_swapped, temp_frame = self.swap_faces(copyframe, temp_frame)
+        if num_swapped > 0:
+            return rotate_anticlockwise(temp_frame)
+        
+        copyframe = frame.copy()
+        copyframe = rotate_anticlockwise(copyframe)
+        temp_frame = copyframe.copy()
+        num_swapped, temp_frame = self.swap_faces(copyframe, temp_frame)
+        if num_swapped > 0:
+            return rotate_clockwise(temp_frame)
+        del copyframe
+        return frame
+        
+
+
+    def swap_faces(self, frame, temp_frame):
+        num_faces_found = 0
+
+        if self.options.swap_mode == "first":
+            face = get_first_face(frame)
+
+            if face is None:
+                return num_faces_found, frame
+            
+            num_faces_found += 1
+            temp_frame = self.process_face(self.options.selected_index, face, temp_frame)
+        else:
+            faces = get_all_faces(frame)
+            if faces is None:
+                return num_faces_found, frame
+            
+            if self.options.swap_mode == "all":
+                for face in faces:
+                    num_faces_found += 1
+                    temp_frame = self.process_face(self.options.selected_index, face, temp_frame)
+                    del face
+            
+            elif self.options.swap_mode == "selected":
+                num_targetfaces = len(self.target_face_datas) 
+                use_index = num_targetfaces == 1
+                for i,tf in enumerate(self.target_face_datas):
+                    for face in faces:
+                        if compute_cosine_distance(tf.embedding, face.embedding) <= self.options.face_distance_threshold:
+                            if i < len(self.input_face_datas):
+                                if use_index:
+                                    temp_frame = self.process_face(self.options.selected_index, face, temp_frame)
+                                else:
+                                    temp_frame = self.process_face(i, face, temp_frame)
+                                num_faces_found += 1
+                            del face
+                            if not roop.globals.vr_mode and num_faces_found == num_targetfaces:
+                                break
+            elif self.options.swap_mode == "all_female" or self.options.swap_mode == "all_male":
+                gender = 'F' if self.options.swap_mode == "all_female" else 'M'
+                for face in faces:
+                    if face.sex == gender:
+                        num_faces_found += 1
+                        temp_frame = self.process_face(self.options.selected_index, face, temp_frame)
+                    del face
+
+        if roop.globals.vr_mode and num_faces_found % 2 > 0:
+            # stereo image, there has to be an even number of faces
+            num_faces_found = 0
+            return num_faces_found, frame
+        if num_faces_found == 0:
+            return num_faces_found, frame
+
+        #maskprocessor = next((x for x in self.processors if x.type == 'mask'), None)
+
+        if self.options.imagemask is not None and self.options.imagemask.shape == frame.shape:
+            temp_frame = self.simple_blend_with_mask(temp_frame, frame, self.options.imagemask)
+        return num_faces_found, temp_frame
+
+
+    def rotation_action(self, original_face:Face, frame:Frame):
+        (height, width) = frame.shape[:2]
+
+        bounding_box_width = original_face.bbox[2] - original_face.bbox[0]
+        bounding_box_height = original_face.bbox[3] - original_face.bbox[1]
+        horizontal_face = bounding_box_width > bounding_box_height
+
+        center_x = width // 2.0
+        start_x = original_face.bbox[0]
+        end_x = original_face.bbox[2]
+        bbox_center_x = start_x + (bounding_box_width // 2.0)
+
+        # need to leverage the array of landmarks as decribed here:
+        # https://github.com/deepinsight/insightface/tree/master/alignment/coordinate_reg
+        # basically, we should be able to check for the relative position of eyes and nose
+        # then use that to determine which way the face is actually facing when in a horizontal position
+        # and use that to determine the correct rotation_action
+
+        forehead_x = original_face.landmark_2d_106[72][0]
+        chin_x = original_face.landmark_2d_106[0][0]
+
+        if horizontal_face:
+            if chin_x < forehead_x:
+                # this is someone lying down with their face like this (:
+                return "rotate_anticlockwise"
+            elif forehead_x < chin_x:
+                # this is someone lying down with their face like this :)
+                return "rotate_clockwise"
+            if bbox_center_x >= center_x:
+                # this is someone lying down with their face in the right hand side of the frame
+                return "rotate_anticlockwise"
+            if bbox_center_x < center_x:
+                # this is someone lying down with their face in the left hand side of the frame
+                return "rotate_clockwise"
+
+        return None
+
+
+    def auto_rotate_frame(self, original_face, frame:Frame):
+        target_face = original_face
+        original_frame = frame
+
+        rotation_action = self.rotation_action(original_face, frame)
+
+        if rotation_action == "rotate_anticlockwise":
+            #face is horizontal, rotating frame anti-clockwise and getting face bounding box from rotated frame
+            frame = rotate_anticlockwise(frame)
+        elif rotation_action == "rotate_clockwise":
+            #face is horizontal, rotating frame clockwise and getting face bounding box from rotated frame
+            frame = rotate_clockwise(frame)
+
+        return target_face, frame, rotation_action
+    
+
+    def auto_unrotate_frame(self, frame:Frame, rotation_action):
+        if rotation_action == "rotate_anticlockwise":
+            return rotate_clockwise(frame)
+        elif rotation_action == "rotate_clockwise":
+            return rotate_anticlockwise(frame)
+        
+        return frame
+
+
+
+    def process_face(self,face_index, target_face:Face, frame:Frame):
+        from roop.face_util import align_crop
+
+        enhanced_frame = None
+        if(len(self.input_face_datas) > 0):
+            inputface = self.input_face_datas[face_index].faces[0]
+        else:
+            inputface = None
+
+        rotation_action = None
+        if roop.globals.autorotate_faces:
+            # check for sideways rotation of face
+            rotation_action = self.rotation_action(target_face, frame)
+            if rotation_action is not None:
+                (startX, startY, endX, endY) = target_face["bbox"].astype("int")
+                width = endX - startX
+                height = endY - startY
+                offs = int(max(width,height) * 0.25)
+                rotcutframe,startX, startY, endX, endY = self.cutout(frame, startX - offs, startY - offs, endX + offs, endY + offs)
+                if rotation_action == "rotate_anticlockwise":
+                    rotcutframe = rotate_anticlockwise(rotcutframe)
+                elif rotation_action == "rotate_clockwise":
+                    rotcutframe = rotate_clockwise(rotcutframe)
+                # rotate image and re-detect face to correct wonky landmarks
+                rotface = get_first_face(rotcutframe)
+                if rotface is None:
+                    rotation_action = None
+                else:
+                    saved_frame = frame.copy()
+                    frame = rotcutframe
+                    target_face = rotface
+
+
+
+        # if roop.globals.vr_mode:
+            # bbox = target_face.bbox
+            # [orig_width, orig_height, _] = frame.shape
+
+            # # Convert bounding box to ints
+            # x1, y1, x2, y2 = map(int, bbox)
+
+            # # Determine the center of the bounding box
+            # x_center = (x1 + x2) / 2
+            # y_center = (y1 + y2) / 2
+
+            # # Normalize coordinates to range [-1, 1]
+            # x_center_normalized = x_center / (orig_width / 2) - 1
+            # y_center_normalized = y_center / (orig_width / 2) - 1
+
+            # # Convert normalized coordinates to spherical (theta, phi)
+            # theta = x_center_normalized * 180  # Theta ranges from -180 to 180 degrees
+            # phi = -y_center_normalized * 90  # Phi ranges from -90 to 90 degrees
+
+            # img = vr.GetPerspective(frame, 90, theta, phi, 1280, 1280)  # Generate perspective image
+
+        fake_frame = None
+        aligned_img, M = align_crop(frame, target_face.kps, 128)
+        fake_frame = aligned_img
+        swap_frame = aligned_img
+        target_face.matrix = M
+        for p in self.processors:
+            if p.type == 'swap':
+                if inputface is not None:
+                    for _ in range(0,self.options.num_swap_steps):
+                        swap_frame = p.Run(inputface, target_face, swap_frame)
+                fake_frame = swap_frame
+                scale_factor = 0.0
+            elif p.type == 'mask':
+                fake_frame = self.process_mask(p, aligned_img, fake_frame)
+            else:
+                enhanced_frame, scale_factor = p.Run(self.input_face_datas[face_index], target_face, fake_frame)
+
+        upscale = 512
+        orig_width = fake_frame.shape[1]
+
+        fake_frame = cv2.resize(fake_frame, (upscale, upscale), cv2.INTER_CUBIC)
+        mask_offsets = (0,0,0,0,1,20) if inputface is None else inputface.mask_offsets
+
+        
+        if enhanced_frame is None:
+            scale_factor = int(upscale / orig_width)
+            result = self.paste_upscale(fake_frame, fake_frame, target_face.matrix, frame, scale_factor, mask_offsets)
+        else:
+            result = self.paste_upscale(fake_frame, enhanced_frame, target_face.matrix, frame, scale_factor, mask_offsets)
+
+        if rotation_action is not None:
+            fake_frame = self.auto_unrotate_frame(result, rotation_action)
+            return self.paste_simple(fake_frame, saved_frame, startX, startY)
+        
+        return result
+
+        
+
+
+    def cutout(self, frame:Frame, start_x, start_y, end_x, end_y):
+        if start_x < 0:
+            start_x = 0
+        if start_y < 0:
+            start_y = 0
+        if end_x > frame.shape[1]:
+            end_x = frame.shape[1]
+        if end_y > frame.shape[0]:
+            end_y = frame.shape[0]
+        return frame[start_y:end_y, start_x:end_x], start_x, start_y, end_x, end_y
+
+    def paste_simple(self, src:Frame, dest:Frame, start_x, start_y):
+        end_x = start_x + src.shape[1]
+        end_y = start_y + src.shape[0]
+
+        start_x, end_x, start_y, end_y = clamp_cut_values(start_x, end_x, start_y, end_y, dest)
+        dest[start_y:end_y, start_x:end_x] = src
+        return dest
+        
+    def simple_blend_with_mask(self, image1, image2, mask):
+        # Blend the images
+        blended_image = image1.astype(np.float32) * (1.0 - mask) + image2.astype(np.float32) * mask
+        return blended_image.astype(np.uint8)
+
+
+    def paste_upscale(self, fake_face, upsk_face, M, target_img, scale_factor, mask_offsets):
+        M_scale = M * scale_factor
+        IM = cv2.invertAffineTransform(M_scale)
+
+        face_matte = np.full((target_img.shape[0],target_img.shape[1]), 255, dtype=np.uint8)
+        # Generate white square sized as a upsk_face
+        img_matte = np.zeros((upsk_face.shape[0],upsk_face.shape[1]), dtype=np.uint8)
+
+        w = img_matte.shape[1]
+        h = img_matte.shape[0]
+
+        top = int(mask_offsets[0] * h)
+        bottom = int(h - (mask_offsets[1] * h))
+        left = int(mask_offsets[2] * w)
+        right = int(w - (mask_offsets[3] * w))
+        img_matte[top:bottom,left:right] = 255
+
+        # Transform white square back to target_img
+        img_matte = cv2.warpAffine(img_matte, IM, (target_img.shape[1], target_img.shape[0]), flags=cv2.INTER_NEAREST, borderValue=0.0) 
+        ##Blacken the edges of face_matte by 1 pixels (so the mask in not expanded on the image edges)
+        img_matte[:1,:] = img_matte[-1:,:] = img_matte[:,:1] = img_matte[:,-1:] = 0
+
+        img_matte = self.blur_area(img_matte, mask_offsets[4], mask_offsets[5])
+        #Normalize images to float values and reshape
+        img_matte = img_matte.astype(np.float32)/255
+        face_matte = face_matte.astype(np.float32)/255
+        img_matte = np.minimum(face_matte, img_matte)
+        if self.options.show_face_area_overlay:
+            # Additional steps for green overlay
+            green_overlay = np.zeros_like(target_img)
+            green_color = [0, 255, 0]  # RGB for green
+            for i in range(3):  # Apply green color where img_matte is not zero
+                green_overlay[:, :, i] = np.where(img_matte > 0, green_color[i], 0)        ##Transform upcaled face back to target_img
+        img_matte = np.reshape(img_matte, [img_matte.shape[0],img_matte.shape[1],1]) 
+        paste_face = cv2.warpAffine(upsk_face, IM, (target_img.shape[1], target_img.shape[0]), borderMode=cv2.BORDER_REPLICATE)
+        if upsk_face is not fake_face:
+            fake_face = cv2.warpAffine(fake_face, IM, (target_img.shape[1], target_img.shape[0]), borderMode=cv2.BORDER_REPLICATE)
+            paste_face = cv2.addWeighted(paste_face, self.options.blend_ratio, fake_face, 1.0 - self.options.blend_ratio, 0)
+
+        # Re-assemble image
+        paste_face = img_matte * paste_face
+        paste_face = paste_face + (1-img_matte) * target_img.astype(np.float32)
+        if self.options.show_face_area_overlay:
+            # Overlay the green overlay on the final image
+            paste_face = cv2.addWeighted(paste_face.astype(np.uint8), 1 - 0.5, green_overlay, 0.5, 0)
+        return paste_face.astype(np.uint8)
+
+
+    def blur_area(self, img_matte, num_erosion_iterations, blur_amount):
+        # Detect the affine transformed white area
+        mask_h_inds, mask_w_inds = np.where(img_matte==255) 
+        # Calculate the size (and diagonal size) of transformed white area width and height boundaries
+        mask_h = np.max(mask_h_inds) - np.min(mask_h_inds) 
+        mask_w = np.max(mask_w_inds) - np.min(mask_w_inds)
+        mask_size = int(np.sqrt(mask_h*mask_w))
+        # Calculate the kernel size for eroding img_matte by kernel (insightface empirical guess for best size was max(mask_size//10,10))
+        # k = max(mask_size//12, 8)
+        k = max(mask_size//(blur_amount // 2) , blur_amount // 2)
+        kernel = np.ones((k,k),np.uint8)
+        img_matte = cv2.erode(img_matte,kernel,iterations = num_erosion_iterations)
+        #Calculate the kernel size for blurring img_matte by blur_size (insightface empirical guess for best size was max(mask_size//20, 5))
+        # k = max(mask_size//24, 4) 
+        k = max(mask_size//blur_amount, blur_amount//5) 
+        kernel_size = (k, k)
+        blur_size = tuple(2*i+1 for i in kernel_size)
+        return cv2.GaussianBlur(img_matte, blur_size, 0)
+
+
+    def process_mask(self, processor, frame:Frame, target:Frame):
+        img_mask = processor.Run(frame, self.options.masking_text)
+        img_mask = cv2.resize(img_mask, (target.shape[1], target.shape[0]))
+        img_mask = np.reshape(img_mask, [img_mask.shape[0],img_mask.shape[1],1])
+
+        if self.options.show_face_masking:
+            result = (1 - img_mask) * frame.astype(np.float32)
+            return np.uint8(result)
+
+
+        target = target.astype(np.float32)
+        result = (1-img_mask) * target
+        result += img_mask * frame.astype(np.float32)
+        return np.uint8(result)
+
+            
+
+
+    def unload_models():
+        pass
+
+
+    def release_resources(self):
+        for p in self.processors:
+            p.Release()
+        self.processors.clear()
+

roop/ProcessOptions.py

@@ -0,0 +1,13 @@
+class ProcessOptions:
+
+    def __init__(self, processordefines:dict, face_distance,  blend_ratio, swap_mode, selected_index, masking_text, imagemask, num_steps, show_face_area, show_mask=False):
+        self.processors = processordefines
+        self.face_distance_threshold = face_distance
+        self.blend_ratio = blend_ratio
+        self.swap_mode = swap_mode
+        self.selected_index = selected_index
+        self.masking_text = masking_text
+        self.imagemask = imagemask
+        self.num_swap_steps = num_steps
+        self.show_face_area_overlay = show_face_area
+        self.show_face_masking = show_mask

roop/capturer.py

@@ -0,0 +1,30 @@
+from typing import Optional
+import cv2
+import numpy as np
+
+from roop.typing import Frame
+
+def get_image_frame(filename: str):
+    try:
+        return cv2.imdecode(np.fromfile(filename, dtype=np.uint8), cv2.IMREAD_COLOR)
+    except:
+        print(f"Exception reading {filename}")
+    return None
+
+    
+def get_video_frame(video_path: str, frame_number: int = 0) -> Optional[Frame]:
+    capture = cv2.VideoCapture(video_path)
+    frame_total = capture.get(cv2.CAP_PROP_FRAME_COUNT)
+    capture.set(cv2.CAP_PROP_POS_FRAMES, min(frame_total, frame_number - 1))
+    has_frame, frame = capture.read()
+    capture.release()
+    if has_frame:
+        return frame
+    return None
+
+
+def get_video_frame_total(video_path: str) -> int:
+    capture = cv2.VideoCapture(video_path)
+    video_frame_total = int(capture.get(cv2.CAP_PROP_FRAME_COUNT))
+    capture.release()
+    return video_frame_total

roop/core.py

@@ -0,0 +1,378 @@
+#!/usr/bin/env python3
+
+import os
+import sys
+import shutil
+# single thread doubles cuda performance - needs to be set before torch import
+if any(arg.startswith('--execution-provider') for arg in sys.argv):
+    os.environ['OMP_NUM_THREADS'] = '1'
+
+import warnings
+from typing import List
+import platform
+import signal
+import torch
+import onnxruntime
+import pathlib
+
+from time import time
+
+import roop.globals
+import roop.metadata
+import roop.utilities as util
+import roop.util_ffmpeg as ffmpeg
+import ui.main as main
+from settings import Settings
+from roop.face_util import extract_face_images
+from roop.ProcessEntry import ProcessEntry
+from roop.ProcessMgr import ProcessMgr
+from roop.ProcessOptions import ProcessOptions
+from roop.capturer import get_video_frame_total
+
+
+clip_text = None
+
+call_display_ui = None
+
+process_mgr = None
+
+
+if 'ROCMExecutionProvider' in roop.globals.execution_providers:
+    del torch
+
+warnings.filterwarnings('ignore', category=FutureWarning, module='insightface')
+warnings.filterwarnings('ignore', category=UserWarning, module='torchvision')
+
+
+def parse_args() -> None:
+    signal.signal(signal.SIGINT, lambda signal_number, frame: destroy())
+    roop.globals.headless = False
+    # Always enable all processors when using GUI
+    if len(sys.argv) > 1:
+        print('No CLI args supported - use Settings Tab instead')
+    roop.globals.frame_processors = ['face_swapper', 'face_enhancer']
+
+
+def encode_execution_providers(execution_providers: List[str]) -> List[str]:
+    return [execution_provider.replace('ExecutionProvider', '').lower() for execution_provider in execution_providers]
+
+
+def decode_execution_providers(execution_providers: List[str]) -> List[str]:
+    return [provider for provider, encoded_execution_provider in zip(onnxruntime.get_available_providers(), encode_execution_providers(onnxruntime.get_available_providers()))
+            if any(execution_provider in encoded_execution_provider for execution_provider in execution_providers)]
+
+
+def suggest_max_memory() -> int:
+    if platform.system().lower() == 'darwin':
+        return 4
+    return 16
+
+
+def suggest_execution_providers() -> List[str]:
+    return encode_execution_providers(onnxruntime.get_available_providers())
+
+
+def suggest_execution_threads() -> int:
+    if 'DmlExecutionProvider' in roop.globals.execution_providers:
+        return 1
+    if 'ROCMExecutionProvider' in roop.globals.execution_providers:
+        return 1
+    return 8
+
+
+def limit_resources() -> None:
+    # limit memory usage
+    if roop.globals.max_memory:
+        memory = roop.globals.max_memory * 1024 ** 3
+        if platform.system().lower() == 'darwin':
+            memory = roop.globals.max_memory * 1024 ** 6
+        if platform.system().lower() == 'windows':
+            import ctypes
+            kernel32 = ctypes.windll.kernel32  # type: ignore[attr-defined]
+            kernel32.SetProcessWorkingSetSize(-1, ctypes.c_size_t(memory), ctypes.c_size_t(memory))
+        else:
+            import resource
+            resource.setrlimit(resource.RLIMIT_DATA, (memory, memory))
+
+
+
+def release_resources() -> None:
+    import gc
+    global process_mgr
+
+    if process_mgr is not None:
+        process_mgr.release_resources()
+        process_mgr = None
+
+    gc.collect()
+    # if 'CUDAExecutionProvider' in roop.globals.execution_providers and torch.cuda.is_available():
+    #     with torch.cuda.device('cuda'):
+    #         torch.cuda.empty_cache()
+    #         torch.cuda.ipc_collect()
+
+
+def pre_check() -> bool:
+    if sys.version_info < (3, 9):
+        update_status('Python version is not supported - please upgrade to 3.9 or higher.')
+        return False
+    
+    download_directory_path = util.resolve_relative_path('../models')
+    util.conditional_download(download_directory_path, ['https://huggingface.co/countfloyd/deepfake/resolve/main/inswapper_128.onnx'])
+    util.conditional_download(download_directory_path, ['https://huggingface.co/countfloyd/deepfake/resolve/main/GFPGANv1.4.onnx'])
+    util.conditional_download(download_directory_path, ['https://github.com/csxmli2016/DMDNet/releases/download/v1/DMDNet.pth'])
+    util.conditional_download(download_directory_path, ['https://huggingface.co/countfloyd/deepfake/resolve/main/GPEN-BFR-512.onnx'])
+    util.conditional_download(download_directory_path, ['https://huggingface.co/countfloyd/deepfake/resolve/main/restoreformer_plus_plus.onnx'])
+    util.conditional_download(download_directory_path, ['https://huggingface.co/countfloyd/deepfake/resolve/main/xseg.onnx'])
+    download_directory_path = util.resolve_relative_path('../models/CLIP')
+    util.conditional_download(download_directory_path, ['https://huggingface.co/countfloyd/deepfake/resolve/main/rd64-uni-refined.pth'])
+    download_directory_path = util.resolve_relative_path('../models/CodeFormer')
+    util.conditional_download(download_directory_path, ['https://huggingface.co/countfloyd/deepfake/resolve/main/CodeFormerv0.1.onnx'])
+    download_directory_path = util.resolve_relative_path('../models/Frame')
+    util.conditional_download(download_directory_path, ['https://huggingface.co/countfloyd/deepfake/resolve/main/deoldify_artistic.onnx'])
+    util.conditional_download(download_directory_path, ['https://huggingface.co/countfloyd/deepfake/resolve/main/deoldify_stable.onnx'])
+    util.conditional_download(download_directory_path, ['https://huggingface.co/countfloyd/deepfake/resolve/main/isnet-general-use.onnx'])
+    util.conditional_download(download_directory_path, ['https://huggingface.co/countfloyd/deepfake/resolve/main/real_esrgan_x4.onnx'])
+    util.conditional_download(download_directory_path, ['https://huggingface.co/countfloyd/deepfake/resolve/main/real_esrgan_x2.onnx'])
+    util.conditional_download(download_directory_path, ['https://huggingface.co/countfloyd/deepfake/resolve/main/lsdir_x4.onnx'])
+
+    if not shutil.which('ffmpeg'):
+       update_status('ffmpeg is not installed.')
+    return True
+
+def set_display_ui(function):
+    global call_display_ui
+
+    call_display_ui = function
+
+
+def update_status(message: str) -> None:
+    global call_display_ui
+
+    print(message)
+    if call_display_ui is not None:
+        call_display_ui(message)
+
+
+
+
+def start() -> None:
+    if roop.globals.headless:
+        print('Headless mode currently unsupported - starting UI!')
+        # faces = extract_face_images(roop.globals.source_path,  (False, 0))
+        # roop.globals.INPUT_FACES.append(faces[roop.globals.source_face_index])
+        # faces = extract_face_images(roop.globals.target_path,  (False, util.has_image_extension(roop.globals.target_path)))
+        # roop.globals.TARGET_FACES.append(faces[roop.globals.target_face_index])
+        # if 'face_enhancer' in roop.globals.frame_processors:
+        #     roop.globals.selected_enhancer = 'GFPGAN'
+       
+    batch_process_regular(None, False, None)
+
+
+def get_processing_plugins(masking_engine):
+    processors = {  "faceswap": {}}
+    if masking_engine is not None:
+        processors.update({masking_engine: {}})
+    
+    if roop.globals.selected_enhancer == 'GFPGAN':
+        processors.update({"gfpgan": {}})
+    elif roop.globals.selected_enhancer == 'Codeformer':
+        processors.update({"codeformer": {}})
+    elif roop.globals.selected_enhancer == 'DMDNet':
+        processors.update({"dmdnet": {}})
+    elif roop.globals.selected_enhancer == 'GPEN':
+        processors.update({"gpen": {}})
+    elif roop.globals.selected_enhancer == 'Restoreformer++':
+        processors.update({"restoreformer++": {}})
+    return processors
+
+
+def live_swap(frame, options):
+    global process_mgr
+
+    if frame is None:
+        return frame
+
+    if process_mgr is None:
+        process_mgr = ProcessMgr(None)
+    
+#    if len(roop.globals.INPUT_FACESETS) <= selected_index:
+#        selected_index = 0
+    process_mgr.initialize(roop.globals.INPUT_FACESETS, roop.globals.TARGET_FACES, options)
+    newframe = process_mgr.process_frame(frame)
+    if newframe is None:
+        return frame
+    return newframe
+
+
+def batch_process_regular(files:list[ProcessEntry], masking_engine:str, new_clip_text:str, use_new_method, imagemask, num_swap_steps, progress, selected_index = 0) -> None:
+    global clip_text, process_mgr
+
+    release_resources()
+    limit_resources()
+    if process_mgr is None:
+        process_mgr = ProcessMgr(progress)
+    mask = imagemask["layers"][0] if imagemask is not None else None
+    if len(roop.globals.INPUT_FACESETS) <= selected_index:
+        selected_index = 0
+    options = ProcessOptions(get_processing_plugins(masking_engine), roop.globals.distance_threshold, roop.globals.blend_ratio, roop.globals.face_swap_mode, selected_index, new_clip_text, mask, num_swap_steps, False)
+    process_mgr.initialize(roop.globals.INPUT_FACESETS, roop.globals.TARGET_FACES, options)
+    batch_process(files, use_new_method)
+    return
+
+def batch_process_with_options(files:list[ProcessEntry], options, progress):
+    global clip_text, process_mgr
+
+    release_resources()
+    limit_resources()
+    if process_mgr is None:
+        process_mgr = ProcessMgr(progress)
+    process_mgr.initialize(roop.globals.INPUT_FACESETS, roop.globals.TARGET_FACES, options)
+    roop.globals.keep_frames = False
+    roop.globals.wait_after_extraction = False
+    roop.globals.skip_audio = False
+    batch_process(files, True)
+
+
+
+def batch_process(files:list[ProcessEntry], use_new_method) -> None:
+    global clip_text, process_mgr
+
+    roop.globals.processing = True
+
+    # limit threads for some providers
+    max_threads = suggest_execution_threads()
+    if max_threads == 1:
+        roop.globals.execution_threads = 1
+
+    imagefiles:list[ProcessEntry] = []
+    videofiles:list[ProcessEntry] = []
+           
+    update_status('Sorting videos/images')
+
+
+    for index, f in enumerate(files):
+        fullname = f.filename
+        if util.has_image_extension(fullname):
+            destination = util.get_destfilename_from_path(fullname, roop.globals.output_path, f'.{roop.globals.CFG.output_image_format}')
+            destination = util.replace_template(destination, index=index)
+            pathlib.Path(os.path.dirname(destination)).mkdir(parents=True, exist_ok=True)
+            f.finalname = destination
+            imagefiles.append(f)
+
+        elif util.is_video(fullname) or util.has_extension(fullname, ['gif']):
+            destination = util.get_destfilename_from_path(fullname, roop.globals.output_path, f'__temp.{roop.globals.CFG.output_video_format}')
+            f.finalname = destination
+            videofiles.append(f)
+
+
+
+    if(len(imagefiles) > 0):
+        update_status('Processing image(s)')
+        origimages = []
+        fakeimages = []
+        for f in imagefiles:
+            origimages.append(f.filename)
+            fakeimages.append(f.finalname)
+
+        process_mgr.run_batch(origimages, fakeimages, roop.globals.execution_threads)
+        origimages.clear()
+        fakeimages.clear()
+
+    if(len(videofiles) > 0):
+        for index,v in enumerate(videofiles):
+            if not roop.globals.processing:
+                end_processing('Processing stopped!')
+                return
+            fps = v.fps if v.fps > 0 else util.detect_fps(v.filename)
+            if v.endframe == 0:
+                v.endframe = get_video_frame_total(v.filename)
+
+            update_status(f'Creating {os.path.basename(v.finalname)} with {fps} FPS...')
+            start_processing = time()
+            if roop.globals.keep_frames or not use_new_method:
+                util.create_temp(v.filename)
+                update_status('Extracting frames...')
+                ffmpeg.extract_frames(v.filename,v.startframe,v.endframe, fps)
+                if not roop.globals.processing:
+                    end_processing('Processing stopped!')
+                    return
+
+                temp_frame_paths = util.get_temp_frame_paths(v.filename)
+                process_mgr.run_batch(temp_frame_paths, temp_frame_paths, roop.globals.execution_threads)
+                if not roop.globals.processing:
+                    end_processing('Processing stopped!')
+                    return
+                if roop.globals.wait_after_extraction:
+                    extract_path = os.path.dirname(temp_frame_paths[0])
+                    util.open_folder(extract_path)
+                    input("Press any key to continue...")
+                    print("Resorting frames to create video")
+                    util.sort_rename_frames(extract_path)                                    
+                
+                ffmpeg.create_video(v.filename, v.finalname, fps)
+                if not roop.globals.keep_frames:
+                    util.delete_temp_frames(temp_frame_paths[0])
+            else:
+                if util.has_extension(v.filename, ['gif']):
+                    skip_audio = True
+                else:
+                    skip_audio = roop.globals.skip_audio
+                process_mgr.run_batch_inmem(v.filename, v.finalname, v.startframe, v.endframe, fps,roop.globals.execution_threads, skip_audio)
+                
+            if not roop.globals.processing:
+                end_processing('Processing stopped!')
+                return
+            
+            video_file_name = v.finalname
+            if os.path.isfile(video_file_name):
+                destination = ''
+                if util.has_extension(v.filename, ['gif']):
+                    gifname = util.get_destfilename_from_path(v.filename, roop.globals.output_path, '.gif')
+                    destination = util.replace_template(gifname, index=index)
+                    pathlib.Path(os.path.dirname(destination)).mkdir(parents=True, exist_ok=True)
+
+                    update_status('Creating final GIF')
+                    ffmpeg.create_gif_from_video(video_file_name, destination)
+                    if os.path.isfile(destination):
+                        os.remove(video_file_name)
+                else:
+                    skip_audio = roop.globals.skip_audio
+                    destination = util.replace_template(video_file_name, index=index)
+                    pathlib.Path(os.path.dirname(destination)).mkdir(parents=True, exist_ok=True)
+
+                    if not skip_audio:
+                        ffmpeg.restore_audio(video_file_name, v.filename, v.startframe, v.endframe, destination)
+                        if os.path.isfile(destination):
+                            os.remove(video_file_name)
+                    else:
+                        shutil.move(video_file_name, destination)
+                update_status(f'\nProcessing {os.path.basename(destination)} took {time() - start_processing} secs')
+
+            else:
+                update_status(f'Failed processing {os.path.basename(v.finalname)}!')
+    end_processing('Finished')
+
+
+def end_processing(msg:str):
+    update_status(msg)
+    roop.globals.target_folder_path = None
+    release_resources()
+
+
+def destroy() -> None:
+    if roop.globals.target_path:
+        util.clean_temp(roop.globals.target_path)
+    release_resources()        
+    sys.exit()
+
+
+def run() -> None:
+    parse_args()
+    if not pre_check():
+        return
+    roop.globals.CFG = Settings('config.yaml')
+    roop.globals.execution_threads = roop.globals.CFG.max_threads
+    roop.globals.video_encoder = roop.globals.CFG.output_video_codec
+    roop.globals.video_quality = roop.globals.CFG.video_quality
+    roop.globals.max_memory = roop.globals.CFG.memory_limit if roop.globals.CFG.memory_limit > 0 else None
+    main.run()

roop/face_util.py

@@ -0,0 +1,306 @@
+import threading
+from typing import Any
+import insightface
+
+import roop.globals
+from roop.typing import Frame, Face
+
+import cv2
+import numpy as np
+from skimage import transform as trans
+from roop.capturer import get_video_frame
+from roop.utilities import resolve_relative_path, conditional_download
+
+FACE_ANALYSER = None
+THREAD_LOCK_ANALYSER = threading.Lock()
+THREAD_LOCK_SWAPPER = threading.Lock()
+FACE_SWAPPER = None
+
+
+def get_face_analyser() -> Any:
+    global FACE_ANALYSER
+
+    with THREAD_LOCK_ANALYSER:
+        if FACE_ANALYSER is None or roop.globals.g_current_face_analysis != roop.globals.g_desired_face_analysis:
+            model_path = resolve_relative_path('..')
+            # removed genderage
+            allowed_modules = roop.globals.g_desired_face_analysis
+            roop.globals.g_current_face_analysis = roop.globals.g_desired_face_analysis
+            if roop.globals.CFG.force_cpu:
+                print("Forcing CPU for Face Analysis")
+                FACE_ANALYSER = insightface.app.FaceAnalysis(
+                    name="buffalo_l",
+                    root=model_path, providers=["CPUExecutionProvider"],allowed_modules=allowed_modules
+                )
+            else:
+                FACE_ANALYSER = insightface.app.FaceAnalysis(
+                    name="buffalo_l", root=model_path, providers=roop.globals.execution_providers,allowed_modules=allowed_modules
+                )
+            FACE_ANALYSER.prepare(
+                ctx_id=0,
+                det_size=(640, 640) if roop.globals.default_det_size else (320, 320),
+            )
+    return FACE_ANALYSER
+
+
+def get_first_face(frame: Frame) -> Any:
+    try:
+        faces = get_face_analyser().get(frame)
+        return min(faces, key=lambda x: x.bbox[0])
+    #   return sorted(faces, reverse=True, key=lambda x: (x.bbox[2] - x.bbox[0]) * (x.bbox[3] - x.bbox[1]))[0]
+    except:
+        return None
+
+
+def get_all_faces(frame: Frame) -> Any:
+    try:
+        faces = get_face_analyser().get(frame)
+        return sorted(faces, key=lambda x: x.bbox[0])
+    except:
+        return None
+
+
+def extract_face_images(source_filename, video_info, extra_padding=-1.0):
+    face_data = []
+    source_image = None
+
+    if video_info[0]:
+        frame = get_video_frame(source_filename, video_info[1])
+        if frame is not None:
+            source_image = frame
+        else:
+            return face_data
+    else:
+        source_image = cv2.imdecode(np.fromfile(source_filename, dtype=np.uint8), cv2.IMREAD_COLOR)
+
+    faces = get_all_faces(source_image)
+    if faces is None:
+        return face_data
+
+    i = 0
+    for face in faces:
+        (startX, startY, endX, endY) = face["bbox"].astype("int")
+        startX, endX, startY, endY = clamp_cut_values(startX, endX, startY, endY, source_image)
+        if extra_padding > 0.0:
+            if source_image.shape[:2] == (512, 512):
+                i += 1
+                face_data.append([face, source_image])
+                continue
+
+            found = False
+            for i in range(1, 3):
+                (startX, startY, endX, endY) = face["bbox"].astype("int")
+                startX, endX, startY, endY = clamp_cut_values(startX, endX, startY, endY, source_image)
+                cutout_padding = extra_padding
+                # top needs extra room for detection
+                padding = int((endY - startY) * cutout_padding)
+                oldY = startY
+                startY -= padding
+
+                factor = 0.25 if i == 1 else 0.5
+                cutout_padding = factor
+                padding = int((endY - oldY) * cutout_padding)
+                endY += padding
+                padding = int((endX - startX) * cutout_padding)
+                startX -= padding
+                endX += padding
+                startX, endX, startY, endY = clamp_cut_values(
+                    startX, endX, startY, endY, source_image
+                )
+                face_temp = source_image[startY:endY, startX:endX]
+                face_temp = resize_image_keep_content(face_temp)
+                testfaces = get_all_faces(face_temp)
+                if testfaces is not None and len(testfaces) > 0:
+                    i += 1
+                    face_data.append([testfaces[0], face_temp])
+                    found = True
+                    break
+
+            if not found:
+                print("No face found after resizing, this shouldn't happen!")
+            continue
+
+        face_temp = source_image[startY:endY, startX:endX]
+        if face_temp.size < 1:
+            continue
+
+        i += 1
+        face_data.append([face, face_temp])
+    return face_data
+
+
+def clamp_cut_values(startX, endX, startY, endY, image):
+    if startX < 0:
+        startX = 0
+    if endX > image.shape[1]:
+        endX = image.shape[1]
+    if startY < 0:
+        startY = 0
+    if endY > image.shape[0]:
+        endY = image.shape[0]
+    return startX, endX, startY, endY
+
+
+
+def face_offset_top(face: Face, offset):
+    face["bbox"][1] += offset
+    face["bbox"][3] += offset
+    lm106 = face.landmark_2d_106
+    add = np.full_like(lm106, [0, offset])
+    face["landmark_2d_106"] = lm106 + add
+    return face
+
+
+def resize_image_keep_content(image, new_width=512, new_height=512):
+    dim = None
+    (h, w) = image.shape[:2]
+    if h > w:
+        r = new_height / float(h)
+        dim = (int(w * r), new_height)
+    else:
+        # Calculate the ratio of the width and construct the dimensions
+        r = new_width / float(w)
+        dim = (new_width, int(h * r))
+    image = cv2.resize(image, dim, interpolation=cv2.INTER_AREA)
+    (h, w) = image.shape[:2]
+    if h == new_height and w == new_width:
+        return image
+    resize_img = np.zeros(shape=(new_height, new_width, 3), dtype=image.dtype)
+    offs = (new_width - w) if h == new_height else (new_height - h)
+    startoffs = int(offs // 2) if offs % 2 == 0 else int(offs // 2) + 1
+    offs = int(offs // 2)
+
+    if h == new_height:
+        resize_img[0:new_height, startoffs : new_width - offs] = image
+    else:
+        resize_img[startoffs : new_height - offs, 0:new_width] = image
+    return resize_img
+
+
+def rotate_image_90(image, rotate=True):
+    if rotate:
+        return np.rot90(image)
+    else:
+        return np.rot90(image, 1, (1, 0))
+
+
+def rotate_anticlockwise(frame):
+    return rotate_image_90(frame)
+
+
+def rotate_clockwise(frame):
+    return rotate_image_90(frame, False)
+
+
+def rotate_image_180(image):
+    return np.flip(image, 0)
+
+
+# alignment code from insightface https://github.com/deepinsight/insightface/blob/master/python-package/insightface/utils/face_align.py
+
+arcface_dst = np.array(
+    [
+        [38.2946, 51.6963],
+        [73.5318, 51.5014],
+        [56.0252, 71.7366],
+        [41.5493, 92.3655],
+        [70.7299, 92.2041],
+    ],
+    dtype=np.float32,
+)
+
+
+def estimate_norm(lmk, image_size=112, mode="arcface"):
+    assert lmk.shape == (5, 2)
+    assert image_size % 112 == 0 or image_size % 128 == 0
+    if image_size % 112 == 0:
+        ratio = float(image_size) / 112.0
+        diff_x = 0
+    else:
+        ratio = float(image_size) / 128.0
+        diff_x = 8.0 * ratio
+    dst = arcface_dst * ratio
+    dst[:, 0] += diff_x
+    tform = trans.SimilarityTransform()
+    tform.estimate(lmk, dst)
+    M = tform.params[0:2, :]
+    return M
+
+
+
+# aligned, M = norm_crop2(f[1], face.kps, 512)
+def align_crop(img, landmark, image_size=112, mode="arcface"):
+    M = estimate_norm(landmark, image_size, mode)
+    warped = cv2.warpAffine(img, M, (image_size, image_size), borderValue=0.0)
+    return warped, M
+
+
+def square_crop(im, S):
+    if im.shape[0] > im.shape[1]:
+        height = S
+        width = int(float(im.shape[1]) / im.shape[0] * S)
+        scale = float(S) / im.shape[0]
+    else:
+        width = S
+        height = int(float(im.shape[0]) / im.shape[1] * S)
+        scale = float(S) / im.shape[1]
+    resized_im = cv2.resize(im, (width, height))
+    det_im = np.zeros((S, S, 3), dtype=np.uint8)
+    det_im[: resized_im.shape[0], : resized_im.shape[1], :] = resized_im
+    return det_im, scale
+
+
+def transform(data, center, output_size, scale, rotation):
+    scale_ratio = scale
+    rot = float(rotation) * np.pi / 180.0
+    # translation = (output_size/2-center[0]*scale_ratio, output_size/2-center[1]*scale_ratio)
+    t1 = trans.SimilarityTransform(scale=scale_ratio)
+    cx = center[0] * scale_ratio
+    cy = center[1] * scale_ratio
+    t2 = trans.SimilarityTransform(translation=(-1 * cx, -1 * cy))
+    t3 = trans.SimilarityTransform(rotation=rot)
+    t4 = trans.SimilarityTransform(translation=(output_size / 2, output_size / 2))
+    t = t1 + t2 + t3 + t4
+    M = t.params[0:2]
+    cropped = cv2.warpAffine(data, M, (output_size, output_size), borderValue=0.0)
+    return cropped, M
+
+
+def trans_points2d(pts, M):
+    new_pts = np.zeros(shape=pts.shape, dtype=np.float32)
+    for i in range(pts.shape[0]):
+        pt = pts[i]
+        new_pt = np.array([pt[0], pt[1], 1.0], dtype=np.float32)
+        new_pt = np.dot(M, new_pt)
+        # print('new_pt', new_pt.shape, new_pt)
+        new_pts[i] = new_pt[0:2]
+
+    return new_pts
+
+
+def trans_points3d(pts, M):
+    scale = np.sqrt(M[0][0] * M[0][0] + M[0][1] * M[0][1])
+    # print(scale)
+    new_pts = np.zeros(shape=pts.shape, dtype=np.float32)
+    for i in range(pts.shape[0]):
+        pt = pts[i]
+        new_pt = np.array([pt[0], pt[1], 1.0], dtype=np.float32)
+        new_pt = np.dot(M, new_pt)
+        # print('new_pt', new_pt.shape, new_pt)
+        new_pts[i][0:2] = new_pt[0:2]
+        new_pts[i][2] = pts[i][2] * scale
+
+    return new_pts
+
+
+def trans_points(pts, M):
+    if pts.shape[1] == 2:
+        return trans_points2d(pts, M)
+    else:
+        return trans_points3d(pts, M)
+    
+def create_blank_image(width, height):
+    img = np.zeros((height, width, 4), dtype=np.uint8)
+    img[:] = [0,0,0,0]
+    return img
+

roop/ffmpeg_writer.py

@@ -0,0 +1,218 @@
+"""
+FFMPEG_Writer - write set of frames to video file
+
+original from
+https://github.com/Zulko/moviepy/blob/master/moviepy/video/io/ffmpeg_writer.py
+
+removed unnecessary dependencies
+
+The MIT License (MIT)
+
+Copyright (c) 2015 Zulko
+Copyright (c) 2023 Janvarev Vladislav
+"""
+
+import os
+import subprocess as sp
+
+PIPE = -1
+STDOUT = -2
+DEVNULL = -3
+
+FFMPEG_BINARY = "ffmpeg"
+
+class FFMPEG_VideoWriter:
+    """ A class for FFMPEG-based video writing.
+
+    A class to write videos using ffmpeg. ffmpeg will write in a large
+    choice of formats.
+
+    Parameters
+    -----------
+
+    filename
+      Any filename like 'video.mp4' etc. but if you want to avoid
+      complications it is recommended to use the generic extension
+      '.avi' for all your videos.
+
+    size
+      Size (width,height) of the output video in pixels.
+
+    fps
+      Frames per second in the output video file.
+
+    codec
+      FFMPEG codec. It seems that in terms of quality the hierarchy is
+      'rawvideo' = 'png' > 'mpeg4' > 'libx264'
+      'png' manages the same lossless quality as 'rawvideo' but yields
+      smaller files. Type ``ffmpeg -codecs`` in a terminal to get a list
+      of accepted codecs.
+
+      Note for default 'libx264': by default the pixel format yuv420p
+      is used. If the video dimensions are not both even (e.g. 720x405)
+      another pixel format is used, and this can cause problem in some
+      video readers.
+
+    audiofile
+      Optional: The name of an audio file that will be incorporated
+      to the video.
+
+    preset
+      Sets the time that FFMPEG will take to compress the video. The slower,
+      the better the compression rate. Possibilities are: ultrafast,superfast,
+      veryfast, faster, fast, medium (default), slow, slower, veryslow,
+      placebo.
+
+    bitrate
+      Only relevant for codecs which accept a bitrate. "5000k" offers
+      nice results in general.
+
+    """
+
+    def __init__(self, filename, size, fps, codec="libx265", crf=14, audiofile=None,
+                 preset="medium", bitrate=None,
+                 logfile=None, threads=None, ffmpeg_params=None):
+
+        if logfile is None:
+            logfile = sp.PIPE
+
+        self.filename = filename
+        self.codec = codec
+        self.ext = self.filename.split(".")[-1]
+        w = size[0] - 1 if size[0] % 2 != 0 else size[0]
+        h = size[1] - 1 if size[1] % 2 != 0 else size[1]
+
+
+        # order is important
+        cmd = [
+            FFMPEG_BINARY,
+            '-hide_banner',
+            '-hwaccel', 'auto',
+            '-y',
+            '-loglevel', 'error' if logfile == sp.PIPE else 'info',
+            '-f', 'rawvideo',
+            '-vcodec', 'rawvideo',
+            '-s', '%dx%d' % (size[0], size[1]),
+            #'-pix_fmt', 'rgba' if withmask else 'rgb24',
+            '-pix_fmt', 'bgr24',
+            '-r', str(fps),
+            '-an', '-i', '-' 
+        ]
+
+        if audiofile is not None:
+            cmd.extend([
+                '-i', audiofile,
+                '-acodec', 'copy'
+            ])
+
+        cmd.extend([
+            '-vcodec', codec,
+            '-crf', str(crf)
+            #'-preset', preset,
+        ])
+        if ffmpeg_params is not None:
+            cmd.extend(ffmpeg_params)
+        if bitrate is not None:
+            cmd.extend([
+                '-b', bitrate
+            ])
+
+        # scale to a resolution divisible by 2 if not even
+        cmd.extend(['-vf', f'scale={w}:{h}' if w != size[0] or h != size[1] else 'colorspace=bt709:iall=bt601-6-625:fast=1'])
+
+        if threads is not None:
+            cmd.extend(["-threads", str(threads)])
+
+        cmd.extend([
+            '-pix_fmt', 'yuv420p',
+
+        ])
+        cmd.extend([
+            filename
+        ])
+
+        test = str(cmd)
+        print(test)
+
+        popen_params = {"stdout": DEVNULL,
+                        "stderr": logfile,
+                        "stdin": sp.PIPE}
+
+        # This was added so that no extra unwanted window opens on windows
+        # when the child process is created
+        if os.name == "nt":
+            popen_params["creationflags"] = 0x08000000  # CREATE_NO_WINDOW
+        
+        self.proc = sp.Popen(cmd, **popen_params)
+
+
+    def write_frame(self, img_array):
+        """ Writes one frame in the file."""
+        try:
+            #if PY3:
+            self.proc.stdin.write(img_array.tobytes())
+            # else:
+            #    self.proc.stdin.write(img_array.tostring())
+        except IOError as err:
+            _, ffmpeg_error = self.proc.communicate()
+            error = (str(err) + ("\n\nroop unleashed error: FFMPEG encountered "
+                                 "the following error while writing file %s:"
+                                 "\n\n %s" % (self.filename, str(ffmpeg_error))))
+
+            if b"Unknown encoder" in ffmpeg_error:
+
+                error = error+("\n\nThe video export "
+                  "failed because FFMPEG didn't find the specified "
+                  "codec for video encoding (%s). Please install "
+                  "this codec or change the codec when calling "
+                  "write_videofile. For instance:\n"
+                  "  >>> clip.write_videofile('myvid.webm', codec='libvpx')")%(self.codec)
+
+            elif b"incorrect codec parameters ?" in ffmpeg_error:
+
+                 error = error+("\n\nThe video export "
+                  "failed, possibly because the codec specified for "
+                  "the video (%s) is not compatible with the given "
+                  "extension (%s). Please specify a valid 'codec' "
+                  "argument in write_videofile. This would be 'libx264' "
+                  "or 'mpeg4' for mp4, 'libtheora' for ogv, 'libvpx for webm. "
+                  "Another possible reason is that the audio codec was not "
+                  "compatible with the video codec. For instance the video "
+                  "extensions 'ogv' and 'webm' only allow 'libvorbis' (default) as a"
+                  "video codec."
+                  )%(self.codec, self.ext)
+
+            elif  b"encoder setup failed" in ffmpeg_error:
+
+                error = error+("\n\nThe video export "
+                  "failed, possibly because the bitrate you specified "
+                  "was too high or too low for the video codec.")
+
+            elif b"Invalid encoder type" in ffmpeg_error:
+
+                error = error + ("\n\nThe video export failed because the codec "
+                  "or file extension you provided is not a video")
+
+
+            raise IOError(error)
+
+    def close(self):
+        if self.proc:
+            self.proc.stdin.close()
+            if self.proc.stderr is not None:
+                self.proc.stderr.close()
+            self.proc.wait()
+
+        self.proc = None
+
+    # Support the Context Manager protocol, to ensure that resources are cleaned up.
+
+    def __enter__(self):
+        return self
+
+    def __exit__(self, exc_type, exc_value, traceback):
+        self.close()
+
+
+
+    

roop/globals.py

@@ -0,0 +1,53 @@
+from settings import Settings
+from typing import List
+
+source_path = None
+target_path = None
+output_path = None
+target_folder_path = None
+
+frame_processors: List[str] = []
+keep_fps = None
+keep_frames = None
+autorotate_faces = None
+vr_mode = None
+skip_audio = None
+wait_after_extraction = None
+many_faces = None
+use_batch = None
+source_face_index = 0
+target_face_index = 0
+face_position = None
+video_encoder = None
+video_quality = None
+max_memory = None
+execution_providers: List[str] = []
+execution_threads = None
+headless = None
+log_level = 'error'
+selected_enhancer = None
+face_swap_mode = None
+blend_ratio = 0.5
+distance_threshold = 0.65
+default_det_size = True
+
+no_face_action = 0
+
+processing = False
+
+g_current_face_analysis = None
+g_desired_face_analysis = None
+
+FACE_ENHANCER = None
+
+INPUT_FACESETS = []
+TARGET_FACES = []
+
+
+IMAGE_CHAIN_PROCESSOR = None
+VIDEO_CHAIN_PROCESSOR = None
+BATCH_IMAGE_CHAIN_PROCESSOR = None
+
+CFG: Settings = None
+
+

roop/metadata.py

@@ -0,0 +1,2 @@
+name = 'roop unleashed'
+version = '4.0.0'

roop/processors/Enhance_CodeFormer.py

@@ -0,0 +1,75 @@
+from typing import Any, List, Callable
+import cv2 
+import numpy as np
+import onnxruntime
+import roop.globals
+
+from roop.typing import Face, Frame, FaceSet
+from roop.utilities import resolve_relative_path
+
+
+# THREAD_LOCK = threading.Lock()
+
+
+class Enhance_CodeFormer():
+    model_codeformer = None
+
+    plugin_options:dict = None
+
+    processorname = 'codeformer'
+    type = 'enhance'
+    
+
+    def Initialize(self, plugin_options:dict):
+        if self.plugin_options is not None:
+            if self.plugin_options["devicename"] != plugin_options["devicename"]:
+                self.Release()
+
+        self.plugin_options = plugin_options
+        if self.model_codeformer is None:
+            # replace Mac mps with cpu for the moment
+            self.devicename = self.plugin_options["devicename"].replace('mps', 'cpu')
+            model_path = resolve_relative_path('../models/CodeFormer/CodeFormerv0.1.onnx')
+            self.model_codeformer = onnxruntime.InferenceSession(model_path, None, providers=roop.globals.execution_providers)
+            self.model_inputs = self.model_codeformer.get_inputs()
+            model_outputs = self.model_codeformer.get_outputs()
+            self.io_binding = self.model_codeformer.io_binding()           
+            self.io_binding.bind_cpu_input(self.model_inputs[1].name, np.array([0.5]))
+            self.io_binding.bind_output(model_outputs[0].name, self.devicename)
+
+
+    def Run(self, source_faceset: FaceSet, target_face: Face, temp_frame: Frame) -> Frame:
+        input_size = temp_frame.shape[1]
+        # preprocess
+        temp_frame = cv2.resize(temp_frame, (512, 512), cv2.INTER_CUBIC)
+        temp_frame = cv2.cvtColor(temp_frame, cv2.COLOR_BGR2RGB)
+        temp_frame = temp_frame.astype('float32') / 255.0
+        temp_frame = (temp_frame - 0.5) / 0.5
+        temp_frame = np.expand_dims(temp_frame, axis=0).transpose(0, 3, 1, 2)
+        
+        self.io_binding.bind_cpu_input(self.model_inputs[0].name, temp_frame.astype(np.float32))
+        self.model_codeformer.run_with_iobinding(self.io_binding)
+        ort_outs = self.io_binding.copy_outputs_to_cpu()
+        result = ort_outs[0][0]
+        del ort_outs
+        
+        # post-process
+        result = result.transpose((1, 2, 0))
+
+        un_min = -1.0
+        un_max = 1.0
+        result = np.clip(result, un_min, un_max)
+        result = (result - un_min) / (un_max - un_min)
+
+        result = cv2.cvtColor(result, cv2.COLOR_RGB2BGR)
+        result = (result * 255.0).round()
+        scale_factor = int(result.shape[1] / input_size)       
+        return result.astype(np.uint8), scale_factor
+
+
+    def Release(self):
+        del self.model_codeformer
+        self.model_codeformer = None
+        del self.io_binding
+        self.io_binding = None
+

roop/processors/Enhance_DMDNet.py

@@ -0,0 +1,898 @@
+from typing import Any, List, Callable
+import cv2 
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.nn.utils.spectral_norm as SpectralNorm
+import threading
+from torchvision.ops import roi_align
+
+from math import sqrt
+
+from torchvision.transforms.functional import normalize
+
+from roop.typing import Face, Frame, FaceSet
+
+
+THREAD_LOCK_DMDNET = threading.Lock()
+
+
+class Enhance_DMDNet():
+    plugin_options:dict = None
+    model_dmdnet = None
+    torchdevice = None
+
+    processorname = 'dmdnet'
+    type = 'enhance'
+
+
+    def Initialize(self, plugin_options:dict):
+        if self.plugin_options is not None:
+            if self.plugin_options["devicename"] != plugin_options["devicename"]:
+                self.Release()
+
+        self.plugin_options = plugin_options
+        if self.model_dmdnet is None:
+            self.model_dmdnet = self.create(self.plugin_options["devicename"])
+            
+
+    # temp_frame already cropped+aligned, bbox not
+    def Run(self, source_faceset: FaceSet, target_face: Face, temp_frame: Frame) -> Frame:
+        input_size = temp_frame.shape[1]
+
+        result = self.enhance_face(source_faceset, temp_frame, target_face)
+        scale_factor = int(result.shape[1] / input_size)       
+        return result.astype(np.uint8), scale_factor
+
+
+    def Release(self):
+        self.model_gfpgan = None
+
+
+    # https://stackoverflow.com/a/67174339
+    def landmarks106_to_68(self, pt106):
+        map106to68=[1,10,12,14,16,3,5,7,0,23,21,19,32,30,28,26,17,
+                        43,48,49,51,50,
+                        102,103,104,105,101,
+                        72,73,74,86,78,79,80,85,84,
+                        35,41,42,39,37,36,
+                        89,95,96,93,91,90,
+                        52,64,63,71,67,68,61,58,59,53,56,55,65,66,62,70,69,57,60,54
+                        ]
+      
+        pt68 = []
+        for i in range(68):
+            index = map106to68[i]
+            pt68.append(pt106[index])
+        return pt68
+
+        
+
+
+    def check_bbox(self, imgs, boxes):
+        boxes = boxes.view(-1, 4, 4)
+        colors = [(0, 255, 0), (0, 255, 0), (255, 255, 0), (255, 0, 0)]
+        i = 0
+        for img, box in zip(imgs, boxes):
+            img = (img + 1)/2 * 255
+            img2 = img.permute(1, 2, 0).float().cpu().flip(2).numpy().copy()
+            for idx, point in enumerate(box):
+                cv2.rectangle(img2, (int(point[0]), int(point[1])), (int(point[2]), int(point[3])), color=colors[idx], thickness=2)
+            cv2.imwrite('dmdnet_{:02d}.png'.format(i), img2)
+            i += 1
+
+
+    def trans_points2d(self, pts, M):
+        new_pts = np.zeros(shape=pts.shape, dtype=np.float32)
+        for i in range(pts.shape[0]):
+            pt = pts[i]
+            new_pt = np.array([pt[0], pt[1], 1.0], dtype=np.float32)
+            new_pt = np.dot(M, new_pt)
+            new_pts[i] = new_pt[0:2]
+
+        return new_pts
+
+
+    def enhance_face(self, ref_faceset: FaceSet, temp_frame, face: Face):
+        # preprocess
+        start_x, start_y, end_x, end_y = map(int, face['bbox'])
+        lm106 = face.landmark_2d_106
+        lq_landmarks = np.asarray(self.landmarks106_to_68(lm106))
+
+        if temp_frame.shape[0] != 512 or temp_frame.shape[1] != 512:
+            # scale to 512x512
+            scale_factor = 512 / temp_frame.shape[1]
+
+            M = face.matrix * scale_factor
+
+            lq_landmarks = self.trans_points2d(lq_landmarks, M)
+            temp_frame = cv2.resize(temp_frame, (512,512), interpolation = cv2.INTER_AREA)
+
+        if temp_frame.ndim == 2:
+            temp_frame = cv2.cvtColor(temp_frame, cv2.COLOR_GRAY2RGB)  # GGG
+        # else:
+        #     temp_frame = cv2.cvtColor(temp_frame, cv2.COLOR_BGR2RGB)  # RGB
+
+        lq = read_img_tensor(temp_frame)
+
+        LQLocs = get_component_location(lq_landmarks)
+        # self.check_bbox(lq, LQLocs.unsqueeze(0))
+
+        # specific, change 1000 to 1 to activate
+        if len(ref_faceset.faces) > 1:
+            SpecificImgs = []
+            SpecificLocs = []
+            for i,face in enumerate(ref_faceset.faces):
+                lm106 = face.landmark_2d_106
+                lq_landmarks = np.asarray(self.landmarks106_to_68(lm106))
+                ref_image = ref_faceset.ref_images[i]
+                if ref_image.shape[0] != 512 or ref_image.shape[1] != 512:
+                    # scale to 512x512
+                    scale_factor = 512 / ref_image.shape[1]
+
+                    M = face.matrix * scale_factor
+
+                    lq_landmarks = self.trans_points2d(lq_landmarks, M)
+                    ref_image = cv2.resize(ref_image, (512,512), interpolation = cv2.INTER_AREA)
+
+                if ref_image.ndim == 2:
+                    temp_frame = cv2.cvtColor(temp_frame, cv2.COLOR_GRAY2RGB)  # GGG
+                # else:
+                #     temp_frame = cv2.cvtColor(temp_frame, cv2.COLOR_BGR2RGB)  # RGB
+
+                ref_tensor = read_img_tensor(ref_image)
+                ref_locs = get_component_location(lq_landmarks)
+                # self.check_bbox(ref_tensor, ref_locs.unsqueeze(0))
+
+                SpecificImgs.append(ref_tensor)
+                SpecificLocs.append(ref_locs.unsqueeze(0))
+
+            SpecificImgs = torch.cat(SpecificImgs, dim=0)
+            SpecificLocs = torch.cat(SpecificLocs, dim=0)
+            # check_bbox(SpecificImgs, SpecificLocs)
+            SpMem256, SpMem128, SpMem64 = self.model_dmdnet.generate_specific_dictionary(sp_imgs = SpecificImgs.to(self.torchdevice), sp_locs = SpecificLocs)
+            SpMem256Para = {}
+            SpMem128Para = {}
+            SpMem64Para = {}
+            for k, v in SpMem256.items():
+                SpMem256Para[k] = v
+            for k, v in SpMem128.items():
+                SpMem128Para[k] = v
+            for k, v in SpMem64.items():
+                SpMem64Para[k] = v
+        else:
+            # generic
+            SpMem256Para, SpMem128Para, SpMem64Para = None, None, None
+
+        with torch.no_grad():
+            with THREAD_LOCK_DMDNET:
+                try:
+                    GenericResult, SpecificResult = self.model_dmdnet(lq = lq.to(self.torchdevice), loc = LQLocs.unsqueeze(0), sp_256 = SpMem256Para, sp_128 = SpMem128Para, sp_64 = SpMem64Para)
+                except Exception as e:
+                    print(f'Error {e} there may be something wrong with the detected component locations.')
+                    return temp_frame
+        
+        if SpecificResult is not None:
+            save_specific = SpecificResult * 0.5 + 0.5
+            save_specific = save_specific.squeeze(0).permute(1, 2, 0).flip(2) # RGB->BGR
+            save_specific = np.clip(save_specific.float().cpu().numpy(), 0, 1) * 255.0
+            temp_frame =  save_specific.astype("uint8")
+            if False:
+                save_generic = GenericResult * 0.5 + 0.5
+                save_generic = save_generic.squeeze(0).permute(1, 2, 0).flip(2) # RGB->BGR
+                save_generic = np.clip(save_generic.float().cpu().numpy(), 0, 1) * 255.0
+                check_lq = lq * 0.5 + 0.5
+                check_lq = check_lq.squeeze(0).permute(1, 2, 0).flip(2) # RGB->BGR
+                check_lq = np.clip(check_lq.float().cpu().numpy(), 0, 1) * 255.0
+                cv2.imwrite('dmdnet_comparison.png', cv2.cvtColor(np.hstack((check_lq, save_generic, save_specific)),cv2.COLOR_RGB2BGR))
+        else:
+            save_generic = GenericResult * 0.5 + 0.5
+            save_generic = save_generic.squeeze(0).permute(1, 2, 0).flip(2) # RGB->BGR
+            save_generic = np.clip(save_generic.float().cpu().numpy(), 0, 1) * 255.0
+            temp_frame =  save_generic.astype("uint8")
+        temp_frame = cv2.cvtColor(temp_frame, cv2.COLOR_RGB2BGR)  # RGB
+        return temp_frame
+
+    
+
+    def create(self, devicename):
+        self.torchdevice = torch.device(devicename)
+        model_dmdnet = DMDNet().to(self.torchdevice)
+        weights = torch.load('./models/DMDNet.pth') 
+        model_dmdnet.load_state_dict(weights, strict=True)
+
+        model_dmdnet.eval()
+        num_params = 0
+        for param in model_dmdnet.parameters():
+            num_params += param.numel()
+        return model_dmdnet
+
+    # print('{:>8s} : {}'.format('Using device', device))
+    # print('{:>8s} : {:.2f}M'.format('Model params', num_params/1e6))
+
+
+
+def read_img_tensor(Img=None): #rgb -1~1 
+    Img = Img.transpose((2, 0, 1))/255.0
+    Img = torch.from_numpy(Img).float()
+    normalize(Img, [0.5,0.5,0.5], [0.5,0.5,0.5], inplace=True)
+    ImgTensor = Img.unsqueeze(0)
+    return ImgTensor
+
+
+def get_component_location(Landmarks, re_read=False):
+    if re_read:
+        ReadLandmark = []
+        with open(Landmarks,'r') as f:
+            for line in f:
+                tmp = [float(i) for i in line.split(' ') if i != '\n']
+                ReadLandmark.append(tmp)
+        ReadLandmark = np.array(ReadLandmark) #
+        Landmarks = np.reshape(ReadLandmark, [-1, 2]) # 68*2
+    Map_LE_B = list(np.hstack((range(17,22), range(36,42))))
+    Map_RE_B = list(np.hstack((range(22,27), range(42,48))))
+    Map_LE = list(range(36,42))
+    Map_RE = list(range(42,48))
+    Map_NO = list(range(29,36))
+    Map_MO = list(range(48,68))
+
+    Landmarks[Landmarks>504]=504
+    Landmarks[Landmarks<8]=8
+    
+    #left eye
+    Mean_LE = np.mean(Landmarks[Map_LE],0)
+    L_LE1 = Mean_LE[1] - np.min(Landmarks[Map_LE_B,1])
+    L_LE1 = L_LE1 * 1.3
+    L_LE2 = L_LE1 / 1.9
+    L_LE_xy = L_LE1 + L_LE2
+    L_LE_lt = [L_LE_xy/2, L_LE1]
+    L_LE_rb = [L_LE_xy/2, L_LE2]
+    Location_LE = np.hstack((Mean_LE - L_LE_lt + 1, Mean_LE + L_LE_rb)).astype(int)
+
+    #right eye
+    Mean_RE = np.mean(Landmarks[Map_RE],0)
+    L_RE1 = Mean_RE[1] - np.min(Landmarks[Map_RE_B,1])
+    L_RE1 = L_RE1 * 1.3
+    L_RE2 = L_RE1 / 1.9
+    L_RE_xy = L_RE1 + L_RE2
+    L_RE_lt = [L_RE_xy/2, L_RE1]
+    L_RE_rb = [L_RE_xy/2, L_RE2]
+    Location_RE = np.hstack((Mean_RE - L_RE_lt + 1, Mean_RE + L_RE_rb)).astype(int)
+
+    #nose
+    Mean_NO = np.mean(Landmarks[Map_NO],0)
+    L_NO1 =( np.max([Mean_NO[0] - Landmarks[31][0], Landmarks[35][0] - Mean_NO[0]])) * 1.25
+    L_NO2 = (Landmarks[33][1] - Mean_NO[1]) * 1.1
+    L_NO_xy = L_NO1 * 2
+    L_NO_lt = [L_NO_xy/2, L_NO_xy - L_NO2]
+    L_NO_rb = [L_NO_xy/2, L_NO2]
+    Location_NO = np.hstack((Mean_NO - L_NO_lt + 1, Mean_NO + L_NO_rb)).astype(int)
+    
+    #mouth
+    Mean_MO = np.mean(Landmarks[Map_MO],0)
+    L_MO = np.max((np.max(np.max(Landmarks[Map_MO],0) - np.min(Landmarks[Map_MO],0))/2,16)) * 1.1
+    MO_O = Mean_MO - L_MO + 1
+    MO_T = Mean_MO + L_MO
+    MO_T[MO_T>510]=510
+    Location_MO = np.hstack((MO_O, MO_T)).astype(int)
+    return torch.cat([torch.FloatTensor(Location_LE).unsqueeze(0), torch.FloatTensor(Location_RE).unsqueeze(0), torch.FloatTensor(Location_NO).unsqueeze(0), torch.FloatTensor(Location_MO).unsqueeze(0)], dim=0)
+
+
+
+
+def calc_mean_std_4D(feat, eps=1e-5):
+    # eps is a small value added to the variance to avoid divide-by-zero.
+    size = feat.size()
+    assert (len(size) == 4)
+    N, C = size[:2]
+    feat_var = feat.view(N, C, -1).var(dim=2) + eps
+    feat_std = feat_var.sqrt().view(N, C, 1, 1)
+    feat_mean = feat.view(N, C, -1).mean(dim=2).view(N, C, 1, 1)
+    return feat_mean, feat_std
+
+def adaptive_instance_normalization_4D(content_feat, style_feat): # content_feat is ref feature, style is degradate feature
+    size = content_feat.size()
+    style_mean, style_std = calc_mean_std_4D(style_feat)
+    content_mean, content_std = calc_mean_std_4D(content_feat)
+    normalized_feat = (content_feat - content_mean.expand(size)) / content_std.expand(size)
+    return normalized_feat * style_std.expand(size) + style_mean.expand(size)
+
+
+def convU(in_channels, out_channels,conv_layer, norm_layer, kernel_size=3, stride=1,dilation=1, bias=True):
+    return nn.Sequential(
+        SpectralNorm(conv_layer(in_channels, out_channels, kernel_size=kernel_size, stride=stride, dilation=dilation, padding=((kernel_size-1)//2)*dilation, bias=bias)),
+        nn.LeakyReLU(0.2),
+        SpectralNorm(conv_layer(out_channels, out_channels, kernel_size=kernel_size, stride=stride, dilation=dilation, padding=((kernel_size-1)//2)*dilation, bias=bias)),
+    )
+    
+
+class MSDilateBlock(nn.Module):
+    def __init__(self, in_channels,conv_layer=nn.Conv2d, norm_layer=nn.BatchNorm2d, kernel_size=3, dilation=[1,1,1,1], bias=True):
+        super(MSDilateBlock, self).__init__()
+        self.conv1 =  convU(in_channels, in_channels,conv_layer, norm_layer, kernel_size,dilation=dilation[0], bias=bias)
+        self.conv2 =  convU(in_channels, in_channels,conv_layer, norm_layer, kernel_size,dilation=dilation[1], bias=bias)
+        self.conv3 =  convU(in_channels, in_channels,conv_layer, norm_layer, kernel_size,dilation=dilation[2], bias=bias)
+        self.conv4 =  convU(in_channels, in_channels,conv_layer, norm_layer, kernel_size,dilation=dilation[3], bias=bias)
+        self.convi =  SpectralNorm(conv_layer(in_channels*4, in_channels, kernel_size=kernel_size, stride=1, padding=(kernel_size-1)//2, bias=bias))
+    def forward(self, x):
+        conv1 = self.conv1(x)
+        conv2 = self.conv2(x)
+        conv3 = self.conv3(x)
+        conv4 = self.conv4(x)
+        cat  = torch.cat([conv1, conv2, conv3, conv4], 1)
+        out = self.convi(cat) + x
+        return out
+
+
+class AdaptiveInstanceNorm(nn.Module):
+    def __init__(self, in_channel):
+        super().__init__()
+        self.norm = nn.InstanceNorm2d(in_channel)
+
+    def forward(self, input, style):
+        style_mean, style_std = calc_mean_std_4D(style)
+        out = self.norm(input)
+        size = input.size()
+        out = style_std.expand(size) * out + style_mean.expand(size)
+        return out
+
+class NoiseInjection(nn.Module):
+    def __init__(self, channel):
+        super().__init__()
+        self.weight = nn.Parameter(torch.zeros(1, channel, 1, 1))
+    def forward(self, image, noise):
+        if noise is None:
+            b, c, h, w = image.shape
+            noise = image.new_empty(b, 1, h, w).normal_()
+        return image + self.weight * noise
+
+class StyledUpBlock(nn.Module):
+    def __init__(self, in_channel, out_channel, kernel_size=3, padding=1,upsample=False, noise_inject=False):
+        super().__init__()
+
+        self.noise_inject = noise_inject
+        if upsample:
+            self.conv1 = nn.Sequential(
+                nn.Upsample(scale_factor=2, mode='bilinear', align_corners=False),
+                SpectralNorm(nn.Conv2d(in_channel, out_channel, kernel_size, padding=padding)),
+                nn.LeakyReLU(0.2),
+            )
+        else:
+            self.conv1 = nn.Sequential(
+                SpectralNorm(nn.Conv2d(in_channel, out_channel, kernel_size, padding=padding)),
+                nn.LeakyReLU(0.2),
+                SpectralNorm(nn.Conv2d(out_channel, out_channel, kernel_size, padding=padding)),
+            )
+        self.convup = nn.Sequential(
+                nn.Upsample(scale_factor=2, mode='bilinear', align_corners=False),
+                SpectralNorm(nn.Conv2d(out_channel, out_channel, kernel_size, padding=padding)),
+                nn.LeakyReLU(0.2),
+                SpectralNorm(nn.Conv2d(out_channel, out_channel, kernel_size, padding=padding)),
+            )
+        if self.noise_inject:
+            self.noise1 = NoiseInjection(out_channel)
+
+        self.lrelu1 = nn.LeakyReLU(0.2)
+
+        self.ScaleModel1 = nn.Sequential(
+            SpectralNorm(nn.Conv2d(in_channel,out_channel,3, 1, 1)),
+            nn.LeakyReLU(0.2),
+            SpectralNorm(nn.Conv2d(out_channel, out_channel, 3, 1, 1))
+        )
+        self.ShiftModel1 = nn.Sequential(
+            SpectralNorm(nn.Conv2d(in_channel,out_channel,3, 1, 1)),
+            nn.LeakyReLU(0.2),
+            SpectralNorm(nn.Conv2d(out_channel, out_channel, 3, 1, 1)),
+        )
+       
+    def forward(self, input, style):
+        out = self.conv1(input)
+        out = self.lrelu1(out)
+        Shift1 = self.ShiftModel1(style)
+        Scale1 = self.ScaleModel1(style)
+        out = out * Scale1 + Shift1
+        if self.noise_inject:
+            out = self.noise1(out, noise=None)
+        outup = self.convup(out)
+        return outup
+
+
+####################################################################
+###############Face Dictionary Generator
+####################################################################
+def AttentionBlock(in_channel):
+    return nn.Sequential(
+        SpectralNorm(nn.Conv2d(in_channel, in_channel, 3, 1, 1)),
+        nn.LeakyReLU(0.2),
+        SpectralNorm(nn.Conv2d(in_channel, in_channel, 3, 1, 1)),
+    )
+
+class DilateResBlock(nn.Module):
+    def __init__(self, dim, dilation=[5,3] ):
+        super(DilateResBlock, self).__init__()
+        self.Res = nn.Sequential(
+            SpectralNorm(nn.Conv2d(dim, dim, 3, 1, ((3-1)//2)*dilation[0], dilation[0])),
+            nn.LeakyReLU(0.2),
+            SpectralNorm(nn.Conv2d(dim, dim, 3, 1, ((3-1)//2)*dilation[1], dilation[1])),
+        )
+    def forward(self, x):
+        out = x + self.Res(x)
+        return out
+
+
+class KeyValue(nn.Module):
+    def __init__(self, indim, keydim, valdim):
+        super(KeyValue, self).__init__()
+        self.Key = nn.Sequential(
+            SpectralNorm(nn.Conv2d(indim, keydim, kernel_size=(3,3), padding=(1,1), stride=1)),
+            nn.LeakyReLU(0.2),
+            SpectralNorm(nn.Conv2d(keydim, keydim, kernel_size=(3,3), padding=(1,1), stride=1)),
+        )
+        self.Value = nn.Sequential(
+            SpectralNorm(nn.Conv2d(indim, valdim, kernel_size=(3,3), padding=(1,1), stride=1)),
+            nn.LeakyReLU(0.2),
+            SpectralNorm(nn.Conv2d(valdim, valdim, kernel_size=(3,3), padding=(1,1), stride=1)),
+        )
+    def forward(self, x):  
+        return self.Key(x), self.Value(x)
+
+class MaskAttention(nn.Module):
+    def __init__(self, indim):
+        super(MaskAttention, self).__init__()
+        self.conv1 = nn.Sequential(
+            SpectralNorm(nn.Conv2d(indim, indim//3, kernel_size=(3,3), padding=(1,1), stride=1)),
+            nn.LeakyReLU(0.2),
+            SpectralNorm(nn.Conv2d(indim//3, indim//3, kernel_size=(3,3), padding=(1,1), stride=1)),
+        )
+        self.conv2 = nn.Sequential(
+            SpectralNorm(nn.Conv2d(indim, indim//3, kernel_size=(3,3), padding=(1,1), stride=1)),
+            nn.LeakyReLU(0.2),
+            SpectralNorm(nn.Conv2d(indim//3, indim//3, kernel_size=(3,3), padding=(1,1), stride=1)),
+        )
+        self.conv3 = nn.Sequential(
+            SpectralNorm(nn.Conv2d(indim, indim//3, kernel_size=(3,3), padding=(1,1), stride=1)),
+            nn.LeakyReLU(0.2),
+            SpectralNorm(nn.Conv2d(indim//3, indim//3, kernel_size=(3,3), padding=(1,1), stride=1)),
+        )
+        self.convCat = nn.Sequential(
+            SpectralNorm(nn.Conv2d(indim//3 * 3, indim, kernel_size=(3,3), padding=(1,1), stride=1)),
+            nn.LeakyReLU(0.2),
+            SpectralNorm(nn.Conv2d(indim, indim, kernel_size=(3,3), padding=(1,1), stride=1)),
+        )
+    def forward(self, x, y, z):
+        c1 = self.conv1(x)
+        c2 = self.conv2(y)
+        c3 = self.conv3(z)
+        return self.convCat(torch.cat([c1,c2,c3], dim=1))
+
+class Query(nn.Module):
+    def __init__(self, indim, quedim):
+        super(Query, self).__init__()
+        self.Query = nn.Sequential(
+            SpectralNorm(nn.Conv2d(indim, quedim, kernel_size=(3,3), padding=(1,1), stride=1)),
+            nn.LeakyReLU(0.2),
+            SpectralNorm(nn.Conv2d(quedim, quedim, kernel_size=(3,3), padding=(1,1), stride=1)),
+        )
+    def forward(self, x):
+        return self.Query(x)
+
+def roi_align_self(input, location, target_size):
+    test = (target_size.item(),target_size.item())
+    return torch.cat([F.interpolate(input[i:i+1,:,location[i,1]:location[i,3],location[i,0]:location[i,2]],test,mode='bilinear',align_corners=False) for i in range(input.size(0))],0)
+
+class FeatureExtractor(nn.Module):
+    def __init__(self, ngf = 64, key_scale = 4):#
+        super().__init__()
+
+        self.key_scale = 4
+        self.part_sizes = np.array([80,80,50,110]) #
+        self.feature_sizes = np.array([256,128,64]) # 
+
+        self.conv1 = nn.Sequential(
+                SpectralNorm(nn.Conv2d(3, ngf, 3, 2, 1)),
+                nn.LeakyReLU(0.2),
+                SpectralNorm(nn.Conv2d(ngf, ngf, 3, 1, 1)),
+            )
+        self.conv2 = nn.Sequential(
+            SpectralNorm(nn.Conv2d(ngf, ngf, 3, 1, 1)),
+            nn.LeakyReLU(0.2),
+            SpectralNorm(nn.Conv2d(ngf, ngf, 3, 1, 1))
+        )
+        self.res1 = DilateResBlock(ngf, [5,3])
+        self.res2 = DilateResBlock(ngf, [5,3])
+
+        
+        self.conv3 = nn.Sequential(
+            SpectralNorm(nn.Conv2d(ngf, ngf*2, 3, 2, 1)),
+            nn.LeakyReLU(0.2),
+            SpectralNorm(nn.Conv2d(ngf*2, ngf*2, 3, 1, 1)),
+            )
+        self.conv4 = nn.Sequential(
+            SpectralNorm(nn.Conv2d(ngf*2, ngf*2, 3, 1, 1)),
+            nn.LeakyReLU(0.2),
+            SpectralNorm(nn.Conv2d(ngf*2, ngf*2, 3, 1, 1))
+        )
+        self.res3 = DilateResBlock(ngf*2, [3,1])
+        self.res4 = DilateResBlock(ngf*2, [3,1])
+
+        self.conv5 = nn.Sequential(
+            SpectralNorm(nn.Conv2d(ngf*2, ngf*4, 3, 2, 1)),
+            nn.LeakyReLU(0.2),
+            SpectralNorm(nn.Conv2d(ngf*4, ngf*4, 3, 1, 1)),
+        )
+        self.conv6 = nn.Sequential(
+            SpectralNorm(nn.Conv2d(ngf*4, ngf*4, 3, 1, 1)),
+            nn.LeakyReLU(0.2),
+            SpectralNorm(nn.Conv2d(ngf*4, ngf*4, 3, 1, 1))
+        )
+        self.res5 = DilateResBlock(ngf*4, [1,1])
+        self.res6 = DilateResBlock(ngf*4, [1,1])
+
+        self.LE_256_Q = Query(ngf, ngf // self.key_scale)
+        self.RE_256_Q = Query(ngf, ngf // self.key_scale)
+        self.MO_256_Q = Query(ngf, ngf // self.key_scale)
+        self.LE_128_Q = Query(ngf * 2, ngf * 2 // self.key_scale)
+        self.RE_128_Q = Query(ngf * 2, ngf * 2 // self.key_scale)
+        self.MO_128_Q = Query(ngf * 2, ngf * 2 // self.key_scale)
+        self.LE_64_Q = Query(ngf * 4, ngf * 4 // self.key_scale)
+        self.RE_64_Q = Query(ngf * 4, ngf * 4 // self.key_scale)
+        self.MO_64_Q = Query(ngf * 4, ngf * 4 // self.key_scale)
+
+
+    def forward(self, img, locs):
+        le_location = locs[:,0,:].int().cpu().numpy()
+        re_location = locs[:,1,:].int().cpu().numpy()
+        no_location = locs[:,2,:].int().cpu().numpy()
+        mo_location = locs[:,3,:].int().cpu().numpy()
+        
+
+        f1_0 = self.conv1(img) 
+        f1_1 = self.res1(f1_0)
+        f2_0 = self.conv2(f1_1)
+        f2_1 = self.res2(f2_0)
+
+        f3_0 = self.conv3(f2_1) 
+        f3_1 = self.res3(f3_0)
+        f4_0 = self.conv4(f3_1)
+        f4_1 = self.res4(f4_0)
+
+        f5_0 = self.conv5(f4_1) 
+        f5_1 = self.res5(f5_0)
+        f6_0 = self.conv6(f5_1)
+        f6_1 = self.res6(f6_0)
+
+
+        ####ROI Align
+        le_part_256 = roi_align_self(f2_1.clone(), le_location//2, self.part_sizes[0]//2)
+        re_part_256 = roi_align_self(f2_1.clone(), re_location//2, self.part_sizes[1]//2)
+        mo_part_256 = roi_align_self(f2_1.clone(), mo_location//2, self.part_sizes[3]//2)
+
+        le_part_128 = roi_align_self(f4_1.clone(), le_location//4, self.part_sizes[0]//4)
+        re_part_128 = roi_align_self(f4_1.clone(), re_location//4, self.part_sizes[1]//4)
+        mo_part_128 = roi_align_self(f4_1.clone(), mo_location//4, self.part_sizes[3]//4)
+
+        le_part_64 = roi_align_self(f6_1.clone(), le_location//8, self.part_sizes[0]//8)
+        re_part_64 = roi_align_self(f6_1.clone(), re_location//8, self.part_sizes[1]//8)
+        mo_part_64 = roi_align_self(f6_1.clone(), mo_location//8, self.part_sizes[3]//8)
+
+
+        le_256_q = self.LE_256_Q(le_part_256)
+        re_256_q = self.RE_256_Q(re_part_256)
+        mo_256_q = self.MO_256_Q(mo_part_256)
+
+        le_128_q = self.LE_128_Q(le_part_128)
+        re_128_q = self.RE_128_Q(re_part_128)
+        mo_128_q = self.MO_128_Q(mo_part_128)
+
+        le_64_q = self.LE_64_Q(le_part_64)
+        re_64_q = self.RE_64_Q(re_part_64)
+        mo_64_q = self.MO_64_Q(mo_part_64)
+
+        return {'f256': f2_1, 'f128': f4_1, 'f64': f6_1,\
+            'le256': le_part_256, 're256': re_part_256, 'mo256': mo_part_256, \
+            'le128': le_part_128, 're128': re_part_128, 'mo128': mo_part_128, \
+            'le64': le_part_64, 're64': re_part_64, 'mo64': mo_part_64, \
+            'le_256_q': le_256_q, 're_256_q': re_256_q, 'mo_256_q': mo_256_q,\
+            'le_128_q': le_128_q, 're_128_q': re_128_q, 'mo_128_q': mo_128_q,\
+            'le_64_q': le_64_q, 're_64_q': re_64_q, 'mo_64_q': mo_64_q}
+
+
+class DMDNet(nn.Module):
+    def __init__(self, ngf = 64, banks_num = 128):
+        super().__init__()
+        self.part_sizes = np.array([80,80,50,110]) # size for 512
+        self.feature_sizes = np.array([256,128,64]) # size for 512
+
+        self.banks_num = banks_num
+        self.key_scale = 4
+
+        self.E_lq = FeatureExtractor(key_scale = self.key_scale)
+        self.E_hq = FeatureExtractor(key_scale = self.key_scale)
+
+        self.LE_256_KV = KeyValue(ngf, ngf // self.key_scale, ngf)
+        self.RE_256_KV = KeyValue(ngf, ngf // self.key_scale, ngf)
+        self.MO_256_KV = KeyValue(ngf, ngf // self.key_scale, ngf)
+
+        self.LE_128_KV = KeyValue(ngf * 2 , ngf * 2 // self.key_scale, ngf * 2)
+        self.RE_128_KV = KeyValue(ngf * 2 , ngf * 2 // self.key_scale, ngf * 2)
+        self.MO_128_KV = KeyValue(ngf * 2 , ngf * 2 // self.key_scale, ngf * 2)
+
+        self.LE_64_KV = KeyValue(ngf * 4 , ngf * 4 // self.key_scale, ngf * 4)
+        self.RE_64_KV = KeyValue(ngf * 4 , ngf * 4 // self.key_scale, ngf * 4)
+        self.MO_64_KV = KeyValue(ngf * 4 , ngf * 4 // self.key_scale, ngf * 4)
+
+
+        self.LE_256_Attention = AttentionBlock(64)
+        self.RE_256_Attention = AttentionBlock(64)
+        self.MO_256_Attention = AttentionBlock(64)
+
+        self.LE_128_Attention = AttentionBlock(128)
+        self.RE_128_Attention = AttentionBlock(128)
+        self.MO_128_Attention = AttentionBlock(128)
+
+        self.LE_64_Attention = AttentionBlock(256)
+        self.RE_64_Attention = AttentionBlock(256)
+        self.MO_64_Attention = AttentionBlock(256)
+
+        self.LE_256_Mask = MaskAttention(64)
+        self.RE_256_Mask = MaskAttention(64)
+        self.MO_256_Mask = MaskAttention(64)
+
+        self.LE_128_Mask = MaskAttention(128)
+        self.RE_128_Mask = MaskAttention(128)
+        self.MO_128_Mask = MaskAttention(128)
+
+        self.LE_64_Mask = MaskAttention(256)
+        self.RE_64_Mask = MaskAttention(256)
+        self.MO_64_Mask = MaskAttention(256)
+
+        self.MSDilate = MSDilateBlock(ngf*4, dilation = [4,3,2,1])
+
+        self.up1 = StyledUpBlock(ngf*4, ngf*2, noise_inject=False) #
+        self.up2 = StyledUpBlock(ngf*2, ngf, noise_inject=False) #
+        self.up3 = StyledUpBlock(ngf, ngf, noise_inject=False) #
+        self.up4 = nn.Sequential( 
+            SpectralNorm(nn.Conv2d(ngf, ngf, 3, 1, 1)),
+            nn.LeakyReLU(0.2),
+            UpResBlock(ngf),
+            UpResBlock(ngf),
+            SpectralNorm(nn.Conv2d(ngf, 3, kernel_size=3, stride=1, padding=1)),
+            nn.Tanh()
+        )
+ 
+        # define generic memory, revise register_buffer to register_parameter for backward update
+        self.register_buffer('le_256_mem_key', torch.randn(128,16,40,40))
+        self.register_buffer('re_256_mem_key', torch.randn(128,16,40,40))
+        self.register_buffer('mo_256_mem_key', torch.randn(128,16,55,55))
+        self.register_buffer('le_256_mem_value', torch.randn(128,64,40,40))
+        self.register_buffer('re_256_mem_value', torch.randn(128,64,40,40))
+        self.register_buffer('mo_256_mem_value', torch.randn(128,64,55,55))
+        
+
+        self.register_buffer('le_128_mem_key', torch.randn(128,32,20,20))
+        self.register_buffer('re_128_mem_key', torch.randn(128,32,20,20))
+        self.register_buffer('mo_128_mem_key', torch.randn(128,32,27,27))
+        self.register_buffer('le_128_mem_value', torch.randn(128,128,20,20))
+        self.register_buffer('re_128_mem_value', torch.randn(128,128,20,20))
+        self.register_buffer('mo_128_mem_value', torch.randn(128,128,27,27))
+
+        self.register_buffer('le_64_mem_key', torch.randn(128,64,10,10))
+        self.register_buffer('re_64_mem_key', torch.randn(128,64,10,10))
+        self.register_buffer('mo_64_mem_key', torch.randn(128,64,13,13))
+        self.register_buffer('le_64_mem_value', torch.randn(128,256,10,10))
+        self.register_buffer('re_64_mem_value', torch.randn(128,256,10,10))
+        self.register_buffer('mo_64_mem_value', torch.randn(128,256,13,13))
+
+    
+    def readMem(self, k, v, q):
+        sim = F.conv2d(q, k)
+        score = F.softmax(sim/sqrt(sim.size(1)), dim=1) #B * S * 1 * 1 6*128
+        sb,sn,sw,sh = score.size()
+        s_m = score.view(sb, -1).unsqueeze(1)#2*1*M
+        vb,vn,vw,vh = v.size()
+        v_in = v.view(vb, -1).repeat(sb,1,1)#2*M*(c*w*h)
+        mem_out = torch.bmm(s_m, v_in).squeeze(1).view(sb, vn, vw,vh)
+        max_inds = torch.argmax(score, dim=1).squeeze()
+        return mem_out, max_inds
+    
+
+    def memorize(self, img, locs):
+        fs = self.E_hq(img, locs)
+        LE256_key, LE256_value = self.LE_256_KV(fs['le256'])
+        RE256_key, RE256_value = self.RE_256_KV(fs['re256'])
+        MO256_key, MO256_value = self.MO_256_KV(fs['mo256'])
+
+        LE128_key, LE128_value = self.LE_128_KV(fs['le128'])
+        RE128_key, RE128_value = self.RE_128_KV(fs['re128'])
+        MO128_key, MO128_value = self.MO_128_KV(fs['mo128'])
+
+        LE64_key, LE64_value = self.LE_64_KV(fs['le64'])
+        RE64_key, RE64_value = self.RE_64_KV(fs['re64'])
+        MO64_key, MO64_value = self.MO_64_KV(fs['mo64'])
+
+        Mem256 = {'LE256Key': LE256_key, 'LE256Value': LE256_value, 'RE256Key': RE256_key, 'RE256Value': RE256_value,'MO256Key': MO256_key, 'MO256Value': MO256_value}
+        Mem128 = {'LE128Key': LE128_key, 'LE128Value': LE128_value, 'RE128Key': RE128_key, 'RE128Value': RE128_value,'MO128Key': MO128_key, 'MO128Value': MO128_value}
+        Mem64 = {'LE64Key': LE64_key, 'LE64Value': LE64_value, 'RE64Key': RE64_key, 'RE64Value': RE64_value,'MO64Key': MO64_key, 'MO64Value': MO64_value}
+ 
+        FS256 = {'LE256F':fs['le256'], 'RE256F':fs['re256'], 'MO256F':fs['mo256']}
+        FS128 = {'LE128F':fs['le128'], 'RE128F':fs['re128'], 'MO128F':fs['mo128']}
+        FS64 = {'LE64F':fs['le64'], 'RE64F':fs['re64'], 'MO64F':fs['mo64']}
+        
+        return Mem256, Mem128, Mem64
+
+    def enhancer(self, fs_in, sp_256=None, sp_128=None, sp_64=None):
+        le_256_q = fs_in['le_256_q']
+        re_256_q = fs_in['re_256_q']
+        mo_256_q = fs_in['mo_256_q']
+
+        le_128_q = fs_in['le_128_q']
+        re_128_q = fs_in['re_128_q']
+        mo_128_q = fs_in['mo_128_q']
+
+        le_64_q = fs_in['le_64_q']
+        re_64_q = fs_in['re_64_q']
+        mo_64_q = fs_in['mo_64_q']
+
+        
+        ####for 256
+        le_256_mem_g, le_256_inds = self.readMem(self.le_256_mem_key, self.le_256_mem_value, le_256_q)
+        re_256_mem_g, re_256_inds = self.readMem(self.re_256_mem_key, self.re_256_mem_value, re_256_q)
+        mo_256_mem_g, mo_256_inds = self.readMem(self.mo_256_mem_key, self.mo_256_mem_value, mo_256_q)
+
+        le_128_mem_g, le_128_inds = self.readMem(self.le_128_mem_key, self.le_128_mem_value, le_128_q)
+        re_128_mem_g, re_128_inds = self.readMem(self.re_128_mem_key, self.re_128_mem_value, re_128_q)
+        mo_128_mem_g, mo_128_inds = self.readMem(self.mo_128_mem_key, self.mo_128_mem_value, mo_128_q)
+
+        le_64_mem_g, le_64_inds = self.readMem(self.le_64_mem_key, self.le_64_mem_value, le_64_q)
+        re_64_mem_g, re_64_inds = self.readMem(self.re_64_mem_key, self.re_64_mem_value, re_64_q)
+        mo_64_mem_g, mo_64_inds = self.readMem(self.mo_64_mem_key, self.mo_64_mem_value, mo_64_q)
+
+        if sp_256 is not None and sp_128 is not None and sp_64 is not None:
+            le_256_mem_s, _ = self.readMem(sp_256['LE256Key'], sp_256['LE256Value'], le_256_q)
+            re_256_mem_s, _ = self.readMem(sp_256['RE256Key'], sp_256['RE256Value'], re_256_q)
+            mo_256_mem_s, _ = self.readMem(sp_256['MO256Key'], sp_256['MO256Value'], mo_256_q)
+            le_256_mask = self.LE_256_Mask(fs_in['le256'],le_256_mem_s,le_256_mem_g)
+            le_256_mem = le_256_mask*le_256_mem_s + (1-le_256_mask)*le_256_mem_g
+            re_256_mask = self.RE_256_Mask(fs_in['re256'],re_256_mem_s,re_256_mem_g)
+            re_256_mem = re_256_mask*re_256_mem_s + (1-re_256_mask)*re_256_mem_g
+            mo_256_mask = self.MO_256_Mask(fs_in['mo256'],mo_256_mem_s,mo_256_mem_g)
+            mo_256_mem = mo_256_mask*mo_256_mem_s + (1-mo_256_mask)*mo_256_mem_g
+
+            le_128_mem_s, _ = self.readMem(sp_128['LE128Key'], sp_128['LE128Value'], le_128_q)
+            re_128_mem_s, _ = self.readMem(sp_128['RE128Key'], sp_128['RE128Value'], re_128_q)
+            mo_128_mem_s, _ = self.readMem(sp_128['MO128Key'], sp_128['MO128Value'], mo_128_q)
+            le_128_mask = self.LE_128_Mask(fs_in['le128'],le_128_mem_s,le_128_mem_g)
+            le_128_mem = le_128_mask*le_128_mem_s + (1-le_128_mask)*le_128_mem_g
+            re_128_mask = self.RE_128_Mask(fs_in['re128'],re_128_mem_s,re_128_mem_g)
+            re_128_mem = re_128_mask*re_128_mem_s + (1-re_128_mask)*re_128_mem_g
+            mo_128_mask = self.MO_128_Mask(fs_in['mo128'],mo_128_mem_s,mo_128_mem_g)
+            mo_128_mem = mo_128_mask*mo_128_mem_s + (1-mo_128_mask)*mo_128_mem_g
+
+            le_64_mem_s, _ = self.readMem(sp_64['LE64Key'], sp_64['LE64Value'], le_64_q)
+            re_64_mem_s, _ = self.readMem(sp_64['RE64Key'], sp_64['RE64Value'], re_64_q)
+            mo_64_mem_s, _ = self.readMem(sp_64['MO64Key'], sp_64['MO64Value'], mo_64_q)
+            le_64_mask = self.LE_64_Mask(fs_in['le64'],le_64_mem_s,le_64_mem_g)
+            le_64_mem = le_64_mask*le_64_mem_s + (1-le_64_mask)*le_64_mem_g
+            re_64_mask = self.RE_64_Mask(fs_in['re64'],re_64_mem_s,re_64_mem_g)
+            re_64_mem = re_64_mask*re_64_mem_s + (1-re_64_mask)*re_64_mem_g
+            mo_64_mask = self.MO_64_Mask(fs_in['mo64'],mo_64_mem_s,mo_64_mem_g)
+            mo_64_mem = mo_64_mask*mo_64_mem_s + (1-mo_64_mask)*mo_64_mem_g
+        else:
+            le_256_mem = le_256_mem_g
+            re_256_mem = re_256_mem_g
+            mo_256_mem = mo_256_mem_g
+            le_128_mem = le_128_mem_g
+            re_128_mem = re_128_mem_g
+            mo_128_mem = mo_128_mem_g
+            le_64_mem = le_64_mem_g
+            re_64_mem = re_64_mem_g
+            mo_64_mem = mo_64_mem_g
+
+        le_256_mem_norm = adaptive_instance_normalization_4D(le_256_mem, fs_in['le256'])
+        re_256_mem_norm = adaptive_instance_normalization_4D(re_256_mem, fs_in['re256'])
+        mo_256_mem_norm = adaptive_instance_normalization_4D(mo_256_mem, fs_in['mo256'])
+        
+        ####for 128
+        le_128_mem_norm = adaptive_instance_normalization_4D(le_128_mem, fs_in['le128'])
+        re_128_mem_norm = adaptive_instance_normalization_4D(re_128_mem, fs_in['re128'])
+        mo_128_mem_norm = adaptive_instance_normalization_4D(mo_128_mem, fs_in['mo128'])
+        
+        ####for 64
+        le_64_mem_norm = adaptive_instance_normalization_4D(le_64_mem, fs_in['le64'])
+        re_64_mem_norm = adaptive_instance_normalization_4D(re_64_mem, fs_in['re64'])
+        mo_64_mem_norm = adaptive_instance_normalization_4D(mo_64_mem, fs_in['mo64'])
+    
+
+        EnMem256 = {'LE256Norm': le_256_mem_norm, 'RE256Norm': re_256_mem_norm, 'MO256Norm': mo_256_mem_norm}
+        EnMem128 = {'LE128Norm': le_128_mem_norm, 'RE128Norm': re_128_mem_norm, 'MO128Norm': mo_128_mem_norm}
+        EnMem64 = {'LE64Norm': le_64_mem_norm, 'RE64Norm': re_64_mem_norm, 'MO64Norm': mo_64_mem_norm}
+        Ind256 = {'LE': le_256_inds, 'RE': re_256_inds, 'MO': mo_256_inds}
+        Ind128 = {'LE': le_128_inds, 'RE': re_128_inds, 'MO': mo_128_inds}
+        Ind64 = {'LE': le_64_inds, 'RE': re_64_inds, 'MO': mo_64_inds}
+        return EnMem256, EnMem128, EnMem64, Ind256, Ind128, Ind64
+
+    def reconstruct(self, fs_in, locs, memstar):
+        le_256_mem_norm, re_256_mem_norm, mo_256_mem_norm = memstar[0]['LE256Norm'], memstar[0]['RE256Norm'], memstar[0]['MO256Norm']
+        le_128_mem_norm, re_128_mem_norm, mo_128_mem_norm = memstar[1]['LE128Norm'], memstar[1]['RE128Norm'], memstar[1]['MO128Norm']
+        le_64_mem_norm, re_64_mem_norm, mo_64_mem_norm = memstar[2]['LE64Norm'], memstar[2]['RE64Norm'], memstar[2]['MO64Norm']
+
+        le_256_final = self.LE_256_Attention(le_256_mem_norm - fs_in['le256']) * le_256_mem_norm + fs_in['le256']
+        re_256_final = self.RE_256_Attention(re_256_mem_norm - fs_in['re256']) * re_256_mem_norm + fs_in['re256']
+        mo_256_final = self.MO_256_Attention(mo_256_mem_norm - fs_in['mo256']) * mo_256_mem_norm + fs_in['mo256']
+        
+        le_128_final = self.LE_128_Attention(le_128_mem_norm - fs_in['le128']) * le_128_mem_norm + fs_in['le128']
+        re_128_final = self.RE_128_Attention(re_128_mem_norm - fs_in['re128']) * re_128_mem_norm + fs_in['re128']
+        mo_128_final = self.MO_128_Attention(mo_128_mem_norm - fs_in['mo128']) * mo_128_mem_norm + fs_in['mo128']
+        
+        le_64_final = self.LE_64_Attention(le_64_mem_norm - fs_in['le64']) * le_64_mem_norm + fs_in['le64']
+        re_64_final = self.RE_64_Attention(re_64_mem_norm - fs_in['re64']) * re_64_mem_norm + fs_in['re64']
+        mo_64_final = self.MO_64_Attention(mo_64_mem_norm - fs_in['mo64']) * mo_64_mem_norm + fs_in['mo64']
+
+
+        le_location = locs[:,0,:]
+        re_location = locs[:,1,:]
+        mo_location = locs[:,3,:]
+
+        # Somehow with latest Torch it doesn't like numpy wrappers anymore
+        
+        # le_location = le_location.cpu().int().numpy()
+        # re_location = re_location.cpu().int().numpy()
+        # mo_location = mo_location.cpu().int().numpy()
+        le_location = le_location.cpu().int()
+        re_location = re_location.cpu().int()
+        mo_location = mo_location.cpu().int()
+
+        up_in_256 = fs_in['f256'].clone()# * 0
+        up_in_128 = fs_in['f128'].clone()# * 0
+        up_in_64 = fs_in['f64'].clone()# * 0
+
+        for i in range(fs_in['f256'].size(0)):
+            up_in_256[i:i+1,:,le_location[i,1]//2:le_location[i,3]//2,le_location[i,0]//2:le_location[i,2]//2] = F.interpolate(le_256_final[i:i+1,:,:,:].clone(), (le_location[i,3]//2-le_location[i,1]//2,le_location[i,2]//2-le_location[i,0]//2),mode='bilinear',align_corners=False)
+            up_in_256[i:i+1,:,re_location[i,1]//2:re_location[i,3]//2,re_location[i,0]//2:re_location[i,2]//2] = F.interpolate(re_256_final[i:i+1,:,:,:].clone(), (re_location[i,3]//2-re_location[i,1]//2,re_location[i,2]//2-re_location[i,0]//2),mode='bilinear',align_corners=False)
+            up_in_256[i:i+1,:,mo_location[i,1]//2:mo_location[i,3]//2,mo_location[i,0]//2:mo_location[i,2]//2] = F.interpolate(mo_256_final[i:i+1,:,:,:].clone(), (mo_location[i,3]//2-mo_location[i,1]//2,mo_location[i,2]//2-mo_location[i,0]//2),mode='bilinear',align_corners=False)
+            
+            up_in_128[i:i+1,:,le_location[i,1]//4:le_location[i,3]//4,le_location[i,0]//4:le_location[i,2]//4] = F.interpolate(le_128_final[i:i+1,:,:,:].clone(), (le_location[i,3]//4-le_location[i,1]//4,le_location[i,2]//4-le_location[i,0]//4),mode='bilinear',align_corners=False)
+            up_in_128[i:i+1,:,re_location[i,1]//4:re_location[i,3]//4,re_location[i,0]//4:re_location[i,2]//4] = F.interpolate(re_128_final[i:i+1,:,:,:].clone(), (re_location[i,3]//4-re_location[i,1]//4,re_location[i,2]//4-re_location[i,0]//4),mode='bilinear',align_corners=False)
+            up_in_128[i:i+1,:,mo_location[i,1]//4:mo_location[i,3]//4,mo_location[i,0]//4:mo_location[i,2]//4] = F.interpolate(mo_128_final[i:i+1,:,:,:].clone(), (mo_location[i,3]//4-mo_location[i,1]//4,mo_location[i,2]//4-mo_location[i,0]//4),mode='bilinear',align_corners=False)
+
+            up_in_64[i:i+1,:,le_location[i,1]//8:le_location[i,3]//8,le_location[i,0]//8:le_location[i,2]//8] = F.interpolate(le_64_final[i:i+1,:,:,:].clone(), (le_location[i,3]//8-le_location[i,1]//8,le_location[i,2]//8-le_location[i,0]//8),mode='bilinear',align_corners=False)
+            up_in_64[i:i+1,:,re_location[i,1]//8:re_location[i,3]//8,re_location[i,0]//8:re_location[i,2]//8] = F.interpolate(re_64_final[i:i+1,:,:,:].clone(), (re_location[i,3]//8-re_location[i,1]//8,re_location[i,2]//8-re_location[i,0]//8),mode='bilinear',align_corners=False)
+            up_in_64[i:i+1,:,mo_location[i,1]//8:mo_location[i,3]//8,mo_location[i,0]//8:mo_location[i,2]//8] = F.interpolate(mo_64_final[i:i+1,:,:,:].clone(), (mo_location[i,3]//8-mo_location[i,1]//8,mo_location[i,2]//8-mo_location[i,0]//8),mode='bilinear',align_corners=False)
+        
+        ms_in_64 = self.MSDilate(fs_in['f64'].clone())
+        fea_up1 = self.up1(ms_in_64, up_in_64)
+        fea_up2 = self.up2(fea_up1, up_in_128) #
+        fea_up3 = self.up3(fea_up2, up_in_256) #
+        output = self.up4(fea_up3) #
+        return output
+
+    def generate_specific_dictionary(self, sp_imgs=None, sp_locs=None):
+        return self.memorize(sp_imgs, sp_locs)
+
+    def forward(self, lq=None, loc=None, sp_256 = None, sp_128 = None, sp_64 = None):
+        try:
+            fs_in = self.E_lq(lq, loc) # low quality images
+        except Exception as e:
+            print(e)
+
+        GeMemNorm256, GeMemNorm128, GeMemNorm64, Ind256, Ind128, Ind64 = self.enhancer(fs_in)
+        GeOut = self.reconstruct(fs_in, loc, memstar = [GeMemNorm256, GeMemNorm128, GeMemNorm64])
+        if sp_256 is not None and sp_128 is not None and sp_64 is not None:
+            GSMemNorm256, GSMemNorm128, GSMemNorm64, _, _, _ = self.enhancer(fs_in, sp_256, sp_128, sp_64)
+            GSOut = self.reconstruct(fs_in, loc, memstar = [GSMemNorm256, GSMemNorm128, GSMemNorm64])
+        else:
+            GSOut = None
+        return GeOut, GSOut
+
+class UpResBlock(nn.Module):
+    def __init__(self, dim, conv_layer = nn.Conv2d, norm_layer = nn.BatchNorm2d):
+        super(UpResBlock, self).__init__()
+        self.Model = nn.Sequential(
+            SpectralNorm(conv_layer(dim, dim, 3, 1, 1)),
+            nn.LeakyReLU(0.2),
+            SpectralNorm(conv_layer(dim, dim, 3, 1, 1)),
+        )
+    def forward(self, x):
+        out = x + self.Model(x)
+        return out

roop/processors/Enhance_GFPGAN.py

@@ -0,0 +1,77 @@
+from typing import Any, List, Callable
+import cv2 
+import numpy as np
+import onnxruntime
+import roop.globals
+
+from roop.typing import Face, Frame, FaceSet
+from roop.utilities import resolve_relative_path
+
+
+# THREAD_LOCK = threading.Lock()
+
+
+class Enhance_GFPGAN():
+    plugin_options:dict = None
+
+    model_gfpgan = None
+    name = None
+    devicename = None
+
+    processorname = 'gfpgan'
+    type = 'enhance'
+
+
+    def Initialize(self, plugin_options:dict):
+        if self.plugin_options is not None:
+            if self.plugin_options["devicename"] != plugin_options["devicename"]:
+                self.Release()
+
+        self.plugin_options = plugin_options
+        if self.model_gfpgan is None:
+            model_path = resolve_relative_path('../models/GFPGANv1.4.onnx')
+            self.model_gfpgan = onnxruntime.InferenceSession(model_path, None, providers=roop.globals.execution_providers)
+            # replace Mac mps with cpu for the moment
+            self.devicename = self.plugin_options["devicename"].replace('mps', 'cpu')
+
+        self.name = self.model_gfpgan.get_inputs()[0].name
+
+    def Run(self, source_faceset: FaceSet, target_face: Face, temp_frame: Frame) -> Frame:
+        # preprocess
+        input_size = temp_frame.shape[1]
+        temp_frame = cv2.resize(temp_frame, (512, 512), cv2.INTER_CUBIC)
+
+        temp_frame = cv2.cvtColor(temp_frame, cv2.COLOR_BGR2RGB)
+        temp_frame = temp_frame.astype('float32') / 255.0
+        temp_frame = (temp_frame - 0.5) / 0.5
+        temp_frame = np.expand_dims(temp_frame, axis=0).transpose(0, 3, 1, 2)
+
+        io_binding = self.model_gfpgan.io_binding()           
+        io_binding.bind_cpu_input("input", temp_frame)
+        io_binding.bind_output("1288", self.devicename)
+        self.model_gfpgan.run_with_iobinding(io_binding)
+        ort_outs = io_binding.copy_outputs_to_cpu()
+        result = ort_outs[0][0]
+
+        # post-process
+        result = np.clip(result, -1, 1)
+        result = (result + 1) / 2
+        result = result.transpose(1, 2, 0) * 255.0
+        result = cv2.cvtColor(result, cv2.COLOR_RGB2BGR)
+        scale_factor = int(result.shape[1] / input_size)       
+        return result.astype(np.uint8), scale_factor
+
+
+    def Release(self):
+        self.model_gfpgan = None
+
+
+
+
+
+
+
+
+
+
+

roop/processors/Enhance_GPEN.py

@@ -0,0 +1,63 @@
+from typing import Any, List, Callable
+import cv2 
+import numpy as np
+import onnxruntime
+import roop.globals
+
+from roop.typing import Face, Frame, FaceSet
+from roop.utilities import resolve_relative_path
+
+
+class Enhance_GPEN():
+    plugin_options:dict = None
+
+    model_gpen = None
+    name = None
+    devicename = None
+
+    processorname = 'gpen'
+    type = 'enhance'
+
+
+    def Initialize(self, plugin_options:dict):
+        if self.plugin_options is not None:
+            if self.plugin_options["devicename"] != plugin_options["devicename"]:
+                self.Release()
+
+        self.plugin_options = plugin_options
+        if self.model_gpen is None:
+            model_path = resolve_relative_path('../models/GPEN-BFR-512.onnx')
+            self.model_gpen = onnxruntime.InferenceSession(model_path, None, providers=roop.globals.execution_providers)
+            # replace Mac mps with cpu for the moment
+            self.devicename = self.plugin_options["devicename"].replace('mps', 'cpu')
+
+        self.name = self.model_gpen.get_inputs()[0].name
+
+    def Run(self, source_faceset: FaceSet, target_face: Face, temp_frame: Frame) -> Frame:
+        # preprocess
+        input_size = temp_frame.shape[1]
+        temp_frame = cv2.resize(temp_frame, (512, 512), cv2.INTER_CUBIC)
+
+        temp_frame = cv2.cvtColor(temp_frame, cv2.COLOR_BGR2RGB)
+        temp_frame = temp_frame.astype('float32') / 255.0
+        temp_frame = (temp_frame - 0.5) / 0.5
+        temp_frame = np.expand_dims(temp_frame, axis=0).transpose(0, 3, 1, 2)
+
+        io_binding = self.model_gpen.io_binding()           
+        io_binding.bind_cpu_input("input", temp_frame)
+        io_binding.bind_output("output", self.devicename)
+        self.model_gpen.run_with_iobinding(io_binding)
+        ort_outs = io_binding.copy_outputs_to_cpu()
+        result = ort_outs[0][0]
+
+        # post-process
+        result = np.clip(result, -1, 1)
+        result = (result + 1) / 2
+        result = result.transpose(1, 2, 0) * 255.0
+        result = cv2.cvtColor(result, cv2.COLOR_RGB2BGR)
+        scale_factor = int(result.shape[1] / input_size)       
+        return result.astype(np.uint8), scale_factor
+
+
+    def Release(self):
+        self.model_gpen = None

roop/processors/Enhance_RestoreFormerPPlus.py

@@ -0,0 +1,64 @@
+from typing import Any, List, Callable
+import cv2 
+import numpy as np
+import onnxruntime
+import roop.globals
+
+from roop.typing import Face, Frame, FaceSet
+from roop.utilities import resolve_relative_path
+
+class Enhance_RestoreFormerPPlus():
+    plugin_options:dict = None
+    model_restoreformerpplus = None
+    devicename = None
+    name = None
+
+    processorname = 'restoreformer++'
+    type = 'enhance'
+    
+
+    def Initialize(self, plugin_options:dict):
+        if self.plugin_options is not None:
+            if self.plugin_options["devicename"] != plugin_options["devicename"]:
+                self.Release()
+
+        self.plugin_options = plugin_options
+        if self.model_restoreformerpplus is None:
+            # replace Mac mps with cpu for the moment
+            self.devicename = self.plugin_options["devicename"].replace('mps', 'cpu')
+            model_path = resolve_relative_path('../models/restoreformer_plus_plus.onnx')
+            self.model_restoreformerpplus = onnxruntime.InferenceSession(model_path, None, providers=roop.globals.execution_providers)
+            self.model_inputs = self.model_restoreformerpplus.get_inputs()
+            model_outputs = self.model_restoreformerpplus.get_outputs()
+            self.io_binding = self.model_restoreformerpplus.io_binding()
+            self.io_binding.bind_output(model_outputs[0].name, self.devicename)
+
+    def Run(self, source_faceset: FaceSet, target_face: Face, temp_frame: Frame) -> Frame:
+        # preprocess
+        input_size = temp_frame.shape[1]
+        temp_frame = cv2.resize(temp_frame, (512, 512), cv2.INTER_CUBIC)
+        temp_frame = cv2.cvtColor(temp_frame, cv2.COLOR_BGR2RGB)
+        temp_frame = temp_frame.astype('float32') / 255.0
+        temp_frame = (temp_frame - 0.5) / 0.5
+        temp_frame = np.expand_dims(temp_frame, axis=0).transpose(0, 3, 1, 2)
+        
+        self.io_binding.bind_cpu_input(self.model_inputs[0].name, temp_frame) # .astype(np.float32)
+        self.model_restoreformerpplus.run_with_iobinding(self.io_binding)
+        ort_outs = self.io_binding.copy_outputs_to_cpu()
+        result = ort_outs[0][0]
+        del ort_outs 
+        
+        result = np.clip(result, -1, 1)
+        result = (result + 1) / 2
+        result = result.transpose(1, 2, 0) * 255.0
+        result = cv2.cvtColor(result, cv2.COLOR_RGB2BGR)
+        scale_factor = int(result.shape[1] / input_size)       
+        return result.astype(np.uint8), scale_factor
+
+
+    def Release(self):
+        del self.model_restoreformerpplus
+        self.model_restoreformerpplus = None
+        del self.io_binding
+        self.io_binding = None
+

roop/processors/FaceSwapInsightFace.py

@@ -0,0 +1,69 @@
+import roop.globals
+import cv2
+import numpy as np
+import onnx
+import onnxruntime
+
+from roop.typing import Face, Frame
+from roop.utilities import resolve_relative_path
+
+
+
+class FaceSwapInsightFace():
+    plugin_options:dict = None
+    model_swap_insightface = None
+
+    processorname = 'faceswap'
+    type = 'swap'
+
+
+    def Initialize(self, plugin_options:dict):
+        if self.plugin_options is not None:
+            if self.plugin_options["devicename"] != plugin_options["devicename"]:
+                self.Release()
+
+        self.plugin_options = plugin_options
+        if self.model_swap_insightface is None:
+            model_path = resolve_relative_path('../models/inswapper_128.onnx')
+            graph = onnx.load(model_path).graph
+            self.emap = onnx.numpy_helper.to_array(graph.initializer[-1])
+            self.devicename = self.plugin_options["devicename"].replace('mps', 'cpu')
+            self.input_mean = 0.0
+            self.input_std = 255.0
+            #cuda_options = {"arena_extend_strategy": "kSameAsRequested", 'cudnn_conv_algo_search': 'DEFAULT'}            
+            sess_options = onnxruntime.SessionOptions()
+            sess_options.enable_cpu_mem_arena = False            
+            self.model_swap_insightface = onnxruntime.InferenceSession(model_path, sess_options, providers=roop.globals.execution_providers)
+
+
+    
+    def Run(self, source_face: Face, target_face: Face, temp_frame: Frame) -> Frame:
+        blob = cv2.dnn.blobFromImage(temp_frame, 1.0 / self.input_std, (128, 128),
+                                      (self.input_mean, self.input_mean, self.input_mean), swapRB=True)
+        latent = source_face.normed_embedding.reshape((1,-1))
+        latent = np.dot(latent, self.emap)
+        latent /= np.linalg.norm(latent)
+        io_binding = self.model_swap_insightface.io_binding()           
+        io_binding.bind_cpu_input("target", blob)
+        io_binding.bind_cpu_input("source", latent)
+        io_binding.bind_output("output", self.devicename)
+        self.model_swap_insightface.run_with_iobinding(io_binding)
+        ort_outs = io_binding.copy_outputs_to_cpu()[0]
+        img_fake = ort_outs.transpose((0,2,3,1))[0]
+        return np.clip(255 * img_fake, 0, 255).astype(np.uint8)[:,:,::-1]
+
+
+        img_fake, M = self.model_swap_insightface.get(temp_frame, target_face, source_face, paste_back=False)
+    #    target_face.matrix = M
+    #    return img_fake 
+
+
+    def Release(self):
+        del self.model_swap_insightface
+        self.model_swap_insightface = None
+
+
+                
+
+
+

roop/processors/Frame_Colorizer.py

@@ -0,0 +1,70 @@
+import cv2 
+import numpy as np
+import onnxruntime
+import roop.globals
+
+from roop.utilities import resolve_relative_path
+from roop.typing import Frame
+
+class Frame_Colorizer():
+    plugin_options:dict = None
+    model_colorizer = None
+    devicename = None
+    prev_type = None
+
+    processorname = 'deoldify'
+    type = 'frame_colorizer'
+    
+
+    def Initialize(self, plugin_options:dict):
+        if self.plugin_options is not None:
+            if self.plugin_options["devicename"] != plugin_options["devicename"]:
+                self.Release()
+
+        self.plugin_options = plugin_options
+        if self.prev_type is not None and self.prev_type != self.plugin_options["subtype"]:
+            self.Release()
+        self.prev_type = self.plugin_options["subtype"]
+        if self.model_colorizer is None:
+            # replace Mac mps with cpu for the moment
+            self.devicename = self.plugin_options["devicename"].replace('mps', 'cpu')
+            if self.prev_type == "deoldify_artistic":
+                model_path = resolve_relative_path('../models/Frame/deoldify_artistic.onnx')
+            elif self.prev_type == "deoldify_stable":
+                model_path = resolve_relative_path('../models/Frame/deoldify_stable.onnx')
+
+            onnxruntime.set_default_logger_severity(3)
+            self.model_colorizer = onnxruntime.InferenceSession(model_path, None, providers=roop.globals.execution_providers)
+            self.model_inputs = self.model_colorizer.get_inputs()
+            model_outputs = self.model_colorizer.get_outputs()
+            self.io_binding = self.model_colorizer.io_binding()
+            self.io_binding.bind_output(model_outputs[0].name, self.devicename)
+
+    def Run(self, input_frame: Frame) -> Frame:
+        temp_frame = cv2.cvtColor(input_frame, cv2.COLOR_BGR2GRAY)
+        temp_frame = cv2.cvtColor(temp_frame, cv2.COLOR_GRAY2RGB)
+        temp_frame = cv2.resize(temp_frame, (256, 256))
+        temp_frame = temp_frame.transpose((2, 0, 1))
+        temp_frame = np.expand_dims(temp_frame, axis=0).astype(np.float32)
+        self.io_binding.bind_cpu_input(self.model_inputs[0].name, temp_frame)
+        self.model_colorizer.run_with_iobinding(self.io_binding)
+        ort_outs = self.io_binding.copy_outputs_to_cpu()
+        result = ort_outs[0][0]
+        del ort_outs
+        colorized_frame = result.transpose(1, 2, 0)
+        colorized_frame = cv2.resize(colorized_frame, (input_frame.shape[1], input_frame.shape[0]))
+        temp_blue_channel, _, _ = cv2.split(input_frame)
+        colorized_frame = cv2.cvtColor(colorized_frame, cv2.COLOR_BGR2RGB).astype(np.uint8)
+        colorized_frame = cv2.cvtColor(colorized_frame, cv2.COLOR_BGR2LAB)
+        _, color_green_channel, color_red_channel = cv2.split(colorized_frame)
+        colorized_frame = cv2.merge((temp_blue_channel, color_green_channel, color_red_channel))
+        colorized_frame = cv2.cvtColor(colorized_frame, cv2.COLOR_LAB2BGR)
+        return colorized_frame.astype(np.uint8)
+
+
+    def Release(self):
+        del self.model_colorizer
+        self.model_colorizer = None
+        del self.io_binding
+        self.io_binding = None
+

roop/processors/Frame_Filter.py

@@ -0,0 +1,105 @@
+import cv2
+import numpy as np
+
+from roop.typing import Frame
+
+class Frame_Filter():
+    processorname = 'generic_filter'
+    type = 'frame_processor'
+
+    plugin_options:dict = None
+
+    c64_palette = np.array([
+            [0, 0, 0],
+            [255, 255, 255],
+            [0x81, 0x33, 0x38],
+            [0x75, 0xce, 0xc8],
+            [0x8e, 0x3c, 0x97],
+            [0x56, 0xac, 0x4d],
+            [0x2e, 0x2c, 0x9b],
+            [0xed, 0xf1, 0x71],
+            [0x8e, 0x50, 0x29],
+            [0x55, 0x38, 0x00],
+            [0xc4, 0x6c, 0x71],
+            [0x4a, 0x4a, 0x4a],
+            [0x7b, 0x7b, 0x7b],
+            [0xa9, 0xff, 0x9f],
+            [0x70, 0x6d, 0xeb],
+            [0xb2, 0xb2, 0xb2]
+        ])
+
+
+    def RenderC64Screen(self, image):
+        # Simply round the color values to the nearest color in the palette
+        image = cv2.resize(image,(320,200))
+        palette = self.c64_palette / 255.0  # Normalize palette
+        img_normalized = image  / 255.0  # Normalize image
+
+        # Calculate the index in the palette that is closest to each pixel in the image
+        indices = np.sqrt(((img_normalized[:, :, None, :] - palette[None, None, :, :]) ** 2).sum(axis=3)).argmin(axis=2)
+        # Map the image to the palette colors
+        mapped_image = palette[indices]
+        return (mapped_image * 255).astype(np.uint8)  # Denormalize and return the image
+
+
+    def RenderDetailEnhance(self, image):
+        return cv2.detailEnhance(image)
+
+    def RenderStylize(self, image):
+        return cv2.stylization(image)
+    
+    def RenderPencilSketch(self, image):
+        imgray, imout = cv2.pencilSketch(image, sigma_s=60, sigma_r=0.07, shade_factor=0.05)
+        return imout
+    
+    def RenderCartoon(self, image):
+        numDownSamples = 2 # number of downscaling steps
+        numBilateralFilters = 7  # number of bilateral filtering steps
+
+        img_color = image
+        for _ in range(numDownSamples):
+            img_color = cv2.pyrDown(img_color)
+        for _ in range(numBilateralFilters):
+            img_color = cv2.bilateralFilter(img_color, 9, 9, 7)
+        for _ in range(numDownSamples):
+            img_color = cv2.pyrUp(img_color)
+        img_gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
+        img_blur = cv2.medianBlur(img_gray, 7)
+        img_edge = cv2.adaptiveThreshold(img_blur, 255,
+            cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, 9, 2)
+        img_edge = cv2.cvtColor(img_edge, cv2.COLOR_GRAY2RGB)
+        if img_color.shape != image.shape:
+            img_color = cv2.resize(img_color, (image.shape[1], image.shape[0]), interpolation=cv2.INTER_LINEAR)        
+        if img_color.shape != img_edge.shape:
+            img_edge = cv2.resize(img_edge, (img_color.shape[1], img_color.shape[0]), interpolation=cv2.INTER_LINEAR)        
+        return cv2.bitwise_and(img_color, img_edge)
+    
+
+    def Initialize(self, plugin_options:dict):
+        if self.plugin_options is not None:
+            if self.plugin_options["devicename"] != plugin_options["devicename"]:
+                self.Release()
+        self.plugin_options = plugin_options
+
+    def Run(self, temp_frame: Frame) -> Frame:
+        subtype = self.plugin_options["subtype"]
+        if subtype == "stylize":
+            return self.RenderStylize(temp_frame).astype(np.uint8)
+        if subtype == "detailenhance":
+            return self.RenderDetailEnhance(temp_frame).astype(np.uint8)
+        if subtype == "pencil":
+            return self.RenderPencilSketch(temp_frame).astype(np.uint8)
+        if subtype == "cartoon":
+            return self.RenderCartoon(temp_frame).astype(np.uint8)
+        if subtype == "C64":
+            return self.RenderC64Screen(temp_frame).astype(np.uint8)
+
+
+    def Release(self):
+        pass
+
+    def getProcessedResolution(self, width, height):
+        if self.plugin_options["subtype"] == "C64":
+            return (320,200)
+        return None
+

roop/processors/Frame_Masking.py

@@ -0,0 +1,71 @@
+import cv2 
+import numpy as np
+import onnxruntime
+import roop.globals
+
+from roop.utilities import resolve_relative_path
+from roop.typing import Frame
+
+class Frame_Masking():
+    plugin_options:dict = None
+    model_masking = None
+    devicename = None
+    name = None
+
+    processorname = 'removebg'
+    type = 'frame_masking'
+    
+
+    def Initialize(self, plugin_options:dict):
+        if self.plugin_options is not None:
+            if self.plugin_options["devicename"] != plugin_options["devicename"]:
+                self.Release()
+
+        self.plugin_options = plugin_options
+        if self.model_masking is None:
+            # replace Mac mps with cpu for the moment
+            self.devicename = self.plugin_options["devicename"]
+            self.devicename = self.devicename.replace('mps', 'cpu')
+            model_path = resolve_relative_path('../models/Frame/isnet-general-use.onnx')
+            self.model_masking = onnxruntime.InferenceSession(model_path, None, providers=roop.globals.execution_providers)
+            self.model_inputs = self.model_masking.get_inputs()
+            model_outputs = self.model_masking.get_outputs()
+            self.io_binding = self.model_masking.io_binding()
+            self.io_binding.bind_output(model_outputs[0].name, self.devicename)
+
+    def Run(self, temp_frame: Frame) -> Frame:
+        # Pre process:Resize, BGR->RGB, float32 cast
+        input_image = cv2.resize(temp_frame, (1024, 1024))
+        input_image = cv2.cvtColor(input_image, cv2.COLOR_BGR2RGB)
+        mean = [0.5, 0.5, 0.5]
+        std = [1.0, 1.0, 1.0]
+        input_image = (input_image / 255.0 - mean) / std
+        input_image = input_image.transpose(2, 0, 1)
+        input_image = np.expand_dims(input_image, axis=0)
+        input_image = input_image.astype('float32')
+        
+        self.io_binding.bind_cpu_input(self.model_inputs[0].name, input_image)
+        self.model_masking.run_with_iobinding(self.io_binding)
+        ort_outs = self.io_binding.copy_outputs_to_cpu()
+        result = ort_outs[0][0]
+        del ort_outs
+        # Post process:squeeze, Sigmoid, Normarize, uint8 cast
+        mask = np.squeeze(result[0])
+        min_value = np.min(mask)
+        max_value = np.max(mask)
+        mask = (mask - min_value) / (max_value - min_value)
+        #mask = np.where(mask < score_th, 0, 1)
+        #mask *= 255
+        mask = cv2.resize(mask, (temp_frame.shape[1], temp_frame.shape[0]), interpolation=cv2.INTER_LINEAR)        
+        mask = np.reshape(mask, [mask.shape[0],mask.shape[1],1])
+        result = mask * temp_frame.astype(np.float32)
+        return result.astype(np.uint8)
+
+
+
+    def Release(self):
+        del self.model_masking
+        self.model_masking = None
+        del self.io_binding
+        self.io_binding = None
+

roop/processors/Frame_Upscale.py

@@ -0,0 +1,131 @@
+import cv2 
+import numpy as np
+import onnxruntime
+import roop.globals
+import threading
+
+from roop.utilities import resolve_relative_path
+from roop.typing import Frame
+
+class Frame_Upscale():
+    plugin_options:dict = None
+    model_upscale = None
+    devicename = None
+    prev_type = None
+
+    processorname = 'upscale'
+    type = 'frame_enhancer'
+
+    THREAD_LOCK_UPSCALE = threading.Lock()
+
+
+    def Initialize(self, plugin_options:dict):
+        if self.plugin_options is not None:
+            if self.plugin_options["devicename"] != plugin_options["devicename"]:
+                self.Release()
+
+        self.plugin_options = plugin_options
+        if self.prev_type is not None and self.prev_type != self.plugin_options["subtype"]:
+            self.Release()
+        self.prev_type = self.plugin_options["subtype"]
+        if self.model_upscale is None:
+            # replace Mac mps with cpu for the moment
+            self.devicename = self.plugin_options["devicename"].replace('mps', 'cpu')
+            if self.prev_type == "esrganx4":
+                model_path = resolve_relative_path('../models/Frame/real_esrgan_x4.onnx')
+                self.scale = 4
+            elif self.prev_type == "esrganx2":
+                model_path = resolve_relative_path('../models/Frame/real_esrgan_x2.onnx')
+                self.scale = 2
+            elif self.prev_type == "lsdirx4":
+                model_path = resolve_relative_path('../models/Frame/lsdir_x4.onnx')
+                self.scale = 4
+
+            self.model_upscale = onnxruntime.InferenceSession(model_path, None, providers=roop.globals.execution_providers)
+            self.model_inputs = self.model_upscale.get_inputs()
+            model_outputs = self.model_upscale.get_outputs()
+            self.io_binding = self.model_upscale.io_binding()
+            self.io_binding.bind_output(model_outputs[0].name, self.devicename)
+
+    def getProcessedResolution(self, width, height):
+        return (width * self.scale, height * self.scale)
+
+# borrowed from facefusion -> https://github.com/facefusion/facefusion
+    def prepare_tile_frame(self, tile_frame : Frame) -> Frame:
+        tile_frame = np.expand_dims(tile_frame[:, :, ::-1], axis = 0)
+        tile_frame = tile_frame.transpose(0, 3, 1, 2)
+        tile_frame = tile_frame.astype(np.float32) / 255
+        return tile_frame
+
+
+    def normalize_tile_frame(self, tile_frame : Frame) -> Frame:
+        tile_frame = tile_frame.transpose(0, 2, 3, 1).squeeze(0) * 255
+        tile_frame = tile_frame.clip(0, 255).astype(np.uint8)[:, :, ::-1]
+        return tile_frame
+
+    def create_tile_frames(self, input_frame : Frame, size):
+        input_frame = np.pad(input_frame, ((size[1], size[1]), (size[1], size[1]), (0, 0)))
+        tile_width = size[0] - 2 * size[2]
+        pad_size_bottom = size[2] + tile_width - input_frame.shape[0] % tile_width
+        pad_size_right = size[2] + tile_width - input_frame.shape[1] % tile_width
+        pad_vision_frame = np.pad(input_frame, ((size[2], pad_size_bottom), (size[2], pad_size_right), (0, 0)))
+        pad_height, pad_width = pad_vision_frame.shape[:2]
+        row_range = range(size[2], pad_height - size[2], tile_width)
+        col_range = range(size[2], pad_width - size[2], tile_width)
+        tile_frames = []
+
+        for row_frame in row_range:
+            top = row_frame - size[2]
+            bottom = row_frame + size[2] + tile_width
+            for column_vision_frame in col_range:
+                left = column_vision_frame - size[2]
+                right = column_vision_frame + size[2] + tile_width
+                tile_frames.append(pad_vision_frame[top:bottom, left:right, :])
+        return tile_frames, pad_width, pad_height
+
+
+    def merge_tile_frames(self, tile_frames, temp_width : int, temp_height : int, pad_width : int, pad_height : int, size) -> Frame:
+        merge_frame = np.zeros((pad_height, pad_width, 3)).astype(np.uint8)
+        tile_width = tile_frames[0].shape[1] - 2 * size[2]
+        tiles_per_row = min(pad_width // tile_width, len(tile_frames))
+
+        for index, tile_frame in enumerate(tile_frames):
+            tile_frame = tile_frame[size[2]:-size[2], size[2]:-size[2]]
+            row_index = index // tiles_per_row
+            col_index = index % tiles_per_row
+            top = row_index * tile_frame.shape[0]
+            bottom = top + tile_frame.shape[0]
+            left = col_index * tile_frame.shape[1]
+            right = left + tile_frame.shape[1]
+            merge_frame[top:bottom, left:right, :] = tile_frame
+        merge_frame = merge_frame[size[1] : size[1] + temp_height, size[1]: size[1] + temp_width, :]
+        return merge_frame
+
+
+    def Run(self, temp_frame: Frame) -> Frame:
+        size = (128, 8, 2)
+        temp_height, temp_width = temp_frame.shape[:2]
+        upscale_tile_frames, pad_width, pad_height = self.create_tile_frames(temp_frame, size)
+
+        for index, tile_frame in enumerate(upscale_tile_frames):
+            tile_frame = self.prepare_tile_frame(tile_frame)
+            with self.THREAD_LOCK_UPSCALE:
+                self.io_binding.bind_cpu_input(self.model_inputs[0].name, tile_frame)
+                self.model_upscale.run_with_iobinding(self.io_binding)
+                ort_outs = self.io_binding.copy_outputs_to_cpu()
+                result = ort_outs[0]
+            upscale_tile_frames[index] = self.normalize_tile_frame(result)
+        final_frame = self.merge_tile_frames(upscale_tile_frames, temp_width * self.scale
+                                                    , temp_height * self.scale
+                                                    , pad_width * self.scale, pad_height * self.scale
+                                                    , (size[0] * self.scale, size[1] * self.scale, size[2] * self.scale))
+        return final_frame.astype(np.uint8)
+
+
+
+    def Release(self):
+        del self.model_upscale
+        self.model_upscale = None
+        del self.io_binding
+        self.io_binding = None
+

roop/processors/Mask_Clip2Seg.py

@@ -0,0 +1,94 @@
+import cv2
+import numpy as np
+import torch
+import threading
+from torchvision import transforms
+from clip.clipseg import CLIPDensePredT
+import numpy as np
+
+from roop.typing import Frame
+
+THREAD_LOCK_CLIP = threading.Lock()
+
+
+class Mask_Clip2Seg():
+    plugin_options:dict = None
+    model_clip = None
+
+    processorname = 'clip2seg'
+    type = 'mask'
+
+
+    def Initialize(self, plugin_options:dict):
+        if self.plugin_options is not None:
+            if self.plugin_options["devicename"] != plugin_options["devicename"]:
+                self.Release()
+
+        self.plugin_options = plugin_options
+        if self.model_clip is None:
+            self.model_clip = CLIPDensePredT(version='ViT-B/16', reduce_dim=64, complex_trans_conv=True)
+            self.model_clip.eval();
+            self.model_clip.load_state_dict(torch.load('models/CLIP/rd64-uni-refined.pth', map_location=torch.device('cpu')), strict=False)
+
+        device = torch.device(self.plugin_options["devicename"])
+        self.model_clip.to(device)
+
+
+    def Run(self, img1, keywords:str) -> Frame:
+        if keywords is None or len(keywords) < 1 or img1 is None:
+            return img1
+        
+        source_image_small = cv2.resize(img1, (256,256))
+        
+        img_mask = np.full((source_image_small.shape[0],source_image_small.shape[1]), 0, dtype=np.float32)
+        mask_border = 1
+        l = 0
+        t = 0
+        r = 1
+        b = 1
+        
+        mask_blur = 5
+        clip_blur = 5
+        
+        img_mask = cv2.rectangle(img_mask, (mask_border+int(l), mask_border+int(t)), 
+                                (256 - mask_border-int(r), 256-mask_border-int(b)), (255, 255, 255), -1)    
+        img_mask = cv2.GaussianBlur(img_mask, (mask_blur*2+1,mask_blur*2+1), 0)    
+        img_mask /= 255
+
+        
+        input_image = source_image_small
+
+        transform = transforms.Compose([
+            transforms.ToTensor(),
+            transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+            transforms.Resize((256, 256)),
+        ])
+        img = transform(input_image).unsqueeze(0)
+
+        thresh = 0.5
+        prompts = keywords.split(',')
+        with THREAD_LOCK_CLIP:
+            with torch.no_grad():
+                preds = self.model_clip(img.repeat(len(prompts),1,1,1), prompts)[0]
+        clip_mask = torch.sigmoid(preds[0][0])
+        for i in range(len(prompts)-1):
+            clip_mask += torch.sigmoid(preds[i+1][0])
+           
+        clip_mask = clip_mask.data.cpu().numpy()
+        np.clip(clip_mask, 0, 1)
+        
+        clip_mask[clip_mask>thresh] = 1.0
+        clip_mask[clip_mask<=thresh] = 0.0
+        kernel = np.ones((5, 5), np.float32)
+        clip_mask = cv2.dilate(clip_mask, kernel, iterations=1)
+        clip_mask = cv2.GaussianBlur(clip_mask, (clip_blur*2+1,clip_blur*2+1), 0)
+       
+        img_mask *= clip_mask
+        img_mask[img_mask<0.0] = 0.0
+        return img_mask
+       
+
+
+    def Release(self):
+        self.model_clip = None
+

roop/processors/Mask_XSeg.py

@@ -0,0 +1,60 @@
+import numpy as np
+import cv2
+import onnxruntime
+import threading
+import roop.globals
+
+from roop.typing import Frame
+from roop.utilities import resolve_relative_path
+
+THREAD_LOCK_CLIP = threading.Lock()
+
+
+class Mask_XSeg():
+    plugin_options:dict = None
+
+    model_xseg = None
+
+    processorname = 'mask_xseg'
+    type = 'mask'
+
+
+    def Initialize(self, plugin_options:dict):
+        if self.plugin_options is not None:
+            if self.plugin_options["devicename"] != plugin_options["devicename"]:
+                self.Release()
+
+        self.plugin_options = plugin_options
+        if self.model_xseg is None:
+            model_path = resolve_relative_path('../models/xseg.onnx')
+            onnxruntime.set_default_logger_severity(3)
+            self.model_xseg = onnxruntime.InferenceSession(model_path, None, providers=roop.globals.execution_providers)
+            self.model_inputs = self.model_xseg.get_inputs()
+            self.model_outputs = self.model_xseg.get_outputs()
+
+            # replace Mac mps with cpu for the moment
+            self.devicename = self.plugin_options["devicename"].replace('mps', 'cpu')
+
+
+    def Run(self, img1, keywords:str) -> Frame:
+        temp_frame = cv2.resize(img1, (256, 256), cv2.INTER_CUBIC)
+        temp_frame = temp_frame.astype('float32') / 255.0
+        temp_frame = temp_frame[None, ...]
+        io_binding = self.model_xseg.io_binding()           
+        io_binding.bind_cpu_input(self.model_inputs[0].name, temp_frame)
+        io_binding.bind_output(self.model_outputs[0].name, self.devicename)
+        self.model_xseg.run_with_iobinding(io_binding)
+        ort_outs = io_binding.copy_outputs_to_cpu()
+        result = ort_outs[0][0]
+        result = np.clip(result, 0, 1.0)
+        result[result < 0.1] = 0
+        # invert values to mask areas to keep
+        result = 1.0 - result
+        return result       
+
+
+    def Release(self):
+        del self.model_xseg
+        self.model_xseg = None
+
+

roop/template_parser.py

@@ -0,0 +1,23 @@
+import re
+from datetime import datetime
+
+template_functions = {
+    "timestamp": lambda data: str(int(datetime.now().timestamp())),
+    "i": lambda data: data.get("index", False),
+    "file": lambda data: data.get("file", False),
+    "date": lambda data: datetime.now().strftime("%Y-%m-%d"),
+    "time": lambda data: datetime.now().strftime("%H-%M-%S"),
+}
+
+
+def parse(text: str, data: dict):
+    pattern = r"\{([^}]+)\}"
+
+    matches = re.findall(pattern, text)
+
+    for match in matches:
+        replacement = template_functions[match](data)
+        if replacement is not False:
+            text = text.replace(f"{{{match}}}", replacement)
+
+    return text

roop/typing.py

@@ -0,0 +1,9 @@
+from typing import Any
+
+from insightface.app.common import Face
+from roop.FaceSet import FaceSet
+import numpy
+
+Face = Face
+FaceSet = FaceSet
+Frame = numpy.ndarray[Any, Any]