Upload folder using huggingface_hub

.flake8

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

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+docs/screenshot.png 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.
+
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+under version 3 of the GNU General Public License into a single
+combined work, and to convey the resulting work. The terms of this
+License will continue to apply to the part which is the covered work,
+but the work with which it is combined will remain governed by version
+3 of the GNU General Public License.
+
+ 14. Revised Versions of this License.
+
+ The Free Software Foundation may publish revised and/or new versions of
+the GNU Affero General Public License from time to time. Such new versions
+will be similar in spirit to the present version, but may differ in detail to
+address new problems or concerns.
+
+ Each version is given a distinguishing version number. If the
+Program specifies that a certain numbered version of the GNU Affero General
+Public License "or any later version" applies to it, you have the
+option of following the terms and conditions either of that numbered
+version or of any later version published by the Free Software
+Foundation. If the Program does not specify a version number of the
+GNU Affero General Public License, you may choose any version ever published
+by the Free Software Foundation.
+
+ If the Program specifies that a proxy can decide which future
+versions of the GNU Affero General Public License can be used, that proxy's
+public statement of acceptance of a version permanently authorizes you
+to choose that version for the Program.
+
+ Later license versions may give you additional or different
+permissions. However, no additional obligations are imposed on any
+author or copyright holder as a result of your choosing to follow a
+later version.
+
+ 15. Disclaimer of Warranty.
+
+ THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY
+APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT
+HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY
+OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO,
+THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
+PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM
+IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF
+ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
+
+ 16. Limitation of Liability.
+
+ IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
+WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
+THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
+GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
+USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
+DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
+PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
+EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
+SUCH DAMAGES.
+
+ 17. Interpretation of Sections 15 and 16.
+
+ If the disclaimer of warranty and limitation of liability provided
+above cannot be given local legal effect according to their terms,
+reviewing courts shall apply local law that most closely approximates
+an absolute waiver of all civil liability in connection with the
+Program, unless a warranty or assumption of liability accompanies a
+copy of the Program in return for a fee.
+
+ END OF TERMS AND CONDITIONS
+
+ How to Apply These Terms to Your New Programs
+
+ If you develop a new program, and you want it to be of the greatest
+possible use to the public, the best way to achieve this is to make it
+free software which everyone can redistribute and change under these terms.
+
+ To do so, attach the following notices to the program. It is safest
+to attach them to the start of each source file to most effectively
+state the exclusion of warranty; and each file should have at least
+the "copyright" line and a pointer to where the full notice is found.
+
+ <one line to give the program's name and a brief idea of what it does.>
+ Copyright (C) <year> <name of author>
+
+ This program is free software: you can redistribute it and/or modify
+ it under the terms of the GNU Affero General Public License as published
+ by the Free Software Foundation, either version 3 of the License, or
+ (at your option) any later version.
+
+ This program is distributed in the hope that it will be useful,
+ but WITHOUT ANY WARRANTY; without even the implied warranty of
+ MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+ GNU Affero General Public License for more details.
+
+ You should have received a copy of the GNU Affero General Public License
+ along with this program. If not, see <https://www.gnu.org/licenses/>.
+
+Also add information on how to contact you by electronic and paper mail.
+
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+interface could display a "Source" link that leads users to an archive
+of the code. There are many ways you could offer source, and different
+solutions will be better for different programs; see section 13 for the
+specific requirements.
+
+ You should also get your employer (if you work as a programmer) or school,
+if any, to sign a "copyright disclaimer" for the program, if necessary.
+For more information on this, and how to apply and follow the GNU AGPL, see
+<https://www.gnu.org/licenses/>.

README.md

@@ -1,12 +1,162 @@
 ---
-title: Roop Unleashed
-emoji: 🏃
-colorFrom: green
-colorTo: gray
+title: roop-unleashed
+app_file: run.py
 sdk: gradio
-sdk_version: 4.36.1
-app_file: app.py
-pinned: false
+sdk_version: 4.32.1
 ---
+# 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!
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

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,

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]

roop/util_ffmpeg.py

@@ -0,0 +1,110 @@
+
+import os
+import subprocess
+import roop.globals
+import roop.utilities as util
+
+from typing import List, Any
+
+def run_ffmpeg(args: List[str]) -> bool:
+ commands = ['ffmpeg', '-hide_banner', '-hwaccel', 'auto', '-y', '-loglevel', roop.globals.log_level]
+ commands.extend(args)
+ print ("Running ffmpeg")
+ try:
+ subprocess.check_output(commands, stderr=subprocess.STDOUT)
+ return True
+ except Exception as e:
+ print("Running ffmpeg failed! Commandline:")
+ print (" ".join(commands))
+ return False
+
+
+
+def cut_video(original_video: str, cut_video: str, start_frame: int, end_frame: int, reencode: bool):
+ fps = util.detect_fps(original_video)
+ start_time = start_frame / fps
+ num_frames = end_frame - start_frame
+
+ if reencode:
+ run_ffmpeg(['-ss', format(start_time, ".2f"), '-i', original_video, '-c:v', roop.globals.video_encoder, '-c:a', 'aac', '-frames:v', str(num_frames), cut_video])
+ else:
+ run_ffmpeg(['-ss', format(start_time, ".2f"), '-i', original_video, '-frames:v', str(num_frames), '-c:v' ,'copy','-c:a' ,'copy', cut_video])
+
+def join_videos(videos: List[str], dest_filename: str, simple: bool):
+ if simple:
+ txtfilename = util.resolve_relative_path('../temp')
+ txtfilename = os.path.join(txtfilename, 'joinvids.txt')
+ with open(txtfilename, "w", encoding="utf-8") as f:
+ for v in videos:
+ v = v.replace('\\', '/')
+ f.write(f"file {v}\n")
+ commands = ['-f', 'concat', '-safe', '0', '-i', f'{txtfilename}', '-vcodec', 'copy', f'{dest_filename}']
+ run_ffmpeg(commands)
+
+ else:
+ inputs = []
+ filter = ''
+ for i,v in enumerate(videos):
+ inputs.append('-i')
+ inputs.append(v)
+ filter += f'[{i}:v:0][{i}:a:0]'
+ run_ffmpeg([" ".join(inputs), '-filter_complex', f'"{filter}concat=n={len(videos)}:v=1:a=1[outv][outa]"', '-map', '"[outv]"', '-map', '"[outa]"', dest_filename]) 
+
+ # filter += f'[{i}:v:0][{i}:a:0]'
+ # run_ffmpeg([" ".join(inputs), '-filter_complex', f'"{filter}concat=n={len(videos)}:v=1:a=1[outv][outa]"', '-map', '"[outv]"', '-map', '"[outa]"', dest_filename]) 
+
+
+
+def extract_frames(target_path : str, trim_frame_start, trim_frame_end, fps : float) -> bool:
+ util.create_temp(target_path)
+ temp_directory_path = util.get_temp_directory_path(target_path)
+ commands = ['-i', target_path, '-q:v', '1', '-pix_fmt', 'rgb24', ]
+ if trim_frame_start is not None and trim_frame_end is not None:
+ commands.extend([ '-vf', 'trim=start_frame=' + str(trim_frame_start) + ':end_frame=' + str(trim_frame_end) + ',fps=' + str(fps) ])
+ commands.extend(['-vsync', '0', os.path.join(temp_directory_path, '%06d.' + roop.globals.CFG.output_image_format)])
+ return run_ffmpeg(commands)
+
+
+def create_video(target_path: str, dest_filename: str, fps: float = 24.0, temp_directory_path: str = None) -> None:
+ if temp_directory_path is None:
+ temp_directory_path = util.get_temp_directory_path(target_path)
+ run_ffmpeg(['-r', str(fps), '-i', os.path.join(temp_directory_path, f'%06d.{roop.globals.CFG.output_image_format}'), '-c:v', roop.globals.video_encoder, '-crf', str(roop.globals.video_quality), '-pix_fmt', 'yuv420p', '-vf', 'colorspace=bt709:iall=bt601-6-625:fast=1', '-y', dest_filename])
+ return dest_filename
+
+
+def create_gif_from_video(video_path: str, gif_path):
+ from roop.capturer import get_video_frame
+
+ fps = util.detect_fps(video_path)
+ frame = get_video_frame(video_path)
+
+ run_ffmpeg(['-i', video_path, '-vf', f'fps={fps},scale={frame.shape[0]}:-1:flags=lanczos,split[s0][s1];[s0]palettegen[p];[s1][p]paletteuse', '-loop', '0', gif_path])
+
+
+def restore_audio(intermediate_video: str, original_video: str, trim_frame_start, trim_frame_end, final_video : str) -> None:
+ fps = util.detect_fps(original_video)
+ commands = [ '-i', intermediate_video ]
+ if trim_frame_start is None and trim_frame_end is None:
+ commands.extend([ '-c:a', 'copy' ])
+ else:
+ # if trim_frame_start is not None:
+ # start_time = trim_frame_start / fps
+ # commands.extend([ '-ss', format(start_time, ".2f")])
+ # else:
+ # commands.extend([ '-ss', '0' ])
+ # if trim_frame_end is not None:
+ # end_time = trim_frame_end / fps
+ # commands.extend([ '-to', format(end_time, ".2f")])
+ # commands.extend([ '-c:a', 'aac' ])
+ if trim_frame_start is not None:
+ start_time = trim_frame_start / fps
+ commands.extend([ '-ss', format(start_time, ".2f")])
+ else:
+ commands.extend([ '-ss', '0' ])
+ if trim_frame_end is not None:
+ end_time = trim_frame_end / fps
+ commands.extend([ '-to', format(end_time, ".2f")])
+ commands.extend([ '-i', original_video, "-c", "copy" ])
+
+ commands.extend([ '-map', '0:v:0', '-map', '1:a:0?', '-shortest', final_video ])
+ run_ffmpeg(commands)

roop/utilities.py

@@ -0,0 +1,339 @@
+import glob
+import mimetypes
+import os
+import platform
+import shutil
+import ssl
+import subprocess
+import sys
+import urllib
+import torch
+import gradio
+import tempfile
+import cv2
+import zipfile
+import traceback
+
+from pathlib import Path
+from typing import List, Any
+from tqdm import tqdm
+from scipy.spatial import distance
+
+import roop.template_parser as template_parser
+
+import roop.globals
+
+TEMP_FILE = "temp.mp4"
+TEMP_DIRECTORY = "temp"
+
+# monkey patch ssl for mac
+if platform.system().lower() == "darwin":
+ ssl._create_default_https_context = ssl._create_unverified_context
+
+
+# https://github.com/facefusion/facefusion/blob/master/facefusion
+def detect_fps(target_path: str) -> float:
+ fps = 24.0
+ cap = cv2.VideoCapture(target_path)
+ if cap.isOpened():
+ fps = cap.get(cv2.CAP_PROP_FPS)
+ cap.release()
+ return fps
+
+
+# Gradio wants Images in RGB
+def convert_to_gradio(image):
+ if image is None:
+ return None
+ return cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+
+
+def sort_filenames_ignore_path(filenames):
+ """Sorts a list of filenames containing a complete path by their filename,
+ while retaining their original path.
+
+ Args:
+ filenames: A list of filenames containing a complete path.
+
+ Returns:
+ A sorted list of filenames containing a complete path.
+ """
+ filename_path_tuples = [
+ (os.path.split(filename)[1], filename) for filename in filenames
+ ]
+ sorted_filename_path_tuples = sorted(filename_path_tuples, key=lambda x: x[0])
+ return [
+ filename_path_tuple[1] for filename_path_tuple in sorted_filename_path_tuples
+ ]
+
+
+def sort_rename_frames(path: str):
+ filenames = os.listdir(path)
+ filenames.sort()
+ for i in range(len(filenames)):
+ of = os.path.join(path, filenames[i])
+ newidx = i + 1
+ new_filename = os.path.join(
+ path, f"{newidx:06d}." + roop.globals.CFG.output_image_format
+ )
+ os.rename(of, new_filename)
+
+
+def get_temp_frame_paths(target_path: str) -> List[str]:
+ temp_directory_path = get_temp_directory_path(target_path)
+ return glob.glob(
+ (
+ os.path.join(
+ glob.escape(temp_directory_path),
+ f"*.{roop.globals.CFG.output_image_format}",
+ )
+ )
+ )
+
+
+def get_temp_directory_path(target_path: str) -> str:
+ target_name, _ = os.path.splitext(os.path.basename(target_path))
+ target_directory_path = os.path.dirname(target_path)
+ return os.path.join(target_directory_path, TEMP_DIRECTORY, target_name)
+
+
+def get_temp_output_path(target_path: str) -> str:
+ temp_directory_path = get_temp_directory_path(target_path)
+ return os.path.join(temp_directory_path, TEMP_FILE)
+
+
+def normalize_output_path(source_path: str, target_path: str, output_path: str) -> Any:
+ if source_path and target_path:
+ source_name, _ = os.path.splitext(os.path.basename(source_path))
+ target_name, target_extension = os.path.splitext(os.path.basename(target_path))
+ if os.path.isdir(output_path):
+ return os.path.join(
+ output_path, source_name + "-" + target_name + target_extension
+ )
+ return output_path
+
+
+def get_destfilename_from_path(
+ srcfilepath: str, destfilepath: str, extension: str
+) -> str:
+ fn, ext = os.path.splitext(os.path.basename(srcfilepath))
+ if "." in extension:
+ return os.path.join(destfilepath, f"{fn}{extension}")
+ return os.path.join(destfilepath, f"{fn}{extension}{ext}")
+
+
+def replace_template(file_path: str, index: int = 0) -> str:
+ fn, ext = os.path.splitext(os.path.basename(file_path))
+
+ # Remove the "__temp" placeholder that was used as a temporary filename
+ fn = fn.replace("__temp", "")
+
+ template = roop.globals.CFG.output_template
+ replaced_filename = template_parser.parse(
+ template, {"index": str(index), "file": fn}
+ )
+
+ return os.path.join(roop.globals.output_path, f"{replaced_filename}{ext}")
+
+
+def create_temp(target_path: str) -> None:
+ temp_directory_path = get_temp_directory_path(target_path)
+ Path(temp_directory_path).mkdir(parents=True, exist_ok=True)
+
+
+def move_temp(target_path: str, output_path: str) -> None:
+ temp_output_path = get_temp_output_path(target_path)
+ if os.path.isfile(temp_output_path):
+ if os.path.isfile(output_path):
+ os.remove(output_path)
+ shutil.move(temp_output_path, output_path)
+
+
+def clean_temp(target_path: str) -> None:
+ temp_directory_path = get_temp_directory_path(target_path)
+ parent_directory_path = os.path.dirname(temp_directory_path)
+ if not roop.globals.keep_frames and os.path.isdir(temp_directory_path):
+ shutil.rmtree(temp_directory_path)
+ if os.path.exists(parent_directory_path) and not os.listdir(parent_directory_path):
+ os.rmdir(parent_directory_path)
+
+
+def delete_temp_frames(filename: str) -> None:
+ dir = os.path.dirname(os.path.dirname(filename))
+ shutil.rmtree(dir)
+
+
+def has_image_extension(image_path: str) -> bool:
+ return image_path.lower().endswith(("png", "jpg", "jpeg", "webp"))
+
+
+def has_extension(filepath: str, extensions: List[str]) -> bool:
+ return filepath.lower().endswith(tuple(extensions))
+
+
+def is_image(image_path: str) -> bool:
+ if image_path and os.path.isfile(image_path):
+ mimetype, _ = mimetypes.guess_type(image_path)
+ return bool(mimetype and mimetype.startswith("image/"))
+ return False
+
+
+def is_video(video_path: str) -> bool:
+ if video_path and os.path.isfile(video_path):
+ mimetype, _ = mimetypes.guess_type(video_path)
+ return bool(mimetype and mimetype.startswith("video/"))
+ return False
+
+
+def conditional_download(download_directory_path: str, urls: List[str]) -> None:
+ if not os.path.exists(download_directory_path):
+ os.makedirs(download_directory_path)
+ for url in urls:
+ download_file_path = os.path.join(
+ download_directory_path, os.path.basename(url)
+ )
+ if not os.path.exists(download_file_path):
+ request = urllib.request.urlopen(url) # type: ignore[attr-defined]
+ total = int(request.headers.get("Content-Length", 0))
+ with tqdm(
+ total=total,
+ desc=f"Downloading {url}",
+ unit="B",
+ unit_scale=True,
+ unit_divisor=1024,
+ ) as progress:
+ urllib.request.urlretrieve(url, download_file_path, reporthook=lambda count, block_size, total_size: progress.update(block_size)) # type: ignore[attr-defined]
+
+
+def get_local_files_from_folder(folder: str) -> List[str]:
+ if not os.path.exists(folder) or not os.path.isdir(folder):
+ return None
+ files = [
+ os.path.join(folder, f)
+ for f in os.listdir(folder)
+ if os.path.isfile(os.path.join(folder, f))
+ ]
+ return files
+
+
+def resolve_relative_path(path: str) -> str:
+ return os.path.abspath(os.path.join(os.path.dirname(__file__), path))
+
+
+def get_device() -> str:
+ if len(roop.globals.execution_providers) < 1:
+ roop.globals.execution_providers = ["CPUExecutionProvider"]
+
+ prov = roop.globals.execution_providers[0]
+ if "CoreMLExecutionProvider" in prov:
+ return "mps"
+ if "CUDAExecutionProvider" in prov or "ROCMExecutionProvider" in prov:
+ return "cuda"
+ if "OpenVINOExecutionProvider" in prov:
+ return "mkl"
+ return "cpu"
+
+
+def str_to_class(module_name, class_name) -> Any:
+ from importlib import import_module
+
+ class_ = None
+ try:
+ module_ = import_module(module_name)
+ try:
+ class_ = getattr(module_, class_name)()
+ except AttributeError:
+ print(f"Class {class_name} does not exist")
+ except ImportError:
+ print(f"Module {module_name} does not exist")
+ return class_
+
+def is_installed(name:str) -> bool:
+ return shutil.which(name);
+
+# Taken from https://stackoverflow.com/a/68842705
+def get_platform() -> str:
+ if sys.platform == "linux":
+ try:
+ proc_version = open("/proc/version").read()
+ if "Microsoft" in proc_version:
+ return "wsl"
+ except:
+ pass
+ return sys.platform
+
+def open_with_default_app(filename:str):
+ if filename == None:
+ return
+ platform = get_platform()
+ if platform == "darwin":
+ subprocess.call(("open", filename))
+ elif platform in ["win64", "win32"]: os.startfile(filename.replace("/", "\\"))
+ elif platform == "wsl":
+ subprocess.call("cmd.exe /C start".split() + [filename])
+ else: # linux variants
+ subprocess.call("xdg-open", filename)
+
+
+def prepare_for_batch(target_files) -> str:
+ print("Preparing temp files")
+ tempfolder = os.path.join(tempfile.gettempdir(), "rooptmp")
+ if os.path.exists(tempfolder):
+ shutil.rmtree(tempfolder)
+ Path(tempfolder).mkdir(parents=True, exist_ok=True)
+ for f in target_files:
+ newname = os.path.basename(f.name)
+ shutil.move(f.name, os.path.join(tempfolder, newname))
+ return tempfolder
+
+
+def zip(files, zipname):
+ with zipfile.ZipFile(zipname, "w") as zip_file:
+ for f in files:
+ zip_file.write(f, os.path.basename(f))
+
+
+def unzip(zipfilename: str, target_path: str):
+ with zipfile.ZipFile(zipfilename, "r") as zip_file:
+ zip_file.extractall(target_path)
+
+
+def mkdir_with_umask(directory):
+ oldmask = os.umask(0)
+ # mode needs octal
+ os.makedirs(directory, mode=0o775, exist_ok=True)
+ os.umask(oldmask)
+
+
+def open_folder(path: str):
+ platform = get_platform()
+ try:
+ if platform == "darwin":
+ subprocess.call(("open", path))
+ elif platform in ["win64", "win32"]:
+ open_with_default_app(path)
+ elif platform == "wsl":
+ subprocess.call("cmd.exe /C start".split() + [path])
+ else: # linux variants
+ subprocess.Popen(["xdg-open", path])
+ except Exception as e:
+ traceback.print_exc()
+ pass
+ # import webbrowser
+ # webbrowser.open(url)
+
+
+def create_version_html() -> str:
+ python_version = ".".join([str(x) for x in sys.version_info[0:3]])
+ versions_html = f"""
+python: <span title="{sys.version}">{python_version}</span>
+•
+torch: {getattr(torch, '__long_version__',torch.__version__)}
+•
+gradio: {gradio.__version__}
+"""
+ return versions_html
+
+
+def compute_cosine_distance(emb1, emb2) -> float:
+ return distance.cosine(emb1, emb2)

roop/virtualcam.py

@@ -0,0 +1,87 @@
+import cv2
+import roop.globals
+import ui.globals
+import pyvirtualcam
+import threading
+import platform
+
+
+cam_active = False
+cam_thread = None
+vcam = None
+
+def virtualcamera(streamobs, cam_num,width,height):
+ from roop.ProcessOptions import ProcessOptions
+ from roop.core import live_swap, get_processing_plugins
+
+ global cam_active
+
+ #time.sleep(2)
+ print('Starting capture')
+ cap = cv2.VideoCapture(cam_num, cv2.CAP_DSHOW if platform.system() != 'Darwin' else cv2.CAP_AVFOUNDATION)
+ if not cap.isOpened():
+ print("Cannot open camera")
+ cap.release()
+ del cap
+ return
+
+ pref_width = width
+ pref_height = height
+ pref_fps_in = 30
+ cap.set(cv2.CAP_PROP_FRAME_WIDTH, pref_width)
+ cap.set(cv2.CAP_PROP_FRAME_HEIGHT, pref_height)
+ cap.set(cv2.CAP_PROP_FPS, pref_fps_in)
+ cam_active = True
+
+ # native format UYVY
+
+ cam = None
+ if streamobs:
+ print('Detecting virtual cam devices')
+ cam = pyvirtualcam.Camera(width=pref_width, height=pref_height, fps=pref_fps_in, fmt=pyvirtualcam.PixelFormat.BGR, print_fps=False)
+ if cam:
+ print(f'Using virtual camera: {cam.device}')
+ print(f'Using {cam.native_fmt}')
+ else:
+ print(f'Not streaming to virtual camera!')
+
+ # always use xseg masking
+ options = ProcessOptions(get_processing_plugins("mask_xseg"), roop.globals.distance_threshold, roop.globals.blend_ratio,
+ "all", 0, None, None, 1, False)
+ while cam_active:
+ ret, frame = cap.read()
+ if not ret:
+ break
+
+ if len(roop.globals.INPUT_FACESETS) > 0:
+ frame = live_swap(frame, options)
+ if cam:
+ cam.send(frame)
+ cam.sleep_until_next_frame()
+ ui.globals.ui_camera_frame = frame
+
+ if cam:
+ cam.close()
+ cap.release()
+ print('Camera stopped')
+
+
+
+def start_virtual_cam(streamobs, cam_number, resolution):
+ global cam_thread, cam_active
+
+ if not cam_active:
+ width, height = map(int, resolution.split('x'))
+ cam_thread = threading.Thread(target=virtualcamera, args=[streamobs, cam_number, width, height])
+ cam_thread.start()
+
+
+
+def stop_virtual_cam():
+ global cam_active, cam_thread
+
+ if cam_active:
+ cam_active = False
+ cam_thread.join()
+ 
+

roop/vr_util.py

@@ -0,0 +1,57 @@
+import cv2
+import numpy as np
+
+# VR Lense Distortion
+# Taken from https://github.com/g0kuvonlange/vrswap
+
+
+def get_perspective(img, FOV, THETA, PHI, height, width):
+ #
+ # THETA is left/right angle, PHI is up/down angle, both in degree
+ #
+ [orig_width, orig_height, _] = img.shape
+ equ_h = orig_height
+ equ_w = orig_width
+ equ_cx = (equ_w - 1) / 2.0
+ equ_cy = (equ_h - 1) / 2.0
+
+ wFOV = FOV
+ hFOV = float(height) / width * wFOV
+
+ w_len = np.tan(np.radians(wFOV / 2.0))
+ h_len = np.tan(np.radians(hFOV / 2.0))
+
+ x_map = np.ones([height, width], np.float32)
+ y_map = np.tile(np.linspace(-w_len, w_len, width), [height, 1])
+ z_map = -np.tile(np.linspace(-h_len, h_len, height), [width, 1]).T
+
+ D = np.sqrt(x_map**2 + y_map**2 + z_map**2)
+ xyz = np.stack((x_map, y_map, z_map), axis=2) / np.repeat(
+ D[:, :, np.newaxis], 3, axis=2
+ )
+
+ y_axis = np.array([0.0, 1.0, 0.0], np.float32)
+ z_axis = np.array([0.0, 0.0, 1.0], np.float32)
+ [R1, _] = cv2.Rodrigues(z_axis * np.radians(THETA))
+ [R2, _] = cv2.Rodrigues(np.dot(R1, y_axis) * np.radians(-PHI))
+
+ xyz = xyz.reshape([height * width, 3]).T
+ xyz = np.dot(R1, xyz)
+ xyz = np.dot(R2, xyz).T
+ lat = np.arcsin(xyz[:, 2])
+ lon = np.arctan2(xyz[:, 1], xyz[:, 0])
+
+ lon = lon.reshape([height, width]) / np.pi * 180
+ lat = -lat.reshape([height, width]) / np.pi * 180
+
+ lon = lon / 180 * equ_cx + equ_cx
+ lat = lat / 90 * equ_cy + equ_cy
+
+ persp = cv2.remap(
+ img,
+ lon.astype(np.float32),
+ lat.astype(np.float32),
+ cv2.INTER_CUBIC,
+ borderMode=cv2.BORDER_WRAP,
+ )
+ return persp