Face fusion + removing background

facefusion

@@ -0,0 +1 @@
+Subproject commit 126845c24c5c60699311d1f822de1a056d83b3bd

handler.py

@@ -19,10 +19,8 @@ from src.models.unet_2d_condition import UNet2DConditionModel
 from src.models.unet_3d import UNet3DConditionModel
 from src.pipelines.pipeline_pose2vid_long import Pose2VideoPipeline
 from src.utils.util import read_frames, get_fps, save_videos_grid
-import roop.globals
-from roop.core import start, decode_execution_providers, suggest_max_memory, suggest_execution_threads
-from roop.utilities import normalize_output_path
-from roop.processors.frame.core import get_frame_processors_modules
+
+import backgroundremover
 
 # import onnxruntime as ort
 import gc
@@ -35,7 +33,7 @@ from rembg import remove
 import onnxruntime as ort
 import shutil
 
-device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
 if device.type != 'cuda':
     raise ValueError("The model requires a GPU for inference.")
@@ -306,26 +304,28 @@ class EndpointHandler():
             ref_image_no_bg_path = os.path.join(video_root, "ref_image_no_bg.png")
             ref_image_no_bg.save(ref_image_no_bg_path)
 
-            pose_output_path = os.path.join(temp_dir, "pose_videos")
+            # pose_output_path = os.path.join(temp_dir, "pose_videos")
 
-            # Run the extract_dwpose_from_vid.py script
-            extract_pose_path = os.path.join(base_dir, 'extract_dwpose_from_vid.py')
-            command = f'python3 {extract_pose_path} --video_root {video_root}'
+            # print("we are number 1")
+            # # Run the extract_dwpose_from_vid.py script
+            # extract_pose_path = os.path.join(base_dir, 'extract_dwpose_from_vid.py')
+            # command = f'python3 {extract_pose_path} --video_root {video_root}'
 
-            # Run the command with shell=True
-            result = subprocess.run(command, shell=True, capture_output=True, text=True)
-            if result.returncode != 0:
-                raise RuntimeError(f"Error running extract_dwpose_from_vid.py: {result.stderr}")
+            # # Run the command with shell=True
+            # result = subprocess.run(command, shell=True, capture_output=True, text=True)
+            # if result.returncode != 0:
+            #     raise RuntimeError(f"Error running extract_dwpose_from_vid.py: {result.stderr}")
+            # print("we are number 2")
 
-            # Locate the extracted pose video
-            save_dir = video_root + "_dwpose"
-            print(f"Expected save directory: {save_dir}")  # Debug statement
-            pose_video_path = os.path.join(save_dir, "downloaded_video.mp4")
+            # # Locate the extracted pose video
+            # save_dir = video_root + "_dwpose"
+            # print(f"Expected save directory: {save_dir}")  # Debug statement
+            # pose_video_path = os.path.join(save_dir, "downloaded_video.mp4")
 
-            if not os.path.exists(pose_video_path):
-                print("Contents of the temporary directory:")
-                self.print_directory_contents(temp_dir)
-                raise FileNotFoundError(f"The pose video was not found at: {pose_video_path}")
+            # if not os.path.exists(pose_video_path):
+            #     print("Contents of the temporary directory:")
+            #     self.print_directory_contents(temp_dir)
+            #     raise FileNotFoundError(f"The pose video was not found at: {pose_video_path}")
 
             # Speed up the pose video by 4x
             # sped_up_pose_video_path = os.path.join(temp_dir, "sped_up_pose_video.mp4")
@@ -365,9 +365,14 @@ class EndpointHandler():
             torch.cuda.empty_cache()
 
             # Perform face swapping
-            # self.print_directory_contents(temp_dir)
-            # swapped_face_video_path = os.path.join(save_dir, "swapped_face_output.mp4")
-            # self._swap_face(cropped_face_path, animation_path, swapped_face_video_path)
+            swapped_face_video_path = os.path.join(save_dir, "swapped_face_output.mp4")
+            
+            # Subprocess call to facefusion for face swapping
+            facefusion_script_path = os.path.join(base_dir, 'facefusion', 'core.py')
+            swap_command = f'python3 {facefusion_script_path} --source {cropped_face_path} --target {animation_path} --output {swapped_face_video_path}'
+            swap_result = subprocess.run(swap_command, shell=True, capture_output=True, text=True)
+            if swap_result.returncode != 0:
+                raise RuntimeError(f"Error running face swap: {swap_result.stderr}")
 
             # Slow down the produced video by 4x
             self.print_directory_contents(temp_dir)
@@ -377,13 +382,23 @@ class EndpointHandler():
             # Clear CUDA cache before RIFE interpolation
             torch.cuda.empty_cache()
 
+
+            #remove background
+            removed_background_output_path = os.path.join(save_dir, "removed_background_result.mp4")
+            remove_background_command = f'python3 ./rembg_video.py {swapped_face_video_path} {removed_background_output_path}'
+            print("Command is " + remove_background_command)
+            remove_background_result = subprocess.run(remove_background_command, shell=True, capture_output=True, text=True)
+            if remove_background_result.returncode != 0:
+                raise RuntimeError(f"Error running removing backgriund: {remove_background_result.stderr}")
+
+
             # Perform RIFE interpolation
             # self.print_directory_contents(temp_dir)
-            rife_output_path = os.path.join(save_dir, "completed_result.mp4")
-            self.run_rife_interpolation(animation_path, rife_output_path, multi=2, scale=0.5)
+            # rife_output_path = os.path.join(save_dir, "completed_result.mp4")
+            # self.run_rife_interpolation(swapped_face_video_path, rife_output_path, multi=2, scale=0.5)
 
             # Encode the final video in base64
-            with open(rife_output_path, "rb") as video_file:
+            with open(removed_background_output_path, "rb") as video_file:
                 video_base64 = base64.b64encode(video_file.read()).decode("utf-8")
 
             torch.cuda.empty_cache()

rembg_video.py

@@ -0,0 +1,81 @@
+import cv2
+import numpy as np
+import time
+import random
+from PIL import Image
+import argparse
+import subprocess
+from transparent_background import Remover
+import os
+import torch
+
+
+def process_video(input_video, output_video, mode='Normal'):
+    if mode == 'Fast':
+        remover = Remover(mode='fast')
+    else:
+        remover = Remover()
+
+    cap = cv2.VideoCapture(input_video)
+    total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))  # Get total frames
+    processed_frames = 0
+    start_time = time.time()
+
+    # Get video properties
+    fps = cap.get(cv2.CAP_PROP_FPS)
+    width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+    height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+
+    # Start ffmpeg subprocess
+    ffmpeg_command = [
+        'ffmpeg',
+        '-y',  # Overwrite output file if it exists
+        '-f', 'rawvideo',
+        '-vcodec', 'rawvideo',
+        '-pix_fmt', 'rgb24',
+        '-s', f'{width}x{height}',  # Size of one frame
+        '-r', str(fps),  # Frames per second
+        '-i', '-',  # Input from pipe
+        '-c:v', 'libx264',
+        '-crf', '0',
+        output_video
+    ]
+
+    proc = subprocess.Popen(ffmpeg_command, stdin=subprocess.PIPE)
+
+    while cap.isOpened():
+        ret, frame = cap.read()
+
+        if not ret:
+            break
+
+        if time.time() - start_time >= 20 * 60 - 5:
+            print("GPU Timeout is coming")
+            cap.release()
+            proc.stdin.close()
+            proc.wait()
+            return output_video
+
+        frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+        img = Image.fromarray(frame).convert('RGB')
+
+        processed_frames += 1
+        print(f"Processing frame {processed_frames}/{total_frames}")
+        out = remover.process(img, type='green')
+        proc.stdin.write(np.array(out).tobytes())
+
+    cap.release()
+    proc.stdin.close()
+    proc.wait()
+
+    print(f"Output video saved to {output_video}")
+    return output_video
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Remove background from video using transparent_background library.")
+    parser.add_argument('input', type=str, help='Input video file path')
+    parser.add_argument('output', type=str, help='Output video file path')
+    parser.add_argument('--mode', type=str, default='Normal', choices=['Fast', 'Normal'], help='Mode of operation')
+    args = parser.parse_args()
+
+    process_video(args.input, args.output, args.mode)

requirements.txt

@@ -29,9 +29,6 @@ controlnet-aux==0.0.7
 diffusers==0.24.0
 omegaconf==2.2.3
 
-# Face swap related dependencies
-facenet-pytorch==2.5.2
-dlib==19.22.0
 
 # Additional dependencies from the first list not present in the second list
 accelerate==0.21.0
@@ -43,11 +40,11 @@ imageio-ffmpeg==0.4.9
 scikit-image==0.21.0
 scikit-learn==1.3.2
 scipy==1.11.4
-torch==2.0.1
+torch==2.0.1+cu118
 torchdiffeq==0.2.3
 torchmetrics==1.2.1
 torchsde==0.2.5
-torchvision==0.15.2
+torchvision==0.15.2+cu118
 
 
 # Additional dependencies for RIFE
@@ -57,5 +54,11 @@ moviepy==1.0.3
 requests==2.32.3
 
 
-rembg
+rembg[gpu]
 mediapipe==0.9.1.0
+
+
+filetype==1.2.0
+
+
+transparent-background

roop-unleashed/.flake8

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

roop-unleashed/LICENSE

@@ -1,661 +0,0 @@
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roop-unleashed/README.md

@@ -1,156 +0,0 @@
-# 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!
-

roop-unleashed/clip/__init__.py

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

roop-unleashed/clip/bpe_simple_vocab_16e6.txt.gz

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

roop-unleashed/clip/clip.py

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

roop-unleashed/clip/clipseg.py

@@ -1,538 +0,0 @@
-import math
-from os.path import basename, dirname, join, isfile
-import torch
-from torch import nn
-from torch.nn import functional as nnf
-from torch.nn.modules.activation import ReLU
-
-
-def get_prompt_list(prompt):
-    if prompt == 'plain':
-        return ['{}']    
-    elif prompt == 'fixed':
-        return ['a photo of a {}.']
-    elif prompt == 'shuffle':
-        return ['a photo of a {}.', 'a photograph of a {}.', 'an image of a {}.', '{}.']
-    elif prompt == 'shuffle+':
-        return ['a photo of a {}.', 'a photograph of a {}.', 'an image of a {}.', '{}.',
-                            'a cropped photo of a {}.', 'a good photo of a {}.', 'a photo of one {}.',
-                            'a bad photo of a {}.', 'a photo of the {}.']
-    else:
-        raise ValueError('Invalid value for prompt')        
-
-
-def forward_multihead_attention(x, b, with_aff=False, attn_mask=None):
-    """ 
-    Simplified version of multihead attention (taken from torch source code but without tons of if clauses). 
-    The mlp and layer norm come from CLIP.
-    x: input.
-    b: multihead attention module. 
-    """
-
-    x_ = b.ln_1(x)
-    q, k, v = nnf.linear(x_, b.attn.in_proj_weight, b.attn.in_proj_bias).chunk(3, dim=-1)
-    tgt_len, bsz, embed_dim = q.size()
-
-    head_dim = embed_dim // b.attn.num_heads
-    scaling = float(head_dim) ** -0.5
-
-    q = q.contiguous().view(tgt_len, bsz * b.attn.num_heads, b.attn.head_dim).transpose(0, 1)
-    k = k.contiguous().view(-1, bsz * b.attn.num_heads, b.attn.head_dim).transpose(0, 1)
-    v = v.contiguous().view(-1, bsz * b.attn.num_heads, b.attn.head_dim).transpose(0, 1)
-
-    q = q * scaling
-
-    attn_output_weights = torch.bmm(q, k.transpose(1, 2)) #  n_heads * batch_size, tokens^2, tokens^2
-    if attn_mask is not None:
-
-
-        attn_mask_type, attn_mask = attn_mask
-        n_heads = attn_output_weights.size(0) // attn_mask.size(0)
-        attn_mask = attn_mask.repeat(n_heads, 1)
-        
-        if attn_mask_type == 'cls_token':
-            # the mask only affects similarities compared to the readout-token.
-            attn_output_weights[:, 0, 1:] = attn_output_weights[:, 0, 1:] * attn_mask[None,...]
-            # attn_output_weights[:, 0, 0] = 0*attn_output_weights[:, 0, 0]
-
-        if attn_mask_type == 'all':
-            # print(attn_output_weights.shape, attn_mask[:, None].shape)
-            attn_output_weights[:, 1:, 1:] = attn_output_weights[:, 1:, 1:] * attn_mask[:, None]
-        
-    
-    attn_output_weights = torch.softmax(attn_output_weights, dim=-1)
-
-    attn_output = torch.bmm(attn_output_weights, v)
-    attn_output = attn_output.transpose(0, 1).contiguous().view(tgt_len, bsz, embed_dim)
-    attn_output = b.attn.out_proj(attn_output)
-
-    x = x + attn_output
-    x = x + b.mlp(b.ln_2(x))
-
-    if with_aff:
-        return x, attn_output_weights
-    else:
-        return x
-
-
-class CLIPDenseBase(nn.Module):
-
-    def __init__(self, version, reduce_cond, reduce_dim, prompt, n_tokens):
-        super().__init__()
-
-        import clip
-
-        # prec = torch.FloatTensor
-        self.clip_model, _ = clip.load(version, device='cpu', jit=False)
-        self.model = self.clip_model.visual
-
-        # if not None, scale conv weights such that we obtain n_tokens.
-        self.n_tokens = n_tokens
-
-        for p in self.clip_model.parameters():
-            p.requires_grad_(False)
-
-        # conditional
-        if reduce_cond is not None:
-            self.reduce_cond = nn.Linear(512, reduce_cond)
-            for p in self.reduce_cond.parameters():
-                p.requires_grad_(False)
-        else:
-            self.reduce_cond = None        
-
-        self.film_mul = nn.Linear(512 if reduce_cond is None else reduce_cond, reduce_dim)
-        self.film_add = nn.Linear(512 if reduce_cond is None else reduce_cond, reduce_dim)
-        
-        self.reduce = nn.Linear(768, reduce_dim)
-
-        self.prompt_list = get_prompt_list(prompt)     
-
-        # precomputed prompts
-        import pickle
-        if isfile('precomputed_prompt_vectors.pickle'):
-            precomp = pickle.load(open('precomputed_prompt_vectors.pickle', 'rb'))
-            self.precomputed_prompts = {k: torch.from_numpy(v) for k, v in precomp.items()}        
-        else:
-            self.precomputed_prompts = dict()
-    
-    def rescaled_pos_emb(self, new_size):
-        assert len(new_size) == 2
-
-        a = self.model.positional_embedding[1:].T.view(1, 768, *self.token_shape)
-        b = nnf.interpolate(a, new_size, mode='bicubic', align_corners=False).squeeze(0).view(768, new_size[0]*new_size[1]).T
-        return torch.cat([self.model.positional_embedding[:1], b])
-
-    def visual_forward(self, x_inp, extract_layers=(), skip=False, mask=None):
-        
-
-        with torch.no_grad():
-
-            inp_size = x_inp.shape[2:]
-
-            if self.n_tokens is not None:
-                stride2 = x_inp.shape[2] // self.n_tokens
-                conv_weight2 = nnf.interpolate(self.model.conv1.weight, (stride2, stride2), mode='bilinear', align_corners=True)
-                x = nnf.conv2d(x_inp, conv_weight2, bias=self.model.conv1.bias, stride=stride2, dilation=self.model.conv1.dilation)
-            else:
-                x = self.model.conv1(x_inp)  # shape = [*, width, grid, grid]
-
-            x = x.reshape(x.shape[0], x.shape[1], -1)  # shape = [*, width, grid ** 2]
-            x = x.permute(0, 2, 1)  # shape = [*, grid ** 2, width]
-
-            x = torch.cat([self.model.class_embedding.to(x.dtype) + torch.zeros(x.shape[0], 1, x.shape[-1], dtype=x.dtype, device=x.device), x], dim=1)  # shape = [*, grid ** 2 + 1, width]
-
-            standard_n_tokens = 50 if self.model.conv1.kernel_size[0] == 32 else 197
-
-            if x.shape[1] != standard_n_tokens:
-                new_shape = int(math.sqrt(x.shape[1]-1))
-                x = x + self.rescaled_pos_emb((new_shape, new_shape)).to(x.dtype)[None,:,:]
-            else:
-                x = x + self.model.positional_embedding.to(x.dtype)
-
-            x = self.model.ln_pre(x)
-
-            x = x.permute(1, 0, 2)  # NLD -> LND
-
-            activations, affinities = [], []
-            for i, res_block in enumerate(self.model.transformer.resblocks):
-                
-                if mask is not None:
-                    mask_layer, mask_type, mask_tensor = mask
-                    if mask_layer == i or mask_layer == 'all':
-                        # import ipdb; ipdb.set_trace()
-                        size = int(math.sqrt(x.shape[0] - 1))
-                        
-                        attn_mask = (mask_type, nnf.interpolate(mask_tensor.unsqueeze(1).float(), (size, size)).view(mask_tensor.shape[0], size * size))
-                        
-                    else:
-                        attn_mask = None
-                else:
-                    attn_mask = None
-
-                x, aff_per_head = forward_multihead_attention(x, res_block, with_aff=True, attn_mask=attn_mask)
-
-                if i in extract_layers:
-                    affinities += [aff_per_head]
-
-                    #if self.n_tokens is not None:
-                    #    activations += [nnf.interpolate(x, inp_size, mode='bilinear', align_corners=True)]
-                    #else:
-                    activations += [x]
-
-                if len(extract_layers) > 0 and i == max(extract_layers) and skip:
-                    print('early skip')
-                    break
-                
-            x = x.permute(1, 0, 2)  # LND -> NLD
-            x = self.model.ln_post(x[:, 0, :])
-
-            if self.model.proj is not None:
-                x = x @ self.model.proj
-
-            return x, activations, affinities
-
-    def sample_prompts(self, words, prompt_list=None):
-
-        prompt_list = prompt_list if prompt_list is not None else self.prompt_list
-
-        prompt_indices = torch.multinomial(torch.ones(len(prompt_list)), len(words), replacement=True)
-        prompts = [prompt_list[i] for i in prompt_indices]
-        return [promt.format(w) for promt, w in zip(prompts, words)]
-
-    def get_cond_vec(self, conditional, batch_size):
-        # compute conditional from a single string
-        if conditional is not None and type(conditional) == str:
-            cond = self.compute_conditional(conditional)
-            cond = cond.repeat(batch_size, 1)
-
-        # compute conditional from string list/tuple
-        elif conditional is not None and type(conditional) in {list, tuple} and type(conditional[0]) == str:
-            assert len(conditional) == batch_size
-            cond = self.compute_conditional(conditional)
-
-        # use conditional directly
-        elif conditional is not None and type(conditional) == torch.Tensor and conditional.ndim == 2:
-            cond = conditional
-
-        # compute conditional from image
-        elif conditional is not None and type(conditional) == torch.Tensor:
-            with torch.no_grad():
-                cond, _, _ = self.visual_forward(conditional)
-        else:
-            raise ValueError('invalid conditional')
-        return cond   
-
-    def compute_conditional(self, conditional):
-        import clip
-
-        dev = next(self.parameters()).device
-
-        if type(conditional) in {list, tuple}:
-            text_tokens = clip.tokenize(conditional).to(dev)
-            cond = self.clip_model.encode_text(text_tokens)
-        else:
-            if conditional in self.precomputed_prompts:
-                cond = self.precomputed_prompts[conditional].float().to(dev)
-            else:
-                text_tokens = clip.tokenize([conditional]).to(dev)
-                cond = self.clip_model.encode_text(text_tokens)[0]
-        
-        if self.shift_vector is not None:
-            return cond + self.shift_vector
-        else:
-            return cond
-
-
-def clip_load_untrained(version):
-    assert version == 'ViT-B/16'
-    from clip.model import CLIP
-    from clip.clip import _MODELS, _download
-    model = torch.jit.load(_download(_MODELS['ViT-B/16'])).eval()
-    state_dict = model.state_dict()
-
-    vision_width = state_dict["visual.conv1.weight"].shape[0]
-    vision_layers = len([k for k in state_dict.keys() if k.startswith("visual.") and k.endswith(".attn.in_proj_weight")])
-    vision_patch_size = state_dict["visual.conv1.weight"].shape[-1]
-    grid_size = round((state_dict["visual.positional_embedding"].shape[0] - 1) ** 0.5)
-    image_resolution = vision_patch_size * grid_size
-    embed_dim = state_dict["text_projection"].shape[1]
-    context_length = state_dict["positional_embedding"].shape[0]
-    vocab_size = state_dict["token_embedding.weight"].shape[0]
-    transformer_width = state_dict["ln_final.weight"].shape[0]
-    transformer_heads = transformer_width // 64
-    transformer_layers = len(set(k.split(".")[2] for k in state_dict if k.startswith(f"transformer.resblocks")))
-
-    return CLIP(embed_dim, image_resolution, vision_layers, vision_width, vision_patch_size, 
-        context_length, vocab_size, transformer_width, transformer_heads, transformer_layers)    
-
-
-class CLIPDensePredT(CLIPDenseBase):
-
-    def __init__(self, version='ViT-B/32', extract_layers=(3, 6, 9), cond_layer=0, reduce_dim=128, n_heads=4, prompt='fixed', 
-                 extra_blocks=0, reduce_cond=None, fix_shift=False,
-                 learn_trans_conv_only=False,  limit_to_clip_only=False, upsample=False, 
-                 add_calibration=False, rev_activations=False, trans_conv=None, n_tokens=None, complex_trans_conv=False):
-        
-        super().__init__(version, reduce_cond, reduce_dim, prompt, n_tokens)
-        # device = 'cpu'
-
-        self.extract_layers = extract_layers
-        self.cond_layer = cond_layer
-        self.limit_to_clip_only = limit_to_clip_only
-        self.process_cond = None
-        self.rev_activations = rev_activations
-        
-        depth = len(extract_layers)
-
-        if add_calibration:
-            self.calibration_conds = 1
-
-        self.upsample_proj = nn.Conv2d(reduce_dim, 1, kernel_size=1) if upsample else None
-
-        self.add_activation1 = True
-
-        self.version = version
-        
-        self.token_shape = {'ViT-B/32': (7, 7), 'ViT-B/16': (14, 14)}[version]
-
-        if fix_shift:
-            # self.shift_vector = nn.Parameter(torch.load(join(dirname(basename(__file__)), 'clip_text_shift_vector.pth')), requires_grad=False)
-            self.shift_vector = nn.Parameter(torch.load(join(dirname(basename(__file__)), 'shift_text_to_vis.pth')), requires_grad=False)
-            # self.shift_vector = nn.Parameter(-1*torch.load(join(dirname(basename(__file__)), 'shift2.pth')), requires_grad=False)
-        else:
-            self.shift_vector = None
-
-        if trans_conv is None:
-            trans_conv_ks = {'ViT-B/32': (32, 32), 'ViT-B/16': (16, 16)}[version]
-        else:
-            # explicitly define transposed conv kernel size
-            trans_conv_ks = (trans_conv, trans_conv)
-
-        if not complex_trans_conv:
-            self.trans_conv = nn.ConvTranspose2d(reduce_dim, 1, trans_conv_ks, stride=trans_conv_ks)
-        else:
-            assert trans_conv_ks[0] == trans_conv_ks[1]
-
-            tp_kernels = (trans_conv_ks[0] // 4, trans_conv_ks[0] // 4)
-
-            self.trans_conv = nn.Sequential(
-                nn.Conv2d(reduce_dim, reduce_dim, kernel_size=3, padding=1),
-                nn.ReLU(),
-                nn.ConvTranspose2d(reduce_dim, reduce_dim // 2, kernel_size=tp_kernels[0], stride=tp_kernels[0]),
-                nn.ReLU(),
-                nn.ConvTranspose2d(reduce_dim // 2, 1, kernel_size=tp_kernels[1], stride=tp_kernels[1]),               
-            )
-
-#        self.trans_conv = nn.ConvTranspose2d(reduce_dim, 1, trans_conv_ks, stride=trans_conv_ks)
-        
-        assert len(self.extract_layers) == depth
-
-        self.reduces = nn.ModuleList([nn.Linear(768, reduce_dim) for _ in range(depth)])
-        self.blocks = nn.ModuleList([nn.TransformerEncoderLayer(d_model=reduce_dim, nhead=n_heads) for _ in range(len(self.extract_layers))])
-        self.extra_blocks = nn.ModuleList([nn.TransformerEncoderLayer(d_model=reduce_dim, nhead=n_heads) for _ in range(extra_blocks)])
-        
-        # refinement and trans conv
-
-        if learn_trans_conv_only:
-            for p in self.parameters():
-                p.requires_grad_(False)
-            
-            for p in self.trans_conv.parameters():
-                p.requires_grad_(True)
-
-        self.prompt_list = get_prompt_list(prompt)
-
-
-    def forward(self, inp_image, conditional=None, return_features=False, mask=None):
-
-        assert type(return_features) == bool
-
-        inp_image = inp_image.to(self.model.positional_embedding.device)
-
-        if mask is not None:
-            raise ValueError('mask not supported')
-
-        # x_inp = normalize(inp_image)
-        x_inp = inp_image
-
-        bs, dev = inp_image.shape[0], x_inp.device
-
-        cond = self.get_cond_vec(conditional, bs)
-
-        visual_q, activations, _ = self.visual_forward(x_inp, extract_layers=[0] + list(self.extract_layers))
-
-        activation1 = activations[0]
-        activations = activations[1:]
-
-        _activations = activations[::-1] if not self.rev_activations else activations
-
-        a = None
-        for i, (activation, block, reduce) in enumerate(zip(_activations, self.blocks, self.reduces)):
-            
-            if a is not None:
-                a = reduce(activation) + a
-            else:
-                a = reduce(activation)
-
-            if i == self.cond_layer:
-                if self.reduce_cond is not None:
-                    cond = self.reduce_cond(cond)
-                
-                a = self.film_mul(cond) * a + self.film_add(cond)
-
-            a = block(a)
-
-        for block in self.extra_blocks:
-            a = a + block(a)
-
-        a = a[1:].permute(1, 2, 0) # rm cls token and -> BS, Feats, Tokens
-
-        size = int(math.sqrt(a.shape[2]))
-
-        a = a.view(bs, a.shape[1], size, size)
-
-        a = self.trans_conv(a)
-
-        if self.n_tokens is not None:
-            a = nnf.interpolate(a, x_inp.shape[2:], mode='bilinear', align_corners=True) 
-
-        if self.upsample_proj is not None:
-            a = self.upsample_proj(a)
-            a = nnf.interpolate(a, x_inp.shape[2:], mode='bilinear')
-
-        if return_features:
-            return a, visual_q, cond, [activation1] + activations
-        else:
-            return a,
-
-
-
-class CLIPDensePredTMasked(CLIPDensePredT):
-
-    def __init__(self, version='ViT-B/32', extract_layers=(3, 6, 9), cond_layer=0, reduce_dim=128, n_heads=4, 
-                 prompt='fixed', extra_blocks=0, reduce_cond=None, fix_shift=False, learn_trans_conv_only=False, 
-                 refine=None, limit_to_clip_only=False, upsample=False, add_calibration=False, n_tokens=None):
-
-        super().__init__(version=version, extract_layers=extract_layers, cond_layer=cond_layer, reduce_dim=reduce_dim, 
-                         n_heads=n_heads, prompt=prompt, extra_blocks=extra_blocks, reduce_cond=reduce_cond, 
-                         fix_shift=fix_shift, learn_trans_conv_only=learn_trans_conv_only,
-                         limit_to_clip_only=limit_to_clip_only, upsample=upsample, add_calibration=add_calibration,
-                         n_tokens=n_tokens)
-
-    def visual_forward_masked(self, img_s, seg_s):
-        return super().visual_forward(img_s, mask=('all', 'cls_token', seg_s))
-
-    def forward(self, img_q, cond_or_img_s, seg_s=None, return_features=False):
-
-        if seg_s is None:
-            cond = cond_or_img_s
-        else:
-            img_s = cond_or_img_s
-
-            with torch.no_grad():
-                cond, _, _ = self.visual_forward_masked(img_s, seg_s)
-
-        return super().forward(img_q, cond, return_features=return_features)
-
-
-
-class CLIPDenseBaseline(CLIPDenseBase):
-
-    def __init__(self, version='ViT-B/32', cond_layer=0, 
-                extract_layer=9, reduce_dim=128, reduce2_dim=None, prompt='fixed', 
-                 reduce_cond=None, limit_to_clip_only=False, n_tokens=None):
-        
-        super().__init__(version, reduce_cond, reduce_dim, prompt, n_tokens)
-        device = 'cpu'
-
-        # self.cond_layer = cond_layer
-        self.extract_layer = extract_layer
-        self.limit_to_clip_only = limit_to_clip_only
-        self.shift_vector = None
-
-        self.token_shape = {'ViT-B/32': (7, 7), 'ViT-B/16': (14, 14)}[version]
-        
-        assert reduce2_dim is not None
-
-        self.reduce2 = nn.Sequential(
-            nn.Linear(reduce_dim, reduce2_dim),
-            nn.ReLU(),
-            nn.Linear(reduce2_dim, reduce_dim)
-        )
-        
-        trans_conv_ks = {'ViT-B/32': (32, 32), 'ViT-B/16': (16, 16)}[version]
-        self.trans_conv = nn.ConvTranspose2d(reduce_dim, 1, trans_conv_ks, stride=trans_conv_ks)
-
-
-    def forward(self, inp_image, conditional=None, return_features=False):
-
-        inp_image = inp_image.to(self.model.positional_embedding.device)
-
-        # x_inp = normalize(inp_image)
-        x_inp = inp_image
-
-        bs, dev = inp_image.shape[0], x_inp.device
-
-        cond = self.get_cond_vec(conditional, bs)
-
-        visual_q, activations, affinities = self.visual_forward(x_inp, extract_layers=[self.extract_layer])
-
-        a = activations[0]
-        a = self.reduce(a)
-        a = self.film_mul(cond) * a + self.film_add(cond)
-
-        if self.reduce2 is not None:
-            a = self.reduce2(a)
-
-        # the original model would execute a transformer block here
-
-        a = a[1:].permute(1, 2, 0) # rm cls token and -> BS, Feats, Tokens
-
-        size = int(math.sqrt(a.shape[2]))
-
-        a = a.view(bs, a.shape[1], size, size)
-        a = self.trans_conv(a)
-
-        if return_features:
-            return a, visual_q, cond, activations
-        else:
-            return a,
-
-
-class CLIPSegMultiLabel(nn.Module):
-
-    def __init__(self, model) -> None:
-        super().__init__()
-
-        from third_party.JoEm.data_loader import get_seen_idx, get_unseen_idx, VOC
-
-        self.pascal_classes = VOC
-
-        from clip.clipseg import CLIPDensePredT
-        from general_utils import load_model
-        # self.clipseg = load_model('rd64-vit16-neg0.2-phrasecut', strict=False)
-        self.clipseg = load_model(model, strict=False)
-        
-        self.clipseg.eval()
-
-    def forward(self, x):
-
-        bs = x.shape[0]
-        out = torch.ones(21, bs, 352, 352).to(x.device) * -10
-
-        for class_id, class_name in enumerate(self.pascal_classes):
-        
-            fac = 3 if class_name == 'background' else 1
-
-            with torch.no_grad():
-                pred = torch.sigmoid(self.clipseg(x, class_name)[0][:,0]) * fac
-
-            out[class_id] += pred
-
-
-        out = out.permute(1, 0, 2, 3)
-
-        return out
-
-        # construct output tensor
-                    

roop-unleashed/clip/model.py

@@ -1,436 +0,0 @@
-from collections import OrderedDict
-from typing import Tuple, Union
-
-import numpy as np
-import torch
-import torch.nn.functional as F
-from torch import nn
-
-
-class Bottleneck(nn.Module):
-    expansion = 4
-
-    def __init__(self, inplanes, planes, stride=1):
-        super().__init__()
-
-        # all conv layers have stride 1. an avgpool is performed after the second convolution when stride > 1
-        self.conv1 = nn.Conv2d(inplanes, planes, 1, bias=False)
-        self.bn1 = nn.BatchNorm2d(planes)
-        self.relu1 = nn.ReLU(inplace=True)
-
-        self.conv2 = nn.Conv2d(planes, planes, 3, padding=1, bias=False)
-        self.bn2 = nn.BatchNorm2d(planes)
-        self.relu2 = nn.ReLU(inplace=True)
-
-        self.avgpool = nn.AvgPool2d(stride) if stride > 1 else nn.Identity()
-
-        self.conv3 = nn.Conv2d(planes, planes * self.expansion, 1, bias=False)
-        self.bn3 = nn.BatchNorm2d(planes * self.expansion)
-        self.relu3 = nn.ReLU(inplace=True)
-
-        self.downsample = None
-        self.stride = stride
-
-        if stride > 1 or inplanes != planes * Bottleneck.expansion:
-            # downsampling layer is prepended with an avgpool, and the subsequent convolution has stride 1
-            self.downsample = nn.Sequential(OrderedDict([
-                ("-1", nn.AvgPool2d(stride)),
-                ("0", nn.Conv2d(inplanes, planes * self.expansion, 1, stride=1, bias=False)),
-                ("1", nn.BatchNorm2d(planes * self.expansion))
-            ]))
-
-    def forward(self, x: torch.Tensor):
-        identity = x
-
-        out = self.relu1(self.bn1(self.conv1(x)))
-        out = self.relu2(self.bn2(self.conv2(out)))
-        out = self.avgpool(out)
-        out = self.bn3(self.conv3(out))
-
-        if self.downsample is not None:
-            identity = self.downsample(x)
-
-        out += identity
-        out = self.relu3(out)
-        return out
-
-
-class AttentionPool2d(nn.Module):
-    def __init__(self, spacial_dim: int, embed_dim: int, num_heads: int, output_dim: int = None):
-        super().__init__()
-        self.positional_embedding = nn.Parameter(torch.randn(spacial_dim ** 2 + 1, embed_dim) / embed_dim ** 0.5)
-        self.k_proj = nn.Linear(embed_dim, embed_dim)
-        self.q_proj = nn.Linear(embed_dim, embed_dim)
-        self.v_proj = nn.Linear(embed_dim, embed_dim)
-        self.c_proj = nn.Linear(embed_dim, output_dim or embed_dim)
-        self.num_heads = num_heads
-
-    def forward(self, x):
-        x = x.flatten(start_dim=2).permute(2, 0, 1)  # NCHW -> (HW)NC
-        x = torch.cat([x.mean(dim=0, keepdim=True), x], dim=0)  # (HW+1)NC
-        x = x + self.positional_embedding[:, None, :].to(x.dtype)  # (HW+1)NC
-        x, _ = F.multi_head_attention_forward(
-            query=x[:1], key=x, value=x,
-            embed_dim_to_check=x.shape[-1],
-            num_heads=self.num_heads,
-            q_proj_weight=self.q_proj.weight,
-            k_proj_weight=self.k_proj.weight,
-            v_proj_weight=self.v_proj.weight,
-            in_proj_weight=None,
-            in_proj_bias=torch.cat([self.q_proj.bias, self.k_proj.bias, self.v_proj.bias]),
-            bias_k=None,
-            bias_v=None,
-            add_zero_attn=False,
-            dropout_p=0,
-            out_proj_weight=self.c_proj.weight,
-            out_proj_bias=self.c_proj.bias,
-            use_separate_proj_weight=True,
-            training=self.training,
-            need_weights=False
-        )
-        return x.squeeze(0)
-
-
-class ModifiedResNet(nn.Module):
-    """
-    A ResNet class that is similar to torchvision's but contains the following changes:
-    - There are now 3 "stem" convolutions as opposed to 1, with an average pool instead of a max pool.
-    - Performs anti-aliasing strided convolutions, where an avgpool is prepended to convolutions with stride > 1
-    - The final pooling layer is a QKV attention instead of an average pool
-    """
-
-    def __init__(self, layers, output_dim, heads, input_resolution=224, width=64):
-        super().__init__()
-        self.output_dim = output_dim
-        self.input_resolution = input_resolution
-
-        # the 3-layer stem
-        self.conv1 = nn.Conv2d(3, width // 2, kernel_size=3, stride=2, padding=1, bias=False)
-        self.bn1 = nn.BatchNorm2d(width // 2)
-        self.relu1 = nn.ReLU(inplace=True)
-        self.conv2 = nn.Conv2d(width // 2, width // 2, kernel_size=3, padding=1, bias=False)
-        self.bn2 = nn.BatchNorm2d(width // 2)
-        self.relu2 = nn.ReLU(inplace=True)
-        self.conv3 = nn.Conv2d(width // 2, width, kernel_size=3, padding=1, bias=False)
-        self.bn3 = nn.BatchNorm2d(width)
-        self.relu3 = nn.ReLU(inplace=True)
-        self.avgpool = nn.AvgPool2d(2)
-
-        # residual layers
-        self._inplanes = width  # this is a *mutable* variable used during construction
-        self.layer1 = self._make_layer(width, layers[0])
-        self.layer2 = self._make_layer(width * 2, layers[1], stride=2)
-        self.layer3 = self._make_layer(width * 4, layers[2], stride=2)
-        self.layer4 = self._make_layer(width * 8, layers[3], stride=2)
-
-        embed_dim = width * 32  # the ResNet feature dimension
-        self.attnpool = AttentionPool2d(input_resolution // 32, embed_dim, heads, output_dim)
-
-    def _make_layer(self, planes, blocks, stride=1):
-        layers = [Bottleneck(self._inplanes, planes, stride)]
-
-        self._inplanes = planes * Bottleneck.expansion
-        for _ in range(1, blocks):
-            layers.append(Bottleneck(self._inplanes, planes))
-
-        return nn.Sequential(*layers)
-
-    def forward(self, x):
-        def stem(x):
-            x = self.relu1(self.bn1(self.conv1(x)))
-            x = self.relu2(self.bn2(self.conv2(x)))
-            x = self.relu3(self.bn3(self.conv3(x)))
-            x = self.avgpool(x)
-            return x
-
-        x = x.type(self.conv1.weight.dtype)
-        x = stem(x)
-        x = self.layer1(x)
-        x = self.layer2(x)
-        x = self.layer3(x)
-        x = self.layer4(x)
-        x = self.attnpool(x)
-
-        return x
-
-
-class LayerNorm(nn.LayerNorm):
-    """Subclass torch's LayerNorm to handle fp16."""
-
-    def forward(self, x: torch.Tensor):
-        orig_type = x.dtype
-        ret = super().forward(x.type(torch.float32))
-        return ret.type(orig_type)
-
-
-class QuickGELU(nn.Module):
-    def forward(self, x: torch.Tensor):
-        return x * torch.sigmoid(1.702 * x)
-
-
-class ResidualAttentionBlock(nn.Module):
-    def __init__(self, d_model: int, n_head: int, attn_mask: torch.Tensor = None):
-        super().__init__()
-
-        self.attn = nn.MultiheadAttention(d_model, n_head)
-        self.ln_1 = LayerNorm(d_model)
-        self.mlp = nn.Sequential(OrderedDict([
-            ("c_fc", nn.Linear(d_model, d_model * 4)),
-            ("gelu", QuickGELU()),
-            ("c_proj", nn.Linear(d_model * 4, d_model))
-        ]))
-        self.ln_2 = LayerNorm(d_model)
-        self.attn_mask = attn_mask
-
-    def attention(self, x: torch.Tensor):
-        self.attn_mask = self.attn_mask.to(dtype=x.dtype, device=x.device) if self.attn_mask is not None else None
-        return self.attn(x, x, x, need_weights=False, attn_mask=self.attn_mask)[0]
-
-    def forward(self, x: torch.Tensor):
-        x = x + self.attention(self.ln_1(x))
-        x = x + self.mlp(self.ln_2(x))
-        return x
-
-
-class Transformer(nn.Module):
-    def __init__(self, width: int, layers: int, heads: int, attn_mask: torch.Tensor = None):
-        super().__init__()
-        self.width = width
-        self.layers = layers
-        self.resblocks = nn.Sequential(*[ResidualAttentionBlock(width, heads, attn_mask) for _ in range(layers)])
-
-    def forward(self, x: torch.Tensor):
-        return self.resblocks(x)
-
-
-class VisionTransformer(nn.Module):
-    def __init__(self, input_resolution: int, patch_size: int, width: int, layers: int, heads: int, output_dim: int):
-        super().__init__()
-        self.input_resolution = input_resolution
-        self.output_dim = output_dim
-        self.conv1 = nn.Conv2d(in_channels=3, out_channels=width, kernel_size=patch_size, stride=patch_size, bias=False)
-
-        scale = width ** -0.5
-        self.class_embedding = nn.Parameter(scale * torch.randn(width))
-        self.positional_embedding = nn.Parameter(scale * torch.randn((input_resolution // patch_size) ** 2 + 1, width))
-        self.ln_pre = LayerNorm(width)
-
-        self.transformer = Transformer(width, layers, heads)
-
-        self.ln_post = LayerNorm(width)
-        self.proj = nn.Parameter(scale * torch.randn(width, output_dim))
-
-    def forward(self, x: torch.Tensor):
-        x = self.conv1(x)  # shape = [*, width, grid, grid]
-        x = x.reshape(x.shape[0], x.shape[1], -1)  # shape = [*, width, grid ** 2]
-        x = x.permute(0, 2, 1)  # shape = [*, grid ** 2, width]
-        x = torch.cat([self.class_embedding.to(x.dtype) + torch.zeros(x.shape[0], 1, x.shape[-1], dtype=x.dtype, device=x.device), x], dim=1)  # shape = [*, grid ** 2 + 1, width]
-        x = x + self.positional_embedding.to(x.dtype)
-        x = self.ln_pre(x)
-
-        x = x.permute(1, 0, 2)  # NLD -> LND
-        x = self.transformer(x)
-        x = x.permute(1, 0, 2)  # LND -> NLD
-
-        x = self.ln_post(x[:, 0, :])
-
-        if self.proj is not None:
-            x = x @ self.proj
-
-        return x
-
-
-class CLIP(nn.Module):
-    def __init__(self,
-                 embed_dim: int,
-                 # vision
-                 image_resolution: int,
-                 vision_layers: Union[Tuple[int, int, int, int], int],
-                 vision_width: int,
-                 vision_patch_size: int,
-                 # text
-                 context_length: int,
-                 vocab_size: int,
-                 transformer_width: int,
-                 transformer_heads: int,
-                 transformer_layers: int
-                 ):
-        super().__init__()
-
-        self.context_length = context_length
-
-        if isinstance(vision_layers, (tuple, list)):
-            vision_heads = vision_width * 32 // 64
-            self.visual = ModifiedResNet(
-                layers=vision_layers,
-                output_dim=embed_dim,
-                heads=vision_heads,
-                input_resolution=image_resolution,
-                width=vision_width
-            )
-        else:
-            vision_heads = vision_width // 64
-            self.visual = VisionTransformer(
-                input_resolution=image_resolution,
-                patch_size=vision_patch_size,
-                width=vision_width,
-                layers=vision_layers,
-                heads=vision_heads,
-                output_dim=embed_dim
-            )
-
-        self.transformer = Transformer(
-            width=transformer_width,
-            layers=transformer_layers,
-            heads=transformer_heads,
-            attn_mask=self.build_attention_mask()
-        )
-
-        self.vocab_size = vocab_size
-        self.token_embedding = nn.Embedding(vocab_size, transformer_width)
-        self.positional_embedding = nn.Parameter(torch.empty(self.context_length, transformer_width))
-        self.ln_final = LayerNorm(transformer_width)
-
-        self.text_projection = nn.Parameter(torch.empty(transformer_width, embed_dim))
-        self.logit_scale = nn.Parameter(torch.ones([]) * np.log(1 / 0.07))
-
-        self.initialize_parameters()
-
-    def initialize_parameters(self):
-        nn.init.normal_(self.token_embedding.weight, std=0.02)
-        nn.init.normal_(self.positional_embedding, std=0.01)
-
-        if isinstance(self.visual, ModifiedResNet):
-            if self.visual.attnpool is not None:
-                std = self.visual.attnpool.c_proj.in_features ** -0.5
-                nn.init.normal_(self.visual.attnpool.q_proj.weight, std=std)
-                nn.init.normal_(self.visual.attnpool.k_proj.weight, std=std)
-                nn.init.normal_(self.visual.attnpool.v_proj.weight, std=std)
-                nn.init.normal_(self.visual.attnpool.c_proj.weight, std=std)
-
-            for resnet_block in [self.visual.layer1, self.visual.layer2, self.visual.layer3, self.visual.layer4]:
-                for name, param in resnet_block.named_parameters():
-                    if name.endswith("bn3.weight"):
-                        nn.init.zeros_(param)
-
-        proj_std = (self.transformer.width ** -0.5) * ((2 * self.transformer.layers) ** -0.5)
-        attn_std = self.transformer.width ** -0.5
-        fc_std = (2 * self.transformer.width) ** -0.5
-        for block in self.transformer.resblocks:
-            nn.init.normal_(block.attn.in_proj_weight, std=attn_std)
-            nn.init.normal_(block.attn.out_proj.weight, std=proj_std)
-            nn.init.normal_(block.mlp.c_fc.weight, std=fc_std)
-            nn.init.normal_(block.mlp.c_proj.weight, std=proj_std)
-
-        if self.text_projection is not None:
-            nn.init.normal_(self.text_projection, std=self.transformer.width ** -0.5)
-
-    def build_attention_mask(self):
-        # lazily create causal attention mask, with full attention between the vision tokens
-        # pytorch uses additive attention mask; fill with -inf
-        mask = torch.empty(self.context_length, self.context_length)
-        mask.fill_(float("-inf"))
-        mask.triu_(1)  # zero out the lower diagonal
-        return mask
-
-    @property
-    def dtype(self):
-        return self.visual.conv1.weight.dtype
-
-    def encode_image(self, image):
-        return self.visual(image.type(self.dtype))
-
-    def encode_text(self, text):
-        x = self.token_embedding(text).type(self.dtype)  # [batch_size, n_ctx, d_model]
-
-        x = x + self.positional_embedding.type(self.dtype)
-        x = x.permute(1, 0, 2)  # NLD -> LND
-        x = self.transformer(x)
-        x = x.permute(1, 0, 2)  # LND -> NLD
-        x = self.ln_final(x).type(self.dtype)
-
-        # x.shape = [batch_size, n_ctx, transformer.width]
-        # take features from the eot embedding (eot_token is the highest number in each sequence)
-        x = x[torch.arange(x.shape[0]), text.argmax(dim=-1)] @ self.text_projection
-
-        return x
-
-    def forward(self, image, text):
-        image_features = self.encode_image(image)
-        text_features = self.encode_text(text)
-
-        # normalized features
-        image_features = image_features / image_features.norm(dim=1, keepdim=True)
-        text_features = text_features / text_features.norm(dim=1, keepdim=True)
-
-        # cosine similarity as logits
-        logit_scale = self.logit_scale.exp()
-        logits_per_image = logit_scale * image_features @ text_features.t()
-        logits_per_text = logits_per_image.t()
-
-        # shape = [global_batch_size, global_batch_size]
-        return logits_per_image, logits_per_text
-
-
-def convert_weights(model: nn.Module):
-    """Convert applicable model parameters to fp16"""
-
-    def _convert_weights_to_fp16(l):
-        if isinstance(l, (nn.Conv1d, nn.Conv2d, nn.Linear)):
-            l.weight.data = l.weight.data.half()
-            if l.bias is not None:
-                l.bias.data = l.bias.data.half()
-
-        if isinstance(l, nn.MultiheadAttention):
-            for attr in [*[f"{s}_proj_weight" for s in ["in", "q", "k", "v"]], "in_proj_bias", "bias_k", "bias_v"]:
-                tensor = getattr(l, attr)
-                if tensor is not None:
-                    tensor.data = tensor.data.half()
-
-        for name in ["text_projection", "proj"]:
-            if hasattr(l, name):
-                attr = getattr(l, name)
-                if attr is not None:
-                    attr.data = attr.data.half()
-
-    model.apply(_convert_weights_to_fp16)
-
-
-def build_model(state_dict: dict):
-    vit = "visual.proj" in state_dict
-
-    if vit:
-        vision_width = state_dict["visual.conv1.weight"].shape[0]
-        vision_layers = len([k for k in state_dict.keys() if k.startswith("visual.") and k.endswith(".attn.in_proj_weight")])
-        vision_patch_size = state_dict["visual.conv1.weight"].shape[-1]
-        grid_size = round((state_dict["visual.positional_embedding"].shape[0] - 1) ** 0.5)
-        image_resolution = vision_patch_size * grid_size
-    else:
-        counts: list = [len(set(k.split(".")[2] for k in state_dict if k.startswith(f"visual.layer{b}"))) for b in [1, 2, 3, 4]]
-        vision_layers = tuple(counts)
-        vision_width = state_dict["visual.layer1.0.conv1.weight"].shape[0]
-        output_width = round((state_dict["visual.attnpool.positional_embedding"].shape[0] - 1) ** 0.5)
-        vision_patch_size = None
-        assert output_width ** 2 + 1 == state_dict["visual.attnpool.positional_embedding"].shape[0]
-        image_resolution = output_width * 32
-
-    embed_dim = state_dict["text_projection"].shape[1]
-    context_length = state_dict["positional_embedding"].shape[0]
-    vocab_size = state_dict["token_embedding.weight"].shape[0]
-    transformer_width = state_dict["ln_final.weight"].shape[0]
-    transformer_heads = transformer_width // 64
-    transformer_layers = len(set(k.split(".")[2] for k in state_dict if k.startswith("transformer.resblocks")))
-
-    model = CLIP(
-        embed_dim,
-        image_resolution, vision_layers, vision_width, vision_patch_size,
-        context_length, vocab_size, transformer_width, transformer_heads, transformer_layers
-    )
-
-    for key in ["input_resolution", "context_length", "vocab_size"]:
-        if key in state_dict:
-            del state_dict[key]
-
-    convert_weights(model)
-    model.load_state_dict(state_dict)
-    return model.eval()

roop-unleashed/clip/simple_tokenizer.py

@@ -1,132 +0,0 @@
-import gzip
-import html
-import os
-from functools import lru_cache
-
-import ftfy
-import regex as re
-
-
-@lru_cache()
-def default_bpe():
-    return os.path.join(os.path.dirname(os.path.abspath(__file__)), "bpe_simple_vocab_16e6.txt.gz")
-
-
-@lru_cache()
-def bytes_to_unicode():
-    """
-    Returns list of utf-8 byte and a corresponding list of unicode strings.
-    The reversible bpe codes work on unicode strings.
-    This means you need a large # of unicode characters in your vocab if you want to avoid UNKs.
-    When you're at something like a 10B token dataset you end up needing around 5K for decent coverage.
-    This is a signficant percentage of your normal, say, 32K bpe vocab.
-    To avoid that, we want lookup tables between utf-8 bytes and unicode strings.
-    And avoids mapping to whitespace/control characters the bpe code barfs on.
-    """
-    bs = list(range(ord("!"), ord("~")+1))+list(range(ord("¡"), ord("¬")+1))+list(range(ord("®"), ord("ÿ")+1))
-    cs = bs[:]
-    n = 0
-    for b in range(2**8):
-        if b not in bs:
-            bs.append(b)
-            cs.append(2**8+n)
-            n += 1
-    cs = [chr(n) for n in cs]
-    return dict(zip(bs, cs))
-
-
-def get_pairs(word):
-    """Return set of symbol pairs in a word.
-    Word is represented as tuple of symbols (symbols being variable-length strings).
-    """
-    pairs = set()
-    prev_char = word[0]
-    for char in word[1:]:
-        pairs.add((prev_char, char))
-        prev_char = char
-    return pairs
-
-
-def basic_clean(text):
-    text = ftfy.fix_text(text)
-    text = html.unescape(html.unescape(text))
-    return text.strip()
-
-
-def whitespace_clean(text):
-    text = re.sub(r'\s+', ' ', text)
-    text = text.strip()
-    return text
-
-
-class SimpleTokenizer(object):
-    def __init__(self, bpe_path: str = default_bpe()):
-        self.byte_encoder = bytes_to_unicode()
-        self.byte_decoder = {v: k for k, v in self.byte_encoder.items()}
-        merges = gzip.open(bpe_path).read().decode("utf-8").split('\n')
-        merges = merges[1:49152-256-2+1]
-        merges = [tuple(merge.split()) for merge in merges]
-        vocab = list(bytes_to_unicode().values())
-        vocab = vocab + [v+'</w>' for v in vocab]
-        for merge in merges:
-            vocab.append(''.join(merge))
-        vocab.extend(['<|startoftext|>', '<|endoftext|>'])
-        self.encoder = dict(zip(vocab, range(len(vocab))))
-        self.decoder = {v: k for k, v in self.encoder.items()}
-        self.bpe_ranks = dict(zip(merges, range(len(merges))))
-        self.cache = {'<|startoftext|>': '<|startoftext|>', '<|endoftext|>': '<|endoftext|>'}
-        self.pat = re.compile(r"""<\|startoftext\|>|<\|endoftext\|>|'s|'t|'re|'ve|'m|'ll|'d|[\p{L}]+|[\p{N}]|[^\s\p{L}\p{N}]+""", re.IGNORECASE)
-
-    def bpe(self, token):
-        if token in self.cache:
-            return self.cache[token]
-        word = tuple(token[:-1]) + ( token[-1] + '</w>',)
-        pairs = get_pairs(word)
-
-        if not pairs:
-            return token+'</w>'
-
-        while True:
-            bigram = min(pairs, key = lambda pair: self.bpe_ranks.get(pair, float('inf')))
-            if bigram not in self.bpe_ranks:
-                break
-            first, second = bigram
-            new_word = []
-            i = 0
-            while i < len(word):
-                try:
-                    j = word.index(first, i)
-                    new_word.extend(word[i:j])
-                    i = j
-                except:
-                    new_word.extend(word[i:])
-                    break
-
-                if word[i] == first and i < len(word)-1 and word[i+1] == second:
-                    new_word.append(first+second)
-                    i += 2
-                else:
-                    new_word.append(word[i])
-                    i += 1
-            new_word = tuple(new_word)
-            word = new_word
-            if len(word) == 1:
-                break
-            else:
-                pairs = get_pairs(word)
-        word = ' '.join(word)
-        self.cache[token] = word
-        return word
-
-    def encode(self, text):
-        bpe_tokens = []
-        text = whitespace_clean(basic_clean(text)).lower()
-        for token in re.findall(self.pat, text):
-            token = ''.join(self.byte_encoder[b] for b in token.encode('utf-8'))
-            bpe_tokens.extend(self.encoder[bpe_token] for bpe_token in self.bpe(token).split(' '))
-        return bpe_tokens
-
-    def decode(self, tokens):
-        text = ''.join([self.decoder[token] for token in tokens])
-        text = bytearray([self.byte_decoder[c] for c in text]).decode('utf-8', errors="replace").replace('</w>', ' ')
-        return text

roop-unleashed/clip/vitseg.py

@@ -1,286 +0,0 @@
-import math
-from posixpath import basename, dirname, join
-# import clip
-from clip.model import convert_weights
-import torch
-import json
-from torch import nn
-from torch.nn import functional as nnf
-from torch.nn.modules import activation
-from torch.nn.modules.activation import ReLU
-from torchvision import transforms
-
-normalize = transforms.Normalize(mean=(0.48145466, 0.4578275, 0.40821073), std=(0.26862954, 0.26130258, 0.27577711))
-
-from torchvision.models import ResNet
-
-
-def process_prompts(conditional, prompt_list, conditional_map):
-    # DEPRECATED
-            
-    # randomly sample a synonym
-    words = [conditional_map[int(i)] for i in conditional]
-    words = [syns[torch.multinomial(torch.ones(len(syns)), 1, replacement=True).item()] for syns in words]
-    words = [w.replace('_', ' ') for w in words]
-
-    if prompt_list is not None:
-        prompt_indices = torch.multinomial(torch.ones(len(prompt_list)), len(words), replacement=True)
-        prompts = [prompt_list[i] for i in prompt_indices]
-    else:
-        prompts = ['a photo of {}'] * (len(words))
-
-    return [promt.format(w) for promt, w in zip(prompts, words)]
-
-
-class VITDenseBase(nn.Module):
-    
-    def rescaled_pos_emb(self, new_size):
-        assert len(new_size) == 2
-
-        a = self.model.positional_embedding[1:].T.view(1, 768, *self.token_shape)
-        b = nnf.interpolate(a, new_size, mode='bicubic', align_corners=False).squeeze(0).view(768, new_size[0]*new_size[1]).T
-        return torch.cat([self.model.positional_embedding[:1], b])
-
-    def visual_forward(self, x_inp, extract_layers=(), skip=False, mask=None):
-        
-        with torch.no_grad():
-
-            x_inp = nnf.interpolate(x_inp, (384, 384))
-
-            x = self.model.patch_embed(x_inp)
-            cls_token = self.model.cls_token.expand(x.shape[0], -1, -1)  # stole cls_tokens impl from Phil Wang, thanks
-            if self.model.dist_token is None:
-                x = torch.cat((cls_token, x), dim=1)
-            else:
-                x = torch.cat((cls_token, self.model.dist_token.expand(x.shape[0], -1, -1), x), dim=1)
-            x = self.model.pos_drop(x + self.model.pos_embed)
-
-            activations = []
-            for i, block in enumerate(self.model.blocks):
-                x = block(x)
-
-                if i in extract_layers:
-                    # permute to be compatible with CLIP
-                    activations += [x.permute(1,0,2)]                
-
-            x = self.model.norm(x)
-            x = self.model.head(self.model.pre_logits(x[:, 0]))
-
-            # again for CLIP compatibility
-            # x = x.permute(1, 0, 2)
-
-        return x, activations, None
-
-    def sample_prompts(self, words, prompt_list=None):
-
-        prompt_list = prompt_list if prompt_list is not None else self.prompt_list
-
-        prompt_indices = torch.multinomial(torch.ones(len(prompt_list)), len(words), replacement=True)
-        prompts = [prompt_list[i] for i in prompt_indices]
-        return [promt.format(w) for promt, w in zip(prompts, words)]
-
-    def get_cond_vec(self, conditional, batch_size):
-        # compute conditional from a single string
-        if conditional is not None and type(conditional) == str:
-            cond = self.compute_conditional(conditional)
-            cond = cond.repeat(batch_size, 1)
-
-        # compute conditional from string list/tuple
-        elif conditional is not None and type(conditional) in {list, tuple} and type(conditional[0]) == str:
-            assert len(conditional) == batch_size
-            cond = self.compute_conditional(conditional)
-
-        # use conditional directly
-        elif conditional is not None and type(conditional) == torch.Tensor and conditional.ndim == 2:
-            cond = conditional
-
-        # compute conditional from image
-        elif conditional is not None and type(conditional) == torch.Tensor:
-            with torch.no_grad():
-                cond, _, _ = self.visual_forward(conditional)
-        else:
-            raise ValueError('invalid conditional')
-        return cond   
-
-    def compute_conditional(self, conditional):
-        import clip
-
-        dev = next(self.parameters()).device
-
-        if type(conditional) in {list, tuple}:
-            text_tokens = clip.tokenize(conditional).to(dev)
-            cond = self.clip_model.encode_text(text_tokens)
-        else:
-            if conditional in self.precomputed_prompts:
-                cond = self.precomputed_prompts[conditional].float().to(dev)
-            else:
-                text_tokens = clip.tokenize([conditional]).to(dev)
-                cond = self.clip_model.encode_text(text_tokens)[0]
-        
-        return cond
-
-
-class VITDensePredT(VITDenseBase):
-
-    def __init__(self, extract_layers=(3, 6, 9), cond_layer=0, reduce_dim=128, n_heads=4, prompt='fixed', 
-                 depth=3, extra_blocks=0, reduce_cond=None, fix_shift=False,
-                 learn_trans_conv_only=False, refine=None, limit_to_clip_only=False, upsample=False, 
-                 add_calibration=False, process_cond=None, not_pretrained=False):
-        super().__init__()
-        # device = 'cpu'
-
-        self.extract_layers = extract_layers
-        self.cond_layer = cond_layer
-        self.limit_to_clip_only = limit_to_clip_only
-        self.process_cond = None
-        
-        if add_calibration:
-            self.calibration_conds = 1
-
-        self.upsample_proj = nn.Conv2d(reduce_dim, 1, kernel_size=1) if upsample else None
-
-        self.add_activation1 = True
-
-        import timm 
-        self.model = timm.create_model('vit_base_patch16_384', pretrained=True)
-        self.model.head = nn.Linear(768, 512 if reduce_cond is None else reduce_cond)
-
-        for p in self.model.parameters():
-            p.requires_grad_(False)
-
-        import clip
-        self.clip_model, _ = clip.load('ViT-B/16', device='cpu', jit=False)
-        # del self.clip_model.visual
-        
-        
-        self.token_shape = (14, 14)
-
-        # conditional
-        if reduce_cond is not None:
-            self.reduce_cond = nn.Linear(512, reduce_cond)
-            for p in self.reduce_cond.parameters():
-                p.requires_grad_(False)
-        else:
-            self.reduce_cond = None
-
-        # self.film = AVAILABLE_BLOCKS['film'](512, 128)
-        self.film_mul = nn.Linear(512 if reduce_cond is None else reduce_cond, reduce_dim)
-        self.film_add = nn.Linear(512 if reduce_cond is None else reduce_cond, reduce_dim)
-        
-        # DEPRECATED
-        # self.conditional_map = {c['id']: c['synonyms'] for c in json.load(open(cond_map))}
-        
-        assert len(self.extract_layers) == depth
-
-        self.reduces = nn.ModuleList([nn.Linear(768, reduce_dim) for _ in range(depth)])
-        self.blocks = nn.ModuleList([nn.TransformerEncoderLayer(d_model=reduce_dim, nhead=n_heads) for _ in range(len(self.extract_layers))])
-        self.extra_blocks = nn.ModuleList([nn.TransformerEncoderLayer(d_model=reduce_dim, nhead=n_heads) for _ in range(extra_blocks)])
-
-        trans_conv_ks = (16, 16)
-        self.trans_conv = nn.ConvTranspose2d(reduce_dim, 1, trans_conv_ks, stride=trans_conv_ks)
-
-        # refinement and trans conv
-
-        if learn_trans_conv_only:
-            for p in self.parameters():
-                p.requires_grad_(False)
-            
-            for p in self.trans_conv.parameters():
-                p.requires_grad_(True)
-
-        if prompt == 'fixed':
-            self.prompt_list = ['a photo of a {}.']
-        elif prompt == 'shuffle':
-            self.prompt_list = ['a photo of a {}.', 'a photograph of a {}.', 'an image of a {}.', '{}.']
-        elif prompt == 'shuffle+':
-            self.prompt_list = ['a photo of a {}.', 'a photograph of a {}.', 'an image of a {}.', '{}.',
-                                'a cropped photo of a {}.', 'a good photo of a {}.', 'a photo of one {}.',
-                                'a bad photo of a {}.', 'a photo of the {}.']
-        elif prompt == 'shuffle_clip':
-            from models.clip_prompts import imagenet_templates
-            self.prompt_list = imagenet_templates
-
-        if process_cond is not None:
-            if process_cond == 'clamp' or process_cond[0] == 'clamp':
-
-                val = process_cond[1] if type(process_cond) in {list, tuple} else 0.2
-
-                def clamp_vec(x):
-                    return torch.clamp(x, -val, val)
-
-                self.process_cond = clamp_vec
-
-            elif process_cond.endswith('.pth'):
-                
-                shift = torch.load(process_cond)
-                def add_shift(x):
-                    return x + shift.to(x.device)
-
-                self.process_cond = add_shift
-
-        import pickle
-        precomp = pickle.load(open('precomputed_prompt_vectors.pickle', 'rb'))
-        self.precomputed_prompts = {k: torch.from_numpy(v) for k, v in precomp.items()}
-
-
-    def forward(self, inp_image, conditional=None, return_features=False, mask=None):
-
-        assert type(return_features) == bool
-
-        # inp_image = inp_image.to(self.model.positional_embedding.device)
-
-        if mask is not None:
-            raise ValueError('mask not supported')
-
-        # x_inp = normalize(inp_image)
-        x_inp = inp_image
-
-        bs, dev = inp_image.shape[0], x_inp.device
-
-        inp_image_size = inp_image.shape[2:]
-
-        cond = self.get_cond_vec(conditional, bs)
-
-        visual_q, activations, _ = self.visual_forward(x_inp, extract_layers=[0] + list(self.extract_layers))
-
-        activation1 = activations[0]
-        activations = activations[1:]
-
-        a = None
-        for i, (activation, block, reduce) in enumerate(zip(activations[::-1], self.blocks, self.reduces)):
-            
-            if a is not None:
-                a = reduce(activation) + a
-            else:
-                a = reduce(activation)
-
-            if i == self.cond_layer:
-                if self.reduce_cond is not None:
-                    cond = self.reduce_cond(cond)
-                
-                a = self.film_mul(cond) * a + self.film_add(cond)
-
-            a = block(a)
-
-        for block in self.extra_blocks:
-            a = a + block(a)
-
-        a = a[1:].permute(1, 2, 0) # rm cls token and -> BS, Feats, Tokens
-
-        size = int(math.sqrt(a.shape[2]))
-
-        a = a.view(bs, a.shape[1], size, size)
-
-        if self.trans_conv is not None:
-            a = self.trans_conv(a)
-
-        if self.upsample_proj is not None:
-            a = self.upsample_proj(a)
-            a = nnf.interpolate(a, x_inp.shape[2:], mode='bilinear')
-
-        a = nnf.interpolate(a, inp_image_size)
-
-        if return_features:
-            return a, visual_q, cond, [activation1] + activations
-        else:
-            return a,

roop-unleashed/config_colab.yaml

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

roop-unleashed/installer/installer.py

@@ -1,87 +0,0 @@
-import argparse
-import glob
-import os
-import shutil
-import site
-import subprocess
-import sys
-
-
-script_dir = os.getcwd()
-
-
-def run_cmd(cmd, capture_output=False, env=None):
-    # Run shell commands
-    return subprocess.run(cmd, shell=True, capture_output=capture_output, env=env)
-
-
-def check_env():
-    # If we have access to conda, we are probably in an environment
-    conda_not_exist = run_cmd("conda", capture_output=True).returncode
-    if conda_not_exist:
-        print("Conda is not installed. Exiting...")
-        sys.exit()
-    
-    # Ensure this is a new environment and not the base environment
-    if os.environ["CONDA_DEFAULT_ENV"] == "base":
-        print("Create an environment for this project and activate it. Exiting...")
-        sys.exit()
-
-
-def install_dependencies():
-    global MY_PATH
-
-    # Install Git and clone repo
-    run_cmd("conda install -y -k git")
-    run_cmd("git clone https://github.com/C0untFloyd/roop-unleashed.git")
-    os.chdir(MY_PATH)
-    run_cmd("git checkout c8643a0532f09f84397aaacf526e66db6455d399")
-    # 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()

roop-unleashed/installer/windows_run.bat

@@ -1,99 +0,0 @@
-@echo off
-
-REM No CLI arguments supported anymore
-set COMMANDLINE_ARGS=
-
-cd /D "%~dp0"
-
-echo "%CD%"| findstr /C:" " >nul && echo This script relies on Miniconda which can not be silently installed under a path with spaces. && goto end
-
-set PATH=%PATH%;%SystemRoot%\system32
-
-@rem config
-set INSTALL_DIR=%cd%\installer_files
-set CONDA_ROOT_PREFIX=%cd%\installer_files\conda
-set INSTALL_ENV_DIR=%cd%\installer_files\env
-set MINICONDA_DOWNLOAD_URL=https://repo.anaconda.com/miniconda/Miniconda3-latest-Windows-x86_64.exe
-set FFMPEG_DOWNLOAD_URL=https://github.com/GyanD/codexffmpeg/releases/download/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

roop-unleashed/models/CLIP/rd64-uni-refined.pth

@@ -1,3 +0,0 @@
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roop-unleashed/models/CodeFormer/CodeFormerv0.1.onnx

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roop-unleashed/models/DMDNet.pth

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roop-unleashed/models/Frame/deoldify_artistic.onnx

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-size 255024891

roop-unleashed/models/Frame/deoldify_stable.onnx

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roop-unleashed/models/Frame/isnet-general-use.onnx

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roop-unleashed/models/Frame/lsdir_x4.onnx

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-size 66938051

roop-unleashed/models/Frame/real_esrgan_x2.onnx

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-size 69524246

roop-unleashed/models/Frame/real_esrgan_x4.onnx

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-size 69464831

roop-unleashed/models/GFPGANv1.4.onnx

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roop-unleashed/models/GPEN-BFR-512.onnx

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roop-unleashed/models/buffalo_l.zip

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-size 288621354

roop-unleashed/models/buffalo_l/1k3d68.onnx

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roop-unleashed/models/buffalo_l/2d106det.onnx

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-size 5030888

roop-unleashed/models/buffalo_l/det_10g.onnx

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roop-unleashed/models/buffalo_l/genderage.onnx

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roop-unleashed/models/buffalo_l/w600k_r50.onnx

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roop-unleashed/models/inswapper_128.onnx

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roop-unleashed/models/restoreformer_plus_plus.onnx

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roop-unleashed/models/xseg.onnx

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roop-unleashed/mypy.ini

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

roop-unleashed/requirements.txt

@@ -1,19 +0,0 @@
---extra-index-url https://download.pytorch.org/whl/cu118
-
-numpy==1.26.4
-gradio==4.29.0
-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==1.17.1; sys_platform == 'darwin' and platform_machine == 'arm64'
-onnxruntime-gpu==1.17.1; sys_platform != 'darwin'
-tqdm==4.66.4
-ftfy
-regex
-pyvirtualcam

roop-unleashed/roop-unleashed.ipynb

@@ -1,208 +0,0 @@
-{
-  "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-unleashed/roop/FaceSet.py

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

roop-unleashed/roop/ProcessEntry.py

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

roop-unleashed/roop/ProcessMgr.py

@@ -1,701 +0,0 @@
-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-unleashed/roop/ProcessOptions.py

@@ -1,13 +0,0 @@
-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