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README.md

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+![Screenshot 2023-08-04 212706](https://github.com/Hillobar/Rope/assets/63615199/114b4073-9a25-42cc-844d-1afc3625907b)
+
+# Rope
+Rope implements the insightface inswapper_128 model with a helpful GUI.
+### Discord link: ###
+[Discord](https://discord.gg/5CxhgRKBdN)
+
+### Features: ###
+* Ugly GUI, but incredible features and fast workflow
+* Fastest face swapper available
+* Real-time video player
+* Occlusion functions
+
+### Changes for Rope - Space Worm: ###
+* Updated video rendering to use Target Video parameters
+* Mousewheel scroll on the time bar to control frame position
+* Added an occluder model (experimental, very fast, make sure you download the new model-link below)
+* Greatly increased performance for larger videos/multiple faces
+* CLIP crashing fixed. Add as many words as you like!
+* Detachable video preview
+* Fixed most bugs related to changing options while playing. Adjust setting on the fly!
+* GFPGAN now renders up to 512x512
+* Status bar (still adding features to this)
+
+### Known bugs: ### 
+* Stop video playback before loading a new video, or bork
+
+### Preview: ###
+![Screenshot 2023-08-05 154156](https://github.com/Hillobar/Rope/assets/63615199/921698ab-af0e-43ca-b669-a2b2537d5c0f)
+### Getting Started: ###
+![Screenshot 2023-08-05 152851](https://github.com/Hillobar/Rope/assets/63615199/68b4ec4e-615f-4fd6-9215-f5a2ae8314b4)
+### Features: ###
+![Screenshot 2023-08-05 152835](https://github.com/Hillobar/Rope/assets/63615199/4e64237e-7d0f-4a83-a738-64b0df206766)
+
+### Disclaimer: ###
+Rope is a personal project that I'm making available to the community as a thank you for all of the contributors ahead of me. I don't have time to troubleshoot or add requested features, so it is provided as-is. Don't look at this code for example of good coding practices. I am primarily focused on performance and my specific use cases. There are plenty of ways to bork the workflow. Please see how to use below.
+
+### Install: ###
+Note: It's only configured for CUDA (Nvidia)
+* Set up a local venv
+  * python.exe -m venv venv
+* Activate your new venv
+  * .\venv\Scripts\activate
+* Install requirements
+  * .\venv\Scripts\pip.exe install -r .\requirements.txt
+* Place [GFPGANv1.4.onnx](https://github.com/Hillobar/Rope/releases/download/Space_Worm/GFPGANv1.4.onnx), [inswapper_128_fp16.onnx](https://github.com/Hillobar/Rope/releases/download/Space_Worm/inswapper_128.fp16.onnx), and [occluder.ckpt](https://github.com/Hillobar/Rope/releases/download/Space_Worm/occluder.ckpt) in the root directory
+* Do this if you've never installed roop or Rope (or any other onnx runtimes):
+  * Install CUDA Toolkit 11.8
+  * Install dependencies:
+  * pip uninstall onnxruntime onnxruntime-gpu
+  * pip install onnxruntime-gpu==1.15.1
+* Double-click on Rope.bat!
+
+### To use: ###
+* Run Rope.bat
+* Set your Target Video, Source Faces, and Video Output folders
+  * Buttons will be gold if they are not set
+  * Only places videos or images in the respective folders. Other files my bork it
+  * Rope creates a JSON file to remember your last set paths
+  * I like to keep my folders <20 or so items. Helps to organize and reduces load times
+* Click on the Load Models button to initialize Rope
+* Select a video to load it into the player
+* Find Target Faces
+  * Adds all faces in the current frame to the Found Faces pane
+  * If a Face is already Found and in the pane, it won't re-add it
+* Click a Source Face
+  * Source Face number will appear
+* Select a Target Face
+  * Target Faces will show assignment number to the Source Face number
+  * Toggle a Target Face to unselect and reassign to currently selected Source Face
+* Continue to select other Source Faces and assign them to Target Faces
+* Click SWAP to enable face swapping
+* Click PLAY to play
+* Click REC to arm recording
+  * Click PLAY to start recording using the current settings
+  * Click PLAY again to stop recording, otherwise it will record to the end of the Target Video
+* Toggle GFPGAN, adjust blending amount
+* Toggle Diffing, adjust blending amount
+* Lower the threshhold if you have multiple Source Faces assigned and they are jumping around. You can also try Clearing and Finding new Target Faces (disable SWAP first)
+* Modify the Masking boundaries
+* Use CLIP to identify objects to swap or not swap (e.g Pos: face, head; Neg: hair, hand), adjust the gain of the words, and set the blur amount around the items
+* Change # threads to match your GPU memory (24GB ~9 threads with GFPGAN on, more threads w/o GFPGAN)
+  * Start with the lowest you think will run and watch your GPU memory.
+  * Once you allocate memory by increasing # threads, you can't un-allocate it by reducing # threads. You will need to restart Rope.
+* In general, always stop the video before changing anything. Otherwise, it might bork. Reassigning faces is okay
+* If it does bork, reload the video (reclick on it). If that doesn't work you'll need to restart
+

Rope.bat

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+call venv\Scripts\activate.bat
+python run_working.py 
+pause

clip/__init__.py

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+from .clip import *

clip/clip.py

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+import hashlib
+import os
+import urllib
+import warnings
+from typing import Any, Union, List
+from pkg_resources import packaging
+
+import torch
+from PIL import Image
+from torchvision.transforms import Compose, Resize, CenterCrop, ToTensor, Normalize
+from tqdm import tqdm
+
+from .model import build_model
+from .simple_tokenizer import SimpleTokenizer as _Tokenizer
+
+try:
+    from torchvision.transforms import InterpolationMode
+    BICUBIC = InterpolationMode.BICUBIC
+except ImportError:
+    BICUBIC = Image.BICUBIC
+
+
+if packaging.version.parse(torch.__version__) < packaging.version.parse("1.7.1"):
+    warnings.warn("PyTorch version 1.7.1 or higher is recommended")
+
+
+__all__ = ["available_models", "load", "tokenize"]
+_tokenizer = _Tokenizer()
+
+_MODELS = {
+    "RN50": "https://openaipublic.azureedge.net/clip/models/afeb0e10f9e5a86da6080e35cf09123aca3b358a0c3e3b6c78a7b63bc04b6762/RN50.pt",
+    "RN101": "https://openaipublic.azureedge.net/clip/models/8fa8567bab74a42d41c5915025a8e4538c3bdbe8804a470a72f30b0d94fab599/RN101.pt",
+    "RN50x4": "https://openaipublic.azureedge.net/clip/models/7e526bd135e493cef0776de27d5f42653e6b4c8bf9e0f653bb11773263205fdd/RN50x4.pt",
+    "RN50x16": "https://openaipublic.azureedge.net/clip/models/52378b407f34354e150460fe41077663dd5b39c54cd0bfd2b27167a4a06ec9aa/RN50x16.pt",
+    "RN50x64": "https://openaipublic.azureedge.net/clip/models/be1cfb55d75a9666199fb2206c106743da0f6468c9d327f3e0d0a543a9919d9c/RN50x64.pt",
+    "ViT-B/32": "https://openaipublic.azureedge.net/clip/models/40d365715913c9da98579312b702a82c18be219cc2a73407c4526f58eba950af/ViT-B-32.pt",
+    "ViT-B/16": "https://openaipublic.azureedge.net/clip/models/5806e77cd80f8b59890b7e101eabd078d9fb84e6937f9e85e4ecb61988df416f/ViT-B-16.pt",
+    "ViT-L/14": "https://openaipublic.azureedge.net/clip/models/b8cca3fd41ae0c99ba7e8951adf17d267cdb84cd88be6f7c2e0eca1737a03836/ViT-L-14.pt",
+    "ViT-L/14@336px": "https://openaipublic.azureedge.net/clip/models/3035c92b350959924f9f00213499208652fc7ea050643e8b385c2dac08641f02/ViT-L-14-336px.pt",
+}
+
+
+def _download(url: str, root: str):
+    os.makedirs(root, exist_ok=True)
+    filename = os.path.basename(url)
+
+    expected_sha256 = url.split("/")[-2]
+    download_target = os.path.join(root, filename)
+
+    if os.path.exists(download_target) and not os.path.isfile(download_target):
+        raise RuntimeError(f"{download_target} exists and is not a regular file")
+
+    if os.path.isfile(download_target):
+        if hashlib.sha256(open(download_target, "rb").read()).hexdigest() == expected_sha256:
+            return download_target
+        else:
+            warnings.warn(f"{download_target} exists, but the SHA256 checksum does not match; re-downloading the file")
+
+    with urllib.request.urlopen(url) as source, open(download_target, "wb") as output:
+        with tqdm(total=int(source.info().get("Content-Length")), ncols=80, unit='iB', unit_scale=True, unit_divisor=1024) as loop:
+            while True:
+                buffer = source.read(8192)
+                if not buffer:
+                    break
+
+                output.write(buffer)
+                loop.update(len(buffer))
+
+    if hashlib.sha256(open(download_target, "rb").read()).hexdigest() != expected_sha256:
+        raise RuntimeError("Model has been downloaded but the SHA256 checksum does not not match")
+
+    return download_target
+
+
+def _convert_image_to_rgb(image):
+    return image.convert("RGB")
+
+
+def _transform(n_px):
+    return Compose([
+        Resize(n_px, interpolation=BICUBIC),
+        CenterCrop(n_px),
+        _convert_image_to_rgb,
+        ToTensor(),
+        Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711)),
+    ])
+
+
+def available_models() -> List[str]:
+    """Returns the names of available CLIP models"""
+    return list(_MODELS.keys())
+
+
+def load(name: str, device: Union[str, torch.device] = "cuda" if torch.cuda.is_available() else "cpu", jit: bool = False, download_root: str = None):
+    """Load a CLIP model
+
+    Parameters
+    ----------
+    name : str
+        A model name listed by `clip.available_models()`, or the path to a model checkpoint containing the state_dict
+
+    device : Union[str, torch.device]
+        The device to put the loaded model
+
+    jit : bool
+        Whether to load the optimized JIT model or more hackable non-JIT model (default).
+
+    download_root: str
+        path to download the model files; by default, it uses "~/.cache/clip"
+
+    Returns
+    -------
+    model : torch.nn.Module
+        The CLIP model
+
+    preprocess : Callable[[PIL.Image], torch.Tensor]
+        A torchvision transform that converts a PIL image into a tensor that the returned model can take as its input
+    """
+    if name in _MODELS:
+        model_path = _download(_MODELS[name], download_root or os.path.expanduser("~/.cache/clip"))
+    elif os.path.isfile(name):
+        model_path = name
+    else:
+        raise RuntimeError(f"Model {name} not found; available models = {available_models()}")
+
+    with open(model_path, 'rb') as opened_file:
+        try:
+            # loading JIT archive
+            model = torch.jit.load(opened_file, map_location=device if jit else "cpu").eval()
+            state_dict = None
+        except RuntimeError:
+            # loading saved state dict
+            if jit:
+                warnings.warn(f"File {model_path} is not a JIT archive. Loading as a state dict instead")
+                jit = False
+            state_dict = torch.load(opened_file, map_location="cpu")
+
+    if not jit:
+        model = build_model(state_dict or model.state_dict()).to(device)
+        if str(device) == "cpu":
+            model.float()
+        return model, _transform(model.visual.input_resolution)
+
+    # patch the device names
+    device_holder = torch.jit.trace(lambda: torch.ones([]).to(torch.device(device)), example_inputs=[])
+    device_node = [n for n in device_holder.graph.findAllNodes("prim::Constant") if "Device" in repr(n)][-1]
+
+    def _node_get(node: torch._C.Node, key: str):
+        """Gets attributes of a node which is polymorphic over return type.
+        
+        From https://github.com/pytorch/pytorch/pull/82628
+        """
+        sel = node.kindOf(key)
+        return getattr(node, sel)(key)
+
+    def patch_device(module):
+        try:
+            graphs = [module.graph] if hasattr(module, "graph") else []
+        except RuntimeError:
+            graphs = []
+
+        if hasattr(module, "forward1"):
+            graphs.append(module.forward1.graph)
+
+        for graph in graphs:
+            for node in graph.findAllNodes("prim::Constant"):
+                if "value" in node.attributeNames() and str(_node_get(node, "value")).startswith("cuda"):
+                    node.copyAttributes(device_node)
+
+    model.apply(patch_device)
+    patch_device(model.encode_image)
+    patch_device(model.encode_text)
+
+    # patch dtype to float32 on CPU
+    if str(device) == "cpu":
+        float_holder = torch.jit.trace(lambda: torch.ones([]).float(), example_inputs=[])
+        float_input = list(float_holder.graph.findNode("aten::to").inputs())[1]
+        float_node = float_input.node()
+
+        def patch_float(module):
+            try:
+                graphs = [module.graph] if hasattr(module, "graph") else []
+            except RuntimeError:
+                graphs = []
+
+            if hasattr(module, "forward1"):
+                graphs.append(module.forward1.graph)
+
+            for graph in graphs:
+                for node in graph.findAllNodes("aten::to"):
+                    inputs = list(node.inputs())
+                    for i in [1, 2]:  # dtype can be the second or third argument to aten::to()
+                        if _node_get(inputs[i].node(), "value") == 5:
+                            inputs[i].node().copyAttributes(float_node)
+
+        model.apply(patch_float)
+        patch_float(model.encode_image)
+        patch_float(model.encode_text)
+
+        model.float()
+
+    return model, _transform(model.input_resolution.item())
+
+
+def tokenize(texts: Union[str, List[str]], context_length: int = 77, truncate: bool = False) -> Union[torch.IntTensor, torch.LongTensor]:
+    """
+    Returns the tokenized representation of given input string(s)
+
+    Parameters
+    ----------
+    texts : Union[str, List[str]]
+        An input string or a list of input strings to tokenize
+
+    context_length : int
+        The context length to use; all CLIP models use 77 as the context length
+
+    truncate: bool
+        Whether to truncate the text in case its encoding is longer than the context length
+
+    Returns
+    -------
+    A two-dimensional tensor containing the resulting tokens, shape = [number of input strings, context_length].
+    We return LongTensor when torch version is <1.8.0, since older index_select requires indices to be long.
+    """
+    if isinstance(texts, str):
+        texts = [texts]
+
+    sot_token = _tokenizer.encoder["<|startoftext|>"]
+    eot_token = _tokenizer.encoder["<|endoftext|>"]
+    all_tokens = [[sot_token] + _tokenizer.encode(text) + [eot_token] for text in texts]
+    if packaging.version.parse(torch.__version__) < packaging.version.parse("1.8.0"):
+        result = torch.zeros(len(all_tokens), context_length, dtype=torch.long)
+    else:
+        result = torch.zeros(len(all_tokens), context_length, dtype=torch.int)
+
+    for i, tokens in enumerate(all_tokens):
+        if len(tokens) > context_length:
+            if truncate:
+                tokens = tokens[:context_length]
+                tokens[-1] = eot_token
+            else:
+                raise RuntimeError(f"Input {texts[i]} is too long for context length {context_length}")
+        result[i, :len(tokens)] = torch.tensor(tokens)
+
+    return result