Add files using upload-large-folder tool

RAVE-main/CIVIT_AI/civit_ai.sh

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+#!/bin/sh
+
+civit_ai=$1 
+
+CWDPATH=$(pwd)
+mkdir $CWDPATH/CIVIT_AI/safetensors
+cd $CWDPATH/CIVIT_AI/safetensors
+
+mkdir $civit_ai
+cd $civit_ai
+wget https://civitai.com/api/download/models/$civit_ai --content-disposition
+
+model_name=$(ls -l | awk '{print $9}')
+model_name=${model_name//$'\n'/}
+model_name2=${model_name//$'.safetensors'/}
+
+eval "$(conda shell.bash hook)"
+conda activate rave
+cd ../..
+python convert.py \
+    --checkpoint_path "$CWDPATH/CIVIT_AI/safetensors/$civit_ai/$model_name" \
+    --dump_path "$CWDPATH/CIVIT_AI/diffusers_models/$civit_ai/$model_name2" \
+    --from_safetensors
+
+rm -rf $CWDPATH/CIVIT_AI/safetensors/
+
+echo "Download is done! Check the diffusers_models folder. $model_name"

RAVE-main/CIVIT_AI/convert.py

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+# coding=utf-8
+# Copyright 2023 The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+""" Conversion script for the LDM checkpoints. """
+
+import argparse
+import importlib
+
+import torch
+
+from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+
+    parser.add_argument(
+        "--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert."
+    )
+    # !wget https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml
+    parser.add_argument(
+        "--original_config_file",
+        default=None,
+        type=str,
+        help="The YAML config file corresponding to the original architecture.",
+    )
+    parser.add_argument(
+        "--num_in_channels",
+        default=None,
+        type=int,
+        help="The number of input channels. If `None` number of input channels will be automatically inferred.",
+    )
+    parser.add_argument(
+        "--scheduler_type",
+        default="pndm",
+        type=str,
+        help="Type of scheduler to use. Should be one of ['pndm', 'lms', 'ddim', 'euler', 'euler-ancestral', 'dpm']",
+    )
+    parser.add_argument(
+        "--pipeline_type",
+        default=None,
+        type=str,
+        help=(
+            "The pipeline type. One of 'FrozenOpenCLIPEmbedder', 'FrozenCLIPEmbedder', 'PaintByExample'"
+            ". If `None` pipeline will be automatically inferred."
+        ),
+    )
+    parser.add_argument(
+        "--image_size",
+        default=None,
+        type=int,
+        help=(
+            "The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2"
+            " Base. Use 768 for Stable Diffusion v2."
+        ),
+    )
+    parser.add_argument(
+        "--prediction_type",
+        default=None,
+        type=str,
+        help=(
+            "The prediction type that the model was trained on. Use 'epsilon' for Stable Diffusion v1.X and Stable"
+            " Diffusion v2 Base. Use 'v_prediction' for Stable Diffusion v2."
+        ),
+    )
+    parser.add_argument(
+        "--extract_ema",
+        action="store_true",
+        help=(
+            "Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights"
+            " or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield"
+            " higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning."
+        ),
+    )
+    parser.add_argument(
+        "--upcast_attention",
+        action="store_true",
+        help=(
+            "Whether the attention computation should always be upcasted. This is necessary when running stable"
+            " diffusion 2.1."
+        ),
+    )
+    parser.add_argument(
+        "--from_safetensors",
+        action="store_true",
+        help="If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.",
+    )
+    parser.add_argument(
+        "--to_safetensors",
+        action="store_true",
+        help="Whether to store pipeline in safetensors format or not.",
+    )
+    parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")
+    parser.add_argument("--device", type=str, help="Device to use (e.g. cpu, cuda:0, cuda:1, etc.)")
+    parser.add_argument(
+        "--stable_unclip",
+        type=str,
+        default=None,
+        required=False,
+        help="Set if this is a stable unCLIP model. One of 'txt2img' or 'img2img'.",
+    )
+    parser.add_argument(
+        "--stable_unclip_prior",
+        type=str,
+        default=None,
+        required=False,
+        help="Set if this is a stable unCLIP txt2img model. Selects which prior to use. If `--stable_unclip` is set to `txt2img`, the karlo prior (https://huggingface.co/kakaobrain/karlo-v1-alpha/tree/main/prior) is selected by default.",
+    )
+    parser.add_argument(
+        "--clip_stats_path",
+        type=str,
+        help="Path to the clip stats file. Only required if the stable unclip model's config specifies `model.params.noise_aug_config.params.clip_stats_path`.",
+        required=False,
+    )
+    parser.add_argument(
+        "--controlnet", action="store_true", default=None, help="Set flag if this is a controlnet checkpoint."
+    )
+    parser.add_argument("--half", action="store_true", help="Save weights in half precision.")
+    parser.add_argument(
+        "--vae_path",
+        type=str,
+        default=None,
+        required=False,
+        help="Set to a path, hub id to an already converted vae to not convert it again.",
+    )
+    parser.add_argument(
+        "--pipeline_class_name",
+        type=str,
+        default=None,
+        required=False,
+        help="Specify the pipeline class name",
+    )
+
+    args = parser.parse_args()
+
+    if args.pipeline_class_name is not None:
+        library = importlib.import_module("diffusers")
+        class_obj = getattr(library, args.pipeline_class_name)
+        pipeline_class = class_obj
+    else:
+        pipeline_class = None
+
+    pipe = download_from_original_stable_diffusion_ckpt(
+        checkpoint_path=args.checkpoint_path,
+        original_config_file=args.original_config_file,
+        # config_files=args.config_files,
+        image_size=args.image_size,
+        prediction_type=args.prediction_type,
+        model_type=args.pipeline_type,
+        extract_ema=args.extract_ema,
+        scheduler_type=args.scheduler_type,
+        num_in_channels=args.num_in_channels,
+        upcast_attention=args.upcast_attention,
+        from_safetensors=args.from_safetensors,
+        device=args.device,
+        stable_unclip=args.stable_unclip,
+        stable_unclip_prior=args.stable_unclip_prior,
+        clip_stats_path=args.clip_stats_path,
+        controlnet=args.controlnet,
+        vae_path=args.vae_path,
+        pipeline_class=pipeline_class,
+    )
+
+    if args.half:
+        pipe.to(torch_dtype=torch.float16)
+
+    if args.controlnet:
+        # only save the controlnet model
+        pipe.controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
+    else:
+        pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)

RAVE-main/pretrained_models/.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

RAVE-main/scripts/run_experiment.py

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+import torch
+import argparse
+import os
+import json
+import sys
+import datetime
+import imageio  # Import imageio for MP4 saving
+sys.path.append(os.getcwd())
+from pipelines.sd_controlnet_rave import RAVE
+from pipelines.sd_multicontrolnet_rave import RAVE_MultiControlNet
+import utils.constants as const
+import utils.video_grid_utils as vgu
+import warnings
+warnings.filterwarnings("ignore")
+import numpy as np
+
+def init_device():
+    """Initialize the device (CUDA if available, else CPU)."""
+    device_name = 'cuda' if torch.cuda.is_available() else 'cpu'
+    device = torch.device(device_name)
+    return device
+
+def init_paths(input_ns, video_name, save_folder):
+    """Initialize paths for video processing based on video name and save folder."""
+    # Set save path directly to the video name (e.g., truck.mp4) under save_folder
+    save_dir = save_folder
+    os.makedirs(save_dir, exist_ok=True)
+    input_ns.save_path = os.path.join(save_dir, video_name)  # Use video_name directly as filename
+
+    # Set video path using the fixed base path and video name
+    input_ns.video_path = f'/home/wangjuntong/video_editing_dataset/all_sourse/{video_name}'
+    
+    # Set Hugging Face ControlNet path based on preprocess_name
+    if '-' in input_ns.preprocess_name:
+        input_ns.hf_cn_path = [const.PREPROCESSOR_DICT[i] for i in input_ns.preprocess_name.split('-')]
+    else:
+        input_ns.hf_cn_path = const.PREPROCESSOR_DICT[input_ns.preprocess_name]
+    input_ns.hf_path = "runwayml/stable-diffusion-v1-5"
+    
+    # Set inverse and control paths (though not used for saving)
+    input_ns.inverse_path = f'{const.GENERATED_DATA_PATH}/inverses/{video_name}/{input_ns.preprocess_name}_{input_ns.model_id}_{input_ns.grid_size}x{input_ns.grid_size}_{input_ns.pad}'
+    input_ns.control_path = f'{const.GENERATED_DATA_PATH}/controls/{video_name}/{input_ns.preprocess_name}_{input_ns.grid_size}x{input_ns.grid_size}_{input_ns.pad}'
+    os.makedirs(input_ns.control_path, exist_ok=True)
+    os.makedirs(input_ns.inverse_path, exist_ok=True)
+    
+    return input_ns
+
+def run(input_ns, video_name, positive_prompts, save_folder):
+    """Run the video editing process with the given parameters."""
+    if 'model_id' not in input_ns.__dict__:
+        input_ns.model_id = "None"
+    device = init_device()
+    input_ns = init_paths(input_ns, video_name, save_folder)
+
+    
+    print(f"Save path: {input_ns.save_path}")
+
+    # Prepare video frames as a grid
+    input_ns.image_pil_list = vgu.prepare_video_to_grid(input_ns.video_path, input_ns.sample_size, input_ns.grid_size, input_ns.pad)
+    input_ns.sample_size = len(input_ns.image_pil_list)
+    print(f'Frame count: {len(input_ns.image_pil_list)}')
+
+    # Choose the appropriate ControlNet class
+    controlnet_class = RAVE_MultiControlNet if '-' in str(input_ns.controlnet_conditioning_scale) else RAVE
+    CN = controlnet_class(device)
+
+    # Initialize models
+    CN.init_models(input_ns.hf_cn_path, input_ns.hf_path, input_ns.preprocess_name, input_ns.model_id)
+    
+    input_dict = vars(input_ns)
+
+    # Run the editing process
+    start_time = datetime.datetime.now()
+    if '-' in str(input_ns.controlnet_conditioning_scale):
+        res_vid, control_vid_1, control_vid_2 = CN(input_dict)
+    else: 
+        res_vid, control_vid = CN(input_dict)
+    end_time = datetime.datetime.now()
+
+    # Convert PIL images to numpy arrays for imageio
+    res_vid_np = [np.array(img) for img in res_vid]
+
+    # Save the result video as MP4
+    imageio.mimwrite(input_ns.save_path, res_vid_np, format='mp4', fps=30, quality=8) 
+
+if __name__ == '__main__':
+    # Parse command-line argument for JSONL file path
+    parser = argparse.ArgumentParser(description='Batch video editing with JSONL input.')
+    parser.add_argument('--jsonl_path', type=str, required=True, help='Path to the JSONL file containing video info')
+    args = parser.parse_args()
+
+    # Fixed parameters
+    fixed_params = {
+        'preprocess_name': 'depth_zoe',
+        'batch_size': 4,
+        'batch_size_vae': 1,
+        'cond_step_start': 0.0,
+        'controlnet_conditioning_scale': 1.0,
+        'controlnet_guidance_end': 1.0,
+        'controlnet_guidance_start': 0.0,
+        'give_control_inversion': True,
+        'grid_size': 3,
+        'sample_size': -1,
+        'pad': 1,
+        'guidance_scale': 7.5,
+        'inversion_prompt': '',
+        'is_ddim_inversion': True,
+        'is_shuffle': True,
+        'negative_prompts': '',
+        'num_inference_steps': 50,
+        'num_inversion_step': 50,
+        'seed': 0,
+        'model_id': 'None'
+    }
+
+    # Read and process each line in the JSONL file
+    with open(args.jsonl_path, 'r') as f:
+        for line in f:
+            data = json.loads(line)
+            video_name = data['video']  # Use video key directly as filename (e.g., "truck.mp4")
+            positive_prompts = data['edit_prompt']
+            save_folder = f'/home/wangjuntong/RAVE-main/outputs/lnk_painting/{video_name.rsplit(".", 1)[0]}'  # Folder named after video without extension
+
+            # Create input namespace with fixed and dynamic parameters
+            input_ns = argparse.Namespace(**fixed_params)
+            input_ns.positive_prompts = positive_prompts
+            input_ns.video_name = video_name
+
+            # Run the editing process
+            run(input_ns, video_name, positive_prompts, save_folder)

vid2vid-zero-main/.gitignore

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+# custom dirs
+checkpoints/
+outputs/
+
+# Initially taken from Github's Python gitignore files
+
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# tests and logs
+tests/fixtures/cached_*_text.txt
+logs/
+lightning_logs/
+lang_code_data/
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+.python-version
+
+# celery beat schedule file
+celerybeat-schedule
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# vscode
+.vs
+.vscode
+
+# Pycharm
+.idea
+
+# TF code
+tensorflow_code
+
+# Models
+proc_data
+
+# examples
+runs
+/runs_old
+/wandb
+/examples/runs
+/examples/**/*.args
+/examples/rag/sweep
+
+# data
+/data
+serialization_dir
+
+# emacs
+*.*~
+debug.env
+
+# vim
+.*.swp
+
+#ctags
+tags
+
+# pre-commit
+.pre-commit*
+
+# .lock
+*.lock
+
+# DS_Store (MacOS)
+.DS_Store
+# RL pipelines may produce mp4 outputs
+*.mp4
+
+# dependencies
+/transformers

vid2vid-zero-main/README.md

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+
+
+
+<div align="center">
+
+<h1>vid2vid-zero for Zero-Shot Video Editing</h1>
+
+<h3><a href="https://arxiv.org/abs/2303.17599">Zero-Shot Video Editing Using Off-The-Shelf Image Diffusion Models</a></h3>
+
+[Wen Wang](https://scholar.google.com/citations?user=1ks0R04AAAAJ&hl=zh-CN)<sup>1*</sup>, &nbsp; [Kangyang Xie](https://github.com/felix-ky)<sup>1*</sup>, &nbsp; [Zide Liu](https://github.com/zideliu)<sup>1*</sup>, &nbsp; [Hao Chen](https://scholar.google.com.au/citations?user=FaOqRpcAAAAJ&hl=en)<sup>1</sup>, &nbsp; [Yue Cao](http://yue-cao.me/)<sup>2</sup>, &nbsp; [Xinlong Wang](https://www.xloong.wang/)<sup>2</sup>, &nbsp; [Chunhua Shen](https://cshen.github.io/)<sup>1</sup>
+
+<sup>1</sup>[ZJU](https://www.zju.edu.cn/english/), &nbsp; <sup>2</sup>[BAAI](https://www.baai.ac.cn/english.html)
+
+<br>
+
+[![Hugging Face Demo](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/BAAI/vid2vid-zero)
+
+
+<image src="docs/vid2vid-zero.png" />
+<br>
+
+</div>
+
+We propose vid2vid-zero, a simple yet effective method for zero-shot video editing. Our vid2vid-zero leverages off-the-shelf image diffusion models, and doesn't require training on any video. At the core of our method is a null-text inversion module for text-to-video alignment, a cross-frame modeling module for temporal consistency, and a spatial regularization module for fidelity to the original video. Without any training, we leverage the dynamic nature of the attention mechanism to enable bi-directional temporal modeling at test time. 
+Experiments and analyses show promising results in editing attributes, subjects, places, etc., in real-world videos. 
+
+
+## Highlights
+
+- Video editing with off-the-shelf image diffusion models.
+
+- No training on any video.
+
+- Promising results in editing attributes, subjects, places, etc., in real-world videos.
+
+## News
+* [2023.4.12] Online Gradio Demo is available [here](https://huggingface.co/spaces/BAAI/vid2vid-zero).
+* [2023.4.11] Add Gradio Demo (runs in local).
+* [2023.4.9] Code released! 
+
+## Installation
+### Requirements
+
+```shell
+pip install -r requirements.txt
+```
+Installing [xformers](https://github.com/facebookresearch/xformers) is highly recommended for improved efficiency and speed on GPUs. 
+
+### Weights
+
+**[Stable Diffusion]** [Stable Diffusion](https://arxiv.org/abs/2112.10752) is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input. The pre-trained Stable Diffusion models can be downloaded from [🤗 Hugging Face](https://huggingface.co) (e.g., [Stable Diffusion v1-4](https://huggingface.co/CompVis/stable-diffusion-v1-4), [v2-1](https://huggingface.co/stabilityai/stable-diffusion-2-1)). We use Stable Diffusion v1-4 by default.
+
+## Zero-shot testing
+
+Simply run:
+
+```bash
+accelerate launch test_vid2vid_zero.py --config path/to/config
+```
+
+For example:
+```bash
+accelerate launch test_vid2vid_zero.py --config configs/car-moving.yaml
+```
+
+## Gradio Demo
+Launch the local demo built with [gradio](https://gradio.app/):
+```bash
+python app.py
+```
+
+Or you can use our online gradio demo [here](https://huggingface.co/spaces/BAAI/vid2vid-zero).
+
+Note that we disable Null-text Inversion and enable fp16 for faster demo response.
+
+## Examples
+<table class="center">
+<tr>
+  <td style="text-align:center;"><b>Input Video</b></td>
+  <td style="text-align:center;"><b>Output Video</b></td>
+  <td style="text-align:center;"><b>Input Video</b></td>
+  <td style="text-align:center;"><b>Output Video</b></td>
+</tr>
+
+<tr>
+  <td width=25% style="text-align:center;color:gray;">"A car is moving on the road"</td>
+  <td width=25% style="text-align:center;">"A Porsche car is moving on the desert"</td>
+  <td width=25% style="text-align:center;color:gray;">"A car is moving on the road"</td>
+  <td width=25% style="text-align:center;">"A jeep car is moving on the snow"</td>
+</tr>
+
+<tr>
+  <td style colspan="2"><img src="examples/jeep-moving_Porsche.gif"></td>
+  <td style colspan="2"><img src="examples/jeep-moving_snow.gif"></td>       
+</tr>
+
+
+<tr>
+  <td width=25% style="text-align:center;color:gray;">"A man is running"</td>
+  <td width=25% style="text-align:center;">"Stephen Curry is running in Time Square"</td>
+  <td width=25% style="text-align:center;color:gray;">"A man is running"</td>
+  <td width=25% style="text-align:center;">"A man is running in New York City"</td>
+</tr>
+
+<tr>
+  <td style colspan="2"><img src="examples/man-running_stephen.gif"></td>
+  <td style colspan="2"><img src="examples/man-running_newyork.gif"></td>       
+</tr>
+
+<tr>
+  <td width=25% style="text-align:center;color:gray;">"A child is riding a bike on the road"</td>
+  <td width=25% style="text-align:center;">"a child is riding a bike on the flooded road"</td>
+  <td width=25% style="text-align:center;color:gray;">"A child is riding a bike on the road"</td>
+  <td width=25% style="text-align:center;">"a lego child is riding a bike on the road.gif"</td>
+</tr>
+
+<tr>
+  <td style colspan="2"><img src="examples/child-riding_flooded.gif"></td>
+  <td style colspan="2"><img src="examples/child-riding_lego.gif"></td>       
+</tr>
+
+<tr>
+  <td width=25% style="text-align:center;color:gray;">"A car is moving on the road"</td>
+  <td width=25% style="text-align:center;">"A car is moving on the snow"</td>
+  <td width=25% style="text-align:center;color:gray;">"A car is moving on the road"</td>
+  <td width=25% style="text-align:center;">"A jeep car is moving on the desert"</td>
+</tr>
+
+<tr>
+  <td style colspan="2"><img src="examples/red-moving_snow.gif"></td>
+  <td style colspan="2"><img src="examples/red-moving_desert.gif"></td>       
+</tr>
+</table>
+
+## Citation
+
+```
+@article{vid2vid-zero,
+  title={Zero-Shot Video Editing Using Off-The-Shelf Image Diffusion Models},
+  author={Wang, Wen and Xie, kangyang and Liu, Zide and Chen, Hao and Cao, Yue and Wang, Xinlong and Shen, Chunhua},
+  journal={arXiv preprint arXiv:2303.17599},
+  year={2023}
+}
+```
+
+## Acknowledgement
+[Tune-A-Video](https://github.com/showlab/Tune-A-Video), [diffusers](https://github.com/huggingface/diffusers), [prompt-to-prompt](https://github.com/google/prompt-to-prompt).
+
+## Contact
+
+**We are hiring** at all levels at BAAI Vision Team, including full-time researchers, engineers and interns. 
+If you are interested in working with us on **foundation model, visual perception and multimodal learning**, please contact [Xinlong Wang](https://www.xloong.wang/) (`wangxinlong@baai.ac.cn`) and [Yue Cao](http://yue-cao.me/) (`caoyue@baai.ac.cn`).

vid2vid-zero-main/app.py

@@ -0,0 +1,66 @@
+# Most code is from https://huggingface.co/spaces/Tune-A-Video-library/Tune-A-Video-Training-UI
+
+#!/usr/bin/env python
+
+from __future__ import annotations
+
+import os
+from subprocess import getoutput
+
+import gradio as gr
+import torch
+
+from gradio_demo.app_running import create_demo
+from gradio_demo.runner import Runner
+
+TITLE = '# [vid2vid-zero](https://github.com/baaivision/vid2vid-zero)'
+
+ORIGINAL_SPACE_ID = 'BAAI/vid2vid-zero'
+SPACE_ID = os.getenv('SPACE_ID', ORIGINAL_SPACE_ID)
+GPU_DATA = getoutput('nvidia-smi')
+
+if os.getenv('SYSTEM') == 'spaces' and SPACE_ID != ORIGINAL_SPACE_ID:
+    SETTINGS = f'<a href="https://huggingface.co/spaces/{SPACE_ID}/settings">Settings</a>'
+else:
+    SETTINGS = 'Settings'
+
+CUDA_NOT_AVAILABLE_WARNING = f'''## Attention - Running on CPU.
+<center>
+You can assign a GPU in the {SETTINGS} tab if you are running this on HF Spaces.
+You can use "T4 small/medium" to run this demo.
+</center>
+'''
+
+HF_TOKEN_NOT_SPECIFIED_WARNING = f'''The environment variable `HF_TOKEN` is not specified. Feel free to specify your Hugging Face token with write permission if you don't want to manually provide it for every run.
+<center>
+You can check and create your Hugging Face tokens <a href="https://huggingface.co/settings/tokens" target="_blank">here</a>.
+You can specify environment variables in the "Repository secrets" section of the {SETTINGS} tab.
+</center>
+'''
+
+HF_TOKEN = os.getenv('HF_TOKEN')
+
+
+def show_warning(warning_text: str) -> gr.Blocks:
+    with gr.Blocks() as demo:
+        with gr.Box():
+            gr.Markdown(warning_text)
+    return demo
+
+
+pipe = None
+runner = Runner(HF_TOKEN)
+
+with gr.Blocks(css='gradio_demo/style.css') as demo:
+    if not torch.cuda.is_available():
+        show_warning(CUDA_NOT_AVAILABLE_WARNING)
+
+    gr.Markdown(TITLE)
+    with gr.Tabs():
+        with gr.TabItem('Zero-shot Testing'):
+            create_demo(runner, pipe)
+
+    if not HF_TOKEN:
+        show_warning(HF_TOKEN_NOT_SPECIFIED_WARNING)
+
+demo.queue(max_size=1).launch(share=False)

vid2vid-zero-main/checkpoints/.gitattributes

@@ -0,0 +1,32 @@
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text

vid2vid-zero-main/configs/Cartoon_kangaroos.yaml

@@ -0,0 +1,37 @@
+pretrained_model_path: checkpoints
+output_dir: "/home/wangjuntong/vid2vid-zero-main/outputs/cartoon_kangaroo/"
+input_data:
+  video_path: "/home/wangjuntong/vid2vid-zero-main/AI_video/A cartoon kangaroo disco dances.mp4"
+  prompt: A cartoon kangaroo disco dances. 
+  n_sample_frames: 8
+  width: 512
+  height: 512
+  sample_start_idx: 0
+  sample_frame_rate: 1
+validation_data:
+  prompts:
+  - A cartoon robot disco dances.
+  - A man disco dances.
+  - A cartoon man disco dances.
+  video_length: 8
+  width: 512
+  height: 512
+  num_inference_steps: 50
+  guidance_scale: 7.5
+  num_inv_steps: 50
+  # args for null-text inv
+  use_null_inv: True
+  null_inner_steps: 1
+  null_base_lr: 1e-2
+  null_uncond_ratio: -0.5
+  null_normal_infer: True
+
+input_batch_size: 1
+seed: 33
+mixed_precision: "no"
+gradient_checkpointing: True
+enable_xformers_memory_efficient_attention: True
+# test-time adaptation
+use_sc_attn: True
+use_st_attn: True
+st_attn_idx: 0

vid2vid-zero-main/configs/black-swan.yaml

@@ -0,0 +1,36 @@
+pretrained_model_path: checkpoints/stable-diffusion-v1-4
+output_dir: outputs/black-swan
+input_data:
+  video_path: data/black-swan.mp4
+  prompt: a blackswan is swimming on the water
+  n_sample_frames: 8
+  width: 512
+  height: 512
+  sample_start_idx: 0
+  sample_frame_rate: 4
+validation_data:
+  prompts:
+  - a black swan is swimming on the water, Van Gogh style
+  - a white swan is swimming on the water
+  video_length: 8
+  width: 512
+  height: 512
+  num_inference_steps: 50
+  guidance_scale: 7.5
+  num_inv_steps: 50
+  # args for null-text inv
+  use_null_inv: True
+  null_inner_steps: 1
+  null_base_lr: 1e-2
+  null_uncond_ratio: -0.5
+  null_normal_infer: True
+
+input_batch_size: 1
+seed: 33
+mixed_precision: "no"
+gradient_checkpointing: True
+enable_xformers_memory_efficient_attention: True
+# test-time adaptation
+use_sc_attn: True
+use_st_attn: True
+st_attn_idx: 0

vid2vid-zero-main/configs/brown-bear.yaml

@@ -0,0 +1,37 @@
+pretrained_model_path: checkpoints/stable-diffusion-v1-4
+output_dir: outputs/brown-bear
+input_data:
+  video_path: data/brown-bear.mp4
+  prompt: a brown bear is sitting on the ground
+  n_sample_frames: 8
+  width: 512
+  height: 512
+  sample_start_idx: 0
+  sample_frame_rate: 1
+validation_data:
+  prompts:
+  - a brown bear is sitting on the grass
+  - a black bear is sitting on the grass
+  - a polar bear is sitting on the ground
+  video_length: 8
+  width: 512
+  height: 512
+  num_inference_steps: 50
+  guidance_scale: 7.5
+  num_inv_steps: 50
+  # args for null-text inv
+  use_null_inv: True
+  null_inner_steps: 1
+  null_base_lr: 1e-2
+  null_uncond_ratio: -0.5
+  null_normal_infer: True
+
+input_batch_size: 1
+seed: 33
+mixed_precision: "no"
+gradient_checkpointing: True
+enable_xformers_memory_efficient_attention: True
+# test-time adaptation
+use_sc_attn: True
+use_st_attn: True
+st_attn_idx: 0

vid2vid-zero-main/configs/car-moving.yaml

@@ -0,0 +1,37 @@
+pretrained_model_path: checkpoints
+output_dir: outputs/car-moving
+input_data:
+  video_path: data/car-moving.mp4
+  prompt: a car is moving on the road
+  n_sample_frames: 8
+  width: 512
+  height: 512
+  sample_start_idx: 0
+  sample_frame_rate: 1
+validation_data:
+  prompts:
+  - a car is moving on the snow
+  - a jeep car is moving on the road
+  - a jeep car is moving on the desert
+  video_length: 8
+  width: 512
+  height: 512
+  num_inference_steps: 50
+  guidance_scale: 7.5
+  num_inv_steps: 50
+  # args for null-text inv
+  use_null_inv: True
+  null_inner_steps: 1
+  null_base_lr: 1e-2
+  null_uncond_ratio: -0.5
+  null_normal_infer: True
+
+input_batch_size: 1
+seed: 33
+mixed_precision: "no"
+gradient_checkpointing: True
+enable_xformers_memory_efficient_attention: True
+# test-time adaptation
+use_sc_attn: True
+use_st_attn: True
+st_attn_idx: 0

vid2vid-zero-main/configs/car-turn.yaml

@@ -0,0 +1,39 @@
+pretrained_model_path: checkpoints/stable-diffusion-v1-4
+output_dir: "outputs/car-turn"
+
+input_data:
+  video_path: "data/car-turn.mp4"
+  prompt: "a jeep car is moving on the road"
+  n_sample_frames: 8
+  width: 512
+  height: 512
+  sample_start_idx: 0
+  sample_frame_rate: 6
+
+validation_data:
+  prompts:
+    - "a jeep car is moving on the beach"
+    - "a jeep car is moving on the snow"
+    - "a Porsche car is moving on the desert"
+  video_length: 8
+  width: 512
+  height: 512
+  num_inference_steps: 50
+  guidance_scale: 7.5
+  num_inv_steps: 50
+  # args for null-text inv
+  use_null_inv: True
+  null_inner_steps: 1
+  null_base_lr: 1e-2
+  null_uncond_ratio: -0.5
+  null_normal_infer: True
+
+input_batch_size: 1
+seed: 33
+mixed_precision: "no"
+gradient_checkpointing: True
+enable_xformers_memory_efficient_attention: True
+# test-time adaptation
+use_sc_attn: True
+use_st_attn: True
+st_attn_idx: 0

vid2vid-zero-main/configs/child-riding.yaml

@@ -0,0 +1,40 @@
+
+pretrained_model_path: checkpoints/stable-diffusion-v1-4
+output_dir: outputs/child-riding
+
+input_data:
+  video_path: data/child-riding.mp4
+  prompt: "a child is riding a bike on the road"
+  n_sample_frames: 8
+  width: 512
+  height: 512
+  sample_start_idx: 0
+  sample_frame_rate: 1
+
+validation_data:
+  # inv_latent: "outputs_2d/car-turn/inv_latents/ddim_latent-0.pt"  # latent inversed w/o SCAttn ! 
+  prompts:
+    - a lego child is riding a bike on the road
+    - a child is riding a bike on the flooded road
+  video_length: 8
+  width: 512
+  height: 512
+  num_inference_steps: 50
+  guidance_scale: 7.5
+  num_inv_steps: 50
+  # args for null-text inv
+  use_null_inv: True
+  null_inner_steps: 1
+  null_base_lr: 1e-2
+  null_uncond_ratio: -0.5
+  null_normal_infer: True
+
+input_batch_size: 1
+seed: 33
+mixed_precision: "no"
+gradient_checkpointing: True
+enable_xformers_memory_efficient_attention: True
+# test-time adaptation
+use_sc_attn: True
+use_st_attn: True
+st_attn_idx: 0

vid2vid-zero-main/configs/cow-walking.yaml

@@ -0,0 +1,37 @@
+pretrained_model_path: checkpoints/stable-diffusion-v1-4
+output_dir: outputs/cow-walking
+input_data:
+  video_path: data/cow-walking.mp4
+  prompt: a cow is walking on the grass
+  n_sample_frames: 8
+  width: 512
+  height: 512
+  sample_start_idx: 0
+  sample_frame_rate: 2
+validation_data:
+  prompts:
+  - a lion is walking on the grass
+  - a dog is walking on the grass
+  - a cow is walking on the snow
+  video_length: 8
+  width: 512
+  height: 512
+  num_inference_steps: 50
+  guidance_scale: 7.5
+  num_inv_steps: 50
+  # args for null-text inv
+  use_null_inv: True
+  null_inner_steps: 1
+  null_base_lr: 1e-2
+  null_uncond_ratio: -0.5
+  null_normal_infer: True
+
+input_batch_size: 1
+seed: 33
+mixed_precision: "no"
+gradient_checkpointing: True
+enable_xformers_memory_efficient_attention: True
+# test-time adaptation
+use_sc_attn: True
+use_st_attn: True
+st_attn_idx: 0

vid2vid-zero-main/configs/dog-walking.yaml

@@ -0,0 +1,35 @@
+pretrained_model_path: checkpoints/stable-diffusion-v1-4
+output_dir: outputs/dog_walking
+input_data:
+  video_path: data/dog-walking.mp4
+  prompt: a dog is walking on the ground
+  n_sample_frames: 8
+  width: 512
+  height: 512
+  sample_start_idx: 15
+  sample_frame_rate: 3
+validation_data:
+  prompts:
+  - a dog is walking on the ground, Van Gogh style
+  video_length: 8
+  width: 512
+  height: 512
+  num_inference_steps: 50
+  guidance_scale: 7.5
+  num_inv_steps: 50
+  # args for null-text inv
+  use_null_inv: True
+  null_inner_steps: 1
+  null_base_lr: 1e-2
+  null_uncond_ratio: -0.5
+  null_normal_infer: True
+
+input_batch_size: 1
+seed: 33
+mixed_precision: "no"
+gradient_checkpointing: True
+enable_xformers_memory_efficient_attention: True
+# test-time adaptation
+use_sc_attn: True
+use_st_attn: True
+st_attn_idx: 0

vid2vid-zero-main/configs/horse-running.yaml

@@ -0,0 +1,36 @@
+pretrained_model_path: checkpoints/stable-diffusion-v1-4
+output_dir: outputs/horse-running
+input_data:
+  video_path: data/horse-running.mp4
+  prompt: a horse is running on the beach
+  n_sample_frames: 8
+  width: 512
+  height: 512
+  sample_start_idx: 0
+  sample_frame_rate: 2
+validation_data:
+  prompts:
+  - a dog is running on the beach
+  - a dog is running on the desert
+  video_length: 8
+  width: 512
+  height: 512
+  num_inference_steps: 50
+  guidance_scale: 7.5
+  num_inv_steps: 50
+  # args for null-text inv
+  use_null_inv: True
+  null_inner_steps: 1
+  null_base_lr: 1e-2
+  null_uncond_ratio: -0.5
+  null_normal_infer: True
+
+input_batch_size: 1
+seed: 33
+mixed_precision: "no"
+gradient_checkpointing: True
+enable_xformers_memory_efficient_attention: True
+# test-time adaptation
+use_sc_attn: True
+use_st_attn: True
+st_attn_idx: 0

vid2vid-zero-main/configs/lion-roaring.yaml

@@ -0,0 +1,37 @@
+pretrained_model_path: checkpoints/stable-diffusion-v1-4
+output_dir: ./outputs/lion-roaring
+input_data:
+  video_path: data/lion-roaring.mp4
+  prompt: a lion is roaring
+  n_sample_frames: 8
+  width: 512
+  height: 512
+  sample_start_idx: 0
+  sample_frame_rate: 2
+validation_data:
+  prompts:
+  - a lego lion is roaring
+  - a wolf is roaring, anime style
+  - a lion is roaring, anime style
+  video_length: 8
+  width: 512
+  height: 512
+  num_inference_steps: 50
+  guidance_scale: 7.5
+  num_inv_steps: 50
+  # args for null-text inv
+  use_null_inv: True
+  null_inner_steps: 1
+  null_base_lr: 1e-2
+  null_uncond_ratio: -0.5
+  null_normal_infer: True
+
+input_batch_size: 1
+seed: 33
+mixed_precision: "no"
+gradient_checkpointing: True
+enable_xformers_memory_efficient_attention: True
+# test-time adaptation
+use_sc_attn: True
+use_st_attn: True
+st_attn_idx: 0

vid2vid-zero-main/configs/man-running.yaml

@@ -0,0 +1,37 @@
+pretrained_model_path: checkpoints/stable-diffusion-v1-4
+output_dir: outputs/man-running
+input_data:
+  video_path: data/man-running.mp4
+  prompt: a man is running
+  n_sample_frames: 8
+  width: 512
+  height: 512
+  sample_start_idx: 25
+  sample_frame_rate: 2
+validation_data:
+  prompts:
+  - Stephen Curry is running in Time Square
+  - a man is running, Van Gogh style
+  - a man is running in New York City
+  video_length: 8
+  width: 512
+  height: 512
+  num_inference_steps: 50
+  guidance_scale: 7.5
+  num_inv_steps: 50
+  # args for null-text inv
+  use_null_inv: True
+  null_inner_steps: 1
+  null_base_lr: 1e-2
+  null_uncond_ratio: -0.5
+  null_normal_infer: True
+
+input_batch_size: 1
+seed: 33
+mixed_precision: "no"
+gradient_checkpointing: True
+enable_xformers_memory_efficient_attention: True
+# test-time adaptation
+use_sc_attn: True
+use_st_attn: True
+st_attn_idx: 0

vid2vid-zero-main/configs/man-surfing.yaml

@@ -0,0 +1,36 @@
+pretrained_model_path: checkpoints/stable-diffusion-v1-4
+output_dir: outputs/man-surfing
+input_data:
+  video_path: data/man-surfing.mp4
+  prompt: a man is surfing
+  n_sample_frames: 8
+  width: 512
+  height: 512
+  sample_start_idx: 0
+  sample_frame_rate: 3
+validation_data:
+  prompts:
+  - a boy is surfing in the desert
+  - Iron Man is surfing is surfing
+  video_length: 8
+  width: 512
+  height: 512
+  num_inference_steps: 50
+  guidance_scale: 7.5
+  num_inv_steps: 50
+  # args for null-text inv
+  use_null_inv: True
+  null_inner_steps: 1
+  null_base_lr: 1e-2
+  null_uncond_ratio: -0.5
+  null_normal_infer: True
+
+input_batch_size: 1
+seed: 33
+mixed_precision: "no"
+gradient_checkpointing: True
+enable_xformers_memory_efficient_attention: True
+# test-time adaptation
+use_sc_attn: True
+use_st_attn: True
+st_attn_idx: 0

vid2vid-zero-main/configs/plane.yaml

@@ -0,0 +1,37 @@
+pretrained_model_path: checkpoints
+output_dir: "/home/wangjuntong/vid2vid-zero-main/outputs/aircraft-landing/"
+input_data:
+  video_path: "/home/wangjuntong/video_editing_dataset/real_video/aircraft-landing.mp4"
+  prompt: "A plane is landing."
+  n_sample_frames: 24
+  width: 512
+  height: 512
+  sample_start_idx: 0
+  sample_frame_rate: 1
+validation_data:
+  prompts:
+  - A helicopter is landing on a helipad.
+  - A small private plane is landing at a rural airfield.
+  - A balck plane is landing at a busy airport.
+  video_length: 24
+  width: 512
+  height: 512
+  num_inference_steps: 50
+  guidance_scale: 7.5
+  num_inv_steps: 50
+  # args for null-text inv
+  use_null_inv: True
+  null_inner_steps: 1
+  null_base_lr: 1e-2
+  null_uncond_ratio: -0.5
+  null_normal_infer: True
+
+input_batch_size: 1
+seed: 33
+mixed_precision: "no"
+gradient_checkpointing: True
+enable_xformers_memory_efficient_attention: True
+# test-time adaptation
+use_sc_attn: True
+use_st_attn: True
+st_attn_idx: 0

vid2vid-zero-main/configs/rabbit-watermelon.yaml

@@ -0,0 +1,40 @@
+pretrained_model_path: checkpoints/stable-diffusion-v1-4
+output_dir: "outputs/rabbit-watermelon"
+
+input_data:
+  video_path: "data/rabbit-watermelon.mp4"
+  prompt: "a rabbit is eating a watermelon"
+  n_sample_frames: 8
+  width: 512
+  height: 512
+  sample_start_idx: 0
+  sample_frame_rate: 6
+
+validation_data:
+  prompts:
+    - "a tiger is eating a watermelon"
+    - "a rabbit is eating an orange"
+    - "a rabbit is eating a pizza"
+    - "a puppy is eating an orange"
+  video_length: 8
+  width: 512
+  height: 512
+  num_inference_steps: 50
+  guidance_scale: 7.5
+  num_inv_steps: 50
+  # args for null-text inv
+  use_null_inv: True
+  null_inner_steps: 1
+  null_base_lr: 1e-2
+  null_uncond_ratio: -0.5
+  null_normal_infer: True
+
+input_batch_size: 1
+seed: 33
+mixed_precision: "no"
+gradient_checkpointing: True
+enable_xformers_memory_efficient_attention: True
+# test-time adaptation
+use_sc_attn: True
+use_st_attn: True
+st_attn_idx: 0