https://github.com/pku-yuangroup/consisid
[CVPR 2025 Highlight🔥] Identity-Preserving Text-to-Video Generation by Frequency Decomposition
https://github.com/pku-yuangroup/consisid
diffusion diffusion-models identity-preserving text-to-video video-generation video-generation-dataset video-generator videogeneration
Last synced: 3 months ago
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[CVPR 2025 Highlight🔥] Identity-Preserving Text-to-Video Generation by Frequency Decomposition
- Host: GitHub
- URL: https://github.com/pku-yuangroup/consisid
- Owner: PKU-YuanGroup
- License: apache-2.0
- Created: 2024-11-15T11:19:39.000Z (11 months ago)
- Default Branch: main
- Last Pushed: 2025-06-08T13:40:21.000Z (4 months ago)
- Last Synced: 2025-06-08T14:31:06.666Z (4 months ago)
- Topics: diffusion, diffusion-models, identity-preserving, text-to-video, video-generation, video-generation-dataset, video-generator, videogeneration
- Language: Python
- Homepage: https://pku-yuangroup.github.io/ConsisID/
- Size: 13.3 MB
- Stars: 706
- Watchers: 12
- Forks: 35
- Open Issues: 31
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
[CVPR 2025 Highlight] Identity-Preserving Text-to-Video Generation by Frequency Decomposition
If you like our project, please give us a star ⭐ on GitHub for the latest update.
[](https://huggingface.co/spaces/BestWishYsh/ConsisID-preview-Space)
[](https://colab.research.google.com/github/camenduru/ConsisID-jupyter/blob/main/ConsisID_jupyter.ipynb)
[](https://huggingface.co/papers/2411.17440)
[](https://arxiv.org/abs/2411.17440)
[](https://pku-yuangroup.github.io/ConsisID/)
[](https://huggingface.co/datasets/BestWishYsh/ConsisID-preview-Data)
[](https://x.com/AdinaYakup/status/1862604191631573122)
[](https://x.com/camenduru/status/1861957812152078701)
[](https://www.youtube.com/watch?v=PhlgC-bI5SQ)
[](https://github.com/PKU-YuanGroup/ConsisID/blob/main/LICENSE)
[](https://github.com/PKU-YuanGroup/ConsisID/)
This repository is the official implementation of ConsisID, a tuning-free DiT-based controllable IPT2V model to keep human-identity consistent in the generated video. The approach draws inspiration from previous studies on frequency analysis of vision/diffusion transformers.
💡 We also have other video generation projects that may interest you ✨.
> [**Open-Sora Plan: Open-Source Large Video Generation Model**](https://arxiv.org/abs/2412.00131)
> Bin Lin, Yunyang Ge and Xinhua Cheng etc.
[](https://github.com/PKU-YuanGroup/Open-Sora-Plan) [](https://github.com/PKU-YuanGroup/Open-Sora-Plan) [](https://arxiv.org/abs/2412.00131)
>
> [**OpenS2V-Nexus: A Detailed Benchmark and Million-Scale Dataset for Subject-to-Video Generation**](https://arxiv.org/abs/2505.20292)
> Shenghai Yuan, Xianyi He and Yufan Deng etc.
> [](https://github.com/PKU-YuanGroup/OpenS2V-Nexus) [](https://github.com/PKU-YuanGroup/OpenS2V-Nexus) [](https://arxiv.org/abs/2505.20292)
>
> [**MagicTime: Time-lapse Video Generation Models as Metamorphic Simulators**](https://arxiv.org/abs/2404.05014)
> Shenghai Yuan, Jinfa Huang and Yujun Shi etc.
> [](https://github.com/PKU-YuanGroup/MagicTime) [](https://github.com/PKU-YuanGroup/MagicTime) [](https://arxiv.org/abs/2404.05014)
>
> [**ChronoMagic-Bench: A Benchmark for Metamorphic Evaluation of Text-to-Time-lapse Video Generation**](https://arxiv.org/abs/2406.18522)
> Shenghai Yuan, Jinfa Huang and Yongqi Xu etc.
> [](https://github.com/PKU-YuanGroup/ChronoMagic-Bench/) [](https://github.com/PKU-YuanGroup/ChronoMagic-Bench/) [](https://arxiv.org/abs/2406.18522)
>
## 📣 News
* ⏳⏳⏳ Release the full code & datasets & weights.
* `[2025.05.27]` 🔥 Introducing [**OpenS2V-Nexus**](https://github.com/PKU-YuanGroup/OpenS2V-Nexus), which consists of: (i) [**OpenS2V-Eval**](https://huggingface.co/spaces/BestWishYsh/OpenS2V-Eval), a fine-grained benchmark, and (ii) [**OpenS2V-5M**](https://huggingface.co/datasets/BestWishYsh/OpenS2V-5M), a million-scale dataset. Welcome to try it!
* `[2025.04.04]` 🔥 Breaking news! ConsisID has been recommended as **CVPR Highlight**.
* `[2025.03.27]` 🔥 We have updated our technical report. Please click [here](https://arxiv.org/abs/2411.17440v3) to view it.
* `[2025.02.27]` 🔥 ConsisID has been accepted by CVPR 2025, and we will update arXiv with more details soon. Stay tuned!
* `[2025.02.16]` 🔥 We have adapted the code for CogVideoX1.5, and you can use our code not only for training ConsisID but also for the CogVideoX-series.
* `[2025.01.19]` 🤗 Thanks [@arrow](https://github.com/a-r-r-o-w), [@yiyixuxu](https://github.com/yiyixuxu), [@hlky](https://github.com/hlky) and [@stevhliu](https://github.com/stevhliu), ConsisID will be merged into [diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/consisid#identity-preserving-text-to-video) in `0.33.0`. So for now, please use `pip install git+https://github.com/huggingface/diffusers.git` to install diffusers dev version. And we have reorganized the code and weight configs, so it's better to update your local files if you have cloned them previously.
* `[2024.12.26]` 🚀 We release the [cache inference code](https://github.com/PKU-YuanGroup/ConsisID/tree/main/tools/cache_inference) for ConsisID powered by [TeaCache](https://github.com/LiewFeng/TeaCache). Thanks [@LiewFeng](https://github.com/LiewFeng) for his help.
* `[2024.12.24]` 🚀 We release the [parallel inference code](https://github.com/PKU-YuanGroup/ConsisID/tree/main/tools/parallel_inference) for ConsisID powered by [xDiT](https://github.com/xdit-project/xDiT). Thanks [@feifeibear](https://github.com/feifeibear) for his help.
* `[2024.12.09]` 🔥We release the [test set](https://huggingface.co/datasets/BestWishYsh/ConsisID-preview-Data/tree/main/eval) and [metric calculation code](https://github.com/PKU-YuanGroup/ConsisID/tree/main/eval) used in the paper, now your can measure the metrics on your own machine. Please refer to [this guide](https://github.com/PKU-YuanGroup/ConsisID/tree/main/eval) for more details.
* `[2024.12.08]` 🔥The code for data preprocessing is out, which is used to obtain the [training data](https://huggingface.co/datasets/BestWishYsh/ConsisID-preview-Data) required by ConsisID, supporting multi-id annotation. Please refer to [this guide](https://github.com/PKU-YuanGroup/ConsisID/tree/main/data_preprocess) for more details.
* `[2024.12.04]` Thanks [@shizi](https://www.bilibili.com/video/BV1v3iUY4EeQ/?vd_source=ae3f2652765c02e41cdd698b311989e3) for providing [🤗Windows-ConsisID](https://huggingface.co/pkuhexianyi/ConsisID-Windows/tree/main) and [🟣Windows-ConsisID](https://www.wisemodel.cn/models/PkuHexianyi/ConsisID-Windows/file), which make it easy to run ConsisID on Windows.
* `[2024.12.01]` 🔥 We provide full text prompts corresponding to all the videos on project page. Click [here](https://github.com/PKU-YuanGroup/ConsisID/blob/main/asserts/prompt.xlsx) to get and try the demo.
* `[2024.11.30]` 🤗 We have fixed the [huggingface demo](https://huggingface.co/spaces/BestWishYsh/ConsisID-preview-Space), welcome to try it.
* `[2024.11.29]` 🏃♂️ The current code and weights are our early versions, and the differences with the latest version in [arxiv](https://github.com/PKU-YuanGroup/ConsisID) can be viewed [here](https://github.com/PKU-YuanGroup/ConsisID/tree/main/util/on_going_module). And we will release the full code in the next few days.
* `[2024.11.28]` Thanks [@camenduru](https://twitter.com/camenduru) for providing [Jupyter Notebook](https://colab.research.google.com/github/camenduru/ConsisID-jupyter/blob/main/ConsisID_jupyter.ipynb) and [@Kijai](https://github.com/kijai) for providing ComfyUI Extension [ComfyUI-ConsisIDWrapper](https://github.com/kijai/ComfyUI-CogVideoXWrapper). If you find related work, please let us know.
* `[2024.11.27]` 🏃♂️ Due to policy restrictions, we only open-source part of the dataset. You can download it by clicking [here](https://huggingface.co/datasets/BestWishYsh/ConsisID-preview-Data). And we will release the data processing code in the next few days.
* `[2024.11.26]` 🔥 We release the arXiv paper for ConsisID, and you can click [here](https://arxiv.org/abs/2411.17440) to see more details.
* `[2024.11.22]` 🔥 **All code & datasets** are coming soon! Stay tuned 👀!
## 😍 Gallery
Identity-Preserving Text-to-Video Generation. (Some best prompts [here](https://github.com/PKU-YuanGroup/ConsisID/blob/main/asserts/prompt.xlsx))
[](https://www.youtube.com/watch?v=PhlgC-bI5SQ)
or you can click here to watch the video.
## 🤗 Demo
### Diffusers API
```bash
!pip install diffusers==0.33.1
import torch
from diffusers import ConsisIDPipeline
from diffusers.pipelines.consisid.consisid_utils import prepare_face_models, process_face_embeddings_infer
from diffusers.utils import export_to_video
from huggingface_hub import snapshot_download
snapshot_download(repo_id="BestWishYsh/ConsisID-preview", local_dir="BestWishYsh/ConsisID-preview")
face_helper_1, face_helper_2, face_clip_model, face_main_model, eva_transform_mean, eva_transform_std = (
prepare_face_models("BestWishYsh/ConsisID-preview", device="cuda", dtype=torch.bfloat16)
)
pipe = ConsisIDPipeline.from_pretrained("BestWishYsh/ConsisID-preview", torch_dtype=torch.bfloat16)
pipe.to("cuda")
# ConsisID works well with long and well-described prompts. Make sure the face in the image is clearly visible (e.g., preferably half-body or full-body).
prompt = "The video captures a boy walking along a city street, filmed in black and white on a classic 35mm camera. His expression is thoughtful, his brow slightly furrowed as if he's lost in contemplation. The film grain adds a textured, timeless quality to the image, evoking a sense of nostalgia. Around him, the cityscape is filled with vintage buildings, cobblestone sidewalks, and softly blurred figures passing by, their outlines faint and indistinct. Streetlights cast a gentle glow, while shadows play across the boy's path, adding depth to the scene. The lighting highlights the boy's subtle smile, hinting at a fleeting moment of curiosity. The overall cinematic atmosphere, complete with classic film still aesthetics and dramatic contrasts, gives the scene an evocative and introspective feel."
image = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/consisid/consisid_input.png?download=true"
id_cond, id_vit_hidden, image, face_kps = process_face_embeddings_infer(
face_helper_1,
face_clip_model,
face_helper_2,
eva_transform_mean,
eva_transform_std,
face_main_model,
"cuda",
torch.bfloat16,
image,
is_align_face=True,
)
video = pipe(
image=image,
prompt=prompt,
num_inference_steps=50,
guidance_scale=6.0,
use_dynamic_cfg=False,
id_vit_hidden=id_vit_hidden,
id_cond=id_cond,
kps_cond=face_kps,
generator=torch.Generator("cuda").manual_seed(42),
)
export_to_video(video.frames[0], "output.mp4", fps=8)
```
### Gradio Web UI
Highly recommend trying out our web demo by the following command, which incorporates all features currently supported by ConsisID. We also provide [online demo](https://huggingface.co/spaces/BestWishYsh/ConsisID-preview-Space) in Hugging Face Spaces.
```bash
python app.py
```
### CLI Inference
```bash
python infer.py --model_path BestWishYsh/ConsisID-preview
```
warning: It is worth noting that even if we use the same seed and prompt but we change a machine, the results will be different.
### Prompt Refiner
ConsisID has high requirements for prompt quality. You can use [GPT-4o](https://chatgpt.com/) to refine the input text prompt, an example is as follows (original prompt: "a man is playing guitar.")
```bash
a man is playing guitar.
Change the sentence above to something like this (add some facial changes, even if they are minor. Don't make the sentence too long):
The video features a man standing next to an airplane, engaged in a conversation on his cell phone. he is wearing sunglasses and a black top, and he appears to be talking seriously. The airplane has a green stripe running along its side, and there is a large engine visible behind his. The man seems to be standing near the entrance of the airplane, possibly preparing to board or just having disembarked. The setting suggests that he might be at an airport or a private airfield. The overall atmosphere of the video is professional and focused, with the man's attire and the presence of the airplane indicating a business or travel context.
```
Some sample prompts are available [here](https://github.com/PKU-YuanGroup/ConsisID/blob/main/asserts/prompt.xlsx).
### GPU Memory Optimization
ConsisID requires about 44 GB of GPU memory to decode 49 frames (6 seconds of video at 8 FPS) with output resolution 720x480 (W x H), which makes it not possible to run on consumer GPUs or free-tier T4 Colab. The following memory optimizations could be used to reduce the memory footprint. For replication, you can refer to [this](https://gist.github.com/SHYuanBest/bc4207c36f454f9e969adbb50eaf8258) script.
| Feature (overlay the previous) | Max Memory Allocated | Max Memory Reserved |
| :----------------------------- | :------------------- | :------------------ |
| - | 37 GB | 44 GB |
| enable_model_cpu_offload | 22 GB | 25 GB |
| enable_sequential_cpu_offload | 16 GB | 22 GB |
| vae.enable_slicing | 16 GB | 22 GB |
| vae.enable_tiling | 5 GB | 7 GB |
```bash
# turn on if you don't have multiple GPUs or enough GPU memory(such as H100)
pipe.enable_model_cpu_offload()
pipe.enable_sequential_cpu_offload()
pipe.vae.enable_slicing()
pipe.vae.enable_tiling()
```
warning: it will cost more time in inference and may also reduce the quality.
## 🚀 Parallel Inference on Multiple GPUs by xDiT
[xDiT](https://github.com/xdit-project/xDiT) is a Scalable Inference Engine for Diffusion Transformers (DiTs) on multi-GPU Clusters. It has successfully provided low-latency parallel inference solutions for a variety of DiTs models. For example, to generate a video with 6 GPUs, you can use the following command:
```
cd tools/parallel_inference
bash run.sh
# run_usp.sh
```
## 🚀 Cache Inference by TeaCache
[TeaCache](https://github.com/LiewFeng/TeaCache) is a training-free caching approach that estimates and leverages the fluctuating differences among model outputs across timesteps, thereby accelerating the inference. For example, you can use the following command:
```
cd tools/cache_inference
bash run.sh
```
## ⚙️ Requirements and Installation
We recommend the requirements as follows.
### Environment
```bash
# 0. Clone the repo
git clone --depth=1 https://github.com/PKU-YuanGroup/ConsisID.git
cd ConsisID
# 1. Create conda environment
conda create -n consisid python=3.11.0
conda activate consisid
# 3. Install PyTorch and other dependencies using conda
# CUDA 11.8
conda install pytorch==2.5.1 torchvision==0.20.1 torchaudio==2.5.1 pytorch-cuda=11.8 -c pytorch -c nvidia
# CUDA 12.1
conda install pytorch==2.5.1 torchvision==0.20.1 torchaudio==2.5.1 pytorch-cuda=12.1 -c pytorch -c nvidia
# 4. Install pip dependencies
pip install -r requirements.txt
```
### Download ConsisID
The weights are available at [🤗HuggingFace](https://huggingface.co/BestWishYsh/ConsisID-preview), [🤖ModelScope](https://modelscope.cn/models/BestWishYSH/ConsisID-preview) and [🟣WiseModel](https://wisemodel.cn/models/SHYuanBest/ConsisID-Preview/file), and will be automatically downloaded when runing `app.py` and `infer.py`, or you can download it with the following commands.
```bash
# way 1
# if you are in china mainland, run this first: export HF_ENDPOINT=https://hf-mirror.com
cd util
python download_weights.py
# way 2
# if you are in china mainland, run this first: export HF_ENDPOINT=https://hf-mirror.com
huggingface-cli download --repo-type model \
BestWishYsh/ConsisID-preview \
--local-dir ckpts
# way 3
modelscope download --model \
BestWishYSH/ConsisID-preview \
--local-dir ckpts
# way 4
git lfs install
git clone https://www.wisemodel.cn/SHYuanBest/ConsisID-Preview.git
```
Once ready, the weights will be organized in this format:
```
📦 ckpts/
├── 📂 data_process/
├── 📂 face_encoder/
├── 📂 scheduler/
├── 📂 text_encoder/
├── 📂 tokenizer/
├── 📂 transformer/
├── 📂 vae/
├── 📄 configuration.json
├── 📄 model_index.json
```
## 🗝️ Training
### Data preprocessing
Please refer to [this guide](https://github.com/PKU-YuanGroup/ConsisID/tree/main/data_preprocess) for how to obtain the [training data](https://huggingface.co/datasets/BestWishYsh/ConsisID-preview-Data) required by ConsisID. If you want to train your own identity-preserving text-to-video generation model, you need to arrange all the dataset in this [format](https://github.com/PKU-YuanGroup/ConsisID/tree/main/asserts/demo_train_data/dataname):
```
📦 datasets/
├── 📂 captions/
│ ├── 📄 dataname_1.json
│ ├── 📄 dataname_2.json
├── 📂 dataname_1/
│ ├── 📂 refine_bbox_jsons/
│ ├── 📂 track_masks_data/
│ ├── 📂 videos/
├── 📂 dataname_2/
│ ├── 📂 refine_bbox_jsons/
│ ├── 📂 track_masks_data/
│ ├── 📂 videos/
├── ...
├── 📄 total_train_data.txt
```
### Video DiT training
First, setting hyperparameters:
- environment (e.g., cuda): [deepspeed_configs](https://github.com/PKU-YuanGroup/ConsisID/tree/main/util/deepspeed_configs)
- training arguments (e.g., batchsize): [train_single_rank.sh](https://github.com/PKU-YuanGroup/ConsisID/blob/main/train_single_rank.sh) or [train_multi_rank.sh](https://github.com/PKU-YuanGroup/ConsisID/blob/main/train_multi_rank.sh)
Then, we run the following bash to start training:
```bash
# For single rank
bash train_single_rank.sh
# For multi rank
bash train_multi_rank.sh
```
## 🙌 Friendly Links
We found some plugins created by community developers. Thanks for their efforts:
- ComfyUI Extension. [ComfyUI-ConsisIDWrapper](https://github.com/kijai/ComfyUI-CogVideoXWrapper) (by [@Kijai](https://github.com/kijai)).
- Jupyter Notebook. [Jupyter-ConsisID](https://colab.research.google.com/github/camenduru/ConsisID-jupyter/blob/main/ConsisID_jupyter.ipynb) (by [@camenduru](https://github.com/camenduru/consisid-tost)).
- Windows Docker. [🤗Windows-ConsisID](https://huggingface.co/pkuhexianyi/ConsisID-Windows/tree/main) and [🟣Windows-ConsisID](https://www.wisemodel.cn/models/PkuHexianyi/ConsisID-Windows/file) (by [@shizi](https://www.bilibili.com/video/BV1v3iUY4EeQ/?vd_source=ae3f2652765c02e41cdd698b311989e3)).
- Diffusres. [Diffusers-ConsisID](https://github.com/huggingface/diffusers) (thanks [@arrow](https://github.com/a-r-r-o-w), [@yiyixuxu](https://github.com/yiyixuxu), [@hlky](https://github.com/hlky) and [@stevhliu](https://github.com/stevhliu) for their help).
- xDiT. [xDiT-ConsisID](https://github.com/xdit-project/xDiT) (thanks [@feifeibear](https://github.com/feifeibear) for his help).
- TeaCache. [TeaCache-ConsisID](https://github.com/LiewFeng/TeaCache) (thanks [@LiewFeng](https://github.com/LiewFeng) for his help).
- [Ingredients](https://github.com/feizc/Ingredients): A powerful way to customize video creations by incorporating multiple specific identity (ID) photos, based on ConsisID.
If you find related work, please let us know.
## 🐳 Dataset
We release the subset of the data used to train ConsisID. The dataset is available at [HuggingFace](https://huggingface.co/datasets/BestWishYsh/ConsisID-preview-Data), or you can download it with the following command. Some samples can be found on our [Project Page](https://pku-yuangroup.github.io/ConsisID/).
```bash
huggingface-cli download --repo-type dataset \
BestWishYsh/ConsisID-preview-Data \
--local-dir BestWishYsh/ConsisID-preview-Data
```
## 🛠️ Evaluation
We release the data used for evaluation in [ConsisID](https://huggingface.co/papers/2411.17440), which is available at [HuggingFace](https://huggingface.co/datasets/BestWishYsh/ConsisID-preview-Data). Please refer to [this guide](https://github.com/PKU-YuanGroup/ConsisID/tree/main/eval) for how to evaluate customized model.
## 👍 Acknowledgement
* This project wouldn't be possible without the following open-sourced repositories: [Open-Sora Plan](https://github.com/PKU-YuanGroup/Open-Sora-Plan), [CogVideoX](https://github.com/THUDM/CogVideo), [EasyAnimate](https://github.com/aigc-apps/EasyAnimate), [CogVideoX-Fun](https://github.com/aigc-apps/CogVideoX-Fun), [IP-Adapter](https://github.com/tencent-ailab/IP-Adapter), [PhotoMaker](https://github.com/TencentARC/PhotoMaker), [UniPortrait](https://github.com/junjiehe96/UniPortrait).
## 🔒 License
* The majority of this project is released under the Apache 2.0 license as found in the [LICENSE](https://github.com/PKU-YuanGroup/ConsisID/blob/main/LICENSE) file.
* The CogVideoX-5B model (Transformers module) is released under the [CogVideoX LICENSE](https://huggingface.co/THUDM/CogVideoX-5b/blob/main/LICENSE).
* The service is a research preview. Please contact us if you find any potential violations. (shyuan-cs@hotmail.com)
## ✏️ Citation
If you find our paper and code useful in your research, please consider giving a star :star: and citation :pencil:.
```BibTeX
@inproceedings{yuan2025identity,
title={Identity-preserving text-to-video generation by frequency decomposition},
author={Yuan, Shenghai and Huang, Jinfa and He, Xianyi and Ge, Yunyang and Shi, Yujun and Chen, Liuhan and Luo, Jiebo and Yuan, Li},
booktitle={Proceedings of the Computer Vision and Pattern Recognition Conference},
pages={12978--12988},
year={2025}
}
```
## 🤝 Contributors
