# ConsisID will be merged into diffusers in the next version. So for now, you should install from source.
pip install --upgrade consisid_eva_clip pyfacer insightface facexlib transformers accelerate imageio-ffmpeg 
pip install git+https://github.com/huggingface/diffusers.git

Run the code

python

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://github.com/PKU-YuanGroup/ConsisID/blob/main/asserts/example_images/2.png?raw=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)

🛠️ Prompt Refiner

ConsisID has high requirements for prompt quality. You can use GPT-4o 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.

💡 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 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.

🙌 Description

Repository: Code, Page, Data
Paper: https://huggingface.co/papers/2411.17440
Point of Contact: Shenghai Yuan

✏️ Citation

If you find our paper and code useful in your research, please consider giving a star and citation.

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

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