docs/source/en/api/pipelines/consisid.md
Identity-Preserving Text-to-Video Generation by Frequency Decomposition from Peking University & University of Rochester & etc, by Shenghai Yuan, Jinfa Huang, Xianyi He, Yunyang Ge, Yujun Shi, Liuhan Chen, Jiebo Luo, Li Yuan.
The abstract from the paper is:
Identity-preserving text-to-video (IPT2V) generation aims to create high-fidelity videos with consistent human identity. It is an important task in video generation but remains an open problem for generative models. This paper pushes the technical frontier of IPT2V in two directions that have not been resolved in the literature: (1) A tuning-free pipeline without tedious case-by-case finetuning, and (2) A frequency-aware heuristic identity-preserving Diffusion Transformer (DiT)-based control scheme. To achieve these goals, we propose ConsisID, a tuning-free DiT-based controllable IPT2V model to keep human-identity consistent in the generated video. Inspired by prior findings in frequency analysis of vision/diffusion transformers, it employs identity-control signals in the frequency domain, where facial features can be decomposed into low-frequency global features (e.g., profile, proportions) and high-frequency intrinsic features (e.g., identity markers that remain unaffected by pose changes). First, from a low-frequency perspective, we introduce a global facial extractor, which encodes the reference image and facial key points into a latent space, generating features enriched with low-frequency information. These features are then integrated into the shallow layers of the network to alleviate training challenges associated with DiT. Second, from a high-frequency perspective, we design a local facial extractor to capture high-frequency details and inject them into the transformer blocks, enhancing the model's ability to preserve fine-grained features. To leverage the frequency information for identity preservation, we propose a hierarchical training strategy, transforming a vanilla pre-trained video generation model into an IPT2V model. Extensive experiments demonstrate that our frequency-aware heuristic scheme provides an optimal control solution for DiT-based models. Thanks to this scheme, our ConsisID achieves excellent results in generating high-quality, identity-preserving videos, making strides towards more effective IPT2V. The model weight of ConsID is publicly available at https://github.com/PKU-YuanGroup/ConsisID.
[!TIP] Make sure to check out the Schedulers guide to learn how to explore the tradeoff between scheduler speed and quality, and see the reuse components across pipelines section to learn how to efficiently load the same components into multiple pipelines.
This pipeline was contributed by SHYuanBest. The original codebase can be found here. The original weights can be found under hf.co/BestWishYsh.
There are two official ConsisID checkpoints for identity-preserving text-to-video.
| checkpoints | recommended inference dtype |
|---|---|
BestWishYsh/ConsisID-preview | torch.bfloat16 |
BestWishYsh/ConsisID-1.5 | torch.bfloat16 |
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 |
Model weights may be stored in separate subfolders on the Hub or locally, in which case, you should use the [~DiffusionPipeline.from_pretrained] method.
# !pip install consisid_eva_clip insightface facexlib
import torch
from diffusers import ConsisIDPipeline
from diffusers.pipelines.consisid.consisid_utils import prepare_face_models, process_face_embeddings_infer
from huggingface_hub import snapshot_download
# Download ckpts
snapshot_download(repo_id="BestWishYsh/ConsisID-preview", local_dir="BestWishYsh/ConsisID-preview")
# Load face helper model to preprocess input face image
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)
# Load consisid base model
pipe = ConsisIDPipeline.from_pretrained("BestWishYsh/ConsisID-preview", torch_dtype=torch.bfloat16)
pipe.to("cuda")
For identity-preserving text-to-video, pass a text prompt and an image contain clear face (e.g., preferably half-body or full-body). By default, ConsisID generates a 720x480 video for the best results.
from diffusers.utils import export_to_video
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)
Learn more about ConsisID with the following resources.
[[autodoc]] ConsisIDPipeline
[[autodoc]] pipelines.consisid.pipeline_output.ConsisIDPipelineOutput