metadata

license: other
license_name: cogvideox
license_link: https://huggingface.co/THUDM/CogVideoX-2b/blob/main/LICENSE
language:
  - en
tags:
  - cogvideox
  - video-generation
  - thudm
inference: false

CogVideoX-2B

📄 中文阅读 | 🌐 Github | 📜 arxiv (coming soon)

Demo Show

Video Gallery with Captions

A detailed wooden toy ship with intricately carved masts and sails is seen gliding smoothly over a plush, blue carpet that mimics the waves of the sea. The ship's hull is painted a rich brown, with tiny windows. The carpet, soft and textured, provides a perfect backdrop, resembling an oceanic expanse. Surrounding the ship are various other toys and children's items, hinting at a playful environment. The scene captures the innocence and imagination of childhood, with the toy ship's journey symbolizing endless adventures in a whimsical, indoor setting.

The camera follows behind a white vintage SUV with a black roof rack as it speeds up a steep dirt road surrounded by pine trees on a steep mountain slope, dust kicks up from it’s tires, the sunlight shines on the SUV as it speeds along the dirt road, casting a warm glow over the scene. The dirt road curves gently into the distance, with no other cars or vehicles in sight. The trees on either side of the road are redwoods, with patches of greenery scattered throughout. The car is seen from the rear following the curve with ease, making it seem as if it is on a rugged drive through the rugged terrain. The dirt road itself is surrounded by steep hills and mountains, with a clear blue sky above with wispy clouds.

A street artist, clad in a worn-out denim jacket and a colorful bandana, stands before a vast concrete wall in the heart, holding a can of spray paint, spray-painting a colorful bird on a mottled wall.

In the haunting backdrop of a war-torn city, where ruins and crumbled walls tell a story of devastation, a poignant close-up frames a young girl. Her face is smudged with ash, a silent testament to the chaos around her. Her eyes glistening with a mix of sorrow and resilience, capturing the raw emotion of a world that has lost its innocence to the ravages of conflict.

Model Introduction

CogVideoX is an open-source video generation model that shares the same origins as 清影. The table below provides a list of the video generation models we currently offer, along with their basic information.

Model Name	CogVideoX-2B (Current Repos)
Supported Prompt Language	English
GPU Memory Required for Inference	21.6GB
GPU Memory Required for Fine-tuning (bs=1)	46.2GB
Prompt Length	226 Tokens
Video Length	6 seconds
Frames Per Second	8 frames
Resolution	720 * 480
Positional Embeddings	Sinusoidal
Quantized Inference	Not Supported
Multi-card Inference	Not Supported

Quick Start 🤗

This model supports deployment using the huggingface diffusers library. You can deploy it by following these steps.

We recommend that you visit our GitHub and check out the relevant prompt optimizations and conversions to get a better experience.

Install the required dependencies

pip install --upgrade opencv-python transformers 
pip install git+https://github.com/huggingface/diffusers.git@32da2e7673cfe0475a47c41b859f5fbd8bf17a40#egg=diffusers # Still in PR

Run the code

import torch
from diffusers import CogVideoXPipeline
from diffusers.utils import export_to_video

prompt = "A girl ridding a bike"

pipe = CogVideoXPipeline.from_pretrained(
    "THUDM/CogVideoX-2b",
    torch_dtype=torch.float16
).to("cuda")

prompt_embeds, _ = pipe.encode_prompt(
    prompt=prompt,
    do_classifier_free_guidance=True,
    num_videos_per_prompt=1,
    max_sequence_length=226,
    device="cuda",
    dtype=torch.float16,
)

video = pipe(
    num_inference_steps=50,
    guidance_scale=6,
    prompt_embeds=prompt_embeds,
).frames[0]

export_to_video(video, "output.mp4", fps=8)

If the generated model appears “all green” and not viewable in the default MAC player, it is a normal phenomenon (due to OpenCV saving video issues). Simply use a different player to view the video.

Explore the Model

Welcome to our github, where you will find:

More detailed technical details and code explanation.
Optimization and conversion of prompt words.
Reasoning and fine-tuning of SAT version models, and even pre-release.
Project update log dynamics, more interactive opportunities.
CogVideoX toolchain to help you better use the model.

Model License

This model is released under the CogVideoX LICENSE.

Citation

The technical report is still being written, stay tuned.