CogVideoX-5B-I2V

Model Introduction

• While testing using the diffusers library, all optimizations included in the diffusers library were enabled. This scheme has not been tested for actual memory usage on devices outside of NVIDIA A100 / H100 architectures. Generally, this scheme can be adapted to all NVIDIA Ampere architecture and above devices. If optimizations are disabled, memory consumption will multiply, with peak memory usage being about 3 times the value in the table. However, speed will increase by about 3-4 times. You can selectively disable some optimizations, including:

pipe.enable_sequential_cpu_offload()
pipe.vae.enable_slicing()
pipe.vae.enable_tiling()

• For multi-GPU inference, the enable_sequential_cpu_offload() optimization needs to be disabled.
• Using INT8 models will slow down inference, which is done to accommodate lower-memory GPUs while maintaining minimal video quality loss, though inference speed will significantly decrease.
• The CogVideoX-2B model was trained in FP16 precision, and all CogVideoX-5B models were trained in BF16 precision. We recommend using the precision in which the model was trained for inference.
• PytorchAO and Optimum-quanto can be used to quantize the text encoder, transformer, and VAE modules to reduce the memory requirements of CogVideoX. This allows the model to run on free T4 Colabs or GPUs with smaller memory! Also, note that TorchAO quantization is fully compatible with torch.compile, which can significantly improve inference speed. FP8 precision must be used on devices with NVIDIA H100 and above, requiring source installation of torch, torchao, diffusers, and accelerate Python packages. CUDA 12.4 is recommended.
• The inference speed tests also used the above memory optimization scheme. Without memory optimization, inference speed increases by about 10%. Only the diffusers version of the model supports quantization.
• The model only supports English input; other languages can be translated into English for use via large model refinement.
• The memory usage of model fine-tuning is tested in an 8 * H100 environment, and the program automatically uses Zero 2 optimization. If a specific number of GPUs is marked in the table, that number or more GPUs must be used for fine-tuning.

• Use SAT for inference and fine-tuning SAT version models. Feel free to visit our GitHub for more details.

Getting Started Quickly 🤗

1. Install the required dependencies

1. Run the code

Quantized Inference

# To get started, PytorchAO needs to be installed from the GitHub source and PyTorch Nightly.
# Source and nightly installation is only required until the next release.

import torch
from diffusers import AutoencoderKLCogVideoX, CogVideoXTransformer3DModel, CogVideoXImageToVideoPipeline
from diffusers.utils import export_to_video, load_image
from transformers import T5EncoderModel
from torchao.quantization import quantize_, int8_weight_only

quantization = int8_weight_only

text_encoder = T5EncoderModel.from_pretrained("THUDM/CogVideoX-5b-I2V", subfolder="text_encoder", torch_dtype=torch.bfloat16)
quantize_(text_encoder, quantization())

transformer = CogVideoXTransformer3DModel.from_pretrained("THUDM/CogVideoX-5b-I2V",subfolder="transformer", torch_dtype=torch.bfloat16)
quantize_(transformer, quantization())

vae = AutoencoderKLCogVideoX.from_pretrained("THUDM/CogVideoX-5b-I2V", subfolder="vae", torch_dtype=torch.bfloat16)
quantize_(vae, quantization())

# Create pipeline and run inference
pipe = CogVideoXImageToVideoPipeline.from_pretrained(
    "THUDM/CogVideoX-5b-I2V",
    text_encoder=text_encoder,
    transformer=transformer,
    vae=vae,
    torch_dtype=torch.bfloat16,
)

pipe.enable_model_cpu_offload()
pipe.vae.enable_tiling()
pipe.vae.enable_slicing()

prompt = "A little girl is riding a bicycle at high speed. Focused, detailed, realistic."
image = load_image(image="input.jpg")
video = pipe(
    prompt=prompt,
    image=image,
    num_videos_per_prompt=1,
    num_inference_steps=50,
    num_frames=49,
    guidance_scale=6,
    generator=torch.Generator(device="cuda").manual_seed(42),
).frames[0]

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

• torchao
• quanto

Further Exploration

1. More detailed technical explanations and code.
2. Optimized prompt examples and conversions.
3. Detailed code for model inference and fine-tuning.
4. Project update logs and more interactive opportunities.
5. CogVideoX toolchain to help you better use the model.
6. INT8 model inference code.

Model License

Citation

@article{yang2024cogvideox,
  title={CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer},
  author={Yang, Zhuoyi and Teng, Jiayan and Zheng, Wendi and Ding, Ming and Huang, Shiyu and Xu, Jiazheng and Yang, Yuanming and Hong, Wenyi and Zhang, Xiaohan and Feng, Guanyu and others},
  journal={arXiv preprint arXiv:2408.06072},
  year={2024}
}