CogVideoX1.5-5B
π δΈζι θ―» | π€ Huggingface Space | π Github | π arxiv
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Model Introduction
CogVideoX is an open-source video generation model similar to QingYing. Below is a table listing information on the video generation models available in this generation:
Model Name | CogVideoX1.5-5B (Current Repository) | CogVideoX1.5-5B-I2V | |||
---|---|---|---|---|---|
Video Resolution | 1360 * 768 | Min(W, H) = 768 768 β€ Max(W, H) β€ 1360 Max(W, H) % 16 = 0 |
|||
Inference Precision | BF16 (recommended), FP16, FP32, FP8*, INT8, not supported INT4 | ||||
Single GPU Inference Memory Consumption | BF16: 9GB minimum* | ||||
Multi-GPU Inference Memory Consumption | BF16: 24GB* |
||||
Inference Speed (Step = 50, BF16) |
Single A100: ~1000 seconds (5-second video) Single H100: ~550 seconds (5-second video) |
||||
Prompt Language | English* | ||||
Max Prompt Length | 224 Tokens | ||||
Video Length | 5 or 10 seconds | ||||
Frame Rate | 16 frames/second |
Data Explanation
- Testing with the
diffusers
library enabled all optimizations included in the library. This scheme has not been tested on non-NVIDIA A100/H100 devices. It should generally work with all NVIDIA Ampere architecture or higher devices. Disabling optimizations can triple VRAM usage but increase speed by 3-4 times. You can selectively disable certain optimizations, including:
pipe.enable_sequential_cpu_offload()
pipe.vae.enable_slicing()
pipe.vae.enable_tiling()
- In multi-GPU inference,
enable_sequential_cpu_offload()
optimization needs to be disabled. - Using an INT8 model reduces inference speed, meeting the requirements of lower VRAM GPUs while retaining minimal video quality degradation, at the cost of significant speed reduction.
- PytorchAO and Optimum-quanto can be
used to quantize the text encoder, Transformer, and VAE modules, reducing CogVideoXβs memory requirements, making it
feasible to run the model on smaller VRAM GPUs. TorchAO quantization is fully compatible with
torch.compile
, significantly improving inference speed.FP8
precision is required for NVIDIA H100 and above, which requires source installation oftorch
,torchao
,diffusers
, andaccelerate
. UsingCUDA 12.4
is recommended. - Inference speed testing also used the above VRAM optimizations, and without optimizations, speed increases by about
10%. Only
diffusers
versions of models support quantization. - Models support English input only; other languages should be translated into English during prompt crafting with a larger model.
Note
- Use SAT for inference and fine-tuning SAT version models. Check our GitHub for more details.
Getting Started Quickly π€
This model supports deployment using the Hugging Face diffusers library. You can follow the steps below to get started.
We recommend that you visit our GitHub to check out prompt optimization and conversion to get a better experience.
- Install the required dependencies
# diffusers (from source)
# transformers>=4.46.2
# accelerate>=1.1.1
# imageio-ffmpeg>=0.5.1
pip install git+https://github.com/huggingface/diffusers
pip install --upgrade transformers accelerate diffusers imageio-ffmpeg
- Run the code
import torch
from diffusers import CogVideoXPipeline
from diffusers.utils import export_to_video
prompt = "A panda, dressed in a small, red jacket and a tiny hat, sits on a wooden stool in a serene bamboo forest. The panda's fluffy paws strum a miniature acoustic guitar, producing soft, melodic tunes. Nearby, a few other pandas gather, watching curiously and some clapping in rhythm. Sunlight filters through the tall bamboo, casting a gentle glow on the scene. The panda's face is expressive, showing concentration and joy as it plays. The background includes a small, flowing stream and vibrant green foliage, enhancing the peaceful and magical atmosphere of this unique musical performance."
pipe = CogVideoXPipeline.from_pretrained(
"THUDM/CogVideoX1.5-5B",
torch_dtype=torch.bfloat16
)
pipe.enable_sequential_cpu_offload()
pipe.vae.enable_tiling()
pipe.vae.enable_slicing()
video = pipe(
prompt=prompt,
num_videos_per_prompt=1,
num_inference_steps=50,
num_frames=81,
guidance_scale=6,
generator=torch.Generator(device="cuda").manual_seed(42),
).frames[0]
export_to_video(video, "output.mp4", fps=8)
Quantized Inference
PytorchAO and Optimum-quanto can be
used to quantize the text encoder, transformer, and VAE modules to reduce CogVideoX's memory requirements. This allows
the model to run on free T4 Colab or GPUs with lower VRAM! Also, note that TorchAO quantization is fully compatible
with torch.compile
, which can significantly accelerate 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
from transformers import T5EncoderModel
from torchao.quantization import quantize_, int8_weight_only
quantization = int8_weight_only
text_encoder = T5EncoderModel.from_pretrained("THUDM/CogVideoX1.5-5B", subfolder="text_encoder",
torch_dtype=torch.bfloat16)
quantize_(text_encoder, quantization())
transformer = CogVideoXTransformer3DModel.from_pretrained("THUDM/CogVideoX1.5-5B", subfolder="transformer",
torch_dtype=torch.bfloat16)
quantize_(transformer, quantization())
vae = AutoencoderKLCogVideoX.from_pretrained("THUDM/CogVideoX1.5-5B", subfolder="vae", torch_dtype=torch.bfloat16)
quantize_(vae, quantization())
# Create pipeline and run inference
pipe = CogVideoXImageToVideoPipeline.from_pretrained(
"THUDM/CogVideoX1.5-5B",
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."
video = pipe(
prompt=prompt,
num_videos_per_prompt=1,
num_inference_steps=50,
num_frames=81,
guidance_scale=6,
generator=torch.Generator(device="cuda").manual_seed(42),
).frames[0]
export_to_video(video, "output.mp4", fps=8)
Additionally, these models can be serialized and stored using PytorchAO in quantized data types to save disk space. You can find examples and benchmarks at the following links:
Further Exploration
Feel free to enter our GitHub, where you'll find:
- More detailed technical explanations and code.
- Optimized prompt examples and conversions.
- Detailed code for model inference and fine-tuning.
- Project update logs and more interactive opportunities.
- CogVideoX toolchain to help you better use the model.
- INT8 model inference code.
Model License
This model is released under the CogVideoX 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}
}
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