CogVideoX-2B
π δΈζι θ―» | π€ Huggingface Space | π Github | π arxiv
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Model Introduction
CogVideoX is an open-source version of the video generation model originating from QingYing. The table below displays the list of video generation models we currently offer, along with their foundational information.
Model Name | CogVideoX-2B (This Repository) | CogVideoX-5B |
---|---|---|
Model Description | Entry-level model, balancing compatibility. Low cost for running and secondary development. | Larger model with higher video generation quality and better visual effects. |
Inference Precision | FP16* (Recommended), BF16, FP32, FP8*, INT8, no support for INT4 | BF16 (Recommended), FP16, FP32, FP8*, INT8, no support for INT4 |
Single GPU VRAM Consumption |
SAT FP16: 18GB diffusers FP16: starting from 4GB* diffusers INT8(torchao): starting from 3.6GB* |
SAT BF16: 26GB diffusers BF16: starting from 5GB* diffusers INT8(torchao): starting from 4.4GB* |
Multi-GPU Inference VRAM Consumption | FP16: 10GB* using diffusers | BF16: 15GB* using diffusers |
Inference Speed (Step = 50, FP/BF16) |
Single A100: ~90 seconds Single H100: ~45 seconds |
Single A100: ~180 seconds Single H100: ~90 seconds |
Fine-tuning Precision | FP16 | BF16 |
Fine-tuning VRAM Consumption (per GPU) | 47 GB (bs=1, LORA) 61 GB (bs=2, LORA) 62GB (bs=1, SFT) |
63 GB (bs=1, LORA) 80 GB (bs=2, LORA) 75GB (bs=1, SFT) |
Prompt Language | English* | |
Prompt Length Limit | 226 Tokens | |
Video Length | 6 Seconds | |
Frame Rate | 8 Frames per Second | |
Video Resolution | 720 x 480, no support for other resolutions (including fine-tuning) | |
Positional Encoding | 3d_sincos_pos_embed | 3d_rope_pos_embed |
Data Explanation
- When testing using the
diffusers
library, all optimizations provided by thediffusers
library were enabled. This solution has not been tested for actual VRAM/memory usage on devices other than NVIDIA A100 / H100. Generally, this solution can be adapted to all devices with NVIDIA Ampere architecture and above. If the optimizations are disabled, VRAM usage will increase significantly, with peak VRAM usage being about 3 times higher than the table shows. However, speed will increase by 3-4 times. You can selectively disable some optimizations, including:
pipe.enable_model_cpu_offload()
pipe.enable_sequential_cpu_offload()
pipe.vae.enable_slicing()
pipe.vae.enable_tiling()
- When performing multi-GPU inference, the
enable_model_cpu_offload()
optimization needs to be disabled. - Using INT8 models will reduce inference speed. This is to ensure that GPUs with lower VRAM can perform inference normally while maintaining minimal video quality loss, though inference speed will decrease significantly.
- The 2B model is trained with
FP16
precision, and the 5B model is trained withBF16
precision. We recommend using the precision the model was trained with for inference. - PytorchAO and Optimum-quanto can be
used to quantize the text encoder, Transformer, and VAE modules to reduce CogVideoX's memory requirements. This makes
it possible to run the model on a free T4 Colab or GPUs with smaller VRAM! It is also worth noting that TorchAO
quantization is fully compatible with
torch.compile
, which can significantly improve inference speed.FP8
precision must be used on devices withNVIDIA H100
or above, which requires installing thetorch
,torchao
,diffusers
, andaccelerate
Python packages from source.CUDA 12.4
is recommended. - The inference speed test also used the above VRAM optimization scheme. Without VRAM 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 during refinement by a large model.
Note
- Using SAT for inference and fine-tuning of SAT version models. Feel free to visit our GitHub for more information.
Quick Start π€
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
# diffusers>=0.30.1
# transformers>=0.44.0
# accelerate>=0.33.0 (suggest install from source)
# imageio-ffmpeg>=0.5.1
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/CogVideoX-2b",
torch_dtype=torch.float16
)
pipe.enable_model_cpu_offload()
pipe.enable_sequential_cpu_offload()
pipe.vae.enable_slicing()
pipe.vae.enable_tiling()
video = pipe(
prompt=prompt,
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)
Quantized Inference
PytorchAO and Optimum-quanto can be
used to quantize the Text Encoder, Transformer and VAE modules to lower the memory requirement of CogVideoX. This makes
it possible to run the model on free-tier T4 Colab or smaller VRAM GPUs as well! It is also worth noting that TorchAO
quantization is fully compatible with torch.compile
, which allows for much faster inference speed.
# To get started, PytorchAO needs to be installed from the GitHub source and PyTorch Nightly.
# Source and nightly installation is only required until next release.
import torch
from diffusers import AutoencoderKLCogVideoX, CogVideoXTransformer3DModel, CogVideoXPipeline
from diffusers.utils import export_to_video
+ from transformers import T5EncoderModel
+ from torchao.quantization import quantize_, int8_weight_only, int8_dynamic_activation_int8_weight
+ quantization = int8_weight_only
+ text_encoder = T5EncoderModel.from_pretrained("THUDM/CogVideoX-5b", subfolder="text_encoder", torch_dtype=torch.bfloat16)
+ quantize_(text_encoder, quantization())
+ transformer = CogVideoXTransformer3DModel.from_pretrained("THUDM/CogVideoX-5b", subfolder="transformer", torch_dtype=torch.bfloat16)
+ quantize_(transformer, quantization())
+ vae = AutoencoderKLCogVideoX.from_pretrained("THUDM/CogVideoX-2b", subfolder="vae", torch_dtype=torch.bfloat16)
+ quantize_(vae, quantization())
# Create pipeline and run inference
pipe = CogVideoXPipeline.from_pretrained(
"THUDM/CogVideoX-2b",
+ text_encoder=text_encoder,
+ transformer=transformer,
+ vae=vae,
torch_dtype=torch.bfloat16,
)
pipe.enable_model_cpu_offload()
pipe.vae.enable_tiling()
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."
video = pipe(
prompt=prompt,
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)
Additionally, the models can be serialized and stored in a quantized datatype to save disk space when using PytorchAO. Find examples and benchmarks at these links:
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.
- INT8 model inference code support.
Model License
The CogVideoX-2B model (including its corresponding Transformers module and VAE module) is released under the Apache 2.0 License.
The CogVideoX-5B model (Transformers module) 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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