CogView3-Plus-3B
Inference Requirements and Model Overview
This model is the DiT version of CogView3, a text-to-image generation model, supporting image generation from 512 to 2048px.
- Resolution: Width and height must meet the range from 512px to 2048px and must be divisible by 32.
- Inference Speed: 1s / step (tested on A100)
- Precision: BF16 / FP32 (FP16 is not supported, as it leads to overflow causing black images)
Memory Consumption
We tested memory consumption at several common resolutions on A100 devices, batchsize=1, BF16, as shown in the table below:
| 分辨率 | enable_model_cpu_offload OFF | enable_model_cpu_offload ON |
|---|---|---|
| 512 * 512 | 19GB | 11GB |
| 720 * 480 | 20GB | 11GB |
| 1024 * 1024 | 23GB | 11GB |
| 1280 * 720 | 24GB | 11GB |
| 2048 * 2048 | 25GB | 11GB |
Quick Start
First, ensure the diffusers library is installed from source.
pip install git+https://github.com/huggingface/diffusers.git
Then, run the following code:
from diffusers import CogView3PlusPipeline
import torch
pipe = CogView3PlusPipeline.from_pretrained("THUDM/CogView3-Plus-3B", torch_dtype=torch.float16).to("cuda")
# Enable it to reduce GPU memory usage
pipe.enable_model_cpu_offload()
pipe.vae.enable_slicing()
pipe.vae.enable_tiling()
prompt = "A vibrant cherry red sports car sits proudly under the gleaming sun, its polished exterior smooth and flawless, casting a mirror-like reflection. The car features a low, aerodynamic body, angular headlights that gaze forward like predatory eyes, and a set of black, high-gloss racing rims that contrast starkly with the red. A subtle hint of chrome embellishes the grille and exhaust, while the tinted windows suggest a luxurious and private interior. The scene conveys a sense of speed and elegance, the car appearing as if it's about to burst into a sprint along a coastal road, with the ocean's azure waves crashing in the background."
image = pipe(
prompt=prompt,
guidance_scale=7.0,
num_images_per_prompt=1,
num_inference_steps=50,
width=1024,
height=1024,
).images[0]
image.save("cogview3.png")
For more content and to download the original SAT weights, please visit our GitHub.
Citation
🌟 If you find our work helpful, feel free to cite our paper and leave a star:
@article{zheng2024cogview3,
title={Cogview3: Finer and faster text-to-image generation via relay diffusion},
author={Zheng, Wendi and Teng, Jiayan and Yang, Zhuoyi and Wang, Weihan and Chen, Jidong and Gu, Xiaotao and Dong, Yuxiao and Ding, Ming and Tang, Jie},
journal={arXiv preprint arXiv:2403.05121},
year={2024}
}
Model License
This Model is released under the Apache 2.0 License.