metadata

license: apache-2.0
license_link: LICENSE.md
language:
  - en
tags:
  - text-to-image
  - image-generation
  - cogview
inference: false

CogView3-Plus-3B

📄 中文阅读 | 🤗 Hugging Face Space | 🌐 Github | 📜 arxiv

📍 Visit Qingyan and API Platform to experience larger-scale commercial video generation models.

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.bfloat16).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.