--- title: CogView4-6B canonical_url: "https://www.modelscope.cn/models/ZhipuAI/CogView4-6B" md_url: "https://www.modelscope.cn/models/ZhipuAI/CogView4-6B.md" repository: ZhipuAI/CogView4-6B last_updated: 2026-02-03 license: apache-2.0 pipeline_tag: text-to-image-synthesis tasks: - text-to-image-synthesis base_model: - THUDM/glm-4-9b base_model_relation: finetune parameters: 15.6B tensor_type: - BF16 library_name: - safetensors - diffusers language: - zh - en downloads: 8033 stars: 37 --- # CogView4-6B > CogView4-6B - ZhipuAI 在 ModelScope 开源的模型。CogView4-6B 🤗 Space | 🌐 Github | 📜 CogView3 Paper Inference Requirements and Model Introduction Resolution: Width and height must be between 512px and 2048px, divisible by 32, and ensure the maximum number of… ZhipuAI/CogView4-6B 是 ModelScope 魔搭社区上的 15.6B 参数text-to-image-synthesis模型，采用 apache-2.0 许可，基于 THUDM/glm-4-9b 构建。 - **Repository**: ZhipuAI/CogView4-6B - **License**: apache-2.0 - **Tasks**: text-to-image-synthesis - **Parameters**: 15.6B - **Base model**: THUDM/glm-4-9b - **Downloads**: 8033 - **Stars**: 37 - **Last updated**: 2026-02-03 Source: https://www.modelscope.cn/models/ZhipuAI/CogView4-6B --- # CogView4-6B

🤗 Space | 🌐 Github | 📜 CogView3 Paper

![img](https://raw.githubusercontent.com/THUDM/CogView4/refs/heads/main/resources/showcase.png) ## Inference Requirements and Model Introduction + Resolution: Width and height must be between `512px` and `2048px`, divisible by `32`, and ensure the maximum number of pixels does not exceed `2^21` px. + Precision: BF16 / FP32 (FP16 is not supported as it will cause overflow resulting in completely black images) Using `BF16` precision with `batchsize=4` for testing, the memory usage is shown in the table below: | Resolution | enable_model_cpu_offload OFF | enable_model_cpu_offload ON | enable_model_cpu_offload ON
Text Encoder 4bit | |-------------|------------------------------|-----------------------------|-----------------------------------------------------| | 512 * 512 | 33GB | 20GB | 13G | | 1280 * 720 | 35GB | 20GB | 13G | | 1024 * 1024 | 35GB | 20GB | 13G | | 1920 * 1280 | 39GB | 20GB | 14G | ## Quick Start First, ensure you install the `diffusers` library from source. ```shell pip install git+https://github.com/huggingface/diffusers.git cd diffusers pip install -e . ``` Then, run the following code: ```python from diffusers import CogView4Pipeline pipe = CogView4Pipeline.from_pretrained("THUDM/CogView4-6B", torch_dtype=torch.bfloat16) # Open it for 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=3.5, num_images_per_prompt=1, num_inference_steps=50, width=1024, height=1024, ).images[0] image.save("cogview4.png") ``` ### Model Metrics We've tested on multiple benchmarks and achieved the following scores: #### DPG-Bench | Model | Overall | Global | Entity | Attribute | Relation | Other | |-----------------|-----------|-----------|-----------|-----------|-----------|-----------| | SDXL | 74.65 | 83.27 | 82.43 | 80.91 | 86.76 | 80.41 | | PixArt-alpha | 71.11 | 74.97 | 79.32 | 78.60 | 82.57 | 76.96 | | SD3-Medium | 84.08 | 87.90 | **91.01** | 88.83 | 80.70 | 88.68 | | DALL-E 3 | 83.50 | **90.97** | 89.61 | 88.39 | 90.58 | 89.83 | | Flux.1-dev | 83.79 | 85.80 | 86.79 | 89.98 | 90.04 | **89.90** | | Janus-Pro-7B | 84.19 | 86.90 | 88.90 | 89.40 | 89.32 | 89.48 | | **CogView4-6B** | **85.13** | 83.85 | 90.35 | **91.17** | **91.14** | 87.29 | #### GenEval | Model | Overall | Single Obj. | Two Obj. | Counting | Colors | Position | Color attribution | |-----------------|----------|-------------|----------|----------|----------|----------|-------------------| | SDXL | 0.55 | 0.98 | 0.74 | 0.39 | 0.85 | 0.15 | 0.23 | | PixArt-alpha | 0.48 | 0.98 | 0.50 | 0.44 | 0.80 | 0.08 | 0.07 | | SD3-Medium | 0.74 | **0.99** | **0.94** | 0.72 | 0.89 | 0.33 | 0.60 | | DALL-E 3 | 0.67 | 0.96 | 0.87 | 0.47 | 0.83 | 0.43 | 0.45 | | Flux.1-dev | 0.66 | 0.98 | 0.79 | **0.73** | 0.77 | 0.22 | 0.45 | | Janus-Pro-7B | **0.80** | **0.99** | 0.89 | 0.59 | **0.90** | **0.79** | **0.66** | | **CogView4-6B** | 0.73 | **0.99** | 0.86 | 0.66 | 0.79 | 0.48 | 0.58 | #### T2I-CompBench | Model | Color | Shape | Texture | 2D-Spatial | 3D-Spatial | Numeracy | Non-spatial Clip | Complex 3-in-1 | |-----------------|------------|------------|------------|------------|------------|------------|------------------|----------------| | SDXL | 0.5879 | 0.4687 | 0.5299 | 0.2133 | 0.3566 | 0.4988 | 0.3119 | 0.3237 | | PixArt-alpha | 0.6690 | 0.4927 | 0.6477 | 0.2064 | 0.3901 | 0.5058 | **0.3197** | 0.3433 | | SD3-Medium | **0.8132** | 0.5885 | **0.7334** | **0.3200** | **0.4084** | 0.6174 | 0.3140 | 0.3771 | | DALL-E 3 | 0.7785 | **0.6205** | 0.7036 | 0.2865 | 0.3744 | 0.5880 | 0.3003 | 0.3773 | | Flux.1-dev | 0.7572 | 0.5066 | 0.6300 | 0.2700 | 0.3992 | 0.6165 | 0.3065 | 0.3628 | | Janus-Pro-7B | 0.5145 | 0.3323 | 0.4069 | 0.1566 | 0.2753 | 0.4406 | 0.3137 | 0.3806 | | **CogView4-6B** | 0.7786 | 0.5880 | 0.6983 | 0.3075 | 0.3708 | **0.6626** | 0.3056 | **0.3869** | ## Chinese Text Accuracy Evaluation | Model | Precision | Recall | F1 Score | Pick@4 | |-----------------|------------|------------|------------|------------| | Kolors | 0.6094 | 0.1886 | 0.2880 | 0.1633 | | **CogView4-6B** | **0.6969** | **0.5532** | **0.6168** | **0.3265** | ## Citation 🌟 If you find our work helpful, please consider citing our paper and leaving valuable stars ``` @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} } ``` ## License This model is released under the [Apache 2.0 License](LICENSE).