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license: apache-2.0
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language:
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- zh
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- en
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base_model:
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- THUDM/glm-4-9b
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pipeline_tag: text-to-image
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library_name: diffusers
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---
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# CogView4-6B
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<p style="text-align: center;">
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<div align="center">
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<img src=https://github.com/THUDM/CogView4/raw/main/resources/logo.svg width="50%"/>
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</div>
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<p align="center">
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<a href="https://huggingface.co/spaces/THUDM-HF-SPACE/CogView4">🤗 Space | </a>
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<a href="https://github.com/THUDM/CogView4">🌐 Github </a> |
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<a href="https://arxiv.org/pdf/2403.05121">📜 CogView3 Paper </a>
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</p>
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## Inference Requirements and Model Introduction
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+ Resolution: Width and height must be between `512px` and `2048px`, divisible by `32`, and ensure the maximum number of
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pixels does not exceed `2^21` px.
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+ Precision: BF16 / FP32 (FP16 is not supported as it will cause overflow resulting in completely black images)
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Using `BF16` precision with `batchsize=4` for testing, the memory usage is shown in the table below:
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| Resolution | enable_model_cpu_offload OFF | enable_model_cpu_offload ON | enable_model_cpu_offload ON </br> Text Encoder 4bit |
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|-------------|------------------------------|-----------------------------|-----------------------------------------------------|
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| 512 * 512 | 33GB | 20GB | 13G |
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| 1280 * 720 | 35GB | 20GB | 13G |
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| 1024 * 1024 | 35GB | 20GB | 13G |
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| 1920 * 1280 | 39GB | 20GB | 14G |
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## Quick Start
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First, ensure you install the `diffusers` library from source.
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```shell
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pip install git+https://github.com/huggingface/diffusers.git
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cd diffusers
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pip install -e .
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```
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Then, run the following code:
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```python
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from diffusers import CogView4Pipeline
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pipe.
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pipe.vae.
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}
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---
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license: apache-2.0
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language:
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- zh
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- en
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base_model:
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- THUDM/glm-4-9b
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pipeline_tag: text-to-image
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library_name: diffusers
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---
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# CogView4-6B
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<p style="text-align: center;">
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<div align="center">
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<img src=https://github.com/THUDM/CogView4/raw/main/resources/logo.svg width="50%"/>
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</div>
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<p align="center">
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<a href="https://huggingface.co/spaces/THUDM-HF-SPACE/CogView4">🤗 Space | </a>
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<a href="https://github.com/THUDM/CogView4">🌐 Github </a> |
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<a href="https://arxiv.org/pdf/2403.05121">📜 CogView3 Paper </a>
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</p>
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## Inference Requirements and Model Introduction
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+ Resolution: Width and height must be between `512px` and `2048px`, divisible by `32`, and ensure the maximum number of
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pixels does not exceed `2^21` px.
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+ Precision: BF16 / FP32 (FP16 is not supported as it will cause overflow resulting in completely black images)
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Using `BF16` precision with `batchsize=4` for testing, the memory usage is shown in the table below:
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| Resolution | enable_model_cpu_offload OFF | enable_model_cpu_offload ON | enable_model_cpu_offload ON </br> Text Encoder 4bit |
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|-------------|------------------------------|-----------------------------|-----------------------------------------------------|
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| 512 * 512 | 33GB | 20GB | 13G |
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| 1280 * 720 | 35GB | 20GB | 13G |
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| 1024 * 1024 | 35GB | 20GB | 13G |
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| 1920 * 1280 | 39GB | 20GB | 14G |
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## Quick Start
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First, ensure you install the `diffusers` library from source.
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```shell
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pip install git+https://github.com/huggingface/diffusers.git
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cd diffusers
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pip install -e .
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```
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Then, run the following code:
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```python
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from diffusers import CogView4Pipeline
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import torch
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pipe = CogView4Pipeline.from_pretrained("THUDM/CogView4-6B", torch_dtype=torch.bfloat16)
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# Open it for reduce GPU memory usage
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pipe.enable_model_cpu_offload()
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pipe.vae.enable_slicing()
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pipe.vae.enable_tiling()
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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."
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image = pipe(
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prompt=prompt,
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guidance_scale=3.5,
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num_images_per_prompt=1,
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num_inference_steps=50,
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width=1024,
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height=1024,
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).images[0]
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image.save("cogview4.png")
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```
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### Model Metrics
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We've tested on multiple benchmarks and achieved the following scores:
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#### DPG-Bench
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| Model | Overall | Global | Entity | Attribute | Relation | Other |
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|-----------------|-----------|-----------|-----------|-----------|-----------|-----------|
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| SDXL | 74.65 | 83.27 | 82.43 | 80.91 | 86.76 | 80.41 |
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| PixArt-alpha | 71.11 | 74.97 | 79.32 | 78.60 | 82.57 | 76.96 |
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| SD3-Medium | 84.08 | 87.90 | **91.01** | 88.83 | 80.70 | 88.68 |
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| DALL-E 3 | 83.50 | **90.97** | 89.61 | 88.39 | 90.58 | 89.83 |
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| Flux.1-dev | 83.79 | 85.80 | 86.79 | 89.98 | 90.04 | **89.90** |
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| Janus-Pro-7B | 84.19 | 86.90 | 88.90 | 89.40 | 89.32 | 89.48 |
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| **CogView4-6B** | **85.13** | 83.85 | 90.35 | **91.17** | **91.14** | 87.29 |
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#### GenEval
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| Model | Overall | Single Obj. | Two Obj. | Counting | Colors | Position | Color attribution |
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|-----------------|----------|-------------|----------|----------|----------|----------|-------------------|
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| SDXL | 0.55 | 0.98 | 0.74 | 0.39 | 0.85 | 0.15 | 0.23 |
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| PixArt-alpha | 0.48 | 0.98 | 0.50 | 0.44 | 0.80 | 0.08 | 0.07 |
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| SD3-Medium | 0.74 | **0.99** | **0.94** | 0.72 | 0.89 | 0.33 | 0.60 |
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| DALL-E 3 | 0.67 | 0.96 | 0.87 | 0.47 | 0.83 | 0.43 | 0.45 |
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| Flux.1-dev | 0.66 | 0.98 | 0.79 | **0.73** | 0.77 | 0.22 | 0.45 |
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| Janus-Pro-7B | **0.80** | **0.99** | 0.89 | 0.59 | **0.90** | **0.79** | **0.66** |
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| **CogView4-6B** | 0.73 | **0.99** | 0.86 | 0.66 | 0.79 | 0.48 | 0.58 |
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#### T2I-CompBench
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| Model | Color | Shape | Texture | 2D-Spatial | 3D-Spatial | Numeracy | Non-spatial Clip | Complex 3-in-1 |
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|-----------------|------------|------------|------------|------------|------------|------------|------------------|----------------|
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| SDXL | 0.5879 | 0.4687 | 0.5299 | 0.2133 | 0.3566 | 0.4988 | 0.3119 | 0.3237 |
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| PixArt-alpha | 0.6690 | 0.4927 | 0.6477 | 0.2064 | 0.3901 | 0.5058 | **0.3197** | 0.3433 |
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| SD3-Medium | **0.8132** | 0.5885 | **0.7334** | **0.3200** | **0.4084** | 0.6174 | 0.3140 | 0.3771 |
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| DALL-E 3 | 0.7785 | **0.6205** | 0.7036 | 0.2865 | 0.3744 | 0.5880 | 0.3003 | 0.3773 |
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| Flux.1-dev | 0.7572 | 0.5066 | 0.6300 | 0.2700 | 0.3992 | 0.6165 | 0.3065 | 0.3628 |
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| Janus-Pro-7B | 0.5145 | 0.3323 | 0.4069 | 0.1566 | 0.2753 | 0.4406 | 0.3137 | 0.3806 |
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| **CogView4-6B** | 0.7786 | 0.5880 | 0.6983 | 0.3075 | 0.3708 | **0.6626** | 0.3056 | **0.3869** |
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## Chinese Text Accuracy Evaluation
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| Model | Precision | Recall | F1 Score | Pick@4 |
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|-----------------|------------|------------|------------|------------|
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| Kolors | 0.6094 | 0.1886 | 0.2880 | 0.1633 |
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| **CogView4-6B** | **0.6969** | **0.5532** | **0.6168** | **0.3265** |
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## Citation
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🌟 If you find our work helpful, please consider citing our paper and leaving valuable stars
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```
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@article{zheng2024cogview3,
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title={Cogview3: Finer and faster text-to-image generation via relay diffusion},
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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},
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journal={arXiv preprint arXiv:2403.05121},
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year={2024}
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}
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```
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## License
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This model is released under the [Apache 2.0 License](LICENSE).
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