https://github.com/thudm/cogview4
CogView4, CogView3-Plus and CogView3(ECCV 2024)
https://github.com/thudm/cogview4
eccv2024 high-resolution image-generation text-to-image
Last synced: about 1 year ago
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CogView4, CogView3-Plus and CogView3(ECCV 2024)
- Host: GitHub
- URL: https://github.com/thudm/cogview4
- Owner: THUDM
- License: apache-2.0
- Created: 2024-09-23T06:38:31.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2025-03-29T07:09:58.000Z (about 1 year ago)
- Last Synced: 2025-04-03T03:59:04.575Z (about 1 year ago)
- Topics: eccv2024, high-resolution, image-generation, text-to-image
- Language: Python
- Homepage:
- Size: 24.1 MB
- Stars: 964
- Watchers: 18
- Forks: 67
- Open Issues: 6
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# CogView4 & CogView3 & CogView-3Plus
[阅读中文版](./README_zh.md)
[日本語で読む](./README_ja.md)
🤗 HuggingFace Space
🤖ModelScope Space
🛠️ZhipuAI MaaS(Faster)
👋 WeChat Community 📚 CogView3 Paper
## Project Updates
- 🔥🔥 ```2025/03/24```: We are launching [CogKit](https://github.com/THUDM/CogKit), a powerful toolkit for fine-tuning and inference of the **CogView4** and **CogVideoX** series, allowing you to fully explore our multimodal generation models.
- ```2025/03/04```: We've adapted and open-sourced the [diffusers](https://github.com/huggingface/diffusers) version
of **CogView-4** model, which has 6B parameters, supports native Chinese input, and Chinese text-to-image generation.
You can try it [online](https://huggingface.co/spaces/THUDM-HF-SPACE/CogView4).
- ```2024/10/13```: We've adapted and open-sourced the [diffusers](https://github.com/huggingface/diffusers) version of
**CogView-3Plus-3B** model. You can try
it [online](https://huggingface.co/spaces/THUDM-HF-SPACE/CogView3-Plus-3B-Space).
- ```2024/9/29```: We've open-sourced **CogView3** and **CogView-3Plus-3B**. **CogView3** is a text-to-image system
based on cascading diffusion, using a relay diffusion framework. **CogView-3Plus** is a series of newly developed
text-to-image models based on Diffusion Transformer.
## Project Plan
- [X] Diffusers workflow adaptation
- [X] Cog series fine-tuning kits (coming soon)
- [ ] ControlNet models and training code
## Community Contributions
We have collected some community projects related to this repository here. These projects are maintained by community members, and we appreciate their contributions.
+ [ComfyUI_CogView4_Wrapper](https://github.com/chflame163/ComfyUI_CogView4_Wrapper) - An implementation of the CogView4 project in ComfyUI.
## Model Introduction
### Model Comparison
Model Name
CogView4
CogView3-Plus-3B
Resolution
512 <= H, W <= 2048
H * W <= 2^{21}
H, W \mod 32 = 0
Inference Precision
Only supports BF16, FP32
Encoder
GLM-4-9B
T5-XXL
Prompt Language
Chinese, English
English
Prompt Length Limit
1024 Tokens
224 Tokens
Download Links
🤗 HuggingFace
🤖 ModelScope
🟣 WiseModel
🤗 HuggingFace
🤖 ModelScope
🟣 WiseModel
### Memory Usage
DIT models are tested with `BF16` precision and `batchsize=4`, with results 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 |
Additionally, we recommend that your device has at least `32GB` of RAM to prevent the process from being killed.
### 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** |
## Inference Model
### Prompt Optimization
Although CogView4 series models are trained with lengthy synthetic image descriptions, we strongly recommend using a
large language model to rewrite prompts before text-to-image generation, which will greatly improve generation quality.
We provide an [example script](inference/prompt_optimize.py). We recommend running this script to refine your prompts.
Note that `CogView4` and `CogView3` models use different few-shot examples for prompt optimization. They need to be
distinguished.
```shell
cd inference
python prompt_optimize.py --api_key "Zhipu AI API Key" --prompt {your prompt} --base_url "https://open.bigmodel.cn/api/paas/v4" --model "glm-4-plus" --cogview_version "cogview4"
```
### Inference Model
Run the model `CogView4-6B` with `BF16` precision:
```python
from diffusers import CogView4Pipeline
import torch
pipe = CogView4Pipeline.from_pretrained("THUDM/CogView4-6B", torch_dtype=torch.bfloat16).to("cuda")
# 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")
```
For more inference code, please check:
1. For using `BNB int4` to load `text encoder` and complete inference code annotations,
check [here](inference/cli_demo_cogview4.py).
2. For using `TorchAO int8 or int4` to load `text encoder & transformer` and complete inference code annotations,
check [here](inference/cli_demo_cogview4_int8.py).
3. For setting up a `gradio` GUI DEMO, check [here](inference/gradio_web_demo.py).
## Fine-tuning
This repository does not contain fine-tuning code, but you can fine-tune using the following two approaches, including both LoRA and SFT:
1. [CogKit](https://github.com/THUDM/CogKit), our officially maintained system-level fine-tuning framework that supports CogView4 and CogVideoX.
2. [finetrainers](https://github.com/a-r-r-o-w/finetrainers), a low-memory solution that enables fine-tuning on a single RTX 4090.
3. If you want to train ControlNet models directly, you can refer to the [training code](https://github.com/huggingface/diffusers/tree/main/examples/cogview4-control) and train your own models.
## License
The code in this repository and the CogView3 models are licensed under [Apache 2.0](./LICENSE).
We welcome and appreciate your code contributions. You can view the contribution
guidelines [here](resources/contribute.md).