CogView4: A Text-to-Image Generation Model for Highly Accurate Multimodal Authoring at Tsinghua University

1. What is CogView4?
CogView4 is developed by the Knowledge Engineering Laboratory of Tsinghua University (THUDM)Multimodal Text-to-Image Generation ModelCogView is based on the self-developed Transformer architecture, which supports the generation of high-quality images from natural language descriptions. As the upgraded version of CogView series, it realizes significant breakthroughs in generation resolution, semantic understanding and Chinese scene adaptability, and is especially good at handling complex Chinese instructions and cultural elements.

2. Core functions and strengths

• High Resolution Generation::
• Supports the generation of 1024 x 1024 pixel HD images with detail comparable to professional designs.
• Improved diffusion modeling techniques to reduce image noise and structural distortion.
• Chinese Scene Optimization::
• Accurately understand idioms, poems and Internet buzzwords to generate contextualized visual content (e.g. "Chinese ink painting", "Cyberpunk Forbidden City").
• Built-in library of Chinese cultural elements (traditional costumes, architectural styles, etc.).
• multimodal control::
• Supports joint text + sketch input for precise composition control.
• You can specify the art style (oil painting/pixel style/3D rendering) to suit different creative needs.
• open source and scalable::
• Provide pre-training model weights and fine-tuning interface, support customized dataset training.
• Compatible with the Hugging Face ecosystem for easy integration into existing AI workflows.

3. Application scenarios

• art: Translate literary descriptions into illustrations, comics, or conceptual design drawings.
• advertising marketing: Quickly generate promotional material that matches the tone of the brand.
• Educational aids: Visualize historical events, scientific principles, and other teaching difficulties.
• game development: Batch generate original scene paintings, character drawings and prop icons.

4. How to use CogView4?

• Quick Experience::

1. Clone the GitHub repository and install PyTorch with related dependencies.
2. Download the pre-trained model and run the example script to input the prompt words (e.g. "Jiangnan water town, drizzling rain, stone slabs and old bridges").
3. alignnum_samplesparameters to generate multiple versions of the results and select the optimal image.

• Advanced Development::
• Use LoRA techniques to fine-tune the model and adapt it to vertical domain requirements (e.g., medical atlas generation).
• Realize batch generation in the cloud through API encapsulation, combined with SDK to access third-party applications.

5. Advantages over comparable tools
Compared with western dominant models such as Stable Diffusion, CogView4 improves the accuracy of Chinese semantic parsing and cultural element reduction by 351 TP3T, and reduces the memory consumption by 701 TP3T through the sparse attention mechanism, which supports the operation of consumer-grade graphics cards.

Summary:

CogView4 sets a new benchmark for multimodal generation with "Chinese-friendly + industrial-grade accuracy", provides content creators, enterprises and researchers with low-cost and highly controllable visual production solutions, and promotes the in-depth application of AIGC technology in localized scenarios.