GLM-Image combines a 9B autoregressive generator with a 7B diffusion decoder for exceptional text rendering and knowledge-intensive generation. Experience the power of 16B parameters optimized for high-fidelity image creation.
Explore in-depth articles about GLM-Image capabilities, techniques, and best practices.
Learn how GLM-Image achieves exceptional text rendering accuracy with the Glyph-byT5 encoder, especially for Chinese characters.
Read MoreDiscover how GLM-Image excels at complex instruction following and factual accuracy for educational and technical content.
Read MoreExplore GLM-Image's block-causal attention mechanism for precise image editing, style transfer, and identity preservation.
Read MoreExperience GLM-Image's powerful capabilities with our free online demo. Generate high-quality images with exceptional text rendering and knowledge-intensive content.
GLM-Image delivers exceptional performance across multiple dimensions, from text rendering to knowledge-intensive generation.
GLM-Image achieves 0.9788 accuracy on Chinese text rendering (LongText-Bench ZH) and 0.9557 on English text. Perfect for creating posters, infographics, and multilingual content with precise text integration.
Combines a 9B autoregressive generator with a 7B diffusion decoder for progressive generation. The model first establishes layout with low-resolution tokens, then adds high-resolution details.
GLM-Image excels at complex instruction following with factual accuracy. Ideal for educational content, technical diagrams, and creative work requiring intricate information representation.
Generate images at native resolutions from 1024px to 2048px. GLM-Image produces print-quality images with exceptional detail and clarity for professional applications.
Leverages block-causal attention for precise image editing capabilities. Transform photos with style transfer, enhance images, and create artistic variations while preserving key details.
Maintain multi-subject consistency across generations. Perfect for character design, brand consistency, and projects requiring recognizable subjects across multiple images.
GLM-Image demonstrates exceptional performance across industry benchmarks, particularly excelling in text rendering accuracy.
| Benchmark | GLM-Image | Competitor Avg | Improvement |
|---|---|---|---|
| CVTG-2K Word Accuracy | 0.9116 | 0.7850 | +16.1% |
| LongText-Bench EN | 0.9557 | 0.8920 | +7.1% |
| LongText-Bench ZH | 0.9788 | 0.8650 | +13.2% |
| OneIG-Bench | 0.528 | 0.512 | +3.1% |
| DPG-Bench | 84.78 | 82.45 | +2.8% |
| TIIF-Bench (Short) | 81.01 | 78.30 | +3.5% |
* Competitor averages based on comparable open-source models. GLM-Image consistently outperforms in text rendering tasks.
Create images with precise text integration in multiple languages, perfect for posters and marketing materials.
Transform images with artistic styles while maintaining subject identity and key visual elements.
Generate knowledge-intensive visuals for educational materials with accurate information representation.
GLM-Image incorporates cutting-edge architectural innovations for superior image generation performance.
16Γ compression ratio with semantic preservation. Superior convergence properties compared to traditional VQVAE approaches.
Hierarchical token generation: low-resolution layout first (~256 tokens), then high-resolution details (1K-4K tokens).
Character-level encoding for exceptional text rendering accuracy, especially for Chinese characters and complex scripts.
Maintains high-frequency details during image editing while reducing computational overhead for efficient processing.
Get started with GLM-Image in minutes. Install the required packages and start generating high-quality images.
pip install git+https://github.com/huggingface/transformers.git
pip install git+https://github.com/huggingface/diffusers.git80GB+ VRAM or multi-GPU setup
Version 3.8 or higher
import torch
from diffusers.pipelines.glm_image import GlmImagePipeline
pipe = GlmImagePipeline.from_pretrained(
"zai-org/GLM-Image",
torch_dtype=torch.bfloat16,
device_map="cuda"
)
prompt = "A beautiful landscape with mountains and a lake"
image = pipe(
prompt=prompt,
height=32 * 32,
width=36 * 32,
num_inference_steps=50,
guidance_scale=1.5
).images[0]
image.save("output.png")Common questions about GLM-Image and its capabilities.
GLM-Image is the first open-source industrial-grade discrete auto-regressive image generation model with 16B parameters (9B autoregressive + 7B diffusion decoder). It excels at text rendering, especially Chinese characters, and knowledge-intensive content generation.
GLM-Image uses the Glyph-byT5 text encoder, which provides exceptional accuracy for text rendering in images. It achieves 0.9788 accuracy on Chinese text (LongText-Bench ZH) and 0.9557 on English text (LongText-Bench EN), outperforming other models.
GLM-Image requires a GPU with 80GB+ VRAM or a multi-GPU setup. It also requires Python 3.8 or higher and the latest stable version of PyTorch. The model's large parameter count (16B) necessitates significant computational resources.
GLM-Image combines a 9B autoregressive generator with a 7B diffusion decoder. The autoregressive component first generates low-resolution tokens (~256) to establish the layout, then the diffusion decoder adds high-resolution details (1K-4K tokens) for the final image.
Yes! GLM-Image is released under the Apache 2.0 license, which allows for commercial use. You can use GLM-Image in your commercial projects, modify it, and distribute it, as long as you comply with the license terms.
Knowledge-intensive generation refers to GLM-Image's ability to follow complex instructions with factual accuracy. This makes it ideal for creating educational content, technical diagrams, and images that require accurate representation of intricate information.
GLM-Image outperforms comparable models in text rendering tasks, achieving 0.9116 on CVTG-2K Word Accuracy (16.1% improvement over competitors). It also excels in Chinese text rendering with 0.9788 accuracy, making it the best choice for multilingual content creation.
Yes, GLM-Image can be fine-tuned for specific domains or styles. The model's architecture supports transfer learning, allowing you to adapt it to your specific needs while maintaining its core capabilities in text rendering and knowledge-intensive generation.