GLM Image
Unlock the power of GLM-Image, the revolutionary hybrid AI architecture (9B AR + 7B DiT). Perfect for dense-text posters, knowledge-intensive illustrations, and precise image-to-image editing.
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Revolutionary Hybrid AR + Diffusion Architecture
GLM-Image is not just another diffusion model. It adopts a unique hybrid architecture combining a 9B-parameter Autoregressive (AR) generator for semantic understanding with a 7B-parameter Diffusion Decoder (DiT) for detail refinement. This dual-engine approach ensures superior global composition while maintaining high-frequency texture details, aligning with mainstream latent diffusion quality but surpassing them in complex reasoning tasks.
SOTA Text Rendering & Typography
Say goodbye to gibberish text in AI images. GLM-Image integrates a Glyph Encoder to achieve state-of-the-art performance in text rendering. Ranking #1 in open-source benchmarks like CVTG-2K and LongText-Bench, it accurately generates coherent sentences, complex Chinese characters, and English labels within images. Perfect for creating commercial posters, book covers, and diagrams where text accuracy is paramount.
Knowledge-Intensive Image Generation
GLM-Image excels in scenarios requiring deep semantic understanding. Thanks to its Autoregressive foundation initialized from GLM-4-9B, the model understands complex, information-dense prompts better than standard diffusion models. Whether you need scientific illustrations, flowcharts, or detailed infographics (like recipe guides or biological diagrams), GLM-Image aligns visual elements logically with the provided knowledge.
Advanced Image-to-Image & Style Transfer
Beyond text-to-image, GLM-Image offers robust Image-to-Image (I2I) capabilities. It supports image editing, style transfer, and identity-preserving generation. By utilizing block-causal attention between reference and generated images, it maintains the subject's high-frequency details while effectively applying new styles or modifying backgrounds (e.g., changing a snowy forest to a subway station) without losing the original essence.
Decoupled RL for Semantic & Visual Perfection
GLM-Image utilizes a post-training decoupled reinforcement learning strategy (GRPO). The AR module is optimized for aesthetics and semantic alignment (instruction following), while the Decoder is fine-tuned for detail fidelity and texture. This ensures that every output is not only visually stunning but also strictly adheres to your prompt's logical requirements.
Commercial Posters
Create ready-to-use advertising posters with accurate brand names, slogans, and product descriptions directly embedded in the image.
Social Media Graphics
Generate visually striking covers and cards for WeChat, Instagram, or blogs that combine aesthetics with readable text elements.
Comic & Storyboards
Maintain multi-subject consistency and identity preservation across different panels for coherent visual storytelling.
Artistic Style Transfer
Transform ordinary photos into specific artistic styles (like sketch, oil painting, or cyberpunk) while keeping the main subject recognizable.
Science & Education Illustrations
Generate accurate anatomical diagrams, chemical structures, or physics principles with correct labeling and logical layouts.
Infographics & Charts
Visualize data processes or step-by-step guides (e.g., cooking recipes, assembly instructions) with clear visual hierarchy.
Presentation Materials
Create custom visuals for PPTs that strictly follow your semantic instructions, avoiding the hallucinations common in other models.
Historical & Cultural Reconstructions
Render culturally specific content (like Chinese calligraphy or traditional artifacts) with high fidelity and understanding.
Background Replacement
Seamlessly swap backgrounds (e.g., product staging) while maintaining perfect lighting and shadow consistency on the subject.
Identity Preservation
Generate new scenarios for a specific character or person without losing their facial features or key identity traits.
Image Extension & Outpainting
Expand the boundaries of your images or change aspect ratios while keeping the semantic context intact.
Virtual Try-On & Editing
Edit specific parts of an image (inpainting) based on text commands, such as changing clothing or adding objects.
Simple Pricing, Professional Results
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