GLM Image | z.AI
4
Overview
- Generate images with precise, readable text and complex data visualizations using a hybrid architecture optimized for text-rendering and knowledge-intensive generation.
- Edit existing images or apply new styles while perfectly preserving the identity of people and objects through identity-preserving generation capabilities.
- Create highly consistent scenes with multiple subjects that maintain their individual characteristics across the entire image.
- Achieve exceptional texture realism and intricate visual details from your prompts via fine-grained reinforcement learning (GRPO) feedback.
- Transform detailed textual descriptions into accurate, high-fidelity visuals with robust semantic understanding and intricate information expression.
Pros & Cons
Pros
- Hybrid autoregressive architecture
- Diffusion decoder
- Text-rendering capabilities
- Knowledge-intensive generation
- High-fidelity image generation
- Detailed image generation
- 9B-parameter autoregressive generator
- Additional visual tokens
- Reinforcement learning algorithm GRPO
- Supports text-to-image generation
- Supports image-to-image generation
- Supports various image-to-image tasks
- Image editing
- Style transfer
- Consistent generation of subjects
- Identity-preserving generation
- High-detail images from textual descriptions
- Quality aligns with latent diffusion
- Post-training system
- Robust semantic understanding
- Accurate in tasks requiring intricate information
- Enhances visual detail
Cons
- Requires GPU with 80GB+ memory
- Image resolution must be divisible by 32
- High runtime cost
- AR model configured with do_sample=True
- Limited inference optimizations
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❓ Frequently Asked Questions
GLM-Image's architecture is a hybrid of autoregressive and diffusion decoder. It incorporates a 9B-parameter autoregressive generator, initialized from GLM-4-9B-0414 with additional visual tokens. This is complemented by a diffusion decoder and a post-training system with the reinforcement learning algorithm GRPO.
GLM-Image handles text-to-image generation tasks by extracting high-detail images from textual descriptions. This is part of its hybrid autoregressive and diffusion decoder architecture. The capabilities are particularly beneficial in scenarios necessitating knowledge-intensive generation.
GLM-Image supports a wide range of image-to-image tasks, including image editing, style transfer, consistent generation of multiple subjects, and identity-preserving generation. It applies its hybrid autoregressive and diffusion decoder architecture to handle these tasks.
The 9B-parameter autoregressive generator serves as the initial segment of GLM-Image's architecture. It initializes from GLM-4-9B-0414 with additional visual tokens to generate a compact encoding, which then expands to create high-resolution image outputs.
The reinforcement learning algorithm GRPO significantly enhances GLM-Image. It is implemented in the post-training system to augment both semantic understanding and visual detail quality, providing modular feedback for improved instruction following, artistic expressiveness, texture realism, and text rendering accuracy.
GLM-Image generates high-fidelity images. It is positioned in line with mainstream latent diffusion approaches in general image generation quality, and can also generate high-detail images from textual descriptions with robust semantic understanding and intricate information expression.
'Latent diffusion' refers to a class of generative models that GLM-Image follows. These models generate images by a stochastic process, which starts at an initial noise level and progressively refines into the final image through a series of diffusion and denoising steps.
The hybrid architecture of GLM-Image offers notable benefits in various scenarios. It allows for impressive performance in tasks that require intricate information expression and robust semantic understanding. It also ensures high-fidelity and detailed image generation, and is adept at handling both text-rendering and knowledge-intensive generation.
GLM-Image enhances visual detail quality through a fine-grained, modular feedback strategy using the GRPO algorithm. This feedback targets specific aspects of detail fidelity and text accuracy, resulting in highly realistic textures and precise text rendering.
In tasks requiring robust semantic understanding, GLM-Image demonstrates impressive performance. A part of its architecture is dedicated to improving instruction following and artistic expressiveness, making it very effective in such tasks.
'Knowledge-intensive generation' in GLM-Image's context means generating images that express or embody large amounts of detailed, specific information. This makes GLM-Image particularly useful in scenarios where complex or nuanced data must be expressed as an image.
GLM-Image provides functionalities for image editing and style transfer as part of its image-to-image generation capabilities. These tasks can be completed with a high level of detail, consistency, and fidelity, thanks to GLM-Image's hybrid architecture and sophisticated generation techniques.
GLM-4-9B-0414 plays a significant role in initializing GLM-Image. This is the model used as a starting point for the generation of GLM-Image's 9B-parameter autoregressive generator. It helps set the stage for the rest of the image generation process.
Visual tokens are components of the model's vocabulary that represent various visual aspects of an image. In GLM-Image, they’re part of the expanded vocabulary used by the 9B-parameter autoregressive generator to produce the initial compact encoding of an image.
High-fidelity images are ones that closely resemble the original image in quality and details. In GLM-Image, they refer to the quality of the images produced by the model — their faithfulness to the input prompt, their heightened detail, and their high resolution.
In GLM-Image, a post-training system is implemented with the reinforcement learning algorithm GRPO. This system helps further augment the model's semantic understanding and visual detail quality after the initial training phase.
GLM-Image handles consistent generation of multiple subjects through multiple image inputs. It is capable of producing consistent generation across various subjects, maintaining the identity of each subject while fulfilling the specificities of the prompt.
'Identity-preserving generation' in GLM-Image means that when generating or modifying images, the identity of the subject — person, object, or element — in the original image is preserved. This makes GLM-Image particularly good at tasks like image editing and generative art.
GLM-Image is developed by zai-org. Their reputation in the AI field aligns with their ability to create highly sophisticated AI models, as exemplified by their work on GLM-Image. Specific reputational details about zai-org aren't given on their website.
GLM-Image ensures detailed image generations through its hybrid autoregressive and diffusion decoder architecture. The use of a 9B-parameter autoregressive generator, a diffusion decoder, and the GRPO reinforcement learning algorithm all contribute to creating images with minute and intricate details.
Users can provide prompts to GLM-Image for image generation through specific commands and parameters in the GLM-Image API. Text prompts for text-to-image generation can be passed in the 'prompt' input. Similarly, image prompts for image-to-image generation tasks can also be specified as inputs.
'Latent diffusion' refers to a class of generative models that GLM-Image follows. These models generate images by a stochastic process, which starts at an initial noise level and progressively refines into the final image through a series of diffusion and denoising steps.
The hybrid architecture of GLM-Image offers notable benefits in various scenarios. It allows for impressive performance in tasks that require intricate information expression and robust semantic understanding. It also ensures high-fidelity and detailed image generation, and is adept at handling both text-rendering and knowledge-intensive generation.
GLM-Image enhances visual detail quality through a fine-grained, modular feedback strategy using the GRPO algorithm. This feedback targets specific aspects of detail fidelity and text accuracy, resulting in highly realistic textures and precise text rendering.
In tasks requiring robust semantic understanding, GLM-Image demonstrates impressive performance. A part of its architecture is dedicated to improving instruction following and artistic expressiveness, making it very effective in such tasks.
'Knowledge-intensive generation' in GLM-Image's context means generating images that express or embody large amounts of detailed, specific information. This makes GLM-Image particularly useful in scenarios where complex or nuanced data must be expressed as an image.
GLM-Image provides functionalities for image editing and style transfer as part of its image-to-image generation capabilities. These tasks can be completed with a high level of detail, consistency, and fidelity, thanks to GLM-Image's hybrid architecture and sophisticated generation techniques.
GLM-4-9B-0414 plays a significant role in initializing GLM-Image. This is the model used as a starting point for the generation of GLM-Image's 9B-parameter autoregressive generator. It helps set the stage for the rest of the image generation process.
Visual tokens are components of the model's vocabulary that represent various visual aspects of an image. In GLM-Image, they’re part of the expanded vocabulary used by the 9B-parameter autoregressive generator to produce the initial compact encoding of an image.
High-fidelity images are ones that closely resemble the original image in quality and details. In GLM-Image, they refer to the quality of the images produced by the model — their faithfulness to the input prompt, their heightened detail, and their high resolution.
In GLM-Image, a post-training system is implemented with the reinforcement learning algorithm GRPO. This system helps further augment the model's semantic understanding and visual detail quality after the initial training phase.
GLM-Image handles consistent generation of multiple subjects through multiple image inputs. It is capable of producing consistent generation across various subjects, maintaining the identity of each subject while fulfilling the specificities of the prompt.
'Identity-preserving generation' in GLM-Image means that when generating or modifying images, the identity of the subject — person, object, or element — in the original image is preserved. This makes GLM-Image particularly good at tasks like image editing and generative art.
GLM-Image is developed by zai-org. Their reputation in the AI field aligns with their ability to create highly sophisticated AI models, as exemplified by their work on GLM-Image. Specific reputational details about zai-org aren't given on their website.
GLM-Image ensures detailed image generations through its hybrid autoregressive and diffusion decoder architecture. The use of a 9B-parameter autoregressive generator, a diffusion decoder, and the GRPO reinforcement learning algorithm all contribute to creating images with minute and intricate details.
Users can provide prompts to GLM-Image for image generation through specific commands and parameters in the GLM-Image API. Text prompts for text-to-image generation can be passed in the 'prompt' input. Similarly, image prompts for image-to-image generation tasks can also be specified as inputs.
Pricing
Pricing model
Freemium
Paid options from
Free tier available
Billing frequency
Monthly
