MOSS-Image
MOSS-Image is an open-weight text-to-image model family for generating images from natural-language prompts and structured layout instructions. Its Base and fine-tuned SFT checkpoints share an inference interface and support prompts of up to 4,096 tokens. Users can describe a scene in text or provide JSON layout data to guide where objects and rendered text appear in the composition. The project includes Python and Gradio workflows, a Diffusers pipeline, model weights, and guidance for local deployment and fine-tuning. It can be used to create illustrations, photorealistic images, posters, and other designed compositions, including images containing specified text. Developers can also use supported image-generation runtimes and a hosted API option. The software is aimed at developers, researchers, and creative practitioners who want to generate or adapt images with layout control.
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GLM-Image
GLM-Image is a next-generation, open source image generation model developed by Z.ai, designed to combine deep language understanding with high-fidelity visual synthesis. Unlike traditional diffusion-only models, it uses a hybrid architecture that integrates an autoregressive language model with a diffusion decoder, enabling it to first reason about the structure, meaning, and relationships within a prompt before generating the image itself. This approach allows GLM-Image to excel in scenarios that require precise semantic control, such as generating infographics, presentation slides, posters, and diagrams with accurate embedded text and complex layouts. With a total of around 16 billion parameters, the model achieves strong performance in rendering readable, correctly placed text within images, an area where many image models struggle, while maintaining detailed visual quality and consistency.
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MiniMax H3
MiniMax H3 is a general-purpose omni-modal generation model that jointly understands multimodal contexts spanning text, images, video, and audio. It generates videos with native stereo sound at up to 2K resolution and 15 seconds in length, delivering content for advertising, branding, ecommerce, product design, UI/UX, gaming, and creative workflows. Users can combine reference types in one instruction, for example, transferring camera movement from a video, placing a character from an image into the scene, and matching vocals from an audio clip, while describing the relationships in natural language. H3 supports text-to-image, text-to-video with jointly generated audio, multi-shot modeling, text-to-audio, and generalized reference and editing across images, videos, and audio. Voice, sound effects, and music are modeled together. The model excels at instruction following, accurate text and brand presentation, and video-to-video motion transfer.
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MAI Image 2.7
MAI Image 2.7 is Microsoft AI’s image-editing model for making targeted changes to existing images while preserving their broader composition. It can restyle or relight a scene, move objects into new settings, revise text and symbols in images, and restore or enhance visual assets. It is aimed at creators and others who need to refine images without rebuilding an entire scene. Preview access is available through MAI Playground, Arena, and Artificial Analysis. The initial preview focuses on image editing, with text-to-image generation planned for a later release. Example uses include changing a product scene, modifying an image’s mood, and making selective edits to individual objects.
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