Search Results for "cursive image text"
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83 projects for "cursive image text" with 2 filters applied:
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LongCat-Image
Foundation model for image generation
...The model excels at both text-to-image generation and instruction-guided image editing, offering users versatile capabilities for creative and practical tasks—whether generating art, mockups, or adjusting existing visuals with fine control. -
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GLM-Image
GLM-Image: Auto-regressive for Dense-knowledge and High-fidelity Image
GLM-Image is an open-source generative AI model designed to create high-fidelity images from text prompts using a hybrid architecture that combines autoregressive semantic understanding with diffusion-based detail refinement. It excels at generating images that include complex layouts and detailed text content, making it especially useful for posters, diagrams, info-graphics, social media graphics, and visual content that requires precise text placement and semantic alignment. ... -
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Z-Image
Image generation model with single-stream diffusion transformer
...The project includes several variants: Z-Image-Turbo, a distilled version optimized for speed and low resource consumption; Z-Image-Base, the full-capacity foundation model; and Z-Image-Edit, fine-tuned for image editing tasks. Despite its compact size, Z-Image produces outputs that closely rival those from much larger models — including strong rendering of bilingual (English and Chinese) text inside images, accurate prompt adherence, and good layout and composition. -
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Qwen-Image-2.1
Qwen's most powerful open-source image generation model
Qwen-Image-2.1 is an open-source 7B-parameter image generation and editing model from the Qwen family. It unifies text-to-image creation and image editing within the same architecture. The model can natively generate transparent RGBA images, edit transparent layers, and extract subjects from photographs. Image editing supports up to 10 reference images for multi-subject composition and identity preservation. -
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FireRed-Image-Edit
General-purpose image editing model that delivers high-fidelity
FireRed-Image-Edit is an open-source general-purpose image editing model and toolset designed to deliver high-fidelity, visually coherent edits across a wide range of editing tasks, from simple object modifications to complex enhancements like restoration and style preservation. It is built on a flexible text-to-image foundation model that has been extended with training paradigms including pretraining, supervised fine-tuning, and reinforcement learning to imbue the system with strong instruction following and editing consistency. ... -
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Qwen-Image-Layered
Qwen-Image-Layered: Layered Decomposition for Inherent Editablity
...By combining text and structured image representations, it aims to facilitate tasks where both descriptive and structural understanding are important, such as detailed image QA, interactive image editing via prompt layers, and image-conditioned generation with structural control. The layered approach supports training signals that help the model learn how visual elements relate to each other and to textual context, rather than simply learning global image embeddings. -
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FLUX.1
Official inference repo for FLUX.1 models
FLUX.1 repository contains inference code and tooling for the FLUX.1 text-to-image diffusion models, enabling developers and researchers to generate and edit images from natural-language prompts using open-weight versions of the model on their own hardware or within custom applications. The project is part of a larger family of FLUX models developed by Black Forest Labs, designed to produce high-quality, detailed visuals from text descriptions with competitive prompt adherence and artistic fidelity. ... -
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HunyuanImage-3.0
A Powerful Native Multimodal Model for Image Generation
HunyuanImage-3.0 is a powerful, native multimodal text-to-image generation model released by Tencent’s Hunyuan team. It unifies multimodal understanding and generation in a single autoregressive framework, combining text and image modalities seamlessly rather than relying on separate image-only diffusion components. It uses a Mixture-of-Experts (MoE) architecture with many expert subnetworks to scale efficiently, deploying only a subset of experts per token, which allows large parameter counts without linear inference cost explosion. ... -
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FLUX.2
Official inference repo for FLUX.2 models
FLUX.2 is a state-of-the-art open-weight image generation and editing model released by Black Forest Labs aimed at bridging the gap between research-grade capabilities and production-ready workflows. The model offers both text-to-image generation and powerful image editing, including editing of multiple reference images, with fidelity, consistency, and realism that push the limits of what open-source generative models have achieved. -
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CLIP
CLIP, Predict the most relevant text snippet given an image
CLIP (Contrastive Language-Image Pretraining) is a neural model that links images and text in a shared embedding space, allowing zero-shot image classification, similarity search, and multimodal alignment. It was trained on large sets of (image, caption) pairs using a contrastive objective: images and their matching text are pulled together in embedding space, while mismatches are pushed apart. -
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MiniMax H3
MiniMax H3 is a general-purpose, omni-modal generative system
MiniMax H3 is an omni-modal generative system for creating synchronized video and native stereo audio from complex multimodal instructions. It can understand combinations of text, images, video, and audio as generation context. Outputs range from four to fifteen seconds at 24 frames per second, with multiple aspect ratios and resolutions up to 2K. H3-Base supports text-to-video, first-frame, last-frame, and first-and-last-frame generation. Ref2VA accepts multiple reference images, videos,... -
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DeepSeek VL2
Mixture-of-Experts Vision-Language Models for Advanced Multimodal
DeepSeek-VL2 is DeepSeek’s vision + language multimodal model—essentially the next-gen successor to their first vision-language models. It combines image and text inputs into a unified embedding / reasoning space so that you can query with text and image jointly (e.g. “What’s going on in this scene?” or “Generate a caption appropriate to context”). The model supports both image understanding (vision tasks) and multimodal reasoning, and is likely used as a component in agent systems to process visual inputs as context for downstream tasks. ... -
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Hunyuan3D-1
A Unified Framework for Text-to-3D and Image-to-3D Generation
Hunyuan3D-1 is an earlier version in the same 3D generation line (the unified framework for text-to-3D and image-to-3D tasks) by Tencent Hunyuan. It provides a framework combining shape generation and texture synthesis, enabling users to create 3D assets from images or text conditions. While less advanced than version 2.1, it laid the foundations for the later PBR, higher resolution, and open-source enhancements. (Note: less detailed public documentation was found for Hunyuan3D-1 compared to 2.1.). ... -
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HiDream-I1
Open-source image generative foundation model
...The model uses a Llama 3.1 text encoder path and requires the proper Hugging Face access setup for automatic downloads. It is useful for researchers, developers, and creative AI builders who want an open text-to-image model with strong benchmark performance and multiple deployment options. -
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MedGemma
Collection of Gemma 3 variants that are trained for performance
MedGemma is a collection of specialized open-source AI models created by Google as part of its Health AI Developer Foundations initiative, built on the Gemma 3 family of transformer models and trained for medical text and image comprehension tasks that help accelerate the development of healthcare-focused AI applications. It includes multiple variants such as a 4 billion-parameter multimodal model that can process both medical images and text and a 27 billion-parameter text-only (and multimodal) model that offers deeper clinical reasoning and understanding at higher capacity, making it suitable for complex tasks like medical question answering, summarization of clinical notes, or generating reports from radiology images. ... -
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LingBot-Video
Scaling Mixture-of-Experts Video Pretraining for Embodied Intelligence
...It is designed to connect video synthesis with physical-world understanding instead of generating only visually appealing clips. The project includes dense and MoE model variants for text-to-image, text-to-video, and text-image-to-video workflows. Its training combines large-scale web video data with more than 70,000 hours of embodied data. A multi-reward system emphasizes aesthetics, physical rationality, and task completion. The repository includes models, inference code, prompt rewriting tools, refiner workflows, and single-GPU or multi-GPU scripts. -
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HunyuanCustom
Multimodal-Driven Architecture for Customized Video Generation
HunyuanCustom is a multimodal video customization framework by Tencent Hunyuan, aimed at generating customized videos featuring particular subjects (people, characters) under flexible conditions, while maintaining subject/identity consistency. It supports conditioning via image, audio, video, and text, and can perform subject replacement in videos, generate avatars speaking given audio, or combine multiple subject images. The architecture builds on HunyuanVideo, with added modules for identity reinforcement and modality-specific condition injection. Text-image fusion module based on LLaVA for improved multimodal understanding. ... -
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DeepSeek-OCR
Contexts Optical Compression
DeepSeek-OCR is an open-source optical character recognition solution built as part of the broader DeepSeek AI vision-language ecosystem. It is designed to extract text from images, PDFs, and scanned documents, and integrates with multimodal capabilities that understand layout, context, and visual elements beyond raw character recognition. The system treats OCR not simply as “read the text” but as “understand what the text is doing in the image”—for example distinguishing captions from body text, interpreting tables, or recognizing handwritten versus printed words. ... -
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HunyuanDiT
Diffusion Transformer with Fine-Grained Chinese Understanding
...LoRA, ControlNet (pose, depth, canny), IP-adapter to extend control over generation. Integration with Gradio for web demos and diffusers / command-line compatibility. Supports multi-turn T2I (text-to-image) interactions so users can iteratively refine their images via dialogue. -
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MiniMind-O
A 0.1B Omni model trained from scratch
MiniMind-O is an educational open-source project for building a small end-to-end Omni model from scratch. It extends the MiniMind family by exploring a model that can handle text, audio, and image inputs while producing text and streaming speech outputs. The project is designed to make multimodal AI training more accessible by keeping the model size small enough for ordinary personal hardware. It includes both mini and full training data paths, allowing learners to run a complete workflow quickly or reproduce the released model setup more closely. ... -
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SAM 3
Code for running inference and finetuning with SAM 3 model
SAM 3 (Segment Anything Model 3) is a unified foundation model for promptable segmentation in both images and videos, capable of detecting, segmenting, and tracking objects. It accepts both text prompts (open-vocabulary concepts like “red car” or “goalkeeper in white”) and visual prompts (points, boxes, masks) and returns high-quality masks, boxes, and scores for the requested concepts. Compared with SAM 2, SAM 3 introduces the ability to exhaustively segment all instances of an... -
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Kimi-K3
Open Frontier Intelligence
Kimi K3 is an open-weight, native multimodal agentic model from Moonshot AI for advanced reasoning, coding, and knowledge work. It uses a 2.8-trillion-parameter mixture-of-experts architecture while activating 104 billion parameters per token. Its design combines Kimi Delta Attention, gated multihead latent attention, Attention Residuals, and Stable LatentMoE routing. The model can process text and images and supports a context window of more than one million tokens. It is built for long... -
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HY-World 2.0
A Multi-Modal World Model for Reconstructing, Generating, Simulation
HY-World 2.0 is a multi-modal world model framework for reconstructing, generating, and simulating navigable 3D worlds from diverse inputs. It accepts text prompts, single-view images, multi-view images, and videos, and produces 3D world representations rather than limiting output to flat video generation. For text and single-image inputs, it generates high-fidelity 3D Gaussian Splatting scenes through a multi-stage pipeline that includes panorama generation, trajectory planning, world expansion, and world composition. ... -
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GLM-OCR
Accurate × Fast × Comprehensive
GLM-OCR is an open-source multimodal optical character recognition (OCR) model built on a GLM-V encoder–decoder foundation that brings robust, accurate document understanding to complex real-world layouts and modalities. Designed to handle text recognition, table parsing, formula extraction, and general information retrieval from documents containing mixed content, GLM-OCR excels across major benchmarks while remaining highly efficient with a relatively compact parameter size (~0.9B), enabling deployment in high-concurrency services and edge environments. The model’s multimodal capabilities allow it to reason across image and text content holistically, capturing structured and unstructured information from pages that include dense tables, seals, code snippets, and varied document graphics. ... -
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fast-stable-diffusion
Fast-stable-diffusion + DreamBooth
...Rather than being a standalone packaged application, this project offers ready-to-use interactive notebooks that install and launch full-feature Stable Diffusion web UIs inside Colab without requiring complex local setups or GPU installations. Users can run both AUTOMATIC1111’s interface and ComfyUI workflows with minimal configuration, experiment with DreamBooth fine-tuning, and explore features like text-to-image generation, inpainting, and image-to-image transformations all within a browser session. Because it is configured for Colab, the project leverages Colab’s hosted GPUs, making it possible to use Stable Diffusion even without a powerful local GPU.
