Showing 3 open source projects for "objects"

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    MOSS-Image

    MOSS-Image

    Official repository for MOSS-Image, a 5B-parameter base DiT model

    ...It creates high-quality photorealistic and anime-style images from natural language descriptions. The model specializes in accurate text rendering and structured visual compositions. Users can control the placement of text, objects, and other elements through JSON-based layouts. It supports detailed prompts of up to 4,000 tokens for complex scenes. Two model variants, Base and SFT, support general generation and improved visual organization. Developers can run inference through Python, Gradio, ComfyUI, SGLang, and vLLM-Omni. Fine-tuning workflows support custom datasets and LoRA adaptation, while configurable memory modes enable deployment on different hardware.
    Downloads: 14 This Week
    Last Update:
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  • 2
    Free AI Watermark Remover - FreeRepair

    Free AI Watermark Remover - FreeRepair

    AI-powered tool to quickly remove watermarks from images flawlessly

    AI Watermark Remover (Free And Open-Source) & Make Blurry Images Clearer Or Larger Tool - FreeRepair, Simulation IOPaint Based On The Django Of Python With No Sign-Up. As a free, open-source, AI-powered tool, FreeRepair makes it easy to remove watermarks, logos, text or clutter from images, and blurry images can be made clearer or larger. No installation, no internet connection, it works out of the box, safe and secure, unlimited.
    5.0 out of 5 stars 1 Review
    Downloads: 34 This Week
    Last Update:
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  • 3
    GANformer

    GANformer

    Generative Adversarial Transformers

    ...The model iteratively propagates information from a set of latent variables to the evolving visual features and vice versa, to support the refinement of each in light of the other and encourage the emergence of compositional representations of objects and scenes. In contrast to the classic transformer architecture, it utilizes multiplicative integration that allows flexible region-based modulation and can thus be seen as a generalization of the successful StyleGAN network. Using the pre-trained models (generated after training for 5-7x less steps than StyleGAN2 models! Training our models for longer will improve the image quality further).
    Downloads: 0 This Week
    Last Update:
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