Showing 3 open source projects for "alignment"

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  • 1
    GLM-Image

    GLM-Image

    GLM-Image: Auto-regressive for Dense-knowledge and High-fidelity Image

    ...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. Because it blends linguistic reasoning with image synthesis, GLM-Image produces visual outputs where semantic relationships and textual accuracy are prioritized alongside artistic style and realism, and its model structure enables it to handle dense visual knowledge tasks that challenge many pure diffusion models. The model’s design and weights are available under an open-source license that encourages experimentation, integration, and deployment across a range of creative workflows.
    Downloads: 1 This Week
    Last Update:
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  • 2
    GLIDE (Text2Im)

    GLIDE (Text2Im)

    GLIDE: a diffusion-based text-conditional image synthesis model

    ...The repository provides both model code and pretrained checkpoints, making it possible for researchers and developers to experiment with text-to-image synthesis. GLIDE includes advanced techniques such as classifier-free guidance, which improves the quality and alignment of generated images with the input text. The project also offers sampling scripts and utilities for exploring how diffusion models can be applied to multimodal tasks. As one of the early diffusion-based text-to-image systems, glide-text2im laid important groundwork for later advances in generative AI research.
    Downloads: 2 This Week
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  • 3
    CLIP Guided Diffusion

    CLIP Guided Diffusion

    A CLI tool/python module for generating images from text

    ...Non-square Generations (experimental) Generate portrait or landscape images by specifying a number to offset the width and/or height. Uses fewer timesteps over the same diffusion schedule. Sacrifices accuracy/alignment for quicker runtime. options: - 25, 50, 150, 250, 500, 1000, ddim25,ddim50,ddim150, ddim250,ddim500,ddim1000 (default: 1000) Prepending a number with ddim will use the ddim scheduler. e.g. ddim25 will use the 25 timstep ddim scheduler. This method may be better at shorter timestep_respacing values. Multiple prompts can be specified with the | character. ...
    Downloads: 2 This Week
    Last Update:
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