Showing 4 open source projects for "stable diffusion"

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    stable-diffusion-videos

    stable-diffusion-videos

    Create videos with Stable Diffusion

    Create videos with Stable Diffusion by exploring the latent space and morphing between text prompts. Try it yourself in Colab.
    Downloads: 13 This Week
    Last Update:
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  • 2
    SimpleTuner

    SimpleTuner

    A general fine-tuning kit geared toward image/video/audio diffusion

    SimpleTuner is an open-source toolkit designed to simplify the fine-tuning of modern diffusion models for generating images, video, and audio. The project focuses on providing a clear and understandable training environment for researchers, developers, and artists who want to customize generative AI models without navigating complex machine learning pipelines. It supports fine-tuning workflows for models such as Stable Diffusion variants and other diffusion architectures, enabling users to adapt pretrained models to specialized datasets or creative tasks. ...
    Downloads: 1 This Week
    Last Update:
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  • 3
    Text-to-image Playground

    Text-to-image Playground

    A playground to generate images from any text prompt using SD

    dalle-playground is an open-source web application that allows users to generate images from natural language text prompts using modern text-to-image generative models. Originally built around DALL-E Mini, the project later transitioned to using Stable Diffusion, enabling more detailed and higher-quality image synthesis. The system combines a backend machine learning service with a browser-based frontend interface that lets users experiment interactively with prompt engineering and generative AI. Developers can run the application locally or deploy it using cloud infrastructure, making it accessible both for experimentation and educational use. ...
    Downloads: 0 This Week
    Last Update:
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  • 4
    OnnxStream

    OnnxStream

    Lightweight inference library for ONNX files, written in C++

    The challenge is to run Stable Diffusion 1.5, which includes a large transformer model with almost 1 billion parameters, on a Raspberry Pi Zero 2, which is a microcomputer with 512MB of RAM, without adding more swap space and without offloading intermediate results on disk. The recommended minimum RAM/VRAM for Stable Diffusion 1.5 is typically 8GB. Generally, major machine learning frameworks and libraries are focused on minimizing inference latency and/or maximizing throughput, all of which at the cost of RAM usage. ...
    Downloads: 24 This Week
    Last Update:
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