Showing 6 open source projects for "diffusion"

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  • 1
    Stable Diffusion Rembg

    Stable Diffusion Rembg

    Removes backgrounds from pictures. Extension for webui

    This project is an extension for the Stable Diffusion Web UI that removes backgrounds from images directly inside the interface. It wraps popular background-removal models so creators can take a generated or uploaded image and isolate the subject with a single click. The workflow is designed to be non-destructive: you can preview, tweak thresholds, and export either a transparent PNG or a masked layer for further editing.
    Downloads: 0 This Week
    Last Update:
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  • 2
    Dream Textures

    Dream Textures

    Stable Diffusion built-in to Blender

    ...Inpaint to fix up images and convert existing textures into seamless ones automatically. Outpaint to increase the size of an image by extending it in any direction. Perform style transfer and create novel animations with Stable Diffusion as a post processing step. Dream Textures has been tested with CUDA and Apple Silicon GPUs. Over 4GB of VRAM is recommended.
    Downloads: 5 This Week
    Last Update:
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  • 3
    Lama Cleaner

    Lama Cleaner

    Image inpainting tool powered by SOTA AI Model

    Image inpainting tool powered by SOTA AI Model. Remove any unwanted object, defect, or people from your pictures or erase and replace(powered by stable diffusion) anything on your pictures. Lama Cleaner is a free, open-source and fully self-hostable inpainting tool powered by state-of-the-art AI models. You can use it to remove any unwanted object, defect, or people from your pictures or erase and replace anything on your pictures. Many AICG creators are using Lama Cleaner to clean-up their work. ...
    Downloads: 52 This Week
    Last Update:
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  • 4
    Stable Diffusion in Docker

    Stable Diffusion in Docker

    Run the Stable Diffusion releases in a Docker container

    Run the Stable Diffusion releases in a Docker container with txt2img, img2img, depth2img, pix2pix, upscale4x, and inpaint. Run the Stable Diffusion releases on Huggingface in a GPU-accelerated Docker container. By default, the pipeline uses the full model and weights which requires a CUDA capable GPU with 8GB+ of VRAM. It should take a few seconds to create one image.
    Downloads: 0 This Week
    Last Update:
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  • Venue management software for the growing attraction Icon
    Venue management software for the growing attraction

    ROLLER is an all-in-one, cloud-based venue management software solution built for attraction businesses.

    At ROLLER, we're passionate about empowering leisure and entertainment businesses to reach new heights of success. With a track record of serving 1,500+ customers across 25 countries, including some of the most renowned names in the attractions industry, including SkyZone, Altitude, American Dream, Uptown Jungle, Flip Out, WhoaZone, Oxygen, Innoflate, and Jumpsquare, we understand the unique needs of playcenters, family entertainment centers, wake parks, water parks, trampoline parks, theme parks, amusement parks, indoor climbing facilities, children's museums, zoos, aquariums and more.
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  • 5
    DALL-E 2 - Pytorch

    DALL-E 2 - Pytorch

    Implementation of DALL-E 2, OpenAI's updated text-to-image synthesis

    Implementation of DALL-E 2, OpenAI's updated text-to-image synthesis neural network, in Pytorch. The main novelty seems to be an extra layer of indirection with the prior network (whether it is an autoregressive transformer or a diffusion network), which predicts an image embedding based on the text embedding from CLIP. Specifically, this repository will only build out the diffusion prior network, as it is the best performing variant (but which incidentally involves a causal transformer as the denoising network) To train DALLE-2 is a 3 step process, with the training of CLIP being the most important. ...
    Downloads: 6 This Week
    Last Update:
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  • 6
    audio-diffusion-pytorch

    audio-diffusion-pytorch

    Audio generation using diffusion models, in PyTorch

    A fully featured audio diffusion library, for PyTorch. Includes models for unconditional audio generation, text-conditional audio generation, diffusion autoencoding, upsampling, and vocoding. The provided models are waveform-based, however, the U-Net (built using a-unet), DiffusionModel, diffusion method, and diffusion samplers are both generic to any dimension and highly customizable to work on other formats.
    Downloads: 2 This Week
    Last Update:
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