Showing 7 open source projects for "python-ldap"

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  • 1
    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...
    Downloads: 23 This Week
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  • 2
    Dream Textures

    Dream Textures

    Stable Diffusion built-in to Blender

    Create textures, concept art, background assets, and more with a simple text prompt. Use the 'Seamless' option to create textures that tile perfectly with no visible seam. Texture entire scenes with 'Project Dream Texture' and depth to image. Re-style animations with the Cycles render pass. Run the models on your machine to iterate without slowdowns from a service. Create textures, concept art, and more with text prompts. Learn how to use the various configuration options to get exactly what...
    Downloads: 3 This Week
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  • 3
    MMEditing

    MMEditing

    MMEditing is a low-level vision toolbox based on PyTorch

    MMEditing is an open-source toolbox for low-level vision. It supports various tasks. MMEditing is a low-level vision toolbox based on PyTorch, supporting super-resolution, inpainting, matting, video interpolation, etc. We decompose the editing framework into different components and one can easily construct a customized editor framework by combining different modules. The toolbox directly supports popular and contemporary inpainting, matting, super-resolution and generation tasks. The...
    Downloads: 1 This Week
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  • 4
    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...
    Downloads: 0 This Week
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    Simply solve complex auth. Easy for devs to set up. Easy for non-devs to use.

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  • 5
    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. On less powerful GPUs you may need to modify some of the options; see the Examples section for more details. If you lack a...
    Downloads: 0 This Week
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  • 6
    Video Object Remover – Frame-Accurate

    Video Object Remover – Frame-Accurate

    🎥 A free & open-source Python tool to remove unwanted objects from videos frame-by-frame using brush masking and AI inpainting (OpenCV + FFmpeg). EXE included.

    Video Object Remover – Frame Accurate Edition is a free and open-source desktop application that helps you remove unwanted objects, logos, or watermarks from videos using brush-based masking and AI inpainting. The tool extracts video frames using FFmpeg, lets you mask objects frame-by-frame, and removes them using OpenCV. Built with Python and Tkinter, it features a modern dark-themed GUI, adjustable brush tool, zoom control, and real-time logging. The cleaned video is rebuilt and exported...
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    Downloads: 83 This Week
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  • 7
    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. Note: no pre-trained models are provided here, this library is meant for research...
    Downloads: 2 This Week
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