Showing 7 open source projects for "python voice synthesis"

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  • 1
    Wan2.1

    Wan2.1

    Wan2.1: Open and Advanced Large-Scale Video Generative Model

    Wan2.1 is a foundational open-source large-scale video generative model developed by the Wan team, providing high-quality video generation from text and images. It employs advanced diffusion-based architectures to produce coherent, temporally consistent videos with realistic motion and visual fidelity. Wan2.1 focuses on efficient video synthesis while maintaining rich semantic and aesthetic detail, enabling applications in content creation, entertainment, and research. The model supports...
    Downloads: 96 This Week
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  • 2
    Story Flicks

    Story Flicks

    Generate high-definition story short videos with one click using AI

    Story Flicks is another open-source project in the AI-assisted video generation / editing space, focused on creating short, story-style videos from script or prompt inputs. It aims to let users generate high-definition short movies or video stories with minimal manual effort, using AI models under the hood to assemble visuals, timing, and possibly narration or subtitles. For creators who want to produce narrative short-form content — whether for social media, storytelling, or prototyping...
    Downloads: 0 This Week
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  • 3
    HunyuanWorld-Voyager

    HunyuanWorld-Voyager

    RGBD video generation model conditioned on camera input

    HunyuanWorld-Voyager is a next-generation video diffusion framework developed by Tencent-Hunyuan for generating world-consistent 3D scene videos from a single input image. By leveraging user-defined camera paths, it enables immersive scene exploration and supports controllable video synthesis with high realism. The system jointly produces aligned RGB and depth video sequences, making it directly applicable to 3D reconstruction tasks. At its core, Voyager integrates a world-consistent video...
    Downloads: 12 This Week
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  • 4
    Wan Move

    Wan Move

    Motion-controllable Video Generation via Latent Trajectory Guidance

    Wan Move is an open-source research codebase for motion-controllable video generation that focuses on enabling fine-grained control of motion within generative video models. It is designed to guide the temporal evolution of visual content by leveraging latent trajectory guidance, allowing users to manipulate how objects move over time without modifying the underlying generative architecture. By representing motion information as dense point trajectories and integrating them into the latent...
    Downloads: 0 This Week
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  • 5
    Video Diffusion - Pytorch

    Video Diffusion - Pytorch

    Implementation of Video Diffusion Models

    Implementation of Video Diffusion Models, Jonathan Ho's new paper extending DDPMs to Video Generation - in Pytorch. Implementation of Video Diffusion Models, Jonathan Ho's new paper extending DDPMs to Video Generation - in Pytorch. It uses a special space-time factored U-net, extending generation from 2D images to 3D videos. 14k for difficult moving mnist (converging much faster and better than NUWA) - wip. Any new developments for text-to-video synthesis will be centralized at...
    Downloads: 2 This Week
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  • 6
    Amiga Memories

    Amiga Memories

    A walk along memory lane

    Amiga Memories is a project (started & released in 2013) that aims to make video programmes that can be published on the internet. The images and sound produced by Amiga Memories are 100% automatically generated. The generator itself is implemented in Squirrel, the 3D rendering is done on GameStart 3D. An Amiga Memories video is mostly based on a narrative. The purpose of the script is to define the spoken and written content. The spoken text will be read by a voice synthesizer (Text To...
    Downloads: 0 This Week
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  • 7
    NÜWA - Pytorch

    NÜWA - Pytorch

    Implementation of NÜWA, attention network for text to video synthesis

    Implementation of NÜWA, state of the art attention network for text-to-video synthesis, in Pytorch. It also contains an extension into video and audio generation, using a dual decoder approach. It seems as though a diffusion-based method has taken the new throne for SOTA. However, I will continue on with NUWA, extending it to use multi-headed codes + hierarchical causal transformer. I think that direction is untapped for improving on this line of work. In the paper, they also present a way...
    Downloads: 0 This Week
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