Showing 7 open source projects for "image steganography code"

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  • 1
    Wan2.2

    Wan2.2

    Wan2.2: Open and Advanced Large-Scale Video Generative Model

    Wan2.2 is a major upgrade to the Wan series of open and advanced large-scale video generative models, incorporating cutting-edge innovations to boost video generation quality and efficiency. It introduces a Mixture-of-Experts (MoE) architecture that splits the denoising process across specialized expert models, increasing total model capacity without raising computational costs. Wan2.2 integrates meticulously curated cinematic aesthetic data, enabling precise control over lighting,...
    Downloads: 105 This Week
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  • 2
    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: 55 This Week
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  • 3
    HunyuanVideo

    HunyuanVideo

    HunyuanVideo: A Systematic Framework For Large Video Generation Model

    HunyuanVideo is a cutting-edge framework designed for large-scale video generation, leveraging advanced AI techniques to synthesize videos from various inputs. It is implemented in PyTorch, providing pre-trained model weights and inference code for efficient deployment. The framework aims to push the boundaries of video generation quality, incorporating multiple innovative approaches to improve the realism and coherence of the generated content. Release of FP8 model weights to reduce GPU...
    Downloads: 16 This Week
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  • 4
    HunyuanVideo-I2V

    HunyuanVideo-I2V

    A Customizable Image-to-Video Model based on HunyuanVideo

    HunyuanVideo-I2V is a customizable image-to-video generation framework from Tencent Hunyuan, built on their HunyuanVideo foundation. It extends video generation so that given a static reference image plus an optional prompt, it generates a video sequence that preserves the reference image’s identity (especially in the first frame) and allows stylized effects via LoRA adapters.
    Downloads: 2 This Week
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  • 5
    Story Flicks

    Story Flicks

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

    ...For creators who want to produce narrative short-form content — whether for social media, storytelling, or prototyping video ideas — story-flicks offers a lightweight, code-backed alternative to complex video editing suites. Because the project is open and modifiable, developers can customize the generation pipeline: adjust story structure, alter rendering parameters, tweak video quality or resolution, or integrate with other AI models (e.g. for audio, voice-over, or image-to-video). It’s especially useful as a starting template or experimentation ground for developers building automated content-creation tools.
    Downloads: 4 This Week
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  • 6
    HunyuanVideo-I2V

    HunyuanVideo-I2V

    A Customizable Image-to-Video Model based on HunyuanVideo

    HunyuanVideo-I2V is a customizable image-to-video generation framework developed by Tencent, extending the capabilities of HunyuanVideo. It allows for high-quality video creation from still images, using PyTorch and providing pre-trained model weights, inference code, and customizable training options. The system includes a LoRA training code for adding special effects and enhancing video realism, aiming to offer versatile and scalable solutions for generating videos from static image inputs.
    Downloads: 2 This Week
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  • 7
    StoryTeller

    StoryTeller

    Multimodal AI Story Teller, built with Stable Diffusion, GPT, etc.

    ...For more advanced use cases, you can also directly interface with Story Teller in Python code.
    Downloads: 1 This Week
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