Open Source Linux AI Video Generators - Page 2
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AI Video Generators for Linux
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StoryTeller
Multimodal AI Story Teller, built with Stable Diffusion, GPT, etc.
A multimodal AI story teller, built with Stable Diffusion, GPT, and neural text-to-speech (TTS). Given a prompt as an opening line of a story, GPT writes the rest of the plot; Stable Diffusion draws an image for each sentence; a TTS model narrates each line, resulting in a fully animated video of a short story, replete with audio and visuals. To develop locally, install dev dependencies and install pre-commit hooks. This will automatically trigger linting and code quality checks before each commit. The final video will be saved as /out/out.mp4, alongside other intermediate images, audio files, and subtitles. For more advanced use cases, you can also directly interface with Story Teller in Python code. -
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Aphantasia
CLIP + FFT/DWT/RGB = text to image/video
This is a collection of text-to-image tools, evolved from the artwork of the same name. Based on CLIP model and Lucent library, with FFT/DWT/RGB parameterizes (no-GAN generation). Illustrip (text-to-video with motion and depth) is added. DWT (wavelets) parameterization is added. Check also colabs below, with VQGAN and SIREN+FFM generators. Tested on Python 3.7 with PyTorch 1.7.1 or 1.8. Generating massive detailed textures, a la deepdream, fullHD/4K resolutions and above, various CLIP models (including multi-language from SBERT), continuous mode to process phrase lists (e.g. illustrating lyrics), pan/zoom motion with smooth interpolation. Direct RGB pixels optimization (very stable) depth-based 3D look (courtesy of deKxi, based on AdaBins), complex queries: text and/or image as main prompts, separate text prompts for style and to subtract (avoid) topics. Starting/resuming process from saved parameters or from an image. -
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DCVGAN
DCVGAN: Depth Conditional Video Generation, ICIP 2019.
This paper proposes a new GAN architecture for video generation with depth videos and color videos. The proposed model explicitly uses the information of depth in a video sequence as additional information for a GAN-based video generation scheme to make the model understands scene dynamics more accurately. The model uses pairs of color video and depth video for training and generates a video using the two steps. Generate the depth video to model the scene dynamics based on the geometrical information. To add appropriate color to the geometrical information of the scene, the domain translation from depth to color is performed for each image. This model has three networks in the generator. In addition, the model has two discriminators. -
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Phenaki - Pytorch
Implementation of Phenaki Video, which uses Mask GIT
Implementation of Phenaki Video, which uses Mask GIT to produce text-guided videos of up to 2 minutes in length, in Pytorch. It will also combine another technique involving a token critic for potentially even better generations. A new paper suggests that instead of relying on the predicted probabilities of each token as a measure of confidence, one can train an extra critic to decide what to iteratively mask during sampling. This repository will also endeavor to allow the researcher to train on text-to-image and then text-to-video. Similarly, for unconditional training, the researcher should be able to first train on images and then fine tune on video. -
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Recurrent Interface Network (RIN)
Implementation of Recurrent Interface Network (RIN)
Implementation of Recurrent Interface Network (RIN), for highly efficient generation of images and video without cascading networks, in Pytorch. The author unawaredly reinvented the induced set-attention block from the set transformers paper. They also combine this with the self-conditioning technique from the Bit Diffusion paper, specifically for the latents. The last ingredient seems to be a new noise function based around the sigmoid, which the author claims is better than cosine scheduler for larger images. The big surprise is that the generations can reach this level of fidelity. Will need to verify this on my own machine. Additionally, we will try adding an extra linear attention on the main branch as well as self-conditioning in the pixel space. The insight of being able to self-condition on any hidden state of the network as well as the newly proposed sigmoid noise schedule are the two main findings. -
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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 Imagen-pytorch. For conditioning on text, they derived text embeddings by first passing the tokenized text through BERT-large. You can also directly pass in the descriptions of the video as strings, if you plan on using BERT-base for text conditioning. This repository also contains a handy Trainer class for training on a folder of gifs. Each gif must be of the correct dimensions image_size and num_frames. -
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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 space of an image-to-video model, the project produces videos with more precise and controllable motion behavior than many existing methods. Wan-Move is particularly notable for eliminating the need for additional motion encoders, instead directly infusing motion cues into spatiotemporal features, which simplifies both training and inference. -
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video2robot
End-to-end pipeline converting generative videos
video2robot is an end-to-end open-source pipeline that converts generative video or prompt-driven motion content into executable humanoid robot motion sequences, enabling researchers and developers to go from high-level action descriptions or videos to robot-ready motion data. The pipeline supports both prompt-to-video generation using models like Veo/Sora and video upload processing, followed by human pose extraction through a 3D pose model and retargeting of that motion to robot joints using a general motion retargeting system. This workflow allows users to generate robot motion files that specify joint angles, root positions, and orientations that can be deployed on supported robot platforms (e.g., Unitree models). Video2robot includes scripts for each stage of the pipeline (generation, extraction, conversion, visualization) and can run as a CLI or through a basic web UI. -
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VideoCrafter2
Overcoming Data Limitations for High-Quality Video Diffusion Models
VideoCrafter is an open-source video generation and editing toolbox designed to create high-quality video content. It features models for both text-to-video and image-to-video generation. The system is optimized for generating videos from textual descriptions or still images, leveraging advanced diffusion models. VideoCrafter2, an upgraded version, improves on its predecessor by enhancing motion dynamics and concept combinations, especially in low-data scenarios. Users can explore a wide range of creative possibilities, producing cinematic videos that combine artistic styles and real-world scenes. -
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NodeTool
Visual AI Workflow Builder
NodeTool is an open‑source, visual AI workflow builder that lets you connect nodes for text, images, audio, video, data, and automation—then run them locally or on the cloud. Build multi‑step agents, RAG systems, and creative media pipelines without coding, inspect execution in real time, and deploy anywhere: home server, private VPC, RunPod, or Cloud Run. With a local‑first design, NodeTool keeps models and data under your control while still supporting providers like OpenAI, Anthropic, Replicate, and HuggingFace. Use templates to get started fast, customize every step, and share workflows as simple apps across desktop and mobile via secure connections. -
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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. -
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HunyuanCustom
Multimodal-Driven Architecture for Customized Video Generation
HunyuanCustom is a multimodal video customization framework by Tencent Hunyuan, aimed at generating customized videos featuring particular subjects (people, characters) under flexible conditions, while maintaining subject/identity consistency. It supports conditioning via image, audio, video, and text, and can perform subject replacement in videos, generate avatars speaking given audio, or combine multiple subject images. The architecture builds on HunyuanVideo, with added modules for identity reinforcement and modality-specific condition injection. Text-image fusion module based on LLaVA for improved multimodal understanding. Applicable to single- and multi-subject scenarios, video editing/replacement, singing avatars etc. -
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NWT - Pytorch (wip)
Implementation of NWT, audio-to-video generation, in Pytorch
Implementation of NWT, audio-to-video generation, in Pytorch. The paper proposes a new discrete latent representation named Memcodes, which can be succinctly described as a type of multi-head hard-attention to learned memory (codebook) key/values. They claim the need for less codes and smaller codebook dimensions in order to achieve better reconstructions. -
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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 to condition the video generation based on segmentation mask(s). You can easily do this as well, given you train a VQGanVAE on the sketches beforehand. Then, you will use NUWASketch instead of NUWA, which can accept the sketch VAE as a reference. This repository will also offer a variant of NUWA that can produce both video and audio. For now, the audio will need to be encoded manually.
