# showlab/tune-a-video **Attribution required: if you use, quote, or summarise this content, you must credit and link back to [awesome-repositories.com](https://awesome-repositories.com/repository/showlab-tune-a-video).** _How this analysis was created: the description and tags below were written by an AI model that read this project's README and public documentation pages; stars, license and language come straight from the GitHub API. The model does not read the source code._ 4,364 stars · 390 forks · Python · Apache-2.0 ## Links - GitHub: https://github.com/showlab/Tune-A-Video - Homepage: https://tuneavideo.github.io - awesome-repositories: https://awesome-repositories.com/repository/showlab-tune-a-video.md ## Description Tune-A-Video is a text-to-video diffusion framework designed to convert pretrained text-to-image diffusion models into video generators. It utilizes a spatio-temporal attention mechanism and single text-video pair training to enable the synthesis of moving sequences from text prompts. The project provides tools for one-shot video personalization, allowing a model to be tuned on a single reference video to preserve specific characters or artistic styles across new generations. It also functions as a video editor that modifies subjects, backgrounds, and styles through noise-sampling prompt guidance and inversion. The framework covers several core capability areas, including structural guidance extraction to preserve the layout and movement of source videos and spatio-temporal content consistency to keep visual elements stable across frames. These processes enable personalized video synthesis and the modification of visual content via guided sampling. ## Tags ### Artificial Intelligence & ML - [Temporal Adaptation](https://awesome-repositories.com/f/artificial-intelligence-ml/generative-ai-resources/diffusion-visual-models/generative-ai-pipelines/text-to-image-generators/temporal-adaptation.md) — Adapts pretrained text-to-image diffusion models for video production by introducing temporal weights into the denoising process. - [Text-to-Video Generators](https://awesome-repositories.com/f/artificial-intelligence-ml/generative-ai-resources/diffusion-visual-models/generative-ai-pipelines/text-to-video-generators.md) — Provides a framework to convert pretrained text-to-image diffusion models into generators that synthesize video from text prompts. ([source](https://cdn.jsdelivr.net/gh/showlab/tune-a-video@main/README.md)) - [Spatio-Temporal Attention](https://awesome-repositories.com/f/artificial-intelligence-ml/attention-mechanisms/spatio-temporal-attention.md) — Implements a spatio-temporal attention mechanism to maintain visual consistency across frames when generating video. ([source](https://cdn.jsdelivr.net/gh/showlab/tune-a-video@main/README.md)) - [Text-Video Pair Tuning](https://awesome-repositories.com/f/artificial-intelligence-ml/generative-ai-resources/diffusion-visual-models/generative-ai-pipelines/text-to-video-generators/text-video-pair-tuning.md) — Optimizes the framework using individual text-video pairs to enable personalized video synthesis. - [Text-to-Video Generation](https://awesome-repositories.com/f/artificial-intelligence-ml/generative-ai-resources/diffusion-visual-models/generative-ai-workflows/text-to-video-generation.md) — Synthesizes moving video sequences from textual prompts by repurposing image diffusion models. ([source](https://cdn.jsdelivr.net/gh/showlab/tune-a-video@main/README.md)) - [Personalized Synthesis](https://awesome-repositories.com/f/artificial-intelligence-ml/generative-ai-resources/generative-ai/text-to-image-synthesis/media-synthesis-from-text/video-synthesis/personalized-synthesis.md) — Implements techniques to preserve specific characters and artistic styles across generated video sequences using one-shot tuning. ([source](https://tuneavideo.github.io)) - [Diffusion Model Adaptations](https://awesome-repositories.com/f/artificial-intelligence-ml/language-model-fine-tuning/partial-layer-fine-tunings/lora-fine-tuning-pipelines/diffusion-model-adaptations.md) — Repurposes image-based diffusion weights for video production by introducing temporal attention. - [Video Structure Inversion](https://awesome-repositories.com/f/artificial-intelligence-ml/structural-guidance/video-structure-inversion.md) — Preserves the layout and movement of source videos by extracting noise latent representations. - [Visual Identity Preservation](https://awesome-repositories.com/f/artificial-intelligence-ml/visual-identity-preservation.md) — Preserves specific characters or visual styles across generated frames using personalized tuning. ([source](https://cdn.jsdelivr.net/gh/showlab/tune-a-video@main/README.md)) - [One-Shot Personalization Tools](https://awesome-repositories.com/f/artificial-intelligence-ml/few-shot-learning-baselines/one-shot-personalization-tools.md) — Adapts generative models to a specific subject using a single reference video example. - [Image-to-Video Model Adaptation](https://awesome-repositories.com/f/artificial-intelligence-ml/generative-ai-resources/diffusion-visual-models/generative-models/latent-diffusion-models/video-diffusion-models/image-to-video-model-adaptation.md) — Converts text-to-image diffusion models into video generators through training on specific sequences. ([source](https://cdn.jsdelivr.net/gh/showlab/tune-a-video@main/README.md)) - [Noise-Sampling Guidance](https://awesome-repositories.com/f/artificial-intelligence-ml/image-generation/image-editing/editing-prompt-generation/noise-sampling-guidance.md) — Directs the diffusion process using edited text prompts to modify specific visual elements within video sequences. - [Temporal Structure Extraction](https://awesome-repositories.com/f/artificial-intelligence-ml/structural-guidance/temporal-structure-extraction.md) — Extracts structural guidance from input videos using inversion to ensure output follows specific motion. ([source](https://tuneavideo.github.io/)) - [Diffusion-Based Editors](https://awesome-repositories.com/f/artificial-intelligence-ml/video-generation/prompt-based-video-editors/diffusion-based-editors.md) — Provides a system for modifying video visual properties using diffusion-based noise sampling and inversion. ### Part of an Awesome List - [One-Shot Video Tuning](https://awesome-repositories.com/f/awesome-lists/ai/model-training-and-fine-tuning/model-fine-tuning/subject-specific-fine-tuning/one-shot-video-tuning.md) — Fine-tunes diffusion models on a single video sequence to learn specific subjects and motions. - [Video Generation](https://awesome-repositories.com/f/awesome-lists/ai/video-generation.md) — One-shot tuning for text-to-video generation. ### Graphics & Multimedia - [Generative Video Editing](https://awesome-repositories.com/f/graphics-multimedia/ai-video-editing-automation/generative-video-editing.md) — Modifies subjects, backgrounds, and artistic styles in existing videos using generative ML transformations. - [Inversion-Based Layout Control](https://awesome-repositories.com/f/graphics-multimedia/ai-video-editing-automation/generative-video-editing/inversion-based-layout-control.md) — Preserves the layout and movement of existing videos while changing visual content via inversion-based guidance. ([source](https://tuneavideo.github.io))