{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-pku-yuangroup--magictime","slug":"pku-yuangroup--magictime","name":"MagicTime","type":"repo","url":"https://pku-yuangroup.github.io/MagicTime/","page_url":"https://unfragile.ai/pku-yuangroup--magictime","categories":["video-generation"],"tags":["diffusion-models","long-video-generation","metamorphic-video-generation","open-sora-plan","text-to-video","time-lapse","time-lapse-dataset","video-generation"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-pku-yuangroup--magictime__cap_0","uri":"capability://image.visual.metamorphic.time.lapse.video.generation.from.text.prompts","name":"metamorphic time-lapse video generation from text prompts","description":"Generates time-lapse videos depicting physical transformations (plant growth, construction, melting) by conditioning a modified Stable Diffusion v1.5 base model with specialized Magic Adapters (spatial and temporal variants) and a Magic Text Encoder trained on metamorphic video datasets. The pipeline encodes text prompts through the Magic Text Encoder, guides diffusion-based frame generation with temporal coherence constraints via the Motion Module, and compiles output frames into coherent video sequences that maintain object identity across significant visual changes.","intents":["Generate time-lapse videos of natural processes like plant growth or ice melting from text descriptions","Create construction or assembly time-lapse sequences from prompts without manual frame-by-frame editing","Produce cooking or transformation videos showing progressive changes over time","Generate metamorphic videos that maintain physical plausibility and object persistence across frames"],"best_for":["Content creators producing time-lapse videos for educational or documentary purposes","Visual effects artists needing rapid prototyping of transformation sequences","Researchers studying temporal coherence in video generation models","Developers building video generation pipelines requiring metamorphic capabilities"],"limitations":["Specialized for metamorphic/transformation content; general-purpose video generation may be less effective","Requires significant VRAM (typically 24GB+ for full quality generation) due to diffusion model size","Generation speed is slow (minutes per video) compared to real-time video systems","Output quality depends heavily on prompt engineering and understanding of metamorphic concepts","Limited to video lengths determined by training data (typically short clips, not feature-length content)"],"requires":["Python 3.8+","PyTorch with CUDA support (for GPU acceleration)","24GB+ VRAM for optimal generation quality","Pre-trained model checkpoints (Stable Diffusion v1.5 base, Motion Module, Magic Adapters)","Text encoder compatible with the Magic Text Encoder architecture"],"input_types":["text (natural language prompts describing metamorphic transformations)","configuration parameters (video dimensions, frame count, seed, sampling steps)"],"output_types":["video files (MP4 or other container formats)","frame sequences (individual PNG/JPG frames)","latent representations (intermediate diffusion states)"],"categories":["image-visual","video-generation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-pku-yuangroup--magictime__cap_1","uri":"capability://image.visual.style.aware.video.generation.via.dreambooth.model.composition","name":"style-aware video generation via dreambooth model composition","description":"Applies visual style transfer to generated videos by composing DreamBooth fine-tuned models with the base diffusion pipeline, allowing users to select from pre-trained style variants that define aesthetic properties (e.g., oil painting, photorealistic, anime) without retraining the entire model. The system loads style-specific DreamBooth checkpoints and integrates them into the diffusion sampling process, enabling consistent stylistic rendering across all generated frames.","intents":["Generate time-lapse videos in specific visual styles (photorealistic, artistic, animated) from a single prompt","Apply consistent aesthetic across multiple video generations without manual post-processing","Create branded content with specific visual identities by leveraging pre-trained style models","Extend metamorphic video generation with diverse artistic interpretations"],"best_for":["Content creators wanting stylistically consistent video outputs","Teams managing brand-specific visual guidelines in video generation","Developers building multi-style video generation pipelines","Users without machine learning expertise who want to apply complex style transformations"],"limitations":["Style quality depends on quality of underlying DreamBooth training data","Adding DreamBooth models increases memory footprint and generation latency","Limited to pre-trained styles; custom styles require additional DreamBooth fine-tuning","Style transfer may conflict with prompt semantics in edge cases, requiring prompt engineering"],"requires":["Pre-trained DreamBooth model checkpoints for desired styles","Base diffusion model and Magic Adapter components","Sufficient VRAM to load both base model and DreamBooth weights simultaneously"],"input_types":["text prompt","style selection (identifier or path to DreamBooth checkpoint)"],"output_types":["styled video files","frame sequences with applied style"],"categories":["image-visual","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-pku-yuangroup--magictime__cap_10","uri":"capability://image.visual.multi.adapter.composition.for.spatial.temporal.generation.control","name":"multi-adapter composition for spatial-temporal generation control","description":"Combines Magic Adapter S (spatial detail focus) and Magic Adapter T (temporal coherence focus) during generation to provide fine-grained control over the balance between visual detail and temporal smoothness. The adapters operate on different aspects of the diffusion process—spatial adapter enhances object details and textures, temporal adapter constrains frame-to-frame consistency—allowing users to tune the trade-off between visual quality and temporal stability.","intents":["Balance visual detail quality with temporal coherence in generated videos","Enhance spatial details in specific regions while maintaining temporal consistency","Control the trade-off between photorealism and smooth motion","Adapt generation to different content types (detailed objects vs. smooth transformations)"],"best_for":["Users requiring fine-grained control over generation quality aspects","Content creators optimizing for specific visual styles","Researchers studying spatial-temporal trade-offs in video generation","Teams with diverse content requirements (detail-heavy vs. motion-smooth)"],"limitations":["Adapter composition adds computational overhead (increased generation time)","Balancing adapters requires experimentation; no automatic optimization","Conflicting adapter objectives may produce suboptimal results","Adapter effectiveness depends on training data and may not generalize to novel content"],"requires":["Pre-trained Magic Adapter S and Magic Adapter T checkpoints","Base diffusion model compatible with both adapters","Configuration parameters specifying adapter weights/influence"],"input_types":["adapter weight parameters (typically 0.0-1.0 range)","text prompts","generation parameters"],"output_types":["videos with balanced spatial-temporal quality","generation metadata including adapter weights used"],"categories":["image-visual","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-pku-yuangroup--magictime__cap_2","uri":"capability://image.visual.modular.motion.module.based.temporal.coherence.enforcement","name":"modular motion module-based temporal coherence enforcement","description":"Ensures temporal consistency across generated video frames by integrating a dedicated Motion Module that operates on latent representations during the diffusion process. The Motion Module constrains frame-to-frame optical flow and appearance consistency, preventing temporal flickering and ensuring smooth transitions between frames depicting transformations. This component works in parallel with spatial diffusion, applying temporal constraints at each sampling step.","intents":["Prevent temporal flickering and jitter in generated video sequences","Maintain smooth object motion and appearance consistency across frames","Ensure that transformations progress naturally without abrupt visual discontinuities","Generate videos with coherent temporal dynamics suitable for time-lapse content"],"best_for":["Developers requiring high-quality temporal coherence in video generation","Content creators producing professional-grade time-lapse videos","Researchers studying temporal modeling in diffusion-based video synthesis","Teams building video generation systems where flicker artifacts are unacceptable"],"limitations":["Motion Module adds computational overhead (~15-25% increase in generation time)","Temporal constraints may limit creative variation if too restrictive","Motion Module effectiveness depends on training data quality and diversity","Cannot recover from severe spatial inconsistencies introduced by base diffusion model"],"requires":["Pre-trained Motion Module checkpoint","Base diffusion model with latent space compatible with Motion Module","Sufficient VRAM to load Motion Module alongside base model"],"input_types":["latent representations from diffusion process","temporal constraint parameters (motion smoothness, consistency strength)"],"output_types":["temporally-coherent latent representations","decoded video frames with reduced flicker"],"categories":["image-visual","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-pku-yuangroup--magictime__cap_3","uri":"capability://text.generation.language.specialized.magic.text.encoder.for.metamorphic.prompt.understanding","name":"specialized magic text encoder for metamorphic prompt understanding","description":"Encodes text prompts into embeddings optimized for metamorphic video generation by using a specialized encoder trained on time-lapse and transformation-focused datasets. Unlike standard CLIP encoders, the Magic Text Encoder learns to represent temporal transformation semantics (growth, melting, construction) and physical process descriptions, enabling the diffusion model to better understand and generate videos depicting meaningful changes over time.","intents":["Encode prompts describing physical transformations in ways that guide accurate video generation","Improve model understanding of temporal progression and process-based descriptions","Enable more natural prompt engineering for metamorphic content without special syntax","Capture semantic nuances of time-lapse and transformation concepts"],"best_for":["Users generating metamorphic videos who want better prompt-to-output alignment","Researchers studying text encoding for temporal video generation","Developers building prompt optimization systems for time-lapse content","Teams requiring domain-specific text understanding for video synthesis"],"limitations":["Specialized for metamorphic content; may underperform on general video generation prompts","Encoding quality depends on training data coverage of transformation types","Cannot handle novel transformation concepts not well-represented in training data","Requires retraining to adapt to new domains or transformation types"],"requires":["Pre-trained Magic Text Encoder checkpoint","Text tokenizer compatible with encoder architecture","Input text in natural language format"],"input_types":["text prompts (natural language descriptions of metamorphic transformations)"],"output_types":["embedding vectors (typically 768-1024 dimensions)","attention maps (optional, for interpretability)"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-pku-yuangroup--magictime__cap_4","uri":"capability://tool.use.integration.interactive.gradio.web.ui.with.real.time.parameter.adjustment","name":"interactive gradio web ui with real-time parameter adjustment","description":"Provides a web-based interface (app.py) for video generation with interactive controls for style selection, prompt input, and parameter tuning (dimensions, frame count, seed, sampling steps). The UI integrates the MagicTimeController class to handle model initialization, loading, and generation orchestration, enabling users to adjust parameters and preview results without command-line interaction or code modification.","intents":["Generate videos through a user-friendly web interface without technical setup","Experiment with different styles and parameters interactively","Preview generated videos in real-time within the browser","Adjust generation parameters (resolution, frame count, randomness) without restarting"],"best_for":["Non-technical users wanting to generate time-lapse videos","Content creators iterating on video generation parameters","Teams deploying MagicTime as a shared service","Researchers prototyping video generation experiments"],"limitations":["Web UI adds latency compared to direct API calls (network overhead)","Real-time preview limited by generation speed (minutes per video)","UI responsiveness depends on server hardware and concurrent user load","Parameter space is simplified compared to CLI, hiding advanced options"],"requires":["Python 3.8+","Gradio library","MagicTime model components and checkpoints","Web browser for accessing the interface","Server with GPU for generation (local or remote)"],"input_types":["text (prompt input field)","dropdown selections (style, model variants)","numeric inputs (dimensions, frame count, seed, steps)"],"output_types":["video preview (embedded in web UI)","downloadable video files","generation logs and metadata"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-pku-yuangroup--magictime__cap_5","uri":"capability://automation.workflow.batch.processing.and.cli.based.video.generation.with.yaml.configuration","name":"batch processing and cli-based video generation with yaml configuration","description":"Enables programmatic video generation through a command-line interface (inference_magictime.py) that accepts YAML configuration files specifying model components, generation parameters, and input/output paths. The CLI supports batch processing of multiple prompts from CSV, JSON, or TXT files, allowing users to define complex generation workflows, optimize settings, and automate video production pipelines without manual UI interaction.","intents":["Generate multiple videos in batch from a list of prompts","Define reusable generation configurations in YAML for reproducibility","Integrate video generation into automated content production pipelines","Optimize generation parameters for specific use cases and save configurations"],"best_for":["Developers building automated video generation pipelines","Content production teams generating large volumes of videos","Researchers running systematic experiments with different configurations","DevOps engineers integrating MagicTime into CI/CD workflows"],"limitations":["Requires familiarity with YAML syntax and CLI tools","Batch processing can be slow for large prompt lists (linear with batch size)","No built-in progress tracking or job queue management for distributed processing","Configuration errors in YAML can cause entire batch to fail without granular error recovery"],"requires":["Python 3.8+","Command-line access to the system","YAML configuration files with proper syntax","Input files (CSV, JSON, TXT) with prompts","Sufficient disk space for output videos"],"input_types":["YAML configuration files","CSV/JSON/TXT files with prompt lists","Command-line arguments for parameter overrides"],"output_types":["video files (batch output)","generation logs and metadata","configuration snapshots (for reproducibility)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-pku-yuangroup--magictime__cap_6","uri":"capability://tool.use.integration.checkpoint.system.with.modular.model.component.loading","name":"checkpoint system with modular model component loading","description":"Manages loading and composition of multiple model components (base model, Motion Module, Magic Adapters, DreamBooth models) through a checkpoint system that tracks model paths and versions. The system loads components on-demand, caches them in memory, and allows dynamic composition of different model variants without restarting the application, enabling efficient resource utilization and flexible model experimentation.","intents":["Load different model variants and components without restarting the application","Compose multiple model components (base + adapters + style models) efficiently","Switch between different model configurations for A/B testing","Manage model versions and ensure reproducibility across runs"],"best_for":["Researchers experimenting with different model combinations","Developers building systems requiring dynamic model switching","Teams managing multiple model versions in production","Users with limited VRAM who need to swap models efficiently"],"limitations":["Loading large models incurs latency (typically 10-30 seconds per component)","Memory caching of multiple models can exceed available VRAM","No automatic cleanup of unused cached models; requires manual memory management","Checkpoint format is proprietary; migrating to other frameworks requires conversion"],"requires":["Model checkpoint files in MagicTime format","Configuration files specifying checkpoint paths","Sufficient VRAM to hold active models (24GB+ recommended)","Disk space for storing multiple model variants"],"input_types":["checkpoint paths (file system paths or URLs)","configuration files specifying model components","model selection parameters"],"output_types":["loaded model objects in memory","model metadata and version information","generation results using composed models"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-pku-yuangroup--magictime__cap_7","uri":"capability://data.processing.analysis.frame.extraction.and.video.captioning.for.dataset.creation","name":"frame extraction and video captioning for dataset creation","description":"Provides data preprocessing utilities for creating metamorphic video datasets by extracting frames from source videos and generating captions using vision-language models. The system processes raw video files into frame sequences and associates them with text descriptions of the transformations, enabling the creation of training data for fine-tuning or evaluating metamorphic video generation models.","intents":["Extract frames from existing time-lapse videos for dataset creation","Generate captions describing metamorphic transformations for training data","Prepare datasets for fine-tuning MagicTime or other video generation models","Create evaluation benchmarks for metamorphic video generation"],"best_for":["Researchers building metamorphic video datasets","Teams fine-tuning MagicTime on domain-specific content","Data engineers preparing training data for video generation models","Developers creating evaluation benchmarks for time-lapse generation"],"limitations":["Frame extraction quality depends on source video quality and codec","Automatic captioning may require manual correction for accuracy","Processing large video collections is computationally expensive and time-consuming","No built-in deduplication or quality filtering of extracted frames"],"requires":["Source video files (MP4, AVI, MOV, etc.)","Vision-language model for captioning (e.g., BLIP, LLaVA)","Sufficient disk space for frame storage","Python 3.8+ with video processing libraries (ffmpeg, opencv)"],"input_types":["video files","frame extraction parameters (sampling rate, resolution)","captioning model selection"],"output_types":["frame sequences (PNG/JPG files)","caption files (JSON, CSV, or text)","metadata files (frame timestamps, video source)"],"categories":["data-processing-analysis","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-pku-yuangroup--magictime__cap_8","uri":"capability://text.generation.language.prompt.engineering.guidance.and.transformation.semantic.understanding","name":"prompt engineering guidance and transformation semantic understanding","description":"Provides documentation and examples for crafting effective prompts that describe metamorphic transformations, including guidance on temporal language, physical process descriptions, and transformation-specific keywords. The system helps users understand how to phrase prompts to maximize model understanding of growth, melting, construction, and other time-lapse phenomena, improving generation quality through better prompt semantics.","intents":["Learn how to write effective prompts for metamorphic video generation","Understand which keywords and phrasings improve transformation depiction","Discover examples of successful prompts for different transformation types","Optimize prompts iteratively based on generation results"],"best_for":["Content creators new to metamorphic video generation","Users wanting to improve generation quality through better prompts","Teams developing prompt optimization strategies","Researchers studying prompt engineering for temporal video generation"],"limitations":["Prompt engineering is empirical; no guaranteed formula for optimal prompts","Guidance may not transfer to novel transformation types not covered in documentation","Optimal prompts vary based on model version and training data","Requires experimentation and iteration to find effective phrasings"],"requires":["Access to MagicTime documentation and examples","Understanding of natural language and transformation concepts","Willingness to experiment with different prompt variations"],"input_types":["text prompts (user-written descriptions)","feedback from generation results"],"output_types":["improved prompts","generation quality metrics","prompt variation examples"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-pku-yuangroup--magictime__cap_9","uri":"capability://automation.workflow.configuration.driven.style.and.parameter.customization","name":"configuration-driven style and parameter customization","description":"Enables users to customize video generation through YAML configuration files that specify model components, generation parameters (resolution, frame count, sampling steps, guidance scale), and style selections. The configuration system decouples user preferences from code, allowing non-technical users to modify generation behavior by editing configuration files without understanding the underlying implementation.","intents":["Customize video generation parameters without modifying code","Create reusable configuration templates for different use cases","Enable non-technical users to adjust generation settings","Version control generation configurations for reproducibility"],"best_for":["Non-technical users wanting to customize generation without coding","Teams managing multiple generation configurations","Researchers documenting experimental settings","DevOps engineers deploying MagicTime with custom configurations"],"limitations":["YAML syntax errors can cause generation failures","Limited validation of configuration values; invalid parameters may cause runtime errors","No GUI for configuration editing; requires text editor","Configuration changes require restarting generation pipeline"],"requires":["YAML configuration files with proper syntax","Text editor for modifying configurations","Understanding of available parameters and their valid ranges"],"input_types":["YAML configuration files","parameter values (numeric, string, boolean)"],"output_types":["loaded configuration objects","validated parameter sets","generation results using specified configuration"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":40,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+","PyTorch with CUDA support (for GPU acceleration)","24GB+ VRAM for optimal generation quality","Pre-trained model checkpoints (Stable Diffusion v1.5 base, Motion Module, Magic Adapters)","Text encoder compatible with the Magic Text Encoder architecture","Pre-trained DreamBooth model checkpoints for desired styles","Base diffusion model and Magic Adapter components","Sufficient VRAM to load both base model and DreamBooth weights simultaneously","Pre-trained Magic Adapter S and Magic Adapter T checkpoints","Base diffusion model compatible with both adapters"],"failure_modes":["Specialized for metamorphic/transformation content; general-purpose video generation may be less effective","Requires significant VRAM (typically 24GB+ for full quality generation) due to diffusion model size","Generation speed is slow (minutes per video) compared to real-time video systems","Output quality depends heavily on prompt engineering and understanding of metamorphic concepts","Limited to video lengths determined by training data (typically short clips, not feature-length content)","Style quality depends on quality of underlying DreamBooth training data","Adding DreamBooth models increases memory footprint and generation latency","Limited to pre-trained styles; custom styles require additional DreamBooth fine-tuning","Style transfer may conflict with prompt semantics in edge cases, requiring prompt engineering","Adapter composition adds computational overhead (increased generation time)","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.4506155000924169,"quality":0.32,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:22.063Z","last_scraped_at":"2026-05-03T13:59:47.981Z","last_commit":"2026-04-14T03:25:38Z"},"community":{"stars":1342,"forks":123,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=pku-yuangroup--magictime","compare_url":"https://unfragile.ai/compare?artifact=pku-yuangroup--magictime"}},"signature":"p8Trkogh8PiEDHxULolkPDGaI9oSPmnjX/7qMdAUbdl9Fc9yLCQToKDLEgqo2me/uXHBapcDmFmJ5vsTGasLDg==","signedAt":"2026-06-21T21:38:32.716Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/pku-yuangroup--magictime","artifact":"https://unfragile.ai/pku-yuangroup--magictime","verify":"https://unfragile.ai/api/v1/verify?slug=pku-yuangroup--magictime","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}