{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-phantom-video--phantom","slug":"phantom-video--phantom","name":"Phantom","type":"repo","url":"https://phantom-video.github.io/Phantom/","page_url":"https://unfragile.ai/phantom-video--phantom","categories":["video-generation"],"tags":["aigc","consistency-models","text-to-video","video-generation"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-phantom-video--phantom__cap_0","uri":"capability://image.visual.subject.consistent.text.to.video.generation.with.cross.modal.alignment","name":"subject-consistent text-to-video generation with cross-modal alignment","description":"Generates videos from text prompts while maintaining consistent subject identity across frames through cross-modal alignment between text embeddings and visual features. The system uses consistency models to enforce temporal coherence and subject preservation, processing text descriptions through a learned alignment mechanism that maps semantic intent to stable visual representations across the entire video sequence.","intents":["Generate videos from text descriptions where the main subject remains visually consistent throughout","Create multi-shot videos with the same character or object appearing consistently across scenes","Produce marketing or creative content where brand identity or character appearance must be preserved"],"best_for":["AI researchers and ML engineers building video generation systems with identity preservation requirements","Content creators needing consistent character representation across generated video sequences","Teams developing AIGC pipelines where subject consistency is critical for narrative coherence"],"limitations":["Requires 16GB+ VRAM for 1.3B model variant, 40GB+ for 14B variant — limits deployment to high-end GPUs","Cross-modal alignment adds computational overhead during inference, increasing generation latency compared to unconstrained video generation","Subject consistency degrades with complex multi-subject scenes or rapid scene transitions not well-represented in training data","No built-in support for fine-grained control over subject appearance variations (e.g., aging, costume changes) within single video"],"requires":["Python 3.8 or higher","PyTorch 2.4.0 or higher","CUDA-compatible GPU with minimum 16GB VRAM for base model","~50GB storage for model weights and dependencies","32GB system RAM minimum recommended"],"input_types":["text prompts (natural language descriptions)","optional reference images for subject specification"],"output_types":["video files (MP4, WebM, or other standard formats)","frame sequences with temporal consistency metadata"],"categories":["image-visual","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-phantom-video--phantom__cap_1","uri":"capability://automation.workflow.multi.gpu.distributed.video.generation.with.fsdp","name":"multi-gpu distributed video generation with fsdp","description":"Distributes video generation inference and training across multiple GPUs using Fully Sharded Data Parallel (FSDP) strategy, enabling larger model variants (14B parameters) to run on 8-GPU clusters by sharding model weights, optimizer states, and gradients across devices. The system automatically manages communication patterns and gradient synchronization to maintain training stability while reducing per-GPU memory requirements.","intents":["Scale video generation to larger model variants that exceed single-GPU memory capacity","Reduce per-GPU memory footprint to enable deployment on more accessible hardware configurations","Accelerate training and inference throughput by parallelizing computation across available GPUs"],"best_for":["ML teams with multi-GPU infrastructure (8+ GPUs) looking to train or deploy large video models","Research labs requiring high-throughput video generation for large-scale experiments","Organizations needing to balance model capacity with hardware constraints through distributed computing"],"limitations":["FSDP introduces inter-GPU communication overhead — typically 15-25% latency increase per generation step compared to single-GPU inference","Requires homogeneous GPU cluster with consistent VRAM and compute capability — heterogeneous setups cause bottlenecks","Network bandwidth between GPUs becomes critical bottleneck; slow interconnects (PCIe) significantly degrade performance vs NVLink","Debugging distributed training/inference is complex; errors in one GPU can cascade across the cluster"],"requires":["PyTorch 2.4.0+ with FSDP support","8 NVIDIA GPUs minimum (A100, H100, or equivalent) for 14B model variant","High-bandwidth GPU interconnect (NVLink preferred, PCIe 4.0+ minimum)","NCCL 2.14+ for collective communication","Distributed training framework setup (torchrun or equivalent)"],"input_types":["text prompts","distributed batch of samples across GPU cluster"],"output_types":["video files generated in parallel across GPUs","training checkpoints with sharded model state"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-phantom-video--phantom__cap_10","uri":"capability://data.processing.analysis.model.variant.performance.profiling.and.benchmarking","name":"model variant performance profiling and benchmarking","description":"Provides utilities to measure inference latency, throughput, memory usage, and quality metrics across different model variants (1.3B vs 14B) and hardware configurations, enabling data-driven decisions about model selection. The system profiles generation time, peak memory consumption, and optionally computes quality metrics (LPIPS, FVD) to quantify the accuracy-efficiency tradeoff between variants.","intents":["Compare inference speed and memory requirements between 1.3B and 14B model variants","Measure quality differences between model sizes to inform deployment decisions","Profile performance on different GPU types to estimate cost-per-video"],"best_for":["ML engineers selecting model variants for production deployment","Researchers quantifying accuracy-efficiency tradeoffs in video generation","Teams optimizing cost-per-video for large-scale generation systems"],"limitations":["Benchmarking requires running full inference pipelines — time-consuming for large-scale studies (hours to days)","Quality metrics (FVD, LPIPS) require reference videos or ground truth — not always available for text-to-video","Performance varies significantly with prompt length, video duration, and batch size — single benchmark may not generalize","Profiling overhead (memory tracking, timing) can affect measured performance by 5-15%"],"requires":["Model variants installed and accessible","Benchmark dataset (text prompts, optional reference videos)","GPU with sufficient VRAM for largest variant","Optional: video quality metric libraries (LPIPS, FVD)"],"input_types":["list of text prompts","model variants to benchmark","hardware configuration"],"output_types":["latency measurements (seconds per video)","throughput metrics (videos per hour)","memory usage profiles (peak VRAM, allocation patterns)","quality scores (if reference videos provided)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-phantom-video--phantom__cap_11","uri":"capability://image.visual.video.output.format.conversion.and.quality.settings","name":"video output format conversion and quality settings","description":"Converts generated video frames to standard output formats (MP4, WebM, etc.) with configurable quality settings including bitrate, codec, and resolution. The system handles frame-to-video encoding, manages output file paths, and supports quality presets (low/medium/high) that trade off file size against visual quality.","intents":["Save generated videos in standard formats compatible with video players and platforms","Control output file size and quality through preset configurations","Batch convert frame sequences to videos with consistent settings"],"best_for":["Content creators needing videos in specific formats for distribution platforms","Teams managing large video archives where file size is a concern","Researchers comparing video quality at different bitrates"],"limitations":["Video encoding is CPU-intensive — can take 30-60 seconds per video depending on resolution and bitrate","Codec availability depends on FFmpeg installation — not all codecs available on all systems","Quality settings are codec-specific — presets may not transfer between H.264 and VP9","No built-in quality assessment — users must manually verify output quality"],"requires":["FFmpeg installed and accessible in system PATH","Generated frame sequences (PNG, JPEG, or raw tensors)","Output directory with write permissions"],"input_types":["frame sequences (list of image files or tensor batch)","quality preset (low/medium/high) or explicit bitrate","output format (MP4, WebM, etc.)"],"output_types":["video files in specified format","encoding metadata (codec, bitrate, duration)"],"categories":["image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-phantom-video--phantom__cap_2","uri":"capability://image.visual.consistency.model.based.fast.video.frame.generation","name":"consistency-model-based fast video frame generation","description":"Generates video frames using consistency models rather than traditional diffusion, enabling single-step or few-step generation by learning to map noisy inputs directly to clean outputs through a consistency function. This approach trades off some quality for dramatically reduced inference time, using a learned ODE trajectory that collapses the diffusion process into fewer sampling steps while maintaining temporal coherence across frames.","intents":["Generate videos with minimal latency for interactive or real-time applications","Reduce computational cost of video generation for large-scale batch processing","Enable video generation on resource-constrained hardware by reducing sampling steps from 50+ to 1-4"],"best_for":["Developers building interactive video generation applications with latency constraints (<5 seconds per video)","Teams running large-scale video generation pipelines where inference cost is a primary concern","Edge deployment scenarios where computational resources are limited"],"limitations":["Consistency models typically produce lower visual quality than full diffusion pipelines — noticeable artifacts in fine details and textures","Training consistency models requires pre-trained diffusion models as teachers, adding complexity to the training pipeline","Subject consistency may degrade with very few sampling steps (1-2 steps) due to insufficient refinement iterations","Hyperparameter tuning for consistency function is sensitive — small changes in boundary conditions significantly impact output quality"],"requires":["Pre-trained diffusion model checkpoint for consistency distillation","PyTorch 2.4.0+","GPU with 16GB+ VRAM for inference","Training data for consistency model distillation (if training custom models)"],"input_types":["text prompts","noise tensors (optional, for deterministic generation)"],"output_types":["video frames generated in 1-4 sampling steps","consistency function parameters for custom implementations"],"categories":["image-visual","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-phantom-video--phantom__cap_3","uri":"capability://automation.workflow.configuration.driven.model.variant.selection.and.inference","name":"configuration-driven model variant selection and inference","description":"Provides a configuration system that abstracts model selection, hyperparameter tuning, and inference settings through structured config files, enabling users to switch between Phantom-Wan-1.3B and Phantom-Wan-14B variants without code changes. The system loads model architectures, weights, and inference parameters from configuration, supporting different GPU memory profiles and inference strategies through declarative configuration rather than imperative code.","intents":["Switch between model sizes (1.3B vs 14B) based on available hardware without modifying inference code","Configure inference parameters (sampling steps, guidance scale, batch size) through config files for reproducibility","Manage different inference strategies (single-GPU vs distributed) through configuration"],"best_for":["ML engineers managing multiple model variants across different hardware configurations","Teams needing reproducible inference configurations for experiments and production deployments","Non-expert users who want to adjust model behavior without understanding underlying code"],"limitations":["Configuration system adds indirection — debugging issues requires tracing through config loading and model initialization logic","Limited to pre-defined model variants; adding new architectures requires modifying configuration schema","No built-in validation of configuration compatibility — invalid configs may fail silently or produce cryptic errors during model loading","Configuration files can become complex with many hyperparameters, making it difficult to understand which settings actually impact output"],"requires":["YAML or JSON configuration files (format depends on implementation)","Model weights for selected variant downloaded and accessible","Python 3.8+ with PyTorch 2.4.0+"],"input_types":["configuration files (YAML/JSON)","model variant identifiers (e.g., 'Phantom-Wan-1.3B')"],"output_types":["loaded model instances with configured hyperparameters","inference results with configuration metadata"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-phantom-video--phantom__cap_4","uri":"capability://automation.workflow.command.line.interface.for.batch.video.generation","name":"command-line interface for batch video generation","description":"Provides a CLI tool (infer.sh) that wraps the video generation pipeline, accepting text prompts and configuration parameters as command-line arguments and orchestrating the full generation workflow including model loading, inference, and output saving. The CLI abstracts away Python API complexity and enables integration with shell scripts, CI/CD pipelines, and batch processing systems through standard command invocation.","intents":["Generate videos in batch from shell scripts or CI/CD pipelines without writing Python code","Integrate video generation into existing command-line workflows and automation systems","Enable non-technical users to generate videos through simple command invocation"],"best_for":["DevOps engineers integrating video generation into CI/CD pipelines","Researchers running batch experiments with multiple text prompts","Teams building command-line tools that need video generation as a subprocess"],"limitations":["CLI argument parsing may not support all model configuration options — complex setups require config files or Python API","Error handling through exit codes and stderr is less informative than Python exceptions — debugging failures requires log file inspection","No built-in progress reporting or streaming output — long-running generations provide no feedback until completion","Batch processing requires external orchestration (GNU parallel, xargs) — no native batching support in CLI"],"requires":["Bash shell (Linux/macOS) or equivalent shell environment","Python 3.8+ with Phantom installed","Model weights downloaded and accessible via configured paths"],"input_types":["command-line arguments (text prompts, config paths, output directories)","environment variables for model paths and CUDA settings"],"output_types":["video files written to specified output directory","exit codes indicating success/failure"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-phantom-video--phantom__cap_5","uri":"capability://automation.workflow.model.checkpoint.loading.and.weight.initialization","name":"model checkpoint loading and weight initialization","description":"Implements model loading logic that deserializes pre-trained weights from checkpoint files, initializes model architecture based on configuration, and validates weight compatibility with the target architecture. The system handles different checkpoint formats, manages device placement (CPU/GPU), and supports partial weight loading for transfer learning scenarios where only specific layers are updated.","intents":["Load pre-trained Phantom models from disk without manual weight mapping","Initialize models with correct architecture and weights for immediate inference","Support fine-tuning workflows by selectively loading weights for specific layers"],"best_for":["Researchers fine-tuning Phantom on custom datasets","Practitioners deploying pre-trained models for inference","Teams managing model versioning and checkpoint management"],"limitations":["Checkpoint files are large (multi-GB) — loading adds 30-60 second startup latency per model","No built-in checkpoint validation — corrupted weights may load without error and cause silent inference failures","Weight format is tied to specific PyTorch versions — checkpoints from older versions may fail to load with newer PyTorch","No automatic weight quantization or compression — full precision weights require full VRAM allocation"],"requires":["Pre-trained checkpoint files in PyTorch format (.pt, .pth, or .safetensors)","PyTorch 2.4.0+ with matching CUDA version","Sufficient disk space for checkpoint files (~10-50GB depending on model variant)","GPU with enough VRAM to hold model weights"],"input_types":["checkpoint file paths","configuration specifying model architecture"],"output_types":["initialized PyTorch model with loaded weights","metadata about loaded checkpoint (version, training steps, etc.)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-phantom-video--phantom__cap_6","uri":"capability://image.visual.temporal.coherence.enforcement.through.frame.to.frame.consistency","name":"temporal coherence enforcement through frame-to-frame consistency","description":"Enforces temporal coherence across video frames by applying consistency constraints between adjacent frames during generation, ensuring smooth transitions and preventing flickering or subject drift. The system uses the cross-modal alignment mechanism to maintain semantic consistency while allowing natural motion and scene changes, applying regularization that penalizes large frame-to-frame differences in subject representation while permitting expected motion.","intents":["Generate videos with smooth, flicker-free transitions between frames","Prevent subject identity drift or sudden appearance changes across the video sequence","Enable natural motion and scene changes while maintaining visual continuity"],"best_for":["Content creators requiring professional-quality video output without temporal artifacts","Researchers studying temporal consistency in generative models","Teams building video generation systems where visual quality is paramount"],"limitations":["Temporal consistency constraints add 10-20% computational overhead during inference","Over-constraining temporal coherence can produce unnatural motion or 'frozen' subjects","Consistency enforcement is frame-pair based — long-range temporal dependencies (20+ frames) may still accumulate drift","No explicit control over motion magnitude — users cannot specify how much subject movement is acceptable"],"requires":["Cross-modal alignment mechanism (built into Phantom architecture)","Temporal consistency loss function in training pipeline","GPU with sufficient VRAM for frame buffering during inference"],"input_types":["video frame sequences","temporal consistency weight (hyperparameter)"],"output_types":["temporally coherent video frames","consistency metrics per frame pair"],"categories":["image-visual","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-phantom-video--phantom__cap_7","uri":"capability://planning.reasoning.inference.time.guidance.and.prompt.conditioning","name":"inference-time guidance and prompt conditioning","description":"Implements classifier-free guidance at inference time, allowing users to control the strength of text prompt conditioning through a guidance scale parameter that interpolates between unconditional and conditional generation. The system computes both conditional (text-guided) and unconditional predictions, then blends them according to guidance scale to balance prompt adherence with output diversity and quality.","intents":["Control how strongly the generated video follows the input text prompt","Trade off between prompt fidelity and output diversity/quality","Fine-tune generation behavior without retraining the model"],"best_for":["Content creators iterating on prompts and wanting to adjust generation behavior","Researchers studying the effect of guidance strength on video quality","Teams deploying models where different users have different guidance preferences"],"limitations":["Guidance requires computing both conditional and unconditional predictions — doubles inference cost compared to unconditional generation","Guidance scale is a continuous hyperparameter with no principled way to select optimal value — requires manual tuning per prompt","Very high guidance scales (>15) often produce artifacts or distorted subjects as the model is pushed beyond its training distribution","Guidance effectiveness varies significantly across different prompt types and model variants"],"requires":["Model trained with classifier-free guidance (unconditional predictions available)","Guidance scale parameter (typically 1.0-15.0, where 1.0 is unconditional)","2x inference compute compared to unconditional generation"],"input_types":["text prompts","guidance scale value (float)"],"output_types":["guided video output","guidance metrics (conditional/unconditional prediction magnitudes)"],"categories":["planning-reasoning","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-phantom-video--phantom__cap_8","uri":"capability://automation.workflow.batch.inference.with.dynamic.batching.and.memory.management","name":"batch inference with dynamic batching and memory management","description":"Processes multiple video generation requests in batches, automatically managing GPU memory allocation and deallocating intermediate tensors to fit multiple samples within available VRAM. The system uses dynamic batching that adjusts batch size based on available memory and prompt length, enabling higher throughput than sequential generation while preventing out-of-memory errors.","intents":["Generate multiple videos efficiently by batching requests and amortizing model loading overhead","Maximize GPU utilization by filling available VRAM with multiple samples","Process large numbers of prompts without manual batch size tuning"],"best_for":["Teams running large-scale video generation experiments with hundreds of prompts","Production systems serving multiple concurrent video generation requests","Researchers benchmarking throughput and cost-per-video metrics"],"limitations":["Dynamic batching adds complexity to error handling — failure in one sample can corrupt batch state","Memory management overhead (tensor allocation/deallocation) adds 5-10% latency per batch","Batch size is limited by longest prompt in batch — heterogeneous prompt lengths reduce effective batch utilization","No built-in prioritization or fairness — large batches can starve small requests"],"requires":["GPU with sufficient VRAM for at least 2 samples (32GB+ recommended for batch size >2)","Dynamic memory allocation support in PyTorch","Batch processing framework (DataLoader or custom batching logic)"],"input_types":["list of text prompts","batch size (auto-tuned or user-specified)"],"output_types":["list of video files","batch processing metrics (throughput, memory usage)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-phantom-video--phantom__cap_9","uri":"capability://image.visual.reference.image.guided.subject.specification","name":"reference image-guided subject specification","description":"Accepts optional reference images that specify the desired appearance of the subject, using image encoders to extract visual features that condition the video generation process alongside text prompts. The system aligns reference image features with text embeddings through the cross-modal alignment mechanism, enabling users to generate videos where the subject matches a provided reference image while following the text description.","intents":["Generate videos of a specific person, character, or object by providing a reference image","Combine text descriptions with visual references for precise subject specification","Enable style transfer or appearance matching from reference images to generated videos"],"best_for":["Content creators who want to generate videos of specific people or objects","Teams building personalized video generation systems","Researchers studying image-to-video generation and appearance consistency"],"limitations":["Reference image quality significantly impacts output quality — low-resolution or ambiguous images produce poor results","Image encoder must be trained to extract features compatible with text embeddings — requires joint training or careful alignment","No explicit control over how much the output should match the reference image vs. follow the text prompt","Reference images with multiple subjects or complex backgrounds can confuse the alignment mechanism"],"requires":["Image encoder (pre-trained or fine-tuned for cross-modal alignment)","Reference image in standard format (PNG, JPG, WebP)","Cross-modal alignment mechanism to bridge image and text features"],"input_types":["text prompts","reference image (optional)"],"output_types":["video with subject appearance matching reference image","alignment confidence scores"],"categories":["image-visual","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":39,"verified":false,"data_access_risk":"high","permissions":["Python 3.8 or higher","PyTorch 2.4.0 or higher","CUDA-compatible GPU with minimum 16GB VRAM for base model","~50GB storage for model weights and dependencies","32GB system RAM minimum recommended","PyTorch 2.4.0+ with FSDP support","8 NVIDIA GPUs minimum (A100, H100, or equivalent) for 14B model variant","High-bandwidth GPU interconnect (NVLink preferred, PCIe 4.0+ minimum)","NCCL 2.14+ for collective communication","Distributed training framework setup (torchrun or equivalent)"],"failure_modes":["Requires 16GB+ VRAM for 1.3B model variant, 40GB+ for 14B variant — limits deployment to high-end GPUs","Cross-modal alignment adds computational overhead during inference, increasing generation latency compared to unconstrained video generation","Subject consistency degrades with complex multi-subject scenes or rapid scene transitions not well-represented in training data","No built-in support for fine-grained control over subject appearance variations (e.g., aging, costume changes) within single video","FSDP introduces inter-GPU communication overhead — typically 15-25% latency increase per generation step compared to single-GPU inference","Requires homogeneous GPU cluster with consistent VRAM and compute capability — heterogeneous setups cause bottlenecks","Network bandwidth between GPUs becomes critical bottleneck; slow interconnects (PCIe) significantly degrade performance vs NVLink","Debugging distributed training/inference is complex; errors in one GPU can cascade across the cluster","Benchmarking requires running full inference pipelines — time-consuming for large-scale studies (hours to days)","Quality metrics (FVD, LPIPS) require reference videos or ground truth — not always available for text-to-video","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.45079481582139247,"quality":0.34,"ecosystem":0.52,"match_graph":0.25,"freshness":0.6,"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":"2025-09-11T14:52:42Z"},"community":{"stars":1501,"forks":97,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=phantom-video--phantom","compare_url":"https://unfragile.ai/compare?artifact=phantom-video--phantom"}},"signature":"2qh0X/ixAfv/JYssfLWfNtuml+wGna1PkzwrnNC3Dk0Ry9GBHO8lbTtKel6+bUlC5wAYJb5Wft+Ztb6W0w9JDg==","signedAt":"2026-06-22T02:35:51.963Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/phantom-video--phantom","artifact":"https://unfragile.ai/phantom-video--phantom","verify":"https://unfragile.ai/api/v1/verify?slug=phantom-video--phantom","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"}}