{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-lightricks--ltx-video","slug":"lightricks--ltx-video","name":"LTX-Video","type":"model","url":"https://ltx.io/model","page_url":"https://unfragile.ai/lightricks--ltx-video","categories":["video-generation"],"tags":["diffusion-models","dit","image-to-video","image-to-video-generation","text-to-video","text-to-video-generation"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-lightricks--ltx-video__cap_0","uri":"capability://image.visual.text.to.video.generation.with.dit.based.diffusion","name":"text-to-video generation with dit-based diffusion","description":"Generates videos directly from natural language prompts using a Diffusion Transformer (DiT) architecture with a rectified flow scheduler. The system encodes text prompts through a language model, then iteratively denoises latent video representations in the causal video autoencoder's latent space, producing 30 FPS video at 1216×704 resolution. Uses spatiotemporal attention mechanisms to maintain temporal coherence across frames while respecting the causal structure of video generation.","intents":["Generate short-form video content from text descriptions without manual filming","Rapidly prototype video ideas for storyboarding and concept validation","Create synthetic video datasets for training or testing purposes"],"best_for":["Content creators and filmmakers prototyping visual concepts","AI researchers benchmarking video generation quality and speed","Developers building video generation APIs or applications"],"limitations":["Generation speed depends on model variant; distilled models trade quality for 10-second HD generation speed","Prompt understanding limited by underlying text encoder; complex narrative instructions may not translate to coherent video","Fixed output resolution of 1216×704; multi-scale pipeline required for higher resolutions adds latency","Temporal consistency degrades beyond ~10 seconds without explicit keyframe conditioning"],"requires":["Python 3.8+","PyTorch 2.0+ with CUDA support (GPU with 16GB+ VRAM recommended)","Model checkpoint file (ltxv-13b-0.9.7-dev.safetensors or variant)","Text encoder weights (typically CLIP or similar)"],"input_types":["text (natural language prompt, 10-500 characters typical)","optional: seed integer for reproducibility"],"output_types":["video file (MP4, WebM, or raw frame tensor)","30 FPS, 1216×704 resolution, 10-second duration default"],"categories":["image-visual","text-to-video-generation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-lightricks--ltx-video__cap_1","uri":"capability://image.visual.image.to.video.animation.with.conditioning.frames","name":"image-to-video animation with conditioning frames","description":"Transforms static images into dynamic videos by conditioning the diffusion process on image embeddings at specified frame positions. The system encodes the input image through the causal video autoencoder, injects it as a conditioning signal at designated temporal positions (e.g., frame 0 for image-to-video), then generates surrounding frames while maintaining visual consistency with the conditioned image. Supports multiple conditioning frames at different temporal positions for keyframe-based animation control.","intents":["Animate still photographs or artwork into short videos with natural motion","Create video transitions between multiple keyframe images","Generate video extensions from a single reference image with text-guided motion"],"best_for":["Photographers and digital artists extending static content into video","Marketing teams creating animated product showcases from product photos","Game developers generating in-between frames for keyframe animation"],"limitations":["Conditioning strength must be balanced; over-conditioning locks output to input image, under-conditioning ignores image entirely","Motion quality degrades if conditioning frames are too dissimilar (e.g., different lighting, angles)","Requires explicit frame indices for conditioning; automatic temporal placement not supported","Image resolution must match or be resized to 1216×704; aspect ratio changes may distort content"],"requires":["Python 3.8+","PyTorch 2.0+ with CUDA support","Model checkpoint (ltxv-13b-0.9.7-dev or variant)","Input image file (PNG, JPEG, WebP; max 1216×704 native resolution)"],"input_types":["image file (PNG, JPEG, WebP)","text prompt describing desired motion or transformation","conditioning_start_frames: integer or list of integers specifying frame positions for conditioning"],"output_types":["video file (MP4, WebM)","30 FPS, 1216×704 resolution, 10 seconds default"],"categories":["image-visual","image-to-video-generation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-lightricks--ltx-video__cap_10","uri":"capability://image.visual.classifier.free.guidance.with.dynamic.guidance.scaling","name":"classifier-free guidance with dynamic guidance scaling","description":"Implements classifier-free guidance (CFG) to improve prompt adherence and video quality by training the model to generate both conditioned and unconditional outputs. During inference, the system computes predictions for both conditioned and unconditional cases, then interpolates between them using a guidance scale parameter. Higher guidance scales increase adherence to conditioning signals (text, images) at the cost of reduced diversity and potential artifacts. The guidance scale can be dynamically adjusted per timestep, enabling stronger guidance early in generation (for structure) and weaker guidance later (for detail).","intents":["Improve adherence to text prompts and conditioning frames","Control the trade-off between prompt fidelity and output diversity","Enable dynamic guidance adjustment for optimized generation quality"],"best_for":["Users requiring high prompt adherence for consistent results","Applications where output diversity is less important than consistency","Researchers studying guidance mechanisms in diffusion models"],"limitations":["High guidance scales (>10) often produce artifacts, oversaturation, or unnatural motion","Guidance requires computing both conditioned and unconditional predictions, doubling inference cost","Optimal guidance scale varies by prompt and model; requires manual tuning for best results","Dynamic guidance scheduling adds complexity; pre-computed schedules may not generalize across prompts"],"requires":["Python 3.8+","PyTorch 2.0+","Model trained with classifier-free guidance (LTX-Video models include this)"],"input_types":["guidance_scale: float (1.0-15.0, typical 7.5-10.0)","optional: guidance_schedule: list of floats for per-timestep scaling"],"output_types":["guided latent predictions: tensor matching input dimensions","guidance contribution: tensor showing guidance influence per timestep"],"categories":["image-visual","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-lightricks--ltx-video__cap_11","uri":"capability://automation.workflow.inference.script.with.configuration.management","name":"inference script with configuration management","description":"Provides a command-line inference interface (inference.py) that orchestrates the complete video generation pipeline with YAML-based configuration management. The script accepts model checkpoints, prompts, conditioning media, and generation parameters, then executes the appropriate pipeline (text-to-video, image-to-video, etc.) based on provided inputs. Configuration files specify model architecture, hyperparameters, and generation settings, enabling reproducible generation and easy model variant switching. The script handles device management, memory optimization, and output formatting automatically.","intents":["Execute video generation from command line without writing custom code","Reproduce generation results using saved configuration files","Switch between model variants (quality, speed, quantization) through configuration changes"],"best_for":["Developers prototyping video generation without building custom pipelines","Researchers running batch generation experiments with configuration sweeps","Teams deploying video generation with standardized configurations"],"limitations":["Command-line interface limits real-time parameter adjustment; requires script restart for changes","Configuration files are YAML; complex conditional logic or dynamic parameters not well-supported","Output formatting is fixed (MP4, WebM); custom output formats require code modification","No built-in progress tracking or cancellation; long generations cannot be interrupted gracefully"],"requires":["Python 3.8+","PyTorch 2.0+ with CUDA support","Model checkpoint file (.safetensors format)","Configuration YAML file matching model variant"],"input_types":["command-line arguments: --ckpt_path, --prompt, --conditioning_media_paths, --conditioning_start_frames, etc.","YAML configuration file specifying model and generation parameters"],"output_types":["video file (MP4 or WebM)","optional: latent tensor, attention maps (if debugging enabled)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-lightricks--ltx-video__cap_12","uri":"capability://data.processing.analysis.vae.encoding.and.patchification.for.efficient.latent.processing","name":"vae encoding and patchification for efficient latent processing","description":"Converts video frames into patch tokens for transformer processing through VAE encoding followed by spatial patchification. The causal video autoencoder encodes video into latent space, then the latent representation is divided into non-overlapping patches (e.g., 16×16 spatial patches), flattened into tokens, and concatenated with temporal dimension. This patchification reduces sequence length by ~256x (16×16 spatial patches) while preserving spatial structure, enabling efficient transformer processing. Patches are then processed through the Transformer3D model, and the output is unpatchified and decoded back to video space.","intents":["Convert video frames into efficient token sequences for transformer processing","Reduce computational complexity of attention mechanisms through spatial patchification","Maintain spatial structure while enabling efficient sequence processing"],"best_for":["Developers building efficient video generation systems","Researchers studying patch-based video processing and tokenization","Teams optimizing attention complexity for long video sequences"],"limitations":["Patch size is fixed (typically 16×16); no adaptive patching based on content","Patchification loses fine spatial details; reconstruction quality depends on patch size","Unpatchification requires careful handling of boundary conditions; edge artifacts may appear","Patch tokens are position-independent; spatial relationships must be learned through attention"],"requires":["Python 3.8+","PyTorch 2.0+","VAE encoder/decoder weights"],"input_types":["latent video tensor (B, T, H, W, D) from VAE encoder","patch_size: integer (typically 16) specifying spatial patch dimensions"],"output_types":["patch tokens: tensor of shape (B, T, (H/patch_size)*(W/patch_size), patch_dim)","unpatchified latent: tensor matching input dimensions after transformer processing"],"categories":["data-processing-analysis","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-lightricks--ltx-video__cap_13","uri":"capability://automation.workflow.model.quantization.and.optimization.for.resource.constrained.deployment","name":"model quantization and optimization for resource-constrained deployment","description":"Provides multiple model variants optimized for different hardware constraints through quantization and distillation. The ltxv-13b-0.9.7-dev-fp8 variant uses 8-bit floating point quantization to reduce model size by ~75% while maintaining quality. The ltxv-13b-0.9.7-distilled variant uses knowledge distillation to create a smaller, faster model suitable for rapid iteration. These variants are loaded through configuration files that specify quantization parameters, enabling easy switching between quality/speed trade-offs. Quantization is applied during model loading; no retraining required.","intents":["Deploy video generation on GPUs with limited VRAM (8-16GB)","Reduce generation latency for real-time or interactive applications","Enable video generation on edge devices or consumer hardware"],"best_for":["Teams deploying video generation on resource-constrained hardware","Applications requiring rapid iteration over quality (distilled models)","Researchers studying model compression and quantization trade-offs"],"limitations":["FP8 quantization reduces quality by ~5-10% compared to full precision; noticeable in fine details","Distilled models are 30-50% faster but produce lower quality video; suitable for prototyping, not final output","Quantization is applied uniformly; no layer-specific or adaptive quantization","Quantized models may have reduced compatibility with custom modifications or fine-tuning"],"requires":["Python 3.8+","PyTorch 2.0+ with CUDA support","Quantized model checkpoint (.safetensors with FP8 weights)","GPU with 8GB+ VRAM (FP8), 16GB+ for full precision"],"input_types":["model_variant: string ('dev', 'distilled', 'dev-fp8')","configuration file specifying quantization parameters"],"output_types":["loaded model with quantized weights","generation quality and speed metrics"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-lightricks--ltx-video__cap_2","uri":"capability://image.visual.video.extension.with.bidirectional.temporal.generation","name":"video extension with bidirectional temporal generation","description":"Extends existing video segments forward or backward in time by conditioning the diffusion process on video frames from the source clip. The system encodes video frames into the causal video autoencoder's latent space, specifies conditioning frame positions, then generates new frames before or after the conditioned segment. Uses the causal attention structure to ensure temporal consistency and prevent information leakage from future frames during backward extension.","intents":["Extend short video clips to longer durations with coherent motion continuation","Generate pre-roll or post-roll footage for existing video segments","Create seamless transitions by extending video in both temporal directions"],"best_for":["Video editors extending footage without reshooting","Content creators filling gaps in video sequences","Researchers studying temporal consistency in video generation"],"limitations":["Backward extension (pre-roll) may show temporal artifacts due to causal attention constraints; forward extension generally more stable","Motion consistency degrades significantly beyond 10 seconds total duration","Requires source video to be in supported format and resolution; transcoding adds latency","Conditioning frame selection is manual; automatic keyframe detection not provided"],"requires":["Python 3.8+","PyTorch 2.0+ with CUDA support","Model checkpoint (ltxv-13b-0.9.7-dev or variant)","Input video file (MP4, WebM, or raw frame sequence)"],"input_types":["video file (MP4, WebM, or frame sequence)","text prompt describing desired motion or scene continuation","conditioning_start_frames: integer specifying which frame(s) from source video to condition on"],"output_types":["extended video file (MP4, WebM)","30 FPS, 1216×704 resolution, up to 20 seconds (10s source + 10s extension)"],"categories":["image-visual","video-generation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-lightricks--ltx-video__cap_3","uri":"capability://image.visual.multi.condition.video.generation.with.keyframe.composition","name":"multi-condition video generation with keyframe composition","description":"Generates videos constrained by multiple conditioning frames at different temporal positions, enabling precise control over video structure and content. The system accepts multiple image or video segments as conditioning inputs, maps them to specified frame indices, then performs diffusion with all constraints active simultaneously. Uses a multi-condition attention mechanism to balance competing constraints and maintain coherence across the entire temporal span while respecting individual conditioning signals.","intents":["Create videos that transition between multiple keyframe images in a specified sequence","Generate video segments that must match specific visual states at multiple points in time","Compose complex video narratives by specifying key visual moments and letting diffusion fill transitions"],"best_for":["Storyboard artists creating animated sequences from keyframe sketches","Video editors composing complex shots with multiple visual constraints","Researchers studying constrained video generation and composition"],"limitations":["Conflicting conditioning constraints (e.g., incompatible motion between keyframes) may produce artifacts or fail to converge","Computational cost scales with number of conditioning frames; 3+ conditions significantly increase generation time","Temporal spacing between conditioning frames must be reasonable (e.g., 2-8 frames apart); too-close spacing over-constrains generation","No automatic conflict detection; invalid constraint combinations require manual adjustment"],"requires":["Python 3.8+","PyTorch 2.0+ with CUDA support","Model checkpoint (ltxv-13b-0.9.7-dev or variant)","Multiple input images or video segments (2-5 typical)"],"input_types":["multiple image files or video segments","text prompt describing overall narrative or motion","conditioning_media_paths: list of file paths","conditioning_start_frames: list of integers specifying frame positions for each condition"],"output_types":["video file (MP4, WebM)","30 FPS, 1216×704 resolution, 10 seconds default"],"categories":["image-visual","video-generation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-lightricks--ltx-video__cap_4","uri":"capability://image.visual.video.to.video.transformation.with.content.preservation","name":"video-to-video transformation with content preservation","description":"Transforms existing video content by conditioning generation on the source video while applying text-guided modifications. The system encodes the source video into latent space, uses it as a conditioning signal, then applies diffusion with a text prompt describing desired transformations (style changes, object modifications, scene alterations). The conditioning strength parameter controls the balance between preserving source content and applying text-guided changes, enabling style transfer, object replacement, or scene reinterpretation while maintaining temporal coherence.","intents":["Apply style transfer or artistic effects to existing video footage","Replace or modify objects in video while maintaining motion and scene structure","Reinterpret video scenes with different lighting, weather, or environmental conditions"],"best_for":["Video editors applying consistent effects across footage","Content creators remixing existing video with new artistic directions","Researchers studying video-to-video translation and style transfer"],"limitations":["Conditioning strength must be carefully tuned; too high preserves source too literally, too low ignores source entirely","Temporal consistency depends on source video quality; low-quality or highly compressed source produces artifacts","Text prompts describing transformations must be specific; vague descriptions may produce unpredictable results","Significant structural changes (e.g., changing camera angle) may fail; conditioning is strongest for style/appearance modifications"],"requires":["Python 3.8+","PyTorch 2.0+ with CUDA support","Model checkpoint (ltxv-13b-0.9.7-dev or variant)","Input video file (MP4, WebM, or frame sequence)"],"input_types":["video file (MP4, WebM, or frame sequence)","text prompt describing desired transformation or style","conditioning_strength: float (0.0-1.0) controlling preservation vs. transformation"],"output_types":["transformed video file (MP4, WebM)","30 FPS, 1216×704 resolution, matching source duration"],"categories":["image-visual","video-generation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-lightricks--ltx-video__cap_5","uri":"capability://data.processing.analysis.causal.video.autoencoder.with.spatiotemporal.compression","name":"causal video autoencoder with spatiotemporal compression","description":"Encodes and decodes videos using a causal video autoencoder (CausalVideoAutoencoder) that compresses video into a latent space while preserving temporal structure. The encoder uses 3D convolutions with causal masking to ensure frames only depend on past frames, reducing spatial resolution by 8x and temporal resolution by 4x while maintaining motion information. The decoder reconstructs video from latent representations with high fidelity. This compression enables efficient diffusion in latent space rather than pixel space, reducing memory requirements and generation time by orders of magnitude.","intents":["Compress video into efficient latent representations for diffusion-based generation","Encode conditioning frames into latent space for efficient conditioning signal injection","Reconstruct high-quality video from latent representations with minimal quality loss"],"best_for":["Developers building video generation systems requiring efficient latent-space operations","Researchers studying video compression and autoencoder architectures","Teams optimizing video generation for memory-constrained environments"],"limitations":["Causal masking prevents bidirectional context; may miss long-range temporal dependencies","Compression ratio (8x spatial, 4x temporal) is fixed; no variable-rate encoding","Reconstruction quality degrades for high-motion or high-frequency content (e.g., fast camera pans, fine textures)","Latent space is not directly interpretable; modifications require diffusion-based refinement"],"requires":["Python 3.8+","PyTorch 2.0+ with CUDA support","Autoencoder checkpoint weights (included with model distribution)"],"input_types":["video tensor (B, C, T, H, W) where B=batch, C=channels (3 for RGB), T=frames, H=height, W=width","video file (MP4, WebM) automatically converted to tensor"],"output_types":["latent tensor (B, D, T', H', W') where D=latent dimension, T'=T/4, H'=H/8, W'=W/8","reconstructed video tensor matching input dimensions"],"categories":["data-processing-analysis","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-lightricks--ltx-video__cap_6","uri":"capability://data.processing.analysis.rectified.flow.scheduler.with.optimized.diffusion.timesteps","name":"rectified flow scheduler with optimized diffusion timesteps","description":"Implements a rectified flow scheduler that optimizes the diffusion process by mapping noise schedules to straight-line trajectories in latent space, enabling fewer denoising steps while maintaining quality. The scheduler computes optimal timestep sequences that minimize the path length through noise space, reducing the number of required inference steps from typical 50-100 down to 20-30 steps. Uses linear interpolation between noise and signal rather than exponential schedules, improving convergence speed and enabling real-time generation without quality degradation.","intents":["Accelerate video generation by reducing required diffusion steps without quality loss","Optimize inference latency for real-time video generation applications","Enable efficient multi-scale generation by reusing timestep schedules across resolutions"],"best_for":["Developers building real-time video generation APIs requiring sub-second latency","Researchers studying diffusion scheduling and optimization","Teams deploying video generation on resource-constrained hardware"],"limitations":["Rectified flow scheduling is optimized for specific noise distributions; custom noise schedules may not benefit equally","Fewer steps may reduce diversity in generated outputs; trade-off between speed and variety","Timestep sequence is pre-computed; dynamic step adjustment during inference not supported","Quality improvements from rectified flow are model-specific; benefits depend on training with rectified flow objective"],"requires":["Python 3.8+","PyTorch 2.0+","Model trained with rectified flow objective (LTX-Video models include this)"],"input_types":["num_inference_steps: integer (typically 20-30 for LTX-Video)","guidance_scale: float (1.0-15.0) for classifier-free guidance strength"],"output_types":["timestep tensor: 1D array of shape (num_inference_steps,)","noise schedule: 1D array mapping timesteps to noise levels"],"categories":["data-processing-analysis","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-lightricks--ltx-video__cap_7","uri":"capability://image.visual.transformer3d.spatiotemporal.attention.with.causal.masking","name":"transformer3d spatiotemporal attention with causal masking","description":"Implements a 3D transformer architecture (Transformer3D) that processes video as spatiotemporal tokens using causal attention mechanisms. The model applies self-attention across spatial dimensions (height, width) and temporal dimensions (frames) simultaneously, with causal masking preventing frames from attending to future frames. Uses grouped query attention and flash attention optimizations to reduce memory overhead and computation time. The architecture enables efficient processing of long video sequences while maintaining temporal coherence through causal constraints.","intents":["Process video tokens with spatiotemporal awareness for coherent video generation","Maintain temporal consistency across frames through causal attention constraints","Scale video generation to longer sequences with efficient attention mechanisms"],"best_for":["Researchers studying transformer architectures for video generation","Developers building video generation systems requiring temporal coherence","Teams optimizing attention mechanisms for video processing efficiency"],"limitations":["Causal masking prevents bidirectional context; may miss long-range dependencies that span many frames","Attention complexity is O(T*H*W) where T=frames, H=height, W=width; very long videos or high resolutions become intractable","Grouped query attention reduces parameters but may lose fine-grained spatial-temporal interactions","Flash attention optimizations are CUDA-specific; CPU inference significantly slower"],"requires":["Python 3.8+","PyTorch 2.0+ with CUDA support","Model checkpoint with Transformer3D weights (included in LTX-Video distribution)"],"input_types":["latent video tensor (B, T, H, W, D) where D=latent dimension","conditioning embeddings (text, image, or video embeddings)"],"output_types":["denoised latent tensor matching input dimensions","attention maps (optional, for visualization)"],"categories":["image-visual","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-lightricks--ltx-video__cap_8","uri":"capability://image.visual.multi.scale.pipeline.with.progressive.resolution.generation","name":"multi-scale pipeline with progressive resolution generation","description":"Implements LTXMultiScalePipeline for generating videos at higher resolutions through progressive multi-pass generation. The system first generates low-resolution video (e.g., 1216×704), then upscales and refines at progressively higher resolutions (e.g., 2432×1408, 4864×2816) using the same diffusion process with additional refinement steps. Each pass conditions on the previous resolution's output, enabling coherent upscaling while adding fine details. This approach avoids the memory and computation overhead of single-pass high-resolution generation.","intents":["Generate high-resolution videos (4K+) without requiring massive GPU memory","Progressively refine video quality through multi-pass generation","Balance generation speed and quality by controlling number of upscaling passes"],"best_for":["Content creators requiring broadcast-quality high-resolution video output","Teams with limited GPU memory needing to generate 4K+ video","Researchers studying progressive generation and upscaling strategies"],"limitations":["Multi-pass generation increases total latency by 2-4x vs. single-pass; not suitable for real-time applications","Upscaling artifacts may accumulate across passes if refinement steps are insufficient","Each upscaling pass requires separate diffusion inference; computational cost scales linearly with number of passes","Conditioning between passes must be carefully tuned; over-conditioning locks details, under-conditioning loses coherence"],"requires":["Python 3.8+","PyTorch 2.0+ with CUDA support","Model checkpoint (ltxv-13b-0.9.7-dev or variant)","GPU with 24GB+ VRAM for 4K generation (16GB minimum for 2K)"],"input_types":["text prompt","optional: conditioning frames or video","target_resolution: tuple (height, width) for final output","num_scales: integer (2-4 typical) for number of upscaling passes"],"output_types":["high-resolution video file (MP4, WebM)","30 FPS, user-specified resolution (up to 4K or higher)"],"categories":["image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-lightricks--ltx-video__cap_9","uri":"capability://text.generation.language.prompt.enhancement.and.semantic.understanding","name":"prompt enhancement and semantic understanding","description":"Processes natural language prompts through semantic enhancement to improve video generation quality and coherence. The system tokenizes prompts, encodes them through a text encoder (typically CLIP or similar), and optionally applies prompt expansion or rewriting to clarify ambiguous descriptions. Enhanced prompts are converted to embeddings that condition the diffusion process. The text encoder's semantic understanding enables the model to interpret complex descriptions, temporal narratives, and stylistic directives, translating them into coherent video generation constraints.","intents":["Translate natural language descriptions into high-quality video generation","Improve prompt clarity through automatic enhancement and expansion","Enable complex narrative and stylistic control through text conditioning"],"best_for":["Content creators without technical video editing skills","Rapid prototyping of video ideas from natural language descriptions","Building user-facing video generation applications with text interfaces"],"limitations":["Prompt understanding is limited by text encoder capacity; very long or complex prompts may be truncated or misunderstood","Ambiguous or contradictory prompts produce unpredictable results; prompt engineering required for consistent quality","Temporal narratives (e.g., 'first X happens, then Y') may not translate to correct frame ordering","Stylistic descriptions (e.g., 'cinematic', 'photorealistic') are interpreted based on training data; results vary by model"],"requires":["Python 3.8+","PyTorch 2.0+","Text encoder model (CLIP or similar, included with LTX-Video)","Tokenizer for text encoding"],"input_types":["text prompt: string (10-500 characters typical)","optional: prompt_enhancement: boolean to enable automatic expansion"],"output_types":["text embeddings: tensor of shape (1, seq_len, embedding_dim)","enhanced prompt: string (if enhancement enabled)"],"categories":["text-generation-language","image-visual"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":36,"verified":false,"data_access_risk":"low","permissions":["Python 3.8+","PyTorch 2.0+ with CUDA support (GPU with 16GB+ VRAM recommended)","Model checkpoint file (ltxv-13b-0.9.7-dev.safetensors or variant)","Text encoder weights (typically CLIP or similar)","PyTorch 2.0+ with CUDA support","Model checkpoint (ltxv-13b-0.9.7-dev or variant)","Input image file (PNG, JPEG, WebP; max 1216×704 native resolution)","PyTorch 2.0+","Model trained with classifier-free guidance (LTX-Video models include this)","Model checkpoint file (.safetensors format)"],"failure_modes":["Generation speed depends on model variant; distilled models trade quality for 10-second HD generation speed","Prompt understanding limited by underlying text encoder; complex narrative instructions may not translate to coherent video","Fixed output resolution of 1216×704; multi-scale pipeline required for higher resolutions adds latency","Temporal consistency degrades beyond ~10 seconds without explicit keyframe conditioning","Conditioning strength must be balanced; over-conditioning locks output to input image, under-conditioning ignores image entirely","Motion quality degrades if conditioning frames are too dissimilar (e.g., different lighting, angles)","Requires explicit frame indices for conditioning; automatic temporal placement not supported","Image resolution must match or be resized to 1216×704; aspect ratio changes may distort content","High guidance scales (>10) often produce artifacts, oversaturation, or unnatural motion","Guidance requires computing both conditioned and unconditional predictions, doubling inference cost","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.34956997842495463,"quality":0.35,"ecosystem":0.5800000000000001,"match_graph":0.25,"freshness":0.6,"weights":{"adoption":0.35,"quality":0.2,"ecosystem":0.1,"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":"2026-05-24T12:16:21.550Z","last_scraped_at":"2026-05-03T13:59:47.981Z","last_commit":"2026-01-05T22:37:07Z"},"community":{"stars":10163,"forks":995,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=lightricks--ltx-video","compare_url":"https://unfragile.ai/compare?artifact=lightricks--ltx-video"}},"signature":"4es0HTz/LkVpwvhQ2I7Dv0QSX4KGfhpFVmYBgIm5KJEa/EBTOWTGRL/Iyvb3sDA+cu72n21nHvJuiZjgAHbyCA==","signedAt":"2026-06-20T13:22:45.124Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/lightricks--ltx-video","artifact":"https://unfragile.ai/lightricks--ltx-video","verify":"https://unfragile.ai/api/v1/verify?slug=lightricks--ltx-video","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"}}