{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-space-lightricks--ltx-video-distilled","slug":"lightricks--ltx-video-distilled","name":"ltx-video-distilled","type":"webapp","url":"https://huggingface.co/spaces/Lightricks/ltx-video-distilled","page_url":"https://unfragile.ai/lightricks--ltx-video-distilled","categories":["video-generation"],"tags":["gradio","mcp-server","region:us"],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hf-space-lightricks--ltx-video-distilled__cap_0","uri":"capability://image.visual.text.to.video.generation.with.distilled.model.inference","name":"text-to-video generation with distilled model inference","description":"Generates short video clips from natural language text prompts using a distilled version of the LTX video model, optimized for reduced computational overhead while maintaining visual quality. The implementation leverages HuggingFace's Spaces infrastructure to run inference serverlessly, accepting text descriptions and outputting MP4 video files through a Gradio web interface that handles request queuing and result streaming.","intents":["Generate short video content from text descriptions without managing GPU infrastructure","Create demo videos for product prototyping or marketing without video editing software","Batch-generate multiple video variations from different text prompts","Integrate text-to-video generation into downstream applications via API"],"best_for":["Content creators prototyping video ideas quickly","Developers building video generation features without GPU access","Teams evaluating text-to-video capabilities before production deployment"],"limitations":["Inference latency typically 30-120 seconds per video due to model size and Spaces resource constraints","Output video length and resolution limited by model architecture (likely 512-768p, 4-8 second clips)","No fine-tuning or custom model weights support — uses only the pre-distilled checkpoint","Concurrent request handling limited by single GPU allocation on Spaces; queue times increase during peak usage","No persistent storage of generated videos; outputs must be downloaded immediately or are discarded"],"requires":["Web browser with modern JavaScript support","Internet connection with sufficient bandwidth for video download (typically 5-50MB per clip)","No API key required for free tier, but rate-limited to prevent abuse"],"input_types":["text (natural language prompt, typically 10-100 words)"],"output_types":["video (MP4 format, H.264 codec)"],"categories":["image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-space-lightricks--ltx-video-distilled__cap_1","uri":"capability://automation.workflow.gradio.based.interactive.web.ui.with.request.queuing","name":"gradio-based interactive web ui with request queuing","description":"Provides a browser-accessible interface built with Gradio that abstracts the underlying model inference pipeline, handling form submission, input validation, asynchronous job queuing, and result display. The Gradio framework automatically generates a responsive web UI from Python function signatures, manages concurrent request handling through a queue system, and streams results back to the client as they complete.","intents":["Access video generation without writing code or managing CLI tools","Share a public demo link with non-technical stakeholders","Test different prompts iteratively with immediate visual feedback","Monitor inference progress and queue status in real-time"],"best_for":["Non-technical users exploring AI capabilities","Researchers sharing reproducible demos with the community","Product teams gathering user feedback on generated content"],"limitations":["Gradio's queue system adds 1-5 second overhead per request for job scheduling","No authentication or access control — public Spaces are accessible to anyone","UI customization limited to Gradio's component library; no custom CSS/JavaScript without forking","Session state not persisted across browser refreshes; no user accounts or history tracking"],"requires":["Gradio 4.0+ (likely bundled in the Spaces environment)","HuggingFace Spaces runtime with Python 3.8+"],"input_types":["text (form input)"],"output_types":["HTML (rendered UI)","video (downloadable MP4)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-space-lightricks--ltx-video-distilled__cap_2","uri":"capability://automation.workflow.huggingface.spaces.serverless.model.hosting.and.execution","name":"huggingface spaces serverless model hosting and execution","description":"Deploys the distilled LTX model on HuggingFace Spaces infrastructure, which provides ephemeral GPU compute, automatic scaling, and public URL exposure without requiring manual server management. The Spaces runtime handles dependency installation from a requirements.txt file, model weight downloading from HuggingFace Hub, and request routing through Gradio's built-in server, with automatic restart on code updates.","intents":["Host a public demo without provisioning cloud infrastructure or managing Docker","Share reproducible model inference with version control (Git-backed Spaces)","Iterate on model prompts and UI without redeploying servers","Leverage free GPU quota for non-commercial research and prototyping"],"best_for":["Academic researchers publishing reproducible demos","Open-source projects demonstrating capabilities to the community","Startups prototyping features before committing to production infrastructure"],"limitations":["Free tier limited to CPU or shared GPU with unpredictable availability; no SLA or guaranteed uptime","Spaces may be suspended if inactive for 48+ hours; requires manual restart","Model weights downloaded fresh on each Spaces restart, adding 2-5 minute cold-start latency","No persistent storage between restarts; temporary files and caches are discarded","Rate-limited to prevent abuse; concurrent requests queued and may timeout after 15-30 minutes","No custom domain support on free tier; URL is auto-generated and may change"],"requires":["HuggingFace account with Spaces access","Git repository (HuggingFace Git or GitHub) for version control","Model weights available on HuggingFace Hub (public or with access token)"],"input_types":["Python code","requirements.txt (dependencies)"],"output_types":["Public URL","Gradio web interface"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-space-lightricks--ltx-video-distilled__cap_3","uri":"capability://memory.knowledge.model.weight.caching.and.lazy.loading.from.huggingface.hub","name":"model weight caching and lazy loading from huggingface hub","description":"Automatically downloads and caches the distilled LTX model weights from HuggingFace Hub on first inference request, using the transformers library's built-in caching mechanism to avoid re-downloading on subsequent requests within the same Spaces session. The implementation likely uses `torch.load()` or `safetensors` to deserialize weights and load them into GPU memory, with fallback to CPU if GPU is unavailable.","intents":["Reduce inference startup time by caching model weights in memory","Avoid redundant downloads of large model files (likely 2-10GB for distilled video models)","Support automatic model updates from HuggingFace Hub without code changes"],"best_for":["Demos with multiple inference requests in a single session","Prototypes where cold-start latency is acceptable but warm-start performance matters"],"limitations":["First inference request incurs 2-5 minute latency for model download and GPU loading","Cache cleared on Spaces restart; subsequent restarts require re-download","No explicit cache invalidation; model updates on Hub may not be reflected until Spaces restarts","GPU memory must accommodate full model size; OOM errors if GPU allocation is insufficient"],"requires":["HuggingFace transformers library (likely 4.30+)","Internet connectivity to HuggingFace Hub","Sufficient disk space in Spaces ephemeral storage (~10-20GB for model + dependencies)"],"input_types":["Model identifier (e.g., 'Lightricks/ltx-video-distilled')"],"output_types":["Loaded model in GPU/CPU memory"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-space-lightricks--ltx-video-distilled__cap_4","uri":"capability://automation.workflow.asynchronous.inference.job.scheduling.and.result.streaming","name":"asynchronous inference job scheduling and result streaming","description":"Implements asynchronous request handling through Gradio's queue system, which decouples user requests from inference execution, allowing multiple users to submit prompts without blocking on model inference. The queue assigns each request a job ID, executes inference in background worker threads/processes, and streams results back to the client via WebSocket or polling, with progress indicators showing queue position and estimated completion time.","intents":["Handle multiple concurrent video generation requests without timeout errors","Provide real-time feedback on job progress and queue status","Prevent UI freezing while waiting for long-running inference (30-120 seconds)"],"best_for":["Public demos expecting variable traffic and concurrent users","Applications where inference latency is acceptable but user experience should remain responsive"],"limitations":["Queue overhead adds 1-5 seconds per request for job scheduling and result retrieval","No persistent job history; completed jobs are discarded after download","Queue depth limited by Spaces memory and worker thread count; excess requests may timeout","No priority queuing or user-based rate limiting; first-come-first-served only","WebSocket connections may drop on network interruptions; client must re-poll for results"],"requires":["Gradio 4.0+ with queue support enabled","Client-side JavaScript for WebSocket or polling (handled by Gradio automatically)"],"input_types":["Text prompt"],"output_types":["Job ID (string)","Progress status (JSON)","Video file (MP4)"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":23,"verified":false,"data_access_risk":"high","permissions":["Web browser with modern JavaScript support","Internet connection with sufficient bandwidth for video download (typically 5-50MB per clip)","No API key required for free tier, but rate-limited to prevent abuse","Gradio 4.0+ (likely bundled in the Spaces environment)","HuggingFace Spaces runtime with Python 3.8+","HuggingFace account with Spaces access","Git repository (HuggingFace Git or GitHub) for version control","Model weights available on HuggingFace Hub (public or with access token)","HuggingFace transformers library (likely 4.30+)","Internet connectivity to HuggingFace Hub"],"failure_modes":["Inference latency typically 30-120 seconds per video due to model size and Spaces resource constraints","Output video length and resolution limited by model architecture (likely 512-768p, 4-8 second clips)","No fine-tuning or custom model weights support — uses only the pre-distilled checkpoint","Concurrent request handling limited by single GPU allocation on Spaces; queue times increase during peak usage","No persistent storage of generated videos; outputs must be downloaded immediately or are discarded","Gradio's queue system adds 1-5 second overhead per request for job scheduling","No authentication or access control — public Spaces are accessible to anyone","UI customization limited to Gradio's component library; no custom CSS/JavaScript without forking","Session state not persisted across browser refreshes; no user accounts or history tracking","Free tier limited to CPU or shared GPU with unpredictable availability; no SLA or guaranteed uptime","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.2,"ecosystem":0.38999999999999996,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.35,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:22.766Z","last_scraped_at":"2026-05-03T14:22:48.012Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=lightricks--ltx-video-distilled","compare_url":"https://unfragile.ai/compare?artifact=lightricks--ltx-video-distilled"}},"signature":"e3po0aUIy73LjOfQmZFgLtp6bBpUs5Zk9Xp6xu1teX/5s+T+tZHVe5RKztDUl+mVXW1+YmGyTeQ1+3mMNAUzBQ==","signedAt":"2026-06-22T06:56:14.229Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/lightricks--ltx-video-distilled","artifact":"https://unfragile.ai/lightricks--ltx-video-distilled","verify":"https://unfragile.ai/api/v1/verify?slug=lightricks--ltx-video-distilled","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"}}