{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-space-microsoft--trellis","slug":"microsoft--trellis","name":"TRELLIS","type":"webapp","url":"https://huggingface.co/spaces/microsoft/TRELLIS","page_url":"https://unfragile.ai/microsoft--trellis","categories":["automation"],"tags":["gradio","region:us"],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hf-space-microsoft--trellis__cap_0","uri":"capability://image.visual.text.to.3d.model.generation.with.multi.stage.diffusion.pipeline","name":"text-to-3d model generation with multi-stage diffusion pipeline","description":"Generates 3D models from natural language text descriptions using a multi-stage diffusion-based architecture that progressively refines geometry and appearance. The system employs a two-phase approach: first generating a coarse 3D representation via latent diffusion, then refining surface details and textures through iterative denoising steps conditioned on the text embedding. This enables conversion of arbitrary text prompts into exportable 3D assets without requiring 3D training data paired with text.","intents":["Generate 3D models from text descriptions for game development or 3D printing","Rapidly prototype 3D assets without 3D modeling expertise","Create diverse 3D variations from a single text prompt for content generation","Export production-ready 3D geometry in standard formats for downstream tools"],"best_for":["Game developers and 3D artists seeking rapid asset generation","Non-technical creators wanting to generate 3D content from descriptions","Teams prototyping 3D-heavy applications without dedicated modeling staff"],"limitations":["Generation quality varies significantly based on prompt specificity and complexity","Single inference pass takes 2-5 minutes depending on refinement iterations","Output geometry may require post-processing in professional 3D tools for production use","Limited control over specific geometric constraints or topology during generation","Memory-intensive inference requires GPU acceleration; CPU-only inference is impractical"],"requires":["Modern GPU with 8GB+ VRAM for reasonable inference speed","Web browser with WebGL support for 3D preview rendering","Text prompt describing desired 3D object characteristics"],"input_types":["text (natural language descriptions)"],"output_types":["3D mesh (GLB/GLTF format)","3D point cloud","Textured 3D model"],"categories":["image-visual","generative-3d"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-space-microsoft--trellis__cap_1","uri":"capability://image.visual.interactive.3d.model.preview.and.manipulation.in.web.browser","name":"interactive 3d model preview and manipulation in web browser","description":"Provides real-time 3D visualization and manipulation of generated models directly in the browser using WebGL-based rendering with orbit controls, lighting adjustment, and material preview. The interface streams the generated 3D asset to a Three.js-based viewer that supports rotation, zoom, pan, and dynamic lighting to inspect geometry quality and texture details without requiring external 3D software.","intents":["Inspect generated 3D models from multiple angles before export","Verify texture quality and geometric accuracy in real-time","Adjust lighting and material properties to evaluate model appearance","Preview models before committing to download or further processing"],"best_for":["Designers and artists evaluating generated assets interactively","Developers prototyping 3D-enabled web applications","Non-technical users wanting immediate visual feedback on generation results"],"limitations":["Browser-based rendering limited to real-time performance on consumer GPUs","No advanced material editing or PBR workflow integration","Limited to single-model viewing; no scene composition or multi-object manipulation","Performance degrades with very high-polygon-count meshes (>1M triangles)"],"requires":["Modern web browser with WebGL 2.0 support","GPU with dedicated VRAM for smooth real-time rendering"],"input_types":["3D mesh (GLB/GLTF format)"],"output_types":["visual rendering (WebGL canvas)","camera parameters (for screenshot capture)"],"categories":["image-visual","user-interface"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-space-microsoft--trellis__cap_2","uri":"capability://data.processing.analysis.3d.model.export.with.format.conversion.and.optimization","name":"3d model export with format conversion and optimization","description":"Exports generated 3D models in standard interchange formats (GLB, GLTF, OBJ) with automatic geometry optimization and texture embedding. The export pipeline applies mesh simplification, vertex quantization, and texture compression to reduce file size while preserving visual quality, enabling seamless integration with game engines, 3D printing software, and other downstream tools.","intents":["Export 3D models for use in game engines (Unity, Unreal Engine)","Prepare models for 3D printing with format conversion and optimization","Share generated assets with team members in standard formats","Integrate generated models into existing 3D pipelines and workflows"],"best_for":["Game developers needing rapid asset pipeline integration","3D printing services and hobbyists preparing models for fabrication","Teams collaborating on 3D content with mixed software stacks"],"limitations":["Export optimization may introduce minor visual artifacts in high-detail regions","No support for advanced material definitions (PBR workflows require post-processing)","File size reduction through quantization may affect precision for engineering applications","Texture resolution fixed at generation time; no post-export upsampling"],"requires":["Generated 3D model in system memory","Sufficient disk space for uncompressed export (typically 10-100MB per model)"],"input_types":["3D mesh (internal representation)"],"output_types":["GLB (binary GLTF with embedded textures)","GLTF (JSON + separate texture files)","OBJ (Wavefront format with MTL materials)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-space-microsoft--trellis__cap_3","uri":"capability://text.generation.language.prompt.to.3d.semantic.understanding.and.conditioning","name":"prompt-to-3d semantic understanding and conditioning","description":"Processes natural language text prompts through a pre-trained vision-language model (likely CLIP or similar) to extract semantic embeddings that condition the 3D generation diffusion process. The system maps arbitrary text descriptions to a learned embedding space that guides geometry and appearance synthesis, enabling intuitive text-based control over 3D model generation without requiring structured 3D descriptors or parameter tuning.","intents":["Generate 3D models from casual, natural language descriptions","Control 3D generation output through semantic concepts rather than technical parameters","Explore variations of a concept by rephrasing text prompts","Enable non-technical users to specify 3D content without 3D domain knowledge"],"best_for":["Content creators and designers without 3D modeling expertise","Rapid prototyping and ideation workflows requiring quick iteration","Applications requiring user-friendly text-based 3D control interfaces"],"limitations":["Semantic understanding limited by training data; unusual or niche concepts may generate poor results","No explicit control over specific geometric properties (size, proportions, symmetry)","Ambiguous prompts may produce inconsistent results across multiple generations","Prompt engineering required for consistent quality; vague descriptions often fail","No support for negative prompts or exclusion-based conditioning"],"requires":["Text prompt (minimum ~5 words for reasonable results)","Pre-trained vision-language model weights loaded in memory"],"input_types":["text (natural language description)"],"output_types":["embedding vector (conditioning signal for diffusion)","3D model (conditioned on embedding)"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-space-microsoft--trellis__cap_4","uri":"capability://planning.reasoning.iterative.refinement.with.multi.step.diffusion.denoising","name":"iterative refinement with multi-step diffusion denoising","description":"Implements a multi-step diffusion denoising process that progressively refines 3D geometry and texture quality through repeated denoising iterations, each conditioned on the text embedding and previous refinement state. The pipeline starts with coarse geometry and iteratively adds detail, surface refinement, and texture information across 20-50 denoising steps, with each step reducing noise and improving coherence.","intents":["Improve 3D model quality through iterative refinement without regeneration","Control generation detail level through step count adjustment","Achieve higher-quality outputs by trading computation time for visual fidelity","Explore generation quality-speed tradeoffs for different use cases"],"best_for":["Production workflows where quality is prioritized over speed","Iterative design processes requiring progressive refinement","Applications with flexible latency budgets (2-5 minute generation acceptable)"],"limitations":["Linear relationship between step count and inference time; doubling steps doubles latency","Diminishing returns after ~40 steps; additional refinement provides minimal quality improvement","No early-stopping mechanism; all steps must complete for final output","Memory usage scales with step count; very high step counts may exceed GPU VRAM"],"requires":["GPU with sufficient VRAM for multi-step inference (8GB+ recommended)","2-5 minutes of compute time per generation"],"input_types":["text embedding (from semantic conditioning)","noise schedule parameters"],"output_types":["refined 3D mesh","textured 3D model"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-space-microsoft--trellis__cap_5","uri":"capability://automation.workflow.batch.generation.with.queue.management.and.result.caching","name":"batch generation with queue management and result caching","description":"Manages multiple concurrent generation requests through a queue-based system that serializes GPU inference while maintaining responsive user feedback. The system caches generation results keyed by prompt hash, enabling instant retrieval of previously generated models for identical prompts without re-computation. Queue management prevents GPU overload and ensures fair resource allocation across simultaneous users.","intents":["Generate multiple 3D models sequentially without manual re-submission","Avoid redundant computation for duplicate prompts across users","Maintain responsive UI while long-running generation completes in background","Scale to multiple concurrent users on shared GPU infrastructure"],"best_for":["Multi-user SaaS applications with shared GPU resources","Batch processing workflows requiring multiple model generations","Applications with variable user load requiring fair resource sharing"],"limitations":["Queue latency adds 10-60 seconds depending on queue depth and GPU availability","Cache hit rate depends on prompt diversity; high-entropy prompts reduce cache effectiveness","No priority queuing; all requests processed in FIFO order regardless of importance","Cache invalidation requires manual intervention; no automatic cache expiration","Single GPU bottleneck limits throughput to ~1-2 models per minute"],"requires":["Shared GPU infrastructure with queue management system","Persistent storage for result caching (filesystem or database)"],"input_types":["text prompt","generation parameters"],"output_types":["3D model (from cache or fresh generation)","queue status (position, estimated wait time)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-space-microsoft--trellis__cap_6","uri":"capability://tool.use.integration.gradio.web.interface.with.real.time.streaming.feedback","name":"gradio web interface with real-time streaming feedback","description":"Exposes the 3D generation pipeline through a Gradio-based web interface that provides real-time feedback during inference, including progress indicators, intermediate generation visualizations, and streaming status updates. The interface abstracts away infrastructure complexity, enabling users to interact with the model through simple text input and visual output without API knowledge or local setup.","intents":["Provide accessible web-based interface for 3D generation without local installation","Enable real-time feedback on generation progress and intermediate results","Share generation capabilities with non-technical users via shareable URL","Prototype and demo 3D generation without building custom frontend"],"best_for":["Researchers and teams sharing models via HuggingFace Spaces","Rapid prototyping and demos requiring minimal frontend development","Non-technical users wanting to experiment with 3D generation"],"limitations":["Gradio interface limited to simple input/output patterns; complex workflows require custom frontend","No authentication or rate limiting built-in; requires external proxy for production use","Streaming updates add ~500ms latency per status message","No persistent session state; each refresh resets generation history","Limited customization of UI appearance and layout"],"requires":["Modern web browser with JavaScript enabled","HuggingFace Spaces account for hosting (or local Gradio server)"],"input_types":["text (via Gradio textbox)","UI interactions (buttons, sliders)"],"output_types":["3D visualization (WebGL canvas)","downloadable 3D files","status messages and progress indicators"],"categories":["tool-use-integration","user-interface"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":23,"verified":false,"data_access_risk":"high","permissions":["Modern GPU with 8GB+ VRAM for reasonable inference speed","Web browser with WebGL support for 3D preview rendering","Text prompt describing desired 3D object characteristics","Modern web browser with WebGL 2.0 support","GPU with dedicated VRAM for smooth real-time rendering","Generated 3D model in system memory","Sufficient disk space for uncompressed export (typically 10-100MB per model)","Text prompt (minimum ~5 words for reasonable results)","Pre-trained vision-language model weights loaded in memory","GPU with sufficient VRAM for multi-step inference (8GB+ recommended)"],"failure_modes":["Generation quality varies significantly based on prompt specificity and complexity","Single inference pass takes 2-5 minutes depending on refinement iterations","Output geometry may require post-processing in professional 3D tools for production use","Limited control over specific geometric constraints or topology during generation","Memory-intensive inference requires GPU acceleration; CPU-only inference is impractical","Browser-based rendering limited to real-time performance on consumer GPUs","No advanced material editing or PBR workflow integration","Limited to single-model viewing; no scene composition or multi-object manipulation","Performance degrades with very high-polygon-count meshes (>1M triangles)","Export optimization may introduce minor visual artifacts in high-detail regions","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.24,"ecosystem":0.36,"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=microsoft--trellis","compare_url":"https://unfragile.ai/compare?artifact=microsoft--trellis"}},"signature":"08q9ALLZ0Zet1IQAqyG6tCLaO8XVbDu51fWbPgultO4E2/xeMDbhscwruoMeb6ZxJEkbFgTx5McDEihqtApUDg==","signedAt":"2026-06-21T14:20:41.819Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/microsoft--trellis","artifact":"https://unfragile.ai/microsoft--trellis","verify":"https://unfragile.ai/api/v1/verify?slug=microsoft--trellis","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"}}