{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-ashawkey--stable-dreamfusion","slug":"ashawkey--stable-dreamfusion","name":"stable-dreamfusion","type":"repo","url":"https://github.com/ashawkey/stable-dreamfusion","page_url":"https://unfragile.ai/ashawkey--stable-dreamfusion","categories":["image-generation"],"tags":["dreamfusion","gui","image-to-3d","nerf","stable-diffusion","text-to-3d"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-ashawkey--stable-dreamfusion__cap_0","uri":"capability://image.visual.text.to.3d.generation.via.score.distillation.sampling","name":"text-to-3d generation via score distillation sampling","description":"Converts natural language text prompts into 3D models by optimizing a Neural Radiance Field (NeRF) using Score Distillation Sampling (SDS) guidance from Stable Diffusion. The system renders 2D views from the NeRF at each training step, computes diffusion model gradients on those renders conditioned on the text prompt, and backpropagates those gradients through the NeRF parameters to iteratively refine the 3D representation without paired 3D training data.","intents":["Generate a 3D model from a text description like 'a ceramic vase with blue glaze'","Create 3D assets for games or VR without manual modeling","Rapidly prototype 3D concepts from natural language specifications","Explore multiple 3D variations from a single text prompt"],"best_for":["3D content creators and game developers seeking rapid prototyping","AI researchers exploring diffusion-based 3D generation","Teams building generative 3D pipelines without 3D training datasets"],"limitations":["Training time is 1-2 hours per model on high-end GPUs (A100/RTX 4090); slower on consumer hardware","Generated geometry may lack fine details and sharp features compared to hand-modeled assets","Text prompts with complex spatial relationships or multiple objects may produce ambiguous results","Requires 24GB+ VRAM for full resolution rendering; lower resolutions reduce quality","No built-in control over specific object parts or fine-grained geometry constraints"],"requires":["Python 3.8+","PyTorch 1.13+ with CUDA 11.7+ for GPU acceleration","Stable Diffusion model weights (1.5, 2.0, or 2.1) loaded via diffusers library","NVIDIA GPU with 24GB+ VRAM (A100, RTX 4090, or equivalent)","~50GB free disk space for model weights and intermediate outputs"],"input_types":["text (natural language prompt, e.g., 'a golden Buddha statue')"],"output_types":["3D NeRF representation (PyTorch model checkpoint)","Mesh file (OBJ, PLY via DMTet extraction)","Rendered 2D images from arbitrary viewpoints"],"categories":["image-visual","3d-generation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ashawkey--stable-dreamfusion__cap_1","uri":"capability://image.visual.image.to.3d.generation.via.zero123.novel.view.synthesis","name":"image-to-3d generation via zero123 novel view synthesis","description":"Generates 3D models from a single reference image by optimizing a NeRF using guidance from the Zero123 model, which performs novel view synthesis. The system renders the NeRF from multiple viewpoints, feeds those renders to Zero123 conditioned on the input image, and uses the diffusion gradients to refine the 3D geometry to be consistent with the reference image across different viewing angles.","intents":["Convert a single product photo into a 3D model for e-commerce or AR applications","Generate 3D reconstructions from real-world object photographs","Create 3D models from artwork or concept art images","Build 3D assets from existing 2D reference images without manual modeling"],"best_for":["E-commerce platforms needing rapid 3D product generation from photos","3D reconstruction pipelines for heritage or museum digitization","AR/VR developers building asset libraries from 2D references","Game studios creating variations of existing 2D concept art"],"limitations":["Requires a clear, well-lit reference image; poor quality inputs produce poor 3D results","Struggles with transparent, reflective, or highly specular materials","Cannot infer occluded geometry (e.g., back of an object if only front is visible)","Zero123 model has limited understanding of extreme viewpoint changes","Training time is 30-60 minutes per image on high-end GPUs"],"requires":["Python 3.8+","PyTorch 1.13+ with CUDA 11.7+","Zero123 model weights (downloaded automatically or pre-cached)","NVIDIA GPU with 24GB+ VRAM","Input image in PNG, JPG, or WebP format (512x512 or higher recommended)"],"input_types":["image (single reference image of an object, e.g., product photo)"],"output_types":["3D NeRF representation (PyTorch checkpoint)","Mesh file (OBJ, PLY via DMTet extraction)","Rendered views from arbitrary camera angles"],"categories":["image-visual","3d-generation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ashawkey--stable-dreamfusion__cap_10","uri":"capability://automation.workflow.training.checkpoint.management.and.resumption","name":"training checkpoint management and resumption","description":"Implements automatic checkpoint saving during training, allowing users to resume interrupted training from the latest checkpoint without losing progress. The system saves NeRF model weights, optimizer state, learning rate schedules, and training iteration count at regular intervals. Users can specify checkpoint frequency and directory, and the training loop automatically loads the latest checkpoint on restart.","intents":["Resume training after hardware failures or interruptions without restarting from scratch","Save intermediate models for comparison and evaluation","Reduce total training time by avoiding redundant computation","Enable long-running training jobs on time-limited compute resources"],"best_for":["Teams running long training jobs (1-2+ hours) on shared or time-limited resources","Developers iterating on model architecture and wanting to preserve progress","Production pipelines requiring reliable checkpoint management"],"limitations":["Checkpoint files are large (500MB-2GB per checkpoint); disk space can be limiting","Resuming from checkpoint requires exact same hardware/software configuration","Optimizer state is hardware-specific; checkpoints may not be portable across GPU types","No built-in checkpoint pruning; old checkpoints must be manually deleted to save space"],"requires":["Python 3.8+","PyTorch 1.13+","Sufficient disk space (at least 5-10GB for multiple checkpoints)","Write permissions to checkpoint directory"],"input_types":["checkpoint directory path","checkpoint save frequency (iterations or minutes)","resume flag (boolean)"],"output_types":["checkpoint files (PyTorch .pt format)","training logs and metrics"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ashawkey--stable-dreamfusion__cap_11","uri":"capability://image.visual.image.preprocessing.and.augmentation.for.guidance","name":"image preprocessing and augmentation for guidance","description":"Provides utilities for preprocessing input images (resizing, normalization, center cropping) and augmenting rendered NeRF outputs (random crops, color jitter, rotation) before feeding to diffusion guidance models. Preprocessing ensures inputs match diffusion model expectations (e.g., 512x512 for Stable Diffusion), while augmentation improves robustness by exposing the NeRF to diverse rendered variations during training.","intents":["Automatically resize and normalize input images to match diffusion model requirements","Improve 3D generation robustness through data augmentation","Handle images of arbitrary aspect ratios and resolutions","Reduce overfitting to specific viewpoints through augmentation"],"best_for":["Developers building robust 3D generation pipelines","Teams handling diverse input image formats and resolutions","Researchers exploring augmentation strategies for 3D generation"],"limitations":["Aggressive augmentation (large crops, rotations) may degrade guidance quality","Preprocessing adds computational overhead to training loop","Augmentation parameters require tuning; suboptimal settings reduce quality","Some augmentations (e.g., color jitter) may be inappropriate for certain object types"],"requires":["Python 3.8+","PyTorch 1.13+","PIL/Pillow for image operations","torchvision for augmentation transforms"],"input_types":["input image (arbitrary resolution and aspect ratio)","target resolution (e.g., 512x512)","augmentation parameters (crop size, jitter strength, rotation range, etc.)"],"output_types":["preprocessed image (normalized, resized)","augmented image (with random crops, jitter, etc.)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ashawkey--stable-dreamfusion__cap_12","uri":"capability://image.visual.taichi.and.cuda.acceleration.backend.selection","name":"taichi and cuda acceleration backend selection","description":"Provides runtime selection between Taichi (CUDA-free, portable) and CUDA-optimized backends for ray marching and grid encoding computation. Taichi is a domain-specific language for high-performance computing that compiles to CUDA, enabling GPU acceleration without explicit CUDA kernel writing. Users select the backend via configuration, and the system automatically uses the appropriate implementation for ray marching, feature encoding, and other compute-intensive operations.","intents":["Run 3D generation on systems without CUDA toolkit installed","Maintain code portability across different GPU architectures","Experiment with different acceleration backends without code changes","Deploy on systems with non-NVIDIA GPUs (via Taichi's multi-backend support)"],"best_for":["Developers targeting diverse hardware platforms","Teams avoiding CUDA toolkit dependency and installation complexity","Researchers exploring different acceleration approaches","Deployment scenarios with restricted CUDA availability"],"limitations":["Taichi backend may be 10-30% slower than hand-optimized CUDA kernels","Taichi compilation adds startup overhead (first run takes 30-60 seconds)","Some advanced CUDA features may not be available in Taichi","Debugging Taichi code is more difficult than standard PyTorch","Taichi support is experimental and may have stability issues"],"requires":["Python 3.8+","PyTorch 1.13+","Taichi 1.0+ (for Taichi backend)","NVIDIA GPU with compute capability 5.0+ (for CUDA backend)","No CUDA toolkit required for Taichi backend (uses Taichi's bundled CUDA)"],"input_types":["backend selection parameter (string: 'cuda' or 'taichi')","compute-intensive operations (ray marching, grid encoding, etc.)"],"output_types":["accelerated computation results (same as input backend)"],"categories":["image-visual","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ashawkey--stable-dreamfusion__cap_2","uri":"capability://image.visual.multi.resolution.grid.encoding.for.accelerated.nerf.rendering","name":"multi-resolution grid encoding for accelerated nerf rendering","description":"Implements the Instant-NGP multi-resolution grid encoding scheme to replace vanilla NeRF's positional encoding, enabling 10-100x faster rendering and training. The system uses a hierarchical grid structure with learnable feature vectors at multiple scales (coarse to fine), allowing the network to efficiently represent high-frequency details without dense MLPs. Ray marching queries the grid at each sample point, interpolating features across resolution levels.","intents":["Reduce NeRF training time from hours to minutes for interactive workflows","Enable real-time or near-real-time 3D model preview during generation","Support higher resolution 3D generation on consumer-grade GPUs","Optimize memory usage to fit larger models on limited VRAM"],"best_for":["Developers building interactive 3D generation tools with tight latency budgets","Teams deploying 3D generation on resource-constrained hardware","Researchers exploring efficient neural 3D representations","Production pipelines requiring fast iteration cycles"],"limitations":["Grid encoding requires careful tuning of grid resolution and feature dimensions; suboptimal settings degrade quality","Memory overhead of storing multi-resolution grids can exceed vanilla NeRF for very large scenes","Grid-based approach may struggle with unbounded or extremely large scenes","Requires CUDA-capable GPU for optimal performance; CPU fallback is extremely slow"],"requires":["Python 3.8+","PyTorch 1.13+","NVIDIA GPU with CUDA 11.7+ (grid encoding is CUDA-optimized)","Taichi or TCNN library for grid encoding backend (automatically installed)"],"input_types":["ray coordinates (3D points sampled along camera rays)"],"output_types":["encoded feature vectors (input to NeRF MLP)","density and color predictions after MLP processing"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ashawkey--stable-dreamfusion__cap_3","uri":"capability://image.visual.perpendicular.negative.sampling.for.multi.view.consistency","name":"perpendicular negative sampling for multi-view consistency","description":"Implements a specialized sampling strategy during SDS guidance to mitigate the 'multi-head' problem where the NeRF generates different geometry from different viewpoints. The system samples negative prompts from viewpoints perpendicular to the current rendering direction, encouraging the model to learn consistent 3D structure rather than view-dependent artifacts. This is applied during diffusion guidance by conditioning on both the positive prompt and perpendicular negative views.","intents":["Improve 3D consistency across multiple viewpoints during generation","Reduce view-dependent artifacts and 'floaters' in generated 3D models","Enhance geometric coherence without manual multi-view constraints","Generate more realistic and stable 3D representations"],"best_for":["Developers prioritizing geometric consistency over speed","Applications requiring high-quality 3D models for rendering or 3D printing","Teams building 3D assets for games or VR where view consistency is critical"],"limitations":["Adds computational overhead (additional diffusion forward passes per training step)","Requires careful tuning of perpendicular sampling angles and weighting","May slightly increase training time (10-20% overhead) compared to standard SDS","Effectiveness depends on the quality of the underlying diffusion model"],"requires":["Python 3.8+","PyTorch 1.13+","Stable Diffusion or other diffusion model for guidance","NVIDIA GPU with 24GB+ VRAM (perpendicular sampling requires additional memory)"],"input_types":["camera viewpoint (3D position and orientation)","text prompt (positive and negative)"],"output_types":["diffusion gradients for NeRF optimization","multi-view consistency scores"],"categories":["image-visual","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ashawkey--stable-dreamfusion__cap_4","uri":"capability://image.visual.dmtet.mesh.extraction.and.refinement","name":"dmtet mesh extraction and refinement","description":"Converts the implicit NeRF representation into an explicit mesh (OBJ, PLY) using Differentiable Marching Tetrahedra (DMTet). The system extracts a signed distance field (SDF) from the NeRF's density predictions, applies marching tetrahedra on a tetrahedral grid to generate a mesh, and optionally refines the mesh geometry through additional optimization. The extracted mesh can be textured, edited, or exported to standard 3D software.","intents":["Export generated 3D models to standard mesh formats for use in game engines or 3D software","Convert implicit NeRF representations into explicit geometry for 3D printing or CAD workflows","Enable post-processing and editing of generated 3D models","Create lightweight mesh assets for real-time rendering instead of NeRF inference"],"best_for":["3D artists and designers needing to edit or refine generated models","Game developers requiring mesh assets for engines like Unity or Unreal","Manufacturing or 3D printing workflows requiring explicit geometry","Teams integrating generated 3D into existing CAD or 3D pipelines"],"limitations":["Mesh extraction loses some detail compared to the original NeRF (resolution limited by tetrahedral grid)","Extracted meshes may have holes, artifacts, or non-manifold geometry requiring cleanup","DMTet refinement adds 10-30 minutes of additional optimization time","Texture extraction from NeRF is not built-in; requires external tools or custom code","Very high-resolution meshes (>1M triangles) may be slow to process or export"],"requires":["Python 3.8+","PyTorch 1.13+","Trained NeRF model checkpoint","NVIDIA GPU with 8GB+ VRAM for extraction","Optional: Meshlab or similar tools for mesh cleanup and inspection"],"input_types":["trained NeRF model (PyTorch checkpoint)","extraction resolution parameter (controls mesh detail)"],"output_types":["mesh file (OBJ, PLY, GLB formats)","vertex positions and face indices","optional: vertex normals and colors"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ashawkey--stable-dreamfusion__cap_5","uri":"capability://image.visual.ray.marching.with.adaptive.step.sampling","name":"ray marching with adaptive step sampling","description":"Implements efficient ray marching through the 3D scene by sampling points along camera rays and querying the NeRF at each sample point. The system uses adaptive step sizing based on density predictions, skipping empty regions and concentrating samples in high-density areas. Ray marching integrates density and color predictions along the ray to produce final pixel colors, with support for both coarse and fine sampling passes for improved quality.","intents":["Render 2D images from arbitrary camera viewpoints for SDS guidance computation","Generate training data for diffusion model guidance during NeRF optimization","Produce high-quality rendered outputs for visualization and evaluation","Enable efficient volumetric rendering without explicit mesh representation"],"best_for":["Developers building NeRF-based 3D generation pipelines","Researchers exploring volumetric rendering and neural representations","Teams requiring efficient rendering of implicit 3D representations"],"limitations":["Ray marching is slower than rasterization-based rendering for explicit meshes","Rendering quality depends on number of samples per ray; more samples = slower but better quality","Adaptive sampling adds complexity and may introduce artifacts if density predictions are noisy","Memory usage scales with image resolution and number of samples per ray","Difficult to achieve real-time rendering on consumer hardware (typically 0.5-2 FPS)"],"requires":["Python 3.8+","PyTorch 1.13+","Trained NeRF model with density and color outputs","NVIDIA GPU with 8GB+ VRAM for reasonable rendering speed"],"input_types":["camera intrinsics (focal length, principal point)","camera extrinsics (position and orientation)","image resolution (width, height)","number of samples per ray (coarse and fine)"],"output_types":["rendered RGB image (same resolution as input)","depth map (optional)","alpha/transparency map (optional)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ashawkey--stable-dreamfusion__cap_6","uri":"capability://image.visual.multi.backend.nerf.architecture.support","name":"multi-backend nerf architecture support","description":"Provides abstracted NeRF implementations across multiple backends (Instant-NGP, Vanilla NeRF, TCNN, Taichi) with a unified interface, allowing users to select the optimal backend for their hardware and performance requirements. Each backend implements the same forward pass interface but with different underlying representations: grid encoding (Instant-NGP), sinusoidal positional encoding (Vanilla), tiny CUDA neural networks (TCNN), or Taichi-based computation (Taichi). Users specify the backend via command-line arguments.","intents":["Choose the fastest NeRF backend for available hardware (GPU vs CPU, CUDA vs non-CUDA)","Trade off speed vs memory usage by selecting appropriate backend","Run on diverse hardware (high-end GPUs, consumer GPUs, CPU-only systems)","Experiment with different NeRF architectures without code changes"],"best_for":["Developers deploying 3D generation across heterogeneous hardware","Researchers comparing NeRF architectures and encoding schemes","Teams needing flexibility in performance vs quality tradeoffs","Users with limited GPU memory or non-NVIDIA hardware"],"limitations":["Instant-NGP backend requires NVIDIA GPU with CUDA; not available on CPU or AMD GPUs","Vanilla NeRF backend is 10-100x slower than Instant-NGP but works on any PyTorch device","TCNN backend requires CUDA and additional library installation","Taichi backend is experimental and may have stability issues","Switching backends mid-training requires restarting from scratch (no checkpoint compatibility)"],"requires":["Python 3.8+","PyTorch 1.13+","Backend-specific dependencies: CUDA 11.7+ for Instant-NGP/TCNN, Taichi for Taichi backend","NVIDIA GPU recommended for Instant-NGP (default); CPU-only possible with Vanilla backend"],"input_types":["backend selection parameter (string: 'instant-ngp', 'vanilla', 'tcnn', 'taichi')","NeRF architecture hyperparameters (hidden dimensions, depth, etc.)"],"output_types":["NeRF model instance with unified forward() interface","density and color predictions for input coordinates"],"categories":["image-visual","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ashawkey--stable-dreamfusion__cap_7","uri":"capability://image.visual.multi.guidance.diffusion.model.integration","name":"multi-guidance diffusion model integration","description":"Provides a modular guidance system supporting multiple diffusion models (Stable Diffusion, Zero123, DeepFloyd IF) through a unified Score Distillation Sampling (SDS) interface. Each guidance module implements the same compute_sds_loss() interface but with model-specific preprocessing, conditioning, and gradient computation. The system loads the appropriate diffusion model based on user selection and applies its gradients to optimize the NeRF.","intents":["Switch between text-to-3D (Stable Diffusion, DeepFloyd IF) and image-to-3D (Zero123) guidance without code changes","Experiment with different diffusion models to compare 3D generation quality","Combine multiple guidance models for hybrid generation (e.g., text + image conditioning)","Integrate new diffusion models by implementing a standard guidance interface"],"best_for":["Researchers exploring different diffusion models for 3D generation","Developers building flexible 3D generation pipelines","Teams wanting to experiment with model combinations","Advanced users implementing custom guidance models"],"limitations":["Each guidance model requires separate model weights (50-100GB total for all models)","Switching guidance models requires restarting training; no mid-training model switching","Different models have different quality characteristics; no automatic model selection","Combining multiple guidance models requires manual weight tuning","Some models (DeepFloyd IF) are slower or require additional dependencies"],"requires":["Python 3.8+","PyTorch 1.13+","Diffusers library 0.16+","Model-specific weights: Stable Diffusion (2GB), Zero123 (2GB), DeepFloyd IF (50GB)","NVIDIA GPU with 24GB+ VRAM for simultaneous NeRF + guidance model inference"],"input_types":["guidance model selection (string: 'sd', 'zero123', 'if')","conditioning input (text prompt for SD/IF, reference image for Zero123)","NeRF rendered views (RGB images)"],"output_types":["SDS loss value (scalar)","gradients for NeRF parameters","intermediate diffusion predictions (optional)"],"categories":["image-visual","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ashawkey--stable-dreamfusion__cap_8","uri":"capability://image.visual.gui.based.interactive.3d.generation.and.preview","name":"gui-based interactive 3d generation and preview","description":"Provides a graphical user interface (built with Gradio or similar) for text-to-3D and image-to-3D generation without command-line interaction. The GUI accepts text prompts or image uploads, displays real-time or periodic preview renders of the NeRF during training, allows parameter adjustment (guidance scale, learning rate, etc.), and enables one-click mesh export. The interface abstracts away command-line complexity for non-technical users.","intents":["Enable non-technical users to generate 3D models without CLI knowledge","Provide real-time visual feedback during 3D generation process","Allow interactive parameter tuning and experimentation","Simplify the workflow from prompt/image to downloadable 3D model"],"best_for":["Non-technical creators and artists wanting to generate 3D models","Teams building web-based 3D generation services","Educators demonstrating 3D generation to students","Rapid prototyping and experimentation workflows"],"limitations":["GUI adds overhead and may reduce responsiveness compared to direct Python API","Real-time preview rendering may be slow on consumer hardware (updates every 30-60 seconds)","Limited parameter exposure compared to CLI; advanced users may need CLI for full control","Web-based GUI requires server infrastructure for deployment","File upload/download bandwidth may be limiting factor for large models"],"requires":["Python 3.8+","PyTorch 1.13+","Gradio 3.0+ (or similar web framework)","All standard Stable-Dreamfusion dependencies (Stable Diffusion, NeRF backends, etc.)","Modern web browser for GUI access"],"input_types":["text prompt (string)","reference image (PNG, JPG, WebP)","generation parameters (guidance scale, learning rate, iterations, etc.)"],"output_types":["preview renders (displayed in browser during generation)","final mesh file (OBJ, PLY, downloadable)","training logs and metrics"],"categories":["image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ashawkey--stable-dreamfusion__cap_9","uri":"capability://image.visual.camera.trajectory.and.multi.view.rendering","name":"camera trajectory and multi-view rendering","description":"Supports rendering the NeRF from multiple camera viewpoints along predefined trajectories (circular orbits, spiral paths, etc.) to generate multi-view image sequences. The system computes camera intrinsics and extrinsics for each viewpoint, performs ray marching from each camera, and outputs a sequence of rendered images. This enables visualization of the 3D model from all angles and generation of training data for downstream tasks.","intents":["Generate 360-degree views of generated 3D models for visualization","Create multi-view image sequences for video or animation","Produce training data for other 3D tasks (e.g., view synthesis, 3D reconstruction)","Validate 3D model quality by inspecting from multiple angles"],"best_for":["Developers building 3D visualization and inspection tools","Teams generating multi-view training datasets","Content creators producing 3D model showcase videos","Researchers evaluating 3D generation quality across viewpoints"],"limitations":["Rendering many views is computationally expensive (linear scaling with number of views)","Trajectory definition requires manual specification or scripting","Output image sequence can be large (100+ views × high resolution = multiple GB)","No built-in video encoding; requires external tools to convert image sequences to video"],"requires":["Python 3.8+","PyTorch 1.13+","Trained NeRF model checkpoint","NVIDIA GPU with 8GB+ VRAM for reasonable rendering speed","Optional: FFmpeg for video encoding"],"input_types":["trained NeRF model","camera trajectory specification (orbit radius, height, number of frames, etc.)","output resolution and format"],"output_types":["image sequence (PNG, JPG, or video file)","camera poses for each frame (optional, for downstream tasks)"],"categories":["image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":45,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+","PyTorch 1.13+ with CUDA 11.7+ for GPU acceleration","Stable Diffusion model weights (1.5, 2.0, or 2.1) loaded via diffusers library","NVIDIA GPU with 24GB+ VRAM (A100, RTX 4090, or equivalent)","~50GB free disk space for model weights and intermediate outputs","PyTorch 1.13+ with CUDA 11.7+","Zero123 model weights (downloaded automatically or pre-cached)","NVIDIA GPU with 24GB+ VRAM","Input image in PNG, JPG, or WebP format (512x512 or higher recommended)","PyTorch 1.13+"],"failure_modes":["Training time is 1-2 hours per model on high-end GPUs (A100/RTX 4090); slower on consumer hardware","Generated geometry may lack fine details and sharp features compared to hand-modeled assets","Text prompts with complex spatial relationships or multiple objects may produce ambiguous results","Requires 24GB+ VRAM for full resolution rendering; lower resolutions reduce quality","No built-in control over specific object parts or fine-grained geometry constraints","Requires a clear, well-lit reference image; poor quality inputs produce poor 3D results","Struggles with transparent, reflective, or highly specular materials","Cannot infer occluded geometry (e.g., back of an object if only front is visible)","Zero123 model has limited understanding of extreme viewpoint changes","Training time is 30-60 minutes per image on high-end GPUs","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.6437430329941016,"quality":0.35,"ecosystem":0.5800000000000001,"match_graph":0.25,"freshness":0.52,"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":"2026-05-24T12:16:21.549Z","last_scraped_at":"2026-05-03T13:58:42.318Z","last_commit":"2023-12-10T23:17:27Z"},"community":{"stars":8831,"forks":775,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=ashawkey--stable-dreamfusion","compare_url":"https://unfragile.ai/compare?artifact=ashawkey--stable-dreamfusion"}},"signature":"/NeURtdoIvnTsa97w+wtQxXo+JHSmMjg/AWXzi/XZB8+HCVbCJiY9ElmkPE1Q82tRayoqfY3+fDc3BGqMtZgCg==","signedAt":"2026-06-22T01:49:57.884Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/ashawkey--stable-dreamfusion","artifact":"https://unfragile.ai/ashawkey--stable-dreamfusion","verify":"https://unfragile.ai/api/v1/verify?slug=ashawkey--stable-dreamfusion","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"}}