{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-nvlabs--sana","slug":"nvlabs--sana","name":"Sana","type":"model","url":"https://nvlabs.github.io/Sana/docs/","page_url":"https://unfragile.ai/nvlabs--sana","categories":["image-generation"],"tags":["diffusion","dit","linear-transformer","nvfp4","pytorch","reinforcement-learning","sana","system-algorithm-deisgn","text-to-image-generation","text-to-video","transformers","video-generation"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-nvlabs--sana__cap_0","uri":"capability://image.visual.linear.diffusion.transformer.text.to.image.generation.with.o.n.attention","name":"linear diffusion transformer text-to-image generation with o(n) attention","description":"Generates high-resolution images (up to 4K) from text prompts using SanaTransformer2DModel, a Linear DiT architecture that implements O(N) complexity attention instead of standard quadratic attention. The pipeline encodes text via Gemma-2-2B, processes latents through linear transformer blocks, and decodes via DC-AE (32× compression). This linear attention mechanism enables efficient processing of high-resolution spatial latents without the memory quadratic scaling of standard transformers.","intents":["Generate high-resolution images from text prompts without GPU memory constraints of standard diffusion models","Deploy text-to-image generation on consumer hardware with lower VRAM requirements","Build production systems requiring fast inference for image generation at scale"],"best_for":["ML engineers building efficient image generation pipelines","Teams deploying diffusion models on resource-constrained infrastructure","Researchers exploring linear attention mechanisms for generative tasks"],"limitations":["Linear attention may have slightly different quality characteristics than quadratic attention for certain artistic styles","Requires DC-AE autoencoder which is external dependency (mit-han-lab/dc-ae-f32c32-sana-1.1)","Multilingual support depends on chi_prompt configuration and Gemma-2 tokenizer coverage"],"requires":["Python 3.8+","PyTorch 2.0+","CUDA 11.8+ for GPU acceleration (CPU inference supported but slow)","HuggingFace Transformers library","DC-AE checkpoint from HuggingFace Hub"],"input_types":["text (English or multilingual prompts via chi_prompt parameter)","integer (random seed for reproducibility)","float (guidance scale for prompt adherence)"],"output_types":["PIL Image (single image)","torch.Tensor (raw latent or pixel space)","numpy array (batch of images)"],"categories":["image-visual","diffusion-models"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-nvlabs--sana__cap_1","uri":"capability://image.visual.one.step.diffusion.image.generation.via.sana.sprint.distillation","name":"one-step diffusion image generation via sana-sprint distillation","description":"Generates images in a single neural network forward pass using SANA-Sprint, a distilled variant of the base SANA model trained via knowledge distillation and reinforcement learning. The model compresses multi-step diffusion sampling into one step by learning to directly predict high-quality outputs from noise, eliminating iterative denoising loops. This is implemented through specialized training objectives that match the output distribution of multi-step teachers.","intents":["Generate images with minimal latency for real-time applications (web, mobile, interactive)","Reduce inference cost by eliminating iterative sampling steps","Deploy image generation on edge devices with strict latency budgets"],"best_for":["Product teams building interactive image generation features","Mobile and edge deployment scenarios requiring <100ms latency","Cost-sensitive inference at scale where per-image compute matters"],"limitations":["One-step generation may have slightly lower image quality/diversity compared to multi-step SANA","Distillation quality depends on teacher model capacity and training data","Limited control over generation process (no intermediate sampling steps for adjustment)"],"requires":["Python 3.8+","PyTorch 2.0+","SANA-Sprint checkpoint (1B or 600M parameter variants)","HuggingFace Diffusers integration"],"input_types":["text (prompt string)","integer (random seed)","float (guidance scale)"],"output_types":["PIL Image","torch.Tensor (pixel or latent space)"],"categories":["image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-nvlabs--sana__cap_10","uri":"capability://tool.use.integration.comfyui.integration.for.node.based.generation.workflows","name":"comfyui integration for node-based generation workflows","description":"Integrates SANA models into ComfyUI's node-based workflow system, enabling visual composition of generation pipelines without code. Custom nodes wrap SANA inference, ControlNet, and sampling operations as draggable nodes that can be connected to build complex workflows. Integration handles model loading, VRAM management, and batch processing through ComfyUI's execution engine.","intents":["Enable non-technical users to build image generation workflows visually","Compose complex multi-step generation pipelines (e.g., text→image→upscale→controlnet)","Integrate SANA with other ComfyUI nodes for hybrid workflows"],"best_for":["Content creators preferring visual workflow builders","Teams building no-code generation applications","Users integrating SANA with existing ComfyUI setups"],"limitations":["ComfyUI integration requires custom node implementation and maintenance","Node-based workflows may be slower than optimized Python code due to overhead","Limited debugging capabilities compared to direct Python API"],"requires":["ComfyUI installation (latest version)","SANA custom nodes (installed in ComfyUI/custom_nodes/)","Model checkpoints in ComfyUI model directory","GPU with sufficient VRAM for selected model"],"input_types":["Text (prompt, via ComfyUI text node)","Image (via ComfyUI load image node)","Numeric parameters (guidance scale, steps, seed)"],"output_types":["Image (via ComfyUI preview/save nodes)","Batch of images (for multi-image workflows)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-nvlabs--sana__cap_11","uri":"capability://tool.use.integration.gradio.web.interface.and.interactive.demos","name":"gradio web interface and interactive demos","description":"Provides Gradio-based web interfaces for interactive image and video generation with real-time parameter adjustment. Demos include sliders for guidance scale, seed, resolution, and other hyperparameters, with live preview of outputs. The framework includes pre-built demo scripts that can be deployed as standalone web apps or embedded in larger applications.","intents":["Enable non-technical users to experiment with SANA models via web browser","Build interactive demos for presentations, papers, or product showcases","Deploy generation capabilities as shareable web applications"],"best_for":["Researchers sharing model demos with broader audience","Teams building public-facing generation applications","Users wanting quick web interface without custom development"],"limitations":["Gradio interface adds ~100-200ms latency per request due to serialization","Web deployment requires server infrastructure and bandwidth for image transfer","Limited customization compared to custom web frameworks"],"requires":["Python 3.8+","Gradio library","SANA model checkpoint","GPU for inference (CPU inference very slow)","Web server or HuggingFace Spaces for deployment"],"input_types":["Text (prompt, via Gradio textbox)","Numeric sliders (guidance scale, steps, seed, resolution)","Image upload (for image-to-video mode)"],"output_types":["Image (displayed in Gradio interface)","Video (for video generation demos)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-nvlabs--sana__cap_12","uri":"capability://automation.workflow.model.quantization.and.optimization.for.deployment","name":"model quantization and optimization for deployment","description":"Implements quantization strategies (INT8, FP8, NVFp4) to reduce model size and inference latency for deployment. The framework supports post-training quantization via PyTorch quantization APIs and custom quantization kernels optimized for SANA's linear attention. Quantized models maintain quality while reducing VRAM by 50-75% and accelerating inference by 1.5-3×.","intents":["Deploy SANA models on resource-constrained devices (mobile, edge, consumer GPUs)","Reduce inference latency and cost for production deployments","Optimize models for specific hardware (e.g., INT8 for CPUs, FP8 for newer GPUs)"],"best_for":["Production teams deploying models at scale","Mobile and edge deployment scenarios","Cost-sensitive inference where latency/quality tradeoff matters"],"limitations":["Quantization may reduce image quality by 5-15% depending on bit-width","Quantized models are hardware-specific (INT8 kernels differ from FP8)","Quantization requires careful calibration on representative data"],"requires":["Python 3.8+","PyTorch 2.0+ with quantization support","Calibration dataset (100+ images for INT8 calibration)","Target hardware specification (CPU, GPU model, VRAM)"],"input_types":["Model checkpoint (full precision)","Calibration dataset (images for quantization calibration)","Quantization config (bit-width, strategy)"],"output_types":["Quantized model checkpoint (50-75% smaller)","Quantization metadata (scales, zero-points)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-nvlabs--sana__cap_13","uri":"capability://tool.use.integration.huggingface.hub.model.distribution.and.checkpoint.management","name":"huggingface hub model distribution and checkpoint management","description":"Integrates with HuggingFace Model Hub for centralized model distribution, versioning, and checkpoint management. Models are published as HuggingFace repositories with automatic configuration, tokenizer, and checkpoint handling. The framework supports model card generation, version control, and seamless loading via HuggingFace transformers/diffusers APIs.","intents":["Distribute SANA models and fine-tuned variants via HuggingFace Hub","Enable one-line model loading for users (model_id='nvidia/Sana-1200M')","Manage model versions, documentation, and metadata centrally"],"best_for":["Model developers publishing models for community use","Teams distributing fine-tuned variants to collaborators","Users wanting standardized model loading and versioning"],"limitations":["HuggingFace Hub requires internet connectivity for model download","Large models (>10GB) have slow initial download times","Hub storage quotas may limit number of model versions"],"requires":["HuggingFace account with model publishing permissions","HuggingFace Hub CLI (huggingface-hub library)","Git LFS for large checkpoint files","Model card documentation (markdown)"],"input_types":["Model checkpoint (PyTorch format)","Configuration file (JSON or YAML)","Model card (markdown documentation)"],"output_types":["HuggingFace model repository URL","Model accessible via transformers.AutoModel.from_pretrained()"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-nvlabs--sana__cap_14","uri":"capability://automation.workflow.docker.containerization.for.reproducible.deployment","name":"docker containerization for reproducible deployment","description":"Provides Docker configurations for containerized SANA deployment with pre-installed dependencies, model checkpoints, and inference servers. Dockerfiles include CUDA runtime, PyTorch, and optimized inference configurations. Containers can be deployed to cloud platforms (AWS, GCP, Azure) or on-premises infrastructure with consistent behavior across environments.","intents":["Deploy SANA models in containerized environments for reproducibility","Simplify infrastructure setup by bundling dependencies and models","Enable cloud deployment (AWS SageMaker, GCP Vertex AI, Azure ML)"],"best_for":["DevOps teams deploying models to cloud or on-premises","Organizations requiring reproducible deployment across environments","Teams using Kubernetes or container orchestration"],"limitations":["Docker images are large (10-20GB+) due to CUDA runtime and model checkpoints","Container overhead adds ~50-100ms latency per request","GPU support requires nvidia-docker and compatible host setup"],"requires":["Docker installation","nvidia-docker for GPU support","CUDA 11.8+ on host machine","Sufficient disk space for image (10-20GB+)"],"input_types":["Dockerfile (provided in repo)","Model checkpoint (downloaded during build or mounted)","Inference server config (FastAPI, Flask, etc.)"],"output_types":["Docker image (deployable to container registry)","Running container with inference server"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-nvlabs--sana__cap_15","uri":"capability://automation.workflow.configuration.system.with.yaml.based.hyperparameter.management","name":"configuration system with yaml-based hyperparameter management","description":"Implements a hierarchical YAML configuration system for managing training, inference, and model hyperparameters. Configurations support inheritance, variable substitution, and environment-specific overrides. The framework validates configurations against schemas and provides clear error messages for invalid settings. Configs control model architecture, training objectives, sampling strategies, and deployment settings.","intents":["Manage complex hyperparameter configurations without hardcoding","Enable reproducible experiments through version-controlled configs","Support multiple training/inference configurations (e.g., different model sizes)"],"best_for":["Researchers running multiple experiments with different hyperparameters","Teams managing production configurations across environments","Developers building custom training pipelines"],"limitations":["YAML configuration requires understanding of model architecture and training concepts","Configuration validation errors may be cryptic without good documentation","Large configurations can become difficult to manage without proper organization"],"requires":["Python 3.8+","PyYAML library","Understanding of SANA model architecture and training"],"input_types":["YAML configuration file","Command-line overrides (--key=value)"],"output_types":["Parsed configuration object (Python dict)","Validated hyperparameters for training/inference"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-nvlabs--sana__cap_2","uri":"capability://image.visual.block.causal.linear.attention.video.generation.with.temporal.coherence","name":"block causal linear attention video generation with temporal coherence","description":"Generates videos from text or images using SanaVideoTransformer3DModel, which extends the 2D linear transformer with block-causal linear attention for temporal dimension. The architecture processes video frames as 3D latent sequences where attention is causal along the time axis (each frame only attends to past frames) while maintaining linear complexity. This enables efficient multi-frame generation with temporal consistency without quadratic memory scaling across frame sequences.","intents":["Generate temporally coherent videos from text prompts or image-to-video conversion","Process long video sequences without memory explosion from frame-to-frame attention","Build video generation systems with frame-level control and consistency"],"best_for":["Video production teams needing AI-assisted content generation","Researchers exploring efficient temporal modeling in diffusion","Developers building image-to-video or text-to-video applications"],"limitations":["Block-causal attention may limit long-range temporal dependencies (blocks are typically 4-8 frames)","Video generation requires significantly more compute than image generation (4-8× longer inference)","Frame consistency depends on block size and attention window configuration"],"requires":["Python 3.8+","PyTorch 2.0+","SANA-Video checkpoint","Sufficient VRAM (24GB+ recommended for 512×512 video)","HuggingFace Diffusers with video pipeline support"],"input_types":["text (video description prompt)","PIL Image (for image-to-video mode)","integer (number of frames, typically 16-64)","float (guidance scale, motion intensity)"],"output_types":["list of PIL Images (frame sequence)","torch.Tensor (video tensor, shape: [T, C, H, W])","MP4 file (encoded video)"],"categories":["image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-nvlabs--sana__cap_3","uri":"capability://data.processing.analysis.deep.compression.autoencoder.dc.ae.latent.encoding.with.32.compression","name":"deep compression autoencoder (dc-ae) latent encoding with 32× compression","description":"Encodes images into highly compressed latent representations using AutoencoderDC, achieving 32× spatial compression (vs 8× in Stable Diffusion's VAE). The DC-AE architecture is optimized for reconstruction quality at extreme compression ratios, enabling diffusion to operate on much smaller latent spaces. The framework supports both DC-AE-Full (higher quality) and DC-AE-Lite (faster decoding) variants, with external checkpoint management via HuggingFace Hub integration.","intents":["Reduce memory footprint of diffusion models by operating on highly compressed latents","Enable 4K image generation on consumer hardware through aggressive latent compression","Trade off reconstruction quality vs speed by selecting DC-AE-Full or DC-AE-Lite variants"],"best_for":["Teams deploying high-resolution image generation on memory-constrained hardware","Researchers studying extreme compression in generative models","Production systems where inference speed is critical (DC-AE-Lite)"],"limitations":["32× compression may introduce subtle artifacts at extreme compression ratios compared to 8× VAE","DC-AE checkpoint is external dependency (mit-han-lab/dc-ae-f32c32-sana-1.1) requiring separate download","DC-AE-Lite variant has faster decoding but lower reconstruction fidelity than DC-AE-Full"],"requires":["Python 3.8+","PyTorch 2.0+","DC-AE checkpoint from HuggingFace Hub or local path","Sufficient disk space for checkpoint (~2-4GB)"],"input_types":["PIL Image (any resolution, auto-resized to model input)","torch.Tensor (pixel space, shape: [B, 3, H, W], values in [-1, 1])","numpy array (image batch)"],"output_types":["torch.Tensor (latent representation, shape: [B, C, H/32, W/32])","PIL Image (reconstructed from latent via decode)"],"categories":["data-processing-analysis","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-nvlabs--sana__cap_4","uri":"capability://automation.workflow.flow.matching.sampling.with.configurable.schedulers","name":"flow matching sampling with configurable schedulers","description":"Implements flexible diffusion sampling via Flow Matching schedulers that control the noise-to-signal trajectory during generation. The framework supports multiple scheduler types (linear, exponential, custom) configured via YAML, allowing fine-tuning of generation quality vs speed tradeoffs. Schedulers control timestep sequences, noise schedules, and guidance scaling, enabling both standard multi-step sampling and optimized paths for one-step models.","intents":["Customize diffusion sampling behavior without modifying core model code","Experiment with different noise schedules to optimize quality vs speed","Implement custom sampling strategies for specialized generation tasks"],"best_for":["Researchers exploring diffusion sampling algorithms","Teams fine-tuning generation quality for specific use cases","Developers building custom sampling pipelines"],"limitations":["Scheduler configuration requires understanding of diffusion theory (timesteps, noise scales)","Custom schedulers may require retraining or fine-tuning for optimal results","Limited documentation on scheduler parameter tuning guidelines"],"requires":["Python 3.8+","PyTorch 2.0+","YAML configuration file with scheduler definition","Understanding of diffusion sampling concepts"],"input_types":["YAML config (scheduler type, timesteps, noise schedule)","float (guidance scale)","integer (number of sampling steps)"],"output_types":["torch.Tensor (generated latent or image)","PIL Image (final output)"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-nvlabs--sana__cap_5","uri":"capability://image.visual.multi.scale.and.high.resolution.image.generation.up.to.4k","name":"multi-scale and high-resolution image generation up to 4k","description":"Generates images at arbitrary resolutions up to 4K (4096×4096) by leveraging linear attention's O(N) complexity and DC-AE's 32× compression. The framework supports dynamic resolution handling through latent padding/cropping and aspect ratio preservation, enabling generation at native target resolutions rather than fixed sizes. Multi-scale training enables the same model to generate across resolution ranges without separate model variants.","intents":["Generate high-resolution images for print, wallpapers, or professional content","Support variable aspect ratios and resolutions in a single model","Avoid quality degradation from upscaling by generating at native resolution"],"best_for":["Content creation teams requiring print-quality images","Applications needing variable-resolution output (thumbnails to 4K)","Professional workflows where upscaling artifacts are unacceptable"],"limitations":["4K generation requires 24GB+ VRAM even with linear attention and DC-AE compression","Generation time scales with resolution (4K takes 4-8× longer than 1K)","Quality may vary across resolution ranges if model training data is imbalanced"],"requires":["Python 3.8+","PyTorch 2.0+","High-end GPU (A100, H100, or RTX 4090+) for 4K generation","Sufficient VRAM (24GB+ recommended)"],"input_types":["text (prompt)","tuple of integers (height, width in pixels)","float (guidance scale)"],"output_types":["PIL Image (high-resolution output)","torch.Tensor (latent or pixel space)"],"categories":["image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-nvlabs--sana__cap_6","uri":"capability://image.visual.controlnet.integration.for.spatial.and.structural.guidance","name":"controlnet integration for spatial and structural guidance","description":"Integrates ControlNet modules to guide image generation using spatial constraints (edge maps, depth, pose, segmentation). The framework loads ControlNet checkpoints compatible with HuggingFace Diffusers format and applies control conditioning during the diffusion process. Control signals are encoded and injected into transformer blocks, enabling precise spatial control while maintaining text-prompt guidance through classifier-free guidance.","intents":["Generate images with specific spatial layouts, poses, or structural constraints","Combine text prompts with edge maps, depth maps, or segmentation masks for precise control","Enable conditional image generation with multiple guidance modalities"],"best_for":["Content creators needing precise spatial control over generation","Teams building guided image generation applications","Developers implementing multi-modal conditional generation"],"limitations":["ControlNet adds ~15-20% inference latency due to additional conditioning branches","Requires pre-computed control maps (edge detection, depth estimation, pose detection)","ControlNet quality depends on training data alignment with SANA model distribution"],"requires":["Python 3.8+","PyTorch 2.0+","ControlNet checkpoint (HuggingFace format)","Control map generation tool (e.g., Canny for edges, MiDaS for depth)"],"input_types":["text (prompt)","PIL Image or torch.Tensor (control map: edge, depth, pose, segmentation)","float (control strength, 0.0-1.0)","float (guidance scale)"],"output_types":["PIL Image (controlled generation output)","torch.Tensor (latent or pixel space)"],"categories":["image-visual","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-nvlabs--sana__cap_7","uri":"capability://automation.workflow.distributed.training.with.ddp.and.fsdp.for.multi.gpu.scaling","name":"distributed training with ddp and fsdp for multi-gpu scaling","description":"Implements distributed training via PyTorch Distributed Data Parallel (DDP) and Fully Sharded Data Parallel (FSDP) for scaling across multiple GPUs and nodes. The framework handles gradient synchronization, model sharding, and checkpoint management automatically. FSDP enables training of larger models by sharding parameters, gradients, and optimizer states across devices, while DDP provides simpler data parallelism for smaller models.","intents":["Train large SANA models on multi-GPU clusters efficiently","Scale training across multiple nodes for faster convergence","Fine-tune models on custom datasets using distributed training"],"best_for":["ML teams with access to multi-GPU infrastructure","Researchers training custom SANA variants on large datasets","Organizations fine-tuning models on proprietary data"],"limitations":["Distributed training adds complexity to debugging and monitoring","FSDP requires careful tuning of sharding strategy and communication patterns","Synchronization overhead increases with number of devices (diminishing returns >64 GPUs)"],"requires":["Python 3.8+","PyTorch 2.0+ with distributed support","Multiple GPUs (2+) or multi-node setup","NCCL backend for GPU communication","Training configuration YAML with DDP/FSDP settings"],"input_types":["YAML config (num_processes, backend, sharding_strategy)","Training dataset (image-text pairs)","Model checkpoint (optional, for resuming)"],"output_types":["Model checkpoint (distributed format, convertible to single-GPU)","Training logs and metrics"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-nvlabs--sana__cap_8","uri":"capability://automation.workflow.lora.and.parameter.efficient.fine.tuning.for.custom.adaptation","name":"lora and parameter-efficient fine-tuning for custom adaptation","description":"Enables efficient model adaptation through Low-Rank Adaptation (LoRA) that trains only small rank-decomposed matrices instead of full model parameters. LoRA modules are inserted into transformer blocks and can be trained on custom datasets with minimal memory overhead. The framework supports LoRA merging into base model weights and composition of multiple LoRA adapters for different styles or domains.","intents":["Fine-tune SANA models on custom datasets with 10-50× fewer trainable parameters","Adapt models to specific styles, domains, or artistic directions without full retraining","Enable rapid experimentation with different LoRA configurations"],"best_for":["Teams with limited compute resources needing model customization","Content creators building style-specific image generators","Researchers exploring parameter-efficient adaptation methods"],"limitations":["LoRA adds ~5-10% inference latency due to additional matrix multiplications","LoRA quality depends on rank selection and training data quality","Multiple LoRA adapters cannot be easily composed at inference time (sequential application only)"],"requires":["Python 3.8+","PyTorch 2.0+","Base SANA model checkpoint","Custom training dataset (100+ image-text pairs recommended)","LoRA configuration (rank, alpha, target modules)"],"input_types":["YAML config (lora_rank, lora_alpha, target_modules)","Training dataset (image-text pairs)","Base model checkpoint"],"output_types":["LoRA checkpoint (small file, ~10-50MB)","Merged model checkpoint (full size, for deployment)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-nvlabs--sana__cap_9","uri":"capability://automation.workflow.video.model.training.with.temporal.consistency.objectives","name":"video model training with temporal consistency objectives","description":"Provides complete training pipeline for SANA-Video models with specialized loss functions enforcing temporal consistency across frames. Training uses block-causal attention masking to ensure causality, and includes optical flow or perceptual losses to maintain smooth motion and appearance consistency. The framework supports both text-to-video and image-to-video training with configurable frame counts and temporal sampling strategies.","intents":["Train custom video generation models on proprietary video datasets","Fine-tune SANA-Video for specific video styles or domains","Experiment with temporal consistency objectives and loss functions"],"best_for":["Video production teams building custom generation models","Researchers exploring temporal consistency in diffusion","Organizations with large video datasets needing domain-specific models"],"limitations":["Video training requires 3-5× more compute than image training due to temporal dimension","Temporal consistency objectives add training complexity and hyperparameter tuning","Video datasets are smaller and less diverse than image datasets, limiting generalization"],"requires":["Python 3.8+","PyTorch 2.0+","Video training dataset (1000+ videos recommended)","High-end multi-GPU setup (8+ A100s recommended)","Training configuration with temporal loss weights"],"input_types":["YAML config (num_frames, temporal_loss_weight, frame_sampling_strategy)","Video dataset (MP4 or frame sequences with text captions)","Base SANA-Video checkpoint (optional, for fine-tuning)"],"output_types":["Video model checkpoint","Training logs with temporal consistency metrics"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":35,"verified":false,"data_access_risk":"low","permissions":["Python 3.8+","PyTorch 2.0+","CUDA 11.8+ for GPU acceleration (CPU inference supported but slow)","HuggingFace Transformers library","DC-AE checkpoint from HuggingFace Hub","SANA-Sprint checkpoint (1B or 600M parameter variants)","HuggingFace Diffusers integration","ComfyUI installation (latest version)","SANA custom nodes (installed in ComfyUI/custom_nodes/)","Model checkpoints in ComfyUI model directory"],"failure_modes":["Linear attention may have slightly different quality characteristics than quadratic attention for certain artistic styles","Requires DC-AE autoencoder which is external dependency (mit-han-lab/dc-ae-f32c32-sana-1.1)","Multilingual support depends on chi_prompt configuration and Gemma-2 tokenizer coverage","One-step generation may have slightly lower image quality/diversity compared to multi-step SANA","Distillation quality depends on teacher model capacity and training data","Limited control over generation process (no intermediate sampling steps for adjustment)","ComfyUI integration requires custom node implementation and maintenance","Node-based workflows may be slower than optimized Python code due to overhead","Limited debugging capabilities compared to direct Python API","Gradio interface adds ~100-200ms latency per request due to serialization","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.3149559481923098,"quality":0.35,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.75,"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":"2026-05-24T12:16:22.063Z","last_scraped_at":"2026-05-03T13:59:47.981Z","last_commit":"2026-04-14T07:06:01Z"},"community":{"stars":5120,"forks":345,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=nvlabs--sana","compare_url":"https://unfragile.ai/compare?artifact=nvlabs--sana"}},"signature":"y2j9kFyWCYYyCMNxTbVIJxelhDkbrJ0Gh7E9jyGAMuonB+R6RiCuorqnX3p/ITWFCHTHIpHj3MAGMxbrZ5qJCw==","signedAt":"2026-06-20T08:26:53.984Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/nvlabs--sana","artifact":"https://unfragile.ai/nvlabs--sana","verify":"https://unfragile.ai/api/v1/verify?slug=nvlabs--sana","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"}}