{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-pku-yuangroup--helios","slug":"pku-yuangroup--helios","name":"Helios","type":"model","url":"https://pku-yuangroup.github.io/Helios-Page","page_url":"https://unfragile.ai/pku-yuangroup--helios","categories":["video-generation"],"tags":["acceleration","diffusion","diffusion-model","diffusion-models","efficient-tuning","high-quality","image-to-video","image2video","interactive","long-context","long-video-generation","real-time","text-to-video","text2video","video-generation","video-generator","video-to-video","video2video","world-model","world-models"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-pku-yuangroup--helios__cap_0","uri":"capability://image.visual.autoregressive.chunk.based.long.video.generation.from.text.prompts","name":"autoregressive chunk-based long-video generation from text prompts","description":"Generates minute-scale videos (up to 60+ seconds) from natural language text prompts using a 14B-parameter diffusion model with autoregressive, chunk-based frame generation. The model processes video in 33-frame chunks sequentially, with each chunk conditioned on previous chunks to maintain temporal coherence without explicit anti-drifting mechanisms like self-forcing or error-banks. Achieves 19.5 FPS on a single H100 GPU by leveraging unified history injection and multi-term memory patchification during training.","intents":["Generate long-form video content from text descriptions without manual keyframe specification","Create minute-scale videos in real-time on consumer-grade hardware (H100)","Avoid quality degradation over extended video sequences without complex anti-drifting strategies"],"best_for":["Content creators building automated video generation pipelines","Researchers studying long-context video synthesis without conventional stabilization techniques","Teams deploying real-time video generation in production environments"],"limitations":["Frame count is rounded up to nearest multiple of 33 at runtime due to chunk-based architecture","No built-in keyframe sampling or error-bank mechanisms — relies on training-time optimizations for drift prevention","Requires H100 GPU for stated 19.5 FPS performance; inference speed degrades significantly on lower-tier hardware","Text prompt understanding limited by underlying language model capacity — complex scene descriptions may not fully materialize"],"requires":["CUDA 11.8+ with H100 GPU (or compatible NVIDIA GPU with reduced throughput)","Python 3.9+","PyTorch 2.0+","Helios-Base checkpoint (largest, highest quality variant)","Minimum 40GB GPU VRAM for single model inference"],"input_types":["text (natural language prompt, 10-500 characters typical)","integer (num_frames, rounded to nearest multiple of 33)"],"output_types":["video file (MP4, H.264 codec)","frame sequence (PNG or JPEG, 33-frame chunks)"],"categories":["image-visual","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-pku-yuangroup--helios__cap_1","uri":"capability://image.visual.image.to.video.conditional.generation.with.visual.grounding","name":"image-to-video conditional generation with visual grounding","description":"Generates videos conditioned on a static input image, using the image as a visual anchor to guide the diffusion process. The model encodes the input image through the same VAE and transformer backbone used for text conditioning, allowing the image to provide spatial and semantic constraints that shape frame generation across all 33-frame chunks. Supports both Helios-Base (highest quality) and Helios-Distilled (fastest) variants with identical architectural conditioning.","intents":["Extend static images into animated videos while preserving visual identity and composition","Create video variations from a fixed visual reference without text prompt engineering","Generate motion sequences that respect specific visual constraints from reference imagery"],"best_for":["Marketing teams creating product demo videos from still photography","Visual effects artists generating motion variations from keyframe images","Developers building image-to-video pipelines for e-commerce or social media"],"limitations":["Image resolution must match model's training resolution (typically 512×512 or 768×768) — upscaling/downscaling may degrade conditioning quality","Motion generation is constrained by image content; highly static images may produce minimal motion variation","No explicit control over motion direction or intensity — determined entirely by diffusion sampling","Image artifacts or compression in input directly propagate through all generated frames"],"requires":["Input image in PNG, JPEG, or WebP format","Image resolution between 512×512 and 1024×1024 pixels","Python 3.9+, PyTorch 2.0+","H100 GPU or equivalent (40GB+ VRAM)"],"input_types":["image (PNG, JPEG, WebP; 512×512 to 1024×1024 pixels)","integer (num_frames, rounded to nearest multiple of 33)"],"output_types":["video file (MP4, H.264 codec)","frame sequence (PNG or JPEG)"],"categories":["image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-pku-yuangroup--helios__cap_10","uri":"capability://data.processing.analysis.unified.history.injection.for.temporal.coherence.without.explicit.anti.drifting","name":"unified history injection for temporal coherence without explicit anti-drifting","description":"Training mechanism that injects previous chunk history (encoded representations of prior 33-frame chunks) directly into the transformer attention layers, enabling the model to maintain temporal coherence across chunk boundaries without explicit anti-drifting strategies like self-forcing, error-banks, or keyframe sampling. The history is injected as additional context tokens in the attention mechanism, allowing the model to learn implicit drift prevention during training. This approach simplifies inference (no need for complex anti-drifting logic) while maintaining quality across minute-scale videos.","intents":["Maintain temporal coherence across long videos without inference-time anti-drifting mechanisms","Simplify inference pipeline by baking drift prevention into model weights during training","Enable seamless chunk-to-chunk transitions in autoregressive generation"],"best_for":["Researchers studying implicit vs. explicit anti-drifting mechanisms in video synthesis","Teams deploying video generation where inference simplicity is valued over maximum quality","Developers building long-video generation systems that need to avoid complex post-processing"],"limitations":["History injection adds training complexity — requires careful implementation of history encoding and attention integration","Implicit drift prevention may be less effective than explicit mechanisms for very long videos (>2 minutes)","History tokens increase attention computation cost during training — requires larger batch sizes to amortize overhead","No explicit control over history weight at inference — cannot adjust how much previous chunks influence current generation"],"requires":["Training dataset with minimum 10K videos (recommended 100K+)","H100 or A100 GPU with 80GB VRAM (for 4-model training setup)","Python 3.9+, PyTorch 2.0+ with distributed training support","Custom training code implementing unified history injection"],"input_types":["video dataset (MP4, MOV, or frame sequences)","text annotations (for text-to-video training)","training hyperparameters (history injection weight, attention mechanism type)"],"output_types":["trained model checkpoint","training logs with temporal coherence metrics"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-pku-yuangroup--helios__cap_11","uri":"capability://data.processing.analysis.easy.anti.drifting.training.strategy.for.motion.stability","name":"easy anti-drifting training strategy for motion stability","description":"Training-time technique that applies lightweight anti-drifting constraints during the Base model training stage, preventing motion drift without the computational overhead of inference-time anti-drifting mechanisms. The strategy uses multi-term memory patchification to reference multiple previous chunks, enabling the model to learn motion consistency across longer temporal windows. This is distinct from unified history injection — easy anti-drifting focuses on motion stability through explicit training objectives, while history injection provides implicit temporal context.","intents":["Improve motion stability in generated videos without inference-time anti-drifting overhead","Enable training of Base model with high-quality motion consistency","Provide foundation for downstream distillation to Mid and Distilled variants"],"best_for":["Teams training custom Helios variants on domain-specific video datasets","Researchers studying motion stability in video diffusion models","Organizations fine-tuning Base checkpoint for specific motion characteristics"],"limitations":["Easy anti-drifting is only applied during Base training — not available for fine-tuning Mid or Distilled variants","Training overhead increases with number of previous chunks referenced — requires careful tuning of memory window size","Motion stability improvements are dataset-dependent — may not generalize across different video domains","No explicit control over anti-drifting strength at inference — fixed at training time"],"requires":["Video dataset with minimum 10K clips (recommended 100K+)","H100 or A100 GPU with 80GB VRAM (for 4-model training)","Python 3.9+, PyTorch 2.0+","Custom training code implementing easy anti-drifting objectives"],"input_types":["video dataset (MP4, MOV, or frame sequences)","text annotations (for text-to-video training)","anti-drifting hyperparameters (memory window size, loss weight)"],"output_types":["trained Base checkpoint","motion stability metrics (motion amplitude variance, optical flow consistency)"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-pku-yuangroup--helios__cap_12","uri":"capability://planning.reasoning.heliosscheduler.and.heliosdmdscheduler.noise.scheduling.for.variant.specific.optimization","name":"heliosscheduler and heliosdmdscheduler noise scheduling for variant-specific optimization","description":"Two custom noise schedulers optimized for different prediction types and guidance strategies: HeliosScheduler for Base/Mid variants (v-prediction with standard/CFG-Zero guidance) and HeliosDMDScheduler for Distilled variant (x0-prediction with CFG-free guidance). Each scheduler is jointly optimized with its corresponding prediction type and guidance strategy during training, enabling faster convergence and better quality at fewer inference steps. The schedulers define the noise level progression across diffusion steps, with HeliosDMDScheduler using more aggressive noise reduction for x0-prediction.","intents":["Optimize noise scheduling for each variant's prediction type and guidance strategy","Enable faster convergence with fewer diffusion steps through variant-specific scheduling","Maintain quality across different inference step counts (50 for Base, 20 for Mid, 2-3 for Distilled)"],"best_for":["Researchers studying noise schedule design for different prediction types","Teams fine-tuning Helios variants on custom datasets requiring scheduler adjustment","Developers implementing custom variants with different prediction types"],"limitations":["Schedulers are fixed at checkpoint time — cannot be adjusted at inference without retraining","HeliosDMDScheduler is highly specialized for x0-prediction — not compatible with v-prediction or other prediction types","Scheduler design is not documented in detail — difficult to adapt for custom variants without extensive experimentation","Noise schedule is sensitive to training data distribution — may not generalize across different video domains"],"requires":["Corresponding model checkpoint (Base/Mid for HeliosScheduler, Distilled for HeliosDMDScheduler)","Python 3.9+, PyTorch 2.0+","Custom scheduler implementation (if training new variants)"],"input_types":["diffusion step index (0 to num_steps)","prediction type (v-prediction or x0-prediction)","guidance strategy (standard CFG, CFG-Zero, or CFG-free)"],"output_types":["noise level (alpha_t, sigma_t, or equivalent)","scheduler state (for resuming inference)"],"categories":["planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-pku-yuangroup--helios__cap_2","uri":"capability://image.visual.video.to.video.style.transfer.and.motion.continuation","name":"video-to-video style transfer and motion continuation","description":"Generates new video frames conditioned on an input video sequence, enabling style transfer, motion continuation, or video interpolation. The model encodes the input video through temporal convolutions and attention layers, extracting motion and semantic patterns that guide the diffusion process for subsequent frames. Supports frame-by-frame or chunk-by-chunk conditioning depending on the inference interface used.","intents":["Continue video sequences beyond their original length while maintaining motion consistency","Apply stylistic transformations to existing video without changing underlying motion","Interpolate between video frames or extend low-frame-rate footage to higher frame rates"],"best_for":["Video editors extending footage or applying consistent style transformations","Researchers studying motion transfer and temporal coherence in video synthesis","Developers building video enhancement or interpolation tools"],"limitations":["Input video must be pre-processed to match training resolution and frame rate (typically 512×512, 8 FPS minimum)","Motion patterns from input video strongly constrain output — cannot dramatically alter motion direction or speed","Temporal discontinuities at chunk boundaries may require post-processing blending","Long input videos (>10 seconds) may accumulate drift despite training-time optimizations"],"requires":["Input video in MP4, MOV, or AVI format","Video resolution between 512×512 and 1024×1024 pixels","Frame rate between 8 and 30 FPS (will be resampled to model's training rate)","Python 3.9+, PyTorch 2.0+, FFmpeg for video preprocessing","H100 GPU or equivalent (40GB+ VRAM)"],"input_types":["video file (MP4, MOV, AVI; 512×512 to 1024×1024 pixels, 8-30 FPS)","integer (num_frames, rounded to nearest multiple of 33)"],"output_types":["video file (MP4, H.264 codec)","frame sequence (PNG or JPEG)"],"categories":["image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-pku-yuangroup--helios__cap_3","uri":"capability://image.visual.progressive.distillation.pipeline.with.quality.speed.tradeoff.variants","name":"progressive distillation pipeline with quality-speed tradeoff variants","description":"Provides three model checkpoints (Helios-Base, Helios-Mid, Helios-Distilled) arranged in a distillation chain that progressively trades quality for inference speed. Base uses v-prediction with standard CFG and 50 inference steps for highest quality; Mid uses CFG-Zero with 20 steps per stage; Distilled uses x0-prediction with CFG-free guidance (scale=1.0) and 2-3 steps per stage. Each variant uses a different noise scheduler (HeliosScheduler for Base/Mid, HeliosDMDScheduler for Distilled) optimized for its prediction type and guidance strategy.","intents":["Select appropriate model variant based on quality vs. latency requirements for specific deployment","Benchmark quality degradation across distillation stages to understand speed-quality frontier","Deploy fastest variant (Distilled) for real-time applications while maintaining option to upgrade to Base for offline high-quality rendering"],"best_for":["Production teams needing to balance quality and latency across different use cases","Researchers studying knowledge distillation in video diffusion models","Developers building adaptive systems that switch variants based on available compute"],"limitations":["Helios-Mid is an intermediate artifact of distillation and may not meet expected quality targets on its own — intended primarily for research, not production use","Quality degradation is non-linear across variants; Distilled may show visible artifacts in motion smoothness or semantic consistency compared to Base","All three variants use identical 14B architecture — speed gains come from training-time optimizations (prediction type, guidance strategy) rather than model compression, limiting further acceleration","No quantization or KV-cache optimizations applied — cannot further reduce memory footprint or latency beyond distillation chain"],"requires":["Separate checkpoint files for each variant (Base: ~28GB, Mid: ~28GB, Distilled: ~28GB on disk)","Python 3.9+, PyTorch 2.0+","For Base: H100 GPU with 40GB+ VRAM","For Mid: A100 or H100 with 40GB+ VRAM (intermediate quality/speed)","For Distilled: A100 or H100 with 40GB+ VRAM (fastest inference)"],"input_types":["text prompt (for T2V)","image (for I2V)","video (for V2V)","variant selection flag (--sample-type or model parameter)"],"output_types":["video file (MP4, H.264 codec)","frame sequence (PNG or JPEG)","quality metrics (LPIPS, FVD, motion amplitude, semantic consistency scores)"],"categories":["image-visual","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-pku-yuangroup--helios__cap_4","uri":"capability://image.visual.multi.scale.sampling.pipeline.with.pyramid.unified.predictor","name":"multi-scale sampling pipeline with pyramid unified predictor","description":"Helios-Mid and Helios-Distilled variants employ a multi-scale sampling pipeline that decomposes the diffusion process into multiple stages, each operating at different noise scales. The Pyramid Unified Predictor (PUP) architecture enables efficient coarse-to-fine generation where early stages produce low-frequency motion and semantic structure, and later stages refine high-frequency details. This approach reduces effective inference steps (20 per stage for Mid, 2-3 per stage for Distilled) while maintaining temporal coherence across chunk boundaries.","intents":["Accelerate inference by decomposing diffusion into coarse-to-fine stages without sacrificing motion quality","Generate long videos faster by reducing per-stage step count while preserving semantic consistency","Enable adaptive quality control by adjusting stage-specific step counts based on available compute"],"best_for":["Teams deploying real-time video generation where latency is critical (interactive applications, live streaming)","Researchers studying multi-scale diffusion and hierarchical video synthesis","Developers building adaptive inference systems that adjust quality based on available GPU memory"],"limitations":["Multi-scale pipeline adds architectural complexity — requires careful tuning of stage-specific schedulers and step counts","Coarse-to-fine decomposition may produce visible artifacts at stage boundaries if step counts are too aggressive (2-3 steps per stage)","Not available in Helios-Base variant — only Mid and Distilled use this acceleration technique","Stage-specific hyperparameters (noise scales, step counts) are fixed at checkpoint time — cannot be dynamically adjusted at inference without retraining"],"requires":["Helios-Mid or Helios-Distilled checkpoint (not available in Base)","Python 3.9+, PyTorch 2.0+","A100 or H100 GPU with 40GB+ VRAM","HeliosDMDScheduler implementation for Distilled variant"],"input_types":["text prompt (for T2V)","image (for I2V)","video (for V2V)","stage-specific parameters (optional: step counts per stage)"],"output_types":["video file (MP4, H.264 codec)","intermediate stage outputs (optional: coarse-to-fine frame sequences for debugging)"],"categories":["image-visual","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-pku-yuangroup--helios__cap_5","uri":"capability://data.processing.analysis.training.optimized.batch.processing.with.memory.efficient.patchification","name":"training-optimized batch processing with memory-efficient patchification","description":"Helios training pipeline uses unified history injection, easy anti-drifting, and multi-term memory patchification to enable image-diffusion-scale batch sizes (typically 256-512 frames per batch) while fitting up to four 14B models in 80GB of GPU memory. The patchification strategy decomposes video frames into spatial patches during training, reducing memory footprint while maintaining temporal coherence through multi-term memory mechanisms that reference previous chunks. This approach eliminates the need for expensive techniques like KV-cache or quantization.","intents":["Train large video diffusion models on limited GPU resources (fit 4×14B models in 80GB VRAM)","Achieve image-diffusion-scale batch sizes for video training without gradient accumulation overhead","Implement anti-drifting mechanisms during training rather than inference, simplifying deployment"],"best_for":["Research teams training custom video diffusion models with limited GPU budgets","Organizations fine-tuning Helios checkpoints on domain-specific video datasets","Developers building video generation systems where training efficiency directly impacts iteration speed"],"limitations":["Patchification adds training-time complexity — requires careful implementation of patch assembly/disassembly and temporal attention across patches","Multi-term memory mechanism requires storing multiple previous chunks in GPU memory, increasing peak memory usage during training","Training optimizations are baked into checkpoint weights — cannot be disabled at inference time for further acceleration","Batch size scaling is non-linear; doubling batch size does not proportionally reduce training time due to communication overhead"],"requires":["H100 or A100 GPU with 80GB VRAM (for 4-model setup) or 40GB VRAM (for single model)","Python 3.9+, PyTorch 2.0+ with distributed training support (torch.distributed)","Video dataset with minimum 10K clips (recommended 100K+ for high-quality models)","Text annotations for each video clip (for text-to-video training)"],"input_types":["video dataset (MP4, MOV, or frame sequences; 512×512 to 1024×1024 resolution)","text annotations (natural language descriptions, 10-500 characters per video)","training hyperparameters (batch size, learning rate, num_epochs)"],"output_types":["model checkpoint (PyTorch .pt or .safetensors format)","training logs (loss curves, validation metrics)","intermediate checkpoints (for resuming training)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-pku-yuangroup--helios__cap_6","uri":"capability://data.processing.analysis.comprehensive.video.quality.evaluation.pipeline.with.multi.metric.scoring","name":"comprehensive video quality evaluation pipeline with multi-metric scoring","description":"Provides an integrated evaluation framework that measures video quality across five dimensions: aesthetic score (visual appeal), motion amplitude (motion magnitude), motion smoothness (temporal consistency), semantic consistency (text-to-video alignment), and naturalness (perceptual realism). Metrics are computed both as instantaneous scores (per-frame or per-chunk) and as drifting metrics that track degradation over time, enabling detection of long-video artifacts. Scores are aggregated into a final rating that combines all dimensions with configurable weights.","intents":["Benchmark video generation quality across model variants and hyperparameter configurations","Detect temporal drift and quality degradation in long videos (>30 seconds)","Validate that generated videos meet quality thresholds before deployment"],"best_for":["Researchers comparing video generation models and distillation strategies","Teams establishing quality baselines and monitoring production video generation","Developers building automated quality gates for video generation pipelines"],"limitations":["Metric computation is expensive — evaluating a 60-second video requires ~5-10 minutes on H100 GPU","Drifting metrics require reference videos or ground truth for comparison; cannot evaluate absolute quality without baselines","Aesthetic and naturalness scores rely on pre-trained CLIP/LPIPS models that may have domain bias (e.g., favor certain visual styles)","Semantic consistency metric requires text encoder alignment with training data — may not generalize to out-of-distribution prompts"],"requires":["Generated video file (MP4, MOV, or frame sequence)","Reference video or ground truth (for drifting metrics)","Text prompt (for semantic consistency evaluation)","Python 3.9+, PyTorch 2.0+","Pre-trained CLIP, LPIPS, and optical flow models (automatically downloaded on first run)","H100 or A100 GPU with 40GB+ VRAM (for efficient metric computation)"],"input_types":["video file (MP4, MOV, or frame sequence; 512×512 to 1024×1024 pixels)","text prompt (for semantic consistency metric)","reference video (optional, for drifting metrics)","evaluation configuration (metric weights, aggregation strategy)"],"output_types":["structured metrics (JSON with per-frame/per-chunk scores)","aggregated rating (0-100 scale)","drifting metric curves (CSV or plot)","quality report (HTML or PDF with visualizations)"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-pku-yuangroup--helios__cap_7","uri":"capability://tool.use.integration.four.interface.inference.abstraction.with.cli.python.api.and.interactive.modes","name":"four-interface inference abstraction with cli, python api, and interactive modes","description":"Exposes video generation through four distinct inference interfaces: (1) shell scripts (helios-{variant}_{task}.sh) for quick command-line usage, (2) Python API for programmatic integration, (3) interactive Gradio web UI for manual exploration, and (4) batch processing interface for large-scale generation. All interfaces support the same three tasks (T2V, I2V, V2V) and three variants (Base, Mid, Distilled) through unified parameter passing, enabling seamless switching between interfaces without code changes.","intents":["Enable quick prototyping via CLI while supporting production deployment via Python API","Allow non-technical users to explore video generation via web UI without code","Support batch processing of thousands of videos through unified interface"],"best_for":["Teams with diverse technical backgrounds (researchers, engineers, product managers) needing different interfaces","Organizations deploying Helios across multiple environments (local development, cloud inference, web services)","Developers building downstream applications that consume Helios as a library"],"limitations":["Four interfaces add maintenance burden — bugs or feature additions must be implemented across all interfaces","Parameter validation is duplicated across interfaces, risking inconsistencies","Interactive Gradio UI has limited customization — cannot easily embed in existing web applications without forking","Batch processing interface requires external job queue (e.g., Celery, Ray) for distributed execution — not built-in"],"requires":["For CLI: bash shell, Python 3.9+, PyTorch 2.0+","For Python API: Python 3.9+, PyTorch 2.0+, importable helios module","For Gradio UI: Python 3.9+, Gradio 4.0+, PyTorch 2.0+","For batch processing: Python 3.9+, job queue system (optional but recommended)"],"input_types":["CLI: command-line arguments (--prompt, --image, --video, --num_frames, --variant)","Python API: function parameters (prompt, image, video, num_frames, variant)","Gradio UI: form inputs (text, file upload, slider)","Batch: JSON configuration file with list of generation tasks"],"output_types":["CLI: video file written to disk, console output with timing","Python API: video tensor or file path, metadata dict","Gradio UI: video displayed in browser, downloadable MP4","Batch: video files in output directory, CSV log with generation metadata"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-pku-yuangroup--helios__cap_8","uri":"capability://image.visual.cfg.zero.guidance.strategy.for.accelerated.inference.without.quality.loss","name":"cfg-zero guidance strategy for accelerated inference without quality loss","description":"Helios-Mid variant uses CFG-Zero (classifier-free guidance with zero guidance scale) instead of standard CFG, reducing the number of forward passes required per diffusion step from 2 (conditional + unconditional) to 1. This is achieved through training-time modifications that condition the model to produce high-quality outputs without explicit guidance scaling, effectively eliminating the guidance overhead while maintaining quality comparable to standard CFG. The technique is enabled by the v-prediction type and HeliosScheduler, which are jointly optimized during training.","intents":["Reduce inference latency by 30-40% compared to standard CFG without sacrificing quality","Enable real-time video generation on mid-range GPUs (A100) by eliminating guidance overhead","Maintain semantic alignment with text prompts without explicit guidance scaling"],"best_for":["Production systems where inference latency is critical (interactive applications, live streaming)","Teams with limited GPU budgets seeking to maximize throughput per GPU","Researchers studying guidance-free diffusion and training-time optimization alternatives to inference-time guidance"],"limitations":["CFG-Zero is only available in Helios-Mid variant — not in Base (uses standard CFG) or Distilled (uses CFG-free with scale=1.0)","Quality is intermediate between Base and Distilled — may not meet high-quality requirements despite faster inference","Guidance scale is fixed at training time (≈1.0) — cannot adjust guidance strength at inference to trade quality for speed","Requires v-prediction type and HeliosScheduler — not compatible with other prediction types or schedulers"],"requires":["Helios-Mid checkpoint (not available in Base or Distilled)","Python 3.9+, PyTorch 2.0+","A100 or H100 GPU with 40GB+ VRAM","HeliosScheduler implementation"],"input_types":["text prompt (for T2V)","image (for I2V)","video (for V2V)","num_frames (rounded to nearest multiple of 33)"],"output_types":["video file (MP4, H.264 codec)","frame sequence (PNG or JPEG)"],"categories":["image-visual","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-pku-yuangroup--helios__cap_9","uri":"capability://image.visual.x0.prediction.with.cfg.free.guidance.for.fastest.inference","name":"x0-prediction with cfg-free guidance for fastest inference","description":"Helios-Distilled variant uses x0-prediction (direct prediction of clean image) with CFG-free guidance (scale=1.0) and HeliosDMDScheduler, enabling the fastest inference path with only 2-3 diffusion steps per stage. Unlike standard CFG which requires dual forward passes, CFG-free guidance operates on a single forward pass with guidance scale fixed at 1.0, eliminating both the guidance computation overhead and the need for unconditional predictions. x0-prediction directly predicts the final clean frame rather than the noise residual, enabling faster convergence with fewer steps.","intents":["Generate videos with minimal latency (sub-second per chunk) for real-time interactive applications","Deploy video generation on resource-constrained environments (A100 with batch processing)","Enable live video generation for streaming or interactive experiences"],"best_for":["Real-time applications requiring sub-second latency (interactive video editing, live streaming)","Mobile or edge deployment scenarios where GPU memory is limited","High-throughput batch processing where latency per video is critical"],"limitations":["Quality is noticeably lower than Base or Mid — visible artifacts in motion smoothness, semantic consistency, and naturalness","x0-prediction is sensitive to noise schedule — requires HeliosDMDScheduler tuning for optimal results","CFG-free guidance (scale=1.0) cannot be adjusted at inference — no quality-speed tradeoff possible","2-3 steps per stage may be insufficient for complex scenes or long videos — drift accumulation increases with video length"],"requires":["Helios-Distilled checkpoint (not available in Base or Mid)","Python 3.9+, PyTorch 2.0+","A100 or H100 GPU with 40GB+ VRAM (or 24GB+ for batch size 1)","HeliosDMDScheduler implementation"],"input_types":["text prompt (for T2V)","image (for I2V)","video (for V2V)","num_frames (rounded to nearest multiple of 33)"],"output_types":["video file (MP4, H.264 codec)","frame sequence (PNG or JPEG)"],"categories":["image-visual"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":33,"verified":false,"data_access_risk":"high","permissions":["CUDA 11.8+ with H100 GPU (or compatible NVIDIA GPU with reduced throughput)","Python 3.9+","PyTorch 2.0+","Helios-Base checkpoint (largest, highest quality variant)","Minimum 40GB GPU VRAM for single model inference","Input image in PNG, JPEG, or WebP format","Image resolution between 512×512 and 1024×1024 pixels","Python 3.9+, PyTorch 2.0+","H100 GPU or equivalent (40GB+ VRAM)","Training dataset with minimum 10K videos (recommended 100K+)"],"failure_modes":["Frame count is rounded up to nearest multiple of 33 at runtime due to chunk-based architecture","No built-in keyframe sampling or error-bank mechanisms — relies on training-time optimizations for drift prevention","Requires H100 GPU for stated 19.5 FPS performance; inference speed degrades significantly on lower-tier hardware","Text prompt understanding limited by underlying language model capacity — complex scene descriptions may not fully materialize","Image resolution must match model's training resolution (typically 512×512 or 768×768) — upscaling/downscaling may degrade conditioning quality","Motion generation is constrained by image content; highly static images may produce minimal motion variation","No explicit control over motion direction or intensity — determined entirely by diffusion sampling","Image artifacts or compression in input directly propagate through all generated frames","History injection adds training complexity — requires careful implementation of history encoding and attention integration","Implicit drift prevention may be less effective than explicit mechanisms for very long videos (>2 minutes)","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.2609861908660292,"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":"2026-04-16T07:54:01Z"},"community":{"stars":1758,"forks":133,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=pku-yuangroup--helios","compare_url":"https://unfragile.ai/compare?artifact=pku-yuangroup--helios"}},"signature":"SHgKNhXlHS/QjgR1HHU58BHhAbW5pivZEyIUNp+Q/+Y0a7Uf6/XsSLF123VOtfCxK+7Af8Nw7b4JsGfbaUVcBQ==","signedAt":"2026-06-20T15:07:05.896Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/pku-yuangroup--helios","artifact":"https://unfragile.ai/pku-yuangroup--helios","verify":"https://unfragile.ai/api/v1/verify?slug=pku-yuangroup--helios","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"}}