Video Models
Post-training for video generation
Fine-tune and evaluate video generation models with the data infrastructure and evaluation workflows they actually need — frame-level annotation, temporal quality metrics, and structured human evaluation.
Video models need video-native tooling
Image tools don't work for video. You need to evaluate temporal consistency, not just per-frame quality. You need to version datasets measured in terabytes, not gigabytes. You need human evaluation workflows that let reviewers compare motion quality, not just static frames.
Most teams cobble this together from scripts, spreadsheets, and shared drives. Mixtrain replaces all of it with a single platform purpose-built for video model development — from data preparation through evaluation to deployment.
Evaluation that captures what metrics miss
FVD and FID tell part of the story. Viewers notice temporal artifacts, motion quality, and physical plausibility that automated metrics can't capture. Mixtrain runs both side-by-side.
Automated
- •FVD, FID per frame, and temporal consistency scores at scale
- •Regression detection across training checkpoints
- •Custom metrics — motion smoothness, scene coherence, physics plausibility
- •Run evaluations on thousands of clips in parallel
Human
- •Side-by-side video comparison with structured preference collection
- •A/B testing workflows with statistical significance tracking
- •Per-dimension ratings: motion, consistency, aesthetics, prompt adherence
- •Aggregate results with inter-annotator agreement and confidence intervals
What you get
Video dataset versioning
Version terabyte-scale video datasets with deduplication and incremental storage. Diff across versions, slice by scene, duration, or resolution.
Frame-level annotation
Annotate with bounding boxes, segmentation masks, temporal events, and quality scores. Build training datasets with precise spatial and temporal labels.
Multi-resolution training
Train across resolutions, aspect ratios, and frame rates. Mixtrain handles resolution-aware batching and data pipeline configuration.
Temporal quality metrics
Measure frame-to-frame coherence, motion smoothness, and temporal artifacts with metrics built for video, not adapted from images.
Distributed fine-tuning
Launch multi-node training jobs with your own scripts. Automatic checkpointing, configurable compute, and full experiment tracking.
Model comparison
Compare outputs across model versions, checkpoints, and hyperparameter configs. Side-by-side video playback with synced timelines.
Scene detection & segmentation
Automatic scene boundary detection, shot classification, and temporal segmentation for training data preparation.
Production export
Export models with optimized inference configs, quality baselines, and regression monitoring. ONNX and TensorRT support.
Start building
Get your video models from prototype to production. Free to start, no credit card required.