Train a LoRA that transforms one audio clip into another, learning a reference→target mapping from paired audio examples.

Train a LoRA that generates video from a start image plus a conditioning audio track, producing motion that matches the sound.

Train a LoRA that continues an audio clip forward in time, generating the audio that follows a short clean prefix.

Train a LoRA that generates the lead-in to an audio clip, extending audio backward in time from its ending.

Train a LoRA that regenerates masked time spans of an audio clip while keeping the rest unchanged.

Train a LoRA for a joint audio+video transformation, conditioned on a reference clip (its video and audio) to produce a matching target clip.

Train a LoRA that regenerates a masked video region (guided by kept pixels and a video reference) while jointly generating audio from an audio reference.

Train a LoRA that continues a video forward in time — supply an opening clip at inference and the model generates what comes next.

Train a LoRA that generates the lead-in to a video, extending a clip backward in time from its ending.

Train an IC-LoRA that transforms one audio clip into another, conditioned at inference on a reference audio clip.

Train an IC-LoRA for a joint audio+video transformation, conditioned on a reference clip's video and audio to produce a matching target.

Train an IC-LoRA that regenerates a masked video region (guided by kept pixels and a video reference) while jointly generating audio from an audio reference.

Train an IC-LoRA that regenerates only the masked region of a video, guided by the kept pixels and a separate reference/control video.

Train a LoRA that generates the video between keyframes — supply first/last (and optional middle) frames at inference and the model fills the in-between motion.

Train a LoRA that expands the video frame outward, keeping an inner rectangle fixed and generating the surrounding region.

Train a LoRA that generates audio (foley / sound design) for a silent video, learning a soundtrack that matches the on-screen action.

Train a LoRA that regenerates only the masked region of a video, guided by both the kept pixels and a separate reference/control video.

Direct continuous, realtime video streams with live prompts while preserving characters, settings, and story continuity.

Direct a continuous world in real time with image references and text prompts.

Chat and reason with NVIDIA Nemotron 3.5 Super, a hybrid Mamba-Transformer mixture-of-experts model with optional step-by-step thinking.

Open, efficient reasoning model from NVIDIA. 30B A3B hybrid Transformer-Mamba MoE, built for enterprise agentic workflows.

Real-time games rendered using VAST's Eden Live 14B

Real-time games rendered using VAST's Eden Live 5B

Train custom LoRAs for Wan-2.2 T2V/I2V 480P