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 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 transforms one audio clip into another, conditioned at inference on a reference audio clip.
Train an IC-LoRA that learns a video-to-video transformation from paired before/after clips, conditioned at inference on a reference (control) video.
Train a LoRA that regenerates masked time spans of an audio clip while keeping the rest unchanged.
Train a LoRA that generates the lead-in to an audio clip, extending audio backward in time from its ending.
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 audio from a text prompt — the audio counterpart of text-to-video — learning a sound or style from your clips.
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 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 regenerates only the masked region of a video, guided by both the kept pixels and a separate reference/control video.
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 transforms one audio clip into another, learning a reference→target mapping from paired audio examples.
Train a LoRA that regenerates a masked region of a video while keeping the rest unchanged, blending the new content with its surroundings.
Train a LoRA that expands the video frame outward, keeping an inner rectangle fixed and generating the surrounding region.
Train a LoRA that generates the lead-in to a video, extending a clip backward in time from its ending.
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 audio (foley / sound design) for a silent video, learning a soundtrack that matches the on-screen action.
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 learns a video-to-video transformation from paired before/after clips, steered at inference by a reference (control) video.
Fine-tune LTX 2.3 to animate a starting image — supply a still plus a prompt at inference and the model generates a video that begins from that frame.
Fine-tune LTX 2.3 on your own clips to teach it a new subject, character, object, or visual style, then generate full videos from a text prompt.
GLM-5.2 is Z-AI's latest flagship model for long-horizon tasks. It marks a substantial leap in long-horizon task capability over its predecessor GLM-5.1 and, for the first time, delivers that capability on…