Marlin is a 2B video VLM tuned for the two questions developers actually want to ask of their videos: what is happening, and when?

MoonDreamNext Batch is a multimodal vision-language model for batch captioning.

Train custom LoRAs for personalization, styles or other use cases on top of Ideogram V4.

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.

Sa2VA is an MLLM capable of question answering, visual prompt understanding, and dense object segmentation at both image and video levels

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.

Train a LoRA that learns a video-to-video transformation from paired before/after clips, steered at inference by a reference (control) video.

Florence-2 is an advanced vision foundation model that uses a prompt-based approach to handle a wide range of vision and vision-language tasks

Train an IC-LoRA that learns a video-to-video transformation from paired before/after clips, conditioned at inference on a reference (control) video.

Florence-2 is an advanced vision foundation model that uses a prompt-based approach to handle a wide range of vision and vision-language tasks

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 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.