LTX Video 2.5 Pro

• 👁️ Pro-tier image-to-video model from Lightricks' LTX-2.5 generation
• 🧠 Lightricks describes a dual-stream diffusion transformer generating video and audio jointly
• 🆕 Successor to LTX-2.3; multi-shot scenes rendered in one pass
• 🔧 Per Lightricks, can edit existing footage and adapt to domains
• 💬 Outputs synchronized audio alongside the generated video
• ⚡ Pro targets final renders; the Fast variant targets quick iteration
• 📚 Open foundation model published by Lightricks, with a hosted API
• 🏢 Lightricks is the studio behind Facetune and LTX Studio

Lightricks

Lightricks is a creative technology company founded in January 2013, widely recognized for its popular selfie-editing app Facetune. Originally focused on mobile video and image editing tools, the company has expanded significantly into generative AI,…

LTX Video 2.5 Pro is the quality-oriented image-to-video variant of Lightricks' LTX-2.5 generation, turning a still input frame into a clip with synchronized audio. Lightricks presents the LTX model as an open foundation system for cinematic audio–video creation, built on an asymmetric dual-stream diffusion transformer that handles both modalities in a single pass rather than bolting sound on afterwards.

Compared with its same-family predecessors — LTX Video 2.3 Full Quality and LTX Video 2.3 Fast — Lightricks positions 2.5 as the recommended starting point, citing multi-shot generation in one pass, the ability to edit existing footage, and domain adaptation as the main reasons to upgrade. The company also notes a migration caveat: custom LoRAs trained against the earlier latent space need retraining for 2.5.

Within the 2.5 release, Pro is the higher-fidelity path intended for final renders, while LTX Video 2.5 Fast covers previews and high-volume iteration across the same API. If your starting point is a written prompt rather than a reference image, LTX Video 2.5 Pro text-to-video handles that mode instead.

Because Lightricks publishes LTX weights openly alongside a hosted generation API, teams can either call the model as a service or run and fine-tune it on their own infrastructure.