LTX Trainer: 10 fine-tunable video effects, and the first agent-driven training loop. LTX just shipped a training framework, not a model. Ten demo LoRAs, open source, and the first time a major video lab has shipped a tool an agent can drive end-to-end, including the training step. Short version What to remember. * 01 LTX Trainer is a training framework, not a model: LoRAs and IC-LoRAs for video, audio, cross-modal, and reference-conditioned workflows * 02 10 demo LoRAs ship with the release (water simulation, ingredients, inpainting, day-to-night, colorization, instant shave, cross-eyed, deblurring, decompression), all trained on LTX-Video with the new trainer * 03 The first major video lab to ship a first-party agentic skill for training, not just prompting * 04 Open source: framework, IC-LoRAs, and the agentic skill are all on GitHub and HuggingFace * 05 Cost reality: pre-trained IC-LoRAs are free, but training a LoRA on a 19B base is hundreds to thousands of dollars in GPU time * 06 Two-layer choice: free IC-LoRAs + rented GPU for custom training, or skip training and pay per generation through a hosted route like fal.ai LTX just shipped a training framework, not a model. That distinction is the entire story, and most of the coverage is going to miss it. On June 17, 2026, Lightricks introduced LTX Trainer, a single framework for training LoRAs and IC-LoRAs across video, audio, cross-modal, and reference-conditioned workflows. Fully open source. GitHub, documentation, and HuggingFace all linked from the announcement. Ten demo effects ship with the release, all of them LoRAs the LTX team trained on top of LTX-Video using their own trainer. The ten effects, straight from LTX's launch thread. Each links to the demo video LTX posted: * Water Simulation. Add rivers, surf, rain, floods, or splashes to any shot while preserving composition and motion. https://x.com/ltx_io/status/2067284780081905685 * Ingredients. Combine characters, props, locations, and styles into fully realized video worlds generated from a reference sheet. https://x.com/ltx_io/status/2067284859668877723 * Inpainting and Outpainting. Extend and modify scenes beyond the original frame. https://x.com/ltx_io/status/2067284934847528965 * Day to Night. Transform daytime footage into nighttime scenes. https://x.com/ltx_io/status/2067285012060537021 * Colorization. Bring grayscale footage back to life. https://x.com/ltx_io/status/2067285088761708813 * Instant Shave. Remove facial hair while preserving identity and expressions. https://x.com/ltx_io/status/2067285165546873140 * Cross-Eyed. Make anyone go cross-eyed while keeping expressions, motion, and framing intact. https://x.com/ltx_io/status/2067285243690930534 * Deblurring. Turn blurry footage into sharp, clean video. https://x.com/ltx_io/status/2067285318454378784 * Decompression. Remove compression artifacts and restore visual quality. https://x.com/ltx_io/status/2067285396728521147 The tenth tweet is the closer, not a new effect: "All trained with LTX Trainer. What will you build?" https://x.com/ltx_io/status/2067285408468337046 (All nine demo videos are LTX's own assets, linked back to the original tweets. If a video breaks, the tweet is the source of truth.) What actually changed. First, the trainer is a framework, not a single skill. It supports video, audio, cross-modal, and reference-conditioned workflows in one place. That means a LoRA you train can be conditioned on a reference image, a reference video, an audio track, or a combination. Most of the existing video model ecosystem is still one-input, one-output. Multi-modal conditioning at the training stage is a real shift. Second, the open-source IC-LoRAs ship with the framework. IC-LoRA, in LTX's vocabulary, is in-context LoRA: the LoRA is conditioned on a reference input at inference time, so a single trained adapter can produce different outputs depending on the reference you feed it. The Ingredients effect (number 2 above) is the cleanest demonstration of the pattern. The LoRA turns a single reference sheet of characters, props, and locations into fully realized video worlds. Third, and the one most people will gloss over: a new agentic skill ships alongside the trainer. The line is buried in the announcement copy, but it is there. "Plus: new agentic skill, flexible conditioning, free IC-LoRAs, fully open source." The agentic skill is what lets an AI agent, not a human in a CLI, drive the training loop. Pick the base model, point it at a dataset, configure conditioning, kick off training, evaluate the output. That is the same shape as Scopeful's own MCP server surface, and it is the first time I have seen a major video lab ship a first-party agent surface for training rather than only for prompting. Why this matters if you are not a researcher. The reason I am writing this on Scopeful, where the beat is pricing and tooling, is that the gap between "an open-source trainer exists" and "you can actually use it" is enormous, and LTX is one of the few teams actually closing it. Most open-source video training setups require you to wire together the base model, the training loop, the conditioning pipeline, and the inference harness yourself. LTX Trainer ships all four as one project. The free IC-LoRAs are reference adapters you can download from HuggingFace, run locally, and use without retraining. The agentic skill exists so an agent, Claude, GPT, whatever you run, can use the trainer the same way it uses any other tool. That is the part with real Scopeful relevance. Scopeful has been saying for a year that the agent layer is the most underexplored surface in creative AI. Most vendors are still shipping prompt UIs. LTX just shipped a tool that an agent can drive end-to-end, including the training step. If the trend holds, every video model lab is going to ship an agent surface within twelve months, and the ones that ship it first will define what "agent-friendly" means in the category. The cost reality. The training framework itself is free and open source. The compute is not. A LoRA fine-tune on a 13B-to-19B parameter video model is not a laptop job. You are looking at a multi-GPU box or a cloud rental, and depending on the dataset size and target rank, the bill lands somewhere between a few hundred and a few thousand dollars per adapter. The 10 LTX-shipped IC-LoRAs save you that cost, because you can download and use them directly. The 11th adapter, the one you train on your own footage, is where the cost lives. If you do not want to train anything, the underlying LTX-Video model is also billable through hosted inference, including via fal.ai, which is what the Scopeful calculator already prices out. The LTX-2 family and the LTX 2.3 family are both in the catalog with per-second pricing. So you have a clean two-layer choice: use a pre-trained IC-LoRA from HuggingFace for free, train your own on rented GPUs, or skip training entirely and pay per generation through a hosted route. What I would actually do. If you are curious, the move is to grab the free IC-LoRAs from HuggingFace and try the Ingredients reference-sheet workflow first. It is the most generally useful of the ten, and the demos are convincing. Pair it with an LTX-Video inference route, either the open-source weights locally or a hosted route, and you can do reference-conditioned video generation today, on a laptop, for the cost of inference only. If you have a use case the free adapters do not cover, train your own. The trainer is the right primitive for that. Budget realistically: a single LoRA on a 19B base is a meaningful spend. Treat it like hiring, not like a subscription. And if you are building an agent that touches video models at all, watch LTX's agentic skill carefully. It is the first time a major video lab has shipped a tool designed to be driven by an agent, not by a prompt. That is the move the rest of the category is going to make, and the projects that adopt the pattern early are the ones that will set the standard. Links. * LTX Trainer GitHub: https://github.com/Lightricks/LTX-Trainer * LTX Trainer documentation: https://docs.ltx.io * LTX IC-LoRAs on HuggingFace: https://huggingface.co/Lightricks * LTX Studio on Scopeful: /tools/ltx-studio * The 10 demo effects, with videos, live in the launch thread: https://x.com/ltx_io/status/2067258965369528397 Scopeful tracks where creative-AI pricing moves, every week. If you would rather see a new model on the day it ships than the week after, the newsletter is for you. Questions & answers. * Is LTX Trainer free? - The framework is free and open source, and the IC-LoRAs that ship with it are free to download from HuggingFace. The cost of training your own LoRA on top of LTX-Video is the GPU time, which is hundreds to thousands of dollars per adapter on rented hardware depending on rank and dataset size. * What is IC-LoRA? - IC-LoRA, in LTX terminology, is in-context LoRA. The LoRA is conditioned on a reference input at inference time, so a single trained adapter can produce different outputs depending on the reference image, video, or audio you feed it. The Ingredients effect in the LTX Trainer launch thread is the clearest demonstration: one reference sheet becomes a fully realized video world. * Do I need to train a LoRA to use LTX Trainer? - No. The ten demo IC-LoRAs (water simulation, inpainting, day-to-night, colorization, and so on) are free to download and use. Train your own only if you have a use case the pre-trained adapters do not cover. * What is the new agentic skill? - A first-party agent surface that lets an AI agent, not a human in a CLI, drive the LTX Trainer training loop end-to-end: pick the base model, point at a dataset, configure conditioning, kick off training, evaluate the output. It is the first time a major video lab has shipped this kind of surface for training rather than only for prompting.

Lightricks to split into two companies as it cuts another 75 jobs. Jerusalem unicorn reshapes itself around AI, as Facetune profitability funds a costly push into video models. 13:19, 01.06.26 The veteran Jerusalem unicorn Lightricks continues to grapple with major operational changes amid the disruption caused by AI video models. Against this backdrop, the company is launching another round of layoffs, less than six months after cutting 85 employees, who at the time represented about 15% of its workforce. Today, the company informed 17% of its employees that they would be laid off, totaling 75 workers. Most of those affected, 55 employees, are based in Israel and work in core software and product development roles. After the current round and previous cuts, Lightricks will employ about 350 people, roughly half its workforce in 2022, before the AI-driven downturn began. Lightricks currently operates two businesses that are expected to be split into two separate companies: the traditional business of its popular photo-editing app Facetune, and a newer video AI division currently operating under the name LTX. Facetune generates around $300 million in revenue, growing at approximately 20% annually, and produces about $100 million in net profit. Despite these strong results, it is expected to face increasing competition as AI-generated tools become cheaper and more widely integrated into consumer apps. For now, Facetune is effectively financing Lightricks' heavy investment in its AI division. According to co-founder and CEO Zeev Farbman, the company has invested about $150 million in the AI business to date. As part of the planned split, Farbman will focus solely on LTX, while Asaf Porat, former deputy CEO of Outbrain, which merged with Teads and is traded on Wall Street, will take over Facetune. During the summer, Facetune will also part ways with CFO Shaul Meridor, who recently announced his decision to enter politics by joining Gadi Eisenkot's "Yashar" party. After the split, Facetune will have about 100 employees, while LTX will employ roughly 250. "Alongside the layoffs, we still have 30 open positions, but we are forced to reduce headcount because the AI modeling activity requires a different kind of talent, AI experts who have actually built models," Farbman said in an interview with Calcalist. "So far, we've mostly recruited such people from Orcam, and maybe now from AI21 as well." He added: "Our CTO, one of the company's founders, has moved to London, and we are beginning to recruit there as well, because, ironically, given the shekel exchange rate, talent there has become cheaper than in Israel." Ironically, even after the previous layoff round in October 2025, Lightricks still maintained that around 30 positions remained open. Lightricks was founded in 2013 by Farbman along with Amit Goldstein, Yaron Inger, Nir Pochter, and Itai Sidon, and was one of Israel's earliest unicorns. The company has raised $335 million to date, but has not raised a new funding round in many years. Its profitable Facetune business continues to fund AI development. However, if the AI division succeeds and scales, the company is expected to eventually seek new capital. Farbman is now fully focused on LTX, which initially developed a video model competing with systems such as OpenAI's Sora. Recently, however, the company shifted toward an enterprise model, selling licenses to its technology rather than operating it itself. This approach reduces the massive computing costs by running models on customers' infrastructure. At the same time, LTX is targeting the emerging field of physical AI, where video models are used to train robots in human actions such as folding laundry, tasks machines still struggle to master. According to Lightricks, LTX is already generating annualized revenues of tens of millions of dollars from licensing its models, primarily from large tech companies and Fortune 500 clients.

Lightricks, an Israeli AI startup backed by $375 million, continues to navigate operational challenges whilst building proprietary multimodal AI models for photo and video creation. Noa Rosenberg Segalovitz, CHRO and CLO, reports the company hasn't experienced feared "brain drain", with some talented individuals who moved abroad actually choosing to return to Israel. Founded in 2013, Lightricks operates as an "AI-native company" where every employee integrates AI tools into daily work. Rather than eliminating roles, AI has transformed them, with professional boundaries dissolving as employees expand their capabilities. The company remains committed to recruiting at all levels, launching its 2026 student recruitment programme. Managing through nearly three years of emergency, Lightricks has mapped employees' personal situations and redistributed workloads accordingly. Recruitment continues uninterrupted, with interviews adapting to circumstances whilst maintaining professionalism.