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Runway launches DIFFUSE, platform for ai-native creatives. News Tue, 22 Sep 2026 16:37:56 UTC 14 hours ago Runway's DIFFUSE connects AI-native creatives with brands, studios, and agencies. Build portfolios, get discovered, and drive AI-enabled media innovation. (Read More) This content is automatically aggregated. Full credit goes to the original publisher (blockchain.news).

Runway Labs tests generative video interfaces that recompose scenes as windows resize. Runway Labs is testing responsive generative video interfaces that create a new scene composition when the viewing window changes size. The experiments show how real-time generation could let creative teams explore proportions and spatial arrangements without treating every display format as a fixed crop of one video. * Window dimensions become model inputs. Resizing the frame triggers a composition for the available space rather than scaling the previous view. * Objects move with the changing layout. Runway claims its model interprets scenes in three dimensions and rearranges objects within the frame. * Scene elements can change form. One example turns stairs into a spiral staircase when the view becomes narrower. * The work remains experimental. The supplied materials provide no public access details, pricing, output specifications, or release schedule. Window resizing generates a new composition instead of scaling the previous view. The central demonstration treats window dimensions as an input. When the viewer changes the frame's proportions, the model generates a composition for the new space rather than scaling the previous view. Runway defines real-time video generation as frame-by-frame synthesis that responds to live input. A completed video, by contrast, is rendered before playback and cannot alter its composition in response to an interaction. The demonstrations show layouts changing as the viewing area shifts between wider and narrower proportions. Runway also claims the model interprets the scene in three dimensions, allowing objects to settle into the available space instead of overlapping or extending beyond the frame. That behavior could make aspect-ratio exploration more interactive during concept development. A creator could test how a generated environment responds to wide, vertical, or unusually shaped displays without starting from one fixed master image. The supplied materials do not describe export options or whether a selected composition can be saved as a conventional video asset. Scene elements can change form as the available space narrows. Runway's examples extend beyond repositioning objects. One demonstration redesigns stairs as a spiral staircase when the viewing area becomes narrower, showing how the generated content can respond through its internal composition rather than through fixed interface elements. This interaction model could support creative experiences in which a scene changes its framing, spatial arrangement, or object design as the display changes. It also offers a different interaction pattern from selecting among predefined layouts. Google's Project Genie provides interactive AI worlds that people can explore. Runway Labs is demonstrating a more focused interface experiment in which the dimensions of the viewing surface affect the generated video composition. Possible applications include interactive entertainment, experiential design, and previsualization. Teams could explore alternative framing or spatial arrangements without rendering a separate completed clip for every variation, although Runway has not announced these applications as products. Low-latency generation provides the technical foundation for responsive scenes. A responsive interface depends on the model producing frames quickly enough for an interaction and its visual result to feel connected. Runway's instant-generation research identifies causal autoregressive generation, model distillation, streaming decoders, and reduced time to first frame as foundations for interactive video. Runway describes Labs as an internal incubator for experimental applications of AI video and General World Models. The company has not identified the responsive-interface work as a separate named product. The demonstrations establish the core interaction concept, but the supplied materials leave practical production questions unanswered. They provide no public access details, pricing, output specifications, or release schedule. The experiment shows how real-time generation could turn a fixed video frame into a scene that continually recomposes itself around the available display.

Runway's new Ruby model converts SDR video into 16-bit HDR ProRes and EXR. Ruby takes a finished SDR clip and converts it up to 16-bit HDR, exporting ProRes files and EXR sequences instead of a flat 8-bit master. Runway says the model, announced on X, works on any uploaded video or generated output up to 30 seconds. * Exports ProRes and EXR delivery files. Ruby returns the containers grading and VFX pipelines already run on, so a converted clip can move into a color or compositing session directly. * Works on uploaded footage or generated clips. Runway says any existing upload or generated output can run through the conversion, up to a 30-second limit per clip. * The model reconstructs the HDR signal. A 16-bit EXR wrapper does not restore camera-original sensor data; Ruby has to rebuild highlight and color detail that an 8-bit SDR source never recorded. * Available now. Ruby is live in the Runway app. Ruby is a conversion model that ingests existing footage. Runway frames Ruby as a conversion model rather than another generator. It ingests footage that already exists, whether a camera clip, a client upload, or output from a generation model, and rewrites it into HDR delivery formats. The announcement lists ProRes and EXR sequences as the outputs, with the pitch that it fits "every format your pipeline already runs on." The 30-second ceiling keeps Ruby at shot and short-sequence scale, not full timelines. That matches how AI-video finishing usually works, where clips get generated or captured a few seconds at a time and assembled later, the same shot-by-shot logic behind the multi-stage ETC HDR pipeline VP Land covered. Runway has not detailed how Ruby holds temporal consistency across a converted clip, so frame-to-frame stability under a grade is worth testing. A 16-bit container is not the same as 16-bit capture. Moving an 8-bit SDR clip into a 16-bit EXR sequence changes the container, but the container alone does not recreate what the sensor never recorded. To fill an HDR signal, the model has to infer highlight rolloff, shadow detail, and wider color that the SDR master clipped or compressed away. It is the same reconstruction bet other converters make, including Beeble SwitchHDR. Runway's demo shows a saturated skateboard sequence and translucent graphics; it does not independently prove recovered latitude, added resolution, or measurable image-quality gains, so treat those as Runway's claims until a real grade confirms them. The useful part for finishing is the output side: a tool that emits ProRes and EXR removes a manual export-and-conform step before color or compositing. Whether the inferred HDR survives an aggressive grade is the practical question for each shot. Ruby, Hyperion 2.5, and Ray3's SDR path are the direct alternatives. Ruby lands in a small group of models doing SDR-to-HDR conversion rather than HDR generation. Topaz Labs takes the closest path: Hyperion 2.5 converts 8-bit SDR into 10-bit ProRes or 16-bit EXR, available inside Astra with Topaz Video support coming. VP Land covered Hyperion 2.5 at launch. Both Ruby and Hyperion start from existing SDR and target the same container outputs, which makes them direct alternatives for a finishing pass. Luma sits slightly to the side. Its Ray3 model is a native 16-bit HDR generator, but Luma also lists an "SDR to Generative HDR" path for generated or recorded standard video, plus EXR export. That conversion path competes with Ruby, while the rest of Ray3 is about generating HDR in the first place. Several nearby tools solve different problems, and the labels matter: * Resolution upscaling adds pixels and detail. Topaz Astra and Starlight do this; they are not HDR converters. * Denoise and restoration clean artifacts and grain, a separate repair stage. * SDR-to-HDR inverse tone mapping expands dynamic range. This is where Ruby, Hyperion, and Ray3's SDR path all sit. * Bit-depth and container conversion moves 8-bit into 16-bit ProRes or EXR wrappers, which Ruby does alongside the tone-mapping step. * Gamut and color-space conversion remaps footage into a wider color space. * Generative enhancement invents new detail that was never in the source. The LTX-2.5 model shows why the categories blur. It generates 4K HDR natively and offers a RAW finishing workflow, so it produces HDR as output rather than converting an SDR input. That puts it next to Ray3's generation side and apart from Ruby's converter role. For AI-video finishing, Ruby gives Runway users a way to leave the app with ProRes or EXR instead of an 8-bit file, and it accepts footage from outside the Runway ecosystem. The open questions are the ones that apply to every inverse-tone-mapping model: how convincing the reconstructed highlights and color look once a colorist pushes them. TOOLS IN THIS STORY