What Is Conditioning in Generative AI (2026 Guide)?

Conditioning is how a generative AI model is guided toward a desired output. By providing inputs such as text prompts, reference images, depth maps, pose skeletons, or audio, practitioners can steer the same underlying model to generate different results.

For video models like LTX, conditioning makes generation controllable and adaptable to real production workflows. This guide explains what conditioning is, the main types of conditioning used in AI video, how these signals influence generation, and how LTX combines them in real-world video workflows.

Conditioning, Defined

Conditioning is any input that guides a generative model toward a desired output. A diffusion model without conditioning generates samples from the data distribution it learned during training.

Add conditioning, and the model generates from the subset of that distribution that best matches the provided input—whether that's a text prompt, reference image, depth map, or still image to animate.

In the LTX open-source codebase, the ltx-core package exposes a dedicated conditioning/ module for preparing latent states and applying image, video, and keyframe conditioning, while the trainer includes a dedicated IC-LoRA mode for learning new conditioning behaviors through efficient fine-tuning.

How Does AI Video Conditioning Work?

AI video conditioning works by integrating external signals into the denoising network, guiding the model at each sampling step. While the underlying principle is consistent across conditioning types, the architectural mechanism varies by signal modality.

Text Conditioning

Text conditioning is the most familiar form: a written prompt describing the scene, subject, style, and camera. LTX routes text through a Gemma 3 multilingual text encoder that aggregates features from multiple decoder layers before projecting them into embeddings for the diffusion transformer.

The video and audio streams receive separate text embeddings from the same prompt—a 4096-dimensional embedding for video and a 2048-dimensional embedding for audio—helping LTX generate synchronized speech with cadence and emotion that align with the visuals.

Image and Keyframe Conditioning

Image conditioning anchors generation to a starting frame. In LTX's image-to-video pipeline, the input image is encoded into the model's latent space and used as the condition for the first frame; subsequent frames are generated to maintain continuity with that anchor.

Keyframe conditioning extends this idea to multiple anchor frames, allowing users to provide a first and final frame—or several keyframes throughout the clip—and have the model interpolate between them.

What About Structural Conditioning?

Structural conditioning passes non-RGB signals—such as depth maps, edge maps, or pose skeletons—into the model to preserve geometry, composition, or motion from a reference input.

LTX supports three structural conditioning modes through its IC-LoRA system: Canny IC-LoRA preserves edges and silhouettes, Depth IC-LoRA preserves scene geometry and camera movement, and Pose IC-LoRA preserves human motion using DWPose skeleton extraction. The reference input is processed into the appropriate structural representation, which guides the diffusion process throughout generation.

Audio Conditioning

Audio conditioning is one of the features that distinguishes LTX from many open-source video models. Its architecture is an asymmetric dual-stream diffusion transformer (14 billion video parameters and 5 billion audio parameters) with shared transformer blocks connected through cross-modal attention.

During generation, the audio and video streams exchange information throughout the network, helping maintain synchronization between generated speech and lip movements.

Classifier-Free Guidance and the Guidance Scale

Classifier-free guidance (CFG) controls the strength of the conditioning signal during generation. At each denoising step, the model produces two predictions: one conditioned on the input and one unconditioned. The final prediction is a weighted combination of the two, controlled by the guidance scale (cfg_scale).

Higher values increase adherence to the conditioning signal, while lower values allow the model greater flexibility and output diversity.

LTX exposes the guidance scale as a configurable parameter in its open-source Python pipelines. As with other diffusion models, higher guidance values can improve prompt adherence but may introduce visual artifacts or reduce motion naturalness, while lower values often produce more natural-looking results at the cost of weaker adherence to the input.

The cfg_scale parameter is available in the open-source pipelines but is not currently exposed through the hosted REST API at docs.ltx.video.

Conditioning in Practice with LTX

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LTX packages conditioning into named pipelines so practitioners don't have to wire up the underlying components manually.

The ltx-pipelines package provides class-based APIs and CLI entry points for text-to-video, image-to-video, audio-to-video, video-to-video, and keyframe interpolation, each exposing a different conditioning interface. Text-to-video and image-to-video are the primary entry-point pipelines, both running as two-stage workflows: a low-resolution stage generates motion, followed by a second stage that upsamples to the final resolution.

These pipelines combine multiple conditioning mechanisms into production-ready workflows, making them easier to use for real-world AI video generation.

IC-LoRA for Specialized Conditioning

IC-LoRA (In-Context LoRA) is LTX's mechanism for extending conditioning capabilities through efficient fine-tuning. The trainer documentation describes it as a training mode that doubles inference sequence length by appending paired reference-target video sequences, allowing the model to learn transformations such as deblurring, colorization, depth control, and pose control.

Pre-trained Canny, Depth, and Pose IC-LoRAs are included in the official workflows, while the LoRA fine-tuning guide explains how to train custom IC-LoRAs for additional conditioning behaviors.

Combining Multiple Conditioning Signals

Many production workflows combine several conditioning signals. A practitioner might anchor the first frame with image conditioning, guide camera movement with Depth IC-LoRA, preserve character motion with Pose IC-LoRA, define the visual style through a text prompt, and generate synchronized dialogue through the audio stream—all within the same generation.

LTX is designed to combine multiple conditioning signals through complementary mechanisms, allowing them to guide generation simultaneously.

The primary practical constraint is GPU memory: the IC-LoRA documentation recommends avoiding multiple structural IC-LoRAs in the same generation. The typical workflow combines a single structural IC-LoRA with text, image, and audio conditioning.

Prerequisites and Hardware

Running LTX conditioning workflows locally requires a supported NVIDIA GPU and the software environment described in the official documentation. The full model is intended for high-memory GPUs, while the distilled variant supports lower-memory hardware primarily through FP8 quantization.

INT8 quantization also exists in the ecosystem, mainly in community-built variants for older GPU architectures without FP8 tensor-core support, and as the documented floor for LoRA fine-tuning on lower-VRAM setups — it isn't the default path for standard distilled-variant inference.

For practitioners without suitable hardware, the hosted REST API at docs.ltx.video provides a lower-barrier alternative, although some open-source parameters—such as the raw cfg_scale value—are not exposed.

Practical Tips for Better Conditioning

Choose the conditioning signal that best matches the aspect of the output you want to control. If the goal is to preserve camera movement or scene geometry, depth conditioning is generally more reliable than describing those characteristics in text. If the goal is to transfer a human performance, pose conditioning provides more explicit control than a text prompt alone. Text conditioning remains highly flexible, while structural conditioning offers more direct control over geometry and motion.

Avoid over-specifying when multiple conditioning signals are active.

A common failure mode in IC-LoRA workflows is describing motion in the text prompt while also providing that motion through a reference video—the two conditioning signals may conflict, reducing output quality. The IC-LoRA documentation recommends keeping prompts focused on subject, style, and environment when motion is already provided by an IC-LoRA.

Frame Counts and the (F-1) % 8 == 0 Rule

Frame counts must satisfy the LTX Video VAE constraint (F-1) % 8 == 0. At 25 fps, valid clip lengths are 9, 17, 25, 33, 41, 49 (1.96 seconds), 57, 65, 73, 81, 89, or 97 frames. A request for "30 frames" does not satisfy the VAE constraint and cannot be processed correctly. Build conditioning workflows around valid frame counts for your target frame rate rather than intuitive round numbers.

Summary

Conditioning is the collection of inputs—text, images, keyframes, structural signals such as depth or pose, audio, and reference videos—that guide a generative AI model toward a desired output. In LTX, different conditioning signals are integrated through specialized architectural mechanisms, allowing them to influence generation in complementary ways.

Classifier-free guidance (cfg_scale) controls the strength of the conditioning signal, while IC-LoRA extends the model with new conditioning behaviors through efficient fine-tuning.

In practice, effective workflows combine multiple conditioning signals while respecting hardware limitations and the Video VAE's (F-1) % 8 == 0 frame constraint.

Conclusion

Conditioning is what makes generative AI controllable for production workflows. LTX provides multiple conditioning mechanisms that give practitioners fine-grained control over different aspects of generation.

A typical workflow begins with text-to-video, adds image conditioning when a specific starting frame is needed, and incorporates IC-LoRA when more explicit structural control is required. For deeper implementation details, see the image-to-video and text-to-video workflow guide.