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name: core-generation description: "Plan and troubleshoot core LTX-2 text/image/video generation workflows in ComfyUI-LTXVideo." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

Core Generation

Use this sub-skill when the user asks how to build, adapt, or debug ordinary LTX-2 video generation in ComfyUI-LTXVideo: text-to-video (T2V), image-to-video (I2V), video-to-video/detailer (V2V), single-stage generation, two-stage latent upsampling, long or tiled generation, low-VRAM loading, VAE decode, latent selection/transition, and basic guide/keyframe placement.

Start with the repo-level model and backend requirements before promising a run. Generation requires a working ComfyUI install, this custom node package, a CUDA-capable runtime for native generation, and user-provided LTX/Gemma/upscaler/LoRA files in the standard ComfyUI model folders. For prompt encoder setup, route to prompt-conditioning.

Route here for

• T2V and I2V graph planning with LTXVBaseSampler, EmptyLTXVLatentVideo, LTXVImgToVideoConditionOnly, standard ComfyUI sampler/sigma/noise nodes, and VAE decode.
• V2V/detailer or continuation flows that reuse input/video latents with LTXVInContextSampler, LTXVExtendSampler, LTXVLoopingSampler, latent selection, or latent transitions.
• Single-stage LTX-2.3 generation and two-stage LTX-2.3 generation with latent upsampler models and a second denoise/decode pass.
• Spatially tiled sampling and tiled VAE decode with LTXVTiledSampler, LTXVTiledVAEDecode, and LTXVSpatioTemporalTiledVAEDecode.
• Low-VRAM model-load sequencing with LowVRAMCheckpointLoader, LowVRAMAudioVAELoader, and LowVRAMLatentUpscaleModelLoader.
• Workflow-specific errors involving missing core model files, wrong guider/conditioning object type, latent shape/frame mismatch, keyframe index constraints, tiling seams, and VRAM pressure.

Do not handle here

• Gemma local/API text encoding, prompt enhancement, saved conditioning files, multimodal guider parameter semantics, or dynamic prompt details: use prompt-conditioning.
• IC-LoRA union control, HDR, audio-only/T2A, DubIt, sparse motion tracks, masks, inpaint, outpaint, pixel spatial upscaling, or ingredients workflows: use specialized-workflows.
• STG/APG tuning, Q8 kernels, VAE/model patchers, PAG/FETA, attention-bank/override, flow-edit, or other experimental tricks: use advanced-control.
• Installation/import failures that occur before nodes appear in ComfyUI: use repo-level troubleshooting.

Operating workflow

1. Classify the recipe. Decide whether the user needs T2V, I2V, V2V/detailing, two-stage upsampled output, long/tiled output, or only decode/latent utilities. Use core workflows for recipe families distilled from native workflow candidates.
2. Check public prerequisites. Confirm the checkpoint, text encoder, optional latent upscaler, optional distilled LoRA, CUDA/VRAM expectations, and ComfyUI model-folder placement. Defer prompt encoder details to prompt-conditioning but keep the folder dependency visible.
3. Choose the latent shape first. For video latents the internal shape is [batch, channels, latent_frames, latent_h, latent_w]; pixel-frame count is derived by the VAE as 1 + (latent_frames - 1) * time_scale. Prefer output dimensions divisible by the VAE spatial scale and ComfyUI node increments.
4. Wire conditioning deliberately. T2V leaves image inputs absent. I2V uses LTXVImgToVideoConditionOnly or sampler optional_cond_images at index 0. Keyframes use guide nodes and explicit indices; guide videos longer than eight frames should start on a frame index divisible by eight.
5. Use a guider that preserves raw positive and negative conditionings. Core samplers extract positive and negative from the guider. If the user has only prompt text, route prompt construction to prompt-conditioning first; if the graph uses STG/APG-specific guiders, route expert parameter tuning to advanced-control.
6. Pick the sampler family. LTXVBaseSampler is the default core T2V/I2V sampler. LTXVInContextSampler starts from guiding latents. LTXVExtendSampler appends new frames with overlap. LTXVTiledSampler divides one latent spatially. LTXVLoopingSampler combines temporal chunks and optional spatial tiles for long outputs.
7. Decode safely. Standard decode is fine for small outputs. Use LTXVTiledVAEDecode for spatial memory pressure and LTXVSpatioTemporalTiledVAEDecode for long videos; choose working_device=cpu only when the user accepts slower decode for lower GPU memory.
8. Troubleshoot before changing semantics. Use troubleshooting for missing files, guider errors, frame-count mismatches, visible seams, or OOM. Do not tell the user to open original example JSONs; answer from the distilled references here.

Core node decision table

Need	Prefer	Key cue
Ordinary T2V	LTXVBaseSampler with empty video latent/no image conditioning	User has prompt only and wants one generated clip.
First-frame I2V	LTXVImgToVideoConditionOnly before sampling, or sampler optional_cond_images index 0	User provides one image to animate.
Keyframes in a clip	LTXVAddGuideAdvanced or sampler optional conditioning images/indices	Multiple images should appear at specific frames.
Latent-guided generation	LTXVInContextSampler or LTXVAddLatentGuide	User already has reference/guiding latents.
Extend a clip	LTXVExtendSampler	User wants more frames after an existing latent/video.
Spatial high resolution	LTXVTiledSampler and/or LTXVTiledVAEDecode	One clip is too large for memory or shows spatial scale needs.
Very long video	LTXVLoopingSampler	Needs temporal chunks, evolving prompts, or long-term coherence.
Low VRAM load order	LowVRAM* loaders chained by dependencies	Model loading peaks before generation starts.

Answering patterns

When answering novice workflow questions:

• Name the recipe family first, then list the minimum node families in order.
• State which model folders must already contain user-provided assets.
• Keep prompt encoder setup as a dependency and link to prompt-conditioning instead of copying Gemma/API details here.
• Explain whether frame numbers are pixel frames or latent frames for the node being discussed.

When answering expert troubleshooting questions:

• Identify whether the failure happens during asset loading, sampling, latent utility wiring, or VAE decode.
• Preserve the user's sampler/scheduler choices unless the error specifically implicates those choices.
• Prefer alignment, overlap, tiling, and latent-shape fixes before changing prompt semantics.
• Mention CUDA/VRAM requirements publicly, but do not assume any private inspection environment or local checkout remains available.

Reference map

• Core workflows: T2V/I2V/V2V recipe patterns, model-folder expectations, single-stage and two-stage families.
• Sampling and latents: sampler inputs, latent frame math, guide/keyframe rules, selection/transition, and noise masks.
• Tiling, looping, low VRAM: long/tiled generation, tiled VAE decode, low-VRAM loader sequencing, and parameter cues.
• Troubleshooting: workflow-specific failures and fixes.
• Root workflow overview and node catalog provide cross-skill context when available.

Native recipe families distilled here

• LTX-2.3 T2V/I2V single-stage: checkpoint + text conditioning + video/audio latent preparation + one denoise pass + tiled VAE decode.
• LTX-2.3 T2V/I2V two-stage: stage-one generation, latent upsampler model, second conditioning/denoise pass, then decode.
• LTX-2.0 V2V detailer: input video/components, LTX checkpoint/LoRA, text conditioning, LTXVLoopingSampler, and spatio-temporal tiled decode.

These native candidates require CUDA and model assets and were used as static recipe evidence only; do not run native ComfyUI workflows or download models from this skill.