LTX-2.5_GGUFs

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What LTX-2.5_GGUFs is

LTX-2.5 22B — GGUF GGUF quantizations of Lightricks' LTX-2.5, converted for ComfyUI with ComfyUI-GGUF. LTX-2.5 generates video and synchronized audio in a single pass — a dual-stream DiT with a 4096-wide video path, a 2048-wide audio path, and cross-modal attention joining them. The bf16 transformer is 39 GB. These quants bring it to 8-22 GB. All original licensing terms and usage restrictions carry over from the base model. The config metadata problem If you convert LTX-2.5 to GGUF yourself, it will not load. These files have the fix baked in; this section explains what it is, because it isn't obvious and it cost a night to find. ComfyUI derives most model dimensions from tensor shapes. For LTX-2.5 it can't: the connector widths, the audio path dimension, and the 9-row modulation tables are only knowable from a config blob stored in the original safetensors metadata. ComfyUI reads it here: GGUF has no metadata field, so every standard conversion drops it. ComfyUI then builds an LTX-2.3-shaped model and pours 2.5 weights into it, producing a wall of shape mismatches: These files carry the full 61-key transformer config as a GGUF KV field named config. ComfyUI-GGUF surfaces every scalar KV as metadata, ComfyUI merges it, and the model builds with the right dimensions. No custom node, no config pasting — the stock Unet Loader (GGUF) just works. Files Grab one file rather than the repo: Companion files The GGUF replaces only the transformer. You also need: Two VAEs. Missing the audio one is the most common reason a render comes out silent. The text encoder is not stock Gemma — it carries LTX's own projections (textembeddingprojection, audioprojector) that map prompt embeddings into the DiT's conditioning. A plain Gemma checkpoint will not substitute. Settings Distilled — few-step schedule, CFG 1.0. Guidance is baked into the weights; applying it again degrades output regardless of quantization. Dev — full step count with real CFG. Use ComfyUI's built-in LTX-2.5 template and swap its model loader for Unet Loader (GGUF). Everything else stays as the template ships it. Conversion notes Converted from the official bf16 transformer with a streaming converter, quantized

Summarised from the published model card. Read the full card on the HuggingFace links below.

Specifications

What it is

How it works

How video models work

Variants

Sizes & precisions

Open weights ship in several sizes and precisions. One page, all the variants — pick the one that fits your GPU. VRAM figures are estimates from model size.

Using it via the API

Call it like any OpenAI endpoint

Once AxForge deploys ltx-2-5-ggufs for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (ltx-2-5-ggufs below is illustrative; you get the exact model name on deployment.)

$ curl -sS https://api.axforge.ai/v1/videos/generations \
  -H "Authorization: Bearer $AXFORGE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"ltx-2-5-ggufs","prompt":"a drone shot over a forest"}'

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Licence

Commercial licence needed

Sources

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