Fast Decode1.33 GB2GB+ VRAMlora

pruna_ltx2.3_vae_comfy_bf16.safetensors

LTX 2.3 Pruna VAE — Fast Decoder (Kijai)

ComfyUI-format conversion of Pruna AI's PrunaVAED — a drop-in replacement video VAE for LTX 2.3 with a pruned, faster decoder. Kijai's one-line summary: 'Faster decode, unchanged encode.' Pruna reports ~1.7× faster decoding and ~50% lower peak decode VRAM versus the stock BF16 video VAE, with the encoder and latent format unchanged, so existing workflows need no graph edits. Video VAE only — keep using LTX23_audio_vae_bf16.safetensors for audio. Place in models/vae/.

Released 2026-07-28 · Source: Kijai/LTX2.3_comfy (HuggingFace)Uploaded by Kijai the same day Pruna AI announced PrunaVAED for LTX-2.3. Kijai's description in the HF discussion thread: 'Faster decode, unchanged encode.'

Download pruna_ltx2.3_vae_comfy_bf16.safetensors

Direct HuggingFace download. 1.33 GB · Free.

Install path: ComfyUI/models/vae/ + pruna_ltx2.3_vae_comfy_bf16.safetensors

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Technical details

pruna_ltx2.3_vae_comfy_bf16.safetensors is Kijai's ComfyUI-layout conversion of PrunaVAED, Pruna AI's accelerated decoder for the LTX 2.3 video VAE. The decoder channel structure is pruned; the encoder side and the latent space are identical to the stock VAE, which is why it is a drop-in swap — the same latents decode through either file.

Native support landed in ComfyUI core (commit c38171d, 'Support Pruna LTX VAE'), so on a current ComfyUI build the standard VAELoader node loads it directly. Older builds need the third-party ComfyUI-PrunaVAED custom node, which remaps the upstream Diffusers-style state dict to the native LTX VAE layout.

It is 1.33 GB BF16 — slightly smaller than the 1.45 GB stock BF16 video VAE — and lives in ComfyUI/models/vae/ alongside taeltx2_3 and the audio VAE. It replaces the video VAE only; audio pipelines still load LTX23_audio_vae_bf16.safetensors separately.

When to choose pruna_ltx2.3_vae_comfy_bf16.safetensors

Choose the Pruna VAE when decode time or decode-step VRAM is the constraint: long or high-resolution clips where the final decode is a large share of total time, or a 16GB card that generates fine but OOMs when decoding. Pruna's headline numbers are ~1.7× faster decode and roughly half the peak decode VRAM against the stock BF16 VAE.

Stick with taeltx2_3.safetensors if you just want the smallest file that every published workflow already references — it is 23 MB and the default everywhere. Stick with LTX23_video_vae_bf16 if you want the unmodified full-fidelity decoder for HDR or archival output and don't care about decode speed.

All three coexist in models/vae/; switching is a dropdown change in the VAELoader node, not a workflow rebuild.

Will this run on my GPU?

Minimum: 2GB VRAM.

GPUVRAMVerdict
RTX 3060 12GB12GBComfortable
RTX 4060 Ti / 4070 (16GB)16GBComfortable
RTX 4070 Ti SUPER / 4080 (16GB)16GBComfortable
RTX 3090 (24GB)24GBComfortable
RTX 4090 (24GB)24GBComfortable
RTX 5090 / A6000 (32GB+)32GBComfortable

Recommendation: Worth it if VAE decode is your bottleneck — long clips, high resolution, or a 16GB card that OOMs at the decode step. Select it in the VAELoader node in place of LTX23_video_vae_bf16 or taeltx2_3; nothing else in the workflow changes.

How to use pruna_ltx2.3_vae_comfy_bf16.safetensors

  1. Download the file from HuggingFace.
  2. Place it in ComfyUI/models/vae/ inside your ComfyUI directory.
  3. Restart ComfyUI (or refresh the model list from the menu).
  4. Load a compatible workflow — see below.

Don't want to run this locally? Try pruna_ltx2.3_vae_comfy_bf16.safetensors online with a free generation — no GPU, no install, ~30 seconds per clip.

ComfyUI says it can't find pruna_ltx2.3_vae_comfy_bf16.safetensors?

Some published workflow JSONs reference this file under a custom subdirectory. If ComfyUI shows a "cannot find model" error and your workflow references one of these path-prefixed variants:

  • vae/pruna_ltx2.3_vae_comfy_bf16.safetensors
  • ltx23\pruna_ltx2.3_vae_comfy_bf16.safetensors

The prefix before the slash or backslash is a subdirectory the workflow author used. The actual file is the same pruna_ltx2.3_vae_comfy_bf16.safetensors — you have two fixes:

  1. Create the matching subdirectory inside ComfyUI/models/vae/ and place the file there. Example: if the workflow references vae/pruna_ltx2.3_vae_comfy_bf16.safetensors, create the corresponding subfolder under ComfyUI/models/vae/ and put pruna_ltx2.3_vae_comfy_bf16.safetensors inside it.
  2. Or open the workflow JSON in a text editor and replace the prefixed string with just pruna_ltx2.3_vae_comfy_bf16.safetensors. ComfyUI then resolves it directly from ComfyUI/models/vae/.

On Windows the separator is \, on macOS/Linux it is / — they refer to the same nested folder regardless of platform.

Common issues

VAELoader: 'pruna_ltx2.3_vae_comfy_bf16.safetensors not in list'

File placed in ComfyUI/models/ root or in checkpoints/ instead of vae/. Fix: Move it to ComfyUI/models/vae/pruna_ltx2.3_vae_comfy_bf16.safetensors and click refresh on the VAELoader node so ComfyUI re-scans.

VAE loads but decode errors with a state-dict / key mismatch

ComfyUI build predates native Pruna LTX VAE support (commit c38171d). Fix: Update ComfyUI to a current build. If you can't update, install the ScryptHunter/ComfyUI-PrunaVAED custom node and use its dedicated loader instead of the stock VAELoader.

ComfyUI doesn't see the file after I downloaded it

Make sure the file is in ComfyUI/models/vae/ (not a subfolder). Restart ComfyUI fully — the menu refresh sometimes misses new files. Filename must match exactly: pruna_ltx2.3_vae_comfy_bf16.safetensors.

CUDA out of memory error when loading the model

pruna_ltx2.3_vae_comfy_bf16.safetensors needs ~2GB VRAM minimum. If you're hitting OOM: • Enable Sequential Offloading in ComfyUI settings • Lower the resolution (768×512 instead of 1280×704) — both dimensions must be divisible by 32 • Reduce frame count (65 frames instead of 161) — must be 8n+1 • Use a smaller variant — see Related models below.

How do I apply this LoRA in ComfyUI?

Load it in a 'LoraLoader' node and connect it after your model loader. Pair this LoRA with the dev base model (not the distilled one) for the right behavior. LoRA strength 1.0 is the trained value — start there.

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