Stubelius Remix v1 (LTX 2.5)
The remix is out of beta. v1 is beta 2 plus one thing I tested properly before baking it in: OmniNFT, a reinforcement-learning fine-tune that rewards video and audio quality, merged in at its trained strength. Everything else is the same as beta 2. It’s still mainly meant for NSFW work but can do SFW (name the clothing in SFW prompts, or you may get unwanted nudity), and the distilled LoRA is still baked in.
What’s new (measured against beta 2 on my test prompts)
Sharper fine detail: +55%. Faces, hair, fabric and backgrounds come out crisper.
A bit more motion: +15%.
Same remix: prompt following, loudness and dialogue unchanged, and dialogue lines still come out word for word. Colour is the same on average, though a few scenes come out less saturated.
Blind A/B: in a blind, AI-judged comparison of full-resolution frames, OmniNFT at the strength v1 bakes in won 29 to 9 against beta 2, with 10 ties.
I tested VBVR too. It cost motion and didn’t improve prompt following, so it isn’t in.
How to run
Put it in
ComfyUI/models/diffusion_modelsand load it with Load Diffusion Model, or pick it in the Models node of Stubelius Ultimate LTX 2.5.CFG 1, 8 steps (plus 3–4 steps if you run an upscale pass). The distilled LoRA and OmniNFT are already baked in, so don’t add either.
Text encoder:
gemma4-12b-with-proj-ltx-2.5-comfy-int8-convrot.safetensors. Lightricks re-uploaded it in mid-August, so download it again if your copy is older. VAEs:ltx-2.5-video-vae-bf16andltx-2.5-audio-vae-bf16.The int8 file needs a ComfyUI recent enough to load Comfy-Org’s int8 LTX 2.5 files.
All files: https://huggingface.co/Stuubs/Stubelius_Remix_Ltx2.5
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🚀 New models and workflows land on Patreon first. Stubelius Remix v1 went to Studio patrons first.
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LTX 2.5 Stubelius Remix — beta 2 out now!
Int8 merged with Distilled lora so you can run it on its own + other finetunes.
LTX 2.5 Stubelius Remix — beta 1
A custom LTX 2.5 checkpoint that brings the 2.3-era stylized/NSFW lineage onto the 2.5 engine — built because existing 2.5 ports lean hard into photoreal humans and lose the 2D/stylized soul.
Method (short version): task-vector delta arithmetic, not a naive merge. Deltas extracted from Sulphur 2 (concept & composition) and 10Eros (2D/stylized prior) against their LTX 2.3 base, then injected into the LTX 2.5 transformer with per-block-depth and per-component weighting — early blocks preserve composition knowledge, late blocks favor the stylized prior, photoreal MLP contributions deliberately suppressed. 4,349 tensors, 48 blocks.
Usage
Load as a diffusion model (UNETLoader / Load Diffusion Model) with your normal LTX 2.5 VAEs + text encoder
Run with the LTX distilled LoRA at 0.6–1.0 strength, 10–12 steps. Without it, output is blurry/underbaked (standard 2.5-dev behavior)
CFG 1 with the distill LoRA; your usual samplers (euler / linear_quadratic tested)
Fully compatible with the Stubelius LTX Director pack (seed hunt, refine, Licon MSR references, IC-LoRA, ingredients)
What it's for
2D / 2.5D / anime-styled characters on the full LTX 2.5 feature set (motion, audio, dialogue). NSFW-capable across styles via the Sulphur/Eros lineage. Beta 1: style balance is a first pass and will evolve — feedback steers beta 2.
Credits
TenStrip — Sulphur 2 and 10Eros, the models this stands on
Lightricks — LTX 2.5 base (LTX-Video community license applies)
Merge method & balance — Stubelius
Adult-capable model — intended for consenting-adult creative work only. Check the base and donor model licenses before commercial use.
Description
Sharper fine detail: +55%. Faces, hair, fabric and backgrounds come out crisper.
A bit more motion: +15%.
Same remix: prompt following, loudness and dialogue unchanged, and dialogue lines still come out word for word. Colour is the same on average, though a few scenes come out less saturated.
Blind A/B: in a blind, AI-judged comparison of full-resolution frames, OmniNFT at the strength v1 bakes in won 29 to 9 against beta 2, with 10 ties.
I tested VBVR too. It cost motion and didn’t improve prompt following, so it isn’t in.