This GGUF file is a direct conversion of Wan-AI/Wan2.2-T2V-A14B Since this is a quantized model, all original licensing terms and usage restrictions remain in effect. Usage The model can be used with the ComfyUI custom node ComfyUI-GGUF by city96 Place model files in ComfyUI/models/unet see the GitHub readme for further installation instructions.
This repository provides quantized GGUF formats of the distilled transformer from Lightricks/LTX-2.5.
Parameters—
Context—
Weights116.9 GB
Licenseother
AccessOpen weights
Monthly Downloads86.5k
Model Card
This repository provides quantized GGUF formats of the distilled transformer from Lightricks/LTX-2.5. These weights are highly optimized for local execution, allowing you to run high-fidelity video and audio generation workflows on hardware with memory constraints while retaining the core visual fidelity of the original base model. LTX-2.5 operates on a split-component architecture. To run these GGUF diffusion models in environments like ComfyUI or local Python pipelines, you must also fetch the official Text Encoders and VAEs directly from the upstream Lightricks/LTX-2.5 repository. Download these from the textencoders directory: gemma4-12b-with-proj-ltx-2.5-bf16.safetensors (26.3 GB)…
Excerpt from the card by Jay, licensed other.
Identity and Version
- Repository
- Abiray/LTX-2.5-Distilled-GGUF
- Publisher
- Jay
- Task
- Text to video
- Modality
- Video
- Library
- Not stated by the source
- Parameters
- Not stated by the source
- Languages
- en, de, es, fr, ja, ko, zh, it
- Revision
- 7b0c2025441f1bf12c18eac375ad21f5e3d3c9e0
- First published
- 2026-08-12
- Last updated
- 2026-08-22
Files and Weights
12 files, 116.9 GB in total. The weights are 7 files totalling 116.9 GB in gguf.
Weights7 files · 116.9 GB
Configuration2 files · 245.1 KB
Documentation1 file · 6.7 KB
Other1 file · 1.1 MB
Repository1 file · 2.0 KB
Every file
| File | Type | Size | SHA-256 |
|---|---|---|---|
| LTX-2.5-Distilled-Q3_K_M.gguf | Weights | 12.9 GB | f593274e67dc |
| LTX-2.5-Distilled-Q3_K_S.gguf | Weights | 12.6 GB | a71473c8b7fe |
| LTX-2.5-Distilled-Q4_K_M.gguf | Weights | 15.7 GB | 0f51eb0d82b1 |
| LTX-2.5-Distilled-Q4_K_S.gguf | Weights | 15.3 GB | 91ad1ccc9c90 |
| LTX-2.5-Distilled-Q5_K_M.gguf | Weights | 18.1 GB | 9cc79799a741 |
| LTX-2.5-Distilled-Q6_K.gguf | Weights | 18.6 GB | ee8835ff8f11 |
| LTX-2.5-Distilled-Q8_0.gguf | Weights | 23.6 GB | 524a1410c647 |
| video_ltx2_5_i2v_GGUF.json | Configuration | 129.6 KB | — |
| video_ltx2_5_t2v_GGUF.json | Configuration | 115.5 KB | — |
| README.md | Documentation | 6.7 KB | — |
| video/LTX_2.5_t2v.mp4 | Other | 1.1 MB | cb33c2250f62 |
| .gitattributes | Repository | 2.0 KB | — |
License and Download
- License
- other
- Access
- Open weights, no gate
- Download size
- 116.9 GB
Download from Jay
Released by Jay through its official repository on Hugging Face. Read the license.
Built From
- Derived from Lightricks/LTX-2.5
- Quantized from Lightricks/LTX-2.5
Memory Requirements
| Precision | Weights in memory |
|---|---|
| As published | 116.9 GB |
Weights only, from the published parameter count; the key-value cache and runtime add to this.
Questions About LTX-2.5-Distilled-GGUF
What license is LTX-2.5-Distilled-GGUF released under?
other, as its publisher declares it. Read the license text before commercial use.
Similar Models
A LoRA for MiniMax-H3 that renders joint video + synchronized stereo audio in as few as 4 sampling steps instead of the usual ~20 — a ~5× sampling speedup — and keeps getting better as you add steps. For most work, use minimaxh3turbov4step600ema.safetensors. It's the markedly better micro-detail (faces, fingers, fine texture), and the over-sharpening / plastic look of the earlier v1 (~850) line is fully resolved. v4 introduced a static-frame enhancement — a big win for static and small-motion content. The one trade-off shows up only at 4 steps with large, fast motion, where v4 can produce motion-smear / trailing ghosting (we're actively fixing this). Two things address it: - Use 6–8 steps.…
This repository contains MiniMax-H3 Turbo LoRAs converted and optimized for ComfyUI: These LoRAs accelerate MiniMax-H3 video and synchronized-audio generation by reducing the required number of sampling steps. Newly added LoRA, located in the experimental/ folder: Manual recommended sigmas: 3-step 1.0, 0.961165, 0.853333, 0.0 4-step 1.0, 0.970874, 0.907249, 0.640000, 0.0 Three LoRAs extracted from VDN-H3 8 step: The main 8-step LoRA works on both FL2VA and Ref2VA. If you are running a pruned base, choose the pruned version that corresponds to your base — the pruned versions need their own matching pruned base. Three dynamically resized BF16 LoRAs are now included. Their source weights were…
Sulphur 2 An uncensored video generation model based on LTX 2.3 supporting both t2v and i2v natively, as well as all of the other ltx 2.3 formats. Follow us on X Join our Discord Support the next version of the project, even just a few dollars would go a long way: Kofi To get started with the model, I recommend downloading either of the dev versions, (fp8mixed or bf16) and downloading the distill lora provided. By the way, I'm aware the workflows contain sulphurfinal right now, just use the lora or use the full models, don't use both at the same time. This model contains a prompt enhancer. The easiest way to get started with the prompt enhancer is by using it on lmstudio. The way to…
Wan2.1 is an open-source suite of video foundation models, compatible with consumer-grade GPUs, that excels in various video generation tasks like text-to-video, image-to-video, and video editing, even supporting visual text generation. Download models using huggingface-cli: You can also download directly from this page. This model is a derivative work of the original model licensed under the Apache 2.0 License, and is therefore distributed under the terms of the same license. Thanks to Patrick Gillespie for creating the ASCII text art tool used in this project https://patorjk.com/software/taag/ Wan-AI for the Wan model https://huggingface.co/Wan-AI/Wan2.1-VACE-1.3B…
Wan2.1 is an open-source suite of video foundation models, compatible with consumer-grade GPUs, that excels in various video generation tasks like text-to-video, image-to-video, and video editing, even supporting visual text generation. Download models using huggingface-cli: You can also download directly from this page. This model is a derivative work of the original model licensed under the Apache 2.0 License, and is therefore distributed under the terms of the same license. Thanks to Patrick Gillespie for creating the ASCII text art tool used in this project https://patorjk.com/software/taag/ Wan-AI for the Wan model https://huggingface.co/Wan-AI/Wan2.1-T2V-1.3B…