Overview

Sulphur 2 base is a foundational text-to-video model designed for developers building generative media pipelines. Unlike high-level consumer apps, this base model provides the raw flexibility needed to fine-tune specific motion dynamics or integrate custom temporal consistency layers. It is particularly suited for developers working on automated content creation, synthetic data generation for computer vision, or dynamic UI backgrounds. Released under the Apache-2.0 license, it offers a permissive framework for commercial deployment without the restrictive overhead of proprietary APIs. While it lacks the polished post-processing of closed-source alternatives, its open nature allows for deeper optimization of inference latency and hardware-specific acceleration.

Highlights

Permissive Apache-2.0 license for commercial flexibility
Foundational text-to-video capabilities for custom fine-tuning
Ideal for synthetic data and automated media pipelines
Open architecture allows for hardware-level inference optimization

Usage

Install

# Install Hugging Face transformers
pip install transformers torch

SDK Usage

# Load model with transformers
from transformers import AutoModel, AutoTokenizer

model = AutoModel.from_pretrained("SulphurAI/Sulphur-2-base")
tokenizer = AutoTokenizer.from_pretrained("SulphurAI/Sulphur-2-base")

Hugging Face Download

We recommend downloading the model via the Hugging Face CLI or Hub SDK.

Guidance：Before downloading, install huggingface_hub with:

Guidance

pip install -U huggingface_hub

CLI Download

Download the full repository

huggingface-cli download SulphurAI/Sulphur-2-base

Download a single file to a local folder (e.g. config.json into ./dir)

huggingface-cli download SulphurAI/Sulphur-2-base config.json --local-dir ./dir

See the official docs for more CLI options

SDK Download

# 模型下载
from huggingface_hub import snapshot_download
model_dir = snapshot_download('SulphurAI/Sulphur-2-base')

Git Download

Make sure git-lfs is installed first

Git Download

git lfs install
git clone https://huggingface.co/SulphurAI/Sulphur-2-base

To skip LFS large-file downloads, use:

Skip LFS

GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/SulphurAI/Sulphur-2-base

Model files are hosted on the Hugging Face Hub — download directly via HF CLI / SDK / Git, not through this site.

PyTorch / Transformers Usage

Install Transformers

pip install -U transformers torch

Load the model and run inference

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained('SulphurAI/Sulphur-2-base')
tokenizer = AutoTokenizer.from_pretrained('SulphurAI/Sulphur-2-base')

Full Documentation

来源: HuggingFace

---
base_model:
- Lightricks/LTX-2.3
library_name: diffusers
pipeline_tag: text-to-video
---

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.

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Join our Discord

Support the next version of the project, even just a few dollars would go a long way: Kofi

As a note, official inference for the model is coming soon.

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Get Started:
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 sulphur_final 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 accomplish this is by going to your model folder inside lmstudio, then opening it up in your file explorer. Create a folder named "Sulphur", then a folder inside that called "promptenhancer". Inside that folder, place the gguf file and the mmproj file. Once you've done that, you should be able to load the prompt enhancer in lmstudio. There is no system prompt for it, just send the text (and an image) you'd like to be enhanced.

*As a note, this readme will contain better setup instructions and how to train on top of the model soon.

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Links

(CivitAI Base Model) -

(CivitAI Quant Model) -

Credits

(TenStrip) — Testing & model merging (His i2v merge of sulphur 2, highly recommend for i2v)

@s1lv3rc01n — Testing & model merging/quantizing (silveroxides)

@mov7162 — Musubi Tuner guidance

And many others, if you'd like to be on the credits and I didn't place you here, message me I likely assumed you didn't want to be here.

Funders

Anonymous funder #1 — Supported the original Sulphur

Anonymous funder #2 — Made Sulphur 2 possible; this model wouldn't exist without them

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Thank you to everyone who contributed.