Instructions to use vantagewithai/Sulphur-2-Base-Split with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use vantagewithai/Sulphur-2-Base-Split with Diffusers:

pip install -U diffusers transformers accelerate

import torch
from diffusers import DiffusionPipeline
from diffusers.utils import load_image, export_to_video

# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("vantagewithai/Sulphur-2-Base-Split", dtype=torch.bfloat16, device_map="cuda")
pipe.to("cuda")

prompt = "A man with short gray hair plays a red electric guitar."
image = load_image(
    "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/guitar-man.png"
)

output = pipe(image=image, prompt=prompt).frames[0]
export_to_video(output, "output.mp4")

llama-cpp-python

How to use vantagewithai/Sulphur-2-Base-Split with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="vantagewithai/Sulphur-2-Base-Split",
	filename="text_encoder/Qwen3.5-sulphur_prompt_enhancer-Q8_0.gguf",
)

llm.create_chat_completion(
	messages = "{\n    \"image\": \"cat.png\",\n    \"prompt\": \"The cat starts to dance\"\n}"
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use vantagewithai/Sulphur-2-Base-Split with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf vantagewithai/Sulphur-2-Base-Split:Q8_0
# Run inference directly in the terminal:
llama-cli -hf vantagewithai/Sulphur-2-Base-Split:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf vantagewithai/Sulphur-2-Base-Split:Q8_0
# Run inference directly in the terminal:
llama-cli -hf vantagewithai/Sulphur-2-Base-Split:Q8_0

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf vantagewithai/Sulphur-2-Base-Split:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf vantagewithai/Sulphur-2-Base-Split:Q8_0

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf vantagewithai/Sulphur-2-Base-Split:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf vantagewithai/Sulphur-2-Base-Split:Q8_0

Use Docker

docker model run hf.co/vantagewithai/Sulphur-2-Base-Split:Q8_0

LM Studio
Jan
Ollama
How to use vantagewithai/Sulphur-2-Base-Split with Ollama:
```
ollama run hf.co/vantagewithai/Sulphur-2-Base-Split:Q8_0
```

Unsloth Studio new

How to use vantagewithai/Sulphur-2-Base-Split with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for vantagewithai/Sulphur-2-Base-Split to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for vantagewithai/Sulphur-2-Base-Split to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for vantagewithai/Sulphur-2-Base-Split to start chatting

Pi new

How to use vantagewithai/Sulphur-2-Base-Split with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf vantagewithai/Sulphur-2-Base-Split:Q8_0

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "vantagewithai/Sulphur-2-Base-Split:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use vantagewithai/Sulphur-2-Base-Split with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf vantagewithai/Sulphur-2-Base-Split:Q8_0

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default vantagewithai/Sulphur-2-Base-Split:Q8_0

Run Hermes

hermes

Docker Model Runner
How to use vantagewithai/Sulphur-2-Base-Split with Docker Model Runner:
```
docker model run hf.co/vantagewithai/Sulphur-2-Base-Split:Q8_0
```

Lemonade

How to use vantagewithai/Sulphur-2-Base-Split with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull vantagewithai/Sulphur-2-Base-Split:Q8_0

Run and chat with the model

lemonade run user.Sulphur-2-Base-Split-Q8_0

List all available models

lemonade list

vantagewithai commited on 24 days ago

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+---
+library_name: diffusers
+license: other
+license_name: ltx-2-community-license-agreement
+license_link: https://github.com/Lightricks/LTX-2/blob/main/LICENSE
+pipeline_tag: image-to-video
+tags:
+- image-to-video
+- text-to-video
+- video-to-video
+- image-text-to-video
+- audio-to-video
+- text-to-audio
+- video-to-audio
+- audio-to-audio
+- text-to-audio-video
+- image-to-audio-video
+- image-text-to-audio-video
+- ltx-2
+- ltx-2-3
+- ltx-video
+- ltxv
+- lightricks
+- sulphur
+- sulphur-2
+---
+**Split Version of Sulphur 2 for ComfyUI**
+**Original model Link:** [https://huggingface.co/SulphurAI/Sulphur-2-base](https://huggingface.co/SulphurAI/Sulphur-2-base)
+**Watch us at Youtube:** [@VantageWithAI](https://www.youtube.com/@vantagewithai)
+**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.
+Join our  **[Discord](https://discord.gg/GSXJhKZ9V)**
+Support the project: **[Kofi](https://ko-fi.com/fusioncow)**
+---
+**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.
+---
+**Credits**
+- **([TenStrip](https://huggingface.co/TenStrip))** — Testing & model merging ([His i2v merge of sulphur 2, highly recommend for i2v](https://huggingface.co/TenStrip/LTX2.3-10Eros))
+- **@s1lv3rc01n** — Testing & model merging/quantizing ([silveroxides](https://huggingface.co/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
+---
+Thank you to everyone who contributed.