Instructions to use vantagewithai/Sulphur-2-Base-Split with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
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
Nan/+-Inf Error?
Whenever I try to generate t2v using one of the quantized ggufs (with no custom audio and no upsampling, normal VAE decoding), I get the following error log:
Model LTXAVTEModel_ prepared for dynamic VRAM loading. 25440MB Staged. 0 patches attached. Force pre-loaded 290 weights: 1497 KB.
gguf qtypes: F32 (2672), BF16 (28), Q4_K (1214), Q6_K (326), Q5_K (204)
model weight dtype torch.bfloat16, manual cast: None
model_type FLUX
Requested to load LTXAV
loaded partially; 9766.55 MB usable, 9721.50 MB loaded, 4178.95 MB offloaded, 45.05 MB buffer reserved, lowvram patches: 0
100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 8/8 [01:34<00:00, 11.84s/it]
Requested to load AudioVAE
Unloaded partially: 697.07 MB freed, 9024.43 MB remains loaded, 45.05 MB buffer reserved, lowvram patches: 79
loaded completely; 693.46 MB loaded, full load: True
VAE load device: cuda:0, offload device: cpu, dtype: torch.bfloat16
Requested to load VideoVAE
Unloaded partially: 4909.06 MB freed, 4115.37 MB remains loaded, 201.05 MB buffer reserved, lowvram patches: 396
Model VideoVAE prepared for dynamic VRAM loading. 1384MB Staged. 0 patches attached.
Input contains (near) NaN/+-Inf
!!! Exception during processing !!! [Errno 22] Invalid argument: 'avcodec_send_frame()'; last error log: [aac] Input contains (near) NaN/+-Inf
Traceback (most recent call last):
File "D:\ComfyUI\ComfyUI\execution.py", line 535, in execute
output_data, output_ui, has_subgraph, has_pending_tasks = await get_output_data(prompt_id, unique_id, obj, input_data_all, execution_block_cb=execution_block_cb, pre_execute_cb=pre_execute_cb, v3_data=v3_data)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\ComfyUI\ComfyUI\execution.py", line 335, in get_output_data
return_values = await _async_map_node_over_list(prompt_id, unique_id, obj, input_data_all, obj.FUNCTION, allow_interrupt=True, execution_block_cb=execution_block_cb, pre_execute_cb=pre_execute_cb, v3_data=v3_data)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\ComfyUI\ComfyUI\execution.py", line 309, in _async_map_node_over_list
await process_inputs(input_dict, i)
File "D:\ComfyUI\ComfyUI\execution.py", line 297, in process_inputs
result = f(**inputs)
File "D:\ComfyUI\ComfyUI\comfy_api\internal__init__.py", line 149, in wrapped_func
return method(locked_class, **inputs)
File "D:\ComfyUI\ComfyUI\comfy_api\latest_io.py", line 1833, in EXECUTE_NORMALIZED
to_return = cls.execute(*args, **kwargs)
File "D:\ComfyUI\ComfyUI\comfy_extras\nodes_video.py", line 108, in execute
video.save_to(
~~~~~~~~~~~~~^
os.path.join(full_output_folder, file),
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
...<2 lines>...
metadata=saved_metadata
^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "D:\ComfyUI\ComfyUI\comfy_api\latest_input_impl\video_types.py", line 517, in save_to
output.mux(audio_stream.encode(frame))
~~~~~~~~~~~~~~~~~~~^^^^^^^
File "av/audio/stream.py", line 18, in av.audio.stream.AudioStream.encode
File "av/audio/stream.py", line 28, in av.audio.stream.AudioStream.encode
File "av/codec/context.pyx", line 411, in av.codec.context.CodecContext.encode
File "av/codec/context.pyx", line 326, in _send_frame_and_recv
File "av/error.pyx", line 424, in av.error.err_check
av.error.ValueError: [Errno 22] Invalid argument: 'avcodec_send_frame()'; last error log: [aac] Input contains (near) NaN/+-Inf
I can't figure out what is wrong, but no output is passed on to the Save Video node.
This was an issue in older ComfyUI version, they fixed it. Please make sure that you are running latest ComfyUI version.