Instructions to use MLXBits/sulphur-2-distill-mlx-q4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use MLXBits/sulphur-2-distill-mlx-q4 with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir sulphur-2-distill-mlx-q4 MLXBits/sulphur-2-distill-mlx-q4
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
File size: 2,496 Bytes
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base_model:
- SulphurAI/Sulphur-2-base
base_model_relation: quantized
pipeline_tag: image-text-to-video
tags:
- quantized
- mlx
- 4bit
- q4
library_name: mlx
---
This repository hosts custom implementations of the LTX2.3 video AI model, refined specifically for high-fidelity generation using the Sulphur 2 architecture. It has been converted to Apples MLX architecture and quantized down to Q4, to maximize memory efficiency. It has been tested on a 32GB M5 Silicon Mac, and that is the lowest recommended RAM for this model.
If you are not on a Mac, this madel variant is not for you.
## Model Overview
This implementation represents a highly optimized workflow built around the LTX2.3 core.
- **Base Model:** LTX2.3
- **Refinement Applied:** Sulphur 2
- **Fusion Detail:** The Sulphur 2 refinements have been successfully fused into the **`transformer-distilled.safetensors`** checkpoint, providing a unified generation experience.
- **Implementation:** MLX Conversion
- **Quantization:** FP4 (Optimized for performance and memory footprint)
- **Target Pipeline:** 8/3 Pipeline (Optimized for generation workflow)
## Usage Guide
### Core Workflow (Recommended)
For the best results and fastest generation times, users should rely on the integrated 8/3 pipeline.
- **Primary Generation:** Use the fused `transformer-distilled.safetensors` checkpoint to access the Sulphur 2 quality enhancements baked into the LTX2.3 base.
- **LoRAs:** No external LoRAs are required when using the fused model for Sulphur 2 quality, but have been included in this repo for convenience.
### Hardware & Compute Notes
- **Primary Platform:** Optimized for macOS compute environments on Apple silicon M-series SOC's.
- **AI Engine:** Built around the MLX framework integration.
## Prompting Guidelines (LTX Specific)
To achieve optimal generation quality with this model, adhere strictly to the following prompting conventions:
1. **Structure:** Aim for a single, flowing paragraph.
2. **Tense:** Use present tense verbs for all actions and movements.
3. **Detail Level:** Match the level of descriptive detail to the intended shot scale (e.g., high detail for close-ups, broader strokes for wide shots).
4. **Flow:** Describe the camera movement relative to the subject matter.
5. **Length Target:** Aim for 4–8 descriptive sentences to maintain focus and coherence.
**Note:** Model coherence (and body horror) has a swift uptake in clips going past ~17 seconds. Test with shorter clips. |