Instructions to use muhammad-taqi512/SLORA-HUNYUAN with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use muhammad-taqi512/SLORA-HUNYUAN with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("muhammad-taqi512/SLORA-HUNYUAN", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
SLORA-HUNYUAN: Open-Source High-Resolution Video Diffusion Model 🎬
SLORA-HUNYUAN is a powerful text-to-video generation model that shares structural familiarity and syntax parity with Tencent/HunyuanVideo. Engineered by Muhammad Taqi, this model employs advanced spatiotemporal modeling and dual-text encoding integration to deliver rich, high-definition cinematic motion sequences.
🌟 Key Features
- Advanced Text-to-Video Engine: Generates highly realistic and temporally consistent video outputs from text descriptions.
- HunyuanVideo Syntax Alignment: Fully compatible with standard diffusion pipeline execution workflows and parameter layouts inspired by
Tencent/HunyuanVideo. - High-Quality Dual Encoders: Supports comprehensive prompt comprehension for superior text-to-video alignment.
- Optimized Diffusion Pipeline: Designed for efficient frame rendering and smooth motion scaling.
💾 Installation & Setup
Ensure your local or cloud environment is configured with PyTorch and the latest Hugging Face ecosystem libraries:
# Clone the repository
git clone [https://huggingface.co/muhammad-taqi512/SLORA-HUNYUAN](https://huggingface.co/muhammad-taqi512/SLORA-HUNYUAN)
cd SLORA-HUNYUAN
# Install required dependencies
pip install -U diffusers transformers accelerate torch torchvision
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