Instructions to use vanzl/DiffusionART-HunyuanVideo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use vanzl/DiffusionART-HunyuanVideo with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("vanzl/DiffusionART-HunyuanVideo", 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
Simplify model introduction
Browse files
README.md
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[Project Page](https://vanzll.github.io/diffusionart-project/) 路
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[Demo](https://vanzll.github.io/diffusionart-project/demo.html)
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HunyuanVideo-13B fine-tuned with **DiffusionART**
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visual quality and motion quality rewards at **7:2:1**, with a pretrained-reference
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MSE regularizer (尾 = 0.1).
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This repository contains the **full transformer weights**, not a LoRA or a
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complete pipeline. The inference entry below downloads the base model's VAE
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[Project Page](https://vanzll.github.io/diffusionart-project/) 路
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[Demo](https://vanzll.github.io/diffusionart-project/demo.html)
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HunyuanVideo-13B fine-tuned with **DiffusionART** for text-to-video generation.
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This repository contains the **full transformer weights**, not a LoRA or a
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complete pipeline. The inference entry below downloads the base model's VAE
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