Instructions to use RaphaelLiu/Pusa-V0.5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use RaphaelLiu/Pusa-V0.5 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("RaphaelLiu/Pusa-V0.5", 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
Dataset?
#2
by eggsbenedicto - opened
Amazing work! Would like to continue on this track. However, your huggingface dataset page appears to be empty. Do I need a dataset for this or am I misunderstanding the training process.
Sorry for the delay. Please check https://huggingface.co/datasets/RaphaelLiu/PusaV0.5_Training. This repository contains the pre-encoded training dataset used for fine-tuning the Pusa-V0.5 video generation model. The dataset consists of 52,695 pre-encoded latent samples derived from VIDGEN-1M, though Pusa-V0.5 was trained using only 16,000 of this dataset.