Instructions to use Tianyi1229/MindCine with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Tianyi1229/MindCine with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Tianyi1229/MindCine", 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
Project Name: MindCine
This repository contains the pre-trained weights and ground truth data required to reproduce the results of our framework. The content is organized into two primary components: the generation backbone and the training data for specific branches.
π File Structure & Description
The repository is structured as follows:
.
βββ Tune-A-Video/ # Pre-trained Weights for the Generation Model
ββββ data/ # Ground Truth Data for Semantic & Perception Branches
βββ LLM_pretrained/ # EEG Foundation Model
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