Instructions to use Revanthraja/Text_to_Vision with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Revanthraja/Text_to_Vision with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Revanthraja/Text_to_Vision", 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
| --- | |
| tags: | |
| - Text-to-Video | |
| license: mit | |
| pipeline_tag: text-to-video | |
| --- | |
| # Text-to-Video Model with Hugging Face Transformers | |
| This repository contains a text-to-video generation model fine-tuned using the Hugging Face Transformers library. The model has been trained on various datasets over approximately 1000 steps to generate video content from textual input. | |
| ## Overview | |
| The text-to-video model developed here is based on Hugging Face's Transformers, specializing in translating textual descriptions into corresponding video sequences. It has been fine-tuned on diverse datasets, enabling it to understand and visualize a wide range of textual prompts, generating relevant video content. | |
| ## Features | |
| - Transforms text input into corresponding video sequences | |
| - Fine-tuned using Hugging Face Transformers with datasets spanning various domains | |
| - Capable of generating diverse video content based on textual descriptions | |
| - Handles nuanced textual prompts to generate meaningful video representations |