Instructions to use johnsonoluwafemi/TextImageGenerationModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use johnsonoluwafemi/TextImageGenerationModel with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("johnsonoluwafemi/TextImageGenerationModel", 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
metadata
license: apache-2.0
language:
- en
- yo
- ig
- ha
base_model:
- openai/clip-vit-large-patch14
- sirbrentmichaelskoda/Auto-GBT-Dream-Team-Model
new_version: deepseek-ai/DeepSeek-R1
library_name: diffusers
tags:
- Text-to-image
- Deep Learning
- GAN
Text-Image Generation Model
This model generates images from textual descriptions using deep neural networks. It uses a [GAN architecture] to map text to images.
How to Use
- Install dependencies using
pip install -r requirements.txt - Load the model using the Hugging Face API.
- Pass a text description to generate an image.
Dataset
The model was trained on the MS COCO dataset, a large-scale dataset containing images and their textual descriptions.