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
A newer version of this model is available: deepseek-ai/DeepSeek-R1
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.
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Model tree for johnsonoluwafemi/TextImageGenerationModel
Base model
openai/clip-vit-large-patch14