Instructions to use JingyeChen22/textdiffuser2_layout_planner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use JingyeChen22/textdiffuser2_layout_planner with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("JingyeChen22/textdiffuser2_layout_planner", 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
- Local Apps Settings
- Draw Things
- DiffusionBee
| pipeline_tag: text-to-image | |
| library_name: diffusers | |
| license: mit | |
| # Model | |
| This repo contains the layout planner model of the paper [TextDiffuser-2: Unleashing the Power of Language Models for Text Rendering](https://huggingface.co/papers/2311.16465). | |
| # Usage | |
| The script [here](https://github.com/microsoft/unilm/tree/master/textdiffuser-2#firecracker-inference) can be used to perform inference with the model. |