Instructions to use iamkaikai/text-rendering with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use iamkaikai/text-rendering with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("iamkaikai/amazing-logos-v5", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("iamkaikai/text-rendering") 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
- Xet hash:
- c001a6ffa295695aaf92c2bbb7c428065fedcf5d1f92d1d5031aceae21b7274f
- Size of remote file:
- 6.59 MB
- SHA256:
- bc8455c486c8d914c9635dcce9f57fb140f9505839a96e80f570fbaeac488930
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.