Instructions to use SteveWCG/trained_buffer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use SteveWCG/trained_buffer with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-3-medium-diffusers", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("SteveWCG/trained_buffer") prompt = "A photo of a bike lane with a white-painted buffer zone separating cyclists from moving car traffic." image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
- Xet hash:
- f935c58e7a990bb0a88385424cd0babab2ca79656e756dc2852c8bbce5c69741
- Size of remote file:
- 44.6 MB
- SHA256:
- 9cee00e1a67ddf6d88477e0f74bdf894a7796060fb3fc7ab0580bdb8f462203a
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