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
- Draw Things
- DiffusionBee
- Xet hash:
- 1010b0b74175b856546e842a8aefd4ba189eb955ec0d8b74702e78cb8e94c994
- Size of remote file:
- 1.06 kB
- SHA256:
- 7fe1486d475272308059b0ad022a7664ecb61d6280cbb365524f40a7cfaaac92
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