Instructions to use timduck8/sj1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use timduck8/sj1 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", torch_dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("timduck8/sj1") prompt = "-" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
- Xet hash:
- bec34927db797776abac2a50e246eb79098eaafbbc39e9c00f4884f8135c9680
- Size of remote file:
- 113 MB
- SHA256:
- c1f64c310059fb0217abe9c4febe13ad598facf25907c049297fad538a8d64df
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