Text-to-Image
Diffusers
TensorBoard
stable-diffusion
stable-diffusion-diffusers
diffusers-training
lora
Instructions to use lamble-lambe/atelie with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use lamble-lambe/atelie with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("fill-in-base-model", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("lamble-lambe/atelie") 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:
- f7a5fd3d3e011af0e836206e99505e480356c393aa9d51d21cd4d7f87e5686e9
- Size of remote file:
- 13 MB
- SHA256:
- 48ae28bcb7c204580f0f5cda9fe75a0d4108c07ab1a2d20d8c89499841117162
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.