Text-to-Image
Diffusers
diffusers-training
lora
template:sd-lora
stable-diffusion-xl
stable-diffusion-xl-diffusers
Instructions to use JawadC/raclette with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use JawadC/raclette 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-xl-base-1.0", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("JawadC/raclette") prompt = "A close-up shot of a person dipping RACLETTE cheese into a warm, golden-brown pan." image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Download image_2.png from JawadC/raclette: direct link, hf CLI and curl.
- Browser
- Download file 1.15 MB
-
https://huggingface.co/JawadC/raclette/resolve/main/image_2.png
- Command line
-
hf download hf://JawadC/raclette/image_2.png
-
curl -L -o image_2.png https://huggingface.co/JawadC/raclette/resolve/main/image_2.png
1.15 MB

- Xet hash:
- 888263e805315e38522f172cc236f3e41f0de39d73dde31e5be0c51c27066cef
- Size of remote file:
- 1.15 MB
- SHA256:
- fa2aa6ca1807153b827c0902dbab087b480e0c241107c92563c27a0e8cfbb6f4
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.