Instructions to use Muniyaraj/output_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Muniyaraj/output_model 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("Muniyaraj/output_model") prompt = "a photo of muniyarajs" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Download image_2.png from Muniyaraj/output_model: direct link, hf CLI and curl.
- Browser
- Download file 1.36 MB
-
https://huggingface.co/Muniyaraj/output_model/resolve/main/image_2.png
- Command line
-
hf download hf://Muniyaraj/output_model/image_2.png
-
curl -L -o image_2.png https://huggingface.co/Muniyaraj/output_model/resolve/main/image_2.png
1.36 MB

- Xet hash:
- cff272ecb8ba522db0841bd653e177ce33626b977560289bbfe43d77873191be
- Size of remote file:
- 1.36 MB
- SHA256:
- edc8cca24b2b20268b4f04acc161f9338d50907c1937ff7a6473260770857a86
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.