Instructions to use deepgoyal19/lora15 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use deepgoyal19/lora15 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("deepgoyal19/mysd1", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("deepgoyal19/lora15") 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
Download checkpoint-320/optimizer.bin from deepgoyal19/lora15: direct link, hf CLI and curl.
- Browser
- Download file 6.59 MB
-
https://huggingface.co/deepgoyal19/lora15/resolve/main/checkpoint-320/optimizer.bin
- Command line
-
hf download hf://deepgoyal19/lora15/checkpoint-320/optimizer.bin
-
curl -L -o optimizer.bin https://huggingface.co/deepgoyal19/lora15/resolve/main/checkpoint-320/optimizer.bin
6.59 MB
- Xet hash:
- a29535f24b06394433d0a3f7f750b5c031c454aea79a241d96dda3883b9f0ad1
- Size of remote file:
- 6.59 MB
- SHA256:
- e47b8fd8bf089bc0bc8152c049e938d1510d989844eecbc9d9cdc3303eaa4a93
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.