Instructions to use erkam/sd-clevr-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use erkam/sd-clevr-lora 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-2", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("erkam/sd-clevr-lora") 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 image_3.png from erkam/sd-clevr-lora: direct link, hf CLI and curl.
- Browser
- Download file 367 kB
-
https://huggingface.co/erkam/sd-clevr-lora/resolve/main/image_3.png
- Command line
-
hf download hf://erkam/sd-clevr-lora/image_3.png
-
curl -L -o image_3.png https://huggingface.co/erkam/sd-clevr-lora/resolve/main/image_3.png
367 kB

- Xet hash:
- 18dc2bc9245d58f52b3ea7a347cbe0292732329215419bbf04e55ee859d9e479
- Size of remote file:
- 367 kB
- SHA256:
- 80a28bc9cf6b74173ccbd9dbfd749a8ecc69b72831552d2e816f14e1c988b577
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.