Instructions to use erkam/sd-clevr-sg2im-objects_cap with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use erkam/sd-clevr-sg2im-objects_cap 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-sg2im-objects_cap") 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 pytorch_lora_weights.bin from erkam/sd-clevr-sg2im-objects_cap: direct link, hf CLI and curl.
- Browser
- Download file 106 MB
-
https://huggingface.co/erkam/sd-clevr-sg2im-objects_cap/resolve/main/pytorch_lora_weights.bin
- Command line
-
hf download hf://erkam/sd-clevr-sg2im-objects_cap/pytorch_lora_weights.bin
-
curl -L -o pytorch_lora_weights.bin https://huggingface.co/erkam/sd-clevr-sg2im-objects_cap/resolve/main/pytorch_lora_weights.bin
106 MB
- Xet hash:
- 23b1f5053772e865035dcc322bf929f608107b4119f79eb1109fe59395e7cd2a
- Size of remote file:
- 106 MB
- SHA256:
- 6f517d5cace73954070edadc1bc084e756d51126d5d2946ce05c83677a5e7cb2
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.