Instructions to use swapnabyna/OUTPUT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use swapnabyna/OUTPUT with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("swapnabyna/OUTPUT") 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-1000/optimizer.bin from swapnabyna/OUTPUT: direct link, hf CLI and curl.
- Browser
- Download file 6.59 MB
-
https://huggingface.co/swapnabyna/OUTPUT/resolve/main/checkpoint-1000/optimizer.bin
- Command line
-
hf download hf://swapnabyna/OUTPUT/checkpoint-1000/optimizer.bin
-
curl -L -o optimizer.bin https://huggingface.co/swapnabyna/OUTPUT/resolve/main/checkpoint-1000/optimizer.bin
6.59 MB
- Xet hash:
- 921a7bd0e15675dd775d2761aff86d343a3aaa6b645ad920d20d432877aeecc9
- Size of remote file:
- 6.59 MB
- SHA256:
- 46e31c8f24ba00f05f8988ea6eed693be1e7c5962d23c917f2b3fcdb6f82c807
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