Instructions to use kraina/map_diffusion_lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use kraina/map_diffusion_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("runwayml/stable-diffusion-v1-5", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("kraina/map_diffusion_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 checkpoint-20584/scheduler.bin from kraina/map_diffusion_lora: direct link, hf CLI and curl.
- Browser
- Download file 563 Bytes
-
https://huggingface.co/kraina/map_diffusion_lora/resolve/main/checkpoint-20584/scheduler.bin
- Command line
-
hf download hf://kraina/map_diffusion_lora/checkpoint-20584/scheduler.bin
-
curl -L -o scheduler.bin https://huggingface.co/kraina/map_diffusion_lora/resolve/main/checkpoint-20584/scheduler.bin
563 Bytes
- Xet hash:
- 3a4f9a75ccd06648fe7cfbc68aae5167cb3665e3e8c008c9c046af93283b5db7
- Size of remote file:
- 563 Bytes
- SHA256:
- 18a7942e428721de225e2322b1c2d9b7247b3de6fe61dffa42f607656d9ca37e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.