Instructions to use rakaaa/tree-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use rakaaa/tree-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("rakaaa/tree-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-300/optimizer.bin from rakaaa/tree-lora: direct link, hf CLI and curl.
- Browser
- Download file 6.59 MB
-
https://huggingface.co/rakaaa/tree-lora/resolve/main/checkpoint-300/optimizer.bin
- Command line
-
hf download hf://rakaaa/tree-lora/checkpoint-300/optimizer.bin
-
curl -L -o optimizer.bin https://huggingface.co/rakaaa/tree-lora/resolve/main/checkpoint-300/optimizer.bin
6.59 MB
- Xet hash:
- ec59c05d9f27f8dd9a72f9b875cbd483bb62d87bdd0f1ddf7734a2144b54e593
- Size of remote file:
- 6.59 MB
- SHA256:
- 9a5e3f04f69ddd00840f28949f65824b8c7c4cbb3fb50fa1939e26e766f756e2
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