Text-to-Image
Diffusers
TensorBoard
stable-diffusion-xl
stable-diffusion-xl-diffusers
diffusers-training
lora
Instructions to use nroggendorff/zelda-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use nroggendorff/zelda-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-xl-base-1.0", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("nroggendorff/zelda-lora") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Download checkpoint-1000/optimizer.bin from nroggendorff/zelda-lora: direct link, hf CLI and curl.
- Browser
- Download file 47.4 MB
-
https://huggingface.co/nroggendorff/zelda-lora/resolve/main/checkpoint-1000/optimizer.bin
- Command line
-
hf download hf://nroggendorff/zelda-lora/checkpoint-1000/optimizer.bin
-
curl -L -o optimizer.bin https://huggingface.co/nroggendorff/zelda-lora/resolve/main/checkpoint-1000/optimizer.bin
47.4 MB
- Xet hash:
- 4ce79a5236e9a5e8152aed7d98ad555aa0ea335472e349aa10cd5f42fca615be
- Size of remote file:
- 47.4 MB
- SHA256:
- d5f0ea5645fe526d3692f8a6ed56365840023d64ac02602c1c30860560a3db25
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