Instructions to use takuma104/lora-test-text-encoder-lora-target with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use takuma104/lora-test-text-encoder-lora-target 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("takuma104/lora-test-text-encoder-lora-target") prompt = "a photo of sks dog" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Download checkpoint-100/optimizer.bin from takuma104/lora-test-text-encoder-lora-target: direct link, hf CLI and curl.
- Browser
- Download file 9.03 MB
-
https://huggingface.co/takuma104/lora-test-text-encoder-lora-target/resolve/main/checkpoint-100/optimizer.bin
- Command line
-
hf download hf://takuma104/lora-test-text-encoder-lora-target/checkpoint-100/optimizer.bin
-
curl -L -o optimizer.bin https://huggingface.co/takuma104/lora-test-text-encoder-lora-target/resolve/main/checkpoint-100/optimizer.bin
9.03 MB
- Xet hash:
- 4f8f21c34cedd65f6ed0b0d2dcc80769ba98934b2ea2503b8cd4eb5f5799fa1f
- Size of remote file:
- 9.03 MB
- SHA256:
- b5f354b46259c21f27cebf360fb0fcc89b67b8f6a4381f0892af6457fabf3fdb
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