Instructions to use dbecker1/test_lora_mdl4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use dbecker1/test_lora_mdl4 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("dbecker1/test_lora_mdl4") 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-500/optimizer.bin from dbecker1/test_lora_mdl4: direct link, hf CLI and curl.
- Browser
- Download file 47.4 MB
-
https://huggingface.co/dbecker1/test_lora_mdl4/resolve/main/checkpoint-500/optimizer.bin
- Command line
-
hf download hf://dbecker1/test_lora_mdl4/checkpoint-500/optimizer.bin
-
curl -L -o optimizer.bin https://huggingface.co/dbecker1/test_lora_mdl4/resolve/main/checkpoint-500/optimizer.bin
47.4 MB
- Xet hash:
- fb771b2807a0261238fd735bda94a4bdf9e90c5f5f96965cfb0bd61716745d21
- Size of remote file:
- 47.4 MB
- SHA256:
- a01237a1ca0897ac4e4bace34a5508da7f078bfb61653c33f7d25ac881fdb30a
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