Instructions to use backnotprop/np_cr_model6 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use backnotprop/np_cr_model6 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("backnotprop/np_cr_model6") prompt = "spiral wave flower,minimalism,white_background,abstract,photoshop generated abstract on a white background" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Download checkpoint-1000/scheduler.bin from backnotprop/np_cr_model6: direct link, hf CLI and curl.
- Browser
- Download file 1.06 kB
-
https://huggingface.co/backnotprop/np_cr_model6/resolve/main/checkpoint-1000/scheduler.bin
- Command line
-
hf download hf://backnotprop/np_cr_model6/checkpoint-1000/scheduler.bin
-
curl -L -o scheduler.bin https://huggingface.co/backnotprop/np_cr_model6/resolve/main/checkpoint-1000/scheduler.bin
1.06 kB
- Xet hash:
- 9aaa9ddbe00fbef84eb91adceb3d2c105cc206a39fb71b185c50265f2eb89b5b
- Size of remote file:
- 1.06 kB
- SHA256:
- 7babe47bfb751f6436f46e92a0639bc1ec9db889e510abc5afa501cfc0de4add
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