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-3000/optimizer.bin from backnotprop/np_cr_model6: direct link, hf CLI and curl.
- Browser
- Download file 4.59 GB
-
https://huggingface.co/backnotprop/np_cr_model6/resolve/main/checkpoint-3000/optimizer.bin
- Command line
-
hf download hf://backnotprop/np_cr_model6/checkpoint-3000/optimizer.bin
-
curl -L -o optimizer.bin https://huggingface.co/backnotprop/np_cr_model6/resolve/main/checkpoint-3000/optimizer.bin
4.59 GB
- Xet hash:
- 7f3264b0a35578d149d8044fe58cf4d3f1917aa66b9b80a7b43588651dd41d5d
- Size of remote file:
- 4.59 GB
- SHA256:
- 554810844de91a6f626cf8ead4f4b5b97bcfcc8adb0c22af6d40946b5a7e7b5a
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