Instructions to use backnotprop/np_cr_model3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use backnotprop/np_cr_model3 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_model3") prompt = "spiral wave flower by <s0><s1>,minimalism,abstract,photoshop generated abstract colorful object mesh" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Download checkpoint-5000/optimizer.bin from backnotprop/np_cr_model3: direct link, hf CLI and curl.
- Browser
- Download file 4.59 GB
-
https://huggingface.co/backnotprop/np_cr_model3/resolve/main/checkpoint-5000/optimizer.bin
- Command line
-
hf download hf://backnotprop/np_cr_model3/checkpoint-5000/optimizer.bin
-
curl -L -o optimizer.bin https://huggingface.co/backnotprop/np_cr_model3/resolve/main/checkpoint-5000/optimizer.bin
4.59 GB
- Xet hash:
- 5a11b1fa700729d5775930913ce3d20df8f166f21564cb53699d6cb5c3333c8b
- Size of remote file:
- 4.59 GB
- SHA256:
- 535800952a538f4d9dfabd72fb4d3c5ef5dc6348def0e9c69a9a99e60e1d3744
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