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-2500/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-2500/optimizer.bin
- Command line
-
hf download hf://backnotprop/np_cr_model3/checkpoint-2500/optimizer.bin
-
curl -L -o optimizer.bin https://huggingface.co/backnotprop/np_cr_model3/resolve/main/checkpoint-2500/optimizer.bin
4.59 GB
- Xet hash:
- 710d71e3ca7acabec4c55ba3a5b9baac6654e643290d08965e80a1d4bac99b12
- Size of remote file:
- 4.59 GB
- SHA256:
- c7a3bfa65f110ab732b14bfadf2e7d6c6b9df7cf0c6078a9e51fb1b60b8d48c6
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