Instructions to use Knisaci/bindu with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Knisaci/bindu with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Knisaci/bindu", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
metadata
license: apache-2.0
datasets:
- HuggingFaceFW/finetranslations
language:
- pa
metrics:
- accuracy
base_model:
- moonshotai/Kimi-K2.5
new_version: zai-org/GLM-4.7-Flash
pipeline_tag: text-classification
library_name: diffusers
tags:
- biology