Instructions to use anu-122006/p3-lora-bk with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use anu-122006/p3-lora-bk with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("nota-ai/bk-sdm-small-2m", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("anu-122006/p3-lora-bk") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
p3-lora-bk: LoRA for BK-SDM-Small-2M (CV Hackathon 2026, P3)
(Paste this as README.md in the Hugging Face repo anu-122006/p3-lora-bk.)
- Base model: nota-ai/bk-sdm-small-2m
- Data: 1,000 COCO train2017 image–caption pairs
- Training: diffusers
train_text_to_image_lora.py; UNet LoRA rank 16; 1,000 steps; batch 4; lr 1e-4; fp16; 512px; NVIDIA T4, 17 min - Use:
pipe.load_lora_weights("anu-122006/p3-lora-bk"); pipe.fuse_lora()on a BK-SDM-Small-2M pipeline - Effect: light style adaptation toward COCO-like everyday photos; see the project's evaluation for measured results
- Licence: CreativeML OpenRAIL-M, inherited from the base model, with the same use restrictions
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Model tree for anu-122006/p3-lora-bk
Base model
nota-ai/bk-sdm-small-2m