Instructions to use ShreyashDhoot/v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ShreyashDhoot/v3 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import load_image # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-inpainting", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("ShreyashDhoot/v3") prompt = "Turn this cat into a dog" input_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png") image = pipe(image=input_image, prompt=prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
ShreyashDhoot/v3
Last updated: 2026-05-15 14:05
Model Description
KTO fine-tuned Stable Diffusion inpainter with LoRA for safety alignment.
Base model: runwayml/stable-diffusion-inpainting
Checkpoints
checkpoint--1000checkpoint--1500checkpoint--2000checkpoint--2500checkpoint--3000checkpoint--3500checkpoint--4000checkpoint--4500checkpoint--50checkpoint--500checkpoint--5000
Example Eval Outputs
Auto-generated by push_to_hf.py
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