How to use from the
Use from the
Diffusers library
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]

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--1000
  • checkpoint--1500
  • checkpoint--2000
  • checkpoint--2500
  • checkpoint--3000
  • checkpoint--3500
  • checkpoint--4000
  • checkpoint--4500
  • checkpoint--50
  • checkpoint--500
  • checkpoint--5000

Example Eval Outputs

eval_step1000_sample0.png eval_step1000_sample1.png eval_step1000_sample2.png eval_step1000_sample3.png eval_step1000_sample4.png eval_step1000_sample5.png eval_step1500_sample0.png eval_step1500_sample1.png


Auto-generated by push_to_hf.py

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