Instructions to use ShreyashDhoot/v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ShreyashDhoot/v2 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/v2") 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
| base_model: runwayml/stable-diffusion-inpainting | |
| tags: | |
| - stable-diffusion | |
| - inpainting | |
| - lora | |
| - kto | |
| - diffusers | |
| license: creativeml-openrail-m | |
| library_name: diffusers | |
| pipeline_tag: image-to-image | |
| # ShreyashDhoot/v2 | |
| **Last updated:** 2026-04-28 06:34 | |
| ## Model Description | |
| KTO fine-tuned Stable Diffusion inpainter with LoRA for safety alignment. | |
| Base model: [`runwayml/stable-diffusion-inpainting`](https://huggingface.co/runwayml/stable-diffusion-inpainting) | |
| ## Checkpoints | |
| - `checkpoint--1000` | |
| - `checkpoint--250` | |
| - `checkpoint--500` | |
| - `checkpoint--750` | |
| ## Example Eval Outputs | |
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| _Auto-generated by push_to_hf.py_ |