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 Settings
- Draw Things
Download eval_outputs/eval_step1000_sample15.png from ShreyashDhoot/v2: direct link, hf CLI and curl.
- Browser
- Download file 476 kB
-
https://huggingface.co/ShreyashDhoot/v2/resolve/main/eval_outputs/eval_step1000_sample15.png
- Command line
-
hf download hf://ShreyashDhoot/v2/eval_outputs/eval_step1000_sample15.png
-
curl -L -o eval_step1000_sample15.png https://huggingface.co/ShreyashDhoot/v2/resolve/main/eval_outputs/eval_step1000_sample15.png
476 kB

- Xet hash:
- 23dcf82bb9287f25a9a49f57a631ea45689f46b9d5161b21b6d751218ef70236
- Size of remote file:
- 476 kB
- SHA256:
- f4098aa6d117b100bc484780e3890b5c26a16617407186ee2ebeaac0b7a16f4d
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