Image-to-Image
Diffusers
StableDiffusionInpaintPipeline
stable-diffusion
stable-diffusion-diffusers
text-guided-to-image-inpainting
endpoints-template
Instructions to use philschmid/stable-diffusion-2-inpainting-endpoint with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use philschmid/stable-diffusion-2-inpainting-endpoint 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("philschmid/stable-diffusion-2-inpainting-endpoint", dtype=torch.bfloat16, device_map="cuda") 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
| { | |
| "_class_name": "StableDiffusionInpaintPipeline", | |
| "_diffusers_version": "0.10.2", | |
| "feature_extractor": [ | |
| null, | |
| null | |
| ], | |
| "requires_safety_checker": false, | |
| "safety_checker": [ | |
| null, | |
| null | |
| ], | |
| "scheduler": [ | |
| "diffusers", | |
| "PNDMScheduler" | |
| ], | |
| "text_encoder": [ | |
| "transformers", | |
| "CLIPTextModel" | |
| ], | |
| "tokenizer": [ | |
| "transformers", | |
| "CLIPTokenizer" | |
| ], | |
| "unet": [ | |
| "diffusers", | |
| "UNet2DConditionModel" | |
| ], | |
| "vae": [ | |
| "diffusers", | |
| "AutoencoderKL" | |
| ] | |
| } | |