README.md

# Stable Diffusion 3 Inpaint Pipeline

| input image | input mask image | output |
|:-------------------------:|:-------------------------:|:-------------------------:|
|<img src="./overture-creations-5sI6fQgYIuo.png" width = "400" /> | <img src="./overture-creations-5sI6fQgYIuo_mask.png" width = "400" /> | <img src="./overture-creations-5sI6fQgYIuo_output.jpg" width = "400" /> |
|<img src="./overture-creations-5sI6fQgYIuo.png" width = "400" /> | <img src="./overture-creations-5sI6fQgYIuo_mask.png" width = "400" /> | <img src="./overture-creations-5sI6fQgYIuo_tiger.jpg" width = "400" /> |
|<img src="./overture-creations-5sI6fQgYIuo.png" width = "400" /> | <img src="./overture-creations-5sI6fQgYIuo_mask.png" width = "400" /> | <img src="./overture-creations-5sI6fQgYIuo_panda.jpg" width = "400" /> |

**Please ensure that the version of diffusers >= 0.29.1**

# Demo
```python
import torch
from torchvision import transforms

from pipeline_stable_diffusion_3_inpaint import StableDiffusion3InpaintPipeline
from diffusers.utils import load_image

def preprocess_image(image):
    image = image.convert("RGB")
    image = transforms.CenterCrop((image.size[1] // 64 * 64, image.size[0] // 64 * 64))(image)
    image = transforms.ToTensor()(image)
    image = image * 2 - 1
    image = image.unsqueeze(0).to("cuda")
    return image

def preprocess_mask(mask):
    mask = mask.convert("L")
    mask = transforms.CenterCrop((mask.size[1] // 64 * 64, mask.size[0] // 64 * 64))(mask)
    mask = transforms.ToTensor()(mask)
    mask = mask.to("cuda")
    return mask

pipe = StableDiffusion3InpaintPipeline.from_pretrained(
    "stabilityai/stable-diffusion-3-medium-diffusers",
    torch_dtype=torch.float16,
).to("cuda")

prompt = "Face of a yellow cat, high resolution, sitting on a park bench"
source_image = load_image(
    "./overture-creations-5sI6fQgYIuo.png"
)
source = preprocess_image(source_image)
mask = preprocess_mask(
    load_image(
        "./overture-creations-5sI6fQgYIuo_mask.png"
    )
)

image = pipe(
    prompt=prompt,
    image=source,
    mask_image=1-mask,
    height=1024,
    width=1024,
    num_inference_steps=28,
    guidance_scale=7.0,
    strength=0.6,
).images[0]

image.save("output.png")
```