import torch
from diffusers import AutoPipelineForInpainting
from diffusers.utils import load_image
# switch to "mps" for apple devices
pipe = AutoPipelineForInpainting.from_pretrained("yuvalkirstain/cat", dtype=torch.float16, device_map="cuda")
img_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png"
mask_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png"
image = load_image(img_url).resize((1024, 1024))
mask_image = load_image(mask_url).resize((1024, 1024))
prompt = "a tiger sitting on a park bench"
generator = torch.Generator(device="cuda").manual_seed(0)
image = pipe(
prompt=prompt,
image=image,
mask_image=mask_image,
guidance_scale=8.0,
num_inference_steps=20, # steps between 15 and 30 work well for us
strength=0.99, # make sure to use `strength` below 1.0
generator=generator,
).images[0]DreamBooth - yuvalkirstain/cat
This is a dreambooth model derived from stabilityai/stable-diffusion-2-inpainting. The weights were trained on Woman in wheelchair with her dog outdoors using DreamBooth. You can find some example images in the following.
DreamBooth for the text encoder was enabled: True.
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