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# %%
# an example script of how to do outpainting with the diffusers inpainting pipeline
# this is basically just the example from
# https://huggingface.co/runwayml/stable-diffusion-inpainting
#%
from diffusers import StableDiffusionInpaintPipeline
from PIL import Image
import numpy as np
import torch
from diffusers import StableDiffusionInpaintPipeline
pipe = StableDiffusionInpaintPipeline.from_pretrained(
"runwayml/stable-diffusion-inpainting",
revision="fp16",
torch_dtype=torch.float16,
)
pipe.to("cuda")
# load the image, extract the mask
rgba = Image.open('primed_image_with_alpha_channel.png')
mask_image = Image.fromarray(np.array(rgba)[:, :, 3] == 0)
# run the pipeline
prompt = "Face of a yellow cat, high resolution, sitting on a park bench."
# image and mask_image should be PIL images.
# The mask structure is white for outpainting and black for keeping as is
image = pipe(
prompt=prompt,
image=rgba,
mask_image=mask_image,
).images[0]
image
# %%
# the vae does lossy encoding, we could get better quality if we pasted the original image into our result.
# this may yield visible edges
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