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import torch
from diffusers import StableDiffusionXLInpaintPipeline
from diffusers.utils import load_image
from stable_diffusion_server.utils import log_time
import numpy as np
import PIL.Image
pipe = StableDiffusionXLInpaintPipeline.from_pretrained(
"models/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
)
pipe.to("cuda")
refiner = StableDiffusionXLInpaintPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-refiner-1.0",
text_encoder_2=pipe.text_encoder_2,
vae=pipe.vae,
torch_dtype=torch.float16,
use_safetensors=True,
variant="fp16",
)
refiner.to("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"
# inpaint_and_upload_image?prompt=majestic tiger sitting on a bench&image_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&save_path=tests/inpaint.webp
# inpainting can be used to upscale to 1080p
init_image = load_image(img_url).convert("RGB")
# mask_image = load_image(mask_url).convert("RGB")
# mask image all ones same shape as init_image
# here's a failed experiment: inpainting cannot be used as style transfer/it doesnt recreate ain image doing a full mask in this way
image_size = init_image.size
ones_of_size = np.ones(image_size, np.uint8) * 255
mask_image = PIL.Image.fromarray(ones_of_size.astype(np.uint8))
# mask_image = torch.ones_like(init_image) * 255
prompt = "A majestic tiger sitting on a bench, castle backdrop elegent anime"
num_inference_steps = 75
high_noise_frac = 0.7
with log_time("inpaint"):
with torch.inference_mode():
image = pipe(
prompt=prompt,
image=init_image,
mask_image=mask_image,
num_inference_steps=num_inference_steps,
denoising_start=high_noise_frac,
output_type="latent",
).images
image = refiner(
prompt=prompt,
image=image,
mask_image=mask_image,
num_inference_steps=num_inference_steps,
denoising_start=high_noise_frac,
).images[0]
image.save("inpaintfull.png")
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