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import cv2
import numpy as np
import torch
from PIL import Image, ImageFilter, ImageOps
from pipeline_PowerPaint import StableDiffusionInpaintPipeline as Pipeline
from power_paint_tokenizer import PowerPaintTokenizer
from diffusers.utils import load_image
def add_task_to_prompt(prompt, negative_prompt, task):
if task == "object-removal":
promptA = prompt + " P_ctxt"
promptB = prompt + " P_ctxt"
negative_promptA = negative_prompt + " P_obj"
negative_promptB = negative_prompt + " P_obj"
elif task == "shape-guided":
promptA = prompt + " P_shape"
promptB = prompt + " P_ctxt"
negative_promptA = negative_prompt
negative_promptB = negative_prompt
elif task == "image-outpainting":
promptA = prompt + " P_ctxt"
promptB = prompt + " P_ctxt"
negative_promptA = negative_prompt + " P_obj"
negative_promptB = negative_prompt + " P_obj"
else:
promptA = prompt + " P_obj"
promptB = prompt + " P_obj"
negative_promptA = negative_prompt
negative_promptB = negative_prompt
return promptA, promptB, negative_promptA, negative_promptB
@torch.inference_mode()
def predict(
pipe,
input_image,
prompt,
fitting_degree,
ddim_steps,
scale,
negative_prompt,
task,
):
width, height = input_image["image"].convert("RGB").size
if width < height:
input_image["image"] = (
input_image["image"].convert("RGB").resize((640, int(height / width * 640)))
)
else:
input_image["image"] = (
input_image["image"].convert("RGB").resize((int(width / height * 640), 640))
)
promptA, promptB, negative_promptA, negative_promptB = add_task_to_prompt(
prompt, negative_prompt, task
)
print(promptA, promptB, negative_promptA, negative_promptB)
img = np.array(input_image["image"].convert("RGB"))
W = int(np.shape(img)[0] - np.shape(img)[0] % 8)
H = int(np.shape(img)[1] - np.shape(img)[1] % 8)
input_image["image"] = input_image["image"].resize((H, W))
input_image["mask"] = input_image["mask"].resize((H, W))
result = pipe(
promptA=promptA,
promptB=promptB,
tradoff=fitting_degree,
tradoff_nag=fitting_degree,
negative_promptA=negative_promptA,
negative_promptB=negative_promptB,
image=input_image["image"].convert("RGB"),
mask_image=input_image["mask"].convert("RGB"),
width=H,
height=W,
guidance_scale=scale,
num_inference_steps=ddim_steps,
).images[0]
mask_np = np.array(input_image["mask"].convert("RGB"))
red = np.array(result).astype("float") * 1
red[:, :, 0] = 180.0
red[:, :, 2] = 0
red[:, :, 1] = 0
result_m = np.array(result)
result_m = Image.fromarray(
(
result_m.astype("float") * (1 - mask_np.astype("float") / 512.0)
+ mask_np.astype("float") / 512.0 * red
).astype("uint8")
)
m_img = (
input_image["mask"].convert("RGB").filter(ImageFilter.GaussianBlur(radius=3))
)
m_img = np.asarray(m_img) / 255.0
img_np = np.asarray(input_image["image"].convert("RGB")) / 255.0
ours_np = np.asarray(result) / 255.0
ours_np = ours_np * m_img + (1 - m_img) * img_np
result_paste = Image.fromarray(np.uint8(ours_np * 255))
dict_res = [input_image["mask"].convert("RGB"), result_m]
dict_out = [input_image["image"].convert("RGB"), result_paste]
return dict_out, dict_res
pipe = Pipeline.from_pretrained(
"Sanster/PowerPaint-V1-stable-diffusion-inpainting",
torch_dtype=torch.float16,
safety_checker=None,
variant="fp16",
)
pipe.tokenizer = PowerPaintTokenizer(pipe.tokenizer)
pipe = pipe.to("mps")
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).convert("RGB")
mask = load_image(mask_url).convert("RGB")
input_image = {"image": image, "mask": mask}
prompt = "Face of a fox sitting on a bench"
negative_prompt = "out of frame, lowres, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, disfigured, gross proportions, malformed limbs, watermark, signature"
fitting_degree = 1
ddim_steps = 30
tasks = [
{
"task": "object-removal",
"guidance_scale": 12,
"prompt": "",
"negative_prompt": "",
},
{
"task": "shape-guided",
"guidance_scale": 7.5,
"prompt": prompt,
"negative_prompt": negative_prompt,
},
{
"task": "inpaint",
"guidance_scale": 7.5,
"prompt": prompt,
"negative_prompt": negative_prompt,
},
{
"task": "image-outpainting",
"guidance_scale": 7.5,
"prompt": "A dog seitting on a bench",
"negative_prompt": negative_prompt,
},
]
for task in tasks:
if task["task"] == "image-outpainting":
margin = 128
input_image["image"] = ImageOps.expand(
input_image["image"],
border=(margin, margin, margin, margin),
fill=(127, 127, 127),
)
outpaint_mask = np.zeros_like(np.asarray(input_image["mask"]))
input_image["mask"] = Image.fromarray(
cv2.copyMakeBorder(
outpaint_mask,
margin,
margin,
margin,
margin,
cv2.BORDER_CONSTANT,
value=(255, 255, 255),
)
)
dict_out, dict_res = predict(
pipe,
input_image,
task["prompt"],
fitting_degree,
ddim_steps,
task["guidance_scale"],
task["negative_prompt"],
task,
)
result_image = dict_out[1]
result_image.save(f"{task['task']}_result.png")
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