IOPaint/lama_cleaner/model/paint_by_example.py
2022-12-11 09:14:28 +08:00

81 lines
2.7 KiB
Python

import random
import PIL
import PIL.Image
import cv2
import numpy as np
import torch
from diffusers import DiffusionPipeline
from lama_cleaner.model.base import InpaintModel
from lama_cleaner.schema import Config
class PaintByExample(InpaintModel):
pad_mod = 8
min_size = 512
def init_model(self, device: torch.device, **kwargs):
use_gpu = device == torch.device('cuda') and torch.cuda.is_available()
torch_dtype = torch.float16 if use_gpu else torch.float32
self.model = DiffusionPipeline.from_pretrained(
"Fantasy-Studio/Paint-by-Example",
torch_dtype=torch_dtype,
)
self.model.enable_attention_slicing()
self.model = self.model.to(device)
def forward(self, image, mask, config: Config):
"""Input image and output image have same size
image: [H, W, C] RGB
mask: [H, W, 1] 255 means area to repaint
return: BGR IMAGE
"""
seed = config.paint_by_example_seed
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
output = self.model(
image=PIL.Image.fromarray(image),
mask_image=PIL.Image.fromarray(mask[:, :, -1], mode="L"),
example_image=config.paint_by_example_example_image,
num_inference_steps=config.paint_by_example_steps,
output_type='np.array',
).images[0]
output = (output * 255).round().astype("uint8")
output = cv2.cvtColor(output, cv2.COLOR_RGB2BGR)
return output
@torch.no_grad()
def __call__(self, image, mask, config: Config):
"""
images: [H, W, C] RGB, not normalized
masks: [H, W]
return: BGR IMAGE
"""
if config.use_croper:
crop_img, crop_mask, (l, t, r, b) = self._apply_cropper(image, mask, config)
crop_image = self._pad_forward(crop_img, crop_mask, config)
inpaint_result = image[:, :, ::-1]
inpaint_result[t:b, l:r, :] = crop_image
else:
inpaint_result = self._pad_forward(image, mask, config)
return inpaint_result
def forward_post_process(self, result, image, mask, config):
if config.paint_by_example_match_histograms:
result = self._match_histograms(result, image[:, :, ::-1], mask)
if config.paint_by_example_mask_blur != 0:
k = 2 * config.paint_by_example_mask_blur + 1
mask = cv2.GaussianBlur(mask, (k, k), 0)
return result, image, mask
@staticmethod
def is_downloaded() -> bool:
# model will be downloaded when app start, and can't switch in frontend settings
return True