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import random
import PIL
import PIL.Image
import cv2
import numpy as np
import torch
from diffusers import DiffusionPipeline
from loguru import logger
from lama_cleaner.helper import resize_max_size
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):
fp16 = not kwargs.get('no_half', False)
use_gpu = device == torch.device('cuda') and torch.cuda.is_available()
torch_dtype = torch.float16 if use_gpu and fp16 else torch.float32
model_kwargs = {"local_files_only": kwargs.get('local_files_only', False)}
if kwargs['disable_nsfw'] or kwargs.get('cpu_offload', False):
logger.info("Disable Paint By Example Model NSFW checker")
model_kwargs.update(dict(
safety_checker=None,
requires_safety_checker=False
))
self.model = DiffusionPipeline.from_pretrained(
"Fantasy-Studio/Paint-by-Example",
torch_dtype=torch_dtype,
**model_kwargs
)
self.model.enable_attention_slicing()
if kwargs.get('enable_xformers', False):
self.model.enable_xformers_memory_efficient_attention()
# TODO: gpu_id
if kwargs.get('cpu_offload', False) and use_gpu:
self.model.image_encoder = self.model.image_encoder.to(device)
self.model.enable_sequential_cpu_offload(gpu_id=0)
else:
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
def _scaled_pad_forward(self, image, mask, config: Config):
longer_side_length = int(config.sd_scale * max(image.shape[:2]))
origin_size = image.shape[:2]
downsize_image = resize_max_size(image, size_limit=longer_side_length)
downsize_mask = resize_max_size(mask, size_limit=longer_side_length)
logger.info(
f"Resize image to do paint_by_example: {image.shape} -> {downsize_image.shape}"
)
inpaint_result = self._pad_forward(downsize_image, downsize_mask, config)
# only paste masked area result
inpaint_result = cv2.resize(
inpaint_result,
(origin_size[1], origin_size[0]),
interpolation=cv2.INTER_CUBIC,
)
original_pixel_indices = mask < 127
inpaint_result[original_pixel_indices] = image[:, :, ::-1][original_pixel_indices]
return inpaint_result
@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._scaled_pad_forward(crop_img, crop_mask, config)
inpaint_result = image[:, :, ::-1]
inpaint_result[t:b, l:r, :] = crop_image
else:
inpaint_result = self._scaled_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
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