83 lines
3.0 KiB
Python
83 lines
3.0 KiB
Python
import PIL
|
|
import PIL.Image
|
|
import cv2
|
|
import torch
|
|
from loguru import logger
|
|
|
|
from lama_cleaner.model.base import DiffusionInpaintModel
|
|
from lama_cleaner.schema import Config
|
|
|
|
|
|
class PaintByExample(DiffusionInpaintModel):
|
|
name = "paint_by_example"
|
|
pad_mod = 8
|
|
min_size = 512
|
|
|
|
def init_model(self, device: torch.device, **kwargs):
|
|
from diffusers import DiffusionPipeline
|
|
|
|
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)
|
|
|
|
@staticmethod
|
|
def download():
|
|
from diffusers import DiffusionPipeline
|
|
|
|
DiffusionPipeline.from_pretrained("Fantasy-Studio/Paint-by-Example")
|
|
|
|
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
|
|
"""
|
|
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",
|
|
generator=torch.manual_seed(config.paint_by_example_seed),
|
|
).images[0]
|
|
|
|
output = (output * 255).round().astype("uint8")
|
|
output = cv2.cvtColor(output, cv2.COLOR_RGB2BGR)
|
|
return output
|
|
|
|
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
|