87 lines
2.8 KiB
Python
87 lines
2.8 KiB
Python
import PIL.Image
|
|
import cv2
|
|
import numpy as np
|
|
import torch
|
|
|
|
from lama_cleaner.model.base import DiffusionInpaintModel
|
|
from lama_cleaner.model.utils import get_scheduler
|
|
from lama_cleaner.schema import Config
|
|
|
|
|
|
class Kandinsky(DiffusionInpaintModel):
|
|
pad_mod = 64
|
|
min_size = 512
|
|
|
|
def init_model(self, device: torch.device, **kwargs):
|
|
from diffusers import AutoPipelineForInpainting
|
|
|
|
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", kwargs["sd_run_local"]),
|
|
"torch_dtype": torch_dtype,
|
|
}
|
|
|
|
self.model = AutoPipelineForInpainting.from_pretrained(
|
|
self.model_name, **model_kwargs
|
|
).to(device)
|
|
|
|
self.callback = kwargs.pop("callback", None)
|
|
|
|
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
|
|
"""
|
|
scheduler_config = self.model.scheduler.config
|
|
scheduler = get_scheduler(config.sd_sampler, scheduler_config)
|
|
self.model.scheduler = scheduler
|
|
|
|
generator = torch.manual_seed(config.sd_seed)
|
|
if config.sd_mask_blur != 0:
|
|
k = 2 * config.sd_mask_blur + 1
|
|
mask = cv2.GaussianBlur(mask, (k, k), 0)[:, :, np.newaxis]
|
|
mask = mask.astype(np.float32) / 255
|
|
img_h, img_w = image.shape[:2]
|
|
|
|
# kandinsky 没有 strength
|
|
output = self.model(
|
|
prompt=config.prompt,
|
|
negative_prompt=config.negative_prompt,
|
|
image=PIL.Image.fromarray(image),
|
|
mask_image=mask[:, :, 0],
|
|
height=img_h,
|
|
width=img_w,
|
|
num_inference_steps=config.sd_steps,
|
|
guidance_scale=config.sd_guidance_scale,
|
|
output_type="np",
|
|
callback=self.callback,
|
|
generator=generator,
|
|
callback_steps=1,
|
|
).images[0]
|
|
|
|
output = (output * 255).round().astype("uint8")
|
|
output = cv2.cvtColor(output, cv2.COLOR_RGB2BGR)
|
|
return output
|
|
|
|
@staticmethod
|
|
def is_downloaded() -> bool:
|
|
# model will be downloaded when app start, and can't switch in frontend settings
|
|
return True
|
|
|
|
|
|
class Kandinsky22(Kandinsky):
|
|
name = "kandinsky2.2"
|
|
model_name = "kandinsky-community/kandinsky-2-2-decoder-inpaint"
|
|
|
|
@staticmethod
|
|
def download():
|
|
from diffusers import AutoPipelineForInpainting
|
|
|
|
AutoPipelineForInpainting.from_pretrained(
|
|
"kandinsky-community/kandinsky-2-2-decoder-inpaint"
|
|
)
|