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from typing import List, Union
import torch
from diffusers import StableDiffusionInpaintPipeline, StableDiffusionXLInpaintPipeline
from internals.pipelines.commons import AbstractPipeline
from internals.util.commons import disable_safety_checker, download_image
from internals.util.config import (
get_hf_cache_dir,
get_hf_token,
get_is_sdxl,
get_inpaint_model_path,
get_model_dir,
)
class InPainter(AbstractPipeline):
__loaded = False
def init(self, pipeline: AbstractPipeline):
self.__base = pipeline
def load(self):
if self.__loaded:
return
if hasattr(self, "__base") and get_inpaint_model_path() == get_model_dir():
self.create(self.__base)
self.__loaded = True
return
if get_is_sdxl():
self.pipe = StableDiffusionXLInpaintPipeline.from_pretrained(
get_inpaint_model_path(),
torch_dtype=torch.float16,
cache_dir=get_hf_cache_dir(),
use_auth_token=get_hf_token(),
).to("cuda")
else:
self.pipe = StableDiffusionInpaintPipeline.from_pretrained(
get_inpaint_model_path(),
torch_dtype=torch.float16,
cache_dir=get_hf_cache_dir(),
use_auth_token=get_hf_token(),
).to("cuda")
disable_safety_checker(self.pipe)
self.__patch()
self.__loaded = True
def create(self, pipeline: AbstractPipeline):
if get_is_sdxl():
self.pipe = StableDiffusionXLInpaintPipeline(**pipeline.pipe.components).to(
"cuda"
)
else:
self.pipe = StableDiffusionInpaintPipeline(**pipeline.pipe.components).to(
"cuda"
)
disable_safety_checker(self.pipe)
self.__patch()
def __patch(self):
if get_is_sdxl():
self.pipe.enable_vae_tiling()
self.pipe.enable_vae_slicing()
self.pipe.enable_xformers_memory_efficient_attention()
@torch.inference_mode()
def process(
self,
image_url: str,
mask_image_url: str,
width: int,
height: int,
seed: int,
prompt: Union[str, List[str]],
negative_prompt: Union[str, List[str]],
num_inference_steps: int,
**kwargs,
):
torch.manual_seed(seed)
input_img = download_image(image_url).resize((width, height))
mask_img = download_image(mask_image_url).resize((width, height))
kwargs = {
"prompt": prompt,
"image": input_img,
"mask_image": mask_img,
"height": height,
"width": width,
"negative_prompt": negative_prompt,
"num_inference_steps": num_inference_steps,
**kwargs,
}
return self.pipe.__call__(**kwargs).images
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