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from __future__ import annotations
import gc
import json
import pathlib
import sys
import gradio as gr
import PIL.Image
import torch
from diffusers import StableDiffusionInpaintPipeline
class InferencePipeline:
def __init__(self):
self.pipe = None
self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
def clear(self) -> None:
del self.pipe
self.pipe = None
torch.cuda.empty_cache()
gc.collect()
def load_pipe(self, realfill_model: str) -> None:
pipe = StableDiffusionInpaintPipeline.from_pretrained(
realfill_model, torch_dtype=torch.float16
).to(self.device)
pipe = pipe.to(self.device)
self.pipe = pipe
def run(
self,
realfill_model: str,
target_image: PIL.Image,
target_mask: PIL.Image,
seed: int,
n_steps: int,
guidance_scale: float,
) -> PIL.Image.Image:
if not torch.cuda.is_available():
raise gr.Error("CUDA is not available.")
self.load_pipe(realfill_model)
image = PIL.Image.open(target_image)
mask_image = PIL.Image.open(target_mask)
generator = torch.Generator(device=self.device).manual_seed(seed)
out = self.pipe(
"a photo of sks",
image=image,
mask_image=mask_image,
num_inference_steps=n_steps,
guidance_scale=guidance_scale,
generator=generator,
).images[0] # type: ignore
erode_kernel = PIL.ImageFilter.MaxFilter(3)
mask_image = mask_image.filter(erode_kernel)
result = PIL.Image.composite(result, out, mask_image)
return result
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