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from __future__ import annotations
import gc
import pathlib
import gradio as gr
import torch
from PIL import Image, ImageFilter
from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler
from huggingface_hub import ModelCard
class InferencePipeline:
def __init__(self, hf_token: str | None = None):
self.hf_token = hf_token
self.pipe = None
self.device = torch.device(
'cuda:0' if torch.cuda.is_available() else 'cpu')
self.model_id = None
def clear(self) -> None:
self.model_id = None
del self.pipe
self.pipe = None
torch.cuda.empty_cache()
gc.collect()
@staticmethod
def check_if_model_is_local(model_id: str) -> bool:
return pathlib.Path(model_id).exists()
@staticmethod
def get_model_card(model_id: str,
hf_token: str | None = None) -> ModelCard:
if InferencePipeline.check_if_model_is_local(model_id):
card_path = (pathlib.Path(model_id) / 'README.md').as_posix()
else:
card_path = model_id
return ModelCard.load(card_path, token=hf_token)
def load_pipe(self, model_id: str) -> None:
if model_id == self.model_id:
return
if self.device.type == 'cpu':
pipe = DiffusionPipeline.from_pretrained(
model_id, use_auth_token=self.hf_token)
else:
pipe = DiffusionPipeline.from_pretrained(
model_id, torch_dtype=torch.float16,
use_auth_token=self.hf_token)
pipe = pipe.to(self.device)
pipe.scheduler = DPMSolverMultistepScheduler.from_config(
pipe.scheduler.config)
self.pipe = pipe
pipe.safety_checker = lambda images, **kwargs: (images, [False] * len(images))
self.model_id = model_id # type: ignore
def run(
self,
model_id: str,
seed: int,
target_image: str,
target_mask: str,
n_steps: int,
guidance_scale: float,
) -> Image.Image:
if not torch.cuda.is_available():
raise gr.Error('CUDA is not available.')
self.load_pipe(model_id)
generator = torch.Generator(device=self.device).manual_seed(seed)
image, mask_image = Image.open(target_image), Image.open(target_mask)
image, mask_image = image.convert("RGB"), mask_image.convert("L")
erode_kernel = ImageFilter.MaxFilter(3)
mask_image = mask_image.filter(erode_kernel)
blur_kernel = ImageFilter.BoxBlur(1)
mask_image = mask_image.filter(blur_kernel)
out = self.pipe(
"a photo of sks",
image=image,
mask_image=mask_image,
num_inference_steps=n_steps,
guidance_scale=guidance_scale,
generator=generator,
) # type: ignore
return out.images[0]
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