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Update cimage.py

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Art Gourieff 2023-06-19 02:54:54 +07:00 committed by GitHub
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commit 58941a52ff
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scripts/cimage.py
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@ -1,18 +1,10 @@
from typing import List, Union, Dict, Set, Tuple from typing import List, Union, Dict, Set, Tuple
# from diffusers.pipelines.stable_diffusion.safety_checker import (
# StableDiffusionSafetyChecker,
# )
from transformers import AutoFeatureExtractor from transformers import AutoFeatureExtractor
import torch import torch
from PIL import Image, ImageFilter from PIL import Image, ImageFilter
import numpy as np import numpy as np
# safety_model_id: str = "CompVis/stable-diffusion-safety-checker"
# safety_feature_extractor: AutoFeatureExtractor = None
# safety_checker: StableDiffusionSafetyChecker = None
def numpy_to_pil(images: np.ndarray) -> List[Image.Image]: def numpy_to_pil(images: np.ndarray) -> List[Image.Image]:
if images.ndim == 3: if images.ndim == 3:
images = images[None, ...] images = images[None, ...]
@ -24,36 +16,15 @@ def numpy_to_pil(images: np.ndarray) -> List[Image.Image]:
def check_image(x_image: np.ndarray) -> Tuple[np.ndarray, List[bool]]: def check_image(x_image: np.ndarray) -> Tuple[np.ndarray, List[bool]]:
global safety_feature_extractor, safety_checker global safety_feature_extractor, safety_checker
# if safety_feature_extractor is None:
# safety_feature_extractor = AutoFeatureExtractor.from_pretrained(safety_model_id)
# safety_checker = StableDiffusionSafetyChecker.from_pretrained(safety_model_id)
# safety_checker_input = safety_feature_extractor(
# images=numpy_to_pil(x_image), return_tensors="pt"
# )
# x_checked_image, hs = safety_checker(
# images=x_image, clip_input=safety_checker_input.pixel_values
# )
# return x_checked_image, hs
return x_image, False return x_image, False
def check_batch(x: torch.Tensor) -> torch.Tensor: def check_batch(x: torch.Tensor) -> torch.Tensor:
x_samples_ddim_numpy = x.cpu().permute(0, 2, 3, 1).numpy() x_samples_ddim_numpy = x.cpu().permute(0, 2, 3, 1).numpy()
# x_checked_image, _ = check_image(x_samples_ddim_numpy)
x_checked_image = x_samples_ddim_numpy x_checked_image = x_samples_ddim_numpy
x = torch.from_numpy(x_checked_image).permute(0, 3, 1, 2) x = torch.from_numpy(x_checked_image).permute(0, 3, 1, 2)
return x return x
def convert_to_sd(img: Image) -> Image: def convert_to_sd(img: Image) -> Image:
# _, hs = check_image(np.array(img))
# if any(hs):
# img = (
# img.resize((int(img.width * 0.1), int(img.height * 0.1)))
# .resize(img.size, Image.BOX)
# .filter(ImageFilter.BLUR)
# )
return img return img