模型地址：https://huggingface.co/CompVis/stable-diffusion-v1-4/tree/main/vae
主要參考:Using-Stable-Diffusion-VAE-to-encode-satellite-images

sd1.4 vae

下載到本地

from diffusers import AutoencoderKL
from PIL import Image
import  torch
import torchvision.transforms as T#  ./huggingface/stable-diffusion-v1-4/vae 切換為任意本地路徑
vae = AutoencoderKL.from_pretrained("./huggingface/stable-diffusion-v1-4/vae",variant='fp16')
# c:\Users\zeng\Downloads\vae_config.jsondef encode_img(input_img):# Single image -> single latent in a batch (so size 1, 4, 64, 64)# Transform the image to a tensor and normalize ittransform = T.Compose([# T.Resize((256, 256)),T.ToTensor()])input_img = transform(input_img)if len(input_img.shape)<4:input_img = input_img.unsqueeze(0)with torch.no_grad():latent = vae.encode(input_img*2 - 1) # Note scalingreturn 0.18215 * latent.latent_dist.sample()def decode_img(latents):# bath of latents -> list of imageslatents = (1 / 0.18215) * latentswith torch.no_grad():image = vae.decode(latents).sampleimage = (image / 2 + 0.5).clamp(0, 1)image = image.detach().cpu()# image = T.Resize(original_size)(image.squeeze())return T.ToPILImage()(image.squeeze())if __name__ == '__main__':# Load an example imageinput_img = Image.open("huge.jpg")original_size = input_img.sizeprint('original_size',original_size)# Encode and decode the imagelatents = encode_img(input_img)reconstructed_img = decode_img(latents)# Save the reconstructed imagereconstructed_img.save("reconstructed_example2.jpg")# Concatenate the original and reconstructed imagesconcatenated_img = Image.new('RGB', (original_size[0] * 2, original_size[1]))concatenated_img.paste(input_img, (0, 0))concatenated_img.paste(reconstructed_img, (original_size[0], 0))# Save the concatenated imageconcatenated_img.save("concatenated_example2.jpg")