label
class label 16
classes | latent
sequence |
|---|---|
55
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11
| [[[1.5244190692901611,1.3618990182876587,1.3037453889846802,1.2363225221633911,1.0086064338684082,1.(...TRUNCATED) |
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Dataset Card for "latent_lsun_church_128px"
Each image is cropped to 128px square and encoded to a 4x16x16 latent representation using the same VAE as that employed by Stable Diffusion
Decoding
from diffusers import AutoencoderKL
from datasets import load_dataset
from PIL import Image
import numpy as np
import torch
# load the dataset
dataset = load_dataset('tglcourse/latent_lsun_church_128px')
# Load the VAE (requires access - see repo model card for info)
vae = AutoencoderKL.from_pretrained("CompVis/stable-diffusion-v1-4", subfolder="vae")
latent = torch.tensor([dataset['train'][0]['latent']]) # To tensor (bs, 4, 16, 16)
latent = (1 / 0.18215) * latent # Scale to match SD implementation
with torch.no_grad():
image = vae.decode(latent).sample[0] # Decode
image = (image / 2 + 0.5).clamp(0, 1) # To (0, 1)
image = image.detach().cpu().permute(1, 2, 0).numpy() # To numpy, channels lsat
image = (image * 255).round().astype("uint8") # (0, 255) and type uint8
image = Image.fromarray(image) # To PIL
image # The resulting PIL image
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