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Browse files- README.md +0 -69
- data/train-00003-of-00005-9250fc9afe771c61.parquet +0 -3
- data/train-00004-of-00005-640709d96f19594a.parquet +0 -3
- data/train-00002-of-00005-8afbdc84a24073cd.parquet β tglcourse--latent_lsun_church_256px/parquet-test.parquet +2 -2
- data/test-00000-of-00001-3db057d5316e30db.parquet β tglcourse--latent_lsun_church_256px/parquet-train-00000-of-00004.parquet +2 -2
- data/train-00000-of-00005-46d4f66e6345be99.parquet β tglcourse--latent_lsun_church_256px/parquet-train-00001-of-00004.parquet +2 -2
- data/train-00001-of-00005-6b76eddc47149307.parquet β tglcourse--latent_lsun_church_256px/parquet-train-00002-of-00004.parquet +2 -2
- tglcourse--latent_lsun_church_256px/parquet-train-00003-of-00004.parquet +3 -0
README.md
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---
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dataset_info:
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features:
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- name: label
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dtype:
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class_label:
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names:
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10: a
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12: c
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13: d
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14: e
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15: f
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- name: latent
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sequence:
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sequence:
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sequence: float32
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splits:
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- name: test
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num_bytes: 106824288
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num_examples: 6312
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- name: train
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num_bytes: 2029441460
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num_examples: 119915
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download_size: 2082210019
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dataset_size: 2136265748
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---
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# Dataset Card for "latent_lsun_church_256px"
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This is derived from https://huggingface.co/datasets/tglcourse/lsun_church_train
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Each image is cropped to 256px square and encoded to a 4x32x32 latent representation using the same VAE as that employed by Stable Diffusion
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Decoding
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```python
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from diffusers import AutoencoderKL
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from datasets import load_dataset
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from PIL import Image
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import numpy as np
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import torch
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# load the dataset
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dataset = load_dataset('tglcourse/latent_lsun_church_256px')
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# Load the VAE (requires access - see repo model card for info)
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vae = AutoencoderKL.from_pretrained("CompVis/stable-diffusion-v1-4", subfolder="vae")
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latent = torch.tensor([dataset['train'][0]['latent']]) # To tensor (bs, 4, 32, 32)
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latent = (1 / 0.18215) * latent # Scale to match SD implementation
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with torch.no_grad():
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image = vae.decode(latent).sample[0] # Decode
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image = (image / 2 + 0.5).clamp(0, 1) # To (0, 1)
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image = image.detach().cpu().permute(1, 2, 0).numpy() # To numpy, channels lsat
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image = (image * 255).round().astype("uint8") # (0, 255) and type uint8
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image = Image.fromarray(image) # To PIL
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image # The resulting PIL image
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```
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data/train-00003-of-00005-9250fc9afe771c61.parquet
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