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7bef20f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 | #!/usr/bin/env python3
"""Simple reconstruction script for VibeToken.
Usage:
# Auto mode (recommended) - automatically determines optimal settings
python reconstruct.py --auto \
--config configs/vibetoken_ll.yaml \
--checkpoint /path/to/checkpoint.bin \
--image assets/example_1.jpg \
--output assets/reconstructed.png
# Manual mode - specify all parameters
python reconstruct.py \
--config configs/vibetoken_ll.yaml \
--checkpoint /path/to/checkpoint.bin \
--image assets/example_1.jpg \
--output assets/reconstructed.png \
--input_height 512 --input_width 512 \
--encoder_patch_size 16,32 \
--decoder_patch_size 16
"""
import argparse
from PIL import Image
from vibetoken import VibeTokenTokenizer, auto_preprocess_image, center_crop_to_multiple
def parse_patch_size(value):
"""Parse patch size from string. Supports single int or tuple (e.g., '16' or '16,32')."""
if value is None:
return None
if ',' in value:
parts = value.split(',')
return (int(parts[0]), int(parts[1]))
return int(value)
def main():
parser = argparse.ArgumentParser(description="VibeToken image reconstruction")
parser.add_argument("--config", type=str, default="configs/vibetoken_ll.yaml",
help="Path to config YAML")
parser.add_argument("--checkpoint", type=str, required=True,
help="Path to model checkpoint")
parser.add_argument("--image", type=str, default="assets/example_1.jpg",
help="Path to input image")
parser.add_argument("--output", type=str, default="./assets/reconstructed.png",
help="Path to output image")
parser.add_argument("--device", type=str, default="cuda",
help="Device (cuda/cpu)")
# Auto mode
parser.add_argument("--auto", action="store_true",
help="Auto mode: automatically determine optimal input resolution and patch sizes")
# Input resolution (optional - resize input before encoding)
parser.add_argument("--input_height", type=int, default=None,
help="Resize input to this height before encoding (default: original)")
parser.add_argument("--input_width", type=int, default=None,
help="Resize input to this width before encoding (default: original)")
# Output resolution (optional - decode to this size)
parser.add_argument("--output_height", type=int, default=None,
help="Decode to this height (default: same as input)")
parser.add_argument("--output_width", type=int, default=None,
help="Decode to this width (default: same as input)")
# Patch sizes (optional) - supports single int or tuple like "16,32"
parser.add_argument("--encoder_patch_size", type=str, default=None,
help="Encoder patch size: single int (e.g., 16) or tuple (e.g., 16,32 for H,W)")
parser.add_argument("--decoder_patch_size", type=str, default=None,
help="Decoder patch size: single int (e.g., 16) or tuple (e.g., 16,32 for H,W)")
args = parser.parse_args()
# Load tokenizer
print(f"Loading tokenizer from {args.config}")
tokenizer = VibeTokenTokenizer.from_config(
args.config,
args.checkpoint,
device=args.device,
)
# Load image
print(f"Loading image from {args.image}")
image = Image.open(args.image).convert("RGB")
original_size = image.size # (W, H)
print(f"Original image size: {original_size[0]}x{original_size[1]}")
if args.auto:
# AUTO MODE - use centralized auto_preprocess_image
print("\n=== AUTO MODE ===")
image, patch_size, info = auto_preprocess_image(image, verbose=True)
input_width, input_height = info["cropped_size"]
output_width, output_height = input_width, input_height
encoder_patch_size = patch_size
decoder_patch_size = patch_size
print("=================\n")
else:
# MANUAL MODE
# Parse patch sizes
encoder_patch_size = parse_patch_size(args.encoder_patch_size)
decoder_patch_size = parse_patch_size(args.decoder_patch_size)
# Resize input if specified
if args.input_width or args.input_height:
input_width = args.input_width or original_size[0]
input_height = args.input_height or original_size[1]
print(f"Resizing input to {input_width}x{input_height}")
image = image.resize((input_width, input_height), Image.LANCZOS)
# Always center crop to ensure dimensions divisible by 32
image = center_crop_to_multiple(image, multiple=32)
input_width, input_height = image.size
if (input_width, input_height) != original_size:
print(f"Center cropped to {input_width}x{input_height} (divisible by 32)")
# Determine output size
output_height = args.output_height or input_height
output_width = args.output_width or input_width
# Encode image to tokens
print("Encoding image to tokens...")
if encoder_patch_size:
print(f" Using encoder patch size: {encoder_patch_size}")
tokens = tokenizer.encode(image, patch_size=encoder_patch_size)
print(f"Token shape: {tokens.shape}")
# Decode back to image
print(f"Decoding to {output_width}x{output_height}...")
if decoder_patch_size:
print(f" Using decoder patch size: {decoder_patch_size}")
reconstructed = tokenizer.decode(
tokens,
height=output_height,
width=output_width,
patch_size=decoder_patch_size
)
print(f"Reconstructed shape: {reconstructed.shape}")
# Convert tensor to PIL and save
output_images = tokenizer.to_pil(reconstructed)
output_images[0].save(args.output)
print(f"Saved reconstructed image to {args.output}")
if __name__ == "__main__":
main()
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