106 lines
4.2 KiB
Python
106 lines
4.2 KiB
Python
from aiia import AIIABase
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from aiunn import aiuNN
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from aiunn import aiuNNTrainer
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import pandas as pd
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import io
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import base64
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from PIL import Image, ImageFile
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from torch.utils.data import Dataset
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from torchvision import transforms
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class UpscaleDataset(Dataset):
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def __init__(self, parquet_files: list, transform=None, samples_per_file=10_000):
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combined_df = pd.DataFrame()
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for parquet_file in parquet_files:
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# Load a subset from each parquet file
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df = pd.read_parquet(parquet_file, columns=['image_512', 'image_1024']).head(samples_per_file)
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combined_df = pd.concat([combined_df, df], ignore_index=True)
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# Validate rows (ensuring each value is bytes or str)
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self.df = combined_df.apply(self._validate_row, axis=1)
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self.transform = transform
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self.failed_indices = set()
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def _validate_row(self, row):
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for col in ['image_512', 'image_1024']:
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if not isinstance(row[col], (bytes, str)):
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raise ValueError(f"Invalid data type in column {col}: {type(row[col])}")
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return row
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def _decode_image(self, data):
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try:
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if isinstance(data, str):
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return base64.b64decode(data)
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elif isinstance(data, bytes):
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return data
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raise ValueError(f"Unsupported data type: {type(data)}")
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except Exception as e:
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raise RuntimeError(f"Decoding failed: {str(e)}")
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def __len__(self):
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return len(self.df)
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def __getitem__(self, idx):
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# If previous call failed for this index, use a different index
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if idx in self.failed_indices:
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return self[(idx + 1) % len(self)]
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try:
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row = self.df.iloc[idx]
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low_res_bytes = self._decode_image(row['image_512'])
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high_res_bytes = self._decode_image(row['image_1024'])
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ImageFile.LOAD_TRUNCATED_IMAGES = True
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# Open image bytes with Pillow and convert to RGBA first
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low_res_rgba = Image.open(io.BytesIO(low_res_bytes)).convert('RGBA')
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high_res_rgba = Image.open(io.BytesIO(high_res_bytes)).convert('RGBA')
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# Create a new RGB image with black background
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low_res_rgb = Image.new("RGB", low_res_rgba.size, (0, 0, 0))
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high_res_rgb = Image.new("RGB", high_res_rgba.size, (0, 0, 0))
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# Composite the original image over the black background
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low_res_rgb.paste(low_res_rgba, mask=low_res_rgba.split()[3])
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high_res_rgb.paste(high_res_rgba, mask=high_res_rgba.split()[3])
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# Now we have true 3-channel RGB images with transparent areas converted to black
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low_res = low_res_rgb
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high_res = high_res_rgb
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# Resize the images to reduce VRAM usage
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low_res = low_res.resize((410, 410), Image.LANCZOS)
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high_res = high_res.resize((820, 820), Image.LANCZOS)
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# If a transform is provided (e.g. conversion to Tensor), apply it
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if self.transform:
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low_res = self.transform(low_res)
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high_res = self.transform(high_res)
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return low_res, high_res
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except Exception as e:
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print(f"\nError at index {idx}: {str(e)}")
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self.failed_indices.add(idx)
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return self[(idx + 1) % len(self)]
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if __name__ =="__main__":
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# Load your base model and upscaler
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pretrained_model_path = "/root/vision/AIIA/AIIA-base-512"
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base_model = AIIABase.load(pretrained_model_path, precision="bf16")
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upscaler = aiuNN(base_model)
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# Create trainer with your dataset class
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trainer = aiuNNTrainer(upscaler, dataset_class=UpscaleDataset)
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# Load data using parameters for your dataset
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dataset_params = {
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'parquet_files': [
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"/root/training_data/vision-dataset/image_upscaler.parquet",
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"/root/training_data/vision-dataset/image_vec_upscaler.parquet"
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],
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'transform': transforms.Compose([transforms.ToTensor()]),
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'samples_per_file': 5000
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}
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trainer.load_data(dataset_params=dataset_params, batch_size=1)
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# Fine-tune the model
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trainer.finetune(output_path="trained_models") |