new upsampler scripts

example.py

@@ -0,0 +1,106 @@
+from aiia import AIIABase
+from aiunn import aiuNN
+from aiunn import aiuNNTrainer
+import pandas as pd
+import io
+import base64
+from PIL import Image, ImageFile
+from torch.utils.data import Dataset
+from torchvision import transforms
+
+
+
+class UpscaleDataset(Dataset):
+    def __init__(self, parquet_files: list, transform=None, samples_per_file=2500):
+        combined_df = pd.DataFrame()
+        for parquet_file in parquet_files:
+            # Load a subset from each parquet file
+            df = pd.read_parquet(parquet_file, columns=['image_512', 'image_1024']).head(samples_per_file)
+            combined_df = pd.concat([combined_df, df], ignore_index=True)
+
+        # Validate rows (ensuring each value is bytes or str)
+        self.df = combined_df.apply(self._validate_row, axis=1)
+        self.transform = transform
+        self.failed_indices = set()
+
+    def _validate_row(self, row):
+        for col in ['image_512', 'image_1024']:
+            if not isinstance(row[col], (bytes, str)):
+                raise ValueError(f"Invalid data type in column {col}: {type(row[col])}")
+        return row
+
+    def _decode_image(self, data):
+        try:
+            if isinstance(data, str):
+                return base64.b64decode(data)
+            elif isinstance(data, bytes):
+                return data
+            raise ValueError(f"Unsupported data type: {type(data)}")
+        except Exception as e:
+            raise RuntimeError(f"Decoding failed: {str(e)}")
+
+    def __len__(self):
+        return len(self.df)
+
+    def __getitem__(self, idx):
+        # If previous call failed for this index, use a different index
+        if idx in self.failed_indices:
+            return self[(idx + 1) % len(self)]
+        try:
+            row = self.df.iloc[idx]
+            low_res_bytes = self._decode_image(row['image_512'])
+            high_res_bytes = self._decode_image(row['image_1024'])
+            ImageFile.LOAD_TRUNCATED_IMAGES = True
+            # Open image bytes with Pillow and convert to RGBA first
+            low_res_rgba = Image.open(io.BytesIO(low_res_bytes)).convert('RGBA')
+            high_res_rgba = Image.open(io.BytesIO(high_res_bytes)).convert('RGBA')
+            
+            # Create a new RGB image with black background
+            low_res_rgb = Image.new("RGB", low_res_rgba.size, (0, 0, 0))
+            high_res_rgb = Image.new("RGB", high_res_rgba.size, (0, 0, 0))
+            
+            # Composite the original image over the black background
+            low_res_rgb.paste(low_res_rgba, mask=low_res_rgba.split()[3])
+            high_res_rgb.paste(high_res_rgba, mask=high_res_rgba.split()[3])
+            
+            # Now we have true 3-channel RGB images with transparent areas converted to black
+            low_res = low_res_rgb
+            high_res = high_res_rgb
+            
+            # Resize the images to reduce VRAM usage
+            low_res = low_res.resize((384, 384), Image.LANCZOS)
+            high_res = high_res.resize((768, 768), Image.LANCZOS)
+            
+            # If a transform is provided (e.g. conversion to Tensor), apply it
+            if self.transform:
+                low_res = self.transform(low_res)
+                high_res = self.transform(high_res)
+            return low_res, high_res
+        except Exception as e:
+            print(f"\nError at index {idx}: {str(e)}")
+            self.failed_indices.add(idx)
+            return self[(idx + 1) % len(self)]
+
+
+if __name__ =="__main__":
+    # Load your base model and upscaler
+    pretrained_model_path = "/root/vision/AIIA/AIIA-base-512"
+    base_model = AIIABase.load(pretrained_model_path, precision="bf16")
+    upscaler = aiuNN(base_model)
+
+    # Create trainer with your dataset class
+    trainer = aiuNNTrainer(upscaler, dataset_class=UpscaleDataset)
+
+    # Load data using parameters for your dataset
+    dataset_params = {
+        'parquet_files': [
+            "/root/training_data/vision-dataset/image_upscaler.parquet",
+            "/root/training_data/vision-dataset/image_vec_upscaler.parquet"
+        ],
+        'transform': transforms.Compose([transforms.ToTensor()]),
+        'samples_per_file': 2500
+    }
+    trainer.load_data(dataset_params=dataset_params, batch_size=1)
+
+    # Fine-tune the model
+    trainer.finetune(output_path="trained_models")

src/aiunn/config.py

@@ -1,7 +1,7 @@
 from aiia import AIIAConfig
 
 
-class UpsamplerConfig(AIIAConfig):
+class aiuNNConfig(AIIAConfig):
     def __init__(
         self,
         base_config=None,