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/content/tetsu_training_data/\n", "#\n", "# Dataset format:\n", "# image.jpg\n", "# image.txt\n", "#\n", "# =============================================================================\n", "\n", "import os\n", "import gc\n", "import sys\n", "import subprocess\n", "import warnings\n", "import shutil\n", "import zipfile\n", "import random\n", "\n", "warnings.filterwarnings(\"ignore\")\n", "\n", "print(\"Installing dependencies...\")\n", "\n", "subprocess.run(\n", " [\n", " sys.executable, \"-m\", \"pip\", \"install\", \"-q\", \"--upgrade\",\n", " \"diffusers>=0.38\",\n", " \"transformers>=5.3\",\n", " \"accelerate>=1.10\",\n", " \"bitsandbytes\",\n", " \"huggingface_hub\",\n", " \"safetensors\",\n", " \"Pillow\",\n", " \"tqdm\"\n", " ],\n", " check=True\n", ")\n", "\n", "import torch\n", "from pathlib import Path\n", "from google.colab import userdata, drive\n", "from huggingface_hub import login\n", "\n", "# -------------------------------------------------------------------------\n", "# AUTHENTICATION\n", "# -------------------------------------------------------------------------\n", "\n", "HF_TOKEN = userdata.get(\"HF_TOKEN\")\n", "\n", "if not HF_TOKEN:\n", " raise RuntimeError(\"HF_TOKEN missing from Colab Secrets.\")\n", "\n", "login(token=HF_TOKEN, add_to_git_credential=False)\n", "\n", "print(\"Hugging Face authentication successful.\")\n", "\n", "# -------------------------------------------------------------------------\n", "# GOOGLE DRIVE\n", "# -------------------------------------------------------------------------\n", "\n", "drive.mount(\"/content/drive\")\n", "\n", "# -------------------------------------------------------------------------\n", "# CONFIGURATION\n", "# -------------------------------------------------------------------------\n", "\n", "REPO_ID = \"lodestones/Kromoss\"\n", "\n", "DATA_ZIP = Path(\n", " \"/content/drive/MyDrive/sm_merged.zip\"\n", ")\n", "\n", "DATA_DIR = Path(\n", " \"/content/sm_training_data\"\n", ")\n", "\n", "TEXT_CACHE_DIR = Path(\n", " \"/content/text_emb_cache\"\n", ")\n", "\n", "DRIVE_DATASET_DIR = Path(\n", " \"/content/drive/MyDrive/kromoss_precomputed_dataset\"\n", ")\n", "\n", "RESOLUTION = 1024\n", "MAX_SEQ_LEN = 4096\n", "\n", "SEED = 42\n", "\n", "random.seed(SEED)\n", "torch.manual_seed(SEED)\n", "torch.cuda.manual_seed_all(SEED)\n", "\n", "assert torch.cuda.is_available()\n", "\n", "print(\"GPU:\", torch.cuda.get_device_name(0))\n", "print(\"VRAM:\", round(\n", " torch.cuda.get_device_properties(0).total_memory / 1024**3, 2\n", "), \"GB\")\n", "\n", "# -------------------------------------------------------------------------\n", "# EXTRACT DATASET\n", "# -------------------------------------------------------------------------\n", "\n", "if not DATA_ZIP.exists():\n", " raise FileNotFoundError(DATA_ZIP)\n", "\n", "if DATA_DIR.exists():\n", " shutil.rmtree(DATA_DIR)\n", "\n", "DATA_DIR.mkdir(parents=True, exist_ok=True)\n", "\n", "print(\"Extracting dataset...\")\n", "\n", "with zipfile.ZipFile(DATA_ZIP, \"r\") as archive:\n", " archive.extractall(DATA_DIR)\n", "\n", "# Flatten one top-level directory if present.\n", "\n", "contents = list(DATA_DIR.iterdir())\n", "\n", "if len(contents) == 1 and contents[0].is_dir():\n", "\n", " root = contents[0]\n", "\n", " for item in list(root.iterdir()):\n", " shutil.move(str(item), str(DATA_DIR / item.name))\n", "\n", " root.rmdir()\n", "\n", "print(\"Dataset extracted.\")\n", "\n", "# -------------------------------------------------------------------------\n", "# COLLECT IMAGE/CAPTION PAIRS\n", "# -------------------------------------------------------------------------\n", "\n", "IMG_EXTS = {\n", " \".jpg\", \".jpeg\", \".png\", \".webp\", \".bmp\"\n", "}\n", "\n", "pairs = []\n", "\n", "for img_path in sorted(DATA_DIR.rglob(\"*\")):\n", "\n", " if img_path.suffix.lower() not in IMG_EXTS:\n", " continue\n", "\n", " txt_path = img_path.with_suffix(\".txt\")\n", "\n", " if not txt_path.exists():\n", " continue\n", "\n", " caption = txt_path.read_text(\n", " encoding=\"utf-8\"\n", " ).strip()\n", "\n", " if caption:\n", " pairs.append((img_path, caption))\n", "\n", "print(\"=\" * 70)\n", "print(\"DATASET SUMMARY\")\n", "print(\"=\" * 70)\n", "\n", "print(\"Images with captions:\", len(pairs))\n", "\n", "assert pairs, \"No image-caption pairs found.\"\n", "\n", "TEXT_CACHE_DIR.mkdir(parents=True, exist_ok=True)\n", "DRIVE_DATASET_DIR.mkdir(parents=True, exist_ok=True)\n", "\n", "print(\"CELL 1 COMPLETE\")\n", "\n", "\n" ], "execution_count": 1, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Installing dependencies...\n", "Hugging Face authentication successful.\n", "Mounted at /content/drive\n", "GPU: Tesla T4\n", "VRAM: 14.56 GB\n", "Extracting dataset...\n", "Dataset extracted.\n", "======================================================================\n", "DATASET SUMMARY\n", "======================================================================\n", "Images with captions: 7\n", "CELL 1 COMPLETE\n" ] } ] }, { "cell_type": "code", "source": [ "# =============================================================================\n", "# KROMOSS – CELL 2: PRECOMPUTE TEXT EMBEDDINGS — FP16\n", "#\n", "# Encoder:\n", "# lodestones/Kromoss/text_encoder\n", "#\n", "# Tokenizer:\n", "# lodestones/Kromoss/tokenizer\n", "#\n", "# Precision:\n", "# 4-bit NF4, FP16 computation\n", "#\n", "# Storage:\n", "# FP16 text embeddings\n", "# BOOL attention masks\n", "#\n", "# Output:\n", "# /content/text_emb_cache/\n", "#\n", "# =============================================================================\n", "\n", "import torch\n", "import gc\n", "\n", "from tqdm.auto import tqdm\n", "from transformers import (\n", " AutoTokenizer,\n", " AutoModel,\n", " BitsAndBytesConfig\n", ")\n", "\n", "# =============================================================================\n", "# CONFIGURATION\n", "# =============================================================================\n", "\n", "device = torch.device(\"cuda\")\n", "\n", "print(\"Loading Kromoss tokenizer...\")\n", "\n", "tokenizer = AutoTokenizer.from_pretrained(\n", " REPO_ID,\n", " subfolder=\"tokenizer\",\n", " trust_remote_code=True,\n", " token=HF_TOKEN\n", ")\n", "\n", "# =============================================================================\n", "# QUANTIZATION\n", "# =============================================================================\n", "\n", "bnb_config = BitsAndBytesConfig(\n", " load_in_4bit=True,\n", " bnb_4bit_quant_type=\"nf4\",\n", " bnb_4bit_compute_dtype=torch.float16,\n", " bnb_4bit_use_double_quant=True\n", ")\n", "\n", "print(\"Loading quantized Kromoss text encoder...\")\n", "\n", "text_encoder = AutoModel.from_pretrained(\n", " REPO_ID,\n", " subfolder=\"text_encoder\",\n", " quantization_config=bnb_config,\n", " torch_dtype=torch.float16,\n", " device_map=\"auto\",\n", " trust_remote_code=True,\n", " token=HF_TOKEN\n", ")\n", "\n", "text_encoder.eval()\n", "text_encoder.requires_grad_(False)\n", "\n", "print(\"Text encoder loaded.\")\n", "\n", "# =============================================================================\n", "# ENCODING FUNCTION\n", "# =============================================================================\n", "\n", "@torch.no_grad()\n", "def encode_prompt(prompt):\n", "\n", " formatted = tokenizer.apply_chat_template(\n", " [\n", " {\n", " \"role\": \"user\",\n", " \"content\": prompt\n", " }\n", " ],\n", " tokenize=False,\n", " add_generation_prompt=True,\n", " enable_thinking=True\n", " )\n", "\n", " tokens = tokenizer(\n", " formatted,\n", " return_tensors=\"pt\",\n", " truncation=True,\n", " max_length=MAX_SEQ_LEN\n", " )\n", "\n", " tokens = tokens.to(device)\n", "\n", " outputs = text_encoder(\n", " **tokens,\n", " use_cache=False,\n", " return_dict=True\n", " )\n", "\n", " embedding = outputs.last_hidden_state\n", " attention_mask = tokens.attention_mask\n", "\n", " # -------------------------------------------------------------------------\n", " # Store as FP16\n", " # -------------------------------------------------------------------------\n", "\n", " embedding = embedding.detach().to(\n", " dtype=torch.float16\n", " ).cpu().contiguous()\n", "\n", " attention_mask = attention_mask.detach().bool().cpu().contiguous()\n", "\n", " # Strong validation before saving.\n", " assert embedding.dtype == torch.float16\n", " assert torch.isfinite(embedding).all()\n", " assert embedding.ndim == 3\n", " assert embedding.shape[0] == 1\n", " assert embedding.shape[-1] == 4096\n", " assert embedding.shape[1] == attention_mask.shape[1]\n", "\n", " return embedding, attention_mask\n", "\n", "\n", "# =============================================================================\n", "# PRECOMPUTE ALL CAPTIONS\n", "# =============================================================================\n", "\n", "print(\"=\" * 70)\n", "print(\"PRECOMPUTING TEXT EMBEDDINGS — FP16\")\n", "print(\"=\" * 70)\n", "\n", "for index, (img_path, caption) in enumerate(\n", " tqdm(pairs, desc=\"Text embeddings\")\n", "):\n", "\n", " output_file = TEXT_CACHE_DIR / f\"{index:08d}.pt\"\n", "\n", " if output_file.exists():\n", " continue\n", "\n", " embedding, attention_mask = encode_prompt(caption)\n", "\n", " torch.save(\n", " {\n", " \"img\": str(img_path),\n", " \"caption\": caption,\n", " \"emb\": embedding,\n", " \"mask\": attention_mask\n", " },\n", " output_file\n", " )\n", "\n", " del embedding\n", " del attention_mask\n", "\n", " if index % 25 == 0:\n", " gc.collect()\n", " torch.cuda.empty_cache()\n", "\n", "print(\"=\" * 70)\n", "print(\"TEXT EMBEDDING PRECOMPUTATION COMPLETE\")\n", "print(\"Files:\", len(list(TEXT_CACHE_DIR.glob(\"*.pt\"))))\n", "print(\"=\" * 70)\n", "\n", "# =============================================================================\n", "# UNLOAD TEXT ENCODER\n", "# =============================================================================\n", "\n", "del text_encoder\n", "del tokenizer\n", "del bnb_config\n", "\n", "gc.collect()\n", "torch.cuda.empty_cache()\n", "torch.cuda.ipc_collect()\n", "\n", "print(\n", " \"VRAM after text encoder unload:\",\n", " round(torch.cuda.memory_allocated() / 1024**3, 2),\n", " \"GB\"\n", ")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 632, "referenced_widgets": [ "756a0ea57c9a40c9b74e1e06e36ae915", "57988b1270a44e8487607bdee013084f", "518eae491bb6491ea7f99b641d7c5e37", "624c4014c411495a8bd3b7e847de097b", "1b83fa9958f048af8dabdd4c52fd4a62", "c010d213e90e4f6eb58e2022ee365650", "ab0d4d3c97ad49d391b3ac963bb0e2c5", "436ff1ec3e4843a6a7ab34d2833b2565", "d4a7631614724891a5b50ef14dae2613", "af9e77299e0943eb849bce9bb3baf9dd", "1b37868287ac4a5f9455a39e510602ce", "0bdb55a294964d499955c1b05fef04bd", "bac978cfa8a14aaa9430ca4b5d7d4693", "4aad4f503c6143e0a8a81196e7710557", 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"d578772ed63a4a3abff3f6eef3ede9b7", "3ae7002ccc3f49d386e432bd1fe43fe2", "3d41a44dbf984e7e9f925ff6ae340ad7", "c35f58da7f6a43a69326eb23aab7f67e", "0c080e4d0f0b4e80b2c3a2b20d942b12", "14eb439e43894fa2bfd61a330091c597", "b449b8b86f2542369bf6184cfb2acf49", "83a54b06223d43ffa433925075b05a85", "73fa13aae17548e68bdcaa7588d309cf", "4f3af06d92984abc968f69bf14e1f645", "8343248c5a8d421391d202ecd2d6527b", "4cd04123a6274fda8cb23db840705a35", "6459791ebbe044adbf7a8c58a0d9ad62", "cfc14774176343b2b05b2228b59acb10", "a2cc906bf480467292c78bdb79906d1a", "8d38f10001684637aa21342c6032d148", "ddcc79a83b8345c9a57b0922bc071087", "7d16866952334f43aa956cb1136ac667", "b7728a740a5842a8a9380a252a085547", "d3f9d70624674cea816b2d24c863cecb", "97cd11c379d842a9be17159aa07b060a", "a7bca5e553a64c09b8b8f2a879ab4b32", "94b7947c802849f98b28daaaa9d801ee", "5b1b78acfc7a48b1a464b9de178029dc", "9d83962817184333a32ceb4a6360e417", "b0326071568e45aebd4b215398a6c1dc", "3f4f476c4e5c42029c4f2612e7ae5154", "47eebe3f8a794e2abd820084c1e7d1cd", "426dee379f8b4af397e1906427e4a0e3", "fabb292bf5e740e6ad96bae0718d73a1", "3aeea0ebbbd94bebb44cf26af123cf30", "857b301b54684727bba90d752bfb86c0", "e000b826ff7a482797d4d1dc76dc9a95", "5ed19e8edf714079a0b02786195e802b", "cc8b45bb674c4080a49b6f1438a976fa", "22a749d6f9de4b06850b9010ad38400c", "5833dbdb0dea4fa8bb4e5b57b4016c1e", "f434483ec6f44d0492c12a6064ed3716", "455a392d429f41239d776c374a13b3e6", "49d56f5c2bf74bd0aeee202ea11bfd3a", "d7984fd7be0e4422ade74b1f8696f55f", "a315e61a06c243d58e40f542e5981f09", "9b10b876f8a9460a9eeafcf6194c33df", "bd19502f139e4ca0a4e86c62ab0f2b41", "f51ee87a16f94d1382377df0f812f55f", "df0543a9d4b04ee0a6ca6dc21e8052ec", "0b4fb0bdf1c2473584f99b7e4aadc0ca", "1eb9f81fd70443909d9d8136404718a7", "cd3ef18d5d1e4d70b30757aba1a47f80", "4aa9e9f6d4e043ba8f8fd9aa0dc27f00", "74681c2f10864ee2b01d07ff0d1f1ccd", "34b64084481143889e73cf7e18adf5c9", "a2f74289866a4391ba9617656e1b2815", "e058819128c940938f7280b40b0e46f0", "614638d1042048b0bee01ef7dd0d634a", "006b54ab474b4061b8183c5079bd7c9e", "6d8e34dabe1e4768902120eb5ad61419", "fb997b8d95b7400498618638b542b9c6", "39da7dfd9ae945a9b27721cb30d59312", "6a50e0e0ae5a4bb2b1bd04ca687dfafb", "687a3bb0deba4a4087f1408e7b014710", "2f1ea3daa8f4432e800c201c968712e6", "18b190d2dbb84a1ca2042cf9880f924d", "29d77977f83e410ca9857d73290e99cd", "d5c289a91821443e8d11dca4b5efc95f", "0517110b8a154ad89533caf45ec2e721", "6ecff5fa5da44af8abcaf501dd51bcf8", "40f4bf07388c44e6bb2f5f13502829b3", "fc9c9d10d4b045e58b88ff6f6bd6b25c", "3e4a726331364076ab7709db5a4aa302", "eec6fcd914634adfbe0691bbd212f20b", "573b410ec7324eeeab0d39caa72ec4fd", "e09a87254c1f441aa8372339e1e35747", "5a9186de66cb44a5b1c3cd9fcd5ad768", "07e1e068917e45d0837eaa4cc269605d", "4a104ac4031645868866b0ed0eab4a19", "37a5625ffc634afeaec1925ebb785e7e", "eeabd46d857d4d8296e592bbec8b4523", "fc87667c7c884b7b9ddb26817441eb44", "b2eb4ac6500249dcb96c08af77433827" ] }, "id": "kSqXs5HX7HwV", "outputId": "f0b70837-788f-40fb-948d-962992cbae9a" }, "execution_count": 2, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Loading Kromoss tokenizer...\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "tokenizer_config.json: 0%| | 0.00/1.12k [00:00 128 channels\n", "# 2. BatchNorm inverse normalization\n", "#\n", "# FINAL OUTPUT:\n", "# image_latents: [128, 64, 64] FP16\n", "# encoder_hidden_states: [sequence_length, 4096] FP16\n", "# encoder_attention_mask: [sequence_length] BOOL\n", "#\n", "# OUTPUT:\n", "# /content/drive/MyDrive/kromoss_precomputed_dataset.zip\n", "#\n", "# =============================================================================\n", "\n", "import torch\n", "import gc\n", "import os\n", "import shutil\n", "import zipfile\n", "\n", "from pathlib import Path\n", "from PIL import Image, ImageOps\n", "from tqdm.auto import tqdm\n", "from torchvision import transforms\n", "from diffusers import AutoencoderKLFlux2\n", "\n", "# =============================================================================\n", "# CONFIGURATION\n", "# =============================================================================\n", "\n", "device = torch.device(\"cuda\")\n", "\n", "# Local temporary staging area for individual files before zipping\n", "LOCAL_STAGE_DIR = Path(\"/content/kromoss_local_stage\")\n", "ZIP_OUTPUT_PATH = Path(\"/content/drive/MyDrive/kromoss_precomputed_dataset.zip\")\n", "\n", "assert TEXT_CACHE_DIR.exists(), \"Text embedding cache missing.\"\n", "assert len(pairs) > 0, \"No image-caption pairs available.\"\n", "\n", "# Prepare local clean staging area\n", "if LOCAL_STAGE_DIR.exists():\n", " shutil.rmtree(LOCAL_STAGE_DIR)\n", "LOCAL_STAGE_DIR.mkdir(parents=True, exist_ok=True)\n", "\n", "print(\"=\" * 75)\n", "print(\"KROMOSS VAE PRECOMPUTATION — FP16 STORAGE (ZIP MODE)\")\n", "print(\"=\" * 75)\n", "\n", "gc.collect()\n", "torch.cuda.empty_cache()\n", "\n", "# =============================================================================\n", "# LOAD VAE\n", "# =============================================================================\n", "\n", "print(\"Loading Kromoss VAE in FP32...\")\n", "\n", "vae = AutoencoderKLFlux2.from_pretrained(\n", " REPO_ID,\n", " subfolder=\"vae\",\n", " torch_dtype=torch.float32,\n", " token=HF_TOKEN\n", ").to(device)\n", "\n", "vae.eval()\n", "vae.requires_grad_(False)\n", "\n", "assert not vae.training\n", "assert next(vae.parameters()).dtype == torch.float32\n", "\n", "print(\"VAE loaded.\")\n", "print(\"VAE dtype:\", next(vae.parameters()).dtype)\n", "\n", "# =============================================================================\n", "# BATCH NORMALIZATION STATISTICS\n", "# =============================================================================\n", "\n", "assert hasattr(vae, \"bn\"), \"Missing VAE BatchNorm layer.\"\n", "\n", "BN_MEAN = vae.bn.running_mean.detach().to(\n", " device=device,\n", " dtype=torch.float32\n", ").view(1, -1, 1, 1)\n", "\n", "BN_VAR = vae.bn.running_var.detach().to(\n", " device=device,\n", " dtype=torch.float32\n", ").view(1, -1, 1, 1)\n", "\n", "BN_EPS = vae.config.batch_norm_eps\n", "\n", "assert BN_MEAN.shape == (1, 128, 1, 1)\n", "assert BN_VAR.shape == (1, 128, 1, 1)\n", "\n", "assert torch.all(torch.isfinite(BN_MEAN))\n", "assert torch.all(torch.isfinite(BN_VAR))\n", "assert torch.all(BN_VAR >= 0)\n", "\n", "BN_STD = torch.sqrt(BN_VAR + BN_EPS)\n", "\n", "assert torch.all(BN_STD > 0)\n", "\n", "print(\"BatchNorm verified.\")\n", "print(\"Channels:\", BN_MEAN.shape[1])\n", "print(\"Epsilon:\", BN_EPS)\n", "\n", "# =============================================================================\n", "# IMAGE TRANSFORMS\n", "# =============================================================================\n", "\n", "image_transforms = transforms.Compose([\n", " transforms.Resize(\n", " RESOLUTION,\n", " interpolation=transforms.InterpolationMode.LANCZOS\n", " ),\n", " transforms.CenterCrop(RESOLUTION),\n", " transforms.ToTensor(),\n", " transforms.Normalize(\n", " [0.5, 0.5, 0.5],\n", " [0.5, 0.5, 0.5]\n", " )\n", "])\n", "\n", "# =============================================================================\n", "# KROMOSS LATENT ENCODING\n", "# =============================================================================\n", "\n", "@torch.no_grad()\n", "def encode_kromoss_latents(pixel_values):\n", " posterior = vae.encode(pixel_values).latent_dist\n", " z = posterior.sample()\n", "\n", " assert z.ndim == 4\n", " assert z.shape[1] == 32, f\"Unexpected raw VAE channels: {z.shape}\"\n", "\n", " B, C, H, W = z.shape\n", " assert H % 2 == 0\n", " assert W % 2 == 0\n", "\n", " z = (\n", " z.reshape(B, C, H // 2, 2, W // 2, 2)\n", " .permute(0, 1, 3, 5, 2, 4)\n", " .reshape(B, C * 4, H // 2, W // 2)\n", " )\n", "\n", " assert z.shape == (B, 128, H // 2, W // 2), f\"Incorrect packed latent: {z.shape}\"\n", " z = (z - BN_MEAN) / BN_STD\n", " assert torch.isfinite(z).all(), \"NaN or Inf detected in latent tensor.\"\n", " return z\n", "\n", "# =============================================================================\n", "# LOAD TEXT CACHE\n", "# =============================================================================\n", "\n", "cached_text_files = sorted(TEXT_CACHE_DIR.glob(\"*.pt\"))\n", "\n", "assert len(cached_text_files) == len(pairs), (\n", " f\"Cache mismatch: {len(cached_text_files)} embeddings for {len(pairs)} image-caption pairs.\"\n", ")\n", "\n", "print(\"=\" * 75)\n", "print(\"DATASET VALIDATION + VAE ENCODING (STAGE LOCAL)\")\n", "print(\"=\" * 75)\n", "\n", "print(\"Image-caption pairs:\", len(pairs))\n", "print(\"Text cache files:\", len(cached_text_files))\n", "print(\"Local Staging Dir:\", LOCAL_STAGE_DIR)\n", "\n", "# =============================================================================\n", "# PROCESS DATASET\n", "# =============================================================================\n", "\n", "successful = 0\n", "failed = []\n", "\n", "for text_file in tqdm(cached_text_files, desc=\"Kromoss VAE encoding\"):\n", " output_file = LOCAL_STAGE_DIR / text_file.name\n", "\n", " try:\n", " text_data = torch.load(text_file, map_location=\"cpu\", weights_only=False)\n", " img_path = Path(text_data[\"img\"])\n", " caption = text_data[\"caption\"]\n", " embedding = text_data[\"emb\"].squeeze(0)\n", " attention_mask = text_data[\"mask\"].squeeze(0).bool()\n", "\n", " assert embedding.ndim == 2\n", " assert embedding.shape[-1] == 4096, f\"Incorrect text dimension: {embedding.shape}\"\n", " assert embedding.shape[0] == attention_mask.shape[0]\n", " assert embedding.dtype == torch.float16\n", " assert torch.isfinite(embedding).all()\n", " assert img_path.exists(), f\"Missing image: {img_path}\"\n", "\n", " with Image.open(img_path) as source:\n", " img = ImageOps.exif_transpose(source).convert(\"RGB\")\n", " pixel_values = image_transforms(img).unsqueeze(0).to(device=device, dtype=torch.float32)\n", "\n", " assert pixel_values.shape == (1, 3, RESOLUTION, RESOLUTION)\n", "\n", " image_latents = encode_kromoss_latents(pixel_values)\n", " assert image_latents.shape == (1, 128, 64, 64)\n", "\n", " image_latents_fp16 = image_latents.squeeze(0).to(torch.float16).cpu().contiguous()\n", " embedding_fp16 = embedding.to(torch.float16).cpu().contiguous()\n", "\n", " assert image_latents_fp16.shape == (128, 64, 64)\n", " assert image_latents_fp16.dtype == torch.float16\n", " assert embedding_fp16.dtype == torch.float16\n", " assert torch.isfinite(image_latents_fp16).all()\n", " assert torch.isfinite(embedding_fp16).all()\n", "\n", " precomputed_sample = {\n", " \"image_latents\": image_latents_fp16,\n", " \"encoder_hidden_states\": embedding_fp16,\n", " \"encoder_attention_mask\": attention_mask.cpu().contiguous(),\n", " \"caption\": caption\n", " }\n", "\n", " temp_file = output_file.with_suffix(\".tmp\")\n", " torch.save(precomputed_sample, temp_file)\n", " os.replace(temp_file, output_file)\n", " successful += 1\n", "\n", " del text_data\n", " del precomputed_sample\n", " del image_latents\n", " del image_latents_fp16\n", " del embedding\n", " del embedding_fp16\n", " del attention_mask\n", " del pixel_values\n", "\n", " if successful % 20 == 0:\n", " gc.collect()\n", " torch.cuda.empty_cache()\n", "\n", " except Exception as e:\n", " failed.append((text_file.name, repr(e)))\n", " print(f\"\\nERROR processing {text_file.name}: {e}\")\n", " gc.collect()\n", " torch.cuda.empty_cache()\n", "\n", "# =============================================================================\n", "# RELEASE VAE\n", "# =============================================================================\n", "\n", "print(\"\\nUnloading VAE...\")\n", "\n", "del vae\n", "del BN_MEAN\n", "del BN_VAR\n", "del BN_STD\n", "\n", "gc.collect()\n", "torch.cuda.empty_cache()\n", "torch.cuda.ipc_collect()\n", "\n", "# =============================================================================\n", "# ZIP AND SAVE TO DRIVE\n", "# =============================================================================\n", "\n", "if not failed and successful == len(pairs):\n", " print(f\"\\nZipping {successful} files to {ZIP_OUTPUT_PATH}...\")\n", " ZIP_OUTPUT_PATH.parent.mkdir(parents=True, exist_ok=True)\n", "\n", " with zipfile.ZipFile(ZIP_OUTPUT_PATH, \"w\", zipfile.ZIP_DEFLATED) as zipf:\n", " for file_path in sorted(LOCAL_STAGE_DIR.glob(\"*.pt\")):\n", " zipf.write(file_path, arcname=file_path.name)\n", "\n", " print(\"Zipping finished. Cleaning up temporary stage...\")\n", " shutil.rmtree(LOCAL_STAGE_DIR)\n", "else:\n", " print(\"\\nWARNING: Encoding failed or was incomplete. Skipping ZIP creation.\")\n", "\n", "# =============================================================================\n", "# FINAL REPORT\n", "# =============================================================================\n", "\n", "print(\"\\n\" + \"=\" * 75)\n", "print(\"KROMOSS PRECOMPUTATION COMPLETE\")\n", "print(\"=\" * 75)\n", "\n", "print(\"Successfully processed:\", successful)\n", "print(\"Failed:\", len(failed))\n", "\n", "if ZIP_OUTPUT_PATH.exists():\n", " print(f\"Output ZIP file: {ZIP_OUTPUT_PATH} ({round(ZIP_OUTPUT_PATH.stat().st_size / 1024**2, 2)} MB)\")\n", "else:\n", " print(\"Output ZIP file not generated due to errors.\")\n", "\n", "print(\"VRAM remaining:\", round(torch.cuda.memory_allocated() / 1024**3, 2), \"GB\")\n", "\n", "if failed:\n", " print(\"\\nFailed samples:\")\n", " for name, error in failed:\n", " print(name, \":\", error)\n", "print(\"=\" * 75)\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 686, "referenced_widgets": [ "6112daf526d34bcca41ba8cefe1f0de3", "48e2697ca2d34d48bae47672b389fdec", "07e251ff5ddd46eab2a18c619af91d5a", "188f7e141b0b433189ab7102b98ecf07", "4015365ed60c447b9d5dda572cd0236d", "f6fc26364d8c4dc3a5210b471813f9f2", "54778a40879f478c905902129f4bde86", "414256ea493c4e03af23961d4bcdb025", "97431ff874f84f6698aff0cb882439a5", "d92ac3655ab04abc83e99398707e608a", "bfb8dbb7b8cf4894b8f754943586ff0e", "b34671dd082a429d8af9bad8652642a7", "6733d280ec694bdda16575e169c3d2f0", "e15f4b7af5d64c1bbff6ed149048ca4a", "2747d8dcc90b4e2bb9e45eb50e9afe61", "5202ce7e204d4d4ca6b24e88aa16d062", "627024a7f2a74eedb3ff8c393a3118a1", "b8cb2d2368a94d47a282edf18185e120", "b11b41dfc0744480a9d99a5edb3d3ba5", "d56fb8f4b20b42ee933d46c072d07545", "df0f90a5c89d4745819082735e19d8ca", "eb3db520a457416183ee865f060c2e8a", "ffcd6318e25449abba3af2476c166232", "c24b175a3f8146f7946e4ad9c1357a89", "3bfd811608a24c7296ea1ddaa0a9ab5d", "52d98658965148feaa054a7c42920aac", "33f6c545d57743e2a90037e3e9c7ec25", "46466192dfa04ca48bd11cec386c2f1d", "5b2887c3d9544619a0b82fce8a7511d8", "01cbfbac4a314e0489e7e75877c0f5ac", "801cce96b0254aa9b281b845d8d4bcf9", "d665c3fd51874781a21115e421629a7b", "231ac161d3004494a784baaaab007f1f", "768fdc296e5e493999b0fd4be687030a", "abfb516d15f9408db9ab625a92851289", "8c413116974546089da5cc84e5d0a6ca", "33f4f617a9d643c083cedd33f4ec3487", "3c95fc33a11948a0aac904529e51b3a8", "32527b6226584beead4b408d769e2041", "908b3f090d33469d93407b6ba5a69583", "096454b99c3e494793c745564855eb0f", "a2fab398405a4bc4884900a293d2f2aa", "a1aa271def384aaf9353777ec2c214fa", "cccd3d7676794e98af3f6b0f78a64cb7" ] }, "id": "GP4EQlYi-I-G", "outputId": "bfff0f95-4be1-43f6-ff15-56c3bb69b6e1" }, "execution_count": 3, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "===========================================================================\n", "KROMOSS VAE PRECOMPUTATION — FP16 STORAGE (ZIP MODE)\n", "===========================================================================\n", "Loading Kromoss VAE in FP32...\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "config.json: 0%| | 0.00/838 [00:00 min: {latent_means.min().item():.6f} | max: {latent_means.max().item():.6f} | mean: {latent_means.mean().item():.6f}\")\n", " print(f\"LATENT STD DATASET -> min: {latent_stds.min().item():.6f} | max: {latent_stds.max().item():.6f} | mean: {latent_stds.mean().item():.6f}\")\n", " print(f\"EMBEDDING STD DATASET -> min: {embedding_stds.min().item():.6f} | max: {embedding_stds.max().item():.6f} | mean: {embedding_stds.mean().item():.6f}\")\n", "\n", "# =============================================================================\n", "# FINAL SUMMARY\n", "# =============================================================================\n", "\n", "print(\"\\n\" + \"=\" * 95)\n", "print(\"VALIDATION SUMMARY\")\n", "print(\"=\" * 95)\n", "print(\"Total files checked inside ZIP:\", len(files))\n", "print(\"Successful:\", len(files) - len(errors))\n", "print(\"Failed:\", len(errors))\n", "\n", "if errors:\n", " print(\"\\nFAILED FILES:\")\n", " for name, reason in errors:\n", " print(name, \":\", reason)\n", " raise RuntimeError(f\"Dataset validation failed for {len(errors)} samples inside ZIP.\")\n", "\n", "print(\"\\nALL SAMPLES PASSED STRUCTURAL VALIDATION DIRECTLY INSIDE ZIP.\")\n", "print(\"=\" * 95)\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "7gxdlE5XCpJS", "outputId": "b808dcb0-523c-4af6-fada-82613dadd07c" }, "execution_count": 7, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "===============================================================================================\n", "KROMOSS PRECOMPUTED DATASET — STRONG ZIP-CONTAINER VALIDATION\n", "===============================================================================================\n", "Archive Path: /content/drive/MyDrive/kromoss_precomputed_dataset.zip\n", "Total samples in ZIP: 7\n", "Expected samples: 7\n", "===============================================================================================\n", "\n", "SAMPLE 1/7\n", "-----------------------------------------------------------------------------------------------\n", "Archive Member: 00000000.pt\n", "Latents: (128, 64, 64) | torch.float16\n", "Text embeddings: (586, 4096) | torch.float16\n", "Attention mask: (586,) | torch.bool\n", "Valid text tokens: 586 / 586\n", " latent global mean = -0.042176\n", " latent global std = 0.963638\n", " channel mean range = [-0.725479, 0.470207]\n", " emb global mean = 0.010543\n", "Status: PASSED\n", "\n", "SAMPLE 2/7\n", "-----------------------------------------------------------------------------------------------\n", "Archive Member: 00000001.pt\n", "Latents: (128, 64, 64) | torch.float16\n", "Text embeddings: (547, 4096) | torch.float16\n", "Attention mask: (547,) | torch.bool\n", "Valid text tokens: 547 / 547\n", " latent global mean = -0.082856\n", " latent global std = 0.963638\n", " channel mean range = [-0.652561, 0.600844]\n", " emb global mean = 0.011204\n", "Status: PASSED\n", "\n", "SAMPLE 3/7\n", "-----------------------------------------------------------------------------------------------\n", "Archive Member: 00000002.pt\n", "Latents: (128, 64, 64) | torch.float16\n", "Text embeddings: (594, 4096) | torch.float16\n", "Attention mask: (594,) | torch.bool\n", "Valid text tokens: 594 / 594\n", " latent global mean = -0.031509\n", " latent global std = 0.939163\n", " channel mean range = [-0.728995, 0.676631]\n", " emb global mean = 0.012614\n", "Status: PASSED\n", "\n", "SAMPLE 4/7\n", "-----------------------------------------------------------------------------------------------\n", "Archive Member: 00000003.pt\n", "Latents: (128, 64, 64) | torch.float16\n", "Text embeddings: (632, 4096) | torch.float16\n", "Attention mask: (632,) | torch.bool\n", "Valid text tokens: 632 / 632\n", " latent global mean = -0.058849\n", " latent global std = 0.938984\n", " channel mean range = [-0.650008, 0.402342]\n", " emb global mean = 0.014512\n", "Status: PASSED\n", "\n", "SAMPLE 5/7\n", "-----------------------------------------------------------------------------------------------\n", "Archive Member: 00000004.pt\n", "Latents: (128, 64, 64) | torch.float16\n", "Text embeddings: (610, 4096) | torch.float16\n", "Attention mask: (610,) | torch.bool\n", "Valid text tokens: 610 / 610\n", " latent global mean = -0.017558\n", " latent global std = 0.975084\n", " channel mean range = [-0.631518, 0.658803]\n", " emb global mean = 0.013341\n", "Status: PASSED\n", "\n", "SAMPLE 6/7\n", "-----------------------------------------------------------------------------------------------\n", "Archive Member: 00000005.pt\n", "Latents: (128, 64, 64) | torch.float16\n", "Text embeddings: (654, 4096) | torch.float16\n", "Attention mask: (654,) | torch.bool\n", "Valid text tokens: 654 / 654\n", " latent global mean = -0.003240\n", " latent global std = 0.970846\n", " channel mean range = [-0.438728, 0.612600]\n", " emb global mean = 0.012943\n", "Status: PASSED\n", "\n", "SAMPLE 7/7\n", "-----------------------------------------------------------------------------------------------\n", "Archive Member: 00000006.pt\n", "Latents: (128, 64, 64) | torch.float16\n", "Text embeddings: (622, 4096) | torch.float16\n", "Attention mask: (622,) | torch.bool\n", "Valid text tokens: 622 / 622\n", " latent global mean = -0.054485\n", " latent global std = 0.951218\n", " channel mean range = [-0.913750, 0.496397]\n", " emb global mean = 0.010087\n", "Status: PASSED\n", "\n", "===============================================================================================\n", "CROSS-SAMPLE STATISTICAL ANALYSIS\n", "===============================================================================================\n", "LATENT MEAN DATASET -> min: -0.082856 | max: -0.003240 | mean: -0.041525\n", "LATENT STD DATASET -> min: 0.938984 | max: 0.975084 | mean: 0.957510\n", "EMBEDDING STD DATASET -> min: 1.946023 | max: 1.962517 | mean: 1.955081\n", "\n", "===============================================================================================\n", "VALIDATION SUMMARY\n", "===============================================================================================\n", "Total files checked inside ZIP: 7\n", "Successful: 7\n", "Failed: 0\n", "\n", "ALL SAMPLES PASSED STRUCTURAL VALIDATION DIRECTLY INSIDE ZIP.\n", "===============================================================================================\n" ] } ] }, { "cell_type": "code", "source": [], "metadata": { "id": "r7ywE342Cr7X" }, "execution_count": null, "outputs": [] } ] }