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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "4b23e3a6-544b-4e8c-b37c-70d8d5f04e46",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Looking in indexes: https://download.pytorch.org/whl/cu121\n",
"Collecting torch\n",
" Downloading https://download.pytorch.org/whl/cu121/torch-2.5.1%2Bcu121-cp312-cp312-linux_x86_64.whl (780.4 MB)\n",
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"Installing collected packages: typing-inspection, smmap, sentry-sdk, pydantic-core, click, annotated-types, pydantic, gitdb, gitpython, wandb\n",
"Successfully installed annotated-types-0.7.0 click-8.2.1 gitdb-4.0.12 gitpython-3.1.44 pydantic-2.11.7 pydantic-core-2.33.2 sentry-sdk-2.33.0 smmap-5.0.2 typing-inspection-0.4.1 wandb-0.21.0\n",
"\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
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"\u001b[?25hInstalling collected packages: numpy, lazy-loader, tifffile, scipy, opencv-python, imageio, scikit-image\n",
" Attempting uninstall: numpy\n",
" Found existing installation: numpy 2.3.1\n",
" Uninstalling numpy-2.3.1:\n",
" Successfully uninstalled numpy-2.3.1\n",
"Successfully installed imageio-2.37.0 lazy-loader-0.4 numpy-2.2.6 opencv-python-4.12.0.88 scikit-image-0.25.2 scipy-1.16.0 tifffile-2025.6.11\n",
"\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
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"\u001b[0m"
]
}
],
"source": [
"# Install required packages\n",
"!pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121\n",
"!pip install diffusers transformers accelerate\n",
"!pip install datasets pillow matplotlib tqdm\n",
"!pip install wandb tensorboard\n",
"!pip install opencv-python scikit-image\n",
"!pip install einops"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "a66917c9-173b-4547-baa6-56f303707025",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Filesystem Size Used Avail Use% Mounted on\n",
"overlay 131G 8.9G 123G 7% /\n",
"tmpfs 64M 0 64M 0% /dev\n",
"shm 15G 0 15G 0% /dev/shm\n",
"/dev/loop6 1.7T 386G 1.3T 24% /etc/hosts\n",
"/dev/nvme0n1p2 1.9T 562G 1.2T 32% /usr/bin/nvidia-smi\n",
"tmpfs 13G 2.9M 13G 1% /run/nvidia-persistenced/socket\n",
"tmpfs 63G 0 63G 0% /sys/fs/cgroup\n",
"tmpfs 63G 0 63G 0% /proc/asound\n",
"tmpfs 63G 0 63G 0% /proc/acpi\n",
"tmpfs 63G 0 63G 0% /proc/scsi\n",
"tmpfs 63G 0 63G 0% /sys/firmware\n",
"tmpfs 63G 0 63G 0% /sys/devices/virtual/powercap\n"
]
}
],
"source": [
"!df -h"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "88aec5bc-9914-44ee-8ab9-4b46378ee61a",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Using device: cuda\n",
"GPU: NVIDIA GeForce RTX 3060\n",
"CUDA Version: 12.1\n",
"Available VRAM: 11.66 GB\n",
"Current VRAM usage: 0.00 GB\n"
]
}
],
"source": [
"import torch\n",
"import torch.nn as nn\n",
"import torch.nn.functional as F\n",
"from torch.utils.data import DataLoader, Dataset\n",
"import torchvision.transforms as transforms\n",
"from torchvision import datasets\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from tqdm import tqdm\n",
"import os\n",
"import math\n",
"from PIL import Image\n",
"import random\n",
"\n",
"# Check GPU setup\n",
"device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
"print(f\"Using device: {device}\")\n",
"\n",
"if torch.cuda.is_available():\n",
" print(f\"GPU: {torch.cuda.get_device_name(0)}\")\n",
" print(f\"CUDA Version: {torch.version.cuda}\")\n",
" print(f\"Available VRAM: {torch.cuda.get_device_properties(0).total_memory / 1024**3:.2f} GB\")\n",
" print(f\"Current VRAM usage: {torch.cuda.memory_allocated(0) / 1024**3:.2f} GB\")\n",
"\n",
"# Set random seeds for reproducibility\n",
"torch.manual_seed(42)\n",
"np.random.seed(42)\n",
"random.seed(42)\n",
"if torch.cuda.is_available():\n",
" torch.cuda.manual_seed(42)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "bea9f729-e07e-4e17-996b-608fb7aff7af",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Package Version\n",
"------------------------- --------------\n",
"absl-py 2.3.0\n",
"accelerate 1.9.0\n",
"aiohappyeyeballs 2.6.1\n",
"aiohttp 3.12.14\n",
"aiosignal 1.4.0\n",
"annotated-types 0.7.0\n",
"anyio 4.9.0\n",
"argon2-cffi 25.1.0\n",
"argon2-cffi-bindings 21.2.0\n",
"arrow 1.3.0\n",
"asttokens 3.0.0\n",
"async-lru 2.0.5\n",
"attrs 25.3.0\n",
"babel 2.17.0\n",
"bash_kernel 0.10.0\n",
"beautifulsoup4 4.13.4\n",
"bleach 6.2.0\n",
"blinker 1.7.0\n",
"certifi 2025.6.15\n",
"cffi 1.17.1\n",
"charset-normalizer 3.4.2\n",
"click 8.2.1\n",
"comm 0.2.2\n",
"conda-pack 0.8.1\n",
"contourpy 1.3.2\n",
"cryptography 41.0.7\n",
"cycler 0.12.1\n",
"datasets 4.0.0\n",
"dbus-python 1.3.2\n",
"debugpy 1.8.14\n",
"decorator 5.2.1\n",
"defusedxml 0.7.1\n",
"diffusers 0.34.0\n",
"dill 0.3.8\n",
"distro 1.9.0\n",
"einops 0.8.1\n",
"executing 2.2.0\n",
"fastjsonschema 2.21.1\n",
"filelock 3.13.1\n",
"filetype 1.2.0\n",
"fonttools 4.59.0\n",
"fqdn 1.5.1\n",
"frozenlist 1.7.0\n",
"fsspec 2024.6.1\n",
"gitdb 4.0.12\n",
"GitPython 3.1.44\n",
"grpcio 1.73.1\n",
"h11 0.16.0\n",
"hf-xet 1.1.5\n",
"httpcore 1.0.9\n",
"httplib2 0.20.4\n",
"httpx 0.28.1\n",
"huggingface-hub 0.33.4\n",
"idna 3.10\n",
"imageio 2.37.0\n",
"importlib_metadata 8.7.0\n",
"iniconfig 2.1.0\n",
"iotop 0.6\n",
"ipykernel 6.29.5\n",
"ipython 9.3.0\n",
"ipython_pygments_lexers 1.1.1\n",
"ipywidgets 8.1.7\n",
"isoduration 20.11.0\n",
"jedi 0.19.2\n",
"Jinja2 3.1.6\n",
"json5 0.12.0\n",
"jsonpointer 3.0.0\n",
"jsonschema 4.24.0\n",
"jsonschema-specifications 2025.4.1\n",
"jupyter 1.1.1\n",
"jupyter-archive 3.4.0\n",
"jupyter_client 8.6.3\n",
"jupyter-console 6.6.3\n",
"jupyter_core 5.8.1\n",
"jupyter-events 0.12.0\n",
"jupyter-http-over-ws 0.0.8\n",
"jupyter-lsp 2.2.5\n",
"jupyter_server 2.16.0\n",
"jupyter_server_terminals 0.5.3\n",
"jupyterlab 4.4.4\n",
"jupyterlab_pygments 0.3.0\n",
"jupyterlab_server 2.27.3\n",
"jupyterlab_widgets 3.0.15\n",
"kiwisolver 1.4.8\n",
"launchpadlib 1.11.0\n",
"lazr.restfulclient 0.14.6\n",
"lazr.uri 1.0.6\n",
"lazy_loader 0.4\n",
"Markdown 3.8.2\n",
"MarkupSafe 3.0.2\n",
"matplotlib 3.10.3\n",
"matplotlib-inline 0.1.7\n",
"mistune 3.1.3\n",
"mpmath 1.3.0\n",
"multidict 6.6.3\n",
"multiprocess 0.70.16\n",
"nbclient 0.10.2\n",
"nbconvert 7.16.6\n",
"nbformat 5.10.4\n",
"nbzip 0.1.0\n",
"nest-asyncio 1.6.0\n",
"networkx 3.3\n",
"notebook 7.4.3\n",
"notebook_shim 0.2.4\n",
"numpy 2.2.6\n",
"nvidia-cublas-cu12 12.1.3.1\n",
"nvidia-cuda-cupti-cu12 12.1.105\n",
"nvidia-cuda-nvrtc-cu12 12.1.105\n",
"nvidia-cuda-runtime-cu12 12.1.105\n",
"nvidia-cudnn-cu12 9.1.0.70\n",
"nvidia-cufft-cu12 11.0.2.54\n",
"nvidia-curand-cu12 10.3.2.106\n",
"nvidia-cusolver-cu12 11.4.5.107\n",
"nvidia-cusparse-cu12 12.1.0.106\n",
"nvidia-nccl-cu12 2.21.5\n",
"nvidia-nvjitlink-cu12 12.1.105\n",
"nvidia-nvtx-cu12 12.1.105\n",
"oauthlib 3.2.2\n",
"opencv-python 4.12.0.88\n",
"overrides 7.7.0\n",
"packaging 25.0\n",
"pandas 2.3.1\n",
"pandocfilters 1.5.1\n",
"parso 0.8.4\n",
"pexpect 4.9.0\n",
"pillow 11.0.0\n",
"pip 24.0\n",
"platformdirs 4.3.8\n",
"pluggy 1.6.0\n",
"prometheus_client 0.22.1\n",
"prompt_toolkit 3.0.51\n",
"propcache 0.3.2\n",
"protobuf 6.31.1\n",
"psutil 7.0.0\n",
"ptyprocess 0.7.0\n",
"pure_eval 0.2.3\n",
"pyarrow 21.0.0\n",
"pycparser 2.22\n",
"pydantic 2.11.7\n",
"pydantic_core 2.33.2\n",
"Pygments 2.19.2\n",
"PyGObject 3.48.2\n",
"PyJWT 2.7.0\n",
"pyparsing 3.1.1\n",
"pytest 8.4.1\n",
"python-apt 2.7.7+ubuntu4\n",
"python-dateutil 2.9.0.post0\n",
"python-json-logger 3.3.0\n",
"pytz 2025.2\n",
"PyYAML 6.0.2\n",
"pyzmq 27.0.0\n",
"referencing 0.36.2\n",
"regex 2024.11.6\n",
"requests 2.32.4\n",
"rfc3339-validator 0.1.4\n",
"rfc3986-validator 0.1.1\n",
"rpds-py 0.25.1\n",
"safetensors 0.5.3\n",
"scikit-image 0.25.2\n",
"scipy 1.16.0\n",
"Send2Trash 1.8.3\n",
"sentry-sdk 2.33.0\n",
"setuptools 68.1.2\n",
"six 1.16.0\n",
"smmap 5.0.2\n",
"sniffio 1.3.1\n",
"soupsieve 2.7\n",
"stack-data 0.6.3\n",
"supervisor 4.2.5\n",
"sympy 1.13.1\n",
"tensorboard 2.19.0\n",
"tensorboard-data-server 0.7.2\n",
"terminado 0.18.1\n",
"tifffile 2025.6.11\n",
"tinycss2 1.4.0\n",
"tokenizers 0.21.2\n",
"torch 2.5.1+cu121\n",
"torchaudio 2.5.1+cu121\n",
"torchvision 0.20.1+cu121\n",
"tornado 6.5.1\n",
"tqdm 4.67.1\n",
"traitlets 5.14.3\n",
"transformers 4.53.2\n",
"triton 3.1.0\n",
"types-python-dateutil 2.9.0.20250516\n",
"typing_extensions 4.14.0\n",
"typing-inspection 0.4.1\n",
"tzdata 2025.2\n",
"uri-template 1.3.0\n",
"urllib3 2.5.0\n",
"uv 0.7.16\n",
"wadllib 1.3.6\n",
"wandb 0.21.0\n",
"wcwidth 0.2.13\n",
"webcolors 24.11.1\n",
"webencodings 0.5.1\n",
"websocket-client 1.8.0\n",
"Werkzeug 3.1.3\n",
"wheel 0.42.0\n",
"widgetsnbextension 4.0.14\n",
"xxhash 3.5.0\n",
"yarl 1.20.1\n",
"zipp 3.23.0\n"
]
}
],
"source": [
"!pip list"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "aeb74a18-747e-40ef-8229-0de619fa3859",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Downloading CIFAR-10 dataset...\n",
"Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./data/cifar-10-python.tar.gz\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 170M/170M [00:14<00:00, 11.9MB/s] \n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Extracting ./data/cifar-10-python.tar.gz to ./data\n",
"Dataset downloaded successfully!\n",
"Number of training images: 50000\n",
"Image shape: torch.Size([3, 32, 32])\n",
"Classes: ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n",
"Batch size: 128\n",
"Number of batches: 391\n"
]
},
{
"data": {
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"text/plain": [
"<Figure size 1200x600 with 8 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Step 3: Download and prepare CIFAR-10 dataset\n",
"from torchvision import datasets, transforms\n",
"\n",
"# Define data transforms\n",
"transform = transforms.Compose([\n",
" transforms.Resize(32), # CIFAR-10 is 32x32\n",
" transforms.ToTensor(),\n",
" transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)) # Normalize to [-1, 1]\n",
"])\n",
"\n",
"# Download CIFAR-10 dataset\n",
"print(\"Downloading CIFAR-10 dataset...\")\n",
"train_dataset = datasets.CIFAR10(\n",
" root='./data', \n",
" train=True, \n",
" download=True, \n",
" transform=transform\n",
")\n",
"\n",
"print(f\"Dataset downloaded successfully!\")\n",
"print(f\"Number of training images: {len(train_dataset)}\")\n",
"print(f\"Image shape: {train_dataset[0][0].shape}\")\n",
"print(f\"Classes: {train_dataset.classes}\")\n",
"\n",
"# Create dataloader\n",
"batch_size = 128 # Good for your 12GB VRAM\n",
"train_loader = DataLoader(\n",
" train_dataset, \n",
" batch_size=batch_size, \n",
" shuffle=True, \n",
" num_workers=4,\n",
" pin_memory=True\n",
")\n",
"\n",
"print(f\"Batch size: {batch_size}\")\n",
"print(f\"Number of batches: {len(train_loader)}\")\n",
"\n",
"# Visualize some samples\n",
"def show_samples(dataset, num_samples=8):\n",
" fig, axes = plt.subplots(2, 4, figsize=(12, 6))\n",
" for i in range(num_samples):\n",
" img, label = dataset[i]\n",
" # Convert from [-1, 1] to [0, 1] for display\n",
" img = (img + 1) / 2\n",
" img = img.permute(1, 2, 0)\n",
" \n",
" row, col = i // 4, i % 4\n",
" axes[row, col].imshow(img)\n",
" axes[row, col].set_title(f'Class: {dataset.classes[label]}')\n",
" axes[row, col].axis('off')\n",
" \n",
" plt.tight_layout()\n",
" plt.show()\n",
"\n",
"show_samples(train_dataset)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "2a4d4d2c-8119-41a3-be04-22d98bbff38a",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Diffusion model components defined successfully!\n",
"Next: U-Net architecture\n"
]
}
],
"source": [
"# Step 4: Define the U-Net architecture for diffusion model\n",
"\n",
"class TimeEmbedding(nn.Module):\n",
" def __init__(self, dim):\n",
" super().__init__()\n",
" self.dim = dim\n",
" \n",
" def forward(self, time):\n",
" device = time.device\n",
" half_dim = self.dim // 2\n",
" embeddings = math.log(10000) / (half_dim - 1)\n",
" embeddings = torch.exp(torch.arange(half_dim, device=device) * -embeddings)\n",
" embeddings = time[:, None] * embeddings[None, :]\n",
" embeddings = torch.cat((embeddings.sin(), embeddings.cos()), dim=-1)\n",
" return embeddings\n",
"\n",
"class ResidualBlock(nn.Module):\n",
" def __init__(self, in_channels, out_channels, time_emb_dim, dropout=0.1):\n",
" super().__init__()\n",
" self.time_mlp = nn.Linear(time_emb_dim, out_channels)\n",
" \n",
" self.block1 = nn.Sequential(\n",
" nn.GroupNorm(8, in_channels),\n",
" nn.SiLU(),\n",
" nn.Conv2d(in_channels, out_channels, 3, padding=1),\n",
" )\n",
" \n",
" self.block2 = nn.Sequential(\n",
" nn.GroupNorm(8, out_channels),\n",
" nn.SiLU(),\n",
" nn.Dropout(dropout),\n",
" nn.Conv2d(out_channels, out_channels, 3, padding=1),\n",
" )\n",
" \n",
" if in_channels != out_channels:\n",
" self.shortcut = nn.Conv2d(in_channels, out_channels, 1)\n",
" else:\n",
" self.shortcut = nn.Identity()\n",
" \n",
" def forward(self, x, time_emb):\n",
" h = self.block1(x)\n",
" time_emb = self.time_mlp(time_emb)\n",
" h = h + time_emb[:, :, None, None]\n",
" h = self.block2(h)\n",
" return h + self.shortcut(x)\n",
"\n",
"class AttentionBlock(nn.Module):\n",
" def __init__(self, channels):\n",
" super().__init__()\n",
" self.channels = channels\n",
" self.group_norm = nn.GroupNorm(8, channels)\n",
" self.q = nn.Conv2d(channels, channels, 1)\n",
" self.k = nn.Conv2d(channels, channels, 1)\n",
" self.v = nn.Conv2d(channels, channels, 1)\n",
" self.proj_out = nn.Conv2d(channels, channels, 1)\n",
" \n",
" def forward(self, x):\n",
" B, C, H, W = x.shape\n",
" h = self.group_norm(x)\n",
" q = self.q(h)\n",
" k = self.k(h)\n",
" v = self.v(h)\n",
" \n",
" q = q.reshape(B, C, H*W).permute(0, 2, 1)\n",
" k = k.reshape(B, C, H*W)\n",
" v = v.reshape(B, C, H*W).permute(0, 2, 1)\n",
" \n",
" attn = torch.bmm(q, k) * (int(C) ** (-0.5))\n",
" attn = F.softmax(attn, dim=2)\n",
" \n",
" h = torch.bmm(attn, v)\n",
" h = h.permute(0, 2, 1).reshape(B, C, H, W)\n",
" h = self.proj_out(h)\n",
" \n",
" return x + h\n",
"\n",
"print(\"Diffusion model components defined successfully!\")\n",
"print(\"Next: U-Net architecture\")"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "1ea769ea-8e47-47fb-add3-f4139afcc5b9",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Model created with 16,808,835 parameters\n",
"Test input shape: torch.Size([4, 3, 32, 32])\n",
"Test output shape: torch.Size([4, 3, 32, 32])\n",
"Model forward pass successful!\n",
"VRAM usage after model creation: 0.22 GB\n"
]
}
],
"source": [
"# Step 5: Simplified Working U-Net Model\n",
"\n",
"class SimpleUNet(nn.Module):\n",
" def __init__(self, in_channels=3, out_channels=3, time_emb_dim=128):\n",
" super().__init__()\n",
" \n",
" # Time embedding\n",
" self.time_embedding = TimeEmbedding(time_emb_dim)\n",
" self.time_mlp = nn.Sequential(\n",
" nn.Linear(time_emb_dim, time_emb_dim * 4),\n",
" nn.SiLU(),\n",
" nn.Linear(time_emb_dim * 4, time_emb_dim * 4),\n",
" )\n",
" \n",
" # Encoder\n",
" self.conv1 = nn.Conv2d(in_channels, 64, 3, padding=1)\n",
" self.res1 = ResidualBlock(64, 64, time_emb_dim * 4)\n",
" self.down1 = nn.Conv2d(64, 64, 3, stride=2, padding=1) # 32->16\n",
" \n",
" self.res2 = ResidualBlock(64, 128, time_emb_dim * 4)\n",
" self.down2 = nn.Conv2d(128, 128, 3, stride=2, padding=1) # 16->8\n",
" \n",
" self.res3 = ResidualBlock(128, 256, time_emb_dim * 4)\n",
" self.down3 = nn.Conv2d(256, 256, 3, stride=2, padding=1) # 8->4\n",
" \n",
" # Middle\n",
" self.mid1 = ResidualBlock(256, 512, time_emb_dim * 4)\n",
" self.mid_attn = AttentionBlock(512)\n",
" self.mid2 = ResidualBlock(512, 512, time_emb_dim * 4)\n",
" \n",
" # Decoder\n",
" self.up3 = nn.ConvTranspose2d(512, 256, 3, stride=2, padding=1, output_padding=1) # 4->8\n",
" self.res_up3 = ResidualBlock(256 + 256, 256, time_emb_dim * 4)\n",
" \n",
" self.up2 = nn.ConvTranspose2d(256, 128, 3, stride=2, padding=1, output_padding=1) # 8->16\n",
" self.res_up2 = ResidualBlock(128 + 128, 128, time_emb_dim * 4)\n",
" \n",
" self.up1 = nn.ConvTranspose2d(128, 64, 3, stride=2, padding=1, output_padding=1) # 16->32\n",
" self.res_up1 = ResidualBlock(64 + 64, 64, time_emb_dim * 4)\n",
" \n",
" # Output\n",
" self.output = nn.Sequential(\n",
" nn.GroupNorm(8, 64),\n",
" nn.SiLU(),\n",
" nn.Conv2d(64, out_channels, 3, padding=1),\n",
" )\n",
" \n",
" def forward(self, x, time):\n",
" # Time embedding\n",
" time_emb = self.time_embedding(time)\n",
" time_emb = self.time_mlp(time_emb)\n",
" \n",
" # Encoder\n",
" x1 = self.conv1(x)\n",
" x1 = self.res1(x1, time_emb)\n",
" \n",
" x2 = self.down1(x1)\n",
" x2 = self.res2(x2, time_emb)\n",
" \n",
" x3 = self.down2(x2)\n",
" x3 = self.res3(x3, time_emb)\n",
" \n",
" x4 = self.down3(x3)\n",
" \n",
" # Middle\n",
" x4 = self.mid1(x4, time_emb)\n",
" x4 = self.mid_attn(x4)\n",
" x4 = self.mid2(x4, time_emb)\n",
" \n",
" # Decoder\n",
" x = self.up3(x4)\n",
" x = torch.cat([x, x3], dim=1)\n",
" x = self.res_up3(x, time_emb)\n",
" \n",
" x = self.up2(x)\n",
" x = torch.cat([x, x2], dim=1)\n",
" x = self.res_up2(x, time_emb)\n",
" \n",
" x = self.up1(x)\n",
" x = torch.cat([x, x1], dim=1)\n",
" x = self.res_up1(x, time_emb)\n",
" \n",
" return self.output(x)\n",
"\n",
"# Initialize model\n",
"model = SimpleUNet().to(device)\n",
"print(f\"Model created with {sum(p.numel() for p in model.parameters()):,} parameters\")\n",
"\n",
"# Test model forward pass\n",
"with torch.no_grad():\n",
" test_x = torch.randn(4, 3, 32, 32).to(device)\n",
" test_t = torch.randint(0, 1000, (4,)).to(device)\n",
" test_output = model(test_x, test_t)\n",
" print(f\"Test input shape: {test_x.shape}\")\n",
" print(f\"Test output shape: {test_output.shape}\")\n",
" print(\"Model forward pass successful!\")\n",
" \n",
"# Check memory usage\n",
"print(f\"VRAM usage after model creation: {torch.cuda.memory_allocated(0) / 1024**3:.2f} GB\")"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "52a6b46c-f17e-426c-80f5-1042f5529e44",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Diffusion scheduler created successfully!\n"
]
},
{
"data": {
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"text/plain": [
"<Figure size 1200x600 with 8 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Forward diffusion process test completed!\n"
]
}
],
"source": [
"# Step 6: Define the Diffusion Process (Fixed)\n",
"\n",
"class DDPMScheduler:\n",
" def __init__(self, num_timesteps=1000, beta_start=0.0001, beta_end=0.02, device='cuda'):\n",
" self.num_timesteps = num_timesteps\n",
" self.device = device\n",
" \n",
" # Linear beta schedule\n",
" self.betas = torch.linspace(beta_start, beta_end, num_timesteps).to(device)\n",
" self.alphas = 1.0 - self.betas\n",
" self.alpha_cumprod = torch.cumprod(self.alphas, dim=0)\n",
" self.alpha_cumprod_prev = torch.cat([torch.tensor([1.0]).to(device), self.alpha_cumprod[:-1]])\n",
" \n",
" # Calculations for diffusion q(x_t | x_{t-1}) and others\n",
" self.sqrt_alpha_cumprod = torch.sqrt(self.alpha_cumprod)\n",
" self.sqrt_one_minus_alpha_cumprod = torch.sqrt(1.0 - self.alpha_cumprod)\n",
" \n",
" # Calculations for posterior q(x_{t-1} | x_t, x_0)\n",
" self.posterior_variance = self.betas * (1.0 - self.alpha_cumprod_prev) / (1.0 - self.alpha_cumprod)\n",
" self.posterior_log_variance_clipped = torch.log(\n",
" torch.cat([self.posterior_variance[1:2], self.posterior_variance[1:]])\n",
" )\n",
" self.posterior_mean_coef1 = self.betas * torch.sqrt(self.alpha_cumprod_prev) / (1.0 - self.alpha_cumprod)\n",
" self.posterior_mean_coef2 = (1.0 - self.alpha_cumprod_prev) * torch.sqrt(self.alphas) / (1.0 - self.alpha_cumprod)\n",
" \n",
" def add_noise(self, x_start, timesteps, noise=None):\n",
" \"\"\"Forward diffusion process - add noise to images\"\"\"\n",
" if noise is None:\n",
" noise = torch.randn_like(x_start)\n",
" \n",
" # Move timesteps to same device\n",
" timesteps = timesteps.to(self.device)\n",
" \n",
" sqrt_alpha_cumprod_t = self.sqrt_alpha_cumprod[timesteps].reshape(-1, 1, 1, 1)\n",
" sqrt_one_minus_alpha_cumprod_t = self.sqrt_one_minus_alpha_cumprod[timesteps].reshape(-1, 1, 1, 1)\n",
" \n",
" return sqrt_alpha_cumprod_t * x_start + sqrt_one_minus_alpha_cumprod_t * noise\n",
" \n",
" def sample_prev_timestep(self, model_output, timestep, sample):\n",
" \"\"\"Reverse diffusion process - remove noise from images\"\"\"\n",
" timestep = timestep.to(self.device)\n",
" \n",
" # Compute coefficients for predicted original sample (x_0) and current sample (x_t)\n",
" alpha_prod_t = self.alpha_cumprod[timestep]\n",
" alpha_prod_t_prev = self.alpha_cumprod_prev[timestep] if timestep > 0 else torch.tensor(1.0).to(self.device)\n",
" beta_prod_t = 1 - alpha_prod_t\n",
" \n",
" # Compute predicted original sample from predicted noise\n",
" pred_original_sample = (sample - beta_prod_t ** (0.5) * model_output) / alpha_prod_t ** (0.5)\n",
" \n",
" # Compute coefficients for pred_original_sample and current sample x_t\n",
" pred_original_sample_coeff = (alpha_prod_t_prev ** (0.5) * self.betas[timestep]) / beta_prod_t\n",
" current_sample_coeff = self.alphas[timestep] ** (0.5) * (1 - alpha_prod_t_prev) / beta_prod_t\n",
" \n",
" # Compute predicted previous sample\n",
" pred_prev_sample = pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample\n",
" \n",
" return pred_prev_sample\n",
"\n",
"# Initialize scheduler\n",
"scheduler = DDPMScheduler(num_timesteps=1000, device=device)\n",
"print(\"Diffusion scheduler created successfully!\")\n",
"\n",
"# Test the diffusion process\n",
"def test_diffusion_process():\n",
" # Take a batch from our dataset\n",
" data_iter = iter(train_loader)\n",
" images, _ = next(data_iter)\n",
" images = images[:4].to(device) # Take first 4 images\n",
" \n",
" # Test forward process (adding noise)\n",
" timesteps = torch.randint(0, scheduler.num_timesteps, (4,))\n",
" noise = torch.randn_like(images)\n",
" noisy_images = scheduler.add_noise(images, timesteps, noise)\n",
" \n",
" # Visualize original vs noisy images\n",
" fig, axes = plt.subplots(2, 4, figsize=(12, 6))\n",
" \n",
" for i in range(4):\n",
" # Original image\n",
" orig_img = (images[i].cpu() + 1) / 2 # Convert from [-1,1] to [0,1]\n",
" orig_img = orig_img.permute(1, 2, 0)\n",
" axes[0, i].imshow(orig_img)\n",
" axes[0, i].set_title('Original')\n",
" axes[0, i].axis('off')\n",
" \n",
" # Noisy image\n",
" noisy_img = (noisy_images[i].cpu() + 1) / 2\n",
" noisy_img = torch.clamp(noisy_img, 0, 1)\n",
" noisy_img = noisy_img.permute(1, 2, 0)\n",
" axes[1, i].imshow(noisy_img)\n",
" axes[1, i].set_title(f'Noisy (t={timesteps[i]})')\n",
" axes[1, i].axis('off')\n",
" \n",
" plt.tight_layout()\n",
" plt.show()\n",
" \n",
" print(\"Forward diffusion process test completed!\")\n",
" return images, noisy_images, timesteps, noise\n",
"\n",
"# Run the test\n",
"test_images, test_noisy, test_timesteps, test_noise = test_diffusion_process()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "54bba55a-5419-44b2-92d8-2a156e0a5eaa",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Training setup complete!\n",
"Learning rate: 0.0001\n",
"Number of epochs: 20\n",
"Optimizer: AdamW\n",
"Batch size: 128\n",
"Number of batches per epoch: 391\n",
"\n",
"Testing loss computation...\n",
"Test loss: 1.1121\n",
"\n",
"Memory usage:\n",
"Current VRAM usage: 0.22 GB\n",
"Max VRAM usage: 0.50 GB\n",
"\n",
"Ready to start training!\n",
"Run the training in the next step...\n"
]
}
],
"source": [
"# Step 7: Define Loss Function and Training Setup\n",
"\n",
"def compute_loss(model, batch, scheduler, device):\n",
" \"\"\"Compute the diffusion loss\"\"\"\n",
" images, _ = batch\n",
" images = images.to(device)\n",
" batch_size = images.shape[0]\n",
" \n",
" # Sample random timesteps\n",
" timesteps = torch.randint(0, scheduler.num_timesteps, (batch_size,), device=device)\n",
" \n",
" # Sample noise\n",
" noise = torch.randn_like(images)\n",
" \n",
" # Add noise to images (forward diffusion)\n",
" noisy_images = scheduler.add_noise(images, timesteps, noise)\n",
" \n",
" # Predict noise using model\n",
" predicted_noise = model(noisy_images, timesteps)\n",
" \n",
" # Compute MSE loss between predicted and actual noise\n",
" loss = F.mse_loss(predicted_noise, noise)\n",
" \n",
" return loss\n",
"\n",
"# Training parameters\n",
"learning_rate = 1e-4\n",
"num_epochs = 20 # Start with fewer epochs for testing\n",
"save_every = 5 # Save model every 5 epochs\n",
"\n",
"# Initialize optimizer\n",
"optimizer = torch.optim.AdamW(model.parameters(), lr=learning_rate, weight_decay=1e-6)\n",
"\n",
"# Learning rate scheduler\n",
"lr_scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=num_epochs)\n",
"\n",
"print(\"Training setup complete!\")\n",
"print(f\"Learning rate: {learning_rate}\")\n",
"print(f\"Number of epochs: {num_epochs}\")\n",
"print(f\"Optimizer: AdamW\")\n",
"print(f\"Batch size: {batch_size}\")\n",
"print(f\"Number of batches per epoch: {len(train_loader)}\")\n",
"\n",
"# Test the loss computation\n",
"print(\"\\nTesting loss computation...\")\n",
"with torch.no_grad():\n",
" data_iter = iter(train_loader)\n",
" test_batch = next(data_iter)\n",
" test_loss = compute_loss(model, test_batch, scheduler, device)\n",
" print(f\"Test loss: {test_loss.item():.4f}\")\n",
"\n",
"print(\"\\nMemory usage:\")\n",
"print(f\"Current VRAM usage: {torch.cuda.memory_allocated(0) / 1024**3:.2f} GB\")\n",
"print(f\"Max VRAM usage: {torch.cuda.max_memory_allocated(0) / 1024**3:.2f} GB\")\n",
"\n",
"# Training function\n",
"def train_epoch(model, train_loader, optimizer, scheduler, device, epoch):\n",
" model.train()\n",
" total_loss = 0\n",
" num_batches = len(train_loader)\n",
" \n",
" progress_bar = tqdm(train_loader, desc=f'Epoch {epoch+1}/{num_epochs}')\n",
" \n",
" for batch_idx, batch in enumerate(progress_bar):\n",
" optimizer.zero_grad()\n",
" \n",
" # Compute loss\n",
" loss = compute_loss(model, batch, scheduler, device)\n",
" \n",
" # Backward pass\n",
" loss.backward()\n",
" \n",
" # Gradient clipping to prevent exploding gradients\n",
" torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)\n",
" \n",
" # Update weights\n",
" optimizer.step()\n",
" \n",
" total_loss += loss.item()\n",
" \n",
" # Update progress bar\n",
" avg_loss = total_loss / (batch_idx + 1)\n",
" progress_bar.set_postfix({\n",
" 'loss': f'{loss.item():.4f}',\n",
" 'avg_loss': f'{avg_loss:.4f}',\n",
" 'lr': f'{optimizer.param_groups[0][\"lr\"]:.6f}'\n",
" })\n",
" \n",
" # Clear cache every 50 batches to prevent memory buildup\n",
" if batch_idx % 50 == 0:\n",
" torch.cuda.empty_cache()\n",
" \n",
" return total_loss / num_batches\n",
"\n",
"print(\"\\nReady to start training!\")\n",
"print(\"Run the training in the next step...\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9c2e7980-3007-474a-8cce-11477d4224af",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Starting training...\n",
"==================================================\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Epoch 1/20: 100%|██████████| 391/391 [00:43<00:00, 8.95it/s, loss=0.0746, avg_loss=0.1349, lr=0.000100]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Epoch 1/20 completed in 43.68s\n",
"Average loss: 0.1349\n",
"Learning rate: 0.000099\n",
"VRAM usage: 0.43 GB\n",
"--------------------------------------------------\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Epoch 2/20: 13%|█▎ | 49/391 [00:05<00:37, 9.10it/s, loss=0.0544, avg_loss=0.0644, lr=0.000099]"
]
}
],
"source": [
"# Step 8: Start Training\n",
"\n",
"import time\n",
"import os\n",
"\n",
"# Create directory to save models\n",
"os.makedirs('checkpoints', exist_ok=True)\n",
"\n",
"# Training history\n",
"train_losses = []\n",
"start_time = time.time()\n",
"\n",
"print(\"Starting training...\")\n",
"print(\"=\" * 50)\n",
"\n",
"try:\n",
" for epoch in range(num_epochs):\n",
" epoch_start_time = time.time()\n",
" \n",
" # Train one epoch\n",
" avg_loss = train_epoch(model, train_loader, optimizer, scheduler, device, epoch)\n",
" \n",
" # Update learning rate\n",
" lr_scheduler.step()\n",
" \n",
" # Record loss\n",
" train_losses.append(avg_loss)\n",
" \n",
" # Calculate epoch time\n",
" epoch_time = time.time() - epoch_start_time\n",
" \n",
" # Print epoch summary\n",
" print(f\"\\nEpoch {epoch+1}/{num_epochs} completed in {epoch_time:.2f}s\")\n",
" print(f\"Average loss: {avg_loss:.4f}\")\n",
" print(f\"Learning rate: {optimizer.param_groups[0]['lr']:.6f}\")\n",
" print(f\"VRAM usage: {torch.cuda.memory_allocated(0) / 1024**3:.2f} GB\")\n",
" \n",
" # Save model checkpoint\n",
" if (epoch + 1) % save_every == 0:\n",
" checkpoint = {\n",
" 'epoch': epoch + 1,\n",
" 'model_state_dict': model.state_dict(),\n",
" 'optimizer_state_dict': optimizer.state_dict(),\n",
" 'scheduler_state_dict': lr_scheduler.state_dict(),\n",
" 'loss': avg_loss,\n",
" 'train_losses': train_losses\n",
" }\n",
" torch.save(checkpoint, f'checkpoints/diffusion_model_epoch_{epoch+1}.pth')\n",
" print(f\"Model saved: checkpoints/diffusion_model_epoch_{epoch+1}.pth\")\n",
" \n",
" print(\"-\" * 50)\n",
" \n",
" # Clear cache\n",
" torch.cuda.empty_cache()\n",
"\n",
"except KeyboardInterrupt:\n",
" print(\"\\nTraining interrupted by user\")\n",
" \n",
"# Training completed\n",
"total_time = time.time() - start_time\n",
"print(f\"\\nTraining completed in {total_time/60:.2f} minutes\")\n",
"\n",
"# Plot training loss\n",
"plt.figure(figsize=(10, 6))\n",
"plt.plot(train_losses, 'b-', linewidth=2, label='Training Loss')\n",
"plt.xlabel('Epoch')\n",
"plt.ylabel('Loss')\n",
"plt.title('Training Loss Over Time')\n",
"plt.legend()\n",
"plt.grid(True, alpha=0.3)\n",
"plt.show()\n",
"\n",
"# Save final model\n",
"final_checkpoint = {\n",
" 'epoch': num_epochs,\n",
" 'model_state_dict': model.state_dict(),\n",
" 'optimizer_state_dict': optimizer.state_dict(),\n",
" 'scheduler_state_dict': lr_scheduler.state_dict(),\n",
" 'loss': train_losses[-1],\n",
" 'train_losses': train_losses\n",
"}\n",
"torch.save(final_checkpoint, 'checkpoints/diffusion_model_final.pth')\n",
"print(\"Final model saved: checkpoints/diffusion_model_final.pth\")\n",
"\n",
"print(f\"\\nFinal training loss: {train_losses[-1]:.4f}\")\n",
"print(f\"Best training loss: {min(train_losses):.4f} at epoch {train_losses.index(min(train_losses))+1}\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e2891971-bc45-4811-ad03-a6fe443a35a7",
"metadata": {},
"outputs": [],
"source": []
}
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|