diff --git a/.gitattributes b/.gitattributes
index 6ecdafb819e5f2473034facb62862b3c25234ba4..fda315d816776633c29e95601c04cff35f665aad 100644
--- a/.gitattributes
+++ b/.gitattributes
@@ -135,3 +135,4 @@ Vaani/output_image2.png filter=lfs diff=lfs merge=lfs -text
 Vaani/sampleJSON.csv filter=lfs diff=lfs merge=lfs -text
 Vaani/sampleJSON.json filter=lfs diff=lfs merge=lfs -text
 tools/__pycache__/pynvml.cpython-312.pyc filter=lfs diff=lfs merge=lfs -text
+Vaani/VaaniLDM/samplesH/x0_0.png filter=lfs diff=lfs merge=lfs -text
diff --git a/Vaani/SDFT/_2.ipynb b/Vaani/SDFT/_2.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..84d7154a385ff2bdab676a8b3c8a688c1b0aa39c
--- /dev/null
+++ b/Vaani/SDFT/_2.ipynb
@@ -0,0 +1,675 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "aab59bea",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'cuda'"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "import torch.optim as optim\n",
+    "from torch.utils.data import Dataset, DataLoader\n",
+    "from torchvision import transforms\n",
+    "from torchvision.transforms import v2\n",
+    "from PIL import Image\n",
+    "from diffusers import StableDiffusionPipeline\n",
+    "from diffusers.optimization import get_scheduler\n",
+    "from torch import nn\n",
+    "import torch.nn.functional as F\n",
+    "import os\n",
+    "import pandas as pd\n",
+    "from tqdm import trange, tqdm\n",
+    "\n",
+    "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"1\"\n",
+    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
+    "device"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "8f13b66f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# import torch\n",
+    "# import torch.nn as nn\n",
+    "# import torch.nn.functional as F\n",
+    "\n",
+    "# audio_embed_dim = 1280\n",
+    "# output_dim = 768\n",
+    "# device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
+    "\n",
+    "# context_projector = nn.Sequential(\n",
+    "#     nn.Linear(audio_embed_dim, 320),\n",
+    "#     nn.SiLU(),\n",
+    "#     nn.Linear(320, output_dim)\n",
+    "# ).to(device).half()\n",
+    "\n",
+    "# # Dummy input\n",
+    "# audio_embedding = dummy_audio = torch.zeros(10, 1500, 1280, device=device, dtype=torch.float16)\n",
+    "# print(audio_embedding.shape)  # [10, 1500, 1280]\n",
+    "\n",
+    "# # Project audio to [10, 1500, 768]\n",
+    "# projected = context_projector(audio_embedding)\n",
+    "# print(projected.shape)  # [10, 1500, 768]\n",
+    "\n",
+    "# # Compute attention scores: reduce feature dim to scalar per time step\n",
+    "# attn_scores = projected.mean(dim=2)               # [10, 1500]\n",
+    "# attn_weights = F.softmax(attn_scores, dim=1)      # [10, 1500]\n",
+    "# attn_weights = attn_weights.unsqueeze(2)          # [10, 1500, 1]\n",
+    "\n",
+    "# # Weighted average\n",
+    "# pooled = (projected * attn_weights).sum(dim=1, keepdim=True)  # [10, 1, 768]\n",
+    "# print(pooled.shape)  # Final shape: [10, 1, 768]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d32b7d9d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# === Helpers ===\n",
+    "def walkDIR(folder_path, include=None):\n",
+    "    file_list = []\n",
+    "    for root, _, files in os.walk(folder_path):\n",
+    "        for file in files:\n",
+    "            if include is None or any(file.endswith(ext) for ext in include):\n",
+    "                file_list.append(os.path.join(root, file))\n",
+    "    print(\"Files found:\", len(file_list))\n",
+    "    return file_list\n",
+    "\n",
+    "# === Dataset Class ===\n",
+    "class VaaniDataset(torch.utils.data.Dataset):\n",
+    "    def __init__(self, files_paths, im_size):\n",
+    "        self.files_paths = files_paths\n",
+    "        self.im_size = im_size\n",
+    "\n",
+    "    def __len__(self):\n",
+    "        return len(self.files_paths)\n",
+    "\n",
+    "    def __getitem__(self, idx):\n",
+    "        # image = tv.io.read_image(self.files_paths[idx], mode=tv.io.ImageReadMode.RGB)\n",
+    "        image = Image.open(self.files_paths[idx]).convert(\"RGB\")\n",
+    "        image = v2.ToImage()(image)\n",
+    "        # image = tv.io.decode_image(self.files_paths[idx], mode=tv.io.ImageReadMode.RGB)\n",
+    "        image = v2.Resize((self.im_size, self.im_size))(image)\n",
+    "        image = v2.ToDtype(torch.float32, scale=True)(image)\n",
+    "        # image = 2*image - 1\n",
+    "        return image\n",
+    "\n",
+    "\n",
+    "def create_dataloader(dataset, batch_size, debug=False, val_split=0.1, num_workers=4):\n",
+    "    if debug:\n",
+    "        s = 0.001\n",
+    "        dataset, _ = torch.utils.data.random_split(dataset, [s, 1-s], torch.manual_seed(42))\n",
+    "        print(\"Length of Train dataset:\", len(dataset))\n",
+    "\n",
+    "    train_dataloader = DataLoader(\n",
+    "        dataset, \n",
+    "        batch_size=batch_size, \n",
+    "        shuffle=True, \n",
+    "        num_workers=num_workers,\n",
+    "        pin_memory=True,\n",
+    "        drop_last=True,\n",
+    "        persistent_workers=True\n",
+    "    )\n",
+    "    \n",
+    "    images = next(iter(train_dataloader))\n",
+    "    print('Total Batches:', len(train_dataloader))\n",
+    "    print('BATCH SHAPE:', images.shape)\n",
+    "    return train_dataloader\n",
+    "\n",
+    "# === Audio Context Projector ===\n",
+    "# class AudioContextProjector(nn.Module):\n",
+    "#     def __init__(self, audio_embed_dim):\n",
+    "#         super().__init__()\n",
+    "#         self.audio_embed_dim = audio_embed_dim\n",
+    "#         self.context_projector = nn.Sequential(\n",
+    "#             nn.Linear(audio_embed_dim, 320),\n",
+    "#             nn.SiLU(),\n",
+    "#             nn.Linear(320, 1)\n",
+    "#         )\n",
+    "\n",
+    "#     def forward(self, audio_embedding):\n",
+    "#         if audio_embedding.size(-1) != self.audio_embed_dim:\n",
+    "#           raise ValueError(f\"Expected audio embedding dim {self.audio_embed_dim}, got {audio_embedding.size(-1)}\")\n",
+    "#         weights = self.context_projector(audio_embedding)  # [B, T, 1]\n",
+    "#         weights = torch.softmax(weights, dim=1)            # [B, T, 1]\n",
+    "#         pooled = (audio_embedding * weights).sum(dim=1)    # [B, 1280]\n",
+    "#         return pooled.unsqueeze(1)                         # [B, 1, 1280]\n",
+    "# class AudioContextProjector(nn.Module):\n",
+    "#     def __init__(self, audio_embed_dim=1280, output_dim=768):  # Add output_dim for flexibility\n",
+    "#         super().__init__()\n",
+    "#         self.audio_embed_dim = audio_embed_dim\n",
+    "#         self.context_projector = nn.Sequential(\n",
+    "#             nn.Linear(audio_embed_dim, 320),\n",
+    "#             nn.SiLU(),\n",
+    "#             nn.Linear(320, output_dim)  # Output 768 to match UNet's expectation\n",
+    "#         )\n",
+    "\n",
+    "#     def forward(self, audio_embedding):\n",
+    "#         if audio_embedding.size(-1) != self.audio_embed_dim:\n",
+    "#             raise ValueError(f\"Expected audio embedding dim {self.audio_embed_dim}, got {audio_embedding.size(-1)}\")\n",
+    "#         weights = self.context_projector(audio_embedding)  # [B, T, 768]\n",
+    "#         weights = torch.softmax(pooled, dim=1)            # [B, T, 768]\n",
+    "#         pooled = (audio_embedding * weights).sum(dim=1)    # [B, 768]\n",
+    "#         return pooled.unsqueeze(1)                         # [B, 1, 768]\n",
+    "class AudioContextProjector(nn.Module):\n",
+    "    def __init__(self, audio_embed_dim=1280, output_dim=768):\n",
+    "        super().__init__()\n",
+    "        self.audio_embed_dim = audio_embed_dim\n",
+    "        self.output_dim = output_dim\n",
+    "        self.context_projector = nn.Sequential(\n",
+    "            nn.Linear(audio_embed_dim, 320),\n",
+    "            nn.SiLU(),\n",
+    "            nn.Linear(320, output_dim)  # Output 768 to match UNet's expectation\n",
+    "        )\n",
+    "\n",
+    "    def forward(self, audio_embedding):\n",
+    "        if audio_embedding.size(-1) != self.audio_embed_dim:\n",
+    "            raise ValueError(f\"Expected audio embedding dim {self.audio_embed_dim}, got {audio_embedding.size(-1)}\")\n",
+    "\n",
+    "        # Project to [B, T, 768]\n",
+    "        projected = self.context_projector(audio_embedding)  # [B, T, 768]\n",
+    "\n",
+    "        # Compute scalar attention scores per timestep\n",
+    "        attn_scores = projected.mean(dim=2)                 # [B, T]\n",
+    "        attn_weights = F.softmax(attn_scores, dim=1)        # [B, T]\n",
+    "        attn_weights = attn_weights.unsqueeze(2)            # [B, T, 1]\n",
+    "\n",
+    "        # Apply attention to the projected embeddings\n",
+    "        pooled = (projected * attn_weights).sum(dim=1, keepdim=True)  # [B, 1, 768]\n",
+    "        return pooled\n",
+    "\n",
+    "\n",
+    "\n",
+    "# === Inference Function ===\n",
+    "def run_inference(pipe, unet, vae, device, context_hidden_states, save_path=\"inference_output.png\"):\n",
+    "    pipe.unet = unet\n",
+    "    pipe.vae = vae\n",
+    "    pipe.to(device)\n",
+    "\n",
+    "    batch_size = 1\n",
+    "    latents = torch.randn((batch_size, pipe.unet.in_channels, 64, 64), device=device, dtype=torch.float16)\n",
+    "    # latents = torch.randn((batch_size, pipe.unet.config.in_channels, 64, 64), device=device, dtype=torch.float16)\n",
+    "    pipe.scheduler.set_timesteps(50)\n",
+    "    latents = latents * pipe.scheduler.init_noise_sigma\n",
+    "  \n",
+    "    expected_shape = (batch_size, 1, 768)  # Adjust based on model\n",
+    "    if context_hidden_states.shape != expected_shape:\n",
+    "        raise ValueError(f\"Expected context_hidden_states shape {expected_shape}, got {context_hidden_states.shape}\")\n",
+    "      \n",
+    "    for t in pipe.scheduler.timesteps:\n",
+    "        with torch.no_grad():\n",
+    "            noise_pred = pipe.unet(latents, t, encoder_hidden_states=context_hidden_states).sample\n",
+    "        latents = pipe.scheduler.step(noise_pred, t, latents).prev_sample\n",
+    "\n",
+    "    # latents = 1 / 0.18215 * latents\n",
+    "    latents = 1 / pipe.vae.config.scaling_factor * latents\n",
+    "    with torch.no_grad():\n",
+    "        image = pipe.vae.decode(latents).sample\n",
+    "\n",
+    "    image = (image / 2 + 0.5).clamp(0, 1)\n",
+    "    image = image.cpu().permute(0, 2, 3, 1).numpy()[0]\n",
+    "    image = Image.fromarray((image * 255).astype(\"uint8\"))\n",
+    "    image.save(save_path)\n",
+    "    print(f\"Inference image saved to {save_path}\")\n",
+    "\n",
+    "\n",
+    "# === Load Pipeline ===\n",
+    "def load_pipeline(model_id, device):\n",
+    "    pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16).to(device)\n",
+    "    unet = pipe.unet\n",
+    "    vae = pipe.vae\n",
+    "    return pipe, unet, vae\n",
+    "\n",
+    "# === Freeze Layers Function ===\n",
+    "def freeze_vae_layers(vae):\n",
+    "    vae.encoder.requires_grad_(False)\n",
+    "    vae.quant_conv.requires_grad_(False)\n",
+    "    vae.decoder.requires_grad_(True)\n",
+    "    vae.post_quant_conv.requires_grad_(True)\n",
+    "\n",
+    "def freeze_unet_layers(unet):\n",
+    "    for name, param in unet.named_parameters():\n",
+    "        if \"attn2\" in name or \"conv2\" in name:\n",
+    "            param.requires_grad = True\n",
+    "        else:\n",
+    "            param.requires_grad = False\n",
+    "\n",
+    "# === Optimizer Setup ===\n",
+    "def setup_optimizer(vae, unet, projector, lr):\n",
+    "    params_to_optimize = list(filter(lambda p: p.requires_grad, vae.parameters())) + \\\n",
+    "                         list(filter(lambda p: p.requires_grad, unet.parameters())) + \\\n",
+    "                         list(filter(lambda p: p.requires_grad, projector.parameters()))\n",
+    "    optimizer = optim.AdamW(params_to_optimize, lr=lr)\n",
+    "    return optimizer\n",
+    "\n",
+    "\n",
+    "# === Gradient Accumulation Function ===\n",
+    "def accumulate_gradients(optimizer, loss, gradient_accumulation_steps, step, dataloader):\n",
+    "    loss = loss / gradient_accumulation_steps\n",
+    "    loss.backward()\n",
+    "    if (step + 1) % gradient_accumulation_steps == 0 or (step + 1) == len(dataloader):\n",
+    "        optimizer.step()\n",
+    "        optimizer.zero_grad()\n",
+    "\n",
+    "# === Save Checkpoint Function ===\n",
+    "def save_checkpoint(epoch, unet, vae, projector, optimizer, checkpoint_path):\n",
+    "    # checkpoint_path = f\"{save_dir}/checkpoint.pth\"\n",
+    "    torch.save({\n",
+    "        'epoch': epoch,\n",
+    "        'unet': unet.state_dict(),\n",
+    "        'vae': vae.state_dict(),\n",
+    "        'projector': projector.state_dict(),\n",
+    "        'optimizer': optimizer.state_dict(),\n",
+    "    }, checkpoint_path)\n",
+    "    print(f\"Checkpoint saved to {checkpoint_path}\")\n",
+    "\n",
+    "# === Resume from Checkpoint Function ===\n",
+    "def resume_from_checkpoint(checkpoint_path, unet, vae, projector, optimizer):\n",
+    "    if os.path.exists(checkpoint_path):\n",
+    "        checkpoint = torch.load(checkpoint_path, map_location='cpu')\n",
+    "        unet.load_state_dict(checkpoint['unet'])\n",
+    "        vae.load_state_dict(checkpoint['vae'])\n",
+    "        projector.load_state_dict(checkpoint['projector'])\n",
+    "        optimizer.load_state_dict(checkpoint['optimizer'])\n",
+    "        start_epoch = checkpoint['epoch'] + 1\n",
+    "        print(f\"Resuming training from epoch {start_epoch}...\")\n",
+    "        return start_epoch\n",
+    "    else:\n",
+    "        print(\"No checkpoint found, starting from scratch.\")\n",
+    "        return 0\n",
+    "\n",
+    "\n",
+    "# === Training Loop Function ===\n",
+    "def train_loop(dataloader, unet, vae, optimizer, gradient_accumulation_steps, device, num_epochs, samples_path, checkpoint_path, pipe, projector):\n",
+    "    start_epoch = resume_from_checkpoint(checkpoint_path, unet, vae, projector, optimizer)\n",
+    "\n",
+    "    for epoch in trange(start_epoch, num_epochs, colour='red', desc=f'{device}-training', ncols=100):\n",
+    "        unet.train()\n",
+    "        vae.train()\n",
+    "        projector.train()\n",
+    "        total_loss = 0\n",
+    "        step = 0\n",
+    "        \n",
+    "        for image in tqdm(dataloader, colour='green', desc=f'{device}-batch', ncols=100):\n",
+    "            # print(\"step:\", step)\n",
+    "            image = image.to(device, dtype=torch.float16)\n",
+    "\n",
+    "            latents = vae.encode(image).latent_dist.sample() * 0.18215\n",
+    "            noise = torch.randn_like(latents)\n",
+    "            # timesteps = torch.randint(0, 1000, (latents.shape[0],), device=device).long()\n",
+    "            timesteps = torch.randint(0, pipe.scheduler.config.num_train_timesteps, (latents.shape[0],), device=device).long()\n",
+    "\n",
+    "            # === Use dummy audio embedding ===\n",
+    "            dummy_audio = torch.zeros(image.size(0), 1500, 1280, device=device, dtype=torch.float16)\n",
+    "            context_hidden_states = projector(dummy_audio)\n",
+    "\n",
+    "            # print(\"Model IP\")\n",
+    "            noise_pred = unet(latents + noise, timesteps, encoder_hidden_states=context_hidden_states).sample\n",
+    "            # print(\"Model OP\")\n",
+    "\n",
+    "            loss = nn.MSELoss()(noise_pred, noise)\n",
+    "            total_loss += loss.item()\n",
+    "\n",
+    "            step += 1\n",
+    "            accumulate_gradients(optimizer, loss, gradient_accumulation_steps, step, dataloader)\n",
+    "\n",
+    "        avg_loss = total_loss / len(dataloader)\n",
+    "        print(f\"Epoch {epoch + 1} | Avg Loss: {avg_loss:.6f}\")\n",
+    "\n",
+    "        save_checkpoint(epoch, unet, vae, projector, optimizer, checkpoint_path)\n",
+    "        run_inference(pipe, unet, vae, device, context_hidden_states, save_path=f\"{samples_path}/inference_epoch{epoch + 1}.png\")\n",
+    "\n",
+    "    print(\"\\n✅ Fine-tuning complete.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "9ad5f6a3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# === Main Function ===\n",
+    "def main():\n",
+    "    model_id = \"runwayml/stable-diffusion-v1-5\"\n",
+    "    device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
+    "    lr = 1e-5\n",
+    "    num_epochs = 10\n",
+    "    batch_size = 16\n",
+    "    debug = False\n",
+    "    gradient_accumulation_steps = 1\n",
+    "    \n",
+    "    os.makedirs(f\"./checkpoints\", exist_ok=True)\n",
+    "    os.makedirs(f\"./samples\", exist_ok=True)\n",
+    "    checkpoint_path = f\"./checkpoints/checkpoint.pth\"\n",
+    "    samples_path = f\"./samples\"\n",
+    "    image_dir = \"/scratch/IITB/ai-at-ieor/23m1521/datasets/Vaani/Images\"\n",
+    "\n",
+    "    pipe, unet, vae = load_pipeline(model_id, device)\n",
+    "    freeze_vae_layers(vae)\n",
+    "    freeze_unet_layers(unet)\n",
+    "    projector = AudioContextProjector(audio_embed_dim=1280, output_dim=768).to(device).half()\n",
+    "    optimizer = setup_optimizer(vae, unet, projector, lr)\n",
+    "\n",
+    "    # === Dataset & Dataloader ===\n",
+    "    files = walkDIR(image_dir, include=['.png', '.jpeg', '.jpg'])\n",
+    "    dataset = VaaniDataset(files_paths=files, im_size=256)\n",
+    "    image = dataset[2]\n",
+    "    print('IMAGE SHAPE:', image.shape, \"Dataset len:\", len(dataset))\n",
+    "    dataloader = create_dataloader(dataset, batch_size, debug=debug)\n",
+    "\n",
+    "    train_loop(dataloader, unet, vae, optimizer, gradient_accumulation_steps, device, num_epochs, samples_path, checkpoint_path, pipe, projector)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "e71b4ba9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Couldn't connect to the Hub: (MaxRetryError('HTTPSConnectionPool(host=\\'huggingface.co\\', port=443): Max retries exceeded with url: /api/models/runwayml/stable-diffusion-v1-5 (Caused by NameResolutionError(\"<urllib3.connection.HTTPSConnection object at 0x7fd9a9445c40>: Failed to resolve \\'huggingface.co\\' ([Errno -2] Name or service not known)\"))'), '(Request ID: bcd4fcc3-8634-4bfe-8454-3b4dbdcc1222)').\n",
+      "Will try to load from local cache.\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1014662fa9c44a00b0e9e6b3d1e9747d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Loading pipeline components...:   0%|          | 0/7 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Files found: 128807\n",
+      "IMAGE SHAPE: torch.Size([3, 256, 256]) Dataset len: 128807\n",
+      "Total Batches: 8050\n",
+      "BATCH SHAPE: torch.Size([16, 3, 256, 256])\n",
+      "No checkpoint found, starting from scratch.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "cuda-training:   0%|\u001b[31m                                                         \u001b[0m| 0/10 [00:00<?, ?it/s]\u001b[0m"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "step: 0\n",
+      "Model IP\n",
+      "Model OP\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": []
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "step: 1\n",
+      "Model IP\n",
+      "Model OP\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": []
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "step: 2\n",
+      "Model IP\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": []
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model OP\n",
+      "step: 3\n",
+      "Model IP\n",
+      "Model OP\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": []
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "step: 4\n",
+      "Model IP\n",
+      "Model OP\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": []
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "step: 5\n",
+      "Model IP\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": []
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model OP\n",
+      "step: 6\n",
+      "Model IP\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": []
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model OP\n",
+      "step: 7\n",
+      "Model IP\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": []
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model OP\n",
+      "step: 8\n",
+      "Model IP\n",
+      "Model OP\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": []
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "step: 9\n",
+      "Model IP\n",
+      "Model OP\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": []
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "step: 10\n",
+      "Model IP\n",
+      "Model OP\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": []
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "step: 11\n",
+      "Model IP\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": []
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model OP\n",
+      "step: 12\n",
+      "Model IP\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": []
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model OP\n",
+      "step: 13\n",
+      "Model IP\n",
+      "Model OP\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "cuda-batch:   0%|\u001b[32m                                               \u001b[0m| 14/8050 [00:06<1:02:22,  2.15it/s]\u001b[0m\n",
+      "cuda-training:   0%|\u001b[31m                                                         \u001b[0m| 0/10 [00:06<?, ?it/s]\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "step: 14\n"
+     ]
+    },
+    {
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+      "\u001b[31mKeyboardInterrupt\u001b[39m                         Traceback (most recent call last)",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[5]\u001b[39m\u001b[32m, line 2\u001b[39m\n\u001b[32m      1\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[34m__name__\u001b[39m == \u001b[33m\"\u001b[39m\u001b[33m__main__\u001b[39m\u001b[33m\"\u001b[39m:\n\u001b[32m----> \u001b[39m\u001b[32m2\u001b[39m     \u001b[43mmain\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[4]\u001b[39m\u001b[32m, line 30\u001b[39m, in \u001b[36mmain\u001b[39m\u001b[34m()\u001b[39m\n\u001b[32m     27\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33m'\u001b[39m\u001b[33mIMAGE SHAPE:\u001b[39m\u001b[33m'\u001b[39m, image.shape, \u001b[33m\"\u001b[39m\u001b[33mDataset len:\u001b[39m\u001b[33m\"\u001b[39m, \u001b[38;5;28mlen\u001b[39m(dataset))\n\u001b[32m     28\u001b[39m dataloader = create_dataloader(dataset, batch_size, debug=debug)\n\u001b[32m---> \u001b[39m\u001b[32m30\u001b[39m \u001b[43mtrain_loop\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdataloader\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43munet\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvae\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moptimizer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgradient_accumulation_steps\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdevice\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnum_epochs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msamples_path\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcheckpoint_path\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpipe\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprojector\u001b[49m\u001b[43m)\u001b[49m\n",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[3]\u001b[39m\u001b[32m, line 228\u001b[39m, in \u001b[36mtrain_loop\u001b[39m\u001b[34m(dataloader, unet, vae, optimizer, gradient_accumulation_steps, device, num_epochs, samples_path, checkpoint_path, pipe, projector)\u001b[39m\n\u001b[32m    226\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m image \u001b[38;5;129;01min\u001b[39;00m tqdm(dataloader, colour=\u001b[33m'\u001b[39m\u001b[33mgreen\u001b[39m\u001b[33m'\u001b[39m, desc=\u001b[33mf\u001b[39m\u001b[33m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mdevice\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m-batch\u001b[39m\u001b[33m'\u001b[39m, ncols=\u001b[32m100\u001b[39m):\n\u001b[32m    227\u001b[39m     \u001b[38;5;28mprint\u001b[39m(\u001b[33m\"\u001b[39m\u001b[33mstep:\u001b[39m\u001b[33m\"\u001b[39m, step)\n\u001b[32m--> \u001b[39m\u001b[32m228\u001b[39m     image = \u001b[43mimage\u001b[49m\u001b[43m.\u001b[49m\u001b[43mto\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdevice\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m=\u001b[49m\u001b[43mtorch\u001b[49m\u001b[43m.\u001b[49m\u001b[43mfloat16\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    230\u001b[39m     latents = vae.encode(image).latent_dist.sample() * \u001b[32m0.18215\u001b[39m\n\u001b[32m    231\u001b[39m     noise = torch.randn_like(latents)\n",
+      "\u001b[31mKeyboardInterrupt\u001b[39m: "
+     ]
+    }
+   ],
+   "source": [
+    "if __name__ == \"__main__\":\n",
+    "    main()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Vaani/SDFT/_2_.py b/Vaani/SDFT/_2_.py
new file mode 100644
index 0000000000000000000000000000000000000000..cf32f91cdd212495994c78095ed0caff5f4373f3
--- /dev/null
+++ b/Vaani/SDFT/_2_.py
@@ -0,0 +1,345 @@
+import torch
+import torch.optim as optim
+from torch.utils.data import Dataset, DataLoader
+from torchvision import transforms
+from torchvision.transforms import v2
+from PIL import Image
+from diffusers import StableDiffusionPipeline
+from diffusers.optimization import get_scheduler
+from torch import nn
+import torch.nn.functional as F
+import os
+import pandas as pd
+from tqdm import trange, tqdm
+
+os.environ["CUDA_VISIBLE_DEVICES"] = "1"
+device = "cuda" if torch.cuda.is_available() else "cpu"
+device
+
+
+# import torch
+# import torch.nn as nn
+# import torch.nn.functional as F
+
+# audio_embed_dim = 1280
+# output_dim = 768
+# device = 'cuda' if torch.cuda.is_available() else 'cpu'
+
+# context_projector = nn.Sequential(
+#     nn.Linear(audio_embed_dim, 320),
+#     nn.SiLU(),
+#     nn.Linear(320, output_dim)
+# ).to(device).half()
+
+# # Dummy input
+# audio_embedding = dummy_audio = torch.zeros(10, 1500, 1280, device=device, dtype=torch.float16)
+# print(audio_embedding.shape)  # [10, 1500, 1280]
+
+# # Project audio to [10, 1500, 768]
+# projected = context_projector(audio_embedding)
+# print(projected.shape)  # [10, 1500, 768]
+
+# # Compute attention scores: reduce feature dim to scalar per time step
+# attn_scores = projected.mean(dim=2)               # [10, 1500]
+# attn_weights = F.softmax(attn_scores, dim=1)      # [10, 1500]
+# attn_weights = attn_weights.unsqueeze(2)          # [10, 1500, 1]
+
+# # Weighted average
+# pooled = (projected * attn_weights).sum(dim=1, keepdim=True)  # [10, 1, 768]
+# print(pooled.shape)  # Final shape: [10, 1, 768]
+
+
+
+# === Helpers ===
+def walkDIR(folder_path, include=None):
+    file_list = []
+    for root, _, files in os.walk(folder_path):
+        for file in files:
+            if include is None or any(file.endswith(ext) for ext in include):
+                file_list.append(os.path.join(root, file))
+    print("Files found:", len(file_list))
+    return file_list
+
+# === Dataset Class ===
+class VaaniDataset(torch.utils.data.Dataset):
+    def __init__(self, files_paths, im_size):
+        self.files_paths = files_paths
+        self.im_size = im_size
+
+    def __len__(self):
+        return len(self.files_paths)
+
+    def __getitem__(self, idx):
+        # image = tv.io.read_image(self.files_paths[idx], mode=tv.io.ImageReadMode.RGB)
+        image = Image.open(self.files_paths[idx]).convert("RGB")
+        image = v2.ToImage()(image)
+        # image = tv.io.decode_image(self.files_paths[idx], mode=tv.io.ImageReadMode.RGB)
+        image = v2.Resize((self.im_size, self.im_size))(image)
+        image = v2.ToDtype(torch.float32, scale=True)(image)
+        # image = 2*image - 1
+        return image
+
+
+def create_dataloader(dataset, batch_size, debug=False, val_split=0.1, num_workers=4):
+    if debug:
+        s = 0.001
+        dataset, _ = torch.utils.data.random_split(dataset, [s, 1-s], torch.manual_seed(42))
+        print("Length of Train dataset:", len(dataset))
+
+    train_dataloader = DataLoader(
+        dataset, 
+        batch_size=batch_size, 
+        shuffle=True, 
+        num_workers=num_workers,
+        pin_memory=True,
+        drop_last=True,
+        persistent_workers=True
+    )
+    
+    images = next(iter(train_dataloader))
+    print('Total Batches:', len(train_dataloader))
+    print('BATCH SHAPE:', images.shape)
+    return train_dataloader
+
+# === Audio Context Projector ===
+# class AudioContextProjector(nn.Module):
+#     def __init__(self, audio_embed_dim):
+#         super().__init__()
+#         self.audio_embed_dim = audio_embed_dim
+#         self.context_projector = nn.Sequential(
+#             nn.Linear(audio_embed_dim, 320),
+#             nn.SiLU(),
+#             nn.Linear(320, 1)
+#         )
+
+#     def forward(self, audio_embedding):
+#         if audio_embedding.size(-1) != self.audio_embed_dim:
+#           raise ValueError(f"Expected audio embedding dim {self.audio_embed_dim}, got {audio_embedding.size(-1)}")
+#         weights = self.context_projector(audio_embedding)  # [B, T, 1]
+#         weights = torch.softmax(weights, dim=1)            # [B, T, 1]
+#         pooled = (audio_embedding * weights).sum(dim=1)    # [B, 1280]
+#         return pooled.unsqueeze(1)                         # [B, 1, 1280]
+# class AudioContextProjector(nn.Module):
+#     def __init__(self, audio_embed_dim=1280, output_dim=768):  # Add output_dim for flexibility
+#         super().__init__()
+#         self.audio_embed_dim = audio_embed_dim
+#         self.context_projector = nn.Sequential(
+#             nn.Linear(audio_embed_dim, 320),
+#             nn.SiLU(),
+#             nn.Linear(320, output_dim)  # Output 768 to match UNet's expectation
+#         )
+
+#     def forward(self, audio_embedding):
+#         if audio_embedding.size(-1) != self.audio_embed_dim:
+#             raise ValueError(f"Expected audio embedding dim {self.audio_embed_dim}, got {audio_embedding.size(-1)}")
+#         weights = self.context_projector(audio_embedding)  # [B, T, 768]
+#         weights = torch.softmax(pooled, dim=1)            # [B, T, 768]
+#         pooled = (audio_embedding * weights).sum(dim=1)    # [B, 768]
+#         return pooled.unsqueeze(1)                         # [B, 1, 768]
+class AudioContextProjector(nn.Module):
+    def __init__(self, audio_embed_dim=1280, output_dim=768):
+        super().__init__()
+        self.audio_embed_dim = audio_embed_dim
+        self.output_dim = output_dim
+        self.context_projector = nn.Sequential(
+            nn.Linear(audio_embed_dim, 320),
+            nn.SiLU(),
+            nn.Linear(320, output_dim)  # Output 768 to match UNet's expectation
+        )
+
+    def forward(self, audio_embedding):
+        if audio_embedding.size(-1) != self.audio_embed_dim:
+            raise ValueError(f"Expected audio embedding dim {self.audio_embed_dim}, got {audio_embedding.size(-1)}")
+
+        # Project to [B, T, 768]
+        projected = self.context_projector(audio_embedding)  # [B, T, 768]
+
+        # Compute scalar attention scores per timestep
+        attn_scores = projected.mean(dim=2)                 # [B, T]
+        attn_weights = F.softmax(attn_scores, dim=1)        # [B, T]
+        attn_weights = attn_weights.unsqueeze(2)            # [B, T, 1]
+
+        # Apply attention to the projected embeddings
+        pooled = (projected * attn_weights).sum(dim=1, keepdim=True)  # [B, 1, 768]
+        return pooled
+
+
+
+# === Inference Function ===
+def run_inference(pipe, unet, vae, device, context_hidden_states, save_path="inference_output.png"):
+    pipe.unet = unet
+    pipe.vae = vae
+    pipe.to(device)
+
+    batch_size = 1
+    latents = torch.randn((batch_size, pipe.unet.in_channels, 64, 64), device=device, dtype=torch.float16)
+    # latents = torch.randn((batch_size, pipe.unet.config.in_channels, 64, 64), device=device, dtype=torch.float16)
+    pipe.scheduler.set_timesteps(50)
+    latents = latents * pipe.scheduler.init_noise_sigma
+  
+    expected_shape = (batch_size, 1, 768)  # Adjust based on model
+    if context_hidden_states.shape != expected_shape:
+        raise ValueError(f"Expected context_hidden_states shape {expected_shape}, got {context_hidden_states.shape}")
+      
+    for t in pipe.scheduler.timesteps:
+        with torch.no_grad():
+            noise_pred = pipe.unet(latents, t, encoder_hidden_states=context_hidden_states).sample
+        latents = pipe.scheduler.step(noise_pred, t, latents).prev_sample
+
+    # latents = 1 / 0.18215 * latents
+    latents = 1 / pipe.vae.config.scaling_factor * latents
+    with torch.no_grad():
+        image = pipe.vae.decode(latents).sample
+
+    image = (image / 2 + 0.5).clamp(0, 1)
+    image = image.cpu().permute(0, 2, 3, 1).numpy()[0]
+    image = Image.fromarray((image * 255).astype("uint8"))
+    image.save(save_path)
+    print(f"Inference image saved to {save_path}")
+
+
+# === Load Pipeline ===
+def load_pipeline(model_id, device):
+    pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16).to(device)
+    unet = pipe.unet
+    vae = pipe.vae
+    return pipe, unet, vae
+
+# === Freeze Layers Function ===
+def freeze_vae_layers(vae):
+    vae.encoder.requires_grad_(False)
+    vae.quant_conv.requires_grad_(False)
+    vae.decoder.requires_grad_(True)
+    vae.post_quant_conv.requires_grad_(True)
+
+def freeze_unet_layers(unet):
+    for name, param in unet.named_parameters():
+        if "attn2" in name or "conv2" in name:
+            param.requires_grad = True
+        else:
+            param.requires_grad = False
+
+# === Optimizer Setup ===
+def setup_optimizer(vae, unet, projector, lr):
+    params_to_optimize = list(filter(lambda p: p.requires_grad, vae.parameters())) + \
+                         list(filter(lambda p: p.requires_grad, unet.parameters())) + \
+                         list(filter(lambda p: p.requires_grad, projector.parameters()))
+    optimizer = optim.AdamW(params_to_optimize, lr=lr)
+    return optimizer
+
+
+# === Gradient Accumulation Function ===
+def accumulate_gradients(optimizer, loss, gradient_accumulation_steps, step, dataloader):
+    loss = loss / gradient_accumulation_steps
+    loss.backward()
+    if (step + 1) % gradient_accumulation_steps == 0 or (step + 1) == len(dataloader):
+        optimizer.step()
+        optimizer.zero_grad()
+
+# === Save Checkpoint Function ===
+def save_checkpoint(epoch, unet, vae, projector, optimizer, checkpoint_path):
+    # checkpoint_path = f"{save_dir}/checkpoint.pth"
+    torch.save({
+        'epoch': epoch,
+        'unet': unet.state_dict(),
+        'vae': vae.state_dict(),
+        'projector': projector.state_dict(),
+        'optimizer': optimizer.state_dict(),
+    }, checkpoint_path)
+    print(f"Checkpoint saved to {checkpoint_path}")
+
+# === Resume from Checkpoint Function ===
+def resume_from_checkpoint(checkpoint_path, unet, vae, projector, optimizer):
+    if os.path.exists(checkpoint_path):
+        checkpoint = torch.load(checkpoint_path, map_location='cpu')
+        unet.load_state_dict(checkpoint['unet'])
+        vae.load_state_dict(checkpoint['vae'])
+        projector.load_state_dict(checkpoint['projector'])
+        optimizer.load_state_dict(checkpoint['optimizer'])
+        start_epoch = checkpoint['epoch'] + 1
+        print(f"Resuming training from epoch {start_epoch}...")
+        return start_epoch
+    else:
+        print("No checkpoint found, starting from scratch.")
+        return 0
+
+
+# === Training Loop Function ===
+def train_loop(dataloader, unet, vae, optimizer, gradient_accumulation_steps, device, num_epochs, samples_path, checkpoint_path, pipe, projector):
+    start_epoch = resume_from_checkpoint(checkpoint_path, unet, vae, projector, optimizer)
+
+    for epoch in trange(start_epoch, num_epochs, colour='red', desc=f'{device}-training', dynamic_ncols=True):
+        unet.train()
+        vae.train()
+        projector.train()
+        total_loss = 0
+        step = 0
+        
+        for image in tqdm(dataloader, colour='green', desc=f'{device}-batch', dynamic_ncols=True):
+            # print("step:", step)
+            image = image.to(device, dtype=torch.float16)
+
+            latents = vae.encode(image).latent_dist.sample() * 0.18215
+            noise = torch.randn_like(latents)
+            # timesteps = torch.randint(0, 1000, (latents.shape[0],), device=device).long()
+            timesteps = torch.randint(0, pipe.scheduler.config.num_train_timesteps, (latents.shape[0],), device=device).long()
+
+            # === Use dummy audio embedding ===
+            dummy_audio = torch.zeros(image.size(0), 1500, 1280, device=device, dtype=torch.float16)
+            context_hidden_states = projector(dummy_audio)
+
+            # print("Model IP")
+            noise_pred = unet(latents + noise, timesteps, encoder_hidden_states=context_hidden_states).sample
+            # print("Model OP")
+
+            loss = nn.MSELoss()(noise_pred, noise)
+            total_loss += loss.item()
+
+            step += 1
+            accumulate_gradients(optimizer, loss, gradient_accumulation_steps, step, dataloader)
+
+        avg_loss = total_loss / len(dataloader)
+        print(f"Epoch {epoch + 1} | Avg Loss: {avg_loss:.6f}")
+
+        save_checkpoint(epoch, unet, vae, projector, optimizer, checkpoint_path)
+        run_inference(pipe, unet, vae, device, context_hidden_states, save_path=f"{samples_path}/inference_epoch{epoch + 1}.png")
+
+    print("\n✅ Fine-tuning complete.")
+
+
+# === Main Function ===
+def main():
+    model_id = "runwayml/stable-diffusion-v1-5"
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    lr = 1e-5
+    num_epochs = 10
+    batch_size = 16
+    debug = False
+    gradient_accumulation_steps = 1
+    
+    os.makedirs(f"./checkpoints", exist_ok=True)
+    os.makedirs(f"./samples", exist_ok=True)
+    checkpoint_path = f"./checkpoints/checkpoint.pth"
+    samples_path = f"./samples"
+    image_dir = "/scratch/IITB/ai-at-ieor/23m1521/datasets/Vaani/Images"
+
+    pipe, unet, vae = load_pipeline(model_id, device)
+    freeze_vae_layers(vae)
+    freeze_unet_layers(unet)
+    projector = AudioContextProjector(audio_embed_dim=1280, output_dim=768).to(device).half()
+    optimizer = setup_optimizer(vae, unet, projector, lr)
+
+    # === Dataset & Dataloader ===
+    files = walkDIR(image_dir, include=['.png', '.jpeg', '.jpg'])
+    dataset = VaaniDataset(files_paths=files, im_size=256)
+    image = dataset[2]
+    print('IMAGE SHAPE:', image.shape, "Dataset len:", len(dataset))
+    dataloader = create_dataloader(dataset, batch_size, debug=debug)
+
+    train_loop(dataloader, unet, vae, optimizer, gradient_accumulation_steps, device, num_epochs, samples_path, checkpoint_path, pipe, projector)
+
+
+if __name__ == "__main__":
+    main()
+
+
diff --git a/Vaani/SDFT/_2_DDP.py b/Vaani/SDFT/_2_DDP.py
new file mode 100644
index 0000000000000000000000000000000000000000..cef1b872bef45db8f849d95abdeb171cb4bc1990
--- /dev/null
+++ b/Vaani/SDFT/_2_DDP.py
@@ -0,0 +1,316 @@
+import torch
+import torch.optim as optim
+from torch.utils.data import Dataset, DataLoader
+from torchvision import transforms
+from torchvision.transforms import v2
+from PIL import Image
+from diffusers import StableDiffusionPipeline
+from diffusers.optimization import get_scheduler
+from torch import nn
+import torch.nn.functional as F
+import os
+import pandas as pd
+from tqdm import trange, tqdm
+# DDP Imports
+import torch.distributed as dist
+from torch.nn.parallel import DistributedDataParallel as DDP
+from torch.utils.data.distributed import DistributedSampler
+import torch.multiprocessing as mp
+
+# Set CUDA_VISIBLE_DEVICES
+# os.environ["CUDA_VISIBLE_DEVICES"] = "1"
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+# === Helpers ===
+def walkDIR(folder_path, include=None):
+    file_list = []
+    for root, _, files in os.walk(folder_path):
+        for file in files:
+            if include is None or any(file.endswith(ext) for ext in include):
+                file_list.append(os.path.join(root, file))
+    print("Files found:", len(file_list))
+    return file_list
+
+# === Dataset Class ===
+class VaaniDataset(torch.utils.data.Dataset):
+    def __init__(self, files_paths, im_size):
+        self.files_paths = files_paths
+        self.im_size = im_size
+
+    def __len__(self):
+        return len(self.files_paths)
+
+    def __getitem__(self, idx):
+        image = Image.open(self.files_paths[idx]).convert("RGB")
+        image = v2.ToImage()(image)
+        image = v2.Resize((self.im_size, self.im_size))(image)
+        image = v2.ToDtype(torch.float32, scale=True)(image)
+        return image
+
+# === Modified create_dataloader for DDP and single GPU ===
+def create_dataloader(dataset, batch_size, debug=False, val_split=0.1, num_workers=4, rank=None, is_distributed=False):
+    if debug:
+        s = 0.001
+        dataset, _ = torch.utils.data.random_split(dataset, [s, 1-s], torch.manual_seed(42))
+        print(f"{'Rank ' + str(rank) + ': ' if rank is not None else ''}Length of Train dataset: {len(dataset)}")
+
+    # Use DistributedSampler only if DDP is active
+    sampler = DistributedSampler(dataset, shuffle=True) if is_distributed else None
+    train_dataloader = DataLoader(
+        dataset,
+        batch_size=batch_size,
+        shuffle=(sampler is None),
+        sampler=sampler,
+        num_workers=num_workers,
+        pin_memory=True,
+        drop_last=True,
+        persistent_workers=True
+    )
+    
+    images = next(iter(train_dataloader))
+    if rank is not None:
+        print(f"Rank {rank}: Total Batches: {len(train_dataloader)}")
+        print(f"Rank {rank}: BATCH SHAPE: {images.shape}")
+    else:
+        print(f"Total Batches: {len(train_dataloader)}")
+        print(f"BATCH SHAPE: {images.shape}")
+    return train_dataloader
+
+# === Audio Context Projector ===
+class AudioContextProjector(nn.Module):
+    def __init__(self, audio_embed_dim=1280, output_dim=768):
+        super().__init__()
+        self.audio_embed_dim = audio_embed_dim
+        self.output_dim = output_dim
+        self.context_projector = nn.Sequential(
+            nn.Linear(audio_embed_dim, 320),
+            nn.SiLU(),
+            nn.Linear(320, output_dim)
+        )
+
+    def forward(self, audio_embedding):
+        if audio_embedding.size(-1) != self.audio_embed_dim:
+            raise ValueError(f"Expected audio embedding dim {self.audio_embed_dim}, got {audio_embedding.size(-1)}")
+        projected = self.context_projector(audio_embedding)
+        attn_scores = projected.mean(dim=2)
+        attn_weights = F.softmax(attn_scores, dim=1)
+        attn_weights = attn_weights.unsqueeze(2)
+        pooled = (projected * attn_weights).sum(dim=1, keepdim=True)
+        return pooled
+
+# === Inference Function ===
+def run_inference(pipe, unet, vae, device, context_hidden_states, save_path="inference_output.png", rank=0):
+    if rank != 0:  # Only rank-0 or single-GPU process runs inference
+        return
+    pipe.unet = unet.module if isinstance(unet, DDP) else unet
+    pipe.vae = vae.module if isinstance(vae, DDP) else vae
+    pipe.to(device)
+
+    batch_size = 1
+    latents = torch.randn((batch_size, pipe.unet.in_channels, 64, 64), device=device, dtype=torch.float16)
+    pipe.scheduler.set_timesteps(50)
+    latents = latents * pipe.scheduler.init_noise_sigma
+  
+    expected_shape = (batch_size, 1, 768)
+    if context_hidden_states.shape != expected_shape:
+        raise ValueError(f"Expected context_hidden_states shape {expected_shape}, got {context_hidden_states.shape}")
+      
+    for t in pipe.scheduler.timesteps:
+        with torch.no_grad():
+            noise_pred = pipe.unet(latents, t, encoder_hidden_states=context_hidden_states).sample
+        latents = pipe.scheduler.step(noise_pred, t, latents).prev_sample
+
+    latents = 1 / pipe.vae.config.scaling_factor * latents
+    with torch.no_grad():
+        image = pipe.vae.decode(latents).sample
+
+    image = (image / 2 + 0.5).clamp(0, 1)
+    image = image.cpu().permute(0, 2, 3, 1).numpy()[0]
+    image = Image.fromarray((image * 255).astype("uint8"))
+    image.save(save_path)
+    print(f"{'Rank ' + str(rank) + ': ' if rank != 0 else ''}Inference image saved to {save_path}")
+
+# === Load Pipeline ===
+def load_pipeline(model_id, device):
+    pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16).to(device)
+    unet = pipe.unet
+    vae = pipe.vae
+    return pipe, unet, vae
+
+# === Freeze Layers Function ===
+def freeze_vae_layers(vae):
+    vae.encoder.requires_grad_(False)
+    vae.quant_conv.requires_grad_(False)
+    vae.decoder.requires_grad_(True)
+    vae.post_quant_conv.requires_grad_(True)
+
+def freeze_unet_layers(unet):
+    for name, param in unet.named_parameters():
+        if "attn2" in name or "conv2" in name:
+            param.requires_grad = True
+        else:
+            param.requires_grad = False
+
+# === Optimizer Setup ===
+def setup_optimizer(vae, unet, projector, lr):
+    params_to_optimize = list(filter(lambda p: p.requires_grad, vae.parameters())) + \
+                         list(filter(lambda p: p.requires_grad, unet.parameters())) + \
+                         list(filter(lambda p: p.requires_grad, projector.parameters()))
+    optimizer = optim.AdamW(params_to_optimize, lr=lr)
+    return optimizer
+
+# === Gradient Accumulation Function ===
+def accumulate_gradients(optimizer, loss, gradient_accumulation_steps, step, dataloader):
+    loss = loss / gradient_accumulation_steps
+    loss.backward()
+    if (step + 1) % gradient_accumulation_steps == 0 or (step + 1) == len(dataloader):
+        optimizer.step()
+        optimizer.zero_grad()
+
+# === Save Checkpoint Function ===
+def save_checkpoint(epoch, unet, vae, projector, optimizer, checkpoint_path, rank=0):
+    if rank != 0:  # Only rank-0 or single-GPU process saves checkpoint
+        return
+    torch.save({
+        'epoch': epoch,
+        'unet': unet.module.state_dict() if isinstance(unet, DDP) else unet.state_dict(),
+        'vae': vae.module.state_dict() if isinstance(vae, DDP) else vae.state_dict(),
+        'projector': projector.module.state_dict() if isinstance(projector, DDP) else projector.state_dict(),
+        'optimizer': optimizer.state_dict(),
+    }, checkpoint_path)
+    print(f"{'Rank ' + str(rank) + ': ' if rank != 0 else ''}Checkpoint saved to {checkpoint_path}")
+
+# === Resume from Checkpoint Function ===
+def resume_from_checkpoint(checkpoint_path, unet, vae, projector, optimizer, rank=0):
+    if rank != 0:  # Only rank-0 or single-GPU process loads checkpoint
+        return 0
+    if os.path.exists(checkpoint_path):
+        checkpoint = torch.load(checkpoint_path, map_location='cpu')
+        unet.load_state_dict(checkpoint['unet'])
+        vae.load_state_dict(checkpoint['vae'])
+        projector.load_state_dict(checkpoint['projector'])
+        optimizer.load_state_dict(checkpoint['optimizer'])
+        start_epoch = checkpoint['epoch'] + 1
+        print(f"{'Rank ' + str(rank) + ': ' if rank != 0 else ''}Resuming training from epoch {start_epoch}...")
+        return start_epoch
+    else:
+        print(f"{'Rank ' + str(rank) + ': ' if rank != 0 else ''}No checkpoint found, starting from scratch.")
+        return 0
+
+# === Training Loop Function ===
+def train_loop(dataloader, unet, vae, optimizer, gradient_accumulation_steps, device, num_epochs, samples_path, checkpoint_path, pipe, projector, rank=0, is_distributed=False):
+    start_epoch = resume_from_checkpoint(checkpoint_path, unet, vae, projector, optimizer, rank)
+
+    for epoch in trange(start_epoch, num_epochs, colour='red', desc=f"{'Rank ' + str(rank) + ' ' if rank != 0 else ''}{device}-training", dynamic_ncols=True):
+        unet.train()
+        vae.train()
+        projector.train()
+        total_loss = 0
+        step = 0
+        
+        # Reset sampler for each epoch if using DistributedSampler
+        if is_distributed and isinstance(dataloader.sampler, DistributedSampler):
+            dataloader.sampler.set_epoch(epoch)
+        
+        for image in tqdm(dataloader, colour='green', desc=f"{'Rank ' + str(rank) + ' ' if rank != 0 else ''}{device}-batch", dynamic_ncols=True):
+            image = image.to(device, dtype=torch.float16)
+
+            latents = vae.encode(image).latent_dist.sample() * 0.18215
+            noise = torch.randn_like(latents)
+            timesteps = torch.randint(0, pipe.scheduler.config.num_train_timesteps, (latents.shape[0],), device=device).long()
+
+            dummy_audio = torch.zeros(image.size(0), 1500, 1280, device=device, dtype=torch.float16)
+            context_hidden_states = projector(dummy_audio)
+
+            noise_pred = unet(latents + noise, timesteps, encoder_hidden_states=context_hidden_states).sample
+            loss = nn.MSELoss()(noise_pred, noise)
+            total_loss += loss.item()
+
+            step += 1
+            accumulate_gradients(optimizer, loss, gradient_accumulation_steps, step, dataloader)
+
+        # Aggregate loss for DDP
+        if is_distributed:
+            total_loss_tensor = torch.tensor(total_loss, device=device)
+            dist.all_reduce(total_loss_tensor, op=dist.ReduceOp.SUM)
+            avg_loss = total_loss_tensor.item() / (len(dataloader) * dist.get_world_size())
+        else:
+            avg_loss = total_loss / len(dataloader)
+
+        if rank == 0:
+            print(f"{'Rank ' + str(rank) + ': ' if rank != 0 else ''}Epoch {epoch + 1} | Avg Loss: {avg_loss:.6f}")
+
+        save_checkpoint(epoch, unet, vae, projector, optimizer, checkpoint_path, rank)
+        run_inference(pipe, unet, vae, device, context_hidden_states, 
+                      save_path=f"{samples_path}/inference_epoch{epoch + 1}{'_rank' + str(rank) if rank != 0 else ''}.png", 
+                      rank=rank)
+
+    if rank == 0:
+        print(f"{'Rank ' + str(rank) + ': ' if rank != 0 else ''}✅ Fine-tuning complete.")
+
+# === DDP Setup Function ===
+def setup_ddp(rank, world_size):
+    os.environ['MASTER_ADDR'] = 'localhost'
+    os.environ['MASTER_PORT'] = '12355'
+    dist.init_process_group("nccl", rank=rank, world_size=world_size)
+    torch.cuda.set_device(rank)
+
+# === Main Function ===
+def main(rank=0, world_size=1, is_distributed=False):
+    if is_distributed:
+        setup_ddp(rank, world_size)
+        device = torch.device(f"cuda:{rank}")
+    else:
+        device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+    model_id = "runwayml/stable-diffusion-v1-5"
+    lr = 1e-5
+    num_epochs = 10
+    batch_size = 16
+    debug = False
+    gradient_accumulation_steps = 1
+    
+    if rank == 0:
+        os.makedirs(f"./checkpoints", exist_ok=True)
+        os.makedirs(f"./samples", exist_ok=True)
+    if is_distributed:
+        dist.barrier()
+
+    checkpoint_path = f"./checkpoints/checkpoint{'_rank' + str(rank) if is_distributed else ''}.pth"
+    samples_path = f"./samples"
+    image_dir = "/scratch/IITB/ai-at-ieor/23m1521/datasets/Vaani/Images"
+
+    pipe, unet, vae = load_pipeline(model_id, device)
+    freeze_vae_layers(vae)
+    freeze_unet_layers(unet)
+    projector = AudioContextProjector(audio_embed_dim=1280, output_dim=768).to(device).half()
+
+    if is_distributed:
+        unet = DDP(unet, device_ids=[rank])
+        vae = DDP(vae, device_ids=[rank])
+        projector = DDP(projector, device_ids=[rank])
+
+    optimizer = setup_optimizer(vae, unet, projector, lr)
+
+    files = walkDIR(image_dir, include=['.png', '.jpeg', '.jpg'])
+    dataset = VaaniDataset(files_paths=files, im_size=256)
+    if rank == 0:
+        image = dataset[2]
+        print(f"{'Rank ' + str(rank) + ': ' if rank != 0 else ''}IMAGE SHAPE: {image.shape}, Dataset len: {len(dataset)}")
+
+    dataloader = create_dataloader(dataset, batch_size, debug=debug, rank=rank, is_distributed=is_distributed)
+
+    train_loop(dataloader, unet, vae, optimizer, gradient_accumulation_steps, device, num_epochs, 
+               samples_path, checkpoint_path, pipe, projector, rank, is_distributed)
+
+    if is_distributed:
+        dist.destroy_process_group()
+
+# === Entry Point ===
+if __name__ == "__main__":
+    world_size = torch.cuda.device_count()
+    print(f"Detected {world_size} GPU(s)")
+    if world_size > 1:
+        mp.spawn(main, args=(world_size, True), nprocs=world_size, join=True)
+    else:
+        main(rank=0, world_size=1, is_distributed=False)
\ No newline at end of file
diff --git a/Vaani/SDFT/checkpoints/checkpoint.pth b/Vaani/SDFT/checkpoints/checkpoint.pth
new file mode 100644
index 0000000000000000000000000000000000000000..f7c3f77f5cce17d3129e22bf8a482b77f2ded7d5
--- /dev/null
+++ b/Vaani/SDFT/checkpoints/checkpoint.pth
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:79469e5ae61b7894df2b96cdb09b873f9d0e2282f8b85d4195c5dbd16e182891
+size 2866661866
diff --git a/Vaani/SDFT/download_model.py b/Vaani/SDFT/download_model.py
new file mode 100644
index 0000000000000000000000000000000000000000..15195c4c0f7205d681fe696b773f8e2be2d610f2
--- /dev/null
+++ b/Vaani/SDFT/download_model.py
@@ -0,0 +1,13 @@
+import torch
+from diffusers import StableDiffusionPipeline, UNet2DConditionModel, StableDiffusion3Pipeline
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+print("device:", device)
+
+# pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16)
+# pipe
+# del pipe
+
+pipe = StableDiffusion3Pipeline.from_pretrained("stabilityai/stable-diffusion-3.5-medium", torch_dtype=torch.bfloat16)
+pipe
+# del pipe
\ No newline at end of file
diff --git a/Vaani/SDFT/vaani-stablediffusion-finetune-kaggle.ipynb b/Vaani/SDFT/vaani-stablediffusion-finetune-kaggle.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..74464eba5adb681c979ddc76ae429d04c932bb7a
--- /dev/null
+++ b/Vaani/SDFT/vaani-stablediffusion-finetune-kaggle.ipynb
@@ -0,0 +1,650 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "trusted": true
+   },
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "from torch import nn\n",
+    "from torch.utils.data import Dataset, DataLoader\n",
+    "from transformers import CLIPTextModel, CLIPTokenizer\n",
+    "from diffusers import StableDiffusionPipeline, UNet2DConditionModel\n",
+    "from diffusers.optimization import get_scheduler\n",
+    "from accelerate import Accelerator\n",
+    "import torchaudio"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "trusted": true
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Couldn't connect to the Hub: (MaxRetryError('HTTPSConnectionPool(host=\\'huggingface.co\\', port=443): Max retries exceeded with url: /api/models/runwayml/stable-diffusion-v1-5 (Caused by NameResolutionError(\"<urllib3.connection.HTTPSConnection object at 0x7f99cc2c77d0>: Failed to resolve \\'huggingface.co\\' ([Errno -2] Name or service not known)\"))'), '(Request ID: 85a7f948-b1d1-4bb4-be97-0eaea2bfd0f8)').\n",
+      "Will try to load from local cache.\n",
+      "Loading pipeline components...: 100%|██████████| 7/7 [00:43<00:00,  6.22s/it]\n"
+     ]
+    }
+   ],
+   "source": [
+    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
+    "pipe = StableDiffusionPipeline.from_pretrained(\"runwayml/stable-diffusion-v1-5\", torch_dtype=torch.float16).to(device)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "trusted": true
+   },
+   "outputs": [],
+   "source": [
+    "unet = pipe.unet\n",
+    "vae = pipe.vae\n",
+    "tokenizer = pipe.tokenizer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "trusted": true
+   },
+   "outputs": [],
+   "source": [
+    "# Your text prompt\n",
+    "prompt = \"a photo of an astronaut riding a horse on mars\"\n",
+    "\n",
+    "# Generate image\n",
+    "with torch.autocast(\"cuda\"):\n",
+    "    image = pipe(prompt).images[0]\n",
+    "\n",
+    "# Show or save the result\n",
+    "image.show()  # Opens in default image viewer\n",
+    "image.save(\"astronaut_horse_mars.png\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<hr style=\"height:4px;border:none;color:#ff0000;background-color:#ff0000;\">"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-05-14T14:30:58.653987Z",
+     "iopub.status.busy": "2025-05-14T14:30:58.653745Z",
+     "iopub.status.idle": "2025-05-14T14:30:58.658276Z",
+     "shell.execute_reply": "2025-05-14T14:30:58.657649Z",
+     "shell.execute_reply.started": "2025-05-14T14:30:58.653970Z"
+    },
+    "trusted": true
+   },
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "from torch.utils.data import Dataset, DataLoader\n",
+    "from torchvision import transforms\n",
+    "from torchvision.transforms import v2\n",
+    "from PIL import Image\n",
+    "from diffusers import StableDiffusionPipeline\n",
+    "from diffusers.optimization import get_scheduler\n",
+    "from accelerate import Accelerator\n",
+    "from torch import nn\n",
+    "import os\n",
+    "import pandas as pd\n",
+    "from tqdm import trange, tqdm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-05-14T14:30:58.659588Z",
+     "iopub.status.busy": "2025-05-14T14:30:58.658976Z",
+     "iopub.status.idle": "2025-05-14T14:31:23.063776Z",
+     "shell.execute_reply": "2025-05-14T14:31:23.063145Z",
+     "shell.execute_reply.started": "2025-05-14T14:30:58.659571Z"
+    },
+    "trusted": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Files found: 128807\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>image_path</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>/kaggle/input/vaani-images-tar/Images/IISc_Vaa...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>/kaggle/input/vaani-images-tar/Images/IISc_Vaa...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>/kaggle/input/vaani-images-tar/Images/IISc_Vaa...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>/kaggle/input/vaani-images-tar/Images/IISc_Vaa...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>/kaggle/input/vaani-images-tar/Images/IISc_Vaa...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>128802</th>\n",
+       "      <td>/kaggle/input/vaani-images-tar/Images/IISc_Vaa...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>128803</th>\n",
+       "      <td>/kaggle/input/vaani-images-tar/Images/IISc_Vaa...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>128804</th>\n",
+       "      <td>/kaggle/input/vaani-images-tar/Images/IISc_Vaa...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>128805</th>\n",
+       "      <td>/kaggle/input/vaani-images-tar/Images/IISc_Vaa...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>128806</th>\n",
+       "      <td>/kaggle/input/vaani-images-tar/Images/IISc_Vaa...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>128807 rows × 1 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                               image_path\n",
+       "0       /kaggle/input/vaani-images-tar/Images/IISc_Vaa...\n",
+       "1       /kaggle/input/vaani-images-tar/Images/IISc_Vaa...\n",
+       "2       /kaggle/input/vaani-images-tar/Images/IISc_Vaa...\n",
+       "3       /kaggle/input/vaani-images-tar/Images/IISc_Vaa...\n",
+       "4       /kaggle/input/vaani-images-tar/Images/IISc_Vaa...\n",
+       "...                                                   ...\n",
+       "128802  /kaggle/input/vaani-images-tar/Images/IISc_Vaa...\n",
+       "128803  /kaggle/input/vaani-images-tar/Images/IISc_Vaa...\n",
+       "128804  /kaggle/input/vaani-images-tar/Images/IISc_Vaa...\n",
+       "128805  /kaggle/input/vaani-images-tar/Images/IISc_Vaa...\n",
+       "128806  /kaggle/input/vaani-images-tar/Images/IISc_Vaa...\n",
+       "\n",
+       "[128807 rows x 1 columns]"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "IMAGES_PATH = r\"/kaggle/input/vaani-images-tar/Images\"\n",
+    "\n",
+    "def walkDIR(folder_path, include=None):\n",
+    "    file_list = []\n",
+    "    for root, _, files in os.walk(folder_path):\n",
+    "        for file in files:\n",
+    "            if include is None or any(file.endswith(ext) for ext in include):\n",
+    "                file_list.append(os.path.join(root, file))\n",
+    "    print(\"Files found:\", len(file_list))\n",
+    "    return file_list\n",
+    "\n",
+    "files = walkDIR(IMAGES_PATH, include=['.png', '.jpeg', '.jpg'])\n",
+    "df = pd.DataFrame(files, columns=['image_path'])\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-05-14T14:31:23.065017Z",
+     "iopub.status.busy": "2025-05-14T14:31:23.064553Z",
+     "iopub.status.idle": "2025-05-14T14:31:23.086417Z",
+     "shell.execute_reply": "2025-05-14T14:31:23.085628Z",
+     "shell.execute_reply.started": "2025-05-14T14:31:23.064991Z"
+    },
+    "trusted": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "IMAGE SHAPE: torch.Size([3, 256, 256])\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "128807"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "class VaaniDataset(torch.utils.data.Dataset):\n",
+    "    def __init__(self, files_paths, im_size):\n",
+    "        self.files_paths = files_paths\n",
+    "        self.im_size = im_size\n",
+    "\n",
+    "    def __len__(self):\n",
+    "        return len(self.files_paths)\n",
+    "\n",
+    "    def __getitem__(self, idx):\n",
+    "        # image = tv.io.read_image(self.files_paths[idx], mode=tv.io.ImageReadMode.RGB)\n",
+    "        image = Image.open(self.files_paths[idx]).convert(\"RGB\")\n",
+    "        image = v2.ToImage()(image)\n",
+    "        # image = tv.io.decode_image(self.files_paths[idx], mode=tv.io.ImageReadMode.RGB)\n",
+    "        image = v2.Resize((self.im_size, self.im_size))(image)\n",
+    "        image = v2.ToDtype(torch.float32, scale=True)(image)\n",
+    "        # image = 2*image - 1\n",
+    "        return image\n",
+    "\n",
+    "dataset = VaaniDataset(files_paths=files, im_size=256)\n",
+    "image = dataset[2]\n",
+    "print('IMAGE SHAPE:', image.shape)\n",
+    "len(dataset)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-05-14T14:31:23.087483Z",
+     "iopub.status.busy": "2025-05-14T14:31:23.087211Z",
+     "iopub.status.idle": "2025-05-14T14:31:23.468810Z",
+     "shell.execute_reply": "2025-05-14T14:31:23.465992Z",
+     "shell.execute_reply.started": "2025-05-14T14:31:23.087458Z"
+    },
+    "trusted": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Length of Train dataset: 129\n",
+      "BATCH SHAPE: torch.Size([2, 3, 256, 256])\n"
+     ]
+    }
+   ],
+   "source": [
+    "debug = True\n",
+    "\n",
+    "if debug:\n",
+    "    s = 0.001\n",
+    "    dataset, _ = torch.utils.data.random_split(dataset, [s, 1-s], torch.manual_seed(42))\n",
+    "    print(\"Length of Train dataset:\", len(dataset))\n",
+    "\n",
+    "BATCH_SIZE = 2\n",
+    "\n",
+    "dataloader = torch.utils.data.DataLoader(\n",
+    "    dataset, \n",
+    "    batch_size=BATCH_SIZE, \n",
+    "    shuffle=True, \n",
+    "    num_workers=4,\n",
+    "    pin_memory=True,\n",
+    "    drop_last=True,\n",
+    "    persistent_workers=True\n",
+    ")\n",
+    "\n",
+    "images = next(iter(dataloader))\n",
+    "print('BATCH SHAPE:', images.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-05-14T14:31:59.796334Z",
+     "iopub.status.busy": "2025-05-14T14:31:59.795660Z",
+     "iopub.status.idle": "2025-05-14T14:31:59.800889Z",
+     "shell.execute_reply": "2025-05-14T14:31:59.800295Z",
+     "shell.execute_reply.started": "2025-05-14T14:31:59.796311Z"
+    },
+    "trusted": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "64"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(dataloader)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-05-14T14:31:23.470858Z",
+     "iopub.status.busy": "2025-05-14T14:31:23.470503Z",
+     "iopub.status.idle": "2025-05-14T14:31:28.213003Z",
+     "shell.execute_reply": "2025-05-14T14:31:28.212168Z",
+     "shell.execute_reply.started": "2025-05-14T14:31:23.470801Z"
+    },
+    "scrolled": true,
+    "trusted": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "28c0c220b2cf45968b4abdecf3936bc9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Loading pipeline components...:   0%|          | 0/7 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Load pretrained Stable Diffusion\n",
+    "pipe = StableDiffusionPipeline.from_pretrained(\n",
+    "    \"runwayml/stable-diffusion-v1-5\", \n",
+    "    torch_dtype=torch.float16\n",
+    ").to(\"cuda\")\n",
+    "\n",
+    "unet = pipe.unet\n",
+    "vae = pipe.vae\n",
+    "scheduler = pipe.scheduler"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-05-14T14:31:39.847880Z",
+     "iopub.status.busy": "2025-05-14T14:31:39.847601Z",
+     "iopub.status.idle": "2025-05-14T14:31:39.868068Z",
+     "shell.execute_reply": "2025-05-14T14:31:39.867331Z",
+     "shell.execute_reply.started": "2025-05-14T14:31:39.847863Z"
+    },
+    "trusted": true
+   },
+   "outputs": [],
+   "source": [
+    "# Optimizer and scheduler\n",
+    "optimizer = torch.optim.AdamW(unet.parameters(), lr=1e-5)\n",
+    "lr_scheduler = get_scheduler(\"linear\", optimizer=optimizer, num_warmup_steps=100, num_training_steps=1000)\n",
+    "\n",
+    "\n",
+    "accelerator = Accelerator()\n",
+    "unet, optimizer, dataloader = accelerator.prepare(unet, optimizer, dataloader)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-05-14T14:31:42.759171Z",
+     "iopub.status.busy": "2025-05-14T14:31:42.758886Z",
+     "iopub.status.idle": "2025-05-14T14:31:42.763012Z",
+     "shell.execute_reply": "2025-05-14T14:31:42.762387Z",
+     "shell.execute_reply.started": "2025-05-14T14:31:42.759152Z"
+    },
+    "trusted": true
+   },
+   "outputs": [],
+   "source": [
+    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-05-14T14:40:23.644302Z",
+     "iopub.status.busy": "2025-05-14T14:40:23.643598Z",
+     "iopub.status.idle": "2025-05-14T14:40:23.648831Z",
+     "shell.execute_reply": "2025-05-14T14:40:23.648151Z",
+     "shell.execute_reply.started": "2025-05-14T14:40:23.644244Z"
+    },
+    "trusted": true
+   },
+   "outputs": [],
+   "source": [
+    "EPOCHS = 100"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-05-14T14:40:35.244187Z",
+     "iopub.status.busy": "2025-05-14T14:40:35.243686Z"
+    },
+    "scrolled": true,
+    "trusted": true
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 84%|\u001b[32m████████████████████████████████████████████████████          \u001b[0m| 84/100 [39:16<07:28, 28.02s/it]\u001b[0m"
+     ]
+    }
+   ],
+   "source": [
+    "# Start training loop\n",
+    "for epoch in trange(EPOCHS, ncols=100, colour='green'):\n",
+    "    for step, images in enumerate(dataloader):\n",
+    "        images = images.to(device, dtype=torch.float16)\n",
+    "\n",
+    "        # Encode images to latents\n",
+    "        latents = vae.encode(images).latent_dist.sample()\n",
+    "        latents = latents * 0.18215\n",
+    "\n",
+    "        # Sample noise and timesteps\n",
+    "        noise = torch.randn_like(latents)\n",
+    "        timesteps = torch.randint(0, scheduler.config.num_train_timesteps, (latents.shape[0],), device=device).long()\n",
+    "        noisy_latents = scheduler.add_noise(latents, noise, timesteps)\n",
+    "\n",
+    "        # Use zeroed audio embedding (like a null conditioning vector)\n",
+    "        batch_size = images.shape[0]\n",
+    "        cond_dim = pipe.text_encoder.config.hidden_size  # 768 for SD 1.5\n",
+    "        null_emb = torch.zeros((batch_size, 77, cond_dim), device=device, dtype=torch.float16)\n",
+    "\n",
+    "        # Predict noise\n",
+    "        noise_pred = unet(noisy_latents, timesteps, encoder_hidden_states=null_emb).sample\n",
+    "\n",
+    "        # Loss and backward\n",
+    "        loss = nn.MSELoss()(noise_pred, noise)\n",
+    "        accelerator.backward(loss)\n",
+    "        optimizer.step()\n",
+    "        lr_scheduler.step()\n",
+    "        optimizer.zero_grad()\n",
+    "\n",
+    "        # if step % 10 == 0:\n",
+    "        #     print(f\"Epoch {epoch}, Step {step}, Loss: {loss.item():.4f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Sampling"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "trusted": true
+   },
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "from diffusers import StableDiffusionPipeline, DDIMScheduler\n",
+    "from PIL import Image\n",
+    "\n",
+    "# Load pretrained (or fine-tuned) Stable Diffusion\n",
+    "pipe = StableDiffusionPipeline.from_pretrained(\n",
+    "    \"runwayml/stable-diffusion-v1-5\",\n",
+    "    torch_dtype=torch.float16,\n",
+    ")\n",
+    "pipe.to(\"cuda\")\n",
+    "\n",
+    "# Optionally load fine-tuned weights\n",
+    "pipe.unet.load_state_dict(torch.load(\"path/to/fine_tuned_unet.pth\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "trusted": true
+   },
+   "outputs": [],
+   "source": [
+    "# Prepare dummy zero embedding\n",
+    "batch_size = 1\n",
+    "seq_len = 77  # number of tokens (CLIP text length)\n",
+    "embed_dim = pipe.text_encoder.config.hidden_size  # 768 for CLIP\n",
+    "null_emb = torch.zeros((batch_size, seq_len, embed_dim), device=\"cuda\", dtype=torch.float16)\n",
+    "\n",
+    "# Sample initial noise\n",
+    "latents = torch.randn((batch_size, pipe.unet.in_channels, 64, 64), device=\"cuda\", dtype=torch.float16)\n",
+    "\n",
+    "# Use DDIM or default scheduler\n",
+    "pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)\n",
+    "\n",
+    "# Denoising loop\n",
+    "num_inference_steps = 50\n",
+    "pipe.scheduler.set_timesteps(num_inference_steps)\n",
+    "latents = latents * pipe.scheduler.init_noise_sigma\n",
+    "\n",
+    "for t in pipe.scheduler.timesteps:\n",
+    "    # Predict noise using zero embedding\n",
+    "    with torch.no_grad():\n",
+    "        noise_pred = pipe.unet(latents, t, encoder_hidden_states=null_emb).sample\n",
+    "\n",
+    "    # Compute the previous noisy sample\n",
+    "    latents = pipe.scheduler.step(noise_pred, t, latents).prev_sample\n",
+    "\n",
+    "# Decode latents to image\n",
+    "latents = 1 / 0.18215 * latents\n",
+    "with torch.no_grad():\n",
+    "    image = pipe.vae.decode(latents).sample\n",
+    "\n",
+    "# Convert to PIL\n",
+    "image = (image / 2 + 0.5).clamp(0, 1)\n",
+    "image = image.cpu().permute(0, 2, 3, 1).numpy()[0]\n",
+    "image = Image.fromarray((image * 255).astype(\"uint8\"))\n",
+    "\n",
+    "# Save or show image\n",
+    "image.save(\"zero_condition_output.png\")\n",
+    "image.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kaggle": {
+   "accelerator": "nvidiaTeslaT4",
+   "dataSources": [
+    {
+     "datasetId": 6964433,
+     "sourceId": 11161218,
+     "sourceType": "datasetVersion"
+    }
+   ],
+   "dockerImageVersionId": 31041,
+   "isGpuEnabled": true,
+   "isInternetEnabled": true,
+   "language": "python",
+   "sourceType": "notebook"
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/Vaani/VaaniLDM/ddpm_ckpt_epoch31.pt b/Vaani/VaaniLDM/ddpm_ckpt_epoch31.pt
new file mode 100644
index 0000000000000000000000000000000000000000..edd13808ba6bfc27b906ac469cc83a78e764a57e
--- /dev/null
+++ b/Vaani/VaaniLDM/ddpm_ckpt_epoch31.pt
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:339fa2458d3ead55689b3e219be0223ddc515874a4c03bb67bce527527076073
+size 593243562
diff --git a/Vaani/VaaniLDM/ddpm_ckpt_epoch32.pt b/Vaani/VaaniLDM/ddpm_ckpt_epoch32.pt
new file mode 100644
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diff --git a/Vaani/_7.1_Vaani-LDM-High-Pre.py b/Vaani/_7.1_Vaani-LDM-High-Pre.py
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--- /dev/null
+++ b/Vaani/_7.1_Vaani-LDM-High-Pre.py
@@ -0,0 +1,2000 @@
+# ==================================================================
+#             L A T E N T   D I F F U S I O N   M O D E L
+# ==================================================================
+# Author    : Ashish Kumar Uchadiya
+# Created   : May 11, 2025
+# Description: This script implements the training of a VQ-VAE model for
+# image reconstruction, integrated with Latent Diffusion Models (LDMs) and
+# audio conditioning. The VQ-VAE maps images to a discrete latent space, 
+# which is then modeled by the LDM for learning a diffusion process over the 
+# compressed representation. Audio features are used as conditioning inputs 
+# to guide the generation process. The training minimizes a combination of 
+# LPIPS (Learned Perceptual Image Patch Similarity) loss for perceptual 
+# fidelity and PatchGAN loss to enforce local realism. This setup enables 
+# efficient and semantically-aware generation of high-quality images driven 
+# by audio cues.
+# ==================================================================
+#                         I M P O R T S
+# ==================================================================
+
+
+import os
+
+import torch
+import torch.nn as nn
+import numpy as np
+from collections import namedtuple
+
+import pandas as pd
+import torchvision as tv
+from torchvision.transforms import v2
+from tqdm import tqdm, trange
+import matplotlib.pyplot as plt
+
+import re
+import glob
+import sys
+import yaml
+import random
+import datetime
+import torch.hub
+from torch.utils.data import Dataset, DataLoader
+from torchvision.utils import make_grid
+
+print("TIME:", datetime.datetime.now())
+# os.environ["CUDA_VISIBLE_DEVICES"] = f"{sys.argv[2]}"
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+print("DEVICE:", device)
+
+
+# ==================================================================
+#                         H E L P E R S
+# ==================================================================
+from typing import Any
+from argparse import Namespace
+import typing
+
+
+class DotDict(Namespace):
+    """A simple class that builds upon `argparse.Namespace`
+    in order to make chained attributes possible."""
+
+    def __init__(self, temp=False, key=None, parent=None) -> None:
+        self._temp = temp
+        self._key = key
+        self._parent = parent
+
+    def __eq__(self, other):
+        if not isinstance(other, DotDict):
+            return NotImplemented
+        return vars(self) == vars(other)
+
+    def __getattr__(self, __name: str) -> Any:
+        if __name not in self.__dict__ and not self._temp:
+            self.__dict__[__name] = DotDict(temp=True, key=__name, parent=self)
+        else:
+            del self._parent.__dict__[self._key]
+            raise AttributeError("No attribute '%s'" % __name)
+        return self.__dict__[__name]
+
+    def __repr__(self) -> str:
+        item_keys = [k for k in self.__dict__ if not k.startswith("_")]
+
+        if len(item_keys) == 0:
+            return "DotDict()"
+        elif len(item_keys) == 1:
+            key = item_keys[0]
+            val = self.__dict__[key]
+            return "DotDict(%s=%s)" % (key, repr(val))
+        else:
+            return "DotDict(%s)" % ", ".join(
+                "%s=%s" % (key, repr(val)) for key, val in self.__dict__.items()
+            )
+
+    @classmethod
+    def from_dict(cls, original: typing.Mapping[str, any]) -> "DotDict":
+        """Create a DotDict from a (possibly nested) dict `original`.
+        Warning: this method should not be used on very deeply nested inputs,
+        since it's recursively traversing the nested dictionary values.
+        """
+        dd = DotDict()
+        for key, value in original.items():
+            if isinstance(value, typing.Mapping):
+                value = cls.from_dict(value)
+            setattr(dd, key, value)
+        return dd
+    
+    
+# ==================================================================
+#                           L P I P S 
+# ==================================================================
+class vgg16(nn.Module):
+    def __init__(self):
+        super(vgg16, self).__init__()
+        vgg_pretrained_features = tv.models.vgg16(
+            weights=tv.models.VGG16_Weights.IMAGENET1K_V1
+        ).features
+        self.slice1 = torch.nn.Sequential()
+        self.slice2 = torch.nn.Sequential()
+        self.slice3 = torch.nn.Sequential()
+        self.slice4 = torch.nn.Sequential()
+        self.slice5 = torch.nn.Sequential()
+        self.N_slices = 5
+        for x in range(4):
+            self.slice1.add_module(str(x), vgg_pretrained_features[x])
+        for x in range(4, 9):
+            self.slice2.add_module(str(x), vgg_pretrained_features[x])
+        for x in range(9, 16):
+            self.slice3.add_module(str(x), vgg_pretrained_features[x])
+        for x in range(16, 23):
+            self.slice4.add_module(str(x), vgg_pretrained_features[x])
+        for x in range(23, 30):
+            self.slice5.add_module(str(x), vgg_pretrained_features[x])
+            
+        self.eval()
+        for param in self.parameters():
+            param.requires_grad = False
+
+    def forward(self, X):
+        h1 = self.slice1(X)
+        h2 = self.slice2(h1)
+        h3 = self.slice3(h2)
+        h4 = self.slice4(h3)
+        h5 = self.slice5(h4)
+        vgg_outputs = namedtuple("VggOutputs", ['h1', 'h2', 'h3', 'h4', 'h5'])
+        out = vgg_outputs(h1, h2, h3, h4, h5)
+        return out
+
+
+def _spatial_average(in_tens, keepdim=True):
+    return in_tens.mean([2, 3], keepdim=keepdim)
+
+
+def _normalize_tensor(in_feat, eps= 1e-8):
+    norm_factor = torch.sqrt(eps + torch.sum(in_feat**2, dim=1, keepdim=True))
+    return in_feat / norm_factor
+
+
+class ScalingLayer(nn.Module):
+    def __init__(self):
+        super(ScalingLayer, self).__init__()
+        # Imagnet normalization for (0-1)
+        # mean = [0.485, 0.456, 0.406]
+        # std = [0.229, 0.224, 0.225]
+
+        self.register_buffer('shift', torch.Tensor([-.030, -.088, -.188])[None, :, None, None])
+        self.register_buffer('scale', torch.Tensor([.458, .448, .450])[None, :, None, None])
+
+    def forward(self, inp):
+        return (inp - self.shift) / self.scale
+
+
+class NetLinLayer(nn.Module):
+    ''' A single linear layer which does a 1x1 conv '''
+    def __init__(self, chn_in, chn_out=1, use_dropout=False):
+        super(NetLinLayer, self).__init__()
+        layers = [nn.Dropout(), ] if (use_dropout) else []
+        layers += [nn.Conv2d(chn_in, chn_out, 1, stride=1, padding=0, bias=False), ]
+        self.model = nn.Sequential(*layers)
+
+    def forward(self, x):
+        return self.model(x)
+
+
+class LPIPS(nn.Module):
+    def __init__(self, net='vgg', version='0.1', use_dropout=True):
+        super(LPIPS, self).__init__()
+        self.version = version
+        self.scaling_layer = ScalingLayer()
+        self.chns = [64, 128, 256, 512, 512]
+        self.L = len(self.chns)
+        self.net = vgg16()
+        self.lin0 = NetLinLayer(self.chns[0], use_dropout=use_dropout)
+        self.lin1 = NetLinLayer(self.chns[1], use_dropout=use_dropout)
+        self.lin2 = NetLinLayer(self.chns[2], use_dropout=use_dropout)
+        self.lin3 = NetLinLayer(self.chns[3], use_dropout=use_dropout)
+        self.lin4 = NetLinLayer(self.chns[4], use_dropout=use_dropout)
+        self.lins = nn.ModuleList([self.lin0, self.lin1, self.lin2, self.lin3, self.lin4])
+
+        # --- Orignal url --------------------
+        # weights_url = f"https://github.com/richzhang/PerceptualSimilarity/raw/master/lpips/weights/v{version}/{net}.pth"
+        
+        # --- Orignal Forked url -------------
+        weights_url = f"https://github.com/akuresonite/PerceptualSimilarity-Forked/raw/master/lpips/weights/v{version}/{net}.pth"
+        
+        # --- Orignal torchmetric url --------
+        # weights_url = "https://github.com/Lightning-AI/torchmetrics/raw/master/src/torchmetrics/functional/image/lpips_models/vgg.pth"
+        
+        state_dict = torch.hub.load_state_dict_from_url(weights_url, map_location='cpu')
+        self.load_state_dict(state_dict, strict=False)
+        
+        self.eval()
+        for param in self.parameters():
+            param.requires_grad = False
+
+    def forward(self, in0, in1, normalize=False):
+        # Scale the inputs to -1 to +1 range if input in [0,1]
+        if normalize:
+            in0 = 2 * in0 - 1
+            in1 = 2 * in1 - 1
+
+        in0_input, in1_input = self.scaling_layer(in0), self.scaling_layer(in1)
+        # in0_input, in1_input = in0, in1
+        
+        outs0, outs1 = self.net.forward(in0_input), self.net.forward(in1_input)
+        
+        diffs = {}
+        for kk in range(self.L):
+            feats0 = _normalize_tensor(outs0[kk])
+            feats1 = _normalize_tensor(outs1[kk])
+            diffs[kk] = (feats0 - feats1) ** 2
+            
+        res = [_spatial_average(self.lins[kk](diffs[kk]), keepdim=True) for kk in range(self.L)]
+        val = sum(res)
+        return val.reshape(-1)
+
+
+# ==================================================================
+#             P A T C H - G A N - D I S C R I M I N A T O R
+# ==================================================================
+class Discriminator(nn.Module):
+    r"""
+    PatchGAN Discriminator.
+    Rather than taking IMG_CHANNELSxIMG_HxIMG_W all the way to
+    1 scalar value , we instead predict grid of values.
+    Where each grid is prediction of how likely
+    the discriminator thinks that the image patch corresponding
+    to the grid cell is real
+    """
+
+    def __init__(
+        self,
+        im_channels=3,
+        conv_channels=[64, 128, 256],
+        kernels=[4, 4, 4, 4],
+        strides=[2, 2, 2, 1],
+        paddings=[1, 1, 1, 1],
+    ):
+        super().__init__()
+        self.im_channels = im_channels
+        activation = nn.LeakyReLU(0.2)
+        layers_dim = [self.im_channels] + conv_channels + [1]
+        self.layers = nn.ModuleList(
+            [
+                nn.Sequential(
+                    nn.Conv2d(
+                        layers_dim[i],
+                        layers_dim[i + 1],
+                        kernel_size=kernels[i],
+                        stride=strides[i],
+                        padding=paddings[i],
+                        bias=False if i != 0 else True,
+                    ),
+                    (
+                        nn.BatchNorm2d(layers_dim[i + 1])
+                        if i != len(layers_dim) - 2 and i != 0
+                        else nn.Identity()
+                    ),
+                    activation if i != len(layers_dim) - 2 else nn.Identity(),
+                )
+                for i in range(len(layers_dim) - 1)
+            ]
+        )
+
+    def forward(self, x):
+        out = x
+        for layer in self.layers:
+            out = layer(out)
+        return out
+
+
+
+# ==================================================================
+#                      D O W E - B L O C K
+# ==================================================================
+class DownBlock(nn.Module):
+    r"""
+    Down conv block with attention.
+    Sequence of following block
+    1. Resnet block with time embedding
+    2. Attention block
+    3. Downsample
+    """
+
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        t_emb_dim,
+        down_sample,
+        num_heads,
+        num_layers,
+        attn,
+        norm_channels,
+        cross_attn=False,
+        context_dim=None,
+    ):
+        super().__init__()
+        self.num_layers = num_layers
+        self.down_sample = down_sample
+        self.attn = attn
+        self.context_dim = context_dim
+        self.cross_attn = cross_attn
+        self.t_emb_dim = t_emb_dim
+        self.resnet_conv_first = nn.ModuleList(
+            [
+                nn.Sequential(
+                    nn.GroupNorm(norm_channels, in_channels if i == 0 else out_channels),
+                    nn.SiLU(),
+                    nn.Conv2d(
+                        in_channels if i == 0 else out_channels,
+                        out_channels,
+                        kernel_size=3,
+                        stride=1,
+                        padding=1,
+                    ),
+                )
+                for i in range(num_layers)
+            ]
+        )
+        if self.t_emb_dim is not None:
+            self.t_emb_layers = nn.ModuleList(
+                [
+                    nn.Sequential(nn.SiLU(), nn.Linear(self.t_emb_dim, out_channels))
+                    for _ in range(num_layers)
+                ]
+            )
+        self.resnet_conv_second = nn.ModuleList(
+            [
+                nn.Sequential(
+                    nn.GroupNorm(norm_channels, out_channels),
+                    nn.SiLU(),
+                    nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1),
+                )
+                for _ in range(num_layers)
+            ]
+        )
+
+        if self.attn:
+            self.attention_norms = nn.ModuleList(
+                [nn.GroupNorm(norm_channels, out_channels) for _ in range(num_layers)]
+            )
+
+            self.attentions = nn.ModuleList(
+                [
+                    nn.MultiheadAttention(out_channels, num_heads, batch_first=True)
+                    for _ in range(num_layers)
+                ]
+            )
+        if self.cross_attn:
+            assert context_dim is not None, "Context Dimension must be passed for cross attention"
+            self.cross_attention_norms = nn.ModuleList(
+                [nn.GroupNorm(norm_channels, out_channels) for _ in range(num_layers)]
+            )
+            self.cross_attentions = nn.ModuleList(
+                [
+                    nn.MultiheadAttention(out_channels, num_heads, batch_first=True)
+                    for _ in range(num_layers)
+                ]
+            )
+            self.context_proj = nn.ModuleList(
+                [nn.Linear(context_dim, out_channels) for _ in range(num_layers)]
+            )
+        self.residual_input_conv = nn.ModuleList(
+            [
+                nn.Conv2d(in_channels if i == 0 else out_channels, out_channels, kernel_size=1)
+                for i in range(num_layers)
+            ]
+        )
+        self.down_sample_conv = (
+            nn.Conv2d(out_channels, out_channels, 4, 2, 1) if self.down_sample else nn.Identity()
+        )
+
+    def forward(self, x, t_emb=None, context=None):
+        out = x
+        for i in range(self.num_layers):
+            # Resnet block of Unet
+
+            resnet_input = out
+            out = self.resnet_conv_first[i](out)
+            if self.t_emb_dim is not None:
+                out = out + self.t_emb_layers[i](t_emb)[:, :, None, None]
+            out = self.resnet_conv_second[i](out)
+            out = out + self.residual_input_conv[i](resnet_input)
+
+            if self.attn:
+                # Attention block of Unet
+
+                batch_size, channels, h, w = out.shape
+                in_attn = out.reshape(batch_size, channels, h * w)
+                in_attn = self.attention_norms[i](in_attn)
+                in_attn = in_attn.transpose(1, 2)
+                out_attn, _ = self.attentions[i](in_attn, in_attn, in_attn)
+                out_attn = out_attn.transpose(1, 2).reshape(batch_size, channels, h, w)
+                out = out + out_attn
+            if self.cross_attn:
+                assert (
+                    context is not None
+                ), "context cannot be None if cross attention layers are used"
+                batch_size, channels, h, w = out.shape
+                in_attn = out.reshape(batch_size, channels, h * w)
+                in_attn = self.cross_attention_norms[i](in_attn)
+                in_attn = in_attn.transpose(1, 2)
+                assert context.shape[0] == x.shape[0] and context.shape[-1] == self.context_dim
+                context_proj = self.context_proj[i](context)
+                out_attn, _ = self.cross_attentions[i](in_attn, context_proj, context_proj)
+                out_attn = out_attn.transpose(1, 2).reshape(batch_size, channels, h, w)
+                out = out + out_attn
+        # Downsample
+
+        out = self.down_sample_conv(out)
+        return out
+
+
+
+# ==================================================================
+#                        M I D - B L O C K
+# ==================================================================
+class MidBlock(nn.Module):
+    r"""
+    Mid conv block with attention.
+    Sequence of following blocks
+    1. Resnet block with time embedding
+    2. Attention block
+    3. Resnet block with time embedding
+    """
+
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        t_emb_dim,
+        num_heads,
+        num_layers,
+        norm_channels,
+        cross_attn=None,
+        context_dim=None,
+    ):
+        super().__init__()
+        self.num_layers = num_layers
+        self.t_emb_dim = t_emb_dim
+        self.context_dim = context_dim
+        self.cross_attn = cross_attn
+        self.resnet_conv_first = nn.ModuleList(
+            [
+                nn.Sequential(
+                    nn.GroupNorm(norm_channels, in_channels if i == 0 else out_channels),
+                    nn.SiLU(),
+                    nn.Conv2d(
+                        in_channels if i == 0 else out_channels,
+                        out_channels,
+                        kernel_size=3,
+                        stride=1,
+                        padding=1,
+                    ),
+                )
+                for i in range(num_layers + 1)
+            ]
+        )
+
+        if self.t_emb_dim is not None:
+            self.t_emb_layers = nn.ModuleList(
+                [
+                    nn.Sequential(nn.SiLU(), nn.Linear(t_emb_dim, out_channels))
+                    for _ in range(num_layers + 1)
+                ]
+            )
+        self.resnet_conv_second = nn.ModuleList(
+            [
+                nn.Sequential(
+                    nn.GroupNorm(norm_channels, out_channels),
+                    nn.SiLU(),
+                    nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1),
+                )
+                for _ in range(num_layers + 1)
+            ]
+        )
+
+        self.attention_norms = nn.ModuleList(
+            [nn.GroupNorm(norm_channels, out_channels) for _ in range(num_layers)]
+        )
+
+        self.attentions = nn.ModuleList(
+            [
+                nn.MultiheadAttention(out_channels, num_heads, batch_first=True)
+                for _ in range(num_layers)
+            ]
+        )
+        if self.cross_attn:
+            assert context_dim is not None, "Context Dimension must be passed for cross attention"
+            self.cross_attention_norms = nn.ModuleList(
+                [nn.GroupNorm(norm_channels, out_channels) for _ in range(num_layers)]
+            )
+            self.cross_attentions = nn.ModuleList(
+                [
+                    nn.MultiheadAttention(out_channels, num_heads, batch_first=True)
+                    for _ in range(num_layers)
+                ]
+            )
+            self.context_proj = nn.ModuleList(
+                [nn.Linear(context_dim, out_channels) for _ in range(num_layers)]
+            )
+        self.residual_input_conv = nn.ModuleList(
+            [
+                nn.Conv2d(in_channels if i == 0 else out_channels, out_channels, kernel_size=1)
+                for i in range(num_layers + 1)
+            ]
+        )
+
+    def forward(self, x, t_emb=None, context=None):
+        out = x
+
+        # First resnet block
+        resnet_input = out
+        out = self.resnet_conv_first[0](out)
+        if self.t_emb_dim is not None:
+            out = out + self.t_emb_layers[0](t_emb)[:, :, None, None]
+        out = self.resnet_conv_second[0](out)
+        out = out + self.residual_input_conv[0](resnet_input)
+
+        for i in range(self.num_layers):
+            # Attention Block
+            batch_size, channels, h, w = out.shape
+            in_attn = out.reshape(batch_size, channels, h * w)
+            in_attn = self.attention_norms[i](in_attn)
+            in_attn = in_attn.transpose(1, 2)
+            out_attn, _ = self.attentions[i](in_attn, in_attn, in_attn)
+            out_attn = out_attn.transpose(1, 2).reshape(batch_size, channels, h, w)
+            out = out + out_attn
+
+            if self.cross_attn:
+                assert (
+                    context is not None
+                ), "context cannot be None if cross attention layers are used"
+                batch_size, channels, h, w = out.shape
+                in_attn = out.reshape(batch_size, channels, h * w)
+                in_attn = self.cross_attention_norms[i](in_attn)
+                in_attn = in_attn.transpose(1, 2)
+                assert context.shape[0] == x.shape[0] and context.shape[-1] == self.context_dim
+                context_proj = self.context_proj[i](context)
+                out_attn, _ = self.cross_attentions[i](in_attn, context_proj, context_proj)
+                out_attn = out_attn.transpose(1, 2).reshape(batch_size, channels, h, w)
+                out = out + out_attn
+            
+            # Resnet Block
+            resnet_input = out
+            out = self.resnet_conv_first[i + 1](out)
+            if self.t_emb_dim is not None:
+                out = out + self.t_emb_layers[i + 1](t_emb)[:, :, None, None]
+            out = self.resnet_conv_second[i + 1](out)
+            out = out + self.residual_input_conv[i + 1](resnet_input)
+        return out
+
+
+# ==================================================================
+#                        U P - B L O C K
+# ==================================================================
+class UpBlock(nn.Module):
+    r"""
+    Up conv block with attention.
+    Sequence of following blocks
+    1. Upsample
+    1. Concatenate Down block output
+    2. Resnet block with time embedding
+    3. Attention Block
+    """
+
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        t_emb_dim,
+        up_sample,
+        num_heads,
+        num_layers,
+        attn,
+        norm_channels,
+    ):
+        super().__init__()
+        self.num_layers = num_layers
+        self.up_sample = up_sample
+        self.t_emb_dim = t_emb_dim
+        self.attn = attn
+        self.resnet_conv_first = nn.ModuleList(
+            [
+                nn.Sequential(
+                    nn.GroupNorm(norm_channels, in_channels if i == 0 else out_channels),
+                    nn.SiLU(),
+                    nn.Conv2d(
+                        in_channels if i == 0 else out_channels,
+                        out_channels,
+                        kernel_size=3,
+                        stride=1,
+                        padding=1,
+                    ),
+                )
+                for i in range(num_layers)
+            ]
+        )
+
+        if self.t_emb_dim is not None:
+            self.t_emb_layers = nn.ModuleList(
+                [
+                    nn.Sequential(nn.SiLU(), nn.Linear(t_emb_dim, out_channels))
+                    for _ in range(num_layers)
+                ]
+            )
+        self.resnet_conv_second = nn.ModuleList(
+            [
+                nn.Sequential(
+                    nn.GroupNorm(norm_channels, out_channels),
+                    nn.SiLU(),
+                    nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1),
+                )
+                for _ in range(num_layers)
+            ]
+        )
+        if self.attn:
+            self.attention_norms = nn.ModuleList(
+                [nn.GroupNorm(norm_channels, out_channels) for _ in range(num_layers)]
+            )
+
+            self.attentions = nn.ModuleList(
+                [
+                    nn.MultiheadAttention(out_channels, num_heads, batch_first=True)
+                    for _ in range(num_layers)
+                ]
+            )
+        self.residual_input_conv = nn.ModuleList(
+            [
+                nn.Conv2d(in_channels if i == 0 else out_channels, out_channels, kernel_size=1)
+                for i in range(num_layers)
+            ]
+        )
+        self.up_sample_conv = (
+            nn.ConvTranspose2d(in_channels, in_channels, 4, 2, 1)
+            if self.up_sample
+            else nn.Identity()
+        )
+
+    def forward(self, x, out_down=None, t_emb=None):
+        # Upsample
+
+        x = self.up_sample_conv(x)
+
+        # Concat with Downblock output
+
+        if out_down is not None:
+            x = torch.cat([x, out_down], dim=1)
+        out = x
+        for i in range(self.num_layers):
+            # Resnet Block
+
+            resnet_input = out
+            out = self.resnet_conv_first[i](out)
+            if self.t_emb_dim is not None:
+                out = out + self.t_emb_layers[i](t_emb)[:, :, None, None]
+            out = self.resnet_conv_second[i](out)
+            out = out + self.residual_input_conv[i](resnet_input)
+
+            # Self Attention
+
+            if self.attn:
+                batch_size, channels, h, w = out.shape
+                in_attn = out.reshape(batch_size, channels, h * w)
+                in_attn = self.attention_norms[i](in_attn)
+                in_attn = in_attn.transpose(1, 2)
+                out_attn, _ = self.attentions[i](in_attn, in_attn, in_attn)
+                out_attn = out_attn.transpose(1, 2).reshape(batch_size, channels, h, w)
+                out = out + out_attn
+        return out
+
+
+# ==================================================================
+#                           V Q - V A E
+# ==================================================================
+class VQVAE(nn.Module):
+    def __init__(self, im_channels, model_config):
+        super().__init__()
+        self.down_channels = model_config.down_channels
+        self.mid_channels = model_config.mid_channels
+        self.down_sample = model_config.down_sample
+        self.num_down_layers = model_config.num_down_layers
+        self.num_mid_layers = model_config.num_mid_layers
+        self.num_up_layers = model_config.num_up_layers
+
+        # To disable attention in Downblock of Encoder and Upblock of Decoder
+        self.attns = model_config.attn_down
+
+        # Latent Dimension
+        self.z_channels = model_config.z_channels
+        self.codebook_size = model_config.codebook_size
+        self.norm_channels = model_config.norm_channels
+        self.num_heads = model_config.num_heads
+
+        # Assertion to validate the channel information
+        assert self.mid_channels[0] == self.down_channels[-1]
+        assert self.mid_channels[-1] == self.down_channels[-1]
+        assert len(self.down_sample) == len(self.down_channels) - 1
+        assert len(self.attns) == len(self.down_channels) - 1
+
+        # Wherever we use downsampling in encoder correspondingly use
+        # upsampling in decoder
+        self.up_sample = list(reversed(self.down_sample))
+
+        ##################### Encoder ######################
+        self.encoder_conv_in = nn.Conv2d(
+            im_channels, self.down_channels[0], kernel_size=3, padding=(1, 1)
+        )
+
+        # Downblock + Midblock
+        self.encoder_layers = nn.ModuleList([])
+        for i in range(len(self.down_channels) - 1):
+            self.encoder_layers.append(
+                DownBlock(
+                    self.down_channels[i],
+                    self.down_channels[i + 1],
+                    t_emb_dim=None,
+                    down_sample=self.down_sample[i],
+                    num_heads=self.num_heads,
+                    num_layers=self.num_down_layers,
+                    attn=self.attns[i],
+                    norm_channels=self.norm_channels,
+                )
+            )
+        self.encoder_mids = nn.ModuleList([])
+        for i in range(len(self.mid_channels) - 1):
+            self.encoder_mids.append(
+                MidBlock(
+                    self.mid_channels[i],
+                    self.mid_channels[i + 1],
+                    t_emb_dim=None,
+                    num_heads=self.num_heads,
+                    num_layers=self.num_mid_layers,
+                    norm_channels=self.norm_channels,
+                )
+            )
+        self.encoder_norm_out = nn.GroupNorm(self.norm_channels, self.down_channels[-1])
+        self.encoder_conv_out = nn.Conv2d(
+            self.down_channels[-1], self.z_channels, kernel_size=3, padding=1
+        )
+
+        # Pre Quantization Convolution
+        self.pre_quant_conv = nn.Conv2d(self.z_channels, self.z_channels, kernel_size=1)
+
+        # Codebook
+        self.embedding = nn.Embedding(self.codebook_size, self.z_channels)
+        ####################################################
+
+        ##################### Decoder ######################
+
+        # Post Quantization Convolution
+        self.post_quant_conv = nn.Conv2d(self.z_channels, self.z_channels, kernel_size=1)
+        self.decoder_conv_in = nn.Conv2d(
+            self.z_channels, self.mid_channels[-1], kernel_size=3, padding=(1, 1)
+        )
+
+        # Midblock + Upblock
+        self.decoder_mids = nn.ModuleList([])
+        for i in reversed(range(1, len(self.mid_channels))):
+            self.decoder_mids.append(
+                MidBlock(
+                    self.mid_channels[i],
+                    self.mid_channels[i - 1],
+                    t_emb_dim=None,
+                    num_heads=self.num_heads,
+                    num_layers=self.num_mid_layers,
+                    norm_channels=self.norm_channels,
+                )
+            )
+        self.decoder_layers = nn.ModuleList([])
+        for i in reversed(range(1, len(self.down_channels))):
+            self.decoder_layers.append(
+                UpBlock(
+                    self.down_channels[i],
+                    self.down_channels[i - 1],
+                    t_emb_dim=None,
+                    up_sample=self.down_sample[i - 1],
+                    num_heads=self.num_heads,
+                    num_layers=self.num_up_layers,
+                    attn=self.attns[i - 1],
+                    norm_channels=self.norm_channels,
+                )
+            )
+        self.decoder_norm_out = nn.GroupNorm(self.norm_channels, self.down_channels[0])
+        self.decoder_conv_out = nn.Conv2d(
+            self.down_channels[0], im_channels, kernel_size=3, padding=1
+        )
+
+    def quantize(self, x):
+        B, C, H, W = x.shape
+
+        # B, C, H, W -> B, H, W, C
+        x = x.permute(0, 2, 3, 1)
+
+        # B, H, W, C -> B, H*W, C
+        x = x.reshape(x.size(0), -1, x.size(-1))
+
+        # Find nearest embedding/codebook vector
+        # dist between (B, H*W, C) and (B, K, C) -> (B, H*W, K)
+        dist = torch.cdist(x, self.embedding.weight[None, :].repeat((x.size(0), 1, 1)))
+        # (B, H*W)
+        min_encoding_indices = torch.argmin(dist, dim=-1)
+
+        # Replace encoder output with nearest codebook
+        # quant_out -> B*H*W, C
+        quant_out = torch.index_select(self.embedding.weight, 0, min_encoding_indices.view(-1))
+
+        # x -> B*H*W, C
+        x = x.reshape((-1, x.size(-1)))
+        commmitment_loss = torch.mean((quant_out.detach() - x) ** 2)
+        codebook_loss = torch.mean((quant_out - x.detach()) ** 2)
+        quantize_losses = {"codebook_loss": codebook_loss, "commitment_loss": commmitment_loss}
+        # Straight through estimation
+        quant_out = x + (quant_out - x).detach()
+
+        # quant_out -> B, C, H, W
+        quant_out = quant_out.reshape((B, H, W, C)).permute(0, 3, 1, 2)
+        min_encoding_indices = min_encoding_indices.reshape(
+            (-1, quant_out.size(-2), quant_out.size(-1))
+        )
+        return quant_out, quantize_losses, min_encoding_indices
+
+    def encode(self, x):
+        out = self.encoder_conv_in(x)
+        for idx, down in enumerate(self.encoder_layers):
+            out = down(out)
+        for mid in self.encoder_mids:
+            out = mid(out)
+        out = self.encoder_norm_out(out)
+        out = nn.SiLU()(out)
+        out = self.encoder_conv_out(out)
+        out = self.pre_quant_conv(out)
+        out, quant_losses, _ = self.quantize(out)
+        return out, quant_losses
+
+    def decode(self, z):
+        out = z
+        out = self.post_quant_conv(out)
+        out = self.decoder_conv_in(out)
+        for mid in self.decoder_mids:
+            out = mid(out)
+        for idx, up in enumerate(self.decoder_layers):
+            out = up(out)
+        out = self.decoder_norm_out(out)
+        out = nn.SiLU()(out)
+        out = self.decoder_conv_out(out)
+        return out
+
+    def forward(self, x):
+        '''out: [B, 3, 256, 256]
+        z: [B, 3, 64, 64]
+        quant_losses: {
+            codebook_loss: 0.0681,
+            commitment_loss: 0.0681
+        }
+        '''
+        z, quant_losses = self.encode(x)
+        out = self.decode(z)
+        return out, z, quant_losses
+
+
+# ==================================================================
+#                   C O N F I G U R A T I O N
+# ==================================================================
+import pprint
+config_path = "/home/IITB/ai-at-ieor/23m1521/ashish/MTP/Vaani/config-LDM-High-Pre.yaml"
+# config_path = sys.argv[1]
+with open(config_path, 'r') as file:
+    Config = yaml.safe_load(file)
+    pprint.pprint(Config, width=120)
+
+Config = DotDict.from_dict(Config)
+dataset_config = Config.dataset_params
+diffusion_config = Config.diffusion_params
+model_config = Config.model_params
+train_config = Config.train_params
+paths = Config.paths
+
+
+# ==================================================================
+#                   V A A N I - D A T A S E T
+# ==================================================================
+IMAGES_PATH = paths.images_dir
+
+def walkDIR(folder_path, include=None):
+    file_list = []
+    for root, _, files in os.walk(folder_path):
+        for file in files:
+            if include is None or any(file.endswith(ext) for ext in include):
+                file_list.append(os.path.join(root, file))
+    print("Files found:", len(file_list))
+    return file_list
+
+files = walkDIR(IMAGES_PATH, include=['.png', '.jpeg', '.jpg'])
+df = pd.DataFrame(files, columns=['image_path'])
+
+class VaaniDataset(torch.utils.data.Dataset):
+    def __init__(self, files_paths, im_size):
+        self.files_paths = files_paths
+        self.im_size = im_size
+
+    def __len__(self):
+        return len(self.files_paths)
+
+    def __getitem__(self, idx):
+        image = tv.io.read_image(self.files_paths[idx], mode=tv.io.ImageReadMode.RGB)
+        # image = tv.io.decode_image(self.files_paths[idx], mode=tv.io.ImageReadMode.RGB)
+        image = v2.Resize((self.im_size,self.im_size))(image)
+        image = v2.ToDtype(torch.float32, scale=True)(image)
+        # image = 2*image - 1
+        return image
+
+dataset = VaaniDataset(files_paths=files, im_size=dataset_config.im_size)
+image = dataset[2]
+print('IMAGE SHAPE:', image.shape)
+if train_config.debug:
+    s = 0.001
+    dataset, _ = torch.utils.data.random_split(dataset, [s, 1-s], torch.manual_seed(42))
+    print("Length of Train dataset:", len(dataset))
+
+if sys.argv[1] == "train_vae":
+    BATCH_SIZE = train_config.autoencoder_batch_size
+elif sys.argv[1] == "train_ldm":
+    BATCH_SIZE = train_config.ldm_batch_size
+
+dataloader = torch.utils.data.DataLoader(
+    dataset, 
+    batch_size=BATCH_SIZE, 
+    shuffle=True, 
+    num_workers=48,
+    pin_memory=True,
+    drop_last=True,
+    persistent_workers=True
+)
+
+images = next(iter(dataloader))
+print('BATCH SHAPE:', images.shape)
+
+
+# ==================================================================
+#               M O D E L - I N I T I L I Z A T I O N
+# ==================================================================
+dataset_config = Config.dataset_params
+autoencoder_config = Config.autoencoder_params
+train_config = Config.train_params
+
+# model = VQVAE(im_channels=dataset_config.im_channels, 
+#               model_config=autoencoder_config).to(device)
+
+# model_output = model(images.to(device))
+# print('MODEL OUTPUT:')
+# print(model_output[0].shape, model_output[1].shape, model_output[2])
+
+# from torchinfo import summary
+# summary(model=model,
+#         input_data=images.to(device),
+#         # input_size = (1, 3, config.IMAGE_SIZE, config.IMAGE_SIZE),
+#         col_names = ["input_size", "output_size", "num_params", "trainable", "params_percent"],
+#         col_width=20,
+#         row_settings=["var_names"],
+#         depth = 6,
+#         # device=device
+# )
+# exit()
+
+
+# ==================================================================
+#                  V Q - V A E - T R A I N I N G
+# ==================================================================
+# python your_script.py 2>&1 > training.log
+import time
+
+def format_time(t1, t2):
+    elapsed_time = t2 - t1
+    if elapsed_time < 60:
+        return f"{elapsed_time:.2f} seconds"
+    elif elapsed_time < 3600:
+        minutes = elapsed_time // 60
+        seconds = elapsed_time % 60
+        return f"{minutes:.0f} minutes {seconds:.2f} seconds"
+    elif elapsed_time < 86400:
+        hours = elapsed_time // 3600
+        remainder = elapsed_time % 3600
+        minutes = remainder // 60
+        seconds = remainder % 60
+        return f"{hours:.0f} hours {minutes:.0f} minutes {seconds:.2f} seconds"
+    else:
+        days = elapsed_time // 86400
+        remainder = elapsed_time % 86400
+        hours = remainder // 3600
+        remainder = remainder % 3600
+        minutes = remainder // 60
+        seconds = remainder % 60
+        return f"{days:.0f} days {hours:.0f} hours {minutes:.0f} minutes {seconds:.2f} seconds"
+
+
+def find_checkpoints(checkpoint_path):
+    directory = os.path.dirname(checkpoint_path)
+    prefix = os.path.basename(checkpoint_path)
+    pattern = re.compile(rf"{re.escape(prefix)}_epoch(\d+)\.pt$")
+
+    try:
+        files = os.listdir(directory)
+    except FileNotFoundError:
+        return []
+
+    return [
+        os.path.join(directory, f)
+        for f in files if pattern.match(f)
+    ]
+    
+    
+def save_vae_checkpoint(
+    total_steps, epoch, model, discriminator, optimizer_d,
+    optimizer_g, metrics, checkpoint_path, logs, total_training_time
+):
+    checkpoint = {
+        "total_steps": total_steps,
+        "epoch": epoch,
+        "model_state_dict": model.state_dict(),
+        "discriminator_state_dict": discriminator.state_dict(),
+        "optimizer_d_state_dict": optimizer_d.state_dict(),
+        "optimizer_g_state_dict": optimizer_g.state_dict(),
+        "metrics": metrics,
+        "logs": logs,
+        "total_training_time": total_training_time
+    }
+    checkpoint_file = f"{checkpoint_path}_epoch{epoch}.pt"
+    torch.save(checkpoint, checkpoint_file)
+    print(f"VQVAE Checkpoint saved at {checkpoint_file}")
+    all_ckpts = glob.glob(f"{checkpoint_path}_epoch*.pt")
+    # all_ckpts = find_checkpoints(checkpoint_path)    
+    def extract_epoch(filename):
+        match = re.search(r"_epoch(\d+)\.pt", filename)
+        return int(match.group(1)) if match else -1
+    all_ckpts = sorted(all_ckpts, key=extract_epoch)
+    for old_ckpt in all_ckpts[:-2]:
+        os.remove(old_ckpt)
+        print(f"Removed old VQVAE checkpoint: {old_ckpt}")
+
+
+
+def load_vae_checkpoint(checkpoint_path, model, discriminator, optimizer_d, optimizer_g, device=device):
+    all_ckpts = glob.glob(f"{checkpoint_path}_epoch*.pt")
+    # all_ckpts = find_checkpoints(checkpoint_path)    
+    if not all_ckpts:
+        print("No VQVAE checkpoint found. Starting from scratch.")
+        return 0, 0, None, [], 0
+    def extract_epoch(filename):
+        match = re.search(r"_epoch(\d+)\.pt", filename)
+        return int(match.group(1)) if match else -1
+    all_ckpts = sorted(all_ckpts, key=extract_epoch)
+    latest_ckpt = all_ckpts[-1]
+    if os.path.exists(latest_ckpt):
+        checkpoint = torch.load(latest_ckpt, map_location=device)
+        model.load_state_dict(checkpoint["model_state_dict"])
+        discriminator.load_state_dict(checkpoint["discriminator_state_dict"])
+        optimizer_d.load_state_dict(checkpoint["optimizer_d_state_dict"])
+        optimizer_g.load_state_dict(checkpoint["optimizer_g_state_dict"])
+        total_steps = checkpoint["total_steps"]
+        epoch = checkpoint["epoch"]
+        metrics = checkpoint["metrics"]
+        logs = checkpoint.get("logs", [])
+        total_training_time = checkpoint.get("total_training_time", 0)
+        print(f"VQVAE Checkpoint loaded from {latest_ckpt}. Resuming from epoch {epoch + 1}, step {total_steps}")
+        return total_steps, epoch + 1, metrics, logs, total_training_time
+    else:
+        print("No VQVAE checkpoint found. Starting from scratch.")
+        return 0, 0, None, [], 0
+
+from PIL import Image
+def inference(model, dataset, save_path, epoch, device="cuda", sample_size=8):
+    if not os.path.exists(save_path):
+        os.makedirs(save_path)
+
+    image_tensors = []
+    for i in range(sample_size):
+        image_tensors.append(dataset[i].unsqueeze(0))
+
+    image_tensors = torch.cat(image_tensors, dim=0).to(device)
+    with torch.no_grad():
+        outputs, _, _ = model(image_tensors)
+
+        save_input = image_tensors.detach().cpu()
+        save_output = outputs.detach().cpu()
+
+        grid = make_grid(torch.cat([save_input, save_output], dim=0), nrow=sample_size)
+
+        np_img = (grid * 255).byte().numpy().transpose(1, 2, 0)
+        combined_image = Image.fromarray(np_img)
+        combined_image.save("output_image.png")
+        
+        # combined_image = tv.transforms.ToPILImage()(grid)
+        combined_image.save(os.path.join(save_path, f"reconstructed_images_EP-{epoch}_{sample_size}.png"))
+
+    print(f"Reconstructed images saved at: {save_path}")    
+
+
+def trainVAE(Config, dataloader):
+    dataset_config = Config.dataset_params
+    autoencoder_config = Config.autoencoder_params
+    train_config = Config.train_params
+    paths = Config.paths
+
+    seed = train_config.seed
+    torch.manual_seed(seed)
+    np.random.seed(seed)
+    random.seed(seed)
+    if device == "cuda":
+        torch.cuda.manual_seed_all(seed)
+
+    model = VQVAE(im_channels=dataset_config.im_channels, model_config=autoencoder_config).to(device)
+    discriminator = Discriminator(im_channels=dataset_config.im_channels).to(device)
+
+    optimizer_d = torch.optim.AdamW(discriminator.parameters(), lr=train_config.autoencoder_lr, betas=(0.5, 0.999))
+    optimizer_g = torch.optim.AdamW(model.parameters(), lr=train_config.autoencoder_lr, betas=(0.5, 0.999))
+
+    checkpoint_path = os.path.join(train_config.task_name, "vqvae_ckpt.pth")
+    (total_steps, start_epoch, 
+     metrics, logs, total_training_time) = load_vae_checkpoint(checkpoint_path, 
+                                                               model, discriminator, 
+                                                               optimizer_d, optimizer_g)
+
+    if not os.path.exists(train_config.task_name):
+        os.mkdir(train_config.task_name)
+
+    num_epochs = train_config.autoencoder_epochs
+    recon_criterion = torch.nn.MSELoss()
+    disc_criterion = torch.nn.MSELoss()
+    lpips_model = LPIPS().eval().to(device)
+
+    acc_steps = train_config.autoencoder_acc_steps
+    disc_step_start = train_config.disc_start
+
+    start_time_total = time.time() - total_training_time
+
+    for epoch_idx in trange(start_epoch, num_epochs, colour='red', dynamic_ncols=True):
+        start_time_epoch = time.time()
+        epoch_log = []
+
+        for images in tqdm(dataloader, colour='green', dynamic_ncols=True):
+            batch_start_time = time.time()
+            total_steps += 1
+
+            images = images.to(device)
+            model_output = model(images)
+            output, z, quantize_losses = model_output
+
+            recon_loss = recon_criterion(output, images) / acc_steps
+
+            g_loss = (
+                recon_loss
+                + (train_config.codebook_weight * quantize_losses["codebook_loss"] / acc_steps)
+                + (train_config.commitment_beta * quantize_losses["commitment_loss"] / acc_steps)
+            )
+
+            if total_steps > disc_step_start:
+                disc_fake_pred = discriminator(output)
+                disc_fake_loss = disc_criterion(disc_fake_pred, torch.ones_like(disc_fake_pred))
+                g_loss += train_config.disc_weight * disc_fake_loss / acc_steps
+
+            lpips_loss = torch.mean(lpips_model(output, images)) / acc_steps
+            g_loss += train_config.perceptual_weight * lpips_loss
+
+            g_loss.backward()
+
+            if total_steps % acc_steps == 0:
+                optimizer_g.step()
+                optimizer_g.zero_grad()
+
+            if total_steps > disc_step_start:
+                disc_fake_pred = discriminator(output.detach())
+                disc_real_pred = discriminator(images)
+                
+                # disc_loss = (disc_criterion(disc_fake_pred, torch.zeros_like(disc_fake_pred)) +
+                #              disc_criterion(disc_real_pred, torch.ones_like(disc_real_pred))) / 2 / acc_steps
+                
+                disc_fake_loss = disc_criterion(disc_fake_pred, torch.zeros(disc_fake_pred.shape, device=disc_fake_pred.device))
+                disc_real_loss = disc_criterion(disc_real_pred, torch.ones(disc_real_pred.shape, device=disc_real_pred.device))
+                
+                disc_loss = train_config.disc_weight * (disc_fake_loss + disc_real_loss) / 2 / acc_steps
+                
+                disc_loss.backward()
+
+                if total_steps % acc_steps == 0:
+                    optimizer_d.step()
+                    optimizer_d.zero_grad()
+                    
+            if total_steps % acc_steps == 0:
+                optimizer_g.step()
+                optimizer_g.zero_grad()
+
+            batch_time = time.time() - batch_start_time
+            epoch_log.append(format_time(0, batch_time))
+
+        optimizer_d.step()
+        optimizer_d.zero_grad()
+        optimizer_g.step()
+        optimizer_g.zero_grad()
+        
+        epoch_time = time.time() - start_time_epoch
+        logs.append({"epoch": epoch_idx + 1, "epoch_time": format_time(0, epoch_time), "batch_times": epoch_log})
+
+        total_training_time = time.time() - start_time_total
+
+        save_vae_checkpoint(total_steps, epoch_idx + 1, model, discriminator, optimizer_d, optimizer_g, metrics, checkpoint_path, logs, total_training_time)
+        recon_save_path = os.path.join(train_config.task_name, 'vqvae_recon')
+        inference(model, dataset, recon_save_path, epoch=epoch_idx, device=device, sample_size=16)
+
+    print("Training completed.")
+
+
+
+
+# ==================================================================
+#          S T A R T I N G - V Q - V A E - T R A I N I N G
+# ==================================================================
+# trainVAE(Config, dataloader)
+
+# python Vaani-VQVAE-Main.py | tee AE-training.log
+# python Vaani-VQVAE-Main.py > AE-training.log 2>&1
+
+
+
+
+
+# ==================================================================
+#            L I N E A R - N O I S E - S C H E D U L E R
+# ==================================================================
+class LinearNoiseScheduler:
+    r"""
+    Class for the linear noise scheduler that is used in DDPM.
+    """
+
+    def __init__(self, num_timesteps, beta_start, beta_end):
+        
+        self.num_timesteps = num_timesteps
+        self.beta_start = beta_start
+        self.beta_end = beta_end
+        # Mimicking how compvis repo creates schedule
+        self.betas = (
+                torch.linspace(beta_start ** 0.5, beta_end ** 0.5, num_timesteps) ** 2
+        )
+        self.alphas = 1. - self.betas
+        self.alpha_cum_prod = torch.cumprod(self.alphas, dim=0)
+        self.sqrt_alpha_cum_prod = torch.sqrt(self.alpha_cum_prod)
+        self.sqrt_one_minus_alpha_cum_prod = torch.sqrt(1 - self.alpha_cum_prod)
+
+    def add_noise(self, original, noise, t):
+        r"""
+        Forward method for diffusion
+        :param original: Image on which noise is to be applied
+        :param noise: Random Noise Tensor (from normal dist)
+        :param t: timestep of the forward process of shape -> (B,)
+        :return:
+        """
+        original_shape = original.shape
+        batch_size = original_shape[0]
+
+        sqrt_alpha_cum_prod = self.sqrt_alpha_cum_prod.to(original.device)[t].reshape(batch_size)
+        sqrt_one_minus_alpha_cum_prod = self.sqrt_one_minus_alpha_cum_prod.to(original.device)[t].reshape(batch_size)
+
+        # Reshape till (B,) becomes (B,1,1,1) if image is (B,C,H,W)
+        for _ in range(len(original_shape) - 1):
+            sqrt_alpha_cum_prod = sqrt_alpha_cum_prod.unsqueeze(-1)
+        for _ in range(len(original_shape) - 1):
+            sqrt_one_minus_alpha_cum_prod = sqrt_one_minus_alpha_cum_prod.unsqueeze(-1)
+
+        # Apply and Return Forward process equation
+        return (sqrt_alpha_cum_prod.to(original.device) * original
+                + sqrt_one_minus_alpha_cum_prod.to(original.device) * noise)
+
+    def sample_prev_timestep(self, xt, noise_pred, t):
+        r"""
+            Use the noise prediction by model to get
+            xt-1 using xt and the nosie predicted
+        :param xt: current timestep sample
+        :param noise_pred: model noise prediction
+        :param t: current timestep we are at
+        :return:
+        """
+        x0 = ((xt - (self.sqrt_one_minus_alpha_cum_prod.to(xt.device)[t] * noise_pred)) /
+              torch.sqrt(self.alpha_cum_prod.to(xt.device)[t]))
+        x0 = torch.clamp(x0, -1., 1.)
+
+        mean = xt - ((self.betas.to(xt.device)[t]) * noise_pred) / (self.sqrt_one_minus_alpha_cum_prod.to(xt.device)[t])
+        mean = mean / torch.sqrt(self.alphas.to(xt.device)[t])
+
+        if t == 0:
+            return mean, x0
+        else:
+            variance = (1 - self.alpha_cum_prod.to(xt.device)[t - 1]) / (1.0 - self.alpha_cum_prod.to(xt.device)[t])
+            variance = variance * self.betas.to(xt.device)[t]
+            sigma = variance ** 0.5
+            z = torch.randn(xt.shape).to(xt.device)
+
+            # OR
+            # variance = self.betas[t]
+            # sigma = variance ** 0.5
+            # z = torch.randn(xt.shape).to(xt.device)
+            return mean + sigma * z, x0
+
+
+
+# ==================================================================
+#                    T I M E - E M B E D D I N G
+# ==================================================================
+def get_time_embedding(time_steps, temb_dim):
+    r"""
+    Convert time steps tensor into an embedding using the
+    sinusoidal time embedding formula
+    :param time_steps: 1D tensor of length batch size
+    :param temb_dim: Dimension of the embedding
+    :return: BxD embedding representation of B time steps
+    """
+    assert temb_dim % 2 == 0, "time embedding dimension must be divisible by 2"
+
+    # factor = 10000^(2i/d_model)
+    factor = 10000 ** ((torch.arange(
+        start=0, end=temb_dim // 2, dtype=torch.float32, device=time_steps.device) / (temb_dim // 2))
+    )
+
+    # pos / factor
+    # timesteps B -> B, 1 -> B, temb_dim
+    t_emb = time_steps[:, None].repeat(1, temb_dim // 2) / factor
+    t_emb = torch.cat([torch.sin(t_emb), torch.cos(t_emb)], dim=-1)
+    return t_emb
+
+
+
+
+
+# ==================================================================
+#               L D M - U N E T - U P - B L O C K
+# ==================================================================
+class UpBlockUnet(nn.Module):
+    r"""
+    Up conv block with attention.
+    Sequence of following blocks
+    1. Upsample
+    1. Concatenate Down block output
+    2. Resnet block with time embedding
+    3. Attention Block
+    """
+
+    def __init__(self, in_channels, out_channels, t_emb_dim, up_sample,
+                 num_heads, num_layers, norm_channels, cross_attn=False, context_dim=None):
+        super().__init__()
+        self.num_layers = num_layers
+        self.up_sample = up_sample
+        self.t_emb_dim = t_emb_dim
+        self.cross_attn = cross_attn
+        self.context_dim = context_dim
+        self.resnet_conv_first = nn.ModuleList(
+            [
+                nn.Sequential(
+                    nn.GroupNorm(norm_channels, in_channels if i == 0 else out_channels),
+                    nn.SiLU(),
+                    nn.Conv2d(in_channels if i == 0 else out_channels, out_channels, kernel_size=3, stride=1,
+                              padding=1),
+                )
+                for i in range(num_layers)
+            ]
+        )
+
+        if self.t_emb_dim is not None:
+            self.t_emb_layers = nn.ModuleList([
+                nn.Sequential(
+                    nn.SiLU(),
+                    nn.Linear(t_emb_dim, out_channels)
+                )
+                for _ in range(num_layers)
+            ])
+
+        self.resnet_conv_second = nn.ModuleList(
+            [
+                nn.Sequential(
+                    nn.GroupNorm(norm_channels, out_channels),
+                    nn.SiLU(),
+                    nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1),
+                )
+                for _ in range(num_layers)
+            ]
+        )
+
+        self.attention_norms = nn.ModuleList(
+            [
+                nn.GroupNorm(norm_channels, out_channels)
+                for _ in range(num_layers)
+            ]
+        )
+
+        self.attentions = nn.ModuleList(
+            [
+                nn.MultiheadAttention(out_channels, num_heads, batch_first=True)
+                for _ in range(num_layers)
+            ]
+        )
+
+        if self.cross_attn:
+            assert context_dim is not None, "Context Dimension must be passed for cross attention"
+            self.cross_attention_norms = nn.ModuleList(
+                [nn.GroupNorm(norm_channels, out_channels)
+                 for _ in range(num_layers)]
+            )
+            self.cross_attentions = nn.ModuleList(
+                [nn.MultiheadAttention(out_channels, num_heads, batch_first=True)
+                 for _ in range(num_layers)]
+            )
+            self.context_proj = nn.ModuleList(
+                [nn.Linear(context_dim, out_channels)
+                 for _ in range(num_layers)]
+            )
+        self.residual_input_conv = nn.ModuleList(
+            [
+                nn.Conv2d(in_channels if i == 0 else out_channels, out_channels, kernel_size=1)
+                for i in range(num_layers)
+            ]
+        )
+        self.up_sample_conv = nn.ConvTranspose2d(in_channels // 2, in_channels // 2,
+                                                 4, 2, 1) \
+            if self.up_sample else nn.Identity()
+
+    def forward(self, x, out_down=None, t_emb=None, context=None):
+        x = self.up_sample_conv(x)
+        if out_down is not None:
+            x = torch.cat([x, out_down], dim=1)
+
+        out = x
+        for i in range(self.num_layers):
+            # --- Resnet --------------------
+            resnet_input = out
+            out = self.resnet_conv_first[i](out)
+            if self.t_emb_dim is not None:
+                out = out + self.t_emb_layers[i](t_emb)[:, :, None, None]
+            out = self.resnet_conv_second[i](out)
+            out = out + self.residual_input_conv[i](resnet_input)
+            
+            # --- Self Attention ------------
+            batch_size, channels, h, w = out.shape
+            in_attn = out.reshape(batch_size, channels, h * w)
+            in_attn = self.attention_norms[i](in_attn)
+            in_attn = in_attn.transpose(1, 2)
+            out_attn, _ = self.attentions[i](in_attn, in_attn, in_attn)
+            out_attn = out_attn.transpose(1, 2).reshape(batch_size, channels, h, w)
+            out = out + out_attn
+            
+            # --- Cross Attention -----------
+            if self.cross_attn:
+                assert context is not None, "context cannot be None if cross attention layers are used"
+                batch_size, channels, h, w = out.shape
+                in_attn = out.reshape(batch_size, channels, h * w)
+                in_attn = self.cross_attention_norms[i](in_attn)
+                in_attn = in_attn.transpose(1, 2)
+                assert len(context.shape) == 3, \
+                    "Context shape does not match B,_,CONTEXT_DIM"
+                assert context.shape[0] == x.shape[0] and context.shape[-1] == self.context_dim,\
+                    "Context shape does not match B,_,CONTEXT_DIM"
+                context_proj = self.context_proj[i](context)
+                out_attn, _ = self.cross_attentions[i](in_attn, context_proj, context_proj)
+                out_attn = out_attn.transpose(1, 2).reshape(batch_size, channels, h, w)
+                out = out + out_attn
+
+        return out
+
+
+
+# ==================================================================
+#                         L D M - U N E T
+# ==================================================================
+class Unet(nn.Module):
+    r"""
+    Unet model comprising
+    Down blocks, Midblocks and Uplocks
+    """
+
+    def __init__(self, im_channels, model_config):
+        super().__init__()
+        self.down_channels = model_config.down_channels
+        self.mid_channels = model_config.mid_channels
+        self.t_emb_dim = model_config.time_emb_dim
+        self.down_sample = model_config.down_sample
+        self.num_down_layers = model_config.num_down_layers
+        self.num_mid_layers = model_config.num_mid_layers
+        self.num_up_layers = model_config.num_up_layers
+        self.attns = model_config.attn_down
+        self.norm_channels = model_config.norm_channels
+        self.num_heads = model_config.num_heads
+        self.conv_out_channels = model_config.conv_out_channels
+
+        assert self.mid_channels[0] == self.down_channels[-1]
+        assert self.mid_channels[-1] == self.down_channels[-2]
+        assert len(self.down_sample) == len(self.down_channels) - 1
+        assert len(self.attns) == len(self.down_channels) - 1
+        
+        self.condition_config = model_config.condition_config
+        self.cond = condition_types = self.condition_config.condition_types
+        if 'audio' in condition_types:
+            self.audio_cond = True
+            self.audio_embed_dim = self.condition_config.audio_condition_config.audio_embed_dim
+
+        # Initial projection from sinusoidal time embedding
+        self.t_proj = nn.Sequential(
+            nn.Linear(self.t_emb_dim, self.t_emb_dim),
+            nn.SiLU(),
+            nn.Linear(self.t_emb_dim, self.t_emb_dim),
+        )
+        
+        # Context projection for whisper Encoder last hidden state
+        # [B, 1500, 1280] -> [B, 1280]
+        self.context_projector = nn.Sequential(
+            nn.Linear(self.audio_embed_dim, 320),
+            nn.SiLU(),
+            nn.Linear(320, 1)
+        )
+
+        self.up_sample = list(reversed(self.down_sample))
+        self.conv_in = nn.Conv2d(im_channels, self.down_channels[0], kernel_size=3, padding=1)
+
+        # --::----- D O W N - B l O C K S ----------------::--------------::----------------
+        self.downs = nn.ModuleList([])
+        for i in range(len(self.down_channels) - 1):
+            # Cross Attention and Context Dim only needed if text or audio condition is present
+            self.downs.append(
+                DownBlock(
+                    self.down_channels[i],
+                    self.down_channels[i + 1],
+                    self.t_emb_dim,
+                    down_sample=self.down_sample[i],
+                    num_heads=self.num_heads,
+                    num_layers=self.num_down_layers,
+                    attn=self.attns[i],
+                    norm_channels=self.norm_channels,
+                    cross_attn=self.audio_cond,
+                    context_dim=self.audio_embed_dim
+                )
+            )
+
+        # --::----- M I D - B l O C K S ----------------::--------------::----------------
+        self.mids = nn.ModuleList([])
+        for i in range(len(self.mid_channels) - 1):
+            self.mids.append(
+                MidBlock(
+                    self.mid_channels[i],
+                    self.mid_channels[i + 1],
+                    self.t_emb_dim,
+                    num_heads=self.num_heads,
+                    num_layers=self.num_mid_layers,
+                    norm_channels=self.norm_channels,
+                    cross_attn=self.audio_cond,
+                    context_dim=self.audio_embed_dim
+                )
+            )
+
+        # --::----- U P - B l O C K S ----------------::--------------::----------------
+        self.ups = nn.ModuleList([])
+        for i in reversed(range(len(self.down_channels) - 1)):
+            self.ups.append(
+                UpBlockUnet(
+                    self.down_channels[i] * 2,
+                    self.down_channels[i - 1] if i != 0 else self.conv_out_channels,
+                    self.t_emb_dim,
+                    up_sample=self.down_sample[i],
+                    num_heads=self.num_heads,
+                    num_layers=self.num_up_layers,
+                    norm_channels=self.norm_channels,
+                    cross_attn=self.audio_cond,
+                    context_dim=self.audio_embed_dim
+                )
+            )
+
+        self.norm_out = nn.GroupNorm(self.norm_channels, self.conv_out_channels)
+        self.conv_out = nn.Conv2d(self.conv_out_channels, im_channels, kernel_size=3, padding=1)
+
+    def forward(self, x, t, cond_input=None):
+        # Shapes assuming downblocks are [C1, C2, C3, C4]
+        # Shapes assuming midblocks are [C4, C4, C3]
+        # Shapes assuming downsamples are [True, True, False]
+        # B x C x H x W
+        out = self.conv_in(x)
+        # B x C1 x H x W
+
+        # t_emb -> B x t_emb_dim
+        t_emb = get_time_embedding(torch.as_tensor(t).long(), self.t_emb_dim)
+        t_emb = self.t_proj(t_emb)
+        
+        # --- Conditioning ---------------
+        if self.audio_cond:
+            # context_hidden_states = cond_input
+            # print(self.audio_cond, cond_input.shape)
+            
+            last_hidden_state = cond_input
+            weights = self.context_projector(last_hidden_state)
+            weights = torch.softmax(weights, dim=1)   # Normalize across time
+            pooled_embedding = (last_hidden_state * weights).sum(dim=1)  # [1, 512]
+            context_hidden_states = pooled_embedding.unsqueeze(1)
+    
+            # print(context_hidden_states.shape)
+            # exit()
+
+        # --- Down Pass ------------------
+        down_outs = []
+        for idx, down in enumerate(self.downs):
+            down_outs.append(out)
+            out = down(out, t_emb, context_hidden_states)
+        # down_outs  [B x C1 x H x W, B x C2 x H/2 x W/2, B x C3 x H/4 x W/4]
+        # out B x C4 x H/4 x W/4
+
+        # --- Mid Pass ------------------
+        for mid in self.mids:
+            out = mid(out, t_emb, context_hidden_states)
+        # out B x C3 x H/4 x W/4
+
+        # --- Up Pass ------------------
+        for up in self.ups:
+            down_out = down_outs.pop()
+            out = up(out, down_out, t_emb, context_hidden_states)
+            # out [B x C2 x H/4 x W/4, B x C1 x H/2 x W/2, B x 16 x H x W]
+        
+        out = self.norm_out(out)
+        out = nn.SiLU()(out)
+        out = self.conv_out(out)
+        # out B x C x H x W
+        return out
+    
+    
+
+# ==================================================================
+#                     L D M - T R A I N I N G
+# ==================================================================
+def find_checkpoints(checkpoint_path):
+    directory = os.path.dirname(checkpoint_path)
+    prefix = os.path.basename(checkpoint_path)
+    pattern = re.compile(rf"{re.escape(prefix)}_epoch(\d+)\.pt$")
+
+    try:
+        files = os.listdir(directory)
+    except FileNotFoundError:
+        return []
+
+    return [
+        os.path.join(directory, f)
+        for f in files if pattern.match(f)
+    ]
+
+def save_ldm_checkpoint(checkpoint_path,
+    total_steps, epoch, model, optimizer, 
+    metrics, logs, total_training_time
+    ):
+    checkpoint = {
+        "total_steps": total_steps,
+        "epoch": epoch,
+        "model_state_dict": model.state_dict(),
+        "optimizer_state_dict": optimizer.state_dict(),
+        "metrics": metrics,
+        "logs": logs,
+        "total_training_time": total_training_time
+    }
+    checkpoint_file = f"{checkpoint_path}_epoch{epoch}.pt"
+    torch.save(checkpoint, checkpoint_file)
+    print(f"LDM Checkpoint saved at {checkpoint_file}")
+    all_ckpts = glob.glob(f"{checkpoint_path}_epoch*.pt")
+    # all_ckpts = find_checkpoints(checkpoint_path)
+    def extract_epoch(filename):
+        match = re.search(r"_epoch(\d+)\.pt", filename)
+        return int(match.group(1)) if match else -1
+    all_ckpts = sorted(all_ckpts, key=extract_epoch)
+    for old_ckpt in all_ckpts[:-2]:
+        os.remove(old_ckpt)
+        print(f"Removed old LDM checkpoint: {old_ckpt}")
+
+def load_ldm_checkpoint(checkpoint_path, model, optimizer):
+    all_ckpts = glob.glob(f"{checkpoint_path}_epoch*.pt")
+    # all_ckpts = find_checkpoints(checkpoint_path)    
+    if not all_ckpts:
+        print("No LDM checkpoint found. Starting from scratch.")
+        return 0, 0, None, [], 0
+    def extract_epoch(filename):
+        match = re.search(r"_epoch(\d+)\.pt", filename)
+        return int(match.group(1)) if match else -1
+    all_ckpts = sorted(all_ckpts, key=extract_epoch)
+    latest_ckpt = all_ckpts[-1]
+    if os.path.exists(latest_ckpt):
+        checkpoint = torch.load(latest_ckpt, map_location=device)
+        model.load_state_dict(checkpoint["model_state_dict"])
+        optimizer.load_state_dict(checkpoint["optimizer_state_dict"])
+        total_steps = checkpoint["total_steps"]
+        epoch = checkpoint["epoch"]
+        metrics = checkpoint["metrics"]
+        logs = checkpoint.get("logs", [])
+        total_training_time = checkpoint.get("total_training_time", 0)
+        print(f"LDM Checkpoint loaded from {latest_ckpt}. Resuming from epoch {epoch + 1}, step {total_steps}")
+        return total_steps, epoch + 1, metrics, logs, total_training_time
+    else:
+        print("No LDM checkpoint found. Starting from scratch.")
+        return 0, 0, None, [], 0
+    
+def load_ldm_vae_checkpoint(checkpoint_path, vae, device=device):
+    # all_ckpts = glob.glob(f"{checkpoint_path}_epoch*.pt")
+    all_ckpts = find_checkpoints(checkpoint_path)    
+    if not all_ckpts:
+        print("No VQVAE checkpoint found.")
+        return 0, 0, None, [], 0
+    def extract_epoch(filename):
+        match = re.search(r"_epoch(\d+)\.pt", filename)
+        return int(match.group(1)) if match else -1
+    all_ckpts = sorted(all_ckpts, key=extract_epoch)
+    latest_ckpt = all_ckpts[-1]
+    if os.path.exists(latest_ckpt):
+        checkpoint = torch.load(latest_ckpt, map_location=device)
+        vae.load_state_dict(checkpoint["model_state_dict"])
+        total_steps = checkpoint["total_steps"]
+        epoch = checkpoint["epoch"]
+        print(f"VQVAE Checkpoint loaded from {latest_ckpt} at epoch {epoch + 1} & step {total_steps}")
+    
+def trainLDM(Config, dataloader):
+    diffusion_config = Config.diffusion_params
+    dataset_config = Config.dataset_params
+    diffusion_model_config = Config.ldm_params
+    autoencoder_model_config = Config.autoencoder_params
+    train_config = Config.train_params
+    condition_config = diffusion_model_config.condition_config
+    
+    seed = train_config.seed
+    torch.manual_seed(seed)
+    np.random.seed(seed)
+    random.seed(seed)
+    if device == "cuda":
+        torch.cuda.manual_seed_all(seed)
+    
+    vqvae_device = "cuda:1"
+    ldm_device = "cuda:0"
+    # ldm_device = vqvae_device = device
+
+    # scheduler = CosineNoiseScheduler(diffusion_config.num_timesteps)
+    scheduler = LinearNoiseScheduler(num_timesteps=diffusion_config.num_timesteps,
+                                     beta_start=diffusion_config.beta_start,
+                                     beta_end=diffusion_config.beta_end)
+    
+    if not train_config.ldm_pretraining:
+        if condition_config is not None:
+            condition_types = condition_config.condition_types
+            if 'audio' in condition_types:
+                from msclap import CLAP # type: ignore
+                audio_model = CLAP(version = '2023', use_cuda=(True if "cuda" in  device else False))
+            
+    model = Unet(im_channels=autoencoder_model_config.z_channels, model_config=diffusion_model_config).to(device)  
+    optimizer = torch.optim.AdamW(model.parameters(), lr=train_config.ldm_lr)
+    criterion = torch.nn.MSELoss()
+    num_epochs = train_config.ldm_epochs
+    
+    checkpoint_path = os.path.join(os.getcwd(), train_config.task_name, "ldmH_ckpt")
+    (total_steps, start_epoch, metrics, logs, 
+     total_training_time) = load_ldm_checkpoint(checkpoint_path, model, optimizer)
+    
+    vae = VQVAE(im_channels=dataset_config.im_channels, model_config=autoencoder_model_config).eval().to(vqvae_device)
+    vae_checkpoint_path = os.path.join(os.getcwd(), train_config.task_name, "vqvae_ckpt")
+    load_ldm_vae_checkpoint(vae_checkpoint_path, vae, vqvae_device)
+    for param in vae.parameters():
+        param.requires_grad = False
+    vae.eval()
+
+    if not os.path.exists(train_config.task_name):
+        os.makedirs(train_config.task_name, exist_ok=True)
+    
+    acc_steps = train_config.ldm_acc_steps
+    disc_step_start = train_config.disc_start
+    start_time_total = time.time() - total_training_time
+        
+    model.train()
+    optimizer.zero_grad()
+    for epoch_idx in trange(start_epoch, num_epochs, desc=f"{device}-LDM Epoch", colour='red', dynamic_ncols=True):
+        start_time_epoch = time.time()
+        losses = []
+        epoch_log = []
+        
+        # Load latest vqvae checkpoints
+        vae_checkpoint_path = os.path.join(os.getcwd(), train_config.task_name, "vqvae_ckpt")
+        load_ldm_vae_checkpoint(vae_checkpoint_path, vae, vqvae_device)
+        for param in vae.parameters():
+            param.requires_grad = False
+        vae.eval()
+        
+        # for images, cond_input in tqdm(dataloader, colour='green', dynamic_ncols=True):
+        for images in tqdm(dataloader, colour='green', dynamic_ncols=True):
+            cond_input = None
+            batch_start_time = time.time()
+            total_steps += 1
+            batch_size = images.shape[0]
+            
+            # images = images.to(device)
+            with torch.no_grad():
+                images, _ = vae.encode(images.to(vqvae_device))
+                images = images.to(ldm_device)
+                
+            # Conditional Input
+            audio_embed_dim = condition_config.audio_condition_config.audio_embed_dim
+            # empty_audio_embedding = torch.zeros(audio_embed_dim, device=device).float().unsqueeze(0).repeat(batch_size, 1).unsqueeze(1)
+            # empty_audio_embedding = torch.zeros((1500,1280), device=device).float().unsqueeze(0).repeat(batch_size, 1).unsqueeze(1)
+            empty_audio_embedding = torch.zeros((batch_size, 1500, 1280), device=device).float()
+            if not train_config.ldm_pretraining:
+                if 'audio' in condition_types:
+                    with torch.no_grad():
+                        audio_embeddings = audio_model.get_audio_embeddings(cond_input)
+                        text_drop_prob = condition_config.audio_condition_config.cond_drop_prob
+                        text_drop_mask = torch.zeros((images.shape[0]), device=images.device).float().uniform_(0, 1) < text_drop_prob
+                        audio_embeddings[text_drop_mask, :, :] = empty_audio_embedding[0]
+            else:
+                audio_embeddings = empty_audio_embedding
+                
+            # Sample random noise
+            noise = torch.randn_like(images).to(device)
+
+            # Sample timestep
+            t = torch.randint(0, diffusion_config.num_timesteps, (images.shape[0],)).to(device)
+
+            # Add noise to images according to timestep
+            noisy_images = scheduler.add_noise(images, noise, t)
+            noise_pred = model(noisy_images, t, cond_input=audio_embeddings)
+
+            loss = criterion(noise_pred, noise)
+            losses.append(loss.item())
+            loss = loss / acc_steps
+            loss.backward()
+            
+            if total_steps % acc_steps == 0:
+                optimizer.step()
+                optimizer.zero_grad()
+                
+        if total_steps % acc_steps == 0:
+            optimizer.step()
+            optimizer.zero_grad()
+            
+        print(f'Finished epoch:{epoch_idx + 1}/{num_epochs} | Loss : {np.mean(losses):.4f}')
+
+        epoch_time = time.time() - start_time_epoch
+        logs.append({"epoch": epoch_idx + 1, "epoch_time": format_time(0, epoch_time), "batch_times": epoch_log})
+
+        total_training_time = time.time() - start_time_total
+        save_ldm_checkpoint(checkpoint_path, total_steps, epoch_idx + 1, model, optimizer, metrics, logs, total_training_time)
+
+        
+        infer(Config)
+
+        # Checking to conntinue training
+        train_continue = DotDict.from_dict(yaml.safe_load(open(config_path, 'r')))
+        if train_continue.training.continue_ldm == False:
+            print('LDM Training Stoped ...')
+            break
+
+    print('Done Training ...')
+
+
+
+# ==================================================================
+#                     L D M - S A M P L I N G
+# ==================================================================
+def sample(model, scheduler, train_config, diffusion_model_config,
+               autoencoder_model_config, diffusion_config, dataset_config,
+               vae, audio_model
+    ):
+    r"""
+    Sample stepwise by going backward one timestep at a time.
+    We save the x0 predictions
+    """
+    im_size = dataset_config.im_size // 2**sum(autoencoder_model_config.down_sample)
+    xt = torch.randn((train_config.num_samples,
+                      autoencoder_model_config.z_channels,
+                      im_size,
+                      im_size)).to(device)
+
+    audio_embed_dim = diffusion_model_config.condition_config.audio_condition_config.audio_embed_dim
+    # empty_audio_embedding = torch.zeros(audio_embed_dim, device=device).float()
+    # empty_audio_embedding = torch.zeros(audio_embed_dim, device=device).float().unsqueeze(0)
+    # empty_audio_embedding = empty_audio_embedding.repeat(train_config.num_samples, 1).unsqueeze(1)
+    empty_audio_embedding = torch.zeros((train_config.num_samples, 1500, 1280), device=device).float()
+    if not train_config.ldm_pretraining:
+        # Create Conditional input
+        pass
+    else:
+        audio_embeddings = empty_audio_embedding
+    
+    uncond_input = empty_audio_embedding
+    cond_input = audio_embeddings
+    
+    save_count = 0
+    for i in tqdm(reversed(range(diffusion_config.num_timesteps)), 
+                  total=diffusion_config.num_timesteps,
+                  colour='blue', desc="Sampling", dynamic_ncols=True):
+        # Get prediction of noise
+        t = (torch.ones((xt.shape[0],)) * i).long().to(device)
+        # t = torch.as_tensor(i).unsqueeze(0).to(device)
+        noise_pred_cond = model(xt, t, cond_input)
+        
+        cf_guidance_scale = train_config.cf_guidance_scale
+        if cf_guidance_scale > 1:
+            noise_pred_uncond = model(xt, t, uncond_input)
+            noise_pred = noise_pred_uncond + cf_guidance_scale * (noise_pred_cond - noise_pred_uncond)
+        else:
+            noise_pred = noise_pred_cond
+
+        # Use scheduler to get x0 and xt-1
+        xt, x0_pred = scheduler.sample_prev_timestep(xt, noise_pred, torch.as_tensor(i).to(device))
+
+        # Save x0
+        #ims = torch.clamp(xt, -1., 1.).detach().cpu()
+        if i == 0:
+            # Decode ONLY the final iamge to save time
+            ims = vae.decode(xt)
+        else:
+            # ims = xt
+            ims = x0_pred
+
+        ims = torch.clamp(ims, -1., 1.).detach().cpu()
+        ims = (ims + 1) / 2
+        grid = make_grid(ims, nrow=train_config.num_grid_rows)
+        # img = tv.transforms.ToPILImage()(grid)
+        np_img = (grid * 255).byte().numpy().transpose(1, 2, 0)
+        img = Image.fromarray(np_img)
+
+        if not os.path.exists(os.path.join(train_config.task_name, 'samplesH')):
+            os.makedirs(os.path.join(train_config.task_name, 'samplesH'), exist_ok=True)
+        
+        img.save(os.path.join(train_config.task_name, 'samplesH', 'x0_{}.png'.format(i)))
+        img.close()
+
+
+def infer(Config):
+    diffusion_config = Config.diffusion_params
+    dataset_config = Config.dataset_params
+    diffusion_model_config = Config.ldm_params
+    autoencoder_model_config = Config.autoencoder_params
+    train_config = Config.train_params
+
+    # scheduler = CosineNoiseScheduler(diffusion_config.num_timesteps)
+    scheduler = LinearNoiseScheduler(num_timesteps=diffusion_config.num_timesteps,
+                                     beta_start=diffusion_config.beta_start,
+                                     beta_end=diffusion_config.beta_end)
+
+    model = Unet(im_channels=autoencoder_model_config.z_channels,
+                 model_config=diffusion_model_config).eval().to(device)
+    vae = VQVAE(im_channels=dataset_config.im_channels, 
+                model_config=autoencoder_model_config).eval().to(device)
+
+    if os.path.exists(os.path.join(train_config.task_name, train_config.ldm_ckpt_name)):
+        checkpoint_path = os.path.join(train_config.task_name, train_config.ldm_ckpt_name)
+        checkpoint = torch.load(checkpoint_path, map_location=device)
+        model.load_state_dict(checkpoint["model_state_dict"])
+        vae.load_state_dict(checkpoint["vae_state_dict"])
+        print('Loaded unet & vae checkpoint')
+    
+    # Create output directories
+    if not os.path.exists(train_config.task_name):
+        os.makedirs(train_config.task_name, exist_ok=True)
+    
+    with torch.no_grad():
+        sample(model, scheduler, train_config, diffusion_model_config,
+               autoencoder_model_config, diffusion_config, dataset_config, vae, None)
+
+
+
+
+# ==================================================================
+#            S T A R T I N G - L D M - T R A I N I N G
+# ==================================================================
+# trainLDM(Config, dataloader)
+
+    
+
+if sys.argv[1] == 'train_vae':
+    trainVAE(Config, dataloader)
+elif sys.argv[1] == 'train_ldm':
+    trainLDM(Config, dataloader)
+else:
+    infer(Config)
+    
+    
+
+# git add . && git commit -m "LDM" && git push -u origin master
+# huggingface-cli upload alpha31476/Vaani-Audio2Img-LDM . --commit-message "SDFT"
\ No newline at end of file
diff --git a/Vaani/_7_Vaani-LDM-Pre.py b/Vaani/_7_Vaani-LDM-Pre.py
index d21606c6b09a85dd7e11aecd71e71117ad123283..df74121f3c2068678d2906b951690d90113ed5da 100644
--- a/Vaani/_7_Vaani-LDM-Pre.py
+++ b/Vaani/_7_Vaani-LDM-Pre.py
@@ -42,7 +42,7 @@ from torch.utils.data import Dataset, DataLoader
 from torchvision.utils import make_grid
 
 print("TIME:", datetime.datetime.now())
-# os.environ["CUDA_VISIBLE_DEVICES"] = f"{sys.argv[2]}"
+os.environ["CUDA_VISIBLE_DEVICES"] = f"{sys.argv[2]}"
 device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
 print("DEVICE:", device)
 
@@ -1730,9 +1730,9 @@ def trainLDM(Config, dataloader):
     if device == "cuda":
         torch.cuda.manual_seed_all(seed)
     
-    vqvae_device = "cuda:1"
-    ldm_device = "cuda:0"
-    # ldm_device = vqvae_device = device
+    vqvae_device = "cuda:0"
+    ldm_device = "cuda:1"
+    ldm_device = vqvae_device = device
 
     # scheduler = CosineNoiseScheduler(diffusion_config.num_timesteps)
     scheduler = LinearNoiseScheduler(num_timesteps=diffusion_config.num_timesteps,
diff --git a/Vaani/config-LDM-High-Pre.yaml b/Vaani/config-LDM-High-Pre.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..f1b6668c606e6b1eb02080f5586e39284e610944
--- /dev/null
+++ b/Vaani/config-LDM-High-Pre.yaml
@@ -0,0 +1,77 @@
+dataset_params:
+  im_channels: 3
+  im_size: 128
+
+paths:
+  images_dir: "/scratch/IITB/ai-at-ieor/23m1521/datasets/Vaani/Images"
+
+diffusion_params:
+  num_timesteps: 1000
+  beta_start: 0.00085
+  beta_end: 0.012
+
+ldm_params:
+  down_channels: [ 128, 384, 512, 768 ]
+  mid_channels: [ 768, 512 ]
+  down_sample: [ True, True, True ]
+  attn_down: [ True, True, True ]
+  time_emb_dim: 512
+  norm_channels: 32
+  num_heads: 16
+  conv_out_channels: 128
+  num_down_layers: 4
+  num_mid_layers: 4
+  num_up_layers: 4
+  condition_config:
+    condition_types: [ 'audio' ]
+    audio_condition_config:
+      text_embed_model: 'whisper'
+      train_audio_embed_model: False
+      audio_embed_dim: 1280
+      cond_drop_prob: 0.1
+
+
+autoencoder_params:
+  z_channels: 3
+  codebook_size: 20
+  down_channels: [ 32, 64, 128 ]
+  mid_channels: [ 128, 128 ]
+  down_sample: [ True, True ]
+  attn_down: [ False, False ]
+  norm_channels: 32
+  num_heads: 16
+  num_down_layers: 4
+  num_mid_layers: 4
+  num_up_layers: 4
+
+train_params:
+  seed: 4422
+  task_name: 'VaaniLDM'
+  ldm_batch_size: 32
+  ldm_pretraining: True
+  debug: False
+  autoencoder_batch_size: 80
+  disc_start: 1000
+  disc_weight: 0.5
+  codebook_weight: 1
+  commitment_beta: 0.2
+  perceptual_weight: 1
+  kl_weight: 0.000005
+  ldm_epochs: 50
+  autoencoder_epochs: 30
+  num_samples: 9
+  num_grid_rows: 3
+  ldm_lr: 0.00001
+  autoencoder_lr: 0.0001
+  ldm_acc_steps: 1
+  autoencoder_acc_steps: 1
+  autoencoder_img_save_steps: 8
+  save_latents: True
+  cf_guidance_scale : 1.0
+  vqvae_latent_dir_name: 'vqvae_latents'
+  ldm_ckpt_name: 'ddpm_ckpt.pth'
+  vqvae_ckpt_name: 'vqvae_ckpt.pth'
+
+training:
+  continue_vqvae: True
+  continue_ldm: True
diff --git a/Vaani/config-LDM-Pre.yaml b/Vaani/config-LDM-Pre.yaml
index 90f684f7a553adccb8c4e6e449b4b888a6767a45..d845e6b18ba89146ba37996d99fdb44ae2a39a6c 100644
--- a/Vaani/config-LDM-Pre.yaml
+++ b/Vaani/config-LDM-Pre.yaml
@@ -47,7 +47,7 @@ autoencoder_params:
 train_params:
   seed: 4422
   task_name: 'VaaniLDM'
-  ldm_batch_size: 32
+  ldm_batch_size: 16
   ldm_pretraining: True
   debug: False
   autoencoder_batch_size: 80
@@ -57,7 +57,7 @@ train_params:
   commitment_beta: 0.2
   perceptual_weight: 1
   kl_weight: 0.000005
-  ldm_epochs: 50
+  ldm_epochs: 60
   autoencoder_epochs: 30
   num_samples: 9
   num_grid_rows: 3