Upload lora_forward_pass_test.ipynb

lora_forward_pass_test.ipynb

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-            "\u001b[?25h✅ Dependencies installed\n",
-            "✅ Logged into Hugging Face\n"
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-            "Flax classes are deprecated and will be removed in Diffusers v1.0.0. We recommend migrating to PyTorch classes or pinning your version of Diffusers.\n",
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+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "9ul9xNLYEzTj",
+        "outputId": "eab2f146-2576-4a03-966b-078b112bebe8"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Mounted at /content/drive\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.2/3.2 MB\u001b[0m \u001b[31m57.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25h"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "WARNING:torchao:Skipping import of cpp extensions due to incompatible torch version. Please upgrade to torch >= 2.11.0 (found 2.10.0+cu128).\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "✅ torchao updated to version: 0.17.0\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m113.2/113.2 kB\u001b[0m \u001b[31m5.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25h✅ Dependencies installed\n",
+            "✅ Logged into Hugging Face\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "Unable to import `torchao` Tensor objects. This may affect loading checkpoints serialized with `torchao`\n",
+            "Flax classes are deprecated and will be removed in Diffusers v1.0.0. We recommend migrating to PyTorch classes or pinning your version of Diffusers.\n",
+            "Flax classes are deprecated and will be removed in Diffusers v1.0.0. We recommend migrating to PyTorch classes or pinning your version of Diffusers.\n"
+          ]
+        }
+      ],
+      "source": [
+        "#@markdown # CELL 1 — Setup environment + login\n",
+        "\n",
+        "from google.colab import drive, userdata\n",
+        "from huggingface_hub import login\n",
+        "import torch, os, gc\n",
+        "\n",
+        "drive.mount(\"/content/drive\")\n",
+        "\n",
+        "hf_token = userdata.get(\"HF_TOKEN\")\n",
+        "if hf_token:\n",
+        "    login(token=hf_token)\n",
+        "else:\n",
+        "    raise ValueError(\"HF_TOKEN not found in Google Colab secrets\")\n",
+        "#----#\n",
+        "\n",
+        "!pip uninstall -q torchao -y\n",
+        "!pip install -q torchao==0.17.0 --no-deps\n",
+        "\n",
+        "import torchao\n",
+        "print(f\"✅ torchao updated to version: {torchao.__version__}\")\n",
+        "\n",
+        "!pip install -q safetensors huggingface_hub diffusers transformers accelerate sdnq\n",
+        "\n",
+        "#images for testing\n",
+        "# Resize so longest side is no greater than 1024px prior to\n",
+        "#prior to encoding into VAE\n",
+        "image1_path = '/content/drive/MyDrive/image1.jpg'\n",
+        "image2_path = '/content/drive/MyDrive/image2.jpg'\n",
+        "\n",
+        "# Test prompts\n",
+        "prompt1= 'a cute dog on grass'\n",
+        "prompt2 = 'a golden retriever on a road'\n",
+        "#-----#\n",
+        "\n",
+        "print(\"✅ Dependencies installed\")\n",
+        "\n",
+        "print(\"✅ Logged into Hugging Face\")\n",
+        "\n",
+        "import gc\n",
+        "import torch\n",
+        "from diffusers import Flux2KleinPipeline\n",
+        "#----#\n",
+        "from sdnq import SDNQConfig\n",
+        "from sdnq.common import use_torch_compile as triton_is_available\n",
+        "from sdnq.training import convert_sdnq_model_to_training\n",
+        "from sdnq.training import sdnq_training_post_load_quant\n",
+        "from sdnq.loader import apply_sdnq_options_to_model\n",
+        "#----#\n",
+        "from functools import partial\n",
+        "from safetensors.torch import load_file, save_file\n",
+        "from huggingface_hub import hf_hub_download\n",
+        "import hashlib\n",
+        "from pathlib import Path\n",
+        "\n",
+        "import torch\n",
+        "from transformers import BitsAndBytesConfig\n",
+        "\n",
+        "\n",
+        "# Patch function\n",
+        "def patch_encode_prompt(pipe, cached_prompt_embeds, cached_text_ids, gpu_id):\n",
+        "    def patched(self, prompt=None, device=None, num_images_per_prompt=1, prompt_embeds=None, **kwargs):\n",
+        "        device = device or torch.device(f\"cuda:{gpu_id}\")\n",
+        "        if prompt_embeds is None:\n",
+        "            emb = cached_prompt_embeds.to(device)\n",
+        "            emb = emb.repeat(1, num_images_per_prompt, 1)\n",
+        "            emb = emb.view(emb.shape[0] * num_images_per_prompt, emb.shape[1], -1)\n",
+        "            tids = cached_text_ids.to(device)\n",
+        "            return emb, tids\n",
+        "        return self._original_encode_prompt(prompt, device, num_images_per_prompt, prompt_embeds, **kwargs)\n",
+        "\n",
+        "    if not hasattr(pipe, '_original_encode_prompt'):\n",
+        "        pipe._original_encode_prompt = pipe.encode_prompt\n",
+        "    pipe.encode_prompt = partial(patched, pipe)\n",
+        "    return pipe\n",
+        "\n",
+        "\n",
+        "# Load single pipe\n",
+        "def load_klein_pipe(gpu_id: int, pipe_id: int):\n",
+        "    torch.cuda.set_device(gpu_id)\n",
+        "\n",
+        "    print(f\" [GPU {gpu_id} - Pipe {pipe_id}] Loading...\")\n",
+        "\n",
+        "    MODEL_ID = 'codeShare/Flux.2-Klein-4B-SDNQ-4bit'\n",
+        "\n",
+        "    edit_prompt = 'this is a test'\n",
+        "    pipe = Flux2KleinPipeline.from_pretrained(\n",
+        "        MODEL_ID,\n",
+        "        torch_dtype=torch.float16,\n",
+        "        low_cpu_mem_usage=True,\n",
+        "        device_map=\"cpu\",\n",
+        "    )\n",
+        "\n",
+        "    # SDNQ + Prompt cache\n",
+        "    pipe.transformer = apply_sdnq_options_to_model(pipe.transformer, use_quantized_matmul=False)\n",
+        "\n",
+        "    # Prepare text_encoder\n",
+        "    pipe.text_encoder = pipe.text_encoder.to(f\"cuda:{gpu_id}\")\n",
+        "\n",
+        "    #if False:\n",
+        "      #with torch.inference_mode():\n",
+        "      #    prompt_embeds, text_ids = pipe.encode_prompt(edit_prompt, device=f\"cuda:{gpu_id}\", num_images_per_prompt=1)\n",
+        "\n",
+        "      #pipe.edit_prompt_embeds = prompt_embeds.cpu()\n",
+        "      #pipe.edit_text_ids = text_ids.cpu()\n",
+        "      #pipe = patch_encode_prompt(pipe, pipe.edit_prompt_embeds, pipe.edit_text_ids, gpu_id)\n",
+        "      #del pipe.text_encoder\n",
+        "      #del pipe.tokenizer\n",
+        "    #-----#\n",
+        "    gc.collect()\n",
+        "    torch.cuda.empty_cache()\n",
+        "\n",
+        "    pipe.transformer.to(f\"cuda:{gpu_id}\")\n",
+        "    # pipe.enable_model_cpu_offload(gpu_id=gpu_id) # Removed: This can interfere with VAE device placement during training\n",
+        "\n",
+        "    # VAE stuffs\n",
+        "    pipe.vae = pipe.vae.to(f\"cuda:{gpu_id}\")\n",
+        "    #pipe.vae.enable_slicing()\n",
+        "    #pipe.vae.enable_tiling()\n",
+        "    #pipe.enable_attention_slicing(1)\n",
+        "\n",
+        "    print(f\" [GPU {gpu_id} - Pipe {pipe_id}] Ready | VRAM: {torch.cuda.memory_allocated(gpu_id)/1e9:.2f} GB\")\n",
+        "    return pipe\n",
+        "#----#\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "gc.collect()\n",
+        "torch.cuda.empty_cache()\n",
+        "NUM_GPUS = torch.cuda.device_count()\n",
+        "PIPE_NUM = 1\n",
+        "for gpu in range(NUM_GPUS):\n",
+        "  pipe = load_klein_pipe(gpu , PIPE_NUM)\n",
+        "  print(f'loaded pipe to {pipe.device}')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 266,
+          "referenced_widgets": [
+            "16577fe1698040568ea4dba67a1fa7d0",
+            "b85487705df34c2e867fcbb7024b4b5b",
+            "c0ab2e603303426f879e8f0abf4b7393",
+            "fd28a908626645819424f1afe289375d",
+            "475e1827136247a6b58d353ea694bd19",
+            "4d7f3b4f3fee49ac9092af94f2b2be1f",
+            "1a9301da3ba34b7fbd3c18f826b058fd",
+            "15999cdf364c48aaba6bc3d99dad3a63",
+            "e953a19bb91b4ed5aa4e4244e3c91405",
+            "9261c32d28854c9eb50a2e5581de4caf",
+            "f1083e6575824427be954bc52a119423",
+            "f3d3ac18dc9c4ddeb1ed19f0fdfffe46",
+            "2cb257c3b846484c90e27e38571252d0",
+            "7c93c8bd0be445c68254272da976888f",
+            "7d2113f0421f4880a12d5bb0308fef49",
+            "b91eefa3c4e84d73bcaab6f0d8bd93bc",
+            "37ffe4997f1647eb81e7a7d78107fdf1",
+            "1f19f094677e4e97ba4998adad062b69",
+            "a6457a01fe5d47ff804d0d78a3fadd1e",
+            "8f8355073ff140f38077ab6a29c0ae0d",
+            "3dd5809a6c0c4c43b1d5166496a9fab6",
+            "23d0690c63194efa8d77311d9812eb96",
+            "fa76c5a087f34b53956918bea856e80b",
+            "b7101eaa248c48a6ac29a28d44efdfdd",
+            "fde9d91e04b24c04b27e6a9da56aeb61",
+            "66a0f92f4bff492c937a9d32f60d0cb5",
+            "295e8356948745e28fe33dd29b690aab",
+            "9173730e1275476e8de4c9a7da30ae24",
+            "8c5932aebc2e4968bee9bf47eec8057a",
+            "66e49d85a7014dacbc318467ebf45be8",
+            "8d929c0f97504fb6af4c503d333e4512",
+            "ac114e56e12b410f83cbaca782ea72f1",
+            "69c8a7fdc49145eca068262d61f37775",
+            "ea7307ba2daa475ca4e55214b16239a4",
+            "26da14b2a7d642ef9bbbe8ef15eaf8f5",
+            "75ad91ac5d3143379a808487fdffd264",
+            "a8ca808287bd4a6585186f9e9d49f4a3",
+            "78a66c7278dc4f27b949715a3dff75b9",
+            "3233901ab9aa474b9696a43707daa7cb",
+            "0e36ef86c40c4c3fb7893c9ee788772f",
+            "2fd194e0e12045aab0d91c73a399b2ce",
+            "adc12d3c20e74011a45127609dcb1639",
+            "38c495f402a942f88f1121c2b5c21c3c",
+            "26de88ff85364c44b8df7b9061f7e1e4",
+            "e06ac5a2b11f4e409eeb7e7cc19ae434",
+            "ac55b36af4644c84a81a19d70cc1335e",
+            "5772d2b30bf1470fbe7f9f5e9f25372b",
+            "1c243e051a8b4278b28c9c59677a0112",
+            "f3467296dd0140c2a84df2a037135795",
+            "ee48d14ef87b4c11adf44bf3daf76197",
+            "1e3e3d2a22e847698af5ee2f8f6ce22d",
+            "fb6380258dca43f691d06b4d30459ca4",
+            "379712892eba4a9d845520060b163a7b",
+            "0f515e64cbff4c19be9ca26e3ceb48df",
+            "c143b49f768c496181029f9be37dc492"
+          ]
+        },
+        "id": "hI4jZjB3ywqi",
+        "outputId": "ec31f0f2-af05-4e3e-ca2c-29657e937928"
+      },
+      "execution_count": 2,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            " [GPU 0 - Pipe 1] Loading...\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "model_index.json:   0%|          | 0.00/446 [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "16577fe1698040568ea4dba67a1fa7d0"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Downloading (incomplete total...): 0.00B [00:00, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "f3d3ac18dc9c4ddeb1ed19f0fdfffe46"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Fetching 11 files:   0%|          | 0/11 [00:00<?, ?it/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "fa76c5a087f34b53956918bea856e80b"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Loading pipeline components...:   0%|          | 0/5 [00:00<?, ?it/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "ea7307ba2daa475ca4e55214b16239a4"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "The config attributes {'decoder_block_out_channels': None} were passed to AutoencoderKLFlux2, but are not expected and will be ignored. Please verify your config.json configuration file.\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Loading weights:   0%|          | 0/902 [00:00<?, ?it/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "e06ac5a2b11f4e409eeb7e7cc19ae434"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            " [GPU 0 - Pipe 1] Ready | VRAM: 5.95 GB\n",
+            "loaded pipe to cuda:0\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
       "source": [
-        "#@markdown # CELL 1 — Setup environment + login\n",
-        "\n",
-        "from google.colab import drive, userdata\n",
-        "from huggingface_hub import login\n",
-        "import torch, os, gc\n",
-        "\n",
-        "drive.mount(\"/content/drive\")\n",
-        "\n",
-        "hf_token = userdata.get(\"HF_TOKEN\")\n",
-        "if hf_token:\n",
-        "    login(token=hf_token)\n",
-        "else:\n",
-        "    raise ValueError(\"HF_TOKEN not found in Google Colab secrets\")\n",
+        "pipe.transformer = pipe.transformer.to('cuda')\n",
+        "gc.collect()\n",
+        "torch.cuda.empty_cache()\n",
+        "from peft import prepare_model_for_kbit_training\n",
+        "pipe.transformer = prepare_model_for_kbit_training(pipe.transformer)"
+      ],
+      "metadata": {
+        "id": "sEhh282dy1Ls"
+      },
+      "execution_count": 3,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from peft import LoraConfig\n",
         "\n",
-        "#@markdown # CELL 2 — Install dependencies\n",
+        "config = LoraConfig(\n",
+        "    r=16,\n",
+        "    lora_alpha=8,\n",
+        "    target_modules=[\n",
+        "    \"attn.to_q\", \"attn.to_k\", \"attn.to_v\",\n",
+        "    \"ff.linear_in\", \"ff.linear_out\",\n",
+        "    \"ff_context.linear_in\", \"ff_context.linear_out\",\n",
+        "    \"attn.to_qkv_mlp_proj\",\n",
+        "],\n",
+        "    lora_dropout=0.05,\n",
+        "    bias=\"none\",\n",
+        ")"
+      ],
+      "metadata": {
+        "id": "7w3yiYzhuo0A"
+      },
+      "execution_count": 4,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from peft import get_peft_model\n",
         "\n",
-        "!pip install -q safetensors huggingface_hub diffusers transformers accelerate sdnq\n",
+        "pipe.transformer = get_peft_model(pipe.transformer, config)"
+      ],
+      "metadata": {
+        "id": "qTSuLZU7uzTk"
+      },
+      "execution_count": 5,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "pipe.transformer.print_trainable_parameters()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "_6k8-Z0twPn-",
+        "outputId": "3f3cc893-1e91-4b77-8121-7a741ae177dd"
+      },
+      "execution_count": 6,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "trainable params: 16,711,680 || all params: 2,167,021,056 || trainable%: 0.7712\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import torch\n",
+        "import torch.nn.functional as F\n",
+        "from torch.utils.data import Dataset, DataLoader\n",
+        "import numpy as np\n",
+        "from PIL import Image\n",
+        "import torchvision.transforms as T\n",
+        "from tqdm.auto import tqdm\n",
+        "import gc\n",
         "\n",
-        "#images for testing\n",
-        "# Resize so longest side is no greater than 1024px prior to\n",
-        "#prior to encoding into VAE\n",
+        "# ====================== YOUR DATA ======================\n",
         "image1_path = '/content/drive/MyDrive/image1.jpg'\n",
         "image2_path = '/content/drive/MyDrive/image2.jpg'\n",
         "\n",
-        "# Test prompts\n",
-        "prompt1= 'a cute dog on grass'\n",
+        "prompt1 = 'a cute dog on grass'\n",
         "prompt2 = 'a golden retriever on a road'\n",
-        "#-----#\n",
         "\n",
-        "print(\"✅ Dependencies installed\")\n",
+        "image_paths = [image1_path, image2_path]\n",
+        "instance_prompts = [prompt1, prompt2]\n",
         "\n",
-        "print(\"✅ Logged into Hugging Face\")\n",
+        "# ====================== DATASET ======================\n",
+        "class Flux2KleinDataset(Dataset):\n",
+        "    def __init__(self, image_paths, prompts, resolution=512):\n",
+        "        self.images = [Image.open(p).convert(\"RGB\") for p in image_paths]\n",
+        "        self.prompts = prompts\n",
+        "        self.transform = T.Compose([\n",
+        "            T.Resize(resolution, interpolation=T.InterpolationMode.BILINEAR),\n",
+        "            T.CenterCrop(resolution),\n",
+        "            T.ToTensor(),  # [0,1]\n",
+        "        ])\n",
         "\n",
-        "import gc\n",
-        "import torch\n",
-        "from diffusers import Flux2KleinPipeline\n",
-        "#----#\n",
-        "from sdnq import SDNQConfig\n",
-        "from sdnq.common import use_torch_compile as triton_is_available\n",
-        "from sdnq.training import convert_sdnq_model_to_training\n",
-        "from sdnq.training import sdnq_training_post_load_quant\n",
-        "from sdnq.loader import apply_sdnq_options_to_model\n",
-        "#----#\n",
-        "from functools import partial\n",
-        "from safetensors.torch import load_file, save_file\n",
-        "from huggingface_hub import hf_hub_download\n",
-        "import hashlib\n",
-        "from pathlib import Path\n",
+        "    def __len__(self):\n",
+        "        return len(self.images)\n",
+        "\n",
+        "    def __getitem__(self, idx):\n",
+        "        image = self.transform(self.images[idx])\n",
+        "        return image, self.prompts[idx]\n",
         "\n",
-        "#SD NExt Quantization uint4 FLux2 Klein 4B model https://github.com/Disty0/sdnq\n",
-        "MODEL_ID = 'codeShare/Flux.2-Klein-4B-SDNQ-4bit'\n",
+        "dataset = Flux2KleinDataset(image_paths, instance_prompts, resolution=512)\n",
+        "dataloader = DataLoader(dataset, batch_size=1, shuffle=True)\n",
         "\n",
-        "pipe = Flux2KleinPipeline.from_pretrained(\n",
-        "    MODEL_ID,\n",
-        "    torch_dtype=torch.float16,\n",
-        "    low_cpu_mem_usage=True,\n",
-        "    device_map=\"cpu\",\n",
-        ")\n",
-        "\n"
+        "# ====================== TRAINING SETUP ======================\n",
+        "device = pipe.device\n",
+        "dtype = torch.float16  # or bfloat16 if supported\n",
+        "\n",
+        "pipe.vae.to(device, dtype=dtype)\n",
+        "pipe.transformer.train()\n",
+        "\n",
+        "# Optional: Enable gradient checkpointing for memory savings\n",
+        "#pipe.transformer.gradient_checkpointing_enable()\n",
+        "\n",
+        "optimizer = torch.optim.AdamW(pipe.transformer.parameters(), lr=1e-4, weight_decay=0.01)\n",
+        "num_epochs = 5\n",
+        "accumulation_steps = 2\n",
+        "\n",
+        "print(\"Starting training...\")\n",
+        "\n",
+        "for epoch in range(num_epochs):\n",
+        "    epoch_loss = 0.0\n",
+        "    optimizer.zero_grad()\n",
+        "\n",
+        "    for step, (image, prompt) in enumerate(tqdm(dataloader)):\n",
+        "        # image: [B, C, H, W] in [0,1]\n",
+        "        image = image.to(device, dtype=dtype)\n",
+        "\n",
+        "        # ====================== VAE ENCODE ======================\n",
+        "        with torch.no_grad():\n",
+        "            # FLUX VAE expects [-1, 1]\n",
+        "            latents = pipe.vae.encode(image * 2 - 1).latent_dist.sample()\n",
+        "            # No scaling factor as per your note\n",
+        "\n",
+        "        # ====================== TEXT ENCODING ======================\n",
+        "        with torch.no_grad():\n",
+        "            # prompt is a tuple from DataLoader → convert to list\n",
+        "            text_inputs = pipe.tokenizer(\n",
+        "                list(prompt), padding=True, truncation=True,\n",
+        "                max_length=512, return_tensors=\"pt\"\n",
+        "            ).to(device)\n",
+        "\n",
+        "            text_embeds = pipe.text_encoder(text_inputs.input_ids)\n",
+        "\n",
+        "        # ====================== RECTIFIED FLOW NOISE ======================\n",
+        "        noise = torch.randn_like(latents)\n",
+        "        # Uniform timestep in [0,1]\n",
+        "        t = torch.rand((latents.shape[0],), device=device, dtype=dtype)\n",
+        "\n",
+        "        # z_t = (1-t) * z0 + t * noise\n",
+        "        noisy_latents = (1.0 - t.view(-1, 1, 1, 1)) * latents + \\\n",
+        "                        t.view(-1, 1, 1, 1) * noise\n",
+        "\n",
+        "        # ====================== PACKING (Flux requirement) ======================\n",
+        "        # 2x2 packing\n",
+        "        b, c, h, w = noisy_latents.shape\n",
+        "        noisy_latents = noisy_latents.view(b, c, h//2, 2, w//2, 2)\n",
+        "        noisy_latents = noisy_latents.permute(0, 2, 4, 1, 3, 5).contiguous()\n",
+        "        noisy_latents = noisy_latents.view(b, (h//2)*(w//2), c*4)\n",
+        "\n",
+        "        # ====================== POSITION IDS ======================\n",
+        "        def get_img_ids(h, w, device):\n",
+        "            h = h // 2\n",
+        "            w = w // 2\n",
+        "            img_ids = torch.zeros((h, w, 3), device=device)\n",
+        "            img_ids[..., 1] = torch.arange(h, device=device)[:, None]\n",
+        "            img_ids[..., 2] = torch.arange(w, device=device)[None, :]\n",
+        "            img_ids = img_ids.view(-1, 3)\n",
+        "            return img_ids\n",
+        "\n",
+        "        img_ids = get_img_ids(h, w, device)\n",
+        "\n",
+        "        # ====================== FORWARD PASS ======================\n",
+        "        noise_pred = pipe.transformer(\n",
+        "            hidden_states=noisy_latents,\n",
+        "            timestep=t,                    # [0,1] range\n",
+        "            encoder_hidden_states=text_embeds.to(device=device),\n",
+        "            pooled_projections=pooled_embeds,  # Pooled_embeds are not defined!\n",
+        "            txt_ids=txt_ids,\n",
+        "            img_ids=img_ids,\n",
+        "            guidance=torch.tensor([1.0], device=device, dtype=dtype).expand(latents.shape[0]),\n",
+        "            return_dict=False,\n",
+        "        )[0]\n",
+        "\n",
+        "        # ====================== UNPACK ======================\n",
+        "        noise_pred = noise_pred.view(b, h//2, w//2, c, 2, 2)\n",
+        "        noise_pred = noise_pred.permute(0, 3, 1, 4, 2, 5).contiguous()\n",
+        "        noise_pred = noise_pred.view(b, c, h, w)\n",
+        "\n",
+        "        # ====================== LOSS ======================\n",
+        "        # Target is velocity / noise direction\n",
+        "        target = noise - latents\n",
+        "        loss = F.mse_loss(noise_pred, target)\n",
+        "        loss = loss / accumulation_steps\n",
+        "        loss.backward()\n",
+        "\n",
+        "        epoch_loss += loss.item() * accumulation_steps\n",
+        "\n",
+        "        if (step + 1) % accumulation_steps == 0:\n",
+        "            optimizer.step()\n",
+        "            optimizer.zero_grad()\n",
+        "            gc.collect()\n",
+        "            torch.cuda.empty_cache()\n",
+        "\n",
+        "    print(f\"Epoch {epoch+1}/{num_epochs} - Loss: {epoch_loss / len(dataloader):.6f}\")\n",
+        "\n",
+        "# ====================== SAVE LoRA ======================\n",
+        "pipe.transformer.save_pretrained(\"/content/flux_klein_lora\")\n",
+        "print(\"✅ Training finished! LoRA saved to /content/flux_klein_lora\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 260,
+          "referenced_widgets": [
+            "a2a88573b3d3449a94b3366aace406a5",
+            "6733ac1df676444cbccdd67bb13fe3c8",
+            "c37c5c2c5c384471a15319bb64b8edfe",
+            "d6e383d9c49742cba46fe1509c9e2983",
+            "fd8214ff18c44963bb180222d97aaa04",
+            "eedc0de0634b4e1eacb4b5922b7c31cd",
+            "912a18039ccb42a2bc27ef256f4d85c3",
+            "443f1bc17a6f438c8fb184420dadcd36",
+            "b7cff5bb7aac40ea94a15a3a1e1abcfe",
+            "d214361c6fba487e89b1f9c839a9616d",
+            "6cb69a3ff71a4ec4b618be04b5da3632"
+          ]
+        },
+        "id": "x-HYFzLdwj0x",
+        "outputId": "4dde2b42-89af-4cfa-9d54-bc94c3546aa4"
+      },
+      "execution_count": 11,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Starting training...\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "  0%|          | 0/2 [00:00<?, ?it/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "a2a88573b3d3449a94b3366aace406a5"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "error",
+          "ename": "AttributeError",
+          "evalue": "'CausalLMOutputWithPast' object has no attribute 'to'",
+          "traceback": [
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+            "\u001b[0;32m/tmp/ipykernel_1188/1446239539.py\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[1;32m    111\u001b[0m             \u001b[0mhidden_states\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnoisy_latents\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    112\u001b[0m             \u001b[0mtimestep\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m                    \u001b[0;31m# [0,1] range\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 113\u001b[0;31m             \u001b[0mencoder_hidden_states\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtext_embeds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    114\u001b[0m             \u001b[0mpooled_projections\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpooled_embeds\u001b[0m\u001b[0;34m,\u001b[0m  \u001b[0;31m# Pooled_embeds are not defined!\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    115\u001b[0m             \u001b[0mtxt_ids\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtxt_ids\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;31mAttributeError\u001b[0m: 'CausalLMOutputWithPast' object has no attribute 'to'"
+          ]
+        }
       ]
     },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# OLD CODE"
+      ],
+      "metadata": {
+        "id": "u6BXluFgu5Rl"
+      }
+    },
     {
       "cell_type": "code",
       "source": [
@@ -2352,7 +3102,7 @@
         "id": "sQamMz50JoXM",
         "outputId": "fe17c7f2-6a9c-4c34-ab26-215c3cfe8f04"
       },
-      "execution_count": 2,
+      "execution_count": null,
       "outputs": [
         {
           "output_type": "stream",
@@ -2412,7 +3162,7 @@
         "id": "FNma-jSihzrP",
         "outputId": "79dbfbee-948f-4f98-f385-913934996ee0"
       },
-      "execution_count": 3,
+      "execution_count": null,
       "outputs": [
         {
           "output_type": "stream",
@@ -2455,15 +3205,7 @@
     },
     {
       "cell_type": "code",
-      "source": [
-        "#@markdown # CELL 3.5 — Update torchao (Critical Fix)\n",
-        "\n",
-        "!pip uninstall -q torchao -y\n",
-        "!pip install -q torchao==0.17.0 --no-deps\n",
-        "\n",
-        "import torchao\n",
-        "print(f\"✅ torchao updated to version: {torchao.__version__}\")"
-      ],
+      "source": [],
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -2471,7 +3213,7 @@
         "id": "_fmTXBufjVLz",
         "outputId": "69164b6a-9ea7-4c56-d2c8-078812c4e6ad"
       },
-      "execution_count": 7,
+      "execution_count": null,
       "outputs": [
         {
           "output_type": "stream",
@@ -2521,7 +3263,7 @@
         "id": "jTb-ifqeh5OC",
         "outputId": "61e228c3-85e7-40f8-c2f6-52bc68080f5d"
       },
-      "execution_count": 10,
+      "execution_count": null,
       "outputs": [
         {
           "output_type": "stream",
@@ -2660,7 +3402,7 @@
         "id": "7p26uoeIjAe4",
         "outputId": "cd9488cc-ab59-4ed6-e58c-92fba3aee489"
       },
-      "execution_count": 12,
+      "execution_count": null,
       "outputs": [
         {
           "output_type": "stream",
@@ -2793,9 +3535,7 @@
     },
     {
       "cell_type": "markdown",
-      "source": [
-        "# OLD CODE"
-      ],
+      "source": [],
       "metadata": {
         "id": "4MRDxrA5hZus"
       }
@@ -2849,7 +3589,7 @@
         "id": "Gz6zaIR9GPY-",
         "outputId": "74cea392-32ce-4cf9-b07f-e14a9a53453e"
       },
-      "execution_count": 3,
+      "execution_count": null,
       "outputs": [
         {
           "output_type": "stream",
@@ -2911,7 +3651,7 @@
         "id": "O76DmAIFL_rL",
         "outputId": "81655137-8cd0-41f6-a1dc-cf3ae9eb2bfd"
       },
-      "execution_count": 4,
+      "execution_count": null,
       "outputs": [
         {
           "output_type": "stream",
@@ -2935,7 +3675,7 @@
       "metadata": {
         "id": "xy9HRVAqMJBf"
       },
-      "execution_count": 15,
+      "execution_count": null,
       "outputs": []
     },
     {
@@ -3015,7 +3755,7 @@
         "id": "gXdtTyDqMIN_",
         "outputId": "7f181167-269e-46e7-bdf5-616debd658ff"
       },
-      "execution_count": 24,
+      "execution_count": null,
       "outputs": [
         {
           "output_type": "stream",