{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "a58c955f" }, "source": [ "# SDXL Text Embedding Generator\n", "This notebook mounts Google Drive, loads CLIP-L and CLIP-G using your Hugging Face token, and generates dual text embeddings formatted for SDXL models saved to Drive as `.safetensors` files." ] }, { "cell_type": "code", "source": [ "#@title SDXL Text Embedding Generator (TI format)\n", "# Mount Google Drive & Install\n", "from google.colab import drive\n", "import os\n", "drive.mount('/content/drive')\n", "\n", "!pip install -q transformers huggingface_hub safetensors torch\n", "\n", "#@title Load Models\n", "import torch\n", "from google.colab import userdata\n", "from huggingface_hub import login\n", "from transformers import CLIPTextModel, CLIPTokenizer, CLIPTextModelWithProjection\n", "from safetensors.torch import save_file\n", "\n", "# Login (optional but recommended)\n", "try:\n", " hf_token = userdata.get('HF_TOKEN')\n", " login(token=hf_token)\n", "except Exception as e:\n", " print(f\"HF token warning: {e}\")\n", "\n", "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", "print(f\"Using device: {device}\")\n", "\n", "# Correct model IDs\n", "clip_l_id = \"openai/clip-vit-large-patch14\"\n", "clip_g_id = \"laion/CLIP-ViT-bigG-14-laion2B-39B-b160k\"\n", "\n", "print(\"Loading CLIP-L...\")\n", "tokenizer_l = CLIPTokenizer.from_pretrained(clip_l_id)\n", "text_encoder_l = CLIPTextModel.from_pretrained(clip_l_id).to(device)\n", "text_encoder_l.eval()\n", "\n", "print(\"Loading CLIP-G...\")\n", "tokenizer_g = CLIPTokenizer.from_pretrained(clip_g_id)\n", "text_encoder_g = CLIPTextModelWithProjection.from_pretrained(clip_g_id).to(device)\n", "text_encoder_g.eval()\n", "\n", "print(\"Models loaded.\")" ], "metadata": { "id": "3L8XEw6RoRvF" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "#@title Generate Embedding (fixed + bfloat16)\n", "#@markdown ### Settings\n", "text_to_encode = \"A cinematic shot of a majestic castle on a mountain\" #@param {type:\"string\"}\n", "embedding_file_name = \"castle_embedding\" #@param {type:\"string\"}\n", "keep_special_tokens = False #@param {type:\"boolean\"}\n", "#@markdown Keep `False` for a short embedding that leaves room for other prompt text.\n", "\n", "# Tokenize\n", "inputs_l = tokenizer_l(\n", " [text_to_encode],\n", " padding=\"max_length\",\n", " max_length=77,\n", " truncation=True,\n", " return_tensors=\"pt\"\n", ").to(device)\n", "\n", "inputs_g = tokenizer_g(\n", " [text_to_encode],\n", " padding=\"max_length\",\n", " max_length=77,\n", " truncation=True,\n", " return_tensors=\"pt\"\n", ").to(device)\n", "\n", "with torch.no_grad():\n", " emb_l = text_encoder_l.get_input_embeddings()(inputs_l.input_ids) # [1, 77, 768]\n", " emb_g = text_encoder_g.get_input_embeddings()(inputs_g.input_ids) # [1, 77, 1280]\n", "\n", "# ---- Better token selection ----\n", "if not keep_special_tokens:\n", " # CLIP pad token is almost always 0\n", " pad_id_l = tokenizer_l.pad_token_id if tokenizer_l.pad_token_id is not None else 0\n", " pad_id_g = tokenizer_g.pad_token_id if tokenizer_g.pad_token_id is not None else 0\n", "\n", " # Keep only non-padding tokens\n", " mask_l = inputs_l.input_ids[0] != pad_id_l\n", " mask_g = inputs_g.input_ids[0] != pad_id_g\n", "\n", " emb_l = emb_l[0, mask_l] # [N, 768]\n", " emb_g = emb_g[0, mask_g] # [N, 1280]\n", "\n", " # Safety: if somehow empty, fall back to full sequence\n", " if emb_l.shape[0] == 0 or emb_g.shape[0] == 0:\n", " print(\"Warning: mask produced empty tensors – falling back to full 77 tokens\")\n", " emb_l = emb_l # already squeezed? wait, re-fetch\n", " emb_l = text_encoder_l.get_input_embeddings()(inputs_l.input_ids).squeeze(0)\n", " emb_g = text_encoder_g.get_input_embeddings()(inputs_g.input_ids).squeeze(0)\n", "else:\n", " emb_l = emb_l.squeeze(0)\n", " emb_g = emb_g.squeeze(0)\n", "\n", "print(f\"CLIP-L shape : {emb_l.shape} dtype: {emb_l.dtype}\")\n", "print(f\"CLIP-G shape : {emb_g.shape} dtype: {emb_g.dtype}\")\n", "print(f\"Number of tokens kept: L={emb_l.shape[0]}, G={emb_g.shape[0]}\")\n", "\n", "# ---- Save as bfloat16 ----\n", "save_dir = \"/content/drive/MyDrive/SDXL_Embeddings\"\n", "os.makedirs(save_dir, exist_ok=True)\n", "save_path = os.path.join(save_dir, f\"{embedding_file_name}.safetensors\")\n", "\n", "tensors_to_save = {\n", " \"clip_l\": emb_l.cpu().to(torch.bfloat16).contiguous(),\n", " \"clip_g\": emb_g.cpu().to(torch.bfloat16).contiguous()\n", "}\n", "\n", "save_file(tensors_to_save, save_path)\n", "\n", "# Quick size check\n", "file_size = os.path.getsize(save_path)\n", "print(f\"\\nSuccessfully saved: {save_path}\")\n", "print(f\"File size: {file_size:,} bytes ({file_size/1024:.1f} KB)\")\n", "print(\"You can now use it as: embedding:castle_embedding\")" ], "metadata": { "id": "XquwZtTIow0p" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "from google.colab import drive\n", "drive.mount('/content/drive')" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "iJY1Ffk6w3_7", "outputId": "6363a50e-5c94-4e11-f225-fe4b1aa20ad4" }, "execution_count": 2, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Mounted at /content/drive\n" ] } ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "e89db599", "outputId": "aa59584c-4fa0-4386-ec37-50ecf0064bb6" }, "source": [ "import os\n", "import torch\n", "from safetensors.torch import load_file\n", "\n", "# Define paths\n", "save_dir = \"/content/drive/MyDrive/SDXL_Embeddings\"\n", "file_blabla = os.path.join(save_dir, \"blabla.safetensors\")\n", "file_ladybug = os.path.join(save_dir, \"Ladybug.safetensors\")\n", "\n", "def check_embedding(filepath):\n", " if not os.path.exists(filepath):\n", " print(f\"[-] File not found: {filepath}\")\n", " return None\n", "\n", " try:\n", " tensors = load_file(filepath)\n", " print(f\"\\n[+] Successfully loaded: {os.path.basename(filepath)}\")\n", " for key, tensor in tensors.items():\n", " print(f\" Key: '{key}' | Shape: {list(tensor.shape)} | Dtype: {tensor.dtype} | Device: {tensor.device}\")\n", " return tensors\n", " except Exception as e:\n", " print(f\"[-] Error loading {os.path.basename(filepath)}: {e}\")\n", " return None\n", "\n", "# Load and display structural details of both files\n", "print(\"=== Inspecting Embeddings ===\")\n", "tensors_blabla = check_embedding(file_blabla)\n", "tensors_ladybug = check_embedding(file_ladybug)\n", "\n", "# Verification logic\n", "if tensors_blabla is not None and tensors_ladybug is not None:\n", " print(\"\\n=== Verification Results ===\")\n", " mismatch = False\n", "\n", " # Check if they share the same keys\n", " keys_blabla = set(tensors_blabla.keys())\n", " keys_ladybug = set(tensors_ladybug.keys())\n", "\n", " if keys_blabla != keys_ladybug:\n", " print(f\"[!] Key mismatch!\")\n", " print(f\" 'blabla' keys: {keys_blabla}\")\n", " print(f\" 'Ladybug' keys: {keys_ladybug}\")\n", " mismatch = True\n", " else:\n", " for key in keys_blabla:\n", " shape_b = tensors_blabla[key].shape\n", " shape_l = tensors_ladybug[key].shape\n", " dtype_b = tensors_blabla[key].dtype\n", " dtype_l = tensors_ladybug[key].dtype\n", "\n", " if shape_b != shape_l:\n", " print(f\"[!] Dimension mismatch for key '{key}':\")\n", " print(f\" blabla: {list(shape_b)}\")\n", " print(f\" Ladybug: {list(shape_l)}\")\n", " mismatch = True\n", "\n", " if dtype_b != dtype_l:\n", " print(f\"[!] Dtype mismatch for key '{key}':\")\n", " print(f\" blabla: {dtype_b}\")\n", " print(f\" Ladybug: {dtype_l}\")\n", " mismatch = True\n", "\n", " if not mismatch:\n", " print(\"[✔] Success! All keys, shapes, and data types match perfectly between 'blabla.safetensors' and 'Ladybug.safetensors'.\")\n", " else:\n", " print(\"[X] Verification failed. There are structural or dimension discrepancies between the files.\")\n", "else:\n", " print(\"\\n[-] Could not complete verification because one or both files were missing or invalid.\")" ], "execution_count": 3, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "=== Inspecting Embeddings ===\n", "\n", "[+] Successfully loaded: blabla.safetensors\n", " Key: 'clip_g' | Shape: [12, 1280] | Dtype: torch.bfloat16 | Device: cpu\n", " Key: 'clip_l' | Shape: [12, 768] | Dtype: torch.bfloat16 | Device: cpu\n", "\n", "[+] Successfully loaded: Ladybug.safetensors\n", " Key: 'clip_g' | Shape: [28, 1280] | Dtype: torch.float32 | Device: cpu\n", " Key: 'clip_l' | Shape: [28, 768] | Dtype: torch.float32 | Device: cpu\n", "\n", "=== Verification Results ===\n", "[!] Dimension mismatch for key 'clip_l':\n", " blabla: [12, 768]\n", " Ladybug: [28, 768]\n", "[!] Dtype mismatch for key 'clip_l':\n", " blabla: torch.bfloat16\n", " Ladybug: torch.float32\n", "[!] Dimension mismatch for key 'clip_g':\n", " blabla: [12, 1280]\n", " Ladybug: [28, 1280]\n", "[!] Dtype mismatch for key 'clip_g':\n", " blabla: torch.bfloat16\n", " Ladybug: torch.float32\n", "[X] Verification failed. There are structural or dimension discrepancies between the files.\n" ] } ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000, "referenced_widgets": [ "1828fce2f9f54cd89e3cf02185ad4330", "20b429ee0109422e8b66e57d8074f571", "46fd15340f344cc885b732e1921b817f", "28f84a5cbdb0470f9a2d4671c0d08c38", "e1f890415c7c44c5830199734b52c023", "9c4ec78800f54edf95d882c29da07150", "9721b7f2db4a482e94c97b29803528d8", "fd5553fc1f954bd18141be3e56bbc18c", "0f1cd9bc455a43eaa98419f65ed4b689", "1d0e118963ec4a24a177caed0d1d951b", "4cf6a52f04d4403f8269261e650d1a42", "1015d0958ca24a0c9f42d2838afd247c", "5f0471aa66c2448585b088f0e06b393a", "d6d184f859d4478a925fb402f52433bb", "99abdcf0ed6944afa4b3f6e3a9b55279", "2af8d7875f2142498f621220dbb594d9", "7e484bf49003487fbf0080f0d4452283", "450d8ef79073482ca93c1f5b59a3f791", "5e2c28fd788e4703abf7aae1d01a4d3b", "1ecd7507ab1d4a24b636a804f8a93ab1", "63856296196c4c7b94cd9a162a12a681", "60c158cd5f29436d950b5572651b5c8d", "c760577e5eb746fe83973ea5d8774ac3", "23f94e6313ab45c3af2b4ebc7a06719f", "bc6230a40bbd411287a822900c805940", "5a8f411d6bf44b95b6f211b3af0b3e94", "5b66295325a9470eb29b663a6484f860", "0251a004630d4dc496a95919654bc4e0", "aeb48d0a66734f108c1f1db82df67ce5", "710866d7318b44d998bba700e0d5c683", "656fb2e6756b491a9e3f066bef6b6b71", "4e9bdbc950ff40fb8ac4a8aeee0c0415", "3b97e7c98c7e449793be1aa11e043ba9", "b16914af7c3f4abe943ddca4c48e0436", "b164952daf764fa4955cdbb0f5903cbd", "2c3bb55145654ae2a1d3cb18c8843b46", "059b58c0d84d49bb8865ba734733b60c", "f7b41e169a0d4ed494815b0f7231f546", "5cd0c8cdf82547cc80dfd47c89f7c303", "5f51ca6dddeb444d8dffb0157040abd7", "77a0dbc33a90467cb50c7abbdee312fb", "2c959dcfc56041c4970508bbdc48ade7", "42a13c45f3d748729102b6e1d8922513", "4b52a040a0074623ad4bda2db2f12d65" ] }, "id": "a5c02c3f", "outputId": "00b47176-91a9-4b6f-ec1d-0281667dc8af" }, "source": [ "import os\n", "import gc\n", "import torch\n", "from PIL import Image\n", "from transformers import CLIPTokenizer, CLIPTextModel, CLIPTextModelWithProjection, CLIPProcessor, CLIPVisionModelWithProjection\n", "from safetensors.torch import save_file\n", "\n", "# --- Configurations ---\n", "prompt = \"this is a a blue rabbit on a pink mountain holding a yellow flower\"\n", "save_dir = \"/content/drive/MyDrive/SDXL_Embeddings\"\n", "save_path = os.path.join(save_dir, \"blabla.safetensors\")\n", "image_path = os.path.join(save_dir, \"rabbit_image.png\")\n", "\n", "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", "clip_l_id = \"openai/clip-vit-large-patch14\"\n", "clip_g_id = \"laion/CLIP-ViT-bigG-14-laion2B-39B-b160k\"\n", "\n", "# Helper to clear VRAM thoroughly\n", "def clear_vram():\n", " gc.collect()\n", " if torch.cuda.is_available():\n", " torch.cuda.empty_cache()\n", " torch.cuda.ipc_collect()\n", "\n", "# Clear VRAM initially\n", "clear_vram()\n", "\n", "# --- Step 1: CLIP-L Text Processing ---\n", "print(\"[+] Loading CLIP-L Text Model...\")\n", "tokenizer_l = CLIPTokenizer.from_pretrained(clip_l_id)\n", "text_encoder_l = CLIPTextModel.from_pretrained(clip_l_id).to(device).eval()\n", "\n", "inputs_l = tokenizer_l(\n", " [prompt],\n", " padding=\"max_length\",\n", " max_length=77,\n", " truncation=True,\n", " return_tensors=\"pt\"\n", ").to(device)\n", "\n", "with torch.no_grad():\n", " emb_l = text_encoder_l.get_input_embeddings()(inputs_l.input_ids)\n", "\n", "pad_id_l = tokenizer_l.pad_token_id if tokenizer_l.pad_token_id is not None else 0\n", "mask_l = inputs_l.input_ids[0] != pad_id_l\n", "num_valid_tokens_l = int(mask_l.sum().item())\n", "\n", "final_emb_l = torch.zeros((28, 768), dtype=torch.float32)\n", "valid_l = emb_l[0, mask_l][:28].cpu().to(torch.float32)\n", "final_emb_l[:valid_l.shape[0]] = valid_l\n", "\n", "# Free CLIP-L Text Model immediately\n", "del text_encoder_l, tokenizer_l, emb_l, inputs_l\n", "clear_vram()\n", "\n", "# --- Step 2: CLIP-G Text Processing ---\n", "print(\"[+] Loading CLIP-G Text Model...\")\n", "tokenizer_g = CLIPTokenizer.from_pretrained(clip_g_id)\n", "text_encoder_g = CLIPTextModelWithProjection.from_pretrained(clip_g_id).to(device).eval()\n", "\n", "inputs_g = tokenizer_g(\n", " [prompt],\n", " padding=\"max_length\",\n", " max_length=77,\n", " truncation=True,\n", " return_tensors=\"pt\"\n", ").to(device)\n", "\n", "with torch.no_grad():\n", " emb_g = text_encoder_g.get_input_embeddings()(inputs_g.input_ids)\n", "\n", "pad_id_g = tokenizer_g.pad_token_id if tokenizer_g.pad_token_id is not None else 0\n", "mask_g = inputs_g.input_ids[0] != pad_id_g\n", "num_valid_tokens_g = int(mask_g.sum().item())\n", "\n", "final_emb_g = torch.zeros((28, 1280), dtype=torch.float32)\n", "valid_g = emb_g[0, mask_g][:28].cpu().to(torch.float32)\n", "final_emb_g[:valid_g.shape[0]] = valid_g\n", "\n", "# Free CLIP-G Text Model immediately\n", "del text_encoder_g, tokenizer_g, emb_g, inputs_g\n", "clear_vram()\n", "\n", "# Save the generated embedding file\n", "tensors_to_save = {\n", " \"clip_l\": final_emb_l,\n", " \"clip_g\": final_emb_g\n", "}\n", "save_file(tensors_to_save, save_path)\n", "print(f\"[+] Saved new blabla.safetensors (float32) with shapes: L -> {list(final_emb_l.shape)}, G -> {list(final_emb_g.shape)}\")\n", "\n", "# --- Step 3: Extract Image Embeddings & Calculate Cosine Similarity ---\n", "if not os.path.exists(image_path):\n", " print(f\"[-] Error: '{image_path}' not found. Cannot verify with cosine similarity.\")\n", "else:\n", " print(\"\\n[+] Loading image...\")\n", " image = Image.open(image_path).convert(\"RGB\")\n", "\n", " # 3.1: Verify CLIP-L Vision (Run in FP16 to save memory)\n", " print(\"[+] Loading CLIP-L Vision Projection Model (FP16)...\")\n", " vision_model_l = CLIPVisionModelWithProjection.from_pretrained(\n", " clip_l_id,\n", " torch_dtype=torch.float16\n", " ).to(device).eval()\n", " processor_l = CLIPProcessor.from_pretrained(clip_l_id)\n", "\n", " with torch.no_grad():\n", " img_inputs_l = processor_l(images=image, return_tensors=\"pt\").to(device)\n", " img_inputs_l = {k: v.to(torch.float16) if v.dtype == torch.float32 else v for k, v in img_inputs_l.items()}\n", " img_features_l = vision_model_l(**img_inputs_l).image_embeds[0].to(torch.float32).cpu()\n", "\n", " # Only mean-pool over the actual non-padded tokens to get correct similarity!\n", " text_mean_l = final_emb_l[:num_valid_tokens_l].mean(dim=0)\n", " similarity_l = torch.nn.functional.cosine_similarity(text_mean_l, img_features_l, dim=0).item()\n", "\n", " # Free CLIP-L Vision immediately\n", " del vision_model_l, processor_l, img_inputs_l, img_features_l, text_mean_l\n", " clear_vram()\n", "\n", " # 3.2: Verify CLIP-G Vision (Run in FP16 to prevent OOM)\n", " print(\"[+] Loading CLIP-G Vision Projection Model (FP16)...\")\n", " vision_model_g = CLIPVisionModelWithProjection.from_pretrained(\n", " clip_g_id,\n", " torch_dtype=torch.float16\n", " ).to(device).eval()\n", " processor_g = CLIPProcessor.from_pretrained(clip_g_id)\n", "\n", " with torch.no_grad():\n", " img_inputs_g = processor_g(images=image, return_tensors=\"pt\").to(device)\n", " img_inputs_g = {k: v.to(torch.float16) if v.dtype == torch.float32 else v for k, v in img_inputs_g.items()}\n", " img_features_g = vision_model_g(**img_inputs_g).image_embeds[0].to(torch.float32).cpu()\n", "\n", " # Only mean-pool over the actual non-padded tokens to get correct similarity!\n", " text_mean_g = final_emb_g[:num_valid_tokens_g].mean(dim=0)\n", " similarity_g = torch.nn.functional.cosine_similarity(text_mean_g, img_features_g, dim=0).item()\n", "\n", " # Free CLIP-G Vision\n", " del vision_model_g, processor_g, img_inputs_g, img_features_g, text_mean_g\n", " clear_vram()\n", "\n", " print(\"\\n=== Verification (Cosine Similarity to Image) ===\")\n", " print(f\"CLIP-L Sim (Non-Padded Mean): {similarity_l:.4f}\")\n", " print(f\"CLIP-G Sim (Non-Padded Mean): {similarity_g:.4f}\")" ], "execution_count": 11, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "[+] Loading CLIP-L Text Model...\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "Loading weights: 0%| | 0/196 [00:00 [28, 768], G -> [28, 1280]\n", "\n", "[+] Loading image...\n", "[+] Loading CLIP-L Vision Projection Model (FP16)...\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "Loading weights: 0%| | 0/392 [00:00 0.1 and similarity_g > 0.1:\n", " tensors_to_save = {\n", " \"clip_l\": final_emb_l,\n", " \"clip_g\": final_emb_g\n", " }\n", " save_file(tensors_to_save, save_path)\n", " print(f\"\\n[✔] Success! Saved verified blabla.safetensors to {save_path}\")\n", " else:\n", " print(\"\\n[X] Warning: Similarity score was too low. The embedding has not been saved.\")" ], "execution_count": 12, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "[+] Extracting Contextualized CLIP-L Text Embeddings...\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "Loading weights: 0%| | 0/196 [00:00 0.1 and similarity_g > 0.1:\n", " tensors_to_save = {\n", " \"clip_l\": final_emb_l,\n", " \"clip_g\": final_emb_g\n", " }\n", " save_file(tensors_to_save, save_path)\n", " print(f\"\\n[✔] Verification Passed! Saved validated blabla.safetensors to {save_path}\")\n", " else:\n", " print(\"\\n[X] Verification Failed: Cosine similarity to the target image is too low. File was NOT saved.\")" ], "execution_count": 2, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "[+] Extracting Contextualized CLIP-L Text Embeddings...\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "Loading weights: 0%| | 0/196 [00:00 0.1 and similarity_g > 0.1:\n", " tensors_to_save = {\n", " \"clip_l\": final_emb_l,\n", " \"clip_g\": final_emb_g\n", " }\n", " save_file(tensors_to_save, save_path)\n", " print(f\"\\n[✔] Verification Passed! Saved validated blabla.safetensors to {save_path}\")\n", " else:\n", " print(\"\\n[X] Verification Failed: Cosine similarity is below the 0.1 threshold. Embedding was NOT saved.\")" ], "execution_count": null, "outputs": [] } ], "metadata": { "colab": { "provenance": [], "gpuType": "T4" }, "kernelspec": { "display_name": "Python 3", "name": "python3" }, "language_info": { "name": "python" }, "accelerator": "GPU", "widgets": { "application/vnd.jupyter.widget-state+json": { "version_major": 2, "version_minor": 0, "state": { "1828fce2f9f54cd89e3cf02185ad4330": { "model_module": "@jupyter-widgets/controls", "model_name": "HBoxModel", "model_module_version": "1.5.0", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HBoxView", "box_style": "", "children": [ "IPY_MODEL_20b429ee0109422e8b66e57d8074f571", "IPY_MODEL_46fd15340f344cc885b732e1921b817f", "IPY_MODEL_28f84a5cbdb0470f9a2d4671c0d08c38" ], "layout": 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