diff --git "a/Pony_Illustrious_SDXL_Text_Embedding_Generator.ipynb" "b/Pony_Illustrious_SDXL_Text_Embedding_Generator.ipynb" --- "a/Pony_Illustrious_SDXL_Text_Embedding_Generator.ipynb" +++ "b/Pony_Illustrious_SDXL_Text_Embedding_Generator.ipynb" @@ -63,11 +63,12 @@ { "cell_type": "code", "source": [ - "#@title Generate Embedding\n", + "#@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 # Keep false. This setting minimizes the embedding sized. If true , the embedding is 77 tokens long , leaving no room for the user to prompt anything with it inside a 77 token batch encoding. Keep in mind , a 77 token embedding will still sort of work , as the prompt will extend to 154 tokens and take the average of the two 77 token batch encodings\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", @@ -87,40 +88,52 @@ ").to(device)\n", "\n", "with torch.no_grad():\n", - " # Get INPUT embeddings (token embedding layer) – this is what TI files use\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", - "# Optional: strip special tokens (BOS, EOS, padding)\n", + "# ---- Better token selection ----\n", "if not keep_special_tokens:\n", - " # Find actual content tokens (non-pad)\n", - " # CLIP pad token is usually 0\n", - " mask_l = inputs_l.input_ids[0] != tokenizer_l.pad_token_id\n", - " mask_g = inputs_g.input_ids[0] != tokenizer_g.pad_token_id\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", - " # Also drop BOS (usually first token) and EOS if present\n", - " # Safer approach: keep everything that is not pad\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) # [77, 768]\n", - " emb_g = emb_g.squeeze(0) # [77, 1280]\n", + " emb_l = emb_l.squeeze(0)\n", + " emb_g = emb_g.squeeze(0)\n", "\n", - "print(f\"CLIP-L shape: {emb_l.shape}\")\n", - "print(f\"CLIP-G shape: {emb_g.shape}\")\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\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().contiguous(),\n", - " \"clip_g\": emb_g.cpu().contiguous()\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": { @@ -128,6 +141,1804 @@ }, "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. 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