diff --git "a/joy_caption_alpha_one_jupyter.ipynb" "b/joy_caption_alpha_one_jupyter.ipynb" --- "a/joy_caption_alpha_one_jupyter.ipynb" +++ "b/joy_caption_alpha_one_jupyter.ipynb" @@ -2,25 +2,13 @@ "cells": [ { "cell_type": "code", - "source": [ - "import os\n", - "home_directory = '/content/'\n", - "using_Kaggle = os.environ.get('KAGGLE_URL_BASE','')\n", - "if using_Kaggle : home_directory = '/kaggle/working/'\n", - "%cd {home_directory}\n", - "\n", - "def my_mkdirs(folder):\n", - " if os.path.exists(folder)==False:\n", - " os.makedirs(folder)\n", - "\n", - "my_mkdirs(f'{home_directory}tmp')" - ], + "source": [], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "kLmXfHXy80BY", - "outputId": "171efb10-d9ae-479a-be16-7ddf1888bcb2" + "outputId": "d99480a2-f2a2-43ec-fe12-fce0a740bae3" }, "execution_count": 1, "outputs": [ @@ -41,7 +29,7 @@ "colab": { "base_uri": "https://localhost:8080/" }, - "outputId": "c5c49f72-107b-40cb-d180-a0442a5de70c" + "outputId": "bedb6702-9231-45b1-f071-904527abd4b9" }, "outputs": [ { @@ -100,7 +88,7 @@ "Download Results:\n", "gid |stat|avg speed |path/URI\n", "======+====+===========+=======================================================\n", - "63edad|\u001b[1;32mOK\u001b[0m | 49KiB/s|/content/joy/text_model/adapter_config.json\n", + "1f6e49|\u001b[1;32mOK\u001b[0m | 106KiB/s|/content/joy/text_model/adapter_config.json\n", "\n", "Status Legend:\n", "(OK):download completed.\n", @@ -108,7 +96,7 @@ "Download Results:\n", "gid |stat|avg speed |path/URI\n", "======+====+===========+=======================================================\n", - "c5c8aa|\u001b[1;32mOK\u001b[0m | 110MiB/s|/content/joy/text_model/adapter_model.safetensors\n", + "ec6147|\u001b[1;32mOK\u001b[0m | 42MiB/s|/content/joy/text_model/adapter_model.safetensors\n", "\n", "Status Legend:\n", "(OK):download completed.\n", @@ -116,7 +104,7 @@ "Download Results:\n", "gid |stat|avg speed |path/URI\n", "======+====+===========+=======================================================\n", - "60e2f9|\u001b[1;32mOK\u001b[0m | 159MiB/s|/content/joy/clip_model.pt\n", + "ce51a4|\u001b[1;32mOK\u001b[0m | 171MiB/s|/content/joy/clip_model.pt\n", "\n", "Status Legend:\n", "(OK):download completed.\n", @@ -124,7 +112,7 @@ "Download Results:\n", "gid |stat|avg speed |path/URI\n", "======+====+===========+=======================================================\n", - "a50c87|\u001b[1;32mOK\u001b[0m | 101KiB/s|/content/joy/config.yaml\n", + "a87c89|\u001b[1;32mOK\u001b[0m | 89KiB/s|/content/joy/config.yaml\n", "\n", "Status Legend:\n", "(OK):download completed.\n", @@ -132,7 +120,7 @@ "Download Results:\n", "gid |stat|avg speed |path/URI\n", "======+====+===========+=======================================================\n", - "44ace6|\u001b[1;32mOK\u001b[0m | 129MiB/s|/content/joy/image_adapter.pt\n", + "079f74|\u001b[1;32mOK\u001b[0m | 51MiB/s|/content/joy/image_adapter.pt\n", "\n", "Status Legend:\n", "(OK):download completed.\n", @@ -178,7 +166,7 @@ "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.11/dist-packages (from requests->huggingface-hub>=0.25.0->peft) (2.3.0)\n", "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.11/dist-packages (from requests->huggingface-hub>=0.25.0->peft) (2024.12.14)\n", "Downloading bitsandbytes-0.45.0-py3-none-manylinux_2_24_x86_64.whl (69.1 MB)\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m69.1/69.1 MB\u001b[0m \u001b[31m9.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m69.1/69.1 MB\u001b[0m \u001b[31m10.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[?25hInstalling collected packages: bitsandbytes\n", "Successfully installed bitsandbytes-0.45.0\n" ] @@ -197,11 +185,517 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, 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"cc12ccdf765b4ec5bd5d638f88147617", + "1a67e893929e42a796e25474d22f5ba8", + "8cda68adc80d434bbcc16e508a6cdfa0", + "38f9b47043e94b5ea34dfb93d0881b87", + "edab6fbf75cc4f91b968feab2c761025", + "4463a58874b143c38622940323430cbe", + "763b3aae3fab442e81b1178f596b63d8" + ] + } }, - "outputs": [], + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "preprocessor_config.json: 0%| | 0.00/368 [00:00:70: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", + " checkpoint = torch.load(\"/content/joy/clip_model.pt\", map_location='cpu')\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "tokenizer_config.json: 0%| | 0.00/50.6k [00:00:83: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", + " image_adapter.load_state_dict(torch.load(\"/content/joy/image_adapter.pt\", map_location=\"cpu\"))\n" + ] + } + ], "source": [ "from huggingface_hub import InferenceClient\n", "from torch import nn\n", @@ -344,16 +838,605 @@ { "cell_type": "code", "source": [ + "import os\n", + "from PIL import Image\n", + "home_directory = '/content/'\n", + "using_Kaggle = os.environ.get('KAGGLE_URL_BASE','')\n", + "if using_Kaggle : home_directory = '/kaggle/working/'\n", + "%cd {home_directory}\n", + "\n", + "def my_mkdirs(folder):\n", + " if os.path.exists(folder)==False:\n", + " os.makedirs(folder)\n", + "\n", + "\n", + "tgt_directory = f'{home_directory}tmp'\n", + "my_mkdirs(f'{tgt_directory}')\n", + "\n", + "tgt_folder = '/content/'\n", + "suffixes = ['.png', '.jpeg' , '.webp' , '.jpg']\n", + "for filename in os.listdir(tgt_folder):\n", + " for suffix in suffixes:\n", + " if not filename.find(suffix)>-1: continue\n", + " print(filename)\n", + " %cd {home_directory}\n", + " input_image = Image.open(f\"{filename}\").convert('RGB')\n", + " %cd {tgt_directory}\n", + " num = int(f'{filename}'.replace(suffix,''))\n", + " input_image.save(f'{num-1}.png', \"PNG\")" + ], + "metadata": { + "id": "yVnuCVjNNdim", + "outputId": "f04c5cdd-48df-4afd-d3cf-b43332061064", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "execution_count": 9, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "/content\n", + "99.png\n", + "/content\n", + "/content/tmp\n", + "130.png\n", + "/content\n", + "/content/tmp\n", + "45.png\n", + "/content\n", + "/content/tmp\n", + "16.png\n", + "/content\n", + "/content/tmp\n", + "139.png\n", + "/content\n", + "/content/tmp\n", + "5.png\n", + "/content\n", + "/content/tmp\n", + "88.png\n", + "/content\n", + "/content/tmp\n", + "154.png\n", + "/content\n", + "/content/tmp\n", + "9.png\n", + "/content\n", + "/content/tmp\n", + 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"metadata": { + "id": "0C4KHOeN7V_O", + "outputId": "c8559382-6a1d-4df2-84e0-a78017f7e9e4", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 53 + } + }, + "execution_count": 10, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "/content\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "'/content/prompts.zip'" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "string" + } + }, + "metadata": {}, + "execution_count": 10 + } + ] + }, + { + "cell_type": "code", + "source": [ + "from PIL import Image\n", + "\n", + "# Open an existing image\n", + "image = Image.open('new_york_city.jpg')\n", + "\n", + "# Save the image in a different format\n", + "\n", + "\n", "suffix = 'png'\n", - "%cd /content/tmp\n", - "for number in range(81):\n", - " input_image = Image.open(f\"/content/{number+1}.{suffix}\").convert('RGB')\n", - " caption = stream_chat(input_image, \"descriptive\", \"formal\", \"any\")\n", - " f = open(f\"{number}.txt\", \"w\")\n", - " f.write(f'{caption}')\n", - " f.close()\n", - " print(f\"...\\n\\n...caption for {number+1}.{suffix}\\n\\n...\")\n", - " print(caption)\n", + "for number in range(164):\n", + " if number<84:continue\n", + " try:\n", + " %cd /content/\n", + " input_image = Image.open(f\"/content/{number+1}.{suffix}\").convert('RGB')\n", + " caption = stream_chat(input_image, \"descriptive\", \"formal\", \"any\")\n", + " %cd /content/tmp\n", + " f = open(f\"{number+1}.txt\", \"w\")\n", + " f.write(f'{caption}')\n", + " f.close()\n", + " print(f\"...\\n\\n...caption for {number+1}.{suffix}\\n\\n...\")\n", + " print(caption)\n", + " except:\n", + " continue\n", "#----#\n" ], "metadata": { @@ -371,35 +1454,660 @@ ], "metadata": { "id": "E7t75uf3GUVP", - "outputId": "012ac641-7eb8-4ba7-933b-dc4afa1fc203", + "outputId": "894bd2ab-3a7e-4856-88d7-371fe32bba04", "colab": { "base_uri": "https://localhost:8080/", "height": 53 } }, - "execution_count": 7, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "/content\n" - ] - }, - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "'/content/prompts.zip'" - ], - "application/vnd.google.colaboratory.intrinsic+json": { - "type": "string" - } - }, - "metadata": {}, - "execution_count": 7 - } - ] + "execution_count": 7, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "/content\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "'/content/prompts.zip'" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "string" + } + }, + "metadata": {}, + "execution_count": 7 + } + ] + }, + { + "cell_type": "code", + "source": [ + "#initialize\n", + "import torch\n", + "from safetensors.torch import load_file, save_file\n", + "from google.colab import drive\n", + "drive.mount('/content/drive')" + ], + "metadata": { + "id": "qMLhG1PBYHba", + "outputId": "fb0469d2-a406-4015-87a2-0386c6928071", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "execution_count": 1, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Mounted at /content/drive\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "import torch\n", + "from safetensors.torch import load_file, save_file\n", + "import torch.nn as nn\n", + "from torch import linalg as LA\n", + "import os\n", + "import math\n", + "import random\n", + "import numpy as np\n", + "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n", + "\n", + "# This method rescales a _lora to a given ratio. I haven't tested it\n", + "# yet but this is more or less how it works\n", + "def rescale_and_save(_lora , savefile_name, new_ratio):\n", + " count = 0\n", + " lora = {}\n", + " for key in _lora:count = count + 1\n", + " NUM_ITEMS = count\n", + " count = 0\n", + " decimals = 6\n", + " for key in _lora:\n", + " if not f'{key}'.find('alpha') > -1: continue\n", + " alpha = f'{key}'\n", + " up = f'{key}'.replace('alpha' , 'lora_up.weight')\n", + " down = f'{key}'.replace('alpha' , 'lora_down.weight')\n", + " #------#\n", + " rank = _lora[f'{down}'].shape[0]\n", + " new_alpha = torch.tensor(new_ratio*rank).to(device = device , dtype=torch.float32)\n", + " lora[up] = torch.round(torch.sqrt(_lora[alpha]/new_alpha)*_lora[up], decimals = decimals).to(device = device , dtype=torch.float32)\n", + " lora[down] = torch.round(torch.sqrt(_lora[alpha]/new_alpha)*_lora[down], decimals = decimals).to(device = device , dtype=torch.float32)\n", + " #-----#\n", + " lora[alpha] = (new_alpha/_lora[alpha])*_lora[alpha].to(device = device , dtype=torch.float32)\n", + " count = count + 3\n", + " print(f'{count} / {NUM_ITEMS}')\n", + " #--------#\n", + " print(f'done!')\n", + " print(f'casting params to fp16....')\n", + " for key in lora: lora[f'{key}'] = lora[f'{key}'].to(device = device , dtype=torch.float16)\n", + " #-------#\n", + " print(f'done!')\n", + " print(f'saving {savefile_name}...')\n", + " save_file(lora , f'{savefile_name}')\n", + " #-----------#\n", + "\n", + "tgt = load_file(\"/content/drive/MyDrive/'Your Name (Makoto Shinkai)' - v3 - [STYLE] [LORA] [FLUX] - spectrum_0008 by 'AI_Characters'.safetensors\")\n", + "for key in tgt:\n", + " tgt[f'{key}'] = tgt[f'{key}'].to(device = device , dtype=torch.float32)\n", + "\n", + "\n", + "if False:\n", + " for key in tgt:\n", + " if f'{key}'.find('alpha')>-1: print(tgt[f'{key}'])\n", + " print(f\" {key} : {tgt[f'{key}'].shape}\")\n", + "\n", + "name = 'shinkai'\n", + "savefile_name = f'{name}.safetensors'\n", + "new_ratio = 0.5\n", + "\n", + "\n", + "if True:\n", + " rescale_and_save(tgt , savefile_name, new_ratio)\n", + " #(alpha/scale) = (32/16)\n", + "\n", + " tgt = load_file(f'{savefile_name}')\n", + " for key in tgt:\n", + " if f'{key}'.find('alpha')>-1: print(tgt[f'{key}'])\n", + " print(f\" {key} : {tgt[f'{key}'].shape}\")" + ], + "metadata": { + "id": "XoOQgjdVYLgs" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "import torch\n", + "from safetensors.torch import load_file, save_file\n", + "import torch.nn as nn\n", + "from torch import linalg as LA\n", + "import os\n", + "import math\n", + "import random\n", + "import numpy as np\n", + "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n", + "\n", + "# filter_and_save\n", + "# Use this method to change the scale = (rank/alpha) value of a given _lora\n", + "# This method will also eliminate noise. All values < zero_threshold * e-6 will be set to 0\n", + "# in the delta_W of this LoRa. The processed LoRa will be saved as a .safetensor file in fp16\n", + "# The rank of the LoRa affect the file size. At rank 32 the filesize is 300 MB , at rank 16 the filesize is 150 MB and so on.\n", + "#\n", + "# When merging LoRa , it is important that\n", + "# a) the scale of all the merged LoRas are the same. I use the scale = (alpha/rank) = 0.5 at all times.\n", + "# For rank 32 , the alpha must be 16 , for example.\n", + "#\n", + "# b) The rank of the merged LoRas should be 32 or below , any larger values might trigger a 'Out of Memory' error on Google Colab GPU:s\n", + "# --------------\n", + "# _lora - The lora which you wish to process\n", + "# savefile_name - The name of the savefile to be created. Make sure the savefile_name ends with the '.safetensors' suffix\n", + "# new_rank - The rank you wish to set the LoRa to\n", + "# new_alpha - The alpha value you wish to set the LoRa to. For proper scaling ,\n", + "# set the alpha value to half the value of the rank so (alpha/rank) = 0.5\n", + "# This is a very common scale for trained LoRa\n", + "#\n", + "# zero_threshold - All values < zero_threshold * e-6 will be set to 0\n", + "# in the delta_W of this LoRa. This is useful to eliminate 'junk' in the output of the\n", + "# Lora when scaling it to strength above 0.8. A high zero_threshold will also make the Lora more compatible with other LoRa\n", + "# , at the expense of making the LoRa less true to the originally trained image output.\n", + "#\n", + "def filter_and_save(_lora , savefile_name, new_rank , new_alpha, zero_threshold):\n", + " lora = {}\n", + " count = 0\n", + " for key in _lora:count = count + 1\n", + " NUM_ITEMS = count\n", + " count = 0\n", + " thresh = zero_threshold*0.000001 # 1e-6\n", + " #-------#\n", + " for key in _lora:\n", + " if f'{key}'.find('alpha') > -1:\n", + " lora[f'{key}'] = torch.tensor(new_alpha).to(device = device , dtype = torch.float32)\n", + " count = count + 1\n", + " print(f'{count} / {NUM_ITEMS}')\n", + " continue\n", + " #------#\n", + " if not f'{key}'.find('lora_down') > -1: continue\n", + " up = f'{key}'.replace('lora_down' , 'lora_up')\n", + " down = f'{key}'\n", + " #-------#\n", + " delta_W = torch.matmul(_lora[up],_lora[down]).to(device = device , dtype=torch.float32)\n", + " #---#\n", + " N = delta_W.numel()\n", + " y = delta_W.flatten().to(device = device , dtype=torch.float32)\n", + " values,indices = torch.sort(y, descending = False) # smallest -> largest elements\n", + " y = torch.zeros(y.shape).to(device = device , dtype=torch.float32)\n", + " y[indices[values>thresh]] = 1\n", + " y[indices[values<-thresh]] = 1\n", + " y = y.unflatten(0,delta_W.shape).to(device = device , dtype=torch.float32)\n", + " delta_W = torch.mul(delta_W,y).to(device = device , dtype=torch.float32)\n", + " #------#\n", + " tmp={}\n", + " tmp['u'], tmp['s'], tmp['Vh'] = torch.svd(delta_W)\n", + " tmp['u'] = tmp['u'][:,: new_rank]\n", + " tmp['s'] = tmp['s'][: new_rank]\n", + " #-------#\n", + " tmp['u'] = torch.round(torch.matmul(tmp['u'], torch.diag(tmp['s'])),decimals=6)\n", + " tmp['Vh'] = torch.round(tmp['Vh'].t()[: new_rank,:],decimals=6)\n", + " #-------#\n", + " for key in tmp:tmp[f'{key}'] = tmp[f'{key}'].contiguous()\n", + " lora[up] = tmp['u'].to(device = device , dtype=torch.float32)\n", + " lora[down] = tmp['Vh'].to(device = device , dtype=torch.float32)\n", + " #-------#\n", + " count = count + 2\n", + " print(f'{count} / {NUM_ITEMS}')\n", + " #-------#\n", + " print(f'done!')\n", + " print(f'casting params to fp16....')\n", + " for key in _lora: lora[f'{key}'] = lora[f'{key}'].to(device = device , dtype=torch.float16)\n", + " #-------#\n", + " print(f'done!')\n", + " print(f'saving {savefile_name}...')\n", + " save_file(lora , f'{savefile_name}')\n", + "#--------#\n", + "\n", + "# count_zeros\n", + "# Use this method to guage how large zero_threshold you should set for a given Lora.\n", + "# This function can serve as a 'preview' prior to running either the filter_and_save or\n", + "# merge_and_save methods. Since it does not use SVD to re-pack the LoRa\n", + "# , you can run this method on a non-GPU instance on the Colab\n", + "#-----------\n", + "# _lora - The lora which you wish to process\n", + "# zero_threshold - All values < zero_threshold * e-6 will be set to 0\n", + "def count_zeros(_lora, zero_threshold):\n", + " count = 0\n", + " for key in _lora:count = count + 1\n", + " NUM_ITEMS = count\n", + " count = 0\n", + " #-----#\n", + " thresh = zero_threshold*0.000001 # 1e-6\n", + "\n", + " print(f'at zero_threshold = {zero_threshold}e-6 :')\n", + " for key in _lora:\n", + " if f'{key}'.find('alpha') > -1:\n", + " count = count + 1\n", + " continue\n", + " #------#\n", + " if not f'{key}'.find('lora_down') > -1: continue\n", + " up = f'{key}'.replace('lora_down' , 'lora_up')\n", + " down = f'{key}'\n", + " #-------#\n", + " delta_W = torch.matmul(_lora[up],_lora[down]).to(device = device , dtype=torch.float32)\n", + " N = delta_W.numel()\n", + " y = delta_W.flatten().to(device = device , dtype=torch.float32)\n", + " values,indices = torch.sort(y, descending = False) # smallest -> largest elements\n", + " y = torch.ones(y.shape).to(device = device , dtype=torch.float32)\n", + " y[indices[values>thresh]] = 0\n", + " neg_pcnt = round((100*torch.sum(y) / N).item(),2)\n", + " y[indices[values<-thresh]] = 0\n", + " count = count + 2\n", + " pcnt = round((100*torch.sum(y) / N).item(),2)\n", + " neg_pcnt = round(neg_pcnt - pcnt,2) # remove zero % from neg_pcnt\n", + " pos_pcnt = round(100- pcnt - neg_pcnt,2)\n", + " print(f'at {count} / {NUM_ITEMS} : {pcnt} % zeros ,{pos_pcnt} % pos. , {neg_pcnt} % neg ')\n", + " #------#\n", + "#-----#\n", + "\n", + "# This method rescales a _lora to a given ratio. I haven't tested it\n", + "# But this is more or less how it works\n", + "def rescale_and_save(_lora , savefile_name, new_ratio):\n", + " count = 0\n", + " lora = {}\n", + " for key in _lora:count = count + 1\n", + " NUM_ITEMS = count\n", + " count = 0\n", + " decimals = 6\n", + " for key in _lora:\n", + " if not f'{key}'.find('alpha') > -1: continue\n", + " alpha = f'{key}'\n", + " up = f'{key}'.replace('alpha' , 'lora_up')\n", + " down = f'{key}'.replace('alpha' , 'lora_down')\n", + " #------#\n", + " rank = torch.matmul(_lora[up]*0,_lora[down]*0).shape[0]\n", + " new_alpha = torch.tensor(new_ratio*rank).to(device = device , dtype=torch.float32)\n", + " lora[up] = torch.round(torch.sqrt(_lora[alpha]/new_alpha)*_lora[up], decimals = decimals).to(device = device , dtype=torch.float32)\n", + " lora[down] = torch.round(torch.sqrt(_lora[alpha]/new_alpha)*_lora[down], decimals = decimals).to(device = device , dtype=torch.float32)\n", + " lora[alpha] = (new_alpha/_lora[alpha])*_lora[alpha].to(device = device , dtype=torch.float32)\n", + " count = count + 3\n", + " print(f'{count} / {NUM_ITEMS}')\n", + " #--------#\n", + " print(f'done!')\n", + " print(f'casting params to fp16....')\n", + " for key in lora: lora[f'{key}'] = lora[f'{key}'].to(device = device , dtype=torch.float16)\n", + " #-------#\n", + " print(f'done!')\n", + " print(f'saving {savefile_name}...')\n", + " save_file(lora , f'{savefile_name}')\n", + " #-----------#\n", + "\n", + "# merge_and_save\n", + "# This method uses a general neural net merging method known as TIES - which is an loose abbreviation for\n", + "# 'Trim Elect Sign & Merge' according to the paper : https://arxiv.org/pdf/2306.01708\n", + "#------------#\n", + "# _lora1 - The lora which you wish to process.\n", + "# _lora2 - The lora which you wish to process.\n", + "# _lora3 - The lora which you wish to process.\n", + "\n", + "# NOTE about loras :\n", + "#_lora1 , _lora2 and lora_3 can have different ranks.\n", + "# Make sure the scale of all three loras are the same\n", + "\n", + "#The scale is defined as (alpha/rank) and should be 0.5\n", + "# If the alpha value is too high or too low , for example if (alpha/rank) = 1\n", + "# then run rescale_and_save(_lora , savefile_name, new_alpha) , where new_alpha is given by the rank of the LoRa\n", + "# For example , a LoRa of rank 32 must have an alpha value of 16 for the scale = (alpha/rank) = 0.5 to be True\n", + "\n", + "# However , make sure each lora rank is equal or below 32 ,\n", + "#or that the sum of ranks does not exceed 3*32 = 96 , to not exceed GPU requirements on Google Colab. Slightly higher values might be fine.\n", + "# Haven't tested it since I prefer merging LoRa at rank 32\n", + "\n", + "# savefile_name - The name of the savefile to be created. Make sure the savefile_name ends with the '.safetensors' suffix\n", + "# new_rank - The rank you wish to set the LoRa to\n", + "# new_alpha - The alpha value you wish to set the LoRa to. For proper scaling ,\n", + "# set the alpha value to half the value of the rank so (alpha/rank) = 0.5\n", + "# This is a very common scale for trained LoRa\n", + "#\n", + "# zero_threshold - All values < zero_threshold * e-6 will be set to 0\n", + "# in the delta_W of this LoRa. This is useful to eliminate 'junk' in the output of the\n", + "# Lora when scaling it to strength above 0.8. A high zero_threshold will also make the Lora more compatible with other LoRa\n", + "import torch\n", + "from safetensors.torch import load_file, save_file\n", + "import torch.nn as nn\n", + "from torch import linalg as LA\n", + "import os\n", + "import math\n", + "import random\n", + "import numpy as np\n", + "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n", + "\n", + "# filter_and_save\n", + "# Use this method to change the scale = (rank/alpha) value of a given _lora\n", + "# This method will also eliminate noise. All values < zero_threshold * e-6 will be set to 0\n", + "# in the delta_W of this LoRa. The processed LoRa will be saved as a .safetensor file in fp16\n", + "# The rank of the LoRa affect the file size. At rank 32 the filesize is 300 MB , at rank 16 the filesize is 150 MB and so on.\n", + "#\n", + "# When merging LoRa , it is important that\n", + "# a) the scale of all the merged LoRas are the same. I use the scale = (alpha/rank) = 0.5 at all times.\n", + "# For rank 32 , the alpha must be 16 , for example.\n", + "#\n", + "# b) The rank of the merged LoRas should be 32 or below , any larger values might trigger a 'Out of Memory' error on Google Colab GPU:s\n", + "# --------------\n", + "# _lora - The lora which you wish to process\n", + "# savefile_name - The name of the savefile to be created. Make sure the savefile_name ends with the '.safetensors' suffix\n", + "# new_rank - The rank you wish to set the LoRa to\n", + "# new_alpha - The alpha value you wish to set the LoRa to. For proper scaling ,\n", + "# set the alpha value to half the value of the rank so (alpha/rank) = 0.5\n", + "# This is a very common scale for trained LoRa\n", + "#\n", + "# zero_threshold - All values < zero_threshold * e-6 will be set to 0\n", + "# in the delta_W of this LoRa. This is useful to eliminate 'junk' in the output of the\n", + "# Lora when scaling it to strength above 0.8. A high zero_threshold will also make the Lora more compatible with other LoRa\n", + "# , at the expense of making the LoRa less true to the originally trained image output.\n", + "#\n", + "def filter_and_save(_lora , savefile_name, new_rank , new_alpha, zero_threshold):\n", + " lora = {}\n", + " count = 0\n", + " for key in _lora:count = count + 1\n", + " NUM_ITEMS = count\n", + " count = 0\n", + " thresh = zero_threshold*0.000001 # 1e-6\n", + " #-------#\n", + " for key in _lora:\n", + " if f'{key}'.find('alpha') > -1:\n", + " lora[f'{key}'] = torch.tensor(new_alpha).to(device = device , dtype = torch.float32)\n", + " count = count + 1\n", + " print(f'{count} / {NUM_ITEMS}')\n", + " continue\n", + " #------#\n", + " if not f'{key}'.find('lora_down') > -1: continue\n", + " up = f'{key}'.replace('lora_down' , 'lora_up')\n", + " down = f'{key}'\n", + " #-------#\n", + " delta_W = torch.matmul(_lora[up],_lora[down]).to(device = device , dtype=torch.float32)\n", + " #---#\n", + " N = delta_W.numel()\n", + " y = delta_W.flatten().to(device = device , dtype=torch.float32)\n", + " values,indices = torch.sort(y, descending = False) # smallest -> largest elements\n", + " y = torch.zeros(y.shape).to(device = device , dtype=torch.float32)\n", + " y[indices[values>thresh]] = 1\n", + " y[indices[values<-thresh]] = 1\n", + " y = y.unflatten(0,delta_W.shape).to(device = device , dtype=torch.float32)\n", + " delta_W = torch.mul(delta_W,y).to(device = device , dtype=torch.float32)\n", + " #------#\n", + " tmp={}\n", + " tmp['u'], tmp['s'], tmp['Vh'] = torch.svd(delta_W)\n", + " tmp['u'] = tmp['u'][:,: new_rank]\n", + " tmp['s'] = tmp['s'][: new_rank]\n", + " #-------#\n", + " tmp['u'] = torch.round(torch.matmul(tmp['u'], torch.diag(tmp['s'])),decimals=6)\n", + " tmp['Vh'] = torch.round(tmp['Vh'].t()[: new_rank,:],decimals=6)\n", + " #-------#\n", + " for key in tmp:tmp[f'{key}'] = tmp[f'{key}'].contiguous()\n", + " lora[up] = tmp['u'].to(device = device , dtype=torch.float32)\n", + " lora[down] = tmp['Vh'].to(device = device , dtype=torch.float32)\n", + " #-------#\n", + " count = count + 2\n", + " print(f'{count} / {NUM_ITEMS} , rank {_lora[down].shape[0]}')\n", + " #-------#\n", + " print(f'done!')\n", + " print(f'casting params to fp16....')\n", + " for key in _lora: lora[f'{key}'] = lora[f'{key}'].to(device = device , dtype=torch.float16)\n", + " #-------#\n", + " print(f'done!')\n", + " print(f'saving {savefile_name}...')\n", + " save_file(lora , f'{savefile_name}')\n", + "#--------#\n", + "\n", + "# count_zeros\n", + "# Use this method to guage how large zero_threshold you should set for a given Lora.\n", + "# This function can serve as a 'preview' prior to running either the filter_and_save or\n", + "# merge_and_save methods. Since it does not use SVD to re-pack the LoRa\n", + "# , you can run this method on a non-GPU instance on the Colab\n", + "#-----------\n", + "# _lora - The lora which you wish to process\n", + "# zero_threshold - All values < zero_threshold * e-6 will be set to 0\n", + "def count_zeros(_lora, zero_threshold):\n", + " count = 0\n", + " for key in _lora:count = count + 1\n", + " NUM_ITEMS = count\n", + " count = 0\n", + " #-----#\n", + " thresh = zero_threshold*0.000001 # 1e-6\n", + "\n", + " print(f'at zero_threshold = {zero_threshold}e-6 :')\n", + " for key in _lora:\n", + " if f'{key}'.find('alpha') > -1:\n", + " count = count + 1\n", + " continue\n", + " #------#\n", + " if not f'{key}'.find('lora_down') > -1: continue\n", + " up = f'{key}'.replace('lora_down' , 'lora_up')\n", + " down = f'{key}'\n", + " #-------#\n", + " delta_W = torch.matmul(_lora[up],_lora[down]).to(device = device , dtype=torch.float32)\n", + " N = delta_W.numel()\n", + " y = delta_W.flatten().to(device = device , dtype=torch.float32)\n", + " values,indices = torch.sort(y, descending = False) # smallest -> largest elements\n", + " y = torch.ones(y.shape).to(device = device , dtype=torch.float32)\n", + " y[indices[values>thresh]] = 0\n", + " neg_pcnt = round((100*torch.sum(y) / N).item(),2)\n", + " y[indices[values<-thresh]] = 0\n", + " count = count + 2\n", + " pcnt = round((100*torch.sum(y) / N).item(),2)\n", + " neg_pcnt = round(neg_pcnt - pcnt,2) # remove zero % from neg_pcnt\n", + " pos_pcnt = round(100- pcnt - neg_pcnt,2)\n", + " print(f'at {count} / {NUM_ITEMS} : {pcnt} % zeros ,{pos_pcnt} % pos. , {neg_pcnt} % neg ')\n", + " #------#\n", + "#-----#\n", + "\n", + "# This method rescales a _lora to a given ratio. I haven't tested it\n", + "# yet but this is more or less how it works\n", + "def rescale_and_save(_lora , savefile_name, new_ratio):\n", + " count = 0\n", + " lora = {}\n", + " for key in _lora:count = count + 1\n", + " NUM_ITEMS = count\n", + " count = 0\n", + " decimals = 6\n", + " for key in _lora:\n", + " if not f'{key}'.find('alpha') > -1: continue\n", + " alpha = f'{key}'\n", + " up = f'{key}'.replace('alpha' , 'lora_up')\n", + " down = f'{key}'.replace('alpha' , 'lora_down')\n", + " #------#\n", + " rank = lora[down].shape[0]\n", + " new_alpha = torch.tensor(new_ratio*rank).to(device = device , dtype=torch.float32)\n", + " lora[up] = torch.round(torch.sqrt(_lora[alpha]/new_alpha)*_lora[up], decimals = decimals).to(device = device , dtype=torch.float32)\n", + " lora[down] = torch.round(torch.sqrt(_lora[alpha]/new_alpha)*_lora[down], decimals = decimals).to(device = device , dtype=torch.float32)\n", + " lora[alpha] = (new_alpha/_lora[alpha])*_lora[alpha].to(device = device , dtype=torch.float32)\n", + " count = count + 3\n", + " print(f'{count} / {NUM_ITEMS}')\n", + " #--------#\n", + " print(f'done!')\n", + " print(f'casting params to fp16....')\n", + " for key in lora: lora[f'{key}'] = lora[f'{key}'].to(device = device , dtype=torch.float16)\n", + " #-------#\n", + " print(f'done!')\n", + " print(f'saving {savefile_name}...')\n", + " save_file(lora , f'{savefile_name}')\n", + " #-----------#\n", + "\n", + "# merge_and_save\n", + "# This method uses a general neural net merging method known as TIES - which is an loose abbreviation for\n", + "# 'Trim Elect Sign & Merge' according to the paper : https://arxiv.org/pdf/2306.01708\n", + "#------------#\n", + "# _lora1 - The lora which you wish to process.\n", + "# _lora2 - The lora which you wish to process.\n", + "# _lora3 - The lora which you wish to process.\n", + "\n", + "# NOTE about loras :\n", + "#_lora1 , _lora2 and lora_3 can have different ranks.\n", + "# Make sure the scale of all three loras are the same\n", + "\n", + "#The scale is defined as (alpha/rank) and should be 0.5\n", + "# If the alpha value is too high or too low , for example if (alpha/rank) = 1\n", + "# then run rescale_and_save(_lora , savefile_name, new_alpha) , where new_alpha is given by the rank of the LoRa\n", + "# For example , a LoRa of rank 32 must have an alpha value of 16 for the scale = (alpha/rank) = 0.5 to be True\n", + "\n", + "# Make sure the sum of ranks for _lora1,_lora2 and _lora3 does not exceed 3*32 = 96 , to not exceed GPU requirements on Google Colab. Slightly higher values might be fine.\n", + "# Haven't tested it since I prefer merging LoRa at rank 32\n", + "\n", + "# savefile_name - The name of the savefile to be created. Make sure the savefile_name ends with the '.safetensors' suffix\n", + "# new_rank - The rank you wish to set the LoRa to\n", + "# new_alpha - The alpha value you wish to set the LoRa to. For proper scaling ,\n", + "# set the alpha value to half the value of the rank so (alpha/rank) = 0.5\n", + "# This is a very common scale for trained LoRa\n", + "#\n", + "# zero_threshold - All values < zero_threshold * e-6 will be set to 0\n", + "# in the delta_W of this LoRa. This is useful to eliminate 'junk' persists in the delta_W after training. A high resolution size will also make the Lora more compatible with other LoRa\n", + "# , at the expense of making the LoRa less true to the originally trained image output.\n", + "def merge_and_save(_lora1 , _lora2 , _lora3, savefile_name, new_rank , new_alpha, zero_threshold):\n", + " lora = {}\n", + "\n", + "\n", + "\n", + " count = 0\n", + " for key in _lora1:count = count + 1\n", + " NUM_ITEMS = count\n", + " count = 0\n", + " thresh = zero_threshold*0.000001 # 1e-6\n", + " decimals = 2\n", + "\n", + " #-------#\n", + " for key in _lora1:\n", + " if not key in _lora2: continue\n", + " if not key in _lora3: continue\n", + " if f'{key}'.find('alpha') > -1:\n", + " lora[f'{key}'] = torch.tensor(new_alpha).to(device = device , dtype = torch.float32)\n", + " count = count + 1\n", + " print(f'{count} / {NUM_ITEMS}')\n", + " continue\n", + " #------#\n", + " if not f'{key}'.find('lora_down') > -1: continue\n", + " up = f'{key}'.replace('lora_down' , 'lora_up')\n", + " down = f'{key}'\n", + " #-------#\n", + "\n", + " # Setup\n", + " delta_W = torch.matmul(_lora1[up]*0,_lora1[down]*0).to(device = device, dtype=torch.float32)\n", + " tgt_shape = delta_W.shape\n", + " N = delta_W.numel()\n", + " delta_W = torch.zeros(N).to(device = device , dtype=torch.float32)\n", + " #-----#\n", + "\n", + " #Positives\n", + " Y = torch.zeros(3,N).to(device = device , dtype=torch.float32)\n", + " Y[0] = torch.matmul(_lora1[up],_lora1[down]).flatten().to(device = device , dtype=torch.float32)\n", + " Y[1] = torch.matmul(_lora2[up],_lora2[down]).flatten().to(device = device , dtype=torch.float32)\n", + " Y[2] = torch.matmul(_lora3[up],_lora3[down]).flatten().to(device = device , dtype=torch.float32)\n", + " Y[torch.abs(Y)0,dim=1) + 0.001\n", + " elect = torch.sum(Y<0,dim=1) + 0.001\n", + " elect = (num>=elect)\n", + " Y[Y<0] = 0\n", + " Y = torch.sum(Y, dim=1).to(device = device , dtype=torch.float32)\n", + " delta_W[elect] = torch.round((Y[elect]/num[elect]),decimals=decimals).to(device = device , dtype=torch.float32)\n", + " #-----#\n", + "\n", + " #Negatives\n", + " Y = torch.zeros(3,N).to(device = device , dtype=torch.float32)\n", + " Y[0] = torch.matmul(_lora1[up],_lora1[down]).flatten().to(device = device , dtype=torch.float32)\n", + " Y[1] = torch.matmul(_lora2[up],_lora2[down]).flatten().to(device = device , dtype=torch.float32)\n", + " Y[2] = torch.matmul(_lora3[up],_lora3[down]).flatten().to(device = device , dtype=torch.float32)\n", + " Y[torch.abs(Y)0,dim=1) + 0.1\n", + " elect = (elect0] = 0\n", + " Y = torch.sum(Y, dim=1).to(device = device , dtype=torch.float32)\n", + " delta_W[elect] = torch.round(Y[elect]/num[elect],decimals=decimals).to(device = device , dtype=torch.float32)\n", + " #----#\n", + "\n", + " # Free up memory prior to SVD\n", + " delta_W = delta_W.unflatten(0,tgt_shape).to(device = device , dtype=torch.float32)\n", + " delta_W = delta_W.clone().detach()\n", + " Y = {}\n", + " num = {}\n", + " elect = {}\n", + " #-----#\n", + "\n", + " # Run SVD (Single Value Decomposition)\n", + " #to get the new lora_up and lora_down for delta_W\n", + " tmp={}\n", + " tmp['u'], tmp['s'], tmp['Vh'] = torch.svd(delta_W)\n", + " tmp['u'] = tmp['u'][:,: new_rank]\n", + " tmp['s'] = tmp['s'][: new_rank]\n", + " tmp['u'] = torch.matmul(tmp['u'], torch.diag(tmp['s']))\n", + " tmp['Vh'] = tmp['Vh'].t()[: new_rank,:]\n", + " for key in tmp:tmp[f'{key}'] = tmp[f'{key}'].contiguous()\n", + " lora[up] = torch.round(tmp['u'],decimals=decimals).to(device = device , dtype=torch.float32)\n", + " lora[down] = torch.round(tmp['Vh'],decimals=decimals).to(device = device , dtype=torch.float32)\n", + " #-------#\n", + "\n", + " count = count + 2\n", + " print(f'{count} / {NUM_ITEMS}')\n", + " #----#\n", + " #--------#\n", + " print(f'done!')\n", + " print(f'casting params to fp16....')\n", + " for key in lora: lora[f'{key}'] = lora[f'{key}'].to(device = device , dtype=torch.float16)\n", + " #-------#\n", + " print(f'done!')\n", + " print(f'saving {savefile_name}...')\n", + " save_file(lora , f'{savefile_name}')\n", + "#------#\n", + "\n", + "new_rank = 16\n", + "new_alpha = math.floor(new_rank/2)\n", + "zero_threshold = 200 # e-6\n", + "name = 'tuff_nuff_camel'\n", + "a = load_file('/content/tuff.safetensors')\n", + "b = load_file('/content/nuff.safetensors')\n", + "c = load_file('/content/camel.safetensors')\n", + "savefile_name = f'{name}_{zero_threshold}_r{new_rank}_a{new_alpha}.safetensors'\n", + "\n", + "count = 0\n", + "for key in a:\n", + " a[f'{key}'] = a[f'{key}'].to(device = device , dtype = torch.float32)\n", + " count = count + 1\n", + "print(f'num keys for a : {count}')\n", + "count = 0\n", + "for key in b:\n", + " b[f'{key}'] = b[f'{key}'].to(device = device , dtype = torch.float32)\n", + " count = count + 1\n", + "print(f'num keys for b : {count}')\n", + "count = 0\n", + "for key in c:\n", + " c[f'{key}'] = c[f'{key}'].to(device = device , dtype = torch.float32)\n", + " count = count + 1\n", + "print(f'num keys for c : {count}')\n", + "\n", + "tgt = {}\n", + "#-----#\n", + "print(f'for {name}.safetensors at scale = (rank/alpha) = 0.5')\n", + "merge_and_save(b,c,a, savefile_name, new_rank , new_alpha, zero_threshold)\n", + "\n", + "#filter_and_save(a , savefile_name, new_rank , new_alpha, zero_threshold)\n" + ], + "metadata": { + "id": "7ZSHM5embMd0" + }, + "execution_count": null, + "outputs": [] }, { "cell_type": "code", @@ -2529,9 +4237,7 @@ } ], "metadata": { - "accelerator": "GPU", "colab": { - "gpuType": "T4", "provenance": [] }, "kernelspec": { @@ -2540,6 +4246,6508 @@ }, "language_info": { "name": "python" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "b8bb9e2634654ee2a72e69988c129d9e": { + "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": 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