\n","#@markdown Your project name can't contain spaces but it can contain a single / to make a subfolder in your dataset.\n","project_name = \"poyo_Illustrious\" #@param {type:\"string\"}\n","project_name = project_name.strip()\n","#@markdown The folder structure doesn't matter and is purely for comfort. Make sure to always pick the same one. I like organizing by project.\n","folder_structure = \"Organize by project (MyDrive/Loras/project_name/dataset)\" #@param [\"Organize by category (MyDrive/lora_training/datasets/project_name)\", \"Organize by project (MyDrive/Loras/project_name/dataset)\"]\n","\n","if not project_name or any(c in project_name for c in \" .()\\\"'\\\\\") or project_name.count(\"/\") > 1:\n"," print(\"Please write a valid project_name.\")\n","else:\n"," if COLAB and not os.path.exists('/content/drive'):\n"," from google.colab import drive\n"," print(\"๐ Connecting to Google Drive...\")\n"," drive.mount('/content/drive')\n","\n"," project_base = project_name if \"/\" not in project_name else project_name[:project_name.rfind(\"/\")]\n"," project_subfolder = project_name if \"/\" not in project_name else project_name[project_name.rfind(\"/\")+1:]\n","\n"," root_dir = \"/content\" if COLAB else \"~/Loras\"\n"," deps_dir = os.path.join(root_dir, \"deps\")\n","\n"," if \"/Loras\" in folder_structure:\n"," main_dir = os.path.join(root_dir, \"drive/MyDrive/Loras\") if COLAB else root_dir\n"," config_folder = os.path.join(main_dir, project_base)\n"," images_folder = os.path.join(main_dir, project_base, \"dataset\")\n"," if \"/\" in project_name:\n"," images_folder = os.path.join(images_folder, project_subfolder)\n"," else:\n"," main_dir = os.path.join(root_dir, \"drive/MyDrive/lora_training\") if COLAB else root_dir\n"," config_folder = os.path.join(main_dir, \"config\", project_name)\n"," images_folder = os.path.join(main_dir, \"datasets\", project_name)\n","\n"," for dir in [main_dir, deps_dir, images_folder, config_folder]:\n"," os.makedirs(dir, exist_ok=True)\n","\n"," print(f\"โ Project {project_name} is ready!\")\n"," step1_installed_flag = True\n"]},{"cell_type":"code","execution_count":null,"metadata":{"cellView":"form","id":"afu5dCKTV31E","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1742837397064,"user_tz":-240,"elapsed":12122,"user":{"displayName":"ะะปัะณะฐ","userId":"01931168844080915348"}},"outputId":"57606a01-0b94-4457-84a8-2d91b443f1ab"},"outputs":[{"output_type":"stream","name":"stdout","text":["๐ท No results found\n"]}],"source":["if \"step1_installed_flag\" not in globals():\n"," raise Exception(\"Please run step 1 first!\")\n","\n","import json\n","import time\n","from urllib.request import urlopen, Request\n","\n","#@markdown ### 2๏ธโฃ Scrape images from Gelbooru\n","\n","#@markdown We will grab images from the popular anime gallery [Gelbooru](https://gelbooru.com/). Images are sorted by tags, including poses, scenes, character traits, character names, artists, etc.
\n","#@markdown * If you instead want to use your own images, upload them to your Google Drive's `Loras/project_name/dataset` folder.\n","#@markdown * If you instead want to download screencaps of anime episodes, try [this other colab by another person](https://colab.research.google.com/drive/1oBSntB40BKzNmKceXUlkXzujzdQw-Ci7). It's more complicated though.\n","\n","#@markdown Up to 1000 images may be downloaded by this step in just one minute. Don't abuse it.
\n","#@markdown Your target tags should include the relevant tags for your character/concept/artstyle, and exclude undesired tags (for example, explicit images may affect learning).\n","#@markdown Separate words with underscores, separate tags with spaces, and use - to exclude a tag. You can also include a minimum score: `score:>10`\n","tags = \"1boy -sex -bdsm -loli -greyscale -monochrome milkytiger\" #@param {type:\"string\"}\n","##@markdown If an image is bigger than this resolution a smaller version will be downloaded instead.\n","max_resolution = 3072 #param {type:\"slider\", min:1024, max:8196, step:1024}\n","##@markdown Posts with a parent post are often minor variations of the same image.\n","include_posts_with_parent = True #param {type:\"boolean\"}\n","\n","tags = tags.replace(\" \", \"+\")\\\n"," .replace(\"(\", \"%28\")\\\n"," .replace(\")\", \"%29\")\\\n"," .replace(\":\", \"%3a\")\\\n"," .replace(\"&\", \"%26\")\\\n","\n","url = \"https://gelbooru.com/index.php?page=dapi&json=1&s=post&q=index&limit=100&tags={}\".format(tags)\n","user_agent = \"Mozilla/5.0 AppleWebKit/537.36 (KHTML, like Gecko; compatible; Googlebot/2.1; +http://www.google.com/bot.html) Chrome/93.0.4577.83 Safari/537.36\"\n","limit = 100 # hardcoded by gelbooru\n","total_limit = 1000 # you can edit this if you want but I wouldn't recommend it\n","supported_types = (\".png\", \".jpg\", \".jpeg\")\n","\n","def ubuntu_deps():\n"," print(\"๐ญ Installing dependencies...\\n\")\n"," !apt -y install aria2\n"," return not get_ipython().__dict__['user_ns']['_exit_code']\n","\n","if \"step2_installed_flag\" not in globals():\n"," if ubuntu_deps():\n"," clear_output()\n"," step2_installed_flag = True\n"," else:\n"," print(\"โ Error installing dependencies, attempting to continue anyway...\")\n","\n","def get_json(url):\n"," with urlopen(Request(url, headers={\"User-Agent\": user_agent})) as page:\n"," return json.load(page)\n","\n","def filter_images(data):\n"," return [p[\"file_url\"] if p[\"width\"]*p[\"height\"] <= max_resolution**2 else p[\"sample_url\"]\n"," for p in data[\"post\"]\n"," if (p[\"parent_id\"] == 0 or include_posts_with_parent)\n"," and p[\"file_url\"].lower().endswith(supported_types)]\n","\n","def download_images():\n"," data = get_json(url)\n"," count = data[\"@attributes\"][\"count\"]\n","\n"," if count == 0:\n"," print(\"๐ท No results found\")\n"," return\n","\n"," print(f\"๐ฏ Found {count} results\")\n"," test_url = \"https://gelbooru.com/index.php?page=post&s=list&tags={}\".format(tags)\n"," display(Markdown(f\"[Click here to open in browser!]({test_url})\"))\n"," print (f\"๐ฝ Will download to {images_folder.replace('/content/drive/', '')} (A confirmation box should appear below, otherwise run this cell again)\")\n"," inp = input(\"โ Enter the word 'yes' if you want to proceed with the download: \")\n","\n"," if inp.lower().strip() != 'yes':\n"," print(\"โ Download cancelled\")\n"," return\n","\n"," print(\"๐ฉ Grabbing image list...\")\n","\n"," image_urls = set()\n"," image_urls = image_urls.union(filter_images(data))\n"," for i in range(total_limit // limit):\n"," count -= limit\n"," if count <= 0:\n"," break\n"," time.sleep(0.1)\n"," image_urls = image_urls.union(filter_images(get_json(url+f\"&pid={i+1}\")))\n","\n"," scrape_file = os.path.join(config_folder, f\"scrape_{project_subfolder}.txt\")\n"," with open(scrape_file, \"w\") as f:\n"," f.write(\"\\n\".join(image_urls))\n","\n"," print(f\"๐ Saved links to {scrape_file}\\n\\n๐ Downloading images...\\n\")\n"," old_img_count = len([f for f in os.listdir(images_folder) if f.lower().endswith(supported_types)])\n","\n"," os.chdir(images_folder)\n"," !aria2c --console-log-level=warn -c -x 16 -k 1M -s 16 -i {scrape_file}\n","\n"," new_img_count = len([f for f in os.listdir(images_folder) if f.lower().endswith(supported_types)])\n"," print(f\"\\nโ Downloaded {new_img_count - old_img_count} images.\")\n","\n","download_images()\n"]},{"cell_type":"code","execution_count":null,"metadata":{"cellView":"form","id":"b218DEEMpwzB","colab":{"base_uri":"https://localhost:8080/"},"outputId":"5be42034-58fa-4a52-e174-7cf7a3f7f038"},"outputs":[{"output_type":"stream","name":"stdout","text":["\n","๐ฟ Analyzing dataset...\n","\n"," 100% |โโโโโโโโโโโโโโโโโ| 187/187 [86.3ms elapsed, 0s remaining, 2.2K samples/s] \n"]},{"output_type":"stream","name":"stderr","text":["INFO:eta.core.utils: 100% |โโโโโโโโโโโโโโโโโ| 187/187 [86.3ms elapsed, 0s remaining, 2.2K samples/s] \n"]},{"output_type":"stream","name":"stdout","text":["Downloading model from 'https://openaipublic.azureedge.net/clip/models/40d365715913c9da98579312b702a82c18be219cc2a73407c4526f58eba950af/ViT-B-32.pt'...\n"]},{"output_type":"stream","name":"stderr","text":["INFO:fiftyone.core.models:Downloading model from 'https://openaipublic.azureedge.net/clip/models/40d365715913c9da98579312b702a82c18be219cc2a73407c4526f58eba950af/ViT-B-32.pt'...\n"]},{"output_type":"stream","name":"stdout","text":[" 100% |โโโโโโ| 2.6Gb/2.6Gb [4.9s elapsed, 0s remaining, 661.0Mb/s] \n"]},{"output_type":"stream","name":"stderr","text":["INFO:eta.core.utils: 100% |โโโโโโ| 2.6Gb/2.6Gb [4.9s elapsed, 0s remaining, 661.0Mb/s] \n"]},{"output_type":"stream","name":"stdout","text":["Downloading CLIP tokenizer...\n"]},{"output_type":"stream","name":"stderr","text":["INFO:fiftyone.utils.clip.zoo:Downloading CLIP tokenizer...\n"]},{"output_type":"stream","name":"stdout","text":[" 100% |โโโโโ| 10.4Mb/10.4Mb [18.3ms elapsed, 0s remaining, 566.2Mb/s] \n"]},{"output_type":"stream","name":"stderr","text":["INFO:eta.core.utils: 100% |โโโโโ| 10.4Mb/10.4Mb [18.3ms elapsed, 0s remaining, 566.2Mb/s] \n"]}],"source":["if \"step1_installed_flag\" not in globals():\n"," raise Exception(\"Please run step 1 first!\")\n","\n","#@markdown ### 3๏ธโฃ Curate your images\n","#@markdown We will find duplicate images with the FiftyOne AI, and mark them with `delete`.
\n","#@markdown Then, an interactive area will appear below this cell that lets you visualize all your images and manually mark with `delete` to the ones you don't like.
\n","#@markdown If the interactive area appears blank for over a minute, try enabling cookies and removing tracking protection for the Google Colab website, as they may break it.\n","#@markdown Regardless, you can save your changes by sending Enter in the input box above the interactive area.
\n","#@markdown This is how similar 2 images must be to be marked for deletion. I recommend 0.97 to 0.99:\n","similarity_threshold = 0.985 #@param {type:\"number\"}\n","\n","\n","os.chdir(root_dir)\n","model_name = \"clip-vit-base32-torch\"\n","supported_types = (\".png\", \".jpg\", \".jpeg\")\n","img_count = len(os.listdir(images_folder))\n","batch_size = min(250, img_count)\n","\n","if \"step3_installed_flag\" not in globals():\n"," print(\"๐ญ Installing dependencies...\\n\")\n"," !pip -q install fiftyone ftfy\n"," !pip -q install fiftyone-db-ubuntu2204\n"," if not get_ipython().__dict__['user_ns']['_exit_code']:\n"," clear_output()\n"," step3_installed_flag = True\n"," else:\n"," print(\"โ Error installing dependencies, attempting to continue anyway...\")\n","\n","import numpy as np\n","import fiftyone as fo\n","import fiftyone.zoo as foz\n","from fiftyone import ViewField as F\n","from sklearn.metrics.pairwise import cosine_similarity\n","\n","non_images = [f for f in os.listdir(images_folder) if not f.lower().endswith(supported_types)]\n","if non_images:\n"," print(f\"๐ฅ Error: Found non-image file {non_images[0]} - This program doesn't allow it. Sorry! Use the Extras at the bottom to clean the folder.\")\n","elif img_count == 0:\n"," print(f\"๐ฅ Error: No images found in {images_folder}\")\n","else:\n"," print(\"\\n๐ฟ Analyzing dataset...\\n\")\n"," dataset = fo.Dataset.from_dir(images_folder, dataset_type=fo.types.ImageDirectory)\n"," model = foz.load_zoo_model(model_name)\n"," embeddings = dataset.compute_embeddings(model, batch_size=batch_size)\n","\n"," batch_embeddings = np.array_split(embeddings, batch_size)\n"," similarity_matrices = []\n"," max_size_x = max(array.shape[0] for array in batch_embeddings)\n"," max_size_y = max(array.shape[1] for array in batch_embeddings)\n","\n"," for i, batch_embedding in enumerate(batch_embeddings):\n"," similarity = cosine_similarity(batch_embedding)\n"," #Pad 0 for np.concatenate\n"," padded_array = np.zeros((max_size_x, max_size_y))\n"," padded_array[0:similarity.shape[0], 0:similarity.shape[1]] = similarity\n"," similarity_matrices.append(padded_array)\n","\n"," similarity_matrix = np.concatenate(similarity_matrices, axis=0)\n"," similarity_matrix = similarity_matrix[0:embeddings.shape[0], 0:embeddings.shape[0]]\n","\n"," similarity_matrix = cosine_similarity(embeddings)\n"," similarity_matrix -= np.identity(len(similarity_matrix))\n","\n"," dataset.match(F(\"max_similarity\") > similarity_threshold)\n"," dataset.tags = [\"delete\", \"has_duplicates\"]\n","\n"," id_map = [s.id for s in dataset.select_fields([\"id\"])]\n"," samples_to_remove = set()\n"," samples_to_keep = set()\n","\n"," for idx, sample in enumerate(dataset):\n"," if sample.id not in samples_to_remove:\n"," # Keep the first instance of two duplicates\n"," samples_to_keep.add(sample.id)\n","\n"," dup_idxs = np.where(similarity_matrix[idx] > similarity_threshold)[0]\n"," for dup in dup_idxs:\n"," # We kept the first instance so remove all other duplicates\n"," samples_to_remove.add(id_map[dup])\n","\n"," if len(dup_idxs) > 0:\n"," sample.tags.append(\"has_duplicates\")\n"," sample.save()\n"," else:\n"," sample.tags.append(\"delete\")\n"," sample.save()\n","\n"," clear_output()\n","\n"," sidebar_groups = fo.DatasetAppConfig.default_sidebar_groups(dataset)\n"," for group in sidebar_groups[1:]:\n"," group.expanded = False\n"," dataset.app_config.sidebar_groups = sidebar_groups\n"," dataset.save()\n"," session = fo.launch_app(dataset)\n","\n"," print(\"โ Wait a minute for the session to load. If it doesn't, read above.\")\n"," print(\"โ When it's ready, you'll see a grid of your images.\")\n"," print(\"โ On the left side enable \\\"sample tags\\\" to visualize the images marked for deletion.\")\n"," print(\"โ You can mark your own images with the \\\"delete\\\" label by selecting them and pressing the tag icon at the top.\")\n"," input(\"โญ When you're done, enter something here to save your changes: \")\n","\n"," print(\"๐พ Saving...\")\n","\n"," marked = [s for s in dataset if \"delete\" in s.tags]\n"," dataset.remove_samples(marked)\n"," previous_folder = images_folder[:images_folder.rfind(\"/\")]\n"," dataset.export(export_dir=os.path.join(images_folder, project_subfolder), dataset_type=fo.types.ImageDirectory)\n","\n"," temp_suffix = \"_temp\"\n"," !mv {images_folder} {images_folder}{temp_suffix}\n"," !mv {images_folder}{temp_suffix}/{project_subfolder} {images_folder}\n"," !rm -r {images_folder}{temp_suffix}\n","\n"," session.refresh()\n"," fo.close_app()\n"," clear_output()\n","\n"," print(f\"\\nโ Removed {len(marked)} images from dataset. You now have {len(os.listdir(images_folder))} images.\")\n"]},{"cell_type":"code","source":["!pip install transformers -U"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"3CzwS7CjD4HG","executionInfo":{"status":"ok","timestamp":1735033771925,"user_tz":-240,"elapsed":7126,"user":{"displayName":"ะกะตัะณะตะน ะัะธะณะพััะตะฒ (Sergey004)","userId":"04354315389975155845"}},"outputId":"eb4b43dd-0e49-46eb-fc62-a89f039500a4"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (4.47.1)\n","Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers) (3.16.1)\n","Requirement already satisfied: huggingface-hub<1.0,>=0.24.0 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.27.0)\n","Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (1.26.4)\n","Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from transformers) (24.2)\n","Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (6.0.2)\n","Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (2024.11.6)\n","Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers) (2.32.3)\n","Requirement already satisfied: tokenizers<0.22,>=0.21 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.21.0)\n","Requirement already satisfied: safetensors>=0.4.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.4.5)\n","Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers) (4.67.1)\n","Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.24.0->transformers) (2024.10.0)\n","Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.24.0->transformers) (4.12.2)\n","Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.4.0)\n","Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.10)\n","Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2.2.3)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2024.12.14)\n"]}]},{"cell_type":"code","execution_count":2,"metadata":{"cellView":"form","id":"sl4FD7Mz-uea","colab":{"base_uri":"https://localhost:8080/"},"outputId":"435e49e9-813b-47d7-f523-c6552a641532","executionInfo":{"status":"ok","timestamp":1743520101603,"user_tz":-240,"elapsed":398020,"user":{"displayName":"ะกะตัะณะตะน ะัะธะณะพััะตะฒ (Sergey004)","userId":"04354315389975155845"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["๐ Tagging complete. Here are the top 50 tags in your dataset:\n","animal ears (35)\n","male focus (35)\n","furry male (35)\n","solo (34)\n","short hair (34)\n","1boy (34)\n","furry (34)\n","yellow eyes (33)\n","white hair (33)\n","looking at viewer (27)\n","blush (26)\n","mouse ears (26)\n","long sleeves (22)\n","sweater (22)\n","body fur (21)\n","tail (20)\n","white fur (20)\n","closed mouth (18)\n","black sweater (16)\n","upper body (16)\n","simple background (15)\n","standing (14)\n","thick eyebrows (13)\n","grey background (13)\n","pants (12)\n","outdoors (11)\n","black pants (10)\n","mouse tail (10)\n","cowboy shot (9)\n","smile (9)\n","male child (7)\n","holding (7)\n","open mouth (7)\n","shirt (7)\n","animal hands (7)\n",":< (7)\n","indoors (6)\n","potted plant (6)\n","full body (6)\n",":3 (6)\n","plant (5)\n","portrait (5)\n","chibi (5)\n","day (5)\n","tree (5)\n","white sweater (5)\n","blurry (5)\n","whiskers (5)\n","night (5)\n","english text (4)\n"]}],"source":["if \"step1_installed_flag\" not in globals():\n"," raise Exception(\"Please run step 1 first!\")\n","\n","#@markdown ### 4๏ธโฃ Tag your images\n","#@markdown We will be using AI to automatically tag your images, specifically [Waifu Diffusion](https://huggingface.co/SmilingWolf/wd-v1-4-swinv2-tagger-v2) in the case of anime and [BLIP](https://huggingface.co/spaces/Salesforce/BLIP) in the case of photos.\n","#@markdown Giving tags/captions to your images allows for much better training. This process should take a couple minutes.
\n","method = \"Anime tags\" #@param [\"Anime tags\", \"Photo captions\"]\n","#@markdown **Anime:** The threshold is the minimum level of confidence the tagger must have in order to include a tag. Lower threshold = More tags. Recommended 0.35 to 0.5\n","tag_threshold = 0.35 #@param {type:\"slider\", min:0.0, max:1.0, step:0.01}\n","blacklist_tags = \"bangs, breasts, multicolored hair, two-tone hair, gradient hair, virtual youtuber, parody, style parody, official alternate costume, official alternate hairstyle, official alternate hair length, alternate costume, alternate hairstyle, alternate hair length, alternate hair color\" #@param {type:\"string\"}\n","#@markdown **Photos:** The minimum and maximum length of tokens/words in each caption.\n","caption_min = 10 #@param {type:\"number\"}\n","caption_max = 75 #@param {type:\"number\"}\n","\n","%env PYTHONPATH=/env/python\n","os.chdir(root_dir)\n","kohya = \"/content/kohya-trainer\"\n","if not os.path.exists(kohya):\n"," !git clone https://github.com/kohya-ss/sd-scripts {kohya}\n"," os.chdir(kohya)\n"," !git reset --hard 9a67e0df390033a89f17e70df5131393692c2a55\n"," os.chdir(root_dir)\n","\n","if \"tags\" in method:\n"," if \"step4a_installed_flag\" not in globals() or \"step4b_installed_flag\" in globals():\n"," print(\"\\n๐ญ Installing dependencies...\\n\")\n"," !pip install accelerate==0.25.0 diffusers[torch]==0.25.0 einops==0.6.0 tensorflow==2.15.0 \\\n"," keras==2.15.0 transformers safetensors huggingface-hub==0.22.0 torchvision albumentations \\\n"," jax==0.4.23 jaxlib==0.4.23 flax==0.7.5\n"," if not get_ipython().__dict__['user_ns']['_exit_code']:\n"," clear_output()\n"," step4a_installed_flag = True\n"," else:\n"," print(\"โ Error installing dependencies, trying to continue anyway...\")\n","\n"," print(\"\\n๐ถโโ๏ธ Launching program...\\n\")\n","\n"," os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'\n"," %env PYTHONPATH={kohya}\n"," !python {kohya}/finetune/tag_images_by_wd14_tagger.py \\\n"," {images_folder} \\\n"," --repo_id=SmilingWolf/wd-v1-4-swinv2-tagger-v2 \\\n"," --model_dir={root_dir} \\\n"," --thresh={tag_threshold} \\\n"," --batch_size=8 \\\n"," --caption_extension=.txt \\\n"," --force_download\n","\n"," if not get_ipython().__dict__['user_ns']['_exit_code']:\n"," print(\"removing underscores and blacklist...\")\n"," blacklisted_tags = [t.strip() for t in blacklist_tags.split(\",\")]\n"," from collections import Counter\n"," top_tags = Counter()\n"," for txt in [f for f in os.listdir(images_folder) if f.lower().endswith(\".txt\")]:\n"," with open(os.path.join(images_folder, txt), 'r') as f:\n"," tags = [t.strip() for t in f.read().split(\",\")]\n"," tags = [t.replace(\"_\", \" \") if len(t) > 3 else t for t in tags]\n"," tags = [t for t in tags if t not in blacklisted_tags]\n"," top_tags.update(tags)\n"," with open(os.path.join(images_folder, txt), 'w') as f:\n"," f.write(\", \".join(tags))\n","\n"," %env PYTHONPATH=/env/python\n"," clear_output()\n"," print(f\"๐ Tagging complete. Here are the top 50 tags in your dataset:\")\n"," print(\"\\n\".join(f\"{k} ({v})\" for k, v in top_tags.most_common(50)))\n","\n","\n","else: # Photos\n"," if \"step4b_installed_flag\" not in globals() or \"step4a_installed_flag\" in globals():\n"," print(\"\\n๐ญ Installing dependencies...\\n\")\n"," !pip install timm==0.6.12 fairscale==0.4.13 transformers==4.26.0 requests \\\n"," accelerate==0.25.0 diffusers[torch]==0.25.0 einops==0.6.0 safetensors \\\n"," jax==0.4.23 jaxlib==0.4.23 flax==0.7.5 huggingface-hub==0.22.0\n"," if not get_ipython().__dict__['user_ns']['_exit_code']:\n"," clear_output()\n"," step4b_installed_flag = True\n"," else:\n"," print(\"โ Error installing dependencies, trying to continue anyway...\")\n","\n"," print(\"\\n๐ถโโ๏ธ Launching program...\\n\")\n","\n"," os.chdir(kohya)\n"," %env PYTHONPATH={kohya}\n"," !python {kohya}/finetune/make_captions.py \\\n"," {images_folder} \\\n"," --beam_search \\\n"," --max_data_loader_n_workers=2 \\\n"," --batch_size=1 \\\n"," --min_length={caption_min} \\\n"," --max_length={caption_max} \\\n"," --caption_extension=.txt\n","\n"," if not get_ipython().__dict__['user_ns']['_exit_code']:\n"," import random\n"," captions = [f for f in os.listdir(images_folder) if f.lower().endswith(\".txt\")]\n"," sample = []\n"," for txt in random.sample(captions, min(10, len(captions))):\n"," with open(os.path.join(images_folder, txt), 'r') as f:\n"," sample.append(f.read())\n","\n"," os.chdir(root_dir)\n"," %env PYTHONPATH=/env/python\n"," clear_output()\n"," print(f\"๐ Captioning complete. Here are {len(sample)} example captions from your dataset:\")\n"," print(\"\".join(sample))\n","\n"]},{"cell_type":"code","execution_count":3,"metadata":{"cellView":"form","id":"WBFik7accyDz","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1743520101924,"user_tz":-240,"elapsed":315,"user":{"displayName":"ะกะตัะณะตะน ะัะธะณะพััะตะฒ (Sergey004)","userId":"04354315389975155845"}},"outputId":"88d9f071-f37f-4ae2-dd1f-89b7240f60d1"},"outputs":[{"output_type":"stream","name":"stdout","text":["\n","๐ Applied new activation tag(s): poyo\n","\n","๐ฎ Removed 128 tags.\n","\n","โ Done! Check your updated tags in the Extras below.\n"]}],"source":["if \"step1_installed_flag\" not in globals():\n"," raise Exception(\"Please run step 1 first!\")\n","\n","#@markdown ### 5๏ธโฃ Curate your tags\n","#@markdown Modify your dataset's tags. You can run this cell multiple times with different parameters.
\n","\n","#@markdown Put an activation tag at the start of every text file. This is useful to make learning better and activate your Lora easier. Set `keep_tokens` to 1 when training.
\n","#@markdown Common tags that are removed such as hair color, etc. will be \"absorbed\" by your activation tag.\n","global_activation_tag = \"poyo\" #@param {type:\"string\"}\n","remove_tags = \"candy, musical note, gradient, white background, background, green eyes, heart, gradient background, solo, artist name, traditional media, multicolored background, checkered background, purple background, looking at viewer, simple background, male focus, brown eyes, feet out of frame, underwear only, window, sitting, couch, night sky, night, starry sky, brown fur\" #@param {type:\"string\"}\n","#@markdown \n","\n","#@markdown In this advanced section, you can search text files containing matching tags, and replace them with less/more/different tags. If you select the checkbox below, any extra tags will be put at the start of the file, letting you assign different activation tags to different parts of your dataset. Still, you may want a more advanced tool for this.\n","search_tags = \"\" #@param {type:\"string\"}\n","replace_with = \"\" #@param {type:\"string\"}\n","search_mode = \"OR\" #@param [\"OR\", \"AND\"]\n","new_becomes_activation_tag = False #@param {type:\"boolean\"}\n","#@markdown These may be useful sometimes. Will remove existing activation tags, be careful.\n","sort_alphabetically = False #@param {type:\"boolean\"}\n","remove_duplicates = False #@param {type:\"boolean\"}\n","\n","def split_tags(tagstr):\n"," return [s.strip() for s in tagstr.split(\",\") if s.strip()]\n","\n","activation_tag_list = split_tags(global_activation_tag)\n","remove_tags_list = split_tags(remove_tags)\n","search_tags_list = split_tags(search_tags)\n","replace_with_list = split_tags(replace_with)\n","replace_new_list = [t for t in replace_with_list if t not in search_tags_list]\n","\n","replace_with_list = [t for t in replace_with_list if t not in replace_new_list]\n","replace_new_list.reverse()\n","activation_tag_list.reverse()\n","\n","remove_count = 0\n","replace_count = 0\n","\n","for txt in [f for f in os.listdir(images_folder) if f.lower().endswith(\".txt\")]:\n","\n"," with open(os.path.join(images_folder, txt), 'r') as f:\n"," tags = [s.strip() for s in f.read().split(\",\")]\n","\n"," if remove_duplicates:\n"," tags = list(set(tags))\n"," if sort_alphabetically:\n"," tags.sort()\n","\n"," for rem in remove_tags_list:\n"," if rem in tags:\n"," remove_count += 1\n"," tags.remove(rem)\n","\n"," if \"AND\" in search_mode and all(r in tags for r in search_tags_list) \\\n"," or \"OR\" in search_mode and any(r in tags for r in search_tags_list):\n"," replace_count += 1\n"," for rem in search_tags_list:\n"," if rem in tags:\n"," tags.remove(rem)\n"," for add in replace_with_list:\n"," if add not in tags:\n"," tags.append(add)\n"," for new in replace_new_list:\n"," if new_becomes_activation_tag:\n"," if new in tags:\n"," tags.remove(new)\n"," tags.insert(0, new)\n"," else:\n"," if new not in tags:\n"," tags.append(new)\n","\n"," for act in activation_tag_list:\n"," if act in tags:\n"," tags.remove(act)\n"," tags.insert(0, act)\n","\n"," with open(os.path.join(images_folder, txt), 'w') as f:\n"," f.write(\", \".join(tags))\n","\n","if global_activation_tag:\n"," print(f\"\\n๐ Applied new activation tag(s): {', '.join(activation_tag_list)}\")\n","if remove_tags:\n"," print(f\"\\n๐ฎ Removed {remove_count} tags.\")\n","if search_tags:\n"," print(f\"\\n๐ซ Replaced in {replace_count} files.\")\n","print(\"\\nโ
Done! Check your updated tags in the Extras below.\")\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"HuJB7BGAyZCw","cellView":"form","colab":{"base_uri":"https://localhost:8080/","height":58},"executionInfo":{"status":"ok","timestamp":1739256609188,"user_tz":-240,"elapsed":19,"user":{"displayName":"ะะปัะณะฐ","userId":"01931168844080915348"}},"outputId":"60dd9b50-d526-4e32-e1ac-02db4481d457"},"outputs":[{"output_type":"display_data","data":{"text/plain":[" \n","shuffle_tags = True #@param {type:\"boolean\"}\n","shuffle_caption = shuffle_tags\n","activation_tags = \"1\" #@param [0,1,2,3]\n","keep_tokens = int(activation_tags)\n","\n","#@markdown ### โถ๏ธ Steps \n","#@markdown Your images will repeat this number of times during training. I recommend that your images multiplied by their repeats is around 100, or 1 repeat with more than 100 images.\n","num_repeats = 2 #@param {type:\"number\"}\n","#@markdown Choose how long you want to train for. A good starting point is around 10 epochs or around 2000 steps. \n","#@markdown One epoch is a number of steps equal to: your number of images multiplied by their repeats, divided by batch size. \n","preferred_unit = \"Epochs\" #@param [\"Epochs\", \"Steps\"]\n","how_many = 10 #@param {type:\"number\"}\n","max_train_epochs = how_many if preferred_unit == \"Epochs\" else None\n","max_train_steps = how_many if preferred_unit == \"Steps\" else None\n","#@markdown Saving more epochs will let you compare your Lora's progress better.\n","save_every_n_epochs = 1 #@param {type:\"number\"}\n","keep_only_last_n_epochs = 10 #@param {type:\"number\"}\n","if not save_every_n_epochs:\n"," save_every_n_epochs = max_train_epochs\n","if not keep_only_last_n_epochs:\n"," keep_only_last_n_epochs = max_train_epochs\n","\n","#@markdown ### โถ๏ธ Learning\n","#@markdown The learning rate is the most important for your results. If your Lora produces black images, lower the unet and text encoder to 1e-4 and 1e-5 respectively, or even lower. \n","#@markdown If you're training a style you can choose to set the text encoder to 0.\n","unet_lr = 3e-4 #@param {type:\"number\"}\n","text_encoder_lr = 6e-5 #@param {type:\"number\"}\n","#@markdown The scheduler is the algorithm that guides the learning rate. If you're not sure, pick `constant` and ignore the number. I personally recommend `cosine_with_restarts` with 3 restarts.\n","lr_scheduler = \"cosine_with_restarts\" #@param [\"constant\", \"cosine\", \"cosine_with_restarts\", \"constant_with_warmup\", \"linear\", \"polynomial\", \"rex\"]\n","lr_scheduler_number = 3 #@param {type:\"number\"}\n","#@markdown Steps spent \"warming up\" the learning rate during training for efficiency. I recommend leaving it at 5%.\n","lr_warmup_ratio = 0.05 #@param {type:\"slider\", min:0.0, max:0.2, step:0.01}\n","lr_warmup_steps = 0\n","#@markdown These settings may produce better results. min_snr_gamma adjusts loss over time. ip_noise_gamma adjusts random noise.\n","min_snr_gamma_enabled = True #@param {type:\"boolean\"}\n","min_snr_gamma = 8.0 #@param {type:\"slider\", min:4, max:16.0, step:0.5}\n","ip_noise_gamma_enabled = False #@param {type:\"boolean\"}\n","ip_noise_gamma = 0.05 #@param {type:\"slider\", min:0.05, max:0.1, step:0.01}\n","#@markdown Multinoise may help with color balance (darker darks, lighter lights).\n","multinoise = True #@param {type:\"boolean\"}\n","\n","#@markdown ### โถ๏ธ Structure\n","#@markdown LoRA is the classic type and good for a variety of purposes. LoCon is good with artstyles as it has more layers to learn more aspects of the dataset.\n","lora_type = \"LoRA\" #@param [\"LoRA\", \"LoCon\"]\n","\n","#@markdown Below are some recommended XL values for the following settings:\n","\n","#@markdown | type | network_dim | network_alpha | conv_dim | conv_alpha |\n","#@markdown | :---: | :---: | :---: | :---: | :---: |\n","#@markdown | Regular LoRA | 8 | 4 | | |\n","#@markdown | Style LoCon | 16 | 8 | 16 | 8 |\n","\n","#@markdown More dim means larger Lora, it can hold more information but more isn't always better.\n","network_dim = 32 #@param {type:\"slider\", min:1, max:32, step:1}\n","network_alpha = 16 #@param {type:\"slider\", min:1, max:32, step:1}\n","#@markdown The following two values only apply to the additional layers of LoCon.\n","conv_dim = 16 #@param {type:\"slider\", min:1, max:32, step:1}\n","conv_alpha = 8 #@param {type:\"slider\", min:1, max:32, step:1}\n","\n","network_module = \"networks.lora\"\n","network_args = None\n","if lora_type.lower() == \"locon\":\n"," network_args = [f\"conv_dim={conv_dim}\", f\"conv_alpha={conv_alpha}\"]\n","\n","#@markdown ### โถ๏ธ Training\n","#@markdown Adjust these parameters depending on your colab configuration.\n","#@markdown\n","#@markdown Higher batch size is often faster but uses more memory.\n","train_batch_size = 4 #@param {type:\"slider\", min:1, max:16, step:1}\n","#@markdown I have found no substantial difference between sdpa and xformers.\n","cross_attention = \"sdpa\" #@param [\"sdpa\", \"xformers\"]\n","#@markdown Use `full fp16` for lowest memory usage. The `bf16` options may not work correctly on the free GPU tier. \n","#@markdown The Lora will be trained with the selected precision, but will always be saved in fp16 format for compatibility reasons.\n","precision = \"full fp16\" #@param [\"float\", \"full fp16\", \"full bf16\", \"mixed fp16\", \"mixed bf16\"]\n","#@markdown Caching latents to drive will add a 250KB file next to each image but will use considerably less memory.\n","cache_latents = True #@param {type:\"boolean\"}\n","cache_latents_to_drive = True #@param {type:\"boolean\"}\n","#@markdown The following option will turn off shuffle_tags and disable text encoder training.\n","cache_text_encoder_outputs = False # @param {type:\"boolean\"}\n","\n","mixed_precision = \"no\"\n","if \"fp16\" in precision:\n"," mixed_precision = \"fp16\"\n","elif \"bf16\" in precision:\n"," mixed_precision = \"bf16\"\n","full_precision = \"full\" in precision\n","\n","#@markdown ### โถ๏ธ Advanced\n","#@markdown The optimizer is the algorithm used for training. AdamW8bit is the default and works great, while Prodigy manages learning rate automatically and may have several advantages such as training faster due to needing less steps as well as working better for small datasets.\n","optimizer = \"AdamW8bit\" #@param [\"AdamW8bit\", \"Prodigy\", \"DAdaptation\", \"DadaptAdam\", \"DadaptLion\", \"AdamW\", \"Lion\", \"SGDNesterov\", \"SGDNesterov8bit\", \"AdaFactor\", \"Came\"]\n","#@markdown Recommended args for AdamW8bit: `weight_decay=0.1 betas=[0.9,0.99]` \n","#@markdown Recommended args for Prodigy: `decouple=True weight_decay=0.01 betas=[0.9,0.999] d_coef=2 use_bias_correction=True safeguard_warmup=True` \n","#@markdown Recommended args for AdaFactor: `scale_parameter=False relative_step=False warmup_init=False` \n","#@markdown Recommended args for Came: `weight_decay=0.04` \n","#@markdown If Dadapt or Prodigy are selected and the recommended box is checked, the following values will override any previous settings: \n","#@markdown `unet_lr=0.75`, `text_encoder_lr=0.75`, `network_alpha=network_dim`, `full_precision=False` \n","recommended_values = True #@param {type:\"boolean\"}\n","#@markdown Alternatively set your own optimizer arguments separated by spaces (not commas). Recommended box must be disabled.\n","optimizer_args = \"\" #@param {type:\"string\"}\n","optimizer_args = [a.strip() for a in optimizer_args.split(' ') if a]\n","\n","if recommended_values:\n"," if any(opt in optimizer.lower() for opt in [\"dadapt\", \"prodigy\"]):\n"," unet_lr = 0.75\n"," text_encoder_lr = 0.75\n"," network_alpha = network_dim\n"," full_precision = False\n"," if optimizer == \"Prodigy\":\n"," optimizer_args = [\"decouple=True\", \"weight_decay=0.01\", \"betas=[0.9,0.999]\", \"d_coef=2\", \"use_bias_correction=True\", \"safeguard_warmup=True\"]\n"," elif optimizer == \"AdamW8bit\":\n"," optimizer_args = [\"weight_decay=0.1\", \"betas=[0.9,0.99]\"]\n"," elif optimizer == \"AdaFactor\":\n"," optimizer_args = [\"scale_parameter=False\", \"relative_step=False\", \"warmup_init=False\"]\n"," elif optimizer == \"Came\":\n"," optimizer_args = [\"weight_decay=0.04\"]\n","\n","if optimizer == \"Came\":\n"," optimizer = \"LoraEasyCustomOptimizer.came.CAME\"\n","\n","lr_scheduler_type = None\n","lr_scheduler_args = None\n","lr_scheduler_num_cycles = lr_scheduler_number\n","lr_scheduler_power = lr_scheduler_number\n","\n","if \"rex\" in lr_scheduler:\n"," lr_scheduler = \"cosine\"\n"," lr_scheduler_type = \"LoraEasyCustomOptimizer.RexAnnealingWarmRestarts.RexAnnealingWarmRestarts\"\n"," lr_scheduler_args = [\"min_lr=1e-9\", \"gamma=0.9\", \"d=0.9\"]\n","\n","# Misc\n","seed = 42\n","gradient_accumulation_steps = 1\n","bucket_reso_steps = 64\n","min_bucket_reso = 256\n","max_bucket_reso = 4096\n","\n","\n","#@markdown ### โถ๏ธ Ready\n","#@markdown You can now run this cell to cook your Lora. Good luck! \n","\n","\n","# ๐ฉโ๐ป Cool code goes here\n","\n","root_dir = \"/content\"\n","trainer_dir = os.path.join(root_dir, \"trainer\")\n","kohya_dir = os.path.join(trainer_dir, \"sd_scripts\")\n","\n","venv_python = os.path.join(kohya_dir, \"venv/bin/python\")\n","venv_pip = os.path.join(kohya_dir, \"venv/bin/pip\")\n","train_network = os.path.join(kohya_dir, \"sdxl_train_network.py\")\n","\n","if \"/Loras\" in folder_structure:\n"," main_dir = os.path.join(root_dir, \"drive/MyDrive/Loras\")\n"," log_folder = os.path.join(main_dir, \"_logs\")\n"," config_folder = os.path.join(main_dir, project_name)\n"," images_folder = os.path.join(main_dir, project_name, \"dataset\")\n"," output_folder = os.path.join(main_dir, project_name, \"output\")\n","else:\n"," main_dir = os.path.join(root_dir, \"drive/MyDrive/lora_training\")\n"," images_folder = os.path.join(main_dir, \"datasets\", project_name)\n"," output_folder = os.path.join(main_dir, \"output\", project_name)\n"," config_folder = os.path.join(main_dir, \"config\", project_name)\n"," log_folder = os.path.join(main_dir, \"log\")\n","\n","config_file = os.path.join(config_folder, \"training_config.toml\")\n","dataset_config_file = os.path.join(config_folder, \"dataset_config.toml\")\n","\n","\n","def install_trainer():\n"," !apt -y update -qq\n"," !apt install -y python3.10-venv aria2 -qq\n"," !git clone https://github.com/derrian-distro/LoRA_Easy_Training_scripts_Backend {trainer_dir}\n"," !chmod 755 /content/trainer/colab_install.sh\n"," os.chdir(trainer_dir)\n"," !./colab_install.sh\n","\n"," # fix logging\n"," !{venv_pip} uninstall -y rich\n","\n"," # patch kohya\n"," os.chdir(kohya_dir)\n"," if LOAD_TRUNCATED_IMAGES:\n"," !sed -i 's/from PIL import Image/from PIL import Image, ImageFile\\nImageFile.LOAD_TRUNCATED_IMAGES=True/g' library/train_util.py # fix truncated jpegs error\n"," if BETTER_EPOCH_NAMES:\n"," !sed -i 's/{:06d}/{:02d}/g' library/train_util.py # make epoch names shorter\n"," !sed -i 's/\".\" + args.save_model_as)/\"-{:02d}.\".format(num_train_epochs) + args.save_model_as)/g' train_network.py # name of the last epoch will match the rest\n"," if FIX_DIFFUSERS:\n"," deprecation_utils = os.path.join(kohya_dir, \"venv/lib/python3.10/site-packages/diffusers/utils/deprecation_utils.py\")\n"," !sed -i 's/if version.parse/if False:#/g' {deprecation_utils}\n"," if wandb_key:\n"," !sed -i 's/accelerator.log(logs, step=epoch + 1)//g' train_network.py # fix warning\n"," !sed -i 's/accelerator.log(logs, step=epoch + 1)//g' sdxl_train.py # fix warning\n","\n"," os.environ[\"TF_CPP_MIN_LOG_LEVEL\"] = \"3\"\n"," os.environ[\"BITSANDBYTES_NOWELCOME\"] = \"1\"\n"," os.environ[\"SAFETENSORS_FAST_GPU\"] = \"1\"\n"," os.environ[\"PYTHONWARNINGS\"] = \"ignore\"\n"," os.chdir(root_dir)\n","\n","\n","def validate_dataset():\n"," global lr_warmup_steps, lr_warmup_ratio, caption_extension, keep_tokens, model_url\n"," supported_types = (\".png\", \".jpg\", \".jpeg\", \".webp\", \".bmp\")\n","\n"," if model_url.startswith(\"/content/drive/\") and not os.path.exists(model_url):\n"," print(\"๐ฅ Error: The custom training model you specified was not found in your Google Drive.\")\n"," return\n","\n"," print(\"\\n๐ฟ Checking dataset...\")\n"," if not project_name.strip() or any(c in project_name for c in \" .()\\\"'\\\\/\"):\n"," print(\"๐ฅ Error: Please choose a valid project name.\")\n"," return\n","\n"," # Find the folders and files\n"," if custom_dataset:\n"," try:\n"," datconf = toml.loads(custom_dataset)\n"," datasets = [d for d in datconf[\"datasets\"][0][\"subsets\"]]\n"," except:\n"," print(f\"๐ฅ Error: Your custom dataset is invalid or contains an error! Please check the original template.\")\n"," return\n"," reg = [d.get(\"image_dir\") for d in datasets if d.get(\"is_reg\", False)]\n"," datasets_dict = {d[\"image_dir\"]: d[\"num_repeats\"] for d in datasets}\n"," folders = datasets_dict.keys()\n"," files = [f for folder in folders for f in os.listdir(folder)]\n"," images_repeats = {folder: (len([f for f in os.listdir(folder) if f.lower().endswith(supported_types)]), datasets_dict[folder]) for folder in folders}\n"," else:\n"," reg = []\n"," folders = [images_folder]\n"," files = os.listdir(images_folder)\n"," images_repeats = {images_folder: (len([f for f in files if f.lower().endswith(supported_types)]), num_repeats)}\n","\n"," # Validation\n"," for folder in folders:\n"," if not os.path.exists(folder):\n"," print(f\"๐ฅ Error: The folder {folder.replace('/content/drive/', '')} doesn't exist.\")\n"," return\n"," for folder, (img, rep) in images_repeats.items():\n"," if not img:\n"," print(f\"๐ฅ Error: Your {folder.replace('/content/drive/', '')} folder is empty.\")\n"," return\n"," test_files = []\n"," for f in files:\n"," if not f.lower().endswith((caption_extension, \".npz\")) and not f.lower().endswith(supported_types):\n"," print(f\"๐ฅ Error: Invalid file in dataset: \\\"{f}\\\". Aborting.\")\n"," return\n"," for ff in test_files:\n"," if f.endswith(supported_types) and ff.endswith(supported_types) \\\n"," and os.path.splitext(f)[0] == os.path.splitext(ff)[0]:\n"," print(f\"๐ฅ Error: The files {f} and {ff} cannot have the same name. Aborting.\")\n"," return\n"," test_files.append(f)\n","\n"," if caption_extension and not [txt for txt in files if txt.lower().endswith(caption_extension)]:\n"," caption_extension = \"\"\n"," if continue_from_lora and not (continue_from_lora.endswith(\".safetensors\") and os.path.exists(continue_from_lora)):\n"," print(f\"๐ฅ Error: Invalid path to existing Lora. Example: /content/drive/MyDrive/Loras/example.safetensors\")\n"," return\n","\n"," # Show estimations to the user\n","\n"," pre_steps_per_epoch = sum(img*rep for (img, rep) in images_repeats.values())\n"," steps_per_epoch = pre_steps_per_epoch/train_batch_size\n"," total_steps = max_train_steps or int(max_train_epochs*steps_per_epoch)\n"," estimated_epochs = int(total_steps/steps_per_epoch)\n"," lr_warmup_steps = int(total_steps*lr_warmup_ratio)\n","\n"," for folder, (img, rep) in images_repeats.items():\n"," print(\"๐\"+folder.replace(\"/content/drive/\", \"\") + (\" (Regularization)\" if folder in reg else \"\"))\n"," print(f\"๐ Found {img} images with {rep} repeats, equaling {img*rep} steps.\")\n"," print(f\"๐ Divide {pre_steps_per_epoch} steps by {train_batch_size} batch size to get {steps_per_epoch} steps per epoch.\")\n"," if max_train_epochs:\n"," print(f\"๐ฎ There will be {max_train_epochs} epochs, for around {total_steps} total training steps.\")\n"," else:\n"," print(f\"๐ฎ There will be {total_steps} steps, divided into {estimated_epochs} epochs and then some.\")\n","\n"," if total_steps > 10000:\n"," print(\"๐ฅ Error: Your total steps are too high. You probably made a mistake. Aborting...\")\n"," return\n","\n"," return True\n","\n","\n","def create_config():\n"," global dataset_config_file, config_file, model_file\n","\n"," if override_config_file:\n"," config_file = override_config_file\n"," print(f\"\\nโญ Using custom config file {config_file}\")\n"," else:\n"," config_dict = {\n"," \"network_arguments\": {\n"," \"unet_lr\": unet_lr,\n"," \"text_encoder_lr\": text_encoder_lr if not cache_text_encoder_outputs else 0,\n"," \"network_dim\": network_dim,\n"," \"network_alpha\": network_alpha,\n"," \"network_module\": network_module,\n"," \"network_args\": network_args,\n"," \"network_train_unet_only\": text_encoder_lr == 0 or cache_text_encoder_outputs,\n"," \"network_weights\": continue_from_lora or None\n"," },\n"," \"optimizer_arguments\": {\n"," \"learning_rate\": unet_lr,\n"," \"lr_scheduler\": lr_scheduler,\n"," \"lr_scheduler_type\": lr_scheduler_type,\n"," \"lr_scheduler_args\": lr_scheduler_args,\n"," \"lr_scheduler_num_cycles\": lr_scheduler_num_cycles if lr_scheduler == \"cosine_with_restarts\" else None,\n"," \"lr_scheduler_power\": lr_scheduler_power if lr_scheduler == \"polynomial\" else None,\n"," \"lr_warmup_steps\": lr_warmup_steps if lr_scheduler not in (\"cosine\", \"constant\") else None,\n"," \"optimizer_type\": optimizer,\n"," \"optimizer_args\": optimizer_args or None,\n"," \"loss_type\": \"l2\",\n"," \"max_grad_norm\": 1.0,\n"," },\n"," \"training_arguments\": {\n"," \"lowram\": LOWRAM,\n"," \"pretrained_model_name_or_path\": model_file,\n"," \"vae\": vae_file,\n"," \"max_train_steps\": max_train_steps,\n"," \"max_train_epochs\": max_train_epochs,\n"," \"train_batch_size\": train_batch_size,\n"," \"seed\": seed,\n"," \"max_token_length\": 225,\n"," \"xformers\": cross_attention == \"xformers\",\n"," \"sdpa\": cross_attention == \"sdpa\",\n"," \"min_snr_gamma\": min_snr_gamma if min_snr_gamma_enabled else None,\n"," \"ip_noise_gamma\": ip_noise_gamma if ip_noise_gamma_enabled else None,\n"," \"no_half_vae\": True,\n"," \"gradient_checkpointing\": True,\n"," \"gradient_accumulation_steps\": gradient_accumulation_steps,\n"," \"max_data_loader_n_workers\": 1,\n"," \"persistent_data_loader_workers\": True,\n"," \"mixed_precision\": mixed_precision,\n"," \"full_fp16\": mixed_precision == \"fp16\" and full_precision,\n"," \"full_bf16\": mixed_precision == \"bf16\" and full_precision,\n"," \"cache_latents\": cache_latents,\n"," \"cache_latents_to_disk\": cache_latents_to_drive,\n"," \"cache_text_encoder_outputs\": cache_text_encoder_outputs,\n"," \"min_timestep\": 0,\n"," \"max_timestep\": 1000,\n"," \"prior_loss_weight\": 1.0,\n"," \"multires_noise_iterations\": 6 if multinoise else None,\n"," \"multires_noise_discount\": 0.3 if multinoise else None,\n"," \"v_parameterization\": vpred or None,\n"," \"scale_v_pred_loss_like_noise_pred\": vpred or None,\n"," \"zero_terminal_snr\": vpred or None,\n"," },\n"," \"saving_arguments\": {\n"," \"save_precision\": \"fp16\",\n"," \"save_model_as\": \"safetensors\",\n"," \"save_every_n_epochs\": save_every_n_epochs,\n"," \"save_last_n_epochs\": keep_only_last_n_epochs,\n"," \"output_name\": project_name,\n"," \"output_dir\": output_folder,\n"," \"log_prefix\": project_name,\n"," \"logging_dir\": log_folder,\n"," \"wandb_api_key\": wandb_key or None,\n"," \"log_with\": \"wandb\" if wandb_key else None,\n"," }\n"," }\n","\n"," for key in config_dict:\n"," if isinstance(config_dict[key], dict):\n"," config_dict[key] = {k: v for k, v in config_dict[key].items() if v is not None}\n","\n"," with open(config_file, \"w\") as f:\n"," f.write(toml.dumps(config_dict))\n"," print(f\"\\n๐ Config saved to {config_file}\")\n","\n"," if override_dataset_config_file:\n"," dataset_config_file = override_dataset_config_file\n"," print(f\"โญ Using custom dataset config file {dataset_config_file}\")\n"," else:\n"," dataset_config_dict = {\n"," \"general\": {\n"," \"resolution\": resolution,\n"," \"shuffle_caption\": shuffle_caption and not cache_text_encoder_outputs,\n"," \"keep_tokens\": keep_tokens,\n"," \"flip_aug\": False,\n"," \"caption_extension\": caption_extension,\n"," \"enable_bucket\": True,\n"," \"bucket_no_upscale\": False,\n"," \"bucket_reso_steps\": bucket_reso_steps,\n"," \"min_bucket_reso\": min_bucket_reso,\n"," \"max_bucket_reso\": max_bucket_reso,\n"," },\n"," \"datasets\": toml.loads(custom_dataset)[\"datasets\"] if custom_dataset else [\n"," {\n"," \"subsets\": [\n"," {\n"," \"num_repeats\": num_repeats,\n"," \"image_dir\": images_folder,\n"," \"class_tokens\": None if caption_extension else project_name\n"," }\n"," ]\n"," }\n"," ]\n"," }\n","\n"," for key in dataset_config_dict:\n"," if isinstance(dataset_config_dict[key], dict):\n"," dataset_config_dict[key] = {k: v for k, v in dataset_config_dict[key].items() if v is not None}\n","\n"," with open(dataset_config_file, \"w\") as f:\n"," f.write(toml.dumps(dataset_config_dict))\n"," print(f\"๐ Dataset config saved to {dataset_config_file}\")\n","\n","\n","def download_model():\n"," global old_model_url, model_url, model_file, vae_url, vae_file\n"," real_model_url = model_url # There was a reason for having a separate variable but I forgot what it was.\n","\n"," if real_model_url.startswith(\"/content/drive/\"):\n"," # Local model, already checked to exist\n"," model_file = real_model_url\n"," print(f\"๐ Using local model file: {model_file}\")\n"," # Validation\n"," if model_file.lower().endswith(\".safetensors\"):\n"," from safetensors.torch import load_file as load_safetensors\n"," try:\n"," test = load_safetensors(model_file)\n"," del test\n"," except:\n"," return False\n"," elif model_file.lower().endswith(\".ckpt\"):\n"," from torch import load as load_ckpt\n"," try:\n"," test = load_ckpt(model_file)\n"," del test\n"," except:\n"," return False\n"," return True\n","\n"," else:\n"," # Downloadable model\n"," if load_diffusers:\n"," if 'huggingface.co' in real_model_url:\n"," match = re.search(r'huggingface\\.co/([^/]+)/([^/]+)', real_model_url)\n"," if match:\n"," username = match.group(1)\n"," model_name = match.group(2)\n"," model_file = f\"{username}/{model_name}\"\n"," from huggingface_hub import HfFileSystem\n"," fs = HfFileSystem()\n"," existing_folders = set(fs.ls(model_file, detail=False))\n"," necessary_folders = [ \"scheduler\", \"text_encoder\", \"text_encoder_2\", \"tokenizer\", \"tokenizer_2\", \"unet\", \"vae\" ]\n"," if all(f\"{model_file}/{folder}\" in existing_folders for folder in necessary_folders):\n"," print(\"๐ Diffusers model identified.\") # Will be handled by kohya\n"," return True\n"," raise ValueError(\"๐ฅ Failed to load Diffusers model. If this model is not Diffusers, have you tried turning it off at the top of the colab?\")\n","\n"," # Define local filename\n"," if not model_file or old_model_url and old_model_url != model_url:\n"," if real_model_url.lower().endswith((\".ckpt\", \".safetensors\")):\n"," model_file = f\"/content{real_model_url[real_model_url.rfind('/'):]}\"\n"," else:\n"," model_file = \"/content/downloaded_model.safetensors\"\n"," if os.path.exists(model_file):\n"," !rm \"{model_file}\"\n","\n"," # HuggingFace\n"," if m := re.search(r\"(?:https?://)?(?:www\\.)?huggingface\\.co/[^/]+/[^/]+/blob\", real_model_url):\n"," real_model_url = real_model_url.replace(\"blob\", \"resolve\")\n"," # Civitai\n"," elif m := re.search(r\"(?:https?://)?(?:www\\\\.)?civitai\\.com/models/([0-9]+)(/[A-Za-z0-9-_]+)?\", real_model_url):\n"," if m.group(2):\n"," model_file = f\"/content{m.group(2)}.safetensors\"\n"," if m := re.search(r\"modelVersionId=([0-9]+)\", real_model_url):\n"," real_model_url = f\"https://civitai.com/api/download/models/{m.group(1)}\"\n"," else:\n"," raise ValueError(\"๐ฅ optional_custom_training_model contains a civitai link, but the link doesn't include a modelVersionId. You can also right click the download button to copy the direct download link.\")\n","\n"," # Download checkpoint\n"," !aria2c \"{real_model_url}\" --console-log-level=warn -c -s 16 -x 16 -k 10M -d / -o \"{model_file}\"\n","\n"," # Download VAE\n"," if not os.path.exists(vae_file):\n"," !aria2c \"{vae_url}\" --console-log-level=warn -c -s 16 -x 16 -k 10M -d / -o \"{vae_file}\"\n","\n"," # Validation\n","\n"," if model_file.lower().endswith(\".safetensors\"):\n"," from safetensors.torch import load_file as load_safetensors\n"," try:\n"," test = load_safetensors(model_file)\n"," del test\n"," except:\n"," new_model_file = os.path.splitext(model_file)[0]+\".ckpt\"\n"," !mv \"{model_file}\" \"{new_model_file}\"\n"," model_file = new_model_file\n"," print(f\"Renamed model to {os.path.splitext(model_file)[0]}.ckpt\")\n","\n"," if model_file.lower().endswith(\".ckpt\"):\n"," from torch import load as load_ckpt\n"," try:\n"," test = load_ckpt(model_file)\n"," del test\n"," except:\n"," return False\n","\n"," return True\n","\n","\n","def calculate_rex_steps():\n"," # https://github.com/derrian-distro/LoRA_Easy_Training_scripts_Backend/blob/c34084b0435e6e19bb7a01ac1ecbadd185ee8c1e/utils/validation.py#L268\n"," global max_train_steps\n"," print(\"\\n๐ค Calculating Rex steps\")\n"," if max_train_steps:\n"," calculated_max_steps = max_train_steps\n"," else:\n"," from library.train_util import BucketManager\n"," from PIL import Image\n"," from pathlib import Path\n"," import math\n","\n"," with open(dataset_config_file, \"r\") as f:\n"," subsets = toml.load(f)[\"datasets\"][0][\"subsets\"]\n","\n"," supported_types = [\".png\", \".jpg\", \".jpeg\", \".webp\", \".bmp\"]\n"," res = (resolution, resolution)\n"," bucketManager = BucketManager(False, res, min_bucket_reso, max_bucket_reso, bucket_reso_steps)\n"," bucketManager.make_buckets()\n"," for subset in subsets:\n"," for image in Path(subset[\"image_dir\"]).iterdir():\n"," if image.suffix not in supported_types:\n"," continue\n"," with Image.open(image) as img:\n"," bucket_reso, _, _ = bucketManager.select_bucket(img.width, img.height)\n"," for _ in range(subset[\"num_repeats\"]):\n"," bucketManager.add_image(bucket_reso, image)\n"," steps_before_acc = sum(math.ceil(len(bucket) / train_batch_size) for bucket in bucketManager.buckets)\n"," calculated_max_steps = math.ceil(steps_before_acc / gradient_accumulation_steps) * max_train_epochs\n"," del bucketManager\n","\n"," cycle_steps = calculated_max_steps // (lr_scheduler_num_cycles or 1)\n"," print(f\" cycle steps: {cycle_steps}\")\n"," lr_scheduler_args.append(f\"first_cycle_max_steps={cycle_steps}\")\n","\n"," warmup_steps = round(calculated_max_steps * lr_warmup_ratio) // (lr_scheduler_num_cycles or 1)\n"," if warmup_steps > 0:\n"," print(f\" warmup steps: {warmup_steps}\")\n"," lr_scheduler_args.append(f\"warmup_steps={warmup_steps}\")\n","\n","\n","def main():\n"," global dependencies_installed\n","\n"," if not os.path.exists('/content/drive'):\n"," from google.colab import drive\n"," print(\"๐ Connecting to Google Drive...\")\n"," drive.mount('/content/drive')\n","\n"," for dir in (main_dir, trainer_dir, log_folder, images_folder, output_folder, config_folder):\n"," os.makedirs(dir, exist_ok=True)\n","\n"," if not validate_dataset():\n"," return\n","\n"," if not dependencies_installed:\n"," print(\"\\n๐ญ Installing trainer...\\n\")\n"," t0 = time()\n"," install_trainer()\n"," t1 = time()\n"," dependencies_installed = True\n"," print(f\"\\nโ
Installation finished in {int(t1-t0)} seconds.\")\n"," else:\n"," print(\"\\nโ
Dependencies already installed.\")\n","\n"," if old_model_url != model_url or not model_file or not os.path.exists(model_file):\n"," print(\"\\n๐ Getting model...\")\n"," if not download_model():\n"," print(\"\\n๐ฅ Error: The model you specified is invalid or corrupted.\"\n"," \"\\nIf you're using an URL, please check that the model is accessible without being logged in.\"\n"," \"\\nYou can try civitai or huggingface URLs, or a path in your Google Drive starting with /content/drive/MyDrive\")\n"," return\n"," print()\n"," else:\n"," print(\"\\n๐ Model already downloaded.\\n\")\n","\n"," if lr_scheduler_type:\n"," create_config()\n"," os.chdir(kohya_dir)\n"," calculate_rex_steps()\n"," os.chdir(root_dir)\n","\n"," create_config()\n","\n"," print(\"\\nโญ Starting trainer...\\n\")\n","\n"," os.chdir(kohya_dir)\n"," !{venv_python} {train_network} --config_file={config_file} --dataset_config={dataset_config_file}\n"," os.chdir(root_dir)\n","\n"," if not get_ipython().__dict__['user_ns']['_exit_code']:\n"," display(Markdown(\"### โ
Done! [Go download your Lora from Google Drive](https://drive.google.com/drive/my-drive)\\n\"\n"," \"### There will be several files, you should try the latest version (the file with the largest number next to it)\"))\n","\n","main()\n"]},{"cell_type":"markdown","metadata":{"id":"mBMUJ7BuvNcn"},"source":["## *๏ธโฃ Extras\n","\n","You can run these before starting the training."]},{"cell_type":"markdown","metadata":{"id":"Wd4916Eu1tb9"},"source":["### ๐ Multiple folders in dataset\n","Below is a template allowing you to define multiple folders in your dataset. You must include the location of each folder and you can set different number of repeats for each one. To add more folders simply copy and paste the sections starting with `[[datasets.subsets]]`.\n","\n","When enabling this, the number of repeats set in the main cell will be ignored, and the main folder set by the project name will also be ignored.\n","\n","You can make one of them a regularization folder by adding `is_reg = true` \n","You can also set different `keep_tokens`, `flip_aug`, etc."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Y037lagnJWmn"},"outputs":[],"source":["custom_dataset = \"\"\"\n","[[datasets]]\n","\n","[[datasets.subsets]]\n","image_dir = \"/content/drive/MyDrive/Loras/example/dataset/good_images\"\n","num_repeats = 3\n","\n","[[datasets.subsets]]\n","image_dir = \"/content/drive/MyDrive/Loras/example/dataset/normal_images\"\n","num_repeats = 1\n","\n","\"\"\""]},{"cell_type":"code","execution_count":null,"metadata":{"id":"W84Jxf-U2TIU"},"outputs":[],"source":["custom_dataset = None"]},{"cell_type":"code","execution_count":null,"metadata":{"cellView":"form","id":"WDjkp4scvPgE"},"outputs":[],"source":["#@markdown ### ๐ Unzip dataset\n","#@markdown It's much slower to upload individual files to your Drive, so you may want to upload a zip if you have your dataset in your computer.\n","zip = \"/content/drive/MyDrive/my_dataset.zip\" #@param {type:\"string\"}\n","extract_to = \"/content/drive/MyDrive/Loras/example/dataset\" #@param {type:\"string\"}\n","\n","import os, zipfile\n","\n","if not os.path.exists('/content/drive'):\n"," from google.colab import drive\n"," print(\"๐ Connecting to Google Drive...\")\n"," drive.mount('/content/drive')\n","\n","os.makedirs(extract_to, exist_ok=True)\n","\n","with zipfile.ZipFile(zip, 'r') as f:\n"," f.extractall(extract_to)\n","\n","print(\"โ
Done\")\n"]},{"cell_type":"code","execution_count":null,"metadata":{"cellView":"form","id":"aKWlpsG0jrX3"},"outputs":[],"source":["#@markdown ### ๐ข Count datasets\n","#@markdown Google Drive makes it impossible to count the files in a folder, so this will show you the file counts in all folders and subfolders.\n","folder = \"/content/drive/MyDrive/Loras\" #@param {type:\"string\"}\n","\n","import os\n","from google.colab import drive\n","\n","if not os.path.exists('/content/drive'):\n"," print(\"๐ Connecting to Google Drive...\\n\")\n"," drive.mount('/content/drive')\n","\n","tree = {}\n","exclude = (\"_logs\", \"/output\")\n","for i, (root, dirs, files) in enumerate(os.walk(folder, topdown=True)):\n"," dirs[:] = [d for d in dirs if all(ex not in d for ex in exclude)]\n"," images = len([f for f in files if f.lower().endswith((\".png\", \".jpg\", \".jpeg\"))])\n"," captions = len([f for f in files if f.lower().endswith(\".txt\")])\n"," others = len(files) - images - captions\n"," path = root[folder.rfind(\"/\")+1:]\n"," tree[path] = None if not images else f\"{images:>4} images | {captions:>4} captions |\"\n"," if tree[path] and others:\n"," tree[path] += f\" {others:>4} other files\"\n","\n","pad = max(len(k) for k in tree)\n","print(\"\\n\".join(f\"๐{k.ljust(pad)} | {v}\" for k, v in tree.items() if v))\n"]},{"cell_type":"code","execution_count":null,"metadata":{"cellView":"form","id":"-cnM6xM_E6Jx"},"outputs":[],"source":["#@markdown ### โช๏ธ Continue\n","\n","#@markdown Here you can write a path in your Google Drive to load an existing Lora file to continue training on. \n","#@markdown **Warning:** It's not the same as one long training session. The epochs start from scratch, and it may have worse results.\n","continue_from_lora = \"\" #@param {type:\"string\"}\n","if continue_from_lora and not continue_from_lora.startswith(\"/content/drive/MyDrive\"):\n"," import os\n"," continue_from_lora = os.path.join(\"/content/drive/MyDrive\", continue_from_lora)\n"]},{"cell_type":"markdown","metadata":{"id":"YcTjh07x90Ro"},"source":["# ๐ Plot training results\n","You can do this after running the trainer. You don't need this unless you know what you're doing. \n","The first cell below may fail to load all your logs. Keep trying the second cell until all data has loaded."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Z_TRI3eX90Rp"},"outputs":[],"source":["%load_ext tensorboard\n","%tensorboard --logdir={log_folder}/"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"6rM5SLq990Rp"},"outputs":[],"source":["from tensorboard import notebook\n","notebook.display(port=6006, height=800)"]}],"metadata":{"accelerator":"GPU","colab":{"gpuType":"T4","provenance":[{"file_id":"1XDv7gpljNdzMBzDXubSKIacOqboU1wT8","timestamp":1739624016353},{"file_id":"https://github.com/hollowstrawberry/kohya-colab/blob/main/Lora_Trainer_XL.ipynb","timestamp":1739180056829}]},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"name":"python"}},"nbformat":4,"nbformat_minor":0}
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