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Playground_V2_5_Lora_Creator.ipynb

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+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "#**PLAYGROUND V2.5 LORA CREATOR - Adapted from SD XL Lora training by linoytsaban*"
+      ],
+      "metadata": {
+        "id": "RvbNnswz1Bwy"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Install necessary libs."
+      ],
+      "metadata": {
+        "id": "Pxl1NJ4ew_22"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "krdOGpbewX6D"
+      },
+      "outputs": [],
+      "source": [
+        "!pip install huggingface_hub datasets pillow xformers bitsandbytes transformers accelerate wandb dadaptation prodigyopt torch -q\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!pip install peft -q"
+      ],
+      "metadata": {
+        "id": "LmsPVnS8xpWB"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!pip install git+https://github.com/huggingface/diffusers.git -q"
+      ],
+      "metadata": {
+        "id": "yAKkbjNsxp4n"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Downloading the script!"
+      ],
+      "metadata": {
+        "id": "gYT0P8sMxtOE"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!wget https://raw.githubusercontent.com/huggingface/diffusers/main/examples/advanced_diffusion_training/train_dreambooth_lora_sdxl_advanced.py"
+      ],
+      "metadata": {
+        "id": "ehtWgVcdxwJT"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!wget https://gist.githubusercontent.com/artificialguybr/a1c58ad578d0446d493c9793093196e1/raw/b8cede5d9ad5f9911de70ac187c6b078eea693c8/sd-dataset-to-hf-dataset.py"
+      ],
+      "metadata": {
+        "id": "rvrh-vLexwlI"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!accelerate config default"
+      ],
+      "metadata": {
+        "id": "cepNwXbXyBa-"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Login with your HuggingFace Account!"
+      ],
+      "metadata": {
+        "id": "PTJdDX4AxE2m"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from huggingface_hub import notebook_login\n",
+        "notebook_login()"
+      ],
+      "metadata": {
+        "id": "nKtKBrgVxGpb"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Convert your currently SD XL (Kohya) style Dataset to HF Dataset."
+      ],
+      "metadata": {
+        "id": "HLDTiVlJyI4g"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Required imports\n",
+        "import os\n",
+        "from huggingface_hub import HfApi, login\n",
+        "from datasets import Dataset, Features, Value, Image\n",
+        "from PIL import Image as PILImage\n",
+        "import io\n",
+        "\n",
+        "## Configuration\n",
+        "# Define the directory where your images and texts are stored\n",
+        "data_folder = \"\"  # Example: \"/content/drive/MyDrive/dataset\"\n",
+        "\n",
+        "# Name your dataset as desired\n",
+        "dataset_name = \"\"  # Example: \"my-first-dataset\"\n",
+        "dataset_repo_id = \"\"  # Example: \"your_username/my-first-dataset\"\n",
+        "hf_token = \"\"  # Your Hugging Face token\n",
+        "\n",
+        "## Initialize the Hugging Face API\n",
+        "api = HfApi()\n",
+        "login(hf_token)\n",
+        "\n",
+        "## Define the dataset features\n",
+        "features = Features({\n",
+        "    \"image\": Image(),\n",
+        "    \"text\": Value(\"string\"),\n",
+        "})\n",
+        "\n",
+        "## Create a dictionary to store the corresponding images and texts\n",
+        "data = {\"image\": [], \"text\": []}\n",
+        "\n",
+        "## Accepted image file extensions\n",
+        "image_extensions = [\".png\", \".jpg\", \".jpeg\", \".gif\"]\n",
+        "\n",
+        "## Iterate through image files\n",
+        "for filename in os.listdir(data_folder):\n",
+        "    # Check if the file has an accepted image extension\n",
+        "    if any(filename.lower().endswith(ext) for ext in image_extensions):\n",
+        "        image_path = os.path.join(data_folder, filename)\n",
+        "        txt_filename = os.path.splitext(filename)[0] + \".txt\"\n",
+        "        txt_path = os.path.join(data_folder, txt_filename)\n",
+        "\n",
+        "        if os.path.exists(txt_path):\n",
+        "            # Read the content of the text file\n",
+        "            with open(txt_path, \"r\") as txt_file:\n",
+        "                text = txt_file.read()\n",
+        "\n",
+        "            # Load the image using the pillow library\n",
+        "            with PILImage.open(image_path) as image:\n",
+        "                # Convert the image to the format expected by Hugging Face Datasets\n",
+        "                image_bytes = io.BytesIO()\n",
+        "                image.save(image_bytes, format='PNG')\n",
+        "                image_bytes = image_bytes.getvalue()\n",
+        "\n",
+        "            # Add the image and text to the dictionary\n",
+        "            data[\"image\"].append(image_bytes)\n",
+        "            data[\"text\"].append(text)\n",
+        "\n",
+        "## Create the dataset on Hugging Face\n",
+        "dataset = Dataset.from_dict(data, features=features)\n",
+        "dataset.push_to_hub(dataset_repo_id, private=True)  # Set private=False if you want the dataset to be public\n",
+        "\n",
+        "print(\"Dataset successfully uploaded to Hugging Face!\")\n"
+      ],
+      "metadata": {
+        "id": "jYTgzfRwyDfL"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!pip install python-slugify\n",
+        "from slugify import slugify\n",
+        "\n",
+        "model_name = \"3d icon SDXL LoRA\" # @param\n",
+        "output_dir = slugify(model_name)"
+      ],
+      "metadata": {
+        "id": "I-EcR3g6zS7o"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# **Instance & Validation Prompt**\n",
+        "* `instance_prompt` -\n",
+        "    * when custom captions are enabled this prompt is still used in case there are missing captions, as well as in the model's readme.\n",
+        "    * If custom captions are not used, this prompt will be used as the caption for all training images.\n",
+        "* `validation_prompt` -\n",
+        "    * this prompt is used to generate images throught the training process, this way you can see the models learning curve during training.\n",
+        "    * you can also change `num_validation_images` (4 by default) and `validation_epochs` (50 by default) to control the amount images generated with the validation prompt, and the number of ephochs between each dreambooth validation.  "
+      ],
+      "metadata": {
+        "id": "LyuzvbWIzbXC"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "instance_prompt = \"3d icon in the style of TOK\" # @param\n",
+        "validation_prompt = \"a TOK icon of an astronaut riding a horse, in the style of TOK\" # @param"
+      ],
+      "metadata": {
+        "id": "xIopxUKHzYcU"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**Set your LoRA rank**\n",
+        "The rank of your LoRA is linked to its expressiveness.\n",
+        "The bigger the rank the closer we are to regular dreambooth, and in theory we have more expressive power (and heavier weights).\n",
+        "\n",
+        "For a very simple concept that you have a good high quality image set for (e.g. a pet, a generic object), a rank as low as 4 can be enough to get great results. We reccomend going between 8 and 64 depending on your concept and how much of a priortiy it is for you to keep the LoRA small or not.  "
+      ],
+      "metadata": {
+        "id": "sb0kvKjtzh9w"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "rank = 8 # @param"
+      ],
+      "metadata": {
+        "id": "58pTgSOIziw3"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "#Dataset Name/Local/HF"
+      ],
+      "metadata": {
+        "id": "DviZbC9mz0RX"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "dataset_name=./3d_icon # @param\n",
+        "caption_column=prompt # @param"
+      ],
+      "metadata": {
+        "id": "VZnq1H7fz2dM"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#!/usr/bin/env bash\n",
+        "!accelerate launch train_dreambooth_lora_sdxl_advanced.py \\\n",
+        "  --pretrained_model_name_or_path=\"playgroundai/playground-v2.5-1024px-aesthetic\" \\\n",
+        "  --dataset_name=\"$dataset_name\" \\\n",
+        "  --instance_prompt=\"$instance_prompt\" \\\n",
+        "  --validation_prompt=\"$validation_prompt\" \\\n",
+        "  --output_dir=\"$output_dir\" \\\n",
+        "  --caption_column=\"$caption_column\" \\\n",
+        "  --do_edm_style_training \\\n",
+        "  --mixed_precision=\"bf16\" \\\n",
+        "  --resolution=1024 \\\n",
+        "  --train_batch_size=3 \\\n",
+        "  --repeats=1 \\\n",
+        "  --report_to=\"wandb\"\\\n",
+        "  --gradient_accumulation_steps=1 \\\n",
+        "  --gradient_checkpointing \\\n",
+        "  --learning_rate=1e-5 \\\n",
+        "  --optimizer=\"AdamW\"\\\n",
+        "  --lr_scheduler=\"constant\" \\\n",
+        "  --rank=\"$rank\" \\\n",
+        "  --max_train_steps=2000 \\\n",
+        "  --checkpointing_steps=2000 \\\n",
+        "  --seed=\"0\" \\\n",
+        "  --push_to_hub"
+      ],
+      "metadata": {
+        "id": "P3IAGgF9zmig"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from huggingface_hub import whoami\n",
+        "from pathlib import Path\n",
+        "from IPython.display import display, Markdown\n",
+        "\n",
+        "username = whoami(token=Path(\"/root/.cache/huggingface/\"))[\"name\"]\n",
+        "repo_id = f\"{username}/{output_dir}\"\n",
+        "\n",
+        "link_to_model = f\"https://huggingface.co/{repo_id}\"\n",
+        "\n",
+        "display(Markdown(\"### Your model has finished training.\\nAccess it here: {}\".format(link_to_model)))"
+      ],
+      "metadata": {
+        "id": "NkY5Sviy0fHJ"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "#LOAD INFERENCE"
+      ],
+      "metadata": {
+        "id": "t8gtC0kf0WX0"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import torch\n",
+        "from huggingface_hub import hf_hub_download, upload_file\n",
+        "from diffusers import DiffusionPipeline\n",
+        "from diffusers.models import AutoencoderKL\n",
+        "from safetensors.torch import load_file\n",
+        "\n",
+        "pipe = DiffusionPipeline.from_pretrained(\n",
+        "    \"playgroundai/playground-v2.5-1024px-aesthetic\",\n",
+        "    torch_dtype=torch.float16,\n",
+        "    variant=\"fp16\",\n",
+        ").to(\"cuda\")\n",
+        "\n",
+        "pipe.load_lora_weights(repo_id, weight_name=\"pytorch_lora_weights.safetensors\")\n"
+      ],
+      "metadata": {
+        "id": "Ib7aaWVQ0mdT"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}

abdul_halim_default_playground2_5_lcm_sdxl_face_detailer_comfyworkflows.json

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+{
+  "last_node_id": 20,
+  "last_link_id": 34,
+  "nodes": [
+    {
+      "id": 11,
+      "type": "ModelSamplingContinuousEDM",
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+      "size": {
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+      "inputs": [
+        {
+          "name": "model",
+          "type": "MODEL",
+          "link": 12
+        }
+      ],
+      "outputs": [
+        {
+          "name": "MODEL",
+          "type": "MODEL",
+          "links": [
+            15,
+            20
+          ],
+          "shape": 3,
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "ModelSamplingContinuousEDM"
+      },
+      "widgets_values": [
+        "edm_playground_v2.5",
+        120,
+        0.002
+      ]
+    },
+    {
+      "id": 9,
+      "type": "SaveImage",
+      "pos": [
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+      "size": {
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+        "1": 748.4672241210938
+      },
+      "flags": {},
+      "order": 13,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "images",
+          "type": "IMAGE",
+          "link": 28
+        }
+      ],
+      "properties": {},
+      "widgets_values": [
+        "ComfyUI"
+      ]
+    },
+    {
+      "id": 17,
+      "type": "LoraLoaderModelOnly",
+      "pos": [
+        152,
+        352
+      ],
+      "size": {
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+        "1": 98.5372543334961
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+      "flags": {},
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+      "mode": 0,
+      "inputs": [
+        {
+          "name": "model",
+          "type": "MODEL",
+          "link": 19
+        }
+      ],
+      "outputs": [
+        {
+          "name": "MODEL",
+          "type": "MODEL",
+          "links": [],
+          "shape": 3,
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "LoraLoaderModelOnly"
+      },
+      "widgets_values": [
+        "glowneon_xl_v1.safetensors",
+        1
+      ]
+    },
+    {
+      "id": 10,
+      "type": "LoraLoaderModelOnly",
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+      "inputs": [
+        {
+          "name": "model",
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+          "link": 10
+        }
+      ],
+      "outputs": [
+        {
+          "name": "MODEL",
+          "type": "MODEL",
+          "links": [
+            31
+          ],
+          "shape": 3,
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "LoraLoaderModelOnly"
+      },
+      "widgets_values": [
+        "lcm-sdxl.safetensors",
+        1
+      ]
+    },
+    {
+      "id": 4,
+      "type": "CheckpointLoaderSimple",
+      "pos": [
+        148,
+        35.58000091552734
+      ],
+      "size": {
+        "0": 361.0586853027344,
+        "1": 98
+      },
+      "flags": {},
+      "order": 0,
+      "mode": 0,
+      "outputs": [
+        {
+          "name": "MODEL",
+          "type": "MODEL",
+          "links": [
+            10,
+            12,
+            19
+          ],
+          "slot_index": 0
+        },
+        {
+          "name": "CLIP",
+          "type": "CLIP",
+          "links": [
+            3,
+            5,
+            21,
+            32
+          ],
+          "slot_index": 1
+        },
+        {
+          "name": "VAE",
+          "type": "VAE",
+          "links": [],
+          "slot_index": 2
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "CheckpointLoaderSimple"
+      },
+      "widgets_values": [
+        "playground-v2.5-1024px-aesthetic.fp16.safetensors"
+      ]
+    },
+    {
+      "id": 8,
+      "type": "VAEDecode",
+      "pos": [
+        1356,
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+      ],
+      "size": [
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+        46
+      ],
+      "flags": {},
+      "order": 11,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "samples",
+          "type": "LATENT",
+          "link": 7
+        },
+        {
+          "name": "vae",
+          "type": "VAE",
+          "link": 18
+        }
+      ],
+      "outputs": [
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            27
+          ],
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "VAEDecode"
+      }
+    },
+    {
+      "id": 19,
+      "type": "UltralyticsDetectorProvider",
+      "pos": [
+        1352,
+        142
+      ],
+      "size": [
+        379.2158163330075,
+        83.99736726074212
+      ],
+      "flags": {},
+      "order": 1,
+      "mode": 0,
+      "outputs": [
+        {
+          "name": "BBOX_DETECTOR",
+          "type": "BBOX_DETECTOR",
+          "links": [
+            25
+          ],
+          "shape": 3
+        },
+        {
+          "name": "SEGM_DETECTOR",
+          "type": "SEGM_DETECTOR",
+          "links": null,
+          "shape": 3
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "UltralyticsDetectorProvider"
+      },
+      "widgets_values": [
+        "bbox/face_yolov8m.pt"
+      ]
+    },
+    {
+      "id": 18,
+      "type": "ToDetailerPipeSDXL",
+      "pos": [
+        1350,
+        272
+      ],
+      "size": [
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+      ],
+      "flags": {},
+      "order": 10,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "model",
+          "type": "MODEL",
+          "link": 20
+        },
+        {
+          "name": "clip",
+          "type": "CLIP",
+          "link": 21
+        },
+        {
+          "name": "vae",
+          "type": "VAE",
+          "link": 22
+        },
+        {
+          "name": "positive",
+          "type": "CONDITIONING",
+          "link": 23
+        },
+        {
+          "name": "negative",
+          "type": "CONDITIONING",
+          "link": 24
+        },
+        {
+          "name": "refiner_model",
+          "type": "MODEL",
+          "link": 31
+        },
+        {
+          "name": "refiner_clip",
+          "type": "CLIP",
+          "link": 32
+        },
+        {
+          "name": "refiner_positive",
+          "type": "CONDITIONING",
+          "link": 33
+        },
+        {
+          "name": "refiner_negative",
+          "type": "CONDITIONING",
+          "link": 34
+        },
+        {
+          "name": "bbox_detector",
+          "type": "BBOX_DETECTOR",
+          "link": 25,
+          "slot_index": 9
+        },
+        {
+          "name": "sam_model_opt",
+          "type": "SAM_MODEL",
+          "link": null
+        },
+        {
+          "name": "segm_detector_opt",
+          "type": "SEGM_DETECTOR",
+          "link": null
+        },
+        {
+          "name": "detailer_hook",
+          "type": "DETAILER_HOOK",
+          "link": null
+        }
+      ],
+      "outputs": [
+        {
+          "name": "detailer_pipe",
+          "type": "DETAILER_PIPE",
+          "links": [
+            26
+          ],
+          "shape": 3,
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "ToDetailerPipeSDXL"
+      },
+      "widgets_values": [
+        "",
+        "Select the LoRA to add to the text",
+        "Select the Wildcard to add to the text"
+      ]
+    },
+    {
+      "id": 14,
+      "type": "SDXLEmptyLatentSizePicker+",
+      "pos": [
+        545,
+        615
+      ],
+      "size": [
+        399.65877504516607,
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