{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook shows how to use the adapter merging methods from `peft` and apply them image generation models using `diffusers`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QaEZ3dPgGtza"
   },
   "source": [
    "## Turn `diffusers` LoRA checkpoints into `PeftModel`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "KwvBjN-e62ts",
    "outputId": "19c58dc7-95db-49c6-beb8-3ade1a8fe284"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/1.9 MB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r",
      "\u001b[2K     \u001b[91m━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.2/1.9 MB\u001b[0m \u001b[31m6.6 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.9/1.9 MB\u001b[0m \u001b[31m32.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m280.0/280.0 kB\u001b[0m \u001b[31m34.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m8.4/8.4 MB\u001b[0m \u001b[31m100.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25h  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
      "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
      "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
      "  Building wheel for peft (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n"
     ]
    }
   ],
   "source": [
    "!pip install diffusers accelerate transformers -U -q\n",
    "!pip install git+https://github.com/huggingface/peft -q"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "5S64sUQhJqB3"
   },
   "outputs": [],
   "source": [
    "from google.colab import userdata\n",
    "TOKEN = userdata.get(\"HF_TOKEN\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 150,
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    },
    "id": "1YH9xWDcyhaa",
    "outputId": "8b7b32f4-4b77-411e-f499-7b2cf7650613"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The cache for model files in Transformers v4.22.0 has been updated. Migrating your old cache. This is a one-time only operation. You can interrupt this and resume the migration later on by calling `transformers.utils.move_cache()`.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c9f764b5036042af9f1505e3729cbc32",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "67567eea233b423c8acd62773a4adb30",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "unet/config.json:   0%|          | 0.00/1.68k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b27ac2aaff694dd5999ab2cba91195da",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "diffusion_pytorch_model.fp16.safetensors:   0%|          | 0.00/5.14G [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from diffusers import UNet2DConditionModel\n",
    "import torch\n",
    "\n",
    "model_id = \"stabilityai/stable-diffusion-xl-base-1.0\"\n",
    "unet = UNet2DConditionModel.from_pretrained(\n",
    "    model_id, subfolder=\"unet\", torch_dtype=torch.float16, use_safetensors=True, variant=\"fp16\"\n",
    ").to(\"cuda\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "-kfTBaLR1Mp-"
   },
   "outputs": [],
   "source": [
    "# So that we can populate it later.\n",
    "import copy\n",
    "\n",
    "sdxl_unet = copy.deepcopy(unet)"
   ]
  },
  {
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    "id": "EMTVH9cLEZYi",
    "outputId": "7a24b4b0-71f6-4c65-a242-fb3d502da6a8"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0b7ffee735044ece90f010c6771d2d69",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "85f22e78d47641d19efb6e6d62f6a014",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Fetching 17 files:   0%|          | 0/17 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "efd33121bdfc4ce195a07c9ef523a477",
       "version_major": 2,
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "54ab158c643540abb8b3a96c1ae3ecab",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "model_id": "b25e822b3f77431fb72b4780067c90d9",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "model_id": "40ff502d2d5c40378c25cf84dd3323c8",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "model_id": "a16016987b6145b69caaac6712d72835",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "model_id": "a9ac3b2188594c19885bcdb9a659ecde",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fd0ff16b68b2488d8c31ebe700dee9c9",
       "version_major": 2,
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "model_id": "8840288388ae4162884feef9c8e776f7",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "cb89ecd5a8c14051985495da1797a202",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "model_id": "4fa4f545258b4d6ca014a4c84ec4b24a",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "29b67774c6944bc7ab93e7ba0eaf867f",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c80901426c87439481078b9da2e0c772",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "aae194e7f71f4eba80ffd18f91f083a8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "766567be45eb4db6b8d7a3364566c1dc",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "22e894ba852e4072a1f63a83b3a98b16",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "diffusion_pytorch_model.fp16.safetensors:   0%|          | 0.00/167M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d1e4d2fd70e644f986104c998db7e53b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Loading pipeline components...:   0%|          | 0/7 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Load the pipeline too.\n",
    "from diffusers import DiffusionPipeline\n",
    "\n",
    "pipe = DiffusionPipeline.from_pretrained(\n",
    "    model_id, variant=\"fp16\", torch_dtype=torch.float16, unet=unet\n",
    ").to(\"cuda\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 49,
     "referenced_widgets": [
      "5e677518051c43768dbf06243701c817",
      "d727913663634e368ade4a7dc64fe74b",
      "eb73095c804a4272856fe348fa3cb1e9",
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      "c8156b0cc68e4b3693dcabc530a4ea9a",
      "d987907f09084d44b452f939aadff65e",
      "e976b994189343f5ba7d762ef92c79e2",
      "9810873713024e79ae6d338dfeae5876",
      "2e18ce21c01a4a3ca992622957e7d297",
      "2db944a049a04426bba181fddb2801b1",
      "683863a313034025ab99fab5810f39c7"
     ]
    },
    "id": "D5hL5156zPis",
    "outputId": "2510b8e7-c030-40f8-dcd2-ef76fc8529c6"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5e677518051c43768dbf06243701c817",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "toy_face_sdxl.safetensors:   0%|          | 0.00/171M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Only UNet\n",
    "pipe.load_lora_weights(\"CiroN2022/toy-face\", weight_name=\"toy_face_sdxl.safetensors\", adapter_name=\"toy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "p-7YWoOs02La"
   },
   "outputs": [],
   "source": [
    "from peft import get_peft_model, LoraConfig\n",
    "\n",
    "toy_peft_model = get_peft_model(\n",
    "    sdxl_unet,\n",
    "    pipe.unet.peft_config[\"toy\"],\n",
    "    adapter_name=\"toy\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 101,
     "referenced_widgets": [
      "c6bdeef396174d51af9eee277752bec7",
      "685797f8907c47ffab4fe7a81ca22e63",
      "414f8301f76043758f69bbfb6960072d",
      "23c0492f021a4b60b1d84b0b82d15378",
      "550b3ad10fcb422eb66f71ad95988616",
      "222da37b5af14d60814c65cdd1ea20be",
      "6a2b5bbd4afe4e0cabe39e95ccf528be",
      "7ba112dcece64386bdbac6837004891b",
      "2ffbf400ff1f4cc9b358bb10c6b9d99f",
      "5405d8a1aa1d411b895b0523fb8e4ce7",
      "04846af1def142ffad328d434ba228fe"
     ]
    },
    "id": "a_2n4Odz2a0c",
    "outputId": "4b3b801b-649f-4c69-b75c-f800ac75c17f"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c6bdeef396174d51af9eee277752bec7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "README.md:   0%|          | 0.00/5.12k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
      },
      "text/plain": [
       "CommitInfo(commit_url='https://huggingface.co/sayakpaul/toy_peft_model-new/commit/85a43b6eae5c1634827e0457a45220da8cf17d75', commit_message='Upload model', commit_description='', oid='85a43b6eae5c1634827e0457a45220da8cf17d75', pr_url=None, pr_revision=None, pr_num=None)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "original_state_dict = {f\"base_model.model.{k}\": v for k, v in pipe.unet.state_dict().items()}\n",
    "\n",
    "toy_peft_model.load_state_dict(original_state_dict, strict=True)\n",
    "toy_peft_model.push_to_hub(\"toy_peft_model-new\", token=TOKEN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "z1DWL0X12rxD"
   },
   "outputs": [],
   "source": [
    "pipe.delete_adapters(\"toy\")\n",
    "sdxl_unet.delete_adapters(\"toy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 49,
     "referenced_widgets": [
      "d18c97fe685e4be080125ae770526255",
      "e44c018bc76c48cd8738fee5966767ce",
      "57b491647f1e49cea7ce34774a963936",
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      "99717def9b6b4afe8a411a3bb83320c9"
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    },
    "id": "9PW-SfwH5L7e",
    "outputId": "90721ffa-faa5-4628-994a-7b719a4ef02c"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d18c97fe685e4be080125ae770526255",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "pixel-art-xl.safetensors:   0%|          | 0.00/171M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pipe.load_lora_weights(\"nerijs/pixel-art-xl\", weight_name=\"pixel-art-xl.safetensors\", adapter_name=\"pixel\")\n",
    "pipe.set_adapters(adapter_names=\"pixel\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 101,
     "referenced_widgets": [
      "adb2daf9d62f49f8ab1f0144b717d41e",
      "bba38c266ceb4f30bb4bc1eaf5e3aa96",
      "6926c8dd4e4e46d089bb387333691df7",
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      "bf6e103b43844f17968eadf223a42acb",
      "adf53d97af214cceaae895c8abfbd909",
      "9d74948f7bcb498c9295e41e690a0a8d",
      "d872c3900b8b4275b2224c9ec5e7d78f",
      "4fbaa1bd51bf4b1e90337a435604d2bd"
     ]
    },
    "id": "jHSb-iIf7IEb",
    "outputId": "29124d4c-b58f-4f0e-c59b-d79b44cb162f"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "adb2daf9d62f49f8ab1f0144b717d41e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "adapter_model.safetensors:   0%|          | 0.00/170M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
      },
      "text/plain": [
       "CommitInfo(commit_url='https://huggingface.co/sayakpaul/pixel_peft_model-new/commit/02cfa783ac39e0dc8de871298f8d6632ac796b0d', commit_message='Upload model', commit_description='', oid='02cfa783ac39e0dc8de871298f8d6632ac796b0d', pr_url=None, pr_revision=None, pr_num=None)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pixel_peft_model = get_peft_model(\n",
    "    sdxl_unet,\n",
    "    pipe.unet.peft_config[\"pixel\"],\n",
    "    adapter_name=\"pixel\"\n",
    ")\n",
    "\n",
    "original_state_dict = {f\"base_model.model.{k}\": v for k, v in pipe.unet.state_dict().items()}\n",
    "pixel_peft_model.load_state_dict(original_state_dict, strict=True)\n",
    "pixel_peft_model.push_to_hub(\"pixel_peft_model-new\", token=TOKEN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "yoPzMtyqG2ZO"
   },
   "outputs": [],
   "source": [
    "del pipe, sdxl_unet, toy_peft_model, pixel_peft_model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Zis3zKZpGy8w"
   },
   "source": [
    "## Weighted adapter inference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 145,
     "referenced_widgets": [
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      "a48d3ad24e9744f7898d1f2c5a696ea2",
      "f29673e57d174839a0bde70bfa165715",
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      "05700817adac4fdda6c95dd00eaeae38",
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      "0bca833a6aa74ebaa8f69feb738806bf",
      "6bdb9b0b68c24b84a748c18ed927a8d3",
      "355efc45ddaf42498d72d6134a28c87b",
      "8b6464ce614c4aa29ac66ecce29b6cbf",
      "bc07cdaad5b64fb3b0e1f8c214bba813",
      "9e2e87c131a140a2a37dfdf483d27ced",
      "47468a75637d436f849283c295a74ab6",
      "af5003cf40ae4dfaa0660f247598856e"
     ]
    },
    "id": "gEqT1vFtG0_e",
    "outputId": "282ce865-c653-4912-e497-ff825c896ae7"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d74b1c667d42472b865ee1cbefc33a60",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "toy/adapter_config.json:   0%|          | 0.00/47.3k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "153a93d930df4ee396e03f1aaa6f04f1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "adapter_model.safetensors:   0%|          | 0.00/170M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5c3d142907404cef8fd624839a166530",
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       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2c352a90375443da835eef55b3e63303",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from peft import PeftModel\n",
    "\n",
    "base_unet = UNet2DConditionModel.from_pretrained(\n",
    "    model_id, subfolder=\"unet\", torch_dtype=torch.float16, use_safetensors=True, variant=\"fp16\"\n",
    ").to(\"cuda\")\n",
    "\n",
    "toy_id = \"sayakpaul/toy_peft_model-new\"\n",
    "model = PeftModel.from_pretrained(base_unet, toy_id, use_safetensors=True, subfolder=\"toy\", adapter_name=\"toy\")\n",
    "model.load_adapter(\"sayakpaul/pixel_peft_model-new\", use_safetensors=True, subfolder=\"pixel\", adapter_name=\"pixel\")\n",
    "\n",
    "# https://huggingface.co/docs/peft/main/en/package_reference/lora#peft.LoraModel.add_weighted_adapter\n",
    "model.add_weighted_adapter(\n",
    "    adapters=[\"toy\", \"pixel\"],\n",
    "    weights=[0.7, 0.3],\n",
    "    combination_type=\"linear\",\n",
    "    adapter_name=\"toy-pixel\"\n",
    ")\n",
    "model.set_adapters(\"toy-pixel\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 186
    },
    "id": "QStyurhKsP_g",
    "outputId": "5e3a2627-27a2-4771-e62f-1b81ded2b87e"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-width:800px; border: 1px solid var(--colab-border-color);\"><style>\n",
       "      pre.function-repr-contents {\n",
       "        overflow-x: auto;\n",
       "        padding: 8px 12px;\n",
       "        max-height: 500px;\n",
       "      }\n",
       "\n",
       "      pre.function-repr-contents.function-repr-contents-collapsed {\n",
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       "    </style>\n",
       "    <pre style=\"white-space: initial; background:\n",
       "         var(--colab-secondary-surface-color); padding: 8px 12px;\n",
       "         border-bottom: 1px solid var(--colab-border-color);\"><b>diffusers.models.unets.unet_2d_condition.UNet2DConditionModel</b><br/>def _wrapped_call_impl(*args, **kwargs)</pre><pre class=\"function-repr-contents function-repr-contents-collapsed\" style=\"\"><a class=\"filepath\" style=\"display:none\" href=\"#\">/usr/local/lib/python3.10/dist-packages/diffusers/models/unets/unet_2d_condition.py</a>A conditional 2D UNet model that takes a noisy sample, conditional state, and a timestep and returns a sample\n",
       "shaped output.\n",
       "\n",
       "This model inherits from [`ModelMixin`]. Check the superclass documentation for it&#x27;s generic methods implemented\n",
       "for all models (such as downloading or saving).\n",
       "\n",
       "Parameters:\n",
       "    sample_size (`int` or `Tuple[int, int]`, *optional*, defaults to `None`):\n",
       "        Height and width of input/output sample.\n",
       "    in_channels (`int`, *optional*, defaults to 4): Number of channels in the input sample.\n",
       "    out_channels (`int`, *optional*, defaults to 4): Number of channels in the output.\n",
       "    center_input_sample (`bool`, *optional*, defaults to `False`): Whether to center the input sample.\n",
       "    flip_sin_to_cos (`bool`, *optional*, defaults to `False`):\n",
       "        Whether to flip the sin to cos in the time embedding.\n",
       "    freq_shift (`int`, *optional*, defaults to 0): The frequency shift to apply to the time embedding.\n",
       "    down_block_types (`Tuple[str]`, *optional*, defaults to `(&quot;CrossAttnDownBlock2D&quot;, &quot;CrossAttnDownBlock2D&quot;, &quot;CrossAttnDownBlock2D&quot;, &quot;DownBlock2D&quot;)`):\n",
       "        The tuple of downsample blocks to use.\n",
       "    mid_block_type (`str`, *optional*, defaults to `&quot;UNetMidBlock2DCrossAttn&quot;`):\n",
       "        Block type for middle of UNet, it can be one of `UNetMidBlock2DCrossAttn`, `UNetMidBlock2D`, or\n",
       "        `UNetMidBlock2DSimpleCrossAttn`. If `None`, the mid block layer is skipped.\n",
       "    up_block_types (`Tuple[str]`, *optional*, defaults to `(&quot;UpBlock2D&quot;, &quot;CrossAttnUpBlock2D&quot;, &quot;CrossAttnUpBlock2D&quot;, &quot;CrossAttnUpBlock2D&quot;)`):\n",
       "        The tuple of upsample blocks to use.\n",
       "    only_cross_attention(`bool` or `Tuple[bool]`, *optional*, default to `False`):\n",
       "        Whether to include self-attention in the basic transformer blocks, see\n",
       "        [`~models.attention.BasicTransformerBlock`].\n",
       "    block_out_channels (`Tuple[int]`, *optional*, defaults to `(320, 640, 1280, 1280)`):\n",
       "        The tuple of output channels for each block.\n",
       "    layers_per_block (`int`, *optional*, defaults to 2): The number of layers per block.\n",
       "    downsample_padding (`int`, *optional*, defaults to 1): The padding to use for the downsampling convolution.\n",
       "    mid_block_scale_factor (`float`, *optional*, defaults to 1.0): The scale factor to use for the mid block.\n",
       "    dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.\n",
       "    act_fn (`str`, *optional*, defaults to `&quot;silu&quot;`): The activation function to use.\n",
       "    norm_num_groups (`int`, *optional*, defaults to 32): The number of groups to use for the normalization.\n",
       "        If `None`, normalization and activation layers is skipped in post-processing.\n",
       "    norm_eps (`float`, *optional*, defaults to 1e-5): The epsilon to use for the normalization.\n",
       "    cross_attention_dim (`int` or `Tuple[int]`, *optional*, defaults to 1280):\n",
       "        The dimension of the cross attention features.\n",
       "    transformer_layers_per_block (`int`, `Tuple[int]`, or `Tuple[Tuple]` , *optional*, defaults to 1):\n",
       "        The number of transformer blocks of type [`~models.attention.BasicTransformerBlock`]. Only relevant for\n",
       "        [`~models.unet_2d_blocks.CrossAttnDownBlock2D`], [`~models.unet_2d_blocks.CrossAttnUpBlock2D`],\n",
       "        [`~models.unet_2d_blocks.UNetMidBlock2DCrossAttn`].\n",
       "   reverse_transformer_layers_per_block : (`Tuple[Tuple]`, *optional*, defaults to None):\n",
       "        The number of transformer blocks of type [`~models.attention.BasicTransformerBlock`], in the upsampling\n",
       "        blocks of the U-Net. Only relevant if `transformer_layers_per_block` is of type `Tuple[Tuple]` and for\n",
       "        [`~models.unet_2d_blocks.CrossAttnDownBlock2D`], [`~models.unet_2d_blocks.CrossAttnUpBlock2D`],\n",
       "        [`~models.unet_2d_blocks.UNetMidBlock2DCrossAttn`].\n",
       "    encoder_hid_dim (`int`, *optional*, defaults to None):\n",
       "        If `encoder_hid_dim_type` is defined, `encoder_hidden_states` will be projected from `encoder_hid_dim`\n",
       "        dimension to `cross_attention_dim`.\n",
       "    encoder_hid_dim_type (`str`, *optional*, defaults to `None`):\n",
       "        If given, the `encoder_hidden_states` and potentially other embeddings are down-projected to text\n",
       "        embeddings of dimension `cross_attention` according to `encoder_hid_dim_type`.\n",
       "    attention_head_dim (`int`, *optional*, defaults to 8): The dimension of the attention heads.\n",
       "    num_attention_heads (`int`, *optional*):\n",
       "        The number of attention heads. If not defined, defaults to `attention_head_dim`\n",
       "    resnet_time_scale_shift (`str`, *optional*, defaults to `&quot;default&quot;`): Time scale shift config\n",
       "        for ResNet blocks (see [`~models.resnet.ResnetBlock2D`]). Choose from `default` or `scale_shift`.\n",
       "    class_embed_type (`str`, *optional*, defaults to `None`):\n",
       "        The type of class embedding to use which is ultimately summed with the time embeddings. Choose from `None`,\n",
       "        `&quot;timestep&quot;`, `&quot;identity&quot;`, `&quot;projection&quot;`, or `&quot;simple_projection&quot;`.\n",
       "    addition_embed_type (`str`, *optional*, defaults to `None`):\n",
       "        Configures an optional embedding which will be summed with the time embeddings. Choose from `None` or\n",
       "        &quot;text&quot;. &quot;text&quot; will use the `TextTimeEmbedding` layer.\n",
       "    addition_time_embed_dim: (`int`, *optional*, defaults to `None`):\n",
       "        Dimension for the timestep embeddings.\n",
       "    num_class_embeds (`int`, *optional*, defaults to `None`):\n",
       "        Input dimension of the learnable embedding matrix to be projected to `time_embed_dim`, when performing\n",
       "        class conditioning with `class_embed_type` equal to `None`.\n",
       "    time_embedding_type (`str`, *optional*, defaults to `positional`):\n",
       "        The type of position embedding to use for timesteps. Choose from `positional` or `fourier`.\n",
       "    time_embedding_dim (`int`, *optional*, defaults to `None`):\n",
       "        An optional override for the dimension of the projected time embedding.\n",
       "    time_embedding_act_fn (`str`, *optional*, defaults to `None`):\n",
       "        Optional activation function to use only once on the time embeddings before they are passed to the rest of\n",
       "        the UNet. Choose from `silu`, `mish`, `gelu`, and `swish`.\n",
       "    timestep_post_act (`str`, *optional*, defaults to `None`):\n",
       "        The second activation function to use in timestep embedding. Choose from `silu`, `mish` and `gelu`.\n",
       "    time_cond_proj_dim (`int`, *optional*, defaults to `None`):\n",
       "        The dimension of `cond_proj` layer in the timestep embedding.\n",
       "    conv_in_kernel (`int`, *optional*, default to `3`): The kernel size of `conv_in` layer. conv_out_kernel (`int`,\n",
       "    *optional*, default to `3`): The kernel size of `conv_out` layer. projection_class_embeddings_input_dim (`int`,\n",
       "    *optional*): The dimension of the `class_labels` input when\n",
       "        `class_embed_type=&quot;projection&quot;`. Required when `class_embed_type=&quot;projection&quot;`.\n",
       "    class_embeddings_concat (`bool`, *optional*, defaults to `False`): Whether to concatenate the time\n",
       "        embeddings with the class embeddings.\n",
       "    mid_block_only_cross_attention (`bool`, *optional*, defaults to `None`):\n",
       "        Whether to use cross attention with the mid block when using the `UNetMidBlock2DSimpleCrossAttn`. If\n",
       "        `only_cross_attention` is given as a single boolean and `mid_block_only_cross_attention` is `None`, the\n",
       "        `only_cross_attention` value is used as the value for `mid_block_only_cross_attention`. Default to `False`\n",
       "        otherwise.</pre>\n",
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      ],
      "text/plain": [
       "diffusers.models.unets.unet_2d_condition.UNet2DConditionModel"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(model.base_model.model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000,
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    },
    "id": "iHwVV8f6s1EC",
    "outputId": "47cb80da-266e-40c2-cfc1-3f3e5421b50b"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "28a5059a2cc445d783e94ad5d83a0748",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Loading pipeline components...:   0%|          | 0/7 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "64029a1e70e040b49e38d38bd36823fd",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/30 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "[encoded data, 302908 characters]\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=1024x1024>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = model.to(dtype=torch.float16, device=\"cuda\")\n",
    "\n",
    "pipe = DiffusionPipeline.from_pretrained(\n",
    "    model_id, unet=model, variant=\"fp16\", torch_dtype=torch.float16,\n",
    ").to(\"cuda\")\n",
    "\n",
    "prompt = \"toy_face of a hacker with a hoodie, pixel art\"\n",
    "image = pipe(prompt, num_inference_steps=30, generator=torch.manual_seed(0)).images[0]\n",
    "image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "adLnc7sMRZlq"
   },
   "outputs": [],
   "source": [
    "del pipe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "nIwIQK5zRX25"
   },
   "outputs": [],
   "source": [
    "base_unet = UNet2DConditionModel.from_pretrained(\n",
    "    model_id, subfolder=\"unet\", torch_dtype=torch.float16, use_safetensors=True, variant=\"fp16\"\n",
    ").to(\"cuda\")\n",
    "\n",
    "toy_id = \"sayakpaul/toy_peft_model-new\"\n",
    "model = PeftModel.from_pretrained(base_unet, toy_id, use_safetensors=True, subfolder=\"toy\", adapter_name=\"toy\")\n",
    "model.load_adapter(\"sayakpaul/pixel_peft_model-new\", use_safetensors=True, subfolder=\"pixel\", adapter_name=\"pixel\")\n",
    "\n",
    "# https://huggingface.co/docs/peft/main/en/package_reference/lora#peft.LoraModel.add_weighted_adapter\n",
    "model.add_weighted_adapter(\n",
    "    adapters=[\"toy\", \"pixel\"],\n",
    "    weights=[0.5, 0.5],\n",
    "    combination_type=\"cat\",\n",
    "    adapter_name=\"toy-pixel\"\n",
    ")\n",
    "model.set_adapters(\"toy-pixel\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000,
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    },
    "id": "29iGITdnRhFG",
    "outputId": "dc6a1e54-3f76-457e-da1b-e8677be5c31f"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "65bf3df199b44763aa223fee96889e17",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Loading pipeline components...:   0%|          | 0/7 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "65b15618abfd4e6f9fafd64813e86ace",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/30 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "[encoded data, 334280 characters]\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=1024x1024>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = model.to(dtype=torch.float16, device=\"cuda\")\n",
    "\n",
    "pipe = DiffusionPipeline.from_pretrained(\n",
    "   model_id, unet=model, variant=\"fp16\", torch_dtype=torch.float16,\n",
    ").to(\"cuda\")\n",
    "\n",
    "prompt = \"toy_face of a hacker with a hoodie, pixel art\"\n",
    "image = pipe(prompt, num_inference_steps=30, generator=torch.manual_seed(0)).images[0]\n",
    "image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
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       "Loading pipeline components...:   0%|          | 0/7 [00:00<?, ?it/s]"
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    "\n",
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    ").to(\"cuda\")\n",
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