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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "acab479f",
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"\n",
"import torch\n",
"from accelerate.logging import get_logger\n",
"from diffusers import StableDiffusionPipeline\n",
"from diffusers.utils import check_min_version\n",
"\n",
"from peft import PeftModel\n",
"\n",
"# Will error if the minimal version of diffusers is not installed. Remove at your own risks.\n",
"check_min_version(\"0.10.0.dev0\")\n",
"\n",
"logger = get_logger(__name__)\n",
"\n",
"MODEL_NAME = \"stabilityai/stable-diffusion-2-1\"\n",
"# MODEL_NAME=\"runwayml/stable-diffusion-v1-5\"\n",
"\n",
"PEFT_TYPE=\"boft\"\n",
"BLOCK_NUM=8\n",
"BLOCK_SIZE=0\n",
"N_BUTTERFLY_FACTOR=1\n",
"SELECTED_SUBJECT=\"backpack\"\n",
"EPOCH_IDX = 200\n",
"\n",
"PROJECT_NAME=f\"dreambooth_{PEFT_TYPE}\"\n",
"RUN_NAME=f\"{SELECTED_SUBJECT}_{PEFT_TYPE}_{BLOCK_NUM}{BLOCK_SIZE}{N_BUTTERFLY_FACTOR}\"\n",
"OUTPUT_DIR=f\"./data/output/{PEFT_TYPE}\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "06cfd506",
"metadata": {},
"outputs": [],
"source": [
"def get_boft_sd_pipeline(\n",
" ckpt_dir, base_model_name_or_path=None, epoch=int, dtype=torch.float32, device=\"auto\", adapter_name=\"default\"\n",
"):\n",
" if device == \"auto\":\n",
" device = torch.accelerator.current_accelerator().type if hasattr(torch, \"accelerator\") else \"cuda\"\n",
"\n",
" if base_model_name_or_path is None:\n",
" raise ValueError(\"Please specify the base model name or path\")\n",
"\n",
" pipe = StableDiffusionPipeline.from_pretrained(\n",
" base_model_name_or_path, torch_dtype=dtype, requires_safety_checker=False\n",
" ).to(device)\n",
" \n",
" load_adapter(pipe, ckpt_dir, epoch, adapter_name)\n",
"\n",
" if dtype in (torch.float16, torch.bfloat16):\n",
" pipe.unet.half()\n",
" pipe.text_encoder.half()\n",
"\n",
" pipe.to(device)\n",
" return pipe\n",
"\n",
"\n",
"def load_adapter(pipe, ckpt_dir, epoch, adapter_name=\"default\"):\n",
" \n",
" unet_sub_dir = os.path.join(ckpt_dir, f\"unet/{epoch}\", adapter_name)\n",
" text_encoder_sub_dir = os.path.join(ckpt_dir, f\"text_encoder/{epoch}\", adapter_name)\n",
" \n",
" if isinstance(pipe.unet, PeftModel):\n",
" pipe.unet.load_adapter(unet_sub_dir, adapter_name=adapter_name)\n",
" else:\n",
" pipe.unet = PeftModel.from_pretrained(pipe.unet, unet_sub_dir, adapter_name=adapter_name)\n",
" \n",
" if os.path.exists(text_encoder_sub_dir):\n",
" if isinstance(pipe.text_encoder, PeftModel):\n",
" pipe.text_encoder.load_adapter(text_encoder_sub_dir, adapter_name=adapter_name)\n",
" else:\n",
" pipe.text_encoder = PeftModel.from_pretrained(pipe.text_encoder, text_encoder_sub_dir, adapter_name=adapter_name)\n",
" \n",
"\n",
"def set_adapter(pipe, adapter_name):\n",
" pipe.unet.set_adapter(adapter_name)\n",
" if isinstance(pipe.text_encoder, PeftModel):\n",
" pipe.text_encoder.set_adapter(adapter_name)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "98a0d8ac",
"metadata": {},
"outputs": [],
"source": [
"prompt = \"a photo of sks backpack on a wooden floor\"\n",
"negative_prompt = \"low quality, blurry, unfinished\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d4e888d2",
"metadata": {},
"outputs": [],
"source": [
"%%time\n",
"pipe = get_boft_sd_pipeline(OUTPUT_DIR, MODEL_NAME, EPOCH_IDX, adapter_name=RUN_NAME)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f1c1a1c0",
"metadata": {},
"outputs": [],
"source": [
"%%time\n",
"image = pipe(prompt, num_inference_steps=50, guidance_scale=7, negative_prompt=negative_prompt).images[0]\n",
"image"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3a1aafdf-8cf7-4e47-9471-26478034245e",
"metadata": {},
"outputs": [],
"source": [
"# load and reset another adapter\n",
"# WARNING: requires training DreamBooth with `boft_bias=None`\n",
"\n",
"SELECTED_SUBJECT=\"dog\"\n",
"EPOCH_IDX = 200\n",
"RUN_NAME=f\"{SELECTED_SUBJECT}_{PEFT_TYPE}_{BLOCK_NUM}{BLOCK_SIZE}{N_BUTTERFLY_FACTOR}\"\n",
"\n",
"load_adapter(pipe, OUTPUT_DIR, epoch=EPOCH_IDX, adapter_name=RUN_NAME)\n",
"set_adapter(pipe, adapter_name=RUN_NAME)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c7091ad0-2005-4528-afc1-4f9d70a9a535",
"metadata": {},
"outputs": [],
"source": [
"%%time\n",
"prompt = \"a photo of sks dog running on the beach\"\n",
"negative_prompt = \"low quality, blurry, unfinished\"\n",
"image = pipe(prompt, num_inference_steps=50, guidance_scale=7, negative_prompt=negative_prompt).images[0]\n",
"image"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python [conda env:peft] *",
"language": "python",
"name": "conda-env-peft-py"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.13"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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