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takuma104/scratchpad.ipynb

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scratchpad.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/takuma104/6afb6f694b2ef05e79f2d72df8e8b273/scratchpad.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "lIYdn1woOS1n",
"outputId": "35b76d37-5ab5-4238-aedd-d25671a9c181"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m934.9/934.9 kB\u001b[0m \u001b[31m9.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.1/7.1 MB\u001b[0m \u001b[31m50.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m219.1/219.1 kB\u001b[0m \u001b[31m11.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m224.5/224.5 kB\u001b[0m \u001b[31m13.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.8/7.8 MB\u001b[0m \u001b[31m25.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25h"
]
}
],
"source": [
"!pip install diffusers transformers accelerate -q"
]
},
{
"cell_type": "code",
"source": [
"from diffusers.utils import TEXT_ENCODER_TARGET_MODULES\n",
"from transformers import CLIPTextModel"
],
"metadata": {
"id": "oM-NVFVUBoUl"
},
"execution_count": 2,
"outputs": []
},
{
"cell_type": "code",
"source": [
"text_encoder = CLIPTextModel.from_pretrained(\n",
" \"runwayml/stable-diffusion-v1-5\", subfolder=\"text_encoder\"\n",
")"
],
"metadata": {
"id": "z09CDISlBs_M",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 81,
"referenced_widgets": [
"102b75c9c1534896833114cfb8158774",
"28afa7cadfcd4b0ba362ea71bc499ba2",
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"0266f70e467b42fb8bb47a04c910f702",
"8a12339a55e84209a60b55bc4e6802e1",
"e419fcf6b0c043bfadcb2ba633c5b5db",
"4db4a888820049f4812a0b17d84b3d11",
"b689cd05757c4c6591110ade168b9aa4",
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"8e7b68d53e1b470a94e572e37ca570f4",
"5f99181b1c2d40a99cbfe4cc92698e93",
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]
},
"outputId": "f1f2fd41-6830-4e4f-b680-82e55c89fa84"
},
"execution_count": 3,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"Downloading (…)_encoder/config.json: 0%| | 0.00/617 [00:00<?, ?B/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "102b75c9c1534896833114cfb8158774"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Downloading pytorch_model.bin: 0%| | 0.00/492M [00:00<?, ?B/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "a9264917ab7540df945a25449fab46bb"
}
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"TEXT_ENCODER_TARGET_MODULES\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "UU7JcZ6zMhbg",
"outputId": "3cd97616-6ef5-4701-fd87-e1994cec7d03"
},
"execution_count": 14,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"['q_proj', 'v_proj', 'k_proj', 'out_proj']"
]
},
"metadata": {},
"execution_count": 14
}
]
},
{
"cell_type": "code",
"source": [
"from diffusers.models.attention_processor import LoRAAttnProcessor\n",
"from diffusers.loaders import AttnProcsLayers, LoraLoaderMixin\n",
"\n",
"# From https://github.com/huggingface/diffusers/blob/29b1325a5ae28fa8d7f459b372582287ffc571e5/examples/dreambooth/train_dreambooth_lora.py#L843-L849\n",
"text_lora_attn_procs = {}\n",
"for name, module in text_encoder.named_modules():\n",
" # if any(x in name for x in TEXT_ENCODER_TARGET_MODULES):\n",
" if name.endswith('self_attn'):\n",
" print(name)\n",
" text_lora_attn_procs[name] = LoRAAttnProcessor(\n",
" hidden_size=module.out_proj.out_features, cross_attention_dim=None\n",
" )\n",
"text_encoder_lora_layers = AttnProcsLayers(text_lora_attn_procs)"
],
"metadata": {
"id": "WIJQVDPdAUIL",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "b7ff3e27-12a9-44af-dcef-0e849619c785"
},
"execution_count": 22,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"text_model.encoder.layers.0.self_attn\n",
"text_model.encoder.layers.1.self_attn\n",
"text_model.encoder.layers.2.self_attn\n",
"text_model.encoder.layers.3.self_attn\n",
"text_model.encoder.layers.4.self_attn\n",
"text_model.encoder.layers.5.self_attn\n",
"text_model.encoder.layers.6.self_attn\n",
"text_model.encoder.layers.7.self_attn\n",
"text_model.encoder.layers.8.self_attn\n",
"text_model.encoder.layers.9.self_attn\n",
"text_model.encoder.layers.10.self_attn\n",
"text_model.encoder.layers.11.self_attn\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"len(text_encoder_lora_layers.state_dict())"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "pMETaPwPCR8Y",
"outputId": "8d9af223-5e87-401d-decd-0769f1603b87"
},
"execution_count": 23,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"96"
]
},
"metadata": {},
"execution_count": 23
}
]
},
{
"cell_type": "code",
"source": [
"# From https://github.com/huggingface/diffusers/blob/9b14ce397e53fc5f5b909b07b6e992a2afe8e3af/src/diffusers/loaders.py#LL1095C13-L1098C14\n",
"text_encoder_name = \"text_encoder\"\n",
"\n",
"text_encoder_lora_state_dict = {\n",
" f\"{text_encoder_name}.{module_name}\": param\n",
" for module_name, param in text_encoder_lora_layers.state_dict().items()\n",
"}"
],
"metadata": {
"id": "vwDpCVpKCnqJ"
},
"execution_count": 24,
"outputs": []
},
{
"cell_type": "code",
"source": [
"loaded_text_encoder_lora_state_dict = {\n",
" k: v for k, v in text_encoder_lora_state_dict.items() if k.startswith(\"text_encoder\")\n",
"}\n",
"assert len(loaded_text_encoder_lora_state_dict) == len(text_encoder_lora_layers.state_dict())"
],
"metadata": {
"id": "eMAQxAzDDm61"
},
"execution_count": 25,
"outputs": []
},
{
"cell_type": "code",
"source": [
"text_lora_attn_procs.keys()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "pjQ1BrdWxKBS",
"outputId": "9ae8f43c-da1a-483b-faa8-de5b46069ee5"
},
"execution_count": 26,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"dict_keys(['text_model.encoder.layers.0.self_attn', 'text_model.encoder.layers.1.self_attn', 'text_model.encoder.layers.2.self_attn', 'text_model.encoder.layers.3.self_attn', 'text_model.encoder.layers.4.self_attn', 'text_model.encoder.layers.5.self_attn', 'text_model.encoder.layers.6.self_attn', 'text_model.encoder.layers.7.self_attn', 'text_model.encoder.layers.8.self_attn', 'text_model.encoder.layers.9.self_attn', 'text_model.encoder.layers.10.self_attn', 'text_model.encoder.layers.11.self_attn'])"
]
},
"metadata": {},
"execution_count": 26
}
]
},
{
"cell_type": "code",
"source": [
"text_encoder_lora_layers.state_dict().keys()"
],
"metadata": {
"id": "exguUXx1Fp6x",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "2e2a0ed9-f288-4815-acae-463a33a88512"
},
"execution_count": 27,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"dict_keys(['text_model.encoder.layers.0.self_attn.to_q_lora.down.weight', 'text_model.encoder.layers.0.self_attn.to_q_lora.up.weight', 'text_model.encoder.layers.0.self_attn.to_k_lora.down.weight', 'text_model.encoder.layers.0.self_attn.to_k_lora.up.weight', 'text_model.encoder.layers.0.self_attn.to_v_lora.down.weight', 'text_model.encoder.layers.0.self_attn.to_v_lora.up.weight', 'text_model.encoder.layers.0.self_attn.to_out_lora.down.weight', 'text_model.encoder.layers.0.self_attn.to_out_lora.up.weight', 'text_model.encoder.layers.1.self_attn.to_q_lora.down.weight', 'text_model.encoder.layers.1.self_attn.to_q_lora.up.weight', 'text_model.encoder.layers.1.self_attn.to_k_lora.down.weight', 'text_model.encoder.layers.1.self_attn.to_k_lora.up.weight', 'text_model.encoder.layers.1.self_attn.to_v_lora.down.weight', 'text_model.encoder.layers.1.self_attn.to_v_lora.up.weight', 'text_model.encoder.layers.1.self_attn.to_out_lora.down.weight', 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]
},
"metadata": {},
"execution_count": 27
}
]
},
{
"cell_type": "code",
"source": [],
"metadata": {
"id": "HgTLi0hnwmDD"
},
"execution_count": 9,
"outputs": []
}
],
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