{
"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,
"referenced_widgets": [
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"d99a364420454ba5bfd510d1226b94af",
"d33c90e8ea0945d397659f6a90cf51b6",
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"2c75632d8fdc4458814055172d1d72c3",
"2f016774e7854ef589442734d0bb2f08",
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"989c54778eb7469fb91e4337d6f49b0b",
"439cf6c6f9e845cea4008e3219454ff4",
"460c3c96d1724713b78bddcfc1f3eb97"
]
},
"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\n",
" diffusers.models.unets.unet_2d_condition.UNet2DConditionModel
def _wrapped_call_impl(*args, **kwargs)
/usr/local/lib/python3.10/dist-packages/diffusers/models/unets/unet_2d_condition.pyA 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'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 `("CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "DownBlock2D")`):\n",
" The tuple of downsample blocks to use.\n",
" mid_block_type (`str`, *optional*, defaults to `"UNetMidBlock2DCrossAttn"`):\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 `("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D")`):\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 `"silu"`): 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 `"default"`): 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",
" `"timestep"`, `"identity"`, `"projection"`, or `"simple_projection"`.\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",
" "text". "text" 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="projection"`. Required when `class_embed_type="projection"`.\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.\n",
" \n",
" "
],
"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,
"referenced_widgets": [
"28a5059a2cc445d783e94ad5d83a0748",
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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"
]
},
"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,
"referenced_widgets": [
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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"
]
},
"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/",
"height": 1000,
"referenced_widgets": [
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