Upload lora-scripts/sd-scripts/networks/extract_lora_from_models.py with huggingface_hub

lora-scripts/sd-scripts/networks/extract_lora_from_models.py

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+# extract approximating LoRA by svd from two SD models
+# The code is based on https://github.com/cloneofsimo/lora/blob/develop/lora_diffusion/cli_svd.py
+# Thanks to cloneofsimo!
+
+import argparse
+import json
+import os
+import time
+import torch
+from safetensors.torch import load_file, save_file
+from tqdm import tqdm
+from library import sai_model_spec, model_util, sdxl_model_util
+import lora
+from library.utils import setup_logging
+setup_logging()
+import logging
+logger = logging.getLogger(__name__)
+
+# CLAMP_QUANTILE = 0.99
+# MIN_DIFF = 1e-1
+
+
+def save_to_file(file_name, model, state_dict, dtype):
+    if dtype is not None:
+        for key in list(state_dict.keys()):
+            if type(state_dict[key]) == torch.Tensor:
+                state_dict[key] = state_dict[key].to(dtype)
+
+    if os.path.splitext(file_name)[1] == ".safetensors":
+        save_file(model, file_name)
+    else:
+        torch.save(model, file_name)
+
+
+def svd(
+    model_org=None,
+    model_tuned=None,
+    save_to=None,
+    dim=4,
+    v2=None,
+    sdxl=None,
+    conv_dim=None,
+    v_parameterization=None,
+    device=None,
+    save_precision=None,
+    clamp_quantile=0.99,
+    min_diff=0.01,
+    no_metadata=False,
+    load_precision=None,
+    load_original_model_to=None,
+    load_tuned_model_to=None,
+):
+    def str_to_dtype(p):
+        if p == "float":
+            return torch.float
+        if p == "fp16":
+            return torch.float16
+        if p == "bf16":
+            return torch.bfloat16
+        return None
+
+    assert v2 != sdxl or (not v2 and not sdxl), "v2 and sdxl cannot be specified at the same time / v2とsdxlは同時に指定できません"
+    if v_parameterization is None:
+        v_parameterization = v2
+
+    load_dtype = str_to_dtype(load_precision) if load_precision else None
+    save_dtype = str_to_dtype(save_precision)
+    work_device = "cpu"
+
+    # load models
+    if not sdxl:
+        logger.info(f"loading original SD model : {model_org}")
+        text_encoder_o, _, unet_o = model_util.load_models_from_stable_diffusion_checkpoint(v2, model_org)
+        text_encoders_o = [text_encoder_o]
+        if load_dtype is not None:
+            text_encoder_o = text_encoder_o.to(load_dtype)
+            unet_o = unet_o.to(load_dtype)
+
+        logger.info(f"loading tuned SD model : {model_tuned}")
+        text_encoder_t, _, unet_t = model_util.load_models_from_stable_diffusion_checkpoint(v2, model_tuned)
+        text_encoders_t = [text_encoder_t]
+        if load_dtype is not None:
+            text_encoder_t = text_encoder_t.to(load_dtype)
+            unet_t = unet_t.to(load_dtype)
+
+        model_version = model_util.get_model_version_str_for_sd1_sd2(v2, v_parameterization)
+    else:
+        device_org = load_original_model_to if load_original_model_to else "cpu"
+        device_tuned = load_tuned_model_to if load_tuned_model_to else "cpu"
+
+        logger.info(f"loading original SDXL model : {model_org}")
+        text_encoder_o1, text_encoder_o2, _, unet_o, _, _ = sdxl_model_util.load_models_from_sdxl_checkpoint(
+            sdxl_model_util.MODEL_VERSION_SDXL_BASE_V1_0, model_org, device_org
+        )
+        text_encoders_o = [text_encoder_o1, text_encoder_o2]
+        if load_dtype is not None:
+            text_encoder_o1 = text_encoder_o1.to(load_dtype)
+            text_encoder_o2 = text_encoder_o2.to(load_dtype)
+            unet_o = unet_o.to(load_dtype)
+
+        logger.info(f"loading original SDXL model : {model_tuned}")
+        text_encoder_t1, text_encoder_t2, _, unet_t, _, _ = sdxl_model_util.load_models_from_sdxl_checkpoint(
+            sdxl_model_util.MODEL_VERSION_SDXL_BASE_V1_0, model_tuned, device_tuned
+        )
+        text_encoders_t = [text_encoder_t1, text_encoder_t2]
+        if load_dtype is not None:
+            text_encoder_t1 = text_encoder_t1.to(load_dtype)
+            text_encoder_t2 = text_encoder_t2.to(load_dtype)
+            unet_t = unet_t.to(load_dtype)
+
+        model_version = sdxl_model_util.MODEL_VERSION_SDXL_BASE_V1_0
+
+    # create LoRA network to extract weights: Use dim (rank) as alpha
+    if conv_dim is None:
+        kwargs = {}
+    else:
+        kwargs = {"conv_dim": conv_dim, "conv_alpha": conv_dim}
+
+    lora_network_o = lora.create_network(1.0, dim, dim, None, text_encoders_o, unet_o, **kwargs)
+    lora_network_t = lora.create_network(1.0, dim, dim, None, text_encoders_t, unet_t, **kwargs)
+    assert len(lora_network_o.text_encoder_loras) == len(
+        lora_network_t.text_encoder_loras
+    ), f"model version is different (SD1.x vs SD2.x) / それぞれのモデルのバージョンが違います（SD1.xベースとSD2.xベース） "
+
+    # get diffs
+    diffs = {}
+    text_encoder_different = False
+    for i, (lora_o, lora_t) in enumerate(zip(lora_network_o.text_encoder_loras, lora_network_t.text_encoder_loras)):
+        lora_name = lora_o.lora_name
+        module_o = lora_o.org_module
+        module_t = lora_t.org_module
+        diff = module_t.weight.to(work_device) - module_o.weight.to(work_device)
+
+        # clear weight to save memory
+        module_o.weight = None
+        module_t.weight = None
+
+        # Text Encoder might be same
+        if not text_encoder_different and torch.max(torch.abs(diff)) > min_diff:
+            text_encoder_different = True
+            logger.info(f"Text encoder is different. {torch.max(torch.abs(diff))} > {min_diff}")
+
+        diffs[lora_name] = diff
+
+    # clear target Text Encoder to save memory
+    for text_encoder in text_encoders_t:
+        del text_encoder
+
+    if not text_encoder_different:
+        logger.warning("Text encoder is same. Extract U-Net only.")
+        lora_network_o.text_encoder_loras = []
+        diffs = {}  # clear diffs
+
+    for i, (lora_o, lora_t) in enumerate(zip(lora_network_o.unet_loras, lora_network_t.unet_loras)):
+        lora_name = lora_o.lora_name
+        module_o = lora_o.org_module
+        module_t = lora_t.org_module
+        diff = module_t.weight.to(work_device) - module_o.weight.to(work_device)
+
+        # clear weight to save memory
+        module_o.weight = None
+        module_t.weight = None
+
+        diffs[lora_name] = diff
+
+    # clear LoRA network, target U-Net to save memory
+    del lora_network_o
+    del lora_network_t
+    del unet_t
+
+    # make LoRA with svd
+    logger.info("calculating by svd")
+    lora_weights = {}
+    with torch.no_grad():
+        for lora_name, mat in tqdm(list(diffs.items())):
+            if args.device:
+                mat = mat.to(args.device)
+            mat = mat.to(torch.float)  # calc by float
+
+            # if conv_dim is None, diffs do not include LoRAs for conv2d-3x3
+            conv2d = len(mat.size()) == 4
+            kernel_size = None if not conv2d else mat.size()[2:4]
+            conv2d_3x3 = conv2d and kernel_size != (1, 1)
+
+            rank = dim if not conv2d_3x3 or conv_dim is None else conv_dim
+            out_dim, in_dim = mat.size()[0:2]
+
+            if device:
+                mat = mat.to(device)
+
+            # logger.info(lora_name, mat.size(), mat.device, rank, in_dim, out_dim)
+            rank = min(rank, in_dim, out_dim)  # LoRA rank cannot exceed the original dim
+
+            if conv2d:
+                if conv2d_3x3:
+                    mat = mat.flatten(start_dim=1)
+                else:
+                    mat = mat.squeeze()
+
+            U, S, Vh = torch.linalg.svd(mat)
+
+            U = U[:, :rank]
+            S = S[:rank]
+            U = U @ torch.diag(S)
+
+            Vh = Vh[:rank, :]
+
+            dist = torch.cat([U.flatten(), Vh.flatten()])
+            hi_val = torch.quantile(dist, clamp_quantile)
+            low_val = -hi_val
+
+            U = U.clamp(low_val, hi_val)
+            Vh = Vh.clamp(low_val, hi_val)
+
+            if conv2d:
+                U = U.reshape(out_dim, rank, 1, 1)
+                Vh = Vh.reshape(rank, in_dim, kernel_size[0], kernel_size[1])
+
+            U = U.to(work_device, dtype=save_dtype).contiguous()
+            Vh = Vh.to(work_device, dtype=save_dtype).contiguous()
+
+            lora_weights[lora_name] = (U, Vh)
+
+    # make state dict for LoRA
+    lora_sd = {}
+    for lora_name, (up_weight, down_weight) in lora_weights.items():
+        lora_sd[lora_name + ".lora_up.weight"] = up_weight
+        lora_sd[lora_name + ".lora_down.weight"] = down_weight
+        lora_sd[lora_name + ".alpha"] = torch.tensor(down_weight.size()[0])
+
+    # load state dict to LoRA and save it
+    lora_network_save, lora_sd = lora.create_network_from_weights(1.0, None, None, text_encoders_o, unet_o, weights_sd=lora_sd)
+    lora_network_save.apply_to(text_encoders_o, unet_o)  # create internal module references for state_dict
+
+    info = lora_network_save.load_state_dict(lora_sd)
+    logger.info(f"Loading extracted LoRA weights: {info}")
+
+    dir_name = os.path.dirname(save_to)
+    if dir_name and not os.path.exists(dir_name):
+        os.makedirs(dir_name, exist_ok=True)
+
+    # minimum metadata
+    net_kwargs = {}
+    if conv_dim is not None:
+        net_kwargs["conv_dim"] = str(conv_dim)
+        net_kwargs["conv_alpha"] = str(float(conv_dim))
+
+    metadata = {
+        "ss_v2": str(v2),
+        "ss_base_model_version": model_version,
+        "ss_network_module": "networks.lora",
+        "ss_network_dim": str(dim),
+        "ss_network_alpha": str(float(dim)),
+        "ss_network_args": json.dumps(net_kwargs),
+    }
+
+    if not no_metadata:
+        title = os.path.splitext(os.path.basename(save_to))[0]
+        sai_metadata = sai_model_spec.build_metadata(None, v2, v_parameterization, sdxl, True, False, time.time(), title=title)
+        metadata.update(sai_metadata)
+
+    lora_network_save.save_weights(save_to, save_dtype, metadata)
+    logger.info(f"LoRA weights are saved to: {save_to}")
+
+
+def setup_parser() -> argparse.ArgumentParser:
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--v2", action="store_true", help="load Stable Diffusion v2.x model / Stable Diffusion 2.xのモデルを読み込む")
+    parser.add_argument(
+        "--v_parameterization",
+        action="store_true",
+        default=None,
+        help="make LoRA metadata for v-parameterization (default is same to v2) / 作成するLoRAのメタデータにv-parameterization用と設定する（省略時はv2と同じ）",
+    )
+    parser.add_argument(
+        "--sdxl", action="store_true", help="load Stable Diffusion SDXL base model / Stable Diffusion SDXL baseのモデルを読み込む"
+    )
+    parser.add_argument(
+        "--load_precision",
+        type=str,
+        default=None,
+        choices=[None, "float", "fp16", "bf16"],
+        help="precision in loading, model default if omitted / 読み込み時に精度を変更して読み込む、省略時はモデルファイルによる"
+    )
+    parser.add_argument(
+        "--save_precision",
+        type=str,
+        default=None,
+        choices=[None, "float", "fp16", "bf16"],
+        help="precision in saving, same to merging if omitted / 保存時に精度を変更して保存する、省略時はfloat",
+    )
+    parser.add_argument(
+        "--model_org",
+        type=str,
+        default=None,
+        required=True,
+        help="Stable Diffusion original model: ckpt or safetensors file / 元モデル、ckptまたはsafetensors",
+    )
+    parser.add_argument(
+        "--model_tuned",
+        type=str,
+        default=None,
+        required=True,
+        help="Stable Diffusion tuned model, LoRA is difference of `original to tuned`: ckpt or safetensors file / 派生モデル（生成されるLoRAは元→派生の差分になります）、ckptまたはsafetensors",
+    )
+    parser.add_argument(
+        "--save_to",
+        type=str,
+        default=None,
+        required=True,
+        help="destination file name: ckpt or safetensors file / 保存先のファイル名、ckptまたはsafetensors",
+    )
+    parser.add_argument("--dim", type=int, default=4, help="dimension (rank) of LoRA (default 4) / LoRAの次元数（rank）（デフォルト4）")
+    parser.add_argument(
+        "--conv_dim",
+        type=int,
+        default=None,
+        help="dimension (rank) of LoRA for Conv2d-3x3 (default None, disabled) / LoRAのConv2d-3x3の次元数（rank）（デフォルトNone、適用なし）",
+    )
+    parser.add_argument("--device", type=str, default=None, help="device to use, cuda for GPU / 計算を行うデバイス、cuda でGPUを使う")
+    parser.add_argument(
+        "--clamp_quantile",
+        type=float,
+        default=0.99,
+        help="Quantile clamping value, float, (0-1). Default = 0.99 / 値をクランプするための分位点、float、(0-1)。デフォルトは0.99",
+    )
+    parser.add_argument(
+        "--min_diff",
+        type=float,
+        default=0.01,
+        help="Minimum difference between finetuned model and base to consider them different enough to extract, float, (0-1). Default = 0.01 /"
+        + "LoRAを抽出するために元モデルと派生モデルの差分の最小値、float、(0-1)。デフォルトは0.01",
+    )
+    parser.add_argument(
+        "--no_metadata",
+        action="store_true",
+        help="do not save sai modelspec metadata (minimum ss_metadata for LoRA is saved) / "
+        + "sai modelspecのメタデータを保存しない（LoRAの最低限のss_metadataは保存される）",
+    )
+    parser.add_argument(
+        "--load_original_model_to",
+        type=str,
+        default=None,
+        help="location to load original model, cpu or cuda, cuda:0, etc, default is cpu, only for SDXL / 元モデル読み込み先、cpuまたはcuda、cuda:0など、省略時はcpu、SDXLのみ有効",
+    )
+    parser.add_argument(
+        "--load_tuned_model_to",
+        type=str,
+        default=None,
+        help="location to load tuned model, cpu or cuda, cuda:0, etc, default is cpu, only for SDXL / 派生モデル読み込み先、cpuまたはcuda、cuda:0など、省略時はcpu、SDXLのみ有効",
+    )
+
+    return parser
+
+
+if __name__ == "__main__":
+    parser = setup_parser()
+
+    args = parser.parse_args()
+    svd(**vars(args))