add merge LoRA script

README.md

@@ -11,6 +11,8 @@ The command to install PyTorch is as follows:
 
 Aug 16, 2024: 
 
+Added a script `networks/flux_merge_lora.py` to merge LoRA into FLUX.1 checkpoint. See [Merge LoRA to FLUX.1 checkpoint](#merge-lora-to-flux1-checkpoint) for details.
+
 FLUX.1 schnell model based training is now supported (but not tested). If the name of the model file contains `schnell`, the model is treated as a schnell model. 
 
 Added `--t5xxl_max_token_length` option to specify the maximum token length of T5XXL. The default is 512 in dev and 256 in schnell.
@@ -80,6 +82,28 @@ Aug 12: `--interactive` option is now working.
 python flux_minimal_inference.py --ckpt flux1-dev.sft --clip_l sd3/clip_l.safetensors --t5xxl sd3/t5xxl_fp16.safetensors --ae ae.sft --dtype bf16 --prompt "a cat holding a sign that says hello world" --out path/to/output/dir --seed 1 --flux_dtype fp8 --offload --lora lora-flux-name.safetensors;1.0
 ```
 
+### Merge LoRA to FLUX.1 checkpoint
+
+`networks/flux_merge_lora.py` merges LoRA to FLUX.1 checkpoint. __The script is experimental.__ 
+
+```
+python networks/flux_merge_lora.py --flux_model flux1-dev.sft --save_to output.safetensors --models lora1.safetensors --ratios 2.0 --save_precision fp16 --loading_device cuda --working_device cpu
+```
+
+You can also merge multiple LoRA models into a FLUX.1 model. Specify multiple LoRA models in `--models`. Specify the same number of ratios in `--ratios`.
+
+`--loading_device` is the device to load the LoRA models. `--working_device` is the device to merge (calculate) the models. Default is `cpu` for both. Loading / working device examples are below (in the case of `--save_precision fp16` or `--save_precision bf16`):
+
+- 'cpu' / 'cpu': Uses >50GB of RAM, but works on any machine.
+- 'cuda' / 'cpu': Uses 24GB of VRAM, but requires 30GB of RAM.
+- 'cuda' / 'cuda': Uses 30GB of VRAM, but requires 30GB of RAM, faster than 'cuda' / 'cpu'.
+
+In the case of LoRA models are trained with `bf16`, we are not sure which is better, `fp16` or `bf16` for `--save_precision`.
+
+The script can merge multiple LoRA models. If you want to merge multiple LoRA models, specify `--concat` option to work the merged LoRA model properly.
+
+```
+
 ## SD3 training
 
 SD3 training is done with `sd3_train.py`. 

library/train_util.py

@@ -3160,7 +3160,7 @@ SS_METADATA_MINIMUM_KEYS = [
 
 
 def build_minimum_network_metadata(
-    v2: Optional[bool],
+    v2: Optional[str],
     base_model: Optional[str],
     network_module: str,
     network_dim: str,

networks/flux_merge_lora.py

@@ -0,0 +1,361 @@
+import math
+import argparse
+import os
+import time
+import torch
+from safetensors import safe_open
+from safetensors.torch import load_file, save_file
+from tqdm import tqdm
+from library import sai_model_spec, train_util
+import networks.lora_flux as lora_flux
+from library.utils import setup_logging
+
+setup_logging()
+import logging
+
+logger = logging.getLogger(__name__)
+
+
+def load_state_dict(file_name, dtype):
+    if os.path.splitext(file_name)[1] == ".safetensors":
+        sd = load_file(file_name)
+        metadata = train_util.load_metadata_from_safetensors(file_name)
+    else:
+        sd = torch.load(file_name, map_location="cpu")
+        metadata = {}
+
+    for key in list(sd.keys()):
+        if type(sd[key]) == torch.Tensor:
+            sd[key] = sd[key].to(dtype)
+
+    return sd, metadata
+
+
+def save_to_file(file_name, state_dict, dtype, metadata):
+    if dtype is not None:
+        logger.info(f"converting to {dtype}...")
+        for key in list(state_dict.keys()):
+            if type(state_dict[key]) == torch.Tensor:
+                state_dict[key] = state_dict[key].to(dtype)
+
+    logger.info(f"saving to: {file_name}")
+    save_file(state_dict, file_name, metadata=metadata)
+
+
+def merge_to_flux_model(loading_device, working_device, flux_model, models, ratios, merge_dtype, save_dtype):
+    # create module map without loading state_dict
+    logger.info(f"loading keys from FLUX.1 model: {flux_model}")
+    lora_name_to_module_key = {}
+    with safe_open(flux_model, framework="pt", device=loading_device) as flux_file:
+        keys = list(flux_file.keys())
+        for key in keys:
+            if key.endswith(".weight"):
+                module_name = ".".join(key.split(".")[:-1])
+                lora_name = lora_flux.LoRANetwork.LORA_PREFIX_FLUX + "_" + module_name.replace(".", "_")
+                lora_name_to_module_key[lora_name] = key
+
+    flux_state_dict = load_file(flux_model, device=loading_device)
+    for model, ratio in zip(models, ratios):
+        logger.info(f"loading: {model}")
+        lora_sd, _ = load_state_dict(model, merge_dtype)  # loading on CPU
+
+        logger.info(f"merging...")
+        for key in tqdm(lora_sd.keys()):
+            if "lora_down" in key:
+                lora_name = key[: key.rfind(".lora_down")]
+                up_key = key.replace("lora_down", "lora_up")
+                alpha_key = key[: key.index("lora_down")] + "alpha"
+
+                if lora_name not in lora_name_to_module_key:
+                    logger.warning(f"no module found for LoRA weight: {key}.  LoRA for Text Encoder is not supported yet.")
+                    continue
+
+                down_weight = lora_sd[key]
+                up_weight = lora_sd[up_key]
+
+                dim = down_weight.size()[0]
+                alpha = lora_sd.get(alpha_key, dim)
+                scale = alpha / dim
+
+                # W <- W + U * D
+                module_weight_key = lora_name_to_module_key[lora_name]
+                if module_weight_key not in flux_state_dict:
+                    weight = flux_file.get_tensor(module_weight_key)
+                else:
+                    weight = flux_state_dict[module_weight_key]
+
+                weight = weight.to(working_device, merge_dtype)
+                up_weight = up_weight.to(working_device, merge_dtype)
+                down_weight = down_weight.to(working_device, merge_dtype)
+
+                # logger.info(module_name, down_weight.size(), up_weight.size())
+                if len(weight.size()) == 2:
+                    # linear
+                    weight = weight + ratio * (up_weight @ down_weight) * scale
+                elif down_weight.size()[2:4] == (1, 1):
+                    # conv2d 1x1
+                    weight = (
+                        weight
+                        + ratio
+                        * (up_weight.squeeze(3).squeeze(2) @ down_weight.squeeze(3).squeeze(2)).unsqueeze(2).unsqueeze(3)
+                        * scale
+                    )
+                else:
+                    # conv2d 3x3
+                    conved = torch.nn.functional.conv2d(down_weight.permute(1, 0, 2, 3), up_weight).permute(1, 0, 2, 3)
+                    # logger.info(conved.size(), weight.size(), module.stride, module.padding)
+                    weight = weight + ratio * conved * scale
+
+                flux_state_dict[module_weight_key] = weight.to(loading_device, save_dtype)
+                del up_weight
+                del down_weight
+                del weight
+
+    return flux_state_dict
+
+
+def merge_lora_models(models, ratios, merge_dtype, concat=False, shuffle=False):
+    base_alphas = {}  # alpha for merged model
+    base_dims = {}
+
+    merged_sd = {}
+    base_model = None
+    for model, ratio in zip(models, ratios):
+        logger.info(f"loading: {model}")
+        lora_sd, lora_metadata = load_state_dict(model, merge_dtype)
+
+        if lora_metadata is not None:
+            if base_model is None:
+                base_model = lora_metadata.get(train_util.SS_METADATA_KEY_BASE_MODEL_VERSION, None)
+
+        # get alpha and dim
+        alphas = {}  # alpha for current model
+        dims = {}  # dims for current model
+        for key in lora_sd.keys():
+            if "alpha" in key:
+                lora_module_name = key[: key.rfind(".alpha")]
+                alpha = float(lora_sd[key].detach().numpy())
+                alphas[lora_module_name] = alpha
+                if lora_module_name not in base_alphas:
+                    base_alphas[lora_module_name] = alpha
+            elif "lora_down" in key:
+                lora_module_name = key[: key.rfind(".lora_down")]
+                dim = lora_sd[key].size()[0]
+                dims[lora_module_name] = dim
+                if lora_module_name not in base_dims:
+                    base_dims[lora_module_name] = dim
+
+        for lora_module_name in dims.keys():
+            if lora_module_name not in alphas:
+                alpha = dims[lora_module_name]
+                alphas[lora_module_name] = alpha
+                if lora_module_name not in base_alphas:
+                    base_alphas[lora_module_name] = alpha
+
+        logger.info(f"dim: {list(set(dims.values()))}, alpha: {list(set(alphas.values()))}")
+
+        # merge
+        logger.info(f"merging...")
+        for key in tqdm(lora_sd.keys()):
+            if "alpha" in key:
+                continue
+
+            if "lora_up" in key and concat:
+                concat_dim = 1
+            elif "lora_down" in key and concat:
+                concat_dim = 0
+            else:
+                concat_dim = None
+
+            lora_module_name = key[: key.rfind(".lora_")]
+
+            base_alpha = base_alphas[lora_module_name]
+            alpha = alphas[lora_module_name]
+
+            scale = math.sqrt(alpha / base_alpha) * ratio
+            scale = abs(scale) if "lora_up" in key else scale  # マイナスの重みに対応する。
+
+            if key in merged_sd:
+                assert (
+                    merged_sd[key].size() == lora_sd[key].size() or concat_dim is not None
+                ), f"weights shape mismatch, different dims? / 重みのサイズが合いません。dimが異なる可能性があります。"
+                if concat_dim is not None:
+                    merged_sd[key] = torch.cat([merged_sd[key], lora_sd[key] * scale], dim=concat_dim)
+                else:
+                    merged_sd[key] = merged_sd[key] + lora_sd[key] * scale
+            else:
+                merged_sd[key] = lora_sd[key] * scale
+
+    # set alpha to sd
+    for lora_module_name, alpha in base_alphas.items():
+        key = lora_module_name + ".alpha"
+        merged_sd[key] = torch.tensor(alpha)
+        if shuffle:
+            key_down = lora_module_name + ".lora_down.weight"
+            key_up = lora_module_name + ".lora_up.weight"
+            dim = merged_sd[key_down].shape[0]
+            perm = torch.randperm(dim)
+            merged_sd[key_down] = merged_sd[key_down][perm]
+            merged_sd[key_up] = merged_sd[key_up][:, perm]
+
+    logger.info("merged model")
+    logger.info(f"dim: {list(set(base_dims.values()))}, alpha: {list(set(base_alphas.values()))}")
+
+    # check all dims are same
+    dims_list = list(set(base_dims.values()))
+    alphas_list = list(set(base_alphas.values()))
+    all_same_dims = True
+    all_same_alphas = True
+    for dims in dims_list:
+        if dims != dims_list[0]:
+            all_same_dims = False
+            break
+    for alphas in alphas_list:
+        if alphas != alphas_list[0]:
+            all_same_alphas = False
+            break
+
+    # build minimum metadata
+    dims = f"{dims_list[0]}" if all_same_dims else "Dynamic"
+    alphas = f"{alphas_list[0]}" if all_same_alphas else "Dynamic"
+    metadata = train_util.build_minimum_network_metadata(str(False), base_model, "networks.lora", dims, alphas, None)
+
+    return merged_sd, metadata
+
+
+def merge(args):
+    assert len(args.models) == len(
+        args.ratios
+    ), f"number of models must be equal to number of ratios / モデルの数と重みの数は合わせてください"
+
+    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
+
+    merge_dtype = str_to_dtype(args.precision)
+    save_dtype = str_to_dtype(args.save_precision)
+    if save_dtype is None:
+        save_dtype = merge_dtype
+
+    dest_dir = os.path.dirname(args.save_to)
+    if not os.path.exists(dest_dir):
+        logger.info(f"creating directory: {dest_dir}")
+        os.makedirs(dest_dir)
+
+    if args.flux_model is not None:
+        state_dict = merge_to_flux_model(
+            args.loading_device, args.working_device, args.flux_model, args.models, args.ratios, merge_dtype, save_dtype
+        )
+
+        if args.no_metadata:
+            sai_metadata = None
+        else:
+            merged_from = sai_model_spec.build_merged_from([args.flux_model] + args.models)
+            title = os.path.splitext(os.path.basename(args.save_to))[0]
+            sai_metadata = sai_model_spec.build_metadata(
+                None, False, False, False, False, False, time.time(), title=title, merged_from=merged_from, flux="dev"
+            )
+
+        logger.info(f"saving FLUX model to: {args.save_to}")
+        save_to_file(args.save_to, state_dict, save_dtype, sai_metadata)
+
+    else:
+        state_dict, metadata = merge_lora_models(args.models, args.ratios, merge_dtype, args.concat, args.shuffle)
+
+        logger.info(f"calculating hashes and creating metadata...")
+
+        model_hash, legacy_hash = train_util.precalculate_safetensors_hashes(state_dict, metadata)
+        metadata["sshs_model_hash"] = model_hash
+        metadata["sshs_legacy_hash"] = legacy_hash
+
+        if not args.no_metadata:
+            merged_from = sai_model_spec.build_merged_from(args.models)
+            title = os.path.splitext(os.path.basename(args.save_to))[0]
+            sai_metadata = sai_model_spec.build_metadata(
+                state_dict, False, False, False, True, False, time.time(), title=title, merged_from=merged_from, flux="dev"
+            )
+            metadata.update(sai_metadata)
+
+        logger.info(f"saving model to: {args.save_to}")
+        save_to_file(args.save_to, state_dict, save_dtype, metadata)
+
+
+def setup_parser() -> argparse.ArgumentParser:
+    parser = argparse.ArgumentParser()
+    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、省略時はマージ時の精度と同じ",
+    )
+    parser.add_argument(
+        "--precision",
+        type=str,
+        default="float",
+        choices=["float", "fp16", "bf16"],
+        help="precision in merging (float is recommended) / マージの計算時の精度（floatを推奨）",
+    )
+    parser.add_argument(
+        "--flux_model",
+        type=str,
+        default=None,
+        help="FLUX.1 model to load, merge LoRA models if omitted / 読み込むモデル、指定しない場合はLoRAモデルをマージする",
+    )
+    parser.add_argument(
+        "--loading_device",
+        type=str,
+        default="cpu",
+        help="device to load FLUX.1 model. LoRA models are loaded on CPU / FLUX.1モデルを読み込むデバイス。LoRAモデルはCPUで読み込まれます",
+    )
+    parser.add_argument(
+        "--working_device",
+        type=str,
+        default="cpu",
+        help="device to work (merge). Merging LoRA models are done on CPU."
+        + " / 作業（マージ）するデバイス。LoRAモデルのマージはCPUで行われます。",
+    )
+    parser.add_argument(
+        "--save_to",
+        type=str,
+        default=None,
+        help="destination file name: safetensors file / 保存先のファイル名、safetensorsファイル",
+    )
+    parser.add_argument(
+        "--models",
+        type=str,
+        nargs="*",
+        help="LoRA models to merge: safetensors file / マージするLoRAモデル、safetensorsファイル",
+    )
+    parser.add_argument("--ratios", type=float, nargs="*", help="ratios for each model / それぞれのLoRAモデルの比率")
+    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(
+        "--concat",
+        action="store_true",
+        help="concat lora instead of merge (The dim(rank) of the output LoRA is the sum of the input dims) / "
+        + "マージの代わりに結合する（LoRAのdim(rank)は入力dimの合計になる）",
+    )
+    parser.add_argument(
+        "--shuffle",
+        action="store_true",
+        help="shuffle lora weight./ " + "LoRAの重みをシャッフルする",
+    )
+
+    return parser
+
+
+if __name__ == "__main__":
+    parser = setup_parser()
+
+    args = parser.parse_args()
+    merge(args)