Create lora_loading_patch.py

lora_loading_patch.py

@@ -0,0 +1,116 @@
+from diffusers.utils import (
+    convert_unet_state_dict_to_peft,
+    get_peft_kwargs,
+    is_peft_version,
+    get_adapter_name,
+    logging,
+)
+
+logger = logging.get_logger(__name__)
+
+
+# patching inject_adapter_in_model and load_peft_state_dict with low_cpu_mem_usage=True until it's merged into diffusers
+def load_lora_into_transformer(
+    cls, state_dict, network_alphas, transformer, adapter_name=None, _pipeline=None
+):
+    """
+    This will load the LoRA layers specified in `state_dict` into `transformer`.
+
+    Parameters:
+        state_dict (`dict`):
+            A standard state dict containing the lora layer parameters. The keys can either be indexed directly
+            into the unet or prefixed with an additional `unet` which can be used to distinguish between text
+            encoder lora layers.
+        network_alphas (`Dict[str, float]`):
+            The value of the network alpha used for stable learning and preventing underflow. This value has the
+            same meaning as the `--network_alpha` option in the kohya-ss trainer script. Refer to [this
+            link](https://github.com/darkstorm2150/sd-scripts/blob/main/docs/train_network_README-en.md#execute-learning).
+        transformer (`SD3Transformer2DModel`):
+            The Transformer model to load the LoRA layers into.
+        adapter_name (`str`, *optional*):
+            Adapter name to be used for referencing the loaded adapter model. If not specified, it will use
+            `default_{i}` where i is the total number of adapters being loaded.
+    """
+    from peft import LoraConfig, inject_adapter_in_model, set_peft_model_state_dict
+
+    keys = list(state_dict.keys())
+
+    transformer_keys = [k for k in keys if k.startswith(cls.transformer_name)]
+    state_dict = {
+        k.replace(f"{cls.transformer_name}.", ""): v
+        for k, v in state_dict.items()
+        if k in transformer_keys
+    }
+
+    if len(state_dict.keys()) > 0:
+        # check with first key if is not in peft format
+        first_key = next(iter(state_dict.keys()))
+        if "lora_A" not in first_key:
+            state_dict = convert_unet_state_dict_to_peft(state_dict)
+
+        if adapter_name in getattr(transformer, "peft_config", {}):
+            raise ValueError(
+                f"Adapter name {adapter_name} already in use in the transformer - please select a new adapter name."
+            )
+
+        rank = {}
+        for key, val in state_dict.items():
+            if "lora_B" in key:
+                rank[key] = val.shape[1]
+
+        if network_alphas is not None and len(network_alphas) >= 1:
+            prefix = cls.transformer_name
+            alpha_keys = [
+                k
+                for k in network_alphas.keys()
+                if k.startswith(prefix) and k.split(".")[0] == prefix
+            ]
+            network_alphas = {
+                k.replace(f"{prefix}.", ""): v
+                for k, v in network_alphas.items()
+                if k in alpha_keys
+            }
+
+        lora_config_kwargs = get_peft_kwargs(
+            rank, network_alpha_dict=network_alphas, peft_state_dict=state_dict
+        )
+        if "use_dora" in lora_config_kwargs:
+            if lora_config_kwargs["use_dora"] and is_peft_version("<", "0.9.0"):
+                raise ValueError(
+                    "You need `peft` 0.9.0 at least to use DoRA-enabled LoRAs. Please upgrade your installation of `peft`."
+                )
+            else:
+                lora_config_kwargs.pop("use_dora")
+        lora_config = LoraConfig(**lora_config_kwargs)
+
+        # adapter_name
+        if adapter_name is None:
+            adapter_name = get_adapter_name(transformer)
+
+        # In case the pipeline has been already offloaded to CPU - temporarily remove the hooks
+        # otherwise loading LoRA weights will lead to an error
+        is_model_cpu_offload, is_sequential_cpu_offload = (
+            cls._optionally_disable_offloading(_pipeline)
+        )
+
+        inject_adapter_in_model(
+            lora_config, transformer, adapter_name=adapter_name, low_cpu_mem_usage=True
+        )
+        incompatible_keys = set_peft_model_state_dict(
+            transformer, state_dict, adapter_name, low_cpu_mem_usage=True
+        )
+
+        if incompatible_keys is not None:
+            # check only for unexpected keys
+            unexpected_keys = getattr(incompatible_keys, "unexpected_keys", None)
+            if unexpected_keys:
+                logger.warning(
+                    f"Loading adapter weights from state_dict led to unexpected keys not found in the model: "
+                    f" {unexpected_keys}. "
+                )
+
+        # Offload back.
+        if is_model_cpu_offload:
+            _pipeline.enable_model_cpu_offload()
+        elif is_sequential_cpu_offload:
+            _pipeline.enable_sequential_cpu_offload()