PEFT

Diffusers supports loading adapters such as LoRA with the PEFT library with the loaders.peft.PeftAdapterMixin class. This allows modeling classes in Diffusers like UNet2DConditionModel, SD3Transformer2DModel to operate with an adapter.

Tip

Refer to the Inference with PEFT tutorial for an overview of how to use PEFT in Diffusers for inference.

mindone.diffusers.loaders.peft.PeftAdapterMixin

A class containing all functions for loading and using adapters weights that are supported in PEFT library. For more details about adapters and injecting them in a base model, check out the PEFT documentation.

Install the latest version of PEFT, and use this mixin to:

• Attach new adapters in the model.
• Attach multiple adapters and iteratively activate/deactivate them.
• Activate/deactivate all adapters from the model.
• Get a list of the active adapters.

Source code in mindone/diffusers/loaders/peft.py

class PeftAdapterMixin:
    """
    A class containing all functions for loading and using adapters weights that are supported in PEFT library. For
    more details about adapters and injecting them in a base model, check out the PEFT
    [documentation](https://huggingface.co/docs/peft/index).

    Install the latest version of PEFT, and use this mixin to:

    - Attach new adapters in the model.
    - Attach multiple adapters and iteratively activate/deactivate them.
    - Activate/deactivate all adapters from the model.
    - Get a list of the active adapters.
    """

    _hf_peft_config_loaded = False

    def set_adapters(
        self,
        adapter_names: Union[List[str], str],
        weights: Optional[Union[float, Dict, List[float], List[Dict], List[None]]] = None,
    ):
        """
        Set the currently active adapters for use in the UNet.

        Args:
            adapter_names (`List[str]` or `str`):
                The names of the adapters to use.
            adapter_weights (`Union[List[float], float]`, *optional*):
                The adapter(s) weights to use with the UNet. If `None`, the weights are set to `1.0` for all the
                adapters.

        Example:

        ```py
        from mindone.diffusers import AutoPipelineForText2Image
        import mindspore

        pipeline = AutoPipelineForText2Image.from_pretrained(
            "stabilityai/stable-diffusion-xl-base-1.0", mindspore_dtype=mindspore.float16
        ).to("cuda")
        pipeline.load_lora_weights(
            "jbilcke-hf/sdxl-cinematic-1", weight_name="pytorch_lora_weights.safetensors", adapter_name="cinematic"
        )
        pipeline.load_lora_weights("nerijs/pixel-art-xl", weight_name="pixel-art-xl.safetensors", adapter_name="pixel")
        pipeline.set_adapters(["cinematic", "pixel"], adapter_weights=[0.5, 0.5])
        ```
        """
        adapter_names = [adapter_names] if isinstance(adapter_names, str) else adapter_names

        # Expand weights into a list, one entry per adapter
        # examples for e.g. 2 adapters:  [{...}, 7] -> [7,7] ; None -> [None, None]
        if not isinstance(weights, list):
            weights = [weights] * len(adapter_names)

        if len(adapter_names) != len(weights):
            raise ValueError(
                f"Length of adapter names {len(adapter_names)} is not equal to the length of their weights {len(weights)}."
            )

        # Set None values to default of 1.0
        # e.g. [{...}, 7] -> [{...}, 7] ; [None, None] -> [1.0, 1.0]
        weights = [w if w is not None else 1.0 for w in weights]

        # e.g. [{...}, 7] -> [{expanded dict...}, 7]
        scale_expansion_fn = _SET_ADAPTER_SCALE_FN_MAPPING[self.__class__.__name__]
        weights = scale_expansion_fn(self, weights)

        set_weights_and_activate_adapters(self, adapter_names, weights)

    def add_adapter(self, adapter_config, adapter_name: str = "default") -> None:
        r"""
        Adds a new adapter to the current model for training. If no adapter name is passed, a default name is assigned
        to the adapter to follow the convention of the PEFT library.

        If you are not familiar with adapters and PEFT methods, we invite you to read more about them in the PEFT
        [documentation](https://huggingface.co/docs/peft).

        Args:
            adapter_config (`[~peft.PeftConfig]`):
                The configuration of the adapter to add; supported adapters are non-prefix tuning and adaption prompt
                methods.
            adapter_name (`str`, *optional*, defaults to `"default"`):
                The name of the adapter to add. If no name is passed, a default name is assigned to the adapter.
        """
        from mindone.diffusers._peft import PeftConfig, inject_adapter_in_model

        if not self._hf_peft_config_loaded:
            self._hf_peft_config_loaded = True
        elif adapter_name in self.peft_config:
            raise ValueError(f"Adapter with name {adapter_name} already exists. Please use a different name.")

        if not isinstance(adapter_config, PeftConfig):
            raise ValueError(f"adapter_config should be an instance of PeftConfig. Got {type(adapter_config)} instead.")

        # Unlike transformers, here we don't need to retrieve the name_or_path of the unet as the loading logic is
        # handled by the `load_lora_layers` or `StableDiffusionLoraLoaderMixin`. Therefore we set it to `None` here.
        adapter_config.base_model_name_or_path = None
        inject_adapter_in_model(adapter_config, self, adapter_name)
        self.set_adapter(adapter_name)

    def set_adapter(self, adapter_name: Union[str, List[str]]) -> None:
        """
        Sets a specific adapter by forcing the model to only use that adapter and disables the other adapters.

        If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT
        [documentation](https://huggingface.co/docs/peft).

        Args:
            adapter_name (Union[str, List[str]])):
                The list of adapters to set or the adapter name in the case of a single adapter.
        """
        if not self._hf_peft_config_loaded:
            raise ValueError("No adapter loaded. Please load an adapter first.")

        if isinstance(adapter_name, str):
            adapter_name = [adapter_name]

        missing = set(adapter_name) - set(self.peft_config)
        if len(missing) > 0:
            raise ValueError(
                f"Following adapter(s) could not be found: {', '.join(missing)}. Make sure you are passing the correct adapter name(s)."
                f" current loaded adapters are: {list(self.peft_config.keys())}"
            )

        from mindone.diffusers._peft.tuners.tuners_utils import BaseTunerLayer

        _adapters_has_been_set = False

        for _, module in self.cells_and_names():
            if isinstance(module, BaseTunerLayer):
                if hasattr(module, "set_adapter"):
                    module.set_adapter(adapter_name)
                elif not hasattr(module, "set_adapter"):
                    raise RuntimeError("'BaseTunerLayer' object has no attribute 'set_adapter'")
                _adapters_has_been_set = True

        if not _adapters_has_been_set:
            raise ValueError(
                "Did not succeeded in setting the adapter. Please make sure you are using a model that supports adapters."
            )

    def disable_adapters(self) -> None:
        r"""
        Disable all adapters attached to the model and fallback to inference with the base model only.

        If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT
        [documentation](https://huggingface.co/docs/peft).
        """
        if not self._hf_peft_config_loaded:
            raise ValueError("No adapter loaded. Please load an adapter first.")

        from mindone.diffusers._peft.tuners.tuners_utils import BaseTunerLayer

        for _, module in self.cells_and_names():
            if isinstance(module, BaseTunerLayer):
                if hasattr(module, "enable_adapters"):
                    module.enable_adapters(enabled=False)
                else:
                    raise RuntimeError("'BaseTunerLayer' object has no attribute 'enable_adapters'")

    def enable_adapters(self) -> None:
        """
        Enable adapters that are attached to the model. The model uses `self.active_adapters()` to retrieve the list of
        adapters to enable.

        If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT
        [documentation](https://huggingface.co/docs/peft).
        """
        if not self._hf_peft_config_loaded:
            raise ValueError("No adapter loaded. Please load an adapter first.")

        from mindone.diffusers._peft.tuners.tuners_utils import BaseTunerLayer

        for _, module in self.cells_and_names():
            if isinstance(module, BaseTunerLayer):
                if hasattr(module, "enable_adapters"):
                    module.enable_adapters(enabled=True)
                else:
                    raise RuntimeError("'BaseTunerLayer' object has no attribute 'enable_adapters'")

    def active_adapters(self) -> List[str]:
        """
        Gets the current list of active adapters of the model.

        If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT
        [documentation](https://huggingface.co/docs/peft).
        """
        if not self._hf_peft_config_loaded:
            raise ValueError("No adapter loaded. Please load an adapter first.")

        from mindone.diffusers._peft.tuners.tuners_utils import BaseTunerLayer

        for _, module in self.cells_and_names():
            if isinstance(module, BaseTunerLayer):
                return module.active_adapter

    def fuse_lora(self, lora_scale=1.0, safe_fusing=False, adapter_names=None):
        self.lora_scale = lora_scale
        self._safe_fusing = safe_fusing
        self.apply(partial(self._fuse_lora_apply, adapter_names=adapter_names))

    def _fuse_lora_apply(self, module, adapter_names=None):
        from mindone.diffusers._peft.tuners.tuners_utils import BaseTunerLayer

        merge_kwargs = {"safe_merge": self._safe_fusing}

        if isinstance(module, BaseTunerLayer):
            if self.lora_scale != 1.0:
                module.scale_layer(self.lora_scale)

            # For BC with previous PEFT versions, we need to check the signature
            # of the `merge` method to see if it supports the `adapter_names` argument.
            supported_merge_kwargs = list(inspect.signature(module.merge).parameters)
            if "adapter_names" in supported_merge_kwargs:
                merge_kwargs["adapter_names"] = adapter_names
            elif "adapter_names" not in supported_merge_kwargs and adapter_names is not None:
                raise RuntimeError("The `adapter_names` argument is not supported with `BaseTunerLayer.merge`")

            module.merge(**merge_kwargs)

    def unfuse_lora(self):
        self.apply(self._unfuse_lora_apply)

    def _unfuse_lora_apply(self, module):
        from mindone.diffusers._peft.tuners.tuners_utils import BaseTunerLayer

        if isinstance(module, BaseTunerLayer):
            module.unmerge()

    def unload_lora(self):
        from ..utils import recurse_remove_peft_layers

        recurse_remove_peft_layers(self)
        if hasattr(self, "peft_config"):
            del self.peft_config

    def disable_lora(self):
        """
        Disable the UNet's active LoRA layers.

        Example:

        ```py
        from mindone.diffusers import AutoPipelineForText2Image
        import mindspore

        pipeline = AutoPipelineForText2Image.from_pretrained(
            "stabilityai/stable-diffusion-xl-base-1.0", mindspore_dtype=mindspore.float16
        )
        pipeline.load_lora_weights(
            "jbilcke-hf/sdxl-cinematic-1", weight_name="pytorch_lora_weights.safetensors", adapter_name="cinematic"
        )
        pipeline.disable_lora()
        ```
        """
        set_adapter_layers(self, enabled=False)

    def enable_lora(self):
        """
        Enable the UNet's active LoRA layers.

        Example:

        ```py
        from mindone.diffusers import AutoPipelineForText2Image
        import mindspore

        pipeline = AutoPipelineForText2Image.from_pretrained(
            "stabilityai/stable-diffusion-xl-base-1.0", mindspore_dtype=mindspore.float16
        ).to("cuda")
        pipeline.load_lora_weights(
            "jbilcke-hf/sdxl-cinematic-1", weight_name="pytorch_lora_weights.safetensors", adapter_name="cinematic"
        )
        pipeline.enable_lora()
        ```
        """
        set_adapter_layers(self, enabled=True)

    def delete_adapters(self, adapter_names: Union[List[str], str]):
        """
        Delete an adapter's LoRA layers from the UNet.

        Args:
            adapter_names (`Union[List[str], str]`):
                The names (single string or list of strings) of the adapter to delete.

        Example:

        ```py
        from mindone.diffusers import AutoPipelineForText2Image
        import mindspore

        pipeline = AutoPipelineForText2Image.from_pretrained(
            "stabilityai/stable-diffusion-xl-base-1.0", mindspore_dtype=mindspore.float16
        ).to("cuda")
        pipeline.load_lora_weights(
            "jbilcke-hf/sdxl-cinematic-1", weight_name="pytorch_lora_weights.safetensors", adapter_names="cinematic"
        )
        pipeline.delete_adapters("cinematic")
        ```
        """
        if isinstance(adapter_names, str):
            adapter_names = [adapter_names]

        for adapter_name in adapter_names:
            delete_adapter_layers(self, adapter_name)

            # Pop also the corresponding adapter from the config
            if hasattr(self, "peft_config"):
                self.peft_config.pop(adapter_name, None)

mindone.diffusers.loaders.peft.PeftAdapterMixin.active_adapters()

Gets the current list of active adapters of the model.

If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT documentation.

Source code in mindone/diffusers/loaders/peft.py

def active_adapters(self) -> List[str]:
    """
    Gets the current list of active adapters of the model.

    If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT
    [documentation](https://huggingface.co/docs/peft).
    """
    if not self._hf_peft_config_loaded:
        raise ValueError("No adapter loaded. Please load an adapter first.")

    from mindone.diffusers._peft.tuners.tuners_utils import BaseTunerLayer

    for _, module in self.cells_and_names():
        if isinstance(module, BaseTunerLayer):
            return module.active_adapter

mindone.diffusers.loaders.peft.PeftAdapterMixin.add_adapter(adapter_config, adapter_name='default')

Adds a new adapter to the current model for training. If no adapter name is passed, a default name is assigned to the adapter to follow the convention of the PEFT library.

If you are not familiar with adapters and PEFT methods, we invite you to read more about them in the PEFT documentation.

PARAMETER	DESCRIPTION
adapter_config	The configuration of the adapter to add; supported adapters are non-prefix tuning and adaption prompt methods. TYPE: `[~peft.PeftConfig]`
adapter_name	The name of the adapter to add. If no name is passed, a default name is assigned to the adapter. TYPE: `str`, *optional*, defaults to `"default"` DEFAULT: 'default'

Source code in mindone/diffusers/loaders/peft.py

def add_adapter(self, adapter_config, adapter_name: str = "default") -> None:
    r"""
    Adds a new adapter to the current model for training. If no adapter name is passed, a default name is assigned
    to the adapter to follow the convention of the PEFT library.

    If you are not familiar with adapters and PEFT methods, we invite you to read more about them in the PEFT
    [documentation](https://huggingface.co/docs/peft).

    Args:
        adapter_config (`[~peft.PeftConfig]`):
            The configuration of the adapter to add; supported adapters are non-prefix tuning and adaption prompt
            methods.
        adapter_name (`str`, *optional*, defaults to `"default"`):
            The name of the adapter to add. If no name is passed, a default name is assigned to the adapter.
    """
    from mindone.diffusers._peft import PeftConfig, inject_adapter_in_model

    if not self._hf_peft_config_loaded:
        self._hf_peft_config_loaded = True
    elif adapter_name in self.peft_config:
        raise ValueError(f"Adapter with name {adapter_name} already exists. Please use a different name.")

    if not isinstance(adapter_config, PeftConfig):
        raise ValueError(f"adapter_config should be an instance of PeftConfig. Got {type(adapter_config)} instead.")

    # Unlike transformers, here we don't need to retrieve the name_or_path of the unet as the loading logic is
    # handled by the `load_lora_layers` or `StableDiffusionLoraLoaderMixin`. Therefore we set it to `None` here.
    adapter_config.base_model_name_or_path = None
    inject_adapter_in_model(adapter_config, self, adapter_name)
    self.set_adapter(adapter_name)

mindone.diffusers.loaders.peft.PeftAdapterMixin.delete_adapters(adapter_names)

Delete an adapter's LoRA layers from the UNet.

PARAMETER	DESCRIPTION
adapter_names	The names (single string or list of strings) of the adapter to delete. TYPE: `Union[List[str], str]`

from mindone.diffusers import AutoPipelineForText2Image
import mindspore

pipeline = AutoPipelineForText2Image.from_pretrained(
    "stabilityai/stable-diffusion-xl-base-1.0", mindspore_dtype=mindspore.float16
).to("cuda")
pipeline.load_lora_weights(
    "jbilcke-hf/sdxl-cinematic-1", weight_name="pytorch_lora_weights.safetensors", adapter_names="cinematic"
)
pipeline.delete_adapters("cinematic")

Source code in mindone/diffusers/loaders/peft.py

def delete_adapters(self, adapter_names: Union[List[str], str]):
    """
    Delete an adapter's LoRA layers from the UNet.

    Args:
        adapter_names (`Union[List[str], str]`):
            The names (single string or list of strings) of the adapter to delete.

    Example:

    ```py
    from mindone.diffusers import AutoPipelineForText2Image
    import mindspore

    pipeline = AutoPipelineForText2Image.from_pretrained(
        "stabilityai/stable-diffusion-xl-base-1.0", mindspore_dtype=mindspore.float16
    ).to("cuda")
    pipeline.load_lora_weights(
        "jbilcke-hf/sdxl-cinematic-1", weight_name="pytorch_lora_weights.safetensors", adapter_names="cinematic"
    )
    pipeline.delete_adapters("cinematic")
    ```
    """
    if isinstance(adapter_names, str):
        adapter_names = [adapter_names]

    for adapter_name in adapter_names:
        delete_adapter_layers(self, adapter_name)

        # Pop also the corresponding adapter from the config
        if hasattr(self, "peft_config"):
            self.peft_config.pop(adapter_name, None)

mindone.diffusers.loaders.peft.PeftAdapterMixin.disable_adapters()

Disable all adapters attached to the model and fallback to inference with the base model only.

If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT documentation.

Source code in mindone/diffusers/loaders/peft.py

def disable_adapters(self) -> None:
    r"""
    Disable all adapters attached to the model and fallback to inference with the base model only.

    If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT
    [documentation](https://huggingface.co/docs/peft).
    """
    if not self._hf_peft_config_loaded:
        raise ValueError("No adapter loaded. Please load an adapter first.")

    from mindone.diffusers._peft.tuners.tuners_utils import BaseTunerLayer

    for _, module in self.cells_and_names():
        if isinstance(module, BaseTunerLayer):
            if hasattr(module, "enable_adapters"):
                module.enable_adapters(enabled=False)
            else:
                raise RuntimeError("'BaseTunerLayer' object has no attribute 'enable_adapters'")

mindone.diffusers.loaders.peft.PeftAdapterMixin.disable_lora()

Disable the UNet's active LoRA layers.

Example:

from mindone.diffusers import AutoPipelineForText2Image
import mindspore

pipeline = AutoPipelineForText2Image.from_pretrained(
    "stabilityai/stable-diffusion-xl-base-1.0", mindspore_dtype=mindspore.float16
)
pipeline.load_lora_weights(
    "jbilcke-hf/sdxl-cinematic-1", weight_name="pytorch_lora_weights.safetensors", adapter_name="cinematic"
)
pipeline.disable_lora()

Source code in mindone/diffusers/loaders/peft.py

def disable_lora(self):
    """
    Disable the UNet's active LoRA layers.

    Example:

    ```py
    from mindone.diffusers import AutoPipelineForText2Image
    import mindspore

    pipeline = AutoPipelineForText2Image.from_pretrained(
        "stabilityai/stable-diffusion-xl-base-1.0", mindspore_dtype=mindspore.float16
    )
    pipeline.load_lora_weights(
        "jbilcke-hf/sdxl-cinematic-1", weight_name="pytorch_lora_weights.safetensors", adapter_name="cinematic"
    )
    pipeline.disable_lora()
    ```
    """
    set_adapter_layers(self, enabled=False)

mindone.diffusers.loaders.peft.PeftAdapterMixin.enable_adapters()

Enable adapters that are attached to the model. The model uses self.active_adapters() to retrieve the list of adapters to enable.

If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT documentation.

Source code in mindone/diffusers/loaders/peft.py

def enable_adapters(self) -> None:
    """
    Enable adapters that are attached to the model. The model uses `self.active_adapters()` to retrieve the list of
    adapters to enable.

    If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT
    [documentation](https://huggingface.co/docs/peft).
    """
    if not self._hf_peft_config_loaded:
        raise ValueError("No adapter loaded. Please load an adapter first.")

    from mindone.diffusers._peft.tuners.tuners_utils import BaseTunerLayer

    for _, module in self.cells_and_names():
        if isinstance(module, BaseTunerLayer):
            if hasattr(module, "enable_adapters"):
                module.enable_adapters(enabled=True)
            else:
                raise RuntimeError("'BaseTunerLayer' object has no attribute 'enable_adapters'")

mindone.diffusers.loaders.peft.PeftAdapterMixin.enable_lora()

Enable the UNet's active LoRA layers.

Example:

from mindone.diffusers import AutoPipelineForText2Image
import mindspore

pipeline = AutoPipelineForText2Image.from_pretrained(
    "stabilityai/stable-diffusion-xl-base-1.0", mindspore_dtype=mindspore.float16
).to("cuda")
pipeline.load_lora_weights(
    "jbilcke-hf/sdxl-cinematic-1", weight_name="pytorch_lora_weights.safetensors", adapter_name="cinematic"
)
pipeline.enable_lora()

Source code in mindone/diffusers/loaders/peft.py

def enable_lora(self):
    """
    Enable the UNet's active LoRA layers.

    Example:

    ```py
    from mindone.diffusers import AutoPipelineForText2Image
    import mindspore

    pipeline = AutoPipelineForText2Image.from_pretrained(
        "stabilityai/stable-diffusion-xl-base-1.0", mindspore_dtype=mindspore.float16
    ).to("cuda")
    pipeline.load_lora_weights(
        "jbilcke-hf/sdxl-cinematic-1", weight_name="pytorch_lora_weights.safetensors", adapter_name="cinematic"
    )
    pipeline.enable_lora()
    ```
    """
    set_adapter_layers(self, enabled=True)

mindone.diffusers.loaders.peft.PeftAdapterMixin.set_adapter(adapter_name)

Sets a specific adapter by forcing the model to only use that adapter and disables the other adapters.

If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT documentation.

PARAMETER	DESCRIPTION
adapter_name	The list of adapters to set or the adapter name in the case of a single adapter. TYPE: Union[str, List[str]]

Source code in mindone/diffusers/loaders/peft.py

def set_adapter(self, adapter_name: Union[str, List[str]]) -> None:
    """
    Sets a specific adapter by forcing the model to only use that adapter and disables the other adapters.

    If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT
    [documentation](https://huggingface.co/docs/peft).

    Args:
        adapter_name (Union[str, List[str]])):
            The list of adapters to set or the adapter name in the case of a single adapter.
    """
    if not self._hf_peft_config_loaded:
        raise ValueError("No adapter loaded. Please load an adapter first.")

    if isinstance(adapter_name, str):
        adapter_name = [adapter_name]

    missing = set(adapter_name) - set(self.peft_config)
    if len(missing) > 0:
        raise ValueError(
            f"Following adapter(s) could not be found: {', '.join(missing)}. Make sure you are passing the correct adapter name(s)."
            f" current loaded adapters are: {list(self.peft_config.keys())}"
        )

    from mindone.diffusers._peft.tuners.tuners_utils import BaseTunerLayer

    _adapters_has_been_set = False

    for _, module in self.cells_and_names():
        if isinstance(module, BaseTunerLayer):
            if hasattr(module, "set_adapter"):
                module.set_adapter(adapter_name)
            elif not hasattr(module, "set_adapter"):
                raise RuntimeError("'BaseTunerLayer' object has no attribute 'set_adapter'")
            _adapters_has_been_set = True

    if not _adapters_has_been_set:
        raise ValueError(
            "Did not succeeded in setting the adapter. Please make sure you are using a model that supports adapters."
        )

mindone.diffusers.loaders.peft.PeftAdapterMixin.set_adapters(adapter_names, weights=None)

Set the currently active adapters for use in the UNet.

PARAMETER	DESCRIPTION
adapter_names	The names of the adapters to use. TYPE: `List[str]` or `str`
adapter_weights	The adapter(s) weights to use with the UNet. If None, the weights are set to 1.0 for all the adapters. TYPE: `Union[List[float], float]`, *optional*

from mindone.diffusers import AutoPipelineForText2Image
import mindspore

pipeline = AutoPipelineForText2Image.from_pretrained(
    "stabilityai/stable-diffusion-xl-base-1.0", mindspore_dtype=mindspore.float16
).to("cuda")
pipeline.load_lora_weights(
    "jbilcke-hf/sdxl-cinematic-1", weight_name="pytorch_lora_weights.safetensors", adapter_name="cinematic"
)
pipeline.load_lora_weights("nerijs/pixel-art-xl", weight_name="pixel-art-xl.safetensors", adapter_name="pixel")
pipeline.set_adapters(["cinematic", "pixel"], adapter_weights=[0.5, 0.5])

Source code in mindone/diffusers/loaders/peft.py

def set_adapters(
    self,
    adapter_names: Union[List[str], str],
    weights: Optional[Union[float, Dict, List[float], List[Dict], List[None]]] = None,
):
    """
    Set the currently active adapters for use in the UNet.

    Args:
        adapter_names (`List[str]` or `str`):
            The names of the adapters to use.
        adapter_weights (`Union[List[float], float]`, *optional*):
            The adapter(s) weights to use with the UNet. If `None`, the weights are set to `1.0` for all the
            adapters.

    Example:

    ```py
    from mindone.diffusers import AutoPipelineForText2Image
    import mindspore

    pipeline = AutoPipelineForText2Image.from_pretrained(
        "stabilityai/stable-diffusion-xl-base-1.0", mindspore_dtype=mindspore.float16
    ).to("cuda")
    pipeline.load_lora_weights(
        "jbilcke-hf/sdxl-cinematic-1", weight_name="pytorch_lora_weights.safetensors", adapter_name="cinematic"
    )
    pipeline.load_lora_weights("nerijs/pixel-art-xl", weight_name="pixel-art-xl.safetensors", adapter_name="pixel")
    pipeline.set_adapters(["cinematic", "pixel"], adapter_weights=[0.5, 0.5])
    ```
    """
    adapter_names = [adapter_names] if isinstance(adapter_names, str) else adapter_names

    # Expand weights into a list, one entry per adapter
    # examples for e.g. 2 adapters:  [{...}, 7] -> [7,7] ; None -> [None, None]
    if not isinstance(weights, list):
        weights = [weights] * len(adapter_names)

    if len(adapter_names) != len(weights):
        raise ValueError(
            f"Length of adapter names {len(adapter_names)} is not equal to the length of their weights {len(weights)}."
        )

    # Set None values to default of 1.0
    # e.g. [{...}, 7] -> [{...}, 7] ; [None, None] -> [1.0, 1.0]
    weights = [w if w is not None else 1.0 for w in weights]

    # e.g. [{...}, 7] -> [{expanded dict...}, 7]
    scale_expansion_fn = _SET_ADAPTER_SCALE_FN_MAPPING[self.__class__.__name__]
    weights = scale_expansion_fn(self, weights)

    set_weights_and_activate_adapters(self, adapter_names, weights)