Support Kohya-ss style LoRA file format (in a limited capacity) (#3437)

* add _convert_kohya_lora_to_diffusers

* make style

* add scaffold

* match result: unet attention only

* fix monkey-patch for text_encoder

* with CLIPAttention

While the terrible images are no longer produced,
the results do not match those from the hook ver.
This may be due to not setting the network_alpha value.

* add to support network_alpha

* generate diff image

* fix monkey-patch for text_encoder

* add test_text_encoder_lora_monkey_patch()

* verify that it's okay to release the attn_procs

* fix closure version

* add comment

* Revert "fix monkey-patch for text_encoder"

This reverts commit bb9c61e6faecc1935c9c4319c77065837655d616.

* Fix to reuse utility functions

* make LoRAAttnProcessor targets to self_attn

* fix LoRAAttnProcessor target

* make style

* fix split key

* Update src/diffusers/loaders.py

* remove TEXT_ENCODER_TARGET_MODULES loop

* add print memory usage

* remove test_kohya_loras_scaffold.py

* add: doc on LoRA civitai

* remove print statement and refactor in the doc.

* fix state_dict test for kohya-ss style lora

* Apply suggestions from code review
Co-authored-by: Takuma Mori <takuma104@gmail.com>

---------
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>

docs/source/en/training/lora.mdx

@@ -272,4 +272,75 @@ Note that the use of [`~diffusers.loaders.LoraLoaderMixin.load_lora_weights`] is
 * LoRA parameters that have separate identifiers for the UNet and the text encoder such as: [`"sayakpaul/dreambooth"`](https://huggingface.co/sayakpaul/dreambooth).
 
 **Note** that it is possible to provide a local directory path to [`~diffusers.loaders.LoraLoaderMixin.load_lora_weights`] as well as [`~diffusers.loaders.UNet2DConditionLoadersMixin.load_attn_procs`]. To know about the supported inputs,
-refer to the respective docstrings.
\ No newline at end of file
+refer to the respective docstrings.
+
+## Supporting A1111 themed LoRA checkpoints from Diffusers
+
+To provide seamless interoperability with A1111 to our users, we support loading A1111 formatted
+LoRA checkpoints using [`~diffusers.loaders.LoraLoaderMixin.load_lora_weights`] in a limited capacity.
+In this section, we explain how to load an A1111 formatted LoRA checkpoint from [CivitAI](https://civitai.com/)
+in Diffusers and perform inference with it. 
+
+First, download a checkpoint. We'll use
+[this one](https://civitai.com/models/13239/light-and-shadow) for demonstration purposes. 
+
+```bash
+wget https://civitai.com/api/download/models/15603 -O light_and_shadow.safetensors
+```
+
+Next, we initialize a [`~DiffusionPipeline`]:
+
+```python 
+import torch
+
+from diffusers import StableDiffusionPipeline, DPMSolverMultistepScheduler
+
+pipeline = StableDiffusionPipeline.from_pretrained(
+    "gsdf/Counterfeit-V2.5", torch_dtype=torch.float16, safety_checker=None
+).to("cuda")
+pipeline.scheduler = DPMSolverMultistepScheduler.from_config(
+    pipeline.scheduler.config, use_karras_sigmas=True
+)
+```
+
+We then load the checkpoint downloaded from CivitAI: 
+
+```python 
+pipeline.load_lora_weights(".", weight_name="light_and_shadow.safetensors")
+```
+
+<Tip warning={true}>
+
+If you're loading a checkpoint in the `safetensors` format, please ensure you have `safetensors` installed.
+
+</Tip>
+
+And then it's time for running inference: 
+
+```python 
+prompt = "masterpiece, best quality, 1girl, at dusk"
+negative_prompt = ("(low quality, worst quality:1.4), (bad anatomy), (inaccurate limb:1.2), "
+                   "bad composition, inaccurate eyes, extra digit, fewer digits, (extra arms:1.2), large breasts")
+
+images = pipeline(prompt=prompt, 
+    negative_prompt=negative_prompt, 
+    width=512, 
+    height=768, 
+    num_inference_steps=15, 
+    num_images_per_prompt=4,
+    generator=torch.manual_seed(0)
+).images
+```
+
+Below is a comparison between the LoRA and the non-LoRA results:
+
+![lora_non_lora](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/lora_non_lora_comparison.png)
+
+You have a similar checkpoint stored on the Hugging Face Hub, you can load it
+directly with [`~diffusers.loaders.LoraLoaderMixin.load_lora_weights`] like so: 
+
+```python 
+lora_model_id = "sayakpaul/civitai-light-shadow-lora"
+lora_filename = "light_and_shadow.safetensors"
+pipeline.load_lora_weights(lora_model_id, weight_name=lora_filename)
+```
\ No newline at end of file

examples/dreambooth/train_dreambooth_lora.py

@@ -58,7 +58,7 @@ from diffusers.models.attention_processor import (
     SlicedAttnAddedKVProcessor,
 )
 from diffusers.optimization import get_scheduler
-from diffusers.utils import TEXT_ENCODER_TARGET_MODULES, check_min_version, is_wandb_available
+from diffusers.utils import TEXT_ENCODER_ATTN_MODULE, check_min_version, is_wandb_available
 from diffusers.utils.import_utils import is_xformers_available
 from diffusers.utils.torch_utils import randn_tensor
 
@@ -861,9 +861,9 @@ def main(args):
     if args.train_text_encoder:
         text_lora_attn_procs = {}
         for name, module in text_encoder.named_modules():
-            if any(x in name for x in TEXT_ENCODER_TARGET_MODULES):
+            if name.endswith(TEXT_ENCODER_ATTN_MODULE):
                 text_lora_attn_procs[name] = LoRAAttnProcessor(
-                    hidden_size=module.out_features, cross_attention_dim=None
+                    hidden_size=module.out_proj.out_features, cross_attention_dim=None
                 )
         text_encoder_lora_layers = AttnProcsLayers(text_lora_attn_procs)
         temp_pipeline = DiffusionPipeline.from_pretrained(

src/diffusers/loaders.py

@@ -72,8 +72,8 @@ class AttnProcsLayers(torch.nn.Module):
         self.mapping = dict(enumerate(state_dict.keys()))
         self.rev_mapping = {v: k for k, v in enumerate(state_dict.keys())}
 
-        # .processor for unet, .k_proj, ".q_proj", ".v_proj", and ".out_proj" for text encoder
-        self.split_keys = [".processor", ".k_proj", ".q_proj", ".v_proj", ".out_proj"]
+        # .processor for unet, .self_attn for text encoder
+        self.split_keys = [".processor", ".self_attn"]
 
         # we add a hook to state_dict() and load_state_dict() so that the
         # naming fits with `unet.attn_processors`
@@ -182,6 +182,9 @@ class UNet2DConditionLoadersMixin:
         subfolder = kwargs.pop("subfolder", None)
         weight_name = kwargs.pop("weight_name", None)
         use_safetensors = kwargs.pop("use_safetensors", None)
+        # This value has the same meaning as the `--network_alpha` option in the kohya-ss trainer script.
+        # See https://github.com/darkstorm2150/sd-scripts/blob/main/docs/train_network_README-en.md#execute-learning
+        network_alpha = kwargs.pop("network_alpha", None)
 
         if use_safetensors and not is_safetensors_available():
             raise ValueError(
@@ -287,7 +290,10 @@ class UNet2DConditionLoadersMixin:
                         attn_processor_class = LoRAAttnProcessor
 
                 attn_processors[key] = attn_processor_class(
-                    hidden_size=hidden_size, cross_attention_dim=cross_attention_dim, rank=rank
+                    hidden_size=hidden_size,
+                    cross_attention_dim=cross_attention_dim,
+                    rank=rank,
+                    network_alpha=network_alpha,
                 )
                 attn_processors[key].load_state_dict(value_dict)
         elif is_custom_diffusion:
@@ -774,6 +780,8 @@ class LoraLoaderMixin:
 
         <Tip warning={true}>
 
+        We support loading A1111 formatted LoRA checkpoints in a limited capacity.
+
         This function is experimental and might change in the future.
 
         </Tip>
@@ -898,6 +906,11 @@ class LoraLoaderMixin:
         else:
             state_dict = pretrained_model_name_or_path_or_dict
 
+        # Convert kohya-ss Style LoRA attn procs to diffusers attn procs
+        network_alpha = None
+        if all((k.startswith("lora_te_") or k.startswith("lora_unet_")) for k in state_dict.keys()):
+            state_dict, network_alpha = self._convert_kohya_lora_to_diffusers(state_dict)
+
         # If the serialization format is new (introduced in https://github.com/huggingface/diffusers/pull/2918),
         # then the `state_dict` keys should have `self.unet_name` and/or `self.text_encoder_name` as
         # their prefixes.
@@ -909,7 +922,7 @@ class LoraLoaderMixin:
             unet_lora_state_dict = {
                 k.replace(f"{self.unet_name}.", ""): v for k, v in state_dict.items() if k in unet_keys
             }
-            self.unet.load_attn_procs(unet_lora_state_dict)
+            self.unet.load_attn_procs(unet_lora_state_dict, network_alpha=network_alpha)
 
             # Load the layers corresponding to text encoder and make necessary adjustments.
             text_encoder_keys = [k for k in keys if k.startswith(self.text_encoder_name)]
@@ -918,7 +931,9 @@ class LoraLoaderMixin:
                 k.replace(f"{self.text_encoder_name}.", ""): v for k, v in state_dict.items() if k in text_encoder_keys
             }
             if len(text_encoder_lora_state_dict) > 0:
-                attn_procs_text_encoder = self._load_text_encoder_attn_procs(text_encoder_lora_state_dict)
+                attn_procs_text_encoder = self._load_text_encoder_attn_procs(
+                    text_encoder_lora_state_dict, network_alpha=network_alpha
+                )
                 self._modify_text_encoder(attn_procs_text_encoder)
 
                 # save lora attn procs of text encoder so that it can be easily retrieved
@@ -954,14 +969,20 @@ class LoraLoaderMixin:
                 module = self.text_encoder.get_submodule(name)
                 # Construct a new function that performs the LoRA merging. We will monkey patch
                 # this forward pass.
-                lora_layer = getattr(attn_processors[name], self._get_lora_layer_attribute(name))
+                attn_processor_name = ".".join(name.split(".")[:-1])
+                lora_layer = getattr(attn_processors[attn_processor_name], self._get_lora_layer_attribute(name))
                 old_forward = module.forward
 
-                def new_forward(x):
-                    return old_forward(x) + lora_layer(x)
+                # create a new scope that locks in the old_forward, lora_layer value for each new_forward function
+                # for more detail, see https://github.com/huggingface/diffusers/pull/3490#issuecomment-1555059060
+                def make_new_forward(old_forward, lora_layer):
+                    def new_forward(x):
+                        return old_forward(x) + lora_layer(x)
+
+                    return new_forward
 
                 # Monkey-patch.
-                module.forward = new_forward
+                module.forward = make_new_forward(old_forward, lora_layer)
 
     def _get_lora_layer_attribute(self, name: str) -> str:
         if "q_proj" in name:
@@ -1048,6 +1069,7 @@ class LoraLoaderMixin:
         subfolder = kwargs.pop("subfolder", None)
         weight_name = kwargs.pop("weight_name", None)
         use_safetensors = kwargs.pop("use_safetensors", None)
+        network_alpha = kwargs.pop("network_alpha", None)
 
         if use_safetensors and not is_safetensors_available():
             raise ValueError(
@@ -1125,7 +1147,10 @@ class LoraLoaderMixin:
                 hidden_size = value_dict["to_k_lora.up.weight"].shape[0]
 
                 attn_processors[key] = LoRAAttnProcessor(
-                    hidden_size=hidden_size, cross_attention_dim=cross_attention_dim, rank=rank
+                    hidden_size=hidden_size,
+                    cross_attention_dim=cross_attention_dim,
+                    rank=rank,
+                    network_alpha=network_alpha,
                 )
                 attn_processors[key].load_state_dict(value_dict)
 
@@ -1219,6 +1244,56 @@ class LoraLoaderMixin:
         save_function(state_dict, os.path.join(save_directory, weight_name))
         logger.info(f"Model weights saved in {os.path.join(save_directory, weight_name)}")
 
+    def _convert_kohya_lora_to_diffusers(self, state_dict):
+        unet_state_dict = {}
+        te_state_dict = {}
+        network_alpha = None
+
+        for key, value in state_dict.items():
+            if "lora_down" in key:
+                lora_name = key.split(".")[0]
+                lora_name_up = lora_name + ".lora_up.weight"
+                lora_name_alpha = lora_name + ".alpha"
+                if lora_name_alpha in state_dict:
+                    alpha = state_dict[lora_name_alpha].item()
+                    if network_alpha is None:
+                        network_alpha = alpha
+                    elif network_alpha != alpha:
+                        raise ValueError("Network alpha is not consistent")
+
+                if lora_name.startswith("lora_unet_"):
+                    diffusers_name = key.replace("lora_unet_", "").replace("_", ".")
+                    diffusers_name = diffusers_name.replace("down.blocks", "down_blocks")
+                    diffusers_name = diffusers_name.replace("mid.block", "mid_block")
+                    diffusers_name = diffusers_name.replace("up.blocks", "up_blocks")
+                    diffusers_name = diffusers_name.replace("transformer.blocks", "transformer_blocks")
+                    diffusers_name = diffusers_name.replace("to.q.lora", "to_q_lora")
+                    diffusers_name = diffusers_name.replace("to.k.lora", "to_k_lora")
+                    diffusers_name = diffusers_name.replace("to.v.lora", "to_v_lora")
+                    diffusers_name = diffusers_name.replace("to.out.0.lora", "to_out_lora")
+                    if "transformer_blocks" in diffusers_name:
+                        if "attn1" in diffusers_name or "attn2" in diffusers_name:
+                            diffusers_name = diffusers_name.replace("attn1", "attn1.processor")
+                            diffusers_name = diffusers_name.replace("attn2", "attn2.processor")
+                            unet_state_dict[diffusers_name] = value
+                            unet_state_dict[diffusers_name.replace(".down.", ".up.")] = state_dict[lora_name_up]
+                elif lora_name.startswith("lora_te_"):
+                    diffusers_name = key.replace("lora_te_", "").replace("_", ".")
+                    diffusers_name = diffusers_name.replace("text.model", "text_model")
+                    diffusers_name = diffusers_name.replace("self.attn", "self_attn")
+                    diffusers_name = diffusers_name.replace("q.proj.lora", "to_q_lora")
+                    diffusers_name = diffusers_name.replace("k.proj.lora", "to_k_lora")
+                    diffusers_name = diffusers_name.replace("v.proj.lora", "to_v_lora")
+                    diffusers_name = diffusers_name.replace("out.proj.lora", "to_out_lora")
+                    if "self_attn" in diffusers_name:
+                        te_state_dict[diffusers_name] = value
+                        te_state_dict[diffusers_name.replace(".down.", ".up.")] = state_dict[lora_name_up]
+
+        unet_state_dict = {f"{UNET_NAME}.{module_name}": params for module_name, params in unet_state_dict.items()}
+        te_state_dict = {f"{TEXT_ENCODER_NAME}.{module_name}": params for module_name, params in te_state_dict.items()}
+        new_state_dict = {**unet_state_dict, **te_state_dict}
+        return new_state_dict, network_alpha
+
 
 class FromCkptMixin:
     """This helper class allows to directly load .ckpt stable diffusion file_extension

src/diffusers/models/attention_processor.py

@@ -508,7 +508,7 @@ class AttnProcessor:
 
 
 class LoRALinearLayer(nn.Module):
-    def __init__(self, in_features, out_features, rank=4):
+    def __init__(self, in_features, out_features, rank=4, network_alpha=None):
         super().__init__()
 
         if rank > min(in_features, out_features):
@@ -516,6 +516,10 @@ class LoRALinearLayer(nn.Module):
 
         self.down = nn.Linear(in_features, rank, bias=False)
         self.up = nn.Linear(rank, out_features, bias=False)
+        # This value has the same meaning as the `--network_alpha` option in the kohya-ss trainer script.
+        # See https://github.com/darkstorm2150/sd-scripts/blob/main/docs/train_network_README-en.md#execute-learning
+        self.network_alpha = network_alpha
+        self.rank = rank
 
         nn.init.normal_(self.down.weight, std=1 / rank)
         nn.init.zeros_(self.up.weight)
@@ -527,6 +531,9 @@ class LoRALinearLayer(nn.Module):
         down_hidden_states = self.down(hidden_states.to(dtype))
         up_hidden_states = self.up(down_hidden_states)
 
+        if self.network_alpha is not None:
+            up_hidden_states *= self.network_alpha / self.rank
+
         return up_hidden_states.to(orig_dtype)
 
 
@@ -543,17 +550,17 @@ class LoRAAttnProcessor(nn.Module):
             The dimension of the LoRA update matrices.
     """
 
-    def __init__(self, hidden_size, cross_attention_dim=None, rank=4):
+    def __init__(self, hidden_size, cross_attention_dim=None, rank=4, network_alpha=None):
         super().__init__()
 
         self.hidden_size = hidden_size
         self.cross_attention_dim = cross_attention_dim
         self.rank = rank
 
-        self.to_q_lora = LoRALinearLayer(hidden_size, hidden_size, rank)
-        self.to_k_lora = LoRALinearLayer(cross_attention_dim or hidden_size, hidden_size, rank)
-        self.to_v_lora = LoRALinearLayer(cross_attention_dim or hidden_size, hidden_size, rank)
-        self.to_out_lora = LoRALinearLayer(hidden_size, hidden_size, rank)
+        self.to_q_lora = LoRALinearLayer(hidden_size, hidden_size, rank, network_alpha)
+        self.to_k_lora = LoRALinearLayer(cross_attention_dim or hidden_size, hidden_size, rank, network_alpha)
+        self.to_v_lora = LoRALinearLayer(cross_attention_dim or hidden_size, hidden_size, rank, network_alpha)
+        self.to_out_lora = LoRALinearLayer(hidden_size, hidden_size, rank, network_alpha)
 
     def __call__(
         self, attn: Attention, hidden_states, encoder_hidden_states=None, attention_mask=None, scale=1.0, temb=None
@@ -838,19 +845,19 @@ class LoRAAttnAddedKVProcessor(nn.Module):
             The dimension of the LoRA update matrices.
     """
 
-    def __init__(self, hidden_size, cross_attention_dim=None, rank=4):
+    def __init__(self, hidden_size, cross_attention_dim=None, rank=4, network_alpha=None):
         super().__init__()
 
         self.hidden_size = hidden_size
         self.cross_attention_dim = cross_attention_dim
         self.rank = rank
 
-        self.to_q_lora = LoRALinearLayer(hidden_size, hidden_size, rank)
-        self.add_k_proj_lora = LoRALinearLayer(cross_attention_dim or hidden_size, hidden_size, rank)
-        self.add_v_proj_lora = LoRALinearLayer(cross_attention_dim or hidden_size, hidden_size, rank)
-        self.to_k_lora = LoRALinearLayer(hidden_size, hidden_size, rank)
-        self.to_v_lora = LoRALinearLayer(hidden_size, hidden_size, rank)
-        self.to_out_lora = LoRALinearLayer(hidden_size, hidden_size, rank)
+        self.to_q_lora = LoRALinearLayer(hidden_size, hidden_size, rank, network_alpha)
+        self.add_k_proj_lora = LoRALinearLayer(cross_attention_dim or hidden_size, hidden_size, rank, network_alpha)
+        self.add_v_proj_lora = LoRALinearLayer(cross_attention_dim or hidden_size, hidden_size, rank, network_alpha)
+        self.to_k_lora = LoRALinearLayer(hidden_size, hidden_size, rank, network_alpha)
+        self.to_v_lora = LoRALinearLayer(hidden_size, hidden_size, rank, network_alpha)
+        self.to_out_lora = LoRALinearLayer(hidden_size, hidden_size, rank, network_alpha)
 
     def __call__(self, attn: Attention, hidden_states, encoder_hidden_states=None, attention_mask=None, scale=1.0):
         residual = hidden_states
@@ -1157,7 +1164,9 @@ class LoRAXFormersAttnProcessor(nn.Module):
             operator.
     """
 
-    def __init__(self, hidden_size, cross_attention_dim, rank=4, attention_op: Optional[Callable] = None):
+    def __init__(
+        self, hidden_size, cross_attention_dim, rank=4, attention_op: Optional[Callable] = None, network_alpha=None
+    ):
         super().__init__()
 
         self.hidden_size = hidden_size
@@ -1165,10 +1174,10 @@ class LoRAXFormersAttnProcessor(nn.Module):
         self.rank = rank
         self.attention_op = attention_op
 
-        self.to_q_lora = LoRALinearLayer(hidden_size, hidden_size, rank)
-        self.to_k_lora = LoRALinearLayer(cross_attention_dim or hidden_size, hidden_size, rank)
-        self.to_v_lora = LoRALinearLayer(cross_attention_dim or hidden_size, hidden_size, rank)
-        self.to_out_lora = LoRALinearLayer(hidden_size, hidden_size, rank)
+        self.to_q_lora = LoRALinearLayer(hidden_size, hidden_size, rank, network_alpha)
+        self.to_k_lora = LoRALinearLayer(cross_attention_dim or hidden_size, hidden_size, rank, network_alpha)
+        self.to_v_lora = LoRALinearLayer(cross_attention_dim or hidden_size, hidden_size, rank, network_alpha)
+        self.to_out_lora = LoRALinearLayer(hidden_size, hidden_size, rank, network_alpha)
 
     def __call__(
         self, attn: Attention, hidden_states, encoder_hidden_states=None, attention_mask=None, scale=1.0, temb=None

src/diffusers/utils/__init__.py

@@ -30,6 +30,7 @@ from .constants import (
     ONNX_EXTERNAL_WEIGHTS_NAME,
     ONNX_WEIGHTS_NAME,
     SAFETENSORS_WEIGHTS_NAME,
+    TEXT_ENCODER_ATTN_MODULE,
     TEXT_ENCODER_TARGET_MODULES,
     WEIGHTS_NAME,
 )

src/diffusers/utils/constants.py

@@ -31,3 +31,4 @@ DIFFUSERS_DYNAMIC_MODULE_NAME = "diffusers_modules"
 HF_MODULES_CACHE = os.getenv("HF_MODULES_CACHE", os.path.join(hf_cache_home, "modules"))
 DEPRECATED_REVISION_ARGS = ["fp16", "non-ema"]
 TEXT_ENCODER_TARGET_MODULES = ["q_proj", "v_proj", "k_proj", "out_proj"]
+TEXT_ENCODER_ATTN_MODULE = ".self_attn"

tests/models/test_lora_layers.py

@@ -12,6 +12,7 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+import gc
 import os
 import tempfile
 import unittest
@@ -30,7 +31,7 @@ from diffusers.models.attention_processor import (
     LoRAXFormersAttnProcessor,
     XFormersAttnProcessor,
 )
-from diffusers.utils import TEXT_ENCODER_TARGET_MODULES, floats_tensor, torch_device
+from diffusers.utils import TEXT_ENCODER_ATTN_MODULE, floats_tensor, torch_device
 
 
 def create_unet_lora_layers(unet: nn.Module):
@@ -50,15 +51,35 @@ def create_unet_lora_layers(unet: nn.Module):
     return lora_attn_procs, unet_lora_layers
 
 
-def create_text_encoder_lora_layers(text_encoder: nn.Module):
+def create_text_encoder_lora_attn_procs(text_encoder: nn.Module):
     text_lora_attn_procs = {}
     for name, module in text_encoder.named_modules():
-        if any(x in name for x in TEXT_ENCODER_TARGET_MODULES):
-            text_lora_attn_procs[name] = LoRAAttnProcessor(hidden_size=module.out_features, cross_attention_dim=None)
+        if name.endswith(TEXT_ENCODER_ATTN_MODULE):
+            text_lora_attn_procs[name] = LoRAAttnProcessor(
+                hidden_size=module.out_proj.out_features, cross_attention_dim=None
+            )
+    return text_lora_attn_procs
+
+
+def create_text_encoder_lora_layers(text_encoder: nn.Module):
+    text_lora_attn_procs = create_text_encoder_lora_attn_procs(text_encoder)
     text_encoder_lora_layers = AttnProcsLayers(text_lora_attn_procs)
     return text_encoder_lora_layers
 
 
+def set_lora_up_weights(text_lora_attn_procs, randn_weight=False):
+    for _, attn_proc in text_lora_attn_procs.items():
+        # set up.weights
+        for layer_name, layer_module in attn_proc.named_modules():
+            if layer_name.endswith("_lora"):
+                weight = (
+                    torch.randn_like(layer_module.up.weight)
+                    if randn_weight
+                    else torch.zeros_like(layer_module.up.weight)
+                )
+                layer_module.up.weight = torch.nn.Parameter(weight)
+
+
 class LoraLoaderMixinTests(unittest.TestCase):
     def get_dummy_components(self):
         torch.manual_seed(0)
@@ -220,6 +241,64 @@ class LoraLoaderMixinTests(unittest.TestCase):
         # Outputs shouldn't match.
         self.assertFalse(torch.allclose(torch.from_numpy(orig_image_slice), torch.from_numpy(lora_image_slice)))
 
+    # copied from: https://colab.research.google.com/gist/sayakpaul/df2ef6e1ae6d8c10a49d859883b10860/scratchpad.ipynb
+    def get_dummy_tokens(self):
+        max_seq_length = 77
+
+        inputs = torch.randint(2, 56, size=(1, max_seq_length), generator=torch.manual_seed(0))
+
+        prepared_inputs = {}
+        prepared_inputs["input_ids"] = inputs
+        return prepared_inputs
+
+    def test_text_encoder_lora_monkey_patch(self):
+        pipeline_components, _ = self.get_dummy_components()
+        pipe = StableDiffusionPipeline(**pipeline_components)
+
+        dummy_tokens = self.get_dummy_tokens()
+
+        # inference without lora
+        outputs_without_lora = pipe.text_encoder(**dummy_tokens)[0]
+        assert outputs_without_lora.shape == (1, 77, 32)
+
+        # create lora_attn_procs with zeroed out up.weights
+        text_attn_procs = create_text_encoder_lora_attn_procs(pipe.text_encoder)
+        set_lora_up_weights(text_attn_procs, randn_weight=False)
+
+        # monkey patch
+        pipe._modify_text_encoder(text_attn_procs)
+
+        # verify that it's okay to release the text_attn_procs which holds the LoRAAttnProcessor.
+        del text_attn_procs
+        gc.collect()
+
+        # inference with lora
+        outputs_with_lora = pipe.text_encoder(**dummy_tokens)[0]
+        assert outputs_with_lora.shape == (1, 77, 32)
+
+        assert torch.allclose(
+            outputs_without_lora, outputs_with_lora
+        ), "lora_up_weight are all zero, so the lora outputs should be the same to without lora outputs"
+
+        # create lora_attn_procs with randn up.weights
+        text_attn_procs = create_text_encoder_lora_attn_procs(pipe.text_encoder)
+        set_lora_up_weights(text_attn_procs, randn_weight=True)
+
+        # monkey patch
+        pipe._modify_text_encoder(text_attn_procs)
+
+        # verify that it's okay to release the text_attn_procs which holds the LoRAAttnProcessor.
+        del text_attn_procs
+        gc.collect()
+
+        # inference with lora
+        outputs_with_lora = pipe.text_encoder(**dummy_tokens)[0]
+        assert outputs_with_lora.shape == (1, 77, 32)
+
+        assert not torch.allclose(
+            outputs_without_lora, outputs_with_lora
+        ), "lora_up_weight are not zero, so the lora outputs should be different to without lora outputs"
+
     def create_lora_weight_file(self, tmpdirname):
         _, lora_components = self.get_dummy_components()
         LoraLoaderMixin.save_lora_weights(