check

examples/text_to_image/train_text_to_image_lora.py

@@ -42,8 +42,8 @@ import diffusers
 from diffusers import AutoencoderKL, DDPMScheduler, DiffusionPipeline, UNet2DConditionModel
 from diffusers.models.lora import LoRALinearLayer
 from diffusers.optimization import get_scheduler
-from diffusers.training_utils import compute_snr
-from diffusers.utils import check_min_version, is_wandb_available
+from diffusers.training_utils import compute_snr, replace_linear_cls
+from diffusers.utils import check_min_version, is_peft_available, is_wandb_available
 from diffusers.utils.import_utils import is_xformers_available
 
 
@@ -466,6 +466,7 @@ def main():
     unet = UNet2DConditionModel.from_pretrained(
         args.pretrained_model_name_or_path, subfolder="unet", revision=args.revision, variant=args.variant
     )
+
     # freeze parameters of models to save more memory
     unet.requires_grad_(False)
     vae.requires_grad_(False)
@@ -480,10 +481,14 @@ def main():
         weight_dtype = torch.bfloat16
 
     # Move unet, vae and text_encoder to device and cast to weight_dtype
-    unet.to(accelerator.device, dtype=weight_dtype)
+    # unet.to(accelerator.device, dtype=weight_dtype)
     vae.to(accelerator.device, dtype=weight_dtype)
     text_encoder.to(accelerator.device, dtype=weight_dtype)
 
+    # Replace the `nn.Linear` layers with `LoRACompatibleLinear` layers.
+    if is_peft_available():
+        replace_linear_cls(unet)
+
     # now we will add new LoRA weights to the attention layers
     # It's important to realize here how many attention weights will be added and of which sizes
     # The sizes of the attention layers consist only of two different variables:
@@ -700,10 +705,14 @@ def main():
     )
 
     # Prepare everything with our `accelerator`.
-    unet_lora_parameters, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
-        unet_lora_parameters, optimizer, train_dataloader, lr_scheduler
+    # unet_lora_parameters, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
+    #     unet_lora_parameters, optimizer, train_dataloader, lr_scheduler
+    # )
+    unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
+        unet, optimizer, train_dataloader, lr_scheduler
     )
 
+
     # We need to recalculate our total training steps as the size of the training dataloader may have changed.
     num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
     if overrode_max_train_steps:

src/diffusers/models/__init__.py

@@ -33,8 +33,8 @@ if is_torch_available():
     _import_structure["consistency_decoder_vae"] = ["ConsistencyDecoderVAE"]
     _import_structure["controlnet"] = ["ControlNetModel"]
     _import_structure["dual_transformer_2d"] = ["DualTransformer2DModel"]
-    _import_structure["modeling_utils"] = ["ModelMixin"]
     _import_structure["embeddings"] = ["ImageProjection"]
+    _import_structure["modeling_utils"] = ["ModelMixin"]
     _import_structure["prior_transformer"] = ["PriorTransformer"]
     _import_structure["t5_film_transformer"] = ["T5FilmDecoder"]
     _import_structure["transformer_2d"] = ["Transformer2DModel"]

src/diffusers/models/activations.py

@@ -88,6 +88,7 @@ class GEGLU(nn.Module):
     def __init__(self, dim_in: int, dim_out: int, bias: bool = True):
         super().__init__()
         linear_cls = LoRACompatibleLinear if not USE_PEFT_BACKEND else nn.Linear
+        self.linear_cls = linear_cls
 
         self.proj = linear_cls(dim_in, dim_out * 2, bias=bias)
 

src/diffusers/models/attention_processor.py

@@ -175,11 +175,8 @@ class Attention(nn.Module):
                 f"unknown cross_attention_norm: {cross_attention_norm}. Should be None, 'layer_norm' or 'group_norm'"
             )
 
-        if USE_PEFT_BACKEND:
-            linear_cls = nn.Linear
-        else:
-            linear_cls = LoRACompatibleLinear
-
+        linear_cls = nn.Linear if USE_PEFT_BACKEND else LoRACompatibleLinear
+        self.linear_cls = linear_cls
         self.to_q = linear_cls(query_dim, self.inner_dim, bias=bias)
 
         if not self.only_cross_attention:

src/diffusers/models/embeddings.py

@@ -200,6 +200,7 @@ class TimestepEmbedding(nn.Module):
     ):
         super().__init__()
         linear_cls = nn.Linear if USE_PEFT_BACKEND else LoRACompatibleLinear
+        self.linear_cls = linear_cls
 
         self.linear_1 = linear_cls(in_channels, time_embed_dim)
 

src/diffusers/models/resnet.py

@@ -649,6 +649,7 @@ class ResnetBlock2D(nn.Module):
         self.skip_time_act = skip_time_act
 
         linear_cls = nn.Linear if USE_PEFT_BACKEND else LoRACompatibleLinear
+        self.linear_cls = linear_cls
         conv_cls = nn.Conv2d if USE_PEFT_BACKEND else LoRACompatibleConv
 
         if groups_out is None:

src/diffusers/models/transformer_2d.py

@@ -107,6 +107,7 @@ class Transformer2DModel(ModelMixin, ConfigMixin):
 
         conv_cls = nn.Conv2d if USE_PEFT_BACKEND else LoRACompatibleConv
         linear_cls = nn.Linear if USE_PEFT_BACKEND else LoRACompatibleLinear
+        self.linear_cls = linear_cls
 
         # 1. Transformer2DModel can process both standard continuous images of shape `(batch_size, num_channels, width, height)` as well as quantized image embeddings of shape `(batch_size, num_image_vectors)`
         # Define whether input is continuous or discrete depending on configuration

src/diffusers/pipelines/wuerstchen/modeling_wuerstchen_common.py

@@ -35,6 +35,7 @@ class TimestepBlock(nn.Module):
     def __init__(self, c, c_timestep):
         super().__init__()
         linear_cls = nn.Linear if USE_PEFT_BACKEND else LoRACompatibleLinear
+        self.linear_cls = linear_cls
         self.mapper = linear_cls(c_timestep, c * 2)
 
     def forward(self, x, t):

src/diffusers/pipelines/wuerstchen/modeling_wuerstchen_prior.py

@@ -43,6 +43,7 @@ class WuerstchenPrior(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin):
         super().__init__()
         conv_cls = nn.Conv2d if USE_PEFT_BACKEND else LoRACompatibleConv
         linear_cls = nn.Linear if USE_PEFT_BACKEND else LoRACompatibleLinear
+        self.linear_cls = linear_cls
 
         self.c_r = c_r
         self.projection = conv_cls(c_in, c, kernel_size=1)

src/diffusers/training_utils.py

@@ -7,6 +7,7 @@ import numpy as np
 import torch
 
 from .models import UNet2DConditionModel
+from .models.lora import LoRACompatibleLinear
 from .utils import deprecate, is_transformers_available
 
 
@@ -53,6 +54,24 @@ def compute_snr(noise_scheduler, timesteps):
     return snr
 
 
+@torch.no_grad()
+def replace_linear_cls(model):
+    for name, module in model.named_children():
+        if isinstance(module, torch.nn.Linear):
+            bias = True if hasattr(module, "bias") and getattr(module, "bias", None) is not None else False
+            new_linear_cls = LoRACompatibleLinear(module.in_features, module.out_features, bias=bias)          
+            new_linear_cls.weight.copy_(module.weight.data)
+            new_linear_cls.weight.data.to(device=module.weight.data.device, dtype=module.weight.data.dtype)
+            if bias:
+                new_linear_cls.bias.copy_(module.bias.data)
+                new_linear_cls.bias.data.to(device=module.bias.data.device, dtype=module.bias.data.dtype)
+            setattr(model, name, new_linear_cls)
+        
+        elif len(list(module.children())) > 0:
+            # Recursively apply the same operation to child modules
+            replace_linear_cls(module)
+
+
 def unet_lora_state_dict(unet: UNet2DConditionModel) -> Dict[str, torch.Tensor]:
     r"""
     Returns: