[LoRA] use the PyTorch classes wherever needed and start depcrecation cycles

src/diffusers/models/activations.py

@@ -17,9 +17,6 @@
 import torch.nn.functional as F
 from torch import nn
 
-from ..utils import USE_PEFT_BACKEND
-from .lora import LoRACompatibleLinear
-
 
 ACTIVATION_FUNCTIONS = {
     "swish": nn.SiLU(),
@@ -87,19 +84,16 @@ 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.proj = linear_cls(dim_in, dim_out * 2, bias=bias)
+        self.proj = nn.Linear(dim_in, dim_out * 2, bias=bias)
 
     def gelu(self, gate: torch.Tensor) -> torch.Tensor:
         if gate.device.type != "mps":
             return F.gelu(gate)
         # mps: gelu is not implemented for float16
         return F.gelu(gate.to(dtype=torch.float32)).to(dtype=gate.dtype)
 
-    def forward(self, hidden_states, scale: float = 1.0):
-        args = () if USE_PEFT_BACKEND else (scale,)
-        hidden_states, gate = self.proj(hidden_states, *args).chunk(2, dim=-1)
+    def forward(self, hidden_states):
+        hidden_states, gate = self.proj(hidden_states).chunk(2, dim=-1)
         return hidden_states * self.gelu(gate)
 
 

src/diffusers/models/attention.py

@@ -17,37 +17,29 @@
 import torch.nn.functional as F
 from torch import nn
 
-from ..utils import USE_PEFT_BACKEND
+from ..utils import logging
 from ..utils.torch_utils import maybe_allow_in_graph
 from .activations import GEGLU, GELU, ApproximateGELU
 from .attention_processor import Attention
 from .embeddings import SinusoidalPositionalEmbedding
-from .lora import LoRACompatibleLinear
 from .normalization import AdaLayerNorm, AdaLayerNormContinuous, AdaLayerNormZero, RMSNorm
 
 
-def _chunked_feed_forward(
-    ff: nn.Module, hidden_states: torch.Tensor, chunk_dim: int, chunk_size: int, lora_scale: Optional[float] = None
-):
+logger = logging.get_logger(__name__)
+
+
+def _chunked_feed_forward(ff: nn.Module, hidden_states: torch.Tensor, chunk_dim: int, chunk_size: int):
     # "feed_forward_chunk_size" can be used to save memory
     if hidden_states.shape[chunk_dim] % chunk_size != 0:
         raise ValueError(
             f"`hidden_states` dimension to be chunked: {hidden_states.shape[chunk_dim]} has to be divisible by chunk size: {chunk_size}. Make sure to set an appropriate `chunk_size` when calling `unet.enable_forward_chunking`."
         )
 
     num_chunks = hidden_states.shape[chunk_dim] // chunk_size
-    if lora_scale is None:
-        ff_output = torch.cat(
-            [ff(hid_slice) for hid_slice in hidden_states.chunk(num_chunks, dim=chunk_dim)],
-            dim=chunk_dim,
-        )
-    else:
-        # TOOD(Patrick): LoRA scale can be removed once PEFT refactor is complete
-        ff_output = torch.cat(
-            [ff(hid_slice, scale=lora_scale) for hid_slice in hidden_states.chunk(num_chunks, dim=chunk_dim)],
-            dim=chunk_dim,
-        )
-
+    ff_output = torch.cat(
+        [ff(hid_slice) for hid_slice in hidden_states.chunk(num_chunks, dim=chunk_dim)],
+        dim=chunk_dim,
+    )
     return ff_output
 
 
@@ -299,6 +291,10 @@ def forward(
         class_labels: Optional[torch.LongTensor] = None,
         added_cond_kwargs: Optional[Dict[str, torch.Tensor]] = None,
     ) -> torch.FloatTensor:
+        if cross_attention_kwargs is not None:
+            if cross_attention_kwargs.get("scale", None) is not None:
+                logger.warn("Passing `scale` to `cross_attention_kwargs` is depcrecated.")
+
         # Notice that normalization is always applied before the real computation in the following blocks.
         # 0. Self-Attention
         batch_size = hidden_states.shape[0]
@@ -326,10 +322,7 @@ def forward(
         if self.pos_embed is not None:
             norm_hidden_states = self.pos_embed(norm_hidden_states)
 
-        # 1. Retrieve lora scale.
-        lora_scale = cross_attention_kwargs.get("scale", 1.0) if cross_attention_kwargs is not None else 1.0
-
-        # 2. Prepare GLIGEN inputs
+        # 1. Prepare GLIGEN inputs
         cross_attention_kwargs = cross_attention_kwargs.copy() if cross_attention_kwargs is not None else {}
         gligen_kwargs = cross_attention_kwargs.pop("gligen", None)
 
@@ -348,7 +341,7 @@ def forward(
         if hidden_states.ndim == 4:
             hidden_states = hidden_states.squeeze(1)
 
-        # 2.5 GLIGEN Control
+        # 1.2 GLIGEN Control
         if gligen_kwargs is not None:
             hidden_states = self.fuser(hidden_states, gligen_kwargs["objs"])
 
@@ -394,11 +387,9 @@ def forward(
 
         if self._chunk_size is not None:
             # "feed_forward_chunk_size" can be used to save memory
-            ff_output = _chunked_feed_forward(
-                self.ff, norm_hidden_states, self._chunk_dim, self._chunk_size, lora_scale=lora_scale
-            )
+            ff_output = _chunked_feed_forward(self.ff, norm_hidden_states, self._chunk_dim, self._chunk_size)
         else:
-            ff_output = self.ff(norm_hidden_states, scale=lora_scale)
+            ff_output = self.ff(norm_hidden_states)
 
         if self.norm_type == "ada_norm_zero":
             ff_output = gate_mlp.unsqueeze(1) * ff_output
@@ -643,7 +634,7 @@ def __init__(
         if inner_dim is None:
             inner_dim = int(dim * mult)
         dim_out = dim_out if dim_out is not None else dim
-        linear_cls = LoRACompatibleLinear if not USE_PEFT_BACKEND else nn.Linear
+        linear_cls = nn.Linear
 
         if activation_fn == "gelu":
             act_fn = GELU(dim, inner_dim, bias=bias)
@@ -665,11 +656,7 @@ def __init__(
         if final_dropout:
             self.net.append(nn.Dropout(dropout))
 
-    def forward(self, hidden_states: torch.Tensor, scale: float = 1.0) -> torch.Tensor:
-        compatible_cls = (GEGLU,) if USE_PEFT_BACKEND else (GEGLU, LoRACompatibleLinear)
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
         for module in self.net:
-            if isinstance(module, compatible_cls):
-                hidden_states = module(hidden_states, scale)
-            else:
-                hidden_states = module(hidden_states)
+            hidden_states = module(hidden_states)
         return hidden_states