update

Author: a-r-r-o-w

src/diffusers/models/transformers/transformer_wan.py

@@ -21,10 +21,9 @@
 
 from ...configuration_utils import ConfigMixin, register_to_config
 from ...loaders import FromOriginalModelMixin, PeftAdapterMixin
-from ...utils import USE_PEFT_BACKEND, logging, scale_lora_layers, unscale_lora_layers
+from ...utils import USE_PEFT_BACKEND, deprecate, logging, scale_lora_layers, unscale_lora_layers
 from ...utils.torch_utils import maybe_allow_in_graph
-from ..attention import FeedForward
-from ..attention_processor import Attention
+from ..attention import AttentionMixin, AttentionModuleMixin, FeedForward
 from ..cache_utils import CacheMixin
 from ..embeddings import PixArtAlphaTextProjection, TimestepEmbedding, Timesteps, get_1d_rotary_pos_embed
 from ..modeling_outputs import Transformer2DModelOutput
@@ -35,36 +34,61 @@
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
 
 
-class WanAttnProcessor2_0:
+class WanAttnProcessor:
     def __init__(self):
         if not hasattr(F, "scaled_dot_product_attention"):
-            raise ImportError("WanAttnProcessor2_0 requires PyTorch 2.0. To use it, please upgrade PyTorch to 2.0.")
+            raise ImportError(
+                "WanAttnProcessor2_0 requires PyTorch 2.0. To use it, please upgrade PyTorch to version 2.0 or higher."
+            )
+
+    def get_qkv_projections(
+        self, attn: "WanAttention", hidden_states: torch.Tensor, encoder_hidden_states: torch.Tensor
+    ):
+        # encoder_hidden_states is only passed for cross-attention
+        if encoder_hidden_states is None:
+            encoder_hidden_states = hidden_states
+
+        if attn.fused_projections:
+            if attn.cross_attention_dim_head is None:
+                # In self-attention layers, we can fuse the entire QKV projection into a single linear
+                query, key, value = attn.to_qkv(hidden_states).chunk(3, dim=-1)
+            else:
+                # In cross-attention layers, we can only fuse the KV projections into a single linear
+                query = attn.to_q(hidden_states)
+                key, value = attn.to_kv(encoder_hidden_states).chunk(2, dim=-1)
+        else:
+            query = attn.to_q(hidden_states)
+            key = attn.to_k(encoder_hidden_states)
+            value = attn.to_v(encoder_hidden_states)
+        return query, key, value
+
+    def get_added_kv_projections(self, attn: "WanAttention", encoder_hidden_states_img: torch.Tensor):
+        if attn.fused_projections:
+            key_img, value_img = attn.to_added_kv(encoder_hidden_states_img).chunk(2, dim=-1)
+        else:
+            key_img = attn.add_k_proj(encoder_hidden_states_img)
+            value_img = attn.add_v_proj(encoder_hidden_states_img)
+        return key_img, value_img
 
     def __call__(
         self,
-        attn: Attention,
+        attn: "WanAttention",
         hidden_states: torch.Tensor,
         encoder_hidden_states: Optional[torch.Tensor] = None,
         attention_mask: Optional[torch.Tensor] = None,
-        rotary_emb: Optional[torch.Tensor] = None,
+        rotary_emb: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
     ) -> torch.Tensor:
         encoder_hidden_states_img = None
         if attn.add_k_proj is not None:
             # 512 is the context length of the text encoder, hardcoded for now
             image_context_length = encoder_hidden_states.shape[1] - 512
             encoder_hidden_states_img = encoder_hidden_states[:, :image_context_length]
             encoder_hidden_states = encoder_hidden_states[:, image_context_length:]
-        if encoder_hidden_states is None:
-            encoder_hidden_states = hidden_states
 
-        query = attn.to_q(hidden_states)
-        key = attn.to_k(encoder_hidden_states)
-        value = attn.to_v(encoder_hidden_states)
+        query, key, value = self.get_qkv_projections(attn, hidden_states, encoder_hidden_states)
 
-        if attn.norm_q is not None:
-            query = attn.norm_q(query)
-        if attn.norm_k is not None:
-            key = attn.norm_k(key)
+        query = attn.norm_q(query)
+        key = attn.norm_k(key)
 
         query = query.unflatten(2, (attn.heads, -1)).transpose(1, 2)
         key = key.unflatten(2, (attn.heads, -1)).transpose(1, 2)
@@ -92,9 +116,8 @@ def apply_rotary_emb(
         # I2V task
         hidden_states_img = None
         if encoder_hidden_states_img is not None:
-            key_img = attn.add_k_proj(encoder_hidden_states_img)
+            key_img, value_img = self.get_added_kv_projections(attn, encoder_hidden_states_img)
             key_img = attn.norm_added_k(key_img)
-            value_img = attn.add_v_proj(encoder_hidden_states_img)
 
             key_img = key_img.unflatten(2, (attn.heads, -1)).transpose(1, 2)
             value_img = value_img.unflatten(2, (attn.heads, -1)).transpose(1, 2)
@@ -119,6 +142,119 @@ def apply_rotary_emb(
         return hidden_states
 
 
+class WanAttnProcessor2_0:
+    def __new__(cls, *args, **kwargs):
+        deprecation_message = (
+            "The WanAttnProcessor2_0 class is deprecated and will be removed in a future version. "
+            "Please use WanAttnProcessor instead. "
+        )
+        deprecate("WanAttnProcessor2_0", "1.0.0", deprecation_message, standard_warn=False)
+        return WanAttnProcessor(*args, **kwargs)
+
+
+class WanAttention(torch.nn.Module, AttentionModuleMixin):
+    _default_processor_cls = WanAttnProcessor
+    _available_processors = [WanAttnProcessor]
+
+    def __init__(
+        self,
+        dim: int,
+        heads: int = 8,
+        dim_head: int = 64,
+        eps: float = 1e-5,
+        dropout: float = 0.0,
+        added_kv_proj_dim: Optional[int] = None,
+        cross_attention_dim_head: Optional[int] = None,
+        processor=None,
+    ):
+        super().__init__()
+
+        self.inner_dim = dim_head * heads
+        self.heads = heads
+        self.added_kv_proj_dim = added_kv_proj_dim
+        self.cross_attention_dim_head = cross_attention_dim_head
+        self.kv_inner_dim = self.inner_dim if cross_attention_dim_head is None else cross_attention_dim_head * heads
+
+        self.to_q = torch.nn.Linear(dim, self.inner_dim, bias=True)
+        self.to_k = torch.nn.Linear(dim, self.kv_inner_dim, bias=True)
+        self.to_v = torch.nn.Linear(dim, self.kv_inner_dim, bias=True)
+        self.to_out = torch.nn.ModuleList(
+            [
+                torch.nn.Linear(self.inner_dim, dim, bias=True),
+                torch.nn.Dropout(dropout),
+            ]
+        )
+        self.norm_q = torch.nn.RMSNorm(dim_head * heads, eps=eps, elementwise_affine=True)
+        self.norm_k = torch.nn.RMSNorm(dim_head * heads, eps=eps, elementwise_affine=True)
+
+        self.add_k_proj = self.add_v_proj = None
+        if added_kv_proj_dim is not None:
+            self.add_k_proj = torch.nn.Linear(added_kv_proj_dim, self.inner_dim, bias=True)
+            self.add_v_proj = torch.nn.Linear(added_kv_proj_dim, self.inner_dim, bias=True)
+            self.norm_added_k = torch.nn.RMSNorm(dim_head * heads, eps=eps)
+
+        self.set_processor(processor)
+
+    def fuse_projections(self):
+        if getattr(self, "fused_projections", False):
+            return
+
+        if self.cross_attention_dim_head is None:
+            concatenated_weights = torch.cat([self.to_q.weight.data, self.to_k.weight.data, self.to_v.weight.data])
+            concatenated_bias = torch.cat([self.to_q.bias.data, self.to_k.bias.data, self.to_v.bias.data])
+            out_features, in_features = concatenated_weights.shape
+            with torch.device("meta"):
+                self.to_qkv = nn.Linear(in_features, out_features, bias=True)
+            self.to_qkv.load_state_dict(
+                {"weight": concatenated_weights, "bias": concatenated_bias}, strict=True, assign=True
+            )
+        else:
+            concatenated_weights = torch.cat([self.to_k.weight.data, self.to_v.weight.data])
+            concatenated_bias = torch.cat([self.to_k.bias.data, self.to_v.bias.data])
+            out_features, in_features = concatenated_weights.shape
+            with torch.device("meta"):
+                self.to_kv = nn.Linear(in_features, out_features, bias=True)
+            self.to_kv.load_state_dict(
+                {"weight": concatenated_weights, "bias": concatenated_bias}, strict=True, assign=True
+            )
+
+        if self.added_kv_proj_dim is not None:
+            concatenated_weights = torch.cat([self.add_k_proj.weight.data, self.add_v_proj.weight.data])
+            concatenated_bias = torch.cat([self.add_k_proj.bias.data, self.add_v_proj.bias.data])
+            out_features, in_features = concatenated_weights.shape
+            with torch.device("meta"):
+                self.to_added_kv = nn.Linear(in_features, out_features, bias=True)
+            self.to_added_kv.load_state_dict(
+                {"weight": concatenated_weights, "bias": concatenated_bias}, strict=True, assign=True
+            )
+
+        self.fused_projections = True
+
+    @torch.no_grad()
+    def unfuse_projections(self):
+        if not getattr(self, "fused_projections", False):
+            return
+
+        if hasattr(self, "to_qkv"):
+            delattr(self, "to_qkv")
+        if hasattr(self, "to_kv"):
+            delattr(self, "to_kv")
+        if hasattr(self, "to_added_kv"):
+            delattr(self, "to_added_kv")
+
+        self.fused_projections = False
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        encoder_hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        rotary_emb: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
+        **kwargs,
+    ) -> torch.Tensor:
+        return self.processor(self, hidden_states, encoder_hidden_states, attention_mask, rotary_emb, **kwargs)
+
+
 class WanImageEmbedding(torch.nn.Module):
     def __init__(self, in_features: int, out_features: int, pos_embed_seq_len=None):
         super().__init__()
@@ -266,33 +402,24 @@ def __init__(
 
         # 1. Self-attention
         self.norm1 = FP32LayerNorm(dim, eps, elementwise_affine=False)
-        self.attn1 = Attention(
-            query_dim=dim,
+        self.attn1 = WanAttention(
+            dim=dim,
             heads=num_heads,
-            kv_heads=num_heads,
             dim_head=dim // num_heads,
-            qk_norm=qk_norm,
             eps=eps,
-            bias=True,
-            cross_attention_dim=None,
-            out_bias=True,
-            processor=WanAttnProcessor2_0(),
+            cross_attention_dim_head=None,
+            processor=WanAttnProcessor(),
         )
 
         # 2. Cross-attention
-        self.attn2 = Attention(
-            query_dim=dim,
+        self.attn2 = WanAttention(
+            dim=dim,
             heads=num_heads,
-            kv_heads=num_heads,
             dim_head=dim // num_heads,
-            qk_norm=qk_norm,
             eps=eps,
-            bias=True,
-            cross_attention_dim=None,
-            out_bias=True,
             added_kv_proj_dim=added_kv_proj_dim,
-            added_proj_bias=True,
-            processor=WanAttnProcessor2_0(),
+            cross_attention_dim_head=dim // num_heads,
+            processor=WanAttnProcessor(),
         )
         self.norm2 = FP32LayerNorm(dim, eps, elementwise_affine=True) if cross_attn_norm else nn.Identity()
 
@@ -315,12 +442,12 @@ def forward(
 
         # 1. Self-attention
         norm_hidden_states = (self.norm1(hidden_states.float()) * (1 + scale_msa) + shift_msa).type_as(hidden_states)
-        attn_output = self.attn1(hidden_states=norm_hidden_states, rotary_emb=rotary_emb)
+        attn_output = self.attn1(norm_hidden_states, None, None, rotary_emb)
         hidden_states = (hidden_states.float() + attn_output * gate_msa).type_as(hidden_states)
 
         # 2. Cross-attention
         norm_hidden_states = self.norm2(hidden_states.float()).type_as(hidden_states)
-        attn_output = self.attn2(hidden_states=norm_hidden_states, encoder_hidden_states=encoder_hidden_states)
+        attn_output = self.attn2(norm_hidden_states, encoder_hidden_states, None, None)
         hidden_states = hidden_states + attn_output
 
         # 3. Feed-forward
@@ -333,7 +460,9 @@ def forward(
         return hidden_states
 
 
-class WanTransformer3DModel(ModelMixin, ConfigMixin, PeftAdapterMixin, FromOriginalModelMixin, CacheMixin):
+class WanTransformer3DModel(
+    ModelMixin, ConfigMixin, PeftAdapterMixin, FromOriginalModelMixin, CacheMixin, AttentionMixin
+):
     r"""
     A Transformer model for video-like data used in the Wan model.
 

src/diffusers/models/transformers/transformer_wan_vace.py

@@ -22,12 +22,17 @@
 from ...loaders import FromOriginalModelMixin, PeftAdapterMixin
 from ...utils import USE_PEFT_BACKEND, logging, scale_lora_layers, unscale_lora_layers
 from ..attention import FeedForward
-from ..attention_processor import Attention
 from ..cache_utils import CacheMixin
 from ..modeling_outputs import Transformer2DModelOutput
 from ..modeling_utils import ModelMixin
 from ..normalization import FP32LayerNorm
-from .transformer_wan import WanAttnProcessor2_0, WanRotaryPosEmbed, WanTimeTextImageEmbedding, WanTransformerBlock
+from .transformer_wan import (
+    WanAttention,
+    WanAttnProcessor,
+    WanRotaryPosEmbed,
+    WanTimeTextImageEmbedding,
+    WanTransformerBlock,
+)
 
 
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
@@ -55,33 +60,22 @@ def __init__(
 
         # 2. Self-attention
         self.norm1 = FP32LayerNorm(dim, eps, elementwise_affine=False)
-        self.attn1 = Attention(
-            query_dim=dim,
+        self.attn1 = WanAttention(
+            dim=dim,
             heads=num_heads,
-            kv_heads=num_heads,
             dim_head=dim // num_heads,
-            qk_norm=qk_norm,
             eps=eps,
-            bias=True,
-            cross_attention_dim=None,
-            out_bias=True,
-            processor=WanAttnProcessor2_0(),
+            processor=WanAttnProcessor(),
         )
 
         # 3. Cross-attention
-        self.attn2 = Attention(
-            query_dim=dim,
+        self.attn2 = WanAttention(
+            dim=dim,
             heads=num_heads,
-            kv_heads=num_heads,
             dim_head=dim // num_heads,
-            qk_norm=qk_norm,
             eps=eps,
-            bias=True,
-            cross_attention_dim=None,
-            out_bias=True,
             added_kv_proj_dim=added_kv_proj_dim,
-            added_proj_bias=True,
-            processor=WanAttnProcessor2_0(),
+            processor=WanAttnProcessor(),
         )
         self.norm2 = FP32LayerNorm(dim, eps, elementwise_affine=True) if cross_attn_norm else nn.Identity()