Feature IP Adapter Xformers Attention Processor

src/diffusers/loaders/ip_adapter.py

@@ -33,16 +33,15 @@
 
 
 if is_transformers_available():
-    from transformers import (
-        CLIPImageProcessor,
-        CLIPVisionModelWithProjection,
-    )
+
+    from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
 
     from ..models.attention_processor import (
         AttnProcessor,
         AttnProcessor2_0,
         IPAdapterAttnProcessor,
         IPAdapterAttnProcessor2_0,
+        IPAdapterXFormersAttnProcessor,
     )
 
 logger = logging.get_logger(__name__)
@@ -284,7 +283,7 @@ def set_ip_adapter_scale(self, scale):
         scale_configs = _maybe_expand_lora_scales(unet, scale, default_scale=0.0)
 
         for attn_name, attn_processor in unet.attn_processors.items():
-            if isinstance(attn_processor, (IPAdapterAttnProcessor, IPAdapterAttnProcessor2_0)):
+            if isinstance(attn_processor, (IPAdapterAttnProcessor, IPAdapterAttnProcessor2_0, IPAdapterXFormersAttnProcessor)):
                 if len(scale_configs) != len(attn_processor.scale):
                     raise ValueError(
                         f"Cannot assign {len(scale_configs)} scale_configs to "
@@ -342,7 +341,7 @@ def unload_ip_adapter(self):
             )
             attn_procs[name] = (
                 attn_processor_class
-                if isinstance(value, (IPAdapterAttnProcessor, IPAdapterAttnProcessor2_0))
+                if isinstance(value, (IPAdapterAttnProcessor, IPAdapterAttnProcessor2_0, IPAdapterXFormersAttnProcessor))
                 else value.__class__()
             )
         self.unet.set_attn_processor(attn_procs)

src/diffusers/loaders/unet.py

@@ -765,6 +765,7 @@ def _convert_ip_adapter_attn_to_diffusers(self, state_dicts, low_cpu_mem_usage=F
         from ..models.attention_processor import (
             IPAdapterAttnProcessor,
             IPAdapterAttnProcessor2_0,
+            IPAdapterXFormersAttnProcessor,
         )
 
         if low_cpu_mem_usage:
@@ -804,9 +805,11 @@ def _convert_ip_adapter_attn_to_diffusers(self, state_dicts, low_cpu_mem_usage=F
             if cross_attention_dim is None or "motion_modules" in name:
                 attn_processor_class = self.attn_processors[name].__class__
                 attn_procs[name] = attn_processor_class()
-
             else:
-                attn_processor_class = (
+                if ('XFormers' in str(self.attn_processors[name].__class__)):
+                    attn_processor_class = (IPAdapterXFormersAttnProcessor)
+                else:
+                    attn_processor_class = (
                     IPAdapterAttnProcessor2_0 if hasattr(F, "scaled_dot_product_attention") else IPAdapterAttnProcessor
                 )
                 num_image_text_embeds = []

src/diffusers/models/attention_processor.py

@@ -318,7 +318,10 @@ def set_use_memory_efficient_attention_xformers(
                 XFormersAttnAddedKVProcessor,
             ),
         )
-
+        is_ip_adapter = hasattr(self, "processor") and isinstance(
+            self.processor,
+            (IPAdapterAttnProcessor, IPAdapterAttnProcessor2_0, IPAdapterXFormersAttnProcessor),
+        )
         if use_memory_efficient_attention_xformers:
             if is_added_kv_processor and is_custom_diffusion:
                 raise NotImplementedError(
@@ -368,6 +371,15 @@ def set_use_memory_efficient_attention_xformers(
                     "Memory efficient attention with `xformers` might currently not work correctly if an attention mask is required for the attention operation."
                 )
                 processor = XFormersAttnAddedKVProcessor(attention_op=attention_op)
+            elif is_ip_adapter:
+                processor = IPAdapterXFormersAttnProcessor(hidden_size=self.processor.hidden_size,
+                    cross_attention_dim=self.processor.cross_attention_dim,
+                    scale=self.processor.scale,
+                    num_tokens=self.processor.num_tokens,
+                    attention_op=attention_op)
+                processor.load_state_dict(self.processor.state_dict())
+                if hasattr(self.processor, "to_k_ip"):
+                    processor.to(device=self.processor.to_k_ip[0].weight.device, dtype=self.processor.to_k_ip[0].weight.dtype)
             else:
                 processor = XFormersAttnProcessor(attention_op=attention_op)
         else:
@@ -4542,6 +4554,221 @@ def __call__(
         return hidden_states
 
 
+class IPAdapterXFormersAttnProcessor(torch.nn.Module):
+    r"""
+    Attention processor for IP-Adapter using xFormers.
+
+    Args:
+        hidden_size (`int`):
+            The hidden size of the attention layer.
+        cross_attention_dim (`int`):
+            The number of channels in the `encoder_hidden_states`.
+        num_tokens (`int`, `Tuple[int]` or `List[int]`, defaults to `(4,)`):
+            The context length of the image features.
+        scale (`float` or `List[float]`, defaults to 1.0):
+            the weight scale of image prompt.
+        attention_op (`Callable`, *optional*, defaults to `None`):
+            The base
+            [operator](https://facebookresearch.github.io/xformers/components/ops.html#xformers.ops.AttentionOpBase) to use
+            as the attention operator. It is recommended to set to `None`, and allow xFormers to choose the best operator.
+    """
+    def __init__(self, hidden_size, cross_attention_dim=None, num_tokens=(4,), scale=1.0, attention_op: Optional[Callable] = None):
+        super().__init__()
+
+        self.hidden_size = hidden_size
+        self.cross_attention_dim = cross_attention_dim
+        self.attention_op = attention_op
+
+        if not isinstance(num_tokens, (tuple, list)):
+            num_tokens = [num_tokens]
+        self.num_tokens = num_tokens
+
+        if not isinstance(scale, list):
+            scale = [scale] * len(num_tokens)
+        if len(scale) != len(num_tokens):
+            raise ValueError("`scale` should be a list of integers with the same length as `num_tokens`.")
+        self.scale = scale
+
+        self.to_k_ip = nn.ModuleList(
+            [nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False) for _ in range(len(num_tokens))]
+        )
+        self.to_v_ip = nn.ModuleList(
+            [nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False) for _ in range(len(num_tokens))]
+        )
+
+    def __call__(
+        self,
+        attn: Attention,
+        hidden_states: torch.FloatTensor,
+        encoder_hidden_states: Optional[torch.FloatTensor] = None,
+        attention_mask: Optional[torch.FloatTensor] = None,
+        temb: Optional[torch.FloatTensor] = None,
+        scale: float = 1.0,
+        ip_adapter_masks: Optional[torch.FloatTensor] = None,
+    ):
+        residual = hidden_states
+
+        # separate ip_hidden_states from encoder_hidden_states
+        if encoder_hidden_states is not None:
+            if isinstance(encoder_hidden_states, tuple):
+                encoder_hidden_states, ip_hidden_states = encoder_hidden_states
+            else:
+                deprecation_message = (
+                    "You have passed a tensor as `encoder_hidden_states`. This is deprecated and will be removed in a future release."
+                    " Please make sure to update your script to pass `encoder_hidden_states` as a tuple to suppress this warning."
+                )
+                deprecate("encoder_hidden_states not a tuple", "1.0.0", deprecation_message, standard_warn=False)
+                end_pos = encoder_hidden_states.shape[1] - self.num_tokens[0]
+                encoder_hidden_states, ip_hidden_states = (
+                    encoder_hidden_states[:, :end_pos, :],
+                    [encoder_hidden_states[:, end_pos:, :]],
+                )
+
+        if attn.spatial_norm is not None:
+            hidden_states = attn.spatial_norm(hidden_states, temb)
+
+        input_ndim = hidden_states.ndim
+
+        if input_ndim == 4:
+            batch_size, channel, height, width = hidden_states.shape
+            hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
+
+        batch_size, sequence_length, _ = (
+            hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
+        )
+
+        if attention_mask is not None:
+            attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
+            # expand our mask's singleton query_tokens dimension:
+            #   [batch*heads,            1, key_tokens] ->
+            #   [batch*heads, query_tokens, key_tokens]
+            # so that it can be added as a bias onto the attention scores that xformers computes:
+            #   [batch*heads, query_tokens, key_tokens]
+            # we do this explicitly because xformers doesn't broadcast the singleton dimension for us.
+            _, query_tokens, _ = hidden_states.shape
+            attention_mask = attention_mask.expand(-1, query_tokens, -1)
+
+        if attn.group_norm is not None:
+            hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
+
+        query = attn.to_q(hidden_states)
+
+        if encoder_hidden_states is None:
+            encoder_hidden_states = hidden_states
+        elif attn.norm_cross:
+            encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
+
+        key = attn.to_k(encoder_hidden_states)
+        value = attn.to_v(encoder_hidden_states)
+
+        query = attn.head_to_batch_dim(query).contiguous()
+        key = attn.head_to_batch_dim(key).contiguous()
+        value = attn.head_to_batch_dim(value).contiguous()
+
+        hidden_states = xformers.ops.memory_efficient_attention(query, key, value, attn_bias=attention_mask, op=self.attention_op)
+        hidden_states = hidden_states.to(query.dtype)
+        hidden_states = attn.batch_to_head_dim(hidden_states)
+
+        if ip_hidden_states:
+            if ip_adapter_masks is not None:
+                if not isinstance(ip_adapter_masks, List):
+                    # for backward compatibility, we accept `ip_adapter_mask` as a tensor of shape [num_ip_adapter, 1, height, width]
+                    ip_adapter_masks = list(ip_adapter_masks.unsqueeze(1))
+                if not (len(ip_adapter_masks) == len(self.scale) == len(ip_hidden_states)):
+                    raise ValueError(
+                        f"Length of ip_adapter_masks array ({len(ip_adapter_masks)}) must match "
+                        f"length of self.scale array ({len(self.scale)}) and number of ip_hidden_states "
+                        f"({len(ip_hidden_states)})"
+                    )
+                else:
+                    for index, (mask, scale, ip_state) in enumerate(zip(ip_adapter_masks, self.scale, ip_hidden_states)):
+                        if mask is None:
+                            continue
+                        if not isinstance(mask, torch.Tensor) or mask.ndim != 4:
+                            raise ValueError(
+                                "Each element of the ip_adapter_masks array should be a tensor with shape "
+                                "[1, num_images_for_ip_adapter, height, width]."
+                                " Please use `IPAdapterMaskProcessor` to preprocess your mask"
+                            )
+                        if mask.shape[1] != ip_state.shape[1]:
+                            raise ValueError(
+                                f"Number of masks ({mask.shape[1]}) does not match "
+                                f"number of ip images ({ip_state.shape[1]}) at index {index}"
+                            )
+                        if isinstance(scale, list) and not len(scale) == mask.shape[1]:
+                            raise ValueError(
+                                f"Number of masks ({mask.shape[1]}) does not match "
+                                f"number of scales ({len(scale)}) at index {index}"
+                            )
+            else:
+                ip_adapter_masks = [None] * len(self.scale)
+
+            # for ip-adapter
+            for current_ip_hidden_states, scale, to_k_ip, to_v_ip, mask in zip(
+                ip_hidden_states, self.scale, self.to_k_ip, self.to_v_ip, ip_adapter_masks
+            ):
+                skip = False
+                if isinstance(scale, list):
+                    if all(s == 0 for s in scale):
+                        skip = True
+                elif scale == 0:
+                    skip = True
+                if not skip:
+                    if mask is not None:
+                        mask = mask.to(torch.float16)
+                        if not isinstance(scale, list):
+                            scale = [scale] * mask.shape[1]
+
+                        current_num_images = mask.shape[1]
+                        for i in range(current_num_images):
+                            ip_key = to_k_ip(current_ip_hidden_states[:, i, :, :])
+                            ip_value = to_v_ip(current_ip_hidden_states[:, i, :, :])
+
+                            ip_key = attn.head_to_batch_dim(ip_key).contiguous()
+                            ip_value = attn.head_to_batch_dim(ip_value).contiguous()
+
+                            _current_ip_hidden_states = xformers.ops.memory_efficient_attention(query, ip_key, ip_value, op=self.attention_op)
+                            _current_ip_hidden_states = _current_ip_hidden_states.to(query.dtype)
+                            _current_ip_hidden_states = attn.batch_to_head_dim(_current_ip_hidden_states)
+
+                            mask_downsample = IPAdapterMaskProcessor.downsample(
+                                mask[:, i, :, :],
+                                batch_size,
+                                _current_ip_hidden_states.shape[1],
+                                _current_ip_hidden_states.shape[2],
+                            )
+
+                            mask_downsample = mask_downsample.to(dtype=query.dtype, device=query.device)
+                            hidden_states = hidden_states + scale[i] * (_current_ip_hidden_states * mask_downsample)
+                    else:
+                        ip_key = to_k_ip(current_ip_hidden_states)
+                        ip_value = to_v_ip(current_ip_hidden_states)
+
+                        ip_key = attn.head_to_batch_dim(ip_key).contiguous()
+                        ip_value = attn.head_to_batch_dim(ip_value).contiguous()
+
+                        current_ip_hidden_states = xformers.ops.memory_efficient_attention(query, ip_key, ip_value, op=self.attention_op)
+                        current_ip_hidden_states = current_ip_hidden_states.to(query.dtype)
+                        current_ip_hidden_states = attn.batch_to_head_dim(current_ip_hidden_states)
+
+                        hidden_states = hidden_states + scale * current_ip_hidden_states
+
+        # linear proj
+        hidden_states = attn.to_out[0](hidden_states)
+        # dropout
+        hidden_states = attn.to_out[1](hidden_states)
+
+        if input_ndim == 4:
+            hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
+
+        if attn.residual_connection:
+            hidden_states = hidden_states + residual
+
+        hidden_states = hidden_states / attn.rescale_output_factor
+
+        return hidden_states
+
+
 class PAGIdentitySelfAttnProcessor2_0:
     r"""
     Processor for implementing PAG using scaled dot-product attention (enabled by default if you're using PyTorch 2.0).