Fix handling of scales with multiple IP-Adapters.

invokeai/backend/ip_adapter/attention_processor.py

@@ -9,6 +9,7 @@ import torch.nn.functional as F
 from diffusers.models.attention_processor import AttnProcessor2_0 as DiffusersAttnProcessor2_0
 
 from invokeai.backend.ip_adapter.ip_attention_weights import IPAttentionProcessorWeights
+from invokeai.backend.ip_adapter.scales import Scales
 
 
 # Create a version of AttnProcessor2_0 that is a sub-class of nn.Module. This is required for IP-Adapter state_dict
@@ -47,13 +48,16 @@ class IPAttnProcessor2_0(torch.nn.Module):
             the weight scale of image prompt.
     """
 
-    def __init__(self, weights: list[IPAttentionProcessorWeights]):
+    def __init__(self, weights: list[IPAttentionProcessorWeights], scales: Scales):
         super().__init__()
 
         if not hasattr(F, "scaled_dot_product_attention"):
             raise ImportError("AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")
 
-        self.weights = weights
+        assert len(weights) == len(scales)
+
+        self._weights = weights
+        self._scales = scales
 
     def __call__(
         self,
@@ -119,9 +123,11 @@ class IPAttnProcessor2_0(torch.nn.Module):
             # If encoder_hidden_states is not None, then we are doing cross-attention, not self-attention. In this case,
             # we will apply IP-Adapter conditioning. We validate the inputs for IP-Adapter conditioning here.
             assert ip_adapter_image_prompt_embeds is not None
-            assert len(ip_adapter_image_prompt_embeds) == len(self.weights)
+            assert len(ip_adapter_image_prompt_embeds) == len(self._weights)
 
-            for ipa_embed, ipa_weights in zip(ip_adapter_image_prompt_embeds, self.weights):
+            for ipa_embed, ipa_weights, scale in zip(
+                ip_adapter_image_prompt_embeds, self._weights, self._scales.scales
+            ):
                 # The batch dimensions should match.
                 assert ipa_embed.shape[0] == encoder_hidden_states.shape[0]
                 # The channel dimensions should match.
@@ -144,7 +150,7 @@ class IPAttnProcessor2_0(torch.nn.Module):
                 ip_hidden_states = ip_hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
                 ip_hidden_states = ip_hidden_states.to(query.dtype)
 
-                hidden_states = hidden_states + ipa_weights.scale * ip_hidden_states
+                hidden_states = hidden_states + scale * ip_hidden_states
 
         # linear proj
         hidden_states = attn.to_out[0](hidden_states)

invokeai/backend/ip_adapter/ip_attention_weights.py

@@ -8,9 +8,8 @@ class IPAttentionProcessorWeights(torch.nn.Module):
     method.
     """
 
-    def __init__(self, in_dim: int, out_dim: int, scale: float = 1.0):
+    def __init__(self, in_dim: int, out_dim: int):
         super().__init__()
-        self.scale = scale
         self.to_k_ip = torch.nn.Linear(in_dim, out_dim, bias=False)
         self.to_v_ip = torch.nn.Linear(in_dim, out_dim, bias=False)
 
@@ -26,11 +25,6 @@ class IPAttentionWeights(torch.nn.Module):
         super().__init__()
         self._weights = weights
 
-    def set_scale(self, scale: float):
-        """Set the scale (a.k.a. 'weight') for all of the `IPAttentionProcessorWeights` in this collection."""
-        for w in self._weights.values():
-            w.scale = scale
-
     def get_attention_processor_weights(self, idx: int) -> IPAttentionProcessorWeights:
         """Get the `IPAttentionProcessorWeights` for the idx'th attention processor."""
         # Cast to int first, because we expect the key to represent an int. Then cast back to str, because

invokeai/backend/ip_adapter/scales.py

@@ -0,0 +1,19 @@
+class Scales:
+    """The IP-Adapter scales for a patched UNet. This object can be used to dynamically change the scales for a patched
+    UNet.
+    """
+
+    def __init__(self, scales: list[float]):
+        self._scales = scales
+
+    @property
+    def scales(self):
+        return self._scales
+
+    @scales.setter
+    def scales(self, scales: list[float]):
+        assert len(scales) == len(self._scales)
+        self._scales = scales
+
+    def __len__(self):
+        return len(self._scales)

invokeai/backend/ip_adapter/unet_patcher.py

@@ -4,9 +4,10 @@ from diffusers.models import UNet2DConditionModel
 
 from invokeai.backend.ip_adapter.attention_processor import AttnProcessor2_0, IPAttnProcessor2_0
 from invokeai.backend.ip_adapter.ip_adapter import IPAdapter
+from invokeai.backend.ip_adapter.scales import Scales
 
 
-def _prepare_attention_processors(unet: UNet2DConditionModel, ip_adapters: list[IPAdapter]):
+def _prepare_attention_processors(unet: UNet2DConditionModel, ip_adapters: list[IPAdapter], scales: Scales):
     """Prepare a dict of attention processors that can be injected into a unet, and load the IP-Adapter attention
     weights into them.
 
@@ -32,15 +33,22 @@ def _prepare_attention_processors(unet: UNet2DConditionModel, ip_adapters: list[
         else:
             # Collect the weights from each IP Adapter for the idx'th attention processor.
             attn_procs[name] = IPAttnProcessor2_0(
-                [ip_adapter.attn_weights.get_attention_processor_weights(idx) for ip_adapter in ip_adapters]
+                [ip_adapter.attn_weights.get_attention_processor_weights(idx) for ip_adapter in ip_adapters], scales
             )
     return attn_procs
 
 
 @contextmanager
 def apply_ip_adapter_attention(unet: UNet2DConditionModel, ip_adapters: list[IPAdapter]):
-    """A context manager that patches `unet` with IP-Adapter attention processors."""
+    """A context manager that patches `unet` with IP-Adapter attention processors.
-    attn_procs = _prepare_attention_processors(unet, ip_adapters)
+
+    Yields:
+        Scales: The Scales object, which can be used to dynamically alter the scales of the
+            IP-Adapters.
+    """
+    scales = Scales([1.0] * len(ip_adapters))
+
+    attn_procs = _prepare_attention_processors(unet, ip_adapters, scales)
 
     orig_attn_processors = unet.attn_processors
 
@@ -49,6 +57,6 @@ def apply_ip_adapter_attention(unet: UNet2DConditionModel, ip_adapters: list[IPA
         # passed dict. So, if you wanted to keep the dict for future use, you'd have to make a moderately-shallow copy
         # of it. E.g. `attn_procs_copy = {k: v for k, v in attn_procs.items()}`.
         unet.set_attn_processor(attn_procs)
-        yield None
+        yield scales
     finally:
         unet.set_attn_processor(orig_attn_processors)

invokeai/backend/stable_diffusion/diffusers_pipeline.py

@@ -24,7 +24,7 @@ from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
 
 from invokeai.app.services.config import InvokeAIAppConfig
 from invokeai.backend.ip_adapter.ip_adapter import IPAdapter
-from invokeai.backend.ip_adapter.unet_patcher import apply_ip_adapter_attention
+from invokeai.backend.ip_adapter.unet_patcher import Scales, apply_ip_adapter_attention
 from invokeai.backend.stable_diffusion.diffusion.conditioning_data import ConditioningData
 
 from ..util import auto_detect_slice_size, normalize_device
@@ -426,7 +426,7 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
             return latents, attention_map_saver
 
         if conditioning_data.extra is not None and conditioning_data.extra.wants_cross_attention_control:
-            attn_ctx = self.invokeai_diffuser.custom_attention_context(
+            attn_ctx_mgr = self.invokeai_diffuser.custom_attention_context(
                 self.invokeai_diffuser.model,
                 extra_conditioning_info=conditioning_data.extra,
                 step_count=len(self.scheduler.timesteps),
@@ -435,14 +435,14 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
         elif ip_adapter_data is not None:
             # TODO(ryand): Should we raise an exception if both custom attention and IP-Adapter attention are active?
             # As it is now, the IP-Adapter will silently be skipped.
-            attn_ctx = apply_ip_adapter_attention(
+            attn_ctx_mgr = apply_ip_adapter_attention(
                 unet=self.invokeai_diffuser.model, ip_adapters=[ipa.ip_adapter_model for ipa in ip_adapter_data]
             )
             self.use_ip_adapter = True
         else:
-            attn_ctx = nullcontext()
+            attn_ctx_mgr = nullcontext()
 
-        with attn_ctx:
+        with attn_ctx_mgr as attn_ctx:
             if callback is not None:
                 callback(
                     PipelineIntermediateState(
@@ -467,6 +467,7 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
                     control_data=control_data,
                     ip_adapter_data=ip_adapter_data,
                     t2i_adapter_data=t2i_adapter_data,
+                    attn_ctx=attn_ctx,
                 )
                 latents = step_output.prev_sample
 
@@ -514,6 +515,7 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
         control_data: List[ControlNetData] = None,
         ip_adapter_data: Optional[list[IPAdapterData]] = None,
         t2i_adapter_data: Optional[list[T2IAdapterData]] = None,
+        attn_ctx: Optional[Scales] = None,
     ):
         # invokeai_diffuser has batched timesteps, but diffusers schedulers expect a single value
         timestep = t[0]
@@ -526,7 +528,7 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
 
         # handle IP-Adapter
         if self.use_ip_adapter and ip_adapter_data is not None:  # somewhat redundant but logic is clearer
-            for single_ip_adapter_data in ip_adapter_data:
+            for i, single_ip_adapter_data in enumerate(ip_adapter_data):
                 first_adapter_step = math.floor(single_ip_adapter_data.begin_step_percent * total_step_count)
                 last_adapter_step = math.ceil(single_ip_adapter_data.end_step_percent * total_step_count)
                 weight = (
@@ -536,10 +538,10 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
                 )
                 if step_index >= first_adapter_step and step_index <= last_adapter_step:
                     # Only apply this IP-Adapter if the current step is within the IP-Adapter's begin/end step range.
-                    single_ip_adapter_data.ip_adapter_model.attn_weights.set_scale(weight)
+                    attn_ctx.scales[i] = weight
                 else:
                     # Otherwise, set the IP-Adapter's scale to 0, so it has no effect.
-                    single_ip_adapter_data.ip_adapter_model.attn_weights.set_scale(0.0)
+                    attn_ctx.scales[i] = weight
 
         # Handle ControlNet(s) and T2I-Adapter(s)
         down_block_additional_residuals = None