[refactor embeddings] gligen + ip-adapter

src/diffusers/loaders/unet.py

@@ -24,7 +24,7 @@
 from huggingface_hub.utils import validate_hf_hub_args
 from torch import nn
 
-from ..models.embeddings import ImageProjection, MLPProjection, Resampler
+from ..models.embeddings import ImageProjection, IPAdapterFullImageProjection, IPAdapterPlusImageProjection
 from ..models.modeling_utils import _LOW_CPU_MEM_USAGE_DEFAULT, load_model_dict_into_meta
 from ..utils import (
     USE_PEFT_BACKEND,
@@ -712,7 +712,7 @@ def _convert_ip_adapter_image_proj_to_diffusers(self, state_dict):
             clip_embeddings_dim = state_dict["proj.0.weight"].shape[0]
             cross_attention_dim = state_dict["proj.3.weight"].shape[0]
 
-            image_projection = MLPProjection(
+            image_projection = IPAdapterFullImageProjection(
                 cross_attention_dim=cross_attention_dim, image_embed_dim=clip_embeddings_dim
             )
 
@@ -730,7 +730,7 @@ def _convert_ip_adapter_image_proj_to_diffusers(self, state_dict):
             hidden_dims = state_dict["latents"].shape[2]
             heads = state_dict["layers.0.0.to_q.weight"].shape[0] // 64
 
-            image_projection = Resampler(
+            image_projection = IPAdapterPlusImageProjection(
                 embed_dims=embed_dims,
                 output_dims=output_dims,
                 hidden_dims=hidden_dims,
@@ -780,7 +780,7 @@ def _load_ip_adapter_weights(self, state_dict):
             num_image_text_embeds = state_dict["image_proj"]["latents"].shape[1]
 
         # Set encoder_hid_proj after loading ip_adapter weights,
-        # because `Resampler` also has `attn_processors`.
+        # because `IPAdapterPlusImageProjection` also has `attn_processors`.
         self.encoder_hid_proj = None
 
         # set ip-adapter cross-attention processors & load state_dict

src/diffusers/models/embeddings.py

@@ -462,7 +462,7 @@ def forward(self, image_embeds: torch.FloatTensor):
         return image_embeds
 
 
-class MLPProjection(nn.Module):
+class IPAdapterFullImageProjection(nn.Module):
     def __init__(self, image_embed_dim=1024, cross_attention_dim=1024):
         super().__init__()
         from .attention import FeedForward
@@ -621,29 +621,34 @@ def shape(x):
         return a[:, 0, :]  # cls_token
 
 
-class FourierEmbedder(nn.Module):
-    def __init__(self, num_freqs=64, temperature=100):
-        super().__init__()
+def get_fourier_embeds_from_boundingbox(embed_dim, box):
+    """
+    Args:
+        embed_dim: int
+        box: a 3-D tensor [B x N x 4] representing the bounding boxes for GLIGEN pipeline
+    Returns:
+        [B x N x embed_dim] tensor of positional embeddings
+    """
+
+    batch_size, num_boxes = box.shape[:2]
 
-        self.num_freqs = num_freqs
-        self.temperature = temperature
+    emb = 100 ** (torch.arange(embed_dim) / embed_dim)
+    emb = emb[None, None, None].to(device=box.device, dtype=box.dtype)
+    emb = emb * box.unsqueeze(-1)
 
-        freq_bands = temperature ** (torch.arange(num_freqs) / num_freqs)
-        freq_bands = freq_bands[None, None, None]
-        self.register_buffer("freq_bands", freq_bands, persistent=False)
+    emb = torch.stack((emb.sin(), emb.cos()), dim=-1)
+    emb = emb.permute(0, 1, 3, 4, 2).reshape(batch_size, num_boxes, embed_dim * 2 * 4)
 
-    def __call__(self, x):
-        x = self.freq_bands * x.unsqueeze(-1)
-        return torch.stack((x.sin(), x.cos()), dim=-1).permute(0, 1, 3, 4, 2).reshape(*x.shape[:2], -1)
+    return emb
 
 
-class PositionNet(nn.Module):
+class GLIGENTextBoundingboxProjection(nn.Module):
     def __init__(self, positive_len, out_dim, feature_type="text-only", fourier_freqs=8):
         super().__init__()
         self.positive_len = positive_len
         self.out_dim = out_dim
 
-        self.fourier_embedder = FourierEmbedder(num_freqs=fourier_freqs)
+        self.fourier_embedder_dim = fourier_freqs
         self.position_dim = fourier_freqs * 2 * 4  # 2: sin/cos, 4: xyxy
 
         if isinstance(out_dim, tuple):
@@ -692,7 +697,7 @@ def forward(
         masks = masks.unsqueeze(-1)
 
         # embedding position (it may includes padding as placeholder)
-        xyxy_embedding = self.fourier_embedder(boxes)  # B*N*4 -> B*N*C
+        xyxy_embedding = get_fourier_embeds_from_boundingbox(self.fourier_embedder_dim, boxes)  # B*N*4 -> B*N*C
 
         # learnable null embedding
         xyxy_null = self.null_position_feature.view(1, 1, -1)
@@ -787,7 +792,7 @@ def forward(self, caption):
         return hidden_states
 
 
-class Resampler(nn.Module):
+class IPAdapterPlusImageProjection(nn.Module):
     """Resampler of IP-Adapter Plus.
 
     Args:

src/diffusers/models/unet_2d_condition.py

@@ -32,10 +32,10 @@
 )
 from .embeddings import (
     GaussianFourierProjection,
+    GLIGENTextBoundingboxProjection,
     ImageHintTimeEmbedding,
     ImageProjection,
     ImageTimeEmbedding,
-    PositionNet,
     TextImageProjection,
     TextImageTimeEmbedding,
     TextTimeEmbedding,
@@ -615,7 +615,7 @@ def __init__(
                 positive_len = cross_attention_dim[0]
 
             feature_type = "text-only" if attention_type == "gated" else "text-image"
-            self.position_net = PositionNet(
+            self.position_net = GLIGENTextBoundingboxProjection(
                 positive_len=positive_len, out_dim=cross_attention_dim, feature_type=feature_type
             )
 

src/diffusers/pipelines/deprecated/versatile_diffusion/modeling_text_unet.py

@@ -187,7 +187,7 @@ def __call__(self, x):
         return torch.stack((x.sin(), x.cos()), dim=-1).permute(0, 1, 3, 4, 2).reshape(*x.shape[:2], -1)
 
 
-class PositionNet(nn.Module):
+class GLIGENTextBoundingboxProjection(nn.Module):
     def __init__(self, positive_len, out_dim, feature_type, fourier_freqs=8):
         super().__init__()
         self.positive_len = positive_len
@@ -820,7 +820,7 @@ def __init__(
                 positive_len = cross_attention_dim[0]
 
             feature_type = "text-only" if attention_type == "gated" else "text-image"
-            self.position_net = PositionNet(
+            self.position_net = GLIGENTextBoundingboxProjection(
                 positive_len=positive_len, out_dim=cross_attention_dim, feature_type=feature_type
             )
 

src/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen.py

@@ -730,7 +730,7 @@ def __call__(
             )
             gligen_phrases = gligen_phrases[:max_objs]
             gligen_boxes = gligen_boxes[:max_objs]
-        # prepare batched input to the PositionNet (boxes, phrases, mask)
+        # prepare batched input to the GLIGENTextBoundingboxProjection (boxes, phrases, mask)
         # Get tokens for phrases from pre-trained CLIPTokenizer
         tokenizer_inputs = self.tokenizer(gligen_phrases, padding=True, return_tensors="pt").to(device)
         # For the token, we use the same pre-trained text encoder

tests/models/test_models_unet_2d_condition.py

@@ -26,7 +26,7 @@
 
 from diffusers import UNet2DConditionModel
 from diffusers.models.attention_processor import CustomDiffusionAttnProcessor, IPAdapterAttnProcessor
-from diffusers.models.embeddings import ImageProjection, Resampler
+from diffusers.models.embeddings import ImageProjection, IPAdapterPlusImageProjection
 from diffusers.utils import logging
 from diffusers.utils.import_utils import is_xformers_available
 from diffusers.utils.testing_utils import (
@@ -133,7 +133,7 @@ def create_ip_adapter_plus_state_dict(model):
 
     # "image_proj" (ImageProjection layer weights)
     cross_attention_dim = model.config["cross_attention_dim"]
-    image_projection = Resampler(
+    image_projection = IPAdapterPlusImageProjection(
         embed_dims=cross_attention_dim, output_dims=cross_attention_dim, dim_head=32, heads=2, num_queries=4
     )