Improve control net block index for sd3 (#9758)

* improve control net index

---------

Co-authored-by: YiYi Xu <yixu310@gmail.com>

Author: linjiapro

src/diffusers/models/controlnets/controlnet_sd3.py

@@ -56,6 +56,8 @@ def __init__(
         out_channels: int = 16,
         pos_embed_max_size: int = 96,
         extra_conditioning_channels: int = 0,
+        dual_attention_layers: Tuple[int, ...] = (),
+        qk_norm: Optional[str] = None,
     ):
         super().__init__()
         default_out_channels = in_channels
@@ -84,6 +86,8 @@ def __init__(
                     num_attention_heads=num_attention_heads,
                     attention_head_dim=self.config.attention_head_dim,
                     context_pre_only=False,
+                    qk_norm=qk_norm,
+                    use_dual_attention=True if i in dual_attention_layers else False,
                 )
                 for i in range(num_layers)
             ]
@@ -248,7 +252,7 @@ def from_transformer(
         config = transformer.config
         config["num_layers"] = num_layers or config.num_layers
         config["extra_conditioning_channels"] = num_extra_conditioning_channels
-        controlnet = cls(**config)
+        controlnet = cls.from_config(config)
 
         if load_weights_from_transformer:
             controlnet.pos_embed.load_state_dict(transformer.pos_embed.state_dict())

src/diffusers/models/transformers/transformer_sd3.py

@@ -15,6 +15,7 @@
 
 from typing import Any, Dict, List, Optional, Tuple, Union
 
+import numpy as np
 import torch
 import torch.nn as nn
 
@@ -349,7 +350,8 @@ def custom_forward(*inputs):
 
             # controlnet residual
             if block_controlnet_hidden_states is not None and block.context_pre_only is False:
-                interval_control = len(self.transformer_blocks) // len(block_controlnet_hidden_states)
+                interval_control = len(self.transformer_blocks) / len(block_controlnet_hidden_states)
+                interval_control = int(np.ceil(interval_control))
                 hidden_states = hidden_states + block_controlnet_hidden_states[index_block // interval_control]
 
         hidden_states = self.norm_out(hidden_states, temb)

tests/pipelines/controlnet_sd3/test_controlnet_sd3.py

@@ -15,6 +15,7 @@
 
 import gc
 import unittest
+from typing import Optional
 
 import numpy as np
 import pytest
@@ -59,7 +60,7 @@ class StableDiffusion3ControlNetPipelineFastTests(unittest.TestCase, PipelineTes
     )
     batch_params = frozenset(["prompt", "negative_prompt"])
 
-    def get_dummy_components(self):
+    def get_dummy_components(self, num_controlnet_layers: int = 3, qk_norm: Optional[str] = "rms_norm"):
         torch.manual_seed(0)
         transformer = SD3Transformer2DModel(
             sample_size=32,
@@ -72,14 +73,15 @@ def get_dummy_components(self):
             caption_projection_dim=32,
             pooled_projection_dim=64,
             out_channels=8,
+            qk_norm=qk_norm,
         )
 
         torch.manual_seed(0)
         controlnet = SD3ControlNetModel(
             sample_size=32,
             patch_size=1,
             in_channels=8,
-            num_layers=1,
+            num_layers=num_controlnet_layers,
             attention_head_dim=8,
             num_attention_heads=4,
             joint_attention_dim=32,