From c366b5a817c0671ef9967a58cf6fcec10952f3ae Mon Sep 17 00:00:00 2001
From: DN6 <dhruv.nair@gmail.com>
Date: Thu, 11 Dec 2025 13:37:06 +0530
Subject: [PATCH] update

---
 .../test_models_transformer_flux.py           | 98 ++++++++++---------
 1 file changed, 50 insertions(+), 48 deletions(-)

diff --git a/tests/models/transformers/test_models_transformer_flux.py b/tests/models/transformers/test_models_transformer_flux.py
index 43e02db448..3019308831 100644
--- a/tests/models/transformers/test_models_transformer_flux.py
+++ b/tests/models/transformers/test_models_transformer_flux.py
@@ -45,6 +45,55 @@ from ..testing_utils import (
 enable_full_determinism()
 
 
+# TODO: This standalone function maintains backward compatibility with pipeline tests
+# (tests/pipelines/test_pipelines_common.py) and will be refactored.
+def create_flux_ip_adapter_state_dict(model) -> dict[str, dict[str, Any]]:
+    """Create a dummy IP Adapter state dict for Flux transformer testing."""
+    ip_cross_attn_state_dict = {}
+    key_id = 0
+
+    for name in model.attn_processors.keys():
+        if name.startswith("single_transformer_blocks"):
+            continue
+
+        joint_attention_dim = model.config["joint_attention_dim"]
+        hidden_size = model.config["num_attention_heads"] * model.config["attention_head_dim"]
+        sd = FluxIPAdapterAttnProcessor(
+            hidden_size=hidden_size, cross_attention_dim=joint_attention_dim, scale=1.0
+        ).state_dict()
+        ip_cross_attn_state_dict.update(
+            {
+                f"{key_id}.to_k_ip.weight": sd["to_k_ip.0.weight"],
+                f"{key_id}.to_v_ip.weight": sd["to_v_ip.0.weight"],
+                f"{key_id}.to_k_ip.bias": sd["to_k_ip.0.bias"],
+                f"{key_id}.to_v_ip.bias": sd["to_v_ip.0.bias"],
+            }
+        )
+        key_id += 1
+
+    image_projection = ImageProjection(
+        cross_attention_dim=model.config["joint_attention_dim"],
+        image_embed_dim=(
+            model.config["pooled_projection_dim"] if "pooled_projection_dim" in model.config.keys() else 768
+        ),
+        num_image_text_embeds=4,
+    )
+
+    ip_image_projection_state_dict = {}
+    sd = image_projection.state_dict()
+    ip_image_projection_state_dict.update(
+        {
+            "proj.weight": sd["image_embeds.weight"],
+            "proj.bias": sd["image_embeds.bias"],
+            "norm.weight": sd["norm.weight"],
+            "norm.bias": sd["norm.bias"],
+        }
+    )
+
+    del sd
+    return {"image_proj": ip_image_projection_state_dict, "ip_adapter": ip_cross_attn_state_dict}
+
+
 class FluxTransformerTesterConfig:
     model_class = FluxTransformer2DModel
     pretrained_model_name_or_path = "hf-internal-testing/tiny-flux-pipe"
@@ -169,54 +218,7 @@ class TestFluxTransformerIPAdapter(FluxTransformerTesterConfig, IPAdapterTesterM
         return inputs_dict
 
     def create_ip_adapter_state_dict(self, model: Any) -> dict[str, dict[str, Any]]:
-        from diffusers.models.transformers.transformer_flux import FluxIPAdapterAttnProcessor
-
-        ip_cross_attn_state_dict = {}
-        key_id = 0
-
-        for name in model.attn_processors.keys():
-            if name.startswith("single_transformer_blocks"):
-                continue
-
-            joint_attention_dim = model.config["joint_attention_dim"]
-            hidden_size = model.config["num_attention_heads"] * model.config["attention_head_dim"]
-            sd = FluxIPAdapterAttnProcessor(
-                hidden_size=hidden_size, cross_attention_dim=joint_attention_dim, scale=1.0
-            ).state_dict()
-            ip_cross_attn_state_dict.update(
-                {
-                    f"{key_id}.to_k_ip.weight": sd["to_k_ip.0.weight"],
-                    f"{key_id}.to_v_ip.weight": sd["to_v_ip.0.weight"],
-                    f"{key_id}.to_k_ip.bias": sd["to_k_ip.0.bias"],
-                    f"{key_id}.to_v_ip.bias": sd["to_v_ip.0.bias"],
-                }
-            )
-
-            key_id += 1
-
-        image_projection = ImageProjection(
-            cross_attention_dim=model.config["joint_attention_dim"],
-            image_embed_dim=(
-                model.config["pooled_projection_dim"] if "pooled_projection_dim" in model.config.keys() else 768
-            ),
-            num_image_text_embeds=4,
-        )
-
-        ip_image_projection_state_dict = {}
-        sd = image_projection.state_dict()
-        ip_image_projection_state_dict.update(
-            {
-                "proj.weight": sd["image_embeds.weight"],
-                "proj.bias": sd["image_embeds.bias"],
-                "norm.weight": sd["norm.weight"],
-                "norm.bias": sd["norm.bias"],
-            }
-        )
-
-        del sd
-        ip_state_dict = {}
-        ip_state_dict.update({"image_proj": ip_image_projection_state_dict, "ip_adapter": ip_cross_attn_state_dict})
-        return ip_state_dict
+        return create_flux_ip_adapter_state_dict(model)
 
 
 class TestFluxTransformerLoRA(FluxTransformerTesterConfig, LoraTesterMixin):