fix tests

src/diffusers/pipelines/chroma/pipeline_chroma.py

@@ -675,6 +675,13 @@ class ChromaPipeline(
             batch_size = 1
         elif prompt is not None and isinstance(prompt, list):
             batch_size = len(prompt)
+            if negative_prompt is not None and isinstance(negative_prompt, str):
+                negative_prompt = [negative_prompt] * batch_size
+            elif negative_prompt is not None and isinstance(negative_prompt, list):
+                if len(negative_prompt) == 1:
+                    negative_prompt = [negative_prompt] * batch_size
+                else:
+                    raise ValueError("prompt and negative_prompt are lists of unequal size")
         else:
             batch_size = prompt_embeds.shape[0]
 

tests/models/transformers/test_models_transformer_chroma.py

@@ -82,7 +82,7 @@ class ChromaTransformerTests(ModelTesterMixin, unittest.TestCase):
     model_class = ChromaTransformer2DModel
     main_input_name = "hidden_states"
     # We override the items here because the transformer under consideration is small.
-    model_split_percents = [0.7, 0.6, 0.6]
+    model_split_percents = [0.8, 0.7, 0.7]
 
     # Skip setting testing with default: AttnProcessor
     uses_custom_attn_processor = True

tests/models/transformers/test_models_transformer_flux.py

@@ -57,7 +57,9 @@ def create_flux_ip_adapter_state_dict(model):
 
     image_projection = ImageProjection(
         cross_attention_dim=model.config["joint_attention_dim"],
-        image_embed_dim=model.config["pooled_projection_dim"],
+        image_embed_dim=(
+            model.config["pooled_projection_dim"] if "pooled_projection_dim" in model.config.keys() else 768
+        ),
         num_image_text_embeds=4,
     )
 

tests/pipelines/test_pipelines_common.py

@@ -544,7 +544,11 @@ class FluxIPAdapterTesterMixin:
         components = self.get_dummy_components()
         pipe = self.pipeline_class(**components).to(torch_device)
         pipe.set_progress_bar_config(disable=None)
-        image_embed_dim = pipe.transformer.config.pooled_projection_dim or 768
+        image_embed_dim = (
+            pipe.transformer.config.pooled_projection_dim
+            if hasattr(pipe.transformer.config, "pooled_projection_dim")
+            else 768
+        )
 
         # forward pass without ip adapter
         inputs = self._modify_inputs_for_ip_adapter_test(self.get_dummy_inputs(torch_device))