update

Author: DN6

src/diffusers/pipelines/chroma/pipeline_chroma.py

@@ -214,7 +214,7 @@ def _get_t5_prompt_embeds(
 
         text_inputs = self.tokenizer(
             prompt,
-            padding=True,
+            padding="max_length",
             max_length=max_sequence_length,
             truncation=True,
             return_length=False,
@@ -286,6 +286,11 @@ def encode_prompt(
 
         prompt = [prompt] if isinstance(prompt, str) else prompt
 
+        if prompt is not None:
+            batch_size = len(prompt)
+        else:
+            batch_size = prompt_embeds.shape[0]
+
         if prompt_embeds is None:
             prompt_embeds = self._get_t5_prompt_embeds(
                 prompt=prompt,
@@ -295,6 +300,8 @@ def encode_prompt(
             )
 
         if negative_prompt_embeds is None:
+            negative_prompt = negative_prompt or ""
+            negative_prompt = batch_size * [negative_prompt] if isinstance(negative_prompt, str) else negative_prompt
             negative_prompt_embeds = self._get_t5_prompt_embeds(
                 prompt=negative_prompt,
                 num_images_per_prompt=num_images_per_prompt,

tests/pipelines/chroma/test_pipeline_chroma.py

@@ -4,8 +4,15 @@
 import torch
 from transformers import AutoTokenizer, T5EncoderModel
 
-from diffusers import AutoencoderKL, ChromaPipeline, ChromaTransformer2DModel, FlowMatchEulerDiscreteScheduler
-from diffusers.utils.testing_utils import torch_device
+from diffusers import (
+    AutoencoderKL,
+    ChromaPipeline,
+    ChromaTransformer2DModel,
+    FlowMatchEulerDiscreteScheduler,
+)
+from diffusers.utils.testing_utils import (
+    torch_device,
+)
 
 from ..test_pipelines_common import (
     FluxIPAdapterTesterMixin,
@@ -22,9 +29,6 @@ class ChromaPipelineFastTests(
 ):
     pipeline_class = ChromaPipeline
     params = frozenset(["prompt", "height", "width", "guidance_scale", "prompt_embeds"])
-
-    pipeline_class = ChromaPipeline
-    params = frozenset(["prompt", "negative_prompt", "height", "width", "guidance_scale", "prompt_embeds"])
     batch_params = frozenset(["prompt"])
 
     # there is no xformers processor for Flux
@@ -39,14 +43,13 @@ def get_dummy_components(self, num_layers: int = 1, num_single_layers: int = 1):
             in_channels=4,
             num_layers=num_layers,
             num_single_layers=num_single_layers,
-            attention_head_dim=4,
-            num_attention_heads=4,
+            attention_head_dim=16,
+            num_attention_heads=2,
             joint_attention_dim=32,
             axes_dims_rope=[4, 4, 8],
-            approximator_in_factor=1,
             approximator_hidden_dim=32,
-            approximator_out_dim=64,
-            approximator_layers=5,
+            approximator_layers=1,
+            approximator_num_channels=16,
         )
 
         torch.manual_seed(0)