Fix issue with prompt embeds and latents in SD Cascade Decoder with multiple image embeddings for a single prompt.

src/diffusers/pipelines/stable_cascade/pipeline_stable_cascade.py

@@ -100,8 +100,10 @@ def __init__(
         )
         self.register_to_config(latent_dim_scale=latent_dim_scale)
 
-    def prepare_latents(self, image_embeddings, num_images_per_prompt, dtype, device, generator, latents, scheduler):
-        batch_size, channels, height, width = image_embeddings.shape
+    def prepare_latents(
+        self, batch_size, image_embeddings, num_images_per_prompt, dtype, device, generator, latents, scheduler
+    ):
+        _, channels, height, width = image_embeddings.shape
         latents_shape = (
             batch_size * num_images_per_prompt,
             4,
@@ -383,7 +385,19 @@ def __call__(
         )
         if isinstance(image_embeddings, list):
             image_embeddings = torch.cat(image_embeddings, dim=0)
-        batch_size = image_embeddings.shape[0]
+
+        if prompt is not None and isinstance(prompt, str):
+            batch_size = 1
+        elif prompt is not None and isinstance(prompt, list):
+            batch_size = len(prompt)
+        else:
+            batch_size = prompt_embeds.shape[0]
+
+        # Compute the effective number of images per prompt
+        # We must account for the fact that the image embeddings from the prior can be generated with num_images_per_prompt > 1
+        # This results in a case where a single prompt is associated with multiple image embeddings
+        # Divide the number of image embeddings by the batch size to determine if this is the case.
+        num_images_per_prompt = num_images_per_prompt * (image_embeddings.shape[0] // batch_size)
 
         # 2. Encode caption
         if prompt_embeds is None and negative_prompt_embeds is None:
@@ -417,7 +431,7 @@ def __call__(
 
         # 5. Prepare latents
         latents = self.prepare_latents(
-            image_embeddings, num_images_per_prompt, dtype, device, generator, latents, self.scheduler
+            batch_size, image_embeddings, num_images_per_prompt, dtype, device, generator, latents, self.scheduler
         )
 
         # 6. Run denoising loop

tests/pipelines/stable_cascade/test_stable_cascade_decoder.py

@@ -33,6 +33,7 @@
     slow,
     torch_device,
 )
+from diffusers.utils.torch_utils import randn_tensor
 
 from ..test_pipelines_common import PipelineTesterMixin
 
@@ -246,6 +247,66 @@ def test_stable_cascade_decoder_prompt_embeds(self):
 
         assert np.abs(decoder_output_prompt.images - decoder_output_prompt_embeds.images).max() < 1e-5
 
+    def test_stable_cascade_decoder_single_prompt_multiple_image_embeddings(self):
+        device = "cpu"
+        components = self.get_dummy_components()
+
+        pipe = StableCascadeDecoderPipeline(**components)
+        pipe.set_progress_bar_config(disable=None)
+
+        prior_num_images_per_prompt = 2
+        decoder_num_images_per_prompt = 2
+        prompt = ["a cat"]
+        batch_size = len(prompt)
+
+        generator = torch.Generator(device)
+        image_embeddings = randn_tensor(
+            (batch_size * prior_num_images_per_prompt, 4, 4, 4), generator=generator.manual_seed(0)
+        )
+        decoder_output = pipe(
+            image_embeddings=image_embeddings,
+            prompt=prompt,
+            num_inference_steps=1,
+            output_type="np",
+            guidance_scale=0.0,
+            generator=generator.manual_seed(0),
+            num_images_per_prompt=decoder_num_images_per_prompt,
+        )
+
+        assert decoder_output.images.shape[0] == (
+            batch_size * prior_num_images_per_prompt * decoder_num_images_per_prompt
+        )
+
+    def test_stable_cascade_decoder_single_prompt_multiple_image_embeddings_with_guidance(self):
+        device = "cpu"
+        components = self.get_dummy_components()
+
+        pipe = StableCascadeDecoderPipeline(**components)
+        pipe.set_progress_bar_config(disable=None)
+
+        prior_num_images_per_prompt = 2
+        decoder_num_images_per_prompt = 2
+        prompt = ["a cat"]
+        batch_size = len(prompt)
+
+        generator = torch.Generator(device)
+        image_embeddings = randn_tensor(
+            (batch_size * prior_num_images_per_prompt, 4, 4, 4), generator=generator.manual_seed(0)
+        )
+        decoder_output = pipe(
+            image_embeddings=image_embeddings,
+            prompt=prompt,
+            num_inference_steps=1,
+            output_type="np",
+            guidance_scale=2.0,
+            generator=generator.manual_seed(0),
+            num_images_per_prompt=decoder_num_images_per_prompt,
+        )
+
+        assert decoder_output.images.shape[0] == (
+            batch_size * prior_num_images_per_prompt * decoder_num_images_per_prompt
+        )
+
 
 @slow
 @require_torch_gpu