Cast height, width to int inside prepare latents (#7691)

update

examples/community/composable_stable_diffusion.py

@@ -321,7 +321,12 @@ class ComposableStableDiffusionPipeline(DiffusionPipeline, StableDiffusionMixin)
             )
 
     def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):
-        shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor)
+        shape = (
+            batch_size,
+            num_channels_latents,
+            int(height) // self.vae_scale_factor,
+            int(width) // self.vae_scale_factor,
+        )
         if latents is None:
             if device.type == "mps":
                 # randn does not work reproducibly on mps

examples/community/gluegen.py

@@ -500,7 +500,12 @@ class GlueGenStableDiffusionPipeline(DiffusionPipeline, StableDiffusionMixin, Lo
                 )
 
     def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):
-        shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor)
+        shape = (
+            batch_size,
+            num_channels_latents,
+            int(height) // self.vae_scale_factor,
+            int(width) // self.vae_scale_factor,
+        )
         if isinstance(generator, list) and len(generator) != batch_size:
             raise ValueError(
                 f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"

examples/community/instaflow_one_step.py

@@ -468,7 +468,12 @@ class InstaFlowPipeline(
                 )
 
     def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):
-        shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor)
+        shape = (
+            batch_size,
+            num_channels_latents,
+            int(height) // self.vae_scale_factor,
+            int(width) // self.vae_scale_factor,
+        )
         if isinstance(generator, list) and len(generator) != batch_size:
             raise ValueError(
                 f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"

examples/community/ip_adapter_face_id.py

@@ -753,7 +753,12 @@ class IPAdapterFaceIDStableDiffusionPipeline(
                 )
 
     def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):
-        shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor)
+        shape = (
+            batch_size,
+            num_channels_latents,
+            int(height) // self.vae_scale_factor,
+            int(width) // self.vae_scale_factor,
+        )
         if isinstance(generator, list) and len(generator) != batch_size:
             raise ValueError(
                 f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"

examples/community/latent_consistency_img2img.py

@@ -177,7 +177,12 @@ class LatentConsistencyModelImg2ImgPipeline(DiffusionPipeline):
         latents=None,
         generator=None,
     ):
-        shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor)
+        shape = (
+            batch_size,
+            num_channels_latents,
+            int(height) // self.vae_scale_factor,
+            int(width) // self.vae_scale_factor,
+        )
 
         if not isinstance(image, (torch.Tensor, PIL.Image.Image, list)):
             raise ValueError(

examples/community/latent_consistency_interpolate.py

@@ -472,7 +472,12 @@ class LatentConsistencyModelWalkPipeline(
 
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents
     def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):
-        shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor)
+        shape = (
+            batch_size,
+            num_channels_latents,
+            int(height) // self.vae_scale_factor,
+            int(width) // self.vae_scale_factor,
+        )
         if isinstance(generator, list) and len(generator) != batch_size:
             raise ValueError(
                 f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"

examples/community/latent_consistency_txt2img.py

@@ -163,7 +163,12 @@ class LatentConsistencyModelPipeline(DiffusionPipeline):
         return image, has_nsfw_concept
 
     def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, latents=None):
-        shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor)
+        shape = (
+            batch_size,
+            num_channels_latents,
+            int(height) // self.vae_scale_factor,
+            int(width) // self.vae_scale_factor,
+        )
         if latents is None:
             latents = torch.randn(shape, dtype=dtype).to(device)
         else:

examples/community/lpw_stable_diffusion.py

@@ -726,7 +726,12 @@ class StableDiffusionLongPromptWeightingPipeline(
     ):
         if image is None:
             batch_size = batch_size * num_images_per_prompt
-            shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor)
+            shape = (
+                batch_size,
+                num_channels_latents,
+                int(height) // self.vae_scale_factor,
+                int(width) // self.vae_scale_factor,
+            )
             if isinstance(generator, list) and len(generator) != batch_size:
                 raise ValueError(
                     f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"

examples/community/lpw_stable_diffusion_xl.py

@@ -1060,7 +1060,12 @@ class SDXLLongPromptWeightingPipeline(
         batch_size *= num_images_per_prompt
 
         if image is None:
-            shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor)
+            shape = (
+                batch_size,
+                num_channels_latents,
+                int(height) // self.vae_scale_factor,
+                int(width) // self.vae_scale_factor,
+            )
             if isinstance(generator, list) and len(generator) != batch_size:
                 raise ValueError(
                     f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
@@ -1140,7 +1145,12 @@ class SDXLLongPromptWeightingPipeline(
             return latents
 
         else:
-            shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor)
+            shape = (
+                batch_size,
+                num_channels_latents,
+                int(height) // self.vae_scale_factor,
+                int(width) // self.vae_scale_factor,
+            )
             if isinstance(generator, list) and len(generator) != batch_size:
                 raise ValueError(
                     f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"

examples/community/pipeline_demofusion_sdxl.py

@@ -477,7 +477,12 @@ class DemoFusionSDXLPipeline(
 
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents
     def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):
-        shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor)
+        shape = (
+            batch_size,
+            num_channels_latents,
+            int(height) // self.vae_scale_factor,
+            int(width) // self.vae_scale_factor,
+        )
         if isinstance(generator, list) and len(generator) != batch_size:
             raise ValueError(
                 f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"

examples/community/pipeline_sdxl_style_aligned.py

@@ -919,7 +919,12 @@ class StyleAlignedSDXLPipeline(
         batch_size *= num_images_per_prompt
 
         if image is None:
-            shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor)
+            shape = (
+                batch_size,
+                num_channels_latents,
+                int(height) // self.vae_scale_factor,
+                int(width) // self.vae_scale_factor,
+            )
             if isinstance(generator, list) and len(generator) != batch_size:
                 raise ValueError(
                     f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
@@ -999,7 +1004,12 @@ class StyleAlignedSDXLPipeline(
             return latents
 
         else:
-            shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor)
+            shape = (
+                batch_size,
+                num_channels_latents,
+                int(height) // self.vae_scale_factor,
+                int(width) // self.vae_scale_factor,
+            )
             if isinstance(generator, list) and len(generator) != batch_size:
                 raise ValueError(
                     f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"

examples/community/pipeline_stable_diffusion_pag.py

@@ -857,7 +857,12 @@ class StableDiffusionPAGPipeline(
             )
 
     def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):
-        shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor)
+        shape = (
+            batch_size,
+            num_channels_latents,
+            int(height) // self.vae_scale_factor,
+            int(width) // self.vae_scale_factor,
+        )
         if isinstance(generator, list) and len(generator) != batch_size:
             raise ValueError(
                 f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"

examples/community/pipeline_stable_diffusion_xl_controlnet_adapter.py

@@ -751,7 +751,12 @@ class StableDiffusionXLControlNetAdapterPipeline(
 
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents
     def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):
-        shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor)
+        shape = (
+            batch_size,
+            num_channels_latents,
+            int(height) // self.vae_scale_factor,
+            int(width) // self.vae_scale_factor,
+        )
         if isinstance(generator, list) and len(generator) != batch_size:
             raise ValueError(
                 f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"

examples/community/pipeline_stable_diffusion_xl_ipex.py

@@ -614,7 +614,12 @@ class StableDiffusionXLPipelineIpex(
 
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents
     def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):
-        shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor)
+        shape = (
+            batch_size,
+            num_channels_latents,
+            int(height) // self.vae_scale_factor,
+            int(width) // self.vae_scale_factor,
+        )
         if isinstance(generator, list) and len(generator) != batch_size:
             raise ValueError(
                 f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"

examples/community/pipeline_zero1to3.py

@@ -497,7 +497,12 @@ class Zero1to3StableDiffusionPipeline(DiffusionPipeline, StableDiffusionMixin):
             )
 
     def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):
-        shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor)
+        shape = (
+            batch_size,
+            num_channels_latents,
+            int(height) // self.vae_scale_factor,
+            int(width) // self.vae_scale_factor,
+        )
         if isinstance(generator, list) and len(generator) != batch_size:
             raise ValueError(
                 f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"

examples/community/stable_diffusion_controlnet_inpaint.py

@@ -635,7 +635,12 @@ class StableDiffusionControlNetInpaintPipeline(DiffusionPipeline, StableDiffusio
             )
 
     def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):
-        shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor)
+        shape = (
+            batch_size,
+            num_channels_latents,
+            int(height) // self.vae_scale_factor,
+            int(width) // self.vae_scale_factor,
+        )
         if isinstance(generator, list) and len(generator) != batch_size:
             raise ValueError(
                 f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"

examples/community/stable_diffusion_ipex.py

@@ -533,7 +533,12 @@ class StableDiffusionIPEXPipeline(
                 )
 
     def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):
-        shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor)
+        shape = (
+            batch_size,
+            num_channels_latents,
+            int(height) // self.vae_scale_factor,
+            int(width) // self.vae_scale_factor,
+        )
         if isinstance(generator, list) and len(generator) != batch_size:
             raise ValueError(
                 f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"

examples/community/stable_diffusion_reference.py

@@ -609,7 +609,12 @@ class StableDiffusionReferencePipeline(
         Returns:
             torch.Tensor: The prepared latent vectors.
         """
-        shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor)
+        shape = (
+            batch_size,
+            num_channels_latents,
+            int(height) // self.vae_scale_factor,
+            int(width) // self.vae_scale_factor,
+        )
         if isinstance(generator, list) and len(generator) != batch_size:
             raise ValueError(
                 f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"

examples/research_projects/promptdiffusion/pipeline_prompt_diffusion.py

@@ -789,7 +789,12 @@ class PromptDiffusionPipeline(DiffusionPipeline, TextualInversionLoaderMixin, Lo
 
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents
     def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):
-        shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor)
+        shape = (
+            batch_size,
+            num_channels_latents,
+            int(height) // self.vae_scale_factor,
+            int(width) // self.vae_scale_factor,
+        )
         if isinstance(generator, list) and len(generator) != batch_size:
             raise ValueError(
                 f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"

examples/research_projects/rdm/pipeline_rdm.py

@@ -123,7 +123,12 @@ class RDMPipeline(DiffusionPipeline, StableDiffusionMixin):
         return image_embeddings
 
     def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):
-        shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor)
+        shape = (
+            batch_size,
+            num_channels_latents,
+            int(height) // self.vae_scale_factor,
+            int(width) // self.vae_scale_factor,
+        )
         if isinstance(generator, list) and len(generator) != batch_size:
             raise ValueError(
                 f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"