[SDXL] Fix uncaught error with image to image

src/diffusers/pipelines/controlnet/pipeline_controlnet_img2img.py

@@ -824,6 +824,13 @@ def prepare_latents(self, image, timestep, batch_size, num_images_per_prompt, dt
                 )
 
             elif isinstance(generator, list):
+                if image.shape[0] < batch_size and batch_size % image.shape[0] == 0:
+                    image = torch.cat([image] * (batch_size // image.shape[0]), dim=0)
+                elif image.shape[0] < batch_size and batch_size % image.shape[0] != 0:
+                    raise ValueError(
+                        f"Cannot duplicate `image` of batch size {image.shape[0]} to effective batch_size {batch_size} "
+                    )
+
                 init_latents = [
                     retrieve_latents(self.vae.encode(image[i : i + 1]), generator=generator[i])
                     for i in range(batch_size)

src/diffusers/pipelines/controlnet/pipeline_controlnet_sd_xl_img2img.py

@@ -930,6 +930,13 @@ def prepare_latents(
                 )
 
             elif isinstance(generator, list):
+                if image.shape[0] < batch_size and batch_size % image.shape[0] == 0:
+                    image = torch.cat([image] * (batch_size // image.shape[0]), dim=0)
+                elif image.shape[0] < batch_size and batch_size % image.shape[0] != 0:
+                    raise ValueError(
+                        f"Cannot duplicate `image` of batch size {image.shape[0]} to effective batch_size {batch_size} "
+                    )
+
                 init_latents = [
                     retrieve_latents(self.vae.encode(image[i : i + 1]), generator=generator[i])
                     for i in range(batch_size)

src/diffusers/pipelines/kolors/pipeline_kolors_img2img.py

@@ -528,6 +528,13 @@ def prepare_latents(
                 )
 
             elif isinstance(generator, list):
+                if image.shape[0] < batch_size and batch_size % image.shape[0] == 0:
+                    image = torch.cat([image] * (batch_size // image.shape[0]), dim=0)
+                elif image.shape[0] < batch_size and batch_size % image.shape[0] != 0:
+                    raise ValueError(
+                        f"Cannot duplicate `image` of batch size {image.shape[0]} to effective batch_size {batch_size} "
+                    )
+
                 init_latents = [
                     retrieve_latents(self.vae.encode(image[i : i + 1]), generator=generator[i])
                     for i in range(batch_size)

src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_img2img.py

@@ -520,6 +520,13 @@ def prepare_latents(self, image, timestep, batch_size, num_images_per_prompt, dt
                 )
 
             elif isinstance(generator, list):
+                if image.shape[0] < batch_size and batch_size % image.shape[0] == 0:
+                    image = torch.cat([image] * (batch_size // image.shape[0]), dim=0)
+                elif image.shape[0] < batch_size and batch_size % image.shape[0] != 0:
+                    raise ValueError(
+                        f"Cannot duplicate `image` of batch size {image.shape[0]} to effective batch_size {batch_size} "
+                    )
+
                 init_latents = [
                     retrieve_latents(self.vae.encode(image[i : i + 1]), generator=generator[i])
                     for i in range(batch_size)

src/diffusers/pipelines/pag/pipeline_pag_sd_xl_img2img.py

@@ -719,6 +719,13 @@ def prepare_latents(
                 )
 
             elif isinstance(generator, list):
+                if image.shape[0] < batch_size and batch_size % image.shape[0] == 0:
+                    image = torch.cat([image] * (batch_size // image.shape[0]), dim=0)
+                elif image.shape[0] < batch_size and batch_size % image.shape[0] != 0:
+                    raise ValueError(
+                        f"Cannot duplicate `image` of batch size {image.shape[0]} to effective batch_size {batch_size} "
+                    )
+
                 init_latents = [
                     retrieve_latents(self.vae.encode(image[i : i + 1]), generator=generator[i])
                     for i in range(batch_size)

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_depth2img.py

@@ -494,6 +494,13 @@ def prepare_latents(self, image, timestep, batch_size, num_images_per_prompt, dt
                 )
 
             elif isinstance(generator, list):
+                if image.shape[0] < batch_size and batch_size % image.shape[0] == 0:
+                    image = torch.cat([image] * (batch_size // image.shape[0]), dim=0)
+                elif image.shape[0] < batch_size and batch_size % image.shape[0] != 0:
+                    raise ValueError(
+                        f"Cannot duplicate `image` of batch size {image.shape[0]} to effective batch_size {batch_size} "
+                    )
+
                 init_latents = [
                     retrieve_latents(self.vae.encode(image[i : i + 1]), generator=generator[i])
                     for i in range(batch_size)

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_img2img.py

@@ -740,6 +740,13 @@ def prepare_latents(self, image, timestep, batch_size, num_images_per_prompt, dt
                 )
 
             elif isinstance(generator, list):
+                if image.shape[0] < batch_size and batch_size % image.shape[0] == 0:
+                    image = torch.cat([image] * (batch_size // image.shape[0]), dim=0)
+                elif image.shape[0] < batch_size and batch_size % image.shape[0] != 0:
+                    raise ValueError(
+                        f"Cannot duplicate `image` of batch size {image.shape[0]} to effective batch_size {batch_size} "
+                    )
+
                 init_latents = [
                     retrieve_latents(self.vae.encode(image[i : i + 1]), generator=generator[i])
                     for i in range(batch_size)

src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_img2img.py

@@ -710,6 +710,13 @@ def prepare_latents(
                 )
 
             elif isinstance(generator, list):
+                if image.shape[0] < batch_size and batch_size % image.shape[0] == 0:
+                    image = torch.cat([image] * (batch_size // image.shape[0]), dim=0)
+                elif image.shape[0] < batch_size and batch_size % image.shape[0] != 0:
+                    raise ValueError(
+                        f"Cannot duplicate `image` of batch size {image.shape[0]} to effective batch_size {batch_size} "
+                    )
+
                 init_latents = [
                     retrieve_latents(self.vae.encode(image[i : i + 1]), generator=generator[i])
                     for i in range(batch_size)