update StableDiffusion3Img2ImgPipeline.add image size validation

src/diffusers/pipelines/pag/pipeline_pag_sd_3_img2img.py

@@ -549,6 +549,8 @@ def check_inputs(
         prompt,
         prompt_2,
         prompt_3,
+        height,
+        width,
         strength,
         negative_prompt=None,
         negative_prompt_2=None,
@@ -560,6 +562,15 @@ def check_inputs(
         callback_on_step_end_tensor_inputs=None,
         max_sequence_length=None,
     ):
+        if (
+            height % (self.vae_scale_factor * self.patch_size) != 0
+            or width % (self.vae_scale_factor * self.patch_size) != 0
+        ):
+            raise ValueError(
+                f"`height` and `width` have to be divisible by {self.vae_scale_factor * self.patch_size} but are {height} and {width}."
+                f"You can use height {height - height % (self.vae_scale_factor * self.patch_size)} and width {width - width % (self.vae_scale_factor * self.patch_size)}."
+            )
+
         if strength < 0 or strength > 1:
             raise ValueError(f"The value of strength should in [0.0, 1.0] but is {strength}")
 
@@ -730,6 +741,8 @@ def __call__(
         prompt: Union[str, List[str]] = None,
         prompt_2: Optional[Union[str, List[str]]] = None,
         prompt_3: Optional[Union[str, List[str]]] = None,
+        height: Optional[int] = None,
+        width: Optional[int] = None,
         image: PipelineImageInput = None,
         strength: float = 0.6,
         num_inference_steps: int = 50,
@@ -860,11 +873,15 @@ def __call__(
             [`~pipelines.stable_diffusion_3.StableDiffusion3PipelineOutput`] if `return_dict` is True, otherwise a
             `tuple`. When returning a tuple, the first element is a list with the generated images.
         """
+        height = height or self.default_sample_size * self.vae_scale_factor
+        width = width or self.default_sample_size * self.vae_scale_factor
         # 1. Check inputs. Raise error if not correct
         self.check_inputs(
             prompt,
             prompt_2,
             prompt_3,
+            height,
+            width,
             strength,
             negative_prompt=negative_prompt,
             negative_prompt_2=negative_prompt_2,
@@ -933,7 +950,7 @@ def __call__(
             pooled_prompt_embeds = torch.cat([negative_pooled_prompt_embeds, pooled_prompt_embeds], dim=0)
 
         # 3. Preprocess image
-        image = self.image_processor.preprocess(image)
+        image = self.image_processor.preprocess(image, height=height, width=width)
 
         # 4. Prepare timesteps
         timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, num_inference_steps, device, sigmas=sigmas)

src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3_img2img.py

@@ -218,6 +218,9 @@ def __init__(
         )
         self.tokenizer_max_length = self.tokenizer.model_max_length
         self.default_sample_size = self.transformer.config.sample_size
+        self.patch_size = (
+            self.transformer.config.patch_size if hasattr(self, "transformer") and self.transformer is not None else 2
+        )
 
     # Copied from diffusers.pipelines.stable_diffusion_3.pipeline_stable_diffusion_3.StableDiffusion3Pipeline._get_t5_prompt_embeds
     def _get_t5_prompt_embeds(
@@ -531,6 +534,8 @@ def check_inputs(
         prompt,
         prompt_2,
         prompt_3,
+        height,
+        width,
         strength,
         negative_prompt=None,
         negative_prompt_2=None,
@@ -542,6 +547,15 @@ def check_inputs(
         callback_on_step_end_tensor_inputs=None,
         max_sequence_length=None,
     ):
+        if (
+            height % (self.vae_scale_factor * self.patch_size) != 0
+            or width % (self.vae_scale_factor * self.patch_size) != 0
+        ):
+            raise ValueError(
+                f"`height` and `width` have to be divisible by {self.vae_scale_factor * self.patch_size} but are {height} and {width}."
+                f"You can use height {height - height % (self.vae_scale_factor * self.patch_size)} and width {width - width % (self.vae_scale_factor * self.patch_size)}."
+            )
+
         if strength < 0 or strength > 1:
             raise ValueError(f"The value of strength should in [0.0, 1.0] but is {strength}")
 
@@ -710,6 +724,8 @@ def __call__(
         prompt: Union[str, List[str]] = None,
         prompt_2: Optional[Union[str, List[str]]] = None,
         prompt_3: Optional[Union[str, List[str]]] = None,
+        height: Optional[int] = None,
+        width: Optional[int] = None,
         image: PipelineImageInput = None,
         strength: float = 0.6,
         num_inference_steps: int = 50,
@@ -824,12 +840,16 @@ def __call__(
             [`~pipelines.stable_diffusion_3.StableDiffusion3PipelineOutput`] if `return_dict` is True, otherwise a
             `tuple`. When returning a tuple, the first element is a list with the generated images.
         """
+        height = height or self.default_sample_size * self.vae_scale_factor
+        width = width or self.default_sample_size * self.vae_scale_factor
 
         # 1. Check inputs. Raise error if not correct
         self.check_inputs(
             prompt,
             prompt_2,
             prompt_3,
+            height,
+            width,
             strength,
             negative_prompt=negative_prompt,
             negative_prompt_2=negative_prompt_2,
@@ -890,7 +910,7 @@ def __call__(
             pooled_prompt_embeds = torch.cat([negative_pooled_prompt_embeds, pooled_prompt_embeds], dim=0)
 
         # 3. Preprocess image
-        image = self.image_processor.preprocess(image)
+        image = self.image_processor.preprocess(image, height=height, width=width)
 
         # 4. Prepare timesteps
         timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, num_inference_steps, device, sigmas=sigmas)

src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3_inpaint.py

@@ -224,6 +224,9 @@ def __init__(
         )
         self.tokenizer_max_length = self.tokenizer.model_max_length
         self.default_sample_size = self.transformer.config.sample_size
+        self.patch_size = (
+            self.transformer.config.patch_size if hasattr(self, "transformer") and self.transformer is not None else 2
+        )
 
     # Copied from diffusers.pipelines.stable_diffusion_3.pipeline_stable_diffusion_3.StableDiffusion3Pipeline._get_t5_prompt_embeds
     def _get_t5_prompt_embeds(
@@ -538,6 +541,8 @@ def check_inputs(
         prompt,
         prompt_2,
         prompt_3,
+        height,
+        width,
         strength,
         negative_prompt=None,
         negative_prompt_2=None,
@@ -549,6 +554,15 @@ def check_inputs(
         callback_on_step_end_tensor_inputs=None,
         max_sequence_length=None,
     ):
+        if (
+            height % (self.vae_scale_factor * self.patch_size) != 0
+            or width % (self.vae_scale_factor * self.patch_size) != 0
+        ):
+            raise ValueError(
+                f"`height` and `width` have to be divisible by {self.vae_scale_factor * self.patch_size} but are {height} and {width}."
+                f"You can use height {height - height % (self.vae_scale_factor * self.patch_size)} and width {width - width % (self.vae_scale_factor * self.patch_size)}."
+            )
+
         if strength < 0 or strength > 1:
             raise ValueError(f"The value of strength should in [0.0, 1.0] but is {strength}")
 
@@ -953,6 +967,8 @@ def __call__(
             prompt,
             prompt_2,
             prompt_3,
+            height,
+            width,
             strength,
             negative_prompt=negative_prompt,
             negative_prompt_2=negative_prompt_2,