up

src/diffusers/modular_pipelines/stable_diffusion_xl/before_denoise.py

@@ -607,8 +607,8 @@ class StableDiffusionXLInpaintPrepareLatentsStep(PipelineBlock):
             ),
         ]
 
+    @staticmethod
     def prepare_latents(
-        self,
         image_latents,
         scheduler,
         dtype,
@@ -760,6 +760,7 @@ class StableDiffusionXLImg2ImgPrepareLatentsStep(PipelineBlock):
         if not (image_latents.shape[0] == 1 or image_latents.shape[0] == batch_size):
             raise ValueError(f"image_latents should have have batch size 1 or {batch_size}, but got {image_latents.shape[0]}")
     
+    @staticmethod
     def prepare_latents(image_latents, scheduler, timestep, dtype, device, generator=None):
         if isinstance(generator, list) and len(generator) != image_latents.shape[0]:
             raise ValueError(
@@ -975,6 +976,7 @@ class StableDiffusionXLImg2ImgPrepareAdditionalConditioningStep(PipelineBlock):
                 type_hint=int,
                 description="Number of prompts, the final batch size of model inputs should be batch_size * num_images_per_prompt. Can be generated in input step.",
             ),
+            InputParam("dtype", type_hint=torch.dtype, description="The dtype of the model inputs, can be generated in input step."),
         ]
 
     @property
@@ -992,7 +994,6 @@ class StableDiffusionXLImg2ImgPrepareAdditionalConditioningStep(PipelineBlock):
                 kwargs_type="guider_input_fields",
                 description="The negative time ids to condition the denoising process",
             ),
-            OutputParam("timestep_cond", type_hint=torch.Tensor, description="The timestep cond to use for LCM"),
         ]
 
     @staticmethod
@@ -1136,6 +1137,11 @@ class StableDiffusionXLPrepareAdditionalConditioningStep(PipelineBlock):
                 type_hint=int,
                 description="Number of prompts, the final batch size of model inputs should be batch_size * num_images_per_prompt. Can be generated in input step.",
             ),
+            InputParam(
+                "dtype",
+                type_hint=torch.dtype,
+                description="The dtype of the model inputs. Can be generated in input step.",
+            ),
         ]
 
     @property
@@ -1187,8 +1193,8 @@ class StableDiffusionXLPrepareAdditionalConditioningStep(PipelineBlock):
         _, _, height_latents, width_latents = block_state.latents.shape
         height = height_latents * components.vae_scale_factor
         width = width_latents * components.vae_scale_factor
-        original_size = block_state.original_size or (block_state.height, block_state.width)
-        target_size = block_state.target_size or (block_state.height, block_state.width)
+        original_size = block_state.original_size or (height, width)
+        target_size = block_state.target_size or (height, width)
 
 
         block_state.add_time_ids = self._get_add_time_ids(

src/diffusers/modular_pipelines/stable_diffusion_xl/decoders.py

@@ -139,7 +139,7 @@ class StableDiffusionXLDecodeStep(PipelineBlock):
             block_state.images = components.vae.decode(latents, return_dict=False)[0]
 
             # cast back to fp16 if needed
-            if block_state.needs_upcasting:
+            if needs_upcasting:
                 components.vae.to(dtype=torch.float16)
         else:
             block_state.images = block_state.latents

src/diffusers/modular_pipelines/stable_diffusion_xl/encoders.py

@@ -39,6 +39,8 @@ from ..modular_pipeline import PipelineBlock, PipelineState
 from ..modular_pipeline_utils import ComponentSpec, ConfigSpec, InputParam, OutputParam
 from .modular_pipeline import StableDiffusionXLModularPipeline
 
+from PIL import Image
+
 
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
 
@@ -504,11 +506,11 @@ class StableDiffusionXLTextEncoderStep(PipelineBlock):
             negative_prompt_embeds = torch.concat(negative_prompt_embeds_list, dim=-1)
             negative_pooled_prompt_embeds = torch.concat(negative_pooled_prompt_embeds_list, dim=0)
 
-        prompt_embeds = prompt_embeds.to(dtype, device=device)
-        pooled_prompt_embeds = pooled_prompt_embeds.to(dtype, device=device)
+        prompt_embeds = prompt_embeds.to(dtype=dtype, device=device)
+        pooled_prompt_embeds = pooled_prompt_embeds.to(dtype=dtype, device=device)
         if requires_unconditional_embeds:
-            negative_prompt_embeds = negative_prompt_embeds.to(dtype, device=device)
-            negative_pooled_prompt_embeds = negative_pooled_prompt_embeds.to(dtype, device=device)
+            negative_prompt_embeds = negative_prompt_embeds.to(dtype=dtype, device=device)
+            negative_pooled_prompt_embeds = negative_pooled_prompt_embeds.to(dtype=dtype, device=device)
 
         for text_encoder in text_encoders:
             if isinstance(components, StableDiffusionXLLoraLoaderMixin) and USE_PEFT_BACKEND:
@@ -687,12 +689,12 @@ class StableDiffusionXLInpaintVaeEncoderStep(PipelineBlock):
     
     def check_inputs(self, image, mask_image, padding_mask_crop):
         
-        if padding_mask_crop is not None and not isinstance(image, PIL.Image.Image):
+        if padding_mask_crop is not None and not isinstance(image, Image.Image):
             raise ValueError(
                 f"The image should be a PIL image when inpainting mask crop, but is of type {type(image)}."
             )
 
-        if padding_mask_crop is not None and not isinstance(mask_image, PIL.Image.Image):
+        if padding_mask_crop is not None and not isinstance(mask_image, Image.Image):
             raise ValueError(
                 f"The mask image should be a PIL image when inpainting mask crop, but is of type"
                 f" {type(mask_image)}."
@@ -707,10 +709,8 @@ class StableDiffusionXLInpaintVaeEncoderStep(PipelineBlock):
         dtype = block_state.dtype if block_state.dtype is not None else components.vae.dtype
         device = components._execution_device
 
-        if block_state.height is None:
-            height = components.default_height
-        if block_state.width is None:
-            width = components.default_width
+        height = block_state.height if block_state.height is not None else components.default_height
+        width = block_state.width if block_state.width is not None else components.default_width
 
         if block_state.padding_mask_crop is not None:
             block_state.crops_coords = components.mask_processor.get_crop_region(
@@ -725,21 +725,21 @@ class StableDiffusionXLInpaintVaeEncoderStep(PipelineBlock):
             block_state.image,
             height=height,
             width=width,
-            crops_coords=crops_coords,
+            crops_coords=block_state.crops_coords,
             resize_mode=resize_mode,
         )
 
         image = image.to(dtype=torch.float32)
 
-        mask = components.mask_processor.preprocess(
+        mask_image = components.mask_processor.preprocess(
             block_state.mask_image,
             height=height,
             width=width,
             resize_mode=resize_mode,
-            crops_coords=crops_coords,
+            crops_coords=block_state.crops_coords,
         )
         
-        masked_image = image * (block_state.mask_latents < 0.5)
+        masked_image = image * (mask_image < 0.5)
 
         # Prepare image latent variables
         block_state.image_latents = encode_vae_image(
@@ -762,7 +762,7 @@ class StableDiffusionXLInpaintVaeEncoderStep(PipelineBlock):
         # resize mask to match the image latents
         _, _, height_latents, width_latents = block_state.image_latents.shape
         block_state.mask = torch.nn.functional.interpolate(
-            mask, 
+            mask_image, 
             size=(height_latents, width_latents), 
         )
         block_state.mask = block_state.mask.to(dtype=dtype, device=device)

src/diffusers/modular_pipelines/stable_diffusion_xl/modular_pipeline.py

@@ -95,11 +95,15 @@ class StableDiffusionXLModularPipeline(
         # by default, always prepare unconditional embeddings
         requires_unconditional_embeds = True
 
-        if hasattr(self, "unet") and self.unet is not None and self.unet.config.time_cond_proj_dim is None:
+        if hasattr(self, "unet") and self.unet is not None and self.unet.config.time_cond_proj_dim is not None:
+            # LCM
             requires_unconditional_embeds = False
 
         elif hasattr(self, "guider") and self.guider is not None:
             requires_unconditional_embeds = self.guider.num_conditions > 1
+        
+        elif not hasattr(self, "guider") or self.guider is None:
+            requires_unconditional_embeds = False
 
         return requires_unconditional_embeds