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README.md

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+---
+license: apache-2.0
+---
+
+default image resizing method for wan 14B image-to-video pipelines (for both wan2.1 and wan 2.2 14B)
+```py
+from diffusers import ModularPipeline
+image_processor = ModularPipeline.from_pretrained("YiYiXu/WanImageProcessor14B", trust_remote_code=True)
+image = image_processor(
+    image="https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/wan_i2v_input.JPG",
+    output="processed_image"
+)
+```
+
+for wan 2.2 5B, the default method is here https://huggingface.co/YiYiXu/WanImageProcessor
+
+this is the code to resize
+
+```py
+
+image = load_image(
+    "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/astronaut.jpg"
+)
+max_area = 720 * 1280
+aspect_ratio = image.height / image.width
+mod_value = pipe.vae_scale_factor_spatial * pipe.transformer.config.patch_size[1]
+height = round(np.sqrt(max_area * aspect_ratio)) // mod_value * mod_value
+width = round(np.sqrt(max_area / aspect_ratio)) // mod_value * mod_value
+image = image.resize((width, height))
+```

block.py

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+from diffusers.modular_pipelines import (
+    ModularPipelineBlocks,
+    InputParam,
+    OutputParam,
+    ConfigSpec,
+)
+
+from diffusers.utils import load_image
+from PIL import Image
+from typing import Union, Tuple
+import numpy as np
+
+
+
+class Wan14BImageProcessor(ModularPipelineBlocks):
+
+    @property
+    def description(self):
+        return "default Image Processor for wan14B i2v (for both Wan2.1 and Wan2.2, it resizes image"
+
+    @property
+    def inputs(self):
+        return [
+            InputParam(name="image", type_hint=Union[Image.Image, str], description= "the Image to process"),
+            InputParam(name="max_area", type_hint=int, description= "the maximum area of the Image to process")
+        ]
+    
+    @property
+    def intermediate_outputs(self):
+        return [
+            OutputParam(name="processed_image", type_hint=Image.Image, description= "the processed Image"),
+        ]
+    
+    @property
+    def expected_configs(self):
+        return [
+            ConfigSpec(name="patch_size", default=(1, 2, 2)),
+            ConfigSpec(name="vae_stride", default=(4, 8, 8)),
+        ]
+    
+    def __call__(self, components, state):
+
+        block_state = self.get_block_state(state)
+
+        if isinstance(block_state.image, str):
+            image = load_image(block_state.image).convert("RGB")
+        elif isinstance(block_state.image, Image.Image):
+            image = block_state.image
+        else:
+            raise ValueError(f"Invalid image type: {type(block_state.image)}; only support PIL Image or url string")
+
+        if block_state.max_area is None:
+            max_area = 480 * 832
+        else:
+            max_area = block_state.max_area
+
+        aspect_ratio = image.height / image.width
+        mod_value_height = components.vae_stride[1] * components.patch_size[1]
+        mod_value_width = components.vae_stride[2] * components.patch_size[2]
+        height = round(np.sqrt(max_area * aspect_ratio)) // mod_value_height * mod_value_height
+        width = round(np.sqrt(max_area / aspect_ratio)) // mod_value_width * mod_value_width
+        resized_image = image.resize((width, height))
+        
+
+        block_state.processed_image = resized_image
+
+        print(f" initial image size: {image.size}")
+        print(f" processed image size: {resized_image.size}")
+        
+
+        self.set_block_state(state, block_state)
+        return components, state

modular_config.json

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+{
+  "_class_name": "Wan14BImageProcessor",
+  "_diffusers_version": "0.35.0.dev0",
+  "auto_map": {
+    "ModularPipelineBlocks": "block.Wan14BImageProcessor"
+  }
+}

modular_model_index.json

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+{
+  "_blocks_class_name": "Wan14BImageProcessor",
+  "_class_name": "ModularPipeline",
+  "_diffusers_version": "0.35.0.dev0",
+  "patch_size": [
+    1,
+    2,
+    2
+  ],
+  "vae_stride": [
+    4,
+    8,
+    8
+  ]
+}