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from diffusers.modular_pipelines import (
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PipelineBlock,
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InputParam,
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OutputParam,
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ConfigSpec,
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)
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from diffusers.utils import load_image
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from PIL import Image
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from typing import Union, Tuple
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def best_output_size(w, h, dw, dh, expected_area):
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ratio = w / h
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ow = (expected_area * ratio)**0.5
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oh = expected_area / ow
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ow1 = int(ow // dw * dw)
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oh1 = int(expected_area / ow1 // dh * dh)
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assert ow1 % dw == 0 and oh1 % dh == 0 and ow1 * oh1 <= expected_area
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ratio1 = ow1 / oh1
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oh2 = int(oh // dh * dh)
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ow2 = int(expected_area / oh2 // dw * dw)
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assert oh2 % dh == 0 and ow2 % dw == 0 and ow2 * oh2 <= expected_area
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ratio2 = ow2 / oh2
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if max(ratio / ratio1, ratio1 / ratio) < max(ratio / ratio2,
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ratio2 / ratio):
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return ow1, oh1
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else:
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return ow2, oh2
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class Wan225BI2VImageProcessor(PipelineBlock):
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@property
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def description(self):
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return "default Image Processor for wan2.2 5b i2v, it resizes the image to the best output size and center-crop it"
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@property
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def inputs(self):
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return [
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InputParam(name="image", type_hint=Union[Image.Image, str], description= "the Image to process"),
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InputParam(name="max_area", type_hint=int, description= "the maximum area of the Image to process")
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]
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@property
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def intermediate_outputs(self):
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return [
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OutputParam(name="processed_image", type_hint=Image.Image, description= "the processed Image"),
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]
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@property
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def expected_configs(self):
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return [
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ConfigSpec(name="patch_size", default=(1, 2, 2)),
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ConfigSpec(name="vae_stride", default=(4, 16, 16)),
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]
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def __call__(self, components, state):
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block_state = self.get_block_state(state)
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if isinstance(block_state.image, str):
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image = load_image(block_state.image).convert("RGB")
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elif isinstance(block_state.image, Image.Image):
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image = block_state.image
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else:
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raise ValueError(f"Invalid image type: {type(block_state.image)}; only support PIL Image or url string")
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ih, iw = image.height, image.width
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dh, dw = components.config.patch_size[1] * components.config.vae_stride[1], components.config.patch_size[2] * components.config.vae_stride[2]
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ow, oh = best_output_size(iw, ih, dw, dh, block_state.max_area)
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scale = max(ow / iw, oh / ih)
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resized_image = image.resize((round(iw * scale), round(ih * scale)), Image.LANCZOS)
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x1 = (resized_image.width - ow) // 2
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y1 = (resized_image.height - oh) // 2
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cropped_image = resized_image.crop((x1, y1, x1 + ow, y1 + oh))
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assert cropped_image.width == ow and cropped_image.height == oh
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block_state.processed_image = cropped_image
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print(f" initial image size: {image.size}")
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print(f" processed image size: {cropped_image.size}")
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self.set_block_state(state, block_state)
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return components, state
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