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fix(nodes): fix t2i adapter model loading
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@ -509,19 +509,20 @@ class DenoiseLatentsInvocation(BaseInvocation):
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t2i_adapter_data = []
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for t2i_adapter_field in t2i_adapter:
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t2i_adapter_model_info = context.models.load(key=t2i_adapter_field.t2i_adapter_model.key)
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t2i_adapter_model_config = context.models.get_config(key=t2i_adapter_field.t2i_adapter_model.key)
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t2i_adapter_loaded_model = context.models.load(key=t2i_adapter_field.t2i_adapter_model.key)
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image = context.images.get_pil(t2i_adapter_field.image.image_name)
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# The max_unet_downscale is the maximum amount that the UNet model downscales the latent image internally.
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if t2i_adapter_model_info.base == BaseModelType.StableDiffusion1:
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if t2i_adapter_model_config.base == BaseModelType.StableDiffusion1:
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max_unet_downscale = 8
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elif t2i_adapter_model_info.base == BaseModelType.StableDiffusionXL:
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elif t2i_adapter_model_config.base == BaseModelType.StableDiffusionXL:
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max_unet_downscale = 4
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else:
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raise ValueError(f"Unexpected T2I-Adapter base model type: '{t2i_adapter_model_info.base}'.")
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raise ValueError(f"Unexpected T2I-Adapter base model type: '{t2i_adapter_model_config.base}'.")
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t2i_adapter_model: T2IAdapter
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with t2i_adapter_model_info as t2i_adapter_model:
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with t2i_adapter_loaded_model as t2i_adapter_model:
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total_downscale_factor = t2i_adapter_model.total_downscale_factor
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# Resize the T2I-Adapter input image.
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