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23 | 23 |
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24 | 24 | from diffusers.image_processor import PipelineImageInput, VaeImageProcessor
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25 | 25 | from diffusers.loaders import IPAdapterMixin, LoraLoaderMixin, TextualInversionLoaderMixin
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26 |
| -from diffusers.models import AutoencoderKL, ControlNetModel, ImageProjection, UNet2DConditionModel, UNetMotionModel |
| 26 | +from diffusers.models import AutoencoderKL, ControlNetModel, UNet2DConditionModel, UNetMotionModel |
27 | 27 | from diffusers.models.lora import adjust_lora_scale_text_encoder
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28 | 28 | from diffusers.models.unets.unet_motion_model import MotionAdapter
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29 | 29 | from diffusers.pipelines.animatediff.pipeline_output import AnimateDiffPipelineOutput
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@@ -400,12 +400,11 @@ def prepare_ip_adapter_image_embeds(
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400 | 400 | )
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401 | 401 |
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402 | 402 | image_embeds = []
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403 |
| - for single_ip_adapter_image, image_proj_layer in zip( |
| 403 | + for single_ip_adapter_image, _ in zip( |
404 | 404 | ip_adapter_image, self.unet.encoder_hid_proj.image_projection_layers
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405 | 405 | ):
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406 |
| - output_hidden_state = not isinstance(image_proj_layer, ImageProjection) |
407 | 406 | single_image_embeds, single_negative_image_embeds = self.encode_image(
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408 |
| - single_ip_adapter_image, device, 1, output_hidden_state |
| 407 | + single_ip_adapter_image, device, 1 |
409 | 408 | )
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410 | 409 | single_image_embeds = torch.stack([single_image_embeds] * num_images_per_prompt, dim=0)
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411 | 410 | single_negative_image_embeds = torch.stack(
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