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update IP-adapter code in UNetMotionModel #6828

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merged 1 commit into from
Feb 5, 2024
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update IP-adapter code in UNetMotionModel #6828

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8 changes: 4 additions & 4 deletions src/diffusers/models/unets/unet_motion_model.py
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Original file line number Diff line number Diff line change
Expand Up @@ -792,17 +792,17 @@ def forward(

emb = self.time_embedding(t_emb, timestep_cond)
emb = emb.repeat_interleave(repeats=num_frames, dim=0)
encoder_hidden_states = encoder_hidden_states.repeat_interleave(repeats=num_frames, dim=0)

if self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == "ip_image_proj":
if "image_embeds" not in added_cond_kwargs:
raise ValueError(
f"{self.__class__} has the config param `encoder_hid_dim_type` set to 'ip_image_proj' which requires the keyword argument `image_embeds` to be passed in `added_conditions`"
)
image_embeds = added_cond_kwargs.get("image_embeds")
image_embeds = self.encoder_hid_proj(image_embeds).to(encoder_hidden_states.dtype)
encoder_hidden_states = torch.cat([encoder_hidden_states, image_embeds], dim=1)

encoder_hidden_states = encoder_hidden_states.repeat_interleave(repeats=num_frames, dim=0)
image_embeds = self.encoder_hid_proj(image_embeds)
image_embeds = [image_embed.repeat_interleave(repeats=num_frames, dim=0) for image_embed in image_embeds]
encoder_hidden_states = (encoder_hidden_states, image_embeds)

# 2. pre-process
sample = sample.permute(0, 2, 1, 3, 4).reshape((sample.shape[0] * num_frames, -1) + sample.shape[3:])
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