fix some bug in import

cogvideox/models/transformer3d.py

@@ -27,7 +27,7 @@ from diffusers.utils import is_torch_version, logging
 from diffusers.utils.torch_utils import maybe_allow_in_graph
 from diffusers.models.attention import Attention, FeedForward
 from diffusers.models.attention_processor import AttentionProcessor, CogVideoXAttnProcessor2_0, FusedCogVideoXAttnProcessor2_0
-from diffusers.models.embeddings import CogVideoXPatchEmbed, TimestepEmbedding, Timesteps, get_3d_sincos_pos_embed
+from diffusers.models.embeddings import TimestepEmbedding, Timesteps, get_3d_sincos_pos_embed
 from diffusers.models.modeling_outputs import Transformer2DModelOutput
 from diffusers.models.modeling_utils import ModelMixin
 from diffusers.models.normalization import AdaLayerNorm, CogVideoXLayerNormZero
@@ -35,6 +35,44 @@ from diffusers.models.normalization import AdaLayerNorm, CogVideoXLayerNormZero
 
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
 
+class CogVideoXPatchEmbed(nn.Module):
+    def __init__(
+        self,
+        patch_size: int = 2,
+        in_channels: int = 16,
+        embed_dim: int = 1920,
+        text_embed_dim: int = 4096,
+        bias: bool = True,
+    ) -> None:
+        super().__init__()
+        self.patch_size = patch_size
+
+        self.proj = nn.Conv2d(
+            in_channels, embed_dim, kernel_size=(patch_size, patch_size), stride=patch_size, bias=bias
+        )
+        self.text_proj = nn.Linear(text_embed_dim, embed_dim)
+
+    def forward(self, text_embeds: torch.Tensor, image_embeds: torch.Tensor):
+        r"""
+        Args:
+            text_embeds (`torch.Tensor`):
+                Input text embeddings. Expected shape: (batch_size, seq_length, embedding_dim).
+            image_embeds (`torch.Tensor`):
+                Input image embeddings. Expected shape: (batch_size, num_frames, channels, height, width).
+        """
+        text_embeds = self.text_proj(text_embeds)
+
+        batch, num_frames, channels, height, width = image_embeds.shape
+        image_embeds = image_embeds.reshape(-1, channels, height, width)
+        image_embeds = self.proj(image_embeds)
+        image_embeds = image_embeds.view(batch, num_frames, *image_embeds.shape[1:])
+        image_embeds = image_embeds.flatten(3).transpose(2, 3)  # [batch, num_frames, height x width, channels]
+        image_embeds = image_embeds.flatten(1, 2)  # [batch, num_frames x height x width, channels]
+
+        embeds = torch.cat(
+            [text_embeds, image_embeds], dim=1
+        ).contiguous()  # [batch, seq_length + num_frames x height x width, channels]
+        return embeds
 
 @maybe_allow_in_graph
 class CogVideoXBlock(nn.Module):

cogvideox/ui/ui.py

@@ -1132,7 +1132,7 @@ def post_eas(
 
 
 class CogVideoX_I2VController_EAS:
-    def __init__(self, edition, config_path, model_name, savedir_sample):
+    def __init__(self, model_name, savedir_sample):
         self.savedir_sample = savedir_sample
         os.makedirs(self.savedir_sample, exist_ok=True)
 

requirements.txt

@@ -24,5 +24,5 @@ func_timeout
 deepspeed
 accelerate>=0.25.0
 gradio>=3.41.2
-diffusers>=0.28.2
+diffusers>=0.30.1
 transformers>=4.37.2