Fix open_clip batch_first compatibility via auto-applied patch

Newer open_clip creates nn.MultiheadAttention with batch_first=True,
but STAR's embedder unconditionally permutes to [seq, batch, embed].
This causes a RuntimeError in the text encoder (attn_mask shape
mismatch). The patch detects batch_first at runtime and only permutes
when needed.

Patches in patches/ are auto-applied to the STAR submodule on startup
and skip gracefully if already applied.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

patches/openclip_batch_first.patch

@@ -0,0 +1,24 @@
+diff --git a/video_to_video/modules/embedder.py b/video_to_video/modules/embedder.py
+index 9b2e760..29cc0fd 100644
+--- a/video_to_video/modules/embedder.py
++++ b/video_to_video/modules/embedder.py
+@@ -54,9 +54,17 @@ class FrozenOpenCLIPEmbedder(nn.Module):
+     def encode_with_transformer(self, text):
+         x = self.model.token_embedding(text)
+         x = x + self.model.positional_embedding
+-        x = x.permute(1, 0, 2)
++        # Newer open_clip sets batch_first=True on MHA, so the resblocks
++        # expect [batch, seq, embed].  Older versions use batch_first=False
++        # and expect [seq, batch, embed].  Only permute for the old layout.
++        needs_permute = not getattr(
++            self.model.transformer.resblocks[0].attn, "batch_first", False
++        )
++        if needs_permute:
++            x = x.permute(1, 0, 2)
+         x = self.text_transformer_forward(x, attn_mask=self.model.attn_mask)
+-        x = x.permute(1, 0, 2)
++        if needs_permute:
++            x = x.permute(1, 0, 2)
+         x = self.model.ln_final(x)
+         return x
+