Conditional padding

utils/cond_functions.py

@@ -1,7 +1,9 @@
 import torch
 
+EMPTY_PROMPT = ""
 
-def copy_cond(cond: list[list[torch.Tensor | dict]], device="cpu"):
+
+def copy_cond(cond: list[tuple[torch.Tensor, dict]], device="cpu") -> list[list[torch.Tensor | dict]]:
     cond_copy = [[]]
     t_copy = cond_copy[0]
     for t in cond:
@@ -11,3 +13,25 @@ def copy_cond(cond: list[list[torch.Tensor | dict]], device="cpu"):
             if "pooled_output" in t[1]:
                 t_copy[1]["pooled_output"] = t[1]["pooled_output"].detach().clone().to(device=device)
     return cond_copy
+
+
+def cat_cond(clip, *conds: list[list[torch.Tensor | dict]], device="cpu") -> list[list[torch.Tensor | dict]]:
+    output = copy_cond(conds[0], device=device)
+    cond_tensors = list(map(lambda c: c[0][0], conds))
+
+    chunk_size = 77
+    chunks = list(map(lambda c: c.shape[1] // chunk_size, cond_tensors))
+    chunk_min, chunk_max = min(chunks), max(chunks)
+
+    if chunk_min != chunk_max:
+        empty_tokens = clip.tokenize(EMPTY_PROMPT)
+        empty_cond: torch.Tensor = clip.encode_from_tokens(empty_tokens, return_pooled=False)
+
+        for i, cond in enumerate(cond_tensors):
+            chunk_count = chunks[i]
+            empty_pad = empty_cond.repeat(1, chunk_max - chunk_count, 1)
+            cond_tensors[i] = torch.cat([cond.to(device=device), empty_pad.to(device=device)], dim=1)
+
+    output[0][0] = torch.cat(cond_tensors).to(device=device)
+
+    return output