fix: Hunyuan3D 2.1 batch size crashes in attention and forward pass

- CrossAttention.forward: hardcoded `1` in kv.view() replaced with actual batch size `b`
- Attention.forward: hardcoded `1` in qkv_combined.view() replaced with actual batch size `B`
- HunYuanDiTPlain.forward: context.chunk(2) and output.chunk(2) now guarded with
  shape[0] >= 2 check to avoid crash when running without negative conditioning

Fixes Comfy-Org/ComfyUI#10142

comfy/ldm/hunyuan3dv2_1/hunyuandit.py

@@ -328,7 +328,7 @@ class CrossAttention(nn.Module):
         kv = torch.cat((k, v), dim=-1)
         split_size = kv.shape[-1] // self.num_heads // 2
 
-        kv = kv.view(1, -1, self.num_heads, split_size * 2)
+        kv = kv.view(b, -1, self.num_heads, split_size * 2)
         k, v = torch.split(kv, split_size, dim=-1)
 
         q = q.view(b, s1, self.num_heads, self.head_dim)
@@ -398,7 +398,7 @@ class Attention(nn.Module):
         qkv_combined = torch.cat((query, key, value), dim=-1)
         split_size = qkv_combined.shape[-1] // self.num_heads // 3
 
-        qkv = qkv_combined.view(1, -1, self.num_heads, split_size * 3)
+        qkv = qkv_combined.view(B, -1, self.num_heads, split_size * 3)
         query, key, value = torch.split(qkv, split_size, dim=-1)
 
         query = query.reshape(B, N, self.num_heads, self.head_dim)
@@ -607,9 +607,9 @@ class HunYuanDiTPlain(nn.Module):
     def forward(self, x, t, context, transformer_options = {}, **kwargs):
 
         x = x.movedim(-1, -2)
-        uncond_emb, cond_emb = context.chunk(2, dim = 0)
-
-        context = torch.cat([cond_emb, uncond_emb], dim = 0)
+        if context.shape[0] >= 2:
+            uncond_emb, cond_emb = context.chunk(2, dim = 0)
+            context = torch.cat([cond_emb, uncond_emb], dim = 0)
         main_condition = context
 
         t = 1.0 - t
@@ -657,5 +657,8 @@ class HunYuanDiTPlain(nn.Module):
         output = self.final_layer(combined)
         output =  output.movedim(-2, -1) * (-1.0)
 
-        cond_emb, uncond_emb = output.chunk(2, dim = 0)
-        return torch.cat([uncond_emb, cond_emb])
+        if output.shape[0] >= 2:
+            cond_emb, uncond_emb = output.chunk(2, dim = 0)
+            return torch.cat([uncond_emb, cond_emb])
+        else:
+            return output