Allow using more than one start_image with the Fun InP models

nodes.py

@@ -1396,7 +1396,7 @@ class WanVideoImageToVideoEncode:
         mask = torch.zeros(1, base_frames, lat_h, lat_w, device=device)
         
         if start_image is not None:
-            mask[:, 0] = 1  # First frame
+            mask[:, 0:start_image.shape[0]] = 1  # First frame
         if end_image is not None and not fun_model:
             mask[:, -1] = 1  # End frame if exists
 
@@ -1429,7 +1429,7 @@ class WanVideoImageToVideoEncode:
         vae.to(device)
 
         if start_image is not None and end_image is None:
-            zero_frames = torch.zeros(3, num_frames-1, H, W, device=device)
+            zero_frames = torch.zeros(3, num_frames-start_image.shape[0], H, W, device=device)
             concatenated = torch.cat([resized_start_image.to(device), zero_frames], dim=1)
         elif start_image is None and end_image is not None:
             zero_frames = torch.zeros(3, num_frames-1, H, W, device=device)

wanvideo/modules/attention.py

@@ -184,9 +184,9 @@ def attention(
         #     )
         attn_mask = None
 
-        q = q.transpose(1, 2).to(dtype)
-        k = k.transpose(1, 2).to(dtype)
-        v = v.transpose(1, 2).to(dtype)
+        q = q.transpose(1, 2)#.to(dtype)
+        k = k.transpose(1, 2)#.to(dtype)
+        v = v.transpose(1, 2)#.to(dtype)
 
         out = torch.nn.functional.scaled_dot_product_attention(
             q, k, v, attn_mask=attn_mask, is_causal=causal, dropout_p=dropout_p)
@@ -196,9 +196,9 @@ def attention(
     elif attention_mode == 'sageattn':
         attn_mask = None
 
-        q = q.transpose(1, 2).to(dtype)
-        k = k.transpose(1, 2).to(dtype)
-        v = v.transpose(1, 2).to(dtype)
+        q = q.transpose(1, 2)#.to(dtype)
+        k = k.transpose(1, 2)#.to(dtype)
+        v = v.transpose(1, 2)#.to(dtype)
 
         out = sageattn_func(
             q, k, v, attn_mask=attn_mask, is_causal=causal, dropout_p=dropout_p)