Reimplement img2seq & seq2img in PRX to enable ONNX build without Col2Im (incompatible with TensorRT).

Author: WaterKnight1998

src/diffusers/models/transformers/transformer_prx.py

@@ -532,7 +532,19 @@ def img2seq(img: torch.Tensor, patch_size: int) -> torch.Tensor:
             Flattened patch sequence of shape `(B, L, C * patch_size * patch_size)`, where `L = (H // patch_size) * (W
             // patch_size)` is the number of patches.
     """
-    return unfold(img, kernel_size=patch_size, stride=patch_size).transpose(1, 2)
+    b, c, h, w = img.shape
+    p = patch_size
+    
+    # Reshape to (B, C, H//p, p, W//p, p) separating grid and patch dimensions
+    img = img.reshape(b, c, h // p, p, w // p, p)
+    
+    # Permute to (B, H//p, W//p, C, p, p) using einsum
+    # n=batch, c=channels, h=grid_height, p=patch_height, w=grid_width, q=patch_width
+    img = torch.einsum("nchpwq->nhwcpq", img)
+    
+    # Flatten to (B, L, C * p * p)
+    img = img.reshape(b, -1, c * p * p)
+    return img
 
 
 def seq2img(seq: torch.Tensor, patch_size: int, shape: torch.Tensor) -> torch.Tensor:
@@ -554,12 +566,26 @@ def seq2img(seq: torch.Tensor, patch_size: int, shape: torch.Tensor) -> torch.Te
             Reconstructed image tensor of shape `(B, C, H, W)`.
     """
     if isinstance(shape, tuple):
-        shape = shape[-2:]
+        h, w = shape[-2:]
     elif isinstance(shape, torch.Tensor):
-        shape = (int(shape[0]), int(shape[1]))
+        h, w = (int(shape[0]), int(shape[1]))
     else:
         raise NotImplementedError(f"shape type {type(shape)} not supported")
-    return fold(seq.transpose(1, 2), shape, kernel_size=patch_size, stride=patch_size)
+    
+    b, l, d = seq.shape
+    p = patch_size
+    c = d // (p * p)
+    
+    # Reshape back to grid structure: (B, H//p, W//p, C, p, p)
+    seq = seq.reshape(b, h // p, w // p, c, p, p)
+    
+    # Permute back to image layout: (B, C, H//p, p, W//p, p)
+    # n=batch, h=grid_height, w=grid_width, c=channels, p=patch_height, q=patch_width
+    seq = torch.einsum("nhwcpq->nchpwq", seq)
+    
+    # Final reshape to (B, C, H, W)
+    seq = seq.reshape(b, c, h, w)
+    return seq
 
 
 class PRXTransformer2DModel(ModelMixin, ConfigMixin, AttentionMixin):