From 3d02cd543ef3101d821cb09c8fcab23c6e7ead33 Mon Sep 17 00:00:00 2001
From: David Lacalle Castillo
 <41203448+WaterKnight1998@users.noreply.github.com>
Date: Mon, 8 Dec 2025 13:12:17 +0100
Subject: [PATCH] [PRX] Improve model compilation (#12787)

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

* Apply style fixes

---------

Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
---
 .../models/transformers/transformer_prx.py    | 35 ++++++++++++++++---
 1 file changed, 30 insertions(+), 5 deletions(-)

diff --git a/src/diffusers/models/transformers/transformer_prx.py b/src/diffusers/models/transformers/transformer_prx.py
index 18ec650bac..a87c120fdc 100644
--- a/src/diffusers/models/transformers/transformer_prx.py
+++ b/src/diffusers/models/transformers/transformer_prx.py
@@ -16,7 +16,6 @@ from typing import Any, Dict, List, Optional, Tuple, Union
 
 import torch
 from torch import nn
-from torch.nn.functional import fold, unfold
 
 from ...configuration_utils import ConfigMixin, register_to_config
 from ...utils import logging
@@ -532,7 +531,19 @@ 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 +565,26 @@ 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):