Merge branch 'main' into readme-spelling-sana

Author: sayakpaul

examples/community/pipeline_hunyuandit_differential_img2img.py

@@ -1008,6 +1008,8 @@ def __call__(
             self.transformer.inner_dim // self.transformer.num_heads,
             grid_crops_coords,
             (grid_height, grid_width),
+            device=device,
+            output_type="pt",
         )
 
         style = torch.tensor([0], device=device)

src/diffusers/models/embeddings.py

@@ -957,7 +957,57 @@ def get_3d_rotary_pos_embed_allegro(
     return freqs_t, freqs_h, freqs_w, grid_t, grid_h, grid_w
 
 
-def get_2d_rotary_pos_embed(embed_dim, crops_coords, grid_size, use_real=True):
+def get_2d_rotary_pos_embed(
+    embed_dim, crops_coords, grid_size, use_real=True, device: Optional[torch.device] = None, output_type: str = "np"
+):
+    """
+    RoPE for image tokens with 2d structure.
+
+    Args:
+    embed_dim: (`int`):
+        The embedding dimension size
+    crops_coords (`Tuple[int]`)
+        The top-left and bottom-right coordinates of the crop.
+    grid_size (`Tuple[int]`):
+        The grid size of the positional embedding.
+    use_real (`bool`):
+        If True, return real part and imaginary part separately. Otherwise, return complex numbers.
+    device: (`torch.device`, **optional**):
+        The device used to create tensors.
+
+    Returns:
+        `torch.Tensor`: positional embedding with shape `( grid_size * grid_size, embed_dim/2)`.
+    """
+    if output_type == "np":
+        deprecation_message = (
+            "`get_2d_sincos_pos_embed` uses `torch` and supports `device`."
+            " `from_numpy` is no longer required."
+            "  Pass `output_type='pt' to use the new version now."
+        )
+        deprecate("output_type=='np'", "0.33.0", deprecation_message, standard_warn=False)
+        return _get_2d_rotary_pos_embed_np(
+            embed_dim=embed_dim,
+            crops_coords=crops_coords,
+            grid_size=grid_size,
+            use_real=use_real,
+        )
+    start, stop = crops_coords
+    # scale end by (steps−1)/steps matches np.linspace(..., endpoint=False)
+    grid_h = torch.linspace(
+        start[0], stop[0] * (grid_size[0] - 1) / grid_size[0], grid_size[0], device=device, dtype=torch.float32
+    )
+    grid_w = torch.linspace(
+        start[1], stop[1] * (grid_size[1] - 1) / grid_size[1], grid_size[1], device=device, dtype=torch.float32
+    )
+    grid = torch.meshgrid(grid_w, grid_h, indexing="xy")
+    grid = torch.stack(grid, dim=0)  # [2, W, H]
+
+    grid = grid.reshape([2, 1, *grid.shape[1:]])
+    pos_embed = get_2d_rotary_pos_embed_from_grid(embed_dim, grid, use_real=use_real)
+    return pos_embed
+
+
+def _get_2d_rotary_pos_embed_np(embed_dim, crops_coords, grid_size, use_real=True):
     """
     RoPE for image tokens with 2d structure.
 

src/diffusers/pipelines/controlnet_hunyuandit/pipeline_hunyuandit_controlnet.py

@@ -925,7 +925,11 @@ def __call__(
         base_size = 512 // 8 // self.transformer.config.patch_size
         grid_crops_coords = get_resize_crop_region_for_grid((grid_height, grid_width), base_size)
         image_rotary_emb = get_2d_rotary_pos_embed(
-            self.transformer.inner_dim // self.transformer.num_heads, grid_crops_coords, (grid_height, grid_width)
+            self.transformer.inner_dim // self.transformer.num_heads,
+            grid_crops_coords,
+            (grid_height, grid_width),
+            device=device,
+            output_type="pt",
         )
 
         style = torch.tensor([0], device=device)

src/diffusers/pipelines/hunyuandit/pipeline_hunyuandit.py

@@ -798,7 +798,11 @@ def __call__(
         base_size = 512 // 8 // self.transformer.config.patch_size
         grid_crops_coords = get_resize_crop_region_for_grid((grid_height, grid_width), base_size)
         image_rotary_emb = get_2d_rotary_pos_embed(
-            self.transformer.inner_dim // self.transformer.num_heads, grid_crops_coords, (grid_height, grid_width)
+            self.transformer.inner_dim // self.transformer.num_heads,
+            grid_crops_coords,
+            (grid_height, grid_width),
+            device=device,
+            output_type="pt",
         )
 
         style = torch.tensor([0], device=device)

src/diffusers/pipelines/ltx/pipeline_ltx.py

@@ -1,4 +1,4 @@
-# Copyright 2024 Black Forest Labs and The HuggingFace Team. All rights reserved.
+# Copyright 2024 Lightricks and The HuggingFace Team. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

src/diffusers/pipelines/ltx/pipeline_ltx_image2video.py

@@ -1,4 +1,4 @@
-# Copyright 2024 Black Forest Labs and The HuggingFace Team. All rights reserved.
+# Copyright 2024 Lightricks and The HuggingFace Team. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

src/diffusers/pipelines/mochi/pipeline_mochi.py

@@ -1,4 +1,4 @@
-# Copyright 2024 Black Forest Labs and The HuggingFace Team. All rights reserved.
+# Copyright 2024 Genmo and The HuggingFace Team. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

src/diffusers/pipelines/pag/pipeline_pag_hunyuandit.py

@@ -818,7 +818,11 @@ def __call__(
         base_size = 512 // 8 // self.transformer.config.patch_size
         grid_crops_coords = get_resize_crop_region_for_grid((grid_height, grid_width), base_size)
         image_rotary_emb = get_2d_rotary_pos_embed(
-            self.transformer.inner_dim // self.transformer.num_heads, grid_crops_coords, (grid_height, grid_width)
+            self.transformer.inner_dim // self.transformer.num_heads,
+            grid_crops_coords,
+            (grid_height, grid_width),
+            device=device,
+            output_type="pt",
         )
 
         style = torch.tensor([0], device=device)