Merge branch 'main' into loading_method_integration

Author: suzukimain

src/diffusers/models/embeddings.py

@@ -84,6 +84,78 @@ def get_3d_sincos_pos_embed(
     temporal_size: int,
     spatial_interpolation_scale: float = 1.0,
     temporal_interpolation_scale: float = 1.0,
+    device: Optional[torch.device] = None,
+    output_type: str = "np",
+) -> torch.Tensor:
+    r"""
+    Creates 3D sinusoidal positional embeddings.
+
+    Args:
+        embed_dim (`int`):
+            The embedding dimension of inputs. It must be divisible by 16.
+        spatial_size (`int` or `Tuple[int, int]`):
+            The spatial dimension of positional embeddings. If an integer is provided, the same size is applied to both
+            spatial dimensions (height and width).
+        temporal_size (`int`):
+            The temporal dimension of postional embeddings (number of frames).
+        spatial_interpolation_scale (`float`, defaults to 1.0):
+            Scale factor for spatial grid interpolation.
+        temporal_interpolation_scale (`float`, defaults to 1.0):
+            Scale factor for temporal grid interpolation.
+
+    Returns:
+        `torch.Tensor`:
+            The 3D sinusoidal positional embeddings of shape `[temporal_size, spatial_size[0] * spatial_size[1],
+            embed_dim]`.
+    """
+    if output_type == "np":
+        return _get_3d_sincos_pos_embed_np(
+            embed_dim=embed_dim,
+            spatial_size=spatial_size,
+            temporal_size=temporal_size,
+            spatial_interpolation_scale=spatial_interpolation_scale,
+            temporal_interpolation_scale=temporal_interpolation_scale,
+        )
+    if embed_dim % 4 != 0:
+        raise ValueError("`embed_dim` must be divisible by 4")
+    if isinstance(spatial_size, int):
+        spatial_size = (spatial_size, spatial_size)
+
+    embed_dim_spatial = 3 * embed_dim // 4
+    embed_dim_temporal = embed_dim // 4
+
+    # 1. Spatial
+    grid_h = torch.arange(spatial_size[1], device=device, dtype=torch.float32) / spatial_interpolation_scale
+    grid_w = torch.arange(spatial_size[0], device=device, dtype=torch.float32) / spatial_interpolation_scale
+    grid = torch.meshgrid(grid_w, grid_h, indexing="xy")  # here w goes first
+    grid = torch.stack(grid, dim=0)
+
+    grid = grid.reshape([2, 1, spatial_size[1], spatial_size[0]])
+    pos_embed_spatial = get_2d_sincos_pos_embed_from_grid(embed_dim_spatial, grid, output_type="pt")
+
+    # 2. Temporal
+    grid_t = torch.arange(temporal_size, device=device, dtype=torch.float32) / temporal_interpolation_scale
+    pos_embed_temporal = get_1d_sincos_pos_embed_from_grid(embed_dim_temporal, grid_t, output_type="pt")
+
+    # 3. Concat
+    pos_embed_spatial = pos_embed_spatial[None, :, :]
+    pos_embed_spatial = pos_embed_spatial.repeat_interleave(temporal_size, dim=0)  # [T, H*W, D // 4 * 3]
+
+    pos_embed_temporal = pos_embed_temporal[:, None, :]
+    pos_embed_temporal = pos_embed_temporal.repeat_interleave(
+        spatial_size[0] * spatial_size[1], dim=1
+    )  # [T, H*W, D // 4]
+
+    pos_embed = torch.concat([pos_embed_temporal, pos_embed_spatial], dim=-1)  # [T, H*W, D]
+    return pos_embed
+
+
+def _get_3d_sincos_pos_embed_np(
+    embed_dim: int,
+    spatial_size: Union[int, Tuple[int, int]],
+    temporal_size: int,
+    spatial_interpolation_scale: float = 1.0,
+    temporal_interpolation_scale: float = 1.0,
 ) -> np.ndarray:
     r"""
     Creates 3D sinusoidal positional embeddings.
@@ -106,6 +178,12 @@ def get_3d_sincos_pos_embed(
             The 3D sinusoidal positional embeddings of shape `[temporal_size, spatial_size[0] * spatial_size[1],
             embed_dim]`.
     """
+    deprecation_message = (
+        "`get_3d_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)
     if embed_dim % 4 != 0:
         raise ValueError("`embed_dim` must be divisible by 4")
     if isinstance(spatial_size, int):
@@ -139,6 +217,143 @@ def get_3d_sincos_pos_embed(
 
 
 def get_2d_sincos_pos_embed(
+    embed_dim,
+    grid_size,
+    cls_token=False,
+    extra_tokens=0,
+    interpolation_scale=1.0,
+    base_size=16,
+    device: Optional[torch.device] = None,
+    output_type: str = "np",
+):
+    """
+    Creates 2D sinusoidal positional embeddings.
+
+    Args:
+        embed_dim (`int`):
+            The embedding dimension.
+        grid_size (`int`):
+            The size of the grid height and width.
+        cls_token (`bool`, defaults to `False`):
+            Whether or not to add a classification token.
+        extra_tokens (`int`, defaults to `0`):
+            The number of extra tokens to add.
+        interpolation_scale (`float`, defaults to `1.0`):
+            The scale of the interpolation.
+
+    Returns:
+        pos_embed (`torch.Tensor`):
+            Shape is either `[grid_size * grid_size, embed_dim]` if not using cls_token, or `[1 + grid_size*grid_size,
+            embed_dim]` if using cls_token
+    """
+    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_sincos_pos_embed_np(
+            embed_dim=embed_dim,
+            grid_size=grid_size,
+            cls_token=cls_token,
+            extra_tokens=extra_tokens,
+            interpolation_scale=interpolation_scale,
+            base_size=base_size,
+        )
+    if isinstance(grid_size, int):
+        grid_size = (grid_size, grid_size)
+
+    grid_h = (
+        torch.arange(grid_size[0], device=device, dtype=torch.float32)
+        / (grid_size[0] / base_size)
+        / interpolation_scale
+    )
+    grid_w = (
+        torch.arange(grid_size[1], device=device, dtype=torch.float32)
+        / (grid_size[1] / base_size)
+        / interpolation_scale
+    )
+    grid = torch.meshgrid(grid_w, grid_h, indexing="xy")  # here w goes first
+    grid = torch.stack(grid, dim=0)
+
+    grid = grid.reshape([2, 1, grid_size[1], grid_size[0]])
+    pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid, output_type=output_type)
+    if cls_token and extra_tokens > 0:
+        pos_embed = torch.concat([torch.zeros([extra_tokens, embed_dim]), pos_embed], dim=0)
+    return pos_embed
+
+
+def get_2d_sincos_pos_embed_from_grid(embed_dim, grid, output_type="np"):
+    r"""
+    This function generates 2D sinusoidal positional embeddings from a grid.
+
+    Args:
+        embed_dim (`int`): The embedding dimension.
+        grid (`torch.Tensor`): Grid of positions with shape `(H * W,)`.
+
+    Returns:
+        `torch.Tensor`: The 2D sinusoidal positional embeddings with shape `(H * W, embed_dim)`
+    """
+    if output_type == "np":
+        deprecation_message = (
+            "`get_2d_sincos_pos_embed_from_grid` 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_sincos_pos_embed_from_grid_np(
+            embed_dim=embed_dim,
+            grid=grid,
+        )
+    if embed_dim % 2 != 0:
+        raise ValueError("embed_dim must be divisible by 2")
+
+    # use half of dimensions to encode grid_h
+    emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[0], output_type=output_type)  # (H*W, D/2)
+    emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[1], output_type=output_type)  # (H*W, D/2)
+
+    emb = torch.concat([emb_h, emb_w], dim=1)  # (H*W, D)
+    return emb
+
+
+def get_1d_sincos_pos_embed_from_grid(embed_dim, pos, output_type="np"):
+    """
+    This function generates 1D positional embeddings from a grid.
+
+    Args:
+        embed_dim (`int`): The embedding dimension `D`
+        pos (`torch.Tensor`): 1D tensor of positions with shape `(M,)`
+
+    Returns:
+        `torch.Tensor`: Sinusoidal positional embeddings of shape `(M, D)`.
+    """
+    if output_type == "np":
+        deprecation_message = (
+            "`get_1d_sincos_pos_embed_from_grid` 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_1d_sincos_pos_embed_from_grid_np(embed_dim=embed_dim, pos=pos)
+    if embed_dim % 2 != 0:
+        raise ValueError("embed_dim must be divisible by 2")
+
+    omega = torch.arange(embed_dim // 2, device=pos.device, dtype=torch.float64)
+    omega /= embed_dim / 2.0
+    omega = 1.0 / 10000**omega  # (D/2,)
+
+    pos = pos.reshape(-1)  # (M,)
+    out = torch.outer(pos, omega)  # (M, D/2), outer product
+
+    emb_sin = torch.sin(out)  # (M, D/2)
+    emb_cos = torch.cos(out)  # (M, D/2)
+
+    emb = torch.concat([emb_sin, emb_cos], dim=1)  # (M, D)
+    return emb
+
+
+def get_2d_sincos_pos_embed_np(
     embed_dim, grid_size, cls_token=False, extra_tokens=0, interpolation_scale=1.0, base_size=16
 ):
     """
@@ -170,13 +385,13 @@ def get_2d_sincos_pos_embed(
     grid = np.stack(grid, axis=0)
 
     grid = grid.reshape([2, 1, grid_size[1], grid_size[0]])
-    pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid)
+    pos_embed = get_2d_sincos_pos_embed_from_grid_np(embed_dim, grid)
     if cls_token and extra_tokens > 0:
         pos_embed = np.concatenate([np.zeros([extra_tokens, embed_dim]), pos_embed], axis=0)
     return pos_embed
 
 
-def get_2d_sincos_pos_embed_from_grid(embed_dim, grid):
+def get_2d_sincos_pos_embed_from_grid_np(embed_dim, grid):
     r"""
     This function generates 2D sinusoidal positional embeddings from a grid.
 
@@ -191,14 +406,14 @@ def get_2d_sincos_pos_embed_from_grid(embed_dim, grid):
         raise ValueError("embed_dim must be divisible by 2")
 
     # use half of dimensions to encode grid_h
-    emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[0])  # (H*W, D/2)
-    emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[1])  # (H*W, D/2)
+    emb_h = get_1d_sincos_pos_embed_from_grid_np(embed_dim // 2, grid[0])  # (H*W, D/2)
+    emb_w = get_1d_sincos_pos_embed_from_grid_np(embed_dim // 2, grid[1])  # (H*W, D/2)
 
     emb = np.concatenate([emb_h, emb_w], axis=1)  # (H*W, D)
     return emb
 
 
-def get_1d_sincos_pos_embed_from_grid(embed_dim, pos):
+def get_1d_sincos_pos_embed_from_grid_np(embed_dim, pos):
     """
     This function generates 1D positional embeddings from a grid.
 
@@ -288,10 +503,14 @@ def __init__(
             self.pos_embed = None
         elif pos_embed_type == "sincos":
             pos_embed = get_2d_sincos_pos_embed(
-                embed_dim, grid_size, base_size=self.base_size, interpolation_scale=self.interpolation_scale
+                embed_dim,
+                grid_size,
+                base_size=self.base_size,
+                interpolation_scale=self.interpolation_scale,
+                output_type="pt",
             )
             persistent = True if pos_embed_max_size else False
-            self.register_buffer("pos_embed", torch.from_numpy(pos_embed).float().unsqueeze(0), persistent=persistent)
+            self.register_buffer("pos_embed", pos_embed.float().unsqueeze(0), persistent=persistent)
         else:
             raise ValueError(f"Unsupported pos_embed_type: {pos_embed_type}")
 
@@ -341,8 +560,10 @@ def forward(self, latent):
                     grid_size=(height, width),
                     base_size=self.base_size,
                     interpolation_scale=self.interpolation_scale,
+                    device=latent.device,
+                    output_type="pt",
                 )
-                pos_embed = torch.from_numpy(pos_embed).float().unsqueeze(0).to(latent.device)
+                pos_embed = pos_embed.float().unsqueeze(0)
             else:
                 pos_embed = self.pos_embed
 
@@ -453,7 +674,9 @@ def __init__(
             pos_embedding = self._get_positional_embeddings(sample_height, sample_width, sample_frames)
             self.register_buffer("pos_embedding", pos_embedding, persistent=persistent)
 
-    def _get_positional_embeddings(self, sample_height: int, sample_width: int, sample_frames: int) -> torch.Tensor:
+    def _get_positional_embeddings(
+        self, sample_height: int, sample_width: int, sample_frames: int, device: Optional[torch.device] = None
+    ) -> torch.Tensor:
         post_patch_height = sample_height // self.patch_size
         post_patch_width = sample_width // self.patch_size
         post_time_compression_frames = (sample_frames - 1) // self.temporal_compression_ratio + 1
@@ -465,8 +688,10 @@ def _get_positional_embeddings(self, sample_height: int, sample_width: int, samp
             post_time_compression_frames,
             self.spatial_interpolation_scale,
             self.temporal_interpolation_scale,
+            device=device,
+            output_type="pt",
         )
-        pos_embedding = torch.from_numpy(pos_embedding).flatten(0, 1)
+        pos_embedding = pos_embedding.flatten(0, 1)
         joint_pos_embedding = torch.zeros(
             1, self.max_text_seq_length + num_patches, self.embed_dim, requires_grad=False
         )
@@ -521,8 +746,10 @@ def forward(self, text_embeds: torch.Tensor, image_embeds: torch.Tensor):
                 or self.sample_width != width
                 or self.sample_frames != pre_time_compression_frames
             ):
-                pos_embedding = self._get_positional_embeddings(height, width, pre_time_compression_frames)
-                pos_embedding = pos_embedding.to(embeds.device, dtype=embeds.dtype)
+                pos_embedding = self._get_positional_embeddings(
+                    height, width, pre_time_compression_frames, device=embeds.device
+                )
+                pos_embedding = pos_embedding.to(dtype=embeds.dtype)
             else:
                 pos_embedding = self.pos_embedding
 
@@ -552,9 +779,11 @@ def __init__(
         # Linear projection for text embeddings
         self.text_proj = nn.Linear(text_hidden_size, hidden_size)
 
-        pos_embed = get_2d_sincos_pos_embed(hidden_size, pos_embed_max_size, base_size=pos_embed_max_size)
+        pos_embed = get_2d_sincos_pos_embed(
+            hidden_size, pos_embed_max_size, base_size=pos_embed_max_size, output_type="pt"
+        )
         pos_embed = pos_embed.reshape(pos_embed_max_size, pos_embed_max_size, hidden_size)
-        self.register_buffer("pos_embed", torch.from_numpy(pos_embed).float(), persistent=False)
+        self.register_buffer("pos_embed", pos_embed.float(), persistent=False)
 
     def forward(self, hidden_states: torch.Tensor, encoder_hidden_states: torch.Tensor) -> torch.Tensor:
         batch_size, channel, height, width = hidden_states.shape

src/diffusers/models/transformers/latte_transformer_3d.py

@@ -156,9 +156,9 @@ def __init__(
 
         # define temporal positional embedding
         temp_pos_embed = get_1d_sincos_pos_embed_from_grid(
-            inner_dim, torch.arange(0, video_length).unsqueeze(1)
+            inner_dim, torch.arange(0, video_length).unsqueeze(1), output_type="pt"
         )  # 1152 hidden size
-        self.register_buffer("temp_pos_embed", torch.from_numpy(temp_pos_embed).float().unsqueeze(0), persistent=False)
+        self.register_buffer("temp_pos_embed", temp_pos_embed.float().unsqueeze(0), persistent=False)
 
         self.gradient_checkpointing = False
 

src/diffusers/models/unets/unet_3d_blocks.py

@@ -1375,6 +1375,7 @@ def forward(
         res_hidden_states_tuple: Tuple[torch.Tensor, ...],
         temb: Optional[torch.Tensor] = None,
         image_only_indicator: Optional[torch.Tensor] = None,
+        upsample_size: Optional[int] = None,
     ) -> torch.Tensor:
         for resnet in self.resnets:
             # pop res hidden states
@@ -1415,7 +1416,7 @@ def custom_forward(*inputs):
 
         if self.upsamplers is not None:
             for upsampler in self.upsamplers:
-                hidden_states = upsampler(hidden_states)
+                hidden_states = upsampler(hidden_states, upsample_size)
 
         return hidden_states
 
@@ -1485,6 +1486,7 @@ def forward(
         temb: Optional[torch.Tensor] = None,
         encoder_hidden_states: Optional[torch.Tensor] = None,
         image_only_indicator: Optional[torch.Tensor] = None,
+        upsample_size: Optional[int] = None,
     ) -> torch.Tensor:
         for resnet, attn in zip(self.resnets, self.attentions):
             # pop res hidden states
@@ -1533,6 +1535,6 @@ def custom_forward(*inputs):
 
         if self.upsamplers is not None:
             for upsampler in self.upsamplers:
-                hidden_states = upsampler(hidden_states)
+                hidden_states = upsampler(hidden_states, upsample_size)
 
         return hidden_states