Delete embeddings_pixcell.py

embeddings_pixcell.py

@@ -1,230 +0,0 @@
-# Copyright 2024 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.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-import math
-from typing import List, Optional, Tuple, Union
-
-import numpy as np
-import torch
-import torch.nn.functional as F
-from torch import nn
-
-from diffusers.models.activations import deprecate, FP32SiLU
-
-
-def pixcell_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,
-    phase=0,
-    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)
-        raise ValueError("Not supported")
-    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 = pixcell_get_2d_sincos_pos_embed_from_grid(embed_dim, grid, phase=phase, 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 pixcell_get_2d_sincos_pos_embed_from_grid(embed_dim, grid, phase=0, 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)
-        raise ValueError("Not supported")
-    if embed_dim % 2 != 0:
-        raise ValueError("embed_dim must be divisible by 2")
-
-    # use half of dimensions to encode grid_h
-    emb_h = pixcell_get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[0], phase=phase, output_type=output_type)  # (H*W, D/2)
-    emb_w = pixcell_get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[1], phase=phase, output_type=output_type)  # (H*W, D/2)
-
-    emb = torch.concat([emb_h, emb_w], dim=1)  # (H*W, D)
-    return emb
-
-
-def pixcell_get_1d_sincos_pos_embed_from_grid(embed_dim, pos, phase=0, 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'", "0.34.0", deprecation_message, standard_warn=False)
-        raise ValueError("Not supported")
-    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) + phase  # (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
-
-
-class PixcellUNIProjection(nn.Module):
-    """
-    Projects UNI embeddings. Also handles dropout for classifier-free guidance.
-
-    Adapted from https://github.com/PixArt-alpha/PixArt-alpha/blob/master/diffusion/model/nets/PixArt_blocks.py
-    """
-
-    def __init__(self, in_features, hidden_size, out_features=None, act_fn="gelu_tanh", num_tokens=1):
-        super().__init__()
-        if out_features is None:
-            out_features = hidden_size
-        self.linear_1 = nn.Linear(in_features=in_features, out_features=hidden_size, bias=True)
-        if act_fn == "gelu_tanh":
-            self.act_1 = nn.GELU(approximate="tanh")
-        elif act_fn == "silu":
-            self.act_1 = nn.SiLU()
-        elif act_fn == "silu_fp32":
-            self.act_1 = FP32SiLU()
-        else:
-            raise ValueError(f"Unknown activation function: {act_fn}")
-        self.linear_2 = nn.Linear(in_features=hidden_size, out_features=out_features, bias=True)
-
-        self.register_buffer("uncond_embedding", nn.Parameter(torch.randn(num_tokens, in_features) / in_features ** 0.5))
-
-    def forward(self, caption):
-        hidden_states = self.linear_1(caption)
-        hidden_states = self.act_1(hidden_states)
-        hidden_states = self.linear_2(hidden_states)
-        return hidden_states
-
-class UNIPosEmbed(nn.Module):
-    """
-    Adds positional embeddings to the UNI conditions.
-
-    Args:
-        height (`int`, defaults to `224`): The height of the image.
-        width (`int`, defaults to `224`): The width of the image.
-        patch_size (`int`, defaults to `16`): The size of the patches.
-        in_channels (`int`, defaults to `3`): The number of input channels.
-        embed_dim (`int`, defaults to `768`): The output dimension of the embedding.
-        layer_norm (`bool`, defaults to `False`): Whether or not to use layer normalization.
-        flatten (`bool`, defaults to `True`): Whether or not to flatten the output.
-        bias (`bool`, defaults to `True`): Whether or not to use bias.
-        interpolation_scale (`float`, defaults to `1`): The scale of the interpolation.
-        pos_embed_type (`str`, defaults to `"sincos"`): The type of positional embedding.
-        pos_embed_max_size (`int`, defaults to `None`): The maximum size of the positional embedding.
-    """
-
-    def __init__(
-        self,
-        height=1,
-        width=1,
-        base_size=16,
-        embed_dim=768,
-        interpolation_scale=1,
-        pos_embed_type="sincos",
-    ):
-        super().__init__()
-
-        num_embeds = height*width
-        grid_size = int(num_embeds ** 0.5)
-
-        if pos_embed_type == "sincos":
-            y_pos_embed = pixcell_get_2d_sincos_pos_embed(
-                embed_dim,
-                grid_size,
-                base_size=base_size,
-                interpolation_scale=interpolation_scale,
-                output_type="pt",
-                phase = base_size // num_embeds
-            )
-            self.register_buffer("y_pos_embed", y_pos_embed.float().unsqueeze(0))
-        else:
-            raise ValueError("`pos_embed_type` not supported")
-
-    def forward(self, uni_embeds):
-        return (uni_embeds + self.y_pos_embed).to(uni_embeds.dtype)
-