Delete pipeline.py

pipeline.py

@@ -1,1122 +0,0 @@
-# Copyright 2024 PixArt-Sigma Authors 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.
-# 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 inspect
-import urllib.parse as ul
-from typing import Callable, List, Optional, Tuple, Union
-
-import torch
-from torch import nn
-
-from diffusers.image_processor import PixArtImageProcessor
-from diffusers.models import AutoencoderKL
-from diffusers.schedulers import DPMSolverMultistepScheduler
-from diffusers.utils import (
-    BACKENDS_MAPPING,
-    deprecate,
-    logging,
-    replace_example_docstring,
-)
-from diffusers.utils.torch_utils import randn_tensor
-from diffusers.pipelines.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
-
-from pixcell_transformer_2d import PixCellTransformer2DModel
-
-
-from typing import Any, Dict, Optional, Union
-
-
-from diffusers.configuration_utils import ConfigMixin, register_to_config
-from diffusers.utils import is_torch_version, logging
-from diffusers.models.attention import BasicTransformerBlock
-from diffusers.models.attention_processor import Attention, AttentionProcessor, AttnProcessor, FusedAttnProcessor2_0
-from diffusers.models.embeddings import PatchEmbed
-from diffusers.models.modeling_outputs import Transformer2DModelOutput
-from diffusers.models.modeling_utils import ModelMixin
-from diffusers.models.normalization import AdaLayerNormSingle
-
-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)
-
-
-
-logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
-
-
-class PixCellTransformer2DModel(ModelMixin, ConfigMixin):
-    r"""
-    A 2D Transformer model as introduced in PixArt family of models (https://arxiv.org/abs/2310.00426,
-    https://arxiv.org/abs/2403.04692). Modified for the pathology domain.
-
-    Parameters:
-        num_attention_heads (int, optional, defaults to 16): The number of heads to use for multi-head attention.
-        attention_head_dim (int, optional, defaults to 72): The number of channels in each head.
-        in_channels (int, defaults to 4): The number of channels in the input.
-        out_channels (int, optional):
-            The number of channels in the output. Specify this parameter if the output channel number differs from the
-            input.
-        num_layers (int, optional, defaults to 28): The number of layers of Transformer blocks to use.
-        dropout (float, optional, defaults to 0.0): The dropout probability to use within the Transformer blocks.
-        norm_num_groups (int, optional, defaults to 32):
-            Number of groups for group normalization within Transformer blocks.
-        cross_attention_dim (int, optional):
-            The dimensionality for cross-attention layers, typically matching the encoder's hidden dimension.
-        attention_bias (bool, optional, defaults to True):
-            Configure if the Transformer blocks' attention should contain a bias parameter.
-        sample_size (int, defaults to 128):
-            The width of the latent images. This parameter is fixed during training.
-        patch_size (int, defaults to 2):
-            Size of the patches the model processes, relevant for architectures working on non-sequential data.
-        activation_fn (str, optional, defaults to "gelu-approximate"):
-            Activation function to use in feed-forward networks within Transformer blocks.
-        num_embeds_ada_norm (int, optional, defaults to 1000):
-            Number of embeddings for AdaLayerNorm, fixed during training and affects the maximum denoising steps during
-            inference.
-        upcast_attention (bool, optional, defaults to False):
-            If true, upcasts the attention mechanism dimensions for potentially improved performance.
-        norm_type (str, optional, defaults to "ada_norm_zero"):
-            Specifies the type of normalization used, can be 'ada_norm_zero'.
-        norm_elementwise_affine (bool, optional, defaults to False):
-            If true, enables element-wise affine parameters in the normalization layers.
-        norm_eps (float, optional, defaults to 1e-6):
-            A small constant added to the denominator in normalization layers to prevent division by zero.
-        interpolation_scale (int, optional): Scale factor to use during interpolating the position embeddings.
-        use_additional_conditions (bool, optional): If we're using additional conditions as inputs.
-        attention_type (str, optional, defaults to "default"): Kind of attention mechanism to be used.
-        caption_channels (int, optional, defaults to None):
-            Number of channels to use for projecting the caption embeddings.
-        use_linear_projection (bool, optional, defaults to False):
-            Deprecated argument. Will be removed in a future version.
-        num_vector_embeds (bool, optional, defaults to False):
-            Deprecated argument. Will be removed in a future version.
-    """
-
-    _supports_gradient_checkpointing = True
-    _no_split_modules = ["BasicTransformerBlock", "PatchEmbed"]
-
-    @register_to_config
-    def __init__(
-        self,
-        num_attention_heads: int = 16,
-        attention_head_dim: int = 72,
-        in_channels: int = 4,
-        out_channels: Optional[int] = 8,
-        num_layers: int = 28,
-        dropout: float = 0.0,
-        norm_num_groups: int = 32,
-        cross_attention_dim: Optional[int] = 1152,
-        attention_bias: bool = True,
-        sample_size: int = 128,
-        patch_size: int = 2,
-        activation_fn: str = "gelu-approximate",
-        num_embeds_ada_norm: Optional[int] = 1000,
-        upcast_attention: bool = False,
-        norm_type: str = "ada_norm_single",
-        norm_elementwise_affine: bool = False,
-        norm_eps: float = 1e-6,
-        interpolation_scale: Optional[int] = None,
-        use_additional_conditions: Optional[bool] = None,
-        caption_channels: Optional[int] = None,
-        caption_num_tokens: int = 1,
-        attention_type: Optional[str] = "default",
-    ):
-        super().__init__()
-
-        # Validate inputs.
-        if norm_type != "ada_norm_single":
-            raise NotImplementedError(
-                f"Forward pass is not implemented when `patch_size` is not None and `norm_type` is '{norm_type}'."
-            )
-        elif norm_type == "ada_norm_single" and num_embeds_ada_norm is None:
-            raise ValueError(
-                f"When using a `patch_size` and this `norm_type` ({norm_type}), `num_embeds_ada_norm` cannot be None."
-            )
-
-        # Set some common variables used across the board.
-        self.attention_head_dim = attention_head_dim
-        self.inner_dim = self.config.num_attention_heads * self.config.attention_head_dim
-        self.out_channels = in_channels if out_channels is None else out_channels
-        if use_additional_conditions is None:
-            if sample_size == 128:
-                use_additional_conditions = True
-            else:
-                use_additional_conditions = False
-        self.use_additional_conditions = use_additional_conditions
-
-        self.gradient_checkpointing = False
-
-        # 2. Initialize the position embedding and transformer blocks.
-        self.height = self.config.sample_size
-        self.width = self.config.sample_size
-
-        interpolation_scale = (
-            self.config.interpolation_scale
-            if self.config.interpolation_scale is not None
-            else max(self.config.sample_size // 64, 1)
-        )
-        self.pos_embed = PatchEmbed(
-            height=self.config.sample_size,
-            width=self.config.sample_size,
-            patch_size=self.config.patch_size,
-            in_channels=self.config.in_channels,
-            embed_dim=self.inner_dim,
-            interpolation_scale=interpolation_scale,
-        )
-
-        self.transformer_blocks = nn.ModuleList(
-            [
-                BasicTransformerBlock(
-                    self.inner_dim,
-                    self.config.num_attention_heads,
-                    self.config.attention_head_dim,
-                    dropout=self.config.dropout,
-                    cross_attention_dim=self.config.cross_attention_dim,
-                    activation_fn=self.config.activation_fn,
-                    num_embeds_ada_norm=self.config.num_embeds_ada_norm,
-                    attention_bias=self.config.attention_bias,
-                    upcast_attention=self.config.upcast_attention,
-                    norm_type=norm_type,
-                    norm_elementwise_affine=self.config.norm_elementwise_affine,
-                    norm_eps=self.config.norm_eps,
-                    attention_type=self.config.attention_type,
-                )
-                for _ in range(self.config.num_layers)
-            ]
-        )
-
-        # Initialize the positional embedding for the conditions for >1 UNI embeddings 
-        if self.config.caption_num_tokens == 1:
-            self.y_pos_embed = None
-        else:
-            # 1:1 aspect ratio
-            self.uni_height = int(self.config.caption_num_tokens ** 0.5)
-            self.uni_width = int(self.config.caption_num_tokens ** 0.5)
-
-            self.y_pos_embed = UNIPosEmbed(
-                height=self.uni_height,
-                width=self.uni_width,
-                base_size=self.config.sample_size // self.config.patch_size,
-                embed_dim=self.config.caption_channels,
-                interpolation_scale=2, # Should this be fixed?
-                pos_embed_type="sincos", # This is fixed
-            )
-
-        # 3. Output blocks.
-        self.norm_out = nn.LayerNorm(self.inner_dim, elementwise_affine=False, eps=1e-6)
-        self.scale_shift_table = nn.Parameter(torch.randn(2, self.inner_dim) / self.inner_dim**0.5)
-        self.proj_out = nn.Linear(self.inner_dim, self.config.patch_size * self.config.patch_size * self.out_channels)
-
-        self.adaln_single = AdaLayerNormSingle(
-            self.inner_dim, use_additional_conditions=self.use_additional_conditions
-        )
-        self.caption_projection = None
-        if self.config.caption_channels is not None:
-            self.caption_projection = PixcellUNIProjection(
-                in_features=self.config.caption_channels, hidden_size=self.inner_dim, num_tokens=self.config.caption_num_tokens,
-            )
-
-    def _set_gradient_checkpointing(self, module, value=False):
-        if hasattr(module, "gradient_checkpointing"):
-            module.gradient_checkpointing = value
-
-    @property
-    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.attn_processors
-    def attn_processors(self) -> Dict[str, AttentionProcessor]:
-        r"""
-        Returns:
-            `dict` of attention processors: A dictionary containing all attention processors used in the model with
-            indexed by its weight name.
-        """
-        # set recursively
-        processors = {}
-
-        def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttentionProcessor]):
-            if hasattr(module, "get_processor"):
-                processors[f"{name}.processor"] = module.get_processor()
-
-            for sub_name, child in module.named_children():
-                fn_recursive_add_processors(f"{name}.{sub_name}", child, processors)
-
-            return processors
-
-        for name, module in self.named_children():
-            fn_recursive_add_processors(name, module, processors)
-
-        return processors
-
-    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_attn_processor
-    def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]):
-        r"""
-        Sets the attention processor to use to compute attention.
-
-        Parameters:
-            processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`):
-                The instantiated processor class or a dictionary of processor classes that will be set as the processor
-                for **all** `Attention` layers.
-
-                If `processor` is a dict, the key needs to define the path to the corresponding cross attention
-                processor. This is strongly recommended when setting trainable attention processors.
-
-        """
-        count = len(self.attn_processors.keys())
-
-        if isinstance(processor, dict) and len(processor) != count:
-            raise ValueError(
-                f"A dict of processors was passed, but the number of processors {len(processor)} does not match the"
-                f" number of attention layers: {count}. Please make sure to pass {count} processor classes."
-            )
-
-        def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):
-            if hasattr(module, "set_processor"):
-                if not isinstance(processor, dict):
-                    module.set_processor(processor)
-                else:
-                    module.set_processor(processor.pop(f"{name}.processor"))
-
-            for sub_name, child in module.named_children():
-                fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor)
-
-        for name, module in self.named_children():
-            fn_recursive_attn_processor(name, module, processor)
-
-    def set_default_attn_processor(self):
-        """
-        Disables custom attention processors and sets the default attention implementation.
-
-        Safe to just use `AttnProcessor()` as PixArt doesn't have any exotic attention processors in default model.
-        """
-        self.set_attn_processor(AttnProcessor())
-
-    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.fuse_qkv_projections
-    def fuse_qkv_projections(self):
-        """
-        Enables fused QKV projections. For self-attention modules, all projection matrices (i.e., query, key, value)
-        are fused. For cross-attention modules, key and value projection matrices are fused.
-
-        <Tip warning={true}>
-
-        This API is 🧪 experimental.
-
-        </Tip>
-        """
-        self.original_attn_processors = None
-
-        for _, attn_processor in self.attn_processors.items():
-            if "Added" in str(attn_processor.__class__.__name__):
-                raise ValueError("`fuse_qkv_projections()` is not supported for models having added KV projections.")
-
-        self.original_attn_processors = self.attn_processors
-
-        for module in self.modules():
-            if isinstance(module, Attention):
-                module.fuse_projections(fuse=True)
-
-        self.set_attn_processor(FusedAttnProcessor2_0())
-
-    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.unfuse_qkv_projections
-    def unfuse_qkv_projections(self):
-        """Disables the fused QKV projection if enabled.
-
-        <Tip warning={true}>
-
-        This API is 🧪 experimental.
-
-        </Tip>
-
-        """
-        if self.original_attn_processors is not None:
-            self.set_attn_processor(self.original_attn_processors)
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        encoder_hidden_states: Optional[torch.Tensor] = None,
-        timestep: Optional[torch.LongTensor] = None,
-        added_cond_kwargs: Dict[str, torch.Tensor] = None,
-        cross_attention_kwargs: Dict[str, Any] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        encoder_attention_mask: Optional[torch.Tensor] = None,
-        return_dict: bool = True,
-    ):
-        """
-        The [`PixCellTransformer2DModel`] forward method.
-
-        Args:
-            hidden_states (`torch.FloatTensor` of shape `(batch size, channel, height, width)`):
-                Input `hidden_states`.
-            encoder_hidden_states (`torch.FloatTensor` of shape `(batch size, sequence len, embed dims)`, *optional*):
-                Conditional embeddings for cross attention layer. If not given, cross-attention defaults to
-                self-attention.
-            timestep (`torch.LongTensor`, *optional*):
-                Used to indicate denoising step. Optional timestep to be applied as an embedding in `AdaLayerNorm`.
-            added_cond_kwargs: (`Dict[str, Any]`, *optional*): Additional conditions to be used as inputs.
-            cross_attention_kwargs ( `Dict[str, Any]`, *optional*):
-                A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
-                `self.processor` in
-                [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
-            attention_mask ( `torch.Tensor`, *optional*):
-                An attention mask of shape `(batch, key_tokens)` is applied to `encoder_hidden_states`. If `1` the mask
-                is kept, otherwise if `0` it is discarded. Mask will be converted into a bias, which adds large
-                negative values to the attention scores corresponding to "discard" tokens.
-            encoder_attention_mask ( `torch.Tensor`, *optional*):
-                Cross-attention mask applied to `encoder_hidden_states`. Two formats supported:
-
-                    * Mask `(batch, sequence_length)` True = keep, False = discard.
-                    * Bias `(batch, 1, sequence_length)` 0 = keep, -10000 = discard.
-
-                If `ndim == 2`: will be interpreted as a mask, then converted into a bias consistent with the format
-                above. This bias will be added to the cross-attention scores.
-            return_dict (`bool`, *optional*, defaults to `True`):
-                Whether or not to return a [`~models.unets.unet_2d_condition.UNet2DConditionOutput`] instead of a plain
-                tuple.
-
-        Returns:
-            If `return_dict` is True, an [`~models.transformer_2d.Transformer2DModelOutput`] is returned, otherwise a
-            `tuple` where the first element is the sample tensor.
-        """
-        if self.use_additional_conditions and added_cond_kwargs is None:
-            raise ValueError("`added_cond_kwargs` cannot be None when using additional conditions for `adaln_single`.")
-
-        # ensure attention_mask is a bias, and give it a singleton query_tokens dimension.
-        #   we may have done this conversion already, e.g. if we came here via UNet2DConditionModel#forward.
-        #   we can tell by counting dims; if ndim == 2: it's a mask rather than a bias.
-        # expects mask of shape:
-        #   [batch, key_tokens]
-        # adds singleton query_tokens dimension:
-        #   [batch,                    1, key_tokens]
-        # this helps to broadcast it as a bias over attention scores, which will be in one of the following shapes:
-        #   [batch,  heads, query_tokens, key_tokens] (e.g. torch sdp attn)
-        #   [batch * heads, query_tokens, key_tokens] (e.g. xformers or classic attn)
-        if attention_mask is not None and attention_mask.ndim == 2:
-            # assume that mask is expressed as:
-            #   (1 = keep,      0 = discard)
-            # convert mask into a bias that can be added to attention scores:
-            #       (keep = +0,     discard = -10000.0)
-            attention_mask = (1 - attention_mask.to(hidden_states.dtype)) * -10000.0
-            attention_mask = attention_mask.unsqueeze(1)
-
-        # convert encoder_attention_mask to a bias the same way we do for attention_mask
-        if encoder_attention_mask is not None and encoder_attention_mask.ndim == 2:
-            encoder_attention_mask = (1 - encoder_attention_mask.to(hidden_states.dtype)) * -10000.0
-            encoder_attention_mask = encoder_attention_mask.unsqueeze(1)
-
-        # 1. Input
-        batch_size = hidden_states.shape[0]
-        height, width = (
-            hidden_states.shape[-2] // self.config.patch_size,
-            hidden_states.shape[-1] // self.config.patch_size,
-        )
-        hidden_states = self.pos_embed(hidden_states)
-
-        timestep, embedded_timestep = self.adaln_single(
-            timestep, added_cond_kwargs, batch_size=batch_size, hidden_dtype=hidden_states.dtype
-        )
-
-        if self.caption_projection is not None:
-            # Add positional embeddings to conditions if >1 UNI are given
-            if self.y_pos_embed is not None:
-                encoder_hidden_states = self.y_pos_embed(encoder_hidden_states)
-            encoder_hidden_states = self.caption_projection(encoder_hidden_states)
-            encoder_hidden_states = encoder_hidden_states.view(batch_size, -1, hidden_states.shape[-1])
-
-        # 2. Blocks
-        for block in self.transformer_blocks:
-            if torch.is_grad_enabled() and self.gradient_checkpointing:
-
-                def create_custom_forward(module, return_dict=None):
-                    def custom_forward(*inputs):
-                        if return_dict is not None:
-                            return module(*inputs, return_dict=return_dict)
-                        else:
-                            return module(*inputs)
-
-                    return custom_forward
-
-                ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
-                hidden_states = torch.utils.checkpoint.checkpoint(
-                    create_custom_forward(block),
-                    hidden_states,
-                    attention_mask,
-                    encoder_hidden_states,
-                    encoder_attention_mask,
-                    timestep,
-                    cross_attention_kwargs,
-                    None,
-                    **ckpt_kwargs,
-                )
-            else:
-                hidden_states = block(
-                    hidden_states,
-                    attention_mask=attention_mask,
-                    encoder_hidden_states=encoder_hidden_states,
-                    encoder_attention_mask=encoder_attention_mask,
-                    timestep=timestep,
-                    cross_attention_kwargs=cross_attention_kwargs,
-                    class_labels=None,
-                )
-
-        # 3. Output
-        shift, scale = (
-            self.scale_shift_table[None] + embedded_timestep[:, None].to(self.scale_shift_table.device)
-        ).chunk(2, dim=1)
-        hidden_states = self.norm_out(hidden_states)
-        # Modulation
-        hidden_states = hidden_states * (1 + scale.to(hidden_states.device)) + shift.to(hidden_states.device)
-        hidden_states = self.proj_out(hidden_states)
-        hidden_states = hidden_states.squeeze(1)
-
-        # unpatchify
-        hidden_states = hidden_states.reshape(
-            shape=(-1, height, width, self.config.patch_size, self.config.patch_size, self.out_channels)
-        )
-        hidden_states = torch.einsum("nhwpqc->nchpwq", hidden_states)
-        output = hidden_states.reshape(
-            shape=(-1, self.out_channels, height * self.config.patch_size, width * self.config.patch_size)
-        )
-
-        if not return_dict:
-            return (output,)
-
-        return Transformer2DModelOutput(sample=output)
-
-
-
-
-logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
-
-
-EXAMPLE_DOC_STRING = """
-    Examples:
-        ```py
-        >>> import torch
-        >>> from diffusers import PixCellSigmaPipeline
-
-        >>> # You can replace the checkpoint id with "PixArt-alpha/PixArt-Sigma-XL-2-512-MS" too.
-        >>> pipe = PixArtSigmaPipeline.from_pretrained(
-        ...     "PixArt-alpha/PixArt-Sigma-XL-2-1024-MS", torch_dtype=torch.float16
-        ... )
-        >>> # Enable memory optimizations.
-        >>> # pipe.enable_model_cpu_offload()
-
-        >>> prompt = "A small cactus with a happy face in the Sahara desert."
-        >>> image = pipe(prompt).images[0]
-        ```
-"""
-
-
-# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.retrieve_timesteps
-def retrieve_timesteps(
-    scheduler,
-    num_inference_steps: Optional[int] = None,
-    device: Optional[Union[str, torch.device]] = None,
-    timesteps: Optional[List[int]] = None,
-    sigmas: Optional[List[float]] = None,
-    **kwargs,
-):
-    r"""
-    Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles
-    custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`.
-
-    Args:
-        scheduler (`SchedulerMixin`):
-            The scheduler to get timesteps from.
-        num_inference_steps (`int`):
-            The number of diffusion steps used when generating samples with a pre-trained model. If used, `timesteps`
-            must be `None`.
-        device (`str` or `torch.device`, *optional*):
-            The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.
-        timesteps (`List[int]`, *optional*):
-            Custom timesteps used to override the timestep spacing strategy of the scheduler. If `timesteps` is passed,
-            `num_inference_steps` and `sigmas` must be `None`.
-        sigmas (`List[float]`, *optional*):
-            Custom sigmas used to override the timestep spacing strategy of the scheduler. If `sigmas` is passed,
-            `num_inference_steps` and `timesteps` must be `None`.
-
-    Returns:
-        `Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the
-        second element is the number of inference steps.
-    """
-    if timesteps is not None and sigmas is not None:
-        raise ValueError("Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values")
-    if timesteps is not None:
-        accepts_timesteps = "timesteps" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())
-        if not accepts_timesteps:
-            raise ValueError(
-                f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
-                f" timestep schedules. Please check whether you are using the correct scheduler."
-            )
-        scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs)
-        timesteps = scheduler.timesteps
-        num_inference_steps = len(timesteps)
-    elif sigmas is not None:
-        accept_sigmas = "sigmas" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())
-        if not accept_sigmas:
-            raise ValueError(
-                f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
-                f" sigmas schedules. Please check whether you are using the correct scheduler."
-            )
-        scheduler.set_timesteps(sigmas=sigmas, device=device, **kwargs)
-        timesteps = scheduler.timesteps
-        num_inference_steps = len(timesteps)
-    else:
-        scheduler.set_timesteps(num_inference_steps, device=device, **kwargs)
-        timesteps = scheduler.timesteps
-    return timesteps, num_inference_steps
-
-
-class PixCellPipeline(DiffusionPipeline):
-    r"""
-    Pipeline for SSL-to-image generation using PixCell.
-    """
-
-    model_cpu_offload_seq = "transformer->vae"
-
-    def __init__(
-        self,
-        vae: AutoencoderKL,
-        transformer: PixCellTransformer2DModel,
-        scheduler: DPMSolverMultistepScheduler,
-    ):
-        super().__init__()
-
-        self.register_modules(
-            vae=vae, transformer=transformer, scheduler=scheduler
-        )
-
-        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
-        self.image_processor = PixArtImageProcessor(vae_scale_factor=self.vae_scale_factor)
-
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
-    def prepare_extra_step_kwargs(self, generator, eta):
-        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
-        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
-        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
-        # and should be between [0, 1]
-
-        accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
-        extra_step_kwargs = {}
-        if accepts_eta:
-            extra_step_kwargs["eta"] = eta
-
-        # check if the scheduler accepts generator
-        accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
-        if accepts_generator:
-            extra_step_kwargs["generator"] = generator
-        return extra_step_kwargs
-
-    def get_unconditional_embedding(self, batch_size=1):
-        # Unconditional embedding is learned
-        uncond = self.transformer.caption_projection.uncond_embedding.clone().tile(batch_size,1,1)
-        return uncond
-
-    # Copied from diffusers.pipelines.pixart_alpha.pipeline_pixart_alpha.PixArtAlphaPipeline.check_inputs
-    def check_inputs(
-        self,
-        height,
-        width,
-        callback_steps,
-        uni_embeds=None,
-        negative_uni_embeds=None,
-        guidance_scale=None,
-    ):
-        if height % 8 != 0 or width % 8 != 0:
-            raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
-
-        if (callback_steps is None) or (
-            callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
-        ):
-            raise ValueError(
-                f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
-                f" {type(callback_steps)}."
-            )
-
-        if uni_embeds is None:
-            raise ValueError(
-                "Provide a UNI embedding `uni_embeds`."
-            )
-        elif len(uni_embeds.shape) != 3:
-            raise ValueError(
-                "UNI embedding given is not in (B,N,D)."
-            )
-        elif uni_embeds.shape[1] != self.transformer.config.caption_num_tokens:
-            raise ValueError(
-                f"Number of UNI embeddings must match the ones used in training ({self.transformer.config.caption_num_tokens})."
-            )
-        elif uni_embeds.shape[2] != self.transformer.config.caption_channels:
-            raise ValueError(
-                "UNI embedding given has incorrect dimenions."
-            )
-
-        if guidance_scale > 1.0:
-            if negative_uni_embeds is None:
-                raise ValueError(
-                    "Provide a negative UNI embedding `negative_uni_embeds`."
-                )
-            elif len(negative_uni_embeds.shape) != 3:
-                raise ValueError(
-                    "Negative UNI embedding given is not in (B,N,D)."
-                )
-            elif negative_uni_embeds.shape[1] != self.transformer.config.caption_num_tokens:
-                raise ValueError(
-                    f"Number of negative UNI embeddings must match the ones used in training ({self.transformer.config.caption_num_tokens})."
-                )
-            elif negative_uni_embeds.shape[2] != self.transformer.config.caption_channels:
-                raise ValueError(
-                    "Negative UNI embedding given has incorrect dimenions."
-                )
-
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents
-    def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):
-        shape = (
-            batch_size,
-            num_channels_latents,
-            int(height) // self.vae_scale_factor,
-            int(width) // self.vae_scale_factor,
-        )
-        if isinstance(generator, list) and len(generator) != batch_size:
-            raise ValueError(
-                f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
-                f" size of {batch_size}. Make sure the batch size matches the length of the generators."
-            )
-
-        if latents is None:
-            latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
-        else:
-            latents = latents.to(device)
-
-        # scale the initial noise by the standard deviation required by the scheduler
-        latents = latents * self.scheduler.init_noise_sigma
-        return latents
-
-    @torch.no_grad()
-    @replace_example_docstring(EXAMPLE_DOC_STRING)
-    def __call__(
-        self,
-        num_inference_steps: int = 20,
-        timesteps: List[int] = None,
-        sigmas: List[float] = None,
-        guidance_scale: float = 1.5,
-        num_images_per_prompt: Optional[int] = 1,
-        height: Optional[int] = None,
-        width: Optional[int] = None,
-        eta: float = 0.0,
-        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
-        latents: Optional[torch.Tensor] = None,
-        uni_embeds: Optional[torch.Tensor] = None,
-        negative_uni_embeds: Optional[torch.Tensor] = None,
-        output_type: Optional[str] = "pil",
-        return_dict: bool = True,
-        callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,
-        callback_steps: int = 1,
-        **kwargs,
-    ) -> Union[ImagePipelineOutput, Tuple]:
-        """
-        Function invoked when calling the pipeline for generation.
-
-        Args:
-            num_inference_steps (`int`, *optional*, defaults to 100):
-                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
-                expense of slower inference.
-            timesteps (`List[int]`, *optional*):
-                Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument
-                in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is
-                passed will be used. Must be in descending order.
-            sigmas (`List[float]`, *optional*):
-                Custom sigmas to use for the denoising process with schedulers which support a `sigmas` argument in
-                their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is passed
-                will be used.
-            guidance_scale (`float`, *optional*, defaults to 4.5):
-                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
-                `guidance_scale` is defined as `w` of equation 2. of [Imagen
-                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
-                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
-                usually at the expense of lower image quality.
-            num_images_per_prompt (`int`, *optional*, defaults to 1):
-                The number of images to generate per prompt.
-            height (`int`, *optional*, defaults to self.unet.config.sample_size):
-                The height in pixels of the generated image.
-            width (`int`, *optional*, defaults to self.unet.config.sample_size):
-                The width in pixels of the generated image.
-            eta (`float`, *optional*, defaults to 0.0):
-                Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
-                [`schedulers.DDIMScheduler`], will be ignored for others.
-            generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
-                One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
-                to make generation deterministic.
-            latents (`torch.Tensor`, *optional*):
-                Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
-                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
-                tensor will ge generated by sampling using the supplied random `generator`.
-            uni_embeds (`torch.Tensor`, *optional*):
-                Pre-generated UNI embeddings. 
-            negative_uni_embeds (`torch.Tensor`, *optional*):
-                Pre-generated negative UNI embeddings.
-            output_type (`str`, *optional*, defaults to `"pil"`):
-                The output format of the generate image. Choose between
-                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
-            return_dict (`bool`, *optional*, defaults to `True`):
-                Whether or not to return a [`~pipelines.stable_diffusion.IFPipelineOutput`] instead of a plain tuple.
-            callback (`Callable`, *optional*):
-                A function that will be called every `callback_steps` steps during inference. The function will be
-                called with the following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.
-            callback_steps (`int`, *optional*, defaults to 1):
-                The frequency at which the `callback` function will be called. If not specified, the callback will be
-                called at every step.
-
-        Examples:
-
-        Returns:
-            [`~pipelines.ImagePipelineOutput`] or `tuple`:
-                If `return_dict` is `True`, [`~pipelines.ImagePipelineOutput`] is returned, otherwise a `tuple` is
-                returned where the first element is a list with the generated images
-        """
-        # 1. Check inputs. Raise error if not correct
-        height = height or self.transformer.config.sample_size * self.vae_scale_factor
-        width = width or self.transformer.config.sample_size * self.vae_scale_factor
-
-        self.check_inputs(
-            height,
-            width,
-            callback_steps,
-            uni_embeds,
-            negative_uni_embeds,
-            guidance_scale,
-        )
-
-        # 2. Default height and width to transformer
-        batch_size = uni_embeds.shape[0]
-
-        device = self._execution_device
-
-        # 3. Handle UNI conditioning
-
-        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
-        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
-        # corresponds to doing no classifier free guidance.
-        do_classifier_free_guidance = guidance_scale > 1.0
-
-        uni_embeds = uni_embeds.repeat_interleave(num_images_per_prompt, dim=0)
-        if do_classifier_free_guidance:
-            negative_uni_embeds = negative_uni_embeds.repeat_interleave(num_images_per_prompt, dim=0)
-            uni_embeds = torch.cat([negative_uni_embeds, uni_embeds], dim=0)
-
-
-        # 4. Prepare timesteps
-        timesteps, num_inference_steps = retrieve_timesteps(
-            self.scheduler, num_inference_steps, device, timesteps, sigmas
-        )
-
-        # 5. Prepare latents.
-        latent_channels = self.transformer.config.in_channels
-        latents = self.prepare_latents(
-            batch_size * num_images_per_prompt,
-            latent_channels,
-            height,
-            width,
-            uni_embeds.dtype,
-            device,
-            generator,
-            latents,
-        )
-
-        # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
-        extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
-
-        added_cond_kwargs = {}
-
-        # 7. Denoising loop
-        num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
-
-        with self.progress_bar(total=num_inference_steps) as progress_bar:
-            for i, t in enumerate(timesteps):
-                latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
-                latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
-
-                current_timestep = t
-                if not torch.is_tensor(current_timestep):
-                    # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
-                    # This would be a good case for the `match` statement (Python 3.10+)
-                    is_mps = latent_model_input.device.type == "mps"
-                    if isinstance(current_timestep, float):
-                        dtype = torch.float32 if is_mps else torch.float64
-                    else:
-                        dtype = torch.int32 if is_mps else torch.int64
-                    current_timestep = torch.tensor([current_timestep], dtype=dtype, device=latent_model_input.device)
-                elif len(current_timestep.shape) == 0:
-                    current_timestep = current_timestep[None].to(latent_model_input.device)
-                # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
-                current_timestep = current_timestep.expand(latent_model_input.shape[0])
-
-                # predict noise model_output
-                noise_pred = self.transformer(
-                    latent_model_input,
-                    encoder_hidden_states=uni_embeds,
-                    timestep=current_timestep,
-                    added_cond_kwargs=added_cond_kwargs,
-                    return_dict=False,
-                )[0]
-
-                # perform guidance
-                if do_classifier_free_guidance:
-                    noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
-                    noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
-
-                # learned sigma
-                if self.transformer.config.out_channels // 2 == latent_channels:
-                    noise_pred = noise_pred.chunk(2, dim=1)[0]
-                else:
-                    noise_pred = noise_pred
-
-                # compute previous image: x_t -> x_t-1
-                latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
-
-                # call the callback, if provided
-                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
-                    progress_bar.update()
-                    if callback is not None and i % callback_steps == 0:
-                        step_idx = i // getattr(self.scheduler, "order", 1)
-                        callback(step_idx, t, latents)
-
-        if not output_type == "latent":
-            vae_scale = self.vae.config.scaling_factor
-            vae_shift = getattr(self.vae.config, "shift_factor", 0)
-
-            image = self.vae.decode((latents / vae_scale) + vae_shift, return_dict=False)[0]
-
-        else:
-            image = latents
-
-        if not output_type == "latent":
-            image = self.image_processor.postprocess(image, output_type=output_type)
-
-        # Offload all models
-        self.maybe_free_model_hooks()
-
-        if not return_dict:
-            return (image,)
-
-        return ImagePipelineOutput(images=image)