Delete src

src/attentionhacked_garmnet.py

@@ -1,670 +0,0 @@
-# Copyright 2023 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.
-from typing import Any, Dict, Optional
-
-import torch
-import torch.nn.functional as F
-from torch import nn
-
-from diffusers.utils import USE_PEFT_BACKEND
-from diffusers.utils.torch_utils import maybe_allow_in_graph
-from diffusers.models.activations import GEGLU, GELU, ApproximateGELU
-from diffusers.models.attention_processor import Attention
-from diffusers.models.embeddings import SinusoidalPositionalEmbedding
-from diffusers.models.lora import LoRACompatibleLinear
-from diffusers.models.normalization import AdaLayerNorm, AdaLayerNormContinuous, AdaLayerNormZero, RMSNorm
-
-
-def _chunked_feed_forward(
-    ff: nn.Module, hidden_states: torch.Tensor, chunk_dim: int, chunk_size: int, lora_scale: Optional[float] = None
-):
-    # "feed_forward_chunk_size" can be used to save memory
-    if hidden_states.shape[chunk_dim] % chunk_size != 0:
-        raise ValueError(
-            f"`hidden_states` dimension to be chunked: {hidden_states.shape[chunk_dim]} has to be divisible by chunk size: {chunk_size}. Make sure to set an appropriate `chunk_size` when calling `unet.enable_forward_chunking`."
-        )
-
-    num_chunks = hidden_states.shape[chunk_dim] // chunk_size
-    if lora_scale is None:
-        ff_output = torch.cat(
-            [ff(hid_slice) for hid_slice in hidden_states.chunk(num_chunks, dim=chunk_dim)],
-            dim=chunk_dim,
-        )
-    else:
-        # TOOD(Patrick): LoRA scale can be removed once PEFT refactor is complete
-        ff_output = torch.cat(
-            [ff(hid_slice, scale=lora_scale) for hid_slice in hidden_states.chunk(num_chunks, dim=chunk_dim)],
-            dim=chunk_dim,
-        )
-
-    return ff_output
-
-
-@maybe_allow_in_graph
-class GatedSelfAttentionDense(nn.Module):
-    r"""
-    A gated self-attention dense layer that combines visual features and object features.
-
-    Parameters:
-        query_dim (`int`): The number of channels in the query.
-        context_dim (`int`): The number of channels in the context.
-        n_heads (`int`): The number of heads to use for attention.
-        d_head (`int`): The number of channels in each head.
-    """
-
-    def __init__(self, query_dim: int, context_dim: int, n_heads: int, d_head: int):
-        super().__init__()
-
-        # we need a linear projection since we need cat visual feature and obj feature
-        self.linear = nn.Linear(context_dim, query_dim)
-
-        self.attn = Attention(query_dim=query_dim, heads=n_heads, dim_head=d_head)
-        self.ff = FeedForward(query_dim, activation_fn="geglu")
-
-        self.norm1 = nn.LayerNorm(query_dim)
-        self.norm2 = nn.LayerNorm(query_dim)
-
-        self.register_parameter("alpha_attn", nn.Parameter(torch.tensor(0.0)))
-        self.register_parameter("alpha_dense", nn.Parameter(torch.tensor(0.0)))
-
-        self.enabled = True
-
-    def forward(self, x: torch.Tensor, objs: torch.Tensor) -> torch.Tensor:
-        if not self.enabled:
-            return x
-
-        n_visual = x.shape[1]
-        objs = self.linear(objs)
-
-        x = x + self.alpha_attn.tanh() * self.attn(self.norm1(torch.cat([x, objs], dim=1)))[:, :n_visual, :]
-        x = x + self.alpha_dense.tanh() * self.ff(self.norm2(x))
-
-        return x
-
-
-@maybe_allow_in_graph
-class BasicTransformerBlock(nn.Module):
-    r"""
-    A basic Transformer block.
-
-    Parameters:
-        dim (`int`): The number of channels in the input and output.
-        num_attention_heads (`int`): The number of heads to use for multi-head attention.
-        attention_head_dim (`int`): The number of channels in each head.
-        dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.
-        cross_attention_dim (`int`, *optional*): The size of the encoder_hidden_states vector for cross attention.
-        activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to be used in feed-forward.
-        num_embeds_ada_norm (:
-            obj: `int`, *optional*): The number of diffusion steps used during training. See `Transformer2DModel`.
-        attention_bias (:
-            obj: `bool`, *optional*, defaults to `False`): Configure if the attentions should contain a bias parameter.
-        only_cross_attention (`bool`, *optional*):
-            Whether to use only cross-attention layers. In this case two cross attention layers are used.
-        double_self_attention (`bool`, *optional*):
-            Whether to use two self-attention layers. In this case no cross attention layers are used.
-        upcast_attention (`bool`, *optional*):
-            Whether to upcast the attention computation to float32. This is useful for mixed precision training.
-        norm_elementwise_affine (`bool`, *optional*, defaults to `True`):
-            Whether to use learnable elementwise affine parameters for normalization.
-        norm_type (`str`, *optional*, defaults to `"layer_norm"`):
-            The normalization layer to use. Can be `"layer_norm"`, `"ada_norm"` or `"ada_norm_zero"`.
-        final_dropout (`bool` *optional*, defaults to False):
-            Whether to apply a final dropout after the last feed-forward layer.
-        attention_type (`str`, *optional*, defaults to `"default"`):
-            The type of attention to use. Can be `"default"` or `"gated"` or `"gated-text-image"`.
-        positional_embeddings (`str`, *optional*, defaults to `None`):
-            The type of positional embeddings to apply to.
-        num_positional_embeddings (`int`, *optional*, defaults to `None`):
-            The maximum number of positional embeddings to apply.
-    """
-
-    def __init__(
-        self,
-        dim: int,
-        num_attention_heads: int,
-        attention_head_dim: int,
-        dropout=0.0,
-        cross_attention_dim: Optional[int] = None,
-        activation_fn: str = "geglu",
-        num_embeds_ada_norm: Optional[int] = None,
-        attention_bias: bool = False,
-        only_cross_attention: bool = False,
-        double_self_attention: bool = False,
-        upcast_attention: bool = False,
-        norm_elementwise_affine: bool = True,
-        norm_type: str = "layer_norm",  # 'layer_norm', 'ada_norm', 'ada_norm_zero', 'ada_norm_single'
-        norm_eps: float = 1e-5,
-        final_dropout: bool = False,
-        attention_type: str = "default",
-        positional_embeddings: Optional[str] = None,
-        num_positional_embeddings: Optional[int] = None,
-        ada_norm_continous_conditioning_embedding_dim: Optional[int] = None,
-        ada_norm_bias: Optional[int] = None,
-        ff_inner_dim: Optional[int] = None,
-        ff_bias: bool = True,
-        attention_out_bias: bool = True,
-    ):
-        super().__init__()
-        self.only_cross_attention = only_cross_attention
-
-        self.use_ada_layer_norm_zero = (num_embeds_ada_norm is not None) and norm_type == "ada_norm_zero"
-        self.use_ada_layer_norm = (num_embeds_ada_norm is not None) and norm_type == "ada_norm"
-        self.use_ada_layer_norm_single = norm_type == "ada_norm_single"
-        self.use_layer_norm = norm_type == "layer_norm"
-        self.use_ada_layer_norm_continuous = norm_type == "ada_norm_continuous"
-
-        if norm_type in ("ada_norm", "ada_norm_zero") and num_embeds_ada_norm is None:
-            raise ValueError(
-                f"`norm_type` is set to {norm_type}, but `num_embeds_ada_norm` is not defined. Please make sure to"
-                f" define `num_embeds_ada_norm` if setting `norm_type` to {norm_type}."
-            )
-
-        if positional_embeddings and (num_positional_embeddings is None):
-            raise ValueError(
-                "If `positional_embedding` type is defined, `num_positition_embeddings` must also be defined."
-            )
-
-        if positional_embeddings == "sinusoidal":
-            self.pos_embed = SinusoidalPositionalEmbedding(dim, max_seq_length=num_positional_embeddings)
-        else:
-            self.pos_embed = None
-
-        # Define 3 blocks. Each block has its own normalization layer.
-        # 1. Self-Attn
-        if self.use_ada_layer_norm:
-            self.norm1 = AdaLayerNorm(dim, num_embeds_ada_norm)
-        elif self.use_ada_layer_norm_zero:
-            self.norm1 = AdaLayerNormZero(dim, num_embeds_ada_norm)
-        elif self.use_ada_layer_norm_continuous:
-            self.norm1 = AdaLayerNormContinuous(
-                dim,
-                ada_norm_continous_conditioning_embedding_dim,
-                norm_elementwise_affine,
-                norm_eps,
-                ada_norm_bias,
-                "rms_norm",
-            )
-        else:
-            self.norm1 = nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine, eps=norm_eps)
-
-        self.attn1 = Attention(
-            query_dim=dim,
-            heads=num_attention_heads,
-            dim_head=attention_head_dim,
-            dropout=dropout,
-            bias=attention_bias,
-            cross_attention_dim=cross_attention_dim if only_cross_attention else None,
-            upcast_attention=upcast_attention,
-            out_bias=attention_out_bias,
-        )
-
-        # 2. Cross-Attn
-        if cross_attention_dim is not None or double_self_attention:
-            # We currently only use AdaLayerNormZero for self attention where there will only be one attention block.
-            # I.e. the number of returned modulation chunks from AdaLayerZero would not make sense if returned during
-            # the second cross attention block.
-            if self.use_ada_layer_norm:
-                self.norm2 = AdaLayerNorm(dim, num_embeds_ada_norm)
-            elif self.use_ada_layer_norm_continuous:
-                self.norm2 = AdaLayerNormContinuous(
-                    dim,
-                    ada_norm_continous_conditioning_embedding_dim,
-                    norm_elementwise_affine,
-                    norm_eps,
-                    ada_norm_bias,
-                    "rms_norm",
-                )
-            else:
-                self.norm2 = nn.LayerNorm(dim, norm_eps, norm_elementwise_affine)
-
-            self.attn2 = Attention(
-                query_dim=dim,
-                cross_attention_dim=cross_attention_dim if not double_self_attention else None,
-                heads=num_attention_heads,
-                dim_head=attention_head_dim,
-                dropout=dropout,
-                bias=attention_bias,
-                upcast_attention=upcast_attention,
-                out_bias=attention_out_bias,
-            )  # is self-attn if encoder_hidden_states is none
-        else:
-            self.norm2 = None
-            self.attn2 = None
-
-        # 3. Feed-forward
-        if self.use_ada_layer_norm_continuous:
-            self.norm3 = AdaLayerNormContinuous(
-                dim,
-                ada_norm_continous_conditioning_embedding_dim,
-                norm_elementwise_affine,
-                norm_eps,
-                ada_norm_bias,
-                "layer_norm",
-            )
-        elif not self.use_ada_layer_norm_single:
-            self.norm3 = nn.LayerNorm(dim, norm_eps, norm_elementwise_affine)
-
-        self.ff = FeedForward(
-            dim,
-            dropout=dropout,
-            activation_fn=activation_fn,
-            final_dropout=final_dropout,
-            inner_dim=ff_inner_dim,
-            bias=ff_bias,
-        )
-
-        # 4. Fuser
-        if attention_type == "gated" or attention_type == "gated-text-image":
-            self.fuser = GatedSelfAttentionDense(dim, cross_attention_dim, num_attention_heads, attention_head_dim)
-
-        # 5. Scale-shift for PixArt-Alpha.
-        if self.use_ada_layer_norm_single:
-            self.scale_shift_table = nn.Parameter(torch.randn(6, dim) / dim**0.5)
-
-        # let chunk size default to None
-        self._chunk_size = None
-        self._chunk_dim = 0
-
-    def set_chunk_feed_forward(self, chunk_size: Optional[int], dim: int = 0):
-        # Sets chunk feed-forward
-        self._chunk_size = chunk_size
-        self._chunk_dim = dim
-
-    def forward(
-        self,
-        hidden_states: torch.FloatTensor,
-        attention_mask: Optional[torch.FloatTensor] = None,
-        encoder_hidden_states: Optional[torch.FloatTensor] = None,
-        encoder_attention_mask: Optional[torch.FloatTensor] = None,
-        timestep: Optional[torch.LongTensor] = None,
-        cross_attention_kwargs: Dict[str, Any] = None,
-        class_labels: Optional[torch.LongTensor] = None,
-        added_cond_kwargs: Optional[Dict[str, torch.Tensor]] = None,
-    ) -> torch.FloatTensor:
-        # Notice that normalization is always applied before the real computation in the following blocks.
-        # 0. Self-Attention
-        batch_size = hidden_states.shape[0]
-        if self.use_ada_layer_norm:
-            norm_hidden_states = self.norm1(hidden_states, timestep)
-        elif self.use_ada_layer_norm_zero:
-            norm_hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.norm1(
-                hidden_states, timestep, class_labels, hidden_dtype=hidden_states.dtype
-            )
-        elif self.use_layer_norm:
-            norm_hidden_states = self.norm1(hidden_states)
-        elif self.use_ada_layer_norm_continuous:
-            norm_hidden_states = self.norm1(hidden_states, added_cond_kwargs["pooled_text_emb"])
-        elif self.use_ada_layer_norm_single:
-            shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = (
-                self.scale_shift_table[None] + timestep.reshape(batch_size, 6, -1)
-            ).chunk(6, dim=1)
-            norm_hidden_states = self.norm1(hidden_states)
-            norm_hidden_states = norm_hidden_states * (1 + scale_msa) + shift_msa
-            norm_hidden_states = norm_hidden_states.squeeze(1)
-        else:
-            raise ValueError("Incorrect norm used")
-
-        if self.pos_embed is not None:
-            norm_hidden_states = self.pos_embed(norm_hidden_states)
-
-        garment_features = []
-        garment_features.append(norm_hidden_states)
-
-        # 1. Retrieve lora scale.
-        lora_scale = cross_attention_kwargs.get("scale", 1.0) if cross_attention_kwargs is not None else 1.0
-
-        # 2. Prepare GLIGEN inputs
-        cross_attention_kwargs = cross_attention_kwargs.copy() if cross_attention_kwargs is not None else {}
-        gligen_kwargs = cross_attention_kwargs.pop("gligen", None)
-
-        attn_output = self.attn1(
-            norm_hidden_states,
-            encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None,
-            attention_mask=attention_mask,
-            **cross_attention_kwargs,
-        )
-        if self.use_ada_layer_norm_zero:
-            attn_output = gate_msa.unsqueeze(1) * attn_output
-        elif self.use_ada_layer_norm_single:
-            attn_output = gate_msa * attn_output
-
-        hidden_states = attn_output + hidden_states
-        if hidden_states.ndim == 4:
-            hidden_states = hidden_states.squeeze(1)
-
-        # 2.5 GLIGEN Control
-        if gligen_kwargs is not None:
-            hidden_states = self.fuser(hidden_states, gligen_kwargs["objs"])
-
-        # 3. Cross-Attention
-        if self.attn2 is not None:
-            if self.use_ada_layer_norm:
-                norm_hidden_states = self.norm2(hidden_states, timestep)
-            elif self.use_ada_layer_norm_zero or self.use_layer_norm:
-                norm_hidden_states = self.norm2(hidden_states)
-            elif self.use_ada_layer_norm_single:
-                # For PixArt norm2 isn't applied here:
-                # https://github.com/PixArt-alpha/PixArt-alpha/blob/0f55e922376d8b797edd44d25d0e7464b260dcab/diffusion/model/nets/PixArtMS.py#L70C1-L76C103
-                norm_hidden_states = hidden_states
-            elif self.use_ada_layer_norm_continuous:
-                norm_hidden_states = self.norm2(hidden_states, added_cond_kwargs["pooled_text_emb"])
-            else:
-                raise ValueError("Incorrect norm")
-
-            if self.pos_embed is not None and self.use_ada_layer_norm_single is False:
-                norm_hidden_states = self.pos_embed(norm_hidden_states)
-
-            attn_output = self.attn2(
-                norm_hidden_states,
-                encoder_hidden_states=encoder_hidden_states,
-                attention_mask=encoder_attention_mask,
-                **cross_attention_kwargs,
-            )
-            hidden_states = attn_output + hidden_states
-
-        # 4. Feed-forward
-        if self.use_ada_layer_norm_continuous:
-            norm_hidden_states = self.norm3(hidden_states, added_cond_kwargs["pooled_text_emb"])
-        elif not self.use_ada_layer_norm_single:
-            norm_hidden_states = self.norm3(hidden_states)
-
-        if self.use_ada_layer_norm_zero:
-            norm_hidden_states = norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]
-
-        if self.use_ada_layer_norm_single:
-            norm_hidden_states = self.norm2(hidden_states)
-            norm_hidden_states = norm_hidden_states * (1 + scale_mlp) + shift_mlp
-
-        if self._chunk_size is not None:
-            # "feed_forward_chunk_size" can be used to save memory
-            ff_output = _chunked_feed_forward(
-                self.ff, norm_hidden_states, self._chunk_dim, self._chunk_size, lora_scale=lora_scale
-            )
-        else:
-            ff_output = self.ff(norm_hidden_states, scale=lora_scale)
-
-        if self.use_ada_layer_norm_zero:
-            ff_output = gate_mlp.unsqueeze(1) * ff_output
-        elif self.use_ada_layer_norm_single:
-            ff_output = gate_mlp * ff_output
-
-        hidden_states = ff_output + hidden_states
-        if hidden_states.ndim == 4:
-            hidden_states = hidden_states.squeeze(1)
-
-        return hidden_states, garment_features
-
-
-@maybe_allow_in_graph
-class TemporalBasicTransformerBlock(nn.Module):
-    r"""
-    A basic Transformer block for video like data.
-
-    Parameters:
-        dim (`int`): The number of channels in the input and output.
-        time_mix_inner_dim (`int`): The number of channels for temporal attention.
-        num_attention_heads (`int`): The number of heads to use for multi-head attention.
-        attention_head_dim (`int`): The number of channels in each head.
-        cross_attention_dim (`int`, *optional*): The size of the encoder_hidden_states vector for cross attention.
-    """
-
-    def __init__(
-        self,
-        dim: int,
-        time_mix_inner_dim: int,
-        num_attention_heads: int,
-        attention_head_dim: int,
-        cross_attention_dim: Optional[int] = None,
-    ):
-        super().__init__()
-        self.is_res = dim == time_mix_inner_dim
-
-        self.norm_in = nn.LayerNorm(dim)
-
-        # Define 3 blocks. Each block has its own normalization layer.
-        # 1. Self-Attn
-        self.norm_in = nn.LayerNorm(dim)
-        self.ff_in = FeedForward(
-            dim,
-            dim_out=time_mix_inner_dim,
-            activation_fn="geglu",
-        )
-
-        self.norm1 = nn.LayerNorm(time_mix_inner_dim)
-        self.attn1 = Attention(
-            query_dim=time_mix_inner_dim,
-            heads=num_attention_heads,
-            dim_head=attention_head_dim,
-            cross_attention_dim=None,
-        )
-
-        # 2. Cross-Attn
-        if cross_attention_dim is not None:
-            # We currently only use AdaLayerNormZero for self attention where there will only be one attention block.
-            # I.e. the number of returned modulation chunks from AdaLayerZero would not make sense if returned during
-            # the second cross attention block.
-            self.norm2 = nn.LayerNorm(time_mix_inner_dim)
-            self.attn2 = Attention(
-                query_dim=time_mix_inner_dim,
-                cross_attention_dim=cross_attention_dim,
-                heads=num_attention_heads,
-                dim_head=attention_head_dim,
-            )  # is self-attn if encoder_hidden_states is none
-        else:
-            self.norm2 = None
-            self.attn2 = None
-
-        # 3. Feed-forward
-        self.norm3 = nn.LayerNorm(time_mix_inner_dim)
-        self.ff = FeedForward(time_mix_inner_dim, activation_fn="geglu")
-
-        # let chunk size default to None
-        self._chunk_size = None
-        self._chunk_dim = None
-
-    def set_chunk_feed_forward(self, chunk_size: Optional[int], **kwargs):
-        # Sets chunk feed-forward
-        self._chunk_size = chunk_size
-        # chunk dim should be hardcoded to 1 to have better speed vs. memory trade-off
-        self._chunk_dim = 1
-
-    def forward(
-        self,
-        hidden_states: torch.FloatTensor,
-        num_frames: int,
-        encoder_hidden_states: Optional[torch.FloatTensor] = None,
-    ) -> torch.FloatTensor:
-        # Notice that normalization is always applied before the real computation in the following blocks.
-        # 0. Self-Attention
-        batch_size = hidden_states.shape[0]
-
-        batch_frames, seq_length, channels = hidden_states.shape
-        batch_size = batch_frames // num_frames
-
-        hidden_states = hidden_states[None, :].reshape(batch_size, num_frames, seq_length, channels)
-        hidden_states = hidden_states.permute(0, 2, 1, 3)
-        hidden_states = hidden_states.reshape(batch_size * seq_length, num_frames, channels)
-
-        residual = hidden_states
-        hidden_states = self.norm_in(hidden_states)
-
-        if self._chunk_size is not None:
-            hidden_states = _chunked_feed_forward(self.ff_in, hidden_states, self._chunk_dim, self._chunk_size)
-        else:
-            hidden_states = self.ff_in(hidden_states)
-
-        if self.is_res:
-            hidden_states = hidden_states + residual
-
-        norm_hidden_states = self.norm1(hidden_states)
-        attn_output = self.attn1(norm_hidden_states, encoder_hidden_states=None)
-        hidden_states = attn_output + hidden_states
-
-        # 3. Cross-Attention
-        if self.attn2 is not None:
-            norm_hidden_states = self.norm2(hidden_states)
-            attn_output = self.attn2(norm_hidden_states, encoder_hidden_states=encoder_hidden_states)
-            hidden_states = attn_output + hidden_states
-
-        # 4. Feed-forward
-        norm_hidden_states = self.norm3(hidden_states)
-
-        if self._chunk_size is not None:
-            ff_output = _chunked_feed_forward(self.ff, norm_hidden_states, self._chunk_dim, self._chunk_size)
-        else:
-            ff_output = self.ff(norm_hidden_states)
-
-        if self.is_res:
-            hidden_states = ff_output + hidden_states
-        else:
-            hidden_states = ff_output
-
-        hidden_states = hidden_states[None, :].reshape(batch_size, seq_length, num_frames, channels)
-        hidden_states = hidden_states.permute(0, 2, 1, 3)
-        hidden_states = hidden_states.reshape(batch_size * num_frames, seq_length, channels)
-
-        return hidden_states
-
-
-class SkipFFTransformerBlock(nn.Module):
-    def __init__(
-        self,
-        dim: int,
-        num_attention_heads: int,
-        attention_head_dim: int,
-        kv_input_dim: int,
-        kv_input_dim_proj_use_bias: bool,
-        dropout=0.0,
-        cross_attention_dim: Optional[int] = None,
-        attention_bias: bool = False,
-        attention_out_bias: bool = True,
-    ):
-        super().__init__()
-        if kv_input_dim != dim:
-            self.kv_mapper = nn.Linear(kv_input_dim, dim, kv_input_dim_proj_use_bias)
-        else:
-            self.kv_mapper = None
-
-        self.norm1 = RMSNorm(dim, 1e-06)
-
-        self.attn1 = Attention(
-            query_dim=dim,
-            heads=num_attention_heads,
-            dim_head=attention_head_dim,
-            dropout=dropout,
-            bias=attention_bias,
-            cross_attention_dim=cross_attention_dim,
-            out_bias=attention_out_bias,
-        )
-
-        self.norm2 = RMSNorm(dim, 1e-06)
-
-        self.attn2 = Attention(
-            query_dim=dim,
-            cross_attention_dim=cross_attention_dim,
-            heads=num_attention_heads,
-            dim_head=attention_head_dim,
-            dropout=dropout,
-            bias=attention_bias,
-            out_bias=attention_out_bias,
-        )
-
-    def forward(self, hidden_states, encoder_hidden_states, cross_attention_kwargs):
-        cross_attention_kwargs = cross_attention_kwargs.copy() if cross_attention_kwargs is not None else {}
-
-        if self.kv_mapper is not None:
-            encoder_hidden_states = self.kv_mapper(F.silu(encoder_hidden_states))
-
-        norm_hidden_states = self.norm1(hidden_states)
-
-        attn_output = self.attn1(
-            norm_hidden_states,
-            encoder_hidden_states=encoder_hidden_states,
-            **cross_attention_kwargs,
-        )
-
-        hidden_states = attn_output + hidden_states
-
-        norm_hidden_states = self.norm2(hidden_states)
-
-        attn_output = self.attn2(
-            norm_hidden_states,
-            encoder_hidden_states=encoder_hidden_states,
-            **cross_attention_kwargs,
-        )
-
-        hidden_states = attn_output + hidden_states
-
-        return hidden_states
-
-
-class FeedForward(nn.Module):
-    r"""
-    A feed-forward layer.
-
-    Parameters:
-        dim (`int`): The number of channels in the input.
-        dim_out (`int`, *optional*): The number of channels in the output. If not given, defaults to `dim`.
-        mult (`int`, *optional*, defaults to 4): The multiplier to use for the hidden dimension.
-        dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.
-        activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to be used in feed-forward.
-        final_dropout (`bool` *optional*, defaults to False): Apply a final dropout.
-        bias (`bool`, defaults to True): Whether to use a bias in the linear layer.
-    """
-
-    def __init__(
-        self,
-        dim: int,
-        dim_out: Optional[int] = None,
-        mult: int = 4,
-        dropout: float = 0.0,
-        activation_fn: str = "geglu",
-        final_dropout: bool = False,
-        inner_dim=None,
-        bias: bool = True,
-    ):
-        super().__init__()
-        if inner_dim is None:
-            inner_dim = int(dim * mult)
-        dim_out = dim_out if dim_out is not None else dim
-        linear_cls = LoRACompatibleLinear if not USE_PEFT_BACKEND else nn.Linear
-
-        if activation_fn == "gelu":
-            act_fn = GELU(dim, inner_dim, bias=bias)
-        if activation_fn == "gelu-approximate":
-            act_fn = GELU(dim, inner_dim, approximate="tanh", bias=bias)
-        elif activation_fn == "geglu":
-            act_fn = GEGLU(dim, inner_dim, bias=bias)
-        elif activation_fn == "geglu-approximate":
-            act_fn = ApproximateGELU(dim, inner_dim, bias=bias)
-
-        self.net = nn.ModuleList([])
-        # project in
-        self.net.append(act_fn)
-        # project dropout
-        self.net.append(nn.Dropout(dropout))
-        # project out
-        self.net.append(linear_cls(inner_dim, dim_out, bias=bias))
-        # FF as used in Vision Transformer, MLP-Mixer, etc. have a final dropout
-        if final_dropout:
-            self.net.append(nn.Dropout(dropout))
-
-    def forward(self, hidden_states: torch.Tensor, scale: float = 1.0) -> torch.Tensor:
-        compatible_cls = (GEGLU,) if USE_PEFT_BACKEND else (GEGLU, LoRACompatibleLinear)
-        for module in self.net:
-            if isinstance(module, compatible_cls):
-                hidden_states = module(hidden_states, scale)
-            else:
-                hidden_states = module(hidden_states)
-        return hidden_states

src/attentionhacked_tryon.py

@@ -1,679 +0,0 @@
-# Copyright 2023 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.
-from typing import Any, Dict, Optional
-
-import torch
-import torch.nn.functional as F
-from torch import nn
-
-from diffusers.utils import USE_PEFT_BACKEND
-from diffusers.utils.torch_utils import maybe_allow_in_graph
-from diffusers.models.activations import GEGLU, GELU, ApproximateGELU
-from diffusers.models.attention_processor import Attention
-from diffusers.models.embeddings import SinusoidalPositionalEmbedding
-from diffusers.models.lora import LoRACompatibleLinear
-from diffusers.models.normalization import AdaLayerNorm, AdaLayerNormContinuous, AdaLayerNormZero, RMSNorm
-
-
-def _chunked_feed_forward(
-    ff: nn.Module, hidden_states: torch.Tensor, chunk_dim: int, chunk_size: int, lora_scale: Optional[float] = None
-):
-    # "feed_forward_chunk_size" can be used to save memory
-    if hidden_states.shape[chunk_dim] % chunk_size != 0:
-        raise ValueError(
-            f"`hidden_states` dimension to be chunked: {hidden_states.shape[chunk_dim]} has to be divisible by chunk size: {chunk_size}. Make sure to set an appropriate `chunk_size` when calling `unet.enable_forward_chunking`."
-        )
-
-    num_chunks = hidden_states.shape[chunk_dim] // chunk_size
-    if lora_scale is None:
-        ff_output = torch.cat(
-            [ff(hid_slice) for hid_slice in hidden_states.chunk(num_chunks, dim=chunk_dim)],
-            dim=chunk_dim,
-        )
-    else:
-        # TOOD(Patrick): LoRA scale can be removed once PEFT refactor is complete
-        ff_output = torch.cat(
-            [ff(hid_slice, scale=lora_scale) for hid_slice in hidden_states.chunk(num_chunks, dim=chunk_dim)],
-            dim=chunk_dim,
-        )
-
-    return ff_output
-
-
-@maybe_allow_in_graph
-class GatedSelfAttentionDense(nn.Module):
-    r"""
-    A gated self-attention dense layer that combines visual features and object features.
-
-    Parameters:
-        query_dim (`int`): The number of channels in the query.
-        context_dim (`int`): The number of channels in the context.
-        n_heads (`int`): The number of heads to use for attention.
-        d_head (`int`): The number of channels in each head.
-    """
-
-    def __init__(self, query_dim: int, context_dim: int, n_heads: int, d_head: int):
-        super().__init__()
-
-        # we need a linear projection since we need cat visual feature and obj feature
-        self.linear = nn.Linear(context_dim, query_dim)
-
-        self.attn = Attention(query_dim=query_dim, heads=n_heads, dim_head=d_head)
-        self.ff = FeedForward(query_dim, activation_fn="geglu")
-
-        self.norm1 = nn.LayerNorm(query_dim)
-        self.norm2 = nn.LayerNorm(query_dim)
-
-        self.register_parameter("alpha_attn", nn.Parameter(torch.tensor(0.0)))
-        self.register_parameter("alpha_dense", nn.Parameter(torch.tensor(0.0)))
-
-        self.enabled = True
-
-    def forward(self, x: torch.Tensor, objs: torch.Tensor) -> torch.Tensor:
-        if not self.enabled:
-            return x
-
-        n_visual = x.shape[1]
-        objs = self.linear(objs)
-
-        x = x + self.alpha_attn.tanh() * self.attn(self.norm1(torch.cat([x, objs], dim=1)))[:, :n_visual, :]
-        x = x + self.alpha_dense.tanh() * self.ff(self.norm2(x))
-
-        return x
-
-
-@maybe_allow_in_graph
-class BasicTransformerBlock(nn.Module):
-    r"""
-    A basic Transformer block.
-
-    Parameters:
-        dim (`int`): The number of channels in the input and output.
-        num_attention_heads (`int`): The number of heads to use for multi-head attention.
-        attention_head_dim (`int`): The number of channels in each head.
-        dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.
-        cross_attention_dim (`int`, *optional*): The size of the encoder_hidden_states vector for cross attention.
-        activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to be used in feed-forward.
-        num_embeds_ada_norm (:
-            obj: `int`, *optional*): The number of diffusion steps used during training. See `Transformer2DModel`.
-        attention_bias (:
-            obj: `bool`, *optional*, defaults to `False`): Configure if the attentions should contain a bias parameter.
-        only_cross_attention (`bool`, *optional*):
-            Whether to use only cross-attention layers. In this case two cross attention layers are used.
-        double_self_attention (`bool`, *optional*):
-            Whether to use two self-attention layers. In this case no cross attention layers are used.
-        upcast_attention (`bool`, *optional*):
-            Whether to upcast the attention computation to float32. This is useful for mixed precision training.
-        norm_elementwise_affine (`bool`, *optional*, defaults to `True`):
-            Whether to use learnable elementwise affine parameters for normalization.
-        norm_type (`str`, *optional*, defaults to `"layer_norm"`):
-            The normalization layer to use. Can be `"layer_norm"`, `"ada_norm"` or `"ada_norm_zero"`.
-        final_dropout (`bool` *optional*, defaults to False):
-            Whether to apply a final dropout after the last feed-forward layer.
-        attention_type (`str`, *optional*, defaults to `"default"`):
-            The type of attention to use. Can be `"default"` or `"gated"` or `"gated-text-image"`.
-        positional_embeddings (`str`, *optional*, defaults to `None`):
-            The type of positional embeddings to apply to.
-        num_positional_embeddings (`int`, *optional*, defaults to `None`):
-            The maximum number of positional embeddings to apply.
-    """
-
-    def __init__(
-        self,
-        dim: int,
-        num_attention_heads: int,
-        attention_head_dim: int,
-        dropout=0.0,
-        cross_attention_dim: Optional[int] = None,
-        activation_fn: str = "geglu",
-        num_embeds_ada_norm: Optional[int] = None,
-        attention_bias: bool = False,
-        only_cross_attention: bool = False,
-        double_self_attention: bool = False,
-        upcast_attention: bool = False,
-        norm_elementwise_affine: bool = True,
-        norm_type: str = "layer_norm",  # 'layer_norm', 'ada_norm', 'ada_norm_zero', 'ada_norm_single'
-        norm_eps: float = 1e-5,
-        final_dropout: bool = False,
-        attention_type: str = "default",
-        positional_embeddings: Optional[str] = None,
-        num_positional_embeddings: Optional[int] = None,
-        ada_norm_continous_conditioning_embedding_dim: Optional[int] = None,
-        ada_norm_bias: Optional[int] = None,
-        ff_inner_dim: Optional[int] = None,
-        ff_bias: bool = True,
-        attention_out_bias: bool = True,
-    ):
-        super().__init__()
-        self.only_cross_attention = only_cross_attention
-
-        self.use_ada_layer_norm_zero = (num_embeds_ada_norm is not None) and norm_type == "ada_norm_zero"
-        self.use_ada_layer_norm = (num_embeds_ada_norm is not None) and norm_type == "ada_norm"
-        self.use_ada_layer_norm_single = norm_type == "ada_norm_single"
-        self.use_layer_norm = norm_type == "layer_norm"
-        self.use_ada_layer_norm_continuous = norm_type == "ada_norm_continuous"
-
-        if norm_type in ("ada_norm", "ada_norm_zero") and num_embeds_ada_norm is None:
-            raise ValueError(
-                f"`norm_type` is set to {norm_type}, but `num_embeds_ada_norm` is not defined. Please make sure to"
-                f" define `num_embeds_ada_norm` if setting `norm_type` to {norm_type}."
-            )
-
-        if positional_embeddings and (num_positional_embeddings is None):
-            raise ValueError(
-                "If `positional_embedding` type is defined, `num_positition_embeddings` must also be defined."
-            )
-
-        if positional_embeddings == "sinusoidal":
-            self.pos_embed = SinusoidalPositionalEmbedding(dim, max_seq_length=num_positional_embeddings)
-        else:
-            self.pos_embed = None
-
-        # Define 3 blocks. Each block has its own normalization layer.
-        # 1. Self-Attn
-        if self.use_ada_layer_norm:
-            self.norm1 = AdaLayerNorm(dim, num_embeds_ada_norm)
-        elif self.use_ada_layer_norm_zero:
-            self.norm1 = AdaLayerNormZero(dim, num_embeds_ada_norm)
-        elif self.use_ada_layer_norm_continuous:
-            self.norm1 = AdaLayerNormContinuous(
-                dim,
-                ada_norm_continous_conditioning_embedding_dim,
-                norm_elementwise_affine,
-                norm_eps,
-                ada_norm_bias,
-                "rms_norm",
-            )
-        else:
-            self.norm1 = nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine, eps=norm_eps)
-
-        self.attn1 = Attention(
-            query_dim=dim,
-            heads=num_attention_heads,
-            dim_head=attention_head_dim,
-            dropout=dropout,
-            bias=attention_bias,
-            cross_attention_dim=cross_attention_dim if only_cross_attention else None,
-            upcast_attention=upcast_attention,
-            out_bias=attention_out_bias,
-        )
-
-        # 2. Cross-Attn
-        if cross_attention_dim is not None or double_self_attention:
-            # We currently only use AdaLayerNormZero for self attention where there will only be one attention block.
-            # I.e. the number of returned modulation chunks from AdaLayerZero would not make sense if returned during
-            # the second cross attention block.
-            if self.use_ada_layer_norm:
-                self.norm2 = AdaLayerNorm(dim, num_embeds_ada_norm)
-            elif self.use_ada_layer_norm_continuous:
-                self.norm2 = AdaLayerNormContinuous(
-                    dim,
-                    ada_norm_continous_conditioning_embedding_dim,
-                    norm_elementwise_affine,
-                    norm_eps,
-                    ada_norm_bias,
-                    "rms_norm",
-                )
-            else:
-                self.norm2 = nn.LayerNorm(dim, norm_eps, norm_elementwise_affine)
-
-            self.attn2 = Attention(
-                query_dim=dim,
-                cross_attention_dim=cross_attention_dim if not double_self_attention else None,
-                heads=num_attention_heads,
-                dim_head=attention_head_dim,
-                dropout=dropout,
-                bias=attention_bias,
-                upcast_attention=upcast_attention,
-                out_bias=attention_out_bias,
-            )  # is self-attn if encoder_hidden_states is none
-        else:
-            self.norm2 = None
-            self.attn2 = None
-
-        # 3. Feed-forward
-        if self.use_ada_layer_norm_continuous:
-            self.norm3 = AdaLayerNormContinuous(
-                dim,
-                ada_norm_continous_conditioning_embedding_dim,
-                norm_elementwise_affine,
-                norm_eps,
-                ada_norm_bias,
-                "layer_norm",
-            )
-        elif not self.use_ada_layer_norm_single:
-            self.norm3 = nn.LayerNorm(dim, norm_eps, norm_elementwise_affine)
-
-        self.ff = FeedForward(
-            dim,
-            dropout=dropout,
-            activation_fn=activation_fn,
-            final_dropout=final_dropout,
-            inner_dim=ff_inner_dim,
-            bias=ff_bias,
-        )
-
-        # 4. Fuser
-        if attention_type == "gated" or attention_type == "gated-text-image":
-            self.fuser = GatedSelfAttentionDense(dim, cross_attention_dim, num_attention_heads, attention_head_dim)
-
-        # 5. Scale-shift for PixArt-Alpha.
-        if self.use_ada_layer_norm_single:
-            self.scale_shift_table = nn.Parameter(torch.randn(6, dim) / dim**0.5)
-
-        # let chunk size default to None
-        self._chunk_size = None
-        self._chunk_dim = 0
-
-    def set_chunk_feed_forward(self, chunk_size: Optional[int], dim: int = 0):
-        # Sets chunk feed-forward
-        self._chunk_size = chunk_size
-        self._chunk_dim = dim
-
-    def forward(
-        self,
-        hidden_states: torch.FloatTensor,
-        attention_mask: Optional[torch.FloatTensor] = None,
-        encoder_hidden_states: Optional[torch.FloatTensor] = None,
-        encoder_attention_mask: Optional[torch.FloatTensor] = None,
-        timestep: Optional[torch.LongTensor] = None,
-        cross_attention_kwargs: Dict[str, Any] = None,
-        class_labels: Optional[torch.LongTensor] = None,
-        garment_features=None,
-        curr_garment_feat_idx=0,
-        added_cond_kwargs: Optional[Dict[str, torch.Tensor]] = None,
-    ) -> torch.FloatTensor:
-        # Notice that normalization is always applied before the real computation in the following blocks.
-        # 0. Self-Attention
-        batch_size = hidden_states.shape[0]
-
-        
-        
-        if self.use_ada_layer_norm:
-            norm_hidden_states = self.norm1(hidden_states, timestep)
-        elif self.use_ada_layer_norm_zero:
-            norm_hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.norm1(
-                hidden_states, timestep, class_labels, hidden_dtype=hidden_states.dtype
-            )
-        elif self.use_layer_norm:
-            norm_hidden_states = self.norm1(hidden_states)
-        elif self.use_ada_layer_norm_continuous:
-            norm_hidden_states = self.norm1(hidden_states, added_cond_kwargs["pooled_text_emb"])
-        elif self.use_ada_layer_norm_single:
-            shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = (
-                self.scale_shift_table[None] + timestep.reshape(batch_size, 6, -1)
-            ).chunk(6, dim=1)
-            norm_hidden_states = self.norm1(hidden_states)
-            norm_hidden_states = norm_hidden_states * (1 + scale_msa) + shift_msa
-            norm_hidden_states = norm_hidden_states.squeeze(1)
-        else:
-            raise ValueError("Incorrect norm used")
-
-        if self.pos_embed is not None:
-            norm_hidden_states = self.pos_embed(norm_hidden_states)
-
-        # 1. Retrieve lora scale.
-        lora_scale = cross_attention_kwargs.get("scale", 1.0) if cross_attention_kwargs is not None else 1.0
-
-        # 2. Prepare GLIGEN inputs
-        cross_attention_kwargs = cross_attention_kwargs.copy() if cross_attention_kwargs is not None else {}
-        gligen_kwargs = cross_attention_kwargs.pop("gligen", None)
-
-
-        modify_norm_hidden_states = torch.cat([norm_hidden_states,garment_features[curr_garment_feat_idx]], dim=1)
-        curr_garment_feat_idx +=1
-        attn_output = self.attn1(
-            #norm_hidden_states,
-            modify_norm_hidden_states,
-            encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None,
-            attention_mask=attention_mask,
-            **cross_attention_kwargs,
-        )
-        if self.use_ada_layer_norm_zero:
-            attn_output = gate_msa.unsqueeze(1) * attn_output
-        elif self.use_ada_layer_norm_single:
-            attn_output = gate_msa * attn_output
-
-        hidden_states = attn_output[:,:hidden_states.shape[-2],:] + hidden_states
-
-
-
-
-        if hidden_states.ndim == 4:
-            hidden_states = hidden_states.squeeze(1)
-
-        # 2.5 GLIGEN Control
-        if gligen_kwargs is not None:
-            hidden_states = self.fuser(hidden_states, gligen_kwargs["objs"])
-
-        # 3. Cross-Attention
-        if self.attn2 is not None:
-            if self.use_ada_layer_norm:
-                norm_hidden_states = self.norm2(hidden_states, timestep)
-            elif self.use_ada_layer_norm_zero or self.use_layer_norm:
-                norm_hidden_states = self.norm2(hidden_states)
-            elif self.use_ada_layer_norm_single:
-                # For PixArt norm2 isn't applied here:
-                # https://github.com/PixArt-alpha/PixArt-alpha/blob/0f55e922376d8b797edd44d25d0e7464b260dcab/diffusion/model/nets/PixArtMS.py#L70C1-L76C103
-                norm_hidden_states = hidden_states
-            elif self.use_ada_layer_norm_continuous:
-                norm_hidden_states = self.norm2(hidden_states, added_cond_kwargs["pooled_text_emb"])
-            else:
-                raise ValueError("Incorrect norm")
-
-            if self.pos_embed is not None and self.use_ada_layer_norm_single is False:
-                norm_hidden_states = self.pos_embed(norm_hidden_states)
-
-            attn_output = self.attn2(
-                norm_hidden_states,
-                encoder_hidden_states=encoder_hidden_states,
-                attention_mask=encoder_attention_mask,
-                **cross_attention_kwargs,
-            )
-            hidden_states = attn_output + hidden_states
-
-        # 4. Feed-forward
-        if self.use_ada_layer_norm_continuous:
-            norm_hidden_states = self.norm3(hidden_states, added_cond_kwargs["pooled_text_emb"])
-        elif not self.use_ada_layer_norm_single:
-            norm_hidden_states = self.norm3(hidden_states)
-
-        if self.use_ada_layer_norm_zero:
-            norm_hidden_states = norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]
-
-        if self.use_ada_layer_norm_single:
-            norm_hidden_states = self.norm2(hidden_states)
-            norm_hidden_states = norm_hidden_states * (1 + scale_mlp) + shift_mlp
-
-        if self._chunk_size is not None:
-            # "feed_forward_chunk_size" can be used to save memory
-            ff_output = _chunked_feed_forward(
-                self.ff, norm_hidden_states, self._chunk_dim, self._chunk_size, lora_scale=lora_scale
-            )
-        else:
-            ff_output = self.ff(norm_hidden_states, scale=lora_scale)
-
-        if self.use_ada_layer_norm_zero:
-            ff_output = gate_mlp.unsqueeze(1) * ff_output
-        elif self.use_ada_layer_norm_single:
-            ff_output = gate_mlp * ff_output
-
-        hidden_states = ff_output + hidden_states
-        if hidden_states.ndim == 4:
-            hidden_states = hidden_states.squeeze(1)
-        return hidden_states,curr_garment_feat_idx
-
-
-@maybe_allow_in_graph
-class TemporalBasicTransformerBlock(nn.Module):
-    r"""
-    A basic Transformer block for video like data.
-
-    Parameters:
-        dim (`int`): The number of channels in the input and output.
-        time_mix_inner_dim (`int`): The number of channels for temporal attention.
-        num_attention_heads (`int`): The number of heads to use for multi-head attention.
-        attention_head_dim (`int`): The number of channels in each head.
-        cross_attention_dim (`int`, *optional*): The size of the encoder_hidden_states vector for cross attention.
-    """
-
-    def __init__(
-        self,
-        dim: int,
-        time_mix_inner_dim: int,
-        num_attention_heads: int,
-        attention_head_dim: int,
-        cross_attention_dim: Optional[int] = None,
-    ):
-        super().__init__()
-        self.is_res = dim == time_mix_inner_dim
-
-        self.norm_in = nn.LayerNorm(dim)
-
-        # Define 3 blocks. Each block has its own normalization layer.
-        # 1. Self-Attn
-        self.norm_in = nn.LayerNorm(dim)
-        self.ff_in = FeedForward(
-            dim,
-            dim_out=time_mix_inner_dim,
-            activation_fn="geglu",
-        )
-
-        self.norm1 = nn.LayerNorm(time_mix_inner_dim)
-        self.attn1 = Attention(
-            query_dim=time_mix_inner_dim,
-            heads=num_attention_heads,
-            dim_head=attention_head_dim,
-            cross_attention_dim=None,
-        )
-
-        # 2. Cross-Attn
-        if cross_attention_dim is not None:
-            # We currently only use AdaLayerNormZero for self attention where there will only be one attention block.
-            # I.e. the number of returned modulation chunks from AdaLayerZero would not make sense if returned during
-            # the second cross attention block.
-            self.norm2 = nn.LayerNorm(time_mix_inner_dim)
-            self.attn2 = Attention(
-                query_dim=time_mix_inner_dim,
-                cross_attention_dim=cross_attention_dim,
-                heads=num_attention_heads,
-                dim_head=attention_head_dim,
-            )  # is self-attn if encoder_hidden_states is none
-        else:
-            self.norm2 = None
-            self.attn2 = None
-
-        # 3. Feed-forward
-        self.norm3 = nn.LayerNorm(time_mix_inner_dim)
-        self.ff = FeedForward(time_mix_inner_dim, activation_fn="geglu")
-
-        # let chunk size default to None
-        self._chunk_size = None
-        self._chunk_dim = None
-
-    def set_chunk_feed_forward(self, chunk_size: Optional[int], **kwargs):
-        # Sets chunk feed-forward
-        self._chunk_size = chunk_size
-        # chunk dim should be hardcoded to 1 to have better speed vs. memory trade-off
-        self._chunk_dim = 1
-
-    def forward(
-        self,
-        hidden_states: torch.FloatTensor,
-        num_frames: int,
-        encoder_hidden_states: Optional[torch.FloatTensor] = None,
-    ) -> torch.FloatTensor:
-        # Notice that normalization is always applied before the real computation in the following blocks.
-        # 0. Self-Attention
-        batch_size = hidden_states.shape[0]
-
-        batch_frames, seq_length, channels = hidden_states.shape
-        batch_size = batch_frames // num_frames
-
-        hidden_states = hidden_states[None, :].reshape(batch_size, num_frames, seq_length, channels)
-        hidden_states = hidden_states.permute(0, 2, 1, 3)
-        hidden_states = hidden_states.reshape(batch_size * seq_length, num_frames, channels)
-
-        residual = hidden_states
-        hidden_states = self.norm_in(hidden_states)
-
-        if self._chunk_size is not None:
-            hidden_states = _chunked_feed_forward(self.ff_in, hidden_states, self._chunk_dim, self._chunk_size)
-        else:
-            hidden_states = self.ff_in(hidden_states)
-
-        if self.is_res:
-            hidden_states = hidden_states + residual
-
-        norm_hidden_states = self.norm1(hidden_states)
-        attn_output = self.attn1(norm_hidden_states, encoder_hidden_states=None)
-        hidden_states = attn_output + hidden_states
-
-        # 3. Cross-Attention
-        if self.attn2 is not None:
-            norm_hidden_states = self.norm2(hidden_states)
-            attn_output = self.attn2(norm_hidden_states, encoder_hidden_states=encoder_hidden_states)
-            hidden_states = attn_output + hidden_states
-
-        # 4. Feed-forward
-        norm_hidden_states = self.norm3(hidden_states)
-
-        if self._chunk_size is not None:
-            ff_output = _chunked_feed_forward(self.ff, norm_hidden_states, self._chunk_dim, self._chunk_size)
-        else:
-            ff_output = self.ff(norm_hidden_states)
-
-        if self.is_res:
-            hidden_states = ff_output + hidden_states
-        else:
-            hidden_states = ff_output
-
-        hidden_states = hidden_states[None, :].reshape(batch_size, seq_length, num_frames, channels)
-        hidden_states = hidden_states.permute(0, 2, 1, 3)
-        hidden_states = hidden_states.reshape(batch_size * num_frames, seq_length, channels)
-
-        return hidden_states
-
-
-class SkipFFTransformerBlock(nn.Module):
-    def __init__(
-        self,
-        dim: int,
-        num_attention_heads: int,
-        attention_head_dim: int,
-        kv_input_dim: int,
-        kv_input_dim_proj_use_bias: bool,
-        dropout=0.0,
-        cross_attention_dim: Optional[int] = None,
-        attention_bias: bool = False,
-        attention_out_bias: bool = True,
-    ):
-        super().__init__()
-        if kv_input_dim != dim:
-            self.kv_mapper = nn.Linear(kv_input_dim, dim, kv_input_dim_proj_use_bias)
-        else:
-            self.kv_mapper = None
-
-        self.norm1 = RMSNorm(dim, 1e-06)
-
-        self.attn1 = Attention(
-            query_dim=dim,
-            heads=num_attention_heads,
-            dim_head=attention_head_dim,
-            dropout=dropout,
-            bias=attention_bias,
-            cross_attention_dim=cross_attention_dim,
-            out_bias=attention_out_bias,
-        )
-
-        self.norm2 = RMSNorm(dim, 1e-06)
-
-        self.attn2 = Attention(
-            query_dim=dim,
-            cross_attention_dim=cross_attention_dim,
-            heads=num_attention_heads,
-            dim_head=attention_head_dim,
-            dropout=dropout,
-            bias=attention_bias,
-            out_bias=attention_out_bias,
-        )
-
-    def forward(self, hidden_states, encoder_hidden_states, cross_attention_kwargs):
-        cross_attention_kwargs = cross_attention_kwargs.copy() if cross_attention_kwargs is not None else {}
-
-        if self.kv_mapper is not None:
-            encoder_hidden_states = self.kv_mapper(F.silu(encoder_hidden_states))
-
-        norm_hidden_states = self.norm1(hidden_states)
-
-        attn_output = self.attn1(
-            norm_hidden_states,
-            encoder_hidden_states=encoder_hidden_states,
-            **cross_attention_kwargs,
-        )
-
-        hidden_states = attn_output + hidden_states
-
-        norm_hidden_states = self.norm2(hidden_states)
-
-        attn_output = self.attn2(
-            norm_hidden_states,
-            encoder_hidden_states=encoder_hidden_states,
-            **cross_attention_kwargs,
-        )
-
-        hidden_states = attn_output + hidden_states
-
-        return hidden_states
-
-
-class FeedForward(nn.Module):
-    r"""
-    A feed-forward layer.
-
-    Parameters:
-        dim (`int`): The number of channels in the input.
-        dim_out (`int`, *optional*): The number of channels in the output. If not given, defaults to `dim`.
-        mult (`int`, *optional*, defaults to 4): The multiplier to use for the hidden dimension.
-        dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.
-        activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to be used in feed-forward.
-        final_dropout (`bool` *optional*, defaults to False): Apply a final dropout.
-        bias (`bool`, defaults to True): Whether to use a bias in the linear layer.
-    """
-
-    def __init__(
-        self,
-        dim: int,
-        dim_out: Optional[int] = None,
-        mult: int = 4,
-        dropout: float = 0.0,
-        activation_fn: str = "geglu",
-        final_dropout: bool = False,
-        inner_dim=None,
-        bias: bool = True,
-    ):
-        super().__init__()
-        if inner_dim is None:
-            inner_dim = int(dim * mult)
-        dim_out = dim_out if dim_out is not None else dim
-        linear_cls = LoRACompatibleLinear if not USE_PEFT_BACKEND else nn.Linear
-
-        if activation_fn == "gelu":
-            act_fn = GELU(dim, inner_dim, bias=bias)
-        if activation_fn == "gelu-approximate":
-            act_fn = GELU(dim, inner_dim, approximate="tanh", bias=bias)
-        elif activation_fn == "geglu":
-            act_fn = GEGLU(dim, inner_dim, bias=bias)
-        elif activation_fn == "geglu-approximate":
-            act_fn = ApproximateGELU(dim, inner_dim, bias=bias)
-
-        self.net = nn.ModuleList([])
-        # project in
-        self.net.append(act_fn)
-        # project dropout
-        self.net.append(nn.Dropout(dropout))
-        # project out
-        self.net.append(linear_cls(inner_dim, dim_out, bias=bias))
-        # FF as used in Vision Transformer, MLP-Mixer, etc. have a final dropout
-        if final_dropout:
-            self.net.append(nn.Dropout(dropout))
-
-    def forward(self, hidden_states: torch.Tensor, scale: float = 1.0) -> torch.Tensor:
-        compatible_cls = (GEGLU,) if USE_PEFT_BACKEND else (GEGLU, LoRACompatibleLinear)
-        for module in self.net:
-            if isinstance(module, compatible_cls):
-                hidden_states = module(hidden_states, scale)
-            else:
-                hidden_states = module(hidden_states)
-        return hidden_states

src/transformerhacked_garmnet.py

@@ -1,460 +0,0 @@
-# Copyright 2023 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.
-from dataclasses import dataclass
-from typing import Any, Dict, Optional
-
-import torch
-import torch.nn.functional as F
-from torch import nn
-
-from diffusers.configuration_utils import ConfigMixin, register_to_config
-from diffusers.models.embeddings import ImagePositionalEmbeddings
-from diffusers.utils import USE_PEFT_BACKEND, BaseOutput, deprecate, is_torch_version
-from src.attentionhacked_garmnet import BasicTransformerBlock
-from diffusers.models.embeddings import PatchEmbed, PixArtAlphaTextProjection
-from diffusers.models.lora import LoRACompatibleConv, LoRACompatibleLinear
-from diffusers.models.modeling_utils import ModelMixin
-from diffusers.models.normalization import AdaLayerNormSingle
-
-
-@dataclass
-class Transformer2DModelOutput(BaseOutput):
-    """
-    The output of [`Transformer2DModel`].
-
-    Args:
-        sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` or `(batch size, num_vector_embeds - 1, num_latent_pixels)` if [`Transformer2DModel`] is discrete):
-            The hidden states output conditioned on the `encoder_hidden_states` input. If discrete, returns probability
-            distributions for the unnoised latent pixels.
-    """
-
-    sample: torch.FloatTensor
-
-
-class Transformer2DModel(ModelMixin, ConfigMixin):
-    """
-    A 2D Transformer model for image-like data.
-
-    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 88): The number of channels in each head.
-        in_channels (`int`, *optional*):
-            The number of channels in the input and output (specify if the input is **continuous**).
-        num_layers (`int`, *optional*, defaults to 1): The number of layers of Transformer blocks to use.
-        dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.
-        cross_attention_dim (`int`, *optional*): The number of `encoder_hidden_states` dimensions to use.
-        sample_size (`int`, *optional*): The width of the latent images (specify if the input is **discrete**).
-            This is fixed during training since it is used to learn a number of position embeddings.
-        num_vector_embeds (`int`, *optional*):
-            The number of classes of the vector embeddings of the latent pixels (specify if the input is **discrete**).
-            Includes the class for the masked latent pixel.
-        activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to use in feed-forward.
-        num_embeds_ada_norm ( `int`, *optional*):
-            The number of diffusion steps used during training. Pass if at least one of the norm_layers is
-            `AdaLayerNorm`. This is fixed during training since it is used to learn a number of embeddings that are
-            added to the hidden states.
-
-            During inference, you can denoise for up to but not more steps than `num_embeds_ada_norm`.
-        attention_bias (`bool`, *optional*):
-            Configure if the `TransformerBlocks` attention should contain a bias parameter.
-    """
-
-    _supports_gradient_checkpointing = True
-
-    @register_to_config
-    def __init__(
-        self,
-        num_attention_heads: int = 16,
-        attention_head_dim: int = 88,
-        in_channels: Optional[int] = None,
-        out_channels: Optional[int] = None,
-        num_layers: int = 1,
-        dropout: float = 0.0,
-        norm_num_groups: int = 32,
-        cross_attention_dim: Optional[int] = None,
-        attention_bias: bool = False,
-        sample_size: Optional[int] = None,
-        num_vector_embeds: Optional[int] = None,
-        patch_size: Optional[int] = None,
-        activation_fn: str = "geglu",
-        num_embeds_ada_norm: Optional[int] = None,
-        use_linear_projection: bool = False,
-        only_cross_attention: bool = False,
-        double_self_attention: bool = False,
-        upcast_attention: bool = False,
-        norm_type: str = "layer_norm",
-        norm_elementwise_affine: bool = True,
-        norm_eps: float = 1e-5,
-        attention_type: str = "default",
-        caption_channels: int = None,
-    ):
-        super().__init__()
-        self.use_linear_projection = use_linear_projection
-        self.num_attention_heads = num_attention_heads
-        self.attention_head_dim = attention_head_dim
-        inner_dim = num_attention_heads * attention_head_dim
-
-        conv_cls = nn.Conv2d if USE_PEFT_BACKEND else LoRACompatibleConv
-        linear_cls = nn.Linear if USE_PEFT_BACKEND else LoRACompatibleLinear
-
-        # 1. Transformer2DModel can process both standard continuous images of shape `(batch_size, num_channels, width, height)` as well as quantized image embeddings of shape `(batch_size, num_image_vectors)`
-        # Define whether input is continuous or discrete depending on configuration
-        self.is_input_continuous = (in_channels is not None) and (patch_size is None)
-        self.is_input_vectorized = num_vector_embeds is not None
-        self.is_input_patches = in_channels is not None and patch_size is not None
-
-        if norm_type == "layer_norm" and num_embeds_ada_norm is not None:
-            deprecation_message = (
-                f"The configuration file of this model: {self.__class__} is outdated. `norm_type` is either not set or"
-                " incorrectly set to `'layer_norm'`.Make sure to set `norm_type` to `'ada_norm'` in the config."
-                " Please make sure to update the config accordingly as leaving `norm_type` might led to incorrect"
-                " results in future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it"
-                " would be very nice if you could open a Pull request for the `transformer/config.json` file"
-            )
-            deprecate("norm_type!=num_embeds_ada_norm", "1.0.0", deprecation_message, standard_warn=False)
-            norm_type = "ada_norm"
-
-        if self.is_input_continuous and self.is_input_vectorized:
-            raise ValueError(
-                f"Cannot define both `in_channels`: {in_channels} and `num_vector_embeds`: {num_vector_embeds}. Make"
-                " sure that either `in_channels` or `num_vector_embeds` is None."
-            )
-        elif self.is_input_vectorized and self.is_input_patches:
-            raise ValueError(
-                f"Cannot define both `num_vector_embeds`: {num_vector_embeds} and `patch_size`: {patch_size}. Make"
-                " sure that either `num_vector_embeds` or `num_patches` is None."
-            )
-        elif not self.is_input_continuous and not self.is_input_vectorized and not self.is_input_patches:
-            raise ValueError(
-                f"Has to define `in_channels`: {in_channels}, `num_vector_embeds`: {num_vector_embeds}, or patch_size:"
-                f" {patch_size}. Make sure that `in_channels`, `num_vector_embeds` or `num_patches` is not None."
-            )
-
-        # 2. Define input layers
-        if self.is_input_continuous:
-            self.in_channels = in_channels
-
-            self.norm = torch.nn.GroupNorm(num_groups=norm_num_groups, num_channels=in_channels, eps=1e-6, affine=True)
-            if use_linear_projection:
-                self.proj_in = linear_cls(in_channels, inner_dim)
-            else:
-                self.proj_in = conv_cls(in_channels, inner_dim, kernel_size=1, stride=1, padding=0)
-        elif self.is_input_vectorized:
-            assert sample_size is not None, "Transformer2DModel over discrete input must provide sample_size"
-            assert num_vector_embeds is not None, "Transformer2DModel over discrete input must provide num_embed"
-
-            self.height = sample_size
-            self.width = sample_size
-            self.num_vector_embeds = num_vector_embeds
-            self.num_latent_pixels = self.height * self.width
-
-            self.latent_image_embedding = ImagePositionalEmbeddings(
-                num_embed=num_vector_embeds, embed_dim=inner_dim, height=self.height, width=self.width
-            )
-        elif self.is_input_patches:
-            assert sample_size is not None, "Transformer2DModel over patched input must provide sample_size"
-
-            self.height = sample_size
-            self.width = sample_size
-
-            self.patch_size = patch_size
-            interpolation_scale = self.config.sample_size // 64  # => 64 (= 512 pixart) has interpolation scale 1
-            interpolation_scale = max(interpolation_scale, 1)
-            self.pos_embed = PatchEmbed(
-                height=sample_size,
-                width=sample_size,
-                patch_size=patch_size,
-                in_channels=in_channels,
-                embed_dim=inner_dim,
-                interpolation_scale=interpolation_scale,
-            )
-
-        # 3. Define transformers blocks
-        self.transformer_blocks = nn.ModuleList(
-            [
-                BasicTransformerBlock(
-                    inner_dim,
-                    num_attention_heads,
-                    attention_head_dim,
-                    dropout=dropout,
-                    cross_attention_dim=cross_attention_dim,
-                    activation_fn=activation_fn,
-                    num_embeds_ada_norm=num_embeds_ada_norm,
-                    attention_bias=attention_bias,
-                    only_cross_attention=only_cross_attention,
-                    double_self_attention=double_self_attention,
-                    upcast_attention=upcast_attention,
-                    norm_type=norm_type,
-                    norm_elementwise_affine=norm_elementwise_affine,
-                    norm_eps=norm_eps,
-                    attention_type=attention_type,
-                )
-                for d in range(num_layers)
-            ]
-        )
-
-        # 4. Define output layers
-        self.out_channels = in_channels if out_channels is None else out_channels
-        if self.is_input_continuous:
-            # TODO: should use out_channels for continuous projections
-            if use_linear_projection:
-                self.proj_out = linear_cls(inner_dim, in_channels)
-            else:
-                self.proj_out = conv_cls(inner_dim, in_channels, kernel_size=1, stride=1, padding=0)
-        elif self.is_input_vectorized:
-            self.norm_out = nn.LayerNorm(inner_dim)
-            self.out = nn.Linear(inner_dim, self.num_vector_embeds - 1)
-        elif self.is_input_patches and norm_type != "ada_norm_single":
-            self.norm_out = nn.LayerNorm(inner_dim, elementwise_affine=False, eps=1e-6)
-            self.proj_out_1 = nn.Linear(inner_dim, 2 * inner_dim)
-            self.proj_out_2 = nn.Linear(inner_dim, patch_size * patch_size * self.out_channels)
-        elif self.is_input_patches and norm_type == "ada_norm_single":
-            self.norm_out = nn.LayerNorm(inner_dim, elementwise_affine=False, eps=1e-6)
-            self.scale_shift_table = nn.Parameter(torch.randn(2, inner_dim) / inner_dim**0.5)
-            self.proj_out = nn.Linear(inner_dim, patch_size * patch_size * self.out_channels)
-
-        # 5. PixArt-Alpha blocks.
-        self.adaln_single = None
-        self.use_additional_conditions = False
-        if norm_type == "ada_norm_single":
-            self.use_additional_conditions = self.config.sample_size == 128
-            # TODO(Sayak, PVP) clean this, for now we use sample size to determine whether to use
-            # additional conditions until we find better name
-            self.adaln_single = AdaLayerNormSingle(inner_dim, use_additional_conditions=self.use_additional_conditions)
-
-        self.caption_projection = None
-        if caption_channels is not None:
-            self.caption_projection = PixArtAlphaTextProjection(in_features=caption_channels, hidden_size=inner_dim)
-
-        self.gradient_checkpointing = False
-
-    def _set_gradient_checkpointing(self, module, value=False):
-        if hasattr(module, "gradient_checkpointing"):
-            module.gradient_checkpointing = value
-
-    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,
-        class_labels: Optional[torch.LongTensor] = 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 [`Transformer2DModel`] forward method.
-
-        Args:
-            hidden_states (`torch.LongTensor` of shape `(batch size, num latent pixels)` if discrete, `torch.FloatTensor` of shape `(batch size, channel, height, width)` if continuous):
-                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`.
-            class_labels ( `torch.LongTensor` of shape `(batch size, num classes)`, *optional*):
-                Used to indicate class labels conditioning. Optional class labels to be applied as an embedding in
-                `AdaLayerZeroNorm`.
-            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.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.
-        """
-        # 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)
-
-        # Retrieve lora scale.
-        lora_scale = cross_attention_kwargs.get("scale", 1.0) if cross_attention_kwargs is not None else 1.0
-
-        # 1. Input
-        if self.is_input_continuous:
-            batch, _, height, width = hidden_states.shape
-            residual = hidden_states
-
-            hidden_states = self.norm(hidden_states)
-            if not self.use_linear_projection:
-                hidden_states = (
-                    self.proj_in(hidden_states, scale=lora_scale)
-                    if not USE_PEFT_BACKEND
-                    else self.proj_in(hidden_states)
-                )
-                inner_dim = hidden_states.shape[1]
-                hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch, height * width, inner_dim)
-            else:
-                inner_dim = hidden_states.shape[1]
-                hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch, height * width, inner_dim)
-                hidden_states = (
-                    self.proj_in(hidden_states, scale=lora_scale)
-                    if not USE_PEFT_BACKEND
-                    else self.proj_in(hidden_states)
-                )
-
-        elif self.is_input_vectorized:
-            hidden_states = self.latent_image_embedding(hidden_states)
-        elif self.is_input_patches:
-            height, width = hidden_states.shape[-2] // self.patch_size, hidden_states.shape[-1] // self.patch_size
-            hidden_states = self.pos_embed(hidden_states)
-
-            if self.adaln_single is not None:
-                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`."
-                    )
-                batch_size = hidden_states.shape[0]
-                timestep, embedded_timestep = self.adaln_single(
-                    timestep, added_cond_kwargs, batch_size=batch_size, hidden_dtype=hidden_states.dtype
-                )
-
-        # 2. Blocks
-        if self.caption_projection is not None:
-            batch_size = hidden_states.shape[0]
-            encoder_hidden_states = self.caption_projection(encoder_hidden_states)
-            encoder_hidden_states = encoder_hidden_states.view(batch_size, -1, hidden_states.shape[-1])
-
-        garment_features = []
-        for block in self.transformer_blocks:
-            if self.training 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,out_garment_feat = torch.utils.checkpoint.checkpoint(
-                    create_custom_forward(block),
-                    hidden_states,
-                    attention_mask,
-                    encoder_hidden_states,
-                    encoder_attention_mask,
-                    timestep,
-                    cross_attention_kwargs,
-                    class_labels,
-                    **ckpt_kwargs,
-                )
-            else:
-                hidden_states,out_garment_feat = 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=class_labels,
-                )
-            garment_features += out_garment_feat
-        # 3. Output
-        if self.is_input_continuous:
-            if not self.use_linear_projection:
-                hidden_states = hidden_states.reshape(batch, height, width, inner_dim).permute(0, 3, 1, 2).contiguous()
-                hidden_states = (
-                    self.proj_out(hidden_states, scale=lora_scale)
-                    if not USE_PEFT_BACKEND
-                    else self.proj_out(hidden_states)
-                )
-            else:
-                hidden_states = (
-                    self.proj_out(hidden_states, scale=lora_scale)
-                    if not USE_PEFT_BACKEND
-                    else self.proj_out(hidden_states)
-                )
-                hidden_states = hidden_states.reshape(batch, height, width, inner_dim).permute(0, 3, 1, 2).contiguous()
-
-            output = hidden_states + residual
-        elif self.is_input_vectorized:
-            hidden_states = self.norm_out(hidden_states)
-            logits = self.out(hidden_states)
-            # (batch, self.num_vector_embeds - 1, self.num_latent_pixels)
-            logits = logits.permute(0, 2, 1)
-
-            # log(p(x_0))
-            output = F.log_softmax(logits.double(), dim=1).float()
-
-        if self.is_input_patches:
-            if self.config.norm_type != "ada_norm_single":
-                conditioning = self.transformer_blocks[0].norm1.emb(
-                    timestep, class_labels, hidden_dtype=hidden_states.dtype
-                )
-                shift, scale = self.proj_out_1(F.silu(conditioning)).chunk(2, dim=1)
-                hidden_states = self.norm_out(hidden_states) * (1 + scale[:, None]) + shift[:, None]
-                hidden_states = self.proj_out_2(hidden_states)
-            elif self.config.norm_type == "ada_norm_single":
-                shift, scale = (self.scale_shift_table[None] + embedded_timestep[:, None]).chunk(2, dim=1)
-                hidden_states = self.norm_out(hidden_states)
-                # Modulation
-                hidden_states = hidden_states * (1 + scale) + shift
-                hidden_states = self.proj_out(hidden_states)
-                hidden_states = hidden_states.squeeze(1)
-
-            # unpatchify
-            if self.adaln_single is None:
-                height = width = int(hidden_states.shape[1] ** 0.5)
-            hidden_states = hidden_states.reshape(
-                shape=(-1, height, width, self.patch_size, self.patch_size, self.out_channels)
-            )
-            hidden_states = torch.einsum("nhwpqc->nchpwq", hidden_states)
-            output = hidden_states.reshape(
-                shape=(-1, self.out_channels, height * self.patch_size, width * self.patch_size)
-            )
-
-        if not return_dict:
-            return (output,) ,garment_features
-
-        return Transformer2DModelOutput(sample=output),garment_features

src/transformerhacked_tryon.py

@@ -1,467 +0,0 @@
-# Copyright 2023 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.
-from dataclasses import dataclass
-from typing import Any, Dict, Optional
-
-import torch
-import torch.nn.functional as F
-from torch import nn
-
-from diffusers.configuration_utils import ConfigMixin, register_to_config
-from diffusers.models.embeddings import ImagePositionalEmbeddings
-from diffusers.utils import USE_PEFT_BACKEND, BaseOutput, deprecate, is_torch_version
-from src.attentionhacked_tryon import BasicTransformerBlock
-from diffusers.models.embeddings import PatchEmbed, PixArtAlphaTextProjection
-from diffusers.models.lora import LoRACompatibleConv, LoRACompatibleLinear
-from diffusers.models.modeling_utils import ModelMixin
-from diffusers.models.normalization import AdaLayerNormSingle
-
-
-@dataclass
-class Transformer2DModelOutput(BaseOutput):
-    """
-    The output of [`Transformer2DModel`].
-
-    Args:
-        sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` or `(batch size, num_vector_embeds - 1, num_latent_pixels)` if [`Transformer2DModel`] is discrete):
-            The hidden states output conditioned on the `encoder_hidden_states` input. If discrete, returns probability
-            distributions for the unnoised latent pixels.
-    """
-
-    sample: torch.FloatTensor
-
-
-class Transformer2DModel(ModelMixin, ConfigMixin):
-    """
-    A 2D Transformer model for image-like data.
-
-    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 88): The number of channels in each head.
-        in_channels (`int`, *optional*):
-            The number of channels in the input and output (specify if the input is **continuous**).
-        num_layers (`int`, *optional*, defaults to 1): The number of layers of Transformer blocks to use.
-        dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.
-        cross_attention_dim (`int`, *optional*): The number of `encoder_hidden_states` dimensions to use.
-        sample_size (`int`, *optional*): The width of the latent images (specify if the input is **discrete**).
-            This is fixed during training since it is used to learn a number of position embeddings.
-        num_vector_embeds (`int`, *optional*):
-            The number of classes of the vector embeddings of the latent pixels (specify if the input is **discrete**).
-            Includes the class for the masked latent pixel.
-        activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to use in feed-forward.
-        num_embeds_ada_norm ( `int`, *optional*):
-            The number of diffusion steps used during training. Pass if at least one of the norm_layers is
-            `AdaLayerNorm`. This is fixed during training since it is used to learn a number of embeddings that are
-            added to the hidden states.
-
-            During inference, you can denoise for up to but not more steps than `num_embeds_ada_norm`.
-        attention_bias (`bool`, *optional*):
-            Configure if the `TransformerBlocks` attention should contain a bias parameter.
-    """
-
-    _supports_gradient_checkpointing = True
-
-    @register_to_config
-    def __init__(
-        self,
-        num_attention_heads: int = 16,
-        attention_head_dim: int = 88,
-        in_channels: Optional[int] = None,
-        out_channels: Optional[int] = None,
-        num_layers: int = 1,
-        dropout: float = 0.0,
-        norm_num_groups: int = 32,
-        cross_attention_dim: Optional[int] = None,
-        attention_bias: bool = False,
-        sample_size: Optional[int] = None,
-        num_vector_embeds: Optional[int] = None,
-        patch_size: Optional[int] = None,
-        activation_fn: str = "geglu",
-        num_embeds_ada_norm: Optional[int] = None,
-        use_linear_projection: bool = False,
-        only_cross_attention: bool = False,
-        double_self_attention: bool = False,
-        upcast_attention: bool = False,
-        norm_type: str = "layer_norm",
-        norm_elementwise_affine: bool = True,
-        norm_eps: float = 1e-5,
-        attention_type: str = "default",
-        caption_channels: int = None,
-    ):
-        super().__init__()
-        self.use_linear_projection = use_linear_projection
-        self.num_attention_heads = num_attention_heads
-        self.attention_head_dim = attention_head_dim
-        inner_dim = num_attention_heads * attention_head_dim
-
-        conv_cls = nn.Conv2d if USE_PEFT_BACKEND else LoRACompatibleConv
-        linear_cls = nn.Linear if USE_PEFT_BACKEND else LoRACompatibleLinear
-
-        # 1. Transformer2DModel can process both standard continuous images of shape `(batch_size, num_channels, width, height)` as well as quantized image embeddings of shape `(batch_size, num_image_vectors)`
-        # Define whether input is continuous or discrete depending on configuration
-        self.is_input_continuous = (in_channels is not None) and (patch_size is None)
-        self.is_input_vectorized = num_vector_embeds is not None
-        self.is_input_patches = in_channels is not None and patch_size is not None
-
-        if norm_type == "layer_norm" and num_embeds_ada_norm is not None:
-            deprecation_message = (
-                f"The configuration file of this model: {self.__class__} is outdated. `norm_type` is either not set or"
-                " incorrectly set to `'layer_norm'`.Make sure to set `norm_type` to `'ada_norm'` in the config."
-                " Please make sure to update the config accordingly as leaving `norm_type` might led to incorrect"
-                " results in future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it"
-                " would be very nice if you could open a Pull request for the `transformer/config.json` file"
-            )
-            deprecate("norm_type!=num_embeds_ada_norm", "1.0.0", deprecation_message, standard_warn=False)
-            norm_type = "ada_norm"
-
-        if self.is_input_continuous and self.is_input_vectorized:
-            raise ValueError(
-                f"Cannot define both `in_channels`: {in_channels} and `num_vector_embeds`: {num_vector_embeds}. Make"
-                " sure that either `in_channels` or `num_vector_embeds` is None."
-            )
-        elif self.is_input_vectorized and self.is_input_patches:
-            raise ValueError(
-                f"Cannot define both `num_vector_embeds`: {num_vector_embeds} and `patch_size`: {patch_size}. Make"
-                " sure that either `num_vector_embeds` or `num_patches` is None."
-            )
-        elif not self.is_input_continuous and not self.is_input_vectorized and not self.is_input_patches:
-            raise ValueError(
-                f"Has to define `in_channels`: {in_channels}, `num_vector_embeds`: {num_vector_embeds}, or patch_size:"
-                f" {patch_size}. Make sure that `in_channels`, `num_vector_embeds` or `num_patches` is not None."
-            )
-
-        # 2. Define input layers
-        if self.is_input_continuous:
-            self.in_channels = in_channels
-
-            self.norm = torch.nn.GroupNorm(num_groups=norm_num_groups, num_channels=in_channels, eps=1e-6, affine=True)
-            if use_linear_projection:
-                self.proj_in = linear_cls(in_channels, inner_dim)
-            else:
-                self.proj_in = conv_cls(in_channels, inner_dim, kernel_size=1, stride=1, padding=0)
-        elif self.is_input_vectorized:
-            assert sample_size is not None, "Transformer2DModel over discrete input must provide sample_size"
-            assert num_vector_embeds is not None, "Transformer2DModel over discrete input must provide num_embed"
-
-            self.height = sample_size
-            self.width = sample_size
-            self.num_vector_embeds = num_vector_embeds
-            self.num_latent_pixels = self.height * self.width
-
-            self.latent_image_embedding = ImagePositionalEmbeddings(
-                num_embed=num_vector_embeds, embed_dim=inner_dim, height=self.height, width=self.width
-            )
-        elif self.is_input_patches:
-            assert sample_size is not None, "Transformer2DModel over patched input must provide sample_size"
-
-            self.height = sample_size
-            self.width = sample_size
-
-            self.patch_size = patch_size
-            interpolation_scale = self.config.sample_size // 64  # => 64 (= 512 pixart) has interpolation scale 1
-            interpolation_scale = max(interpolation_scale, 1)
-            self.pos_embed = PatchEmbed(
-                height=sample_size,
-                width=sample_size,
-                patch_size=patch_size,
-                in_channels=in_channels,
-                embed_dim=inner_dim,
-                interpolation_scale=interpolation_scale,
-            )
-
-        # 3. Define transformers blocks
-        self.transformer_blocks = nn.ModuleList(
-            [
-                BasicTransformerBlock(
-                    inner_dim,
-                    num_attention_heads,
-                    attention_head_dim,
-                    dropout=dropout,
-                    cross_attention_dim=cross_attention_dim,
-                    activation_fn=activation_fn,
-                    num_embeds_ada_norm=num_embeds_ada_norm,
-                    attention_bias=attention_bias,
-                    only_cross_attention=only_cross_attention,
-                    double_self_attention=double_self_attention,
-                    upcast_attention=upcast_attention,
-                    norm_type=norm_type,
-                    norm_elementwise_affine=norm_elementwise_affine,
-                    norm_eps=norm_eps,
-                    attention_type=attention_type,
-                )
-                for d in range(num_layers)
-            ]
-        )
-
-        # 4. Define output layers
-        self.out_channels = in_channels if out_channels is None else out_channels
-        if self.is_input_continuous:
-            # TODO: should use out_channels for continuous projections
-            if use_linear_projection:
-                self.proj_out = linear_cls(inner_dim, in_channels)
-            else:
-                self.proj_out = conv_cls(inner_dim, in_channels, kernel_size=1, stride=1, padding=0)
-        elif self.is_input_vectorized:
-            self.norm_out = nn.LayerNorm(inner_dim)
-            self.out = nn.Linear(inner_dim, self.num_vector_embeds - 1)
-        elif self.is_input_patches and norm_type != "ada_norm_single":
-            self.norm_out = nn.LayerNorm(inner_dim, elementwise_affine=False, eps=1e-6)
-            self.proj_out_1 = nn.Linear(inner_dim, 2 * inner_dim)
-            self.proj_out_2 = nn.Linear(inner_dim, patch_size * patch_size * self.out_channels)
-        elif self.is_input_patches and norm_type == "ada_norm_single":
-            self.norm_out = nn.LayerNorm(inner_dim, elementwise_affine=False, eps=1e-6)
-            self.scale_shift_table = nn.Parameter(torch.randn(2, inner_dim) / inner_dim**0.5)
-            self.proj_out = nn.Linear(inner_dim, patch_size * patch_size * self.out_channels)
-
-        # 5. PixArt-Alpha blocks.
-        self.adaln_single = None
-        self.use_additional_conditions = False
-        if norm_type == "ada_norm_single":
-            self.use_additional_conditions = self.config.sample_size == 128
-            # TODO(Sayak, PVP) clean this, for now we use sample size to determine whether to use
-            # additional conditions until we find better name
-            self.adaln_single = AdaLayerNormSingle(inner_dim, use_additional_conditions=self.use_additional_conditions)
-
-        self.caption_projection = None
-        if caption_channels is not None:
-            self.caption_projection = PixArtAlphaTextProjection(in_features=caption_channels, hidden_size=inner_dim)
-
-        self.gradient_checkpointing = False
-
-    def _set_gradient_checkpointing(self, module, value=False):
-        if hasattr(module, "gradient_checkpointing"):
-            module.gradient_checkpointing = value
-
-    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,
-        class_labels: Optional[torch.LongTensor] = None,
-        cross_attention_kwargs: Dict[str, Any] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        encoder_attention_mask: Optional[torch.Tensor] = None,
-        garment_features=None,
-        curr_garment_feat_idx=0,
-        return_dict: bool = True,
-    ):
-        """
-        The [`Transformer2DModel`] forward method.
-
-        Args:
-            hidden_states (`torch.LongTensor` of shape `(batch size, num latent pixels)` if discrete, `torch.FloatTensor` of shape `(batch size, channel, height, width)` if continuous):
-                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`.
-            class_labels ( `torch.LongTensor` of shape `(batch size, num classes)`, *optional*):
-                Used to indicate class labels conditioning. Optional class labels to be applied as an embedding in
-                `AdaLayerZeroNorm`.
-            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.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.
-        """
-        # 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)
-
-        # Retrieve lora scale.
-        lora_scale = cross_attention_kwargs.get("scale", 1.0) if cross_attention_kwargs is not None else 1.0
-
-        # 1. Input
-        if self.is_input_continuous:
-            batch, _, height, width = hidden_states.shape
-            residual = hidden_states
-
-            hidden_states = self.norm(hidden_states)
-            if not self.use_linear_projection:
-                hidden_states = (
-                    self.proj_in(hidden_states, scale=lora_scale)
-                    if not USE_PEFT_BACKEND
-                    else self.proj_in(hidden_states)
-                )
-                inner_dim = hidden_states.shape[1]
-                hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch, height * width, inner_dim)
-            else:
-                inner_dim = hidden_states.shape[1]
-                hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch, height * width, inner_dim)
-                hidden_states = (
-                    self.proj_in(hidden_states, scale=lora_scale)
-                    if not USE_PEFT_BACKEND
-                    else self.proj_in(hidden_states)
-                )
-
-        elif self.is_input_vectorized:
-            hidden_states = self.latent_image_embedding(hidden_states)
-        elif self.is_input_patches:
-            height, width = hidden_states.shape[-2] // self.patch_size, hidden_states.shape[-1] // self.patch_size
-            hidden_states = self.pos_embed(hidden_states)
-
-            if self.adaln_single is not None:
-                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`."
-                    )
-                batch_size = hidden_states.shape[0]
-                timestep, embedded_timestep = self.adaln_single(
-                    timestep, added_cond_kwargs, batch_size=batch_size, hidden_dtype=hidden_states.dtype
-                )
-
-        # 2. Blocks
-        if self.caption_projection is not None:
-            batch_size = hidden_states.shape[0]
-            encoder_hidden_states = self.caption_projection(encoder_hidden_states)
-            encoder_hidden_states = encoder_hidden_states.view(batch_size, -1, hidden_states.shape[-1])
-
-
-        for block in self.transformer_blocks:
-            if self.training 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,curr_garment_feat_idx = torch.utils.checkpoint.checkpoint(
-                    create_custom_forward(block),
-                    hidden_states,
-                    attention_mask,
-                    encoder_hidden_states,
-                    encoder_attention_mask,
-                    timestep,
-                    cross_attention_kwargs,
-                    class_labels,
-                    garment_features,
-                    curr_garment_feat_idx,
-                    **ckpt_kwargs,
-                )
-            else:
-                hidden_states,curr_garment_feat_idx = 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=class_labels,
-                    garment_features=garment_features,
-                    curr_garment_feat_idx=curr_garment_feat_idx,
-                )
-
- 
-        # 3. Output
-        if self.is_input_continuous:
-            if not self.use_linear_projection:
-                hidden_states = hidden_states.reshape(batch, height, width, inner_dim).permute(0, 3, 1, 2).contiguous()
-                hidden_states = (
-                    self.proj_out(hidden_states, scale=lora_scale)
-                    if not USE_PEFT_BACKEND
-                    else self.proj_out(hidden_states)
-                )
-            else:
-                hidden_states = (
-                    self.proj_out(hidden_states, scale=lora_scale)
-                    if not USE_PEFT_BACKEND
-                    else self.proj_out(hidden_states)
-                )
-                hidden_states = hidden_states.reshape(batch, height, width, inner_dim).permute(0, 3, 1, 2).contiguous()
-
-            output = hidden_states + residual
-        elif self.is_input_vectorized:
-            hidden_states = self.norm_out(hidden_states)
-            logits = self.out(hidden_states)
-            # (batch, self.num_vector_embeds - 1, self.num_latent_pixels)
-            logits = logits.permute(0, 2, 1)
-
-            # log(p(x_0))
-            output = F.log_softmax(logits.double(), dim=1).float()
-
-        if self.is_input_patches:
-            if self.config.norm_type != "ada_norm_single":
-                conditioning = self.transformer_blocks[0].norm1.emb(
-                    timestep, class_labels, hidden_dtype=hidden_states.dtype
-                )
-                shift, scale = self.proj_out_1(F.silu(conditioning)).chunk(2, dim=1)
-                hidden_states = self.norm_out(hidden_states) * (1 + scale[:, None]) + shift[:, None]
-                hidden_states = self.proj_out_2(hidden_states)
-            elif self.config.norm_type == "ada_norm_single":
-                shift, scale = (self.scale_shift_table[None] + embedded_timestep[:, None]).chunk(2, dim=1)
-                hidden_states = self.norm_out(hidden_states)
-                # Modulation
-                hidden_states = hidden_states * (1 + scale) + shift
-                hidden_states = self.proj_out(hidden_states)
-                hidden_states = hidden_states.squeeze(1)
-
-            # unpatchify
-            if self.adaln_single is None:
-                height = width = int(hidden_states.shape[1] ** 0.5)
-            hidden_states = hidden_states.reshape(
-                shape=(-1, height, width, self.patch_size, self.patch_size, self.out_channels)
-            )
-            hidden_states = torch.einsum("nhwpqc->nchpwq", hidden_states)
-            output = hidden_states.reshape(
-                shape=(-1, self.out_channels, height * self.patch_size, width * self.patch_size)
-            )
-
-        if not return_dict:
-            return (output,),curr_garment_feat_idx
-
-        return Transformer2DModelOutput(sample=output),curr_garment_feat_idx

src/tryon_pipeline.py

@@ -1,1896 +0,0 @@
-# Copyright 2023 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
-from typing import Any, Callable, Dict, List, Optional, Tuple, Union
-
-import numpy as np
-import PIL.Image
-import torch
-from transformers import (
-    CLIPImageProcessor,
-    CLIPTextModel,
-    CLIPTextModelWithProjection,
-    CLIPTokenizer,
-    CLIPVisionModelWithProjection,
-)
-
-from diffusers.image_processor import PipelineImageInput, VaeImageProcessor
-from diffusers.loaders import (
-    FromSingleFileMixin,
-    IPAdapterMixin,
-    StableDiffusionXLLoraLoaderMixin,
-    TextualInversionLoaderMixin,
-)
-from diffusers.models import AutoencoderKL, ImageProjection, UNet2DConditionModel
-from diffusers.models.attention_processor import (
-    AttnProcessor2_0,
-    FusedAttnProcessor2_0,
-    LoRAAttnProcessor2_0,
-    LoRAXFormersAttnProcessor,
-    XFormersAttnProcessor,
-)
-from diffusers.models.lora import adjust_lora_scale_text_encoder
-from diffusers.schedulers import KarrasDiffusionSchedulers
-from diffusers.utils import (
-    USE_PEFT_BACKEND,
-    deprecate,
-    is_invisible_watermark_available,
-    is_torch_xla_available,
-    logging,
-    replace_example_docstring,
-    scale_lora_layers,
-    unscale_lora_layers,
-)
-from diffusers.utils.torch_utils import randn_tensor
-from diffusers.pipelines.pipeline_utils import DiffusionPipeline
-
-
-
-if is_torch_xla_available():
-    import torch_xla.core.xla_model as xm
-
-    XLA_AVAILABLE = True
-else:
-    XLA_AVAILABLE = False
-
-
-logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
-
-
-EXAMPLE_DOC_STRING = """
-    Examples:
-        ```py
-        >>> import torch
-        >>> from diffusers import StableDiffusionXLInpaintPipeline
-        >>> from diffusers.utils import load_image
-
-        >>> pipe = StableDiffusionXLInpaintPipeline.from_pretrained(
-        ...     "stabilityai/stable-diffusion-xl-base-1.0",
-        ...     torch_dtype=torch.float16,
-        ...     variant="fp16",
-        ...     use_safetensors=True,
-        ... )
-        >>> pipe.to("cuda")
-
-        >>> img_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png"
-        >>> mask_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png"
-
-        >>> init_image = load_image(img_url).convert("RGB")
-        >>> mask_image = load_image(mask_url).convert("RGB")
-
-        >>> prompt = "A majestic tiger sitting on a bench"
-        >>> image = pipe(
-        ...     prompt=prompt, image=init_image, mask_image=mask_image, num_inference_steps=50, strength=0.80
-        ... ).images[0]
-        ```
-"""
-
-
-# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.rescale_noise_cfg
-def rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0):
-    """
-    Rescale `noise_cfg` according to `guidance_rescale`. Based on findings of [Common Diffusion Noise Schedules and
-    Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf). See Section 3.4
-    """
-    std_text = noise_pred_text.std(dim=list(range(1, noise_pred_text.ndim)), keepdim=True)
-    std_cfg = noise_cfg.std(dim=list(range(1, noise_cfg.ndim)), keepdim=True)
-    # rescale the results from guidance (fixes overexposure)
-    noise_pred_rescaled = noise_cfg * (std_text / std_cfg)
-    # mix with the original results from guidance by factor guidance_rescale to avoid "plain looking" images
-    noise_cfg = guidance_rescale * noise_pred_rescaled + (1 - guidance_rescale) * noise_cfg
-    return noise_cfg
-
-
-def mask_pil_to_torch(mask, height, width):
-    # preprocess mask
-    if isinstance(mask, (PIL.Image.Image, np.ndarray)):
-        mask = [mask]
-
-    if isinstance(mask, list) and isinstance(mask[0], PIL.Image.Image):
-        mask = [i.resize((width, height), resample=PIL.Image.LANCZOS) for i in mask]
-        mask = np.concatenate([np.array(m.convert("L"))[None, None, :] for m in mask], axis=0)
-        mask = mask.astype(np.float32) / 255.0
-    elif isinstance(mask, list) and isinstance(mask[0], np.ndarray):
-        mask = np.concatenate([m[None, None, :] for m in mask], axis=0)
-
-    mask = torch.from_numpy(mask)
-    return mask
-
-
-def prepare_mask_and_masked_image(image, mask, height, width, return_image: bool = False):
-    """
-    Prepares a pair (image, mask) to be consumed by the Stable Diffusion pipeline. This means that those inputs will be
-    converted to ``torch.Tensor`` with shapes ``batch x channels x height x width`` where ``channels`` is ``3`` for the
-    ``image`` and ``1`` for the ``mask``.
-
-    The ``image`` will be converted to ``torch.float32`` and normalized to be in ``[-1, 1]``. The ``mask`` will be
-    binarized (``mask > 0.5``) and cast to ``torch.float32`` too.
-
-    Args:
-        image (Union[np.array, PIL.Image, torch.Tensor]): The image to inpaint.
-            It can be a ``PIL.Image``, or a ``height x width x 3`` ``np.array`` or a ``channels x height x width``
-            ``torch.Tensor`` or a ``batch x channels x height x width`` ``torch.Tensor``.
-        mask (_type_): The mask to apply to the image, i.e. regions to inpaint.
-            It can be a ``PIL.Image``, or a ``height x width`` ``np.array`` or a ``1 x height x width``
-            ``torch.Tensor`` or a ``batch x 1 x height x width`` ``torch.Tensor``.
-
-
-    Raises:
-        ValueError: ``torch.Tensor`` images should be in the ``[-1, 1]`` range. ValueError: ``torch.Tensor`` mask
-        should be in the ``[0, 1]`` range. ValueError: ``mask`` and ``image`` should have the same spatial dimensions.
-        TypeError: ``mask`` is a ``torch.Tensor`` but ``image`` is not
-            (ot the other way around).
-
-    Returns:
-        tuple[torch.Tensor]: The pair (mask, masked_image) as ``torch.Tensor`` with 4
-            dimensions: ``batch x channels x height x width``.
-    """
-
-    # checkpoint. TOD(Yiyi) - need to clean this up later
-    deprecation_message = "The prepare_mask_and_masked_image method is deprecated and will be removed in a future version. Please use VaeImageProcessor.preprocess instead"
-    deprecate(
-        "prepare_mask_and_masked_image",
-        "0.30.0",
-        deprecation_message,
-    )
-    if image is None:
-        raise ValueError("`image` input cannot be undefined.")
-
-    if mask is None:
-        raise ValueError("`mask_image` input cannot be undefined.")
-
-    if isinstance(image, torch.Tensor):
-        if not isinstance(mask, torch.Tensor):
-            mask = mask_pil_to_torch(mask, height, width)
-
-        if image.ndim == 3:
-            image = image.unsqueeze(0)
-
-        # Batch and add channel dim for single mask
-        if mask.ndim == 2:
-            mask = mask.unsqueeze(0).unsqueeze(0)
-
-        # Batch single mask or add channel dim
-        if mask.ndim == 3:
-            # Single batched mask, no channel dim or single mask not batched but channel dim
-            if mask.shape[0] == 1:
-                mask = mask.unsqueeze(0)
-
-            # Batched masks no channel dim
-            else:
-                mask = mask.unsqueeze(1)
-
-        assert image.ndim == 4 and mask.ndim == 4, "Image and Mask must have 4 dimensions"
-        # assert image.shape[-2:] == mask.shape[-2:], "Image and Mask must have the same spatial dimensions"
-        assert image.shape[0] == mask.shape[0], "Image and Mask must have the same batch size"
-
-        # Check image is in [-1, 1]
-        # if image.min() < -1 or image.max() > 1:
-        #    raise ValueError("Image should be in [-1, 1] range")
-
-        # Check mask is in [0, 1]
-        if mask.min() < 0 or mask.max() > 1:
-            raise ValueError("Mask should be in [0, 1] range")
-
-        # Binarize mask
-        mask[mask < 0.5] = 0
-        mask[mask >= 0.5] = 1
-
-        # Image as float32
-        image = image.to(dtype=torch.float32)
-    elif isinstance(mask, torch.Tensor):
-        raise TypeError(f"`mask` is a torch.Tensor but `image` (type: {type(image)} is not")
-    else:
-        # preprocess image
-        if isinstance(image, (PIL.Image.Image, np.ndarray)):
-            image = [image]
-        if isinstance(image, list) and isinstance(image[0], PIL.Image.Image):
-            # resize all images w.r.t passed height an width
-            image = [i.resize((width, height), resample=PIL.Image.LANCZOS) for i in image]
-            image = [np.array(i.convert("RGB"))[None, :] for i in image]
-            image = np.concatenate(image, axis=0)
-        elif isinstance(image, list) and isinstance(image[0], np.ndarray):
-            image = np.concatenate([i[None, :] for i in image], axis=0)
-
-        image = image.transpose(0, 3, 1, 2)
-        image = torch.from_numpy(image).to(dtype=torch.float32) / 127.5 - 1.0
-
-        mask = mask_pil_to_torch(mask, height, width)
-        mask[mask < 0.5] = 0
-        mask[mask >= 0.5] = 1
-
-    if image.shape[1] == 4:
-        # images are in latent space and thus can't
-        # be masked set masked_image to None
-        # we assume that the checkpoint is not an inpainting
-        # checkpoint. TOD(Yiyi) - need to clean this up later
-        masked_image = None
-    else:
-        masked_image = image * (mask < 0.5)
-
-    # n.b. ensure backwards compatibility as old function does not return image
-    if return_image:
-        return mask, masked_image, image
-
-    return mask, masked_image
-
-
-# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.retrieve_latents
-def retrieve_latents(
-    encoder_output: torch.Tensor, generator: Optional[torch.Generator] = None, sample_mode: str = "sample"
-):
-    if hasattr(encoder_output, "latent_dist") and sample_mode == "sample":
-        return encoder_output.latent_dist.sample(generator)
-    elif hasattr(encoder_output, "latent_dist") and sample_mode == "argmax":
-        return encoder_output.latent_dist.mode()
-    elif hasattr(encoder_output, "latents"):
-        return encoder_output.latents
-    else:
-        raise AttributeError("Could not access latents of provided encoder_output")
-
-
-# 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,
-    **kwargs,
-):
-    """
-    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 support arbitrary spacing between timesteps. If `None`, then the default
-                timestep spacing strategy of the scheduler is used. If `timesteps` is passed, `num_inference_steps`
-                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:
-        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)
-    else:
-        scheduler.set_timesteps(num_inference_steps, device=device, **kwargs)
-        timesteps = scheduler.timesteps
-    return timesteps, num_inference_steps
-
-
-class StableDiffusionXLInpaintPipeline(
-    DiffusionPipeline,
-    TextualInversionLoaderMixin,
-    StableDiffusionXLLoraLoaderMixin,
-    FromSingleFileMixin,
-    IPAdapterMixin,
-):
-    r"""
-    Pipeline for text-to-image generation using Stable Diffusion XL.
-
-    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
-    library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
-
-    The pipeline also inherits the following loading methods:
-        - [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings
-        - [`~loaders.FromSingleFileMixin.from_single_file`] for loading `.ckpt` files
-        - [`~loaders.StableDiffusionXLLoraLoaderMixin.load_lora_weights`] for loading LoRA weights
-        - [`~loaders.StableDiffusionXLLoraLoaderMixin.save_lora_weights`] for saving LoRA weights
-        - [`~loaders.IPAdapterMixin.load_ip_adapter`] for loading IP Adapters
-
-    Args:
-        vae ([`AutoencoderKL`]):
-            Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
-        text_encoder ([`CLIPTextModel`]):
-            Frozen text-encoder. Stable Diffusion XL uses the text portion of
-            [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
-            the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
-        text_encoder_2 ([` CLIPTextModelWithProjection`]):
-            Second frozen text-encoder. Stable Diffusion XL uses the text and pool portion of
-            [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModelWithProjection),
-            specifically the
-            [laion/CLIP-ViT-bigG-14-laion2B-39B-b160k](https://huggingface.co/laion/CLIP-ViT-bigG-14-laion2B-39B-b160k)
-            variant.
-        tokenizer (`CLIPTokenizer`):
-            Tokenizer of class
-            [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
-        tokenizer_2 (`CLIPTokenizer`):
-            Second Tokenizer of class
-            [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
-        unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
-        scheduler ([`SchedulerMixin`]):
-            A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
-            [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
-        requires_aesthetics_score (`bool`, *optional*, defaults to `"False"`):
-            Whether the `unet` requires a aesthetic_score condition to be passed during inference. Also see the config
-            of `stabilityai/stable-diffusion-xl-refiner-1-0`.
-        force_zeros_for_empty_prompt (`bool`, *optional*, defaults to `"True"`):
-            Whether the negative prompt embeddings shall be forced to always be set to 0. Also see the config of
-            `stabilityai/stable-diffusion-xl-base-1-0`.
-        add_watermarker (`bool`, *optional*):
-            Whether to use the [invisible_watermark library](https://github.com/ShieldMnt/invisible-watermark/) to
-            watermark output images. If not defined, it will default to True if the package is installed, otherwise no
-            watermarker will be used.
-    """
-
-    model_cpu_offload_seq = "text_encoder->text_encoder_2->image_encoder->unet->vae"
-
-    _optional_components = [
-        "tokenizer",
-        "tokenizer_2",
-        "text_encoder",
-        "text_encoder_2",
-        "image_encoder",
-        "feature_extractor",
-        "unet_encoder",
-    ]
-    _callback_tensor_inputs = [
-        "latents",
-        "prompt_embeds",
-        "negative_prompt_embeds",
-        "add_text_embeds",
-        "add_time_ids",
-        "negative_pooled_prompt_embeds",
-        "add_neg_time_ids",
-        "mask",
-        "masked_image_latents",
-    ]
-
-    def __init__(
-        self,
-        vae: AutoencoderKL,
-        text_encoder: CLIPTextModel,
-        text_encoder_2: CLIPTextModelWithProjection,
-        tokenizer: CLIPTokenizer,
-        tokenizer_2: CLIPTokenizer,
-        unet: UNet2DConditionModel,
-        unet_encoder: UNet2DConditionModel,
-        scheduler: KarrasDiffusionSchedulers,
-        image_encoder: CLIPVisionModelWithProjection = None,
-        feature_extractor: CLIPImageProcessor = None,
-        requires_aesthetics_score: bool = False,
-        force_zeros_for_empty_prompt: bool = True,
-    ):
-        super().__init__()
-
-        self.register_modules(
-            vae=vae,
-            text_encoder=text_encoder,
-            text_encoder_2=text_encoder_2,
-            tokenizer=tokenizer,
-            tokenizer_2=tokenizer_2,
-            unet=unet,
-            image_encoder=image_encoder,
-            unet_encoder=unet_encoder,
-            feature_extractor=feature_extractor,
-            scheduler=scheduler,
-        )
-        self.register_to_config(force_zeros_for_empty_prompt=force_zeros_for_empty_prompt)
-        self.register_to_config(requires_aesthetics_score=requires_aesthetics_score)
-        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
-        self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
-        self.mask_processor = VaeImageProcessor(
-            vae_scale_factor=self.vae_scale_factor, do_normalize=False, do_binarize=True, do_convert_grayscale=True
-        )
-
-
-
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_vae_slicing
-    def enable_vae_slicing(self):
-        r"""
-        Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
-        compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.
-        """
-        self.vae.enable_slicing()
-
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_vae_slicing
-    def disable_vae_slicing(self):
-        r"""
-        Disable sliced VAE decoding. If `enable_vae_slicing` was previously enabled, this method will go back to
-        computing decoding in one step.
-        """
-        self.vae.disable_slicing()
-
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_vae_tiling
-    def enable_vae_tiling(self):
-        r"""
-        Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to
-        compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow
-        processing larger images.
-        """
-        self.vae.enable_tiling()
-
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_vae_tiling
-    def disable_vae_tiling(self):
-        r"""
-        Disable tiled VAE decoding. If `enable_vae_tiling` was previously enabled, this method will go back to
-        computing decoding in one step.
-        """
-        self.vae.disable_tiling()
-
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_image
-    def encode_image(self, image, device, num_images_per_prompt, output_hidden_states=None):
-        dtype = next(self.image_encoder.parameters()).dtype
-        # print(image.shape)
-        if not isinstance(image, torch.Tensor):
-            image = self.feature_extractor(image, return_tensors="pt").pixel_values
-
-        image = image.to(device=device, dtype=dtype)
-        if output_hidden_states:
-            image_enc_hidden_states = self.image_encoder(image, output_hidden_states=True).hidden_states[-2]
-            image_enc_hidden_states = image_enc_hidden_states.repeat_interleave(num_images_per_prompt, dim=0)
-            uncond_image_enc_hidden_states = self.image_encoder(
-                torch.zeros_like(image), output_hidden_states=True
-            ).hidden_states[-2]
-            uncond_image_enc_hidden_states = uncond_image_enc_hidden_states.repeat_interleave(
-                num_images_per_prompt, dim=0
-            )
-            return image_enc_hidden_states, uncond_image_enc_hidden_states
-        else:
-            image_embeds = self.image_encoder(image).image_embeds
-            image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0)
-            uncond_image_embeds = torch.zeros_like(image_embeds)
-
-            return image_embeds, uncond_image_embeds
-
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_ip_adapter_image_embeds
-    def prepare_ip_adapter_image_embeds(self, ip_adapter_image, device, num_images_per_prompt):
-        # if not isinstance(ip_adapter_image, list):
-        #     ip_adapter_image = [ip_adapter_image]
-
-        # if len(ip_adapter_image) != len(self.unet.encoder_hid_proj.image_projection_layers):
-        #     raise ValueError(
-        #         f"`ip_adapter_image` must have same length as the number of IP Adapters. Got {len(ip_adapter_image)} images and {len(self.unet.encoder_hid_proj.image_projection_layers)} IP Adapters."
-        #     )
-        output_hidden_state = not isinstance(self.unet.encoder_hid_proj, ImageProjection)
-        # print(output_hidden_state)
-        image_embeds, negative_image_embeds = self.encode_image(
-            ip_adapter_image, device, 1, output_hidden_state
-        )
-        # print(single_image_embeds.shape)
-        # single_image_embeds = torch.stack([single_image_embeds] * num_images_per_prompt, dim=0)
-        # single_negative_image_embeds = torch.stack([single_negative_image_embeds] * num_images_per_prompt, dim=0)
-        # print(single_image_embeds.shape)
-        if self.do_classifier_free_guidance:
-            image_embeds = torch.cat([negative_image_embeds, image_embeds])
-            image_embeds = image_embeds.to(device)
-
-
-        return image_embeds
-
-
-    # Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl.StableDiffusionXLPipeline.encode_prompt
-    def encode_prompt(
-        self,
-        prompt: str,
-        prompt_2: Optional[str] = None,
-        device: Optional[torch.device] = None,
-        num_images_per_prompt: int = 1,
-        do_classifier_free_guidance: bool = True,
-        negative_prompt: Optional[str] = None,
-        negative_prompt_2: Optional[str] = None,
-        prompt_embeds: Optional[torch.FloatTensor] = None,
-        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
-        pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
-        negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
-        lora_scale: Optional[float] = None,
-        clip_skip: Optional[int] = None,
-    ):
-        r"""
-        Encodes the prompt into text encoder hidden states.
-
-        Args:
-            prompt (`str` or `List[str]`, *optional*):
-                prompt to be encoded
-            prompt_2 (`str` or `List[str]`, *optional*):
-                The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is
-                used in both text-encoders
-            device: (`torch.device`):
-                torch device
-            num_images_per_prompt (`int`):
-                number of images that should be generated per prompt
-            do_classifier_free_guidance (`bool`):
-                whether to use classifier free guidance or not
-            negative_prompt (`str` or `List[str]`, *optional*):
-                The prompt or prompts not to guide the image generation. If not defined, one has to pass
-                `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
-                less than `1`).
-            negative_prompt_2 (`str` or `List[str]`, *optional*):
-                The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and
-                `text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders
-            prompt_embeds (`torch.FloatTensor`, *optional*):
-                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
-                provided, text embeddings will be generated from `prompt` input argument.
-            negative_prompt_embeds (`torch.FloatTensor`, *optional*):
-                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
-                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
-                argument.
-            pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
-                Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.
-                If not provided, pooled text embeddings will be generated from `prompt` input argument.
-            negative_pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
-                Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
-                weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt`
-                input argument.
-            lora_scale (`float`, *optional*):
-                A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.
-            clip_skip (`int`, *optional*):
-                Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
-                the output of the pre-final layer will be used for computing the prompt embeddings.
-        """
-        device = device or self._execution_device
-
-        # set lora scale so that monkey patched LoRA
-        # function of text encoder can correctly access it
-        if lora_scale is not None and isinstance(self, StableDiffusionXLLoraLoaderMixin):
-            self._lora_scale = lora_scale
-
-            # dynamically adjust the LoRA scale
-            if self.text_encoder is not None:
-                if not USE_PEFT_BACKEND:
-                    adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)
-                else:
-                    scale_lora_layers(self.text_encoder, lora_scale)
-
-            if self.text_encoder_2 is not None:
-                if not USE_PEFT_BACKEND:
-                    adjust_lora_scale_text_encoder(self.text_encoder_2, lora_scale)
-                else:
-                    scale_lora_layers(self.text_encoder_2, lora_scale)
-
-        prompt = [prompt] if isinstance(prompt, str) else prompt
-
-        if prompt is not None:
-            batch_size = len(prompt)
-        else:
-            batch_size = prompt_embeds.shape[0]
-
-        # Define tokenizers and text encoders
-        tokenizers = [self.tokenizer, self.tokenizer_2] if self.tokenizer is not None else [self.tokenizer_2]
-        text_encoders = (
-            [self.text_encoder, self.text_encoder_2] if self.text_encoder is not None else [self.text_encoder_2]
-        )
-
-        if prompt_embeds is None:
-            prompt_2 = prompt_2 or prompt
-            prompt_2 = [prompt_2] if isinstance(prompt_2, str) else prompt_2
-
-            # textual inversion: procecss multi-vector tokens if necessary
-            prompt_embeds_list = []
-            prompts = [prompt, prompt_2]
-            for prompt, tokenizer, text_encoder in zip(prompts, tokenizers, text_encoders):
-                if isinstance(self, TextualInversionLoaderMixin):
-                    prompt = self.maybe_convert_prompt(prompt, tokenizer)
-
-                text_inputs = tokenizer(
-                    prompt,
-                    padding="max_length",
-                    max_length=tokenizer.model_max_length,
-                    truncation=True,
-                    return_tensors="pt",
-                )
-
-                text_input_ids = text_inputs.input_ids
-                untruncated_ids = tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
-
-                if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
-                    text_input_ids, untruncated_ids
-                ):
-                    removed_text = tokenizer.batch_decode(untruncated_ids[:, tokenizer.model_max_length - 1 : -1])
-                    logger.warning(
-                        "The following part of your input was truncated because CLIP can only handle sequences up to"
-                        f" {tokenizer.model_max_length} tokens: {removed_text}"
-                    )
-
-                prompt_embeds = text_encoder(text_input_ids.to(device), output_hidden_states=True)
-
-                # We are only ALWAYS interested in the pooled output of the final text encoder
-                pooled_prompt_embeds = prompt_embeds[0]
-                if clip_skip is None:
-                    prompt_embeds = prompt_embeds.hidden_states[-2]
-                else:
-                    # "2" because SDXL always indexes from the penultimate layer.
-                    prompt_embeds = prompt_embeds.hidden_states[-(clip_skip + 2)]
-
-                prompt_embeds_list.append(prompt_embeds)
-
-            prompt_embeds = torch.concat(prompt_embeds_list, dim=-1)
-
-        # get unconditional embeddings for classifier free guidance
-        zero_out_negative_prompt = negative_prompt is None and self.config.force_zeros_for_empty_prompt
-        if do_classifier_free_guidance and negative_prompt_embeds is None and zero_out_negative_prompt:
-            negative_prompt_embeds = torch.zeros_like(prompt_embeds)
-            negative_pooled_prompt_embeds = torch.zeros_like(pooled_prompt_embeds)
-        elif do_classifier_free_guidance and negative_prompt_embeds is None:
-            negative_prompt = negative_prompt or ""
-            negative_prompt_2 = negative_prompt_2 or negative_prompt
-
-            # normalize str to list
-            negative_prompt = batch_size * [negative_prompt] if isinstance(negative_prompt, str) else negative_prompt
-            negative_prompt_2 = (
-                batch_size * [negative_prompt_2] if isinstance(negative_prompt_2, str) else negative_prompt_2
-            )
-
-            uncond_tokens: List[str]
-            if prompt is not None and type(prompt) is not type(negative_prompt):
-                raise TypeError(
-                    f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
-                    f" {type(prompt)}."
-                )
-            elif batch_size != len(negative_prompt):
-                raise ValueError(
-                    f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
-                    f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
-                    " the batch size of `prompt`."
-                )
-            else:
-                uncond_tokens = [negative_prompt, negative_prompt_2]
-
-            negative_prompt_embeds_list = []
-            for negative_prompt, tokenizer, text_encoder in zip(uncond_tokens, tokenizers, text_encoders):
-                if isinstance(self, TextualInversionLoaderMixin):
-                    negative_prompt = self.maybe_convert_prompt(negative_prompt, tokenizer)
-
-                max_length = prompt_embeds.shape[1]
-                uncond_input = tokenizer(
-                    negative_prompt,
-                    padding="max_length",
-                    max_length=max_length,
-                    truncation=True,
-                    return_tensors="pt",
-                )
-
-                negative_prompt_embeds = text_encoder(
-                    uncond_input.input_ids.to(device),
-                    output_hidden_states=True,
-                )
-                # We are only ALWAYS interested in the pooled output of the final text encoder
-                negative_pooled_prompt_embeds = negative_prompt_embeds[0]
-                negative_prompt_embeds = negative_prompt_embeds.hidden_states[-2]
-
-                negative_prompt_embeds_list.append(negative_prompt_embeds)
-
-            negative_prompt_embeds = torch.concat(negative_prompt_embeds_list, dim=-1)
-
-        if self.text_encoder_2 is not None:
-            prompt_embeds = prompt_embeds.to(dtype=self.text_encoder_2.dtype, device=device)
-        else:
-            prompt_embeds = prompt_embeds.to(dtype=self.unet.dtype, device=device)
-
-        bs_embed, seq_len, _ = prompt_embeds.shape
-        # duplicate text embeddings for each generation per prompt, using mps friendly method
-        prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
-        prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
-
-        if do_classifier_free_guidance:
-            # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
-            seq_len = negative_prompt_embeds.shape[1]
-
-            if self.text_encoder_2 is not None:
-                negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder_2.dtype, device=device)
-            else:
-                negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.unet.dtype, device=device)
-
-            negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
-            negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
-
-        pooled_prompt_embeds = pooled_prompt_embeds.repeat(1, num_images_per_prompt).view(
-            bs_embed * num_images_per_prompt, -1
-        )
-        if do_classifier_free_guidance:
-            negative_pooled_prompt_embeds = negative_pooled_prompt_embeds.repeat(1, num_images_per_prompt).view(
-                bs_embed * num_images_per_prompt, -1
-            )
-
-        if self.text_encoder is not None:
-            if isinstance(self, StableDiffusionXLLoraLoaderMixin) and USE_PEFT_BACKEND:
-                # Retrieve the original scale by scaling back the LoRA layers
-                unscale_lora_layers(self.text_encoder, lora_scale)
-
-        if self.text_encoder_2 is not None:
-            if isinstance(self, StableDiffusionXLLoraLoaderMixin) and USE_PEFT_BACKEND:
-                # Retrieve the original scale by scaling back the LoRA layers
-                unscale_lora_layers(self.text_encoder_2, lora_scale)
-
-        return prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds
-
-    # 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 check_inputs(
-        self,
-        prompt,
-        prompt_2,
-        image,
-        mask_image,
-        height,
-        width,
-        strength,
-        callback_steps,
-        output_type,
-        negative_prompt=None,
-        negative_prompt_2=None,
-        prompt_embeds=None,
-        negative_prompt_embeds=None,
-        callback_on_step_end_tensor_inputs=None,
-        padding_mask_crop=None,
-    ):
-        if strength < 0 or strength > 1:
-            raise ValueError(f"The value of strength should in [0.0, 1.0] but is {strength}")
-
-        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 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 callback_on_step_end_tensor_inputs is not None and not all(
-            k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs
-        ):
-            raise ValueError(
-                f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}"
-            )
-
-        if prompt is not None and prompt_embeds is not None:
-            raise ValueError(
-                f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
-                " only forward one of the two."
-            )
-        elif prompt_2 is not None and prompt_embeds is not None:
-            raise ValueError(
-                f"Cannot forward both `prompt_2`: {prompt_2} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
-                " only forward one of the two."
-            )
-        elif prompt is None and prompt_embeds is None:
-            raise ValueError(
-                "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
-            )
-        elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
-            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
-        elif prompt_2 is not None and (not isinstance(prompt_2, str) and not isinstance(prompt_2, list)):
-            raise ValueError(f"`prompt_2` has to be of type `str` or `list` but is {type(prompt_2)}")
-
-        if negative_prompt is not None and negative_prompt_embeds is not None:
-            raise ValueError(
-                f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
-                f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
-            )
-        elif negative_prompt_2 is not None and negative_prompt_embeds is not None:
-            raise ValueError(
-                f"Cannot forward both `negative_prompt_2`: {negative_prompt_2} and `negative_prompt_embeds`:"
-                f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
-            )
-
-        if prompt_embeds is not None and negative_prompt_embeds is not None:
-            if prompt_embeds.shape != negative_prompt_embeds.shape:
-                raise ValueError(
-                    "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
-                    f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
-                    f" {negative_prompt_embeds.shape}."
-                )
-        if padding_mask_crop is not None:
-            if not isinstance(image, PIL.Image.Image):
-                raise ValueError(
-                    f"The image should be a PIL image when inpainting mask crop, but is of type" f" {type(image)}."
-                )
-            if not isinstance(mask_image, PIL.Image.Image):
-                raise ValueError(
-                    f"The mask image should be a PIL image when inpainting mask crop, but is of type"
-                    f" {type(mask_image)}."
-                )
-            if output_type != "pil":
-                raise ValueError(f"The output type should be PIL when inpainting mask crop, but is" f" {output_type}.")
-
-    def prepare_latents(
-        self,
-        batch_size,
-        num_channels_latents,
-        height,
-        width,
-        dtype,
-        device,
-        generator,
-        latents=None,
-        image=None,
-        timestep=None,
-        is_strength_max=True,
-        add_noise=True,
-        return_noise=False,
-        return_image_latents=False,
-    ):
-        shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, 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 (image is None or timestep is None) and not is_strength_max:
-            raise ValueError(
-                "Since strength < 1. initial latents are to be initialised as a combination of Image + Noise."
-                "However, either the image or the noise timestep has not been provided."
-            )
-
-        if image.shape[1] == 4:
-            image_latents = image.to(device=device, dtype=dtype)
-            image_latents = image_latents.repeat(batch_size // image_latents.shape[0], 1, 1, 1)
-        elif return_image_latents or (latents is None and not is_strength_max):
-            image = image.to(device=device, dtype=dtype)
-            image_latents = self._encode_vae_image(image=image, generator=generator)
-            image_latents = image_latents.repeat(batch_size // image_latents.shape[0], 1, 1, 1)
-
-        if latents is None and add_noise:
-            noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
-            # if strength is 1. then initialise the latents to noise, else initial to image + noise
-            latents = noise if is_strength_max else self.scheduler.add_noise(image_latents, noise, timestep)
-            # if pure noise then scale the initial latents by the  Scheduler's init sigma
-            latents = latents * self.scheduler.init_noise_sigma if is_strength_max else latents
-        elif add_noise:
-            noise = latents.to(device)
-            latents = noise * self.scheduler.init_noise_sigma
-        else:
-            noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
-            latents = image_latents.to(device)
-
-        outputs = (latents,)
-
-        if return_noise:
-            outputs += (noise,)
-
-        if return_image_latents:
-            outputs += (image_latents,)
-
-        return outputs
-
-    def _encode_vae_image(self, image: torch.Tensor, generator: torch.Generator):
-        dtype = image.dtype
-        if self.vae.config.force_upcast:
-            image = image.float()
-            self.vae.to(dtype=torch.float32)
-
-        if isinstance(generator, list):
-            image_latents = [
-                retrieve_latents(self.vae.encode(image[i : i + 1]), generator=generator[i])
-                for i in range(image.shape[0])
-            ]
-            image_latents = torch.cat(image_latents, dim=0)
-        else:
-            image_latents = retrieve_latents(self.vae.encode(image), generator=generator)
-
-        if self.vae.config.force_upcast:
-            self.vae.to(dtype)
-
-        image_latents = image_latents.to(dtype)
-        image_latents = self.vae.config.scaling_factor * image_latents
-
-        return image_latents
-
-    def prepare_mask_latents(
-        self, mask, masked_image, batch_size, height, width, dtype, device, generator, do_classifier_free_guidance
-    ):
-        # resize the mask to latents shape as we concatenate the mask to the latents
-        # we do that before converting to dtype to avoid breaking in case we're using cpu_offload
-        # and half precision
-        mask = torch.nn.functional.interpolate(
-            mask, size=(height // self.vae_scale_factor, width // self.vae_scale_factor)
-        )
-        mask = mask.to(device=device, dtype=dtype)
-
-        # duplicate mask and masked_image_latents for each generation per prompt, using mps friendly method
-        if mask.shape[0] < batch_size:
-            if not batch_size % mask.shape[0] == 0:
-                raise ValueError(
-                    "The passed mask and the required batch size don't match. Masks are supposed to be duplicated to"
-                    f" a total batch size of {batch_size}, but {mask.shape[0]} masks were passed. Make sure the number"
-                    " of masks that you pass is divisible by the total requested batch size."
-                )
-            mask = mask.repeat(batch_size // mask.shape[0], 1, 1, 1)
-
-        mask = torch.cat([mask] * 2) if do_classifier_free_guidance else mask
-        if masked_image is not None and masked_image.shape[1] == 4:
-            masked_image_latents = masked_image
-        else:
-            masked_image_latents = None
-
-        if masked_image is not None:
-            if masked_image_latents is None:
-                masked_image = masked_image.to(device=device, dtype=dtype)
-                masked_image_latents = self._encode_vae_image(masked_image, generator=generator)
-
-            if masked_image_latents.shape[0] < batch_size:
-                if not batch_size % masked_image_latents.shape[0] == 0:
-                    raise ValueError(
-                        "The passed images and the required batch size don't match. Images are supposed to be duplicated"
-                        f" to a total batch size of {batch_size}, but {masked_image_latents.shape[0]} images were passed."
-                        " Make sure the number of images that you pass is divisible by the total requested batch size."
-                    )
-                masked_image_latents = masked_image_latents.repeat(
-                    batch_size // masked_image_latents.shape[0], 1, 1, 1
-                )
-
-            masked_image_latents = (
-                torch.cat([masked_image_latents] * 2) if do_classifier_free_guidance else masked_image_latents
-            )
-
-            # aligning device to prevent device errors when concating it with the latent model input
-            masked_image_latents = masked_image_latents.to(device=device, dtype=dtype)
-
-        return mask, masked_image_latents
-
-    # Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl_img2img.StableDiffusionXLImg2ImgPipeline.get_timesteps
-    def get_timesteps(self, num_inference_steps, strength, device, denoising_start=None):
-        # get the original timestep using init_timestep
-        if denoising_start is None:
-            init_timestep = min(int(num_inference_steps * strength), num_inference_steps)
-            t_start = max(num_inference_steps - init_timestep, 0)
-        else:
-            t_start = 0
-
-        timesteps = self.scheduler.timesteps[t_start * self.scheduler.order :]
-
-        # Strength is irrelevant if we directly request a timestep to start at;
-        # that is, strength is determined by the denoising_start instead.
-        if denoising_start is not None:
-            discrete_timestep_cutoff = int(
-                round(
-                    self.scheduler.config.num_train_timesteps
-                    - (denoising_start * self.scheduler.config.num_train_timesteps)
-                )
-            )
-
-            num_inference_steps = (timesteps < discrete_timestep_cutoff).sum().item()
-            if self.scheduler.order == 2 and num_inference_steps % 2 == 0:
-                # if the scheduler is a 2nd order scheduler we might have to do +1
-                # because `num_inference_steps` might be even given that every timestep
-                # (except the highest one) is duplicated. If `num_inference_steps` is even it would
-                # mean that we cut the timesteps in the middle of the denoising step
-                # (between 1st and 2nd devirative) which leads to incorrect results. By adding 1
-                # we ensure that the denoising process always ends after the 2nd derivate step of the scheduler
-                num_inference_steps = num_inference_steps + 1
-
-            # because t_n+1 >= t_n, we slice the timesteps starting from the end
-            timesteps = timesteps[-num_inference_steps:]
-            return timesteps, num_inference_steps
-
-        return timesteps, num_inference_steps - t_start
-
-    # Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl_img2img.StableDiffusionXLImg2ImgPipeline._get_add_time_ids
-    def _get_add_time_ids(
-        self,
-        original_size,
-        crops_coords_top_left,
-        target_size,
-        aesthetic_score,
-        negative_aesthetic_score,
-        negative_original_size,
-        negative_crops_coords_top_left,
-        negative_target_size,
-        dtype,
-        text_encoder_projection_dim=None,
-    ):
-        if self.config.requires_aesthetics_score:
-            add_time_ids = list(original_size + crops_coords_top_left + (aesthetic_score,))
-            add_neg_time_ids = list(
-                negative_original_size + negative_crops_coords_top_left + (negative_aesthetic_score,)
-            )
-        else:
-            add_time_ids = list(original_size + crops_coords_top_left + target_size)
-            add_neg_time_ids = list(negative_original_size + crops_coords_top_left + negative_target_size)
-
-        passed_add_embed_dim = (
-            self.unet.config.addition_time_embed_dim * len(add_time_ids) + text_encoder_projection_dim
-        )
-        expected_add_embed_dim = self.unet.add_embedding.linear_1.in_features
-
-        if (
-            expected_add_embed_dim > passed_add_embed_dim
-            and (expected_add_embed_dim - passed_add_embed_dim) == self.unet.config.addition_time_embed_dim
-        ):
-            raise ValueError(
-                f"Model expects an added time embedding vector of length {expected_add_embed_dim}, but a vector of {passed_add_embed_dim} was created. Please make sure to enable `requires_aesthetics_score` with `pipe.register_to_config(requires_aesthetics_score=True)` to make sure `aesthetic_score` {aesthetic_score} and `negative_aesthetic_score` {negative_aesthetic_score} is correctly used by the model."
-            )
-        elif (
-            expected_add_embed_dim < passed_add_embed_dim
-            and (passed_add_embed_dim - expected_add_embed_dim) == self.unet.config.addition_time_embed_dim
-        ):
-            raise ValueError(
-                f"Model expects an added time embedding vector of length {expected_add_embed_dim}, but a vector of {passed_add_embed_dim} was created. Please make sure to disable `requires_aesthetics_score` with `pipe.register_to_config(requires_aesthetics_score=False)` to make sure `target_size` {target_size} is correctly used by the model."
-            )
-        elif expected_add_embed_dim != passed_add_embed_dim:
-            raise ValueError(
-                f"Model expects an added time embedding vector of length {expected_add_embed_dim}, but a vector of {passed_add_embed_dim} was created. The model has an incorrect config. Please check `unet.config.time_embedding_type` and `text_encoder_2.config.projection_dim`."
-            )
-
-        add_time_ids = torch.tensor([add_time_ids], dtype=dtype)
-        add_neg_time_ids = torch.tensor([add_neg_time_ids], dtype=dtype)
-
-        return add_time_ids, add_neg_time_ids
-
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_upscale.StableDiffusionUpscalePipeline.upcast_vae
-    def upcast_vae(self):
-        dtype = self.vae.dtype
-        self.vae.to(dtype=torch.float32)
-        use_torch_2_0_or_xformers = isinstance(
-            self.vae.decoder.mid_block.attentions[0].processor,
-            (
-                AttnProcessor2_0,
-                XFormersAttnProcessor,
-                LoRAXFormersAttnProcessor,
-                LoRAAttnProcessor2_0,
-            ),
-        )
-        # if xformers or torch_2_0 is used attention block does not need
-        # to be in float32 which can save lots of memory
-        if use_torch_2_0_or_xformers:
-            self.vae.post_quant_conv.to(dtype)
-            self.vae.decoder.conv_in.to(dtype)
-            self.vae.decoder.mid_block.to(dtype)
-
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_freeu
-    def enable_freeu(self, s1: float, s2: float, b1: float, b2: float):
-        r"""Enables the FreeU mechanism as in https://arxiv.org/abs/2309.11497.
-
-        The suffixes after the scaling factors represent the stages where they are being applied.
-
-        Please refer to the [official repository](https://github.com/ChenyangSi/FreeU) for combinations of the values
-        that are known to work well for different pipelines such as Stable Diffusion v1, v2, and Stable Diffusion XL.
-
-        Args:
-            s1 (`float`):
-                Scaling factor for stage 1 to attenuate the contributions of the skip features. This is done to
-                mitigate "oversmoothing effect" in the enhanced denoising process.
-            s2 (`float`):
-                Scaling factor for stage 2 to attenuate the contributions of the skip features. This is done to
-                mitigate "oversmoothing effect" in the enhanced denoising process.
-            b1 (`float`): Scaling factor for stage 1 to amplify the contributions of backbone features.
-            b2 (`float`): Scaling factor for stage 2 to amplify the contributions of backbone features.
-        """
-        if not hasattr(self, "unet"):
-            raise ValueError("The pipeline must have `unet` for using FreeU.")
-        self.unet.enable_freeu(s1=s1, s2=s2, b1=b1, b2=b2)
-
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_freeu
-    def disable_freeu(self):
-        """Disables the FreeU mechanism if enabled."""
-        self.unet.disable_freeu()
-
-    # Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl.StableDiffusionXLPipeline.fuse_qkv_projections
-    def fuse_qkv_projections(self, unet: bool = True, vae: bool = True):
-        """
-        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>
-
-        Args:
-            unet (`bool`, defaults to `True`): To apply fusion on the UNet.
-            vae (`bool`, defaults to `True`): To apply fusion on the VAE.
-        """
-        self.fusing_unet = False
-        self.fusing_vae = False
-
-        if unet:
-            self.fusing_unet = True
-            self.unet.fuse_qkv_projections()
-            self.unet.set_attn_processor(FusedAttnProcessor2_0())
-
-        if vae:
-            if not isinstance(self.vae, AutoencoderKL):
-                raise ValueError("`fuse_qkv_projections()` is only supported for the VAE of type `AutoencoderKL`.")
-
-            self.fusing_vae = True
-            self.vae.fuse_qkv_projections()
-            self.vae.set_attn_processor(FusedAttnProcessor2_0())
-
-    # Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl.StableDiffusionXLPipeline.unfuse_qkv_projections
-    def unfuse_qkv_projections(self, unet: bool = True, vae: bool = True):
-        """Disable QKV projection fusion if enabled.
-
-        <Tip warning={true}>
-
-        This API is 🧪 experimental.
-
-        </Tip>
-
-        Args:
-            unet (`bool`, defaults to `True`): To apply fusion on the UNet.
-            vae (`bool`, defaults to `True`): To apply fusion on the VAE.
-
-        """
-        if unet:
-            if not self.fusing_unet:
-                logger.warning("The UNet was not initially fused for QKV projections. Doing nothing.")
-            else:
-                self.unet.unfuse_qkv_projections()
-                self.fusing_unet = False
-
-        if vae:
-            if not self.fusing_vae:
-                logger.warning("The VAE was not initially fused for QKV projections. Doing nothing.")
-            else:
-                self.vae.unfuse_qkv_projections()
-                self.fusing_vae = False
-
-    # Copied from diffusers.pipelines.latent_consistency_models.pipeline_latent_consistency_text2img.LatentConsistencyModelPipeline.get_guidance_scale_embedding
-    def get_guidance_scale_embedding(self, w, embedding_dim=512, dtype=torch.float32):
-        """
-        See https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298
-
-        Args:
-            timesteps (`torch.Tensor`):
-                generate embedding vectors at these timesteps
-            embedding_dim (`int`, *optional*, defaults to 512):
-                dimension of the embeddings to generate
-            dtype:
-                data type of the generated embeddings
-
-        Returns:
-            `torch.FloatTensor`: Embedding vectors with shape `(len(timesteps), embedding_dim)`
-        """
-        assert len(w.shape) == 1
-        w = w * 1000.0
-
-        half_dim = embedding_dim // 2
-        emb = torch.log(torch.tensor(10000.0)) / (half_dim - 1)
-        emb = torch.exp(torch.arange(half_dim, dtype=dtype) * -emb)
-        emb = w.to(dtype)[:, None] * emb[None, :]
-        emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)
-        if embedding_dim % 2 == 1:  # zero pad
-            emb = torch.nn.functional.pad(emb, (0, 1))
-        assert emb.shape == (w.shape[0], embedding_dim)
-        return emb
-
-    @property
-    def guidance_scale(self):
-        return self._guidance_scale
-
-    @property
-    def guidance_rescale(self):
-        return self._guidance_rescale
-
-    @property
-    def clip_skip(self):
-        return self._clip_skip
-
-    # 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.
-    @property
-    def do_classifier_free_guidance(self):
-        return self._guidance_scale > 1 and self.unet.config.time_cond_proj_dim is None
-
-    @property
-    def cross_attention_kwargs(self):
-        return self._cross_attention_kwargs
-
-    @property
-    def denoising_end(self):
-        return self._denoising_end
-
-    @property
-    def denoising_start(self):
-        return self._denoising_start
-
-    @property
-    def num_timesteps(self):
-        return self._num_timesteps
-
-    @property
-    def interrupt(self):
-        return self._interrupt
-
-    @torch.no_grad()
-    @replace_example_docstring(EXAMPLE_DOC_STRING)
-    def __call__(
-        self,
-        prompt: Union[str, List[str]] = None,
-        prompt_2: Optional[Union[str, List[str]]] = None,
-        image: PipelineImageInput = None,
-        mask_image: PipelineImageInput = None,
-        masked_image_latents: torch.FloatTensor = None,
-        height: Optional[int] = None,
-        width: Optional[int] = None,
-        padding_mask_crop: Optional[int] = None,
-        strength: float = 0.9999,
-        num_inference_steps: int = 50,
-        timesteps: List[int] = None,
-        denoising_start: Optional[float] = None,
-        denoising_end: Optional[float] = None,
-        guidance_scale: float = 7.5,
-        negative_prompt: Optional[Union[str, List[str]]] = None,
-        negative_prompt_2: Optional[Union[str, List[str]]] = None,
-        num_images_per_prompt: Optional[int] = 1,
-        eta: float = 0.0,
-        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
-        latents: Optional[torch.FloatTensor] = None,
-        prompt_embeds: Optional[torch.FloatTensor] = None,
-        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
-        pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
-        negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
-        ip_adapter_image: Optional[PipelineImageInput] = None,
-        output_type: Optional[str] = "pil",
-        cloth =None,
-        pose_img = None,
-        text_embeds_cloth=None,
-        return_dict: bool = True,
-        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
-        guidance_rescale: float = 0.0,
-        original_size: Tuple[int, int] = None,
-        crops_coords_top_left: Tuple[int, int] = (0, 0),
-        target_size: Tuple[int, int] = None,
-        negative_original_size: Optional[Tuple[int, int]] = None,
-        negative_crops_coords_top_left: Tuple[int, int] = (0, 0),
-        negative_target_size: Optional[Tuple[int, int]] = None,
-        aesthetic_score: float = 6.0,
-        negative_aesthetic_score: float = 2.5,
-        clip_skip: Optional[int] = None,
-        pooled_prompt_embeds_c=None,
-        callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
-        callback_on_step_end_tensor_inputs: List[str] = ["latents"],
-        **kwargs,
-    ):
-        r"""
-        Function invoked when calling the pipeline for generation.
-
-        Args:
-            prompt (`str` or `List[str]`, *optional*):
-                The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
-                instead.
-            prompt_2 (`str` or `List[str]`, *optional*):
-                The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is
-                used in both text-encoders
-            image (`PIL.Image.Image`):
-                `Image`, or tensor representing an image batch which will be inpainted, *i.e.* parts of the image will
-                be masked out with `mask_image` and repainted according to `prompt`.
-            mask_image (`PIL.Image.Image`):
-                `Image`, or tensor representing an image batch, to mask `image`. White pixels in the mask will be
-                repainted, while black pixels will be preserved. If `mask_image` is a PIL image, it will be converted
-                to a single channel (luminance) before use. If it's a tensor, it should contain one color channel (L)
-                instead of 3, so the expected shape would be `(B, H, W, 1)`.
-            height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
-                The height in pixels of the generated image. This is set to 1024 by default for the best results.
-                Anything below 512 pixels won't work well for
-                [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0)
-                and checkpoints that are not specifically fine-tuned on low resolutions.
-            width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
-                The width in pixels of the generated image. This is set to 1024 by default for the best results.
-                Anything below 512 pixels won't work well for
-                [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0)
-                and checkpoints that are not specifically fine-tuned on low resolutions.
-            padding_mask_crop (`int`, *optional*, defaults to `None`):
-                The size of margin in the crop to be applied to the image and masking. If `None`, no crop is applied to image and mask_image. If
-                `padding_mask_crop` is not `None`, it will first find a rectangular region with the same aspect ration of the image and
-                contains all masked area, and then expand that area based on `padding_mask_crop`. The image and mask_image will then be cropped based on
-                the expanded area before resizing to the original image size for inpainting. This is useful when the masked area is small while the image is large
-                and contain information inreleant for inpainging, such as background.
-            strength (`float`, *optional*, defaults to 0.9999):
-                Conceptually, indicates how much to transform the masked portion of the reference `image`. Must be
-                between 0 and 1. `image` will be used as a starting point, adding more noise to it the larger the
-                `strength`. The number of denoising steps depends on the amount of noise initially added. When
-                `strength` is 1, added noise will be maximum and the denoising process will run for the full number of
-                iterations specified in `num_inference_steps`. A value of 1, therefore, essentially ignores the masked
-                portion of the reference `image`. Note that in the case of `denoising_start` being declared as an
-                integer, the value of `strength` will be ignored.
-            num_inference_steps (`int`, *optional*, defaults to 50):
-                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.
-            denoising_start (`float`, *optional*):
-                When specified, indicates the fraction (between 0.0 and 1.0) of the total denoising process to be
-                bypassed before it is initiated. Consequently, the initial part of the denoising process is skipped and
-                it is assumed that the passed `image` is a partly denoised image. Note that when this is specified,
-                strength will be ignored. The `denoising_start` parameter is particularly beneficial when this pipeline
-                is integrated into a "Mixture of Denoisers" multi-pipeline setup, as detailed in [**Refining the Image
-                Output**](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/stable_diffusion_xl#refining-the-image-output).
-            denoising_end (`float`, *optional*):
-                When specified, determines the fraction (between 0.0 and 1.0) of the total denoising process to be
-                completed before it is intentionally prematurely terminated. As a result, the returned sample will
-                still retain a substantial amount of noise (ca. final 20% of timesteps still needed) and should be
-                denoised by a successor pipeline that has `denoising_start` set to 0.8 so that it only denoises the
-                final 20% of the scheduler. The denoising_end parameter should ideally be utilized when this pipeline
-                forms a part of a "Mixture of Denoisers" multi-pipeline setup, as elaborated in [**Refining the Image
-                Output**](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/stable_diffusion_xl#refining-the-image-output).
-            guidance_scale (`float`, *optional*, defaults to 7.5):
-                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.
-            negative_prompt (`str` or `List[str]`, *optional*):
-                The prompt or prompts not to guide the image generation. If not defined, one has to pass
-                `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
-                less than `1`).
-            negative_prompt_2 (`str` or `List[str]`, *optional*):
-                The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and
-                `text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders
-            prompt_embeds (`torch.FloatTensor`, *optional*):
-                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
-                provided, text embeddings will be generated from `prompt` input argument.
-            negative_prompt_embeds (`torch.FloatTensor`, *optional*):
-                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
-                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
-                argument.
-            pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
-                Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.
-                If not provided, pooled text embeddings will be generated from `prompt` input argument.
-            negative_pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
-                Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
-                weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt`
-                input argument.
-            ip_adapter_image: (`PipelineImageInput`, *optional*): Optional image input to work with IP Adapters.
-            num_images_per_prompt (`int`, *optional*, defaults to 1):
-                The number of images to generate per prompt.
-            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`, *optional*):
-                One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
-                to make generation deterministic.
-            latents (`torch.FloatTensor`, *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`.
-            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.StableDiffusionPipelineOutput`] instead of a
-                plain tuple.
-            cross_attention_kwargs (`dict`, *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).
-            original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
-                If `original_size` is not the same as `target_size` the image will appear to be down- or upsampled.
-                `original_size` defaults to `(height, width)` if not specified. Part of SDXL's micro-conditioning as
-                explained in section 2.2 of
-                [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
-            crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)):
-                `crops_coords_top_left` can be used to generate an image that appears to be "cropped" from the position
-                `crops_coords_top_left` downwards. Favorable, well-centered images are usually achieved by setting
-                `crops_coords_top_left` to (0, 0). Part of SDXL's micro-conditioning as explained in section 2.2 of
-                [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
-            target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
-                For most cases, `target_size` should be set to the desired height and width of the generated image. If
-                not specified it will default to `(height, width)`. Part of SDXL's micro-conditioning as explained in
-                section 2.2 of [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
-            negative_original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
-                To negatively condition the generation process based on a specific image resolution. Part of SDXL's
-                micro-conditioning as explained in section 2.2 of
-                [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more
-                information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
-            negative_crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)):
-                To negatively condition the generation process based on a specific crop coordinates. Part of SDXL's
-                micro-conditioning as explained in section 2.2 of
-                [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more
-                information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
-            negative_target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
-                To negatively condition the generation process based on a target image resolution. It should be as same
-                as the `target_size` for most cases. Part of SDXL's micro-conditioning as explained in section 2.2 of
-                [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more
-                information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
-            aesthetic_score (`float`, *optional*, defaults to 6.0):
-                Used to simulate an aesthetic score of the generated image by influencing the positive text condition.
-                Part of SDXL's micro-conditioning as explained in section 2.2 of
-                [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
-            negative_aesthetic_score (`float`, *optional*, defaults to 2.5):
-                Part of SDXL's micro-conditioning as explained in section 2.2 of
-                [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). Can be used to
-                simulate an aesthetic score of the generated image by influencing the negative text condition.
-            clip_skip (`int`, *optional*):
-                Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
-                the output of the pre-final layer will be used for computing the prompt embeddings.
-            callback_on_step_end (`Callable`, *optional*):
-                A function that calls at the end of each denoising steps during the inference. The function is called
-                with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,
-                callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by
-                `callback_on_step_end_tensor_inputs`.
-            callback_on_step_end_tensor_inputs (`List`, *optional*):
-                The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
-                will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
-                `._callback_tensor_inputs` attribute of your pipeline class.
-
-        Examples:
-
-        Returns:
-            [`~pipelines.stable_diffusion.StableDiffusionXLPipelineOutput`] or `tuple`:
-            [`~pipelines.stable_diffusion.StableDiffusionXLPipelineOutput`] if `return_dict` is True, otherwise a
-            `tuple. `tuple. When returning a tuple, the first element is a list with the generated images.
-        """
-
-        callback = kwargs.pop("callback", None)
-        callback_steps = kwargs.pop("callback_steps", None)
-
-        if callback is not None:
-            deprecate(
-                "callback",
-                "1.0.0",
-                "Passing `callback` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`",
-            )
-        if callback_steps is not None:
-            deprecate(
-                "callback_steps",
-                "1.0.0",
-                "Passing `callback_steps` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`",
-            )
-
-        # 0. Default height and width to unet
-        height = height or self.unet.config.sample_size * self.vae_scale_factor
-        width = width or self.unet.config.sample_size * self.vae_scale_factor
-
-        # 1. Check inputs
-        self.check_inputs(
-            prompt,
-            prompt_2,
-            image,
-            mask_image,
-            height,
-            width,
-            strength,
-            callback_steps,
-            output_type,
-            negative_prompt,
-            negative_prompt_2,
-            prompt_embeds,
-            negative_prompt_embeds,
-            callback_on_step_end_tensor_inputs,
-            padding_mask_crop,
-        )
-
-        self._guidance_scale = guidance_scale
-        self._guidance_rescale = guidance_rescale
-        self._clip_skip = clip_skip
-        self._cross_attention_kwargs = cross_attention_kwargs
-        self._denoising_end = denoising_end
-        self._denoising_start = denoising_start
-        self._interrupt = False
-
-        # 2. Define call parameters
-        if prompt is not None and isinstance(prompt, str):
-            batch_size = 1
-        elif prompt is not None and isinstance(prompt, list):
-            batch_size = len(prompt)
-        else:
-            batch_size = prompt_embeds.shape[0]
-
-        device = self._execution_device
-
-        # 3. Encode input prompt
-        text_encoder_lora_scale = (
-            self.cross_attention_kwargs.get("scale", None) if self.cross_attention_kwargs is not None else None
-        )
-
-        (
-            prompt_embeds,
-            negative_prompt_embeds,
-            pooled_prompt_embeds,
-            negative_pooled_prompt_embeds,
-        ) = self.encode_prompt(
-            prompt=prompt,
-            prompt_2=prompt_2,
-            device=device,
-            num_images_per_prompt=num_images_per_prompt,
-            do_classifier_free_guidance=self.do_classifier_free_guidance,
-            negative_prompt=negative_prompt,
-            negative_prompt_2=negative_prompt_2,
-            prompt_embeds=prompt_embeds,
-            negative_prompt_embeds=negative_prompt_embeds,
-            pooled_prompt_embeds=pooled_prompt_embeds,
-            negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
-            lora_scale=text_encoder_lora_scale,
-            clip_skip=self.clip_skip,
-        )
-
-        # 4. set timesteps
-        def denoising_value_valid(dnv):
-            return isinstance(self.denoising_end, float) and 0 < dnv < 1
-
-        timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, num_inference_steps, device, timesteps)
-        timesteps, num_inference_steps = self.get_timesteps(
-            num_inference_steps,
-            strength,
-            device,
-            denoising_start=self.denoising_start if denoising_value_valid else None,
-        )
-        # check that number of inference steps is not < 1 - as this doesn't make sense
-        if num_inference_steps < 1:
-            raise ValueError(
-                f"After adjusting the num_inference_steps by strength parameter: {strength}, the number of pipeline"
-                f"steps is {num_inference_steps} which is < 1 and not appropriate for this pipeline."
-            )
-        # at which timestep to set the initial noise (n.b. 50% if strength is 0.5)
-        latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)
-        # create a boolean to check if the strength is set to 1. if so then initialise the latents with pure noise
-        is_strength_max = strength == 1.0
-
-        # 5. Preprocess mask and image
-        if padding_mask_crop is not None:
-            crops_coords = self.mask_processor.get_crop_region(mask_image, width, height, pad=padding_mask_crop)
-            resize_mode = "fill"
-        else:
-            crops_coords = None
-            resize_mode = "default"
-
-        original_image = image
-        init_image = self.image_processor.preprocess(
-            image, height=height, width=width, crops_coords=crops_coords, resize_mode=resize_mode
-        )
-        init_image = init_image.to(dtype=torch.float32)
-
-        mask = self.mask_processor.preprocess(
-            mask_image, height=height, width=width, resize_mode=resize_mode, crops_coords=crops_coords
-        )
-        if masked_image_latents is not None:
-            masked_image = masked_image_latents
-        elif init_image.shape[1] == 4:
-            # if images are in latent space, we can't mask it
-            masked_image = None
-        else:
-            masked_image = init_image * (mask < 0.5)
-
-        # 6. Prepare latent variables
-        num_channels_latents = self.vae.config.latent_channels
-        num_channels_unet = self.unet.config.in_channels
-        return_image_latents = num_channels_unet == 4
-
-        add_noise = True if self.denoising_start is None else False
-        latents_outputs = self.prepare_latents(
-            batch_size * num_images_per_prompt,
-            num_channels_latents,
-            height,
-            width,
-            prompt_embeds.dtype,
-            device,
-            generator,
-            latents,
-            image=init_image,
-            timestep=latent_timestep,
-            is_strength_max=is_strength_max,
-            add_noise=add_noise,
-            return_noise=True,
-            return_image_latents=return_image_latents,
-        )
-
-        if return_image_latents:
-            latents, noise, image_latents = latents_outputs
-        else:
-            latents, noise = latents_outputs
-
-        # 7. Prepare mask latent variables
-        mask, masked_image_latents = self.prepare_mask_latents(
-            mask,
-            masked_image,
-            batch_size * num_images_per_prompt,
-            height,
-            width,
-            prompt_embeds.dtype,
-            device,
-            generator,
-            self.do_classifier_free_guidance,
-        )
-        pose_img = pose_img.to(device=device, dtype=prompt_embeds.dtype)
-
-        pose_img = self.vae.encode(pose_img).latent_dist.sample()
-        pose_img = pose_img * self.vae.config.scaling_factor
-
-        # pose_img = self._encode_vae_image(pose_img, generator=generator)
-
-        pose_img = (
-                torch.cat([pose_img] * 2) if self.do_classifier_free_guidance else pose_img
-        )
-        cloth = self._encode_vae_image(cloth, generator=generator)
-
-        # # 8. Check that sizes of mask, masked image and latents match
-        # if num_channels_unet == 9:
-        #     # default case for runwayml/stable-diffusion-inpainting
-        #     num_channels_mask = mask.shape[1]
-        #     num_channels_masked_image = masked_image_latents.shape[1]
-        #     if num_channels_latents + num_channels_mask + num_channels_masked_image != self.unet.config.in_channels:
-        #         raise ValueError(
-        #             f"Incorrect configuration settings! The config of `pipeline.unet`: {self.unet.config} expects"
-        #             f" {self.unet.config.in_channels} but received `num_channels_latents`: {num_channels_latents} +"
-        #             f" `num_channels_mask`: {num_channels_mask} + `num_channels_masked_image`: {num_channels_masked_image}"
-        #             f" = {num_channels_latents+num_channels_masked_image+num_channels_mask}. Please verify the config of"
-        #             " `pipeline.unet` or your `mask_image` or `image` input."
-        #         )
-        # elif num_channels_unet != 4:
-        #     raise ValueError(
-        #         f"The unet {self.unet.__class__} should have either 4 or 9 input channels, not {self.unet.config.in_channels}."
-        #     )
-        # 8.1 Prepare extra step kwargs.
-        extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
-
-        # 9. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
-        height, width = latents.shape[-2:]
-        height = height * self.vae_scale_factor
-        width = width * self.vae_scale_factor
-
-        original_size = original_size or (height, width)
-        target_size = target_size or (height, width)
-
-        # 10. Prepare added time ids & embeddings
-        if negative_original_size is None:
-            negative_original_size = original_size
-        if negative_target_size is None:
-            negative_target_size = target_size
-
-        add_text_embeds = pooled_prompt_embeds
-        if self.text_encoder_2 is None:
-            text_encoder_projection_dim = int(pooled_prompt_embeds.shape[-1])
-        else:
-            text_encoder_projection_dim = self.text_encoder_2.config.projection_dim
-
-        add_time_ids, add_neg_time_ids = self._get_add_time_ids(
-            original_size,
-            crops_coords_top_left,
-            target_size,
-            aesthetic_score,
-            negative_aesthetic_score,
-            negative_original_size,
-            negative_crops_coords_top_left,
-            negative_target_size,
-            dtype=prompt_embeds.dtype,
-            text_encoder_projection_dim=text_encoder_projection_dim,
-        )
-        add_time_ids = add_time_ids.repeat(batch_size * num_images_per_prompt, 1)
-
-        if self.do_classifier_free_guidance:
-            prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
-            add_text_embeds = torch.cat([negative_pooled_prompt_embeds, add_text_embeds], dim=0)
-            add_neg_time_ids = add_neg_time_ids.repeat(batch_size * num_images_per_prompt, 1)
-            add_time_ids = torch.cat([add_neg_time_ids, add_time_ids], dim=0)
-
-        prompt_embeds = prompt_embeds.to(device)
-        add_text_embeds = add_text_embeds.to(device)
-        add_time_ids = add_time_ids.to(device)
-
-        if ip_adapter_image is not None:
-            image_embeds = self.prepare_ip_adapter_image_embeds(
-                ip_adapter_image, device, batch_size * num_images_per_prompt
-            )
-
-            #project outside for loop
-            image_embeds = self.unet.encoder_hid_proj(image_embeds).to(prompt_embeds.dtype)
-
-
-        # 11. Denoising loop
-        num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
-
-        if (
-            self.denoising_end is not None
-            and self.denoising_start is not None
-            and denoising_value_valid(self.denoising_end)
-            and denoising_value_valid(self.denoising_start)
-            and self.denoising_start >= self.denoising_end
-        ):
-            raise ValueError(
-                f"`denoising_start`: {self.denoising_start} cannot be larger than or equal to `denoising_end`: "
-                + f" {self.denoising_end} when using type float."
-            )
-        elif self.denoising_end is not None and denoising_value_valid(self.denoising_end):
-            discrete_timestep_cutoff = int(
-                round(
-                    self.scheduler.config.num_train_timesteps
-                    - (self.denoising_end * self.scheduler.config.num_train_timesteps)
-                )
-            )
-            num_inference_steps = len(list(filter(lambda ts: ts >= discrete_timestep_cutoff, timesteps)))
-            timesteps = timesteps[:num_inference_steps]
-
-        # 11.1 Optionally get Guidance Scale Embedding
-        timestep_cond = None
-        if self.unet.config.time_cond_proj_dim is not None:
-            guidance_scale_tensor = torch.tensor(self.guidance_scale - 1).repeat(batch_size * num_images_per_prompt)
-            timestep_cond = self.get_guidance_scale_embedding(
-                guidance_scale_tensor, embedding_dim=self.unet.config.time_cond_proj_dim
-            ).to(device=device, dtype=latents.dtype)
-
-
-
-        self._num_timesteps = len(timesteps)
-        with self.progress_bar(total=num_inference_steps) as progress_bar:
-            for i, t in enumerate(timesteps):
-                if self.interrupt:
-                    continue
-                # expand the latents if we are doing classifier free guidance
-                latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents
-
-                # concat latents, mask, masked_image_latents in the channel dimension
-                latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
-
-
-                # bsz = mask.shape[0]
-                if num_channels_unet == 13:
-                    latent_model_input = torch.cat([latent_model_input, mask, masked_image_latents,pose_img], dim=1)
-
-                # if num_channels_unet == 9:
-                #     latent_model_input = torch.cat([latent_model_input, mask, masked_image_latents], dim=1)
-
-                # predict the noise residual
-                added_cond_kwargs = {"text_embeds": add_text_embeds, "time_ids": add_time_ids}
-                if ip_adapter_image is not None:
-                    added_cond_kwargs["image_embeds"] = image_embeds
-                # down,reference_features = self.UNet_Encoder(cloth,t, text_embeds_cloth,added_cond_kwargs= {"text_embeds": pooled_prompt_embeds_c, "time_ids": add_time_ids},return_dict=False)
-                down,reference_features = self.unet_encoder(cloth,t, text_embeds_cloth,return_dict=False)
-                # print(type(reference_features))
-                # print(reference_features)
-                reference_features = list(reference_features)
-                # print(len(reference_features))
-                # for elem in reference_features:
-                #     print(elem.shape)
-                # exit(1)
-                if self.do_classifier_free_guidance:
-                    reference_features = [torch.cat([torch.zeros_like(d), d]) for d in reference_features]
-
-
-                noise_pred = self.unet(
-                    latent_model_input,
-                    t,
-                    encoder_hidden_states=prompt_embeds,
-                    timestep_cond=timestep_cond,
-                    cross_attention_kwargs=self.cross_attention_kwargs,
-                    added_cond_kwargs=added_cond_kwargs,
-                    return_dict=False,
-                    garment_features=reference_features,
-                )[0]
-                # noise_pred = self.unet(latent_model_input, t, 
-                #                             prompt_embeds,timestep_cond=timestep_cond,cross_attention_kwargs=self.cross_attention_kwargs,added_cond_kwargs=added_cond_kwargs,down_block_additional_attn=down ).sample
-
-
-                # perform guidance
-                if self.do_classifier_free_guidance:
-                    noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
-                    noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond)
-
-                if self.do_classifier_free_guidance and self.guidance_rescale > 0.0:
-                    # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf
-                    noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=self.guidance_rescale)
-
-                # compute the previous noisy sample x_t -> x_t-1
-                latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
-
-                if num_channels_unet == 4:
-                    init_latents_proper = image_latents
-                    if self.do_classifier_free_guidance:
-                        init_mask, _ = mask.chunk(2)
-                    else:
-                        init_mask = mask
-
-                    if i < len(timesteps) - 1:
-                        noise_timestep = timesteps[i + 1]
-                        init_latents_proper = self.scheduler.add_noise(
-                            init_latents_proper, noise, torch.tensor([noise_timestep])
-                        )
-
-                    latents = (1 - init_mask) * init_latents_proper + init_mask * latents
-
-                if callback_on_step_end is not None:
-                    callback_kwargs = {}
-                    for k in callback_on_step_end_tensor_inputs:
-                        callback_kwargs[k] = locals()[k]
-                    callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)
-
-                    latents = callback_outputs.pop("latents", latents)
-                    prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
-                    negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds)
-                    add_text_embeds = callback_outputs.pop("add_text_embeds", add_text_embeds)
-                    negative_pooled_prompt_embeds = callback_outputs.pop(
-                        "negative_pooled_prompt_embeds", negative_pooled_prompt_embeds
-                    )
-                    add_time_ids = callback_outputs.pop("add_time_ids", add_time_ids)
-                    add_neg_time_ids = callback_outputs.pop("add_neg_time_ids", add_neg_time_ids)
-                    mask = callback_outputs.pop("mask", mask)
-                    masked_image_latents = callback_outputs.pop("masked_image_latents", masked_image_latents)
-
-                # 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 XLA_AVAILABLE:
-                    xm.mark_step()
-
-        if not output_type == "latent":
-            # make sure the VAE is in float32 mode, as it overflows in float16
-            needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast
-
-            if needs_upcasting:
-                self.upcast_vae()
-                latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)
-
-            image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
-
-            # cast back to fp16 if needed
-            if needs_upcasting:
-                self.vae.to(dtype=torch.float16)
-        # else:
-        #     return StableDiffusionXLPipelineOutput(images=latents)
-
-
-        image = self.image_processor.postprocess(image, output_type=output_type)
-
-        if padding_mask_crop is not None:
-            image = [self.image_processor.apply_overlay(mask_image, original_image, i, crops_coords) for i in image]
-
-        # Offload all models
-        self.maybe_free_model_hooks()
-
-        # if not return_dict:
-        return (image,)
-
-        # return StableDiffusionXLPipelineOutput(images=image)

src/unet_hacked_garmnet.py

@@ -1,1284 +0,0 @@
-# Copyright 2023 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.
-from dataclasses import dataclass
-from typing import Any, Dict, List, Optional, Tuple, Union
-
-import torch
-import torch.nn as nn
-import torch.utils.checkpoint
-
-from diffusers.configuration_utils import ConfigMixin, register_to_config
-from diffusers.loaders import UNet2DConditionLoadersMixin
-from diffusers.utils import USE_PEFT_BACKEND, BaseOutput, deprecate, logging, scale_lora_layers, unscale_lora_layers
-from diffusers.models.activations import get_activation
-from diffusers.models.attention_processor import (
-    ADDED_KV_ATTENTION_PROCESSORS,
-    CROSS_ATTENTION_PROCESSORS,
-    Attention,
-    AttentionProcessor,
-    AttnAddedKVProcessor,
-    AttnProcessor,
-)
-from einops import rearrange
-
-from diffusers.models.embeddings import (
-    GaussianFourierProjection,
-    ImageHintTimeEmbedding,
-    ImageProjection,
-    ImageTimeEmbedding,
-    PositionNet,
-    TextImageProjection,
-    TextImageTimeEmbedding,
-    TextTimeEmbedding,
-    TimestepEmbedding,
-    Timesteps,
-)
-from diffusers.models.modeling_utils import ModelMixin
-from src.unet_block_hacked_garmnet import (
-    UNetMidBlock2D,
-    UNetMidBlock2DCrossAttn,
-    UNetMidBlock2DSimpleCrossAttn,
-    get_down_block,
-    get_up_block,
-)
-from diffusers.models.resnet import Downsample2D, FirDownsample2D, FirUpsample2D, KDownsample2D, KUpsample2D, ResnetBlock2D, Upsample2D
-from diffusers.models.transformer_2d import Transformer2DModel
-
-
-logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
-
-
-def zero_module(module):
-    for p in module.parameters():
-        nn.init.zeros_(p)
-    return module
-
-@dataclass
-class UNet2DConditionOutput(BaseOutput):
-    """
-    The output of [`UNet2DConditionModel`].
-
-    Args:
-        sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
-            The hidden states output conditioned on `encoder_hidden_states` input. Output of last layer of model.
-    """
-
-    sample: torch.FloatTensor = None
-
-
-class UNet2DConditionModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin):
-    r"""
-    A conditional 2D UNet model that takes a noisy sample, conditional state, and a timestep and returns a sample
-    shaped output.
-
-    This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemented
-    for all models (such as downloading or saving).
-
-    Parameters:
-        sample_size (`int` or `Tuple[int, int]`, *optional*, defaults to `None`):
-            Height and width of input/output sample.
-        in_channels (`int`, *optional*, defaults to 4): Number of channels in the input sample.
-        out_channels (`int`, *optional*, defaults to 4): Number of channels in the output.
-        center_input_sample (`bool`, *optional*, defaults to `False`): Whether to center the input sample.
-        flip_sin_to_cos (`bool`, *optional*, defaults to `False`):
-            Whether to flip the sin to cos in the time embedding.
-        freq_shift (`int`, *optional*, defaults to 0): The frequency shift to apply to the time embedding.
-        down_block_types (`Tuple[str]`, *optional*, defaults to `("CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "DownBlock2D")`):
-            The tuple of downsample blocks to use.
-        mid_block_type (`str`, *optional*, defaults to `"UNetMidBlock2DCrossAttn"`):
-            Block type for middle of UNet, it can be one of `UNetMidBlock2DCrossAttn`, `UNetMidBlock2D`, or
-            `UNetMidBlock2DSimpleCrossAttn`. If `None`, the mid block layer is skipped.
-        up_block_types (`Tuple[str]`, *optional*, defaults to `("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D")`):
-            The tuple of upsample blocks to use.
-        only_cross_attention(`bool` or `Tuple[bool]`, *optional*, default to `False`):
-            Whether to include self-attention in the basic transformer blocks, see
-            [`~models.attention.BasicTransformerBlock`].
-        block_out_channels (`Tuple[int]`, *optional*, defaults to `(320, 640, 1280, 1280)`):
-            The tuple of output channels for each block.
-        layers_per_block (`int`, *optional*, defaults to 2): The number of layers per block.
-        downsample_padding (`int`, *optional*, defaults to 1): The padding to use for the downsampling convolution.
-        mid_block_scale_factor (`float`, *optional*, defaults to 1.0): The scale factor to use for the mid block.
-        dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.
-        act_fn (`str`, *optional*, defaults to `"silu"`): The activation function to use.
-        norm_num_groups (`int`, *optional*, defaults to 32): The number of groups to use for the normalization.
-            If `None`, normalization and activation layers is skipped in post-processing.
-        norm_eps (`float`, *optional*, defaults to 1e-5): The epsilon to use for the normalization.
-        cross_attention_dim (`int` or `Tuple[int]`, *optional*, defaults to 1280):
-            The dimension of the cross attention features.
-        transformer_layers_per_block (`int`, `Tuple[int]`, or `Tuple[Tuple]` , *optional*, defaults to 1):
-            The number of transformer blocks of type [`~models.attention.BasicTransformerBlock`]. Only relevant for
-            [`~models.unet_2d_blocks.CrossAttnDownBlock2D`], [`~models.unet_2d_blocks.CrossAttnUpBlock2D`],
-            [`~models.unet_2d_blocks.UNetMidBlock2DCrossAttn`].
-       reverse_transformer_layers_per_block : (`Tuple[Tuple]`, *optional*, defaults to None):
-            The number of transformer blocks of type [`~models.attention.BasicTransformerBlock`], in the upsampling
-            blocks of the U-Net. Only relevant if `transformer_layers_per_block` is of type `Tuple[Tuple]` and for
-            [`~models.unet_2d_blocks.CrossAttnDownBlock2D`], [`~models.unet_2d_blocks.CrossAttnUpBlock2D`],
-            [`~models.unet_2d_blocks.UNetMidBlock2DCrossAttn`].
-        encoder_hid_dim (`int`, *optional*, defaults to None):
-            If `encoder_hid_dim_type` is defined, `encoder_hidden_states` will be projected from `encoder_hid_dim`
-            dimension to `cross_attention_dim`.
-        encoder_hid_dim_type (`str`, *optional*, defaults to `None`):
-            If given, the `encoder_hidden_states` and potentially other embeddings are down-projected to text
-            embeddings of dimension `cross_attention` according to `encoder_hid_dim_type`.
-        attention_head_dim (`int`, *optional*, defaults to 8): The dimension of the attention heads.
-        num_attention_heads (`int`, *optional*):
-            The number of attention heads. If not defined, defaults to `attention_head_dim`
-        resnet_time_scale_shift (`str`, *optional*, defaults to `"default"`): Time scale shift config
-            for ResNet blocks (see [`~models.resnet.ResnetBlock2D`]). Choose from `default` or `scale_shift`.
-        class_embed_type (`str`, *optional*, defaults to `None`):
-            The type of class embedding to use which is ultimately summed with the time embeddings. Choose from `None`,
-            `"timestep"`, `"identity"`, `"projection"`, or `"simple_projection"`.
-        addition_embed_type (`str`, *optional*, defaults to `None`):
-            Configures an optional embedding which will be summed with the time embeddings. Choose from `None` or
-            "text". "text" will use the `TextTimeEmbedding` layer.
-        addition_time_embed_dim: (`int`, *optional*, defaults to `None`):
-            Dimension for the timestep embeddings.
-        num_class_embeds (`int`, *optional*, defaults to `None`):
-            Input dimension of the learnable embedding matrix to be projected to `time_embed_dim`, when performing
-            class conditioning with `class_embed_type` equal to `None`.
-        time_embedding_type (`str`, *optional*, defaults to `positional`):
-            The type of position embedding to use for timesteps. Choose from `positional` or `fourier`.
-        time_embedding_dim (`int`, *optional*, defaults to `None`):
-            An optional override for the dimension of the projected time embedding.
-        time_embedding_act_fn (`str`, *optional*, defaults to `None`):
-            Optional activation function to use only once on the time embeddings before they are passed to the rest of
-            the UNet. Choose from `silu`, `mish`, `gelu`, and `swish`.
-        timestep_post_act (`str`, *optional*, defaults to `None`):
-            The second activation function to use in timestep embedding. Choose from `silu`, `mish` and `gelu`.
-        time_cond_proj_dim (`int`, *optional*, defaults to `None`):
-            The dimension of `cond_proj` layer in the timestep embedding.
-        conv_in_kernel (`int`, *optional*, default to `3`): The kernel size of `conv_in` layer. conv_out_kernel (`int`,
-        *optional*, default to `3`): The kernel size of `conv_out` layer. projection_class_embeddings_input_dim (`int`,
-        *optional*): The dimension of the `class_labels` input when
-            `class_embed_type="projection"`. Required when `class_embed_type="projection"`.
-        class_embeddings_concat (`bool`, *optional*, defaults to `False`): Whether to concatenate the time
-            embeddings with the class embeddings.
-        mid_block_only_cross_attention (`bool`, *optional*, defaults to `None`):
-            Whether to use cross attention with the mid block when using the `UNetMidBlock2DSimpleCrossAttn`. If
-            `only_cross_attention` is given as a single boolean and `mid_block_only_cross_attention` is `None`, the
-            `only_cross_attention` value is used as the value for `mid_block_only_cross_attention`. Default to `False`
-            otherwise.
-    """
-
-    _supports_gradient_checkpointing = True
-
-    @register_to_config
-    def __init__(
-        self,
-        sample_size: Optional[int] = None,
-        in_channels: int = 4,
-        out_channels: int = 4,
-        center_input_sample: bool = False,
-        flip_sin_to_cos: bool = True,
-        freq_shift: int = 0,
-        down_block_types: Tuple[str] = (
-            "CrossAttnDownBlock2D",
-            "CrossAttnDownBlock2D",
-            "CrossAttnDownBlock2D",
-            "DownBlock2D",
-        ),
-        mid_block_type: Optional[str] = "UNetMidBlock2DCrossAttn",
-        up_block_types: Tuple[str] = ("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D"),
-        only_cross_attention: Union[bool, Tuple[bool]] = False,
-        block_out_channels: Tuple[int] = (320, 640, 1280, 1280),
-        layers_per_block: Union[int, Tuple[int]] = 2,
-        downsample_padding: int = 1,
-        mid_block_scale_factor: float = 1,
-        dropout: float = 0.0,
-        act_fn: str = "silu",
-        norm_num_groups: Optional[int] = 32,
-        norm_eps: float = 1e-5,
-        cross_attention_dim: Union[int, Tuple[int]] = 1280,
-        transformer_layers_per_block: Union[int, Tuple[int], Tuple[Tuple]] = 1,
-        reverse_transformer_layers_per_block: Optional[Tuple[Tuple[int]]] = None,
-        encoder_hid_dim: Optional[int] = None,
-        encoder_hid_dim_type: Optional[str] = None,
-        attention_head_dim: Union[int, Tuple[int]] = 8,
-        num_attention_heads: Optional[Union[int, Tuple[int]]] = None,
-        dual_cross_attention: bool = False,
-        use_linear_projection: bool = False,
-        class_embed_type: Optional[str] = None,
-        addition_embed_type: Optional[str] = None,
-        addition_time_embed_dim: Optional[int] = None,
-        num_class_embeds: Optional[int] = None,
-        upcast_attention: bool = False,
-        resnet_time_scale_shift: str = "default",
-        resnet_skip_time_act: bool = False,
-        resnet_out_scale_factor: int = 1.0,
-        time_embedding_type: str = "positional",
-        time_embedding_dim: Optional[int] = None,
-        time_embedding_act_fn: Optional[str] = None,
-        timestep_post_act: Optional[str] = None,
-        time_cond_proj_dim: Optional[int] = None,
-        conv_in_kernel: int = 3,
-        conv_out_kernel: int = 3,
-        projection_class_embeddings_input_dim: Optional[int] = None,
-        attention_type: str = "default",
-        class_embeddings_concat: bool = False,
-        mid_block_only_cross_attention: Optional[bool] = None,
-        cross_attention_norm: Optional[str] = None,
-        addition_embed_type_num_heads=64,
-    ):
-        super().__init__()
-
-        self.sample_size = sample_size
-
-        if num_attention_heads is not None:
-            raise ValueError(
-                "At the moment it is not possible to define the number of attention heads via `num_attention_heads` because of a naming issue as described in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131. Passing `num_attention_heads` will only be supported in diffusers v0.19."
-            )
-
-        # If `num_attention_heads` is not defined (which is the case for most models)
-        # it will default to `attention_head_dim`. This looks weird upon first reading it and it is.
-        # The reason for this behavior is to correct for incorrectly named variables that were introduced
-        # when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131
-        # Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking
-        # which is why we correct for the naming here.
-        num_attention_heads = num_attention_heads or attention_head_dim
-
-        # Check inputs
-        if len(down_block_types) != len(up_block_types):
-            raise ValueError(
-                f"Must provide the same number of `down_block_types` as `up_block_types`. `down_block_types`: {down_block_types}. `up_block_types`: {up_block_types}."
-            )
-
-        if len(block_out_channels) != len(down_block_types):
-            raise ValueError(
-                f"Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}."
-            )
-
-        if not isinstance(only_cross_attention, bool) and len(only_cross_attention) != len(down_block_types):
-            raise ValueError(
-                f"Must provide the same number of `only_cross_attention` as `down_block_types`. `only_cross_attention`: {only_cross_attention}. `down_block_types`: {down_block_types}."
-            )
-
-        if not isinstance(num_attention_heads, int) and len(num_attention_heads) != len(down_block_types):
-            raise ValueError(
-                f"Must provide the same number of `num_attention_heads` as `down_block_types`. `num_attention_heads`: {num_attention_heads}. `down_block_types`: {down_block_types}."
-            )
-
-        if not isinstance(attention_head_dim, int) and len(attention_head_dim) != len(down_block_types):
-            raise ValueError(
-                f"Must provide the same number of `attention_head_dim` as `down_block_types`. `attention_head_dim`: {attention_head_dim}. `down_block_types`: {down_block_types}."
-            )
-
-        if isinstance(cross_attention_dim, list) and len(cross_attention_dim) != len(down_block_types):
-            raise ValueError(
-                f"Must provide the same number of `cross_attention_dim` as `down_block_types`. `cross_attention_dim`: {cross_attention_dim}. `down_block_types`: {down_block_types}."
-            )
-
-        if not isinstance(layers_per_block, int) and len(layers_per_block) != len(down_block_types):
-            raise ValueError(
-                f"Must provide the same number of `layers_per_block` as `down_block_types`. `layers_per_block`: {layers_per_block}. `down_block_types`: {down_block_types}."
-            )
-        if isinstance(transformer_layers_per_block, list) and reverse_transformer_layers_per_block is None:
-            for layer_number_per_block in transformer_layers_per_block:
-                if isinstance(layer_number_per_block, list):
-                    raise ValueError("Must provide 'reverse_transformer_layers_per_block` if using asymmetrical UNet.")
-
-        # input
-        conv_in_padding = (conv_in_kernel - 1) // 2
-        self.conv_in = nn.Conv2d(
-            in_channels, block_out_channels[0], kernel_size=conv_in_kernel, padding=conv_in_padding
-        )
-
-        # time
-        if time_embedding_type == "fourier":
-            time_embed_dim = time_embedding_dim or block_out_channels[0] * 2
-            if time_embed_dim % 2 != 0:
-                raise ValueError(f"`time_embed_dim` should be divisible by 2, but is {time_embed_dim}.")
-            self.time_proj = GaussianFourierProjection(
-                time_embed_dim // 2, set_W_to_weight=False, log=False, flip_sin_to_cos=flip_sin_to_cos
-            )
-            timestep_input_dim = time_embed_dim
-        elif time_embedding_type == "positional":
-            time_embed_dim = time_embedding_dim or block_out_channels[0] * 4
-
-            self.time_proj = Timesteps(block_out_channels[0], flip_sin_to_cos, freq_shift)
-            timestep_input_dim = block_out_channels[0]
-        else:
-            raise ValueError(
-                f"{time_embedding_type} does not exist. Please make sure to use one of `fourier` or `positional`."
-            )
-
-        self.time_embedding = TimestepEmbedding(
-            timestep_input_dim,
-            time_embed_dim,
-            act_fn=act_fn,
-            post_act_fn=timestep_post_act,
-            cond_proj_dim=time_cond_proj_dim,
-        )
-
-        if encoder_hid_dim_type is None and encoder_hid_dim is not None:
-            encoder_hid_dim_type = "text_proj"
-            self.register_to_config(encoder_hid_dim_type=encoder_hid_dim_type)
-            logger.info("encoder_hid_dim_type defaults to 'text_proj' as `encoder_hid_dim` is defined.")
-
-        if encoder_hid_dim is None and encoder_hid_dim_type is not None:
-            raise ValueError(
-                f"`encoder_hid_dim` has to be defined when `encoder_hid_dim_type` is set to {encoder_hid_dim_type}."
-            )
-
-        if encoder_hid_dim_type == "text_proj":
-            self.encoder_hid_proj = nn.Linear(encoder_hid_dim, cross_attention_dim)
-        elif encoder_hid_dim_type == "text_image_proj":
-            # image_embed_dim DOESN'T have to be `cross_attention_dim`. To not clutter the __init__ too much
-            # they are set to `cross_attention_dim` here as this is exactly the required dimension for the currently only use
-            # case when `addition_embed_type == "text_image_proj"` (Kadinsky 2.1)`
-            self.encoder_hid_proj = TextImageProjection(
-                text_embed_dim=encoder_hid_dim,
-                image_embed_dim=cross_attention_dim,
-                cross_attention_dim=cross_attention_dim,
-            )
-        elif encoder_hid_dim_type == "image_proj":
-            # Kandinsky 2.2
-            self.encoder_hid_proj = ImageProjection(
-                image_embed_dim=encoder_hid_dim,
-                cross_attention_dim=cross_attention_dim,
-            )
-        elif encoder_hid_dim_type is not None:
-            raise ValueError(
-                f"encoder_hid_dim_type: {encoder_hid_dim_type} must be None, 'text_proj' or 'text_image_proj'."
-            )
-        else:
-            self.encoder_hid_proj = None
-
-        # class embedding
-        if class_embed_type is None and num_class_embeds is not None:
-            self.class_embedding = nn.Embedding(num_class_embeds, time_embed_dim)
-        elif class_embed_type == "timestep":
-            self.class_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim, act_fn=act_fn)
-        elif class_embed_type == "identity":
-            self.class_embedding = nn.Identity(time_embed_dim, time_embed_dim)
-        elif class_embed_type == "projection":
-            if projection_class_embeddings_input_dim is None:
-                raise ValueError(
-                    "`class_embed_type`: 'projection' requires `projection_class_embeddings_input_dim` be set"
-                )
-            # The projection `class_embed_type` is the same as the timestep `class_embed_type` except
-            # 1. the `class_labels` inputs are not first converted to sinusoidal embeddings
-            # 2. it projects from an arbitrary input dimension.
-            #
-            # Note that `TimestepEmbedding` is quite general, being mainly linear layers and activations.
-            # When used for embedding actual timesteps, the timesteps are first converted to sinusoidal embeddings.
-            # As a result, `TimestepEmbedding` can be passed arbitrary vectors.
-            self.class_embedding = TimestepEmbedding(projection_class_embeddings_input_dim, time_embed_dim)
-        elif class_embed_type == "simple_projection":
-            if projection_class_embeddings_input_dim is None:
-                raise ValueError(
-                    "`class_embed_type`: 'simple_projection' requires `projection_class_embeddings_input_dim` be set"
-                )
-            self.class_embedding = nn.Linear(projection_class_embeddings_input_dim, time_embed_dim)
-        else:
-            self.class_embedding = None
-
-        if addition_embed_type == "text":
-            if encoder_hid_dim is not None:
-                text_time_embedding_from_dim = encoder_hid_dim
-            else:
-                text_time_embedding_from_dim = cross_attention_dim
-
-            self.add_embedding = TextTimeEmbedding(
-                text_time_embedding_from_dim, time_embed_dim, num_heads=addition_embed_type_num_heads
-            )
-        elif addition_embed_type == "text_image":
-            # text_embed_dim and image_embed_dim DON'T have to be `cross_attention_dim`. To not clutter the __init__ too much
-            # they are set to `cross_attention_dim` here as this is exactly the required dimension for the currently only use
-            # case when `addition_embed_type == "text_image"` (Kadinsky 2.1)`
-            self.add_embedding = TextImageTimeEmbedding(
-                text_embed_dim=cross_attention_dim, image_embed_dim=cross_attention_dim, time_embed_dim=time_embed_dim
-            )
-        elif addition_embed_type == "text_time":
-            self.add_time_proj = Timesteps(addition_time_embed_dim, flip_sin_to_cos, freq_shift)
-            self.add_embedding = TimestepEmbedding(projection_class_embeddings_input_dim, time_embed_dim)
-        elif addition_embed_type == "image":
-            # Kandinsky 2.2
-            self.add_embedding = ImageTimeEmbedding(image_embed_dim=encoder_hid_dim, time_embed_dim=time_embed_dim)
-        elif addition_embed_type == "image_hint":
-            # Kandinsky 2.2 ControlNet
-            self.add_embedding = ImageHintTimeEmbedding(image_embed_dim=encoder_hid_dim, time_embed_dim=time_embed_dim)
-        elif addition_embed_type is not None:
-            raise ValueError(f"addition_embed_type: {addition_embed_type} must be None, 'text' or 'text_image'.")
-
-        if time_embedding_act_fn is None:
-            self.time_embed_act = None
-        else:
-            self.time_embed_act = get_activation(time_embedding_act_fn)
-
-        self.down_blocks = nn.ModuleList([])
-        self.up_blocks = nn.ModuleList([])
-
-        if isinstance(only_cross_attention, bool):
-            if mid_block_only_cross_attention is None:
-                mid_block_only_cross_attention = only_cross_attention
-
-            only_cross_attention = [only_cross_attention] * len(down_block_types)
-
-        if mid_block_only_cross_attention is None:
-            mid_block_only_cross_attention = False
-
-        if isinstance(num_attention_heads, int):
-            num_attention_heads = (num_attention_heads,) * len(down_block_types)
-
-        if isinstance(attention_head_dim, int):
-            attention_head_dim = (attention_head_dim,) * len(down_block_types)
-
-        if isinstance(cross_attention_dim, int):
-            cross_attention_dim = (cross_attention_dim,) * len(down_block_types)
-
-        if isinstance(layers_per_block, int):
-            layers_per_block = [layers_per_block] * len(down_block_types)
-
-        if isinstance(transformer_layers_per_block, int):
-            transformer_layers_per_block = [transformer_layers_per_block] * len(down_block_types)
-        if class_embeddings_concat:
-            # The time embeddings are concatenated with the class embeddings. The dimension of the
-            # time embeddings passed to the down, middle, and up blocks is twice the dimension of the
-            # regular time embeddings
-            blocks_time_embed_dim = time_embed_dim * 2
-        else:
-            blocks_time_embed_dim = time_embed_dim
-
-        # down
-        output_channel = block_out_channels[0]
-        for i, down_block_type in enumerate(down_block_types):
-            input_channel = output_channel
-            output_channel = block_out_channels[i]
-            is_final_block = i == len(block_out_channels) - 1
-
-            down_block = get_down_block(
-                down_block_type,
-                num_layers=layers_per_block[i],
-                transformer_layers_per_block=transformer_layers_per_block[i],
-                in_channels=input_channel,
-                out_channels=output_channel,
-                temb_channels=blocks_time_embed_dim,
-                add_downsample=not is_final_block,
-                resnet_eps=norm_eps,
-                resnet_act_fn=act_fn,
-                resnet_groups=norm_num_groups,
-                cross_attention_dim=cross_attention_dim[i],
-                num_attention_heads=num_attention_heads[i],
-                downsample_padding=downsample_padding,
-                dual_cross_attention=dual_cross_attention,
-                use_linear_projection=use_linear_projection,
-                only_cross_attention=only_cross_attention[i],
-                upcast_attention=upcast_attention,
-                resnet_time_scale_shift=resnet_time_scale_shift,
-                attention_type=attention_type,
-                resnet_skip_time_act=resnet_skip_time_act,
-                resnet_out_scale_factor=resnet_out_scale_factor,
-                cross_attention_norm=cross_attention_norm,
-                attention_head_dim=attention_head_dim[i] if attention_head_dim[i] is not None else output_channel,
-                dropout=dropout,
-            )
-            self.down_blocks.append(down_block)
-
-        # mid
-        if mid_block_type == "UNetMidBlock2DCrossAttn":
-            self.mid_block = UNetMidBlock2DCrossAttn(
-                transformer_layers_per_block=transformer_layers_per_block[-1],
-                in_channels=block_out_channels[-1],
-                temb_channels=blocks_time_embed_dim,
-                dropout=dropout,
-                resnet_eps=norm_eps,
-                resnet_act_fn=act_fn,
-                output_scale_factor=mid_block_scale_factor,
-                resnet_time_scale_shift=resnet_time_scale_shift,
-                cross_attention_dim=cross_attention_dim[-1],
-                num_attention_heads=num_attention_heads[-1],
-                resnet_groups=norm_num_groups,
-                dual_cross_attention=dual_cross_attention,
-                use_linear_projection=use_linear_projection,
-                upcast_attention=upcast_attention,
-                attention_type=attention_type,
-            )
-        elif mid_block_type == "UNetMidBlock2DSimpleCrossAttn":
-            self.mid_block = UNetMidBlock2DSimpleCrossAttn(
-                in_channels=block_out_channels[-1],
-                temb_channels=blocks_time_embed_dim,
-                dropout=dropout,
-                resnet_eps=norm_eps,
-                resnet_act_fn=act_fn,
-                output_scale_factor=mid_block_scale_factor,
-                cross_attention_dim=cross_attention_dim[-1],
-                attention_head_dim=attention_head_dim[-1],
-                resnet_groups=norm_num_groups,
-                resnet_time_scale_shift=resnet_time_scale_shift,
-                skip_time_act=resnet_skip_time_act,
-                only_cross_attention=mid_block_only_cross_attention,
-                cross_attention_norm=cross_attention_norm,
-            )
-        elif mid_block_type == "UNetMidBlock2D":
-            self.mid_block = UNetMidBlock2D(
-                in_channels=block_out_channels[-1],
-                temb_channels=blocks_time_embed_dim,
-                dropout=dropout,
-                num_layers=0,
-                resnet_eps=norm_eps,
-                resnet_act_fn=act_fn,
-                output_scale_factor=mid_block_scale_factor,
-                resnet_groups=norm_num_groups,
-                resnet_time_scale_shift=resnet_time_scale_shift,
-                add_attention=False,
-            )
-        elif mid_block_type is None:
-            self.mid_block = None
-        else:
-            raise ValueError(f"unknown mid_block_type : {mid_block_type}")
-
-        # count how many layers upsample the images
-        self.num_upsamplers = 0
-
-        # up
-        reversed_block_out_channels = list(reversed(block_out_channels))
-        reversed_num_attention_heads = list(reversed(num_attention_heads))
-        reversed_layers_per_block = list(reversed(layers_per_block))
-        reversed_cross_attention_dim = list(reversed(cross_attention_dim))
-        reversed_transformer_layers_per_block = (
-            list(reversed(transformer_layers_per_block))
-            if reverse_transformer_layers_per_block is None
-            else reverse_transformer_layers_per_block
-        )
-        only_cross_attention = list(reversed(only_cross_attention))
-
-        output_channel = reversed_block_out_channels[0]
-        for i, up_block_type in enumerate(up_block_types):
-            is_final_block = i == len(block_out_channels) - 1
-
-            prev_output_channel = output_channel
-            output_channel = reversed_block_out_channels[i]
-            input_channel = reversed_block_out_channels[min(i + 1, len(block_out_channels) - 1)]
-
-            # add upsample block for all BUT final layer
-            if not is_final_block:
-                add_upsample = True
-                self.num_upsamplers += 1
-            else:
-                add_upsample = False
-            up_block = get_up_block(
-                up_block_type,
-                num_layers=reversed_layers_per_block[i] + 1,
-                transformer_layers_per_block=reversed_transformer_layers_per_block[i],
-                in_channels=input_channel,
-                out_channels=output_channel,
-                prev_output_channel=prev_output_channel,
-                temb_channels=blocks_time_embed_dim,
-                add_upsample=add_upsample,
-                resnet_eps=norm_eps,
-                resnet_act_fn=act_fn,
-                resolution_idx=i,
-                resnet_groups=norm_num_groups,
-                cross_attention_dim=reversed_cross_attention_dim[i],
-                num_attention_heads=reversed_num_attention_heads[i],
-                dual_cross_attention=dual_cross_attention,
-                use_linear_projection=use_linear_projection,
-                only_cross_attention=only_cross_attention[i],
-                upcast_attention=upcast_attention,
-                resnet_time_scale_shift=resnet_time_scale_shift,
-                attention_type=attention_type,
-                resnet_skip_time_act=resnet_skip_time_act,
-                resnet_out_scale_factor=resnet_out_scale_factor,
-                cross_attention_norm=cross_attention_norm,
-                attention_head_dim=attention_head_dim[i] if attention_head_dim[i] is not None else output_channel,
-                dropout=dropout,
-            )
-
-            self.up_blocks.append(up_block)
-            prev_output_channel = output_channel
-
-
-
-
-        # encode_output_chs = [
-        #     # 320,
-        #     # 320,
-        #     # 320,
-        #     1280, 
-        #     1280, 
-        #     1280, 
-        #     1280,
-        #     640,
-        #     640
-        # ]
-
-        # encode_output_chs2 = [
-        #     # 320,
-        #     # 320,
-        #     # 320,
-        #     1280, 
-        #     1280,
-        #     640, 
-        #     640, 
-        #     640,
-        #     320
-        # ]
-
-        # encode_num_head_chs3 = [
-        #     # 5,
-        #     # 5,
-        #     # 10,
-        #     20,
-        #     20, 
-        #     20,
-        #     10,
-        #     10, 
-        #     10 
-        # ]
-
-
-        # encode_num_layers_chs4 = [
-        #     # 1,
-        #     # 1,
-        #     # 2,
-        #     10,
-        #     10, 
-        #     10,
-        #     2,
-        #     2, 
-        #     2 
-        # ]
-
-
-        # self.warp_blks = nn.ModuleList([])
-        # self.warp_zeros = nn.ModuleList([])
-
-        # for in_ch, cont_ch,num_head,num_layers in zip(encode_output_chs, encode_output_chs2,encode_num_head_chs3,encode_num_layers_chs4):
-        #     # dim_head = in_ch // self.num_heads
-        #     # dim_head = dim_head // dim_head_denorm
-
-        #     self.warp_blks.append(Transformer2DModel(
-        #     num_attention_heads=num_head,
-        #     attention_head_dim=64,
-        #     in_channels=in_ch,
-        #     num_layers = num_layers,
-        #     cross_attention_dim = cont_ch,
-        #     ))
-            
-        #     self.warp_zeros.append(zero_module(nn.Conv2d(in_ch, in_ch, 1, padding=0)))
-
-
-
-        # out
-        if norm_num_groups is not None:
-            self.conv_norm_out = nn.GroupNorm(
-                num_channels=block_out_channels[0], num_groups=norm_num_groups, eps=norm_eps
-            )
-
-            self.conv_act = get_activation(act_fn)
-
-        else:
-            self.conv_norm_out = None
-            self.conv_act = None
-
-        conv_out_padding = (conv_out_kernel - 1) // 2
-        self.conv_out = nn.Conv2d(
-            block_out_channels[0], out_channels, kernel_size=conv_out_kernel, padding=conv_out_padding
-        )
-
-        if attention_type in ["gated", "gated-text-image"]:
-            positive_len = 768
-            if isinstance(cross_attention_dim, int):
-                positive_len = cross_attention_dim
-            elif isinstance(cross_attention_dim, tuple) or isinstance(cross_attention_dim, list):
-                positive_len = cross_attention_dim[0]
-
-            feature_type = "text-only" if attention_type == "gated" else "text-image"
-            self.position_net = PositionNet(
-                positive_len=positive_len, out_dim=cross_attention_dim, feature_type=feature_type
-            )
-
-
-
-
-    @property
-    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(return_deprecated_lora=True)
-
-            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
-
-    def set_attn_processor(
-        self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]], _remove_lora=False
-    ):
-        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, _remove_lora=_remove_lora)
-                else:
-                    module.set_processor(processor.pop(f"{name}.processor"), _remove_lora=_remove_lora)
-
-            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.
-        """
-        if all(proc.__class__ in ADDED_KV_ATTENTION_PROCESSORS for proc in self.attn_processors.values()):
-            processor = AttnAddedKVProcessor()
-        elif all(proc.__class__ in CROSS_ATTENTION_PROCESSORS for proc in self.attn_processors.values()):
-            processor = AttnProcessor()
-        else:
-            raise ValueError(
-                f"Cannot call `set_default_attn_processor` when attention processors are of type {next(iter(self.attn_processors.values()))}"
-            )
-
-        self.set_attn_processor(processor, _remove_lora=True)
-
-    def set_attention_slice(self, slice_size):
-        r"""
-        Enable sliced attention computation.
-
-        When this option is enabled, the attention module splits the input tensor in slices to compute attention in
-        several steps. This is useful for saving some memory in exchange for a small decrease in speed.
-
-        Args:
-            slice_size (`str` or `int` or `list(int)`, *optional*, defaults to `"auto"`):
-                When `"auto"`, input to the attention heads is halved, so attention is computed in two steps. If
-                `"max"`, maximum amount of memory is saved by running only one slice at a time. If a number is
-                provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim`
-                must be a multiple of `slice_size`.
-        """
-        sliceable_head_dims = []
-
-        def fn_recursive_retrieve_sliceable_dims(module: torch.nn.Module):
-            if hasattr(module, "set_attention_slice"):
-                sliceable_head_dims.append(module.sliceable_head_dim)
-
-            for child in module.children():
-                fn_recursive_retrieve_sliceable_dims(child)
-
-        # retrieve number of attention layers
-        for module in self.children():
-            fn_recursive_retrieve_sliceable_dims(module)
-
-        num_sliceable_layers = len(sliceable_head_dims)
-
-        if slice_size == "auto":
-            # half the attention head size is usually a good trade-off between
-            # speed and memory
-            slice_size = [dim // 2 for dim in sliceable_head_dims]
-        elif slice_size == "max":
-            # make smallest slice possible
-            slice_size = num_sliceable_layers * [1]
-
-        slice_size = num_sliceable_layers * [slice_size] if not isinstance(slice_size, list) else slice_size
-
-        if len(slice_size) != len(sliceable_head_dims):
-            raise ValueError(
-                f"You have provided {len(slice_size)}, but {self.config} has {len(sliceable_head_dims)} different"
-                f" attention layers. Make sure to match `len(slice_size)` to be {len(sliceable_head_dims)}."
-            )
-
-        for i in range(len(slice_size)):
-            size = slice_size[i]
-            dim = sliceable_head_dims[i]
-            if size is not None and size > dim:
-                raise ValueError(f"size {size} has to be smaller or equal to {dim}.")
-
-        # Recursively walk through all the children.
-        # Any children which exposes the set_attention_slice method
-        # gets the message
-        def fn_recursive_set_attention_slice(module: torch.nn.Module, slice_size: List[int]):
-            if hasattr(module, "set_attention_slice"):
-                module.set_attention_slice(slice_size.pop())
-
-            for child in module.children():
-                fn_recursive_set_attention_slice(child, slice_size)
-
-        reversed_slice_size = list(reversed(slice_size))
-        for module in self.children():
-            fn_recursive_set_attention_slice(module, reversed_slice_size)
-
-    def _set_gradient_checkpointing(self, module, value=False):
-        if hasattr(module, "gradient_checkpointing"):
-            module.gradient_checkpointing = value
-
-    def enable_freeu(self, s1, s2, b1, b2):
-        r"""Enables the FreeU mechanism from https://arxiv.org/abs/2309.11497.
-
-        The suffixes after the scaling factors represent the stage blocks where they are being applied.
-
-        Please refer to the [official repository](https://github.com/ChenyangSi/FreeU) for combinations of values that
-        are known to work well for different pipelines such as Stable Diffusion v1, v2, and Stable Diffusion XL.
-
-        Args:
-            s1 (`float`):
-                Scaling factor for stage 1 to attenuate the contributions of the skip features. This is done to
-                mitigate the "oversmoothing effect" in the enhanced denoising process.
-            s2 (`float`):
-                Scaling factor for stage 2 to attenuate the contributions of the skip features. This is done to
-                mitigate the "oversmoothing effect" in the enhanced denoising process.
-            b1 (`float`): Scaling factor for stage 1 to amplify the contributions of backbone features.
-            b2 (`float`): Scaling factor for stage 2 to amplify the contributions of backbone features.
-        """
-        for i, upsample_block in enumerate(self.up_blocks):
-            setattr(upsample_block, "s1", s1)
-            setattr(upsample_block, "s2", s2)
-            setattr(upsample_block, "b1", b1)
-            setattr(upsample_block, "b2", b2)
-
-    def disable_freeu(self):
-        """Disables the FreeU mechanism."""
-        freeu_keys = {"s1", "s2", "b1", "b2"}
-        for i, upsample_block in enumerate(self.up_blocks):
-            for k in freeu_keys:
-                if hasattr(upsample_block, k) or getattr(upsample_block, k, None) is not None:
-                    setattr(upsample_block, k, None)
-
-    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)
-
-    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,
-        sample: torch.FloatTensor,
-        timestep: Union[torch.Tensor, float, int],
-        encoder_hidden_states: torch.Tensor,
-        class_labels: Optional[torch.Tensor] = None,
-        timestep_cond: Optional[torch.Tensor] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
-        added_cond_kwargs: Optional[Dict[str, torch.Tensor]] = None,
-        down_block_additional_residuals: Optional[Tuple[torch.Tensor]] = None,
-        mid_block_additional_residual: Optional[torch.Tensor] = None,
-        down_intrablock_additional_residuals: Optional[Tuple[torch.Tensor]] = None,
-        encoder_attention_mask: Optional[torch.Tensor] = None,
-        return_dict: bool = True,
-    ) -> Union[UNet2DConditionOutput, Tuple]:
-        r"""
-        The [`UNet2DConditionModel`] forward method.
-
-        Args:
-            sample (`torch.FloatTensor`):
-                The noisy input tensor with the following shape `(batch, channel, height, width)`.
-            timestep (`torch.FloatTensor` or `float` or `int`): The number of timesteps to denoise an input.
-            encoder_hidden_states (`torch.FloatTensor`):
-                The encoder hidden states with shape `(batch, sequence_length, feature_dim)`.
-            class_labels (`torch.Tensor`, *optional*, defaults to `None`):
-                Optional class labels for conditioning. Their embeddings will be summed with the timestep embeddings.
-            timestep_cond: (`torch.Tensor`, *optional*, defaults to `None`):
-                Conditional embeddings for timestep. If provided, the embeddings will be summed with the samples passed
-                through the `self.time_embedding` layer to obtain the timestep embeddings.
-            attention_mask (`torch.Tensor`, *optional*, defaults to `None`):
-                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.
-            cross_attention_kwargs (`dict`, *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).
-            added_cond_kwargs: (`dict`, *optional*):
-                A kwargs dictionary containing additional embeddings that if specified are added to the embeddings that
-                are passed along to the UNet blocks.
-            down_block_additional_residuals: (`tuple` of `torch.Tensor`, *optional*):
-                A tuple of tensors that if specified are added to the residuals of down unet blocks.
-            mid_block_additional_residual: (`torch.Tensor`, *optional*):
-                A tensor that if specified is added to the residual of the middle unet block.
-            encoder_attention_mask (`torch.Tensor`):
-                A cross-attention mask of shape `(batch, sequence_length)` is applied to `encoder_hidden_states`. If
-                `True` the mask is kept, otherwise if `False` it is discarded. Mask will be converted into a bias,
-                which adds large negative values to the attention scores corresponding to "discard" tokens.
-            return_dict (`bool`, *optional*, defaults to `True`):
-                Whether or not to return a [`~models.unet_2d_condition.UNet2DConditionOutput`] instead of a plain
-                tuple.
-            cross_attention_kwargs (`dict`, *optional*):
-                A kwargs dictionary that if specified is passed along to the [`AttnProcessor`].
-            added_cond_kwargs: (`dict`, *optional*):
-                A kwargs dictionary containin additional embeddings that if specified are added to the embeddings that
-                are passed along to the UNet blocks.
-            down_block_additional_residuals (`tuple` of `torch.Tensor`, *optional*):
-                additional residuals to be added to UNet long skip connections from down blocks to up blocks for
-                example from ControlNet side model(s)
-            mid_block_additional_residual (`torch.Tensor`, *optional*):
-                additional residual to be added to UNet mid block output, for example from ControlNet side model
-            down_intrablock_additional_residuals (`tuple` of `torch.Tensor`, *optional*):
-                additional residuals to be added within UNet down blocks, for example from T2I-Adapter side model(s)
-
-        Returns:
-            [`~models.unet_2d_condition.UNet2DConditionOutput`] or `tuple`:
-                If `return_dict` is True, an [`~models.unet_2d_condition.UNet2DConditionOutput`] is returned, otherwise
-                a `tuple` is returned where the first element is the sample tensor.
-        """
-        # By default samples have to be AT least a multiple of the overall upsampling factor.
-        # The overall upsampling factor is equal to 2 ** (# num of upsampling layers).
-        # However, the upsampling interpolation output size can be forced to fit any upsampling size
-        # on the fly if necessary.
-        default_overall_up_factor = 2**self.num_upsamplers
-
-        # upsample size should be forwarded when sample is not a multiple of `default_overall_up_factor`
-        forward_upsample_size = False
-        upsample_size = None
-
-        for dim in sample.shape[-2:]:
-            if dim % default_overall_up_factor != 0:
-                # Forward upsample size to force interpolation output size.
-                forward_upsample_size = True
-                break
-
-        # ensure attention_mask is a bias, and give it a singleton query_tokens dimension
-        # 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:
-            # 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(sample.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:
-            encoder_attention_mask = (1 - encoder_attention_mask.to(sample.dtype)) * -10000.0
-            encoder_attention_mask = encoder_attention_mask.unsqueeze(1)
-
-        # 0. center input if necessary
-        if self.config.center_input_sample:
-            sample = 2 * sample - 1.0
-
-        # 1. time
-        timesteps = timestep
-        if not torch.is_tensor(timesteps):
-            # 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 = sample.device.type == "mps"
-            if isinstance(timestep, float):
-                dtype = torch.float32 if is_mps else torch.float64
-            else:
-                dtype = torch.int32 if is_mps else torch.int64
-            timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device)
-        elif len(timesteps.shape) == 0:
-            timesteps = timesteps[None].to(sample.device)
-
-        # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
-        timesteps = timesteps.expand(sample.shape[0])
-
-        t_emb = self.time_proj(timesteps)
-
-        # `Timesteps` does not contain any weights and will always return f32 tensors
-        # but time_embedding might actually be running in fp16. so we need to cast here.
-        # there might be better ways to encapsulate this.
-        t_emb = t_emb.to(dtype=sample.dtype)
-
-        emb = self.time_embedding(t_emb, timestep_cond)
-        aug_emb = None
-
-        if self.class_embedding is not None:
-            if class_labels is None:
-                raise ValueError("class_labels should be provided when num_class_embeds > 0")
-
-            if self.config.class_embed_type == "timestep":
-                class_labels = self.time_proj(class_labels)
-
-                # `Timesteps` does not contain any weights and will always return f32 tensors
-                # there might be better ways to encapsulate this.
-                class_labels = class_labels.to(dtype=sample.dtype)
-
-            class_emb = self.class_embedding(class_labels).to(dtype=sample.dtype)
-
-            if self.config.class_embeddings_concat:
-                emb = torch.cat([emb, class_emb], dim=-1)
-            else:
-                emb = emb + class_emb
-
-        if self.config.addition_embed_type == "text":
-            aug_emb = self.add_embedding(encoder_hidden_states)
-        elif self.config.addition_embed_type == "text_image":
-            # Kandinsky 2.1 - style
-            if "image_embeds" not in added_cond_kwargs:
-                raise ValueError(
-                    f"{self.__class__} has the config param `addition_embed_type` set to 'text_image' which requires the keyword argument `image_embeds` to be passed in `added_cond_kwargs`"
-                )
-
-            image_embs = added_cond_kwargs.get("image_embeds")
-            text_embs = added_cond_kwargs.get("text_embeds", encoder_hidden_states)
-            aug_emb = self.add_embedding(text_embs, image_embs)
-        elif self.config.addition_embed_type == "text_time":
-            # SDXL - style
-            if "text_embeds" not in added_cond_kwargs:
-                raise ValueError(
-                    f"{self.__class__} has the config param `addition_embed_type` set to 'text_time' which requires the keyword argument `text_embeds` to be passed in `added_cond_kwargs`"
-                )
-            text_embeds = added_cond_kwargs.get("text_embeds")
-            if "time_ids" not in added_cond_kwargs:
-                raise ValueError(
-                    f"{self.__class__} has the config param `addition_embed_type` set to 'text_time' which requires the keyword argument `time_ids` to be passed in `added_cond_kwargs`"
-                )
-            time_ids = added_cond_kwargs.get("time_ids")
-            time_embeds = self.add_time_proj(time_ids.flatten())
-            time_embeds = time_embeds.reshape((text_embeds.shape[0], -1))
-            add_embeds = torch.concat([text_embeds, time_embeds], dim=-1)
-            add_embeds = add_embeds.to(emb.dtype)
-            aug_emb = self.add_embedding(add_embeds)
-        elif self.config.addition_embed_type == "image":
-            # Kandinsky 2.2 - style
-            if "image_embeds" not in added_cond_kwargs:
-                raise ValueError(
-                    f"{self.__class__} has the config param `addition_embed_type` set to 'image' which requires the keyword argument `image_embeds` to be passed in `added_cond_kwargs`"
-                )
-            image_embs = added_cond_kwargs.get("image_embeds")
-            aug_emb = self.add_embedding(image_embs)
-        elif self.config.addition_embed_type == "image_hint":
-            # Kandinsky 2.2 - style
-            if "image_embeds" not in added_cond_kwargs or "hint" not in added_cond_kwargs:
-                raise ValueError(
-                    f"{self.__class__} has the config param `addition_embed_type` set to 'image_hint' which requires the keyword arguments `image_embeds` and `hint` to be passed in `added_cond_kwargs`"
-                )
-            image_embs = added_cond_kwargs.get("image_embeds")
-            hint = added_cond_kwargs.get("hint")
-            aug_emb, hint = self.add_embedding(image_embs, hint)
-            sample = torch.cat([sample, hint], dim=1)
-
-        emb = emb + aug_emb if aug_emb is not None else emb
-
-        if self.time_embed_act is not None:
-            emb = self.time_embed_act(emb)
-
-        if self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == "text_proj":
-            encoder_hidden_states = self.encoder_hid_proj(encoder_hidden_states)
-        elif self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == "text_image_proj":
-            # Kadinsky 2.1 - style
-            if "image_embeds" not in added_cond_kwargs:
-                raise ValueError(
-                    f"{self.__class__} has the config param `encoder_hid_dim_type` set to 'text_image_proj' which requires the keyword argument `image_embeds` to be passed in  `added_conditions`"
-                )
-
-            image_embeds = added_cond_kwargs.get("image_embeds")
-            encoder_hidden_states = self.encoder_hid_proj(encoder_hidden_states, image_embeds)
-        elif self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == "image_proj":
-            # Kandinsky 2.2 - style
-            if "image_embeds" not in added_cond_kwargs:
-                raise ValueError(
-                    f"{self.__class__} has the config param `encoder_hid_dim_type` set to 'image_proj' which requires the keyword argument `image_embeds` to be passed in  `added_conditions`"
-                )
-            image_embeds = added_cond_kwargs.get("image_embeds")
-            encoder_hidden_states = self.encoder_hid_proj(image_embeds)
-        elif self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == "ip_image_proj":
-            if "image_embeds" not in added_cond_kwargs:
-                raise ValueError(
-                    f"{self.__class__} has the config param `encoder_hid_dim_type` set to 'ip_image_proj' which requires the keyword argument `image_embeds` to be passed in  `added_conditions`"
-                )
-            image_embeds = added_cond_kwargs.get("image_embeds")
-            image_embeds = self.encoder_hid_proj(image_embeds).to(encoder_hidden_states.dtype)
-            encoder_hidden_states = torch.cat([encoder_hidden_states, image_embeds], dim=1)
-
-        # 2. pre-process
-        sample = self.conv_in(sample)
-        garment_features=[]
-
-        # 2.5 GLIGEN position net
-        if cross_attention_kwargs is not None and cross_attention_kwargs.get("gligen", None) is not None:
-            cross_attention_kwargs = cross_attention_kwargs.copy()
-            gligen_args = cross_attention_kwargs.pop("gligen")
-            cross_attention_kwargs["gligen"] = {"objs": self.position_net(**gligen_args)}
-
-
-        # 3. down
-        lora_scale = cross_attention_kwargs.get("scale", 1.0) if cross_attention_kwargs is not None else 1.0
-        if USE_PEFT_BACKEND:
-            # weight the lora layers by setting `lora_scale` for each PEFT layer
-            scale_lora_layers(self, lora_scale)
-
-        is_controlnet = mid_block_additional_residual is not None and down_block_additional_residuals is not None
-        # using new arg down_intrablock_additional_residuals for T2I-Adapters, to distinguish from controlnets
-        is_adapter = down_intrablock_additional_residuals is not None
-        # maintain backward compatibility for legacy usage, where
-        #       T2I-Adapter and ControlNet both use down_block_additional_residuals arg
-        #       but can only use one or the other
-        if not is_adapter and mid_block_additional_residual is None and down_block_additional_residuals is not None:
-            deprecate(
-                "T2I should not use down_block_additional_residuals",
-                "1.3.0",
-                "Passing intrablock residual connections with `down_block_additional_residuals` is deprecated \
-                       and will be removed in diffusers 1.3.0.  `down_block_additional_residuals` should only be used \
-                       for ControlNet. Please make sure use `down_intrablock_additional_residuals` instead. ",
-                standard_warn=False,
-            )
-            down_intrablock_additional_residuals = down_block_additional_residuals
-            is_adapter = True
-
-        down_block_res_samples = (sample,)
-        for downsample_block in self.down_blocks:
-            if hasattr(downsample_block, "has_cross_attention") and downsample_block.has_cross_attention:
-                # For t2i-adapter CrossAttnDownBlock2D
-                additional_residuals = {}
-                if is_adapter and len(down_intrablock_additional_residuals) > 0:
-                    additional_residuals["additional_residuals"] = down_intrablock_additional_residuals.pop(0)
-
-                sample, res_samples,out_garment_feat = downsample_block(
-                    hidden_states=sample,
-                    temb=emb,
-                    encoder_hidden_states=encoder_hidden_states,
-                    attention_mask=attention_mask,
-                    cross_attention_kwargs=cross_attention_kwargs,
-                    encoder_attention_mask=encoder_attention_mask,
-                    **additional_residuals,
-                )
-                garment_features += out_garment_feat
-            else:
-                sample, res_samples = downsample_block(hidden_states=sample, temb=emb, scale=lora_scale)
-                if is_adapter and len(down_intrablock_additional_residuals) > 0:
-                    sample += down_intrablock_additional_residuals.pop(0)
-
-            down_block_res_samples += res_samples
-        
-
-        if is_controlnet:
-            new_down_block_res_samples = ()
-
-            for down_block_res_sample, down_block_additional_residual in zip(
-                down_block_res_samples, down_block_additional_residuals
-            ):
-                down_block_res_sample = down_block_res_sample + down_block_additional_residual
-                new_down_block_res_samples = new_down_block_res_samples + (down_block_res_sample,)
-
-            down_block_res_samples = new_down_block_res_samples
-
-        # 4. mid
-        if self.mid_block is not None:
-            if hasattr(self.mid_block, "has_cross_attention") and self.mid_block.has_cross_attention:
-                sample,out_garment_feat = self.mid_block(
-                    sample,
-                    emb,
-                    encoder_hidden_states=encoder_hidden_states,
-                    attention_mask=attention_mask,
-                    cross_attention_kwargs=cross_attention_kwargs,
-                    encoder_attention_mask=encoder_attention_mask,
-                )
-                garment_features += out_garment_feat
-
-            else:
-                sample = self.mid_block(sample, emb)
-
-            # To support T2I-Adapter-XL
-            if (
-                is_adapter
-                and len(down_intrablock_additional_residuals) > 0
-                and sample.shape == down_intrablock_additional_residuals[0].shape
-            ):
-                sample += down_intrablock_additional_residuals.pop(0)
-
-        if is_controlnet:
-            sample = sample + mid_block_additional_residual
-
-
-
-        # 5. up
-        for i, upsample_block in enumerate(self.up_blocks):
-            is_final_block = i == len(self.up_blocks) - 1
-
-            res_samples = down_block_res_samples[-len(upsample_block.resnets) :]
-            down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)]
-
-            # if we have not reached the final block and need to forward the
-            # upsample size, we do it here
-            if not is_final_block and forward_upsample_size:
-                upsample_size = down_block_res_samples[-1].shape[2:]
-
-            if hasattr(upsample_block, "has_cross_attention") and upsample_block.has_cross_attention:
-                sample,out_garment_feat = upsample_block(
-                    hidden_states=sample,
-                    temb=emb,
-                    res_hidden_states_tuple=res_samples,
-                    encoder_hidden_states=encoder_hidden_states,
-                    cross_attention_kwargs=cross_attention_kwargs,
-                    upsample_size=upsample_size,
-                    attention_mask=attention_mask,
-                    encoder_attention_mask=encoder_attention_mask,
-                )
-                garment_features += out_garment_feat
-
-
-        if not return_dict:
-            return (sample,),garment_features
-
-        return UNet2DConditionOutput(sample=sample),garment_features

src/unet_hacked_tryon.py

@@ -1,1395 +0,0 @@
-# Copyright 2023 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.
-from dataclasses import dataclass
-from typing import Any, Dict, List, Optional, Tuple, Union
-
-import torch
-import torch.nn as nn
-import torch.utils.checkpoint
-
-from diffusers.configuration_utils import ConfigMixin, register_to_config
-from diffusers.loaders import UNet2DConditionLoadersMixin
-from diffusers.utils import USE_PEFT_BACKEND, BaseOutput, deprecate, logging, scale_lora_layers, unscale_lora_layers
-from diffusers.models.activations import get_activation
-from diffusers.models.attention_processor import (
-    ADDED_KV_ATTENTION_PROCESSORS,
-    CROSS_ATTENTION_PROCESSORS,
-    Attention,
-    AttentionProcessor,
-    AttnAddedKVProcessor,
-    AttnProcessor,
-)
-from einops import rearrange
-
-from diffusers.models.embeddings import (
-    GaussianFourierProjection,
-    ImageHintTimeEmbedding,
-    ImageProjection,
-    ImageTimeEmbedding,
-    PositionNet,
-    TextImageProjection,
-    TextImageTimeEmbedding,
-    TextTimeEmbedding,
-    TimestepEmbedding,
-    Timesteps,
-)
-
-
-from diffusers.models.modeling_utils import ModelMixin
-from src.unet_block_hacked_tryon import (
-    UNetMidBlock2D,
-    UNetMidBlock2DCrossAttn,
-    UNetMidBlock2DSimpleCrossAttn,
-    get_down_block,
-    get_up_block,
-)
-from diffusers.models.resnet import Downsample2D, FirDownsample2D, FirUpsample2D, KDownsample2D, KUpsample2D, ResnetBlock2D, Upsample2D
-from diffusers.models.transformer_2d import Transformer2DModel
-import math
-
-from ip_adapter.ip_adapter import Resampler
-
-
-logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
-
-
-# def FeedForward(dim, mult=4):
-#     inner_dim = int(dim * mult)
-#     return nn.Sequential(
-#         nn.LayerNorm(dim),
-#         nn.Linear(dim, inner_dim, bias=False),
-#         nn.GELU(),
-#         nn.Linear(inner_dim, dim, bias=False),
-#     )
-
-
-
-# def reshape_tensor(x, heads):
-#     bs, length, width = x.shape
-#     # (bs, length, width) --> (bs, length, n_heads, dim_per_head)
-#     x = x.view(bs, length, heads, -1)
-#     # (bs, length, n_heads, dim_per_head) --> (bs, n_heads, length, dim_per_head)
-#     x = x.transpose(1, 2)
-#     # (bs, n_heads, length, dim_per_head) --> (bs*n_heads, length, dim_per_head)
-#     x = x.reshape(bs, heads, length, -1)
-#     return x
-
-
-# class PerceiverAttention(nn.Module):
-#     def __init__(self, *, dim, dim_head=64, heads=8):
-#         super().__init__()
-#         self.scale = dim_head**-0.5
-#         self.dim_head = dim_head
-#         self.heads = heads
-#         inner_dim = dim_head * heads
-
-#         self.norm1 = nn.LayerNorm(dim)
-#         self.norm2 = nn.LayerNorm(dim)
-
-#         self.to_q = nn.Linear(dim, inner_dim, bias=False)
-#         self.to_kv = nn.Linear(dim, inner_dim * 2, bias=False)
-#         self.to_out = nn.Linear(inner_dim, dim, bias=False)
-
-#     def forward(self, x, latents):
-#         """
-#         Args:
-#             x (torch.Tensor): image features
-#                 shape (b, n1, D)
-#             latent (torch.Tensor): latent features
-#                 shape (b, n2, D)
-#         """
-#         x = self.norm1(x)
-#         latents = self.norm2(latents)
-
-#         b, l, _ = latents.shape
-
-#         q = self.to_q(latents)
-#         kv_input = torch.cat((x, latents), dim=-2)
-#         k, v = self.to_kv(kv_input).chunk(2, dim=-1)
-
-#         q = reshape_tensor(q, self.heads)
-#         k = reshape_tensor(k, self.heads)
-#         v = reshape_tensor(v, self.heads)
-
-#         # attention
-#         scale = 1 / math.sqrt(math.sqrt(self.dim_head))
-#         weight = (q * scale) @ (k * scale).transpose(-2, -1)  # More stable with f16 than dividing afterwards
-#         weight = torch.softmax(weight.float(), dim=-1).type(weight.dtype)
-#         out = weight @ v
-
-#         out = out.permute(0, 2, 1, 3).reshape(b, l, -1)
-
-#         return self.to_out(out)
-
-
-# class Resampler(nn.Module):
-#     def __init__(
-#         self,
-#         dim=1024,
-#         depth=8,
-#         dim_head=64,
-#         heads=16,
-#         num_queries=8,
-#         embedding_dim=768,
-#         output_dim=1024,
-#         ff_mult=4,
-#         max_seq_len: int = 257,  # CLIP tokens + CLS token
-#         apply_pos_emb: bool = False,
-#         num_latents_mean_pooled: int = 0,  # number of latents derived from mean pooled representation of the sequence
-#     ):
-#         super().__init__()
-
-#         self.latents = nn.Parameter(torch.randn(1, num_queries, dim) / dim**0.5)
-
-#         self.proj_in = nn.Linear(embedding_dim, dim)
-
-#         self.proj_out = nn.Linear(dim, output_dim)
-#         self.norm_out = nn.LayerNorm(output_dim)
-
-#         self.layers = nn.ModuleList([])
-#         for _ in range(depth):
-#             self.layers.append(
-#                 nn.ModuleList(
-#                     [
-#                         PerceiverAttention(dim=dim, dim_head=dim_head, heads=heads),
-#                         FeedForward(dim=dim, mult=ff_mult),
-#                     ]
-#                 )
-#             )
-
-#     def forward(self, x):
-
-#         latents = self.latents.repeat(x.size(0), 1, 1)
-
-#         x = self.proj_in(x)
-
-
-#         for attn, ff in self.layers:
-#             latents = attn(x, latents) + latents
-#             latents = ff(latents) + latents
-
-#         latents = self.proj_out(latents)
-#         return self.norm_out(latents)
-
-
-def zero_module(module):
-    for p in module.parameters():
-        nn.init.zeros_(p)
-    return module
-
-@dataclass
-class UNet2DConditionOutput(BaseOutput):
-    """
-    The output of [`UNet2DConditionModel`].
-
-    Args:
-        sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
-            The hidden states output conditioned on `encoder_hidden_states` input. Output of last layer of model.
-    """
-
-    sample: torch.FloatTensor = None
-
-
-class UNet2DConditionModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin):
-    r"""
-    A conditional 2D UNet model that takes a noisy sample, conditional state, and a timestep and returns a sample
-    shaped output.
-
-    This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemented
-    for all models (such as downloading or saving).
-
-    Parameters:
-        sample_size (`int` or `Tuple[int, int]`, *optional*, defaults to `None`):
-            Height and width of input/output sample.
-        in_channels (`int`, *optional*, defaults to 4): Number of channels in the input sample.
-        out_channels (`int`, *optional*, defaults to 4): Number of channels in the output.
-        center_input_sample (`bool`, *optional*, defaults to `False`): Whether to center the input sample.
-        flip_sin_to_cos (`bool`, *optional*, defaults to `False`):
-            Whether to flip the sin to cos in the time embedding.
-        freq_shift (`int`, *optional*, defaults to 0): The frequency shift to apply to the time embedding.
-        down_block_types (`Tuple[str]`, *optional*, defaults to `("CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "DownBlock2D")`):
-            The tuple of downsample blocks to use.
-        mid_block_type (`str`, *optional*, defaults to `"UNetMidBlock2DCrossAttn"`):
-            Block type for middle of UNet, it can be one of `UNetMidBlock2DCrossAttn`, `UNetMidBlock2D`, or
-            `UNetMidBlock2DSimpleCrossAttn`. If `None`, the mid block layer is skipped.
-        up_block_types (`Tuple[str]`, *optional*, defaults to `("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D")`):
-            The tuple of upsample blocks to use.
-        only_cross_attention(`bool` or `Tuple[bool]`, *optional*, default to `False`):
-            Whether to include self-attention in the basic transformer blocks, see
-            [`~models.attention.BasicTransformerBlock`].
-        block_out_channels (`Tuple[int]`, *optional*, defaults to `(320, 640, 1280, 1280)`):
-            The tuple of output channels for each block.
-        layers_per_block (`int`, *optional*, defaults to 2): The number of layers per block.
-        downsample_padding (`int`, *optional*, defaults to 1): The padding to use for the downsampling convolution.
-        mid_block_scale_factor (`float`, *optional*, defaults to 1.0): The scale factor to use for the mid block.
-        dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.
-        act_fn (`str`, *optional*, defaults to `"silu"`): The activation function to use.
-        norm_num_groups (`int`, *optional*, defaults to 32): The number of groups to use for the normalization.
-            If `None`, normalization and activation layers is skipped in post-processing.
-        norm_eps (`float`, *optional*, defaults to 1e-5): The epsilon to use for the normalization.
-        cross_attention_dim (`int` or `Tuple[int]`, *optional*, defaults to 1280):
-            The dimension of the cross attention features.
-        transformer_layers_per_block (`int`, `Tuple[int]`, or `Tuple[Tuple]` , *optional*, defaults to 1):
-            The number of transformer blocks of type [`~models.attention.BasicTransformerBlock`]. Only relevant for
-            [`~models.unet_2d_blocks.CrossAttnDownBlock2D`], [`~models.unet_2d_blocks.CrossAttnUpBlock2D`],
-            [`~models.unet_2d_blocks.UNetMidBlock2DCrossAttn`].
-       reverse_transformer_layers_per_block : (`Tuple[Tuple]`, *optional*, defaults to None):
-            The number of transformer blocks of type [`~models.attention.BasicTransformerBlock`], in the upsampling
-            blocks of the U-Net. Only relevant if `transformer_layers_per_block` is of type `Tuple[Tuple]` and for
-            [`~models.unet_2d_blocks.CrossAttnDownBlock2D`], [`~models.unet_2d_blocks.CrossAttnUpBlock2D`],
-            [`~models.unet_2d_blocks.UNetMidBlock2DCrossAttn`].
-        encoder_hid_dim (`int`, *optional*, defaults to None):
-            If `encoder_hid_dim_type` is defined, `encoder_hidden_states` will be projected from `encoder_hid_dim`
-            dimension to `cross_attention_dim`.
-        encoder_hid_dim_type (`str`, *optional*, defaults to `None`):
-            If given, the `encoder_hidden_states` and potentially other embeddings are down-projected to text
-            embeddings of dimension `cross_attention` according to `encoder_hid_dim_type`.
-        attention_head_dim (`int`, *optional*, defaults to 8): The dimension of the attention heads.
-        num_attention_heads (`int`, *optional*):
-            The number of attention heads. If not defined, defaults to `attention_head_dim`
-        resnet_time_scale_shift (`str`, *optional*, defaults to `"default"`): Time scale shift config
-            for ResNet blocks (see [`~models.resnet.ResnetBlock2D`]). Choose from `default` or `scale_shift`.
-        class_embed_type (`str`, *optional*, defaults to `None`):
-            The type of class embedding to use which is ultimately summed with the time embeddings. Choose from `None`,
-            `"timestep"`, `"identity"`, `"projection"`, or `"simple_projection"`.
-        addition_embed_type (`str`, *optional*, defaults to `None`):
-            Configures an optional embedding which will be summed with the time embeddings. Choose from `None` or
-            "text". "text" will use the `TextTimeEmbedding` layer.
-        addition_time_embed_dim: (`int`, *optional*, defaults to `None`):
-            Dimension for the timestep embeddings.
-        num_class_embeds (`int`, *optional*, defaults to `None`):
-            Input dimension of the learnable embedding matrix to be projected to `time_embed_dim`, when performing
-            class conditioning with `class_embed_type` equal to `None`.
-        time_embedding_type (`str`, *optional*, defaults to `positional`):
-            The type of position embedding to use for timesteps. Choose from `positional` or `fourier`.
-        time_embedding_dim (`int`, *optional*, defaults to `None`):
-            An optional override for the dimension of the projected time embedding.
-        time_embedding_act_fn (`str`, *optional*, defaults to `None`):
-            Optional activation function to use only once on the time embeddings before they are passed to the rest of
-            the UNet. Choose from `silu`, `mish`, `gelu`, and `swish`.
-        timestep_post_act (`str`, *optional*, defaults to `None`):
-            The second activation function to use in timestep embedding. Choose from `silu`, `mish` and `gelu`.
-        time_cond_proj_dim (`int`, *optional*, defaults to `None`):
-            The dimension of `cond_proj` layer in the timestep embedding.
-        conv_in_kernel (`int`, *optional*, default to `3`): The kernel size of `conv_in` layer. conv_out_kernel (`int`,
-        *optional*, default to `3`): The kernel size of `conv_out` layer. projection_class_embeddings_input_dim (`int`,
-        *optional*): The dimension of the `class_labels` input when
-            `class_embed_type="projection"`. Required when `class_embed_type="projection"`.
-        class_embeddings_concat (`bool`, *optional*, defaults to `False`): Whether to concatenate the time
-            embeddings with the class embeddings.
-        mid_block_only_cross_attention (`bool`, *optional*, defaults to `None`):
-            Whether to use cross attention with the mid block when using the `UNetMidBlock2DSimpleCrossAttn`. If
-            `only_cross_attention` is given as a single boolean and `mid_block_only_cross_attention` is `None`, the
-            `only_cross_attention` value is used as the value for `mid_block_only_cross_attention`. Default to `False`
-            otherwise.
-    """
-
-    _supports_gradient_checkpointing = True
-
-    @register_to_config
-    def __init__(
-        self,
-        sample_size: Optional[int] = None,
-        in_channels: int = 4,
-        out_channels: int = 4,
-        center_input_sample: bool = False,
-        flip_sin_to_cos: bool = True,
-        freq_shift: int = 0,
-        down_block_types: Tuple[str] = (
-            "CrossAttnDownBlock2D",
-            "CrossAttnDownBlock2D",
-            "CrossAttnDownBlock2D",
-            "DownBlock2D",
-        ),
-        mid_block_type: Optional[str] = "UNetMidBlock2DCrossAttn",
-        up_block_types: Tuple[str] = ("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D"),
-        only_cross_attention: Union[bool, Tuple[bool]] = False,
-        block_out_channels: Tuple[int] = (320, 640, 1280, 1280),
-        layers_per_block: Union[int, Tuple[int]] = 2,
-        downsample_padding: int = 1,
-        mid_block_scale_factor: float = 1,
-        dropout: float = 0.0,
-        act_fn: str = "silu",
-        norm_num_groups: Optional[int] = 32,
-        norm_eps: float = 1e-5,
-        cross_attention_dim: Union[int, Tuple[int]] = 1280,
-        transformer_layers_per_block: Union[int, Tuple[int], Tuple[Tuple]] = 1,
-        reverse_transformer_layers_per_block: Optional[Tuple[Tuple[int]]] = None,
-        encoder_hid_dim: Optional[int] = None,
-        encoder_hid_dim_type: Optional[str] = None,
-        attention_head_dim: Union[int, Tuple[int]] = 8,
-        num_attention_heads: Optional[Union[int, Tuple[int]]] = None,
-        dual_cross_attention: bool = False,
-        use_linear_projection: bool = False,
-        class_embed_type: Optional[str] = None,
-        addition_embed_type: Optional[str] = None,
-        addition_time_embed_dim: Optional[int] = None,
-        num_class_embeds: Optional[int] = None,
-        upcast_attention: bool = False,
-        resnet_time_scale_shift: str = "default",
-        resnet_skip_time_act: bool = False,
-        resnet_out_scale_factor: int = 1.0,
-        time_embedding_type: str = "positional",
-        time_embedding_dim: Optional[int] = None,
-        time_embedding_act_fn: Optional[str] = None,
-        timestep_post_act: Optional[str] = None,
-        time_cond_proj_dim: Optional[int] = None,
-        conv_in_kernel: int = 3,
-        conv_out_kernel: int = 3,
-        projection_class_embeddings_input_dim: Optional[int] = None,
-        attention_type: str = "default",
-        class_embeddings_concat: bool = False,
-        mid_block_only_cross_attention: Optional[bool] = None,
-        cross_attention_norm: Optional[str] = None,
-        addition_embed_type_num_heads=64,
-    ):
-        super().__init__()
-
-        self.sample_size = sample_size
-
-        if num_attention_heads is not None:
-            raise ValueError(
-                "At the moment it is not possible to define the number of attention heads via `num_attention_heads` because of a naming issue as described in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131. Passing `num_attention_heads` will only be supported in diffusers v0.19."
-            )
-
-        # If `num_attention_heads` is not defined (which is the case for most models)
-        # it will default to `attention_head_dim`. This looks weird upon first reading it and it is.
-        # The reason for this behavior is to correct for incorrectly named variables that were introduced
-        # when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131
-        # Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking
-        # which is why we correct for the naming here.
-        num_attention_heads = num_attention_heads or attention_head_dim
-
-        # Check inputs
-        if len(down_block_types) != len(up_block_types):
-            raise ValueError(
-                f"Must provide the same number of `down_block_types` as `up_block_types`. `down_block_types`: {down_block_types}. `up_block_types`: {up_block_types}."
-            )
-
-        if len(block_out_channels) != len(down_block_types):
-            raise ValueError(
-                f"Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}."
-            )
-
-        if not isinstance(only_cross_attention, bool) and len(only_cross_attention) != len(down_block_types):
-            raise ValueError(
-                f"Must provide the same number of `only_cross_attention` as `down_block_types`. `only_cross_attention`: {only_cross_attention}. `down_block_types`: {down_block_types}."
-            )
-
-        if not isinstance(num_attention_heads, int) and len(num_attention_heads) != len(down_block_types):
-            raise ValueError(
-                f"Must provide the same number of `num_attention_heads` as `down_block_types`. `num_attention_heads`: {num_attention_heads}. `down_block_types`: {down_block_types}."
-            )
-
-        if not isinstance(attention_head_dim, int) and len(attention_head_dim) != len(down_block_types):
-            raise ValueError(
-                f"Must provide the same number of `attention_head_dim` as `down_block_types`. `attention_head_dim`: {attention_head_dim}. `down_block_types`: {down_block_types}."
-            )
-
-        if isinstance(cross_attention_dim, list) and len(cross_attention_dim) != len(down_block_types):
-            raise ValueError(
-                f"Must provide the same number of `cross_attention_dim` as `down_block_types`. `cross_attention_dim`: {cross_attention_dim}. `down_block_types`: {down_block_types}."
-            )
-
-        if not isinstance(layers_per_block, int) and len(layers_per_block) != len(down_block_types):
-            raise ValueError(
-                f"Must provide the same number of `layers_per_block` as `down_block_types`. `layers_per_block`: {layers_per_block}. `down_block_types`: {down_block_types}."
-            )
-        if isinstance(transformer_layers_per_block, list) and reverse_transformer_layers_per_block is None:
-            for layer_number_per_block in transformer_layers_per_block:
-                if isinstance(layer_number_per_block, list):
-                    raise ValueError("Must provide 'reverse_transformer_layers_per_block` if using asymmetrical UNet.")
-
-        # input
-        conv_in_padding = (conv_in_kernel - 1) // 2
-        self.conv_in = nn.Conv2d(
-            in_channels, block_out_channels[0], kernel_size=conv_in_kernel, padding=conv_in_padding
-        )
-
-        # time
-        if time_embedding_type == "fourier":
-            time_embed_dim = time_embedding_dim or block_out_channels[0] * 2
-            if time_embed_dim % 2 != 0:
-                raise ValueError(f"`time_embed_dim` should be divisible by 2, but is {time_embed_dim}.")
-            self.time_proj = GaussianFourierProjection(
-                time_embed_dim // 2, set_W_to_weight=False, log=False, flip_sin_to_cos=flip_sin_to_cos
-            )
-            timestep_input_dim = time_embed_dim
-        elif time_embedding_type == "positional":
-            time_embed_dim = time_embedding_dim or block_out_channels[0] * 4
-
-            self.time_proj = Timesteps(block_out_channels[0], flip_sin_to_cos, freq_shift)
-            timestep_input_dim = block_out_channels[0]
-        else:
-            raise ValueError(
-                f"{time_embedding_type} does not exist. Please make sure to use one of `fourier` or `positional`."
-            )
-
-        self.time_embedding = TimestepEmbedding(
-            timestep_input_dim,
-            time_embed_dim,
-            act_fn=act_fn,
-            post_act_fn=timestep_post_act,
-            cond_proj_dim=time_cond_proj_dim,
-        )
-
-        if encoder_hid_dim_type is None and encoder_hid_dim is not None:
-            encoder_hid_dim_type = "text_proj"
-            self.register_to_config(encoder_hid_dim_type=encoder_hid_dim_type)
-            logger.info("encoder_hid_dim_type defaults to 'text_proj' as `encoder_hid_dim` is defined.")
-
-        if encoder_hid_dim is None and encoder_hid_dim_type is not None:
-            raise ValueError(
-                f"`encoder_hid_dim` has to be defined when `encoder_hid_dim_type` is set to {encoder_hid_dim_type}."
-            )
-
-        if encoder_hid_dim_type == "text_proj":
-            self.encoder_hid_proj = nn.Linear(encoder_hid_dim, cross_attention_dim)
-        elif encoder_hid_dim_type == "text_image_proj":
-            # image_embed_dim DOESN'T have to be `cross_attention_dim`. To not clutter the __init__ too much
-            # they are set to `cross_attention_dim` here as this is exactly the required dimension for the currently only use
-            # case when `addition_embed_type == "text_image_proj"` (Kadinsky 2.1)`
-            self.encoder_hid_proj = TextImageProjection(
-                text_embed_dim=encoder_hid_dim,
-                image_embed_dim=cross_attention_dim,
-                cross_attention_dim=cross_attention_dim,
-            )
-        elif encoder_hid_dim_type == "image_proj":
-            # Kandinsky 2.2
-            self.encoder_hid_proj = ImageProjection(
-                image_embed_dim=encoder_hid_dim,
-                cross_attention_dim=cross_attention_dim,
-            )
-        elif encoder_hid_dim_type == "ip_image_proj":
-            # Kandinsky 2.2
-            self.encoder_hid_proj = Resampler(
-                dim=1280,
-                depth=4,
-                dim_head=64,
-                heads=20,
-                num_queries=16,
-                embedding_dim=encoder_hid_dim,
-                output_dim=self.config.cross_attention_dim,
-                ff_mult=4,
-            )
-                                    
-            
-        elif encoder_hid_dim_type is not None:
-            raise ValueError(
-                f"encoder_hid_dim_type: {encoder_hid_dim_type} must be None, 'text_proj' or 'text_image_proj'."
-            )
-        else:
-            self.encoder_hid_proj = None
-
-        # class embedding
-        if class_embed_type is None and num_class_embeds is not None:
-            self.class_embedding = nn.Embedding(num_class_embeds, time_embed_dim)
-        elif class_embed_type == "timestep":
-            self.class_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim, act_fn=act_fn)
-        elif class_embed_type == "identity":
-            self.class_embedding = nn.Identity(time_embed_dim, time_embed_dim)
-        elif class_embed_type == "projection":
-            if projection_class_embeddings_input_dim is None:
-                raise ValueError(
-                    "`class_embed_type`: 'projection' requires `projection_class_embeddings_input_dim` be set"
-                )
-            # The projection `class_embed_type` is the same as the timestep `class_embed_type` except
-            # 1. the `class_labels` inputs are not first converted to sinusoidal embeddings
-            # 2. it projects from an arbitrary input dimension.
-            #
-            # Note that `TimestepEmbedding` is quite general, being mainly linear layers and activations.
-            # When used for embedding actual timesteps, the timesteps are first converted to sinusoidal embeddings.
-            # As a result, `TimestepEmbedding` can be passed arbitrary vectors.
-            self.class_embedding = TimestepEmbedding(projection_class_embeddings_input_dim, time_embed_dim)
-        elif class_embed_type == "simple_projection":
-            if projection_class_embeddings_input_dim is None:
-                raise ValueError(
-                    "`class_embed_type`: 'simple_projection' requires `projection_class_embeddings_input_dim` be set"
-                )
-            self.class_embedding = nn.Linear(projection_class_embeddings_input_dim, time_embed_dim)
-        else:
-            self.class_embedding = None
-
-        if addition_embed_type == "text":
-            if encoder_hid_dim is not None:
-                text_time_embedding_from_dim = encoder_hid_dim
-            else:
-                text_time_embedding_from_dim = cross_attention_dim
-
-            self.add_embedding = TextTimeEmbedding(
-                text_time_embedding_from_dim, time_embed_dim, num_heads=addition_embed_type_num_heads
-            )
-        elif addition_embed_type == "text_image":
-            # text_embed_dim and image_embed_dim DON'T have to be `cross_attention_dim`. To not clutter the __init__ too much
-            # they are set to `cross_attention_dim` here as this is exactly the required dimension for the currently only use
-            # case when `addition_embed_type == "text_image"` (Kadinsky 2.1)`
-            self.add_embedding = TextImageTimeEmbedding(
-                text_embed_dim=cross_attention_dim, image_embed_dim=cross_attention_dim, time_embed_dim=time_embed_dim
-            )
-        elif addition_embed_type == "text_time":
-            self.add_time_proj = Timesteps(addition_time_embed_dim, flip_sin_to_cos, freq_shift)
-            self.add_embedding = TimestepEmbedding(projection_class_embeddings_input_dim, time_embed_dim)
-        elif addition_embed_type == "image":
-            # Kandinsky 2.2
-            self.add_embedding = ImageTimeEmbedding(image_embed_dim=encoder_hid_dim, time_embed_dim=time_embed_dim)
-        elif addition_embed_type == "image_hint":
-            # Kandinsky 2.2 ControlNet
-            self.add_embedding = ImageHintTimeEmbedding(image_embed_dim=encoder_hid_dim, time_embed_dim=time_embed_dim)
-        elif addition_embed_type is not None:
-            raise ValueError(f"addition_embed_type: {addition_embed_type} must be None, 'text' or 'text_image'.")
-
-        if time_embedding_act_fn is None:
-            self.time_embed_act = None
-        else:
-            self.time_embed_act = get_activation(time_embedding_act_fn)
-
-        self.down_blocks = nn.ModuleList([])
-        self.up_blocks = nn.ModuleList([])
-
-        if isinstance(only_cross_attention, bool):
-            if mid_block_only_cross_attention is None:
-                mid_block_only_cross_attention = only_cross_attention
-
-            only_cross_attention = [only_cross_attention] * len(down_block_types)
-
-        if mid_block_only_cross_attention is None:
-            mid_block_only_cross_attention = False
-
-        if isinstance(num_attention_heads, int):
-            num_attention_heads = (num_attention_heads,) * len(down_block_types)
-
-        if isinstance(attention_head_dim, int):
-            attention_head_dim = (attention_head_dim,) * len(down_block_types)
-
-        if isinstance(cross_attention_dim, int):
-            cross_attention_dim = (cross_attention_dim,) * len(down_block_types)
-
-        if isinstance(layers_per_block, int):
-            layers_per_block = [layers_per_block] * len(down_block_types)
-
-        if isinstance(transformer_layers_per_block, int):
-            transformer_layers_per_block = [transformer_layers_per_block] * len(down_block_types)
-        if class_embeddings_concat:
-            # The time embeddings are concatenated with the class embeddings. The dimension of the
-            # time embeddings passed to the down, middle, and up blocks is twice the dimension of the
-            # regular time embeddings
-            blocks_time_embed_dim = time_embed_dim * 2
-        else:
-            blocks_time_embed_dim = time_embed_dim
-
-        # down
-        output_channel = block_out_channels[0]
-        for i, down_block_type in enumerate(down_block_types):
-            input_channel = output_channel
-            output_channel = block_out_channels[i]
-            is_final_block = i == len(block_out_channels) - 1
-
-            down_block = get_down_block(
-                down_block_type,
-                num_layers=layers_per_block[i],
-                transformer_layers_per_block=transformer_layers_per_block[i],
-                in_channels=input_channel,
-                out_channels=output_channel,
-                temb_channels=blocks_time_embed_dim,
-                add_downsample=not is_final_block,
-                resnet_eps=norm_eps,
-                resnet_act_fn=act_fn,
-                resnet_groups=norm_num_groups,
-                cross_attention_dim=cross_attention_dim[i],
-                num_attention_heads=num_attention_heads[i],
-                downsample_padding=downsample_padding,
-                dual_cross_attention=dual_cross_attention,
-                use_linear_projection=use_linear_projection,
-                only_cross_attention=only_cross_attention[i],
-                upcast_attention=upcast_attention,
-                resnet_time_scale_shift=resnet_time_scale_shift,
-                attention_type=attention_type,
-                resnet_skip_time_act=resnet_skip_time_act,
-                resnet_out_scale_factor=resnet_out_scale_factor,
-                cross_attention_norm=cross_attention_norm,
-                attention_head_dim=attention_head_dim[i] if attention_head_dim[i] is not None else output_channel,
-                dropout=dropout,
-            )
-            self.down_blocks.append(down_block)
-
-        # mid
-        if mid_block_type == "UNetMidBlock2DCrossAttn":
-            self.mid_block = UNetMidBlock2DCrossAttn(
-                transformer_layers_per_block=transformer_layers_per_block[-1],
-                in_channels=block_out_channels[-1],
-                temb_channels=blocks_time_embed_dim,
-                dropout=dropout,
-                resnet_eps=norm_eps,
-                resnet_act_fn=act_fn,
-                output_scale_factor=mid_block_scale_factor,
-                resnet_time_scale_shift=resnet_time_scale_shift,
-                cross_attention_dim=cross_attention_dim[-1],
-                num_attention_heads=num_attention_heads[-1],
-                resnet_groups=norm_num_groups,
-                dual_cross_attention=dual_cross_attention,
-                use_linear_projection=use_linear_projection,
-                upcast_attention=upcast_attention,
-                attention_type=attention_type,
-            )
-        elif mid_block_type == "UNetMidBlock2DSimpleCrossAttn":
-            self.mid_block = UNetMidBlock2DSimpleCrossAttn(
-                in_channels=block_out_channels[-1],
-                temb_channels=blocks_time_embed_dim,
-                dropout=dropout,
-                resnet_eps=norm_eps,
-                resnet_act_fn=act_fn,
-                output_scale_factor=mid_block_scale_factor,
-                cross_attention_dim=cross_attention_dim[-1],
-                attention_head_dim=attention_head_dim[-1],
-                resnet_groups=norm_num_groups,
-                resnet_time_scale_shift=resnet_time_scale_shift,
-                skip_time_act=resnet_skip_time_act,
-                only_cross_attention=mid_block_only_cross_attention,
-                cross_attention_norm=cross_attention_norm,
-            )
-        elif mid_block_type == "UNetMidBlock2D":
-            self.mid_block = UNetMidBlock2D(
-                in_channels=block_out_channels[-1],
-                temb_channels=blocks_time_embed_dim,
-                dropout=dropout,
-                num_layers=0,
-                resnet_eps=norm_eps,
-                resnet_act_fn=act_fn,
-                output_scale_factor=mid_block_scale_factor,
-                resnet_groups=norm_num_groups,
-                resnet_time_scale_shift=resnet_time_scale_shift,
-                add_attention=False,
-            )
-        elif mid_block_type is None:
-            self.mid_block = None
-        else:
-            raise ValueError(f"unknown mid_block_type : {mid_block_type}")
-
-        # count how many layers upsample the images
-        self.num_upsamplers = 0
-
-        # up
-        reversed_block_out_channels = list(reversed(block_out_channels))
-        reversed_num_attention_heads = list(reversed(num_attention_heads))
-        reversed_layers_per_block = list(reversed(layers_per_block))
-        reversed_cross_attention_dim = list(reversed(cross_attention_dim))
-        reversed_transformer_layers_per_block = (
-            list(reversed(transformer_layers_per_block))
-            if reverse_transformer_layers_per_block is None
-            else reverse_transformer_layers_per_block
-        )
-        only_cross_attention = list(reversed(only_cross_attention))
-
-        output_channel = reversed_block_out_channels[0]
-        for i, up_block_type in enumerate(up_block_types):
-            is_final_block = i == len(block_out_channels) - 1
-
-            prev_output_channel = output_channel
-            output_channel = reversed_block_out_channels[i]
-            input_channel = reversed_block_out_channels[min(i + 1, len(block_out_channels) - 1)]
-
-            # add upsample block for all BUT final layer
-            if not is_final_block:
-                add_upsample = True
-                self.num_upsamplers += 1
-            else:
-                add_upsample = False
-            up_block = get_up_block(
-                up_block_type,
-                num_layers=reversed_layers_per_block[i] + 1,
-                transformer_layers_per_block=reversed_transformer_layers_per_block[i],
-                in_channels=input_channel,
-                out_channels=output_channel,
-                prev_output_channel=prev_output_channel,
-                temb_channels=blocks_time_embed_dim,
-                add_upsample=add_upsample,
-                resnet_eps=norm_eps,
-                resnet_act_fn=act_fn,
-                resolution_idx=i,
-                resnet_groups=norm_num_groups,
-                cross_attention_dim=reversed_cross_attention_dim[i],
-                num_attention_heads=reversed_num_attention_heads[i],
-                dual_cross_attention=dual_cross_attention,
-                use_linear_projection=use_linear_projection,
-                only_cross_attention=only_cross_attention[i],
-                upcast_attention=upcast_attention,
-                resnet_time_scale_shift=resnet_time_scale_shift,
-                attention_type=attention_type,
-                resnet_skip_time_act=resnet_skip_time_act,
-                resnet_out_scale_factor=resnet_out_scale_factor,
-                cross_attention_norm=cross_attention_norm,
-                attention_head_dim=attention_head_dim[i] if attention_head_dim[i] is not None else output_channel,
-                dropout=dropout,
-            )
-
-            self.up_blocks.append(up_block)
-            prev_output_channel = output_channel
-
-
-
-
-        # out
-        if norm_num_groups is not None:
-            self.conv_norm_out = nn.GroupNorm(
-                num_channels=block_out_channels[0], num_groups=norm_num_groups, eps=norm_eps
-            )
-
-            self.conv_act = get_activation(act_fn)
-
-        else:
-            self.conv_norm_out = None
-            self.conv_act = None
-
-        conv_out_padding = (conv_out_kernel - 1) // 2
-        self.conv_out = nn.Conv2d(
-            block_out_channels[0], out_channels, kernel_size=conv_out_kernel, padding=conv_out_padding
-        )
-
-        if attention_type in ["gated", "gated-text-image"]:
-            positive_len = 768
-            if isinstance(cross_attention_dim, int):
-                positive_len = cross_attention_dim
-            elif isinstance(cross_attention_dim, tuple) or isinstance(cross_attention_dim, list):
-                positive_len = cross_attention_dim[0]
-
-            feature_type = "text-only" if attention_type == "gated" else "text-image"
-            self.position_net = PositionNet(
-                positive_len=positive_len, out_dim=cross_attention_dim, feature_type=feature_type
-            )
-
-
-
-        from ip_adapter.attention_processor import IPAttnProcessor2_0 as IPAttnProcessor, AttnProcessor2_0 as AttnProcessor
-
-        attn_procs = {}
-        for name in self.attn_processors.keys():
-            cross_attention_dim = None if name.endswith("attn1.processor") else self.config.cross_attention_dim
-            if name.startswith("mid_block"):
-                hidden_size = self.config.block_out_channels[-1]
-            elif name.startswith("up_blocks"):
-                block_id = int(name[len("up_blocks.")])
-                hidden_size = list(reversed(self.config.block_out_channels))[block_id]
-            elif name.startswith("down_blocks"):
-                block_id = int(name[len("down_blocks.")])
-                hidden_size = self.config.block_out_channels[block_id]
-            if cross_attention_dim is None:
-                attn_procs[name] = AttnProcessor()
-            else:
-                layer_name = name.split(".processor")[0]
-                attn_procs[name] = IPAttnProcessor(hidden_size=hidden_size, cross_attention_dim=cross_attention_dim, num_tokens=16)
-        self.set_attn_processor(attn_procs)
-
-
-    @property
-    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(return_deprecated_lora=True)
-
-            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
-
-    def set_attn_processor(
-        self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]], _remove_lora=False
-    ):
-        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, _remove_lora=_remove_lora)
-                else:
-                    module.set_processor(processor.pop(f"{name}.processor"), _remove_lora=_remove_lora)
-
-            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.
-        """
-        if all(proc.__class__ in ADDED_KV_ATTENTION_PROCESSORS for proc in self.attn_processors.values()):
-            processor = AttnAddedKVProcessor()
-        elif all(proc.__class__ in CROSS_ATTENTION_PROCESSORS for proc in self.attn_processors.values()):
-            processor = AttnProcessor()
-        else:
-            raise ValueError(
-                f"Cannot call `set_default_attn_processor` when attention processors are of type {next(iter(self.attn_processors.values()))}"
-            )
-
-        self.set_attn_processor(processor, _remove_lora=True)
-
-    def set_attention_slice(self, slice_size):
-        r"""
-        Enable sliced attention computation.
-
-        When this option is enabled, the attention module splits the input tensor in slices to compute attention in
-        several steps. This is useful for saving some memory in exchange for a small decrease in speed.
-
-        Args:
-            slice_size (`str` or `int` or `list(int)`, *optional*, defaults to `"auto"`):
-                When `"auto"`, input to the attention heads is halved, so attention is computed in two steps. If
-                `"max"`, maximum amount of memory is saved by running only one slice at a time. If a number is
-                provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim`
-                must be a multiple of `slice_size`.
-        """
-        sliceable_head_dims = []
-
-        def fn_recursive_retrieve_sliceable_dims(module: torch.nn.Module):
-            if hasattr(module, "set_attention_slice"):
-                sliceable_head_dims.append(module.sliceable_head_dim)
-
-            for child in module.children():
-                fn_recursive_retrieve_sliceable_dims(child)
-
-        # retrieve number of attention layers
-        for module in self.children():
-            fn_recursive_retrieve_sliceable_dims(module)
-
-        num_sliceable_layers = len(sliceable_head_dims)
-
-        if slice_size == "auto":
-            # half the attention head size is usually a good trade-off between
-            # speed and memory
-            slice_size = [dim // 2 for dim in sliceable_head_dims]
-        elif slice_size == "max":
-            # make smallest slice possible
-            slice_size = num_sliceable_layers * [1]
-
-        slice_size = num_sliceable_layers * [slice_size] if not isinstance(slice_size, list) else slice_size
-
-        if len(slice_size) != len(sliceable_head_dims):
-            raise ValueError(
-                f"You have provided {len(slice_size)}, but {self.config} has {len(sliceable_head_dims)} different"
-                f" attention layers. Make sure to match `len(slice_size)` to be {len(sliceable_head_dims)}."
-            )
-
-        for i in range(len(slice_size)):
-            size = slice_size[i]
-            dim = sliceable_head_dims[i]
-            if size is not None and size > dim:
-                raise ValueError(f"size {size} has to be smaller or equal to {dim}.")
-
-        # Recursively walk through all the children.
-        # Any children which exposes the set_attention_slice method
-        # gets the message
-        def fn_recursive_set_attention_slice(module: torch.nn.Module, slice_size: List[int]):
-            if hasattr(module, "set_attention_slice"):
-                module.set_attention_slice(slice_size.pop())
-
-            for child in module.children():
-                fn_recursive_set_attention_slice(child, slice_size)
-
-        reversed_slice_size = list(reversed(slice_size))
-        for module in self.children():
-            fn_recursive_set_attention_slice(module, reversed_slice_size)
-
-    def _set_gradient_checkpointing(self, module, value=False):
-        if hasattr(module, "gradient_checkpointing"):
-            module.gradient_checkpointing = value
-
-    def enable_freeu(self, s1, s2, b1, b2):
-        r"""Enables the FreeU mechanism from https://arxiv.org/abs/2309.11497.
-
-        The suffixes after the scaling factors represent the stage blocks where they are being applied.
-
-        Please refer to the [official repository](https://github.com/ChenyangSi/FreeU) for combinations of values that
-        are known to work well for different pipelines such as Stable Diffusion v1, v2, and Stable Diffusion XL.
-
-        Args:
-            s1 (`float`):
-                Scaling factor for stage 1 to attenuate the contributions of the skip features. This is done to
-                mitigate the "oversmoothing effect" in the enhanced denoising process.
-            s2 (`float`):
-                Scaling factor for stage 2 to attenuate the contributions of the skip features. This is done to
-                mitigate the "oversmoothing effect" in the enhanced denoising process.
-            b1 (`float`): Scaling factor for stage 1 to amplify the contributions of backbone features.
-            b2 (`float`): Scaling factor for stage 2 to amplify the contributions of backbone features.
-        """
-        for i, upsample_block in enumerate(self.up_blocks):
-            setattr(upsample_block, "s1", s1)
-            setattr(upsample_block, "s2", s2)
-            setattr(upsample_block, "b1", b1)
-            setattr(upsample_block, "b2", b2)
-
-    def disable_freeu(self):
-        """Disables the FreeU mechanism."""
-        freeu_keys = {"s1", "s2", "b1", "b2"}
-        for i, upsample_block in enumerate(self.up_blocks):
-            for k in freeu_keys:
-                if hasattr(upsample_block, k) or getattr(upsample_block, k, None) is not None:
-                    setattr(upsample_block, k, None)
-
-    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)
-
-    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,
-        sample: torch.FloatTensor,
-        timestep: Union[torch.Tensor, float, int],
-        encoder_hidden_states: torch.Tensor,
-        class_labels: Optional[torch.Tensor] = None,
-        timestep_cond: Optional[torch.Tensor] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
-        added_cond_kwargs: Optional[Dict[str, torch.Tensor]] = None,
-        down_block_additional_residuals: Optional[Tuple[torch.Tensor]] = None,
-        mid_block_additional_residual: Optional[torch.Tensor] = None,
-        down_intrablock_additional_residuals: Optional[Tuple[torch.Tensor]] = None,
-        encoder_attention_mask: Optional[torch.Tensor] = None,
-        return_dict: bool = True,
-        garment_features: Optional[Tuple[torch.Tensor]] = None,
-    ) -> Union[UNet2DConditionOutput, Tuple]:
-        r"""
-        The [`UNet2DConditionModel`] forward method.
-
-        Args:
-            sample (`torch.FloatTensor`):
-                The noisy input tensor with the following shape `(batch, channel, height, width)`.
-            timestep (`torch.FloatTensor` or `float` or `int`): The number of timesteps to denoise an input.
-            encoder_hidden_states (`torch.FloatTensor`):
-                The encoder hidden states with shape `(batch, sequence_length, feature_dim)`.
-            class_labels (`torch.Tensor`, *optional*, defaults to `None`):
-                Optional class labels for conditioning. Their embeddings will be summed with the timestep embeddings.
-            timestep_cond: (`torch.Tensor`, *optional*, defaults to `None`):
-                Conditional embeddings for timestep. If provided, the embeddings will be summed with the samples passed
-                through the `self.time_embedding` layer to obtain the timestep embeddings.
-            attention_mask (`torch.Tensor`, *optional*, defaults to `None`):
-                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.
-            cross_attention_kwargs (`dict`, *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).
-            added_cond_kwargs: (`dict`, *optional*):
-                A kwargs dictionary containing additional embeddings that if specified are added to the embeddings that
-                are passed along to the UNet blocks.
-            down_block_additional_residuals: (`tuple` of `torch.Tensor`, *optional*):
-                A tuple of tensors that if specified are added to the residuals of down unet blocks.
-            mid_block_additional_residual: (`torch.Tensor`, *optional*):
-                A tensor that if specified is added to the residual of the middle unet block.
-            encoder_attention_mask (`torch.Tensor`):
-                A cross-attention mask of shape `(batch, sequence_length)` is applied to `encoder_hidden_states`. If
-                `True` the mask is kept, otherwise if `False` it is discarded. Mask will be converted into a bias,
-                which adds large negative values to the attention scores corresponding to "discard" tokens.
-            return_dict (`bool`, *optional*, defaults to `True`):
-                Whether or not to return a [`~models.unet_2d_condition.UNet2DConditionOutput`] instead of a plain
-                tuple.
-            cross_attention_kwargs (`dict`, *optional*):
-                A kwargs dictionary that if specified is passed along to the [`AttnProcessor`].
-            added_cond_kwargs: (`dict`, *optional*):
-                A kwargs dictionary containin additional embeddings that if specified are added to the embeddings that
-                are passed along to the UNet blocks.
-            down_block_additional_residuals (`tuple` of `torch.Tensor`, *optional*):
-                additional residuals to be added to UNet long skip connections from down blocks to up blocks for
-                example from ControlNet side model(s)
-            mid_block_additional_residual (`torch.Tensor`, *optional*):
-                additional residual to be added to UNet mid block output, for example from ControlNet side model
-            down_intrablock_additional_residuals (`tuple` of `torch.Tensor`, *optional*):
-                additional residuals to be added within UNet down blocks, for example from T2I-Adapter side model(s)
-
-        Returns:
-            [`~models.unet_2d_condition.UNet2DConditionOutput`] or `tuple`:
-                If `return_dict` is True, an [`~models.unet_2d_condition.UNet2DConditionOutput`] is returned, otherwise
-                a `tuple` is returned where the first element is the sample tensor.
-        """
-        # By default samples have to be AT least a multiple of the overall upsampling factor.
-        # The overall upsampling factor is equal to 2 ** (# num of upsampling layers).
-        # However, the upsampling interpolation output size can be forced to fit any upsampling size
-        # on the fly if necessary.
-        default_overall_up_factor = 2**self.num_upsamplers
-
-        # upsample size should be forwarded when sample is not a multiple of `default_overall_up_factor`
-        forward_upsample_size = False
-        upsample_size = None
-
-        for dim in sample.shape[-2:]:
-            if dim % default_overall_up_factor != 0:
-                # Forward upsample size to force interpolation output size.
-                forward_upsample_size = True
-                break
-        # ensure attention_mask is a bias, and give it a singleton query_tokens dimension
-        # 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:
-            # 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(sample.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:
-            encoder_attention_mask = (1 - encoder_attention_mask.to(sample.dtype)) * -10000.0
-            encoder_attention_mask = encoder_attention_mask.unsqueeze(1)
-
-        # 0. center input if necessary
-        if self.config.center_input_sample:
-            sample = 2 * sample - 1.0
-
-        # 1. time
-        timesteps = timestep
-        if not torch.is_tensor(timesteps):
-            # 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 = sample.device.type == "mps"
-            if isinstance(timestep, float):
-                dtype = torch.float32 if is_mps else torch.float64
-            else:
-                dtype = torch.int32 if is_mps else torch.int64
-            timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device)
-        elif len(timesteps.shape) == 0:
-            timesteps = timesteps[None].to(sample.device)
-
-        # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
-        timesteps = timesteps.expand(sample.shape[0])
-
-        t_emb = self.time_proj(timesteps)
-
-        # `Timesteps` does not contain any weights and will always return f32 tensors
-        # but time_embedding might actually be running in fp16. so we need to cast here.
-        # there might be better ways to encapsulate this.
-        t_emb = t_emb.to(dtype=sample.dtype)
-
-        emb = self.time_embedding(t_emb, timestep_cond)
-        aug_emb = None
-
-        if self.class_embedding is not None:
-            if class_labels is None:
-                raise ValueError("class_labels should be provided when num_class_embeds > 0")
-
-            if self.config.class_embed_type == "timestep":
-                class_labels = self.time_proj(class_labels)
-
-                # `Timesteps` does not contain any weights and will always return f32 tensors
-                # there might be better ways to encapsulate this.
-                class_labels = class_labels.to(dtype=sample.dtype)
-
-            class_emb = self.class_embedding(class_labels).to(dtype=sample.dtype)
-
-            if self.config.class_embeddings_concat:
-                emb = torch.cat([emb, class_emb], dim=-1)
-            else:
-                emb = emb + class_emb
-
-        if self.config.addition_embed_type == "text":
-            aug_emb = self.add_embedding(encoder_hidden_states)
-        elif self.config.addition_embed_type == "text_image":
-            # Kandinsky 2.1 - style
-            if "image_embeds" not in added_cond_kwargs:
-                raise ValueError(
-                    f"{self.__class__} has the config param `addition_embed_type` set to 'text_image' which requires the keyword argument `image_embeds` to be passed in `added_cond_kwargs`"
-                )
-
-            image_embs = added_cond_kwargs.get("image_embeds")
-            text_embs = added_cond_kwargs.get("text_embeds", encoder_hidden_states)
-            aug_emb = self.add_embedding(text_embs, image_embs)
-        elif self.config.addition_embed_type == "text_time":
-            # SDXL - style
-            if "text_embeds" not in added_cond_kwargs:
-                raise ValueError(
-                    f"{self.__class__} has the config param `addition_embed_type` set to 'text_time' which requires the keyword argument `text_embeds` to be passed in `added_cond_kwargs`"
-                )
-            text_embeds = added_cond_kwargs.get("text_embeds")
-            if "time_ids" not in added_cond_kwargs:
-                raise ValueError(
-                    f"{self.__class__} has the config param `addition_embed_type` set to 'text_time' which requires the keyword argument `time_ids` to be passed in `added_cond_kwargs`"
-                )
-            time_ids = added_cond_kwargs.get("time_ids")
-            time_embeds = self.add_time_proj(time_ids.flatten())
-            time_embeds = time_embeds.reshape((text_embeds.shape[0], -1))
-            add_embeds = torch.concat([text_embeds, time_embeds], dim=-1)
-            add_embeds = add_embeds.to(emb.dtype)
-            aug_emb = self.add_embedding(add_embeds)
-        elif self.config.addition_embed_type == "image":
-            # Kandinsky 2.2 - style
-            if "image_embeds" not in added_cond_kwargs:
-                raise ValueError(
-                    f"{self.__class__} has the config param `addition_embed_type` set to 'image' which requires the keyword argument `image_embeds` to be passed in `added_cond_kwargs`"
-                )
-            image_embs = added_cond_kwargs.get("image_embeds")
-            aug_emb = self.add_embedding(image_embs)
-        elif self.config.addition_embed_type == "image_hint":
-            # Kandinsky 2.2 - style
-            if "image_embeds" not in added_cond_kwargs or "hint" not in added_cond_kwargs:
-                raise ValueError(
-                    f"{self.__class__} has the config param `addition_embed_type` set to 'image_hint' which requires the keyword arguments `image_embeds` and `hint` to be passed in `added_cond_kwargs`"
-                )
-            image_embs = added_cond_kwargs.get("image_embeds")
-            hint = added_cond_kwargs.get("hint")
-            aug_emb, hint = self.add_embedding(image_embs, hint)
-            sample = torch.cat([sample, hint], dim=1)
-
-        emb = emb + aug_emb if aug_emb is not None else emb
-
-        if self.time_embed_act is not None:
-            emb = self.time_embed_act(emb)
-
-        if self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == "text_proj":
-            encoder_hidden_states = self.encoder_hid_proj(encoder_hidden_states)
-        elif self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == "text_image_proj":
-            # Kadinsky 2.1 - style
-            if "image_embeds" not in added_cond_kwargs:
-                raise ValueError(
-                    f"{self.__class__} has the config param `encoder_hid_dim_type` set to 'text_image_proj' which requires the keyword argument `image_embeds` to be passed in  `added_conditions`"
-                )
-
-            image_embeds = added_cond_kwargs.get("image_embeds")
-            encoder_hidden_states = self.encoder_hid_proj(encoder_hidden_states, image_embeds)
-        elif self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == "image_proj":
-            # Kandinsky 2.2 - style
-            if "image_embeds" not in added_cond_kwargs:
-                raise ValueError(
-                    f"{self.__class__} has the config param `encoder_hid_dim_type` set to 'image_proj' which requires the keyword argument `image_embeds` to be passed in  `added_conditions`"
-                )
-            image_embeds = added_cond_kwargs.get("image_embeds")
-            encoder_hidden_states = self.encoder_hid_proj(image_embeds)
-        elif self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == "ip_image_proj":
-            if "image_embeds" not in added_cond_kwargs:
-                raise ValueError(
-                    f"{self.__class__} has the config param `encoder_hid_dim_type` set to 'ip_image_proj' which requires the keyword argument `image_embeds` to be passed in  `added_conditions`"
-                )
-            image_embeds = added_cond_kwargs.get("image_embeds")
-            # print(image_embeds.shape)
-            # image_embeds = self.encoder_hid_proj(image_embeds).to(encoder_hidden_states.dtype)
-            encoder_hidden_states = torch.cat([encoder_hidden_states, image_embeds], dim=1)
-
-        # 2. pre-process
-        sample = self.conv_in(sample)
-
-        # 2.5 GLIGEN position net
-        if cross_attention_kwargs is not None and cross_attention_kwargs.get("gligen", None) is not None:
-            cross_attention_kwargs = cross_attention_kwargs.copy()
-            gligen_args = cross_attention_kwargs.pop("gligen")
-            cross_attention_kwargs["gligen"] = {"objs": self.position_net(**gligen_args)}
-
-
-        curr_garment_feat_idx = 0
-
-
-        # 3. down
-        lora_scale = cross_attention_kwargs.get("scale", 1.0) if cross_attention_kwargs is not None else 1.0
-        if USE_PEFT_BACKEND:
-            # weight the lora layers by setting `lora_scale` for each PEFT layer
-            scale_lora_layers(self, lora_scale)
-
-        is_controlnet = mid_block_additional_residual is not None and down_block_additional_residuals is not None
-        # using new arg down_intrablock_additional_residuals for T2I-Adapters, to distinguish from controlnets
-        is_adapter = down_intrablock_additional_residuals is not None
-        # maintain backward compatibility for legacy usage, where
-        #       T2I-Adapter and ControlNet both use down_block_additional_residuals arg
-        #       but can only use one or the other
-        if not is_adapter and mid_block_additional_residual is None and down_block_additional_residuals is not None:
-            deprecate(
-                "T2I should not use down_block_additional_residuals",
-                "1.3.0",
-                "Passing intrablock residual connections with `down_block_additional_residuals` is deprecated \
-                       and will be removed in diffusers 1.3.0.  `down_block_additional_residuals` should only be used \
-                       for ControlNet. Please make sure use `down_intrablock_additional_residuals` instead. ",
-                standard_warn=False,
-            )
-            down_intrablock_additional_residuals = down_block_additional_residuals
-            is_adapter = True
-
-        down_block_res_samples = (sample,)
-        for downsample_block in self.down_blocks:
-            if hasattr(downsample_block, "has_cross_attention") and downsample_block.has_cross_attention:
-                # For t2i-adapter CrossAttnDownBlock2D
-                additional_residuals = {}
-                if is_adapter and len(down_intrablock_additional_residuals) > 0:
-                    additional_residuals["additional_residuals"] = down_intrablock_additional_residuals.pop(0)
-
-                sample, res_samples,curr_garment_feat_idx = downsample_block(
-                    hidden_states=sample,
-                    temb=emb,
-                    encoder_hidden_states=encoder_hidden_states,
-                    attention_mask=attention_mask,
-                    cross_attention_kwargs=cross_attention_kwargs,
-                    encoder_attention_mask=encoder_attention_mask,
-                    garment_features=garment_features,
-                    curr_garment_feat_idx=curr_garment_feat_idx,
-                    **additional_residuals,
-                )
-            else:
-                sample, res_samples = downsample_block(hidden_states=sample, temb=emb, scale=lora_scale)
-                if is_adapter and len(down_intrablock_additional_residuals) > 0:
-                    sample += down_intrablock_additional_residuals.pop(0)
-
-            down_block_res_samples += res_samples
-
-
-        if is_controlnet:
-            new_down_block_res_samples = ()
-
-            for down_block_res_sample, down_block_additional_residual in zip(
-                down_block_res_samples, down_block_additional_residuals
-            ):
-                down_block_res_sample = down_block_res_sample + down_block_additional_residual
-                new_down_block_res_samples = new_down_block_res_samples + (down_block_res_sample,)
-
-            down_block_res_samples = new_down_block_res_samples
-
-        # 4. mid
-        if self.mid_block is not None:
-            if hasattr(self.mid_block, "has_cross_attention") and self.mid_block.has_cross_attention:
-                sample ,curr_garment_feat_idx= self.mid_block(
-                    sample,
-                    emb,
-                    encoder_hidden_states=encoder_hidden_states,
-                    attention_mask=attention_mask,
-                    cross_attention_kwargs=cross_attention_kwargs,
-                    encoder_attention_mask=encoder_attention_mask,
-                    garment_features=garment_features,
-                    curr_garment_feat_idx=curr_garment_feat_idx,
-                )
-            else:
-                sample = self.mid_block(sample, emb)
-
-            # To support T2I-Adapter-XL
-            if (
-                is_adapter
-                and len(down_intrablock_additional_residuals) > 0
-                and sample.shape == down_intrablock_additional_residuals[0].shape
-            ):
-                sample += down_intrablock_additional_residuals.pop(0)
-
-        if is_controlnet:
-            sample = sample + mid_block_additional_residual
-
-
-
-        # 5. up
-        for i, upsample_block in enumerate(self.up_blocks):
-            is_final_block = i == len(self.up_blocks) - 1
-
-            res_samples = down_block_res_samples[-len(upsample_block.resnets) :]
-            down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)]
-
-            # if we have not reached the final block and need to forward the
-            # upsample size, we do it here
-            if not is_final_block and forward_upsample_size:
-                upsample_size = down_block_res_samples[-1].shape[2:]
-
-            if hasattr(upsample_block, "has_cross_attention") and upsample_block.has_cross_attention:
-                sample ,curr_garment_feat_idx= upsample_block(
-                    hidden_states=sample,
-                    temb=emb,
-                    res_hidden_states_tuple=res_samples,
-                    encoder_hidden_states=encoder_hidden_states,
-                    cross_attention_kwargs=cross_attention_kwargs,
-                    upsample_size=upsample_size,
-                    attention_mask=attention_mask,
-                    encoder_attention_mask=encoder_attention_mask,
-                    garment_features=garment_features,
-                    curr_garment_feat_idx=curr_garment_feat_idx,
-                )
-
-            else:
-                sample = upsample_block(
-                    hidden_states=sample,
-                    temb=emb,
-                    res_hidden_states_tuple=res_samples,
-                    upsample_size=upsample_size,
-                    scale=lora_scale,
-                )
-        # 6. post-process
-        if self.conv_norm_out:
-            sample = self.conv_norm_out(sample)
-            sample = self.conv_act(sample)
-        sample = self.conv_out(sample)
-
-        if USE_PEFT_BACKEND:
-            # remove `lora_scale` from each PEFT layer
-            unscale_lora_layers(self, lora_scale)
-
-        if not return_dict:
-            return (sample,)
-
-        return UNet2DConditionOutput(sample=sample)