diff --git a/MagicQuill/.DS_Store b/MagicQuill/.DS_Store
deleted file mode 100644
index 92f255efe283219850b962b09db1053a14ccd5ca..0000000000000000000000000000000000000000
Binary files a/MagicQuill/.DS_Store and /dev/null differ
diff --git a/MagicQuill/brushnet/brushnet.json b/MagicQuill/brushnet/brushnet.json
deleted file mode 100644
index 65713bfcd0113271496bd06fe6b57299822e0f76..0000000000000000000000000000000000000000
--- a/MagicQuill/brushnet/brushnet.json
+++ /dev/null
@@ -1,58 +0,0 @@
-{
-  "_class_name": "BrushNetModel",
-  "_diffusers_version": "0.27.0.dev0",
-  "_name_or_path": "runs/logs/brushnet_randommask/checkpoint-100000",
-  "act_fn": "silu",
-  "addition_embed_type": null,
-  "addition_embed_type_num_heads": 64,
-  "addition_time_embed_dim": null,
-  "attention_head_dim": 8,
-  "block_out_channels": [
-    320,
-    640,
-    1280,
-    1280
-  ],
-  "brushnet_conditioning_channel_order": "rgb",
-  "class_embed_type": null,
-  "conditioning_channels": 5,
-  "conditioning_embedding_out_channels": [
-    16,
-    32,
-    96,
-    256
-  ],
-  "cross_attention_dim": 768,
-  "down_block_types": [
-    "DownBlock2D",
-    "DownBlock2D",
-    "DownBlock2D",
-    "DownBlock2D"
-  ],
-  "downsample_padding": 1,
-  "encoder_hid_dim": null,
-  "encoder_hid_dim_type": null,
-  "flip_sin_to_cos": true,
-  "freq_shift": 0,
-  "global_pool_conditions": false,
-  "in_channels": 4,
-  "layers_per_block": 2,
-  "mid_block_scale_factor": 1,
-  "mid_block_type": "MidBlock2D",
-  "norm_eps": 1e-05,
-  "norm_num_groups": 32,
-  "num_attention_heads": null,
-  "num_class_embeds": null,
-  "only_cross_attention": false,
-  "projection_class_embeddings_input_dim": null,
-  "resnet_time_scale_shift": "default",
-  "transformer_layers_per_block": 1,
-  "up_block_types": [
-    "UpBlock2D",
-    "UpBlock2D",
-    "UpBlock2D",
-    "UpBlock2D"
-  ],
-  "upcast_attention": false,
-  "use_linear_projection": false
-}
diff --git a/MagicQuill/brushnet/brushnet.py b/MagicQuill/brushnet/brushnet.py
deleted file mode 100644
index aed1cfde30b1ab27286066746058b7b1afcd8a84..0000000000000000000000000000000000000000
--- a/MagicQuill/brushnet/brushnet.py
+++ /dev/null
@@ -1,949 +0,0 @@
-from dataclasses import dataclass
-from typing import Any, Dict, List, Optional, Tuple, Union
-
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from diffusers.configuration_utils import ConfigMixin, register_to_config
-from diffusers.utils import BaseOutput, logging
-from diffusers.models.attention_processor import (
-    ADDED_KV_ATTENTION_PROCESSORS,
-    CROSS_ATTENTION_PROCESSORS,
-    AttentionProcessor,
-    AttnAddedKVProcessor,
-    AttnProcessor,
-)
-from diffusers.models.embeddings import TextImageProjection, TextImageTimeEmbedding, TextTimeEmbedding, TimestepEmbedding, Timesteps
-from diffusers.models.modeling_utils import ModelMixin
-
-from .unet_2d_blocks import (
-    CrossAttnDownBlock2D,
-    DownBlock2D,
-    UNetMidBlock2D,
-    UNetMidBlock2DCrossAttn,
-    get_down_block,
-    get_mid_block,
-    get_up_block,
-    MidBlock2D
-)
-
-from .unet_2d_condition import UNet2DConditionModel
-
-
-logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
-
-
-@dataclass
-class BrushNetOutput(BaseOutput):
-    """
-    The output of [`BrushNetModel`].
-
-    Args:
-        up_block_res_samples (`tuple[torch.Tensor]`):
-            A tuple of upsample activations at different resolutions for each upsampling block. Each tensor should
-            be of shape `(batch_size, channel * resolution, height //resolution, width // resolution)`. Output can be
-            used to condition the original UNet's upsampling activations.
-        down_block_res_samples (`tuple[torch.Tensor]`):
-            A tuple of downsample activations at different resolutions for each downsampling block. Each tensor should
-            be of shape `(batch_size, channel * resolution, height //resolution, width // resolution)`. Output can be
-            used to condition the original UNet's downsampling activations.
-        mid_down_block_re_sample (`torch.Tensor`):
-            The activation of the midde block (the lowest sample resolution). Each tensor should be of shape
-            `(batch_size, channel * lowest_resolution, height // lowest_resolution, width // lowest_resolution)`.
-            Output can be used to condition the original UNet's middle block activation.
-    """
-
-    up_block_res_samples: Tuple[torch.Tensor]
-    down_block_res_samples: Tuple[torch.Tensor]
-    mid_block_res_sample: torch.Tensor
-
-
-class BrushNetModel(ModelMixin, ConfigMixin):
-    """
-    A BrushNet model.
-
-    Args:
-        in_channels (`int`, defaults to 4):
-            The number of channels in the input sample.
-        flip_sin_to_cos (`bool`, defaults to `True`):
-            Whether to flip the sin to cos in the time embedding.
-        freq_shift (`int`, defaults to 0):
-            The frequency shift to apply to the time embedding.
-        down_block_types (`tuple[str]`, 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 (`Union[bool, Tuple[bool]]`, defaults to `False`):
-        block_out_channels (`tuple[int]`, defaults to `(320, 640, 1280, 1280)`):
-            The tuple of output channels for each block.
-        layers_per_block (`int`, defaults to 2):
-            The number of layers per block.
-        downsample_padding (`int`, defaults to 1):
-            The padding to use for the downsampling convolution.
-        mid_block_scale_factor (`float`, defaults to 1):
-            The scale factor to use for the mid block.
-        act_fn (`str`, 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`, defaults to 1e-5):
-            The epsilon to use for the normalization.
-        cross_attention_dim (`int`, defaults to 1280):
-            The dimension of the cross attention features.
-        transformer_layers_per_block (`int` or `Tuple[int]`, *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`].
-        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 (`Union[int, Tuple[int]]`, defaults to 8):
-            The dimension of the attention heads.
-        use_linear_projection (`bool`, defaults to `False`):
-        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.
-        num_class_embeds (`int`, *optional*, defaults to 0):
-            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`.
-        upcast_attention (`bool`, defaults to `False`):
-        resnet_time_scale_shift (`str`, defaults to `"default"`):
-            Time scale shift config for ResNet blocks (see `ResnetBlock2D`). Choose from `default` or `scale_shift`.
-        projection_class_embeddings_input_dim (`int`, *optional*, defaults to `None`):
-            The dimension of the `class_labels` input when `class_embed_type="projection"`. Required when
-            `class_embed_type="projection"`.
-        brushnet_conditioning_channel_order (`str`, defaults to `"rgb"`):
-            The channel order of conditional image. Will convert to `rgb` if it's `bgr`.
-        conditioning_embedding_out_channels (`tuple[int]`, *optional*, defaults to `(16, 32, 96, 256)`):
-            The tuple of output channel for each block in the `conditioning_embedding` layer.
-        global_pool_conditions (`bool`, defaults to `False`):
-            TODO(Patrick) - unused parameter.
-        addition_embed_type_num_heads (`int`, defaults to 64):
-            The number of heads to use for the `TextTimeEmbedding` layer.
-    """
-
-    _supports_gradient_checkpointing = True
-
-    @register_to_config
-    def __init__(
-        self,
-        in_channels: int = 4,
-        conditioning_channels: int = 5,
-        flip_sin_to_cos: bool = True,
-        freq_shift: int = 0,
-        down_block_types: Tuple[str, ...] = (
-            "DownBlock2D",
-            "DownBlock2D",
-            "DownBlock2D",
-            "DownBlock2D",
-        ),
-        mid_block_type: Optional[str] = "UNetMidBlock2D",
-        up_block_types: Tuple[str, ...] = (
-            "UpBlock2D", 
-            "UpBlock2D", 
-            "UpBlock2D", 
-            "UpBlock2D",
-        ),
-        only_cross_attention: Union[bool, Tuple[bool]] = False,
-        block_out_channels: Tuple[int, ...] = (320, 640, 1280, 1280),
-        layers_per_block: int = 2,
-        downsample_padding: int = 1,
-        mid_block_scale_factor: float = 1,
-        act_fn: str = "silu",
-        norm_num_groups: Optional[int] = 32,
-        norm_eps: float = 1e-5,
-        cross_attention_dim: int = 1280,
-        transformer_layers_per_block: Union[int, Tuple[int, ...]] = 1,
-        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,
-        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",
-        projection_class_embeddings_input_dim: Optional[int] = None,
-        brushnet_conditioning_channel_order: str = "rgb",
-        conditioning_embedding_out_channels: Optional[Tuple[int, ...]] = (16, 32, 96, 256),
-        global_pool_conditions: bool = False,
-        addition_embed_type_num_heads: int = 64,
-    ):
-        super().__init__()
-
-        # 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 isinstance(transformer_layers_per_block, int):
-            transformer_layers_per_block = [transformer_layers_per_block] * len(down_block_types)
-
-        # input
-        conv_in_kernel = 3
-        conv_in_padding = (conv_in_kernel - 1) // 2
-        self.conv_in_condition = nn.Conv2d(
-            in_channels+conditioning_channels, block_out_channels[0], kernel_size=conv_in_kernel, padding=conv_in_padding
-        )
-
-        # time
-        time_embed_dim = 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]
-        self.time_embedding = TimestepEmbedding(
-            timestep_input_dim,
-            time_embed_dim,
-            act_fn=act_fn,
-        )
-
-        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 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)
-        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)
-        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 is not None:
-            raise ValueError(f"addition_embed_type: {addition_embed_type} must be None, 'text' or 'text_image'.")
-
-        self.down_blocks = nn.ModuleList([])
-        self.brushnet_down_blocks = nn.ModuleList([])
-
-        if isinstance(only_cross_attention, bool):
-            only_cross_attention = [only_cross_attention] * len(down_block_types)
-
-        if isinstance(attention_head_dim, int):
-            attention_head_dim = (attention_head_dim,) * len(down_block_types)
-
-        if isinstance(num_attention_heads, int):
-            num_attention_heads = (num_attention_heads,) * len(down_block_types)
-
-        # down
-        output_channel = block_out_channels[0]
-
-        brushnet_block = nn.Conv2d(output_channel, output_channel, kernel_size=1)
-        brushnet_block = zero_module(brushnet_block)
-        self.brushnet_down_blocks.append(brushnet_block)
-
-        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,
-                transformer_layers_per_block=transformer_layers_per_block[i],
-                in_channels=input_channel,
-                out_channels=output_channel,
-                temb_channels=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,
-                num_attention_heads=num_attention_heads[i],
-                attention_head_dim=attention_head_dim[i] if attention_head_dim[i] is not None else output_channel,
-                downsample_padding=downsample_padding,
-                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,
-            )
-            self.down_blocks.append(down_block)
-
-            for _ in range(layers_per_block):
-                brushnet_block = nn.Conv2d(output_channel, output_channel, kernel_size=1)
-                brushnet_block = zero_module(brushnet_block)
-                self.brushnet_down_blocks.append(brushnet_block)
-
-            if not is_final_block:
-                brushnet_block = nn.Conv2d(output_channel, output_channel, kernel_size=1)
-                brushnet_block = zero_module(brushnet_block)
-                self.brushnet_down_blocks.append(brushnet_block)
-
-        # mid
-        mid_block_channel = block_out_channels[-1]
-
-        brushnet_block = nn.Conv2d(mid_block_channel, mid_block_channel, kernel_size=1)
-        brushnet_block = zero_module(brushnet_block)
-        self.brushnet_mid_block = brushnet_block
-
-        self.mid_block = get_mid_block(
-                mid_block_type,
-                transformer_layers_per_block=transformer_layers_per_block[-1],
-                in_channels=mid_block_channel,
-                temb_channels=time_embed_dim,
-                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,
-                num_attention_heads=num_attention_heads[-1],
-                resnet_groups=norm_num_groups,
-                use_linear_projection=use_linear_projection,
-                upcast_attention=upcast_attention,
-        )
-
-        # 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_transformer_layers_per_block = (list(reversed(transformer_layers_per_block)))
-        only_cross_attention = list(reversed(only_cross_attention))
-
-        output_channel = reversed_block_out_channels[0]
-        
-        self.up_blocks = nn.ModuleList([])
-        self.brushnet_up_blocks = nn.ModuleList([])
-
-        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=layers_per_block+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=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=cross_attention_dim,
-                num_attention_heads=reversed_num_attention_heads[i],
-                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_head_dim=attention_head_dim[i] if attention_head_dim[i] is not None else output_channel,
-            )
-            self.up_blocks.append(up_block)
-            prev_output_channel = output_channel
-            
-            for _ in range(layers_per_block+1):
-                brushnet_block = nn.Conv2d(output_channel, output_channel, kernel_size=1)
-                brushnet_block = zero_module(brushnet_block)
-                self.brushnet_up_blocks.append(brushnet_block)
-
-            if not is_final_block:
-                brushnet_block = nn.Conv2d(output_channel, output_channel, kernel_size=1)
-                brushnet_block = zero_module(brushnet_block)
-                self.brushnet_up_blocks.append(brushnet_block)
-
-
-    @classmethod
-    def from_unet(
-        cls,
-        unet: UNet2DConditionModel,
-        brushnet_conditioning_channel_order: str = "rgb",
-        conditioning_embedding_out_channels: Optional[Tuple[int, ...]] = (16, 32, 96, 256),
-        load_weights_from_unet: bool = True,
-        conditioning_channels: int = 5,
-    ):
-        r"""
-        Instantiate a [`BrushNetModel`] from [`UNet2DConditionModel`].
-
-        Parameters:
-            unet (`UNet2DConditionModel`):
-                The UNet model weights to copy to the [`BrushNetModel`]. All configuration options are also copied
-                where applicable.
-        """
-        transformer_layers_per_block = (
-            unet.config.transformer_layers_per_block if "transformer_layers_per_block" in unet.config else 1
-        )
-        encoder_hid_dim = unet.config.encoder_hid_dim if "encoder_hid_dim" in unet.config else None
-        encoder_hid_dim_type = unet.config.encoder_hid_dim_type if "encoder_hid_dim_type" in unet.config else None
-        addition_embed_type = unet.config.addition_embed_type if "addition_embed_type" in unet.config else None
-        addition_time_embed_dim = (
-            unet.config.addition_time_embed_dim if "addition_time_embed_dim" in unet.config else None
-        )
-
-        brushnet = cls(
-            in_channels=unet.config.in_channels,
-            conditioning_channels=conditioning_channels,
-            flip_sin_to_cos=unet.config.flip_sin_to_cos,
-            freq_shift=unet.config.freq_shift,
-            down_block_types=["DownBlock2D" for block_name in unet.config.down_block_types],
-            mid_block_type='MidBlock2D',
-            up_block_types=["UpBlock2D" for block_name in unet.config.down_block_types],
-            only_cross_attention=unet.config.only_cross_attention,
-            block_out_channels=unet.config.block_out_channels,
-            layers_per_block=unet.config.layers_per_block,
-            downsample_padding=unet.config.downsample_padding,
-            mid_block_scale_factor=unet.config.mid_block_scale_factor,
-            act_fn=unet.config.act_fn,
-            norm_num_groups=unet.config.norm_num_groups,
-            norm_eps=unet.config.norm_eps,
-            cross_attention_dim=unet.config.cross_attention_dim,
-            transformer_layers_per_block=transformer_layers_per_block,
-            encoder_hid_dim=encoder_hid_dim,
-            encoder_hid_dim_type=encoder_hid_dim_type,
-            attention_head_dim=unet.config.attention_head_dim,
-            num_attention_heads=unet.config.num_attention_heads,
-            use_linear_projection=unet.config.use_linear_projection,
-            class_embed_type=unet.config.class_embed_type,
-            addition_embed_type=addition_embed_type,
-            addition_time_embed_dim=addition_time_embed_dim,
-            num_class_embeds=unet.config.num_class_embeds,
-            upcast_attention=unet.config.upcast_attention,
-            resnet_time_scale_shift=unet.config.resnet_time_scale_shift,
-            projection_class_embeddings_input_dim=unet.config.projection_class_embeddings_input_dim,
-            brushnet_conditioning_channel_order=brushnet_conditioning_channel_order,
-            conditioning_embedding_out_channels=conditioning_embedding_out_channels,
-        )
-
-        if load_weights_from_unet:
-            conv_in_condition_weight=torch.zeros_like(brushnet.conv_in_condition.weight)
-            conv_in_condition_weight[:,:4,...]=unet.conv_in.weight
-            conv_in_condition_weight[:,4:8,...]=unet.conv_in.weight
-            brushnet.conv_in_condition.weight=torch.nn.Parameter(conv_in_condition_weight)
-            brushnet.conv_in_condition.bias=unet.conv_in.bias
-
-            brushnet.time_proj.load_state_dict(unet.time_proj.state_dict())
-            brushnet.time_embedding.load_state_dict(unet.time_embedding.state_dict())
-
-            if brushnet.class_embedding:
-                brushnet.class_embedding.load_state_dict(unet.class_embedding.state_dict())
-
-            brushnet.down_blocks.load_state_dict(unet.down_blocks.state_dict(),strict=False)
-            brushnet.mid_block.load_state_dict(unet.mid_block.state_dict(),strict=False)
-            brushnet.up_blocks.load_state_dict(unet.up_blocks.state_dict(),strict=False)
-
-        return brushnet
-
-    @property
-    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.attn_processors
-    def attn_processors(self) -> Dict[str, AttentionProcessor]:
-        r"""
-        Returns:
-            `dict` of attention processors: A dictionary containing all attention processors used in the model with
-            indexed by its weight name.
-        """
-        # set recursively
-        processors = {}
-
-        def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttentionProcessor]):
-            if hasattr(module, "get_processor"):
-                processors[f"{name}.processor"] = module.get_processor(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
-
-    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_attn_processor
-    def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]):
-        r"""
-        Sets the attention processor to use to compute attention.
-
-        Parameters:
-            processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`):
-                The instantiated processor class or a dictionary of processor classes that will be set as the processor
-                for **all** `Attention` layers.
-
-                If `processor` is a dict, the key needs to define the path to the corresponding cross attention
-                processor. This is strongly recommended when setting trainable attention processors.
-
-        """
-        count = len(self.attn_processors.keys())
-
-        if isinstance(processor, dict) and len(processor) != count:
-            raise ValueError(
-                f"A dict of processors was passed, but the number of processors {len(processor)} does not match the"
-                f" number of attention layers: {count}. Please make sure to pass {count} processor classes."
-            )
-
-        def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):
-            if hasattr(module, "set_processor"):
-                if not isinstance(processor, dict):
-                    module.set_processor(processor)
-                else:
-                    module.set_processor(processor.pop(f"{name}.processor"))
-
-            for sub_name, child in module.named_children():
-                fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor)
-
-        for name, module in self.named_children():
-            fn_recursive_attn_processor(name, module, processor)
-
-    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_default_attn_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)
-
-    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_attention_slice
-    def set_attention_slice(self, slice_size: Union[str, int, List[int]]) -> None:
-        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: bool = False) -> None:
-        if isinstance(module, (CrossAttnDownBlock2D, DownBlock2D)):
-            module.gradient_checkpointing = value
-
-    def forward(
-        self,
-        sample: torch.FloatTensor,
-        encoder_hidden_states: torch.Tensor,
-        brushnet_cond: torch.FloatTensor,
-        timestep = None,
-        time_emb = None,
-        conditioning_scale: float = 1.0,
-        class_labels: Optional[torch.Tensor] = None,
-        timestep_cond: Optional[torch.Tensor] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        added_cond_kwargs: Optional[Dict[str, torch.Tensor]] = None,
-        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
-        guess_mode: bool = False,
-        return_dict: bool = True,
-        debug = False,
-    ) -> Union[BrushNetOutput, Tuple[Tuple[torch.FloatTensor, ...], torch.FloatTensor]]:
-        """
-        The [`BrushNetModel`] forward method.
-
-        Args:
-            sample (`torch.FloatTensor`):
-                The noisy input tensor.
-            timestep (`Union[torch.Tensor, float, int]`):
-                The number of timesteps to denoise an input.
-            encoder_hidden_states (`torch.Tensor`):
-                The encoder hidden states.
-            brushnet_cond (`torch.FloatTensor`):
-                The conditional input tensor of shape `(batch_size, sequence_length, hidden_size)`.
-            conditioning_scale (`float`, defaults to `1.0`):
-                The scale factor for BrushNet outputs.
-            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`):
-                Additional conditional embeddings for timestep. If provided, the embeddings will be summed with the
-                timestep_embedding passed through the `self.time_embedding` layer to obtain the final 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.
-            added_cond_kwargs (`dict`):
-                Additional conditions for the Stable Diffusion XL UNet.
-            cross_attention_kwargs (`dict[str]`, *optional*, defaults to `None`):
-                A kwargs dictionary that if specified is passed along to the `AttnProcessor`.
-            guess_mode (`bool`, defaults to `False`):
-                In this mode, the BrushNet encoder tries its best to recognize the input content of the input even if
-                you remove all prompts. A `guidance_scale` between 3.0 and 5.0 is recommended.
-            return_dict (`bool`, defaults to `True`):
-                Whether or not to return a [`~models.brushnet.BrushNetOutput`] instead of a plain tuple.
-
-        Returns:
-            [`~models.brushnet.BrushNetOutput`] **or** `tuple`:
-                If `return_dict` is `True`, a [`~models.brushnet.BrushNetOutput`] is returned, otherwise a tuple is
-                returned where the first element is the sample tensor.
-        """
-
-        # check channel order
-        channel_order = self.config.brushnet_conditioning_channel_order
-
-        if channel_order == "rgb":
-            # in rgb order by default
-            ...
-        elif channel_order == "bgr":
-            brushnet_cond = torch.flip(brushnet_cond, dims=[1])
-        else:
-            raise ValueError(f"unknown `brushnet_conditioning_channel_order`: {channel_order}")
-
-        # prepare attention_mask
-        if attention_mask is not None:
-            attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0
-            attention_mask = attention_mask.unsqueeze(1)
-
-        if timestep is None and time_emb is None:
-            raise ValueError(f"`timestep` and `emb` are both None")
-        
-        #print("BN: sample.device", sample.device)
-        #print("BN: TE.device", self.time_embedding.linear_1.weight.device)
-
-        if timestep is not None:
-            # 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)
-
-            #print("t_emb.device =",t_emb.device)
-
-            emb = self.time_embedding(t_emb, timestep_cond)
-            aug_emb = None
-
-            #print('emb.shape', emb.shape)
-
-            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)
-
-                class_emb = self.class_embedding(class_labels).to(dtype=self.dtype)
-                emb = emb + class_emb
-
-            if self.config.addition_embed_type is not None:
-                if self.config.addition_embed_type == "text":
-                    aug_emb = self.add_embedding(encoder_hidden_states)
-
-                elif self.config.addition_embed_type == "text_time":
-                    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)
-
-                    #print('text_embeds', text_embeds.shape, 'time_ids', time_ids.shape, 'time_embeds', time_embeds.shape, 'add__embeds', add_embeds.shape, 'aug_emb', aug_emb.shape)
-
-            emb = emb + aug_emb if aug_emb is not None else emb
-        else:
-            emb = time_emb
-        
-        # 2. pre-process
-
-        brushnet_cond=torch.concat([sample,brushnet_cond],1)
-        sample = self.conv_in_condition(brushnet_cond)
-
-        # 3. down
-        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:
-                sample, res_samples = downsample_block(
-                    hidden_states=sample,
-                    temb=emb,
-                    encoder_hidden_states=encoder_hidden_states,
-                    attention_mask=attention_mask,
-                    cross_attention_kwargs=cross_attention_kwargs,
-                )
-            else:
-                sample, res_samples = downsample_block(hidden_states=sample, temb=emb)
-
-            down_block_res_samples += res_samples
-
-        # 4. PaintingNet down blocks
-        brushnet_down_block_res_samples = ()
-        for down_block_res_sample, brushnet_down_block in zip(down_block_res_samples, self.brushnet_down_blocks):
-            down_block_res_sample = brushnet_down_block(down_block_res_sample)
-            brushnet_down_block_res_samples = brushnet_down_block_res_samples + (down_block_res_sample,)
-
-
-        # 5. mid
-        if self.mid_block is not None:
-            if hasattr(self.mid_block, "has_cross_attention") and self.mid_block.has_cross_attention:
-                sample = self.mid_block(
-                    sample,
-                    emb,
-                    encoder_hidden_states=encoder_hidden_states,
-                    attention_mask=attention_mask,
-                    cross_attention_kwargs=cross_attention_kwargs,
-                )
-            else:
-                sample = self.mid_block(sample, emb)
-
-        # 6. BrushNet mid blocks
-        brushnet_mid_block_res_sample = self.brushnet_mid_block(sample)
-
-        # 7. up
-        up_block_res_samples = ()
-        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:
-                upsample_size = down_block_res_samples[-1].shape[2:]
-
-            if hasattr(upsample_block, "has_cross_attention") and upsample_block.has_cross_attention:
-                sample, up_res_samples = 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,
-                    return_res_samples=True
-                )
-            else:
-                sample, up_res_samples = upsample_block(
-                    hidden_states=sample,
-                    temb=emb,
-                    res_hidden_states_tuple=res_samples,
-                    upsample_size=upsample_size,
-                    return_res_samples=True
-                )
-
-            up_block_res_samples += up_res_samples
-
-        # 8. BrushNet up blocks
-        brushnet_up_block_res_samples = ()
-        for up_block_res_sample, brushnet_up_block in zip(up_block_res_samples, self.brushnet_up_blocks):
-            up_block_res_sample = brushnet_up_block(up_block_res_sample)
-            brushnet_up_block_res_samples = brushnet_up_block_res_samples + (up_block_res_sample,)
-
-        # 6. scaling
-        if guess_mode and not self.config.global_pool_conditions:
-            scales = torch.logspace(-1, 0, len(brushnet_down_block_res_samples) + 1 + len(brushnet_up_block_res_samples), device=sample.device)  # 0.1 to 1.0
-            scales = scales * conditioning_scale
-
-            brushnet_down_block_res_samples = [sample * scale for sample, scale in zip(brushnet_down_block_res_samples, scales[:len(brushnet_down_block_res_samples)])]
-            brushnet_mid_block_res_sample = brushnet_mid_block_res_sample * scales[len(brushnet_down_block_res_samples)]
-            brushnet_up_block_res_samples = [sample * scale for sample, scale in zip(brushnet_up_block_res_samples, scales[len(brushnet_down_block_res_samples)+1:])]
-        else:
-            brushnet_down_block_res_samples = [sample * conditioning_scale for sample in brushnet_down_block_res_samples]
-            brushnet_mid_block_res_sample = brushnet_mid_block_res_sample * conditioning_scale
-            brushnet_up_block_res_samples = [sample * conditioning_scale for sample in brushnet_up_block_res_samples]
-
-
-        if self.config.global_pool_conditions:
-            brushnet_down_block_res_samples = [
-                torch.mean(sample, dim=(2, 3), keepdim=True) for sample in brushnet_down_block_res_samples
-            ]
-            brushnet_mid_block_res_sample = torch.mean(brushnet_mid_block_res_sample, dim=(2, 3), keepdim=True)
-            brushnet_up_block_res_samples = [
-                torch.mean(sample, dim=(2, 3), keepdim=True) for sample in brushnet_up_block_res_samples
-            ]
-
-        if not return_dict:
-            return (brushnet_down_block_res_samples, brushnet_mid_block_res_sample, brushnet_up_block_res_samples)
-
-        return BrushNetOutput(
-            down_block_res_samples=brushnet_down_block_res_samples, 
-            mid_block_res_sample=brushnet_mid_block_res_sample,
-            up_block_res_samples=brushnet_up_block_res_samples
-        )
-
-
-def zero_module(module):
-    for p in module.parameters():
-        nn.init.zeros_(p)
-    return module
diff --git a/MagicQuill/brushnet/brushnet_ca.py b/MagicQuill/brushnet/brushnet_ca.py
deleted file mode 100644
index 780a87b23f30e2192a19469c506a22056ea52ba7..0000000000000000000000000000000000000000
--- a/MagicQuill/brushnet/brushnet_ca.py
+++ /dev/null
@@ -1,983 +0,0 @@
-from dataclasses import dataclass
-from typing import Any, Dict, List, Optional, Tuple, Union
-
-import torch
-from torch import nn
-
-from diffusers.configuration_utils import ConfigMixin, register_to_config
-from diffusers.utils import BaseOutput, logging
-from diffusers.models.attention_processor import (
-    ADDED_KV_ATTENTION_PROCESSORS,
-    CROSS_ATTENTION_PROCESSORS,
-    AttentionProcessor,
-    AttnAddedKVProcessor,
-    AttnProcessor,
-)
-from diffusers.models.embeddings import TextImageProjection, TextImageTimeEmbedding, TextTimeEmbedding, TimestepEmbedding, Timesteps
-from diffusers.models.modeling_utils import ModelMixin
-
-from .unet_2d_blocks import (
-    CrossAttnDownBlock2D,
-    DownBlock2D,
-    UNetMidBlock2D,
-    UNetMidBlock2DCrossAttn,
-    get_down_block,
-    get_mid_block,
-    get_up_block,
-    MidBlock2D
-)
-
-from .unet_2d_condition import UNet2DConditionModel
-
-
-logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
-
-
-@dataclass
-class BrushNetOutput(BaseOutput):
-    """
-    The output of [`BrushNetModel`].
-
-    Args:
-        up_block_res_samples (`tuple[torch.Tensor]`):
-            A tuple of upsample activations at different resolutions for each upsampling block. Each tensor should
-            be of shape `(batch_size, channel * resolution, height //resolution, width // resolution)`. Output can be
-            used to condition the original UNet's upsampling activations.
-        down_block_res_samples (`tuple[torch.Tensor]`):
-            A tuple of downsample activations at different resolutions for each downsampling block. Each tensor should
-            be of shape `(batch_size, channel * resolution, height //resolution, width // resolution)`. Output can be
-            used to condition the original UNet's downsampling activations.
-        mid_down_block_re_sample (`torch.Tensor`):
-            The activation of the midde block (the lowest sample resolution). Each tensor should be of shape
-            `(batch_size, channel * lowest_resolution, height // lowest_resolution, width // lowest_resolution)`.
-            Output can be used to condition the original UNet's middle block activation.
-    """
-
-    up_block_res_samples: Tuple[torch.Tensor]
-    down_block_res_samples: Tuple[torch.Tensor]
-    mid_block_res_sample: torch.Tensor
-
-
-class BrushNetModel(ModelMixin, ConfigMixin):
-    """
-    A BrushNet model.
-
-    Args:
-        in_channels (`int`, defaults to 4):
-            The number of channels in the input sample.
-        flip_sin_to_cos (`bool`, defaults to `True`):
-            Whether to flip the sin to cos in the time embedding.
-        freq_shift (`int`, defaults to 0):
-            The frequency shift to apply to the time embedding.
-        down_block_types (`tuple[str]`, 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 (`Union[bool, Tuple[bool]]`, defaults to `False`):
-        block_out_channels (`tuple[int]`, defaults to `(320, 640, 1280, 1280)`):
-            The tuple of output channels for each block.
-        layers_per_block (`int`, defaults to 2):
-            The number of layers per block.
-        downsample_padding (`int`, defaults to 1):
-            The padding to use for the downsampling convolution.
-        mid_block_scale_factor (`float`, defaults to 1):
-            The scale factor to use for the mid block.
-        act_fn (`str`, 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`, defaults to 1e-5):
-            The epsilon to use for the normalization.
-        cross_attention_dim (`int`, defaults to 1280):
-            The dimension of the cross attention features.
-        transformer_layers_per_block (`int` or `Tuple[int]`, *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`].
-        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 (`Union[int, Tuple[int]]`, defaults to 8):
-            The dimension of the attention heads.
-        use_linear_projection (`bool`, defaults to `False`):
-        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.
-        num_class_embeds (`int`, *optional*, defaults to 0):
-            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`.
-        upcast_attention (`bool`, defaults to `False`):
-        resnet_time_scale_shift (`str`, defaults to `"default"`):
-            Time scale shift config for ResNet blocks (see `ResnetBlock2D`). Choose from `default` or `scale_shift`.
-        projection_class_embeddings_input_dim (`int`, *optional*, defaults to `None`):
-            The dimension of the `class_labels` input when `class_embed_type="projection"`. Required when
-            `class_embed_type="projection"`.
-        brushnet_conditioning_channel_order (`str`, defaults to `"rgb"`):
-            The channel order of conditional image. Will convert to `rgb` if it's `bgr`.
-        conditioning_embedding_out_channels (`tuple[int]`, *optional*, defaults to `(16, 32, 96, 256)`):
-            The tuple of output channel for each block in the `conditioning_embedding` layer.
-        global_pool_conditions (`bool`, defaults to `False`):
-            TODO(Patrick) - unused parameter.
-        addition_embed_type_num_heads (`int`, defaults to 64):
-            The number of heads to use for the `TextTimeEmbedding` layer.
-    """
-
-    _supports_gradient_checkpointing = True
-
-    @register_to_config
-    def __init__(
-        self,
-        in_channels: int = 4,
-        conditioning_channels: int = 5,
-        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: int = 2,
-        downsample_padding: int = 1,
-        mid_block_scale_factor: float = 1,
-        act_fn: str = "silu",
-        norm_num_groups: Optional[int] = 32,
-        norm_eps: float = 1e-5,
-        cross_attention_dim: int = 1280,
-        transformer_layers_per_block: Union[int, Tuple[int, ...]] = 1,
-        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,
-        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",
-        projection_class_embeddings_input_dim: Optional[int] = None,
-        brushnet_conditioning_channel_order: str = "rgb",
-        conditioning_embedding_out_channels: Optional[Tuple[int, ...]] = (16, 32, 96, 256),
-        global_pool_conditions: bool = False,
-        addition_embed_type_num_heads: int = 64,
-    ):
-        super().__init__()
-
-        # 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 isinstance(transformer_layers_per_block, int):
-            transformer_layers_per_block = [transformer_layers_per_block] * len(down_block_types)
-
-        # input
-        conv_in_kernel = 3
-        conv_in_padding = (conv_in_kernel - 1) // 2
-        self.conv_in_condition = nn.Conv2d(
-            in_channels + conditioning_channels,
-            block_out_channels[0],
-            kernel_size=conv_in_kernel,
-            padding=conv_in_padding,
-        )
-
-        # time
-        time_embed_dim = 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]
-        self.time_embedding = TimestepEmbedding(
-            timestep_input_dim,
-            time_embed_dim,
-            act_fn=act_fn,
-        )
-
-        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 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)
-        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)
-        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 is not None:
-            raise ValueError(f"addition_embed_type: {addition_embed_type} must be None, 'text' or 'text_image'.")
-
-        self.down_blocks = nn.ModuleList([])
-        self.brushnet_down_blocks = nn.ModuleList([])
-
-        if isinstance(only_cross_attention, bool):
-            only_cross_attention = [only_cross_attention] * len(down_block_types)
-
-        if isinstance(attention_head_dim, int):
-            attention_head_dim = (attention_head_dim,) * len(down_block_types)
-
-        if isinstance(num_attention_heads, int):
-            num_attention_heads = (num_attention_heads,) * len(down_block_types)
-
-        # down
-        output_channel = block_out_channels[0]
-
-        brushnet_block = nn.Conv2d(output_channel, output_channel, kernel_size=1)
-        brushnet_block = zero_module(brushnet_block)
-        self.brushnet_down_blocks.append(brushnet_block)
-
-        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,
-                transformer_layers_per_block=transformer_layers_per_block[i],
-                in_channels=input_channel,
-                out_channels=output_channel,
-                temb_channels=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,
-                num_attention_heads=num_attention_heads[i],
-                attention_head_dim=attention_head_dim[i] if attention_head_dim[i] is not None else output_channel,
-                downsample_padding=downsample_padding,
-                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,
-            )
-            self.down_blocks.append(down_block)
-
-            for _ in range(layers_per_block):
-                brushnet_block = nn.Conv2d(output_channel, output_channel, kernel_size=1)
-                brushnet_block = zero_module(brushnet_block)
-                self.brushnet_down_blocks.append(brushnet_block)
-
-            if not is_final_block:
-                brushnet_block = nn.Conv2d(output_channel, output_channel, kernel_size=1)
-                brushnet_block = zero_module(brushnet_block)
-                self.brushnet_down_blocks.append(brushnet_block)
-
-        # mid
-        mid_block_channel = block_out_channels[-1]
-
-        brushnet_block = nn.Conv2d(mid_block_channel, mid_block_channel, kernel_size=1)
-        brushnet_block = zero_module(brushnet_block)
-        self.brushnet_mid_block = brushnet_block
-
-        self.mid_block = get_mid_block(
-            mid_block_type,
-            transformer_layers_per_block=transformer_layers_per_block[-1],
-            in_channels=mid_block_channel,
-            temb_channels=time_embed_dim,
-            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,
-            num_attention_heads=num_attention_heads[-1],
-            resnet_groups=norm_num_groups,
-            use_linear_projection=use_linear_projection,
-            upcast_attention=upcast_attention,
-        )
-
-        # 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_transformer_layers_per_block = list(reversed(transformer_layers_per_block))
-        only_cross_attention = list(reversed(only_cross_attention))
-
-        output_channel = reversed_block_out_channels[0]
-
-        self.up_blocks = nn.ModuleList([])
-        self.brushnet_up_blocks = nn.ModuleList([])
-
-        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=layers_per_block + 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=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=cross_attention_dim,
-                num_attention_heads=reversed_num_attention_heads[i],
-                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_head_dim=attention_head_dim[i] if attention_head_dim[i] is not None else output_channel,
-            )
-            self.up_blocks.append(up_block)
-            prev_output_channel = output_channel
-
-            for _ in range(layers_per_block + 1):
-                brushnet_block = nn.Conv2d(output_channel, output_channel, kernel_size=1)
-                brushnet_block = zero_module(brushnet_block)
-                self.brushnet_up_blocks.append(brushnet_block)
-
-            if not is_final_block:
-                brushnet_block = nn.Conv2d(output_channel, output_channel, kernel_size=1)
-                brushnet_block = zero_module(brushnet_block)
-                self.brushnet_up_blocks.append(brushnet_block)
-
-    @classmethod
-    def from_unet(
-        cls,
-        unet: UNet2DConditionModel,
-        brushnet_conditioning_channel_order: str = "rgb",
-        conditioning_embedding_out_channels: Optional[Tuple[int, ...]] = (16, 32, 96, 256),
-        load_weights_from_unet: bool = True,
-        conditioning_channels: int = 5,
-    ):
-        r"""
-        Instantiate a [`BrushNetModel`] from [`UNet2DConditionModel`].
-
-        Parameters:
-            unet (`UNet2DConditionModel`):
-                The UNet model weights to copy to the [`BrushNetModel`]. All configuration options are also copied
-                where applicable.
-        """
-        transformer_layers_per_block = (
-            unet.config.transformer_layers_per_block if "transformer_layers_per_block" in unet.config else 1
-        )
-        encoder_hid_dim = unet.config.encoder_hid_dim if "encoder_hid_dim" in unet.config else None
-        encoder_hid_dim_type = unet.config.encoder_hid_dim_type if "encoder_hid_dim_type" in unet.config else None
-        addition_embed_type = unet.config.addition_embed_type if "addition_embed_type" in unet.config else None
-        addition_time_embed_dim = (
-            unet.config.addition_time_embed_dim if "addition_time_embed_dim" in unet.config else None
-        )
-
-        brushnet = cls(
-            in_channels=unet.config.in_channels,
-            conditioning_channels=conditioning_channels,
-            flip_sin_to_cos=unet.config.flip_sin_to_cos,
-            freq_shift=unet.config.freq_shift,
-            # down_block_types=['DownBlock2D','DownBlock2D','DownBlock2D','DownBlock2D'],
-            down_block_types=[
-                "CrossAttnDownBlock2D",
-                "CrossAttnDownBlock2D",
-                "CrossAttnDownBlock2D",
-                "DownBlock2D",
-            ],
-            # mid_block_type='MidBlock2D',
-            mid_block_type="UNetMidBlock2DCrossAttn",
-            # up_block_types=['UpBlock2D','UpBlock2D','UpBlock2D','UpBlock2D'],
-            up_block_types=["UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D"],
-            only_cross_attention=unet.config.only_cross_attention,
-            block_out_channels=unet.config.block_out_channels,
-            layers_per_block=unet.config.layers_per_block,
-            downsample_padding=unet.config.downsample_padding,
-            mid_block_scale_factor=unet.config.mid_block_scale_factor,
-            act_fn=unet.config.act_fn,
-            norm_num_groups=unet.config.norm_num_groups,
-            norm_eps=unet.config.norm_eps,
-            cross_attention_dim=unet.config.cross_attention_dim,
-            transformer_layers_per_block=transformer_layers_per_block,
-            encoder_hid_dim=encoder_hid_dim,
-            encoder_hid_dim_type=encoder_hid_dim_type,
-            attention_head_dim=unet.config.attention_head_dim,
-            num_attention_heads=unet.config.num_attention_heads,
-            use_linear_projection=unet.config.use_linear_projection,
-            class_embed_type=unet.config.class_embed_type,
-            addition_embed_type=addition_embed_type,
-            addition_time_embed_dim=addition_time_embed_dim,
-            num_class_embeds=unet.config.num_class_embeds,
-            upcast_attention=unet.config.upcast_attention,
-            resnet_time_scale_shift=unet.config.resnet_time_scale_shift,
-            projection_class_embeddings_input_dim=unet.config.projection_class_embeddings_input_dim,
-            brushnet_conditioning_channel_order=brushnet_conditioning_channel_order,
-            conditioning_embedding_out_channels=conditioning_embedding_out_channels,
-        )
-
-        if load_weights_from_unet:
-            conv_in_condition_weight = torch.zeros_like(brushnet.conv_in_condition.weight)
-            conv_in_condition_weight[:, :4, ...] = unet.conv_in.weight
-            conv_in_condition_weight[:, 4:8, ...] = unet.conv_in.weight
-            brushnet.conv_in_condition.weight = torch.nn.Parameter(conv_in_condition_weight)
-            brushnet.conv_in_condition.bias = unet.conv_in.bias
-
-            brushnet.time_proj.load_state_dict(unet.time_proj.state_dict())
-            brushnet.time_embedding.load_state_dict(unet.time_embedding.state_dict())
-
-            if brushnet.class_embedding:
-                brushnet.class_embedding.load_state_dict(unet.class_embedding.state_dict())
-
-            brushnet.down_blocks.load_state_dict(unet.down_blocks.state_dict(), strict=False)
-            brushnet.mid_block.load_state_dict(unet.mid_block.state_dict(), strict=False)
-            brushnet.up_blocks.load_state_dict(unet.up_blocks.state_dict(), strict=False)
-
-        return brushnet.to(unet.dtype)
-
-    @property
-    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.attn_processors
-    def attn_processors(self) -> Dict[str, AttentionProcessor]:
-        r"""
-        Returns:
-            `dict` of attention processors: A dictionary containing all attention processors used in the model with
-            indexed by its weight name.
-        """
-        # set recursively
-        processors = {}
-
-        def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttentionProcessor]):
-            if hasattr(module, "get_processor"):
-                processors[f"{name}.processor"] = module.get_processor(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
-
-    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_attn_processor
-    def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]):
-        r"""
-        Sets the attention processor to use to compute attention.
-
-        Parameters:
-            processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`):
-                The instantiated processor class or a dictionary of processor classes that will be set as the processor
-                for **all** `Attention` layers.
-
-                If `processor` is a dict, the key needs to define the path to the corresponding cross attention
-                processor. This is strongly recommended when setting trainable attention processors.
-
-        """
-        count = len(self.attn_processors.keys())
-
-        if isinstance(processor, dict) and len(processor) != count:
-            raise ValueError(
-                f"A dict of processors was passed, but the number of processors {len(processor)} does not match the"
-                f" number of attention layers: {count}. Please make sure to pass {count} processor classes."
-            )
-
-        def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):
-            if hasattr(module, "set_processor"):
-                if not isinstance(processor, dict):
-                    module.set_processor(processor)
-                else:
-                    module.set_processor(processor.pop(f"{name}.processor"))
-
-            for sub_name, child in module.named_children():
-                fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor)
-
-        for name, module in self.named_children():
-            fn_recursive_attn_processor(name, module, processor)
-
-    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_default_attn_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)
-
-    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_attention_slice
-    def set_attention_slice(self, slice_size: Union[str, int, List[int]]) -> None:
-        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: bool = False) -> None:
-        if isinstance(module, (CrossAttnDownBlock2D, DownBlock2D)):
-            module.gradient_checkpointing = value
-
-    def forward(
-        self,
-        sample: torch.FloatTensor,
-        timestep: Union[torch.Tensor, float, int],
-        encoder_hidden_states: torch.Tensor,
-        brushnet_cond: torch.FloatTensor,
-        conditioning_scale: float = 1.0,
-        class_labels: Optional[torch.Tensor] = None,
-        timestep_cond: Optional[torch.Tensor] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        added_cond_kwargs: Optional[Dict[str, torch.Tensor]] = None,
-        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
-        guess_mode: bool = False,
-        return_dict: bool = True,
-        debug=False,
-    ) -> Union[BrushNetOutput, Tuple[Tuple[torch.FloatTensor, ...], torch.FloatTensor]]:
-        """
-        The [`BrushNetModel`] forward method.
-
-        Args:
-            sample (`torch.FloatTensor`):
-                The noisy input tensor.
-            timestep (`Union[torch.Tensor, float, int]`):
-                The number of timesteps to denoise an input.
-            encoder_hidden_states (`torch.Tensor`):
-                The encoder hidden states.
-            brushnet_cond (`torch.FloatTensor`):
-                The conditional input tensor of shape `(batch_size, sequence_length, hidden_size)`.
-            conditioning_scale (`float`, defaults to `1.0`):
-                The scale factor for BrushNet outputs.
-            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`):
-                Additional conditional embeddings for timestep. If provided, the embeddings will be summed with the
-                timestep_embedding passed through the `self.time_embedding` layer to obtain the final 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.
-            added_cond_kwargs (`dict`):
-                Additional conditions for the Stable Diffusion XL UNet.
-            cross_attention_kwargs (`dict[str]`, *optional*, defaults to `None`):
-                A kwargs dictionary that if specified is passed along to the `AttnProcessor`.
-            guess_mode (`bool`, defaults to `False`):
-                In this mode, the BrushNet encoder tries its best to recognize the input content of the input even if
-                you remove all prompts. A `guidance_scale` between 3.0 and 5.0 is recommended.
-            return_dict (`bool`, defaults to `True`):
-                Whether or not to return a [`~models.brushnet.BrushNetOutput`] instead of a plain tuple.
-
-        Returns:
-            [`~models.brushnet.BrushNetOutput`] **or** `tuple`:
-                If `return_dict` is `True`, a [`~models.brushnet.BrushNetOutput`] is returned, otherwise a tuple is
-                returned where the first element is the sample tensor.
-        """
-        # check channel order
-        channel_order = self.config.brushnet_conditioning_channel_order
-
-        if channel_order == "rgb":
-            # in rgb order by default
-            ...
-        elif channel_order == "bgr":
-            brushnet_cond = torch.flip(brushnet_cond, dims=[1])
-        else:
-            raise ValueError(f"unknown `brushnet_conditioning_channel_order`: {channel_order}")
-
-        if debug: print('BrushNet CA: attn mask')
-
-        # prepare attention_mask
-        if attention_mask is not None:
-            attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0
-            attention_mask = attention_mask.unsqueeze(1)
-
-        if debug: print('BrushNet CA: time')
-
-        # 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)
-
-            class_emb = self.class_embedding(class_labels).to(dtype=self.dtype)
-            emb = emb + class_emb
-
-        if self.config.addition_embed_type is not None:
-            if self.config.addition_embed_type == "text":
-                aug_emb = self.add_embedding(encoder_hidden_states)
-
-            elif self.config.addition_embed_type == "text_time":
-                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)
-
-        emb = emb + aug_emb if aug_emb is not None else emb
-
-        if debug: print('BrushNet CA: pre-process')
-
-
-        # 2. pre-process
-        brushnet_cond = torch.concat([sample, brushnet_cond], 1)
-        sample = self.conv_in_condition(brushnet_cond)
-
-        if debug: print('BrushNet CA: down')
-
-        # 3. down
-        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:
-                if debug: print('BrushNet CA (down block with XA): ', type(downsample_block))
-                sample, res_samples = downsample_block(
-                    hidden_states=sample,
-                    temb=emb,
-                    encoder_hidden_states=encoder_hidden_states,
-                    attention_mask=attention_mask,
-                    cross_attention_kwargs=cross_attention_kwargs,
-                    debug=debug,
-                )
-            else:
-                if debug: print('BrushNet CA (down block): ', type(downsample_block))
-                sample, res_samples = downsample_block(hidden_states=sample, temb=emb, debug=debug)
-
-            down_block_res_samples += res_samples
-
-        if debug: print('BrushNet CA: PP down')
-
-        # 4. PaintingNet down blocks
-        brushnet_down_block_res_samples = ()
-        for down_block_res_sample, brushnet_down_block in zip(down_block_res_samples, self.brushnet_down_blocks):
-            down_block_res_sample = brushnet_down_block(down_block_res_sample)
-            brushnet_down_block_res_samples = brushnet_down_block_res_samples + (down_block_res_sample,)
-
-        if debug: print('BrushNet CA: PP mid')
-
-        # 5. mid
-        if self.mid_block is not None:
-            if hasattr(self.mid_block, "has_cross_attention") and self.mid_block.has_cross_attention:
-                sample = self.mid_block(
-                    sample,
-                    emb,
-                    encoder_hidden_states=encoder_hidden_states,
-                    attention_mask=attention_mask,
-                    cross_attention_kwargs=cross_attention_kwargs,
-                )
-            else:
-                sample = self.mid_block(sample, emb)
-
-        if debug: print('BrushNet CA: mid')
-
-        # 6. BrushNet mid blocks
-        brushnet_mid_block_res_sample = self.brushnet_mid_block(sample)
-
-        if debug: print('BrushNet CA: PP up')
-
-        # 7. up
-        up_block_res_samples = ()
-        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:
-                upsample_size = down_block_res_samples[-1].shape[2:]
-
-            if hasattr(upsample_block, "has_cross_attention") and upsample_block.has_cross_attention:
-                sample, up_res_samples = 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,
-                    return_res_samples=True,
-                )
-            else:
-                sample, up_res_samples = upsample_block(
-                    hidden_states=sample,
-                    temb=emb,
-                    res_hidden_states_tuple=res_samples,
-                    upsample_size=upsample_size,
-                    return_res_samples=True,
-                )
-
-            up_block_res_samples += up_res_samples
-
-        if debug: print('BrushNet CA: up')
-
-        # 8. BrushNet up blocks
-        brushnet_up_block_res_samples = ()
-        for up_block_res_sample, brushnet_up_block in zip(up_block_res_samples, self.brushnet_up_blocks):
-            up_block_res_sample = brushnet_up_block(up_block_res_sample)
-            brushnet_up_block_res_samples = brushnet_up_block_res_samples + (up_block_res_sample,)
-
-        if debug: print('BrushNet CA: scaling')
-
-        # 6. scaling
-        if guess_mode and not self.config.global_pool_conditions:
-            scales = torch.logspace(
-                -1,
-                0,
-                len(brushnet_down_block_res_samples) + 1 + len(brushnet_up_block_res_samples),
-                device=sample.device,
-            )  # 0.1 to 1.0
-            scales = scales * conditioning_scale
-
-            brushnet_down_block_res_samples = [
-                sample * scale
-                for sample, scale in zip(
-                    brushnet_down_block_res_samples, scales[: len(brushnet_down_block_res_samples)]
-                )
-            ]
-            brushnet_mid_block_res_sample = (
-                brushnet_mid_block_res_sample * scales[len(brushnet_down_block_res_samples)]
-            )
-            brushnet_up_block_res_samples = [
-                sample * scale
-                for sample, scale in zip(
-                    brushnet_up_block_res_samples, scales[len(brushnet_down_block_res_samples) + 1 :]
-                )
-            ]
-        else:
-            brushnet_down_block_res_samples = [
-                sample * conditioning_scale for sample in brushnet_down_block_res_samples
-            ]
-            brushnet_mid_block_res_sample = brushnet_mid_block_res_sample * conditioning_scale
-            brushnet_up_block_res_samples = [sample * conditioning_scale for sample in brushnet_up_block_res_samples]
-
-        if self.config.global_pool_conditions:
-            brushnet_down_block_res_samples = [
-                torch.mean(sample, dim=(2, 3), keepdim=True) for sample in brushnet_down_block_res_samples
-            ]
-            brushnet_mid_block_res_sample = torch.mean(brushnet_mid_block_res_sample, dim=(2, 3), keepdim=True)
-            brushnet_up_block_res_samples = [
-                torch.mean(sample, dim=(2, 3), keepdim=True) for sample in brushnet_up_block_res_samples
-            ]
-
-        if debug: print('BrushNet CA: finish')
-
-        if not return_dict:
-            return (brushnet_down_block_res_samples, brushnet_mid_block_res_sample, brushnet_up_block_res_samples)
-
-        return BrushNetOutput(
-            down_block_res_samples=brushnet_down_block_res_samples,
-            mid_block_res_sample=brushnet_mid_block_res_sample,
-            up_block_res_samples=brushnet_up_block_res_samples,
-        )
-
-
-def zero_module(module):
-    for p in module.parameters():
-        nn.init.zeros_(p)
-    return module
diff --git a/MagicQuill/brushnet/brushnet_xl.json b/MagicQuill/brushnet/brushnet_xl.json
deleted file mode 100644
index c1a3c655549879fb2e9d7441ec71eef5167eac12..0000000000000000000000000000000000000000
--- a/MagicQuill/brushnet/brushnet_xl.json
+++ /dev/null
@@ -1,63 +0,0 @@
-{
-  "_class_name": "BrushNetModel",
-  "_diffusers_version": "0.27.0.dev0",
-  "_name_or_path": "runs/logs/brushnetsdxl_randommask/checkpoint-80000",
-  "act_fn": "silu",
-  "addition_embed_type": "text_time",
-  "addition_embed_type_num_heads": 64,
-  "addition_time_embed_dim": 256,
-  "attention_head_dim": [
-    5,
-    10,
-    20
-  ],
-  "block_out_channels": [
-    320,
-    640,
-    1280
-  ],
-  "brushnet_conditioning_channel_order": "rgb",
-  "class_embed_type": null,
-  "conditioning_channels": 5,
-  "conditioning_embedding_out_channels": [
-    16,
-    32,
-    96,
-    256
-  ],
-  "cross_attention_dim": 2048,
-  "down_block_types": [
-    "DownBlock2D",
-    "DownBlock2D",
-    "DownBlock2D"
-  ],
-  "downsample_padding": 1,
-  "encoder_hid_dim": null,
-  "encoder_hid_dim_type": null,
-  "flip_sin_to_cos": true,
-  "freq_shift": 0,
-  "global_pool_conditions": false,
-  "in_channels": 4,
-  "layers_per_block": 2,
-  "mid_block_scale_factor": 1,
-  "mid_block_type": "MidBlock2D",
-  "norm_eps": 1e-05,
-  "norm_num_groups": 32,
-  "num_attention_heads": null,
-  "num_class_embeds": null,
-  "only_cross_attention": false,
-  "projection_class_embeddings_input_dim": 2816,
-  "resnet_time_scale_shift": "default",
-  "transformer_layers_per_block": [
-    1,
-    2,
-    10
-  ],
-  "up_block_types": [
-    "UpBlock2D",
-    "UpBlock2D",
-    "UpBlock2D"
-  ],
-  "upcast_attention": null,
-  "use_linear_projection": true
-}
diff --git a/MagicQuill/brushnet/powerpaint.json b/MagicQuill/brushnet/powerpaint.json
deleted file mode 100644
index 4d7c73e9f5654cd775db99a0d77234765f808e6c..0000000000000000000000000000000000000000
--- a/MagicQuill/brushnet/powerpaint.json
+++ /dev/null
@@ -1,57 +0,0 @@
-{
-  "_class_name": "BrushNetModel",
-  "_diffusers_version": "0.27.2",
-  "act_fn": "silu",
-  "addition_embed_type": null,
-  "addition_embed_type_num_heads": 64,
-  "addition_time_embed_dim": null,
-  "attention_head_dim": 8,
-  "block_out_channels": [
-    320,
-    640,
-    1280,
-    1280
-  ],
-  "brushnet_conditioning_channel_order": "rgb",
-  "class_embed_type": null,
-  "conditioning_channels": 5,
-  "conditioning_embedding_out_channels": [
-    16,
-    32,
-    96,
-    256
-  ],
-  "cross_attention_dim": 768,
-  "down_block_types": [
-    "CrossAttnDownBlock2D",
-    "CrossAttnDownBlock2D",
-    "CrossAttnDownBlock2D",
-    "DownBlock2D"
-  ],
-  "downsample_padding": 1,
-  "encoder_hid_dim": null,
-  "encoder_hid_dim_type": null,
-  "flip_sin_to_cos": true,
-  "freq_shift": 0,
-  "global_pool_conditions": false,
-  "in_channels": 4,
-  "layers_per_block": 2,
-  "mid_block_scale_factor": 1,
-  "mid_block_type": "UNetMidBlock2DCrossAttn",
-  "norm_eps": 1e-05,
-  "norm_num_groups": 32,
-  "num_attention_heads": null,
-  "num_class_embeds": null,
-  "only_cross_attention": false,
-  "projection_class_embeddings_input_dim": null,
-  "resnet_time_scale_shift": "default",
-  "transformer_layers_per_block": 1,
-  "up_block_types": [
-    "UpBlock2D",
-    "CrossAttnUpBlock2D",
-    "CrossAttnUpBlock2D",
-    "CrossAttnUpBlock2D"
-  ],
-  "upcast_attention": false,
-  "use_linear_projection": false
-}
diff --git a/MagicQuill/brushnet/powerpaint_utils.py b/MagicQuill/brushnet/powerpaint_utils.py
deleted file mode 100644
index bcbb1f715bd33ef79064361be41c99309a176424..0000000000000000000000000000000000000000
--- a/MagicQuill/brushnet/powerpaint_utils.py
+++ /dev/null
@@ -1,496 +0,0 @@
-import copy
-import random
-
-import torch
-import torch.nn as nn
-from transformers import CLIPTokenizer
-from typing import Any, List, Optional, Union
-
-class TokenizerWrapper:
-    """Tokenizer wrapper for CLIPTokenizer. Only support CLIPTokenizer
-    currently. This wrapper is modified from https://github.com/huggingface/dif
-    fusers/blob/e51f19aee82c8dd874b715a09dbc521d88835d68/src/diffusers/loaders.
-    py#L358  # noqa.
-
-    Args:
-        from_pretrained (Union[str, os.PathLike], optional): The *model id*
-            of a pretrained model or a path to a *directory* containing
-            model weights and config. Defaults to None.
-        from_config (Union[str, os.PathLike], optional): The *model id*
-            of a pretrained model or a path to a *directory* containing
-            model weights and config. Defaults to None.
-
-        *args, **kwargs: If `from_pretrained` is passed, *args and **kwargs
-            will be passed to `from_pretrained` function. Otherwise, *args
-            and **kwargs will be used to initialize the model by
-            `self._module_cls(*args, **kwargs)`.
-    """
-
-    def __init__(self, tokenizer: CLIPTokenizer):
-        self.wrapped = tokenizer
-        self.token_map = {}
-
-    def __getattr__(self, name: str) -> Any:
-        if name in self.__dict__:
-            return getattr(self, name)
-        #if name == "wrapped":
-        #    return getattr(self, 'wrapped')#super().__getattr__("wrapped")
-
-        try:
-            return getattr(self.wrapped, name)
-        except AttributeError:
-            raise AttributeError(
-                "'name' cannot be found in both "
-                f"'{self.__class__.__name__}' and "
-                f"'{self.__class__.__name__}.tokenizer'."
-            )
-
-    def try_adding_tokens(self, tokens: Union[str, List[str]], *args, **kwargs):
-        """Attempt to add tokens to the tokenizer.
-
-        Args:
-            tokens (Union[str, List[str]]): The tokens to be added.
-        """
-        num_added_tokens = self.wrapped.add_tokens(tokens, *args, **kwargs)
-        assert num_added_tokens != 0, (
-            f"The tokenizer already contains the token {tokens}. Please pass "
-            "a different `placeholder_token` that is not already in the "
-            "tokenizer."
-        )
-
-    def get_token_info(self, token: str) -> dict:
-        """Get the information of a token, including its start and end index in
-        the current tokenizer.
-
-        Args:
-            token (str): The token to be queried.
-
-        Returns:
-            dict: The information of the token, including its start and end
-                index in current tokenizer.
-        """
-        token_ids = self.__call__(token).input_ids
-        start, end = token_ids[1], token_ids[-2] + 1
-        return {"name": token, "start": start, "end": end}
-
-    def add_placeholder_token(self, placeholder_token: str, *args, num_vec_per_token: int = 1, **kwargs):
-        """Add placeholder tokens to the tokenizer.
-
-        Args:
-            placeholder_token (str): The placeholder token to be added.
-            num_vec_per_token (int, optional): The number of vectors of
-                the added placeholder token.
-            *args, **kwargs: The arguments for `self.wrapped.add_tokens`.
-        """
-        output = []
-        if num_vec_per_token == 1:
-            self.try_adding_tokens(placeholder_token, *args, **kwargs)
-            output.append(placeholder_token)
-        else:
-            output = []
-            for i in range(num_vec_per_token):
-                ith_token = placeholder_token + f"_{i}"
-                self.try_adding_tokens(ith_token, *args, **kwargs)
-                output.append(ith_token)
-
-        for token in self.token_map:
-            if token in placeholder_token:
-                raise ValueError(
-                    f"The tokenizer already has placeholder token {token} "
-                    f"that can get confused with {placeholder_token} "
-                    "keep placeholder tokens independent"
-                )
-        self.token_map[placeholder_token] = output
-
-    def replace_placeholder_tokens_in_text(
-        self, text: Union[str, List[str]], vector_shuffle: bool = False, prop_tokens_to_load: float = 1.0
-    ) -> Union[str, List[str]]:
-        """Replace the keywords in text with placeholder tokens. This function
-        will be called in `self.__call__` and `self.encode`.
-
-        Args:
-            text (Union[str, List[str]]): The text to be processed.
-            vector_shuffle (bool, optional): Whether to shuffle the vectors.
-                Defaults to False.
-            prop_tokens_to_load (float, optional): The proportion of tokens to
-                be loaded. If 1.0, all tokens will be loaded. Defaults to 1.0.
-
-        Returns:
-            Union[str, List[str]]: The processed text.
-        """
-        if isinstance(text, list):
-            output = []
-            for i in range(len(text)):
-                output.append(self.replace_placeholder_tokens_in_text(text[i], vector_shuffle=vector_shuffle))
-            return output
-
-        for placeholder_token in self.token_map:
-            if placeholder_token in text:
-                tokens = self.token_map[placeholder_token]
-                tokens = tokens[: 1 + int(len(tokens) * prop_tokens_to_load)]
-                if vector_shuffle:
-                    tokens = copy.copy(tokens)
-                    random.shuffle(tokens)
-                text = text.replace(placeholder_token, " ".join(tokens))
-        return text
-
-    def replace_text_with_placeholder_tokens(self, text: Union[str, List[str]]) -> Union[str, List[str]]:
-        """Replace the placeholder tokens in text with the original keywords.
-        This function will be called in `self.decode`.
-
-        Args:
-            text (Union[str, List[str]]): The text to be processed.
-
-        Returns:
-            Union[str, List[str]]: The processed text.
-        """
-        if isinstance(text, list):
-            output = []
-            for i in range(len(text)):
-                output.append(self.replace_text_with_placeholder_tokens(text[i]))
-            return output
-
-        for placeholder_token, tokens in self.token_map.items():
-            merged_tokens = " ".join(tokens)
-            if merged_tokens in text:
-                text = text.replace(merged_tokens, placeholder_token)
-        return text
-
-    def __call__(
-        self,
-        text: Union[str, List[str]],
-        *args,
-        vector_shuffle: bool = False,
-        prop_tokens_to_load: float = 1.0,
-        **kwargs,
-    ):
-        """The call function of the wrapper.
-
-        Args:
-            text (Union[str, List[str]]): The text to be tokenized.
-            vector_shuffle (bool, optional): Whether to shuffle the vectors.
-                Defaults to False.
-            prop_tokens_to_load (float, optional): The proportion of tokens to
-                be loaded. If 1.0, all tokens will be loaded. Defaults to 1.0
-            *args, **kwargs: The arguments for `self.wrapped.__call__`.
-        """
-        replaced_text = self.replace_placeholder_tokens_in_text(
-            text, vector_shuffle=vector_shuffle, prop_tokens_to_load=prop_tokens_to_load
-        )
-
-        return self.wrapped.__call__(replaced_text, *args, **kwargs)
-
-    def encode(self, text: Union[str, List[str]], *args, **kwargs):
-        """Encode the passed text to token index.
-
-        Args:
-            text (Union[str, List[str]]): The text to be encode.
-            *args, **kwargs: The arguments for `self.wrapped.__call__`.
-        """
-        replaced_text = self.replace_placeholder_tokens_in_text(text)
-        return self.wrapped(replaced_text, *args, **kwargs)
-
-    def decode(self, token_ids, return_raw: bool = False, *args, **kwargs) -> Union[str, List[str]]:
-        """Decode the token index to text.
-
-        Args:
-            token_ids: The token index to be decoded.
-            return_raw: Whether keep the placeholder token in the text.
-                Defaults to False.
-            *args, **kwargs: The arguments for `self.wrapped.decode`.
-
-        Returns:
-            Union[str, List[str]]: The decoded text.
-        """
-        text = self.wrapped.decode(token_ids, *args, **kwargs)
-        if return_raw:
-            return text
-        replaced_text = self.replace_text_with_placeholder_tokens(text)
-        return replaced_text
-
-    def __repr__(self):
-        """The representation of the wrapper."""
-        s = super().__repr__()
-        prefix = f"Wrapped Module Class: {self._module_cls}\n"
-        prefix += f"Wrapped Module Name: {self._module_name}\n"
-        if self._from_pretrained:
-            prefix += f"From Pretrained: {self._from_pretrained}\n"
-        s = prefix + s
-        return s
-    
-
-class EmbeddingLayerWithFixes(nn.Module):
-    """The revised embedding layer to support external embeddings. This design
-    of this class is inspired by https://github.com/AUTOMATIC1111/stable-
-    diffusion-webui/blob/22bcc7be428c94e9408f589966c2040187245d81/modules/sd_hi
-    jack.py#L224  # noqa.
-
-    Args:
-        wrapped (nn.Emebdding): The embedding layer to be wrapped.
-        external_embeddings (Union[dict, List[dict]], optional): The external
-            embeddings added to this layer. Defaults to None.
-    """
-
-    def __init__(self, wrapped: nn.Embedding, external_embeddings: Optional[Union[dict, List[dict]]] = None):
-        super().__init__()
-        self.wrapped = wrapped
-        self.num_embeddings = wrapped.weight.shape[0]
-
-        self.external_embeddings = []
-        if external_embeddings:
-            self.add_embeddings(external_embeddings)
-
-        self.trainable_embeddings = nn.ParameterDict()
-
-    @property
-    def weight(self):
-        """Get the weight of wrapped embedding layer."""
-        return self.wrapped.weight
-
-    def check_duplicate_names(self, embeddings: List[dict]):
-        """Check whether duplicate names exist in list of 'external
-        embeddings'.
-
-        Args:
-            embeddings (List[dict]): A list of embedding to be check.
-        """
-        names = [emb["name"] for emb in embeddings]
-        assert len(names) == len(set(names)), (
-            "Found duplicated names in 'external_embeddings'. Name list: " f"'{names}'"
-        )
-
-    def check_ids_overlap(self, embeddings):
-        """Check whether overlap exist in token ids of 'external_embeddings'.
-
-        Args:
-            embeddings (List[dict]): A list of embedding to be check.
-        """
-        ids_range = [[emb["start"], emb["end"], emb["name"]] for emb in embeddings]
-        ids_range.sort()  # sort by 'start'
-        # check if 'end' has overlapping
-        for idx in range(len(ids_range) - 1):
-            name1, name2 = ids_range[idx][-1], ids_range[idx + 1][-1]
-            assert ids_range[idx][1] <= ids_range[idx + 1][0], (
-                f"Found ids overlapping between embeddings '{name1}' " f"and '{name2}'."
-            )
-
-    def add_embeddings(self, embeddings: Optional[Union[dict, List[dict]]]):
-        """Add external embeddings to this layer.
-
-        Use case:
-
-        >>> 1. Add token to tokenizer and get the token id.
-        >>> tokenizer = TokenizerWrapper('openai/clip-vit-base-patch32')
-        >>> # 'how much' in kiswahili
-        >>> tokenizer.add_placeholder_tokens('ngapi', num_vec_per_token=4)
-        >>>
-        >>> 2. Add external embeddings to the model.
-        >>> new_embedding = {
-        >>>     'name': 'ngapi',  # 'how much' in kiswahili
-        >>>     'embedding': torch.ones(1, 15) * 4,
-        >>>     'start': tokenizer.get_token_info('kwaheri')['start'],
-        >>>     'end': tokenizer.get_token_info('kwaheri')['end'],
-        >>>     'trainable': False  # if True, will registry as a parameter
-        >>> }
-        >>> embedding_layer = nn.Embedding(10, 15)
-        >>> embedding_layer_wrapper = EmbeddingLayerWithFixes(embedding_layer)
-        >>> embedding_layer_wrapper.add_embeddings(new_embedding)
-        >>>
-        >>> 3. Forward tokenizer and embedding layer!
-        >>> input_text = ['hello, ngapi!', 'hello my friend, ngapi?']
-        >>> input_ids = tokenizer(
-        >>>     input_text, padding='max_length', truncation=True,
-        >>>     return_tensors='pt')['input_ids']
-        >>> out_feat = embedding_layer_wrapper(input_ids)
-        >>>
-        >>> 4. Let's validate the result!
-        >>> assert (out_feat[0, 3: 7] == 2.3).all()
-        >>> assert (out_feat[2, 5: 9] == 2.3).all()
-
-        Args:
-            embeddings (Union[dict, list[dict]]): The external embeddings to
-                be added. Each dict must contain the following 4 fields: 'name'
-                (the name of this embedding), 'embedding' (the embedding
-                tensor), 'start' (the start token id of this embedding), 'end'
-                (the end token id of this embedding). For example:
-                `{name: NAME, start: START, end: END, embedding: torch.Tensor}`
-        """
-        if isinstance(embeddings, dict):
-            embeddings = [embeddings]
-
-        self.external_embeddings += embeddings
-        self.check_duplicate_names(self.external_embeddings)
-        self.check_ids_overlap(self.external_embeddings)
-
-        # set for trainable
-        added_trainable_emb_info = []
-        for embedding in embeddings:
-            trainable = embedding.get("trainable", False)
-            if trainable:
-                name = embedding["name"]
-                embedding["embedding"] = torch.nn.Parameter(embedding["embedding"])
-                self.trainable_embeddings[name] = embedding["embedding"]
-                added_trainable_emb_info.append(name)
-
-        added_emb_info = [emb["name"] for emb in embeddings]
-        added_emb_info = ", ".join(added_emb_info)
-        print(f"Successfully add external embeddings: {added_emb_info}.", "current")
-
-        if added_trainable_emb_info:
-            added_trainable_emb_info = ", ".join(added_trainable_emb_info)
-            print("Successfully add trainable external embeddings: " f"{added_trainable_emb_info}", "current")
-
-    def replace_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
-        """Replace external input ids to 0.
-
-        Args:
-            input_ids (torch.Tensor): The input ids to be replaced.
-
-        Returns:
-            torch.Tensor: The replaced input ids.
-        """
-        input_ids_fwd = input_ids.clone()
-        input_ids_fwd[input_ids_fwd >= self.num_embeddings] = 0
-        return input_ids_fwd
-
-    def replace_embeddings(
-        self, input_ids: torch.Tensor, embedding: torch.Tensor, external_embedding: dict
-    ) -> torch.Tensor:
-        """Replace external embedding to the embedding layer. Noted that, in
-        this function we use `torch.cat` to avoid inplace modification.
-
-        Args:
-            input_ids (torch.Tensor): The original token ids. Shape like
-                [LENGTH, ].
-            embedding (torch.Tensor): The embedding of token ids after
-                `replace_input_ids` function.
-            external_embedding (dict): The external embedding to be replaced.
-
-        Returns:
-            torch.Tensor: The replaced embedding.
-        """
-        new_embedding = []
-
-        name = external_embedding["name"]
-        start = external_embedding["start"]
-        end = external_embedding["end"]
-        target_ids_to_replace = [i for i in range(start, end)]
-        ext_emb = external_embedding["embedding"]
-
-        # do not need to replace
-        if not (input_ids == start).any():
-            return embedding
-
-        # start replace
-        s_idx, e_idx = 0, 0
-        while e_idx < len(input_ids):
-            if input_ids[e_idx] == start:
-                if e_idx != 0:
-                    # add embedding do not need to replace
-                    new_embedding.append(embedding[s_idx:e_idx])
-
-                # check if the next embedding need to replace is valid
-                actually_ids_to_replace = [int(i) for i in input_ids[e_idx : e_idx + end - start]]
-                assert actually_ids_to_replace == target_ids_to_replace, (
-                    f"Invalid 'input_ids' in position: {s_idx} to {e_idx}. "
-                    f"Expect '{target_ids_to_replace}' for embedding "
-                    f"'{name}' but found '{actually_ids_to_replace}'."
-                )
-
-                new_embedding.append(ext_emb)
-
-                s_idx = e_idx + end - start
-                e_idx = s_idx + 1
-            else:
-                e_idx += 1
-
-        if e_idx == len(input_ids):
-            new_embedding.append(embedding[s_idx:e_idx])
-
-        return torch.cat(new_embedding, dim=0)
-
-    def forward(self, input_ids: torch.Tensor, external_embeddings: Optional[List[dict]] = None):
-        """The forward function.
-
-        Args:
-            input_ids (torch.Tensor): The token ids shape like [bz, LENGTH] or
-                [LENGTH, ].
-            external_embeddings (Optional[List[dict]]): The external
-                embeddings. If not passed, only `self.external_embeddings`
-                will be used.  Defaults to None.
-
-        input_ids: shape like [bz, LENGTH] or [LENGTH].
-        """
-        assert input_ids.ndim in [1, 2]
-        if input_ids.ndim == 1:
-            input_ids = input_ids.unsqueeze(0)
-
-        if external_embeddings is None and not self.external_embeddings:
-            return self.wrapped(input_ids)
-
-        input_ids_fwd = self.replace_input_ids(input_ids)
-        inputs_embeds = self.wrapped(input_ids_fwd)
-
-        vecs = []
-
-        if external_embeddings is None:
-            external_embeddings = []
-        elif isinstance(external_embeddings, dict):
-            external_embeddings = [external_embeddings]
-        embeddings = self.external_embeddings + external_embeddings
-
-        for input_id, embedding in zip(input_ids, inputs_embeds):
-            new_embedding = embedding
-            for external_embedding in embeddings:
-                new_embedding = self.replace_embeddings(input_id, new_embedding, external_embedding)
-            vecs.append(new_embedding)
-
-        return torch.stack(vecs)
-
-
-
-def add_tokens(
-    tokenizer, text_encoder, placeholder_tokens: list, initialize_tokens: list = None, num_vectors_per_token: int = 1
-):
-    """Add token for training.
-
-    # TODO: support add tokens as dict, then we can load pretrained tokens.
-    """
-    if initialize_tokens is not None:
-        assert len(initialize_tokens) == len(
-            placeholder_tokens
-        ), "placeholder_token should be the same length as initialize_token"
-    for ii in range(len(placeholder_tokens)):
-        tokenizer.add_placeholder_token(placeholder_tokens[ii], num_vec_per_token=num_vectors_per_token)
-
-    # text_encoder.set_embedding_layer()
-    embedding_layer = text_encoder.text_model.embeddings.token_embedding
-    text_encoder.text_model.embeddings.token_embedding = EmbeddingLayerWithFixes(embedding_layer)
-    embedding_layer = text_encoder.text_model.embeddings.token_embedding
-
-    assert embedding_layer is not None, (
-        "Do not support get embedding layer for current text encoder. " "Please check your configuration."
-    )
-    initialize_embedding = []
-    if initialize_tokens is not None:
-        for ii in range(len(placeholder_tokens)):
-            init_id = tokenizer(initialize_tokens[ii]).input_ids[1]
-            temp_embedding = embedding_layer.weight[init_id]
-            initialize_embedding.append(temp_embedding[None, ...].repeat(num_vectors_per_token, 1))
-    else:
-        for ii in range(len(placeholder_tokens)):
-            init_id = tokenizer("a").input_ids[1]
-            temp_embedding = embedding_layer.weight[init_id]
-            len_emb = temp_embedding.shape[0]
-            init_weight = (torch.rand(num_vectors_per_token, len_emb) - 0.5) / 2.0
-            initialize_embedding.append(init_weight)
-
-    # initialize_embedding  = torch.cat(initialize_embedding,dim=0)
-
-    token_info_all = []
-    for ii in range(len(placeholder_tokens)):
-        token_info = tokenizer.get_token_info(placeholder_tokens[ii])
-        token_info["embedding"] = initialize_embedding[ii]
-        token_info["trainable"] = True
-        token_info_all.append(token_info)
-    embedding_layer.add_embeddings(token_info_all)
diff --git a/MagicQuill/brushnet/unet_2d_blocks.py b/MagicQuill/brushnet/unet_2d_blocks.py
deleted file mode 100644
index 4a083673867f2568d499480f7dcec1480b20ead0..0000000000000000000000000000000000000000
--- a/MagicQuill/brushnet/unet_2d_blocks.py
+++ /dev/null
@@ -1,3907 +0,0 @@
-# Copyright 2024 The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-from typing import Any, Dict, Optional, Tuple, Union
-
-import numpy as np
-import torch
-import torch.nn.functional as F
-from torch import nn
-
-from diffusers.utils import deprecate, is_torch_version, logging
-from diffusers.utils.torch_utils import apply_freeu
-from diffusers.models.activations import get_activation
-from diffusers.models.attention_processor import Attention, AttnAddedKVProcessor, AttnAddedKVProcessor2_0
-from diffusers.models.normalization import AdaGroupNorm
-from diffusers.models.resnet import (
-    Downsample2D,
-    FirDownsample2D,
-    FirUpsample2D,
-    KDownsample2D,
-    KUpsample2D,
-    ResnetBlock2D,
-    ResnetBlockCondNorm2D,
-    Upsample2D,
-)
-from diffusers.models.transformers.dual_transformer_2d import DualTransformer2DModel
-from diffusers.models.transformers.transformer_2d import Transformer2DModel
-
-
-logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
-
-
-def get_down_block(
-    down_block_type: str,
-    num_layers: int,
-    in_channels: int,
-    out_channels: int,
-    temb_channels: int,
-    add_downsample: bool,
-    resnet_eps: float,
-    resnet_act_fn: str,
-    transformer_layers_per_block: int = 1,
-    num_attention_heads: Optional[int] = None,
-    resnet_groups: Optional[int] = None,
-    cross_attention_dim: Optional[int] = None,
-    downsample_padding: Optional[int] = None,
-    dual_cross_attention: bool = False,
-    use_linear_projection: bool = False,
-    only_cross_attention: bool = False,
-    upcast_attention: bool = False,
-    resnet_time_scale_shift: str = "default",
-    attention_type: str = "default",
-    resnet_skip_time_act: bool = False,
-    resnet_out_scale_factor: float = 1.0,
-    cross_attention_norm: Optional[str] = None,
-    attention_head_dim: Optional[int] = None,
-    downsample_type: Optional[str] = None,
-    dropout: float = 0.0,
-):
-    # If attn head dim is not defined, we default it to the number of heads
-    if attention_head_dim is None:
-        logger.warning(
-            f"It is recommended to provide `attention_head_dim` when calling `get_down_block`. Defaulting `attention_head_dim` to {num_attention_heads}."
-        )
-        attention_head_dim = num_attention_heads
-
-    down_block_type = down_block_type[7:] if down_block_type.startswith("UNetRes") else down_block_type
-    if down_block_type == "DownBlock2D":
-        return DownBlock2D(
-            num_layers=num_layers,
-            in_channels=in_channels,
-            out_channels=out_channels,
-            temb_channels=temb_channels,
-            dropout=dropout,
-            add_downsample=add_downsample,
-            resnet_eps=resnet_eps,
-            resnet_act_fn=resnet_act_fn,
-            resnet_groups=resnet_groups,
-            downsample_padding=downsample_padding,
-            resnet_time_scale_shift=resnet_time_scale_shift,
-        )
-    elif down_block_type == "ResnetDownsampleBlock2D":
-        return ResnetDownsampleBlock2D(
-            num_layers=num_layers,
-            in_channels=in_channels,
-            out_channels=out_channels,
-            temb_channels=temb_channels,
-            dropout=dropout,
-            add_downsample=add_downsample,
-            resnet_eps=resnet_eps,
-            resnet_act_fn=resnet_act_fn,
-            resnet_groups=resnet_groups,
-            resnet_time_scale_shift=resnet_time_scale_shift,
-            skip_time_act=resnet_skip_time_act,
-            output_scale_factor=resnet_out_scale_factor,
-        )
-    elif down_block_type == "AttnDownBlock2D":
-        if add_downsample is False:
-            downsample_type = None
-        else:
-            downsample_type = downsample_type or "conv"  # default to 'conv'
-        return AttnDownBlock2D(
-            num_layers=num_layers,
-            in_channels=in_channels,
-            out_channels=out_channels,
-            temb_channels=temb_channels,
-            dropout=dropout,
-            resnet_eps=resnet_eps,
-            resnet_act_fn=resnet_act_fn,
-            resnet_groups=resnet_groups,
-            downsample_padding=downsample_padding,
-            attention_head_dim=attention_head_dim,
-            resnet_time_scale_shift=resnet_time_scale_shift,
-            downsample_type=downsample_type,
-        )
-    elif down_block_type == "CrossAttnDownBlock2D":
-        if cross_attention_dim is None:
-            raise ValueError("cross_attention_dim must be specified for CrossAttnDownBlock2D")
-        return CrossAttnDownBlock2D(
-            num_layers=num_layers,
-            transformer_layers_per_block=transformer_layers_per_block,
-            in_channels=in_channels,
-            out_channels=out_channels,
-            temb_channels=temb_channels,
-            dropout=dropout,
-            add_downsample=add_downsample,
-            resnet_eps=resnet_eps,
-            resnet_act_fn=resnet_act_fn,
-            resnet_groups=resnet_groups,
-            downsample_padding=downsample_padding,
-            cross_attention_dim=cross_attention_dim,
-            num_attention_heads=num_attention_heads,
-            dual_cross_attention=dual_cross_attention,
-            use_linear_projection=use_linear_projection,
-            only_cross_attention=only_cross_attention,
-            upcast_attention=upcast_attention,
-            resnet_time_scale_shift=resnet_time_scale_shift,
-            attention_type=attention_type,
-        )
-    elif down_block_type == "SimpleCrossAttnDownBlock2D":
-        if cross_attention_dim is None:
-            raise ValueError("cross_attention_dim must be specified for SimpleCrossAttnDownBlock2D")
-        return SimpleCrossAttnDownBlock2D(
-            num_layers=num_layers,
-            in_channels=in_channels,
-            out_channels=out_channels,
-            temb_channels=temb_channels,
-            dropout=dropout,
-            add_downsample=add_downsample,
-            resnet_eps=resnet_eps,
-            resnet_act_fn=resnet_act_fn,
-            resnet_groups=resnet_groups,
-            cross_attention_dim=cross_attention_dim,
-            attention_head_dim=attention_head_dim,
-            resnet_time_scale_shift=resnet_time_scale_shift,
-            skip_time_act=resnet_skip_time_act,
-            output_scale_factor=resnet_out_scale_factor,
-            only_cross_attention=only_cross_attention,
-            cross_attention_norm=cross_attention_norm,
-        )
-    elif down_block_type == "SkipDownBlock2D":
-        return SkipDownBlock2D(
-            num_layers=num_layers,
-            in_channels=in_channels,
-            out_channels=out_channels,
-            temb_channels=temb_channels,
-            dropout=dropout,
-            add_downsample=add_downsample,
-            resnet_eps=resnet_eps,
-            resnet_act_fn=resnet_act_fn,
-            downsample_padding=downsample_padding,
-            resnet_time_scale_shift=resnet_time_scale_shift,
-        )
-    elif down_block_type == "AttnSkipDownBlock2D":
-        return AttnSkipDownBlock2D(
-            num_layers=num_layers,
-            in_channels=in_channels,
-            out_channels=out_channels,
-            temb_channels=temb_channels,
-            dropout=dropout,
-            add_downsample=add_downsample,
-            resnet_eps=resnet_eps,
-            resnet_act_fn=resnet_act_fn,
-            attention_head_dim=attention_head_dim,
-            resnet_time_scale_shift=resnet_time_scale_shift,
-        )
-    elif down_block_type == "DownEncoderBlock2D":
-        return DownEncoderBlock2D(
-            num_layers=num_layers,
-            in_channels=in_channels,
-            out_channels=out_channels,
-            dropout=dropout,
-            add_downsample=add_downsample,
-            resnet_eps=resnet_eps,
-            resnet_act_fn=resnet_act_fn,
-            resnet_groups=resnet_groups,
-            downsample_padding=downsample_padding,
-            resnet_time_scale_shift=resnet_time_scale_shift,
-        )
-    elif down_block_type == "AttnDownEncoderBlock2D":
-        return AttnDownEncoderBlock2D(
-            num_layers=num_layers,
-            in_channels=in_channels,
-            out_channels=out_channels,
-            dropout=dropout,
-            add_downsample=add_downsample,
-            resnet_eps=resnet_eps,
-            resnet_act_fn=resnet_act_fn,
-            resnet_groups=resnet_groups,
-            downsample_padding=downsample_padding,
-            attention_head_dim=attention_head_dim,
-            resnet_time_scale_shift=resnet_time_scale_shift,
-        )
-    elif down_block_type == "KDownBlock2D":
-        return KDownBlock2D(
-            num_layers=num_layers,
-            in_channels=in_channels,
-            out_channels=out_channels,
-            temb_channels=temb_channels,
-            dropout=dropout,
-            add_downsample=add_downsample,
-            resnet_eps=resnet_eps,
-            resnet_act_fn=resnet_act_fn,
-        )
-    elif down_block_type == "KCrossAttnDownBlock2D":
-        return KCrossAttnDownBlock2D(
-            num_layers=num_layers,
-            in_channels=in_channels,
-            out_channels=out_channels,
-            temb_channels=temb_channels,
-            dropout=dropout,
-            add_downsample=add_downsample,
-            resnet_eps=resnet_eps,
-            resnet_act_fn=resnet_act_fn,
-            cross_attention_dim=cross_attention_dim,
-            attention_head_dim=attention_head_dim,
-            add_self_attention=True if not add_downsample else False,
-        )
-    raise ValueError(f"{down_block_type} does not exist.")
-
-
-def get_mid_block(
-    mid_block_type: str,
-    temb_channels: int,
-    in_channels: int,
-    resnet_eps: float,
-    resnet_act_fn: str,
-    resnet_groups: int,
-    output_scale_factor: float = 1.0,
-    transformer_layers_per_block: int = 1,
-    num_attention_heads: Optional[int] = None,
-    cross_attention_dim: Optional[int] = None,
-    dual_cross_attention: bool = False,
-    use_linear_projection: bool = False,
-    mid_block_only_cross_attention: bool = False,
-    upcast_attention: bool = False,
-    resnet_time_scale_shift: str = "default",
-    attention_type: str = "default",
-    resnet_skip_time_act: bool = False,
-    cross_attention_norm: Optional[str] = None,
-    attention_head_dim: Optional[int] = 1,
-    dropout: float = 0.0,
-):
-    if mid_block_type == "UNetMidBlock2DCrossAttn":
-        return UNetMidBlock2DCrossAttn(
-            transformer_layers_per_block=transformer_layers_per_block,
-            in_channels=in_channels,
-            temb_channels=temb_channels,
-            dropout=dropout,
-            resnet_eps=resnet_eps,
-            resnet_act_fn=resnet_act_fn,
-            output_scale_factor=output_scale_factor,
-            resnet_time_scale_shift=resnet_time_scale_shift,
-            cross_attention_dim=cross_attention_dim,
-            num_attention_heads=num_attention_heads,
-            resnet_groups=resnet_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":
-        return UNetMidBlock2DSimpleCrossAttn(
-            in_channels=in_channels,
-            temb_channels=temb_channels,
-            dropout=dropout,
-            resnet_eps=resnet_eps,
-            resnet_act_fn=resnet_act_fn,
-            output_scale_factor=output_scale_factor,
-            cross_attention_dim=cross_attention_dim,
-            attention_head_dim=attention_head_dim,
-            resnet_groups=resnet_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":
-        return UNetMidBlock2D(
-            in_channels=in_channels,
-            temb_channels=temb_channels,
-            dropout=dropout,
-            num_layers=0,
-            resnet_eps=resnet_eps,
-            resnet_act_fn=resnet_act_fn,
-            output_scale_factor=output_scale_factor,
-            resnet_groups=resnet_groups,
-            resnet_time_scale_shift=resnet_time_scale_shift,
-            add_attention=False,
-        )
-    elif mid_block_type == "MidBlock2D":
-        return MidBlock2D(
-            in_channels=in_channels,
-            temb_channels=temb_channels,
-            dropout=dropout,
-            resnet_eps=resnet_eps,
-            resnet_act_fn=resnet_act_fn,
-            output_scale_factor=output_scale_factor,
-            resnet_time_scale_shift=resnet_time_scale_shift,
-            resnet_groups=resnet_groups,
-            use_linear_projection=use_linear_projection,
-        )    
-    elif mid_block_type is None:
-        return None
-    else:
-        raise ValueError(f"unknown mid_block_type : {mid_block_type}")
-
-
-def get_up_block(
-    up_block_type: str,
-    num_layers: int,
-    in_channels: int,
-    out_channels: int,
-    prev_output_channel: int,
-    temb_channels: int,
-    add_upsample: bool,
-    resnet_eps: float,
-    resnet_act_fn: str,
-    resolution_idx: Optional[int] = None,
-    transformer_layers_per_block: int = 1,
-    num_attention_heads: Optional[int] = None,
-    resnet_groups: Optional[int] = None,
-    cross_attention_dim: Optional[int] = None,
-    dual_cross_attention: bool = False,
-    use_linear_projection: bool = False,
-    only_cross_attention: bool = False,
-    upcast_attention: bool = False,
-    resnet_time_scale_shift: str = "default",
-    attention_type: str = "default",
-    resnet_skip_time_act: bool = False,
-    resnet_out_scale_factor: float = 1.0,
-    cross_attention_norm: Optional[str] = None,
-    attention_head_dim: Optional[int] = None,
-    upsample_type: Optional[str] = None,
-    dropout: float = 0.0,
-) -> nn.Module:
-    # If attn head dim is not defined, we default it to the number of heads
-    if attention_head_dim is None:
-        logger.warning(
-            f"It is recommended to provide `attention_head_dim` when calling `get_up_block`. Defaulting `attention_head_dim` to {num_attention_heads}."
-        )
-        attention_head_dim = num_attention_heads
-
-    up_block_type = up_block_type[7:] if up_block_type.startswith("UNetRes") else up_block_type
-    if up_block_type == "UpBlock2D":
-        return UpBlock2D(
-            num_layers=num_layers,
-            in_channels=in_channels,
-            out_channels=out_channels,
-            prev_output_channel=prev_output_channel,
-            temb_channels=temb_channels,
-            resolution_idx=resolution_idx,
-            dropout=dropout,
-            add_upsample=add_upsample,
-            resnet_eps=resnet_eps,
-            resnet_act_fn=resnet_act_fn,
-            resnet_groups=resnet_groups,
-            resnet_time_scale_shift=resnet_time_scale_shift,
-        )
-    elif up_block_type == "ResnetUpsampleBlock2D":
-        return ResnetUpsampleBlock2D(
-            num_layers=num_layers,
-            in_channels=in_channels,
-            out_channels=out_channels,
-            prev_output_channel=prev_output_channel,
-            temb_channels=temb_channels,
-            resolution_idx=resolution_idx,
-            dropout=dropout,
-            add_upsample=add_upsample,
-            resnet_eps=resnet_eps,
-            resnet_act_fn=resnet_act_fn,
-            resnet_groups=resnet_groups,
-            resnet_time_scale_shift=resnet_time_scale_shift,
-            skip_time_act=resnet_skip_time_act,
-            output_scale_factor=resnet_out_scale_factor,
-        )
-    elif up_block_type == "CrossAttnUpBlock2D":
-        if cross_attention_dim is None:
-            raise ValueError("cross_attention_dim must be specified for CrossAttnUpBlock2D")
-        return CrossAttnUpBlock2D(
-            num_layers=num_layers,
-            transformer_layers_per_block=transformer_layers_per_block,
-            in_channels=in_channels,
-            out_channels=out_channels,
-            prev_output_channel=prev_output_channel,
-            temb_channels=temb_channels,
-            resolution_idx=resolution_idx,
-            dropout=dropout,
-            add_upsample=add_upsample,
-            resnet_eps=resnet_eps,
-            resnet_act_fn=resnet_act_fn,
-            resnet_groups=resnet_groups,
-            cross_attention_dim=cross_attention_dim,
-            num_attention_heads=num_attention_heads,
-            dual_cross_attention=dual_cross_attention,
-            use_linear_projection=use_linear_projection,
-            only_cross_attention=only_cross_attention,
-            upcast_attention=upcast_attention,
-            resnet_time_scale_shift=resnet_time_scale_shift,
-            attention_type=attention_type,
-        )
-    elif up_block_type == "SimpleCrossAttnUpBlock2D":
-        if cross_attention_dim is None:
-            raise ValueError("cross_attention_dim must be specified for SimpleCrossAttnUpBlock2D")
-        return SimpleCrossAttnUpBlock2D(
-            num_layers=num_layers,
-            in_channels=in_channels,
-            out_channels=out_channels,
-            prev_output_channel=prev_output_channel,
-            temb_channels=temb_channels,
-            resolution_idx=resolution_idx,
-            dropout=dropout,
-            add_upsample=add_upsample,
-            resnet_eps=resnet_eps,
-            resnet_act_fn=resnet_act_fn,
-            resnet_groups=resnet_groups,
-            cross_attention_dim=cross_attention_dim,
-            attention_head_dim=attention_head_dim,
-            resnet_time_scale_shift=resnet_time_scale_shift,
-            skip_time_act=resnet_skip_time_act,
-            output_scale_factor=resnet_out_scale_factor,
-            only_cross_attention=only_cross_attention,
-            cross_attention_norm=cross_attention_norm,
-        )
-    elif up_block_type == "AttnUpBlock2D":
-        if add_upsample is False:
-            upsample_type = None
-        else:
-            upsample_type = upsample_type or "conv"  # default to 'conv'
-
-        return AttnUpBlock2D(
-            num_layers=num_layers,
-            in_channels=in_channels,
-            out_channels=out_channels,
-            prev_output_channel=prev_output_channel,
-            temb_channels=temb_channels,
-            resolution_idx=resolution_idx,
-            dropout=dropout,
-            resnet_eps=resnet_eps,
-            resnet_act_fn=resnet_act_fn,
-            resnet_groups=resnet_groups,
-            attention_head_dim=attention_head_dim,
-            resnet_time_scale_shift=resnet_time_scale_shift,
-            upsample_type=upsample_type,
-        )
-    elif up_block_type == "SkipUpBlock2D":
-        return SkipUpBlock2D(
-            num_layers=num_layers,
-            in_channels=in_channels,
-            out_channels=out_channels,
-            prev_output_channel=prev_output_channel,
-            temb_channels=temb_channels,
-            resolution_idx=resolution_idx,
-            dropout=dropout,
-            add_upsample=add_upsample,
-            resnet_eps=resnet_eps,
-            resnet_act_fn=resnet_act_fn,
-            resnet_time_scale_shift=resnet_time_scale_shift,
-        )
-    elif up_block_type == "AttnSkipUpBlock2D":
-        return AttnSkipUpBlock2D(
-            num_layers=num_layers,
-            in_channels=in_channels,
-            out_channels=out_channels,
-            prev_output_channel=prev_output_channel,
-            temb_channels=temb_channels,
-            resolution_idx=resolution_idx,
-            dropout=dropout,
-            add_upsample=add_upsample,
-            resnet_eps=resnet_eps,
-            resnet_act_fn=resnet_act_fn,
-            attention_head_dim=attention_head_dim,
-            resnet_time_scale_shift=resnet_time_scale_shift,
-        )
-    elif up_block_type == "UpDecoderBlock2D":
-        return UpDecoderBlock2D(
-            num_layers=num_layers,
-            in_channels=in_channels,
-            out_channels=out_channels,
-            resolution_idx=resolution_idx,
-            dropout=dropout,
-            add_upsample=add_upsample,
-            resnet_eps=resnet_eps,
-            resnet_act_fn=resnet_act_fn,
-            resnet_groups=resnet_groups,
-            resnet_time_scale_shift=resnet_time_scale_shift,
-            temb_channels=temb_channels,
-        )
-    elif up_block_type == "AttnUpDecoderBlock2D":
-        return AttnUpDecoderBlock2D(
-            num_layers=num_layers,
-            in_channels=in_channels,
-            out_channels=out_channels,
-            resolution_idx=resolution_idx,
-            dropout=dropout,
-            add_upsample=add_upsample,
-            resnet_eps=resnet_eps,
-            resnet_act_fn=resnet_act_fn,
-            resnet_groups=resnet_groups,
-            attention_head_dim=attention_head_dim,
-            resnet_time_scale_shift=resnet_time_scale_shift,
-            temb_channels=temb_channels,
-        )
-    elif up_block_type == "KUpBlock2D":
-        return KUpBlock2D(
-            num_layers=num_layers,
-            in_channels=in_channels,
-            out_channels=out_channels,
-            temb_channels=temb_channels,
-            resolution_idx=resolution_idx,
-            dropout=dropout,
-            add_upsample=add_upsample,
-            resnet_eps=resnet_eps,
-            resnet_act_fn=resnet_act_fn,
-        )
-    elif up_block_type == "KCrossAttnUpBlock2D":
-        return KCrossAttnUpBlock2D(
-            num_layers=num_layers,
-            in_channels=in_channels,
-            out_channels=out_channels,
-            temb_channels=temb_channels,
-            resolution_idx=resolution_idx,
-            dropout=dropout,
-            add_upsample=add_upsample,
-            resnet_eps=resnet_eps,
-            resnet_act_fn=resnet_act_fn,
-            cross_attention_dim=cross_attention_dim,
-            attention_head_dim=attention_head_dim,
-        )
-
-    raise ValueError(f"{up_block_type} does not exist.")
-
-
-class AutoencoderTinyBlock(nn.Module):
-    """
-    Tiny Autoencoder block used in [`AutoencoderTiny`]. It is a mini residual module consisting of plain conv + ReLU
-    blocks.
-
-    Args:
-        in_channels (`int`): The number of input channels.
-        out_channels (`int`): The number of output channels.
-        act_fn (`str`):
-            ` The activation function to use. Supported values are `"swish"`, `"mish"`, `"gelu"`, and `"relu"`.
-
-    Returns:
-        `torch.FloatTensor`: A tensor with the same shape as the input tensor, but with the number of channels equal to
-        `out_channels`.
-    """
-
-    def __init__(self, in_channels: int, out_channels: int, act_fn: str):
-        super().__init__()
-        act_fn = get_activation(act_fn)
-        self.conv = nn.Sequential(
-            nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1),
-            act_fn,
-            nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1),
-            act_fn,
-            nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1),
-        )
-        self.skip = (
-            nn.Conv2d(in_channels, out_channels, kernel_size=1, bias=False)
-            if in_channels != out_channels
-            else nn.Identity()
-        )
-        self.fuse = nn.ReLU()
-
-    def forward(self, x: torch.FloatTensor) -> torch.FloatTensor:
-        return self.fuse(self.conv(x) + self.skip(x))
-
-
-class UNetMidBlock2D(nn.Module):
-    """
-    A 2D UNet mid-block [`UNetMidBlock2D`] with multiple residual blocks and optional attention blocks.
-
-    Args:
-        in_channels (`int`): The number of input channels.
-        temb_channels (`int`): The number of temporal embedding channels.
-        dropout (`float`, *optional*, defaults to 0.0): The dropout rate.
-        num_layers (`int`, *optional*, defaults to 1): The number of residual blocks.
-        resnet_eps (`float`, *optional*, 1e-6 ): The epsilon value for the resnet blocks.
-        resnet_time_scale_shift (`str`, *optional*, defaults to `default`):
-            The type of normalization to apply to the time embeddings. This can help to improve the performance of the
-            model on tasks with long-range temporal dependencies.
-        resnet_act_fn (`str`, *optional*, defaults to `swish`): The activation function for the resnet blocks.
-        resnet_groups (`int`, *optional*, defaults to 32):
-            The number of groups to use in the group normalization layers of the resnet blocks.
-        attn_groups (`Optional[int]`, *optional*, defaults to None): The number of groups for the attention blocks.
-        resnet_pre_norm (`bool`, *optional*, defaults to `True`):
-            Whether to use pre-normalization for the resnet blocks.
-        add_attention (`bool`, *optional*, defaults to `True`): Whether to add attention blocks.
-        attention_head_dim (`int`, *optional*, defaults to 1):
-            Dimension of a single attention head. The number of attention heads is determined based on this value and
-            the number of input channels.
-        output_scale_factor (`float`, *optional*, defaults to 1.0): The output scale factor.
-
-    Returns:
-        `torch.FloatTensor`: The output of the last residual block, which is a tensor of shape `(batch_size,
-        in_channels, height, width)`.
-
-    """
-
-    def __init__(
-        self,
-        in_channels: int,
-        temb_channels: int,
-        dropout: float = 0.0,
-        num_layers: int = 1,
-        resnet_eps: float = 1e-6,
-        resnet_time_scale_shift: str = "default",  # default, spatial
-        resnet_act_fn: str = "swish",
-        resnet_groups: int = 32,
-        attn_groups: Optional[int] = None,
-        resnet_pre_norm: bool = True,
-        add_attention: bool = True,
-        attention_head_dim: int = 1,
-        output_scale_factor: float = 1.0,
-    ):
-        super().__init__()
-        resnet_groups = resnet_groups if resnet_groups is not None else min(in_channels // 4, 32)
-        self.add_attention = add_attention
-
-        if attn_groups is None:
-            attn_groups = resnet_groups if resnet_time_scale_shift == "default" else None
-
-        # there is always at least one resnet
-        if resnet_time_scale_shift == "spatial":
-            resnets = [
-                ResnetBlockCondNorm2D(
-                    in_channels=in_channels,
-                    out_channels=in_channels,
-                    temb_channels=temb_channels,
-                    eps=resnet_eps,
-                    groups=resnet_groups,
-                    dropout=dropout,
-                    time_embedding_norm="spatial",
-                    non_linearity=resnet_act_fn,
-                    output_scale_factor=output_scale_factor,
-                )
-            ]
-        else:
-            resnets = [
-                ResnetBlock2D(
-                    in_channels=in_channels,
-                    out_channels=in_channels,
-                    temb_channels=temb_channels,
-                    eps=resnet_eps,
-                    groups=resnet_groups,
-                    dropout=dropout,
-                    time_embedding_norm=resnet_time_scale_shift,
-                    non_linearity=resnet_act_fn,
-                    output_scale_factor=output_scale_factor,
-                    pre_norm=resnet_pre_norm,
-                )
-            ]
-        attentions = []
-
-        if attention_head_dim is None:
-            logger.warning(
-                f"It is not recommend to pass `attention_head_dim=None`. Defaulting `attention_head_dim` to `in_channels`: {in_channels}."
-            )
-            attention_head_dim = in_channels
-
-        for _ in range(num_layers):
-            if self.add_attention:
-                attentions.append(
-                    Attention(
-                        in_channels,
-                        heads=in_channels // attention_head_dim,
-                        dim_head=attention_head_dim,
-                        rescale_output_factor=output_scale_factor,
-                        eps=resnet_eps,
-                        norm_num_groups=attn_groups,
-                        spatial_norm_dim=temb_channels if resnet_time_scale_shift == "spatial" else None,
-                        residual_connection=True,
-                        bias=True,
-                        upcast_softmax=True,
-                        _from_deprecated_attn_block=True,
-                    )
-                )
-            else:
-                attentions.append(None)
-
-            if resnet_time_scale_shift == "spatial":
-                resnets.append(
-                    ResnetBlockCondNorm2D(
-                        in_channels=in_channels,
-                        out_channels=in_channels,
-                        temb_channels=temb_channels,
-                        eps=resnet_eps,
-                        groups=resnet_groups,
-                        dropout=dropout,
-                        time_embedding_norm="spatial",
-                        non_linearity=resnet_act_fn,
-                        output_scale_factor=output_scale_factor,
-                    )
-                )
-            else:
-                resnets.append(
-                    ResnetBlock2D(
-                        in_channels=in_channels,
-                        out_channels=in_channels,
-                        temb_channels=temb_channels,
-                        eps=resnet_eps,
-                        groups=resnet_groups,
-                        dropout=dropout,
-                        time_embedding_norm=resnet_time_scale_shift,
-                        non_linearity=resnet_act_fn,
-                        output_scale_factor=output_scale_factor,
-                        pre_norm=resnet_pre_norm,
-                    )
-                )
-
-        self.attentions = nn.ModuleList(attentions)
-        self.resnets = nn.ModuleList(resnets)
-
-    def forward(self, hidden_states: torch.FloatTensor, temb: Optional[torch.FloatTensor] = None) -> torch.FloatTensor:
-        hidden_states = self.resnets[0](hidden_states, temb)
-        for attn, resnet in zip(self.attentions, self.resnets[1:]):
-            if attn is not None:
-                hidden_states = attn(hidden_states, temb=temb)
-            hidden_states = resnet(hidden_states, temb)
-
-        return hidden_states
-
-
-class UNetMidBlock2DCrossAttn(nn.Module):
-    def __init__(
-        self,
-        in_channels: int,
-        temb_channels: int,
-        dropout: float = 0.0,
-        num_layers: int = 1,
-        transformer_layers_per_block: Union[int, Tuple[int]] = 1,
-        resnet_eps: float = 1e-6,
-        resnet_time_scale_shift: str = "default",
-        resnet_act_fn: str = "swish",
-        resnet_groups: int = 32,
-        resnet_pre_norm: bool = True,
-        num_attention_heads: int = 1,
-        output_scale_factor: float = 1.0,
-        cross_attention_dim: int = 1280,
-        dual_cross_attention: bool = False,
-        use_linear_projection: bool = False,
-        upcast_attention: bool = False,
-        attention_type: str = "default",
-    ):
-        super().__init__()
-
-        self.has_cross_attention = True
-        self.num_attention_heads = num_attention_heads
-        resnet_groups = resnet_groups if resnet_groups is not None else min(in_channels // 4, 32)
-
-        # support for variable transformer layers per block
-        if isinstance(transformer_layers_per_block, int):
-            transformer_layers_per_block = [transformer_layers_per_block] * num_layers
-
-        # there is always at least one resnet
-        resnets = [
-            ResnetBlock2D(
-                in_channels=in_channels,
-                out_channels=in_channels,
-                temb_channels=temb_channels,
-                eps=resnet_eps,
-                groups=resnet_groups,
-                dropout=dropout,
-                time_embedding_norm=resnet_time_scale_shift,
-                non_linearity=resnet_act_fn,
-                output_scale_factor=output_scale_factor,
-                pre_norm=resnet_pre_norm,
-            )
-        ]
-        attentions = []
-
-        for i in range(num_layers):
-            if not dual_cross_attention:
-                attentions.append(
-                    Transformer2DModel(
-                        num_attention_heads,
-                        in_channels // num_attention_heads,
-                        in_channels=in_channels,
-                        num_layers=transformer_layers_per_block[i],
-                        cross_attention_dim=cross_attention_dim,
-                        norm_num_groups=resnet_groups,
-                        use_linear_projection=use_linear_projection,
-                        upcast_attention=upcast_attention,
-                        attention_type=attention_type,
-                    )
-                )
-            else:
-                attentions.append(
-                    DualTransformer2DModel(
-                        num_attention_heads,
-                        in_channels // num_attention_heads,
-                        in_channels=in_channels,
-                        num_layers=1,
-                        cross_attention_dim=cross_attention_dim,
-                        norm_num_groups=resnet_groups,
-                    )
-                )
-            resnets.append(
-                ResnetBlock2D(
-                    in_channels=in_channels,
-                    out_channels=in_channels,
-                    temb_channels=temb_channels,
-                    eps=resnet_eps,
-                    groups=resnet_groups,
-                    dropout=dropout,
-                    time_embedding_norm=resnet_time_scale_shift,
-                    non_linearity=resnet_act_fn,
-                    output_scale_factor=output_scale_factor,
-                    pre_norm=resnet_pre_norm,
-                )
-            )
-
-        self.attentions = nn.ModuleList(attentions)
-        self.resnets = nn.ModuleList(resnets)
-
-        self.gradient_checkpointing = False
-
-    def forward(
-        self,
-        hidden_states: torch.FloatTensor,
-        temb: Optional[torch.FloatTensor] = None,
-        encoder_hidden_states: Optional[torch.FloatTensor] = None,
-        attention_mask: Optional[torch.FloatTensor] = None,
-        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
-        encoder_attention_mask: Optional[torch.FloatTensor] = None,
-    ) -> torch.FloatTensor:
-        if cross_attention_kwargs is not None:
-            if cross_attention_kwargs.get("scale", None) is not None:
-                logger.warning("Passing `scale` to `cross_attention_kwargs` is deprecated. `scale` will be ignored.")
-
-        hidden_states = self.resnets[0](hidden_states, temb)
-        for attn, resnet in zip(self.attentions, self.resnets[1:]):
-            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 = attn(
-                    hidden_states,
-                    encoder_hidden_states=encoder_hidden_states,
-                    cross_attention_kwargs=cross_attention_kwargs,
-                    attention_mask=attention_mask,
-                    encoder_attention_mask=encoder_attention_mask,
-                    return_dict=False,
-                )[0]
-                hidden_states = torch.utils.checkpoint.checkpoint(
-                    create_custom_forward(resnet),
-                    hidden_states,
-                    temb,
-                    **ckpt_kwargs,
-                )
-            else:
-                hidden_states = attn(
-                    hidden_states,
-                    encoder_hidden_states=encoder_hidden_states,
-                    cross_attention_kwargs=cross_attention_kwargs,
-                    attention_mask=attention_mask,
-                    encoder_attention_mask=encoder_attention_mask,
-                    return_dict=False,
-                )[0]
-                hidden_states = resnet(hidden_states, temb)
-
-        return hidden_states
-
-
-class UNetMidBlock2DSimpleCrossAttn(nn.Module):
-    def __init__(
-        self,
-        in_channels: int,
-        temb_channels: int,
-        dropout: float = 0.0,
-        num_layers: int = 1,
-        resnet_eps: float = 1e-6,
-        resnet_time_scale_shift: str = "default",
-        resnet_act_fn: str = "swish",
-        resnet_groups: int = 32,
-        resnet_pre_norm: bool = True,
-        attention_head_dim: int = 1,
-        output_scale_factor: float = 1.0,
-        cross_attention_dim: int = 1280,
-        skip_time_act: bool = False,
-        only_cross_attention: bool = False,
-        cross_attention_norm: Optional[str] = None,
-    ):
-        super().__init__()
-
-        self.has_cross_attention = True
-
-        self.attention_head_dim = attention_head_dim
-        resnet_groups = resnet_groups if resnet_groups is not None else min(in_channels // 4, 32)
-
-        self.num_heads = in_channels // self.attention_head_dim
-
-        # there is always at least one resnet
-        resnets = [
-            ResnetBlock2D(
-                in_channels=in_channels,
-                out_channels=in_channels,
-                temb_channels=temb_channels,
-                eps=resnet_eps,
-                groups=resnet_groups,
-                dropout=dropout,
-                time_embedding_norm=resnet_time_scale_shift,
-                non_linearity=resnet_act_fn,
-                output_scale_factor=output_scale_factor,
-                pre_norm=resnet_pre_norm,
-                skip_time_act=skip_time_act,
-            )
-        ]
-        attentions = []
-
-        for _ in range(num_layers):
-            processor = (
-                AttnAddedKVProcessor2_0() if hasattr(F, "scaled_dot_product_attention") else AttnAddedKVProcessor()
-            )
-
-            attentions.append(
-                Attention(
-                    query_dim=in_channels,
-                    cross_attention_dim=in_channels,
-                    heads=self.num_heads,
-                    dim_head=self.attention_head_dim,
-                    added_kv_proj_dim=cross_attention_dim,
-                    norm_num_groups=resnet_groups,
-                    bias=True,
-                    upcast_softmax=True,
-                    only_cross_attention=only_cross_attention,
-                    cross_attention_norm=cross_attention_norm,
-                    processor=processor,
-                )
-            )
-            resnets.append(
-                ResnetBlock2D(
-                    in_channels=in_channels,
-                    out_channels=in_channels,
-                    temb_channels=temb_channels,
-                    eps=resnet_eps,
-                    groups=resnet_groups,
-                    dropout=dropout,
-                    time_embedding_norm=resnet_time_scale_shift,
-                    non_linearity=resnet_act_fn,
-                    output_scale_factor=output_scale_factor,
-                    pre_norm=resnet_pre_norm,
-                    skip_time_act=skip_time_act,
-                )
-            )
-
-        self.attentions = nn.ModuleList(attentions)
-        self.resnets = nn.ModuleList(resnets)
-
-    def forward(
-        self,
-        hidden_states: torch.FloatTensor,
-        temb: Optional[torch.FloatTensor] = None,
-        encoder_hidden_states: Optional[torch.FloatTensor] = None,
-        attention_mask: Optional[torch.FloatTensor] = None,
-        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
-        encoder_attention_mask: Optional[torch.FloatTensor] = None,
-    ) -> torch.FloatTensor:
-        cross_attention_kwargs = cross_attention_kwargs if cross_attention_kwargs is not None else {}
-        if cross_attention_kwargs.get("scale", None) is not None:
-            logger.warning("Passing `scale` to `cross_attention_kwargs` is deprecated. `scale` will be ignored.")
-
-        if attention_mask is None:
-            # if encoder_hidden_states is defined: we are doing cross-attn, so we should use cross-attn mask.
-            mask = None if encoder_hidden_states is None else encoder_attention_mask
-        else:
-            # when attention_mask is defined: we don't even check for encoder_attention_mask.
-            # this is to maintain compatibility with UnCLIP, which uses 'attention_mask' param for cross-attn masks.
-            # TODO: UnCLIP should express cross-attn mask via encoder_attention_mask param instead of via attention_mask.
-            #       then we can simplify this whole if/else block to:
-            #         mask = attention_mask if encoder_hidden_states is None else encoder_attention_mask
-            mask = attention_mask
-
-        hidden_states = self.resnets[0](hidden_states, temb)
-        for attn, resnet in zip(self.attentions, self.resnets[1:]):
-            # attn
-            hidden_states = attn(
-                hidden_states,
-                encoder_hidden_states=encoder_hidden_states,
-                attention_mask=mask,
-                **cross_attention_kwargs,
-            )
-
-            # resnet
-            hidden_states = resnet(hidden_states, temb)
-
-        return hidden_states
-
-
-class MidBlock2D(nn.Module):
-    def __init__(
-        self,
-        in_channels: int,
-        temb_channels: int,
-        dropout: float = 0.0,
-        num_layers: int = 1,
-        resnet_eps: float = 1e-6,
-        resnet_time_scale_shift: str = "default",
-        resnet_act_fn: str = "swish",
-        resnet_groups: int = 32,
-        resnet_pre_norm: bool = True,
-        output_scale_factor: float = 1.0,
-        use_linear_projection: bool = False,
-    ):
-        super().__init__()
-
-        self.has_cross_attention = False
-        resnet_groups = resnet_groups if resnet_groups is not None else min(in_channels // 4, 32)
-
-        # there is always at least one resnet
-        resnets = [
-            ResnetBlock2D(
-                in_channels=in_channels,
-                out_channels=in_channels,
-                temb_channels=temb_channels,
-                eps=resnet_eps,
-                groups=resnet_groups,
-                dropout=dropout,
-                time_embedding_norm=resnet_time_scale_shift,
-                non_linearity=resnet_act_fn,
-                output_scale_factor=output_scale_factor,
-                pre_norm=resnet_pre_norm,
-            )
-        ]
-
-        for i in range(num_layers):
-            resnets.append(
-                ResnetBlock2D(
-                    in_channels=in_channels,
-                    out_channels=in_channels,
-                    temb_channels=temb_channels,
-                    eps=resnet_eps,
-                    groups=resnet_groups,
-                    dropout=dropout,
-                    time_embedding_norm=resnet_time_scale_shift,
-                    non_linearity=resnet_act_fn,
-                    output_scale_factor=output_scale_factor,
-                    pre_norm=resnet_pre_norm,
-                )
-            )
-
-        self.resnets = nn.ModuleList(resnets)
-
-        self.gradient_checkpointing = False
-
-    def forward(
-        self,
-        hidden_states: torch.FloatTensor,
-        temb: Optional[torch.FloatTensor] = None,
-    ) -> torch.FloatTensor:
-        lora_scale = 1.0
-        hidden_states = self.resnets[0](hidden_states, temb, scale=lora_scale)
-        for resnet in self.resnets[1:]:
-            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 = torch.utils.checkpoint.checkpoint(
-                    create_custom_forward(resnet),
-                    hidden_states,
-                    temb,
-                    **ckpt_kwargs,
-                )
-            else:
-                hidden_states = resnet(hidden_states, temb, scale=lora_scale)
-
-        return hidden_states
-
-
-class AttnDownBlock2D(nn.Module):
-    def __init__(
-        self,
-        in_channels: int,
-        out_channels: int,
-        temb_channels: int,
-        dropout: float = 0.0,
-        num_layers: int = 1,
-        resnet_eps: float = 1e-6,
-        resnet_time_scale_shift: str = "default",
-        resnet_act_fn: str = "swish",
-        resnet_groups: int = 32,
-        resnet_pre_norm: bool = True,
-        attention_head_dim: int = 1,
-        output_scale_factor: float = 1.0,
-        downsample_padding: int = 1,
-        downsample_type: str = "conv",
-    ):
-        super().__init__()
-        resnets = []
-        attentions = []
-        self.downsample_type = downsample_type
-
-        if attention_head_dim is None:
-            logger.warning(
-                f"It is not recommend to pass `attention_head_dim=None`. Defaulting `attention_head_dim` to `in_channels`: {out_channels}."
-            )
-            attention_head_dim = out_channels
-
-        for i in range(num_layers):
-            in_channels = in_channels if i == 0 else out_channels
-            resnets.append(
-                ResnetBlock2D(
-                    in_channels=in_channels,
-                    out_channels=out_channels,
-                    temb_channels=temb_channels,
-                    eps=resnet_eps,
-                    groups=resnet_groups,
-                    dropout=dropout,
-                    time_embedding_norm=resnet_time_scale_shift,
-                    non_linearity=resnet_act_fn,
-                    output_scale_factor=output_scale_factor,
-                    pre_norm=resnet_pre_norm,
-                )
-            )
-            attentions.append(
-                Attention(
-                    out_channels,
-                    heads=out_channels // attention_head_dim,
-                    dim_head=attention_head_dim,
-                    rescale_output_factor=output_scale_factor,
-                    eps=resnet_eps,
-                    norm_num_groups=resnet_groups,
-                    residual_connection=True,
-                    bias=True,
-                    upcast_softmax=True,
-                    _from_deprecated_attn_block=True,
-                )
-            )
-
-        self.attentions = nn.ModuleList(attentions)
-        self.resnets = nn.ModuleList(resnets)
-
-        if downsample_type == "conv":
-            self.downsamplers = nn.ModuleList(
-                [
-                    Downsample2D(
-                        out_channels, use_conv=True, out_channels=out_channels, padding=downsample_padding, name="op"
-                    )
-                ]
-            )
-        elif downsample_type == "resnet":
-            self.downsamplers = nn.ModuleList(
-                [
-                    ResnetBlock2D(
-                        in_channels=out_channels,
-                        out_channels=out_channels,
-                        temb_channels=temb_channels,
-                        eps=resnet_eps,
-                        groups=resnet_groups,
-                        dropout=dropout,
-                        time_embedding_norm=resnet_time_scale_shift,
-                        non_linearity=resnet_act_fn,
-                        output_scale_factor=output_scale_factor,
-                        pre_norm=resnet_pre_norm,
-                        down=True,
-                    )
-                ]
-            )
-        else:
-            self.downsamplers = None
-
-    def forward(
-        self,
-        hidden_states: torch.FloatTensor,
-        temb: Optional[torch.FloatTensor] = None,
-        upsample_size: Optional[int] = None,
-        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
-    ) -> Tuple[torch.FloatTensor, Tuple[torch.FloatTensor, ...]]:
-        cross_attention_kwargs = cross_attention_kwargs if cross_attention_kwargs is not None else {}
-        if cross_attention_kwargs.get("scale", None) is not None:
-            logger.warning("Passing `scale` to `cross_attention_kwargs` is deprecated. `scale` will be ignored.")
-
-        output_states = ()
-
-        for resnet, attn in zip(self.resnets, self.attentions):
-            hidden_states = resnet(hidden_states, temb)
-            hidden_states = attn(hidden_states, **cross_attention_kwargs)
-            output_states = output_states + (hidden_states,)
-
-        if self.downsamplers is not None:
-            for downsampler in self.downsamplers:
-                if self.downsample_type == "resnet":
-                    hidden_states = downsampler(hidden_states, temb=temb)
-                else:
-                    hidden_states = downsampler(hidden_states)
-
-            output_states += (hidden_states,)
-
-        return hidden_states, output_states
-
-
-class CrossAttnDownBlock2D(nn.Module):
-    def __init__(
-        self,
-        in_channels: int,
-        out_channels: int,
-        temb_channels: int,
-        dropout: float = 0.0,
-        num_layers: int = 1,
-        transformer_layers_per_block: Union[int, Tuple[int]] = 1,
-        resnet_eps: float = 1e-6,
-        resnet_time_scale_shift: str = "default",
-        resnet_act_fn: str = "swish",
-        resnet_groups: int = 32,
-        resnet_pre_norm: bool = True,
-        num_attention_heads: int = 1,
-        cross_attention_dim: int = 1280,
-        output_scale_factor: float = 1.0,
-        downsample_padding: int = 1,
-        add_downsample: bool = True,
-        dual_cross_attention: bool = False,
-        use_linear_projection: bool = False,
-        only_cross_attention: bool = False,
-        upcast_attention: bool = False,
-        attention_type: str = "default",
-    ):
-        super().__init__()
-        resnets = []
-        attentions = []
-
-        self.has_cross_attention = True
-        self.num_attention_heads = num_attention_heads
-        if isinstance(transformer_layers_per_block, int):
-            transformer_layers_per_block = [transformer_layers_per_block] * num_layers
-
-        for i in range(num_layers):
-            in_channels = in_channels if i == 0 else out_channels
-            resnets.append(
-                ResnetBlock2D(
-                    in_channels=in_channels,
-                    out_channels=out_channels,
-                    temb_channels=temb_channels,
-                    eps=resnet_eps,
-                    groups=resnet_groups,
-                    dropout=dropout,
-                    time_embedding_norm=resnet_time_scale_shift,
-                    non_linearity=resnet_act_fn,
-                    output_scale_factor=output_scale_factor,
-                    pre_norm=resnet_pre_norm,
-                )
-            )
-            if not dual_cross_attention:
-                attentions.append(
-                    Transformer2DModel(
-                        num_attention_heads,
-                        out_channels // num_attention_heads,
-                        in_channels=out_channels,
-                        num_layers=transformer_layers_per_block[i],
-                        cross_attention_dim=cross_attention_dim,
-                        norm_num_groups=resnet_groups,
-                        use_linear_projection=use_linear_projection,
-                        only_cross_attention=only_cross_attention,
-                        upcast_attention=upcast_attention,
-                        attention_type=attention_type,
-                    )
-                )
-            else:
-                attentions.append(
-                    DualTransformer2DModel(
-                        num_attention_heads,
-                        out_channels // num_attention_heads,
-                        in_channels=out_channels,
-                        num_layers=1,
-                        cross_attention_dim=cross_attention_dim,
-                        norm_num_groups=resnet_groups,
-                    )
-                )
-        self.attentions = nn.ModuleList(attentions)
-        self.resnets = nn.ModuleList(resnets)
-
-        if add_downsample:
-            self.downsamplers = nn.ModuleList(
-                [
-                    Downsample2D(
-                        out_channels, use_conv=True, out_channels=out_channels, padding=downsample_padding, name="op"
-                    )
-                ]
-            )
-        else:
-            self.downsamplers = None
-
-        self.gradient_checkpointing = False
-
-    def forward(
-        self,
-        hidden_states: torch.FloatTensor,
-        temb: Optional[torch.FloatTensor] = None,
-        encoder_hidden_states: Optional[torch.FloatTensor] = None,
-        attention_mask: Optional[torch.FloatTensor] = None,
-        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
-        encoder_attention_mask: Optional[torch.FloatTensor] = None,
-        additional_residuals: Optional[torch.FloatTensor] = None,
-        down_block_add_samples: Optional[torch.FloatTensor] = None,
-        debug = False,
-    ) -> Tuple[torch.FloatTensor, Tuple[torch.FloatTensor, ...]]:
-        
-        if debug: print('    XAD2: forward')
-
-        if cross_attention_kwargs is not None:
-            if cross_attention_kwargs.get("scale", None) is not None:
-                logger.warning("Passing `scale` to `cross_attention_kwargs` is deprecated. `scale` will be ignored.")
-
-        output_states = ()
-
-        blocks = list(zip(self.resnets, self.attentions))
-
-        for i, (resnet, attn) in enumerate(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 = torch.utils.checkpoint.checkpoint(
-                    create_custom_forward(resnet),
-                    hidden_states,
-                    temb,
-                    **ckpt_kwargs,
-                )
-                hidden_states = attn(
-                    hidden_states,
-                    encoder_hidden_states=encoder_hidden_states,
-                    cross_attention_kwargs=cross_attention_kwargs,
-                    attention_mask=attention_mask,
-                    encoder_attention_mask=encoder_attention_mask,
-                    return_dict=False,
-                )[0]
-            else:
-                if debug: print('    XAD2: resnet hs #', i, hidden_states.shape)
-                if debug and temb is not None: print('    XAD2: resnet temb #', i, temb.shape)
-
-                hidden_states = resnet(hidden_states, temb)
-
-                if debug: print('    XAD2: attn hs #', i, hidden_states.shape)
-                if debug and encoder_hidden_states is not None: print('    XAD2: attn ehs #', i, encoder_hidden_states.shape)
-
-                hidden_states = attn(
-                    hidden_states,
-                    encoder_hidden_states=encoder_hidden_states,
-                    cross_attention_kwargs=cross_attention_kwargs,
-                    attention_mask=attention_mask,
-                    encoder_attention_mask=encoder_attention_mask,
-                    return_dict=False,
-                )[0]
-
-            # apply additional residuals to the output of the last pair of resnet and attention blocks
-            if i == len(blocks) - 1 and additional_residuals is not None:
-
-                if debug: print('    XAD2: add res', additional_residuals.shape)
-            
-                hidden_states = hidden_states + additional_residuals
-
-            if down_block_add_samples is not None:
-
-                if debug: print('    XAD2: add samples', down_block_add_samples.shape)
-
-                hidden_states = hidden_states + down_block_add_samples.pop(0) 
-
-            if debug: print('    XAD2: output', hidden_states.shape)
-
-            output_states = output_states + (hidden_states,)
-
-        if self.downsamplers is not None:
-            for downsampler in self.downsamplers:
-                hidden_states = downsampler(hidden_states)
-
-            if down_block_add_samples is not None:
-                hidden_states = hidden_states + down_block_add_samples.pop(0) # todo: add before or after
-
-            output_states = output_states + (hidden_states,)
-
-        if debug: 
-            print('    XAD2: finish')
-            for st in output_states:
-                print('    XAD2: ',st.shape)
-
-        return hidden_states, output_states
-
-
-class DownBlock2D(nn.Module):
-    def __init__(
-        self,
-        in_channels: int,
-        out_channels: int,
-        temb_channels: int,
-        dropout: float = 0.0,
-        num_layers: int = 1,
-        resnet_eps: float = 1e-6,
-        resnet_time_scale_shift: str = "default",
-        resnet_act_fn: str = "swish",
-        resnet_groups: int = 32,
-        resnet_pre_norm: bool = True,
-        output_scale_factor: float = 1.0,
-        add_downsample: bool = True,
-        downsample_padding: int = 1,
-    ):
-        super().__init__()
-        resnets = []
-
-        for i in range(num_layers):
-            in_channels = in_channels if i == 0 else out_channels
-            resnets.append(
-                ResnetBlock2D(
-                    in_channels=in_channels,
-                    out_channels=out_channels,
-                    temb_channels=temb_channels,
-                    eps=resnet_eps,
-                    groups=resnet_groups,
-                    dropout=dropout,
-                    time_embedding_norm=resnet_time_scale_shift,
-                    non_linearity=resnet_act_fn,
-                    output_scale_factor=output_scale_factor,
-                    pre_norm=resnet_pre_norm,
-                )
-            )
-
-        self.resnets = nn.ModuleList(resnets)
-
-        if add_downsample:
-            self.downsamplers = nn.ModuleList(
-                [
-                    Downsample2D(
-                        out_channels, use_conv=True, out_channels=out_channels, padding=downsample_padding, name="op"
-                    )
-                ]
-            )
-        else:
-            self.downsamplers = None
-
-        self.gradient_checkpointing = False
-
-    def forward(
-        self, hidden_states: torch.FloatTensor, temb: Optional[torch.FloatTensor] = None,
-        down_block_add_samples: Optional[torch.FloatTensor] = None, *args, **kwargs
-    ) -> Tuple[torch.FloatTensor, Tuple[torch.FloatTensor, ...]]:
-        if len(args) > 0 or kwargs.get("scale", None) is not None:
-            deprecation_message = "The `scale` argument is deprecated and will be ignored. Please remove it, as passing it will raise an error in the future. `scale` should directly be passed while calling the underlying pipeline component i.e., via `cross_attention_kwargs`."
-            deprecate("scale", "1.0.0", deprecation_message)
-
-        output_states = ()
-
-        if kwargs.get("debug", False): print('    D2: forward', hidden_states.shape)
-
-        for resnet in self.resnets:
-            if self.training and self.gradient_checkpointing:
-
-                def create_custom_forward(module):
-                    def custom_forward(*inputs):
-                        return module(*inputs)
-
-                    return custom_forward
-
-                if is_torch_version(">=", "1.11.0"):
-                    hidden_states = torch.utils.checkpoint.checkpoint(
-                        create_custom_forward(resnet), hidden_states, temb, use_reentrant=False
-                    )
-                else:
-                    hidden_states = torch.utils.checkpoint.checkpoint(
-                        create_custom_forward(resnet), hidden_states, temb
-                    )
-            else:
-
-                if kwargs.get("debug", False): print('    D2: resnet', hidden_states.shape)
-
-                hidden_states = resnet(hidden_states, temb)
-
-            if down_block_add_samples is not None:
-                hidden_states = hidden_states + down_block_add_samples.pop(0) 
-
-            output_states = output_states + (hidden_states,)
-
-        if self.downsamplers is not None:
-            for downsampler in self.downsamplers:
-                hidden_states = downsampler(hidden_states)
-
-            if down_block_add_samples is not None:
-                hidden_states = hidden_states + down_block_add_samples.pop(0)  # todo: add before or after
-
-            output_states = output_states + (hidden_states,)
-
-        if kwargs.get("debug", False): print('    D2: finish', hidden_states.shape)
-            
-        return hidden_states, output_states
-
-
-class DownEncoderBlock2D(nn.Module):
-    def __init__(
-        self,
-        in_channels: int,
-        out_channels: int,
-        dropout: float = 0.0,
-        num_layers: int = 1,
-        resnet_eps: float = 1e-6,
-        resnet_time_scale_shift: str = "default",
-        resnet_act_fn: str = "swish",
-        resnet_groups: int = 32,
-        resnet_pre_norm: bool = True,
-        output_scale_factor: float = 1.0,
-        add_downsample: bool = True,
-        downsample_padding: int = 1,
-    ):
-        super().__init__()
-        resnets = []
-
-        for i in range(num_layers):
-            in_channels = in_channels if i == 0 else out_channels
-            if resnet_time_scale_shift == "spatial":
-                resnets.append(
-                    ResnetBlockCondNorm2D(
-                        in_channels=in_channels,
-                        out_channels=out_channels,
-                        temb_channels=None,
-                        eps=resnet_eps,
-                        groups=resnet_groups,
-                        dropout=dropout,
-                        time_embedding_norm="spatial",
-                        non_linearity=resnet_act_fn,
-                        output_scale_factor=output_scale_factor,
-                    )
-                )
-            else:
-                resnets.append(
-                    ResnetBlock2D(
-                        in_channels=in_channels,
-                        out_channels=out_channels,
-                        temb_channels=None,
-                        eps=resnet_eps,
-                        groups=resnet_groups,
-                        dropout=dropout,
-                        time_embedding_norm=resnet_time_scale_shift,
-                        non_linearity=resnet_act_fn,
-                        output_scale_factor=output_scale_factor,
-                        pre_norm=resnet_pre_norm,
-                    )
-                )
-
-        self.resnets = nn.ModuleList(resnets)
-
-        if add_downsample:
-            self.downsamplers = nn.ModuleList(
-                [
-                    Downsample2D(
-                        out_channels, use_conv=True, out_channels=out_channels, padding=downsample_padding, name="op"
-                    )
-                ]
-            )
-        else:
-            self.downsamplers = None
-
-    def forward(self, hidden_states: torch.FloatTensor, *args, **kwargs) -> torch.FloatTensor:
-        if len(args) > 0 or kwargs.get("scale", None) is not None:
-            deprecation_message = "The `scale` argument is deprecated and will be ignored. Please remove it, as passing it will raise an error in the future. `scale` should directly be passed while calling the underlying pipeline component i.e., via `cross_attention_kwargs`."
-            deprecate("scale", "1.0.0", deprecation_message)
-
-        for resnet in self.resnets:
-            hidden_states = resnet(hidden_states, temb=None)
-
-        if self.downsamplers is not None:
-            for downsampler in self.downsamplers:
-                hidden_states = downsampler(hidden_states)
-
-        return hidden_states
-
-
-class AttnDownEncoderBlock2D(nn.Module):
-    def __init__(
-        self,
-        in_channels: int,
-        out_channels: int,
-        dropout: float = 0.0,
-        num_layers: int = 1,
-        resnet_eps: float = 1e-6,
-        resnet_time_scale_shift: str = "default",
-        resnet_act_fn: str = "swish",
-        resnet_groups: int = 32,
-        resnet_pre_norm: bool = True,
-        attention_head_dim: int = 1,
-        output_scale_factor: float = 1.0,
-        add_downsample: bool = True,
-        downsample_padding: int = 1,
-    ):
-        super().__init__()
-        resnets = []
-        attentions = []
-
-        if attention_head_dim is None:
-            logger.warning(
-                f"It is not recommend to pass `attention_head_dim=None`. Defaulting `attention_head_dim` to `in_channels`: {out_channels}."
-            )
-            attention_head_dim = out_channels
-
-        for i in range(num_layers):
-            in_channels = in_channels if i == 0 else out_channels
-            if resnet_time_scale_shift == "spatial":
-                resnets.append(
-                    ResnetBlockCondNorm2D(
-                        in_channels=in_channels,
-                        out_channels=out_channels,
-                        temb_channels=None,
-                        eps=resnet_eps,
-                        groups=resnet_groups,
-                        dropout=dropout,
-                        time_embedding_norm="spatial",
-                        non_linearity=resnet_act_fn,
-                        output_scale_factor=output_scale_factor,
-                    )
-                )
-            else:
-                resnets.append(
-                    ResnetBlock2D(
-                        in_channels=in_channels,
-                        out_channels=out_channels,
-                        temb_channels=None,
-                        eps=resnet_eps,
-                        groups=resnet_groups,
-                        dropout=dropout,
-                        time_embedding_norm=resnet_time_scale_shift,
-                        non_linearity=resnet_act_fn,
-                        output_scale_factor=output_scale_factor,
-                        pre_norm=resnet_pre_norm,
-                    )
-                )
-            attentions.append(
-                Attention(
-                    out_channels,
-                    heads=out_channels // attention_head_dim,
-                    dim_head=attention_head_dim,
-                    rescale_output_factor=output_scale_factor,
-                    eps=resnet_eps,
-                    norm_num_groups=resnet_groups,
-                    residual_connection=True,
-                    bias=True,
-                    upcast_softmax=True,
-                    _from_deprecated_attn_block=True,
-                )
-            )
-
-        self.attentions = nn.ModuleList(attentions)
-        self.resnets = nn.ModuleList(resnets)
-
-        if add_downsample:
-            self.downsamplers = nn.ModuleList(
-                [
-                    Downsample2D(
-                        out_channels, use_conv=True, out_channels=out_channels, padding=downsample_padding, name="op"
-                    )
-                ]
-            )
-        else:
-            self.downsamplers = None
-
-    def forward(self, hidden_states: torch.FloatTensor, *args, **kwargs) -> torch.FloatTensor:
-        if len(args) > 0 or kwargs.get("scale", None) is not None:
-            deprecation_message = "The `scale` argument is deprecated and will be ignored. Please remove it, as passing it will raise an error in the future. `scale` should directly be passed while calling the underlying pipeline component i.e., via `cross_attention_kwargs`."
-            deprecate("scale", "1.0.0", deprecation_message)
-
-        for resnet, attn in zip(self.resnets, self.attentions):
-            hidden_states = resnet(hidden_states, temb=None)
-            hidden_states = attn(hidden_states)
-
-        if self.downsamplers is not None:
-            for downsampler in self.downsamplers:
-                hidden_states = downsampler(hidden_states)
-
-        return hidden_states
-
-
-class AttnSkipDownBlock2D(nn.Module):
-    def __init__(
-        self,
-        in_channels: int,
-        out_channels: int,
-        temb_channels: int,
-        dropout: float = 0.0,
-        num_layers: int = 1,
-        resnet_eps: float = 1e-6,
-        resnet_time_scale_shift: str = "default",
-        resnet_act_fn: str = "swish",
-        resnet_pre_norm: bool = True,
-        attention_head_dim: int = 1,
-        output_scale_factor: float = np.sqrt(2.0),
-        add_downsample: bool = True,
-    ):
-        super().__init__()
-        self.attentions = nn.ModuleList([])
-        self.resnets = nn.ModuleList([])
-
-        if attention_head_dim is None:
-            logger.warning(
-                f"It is not recommend to pass `attention_head_dim=None`. Defaulting `attention_head_dim` to `in_channels`: {out_channels}."
-            )
-            attention_head_dim = out_channels
-
-        for i in range(num_layers):
-            in_channels = in_channels if i == 0 else out_channels
-            self.resnets.append(
-                ResnetBlock2D(
-                    in_channels=in_channels,
-                    out_channels=out_channels,
-                    temb_channels=temb_channels,
-                    eps=resnet_eps,
-                    groups=min(in_channels // 4, 32),
-                    groups_out=min(out_channels // 4, 32),
-                    dropout=dropout,
-                    time_embedding_norm=resnet_time_scale_shift,
-                    non_linearity=resnet_act_fn,
-                    output_scale_factor=output_scale_factor,
-                    pre_norm=resnet_pre_norm,
-                )
-            )
-            self.attentions.append(
-                Attention(
-                    out_channels,
-                    heads=out_channels // attention_head_dim,
-                    dim_head=attention_head_dim,
-                    rescale_output_factor=output_scale_factor,
-                    eps=resnet_eps,
-                    norm_num_groups=32,
-                    residual_connection=True,
-                    bias=True,
-                    upcast_softmax=True,
-                    _from_deprecated_attn_block=True,
-                )
-            )
-
-        if add_downsample:
-            self.resnet_down = ResnetBlock2D(
-                in_channels=out_channels,
-                out_channels=out_channels,
-                temb_channels=temb_channels,
-                eps=resnet_eps,
-                groups=min(out_channels // 4, 32),
-                dropout=dropout,
-                time_embedding_norm=resnet_time_scale_shift,
-                non_linearity=resnet_act_fn,
-                output_scale_factor=output_scale_factor,
-                pre_norm=resnet_pre_norm,
-                use_in_shortcut=True,
-                down=True,
-                kernel="fir",
-            )
-            self.downsamplers = nn.ModuleList([FirDownsample2D(out_channels, out_channels=out_channels)])
-            self.skip_conv = nn.Conv2d(3, out_channels, kernel_size=(1, 1), stride=(1, 1))
-        else:
-            self.resnet_down = None
-            self.downsamplers = None
-            self.skip_conv = None
-
-    def forward(
-        self,
-        hidden_states: torch.FloatTensor,
-        temb: Optional[torch.FloatTensor] = None,
-        skip_sample: Optional[torch.FloatTensor] = None,
-        *args,
-        **kwargs,
-    ) -> Tuple[torch.FloatTensor, Tuple[torch.FloatTensor, ...], torch.FloatTensor]:
-        if len(args) > 0 or kwargs.get("scale", None) is not None:
-            deprecation_message = "The `scale` argument is deprecated and will be ignored. Please remove it, as passing it will raise an error in the future. `scale` should directly be passed while calling the underlying pipeline component i.e., via `cross_attention_kwargs`."
-            deprecate("scale", "1.0.0", deprecation_message)
-
-        output_states = ()
-
-        for resnet, attn in zip(self.resnets, self.attentions):
-            hidden_states = resnet(hidden_states, temb)
-            hidden_states = attn(hidden_states)
-            output_states += (hidden_states,)
-
-        if self.downsamplers is not None:
-            hidden_states = self.resnet_down(hidden_states, temb)
-            for downsampler in self.downsamplers:
-                skip_sample = downsampler(skip_sample)
-
-            hidden_states = self.skip_conv(skip_sample) + hidden_states
-
-            output_states += (hidden_states,)
-
-        return hidden_states, output_states, skip_sample
-
-
-class SkipDownBlock2D(nn.Module):
-    def __init__(
-        self,
-        in_channels: int,
-        out_channels: int,
-        temb_channels: int,
-        dropout: float = 0.0,
-        num_layers: int = 1,
-        resnet_eps: float = 1e-6,
-        resnet_time_scale_shift: str = "default",
-        resnet_act_fn: str = "swish",
-        resnet_pre_norm: bool = True,
-        output_scale_factor: float = np.sqrt(2.0),
-        add_downsample: bool = True,
-        downsample_padding: int = 1,
-    ):
-        super().__init__()
-        self.resnets = nn.ModuleList([])
-
-        for i in range(num_layers):
-            in_channels = in_channels if i == 0 else out_channels
-            self.resnets.append(
-                ResnetBlock2D(
-                    in_channels=in_channels,
-                    out_channels=out_channels,
-                    temb_channels=temb_channels,
-                    eps=resnet_eps,
-                    groups=min(in_channels // 4, 32),
-                    groups_out=min(out_channels // 4, 32),
-                    dropout=dropout,
-                    time_embedding_norm=resnet_time_scale_shift,
-                    non_linearity=resnet_act_fn,
-                    output_scale_factor=output_scale_factor,
-                    pre_norm=resnet_pre_norm,
-                )
-            )
-
-        if add_downsample:
-            self.resnet_down = ResnetBlock2D(
-                in_channels=out_channels,
-                out_channels=out_channels,
-                temb_channels=temb_channels,
-                eps=resnet_eps,
-                groups=min(out_channels // 4, 32),
-                dropout=dropout,
-                time_embedding_norm=resnet_time_scale_shift,
-                non_linearity=resnet_act_fn,
-                output_scale_factor=output_scale_factor,
-                pre_norm=resnet_pre_norm,
-                use_in_shortcut=True,
-                down=True,
-                kernel="fir",
-            )
-            self.downsamplers = nn.ModuleList([FirDownsample2D(out_channels, out_channels=out_channels)])
-            self.skip_conv = nn.Conv2d(3, out_channels, kernel_size=(1, 1), stride=(1, 1))
-        else:
-            self.resnet_down = None
-            self.downsamplers = None
-            self.skip_conv = None
-
-    def forward(
-        self,
-        hidden_states: torch.FloatTensor,
-        temb: Optional[torch.FloatTensor] = None,
-        skip_sample: Optional[torch.FloatTensor] = None,
-        *args,
-        **kwargs,
-    ) -> Tuple[torch.FloatTensor, Tuple[torch.FloatTensor, ...], torch.FloatTensor]:
-        if len(args) > 0 or kwargs.get("scale", None) is not None:
-            deprecation_message = "The `scale` argument is deprecated and will be ignored. Please remove it, as passing it will raise an error in the future. `scale` should directly be passed while calling the underlying pipeline component i.e., via `cross_attention_kwargs`."
-            deprecate("scale", "1.0.0", deprecation_message)
-
-        output_states = ()
-
-        for resnet in self.resnets:
-            hidden_states = resnet(hidden_states, temb)
-            output_states += (hidden_states,)
-
-        if self.downsamplers is not None:
-            hidden_states = self.resnet_down(hidden_states, temb)
-            for downsampler in self.downsamplers:
-                skip_sample = downsampler(skip_sample)
-
-            hidden_states = self.skip_conv(skip_sample) + hidden_states
-
-            output_states += (hidden_states,)
-
-        return hidden_states, output_states, skip_sample
-
-
-class ResnetDownsampleBlock2D(nn.Module):
-    def __init__(
-        self,
-        in_channels: int,
-        out_channels: int,
-        temb_channels: int,
-        dropout: float = 0.0,
-        num_layers: int = 1,
-        resnet_eps: float = 1e-6,
-        resnet_time_scale_shift: str = "default",
-        resnet_act_fn: str = "swish",
-        resnet_groups: int = 32,
-        resnet_pre_norm: bool = True,
-        output_scale_factor: float = 1.0,
-        add_downsample: bool = True,
-        skip_time_act: bool = False,
-    ):
-        super().__init__()
-        resnets = []
-
-        for i in range(num_layers):
-            in_channels = in_channels if i == 0 else out_channels
-            resnets.append(
-                ResnetBlock2D(
-                    in_channels=in_channels,
-                    out_channels=out_channels,
-                    temb_channels=temb_channels,
-                    eps=resnet_eps,
-                    groups=resnet_groups,
-                    dropout=dropout,
-                    time_embedding_norm=resnet_time_scale_shift,
-                    non_linearity=resnet_act_fn,
-                    output_scale_factor=output_scale_factor,
-                    pre_norm=resnet_pre_norm,
-                    skip_time_act=skip_time_act,
-                )
-            )
-
-        self.resnets = nn.ModuleList(resnets)
-
-        if add_downsample:
-            self.downsamplers = nn.ModuleList(
-                [
-                    ResnetBlock2D(
-                        in_channels=out_channels,
-                        out_channels=out_channels,
-                        temb_channels=temb_channels,
-                        eps=resnet_eps,
-                        groups=resnet_groups,
-                        dropout=dropout,
-                        time_embedding_norm=resnet_time_scale_shift,
-                        non_linearity=resnet_act_fn,
-                        output_scale_factor=output_scale_factor,
-                        pre_norm=resnet_pre_norm,
-                        skip_time_act=skip_time_act,
-                        down=True,
-                    )
-                ]
-            )
-        else:
-            self.downsamplers = None
-
-        self.gradient_checkpointing = False
-
-    def forward(
-        self, hidden_states: torch.FloatTensor, temb: Optional[torch.FloatTensor] = None, *args, **kwargs
-    ) -> Tuple[torch.FloatTensor, Tuple[torch.FloatTensor, ...]]:
-        if len(args) > 0 or kwargs.get("scale", None) is not None:
-            deprecation_message = "The `scale` argument is deprecated and will be ignored. Please remove it, as passing it will raise an error in the future. `scale` should directly be passed while calling the underlying pipeline component i.e., via `cross_attention_kwargs`."
-            deprecate("scale", "1.0.0", deprecation_message)
-
-        output_states = ()
-
-        for resnet in self.resnets:
-            if self.training and self.gradient_checkpointing:
-
-                def create_custom_forward(module):
-                    def custom_forward(*inputs):
-                        return module(*inputs)
-
-                    return custom_forward
-
-                if is_torch_version(">=", "1.11.0"):
-                    hidden_states = torch.utils.checkpoint.checkpoint(
-                        create_custom_forward(resnet), hidden_states, temb, use_reentrant=False
-                    )
-                else:
-                    hidden_states = torch.utils.checkpoint.checkpoint(
-                        create_custom_forward(resnet), hidden_states, temb
-                    )
-            else:
-                hidden_states = resnet(hidden_states, temb)
-
-            output_states = output_states + (hidden_states,)
-
-        if self.downsamplers is not None:
-            for downsampler in self.downsamplers:
-                hidden_states = downsampler(hidden_states, temb)
-
-            output_states = output_states + (hidden_states,)
-
-        return hidden_states, output_states
-
-
-class SimpleCrossAttnDownBlock2D(nn.Module):
-    def __init__(
-        self,
-        in_channels: int,
-        out_channels: int,
-        temb_channels: int,
-        dropout: float = 0.0,
-        num_layers: int = 1,
-        resnet_eps: float = 1e-6,
-        resnet_time_scale_shift: str = "default",
-        resnet_act_fn: str = "swish",
-        resnet_groups: int = 32,
-        resnet_pre_norm: bool = True,
-        attention_head_dim: int = 1,
-        cross_attention_dim: int = 1280,
-        output_scale_factor: float = 1.0,
-        add_downsample: bool = True,
-        skip_time_act: bool = False,
-        only_cross_attention: bool = False,
-        cross_attention_norm: Optional[str] = None,
-    ):
-        super().__init__()
-
-        self.has_cross_attention = True
-
-        resnets = []
-        attentions = []
-
-        self.attention_head_dim = attention_head_dim
-        self.num_heads = out_channels // self.attention_head_dim
-
-        for i in range(num_layers):
-            in_channels = in_channels if i == 0 else out_channels
-            resnets.append(
-                ResnetBlock2D(
-                    in_channels=in_channels,
-                    out_channels=out_channels,
-                    temb_channels=temb_channels,
-                    eps=resnet_eps,
-                    groups=resnet_groups,
-                    dropout=dropout,
-                    time_embedding_norm=resnet_time_scale_shift,
-                    non_linearity=resnet_act_fn,
-                    output_scale_factor=output_scale_factor,
-                    pre_norm=resnet_pre_norm,
-                    skip_time_act=skip_time_act,
-                )
-            )
-
-            processor = (
-                AttnAddedKVProcessor2_0() if hasattr(F, "scaled_dot_product_attention") else AttnAddedKVProcessor()
-            )
-
-            attentions.append(
-                Attention(
-                    query_dim=out_channels,
-                    cross_attention_dim=out_channels,
-                    heads=self.num_heads,
-                    dim_head=attention_head_dim,
-                    added_kv_proj_dim=cross_attention_dim,
-                    norm_num_groups=resnet_groups,
-                    bias=True,
-                    upcast_softmax=True,
-                    only_cross_attention=only_cross_attention,
-                    cross_attention_norm=cross_attention_norm,
-                    processor=processor,
-                )
-            )
-        self.attentions = nn.ModuleList(attentions)
-        self.resnets = nn.ModuleList(resnets)
-
-        if add_downsample:
-            self.downsamplers = nn.ModuleList(
-                [
-                    ResnetBlock2D(
-                        in_channels=out_channels,
-                        out_channels=out_channels,
-                        temb_channels=temb_channels,
-                        eps=resnet_eps,
-                        groups=resnet_groups,
-                        dropout=dropout,
-                        time_embedding_norm=resnet_time_scale_shift,
-                        non_linearity=resnet_act_fn,
-                        output_scale_factor=output_scale_factor,
-                        pre_norm=resnet_pre_norm,
-                        skip_time_act=skip_time_act,
-                        down=True,
-                    )
-                ]
-            )
-        else:
-            self.downsamplers = None
-
-        self.gradient_checkpointing = False
-
-    def forward(
-        self,
-        hidden_states: torch.FloatTensor,
-        temb: Optional[torch.FloatTensor] = None,
-        encoder_hidden_states: Optional[torch.FloatTensor] = None,
-        attention_mask: Optional[torch.FloatTensor] = None,
-        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
-        encoder_attention_mask: Optional[torch.FloatTensor] = None,
-    ) -> Tuple[torch.FloatTensor, Tuple[torch.FloatTensor, ...]]:
-        cross_attention_kwargs = cross_attention_kwargs if cross_attention_kwargs is not None else {}
-        if cross_attention_kwargs.get("scale", None) is not None:
-            logger.warning("Passing `scale` to `cross_attention_kwargs` is deprecated. `scale` will be ignored.")
-
-        output_states = ()
-
-        if attention_mask is None:
-            # if encoder_hidden_states is defined: we are doing cross-attn, so we should use cross-attn mask.
-            mask = None if encoder_hidden_states is None else encoder_attention_mask
-        else:
-            # when attention_mask is defined: we don't even check for encoder_attention_mask.
-            # this is to maintain compatibility with UnCLIP, which uses 'attention_mask' param for cross-attn masks.
-            # TODO: UnCLIP should express cross-attn mask via encoder_attention_mask param instead of via attention_mask.
-            #       then we can simplify this whole if/else block to:
-            #         mask = attention_mask if encoder_hidden_states is None else encoder_attention_mask
-            mask = attention_mask
-
-        for resnet, attn in zip(self.resnets, self.attentions):
-            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
-
-                hidden_states = torch.utils.checkpoint.checkpoint(create_custom_forward(resnet), hidden_states, temb)
-                hidden_states = attn(
-                    hidden_states,
-                    encoder_hidden_states=encoder_hidden_states,
-                    attention_mask=mask,
-                    **cross_attention_kwargs,
-                )
-            else:
-                hidden_states = resnet(hidden_states, temb)
-
-                hidden_states = attn(
-                    hidden_states,
-                    encoder_hidden_states=encoder_hidden_states,
-                    attention_mask=mask,
-                    **cross_attention_kwargs,
-                )
-
-            output_states = output_states + (hidden_states,)
-
-        if self.downsamplers is not None:
-            for downsampler in self.downsamplers:
-                hidden_states = downsampler(hidden_states, temb)
-
-            output_states = output_states + (hidden_states,)
-
-        return hidden_states, output_states
-
-
-class KDownBlock2D(nn.Module):
-    def __init__(
-        self,
-        in_channels: int,
-        out_channels: int,
-        temb_channels: int,
-        dropout: float = 0.0,
-        num_layers: int = 4,
-        resnet_eps: float = 1e-5,
-        resnet_act_fn: str = "gelu",
-        resnet_group_size: int = 32,
-        add_downsample: bool = False,
-    ):
-        super().__init__()
-        resnets = []
-
-        for i in range(num_layers):
-            in_channels = in_channels if i == 0 else out_channels
-            groups = in_channels // resnet_group_size
-            groups_out = out_channels // resnet_group_size
-
-            resnets.append(
-                ResnetBlockCondNorm2D(
-                    in_channels=in_channels,
-                    out_channels=out_channels,
-                    dropout=dropout,
-                    temb_channels=temb_channels,
-                    groups=groups,
-                    groups_out=groups_out,
-                    eps=resnet_eps,
-                    non_linearity=resnet_act_fn,
-                    time_embedding_norm="ada_group",
-                    conv_shortcut_bias=False,
-                )
-            )
-
-        self.resnets = nn.ModuleList(resnets)
-
-        if add_downsample:
-            # YiYi's comments- might be able to use FirDownsample2D, look into details later
-            self.downsamplers = nn.ModuleList([KDownsample2D()])
-        else:
-            self.downsamplers = None
-
-        self.gradient_checkpointing = False
-
-    def forward(
-        self, hidden_states: torch.FloatTensor, temb: Optional[torch.FloatTensor] = None, *args, **kwargs
-    ) -> Tuple[torch.FloatTensor, Tuple[torch.FloatTensor, ...]]:
-        if len(args) > 0 or kwargs.get("scale", None) is not None:
-            deprecation_message = "The `scale` argument is deprecated and will be ignored. Please remove it, as passing it will raise an error in the future. `scale` should directly be passed while calling the underlying pipeline component i.e., via `cross_attention_kwargs`."
-            deprecate("scale", "1.0.0", deprecation_message)
-
-        output_states = ()
-
-        for resnet in self.resnets:
-            if self.training and self.gradient_checkpointing:
-
-                def create_custom_forward(module):
-                    def custom_forward(*inputs):
-                        return module(*inputs)
-
-                    return custom_forward
-
-                if is_torch_version(">=", "1.11.0"):
-                    hidden_states = torch.utils.checkpoint.checkpoint(
-                        create_custom_forward(resnet), hidden_states, temb, use_reentrant=False
-                    )
-                else:
-                    hidden_states = torch.utils.checkpoint.checkpoint(
-                        create_custom_forward(resnet), hidden_states, temb
-                    )
-            else:
-                hidden_states = resnet(hidden_states, temb)
-
-            output_states += (hidden_states,)
-
-        if self.downsamplers is not None:
-            for downsampler in self.downsamplers:
-                hidden_states = downsampler(hidden_states)
-
-        return hidden_states, output_states
-
-
-class KCrossAttnDownBlock2D(nn.Module):
-    def __init__(
-        self,
-        in_channels: int,
-        out_channels: int,
-        temb_channels: int,
-        cross_attention_dim: int,
-        dropout: float = 0.0,
-        num_layers: int = 4,
-        resnet_group_size: int = 32,
-        add_downsample: bool = True,
-        attention_head_dim: int = 64,
-        add_self_attention: bool = False,
-        resnet_eps: float = 1e-5,
-        resnet_act_fn: str = "gelu",
-    ):
-        super().__init__()
-        resnets = []
-        attentions = []
-
-        self.has_cross_attention = True
-
-        for i in range(num_layers):
-            in_channels = in_channels if i == 0 else out_channels
-            groups = in_channels // resnet_group_size
-            groups_out = out_channels // resnet_group_size
-
-            resnets.append(
-                ResnetBlockCondNorm2D(
-                    in_channels=in_channels,
-                    out_channels=out_channels,
-                    dropout=dropout,
-                    temb_channels=temb_channels,
-                    groups=groups,
-                    groups_out=groups_out,
-                    eps=resnet_eps,
-                    non_linearity=resnet_act_fn,
-                    time_embedding_norm="ada_group",
-                    conv_shortcut_bias=False,
-                )
-            )
-            attentions.append(
-                KAttentionBlock(
-                    out_channels,
-                    out_channels // attention_head_dim,
-                    attention_head_dim,
-                    cross_attention_dim=cross_attention_dim,
-                    temb_channels=temb_channels,
-                    attention_bias=True,
-                    add_self_attention=add_self_attention,
-                    cross_attention_norm="layer_norm",
-                    group_size=resnet_group_size,
-                )
-            )
-
-        self.resnets = nn.ModuleList(resnets)
-        self.attentions = nn.ModuleList(attentions)
-
-        if add_downsample:
-            self.downsamplers = nn.ModuleList([KDownsample2D()])
-        else:
-            self.downsamplers = None
-
-        self.gradient_checkpointing = False
-
-    def forward(
-        self,
-        hidden_states: torch.FloatTensor,
-        temb: Optional[torch.FloatTensor] = None,
-        encoder_hidden_states: Optional[torch.FloatTensor] = None,
-        attention_mask: Optional[torch.FloatTensor] = None,
-        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
-        encoder_attention_mask: Optional[torch.FloatTensor] = None,
-    ) -> Tuple[torch.FloatTensor, Tuple[torch.FloatTensor, ...]]:
-        cross_attention_kwargs = cross_attention_kwargs if cross_attention_kwargs is not None else {}
-        if cross_attention_kwargs.get("scale", None) is not None:
-            logger.warning("Passing `scale` to `cross_attention_kwargs` is deprecated. `scale` will be ignored.")
-
-        output_states = ()
-
-        for resnet, attn in zip(self.resnets, self.attentions):
-            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 = torch.utils.checkpoint.checkpoint(
-                    create_custom_forward(resnet),
-                    hidden_states,
-                    temb,
-                    **ckpt_kwargs,
-                )
-                hidden_states = attn(
-                    hidden_states,
-                    encoder_hidden_states=encoder_hidden_states,
-                    emb=temb,
-                    attention_mask=attention_mask,
-                    cross_attention_kwargs=cross_attention_kwargs,
-                    encoder_attention_mask=encoder_attention_mask,
-                )
-            else:
-                hidden_states = resnet(hidden_states, temb)
-                hidden_states = attn(
-                    hidden_states,
-                    encoder_hidden_states=encoder_hidden_states,
-                    emb=temb,
-                    attention_mask=attention_mask,
-                    cross_attention_kwargs=cross_attention_kwargs,
-                    encoder_attention_mask=encoder_attention_mask,
-                )
-
-            if self.downsamplers is None:
-                output_states += (None,)
-            else:
-                output_states += (hidden_states,)
-
-        if self.downsamplers is not None:
-            for downsampler in self.downsamplers:
-                hidden_states = downsampler(hidden_states)
-
-        return hidden_states, output_states
-
-
-class AttnUpBlock2D(nn.Module):
-    def __init__(
-        self,
-        in_channels: int,
-        prev_output_channel: int,
-        out_channels: int,
-        temb_channels: int,
-        resolution_idx: int = None,
-        dropout: float = 0.0,
-        num_layers: int = 1,
-        resnet_eps: float = 1e-6,
-        resnet_time_scale_shift: str = "default",
-        resnet_act_fn: str = "swish",
-        resnet_groups: int = 32,
-        resnet_pre_norm: bool = True,
-        attention_head_dim: int = 1,
-        output_scale_factor: float = 1.0,
-        upsample_type: str = "conv",
-    ):
-        super().__init__()
-        resnets = []
-        attentions = []
-
-        self.upsample_type = upsample_type
-
-        if attention_head_dim is None:
-            logger.warning(
-                f"It is not recommend to pass `attention_head_dim=None`. Defaulting `attention_head_dim` to `in_channels`: {out_channels}."
-            )
-            attention_head_dim = out_channels
-
-        for i in range(num_layers):
-            res_skip_channels = in_channels if (i == num_layers - 1) else out_channels
-            resnet_in_channels = prev_output_channel if i == 0 else out_channels
-
-            resnets.append(
-                ResnetBlock2D(
-                    in_channels=resnet_in_channels + res_skip_channels,
-                    out_channels=out_channels,
-                    temb_channels=temb_channels,
-                    eps=resnet_eps,
-                    groups=resnet_groups,
-                    dropout=dropout,
-                    time_embedding_norm=resnet_time_scale_shift,
-                    non_linearity=resnet_act_fn,
-                    output_scale_factor=output_scale_factor,
-                    pre_norm=resnet_pre_norm,
-                )
-            )
-            attentions.append(
-                Attention(
-                    out_channels,
-                    heads=out_channels // attention_head_dim,
-                    dim_head=attention_head_dim,
-                    rescale_output_factor=output_scale_factor,
-                    eps=resnet_eps,
-                    norm_num_groups=resnet_groups,
-                    residual_connection=True,
-                    bias=True,
-                    upcast_softmax=True,
-                    _from_deprecated_attn_block=True,
-                )
-            )
-
-        self.attentions = nn.ModuleList(attentions)
-        self.resnets = nn.ModuleList(resnets)
-
-        if upsample_type == "conv":
-            self.upsamplers = nn.ModuleList([Upsample2D(out_channels, use_conv=True, out_channels=out_channels)])
-        elif upsample_type == "resnet":
-            self.upsamplers = nn.ModuleList(
-                [
-                    ResnetBlock2D(
-                        in_channels=out_channels,
-                        out_channels=out_channels,
-                        temb_channels=temb_channels,
-                        eps=resnet_eps,
-                        groups=resnet_groups,
-                        dropout=dropout,
-                        time_embedding_norm=resnet_time_scale_shift,
-                        non_linearity=resnet_act_fn,
-                        output_scale_factor=output_scale_factor,
-                        pre_norm=resnet_pre_norm,
-                        up=True,
-                    )
-                ]
-            )
-        else:
-            self.upsamplers = None
-
-        self.resolution_idx = resolution_idx
-
-    def forward(
-        self,
-        hidden_states: torch.FloatTensor,
-        res_hidden_states_tuple: Tuple[torch.FloatTensor, ...],
-        temb: Optional[torch.FloatTensor] = None,
-        upsample_size: Optional[int] = None,
-        *args,
-        **kwargs,
-    ) -> torch.FloatTensor:
-        if len(args) > 0 or kwargs.get("scale", None) is not None:
-            deprecation_message = "The `scale` argument is deprecated and will be ignored. Please remove it, as passing it will raise an error in the future. `scale` should directly be passed while calling the underlying pipeline component i.e., via `cross_attention_kwargs`."
-            deprecate("scale", "1.0.0", deprecation_message)
-
-        for resnet, attn in zip(self.resnets, self.attentions):
-            # pop res hidden states
-            res_hidden_states = res_hidden_states_tuple[-1]
-            res_hidden_states_tuple = res_hidden_states_tuple[:-1]
-            hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)
-
-            hidden_states = resnet(hidden_states, temb)
-            hidden_states = attn(hidden_states)
-
-        if self.upsamplers is not None:
-            for upsampler in self.upsamplers:
-                if self.upsample_type == "resnet":
-                    hidden_states = upsampler(hidden_states, temb=temb)
-                else:
-                    hidden_states = upsampler(hidden_states)
-
-        return hidden_states
-
-
-class CrossAttnUpBlock2D(nn.Module):
-    def __init__(
-        self,
-        in_channels: int,
-        out_channels: int,
-        prev_output_channel: int,
-        temb_channels: int,
-        resolution_idx: Optional[int] = None,
-        dropout: float = 0.0,
-        num_layers: int = 1,
-        transformer_layers_per_block: Union[int, Tuple[int]] = 1,
-        resnet_eps: float = 1e-6,
-        resnet_time_scale_shift: str = "default",
-        resnet_act_fn: str = "swish",
-        resnet_groups: int = 32,
-        resnet_pre_norm: bool = True,
-        num_attention_heads: int = 1,
-        cross_attention_dim: int = 1280,
-        output_scale_factor: float = 1.0,
-        add_upsample: bool = True,
-        dual_cross_attention: bool = False,
-        use_linear_projection: bool = False,
-        only_cross_attention: bool = False,
-        upcast_attention: bool = False,
-        attention_type: str = "default",
-    ):
-        super().__init__()
-        resnets = []
-        attentions = []
-
-        self.has_cross_attention = True
-        self.num_attention_heads = num_attention_heads
-
-        if isinstance(transformer_layers_per_block, int):
-            transformer_layers_per_block = [transformer_layers_per_block] * num_layers
-
-        for i in range(num_layers):
-            res_skip_channels = in_channels if (i == num_layers - 1) else out_channels
-            resnet_in_channels = prev_output_channel if i == 0 else out_channels
-
-            resnets.append(
-                ResnetBlock2D(
-                    in_channels=resnet_in_channels + res_skip_channels,
-                    out_channels=out_channels,
-                    temb_channels=temb_channels,
-                    eps=resnet_eps,
-                    groups=resnet_groups,
-                    dropout=dropout,
-                    time_embedding_norm=resnet_time_scale_shift,
-                    non_linearity=resnet_act_fn,
-                    output_scale_factor=output_scale_factor,
-                    pre_norm=resnet_pre_norm,
-                )
-            )
-            if not dual_cross_attention:
-                attentions.append(
-                    Transformer2DModel(
-                        num_attention_heads,
-                        out_channels // num_attention_heads,
-                        in_channels=out_channels,
-                        num_layers=transformer_layers_per_block[i],
-                        cross_attention_dim=cross_attention_dim,
-                        norm_num_groups=resnet_groups,
-                        use_linear_projection=use_linear_projection,
-                        only_cross_attention=only_cross_attention,
-                        upcast_attention=upcast_attention,
-                        attention_type=attention_type,
-                    )
-                )
-            else:
-                attentions.append(
-                    DualTransformer2DModel(
-                        num_attention_heads,
-                        out_channels // num_attention_heads,
-                        in_channels=out_channels,
-                        num_layers=1,
-                        cross_attention_dim=cross_attention_dim,
-                        norm_num_groups=resnet_groups,
-                    )
-                )
-        self.attentions = nn.ModuleList(attentions)
-        self.resnets = nn.ModuleList(resnets)
-
-        if add_upsample:
-            self.upsamplers = nn.ModuleList([Upsample2D(out_channels, use_conv=True, out_channels=out_channels)])
-        else:
-            self.upsamplers = None
-
-        self.gradient_checkpointing = False
-        self.resolution_idx = resolution_idx
-
-    def forward(
-        self,
-        hidden_states: torch.FloatTensor,
-        res_hidden_states_tuple: Tuple[torch.FloatTensor, ...],
-        temb: Optional[torch.FloatTensor] = None,
-        encoder_hidden_states: Optional[torch.FloatTensor] = None,
-        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
-        upsample_size: Optional[int] = None,
-        attention_mask: Optional[torch.FloatTensor] = None,
-        encoder_attention_mask: Optional[torch.FloatTensor] = None,
-        return_res_samples: Optional[bool]=False,
-        up_block_add_samples: Optional[torch.FloatTensor] = None,
-    ) -> torch.FloatTensor:
-        if cross_attention_kwargs is not None:
-            if cross_attention_kwargs.get("scale", None) is not None:
-                logger.warning("Passing `scale` to `cross_attention_kwargs` is deprecated. `scale` will be ignored.")
-
-        is_freeu_enabled = (
-            getattr(self, "s1", None)
-            and getattr(self, "s2", None)
-            and getattr(self, "b1", None)
-            and getattr(self, "b2", None)
-        )
-        if return_res_samples:
-            output_states=()
-
-        for resnet, attn in zip(self.resnets, self.attentions):
-            # pop res hidden states
-            res_hidden_states = res_hidden_states_tuple[-1]
-            res_hidden_states_tuple = res_hidden_states_tuple[:-1]
-
-            # FreeU: Only operate on the first two stages
-            if is_freeu_enabled:
-                hidden_states, res_hidden_states = apply_freeu(
-                    self.resolution_idx,
-                    hidden_states,
-                    res_hidden_states,
-                    s1=self.s1,
-                    s2=self.s2,
-                    b1=self.b1,
-                    b2=self.b2,
-                )
-
-            hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)
-
-            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 = torch.utils.checkpoint.checkpoint(
-                    create_custom_forward(resnet),
-                    hidden_states,
-                    temb,
-                    **ckpt_kwargs,
-                )
-                hidden_states = attn(
-                    hidden_states,
-                    encoder_hidden_states=encoder_hidden_states,
-                    cross_attention_kwargs=cross_attention_kwargs,
-                    attention_mask=attention_mask,
-                    encoder_attention_mask=encoder_attention_mask,
-                    return_dict=False,
-                )[0]
-            else:
-                hidden_states = resnet(hidden_states, temb)
-                hidden_states = attn(
-                    hidden_states,
-                    encoder_hidden_states=encoder_hidden_states,
-                    cross_attention_kwargs=cross_attention_kwargs,
-                    attention_mask=attention_mask,
-                    encoder_attention_mask=encoder_attention_mask,
-                    return_dict=False,
-                )[0]
-            if return_res_samples:
-                output_states = output_states + (hidden_states,)
-            if up_block_add_samples is not None:
-                hidden_states = hidden_states + up_block_add_samples.pop(0) 
-
-        if self.upsamplers is not None:
-            for upsampler in self.upsamplers:
-                hidden_states = upsampler(hidden_states, upsample_size)
-            if return_res_samples:
-                output_states = output_states + (hidden_states,)
-            if up_block_add_samples is not None:
-                hidden_states = hidden_states + up_block_add_samples.pop(0) 
-            
-        if return_res_samples:
-            return hidden_states, output_states
-        else:
-            return hidden_states
-
-class UpBlock2D(nn.Module):
-    def __init__(
-        self,
-        in_channels: int,
-        prev_output_channel: int,
-        out_channels: int,
-        temb_channels: int,
-        resolution_idx: Optional[int] = None,
-        dropout: float = 0.0,
-        num_layers: int = 1,
-        resnet_eps: float = 1e-6,
-        resnet_time_scale_shift: str = "default",
-        resnet_act_fn: str = "swish",
-        resnet_groups: int = 32,
-        resnet_pre_norm: bool = True,
-        output_scale_factor: float = 1.0,
-        add_upsample: bool = True,
-    ):
-        super().__init__()
-        resnets = []
-
-        for i in range(num_layers):
-            res_skip_channels = in_channels if (i == num_layers - 1) else out_channels
-            resnet_in_channels = prev_output_channel if i == 0 else out_channels
-
-            resnets.append(
-                ResnetBlock2D(
-                    in_channels=resnet_in_channels + res_skip_channels,
-                    out_channels=out_channels,
-                    temb_channels=temb_channels,
-                    eps=resnet_eps,
-                    groups=resnet_groups,
-                    dropout=dropout,
-                    time_embedding_norm=resnet_time_scale_shift,
-                    non_linearity=resnet_act_fn,
-                    output_scale_factor=output_scale_factor,
-                    pre_norm=resnet_pre_norm,
-                )
-            )
-
-        self.resnets = nn.ModuleList(resnets)
-
-        if add_upsample:
-            self.upsamplers = nn.ModuleList([Upsample2D(out_channels, use_conv=True, out_channels=out_channels)])
-        else:
-            self.upsamplers = None
-
-        self.gradient_checkpointing = False
-        self.resolution_idx = resolution_idx
-
-    def forward(
-        self,
-        hidden_states: torch.FloatTensor,
-        res_hidden_states_tuple: Tuple[torch.FloatTensor, ...],
-        temb: Optional[torch.FloatTensor] = None,
-        upsample_size: Optional[int] = None,
-        return_res_samples: Optional[bool]=False,
-        up_block_add_samples: Optional[torch.FloatTensor] = None,
-        *args,
-        **kwargs,
-    ) -> torch.FloatTensor:
-        if len(args) > 0 or kwargs.get("scale", None) is not None:
-            deprecation_message = "The `scale` argument is deprecated and will be ignored. Please remove it, as passing it will raise an error in the future. `scale` should directly be passed while calling the underlying pipeline component i.e., via `cross_attention_kwargs`."
-            deprecate("scale", "1.0.0", deprecation_message)
-
-        is_freeu_enabled = (
-            getattr(self, "s1", None)
-            and getattr(self, "s2", None)
-            and getattr(self, "b1", None)
-            and getattr(self, "b2", None)
-        )
-        if return_res_samples:
-            output_states = ()
-
-        for resnet in self.resnets:
-            # pop res hidden states
-            res_hidden_states = res_hidden_states_tuple[-1]
-            res_hidden_states_tuple = res_hidden_states_tuple[:-1]
-
-            # FreeU: Only operate on the first two stages
-            if is_freeu_enabled:
-                hidden_states, res_hidden_states = apply_freeu(
-                    self.resolution_idx,
-                    hidden_states,
-                    res_hidden_states,
-                    s1=self.s1,
-                    s2=self.s2,
-                    b1=self.b1,
-                    b2=self.b2,
-                )
-
-            hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)
-
-            if self.training and self.gradient_checkpointing:
-
-                def create_custom_forward(module):
-                    def custom_forward(*inputs):
-                        return module(*inputs)
-
-                    return custom_forward
-
-                if is_torch_version(">=", "1.11.0"):
-                    hidden_states = torch.utils.checkpoint.checkpoint(
-                        create_custom_forward(resnet), hidden_states, temb, use_reentrant=False
-                    )
-                else:
-                    hidden_states = torch.utils.checkpoint.checkpoint(
-                        create_custom_forward(resnet), hidden_states, temb
-                    )
-            else:
-                hidden_states = resnet(hidden_states, temb)
-
-            if return_res_samples:
-                output_states = output_states + (hidden_states,)
-            if up_block_add_samples is not None:
-                hidden_states = hidden_states + up_block_add_samples.pop(0)  # todo: add before or after
-
-        if self.upsamplers is not None:
-            for upsampler in self.upsamplers:
-                hidden_states = upsampler(hidden_states, upsample_size)
-
-            if return_res_samples:
-                output_states = output_states + (hidden_states,)
-            if up_block_add_samples is not None:
-                hidden_states = hidden_states + up_block_add_samples.pop(0)  # todo: add before or after
-
-        if return_res_samples:
-            return hidden_states, output_states
-        else:
-            return hidden_states
-
-
-class UpDecoderBlock2D(nn.Module):
-    def __init__(
-        self,
-        in_channels: int,
-        out_channels: int,
-        resolution_idx: Optional[int] = None,
-        dropout: float = 0.0,
-        num_layers: int = 1,
-        resnet_eps: float = 1e-6,
-        resnet_time_scale_shift: str = "default",  # default, spatial
-        resnet_act_fn: str = "swish",
-        resnet_groups: int = 32,
-        resnet_pre_norm: bool = True,
-        output_scale_factor: float = 1.0,
-        add_upsample: bool = True,
-        temb_channels: Optional[int] = None,
-    ):
-        super().__init__()
-        resnets = []
-
-        for i in range(num_layers):
-            input_channels = in_channels if i == 0 else out_channels
-
-            if resnet_time_scale_shift == "spatial":
-                resnets.append(
-                    ResnetBlockCondNorm2D(
-                        in_channels=input_channels,
-                        out_channels=out_channels,
-                        temb_channels=temb_channels,
-                        eps=resnet_eps,
-                        groups=resnet_groups,
-                        dropout=dropout,
-                        time_embedding_norm="spatial",
-                        non_linearity=resnet_act_fn,
-                        output_scale_factor=output_scale_factor,
-                    )
-                )
-            else:
-                resnets.append(
-                    ResnetBlock2D(
-                        in_channels=input_channels,
-                        out_channels=out_channels,
-                        temb_channels=temb_channels,
-                        eps=resnet_eps,
-                        groups=resnet_groups,
-                        dropout=dropout,
-                        time_embedding_norm=resnet_time_scale_shift,
-                        non_linearity=resnet_act_fn,
-                        output_scale_factor=output_scale_factor,
-                        pre_norm=resnet_pre_norm,
-                    )
-                )
-
-        self.resnets = nn.ModuleList(resnets)
-
-        if add_upsample:
-            self.upsamplers = nn.ModuleList([Upsample2D(out_channels, use_conv=True, out_channels=out_channels)])
-        else:
-            self.upsamplers = None
-
-        self.resolution_idx = resolution_idx
-
-    def forward(self, hidden_states: torch.FloatTensor, temb: Optional[torch.FloatTensor] = None) -> torch.FloatTensor:
-        for resnet in self.resnets:
-            hidden_states = resnet(hidden_states, temb=temb)
-
-        if self.upsamplers is not None:
-            for upsampler in self.upsamplers:
-                hidden_states = upsampler(hidden_states)
-
-        return hidden_states
-
-
-class AttnUpDecoderBlock2D(nn.Module):
-    def __init__(
-        self,
-        in_channels: int,
-        out_channels: int,
-        resolution_idx: Optional[int] = None,
-        dropout: float = 0.0,
-        num_layers: int = 1,
-        resnet_eps: float = 1e-6,
-        resnet_time_scale_shift: str = "default",
-        resnet_act_fn: str = "swish",
-        resnet_groups: int = 32,
-        resnet_pre_norm: bool = True,
-        attention_head_dim: int = 1,
-        output_scale_factor: float = 1.0,
-        add_upsample: bool = True,
-        temb_channels: Optional[int] = None,
-    ):
-        super().__init__()
-        resnets = []
-        attentions = []
-
-        if attention_head_dim is None:
-            logger.warning(
-                f"It is not recommend to pass `attention_head_dim=None`. Defaulting `attention_head_dim` to `out_channels`: {out_channels}."
-            )
-            attention_head_dim = out_channels
-
-        for i in range(num_layers):
-            input_channels = in_channels if i == 0 else out_channels
-
-            if resnet_time_scale_shift == "spatial":
-                resnets.append(
-                    ResnetBlockCondNorm2D(
-                        in_channels=input_channels,
-                        out_channels=out_channels,
-                        temb_channels=temb_channels,
-                        eps=resnet_eps,
-                        groups=resnet_groups,
-                        dropout=dropout,
-                        time_embedding_norm="spatial",
-                        non_linearity=resnet_act_fn,
-                        output_scale_factor=output_scale_factor,
-                    )
-                )
-            else:
-                resnets.append(
-                    ResnetBlock2D(
-                        in_channels=input_channels,
-                        out_channels=out_channels,
-                        temb_channels=temb_channels,
-                        eps=resnet_eps,
-                        groups=resnet_groups,
-                        dropout=dropout,
-                        time_embedding_norm=resnet_time_scale_shift,
-                        non_linearity=resnet_act_fn,
-                        output_scale_factor=output_scale_factor,
-                        pre_norm=resnet_pre_norm,
-                    )
-                )
-
-            attentions.append(
-                Attention(
-                    out_channels,
-                    heads=out_channels // attention_head_dim,
-                    dim_head=attention_head_dim,
-                    rescale_output_factor=output_scale_factor,
-                    eps=resnet_eps,
-                    norm_num_groups=resnet_groups if resnet_time_scale_shift != "spatial" else None,
-                    spatial_norm_dim=temb_channels if resnet_time_scale_shift == "spatial" else None,
-                    residual_connection=True,
-                    bias=True,
-                    upcast_softmax=True,
-                    _from_deprecated_attn_block=True,
-                )
-            )
-
-        self.attentions = nn.ModuleList(attentions)
-        self.resnets = nn.ModuleList(resnets)
-
-        if add_upsample:
-            self.upsamplers = nn.ModuleList([Upsample2D(out_channels, use_conv=True, out_channels=out_channels)])
-        else:
-            self.upsamplers = None
-
-        self.resolution_idx = resolution_idx
-
-    def forward(self, hidden_states: torch.FloatTensor, temb: Optional[torch.FloatTensor] = None) -> torch.FloatTensor:
-        for resnet, attn in zip(self.resnets, self.attentions):
-            hidden_states = resnet(hidden_states, temb=temb)
-            hidden_states = attn(hidden_states, temb=temb)
-
-        if self.upsamplers is not None:
-            for upsampler in self.upsamplers:
-                hidden_states = upsampler(hidden_states)
-
-        return hidden_states
-
-
-class AttnSkipUpBlock2D(nn.Module):
-    def __init__(
-        self,
-        in_channels: int,
-        prev_output_channel: int,
-        out_channels: int,
-        temb_channels: int,
-        resolution_idx: Optional[int] = None,
-        dropout: float = 0.0,
-        num_layers: int = 1,
-        resnet_eps: float = 1e-6,
-        resnet_time_scale_shift: str = "default",
-        resnet_act_fn: str = "swish",
-        resnet_pre_norm: bool = True,
-        attention_head_dim: int = 1,
-        output_scale_factor: float = np.sqrt(2.0),
-        add_upsample: bool = True,
-    ):
-        super().__init__()
-        self.attentions = nn.ModuleList([])
-        self.resnets = nn.ModuleList([])
-
-        for i in range(num_layers):
-            res_skip_channels = in_channels if (i == num_layers - 1) else out_channels
-            resnet_in_channels = prev_output_channel if i == 0 else out_channels
-
-            self.resnets.append(
-                ResnetBlock2D(
-                    in_channels=resnet_in_channels + res_skip_channels,
-                    out_channels=out_channels,
-                    temb_channels=temb_channels,
-                    eps=resnet_eps,
-                    groups=min(resnet_in_channels + res_skip_channels // 4, 32),
-                    groups_out=min(out_channels // 4, 32),
-                    dropout=dropout,
-                    time_embedding_norm=resnet_time_scale_shift,
-                    non_linearity=resnet_act_fn,
-                    output_scale_factor=output_scale_factor,
-                    pre_norm=resnet_pre_norm,
-                )
-            )
-
-        if attention_head_dim is None:
-            logger.warning(
-                f"It is not recommend to pass `attention_head_dim=None`. Defaulting `attention_head_dim` to `out_channels`: {out_channels}."
-            )
-            attention_head_dim = out_channels
-
-        self.attentions.append(
-            Attention(
-                out_channels,
-                heads=out_channels // attention_head_dim,
-                dim_head=attention_head_dim,
-                rescale_output_factor=output_scale_factor,
-                eps=resnet_eps,
-                norm_num_groups=32,
-                residual_connection=True,
-                bias=True,
-                upcast_softmax=True,
-                _from_deprecated_attn_block=True,
-            )
-        )
-
-        self.upsampler = FirUpsample2D(in_channels, out_channels=out_channels)
-        if add_upsample:
-            self.resnet_up = ResnetBlock2D(
-                in_channels=out_channels,
-                out_channels=out_channels,
-                temb_channels=temb_channels,
-                eps=resnet_eps,
-                groups=min(out_channels // 4, 32),
-                groups_out=min(out_channels // 4, 32),
-                dropout=dropout,
-                time_embedding_norm=resnet_time_scale_shift,
-                non_linearity=resnet_act_fn,
-                output_scale_factor=output_scale_factor,
-                pre_norm=resnet_pre_norm,
-                use_in_shortcut=True,
-                up=True,
-                kernel="fir",
-            )
-            self.skip_conv = nn.Conv2d(out_channels, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
-            self.skip_norm = torch.nn.GroupNorm(
-                num_groups=min(out_channels // 4, 32), num_channels=out_channels, eps=resnet_eps, affine=True
-            )
-            self.act = nn.SiLU()
-        else:
-            self.resnet_up = None
-            self.skip_conv = None
-            self.skip_norm = None
-            self.act = None
-
-        self.resolution_idx = resolution_idx
-
-    def forward(
-        self,
-        hidden_states: torch.FloatTensor,
-        res_hidden_states_tuple: Tuple[torch.FloatTensor, ...],
-        temb: Optional[torch.FloatTensor] = None,
-        skip_sample=None,
-        *args,
-        **kwargs,
-    ) -> Tuple[torch.FloatTensor, torch.FloatTensor]:
-        if len(args) > 0 or kwargs.get("scale", None) is not None:
-            deprecation_message = "The `scale` argument is deprecated and will be ignored. Please remove it, as passing it will raise an error in the future. `scale` should directly be passed while calling the underlying pipeline component i.e., via `cross_attention_kwargs`."
-            deprecate("scale", "1.0.0", deprecation_message)
-
-        for resnet in self.resnets:
-            # pop res hidden states
-            res_hidden_states = res_hidden_states_tuple[-1]
-            res_hidden_states_tuple = res_hidden_states_tuple[:-1]
-            hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)
-
-            hidden_states = resnet(hidden_states, temb)
-
-        hidden_states = self.attentions[0](hidden_states)
-
-        if skip_sample is not None:
-            skip_sample = self.upsampler(skip_sample)
-        else:
-            skip_sample = 0
-
-        if self.resnet_up is not None:
-            skip_sample_states = self.skip_norm(hidden_states)
-            skip_sample_states = self.act(skip_sample_states)
-            skip_sample_states = self.skip_conv(skip_sample_states)
-
-            skip_sample = skip_sample + skip_sample_states
-
-            hidden_states = self.resnet_up(hidden_states, temb)
-
-        return hidden_states, skip_sample
-
-
-class SkipUpBlock2D(nn.Module):
-    def __init__(
-        self,
-        in_channels: int,
-        prev_output_channel: int,
-        out_channels: int,
-        temb_channels: int,
-        resolution_idx: Optional[int] = None,
-        dropout: float = 0.0,
-        num_layers: int = 1,
-        resnet_eps: float = 1e-6,
-        resnet_time_scale_shift: str = "default",
-        resnet_act_fn: str = "swish",
-        resnet_pre_norm: bool = True,
-        output_scale_factor: float = np.sqrt(2.0),
-        add_upsample: bool = True,
-        upsample_padding: int = 1,
-    ):
-        super().__init__()
-        self.resnets = nn.ModuleList([])
-
-        for i in range(num_layers):
-            res_skip_channels = in_channels if (i == num_layers - 1) else out_channels
-            resnet_in_channels = prev_output_channel if i == 0 else out_channels
-
-            self.resnets.append(
-                ResnetBlock2D(
-                    in_channels=resnet_in_channels + res_skip_channels,
-                    out_channels=out_channels,
-                    temb_channels=temb_channels,
-                    eps=resnet_eps,
-                    groups=min((resnet_in_channels + res_skip_channels) // 4, 32),
-                    groups_out=min(out_channels // 4, 32),
-                    dropout=dropout,
-                    time_embedding_norm=resnet_time_scale_shift,
-                    non_linearity=resnet_act_fn,
-                    output_scale_factor=output_scale_factor,
-                    pre_norm=resnet_pre_norm,
-                )
-            )
-
-        self.upsampler = FirUpsample2D(in_channels, out_channels=out_channels)
-        if add_upsample:
-            self.resnet_up = ResnetBlock2D(
-                in_channels=out_channels,
-                out_channels=out_channels,
-                temb_channels=temb_channels,
-                eps=resnet_eps,
-                groups=min(out_channels // 4, 32),
-                groups_out=min(out_channels // 4, 32),
-                dropout=dropout,
-                time_embedding_norm=resnet_time_scale_shift,
-                non_linearity=resnet_act_fn,
-                output_scale_factor=output_scale_factor,
-                pre_norm=resnet_pre_norm,
-                use_in_shortcut=True,
-                up=True,
-                kernel="fir",
-            )
-            self.skip_conv = nn.Conv2d(out_channels, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
-            self.skip_norm = torch.nn.GroupNorm(
-                num_groups=min(out_channels // 4, 32), num_channels=out_channels, eps=resnet_eps, affine=True
-            )
-            self.act = nn.SiLU()
-        else:
-            self.resnet_up = None
-            self.skip_conv = None
-            self.skip_norm = None
-            self.act = None
-
-        self.resolution_idx = resolution_idx
-
-    def forward(
-        self,
-        hidden_states: torch.FloatTensor,
-        res_hidden_states_tuple: Tuple[torch.FloatTensor, ...],
-        temb: Optional[torch.FloatTensor] = None,
-        skip_sample=None,
-        *args,
-        **kwargs,
-    ) -> Tuple[torch.FloatTensor, torch.FloatTensor]:
-        if len(args) > 0 or kwargs.get("scale", None) is not None:
-            deprecation_message = "The `scale` argument is deprecated and will be ignored. Please remove it, as passing it will raise an error in the future. `scale` should directly be passed while calling the underlying pipeline component i.e., via `cross_attention_kwargs`."
-            deprecate("scale", "1.0.0", deprecation_message)
-
-        for resnet in self.resnets:
-            # pop res hidden states
-            res_hidden_states = res_hidden_states_tuple[-1]
-            res_hidden_states_tuple = res_hidden_states_tuple[:-1]
-            hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)
-
-            hidden_states = resnet(hidden_states, temb)
-
-        if skip_sample is not None:
-            skip_sample = self.upsampler(skip_sample)
-        else:
-            skip_sample = 0
-
-        if self.resnet_up is not None:
-            skip_sample_states = self.skip_norm(hidden_states)
-            skip_sample_states = self.act(skip_sample_states)
-            skip_sample_states = self.skip_conv(skip_sample_states)
-
-            skip_sample = skip_sample + skip_sample_states
-
-            hidden_states = self.resnet_up(hidden_states, temb)
-
-        return hidden_states, skip_sample
-
-
-class ResnetUpsampleBlock2D(nn.Module):
-    def __init__(
-        self,
-        in_channels: int,
-        prev_output_channel: int,
-        out_channels: int,
-        temb_channels: int,
-        resolution_idx: Optional[int] = None,
-        dropout: float = 0.0,
-        num_layers: int = 1,
-        resnet_eps: float = 1e-6,
-        resnet_time_scale_shift: str = "default",
-        resnet_act_fn: str = "swish",
-        resnet_groups: int = 32,
-        resnet_pre_norm: bool = True,
-        output_scale_factor: float = 1.0,
-        add_upsample: bool = True,
-        skip_time_act: bool = False,
-    ):
-        super().__init__()
-        resnets = []
-
-        for i in range(num_layers):
-            res_skip_channels = in_channels if (i == num_layers - 1) else out_channels
-            resnet_in_channels = prev_output_channel if i == 0 else out_channels
-
-            resnets.append(
-                ResnetBlock2D(
-                    in_channels=resnet_in_channels + res_skip_channels,
-                    out_channels=out_channels,
-                    temb_channels=temb_channels,
-                    eps=resnet_eps,
-                    groups=resnet_groups,
-                    dropout=dropout,
-                    time_embedding_norm=resnet_time_scale_shift,
-                    non_linearity=resnet_act_fn,
-                    output_scale_factor=output_scale_factor,
-                    pre_norm=resnet_pre_norm,
-                    skip_time_act=skip_time_act,
-                )
-            )
-
-        self.resnets = nn.ModuleList(resnets)
-
-        if add_upsample:
-            self.upsamplers = nn.ModuleList(
-                [
-                    ResnetBlock2D(
-                        in_channels=out_channels,
-                        out_channels=out_channels,
-                        temb_channels=temb_channels,
-                        eps=resnet_eps,
-                        groups=resnet_groups,
-                        dropout=dropout,
-                        time_embedding_norm=resnet_time_scale_shift,
-                        non_linearity=resnet_act_fn,
-                        output_scale_factor=output_scale_factor,
-                        pre_norm=resnet_pre_norm,
-                        skip_time_act=skip_time_act,
-                        up=True,
-                    )
-                ]
-            )
-        else:
-            self.upsamplers = None
-
-        self.gradient_checkpointing = False
-        self.resolution_idx = resolution_idx
-
-    def forward(
-        self,
-        hidden_states: torch.FloatTensor,
-        res_hidden_states_tuple: Tuple[torch.FloatTensor, ...],
-        temb: Optional[torch.FloatTensor] = None,
-        upsample_size: Optional[int] = None,
-        *args,
-        **kwargs,
-    ) -> torch.FloatTensor:
-        if len(args) > 0 or kwargs.get("scale", None) is not None:
-            deprecation_message = "The `scale` argument is deprecated and will be ignored. Please remove it, as passing it will raise an error in the future. `scale` should directly be passed while calling the underlying pipeline component i.e., via `cross_attention_kwargs`."
-            deprecate("scale", "1.0.0", deprecation_message)
-
-        for resnet in self.resnets:
-            # pop res hidden states
-            res_hidden_states = res_hidden_states_tuple[-1]
-            res_hidden_states_tuple = res_hidden_states_tuple[:-1]
-            hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)
-
-            if self.training and self.gradient_checkpointing:
-
-                def create_custom_forward(module):
-                    def custom_forward(*inputs):
-                        return module(*inputs)
-
-                    return custom_forward
-
-                if is_torch_version(">=", "1.11.0"):
-                    hidden_states = torch.utils.checkpoint.checkpoint(
-                        create_custom_forward(resnet), hidden_states, temb, use_reentrant=False
-                    )
-                else:
-                    hidden_states = torch.utils.checkpoint.checkpoint(
-                        create_custom_forward(resnet), hidden_states, temb
-                    )
-            else:
-                hidden_states = resnet(hidden_states, temb)
-
-        if self.upsamplers is not None:
-            for upsampler in self.upsamplers:
-                hidden_states = upsampler(hidden_states, temb)
-
-        return hidden_states
-
-
-class SimpleCrossAttnUpBlock2D(nn.Module):
-    def __init__(
-        self,
-        in_channels: int,
-        out_channels: int,
-        prev_output_channel: int,
-        temb_channels: int,
-        resolution_idx: Optional[int] = None,
-        dropout: float = 0.0,
-        num_layers: int = 1,
-        resnet_eps: float = 1e-6,
-        resnet_time_scale_shift: str = "default",
-        resnet_act_fn: str = "swish",
-        resnet_groups: int = 32,
-        resnet_pre_norm: bool = True,
-        attention_head_dim: int = 1,
-        cross_attention_dim: int = 1280,
-        output_scale_factor: float = 1.0,
-        add_upsample: bool = True,
-        skip_time_act: bool = False,
-        only_cross_attention: bool = False,
-        cross_attention_norm: Optional[str] = None,
-    ):
-        super().__init__()
-        resnets = []
-        attentions = []
-
-        self.has_cross_attention = True
-        self.attention_head_dim = attention_head_dim
-
-        self.num_heads = out_channels // self.attention_head_dim
-
-        for i in range(num_layers):
-            res_skip_channels = in_channels if (i == num_layers - 1) else out_channels
-            resnet_in_channels = prev_output_channel if i == 0 else out_channels
-
-            resnets.append(
-                ResnetBlock2D(
-                    in_channels=resnet_in_channels + res_skip_channels,
-                    out_channels=out_channels,
-                    temb_channels=temb_channels,
-                    eps=resnet_eps,
-                    groups=resnet_groups,
-                    dropout=dropout,
-                    time_embedding_norm=resnet_time_scale_shift,
-                    non_linearity=resnet_act_fn,
-                    output_scale_factor=output_scale_factor,
-                    pre_norm=resnet_pre_norm,
-                    skip_time_act=skip_time_act,
-                )
-            )
-
-            processor = (
-                AttnAddedKVProcessor2_0() if hasattr(F, "scaled_dot_product_attention") else AttnAddedKVProcessor()
-            )
-
-            attentions.append(
-                Attention(
-                    query_dim=out_channels,
-                    cross_attention_dim=out_channels,
-                    heads=self.num_heads,
-                    dim_head=self.attention_head_dim,
-                    added_kv_proj_dim=cross_attention_dim,
-                    norm_num_groups=resnet_groups,
-                    bias=True,
-                    upcast_softmax=True,
-                    only_cross_attention=only_cross_attention,
-                    cross_attention_norm=cross_attention_norm,
-                    processor=processor,
-                )
-            )
-        self.attentions = nn.ModuleList(attentions)
-        self.resnets = nn.ModuleList(resnets)
-
-        if add_upsample:
-            self.upsamplers = nn.ModuleList(
-                [
-                    ResnetBlock2D(
-                        in_channels=out_channels,
-                        out_channels=out_channels,
-                        temb_channels=temb_channels,
-                        eps=resnet_eps,
-                        groups=resnet_groups,
-                        dropout=dropout,
-                        time_embedding_norm=resnet_time_scale_shift,
-                        non_linearity=resnet_act_fn,
-                        output_scale_factor=output_scale_factor,
-                        pre_norm=resnet_pre_norm,
-                        skip_time_act=skip_time_act,
-                        up=True,
-                    )
-                ]
-            )
-        else:
-            self.upsamplers = None
-
-        self.gradient_checkpointing = False
-        self.resolution_idx = resolution_idx
-
-    def forward(
-        self,
-        hidden_states: torch.FloatTensor,
-        res_hidden_states_tuple: Tuple[torch.FloatTensor, ...],
-        temb: Optional[torch.FloatTensor] = None,
-        encoder_hidden_states: Optional[torch.FloatTensor] = None,
-        upsample_size: Optional[int] = None,
-        attention_mask: Optional[torch.FloatTensor] = None,
-        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
-        encoder_attention_mask: Optional[torch.FloatTensor] = None,
-    ) -> torch.FloatTensor:
-        cross_attention_kwargs = cross_attention_kwargs if cross_attention_kwargs is not None else {}
-        if cross_attention_kwargs.get("scale", None) is not None:
-            logger.warning("Passing `scale` to `cross_attention_kwargs` is deprecated. `scale` will be ignored.")
-
-        if attention_mask is None:
-            # if encoder_hidden_states is defined: we are doing cross-attn, so we should use cross-attn mask.
-            mask = None if encoder_hidden_states is None else encoder_attention_mask
-        else:
-            # when attention_mask is defined: we don't even check for encoder_attention_mask.
-            # this is to maintain compatibility with UnCLIP, which uses 'attention_mask' param for cross-attn masks.
-            # TODO: UnCLIP should express cross-attn mask via encoder_attention_mask param instead of via attention_mask.
-            #       then we can simplify this whole if/else block to:
-            #         mask = attention_mask if encoder_hidden_states is None else encoder_attention_mask
-            mask = attention_mask
-
-        for resnet, attn in zip(self.resnets, self.attentions):
-            # resnet
-            # pop res hidden states
-            res_hidden_states = res_hidden_states_tuple[-1]
-            res_hidden_states_tuple = res_hidden_states_tuple[:-1]
-            hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)
-
-            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
-
-                hidden_states = torch.utils.checkpoint.checkpoint(create_custom_forward(resnet), hidden_states, temb)
-                hidden_states = attn(
-                    hidden_states,
-                    encoder_hidden_states=encoder_hidden_states,
-                    attention_mask=mask,
-                    **cross_attention_kwargs,
-                )
-            else:
-                hidden_states = resnet(hidden_states, temb)
-
-                hidden_states = attn(
-                    hidden_states,
-                    encoder_hidden_states=encoder_hidden_states,
-                    attention_mask=mask,
-                    **cross_attention_kwargs,
-                )
-
-        if self.upsamplers is not None:
-            for upsampler in self.upsamplers:
-                hidden_states = upsampler(hidden_states, temb)
-
-        return hidden_states
-
-
-class KUpBlock2D(nn.Module):
-    def __init__(
-        self,
-        in_channels: int,
-        out_channels: int,
-        temb_channels: int,
-        resolution_idx: int,
-        dropout: float = 0.0,
-        num_layers: int = 5,
-        resnet_eps: float = 1e-5,
-        resnet_act_fn: str = "gelu",
-        resnet_group_size: Optional[int] = 32,
-        add_upsample: bool = True,
-    ):
-        super().__init__()
-        resnets = []
-        k_in_channels = 2 * out_channels
-        k_out_channels = in_channels
-        num_layers = num_layers - 1
-
-        for i in range(num_layers):
-            in_channels = k_in_channels if i == 0 else out_channels
-            groups = in_channels // resnet_group_size
-            groups_out = out_channels // resnet_group_size
-
-            resnets.append(
-                ResnetBlockCondNorm2D(
-                    in_channels=in_channels,
-                    out_channels=k_out_channels if (i == num_layers - 1) else out_channels,
-                    temb_channels=temb_channels,
-                    eps=resnet_eps,
-                    groups=groups,
-                    groups_out=groups_out,
-                    dropout=dropout,
-                    non_linearity=resnet_act_fn,
-                    time_embedding_norm="ada_group",
-                    conv_shortcut_bias=False,
-                )
-            )
-
-        self.resnets = nn.ModuleList(resnets)
-
-        if add_upsample:
-            self.upsamplers = nn.ModuleList([KUpsample2D()])
-        else:
-            self.upsamplers = None
-
-        self.gradient_checkpointing = False
-        self.resolution_idx = resolution_idx
-
-    def forward(
-        self,
-        hidden_states: torch.FloatTensor,
-        res_hidden_states_tuple: Tuple[torch.FloatTensor, ...],
-        temb: Optional[torch.FloatTensor] = None,
-        upsample_size: Optional[int] = None,
-        *args,
-        **kwargs,
-    ) -> torch.FloatTensor:
-        if len(args) > 0 or kwargs.get("scale", None) is not None:
-            deprecation_message = "The `scale` argument is deprecated and will be ignored. Please remove it, as passing it will raise an error in the future. `scale` should directly be passed while calling the underlying pipeline component i.e., via `cross_attention_kwargs`."
-            deprecate("scale", "1.0.0", deprecation_message)
-
-        res_hidden_states_tuple = res_hidden_states_tuple[-1]
-        if res_hidden_states_tuple is not None:
-            hidden_states = torch.cat([hidden_states, res_hidden_states_tuple], dim=1)
-
-        for resnet in self.resnets:
-            if self.training and self.gradient_checkpointing:
-
-                def create_custom_forward(module):
-                    def custom_forward(*inputs):
-                        return module(*inputs)
-
-                    return custom_forward
-
-                if is_torch_version(">=", "1.11.0"):
-                    hidden_states = torch.utils.checkpoint.checkpoint(
-                        create_custom_forward(resnet), hidden_states, temb, use_reentrant=False
-                    )
-                else:
-                    hidden_states = torch.utils.checkpoint.checkpoint(
-                        create_custom_forward(resnet), hidden_states, temb
-                    )
-            else:
-                hidden_states = resnet(hidden_states, temb)
-
-        if self.upsamplers is not None:
-            for upsampler in self.upsamplers:
-                hidden_states = upsampler(hidden_states)
-
-        return hidden_states
-
-
-class KCrossAttnUpBlock2D(nn.Module):
-    def __init__(
-        self,
-        in_channels: int,
-        out_channels: int,
-        temb_channels: int,
-        resolution_idx: int,
-        dropout: float = 0.0,
-        num_layers: int = 4,
-        resnet_eps: float = 1e-5,
-        resnet_act_fn: str = "gelu",
-        resnet_group_size: int = 32,
-        attention_head_dim: int = 1,  # attention dim_head
-        cross_attention_dim: int = 768,
-        add_upsample: bool = True,
-        upcast_attention: bool = False,
-    ):
-        super().__init__()
-        resnets = []
-        attentions = []
-
-        is_first_block = in_channels == out_channels == temb_channels
-        is_middle_block = in_channels != out_channels
-        add_self_attention = True if is_first_block else False
-
-        self.has_cross_attention = True
-        self.attention_head_dim = attention_head_dim
-
-        # in_channels, and out_channels for the block (k-unet)
-        k_in_channels = out_channels if is_first_block else 2 * out_channels
-        k_out_channels = in_channels
-
-        num_layers = num_layers - 1
-
-        for i in range(num_layers):
-            in_channels = k_in_channels if i == 0 else out_channels
-            groups = in_channels // resnet_group_size
-            groups_out = out_channels // resnet_group_size
-
-            if is_middle_block and (i == num_layers - 1):
-                conv_2d_out_channels = k_out_channels
-            else:
-                conv_2d_out_channels = None
-
-            resnets.append(
-                ResnetBlockCondNorm2D(
-                    in_channels=in_channels,
-                    out_channels=out_channels,
-                    conv_2d_out_channels=conv_2d_out_channels,
-                    temb_channels=temb_channels,
-                    eps=resnet_eps,
-                    groups=groups,
-                    groups_out=groups_out,
-                    dropout=dropout,
-                    non_linearity=resnet_act_fn,
-                    time_embedding_norm="ada_group",
-                    conv_shortcut_bias=False,
-                )
-            )
-            attentions.append(
-                KAttentionBlock(
-                    k_out_channels if (i == num_layers - 1) else out_channels,
-                    k_out_channels // attention_head_dim
-                    if (i == num_layers - 1)
-                    else out_channels // attention_head_dim,
-                    attention_head_dim,
-                    cross_attention_dim=cross_attention_dim,
-                    temb_channels=temb_channels,
-                    attention_bias=True,
-                    add_self_attention=add_self_attention,
-                    cross_attention_norm="layer_norm",
-                    upcast_attention=upcast_attention,
-                )
-            )
-
-        self.resnets = nn.ModuleList(resnets)
-        self.attentions = nn.ModuleList(attentions)
-
-        if add_upsample:
-            self.upsamplers = nn.ModuleList([KUpsample2D()])
-        else:
-            self.upsamplers = None
-
-        self.gradient_checkpointing = False
-        self.resolution_idx = resolution_idx
-
-    def forward(
-        self,
-        hidden_states: torch.FloatTensor,
-        res_hidden_states_tuple: Tuple[torch.FloatTensor, ...],
-        temb: Optional[torch.FloatTensor] = None,
-        encoder_hidden_states: Optional[torch.FloatTensor] = None,
-        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
-        upsample_size: Optional[int] = None,
-        attention_mask: Optional[torch.FloatTensor] = None,
-        encoder_attention_mask: Optional[torch.FloatTensor] = None,
-    ) -> torch.FloatTensor:
-        res_hidden_states_tuple = res_hidden_states_tuple[-1]
-        if res_hidden_states_tuple is not None:
-            hidden_states = torch.cat([hidden_states, res_hidden_states_tuple], dim=1)
-
-        for resnet, attn in zip(self.resnets, self.attentions):
-            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 = torch.utils.checkpoint.checkpoint(
-                    create_custom_forward(resnet),
-                    hidden_states,
-                    temb,
-                    **ckpt_kwargs,
-                )
-                hidden_states = attn(
-                    hidden_states,
-                    encoder_hidden_states=encoder_hidden_states,
-                    emb=temb,
-                    attention_mask=attention_mask,
-                    cross_attention_kwargs=cross_attention_kwargs,
-                    encoder_attention_mask=encoder_attention_mask,
-                )
-            else:
-                hidden_states = resnet(hidden_states, temb)
-                hidden_states = attn(
-                    hidden_states,
-                    encoder_hidden_states=encoder_hidden_states,
-                    emb=temb,
-                    attention_mask=attention_mask,
-                    cross_attention_kwargs=cross_attention_kwargs,
-                    encoder_attention_mask=encoder_attention_mask,
-                )
-
-        if self.upsamplers is not None:
-            for upsampler in self.upsamplers:
-                hidden_states = upsampler(hidden_states)
-
-        return hidden_states
-
-
-# can potentially later be renamed to `No-feed-forward` attention
-class KAttentionBlock(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.
-        attention_bias (`bool`, *optional*, defaults to `False`):
-            Configure if the attention layers should contain a bias parameter.
-        upcast_attention (`bool`, *optional*, defaults to `False`):
-            Set to `True` to upcast the attention computation to `float32`.
-        temb_channels (`int`, *optional*, defaults to 768):
-            The number of channels in the token embedding.
-        add_self_attention (`bool`, *optional*, defaults to `False`):
-            Set to `True` to add self-attention to the block.
-        cross_attention_norm (`str`, *optional*, defaults to `None`):
-            The type of normalization to use for the cross attention. Can be `None`, `layer_norm`, or `group_norm`.
-        group_size (`int`, *optional*, defaults to 32):
-            The number of groups to separate the channels into for group normalization.
-    """
-
-    def __init__(
-        self,
-        dim: int,
-        num_attention_heads: int,
-        attention_head_dim: int,
-        dropout: float = 0.0,
-        cross_attention_dim: Optional[int] = None,
-        attention_bias: bool = False,
-        upcast_attention: bool = False,
-        temb_channels: int = 768,  # for ada_group_norm
-        add_self_attention: bool = False,
-        cross_attention_norm: Optional[str] = None,
-        group_size: int = 32,
-    ):
-        super().__init__()
-        self.add_self_attention = add_self_attention
-
-        # 1. Self-Attn
-        if add_self_attention:
-            self.norm1 = AdaGroupNorm(temb_channels, dim, max(1, dim // group_size))
-            self.attn1 = Attention(
-                query_dim=dim,
-                heads=num_attention_heads,
-                dim_head=attention_head_dim,
-                dropout=dropout,
-                bias=attention_bias,
-                cross_attention_dim=None,
-                cross_attention_norm=None,
-            )
-
-        # 2. Cross-Attn
-        self.norm2 = AdaGroupNorm(temb_channels, dim, max(1, dim // group_size))
-        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,
-            upcast_attention=upcast_attention,
-            cross_attention_norm=cross_attention_norm,
-        )
-
-    def _to_3d(self, hidden_states: torch.FloatTensor, height: int, weight: int) -> torch.FloatTensor:
-        return hidden_states.permute(0, 2, 3, 1).reshape(hidden_states.shape[0], height * weight, -1)
-
-    def _to_4d(self, hidden_states: torch.FloatTensor, height: int, weight: int) -> torch.FloatTensor:
-        return hidden_states.permute(0, 2, 1).reshape(hidden_states.shape[0], -1, height, weight)
-
-    def forward(
-        self,
-        hidden_states: torch.FloatTensor,
-        encoder_hidden_states: Optional[torch.FloatTensor] = None,
-        # TODO: mark emb as non-optional (self.norm2 requires it).
-        #       requires assessing impact of change to positional param interface.
-        emb: Optional[torch.FloatTensor] = None,
-        attention_mask: Optional[torch.FloatTensor] = None,
-        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
-        encoder_attention_mask: Optional[torch.FloatTensor] = None,
-    ) -> torch.FloatTensor:
-        cross_attention_kwargs = cross_attention_kwargs if cross_attention_kwargs is not None else {}
-        if cross_attention_kwargs.get("scale", None) is not None:
-            logger.warning("Passing `scale` to `cross_attention_kwargs` is deprecated. `scale` will be ignored.")
-
-        # 1. Self-Attention
-        if self.add_self_attention:
-            norm_hidden_states = self.norm1(hidden_states, emb)
-
-            height, weight = norm_hidden_states.shape[2:]
-            norm_hidden_states = self._to_3d(norm_hidden_states, height, weight)
-
-            attn_output = self.attn1(
-                norm_hidden_states,
-                encoder_hidden_states=None,
-                attention_mask=attention_mask,
-                **cross_attention_kwargs,
-            )
-            attn_output = self._to_4d(attn_output, height, weight)
-
-            hidden_states = attn_output + hidden_states
-
-        # 2. Cross-Attention/None
-        norm_hidden_states = self.norm2(hidden_states, emb)
-
-        height, weight = norm_hidden_states.shape[2:]
-        norm_hidden_states = self._to_3d(norm_hidden_states, height, weight)
-        attn_output = self.attn2(
-            norm_hidden_states,
-            encoder_hidden_states=encoder_hidden_states,
-            attention_mask=attention_mask if encoder_hidden_states is None else encoder_attention_mask,
-            **cross_attention_kwargs,
-        )
-        attn_output = self._to_4d(attn_output, height, weight)
-
-        hidden_states = attn_output + hidden_states
-
-        return hidden_states
diff --git a/MagicQuill/brushnet/unet_2d_condition.py b/MagicQuill/brushnet/unet_2d_condition.py
deleted file mode 100644
index 088e0efdba9f481c57137e5413e795fcca74c6a5..0000000000000000000000000000000000000000
--- a/MagicQuill/brushnet/unet_2d_condition.py
+++ /dev/null
@@ -1,1355 +0,0 @@
-# Copyright 2024 The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-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 PeftAdapterMixin, 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 diffusers.models.embeddings import (
-    GaussianFourierProjection,
-    GLIGENTextBoundingboxProjection,
-    ImageHintTimeEmbedding,
-    ImageProjection,
-    ImageTimeEmbedding,
-    TextImageProjection,
-    TextImageTimeEmbedding,
-    TextTimeEmbedding,
-    TimestepEmbedding,
-    Timesteps,
-)
-from diffusers.models.modeling_utils import ModelMixin
-from .unet_2d_blocks import (
-    get_down_block,
-    get_mid_block,
-    get_up_block,
-)
-
-
-logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
-
-
-@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, PeftAdapterMixin):
-    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 `True`):
-            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: float = 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: int = 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
-        self._check_config(
-            down_block_types=down_block_types,
-            up_block_types=up_block_types,
-            only_cross_attention=only_cross_attention,
-            block_out_channels=block_out_channels,
-            layers_per_block=layers_per_block,
-            cross_attention_dim=cross_attention_dim,
-            transformer_layers_per_block=transformer_layers_per_block,
-            reverse_transformer_layers_per_block=reverse_transformer_layers_per_block,
-            attention_head_dim=attention_head_dim,
-            num_attention_heads=num_attention_heads,
-        )
-
-        # 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
-        time_embed_dim, timestep_input_dim = self._set_time_proj(
-            time_embedding_type,
-            block_out_channels=block_out_channels,
-            flip_sin_to_cos=flip_sin_to_cos,
-            freq_shift=freq_shift,
-            time_embedding_dim=time_embedding_dim,
-        )
-
-        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,
-        )
-
-        self._set_encoder_hid_proj(
-            encoder_hid_dim_type,
-            cross_attention_dim=cross_attention_dim,
-            encoder_hid_dim=encoder_hid_dim,
-        )
-
-        # class embedding
-        self._set_class_embedding(
-            class_embed_type,
-            act_fn=act_fn,
-            num_class_embeds=num_class_embeds,
-            projection_class_embeddings_input_dim=projection_class_embeddings_input_dim,
-            time_embed_dim=time_embed_dim,
-            timestep_input_dim=timestep_input_dim,
-        )
-
-        self._set_add_embedding(
-            addition_embed_type,
-            addition_embed_type_num_heads=addition_embed_type_num_heads,
-            addition_time_embed_dim=addition_time_embed_dim,
-            cross_attention_dim=cross_attention_dim,
-            encoder_hid_dim=encoder_hid_dim,
-            flip_sin_to_cos=flip_sin_to_cos,
-            freq_shift=freq_shift,
-            projection_class_embeddings_input_dim=projection_class_embeddings_input_dim,
-            time_embed_dim=time_embed_dim,
-        )
-
-        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
-        self.mid_block = get_mid_block(
-            mid_block_type,
-            temb_channels=blocks_time_embed_dim,
-            in_channels=block_out_channels[-1],
-            resnet_eps=norm_eps,
-            resnet_act_fn=act_fn,
-            resnet_groups=norm_num_groups,
-            output_scale_factor=mid_block_scale_factor,
-            transformer_layers_per_block=transformer_layers_per_block[-1],
-            num_attention_heads=num_attention_heads[-1],
-            cross_attention_dim=cross_attention_dim[-1],
-            dual_cross_attention=dual_cross_attention,
-            use_linear_projection=use_linear_projection,
-            mid_block_only_cross_attention=mid_block_only_cross_attention,
-            upcast_attention=upcast_attention,
-            resnet_time_scale_shift=resnet_time_scale_shift,
-            attention_type=attention_type,
-            resnet_skip_time_act=resnet_skip_time_act,
-            cross_attention_norm=cross_attention_norm,
-            attention_head_dim=attention_head_dim[-1],
-            dropout=dropout,
-        )
-
-        # 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
-        )
-
-        self._set_pos_net_if_use_gligen(attention_type=attention_type, cross_attention_dim=cross_attention_dim)
-
-    def _check_config(
-        self,
-        down_block_types: Tuple[str],
-        up_block_types: Tuple[str],
-        only_cross_attention: Union[bool, Tuple[bool]],
-        block_out_channels: Tuple[int],
-        layers_per_block: Union[int, Tuple[int]],
-        cross_attention_dim: Union[int, Tuple[int]],
-        transformer_layers_per_block: Union[int, Tuple[int], Tuple[Tuple[int]]],
-        reverse_transformer_layers_per_block: bool,
-        attention_head_dim: int,
-        num_attention_heads: Optional[Union[int, Tuple[int]]],
-    ):
-        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.")
-
-    def _set_time_proj(
-        self,
-        time_embedding_type: str,
-        block_out_channels: int,
-        flip_sin_to_cos: bool,
-        freq_shift: float,
-        time_embedding_dim: int,
-    ) -> Tuple[int, int]:
-        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`."
-            )
-
-        return time_embed_dim, timestep_input_dim
-
-    def _set_encoder_hid_proj(
-        self,
-        encoder_hid_dim_type: Optional[str],
-        cross_attention_dim: Union[int, Tuple[int]],
-        encoder_hid_dim: Optional[int],
-    ):
-        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"` (Kandinsky 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
-
-    def _set_class_embedding(
-        self,
-        class_embed_type: Optional[str],
-        act_fn: str,
-        num_class_embeds: Optional[int],
-        projection_class_embeddings_input_dim: Optional[int],
-        time_embed_dim: int,
-        timestep_input_dim: int,
-    ):
-        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
-
-    def _set_add_embedding(
-        self,
-        addition_embed_type: str,
-        addition_embed_type_num_heads: int,
-        addition_time_embed_dim: Optional[int],
-        flip_sin_to_cos: bool,
-        freq_shift: float,
-        cross_attention_dim: Optional[int],
-        encoder_hid_dim: Optional[int],
-        projection_class_embeddings_input_dim: Optional[int],
-        time_embed_dim: int,
-    ):
-        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"` (Kandinsky 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'.")
-
-    def _set_pos_net_if_use_gligen(self, attention_type: str, cross_attention_dim: int):
-        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 = GLIGENTextBoundingboxProjection(
-                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]]):
-        r"""
-        Sets the attention processor to use to compute attention.
-
-        Parameters:
-            processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`):
-                The instantiated processor class or a dictionary of processor classes that will be set as the processor
-                for **all** `Attention` layers.
-
-                If `processor` is a dict, the key needs to define the path to the corresponding cross attention
-                processor. This is strongly recommended when setting trainable attention processors.
-
-        """
-        count = len(self.attn_processors.keys())
-
-        if isinstance(processor, dict) and len(processor) != count:
-            raise ValueError(
-                f"A dict of processors was passed, but the number of processors {len(processor)} does not match the"
-                f" number of attention layers: {count}. Please make sure to pass {count} processor classes."
-            )
-
-        def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):
-            if hasattr(module, "set_processor"):
-                if not isinstance(processor, dict):
-                    module.set_processor(processor)
-                else:
-                    module.set_processor(processor.pop(f"{name}.processor"))
-
-            for sub_name, child in module.named_children():
-                fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor)
-
-        for name, module in self.named_children():
-            fn_recursive_attn_processor(name, module, processor)
-
-    def set_default_attn_processor(self):
-        """
-        Disables custom attention processors and sets the default attention implementation.
-        """
-        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)
-
-    def set_attention_slice(self, slice_size: Union[str, int, List[int]] = "auto"):
-        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: float, s2: float, b1: float, b2: float):
-        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 unload_lora(self):
-        """Unloads LoRA weights."""
-        deprecate(
-            "unload_lora",
-            "0.28.0",
-            "Calling `unload_lora()` is deprecated and will be removed in a future version. Please install `peft` and then call `disable_adapters().",
-        )
-        for module in self.modules():
-            if hasattr(module, "set_lora_layer"):
-                module.set_lora_layer(None)
-
-    def get_time_embed(
-        self, sample: torch.Tensor, timestep: Union[torch.Tensor, float, int]
-    ) -> Optional[torch.Tensor]:
-        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)
-        return t_emb
-
-    def get_class_embed(self, sample: torch.Tensor, class_labels: Optional[torch.Tensor]) -> Optional[torch.Tensor]:
-        class_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)
-        return class_emb
-
-    def get_aug_embed(
-        self, emb: torch.Tensor, encoder_hidden_states: torch.Tensor, added_cond_kwargs: Dict[str, Any]
-    ) -> Optional[torch.Tensor]:
-        aug_emb = None
-        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 = self.add_embedding(image_embs, hint)
-        return aug_emb
-
-    def process_encoder_hidden_states(
-        self, encoder_hidden_states: torch.Tensor, added_cond_kwargs: Dict[str, Any]
-    ) -> torch.Tensor:
-        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":
-            # Kandinsky 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)
-            encoder_hidden_states = (encoder_hidden_states, image_embeds)
-        return encoder_hidden_states
-
-    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,
-        down_block_add_samples: Optional[Tuple[torch.Tensor]] = None,
-        mid_block_add_sample: Optional[Tuple[torch.Tensor]] = None,
-        up_block_add_samples: 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.
-            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)
-            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.unets.unet_2d_condition.UNet2DConditionOutput`] instead of a plain
-                tuple.
-
-        Returns:
-            [`~models.unets.unet_2d_condition.UNet2DConditionOutput`] or `tuple`:
-                If `return_dict` is True, an [`~models.unets.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
-        t_emb = self.get_time_embed(sample=sample, timestep=timestep)
-        emb = self.time_embedding(t_emb, timestep_cond)
-        aug_emb = None
-
-        class_emb = self.get_class_embed(sample=sample, class_labels=class_labels)
-        if class_emb is not None:
-            if self.config.class_embeddings_concat:
-                emb = torch.cat([emb, class_emb], dim=-1)
-            else:
-                emb = emb + class_emb
-
-        aug_emb = self.get_aug_embed(
-            emb=emb, encoder_hidden_states=encoder_hidden_states, added_cond_kwargs=added_cond_kwargs
-        )
-        if self.config.addition_embed_type == "image_hint":
-            aug_emb, hint = aug_emb
-            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)
-
-        encoder_hidden_states = self.process_encoder_hidden_states(
-            encoder_hidden_states=encoder_hidden_states, added_cond_kwargs=added_cond_kwargs
-        )
-
-        # 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)}
-
-        # 3. down
-        # we're popping the `scale` instead of getting it because otherwise `scale` will be propagated
-        # to the internal blocks and will raise deprecation warnings. this will be confusing for our users.
-        if cross_attention_kwargs is not None:
-            cross_attention_kwargs = cross_attention_kwargs.copy()
-            lora_scale = cross_attention_kwargs.pop("scale", 1.0)
-        else:
-            lora_scale = 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
-        is_brushnet = down_block_add_samples is not None and mid_block_add_sample is not None and up_block_add_samples is not None
-        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,)
-
-        if is_brushnet:
-            sample = sample + down_block_add_samples.pop(0)
-
-        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)
-
-                i = len(down_block_add_samples)
-
-                if is_brushnet and len(down_block_add_samples)>0:
-                    additional_residuals["down_block_add_samples"] = [down_block_add_samples.pop(0) 
-                                                        for _ in range(len(downsample_block.resnets)+(downsample_block.downsamplers !=None))]
-
-                sample, res_samples = 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,
-                )
-            else:
-                additional_residuals = {}
-
-                i = len(down_block_add_samples)
-
-                if is_brushnet and len(down_block_add_samples)>0:
-                    additional_residuals["down_block_add_samples"] = [down_block_add_samples.pop(0) 
-                                                        for _ in range(len(downsample_block.resnets)+(downsample_block.downsamplers !=None))]
-
-                sample, res_samples = downsample_block(hidden_states=sample, temb=emb, **additional_residuals)
-                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 = 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,
-                )
-            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
-
-        if is_brushnet:
-            sample = sample + mid_block_add_sample
-
-        # 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:
-                additional_residuals = {}
-
-                i = len(up_block_add_samples)
-
-                if is_brushnet and len(up_block_add_samples)>0:
-                    additional_residuals["up_block_add_samples"] = [up_block_add_samples.pop(0) 
-                                                        for _ in range(len(upsample_block.resnets)+(upsample_block.upsamplers !=None))]
-                
-                sample = 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,
-                    **additional_residuals,
-                )
-            else:
-                additional_residuals = {}
-
-                i = len(up_block_add_samples)
-
-                if is_brushnet and len(up_block_add_samples)>0:
-                    additional_residuals["up_block_add_samples"] = [up_block_add_samples.pop(0) 
-                                                        for _ in range(len(upsample_block.resnets)+(upsample_block.upsamplers !=None))]
-                
-                sample = upsample_block(
-                    hidden_states=sample,
-                    temb=emb,
-                    res_hidden_states_tuple=res_samples,
-                    upsample_size=upsample_size,
-                    **additional_residuals,
-                )
-
-        # 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)
diff --git a/MagicQuill/brushnet_nodes.py b/MagicQuill/brushnet_nodes.py
deleted file mode 100644
index 1b7a4175380d7c3fbe6ae869a19c4b359161dc27..0000000000000000000000000000000000000000
--- a/MagicQuill/brushnet_nodes.py
+++ /dev/null
@@ -1,1094 +0,0 @@
-import os
-import types
-from typing import Tuple
-
-import torch
-import torchvision.transforms as T
-import torch.nn.functional as F
-from accelerate import init_empty_weights, load_checkpoint_and_dispatch
-import sys
-
-import comfy.sd
-import comfy.utils
-import comfy.model_management
-import comfy.sd1_clip
-import comfy.ldm.models.autoencoder
-import comfy.supported_models
-
-import folder_paths
-
-from .model_patch import add_model_patch_option, patch_model_function_wrapper
-from .brushnet.brushnet import BrushNetModel
-from .brushnet.brushnet_ca import BrushNetModel as PowerPaintModel
-from .brushnet.powerpaint_utils import TokenizerWrapper, add_tokens
-
-current_directory = os.path.dirname(os.path.abspath(__file__))
-brushnet_config_file = os.path.join(current_directory, 'brushnet', 'brushnet.json')
-brushnet_xl_config_file = os.path.join(current_directory, 'brushnet', 'brushnet_xl.json')
-powerpaint_config_file = os.path.join(current_directory,'brushnet', 'powerpaint.json')
-
-sd15_scaling_factor = 0.18215
-sdxl_scaling_factor = 0.13025
-
-print(sys.path)
-ModelsToUnload = [comfy.sd1_clip.SD1ClipModel, 
-                  comfy.ldm.models.autoencoder.AutoencoderKL
-                 ]
-
-
-class BrushNetLoader:
-    @classmethod
-    def INPUT_TYPES(self):
-        self.inpaint_files = get_files_with_extension('inpaint')
-        return {"required":
-                    {    
-                        "brushnet": ([file for file in self.inpaint_files], ),
-                        "dtype": (['float16', 'bfloat16', 'float32', 'float64'], ),
-                     },
-                }
-
-    CATEGORY = "inpaint"
-    RETURN_TYPES = ("BRMODEL",)
-    RETURN_NAMES = ("brushnet",)
-
-    FUNCTION = "brushnet_loading"
-
-    def brushnet_loading(self, brushnet, dtype):
-        brushnet_file = os.path.join(self.inpaint_files[brushnet], brushnet)
-        print('BrushNet model file:', brushnet_file)
-        is_SDXL = False
-        is_PP = False
-        sd = comfy.utils.load_torch_file(brushnet_file)
-        brushnet_down_block, brushnet_mid_block, brushnet_up_block, keys = brushnet_blocks(sd)
-        del sd
-        if brushnet_down_block == 24 and brushnet_mid_block == 2 and brushnet_up_block == 30:
-            is_SDXL = False
-            if keys == 322:
-                is_PP = False
-                print('BrushNet model type: SD1.5')
-            else:
-                is_PP = True
-                print('PowerPaint model type: SD1.5')
-        elif brushnet_down_block == 18 and brushnet_mid_block == 2 and brushnet_up_block == 22:
-            print('BrushNet model type: Loading SDXL')
-            is_SDXL = True
-            is_PP = False
-        else:
-            raise Exception("Unknown BrushNet model")
-
-        with init_empty_weights():
-            if is_SDXL:
-                brushnet_config = BrushNetModel.load_config(brushnet_xl_config_file)
-                brushnet_model = BrushNetModel.from_config(brushnet_config)
-            elif is_PP:
-                brushnet_config = PowerPaintModel.load_config(powerpaint_config_file)
-                brushnet_model = PowerPaintModel.from_config(brushnet_config)
-            else:
-                brushnet_config = BrushNetModel.load_config(brushnet_config_file)
-                brushnet_model = BrushNetModel.from_config(brushnet_config)
-
-        if is_PP:
-            print("PowerPaint model file:", brushnet_file)
-        else:
-            print("BrushNet model file:", brushnet_file)
-
-        if dtype == 'float16':
-            torch_dtype = torch.float16
-        elif dtype == 'bfloat16':
-            torch_dtype = torch.bfloat16
-        elif dtype == 'float32':
-            torch_dtype = torch.float32
-        else:
-            torch_dtype = torch.float64
-
-        brushnet_model = load_checkpoint_and_dispatch(
-            brushnet_model,
-            brushnet_file,
-            device_map="sequential",
-            max_memory=None,
-            offload_folder=None,
-            offload_state_dict=False,
-            dtype=torch_dtype,
-            force_hooks=False,
-        )
-
-        if is_PP: 
-            print("PowerPaint model is loaded")
-        elif is_SDXL:
-            print("BrushNet SDXL model is loaded")
-        else:
-            print("BrushNet SD1.5 model is loaded")
-
-        return ({"brushnet": brushnet_model, "SDXL": is_SDXL, "PP": is_PP, "dtype": torch_dtype}, )
-
-
-class PowerPaintCLIPLoader:
-
-    @classmethod
-    def INPUT_TYPES(self):
-        self.inpaint_files = get_files_with_extension('inpaint', ['.bin'])
-        self.clip_files = get_files_with_extension('clip')
-        return {"required":
-                    {    
-                        "base": ([file for file in self.clip_files], ),
-                        "powerpaint": ([file for file in self.inpaint_files], ),
-                     },
-                }
-
-    CATEGORY = "inpaint"
-    RETURN_TYPES = ("CLIP",)
-    RETURN_NAMES = ("clip",)
-
-    FUNCTION = "ppclip_loading"
-
-    def ppclip_loading(self, base, powerpaint):
-        base_CLIP_file = os.path.join(self.clip_files[base], base)
-        pp_CLIP_file = os.path.join(self.inpaint_files[powerpaint], powerpaint)
-
-        pp_clip = comfy.sd.load_clip(ckpt_paths=[base_CLIP_file])
-
-        print('PowerPaint base CLIP file: ', base_CLIP_file)
-
-        pp_tokenizer = TokenizerWrapper(pp_clip.tokenizer.clip_l.tokenizer)
-        pp_text_encoder = pp_clip.patcher.model.clip_l.transformer
-
-        add_tokens(
-            tokenizer = pp_tokenizer,
-            text_encoder = pp_text_encoder,
-            placeholder_tokens = ["P_ctxt", "P_shape", "P_obj"],
-            initialize_tokens = ["a", "a", "a"],
-            num_vectors_per_token = 10,
-        )
-
-        pp_text_encoder.load_state_dict(comfy.utils.load_torch_file(pp_CLIP_file), strict=False)
-
-        print('PowerPaint CLIP file: ', pp_CLIP_file)
-
-        pp_clip.tokenizer.clip_l.tokenizer = pp_tokenizer
-        pp_clip.patcher.model.clip_l.transformer = pp_text_encoder
-
-        return (pp_clip,)
-    
-
-class PowerPaint:
-
-    @classmethod
-    def INPUT_TYPES(s):
-        return {"required":
-                    {    
-                        "model": ("MODEL",),
-                        "vae": ("VAE", ),
-                        "image": ("IMAGE",),
-                        "mask": ("MASK",),
-                        "powerpaint": ("BRMODEL", ),
-                        "clip": ("CLIP", ),
-                        "positive": ("CONDITIONING", ),
-                        "negative": ("CONDITIONING", ),
-                        "fitting" : ("FLOAT", {"default": 1.0, "min": 0.3, "max": 1.0}),
-                        "function": (['text guided', 'shape guided', 'object removal', 'context aware', 'image outpainting'], ),
-                        "scale": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0}),
-                        "start_at": ("INT", {"default": 0, "min": 0, "max": 10000}),
-                        "end_at": ("INT", {"default": 10000, "min": 0, "max": 10000}),
-                        "save_memory": (['none', 'auto', 'max'], ),
-                     },
-        }
-    
-    CATEGORY = "inpaint"
-    RETURN_TYPES = ("MODEL","CONDITIONING","CONDITIONING","LATENT",)
-    RETURN_NAMES = ("model","positive","negative","latent",)
-
-    FUNCTION = "model_update"
-
-    def model_update(self, model, vae, image, mask, powerpaint, clip, positive, negative, fitting, function, scale, start_at, end_at, save_memory):
-
-        is_SDXL, is_PP = check_compatibilty(model, powerpaint)
-        if not is_PP:
-            raise Exception("BrushNet model was loaded, please use BrushNet node")  
-
-        # Make a copy of the model so that we're not patching it everywhere in the workflow.
-        model = model.clone()
-
-        # prepare image and mask
-        # no batches for original image and mask
-        masked_image, mask = prepare_image(image, mask)
-
-        batch = masked_image.shape[0]
-        #width = masked_image.shape[2]
-        #height = masked_image.shape[1]
-
-        if hasattr(model.model.model_config, 'latent_format') and hasattr(model.model.model_config.latent_format, 'scale_factor'):
-            scaling_factor = model.model.model_config.latent_format.scale_factor
-        else:
-            scaling_factor = sd15_scaling_factor
-
-        torch_dtype = powerpaint['dtype']
-
-        # prepare conditioning latents
-        conditioning_latents = get_image_latents(masked_image, mask, vae, scaling_factor)
-        conditioning_latents[0] = conditioning_latents[0].to(dtype=torch_dtype).to(powerpaint['brushnet'].device)
-        conditioning_latents[1] = conditioning_latents[1].to(dtype=torch_dtype).to(powerpaint['brushnet'].device)
-
-        # prepare embeddings
-
-        if function == "object removal":
-            promptA = "P_ctxt"
-            promptB = "P_ctxt"
-            negative_promptA = "P_obj"
-            negative_promptB = "P_obj"
-            print('You should add to positive prompt: "empty scene blur"')
-            #positive = positive + " empty scene blur"
-        elif function == "context aware":
-            promptA = "P_ctxt"
-            promptB = "P_ctxt"
-            negative_promptA = ""
-            negative_promptB = ""
-            #positive = positive + " empty scene"
-            print('You should add to positive prompt: "empty scene"')
-        elif function == "shape guided":
-            promptA = "P_shape"
-            promptB = "P_ctxt"
-            negative_promptA = "P_shape"
-            negative_promptB = "P_ctxt"
-        elif function == "image outpainting":
-            promptA = "P_ctxt"
-            promptB = "P_ctxt"
-            negative_promptA = "P_obj"
-            negative_promptB = "P_obj"
-            #positive = positive + " empty scene"
-            print('You should add to positive prompt: "empty scene"')
-        else:
-            promptA = "P_obj"
-            promptB = "P_obj"
-            negative_promptA = "P_obj"
-            negative_promptB = "P_obj"
-
-        tokens = clip.tokenize(promptA)
-        prompt_embedsA = clip.encode_from_tokens(tokens, return_pooled=False)
-
-        tokens = clip.tokenize(negative_promptA)
-        negative_prompt_embedsA = clip.encode_from_tokens(tokens, return_pooled=False)
-
-        tokens = clip.tokenize(promptB)
-        prompt_embedsB = clip.encode_from_tokens(tokens, return_pooled=False)
-
-        tokens = clip.tokenize(negative_promptB)
-        negative_prompt_embedsB = clip.encode_from_tokens(tokens, return_pooled=False)
-
-        prompt_embeds_pp = (prompt_embedsA * fitting + (1.0 - fitting) * prompt_embedsB).to(dtype=torch_dtype).to(powerpaint['brushnet'].device)
-        negative_prompt_embeds_pp = (negative_prompt_embedsA * fitting + (1.0 - fitting) * negative_prompt_embedsB).to(dtype=torch_dtype).to(powerpaint['brushnet'].device)
-
-        # unload vae and CLIPs
-        del vae
-        del clip
-        for loaded_model in comfy.model_management.current_loaded_models:
-            if type(loaded_model.model.model) in ModelsToUnload:
-                comfy.model_management.current_loaded_models.remove(loaded_model)
-                loaded_model.model_unload()
-                del loaded_model
-
-        # apply patch to model
-
-        brushnet_conditioning_scale = scale
-        control_guidance_start = start_at
-        control_guidance_end = end_at
-
-        if save_memory != 'none':
-            powerpaint['brushnet'].set_attention_slice(save_memory)
-
-        add_brushnet_patch(model, 
-                           powerpaint['brushnet'],
-                           torch_dtype,
-                           conditioning_latents, 
-                           (brushnet_conditioning_scale, control_guidance_start, control_guidance_end), 
-                           negative_prompt_embeds_pp, prompt_embeds_pp, 
-                           None, None, None,
-                           False)
-
-        latent = torch.zeros([batch, 4, conditioning_latents[0].shape[2], conditioning_latents[0].shape[3]], device=powerpaint['brushnet'].device)
-
-        return (model, positive, negative, {"samples":latent},)
-
-    
-class BrushNet:
-
-    @classmethod
-    def INPUT_TYPES(s):
-        return {"required":
-                    {    
-                        "model": ("MODEL",),
-                        "vae": ("VAE", ),
-                        "image": ("IMAGE",),
-                        "mask": ("MASK",),
-                        "brushnet": ("BRMODEL", ),
-                        "positive": ("CONDITIONING", ),
-                        "negative": ("CONDITIONING", ),
-                        "scale": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0}),
-                        "start_at": ("INT", {"default": 0, "min": 0, "max": 10000}),
-                        "end_at": ("INT", {"default": 10000, "min": 0, "max": 10000}),
-                     },
-        }
-
-    CATEGORY = "inpaint"
-    RETURN_TYPES = ("MODEL","CONDITIONING","CONDITIONING","LATENT",)
-    RETURN_NAMES = ("model","positive","negative","latent",)
-
-    FUNCTION = "model_update"
-
-    def model_update(self, model, vae, image, mask, brushnet, positive, negative, scale, start_at, end_at):
-
-        is_SDXL, is_PP = check_compatibilty(model, brushnet)
-
-        if is_PP:
-            raise Exception("PowerPaint model was loaded, please use PowerPaint node")  
-
-        # Make a copy of the model so that we're not patching it everywhere in the workflow.
-        model = model.clone()
-
-        # prepare image and mask
-        # no batches for original image and mask
-        masked_image, mask = prepare_image(image, mask)
-
-        batch = masked_image.shape[0]
-        width = masked_image.shape[2]
-        height = masked_image.shape[1]
-
-        if hasattr(model.model.model_config, 'latent_format') and hasattr(model.model.model_config.latent_format, 'scale_factor'):
-            scaling_factor = model.model.model_config.latent_format.scale_factor
-        elif is_SDXL:
-            scaling_factor = sdxl_scaling_factor
-        else:
-            scaling_factor = sd15_scaling_factor
-
-        torch_dtype = brushnet['dtype']
-
-        # prepare conditioning latents
-        conditioning_latents = get_image_latents(masked_image, mask, vae, scaling_factor)
-        conditioning_latents[0] = conditioning_latents[0].to(dtype=torch_dtype).to(brushnet['brushnet'].device)
-        conditioning_latents[1] = conditioning_latents[1].to(dtype=torch_dtype).to(brushnet['brushnet'].device)
-
-        # unload vae
-        del vae
-        for loaded_model in comfy.model_management.current_loaded_models:
-            if type(loaded_model.model.model) in ModelsToUnload:
-                comfy.model_management.current_loaded_models.remove(loaded_model)
-                loaded_model.model_unload()
-                del loaded_model
-
-        # prepare embeddings
-
-        prompt_embeds = positive[0][0].to(dtype=torch_dtype).to(brushnet['brushnet'].device)
-        negative_prompt_embeds = negative[0][0].to(dtype=torch_dtype).to(brushnet['brushnet'].device)
-
-        max_tokens = max(prompt_embeds.shape[1], negative_prompt_embeds.shape[1])
-        if prompt_embeds.shape[1] < max_tokens:
-            multiplier = max_tokens // 77 - prompt_embeds.shape[1] // 77
-            prompt_embeds = torch.concat([prompt_embeds] + [prompt_embeds[:,-77:,:]] * multiplier, dim=1)
-            print('BrushNet: negative prompt more than 75 tokens:', negative_prompt_embeds.shape, 'multiplying prompt_embeds')
-        if negative_prompt_embeds.shape[1] < max_tokens:
-            multiplier = max_tokens // 77 - negative_prompt_embeds.shape[1] // 77
-            negative_prompt_embeds = torch.concat([negative_prompt_embeds] + [negative_prompt_embeds[:,-77:,:]] * multiplier, dim=1)
-            print('BrushNet: positive prompt more than 75 tokens:', prompt_embeds.shape, 'multiplying negative_prompt_embeds')
-
-        if len(positive[0]) > 1 and 'pooled_output' in positive[0][1] and positive[0][1]['pooled_output'] is not None:
-            pooled_prompt_embeds = positive[0][1]['pooled_output'].to(dtype=torch_dtype).to(brushnet['brushnet'].device)
-        else:
-            print('BrushNet: positive conditioning has not pooled_output')
-            if is_SDXL:
-                print('BrushNet will not produce correct results')
-            pooled_prompt_embeds = torch.empty([2, 1280], device=brushnet['brushnet'].device).to(dtype=torch_dtype)
-
-        if len(negative[0]) > 1 and 'pooled_output' in negative[0][1] and negative[0][1]['pooled_output'] is not None:
-            negative_pooled_prompt_embeds = negative[0][1]['pooled_output'].to(dtype=torch_dtype).to(brushnet['brushnet'].device)
-        else:
-            print('BrushNet: negative conditioning has not pooled_output')
-            if is_SDXL:
-                print('BrushNet will not produce correct results')
-            negative_pooled_prompt_embeds = torch.empty([1, pooled_prompt_embeds.shape[1]], device=brushnet['brushnet'].device).to(dtype=torch_dtype)
-
-        time_ids = torch.FloatTensor([[height, width, 0., 0., height, width]]).to(dtype=torch_dtype).to(brushnet['brushnet'].device)
-
-        if not is_SDXL:
-            pooled_prompt_embeds = None
-            negative_pooled_prompt_embeds = None
-            time_ids = None
-
-        # apply patch to model
-
-        brushnet_conditioning_scale = scale
-        control_guidance_start = start_at
-        control_guidance_end = end_at
-
-        add_brushnet_patch(model, 
-                           brushnet['brushnet'],
-                           torch_dtype,
-                           conditioning_latents, 
-                           (brushnet_conditioning_scale, control_guidance_start, control_guidance_end), 
-                           prompt_embeds, negative_prompt_embeds,
-                           pooled_prompt_embeds, negative_pooled_prompt_embeds, time_ids,
-                           False)
-
-        latent = torch.zeros([batch, 4, conditioning_latents[0].shape[2], conditioning_latents[0].shape[3]], device=brushnet['brushnet'].device)
-
-        return (model, positive, negative, {"samples":latent},)
-
-
-class BlendInpaint:
-
-    @classmethod
-    def INPUT_TYPES(s):
-        return {"required":
-                    {    
-                        "inpaint": ("IMAGE",),
-                        "original": ("IMAGE",),
-                        "mask": ("MASK",),
-                        "kernel": ("INT", {"default": 10, "min": 1, "max": 1000}),
-                        "sigma": ("FLOAT", {"default": 10.0, "min": 0.01, "max": 1000}),
-                     },
-                "optional":
-                    {
-                        "origin": ("VECTOR",),
-                    },
-                }
-
-    CATEGORY = "inpaint"
-    RETURN_TYPES = ("IMAGE","MASK",)
-    RETURN_NAMES = ("image","MASK",)
-
-    FUNCTION = "blend_inpaint"
-
-    def blend_inpaint(self, inpaint: torch.Tensor, original: torch.Tensor, mask, kernel: int, sigma:int, origin=None) -> Tuple[torch.Tensor]:
-
-        original, mask = check_image_mask(original, mask, 'Blend Inpaint')
-
-        if len(inpaint.shape) < 4:
-            # image tensor shape should be [B, H, W, C], but batch somehow is missing
-            inpaint = inpaint[None,:,:,:]
-
-        if inpaint.shape[0] < original.shape[0]:
-            print("Blend Inpaint gets batch of original images (%d) but only (%d) inpaint images" % (original.shape[0], inpaint.shape[0]))
-            original= original[:inpaint.shape[0],:,:]
-            mask = mask[:inpaint.shape[0],:,:]
-
-        if inpaint.shape[0] > original.shape[0]:
-            # batch over inpaint
-            count = 0
-            original_list = []
-            mask_list = []
-            origin_list = []
-            while (count < inpaint.shape[0]):
-                for i in range(original.shape[0]):
-                    original_list.append(original[i][None,:,:,:])
-                    mask_list.append(mask[i][None,:,:])
-                    if origin is not None:
-                        origin_list.append(origin[i][None,:])
-                    count += 1
-                    if count >= inpaint.shape[0]:
-                        break
-            original = torch.concat(original_list, dim=0)
-            mask = torch.concat(mask_list, dim=0)
-            if origin is not None:
-                origin = torch.concat(origin_list, dim=0)
-
-        if kernel % 2 == 0:
-            kernel += 1
-        transform = T.GaussianBlur(kernel_size=(kernel, kernel), sigma=(sigma, sigma))
-
-        ret = []
-        blurred = []
-        for i in range(inpaint.shape[0]):
-            if origin is None:
-                blurred_mask = transform(mask[i][None,None,:,:]).to(original.device).to(original.dtype)
-                blurred.append(blurred_mask[0])
-
-                result = torch.nn.functional.interpolate(
-                    inpaint[i][None,:,:,:].permute(0, 3, 1, 2), 
-                    size=(
-                        original[i].shape[0], 
-                        original[i].shape[1],
-                    )
-                ).permute(0, 2, 3, 1).to(original.device).to(original.dtype)
-            else:
-                # got mask from CutForInpaint
-                height, width, _ = original[i].shape
-                x0 = origin[i][0].item()
-                y0 = origin[i][1].item()
-
-                if mask[i].shape[0] < height or mask[i].shape[1] < width:
-                    padded_mask = F.pad(input=mask[i], pad=(x0, width-x0-mask[i].shape[1], 
-                                                            y0, height-y0-mask[i].shape[0]), mode='constant', value=0)
-                else:
-                    padded_mask = mask[i]
-                blurred_mask = transform(padded_mask[None,None,:,:]).to(original.device).to(original.dtype)
-                blurred.append(blurred_mask[0][0])
-
-                result = F.pad(input=inpaint[i], pad=(0, 0, x0, width-x0-inpaint[i].shape[1], 
-                                                      y0, height-y0-inpaint[i].shape[0]), mode='constant', value=0)
-                result = result[None,:,:,:].to(original.device).to(original.dtype)
-
-            ret.append(original[i] * (1.0 - blurred_mask[0][0][:,:,None]) + result[0] * blurred_mask[0][0][:,:,None])
-
-        return (torch.stack(ret), torch.stack(blurred), )
-
-
-class CutForInpaint:
-
-    @classmethod
-    def INPUT_TYPES(s):
-        return {"required":
-                    {    
-                        "image": ("IMAGE",),
-                        "mask": ("MASK",),
-                        "width": ("INT", {"default": 512, "min": 64, "max": 2048}),
-                        "height": ("INT", {"default": 512, "min": 64, "max": 2048}),
-                     },
-                }
-
-    CATEGORY = "inpaint"
-    RETURN_TYPES = ("IMAGE","MASK","VECTOR",)
-    RETURN_NAMES = ("image","mask","origin",)
-
-    FUNCTION = "cut_for_inpaint"
-
-    def cut_for_inpaint(self, image: torch.Tensor, mask: torch.Tensor, width: int, height: int):
-
-        image, mask = check_image_mask(image, mask, 'BrushNet')
-
-        ret = []
-        msk = []
-        org = []
-        for i in range(image.shape[0]):
-            x0, y0, w, h = cut_with_mask(mask[i], width, height)
-            ret.append((image[i][y0:y0+h,x0:x0+w,:]))
-            msk.append((mask[i][y0:y0+h,x0:x0+w]))
-            org.append(torch.IntTensor([x0,y0]))
-
-        return (torch.stack(ret), torch.stack(msk), torch.stack(org), )
-
-
-#### Utility function
-
-def get_files_with_extension(folder_name, extension=['.safetensors']):
-
-    try:
-        folders = folder_paths.get_folder_paths(folder_name)
-    except:
-        folders = []
-
-    if not folders:
-        folders = [os.path.join(folder_paths.models_dir, folder_name)]
-    if not os.path.isdir(folders[0]):
-        folders = [os.path.join(folder_paths.base_path, folder_name)]
-    if not os.path.isdir(folders[0]):
-        return {}
-    
-    filtered_folders = []
-    for x in folders:
-        if not os.path.isdir(x):
-            continue
-        the_same = False
-        for y in filtered_folders:
-            if os.path.samefile(x, y):
-                the_same = True
-                break
-        if not the_same:
-            filtered_folders.append(x)
-
-    if not filtered_folders:
-        return {}
-
-    output = {}
-    for x in filtered_folders:
-        files, folders_all = folder_paths.recursive_search(x, excluded_dir_names=[".git"])
-        filtered_files = folder_paths.filter_files_extensions(files, extension)
-
-        for f in filtered_files:
-            output[f] = x
-
-    return output
-
-
-# get blocks from state_dict so we could know which model it is
-def brushnet_blocks(sd):
-    brushnet_down_block = 0
-    brushnet_mid_block = 0
-    brushnet_up_block = 0
-    for key in sd:
-        if 'brushnet_down_block' in key:
-            brushnet_down_block += 1
-        if 'brushnet_mid_block' in key:
-            brushnet_mid_block += 1        
-        if 'brushnet_up_block' in key:
-            brushnet_up_block += 1
-    return (brushnet_down_block, brushnet_mid_block, brushnet_up_block, len(sd))
-
-
-# Check models compatibility
-def check_compatibilty(model, brushnet):
-    is_SDXL = False
-    is_PP = False
-    if isinstance(model.model.model_config, comfy.supported_models.SD15):
-        print('Base model type: SD1.5')
-        is_SDXL = False
-        if brushnet["SDXL"]:
-            raise Exception("Base model is SD15, but BrushNet is SDXL type")  
-        if brushnet["PP"]:
-            is_PP = True
-    elif isinstance(model.model.model_config, comfy.supported_models.SDXL):
-        print('Base model type: SDXL')
-        is_SDXL = True
-        if not brushnet["SDXL"]:
-            raise Exception("Base model is SDXL, but BrushNet is SD15 type")    
-    else:
-        print('Base model type: ', type(model.model.model_config))
-        raise Exception("Unsupported model type: " + str(type(model.model.model_config)))
-
-    return (is_SDXL, is_PP)
-
-
-def check_image_mask(image, mask, name):
-    if len(image.shape) < 4:
-        # image tensor shape should be [B, H, W, C], but batch somehow is missing
-        image = image[None,:,:,:]
-    
-    if len(mask.shape) > 3:
-        # mask tensor shape should be [B, H, W] but we get [B, H, W, C], image may be?
-        # take first mask, red channel
-        mask = (mask[:,:,:,0])[:,:,:]
-    elif len(mask.shape) < 3:
-        # mask tensor shape should be [B, H, W] but batch somehow is missing
-        mask = mask[None,:,:]
-
-    if image.shape[0] > mask.shape[0]:
-        print(name, "gets batch of images (%d) but only %d masks" % (image.shape[0], mask.shape[0]))
-        if mask.shape[0] == 1: 
-            print(name, "will copy the mask to fill batch")
-            mask = torch.cat([mask] * image.shape[0], dim=0)
-        else:
-            print(name, "will add empty masks to fill batch")
-            empty_mask = torch.zeros([image.shape[0] - mask.shape[0], mask.shape[1], mask.shape[2]])
-            mask = torch.cat([mask, empty_mask], dim=0)
-    elif image.shape[0] < mask.shape[0]:
-        print(name, "gets batch of images (%d) but too many (%d) masks" % (image.shape[0], mask.shape[0]))
-        mask = mask[:image.shape[0],:,:]
-
-    return (image, mask)
-
-
-# Prepare image and mask
-def prepare_image(image, mask):
-
-    image, mask = check_image_mask(image, mask, 'BrushNet')
-
-    print("BrushNet image.shape =", image.shape, "mask.shape =", mask.shape)
-
-    if mask.shape[2] != image.shape[2] or mask.shape[1] != image.shape[1]:
-        raise Exception("Image and mask should be the same size")
-    
-    # As a suggestion of inferno46n2 (https://github.com/nullquant/ComfyUI-BrushNet/issues/64)
-    mask = mask.round()
-
-    masked_image = image * (1.0 - mask[:,:,:,None])
-
-    return (masked_image, mask)
-
-
-# Get origin of the mask
-def cut_with_mask(mask, width, height):
-    iy, ix = (mask == 1).nonzero(as_tuple=True)
-
-    h0, w0 = mask.shape
-    
-    if iy.numel() == 0:
-        x_c = w0 / 2.0
-        y_c = h0 / 2.0
-    else:
-        x_min = ix.min().item()
-        x_max = ix.max().item()
-        y_min = iy.min().item()
-        y_max = iy.max().item()
-
-        if x_max - x_min > width or y_max - y_min > height:
-            raise Exception("Masked area is bigger than provided dimensions")
-
-        x_c = (x_min + x_max) / 2.0
-        y_c = (y_min + y_max) / 2.0
-    
-    width2 = width / 2.0
-    height2 = height / 2.0
-
-    if w0 <= width:
-        x0 = 0
-        w = w0
-    else:
-        x0 = max(0, x_c - width2)
-        w = width
-        if x0 + width > w0:
-            x0 = w0 - width
-
-    if h0 <= height:
-        y0 = 0
-        h = h0
-    else:
-        y0 = max(0, y_c - height2)
-        h = height
-        if y0 + height > h0:
-            y0 = h0 - height
-
-    return (int(x0), int(y0), int(w), int(h))
-
-
-# Prepare conditioning_latents
-@torch.inference_mode()
-def get_image_latents(masked_image, mask, vae, scaling_factor):
-    processed_image = masked_image.to(vae.device)
-    image_latents = vae.encode(processed_image[:,:,:,:3]) * scaling_factor
-    processed_mask = 1. - mask[:,None,:,:]
-    interpolated_mask = torch.nn.functional.interpolate(
-                processed_mask, 
-                size=(
-                    image_latents.shape[-2], 
-                    image_latents.shape[-1]
-                )
-            )
-    interpolated_mask = interpolated_mask.to(image_latents.device)
-
-    conditioning_latents = [image_latents, interpolated_mask]
-
-    print('BrushNet CL: image_latents shape =', image_latents.shape, 'interpolated_mask shape =', interpolated_mask.shape)
-
-    return conditioning_latents
-
-
-# Main function where magic happens
-@torch.inference_mode()
-def brushnet_inference(x, timesteps, transformer_options, debug):
-    if 'model_patch' not in transformer_options:
-        print('BrushNet inference: there is no model_patch key in transformer_options')
-        return ([], 0, [])
-    mp = transformer_options['model_patch']
-    if 'brushnet' not in mp:
-        print('BrushNet inference: there is no brushnet key in mdel_patch')
-        return ([], 0, [])
-    bo = mp['brushnet']
-    if 'model' not in bo:
-        print('BrushNet inference: there is no model key in brushnet')
-        return ([], 0, [])
-    brushnet = bo['model']
-    if not (isinstance(brushnet, BrushNetModel) or isinstance(brushnet, PowerPaintModel)):
-        print('BrushNet model is not a BrushNetModel class')
-        return ([], 0, [])
-
-    torch_dtype = bo['dtype']
-    cl_list = bo['latents']
-    brushnet_conditioning_scale, control_guidance_start, control_guidance_end = bo['controls']
-    pe = bo['prompt_embeds']
-    npe = bo['negative_prompt_embeds']
-    ppe, nppe, time_ids = bo['add_embeds']
-
-    #do_classifier_free_guidance = mp['free_guidance']
-    do_classifier_free_guidance = len(transformer_options['cond_or_uncond']) > 1
-
-    x = x.detach().clone()
-    x = x.to(torch_dtype).to(brushnet.device)
-
-    timesteps = timesteps.detach().clone()
-    timesteps = timesteps.to(torch_dtype).to(brushnet.device)
-
-    total_steps = mp['total_steps']
-    step = mp['step']
-
-    added_cond_kwargs = {}
-
-    if do_classifier_free_guidance and step == 0:
-        print('BrushNet inference: do_classifier_free_guidance is True')
-
-    sub_idx = None
-    if 'ad_params' in transformer_options and 'sub_idxs' in transformer_options['ad_params']:
-        sub_idx = transformer_options['ad_params']['sub_idxs']
-
-    # we have batch input images
-    batch = cl_list[0].shape[0]
-    # we have incoming latents
-    latents_incoming = x.shape[0]
-    # and we already got some
-    latents_got = bo['latent_id']
-    if step == 0 or batch > 1:
-        print('BrushNet inference, step = %d: image batch = %d, got %d latents, starting from %d' \
-                % (step, batch, latents_incoming, latents_got))
-
-    image_latents = []
-    masks = []
-    prompt_embeds = []
-    negative_prompt_embeds = []
-    pooled_prompt_embeds = []
-    negative_pooled_prompt_embeds = []
-    if sub_idx:
-        # AnimateDiff indexes detected
-        if step == 0:
-            print('BrushNet inference: AnimateDiff indexes detected and applied')
-
-        batch = len(sub_idx)
-
-        if do_classifier_free_guidance:
-            for i in sub_idx:
-                image_latents.append(cl_list[0][i][None,:,:,:])
-                masks.append(cl_list[1][i][None,:,:,:])
-                prompt_embeds.append(pe)
-                negative_prompt_embeds.append(npe)
-                pooled_prompt_embeds.append(ppe)
-                negative_pooled_prompt_embeds.append(nppe)
-            for i in sub_idx:
-                image_latents.append(cl_list[0][i][None,:,:,:])
-                masks.append(cl_list[1][i][None,:,:,:])
-        else:
-            for i in sub_idx:
-                image_latents.append(cl_list[0][i][None,:,:,:])
-                masks.append(cl_list[1][i][None,:,:,:])
-                prompt_embeds.append(pe)
-                pooled_prompt_embeds.append(ppe)
-    else:
-        # do_classifier_free_guidance = 2 passes, 1st pass is cond, 2nd is uncond
-        continue_batch = True
-        for i in range(latents_incoming):
-            number = latents_got + i
-            if number < batch:
-                # 1st pass, cond
-                image_latents.append(cl_list[0][number][None,:,:,:])
-                masks.append(cl_list[1][number][None,:,:,:])
-                prompt_embeds.append(pe)
-                pooled_prompt_embeds.append(ppe)
-            elif do_classifier_free_guidance and number < batch * 2:
-                # 2nd pass, uncond
-                image_latents.append(cl_list[0][number-batch][None,:,:,:])
-                masks.append(cl_list[1][number-batch][None,:,:,:])
-                negative_prompt_embeds.append(npe)
-                negative_pooled_prompt_embeds.append(nppe)
-            else:
-                # latent batch
-                image_latents.append(cl_list[0][0][None,:,:,:])
-                masks.append(cl_list[1][0][None,:,:,:])
-                prompt_embeds.append(pe)
-                pooled_prompt_embeds.append(ppe)
-                latents_got = -i
-                continue_batch = False
-
-        if continue_batch:
-            # we don't have full batch yet
-            if do_classifier_free_guidance:
-                if number < batch * 2 - 1:
-                    bo['latent_id'] = number + 1
-                else:
-                    bo['latent_id'] = 0
-            else:
-                if number < batch - 1:
-                    bo['latent_id'] = number + 1
-                else:
-                    bo['latent_id'] = 0
-        else:
-            bo['latent_id'] = 0
-
-    cl = []
-    for il, m in zip(image_latents, masks):
-        cl.append(torch.concat([il, m], dim=1))
-    cl2apply = torch.concat(cl, dim=0)
-
-    conditioning_latents = cl2apply.to(torch_dtype).to(brushnet.device)
-
-    # print("BrushNet CL: conditioning_latents shape =", conditioning_latents.shape)
-    # print("BrushNet CL: x shape =", x.shape)
-
-    prompt_embeds.extend(negative_prompt_embeds)
-    prompt_embeds = torch.concat(prompt_embeds, dim=0).to(torch_dtype).to(brushnet.device)
-
-    if ppe is not None:
-        added_cond_kwargs = {}
-        added_cond_kwargs['time_ids'] = torch.concat([time_ids] * latents_incoming, dim = 0).to(torch_dtype).to(brushnet.device)
-
-        pooled_prompt_embeds.extend(negative_pooled_prompt_embeds)
-        pooled_prompt_embeds = torch.concat(pooled_prompt_embeds, dim=0).to(torch_dtype).to(brushnet.device)
-        added_cond_kwargs['text_embeds'] = pooled_prompt_embeds
-    else:
-        added_cond_kwargs = None
-
-    if x.shape[2] != conditioning_latents.shape[2] or x.shape[3] != conditioning_latents.shape[3]:
-        if step == 0:
-            print('BrushNet inference: image', conditioning_latents.shape, 'and latent', x.shape, 'have different size, resizing image')
-        conditioning_latents = torch.nn.functional.interpolate(
-            conditioning_latents, size=(
-                x.shape[2], 
-                x.shape[3],
-            ), mode='bicubic',
-        ).to(torch_dtype).to(brushnet.device)
-
-    if step == 0:
-        print('BrushNet inference: sample', x.shape, ', CL', conditioning_latents.shape, 'dtype', torch_dtype)
-
-    if debug: print('BrushNet: step =', step)
-
-    if step < control_guidance_start or step > control_guidance_end:
-        cond_scale = 0.0
-    else:
-        cond_scale = brushnet_conditioning_scale
-
-    return brushnet(x,
-                    encoder_hidden_states=prompt_embeds,
-                    brushnet_cond=conditioning_latents,
-                    timestep = timesteps,
-                    conditioning_scale=cond_scale,
-                    guess_mode=False,
-                    added_cond_kwargs=added_cond_kwargs,
-                    return_dict=False,
-                    debug=debug,
-                )
-
-
-# This is main patch function
-def add_brushnet_patch(model, brushnet, torch_dtype, conditioning_latents, 
-                       controls, 
-                       prompt_embeds, negative_prompt_embeds,
-                       pooled_prompt_embeds, negative_pooled_prompt_embeds, time_ids,
-                       debug):
-    
-    is_SDXL = isinstance(model.model.model_config, comfy.supported_models.SDXL)
-
-    if is_SDXL:
-        input_blocks = [[0, comfy.ops.disable_weight_init.Conv2d],
-                        [1, comfy.ldm.modules.diffusionmodules.openaimodel.ResBlock],
-                        [2, comfy.ldm.modules.diffusionmodules.openaimodel.ResBlock],
-                        [3, comfy.ldm.modules.diffusionmodules.openaimodel.Downsample],
-                        [4, comfy.ldm.modules.attention.SpatialTransformer],
-                        [5, comfy.ldm.modules.attention.SpatialTransformer],
-                        [6, comfy.ldm.modules.diffusionmodules.openaimodel.Downsample],
-                        [7, comfy.ldm.modules.attention.SpatialTransformer],
-                        [8, comfy.ldm.modules.attention.SpatialTransformer]]
-        middle_block  = [0, comfy.ldm.modules.diffusionmodules.openaimodel.ResBlock]
-        output_blocks = [[0, comfy.ldm.modules.attention.SpatialTransformer],
-                        [1, comfy.ldm.modules.attention.SpatialTransformer],
-                        [2, comfy.ldm.modules.attention.SpatialTransformer],
-                        [2, comfy.ldm.modules.diffusionmodules.openaimodel.Upsample],
-                        [3, comfy.ldm.modules.attention.SpatialTransformer],
-                        [4, comfy.ldm.modules.attention.SpatialTransformer],
-                        [5, comfy.ldm.modules.attention.SpatialTransformer],
-                        [5, comfy.ldm.modules.diffusionmodules.openaimodel.Upsample],
-                        [6, comfy.ldm.modules.diffusionmodules.openaimodel.ResBlock],
-                        [7, comfy.ldm.modules.diffusionmodules.openaimodel.ResBlock],
-                        [8, comfy.ldm.modules.diffusionmodules.openaimodel.ResBlock]]
-    else:
-        input_blocks = [[0, comfy.ops.disable_weight_init.Conv2d],
-                        [1, comfy.ldm.modules.attention.SpatialTransformer],
-                        [2, comfy.ldm.modules.attention.SpatialTransformer],
-                        [3, comfy.ldm.modules.diffusionmodules.openaimodel.Downsample],
-                        [4, comfy.ldm.modules.attention.SpatialTransformer],
-                        [5, comfy.ldm.modules.attention.SpatialTransformer],
-                        [6, comfy.ldm.modules.diffusionmodules.openaimodel.Downsample],
-                        [7, comfy.ldm.modules.attention.SpatialTransformer],
-                        [8, comfy.ldm.modules.attention.SpatialTransformer],
-                        [9, comfy.ldm.modules.diffusionmodules.openaimodel.Downsample],
-                        [10, comfy.ldm.modules.diffusionmodules.openaimodel.ResBlock],
-                        [11, comfy.ldm.modules.diffusionmodules.openaimodel.ResBlock]]
-        middle_block  = [0, comfy.ldm.modules.diffusionmodules.openaimodel.ResBlock]
-        output_blocks = [[0, comfy.ldm.modules.diffusionmodules.openaimodel.ResBlock],
-                        [1, comfy.ldm.modules.diffusionmodules.openaimodel.ResBlock],
-                        [2, comfy.ldm.modules.diffusionmodules.openaimodel.ResBlock],
-                        [2, comfy.ldm.modules.diffusionmodules.openaimodel.Upsample],
-                        [3, comfy.ldm.modules.attention.SpatialTransformer],
-                        [4, comfy.ldm.modules.attention.SpatialTransformer],
-                        [5, comfy.ldm.modules.attention.SpatialTransformer],
-                        [5, comfy.ldm.modules.diffusionmodules.openaimodel.Upsample],
-                        [6, comfy.ldm.modules.attention.SpatialTransformer],
-                        [7, comfy.ldm.modules.attention.SpatialTransformer],
-                        [8, comfy.ldm.modules.attention.SpatialTransformer],
-                        [8, comfy.ldm.modules.diffusionmodules.openaimodel.Upsample],
-                        [9, comfy.ldm.modules.attention.SpatialTransformer],
-                        [10, comfy.ldm.modules.attention.SpatialTransformer],
-                        [11, comfy.ldm.modules.attention.SpatialTransformer]]
-
-    def last_layer_index(block, tp):
-        layer_list = []
-        for layer in block:
-            layer_list.append(type(layer))
-        layer_list.reverse()
-        if tp not in layer_list:
-            return -1, layer_list.reverse()
-        return len(layer_list) - 1 - layer_list.index(tp), layer_list
-
-    def brushnet_forward(model, x, timesteps, transformer_options, control):
-        if 'brushnet' not in transformer_options['model_patch']:
-            input_samples = []
-            mid_sample = 0
-            output_samples = []
-        else:    
-            # brushnet inference
-            input_samples, mid_sample, output_samples = brushnet_inference(x, timesteps, transformer_options, debug)
-
-        # give additional samples to blocks
-        for i, tp in input_blocks:
-            idx, layer_list = last_layer_index(model.input_blocks[i], tp)
-            if idx < 0:
-                print("BrushNet can't find", tp, "layer in", i,"input block:", layer_list)
-                continue
-            model.input_blocks[i][idx].add_sample_after = input_samples.pop(0) if input_samples else 0
-
-        idx, layer_list = last_layer_index(model.middle_block, middle_block[1])
-        if idx < 0:
-            print("BrushNet can't find", middle_block[1], "layer in middle block", layer_list)
-        model.middle_block[idx].add_sample_after = mid_sample
-
-        for i, tp in output_blocks:
-            idx, layer_list = last_layer_index(model.output_blocks[i], tp)
-            if idx < 0:
-                print("BrushNet can't find", tp, "layer in", i,"outnput block:", layer_list)
-                continue
-            model.output_blocks[i][idx].add_sample_after = output_samples.pop(0) if output_samples else 0
-
-    patch_model_function_wrapper(model, brushnet_forward)
-
-    to = add_model_patch_option(model)
-    mp = to['model_patch']
-    if 'brushnet' not in mp:
-        mp['brushnet'] = {}
-    bo = mp['brushnet']
-
-    bo['model'] = brushnet
-    bo['dtype'] = torch_dtype
-    bo['latents'] = conditioning_latents
-    bo['controls'] = controls
-    bo['prompt_embeds'] = prompt_embeds
-    bo['negative_prompt_embeds'] = negative_prompt_embeds
-    bo['add_embeds'] = (pooled_prompt_embeds, negative_pooled_prompt_embeds, time_ids)
-    bo['latent_id'] = 0
-
-    # patch layers `forward` so we can apply brushnet
-    def forward_patched_by_brushnet(self, x, *args, **kwargs):
-        h = self.original_forward(x, *args, **kwargs)
-        if hasattr(self, 'add_sample_after') and type(self):
-            to_add = self.add_sample_after
-            if torch.is_tensor(to_add):
-                # interpolate due to RAUNet
-                if h.shape[2] != to_add.shape[2] or h.shape[3] != to_add.shape[3]:
-                    to_add = torch.nn.functional.interpolate(to_add, size=(h.shape[2], h.shape[3]), mode='bicubic')                  
-                h += to_add.to(h.dtype).to(h.device)
-            else:
-                h += self.add_sample_after
-            self.add_sample_after = 0
-        return h
-
-    for i, block in enumerate(model.model.diffusion_model.input_blocks):
-        for j, layer in enumerate(block):
-            if not hasattr(layer, 'original_forward'):
-                layer.original_forward = layer.forward
-            layer.forward = types.MethodType(forward_patched_by_brushnet, layer)
-            layer.add_sample_after = 0
-
-    for j, layer in enumerate(model.model.diffusion_model.middle_block):
-        if not hasattr(layer, 'original_forward'):
-            layer.original_forward = layer.forward
-        layer.forward = types.MethodType(forward_patched_by_brushnet, layer)
-        layer.add_sample_after = 0
-
-    for i, block in enumerate(model.model.diffusion_model.output_blocks):
-        for j, layer in enumerate(block):
-            if not hasattr(layer, 'original_forward'):
-                layer.original_forward = layer.forward
-            layer.forward = types.MethodType(forward_patched_by_brushnet, layer)
-            layer.add_sample_after = 0
diff --git a/MagicQuill/comfy/.DS_Store b/MagicQuill/comfy/.DS_Store
deleted file mode 100644
index 6929da02147a717f7f4ec1fa0a6d2f0a967729d3..0000000000000000000000000000000000000000
Binary files a/MagicQuill/comfy/.DS_Store and /dev/null differ
diff --git a/MagicQuill/comfy/checkpoint_pickle.py b/MagicQuill/comfy/checkpoint_pickle.py
deleted file mode 100644
index 206551d3c1cf0d654c907534629a800196ba138b..0000000000000000000000000000000000000000
--- a/MagicQuill/comfy/checkpoint_pickle.py
+++ /dev/null
@@ -1,13 +0,0 @@
-import pickle
-
-load = pickle.load
-
-class Empty:
-    pass
-
-class Unpickler(pickle.Unpickler):
-    def find_class(self, module, name):
-        #TODO: safe unpickle
-        if module.startswith("pytorch_lightning"):
-            return Empty
-        return super().find_class(module, name)
diff --git a/MagicQuill/comfy/cldm/__pycache__/cldm.cpython-310.pyc b/MagicQuill/comfy/cldm/__pycache__/cldm.cpython-310.pyc
deleted file mode 100644
index 9607a6650170ea6563fd708ba990c622b63f0e78..0000000000000000000000000000000000000000
Binary files a/MagicQuill/comfy/cldm/__pycache__/cldm.cpython-310.pyc and /dev/null differ
diff --git a/MagicQuill/comfy/cldm/cldm.py b/MagicQuill/comfy/cldm/cldm.py
deleted file mode 100644
index 28076dd9251e12f050a280337eaf3b7504710ce0..0000000000000000000000000000000000000000
--- a/MagicQuill/comfy/cldm/cldm.py
+++ /dev/null
@@ -1,313 +0,0 @@
-#taken from: https://github.com/lllyasviel/ControlNet
-#and modified
-
-import torch
-import torch as th
-import torch.nn as nn
-
-from ..ldm.modules.diffusionmodules.util import (
-    zero_module,
-    timestep_embedding,
-)
-
-from ..ldm.modules.attention import SpatialTransformer
-from ..ldm.modules.diffusionmodules.openaimodel import UNetModel, TimestepEmbedSequential, ResBlock, Downsample
-from ..ldm.util import exists
-import comfy.ops
-
-class ControlledUnetModel(UNetModel):
-    #implemented in the ldm unet
-    pass
-
-class ControlNet(nn.Module):
-    def __init__(
-        self,
-        image_size,
-        in_channels,
-        model_channels,
-        hint_channels,
-        num_res_blocks,
-        dropout=0,
-        channel_mult=(1, 2, 4, 8),
-        conv_resample=True,
-        dims=2,
-        num_classes=None,
-        use_checkpoint=False,
-        dtype=torch.float32,
-        num_heads=-1,
-        num_head_channels=-1,
-        num_heads_upsample=-1,
-        use_scale_shift_norm=False,
-        resblock_updown=False,
-        use_new_attention_order=False,
-        use_spatial_transformer=False,    # custom transformer support
-        transformer_depth=1,              # custom transformer support
-        context_dim=None,                 # custom transformer support
-        n_embed=None,                     # custom support for prediction of discrete ids into codebook of first stage vq model
-        legacy=True,
-        disable_self_attentions=None,
-        num_attention_blocks=None,
-        disable_middle_self_attn=False,
-        use_linear_in_transformer=False,
-        adm_in_channels=None,
-        transformer_depth_middle=None,
-        transformer_depth_output=None,
-        attn_precision=None,
-        device=None,
-        operations=comfy.ops.disable_weight_init,
-        **kwargs,
-    ):
-        super().__init__()
-        assert use_spatial_transformer == True, "use_spatial_transformer has to be true"
-        if use_spatial_transformer:
-            assert context_dim is not None, 'Fool!! You forgot to include the dimension of your cross-attention conditioning...'
-
-        if context_dim is not None:
-            assert use_spatial_transformer, 'Fool!! You forgot to use the spatial transformer for your cross-attention conditioning...'
-            # from omegaconf.listconfig import ListConfig
-            # if type(context_dim) == ListConfig:
-            #     context_dim = list(context_dim)
-
-        if num_heads_upsample == -1:
-            num_heads_upsample = num_heads
-
-        if num_heads == -1:
-            assert num_head_channels != -1, 'Either num_heads or num_head_channels has to be set'
-
-        if num_head_channels == -1:
-            assert num_heads != -1, 'Either num_heads or num_head_channels has to be set'
-
-        self.dims = dims
-        self.image_size = image_size
-        self.in_channels = in_channels
-        self.model_channels = model_channels
-
-        if isinstance(num_res_blocks, int):
-            self.num_res_blocks = len(channel_mult) * [num_res_blocks]
-        else:
-            if len(num_res_blocks) != len(channel_mult):
-                raise ValueError("provide num_res_blocks either as an int (globally constant) or "
-                                 "as a list/tuple (per-level) with the same length as channel_mult")
-            self.num_res_blocks = num_res_blocks
-
-        if disable_self_attentions is not None:
-            # should be a list of booleans, indicating whether to disable self-attention in TransformerBlocks or not
-            assert len(disable_self_attentions) == len(channel_mult)
-        if num_attention_blocks is not None:
-            assert len(num_attention_blocks) == len(self.num_res_blocks)
-            assert all(map(lambda i: self.num_res_blocks[i] >= num_attention_blocks[i], range(len(num_attention_blocks))))
-
-        transformer_depth = transformer_depth[:]
-
-        self.dropout = dropout
-        self.channel_mult = channel_mult
-        self.conv_resample = conv_resample
-        self.num_classes = num_classes
-        self.use_checkpoint = use_checkpoint
-        self.dtype = dtype
-        self.num_heads = num_heads
-        self.num_head_channels = num_head_channels
-        self.num_heads_upsample = num_heads_upsample
-        self.predict_codebook_ids = n_embed is not None
-
-        time_embed_dim = model_channels * 4
-        self.time_embed = nn.Sequential(
-            operations.Linear(model_channels, time_embed_dim, dtype=self.dtype, device=device),
-            nn.SiLU(),
-            operations.Linear(time_embed_dim, time_embed_dim, dtype=self.dtype, device=device),
-        )
-
-        if self.num_classes is not None:
-            if isinstance(self.num_classes, int):
-                self.label_emb = nn.Embedding(num_classes, time_embed_dim)
-            elif self.num_classes == "continuous":
-                print("setting up linear c_adm embedding layer")
-                self.label_emb = nn.Linear(1, time_embed_dim)
-            elif self.num_classes == "sequential":
-                assert adm_in_channels is not None
-                self.label_emb = nn.Sequential(
-                    nn.Sequential(
-                        operations.Linear(adm_in_channels, time_embed_dim, dtype=self.dtype, device=device),
-                        nn.SiLU(),
-                        operations.Linear(time_embed_dim, time_embed_dim, dtype=self.dtype, device=device),
-                    )
-                )
-            else:
-                raise ValueError()
-
-        self.input_blocks = nn.ModuleList(
-            [
-                TimestepEmbedSequential(
-                    operations.conv_nd(dims, in_channels, model_channels, 3, padding=1, dtype=self.dtype, device=device)
-                )
-            ]
-        )
-        self.zero_convs = nn.ModuleList([self.make_zero_conv(model_channels, operations=operations, dtype=self.dtype, device=device)])
-
-        self.input_hint_block = TimestepEmbedSequential(
-                    operations.conv_nd(dims, hint_channels, 16, 3, padding=1, dtype=self.dtype, device=device),
-                    nn.SiLU(),
-                    operations.conv_nd(dims, 16, 16, 3, padding=1, dtype=self.dtype, device=device),
-                    nn.SiLU(),
-                    operations.conv_nd(dims, 16, 32, 3, padding=1, stride=2, dtype=self.dtype, device=device),
-                    nn.SiLU(),
-                    operations.conv_nd(dims, 32, 32, 3, padding=1, dtype=self.dtype, device=device),
-                    nn.SiLU(),
-                    operations.conv_nd(dims, 32, 96, 3, padding=1, stride=2, dtype=self.dtype, device=device),
-                    nn.SiLU(),
-                    operations.conv_nd(dims, 96, 96, 3, padding=1, dtype=self.dtype, device=device),
-                    nn.SiLU(),
-                    operations.conv_nd(dims, 96, 256, 3, padding=1, stride=2, dtype=self.dtype, device=device),
-                    nn.SiLU(),
-                    operations.conv_nd(dims, 256, model_channels, 3, padding=1, dtype=self.dtype, device=device)
-        )
-
-        self._feature_size = model_channels
-        input_block_chans = [model_channels]
-        ch = model_channels
-        ds = 1
-        for level, mult in enumerate(channel_mult):
-            for nr in range(self.num_res_blocks[level]):
-                layers = [
-                    ResBlock(
-                        ch,
-                        time_embed_dim,
-                        dropout,
-                        out_channels=mult * model_channels,
-                        dims=dims,
-                        use_checkpoint=use_checkpoint,
-                        use_scale_shift_norm=use_scale_shift_norm,
-                        dtype=self.dtype,
-                        device=device,
-                        operations=operations,
-                    )
-                ]
-                ch = mult * model_channels
-                num_transformers = transformer_depth.pop(0)
-                if num_transformers > 0:
-                    if num_head_channels == -1:
-                        dim_head = ch // num_heads
-                    else:
-                        num_heads = ch // num_head_channels
-                        dim_head = num_head_channels
-                    if legacy:
-                        #num_heads = 1
-                        dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
-                    if exists(disable_self_attentions):
-                        disabled_sa = disable_self_attentions[level]
-                    else:
-                        disabled_sa = False
-
-                    if not exists(num_attention_blocks) or nr < num_attention_blocks[level]:
-                        layers.append(
-                            SpatialTransformer(
-                                ch, num_heads, dim_head, depth=num_transformers, context_dim=context_dim,
-                                disable_self_attn=disabled_sa, use_linear=use_linear_in_transformer,
-                                use_checkpoint=use_checkpoint, attn_precision=attn_precision, dtype=self.dtype, device=device, operations=operations
-                            )
-                        )
-                self.input_blocks.append(TimestepEmbedSequential(*layers))
-                self.zero_convs.append(self.make_zero_conv(ch, operations=operations, dtype=self.dtype, device=device))
-                self._feature_size += ch
-                input_block_chans.append(ch)
-            if level != len(channel_mult) - 1:
-                out_ch = ch
-                self.input_blocks.append(
-                    TimestepEmbedSequential(
-                        ResBlock(
-                            ch,
-                            time_embed_dim,
-                            dropout,
-                            out_channels=out_ch,
-                            dims=dims,
-                            use_checkpoint=use_checkpoint,
-                            use_scale_shift_norm=use_scale_shift_norm,
-                            down=True,
-                            dtype=self.dtype,
-                            device=device,
-                            operations=operations
-                        )
-                        if resblock_updown
-                        else Downsample(
-                            ch, conv_resample, dims=dims, out_channels=out_ch, dtype=self.dtype, device=device, operations=operations
-                        )
-                    )
-                )
-                ch = out_ch
-                input_block_chans.append(ch)
-                self.zero_convs.append(self.make_zero_conv(ch, operations=operations, dtype=self.dtype, device=device))
-                ds *= 2
-                self._feature_size += ch
-
-        if num_head_channels == -1:
-            dim_head = ch // num_heads
-        else:
-            num_heads = ch // num_head_channels
-            dim_head = num_head_channels
-        if legacy:
-            #num_heads = 1
-            dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
-        mid_block = [
-            ResBlock(
-                ch,
-                time_embed_dim,
-                dropout,
-                dims=dims,
-                use_checkpoint=use_checkpoint,
-                use_scale_shift_norm=use_scale_shift_norm,
-                dtype=self.dtype,
-                device=device,
-                operations=operations
-            )]
-        if transformer_depth_middle >= 0:
-            mid_block += [SpatialTransformer(  # always uses a self-attn
-                            ch, num_heads, dim_head, depth=transformer_depth_middle, context_dim=context_dim,
-                            disable_self_attn=disable_middle_self_attn, use_linear=use_linear_in_transformer,
-                            use_checkpoint=use_checkpoint, attn_precision=attn_precision, dtype=self.dtype, device=device, operations=operations
-                        ),
-            ResBlock(
-                ch,
-                time_embed_dim,
-                dropout,
-                dims=dims,
-                use_checkpoint=use_checkpoint,
-                use_scale_shift_norm=use_scale_shift_norm,
-                dtype=self.dtype,
-                device=device,
-                operations=operations
-            )]
-        self.middle_block = TimestepEmbedSequential(*mid_block)
-        self.middle_block_out = self.make_zero_conv(ch, operations=operations, dtype=self.dtype, device=device)
-        self._feature_size += ch
-
-    def make_zero_conv(self, channels, operations=None, dtype=None, device=None):
-        return TimestepEmbedSequential(operations.conv_nd(self.dims, channels, channels, 1, padding=0, dtype=dtype, device=device))
-
-    def forward(self, x, hint, timesteps, context, y=None, **kwargs):
-        t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False).to(x.dtype)
-        emb = self.time_embed(t_emb)
-
-        guided_hint = self.input_hint_block(hint, emb, context)
-
-        outs = []
-
-        hs = []
-        if self.num_classes is not None:
-            assert y.shape[0] == x.shape[0]
-            emb = emb + self.label_emb(y)
-
-        h = x
-        for module, zero_conv in zip(self.input_blocks, self.zero_convs):
-            if guided_hint is not None:
-                h = module(h, emb, context)
-                h += guided_hint
-                guided_hint = None
-            else:
-                h = module(h, emb, context)
-            outs.append(zero_conv(h, emb, context))
-
-        h = self.middle_block(h, emb, context)
-        outs.append(self.middle_block_out(h, emb, context))
-
-        return outs
-
diff --git a/MagicQuill/comfy/cli_args.py b/MagicQuill/comfy/cli_args.py
deleted file mode 100644
index fb0d37ce75081e3f4f38350cd6131c290a3fdd48..0000000000000000000000000000000000000000
--- a/MagicQuill/comfy/cli_args.py
+++ /dev/null
@@ -1,143 +0,0 @@
-import argparse
-import enum
-import comfy.options
-
-class EnumAction(argparse.Action):
-    """
-    Argparse action for handling Enums
-    """
-    def __init__(self, **kwargs):
-        # Pop off the type value
-        enum_type = kwargs.pop("type", None)
-
-        # Ensure an Enum subclass is provided
-        if enum_type is None:
-            raise ValueError("type must be assigned an Enum when using EnumAction")
-        if not issubclass(enum_type, enum.Enum):
-            raise TypeError("type must be an Enum when using EnumAction")
-
-        # Generate choices from the Enum
-        choices = tuple(e.value for e in enum_type)
-        kwargs.setdefault("choices", choices)
-        kwargs.setdefault("metavar", f"[{','.join(list(choices))}]")
-
-        super(EnumAction, self).__init__(**kwargs)
-
-        self._enum = enum_type
-
-    def __call__(self, parser, namespace, values, option_string=None):
-        # Convert value back into an Enum
-        value = self._enum(values)
-        setattr(namespace, self.dest, value)
-
-
-parser = argparse.ArgumentParser()
-
-parser.add_argument("--listen", type=str, default="127.0.0.1", metavar="IP", nargs="?", const="0.0.0.0", help="Specify the IP address to listen on (default: 127.0.0.1). If --listen is provided without an argument, it defaults to 0.0.0.0. (listens on all)")
-parser.add_argument("--port", type=int, default=8188, help="Set the listen port.")
-parser.add_argument("--tls-keyfile", type=str, help="Path to TLS (SSL) key file. Enables TLS, makes app accessible at https://... requires --tls-certfile to function")
-parser.add_argument("--tls-certfile", type=str, help="Path to TLS (SSL) certificate file. Enables TLS, makes app accessible at https://... requires --tls-keyfile to function")
-parser.add_argument("--enable-cors-header", type=str, default=None, metavar="ORIGIN", nargs="?", const="*", help="Enable CORS (Cross-Origin Resource Sharing) with optional origin or allow all with default '*'.")
-parser.add_argument("--max-upload-size", type=float, default=100, help="Set the maximum upload size in MB.")
-
-parser.add_argument("--extra-model-paths-config", type=str, default=None, metavar="PATH", nargs='+', action='append', help="Load one or more extra_model_paths.yaml files.")
-parser.add_argument("--output-directory", type=str, default=None, help="Set the ComfyUI output directory.")
-parser.add_argument("--temp-directory", type=str, default=None, help="Set the ComfyUI temp directory (default is in the ComfyUI directory).")
-parser.add_argument("--input-directory", type=str, default=None, help="Set the ComfyUI input directory.")
-parser.add_argument("--auto-launch", action="store_true", help="Automatically launch ComfyUI in the default browser.")
-parser.add_argument("--disable-auto-launch", action="store_true", help="Disable auto launching the browser.")
-parser.add_argument("--cuda-device", type=int, default=None, metavar="DEVICE_ID", help="Set the id of the cuda device this instance will use.")
-cm_group = parser.add_mutually_exclusive_group()
-cm_group.add_argument("--cuda-malloc", action="store_true", help="Enable cudaMallocAsync (enabled by default for torch 2.0 and up).")
-cm_group.add_argument("--disable-cuda-malloc", action="store_true", help="Disable cudaMallocAsync.")
-
-
-fp_group = parser.add_mutually_exclusive_group()
-fp_group.add_argument("--force-fp32", action="store_true", help="Force fp32 (If this makes your GPU work better please report it).")
-fp_group.add_argument("--force-fp16", action="store_true", help="Force fp16.")
-
-fpunet_group = parser.add_mutually_exclusive_group()
-fpunet_group.add_argument("--bf16-unet", action="store_true", help="Run the UNET in bf16. This should only be used for testing stuff.")
-fpunet_group.add_argument("--fp16-unet", action="store_true", help="Store unet weights in fp16.")
-fpunet_group.add_argument("--fp8_e4m3fn-unet", action="store_true", help="Store unet weights in fp8_e4m3fn.")
-fpunet_group.add_argument("--fp8_e5m2-unet", action="store_true", help="Store unet weights in fp8_e5m2.")
-
-fpvae_group = parser.add_mutually_exclusive_group()
-fpvae_group.add_argument("--fp16-vae", action="store_true", help="Run the VAE in fp16, might cause black images.")
-fpvae_group.add_argument("--fp32-vae", action="store_true", help="Run the VAE in full precision fp32.")
-fpvae_group.add_argument("--bf16-vae", action="store_true", help="Run the VAE in bf16.")
-
-parser.add_argument("--cpu-vae", action="store_true", help="Run the VAE on the CPU.")
-
-fpte_group = parser.add_mutually_exclusive_group()
-fpte_group.add_argument("--fp8_e4m3fn-text-enc", action="store_true", help="Store text encoder weights in fp8 (e4m3fn variant).")
-fpte_group.add_argument("--fp8_e5m2-text-enc", action="store_true", help="Store text encoder weights in fp8 (e5m2 variant).")
-fpte_group.add_argument("--fp16-text-enc", action="store_true", help="Store text encoder weights in fp16.")
-fpte_group.add_argument("--fp32-text-enc", action="store_true", help="Store text encoder weights in fp32.")
-
-parser.add_argument("--force-channels-last", action="store_true", help="Force channels last format when inferencing the models.")
-
-parser.add_argument("--directml", type=int, nargs="?", metavar="DIRECTML_DEVICE", const=-1, help="Use torch-directml.")
-
-parser.add_argument("--disable-ipex-optimize", action="store_true", help="Disables ipex.optimize when loading models with Intel GPUs.")
-
-class LatentPreviewMethod(enum.Enum):
-    NoPreviews = "none"
-    Auto = "auto"
-    Latent2RGB = "latent2rgb"
-    TAESD = "taesd"
-
-parser.add_argument("--preview-method", type=LatentPreviewMethod, default=LatentPreviewMethod.NoPreviews, help="Default preview method for sampler nodes.", action=EnumAction)
-
-attn_group = parser.add_mutually_exclusive_group()
-attn_group.add_argument("--use-split-cross-attention", action="store_true", help="Use the split cross attention optimization. Ignored when xformers is used.")
-attn_group.add_argument("--use-quad-cross-attention", action="store_true", help="Use the sub-quadratic cross attention optimization . Ignored when xformers is used.")
-attn_group.add_argument("--use-pytorch-cross-attention", action="store_true", help="Use the new pytorch 2.0 cross attention function.")
-
-parser.add_argument("--disable-xformers", action="store_true", help="Disable xformers.")
-
-upcast = parser.add_mutually_exclusive_group()
-upcast.add_argument("--force-upcast-attention", action="store_true", help="Force enable attention upcasting, please report if it fixes black images.")
-upcast.add_argument("--dont-upcast-attention", action="store_true", help="Disable all upcasting of attention. Should be unnecessary except for debugging.")
-
-
-vram_group = parser.add_mutually_exclusive_group()
-vram_group.add_argument("--gpu-only", action="store_true", help="Store and run everything (text encoders/CLIP models, etc... on the GPU).")
-vram_group.add_argument("--highvram", action="store_true", help="By default models will be unloaded to CPU memory after being used. This option keeps them in GPU memory.")
-vram_group.add_argument("--normalvram", action="store_true", help="Used to force normal vram use if lowvram gets automatically enabled.")
-vram_group.add_argument("--lowvram", action="store_true", help="Split the unet in parts to use less vram.")
-vram_group.add_argument("--novram", action="store_true", help="When lowvram isn't enough.")
-vram_group.add_argument("--cpu", action="store_true", help="To use the CPU for everything (slow).")
-
-
-parser.add_argument("--disable-smart-memory", action="store_true", help="Force ComfyUI to agressively offload to regular ram instead of keeping models in vram when it can.")
-parser.add_argument("--deterministic", action="store_true", help="Make pytorch use slower deterministic algorithms when it can. Note that this might not make images deterministic in all cases.")
-
-parser.add_argument("--dont-print-server", action="store_true", help="Don't print server output.")
-parser.add_argument("--quick-test-for-ci", action="store_true", help="Quick test for CI.")
-parser.add_argument("--windows-standalone-build", action="store_true", help="Windows standalone build: Enable convenient things that most people using the standalone windows build will probably enjoy (like auto opening the page on startup).")
-
-parser.add_argument("--disable-metadata", action="store_true", help="Disable saving prompt metadata in files.")
-
-parser.add_argument("--multi-user", action="store_true", help="Enables per-user storage.")
-
-parser.add_argument("--verbose", action="store_true", help="Enables more debug prints.")
-
-
-if comfy.options.args_parsing:
-    args = parser.parse_args()
-else:
-    args = parser.parse_args([])
-
-if args.windows_standalone_build:
-    args.auto_launch = True
-
-if args.disable_auto_launch:
-    args.auto_launch = False
-
-import logging
-logging_level = logging.INFO
-if args.verbose:
-    logging_level = logging.DEBUG
-
-logging.basicConfig(format="%(message)s", level=logging_level)
diff --git a/MagicQuill/comfy/clip_config_bigg.json b/MagicQuill/comfy/clip_config_bigg.json
deleted file mode 100644
index 32d82ff39ba66ba0be15ec101993e1c46cc3f7ab..0000000000000000000000000000000000000000
--- a/MagicQuill/comfy/clip_config_bigg.json
+++ /dev/null
@@ -1,23 +0,0 @@
-{
-  "architectures": [
-    "CLIPTextModel"
-  ],
-  "attention_dropout": 0.0,
-  "bos_token_id": 0,
-  "dropout": 0.0,
-  "eos_token_id": 2,
-  "hidden_act": "gelu",
-  "hidden_size": 1280,
-  "initializer_factor": 1.0,
-  "initializer_range": 0.02,
-  "intermediate_size": 5120,
-  "layer_norm_eps": 1e-05,
-  "max_position_embeddings": 77,
-  "model_type": "clip_text_model",
-  "num_attention_heads": 20,
-  "num_hidden_layers": 32,
-  "pad_token_id": 1,
-  "projection_dim": 1280,
-  "torch_dtype": "float32",
-  "vocab_size": 49408
-}
diff --git a/MagicQuill/comfy/clip_model.py b/MagicQuill/comfy/clip_model.py
deleted file mode 100644
index 14f43c5687cb19c62fbaea3481a66f11f3b186c6..0000000000000000000000000000000000000000
--- a/MagicQuill/comfy/clip_model.py
+++ /dev/null
@@ -1,194 +0,0 @@
-import torch
-from comfy.ldm.modules.attention import optimized_attention_for_device
-
-class CLIPAttention(torch.nn.Module):
-    def __init__(self, embed_dim, heads, dtype, device, operations):
-        super().__init__()
-
-        self.heads = heads
-        self.q_proj = operations.Linear(embed_dim, embed_dim, bias=True, dtype=dtype, device=device)
-        self.k_proj = operations.Linear(embed_dim, embed_dim, bias=True, dtype=dtype, device=device)
-        self.v_proj = operations.Linear(embed_dim, embed_dim, bias=True, dtype=dtype, device=device)
-
-        self.out_proj = operations.Linear(embed_dim, embed_dim, bias=True, dtype=dtype, device=device)
-
-    def forward(self, x, mask=None, optimized_attention=None):
-        q = self.q_proj(x)
-        k = self.k_proj(x)
-        v = self.v_proj(x)
-
-        out = optimized_attention(q, k, v, self.heads, mask)
-        return self.out_proj(out)
-
-ACTIVATIONS = {"quick_gelu": lambda a: a * torch.sigmoid(1.702 * a),
-               "gelu": torch.nn.functional.gelu,
-}
-
-class CLIPMLP(torch.nn.Module):
-    def __init__(self, embed_dim, intermediate_size, activation, dtype, device, operations):
-        super().__init__()
-        self.fc1 = operations.Linear(embed_dim, intermediate_size, bias=True, dtype=dtype, device=device)
-        self.activation = ACTIVATIONS[activation]
-        self.fc2 = operations.Linear(intermediate_size, embed_dim, bias=True, dtype=dtype, device=device)
-
-    def forward(self, x):
-        x = self.fc1(x)
-        x = self.activation(x)
-        x = self.fc2(x)
-        return x
-
-class CLIPLayer(torch.nn.Module):
-    def __init__(self, embed_dim, heads, intermediate_size, intermediate_activation, dtype, device, operations):
-        super().__init__()
-        self.layer_norm1 = operations.LayerNorm(embed_dim, dtype=dtype, device=device)
-        self.self_attn = CLIPAttention(embed_dim, heads, dtype, device, operations)
-        self.layer_norm2 = operations.LayerNorm(embed_dim, dtype=dtype, device=device)
-        self.mlp = CLIPMLP(embed_dim, intermediate_size, intermediate_activation, dtype, device, operations)
-
-    def forward(self, x, mask=None, optimized_attention=None):
-        x += self.self_attn(self.layer_norm1(x), mask, optimized_attention)
-        x += self.mlp(self.layer_norm2(x))
-        return x
-
-
-class CLIPEncoder(torch.nn.Module):
-    def __init__(self, num_layers, embed_dim, heads, intermediate_size, intermediate_activation, dtype, device, operations):
-        super().__init__()
-        self.layers = torch.nn.ModuleList([CLIPLayer(embed_dim, heads, intermediate_size, intermediate_activation, dtype, device, operations) for i in range(num_layers)])
-
-    def forward(self, x, mask=None, intermediate_output=None):
-        optimized_attention = optimized_attention_for_device(x.device, mask=mask is not None, small_input=True)
-
-        if intermediate_output is not None:
-            if intermediate_output < 0:
-                intermediate_output = len(self.layers) + intermediate_output
-
-        intermediate = None
-        for i, l in enumerate(self.layers):
-            x = l(x, mask, optimized_attention)
-            if i == intermediate_output:
-                intermediate = x.clone()
-        return x, intermediate
-
-class CLIPEmbeddings(torch.nn.Module):
-    def __init__(self, embed_dim, vocab_size=49408, num_positions=77, dtype=None, device=None):
-        super().__init__()
-        self.token_embedding = torch.nn.Embedding(vocab_size, embed_dim, dtype=dtype, device=device)
-        self.position_embedding = torch.nn.Embedding(num_positions, embed_dim, dtype=dtype, device=device)
-
-    def forward(self, input_tokens):
-        return self.token_embedding(input_tokens) + self.position_embedding.weight
-
-
-class CLIPTextModel_(torch.nn.Module):
-    def __init__(self, config_dict, dtype, device, operations):
-        num_layers = config_dict["num_hidden_layers"]
-        embed_dim = config_dict["hidden_size"]
-        heads = config_dict["num_attention_heads"]
-        intermediate_size = config_dict["intermediate_size"]
-        intermediate_activation = config_dict["hidden_act"]
-
-        super().__init__()
-        self.embeddings = CLIPEmbeddings(embed_dim, dtype=torch.float32, device=device)
-        self.encoder = CLIPEncoder(num_layers, embed_dim, heads, intermediate_size, intermediate_activation, dtype, device, operations)
-        self.final_layer_norm = operations.LayerNorm(embed_dim, dtype=dtype, device=device)
-
-    def forward(self, input_tokens, attention_mask=None, intermediate_output=None, final_layer_norm_intermediate=True):
-        x = self.embeddings(input_tokens)
-        mask = None
-        if attention_mask is not None:
-            mask = 1.0 - attention_mask.to(x.dtype).reshape((attention_mask.shape[0], 1, -1, attention_mask.shape[-1])).expand(attention_mask.shape[0], 1, attention_mask.shape[-1], attention_mask.shape[-1])
-            mask = mask.masked_fill(mask.to(torch.bool), float("-inf"))
-
-        causal_mask = torch.empty(x.shape[1], x.shape[1], dtype=x.dtype, device=x.device).fill_(float("-inf")).triu_(1)
-        if mask is not None:
-            mask += causal_mask
-        else:
-            mask = causal_mask
-
-        x, i = self.encoder(x, mask=mask, intermediate_output=intermediate_output)
-        x = self.final_layer_norm(x)
-        if i is not None and final_layer_norm_intermediate:
-            i = self.final_layer_norm(i)
-
-        pooled_output = x[torch.arange(x.shape[0], device=x.device), input_tokens.to(dtype=torch.int, device=x.device).argmax(dim=-1),]
-        return x, i, pooled_output
-
-class CLIPTextModel(torch.nn.Module):
-    def __init__(self, config_dict, dtype, device, operations):
-        super().__init__()
-        self.num_layers = config_dict["num_hidden_layers"]
-        self.text_model = CLIPTextModel_(config_dict, dtype, device, operations)
-        embed_dim = config_dict["hidden_size"]
-        self.text_projection = operations.Linear(embed_dim, embed_dim, bias=False, dtype=dtype, device=device)
-        self.text_projection.weight.copy_(torch.eye(embed_dim))
-        self.dtype = dtype
-
-    def get_input_embeddings(self):
-        return self.text_model.embeddings.token_embedding
-
-    def set_input_embeddings(self, embeddings):
-        self.text_model.embeddings.token_embedding = embeddings
-
-    def forward(self, *args, **kwargs):
-        x = self.text_model(*args, **kwargs)
-        out = self.text_projection(x[2])
-        return (x[0], x[1], out, x[2])
-
-
-class CLIPVisionEmbeddings(torch.nn.Module):
-    def __init__(self, embed_dim, num_channels=3, patch_size=14, image_size=224, dtype=None, device=None, operations=None):
-        super().__init__()
-        self.class_embedding = torch.nn.Parameter(torch.empty(embed_dim, dtype=dtype, device=device))
-
-        self.patch_embedding = operations.Conv2d(
-            in_channels=num_channels,
-            out_channels=embed_dim,
-            kernel_size=patch_size,
-            stride=patch_size,
-            bias=False,
-            dtype=dtype,
-            device=device
-        )
-
-        num_patches = (image_size // patch_size) ** 2
-        num_positions = num_patches + 1
-        self.position_embedding = torch.nn.Embedding(num_positions, embed_dim, dtype=dtype, device=device)
-
-    def forward(self, pixel_values):
-        embeds = self.patch_embedding(pixel_values).flatten(2).transpose(1, 2)
-        return torch.cat([self.class_embedding.to(embeds.device).expand(pixel_values.shape[0], 1, -1), embeds], dim=1) + self.position_embedding.weight.to(embeds.device)
-
-
-class CLIPVision(torch.nn.Module):
-    def __init__(self, config_dict, dtype, device, operations):
-        super().__init__()
-        num_layers = config_dict["num_hidden_layers"]
-        embed_dim = config_dict["hidden_size"]
-        heads = config_dict["num_attention_heads"]
-        intermediate_size = config_dict["intermediate_size"]
-        intermediate_activation = config_dict["hidden_act"]
-
-        self.embeddings = CLIPVisionEmbeddings(embed_dim, config_dict["num_channels"], config_dict["patch_size"], config_dict["image_size"], dtype=torch.float32, device=device, operations=operations)
-        self.pre_layrnorm = operations.LayerNorm(embed_dim)
-        self.encoder = CLIPEncoder(num_layers, embed_dim, heads, intermediate_size, intermediate_activation, dtype, device, operations)
-        self.post_layernorm = operations.LayerNorm(embed_dim)
-
-    def forward(self, pixel_values, attention_mask=None, intermediate_output=None):
-        x = self.embeddings(pixel_values)
-        x = self.pre_layrnorm(x)
-        #TODO: attention_mask?
-        x, i = self.encoder(x, mask=None, intermediate_output=intermediate_output)
-        pooled_output = self.post_layernorm(x[:, 0, :])
-        return x, i, pooled_output
-
-class CLIPVisionModelProjection(torch.nn.Module):
-    def __init__(self, config_dict, dtype, device, operations):
-        super().__init__()
-        self.vision_model = CLIPVision(config_dict, dtype, device, operations)
-        self.visual_projection = operations.Linear(config_dict["hidden_size"], config_dict["projection_dim"], bias=False)
-
-    def forward(self, *args, **kwargs):
-        x = self.vision_model(*args, **kwargs)
-        out = self.visual_projection(x[2])
-        return (x[0], x[1], out)
diff --git a/MagicQuill/comfy/clip_vision.py b/MagicQuill/comfy/clip_vision.py
deleted file mode 100644
index acc86be855667e2945d39d991783f4fcb707339d..0000000000000000000000000000000000000000
--- a/MagicQuill/comfy/clip_vision.py
+++ /dev/null
@@ -1,117 +0,0 @@
-from .utils import load_torch_file, transformers_convert, state_dict_prefix_replace
-import os
-import torch
-import json
-import logging
-
-import comfy.ops
-import comfy.model_patcher
-import comfy.model_management
-import comfy.utils
-import comfy.clip_model
-
-class Output:
-    def __getitem__(self, key):
-        return getattr(self, key)
-    def __setitem__(self, key, item):
-        setattr(self, key, item)
-
-def clip_preprocess(image, size=224):
-    mean = torch.tensor([ 0.48145466,0.4578275,0.40821073], device=image.device, dtype=image.dtype)
-    std = torch.tensor([0.26862954,0.26130258,0.27577711], device=image.device, dtype=image.dtype)
-    image = image.movedim(-1, 1)
-    if not (image.shape[2] == size and image.shape[3] == size):
-        scale = (size / min(image.shape[2], image.shape[3]))
-        image = torch.nn.functional.interpolate(image, size=(round(scale * image.shape[2]), round(scale * image.shape[3])), mode="bicubic", antialias=True)
-        h = (image.shape[2] - size)//2
-        w = (image.shape[3] - size)//2
-        image = image[:,:,h:h+size,w:w+size]
-    image = torch.clip((255. * image), 0, 255).round() / 255.0
-    return (image - mean.view([3,1,1])) / std.view([3,1,1])
-
-class ClipVisionModel():
-    def __init__(self, json_config):
-        with open(json_config) as f:
-            config = json.load(f)
-
-        self.load_device = comfy.model_management.text_encoder_device()
-        offload_device = comfy.model_management.text_encoder_offload_device()
-        self.dtype = comfy.model_management.text_encoder_dtype(self.load_device)
-        self.model = comfy.clip_model.CLIPVisionModelProjection(config, self.dtype, offload_device, comfy.ops.manual_cast)
-        self.model.eval()
-
-        self.patcher = comfy.model_patcher.ModelPatcher(self.model, load_device=self.load_device, offload_device=offload_device)
-
-    def load_sd(self, sd):
-        return self.model.load_state_dict(sd, strict=False)
-
-    def get_sd(self):
-        return self.model.state_dict()
-
-    def encode_image(self, image):
-        comfy.model_management.load_model_gpu(self.patcher)
-        pixel_values = clip_preprocess(image.to(self.load_device)).float()
-        out = self.model(pixel_values=pixel_values, intermediate_output=-2)
-
-        outputs = Output()
-        outputs["last_hidden_state"] = out[0].to(comfy.model_management.intermediate_device())
-        outputs["image_embeds"] = out[2].to(comfy.model_management.intermediate_device())
-        outputs["penultimate_hidden_states"] = out[1].to(comfy.model_management.intermediate_device())
-        return outputs
-
-def convert_to_transformers(sd, prefix):
-    sd_k = sd.keys()
-    if "{}transformer.resblocks.0.attn.in_proj_weight".format(prefix) in sd_k:
-        keys_to_replace = {
-            "{}class_embedding".format(prefix): "vision_model.embeddings.class_embedding",
-            "{}conv1.weight".format(prefix): "vision_model.embeddings.patch_embedding.weight",
-            "{}positional_embedding".format(prefix): "vision_model.embeddings.position_embedding.weight",
-            "{}ln_post.bias".format(prefix): "vision_model.post_layernorm.bias",
-            "{}ln_post.weight".format(prefix): "vision_model.post_layernorm.weight",
-            "{}ln_pre.bias".format(prefix): "vision_model.pre_layrnorm.bias",
-            "{}ln_pre.weight".format(prefix): "vision_model.pre_layrnorm.weight",
-        }
-
-        for x in keys_to_replace:
-            if x in sd_k:
-                sd[keys_to_replace[x]] = sd.pop(x)
-
-        if "{}proj".format(prefix) in sd_k:
-            sd['visual_projection.weight'] = sd.pop("{}proj".format(prefix)).transpose(0, 1)
-
-        sd = transformers_convert(sd, prefix, "vision_model.", 48)
-    else:
-        replace_prefix = {prefix: ""}
-        sd = state_dict_prefix_replace(sd, replace_prefix)
-    return sd
-
-def load_clipvision_from_sd(sd, prefix="", convert_keys=False):
-    if convert_keys:
-        sd = convert_to_transformers(sd, prefix)
-    if "vision_model.encoder.layers.47.layer_norm1.weight" in sd:
-        json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_config_g.json")
-    elif "vision_model.encoder.layers.30.layer_norm1.weight" in sd:
-        json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_config_h.json")
-    elif "vision_model.encoder.layers.22.layer_norm1.weight" in sd:
-        json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_config_vitl.json")
-    else:
-        return None
-
-    clip = ClipVisionModel(json_config)
-    m, u = clip.load_sd(sd)
-    if len(m) > 0:
-        logging.warning("missing clip vision: {}".format(m))
-    u = set(u)
-    keys = list(sd.keys())
-    for k in keys:
-        if k not in u:
-            t = sd.pop(k)
-            del t
-    return clip
-
-def load(ckpt_path):
-    sd = load_torch_file(ckpt_path)
-    if "visual.transformer.resblocks.0.attn.in_proj_weight" in sd:
-        return load_clipvision_from_sd(sd, prefix="visual.", convert_keys=True)
-    else:
-        return load_clipvision_from_sd(sd)
diff --git a/MagicQuill/comfy/clip_vision_config_g.json b/MagicQuill/comfy/clip_vision_config_g.json
deleted file mode 100644
index 708e7e21ac3513a719d6a49e88e756f5ef7e2c8d..0000000000000000000000000000000000000000
--- a/MagicQuill/comfy/clip_vision_config_g.json
+++ /dev/null
@@ -1,18 +0,0 @@
-{
-  "attention_dropout": 0.0,
-  "dropout": 0.0,
-  "hidden_act": "gelu",
-  "hidden_size": 1664,
-  "image_size": 224,
-  "initializer_factor": 1.0,
-  "initializer_range": 0.02,
-  "intermediate_size": 8192,
-  "layer_norm_eps": 1e-05,
-  "model_type": "clip_vision_model",
-  "num_attention_heads": 16,
-  "num_channels": 3,
-  "num_hidden_layers": 48,
-  "patch_size": 14,
-  "projection_dim": 1280,
-  "torch_dtype": "float32"
-}
diff --git a/MagicQuill/comfy/clip_vision_config_h.json b/MagicQuill/comfy/clip_vision_config_h.json
deleted file mode 100644
index bb71be419a4be0ad5c8c157850de032a65593cb9..0000000000000000000000000000000000000000
--- a/MagicQuill/comfy/clip_vision_config_h.json
+++ /dev/null
@@ -1,18 +0,0 @@
-{
-  "attention_dropout": 0.0,
-  "dropout": 0.0,
-  "hidden_act": "gelu",
-  "hidden_size": 1280,
-  "image_size": 224,
-  "initializer_factor": 1.0,
-  "initializer_range": 0.02,
-  "intermediate_size": 5120,
-  "layer_norm_eps": 1e-05,
-  "model_type": "clip_vision_model",
-  "num_attention_heads": 16,
-  "num_channels": 3,
-  "num_hidden_layers": 32,
-  "patch_size": 14,
-  "projection_dim": 1024,
-  "torch_dtype": "float32"
-}
diff --git a/MagicQuill/comfy/clip_vision_config_vitl.json b/MagicQuill/comfy/clip_vision_config_vitl.json
deleted file mode 100644
index c59b8ed5a4c1f41fbcc9e6811d2c7dfe44273de7..0000000000000000000000000000000000000000
--- a/MagicQuill/comfy/clip_vision_config_vitl.json
+++ /dev/null
@@ -1,18 +0,0 @@
-{
-  "attention_dropout": 0.0,
-  "dropout": 0.0,
-  "hidden_act": "quick_gelu",
-  "hidden_size": 1024,
-  "image_size": 224,
-  "initializer_factor": 1.0,
-  "initializer_range": 0.02,
-  "intermediate_size": 4096,
-  "layer_norm_eps": 1e-05,
-  "model_type": "clip_vision_model",
-  "num_attention_heads": 16,
-  "num_channels": 3,
-  "num_hidden_layers": 24,
-  "patch_size": 14,
-  "projection_dim": 768,
-  "torch_dtype": "float32"
-}
diff --git a/MagicQuill/comfy/conds.py b/MagicQuill/comfy/conds.py
deleted file mode 100644
index 660690af8425209e6cc8d8b3e17185065e269a47..0000000000000000000000000000000000000000
--- a/MagicQuill/comfy/conds.py
+++ /dev/null
@@ -1,83 +0,0 @@
-import torch
-import math
-import comfy.utils
-
-
-def lcm(a, b): #TODO: eventually replace by math.lcm (added in python3.9)
-    return abs(a*b) // math.gcd(a, b)
-
-class CONDRegular:
-    def __init__(self, cond):
-        self.cond = cond
-
-    def _copy_with(self, cond):
-        return self.__class__(cond)
-
-    def process_cond(self, batch_size, device, **kwargs):
-        return self._copy_with(comfy.utils.repeat_to_batch_size(self.cond, batch_size).to(device))
-
-    def can_concat(self, other):
-        if self.cond.shape != other.cond.shape:
-            return False
-        return True
-
-    def concat(self, others):
-        conds = [self.cond]
-        for x in others:
-            conds.append(x.cond)
-        return torch.cat(conds)
-
-class CONDNoiseShape(CONDRegular):
-    def process_cond(self, batch_size, device, area, **kwargs):
-        data = self.cond
-        if area is not None:
-            dims = len(area) // 2
-            for i in range(dims):
-                data = data.narrow(i + 2, area[i + dims], area[i])
-
-        return self._copy_with(comfy.utils.repeat_to_batch_size(data, batch_size).to(device))
-
-
-class CONDCrossAttn(CONDRegular):
-    def can_concat(self, other):
-        s1 = self.cond.shape
-        s2 = other.cond.shape
-        if s1 != s2:
-            if s1[0] != s2[0] or s1[2] != s2[2]: #these 2 cases should not happen
-                return False
-
-            mult_min = lcm(s1[1], s2[1])
-            diff = mult_min // min(s1[1], s2[1])
-            if diff > 4: #arbitrary limit on the padding because it's probably going to impact performance negatively if it's too much
-                return False
-        return True
-
-    def concat(self, others):
-        conds = [self.cond]
-        crossattn_max_len = self.cond.shape[1]
-        for x in others:
-            c = x.cond
-            crossattn_max_len = lcm(crossattn_max_len, c.shape[1])
-            conds.append(c)
-
-        out = []
-        for c in conds:
-            if c.shape[1] < crossattn_max_len:
-                c = c.repeat(1, crossattn_max_len // c.shape[1], 1) #padding with repeat doesn't change result
-            out.append(c)
-        return torch.cat(out)
-
-class CONDConstant(CONDRegular):
-    def __init__(self, cond):
-        self.cond = cond
-
-    def process_cond(self, batch_size, device, **kwargs):
-        return self._copy_with(self.cond)
-
-    def can_concat(self, other):
-        if self.cond != other.cond:
-            return False
-        return True
-
-    def concat(self, others):
-        return self.cond
diff --git a/MagicQuill/comfy/controlnet.py b/MagicQuill/comfy/controlnet.py
deleted file mode 100644
index 8cf4a61a683392e51665a1d41906b3ab22885506..0000000000000000000000000000000000000000
--- a/MagicQuill/comfy/controlnet.py
+++ /dev/null
@@ -1,554 +0,0 @@
-import torch
-import math
-import os
-import logging
-import comfy.utils
-import comfy.model_management
-import comfy.model_detection
-import comfy.model_patcher
-import comfy.ops
-
-import comfy.cldm.cldm
-import comfy.t2i_adapter.adapter
-import comfy.ldm.cascade.controlnet
-
-
-def broadcast_image_to(tensor, target_batch_size, batched_number):
-    current_batch_size = tensor.shape[0]
-    #print(current_batch_size, target_batch_size)
-    if current_batch_size == 1:
-        return tensor
-
-    per_batch = target_batch_size // batched_number
-    tensor = tensor[:per_batch]
-
-    if per_batch > tensor.shape[0]:
-        tensor = torch.cat([tensor] * (per_batch // tensor.shape[0]) + [tensor[:(per_batch % tensor.shape[0])]], dim=0)
-
-    current_batch_size = tensor.shape[0]
-    if current_batch_size == target_batch_size:
-        return tensor
-    else:
-        return torch.cat([tensor] * batched_number, dim=0)
-
-class ControlBase:
-    def __init__(self, device=None):
-        self.cond_hint_original = None
-        self.cond_hint = None
-        self.strength = 1.0
-        self.timestep_percent_range = (0.0, 1.0)
-        self.global_average_pooling = False
-        self.timestep_range = None
-        self.compression_ratio = 8
-        self.upscale_algorithm = 'nearest-exact'
-
-        if device is None:
-            device = comfy.model_management.get_torch_device()
-        self.device = device
-        self.previous_controlnet = None
-
-    def set_cond_hint(self, cond_hint, strength=1.0, timestep_percent_range=(0.0, 1.0)):
-        self.cond_hint_original = cond_hint
-        self.strength = strength
-        self.timestep_percent_range = timestep_percent_range
-        return self
-
-    def pre_run(self, model, percent_to_timestep_function):
-        self.timestep_range = (percent_to_timestep_function(self.timestep_percent_range[0]), percent_to_timestep_function(self.timestep_percent_range[1]))
-        if self.previous_controlnet is not None:
-            self.previous_controlnet.pre_run(model, percent_to_timestep_function)
-
-    def set_previous_controlnet(self, controlnet):
-        self.previous_controlnet = controlnet
-        return self
-
-    def cleanup(self):
-        if self.previous_controlnet is not None:
-            self.previous_controlnet.cleanup()
-        if self.cond_hint is not None:
-            del self.cond_hint
-            self.cond_hint = None
-        self.timestep_range = None
-
-    def get_models(self):
-        out = []
-        if self.previous_controlnet is not None:
-            out += self.previous_controlnet.get_models()
-        return out
-
-    def copy_to(self, c):
-        c.cond_hint_original = self.cond_hint_original
-        c.strength = self.strength
-        c.timestep_percent_range = self.timestep_percent_range
-        c.global_average_pooling = self.global_average_pooling
-        c.compression_ratio = self.compression_ratio
-        c.upscale_algorithm = self.upscale_algorithm
-
-    def inference_memory_requirements(self, dtype):
-        if self.previous_controlnet is not None:
-            return self.previous_controlnet.inference_memory_requirements(dtype)
-        return 0
-
-    def control_merge(self, control_input, control_output, control_prev, output_dtype):
-        out = {'input':[], 'middle':[], 'output': []}
-
-        if control_input is not None:
-            for i in range(len(control_input)):
-                key = 'input'
-                x = control_input[i]
-                if x is not None:
-                    x *= self.strength
-                    if x.dtype != output_dtype:
-                        x = x.to(output_dtype)
-                out[key].insert(0, x)
-
-        if control_output is not None:
-            for i in range(len(control_output)):
-                if i == (len(control_output) - 1):
-                    key = 'middle'
-                    index = 0
-                else:
-                    key = 'output'
-                    index = i
-                x = control_output[i]
-                if x is not None:
-                    if self.global_average_pooling:
-                        x = torch.mean(x, dim=(2, 3), keepdim=True).repeat(1, 1, x.shape[2], x.shape[3])
-
-                    x *= self.strength
-                    if x.dtype != output_dtype:
-                        x = x.to(output_dtype)
-
-                out[key].append(x)
-        if control_prev is not None:
-            for x in ['input', 'middle', 'output']:
-                o = out[x]
-                for i in range(len(control_prev[x])):
-                    prev_val = control_prev[x][i]
-                    if i >= len(o):
-                        o.append(prev_val)
-                    elif prev_val is not None:
-                        if o[i] is None:
-                            o[i] = prev_val
-                        else:
-                            if o[i].shape[0] < prev_val.shape[0]:
-                                o[i] = prev_val + o[i]
-                            else:
-                                o[i] += prev_val
-        return out
-
-class ControlNet(ControlBase):
-    def __init__(self, control_model=None, global_average_pooling=False, device=None, load_device=None, manual_cast_dtype=None):
-        super().__init__(device)
-        self.control_model = control_model
-        self.load_device = load_device
-        if control_model is not None:
-            self.control_model_wrapped = comfy.model_patcher.ModelPatcher(self.control_model, load_device=load_device, offload_device=comfy.model_management.unet_offload_device())
-
-        self.global_average_pooling = global_average_pooling
-        self.model_sampling_current = None
-        self.manual_cast_dtype = manual_cast_dtype
-
-    def get_control(self, x_noisy, t, cond, batched_number):
-        control_prev = None
-        if self.previous_controlnet is not None:
-            control_prev = self.previous_controlnet.get_control(x_noisy, t, cond, batched_number)
-
-        if self.timestep_range is not None:
-            if t[0] > self.timestep_range[0] or t[0] < self.timestep_range[1]:
-                if control_prev is not None:
-                    return control_prev
-                else:
-                    return None
-
-        dtype = self.control_model.dtype
-        if self.manual_cast_dtype is not None:
-            dtype = self.manual_cast_dtype
-
-        output_dtype = x_noisy.dtype
-        if self.cond_hint is None or x_noisy.shape[2] * self.compression_ratio != self.cond_hint.shape[2] or x_noisy.shape[3] * self.compression_ratio != self.cond_hint.shape[3]:
-            if self.cond_hint is not None:
-                del self.cond_hint
-            self.cond_hint = None
-            self.cond_hint = comfy.utils.common_upscale(self.cond_hint_original, x_noisy.shape[3] * self.compression_ratio, x_noisy.shape[2] * self.compression_ratio, self.upscale_algorithm, "center").to(dtype).to(self.device)
-        if x_noisy.shape[0] != self.cond_hint.shape[0]:
-            self.cond_hint = broadcast_image_to(self.cond_hint, x_noisy.shape[0], batched_number)
-
-        context = cond.get('crossattn_controlnet', cond['c_crossattn'])
-        y = cond.get('y', None)
-        if y is not None:
-            y = y.to(dtype)
-        timestep = self.model_sampling_current.timestep(t)
-        x_noisy = self.model_sampling_current.calculate_input(t, x_noisy)
-
-        control = self.control_model(x=x_noisy.to(dtype), hint=self.cond_hint, timesteps=timestep.float(), context=context.to(dtype), y=y)
-        return self.control_merge(None, control, control_prev, output_dtype)
-
-    def copy(self):
-        c = ControlNet(None, global_average_pooling=self.global_average_pooling, load_device=self.load_device, manual_cast_dtype=self.manual_cast_dtype)
-        c.control_model = self.control_model
-        c.control_model_wrapped = self.control_model_wrapped
-        self.copy_to(c)
-        return c
-
-    def get_models(self):
-        out = super().get_models()
-        out.append(self.control_model_wrapped)
-        return out
-
-    def pre_run(self, model, percent_to_timestep_function):
-        super().pre_run(model, percent_to_timestep_function)
-        self.model_sampling_current = model.model_sampling
-
-    def cleanup(self):
-        self.model_sampling_current = None
-        super().cleanup()
-
-class ControlLoraOps:
-    class Linear(torch.nn.Module, comfy.ops.CastWeightBiasOp):
-        def __init__(self, in_features: int, out_features: int, bias: bool = True,
-                    device=None, dtype=None) -> None:
-            factory_kwargs = {'device': device, 'dtype': dtype}
-            super().__init__()
-            self.in_features = in_features
-            self.out_features = out_features
-            self.weight = None
-            self.up = None
-            self.down = None
-            self.bias = None
-
-        def forward(self, input):
-            weight, bias = comfy.ops.cast_bias_weight(self, input)
-            if self.up is not None:
-                return torch.nn.functional.linear(input, weight + (torch.mm(self.up.flatten(start_dim=1), self.down.flatten(start_dim=1))).reshape(self.weight.shape).type(input.dtype), bias)
-            else:
-                return torch.nn.functional.linear(input, weight, bias)
-
-    class Conv2d(torch.nn.Module, comfy.ops.CastWeightBiasOp):
-        def __init__(
-            self,
-            in_channels,
-            out_channels,
-            kernel_size,
-            stride=1,
-            padding=0,
-            dilation=1,
-            groups=1,
-            bias=True,
-            padding_mode='zeros',
-            device=None,
-            dtype=None
-        ):
-            super().__init__()
-            self.in_channels = in_channels
-            self.out_channels = out_channels
-            self.kernel_size = kernel_size
-            self.stride = stride
-            self.padding = padding
-            self.dilation = dilation
-            self.transposed = False
-            self.output_padding = 0
-            self.groups = groups
-            self.padding_mode = padding_mode
-
-            self.weight = None
-            self.bias = None
-            self.up = None
-            self.down = None
-
-
-        def forward(self, input):
-            weight, bias = comfy.ops.cast_bias_weight(self, input)
-            if self.up is not None:
-                return torch.nn.functional.conv2d(input, weight + (torch.mm(self.up.flatten(start_dim=1), self.down.flatten(start_dim=1))).reshape(self.weight.shape).type(input.dtype), bias, self.stride, self.padding, self.dilation, self.groups)
-            else:
-                return torch.nn.functional.conv2d(input, weight, bias, self.stride, self.padding, self.dilation, self.groups)
-
-
-class ControlLora(ControlNet):
-    def __init__(self, control_weights, global_average_pooling=False, device=None):
-        ControlBase.__init__(self, device)
-        self.control_weights = control_weights
-        self.global_average_pooling = global_average_pooling
-
-    def pre_run(self, model, percent_to_timestep_function):
-        super().pre_run(model, percent_to_timestep_function)
-        controlnet_config = model.model_config.unet_config.copy()
-        controlnet_config.pop("out_channels")
-        controlnet_config["hint_channels"] = self.control_weights["input_hint_block.0.weight"].shape[1]
-        self.manual_cast_dtype = model.manual_cast_dtype
-        dtype = model.get_dtype()
-        if self.manual_cast_dtype is None:
-            class control_lora_ops(ControlLoraOps, comfy.ops.disable_weight_init):
-                pass
-        else:
-            class control_lora_ops(ControlLoraOps, comfy.ops.manual_cast):
-                pass
-            dtype = self.manual_cast_dtype
-
-        controlnet_config["operations"] = control_lora_ops
-        controlnet_config["dtype"] = dtype
-        self.control_model = comfy.cldm.cldm.ControlNet(**controlnet_config)
-        self.control_model.to(comfy.model_management.get_torch_device())
-        diffusion_model = model.diffusion_model
-        sd = diffusion_model.state_dict()
-        cm = self.control_model.state_dict()
-
-        for k in sd:
-            weight = sd[k]
-            try:
-                comfy.utils.set_attr_param(self.control_model, k, weight)
-            except:
-                pass
-
-        for k in self.control_weights:
-            if k not in {"lora_controlnet"}:
-                comfy.utils.set_attr_param(self.control_model, k, self.control_weights[k].to(dtype).to(comfy.model_management.get_torch_device()))
-
-    def copy(self):
-        c = ControlLora(self.control_weights, global_average_pooling=self.global_average_pooling)
-        self.copy_to(c)
-        return c
-
-    def cleanup(self):
-        del self.control_model
-        self.control_model = None
-        super().cleanup()
-
-    def get_models(self):
-        out = ControlBase.get_models(self)
-        return out
-
-    def inference_memory_requirements(self, dtype):
-        return comfy.utils.calculate_parameters(self.control_weights) * comfy.model_management.dtype_size(dtype) + ControlBase.inference_memory_requirements(self, dtype)
-
-def load_controlnet(ckpt_path, model=None):
-    controlnet_data = comfy.utils.load_torch_file(ckpt_path, safe_load=True)
-    if "lora_controlnet" in controlnet_data:
-        return ControlLora(controlnet_data)
-
-    controlnet_config = None
-    supported_inference_dtypes = None
-
-    if "controlnet_cond_embedding.conv_in.weight" in controlnet_data: #diffusers format
-        controlnet_config = comfy.model_detection.unet_config_from_diffusers_unet(controlnet_data)
-        diffusers_keys = comfy.utils.unet_to_diffusers(controlnet_config)
-        diffusers_keys["controlnet_mid_block.weight"] = "middle_block_out.0.weight"
-        diffusers_keys["controlnet_mid_block.bias"] = "middle_block_out.0.bias"
-
-        count = 0
-        loop = True
-        while loop:
-            suffix = [".weight", ".bias"]
-            for s in suffix:
-                k_in = "controlnet_down_blocks.{}{}".format(count, s)
-                k_out = "zero_convs.{}.0{}".format(count, s)
-                if k_in not in controlnet_data:
-                    loop = False
-                    break
-                diffusers_keys[k_in] = k_out
-            count += 1
-
-        count = 0
-        loop = True
-        while loop:
-            suffix = [".weight", ".bias"]
-            for s in suffix:
-                if count == 0:
-                    k_in = "controlnet_cond_embedding.conv_in{}".format(s)
-                else:
-                    k_in = "controlnet_cond_embedding.blocks.{}{}".format(count - 1, s)
-                k_out = "input_hint_block.{}{}".format(count * 2, s)
-                if k_in not in controlnet_data:
-                    k_in = "controlnet_cond_embedding.conv_out{}".format(s)
-                    loop = False
-                diffusers_keys[k_in] = k_out
-            count += 1
-
-        new_sd = {}
-        for k in diffusers_keys:
-            if k in controlnet_data:
-                new_sd[diffusers_keys[k]] = controlnet_data.pop(k)
-
-        leftover_keys = controlnet_data.keys()
-        if len(leftover_keys) > 0:
-            logging.warning("leftover keys: {}".format(leftover_keys))
-        controlnet_data = new_sd
-
-    pth_key = 'control_model.zero_convs.0.0.weight'
-    pth = False
-    key = 'zero_convs.0.0.weight'
-    if pth_key in controlnet_data:
-        pth = True
-        key = pth_key
-        prefix = "control_model."
-    elif key in controlnet_data:
-        prefix = ""
-    else:
-        net = load_t2i_adapter(controlnet_data)
-        if net is None:
-            logging.error("error checkpoint does not contain controlnet or t2i adapter data {}".format(ckpt_path))
-        return net
-
-    if controlnet_config is None:
-        model_config = comfy.model_detection.model_config_from_unet(controlnet_data, prefix, True)
-        supported_inference_dtypes = model_config.supported_inference_dtypes
-        controlnet_config = model_config.unet_config
-
-    load_device = comfy.model_management.get_torch_device()
-    if supported_inference_dtypes is None:
-        unet_dtype = comfy.model_management.unet_dtype()
-    else:
-        unet_dtype = comfy.model_management.unet_dtype(supported_dtypes=supported_inference_dtypes)
-
-    manual_cast_dtype = comfy.model_management.unet_manual_cast(unet_dtype, load_device)
-    if manual_cast_dtype is not None:
-        controlnet_config["operations"] = comfy.ops.manual_cast
-    controlnet_config["dtype"] = unet_dtype
-    controlnet_config.pop("out_channels")
-    controlnet_config["hint_channels"] = controlnet_data["{}input_hint_block.0.weight".format(prefix)].shape[1]
-    control_model = comfy.cldm.cldm.ControlNet(**controlnet_config)
-
-    if pth:
-        if 'difference' in controlnet_data:
-            if model is not None:
-                comfy.model_management.load_models_gpu([model])
-                model_sd = model.model_state_dict()
-                for x in controlnet_data:
-                    c_m = "control_model."
-                    if x.startswith(c_m):
-                        sd_key = "diffusion_model.{}".format(x[len(c_m):])
-                        if sd_key in model_sd:
-                            cd = controlnet_data[x]
-                            cd += model_sd[sd_key].type(cd.dtype).to(cd.device)
-            else:
-                logging.warning("WARNING: Loaded a diff controlnet without a model. It will very likely not work.")
-
-        class WeightsLoader(torch.nn.Module):
-            pass
-        w = WeightsLoader()
-        w.control_model = control_model
-        missing, unexpected = w.load_state_dict(controlnet_data, strict=False)
-    else:
-        missing, unexpected = control_model.load_state_dict(controlnet_data, strict=False)
-
-    if len(missing) > 0:
-        logging.warning("missing controlnet keys: {}".format(missing))
-
-    if len(unexpected) > 0:
-        logging.debug("unexpected controlnet keys: {}".format(unexpected))
-
-    global_average_pooling = False
-    filename = os.path.splitext(ckpt_path)[0]
-    if filename.endswith("_shuffle") or filename.endswith("_shuffle_fp16"): #TODO: smarter way of enabling global_average_pooling
-        global_average_pooling = True
-
-    control = ControlNet(control_model, global_average_pooling=global_average_pooling, load_device=load_device, manual_cast_dtype=manual_cast_dtype)
-    return control
-
-class T2IAdapter(ControlBase):
-    def __init__(self, t2i_model, channels_in, compression_ratio, upscale_algorithm, device=None):
-        super().__init__(device)
-        self.t2i_model = t2i_model
-        self.channels_in = channels_in
-        self.control_input = None
-        self.compression_ratio = compression_ratio
-        self.upscale_algorithm = upscale_algorithm
-
-    def scale_image_to(self, width, height):
-        unshuffle_amount = self.t2i_model.unshuffle_amount
-        width = math.ceil(width / unshuffle_amount) * unshuffle_amount
-        height = math.ceil(height / unshuffle_amount) * unshuffle_amount
-        return width, height
-
-    def get_control(self, x_noisy, t, cond, batched_number):
-        control_prev = None
-        if self.previous_controlnet is not None:
-            control_prev = self.previous_controlnet.get_control(x_noisy, t, cond, batched_number)
-
-        if self.timestep_range is not None:
-            if t[0] > self.timestep_range[0] or t[0] < self.timestep_range[1]:
-                if control_prev is not None:
-                    return control_prev
-                else:
-                    return None
-
-        if self.cond_hint is None or x_noisy.shape[2] * self.compression_ratio != self.cond_hint.shape[2] or x_noisy.shape[3] * self.compression_ratio != self.cond_hint.shape[3]:
-            if self.cond_hint is not None:
-                del self.cond_hint
-            self.control_input = None
-            self.cond_hint = None
-            width, height = self.scale_image_to(x_noisy.shape[3] * self.compression_ratio, x_noisy.shape[2] * self.compression_ratio)
-            self.cond_hint = comfy.utils.common_upscale(self.cond_hint_original, width, height, self.upscale_algorithm, "center").float().to(self.device)
-            if self.channels_in == 1 and self.cond_hint.shape[1] > 1:
-                self.cond_hint = torch.mean(self.cond_hint, 1, keepdim=True)
-        if x_noisy.shape[0] != self.cond_hint.shape[0]:
-            self.cond_hint = broadcast_image_to(self.cond_hint, x_noisy.shape[0], batched_number)
-        if self.control_input is None:
-            self.t2i_model.to(x_noisy.dtype)
-            self.t2i_model.to(self.device)
-            self.control_input = self.t2i_model(self.cond_hint.to(x_noisy.dtype))
-            self.t2i_model.cpu()
-
-        control_input = list(map(lambda a: None if a is None else a.clone(), self.control_input))
-        mid = None
-        if self.t2i_model.xl == True:
-            mid = control_input[-1:]
-            control_input = control_input[:-1]
-        return self.control_merge(control_input, mid, control_prev, x_noisy.dtype)
-
-    def copy(self):
-        c = T2IAdapter(self.t2i_model, self.channels_in, self.compression_ratio, self.upscale_algorithm)
-        self.copy_to(c)
-        return c
-
-def load_t2i_adapter(t2i_data):
-    compression_ratio = 8
-    upscale_algorithm = 'nearest-exact'
-
-    if 'adapter' in t2i_data:
-        t2i_data = t2i_data['adapter']
-    if 'adapter.body.0.resnets.0.block1.weight' in t2i_data: #diffusers format
-        prefix_replace = {}
-        for i in range(4):
-            for j in range(2):
-                prefix_replace["adapter.body.{}.resnets.{}.".format(i, j)] = "body.{}.".format(i * 2 + j)
-            prefix_replace["adapter.body.{}.".format(i, j)] = "body.{}.".format(i * 2)
-        prefix_replace["adapter."] = ""
-        t2i_data = comfy.utils.state_dict_prefix_replace(t2i_data, prefix_replace)
-    keys = t2i_data.keys()
-
-    if "body.0.in_conv.weight" in keys:
-        cin = t2i_data['body.0.in_conv.weight'].shape[1]
-        model_ad = comfy.t2i_adapter.adapter.Adapter_light(cin=cin, channels=[320, 640, 1280, 1280], nums_rb=4)
-    elif 'conv_in.weight' in keys:
-        cin = t2i_data['conv_in.weight'].shape[1]
-        channel = t2i_data['conv_in.weight'].shape[0]
-        ksize = t2i_data['body.0.block2.weight'].shape[2]
-        use_conv = False
-        down_opts = list(filter(lambda a: a.endswith("down_opt.op.weight"), keys))
-        if len(down_opts) > 0:
-            use_conv = True
-        xl = False
-        if cin == 256 or cin == 768:
-            xl = True
-        model_ad = comfy.t2i_adapter.adapter.Adapter(cin=cin, channels=[channel, channel*2, channel*4, channel*4][:4], nums_rb=2, ksize=ksize, sk=True, use_conv=use_conv, xl=xl)
-    elif "backbone.0.0.weight" in keys:
-        model_ad = comfy.ldm.cascade.controlnet.ControlNet(c_in=t2i_data['backbone.0.0.weight'].shape[1], proj_blocks=[0, 4, 8, 12, 51, 55, 59, 63])
-        compression_ratio = 32
-        upscale_algorithm = 'bilinear'
-    elif "backbone.10.blocks.0.weight" in keys:
-        model_ad = comfy.ldm.cascade.controlnet.ControlNet(c_in=t2i_data['backbone.0.weight'].shape[1], bottleneck_mode="large", proj_blocks=[0, 4, 8, 12, 51, 55, 59, 63])
-        compression_ratio = 1
-        upscale_algorithm = 'nearest-exact'
-    else:
-        return None
-
-    missing, unexpected = model_ad.load_state_dict(t2i_data)
-    if len(missing) > 0:
-        logging.warning("t2i missing {}".format(missing))
-
-    if len(unexpected) > 0:
-        logging.debug("t2i unexpected {}".format(unexpected))
-
-    return T2IAdapter(model_ad, model_ad.input_channels, compression_ratio, upscale_algorithm)
diff --git a/MagicQuill/comfy/diffusers_convert.py b/MagicQuill/comfy/diffusers_convert.py
deleted file mode 100644
index ed2a45fea586284c7b881a2a7ab46983cd4baafb..0000000000000000000000000000000000000000
--- a/MagicQuill/comfy/diffusers_convert.py
+++ /dev/null
@@ -1,281 +0,0 @@
-import re
-import torch
-import logging
-
-# conversion code from https://github.com/huggingface/diffusers/blob/main/scripts/convert_diffusers_to_original_stable_diffusion.py
-
-# =================#
-# UNet Conversion #
-# =================#
-
-unet_conversion_map = [
-    # (stable-diffusion, HF Diffusers)
-    ("time_embed.0.weight", "time_embedding.linear_1.weight"),
-    ("time_embed.0.bias", "time_embedding.linear_1.bias"),
-    ("time_embed.2.weight", "time_embedding.linear_2.weight"),
-    ("time_embed.2.bias", "time_embedding.linear_2.bias"),
-    ("input_blocks.0.0.weight", "conv_in.weight"),
-    ("input_blocks.0.0.bias", "conv_in.bias"),
-    ("out.0.weight", "conv_norm_out.weight"),
-    ("out.0.bias", "conv_norm_out.bias"),
-    ("out.2.weight", "conv_out.weight"),
-    ("out.2.bias", "conv_out.bias"),
-]
-
-unet_conversion_map_resnet = [
-    # (stable-diffusion, HF Diffusers)
-    ("in_layers.0", "norm1"),
-    ("in_layers.2", "conv1"),
-    ("out_layers.0", "norm2"),
-    ("out_layers.3", "conv2"),
-    ("emb_layers.1", "time_emb_proj"),
-    ("skip_connection", "conv_shortcut"),
-]
-
-unet_conversion_map_layer = []
-# hardcoded number of downblocks and resnets/attentions...
-# would need smarter logic for other networks.
-for i in range(4):
-    # loop over downblocks/upblocks
-
-    for j in range(2):
-        # loop over resnets/attentions for downblocks
-        hf_down_res_prefix = f"down_blocks.{i}.resnets.{j}."
-        sd_down_res_prefix = f"input_blocks.{3 * i + j + 1}.0."
-        unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix))
-
-        if i < 3:
-            # no attention layers in down_blocks.3
-            hf_down_atn_prefix = f"down_blocks.{i}.attentions.{j}."
-            sd_down_atn_prefix = f"input_blocks.{3 * i + j + 1}.1."
-            unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix))
-
-    for j in range(3):
-        # loop over resnets/attentions for upblocks
-        hf_up_res_prefix = f"up_blocks.{i}.resnets.{j}."
-        sd_up_res_prefix = f"output_blocks.{3 * i + j}.0."
-        unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix))
-
-        if i > 0:
-            # no attention layers in up_blocks.0
-            hf_up_atn_prefix = f"up_blocks.{i}.attentions.{j}."
-            sd_up_atn_prefix = f"output_blocks.{3 * i + j}.1."
-            unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix))
-
-    if i < 3:
-        # no downsample in down_blocks.3
-        hf_downsample_prefix = f"down_blocks.{i}.downsamplers.0.conv."
-        sd_downsample_prefix = f"input_blocks.{3 * (i + 1)}.0.op."
-        unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix))
-
-        # no upsample in up_blocks.3
-        hf_upsample_prefix = f"up_blocks.{i}.upsamplers.0."
-        sd_upsample_prefix = f"output_blocks.{3 * i + 2}.{1 if i == 0 else 2}."
-        unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))
-
-hf_mid_atn_prefix = "mid_block.attentions.0."
-sd_mid_atn_prefix = "middle_block.1."
-unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))
-
-for j in range(2):
-    hf_mid_res_prefix = f"mid_block.resnets.{j}."
-    sd_mid_res_prefix = f"middle_block.{2 * j}."
-    unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))
-
-
-def convert_unet_state_dict(unet_state_dict):
-    # buyer beware: this is a *brittle* function,
-    # and correct output requires that all of these pieces interact in
-    # the exact order in which I have arranged them.
-    mapping = {k: k for k in unet_state_dict.keys()}
-    for sd_name, hf_name in unet_conversion_map:
-        mapping[hf_name] = sd_name
-    for k, v in mapping.items():
-        if "resnets" in k:
-            for sd_part, hf_part in unet_conversion_map_resnet:
-                v = v.replace(hf_part, sd_part)
-            mapping[k] = v
-    for k, v in mapping.items():
-        for sd_part, hf_part in unet_conversion_map_layer:
-            v = v.replace(hf_part, sd_part)
-        mapping[k] = v
-    new_state_dict = {v: unet_state_dict[k] for k, v in mapping.items()}
-    return new_state_dict
-
-
-# ================#
-# VAE Conversion #
-# ================#
-
-vae_conversion_map = [
-    # (stable-diffusion, HF Diffusers)
-    ("nin_shortcut", "conv_shortcut"),
-    ("norm_out", "conv_norm_out"),
-    ("mid.attn_1.", "mid_block.attentions.0."),
-]
-
-for i in range(4):
-    # down_blocks have two resnets
-    for j in range(2):
-        hf_down_prefix = f"encoder.down_blocks.{i}.resnets.{j}."
-        sd_down_prefix = f"encoder.down.{i}.block.{j}."
-        vae_conversion_map.append((sd_down_prefix, hf_down_prefix))
-
-    if i < 3:
-        hf_downsample_prefix = f"down_blocks.{i}.downsamplers.0."
-        sd_downsample_prefix = f"down.{i}.downsample."
-        vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))
-
-        hf_upsample_prefix = f"up_blocks.{i}.upsamplers.0."
-        sd_upsample_prefix = f"up.{3 - i}.upsample."
-        vae_conversion_map.append((sd_upsample_prefix, hf_upsample_prefix))
-
-    # up_blocks have three resnets
-    # also, up blocks in hf are numbered in reverse from sd
-    for j in range(3):
-        hf_up_prefix = f"decoder.up_blocks.{i}.resnets.{j}."
-        sd_up_prefix = f"decoder.up.{3 - i}.block.{j}."
-        vae_conversion_map.append((sd_up_prefix, hf_up_prefix))
-
-# this part accounts for mid blocks in both the encoder and the decoder
-for i in range(2):
-    hf_mid_res_prefix = f"mid_block.resnets.{i}."
-    sd_mid_res_prefix = f"mid.block_{i + 1}."
-    vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))
-
-vae_conversion_map_attn = [
-    # (stable-diffusion, HF Diffusers)
-    ("norm.", "group_norm."),
-    ("q.", "query."),
-    ("k.", "key."),
-    ("v.", "value."),
-    ("q.", "to_q."),
-    ("k.", "to_k."),
-    ("v.", "to_v."),
-    ("proj_out.", "to_out.0."),
-    ("proj_out.", "proj_attn."),
-]
-
-
-def reshape_weight_for_sd(w):
-    # convert HF linear weights to SD conv2d weights
-    return w.reshape(*w.shape, 1, 1)
-
-
-def convert_vae_state_dict(vae_state_dict):
-    mapping = {k: k for k in vae_state_dict.keys()}
-    for k, v in mapping.items():
-        for sd_part, hf_part in vae_conversion_map:
-            v = v.replace(hf_part, sd_part)
-        mapping[k] = v
-    for k, v in mapping.items():
-        if "attentions" in k:
-            for sd_part, hf_part in vae_conversion_map_attn:
-                v = v.replace(hf_part, sd_part)
-            mapping[k] = v
-    new_state_dict = {v: vae_state_dict[k] for k, v in mapping.items()}
-    weights_to_convert = ["q", "k", "v", "proj_out"]
-    for k, v in new_state_dict.items():
-        for weight_name in weights_to_convert:
-            if f"mid.attn_1.{weight_name}.weight" in k:
-                logging.debug(f"Reshaping {k} for SD format")
-                new_state_dict[k] = reshape_weight_for_sd(v)
-    return new_state_dict
-
-
-# =========================#
-# Text Encoder Conversion #
-# =========================#
-
-
-textenc_conversion_lst = [
-    # (stable-diffusion, HF Diffusers)
-    ("resblocks.", "text_model.encoder.layers."),
-    ("ln_1", "layer_norm1"),
-    ("ln_2", "layer_norm2"),
-    (".c_fc.", ".fc1."),
-    (".c_proj.", ".fc2."),
-    (".attn", ".self_attn"),
-    ("ln_final.", "transformer.text_model.final_layer_norm."),
-    ("token_embedding.weight", "transformer.text_model.embeddings.token_embedding.weight"),
-    ("positional_embedding", "transformer.text_model.embeddings.position_embedding.weight"),
-]
-protected = {re.escape(x[1]): x[0] for x in textenc_conversion_lst}
-textenc_pattern = re.compile("|".join(protected.keys()))
-
-# Ordering is from https://github.com/pytorch/pytorch/blob/master/test/cpp/api/modules.cpp
-code2idx = {"q": 0, "k": 1, "v": 2}
-
-# This function exists because at the time of writing torch.cat can't do fp8 with cuda
-def cat_tensors(tensors):
-    x = 0
-    for t in tensors:
-        x += t.shape[0]
-
-    shape = [x] + list(tensors[0].shape)[1:]
-    out = torch.empty(shape, device=tensors[0].device, dtype=tensors[0].dtype)
-
-    x = 0
-    for t in tensors:
-        out[x:x + t.shape[0]] = t
-        x += t.shape[0]
-
-    return out
-
-def convert_text_enc_state_dict_v20(text_enc_dict, prefix=""):
-    new_state_dict = {}
-    capture_qkv_weight = {}
-    capture_qkv_bias = {}
-    for k, v in text_enc_dict.items():
-        if not k.startswith(prefix):
-            continue
-        if (
-                k.endswith(".self_attn.q_proj.weight")
-                or k.endswith(".self_attn.k_proj.weight")
-                or k.endswith(".self_attn.v_proj.weight")
-        ):
-            k_pre = k[: -len(".q_proj.weight")]
-            k_code = k[-len("q_proj.weight")]
-            if k_pre not in capture_qkv_weight:
-                capture_qkv_weight[k_pre] = [None, None, None]
-            capture_qkv_weight[k_pre][code2idx[k_code]] = v
-            continue
-
-        if (
-                k.endswith(".self_attn.q_proj.bias")
-                or k.endswith(".self_attn.k_proj.bias")
-                or k.endswith(".self_attn.v_proj.bias")
-        ):
-            k_pre = k[: -len(".q_proj.bias")]
-            k_code = k[-len("q_proj.bias")]
-            if k_pre not in capture_qkv_bias:
-                capture_qkv_bias[k_pre] = [None, None, None]
-            capture_qkv_bias[k_pre][code2idx[k_code]] = v
-            continue
-
-        text_proj = "transformer.text_projection.weight"
-        if k.endswith(text_proj):
-            new_state_dict[k.replace(text_proj, "text_projection")] = v.transpose(0, 1).contiguous()
-        else:
-            relabelled_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], k)
-            new_state_dict[relabelled_key] = v
-
-    for k_pre, tensors in capture_qkv_weight.items():
-        if None in tensors:
-            raise Exception("CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing")
-        relabelled_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], k_pre)
-        new_state_dict[relabelled_key + ".in_proj_weight"] = cat_tensors(tensors)
-
-    for k_pre, tensors in capture_qkv_bias.items():
-        if None in tensors:
-            raise Exception("CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing")
-        relabelled_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], k_pre)
-        new_state_dict[relabelled_key + ".in_proj_bias"] = cat_tensors(tensors)
-
-    return new_state_dict
-
-
-def convert_text_enc_state_dict(text_enc_dict):
-    return text_enc_dict
-
-
diff --git a/MagicQuill/comfy/diffusers_load.py b/MagicQuill/comfy/diffusers_load.py
deleted file mode 100644
index 98b888a19399d5ea847d90e443737c89c9787cce..0000000000000000000000000000000000000000
--- a/MagicQuill/comfy/diffusers_load.py
+++ /dev/null
@@ -1,36 +0,0 @@
-import os
-
-import comfy.sd
-
-def first_file(path, filenames):
-    for f in filenames:
-        p = os.path.join(path, f)
-        if os.path.exists(p):
-            return p
-    return None
-
-def load_diffusers(model_path, output_vae=True, output_clip=True, embedding_directory=None):
-    diffusion_model_names = ["diffusion_pytorch_model.fp16.safetensors", "diffusion_pytorch_model.safetensors", "diffusion_pytorch_model.fp16.bin", "diffusion_pytorch_model.bin"]
-    unet_path = first_file(os.path.join(model_path, "unet"), diffusion_model_names)
-    vae_path = first_file(os.path.join(model_path, "vae"), diffusion_model_names)
-
-    text_encoder_model_names = ["model.fp16.safetensors", "model.safetensors", "pytorch_model.fp16.bin", "pytorch_model.bin"]
-    text_encoder1_path = first_file(os.path.join(model_path, "text_encoder"), text_encoder_model_names)
-    text_encoder2_path = first_file(os.path.join(model_path, "text_encoder_2"), text_encoder_model_names)
-
-    text_encoder_paths = [text_encoder1_path]
-    if text_encoder2_path is not None:
-        text_encoder_paths.append(text_encoder2_path)
-
-    unet = comfy.sd.load_unet(unet_path)
-
-    clip = None
-    if output_clip:
-        clip = comfy.sd.load_clip(text_encoder_paths, embedding_directory=embedding_directory)
-
-    vae = None
-    if output_vae:
-        sd = comfy.utils.load_torch_file(vae_path)
-        vae = comfy.sd.VAE(sd=sd)
-
-    return (unet, clip, vae)
diff --git a/MagicQuill/comfy/extra_samplers/__pycache__/uni_pc.cpython-310.pyc b/MagicQuill/comfy/extra_samplers/__pycache__/uni_pc.cpython-310.pyc
deleted file mode 100644
index aa06b36d34bc3c37015864c481aa43477d2f19ae..0000000000000000000000000000000000000000
Binary files a/MagicQuill/comfy/extra_samplers/__pycache__/uni_pc.cpython-310.pyc and /dev/null differ
diff --git a/MagicQuill/comfy/extra_samplers/uni_pc.py b/MagicQuill/comfy/extra_samplers/uni_pc.py
deleted file mode 100644
index a30d1d03f2e1001f462ce0fa2422a9a16ed279d8..0000000000000000000000000000000000000000
--- a/MagicQuill/comfy/extra_samplers/uni_pc.py
+++ /dev/null
@@ -1,875 +0,0 @@
-#code taken from: https://github.com/wl-zhao/UniPC and modified
-
-import torch
-import torch.nn.functional as F
-import math
-
-from tqdm.auto import trange, tqdm
-
-
-class NoiseScheduleVP:
-    def __init__(
-            self,
-            schedule='discrete',
-            betas=None,
-            alphas_cumprod=None,
-            continuous_beta_0=0.1,
-            continuous_beta_1=20.,
-        ):
-        """Create a wrapper class for the forward SDE (VP type).
-
-        ***
-        Update: We support discrete-time diffusion models by implementing a picewise linear interpolation for log_alpha_t.
-                We recommend to use schedule='discrete' for the discrete-time diffusion models, especially for high-resolution images.
-        ***
-
-        The forward SDE ensures that the condition distribution q_{t|0}(x_t | x_0) = N ( alpha_t * x_0, sigma_t^2 * I ).
-        We further define lambda_t = log(alpha_t) - log(sigma_t), which is the half-logSNR (described in the DPM-Solver paper).
-        Therefore, we implement the functions for computing alpha_t, sigma_t and lambda_t. For t in [0, T], we have:
-
-            log_alpha_t = self.marginal_log_mean_coeff(t)
-            sigma_t = self.marginal_std(t)
-            lambda_t = self.marginal_lambda(t)
-
-        Moreover, as lambda(t) is an invertible function, we also support its inverse function:
-
-            t = self.inverse_lambda(lambda_t)
-
-        ===============================================================
-
-        We support both discrete-time DPMs (trained on n = 0, 1, ..., N-1) and continuous-time DPMs (trained on t in [t_0, T]).
-
-        1. For discrete-time DPMs:
-
-            For discrete-time DPMs trained on n = 0, 1, ..., N-1, we convert the discrete steps to continuous time steps by:
-                t_i = (i + 1) / N
-            e.g. for N = 1000, we have t_0 = 1e-3 and T = t_{N-1} = 1.
-            We solve the corresponding diffusion ODE from time T = 1 to time t_0 = 1e-3.
-
-            Args:
-                betas: A `torch.Tensor`. The beta array for the discrete-time DPM. (See the original DDPM paper for details)
-                alphas_cumprod: A `torch.Tensor`. The cumprod alphas for the discrete-time DPM. (See the original DDPM paper for details)
-
-            Note that we always have alphas_cumprod = cumprod(betas). Therefore, we only need to set one of `betas` and `alphas_cumprod`.
-
-            **Important**:  Please pay special attention for the args for `alphas_cumprod`:
-                The `alphas_cumprod` is the \hat{alpha_n} arrays in the notations of DDPM. Specifically, DDPMs assume that
-                    q_{t_n | 0}(x_{t_n} | x_0) = N ( \sqrt{\hat{alpha_n}} * x_0, (1 - \hat{alpha_n}) * I ).
-                Therefore, the notation \hat{alpha_n} is different from the notation alpha_t in DPM-Solver. In fact, we have
-                    alpha_{t_n} = \sqrt{\hat{alpha_n}},
-                and
-                    log(alpha_{t_n}) = 0.5 * log(\hat{alpha_n}).
-
-
-        2. For continuous-time DPMs:
-
-            We support two types of VPSDEs: linear (DDPM) and cosine (improved-DDPM). The hyperparameters for the noise
-            schedule are the default settings in DDPM and improved-DDPM:
-
-            Args:
-                beta_min: A `float` number. The smallest beta for the linear schedule.
-                beta_max: A `float` number. The largest beta for the linear schedule.
-                cosine_s: A `float` number. The hyperparameter in the cosine schedule.
-                cosine_beta_max: A `float` number. The hyperparameter in the cosine schedule.
-                T: A `float` number. The ending time of the forward process.
-
-        ===============================================================
-
-        Args:
-            schedule: A `str`. The noise schedule of the forward SDE. 'discrete' for discrete-time DPMs,
-                    'linear' or 'cosine' for continuous-time DPMs.
-        Returns:
-            A wrapper object of the forward SDE (VP type).
-        
-        ===============================================================
-
-        Example:
-
-        # For discrete-time DPMs, given betas (the beta array for n = 0, 1, ..., N - 1):
-        >>> ns = NoiseScheduleVP('discrete', betas=betas)
-
-        # For discrete-time DPMs, given alphas_cumprod (the \hat{alpha_n} array for n = 0, 1, ..., N - 1):
-        >>> ns = NoiseScheduleVP('discrete', alphas_cumprod=alphas_cumprod)
-
-        # For continuous-time DPMs (VPSDE), linear schedule:
-        >>> ns = NoiseScheduleVP('linear', continuous_beta_0=0.1, continuous_beta_1=20.)
-
-        """
-
-        if schedule not in ['discrete', 'linear', 'cosine']:
-            raise ValueError("Unsupported noise schedule {}. The schedule needs to be 'discrete' or 'linear' or 'cosine'".format(schedule))
-
-        self.schedule = schedule
-        if schedule == 'discrete':
-            if betas is not None:
-                log_alphas = 0.5 * torch.log(1 - betas).cumsum(dim=0)
-            else:
-                assert alphas_cumprod is not None
-                log_alphas = 0.5 * torch.log(alphas_cumprod)
-            self.total_N = len(log_alphas)
-            self.T = 1.
-            self.t_array = torch.linspace(0., 1., self.total_N + 1)[1:].reshape((1, -1))
-            self.log_alpha_array = log_alphas.reshape((1, -1,))
-        else:
-            self.total_N = 1000
-            self.beta_0 = continuous_beta_0
-            self.beta_1 = continuous_beta_1
-            self.cosine_s = 0.008
-            self.cosine_beta_max = 999.
-            self.cosine_t_max = math.atan(self.cosine_beta_max * (1. + self.cosine_s) / math.pi) * 2. * (1. + self.cosine_s) / math.pi - self.cosine_s
-            self.cosine_log_alpha_0 = math.log(math.cos(self.cosine_s / (1. + self.cosine_s) * math.pi / 2.))
-            self.schedule = schedule
-            if schedule == 'cosine':
-                # For the cosine schedule, T = 1 will have numerical issues. So we manually set the ending time T.
-                # Note that T = 0.9946 may be not the optimal setting. However, we find it works well.
-                self.T = 0.9946
-            else:
-                self.T = 1.
-
-    def marginal_log_mean_coeff(self, t):
-        """
-        Compute log(alpha_t) of a given continuous-time label t in [0, T].
-        """
-        if self.schedule == 'discrete':
-            return interpolate_fn(t.reshape((-1, 1)), self.t_array.to(t.device), self.log_alpha_array.to(t.device)).reshape((-1))
-        elif self.schedule == 'linear':
-            return -0.25 * t ** 2 * (self.beta_1 - self.beta_0) - 0.5 * t * self.beta_0
-        elif self.schedule == 'cosine':
-            log_alpha_fn = lambda s: torch.log(torch.cos((s + self.cosine_s) / (1. + self.cosine_s) * math.pi / 2.))
-            log_alpha_t =  log_alpha_fn(t) - self.cosine_log_alpha_0
-            return log_alpha_t
-
-    def marginal_alpha(self, t):
-        """
-        Compute alpha_t of a given continuous-time label t in [0, T].
-        """
-        return torch.exp(self.marginal_log_mean_coeff(t))
-
-    def marginal_std(self, t):
-        """
-        Compute sigma_t of a given continuous-time label t in [0, T].
-        """
-        return torch.sqrt(1. - torch.exp(2. * self.marginal_log_mean_coeff(t)))
-
-    def marginal_lambda(self, t):
-        """
-        Compute lambda_t = log(alpha_t) - log(sigma_t) of a given continuous-time label t in [0, T].
-        """
-        log_mean_coeff = self.marginal_log_mean_coeff(t)
-        log_std = 0.5 * torch.log(1. - torch.exp(2. * log_mean_coeff))
-        return log_mean_coeff - log_std
-
-    def inverse_lambda(self, lamb):
-        """
-        Compute the continuous-time label t in [0, T] of a given half-logSNR lambda_t.
-        """
-        if self.schedule == 'linear':
-            tmp = 2. * (self.beta_1 - self.beta_0) * torch.logaddexp(-2. * lamb, torch.zeros((1,)).to(lamb))
-            Delta = self.beta_0**2 + tmp
-            return tmp / (torch.sqrt(Delta) + self.beta_0) / (self.beta_1 - self.beta_0)
-        elif self.schedule == 'discrete':
-            log_alpha = -0.5 * torch.logaddexp(torch.zeros((1,)).to(lamb.device), -2. * lamb)
-            t = interpolate_fn(log_alpha.reshape((-1, 1)), torch.flip(self.log_alpha_array.to(lamb.device), [1]), torch.flip(self.t_array.to(lamb.device), [1]))
-            return t.reshape((-1,))
-        else:
-            log_alpha = -0.5 * torch.logaddexp(-2. * lamb, torch.zeros((1,)).to(lamb))
-            t_fn = lambda log_alpha_t: torch.arccos(torch.exp(log_alpha_t + self.cosine_log_alpha_0)) * 2. * (1. + self.cosine_s) / math.pi - self.cosine_s
-            t = t_fn(log_alpha)
-            return t
-
-
-def model_wrapper(
-    model,
-    noise_schedule,
-    model_type="noise",
-    model_kwargs={},
-    guidance_type="uncond",
-    condition=None,
-    unconditional_condition=None,
-    guidance_scale=1.,
-    classifier_fn=None,
-    classifier_kwargs={},
-):
-    """Create a wrapper function for the noise prediction model.
-
-    DPM-Solver needs to solve the continuous-time diffusion ODEs. For DPMs trained on discrete-time labels, we need to
-    firstly wrap the model function to a noise prediction model that accepts the continuous time as the input.
-
-    We support four types of the diffusion model by setting `model_type`:
-
-        1. "noise": noise prediction model. (Trained by predicting noise).
-
-        2. "x_start": data prediction model. (Trained by predicting the data x_0 at time 0).
-
-        3. "v": velocity prediction model. (Trained by predicting the velocity).
-            The "v" prediction is derivation detailed in Appendix D of [1], and is used in Imagen-Video [2].
-
-            [1] Salimans, Tim, and Jonathan Ho. "Progressive distillation for fast sampling of diffusion models."
-                arXiv preprint arXiv:2202.00512 (2022).
-            [2] Ho, Jonathan, et al. "Imagen Video: High Definition Video Generation with Diffusion Models."
-                arXiv preprint arXiv:2210.02303 (2022).
-    
-        4. "score": marginal score function. (Trained by denoising score matching).
-            Note that the score function and the noise prediction model follows a simple relationship:
-            ```
-                noise(x_t, t) = -sigma_t * score(x_t, t)
-            ```
-
-    We support three types of guided sampling by DPMs by setting `guidance_type`:
-        1. "uncond": unconditional sampling by DPMs.
-            The input `model` has the following format:
-            ``
-                model(x, t_input, **model_kwargs) -> noise | x_start | v | score
-            ``
-
-        2. "classifier": classifier guidance sampling [3] by DPMs and another classifier.
-            The input `model` has the following format:
-            ``
-                model(x, t_input, **model_kwargs) -> noise | x_start | v | score
-            `` 
-
-            The input `classifier_fn` has the following format:
-            ``
-                classifier_fn(x, t_input, cond, **classifier_kwargs) -> logits(x, t_input, cond)
-            ``
-
-            [3] P. Dhariwal and A. Q. Nichol, "Diffusion models beat GANs on image synthesis,"
-                in Advances in Neural Information Processing Systems, vol. 34, 2021, pp. 8780-8794.
-
-        3. "classifier-free": classifier-free guidance sampling by conditional DPMs.
-            The input `model` has the following format:
-            ``
-                model(x, t_input, cond, **model_kwargs) -> noise | x_start | v | score
-            `` 
-            And if cond == `unconditional_condition`, the model output is the unconditional DPM output.
-
-            [4] Ho, Jonathan, and Tim Salimans. "Classifier-free diffusion guidance."
-                arXiv preprint arXiv:2207.12598 (2022).
-        
-
-    The `t_input` is the time label of the model, which may be discrete-time labels (i.e. 0 to 999)
-    or continuous-time labels (i.e. epsilon to T).
-
-    We wrap the model function to accept only `x` and `t_continuous` as inputs, and outputs the predicted noise:
-    ``
-        def model_fn(x, t_continuous) -> noise:
-            t_input = get_model_input_time(t_continuous)
-            return noise_pred(model, x, t_input, **model_kwargs)         
-    ``
-    where `t_continuous` is the continuous time labels (i.e. epsilon to T). And we use `model_fn` for DPM-Solver.
-
-    ===============================================================
-
-    Args:
-        model: A diffusion model with the corresponding format described above.
-        noise_schedule: A noise schedule object, such as NoiseScheduleVP.
-        model_type: A `str`. The parameterization type of the diffusion model.
-                    "noise" or "x_start" or "v" or "score".
-        model_kwargs: A `dict`. A dict for the other inputs of the model function.
-        guidance_type: A `str`. The type of the guidance for sampling.
-                    "uncond" or "classifier" or "classifier-free".
-        condition: A pytorch tensor. The condition for the guided sampling.
-                    Only used for "classifier" or "classifier-free" guidance type.
-        unconditional_condition: A pytorch tensor. The condition for the unconditional sampling.
-                    Only used for "classifier-free" guidance type.
-        guidance_scale: A `float`. The scale for the guided sampling.
-        classifier_fn: A classifier function. Only used for the classifier guidance.
-        classifier_kwargs: A `dict`. A dict for the other inputs of the classifier function.
-    Returns:
-        A noise prediction model that accepts the noised data and the continuous time as the inputs.
-    """
-
-    def get_model_input_time(t_continuous):
-        """
-        Convert the continuous-time `t_continuous` (in [epsilon, T]) to the model input time.
-        For discrete-time DPMs, we convert `t_continuous` in [1 / N, 1] to `t_input` in [0, 1000 * (N - 1) / N].
-        For continuous-time DPMs, we just use `t_continuous`.
-        """
-        if noise_schedule.schedule == 'discrete':
-            return (t_continuous - 1. / noise_schedule.total_N) * 1000.
-        else:
-            return t_continuous
-
-    def noise_pred_fn(x, t_continuous, cond=None):
-        if t_continuous.reshape((-1,)).shape[0] == 1:
-            t_continuous = t_continuous.expand((x.shape[0]))
-        t_input = get_model_input_time(t_continuous)
-        output = model(x, t_input, **model_kwargs)
-        if model_type == "noise":
-            return output
-        elif model_type == "x_start":
-            alpha_t, sigma_t = noise_schedule.marginal_alpha(t_continuous), noise_schedule.marginal_std(t_continuous)
-            dims = x.dim()
-            return (x - expand_dims(alpha_t, dims) * output) / expand_dims(sigma_t, dims)
-        elif model_type == "v":
-            alpha_t, sigma_t = noise_schedule.marginal_alpha(t_continuous), noise_schedule.marginal_std(t_continuous)
-            dims = x.dim()
-            return expand_dims(alpha_t, dims) * output + expand_dims(sigma_t, dims) * x
-        elif model_type == "score":
-            sigma_t = noise_schedule.marginal_std(t_continuous)
-            dims = x.dim()
-            return -expand_dims(sigma_t, dims) * output
-
-    def cond_grad_fn(x, t_input):
-        """
-        Compute the gradient of the classifier, i.e. nabla_{x} log p_t(cond | x_t).
-        """
-        with torch.enable_grad():
-            x_in = x.detach().requires_grad_(True)
-            log_prob = classifier_fn(x_in, t_input, condition, **classifier_kwargs)
-            return torch.autograd.grad(log_prob.sum(), x_in)[0]
-
-    def model_fn(x, t_continuous):
-        """
-        The noise predicition model function that is used for DPM-Solver.
-        """
-        if t_continuous.reshape((-1,)).shape[0] == 1:
-            t_continuous = t_continuous.expand((x.shape[0]))
-        if guidance_type == "uncond":
-            return noise_pred_fn(x, t_continuous)
-        elif guidance_type == "classifier":
-            assert classifier_fn is not None
-            t_input = get_model_input_time(t_continuous)
-            cond_grad = cond_grad_fn(x, t_input)
-            sigma_t = noise_schedule.marginal_std(t_continuous)
-            noise = noise_pred_fn(x, t_continuous)
-            return noise - guidance_scale * expand_dims(sigma_t, dims=cond_grad.dim()) * cond_grad
-        elif guidance_type == "classifier-free":
-            if guidance_scale == 1. or unconditional_condition is None:
-                return noise_pred_fn(x, t_continuous, cond=condition)
-            else:
-                x_in = torch.cat([x] * 2)
-                t_in = torch.cat([t_continuous] * 2)
-                c_in = torch.cat([unconditional_condition, condition])
-                noise_uncond, noise = noise_pred_fn(x_in, t_in, cond=c_in).chunk(2)
-                return noise_uncond + guidance_scale * (noise - noise_uncond)
-
-    assert model_type in ["noise", "x_start", "v"]
-    assert guidance_type in ["uncond", "classifier", "classifier-free"]
-    return model_fn
-
-
-class UniPC:
-    def __init__(
-        self,
-        model_fn,
-        noise_schedule,
-        predict_x0=True,
-        thresholding=False,
-        max_val=1.,
-        variant='bh1',
-    ):
-        """Construct a UniPC. 
-
-        We support both data_prediction and noise_prediction.
-        """
-        self.model = model_fn
-        self.noise_schedule = noise_schedule
-        self.variant = variant
-        self.predict_x0 = predict_x0
-        self.thresholding = thresholding
-        self.max_val = max_val
-
-    def dynamic_thresholding_fn(self, x0, t=None):
-        """
-        The dynamic thresholding method. 
-        """
-        dims = x0.dim()
-        p = self.dynamic_thresholding_ratio
-        s = torch.quantile(torch.abs(x0).reshape((x0.shape[0], -1)), p, dim=1)
-        s = expand_dims(torch.maximum(s, self.thresholding_max_val * torch.ones_like(s).to(s.device)), dims)
-        x0 = torch.clamp(x0, -s, s) / s
-        return x0
-
-    def noise_prediction_fn(self, x, t):
-        """
-        Return the noise prediction model.
-        """
-        return self.model(x, t)
-
-    def data_prediction_fn(self, x, t):
-        """
-        Return the data prediction model (with thresholding).
-        """
-        noise = self.noise_prediction_fn(x, t)
-        dims = x.dim()
-        alpha_t, sigma_t = self.noise_schedule.marginal_alpha(t), self.noise_schedule.marginal_std(t)
-        x0 = (x - expand_dims(sigma_t, dims) * noise) / expand_dims(alpha_t, dims)
-        if self.thresholding:
-            p = 0.995   # A hyperparameter in the paper of "Imagen" [1].
-            s = torch.quantile(torch.abs(x0).reshape((x0.shape[0], -1)), p, dim=1)
-            s = expand_dims(torch.maximum(s, self.max_val * torch.ones_like(s).to(s.device)), dims)
-            x0 = torch.clamp(x0, -s, s) / s
-        return x0
-
-    def model_fn(self, x, t):
-        """
-        Convert the model to the noise prediction model or the data prediction model. 
-        """
-        if self.predict_x0:
-            return self.data_prediction_fn(x, t)
-        else:
-            return self.noise_prediction_fn(x, t)
-
-    def get_time_steps(self, skip_type, t_T, t_0, N, device):
-        """Compute the intermediate time steps for sampling.
-        """
-        if skip_type == 'logSNR':
-            lambda_T = self.noise_schedule.marginal_lambda(torch.tensor(t_T).to(device))
-            lambda_0 = self.noise_schedule.marginal_lambda(torch.tensor(t_0).to(device))
-            logSNR_steps = torch.linspace(lambda_T.cpu().item(), lambda_0.cpu().item(), N + 1).to(device)
-            return self.noise_schedule.inverse_lambda(logSNR_steps)
-        elif skip_type == 'time_uniform':
-            return torch.linspace(t_T, t_0, N + 1).to(device)
-        elif skip_type == 'time_quadratic':
-            t_order = 2
-            t = torch.linspace(t_T**(1. / t_order), t_0**(1. / t_order), N + 1).pow(t_order).to(device)
-            return t
-        else:
-            raise ValueError("Unsupported skip_type {}, need to be 'logSNR' or 'time_uniform' or 'time_quadratic'".format(skip_type))
-
-    def get_orders_and_timesteps_for_singlestep_solver(self, steps, order, skip_type, t_T, t_0, device):
-        """
-        Get the order of each step for sampling by the singlestep DPM-Solver.
-        """
-        if order == 3:
-            K = steps // 3 + 1
-            if steps % 3 == 0:
-                orders = [3,] * (K - 2) + [2, 1]
-            elif steps % 3 == 1:
-                orders = [3,] * (K - 1) + [1]
-            else:
-                orders = [3,] * (K - 1) + [2]
-        elif order == 2:
-            if steps % 2 == 0:
-                K = steps // 2
-                orders = [2,] * K
-            else:
-                K = steps // 2 + 1
-                orders = [2,] * (K - 1) + [1]
-        elif order == 1:
-            K = steps
-            orders = [1,] * steps
-        else:
-            raise ValueError("'order' must be '1' or '2' or '3'.")
-        if skip_type == 'logSNR':
-            # To reproduce the results in DPM-Solver paper
-            timesteps_outer = self.get_time_steps(skip_type, t_T, t_0, K, device)
-        else:
-            timesteps_outer = self.get_time_steps(skip_type, t_T, t_0, steps, device)[torch.cumsum(torch.tensor([0,] + orders), 0).to(device)]
-        return timesteps_outer, orders
-
-    def denoise_to_zero_fn(self, x, s):
-        """
-        Denoise at the final step, which is equivalent to solve the ODE from lambda_s to infty by first-order discretization. 
-        """
-        return self.data_prediction_fn(x, s)
-
-    def multistep_uni_pc_update(self, x, model_prev_list, t_prev_list, t, order, **kwargs):
-        if len(t.shape) == 0:
-            t = t.view(-1)
-        if 'bh' in self.variant:
-            return self.multistep_uni_pc_bh_update(x, model_prev_list, t_prev_list, t, order, **kwargs)
-        else:
-            assert self.variant == 'vary_coeff'
-            return self.multistep_uni_pc_vary_update(x, model_prev_list, t_prev_list, t, order, **kwargs)
-
-    def multistep_uni_pc_vary_update(self, x, model_prev_list, t_prev_list, t, order, use_corrector=True):
-        print(f'using unified predictor-corrector with order {order} (solver type: vary coeff)')
-        ns = self.noise_schedule
-        assert order <= len(model_prev_list)
-
-        # first compute rks
-        t_prev_0 = t_prev_list[-1]
-        lambda_prev_0 = ns.marginal_lambda(t_prev_0)
-        lambda_t = ns.marginal_lambda(t)
-        model_prev_0 = model_prev_list[-1]
-        sigma_prev_0, sigma_t = ns.marginal_std(t_prev_0), ns.marginal_std(t)
-        log_alpha_t = ns.marginal_log_mean_coeff(t)
-        alpha_t = torch.exp(log_alpha_t)
-
-        h = lambda_t - lambda_prev_0
-
-        rks = []
-        D1s = []
-        for i in range(1, order):
-            t_prev_i = t_prev_list[-(i + 1)]
-            model_prev_i = model_prev_list[-(i + 1)]
-            lambda_prev_i = ns.marginal_lambda(t_prev_i)
-            rk = (lambda_prev_i - lambda_prev_0) / h
-            rks.append(rk)
-            D1s.append((model_prev_i - model_prev_0) / rk)
-
-        rks.append(1.)
-        rks = torch.tensor(rks, device=x.device)
-
-        K = len(rks)
-        # build C matrix
-        C = []
-
-        col = torch.ones_like(rks)
-        for k in range(1, K + 1):
-            C.append(col)
-            col = col * rks / (k + 1) 
-        C = torch.stack(C, dim=1)
-
-        if len(D1s) > 0:
-            D1s = torch.stack(D1s, dim=1) # (B, K)
-            C_inv_p = torch.linalg.inv(C[:-1, :-1])
-            A_p = C_inv_p
-
-        if use_corrector:
-            print('using corrector')
-            C_inv = torch.linalg.inv(C)
-            A_c = C_inv
-
-        hh = -h if self.predict_x0 else h
-        h_phi_1 = torch.expm1(hh)
-        h_phi_ks = []
-        factorial_k = 1
-        h_phi_k = h_phi_1
-        for k in range(1, K + 2):
-            h_phi_ks.append(h_phi_k)
-            h_phi_k = h_phi_k / hh - 1 / factorial_k
-            factorial_k *= (k + 1)
-
-        model_t = None
-        if self.predict_x0:
-            x_t_ = (
-                sigma_t / sigma_prev_0 * x
-                - alpha_t * h_phi_1 * model_prev_0
-            )
-            # now predictor
-            x_t = x_t_
-            if len(D1s) > 0:
-                # compute the residuals for predictor
-                for k in range(K - 1):
-                    x_t = x_t - alpha_t * h_phi_ks[k + 1] * torch.einsum('bkchw,k->bchw', D1s, A_p[k])
-            # now corrector
-            if use_corrector:
-                model_t = self.model_fn(x_t, t)
-                D1_t = (model_t - model_prev_0)
-                x_t = x_t_
-                k = 0
-                for k in range(K - 1):
-                    x_t = x_t - alpha_t * h_phi_ks[k + 1] * torch.einsum('bkchw,k->bchw', D1s, A_c[k][:-1])
-                x_t = x_t - alpha_t * h_phi_ks[K] * (D1_t * A_c[k][-1])
-        else:
-            log_alpha_prev_0, log_alpha_t = ns.marginal_log_mean_coeff(t_prev_0), ns.marginal_log_mean_coeff(t)
-            x_t_ = (
-                (torch.exp(log_alpha_t - log_alpha_prev_0)) * x
-                - (sigma_t * h_phi_1) * model_prev_0
-            )
-            # now predictor
-            x_t = x_t_
-            if len(D1s) > 0:
-                # compute the residuals for predictor
-                for k in range(K - 1):
-                    x_t = x_t - sigma_t * h_phi_ks[k + 1] * torch.einsum('bkchw,k->bchw', D1s, A_p[k])
-            # now corrector
-            if use_corrector:
-                model_t = self.model_fn(x_t, t)
-                D1_t = (model_t - model_prev_0)
-                x_t = x_t_
-                k = 0
-                for k in range(K - 1):
-                    x_t = x_t - sigma_t * h_phi_ks[k + 1] * torch.einsum('bkchw,k->bchw', D1s, A_c[k][:-1])
-                x_t = x_t - sigma_t * h_phi_ks[K] * (D1_t * A_c[k][-1])
-        return x_t, model_t
-
-    def multistep_uni_pc_bh_update(self, x, model_prev_list, t_prev_list, t, order, x_t=None, use_corrector=True):
-        # print(f'using unified predictor-corrector with order {order} (solver type: B(h))')
-        ns = self.noise_schedule
-        assert order <= len(model_prev_list)
-        dims = x.dim()
-
-        # first compute rks
-        t_prev_0 = t_prev_list[-1]
-        lambda_prev_0 = ns.marginal_lambda(t_prev_0)
-        lambda_t = ns.marginal_lambda(t)
-        model_prev_0 = model_prev_list[-1]
-        sigma_prev_0, sigma_t = ns.marginal_std(t_prev_0), ns.marginal_std(t)
-        log_alpha_prev_0, log_alpha_t = ns.marginal_log_mean_coeff(t_prev_0), ns.marginal_log_mean_coeff(t)
-        alpha_t = torch.exp(log_alpha_t)
-
-        h = lambda_t - lambda_prev_0
-
-        rks = []
-        D1s = []
-        for i in range(1, order):
-            t_prev_i = t_prev_list[-(i + 1)]
-            model_prev_i = model_prev_list[-(i + 1)]
-            lambda_prev_i = ns.marginal_lambda(t_prev_i)
-            rk = ((lambda_prev_i - lambda_prev_0) / h)[0]
-            rks.append(rk)
-            D1s.append((model_prev_i - model_prev_0) / rk)
-
-        rks.append(1.)
-        rks = torch.tensor(rks, device=x.device)
-
-        R = []
-        b = []
-
-        hh = -h[0] if self.predict_x0 else h[0]
-        h_phi_1 = torch.expm1(hh) # h\phi_1(h) = e^h - 1
-        h_phi_k = h_phi_1 / hh - 1
-
-        factorial_i = 1
-
-        if self.variant == 'bh1':
-            B_h = hh
-        elif self.variant == 'bh2':
-            B_h = torch.expm1(hh)
-        else:
-            raise NotImplementedError()
-            
-        for i in range(1, order + 1):
-            R.append(torch.pow(rks, i - 1))
-            b.append(h_phi_k * factorial_i / B_h)
-            factorial_i *= (i + 1)
-            h_phi_k = h_phi_k / hh - 1 / factorial_i 
-
-        R = torch.stack(R)
-        b = torch.tensor(b, device=x.device)
-
-        # now predictor
-        use_predictor = len(D1s) > 0 and x_t is None
-        if len(D1s) > 0:
-            D1s = torch.stack(D1s, dim=1) # (B, K)
-            if x_t is None:
-                # for order 2, we use a simplified version
-                if order == 2:
-                    rhos_p = torch.tensor([0.5], device=b.device)
-                else:
-                    rhos_p = torch.linalg.solve(R[:-1, :-1], b[:-1])
-        else:
-            D1s = None
-
-        if use_corrector:
-            # print('using corrector')
-            # for order 1, we use a simplified version
-            if order == 1:
-                rhos_c = torch.tensor([0.5], device=b.device)
-            else:
-                rhos_c = torch.linalg.solve(R, b)
-
-        model_t = None
-        if self.predict_x0:
-            x_t_ = (
-                expand_dims(sigma_t / sigma_prev_0, dims) * x
-                - expand_dims(alpha_t * h_phi_1, dims)* model_prev_0
-            )
-
-            if x_t is None:
-                if use_predictor:
-                    pred_res = torch.einsum('k,bkchw->bchw', rhos_p, D1s)
-                else:
-                    pred_res = 0
-                x_t = x_t_ - expand_dims(alpha_t * B_h, dims) * pred_res
-
-            if use_corrector:
-                model_t = self.model_fn(x_t, t)
-                if D1s is not None:
-                    corr_res = torch.einsum('k,bkchw->bchw', rhos_c[:-1], D1s)
-                else:
-                    corr_res = 0
-                D1_t = (model_t - model_prev_0)
-                x_t = x_t_ - expand_dims(alpha_t * B_h, dims) * (corr_res + rhos_c[-1] * D1_t)
-        else:
-            x_t_ = (
-                expand_dims(torch.exp(log_alpha_t - log_alpha_prev_0), dims) * x
-                - expand_dims(sigma_t * h_phi_1, dims) * model_prev_0
-            )
-            if x_t is None:
-                if use_predictor:
-                    pred_res = torch.einsum('k,bkchw->bchw', rhos_p, D1s)
-                else:
-                    pred_res = 0
-                x_t = x_t_ - expand_dims(sigma_t * B_h, dims) * pred_res
-
-            if use_corrector:
-                model_t = self.model_fn(x_t, t)
-                if D1s is not None:
-                    corr_res = torch.einsum('k,bkchw->bchw', rhos_c[:-1], D1s)
-                else:
-                    corr_res = 0
-                D1_t = (model_t - model_prev_0)
-                x_t = x_t_ - expand_dims(sigma_t * B_h, dims) * (corr_res + rhos_c[-1] * D1_t)
-        return x_t, model_t
-
-
-    def sample(self, x, timesteps, t_start=None, t_end=None, order=3, skip_type='time_uniform',
-        method='singlestep', lower_order_final=True, denoise_to_zero=False, solver_type='dpm_solver',
-        atol=0.0078, rtol=0.05, corrector=False, callback=None, disable_pbar=False
-    ):
-        # t_0 = 1. / self.noise_schedule.total_N if t_end is None else t_end
-        # t_T = self.noise_schedule.T if t_start is None else t_start
-        device = x.device
-        steps = len(timesteps) - 1
-        if method == 'multistep':
-            assert steps >= order
-            # timesteps = self.get_time_steps(skip_type=skip_type, t_T=t_T, t_0=t_0, N=steps, device=device)
-            assert timesteps.shape[0] - 1 == steps
-            # with torch.no_grad():
-            for step_index in trange(steps, disable=disable_pbar):
-                if step_index == 0:
-                    vec_t = timesteps[0].expand((x.shape[0]))
-                    model_prev_list = [self.model_fn(x, vec_t)]
-                    t_prev_list = [vec_t]
-                elif step_index < order:
-                    init_order = step_index
-                # Init the first `order` values by lower order multistep DPM-Solver.
-                # for init_order in range(1, order):
-                    vec_t = timesteps[init_order].expand(x.shape[0])
-                    x, model_x = self.multistep_uni_pc_update(x, model_prev_list, t_prev_list, vec_t, init_order, use_corrector=True)
-                    if model_x is None:
-                        model_x = self.model_fn(x, vec_t)
-                    model_prev_list.append(model_x)
-                    t_prev_list.append(vec_t)
-                else:
-                    extra_final_step = 0
-                    if step_index == (steps - 1):
-                        extra_final_step = 1
-                    for step in range(step_index, step_index + 1 + extra_final_step):
-                        vec_t = timesteps[step].expand(x.shape[0])
-                        if lower_order_final:
-                            step_order = min(order, steps + 1 - step)
-                        else:
-                            step_order = order
-                        # print('this step order:', step_order)
-                        if step == steps:
-                            # print('do not run corrector at the last step')
-                            use_corrector = False
-                        else:
-                            use_corrector = True
-                        x, model_x =  self.multistep_uni_pc_update(x, model_prev_list, t_prev_list, vec_t, step_order, use_corrector=use_corrector)
-                        for i in range(order - 1):
-                            t_prev_list[i] = t_prev_list[i + 1]
-                            model_prev_list[i] = model_prev_list[i + 1]
-                        t_prev_list[-1] = vec_t
-                        # We do not need to evaluate the final model value.
-                        if step < steps:
-                            if model_x is None:
-                                model_x = self.model_fn(x, vec_t)
-                            model_prev_list[-1] = model_x
-                if callback is not None:
-                    callback({'x': x, 'i': step_index, 'denoised': model_prev_list[-1]})
-        else:
-            raise NotImplementedError()
-        # if denoise_to_zero:
-        #     x = self.denoise_to_zero_fn(x, torch.ones((x.shape[0],)).to(device) * t_0)
-        return x
-
-
-#############################################################
-# other utility functions
-#############################################################
-
-def interpolate_fn(x, xp, yp):
-    """
-    A piecewise linear function y = f(x), using xp and yp as keypoints.
-    We implement f(x) in a differentiable way (i.e. applicable for autograd).
-    The function f(x) is well-defined for all x-axis. (For x beyond the bounds of xp, we use the outmost points of xp to define the linear function.)
-
-    Args:
-        x: PyTorch tensor with shape [N, C], where N is the batch size, C is the number of channels (we use C = 1 for DPM-Solver).
-        xp: PyTorch tensor with shape [C, K], where K is the number of keypoints.
-        yp: PyTorch tensor with shape [C, K].
-    Returns:
-        The function values f(x), with shape [N, C].
-    """
-    N, K = x.shape[0], xp.shape[1]
-    all_x = torch.cat([x.unsqueeze(2), xp.unsqueeze(0).repeat((N, 1, 1))], dim=2)
-    sorted_all_x, x_indices = torch.sort(all_x, dim=2)
-    x_idx = torch.argmin(x_indices, dim=2)
-    cand_start_idx = x_idx - 1
-    start_idx = torch.where(
-        torch.eq(x_idx, 0),
-        torch.tensor(1, device=x.device),
-        torch.where(
-            torch.eq(x_idx, K), torch.tensor(K - 2, device=x.device), cand_start_idx,
-        ),
-    )
-    end_idx = torch.where(torch.eq(start_idx, cand_start_idx), start_idx + 2, start_idx + 1)
-    start_x = torch.gather(sorted_all_x, dim=2, index=start_idx.unsqueeze(2)).squeeze(2)
-    end_x = torch.gather(sorted_all_x, dim=2, index=end_idx.unsqueeze(2)).squeeze(2)
-    start_idx2 = torch.where(
-        torch.eq(x_idx, 0),
-        torch.tensor(0, device=x.device),
-        torch.where(
-            torch.eq(x_idx, K), torch.tensor(K - 2, device=x.device), cand_start_idx,
-        ),
-    )
-    y_positions_expanded = yp.unsqueeze(0).expand(N, -1, -1)
-    start_y = torch.gather(y_positions_expanded, dim=2, index=start_idx2.unsqueeze(2)).squeeze(2)
-    end_y = torch.gather(y_positions_expanded, dim=2, index=(start_idx2 + 1).unsqueeze(2)).squeeze(2)
-    cand = start_y + (x - start_x) * (end_y - start_y) / (end_x - start_x)
-    return cand
-
-
-def expand_dims(v, dims):
-    """
-    Expand the tensor `v` to the dim `dims`.
-
-    Args:
-        `v`: a PyTorch tensor with shape [N].
-        `dim`: a `int`.
-    Returns:
-        a PyTorch tensor with shape [N, 1, 1, ..., 1] and the total dimension is `dims`.
-    """
-    return v[(...,) + (None,)*(dims - 1)]
-
-
-class SigmaConvert:
-    schedule = ""
-    def marginal_log_mean_coeff(self, sigma):
-        return 0.5 * torch.log(1 / ((sigma * sigma) + 1))
-
-    def marginal_alpha(self, t):
-        return torch.exp(self.marginal_log_mean_coeff(t))
-
-    def marginal_std(self, t):
-        return torch.sqrt(1. - torch.exp(2. * self.marginal_log_mean_coeff(t)))
-
-    def marginal_lambda(self, t):
-        """
-        Compute lambda_t = log(alpha_t) - log(sigma_t) of a given continuous-time label t in [0, T].
-        """
-        log_mean_coeff = self.marginal_log_mean_coeff(t)
-        log_std = 0.5 * torch.log(1. - torch.exp(2. * log_mean_coeff))
-        return log_mean_coeff - log_std
-
-def predict_eps_sigma(model, input, sigma_in, **kwargs):
-    sigma = sigma_in.view(sigma_in.shape[:1] + (1,) * (input.ndim - 1))
-    input = input * ((sigma ** 2 + 1.0) ** 0.5)
-    return  (input - model(input, sigma_in, **kwargs)) / sigma
-
-
-def sample_unipc(model, noise, sigmas, extra_args=None, callback=None, disable=False, variant='bh1'):
-        timesteps = sigmas.clone()
-        if sigmas[-1] == 0:
-            timesteps = sigmas[:]
-            timesteps[-1] = 0.001
-        else:
-            timesteps = sigmas.clone()
-        ns = SigmaConvert()
-
-        noise = noise / torch.sqrt(1.0 + timesteps[0] ** 2.0)
-        model_type = "noise"
-
-        model_fn = model_wrapper(
-            lambda input, sigma, **kwargs: predict_eps_sigma(model, input, sigma, **kwargs),
-            ns,
-            model_type=model_type,
-            guidance_type="uncond",
-            model_kwargs=extra_args,
-        )
-
-        order = min(3, len(timesteps) - 2)
-        uni_pc = UniPC(model_fn, ns, predict_x0=True, thresholding=False, variant=variant)
-        x = uni_pc.sample(noise, timesteps=timesteps, skip_type="time_uniform", method="multistep", order=order, lower_order_final=True, callback=callback, disable_pbar=disable)
-        x /= ns.marginal_alpha(timesteps[-1])
-        return x
-
-def sample_unipc_bh2(model, noise, sigmas, extra_args=None, callback=None, disable=False):
-    return sample_unipc(model, noise, sigmas, extra_args, callback, disable, variant='bh2')
\ No newline at end of file
diff --git a/MagicQuill/comfy/gligen.py b/MagicQuill/comfy/gligen.py
deleted file mode 100644
index 592522767e98bbe11b6e5e9411b1f734cbf92b9b..0000000000000000000000000000000000000000
--- a/MagicQuill/comfy/gligen.py
+++ /dev/null
@@ -1,343 +0,0 @@
-import torch
-from torch import nn
-from .ldm.modules.attention import CrossAttention
-from inspect import isfunction
-import comfy.ops
-ops = comfy.ops.manual_cast
-
-def exists(val):
-    return val is not None
-
-
-def uniq(arr):
-    return{el: True for el in arr}.keys()
-
-
-def default(val, d):
-    if exists(val):
-        return val
-    return d() if isfunction(d) else d
-
-
-# feedforward
-class GEGLU(nn.Module):
-    def __init__(self, dim_in, dim_out):
-        super().__init__()
-        self.proj = ops.Linear(dim_in, dim_out * 2)
-
-    def forward(self, x):
-        x, gate = self.proj(x).chunk(2, dim=-1)
-        return x * torch.nn.functional.gelu(gate)
-
-
-class FeedForward(nn.Module):
-    def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.):
-        super().__init__()
-        inner_dim = int(dim * mult)
-        dim_out = default(dim_out, dim)
-        project_in = nn.Sequential(
-            ops.Linear(dim, inner_dim),
-            nn.GELU()
-        ) if not glu else GEGLU(dim, inner_dim)
-
-        self.net = nn.Sequential(
-            project_in,
-            nn.Dropout(dropout),
-            ops.Linear(inner_dim, dim_out)
-        )
-
-    def forward(self, x):
-        return self.net(x)
-
-
-class GatedCrossAttentionDense(nn.Module):
-    def __init__(self, query_dim, context_dim, n_heads, d_head):
-        super().__init__()
-
-        self.attn = CrossAttention(
-            query_dim=query_dim,
-            context_dim=context_dim,
-            heads=n_heads,
-            dim_head=d_head,
-            operations=ops)
-        self.ff = FeedForward(query_dim, glu=True)
-
-        self.norm1 = ops.LayerNorm(query_dim)
-        self.norm2 = ops.LayerNorm(query_dim)
-
-        self.register_parameter('alpha_attn', nn.Parameter(torch.tensor(0.)))
-        self.register_parameter('alpha_dense', nn.Parameter(torch.tensor(0.)))
-
-        # this can be useful: we can externally change magnitude of tanh(alpha)
-        # for example, when it is set to 0, then the entire model is same as
-        # original one
-        self.scale = 1
-
-    def forward(self, x, objs):
-
-        x = x + self.scale * \
-            torch.tanh(self.alpha_attn) * self.attn(self.norm1(x), objs, objs)
-        x = x + self.scale * \
-            torch.tanh(self.alpha_dense) * self.ff(self.norm2(x))
-
-        return x
-
-
-class GatedSelfAttentionDense(nn.Module):
-    def __init__(self, query_dim, context_dim, n_heads, d_head):
-        super().__init__()
-
-        # we need a linear projection since we need cat visual feature and obj
-        # feature
-        self.linear = ops.Linear(context_dim, query_dim)
-
-        self.attn = CrossAttention(
-            query_dim=query_dim,
-            context_dim=query_dim,
-            heads=n_heads,
-            dim_head=d_head,
-            operations=ops)
-        self.ff = FeedForward(query_dim, glu=True)
-
-        self.norm1 = ops.LayerNorm(query_dim)
-        self.norm2 = ops.LayerNorm(query_dim)
-
-        self.register_parameter('alpha_attn', nn.Parameter(torch.tensor(0.)))
-        self.register_parameter('alpha_dense', nn.Parameter(torch.tensor(0.)))
-
-        # this can be useful: we can externally change magnitude of tanh(alpha)
-        # for example, when it is set to 0, then the entire model is same as
-        # original one
-        self.scale = 1
-
-    def forward(self, x, objs):
-
-        N_visual = x.shape[1]
-        objs = self.linear(objs)
-
-        x = x + self.scale * torch.tanh(self.alpha_attn) * self.attn(
-            self.norm1(torch.cat([x, objs], dim=1)))[:, 0:N_visual, :]
-        x = x + self.scale * \
-            torch.tanh(self.alpha_dense) * self.ff(self.norm2(x))
-
-        return x
-
-
-class GatedSelfAttentionDense2(nn.Module):
-    def __init__(self, query_dim, context_dim, n_heads, d_head):
-        super().__init__()
-
-        # we need a linear projection since we need cat visual feature and obj
-        # feature
-        self.linear = ops.Linear(context_dim, query_dim)
-
-        self.attn = CrossAttention(
-            query_dim=query_dim, context_dim=query_dim, dim_head=d_head, operations=ops)
-        self.ff = FeedForward(query_dim, glu=True)
-
-        self.norm1 = ops.LayerNorm(query_dim)
-        self.norm2 = ops.LayerNorm(query_dim)
-
-        self.register_parameter('alpha_attn', nn.Parameter(torch.tensor(0.)))
-        self.register_parameter('alpha_dense', nn.Parameter(torch.tensor(0.)))
-
-        # this can be useful: we can externally change magnitude of tanh(alpha)
-        # for example, when it is set to 0, then the entire model is same as
-        # original one
-        self.scale = 1
-
-    def forward(self, x, objs):
-
-        B, N_visual, _ = x.shape
-        B, N_ground, _ = objs.shape
-
-        objs = self.linear(objs)
-
-        # sanity check
-        size_v = math.sqrt(N_visual)
-        size_g = math.sqrt(N_ground)
-        assert int(size_v) == size_v, "Visual tokens must be square rootable"
-        assert int(size_g) == size_g, "Grounding tokens must be square rootable"
-        size_v = int(size_v)
-        size_g = int(size_g)
-
-        # select grounding token and resize it to visual token size as residual
-        out = self.attn(self.norm1(torch.cat([x, objs], dim=1)))[
-            :, N_visual:, :]
-        out = out.permute(0, 2, 1).reshape(B, -1, size_g, size_g)
-        out = torch.nn.functional.interpolate(
-            out, (size_v, size_v), mode='bicubic')
-        residual = out.reshape(B, -1, N_visual).permute(0, 2, 1)
-
-        # add residual to visual feature
-        x = x + self.scale * torch.tanh(self.alpha_attn) * residual
-        x = x + self.scale * \
-            torch.tanh(self.alpha_dense) * self.ff(self.norm2(x))
-
-        return x
-
-
-class FourierEmbedder():
-    def __init__(self, num_freqs=64, temperature=100):
-
-        self.num_freqs = num_freqs
-        self.temperature = temperature
-        self.freq_bands = temperature ** (torch.arange(num_freqs) / num_freqs)
-
-    @torch.no_grad()
-    def __call__(self, x, cat_dim=-1):
-        "x: arbitrary shape of tensor. dim: cat dim"
-        out = []
-        for freq in self.freq_bands:
-            out.append(torch.sin(freq * x))
-            out.append(torch.cos(freq * x))
-        return torch.cat(out, cat_dim)
-
-
-class PositionNet(nn.Module):
-    def __init__(self, in_dim, out_dim, fourier_freqs=8):
-        super().__init__()
-        self.in_dim = in_dim
-        self.out_dim = out_dim
-
-        self.fourier_embedder = FourierEmbedder(num_freqs=fourier_freqs)
-        self.position_dim = fourier_freqs * 2 * 4  # 2 is sin&cos, 4 is xyxy
-
-        self.linears = nn.Sequential(
-            ops.Linear(self.in_dim + self.position_dim, 512),
-            nn.SiLU(),
-            ops.Linear(512, 512),
-            nn.SiLU(),
-            ops.Linear(512, out_dim),
-        )
-
-        self.null_positive_feature = torch.nn.Parameter(
-            torch.zeros([self.in_dim]))
-        self.null_position_feature = torch.nn.Parameter(
-            torch.zeros([self.position_dim]))
-
-    def forward(self, boxes, masks, positive_embeddings):
-        B, N, _ = boxes.shape
-        masks = masks.unsqueeze(-1)
-        positive_embeddings = positive_embeddings
-
-        # embedding position (it may includes padding as placeholder)
-        xyxy_embedding = self.fourier_embedder(boxes)  # B*N*4 --> B*N*C
-
-        # learnable null embedding
-        positive_null = self.null_positive_feature.to(device=boxes.device, dtype=boxes.dtype).view(1, 1, -1)
-        xyxy_null = self.null_position_feature.to(device=boxes.device, dtype=boxes.dtype).view(1, 1, -1)
-
-        # replace padding with learnable null embedding
-        positive_embeddings = positive_embeddings * \
-            masks + (1 - masks) * positive_null
-        xyxy_embedding = xyxy_embedding * masks + (1 - masks) * xyxy_null
-
-        objs = self.linears(
-            torch.cat([positive_embeddings, xyxy_embedding], dim=-1))
-        assert objs.shape == torch.Size([B, N, self.out_dim])
-        return objs
-
-
-class Gligen(nn.Module):
-    def __init__(self, modules, position_net, key_dim):
-        super().__init__()
-        self.module_list = nn.ModuleList(modules)
-        self.position_net = position_net
-        self.key_dim = key_dim
-        self.max_objs = 30
-        self.current_device = torch.device("cpu")
-
-    def _set_position(self, boxes, masks, positive_embeddings):
-        objs = self.position_net(boxes, masks, positive_embeddings)
-        def func(x, extra_options):
-            key = extra_options["transformer_index"]
-            module = self.module_list[key]
-            return module(x, objs.to(device=x.device, dtype=x.dtype))
-        return func
-
-    def set_position(self, latent_image_shape, position_params, device):
-        batch, c, h, w = latent_image_shape
-        masks = torch.zeros([self.max_objs], device="cpu")
-        boxes = []
-        positive_embeddings = []
-        for p in position_params:
-            x1 = (p[4]) / w
-            y1 = (p[3]) / h
-            x2 = (p[4] + p[2]) / w
-            y2 = (p[3] + p[1]) / h
-            masks[len(boxes)] = 1.0
-            boxes += [torch.tensor((x1, y1, x2, y2)).unsqueeze(0)]
-            positive_embeddings += [p[0]]
-        append_boxes = []
-        append_conds = []
-        if len(boxes) < self.max_objs:
-            append_boxes = [torch.zeros(
-                [self.max_objs - len(boxes), 4], device="cpu")]
-            append_conds = [torch.zeros(
-                [self.max_objs - len(boxes), self.key_dim], device="cpu")]
-
-        box_out = torch.cat(
-            boxes + append_boxes).unsqueeze(0).repeat(batch, 1, 1)
-        masks = masks.unsqueeze(0).repeat(batch, 1)
-        conds = torch.cat(positive_embeddings +
-                          append_conds).unsqueeze(0).repeat(batch, 1, 1)
-        return self._set_position(
-            box_out.to(device),
-            masks.to(device),
-            conds.to(device))
-
-    def set_empty(self, latent_image_shape, device):
-        batch, c, h, w = latent_image_shape
-        masks = torch.zeros([self.max_objs], device="cpu").repeat(batch, 1)
-        box_out = torch.zeros([self.max_objs, 4],
-                              device="cpu").repeat(batch, 1, 1)
-        conds = torch.zeros([self.max_objs, self.key_dim],
-                            device="cpu").repeat(batch, 1, 1)
-        return self._set_position(
-            box_out.to(device),
-            masks.to(device),
-            conds.to(device))
-
-
-def load_gligen(sd):
-    sd_k = sd.keys()
-    output_list = []
-    key_dim = 768
-    for a in ["input_blocks", "middle_block", "output_blocks"]:
-        for b in range(20):
-            k_temp = filter(lambda k: "{}.{}.".format(a, b)
-                            in k and ".fuser." in k, sd_k)
-            k_temp = map(lambda k: (k, k.split(".fuser.")[-1]), k_temp)
-
-            n_sd = {}
-            for k in k_temp:
-                n_sd[k[1]] = sd[k[0]]
-            if len(n_sd) > 0:
-                query_dim = n_sd["linear.weight"].shape[0]
-                key_dim = n_sd["linear.weight"].shape[1]
-
-                if key_dim == 768:  # SD1.x
-                    n_heads = 8
-                    d_head = query_dim // n_heads
-                else:
-                    d_head = 64
-                    n_heads = query_dim // d_head
-
-                gated = GatedSelfAttentionDense(
-                    query_dim, key_dim, n_heads, d_head)
-                gated.load_state_dict(n_sd, strict=False)
-                output_list.append(gated)
-
-    if "position_net.null_positive_feature" in sd_k:
-        in_dim = sd["position_net.null_positive_feature"].shape[0]
-        out_dim = sd["position_net.linears.4.weight"].shape[0]
-
-        class WeightsLoader(torch.nn.Module):
-            pass
-        w = WeightsLoader()
-        w.position_net = PositionNet(in_dim, out_dim)
-        w.load_state_dict(sd, strict=False)
-
-    gligen = Gligen(output_list, w.position_net, key_dim)
-    return gligen
diff --git a/MagicQuill/comfy/k_diffusion/__pycache__/sampling.cpython-310.pyc b/MagicQuill/comfy/k_diffusion/__pycache__/sampling.cpython-310.pyc
deleted file mode 100644
index d73f59b875871a8131a0b5a885cc7db4ac962567..0000000000000000000000000000000000000000
Binary files a/MagicQuill/comfy/k_diffusion/__pycache__/sampling.cpython-310.pyc and /dev/null differ
diff --git a/MagicQuill/comfy/k_diffusion/__pycache__/utils.cpython-310.pyc b/MagicQuill/comfy/k_diffusion/__pycache__/utils.cpython-310.pyc
deleted file mode 100644
index 43a20158030f452a5e5ba9c92b00d33f7d3c4aa0..0000000000000000000000000000000000000000
Binary files a/MagicQuill/comfy/k_diffusion/__pycache__/utils.cpython-310.pyc and /dev/null differ
diff --git a/MagicQuill/comfy/k_diffusion/sampling.py b/MagicQuill/comfy/k_diffusion/sampling.py
deleted file mode 100644
index 5bb991e76a35d49157464731d993772b2cdfb013..0000000000000000000000000000000000000000
--- a/MagicQuill/comfy/k_diffusion/sampling.py
+++ /dev/null
@@ -1,843 +0,0 @@
-import math
-
-from scipy import integrate
-import torch
-from torch import nn
-import torchsde
-from tqdm.auto import trange, tqdm
-
-from . import utils
-
-
-def append_zero(x):
-    return torch.cat([x, x.new_zeros([1])])
-
-
-def get_sigmas_karras(n, sigma_min, sigma_max, rho=7., device='cpu'):
-    """Constructs the noise schedule of Karras et al. (2022)."""
-    ramp = torch.linspace(0, 1, n, device=device)
-    min_inv_rho = sigma_min ** (1 / rho)
-    max_inv_rho = sigma_max ** (1 / rho)
-    sigmas = (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
-    return append_zero(sigmas).to(device)
-
-
-def get_sigmas_exponential(n, sigma_min, sigma_max, device='cpu'):
-    """Constructs an exponential noise schedule."""
-    sigmas = torch.linspace(math.log(sigma_max), math.log(sigma_min), n, device=device).exp()
-    return append_zero(sigmas)
-
-
-def get_sigmas_polyexponential(n, sigma_min, sigma_max, rho=1., device='cpu'):
-    """Constructs an polynomial in log sigma noise schedule."""
-    ramp = torch.linspace(1, 0, n, device=device) ** rho
-    sigmas = torch.exp(ramp * (math.log(sigma_max) - math.log(sigma_min)) + math.log(sigma_min))
-    return append_zero(sigmas)
-
-
-def get_sigmas_vp(n, beta_d=19.9, beta_min=0.1, eps_s=1e-3, device='cpu'):
-    """Constructs a continuous VP noise schedule."""
-    t = torch.linspace(1, eps_s, n, device=device)
-    sigmas = torch.sqrt(torch.exp(beta_d * t ** 2 / 2 + beta_min * t) - 1)
-    return append_zero(sigmas)
-
-
-def to_d(x, sigma, denoised):
-    """Converts a denoiser output to a Karras ODE derivative."""
-    return (x - denoised) / utils.append_dims(sigma, x.ndim)
-
-
-def get_ancestral_step(sigma_from, sigma_to, eta=1.):
-    """Calculates the noise level (sigma_down) to step down to and the amount
-    of noise to add (sigma_up) when doing an ancestral sampling step."""
-    if not eta:
-        return sigma_to, 0.
-    sigma_up = min(sigma_to, eta * (sigma_to ** 2 * (sigma_from ** 2 - sigma_to ** 2) / sigma_from ** 2) ** 0.5)
-    sigma_down = (sigma_to ** 2 - sigma_up ** 2) ** 0.5
-    return sigma_down, sigma_up
-
-
-def default_noise_sampler(x):
-    return lambda sigma, sigma_next: torch.randn_like(x)
-
-
-class BatchedBrownianTree:
-    """A wrapper around torchsde.BrownianTree that enables batches of entropy."""
-
-    def __init__(self, x, t0, t1, seed=None, **kwargs):
-        self.cpu_tree = True
-        if "cpu" in kwargs:
-            self.cpu_tree = kwargs.pop("cpu")
-        t0, t1, self.sign = self.sort(t0, t1)
-        w0 = kwargs.get('w0', torch.zeros_like(x))
-        if seed is None:
-            seed = torch.randint(0, 2 ** 63 - 1, []).item()
-        self.batched = True
-        try:
-            assert len(seed) == x.shape[0]
-            w0 = w0[0]
-        except TypeError:
-            seed = [seed]
-            self.batched = False
-        if self.cpu_tree:
-            self.trees = [torchsde.BrownianTree(t0.cpu(), w0.cpu(), t1.cpu(), entropy=s, **kwargs) for s in seed]
-        else:
-            self.trees = [torchsde.BrownianTree(t0, w0, t1, entropy=s, **kwargs) for s in seed]
-
-    @staticmethod
-    def sort(a, b):
-        return (a, b, 1) if a < b else (b, a, -1)
-
-    def __call__(self, t0, t1):
-        t0, t1, sign = self.sort(t0, t1)
-        if self.cpu_tree:
-            w = torch.stack([tree(t0.cpu().float(), t1.cpu().float()).to(t0.dtype).to(t0.device) for tree in self.trees]) * (self.sign * sign)
-        else:
-            w = torch.stack([tree(t0, t1) for tree in self.trees]) * (self.sign * sign)
-
-        return w if self.batched else w[0]
-
-
-class BrownianTreeNoiseSampler:
-    """A noise sampler backed by a torchsde.BrownianTree.
-
-    Args:
-        x (Tensor): The tensor whose shape, device and dtype to use to generate
-            random samples.
-        sigma_min (float): The low end of the valid interval.
-        sigma_max (float): The high end of the valid interval.
-        seed (int or List[int]): The random seed. If a list of seeds is
-            supplied instead of a single integer, then the noise sampler will
-            use one BrownianTree per batch item, each with its own seed.
-        transform (callable): A function that maps sigma to the sampler's
-            internal timestep.
-    """
-
-    def __init__(self, x, sigma_min, sigma_max, seed=None, transform=lambda x: x, cpu=False):
-        self.transform = transform
-        t0, t1 = self.transform(torch.as_tensor(sigma_min)), self.transform(torch.as_tensor(sigma_max))
-        self.tree = BatchedBrownianTree(x, t0, t1, seed, cpu=cpu)
-
-    def __call__(self, sigma, sigma_next):
-        t0, t1 = self.transform(torch.as_tensor(sigma)), self.transform(torch.as_tensor(sigma_next))
-        return self.tree(t0, t1) / (t1 - t0).abs().sqrt()
-
-
-@torch.no_grad()
-def sample_euler(model, x, sigmas, extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1.):
-    """Implements Algorithm 2 (Euler steps) from Karras et al. (2022)."""
-    extra_args = {} if extra_args is None else extra_args
-    s_in = x.new_ones([x.shape[0]])
-    for i in trange(len(sigmas) - 1, disable=disable):
-        if s_churn > 0:
-            gamma = min(s_churn / (len(sigmas) - 1), 2 ** 0.5 - 1) if s_tmin <= sigmas[i] <= s_tmax else 0.
-            sigma_hat = sigmas[i] * (gamma + 1)
-        else:
-            gamma = 0
-            sigma_hat = sigmas[i]
-
-        if gamma > 0:
-            eps = torch.randn_like(x) * s_noise
-            x = x + eps * (sigma_hat ** 2 - sigmas[i] ** 2) ** 0.5
-        denoised = model(x, sigma_hat * s_in, **extra_args)
-        d = to_d(x, sigma_hat, denoised)
-        if callback is not None:
-            callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigma_hat, 'denoised': denoised})
-        dt = sigmas[i + 1] - sigma_hat
-        # Euler method
-        x = x + d * dt
-    return x
-
-
-@torch.no_grad()
-def sample_euler_ancestral(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None):
-    """Ancestral sampling with Euler method steps."""
-    extra_args = {} if extra_args is None else extra_args
-    noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler
-    s_in = x.new_ones([x.shape[0]])
-    for i in trange(len(sigmas) - 1, disable=disable):
-        denoised = model(x, sigmas[i] * s_in, **extra_args)
-        sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1], eta=eta)
-        if callback is not None:
-            callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
-        d = to_d(x, sigmas[i], denoised)
-        # Euler method
-        dt = sigma_down - sigmas[i]
-        x = x + d * dt
-        if sigmas[i + 1] > 0:
-            x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up
-    return x
-
-
-@torch.no_grad()
-def sample_heun(model, x, sigmas, extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1.):
-    """Implements Algorithm 2 (Heun steps) from Karras et al. (2022)."""
-    extra_args = {} if extra_args is None else extra_args
-    s_in = x.new_ones([x.shape[0]])
-    for i in trange(len(sigmas) - 1, disable=disable):
-        if s_churn > 0:
-            gamma = min(s_churn / (len(sigmas) - 1), 2 ** 0.5 - 1) if s_tmin <= sigmas[i] <= s_tmax else 0.
-            sigma_hat = sigmas[i] * (gamma + 1)
-        else:
-            gamma = 0
-            sigma_hat = sigmas[i]
-
-        sigma_hat = sigmas[i] * (gamma + 1)
-        if gamma > 0:
-            eps = torch.randn_like(x) * s_noise
-            x = x + eps * (sigma_hat ** 2 - sigmas[i] ** 2) ** 0.5
-        denoised = model(x, sigma_hat * s_in, **extra_args)
-        d = to_d(x, sigma_hat, denoised)
-        if callback is not None:
-            callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigma_hat, 'denoised': denoised})
-        dt = sigmas[i + 1] - sigma_hat
-        if sigmas[i + 1] == 0:
-            # Euler method
-            x = x + d * dt
-        else:
-            # Heun's method
-            x_2 = x + d * dt
-            denoised_2 = model(x_2, sigmas[i + 1] * s_in, **extra_args)
-            d_2 = to_d(x_2, sigmas[i + 1], denoised_2)
-            d_prime = (d + d_2) / 2
-            x = x + d_prime * dt
-    return x
-
-
-@torch.no_grad()
-def sample_dpm_2(model, x, sigmas, extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1.):
-    """A sampler inspired by DPM-Solver-2 and Algorithm 2 from Karras et al. (2022)."""
-    extra_args = {} if extra_args is None else extra_args
-    s_in = x.new_ones([x.shape[0]])
-    for i in trange(len(sigmas) - 1, disable=disable):
-        if s_churn > 0:
-            gamma = min(s_churn / (len(sigmas) - 1), 2 ** 0.5 - 1) if s_tmin <= sigmas[i] <= s_tmax else 0.
-            sigma_hat = sigmas[i] * (gamma + 1)
-        else:
-            gamma = 0
-            sigma_hat = sigmas[i]
-
-        if gamma > 0:
-            eps = torch.randn_like(x) * s_noise
-            x = x + eps * (sigma_hat ** 2 - sigmas[i] ** 2) ** 0.5
-        denoised = model(x, sigma_hat * s_in, **extra_args)
-        d = to_d(x, sigma_hat, denoised)
-        if callback is not None:
-            callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigma_hat, 'denoised': denoised})
-        if sigmas[i + 1] == 0:
-            # Euler method
-            dt = sigmas[i + 1] - sigma_hat
-            x = x + d * dt
-        else:
-            # DPM-Solver-2
-            sigma_mid = sigma_hat.log().lerp(sigmas[i + 1].log(), 0.5).exp()
-            dt_1 = sigma_mid - sigma_hat
-            dt_2 = sigmas[i + 1] - sigma_hat
-            x_2 = x + d * dt_1
-            denoised_2 = model(x_2, sigma_mid * s_in, **extra_args)
-            d_2 = to_d(x_2, sigma_mid, denoised_2)
-            x = x + d_2 * dt_2
-    return x
-
-
-@torch.no_grad()
-def sample_dpm_2_ancestral(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None):
-    """Ancestral sampling with DPM-Solver second-order steps."""
-    extra_args = {} if extra_args is None else extra_args
-    noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler
-    s_in = x.new_ones([x.shape[0]])
-    for i in trange(len(sigmas) - 1, disable=disable):
-        denoised = model(x, sigmas[i] * s_in, **extra_args)
-        sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1], eta=eta)
-        if callback is not None:
-            callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
-        d = to_d(x, sigmas[i], denoised)
-        if sigma_down == 0:
-            # Euler method
-            dt = sigma_down - sigmas[i]
-            x = x + d * dt
-        else:
-            # DPM-Solver-2
-            sigma_mid = sigmas[i].log().lerp(sigma_down.log(), 0.5).exp()
-            dt_1 = sigma_mid - sigmas[i]
-            dt_2 = sigma_down - sigmas[i]
-            x_2 = x + d * dt_1
-            denoised_2 = model(x_2, sigma_mid * s_in, **extra_args)
-            d_2 = to_d(x_2, sigma_mid, denoised_2)
-            x = x + d_2 * dt_2
-            x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up
-    return x
-
-
-def linear_multistep_coeff(order, t, i, j):
-    if order - 1 > i:
-        raise ValueError(f'Order {order} too high for step {i}')
-    def fn(tau):
-        prod = 1.
-        for k in range(order):
-            if j == k:
-                continue
-            prod *= (tau - t[i - k]) / (t[i - j] - t[i - k])
-        return prod
-    return integrate.quad(fn, t[i], t[i + 1], epsrel=1e-4)[0]
-
-
-@torch.no_grad()
-def sample_lms(model, x, sigmas, extra_args=None, callback=None, disable=None, order=4):
-    extra_args = {} if extra_args is None else extra_args
-    s_in = x.new_ones([x.shape[0]])
-    sigmas_cpu = sigmas.detach().cpu().numpy()
-    ds = []
-    for i in trange(len(sigmas) - 1, disable=disable):
-        denoised = model(x, sigmas[i] * s_in, **extra_args)
-        d = to_d(x, sigmas[i], denoised)
-        ds.append(d)
-        if len(ds) > order:
-            ds.pop(0)
-        if callback is not None:
-            callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
-        cur_order = min(i + 1, order)
-        coeffs = [linear_multistep_coeff(cur_order, sigmas_cpu, i, j) for j in range(cur_order)]
-        x = x + sum(coeff * d for coeff, d in zip(coeffs, reversed(ds)))
-    return x
-
-
-class PIDStepSizeController:
-    """A PID controller for ODE adaptive step size control."""
-    def __init__(self, h, pcoeff, icoeff, dcoeff, order=1, accept_safety=0.81, eps=1e-8):
-        self.h = h
-        self.b1 = (pcoeff + icoeff + dcoeff) / order
-        self.b2 = -(pcoeff + 2 * dcoeff) / order
-        self.b3 = dcoeff / order
-        self.accept_safety = accept_safety
-        self.eps = eps
-        self.errs = []
-
-    def limiter(self, x):
-        return 1 + math.atan(x - 1)
-
-    def propose_step(self, error):
-        inv_error = 1 / (float(error) + self.eps)
-        if not self.errs:
-            self.errs = [inv_error, inv_error, inv_error]
-        self.errs[0] = inv_error
-        factor = self.errs[0] ** self.b1 * self.errs[1] ** self.b2 * self.errs[2] ** self.b3
-        factor = self.limiter(factor)
-        accept = factor >= self.accept_safety
-        if accept:
-            self.errs[2] = self.errs[1]
-            self.errs[1] = self.errs[0]
-        self.h *= factor
-        return accept
-
-
-class DPMSolver(nn.Module):
-    """DPM-Solver. See https://arxiv.org/abs/2206.00927."""
-
-    def __init__(self, model, extra_args=None, eps_callback=None, info_callback=None):
-        super().__init__()
-        self.model = model
-        self.extra_args = {} if extra_args is None else extra_args
-        self.eps_callback = eps_callback
-        self.info_callback = info_callback
-
-    def t(self, sigma):
-        return -sigma.log()
-
-    def sigma(self, t):
-        return t.neg().exp()
-
-    def eps(self, eps_cache, key, x, t, *args, **kwargs):
-        if key in eps_cache:
-            return eps_cache[key], eps_cache
-        sigma = self.sigma(t) * x.new_ones([x.shape[0]])
-        eps = (x - self.model(x, sigma, *args, **self.extra_args, **kwargs)) / self.sigma(t)
-        if self.eps_callback is not None:
-            self.eps_callback()
-        return eps, {key: eps, **eps_cache}
-
-    def dpm_solver_1_step(self, x, t, t_next, eps_cache=None):
-        eps_cache = {} if eps_cache is None else eps_cache
-        h = t_next - t
-        eps, eps_cache = self.eps(eps_cache, 'eps', x, t)
-        x_1 = x - self.sigma(t_next) * h.expm1() * eps
-        return x_1, eps_cache
-
-    def dpm_solver_2_step(self, x, t, t_next, r1=1 / 2, eps_cache=None):
-        eps_cache = {} if eps_cache is None else eps_cache
-        h = t_next - t
-        eps, eps_cache = self.eps(eps_cache, 'eps', x, t)
-        s1 = t + r1 * h
-        u1 = x - self.sigma(s1) * (r1 * h).expm1() * eps
-        eps_r1, eps_cache = self.eps(eps_cache, 'eps_r1', u1, s1)
-        x_2 = x - self.sigma(t_next) * h.expm1() * eps - self.sigma(t_next) / (2 * r1) * h.expm1() * (eps_r1 - eps)
-        return x_2, eps_cache
-
-    def dpm_solver_3_step(self, x, t, t_next, r1=1 / 3, r2=2 / 3, eps_cache=None):
-        eps_cache = {} if eps_cache is None else eps_cache
-        h = t_next - t
-        eps, eps_cache = self.eps(eps_cache, 'eps', x, t)
-        s1 = t + r1 * h
-        s2 = t + r2 * h
-        u1 = x - self.sigma(s1) * (r1 * h).expm1() * eps
-        eps_r1, eps_cache = self.eps(eps_cache, 'eps_r1', u1, s1)
-        u2 = x - self.sigma(s2) * (r2 * h).expm1() * eps - self.sigma(s2) * (r2 / r1) * ((r2 * h).expm1() / (r2 * h) - 1) * (eps_r1 - eps)
-        eps_r2, eps_cache = self.eps(eps_cache, 'eps_r2', u2, s2)
-        x_3 = x - self.sigma(t_next) * h.expm1() * eps - self.sigma(t_next) / r2 * (h.expm1() / h - 1) * (eps_r2 - eps)
-        return x_3, eps_cache
-
-    def dpm_solver_fast(self, x, t_start, t_end, nfe, eta=0., s_noise=1., noise_sampler=None):
-        noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler
-        if not t_end > t_start and eta:
-            raise ValueError('eta must be 0 for reverse sampling')
-
-        m = math.floor(nfe / 3) + 1
-        ts = torch.linspace(t_start, t_end, m + 1, device=x.device)
-
-        if nfe % 3 == 0:
-            orders = [3] * (m - 2) + [2, 1]
-        else:
-            orders = [3] * (m - 1) + [nfe % 3]
-
-        for i in range(len(orders)):
-            eps_cache = {}
-            t, t_next = ts[i], ts[i + 1]
-            if eta:
-                sd, su = get_ancestral_step(self.sigma(t), self.sigma(t_next), eta)
-                t_next_ = torch.minimum(t_end, self.t(sd))
-                su = (self.sigma(t_next) ** 2 - self.sigma(t_next_) ** 2) ** 0.5
-            else:
-                t_next_, su = t_next, 0.
-
-            eps, eps_cache = self.eps(eps_cache, 'eps', x, t)
-            denoised = x - self.sigma(t) * eps
-            if self.info_callback is not None:
-                self.info_callback({'x': x, 'i': i, 't': ts[i], 't_up': t, 'denoised': denoised})
-
-            if orders[i] == 1:
-                x, eps_cache = self.dpm_solver_1_step(x, t, t_next_, eps_cache=eps_cache)
-            elif orders[i] == 2:
-                x, eps_cache = self.dpm_solver_2_step(x, t, t_next_, eps_cache=eps_cache)
-            else:
-                x, eps_cache = self.dpm_solver_3_step(x, t, t_next_, eps_cache=eps_cache)
-
-            x = x + su * s_noise * noise_sampler(self.sigma(t), self.sigma(t_next))
-
-        return x
-
-    def dpm_solver_adaptive(self, x, t_start, t_end, order=3, rtol=0.05, atol=0.0078, h_init=0.05, pcoeff=0., icoeff=1., dcoeff=0., accept_safety=0.81, eta=0., s_noise=1., noise_sampler=None):
-        noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler
-        if order not in {2, 3}:
-            raise ValueError('order should be 2 or 3')
-        forward = t_end > t_start
-        if not forward and eta:
-            raise ValueError('eta must be 0 for reverse sampling')
-        h_init = abs(h_init) * (1 if forward else -1)
-        atol = torch.tensor(atol)
-        rtol = torch.tensor(rtol)
-        s = t_start
-        x_prev = x
-        accept = True
-        pid = PIDStepSizeController(h_init, pcoeff, icoeff, dcoeff, 1.5 if eta else order, accept_safety)
-        info = {'steps': 0, 'nfe': 0, 'n_accept': 0, 'n_reject': 0}
-
-        while s < t_end - 1e-5 if forward else s > t_end + 1e-5:
-            eps_cache = {}
-            t = torch.minimum(t_end, s + pid.h) if forward else torch.maximum(t_end, s + pid.h)
-            if eta:
-                sd, su = get_ancestral_step(self.sigma(s), self.sigma(t), eta)
-                t_ = torch.minimum(t_end, self.t(sd))
-                su = (self.sigma(t) ** 2 - self.sigma(t_) ** 2) ** 0.5
-            else:
-                t_, su = t, 0.
-
-            eps, eps_cache = self.eps(eps_cache, 'eps', x, s)
-            denoised = x - self.sigma(s) * eps
-
-            if order == 2:
-                x_low, eps_cache = self.dpm_solver_1_step(x, s, t_, eps_cache=eps_cache)
-                x_high, eps_cache = self.dpm_solver_2_step(x, s, t_, eps_cache=eps_cache)
-            else:
-                x_low, eps_cache = self.dpm_solver_2_step(x, s, t_, r1=1 / 3, eps_cache=eps_cache)
-                x_high, eps_cache = self.dpm_solver_3_step(x, s, t_, eps_cache=eps_cache)
-            delta = torch.maximum(atol, rtol * torch.maximum(x_low.abs(), x_prev.abs()))
-            error = torch.linalg.norm((x_low - x_high) / delta) / x.numel() ** 0.5
-            accept = pid.propose_step(error)
-            if accept:
-                x_prev = x_low
-                x = x_high + su * s_noise * noise_sampler(self.sigma(s), self.sigma(t))
-                s = t
-                info['n_accept'] += 1
-            else:
-                info['n_reject'] += 1
-            info['nfe'] += order
-            info['steps'] += 1
-
-            if self.info_callback is not None:
-                self.info_callback({'x': x, 'i': info['steps'] - 1, 't': s, 't_up': s, 'denoised': denoised, 'error': error, 'h': pid.h, **info})
-
-        return x, info
-
-
-@torch.no_grad()
-def sample_dpm_fast(model, x, sigma_min, sigma_max, n, extra_args=None, callback=None, disable=None, eta=0., s_noise=1., noise_sampler=None):
-    """DPM-Solver-Fast (fixed step size). See https://arxiv.org/abs/2206.00927."""
-    if sigma_min <= 0 or sigma_max <= 0:
-        raise ValueError('sigma_min and sigma_max must not be 0')
-    with tqdm(total=n, disable=disable) as pbar:
-        dpm_solver = DPMSolver(model, extra_args, eps_callback=pbar.update)
-        if callback is not None:
-            dpm_solver.info_callback = lambda info: callback({'sigma': dpm_solver.sigma(info['t']), 'sigma_hat': dpm_solver.sigma(info['t_up']), **info})
-        return dpm_solver.dpm_solver_fast(x, dpm_solver.t(torch.tensor(sigma_max)), dpm_solver.t(torch.tensor(sigma_min)), n, eta, s_noise, noise_sampler)
-
-
-@torch.no_grad()
-def sample_dpm_adaptive(model, x, sigma_min, sigma_max, extra_args=None, callback=None, disable=None, order=3, rtol=0.05, atol=0.0078, h_init=0.05, pcoeff=0., icoeff=1., dcoeff=0., accept_safety=0.81, eta=0., s_noise=1., noise_sampler=None, return_info=False):
-    """DPM-Solver-12 and 23 (adaptive step size). See https://arxiv.org/abs/2206.00927."""
-    if sigma_min <= 0 or sigma_max <= 0:
-        raise ValueError('sigma_min and sigma_max must not be 0')
-    with tqdm(disable=disable) as pbar:
-        dpm_solver = DPMSolver(model, extra_args, eps_callback=pbar.update)
-        if callback is not None:
-            dpm_solver.info_callback = lambda info: callback({'sigma': dpm_solver.sigma(info['t']), 'sigma_hat': dpm_solver.sigma(info['t_up']), **info})
-        x, info = dpm_solver.dpm_solver_adaptive(x, dpm_solver.t(torch.tensor(sigma_max)), dpm_solver.t(torch.tensor(sigma_min)), order, rtol, atol, h_init, pcoeff, icoeff, dcoeff, accept_safety, eta, s_noise, noise_sampler)
-    if return_info:
-        return x, info
-    return x
-
-
-@torch.no_grad()
-def sample_dpmpp_2s_ancestral(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None):
-    """Ancestral sampling with DPM-Solver++(2S) second-order steps."""
-    extra_args = {} if extra_args is None else extra_args
-    noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler
-    s_in = x.new_ones([x.shape[0]])
-    sigma_fn = lambda t: t.neg().exp()
-    t_fn = lambda sigma: sigma.log().neg()
-
-    for i in trange(len(sigmas) - 1, disable=disable):
-        denoised = model(x, sigmas[i] * s_in, **extra_args)
-        sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1], eta=eta)
-        if callback is not None:
-            callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
-        if sigma_down == 0:
-            # Euler method
-            d = to_d(x, sigmas[i], denoised)
-            dt = sigma_down - sigmas[i]
-            x = x + d * dt
-        else:
-            # DPM-Solver++(2S)
-            t, t_next = t_fn(sigmas[i]), t_fn(sigma_down)
-            r = 1 / 2
-            h = t_next - t
-            s = t + r * h
-            x_2 = (sigma_fn(s) / sigma_fn(t)) * x - (-h * r).expm1() * denoised
-            denoised_2 = model(x_2, sigma_fn(s) * s_in, **extra_args)
-            x = (sigma_fn(t_next) / sigma_fn(t)) * x - (-h).expm1() * denoised_2
-        # Noise addition
-        if sigmas[i + 1] > 0:
-            x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up
-    return x
-
-
-@torch.no_grad()
-def sample_dpmpp_sde(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, r=1 / 2):
-    """DPM-Solver++ (stochastic)."""
-    if len(sigmas) <= 1:
-        return x
-
-    sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max()
-    seed = extra_args.get("seed", None)
-    noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=seed, cpu=True) if noise_sampler is None else noise_sampler
-    extra_args = {} if extra_args is None else extra_args
-    s_in = x.new_ones([x.shape[0]])
-    sigma_fn = lambda t: t.neg().exp()
-    t_fn = lambda sigma: sigma.log().neg()
-
-    for i in trange(len(sigmas) - 1, disable=disable):
-        denoised = model(x, sigmas[i] * s_in, **extra_args)
-        if callback is not None:
-            callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
-        if sigmas[i + 1] == 0:
-            # Euler method
-            d = to_d(x, sigmas[i], denoised)
-            dt = sigmas[i + 1] - sigmas[i]
-            x = x + d * dt
-        else:
-            # DPM-Solver++
-            t, t_next = t_fn(sigmas[i]), t_fn(sigmas[i + 1])
-            h = t_next - t
-            s = t + h * r
-            fac = 1 / (2 * r)
-
-            # Step 1
-            sd, su = get_ancestral_step(sigma_fn(t), sigma_fn(s), eta)
-            s_ = t_fn(sd)
-            x_2 = (sigma_fn(s_) / sigma_fn(t)) * x - (t - s_).expm1() * denoised
-            x_2 = x_2 + noise_sampler(sigma_fn(t), sigma_fn(s)) * s_noise * su
-            denoised_2 = model(x_2, sigma_fn(s) * s_in, **extra_args)
-
-            # Step 2
-            sd, su = get_ancestral_step(sigma_fn(t), sigma_fn(t_next), eta)
-            t_next_ = t_fn(sd)
-            denoised_d = (1 - fac) * denoised + fac * denoised_2
-            x = (sigma_fn(t_next_) / sigma_fn(t)) * x - (t - t_next_).expm1() * denoised_d
-            x = x + noise_sampler(sigma_fn(t), sigma_fn(t_next)) * s_noise * su
-    return x
-
-
-@torch.no_grad()
-def sample_dpmpp_2m(model, x, sigmas, extra_args=None, callback=None, disable=None):
-    """DPM-Solver++(2M)."""
-    extra_args = {} if extra_args is None else extra_args
-    s_in = x.new_ones([x.shape[0]])
-    sigma_fn = lambda t: t.neg().exp()
-    t_fn = lambda sigma: sigma.log().neg()
-    old_denoised = None
-
-    for i in trange(len(sigmas) - 1, disable=disable):
-        denoised = model(x, sigmas[i] * s_in, **extra_args)
-        if callback is not None:
-            callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
-        t, t_next = t_fn(sigmas[i]), t_fn(sigmas[i + 1])
-        h = t_next - t
-        if old_denoised is None or sigmas[i + 1] == 0:
-            x = (sigma_fn(t_next) / sigma_fn(t)) * x - (-h).expm1() * denoised
-        else:
-            h_last = t - t_fn(sigmas[i - 1])
-            r = h_last / h
-            denoised_d = (1 + 1 / (2 * r)) * denoised - (1 / (2 * r)) * old_denoised
-            x = (sigma_fn(t_next) / sigma_fn(t)) * x - (-h).expm1() * denoised_d
-        old_denoised = denoised
-    return x
-
-@torch.no_grad()
-def sample_dpmpp_2m_sde(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, solver_type='midpoint'):
-    """DPM-Solver++(2M) SDE."""
-    if len(sigmas) <= 1:
-        return x
-
-    if solver_type not in {'heun', 'midpoint'}:
-        raise ValueError('solver_type must be \'heun\' or \'midpoint\'')
-
-    seed = extra_args.get("seed", None)
-    sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max()
-    noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=seed, cpu=True) if noise_sampler is None else noise_sampler
-    extra_args = {} if extra_args is None else extra_args
-    s_in = x.new_ones([x.shape[0]])
-
-    old_denoised = None
-    h_last = None
-    h = None
-
-    for i in trange(len(sigmas) - 1, disable=disable):
-        denoised = model(x, sigmas[i] * s_in, **extra_args)
-        if callback is not None:
-            callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
-        if sigmas[i + 1] == 0:
-            # Denoising step
-            x = denoised
-        else:
-            # DPM-Solver++(2M) SDE
-            t, s = -sigmas[i].log(), -sigmas[i + 1].log()
-            h = s - t
-            eta_h = eta * h
-
-            x = sigmas[i + 1] / sigmas[i] * (-eta_h).exp() * x + (-h - eta_h).expm1().neg() * denoised
-
-            if old_denoised is not None:
-                r = h_last / h
-                if solver_type == 'heun':
-                    x = x + ((-h - eta_h).expm1().neg() / (-h - eta_h) + 1) * (1 / r) * (denoised - old_denoised)
-                elif solver_type == 'midpoint':
-                    x = x + 0.5 * (-h - eta_h).expm1().neg() * (1 / r) * (denoised - old_denoised)
-
-            if eta:
-                x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * sigmas[i + 1] * (-2 * eta_h).expm1().neg().sqrt() * s_noise
-
-        old_denoised = denoised
-        h_last = h
-    return x
-
-@torch.no_grad()
-def sample_dpmpp_3m_sde(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None):
-    """DPM-Solver++(3M) SDE."""
-
-    if len(sigmas) <= 1:
-        return x
-
-    seed = extra_args.get("seed", None)
-    sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max()
-    noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=seed, cpu=True) if noise_sampler is None else noise_sampler
-    extra_args = {} if extra_args is None else extra_args
-    s_in = x.new_ones([x.shape[0]])
-
-    denoised_1, denoised_2 = None, None
-    h, h_1, h_2 = None, None, None
-
-    for i in trange(len(sigmas) - 1, disable=disable):
-        denoised = model(x, sigmas[i] * s_in, **extra_args)
-        if callback is not None:
-            callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
-        if sigmas[i + 1] == 0:
-            # Denoising step
-            x = denoised
-        else:
-            t, s = -sigmas[i].log(), -sigmas[i + 1].log()
-            h = s - t
-            h_eta = h * (eta + 1)
-
-            x = torch.exp(-h_eta) * x + (-h_eta).expm1().neg() * denoised
-
-            if h_2 is not None:
-                r0 = h_1 / h
-                r1 = h_2 / h
-                d1_0 = (denoised - denoised_1) / r0
-                d1_1 = (denoised_1 - denoised_2) / r1
-                d1 = d1_0 + (d1_0 - d1_1) * r0 / (r0 + r1)
-                d2 = (d1_0 - d1_1) / (r0 + r1)
-                phi_2 = h_eta.neg().expm1() / h_eta + 1
-                phi_3 = phi_2 / h_eta - 0.5
-                x = x + phi_2 * d1 - phi_3 * d2
-            elif h_1 is not None:
-                r = h_1 / h
-                d = (denoised - denoised_1) / r
-                phi_2 = h_eta.neg().expm1() / h_eta + 1
-                x = x + phi_2 * d
-
-            if eta:
-                x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * sigmas[i + 1] * (-2 * h * eta).expm1().neg().sqrt() * s_noise
-
-        denoised_1, denoised_2 = denoised, denoised_1
-        h_1, h_2 = h, h_1
-    return x
-
-@torch.no_grad()
-def sample_dpmpp_3m_sde_gpu(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None):
-    if len(sigmas) <= 1:
-        return x
-
-    sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max()
-    noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=extra_args.get("seed", None), cpu=False) if noise_sampler is None else noise_sampler
-    return sample_dpmpp_3m_sde(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, eta=eta, s_noise=s_noise, noise_sampler=noise_sampler)
-
-@torch.no_grad()
-def sample_dpmpp_2m_sde_gpu(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, solver_type='midpoint'):
-    if len(sigmas) <= 1:
-        return x
-
-    sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max()
-    noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=extra_args.get("seed", None), cpu=False) if noise_sampler is None else noise_sampler
-    return sample_dpmpp_2m_sde(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, eta=eta, s_noise=s_noise, noise_sampler=noise_sampler, solver_type=solver_type)
-
-@torch.no_grad()
-def sample_dpmpp_sde_gpu(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, r=1 / 2):
-    if len(sigmas) <= 1:
-        return x
-
-    sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max()
-    noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=extra_args.get("seed", None), cpu=False) if noise_sampler is None else noise_sampler
-    return sample_dpmpp_sde(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, eta=eta, s_noise=s_noise, noise_sampler=noise_sampler, r=r)
-
-
-def DDPMSampler_step(x, sigma, sigma_prev, noise, noise_sampler):
-    alpha_cumprod = 1 / ((sigma * sigma) + 1)
-    alpha_cumprod_prev = 1 / ((sigma_prev * sigma_prev) + 1)
-    alpha = (alpha_cumprod / alpha_cumprod_prev)
-
-    mu = (1.0 / alpha).sqrt() * (x - (1 - alpha) * noise / (1 - alpha_cumprod).sqrt())
-    if sigma_prev > 0:
-        mu += ((1 - alpha) * (1. - alpha_cumprod_prev) / (1. - alpha_cumprod)).sqrt() * noise_sampler(sigma, sigma_prev)
-    return mu
-
-def generic_step_sampler(model, x, sigmas, extra_args=None, callback=None, disable=None, noise_sampler=None, step_function=None):
-    extra_args = {} if extra_args is None else extra_args
-    noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler
-    s_in = x.new_ones([x.shape[0]])
-
-    for i in trange(len(sigmas) - 1, disable=disable):
-        denoised = model(x, sigmas[i] * s_in, **extra_args)
-        if callback is not None:
-            callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
-        x = step_function(x / torch.sqrt(1.0 + sigmas[i] ** 2.0), sigmas[i], sigmas[i + 1], (x - denoised) / sigmas[i], noise_sampler)
-        if sigmas[i + 1] != 0:
-            x *= torch.sqrt(1.0 + sigmas[i + 1] ** 2.0)
-    return x
-
-
-@torch.no_grad()
-def sample_ddpm(model, x, sigmas, extra_args=None, callback=None, disable=None, noise_sampler=None):
-    return generic_step_sampler(model, x, sigmas, extra_args, callback, disable, noise_sampler, DDPMSampler_step)
-
-@torch.no_grad()
-def sample_lcm(model, x, sigmas, extra_args=None, callback=None, disable=None, noise_sampler=None):
-    extra_args = {} if extra_args is None else extra_args
-    noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler
-    s_in = x.new_ones([x.shape[0]])
-    for i in trange(len(sigmas) - 1, disable=disable):
-        denoised = model(x, sigmas[i] * s_in, **extra_args)
-        if callback is not None:
-            callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
-
-        x = denoised
-        if sigmas[i + 1] > 0:
-            x = model.inner_model.inner_model.model_sampling.noise_scaling(sigmas[i + 1], noise_sampler(sigmas[i], sigmas[i + 1]), x)
-    return x
-
-
-
-@torch.no_grad()
-def sample_heunpp2(model, x, sigmas, extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1.):
-    # From MIT licensed: https://github.com/Carzit/sd-webui-samplers-scheduler/
-    extra_args = {} if extra_args is None else extra_args
-    s_in = x.new_ones([x.shape[0]])
-    s_end = sigmas[-1]
-    for i in trange(len(sigmas) - 1, disable=disable):
-        gamma = min(s_churn / (len(sigmas) - 1), 2 ** 0.5 - 1) if s_tmin <= sigmas[i] <= s_tmax else 0.
-        eps = torch.randn_like(x) * s_noise
-        sigma_hat = sigmas[i] * (gamma + 1)
-        if gamma > 0:
-            x = x + eps * (sigma_hat ** 2 - sigmas[i] ** 2) ** 0.5
-        denoised = model(x, sigma_hat * s_in, **extra_args)
-        d = to_d(x, sigma_hat, denoised)
-        if callback is not None:
-            callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigma_hat, 'denoised': denoised})
-        dt = sigmas[i + 1] - sigma_hat
-        if sigmas[i + 1] == s_end:
-            # Euler method
-            x = x + d * dt
-        elif sigmas[i + 2] == s_end:
-
-            # Heun's method
-            x_2 = x + d * dt
-            denoised_2 = model(x_2, sigmas[i + 1] * s_in, **extra_args)
-            d_2 = to_d(x_2, sigmas[i + 1], denoised_2)
-
-            w = 2 * sigmas[0]
-            w2 = sigmas[i+1]/w
-            w1 = 1 - w2
-
-            d_prime = d * w1 + d_2 * w2
-
-
-            x = x + d_prime * dt
-
-        else:
-            # Heun++
-            x_2 = x + d * dt
-            denoised_2 = model(x_2, sigmas[i + 1] * s_in, **extra_args)
-            d_2 = to_d(x_2, sigmas[i + 1], denoised_2)
-            dt_2 = sigmas[i + 2] - sigmas[i + 1]
-
-            x_3 = x_2 + d_2 * dt_2
-            denoised_3 = model(x_3, sigmas[i + 2] * s_in, **extra_args)
-            d_3 = to_d(x_3, sigmas[i + 2], denoised_3)
-
-            w = 3 * sigmas[0]
-            w2 = sigmas[i + 1] / w
-            w3 = sigmas[i + 2] / w
-            w1 = 1 - w2 - w3
-
-            d_prime = w1 * d + w2 * d_2 + w3 * d_3
-            x = x + d_prime * dt
-    return x
diff --git a/MagicQuill/comfy/k_diffusion/utils.py b/MagicQuill/comfy/k_diffusion/utils.py
deleted file mode 100644
index a644df2f3cf82b32ac6e9bf2cb7bfc70c95e05f9..0000000000000000000000000000000000000000
--- a/MagicQuill/comfy/k_diffusion/utils.py
+++ /dev/null
@@ -1,313 +0,0 @@
-from contextlib import contextmanager
-import hashlib
-import math
-from pathlib import Path
-import shutil
-import urllib
-import warnings
-
-from PIL import Image
-import torch
-from torch import nn, optim
-from torch.utils import data
-
-
-def hf_datasets_augs_helper(examples, transform, image_key, mode='RGB'):
-    """Apply passed in transforms for HuggingFace Datasets."""
-    images = [transform(image.convert(mode)) for image in examples[image_key]]
-    return {image_key: images}
-
-
-def append_dims(x, target_dims):
-    """Appends dimensions to the end of a tensor until it has target_dims dimensions."""
-    dims_to_append = target_dims - x.ndim
-    if dims_to_append < 0:
-        raise ValueError(f'input has {x.ndim} dims but target_dims is {target_dims}, which is less')
-    expanded = x[(...,) + (None,) * dims_to_append]
-    # MPS will get inf values if it tries to index into the new axes, but detaching fixes this.
-    # https://github.com/pytorch/pytorch/issues/84364
-    return expanded.detach().clone() if expanded.device.type == 'mps' else expanded
-
-
-def n_params(module):
-    """Returns the number of trainable parameters in a module."""
-    return sum(p.numel() for p in module.parameters())
-
-
-def download_file(path, url, digest=None):
-    """Downloads a file if it does not exist, optionally checking its SHA-256 hash."""
-    path = Path(path)
-    path.parent.mkdir(parents=True, exist_ok=True)
-    if not path.exists():
-        with urllib.request.urlopen(url) as response, open(path, 'wb') as f:
-            shutil.copyfileobj(response, f)
-    if digest is not None:
-        file_digest = hashlib.sha256(open(path, 'rb').read()).hexdigest()
-        if digest != file_digest:
-            raise OSError(f'hash of {path} (url: {url}) failed to validate')
-    return path
-
-
-@contextmanager
-def train_mode(model, mode=True):
-    """A context manager that places a model into training mode and restores
-    the previous mode on exit."""
-    modes = [module.training for module in model.modules()]
-    try:
-        yield model.train(mode)
-    finally:
-        for i, module in enumerate(model.modules()):
-            module.training = modes[i]
-
-
-def eval_mode(model):
-    """A context manager that places a model into evaluation mode and restores
-    the previous mode on exit."""
-    return train_mode(model, False)
-
-
-@torch.no_grad()
-def ema_update(model, averaged_model, decay):
-    """Incorporates updated model parameters into an exponential moving averaged
-    version of a model. It should be called after each optimizer step."""
-    model_params = dict(model.named_parameters())
-    averaged_params = dict(averaged_model.named_parameters())
-    assert model_params.keys() == averaged_params.keys()
-
-    for name, param in model_params.items():
-        averaged_params[name].mul_(decay).add_(param, alpha=1 - decay)
-
-    model_buffers = dict(model.named_buffers())
-    averaged_buffers = dict(averaged_model.named_buffers())
-    assert model_buffers.keys() == averaged_buffers.keys()
-
-    for name, buf in model_buffers.items():
-        averaged_buffers[name].copy_(buf)
-
-
-class EMAWarmup:
-    """Implements an EMA warmup using an inverse decay schedule.
-    If inv_gamma=1 and power=1, implements a simple average. inv_gamma=1, power=2/3 are
-    good values for models you plan to train for a million or more steps (reaches decay
-    factor 0.999 at 31.6K steps, 0.9999 at 1M steps), inv_gamma=1, power=3/4 for models
-    you plan to train for less (reaches decay factor 0.999 at 10K steps, 0.9999 at
-    215.4k steps).
-    Args:
-        inv_gamma (float): Inverse multiplicative factor of EMA warmup. Default: 1.
-        power (float): Exponential factor of EMA warmup. Default: 1.
-        min_value (float): The minimum EMA decay rate. Default: 0.
-        max_value (float): The maximum EMA decay rate. Default: 1.
-        start_at (int): The epoch to start averaging at. Default: 0.
-        last_epoch (int): The index of last epoch. Default: 0.
-    """
-
-    def __init__(self, inv_gamma=1., power=1., min_value=0., max_value=1., start_at=0,
-                 last_epoch=0):
-        self.inv_gamma = inv_gamma
-        self.power = power
-        self.min_value = min_value
-        self.max_value = max_value
-        self.start_at = start_at
-        self.last_epoch = last_epoch
-
-    def state_dict(self):
-        """Returns the state of the class as a :class:`dict`."""
-        return dict(self.__dict__.items())
-
-    def load_state_dict(self, state_dict):
-        """Loads the class's state.
-        Args:
-            state_dict (dict): scaler state. Should be an object returned
-                from a call to :meth:`state_dict`.
-        """
-        self.__dict__.update(state_dict)
-
-    def get_value(self):
-        """Gets the current EMA decay rate."""
-        epoch = max(0, self.last_epoch - self.start_at)
-        value = 1 - (1 + epoch / self.inv_gamma) ** -self.power
-        return 0. if epoch < 0 else min(self.max_value, max(self.min_value, value))
-
-    def step(self):
-        """Updates the step count."""
-        self.last_epoch += 1
-
-
-class InverseLR(optim.lr_scheduler._LRScheduler):
-    """Implements an inverse decay learning rate schedule with an optional exponential
-    warmup. When last_epoch=-1, sets initial lr as lr.
-    inv_gamma is the number of steps/epochs required for the learning rate to decay to
-    (1 / 2)**power of its original value.
-    Args:
-        optimizer (Optimizer): Wrapped optimizer.
-        inv_gamma (float): Inverse multiplicative factor of learning rate decay. Default: 1.
-        power (float): Exponential factor of learning rate decay. Default: 1.
-        warmup (float): Exponential warmup factor (0 <= warmup < 1, 0 to disable)
-            Default: 0.
-        min_lr (float): The minimum learning rate. Default: 0.
-        last_epoch (int): The index of last epoch. Default: -1.
-        verbose (bool): If ``True``, prints a message to stdout for
-            each update. Default: ``False``.
-    """
-
-    def __init__(self, optimizer, inv_gamma=1., power=1., warmup=0., min_lr=0.,
-                 last_epoch=-1, verbose=False):
-        self.inv_gamma = inv_gamma
-        self.power = power
-        if not 0. <= warmup < 1:
-            raise ValueError('Invalid value for warmup')
-        self.warmup = warmup
-        self.min_lr = min_lr
-        super().__init__(optimizer, last_epoch, verbose)
-
-    def get_lr(self):
-        if not self._get_lr_called_within_step:
-            warnings.warn("To get the last learning rate computed by the scheduler, "
-                          "please use `get_last_lr()`.")
-
-        return self._get_closed_form_lr()
-
-    def _get_closed_form_lr(self):
-        warmup = 1 - self.warmup ** (self.last_epoch + 1)
-        lr_mult = (1 + self.last_epoch / self.inv_gamma) ** -self.power
-        return [warmup * max(self.min_lr, base_lr * lr_mult)
-                for base_lr in self.base_lrs]
-
-
-class ExponentialLR(optim.lr_scheduler._LRScheduler):
-    """Implements an exponential learning rate schedule with an optional exponential
-    warmup. When last_epoch=-1, sets initial lr as lr. Decays the learning rate
-    continuously by decay (default 0.5) every num_steps steps.
-    Args:
-        optimizer (Optimizer): Wrapped optimizer.
-        num_steps (float): The number of steps to decay the learning rate by decay in.
-        decay (float): The factor by which to decay the learning rate every num_steps
-            steps. Default: 0.5.
-        warmup (float): Exponential warmup factor (0 <= warmup < 1, 0 to disable)
-            Default: 0.
-        min_lr (float): The minimum learning rate. Default: 0.
-        last_epoch (int): The index of last epoch. Default: -1.
-        verbose (bool): If ``True``, prints a message to stdout for
-            each update. Default: ``False``.
-    """
-
-    def __init__(self, optimizer, num_steps, decay=0.5, warmup=0., min_lr=0.,
-                 last_epoch=-1, verbose=False):
-        self.num_steps = num_steps
-        self.decay = decay
-        if not 0. <= warmup < 1:
-            raise ValueError('Invalid value for warmup')
-        self.warmup = warmup
-        self.min_lr = min_lr
-        super().__init__(optimizer, last_epoch, verbose)
-
-    def get_lr(self):
-        if not self._get_lr_called_within_step:
-            warnings.warn("To get the last learning rate computed by the scheduler, "
-                          "please use `get_last_lr()`.")
-
-        return self._get_closed_form_lr()
-
-    def _get_closed_form_lr(self):
-        warmup = 1 - self.warmup ** (self.last_epoch + 1)
-        lr_mult = (self.decay ** (1 / self.num_steps)) ** self.last_epoch
-        return [warmup * max(self.min_lr, base_lr * lr_mult)
-                for base_lr in self.base_lrs]
-
-
-def rand_log_normal(shape, loc=0., scale=1., device='cpu', dtype=torch.float32):
-    """Draws samples from an lognormal distribution."""
-    return (torch.randn(shape, device=device, dtype=dtype) * scale + loc).exp()
-
-
-def rand_log_logistic(shape, loc=0., scale=1., min_value=0., max_value=float('inf'), device='cpu', dtype=torch.float32):
-    """Draws samples from an optionally truncated log-logistic distribution."""
-    min_value = torch.as_tensor(min_value, device=device, dtype=torch.float64)
-    max_value = torch.as_tensor(max_value, device=device, dtype=torch.float64)
-    min_cdf = min_value.log().sub(loc).div(scale).sigmoid()
-    max_cdf = max_value.log().sub(loc).div(scale).sigmoid()
-    u = torch.rand(shape, device=device, dtype=torch.float64) * (max_cdf - min_cdf) + min_cdf
-    return u.logit().mul(scale).add(loc).exp().to(dtype)
-
-
-def rand_log_uniform(shape, min_value, max_value, device='cpu', dtype=torch.float32):
-    """Draws samples from an log-uniform distribution."""
-    min_value = math.log(min_value)
-    max_value = math.log(max_value)
-    return (torch.rand(shape, device=device, dtype=dtype) * (max_value - min_value) + min_value).exp()
-
-
-def rand_v_diffusion(shape, sigma_data=1., min_value=0., max_value=float('inf'), device='cpu', dtype=torch.float32):
-    """Draws samples from a truncated v-diffusion training timestep distribution."""
-    min_cdf = math.atan(min_value / sigma_data) * 2 / math.pi
-    max_cdf = math.atan(max_value / sigma_data) * 2 / math.pi
-    u = torch.rand(shape, device=device, dtype=dtype) * (max_cdf - min_cdf) + min_cdf
-    return torch.tan(u * math.pi / 2) * sigma_data
-
-
-def rand_split_log_normal(shape, loc, scale_1, scale_2, device='cpu', dtype=torch.float32):
-    """Draws samples from a split lognormal distribution."""
-    n = torch.randn(shape, device=device, dtype=dtype).abs()
-    u = torch.rand(shape, device=device, dtype=dtype)
-    n_left = n * -scale_1 + loc
-    n_right = n * scale_2 + loc
-    ratio = scale_1 / (scale_1 + scale_2)
-    return torch.where(u < ratio, n_left, n_right).exp()
-
-
-class FolderOfImages(data.Dataset):
-    """Recursively finds all images in a directory. It does not support
-    classes/targets."""
-
-    IMG_EXTENSIONS = {'.jpg', '.jpeg', '.png', '.ppm', '.bmp', '.pgm', '.tif', '.tiff', '.webp'}
-
-    def __init__(self, root, transform=None):
-        super().__init__()
-        self.root = Path(root)
-        self.transform = nn.Identity() if transform is None else transform
-        self.paths = sorted(path for path in self.root.rglob('*') if path.suffix.lower() in self.IMG_EXTENSIONS)
-
-    def __repr__(self):
-        return f'FolderOfImages(root="{self.root}", len: {len(self)})'
-
-    def __len__(self):
-        return len(self.paths)
-
-    def __getitem__(self, key):
-        path = self.paths[key]
-        with open(path, 'rb') as f:
-            image = Image.open(f).convert('RGB')
-        image = self.transform(image)
-        return image,
-
-
-class CSVLogger:
-    def __init__(self, filename, columns):
-        self.filename = Path(filename)
-        self.columns = columns
-        if self.filename.exists():
-            self.file = open(self.filename, 'a')
-        else:
-            self.file = open(self.filename, 'w')
-            self.write(*self.columns)
-
-    def write(self, *args):
-        print(*args, sep=',', file=self.file, flush=True)
-
-
-@contextmanager
-def tf32_mode(cudnn=None, matmul=None):
-    """A context manager that sets whether TF32 is allowed on cuDNN or matmul."""
-    cudnn_old = torch.backends.cudnn.allow_tf32
-    matmul_old = torch.backends.cuda.matmul.allow_tf32
-    try:
-        if cudnn is not None:
-            torch.backends.cudnn.allow_tf32 = cudnn
-        if matmul is not None:
-            torch.backends.cuda.matmul.allow_tf32 = matmul
-        yield
-    finally:
-        if cudnn is not None:
-            torch.backends.cudnn.allow_tf32 = cudnn_old
-        if matmul is not None:
-            torch.backends.cuda.matmul.allow_tf32 = matmul_old
diff --git a/MagicQuill/comfy/latent_formats.py b/MagicQuill/comfy/latent_formats.py
deleted file mode 100644
index 4b4a9eda2ca513adf3f6a55db063bb4289be96a3..0000000000000000000000000000000000000000
--- a/MagicQuill/comfy/latent_formats.py
+++ /dev/null
@@ -1,141 +0,0 @@
-import torch
-
-class LatentFormat:
-    scale_factor = 1.0
-    latent_channels = 4
-    latent_rgb_factors = None
-    taesd_decoder_name = None
-
-    def process_in(self, latent):
-        return latent * self.scale_factor
-
-    def process_out(self, latent):
-        return latent / self.scale_factor
-
-class SD15(LatentFormat):
-    def __init__(self, scale_factor=0.18215):
-        self.scale_factor = scale_factor
-        self.latent_rgb_factors = [
-                    #   R        G        B
-                    [ 0.3512,  0.2297,  0.3227],
-                    [ 0.3250,  0.4974,  0.2350],
-                    [-0.2829,  0.1762,  0.2721],
-                    [-0.2120, -0.2616, -0.7177]
-                ]
-        self.taesd_decoder_name = "taesd_decoder"
-
-class SDXL(LatentFormat):
-    scale_factor = 0.13025
-
-    def __init__(self):
-        self.latent_rgb_factors = [
-                    #   R        G        B
-                    [ 0.3920,  0.4054,  0.4549],
-                    [-0.2634, -0.0196,  0.0653],
-                    [ 0.0568,  0.1687, -0.0755],
-                    [-0.3112, -0.2359, -0.2076]
-                ]
-        self.taesd_decoder_name = "taesdxl_decoder"
-
-class SDXL_Playground_2_5(LatentFormat):
-    def __init__(self):
-        self.scale_factor = 0.5
-        self.latents_mean = torch.tensor([-1.6574, 1.886, -1.383, 2.5155]).view(1, 4, 1, 1)
-        self.latents_std = torch.tensor([8.4927, 5.9022, 6.5498, 5.2299]).view(1, 4, 1, 1)
-
-        self.latent_rgb_factors = [
-                    #   R        G        B
-                    [ 0.3920,  0.4054,  0.4549],
-                    [-0.2634, -0.0196,  0.0653],
-                    [ 0.0568,  0.1687, -0.0755],
-                    [-0.3112, -0.2359, -0.2076]
-                ]
-        self.taesd_decoder_name = "taesdxl_decoder"
-
-    def process_in(self, latent):
-        latents_mean = self.latents_mean.to(latent.device, latent.dtype)
-        latents_std = self.latents_std.to(latent.device, latent.dtype)
-        return (latent - latents_mean) * self.scale_factor / latents_std
-
-    def process_out(self, latent):
-        latents_mean = self.latents_mean.to(latent.device, latent.dtype)
-        latents_std = self.latents_std.to(latent.device, latent.dtype)
-        return latent * latents_std / self.scale_factor + latents_mean
-
-
-class SD_X4(LatentFormat):
-    def __init__(self):
-        self.scale_factor = 0.08333
-        self.latent_rgb_factors = [
-            [-0.2340, -0.3863, -0.3257],
-            [ 0.0994,  0.0885, -0.0908],
-            [-0.2833, -0.2349, -0.3741],
-            [ 0.2523, -0.0055, -0.1651]
-        ]
-
-class SC_Prior(LatentFormat):
-    latent_channels = 16
-    def __init__(self):
-        self.scale_factor = 1.0
-        self.latent_rgb_factors = [
-            [-0.0326, -0.0204, -0.0127],
-            [-0.1592, -0.0427,  0.0216],
-            [ 0.0873,  0.0638, -0.0020],
-            [-0.0602,  0.0442,  0.1304],
-            [ 0.0800, -0.0313, -0.1796],
-            [-0.0810, -0.0638, -0.1581],
-            [ 0.1791,  0.1180,  0.0967],
-            [ 0.0740,  0.1416,  0.0432],
-            [-0.1745, -0.1888, -0.1373],
-            [ 0.2412,  0.1577,  0.0928],
-            [ 0.1908,  0.0998,  0.0682],
-            [ 0.0209,  0.0365, -0.0092],
-            [ 0.0448, -0.0650, -0.1728],
-            [-0.1658, -0.1045, -0.1308],
-            [ 0.0542,  0.1545,  0.1325],
-            [-0.0352, -0.1672, -0.2541]
-        ]
-
-class SC_B(LatentFormat):
-    def __init__(self):
-        self.scale_factor = 1.0 / 0.43
-        self.latent_rgb_factors = [
-            [ 0.1121,  0.2006,  0.1023],
-            [-0.2093, -0.0222, -0.0195],
-            [-0.3087, -0.1535,  0.0366],
-            [ 0.0290, -0.1574, -0.4078]
-        ]
-
-class SD3(LatentFormat):
-    latent_channels = 16
-    def __init__(self):
-        self.scale_factor = 1.5305
-        self.shift_factor = 0.0609
-        self.latent_rgb_factors = [
-            [-0.0645,  0.0177,  0.1052],
-            [ 0.0028,  0.0312,  0.0650],
-            [ 0.1848,  0.0762,  0.0360],
-            [ 0.0944,  0.0360,  0.0889],
-            [ 0.0897,  0.0506, -0.0364],
-            [-0.0020,  0.1203,  0.0284],
-            [ 0.0855,  0.0118,  0.0283],
-            [-0.0539,  0.0658,  0.1047],
-            [-0.0057,  0.0116,  0.0700],
-            [-0.0412,  0.0281, -0.0039],
-            [ 0.1106,  0.1171,  0.1220],
-            [-0.0248,  0.0682, -0.0481],
-            [ 0.0815,  0.0846,  0.1207],
-            [-0.0120, -0.0055, -0.0867],
-            [-0.0749, -0.0634, -0.0456],
-            [-0.1418, -0.1457, -0.1259]
-        ]
-        self.taesd_decoder_name = "taesd3_decoder"
-
-    def process_in(self, latent):
-        return (latent - self.shift_factor) * self.scale_factor
-
-    def process_out(self, latent):
-        return (latent / self.scale_factor) + self.shift_factor
-
-class StableAudio1(LatentFormat):
-    latent_channels = 64
diff --git a/MagicQuill/comfy/ldm/.DS_Store b/MagicQuill/comfy/ldm/.DS_Store
deleted file mode 100644
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deleted file mode 100644
index 4d31ebda5f4eae344a83438bacec2896e5d6c7b9..0000000000000000000000000000000000000000
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deleted file mode 100644
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deleted file mode 100644
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diff --git a/MagicQuill/comfy/ldm/audio/__pycache__/embedders.cpython-310.pyc b/MagicQuill/comfy/ldm/audio/__pycache__/embedders.cpython-310.pyc
deleted file mode 100644
index 0e6022d868f4f6a4b121fe3aa48d2c4c1582104e..0000000000000000000000000000000000000000
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diff --git a/MagicQuill/comfy/ldm/audio/autoencoder.py b/MagicQuill/comfy/ldm/audio/autoencoder.py
deleted file mode 100644
index 8123e66a50074d63bea45591f48e44723dbe5ebf..0000000000000000000000000000000000000000
--- a/MagicQuill/comfy/ldm/audio/autoencoder.py
+++ /dev/null
@@ -1,282 +0,0 @@
-# code adapted from: https://github.com/Stability-AI/stable-audio-tools
-
-import torch
-from torch import nn
-from typing import Literal, Dict, Any
-import math
-import comfy.ops
-ops = comfy.ops.disable_weight_init
-
-def vae_sample(mean, scale):
-        stdev = nn.functional.softplus(scale) + 1e-4
-        var = stdev * stdev
-        logvar = torch.log(var)
-        latents = torch.randn_like(mean) * stdev + mean
-
-        kl = (mean * mean + var - logvar - 1).sum(1).mean()
-
-        return latents, kl
-
-class VAEBottleneck(nn.Module):
-    def __init__(self):
-        super().__init__()
-        self.is_discrete = False
-
-    def encode(self, x, return_info=False, **kwargs):
-        info = {}
-
-        mean, scale = x.chunk(2, dim=1)
-
-        x, kl = vae_sample(mean, scale)
-
-        info["kl"] = kl
-
-        if return_info:
-            return x, info
-        else:
-            return x
-
-    def decode(self, x):
-        return x
-
-
-def snake_beta(x, alpha, beta):
-    return x + (1.0 / (beta + 0.000000001)) * pow(torch.sin(x * alpha), 2)
-
-# Adapted from https://github.com/NVIDIA/BigVGAN/blob/main/activations.py under MIT license
-class SnakeBeta(nn.Module):
-
-    def __init__(self, in_features, alpha=1.0, alpha_trainable=True, alpha_logscale=True):
-        super(SnakeBeta, self).__init__()
-        self.in_features = in_features
-
-        # initialize alpha
-        self.alpha_logscale = alpha_logscale
-        if self.alpha_logscale: # log scale alphas initialized to zeros
-            self.alpha = nn.Parameter(torch.zeros(in_features) * alpha)
-            self.beta = nn.Parameter(torch.zeros(in_features) * alpha)
-        else: # linear scale alphas initialized to ones
-            self.alpha = nn.Parameter(torch.ones(in_features) * alpha)
-            self.beta = nn.Parameter(torch.ones(in_features) * alpha)
-
-        # self.alpha.requires_grad = alpha_trainable
-        # self.beta.requires_grad = alpha_trainable
-
-        self.no_div_by_zero = 0.000000001
-
-    def forward(self, x):
-        alpha = self.alpha.unsqueeze(0).unsqueeze(-1).to(x.device) # line up with x to [B, C, T]
-        beta = self.beta.unsqueeze(0).unsqueeze(-1).to(x.device)
-        if self.alpha_logscale:
-            alpha = torch.exp(alpha)
-            beta = torch.exp(beta)
-        x = snake_beta(x, alpha, beta)
-
-        return x
-
-def WNConv1d(*args, **kwargs):
-    try:
-        return torch.nn.utils.parametrizations.weight_norm(ops.Conv1d(*args, **kwargs))
-    except:
-        return torch.nn.utils.weight_norm(ops.Conv1d(*args, **kwargs)) #support pytorch 2.1 and older
-
-def WNConvTranspose1d(*args, **kwargs):
-    try:
-        return torch.nn.utils.parametrizations.weight_norm(ops.ConvTranspose1d(*args, **kwargs))
-    except:
-        return torch.nn.utils.weight_norm(ops.ConvTranspose1d(*args, **kwargs)) #support pytorch 2.1 and older
-
-def get_activation(activation: Literal["elu", "snake", "none"], antialias=False, channels=None) -> nn.Module:
-    if activation == "elu":
-        act = torch.nn.ELU()
-    elif activation == "snake":
-        act = SnakeBeta(channels)
-    elif activation == "none":
-        act = torch.nn.Identity()
-    else:
-        raise ValueError(f"Unknown activation {activation}")
-
-    if antialias:
-        act = Activation1d(act)
-
-    return act
-
-
-class ResidualUnit(nn.Module):
-    def __init__(self, in_channels, out_channels, dilation, use_snake=False, antialias_activation=False):
-        super().__init__()
-
-        self.dilation = dilation
-
-        padding = (dilation * (7-1)) // 2
-
-        self.layers = nn.Sequential(
-            get_activation("snake" if use_snake else "elu", antialias=antialias_activation, channels=out_channels),
-            WNConv1d(in_channels=in_channels, out_channels=out_channels,
-                      kernel_size=7, dilation=dilation, padding=padding),
-            get_activation("snake" if use_snake else "elu", antialias=antialias_activation, channels=out_channels),
-            WNConv1d(in_channels=out_channels, out_channels=out_channels,
-                      kernel_size=1)
-        )
-
-    def forward(self, x):
-        res = x
-
-        #x = checkpoint(self.layers, x)
-        x = self.layers(x)
-
-        return x + res
-
-class EncoderBlock(nn.Module):
-    def __init__(self, in_channels, out_channels, stride, use_snake=False, antialias_activation=False):
-        super().__init__()
-
-        self.layers = nn.Sequential(
-            ResidualUnit(in_channels=in_channels,
-                         out_channels=in_channels, dilation=1, use_snake=use_snake),
-            ResidualUnit(in_channels=in_channels,
-                         out_channels=in_channels, dilation=3, use_snake=use_snake),
-            ResidualUnit(in_channels=in_channels,
-                         out_channels=in_channels, dilation=9, use_snake=use_snake),
-            get_activation("snake" if use_snake else "elu", antialias=antialias_activation, channels=in_channels),
-            WNConv1d(in_channels=in_channels, out_channels=out_channels,
-                      kernel_size=2*stride, stride=stride, padding=math.ceil(stride/2)),
-        )
-
-    def forward(self, x):
-        return self.layers(x)
-
-class DecoderBlock(nn.Module):
-    def __init__(self, in_channels, out_channels, stride, use_snake=False, antialias_activation=False, use_nearest_upsample=False):
-        super().__init__()
-
-        if use_nearest_upsample:
-            upsample_layer = nn.Sequential(
-                nn.Upsample(scale_factor=stride, mode="nearest"),
-                WNConv1d(in_channels=in_channels,
-                        out_channels=out_channels,
-                        kernel_size=2*stride,
-                        stride=1,
-                        bias=False,
-                        padding='same')
-            )
-        else:
-            upsample_layer = WNConvTranspose1d(in_channels=in_channels,
-                               out_channels=out_channels,
-                               kernel_size=2*stride, stride=stride, padding=math.ceil(stride/2))
-
-        self.layers = nn.Sequential(
-            get_activation("snake" if use_snake else "elu", antialias=antialias_activation, channels=in_channels),
-            upsample_layer,
-            ResidualUnit(in_channels=out_channels, out_channels=out_channels,
-                         dilation=1, use_snake=use_snake),
-            ResidualUnit(in_channels=out_channels, out_channels=out_channels,
-                         dilation=3, use_snake=use_snake),
-            ResidualUnit(in_channels=out_channels, out_channels=out_channels,
-                         dilation=9, use_snake=use_snake),
-        )
-
-    def forward(self, x):
-        return self.layers(x)
-
-class OobleckEncoder(nn.Module):
-    def __init__(self,
-                 in_channels=2,
-                 channels=128,
-                 latent_dim=32,
-                 c_mults = [1, 2, 4, 8],
-                 strides = [2, 4, 8, 8],
-                 use_snake=False,
-                 antialias_activation=False
-        ):
-        super().__init__()
-
-        c_mults = [1] + c_mults
-
-        self.depth = len(c_mults)
-
-        layers = [
-            WNConv1d(in_channels=in_channels, out_channels=c_mults[0] * channels, kernel_size=7, padding=3)
-        ]
-
-        for i in range(self.depth-1):
-            layers += [EncoderBlock(in_channels=c_mults[i]*channels, out_channels=c_mults[i+1]*channels, stride=strides[i], use_snake=use_snake)]
-
-        layers += [
-            get_activation("snake" if use_snake else "elu", antialias=antialias_activation, channels=c_mults[-1] * channels),
-            WNConv1d(in_channels=c_mults[-1]*channels, out_channels=latent_dim, kernel_size=3, padding=1)
-        ]
-
-        self.layers = nn.Sequential(*layers)
-
-    def forward(self, x):
-        return self.layers(x)
-
-
-class OobleckDecoder(nn.Module):
-    def __init__(self,
-                 out_channels=2,
-                 channels=128,
-                 latent_dim=32,
-                 c_mults = [1, 2, 4, 8],
-                 strides = [2, 4, 8, 8],
-                 use_snake=False,
-                 antialias_activation=False,
-                 use_nearest_upsample=False,
-                 final_tanh=True):
-        super().__init__()
-
-        c_mults = [1] + c_mults
-
-        self.depth = len(c_mults)
-
-        layers = [
-            WNConv1d(in_channels=latent_dim, out_channels=c_mults[-1]*channels, kernel_size=7, padding=3),
-        ]
-
-        for i in range(self.depth-1, 0, -1):
-            layers += [DecoderBlock(
-                in_channels=c_mults[i]*channels,
-                out_channels=c_mults[i-1]*channels,
-                stride=strides[i-1],
-                use_snake=use_snake,
-                antialias_activation=antialias_activation,
-                use_nearest_upsample=use_nearest_upsample
-                )
-            ]
-
-        layers += [
-            get_activation("snake" if use_snake else "elu", antialias=antialias_activation, channels=c_mults[0] * channels),
-            WNConv1d(in_channels=c_mults[0] * channels, out_channels=out_channels, kernel_size=7, padding=3, bias=False),
-            nn.Tanh() if final_tanh else nn.Identity()
-        ]
-
-        self.layers = nn.Sequential(*layers)
-
-    def forward(self, x):
-        return self.layers(x)
-
-
-class AudioOobleckVAE(nn.Module):
-    def __init__(self,
-                 in_channels=2,
-                 channels=128,
-                 latent_dim=64,
-                 c_mults = [1, 2, 4, 8, 16],
-                 strides = [2, 4, 4, 8, 8],
-                 use_snake=True,
-                 antialias_activation=False,
-                 use_nearest_upsample=False,
-                 final_tanh=False):
-        super().__init__()
-        self.encoder = OobleckEncoder(in_channels, channels, latent_dim * 2, c_mults, strides, use_snake, antialias_activation)
-        self.decoder = OobleckDecoder(in_channels, channels, latent_dim, c_mults, strides, use_snake, antialias_activation,
-                                      use_nearest_upsample=use_nearest_upsample, final_tanh=final_tanh)
-        self.bottleneck = VAEBottleneck()
-
-    def encode(self, x):
-        return self.bottleneck.encode(self.encoder(x))
-
-    def decode(self, x):
-        return self.decoder(self.bottleneck.decode(x))
-
diff --git a/MagicQuill/comfy/ldm/audio/dit.py b/MagicQuill/comfy/ldm/audio/dit.py
deleted file mode 100644
index 1c1112c5e562c7bdef8e8a795f26803a3d398dd1..0000000000000000000000000000000000000000
--- a/MagicQuill/comfy/ldm/audio/dit.py
+++ /dev/null
@@ -1,888 +0,0 @@
-# code adapted from: https://github.com/Stability-AI/stable-audio-tools
-
-from comfy.ldm.modules.attention import optimized_attention
-import typing as tp
-
-import torch
-
-from einops import rearrange
-from torch import nn
-from torch.nn import functional as F
-import math
-
-class FourierFeatures(nn.Module):
-    def __init__(self, in_features, out_features, std=1., dtype=None, device=None):
-        super().__init__()
-        assert out_features % 2 == 0
-        self.weight = nn.Parameter(torch.empty(
-            [out_features // 2, in_features], dtype=dtype, device=device))
-
-    def forward(self, input):
-        f = 2 * math.pi * input @ self.weight.T.to(dtype=input.dtype, device=input.device)
-        return torch.cat([f.cos(), f.sin()], dim=-1)
-
-# norms
-class LayerNorm(nn.Module):
-    def __init__(self, dim, bias=False, fix_scale=False, dtype=None, device=None):
-        """
-        bias-less layernorm has been shown to be more stable. most newer models have moved towards rmsnorm, also bias-less
-        """
-        super().__init__()
-
-        self.gamma = nn.Parameter(torch.empty(dim, dtype=dtype, device=device))
-
-        if bias:
-            self.beta = nn.Parameter(torch.empty(dim, dtype=dtype, device=device))
-        else:
-            self.beta = None
-
-    def forward(self, x):
-        beta = self.beta
-        if self.beta is not None:
-            beta = beta.to(dtype=x.dtype, device=x.device)
-        return F.layer_norm(x, x.shape[-1:], weight=self.gamma.to(dtype=x.dtype, device=x.device), bias=beta)
-
-class GLU(nn.Module):
-    def __init__(
-        self,
-        dim_in,
-        dim_out,
-        activation,
-        use_conv = False,
-        conv_kernel_size = 3,
-        dtype=None,
-        device=None,
-        operations=None,
-    ):
-        super().__init__()
-        self.act = activation
-        self.proj = operations.Linear(dim_in, dim_out * 2, dtype=dtype, device=device) if not use_conv else operations.Conv1d(dim_in, dim_out * 2, conv_kernel_size, padding = (conv_kernel_size // 2), dtype=dtype, device=device)
-        self.use_conv = use_conv
-
-    def forward(self, x):
-        if self.use_conv:
-            x = rearrange(x, 'b n d -> b d n')
-            x = self.proj(x)
-            x = rearrange(x, 'b d n -> b n d')
-        else:
-            x = self.proj(x)
-
-        x, gate = x.chunk(2, dim = -1)
-        return x * self.act(gate)
-
-class AbsolutePositionalEmbedding(nn.Module):
-    def __init__(self, dim, max_seq_len):
-        super().__init__()
-        self.scale = dim ** -0.5
-        self.max_seq_len = max_seq_len
-        self.emb = nn.Embedding(max_seq_len, dim)
-
-    def forward(self, x, pos = None, seq_start_pos = None):
-        seq_len, device = x.shape[1], x.device
-        assert seq_len <= self.max_seq_len, f'you are passing in a sequence length of {seq_len} but your absolute positional embedding has a max sequence length of {self.max_seq_len}'
-
-        if pos is None:
-            pos = torch.arange(seq_len, device = device)
-
-        if seq_start_pos is not None:
-            pos = (pos - seq_start_pos[..., None]).clamp(min = 0)
-
-        pos_emb = self.emb(pos)
-        pos_emb = pos_emb * self.scale
-        return pos_emb
-
-class ScaledSinusoidalEmbedding(nn.Module):
-    def __init__(self, dim, theta = 10000):
-        super().__init__()
-        assert (dim % 2) == 0, 'dimension must be divisible by 2'
-        self.scale = nn.Parameter(torch.ones(1) * dim ** -0.5)
-
-        half_dim = dim // 2
-        freq_seq = torch.arange(half_dim).float() / half_dim
-        inv_freq = theta ** -freq_seq
-        self.register_buffer('inv_freq', inv_freq, persistent = False)
-
-    def forward(self, x, pos = None, seq_start_pos = None):
-        seq_len, device = x.shape[1], x.device
-
-        if pos is None:
-            pos = torch.arange(seq_len, device = device)
-
-        if seq_start_pos is not None:
-            pos = pos - seq_start_pos[..., None]
-
-        emb = torch.einsum('i, j -> i j', pos, self.inv_freq)
-        emb = torch.cat((emb.sin(), emb.cos()), dim = -1)
-        return emb * self.scale
-
-class RotaryEmbedding(nn.Module):
-    def __init__(
-        self,
-        dim,
-        use_xpos = False,
-        scale_base = 512,
-        interpolation_factor = 1.,
-        base = 10000,
-        base_rescale_factor = 1.
-    ):
-        super().__init__()
-        # proposed by reddit user bloc97, to rescale rotary embeddings to longer sequence length without fine-tuning
-        # has some connection to NTK literature
-        # https://www.reddit.com/r/LocalLLaMA/comments/14lz7j5/ntkaware_scaled_rope_allows_llama_models_to_have/
-        base *= base_rescale_factor ** (dim / (dim - 2))
-
-        inv_freq = 1. / (base ** (torch.arange(0, dim, 2).float() / dim))
-        self.register_buffer('inv_freq', inv_freq)
-
-        assert interpolation_factor >= 1.
-        self.interpolation_factor = interpolation_factor
-
-        if not use_xpos:
-            self.register_buffer('scale', None)
-            return
-
-        scale = (torch.arange(0, dim, 2) + 0.4 * dim) / (1.4 * dim)
-
-        self.scale_base = scale_base
-        self.register_buffer('scale', scale)
-
-    def forward_from_seq_len(self, seq_len, device, dtype):
-        # device = self.inv_freq.device
-
-        t = torch.arange(seq_len, device=device, dtype=dtype)
-        return self.forward(t)
-
-    def forward(self, t):
-        # device = self.inv_freq.device
-        device = t.device
-        dtype = t.dtype
-
-        # t = t.to(torch.float32)
-
-        t = t / self.interpolation_factor
-
-        freqs = torch.einsum('i , j -> i j', t, self.inv_freq.to(dtype=dtype, device=device))
-        freqs = torch.cat((freqs, freqs), dim = -1)
-
-        if self.scale is None:
-            return freqs, 1.
-
-        power = (torch.arange(seq_len, device = device) - (seq_len // 2)) / self.scale_base
-        scale = self.scale.to(dtype=dtype, device=device) ** rearrange(power, 'n -> n 1')
-        scale = torch.cat((scale, scale), dim = -1)
-
-        return freqs, scale
-
-def rotate_half(x):
-    x = rearrange(x, '... (j d) -> ... j d', j = 2)
-    x1, x2 = x.unbind(dim = -2)
-    return torch.cat((-x2, x1), dim = -1)
-
-def apply_rotary_pos_emb(t, freqs, scale = 1):
-    out_dtype = t.dtype
-
-    # cast to float32 if necessary for numerical stability
-    dtype = t.dtype #reduce(torch.promote_types, (t.dtype, freqs.dtype, torch.float32))
-    rot_dim, seq_len = freqs.shape[-1], t.shape[-2]
-    freqs, t = freqs.to(dtype), t.to(dtype)
-    freqs = freqs[-seq_len:, :]
-
-    if t.ndim == 4 and freqs.ndim == 3:
-        freqs = rearrange(freqs, 'b n d -> b 1 n d')
-
-    # partial rotary embeddings, Wang et al. GPT-J
-    t, t_unrotated = t[..., :rot_dim], t[..., rot_dim:]
-    t = (t * freqs.cos() * scale) + (rotate_half(t) * freqs.sin() * scale)
-
-    t, t_unrotated = t.to(out_dtype), t_unrotated.to(out_dtype)
-
-    return torch.cat((t, t_unrotated), dim = -1)
-
-class FeedForward(nn.Module):
-    def __init__(
-        self,
-        dim,
-        dim_out = None,
-        mult = 4,
-        no_bias = False,
-        glu = True,
-        use_conv = False,
-        conv_kernel_size = 3,
-        zero_init_output = True,
-        dtype=None,
-        device=None,
-        operations=None,
-    ):
-        super().__init__()
-        inner_dim = int(dim * mult)
-
-        # Default to SwiGLU
-
-        activation = nn.SiLU()
-
-        dim_out = dim if dim_out is None else dim_out
-
-        if glu:
-            linear_in = GLU(dim, inner_dim, activation, dtype=dtype, device=device, operations=operations)
-        else:
-            linear_in = nn.Sequential(
-                Rearrange('b n d -> b d n') if use_conv else nn.Identity(),
-                operations.Linear(dim, inner_dim, bias = not no_bias, dtype=dtype, device=device) if not use_conv else operations.Conv1d(dim, inner_dim, conv_kernel_size, padding = (conv_kernel_size // 2), bias = not no_bias, dtype=dtype, device=device),
-                Rearrange('b n d -> b d n') if use_conv else nn.Identity(),
-                activation
-            )
-
-        linear_out = operations.Linear(inner_dim, dim_out, bias = not no_bias, dtype=dtype, device=device) if not use_conv else operations.Conv1d(inner_dim, dim_out, conv_kernel_size, padding = (conv_kernel_size // 2), bias = not no_bias, dtype=dtype, device=device)
-
-        # # init last linear layer to 0
-        # if zero_init_output:
-        #     nn.init.zeros_(linear_out.weight)
-        #     if not no_bias:
-        #         nn.init.zeros_(linear_out.bias)
-
-
-        self.ff = nn.Sequential(
-            linear_in,
-            Rearrange('b d n -> b n d') if use_conv else nn.Identity(),
-            linear_out,
-            Rearrange('b n d -> b d n') if use_conv else nn.Identity(),
-        )
-
-    def forward(self, x):
-        return self.ff(x)
-
-class Attention(nn.Module):
-    def __init__(
-        self,
-        dim,
-        dim_heads = 64,
-        dim_context = None,
-        causal = False,
-        zero_init_output=True,
-        qk_norm = False,
-        natten_kernel_size = None,
-        dtype=None,
-        device=None,
-        operations=None,
-    ):
-        super().__init__()
-        self.dim = dim
-        self.dim_heads = dim_heads
-        self.causal = causal
-
-        dim_kv = dim_context if dim_context is not None else dim
-
-        self.num_heads = dim // dim_heads
-        self.kv_heads = dim_kv // dim_heads
-
-        if dim_context is not None:
-            self.to_q = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)
-            self.to_kv = operations.Linear(dim_kv, dim_kv * 2, bias=False, dtype=dtype, device=device)
-        else:
-            self.to_qkv = operations.Linear(dim, dim * 3, bias=False, dtype=dtype, device=device)
-
-        self.to_out = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)
-
-        # if zero_init_output:
-        #     nn.init.zeros_(self.to_out.weight)
-
-        self.qk_norm = qk_norm
-
-
-    def forward(
-        self,
-        x,
-        context = None,
-        mask = None,
-        context_mask = None,
-        rotary_pos_emb = None,
-        causal = None
-    ):
-        h, kv_h, has_context = self.num_heads, self.kv_heads, context is not None
-
-        kv_input = context if has_context else x
-
-        if hasattr(self, 'to_q'):
-            # Use separate linear projections for q and k/v
-            q = self.to_q(x)
-            q = rearrange(q, 'b n (h d) -> b h n d', h = h)
-
-            k, v = self.to_kv(kv_input).chunk(2, dim=-1)
-
-            k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h = kv_h), (k, v))
-        else:
-            # Use fused linear projection
-            q, k, v = self.to_qkv(x).chunk(3, dim=-1)
-            q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h = h), (q, k, v))
-
-        # Normalize q and k for cosine sim attention
-        if self.qk_norm:
-            q = F.normalize(q, dim=-1)
-            k = F.normalize(k, dim=-1)
-
-        if rotary_pos_emb is not None and not has_context:
-            freqs, _ = rotary_pos_emb
-
-            q_dtype = q.dtype
-            k_dtype = k.dtype
-
-            q = q.to(torch.float32)
-            k = k.to(torch.float32)
-            freqs = freqs.to(torch.float32)
-
-            q = apply_rotary_pos_emb(q, freqs)
-            k = apply_rotary_pos_emb(k, freqs)
-
-            q = q.to(q_dtype)
-            k = k.to(k_dtype)
-
-        input_mask = context_mask
-
-        if input_mask is None and not has_context:
-            input_mask = mask
-
-        # determine masking
-        masks = []
-        final_attn_mask = None # The mask that will be applied to the attention matrix, taking all masks into account
-
-        if input_mask is not None:
-            input_mask = rearrange(input_mask, 'b j -> b 1 1 j')
-            masks.append(~input_mask)
-
-        # Other masks will be added here later
-
-        if len(masks) > 0:
-            final_attn_mask = ~or_reduce(masks)
-
-        n, device = q.shape[-2], q.device
-
-        causal = self.causal if causal is None else causal
-
-        if n == 1 and causal:
-            causal = False
-
-        if h != kv_h:
-            # Repeat interleave kv_heads to match q_heads
-            heads_per_kv_head = h // kv_h
-            k, v = map(lambda t: t.repeat_interleave(heads_per_kv_head, dim = 1), (k, v))
-
-        out = optimized_attention(q, k, v, h, skip_reshape=True)
-        out = self.to_out(out)
-
-        if mask is not None:
-            mask = rearrange(mask, 'b n -> b n 1')
-            out = out.masked_fill(~mask, 0.)
-
-        return out
-
-class ConformerModule(nn.Module):
-    def __init__(
-        self,
-        dim,
-        norm_kwargs = {},
-    ):
-
-        super().__init__()
-
-        self.dim = dim
-
-        self.in_norm = LayerNorm(dim, **norm_kwargs)
-        self.pointwise_conv = nn.Conv1d(dim, dim, kernel_size=1, bias=False)
-        self.glu = GLU(dim, dim, nn.SiLU())
-        self.depthwise_conv = nn.Conv1d(dim, dim, kernel_size=17, groups=dim, padding=8, bias=False)
-        self.mid_norm = LayerNorm(dim, **norm_kwargs) # This is a batch norm in the original but I don't like batch norm
-        self.swish = nn.SiLU()
-        self.pointwise_conv_2 = nn.Conv1d(dim, dim, kernel_size=1, bias=False)
-
-    def forward(self, x):
-        x = self.in_norm(x)
-        x = rearrange(x, 'b n d -> b d n')
-        x = self.pointwise_conv(x)
-        x = rearrange(x, 'b d n -> b n d')
-        x = self.glu(x)
-        x = rearrange(x, 'b n d -> b d n')
-        x = self.depthwise_conv(x)
-        x = rearrange(x, 'b d n -> b n d')
-        x = self.mid_norm(x)
-        x = self.swish(x)
-        x = rearrange(x, 'b n d -> b d n')
-        x = self.pointwise_conv_2(x)
-        x = rearrange(x, 'b d n -> b n d')
-
-        return x
-
-class TransformerBlock(nn.Module):
-    def __init__(
-            self,
-            dim,
-            dim_heads = 64,
-            cross_attend = False,
-            dim_context = None,
-            global_cond_dim = None,
-            causal = False,
-            zero_init_branch_outputs = True,
-            conformer = False,
-            layer_ix = -1,
-            remove_norms = False,
-            attn_kwargs = {},
-            ff_kwargs = {},
-            norm_kwargs = {},
-            dtype=None,
-            device=None,
-            operations=None,
-    ):
-
-        super().__init__()
-        self.dim = dim
-        self.dim_heads = dim_heads
-        self.cross_attend = cross_attend
-        self.dim_context = dim_context
-        self.causal = causal
-
-        self.pre_norm = LayerNorm(dim, dtype=dtype, device=device, **norm_kwargs) if not remove_norms else nn.Identity()
-
-        self.self_attn = Attention(
-            dim,
-            dim_heads = dim_heads,
-            causal = causal,
-            zero_init_output=zero_init_branch_outputs,
-            dtype=dtype,
-            device=device,
-            operations=operations,
-            **attn_kwargs
-        )
-
-        if cross_attend:
-            self.cross_attend_norm = LayerNorm(dim, dtype=dtype, device=device, **norm_kwargs) if not remove_norms else nn.Identity()
-            self.cross_attn = Attention(
-                dim,
-                dim_heads = dim_heads,
-                dim_context=dim_context,
-                causal = causal,
-                zero_init_output=zero_init_branch_outputs,
-                dtype=dtype,
-                device=device,
-                operations=operations,
-                **attn_kwargs
-            )
-
-        self.ff_norm = LayerNorm(dim, dtype=dtype, device=device, **norm_kwargs) if not remove_norms else nn.Identity()
-        self.ff = FeedForward(dim, zero_init_output=zero_init_branch_outputs, dtype=dtype, device=device, operations=operations,**ff_kwargs)
-
-        self.layer_ix = layer_ix
-
-        self.conformer = ConformerModule(dim, norm_kwargs=norm_kwargs) if conformer else None
-
-        self.global_cond_dim = global_cond_dim
-
-        if global_cond_dim is not None:
-            self.to_scale_shift_gate = nn.Sequential(
-                nn.SiLU(),
-                nn.Linear(global_cond_dim, dim * 6, bias=False)
-            )
-
-            nn.init.zeros_(self.to_scale_shift_gate[1].weight)
-            #nn.init.zeros_(self.to_scale_shift_gate_self[1].bias)
-
-    def forward(
-        self,
-        x,
-        context = None,
-        global_cond=None,
-        mask = None,
-        context_mask = None,
-        rotary_pos_emb = None
-    ):
-        if self.global_cond_dim is not None and self.global_cond_dim > 0 and global_cond is not None:
-
-            scale_self, shift_self, gate_self, scale_ff, shift_ff, gate_ff = self.to_scale_shift_gate(global_cond).unsqueeze(1).chunk(6, dim = -1)
-
-            # self-attention with adaLN
-            residual = x
-            x = self.pre_norm(x)
-            x = x * (1 + scale_self) + shift_self
-            x = self.self_attn(x, mask = mask, rotary_pos_emb = rotary_pos_emb)
-            x = x * torch.sigmoid(1 - gate_self)
-            x = x + residual
-
-            if context is not None:
-                x = x + self.cross_attn(self.cross_attend_norm(x), context = context, context_mask = context_mask)
-
-            if self.conformer is not None:
-                x = x + self.conformer(x)
-
-            # feedforward with adaLN
-            residual = x
-            x = self.ff_norm(x)
-            x = x * (1 + scale_ff) + shift_ff
-            x = self.ff(x)
-            x = x * torch.sigmoid(1 - gate_ff)
-            x = x + residual
-
-        else:
-            x = x + self.self_attn(self.pre_norm(x), mask = mask, rotary_pos_emb = rotary_pos_emb)
-
-            if context is not None:
-                x = x + self.cross_attn(self.cross_attend_norm(x), context = context, context_mask = context_mask)
-
-            if self.conformer is not None:
-                x = x + self.conformer(x)
-
-            x = x + self.ff(self.ff_norm(x))
-
-        return x
-
-class ContinuousTransformer(nn.Module):
-    def __init__(
-        self,
-        dim,
-        depth,
-        *,
-        dim_in = None,
-        dim_out = None,
-        dim_heads = 64,
-        cross_attend=False,
-        cond_token_dim=None,
-        global_cond_dim=None,
-        causal=False,
-        rotary_pos_emb=True,
-        zero_init_branch_outputs=True,
-        conformer=False,
-        use_sinusoidal_emb=False,
-        use_abs_pos_emb=False,
-        abs_pos_emb_max_length=10000,
-        dtype=None,
-        device=None,
-        operations=None,
-        **kwargs
-        ):
-
-        super().__init__()
-
-        self.dim = dim
-        self.depth = depth
-        self.causal = causal
-        self.layers = nn.ModuleList([])
-
-        self.project_in = operations.Linear(dim_in, dim, bias=False, dtype=dtype, device=device) if dim_in is not None else nn.Identity()
-        self.project_out = operations.Linear(dim, dim_out, bias=False, dtype=dtype, device=device) if dim_out is not None else nn.Identity()
-
-        if rotary_pos_emb:
-            self.rotary_pos_emb = RotaryEmbedding(max(dim_heads // 2, 32))
-        else:
-            self.rotary_pos_emb = None
-
-        self.use_sinusoidal_emb = use_sinusoidal_emb
-        if use_sinusoidal_emb:
-            self.pos_emb = ScaledSinusoidalEmbedding(dim)
-
-        self.use_abs_pos_emb = use_abs_pos_emb
-        if use_abs_pos_emb:
-            self.pos_emb = AbsolutePositionalEmbedding(dim, abs_pos_emb_max_length)
-
-        for i in range(depth):
-            self.layers.append(
-                TransformerBlock(
-                    dim,
-                    dim_heads = dim_heads,
-                    cross_attend = cross_attend,
-                    dim_context = cond_token_dim,
-                    global_cond_dim = global_cond_dim,
-                    causal = causal,
-                    zero_init_branch_outputs = zero_init_branch_outputs,
-                    conformer=conformer,
-                    layer_ix=i,
-                    dtype=dtype,
-                    device=device,
-                    operations=operations,
-                    **kwargs
-                )
-            )
-
-    def forward(
-        self,
-        x,
-        mask = None,
-        prepend_embeds = None,
-        prepend_mask = None,
-        global_cond = None,
-        return_info = False,
-        **kwargs
-    ):
-        batch, seq, device = *x.shape[:2], x.device
-
-        info = {
-            "hidden_states": [],
-        }
-
-        x = self.project_in(x)
-
-        if prepend_embeds is not None:
-            prepend_length, prepend_dim = prepend_embeds.shape[1:]
-
-            assert prepend_dim == x.shape[-1], 'prepend dimension must match sequence dimension'
-
-            x = torch.cat((prepend_embeds, x), dim = -2)
-
-            if prepend_mask is not None or mask is not None:
-                mask = mask if mask is not None else torch.ones((batch, seq), device = device, dtype = torch.bool)
-                prepend_mask = prepend_mask if prepend_mask is not None else torch.ones((batch, prepend_length), device = device, dtype = torch.bool)
-
-                mask = torch.cat((prepend_mask, mask), dim = -1)
-
-        # Attention layers
-
-        if self.rotary_pos_emb is not None:
-            rotary_pos_emb = self.rotary_pos_emb.forward_from_seq_len(x.shape[1], dtype=x.dtype, device=x.device)
-        else:
-            rotary_pos_emb = None
-
-        if self.use_sinusoidal_emb or self.use_abs_pos_emb:
-            x = x + self.pos_emb(x)
-
-        # Iterate over the transformer layers
-        for layer in self.layers:
-            x = layer(x, rotary_pos_emb = rotary_pos_emb, global_cond=global_cond, **kwargs)
-            # x = checkpoint(layer, x, rotary_pos_emb = rotary_pos_emb, global_cond=global_cond, **kwargs)
-
-            if return_info:
-                info["hidden_states"].append(x)
-
-        x = self.project_out(x)
-
-        if return_info:
-            return x, info
-
-        return x
-
-class AudioDiffusionTransformer(nn.Module):
-    def __init__(self,
-        io_channels=64,
-        patch_size=1,
-        embed_dim=1536,
-        cond_token_dim=768,
-        project_cond_tokens=False,
-        global_cond_dim=1536,
-        project_global_cond=True,
-        input_concat_dim=0,
-        prepend_cond_dim=0,
-        depth=24,
-        num_heads=24,
-        transformer_type: tp.Literal["continuous_transformer"] = "continuous_transformer",
-        global_cond_type: tp.Literal["prepend", "adaLN"] = "prepend",
-        audio_model="",
-        dtype=None,
-        device=None,
-        operations=None,
-        **kwargs):
-
-        super().__init__()
-
-        self.dtype = dtype
-        self.cond_token_dim = cond_token_dim
-
-        # Timestep embeddings
-        timestep_features_dim = 256
-
-        self.timestep_features = FourierFeatures(1, timestep_features_dim, dtype=dtype, device=device)
-
-        self.to_timestep_embed = nn.Sequential(
-            operations.Linear(timestep_features_dim, embed_dim, bias=True, dtype=dtype, device=device),
-            nn.SiLU(),
-            operations.Linear(embed_dim, embed_dim, bias=True, dtype=dtype, device=device),
-        )
-
-        if cond_token_dim > 0:
-            # Conditioning tokens
-
-            cond_embed_dim = cond_token_dim if not project_cond_tokens else embed_dim
-            self.to_cond_embed = nn.Sequential(
-                operations.Linear(cond_token_dim, cond_embed_dim, bias=False, dtype=dtype, device=device),
-                nn.SiLU(),
-                operations.Linear(cond_embed_dim, cond_embed_dim, bias=False, dtype=dtype, device=device)
-            )
-        else:
-            cond_embed_dim = 0
-
-        if global_cond_dim > 0:
-            # Global conditioning
-            global_embed_dim = global_cond_dim if not project_global_cond else embed_dim
-            self.to_global_embed = nn.Sequential(
-                operations.Linear(global_cond_dim, global_embed_dim, bias=False, dtype=dtype, device=device),
-                nn.SiLU(),
-                operations.Linear(global_embed_dim, global_embed_dim, bias=False, dtype=dtype, device=device)
-            )
-
-        if prepend_cond_dim > 0:
-            # Prepend conditioning
-            self.to_prepend_embed = nn.Sequential(
-                operations.Linear(prepend_cond_dim, embed_dim, bias=False, dtype=dtype, device=device),
-                nn.SiLU(),
-                operations.Linear(embed_dim, embed_dim, bias=False, dtype=dtype, device=device)
-            )
-
-        self.input_concat_dim = input_concat_dim
-
-        dim_in = io_channels + self.input_concat_dim
-
-        self.patch_size = patch_size
-
-        # Transformer
-
-        self.transformer_type = transformer_type
-
-        self.global_cond_type = global_cond_type
-
-        if self.transformer_type == "continuous_transformer":
-
-            global_dim = None
-
-            if self.global_cond_type == "adaLN":
-                # The global conditioning is projected to the embed_dim already at this point
-                global_dim = embed_dim
-
-            self.transformer = ContinuousTransformer(
-                dim=embed_dim,
-                depth=depth,
-                dim_heads=embed_dim // num_heads,
-                dim_in=dim_in * patch_size,
-                dim_out=io_channels * patch_size,
-                cross_attend = cond_token_dim > 0,
-                cond_token_dim = cond_embed_dim,
-                global_cond_dim=global_dim,
-                dtype=dtype,
-                device=device,
-                operations=operations,
-                **kwargs
-            )
-        else:
-            raise ValueError(f"Unknown transformer type: {self.transformer_type}")
-
-        self.preprocess_conv = operations.Conv1d(dim_in, dim_in, 1, bias=False, dtype=dtype, device=device)
-        self.postprocess_conv = operations.Conv1d(io_channels, io_channels, 1, bias=False, dtype=dtype, device=device)
-
-    def _forward(
-        self,
-        x,
-        t,
-        mask=None,
-        cross_attn_cond=None,
-        cross_attn_cond_mask=None,
-        input_concat_cond=None,
-        global_embed=None,
-        prepend_cond=None,
-        prepend_cond_mask=None,
-        return_info=False,
-        **kwargs):
-
-        if cross_attn_cond is not None:
-            cross_attn_cond = self.to_cond_embed(cross_attn_cond)
-
-        if global_embed is not None:
-            # Project the global conditioning to the embedding dimension
-            global_embed = self.to_global_embed(global_embed)
-
-        prepend_inputs = None
-        prepend_mask = None
-        prepend_length = 0
-        if prepend_cond is not None:
-            # Project the prepend conditioning to the embedding dimension
-            prepend_cond = self.to_prepend_embed(prepend_cond)
-
-            prepend_inputs = prepend_cond
-            if prepend_cond_mask is not None:
-                prepend_mask = prepend_cond_mask
-
-        if input_concat_cond is not None:
-
-            # Interpolate input_concat_cond to the same length as x
-            if input_concat_cond.shape[2] != x.shape[2]:
-                input_concat_cond = F.interpolate(input_concat_cond, (x.shape[2], ), mode='nearest')
-
-            x = torch.cat([x, input_concat_cond], dim=1)
-
-        # Get the batch of timestep embeddings
-        timestep_embed = self.to_timestep_embed(self.timestep_features(t[:, None]).to(x.dtype)) # (b, embed_dim)
-
-        # Timestep embedding is considered a global embedding. Add to the global conditioning if it exists
-        if global_embed is not None:
-            global_embed = global_embed + timestep_embed
-        else:
-            global_embed = timestep_embed
-
-        # Add the global_embed to the prepend inputs if there is no global conditioning support in the transformer
-        if self.global_cond_type == "prepend":
-            if prepend_inputs is None:
-                # Prepend inputs are just the global embed, and the mask is all ones
-                prepend_inputs = global_embed.unsqueeze(1)
-                prepend_mask = torch.ones((x.shape[0], 1), device=x.device, dtype=torch.bool)
-            else:
-                # Prepend inputs are the prepend conditioning + the global embed
-                prepend_inputs = torch.cat([prepend_inputs, global_embed.unsqueeze(1)], dim=1)
-                prepend_mask = torch.cat([prepend_mask, torch.ones((x.shape[0], 1), device=x.device, dtype=torch.bool)], dim=1)
-
-            prepend_length = prepend_inputs.shape[1]
-
-        x = self.preprocess_conv(x) + x
-
-        x = rearrange(x, "b c t -> b t c")
-
-        extra_args = {}
-
-        if self.global_cond_type == "adaLN":
-            extra_args["global_cond"] = global_embed
-
-        if self.patch_size > 1:
-            x = rearrange(x, "b (t p) c -> b t (c p)", p=self.patch_size)
-
-        if self.transformer_type == "x-transformers":
-            output = self.transformer(x, prepend_embeds=prepend_inputs, context=cross_attn_cond, context_mask=cross_attn_cond_mask, mask=mask, prepend_mask=prepend_mask, **extra_args, **kwargs)
-        elif self.transformer_type == "continuous_transformer":
-            output = self.transformer(x, prepend_embeds=prepend_inputs, context=cross_attn_cond, context_mask=cross_attn_cond_mask, mask=mask, prepend_mask=prepend_mask, return_info=return_info, **extra_args, **kwargs)
-
-            if return_info:
-                output, info = output
-        elif self.transformer_type == "mm_transformer":
-            output = self.transformer(x, context=cross_attn_cond, mask=mask, context_mask=cross_attn_cond_mask, **extra_args, **kwargs)
-
-        output = rearrange(output, "b t c -> b c t")[:,:,prepend_length:]
-
-        if self.patch_size > 1:
-            output = rearrange(output, "b (c p) t -> b c (t p)", p=self.patch_size)
-
-        output = self.postprocess_conv(output) + output
-
-        if return_info:
-            return output, info
-
-        return output
-
-    def forward(
-        self,
-        x,
-        timestep,
-        context=None,
-        context_mask=None,
-        input_concat_cond=None,
-        global_embed=None,
-        negative_global_embed=None,
-        prepend_cond=None,
-        prepend_cond_mask=None,
-        mask=None,
-        return_info=False,
-        control=None,
-        transformer_options={},
-        **kwargs):
-            return self._forward(
-                x,
-                timestep,
-                cross_attn_cond=context,
-                cross_attn_cond_mask=context_mask,
-                input_concat_cond=input_concat_cond,
-                global_embed=global_embed,
-                prepend_cond=prepend_cond,
-                prepend_cond_mask=prepend_cond_mask,
-                mask=mask,
-                return_info=return_info,
-                **kwargs
-            )
diff --git a/MagicQuill/comfy/ldm/audio/embedders.py b/MagicQuill/comfy/ldm/audio/embedders.py
deleted file mode 100644
index 82a3210c60de10b4294335cd0001cb3e72b68bd6..0000000000000000000000000000000000000000
--- a/MagicQuill/comfy/ldm/audio/embedders.py
+++ /dev/null
@@ -1,108 +0,0 @@
-# code adapted from: https://github.com/Stability-AI/stable-audio-tools
-
-import torch
-import torch.nn as nn
-from torch import Tensor, einsum
-from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, TypeVar, Union
-from einops import rearrange
-import math
-import comfy.ops
-
-class LearnedPositionalEmbedding(nn.Module):
-    """Used for continuous time"""
-
-    def __init__(self, dim: int):
-        super().__init__()
-        assert (dim % 2) == 0
-        half_dim = dim // 2
-        self.weights = nn.Parameter(torch.empty(half_dim))
-
-    def forward(self, x: Tensor) -> Tensor:
-        x = rearrange(x, "b -> b 1")
-        freqs = x * rearrange(self.weights, "d -> 1 d") * 2 * math.pi
-        fouriered = torch.cat((freqs.sin(), freqs.cos()), dim=-1)
-        fouriered = torch.cat((x, fouriered), dim=-1)
-        return fouriered
-
-def TimePositionalEmbedding(dim: int, out_features: int) -> nn.Module:
-    return nn.Sequential(
-        LearnedPositionalEmbedding(dim),
-        comfy.ops.manual_cast.Linear(in_features=dim + 1, out_features=out_features),
-    )
-
-
-class NumberEmbedder(nn.Module):
-    def __init__(
-        self,
-        features: int,
-        dim: int = 256,
-    ):
-        super().__init__()
-        self.features = features
-        self.embedding = TimePositionalEmbedding(dim=dim, out_features=features)
-
-    def forward(self, x: Union[List[float], Tensor]) -> Tensor:
-        if not torch.is_tensor(x):
-            device = next(self.embedding.parameters()).device
-            x = torch.tensor(x, device=device)
-        assert isinstance(x, Tensor)
-        shape = x.shape
-        x = rearrange(x, "... -> (...)")
-        embedding = self.embedding(x)
-        x = embedding.view(*shape, self.features)
-        return x  # type: ignore
-
-
-class Conditioner(nn.Module):
-    def __init__(
-            self,
-            dim: int,
-            output_dim: int,
-            project_out: bool = False
-            ):
-
-        super().__init__()
-
-        self.dim = dim
-        self.output_dim = output_dim
-        self.proj_out = nn.Linear(dim, output_dim) if (dim != output_dim or project_out) else nn.Identity()
-
-    def forward(self, x):
-        raise NotImplementedError()
-
-class NumberConditioner(Conditioner):
-    '''
-        Conditioner that takes a list of floats, normalizes them for a given range, and returns a list of embeddings
-    '''
-    def __init__(self,
-                output_dim: int,
-                min_val: float=0,
-                max_val: float=1
-                ):
-        super().__init__(output_dim, output_dim)
-
-        self.min_val = min_val
-        self.max_val = max_val
-
-        self.embedder = NumberEmbedder(features=output_dim)
-
-    def forward(self, floats, device=None):
-            # Cast the inputs to floats
-            floats = [float(x) for x in floats]
-
-            if device is None:
-                device = next(self.embedder.parameters()).device
-
-            floats = torch.tensor(floats).to(device)
-
-            floats = floats.clamp(self.min_val, self.max_val)
-
-            normalized_floats = (floats - self.min_val) / (self.max_val - self.min_val)
-
-            # Cast floats to same type as embedder
-            embedder_dtype = next(self.embedder.parameters()).dtype
-            normalized_floats = normalized_floats.to(embedder_dtype)
-
-            float_embeds = self.embedder(normalized_floats).unsqueeze(1)
-
-            return [float_embeds, torch.ones(float_embeds.shape[0], 1).to(device)]
diff --git a/MagicQuill/comfy/ldm/cascade/__pycache__/common.cpython-310.pyc b/MagicQuill/comfy/ldm/cascade/__pycache__/common.cpython-310.pyc
deleted file mode 100644
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deleted file mode 100644
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diff --git a/MagicQuill/comfy/ldm/cascade/common.py b/MagicQuill/comfy/ldm/cascade/common.py
deleted file mode 100644
index 124902c09a4599e97a4e4c80f9d83b9d44eab22e..0000000000000000000000000000000000000000
--- a/MagicQuill/comfy/ldm/cascade/common.py
+++ /dev/null
@@ -1,161 +0,0 @@
-"""
-    This file is part of ComfyUI.
-    Copyright (C) 2024 Stability AI
-
-    This program is free software: you can redistribute it and/or modify
-    it under the terms of the GNU General Public License as published by
-    the Free Software Foundation, either version 3 of the License, or
-    (at your option) any later version.
-
-    This program is distributed in the hope that it will be useful,
-    but WITHOUT ANY WARRANTY; without even the implied warranty of
-    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-    GNU General Public License for more details.
-
-    You should have received a copy of the GNU General Public License
-    along with this program.  If not, see <https://www.gnu.org/licenses/>.
-"""
-
-import torch
-import torch.nn as nn
-from comfy.ldm.modules.attention import optimized_attention
-
-class Linear(torch.nn.Linear):
-    def reset_parameters(self):
-        return None
-
-class Conv2d(torch.nn.Conv2d):
-    def reset_parameters(self):
-        return None
-
-class OptimizedAttention(nn.Module):
-    def __init__(self, c, nhead, dropout=0.0, dtype=None, device=None, operations=None):
-        super().__init__()
-        self.heads = nhead
-
-        self.to_q = operations.Linear(c, c, bias=True, dtype=dtype, device=device)
-        self.to_k = operations.Linear(c, c, bias=True, dtype=dtype, device=device)
-        self.to_v = operations.Linear(c, c, bias=True, dtype=dtype, device=device)
-
-        self.out_proj = operations.Linear(c, c, bias=True, dtype=dtype, device=device)
-
-    def forward(self, q, k, v):
-        q = self.to_q(q)
-        k = self.to_k(k)
-        v = self.to_v(v)
-
-        out = optimized_attention(q, k, v, self.heads)
-
-        return self.out_proj(out)
-
-class Attention2D(nn.Module):
-    def __init__(self, c, nhead, dropout=0.0, dtype=None, device=None, operations=None):
-        super().__init__()
-        self.attn = OptimizedAttention(c, nhead, dtype=dtype, device=device, operations=operations)
-        # self.attn = nn.MultiheadAttention(c, nhead, dropout=dropout, bias=True, batch_first=True, dtype=dtype, device=device)
-
-    def forward(self, x, kv, self_attn=False):
-        orig_shape = x.shape
-        x = x.view(x.size(0), x.size(1), -1).permute(0, 2, 1)  # Bx4xHxW -> Bx(HxW)x4
-        if self_attn:
-            kv = torch.cat([x, kv], dim=1)
-        # x = self.attn(x, kv, kv, need_weights=False)[0]
-        x = self.attn(x, kv, kv)
-        x = x.permute(0, 2, 1).view(*orig_shape)
-        return x
-
-
-def LayerNorm2d_op(operations):
-    class LayerNorm2d(operations.LayerNorm):
-        def __init__(self, *args, **kwargs):
-            super().__init__(*args, **kwargs)
-
-        def forward(self, x):
-            return super().forward(x.permute(0, 2, 3, 1)).permute(0, 3, 1, 2)
-    return LayerNorm2d
-
-class GlobalResponseNorm(nn.Module):
-    "from https://github.com/facebookresearch/ConvNeXt-V2/blob/3608f67cc1dae164790c5d0aead7bf2d73d9719b/models/utils.py#L105"
-    def __init__(self, dim, dtype=None, device=None):
-        super().__init__()
-        self.gamma = nn.Parameter(torch.zeros(1, 1, 1, dim, dtype=dtype, device=device))
-        self.beta = nn.Parameter(torch.zeros(1, 1, 1, dim, dtype=dtype, device=device))
-
-    def forward(self, x):
-        Gx = torch.norm(x, p=2, dim=(1, 2), keepdim=True)
-        Nx = Gx / (Gx.mean(dim=-1, keepdim=True) + 1e-6)
-        return self.gamma.to(device=x.device, dtype=x.dtype) * (x * Nx) + self.beta.to(device=x.device, dtype=x.dtype) + x
-
-
-class ResBlock(nn.Module):
-    def __init__(self, c, c_skip=0, kernel_size=3, dropout=0.0, dtype=None, device=None, operations=None):  # , num_heads=4, expansion=2):
-        super().__init__()
-        self.depthwise = operations.Conv2d(c, c, kernel_size=kernel_size, padding=kernel_size // 2, groups=c, dtype=dtype, device=device)
-        #         self.depthwise = SAMBlock(c, num_heads, expansion)
-        self.norm = LayerNorm2d_op(operations)(c, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
-        self.channelwise = nn.Sequential(
-            operations.Linear(c + c_skip, c * 4, dtype=dtype, device=device),
-            nn.GELU(),
-            GlobalResponseNorm(c * 4, dtype=dtype, device=device),
-            nn.Dropout(dropout),
-            operations.Linear(c * 4, c, dtype=dtype, device=device)
-        )
-
-    def forward(self, x, x_skip=None):
-        x_res = x
-        x = self.norm(self.depthwise(x))
-        if x_skip is not None:
-            x = torch.cat([x, x_skip], dim=1)
-        x = self.channelwise(x.permute(0, 2, 3, 1)).permute(0, 3, 1, 2)
-        return x + x_res
-
-
-class AttnBlock(nn.Module):
-    def __init__(self, c, c_cond, nhead, self_attn=True, dropout=0.0, dtype=None, device=None, operations=None):
-        super().__init__()
-        self.self_attn = self_attn
-        self.norm = LayerNorm2d_op(operations)(c, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
-        self.attention = Attention2D(c, nhead, dropout, dtype=dtype, device=device, operations=operations)
-        self.kv_mapper = nn.Sequential(
-            nn.SiLU(),
-            operations.Linear(c_cond, c, dtype=dtype, device=device)
-        )
-
-    def forward(self, x, kv):
-        kv = self.kv_mapper(kv)
-        x = x + self.attention(self.norm(x), kv, self_attn=self.self_attn)
-        return x
-
-
-class FeedForwardBlock(nn.Module):
-    def __init__(self, c, dropout=0.0, dtype=None, device=None, operations=None):
-        super().__init__()
-        self.norm = LayerNorm2d_op(operations)(c, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
-        self.channelwise = nn.Sequential(
-            operations.Linear(c, c * 4, dtype=dtype, device=device),
-            nn.GELU(),
-            GlobalResponseNorm(c * 4, dtype=dtype, device=device),
-            nn.Dropout(dropout),
-            operations.Linear(c * 4, c, dtype=dtype, device=device)
-        )
-
-    def forward(self, x):
-        x = x + self.channelwise(self.norm(x).permute(0, 2, 3, 1)).permute(0, 3, 1, 2)
-        return x
-
-
-class TimestepBlock(nn.Module):
-    def __init__(self, c, c_timestep, conds=['sca'], dtype=None, device=None, operations=None):
-        super().__init__()
-        self.mapper = operations.Linear(c_timestep, c * 2, dtype=dtype, device=device)
-        self.conds = conds
-        for cname in conds:
-            setattr(self, f"mapper_{cname}", operations.Linear(c_timestep, c * 2, dtype=dtype, device=device))
-
-    def forward(self, x, t):
-        t = t.chunk(len(self.conds) + 1, dim=1)
-        a, b = self.mapper(t[0])[:, :, None, None].chunk(2, dim=1)
-        for i, c in enumerate(self.conds):
-            ac, bc = getattr(self, f"mapper_{c}")(t[i + 1])[:, :, None, None].chunk(2, dim=1)
-            a, b = a + ac, b + bc
-        return x * (1 + a) + b
diff --git a/MagicQuill/comfy/ldm/cascade/controlnet.py b/MagicQuill/comfy/ldm/cascade/controlnet.py
deleted file mode 100644
index 5dac5939409a3c9851e768f412eb42a97a9a4381..0000000000000000000000000000000000000000
--- a/MagicQuill/comfy/ldm/cascade/controlnet.py
+++ /dev/null
@@ -1,93 +0,0 @@
-"""
-    This file is part of ComfyUI.
-    Copyright (C) 2024 Stability AI
-
-    This program is free software: you can redistribute it and/or modify
-    it under the terms of the GNU General Public License as published by
-    the Free Software Foundation, either version 3 of the License, or
-    (at your option) any later version.
-
-    This program is distributed in the hope that it will be useful,
-    but WITHOUT ANY WARRANTY; without even the implied warranty of
-    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-    GNU General Public License for more details.
-
-    You should have received a copy of the GNU General Public License
-    along with this program.  If not, see <https://www.gnu.org/licenses/>.
-"""
-
-import torch
-import torchvision
-from torch import nn
-from .common import LayerNorm2d_op
-
-
-class CNetResBlock(nn.Module):
-    def __init__(self, c, dtype=None, device=None, operations=None):
-        super().__init__()
-        self.blocks = nn.Sequential(
-            LayerNorm2d_op(operations)(c, dtype=dtype, device=device),
-            nn.GELU(),
-            operations.Conv2d(c, c, kernel_size=3, padding=1),
-            LayerNorm2d_op(operations)(c, dtype=dtype, device=device),
-            nn.GELU(),
-            operations.Conv2d(c, c, kernel_size=3, padding=1),
-        )
-
-    def forward(self, x):
-        return x + self.blocks(x)
-
-
-class ControlNet(nn.Module):
-    def __init__(self, c_in=3, c_proj=2048, proj_blocks=None, bottleneck_mode=None, dtype=None, device=None, operations=nn):
-        super().__init__()
-        if bottleneck_mode is None:
-            bottleneck_mode = 'effnet'
-        self.proj_blocks = proj_blocks
-        if bottleneck_mode == 'effnet':
-            embd_channels = 1280
-            self.backbone = torchvision.models.efficientnet_v2_s().features.eval()
-            if c_in != 3:
-                in_weights = self.backbone[0][0].weight.data
-                self.backbone[0][0] = operations.Conv2d(c_in, 24, kernel_size=3, stride=2, bias=False, dtype=dtype, device=device)
-                if c_in > 3:
-                    # nn.init.constant_(self.backbone[0][0].weight, 0)
-                    self.backbone[0][0].weight.data[:, :3] = in_weights[:, :3].clone()
-                else:
-                    self.backbone[0][0].weight.data = in_weights[:, :c_in].clone()
-        elif bottleneck_mode == 'simple':
-            embd_channels = c_in
-            self.backbone = nn.Sequential(
-                operations.Conv2d(embd_channels, embd_channels * 4, kernel_size=3, padding=1, dtype=dtype, device=device),
-                nn.LeakyReLU(0.2, inplace=True),
-                operations.Conv2d(embd_channels * 4, embd_channels, kernel_size=3, padding=1, dtype=dtype, device=device),
-            )
-        elif bottleneck_mode == 'large':
-            self.backbone = nn.Sequential(
-                operations.Conv2d(c_in, 4096 * 4, kernel_size=1, dtype=dtype, device=device),
-                nn.LeakyReLU(0.2, inplace=True),
-                operations.Conv2d(4096 * 4, 1024, kernel_size=1, dtype=dtype, device=device),
-                *[CNetResBlock(1024, dtype=dtype, device=device, operations=operations) for _ in range(8)],
-                operations.Conv2d(1024, 1280, kernel_size=1, dtype=dtype, device=device),
-            )
-            embd_channels = 1280
-        else:
-            raise ValueError(f'Unknown bottleneck mode: {bottleneck_mode}')
-        self.projections = nn.ModuleList()
-        for _ in range(len(proj_blocks)):
-            self.projections.append(nn.Sequential(
-                operations.Conv2d(embd_channels, embd_channels, kernel_size=1, bias=False, dtype=dtype, device=device),
-                nn.LeakyReLU(0.2, inplace=True),
-                operations.Conv2d(embd_channels, c_proj, kernel_size=1, bias=False, dtype=dtype, device=device),
-            ))
-            # nn.init.constant_(self.projections[-1][-1].weight, 0)  # zero output projection
-        self.xl = False
-        self.input_channels = c_in
-        self.unshuffle_amount = 8
-
-    def forward(self, x):
-        x = self.backbone(x)
-        proj_outputs = [None for _ in range(max(self.proj_blocks) + 1)]
-        for i, idx in enumerate(self.proj_blocks):
-            proj_outputs[idx] = self.projections[i](x)
-        return proj_outputs
diff --git a/MagicQuill/comfy/ldm/cascade/stage_a.py b/MagicQuill/comfy/ldm/cascade/stage_a.py
deleted file mode 100644
index ca8867eaf35cbc57eb5d925082b7e2bb7b36932d..0000000000000000000000000000000000000000
--- a/MagicQuill/comfy/ldm/cascade/stage_a.py
+++ /dev/null
@@ -1,255 +0,0 @@
-"""
-    This file is part of ComfyUI.
-    Copyright (C) 2024 Stability AI
-
-    This program is free software: you can redistribute it and/or modify
-    it under the terms of the GNU General Public License as published by
-    the Free Software Foundation, either version 3 of the License, or
-    (at your option) any later version.
-
-    This program is distributed in the hope that it will be useful,
-    but WITHOUT ANY WARRANTY; without even the implied warranty of
-    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-    GNU General Public License for more details.
-
-    You should have received a copy of the GNU General Public License
-    along with this program.  If not, see <https://www.gnu.org/licenses/>.
-"""
-
-import torch
-from torch import nn
-from torch.autograd import Function
-
-class vector_quantize(Function):
-    @staticmethod
-    def forward(ctx, x, codebook):
-        with torch.no_grad():
-            codebook_sqr = torch.sum(codebook ** 2, dim=1)
-            x_sqr = torch.sum(x ** 2, dim=1, keepdim=True)
-
-            dist = torch.addmm(codebook_sqr + x_sqr, x, codebook.t(), alpha=-2.0, beta=1.0)
-            _, indices = dist.min(dim=1)
-
-            ctx.save_for_backward(indices, codebook)
-            ctx.mark_non_differentiable(indices)
-
-            nn = torch.index_select(codebook, 0, indices)
-            return nn, indices
-
-    @staticmethod
-    def backward(ctx, grad_output, grad_indices):
-        grad_inputs, grad_codebook = None, None
-
-        if ctx.needs_input_grad[0]:
-            grad_inputs = grad_output.clone()
-        if ctx.needs_input_grad[1]:
-            # Gradient wrt. the codebook
-            indices, codebook = ctx.saved_tensors
-
-            grad_codebook = torch.zeros_like(codebook)
-            grad_codebook.index_add_(0, indices, grad_output)
-
-        return (grad_inputs, grad_codebook)
-
-
-class VectorQuantize(nn.Module):
-    def __init__(self, embedding_size, k, ema_decay=0.99, ema_loss=False):
-        """
-        Takes an input of variable size (as long as the last dimension matches the embedding size).
-        Returns one tensor containing the nearest neigbour embeddings to each of the inputs,
-        with the same size as the input, vq and commitment components for the loss as a touple
-        in the second output and the indices of the quantized vectors in the third:
-        quantized, (vq_loss, commit_loss), indices
-        """
-        super(VectorQuantize, self).__init__()
-
-        self.codebook = nn.Embedding(k, embedding_size)
-        self.codebook.weight.data.uniform_(-1./k, 1./k)
-        self.vq = vector_quantize.apply
-
-        self.ema_decay = ema_decay
-        self.ema_loss = ema_loss
-        if ema_loss:
-            self.register_buffer('ema_element_count', torch.ones(k))
-            self.register_buffer('ema_weight_sum', torch.zeros_like(self.codebook.weight))
-
-    def _laplace_smoothing(self, x, epsilon):
-        n = torch.sum(x)
-        return ((x + epsilon) / (n + x.size(0) * epsilon) * n)
-
-    def _updateEMA(self, z_e_x, indices):
-        mask = nn.functional.one_hot(indices, self.ema_element_count.size(0)).float()
-        elem_count = mask.sum(dim=0)
-        weight_sum = torch.mm(mask.t(), z_e_x)
-
-        self.ema_element_count = (self.ema_decay * self.ema_element_count) + ((1-self.ema_decay) * elem_count)
-        self.ema_element_count = self._laplace_smoothing(self.ema_element_count, 1e-5)
-        self.ema_weight_sum = (self.ema_decay * self.ema_weight_sum) + ((1-self.ema_decay) * weight_sum)
-
-        self.codebook.weight.data = self.ema_weight_sum / self.ema_element_count.unsqueeze(-1)
-
-    def idx2vq(self, idx, dim=-1):
-        q_idx = self.codebook(idx)
-        if dim != -1:
-            q_idx = q_idx.movedim(-1, dim)
-        return q_idx
-
-    def forward(self, x, get_losses=True, dim=-1):
-        if dim != -1:
-            x = x.movedim(dim, -1)
-        z_e_x = x.contiguous().view(-1, x.size(-1)) if len(x.shape) > 2 else x
-        z_q_x, indices = self.vq(z_e_x, self.codebook.weight.detach())
-        vq_loss, commit_loss = None, None
-        if self.ema_loss and self.training:
-            self._updateEMA(z_e_x.detach(), indices.detach())
-        # pick the graded embeddings after updating the codebook in order to have a more accurate commitment loss
-        z_q_x_grd = torch.index_select(self.codebook.weight, dim=0, index=indices)
-        if get_losses:
-            vq_loss = (z_q_x_grd - z_e_x.detach()).pow(2).mean()
-            commit_loss = (z_e_x - z_q_x_grd.detach()).pow(2).mean()
-
-        z_q_x = z_q_x.view(x.shape)
-        if dim != -1:
-            z_q_x = z_q_x.movedim(-1, dim)
-        return z_q_x, (vq_loss, commit_loss), indices.view(x.shape[:-1])
-
-
-class ResBlock(nn.Module):
-    def __init__(self, c, c_hidden):
-        super().__init__()
-        # depthwise/attention
-        self.norm1 = nn.LayerNorm(c, elementwise_affine=False, eps=1e-6)
-        self.depthwise = nn.Sequential(
-            nn.ReplicationPad2d(1),
-            nn.Conv2d(c, c, kernel_size=3, groups=c)
-        )
-
-        # channelwise
-        self.norm2 = nn.LayerNorm(c, elementwise_affine=False, eps=1e-6)
-        self.channelwise = nn.Sequential(
-            nn.Linear(c, c_hidden),
-            nn.GELU(),
-            nn.Linear(c_hidden, c),
-        )
-
-        self.gammas = nn.Parameter(torch.zeros(6), requires_grad=True)
-
-        # Init weights
-        def _basic_init(module):
-            if isinstance(module, nn.Linear) or isinstance(module, nn.Conv2d):
-                torch.nn.init.xavier_uniform_(module.weight)
-                if module.bias is not None:
-                    nn.init.constant_(module.bias, 0)
-
-        self.apply(_basic_init)
-
-    def _norm(self, x, norm):
-        return norm(x.permute(0, 2, 3, 1)).permute(0, 3, 1, 2)
-
-    def forward(self, x):
-        mods = self.gammas
-
-        x_temp = self._norm(x, self.norm1) * (1 + mods[0]) + mods[1]
-        try:
-            x = x + self.depthwise(x_temp) * mods[2]
-        except: #operation not implemented for bf16
-            x_temp = self.depthwise[0](x_temp.float()).to(x.dtype)
-            x = x + self.depthwise[1](x_temp) * mods[2]
-
-        x_temp = self._norm(x, self.norm2) * (1 + mods[3]) + mods[4]
-        x = x + self.channelwise(x_temp.permute(0, 2, 3, 1)).permute(0, 3, 1, 2) * mods[5]
-
-        return x
-
-
-class StageA(nn.Module):
-    def __init__(self, levels=2, bottleneck_blocks=12, c_hidden=384, c_latent=4, codebook_size=8192):
-        super().__init__()
-        self.c_latent = c_latent
-        c_levels = [c_hidden // (2 ** i) for i in reversed(range(levels))]
-
-        # Encoder blocks
-        self.in_block = nn.Sequential(
-            nn.PixelUnshuffle(2),
-            nn.Conv2d(3 * 4, c_levels[0], kernel_size=1)
-        )
-        down_blocks = []
-        for i in range(levels):
-            if i > 0:
-                down_blocks.append(nn.Conv2d(c_levels[i - 1], c_levels[i], kernel_size=4, stride=2, padding=1))
-            block = ResBlock(c_levels[i], c_levels[i] * 4)
-            down_blocks.append(block)
-        down_blocks.append(nn.Sequential(
-            nn.Conv2d(c_levels[-1], c_latent, kernel_size=1, bias=False),
-            nn.BatchNorm2d(c_latent),  # then normalize them to have mean 0 and std 1
-        ))
-        self.down_blocks = nn.Sequential(*down_blocks)
-        self.down_blocks[0]
-
-        self.codebook_size = codebook_size
-        self.vquantizer = VectorQuantize(c_latent, k=codebook_size)
-
-        # Decoder blocks
-        up_blocks = [nn.Sequential(
-            nn.Conv2d(c_latent, c_levels[-1], kernel_size=1)
-        )]
-        for i in range(levels):
-            for j in range(bottleneck_blocks if i == 0 else 1):
-                block = ResBlock(c_levels[levels - 1 - i], c_levels[levels - 1 - i] * 4)
-                up_blocks.append(block)
-            if i < levels - 1:
-                up_blocks.append(
-                    nn.ConvTranspose2d(c_levels[levels - 1 - i], c_levels[levels - 2 - i], kernel_size=4, stride=2,
-                                       padding=1))
-        self.up_blocks = nn.Sequential(*up_blocks)
-        self.out_block = nn.Sequential(
-            nn.Conv2d(c_levels[0], 3 * 4, kernel_size=1),
-            nn.PixelShuffle(2),
-        )
-
-    def encode(self, x, quantize=False):
-        x = self.in_block(x)
-        x = self.down_blocks(x)
-        if quantize:
-            qe, (vq_loss, commit_loss), indices = self.vquantizer.forward(x, dim=1)
-            return qe, x, indices, vq_loss + commit_loss * 0.25
-        else:
-            return x
-
-    def decode(self, x):
-        x = self.up_blocks(x)
-        x = self.out_block(x)
-        return x
-
-    def forward(self, x, quantize=False):
-        qe, x, _, vq_loss = self.encode(x, quantize)
-        x = self.decode(qe)
-        return x, vq_loss
-
-
-class Discriminator(nn.Module):
-    def __init__(self, c_in=3, c_cond=0, c_hidden=512, depth=6):
-        super().__init__()
-        d = max(depth - 3, 3)
-        layers = [
-            nn.utils.spectral_norm(nn.Conv2d(c_in, c_hidden // (2 ** d), kernel_size=3, stride=2, padding=1)),
-            nn.LeakyReLU(0.2),
-        ]
-        for i in range(depth - 1):
-            c_in = c_hidden // (2 ** max((d - i), 0))
-            c_out = c_hidden // (2 ** max((d - 1 - i), 0))
-            layers.append(nn.utils.spectral_norm(nn.Conv2d(c_in, c_out, kernel_size=3, stride=2, padding=1)))
-            layers.append(nn.InstanceNorm2d(c_out))
-            layers.append(nn.LeakyReLU(0.2))
-        self.encoder = nn.Sequential(*layers)
-        self.shuffle = nn.Conv2d((c_hidden + c_cond) if c_cond > 0 else c_hidden, 1, kernel_size=1)
-        self.logits = nn.Sigmoid()
-
-    def forward(self, x, cond=None):
-        x = self.encoder(x)
-        if cond is not None:
-            cond = cond.view(cond.size(0), cond.size(1), 1, 1, ).expand(-1, -1, x.size(-2), x.size(-1))
-            x = torch.cat([x, cond], dim=1)
-        x = self.shuffle(x)
-        x = self.logits(x)
-        return x
diff --git a/MagicQuill/comfy/ldm/cascade/stage_b.py b/MagicQuill/comfy/ldm/cascade/stage_b.py
deleted file mode 100644
index 7c3d8feabd826accc702b6e6e598b61b4a739194..0000000000000000000000000000000000000000
--- a/MagicQuill/comfy/ldm/cascade/stage_b.py
+++ /dev/null
@@ -1,256 +0,0 @@
-"""
-    This file is part of ComfyUI.
-    Copyright (C) 2024 Stability AI
-
-    This program is free software: you can redistribute it and/or modify
-    it under the terms of the GNU General Public License as published by
-    the Free Software Foundation, either version 3 of the License, or
-    (at your option) any later version.
-
-    This program is distributed in the hope that it will be useful,
-    but WITHOUT ANY WARRANTY; without even the implied warranty of
-    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-    GNU General Public License for more details.
-
-    You should have received a copy of the GNU General Public License
-    along with this program.  If not, see <https://www.gnu.org/licenses/>.
-"""
-
-import math
-import torch
-from torch import nn
-from .common import AttnBlock, LayerNorm2d_op, ResBlock, FeedForwardBlock, TimestepBlock
-
-class StageB(nn.Module):
-    def __init__(self, c_in=4, c_out=4, c_r=64, patch_size=2, c_cond=1280, c_hidden=[320, 640, 1280, 1280],
-                 nhead=[-1, -1, 20, 20], blocks=[[2, 6, 28, 6], [6, 28, 6, 2]],
-                 block_repeat=[[1, 1, 1, 1], [3, 3, 2, 2]], level_config=['CT', 'CT', 'CTA', 'CTA'], c_clip=1280,
-                 c_clip_seq=4, c_effnet=16, c_pixels=3, kernel_size=3, dropout=[0, 0, 0.0, 0.0], self_attn=True,
-                 t_conds=['sca'], stable_cascade_stage=None, dtype=None, device=None, operations=None):
-        super().__init__()
-        self.dtype = dtype
-        self.c_r = c_r
-        self.t_conds = t_conds
-        self.c_clip_seq = c_clip_seq
-        if not isinstance(dropout, list):
-            dropout = [dropout] * len(c_hidden)
-        if not isinstance(self_attn, list):
-            self_attn = [self_attn] * len(c_hidden)
-
-        # CONDITIONING
-        self.effnet_mapper = nn.Sequential(
-            operations.Conv2d(c_effnet, c_hidden[0] * 4, kernel_size=1, dtype=dtype, device=device),
-            nn.GELU(),
-            operations.Conv2d(c_hidden[0] * 4, c_hidden[0], kernel_size=1, dtype=dtype, device=device),
-            LayerNorm2d_op(operations)(c_hidden[0], elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
-        )
-        self.pixels_mapper = nn.Sequential(
-            operations.Conv2d(c_pixels, c_hidden[0] * 4, kernel_size=1, dtype=dtype, device=device),
-            nn.GELU(),
-            operations.Conv2d(c_hidden[0] * 4, c_hidden[0], kernel_size=1, dtype=dtype, device=device),
-            LayerNorm2d_op(operations)(c_hidden[0], elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
-        )
-        self.clip_mapper = operations.Linear(c_clip, c_cond * c_clip_seq, dtype=dtype, device=device)
-        self.clip_norm = operations.LayerNorm(c_cond, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
-
-        self.embedding = nn.Sequential(
-            nn.PixelUnshuffle(patch_size),
-            operations.Conv2d(c_in * (patch_size ** 2), c_hidden[0], kernel_size=1, dtype=dtype, device=device),
-            LayerNorm2d_op(operations)(c_hidden[0], elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
-        )
-
-        def get_block(block_type, c_hidden, nhead, c_skip=0, dropout=0, self_attn=True):
-            if block_type == 'C':
-                return ResBlock(c_hidden, c_skip, kernel_size=kernel_size, dropout=dropout, dtype=dtype, device=device, operations=operations)
-            elif block_type == 'A':
-                return AttnBlock(c_hidden, c_cond, nhead, self_attn=self_attn, dropout=dropout, dtype=dtype, device=device, operations=operations)
-            elif block_type == 'F':
-                return FeedForwardBlock(c_hidden, dropout=dropout, dtype=dtype, device=device, operations=operations)
-            elif block_type == 'T':
-                return TimestepBlock(c_hidden, c_r, conds=t_conds, dtype=dtype, device=device, operations=operations)
-            else:
-                raise Exception(f'Block type {block_type} not supported')
-
-        # BLOCKS
-        # -- down blocks
-        self.down_blocks = nn.ModuleList()
-        self.down_downscalers = nn.ModuleList()
-        self.down_repeat_mappers = nn.ModuleList()
-        for i in range(len(c_hidden)):
-            if i > 0:
-                self.down_downscalers.append(nn.Sequential(
-                    LayerNorm2d_op(operations)(c_hidden[i - 1], elementwise_affine=False, eps=1e-6, dtype=dtype, device=device),
-                    operations.Conv2d(c_hidden[i - 1], c_hidden[i], kernel_size=2, stride=2, dtype=dtype, device=device),
-                ))
-            else:
-                self.down_downscalers.append(nn.Identity())
-            down_block = nn.ModuleList()
-            for _ in range(blocks[0][i]):
-                for block_type in level_config[i]:
-                    block = get_block(block_type, c_hidden[i], nhead[i], dropout=dropout[i], self_attn=self_attn[i])
-                    down_block.append(block)
-            self.down_blocks.append(down_block)
-            if block_repeat is not None:
-                block_repeat_mappers = nn.ModuleList()
-                for _ in range(block_repeat[0][i] - 1):
-                    block_repeat_mappers.append(operations.Conv2d(c_hidden[i], c_hidden[i], kernel_size=1, dtype=dtype, device=device))
-                self.down_repeat_mappers.append(block_repeat_mappers)
-
-        # -- up blocks
-        self.up_blocks = nn.ModuleList()
-        self.up_upscalers = nn.ModuleList()
-        self.up_repeat_mappers = nn.ModuleList()
-        for i in reversed(range(len(c_hidden))):
-            if i > 0:
-                self.up_upscalers.append(nn.Sequential(
-                    LayerNorm2d_op(operations)(c_hidden[i], elementwise_affine=False, eps=1e-6, dtype=dtype, device=device),
-                    operations.ConvTranspose2d(c_hidden[i], c_hidden[i - 1], kernel_size=2, stride=2, dtype=dtype, device=device),
-                ))
-            else:
-                self.up_upscalers.append(nn.Identity())
-            up_block = nn.ModuleList()
-            for j in range(blocks[1][::-1][i]):
-                for k, block_type in enumerate(level_config[i]):
-                    c_skip = c_hidden[i] if i < len(c_hidden) - 1 and j == k == 0 else 0
-                    block = get_block(block_type, c_hidden[i], nhead[i], c_skip=c_skip, dropout=dropout[i],
-                                      self_attn=self_attn[i])
-                    up_block.append(block)
-            self.up_blocks.append(up_block)
-            if block_repeat is not None:
-                block_repeat_mappers = nn.ModuleList()
-                for _ in range(block_repeat[1][::-1][i] - 1):
-                    block_repeat_mappers.append(operations.Conv2d(c_hidden[i], c_hidden[i], kernel_size=1, dtype=dtype, device=device))
-                self.up_repeat_mappers.append(block_repeat_mappers)
-
-        # OUTPUT
-        self.clf = nn.Sequential(
-            LayerNorm2d_op(operations)(c_hidden[0], elementwise_affine=False, eps=1e-6, dtype=dtype, device=device),
-            operations.Conv2d(c_hidden[0], c_out * (patch_size ** 2), kernel_size=1, dtype=dtype, device=device),
-            nn.PixelShuffle(patch_size),
-        )
-
-        # --- WEIGHT INIT ---
-    #     self.apply(self._init_weights)  # General init
-    #     nn.init.normal_(self.clip_mapper.weight, std=0.02)  # conditionings
-    #     nn.init.normal_(self.effnet_mapper[0].weight, std=0.02)  # conditionings
-    #     nn.init.normal_(self.effnet_mapper[2].weight, std=0.02)  # conditionings
-    #     nn.init.normal_(self.pixels_mapper[0].weight, std=0.02)  # conditionings
-    #     nn.init.normal_(self.pixels_mapper[2].weight, std=0.02)  # conditionings
-    #     torch.nn.init.xavier_uniform_(self.embedding[1].weight, 0.02)  # inputs
-    #     nn.init.constant_(self.clf[1].weight, 0)  # outputs
-    # 
-    #     # blocks
-    #     for level_block in self.down_blocks + self.up_blocks:
-    #         for block in level_block:
-    #             if isinstance(block, ResBlock) or isinstance(block, FeedForwardBlock):
-    #                 block.channelwise[-1].weight.data *= np.sqrt(1 / sum(blocks[0]))
-    #             elif isinstance(block, TimestepBlock):
-    #                 for layer in block.modules():
-    #                     if isinstance(layer, nn.Linear):
-    #                         nn.init.constant_(layer.weight, 0)
-    # 
-    # def _init_weights(self, m):
-    #     if isinstance(m, (nn.Conv2d, nn.Linear)):
-    #         torch.nn.init.xavier_uniform_(m.weight)
-    #         if m.bias is not None:
-    #             nn.init.constant_(m.bias, 0)
-
-    def gen_r_embedding(self, r, max_positions=10000):
-        r = r * max_positions
-        half_dim = self.c_r // 2
-        emb = math.log(max_positions) / (half_dim - 1)
-        emb = torch.arange(half_dim, device=r.device).float().mul(-emb).exp()
-        emb = r[:, None] * emb[None, :]
-        emb = torch.cat([emb.sin(), emb.cos()], dim=1)
-        if self.c_r % 2 == 1:  # zero pad
-            emb = nn.functional.pad(emb, (0, 1), mode='constant')
-        return emb
-
-    def gen_c_embeddings(self, clip):
-        if len(clip.shape) == 2:
-            clip = clip.unsqueeze(1)
-        clip = self.clip_mapper(clip).view(clip.size(0), clip.size(1) * self.c_clip_seq, -1)
-        clip = self.clip_norm(clip)
-        return clip
-
-    def _down_encode(self, x, r_embed, clip):
-        level_outputs = []
-        block_group = zip(self.down_blocks, self.down_downscalers, self.down_repeat_mappers)
-        for down_block, downscaler, repmap in block_group:
-            x = downscaler(x)
-            for i in range(len(repmap) + 1):
-                for block in down_block:
-                    if isinstance(block, ResBlock) or (
-                            hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module,
-                                                                                  ResBlock)):
-                        x = block(x)
-                    elif isinstance(block, AttnBlock) or (
-                            hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module,
-                                                                                  AttnBlock)):
-                        x = block(x, clip)
-                    elif isinstance(block, TimestepBlock) or (
-                            hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module,
-                                                                                  TimestepBlock)):
-                        x = block(x, r_embed)
-                    else:
-                        x = block(x)
-                if i < len(repmap):
-                    x = repmap[i](x)
-            level_outputs.insert(0, x)
-        return level_outputs
-
-    def _up_decode(self, level_outputs, r_embed, clip):
-        x = level_outputs[0]
-        block_group = zip(self.up_blocks, self.up_upscalers, self.up_repeat_mappers)
-        for i, (up_block, upscaler, repmap) in enumerate(block_group):
-            for j in range(len(repmap) + 1):
-                for k, block in enumerate(up_block):
-                    if isinstance(block, ResBlock) or (
-                            hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module,
-                                                                                  ResBlock)):
-                        skip = level_outputs[i] if k == 0 and i > 0 else None
-                        if skip is not None and (x.size(-1) != skip.size(-1) or x.size(-2) != skip.size(-2)):
-                            x = torch.nn.functional.interpolate(x, skip.shape[-2:], mode='bilinear',
-                                                                align_corners=True)
-                        x = block(x, skip)
-                    elif isinstance(block, AttnBlock) or (
-                            hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module,
-                                                                                  AttnBlock)):
-                        x = block(x, clip)
-                    elif isinstance(block, TimestepBlock) or (
-                            hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module,
-                                                                                  TimestepBlock)):
-                        x = block(x, r_embed)
-                    else:
-                        x = block(x)
-                if j < len(repmap):
-                    x = repmap[j](x)
-            x = upscaler(x)
-        return x
-
-    def forward(self, x, r, effnet, clip, pixels=None, **kwargs):
-        if pixels is None:
-            pixels = x.new_zeros(x.size(0), 3, 8, 8)
-
-        # Process the conditioning embeddings
-        r_embed = self.gen_r_embedding(r).to(dtype=x.dtype)
-        for c in self.t_conds:
-            t_cond = kwargs.get(c, torch.zeros_like(r))
-            r_embed = torch.cat([r_embed, self.gen_r_embedding(t_cond).to(dtype=x.dtype)], dim=1)
-        clip = self.gen_c_embeddings(clip)
-
-        # Model Blocks
-        x = self.embedding(x)
-        x = x + self.effnet_mapper(
-            nn.functional.interpolate(effnet, size=x.shape[-2:], mode='bilinear', align_corners=True))
-        x = x + nn.functional.interpolate(self.pixels_mapper(pixels), size=x.shape[-2:], mode='bilinear',
-                                          align_corners=True)
-        level_outputs = self._down_encode(x, r_embed, clip)
-        x = self._up_decode(level_outputs, r_embed, clip)
-        return self.clf(x)
-
-    def update_weights_ema(self, src_model, beta=0.999):
-        for self_params, src_params in zip(self.parameters(), src_model.parameters()):
-            self_params.data = self_params.data * beta + src_params.data.clone().to(self_params.device) * (1 - beta)
-        for self_buffers, src_buffers in zip(self.buffers(), src_model.buffers()):
-            self_buffers.data = self_buffers.data * beta + src_buffers.data.clone().to(self_buffers.device) * (1 - beta)
diff --git a/MagicQuill/comfy/ldm/cascade/stage_c.py b/MagicQuill/comfy/ldm/cascade/stage_c.py
deleted file mode 100644
index c85da1f01c1d862de5906e73fc746fc92eb51304..0000000000000000000000000000000000000000
--- a/MagicQuill/comfy/ldm/cascade/stage_c.py
+++ /dev/null
@@ -1,273 +0,0 @@
-"""
-    This file is part of ComfyUI.
-    Copyright (C) 2024 Stability AI
-
-    This program is free software: you can redistribute it and/or modify
-    it under the terms of the GNU General Public License as published by
-    the Free Software Foundation, either version 3 of the License, or
-    (at your option) any later version.
-
-    This program is distributed in the hope that it will be useful,
-    but WITHOUT ANY WARRANTY; without even the implied warranty of
-    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-    GNU General Public License for more details.
-
-    You should have received a copy of the GNU General Public License
-    along with this program.  If not, see <https://www.gnu.org/licenses/>.
-"""
-
-import torch
-from torch import nn
-import math
-from .common import AttnBlock, LayerNorm2d_op, ResBlock, FeedForwardBlock, TimestepBlock
-# from .controlnet import ControlNetDeliverer
-
-class UpDownBlock2d(nn.Module):
-    def __init__(self, c_in, c_out, mode, enabled=True, dtype=None, device=None, operations=None):
-        super().__init__()
-        assert mode in ['up', 'down']
-        interpolation = nn.Upsample(scale_factor=2 if mode == 'up' else 0.5, mode='bilinear',
-                                    align_corners=True) if enabled else nn.Identity()
-        mapping = operations.Conv2d(c_in, c_out, kernel_size=1, dtype=dtype, device=device)
-        self.blocks = nn.ModuleList([interpolation, mapping] if mode == 'up' else [mapping, interpolation])
-
-    def forward(self, x):
-        for block in self.blocks:
-            x = block(x)
-        return x
-
-
-class StageC(nn.Module):
-    def __init__(self, c_in=16, c_out=16, c_r=64, patch_size=1, c_cond=2048, c_hidden=[2048, 2048], nhead=[32, 32],
-                 blocks=[[8, 24], [24, 8]], block_repeat=[[1, 1], [1, 1]], level_config=['CTA', 'CTA'],
-                 c_clip_text=1280, c_clip_text_pooled=1280, c_clip_img=768, c_clip_seq=4, kernel_size=3,
-                 dropout=[0.0, 0.0], self_attn=True, t_conds=['sca', 'crp'], switch_level=[False], stable_cascade_stage=None,
-                 dtype=None, device=None, operations=None):
-        super().__init__()
-        self.dtype = dtype
-        self.c_r = c_r
-        self.t_conds = t_conds
-        self.c_clip_seq = c_clip_seq
-        if not isinstance(dropout, list):
-            dropout = [dropout] * len(c_hidden)
-        if not isinstance(self_attn, list):
-            self_attn = [self_attn] * len(c_hidden)
-
-        # CONDITIONING
-        self.clip_txt_mapper = operations.Linear(c_clip_text, c_cond, dtype=dtype, device=device)
-        self.clip_txt_pooled_mapper = operations.Linear(c_clip_text_pooled, c_cond * c_clip_seq, dtype=dtype, device=device)
-        self.clip_img_mapper = operations.Linear(c_clip_img, c_cond * c_clip_seq, dtype=dtype, device=device)
-        self.clip_norm = operations.LayerNorm(c_cond, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
-
-        self.embedding = nn.Sequential(
-            nn.PixelUnshuffle(patch_size),
-            operations.Conv2d(c_in * (patch_size ** 2), c_hidden[0], kernel_size=1, dtype=dtype, device=device),
-            LayerNorm2d_op(operations)(c_hidden[0], elementwise_affine=False, eps=1e-6)
-        )
-
-        def get_block(block_type, c_hidden, nhead, c_skip=0, dropout=0, self_attn=True):
-            if block_type == 'C':
-                return ResBlock(c_hidden, c_skip, kernel_size=kernel_size, dropout=dropout, dtype=dtype, device=device, operations=operations)
-            elif block_type == 'A':
-                return AttnBlock(c_hidden, c_cond, nhead, self_attn=self_attn, dropout=dropout, dtype=dtype, device=device, operations=operations)
-            elif block_type == 'F':
-                return FeedForwardBlock(c_hidden, dropout=dropout, dtype=dtype, device=device, operations=operations)
-            elif block_type == 'T':
-                return TimestepBlock(c_hidden, c_r, conds=t_conds, dtype=dtype, device=device, operations=operations)
-            else:
-                raise Exception(f'Block type {block_type} not supported')
-
-        # BLOCKS
-        # -- down blocks
-        self.down_blocks = nn.ModuleList()
-        self.down_downscalers = nn.ModuleList()
-        self.down_repeat_mappers = nn.ModuleList()
-        for i in range(len(c_hidden)):
-            if i > 0:
-                self.down_downscalers.append(nn.Sequential(
-                    LayerNorm2d_op(operations)(c_hidden[i - 1], elementwise_affine=False, eps=1e-6),
-                    UpDownBlock2d(c_hidden[i - 1], c_hidden[i], mode='down', enabled=switch_level[i - 1], dtype=dtype, device=device, operations=operations)
-                ))
-            else:
-                self.down_downscalers.append(nn.Identity())
-            down_block = nn.ModuleList()
-            for _ in range(blocks[0][i]):
-                for block_type in level_config[i]:
-                    block = get_block(block_type, c_hidden[i], nhead[i], dropout=dropout[i], self_attn=self_attn[i])
-                    down_block.append(block)
-            self.down_blocks.append(down_block)
-            if block_repeat is not None:
-                block_repeat_mappers = nn.ModuleList()
-                for _ in range(block_repeat[0][i] - 1):
-                    block_repeat_mappers.append(operations.Conv2d(c_hidden[i], c_hidden[i], kernel_size=1, dtype=dtype, device=device))
-                self.down_repeat_mappers.append(block_repeat_mappers)
-
-        # -- up blocks
-        self.up_blocks = nn.ModuleList()
-        self.up_upscalers = nn.ModuleList()
-        self.up_repeat_mappers = nn.ModuleList()
-        for i in reversed(range(len(c_hidden))):
-            if i > 0:
-                self.up_upscalers.append(nn.Sequential(
-                    LayerNorm2d_op(operations)(c_hidden[i], elementwise_affine=False, eps=1e-6),
-                    UpDownBlock2d(c_hidden[i], c_hidden[i - 1], mode='up', enabled=switch_level[i - 1], dtype=dtype, device=device, operations=operations)
-                ))
-            else:
-                self.up_upscalers.append(nn.Identity())
-            up_block = nn.ModuleList()
-            for j in range(blocks[1][::-1][i]):
-                for k, block_type in enumerate(level_config[i]):
-                    c_skip = c_hidden[i] if i < len(c_hidden) - 1 and j == k == 0 else 0
-                    block = get_block(block_type, c_hidden[i], nhead[i], c_skip=c_skip, dropout=dropout[i],
-                                      self_attn=self_attn[i])
-                    up_block.append(block)
-            self.up_blocks.append(up_block)
-            if block_repeat is not None:
-                block_repeat_mappers = nn.ModuleList()
-                for _ in range(block_repeat[1][::-1][i] - 1):
-                    block_repeat_mappers.append(operations.Conv2d(c_hidden[i], c_hidden[i], kernel_size=1, dtype=dtype, device=device))
-                self.up_repeat_mappers.append(block_repeat_mappers)
-
-        # OUTPUT
-        self.clf = nn.Sequential(
-            LayerNorm2d_op(operations)(c_hidden[0], elementwise_affine=False, eps=1e-6, dtype=dtype, device=device),
-            operations.Conv2d(c_hidden[0], c_out * (patch_size ** 2), kernel_size=1, dtype=dtype, device=device),
-            nn.PixelShuffle(patch_size),
-        )
-
-        # --- WEIGHT INIT ---
-    #     self.apply(self._init_weights)  # General init
-    #     nn.init.normal_(self.clip_txt_mapper.weight, std=0.02)  # conditionings
-    #     nn.init.normal_(self.clip_txt_pooled_mapper.weight, std=0.02)  # conditionings
-    #     nn.init.normal_(self.clip_img_mapper.weight, std=0.02)  # conditionings
-    #     torch.nn.init.xavier_uniform_(self.embedding[1].weight, 0.02)  # inputs
-    #     nn.init.constant_(self.clf[1].weight, 0)  # outputs
-    # 
-    #     # blocks
-    #     for level_block in self.down_blocks + self.up_blocks:
-    #         for block in level_block:
-    #             if isinstance(block, ResBlock) or isinstance(block, FeedForwardBlock):
-    #                 block.channelwise[-1].weight.data *= np.sqrt(1 / sum(blocks[0]))
-    #             elif isinstance(block, TimestepBlock):
-    #                 for layer in block.modules():
-    #                     if isinstance(layer, nn.Linear):
-    #                         nn.init.constant_(layer.weight, 0)
-    # 
-    # def _init_weights(self, m):
-    #     if isinstance(m, (nn.Conv2d, nn.Linear)):
-    #         torch.nn.init.xavier_uniform_(m.weight)
-    #         if m.bias is not None:
-    #             nn.init.constant_(m.bias, 0)
-
-    def gen_r_embedding(self, r, max_positions=10000):
-        r = r * max_positions
-        half_dim = self.c_r // 2
-        emb = math.log(max_positions) / (half_dim - 1)
-        emb = torch.arange(half_dim, device=r.device).float().mul(-emb).exp()
-        emb = r[:, None] * emb[None, :]
-        emb = torch.cat([emb.sin(), emb.cos()], dim=1)
-        if self.c_r % 2 == 1:  # zero pad
-            emb = nn.functional.pad(emb, (0, 1), mode='constant')
-        return emb
-
-    def gen_c_embeddings(self, clip_txt, clip_txt_pooled, clip_img):
-        clip_txt = self.clip_txt_mapper(clip_txt)
-        if len(clip_txt_pooled.shape) == 2:
-            clip_txt_pooled = clip_txt_pooled.unsqueeze(1)
-        if len(clip_img.shape) == 2:
-            clip_img = clip_img.unsqueeze(1)
-        clip_txt_pool = self.clip_txt_pooled_mapper(clip_txt_pooled).view(clip_txt_pooled.size(0), clip_txt_pooled.size(1) * self.c_clip_seq, -1)
-        clip_img = self.clip_img_mapper(clip_img).view(clip_img.size(0), clip_img.size(1) * self.c_clip_seq, -1)
-        clip = torch.cat([clip_txt, clip_txt_pool, clip_img], dim=1)
-        clip = self.clip_norm(clip)
-        return clip
-
-    def _down_encode(self, x, r_embed, clip, cnet=None):
-        level_outputs = []
-        block_group = zip(self.down_blocks, self.down_downscalers, self.down_repeat_mappers)
-        for down_block, downscaler, repmap in block_group:
-            x = downscaler(x)
-            for i in range(len(repmap) + 1):
-                for block in down_block:
-                    if isinstance(block, ResBlock) or (
-                            hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module,
-                                                                                  ResBlock)):
-                        if cnet is not None:
-                            next_cnet = cnet.pop()
-                            if next_cnet is not None:
-                                x = x + nn.functional.interpolate(next_cnet, size=x.shape[-2:], mode='bilinear',
-                                                                  align_corners=True).to(x.dtype)
-                        x = block(x)
-                    elif isinstance(block, AttnBlock) or (
-                            hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module,
-                                                                                  AttnBlock)):
-                        x = block(x, clip)
-                    elif isinstance(block, TimestepBlock) or (
-                            hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module,
-                                                                                  TimestepBlock)):
-                        x = block(x, r_embed)
-                    else:
-                        x = block(x)
-                if i < len(repmap):
-                    x = repmap[i](x)
-            level_outputs.insert(0, x)
-        return level_outputs
-
-    def _up_decode(self, level_outputs, r_embed, clip, cnet=None):
-        x = level_outputs[0]
-        block_group = zip(self.up_blocks, self.up_upscalers, self.up_repeat_mappers)
-        for i, (up_block, upscaler, repmap) in enumerate(block_group):
-            for j in range(len(repmap) + 1):
-                for k, block in enumerate(up_block):
-                    if isinstance(block, ResBlock) or (
-                            hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module,
-                                                                                  ResBlock)):
-                        skip = level_outputs[i] if k == 0 and i > 0 else None
-                        if skip is not None and (x.size(-1) != skip.size(-1) or x.size(-2) != skip.size(-2)):
-                            x = torch.nn.functional.interpolate(x, skip.shape[-2:], mode='bilinear',
-                                                                align_corners=True)
-                        if cnet is not None:
-                            next_cnet = cnet.pop()
-                            if next_cnet is not None:
-                                x = x + nn.functional.interpolate(next_cnet, size=x.shape[-2:], mode='bilinear',
-                                                                  align_corners=True).to(x.dtype)
-                        x = block(x, skip)
-                    elif isinstance(block, AttnBlock) or (
-                            hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module,
-                                                                                  AttnBlock)):
-                        x = block(x, clip)
-                    elif isinstance(block, TimestepBlock) or (
-                            hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module,
-                                                                                  TimestepBlock)):
-                        x = block(x, r_embed)
-                    else:
-                        x = block(x)
-                if j < len(repmap):
-                    x = repmap[j](x)
-            x = upscaler(x)
-        return x
-
-    def forward(self, x, r, clip_text, clip_text_pooled, clip_img, control=None, **kwargs):
-        # Process the conditioning embeddings
-        r_embed = self.gen_r_embedding(r).to(dtype=x.dtype)
-        for c in self.t_conds:
-            t_cond = kwargs.get(c, torch.zeros_like(r))
-            r_embed = torch.cat([r_embed, self.gen_r_embedding(t_cond).to(dtype=x.dtype)], dim=1)
-        clip = self.gen_c_embeddings(clip_text, clip_text_pooled, clip_img)
-
-        if control is not None:
-            cnet = control.get("input")
-        else:
-            cnet = None
-
-        # Model Blocks
-        x = self.embedding(x)
-        level_outputs = self._down_encode(x, r_embed, clip, cnet)
-        x = self._up_decode(level_outputs, r_embed, clip, cnet)
-        return self.clf(x)
-
-    def update_weights_ema(self, src_model, beta=0.999):
-        for self_params, src_params in zip(self.parameters(), src_model.parameters()):
-            self_params.data = self_params.data * beta + src_params.data.clone().to(self_params.device) * (1 - beta)
-        for self_buffers, src_buffers in zip(self.buffers(), src_model.buffers()):
-            self_buffers.data = self_buffers.data * beta + src_buffers.data.clone().to(self_buffers.device) * (1 - beta)
diff --git a/MagicQuill/comfy/ldm/cascade/stage_c_coder.py b/MagicQuill/comfy/ldm/cascade/stage_c_coder.py
deleted file mode 100644
index 0cb7c49fc90c434553954772cbf522e1f4a88955..0000000000000000000000000000000000000000
--- a/MagicQuill/comfy/ldm/cascade/stage_c_coder.py
+++ /dev/null
@@ -1,95 +0,0 @@
-"""
-    This file is part of ComfyUI.
-    Copyright (C) 2024 Stability AI
-
-    This program is free software: you can redistribute it and/or modify
-    it under the terms of the GNU General Public License as published by
-    the Free Software Foundation, either version 3 of the License, or
-    (at your option) any later version.
-
-    This program is distributed in the hope that it will be useful,
-    but WITHOUT ANY WARRANTY; without even the implied warranty of
-    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-    GNU General Public License for more details.
-
-    You should have received a copy of the GNU General Public License
-    along with this program.  If not, see <https://www.gnu.org/licenses/>.
-"""
-import torch
-import torchvision
-from torch import nn
-
-
-# EfficientNet
-class EfficientNetEncoder(nn.Module):
-    def __init__(self, c_latent=16):
-        super().__init__()
-        self.backbone = torchvision.models.efficientnet_v2_s().features.eval()
-        self.mapper = nn.Sequential(
-            nn.Conv2d(1280, c_latent, kernel_size=1, bias=False),
-            nn.BatchNorm2d(c_latent, affine=False),  # then normalize them to have mean 0 and std 1
-        )
-        self.mean = nn.Parameter(torch.tensor([0.485, 0.456, 0.406]))
-        self.std = nn.Parameter(torch.tensor([0.229, 0.224, 0.225]))
-
-    def forward(self, x):
-        x = x * 0.5 + 0.5
-        x = (x - self.mean.view([3,1,1])) / self.std.view([3,1,1])
-        o = self.mapper(self.backbone(x))
-        return o
-
-
-# Fast Decoder for Stage C latents. E.g. 16 x 24 x 24 -> 3 x 192 x 192
-class Previewer(nn.Module):
-    def __init__(self, c_in=16, c_hidden=512, c_out=3):
-        super().__init__()
-        self.blocks = nn.Sequential(
-            nn.Conv2d(c_in, c_hidden, kernel_size=1),  # 16 channels to 512 channels
-            nn.GELU(),
-            nn.BatchNorm2d(c_hidden),
-
-            nn.Conv2d(c_hidden, c_hidden, kernel_size=3, padding=1),
-            nn.GELU(),
-            nn.BatchNorm2d(c_hidden),
-
-            nn.ConvTranspose2d(c_hidden, c_hidden // 2, kernel_size=2, stride=2),  # 16 -> 32
-            nn.GELU(),
-            nn.BatchNorm2d(c_hidden // 2),
-
-            nn.Conv2d(c_hidden // 2, c_hidden // 2, kernel_size=3, padding=1),
-            nn.GELU(),
-            nn.BatchNorm2d(c_hidden // 2),
-
-            nn.ConvTranspose2d(c_hidden // 2, c_hidden // 4, kernel_size=2, stride=2),  # 32 -> 64
-            nn.GELU(),
-            nn.BatchNorm2d(c_hidden // 4),
-
-            nn.Conv2d(c_hidden // 4, c_hidden // 4, kernel_size=3, padding=1),
-            nn.GELU(),
-            nn.BatchNorm2d(c_hidden // 4),
-
-            nn.ConvTranspose2d(c_hidden // 4, c_hidden // 4, kernel_size=2, stride=2),  # 64 -> 128
-            nn.GELU(),
-            nn.BatchNorm2d(c_hidden // 4),
-
-            nn.Conv2d(c_hidden // 4, c_hidden // 4, kernel_size=3, padding=1),
-            nn.GELU(),
-            nn.BatchNorm2d(c_hidden // 4),
-
-            nn.Conv2d(c_hidden // 4, c_out, kernel_size=1),
-        )
-
-    def forward(self, x):
-        return (self.blocks(x) - 0.5) * 2.0
-
-class StageC_coder(nn.Module):
-    def __init__(self):
-        super().__init__()
-        self.previewer = Previewer()
-        self.encoder = EfficientNetEncoder()
-
-    def encode(self, x):
-        return self.encoder(x)
-
-    def decode(self, x):
-        return self.previewer(x)
diff --git a/MagicQuill/comfy/ldm/models/__pycache__/autoencoder.cpython-310.pyc b/MagicQuill/comfy/ldm/models/__pycache__/autoencoder.cpython-310.pyc
deleted file mode 100644
index 81d431efe1ab89c23b0df5fa48cb159c51a23e6c..0000000000000000000000000000000000000000
Binary files a/MagicQuill/comfy/ldm/models/__pycache__/autoencoder.cpython-310.pyc and /dev/null differ
diff --git a/MagicQuill/comfy/ldm/models/autoencoder.py b/MagicQuill/comfy/ldm/models/autoencoder.py
deleted file mode 100644
index f5f4de2883078dadee058aef437901069588321b..0000000000000000000000000000000000000000
--- a/MagicQuill/comfy/ldm/models/autoencoder.py
+++ /dev/null
@@ -1,226 +0,0 @@
-import torch
-from contextlib import contextmanager
-from typing import Any, Dict, List, Optional, Tuple, Union
-
-from comfy.ldm.modules.distributions.distributions import DiagonalGaussianDistribution
-
-from comfy.ldm.util import instantiate_from_config
-from comfy.ldm.modules.ema import LitEma
-import comfy.ops
-
-class DiagonalGaussianRegularizer(torch.nn.Module):
-    def __init__(self, sample: bool = True):
-        super().__init__()
-        self.sample = sample
-
-    def get_trainable_parameters(self) -> Any:
-        yield from ()
-
-    def forward(self, z: torch.Tensor) -> Tuple[torch.Tensor, dict]:
-        log = dict()
-        posterior = DiagonalGaussianDistribution(z)
-        if self.sample:
-            z = posterior.sample()
-        else:
-            z = posterior.mode()
-        kl_loss = posterior.kl()
-        kl_loss = torch.sum(kl_loss) / kl_loss.shape[0]
-        log["kl_loss"] = kl_loss
-        return z, log
-
-
-class AbstractAutoencoder(torch.nn.Module):
-    """
-    This is the base class for all autoencoders, including image autoencoders, image autoencoders with discriminators,
-    unCLIP models, etc. Hence, it is fairly general, and specific features
-    (e.g. discriminator training, encoding, decoding) must be implemented in subclasses.
-    """
-
-    def __init__(
-        self,
-        ema_decay: Union[None, float] = None,
-        monitor: Union[None, str] = None,
-        input_key: str = "jpg",
-        **kwargs,
-    ):
-        super().__init__()
-
-        self.input_key = input_key
-        self.use_ema = ema_decay is not None
-        if monitor is not None:
-            self.monitor = monitor
-
-        if self.use_ema:
-            self.model_ema = LitEma(self, decay=ema_decay)
-            logpy.info(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.")
-
-    def get_input(self, batch) -> Any:
-        raise NotImplementedError()
-
-    def on_train_batch_end(self, *args, **kwargs):
-        # for EMA computation
-        if self.use_ema:
-            self.model_ema(self)
-
-    @contextmanager
-    def ema_scope(self, context=None):
-        if self.use_ema:
-            self.model_ema.store(self.parameters())
-            self.model_ema.copy_to(self)
-            if context is not None:
-                logpy.info(f"{context}: Switched to EMA weights")
-        try:
-            yield None
-        finally:
-            if self.use_ema:
-                self.model_ema.restore(self.parameters())
-                if context is not None:
-                    logpy.info(f"{context}: Restored training weights")
-
-    def encode(self, *args, **kwargs) -> torch.Tensor:
-        raise NotImplementedError("encode()-method of abstract base class called")
-
-    def decode(self, *args, **kwargs) -> torch.Tensor:
-        raise NotImplementedError("decode()-method of abstract base class called")
-
-    def instantiate_optimizer_from_config(self, params, lr, cfg):
-        logpy.info(f"loading >>> {cfg['target']} <<< optimizer from config")
-        return get_obj_from_str(cfg["target"])(
-            params, lr=lr, **cfg.get("params", dict())
-        )
-
-    def configure_optimizers(self) -> Any:
-        raise NotImplementedError()
-
-
-class AutoencodingEngine(AbstractAutoencoder):
-    """
-    Base class for all image autoencoders that we train, like VQGAN or AutoencoderKL
-    (we also restore them explicitly as special cases for legacy reasons).
-    Regularizations such as KL or VQ are moved to the regularizer class.
-    """
-
-    def __init__(
-        self,
-        *args,
-        encoder_config: Dict,
-        decoder_config: Dict,
-        regularizer_config: Dict,
-        **kwargs,
-    ):
-        super().__init__(*args, **kwargs)
-
-        self.encoder: torch.nn.Module = instantiate_from_config(encoder_config)
-        self.decoder: torch.nn.Module = instantiate_from_config(decoder_config)
-        self.regularization: AbstractRegularizer = instantiate_from_config(
-            regularizer_config
-        )
-
-    def get_last_layer(self):
-        return self.decoder.get_last_layer()
-
-    def encode(
-        self,
-        x: torch.Tensor,
-        return_reg_log: bool = False,
-        unregularized: bool = False,
-    ) -> Union[torch.Tensor, Tuple[torch.Tensor, dict]]:
-        z = self.encoder(x)
-        if unregularized:
-            return z, dict()
-        z, reg_log = self.regularization(z)
-        if return_reg_log:
-            return z, reg_log
-        return z
-
-    def decode(self, z: torch.Tensor, **kwargs) -> torch.Tensor:
-        x = self.decoder(z, **kwargs)
-        return x
-
-    def forward(
-        self, x: torch.Tensor, **additional_decode_kwargs
-    ) -> Tuple[torch.Tensor, torch.Tensor, dict]:
-        z, reg_log = self.encode(x, return_reg_log=True)
-        dec = self.decode(z, **additional_decode_kwargs)
-        return z, dec, reg_log
-
-
-class AutoencodingEngineLegacy(AutoencodingEngine):
-    def __init__(self, embed_dim: int, **kwargs):
-        self.max_batch_size = kwargs.pop("max_batch_size", None)
-        ddconfig = kwargs.pop("ddconfig")
-        super().__init__(
-            encoder_config={
-                "target": "comfy.ldm.modules.diffusionmodules.model.Encoder",
-                "params": ddconfig,
-            },
-            decoder_config={
-                "target": "comfy.ldm.modules.diffusionmodules.model.Decoder",
-                "params": ddconfig,
-            },
-            **kwargs,
-        )
-        self.quant_conv = comfy.ops.disable_weight_init.Conv2d(
-            (1 + ddconfig["double_z"]) * ddconfig["z_channels"],
-            (1 + ddconfig["double_z"]) * embed_dim,
-            1,
-        )
-        self.post_quant_conv = comfy.ops.disable_weight_init.Conv2d(embed_dim, ddconfig["z_channels"], 1)
-        self.embed_dim = embed_dim
-
-    def get_autoencoder_params(self) -> list:
-        params = super().get_autoencoder_params()
-        return params
-
-    def encode(
-        self, x: torch.Tensor, return_reg_log: bool = False
-    ) -> Union[torch.Tensor, Tuple[torch.Tensor, dict]]:
-        if self.max_batch_size is None:
-            z = self.encoder(x)
-            z = self.quant_conv(z)
-        else:
-            N = x.shape[0]
-            bs = self.max_batch_size
-            n_batches = int(math.ceil(N / bs))
-            z = list()
-            for i_batch in range(n_batches):
-                z_batch = self.encoder(x[i_batch * bs : (i_batch + 1) * bs])
-                z_batch = self.quant_conv(z_batch)
-                z.append(z_batch)
-            z = torch.cat(z, 0)
-
-        z, reg_log = self.regularization(z)
-        if return_reg_log:
-            return z, reg_log
-        return z
-
-    def decode(self, z: torch.Tensor, **decoder_kwargs) -> torch.Tensor:
-        if self.max_batch_size is None:
-            dec = self.post_quant_conv(z)
-            dec = self.decoder(dec, **decoder_kwargs)
-        else:
-            N = z.shape[0]
-            bs = self.max_batch_size
-            n_batches = int(math.ceil(N / bs))
-            dec = list()
-            for i_batch in range(n_batches):
-                dec_batch = self.post_quant_conv(z[i_batch * bs : (i_batch + 1) * bs])
-                dec_batch = self.decoder(dec_batch, **decoder_kwargs)
-                dec.append(dec_batch)
-            dec = torch.cat(dec, 0)
-
-        return dec
-
-
-class AutoencoderKL(AutoencodingEngineLegacy):
-    def __init__(self, **kwargs):
-        if "lossconfig" in kwargs:
-            kwargs["loss_config"] = kwargs.pop("lossconfig")
-        super().__init__(
-            regularizer_config={
-                "target": (
-                    "comfy.ldm.models.autoencoder.DiagonalGaussianRegularizer"
-                )
-            },
-            **kwargs,
-        )
diff --git a/MagicQuill/comfy/ldm/modules/__pycache__/attention.cpython-310.pyc b/MagicQuill/comfy/ldm/modules/__pycache__/attention.cpython-310.pyc
deleted file mode 100644
index a44f34018795428c0803f163f12305f415c521d4..0000000000000000000000000000000000000000
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diff --git a/MagicQuill/comfy/ldm/modules/__pycache__/ema.cpython-310.pyc b/MagicQuill/comfy/ldm/modules/__pycache__/ema.cpython-310.pyc
deleted file mode 100644
index 9493eaf691ef0d4ad636b42cdeecb41fdc9019cf..0000000000000000000000000000000000000000
Binary files a/MagicQuill/comfy/ldm/modules/__pycache__/ema.cpython-310.pyc and /dev/null differ
diff --git a/MagicQuill/comfy/ldm/modules/__pycache__/sub_quadratic_attention.cpython-310.pyc b/MagicQuill/comfy/ldm/modules/__pycache__/sub_quadratic_attention.cpython-310.pyc
deleted file mode 100644
index 916aeaa3d201ae74b5e06ece945c57697af20981..0000000000000000000000000000000000000000
Binary files a/MagicQuill/comfy/ldm/modules/__pycache__/sub_quadratic_attention.cpython-310.pyc and /dev/null differ
diff --git a/MagicQuill/comfy/ldm/modules/attention.py b/MagicQuill/comfy/ldm/modules/attention.py
deleted file mode 100644
index 65a8bcf42b81c318e87f4ed19b4f9a43d8f4d610..0000000000000000000000000000000000000000
--- a/MagicQuill/comfy/ldm/modules/attention.py
+++ /dev/null
@@ -1,865 +0,0 @@
-import math
-import torch
-import torch.nn.functional as F
-from torch import nn, einsum
-from einops import rearrange, repeat
-from typing import Optional
-import logging
-
-from .diffusionmodules.util import AlphaBlender, timestep_embedding
-from .sub_quadratic_attention import efficient_dot_product_attention
-
-from comfy import model_management
-
-if model_management.xformers_enabled():
-    import xformers
-    import xformers.ops
-
-from comfy.cli_args import args
-import comfy.ops
-ops = comfy.ops.disable_weight_init
-
-FORCE_UPCAST_ATTENTION_DTYPE = model_management.force_upcast_attention_dtype()
-
-def get_attn_precision(attn_precision):
-    if args.dont_upcast_attention:
-        return None
-    if FORCE_UPCAST_ATTENTION_DTYPE is not None:
-        return FORCE_UPCAST_ATTENTION_DTYPE
-    return attn_precision
-
-def exists(val):
-    return val is not None
-
-
-def uniq(arr):
-    return{el: True for el in arr}.keys()
-
-
-def default(val, d):
-    if exists(val):
-        return val
-    return d
-
-
-def max_neg_value(t):
-    return -torch.finfo(t.dtype).max
-
-
-def init_(tensor):
-    dim = tensor.shape[-1]
-    std = 1 / math.sqrt(dim)
-    tensor.uniform_(-std, std)
-    return tensor
-
-
-# feedforward
-class GEGLU(nn.Module):
-    def __init__(self, dim_in, dim_out, dtype=None, device=None, operations=ops):
-        super().__init__()
-        self.proj = operations.Linear(dim_in, dim_out * 2, dtype=dtype, device=device)
-
-    def forward(self, x):
-        x, gate = self.proj(x).chunk(2, dim=-1)
-        return x * F.gelu(gate)
-
-
-class FeedForward(nn.Module):
-    def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0., dtype=None, device=None, operations=ops):
-        super().__init__()
-        inner_dim = int(dim * mult)
-        dim_out = default(dim_out, dim)
-        project_in = nn.Sequential(
-            operations.Linear(dim, inner_dim, dtype=dtype, device=device),
-            nn.GELU()
-        ) if not glu else GEGLU(dim, inner_dim, dtype=dtype, device=device, operations=operations)
-
-        self.net = nn.Sequential(
-            project_in,
-            nn.Dropout(dropout),
-            operations.Linear(inner_dim, dim_out, dtype=dtype, device=device)
-        )
-
-    def forward(self, x):
-        return self.net(x)
-
-def Normalize(in_channels, dtype=None, device=None):
-    return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True, dtype=dtype, device=device)
-
-def attention_basic(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False):
-    attn_precision = get_attn_precision(attn_precision)
-
-    if skip_reshape:
-        b, _, _, dim_head = q.shape
-    else:
-        b, _, dim_head = q.shape
-        dim_head //= heads
-
-    scale = dim_head ** -0.5
-
-    h = heads
-    if skip_reshape:
-         q, k, v = map(
-            lambda t: t.reshape(b * heads, -1, dim_head),
-            (q, k, v),
-        )
-    else:
-        q, k, v = map(
-            lambda t: t.unsqueeze(3)
-            .reshape(b, -1, heads, dim_head)
-            .permute(0, 2, 1, 3)
-            .reshape(b * heads, -1, dim_head)
-            .contiguous(),
-            (q, k, v),
-        )
-
-    # force cast to fp32 to avoid overflowing
-    if attn_precision == torch.float32:
-        sim = einsum('b i d, b j d -> b i j', q.float(), k.float()) * scale
-    else:
-        sim = einsum('b i d, b j d -> b i j', q, k) * scale
-
-    del q, k
-
-    if exists(mask):
-        if mask.dtype == torch.bool:
-            mask = rearrange(mask, 'b ... -> b (...)') #TODO: check if this bool part matches pytorch attention
-            max_neg_value = -torch.finfo(sim.dtype).max
-            mask = repeat(mask, 'b j -> (b h) () j', h=h)
-            sim.masked_fill_(~mask, max_neg_value)
-        else:
-            if len(mask.shape) == 2:
-                bs = 1
-            else:
-                bs = mask.shape[0]
-            mask = mask.reshape(bs, -1, mask.shape[-2], mask.shape[-1]).expand(b, heads, -1, -1).reshape(-1, mask.shape[-2], mask.shape[-1])
-            sim.add_(mask)
-
-    # attention, what we cannot get enough of
-    sim = sim.softmax(dim=-1)
-
-    out = einsum('b i j, b j d -> b i d', sim.to(v.dtype), v)
-    out = (
-        out.unsqueeze(0)
-        .reshape(b, heads, -1, dim_head)
-        .permute(0, 2, 1, 3)
-        .reshape(b, -1, heads * dim_head)
-    )
-    return out
-
-
-def attention_sub_quad(query, key, value, heads, mask=None, attn_precision=None, skip_reshape=False):
-    attn_precision = get_attn_precision(attn_precision)
-
-    if skip_reshape:
-        b, _, _, dim_head = query.shape
-    else:
-        b, _, dim_head = query.shape
-        dim_head //= heads
-
-    scale = dim_head ** -0.5
-
-    if skip_reshape:
-        query = query.reshape(b * heads, -1, dim_head)
-        value = value.reshape(b * heads, -1, dim_head)
-        key = key.reshape(b * heads, -1, dim_head).movedim(1, 2)
-    else:
-        query = query.unsqueeze(3).reshape(b, -1, heads, dim_head).permute(0, 2, 1, 3).reshape(b * heads, -1, dim_head)
-        value = value.unsqueeze(3).reshape(b, -1, heads, dim_head).permute(0, 2, 1, 3).reshape(b * heads, -1, dim_head)
-        key = key.unsqueeze(3).reshape(b, -1, heads, dim_head).permute(0, 2, 3, 1).reshape(b * heads, dim_head, -1)
-
-
-    dtype = query.dtype
-    upcast_attention = attn_precision == torch.float32 and query.dtype != torch.float32
-    if upcast_attention:
-        bytes_per_token = torch.finfo(torch.float32).bits//8
-    else:
-        bytes_per_token = torch.finfo(query.dtype).bits//8
-    batch_x_heads, q_tokens, _ = query.shape
-    _, _, k_tokens = key.shape
-    qk_matmul_size_bytes = batch_x_heads * bytes_per_token * q_tokens * k_tokens
-
-    mem_free_total, mem_free_torch = model_management.get_free_memory(query.device, True)
-
-    kv_chunk_size_min = None
-    kv_chunk_size = None
-    query_chunk_size = None
-
-    for x in [4096, 2048, 1024, 512, 256]:
-        count = mem_free_total / (batch_x_heads * bytes_per_token * x * 4.0)
-        if count >= k_tokens:
-            kv_chunk_size = k_tokens
-            query_chunk_size = x
-            break
-
-    if query_chunk_size is None:
-        query_chunk_size = 512
-
-    if mask is not None:
-        if len(mask.shape) == 2:
-            bs = 1
-        else:
-            bs = mask.shape[0]
-        mask = mask.reshape(bs, -1, mask.shape[-2], mask.shape[-1]).expand(b, heads, -1, -1).reshape(-1, mask.shape[-2], mask.shape[-1])
-
-    hidden_states = efficient_dot_product_attention(
-        query,
-        key,
-        value,
-        query_chunk_size=query_chunk_size,
-        kv_chunk_size=kv_chunk_size,
-        kv_chunk_size_min=kv_chunk_size_min,
-        use_checkpoint=False,
-        upcast_attention=upcast_attention,
-        mask=mask,
-    )
-
-    hidden_states = hidden_states.to(dtype)
-
-    hidden_states = hidden_states.unflatten(0, (-1, heads)).transpose(1,2).flatten(start_dim=2)
-    return hidden_states
-
-def attention_split(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False):
-    attn_precision = get_attn_precision(attn_precision)
-
-    if skip_reshape:
-        b, _, _, dim_head = q.shape
-    else:
-        b, _, dim_head = q.shape
-        dim_head //= heads
-
-    scale = dim_head ** -0.5
-
-    h = heads
-    if skip_reshape:
-         q, k, v = map(
-            lambda t: t.reshape(b * heads, -1, dim_head),
-            (q, k, v),
-        )
-    else:
-        q, k, v = map(
-            lambda t: t.unsqueeze(3)
-            .reshape(b, -1, heads, dim_head)
-            .permute(0, 2, 1, 3)
-            .reshape(b * heads, -1, dim_head)
-            .contiguous(),
-            (q, k, v),
-        )
-
-    r1 = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype)
-
-    mem_free_total = model_management.get_free_memory(q.device)
-
-    if attn_precision == torch.float32:
-        element_size = 4
-        upcast = True
-    else:
-        element_size = q.element_size()
-        upcast = False
-
-    gb = 1024 ** 3
-    tensor_size = q.shape[0] * q.shape[1] * k.shape[1] * element_size
-    modifier = 3
-    mem_required = tensor_size * modifier
-    steps = 1
-
-
-    if mem_required > mem_free_total:
-        steps = 2**(math.ceil(math.log(mem_required / mem_free_total, 2)))
-        # print(f"Expected tensor size:{tensor_size/gb:0.1f}GB, cuda free:{mem_free_cuda/gb:0.1f}GB "
-        #      f"torch free:{mem_free_torch/gb:0.1f} total:{mem_free_total/gb:0.1f} steps:{steps}")
-
-    if steps > 64:
-        max_res = math.floor(math.sqrt(math.sqrt(mem_free_total / 2.5)) / 8) * 64
-        raise RuntimeError(f'Not enough memory, use lower resolution (max approx. {max_res}x{max_res}). '
-                            f'Need: {mem_required/64/gb:0.1f}GB free, Have:{mem_free_total/gb:0.1f}GB free')
-
-    if mask is not None:
-        if len(mask.shape) == 2:
-            bs = 1
-        else:
-            bs = mask.shape[0]
-        mask = mask.reshape(bs, -1, mask.shape[-2], mask.shape[-1]).expand(b, heads, -1, -1).reshape(-1, mask.shape[-2], mask.shape[-1])
-
-    # print("steps", steps, mem_required, mem_free_total, modifier, q.element_size(), tensor_size)
-    first_op_done = False
-    cleared_cache = False
-    while True:
-        try:
-            slice_size = q.shape[1] // steps if (q.shape[1] % steps) == 0 else q.shape[1]
-            for i in range(0, q.shape[1], slice_size):
-                end = i + slice_size
-                if upcast:
-                    with torch.autocast(enabled=False, device_type = 'cuda'):
-                        s1 = einsum('b i d, b j d -> b i j', q[:, i:end].float(), k.float()) * scale
-                else:
-                    s1 = einsum('b i d, b j d -> b i j', q[:, i:end], k) * scale
-
-                if mask is not None:
-                    if len(mask.shape) == 2:
-                        s1 += mask[i:end]
-                    else:
-                        s1 += mask[:, i:end]
-
-                s2 = s1.softmax(dim=-1).to(v.dtype)
-                del s1
-                first_op_done = True
-
-                r1[:, i:end] = einsum('b i j, b j d -> b i d', s2, v)
-                del s2
-            break
-        except model_management.OOM_EXCEPTION as e:
-            if first_op_done == False:
-                model_management.soft_empty_cache(True)
-                if cleared_cache == False:
-                    cleared_cache = True
-                    logging.warning("out of memory error, emptying cache and trying again")
-                    continue
-                steps *= 2
-                if steps > 64:
-                    raise e
-                logging.warning("out of memory error, increasing steps and trying again {}".format(steps))
-            else:
-                raise e
-
-    del q, k, v
-
-    r1 = (
-        r1.unsqueeze(0)
-        .reshape(b, heads, -1, dim_head)
-        .permute(0, 2, 1, 3)
-        .reshape(b, -1, heads * dim_head)
-    )
-    return r1
-
-BROKEN_XFORMERS = False
-try:
-    x_vers = xformers.__version__
-    # XFormers bug confirmed on all versions from 0.0.21 to 0.0.26 (q with bs bigger than 65535 gives CUDA error)
-    BROKEN_XFORMERS = x_vers.startswith("0.0.2") and not x_vers.startswith("0.0.20")
-except:
-    pass
-
-def attention_xformers(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False):
-    if skip_reshape:
-        b, _, _, dim_head = q.shape
-    else:
-        b, _, dim_head = q.shape
-        dim_head //= heads
-
-    disabled_xformers = False
-
-    if BROKEN_XFORMERS:
-        if b * heads > 65535:
-            disabled_xformers = True
-
-    if not disabled_xformers:
-        if torch.jit.is_tracing() or torch.jit.is_scripting():
-            disabled_xformers = True
-
-    if disabled_xformers:
-        return attention_pytorch(q, k, v, heads, mask)
-
-    if skip_reshape:
-         q, k, v = map(
-            lambda t: t.reshape(b * heads, -1, dim_head),
-            (q, k, v),
-        )
-    else:
-        q, k, v = map(
-            lambda t: t.reshape(b, -1, heads, dim_head),
-            (q, k, v),
-        )
-
-    if mask is not None:
-        pad = 8 - q.shape[1] % 8
-        mask_out = torch.empty([q.shape[0], q.shape[1], q.shape[1] + pad], dtype=q.dtype, device=q.device)
-        mask_out[:, :, :mask.shape[-1]] = mask
-        mask = mask_out[:, :, :mask.shape[-1]]
-
-    out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=mask)
-
-    if skip_reshape:
-        out = (
-            out.unsqueeze(0)
-            .reshape(b, heads, -1, dim_head)
-            .permute(0, 2, 1, 3)
-            .reshape(b, -1, heads * dim_head)
-        )
-    else:
-        out = (
-            out.reshape(b, -1, heads * dim_head)
-        )
-
-    return out
-
-def attention_pytorch(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False):
-    if skip_reshape:
-        b, _, _, dim_head = q.shape
-    else:
-        b, _, dim_head = q.shape
-        dim_head //= heads
-        q, k, v = map(
-            lambda t: t.view(b, -1, heads, dim_head).transpose(1, 2),
-            (q, k, v),
-        )
-
-    out = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=mask, dropout_p=0.0, is_causal=False)
-    out = (
-        out.transpose(1, 2).reshape(b, -1, heads * dim_head)
-    )
-    return out
-
-
-optimized_attention = attention_basic
-
-if model_management.xformers_enabled():
-    logging.info("Using xformers cross attention")
-    optimized_attention = attention_xformers
-elif model_management.pytorch_attention_enabled():
-    logging.info("Using pytorch cross attention")
-    optimized_attention = attention_pytorch
-else:
-    if args.use_split_cross_attention:
-        logging.info("Using split optimization for cross attention")
-        optimized_attention = attention_split
-    else:
-        logging.info("Using sub quadratic optimization for cross attention, if you have memory or speed issues try using: --use-split-cross-attention")
-        optimized_attention = attention_sub_quad
-
-optimized_attention_masked = optimized_attention
-
-def optimized_attention_for_device(device, mask=False, small_input=False):
-    if small_input:
-        if model_management.pytorch_attention_enabled():
-            return attention_pytorch #TODO: need to confirm but this is probably slightly faster for small inputs in all cases
-        else:
-            return attention_basic
-
-    if device == torch.device("cpu"):
-        return attention_sub_quad
-
-    if mask:
-        return optimized_attention_masked
-
-    return optimized_attention
-
-
-class CrossAttention(nn.Module):
-    def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., attn_precision=None, dtype=None, device=None, operations=ops):
-        super().__init__()
-        inner_dim = dim_head * heads
-        context_dim = default(context_dim, query_dim)
-        self.attn_precision = attn_precision
-
-        self.heads = heads
-        self.dim_head = dim_head
-
-        self.to_q = operations.Linear(query_dim, inner_dim, bias=False, dtype=dtype, device=device)
-        self.to_k = operations.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
-        self.to_v = operations.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
-
-        self.to_out = nn.Sequential(operations.Linear(inner_dim, query_dim, dtype=dtype, device=device), nn.Dropout(dropout))
-
-    def forward(self, x, context=None, value=None, mask=None):
-        q = self.to_q(x)
-        context = default(context, x)
-        k = self.to_k(context)
-        if value is not None:
-            v = self.to_v(value)
-            del value
-        else:
-            v = self.to_v(context)
-
-        if mask is None:
-            out = optimized_attention(q, k, v, self.heads, attn_precision=self.attn_precision)
-        else:
-            out = optimized_attention_masked(q, k, v, self.heads, mask, attn_precision=self.attn_precision)
-        return self.to_out(out)
-
-
-class BasicTransformerBlock(nn.Module):
-    def __init__(self, dim, n_heads, d_head, dropout=0., context_dim=None, gated_ff=True, checkpoint=True, ff_in=False, inner_dim=None,
-                 disable_self_attn=False, disable_temporal_crossattention=False, switch_temporal_ca_to_sa=False, attn_precision=None, dtype=None, device=None, operations=ops):
-        super().__init__()
-
-        self.ff_in = ff_in or inner_dim is not None
-        if inner_dim is None:
-            inner_dim = dim
-
-        self.is_res = inner_dim == dim
-        self.attn_precision = attn_precision
-
-        if self.ff_in:
-            self.norm_in = operations.LayerNorm(dim, dtype=dtype, device=device)
-            self.ff_in = FeedForward(dim, dim_out=inner_dim, dropout=dropout, glu=gated_ff, dtype=dtype, device=device, operations=operations)
-
-        self.disable_self_attn = disable_self_attn
-        self.attn1 = CrossAttention(query_dim=inner_dim, heads=n_heads, dim_head=d_head, dropout=dropout,
-                              context_dim=context_dim if self.disable_self_attn else None, attn_precision=self.attn_precision, dtype=dtype, device=device, operations=operations)  # is a self-attention if not self.disable_self_attn
-        self.ff = FeedForward(inner_dim, dim_out=dim, dropout=dropout, glu=gated_ff, dtype=dtype, device=device, operations=operations)
-
-        if disable_temporal_crossattention:
-            if switch_temporal_ca_to_sa:
-                raise ValueError
-            else:
-                self.attn2 = None
-        else:
-            context_dim_attn2 = None
-            if not switch_temporal_ca_to_sa:
-                context_dim_attn2 = context_dim
-
-            self.attn2 = CrossAttention(query_dim=inner_dim, context_dim=context_dim_attn2,
-                                heads=n_heads, dim_head=d_head, dropout=dropout, attn_precision=self.attn_precision, dtype=dtype, device=device, operations=operations)  # is self-attn if context is none
-            self.norm2 = operations.LayerNorm(inner_dim, dtype=dtype, device=device)
-
-        self.norm1 = operations.LayerNorm(inner_dim, dtype=dtype, device=device)
-        self.norm3 = operations.LayerNorm(inner_dim, dtype=dtype, device=device)
-        self.n_heads = n_heads
-        self.d_head = d_head
-        self.switch_temporal_ca_to_sa = switch_temporal_ca_to_sa
-
-    def forward(self, x, context=None, transformer_options={}):
-        extra_options = {}
-        block = transformer_options.get("block", None)
-        block_index = transformer_options.get("block_index", 0)
-        transformer_patches = {}
-        transformer_patches_replace = {}
-
-        for k in transformer_options:
-            if k == "patches":
-                transformer_patches = transformer_options[k]
-            elif k == "patches_replace":
-                transformer_patches_replace = transformer_options[k]
-            else:
-                extra_options[k] = transformer_options[k]
-
-        extra_options["n_heads"] = self.n_heads
-        extra_options["dim_head"] = self.d_head
-        extra_options["attn_precision"] = self.attn_precision
-
-        if self.ff_in:
-            x_skip = x
-            x = self.ff_in(self.norm_in(x))
-            if self.is_res:
-                x += x_skip
-
-        n = self.norm1(x)
-        if self.disable_self_attn:
-            context_attn1 = context
-        else:
-            context_attn1 = None
-        value_attn1 = None
-
-        if "attn1_patch" in transformer_patches:
-            patch = transformer_patches["attn1_patch"]
-            if context_attn1 is None:
-                context_attn1 = n
-            value_attn1 = context_attn1
-            for p in patch:
-                n, context_attn1, value_attn1 = p(n, context_attn1, value_attn1, extra_options)
-
-        if block is not None:
-            transformer_block = (block[0], block[1], block_index)
-        else:
-            transformer_block = None
-        attn1_replace_patch = transformer_patches_replace.get("attn1", {})
-        block_attn1 = transformer_block
-        if block_attn1 not in attn1_replace_patch:
-            block_attn1 = block
-
-        if block_attn1 in attn1_replace_patch:
-            if context_attn1 is None:
-                context_attn1 = n
-                value_attn1 = n
-            n = self.attn1.to_q(n)
-            context_attn1 = self.attn1.to_k(context_attn1)
-            value_attn1 = self.attn1.to_v(value_attn1)
-            n = attn1_replace_patch[block_attn1](n, context_attn1, value_attn1, extra_options)
-            n = self.attn1.to_out(n)
-        else:
-            n = self.attn1(n, context=context_attn1, value=value_attn1)
-
-        if "attn1_output_patch" in transformer_patches:
-            patch = transformer_patches["attn1_output_patch"]
-            for p in patch:
-                n = p(n, extra_options)
-
-        x += n
-        if "middle_patch" in transformer_patches:
-            patch = transformer_patches["middle_patch"]
-            for p in patch:
-                x = p(x, extra_options)
-
-        if self.attn2 is not None:
-            n = self.norm2(x)
-            if self.switch_temporal_ca_to_sa:
-                context_attn2 = n
-            else:
-                context_attn2 = context
-            value_attn2 = None
-            if "attn2_patch" in transformer_patches:
-                patch = transformer_patches["attn2_patch"]
-                value_attn2 = context_attn2
-                for p in patch:
-                    n, context_attn2, value_attn2 = p(n, context_attn2, value_attn2, extra_options)
-
-            attn2_replace_patch = transformer_patches_replace.get("attn2", {})
-            block_attn2 = transformer_block
-            if block_attn2 not in attn2_replace_patch:
-                block_attn2 = block
-
-            if block_attn2 in attn2_replace_patch:
-                if value_attn2 is None:
-                    value_attn2 = context_attn2
-                n = self.attn2.to_q(n)
-                context_attn2 = self.attn2.to_k(context_attn2)
-                value_attn2 = self.attn2.to_v(value_attn2)
-                n = attn2_replace_patch[block_attn2](n, context_attn2, value_attn2, extra_options)
-                n = self.attn2.to_out(n)
-            else:
-                n = self.attn2(n, context=context_attn2, value=value_attn2)
-
-        if "attn2_output_patch" in transformer_patches:
-            patch = transformer_patches["attn2_output_patch"]
-            for p in patch:
-                n = p(n, extra_options)
-
-        x += n
-        if self.is_res:
-            x_skip = x
-        x = self.ff(self.norm3(x))
-        if self.is_res:
-            x += x_skip
-
-        return x
-
-
-class SpatialTransformer(nn.Module):
-    """
-    Transformer block for image-like data.
-    First, project the input (aka embedding)
-    and reshape to b, t, d.
-    Then apply standard transformer action.
-    Finally, reshape to image
-    NEW: use_linear for more efficiency instead of the 1x1 convs
-    """
-    def __init__(self, in_channels, n_heads, d_head,
-                 depth=1, dropout=0., context_dim=None,
-                 disable_self_attn=False, use_linear=False,
-                 use_checkpoint=True, attn_precision=None, dtype=None, device=None, operations=ops):
-        super().__init__()
-        if exists(context_dim) and not isinstance(context_dim, list):
-            context_dim = [context_dim] * depth
-        self.in_channels = in_channels
-        inner_dim = n_heads * d_head
-        self.norm = operations.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True, dtype=dtype, device=device)
-        if not use_linear:
-            self.proj_in = operations.Conv2d(in_channels,
-                                     inner_dim,
-                                     kernel_size=1,
-                                     stride=1,
-                                     padding=0, dtype=dtype, device=device)
-        else:
-            self.proj_in = operations.Linear(in_channels, inner_dim, dtype=dtype, device=device)
-
-        self.transformer_blocks = nn.ModuleList(
-            [BasicTransformerBlock(inner_dim, n_heads, d_head, dropout=dropout, context_dim=context_dim[d],
-                                   disable_self_attn=disable_self_attn, checkpoint=use_checkpoint, attn_precision=attn_precision, dtype=dtype, device=device, operations=operations)
-                for d in range(depth)]
-        )
-        if not use_linear:
-            self.proj_out = operations.Conv2d(inner_dim,in_channels,
-                                                  kernel_size=1,
-                                                  stride=1,
-                                                  padding=0, dtype=dtype, device=device)
-        else:
-            self.proj_out = operations.Linear(in_channels, inner_dim, dtype=dtype, device=device)
-        self.use_linear = use_linear
-
-    def forward(self, x, context=None, transformer_options={}):
-        # note: if no context is given, cross-attention defaults to self-attention
-        if not isinstance(context, list):
-            context = [context] * len(self.transformer_blocks)
-        b, c, h, w = x.shape
-        x_in = x
-        x = self.norm(x)
-        if not self.use_linear:
-            x = self.proj_in(x)
-        x = x.movedim(1, 3).flatten(1, 2).contiguous()
-        if self.use_linear:
-            x = self.proj_in(x)
-        for i, block in enumerate(self.transformer_blocks):
-            transformer_options["block_index"] = i
-            x = block(x, context=context[i], transformer_options=transformer_options)
-        if self.use_linear:
-            x = self.proj_out(x)
-        x = x.reshape(x.shape[0], h, w, x.shape[-1]).movedim(3, 1).contiguous()
-        if not self.use_linear:
-            x = self.proj_out(x)
-        return x + x_in
-
-
-class SpatialVideoTransformer(SpatialTransformer):
-    def __init__(
-        self,
-        in_channels,
-        n_heads,
-        d_head,
-        depth=1,
-        dropout=0.0,
-        use_linear=False,
-        context_dim=None,
-        use_spatial_context=False,
-        timesteps=None,
-        merge_strategy: str = "fixed",
-        merge_factor: float = 0.5,
-        time_context_dim=None,
-        ff_in=False,
-        checkpoint=False,
-        time_depth=1,
-        disable_self_attn=False,
-        disable_temporal_crossattention=False,
-        max_time_embed_period: int = 10000,
-        attn_precision=None,
-        dtype=None, device=None, operations=ops
-    ):
-        super().__init__(
-            in_channels,
-            n_heads,
-            d_head,
-            depth=depth,
-            dropout=dropout,
-            use_checkpoint=checkpoint,
-            context_dim=context_dim,
-            use_linear=use_linear,
-            disable_self_attn=disable_self_attn,
-            attn_precision=attn_precision,
-            dtype=dtype, device=device, operations=operations
-        )
-        self.time_depth = time_depth
-        self.depth = depth
-        self.max_time_embed_period = max_time_embed_period
-
-        time_mix_d_head = d_head
-        n_time_mix_heads = n_heads
-
-        time_mix_inner_dim = int(time_mix_d_head * n_time_mix_heads)
-
-        inner_dim = n_heads * d_head
-        if use_spatial_context:
-            time_context_dim = context_dim
-
-        self.time_stack = nn.ModuleList(
-            [
-                BasicTransformerBlock(
-                    inner_dim,
-                    n_time_mix_heads,
-                    time_mix_d_head,
-                    dropout=dropout,
-                    context_dim=time_context_dim,
-                    # timesteps=timesteps,
-                    checkpoint=checkpoint,
-                    ff_in=ff_in,
-                    inner_dim=time_mix_inner_dim,
-                    disable_self_attn=disable_self_attn,
-                    disable_temporal_crossattention=disable_temporal_crossattention,
-                    attn_precision=attn_precision,
-                    dtype=dtype, device=device, operations=operations
-                )
-                for _ in range(self.depth)
-            ]
-        )
-
-        assert len(self.time_stack) == len(self.transformer_blocks)
-
-        self.use_spatial_context = use_spatial_context
-        self.in_channels = in_channels
-
-        time_embed_dim = self.in_channels * 4
-        self.time_pos_embed = nn.Sequential(
-            operations.Linear(self.in_channels, time_embed_dim, dtype=dtype, device=device),
-            nn.SiLU(),
-            operations.Linear(time_embed_dim, self.in_channels, dtype=dtype, device=device),
-        )
-
-        self.time_mixer = AlphaBlender(
-            alpha=merge_factor, merge_strategy=merge_strategy
-        )
-
-    def forward(
-        self,
-        x: torch.Tensor,
-        context: Optional[torch.Tensor] = None,
-        time_context: Optional[torch.Tensor] = None,
-        timesteps: Optional[int] = None,
-        image_only_indicator: Optional[torch.Tensor] = None,
-        transformer_options={}
-    ) -> torch.Tensor:
-        _, _, h, w = x.shape
-        x_in = x
-        spatial_context = None
-        if exists(context):
-            spatial_context = context
-
-        if self.use_spatial_context:
-            assert (
-                context.ndim == 3
-            ), f"n dims of spatial context should be 3 but are {context.ndim}"
-
-            if time_context is None:
-                time_context = context
-            time_context_first_timestep = time_context[::timesteps]
-            time_context = repeat(
-                time_context_first_timestep, "b ... -> (b n) ...", n=h * w
-            )
-        elif time_context is not None and not self.use_spatial_context:
-            time_context = repeat(time_context, "b ... -> (b n) ...", n=h * w)
-            if time_context.ndim == 2:
-                time_context = rearrange(time_context, "b c -> b 1 c")
-
-        x = self.norm(x)
-        if not self.use_linear:
-            x = self.proj_in(x)
-        x = rearrange(x, "b c h w -> b (h w) c")
-        if self.use_linear:
-            x = self.proj_in(x)
-
-        num_frames = torch.arange(timesteps, device=x.device)
-        num_frames = repeat(num_frames, "t -> b t", b=x.shape[0] // timesteps)
-        num_frames = rearrange(num_frames, "b t -> (b t)")
-        t_emb = timestep_embedding(num_frames, self.in_channels, repeat_only=False, max_period=self.max_time_embed_period).to(x.dtype)
-        emb = self.time_pos_embed(t_emb)
-        emb = emb[:, None, :]
-
-        for it_, (block, mix_block) in enumerate(
-            zip(self.transformer_blocks, self.time_stack)
-        ):
-            transformer_options["block_index"] = it_
-            x = block(
-                x,
-                context=spatial_context,
-                transformer_options=transformer_options,
-            )
-
-            x_mix = x
-            x_mix = x_mix + emb
-
-            B, S, C = x_mix.shape
-            x_mix = rearrange(x_mix, "(b t) s c -> (b s) t c", t=timesteps)
-            x_mix = mix_block(x_mix, context=time_context) #TODO: transformer_options
-            x_mix = rearrange(
-                x_mix, "(b s) t c -> (b t) s c", s=S, b=B // timesteps, c=C, t=timesteps
-            )
-
-            x = self.time_mixer(x_spatial=x, x_temporal=x_mix, image_only_indicator=image_only_indicator)
-
-        if self.use_linear:
-            x = self.proj_out(x)
-        x = rearrange(x, "b (h w) c -> b c h w", h=h, w=w)
-        if not self.use_linear:
-            x = self.proj_out(x)
-        out = x + x_in
-        return out
-
-
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diff --git a/MagicQuill/comfy/ldm/modules/diffusionmodules/mmdit.py b/MagicQuill/comfy/ldm/modules/diffusionmodules/mmdit.py
deleted file mode 100644
index 20d3a321a02ae36943022ba0b831c45d49f6b15d..0000000000000000000000000000000000000000
--- a/MagicQuill/comfy/ldm/modules/diffusionmodules/mmdit.py
+++ /dev/null
@@ -1,962 +0,0 @@
-import logging
-import math
-from typing import Dict, Optional
-
-import numpy as np
-import torch
-import torch.nn as nn
-from .. import attention
-from einops import rearrange, repeat
-
-def default(x, y):
-    if x is not None:
-        return x
-    return y
-
-class Mlp(nn.Module):
-    """ MLP as used in Vision Transformer, MLP-Mixer and related networks
-    """
-    def __init__(
-            self,
-            in_features,
-            hidden_features=None,
-            out_features=None,
-            act_layer=nn.GELU,
-            norm_layer=None,
-            bias=True,
-            drop=0.,
-            use_conv=False,
-            dtype=None,
-            device=None,
-            operations=None,
-    ):
-        super().__init__()
-        out_features = out_features or in_features
-        hidden_features = hidden_features or in_features
-        drop_probs = drop
-        linear_layer = partial(operations.Conv2d, kernel_size=1) if use_conv else operations.Linear
-
-        self.fc1 = linear_layer(in_features, hidden_features, bias=bias, dtype=dtype, device=device)
-        self.act = act_layer()
-        self.drop1 = nn.Dropout(drop_probs)
-        self.norm = norm_layer(hidden_features) if norm_layer is not None else nn.Identity()
-        self.fc2 = linear_layer(hidden_features, out_features, bias=bias, dtype=dtype, device=device)
-        self.drop2 = nn.Dropout(drop_probs)
-
-    def forward(self, x):
-        x = self.fc1(x)
-        x = self.act(x)
-        x = self.drop1(x)
-        x = self.norm(x)
-        x = self.fc2(x)
-        x = self.drop2(x)
-        return x
-
-class PatchEmbed(nn.Module):
-    """ 2D Image to Patch Embedding
-    """
-    dynamic_img_pad: torch.jit.Final[bool]
-
-    def __init__(
-            self,
-            img_size: Optional[int] = 224,
-            patch_size: int = 16,
-            in_chans: int = 3,
-            embed_dim: int = 768,
-            norm_layer = None,
-            flatten: bool = True,
-            bias: bool = True,
-            strict_img_size: bool = True,
-            dynamic_img_pad: bool = True,
-            dtype=None,
-            device=None,
-            operations=None,
-    ):
-        super().__init__()
-        self.patch_size = (patch_size, patch_size)
-        if img_size is not None:
-            self.img_size = (img_size, img_size)
-            self.grid_size = tuple([s // p for s, p in zip(self.img_size, self.patch_size)])
-            self.num_patches = self.grid_size[0] * self.grid_size[1]
-        else:
-            self.img_size = None
-            self.grid_size = None
-            self.num_patches = None
-
-        # flatten spatial dim and transpose to channels last, kept for bwd compat
-        self.flatten = flatten
-        self.strict_img_size = strict_img_size
-        self.dynamic_img_pad = dynamic_img_pad
-
-        self.proj = operations.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size, bias=bias, dtype=dtype, device=device)
-        self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity()
-
-    def forward(self, x):
-        B, C, H, W = x.shape
-        # if self.img_size is not None:
-        #     if self.strict_img_size:
-        #         _assert(H == self.img_size[0], f"Input height ({H}) doesn't match model ({self.img_size[0]}).")
-        #         _assert(W == self.img_size[1], f"Input width ({W}) doesn't match model ({self.img_size[1]}).")
-        #     elif not self.dynamic_img_pad:
-        #         _assert(
-        #             H % self.patch_size[0] == 0,
-        #             f"Input height ({H}) should be divisible by patch size ({self.patch_size[0]})."
-        #         )
-        #         _assert(
-        #             W % self.patch_size[1] == 0,
-        #             f"Input width ({W}) should be divisible by patch size ({self.patch_size[1]})."
-        #         )
-        if self.dynamic_img_pad:
-            pad_h = (self.patch_size[0] - H % self.patch_size[0]) % self.patch_size[0]
-            pad_w = (self.patch_size[1] - W % self.patch_size[1]) % self.patch_size[1]
-            x = torch.nn.functional.pad(x, (0, pad_w, 0, pad_h), mode='reflect')
-        x = self.proj(x)
-        if self.flatten:
-            x = x.flatten(2).transpose(1, 2)  # NCHW -> NLC
-        x = self.norm(x)
-        return x
-
-def modulate(x, shift, scale):
-    if shift is None:
-        shift = torch.zeros_like(scale)
-    return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1)
-
-
-#################################################################################
-#                   Sine/Cosine Positional Embedding Functions                  #
-#################################################################################
-
-
-def get_2d_sincos_pos_embed(
-    embed_dim,
-    grid_size,
-    cls_token=False,
-    extra_tokens=0,
-    scaling_factor=None,
-    offset=None,
-):
-    """
-    grid_size: int of the grid height and width
-    return:
-    pos_embed: [grid_size*grid_size, embed_dim] or [1+grid_size*grid_size, embed_dim] (w/ or w/o cls_token)
-    """
-    grid_h = np.arange(grid_size, dtype=np.float32)
-    grid_w = np.arange(grid_size, dtype=np.float32)
-    grid = np.meshgrid(grid_w, grid_h)  # here w goes first
-    grid = np.stack(grid, axis=0)
-    if scaling_factor is not None:
-        grid = grid / scaling_factor
-    if offset is not None:
-        grid = grid - offset
-
-    grid = grid.reshape([2, 1, grid_size, grid_size])
-    pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid)
-    if cls_token and extra_tokens > 0:
-        pos_embed = np.concatenate(
-            [np.zeros([extra_tokens, embed_dim]), pos_embed], axis=0
-        )
-    return pos_embed
-
-
-def get_2d_sincos_pos_embed_from_grid(embed_dim, grid):
-    assert embed_dim % 2 == 0
-
-    # use half of dimensions to encode grid_h
-    emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[0])  # (H*W, D/2)
-    emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[1])  # (H*W, D/2)
-
-    emb = np.concatenate([emb_h, emb_w], axis=1)  # (H*W, D)
-    return emb
-
-
-def get_1d_sincos_pos_embed_from_grid(embed_dim, pos):
-    """
-    embed_dim: output dimension for each position
-    pos: a list of positions to be encoded: size (M,)
-    out: (M, D)
-    """
-    assert embed_dim % 2 == 0
-    omega = np.arange(embed_dim // 2, dtype=np.float64)
-    omega /= embed_dim / 2.0
-    omega = 1.0 / 10000**omega  # (D/2,)
-
-    pos = pos.reshape(-1)  # (M,)
-    out = np.einsum("m,d->md", pos, omega)  # (M, D/2), outer product
-
-    emb_sin = np.sin(out)  # (M, D/2)
-    emb_cos = np.cos(out)  # (M, D/2)
-
-    emb = np.concatenate([emb_sin, emb_cos], axis=1)  # (M, D)
-    return emb
-
-def get_1d_sincos_pos_embed_from_grid_torch(embed_dim, pos, device=None, dtype=torch.float32):
-    omega = torch.arange(embed_dim // 2, device=device, dtype=dtype)
-    omega /= embed_dim / 2.0
-    omega = 1.0 / 10000**omega  # (D/2,)
-    pos = pos.reshape(-1)  # (M,)
-    out = torch.einsum("m,d->md", pos, omega)  # (M, D/2), outer product
-    emb_sin = torch.sin(out)  # (M, D/2)
-    emb_cos = torch.cos(out)  # (M, D/2)
-    emb = torch.cat([emb_sin, emb_cos], dim=1)  # (M, D)
-    return emb
-
-def get_2d_sincos_pos_embed_torch(embed_dim, w, h, val_center=7.5, val_magnitude=7.5, device=None, dtype=torch.float32):
-    small = min(h, w)
-    val_h = (h / small) * val_magnitude
-    val_w = (w / small) * val_magnitude
-    grid_h, grid_w = torch.meshgrid(torch.linspace(-val_h + val_center, val_h + val_center, h, device=device, dtype=dtype), torch.linspace(-val_w + val_center, val_w + val_center, w, device=device, dtype=dtype), indexing='ij')
-    emb_h = get_1d_sincos_pos_embed_from_grid_torch(embed_dim // 2, grid_h, device=device, dtype=dtype)
-    emb_w = get_1d_sincos_pos_embed_from_grid_torch(embed_dim // 2, grid_w, device=device, dtype=dtype)
-    emb = torch.cat([emb_w, emb_h], dim=1)  # (H*W, D)
-    return emb
-
-
-#################################################################################
-#               Embedding Layers for Timesteps and Class Labels                 #
-#################################################################################
-
-
-class TimestepEmbedder(nn.Module):
-    """
-    Embeds scalar timesteps into vector representations.
-    """
-
-    def __init__(self, hidden_size, frequency_embedding_size=256, dtype=None, device=None, operations=None):
-        super().__init__()
-        self.mlp = nn.Sequential(
-            operations.Linear(frequency_embedding_size, hidden_size, bias=True, dtype=dtype, device=device),
-            nn.SiLU(),
-            operations.Linear(hidden_size, hidden_size, bias=True, dtype=dtype, device=device),
-        )
-        self.frequency_embedding_size = frequency_embedding_size
-
-    @staticmethod
-    def timestep_embedding(t, dim, max_period=10000):
-        """
-        Create sinusoidal timestep embeddings.
-        :param t: a 1-D Tensor of N indices, one per batch element.
-                          These may be fractional.
-        :param dim: the dimension of the output.
-        :param max_period: controls the minimum frequency of the embeddings.
-        :return: an (N, D) Tensor of positional embeddings.
-        """
-        half = dim // 2
-        freqs = torch.exp(
-            -math.log(max_period)
-            * torch.arange(start=0, end=half, dtype=torch.float32, device=t.device)
-            / half
-        )
-        args = t[:, None].float() * freqs[None]
-        embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
-        if dim % 2:
-            embedding = torch.cat(
-                [embedding, torch.zeros_like(embedding[:, :1])], dim=-1
-            )
-        if torch.is_floating_point(t):
-            embedding = embedding.to(dtype=t.dtype)
-        return embedding
-
-    def forward(self, t, dtype, **kwargs):
-        t_freq = self.timestep_embedding(t, self.frequency_embedding_size).to(dtype)
-        t_emb = self.mlp(t_freq)
-        return t_emb
-
-
-class VectorEmbedder(nn.Module):
-    """
-    Embeds a flat vector of dimension input_dim
-    """
-
-    def __init__(self, input_dim: int, hidden_size: int, dtype=None, device=None, operations=None):
-        super().__init__()
-        self.mlp = nn.Sequential(
-            operations.Linear(input_dim, hidden_size, bias=True, dtype=dtype, device=device),
-            nn.SiLU(),
-            operations.Linear(hidden_size, hidden_size, bias=True, dtype=dtype, device=device),
-        )
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        emb = self.mlp(x)
-        return emb
-
-
-#################################################################################
-#                                 Core DiT Model                                #
-#################################################################################
-
-
-def split_qkv(qkv, head_dim):
-    qkv = qkv.reshape(qkv.shape[0], qkv.shape[1], 3, -1, head_dim).movedim(2, 0)
-    return qkv[0], qkv[1], qkv[2]
-
-def optimized_attention(qkv, num_heads):
-    return attention.optimized_attention(qkv[0], qkv[1], qkv[2], num_heads)
-
-class SelfAttention(nn.Module):
-    ATTENTION_MODES = ("xformers", "torch", "torch-hb", "math", "debug")
-
-    def __init__(
-        self,
-        dim: int,
-        num_heads: int = 8,
-        qkv_bias: bool = False,
-        qk_scale: Optional[float] = None,
-        proj_drop: float = 0.0,
-        attn_mode: str = "xformers",
-        pre_only: bool = False,
-        qk_norm: Optional[str] = None,
-        rmsnorm: bool = False,
-        dtype=None,
-        device=None,
-        operations=None,
-    ):
-        super().__init__()
-        self.num_heads = num_heads
-        self.head_dim = dim // num_heads
-
-        self.qkv = operations.Linear(dim, dim * 3, bias=qkv_bias, dtype=dtype, device=device)
-        if not pre_only:
-            self.proj = operations.Linear(dim, dim, dtype=dtype, device=device)
-            self.proj_drop = nn.Dropout(proj_drop)
-        assert attn_mode in self.ATTENTION_MODES
-        self.attn_mode = attn_mode
-        self.pre_only = pre_only
-
-        if qk_norm == "rms":
-            self.ln_q = RMSNorm(self.head_dim, elementwise_affine=True, eps=1.0e-6, dtype=dtype, device=device)
-            self.ln_k = RMSNorm(self.head_dim, elementwise_affine=True, eps=1.0e-6, dtype=dtype, device=device)
-        elif qk_norm == "ln":
-            self.ln_q = operations.LayerNorm(self.head_dim, elementwise_affine=True, eps=1.0e-6, dtype=dtype, device=device)
-            self.ln_k = operations.LayerNorm(self.head_dim, elementwise_affine=True, eps=1.0e-6, dtype=dtype, device=device)
-        elif qk_norm is None:
-            self.ln_q = nn.Identity()
-            self.ln_k = nn.Identity()
-        else:
-            raise ValueError(qk_norm)
-
-    def pre_attention(self, x: torch.Tensor) -> torch.Tensor:
-        B, L, C = x.shape
-        qkv = self.qkv(x)
-        q, k, v = split_qkv(qkv, self.head_dim)
-        q = self.ln_q(q).reshape(q.shape[0], q.shape[1], -1)
-        k = self.ln_k(k).reshape(q.shape[0], q.shape[1], -1)
-        return (q, k, v)
-
-    def post_attention(self, x: torch.Tensor) -> torch.Tensor:
-        assert not self.pre_only
-        x = self.proj(x)
-        x = self.proj_drop(x)
-        return x
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        qkv = self.pre_attention(x)
-        x = optimized_attention(
-            qkv, num_heads=self.num_heads
-        )
-        x = self.post_attention(x)
-        return x
-
-
-class RMSNorm(torch.nn.Module):
-    def __init__(
-        self, dim: int, elementwise_affine: bool = False, eps: float = 1e-6, device=None, dtype=None
-    ):
-        """
-        Initialize the RMSNorm normalization layer.
-        Args:
-            dim (int): The dimension of the input tensor.
-            eps (float, optional): A small value added to the denominator for numerical stability. Default is 1e-6.
-        Attributes:
-            eps (float): A small value added to the denominator for numerical stability.
-            weight (nn.Parameter): Learnable scaling parameter.
-        """
-        super().__init__()
-        self.eps = eps
-        self.learnable_scale = elementwise_affine
-        if self.learnable_scale:
-            self.weight = nn.Parameter(torch.empty(dim, device=device, dtype=dtype))
-        else:
-            self.register_parameter("weight", None)
-
-    def _norm(self, x):
-        """
-        Apply the RMSNorm normalization to the input tensor.
-        Args:
-            x (torch.Tensor): The input tensor.
-        Returns:
-            torch.Tensor: The normalized tensor.
-        """
-        return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
-
-    def forward(self, x):
-        """
-        Forward pass through the RMSNorm layer.
-        Args:
-            x (torch.Tensor): The input tensor.
-        Returns:
-            torch.Tensor: The output tensor after applying RMSNorm.
-        """
-        x = self._norm(x)
-        if self.learnable_scale:
-            return x * self.weight.to(device=x.device, dtype=x.dtype)
-        else:
-            return x
-
-
-class SwiGLUFeedForward(nn.Module):
-    def __init__(
-        self,
-        dim: int,
-        hidden_dim: int,
-        multiple_of: int,
-        ffn_dim_multiplier: Optional[float] = None,
-    ):
-        """
-        Initialize the FeedForward module.
-
-        Args:
-            dim (int): Input dimension.
-            hidden_dim (int): Hidden dimension of the feedforward layer.
-            multiple_of (int): Value to ensure hidden dimension is a multiple of this value.
-            ffn_dim_multiplier (float, optional): Custom multiplier for hidden dimension. Defaults to None.
-
-        Attributes:
-            w1 (ColumnParallelLinear): Linear transformation for the first layer.
-            w2 (RowParallelLinear): Linear transformation for the second layer.
-            w3 (ColumnParallelLinear): Linear transformation for the third layer.
-
-        """
-        super().__init__()
-        hidden_dim = int(2 * hidden_dim / 3)
-        # custom dim factor multiplier
-        if ffn_dim_multiplier is not None:
-            hidden_dim = int(ffn_dim_multiplier * hidden_dim)
-        hidden_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of)
-
-        self.w1 = nn.Linear(dim, hidden_dim, bias=False)
-        self.w2 = nn.Linear(hidden_dim, dim, bias=False)
-        self.w3 = nn.Linear(dim, hidden_dim, bias=False)
-
-    def forward(self, x):
-        return self.w2(nn.functional.silu(self.w1(x)) * self.w3(x))
-
-
-class DismantledBlock(nn.Module):
-    """
-    A DiT block with gated adaptive layer norm (adaLN) conditioning.
-    """
-
-    ATTENTION_MODES = ("xformers", "torch", "torch-hb", "math", "debug")
-
-    def __init__(
-        self,
-        hidden_size: int,
-        num_heads: int,
-        mlp_ratio: float = 4.0,
-        attn_mode: str = "xformers",
-        qkv_bias: bool = False,
-        pre_only: bool = False,
-        rmsnorm: bool = False,
-        scale_mod_only: bool = False,
-        swiglu: bool = False,
-        qk_norm: Optional[str] = None,
-        dtype=None,
-        device=None,
-        operations=None,
-        **block_kwargs,
-    ):
-        super().__init__()
-        assert attn_mode in self.ATTENTION_MODES
-        if not rmsnorm:
-            self.norm1 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
-        else:
-            self.norm1 = RMSNorm(hidden_size, elementwise_affine=False, eps=1e-6)
-        self.attn = SelfAttention(
-            dim=hidden_size,
-            num_heads=num_heads,
-            qkv_bias=qkv_bias,
-            attn_mode=attn_mode,
-            pre_only=pre_only,
-            qk_norm=qk_norm,
-            rmsnorm=rmsnorm,
-            dtype=dtype,
-            device=device,
-            operations=operations
-        )
-        if not pre_only:
-            if not rmsnorm:
-                self.norm2 = operations.LayerNorm(
-                    hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device
-                )
-            else:
-                self.norm2 = RMSNorm(hidden_size, elementwise_affine=False, eps=1e-6)
-        mlp_hidden_dim = int(hidden_size * mlp_ratio)
-        if not pre_only:
-            if not swiglu:
-                self.mlp = Mlp(
-                    in_features=hidden_size,
-                    hidden_features=mlp_hidden_dim,
-                    act_layer=lambda: nn.GELU(approximate="tanh"),
-                    drop=0,
-                    dtype=dtype,
-                    device=device,
-                    operations=operations
-                )
-            else:
-                self.mlp = SwiGLUFeedForward(
-                    dim=hidden_size,
-                    hidden_dim=mlp_hidden_dim,
-                    multiple_of=256,
-                )
-        self.scale_mod_only = scale_mod_only
-        if not scale_mod_only:
-            n_mods = 6 if not pre_only else 2
-        else:
-            n_mods = 4 if not pre_only else 1
-        self.adaLN_modulation = nn.Sequential(
-            nn.SiLU(), operations.Linear(hidden_size, n_mods * hidden_size, bias=True, dtype=dtype, device=device)
-        )
-        self.pre_only = pre_only
-
-    def pre_attention(self, x: torch.Tensor, c: torch.Tensor) -> torch.Tensor:
-        if not self.pre_only:
-            if not self.scale_mod_only:
-                (
-                    shift_msa,
-                    scale_msa,
-                    gate_msa,
-                    shift_mlp,
-                    scale_mlp,
-                    gate_mlp,
-                ) = self.adaLN_modulation(c).chunk(6, dim=1)
-            else:
-                shift_msa = None
-                shift_mlp = None
-                (
-                    scale_msa,
-                    gate_msa,
-                    scale_mlp,
-                    gate_mlp,
-                ) = self.adaLN_modulation(
-                    c
-                ).chunk(4, dim=1)
-            qkv = self.attn.pre_attention(modulate(self.norm1(x), shift_msa, scale_msa))
-            return qkv, (
-                x,
-                gate_msa,
-                shift_mlp,
-                scale_mlp,
-                gate_mlp,
-            )
-        else:
-            if not self.scale_mod_only:
-                (
-                    shift_msa,
-                    scale_msa,
-                ) = self.adaLN_modulation(
-                    c
-                ).chunk(2, dim=1)
-            else:
-                shift_msa = None
-                scale_msa = self.adaLN_modulation(c)
-            qkv = self.attn.pre_attention(modulate(self.norm1(x), shift_msa, scale_msa))
-            return qkv, None
-
-    def post_attention(self, attn, x, gate_msa, shift_mlp, scale_mlp, gate_mlp):
-        assert not self.pre_only
-        x = x + gate_msa.unsqueeze(1) * self.attn.post_attention(attn)
-        x = x + gate_mlp.unsqueeze(1) * self.mlp(
-            modulate(self.norm2(x), shift_mlp, scale_mlp)
-        )
-        return x
-
-    def forward(self, x: torch.Tensor, c: torch.Tensor) -> torch.Tensor:
-        assert not self.pre_only
-        qkv, intermediates = self.pre_attention(x, c)
-        attn = optimized_attention(
-            qkv,
-            num_heads=self.attn.num_heads,
-        )
-        return self.post_attention(attn, *intermediates)
-
-
-def block_mixing(*args, use_checkpoint=True, **kwargs):
-    if use_checkpoint:
-        return torch.utils.checkpoint.checkpoint(
-            _block_mixing, *args, use_reentrant=False, **kwargs
-        )
-    else:
-        return _block_mixing(*args, **kwargs)
-
-
-def _block_mixing(context, x, context_block, x_block, c):
-    context_qkv, context_intermediates = context_block.pre_attention(context, c)
-
-    x_qkv, x_intermediates = x_block.pre_attention(x, c)
-
-    o = []
-    for t in range(3):
-        o.append(torch.cat((context_qkv[t], x_qkv[t]), dim=1))
-    qkv = tuple(o)
-
-    attn = optimized_attention(
-        qkv,
-        num_heads=x_block.attn.num_heads,
-    )
-    context_attn, x_attn = (
-        attn[:, : context_qkv[0].shape[1]],
-        attn[:, context_qkv[0].shape[1] :],
-    )
-
-    if not context_block.pre_only:
-        context = context_block.post_attention(context_attn, *context_intermediates)
-
-    else:
-        context = None
-    x = x_block.post_attention(x_attn, *x_intermediates)
-    return context, x
-
-
-class JointBlock(nn.Module):
-    """just a small wrapper to serve as a fsdp unit"""
-
-    def __init__(
-        self,
-        *args,
-        **kwargs,
-    ):
-        super().__init__()
-        pre_only = kwargs.pop("pre_only")
-        qk_norm = kwargs.pop("qk_norm", None)
-        self.context_block = DismantledBlock(*args, pre_only=pre_only, qk_norm=qk_norm, **kwargs)
-        self.x_block = DismantledBlock(*args, pre_only=False, qk_norm=qk_norm, **kwargs)
-
-    def forward(self, *args, **kwargs):
-        return block_mixing(
-            *args, context_block=self.context_block, x_block=self.x_block, **kwargs
-        )
-
-
-class FinalLayer(nn.Module):
-    """
-    The final layer of DiT.
-    """
-
-    def __init__(
-        self,
-        hidden_size: int,
-        patch_size: int,
-        out_channels: int,
-        total_out_channels: Optional[int] = None,
-        dtype=None,
-        device=None,
-        operations=None,
-    ):
-        super().__init__()
-        self.norm_final = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
-        self.linear = (
-            operations.Linear(hidden_size, patch_size * patch_size * out_channels, bias=True, dtype=dtype, device=device)
-            if (total_out_channels is None)
-            else operations.Linear(hidden_size, total_out_channels, bias=True, dtype=dtype, device=device)
-        )
-        self.adaLN_modulation = nn.Sequential(
-            nn.SiLU(), operations.Linear(hidden_size, 2 * hidden_size, bias=True, dtype=dtype, device=device)
-        )
-
-    def forward(self, x: torch.Tensor, c: torch.Tensor) -> torch.Tensor:
-        shift, scale = self.adaLN_modulation(c).chunk(2, dim=1)
-        x = modulate(self.norm_final(x), shift, scale)
-        x = self.linear(x)
-        return x
-
-class SelfAttentionContext(nn.Module):
-    def __init__(self, dim, heads=8, dim_head=64, dtype=None, device=None, operations=None):
-        super().__init__()
-        dim_head = dim // heads
-        inner_dim = dim
-
-        self.heads = heads
-        self.dim_head = dim_head
-
-        self.qkv = operations.Linear(dim, dim * 3, bias=True, dtype=dtype, device=device)
-
-        self.proj = operations.Linear(inner_dim, dim, dtype=dtype, device=device)
-
-    def forward(self, x):
-        qkv = self.qkv(x)
-        q, k, v = split_qkv(qkv, self.dim_head)
-        x = optimized_attention((q.reshape(q.shape[0], q.shape[1], -1), k, v), self.heads)
-        return self.proj(x)
-
-class ContextProcessorBlock(nn.Module):
-    def __init__(self, context_size, dtype=None, device=None, operations=None):
-        super().__init__()
-        self.norm1 = operations.LayerNorm(context_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
-        self.attn = SelfAttentionContext(context_size, dtype=dtype, device=device, operations=operations)
-        self.norm2 = operations.LayerNorm(context_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
-        self.mlp = Mlp(in_features=context_size, hidden_features=(context_size * 4), act_layer=lambda: nn.GELU(approximate="tanh"), drop=0, dtype=dtype, device=device, operations=operations)
-
-    def forward(self, x):
-        x += self.attn(self.norm1(x))
-        x += self.mlp(self.norm2(x))
-        return x
-
-class ContextProcessor(nn.Module):
-    def __init__(self, context_size, num_layers, dtype=None, device=None, operations=None):
-        super().__init__()
-        self.layers = torch.nn.ModuleList([ContextProcessorBlock(context_size, dtype=dtype, device=device, operations=operations) for i in range(num_layers)])
-        self.norm = operations.LayerNorm(context_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
-
-    def forward(self, x):
-        for i, l in enumerate(self.layers):
-            x = l(x)
-        return self.norm(x)
-
-class MMDiT(nn.Module):
-    """
-    Diffusion model with a Transformer backbone.
-    """
-
-    def __init__(
-        self,
-        input_size: int = 32,
-        patch_size: int = 2,
-        in_channels: int = 4,
-        depth: int = 28,
-        # hidden_size: Optional[int] = None,
-        # num_heads: Optional[int] = None,
-        mlp_ratio: float = 4.0,
-        learn_sigma: bool = False,
-        adm_in_channels: Optional[int] = None,
-        context_embedder_config: Optional[Dict] = None,
-        compile_core: bool = False,
-        use_checkpoint: bool = False,
-        register_length: int = 0,
-        attn_mode: str = "torch",
-        rmsnorm: bool = False,
-        scale_mod_only: bool = False,
-        swiglu: bool = False,
-        out_channels: Optional[int] = None,
-        pos_embed_scaling_factor: Optional[float] = None,
-        pos_embed_offset: Optional[float] = None,
-        pos_embed_max_size: Optional[int] = None,
-        num_patches = None,
-        qk_norm: Optional[str] = None,
-        qkv_bias: bool = True,
-        context_processor_layers = None,
-        context_size = 4096,
-        dtype = None, #TODO
-        device = None,
-        operations = None,
-    ):
-        super().__init__()
-        self.dtype = dtype
-        self.learn_sigma = learn_sigma
-        self.in_channels = in_channels
-        default_out_channels = in_channels * 2 if learn_sigma else in_channels
-        self.out_channels = default(out_channels, default_out_channels)
-        self.patch_size = patch_size
-        self.pos_embed_scaling_factor = pos_embed_scaling_factor
-        self.pos_embed_offset = pos_embed_offset
-        self.pos_embed_max_size = pos_embed_max_size
-
-        # hidden_size = default(hidden_size, 64 * depth)
-        # num_heads = default(num_heads, hidden_size // 64)
-
-        # apply magic --> this defines a head_size of 64
-        self.hidden_size = 64 * depth
-        num_heads = depth
-
-        self.num_heads = num_heads
-
-        self.x_embedder = PatchEmbed(
-            input_size,
-            patch_size,
-            in_channels,
-            self.hidden_size,
-            bias=True,
-            strict_img_size=self.pos_embed_max_size is None,
-            dtype=dtype,
-            device=device,
-            operations=operations
-        )
-        self.t_embedder = TimestepEmbedder(self.hidden_size, dtype=dtype, device=device, operations=operations)
-
-        self.y_embedder = None
-        if adm_in_channels is not None:
-            assert isinstance(adm_in_channels, int)
-            self.y_embedder = VectorEmbedder(adm_in_channels, self.hidden_size, dtype=dtype, device=device, operations=operations)
-
-        if context_processor_layers is not None:
-            self.context_processor = ContextProcessor(context_size, context_processor_layers, dtype=dtype, device=device, operations=operations)
-        else:
-            self.context_processor = None
-
-        self.context_embedder = nn.Identity()
-        if context_embedder_config is not None:
-            if context_embedder_config["target"] == "torch.nn.Linear":
-                self.context_embedder = operations.Linear(**context_embedder_config["params"], dtype=dtype, device=device)
-
-        self.register_length = register_length
-        if self.register_length > 0:
-            self.register = nn.Parameter(torch.randn(1, register_length, self.hidden_size, dtype=dtype, device=device))
-
-        # num_patches = self.x_embedder.num_patches
-        # Will use fixed sin-cos embedding:
-        # just use a buffer already
-        if num_patches is not None:
-            self.register_buffer(
-                "pos_embed",
-                torch.empty(1, num_patches, self.hidden_size, dtype=dtype, device=device),
-            )
-        else:
-            self.pos_embed = None
-
-        self.use_checkpoint = use_checkpoint
-        self.joint_blocks = nn.ModuleList(
-            [
-                JointBlock(
-                    self.hidden_size,
-                    num_heads,
-                    mlp_ratio=mlp_ratio,
-                    qkv_bias=qkv_bias,
-                    attn_mode=attn_mode,
-                    pre_only=i == depth - 1,
-                    rmsnorm=rmsnorm,
-                    scale_mod_only=scale_mod_only,
-                    swiglu=swiglu,
-                    qk_norm=qk_norm,
-                    dtype=dtype,
-                    device=device,
-                    operations=operations
-                )
-                for i in range(depth)
-            ]
-        )
-
-        self.final_layer = FinalLayer(self.hidden_size, patch_size, self.out_channels, dtype=dtype, device=device, operations=operations)
-
-        if compile_core:
-            assert False
-            self.forward_core_with_concat = torch.compile(self.forward_core_with_concat)
-
-    def cropped_pos_embed(self, hw, device=None):
-        p = self.x_embedder.patch_size[0]
-        h, w = hw
-        # patched size
-        h = (h + 1) // p
-        w = (w + 1) // p
-        if self.pos_embed is None:
-            return get_2d_sincos_pos_embed_torch(self.hidden_size, w, h, device=device)
-        assert self.pos_embed_max_size is not None
-        assert h <= self.pos_embed_max_size, (h, self.pos_embed_max_size)
-        assert w <= self.pos_embed_max_size, (w, self.pos_embed_max_size)
-        top = (self.pos_embed_max_size - h) // 2
-        left = (self.pos_embed_max_size - w) // 2
-        spatial_pos_embed = rearrange(
-            self.pos_embed,
-            "1 (h w) c -> 1 h w c",
-            h=self.pos_embed_max_size,
-            w=self.pos_embed_max_size,
-        )
-        spatial_pos_embed = spatial_pos_embed[:, top : top + h, left : left + w, :]
-        spatial_pos_embed = rearrange(spatial_pos_embed, "1 h w c -> 1 (h w) c")
-        # print(spatial_pos_embed, top, left, h, w)
-        # # t = get_2d_sincos_pos_embed_torch(self.hidden_size, w, h, 7.875, 7.875, device=device) #matches exactly for 1024 res
-        # t = get_2d_sincos_pos_embed_torch(self.hidden_size, w, h, 7.5, 7.5, device=device) #scales better
-        # # print(t)
-        # return t
-        return spatial_pos_embed
-
-    def unpatchify(self, x, hw=None):
-        """
-        x: (N, T, patch_size**2 * C)
-        imgs: (N, H, W, C)
-        """
-        c = self.out_channels
-        p = self.x_embedder.patch_size[0]
-        if hw is None:
-            h = w = int(x.shape[1] ** 0.5)
-        else:
-            h, w = hw
-            h = (h + 1) // p
-            w = (w + 1) // p
-        assert h * w == x.shape[1]
-
-        x = x.reshape(shape=(x.shape[0], h, w, p, p, c))
-        x = torch.einsum("nhwpqc->nchpwq", x)
-        imgs = x.reshape(shape=(x.shape[0], c, h * p, w * p))
-        return imgs
-
-    def forward_core_with_concat(
-        self,
-        x: torch.Tensor,
-        c_mod: torch.Tensor,
-        context: Optional[torch.Tensor] = None,
-    ) -> torch.Tensor:
-        if self.register_length > 0:
-            context = torch.cat(
-                (
-                    repeat(self.register, "1 ... -> b ...", b=x.shape[0]),
-                    default(context, torch.Tensor([]).type_as(x)),
-                ),
-                1,
-            )
-
-        # context is B, L', D
-        # x is B, L, D
-        for block in self.joint_blocks:
-            context, x = block(
-                context,
-                x,
-                c=c_mod,
-                use_checkpoint=self.use_checkpoint,
-            )
-
-        x = self.final_layer(x, c_mod)  # (N, T, patch_size ** 2 * out_channels)
-        return x
-
-    def forward(
-        self,
-        x: torch.Tensor,
-        t: torch.Tensor,
-        y: Optional[torch.Tensor] = None,
-        context: Optional[torch.Tensor] = None,
-    ) -> torch.Tensor:
-        """
-        Forward pass of DiT.
-        x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images)
-        t: (N,) tensor of diffusion timesteps
-        y: (N,) tensor of class labels
-        """
-
-        if self.context_processor is not None:
-            context = self.context_processor(context)
-
-        hw = x.shape[-2:]
-        x = self.x_embedder(x) + self.cropped_pos_embed(hw, device=x.device).to(dtype=x.dtype, device=x.device)
-        c = self.t_embedder(t, dtype=x.dtype)  # (N, D)
-        if y is not None and self.y_embedder is not None:
-            y = self.y_embedder(y)  # (N, D)
-            c = c + y  # (N, D)
-
-        if context is not None:
-            context = self.context_embedder(context)
-
-        x = self.forward_core_with_concat(x, c, context)
-
-        x = self.unpatchify(x, hw=hw)  # (N, out_channels, H, W)
-        return x[:,:,:hw[-2],:hw[-1]]
-
-
-class OpenAISignatureMMDITWrapper(MMDiT):
-    def forward(
-        self,
-        x: torch.Tensor,
-        timesteps: torch.Tensor,
-        context: Optional[torch.Tensor] = None,
-        y: Optional[torch.Tensor] = None,
-        **kwargs,
-    ) -> torch.Tensor:
-        return super().forward(x, timesteps, context=context, y=y)
-
diff --git a/MagicQuill/comfy/ldm/modules/diffusionmodules/model.py b/MagicQuill/comfy/ldm/modules/diffusionmodules/model.py
deleted file mode 100644
index 04eb83b2181253e3a88f7945f75e017060e02ebf..0000000000000000000000000000000000000000
--- a/MagicQuill/comfy/ldm/modules/diffusionmodules/model.py
+++ /dev/null
@@ -1,650 +0,0 @@
-# pytorch_diffusion + derived encoder decoder
-import math
-import torch
-import torch.nn as nn
-import numpy as np
-from typing import Optional, Any
-import logging
-
-from comfy import model_management
-import comfy.ops
-ops = comfy.ops.disable_weight_init
-
-if model_management.xformers_enabled_vae():
-    import xformers
-    import xformers.ops
-
-def get_timestep_embedding(timesteps, embedding_dim):
-    """
-    This matches the implementation in Denoising Diffusion Probabilistic Models:
-    From Fairseq.
-    Build sinusoidal embeddings.
-    This matches the implementation in tensor2tensor, but differs slightly
-    from the description in Section 3.5 of "Attention Is All You Need".
-    """
-    assert len(timesteps.shape) == 1
-
-    half_dim = embedding_dim // 2
-    emb = math.log(10000) / (half_dim - 1)
-    emb = torch.exp(torch.arange(half_dim, dtype=torch.float32) * -emb)
-    emb = emb.to(device=timesteps.device)
-    emb = timesteps.float()[:, 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, (0,1,0,0))
-    return emb
-
-
-def nonlinearity(x):
-    # swish
-    return x*torch.sigmoid(x)
-
-
-def Normalize(in_channels, num_groups=32):
-    return ops.GroupNorm(num_groups=num_groups, num_channels=in_channels, eps=1e-6, affine=True)
-
-
-class Upsample(nn.Module):
-    def __init__(self, in_channels, with_conv):
-        super().__init__()
-        self.with_conv = with_conv
-        if self.with_conv:
-            self.conv = ops.Conv2d(in_channels,
-                                        in_channels,
-                                        kernel_size=3,
-                                        stride=1,
-                                        padding=1)
-
-    def forward(self, x):
-        try:
-            x = torch.nn.functional.interpolate(x, scale_factor=2.0, mode="nearest")
-        except: #operation not implemented for bf16
-            b, c, h, w = x.shape
-            out = torch.empty((b, c, h*2, w*2), dtype=x.dtype, layout=x.layout, device=x.device)
-            split = 8
-            l = out.shape[1] // split
-            for i in range(0, out.shape[1], l):
-                out[:,i:i+l] = torch.nn.functional.interpolate(x[:,i:i+l].to(torch.float32), scale_factor=2.0, mode="nearest").to(x.dtype)
-            del x
-            x = out
-
-        if self.with_conv:
-            x = self.conv(x)
-        return x
-
-
-class Downsample(nn.Module):
-    def __init__(self, in_channels, with_conv):
-        super().__init__()
-        self.with_conv = with_conv
-        if self.with_conv:
-            # no asymmetric padding in torch conv, must do it ourselves
-            self.conv = ops.Conv2d(in_channels,
-                                        in_channels,
-                                        kernel_size=3,
-                                        stride=2,
-                                        padding=0)
-
-    def forward(self, x):
-        if self.with_conv:
-            pad = (0,1,0,1)
-            x = torch.nn.functional.pad(x, pad, mode="constant", value=0)
-            x = self.conv(x)
-        else:
-            x = torch.nn.functional.avg_pool2d(x, kernel_size=2, stride=2)
-        return x
-
-
-class ResnetBlock(nn.Module):
-    def __init__(self, *, in_channels, out_channels=None, conv_shortcut=False,
-                 dropout, temb_channels=512):
-        super().__init__()
-        self.in_channels = in_channels
-        out_channels = in_channels if out_channels is None else out_channels
-        self.out_channels = out_channels
-        self.use_conv_shortcut = conv_shortcut
-
-        self.swish = torch.nn.SiLU(inplace=True)
-        self.norm1 = Normalize(in_channels)
-        self.conv1 = ops.Conv2d(in_channels,
-                                     out_channels,
-                                     kernel_size=3,
-                                     stride=1,
-                                     padding=1)
-        if temb_channels > 0:
-            self.temb_proj = ops.Linear(temb_channels,
-                                             out_channels)
-        self.norm2 = Normalize(out_channels)
-        self.dropout = torch.nn.Dropout(dropout, inplace=True)
-        self.conv2 = ops.Conv2d(out_channels,
-                                     out_channels,
-                                     kernel_size=3,
-                                     stride=1,
-                                     padding=1)
-        if self.in_channels != self.out_channels:
-            if self.use_conv_shortcut:
-                self.conv_shortcut = ops.Conv2d(in_channels,
-                                                     out_channels,
-                                                     kernel_size=3,
-                                                     stride=1,
-                                                     padding=1)
-            else:
-                self.nin_shortcut = ops.Conv2d(in_channels,
-                                                    out_channels,
-                                                    kernel_size=1,
-                                                    stride=1,
-                                                    padding=0)
-
-    def forward(self, x, temb):
-        h = x
-        h = self.norm1(h)
-        h = self.swish(h)
-        h = self.conv1(h)
-
-        if temb is not None:
-            h = h + self.temb_proj(self.swish(temb))[:,:,None,None]
-
-        h = self.norm2(h)
-        h = self.swish(h)
-        h = self.dropout(h)
-        h = self.conv2(h)
-
-        if self.in_channels != self.out_channels:
-            if self.use_conv_shortcut:
-                x = self.conv_shortcut(x)
-            else:
-                x = self.nin_shortcut(x)
-
-        return x+h
-
-def slice_attention(q, k, v):
-    r1 = torch.zeros_like(k, device=q.device)
-    scale = (int(q.shape[-1])**(-0.5))
-
-    mem_free_total = model_management.get_free_memory(q.device)
-
-    gb = 1024 ** 3
-    tensor_size = q.shape[0] * q.shape[1] * k.shape[2] * q.element_size()
-    modifier = 3 if q.element_size() == 2 else 2.5
-    mem_required = tensor_size * modifier
-    steps = 1
-
-    if mem_required > mem_free_total:
-        steps = 2**(math.ceil(math.log(mem_required / mem_free_total, 2)))
-
-    while True:
-        try:
-            slice_size = q.shape[1] // steps if (q.shape[1] % steps) == 0 else q.shape[1]
-            for i in range(0, q.shape[1], slice_size):
-                end = i + slice_size
-                s1 = torch.bmm(q[:, i:end], k) * scale
-
-                s2 = torch.nn.functional.softmax(s1, dim=2).permute(0,2,1)
-                del s1
-
-                r1[:, :, i:end] = torch.bmm(v, s2)
-                del s2
-            break
-        except model_management.OOM_EXCEPTION as e:
-            model_management.soft_empty_cache(True)
-            steps *= 2
-            if steps > 128:
-                raise e
-            logging.warning("out of memory error, increasing steps and trying again {}".format(steps))
-
-    return r1
-
-def normal_attention(q, k, v):
-    # compute attention
-    b,c,h,w = q.shape
-
-    q = q.reshape(b,c,h*w)
-    q = q.permute(0,2,1)   # b,hw,c
-    k = k.reshape(b,c,h*w) # b,c,hw
-    v = v.reshape(b,c,h*w)
-
-    r1 = slice_attention(q, k, v)
-    h_ = r1.reshape(b,c,h,w)
-    del r1
-    return h_
-
-def xformers_attention(q, k, v):
-    # compute attention
-    B, C, H, W = q.shape
-    q, k, v = map(
-        lambda t: t.view(B, C, -1).transpose(1, 2).contiguous(),
-        (q, k, v),
-    )
-
-    try:
-        out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None)
-        out = out.transpose(1, 2).reshape(B, C, H, W)
-    except NotImplementedError as e:
-        out = slice_attention(q.view(B, -1, C), k.view(B, -1, C).transpose(1, 2), v.view(B, -1, C).transpose(1, 2)).reshape(B, C, H, W)
-    return out
-
-def pytorch_attention(q, k, v):
-    # compute attention
-    B, C, H, W = q.shape
-    q, k, v = map(
-        lambda t: t.view(B, 1, C, -1).transpose(2, 3).contiguous(),
-        (q, k, v),
-    )
-
-    try:
-        out = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=None, dropout_p=0.0, is_causal=False)
-        out = out.transpose(2, 3).reshape(B, C, H, W)
-    except model_management.OOM_EXCEPTION as e:
-        logging.warning("scaled_dot_product_attention OOMed: switched to slice attention")
-        out = slice_attention(q.view(B, -1, C), k.view(B, -1, C).transpose(1, 2), v.view(B, -1, C).transpose(1, 2)).reshape(B, C, H, W)
-    return out
-
-
-class AttnBlock(nn.Module):
-    def __init__(self, in_channels):
-        super().__init__()
-        self.in_channels = in_channels
-
-        self.norm = Normalize(in_channels)
-        self.q = ops.Conv2d(in_channels,
-                                 in_channels,
-                                 kernel_size=1,
-                                 stride=1,
-                                 padding=0)
-        self.k = ops.Conv2d(in_channels,
-                                 in_channels,
-                                 kernel_size=1,
-                                 stride=1,
-                                 padding=0)
-        self.v = ops.Conv2d(in_channels,
-                                 in_channels,
-                                 kernel_size=1,
-                                 stride=1,
-                                 padding=0)
-        self.proj_out = ops.Conv2d(in_channels,
-                                        in_channels,
-                                        kernel_size=1,
-                                        stride=1,
-                                        padding=0)
-
-        if model_management.xformers_enabled_vae():
-            logging.info("Using xformers attention in VAE")
-            self.optimized_attention = xformers_attention
-        elif model_management.pytorch_attention_enabled():
-            logging.info("Using pytorch attention in VAE")
-            self.optimized_attention = pytorch_attention
-        else:
-            logging.info("Using split attention in VAE")
-            self.optimized_attention = normal_attention
-
-    def forward(self, x):
-        h_ = x
-        h_ = self.norm(h_)
-        q = self.q(h_)
-        k = self.k(h_)
-        v = self.v(h_)
-
-        h_ = self.optimized_attention(q, k, v)
-
-        h_ = self.proj_out(h_)
-
-        return x+h_
-
-
-def make_attn(in_channels, attn_type="vanilla", attn_kwargs=None):
-    return AttnBlock(in_channels)
-
-
-class Model(nn.Module):
-    def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks,
-                 attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels,
-                 resolution, use_timestep=True, use_linear_attn=False, attn_type="vanilla"):
-        super().__init__()
-        if use_linear_attn: attn_type = "linear"
-        self.ch = ch
-        self.temb_ch = self.ch*4
-        self.num_resolutions = len(ch_mult)
-        self.num_res_blocks = num_res_blocks
-        self.resolution = resolution
-        self.in_channels = in_channels
-
-        self.use_timestep = use_timestep
-        if self.use_timestep:
-            # timestep embedding
-            self.temb = nn.Module()
-            self.temb.dense = nn.ModuleList([
-                ops.Linear(self.ch,
-                                self.temb_ch),
-                ops.Linear(self.temb_ch,
-                                self.temb_ch),
-            ])
-
-        # downsampling
-        self.conv_in = ops.Conv2d(in_channels,
-                                       self.ch,
-                                       kernel_size=3,
-                                       stride=1,
-                                       padding=1)
-
-        curr_res = resolution
-        in_ch_mult = (1,)+tuple(ch_mult)
-        self.down = nn.ModuleList()
-        for i_level in range(self.num_resolutions):
-            block = nn.ModuleList()
-            attn = nn.ModuleList()
-            block_in = ch*in_ch_mult[i_level]
-            block_out = ch*ch_mult[i_level]
-            for i_block in range(self.num_res_blocks):
-                block.append(ResnetBlock(in_channels=block_in,
-                                         out_channels=block_out,
-                                         temb_channels=self.temb_ch,
-                                         dropout=dropout))
-                block_in = block_out
-                if curr_res in attn_resolutions:
-                    attn.append(make_attn(block_in, attn_type=attn_type))
-            down = nn.Module()
-            down.block = block
-            down.attn = attn
-            if i_level != self.num_resolutions-1:
-                down.downsample = Downsample(block_in, resamp_with_conv)
-                curr_res = curr_res // 2
-            self.down.append(down)
-
-        # middle
-        self.mid = nn.Module()
-        self.mid.block_1 = ResnetBlock(in_channels=block_in,
-                                       out_channels=block_in,
-                                       temb_channels=self.temb_ch,
-                                       dropout=dropout)
-        self.mid.attn_1 = make_attn(block_in, attn_type=attn_type)
-        self.mid.block_2 = ResnetBlock(in_channels=block_in,
-                                       out_channels=block_in,
-                                       temb_channels=self.temb_ch,
-                                       dropout=dropout)
-
-        # upsampling
-        self.up = nn.ModuleList()
-        for i_level in reversed(range(self.num_resolutions)):
-            block = nn.ModuleList()
-            attn = nn.ModuleList()
-            block_out = ch*ch_mult[i_level]
-            skip_in = ch*ch_mult[i_level]
-            for i_block in range(self.num_res_blocks+1):
-                if i_block == self.num_res_blocks:
-                    skip_in = ch*in_ch_mult[i_level]
-                block.append(ResnetBlock(in_channels=block_in+skip_in,
-                                         out_channels=block_out,
-                                         temb_channels=self.temb_ch,
-                                         dropout=dropout))
-                block_in = block_out
-                if curr_res in attn_resolutions:
-                    attn.append(make_attn(block_in, attn_type=attn_type))
-            up = nn.Module()
-            up.block = block
-            up.attn = attn
-            if i_level != 0:
-                up.upsample = Upsample(block_in, resamp_with_conv)
-                curr_res = curr_res * 2
-            self.up.insert(0, up) # prepend to get consistent order
-
-        # end
-        self.norm_out = Normalize(block_in)
-        self.conv_out = ops.Conv2d(block_in,
-                                        out_ch,
-                                        kernel_size=3,
-                                        stride=1,
-                                        padding=1)
-
-    def forward(self, x, t=None, context=None):
-        #assert x.shape[2] == x.shape[3] == self.resolution
-        if context is not None:
-            # assume aligned context, cat along channel axis
-            x = torch.cat((x, context), dim=1)
-        if self.use_timestep:
-            # timestep embedding
-            assert t is not None
-            temb = get_timestep_embedding(t, self.ch)
-            temb = self.temb.dense[0](temb)
-            temb = nonlinearity(temb)
-            temb = self.temb.dense[1](temb)
-        else:
-            temb = None
-
-        # downsampling
-        hs = [self.conv_in(x)]
-        for i_level in range(self.num_resolutions):
-            for i_block in range(self.num_res_blocks):
-                h = self.down[i_level].block[i_block](hs[-1], temb)
-                if len(self.down[i_level].attn) > 0:
-                    h = self.down[i_level].attn[i_block](h)
-                hs.append(h)
-            if i_level != self.num_resolutions-1:
-                hs.append(self.down[i_level].downsample(hs[-1]))
-
-        # middle
-        h = hs[-1]
-        h = self.mid.block_1(h, temb)
-        h = self.mid.attn_1(h)
-        h = self.mid.block_2(h, temb)
-
-        # upsampling
-        for i_level in reversed(range(self.num_resolutions)):
-            for i_block in range(self.num_res_blocks+1):
-                h = self.up[i_level].block[i_block](
-                    torch.cat([h, hs.pop()], dim=1), temb)
-                if len(self.up[i_level].attn) > 0:
-                    h = self.up[i_level].attn[i_block](h)
-            if i_level != 0:
-                h = self.up[i_level].upsample(h)
-
-        # end
-        h = self.norm_out(h)
-        h = nonlinearity(h)
-        h = self.conv_out(h)
-        return h
-
-    def get_last_layer(self):
-        return self.conv_out.weight
-
-
-class Encoder(nn.Module):
-    def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks,
-                 attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels,
-                 resolution, z_channels, double_z=True, use_linear_attn=False, attn_type="vanilla",
-                 **ignore_kwargs):
-        super().__init__()
-        if use_linear_attn: attn_type = "linear"
-        self.ch = ch
-        self.temb_ch = 0
-        self.num_resolutions = len(ch_mult)
-        self.num_res_blocks = num_res_blocks
-        self.resolution = resolution
-        self.in_channels = in_channels
-
-        # downsampling
-        self.conv_in = ops.Conv2d(in_channels,
-                                       self.ch,
-                                       kernel_size=3,
-                                       stride=1,
-                                       padding=1)
-
-        curr_res = resolution
-        in_ch_mult = (1,)+tuple(ch_mult)
-        self.in_ch_mult = in_ch_mult
-        self.down = nn.ModuleList()
-        for i_level in range(self.num_resolutions):
-            block = nn.ModuleList()
-            attn = nn.ModuleList()
-            block_in = ch*in_ch_mult[i_level]
-            block_out = ch*ch_mult[i_level]
-            for i_block in range(self.num_res_blocks):
-                block.append(ResnetBlock(in_channels=block_in,
-                                         out_channels=block_out,
-                                         temb_channels=self.temb_ch,
-                                         dropout=dropout))
-                block_in = block_out
-                if curr_res in attn_resolutions:
-                    attn.append(make_attn(block_in, attn_type=attn_type))
-            down = nn.Module()
-            down.block = block
-            down.attn = attn
-            if i_level != self.num_resolutions-1:
-                down.downsample = Downsample(block_in, resamp_with_conv)
-                curr_res = curr_res // 2
-            self.down.append(down)
-
-        # middle
-        self.mid = nn.Module()
-        self.mid.block_1 = ResnetBlock(in_channels=block_in,
-                                       out_channels=block_in,
-                                       temb_channels=self.temb_ch,
-                                       dropout=dropout)
-        self.mid.attn_1 = make_attn(block_in, attn_type=attn_type)
-        self.mid.block_2 = ResnetBlock(in_channels=block_in,
-                                       out_channels=block_in,
-                                       temb_channels=self.temb_ch,
-                                       dropout=dropout)
-
-        # end
-        self.norm_out = Normalize(block_in)
-        self.conv_out = ops.Conv2d(block_in,
-                                        2*z_channels if double_z else z_channels,
-                                        kernel_size=3,
-                                        stride=1,
-                                        padding=1)
-
-    def forward(self, x):
-        # timestep embedding
-        temb = None
-        # downsampling
-        h = self.conv_in(x)
-        for i_level in range(self.num_resolutions):
-            for i_block in range(self.num_res_blocks):
-                h = self.down[i_level].block[i_block](h, temb)
-                if len(self.down[i_level].attn) > 0:
-                    h = self.down[i_level].attn[i_block](h)
-            if i_level != self.num_resolutions-1:
-                h = self.down[i_level].downsample(h)
-
-        # middle
-        h = self.mid.block_1(h, temb)
-        h = self.mid.attn_1(h)
-        h = self.mid.block_2(h, temb)
-
-        # end
-        h = self.norm_out(h)
-        h = nonlinearity(h)
-        h = self.conv_out(h)
-        return h
-
-
-class Decoder(nn.Module):
-    def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks,
-                 attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels,
-                 resolution, z_channels, give_pre_end=False, tanh_out=False, use_linear_attn=False,
-                 conv_out_op=ops.Conv2d,
-                 resnet_op=ResnetBlock,
-                 attn_op=AttnBlock,
-                **ignorekwargs):
-        super().__init__()
-        if use_linear_attn: attn_type = "linear"
-        self.ch = ch
-        self.temb_ch = 0
-        self.num_resolutions = len(ch_mult)
-        self.num_res_blocks = num_res_blocks
-        self.resolution = resolution
-        self.in_channels = in_channels
-        self.give_pre_end = give_pre_end
-        self.tanh_out = tanh_out
-
-        # compute in_ch_mult, block_in and curr_res at lowest res
-        in_ch_mult = (1,)+tuple(ch_mult)
-        block_in = ch*ch_mult[self.num_resolutions-1]
-        curr_res = resolution // 2**(self.num_resolutions-1)
-        self.z_shape = (1,z_channels,curr_res,curr_res)
-        logging.debug("Working with z of shape {} = {} dimensions.".format(
-            self.z_shape, np.prod(self.z_shape)))
-
-        # z to block_in
-        self.conv_in = ops.Conv2d(z_channels,
-                                       block_in,
-                                       kernel_size=3,
-                                       stride=1,
-                                       padding=1)
-
-        # middle
-        self.mid = nn.Module()
-        self.mid.block_1 = resnet_op(in_channels=block_in,
-                                       out_channels=block_in,
-                                       temb_channels=self.temb_ch,
-                                       dropout=dropout)
-        self.mid.attn_1 = attn_op(block_in)
-        self.mid.block_2 = resnet_op(in_channels=block_in,
-                                       out_channels=block_in,
-                                       temb_channels=self.temb_ch,
-                                       dropout=dropout)
-
-        # upsampling
-        self.up = nn.ModuleList()
-        for i_level in reversed(range(self.num_resolutions)):
-            block = nn.ModuleList()
-            attn = nn.ModuleList()
-            block_out = ch*ch_mult[i_level]
-            for i_block in range(self.num_res_blocks+1):
-                block.append(resnet_op(in_channels=block_in,
-                                         out_channels=block_out,
-                                         temb_channels=self.temb_ch,
-                                         dropout=dropout))
-                block_in = block_out
-                if curr_res in attn_resolutions:
-                    attn.append(attn_op(block_in))
-            up = nn.Module()
-            up.block = block
-            up.attn = attn
-            if i_level != 0:
-                up.upsample = Upsample(block_in, resamp_with_conv)
-                curr_res = curr_res * 2
-            self.up.insert(0, up) # prepend to get consistent order
-
-        # end
-        self.norm_out = Normalize(block_in)
-        self.conv_out = conv_out_op(block_in,
-                                        out_ch,
-                                        kernel_size=3,
-                                        stride=1,
-                                        padding=1)
-
-    def forward(self, z, **kwargs):
-        #assert z.shape[1:] == self.z_shape[1:]
-        self.last_z_shape = z.shape
-
-        # timestep embedding
-        temb = None
-
-        # z to block_in
-        h = self.conv_in(z)
-
-        # middle
-        h = self.mid.block_1(h, temb, **kwargs)
-        h = self.mid.attn_1(h, **kwargs)
-        h = self.mid.block_2(h, temb, **kwargs)
-
-        # upsampling
-        for i_level in reversed(range(self.num_resolutions)):
-            for i_block in range(self.num_res_blocks+1):
-                h = self.up[i_level].block[i_block](h, temb, **kwargs)
-                if len(self.up[i_level].attn) > 0:
-                    h = self.up[i_level].attn[i_block](h, **kwargs)
-            if i_level != 0:
-                h = self.up[i_level].upsample(h)
-
-        # end
-        if self.give_pre_end:
-            return h
-
-        h = self.norm_out(h)
-        h = nonlinearity(h)
-        h = self.conv_out(h, **kwargs)
-        if self.tanh_out:
-            h = torch.tanh(h)
-        return h
diff --git a/MagicQuill/comfy/ldm/modules/diffusionmodules/openaimodel.py b/MagicQuill/comfy/ldm/modules/diffusionmodules/openaimodel.py
deleted file mode 100644
index ba8fc2c4a0626456256b474049580f597f4e9ca6..0000000000000000000000000000000000000000
--- a/MagicQuill/comfy/ldm/modules/diffusionmodules/openaimodel.py
+++ /dev/null
@@ -1,892 +0,0 @@
-from abc import abstractmethod
-
-import torch as th
-import torch.nn as nn
-import torch.nn.functional as F
-from einops import rearrange
-import logging
-
-from .util import (
-    checkpoint,
-    avg_pool_nd,
-    zero_module,
-    timestep_embedding,
-    AlphaBlender,
-)
-from ..attention import SpatialTransformer, SpatialVideoTransformer, default
-from comfy.ldm.util import exists
-import comfy.ops
-ops = comfy.ops.disable_weight_init
-
-class TimestepBlock(nn.Module):
-    """
-    Any module where forward() takes timestep embeddings as a second argument.
-    """
-
-    @abstractmethod
-    def forward(self, x, emb):
-        """
-        Apply the module to `x` given `emb` timestep embeddings.
-        """
-
-#This is needed because accelerate makes a copy of transformer_options which breaks "transformer_index"
-def forward_timestep_embed(ts, x, emb, context=None, transformer_options={}, output_shape=None, time_context=None, num_video_frames=None, image_only_indicator=None):
-    for layer in ts:
-        if isinstance(layer, VideoResBlock):
-            x = layer(x, emb, num_video_frames, image_only_indicator)
-        elif isinstance(layer, TimestepBlock):
-            x = layer(x, emb)
-        elif isinstance(layer, SpatialVideoTransformer):
-            x = layer(x, context, time_context, num_video_frames, image_only_indicator, transformer_options)
-            if "transformer_index" in transformer_options:
-                transformer_options["transformer_index"] += 1
-        elif isinstance(layer, SpatialTransformer):
-            x = layer(x, context, transformer_options)
-            if "transformer_index" in transformer_options:
-                transformer_options["transformer_index"] += 1
-        elif isinstance(layer, Upsample):
-            x = layer(x, output_shape=output_shape)
-        else:
-            x = layer(x)
-    return x
-
-class TimestepEmbedSequential(nn.Sequential, TimestepBlock):
-    """
-    A sequential module that passes timestep embeddings to the children that
-    support it as an extra input.
-    """
-
-    def forward(self, *args, **kwargs):
-        return forward_timestep_embed(self, *args, **kwargs)
-
-class Upsample(nn.Module):
-    """
-    An upsampling layer with an optional convolution.
-    :param channels: channels in the inputs and outputs.
-    :param use_conv: a bool determining if a convolution is applied.
-    :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
-                 upsampling occurs in the inner-two dimensions.
-    """
-
-    def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1, dtype=None, device=None, operations=ops):
-        super().__init__()
-        self.channels = channels
-        self.out_channels = out_channels or channels
-        self.use_conv = use_conv
-        self.dims = dims
-        if use_conv:
-            self.conv = operations.conv_nd(dims, self.channels, self.out_channels, 3, padding=padding, dtype=dtype, device=device)
-
-    def forward(self, x, output_shape=None):
-        assert x.shape[1] == self.channels
-        if self.dims == 3:
-            shape = [x.shape[2], x.shape[3] * 2, x.shape[4] * 2]
-            if output_shape is not None:
-                shape[1] = output_shape[3]
-                shape[2] = output_shape[4]
-        else:
-            shape = [x.shape[2] * 2, x.shape[3] * 2]
-            if output_shape is not None:
-                shape[0] = output_shape[2]
-                shape[1] = output_shape[3]
-
-        x = F.interpolate(x, size=shape, mode="nearest")
-        if self.use_conv:
-            x = self.conv(x)
-        return x
-
-class Downsample(nn.Module):
-    """
-    A downsampling layer with an optional convolution.
-    :param channels: channels in the inputs and outputs.
-    :param use_conv: a bool determining if a convolution is applied.
-    :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
-                 downsampling occurs in the inner-two dimensions.
-    """
-
-    def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1, dtype=None, device=None, operations=ops):
-        super().__init__()
-        self.channels = channels
-        self.out_channels = out_channels or channels
-        self.use_conv = use_conv
-        self.dims = dims
-        stride = 2 if dims != 3 else (1, 2, 2)
-        if use_conv:
-            self.op = operations.conv_nd(
-                dims, self.channels, self.out_channels, 3, stride=stride, padding=padding, dtype=dtype, device=device
-            )
-        else:
-            assert self.channels == self.out_channels
-            self.op = avg_pool_nd(dims, kernel_size=stride, stride=stride)
-
-    def forward(self, x):
-        assert x.shape[1] == self.channels
-        return self.op(x)
-
-
-class ResBlock(TimestepBlock):
-    """
-    A residual block that can optionally change the number of channels.
-    :param channels: the number of input channels.
-    :param emb_channels: the number of timestep embedding channels.
-    :param dropout: the rate of dropout.
-    :param out_channels: if specified, the number of out channels.
-    :param use_conv: if True and out_channels is specified, use a spatial
-        convolution instead of a smaller 1x1 convolution to change the
-        channels in the skip connection.
-    :param dims: determines if the signal is 1D, 2D, or 3D.
-    :param use_checkpoint: if True, use gradient checkpointing on this module.
-    :param up: if True, use this block for upsampling.
-    :param down: if True, use this block for downsampling.
-    """
-
-    def __init__(
-        self,
-        channels,
-        emb_channels,
-        dropout,
-        out_channels=None,
-        use_conv=False,
-        use_scale_shift_norm=False,
-        dims=2,
-        use_checkpoint=False,
-        up=False,
-        down=False,
-        kernel_size=3,
-        exchange_temb_dims=False,
-        skip_t_emb=False,
-        dtype=None,
-        device=None,
-        operations=ops
-    ):
-        super().__init__()
-        self.channels = channels
-        self.emb_channels = emb_channels
-        self.dropout = dropout
-        self.out_channels = out_channels or channels
-        self.use_conv = use_conv
-        self.use_checkpoint = use_checkpoint
-        self.use_scale_shift_norm = use_scale_shift_norm
-        self.exchange_temb_dims = exchange_temb_dims
-
-        if isinstance(kernel_size, list):
-            padding = [k // 2 for k in kernel_size]
-        else:
-            padding = kernel_size // 2
-
-        self.in_layers = nn.Sequential(
-            operations.GroupNorm(32, channels, dtype=dtype, device=device),
-            nn.SiLU(),
-            operations.conv_nd(dims, channels, self.out_channels, kernel_size, padding=padding, dtype=dtype, device=device),
-        )
-
-        self.updown = up or down
-
-        if up:
-            self.h_upd = Upsample(channels, False, dims, dtype=dtype, device=device)
-            self.x_upd = Upsample(channels, False, dims, dtype=dtype, device=device)
-        elif down:
-            self.h_upd = Downsample(channels, False, dims, dtype=dtype, device=device)
-            self.x_upd = Downsample(channels, False, dims, dtype=dtype, device=device)
-        else:
-            self.h_upd = self.x_upd = nn.Identity()
-
-        self.skip_t_emb = skip_t_emb
-        if self.skip_t_emb:
-            self.emb_layers = None
-            self.exchange_temb_dims = False
-        else:
-            self.emb_layers = nn.Sequential(
-                nn.SiLU(),
-                operations.Linear(
-                    emb_channels,
-                    2 * self.out_channels if use_scale_shift_norm else self.out_channels, dtype=dtype, device=device
-                ),
-            )
-        self.out_layers = nn.Sequential(
-            operations.GroupNorm(32, self.out_channels, dtype=dtype, device=device),
-            nn.SiLU(),
-            nn.Dropout(p=dropout),
-            operations.conv_nd(dims, self.out_channels, self.out_channels, kernel_size, padding=padding, dtype=dtype, device=device)
-            ,
-        )
-
-        if self.out_channels == channels:
-            self.skip_connection = nn.Identity()
-        elif use_conv:
-            self.skip_connection = operations.conv_nd(
-                dims, channels, self.out_channels, kernel_size, padding=padding, dtype=dtype, device=device
-            )
-        else:
-            self.skip_connection = operations.conv_nd(dims, channels, self.out_channels, 1, dtype=dtype, device=device)
-
-    def forward(self, x, emb):
-        """
-        Apply the block to a Tensor, conditioned on a timestep embedding.
-        :param x: an [N x C x ...] Tensor of features.
-        :param emb: an [N x emb_channels] Tensor of timestep embeddings.
-        :return: an [N x C x ...] Tensor of outputs.
-        """
-        return checkpoint(
-            self._forward, (x, emb), self.parameters(), self.use_checkpoint
-        )
-
-
-    def _forward(self, x, emb):
-        if self.updown:
-            in_rest, in_conv = self.in_layers[:-1], self.in_layers[-1]
-            h = in_rest(x)
-            h = self.h_upd(h)
-            x = self.x_upd(x)
-            h = in_conv(h)
-        else:
-            h = self.in_layers(x)
-
-        emb_out = None
-        if not self.skip_t_emb:
-            emb_out = self.emb_layers(emb).type(h.dtype)
-            while len(emb_out.shape) < len(h.shape):
-                emb_out = emb_out[..., None]
-        if self.use_scale_shift_norm:
-            out_norm, out_rest = self.out_layers[0], self.out_layers[1:]
-            h = out_norm(h)
-            if emb_out is not None:
-                scale, shift = th.chunk(emb_out, 2, dim=1)
-                h *= (1 + scale)
-                h += shift
-            h = out_rest(h)
-        else:
-            if emb_out is not None:
-                if self.exchange_temb_dims:
-                    emb_out = emb_out.movedim(1, 2)
-                h = h + emb_out
-            h = self.out_layers(h)
-        return self.skip_connection(x) + h
-
-
-class VideoResBlock(ResBlock):
-    def __init__(
-        self,
-        channels: int,
-        emb_channels: int,
-        dropout: float,
-        video_kernel_size=3,
-        merge_strategy: str = "fixed",
-        merge_factor: float = 0.5,
-        out_channels=None,
-        use_conv: bool = False,
-        use_scale_shift_norm: bool = False,
-        dims: int = 2,
-        use_checkpoint: bool = False,
-        up: bool = False,
-        down: bool = False,
-        dtype=None,
-        device=None,
-        operations=ops
-    ):
-        super().__init__(
-            channels,
-            emb_channels,
-            dropout,
-            out_channels=out_channels,
-            use_conv=use_conv,
-            use_scale_shift_norm=use_scale_shift_norm,
-            dims=dims,
-            use_checkpoint=use_checkpoint,
-            up=up,
-            down=down,
-            dtype=dtype,
-            device=device,
-            operations=operations
-        )
-
-        self.time_stack = ResBlock(
-            default(out_channels, channels),
-            emb_channels,
-            dropout=dropout,
-            dims=3,
-            out_channels=default(out_channels, channels),
-            use_scale_shift_norm=False,
-            use_conv=False,
-            up=False,
-            down=False,
-            kernel_size=video_kernel_size,
-            use_checkpoint=use_checkpoint,
-            exchange_temb_dims=True,
-            dtype=dtype,
-            device=device,
-            operations=operations
-        )
-        self.time_mixer = AlphaBlender(
-            alpha=merge_factor,
-            merge_strategy=merge_strategy,
-            rearrange_pattern="b t -> b 1 t 1 1",
-        )
-
-    def forward(
-        self,
-        x: th.Tensor,
-        emb: th.Tensor,
-        num_video_frames: int,
-        image_only_indicator = None,
-    ) -> th.Tensor:
-        x = super().forward(x, emb)
-
-        x_mix = rearrange(x, "(b t) c h w -> b c t h w", t=num_video_frames)
-        x = rearrange(x, "(b t) c h w -> b c t h w", t=num_video_frames)
-
-        x = self.time_stack(
-            x, rearrange(emb, "(b t) ... -> b t ...", t=num_video_frames)
-        )
-        x = self.time_mixer(
-            x_spatial=x_mix, x_temporal=x, image_only_indicator=image_only_indicator
-        )
-        x = rearrange(x, "b c t h w -> (b t) c h w")
-        return x
-
-
-class Timestep(nn.Module):
-    def __init__(self, dim):
-        super().__init__()
-        self.dim = dim
-
-    def forward(self, t):
-        return timestep_embedding(t, self.dim)
-
-def apply_control(h, control, name):
-    if control is not None and name in control and len(control[name]) > 0:
-        ctrl = control[name].pop()
-        if ctrl is not None:
-            try:
-                h += ctrl
-            except:
-                logging.warning("warning control could not be applied {} {}".format(h.shape, ctrl.shape))
-    return h
-
-class UNetModel(nn.Module):
-    """
-    The full UNet model with attention and timestep embedding.
-    :param in_channels: channels in the input Tensor.
-    :param model_channels: base channel count for the model.
-    :param out_channels: channels in the output Tensor.
-    :param num_res_blocks: number of residual blocks per downsample.
-    :param dropout: the dropout probability.
-    :param channel_mult: channel multiplier for each level of the UNet.
-    :param conv_resample: if True, use learned convolutions for upsampling and
-        downsampling.
-    :param dims: determines if the signal is 1D, 2D, or 3D.
-    :param num_classes: if specified (as an int), then this model will be
-        class-conditional with `num_classes` classes.
-    :param use_checkpoint: use gradient checkpointing to reduce memory usage.
-    :param num_heads: the number of attention heads in each attention layer.
-    :param num_heads_channels: if specified, ignore num_heads and instead use
-                               a fixed channel width per attention head.
-    :param num_heads_upsample: works with num_heads to set a different number
-                               of heads for upsampling. Deprecated.
-    :param use_scale_shift_norm: use a FiLM-like conditioning mechanism.
-    :param resblock_updown: use residual blocks for up/downsampling.
-    :param use_new_attention_order: use a different attention pattern for potentially
-                                    increased efficiency.
-    """
-
-    def __init__(
-        self,
-        image_size,
-        in_channels,
-        model_channels,
-        out_channels,
-        num_res_blocks,
-        dropout=0,
-        channel_mult=(1, 2, 4, 8),
-        conv_resample=True,
-        dims=2,
-        num_classes=None,
-        use_checkpoint=False,
-        dtype=th.float32,
-        num_heads=-1,
-        num_head_channels=-1,
-        num_heads_upsample=-1,
-        use_scale_shift_norm=False,
-        resblock_updown=False,
-        use_new_attention_order=False,
-        use_spatial_transformer=False,    # custom transformer support
-        transformer_depth=1,              # custom transformer support
-        context_dim=None,                 # custom transformer support
-        n_embed=None,                     # custom support for prediction of discrete ids into codebook of first stage vq model
-        legacy=True,
-        disable_self_attentions=None,
-        num_attention_blocks=None,
-        disable_middle_self_attn=False,
-        use_linear_in_transformer=False,
-        adm_in_channels=None,
-        transformer_depth_middle=None,
-        transformer_depth_output=None,
-        use_temporal_resblock=False,
-        use_temporal_attention=False,
-        time_context_dim=None,
-        extra_ff_mix_layer=False,
-        use_spatial_context=False,
-        merge_strategy=None,
-        merge_factor=0.0,
-        video_kernel_size=None,
-        disable_temporal_crossattention=False,
-        max_ddpm_temb_period=10000,
-        attn_precision=None,
-        device=None,
-        operations=ops,
-    ):
-        super().__init__()
-
-        if context_dim is not None:
-            assert use_spatial_transformer, 'Fool!! You forgot to use the spatial transformer for your cross-attention conditioning...'
-            # from omegaconf.listconfig import ListConfig
-            # if type(context_dim) == ListConfig:
-            #     context_dim = list(context_dim)
-
-        if num_heads_upsample == -1:
-            num_heads_upsample = num_heads
-
-        if num_heads == -1:
-            assert num_head_channels != -1, 'Either num_heads or num_head_channels has to be set'
-
-        if num_head_channels == -1:
-            assert num_heads != -1, 'Either num_heads or num_head_channels has to be set'
-
-        self.in_channels = in_channels
-        self.model_channels = model_channels
-        self.out_channels = out_channels
-
-        if isinstance(num_res_blocks, int):
-            self.num_res_blocks = len(channel_mult) * [num_res_blocks]
-        else:
-            if len(num_res_blocks) != len(channel_mult):
-                raise ValueError("provide num_res_blocks either as an int (globally constant) or "
-                                 "as a list/tuple (per-level) with the same length as channel_mult")
-            self.num_res_blocks = num_res_blocks
-
-        if disable_self_attentions is not None:
-            # should be a list of booleans, indicating whether to disable self-attention in TransformerBlocks or not
-            assert len(disable_self_attentions) == len(channel_mult)
-        if num_attention_blocks is not None:
-            assert len(num_attention_blocks) == len(self.num_res_blocks)
-
-        transformer_depth = transformer_depth[:]
-        transformer_depth_output = transformer_depth_output[:]
-
-        self.dropout = dropout
-        self.channel_mult = channel_mult
-        self.conv_resample = conv_resample
-        self.num_classes = num_classes
-        self.use_checkpoint = use_checkpoint
-        self.dtype = dtype
-        self.num_heads = num_heads
-        self.num_head_channels = num_head_channels
-        self.num_heads_upsample = num_heads_upsample
-        self.use_temporal_resblocks = use_temporal_resblock
-        self.predict_codebook_ids = n_embed is not None
-
-        self.default_num_video_frames = None
-
-        time_embed_dim = model_channels * 4
-        self.time_embed = nn.Sequential(
-            operations.Linear(model_channels, time_embed_dim, dtype=self.dtype, device=device),
-            nn.SiLU(),
-            operations.Linear(time_embed_dim, time_embed_dim, dtype=self.dtype, device=device),
-        )
-
-        if self.num_classes is not None:
-            if isinstance(self.num_classes, int):
-                self.label_emb = nn.Embedding(num_classes, time_embed_dim, dtype=self.dtype, device=device)
-            elif self.num_classes == "continuous":
-                logging.debug("setting up linear c_adm embedding layer")
-                self.label_emb = nn.Linear(1, time_embed_dim)
-            elif self.num_classes == "sequential":
-                assert adm_in_channels is not None
-                self.label_emb = nn.Sequential(
-                    nn.Sequential(
-                        operations.Linear(adm_in_channels, time_embed_dim, dtype=self.dtype, device=device),
-                        nn.SiLU(),
-                        operations.Linear(time_embed_dim, time_embed_dim, dtype=self.dtype, device=device),
-                    )
-                )
-            else:
-                raise ValueError()
-
-        self.input_blocks = nn.ModuleList(
-            [
-                TimestepEmbedSequential(
-                    operations.conv_nd(dims, in_channels, model_channels, 3, padding=1, dtype=self.dtype, device=device)
-                )
-            ]
-        )
-        self._feature_size = model_channels
-        input_block_chans = [model_channels]
-        ch = model_channels
-        ds = 1
-
-        def get_attention_layer(
-            ch,
-            num_heads,
-            dim_head,
-            depth=1,
-            context_dim=None,
-            use_checkpoint=False,
-            disable_self_attn=False,
-        ):
-            if use_temporal_attention:
-                return SpatialVideoTransformer(
-                    ch,
-                    num_heads,
-                    dim_head,
-                    depth=depth,
-                    context_dim=context_dim,
-                    time_context_dim=time_context_dim,
-                    dropout=dropout,
-                    ff_in=extra_ff_mix_layer,
-                    use_spatial_context=use_spatial_context,
-                    merge_strategy=merge_strategy,
-                    merge_factor=merge_factor,
-                    checkpoint=use_checkpoint,
-                    use_linear=use_linear_in_transformer,
-                    disable_self_attn=disable_self_attn,
-                    disable_temporal_crossattention=disable_temporal_crossattention,
-                    max_time_embed_period=max_ddpm_temb_period,
-                    attn_precision=attn_precision,
-                    dtype=self.dtype, device=device, operations=operations
-                )
-            else:
-                return SpatialTransformer(
-                                ch, num_heads, dim_head, depth=depth, context_dim=context_dim,
-                                disable_self_attn=disable_self_attn, use_linear=use_linear_in_transformer,
-                                use_checkpoint=use_checkpoint, attn_precision=attn_precision, dtype=self.dtype, device=device, operations=operations
-                            )
-
-        def get_resblock(
-            merge_factor,
-            merge_strategy,
-            video_kernel_size,
-            ch,
-            time_embed_dim,
-            dropout,
-            out_channels,
-            dims,
-            use_checkpoint,
-            use_scale_shift_norm,
-            down=False,
-            up=False,
-            dtype=None,
-            device=None,
-            operations=ops
-        ):
-            if self.use_temporal_resblocks:
-                return VideoResBlock(
-                    merge_factor=merge_factor,
-                    merge_strategy=merge_strategy,
-                    video_kernel_size=video_kernel_size,
-                    channels=ch,
-                    emb_channels=time_embed_dim,
-                    dropout=dropout,
-                    out_channels=out_channels,
-                    dims=dims,
-                    use_checkpoint=use_checkpoint,
-                    use_scale_shift_norm=use_scale_shift_norm,
-                    down=down,
-                    up=up,
-                    dtype=dtype,
-                    device=device,
-                    operations=operations
-                )
-            else:
-                return ResBlock(
-                    channels=ch,
-                    emb_channels=time_embed_dim,
-                    dropout=dropout,
-                    out_channels=out_channels,
-                    use_checkpoint=use_checkpoint,
-                    dims=dims,
-                    use_scale_shift_norm=use_scale_shift_norm,
-                    down=down,
-                    up=up,
-                    dtype=dtype,
-                    device=device,
-                    operations=operations
-                )
-
-        for level, mult in enumerate(channel_mult):
-            for nr in range(self.num_res_blocks[level]):
-                layers = [
-                    get_resblock(
-                        merge_factor=merge_factor,
-                        merge_strategy=merge_strategy,
-                        video_kernel_size=video_kernel_size,
-                        ch=ch,
-                        time_embed_dim=time_embed_dim,
-                        dropout=dropout,
-                        out_channels=mult * model_channels,
-                        dims=dims,
-                        use_checkpoint=use_checkpoint,
-                        use_scale_shift_norm=use_scale_shift_norm,
-                        dtype=self.dtype,
-                        device=device,
-                        operations=operations,
-                    )
-                ]
-                ch = mult * model_channels
-                num_transformers = transformer_depth.pop(0)
-                if num_transformers > 0:
-                    if num_head_channels == -1:
-                        dim_head = ch // num_heads
-                    else:
-                        num_heads = ch // num_head_channels
-                        dim_head = num_head_channels
-                    if legacy:
-                        #num_heads = 1
-                        dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
-                    if exists(disable_self_attentions):
-                        disabled_sa = disable_self_attentions[level]
-                    else:
-                        disabled_sa = False
-
-                    if not exists(num_attention_blocks) or nr < num_attention_blocks[level]:
-                        layers.append(get_attention_layer(
-                                ch, num_heads, dim_head, depth=num_transformers, context_dim=context_dim,
-                                disable_self_attn=disabled_sa, use_checkpoint=use_checkpoint)
-                        )
-                self.input_blocks.append(TimestepEmbedSequential(*layers))
-                self._feature_size += ch
-                input_block_chans.append(ch)
-            if level != len(channel_mult) - 1:
-                out_ch = ch
-                self.input_blocks.append(
-                    TimestepEmbedSequential(
-                        get_resblock(
-                            merge_factor=merge_factor,
-                            merge_strategy=merge_strategy,
-                            video_kernel_size=video_kernel_size,
-                            ch=ch,
-                            time_embed_dim=time_embed_dim,
-                            dropout=dropout,
-                            out_channels=out_ch,
-                            dims=dims,
-                            use_checkpoint=use_checkpoint,
-                            use_scale_shift_norm=use_scale_shift_norm,
-                            down=True,
-                            dtype=self.dtype,
-                            device=device,
-                            operations=operations
-                        )
-                        if resblock_updown
-                        else Downsample(
-                            ch, conv_resample, dims=dims, out_channels=out_ch, dtype=self.dtype, device=device, operations=operations
-                        )
-                    )
-                )
-                ch = out_ch
-                input_block_chans.append(ch)
-                ds *= 2
-                self._feature_size += ch
-
-        if num_head_channels == -1:
-            dim_head = ch // num_heads
-        else:
-            num_heads = ch // num_head_channels
-            dim_head = num_head_channels
-        if legacy:
-            #num_heads = 1
-            dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
-        mid_block = [
-            get_resblock(
-                merge_factor=merge_factor,
-                merge_strategy=merge_strategy,
-                video_kernel_size=video_kernel_size,
-                ch=ch,
-                time_embed_dim=time_embed_dim,
-                dropout=dropout,
-                out_channels=None,
-                dims=dims,
-                use_checkpoint=use_checkpoint,
-                use_scale_shift_norm=use_scale_shift_norm,
-                dtype=self.dtype,
-                device=device,
-                operations=operations
-            )]
-
-        self.middle_block = None
-        if transformer_depth_middle >= -1:
-            if transformer_depth_middle >= 0:
-                mid_block += [get_attention_layer(  # always uses a self-attn
-                                ch, num_heads, dim_head, depth=transformer_depth_middle, context_dim=context_dim,
-                                disable_self_attn=disable_middle_self_attn, use_checkpoint=use_checkpoint
-                            ),
-                get_resblock(
-                    merge_factor=merge_factor,
-                    merge_strategy=merge_strategy,
-                    video_kernel_size=video_kernel_size,
-                    ch=ch,
-                    time_embed_dim=time_embed_dim,
-                    dropout=dropout,
-                    out_channels=None,
-                    dims=dims,
-                    use_checkpoint=use_checkpoint,
-                    use_scale_shift_norm=use_scale_shift_norm,
-                    dtype=self.dtype,
-                    device=device,
-                    operations=operations
-                )]
-            self.middle_block = TimestepEmbedSequential(*mid_block)
-        self._feature_size += ch
-
-        self.output_blocks = nn.ModuleList([])
-        for level, mult in list(enumerate(channel_mult))[::-1]:
-            for i in range(self.num_res_blocks[level] + 1):
-                ich = input_block_chans.pop()
-                layers = [
-                    get_resblock(
-                        merge_factor=merge_factor,
-                        merge_strategy=merge_strategy,
-                        video_kernel_size=video_kernel_size,
-                        ch=ch + ich,
-                        time_embed_dim=time_embed_dim,
-                        dropout=dropout,
-                        out_channels=model_channels * mult,
-                        dims=dims,
-                        use_checkpoint=use_checkpoint,
-                        use_scale_shift_norm=use_scale_shift_norm,
-                        dtype=self.dtype,
-                        device=device,
-                        operations=operations
-                    )
-                ]
-                ch = model_channels * mult
-                num_transformers = transformer_depth_output.pop()
-                if num_transformers > 0:
-                    if num_head_channels == -1:
-                        dim_head = ch // num_heads
-                    else:
-                        num_heads = ch // num_head_channels
-                        dim_head = num_head_channels
-                    if legacy:
-                        #num_heads = 1
-                        dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
-                    if exists(disable_self_attentions):
-                        disabled_sa = disable_self_attentions[level]
-                    else:
-                        disabled_sa = False
-
-                    if not exists(num_attention_blocks) or i < num_attention_blocks[level]:
-                        layers.append(
-                            get_attention_layer(
-                                ch, num_heads, dim_head, depth=num_transformers, context_dim=context_dim,
-                                disable_self_attn=disabled_sa, use_checkpoint=use_checkpoint
-                            )
-                        )
-                if level and i == self.num_res_blocks[level]:
-                    out_ch = ch
-                    layers.append(
-                        get_resblock(
-                            merge_factor=merge_factor,
-                            merge_strategy=merge_strategy,
-                            video_kernel_size=video_kernel_size,
-                            ch=ch,
-                            time_embed_dim=time_embed_dim,
-                            dropout=dropout,
-                            out_channels=out_ch,
-                            dims=dims,
-                            use_checkpoint=use_checkpoint,
-                            use_scale_shift_norm=use_scale_shift_norm,
-                            up=True,
-                            dtype=self.dtype,
-                            device=device,
-                            operations=operations
-                        )
-                        if resblock_updown
-                        else Upsample(ch, conv_resample, dims=dims, out_channels=out_ch, dtype=self.dtype, device=device, operations=operations)
-                    )
-                    ds //= 2
-                self.output_blocks.append(TimestepEmbedSequential(*layers))
-                self._feature_size += ch
-
-        self.out = nn.Sequential(
-            operations.GroupNorm(32, ch, dtype=self.dtype, device=device),
-            nn.SiLU(),
-            zero_module(operations.conv_nd(dims, model_channels, out_channels, 3, padding=1, dtype=self.dtype, device=device)),
-        )
-        if self.predict_codebook_ids:
-            self.id_predictor = nn.Sequential(
-            operations.GroupNorm(32, ch, dtype=self.dtype, device=device),
-            operations.conv_nd(dims, model_channels, n_embed, 1, dtype=self.dtype, device=device),
-            #nn.LogSoftmax(dim=1)  # change to cross_entropy and produce non-normalized logits
-        )
-
-    def forward(self, x, timesteps=None, context=None, y=None, control=None, transformer_options={}, **kwargs):
-        """
-        Apply the model to an input batch.
-        :param x: an [N x C x ...] Tensor of inputs.
-        :param timesteps: a 1-D batch of timesteps.
-        :param context: conditioning plugged in via crossattn
-        :param y: an [N] Tensor of labels, if class-conditional.
-        :return: an [N x C x ...] Tensor of outputs.
-        """
-        transformer_options["original_shape"] = list(x.shape)
-        transformer_options["transformer_index"] = 0
-        transformer_patches = transformer_options.get("patches", {})
-
-        num_video_frames = kwargs.get("num_video_frames", self.default_num_video_frames)
-        image_only_indicator = kwargs.get("image_only_indicator", None)
-        time_context = kwargs.get("time_context", None)
-
-        assert (y is not None) == (
-            self.num_classes is not None
-        ), "must specify y if and only if the model is class-conditional"
-        hs = []
-        t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False).to(x.dtype)
-        emb = self.time_embed(t_emb)
-
-        if self.num_classes is not None:
-            assert y.shape[0] == x.shape[0]
-            emb = emb + self.label_emb(y)
-
-        h = x
-        for id, module in enumerate(self.input_blocks):
-            transformer_options["block"] = ("input", id)
-            h = forward_timestep_embed(module, h, emb, context, transformer_options, time_context=time_context, num_video_frames=num_video_frames, image_only_indicator=image_only_indicator)
-            h = apply_control(h, control, 'input')
-            if "input_block_patch" in transformer_patches:
-                patch = transformer_patches["input_block_patch"]
-                for p in patch:
-                    h = p(h, transformer_options)
-
-            hs.append(h)
-            if "input_block_patch_after_skip" in transformer_patches:
-                patch = transformer_patches["input_block_patch_after_skip"]
-                for p in patch:
-                    h = p(h, transformer_options)
-
-        transformer_options["block"] = ("middle", 0)
-        if self.middle_block is not None:
-            h = forward_timestep_embed(self.middle_block, h, emb, context, transformer_options, time_context=time_context, num_video_frames=num_video_frames, image_only_indicator=image_only_indicator)
-        h = apply_control(h, control, 'middle')
-
-
-        for id, module in enumerate(self.output_blocks):
-            transformer_options["block"] = ("output", id)
-            hsp = hs.pop()
-            hsp = apply_control(hsp, control, 'output')
-
-            if "output_block_patch" in transformer_patches:
-                patch = transformer_patches["output_block_patch"]
-                for p in patch:
-                    h, hsp = p(h, hsp, transformer_options)
-
-            h = th.cat([h, hsp], dim=1)
-            del hsp
-            if len(hs) > 0:
-                output_shape = hs[-1].shape
-            else:
-                output_shape = None
-            h = forward_timestep_embed(module, h, emb, context, transformer_options, output_shape, time_context=time_context, num_video_frames=num_video_frames, image_only_indicator=image_only_indicator)
-        h = h.type(x.dtype)
-        if self.predict_codebook_ids:
-            return self.id_predictor(h)
-        else:
-            return self.out(h)
diff --git a/MagicQuill/comfy/ldm/modules/diffusionmodules/upscaling.py b/MagicQuill/comfy/ldm/modules/diffusionmodules/upscaling.py
deleted file mode 100644
index f5ac7c2f9138d6d34cda735d2201225d46831154..0000000000000000000000000000000000000000
--- a/MagicQuill/comfy/ldm/modules/diffusionmodules/upscaling.py
+++ /dev/null
@@ -1,85 +0,0 @@
-import torch
-import torch.nn as nn
-import numpy as np
-from functools import partial
-
-from .util import extract_into_tensor, make_beta_schedule
-from comfy.ldm.util import default
-
-
-class AbstractLowScaleModel(nn.Module):
-    # for concatenating a downsampled image to the latent representation
-    def __init__(self, noise_schedule_config=None):
-        super(AbstractLowScaleModel, self).__init__()
-        if noise_schedule_config is not None:
-            self.register_schedule(**noise_schedule_config)
-
-    def register_schedule(self, beta_schedule="linear", timesteps=1000,
-                          linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
-        betas = make_beta_schedule(beta_schedule, timesteps, linear_start=linear_start, linear_end=linear_end,
-                                   cosine_s=cosine_s)
-        alphas = 1. - betas
-        alphas_cumprod = np.cumprod(alphas, axis=0)
-        alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1])
-
-        timesteps, = betas.shape
-        self.num_timesteps = int(timesteps)
-        self.linear_start = linear_start
-        self.linear_end = linear_end
-        assert alphas_cumprod.shape[0] == self.num_timesteps, 'alphas have to be defined for each timestep'
-
-        to_torch = partial(torch.tensor, dtype=torch.float32)
-
-        self.register_buffer('betas', to_torch(betas))
-        self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))
-        self.register_buffer('alphas_cumprod_prev', to_torch(alphas_cumprod_prev))
-
-        # calculations for diffusion q(x_t | x_{t-1}) and others
-        self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod)))
-        self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod)))
-        self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod)))
-        self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod)))
-        self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod - 1)))
-
-    def q_sample(self, x_start, t, noise=None, seed=None):
-        if noise is None:
-            if seed is None:
-                noise = torch.randn_like(x_start)
-            else:
-                noise = torch.randn(x_start.size(), dtype=x_start.dtype, layout=x_start.layout, generator=torch.manual_seed(seed)).to(x_start.device)
-        return (extract_into_tensor(self.sqrt_alphas_cumprod.to(x_start.device), t, x_start.shape) * x_start +
-                extract_into_tensor(self.sqrt_one_minus_alphas_cumprod.to(x_start.device), t, x_start.shape) * noise)
-
-    def forward(self, x):
-        return x, None
-
-    def decode(self, x):
-        return x
-
-
-class SimpleImageConcat(AbstractLowScaleModel):
-    # no noise level conditioning
-    def __init__(self):
-        super(SimpleImageConcat, self).__init__(noise_schedule_config=None)
-        self.max_noise_level = 0
-
-    def forward(self, x):
-        # fix to constant noise level
-        return x, torch.zeros(x.shape[0], device=x.device).long()
-
-
-class ImageConcatWithNoiseAugmentation(AbstractLowScaleModel):
-    def __init__(self, noise_schedule_config, max_noise_level=1000, to_cuda=False):
-        super().__init__(noise_schedule_config=noise_schedule_config)
-        self.max_noise_level = max_noise_level
-
-    def forward(self, x, noise_level=None, seed=None):
-        if noise_level is None:
-            noise_level = torch.randint(0, self.max_noise_level, (x.shape[0],), device=x.device).long()
-        else:
-            assert isinstance(noise_level, torch.Tensor)
-        z = self.q_sample(x, noise_level, seed=seed)
-        return z, noise_level
-
-
-
diff --git a/MagicQuill/comfy/ldm/modules/diffusionmodules/util.py b/MagicQuill/comfy/ldm/modules/diffusionmodules/util.py
deleted file mode 100644
index ce14ad5e18cf1c8f821878f395cc1bab50fad476..0000000000000000000000000000000000000000
--- a/MagicQuill/comfy/ldm/modules/diffusionmodules/util.py
+++ /dev/null
@@ -1,306 +0,0 @@
-# adopted from
-# https://github.com/openai/improved-diffusion/blob/main/improved_diffusion/gaussian_diffusion.py
-# and
-# https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py
-# and
-# https://github.com/openai/guided-diffusion/blob/0ba878e517b276c45d1195eb29f6f5f72659a05b/guided_diffusion/nn.py
-#
-# thanks!
-
-
-import os
-import math
-import torch
-import torch.nn as nn
-import numpy as np
-from einops import repeat, rearrange
-
-from comfy.ldm.util import instantiate_from_config
-
-class AlphaBlender(nn.Module):
-    strategies = ["learned", "fixed", "learned_with_images"]
-
-    def __init__(
-        self,
-        alpha: float,
-        merge_strategy: str = "learned_with_images",
-        rearrange_pattern: str = "b t -> (b t) 1 1",
-    ):
-        super().__init__()
-        self.merge_strategy = merge_strategy
-        self.rearrange_pattern = rearrange_pattern
-
-        assert (
-            merge_strategy in self.strategies
-        ), f"merge_strategy needs to be in {self.strategies}"
-
-        if self.merge_strategy == "fixed":
-            self.register_buffer("mix_factor", torch.Tensor([alpha]))
-        elif (
-            self.merge_strategy == "learned"
-            or self.merge_strategy == "learned_with_images"
-        ):
-            self.register_parameter(
-                "mix_factor", torch.nn.Parameter(torch.Tensor([alpha]))
-            )
-        else:
-            raise ValueError(f"unknown merge strategy {self.merge_strategy}")
-
-    def get_alpha(self, image_only_indicator: torch.Tensor, device) -> torch.Tensor:
-        # skip_time_mix = rearrange(repeat(skip_time_mix, 'b -> (b t) () () ()', t=t), '(b t) 1 ... -> b 1 t ...', t=t)
-        if self.merge_strategy == "fixed":
-            # make shape compatible
-            # alpha = repeat(self.mix_factor, '1 -> b () t  () ()', t=t, b=bs)
-            alpha = self.mix_factor.to(device)
-        elif self.merge_strategy == "learned":
-            alpha = torch.sigmoid(self.mix_factor.to(device))
-            # make shape compatible
-            # alpha = repeat(alpha, '1 -> s () ()', s = t * bs)
-        elif self.merge_strategy == "learned_with_images":
-            if image_only_indicator is None:
-                alpha = rearrange(torch.sigmoid(self.mix_factor.to(device)), "... -> ... 1")
-            else:
-                alpha = torch.where(
-                    image_only_indicator.bool(),
-                    torch.ones(1, 1, device=image_only_indicator.device),
-                    rearrange(torch.sigmoid(self.mix_factor.to(image_only_indicator.device)), "... -> ... 1"),
-                )
-            alpha = rearrange(alpha, self.rearrange_pattern)
-            # make shape compatible
-            # alpha = repeat(alpha, '1 -> s () ()', s = t * bs)
-        else:
-            raise NotImplementedError()
-        return alpha
-
-    def forward(
-        self,
-        x_spatial,
-        x_temporal,
-        image_only_indicator=None,
-    ) -> torch.Tensor:
-        alpha = self.get_alpha(image_only_indicator, x_spatial.device)
-        x = (
-            alpha.to(x_spatial.dtype) * x_spatial
-            + (1.0 - alpha).to(x_spatial.dtype) * x_temporal
-        )
-        return x
-
-
-def make_beta_schedule(schedule, n_timestep, linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
-    if schedule == "linear":
-        betas = (
-                torch.linspace(linear_start ** 0.5, linear_end ** 0.5, n_timestep, dtype=torch.float64) ** 2
-        )
-
-    elif schedule == "cosine":
-        timesteps = (
-                torch.arange(n_timestep + 1, dtype=torch.float64) / n_timestep + cosine_s
-        )
-        alphas = timesteps / (1 + cosine_s) * np.pi / 2
-        alphas = torch.cos(alphas).pow(2)
-        alphas = alphas / alphas[0]
-        betas = 1 - alphas[1:] / alphas[:-1]
-        betas = torch.clamp(betas, min=0, max=0.999)
-
-    elif schedule == "squaredcos_cap_v2":  # used for karlo prior
-        # return early
-        return betas_for_alpha_bar(
-            n_timestep,
-            lambda t: math.cos((t + 0.008) / 1.008 * math.pi / 2) ** 2,
-        )
-
-    elif schedule == "sqrt_linear":
-        betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64)
-    elif schedule == "sqrt":
-        betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64) ** 0.5
-    else:
-        raise ValueError(f"schedule '{schedule}' unknown.")
-    return betas
-
-
-def make_ddim_timesteps(ddim_discr_method, num_ddim_timesteps, num_ddpm_timesteps, verbose=True):
-    if ddim_discr_method == 'uniform':
-        c = num_ddpm_timesteps // num_ddim_timesteps
-        ddim_timesteps = np.asarray(list(range(0, num_ddpm_timesteps, c)))
-    elif ddim_discr_method == 'quad':
-        ddim_timesteps = ((np.linspace(0, np.sqrt(num_ddpm_timesteps * .8), num_ddim_timesteps)) ** 2).astype(int)
-    else:
-        raise NotImplementedError(f'There is no ddim discretization method called "{ddim_discr_method}"')
-
-    # assert ddim_timesteps.shape[0] == num_ddim_timesteps
-    # add one to get the final alpha values right (the ones from first scale to data during sampling)
-    steps_out = ddim_timesteps + 1
-    if verbose:
-        print(f'Selected timesteps for ddim sampler: {steps_out}')
-    return steps_out
-
-
-def make_ddim_sampling_parameters(alphacums, ddim_timesteps, eta, verbose=True):
-    # select alphas for computing the variance schedule
-    alphas = alphacums[ddim_timesteps]
-    alphas_prev = np.asarray([alphacums[0]] + alphacums[ddim_timesteps[:-1]].tolist())
-
-    # according the the formula provided in https://arxiv.org/abs/2010.02502
-    sigmas = eta * np.sqrt((1 - alphas_prev) / (1 - alphas) * (1 - alphas / alphas_prev))
-    if verbose:
-        print(f'Selected alphas for ddim sampler: a_t: {alphas}; a_(t-1): {alphas_prev}')
-        print(f'For the chosen value of eta, which is {eta}, '
-              f'this results in the following sigma_t schedule for ddim sampler {sigmas}')
-    return sigmas, alphas, alphas_prev
-
-
-def betas_for_alpha_bar(num_diffusion_timesteps, alpha_bar, max_beta=0.999):
-    """
-    Create a beta schedule that discretizes the given alpha_t_bar function,
-    which defines the cumulative product of (1-beta) over time from t = [0,1].
-    :param num_diffusion_timesteps: the number of betas to produce.
-    :param alpha_bar: a lambda that takes an argument t from 0 to 1 and
-                      produces the cumulative product of (1-beta) up to that
-                      part of the diffusion process.
-    :param max_beta: the maximum beta to use; use values lower than 1 to
-                     prevent singularities.
-    """
-    betas = []
-    for i in range(num_diffusion_timesteps):
-        t1 = i / num_diffusion_timesteps
-        t2 = (i + 1) / num_diffusion_timesteps
-        betas.append(min(1 - alpha_bar(t2) / alpha_bar(t1), max_beta))
-    return np.array(betas)
-
-
-def extract_into_tensor(a, t, x_shape):
-    b, *_ = t.shape
-    out = a.gather(-1, t)
-    return out.reshape(b, *((1,) * (len(x_shape) - 1)))
-
-
-def checkpoint(func, inputs, params, flag):
-    """
-    Evaluate a function without caching intermediate activations, allowing for
-    reduced memory at the expense of extra compute in the backward pass.
-    :param func: the function to evaluate.
-    :param inputs: the argument sequence to pass to `func`.
-    :param params: a sequence of parameters `func` depends on but does not
-                   explicitly take as arguments.
-    :param flag: if False, disable gradient checkpointing.
-    """
-    if flag:
-        args = tuple(inputs) + tuple(params)
-        return CheckpointFunction.apply(func, len(inputs), *args)
-    else:
-        return func(*inputs)
-
-
-class CheckpointFunction(torch.autograd.Function):
-    @staticmethod
-    def forward(ctx, run_function, length, *args):
-        ctx.run_function = run_function
-        ctx.input_tensors = list(args[:length])
-        ctx.input_params = list(args[length:])
-        ctx.gpu_autocast_kwargs = {"enabled": torch.is_autocast_enabled(),
-                                   "dtype": torch.get_autocast_gpu_dtype(),
-                                   "cache_enabled": torch.is_autocast_cache_enabled()}
-        with torch.no_grad():
-            output_tensors = ctx.run_function(*ctx.input_tensors)
-        return output_tensors
-
-    @staticmethod
-    def backward(ctx, *output_grads):
-        ctx.input_tensors = [x.detach().requires_grad_(True) for x in ctx.input_tensors]
-        with torch.enable_grad(), \
-                torch.cuda.amp.autocast(**ctx.gpu_autocast_kwargs):
-            # Fixes a bug where the first op in run_function modifies the
-            # Tensor storage in place, which is not allowed for detach()'d
-            # Tensors.
-            shallow_copies = [x.view_as(x) for x in ctx.input_tensors]
-            output_tensors = ctx.run_function(*shallow_copies)
-        input_grads = torch.autograd.grad(
-            output_tensors,
-            ctx.input_tensors + ctx.input_params,
-            output_grads,
-            allow_unused=True,
-        )
-        del ctx.input_tensors
-        del ctx.input_params
-        del output_tensors
-        return (None, None) + input_grads
-
-
-def timestep_embedding(timesteps, dim, max_period=10000, repeat_only=False):
-    """
-    Create sinusoidal timestep embeddings.
-    :param timesteps: a 1-D Tensor of N indices, one per batch element.
-                      These may be fractional.
-    :param dim: the dimension of the output.
-    :param max_period: controls the minimum frequency of the embeddings.
-    :return: an [N x dim] Tensor of positional embeddings.
-    """
-    if not repeat_only:
-        half = dim // 2
-        freqs = torch.exp(
-            -math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32, device=timesteps.device) / half
-        )
-        args = timesteps[:, None].float() * freqs[None]
-        embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
-        if dim % 2:
-            embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
-    else:
-        embedding = repeat(timesteps, 'b -> b d', d=dim)
-    return embedding
-
-
-def zero_module(module):
-    """
-    Zero out the parameters of a module and return it.
-    """
-    for p in module.parameters():
-        p.detach().zero_()
-    return module
-
-
-def scale_module(module, scale):
-    """
-    Scale the parameters of a module and return it.
-    """
-    for p in module.parameters():
-        p.detach().mul_(scale)
-    return module
-
-
-def mean_flat(tensor):
-    """
-    Take the mean over all non-batch dimensions.
-    """
-    return tensor.mean(dim=list(range(1, len(tensor.shape))))
-
-
-def avg_pool_nd(dims, *args, **kwargs):
-    """
-    Create a 1D, 2D, or 3D average pooling module.
-    """
-    if dims == 1:
-        return nn.AvgPool1d(*args, **kwargs)
-    elif dims == 2:
-        return nn.AvgPool2d(*args, **kwargs)
-    elif dims == 3:
-        return nn.AvgPool3d(*args, **kwargs)
-    raise ValueError(f"unsupported dimensions: {dims}")
-
-
-class HybridConditioner(nn.Module):
-
-    def __init__(self, c_concat_config, c_crossattn_config):
-        super().__init__()
-        self.concat_conditioner = instantiate_from_config(c_concat_config)
-        self.crossattn_conditioner = instantiate_from_config(c_crossattn_config)
-
-    def forward(self, c_concat, c_crossattn):
-        c_concat = self.concat_conditioner(c_concat)
-        c_crossattn = self.crossattn_conditioner(c_crossattn)
-        return {'c_concat': [c_concat], 'c_crossattn': [c_crossattn]}
-
-
-def noise_like(shape, device, repeat=False):
-    repeat_noise = lambda: torch.randn((1, *shape[1:]), device=device).repeat(shape[0], *((1,) * (len(shape) - 1)))
-    noise = lambda: torch.randn(shape, device=device)
-    return repeat_noise() if repeat else noise()
diff --git a/MagicQuill/comfy/ldm/modules/distributions/__init__.py b/MagicQuill/comfy/ldm/modules/distributions/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/MagicQuill/comfy/ldm/modules/distributions/__pycache__/__init__.cpython-310.pyc b/MagicQuill/comfy/ldm/modules/distributions/__pycache__/__init__.cpython-310.pyc
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diff --git a/MagicQuill/comfy/ldm/modules/distributions/__pycache__/distributions.cpython-310.pyc b/MagicQuill/comfy/ldm/modules/distributions/__pycache__/distributions.cpython-310.pyc
deleted file mode 100644
index 1ec2f27d8b30129ef1b7ff9967ee0ecfcfa4ac82..0000000000000000000000000000000000000000
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diff --git a/MagicQuill/comfy/ldm/modules/distributions/distributions.py b/MagicQuill/comfy/ldm/modules/distributions/distributions.py
deleted file mode 100644
index f2b8ef901130efc171aa69742ca0244d94d3f2e9..0000000000000000000000000000000000000000
--- a/MagicQuill/comfy/ldm/modules/distributions/distributions.py
+++ /dev/null
@@ -1,92 +0,0 @@
-import torch
-import numpy as np
-
-
-class AbstractDistribution:
-    def sample(self):
-        raise NotImplementedError()
-
-    def mode(self):
-        raise NotImplementedError()
-
-
-class DiracDistribution(AbstractDistribution):
-    def __init__(self, value):
-        self.value = value
-
-    def sample(self):
-        return self.value
-
-    def mode(self):
-        return self.value
-
-
-class DiagonalGaussianDistribution(object):
-    def __init__(self, parameters, deterministic=False):
-        self.parameters = parameters
-        self.mean, self.logvar = torch.chunk(parameters, 2, dim=1)
-        self.logvar = torch.clamp(self.logvar, -30.0, 20.0)
-        self.deterministic = deterministic
-        self.std = torch.exp(0.5 * self.logvar)
-        self.var = torch.exp(self.logvar)
-        if self.deterministic:
-            self.var = self.std = torch.zeros_like(self.mean).to(device=self.parameters.device)
-
-    def sample(self):
-        x = self.mean + self.std * torch.randn(self.mean.shape).to(device=self.parameters.device)
-        return x
-
-    def kl(self, other=None):
-        if self.deterministic:
-            return torch.Tensor([0.])
-        else:
-            if other is None:
-                return 0.5 * torch.sum(torch.pow(self.mean, 2)
-                                       + self.var - 1.0 - self.logvar,
-                                       dim=[1, 2, 3])
-            else:
-                return 0.5 * torch.sum(
-                    torch.pow(self.mean - other.mean, 2) / other.var
-                    + self.var / other.var - 1.0 - self.logvar + other.logvar,
-                    dim=[1, 2, 3])
-
-    def nll(self, sample, dims=[1,2,3]):
-        if self.deterministic:
-            return torch.Tensor([0.])
-        logtwopi = np.log(2.0 * np.pi)
-        return 0.5 * torch.sum(
-            logtwopi + self.logvar + torch.pow(sample - self.mean, 2) / self.var,
-            dim=dims)
-
-    def mode(self):
-        return self.mean
-
-
-def normal_kl(mean1, logvar1, mean2, logvar2):
-    """
-    source: https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/losses.py#L12
-    Compute the KL divergence between two gaussians.
-    Shapes are automatically broadcasted, so batches can be compared to
-    scalars, among other use cases.
-    """
-    tensor = None
-    for obj in (mean1, logvar1, mean2, logvar2):
-        if isinstance(obj, torch.Tensor):
-            tensor = obj
-            break
-    assert tensor is not None, "at least one argument must be a Tensor"
-
-    # Force variances to be Tensors. Broadcasting helps convert scalars to
-    # Tensors, but it does not work for torch.exp().
-    logvar1, logvar2 = [
-        x if isinstance(x, torch.Tensor) else torch.tensor(x).to(tensor)
-        for x in (logvar1, logvar2)
-    ]
-
-    return 0.5 * (
-        -1.0
-        + logvar2
-        - logvar1
-        + torch.exp(logvar1 - logvar2)
-        + ((mean1 - mean2) ** 2) * torch.exp(-logvar2)
-    )
diff --git a/MagicQuill/comfy/ldm/modules/ema.py b/MagicQuill/comfy/ldm/modules/ema.py
deleted file mode 100644
index bded25019b9bcbcd0260f0b8185f8c7859ca58c4..0000000000000000000000000000000000000000
--- a/MagicQuill/comfy/ldm/modules/ema.py
+++ /dev/null
@@ -1,80 +0,0 @@
-import torch
-from torch import nn
-
-
-class LitEma(nn.Module):
-    def __init__(self, model, decay=0.9999, use_num_upates=True):
-        super().__init__()
-        if decay < 0.0 or decay > 1.0:
-            raise ValueError('Decay must be between 0 and 1')
-
-        self.m_name2s_name = {}
-        self.register_buffer('decay', torch.tensor(decay, dtype=torch.float32))
-        self.register_buffer('num_updates', torch.tensor(0, dtype=torch.int) if use_num_upates
-        else torch.tensor(-1, dtype=torch.int))
-
-        for name, p in model.named_parameters():
-            if p.requires_grad:
-                # remove as '.'-character is not allowed in buffers
-                s_name = name.replace('.', '')
-                self.m_name2s_name.update({name: s_name})
-                self.register_buffer(s_name, p.clone().detach().data)
-
-        self.collected_params = []
-
-    def reset_num_updates(self):
-        del self.num_updates
-        self.register_buffer('num_updates', torch.tensor(0, dtype=torch.int))
-
-    def forward(self, model):
-        decay = self.decay
-
-        if self.num_updates >= 0:
-            self.num_updates += 1
-            decay = min(self.decay, (1 + self.num_updates) / (10 + self.num_updates))
-
-        one_minus_decay = 1.0 - decay
-
-        with torch.no_grad():
-            m_param = dict(model.named_parameters())
-            shadow_params = dict(self.named_buffers())
-
-            for key in m_param:
-                if m_param[key].requires_grad:
-                    sname = self.m_name2s_name[key]
-                    shadow_params[sname] = shadow_params[sname].type_as(m_param[key])
-                    shadow_params[sname].sub_(one_minus_decay * (shadow_params[sname] - m_param[key]))
-                else:
-                    assert not key in self.m_name2s_name
-
-    def copy_to(self, model):
-        m_param = dict(model.named_parameters())
-        shadow_params = dict(self.named_buffers())
-        for key in m_param:
-            if m_param[key].requires_grad:
-                m_param[key].data.copy_(shadow_params[self.m_name2s_name[key]].data)
-            else:
-                assert not key in self.m_name2s_name
-
-    def store(self, parameters):
-        """
-        Save the current parameters for restoring later.
-        Args:
-          parameters: Iterable of `torch.nn.Parameter`; the parameters to be
-            temporarily stored.
-        """
-        self.collected_params = [param.clone() for param in parameters]
-
-    def restore(self, parameters):
-        """
-        Restore the parameters stored with the `store` method.
-        Useful to validate the model with EMA parameters without affecting the
-        original optimization process. Store the parameters before the
-        `copy_to` method. After validation (or model saving), use this to
-        restore the former parameters.
-        Args:
-          parameters: Iterable of `torch.nn.Parameter`; the parameters to be
-            updated with the stored parameters.
-        """
-        for c_param, param in zip(self.collected_params, parameters):
-            param.data.copy_(c_param.data)
diff --git a/MagicQuill/comfy/ldm/modules/encoders/__init__.py b/MagicQuill/comfy/ldm/modules/encoders/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/MagicQuill/comfy/ldm/modules/encoders/__pycache__/__init__.cpython-310.pyc b/MagicQuill/comfy/ldm/modules/encoders/__pycache__/__init__.cpython-310.pyc
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index a566ac9a9493be54bc741558ef231673d52b1e0c..0000000000000000000000000000000000000000
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diff --git a/MagicQuill/comfy/ldm/modules/encoders/__pycache__/noise_aug_modules.cpython-310.pyc b/MagicQuill/comfy/ldm/modules/encoders/__pycache__/noise_aug_modules.cpython-310.pyc
deleted file mode 100644
index 48a940aead8f17d74a707a5b918592276ae4dbb9..0000000000000000000000000000000000000000
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diff --git a/MagicQuill/comfy/ldm/modules/encoders/noise_aug_modules.py b/MagicQuill/comfy/ldm/modules/encoders/noise_aug_modules.py
deleted file mode 100644
index a5d8660301636fde75808cba50afa539cf1162e0..0000000000000000000000000000000000000000
--- a/MagicQuill/comfy/ldm/modules/encoders/noise_aug_modules.py
+++ /dev/null
@@ -1,35 +0,0 @@
-from ..diffusionmodules.upscaling import ImageConcatWithNoiseAugmentation
-from ..diffusionmodules.openaimodel import Timestep
-import torch
-
-class CLIPEmbeddingNoiseAugmentation(ImageConcatWithNoiseAugmentation):
-    def __init__(self, *args, clip_stats_path=None, timestep_dim=256, **kwargs):
-        super().__init__(*args, **kwargs)
-        if clip_stats_path is None:
-            clip_mean, clip_std = torch.zeros(timestep_dim), torch.ones(timestep_dim)
-        else:
-            clip_mean, clip_std = torch.load(clip_stats_path, map_location="cpu")
-        self.register_buffer("data_mean", clip_mean[None, :], persistent=False)
-        self.register_buffer("data_std", clip_std[None, :], persistent=False)
-        self.time_embed = Timestep(timestep_dim)
-
-    def scale(self, x):
-        # re-normalize to centered mean and unit variance
-        x = (x - self.data_mean.to(x.device)) * 1. / self.data_std.to(x.device)
-        return x
-
-    def unscale(self, x):
-        # back to original data stats
-        x = (x * self.data_std.to(x.device)) + self.data_mean.to(x.device)
-        return x
-
-    def forward(self, x, noise_level=None, seed=None):
-        if noise_level is None:
-            noise_level = torch.randint(0, self.max_noise_level, (x.shape[0],), device=x.device).long()
-        else:
-            assert isinstance(noise_level, torch.Tensor)
-        x = self.scale(x)
-        z = self.q_sample(x, noise_level, seed=seed)
-        z = self.unscale(z)
-        noise_level = self.time_embed(noise_level)
-        return z, noise_level
diff --git a/MagicQuill/comfy/ldm/modules/sub_quadratic_attention.py b/MagicQuill/comfy/ldm/modules/sub_quadratic_attention.py
deleted file mode 100644
index 1bc4138c318125047bf7a58237fd8cbf45f2ed72..0000000000000000000000000000000000000000
--- a/MagicQuill/comfy/ldm/modules/sub_quadratic_attention.py
+++ /dev/null
@@ -1,274 +0,0 @@
-# original source:
-#   https://github.com/AminRezaei0x443/memory-efficient-attention/blob/1bc0d9e6ac5f82ea43a375135c4e1d3896ee1694/memory_efficient_attention/attention_torch.py
-# license:
-#   MIT
-# credit:
-#   Amin Rezaei (original author)
-#   Alex Birch (optimized algorithm for 3D tensors, at the expense of removing bias, masking and callbacks)
-# implementation of:
-#   Self-attention Does Not Need O(n2) Memory":
-#   https://arxiv.org/abs/2112.05682v2
-
-from functools import partial
-import torch
-from torch import Tensor
-from torch.utils.checkpoint import checkpoint
-import math
-import logging
-
-try:
-	from typing import Optional, NamedTuple, List, Protocol
-except ImportError:
-	from typing import Optional, NamedTuple, List
-	from typing_extensions import Protocol
-
-from torch import Tensor
-from typing import List
-
-from comfy import model_management
-
-def dynamic_slice(
-    x: Tensor,
-    starts: List[int],
-    sizes: List[int],
-) -> Tensor:
-    slicing = [slice(start, start + size) for start, size in zip(starts, sizes)]
-    return x[slicing]
-
-class AttnChunk(NamedTuple):
-    exp_values: Tensor
-    exp_weights_sum: Tensor
-    max_score: Tensor
-
-class SummarizeChunk(Protocol):
-    @staticmethod
-    def __call__(
-        query: Tensor,
-        key_t: Tensor,
-        value: Tensor,
-    ) -> AttnChunk: ...
-
-class ComputeQueryChunkAttn(Protocol):
-    @staticmethod
-    def __call__(
-        query: Tensor,
-        key_t: Tensor,
-        value: Tensor,
-    ) -> Tensor: ...
-
-def _summarize_chunk(
-    query: Tensor,
-    key_t: Tensor,
-    value: Tensor,
-    scale: float,
-    upcast_attention: bool,
-    mask,
-) -> AttnChunk:
-    if upcast_attention:
-        with torch.autocast(enabled=False, device_type = 'cuda'):
-            query = query.float()
-            key_t = key_t.float()
-            attn_weights = torch.baddbmm(
-                torch.empty(1, 1, 1, device=query.device, dtype=query.dtype),
-                query,
-                key_t,
-                alpha=scale,
-                beta=0,
-            )
-    else:
-        attn_weights = torch.baddbmm(
-            torch.empty(1, 1, 1, device=query.device, dtype=query.dtype),
-            query,
-            key_t,
-            alpha=scale,
-            beta=0,
-        )
-    max_score, _ = torch.max(attn_weights, -1, keepdim=True)
-    max_score = max_score.detach()
-    attn_weights -= max_score
-    if mask is not None:
-        attn_weights += mask
-    torch.exp(attn_weights, out=attn_weights)
-    exp_weights = attn_weights.to(value.dtype)
-    exp_values = torch.bmm(exp_weights, value)
-    max_score = max_score.squeeze(-1)
-    return AttnChunk(exp_values, exp_weights.sum(dim=-1), max_score)
-
-def _query_chunk_attention(
-    query: Tensor,
-    key_t: Tensor,
-    value: Tensor,
-    summarize_chunk: SummarizeChunk,
-    kv_chunk_size: int,
-    mask,
-) -> Tensor:
-    batch_x_heads, k_channels_per_head, k_tokens = key_t.shape
-    _, _, v_channels_per_head = value.shape
-
-    def chunk_scanner(chunk_idx: int, mask) -> AttnChunk:
-        key_chunk = dynamic_slice(
-            key_t,
-            (0, 0, chunk_idx),
-            (batch_x_heads, k_channels_per_head, kv_chunk_size)
-        )
-        value_chunk = dynamic_slice(
-            value,
-            (0, chunk_idx, 0),
-            (batch_x_heads, kv_chunk_size, v_channels_per_head)
-        )
-        if mask is not None:
-            mask = mask[:,:,chunk_idx:chunk_idx + kv_chunk_size]
-
-        return summarize_chunk(query, key_chunk, value_chunk, mask=mask)
-
-    chunks: List[AttnChunk] = [
-        chunk_scanner(chunk, mask) for chunk in torch.arange(0, k_tokens, kv_chunk_size)
-    ]
-    acc_chunk = AttnChunk(*map(torch.stack, zip(*chunks)))
-    chunk_values, chunk_weights, chunk_max = acc_chunk
-
-    global_max, _ = torch.max(chunk_max, 0, keepdim=True)
-    max_diffs = torch.exp(chunk_max - global_max)
-    chunk_values *= torch.unsqueeze(max_diffs, -1)
-    chunk_weights *= max_diffs
-
-    all_values = chunk_values.sum(dim=0)
-    all_weights = torch.unsqueeze(chunk_weights, -1).sum(dim=0)
-    return all_values / all_weights
-
-# TODO: refactor CrossAttention#get_attention_scores to share code with this
-def _get_attention_scores_no_kv_chunking(
-    query: Tensor,
-    key_t: Tensor,
-    value: Tensor,
-    scale: float,
-    upcast_attention: bool,
-    mask,
-) -> Tensor:
-    if upcast_attention:
-        with torch.autocast(enabled=False, device_type = 'cuda'):
-            query = query.float()
-            key_t = key_t.float()
-            attn_scores = torch.baddbmm(
-                torch.empty(1, 1, 1, device=query.device, dtype=query.dtype),
-                query,
-                key_t,
-                alpha=scale,
-                beta=0,
-            )
-    else:
-        attn_scores = torch.baddbmm(
-            torch.empty(1, 1, 1, device=query.device, dtype=query.dtype),
-            query,
-            key_t,
-            alpha=scale,
-            beta=0,
-        )
-
-    if mask is not None:
-        attn_scores += mask
-    try:
-        attn_probs = attn_scores.softmax(dim=-1)
-        del attn_scores
-    except model_management.OOM_EXCEPTION:
-        logging.warning("ran out of memory while running softmax in  _get_attention_scores_no_kv_chunking, trying slower in place softmax instead")
-        attn_scores -= attn_scores.max(dim=-1, keepdim=True).values
-        torch.exp(attn_scores, out=attn_scores)
-        summed = torch.sum(attn_scores, dim=-1, keepdim=True)
-        attn_scores /= summed
-        attn_probs = attn_scores
-
-    hidden_states_slice = torch.bmm(attn_probs.to(value.dtype), value)
-    return hidden_states_slice
-
-class ScannedChunk(NamedTuple):
-    chunk_idx: int
-    attn_chunk: AttnChunk
-
-def efficient_dot_product_attention(
-    query: Tensor,
-    key_t: Tensor,
-    value: Tensor,
-    query_chunk_size=1024,
-    kv_chunk_size: Optional[int] = None,
-    kv_chunk_size_min: Optional[int] = None,
-    use_checkpoint=True,
-    upcast_attention=False,
-    mask = None,
-):
-    """Computes efficient dot-product attention given query, transposed key, and value.
-      This is efficient version of attention presented in
-      https://arxiv.org/abs/2112.05682v2 which comes with O(sqrt(n)) memory requirements.
-      Args:
-        query: queries for calculating attention with shape of
-          `[batch * num_heads, tokens, channels_per_head]`.
-        key_t: keys for calculating attention with shape of
-          `[batch * num_heads, channels_per_head, tokens]`.
-        value: values to be used in attention with shape of
-          `[batch * num_heads, tokens, channels_per_head]`.
-        query_chunk_size: int: query chunks size
-        kv_chunk_size: Optional[int]: key/value chunks size. if None: defaults to sqrt(key_tokens)
-        kv_chunk_size_min: Optional[int]: key/value minimum chunk size. only considered when kv_chunk_size is None. changes `sqrt(key_tokens)` into `max(sqrt(key_tokens), kv_chunk_size_min)`, to ensure our chunk sizes don't get too small (smaller chunks = more chunks = less concurrent work done).
-        use_checkpoint: bool: whether to use checkpointing (recommended True for training, False for inference)
-      Returns:
-        Output of shape `[batch * num_heads, query_tokens, channels_per_head]`.
-      """
-    batch_x_heads, q_tokens, q_channels_per_head = query.shape
-    _, _, k_tokens = key_t.shape
-    scale = q_channels_per_head ** -0.5
-
-    kv_chunk_size = min(kv_chunk_size or int(math.sqrt(k_tokens)), k_tokens)
-    if kv_chunk_size_min is not None:
-        kv_chunk_size = max(kv_chunk_size, kv_chunk_size_min)
-
-    if mask is not None and len(mask.shape) == 2:
-        mask = mask.unsqueeze(0)
-
-    def get_query_chunk(chunk_idx: int) -> Tensor:
-        return dynamic_slice(
-            query,
-            (0, chunk_idx, 0),
-            (batch_x_heads, min(query_chunk_size, q_tokens), q_channels_per_head)
-        )
-
-    def get_mask_chunk(chunk_idx: int) -> Tensor:
-        if mask is None:
-            return None
-        chunk = min(query_chunk_size, q_tokens)
-        return mask[:,chunk_idx:chunk_idx + chunk]
-
-    summarize_chunk: SummarizeChunk = partial(_summarize_chunk, scale=scale, upcast_attention=upcast_attention)
-    summarize_chunk: SummarizeChunk = partial(checkpoint, summarize_chunk) if use_checkpoint else summarize_chunk
-    compute_query_chunk_attn: ComputeQueryChunkAttn = partial(
-        _get_attention_scores_no_kv_chunking,
-        scale=scale,
-        upcast_attention=upcast_attention
-    ) if k_tokens <= kv_chunk_size else (
-        # fast-path for when there's just 1 key-value chunk per query chunk (this is just sliced attention btw)
-        partial(
-            _query_chunk_attention,
-            kv_chunk_size=kv_chunk_size,
-            summarize_chunk=summarize_chunk,
-        )
-    )
-
-    if q_tokens <= query_chunk_size:
-        # fast-path for when there's just 1 query chunk
-        return compute_query_chunk_attn(
-            query=query,
-            key_t=key_t,
-            value=value,
-            mask=mask,
-        )
-    
-    # TODO: maybe we should use torch.empty_like(query) to allocate storage in-advance,
-    # and pass slices to be mutated, instead of torch.cat()ing the returned slices
-    res = torch.cat([
-        compute_query_chunk_attn(
-            query=get_query_chunk(i * query_chunk_size),
-            key_t=key_t,
-            value=value,
-            mask=get_mask_chunk(i * query_chunk_size)
-        ) for i in range(math.ceil(q_tokens / query_chunk_size))
-    ], dim=1)
-    return res
diff --git a/MagicQuill/comfy/ldm/modules/temporal_ae.py b/MagicQuill/comfy/ldm/modules/temporal_ae.py
deleted file mode 100644
index 2992aeafc35ae8ca9e4ecac236810fa5a1fb84ad..0000000000000000000000000000000000000000
--- a/MagicQuill/comfy/ldm/modules/temporal_ae.py
+++ /dev/null
@@ -1,245 +0,0 @@
-import functools
-from typing import Callable, Iterable, Union
-
-import torch
-from einops import rearrange, repeat
-
-import comfy.ops
-ops = comfy.ops.disable_weight_init
-
-from .diffusionmodules.model import (
-    AttnBlock,
-    Decoder,
-    ResnetBlock,
-)
-from .diffusionmodules.openaimodel import ResBlock, timestep_embedding
-from .attention import BasicTransformerBlock
-
-def partialclass(cls, *args, **kwargs):
-    class NewCls(cls):
-        __init__ = functools.partialmethod(cls.__init__, *args, **kwargs)
-
-    return NewCls
-
-
-class VideoResBlock(ResnetBlock):
-    def __init__(
-        self,
-        out_channels,
-        *args,
-        dropout=0.0,
-        video_kernel_size=3,
-        alpha=0.0,
-        merge_strategy="learned",
-        **kwargs,
-    ):
-        super().__init__(out_channels=out_channels, dropout=dropout, *args, **kwargs)
-        if video_kernel_size is None:
-            video_kernel_size = [3, 1, 1]
-        self.time_stack = ResBlock(
-            channels=out_channels,
-            emb_channels=0,
-            dropout=dropout,
-            dims=3,
-            use_scale_shift_norm=False,
-            use_conv=False,
-            up=False,
-            down=False,
-            kernel_size=video_kernel_size,
-            use_checkpoint=False,
-            skip_t_emb=True,
-        )
-
-        self.merge_strategy = merge_strategy
-        if self.merge_strategy == "fixed":
-            self.register_buffer("mix_factor", torch.Tensor([alpha]))
-        elif self.merge_strategy == "learned":
-            self.register_parameter(
-                "mix_factor", torch.nn.Parameter(torch.Tensor([alpha]))
-            )
-        else:
-            raise ValueError(f"unknown merge strategy {self.merge_strategy}")
-
-    def get_alpha(self, bs):
-        if self.merge_strategy == "fixed":
-            return self.mix_factor
-        elif self.merge_strategy == "learned":
-            return torch.sigmoid(self.mix_factor)
-        else:
-            raise NotImplementedError()
-
-    def forward(self, x, temb, skip_video=False, timesteps=None):
-        b, c, h, w = x.shape
-        if timesteps is None:
-            timesteps = b
-
-        x = super().forward(x, temb)
-
-        if not skip_video:
-            x_mix = rearrange(x, "(b t) c h w -> b c t h w", t=timesteps)
-
-            x = rearrange(x, "(b t) c h w -> b c t h w", t=timesteps)
-
-            x = self.time_stack(x, temb)
-
-            alpha = self.get_alpha(bs=b // timesteps).to(x.device)
-            x = alpha * x + (1.0 - alpha) * x_mix
-
-            x = rearrange(x, "b c t h w -> (b t) c h w")
-        return x
-
-
-class AE3DConv(ops.Conv2d):
-    def __init__(self, in_channels, out_channels, video_kernel_size=3, *args, **kwargs):
-        super().__init__(in_channels, out_channels, *args, **kwargs)
-        if isinstance(video_kernel_size, Iterable):
-            padding = [int(k // 2) for k in video_kernel_size]
-        else:
-            padding = int(video_kernel_size // 2)
-
-        self.time_mix_conv = ops.Conv3d(
-            in_channels=out_channels,
-            out_channels=out_channels,
-            kernel_size=video_kernel_size,
-            padding=padding,
-        )
-
-    def forward(self, input, timesteps=None, skip_video=False):
-        if timesteps is None:
-            timesteps = input.shape[0]
-        x = super().forward(input)
-        if skip_video:
-            return x
-        x = rearrange(x, "(b t) c h w -> b c t h w", t=timesteps)
-        x = self.time_mix_conv(x)
-        return rearrange(x, "b c t h w -> (b t) c h w")
-
-
-class AttnVideoBlock(AttnBlock):
-    def __init__(
-        self, in_channels: int, alpha: float = 0, merge_strategy: str = "learned"
-    ):
-        super().__init__(in_channels)
-        # no context, single headed, as in base class
-        self.time_mix_block = BasicTransformerBlock(
-            dim=in_channels,
-            n_heads=1,
-            d_head=in_channels,
-            checkpoint=False,
-            ff_in=True,
-        )
-
-        time_embed_dim = self.in_channels * 4
-        self.video_time_embed = torch.nn.Sequential(
-            ops.Linear(self.in_channels, time_embed_dim),
-            torch.nn.SiLU(),
-            ops.Linear(time_embed_dim, self.in_channels),
-        )
-
-        self.merge_strategy = merge_strategy
-        if self.merge_strategy == "fixed":
-            self.register_buffer("mix_factor", torch.Tensor([alpha]))
-        elif self.merge_strategy == "learned":
-            self.register_parameter(
-                "mix_factor", torch.nn.Parameter(torch.Tensor([alpha]))
-            )
-        else:
-            raise ValueError(f"unknown merge strategy {self.merge_strategy}")
-
-    def forward(self, x, timesteps=None, skip_time_block=False):
-        if skip_time_block:
-            return super().forward(x)
-
-        if timesteps is None:
-            timesteps = x.shape[0]
-
-        x_in = x
-        x = self.attention(x)
-        h, w = x.shape[2:]
-        x = rearrange(x, "b c h w -> b (h w) c")
-
-        x_mix = x
-        num_frames = torch.arange(timesteps, device=x.device)
-        num_frames = repeat(num_frames, "t -> b t", b=x.shape[0] // timesteps)
-        num_frames = rearrange(num_frames, "b t -> (b t)")
-        t_emb = timestep_embedding(num_frames, self.in_channels, repeat_only=False)
-        emb = self.video_time_embed(t_emb)  # b, n_channels
-        emb = emb[:, None, :]
-        x_mix = x_mix + emb
-
-        alpha = self.get_alpha().to(x.device)
-        x_mix = self.time_mix_block(x_mix, timesteps=timesteps)
-        x = alpha * x + (1.0 - alpha) * x_mix  # alpha merge
-
-        x = rearrange(x, "b (h w) c -> b c h w", h=h, w=w)
-        x = self.proj_out(x)
-
-        return x_in + x
-
-    def get_alpha(
-        self,
-    ):
-        if self.merge_strategy == "fixed":
-            return self.mix_factor
-        elif self.merge_strategy == "learned":
-            return torch.sigmoid(self.mix_factor)
-        else:
-            raise NotImplementedError(f"unknown merge strategy {self.merge_strategy}")
-
-
-
-def make_time_attn(
-    in_channels,
-    attn_type="vanilla",
-    attn_kwargs=None,
-    alpha: float = 0,
-    merge_strategy: str = "learned",
-):
-    return partialclass(
-        AttnVideoBlock, in_channels, alpha=alpha, merge_strategy=merge_strategy
-    )
-
-
-class Conv2DWrapper(torch.nn.Conv2d):
-    def forward(self, input: torch.Tensor, **kwargs) -> torch.Tensor:
-        return super().forward(input)
-
-
-class VideoDecoder(Decoder):
-    available_time_modes = ["all", "conv-only", "attn-only"]
-
-    def __init__(
-        self,
-        *args,
-        video_kernel_size: Union[int, list] = 3,
-        alpha: float = 0.0,
-        merge_strategy: str = "learned",
-        time_mode: str = "conv-only",
-        **kwargs,
-    ):
-        self.video_kernel_size = video_kernel_size
-        self.alpha = alpha
-        self.merge_strategy = merge_strategy
-        self.time_mode = time_mode
-        assert (
-            self.time_mode in self.available_time_modes
-        ), f"time_mode parameter has to be in {self.available_time_modes}"
-
-        if self.time_mode != "attn-only":
-            kwargs["conv_out_op"] = partialclass(AE3DConv, video_kernel_size=self.video_kernel_size)
-        if self.time_mode not in ["conv-only", "only-last-conv"]:
-            kwargs["attn_op"] = partialclass(make_time_attn, alpha=self.alpha, merge_strategy=self.merge_strategy)
-        if self.time_mode not in ["attn-only", "only-last-conv"]:
-            kwargs["resnet_op"] = partialclass(VideoResBlock, video_kernel_size=self.video_kernel_size, alpha=self.alpha, merge_strategy=self.merge_strategy)
-
-        super().__init__(*args, **kwargs)
-
-    def get_last_layer(self, skip_time_mix=False, **kwargs):
-        if self.time_mode == "attn-only":
-            raise NotImplementedError("TODO")
-        else:
-            return (
-                self.conv_out.time_mix_conv.weight
-                if not skip_time_mix
-                else self.conv_out.weight
-            )
diff --git a/MagicQuill/comfy/ldm/util.py b/MagicQuill/comfy/ldm/util.py
deleted file mode 100644
index 8c09ca1c72f7ceb3f9d7f9546aae5561baf62b13..0000000000000000000000000000000000000000
--- a/MagicQuill/comfy/ldm/util.py
+++ /dev/null
@@ -1,197 +0,0 @@
-import importlib
-
-import torch
-from torch import optim
-import numpy as np
-
-from inspect import isfunction
-from PIL import Image, ImageDraw, ImageFont
-
-
-def log_txt_as_img(wh, xc, size=10):
-    # wh a tuple of (width, height)
-    # xc a list of captions to plot
-    b = len(xc)
-    txts = list()
-    for bi in range(b):
-        txt = Image.new("RGB", wh, color="white")
-        draw = ImageDraw.Draw(txt)
-        font = ImageFont.truetype('data/DejaVuSans.ttf', size=size)
-        nc = int(40 * (wh[0] / 256))
-        lines = "\n".join(xc[bi][start:start + nc] for start in range(0, len(xc[bi]), nc))
-
-        try:
-            draw.text((0, 0), lines, fill="black", font=font)
-        except UnicodeEncodeError:
-            print("Cant encode string for logging. Skipping.")
-
-        txt = np.array(txt).transpose(2, 0, 1) / 127.5 - 1.0
-        txts.append(txt)
-    txts = np.stack(txts)
-    txts = torch.tensor(txts)
-    return txts
-
-
-def ismap(x):
-    if not isinstance(x, torch.Tensor):
-        return False
-    return (len(x.shape) == 4) and (x.shape[1] > 3)
-
-
-def isimage(x):
-    if not isinstance(x,torch.Tensor):
-        return False
-    return (len(x.shape) == 4) and (x.shape[1] == 3 or x.shape[1] == 1)
-
-
-def exists(x):
-    return x is not None
-
-
-def default(val, d):
-    if exists(val):
-        return val
-    return d() if isfunction(d) else d
-
-
-def mean_flat(tensor):
-    """
-    https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/nn.py#L86
-    Take the mean over all non-batch dimensions.
-    """
-    return tensor.mean(dim=list(range(1, len(tensor.shape))))
-
-
-def count_params(model, verbose=False):
-    total_params = sum(p.numel() for p in model.parameters())
-    if verbose:
-        print(f"{model.__class__.__name__} has {total_params*1.e-6:.2f} M params.")
-    return total_params
-
-
-def instantiate_from_config(config):
-    if not "target" in config:
-        if config == '__is_first_stage__':
-            return None
-        elif config == "__is_unconditional__":
-            return None
-        raise KeyError("Expected key `target` to instantiate.")
-    return get_obj_from_str(config["target"])(**config.get("params", dict()))
-
-
-def get_obj_from_str(string, reload=False):
-    module, cls = string.rsplit(".", 1)
-    if reload:
-        module_imp = importlib.import_module(module)
-        importlib.reload(module_imp)
-    return getattr(importlib.import_module(module, package=None), cls)
-
-
-class AdamWwithEMAandWings(optim.Optimizer):
-    # credit to https://gist.github.com/crowsonkb/65f7265353f403714fce3b2595e0b298
-    def __init__(self, params, lr=1.e-3, betas=(0.9, 0.999), eps=1.e-8,  # TODO: check hyperparameters before using
-                 weight_decay=1.e-2, amsgrad=False, ema_decay=0.9999,   # ema decay to match previous code
-                 ema_power=1., param_names=()):
-        """AdamW that saves EMA versions of the parameters."""
-        if not 0.0 <= lr:
-            raise ValueError("Invalid learning rate: {}".format(lr))
-        if not 0.0 <= eps:
-            raise ValueError("Invalid epsilon value: {}".format(eps))
-        if not 0.0 <= betas[0] < 1.0:
-            raise ValueError("Invalid beta parameter at index 0: {}".format(betas[0]))
-        if not 0.0 <= betas[1] < 1.0:
-            raise ValueError("Invalid beta parameter at index 1: {}".format(betas[1]))
-        if not 0.0 <= weight_decay:
-            raise ValueError("Invalid weight_decay value: {}".format(weight_decay))
-        if not 0.0 <= ema_decay <= 1.0:
-            raise ValueError("Invalid ema_decay value: {}".format(ema_decay))
-        defaults = dict(lr=lr, betas=betas, eps=eps,
-                        weight_decay=weight_decay, amsgrad=amsgrad, ema_decay=ema_decay,
-                        ema_power=ema_power, param_names=param_names)
-        super().__init__(params, defaults)
-
-    def __setstate__(self, state):
-        super().__setstate__(state)
-        for group in self.param_groups:
-            group.setdefault('amsgrad', False)
-
-    @torch.no_grad()
-    def step(self, closure=None):
-        """Performs a single optimization step.
-        Args:
-            closure (callable, optional): A closure that reevaluates the model
-                and returns the loss.
-        """
-        loss = None
-        if closure is not None:
-            with torch.enable_grad():
-                loss = closure()
-
-        for group in self.param_groups:
-            params_with_grad = []
-            grads = []
-            exp_avgs = []
-            exp_avg_sqs = []
-            ema_params_with_grad = []
-            state_sums = []
-            max_exp_avg_sqs = []
-            state_steps = []
-            amsgrad = group['amsgrad']
-            beta1, beta2 = group['betas']
-            ema_decay = group['ema_decay']
-            ema_power = group['ema_power']
-
-            for p in group['params']:
-                if p.grad is None:
-                    continue
-                params_with_grad.append(p)
-                if p.grad.is_sparse:
-                    raise RuntimeError('AdamW does not support sparse gradients')
-                grads.append(p.grad)
-
-                state = self.state[p]
-
-                # State initialization
-                if len(state) == 0:
-                    state['step'] = 0
-                    # Exponential moving average of gradient values
-                    state['exp_avg'] = torch.zeros_like(p, memory_format=torch.preserve_format)
-                    # Exponential moving average of squared gradient values
-                    state['exp_avg_sq'] = torch.zeros_like(p, memory_format=torch.preserve_format)
-                    if amsgrad:
-                        # Maintains max of all exp. moving avg. of sq. grad. values
-                        state['max_exp_avg_sq'] = torch.zeros_like(p, memory_format=torch.preserve_format)
-                    # Exponential moving average of parameter values
-                    state['param_exp_avg'] = p.detach().float().clone()
-
-                exp_avgs.append(state['exp_avg'])
-                exp_avg_sqs.append(state['exp_avg_sq'])
-                ema_params_with_grad.append(state['param_exp_avg'])
-
-                if amsgrad:
-                    max_exp_avg_sqs.append(state['max_exp_avg_sq'])
-
-                # update the steps for each param group update
-                state['step'] += 1
-                # record the step after step update
-                state_steps.append(state['step'])
-
-            optim._functional.adamw(params_with_grad,
-                    grads,
-                    exp_avgs,
-                    exp_avg_sqs,
-                    max_exp_avg_sqs,
-                    state_steps,
-                    amsgrad=amsgrad,
-                    beta1=beta1,
-                    beta2=beta2,
-                    lr=group['lr'],
-                    weight_decay=group['weight_decay'],
-                    eps=group['eps'],
-                    maximize=False)
-
-            cur_ema_decay = min(ema_decay, 1 - state['step'] ** -ema_power)
-            for param, ema_param in zip(params_with_grad, ema_params_with_grad):
-                ema_param.mul_(cur_ema_decay).add_(param.float(), alpha=1 - cur_ema_decay)
-
-        return loss
\ No newline at end of file
diff --git a/MagicQuill/comfy/lora.py b/MagicQuill/comfy/lora.py
deleted file mode 100644
index 082a8b3cba49572b6360539a3ac4fa3660fb7725..0000000000000000000000000000000000000000
--- a/MagicQuill/comfy/lora.py
+++ /dev/null
@@ -1,266 +0,0 @@
-import comfy.utils
-import logging
-
-LORA_CLIP_MAP = {
-    "mlp.fc1": "mlp_fc1",
-    "mlp.fc2": "mlp_fc2",
-    "self_attn.k_proj": "self_attn_k_proj",
-    "self_attn.q_proj": "self_attn_q_proj",
-    "self_attn.v_proj": "self_attn_v_proj",
-    "self_attn.out_proj": "self_attn_out_proj",
-}
-
-
-def load_lora(lora, to_load):
-    patch_dict = {}
-    loaded_keys = set()
-    for x in to_load:
-        alpha_name = "{}.alpha".format(x)
-        alpha = None
-        if alpha_name in lora.keys():
-            alpha = lora[alpha_name].item()
-            loaded_keys.add(alpha_name)
-
-        dora_scale_name = "{}.dora_scale".format(x)
-        dora_scale = None
-        if dora_scale_name in lora.keys():
-            dora_scale = lora[dora_scale_name]
-            loaded_keys.add(dora_scale_name)
-
-        regular_lora = "{}.lora_up.weight".format(x)
-        diffusers_lora = "{}_lora.up.weight".format(x)
-        diffusers2_lora = "{}.lora_B.weight".format(x)
-        diffusers3_lora = "{}.lora.up.weight".format(x)
-        transformers_lora = "{}.lora_linear_layer.up.weight".format(x)
-        A_name = None
-
-        if regular_lora in lora.keys():
-            A_name = regular_lora
-            B_name = "{}.lora_down.weight".format(x)
-            mid_name = "{}.lora_mid.weight".format(x)
-        elif diffusers_lora in lora.keys():
-            A_name = diffusers_lora
-            B_name = "{}_lora.down.weight".format(x)
-            mid_name = None
-        elif diffusers2_lora in lora.keys():
-            A_name = diffusers2_lora
-            B_name = "{}.lora_A.weight".format(x)
-            mid_name = None
-        elif diffusers3_lora in lora.keys():
-            A_name = diffusers3_lora
-            B_name = "{}.lora.down.weight".format(x)
-            mid_name = None
-        elif transformers_lora in lora.keys():
-            A_name = transformers_lora
-            B_name ="{}.lora_linear_layer.down.weight".format(x)
-            mid_name = None
-
-        if A_name is not None:
-            mid = None
-            if mid_name is not None and mid_name in lora.keys():
-                mid = lora[mid_name]
-                loaded_keys.add(mid_name)
-            patch_dict[to_load[x]] = ("lora", (lora[A_name], lora[B_name], alpha, mid, dora_scale))
-            loaded_keys.add(A_name)
-            loaded_keys.add(B_name)
-
-
-        ######## loha
-        hada_w1_a_name = "{}.hada_w1_a".format(x)
-        hada_w1_b_name = "{}.hada_w1_b".format(x)
-        hada_w2_a_name = "{}.hada_w2_a".format(x)
-        hada_w2_b_name = "{}.hada_w2_b".format(x)
-        hada_t1_name = "{}.hada_t1".format(x)
-        hada_t2_name = "{}.hada_t2".format(x)
-        if hada_w1_a_name in lora.keys():
-            hada_t1 = None
-            hada_t2 = None
-            if hada_t1_name in lora.keys():
-                hada_t1 = lora[hada_t1_name]
-                hada_t2 = lora[hada_t2_name]
-                loaded_keys.add(hada_t1_name)
-                loaded_keys.add(hada_t2_name)
-
-            patch_dict[to_load[x]] = ("loha", (lora[hada_w1_a_name], lora[hada_w1_b_name], alpha, lora[hada_w2_a_name], lora[hada_w2_b_name], hada_t1, hada_t2, dora_scale))
-            loaded_keys.add(hada_w1_a_name)
-            loaded_keys.add(hada_w1_b_name)
-            loaded_keys.add(hada_w2_a_name)
-            loaded_keys.add(hada_w2_b_name)
-
-
-        ######## lokr
-        lokr_w1_name = "{}.lokr_w1".format(x)
-        lokr_w2_name = "{}.lokr_w2".format(x)
-        lokr_w1_a_name = "{}.lokr_w1_a".format(x)
-        lokr_w1_b_name = "{}.lokr_w1_b".format(x)
-        lokr_t2_name = "{}.lokr_t2".format(x)
-        lokr_w2_a_name = "{}.lokr_w2_a".format(x)
-        lokr_w2_b_name = "{}.lokr_w2_b".format(x)
-
-        lokr_w1 = None
-        if lokr_w1_name in lora.keys():
-            lokr_w1 = lora[lokr_w1_name]
-            loaded_keys.add(lokr_w1_name)
-
-        lokr_w2 = None
-        if lokr_w2_name in lora.keys():
-            lokr_w2 = lora[lokr_w2_name]
-            loaded_keys.add(lokr_w2_name)
-
-        lokr_w1_a = None
-        if lokr_w1_a_name in lora.keys():
-            lokr_w1_a = lora[lokr_w1_a_name]
-            loaded_keys.add(lokr_w1_a_name)
-
-        lokr_w1_b = None
-        if lokr_w1_b_name in lora.keys():
-            lokr_w1_b = lora[lokr_w1_b_name]
-            loaded_keys.add(lokr_w1_b_name)
-
-        lokr_w2_a = None
-        if lokr_w2_a_name in lora.keys():
-            lokr_w2_a = lora[lokr_w2_a_name]
-            loaded_keys.add(lokr_w2_a_name)
-
-        lokr_w2_b = None
-        if lokr_w2_b_name in lora.keys():
-            lokr_w2_b = lora[lokr_w2_b_name]
-            loaded_keys.add(lokr_w2_b_name)
-
-        lokr_t2 = None
-        if lokr_t2_name in lora.keys():
-            lokr_t2 = lora[lokr_t2_name]
-            loaded_keys.add(lokr_t2_name)
-
-        if (lokr_w1 is not None) or (lokr_w2 is not None) or (lokr_w1_a is not None) or (lokr_w2_a is not None):
-            patch_dict[to_load[x]] = ("lokr", (lokr_w1, lokr_w2, alpha, lokr_w1_a, lokr_w1_b, lokr_w2_a, lokr_w2_b, lokr_t2, dora_scale))
-
-        #glora
-        a1_name = "{}.a1.weight".format(x)
-        a2_name = "{}.a2.weight".format(x)
-        b1_name = "{}.b1.weight".format(x)
-        b2_name = "{}.b2.weight".format(x)
-        if a1_name in lora:
-            patch_dict[to_load[x]] = ("glora", (lora[a1_name], lora[a2_name], lora[b1_name], lora[b2_name], alpha, dora_scale))
-            loaded_keys.add(a1_name)
-            loaded_keys.add(a2_name)
-            loaded_keys.add(b1_name)
-            loaded_keys.add(b2_name)
-
-        w_norm_name = "{}.w_norm".format(x)
-        b_norm_name = "{}.b_norm".format(x)
-        w_norm = lora.get(w_norm_name, None)
-        b_norm = lora.get(b_norm_name, None)
-
-        if w_norm is not None:
-            loaded_keys.add(w_norm_name)
-            patch_dict[to_load[x]] = ("diff", (w_norm,))
-            if b_norm is not None:
-                loaded_keys.add(b_norm_name)
-                patch_dict["{}.bias".format(to_load[x][:-len(".weight")])] = ("diff", (b_norm,))
-
-        diff_name = "{}.diff".format(x)
-        diff_weight = lora.get(diff_name, None)
-        if diff_weight is not None:
-            patch_dict[to_load[x]] = ("diff", (diff_weight,))
-            loaded_keys.add(diff_name)
-
-        diff_bias_name = "{}.diff_b".format(x)
-        diff_bias = lora.get(diff_bias_name, None)
-        if diff_bias is not None:
-            patch_dict["{}.bias".format(to_load[x][:-len(".weight")])] = ("diff", (diff_bias,))
-            loaded_keys.add(diff_bias_name)
-
-    for x in lora.keys():
-        if x not in loaded_keys:
-            logging.warning("lora key not loaded: {}".format(x))
-
-    return patch_dict
-
-def model_lora_keys_clip(model, key_map={}):
-    sdk = model.state_dict().keys()
-
-    text_model_lora_key = "lora_te_text_model_encoder_layers_{}_{}"
-    clip_l_present = False
-    for b in range(32): #TODO: clean up
-        for c in LORA_CLIP_MAP:
-            k = "clip_h.transformer.text_model.encoder.layers.{}.{}.weight".format(b, c)
-            if k in sdk:
-                lora_key = text_model_lora_key.format(b, LORA_CLIP_MAP[c])
-                key_map[lora_key] = k
-                lora_key = "lora_te1_text_model_encoder_layers_{}_{}".format(b, LORA_CLIP_MAP[c])
-                key_map[lora_key] = k
-                lora_key = "text_encoder.text_model.encoder.layers.{}.{}".format(b, c) #diffusers lora
-                key_map[lora_key] = k
-
-            k = "clip_l.transformer.text_model.encoder.layers.{}.{}.weight".format(b, c)
-            if k in sdk:
-                lora_key = text_model_lora_key.format(b, LORA_CLIP_MAP[c])
-                key_map[lora_key] = k
-                lora_key = "lora_te1_text_model_encoder_layers_{}_{}".format(b, LORA_CLIP_MAP[c]) #SDXL base
-                key_map[lora_key] = k
-                clip_l_present = True
-                lora_key = "text_encoder.text_model.encoder.layers.{}.{}".format(b, c) #diffusers lora
-                key_map[lora_key] = k
-
-            k = "clip_g.transformer.text_model.encoder.layers.{}.{}.weight".format(b, c)
-            if k in sdk:
-                if clip_l_present:
-                    lora_key = "lora_te2_text_model_encoder_layers_{}_{}".format(b, LORA_CLIP_MAP[c]) #SDXL base
-                    key_map[lora_key] = k
-                    lora_key = "text_encoder_2.text_model.encoder.layers.{}.{}".format(b, c) #diffusers lora
-                    key_map[lora_key] = k
-                else:
-                    lora_key = "lora_te_text_model_encoder_layers_{}_{}".format(b, LORA_CLIP_MAP[c]) #TODO: test if this is correct for SDXL-Refiner
-                    key_map[lora_key] = k
-                    lora_key = "text_encoder.text_model.encoder.layers.{}.{}".format(b, c) #diffusers lora
-                    key_map[lora_key] = k
-                    lora_key = "lora_prior_te_text_model_encoder_layers_{}_{}".format(b, LORA_CLIP_MAP[c]) #cascade lora: TODO put lora key prefix in the model config
-                    key_map[lora_key] = k
-
-
-    k = "clip_g.transformer.text_projection.weight"
-    if k in sdk:
-        key_map["lora_prior_te_text_projection"] = k #cascade lora?
-        # key_map["text_encoder.text_projection"] = k #TODO: check if other lora have the text_projection too
-        # key_map["lora_te_text_projection"] = k
-
-    return key_map
-
-def model_lora_keys_unet(model, key_map={}):
-    sd = model.state_dict()
-    sdk = sd.keys()
-
-    for k in sdk:
-        if k.startswith("diffusion_model.") and k.endswith(".weight"):
-            key_lora = k[len("diffusion_model."):-len(".weight")].replace(".", "_")
-            key_map["lora_unet_{}".format(key_lora)] = k
-            key_map["lora_prior_unet_{}".format(key_lora)] = k #cascade lora: TODO put lora key prefix in the model config
-
-    diffusers_keys = comfy.utils.unet_to_diffusers(model.model_config.unet_config)
-    for k in diffusers_keys:
-        if k.endswith(".weight"):
-            unet_key = "diffusion_model.{}".format(diffusers_keys[k])
-            key_lora = k[:-len(".weight")].replace(".", "_")
-            key_map["lora_unet_{}".format(key_lora)] = unet_key
-
-            diffusers_lora_prefix = ["", "unet."]
-            for p in diffusers_lora_prefix:
-                diffusers_lora_key = "{}{}".format(p, k[:-len(".weight")].replace(".to_", ".processor.to_"))
-                if diffusers_lora_key.endswith(".to_out.0"):
-                    diffusers_lora_key = diffusers_lora_key[:-2]
-                key_map[diffusers_lora_key] = unet_key
-
-    if isinstance(model, comfy.model_base.SD3): #Diffusers lora SD3
-        for i in range(model.model_config.unet_config.get("depth", 0)):
-            k = "transformer.transformer_blocks.{}.attn.".format(i)
-            qkv = "diffusion_model.joint_blocks.{}.x_block.attn.qkv.weight".format(i)
-            proj = "diffusion_model.joint_blocks.{}.x_block.attn.proj.weight".format(i)
-            if qkv in sd:
-                offset = sd[qkv].shape[0] // 3
-                key_map["{}to_q".format(k)] = (qkv, (0, 0, offset))
-                key_map["{}to_k".format(k)] = (qkv, (0, offset, offset))
-                key_map["{}to_v".format(k)] = (qkv, (0, offset * 2, offset))
-                key_map["{}to_out.0".format(k)] = proj
-
-    return key_map
diff --git a/MagicQuill/comfy/model_base.py b/MagicQuill/comfy/model_base.py
deleted file mode 100644
index f45b375dee5aac1475686828acfada038799b046..0000000000000000000000000000000000000000
--- a/MagicQuill/comfy/model_base.py
+++ /dev/null
@@ -1,629 +0,0 @@
-import torch
-import logging
-from comfy.ldm.modules.diffusionmodules.openaimodel import UNetModel, Timestep
-from comfy.ldm.cascade.stage_c import StageC
-from comfy.ldm.cascade.stage_b import StageB
-from comfy.ldm.modules.encoders.noise_aug_modules import CLIPEmbeddingNoiseAugmentation
-from comfy.ldm.modules.diffusionmodules.upscaling import ImageConcatWithNoiseAugmentation
-from comfy.ldm.modules.diffusionmodules.mmdit import OpenAISignatureMMDITWrapper
-import comfy.ldm.audio.dit
-import comfy.ldm.audio.embedders
-import comfy.model_management
-import comfy.conds
-import comfy.ops
-from enum import Enum
-from . import utils
-import comfy.latent_formats
-import math
-
-class ModelType(Enum):
-    EPS = 1
-    V_PREDICTION = 2
-    V_PREDICTION_EDM = 3
-    STABLE_CASCADE = 4
-    EDM = 5
-    FLOW = 6
-    V_PREDICTION_CONTINUOUS = 7
-
-
-from comfy.model_sampling import EPS, V_PREDICTION, EDM, ModelSamplingDiscrete, ModelSamplingContinuousEDM, StableCascadeSampling, ModelSamplingContinuousV
-
-
-def model_sampling(model_config, model_type):
-    s = ModelSamplingDiscrete
-
-    if model_type == ModelType.EPS:
-        c = EPS
-    elif model_type == ModelType.V_PREDICTION:
-        c = V_PREDICTION
-    elif model_type == ModelType.V_PREDICTION_EDM:
-        c = V_PREDICTION
-        s = ModelSamplingContinuousEDM
-    elif model_type == ModelType.FLOW:
-        c = comfy.model_sampling.CONST
-        s = comfy.model_sampling.ModelSamplingDiscreteFlow
-    elif model_type == ModelType.STABLE_CASCADE:
-        c = EPS
-        s = StableCascadeSampling
-    elif model_type == ModelType.EDM:
-        c = EDM
-        s = ModelSamplingContinuousEDM
-    elif model_type == ModelType.V_PREDICTION_CONTINUOUS:
-        c = V_PREDICTION
-        s = ModelSamplingContinuousV
-
-    class ModelSampling(s, c):
-        pass
-
-    return ModelSampling(model_config)
-
-
-class BaseModel(torch.nn.Module):
-    def __init__(self, model_config, model_type=ModelType.EPS, device=None, unet_model=UNetModel):
-        super().__init__()
-
-        unet_config = model_config.unet_config
-        self.latent_format = model_config.latent_format
-        self.model_config = model_config
-        self.manual_cast_dtype = model_config.manual_cast_dtype
-
-        if not unet_config.get("disable_unet_model_creation", False):
-            if self.manual_cast_dtype is not None:
-                operations = comfy.ops.manual_cast
-            else:
-                operations = comfy.ops.disable_weight_init
-            self.diffusion_model = unet_model(**unet_config, device=device, operations=operations)
-            if comfy.model_management.force_channels_last():
-                self.diffusion_model.to(memory_format=torch.channels_last)
-                logging.debug("using channels last mode for diffusion model")
-        self.model_type = model_type
-        self.model_sampling = model_sampling(model_config, model_type)
-
-        self.adm_channels = unet_config.get("adm_in_channels", None)
-        if self.adm_channels is None:
-            self.adm_channels = 0
-
-        self.concat_keys = ()
-        logging.info("model_type {}".format(model_type.name))
-        logging.debug("adm {}".format(self.adm_channels))
-
-    def apply_model(self, x, t, c_concat=None, c_crossattn=None, control=None, transformer_options={}, **kwargs):
-        sigma = t
-        xc = self.model_sampling.calculate_input(sigma, x)
-        if c_concat is not None:
-            xc = torch.cat([xc] + [c_concat], dim=1)
-
-        context = c_crossattn
-        dtype = self.get_dtype()
-
-        if self.manual_cast_dtype is not None:
-            dtype = self.manual_cast_dtype
-
-        xc = xc.to(dtype)
-        t = self.model_sampling.timestep(t).float()
-        context = context.to(dtype)
-        extra_conds = {}
-        for o in kwargs:
-            extra = kwargs[o]
-            if hasattr(extra, "dtype"):
-                if extra.dtype != torch.int and extra.dtype != torch.long:
-                    extra = extra.to(dtype)
-            extra_conds[o] = extra
-
-        model_output = self.diffusion_model(xc, t, context=context, control=control, transformer_options=transformer_options, **extra_conds).float()
-        return self.model_sampling.calculate_denoised(sigma, model_output, x)
-
-    def get_dtype(self):
-        return self.diffusion_model.dtype
-
-    def is_adm(self):
-        return self.adm_channels > 0
-
-    def encode_adm(self, **kwargs):
-        return None
-
-    def extra_conds(self, **kwargs):
-        out = {}
-        if len(self.concat_keys) > 0:
-            cond_concat = []
-            denoise_mask = kwargs.get("concat_mask", kwargs.get("denoise_mask", None))
-            concat_latent_image = kwargs.get("concat_latent_image", None)
-            if concat_latent_image is None:
-                concat_latent_image = kwargs.get("latent_image", None)
-            else:
-                concat_latent_image = self.process_latent_in(concat_latent_image)
-
-            noise = kwargs.get("noise", None)
-            device = kwargs["device"]
-
-            if concat_latent_image.shape[1:] != noise.shape[1:]:
-                concat_latent_image = utils.common_upscale(concat_latent_image, noise.shape[-1], noise.shape[-2], "bilinear", "center")
-
-            concat_latent_image = utils.resize_to_batch_size(concat_latent_image, noise.shape[0])
-
-            if denoise_mask is not None:
-                if len(denoise_mask.shape) == len(noise.shape):
-                    denoise_mask = denoise_mask[:,:1]
-
-                denoise_mask = denoise_mask.reshape((-1, 1, denoise_mask.shape[-2], denoise_mask.shape[-1]))
-                if denoise_mask.shape[-2:] != noise.shape[-2:]:
-                    denoise_mask = utils.common_upscale(denoise_mask, noise.shape[-1], noise.shape[-2], "bilinear", "center")
-                denoise_mask = utils.resize_to_batch_size(denoise_mask.round(), noise.shape[0])
-
-            for ck in self.concat_keys:
-                if denoise_mask is not None:
-                    if ck == "mask":
-                        cond_concat.append(denoise_mask.to(device))
-                    elif ck == "masked_image":
-                        cond_concat.append(concat_latent_image.to(device)) #NOTE: the latent_image should be masked by the mask in pixel space
-                else:
-                    if ck == "mask":
-                        cond_concat.append(torch.ones_like(noise)[:,:1])
-                    elif ck == "masked_image":
-                        cond_concat.append(self.blank_inpaint_image_like(noise))
-            data = torch.cat(cond_concat, dim=1)
-            out['c_concat'] = comfy.conds.CONDNoiseShape(data)
-
-        adm = self.encode_adm(**kwargs)
-        if adm is not None:
-            out['y'] = comfy.conds.CONDRegular(adm)
-
-        cross_attn = kwargs.get("cross_attn", None)
-        if cross_attn is not None:
-            out['c_crossattn'] = comfy.conds.CONDCrossAttn(cross_attn)
-
-        cross_attn_cnet = kwargs.get("cross_attn_controlnet", None)
-        if cross_attn_cnet is not None:
-            out['crossattn_controlnet'] = comfy.conds.CONDCrossAttn(cross_attn_cnet)
-
-        c_concat = kwargs.get("noise_concat", None)
-        if c_concat is not None:
-            out['c_concat'] = comfy.conds.CONDNoiseShape(c_concat)
-
-        return out
-
-    def load_model_weights(self, sd, unet_prefix=""):
-        to_load = {}
-        keys = list(sd.keys())
-        for k in keys:
-            if k.startswith(unet_prefix):
-                to_load[k[len(unet_prefix):]] = sd.pop(k)
-
-        to_load = self.model_config.process_unet_state_dict(to_load)
-        m, u = self.diffusion_model.load_state_dict(to_load, strict=False)
-        if len(m) > 0:
-            logging.warning("unet missing: {}".format(m))
-
-        if len(u) > 0:
-            logging.warning("unet unexpected: {}".format(u))
-        del to_load
-        return self
-
-    def process_latent_in(self, latent):
-        return self.latent_format.process_in(latent)
-
-    def process_latent_out(self, latent):
-        return self.latent_format.process_out(latent)
-
-    def state_dict_for_saving(self, clip_state_dict=None, vae_state_dict=None, clip_vision_state_dict=None):
-        extra_sds = []
-        if clip_state_dict is not None:
-            extra_sds.append(self.model_config.process_clip_state_dict_for_saving(clip_state_dict))
-        if vae_state_dict is not None:
-            extra_sds.append(self.model_config.process_vae_state_dict_for_saving(vae_state_dict))
-        if clip_vision_state_dict is not None:
-            extra_sds.append(self.model_config.process_clip_vision_state_dict_for_saving(clip_vision_state_dict))
-
-        unet_state_dict = self.diffusion_model.state_dict()
-        unet_state_dict = self.model_config.process_unet_state_dict_for_saving(unet_state_dict)
-
-        if self.model_type == ModelType.V_PREDICTION:
-            unet_state_dict["v_pred"] = torch.tensor([])
-
-        for sd in extra_sds:
-            unet_state_dict.update(sd)
-
-        return unet_state_dict
-
-    def set_inpaint(self):
-        self.concat_keys = ("mask", "masked_image")
-        def blank_inpaint_image_like(latent_image):
-            blank_image = torch.ones_like(latent_image)
-            # these are the values for "zero" in pixel space translated to latent space
-            blank_image[:,0] *= 0.8223
-            blank_image[:,1] *= -0.6876
-            blank_image[:,2] *= 0.6364
-            blank_image[:,3] *= 0.1380
-            return blank_image
-        self.blank_inpaint_image_like = blank_inpaint_image_like
-
-    def memory_required(self, input_shape):
-        if comfy.model_management.xformers_enabled() or comfy.model_management.pytorch_attention_flash_attention():
-            dtype = self.get_dtype()
-            if self.manual_cast_dtype is not None:
-                dtype = self.manual_cast_dtype
-            #TODO: this needs to be tweaked
-            area = input_shape[0] * math.prod(input_shape[2:])
-            return (area * comfy.model_management.dtype_size(dtype) / 50) * (1024 * 1024)
-        else:
-            #TODO: this formula might be too aggressive since I tweaked the sub-quad and split algorithms to use less memory.
-            area = input_shape[0] * math.prod(input_shape[2:])
-            return (((area * 0.6) / 0.9) + 1024) * (1024 * 1024)
-
-
-def unclip_adm(unclip_conditioning, device, noise_augmentor, noise_augment_merge=0.0, seed=None):
-    adm_inputs = []
-    weights = []
-    noise_aug = []
-    for unclip_cond in unclip_conditioning:
-        for adm_cond in unclip_cond["clip_vision_output"].image_embeds:
-            weight = unclip_cond["strength"]
-            noise_augment = unclip_cond["noise_augmentation"]
-            noise_level = round((noise_augmentor.max_noise_level - 1) * noise_augment)
-            c_adm, noise_level_emb = noise_augmentor(adm_cond.to(device), noise_level=torch.tensor([noise_level], device=device), seed=seed)
-            adm_out = torch.cat((c_adm, noise_level_emb), 1) * weight
-            weights.append(weight)
-            noise_aug.append(noise_augment)
-            adm_inputs.append(adm_out)
-
-    if len(noise_aug) > 1:
-        adm_out = torch.stack(adm_inputs).sum(0)
-        noise_augment = noise_augment_merge
-        noise_level = round((noise_augmentor.max_noise_level - 1) * noise_augment)
-        c_adm, noise_level_emb = noise_augmentor(adm_out[:, :noise_augmentor.time_embed.dim], noise_level=torch.tensor([noise_level], device=device))
-        adm_out = torch.cat((c_adm, noise_level_emb), 1)
-
-    return adm_out
-
-class SD21UNCLIP(BaseModel):
-    def __init__(self, model_config, noise_aug_config, model_type=ModelType.V_PREDICTION, device=None):
-        super().__init__(model_config, model_type, device=device)
-        self.noise_augmentor = CLIPEmbeddingNoiseAugmentation(**noise_aug_config)
-
-    def encode_adm(self, **kwargs):
-        unclip_conditioning = kwargs.get("unclip_conditioning", None)
-        device = kwargs["device"]
-        if unclip_conditioning is None:
-            return torch.zeros((1, self.adm_channels))
-        else:
-            return unclip_adm(unclip_conditioning, device, self.noise_augmentor, kwargs.get("unclip_noise_augment_merge", 0.05), kwargs.get("seed", 0) - 10)
-
-def sdxl_pooled(args, noise_augmentor):
-    if "unclip_conditioning" in args:
-        return unclip_adm(args.get("unclip_conditioning", None), args["device"], noise_augmentor, seed=args.get("seed", 0) - 10)[:,:1280]
-    else:
-        return args["pooled_output"]
-
-class SDXLRefiner(BaseModel):
-    def __init__(self, model_config, model_type=ModelType.EPS, device=None):
-        super().__init__(model_config, model_type, device=device)
-        self.embedder = Timestep(256)
-        self.noise_augmentor = CLIPEmbeddingNoiseAugmentation(**{"noise_schedule_config": {"timesteps": 1000, "beta_schedule": "squaredcos_cap_v2"}, "timestep_dim": 1280})
-
-    def encode_adm(self, **kwargs):
-        clip_pooled = sdxl_pooled(kwargs, self.noise_augmentor)
-        width = kwargs.get("width", 768)
-        height = kwargs.get("height", 768)
-        crop_w = kwargs.get("crop_w", 0)
-        crop_h = kwargs.get("crop_h", 0)
-
-        if kwargs.get("prompt_type", "") == "negative":
-            aesthetic_score = kwargs.get("aesthetic_score", 2.5)
-        else:
-            aesthetic_score = kwargs.get("aesthetic_score", 6)
-
-        out = []
-        out.append(self.embedder(torch.Tensor([height])))
-        out.append(self.embedder(torch.Tensor([width])))
-        out.append(self.embedder(torch.Tensor([crop_h])))
-        out.append(self.embedder(torch.Tensor([crop_w])))
-        out.append(self.embedder(torch.Tensor([aesthetic_score])))
-        flat = torch.flatten(torch.cat(out)).unsqueeze(dim=0).repeat(clip_pooled.shape[0], 1)
-        return torch.cat((clip_pooled.to(flat.device), flat), dim=1)
-
-class SDXL(BaseModel):
-    def __init__(self, model_config, model_type=ModelType.EPS, device=None):
-        super().__init__(model_config, model_type, device=device)
-        self.embedder = Timestep(256)
-        self.noise_augmentor = CLIPEmbeddingNoiseAugmentation(**{"noise_schedule_config": {"timesteps": 1000, "beta_schedule": "squaredcos_cap_v2"}, "timestep_dim": 1280})
-
-    def encode_adm(self, **kwargs):
-        clip_pooled = sdxl_pooled(kwargs, self.noise_augmentor)
-        width = kwargs.get("width", 768)
-        height = kwargs.get("height", 768)
-        crop_w = kwargs.get("crop_w", 0)
-        crop_h = kwargs.get("crop_h", 0)
-        target_width = kwargs.get("target_width", width)
-        target_height = kwargs.get("target_height", height)
-
-        out = []
-        out.append(self.embedder(torch.Tensor([height])))
-        out.append(self.embedder(torch.Tensor([width])))
-        out.append(self.embedder(torch.Tensor([crop_h])))
-        out.append(self.embedder(torch.Tensor([crop_w])))
-        out.append(self.embedder(torch.Tensor([target_height])))
-        out.append(self.embedder(torch.Tensor([target_width])))
-        flat = torch.flatten(torch.cat(out)).unsqueeze(dim=0).repeat(clip_pooled.shape[0], 1)
-        return torch.cat((clip_pooled.to(flat.device), flat), dim=1)
-
-class SVD_img2vid(BaseModel):
-    def __init__(self, model_config, model_type=ModelType.V_PREDICTION_EDM, device=None):
-        super().__init__(model_config, model_type, device=device)
-        self.embedder = Timestep(256)
-
-    def encode_adm(self, **kwargs):
-        fps_id = kwargs.get("fps", 6) - 1
-        motion_bucket_id = kwargs.get("motion_bucket_id", 127)
-        augmentation = kwargs.get("augmentation_level", 0)
-
-        out = []
-        out.append(self.embedder(torch.Tensor([fps_id])))
-        out.append(self.embedder(torch.Tensor([motion_bucket_id])))
-        out.append(self.embedder(torch.Tensor([augmentation])))
-
-        flat = torch.flatten(torch.cat(out)).unsqueeze(dim=0)
-        return flat
-
-    def extra_conds(self, **kwargs):
-        out = {}
-        adm = self.encode_adm(**kwargs)
-        if adm is not None:
-            out['y'] = comfy.conds.CONDRegular(adm)
-
-        latent_image = kwargs.get("concat_latent_image", None)
-        noise = kwargs.get("noise", None)
-        device = kwargs["device"]
-
-        if latent_image is None:
-            latent_image = torch.zeros_like(noise)
-
-        if latent_image.shape[1:] != noise.shape[1:]:
-            latent_image = utils.common_upscale(latent_image, noise.shape[-1], noise.shape[-2], "bilinear", "center")
-
-        latent_image = utils.resize_to_batch_size(latent_image, noise.shape[0])
-
-        out['c_concat'] = comfy.conds.CONDNoiseShape(latent_image)
-
-        cross_attn = kwargs.get("cross_attn", None)
-        if cross_attn is not None:
-            out['c_crossattn'] = comfy.conds.CONDCrossAttn(cross_attn)
-
-        if "time_conditioning" in kwargs:
-            out["time_context"] = comfy.conds.CONDCrossAttn(kwargs["time_conditioning"])
-
-        out['num_video_frames'] = comfy.conds.CONDConstant(noise.shape[0])
-        return out
-
-class SV3D_u(SVD_img2vid):
-    def encode_adm(self, **kwargs):
-        augmentation = kwargs.get("augmentation_level", 0)
-
-        out = []
-        out.append(self.embedder(torch.flatten(torch.Tensor([augmentation]))))
-
-        flat = torch.flatten(torch.cat(out)).unsqueeze(dim=0)
-        return flat
-
-class SV3D_p(SVD_img2vid):
-    def __init__(self, model_config, model_type=ModelType.V_PREDICTION_EDM, device=None):
-        super().__init__(model_config, model_type, device=device)
-        self.embedder_512 = Timestep(512)
-
-    def encode_adm(self, **kwargs):
-        augmentation = kwargs.get("augmentation_level", 0)
-        elevation = kwargs.get("elevation", 0) #elevation and azimuth are in degrees here
-        azimuth = kwargs.get("azimuth", 0)
-        noise = kwargs.get("noise", None)
-
-        out = []
-        out.append(self.embedder(torch.flatten(torch.Tensor([augmentation]))))
-        out.append(self.embedder_512(torch.deg2rad(torch.fmod(torch.flatten(90 - torch.Tensor([elevation])), 360.0))))
-        out.append(self.embedder_512(torch.deg2rad(torch.fmod(torch.flatten(torch.Tensor([azimuth])), 360.0))))
-
-        out = list(map(lambda a: utils.resize_to_batch_size(a, noise.shape[0]), out))
-        return torch.cat(out, dim=1)
-
-
-class Stable_Zero123(BaseModel):
-    def __init__(self, model_config, model_type=ModelType.EPS, device=None, cc_projection_weight=None, cc_projection_bias=None):
-        super().__init__(model_config, model_type, device=device)
-        self.cc_projection = comfy.ops.manual_cast.Linear(cc_projection_weight.shape[1], cc_projection_weight.shape[0], dtype=self.get_dtype(), device=device)
-        self.cc_projection.weight.copy_(cc_projection_weight)
-        self.cc_projection.bias.copy_(cc_projection_bias)
-
-    def extra_conds(self, **kwargs):
-        out = {}
-
-        latent_image = kwargs.get("concat_latent_image", None)
-        noise = kwargs.get("noise", None)
-
-        if latent_image is None:
-            latent_image = torch.zeros_like(noise)
-
-        if latent_image.shape[1:] != noise.shape[1:]:
-            latent_image = utils.common_upscale(latent_image, noise.shape[-1], noise.shape[-2], "bilinear", "center")
-
-        latent_image = utils.resize_to_batch_size(latent_image, noise.shape[0])
-
-        out['c_concat'] = comfy.conds.CONDNoiseShape(latent_image)
-
-        cross_attn = kwargs.get("cross_attn", None)
-        if cross_attn is not None:
-            if cross_attn.shape[-1] != 768:
-                cross_attn = self.cc_projection(cross_attn)
-            out['c_crossattn'] = comfy.conds.CONDCrossAttn(cross_attn)
-        return out
-
-class SD_X4Upscaler(BaseModel):
-    def __init__(self, model_config, model_type=ModelType.V_PREDICTION, device=None):
-        super().__init__(model_config, model_type, device=device)
-        self.noise_augmentor = ImageConcatWithNoiseAugmentation(noise_schedule_config={"linear_start": 0.0001, "linear_end": 0.02}, max_noise_level=350)
-
-    def extra_conds(self, **kwargs):
-        out = {}
-
-        image = kwargs.get("concat_image", None)
-        noise = kwargs.get("noise", None)
-        noise_augment = kwargs.get("noise_augmentation", 0.0)
-        device = kwargs["device"]
-        seed = kwargs["seed"] - 10
-
-        noise_level = round((self.noise_augmentor.max_noise_level) * noise_augment)
-
-        if image is None:
-            image = torch.zeros_like(noise)[:,:3]
-
-        if image.shape[1:] != noise.shape[1:]:
-            image = utils.common_upscale(image.to(device), noise.shape[-1], noise.shape[-2], "bilinear", "center")
-
-        noise_level = torch.tensor([noise_level], device=device)
-        if noise_augment > 0:
-            image, noise_level = self.noise_augmentor(image.to(device), noise_level=noise_level, seed=seed)
-
-        image = utils.resize_to_batch_size(image, noise.shape[0])
-
-        out['c_concat'] = comfy.conds.CONDNoiseShape(image)
-        out['y'] = comfy.conds.CONDRegular(noise_level)
-        return out
-
-class IP2P:
-    def extra_conds(self, **kwargs):
-        out = {}
-
-        image = kwargs.get("concat_latent_image", None)
-        noise = kwargs.get("noise", None)
-        device = kwargs["device"]
-
-        if image is None:
-            image = torch.zeros_like(noise)
-
-        if image.shape[1:] != noise.shape[1:]:
-            image = utils.common_upscale(image.to(device), noise.shape[-1], noise.shape[-2], "bilinear", "center")
-
-        image = utils.resize_to_batch_size(image, noise.shape[0])
-
-        out['c_concat'] = comfy.conds.CONDNoiseShape(self.process_ip2p_image_in(image))
-        adm = self.encode_adm(**kwargs)
-        if adm is not None:
-            out['y'] = comfy.conds.CONDRegular(adm)
-        return out
-
-class SD15_instructpix2pix(IP2P, BaseModel):
-    def __init__(self, model_config, model_type=ModelType.EPS, device=None):
-        super().__init__(model_config, model_type, device=device)
-        self.process_ip2p_image_in = lambda image: image
-
-class SDXL_instructpix2pix(IP2P, SDXL):
-    def __init__(self, model_config, model_type=ModelType.EPS, device=None):
-        super().__init__(model_config, model_type, device=device)
-        if model_type == ModelType.V_PREDICTION_EDM:
-            self.process_ip2p_image_in = lambda image: comfy.latent_formats.SDXL().process_in(image) #cosxl ip2p
-        else:
-            self.process_ip2p_image_in = lambda image: image #diffusers ip2p
-
-
-class StableCascade_C(BaseModel):
-    def __init__(self, model_config, model_type=ModelType.STABLE_CASCADE, device=None):
-        super().__init__(model_config, model_type, device=device, unet_model=StageC)
-        self.diffusion_model.eval().requires_grad_(False)
-
-    def extra_conds(self, **kwargs):
-        out = {}
-        clip_text_pooled = kwargs["pooled_output"]
-        if clip_text_pooled is not None:
-            out['clip_text_pooled'] = comfy.conds.CONDRegular(clip_text_pooled)
-
-        if "unclip_conditioning" in kwargs:
-            embeds = []
-            for unclip_cond in kwargs["unclip_conditioning"]:
-                weight = unclip_cond["strength"]
-                embeds.append(unclip_cond["clip_vision_output"].image_embeds.unsqueeze(0) * weight)
-            clip_img = torch.cat(embeds, dim=1)
-        else:
-            clip_img = torch.zeros((1, 1, 768))
-        out["clip_img"] = comfy.conds.CONDRegular(clip_img)
-        out["sca"] = comfy.conds.CONDRegular(torch.zeros((1,)))
-        out["crp"] = comfy.conds.CONDRegular(torch.zeros((1,)))
-
-        cross_attn = kwargs.get("cross_attn", None)
-        if cross_attn is not None:
-            out['clip_text'] = comfy.conds.CONDCrossAttn(cross_attn)
-        return out
-
-
-class StableCascade_B(BaseModel):
-    def __init__(self, model_config, model_type=ModelType.STABLE_CASCADE, device=None):
-        super().__init__(model_config, model_type, device=device, unet_model=StageB)
-        self.diffusion_model.eval().requires_grad_(False)
-
-    def extra_conds(self, **kwargs):
-        out = {}
-        noise = kwargs.get("noise", None)
-
-        clip_text_pooled = kwargs["pooled_output"]
-        if clip_text_pooled is not None:
-            out['clip'] = comfy.conds.CONDRegular(clip_text_pooled)
-
-        #size of prior doesn't really matter if zeros because it gets resized but I still want it to get batched
-        prior = kwargs.get("stable_cascade_prior", torch.zeros((1, 16, (noise.shape[2] * 4) // 42, (noise.shape[3] * 4) // 42), dtype=noise.dtype, layout=noise.layout, device=noise.device))
-
-        out["effnet"] = comfy.conds.CONDRegular(prior)
-        out["sca"] = comfy.conds.CONDRegular(torch.zeros((1,)))
-        return out
-
-
-class SD3(BaseModel):
-    def __init__(self, model_config, model_type=ModelType.FLOW, device=None):
-        super().__init__(model_config, model_type, device=device, unet_model=OpenAISignatureMMDITWrapper)
-
-    def encode_adm(self, **kwargs):
-        return kwargs["pooled_output"]
-
-    def extra_conds(self, **kwargs):
-        out = super().extra_conds(**kwargs)
-        cross_attn = kwargs.get("cross_attn", None)
-        if cross_attn is not None:
-            out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn)
-        return out
-
-    def memory_required(self, input_shape):
-        if comfy.model_management.xformers_enabled() or comfy.model_management.pytorch_attention_flash_attention():
-            dtype = self.get_dtype()
-            if self.manual_cast_dtype is not None:
-                dtype = self.manual_cast_dtype
-            #TODO: this probably needs to be tweaked
-            area = input_shape[0] * input_shape[2] * input_shape[3]
-            return (area * comfy.model_management.dtype_size(dtype) * 0.012) * (1024 * 1024)
-        else:
-            area = input_shape[0] * input_shape[2] * input_shape[3]
-            return (area * 0.3) * (1024 * 1024)
-
-
-class StableAudio1(BaseModel):
-    def __init__(self, model_config, seconds_start_embedder_weights, seconds_total_embedder_weights, model_type=ModelType.V_PREDICTION_CONTINUOUS, device=None):
-        super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.audio.dit.AudioDiffusionTransformer)
-        self.seconds_start_embedder = comfy.ldm.audio.embedders.NumberConditioner(768, min_val=0, max_val=512)
-        self.seconds_total_embedder = comfy.ldm.audio.embedders.NumberConditioner(768, min_val=0, max_val=512)
-        self.seconds_start_embedder.load_state_dict(seconds_start_embedder_weights)
-        self.seconds_total_embedder.load_state_dict(seconds_total_embedder_weights)
-
-    def extra_conds(self, **kwargs):
-        out = {}
-
-        noise = kwargs.get("noise", None)
-        device = kwargs["device"]
-
-        seconds_start = kwargs.get("seconds_start", 0)
-        seconds_total = kwargs.get("seconds_total", int(noise.shape[-1] / 21.53))
-
-        seconds_start_embed = self.seconds_start_embedder([seconds_start])[0].to(device)
-        seconds_total_embed = self.seconds_total_embedder([seconds_total])[0].to(device)
-
-        global_embed = torch.cat([seconds_start_embed, seconds_total_embed], dim=-1).reshape((1, -1))
-        out['global_embed'] = comfy.conds.CONDRegular(global_embed)
-
-        cross_attn = kwargs.get("cross_attn", None)
-        if cross_attn is not None:
-            cross_attn = torch.cat([cross_attn.to(device), seconds_start_embed.repeat((cross_attn.shape[0], 1, 1)), seconds_total_embed.repeat((cross_attn.shape[0], 1, 1))], dim=1)
-            out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn)
-        return out
diff --git a/MagicQuill/comfy/model_detection.py b/MagicQuill/comfy/model_detection.py
deleted file mode 100644
index 4843e6a4a27409fe31b5a3758961169124ed0dfe..0000000000000000000000000000000000000000
--- a/MagicQuill/comfy/model_detection.py
+++ /dev/null
@@ -1,433 +0,0 @@
-import comfy.supported_models
-import comfy.supported_models_base
-import math
-import logging
-
-def count_blocks(state_dict_keys, prefix_string):
-    count = 0
-    while True:
-        c = False
-        for k in state_dict_keys:
-            if k.startswith(prefix_string.format(count)):
-                c = True
-                break
-        if c == False:
-            break
-        count += 1
-    return count
-
-def calculate_transformer_depth(prefix, state_dict_keys, state_dict):
-    context_dim = None
-    use_linear_in_transformer = False
-
-    transformer_prefix = prefix + "1.transformer_blocks."
-    transformer_keys = sorted(list(filter(lambda a: a.startswith(transformer_prefix), state_dict_keys)))
-    if len(transformer_keys) > 0:
-        last_transformer_depth = count_blocks(state_dict_keys, transformer_prefix + '{}')
-        context_dim = state_dict['{}0.attn2.to_k.weight'.format(transformer_prefix)].shape[1]
-        use_linear_in_transformer = len(state_dict['{}1.proj_in.weight'.format(prefix)].shape) == 2
-        time_stack = '{}1.time_stack.0.attn1.to_q.weight'.format(prefix) in state_dict or '{}1.time_mix_blocks.0.attn1.to_q.weight'.format(prefix) in state_dict
-        time_stack_cross = '{}1.time_stack.0.attn2.to_q.weight'.format(prefix) in state_dict or '{}1.time_mix_blocks.0.attn2.to_q.weight'.format(prefix) in state_dict
-        return last_transformer_depth, context_dim, use_linear_in_transformer, time_stack, time_stack_cross
-    return None
-
-def detect_unet_config(state_dict, key_prefix):
-    state_dict_keys = list(state_dict.keys())
-
-    if '{}joint_blocks.0.context_block.attn.qkv.weight'.format(key_prefix) in state_dict_keys: #mmdit model
-        unet_config = {}
-        unet_config["in_channels"] = state_dict['{}x_embedder.proj.weight'.format(key_prefix)].shape[1]
-        patch_size = state_dict['{}x_embedder.proj.weight'.format(key_prefix)].shape[2]
-        unet_config["patch_size"] = patch_size
-        unet_config["out_channels"] = state_dict['{}final_layer.linear.weight'.format(key_prefix)].shape[0] // (patch_size * patch_size)
-
-        unet_config["depth"] = state_dict['{}x_embedder.proj.weight'.format(key_prefix)].shape[0] // 64
-        unet_config["input_size"] = None
-        y_key = '{}y_embedder.mlp.0.weight'.format(key_prefix)
-        if y_key in state_dict_keys:
-            unet_config["adm_in_channels"] = state_dict[y_key].shape[1]
-
-        context_key = '{}context_embedder.weight'.format(key_prefix)
-        if context_key in state_dict_keys:
-            in_features = state_dict[context_key].shape[1]
-            out_features = state_dict[context_key].shape[0]
-            unet_config["context_embedder_config"] = {"target": "torch.nn.Linear", "params": {"in_features": in_features, "out_features": out_features}}
-        num_patches_key = '{}pos_embed'.format(key_prefix)
-        if num_patches_key in state_dict_keys:
-            num_patches = state_dict[num_patches_key].shape[1]
-            unet_config["num_patches"] = num_patches
-            unet_config["pos_embed_max_size"] = round(math.sqrt(num_patches))
-
-        rms_qk = '{}joint_blocks.0.context_block.attn.ln_q.weight'.format(key_prefix)
-        if rms_qk in state_dict_keys:
-            unet_config["qk_norm"] = "rms"
-
-        unet_config["pos_embed_scaling_factor"] = None #unused for inference
-        context_processor = '{}context_processor.layers.0.attn.qkv.weight'.format(key_prefix)
-        if context_processor in state_dict_keys:
-            unet_config["context_processor_layers"] = count_blocks(state_dict_keys, '{}context_processor.layers.'.format(key_prefix) + '{}.')
-        return unet_config
-
-    if '{}clf.1.weight'.format(key_prefix) in state_dict_keys: #stable cascade
-        unet_config = {}
-        text_mapper_name = '{}clip_txt_mapper.weight'.format(key_prefix)
-        if text_mapper_name in state_dict_keys:
-            unet_config['stable_cascade_stage'] = 'c'
-            w = state_dict[text_mapper_name]
-            if w.shape[0] == 1536: #stage c lite
-                unet_config['c_cond'] = 1536
-                unet_config['c_hidden'] = [1536, 1536]
-                unet_config['nhead'] = [24, 24]
-                unet_config['blocks'] = [[4, 12], [12, 4]]
-            elif w.shape[0] == 2048: #stage c full
-                unet_config['c_cond'] = 2048
-        elif '{}clip_mapper.weight'.format(key_prefix) in state_dict_keys:
-            unet_config['stable_cascade_stage'] = 'b'
-            w = state_dict['{}down_blocks.1.0.channelwise.0.weight'.format(key_prefix)]
-            if w.shape[-1] == 640:
-                unet_config['c_hidden'] = [320, 640, 1280, 1280]
-                unet_config['nhead'] = [-1, -1, 20, 20]
-                unet_config['blocks'] = [[2, 6, 28, 6], [6, 28, 6, 2]]
-                unet_config['block_repeat'] = [[1, 1, 1, 1], [3, 3, 2, 2]]
-            elif w.shape[-1] == 576: #stage b lite
-                unet_config['c_hidden'] = [320, 576, 1152, 1152]
-                unet_config['nhead'] = [-1, 9, 18, 18]
-                unet_config['blocks'] = [[2, 4, 14, 4], [4, 14, 4, 2]]
-                unet_config['block_repeat'] = [[1, 1, 1, 1], [2, 2, 2, 2]]
-        return unet_config
-
-    if '{}transformer.rotary_pos_emb.inv_freq'.format(key_prefix) in state_dict_keys: #stable audio dit
-        unet_config = {}
-        unet_config["audio_model"] = "dit1.0"
-        return unet_config
-
-    unet_config = {
-        "use_checkpoint": False,
-        "image_size": 32,
-        "use_spatial_transformer": True,
-        "legacy": False
-    }
-
-    y_input = '{}label_emb.0.0.weight'.format(key_prefix)
-    if y_input in state_dict_keys:
-        unet_config["num_classes"] = "sequential"
-        unet_config["adm_in_channels"] = state_dict[y_input].shape[1]
-    else:
-        unet_config["adm_in_channels"] = None
-
-    model_channels = state_dict['{}input_blocks.0.0.weight'.format(key_prefix)].shape[0]
-    in_channels = state_dict['{}input_blocks.0.0.weight'.format(key_prefix)].shape[1]
-
-    out_key = '{}out.2.weight'.format(key_prefix)
-    if out_key in state_dict:
-        out_channels = state_dict[out_key].shape[0]
-    else:
-        out_channels = 4
-
-    num_res_blocks = []
-    channel_mult = []
-    attention_resolutions = []
-    transformer_depth = []
-    transformer_depth_output = []
-    context_dim = None
-    use_linear_in_transformer = False
-
-    video_model = False
-    video_model_cross = False
-
-    current_res = 1
-    count = 0
-
-    last_res_blocks = 0
-    last_channel_mult = 0
-
-    input_block_count = count_blocks(state_dict_keys, '{}input_blocks'.format(key_prefix) + '.{}.')
-    for count in range(input_block_count):
-        prefix = '{}input_blocks.{}.'.format(key_prefix, count)
-        prefix_output = '{}output_blocks.{}.'.format(key_prefix, input_block_count - count - 1)
-
-        block_keys = sorted(list(filter(lambda a: a.startswith(prefix), state_dict_keys)))
-        if len(block_keys) == 0:
-            break
-
-        block_keys_output = sorted(list(filter(lambda a: a.startswith(prefix_output), state_dict_keys)))
-
-        if "{}0.op.weight".format(prefix) in block_keys: #new layer
-            num_res_blocks.append(last_res_blocks)
-            channel_mult.append(last_channel_mult)
-
-            current_res *= 2
-            last_res_blocks = 0
-            last_channel_mult = 0
-            out = calculate_transformer_depth(prefix_output, state_dict_keys, state_dict)
-            if out is not None:
-                transformer_depth_output.append(out[0])
-            else:
-                transformer_depth_output.append(0)
-        else:
-            res_block_prefix = "{}0.in_layers.0.weight".format(prefix)
-            if res_block_prefix in block_keys:
-                last_res_blocks += 1
-                last_channel_mult = state_dict["{}0.out_layers.3.weight".format(prefix)].shape[0] // model_channels
-
-                out = calculate_transformer_depth(prefix, state_dict_keys, state_dict)
-                if out is not None:
-                    transformer_depth.append(out[0])
-                    if context_dim is None:
-                        context_dim = out[1]
-                        use_linear_in_transformer = out[2]
-                        video_model = out[3]
-                        video_model_cross = out[4]
-                else:
-                    transformer_depth.append(0)
-
-            res_block_prefix = "{}0.in_layers.0.weight".format(prefix_output)
-            if res_block_prefix in block_keys_output:
-                out = calculate_transformer_depth(prefix_output, state_dict_keys, state_dict)
-                if out is not None:
-                    transformer_depth_output.append(out[0])
-                else:
-                    transformer_depth_output.append(0)
-
-
-    num_res_blocks.append(last_res_blocks)
-    channel_mult.append(last_channel_mult)
-    if "{}middle_block.1.proj_in.weight".format(key_prefix) in state_dict_keys:
-        transformer_depth_middle = count_blocks(state_dict_keys, '{}middle_block.1.transformer_blocks.'.format(key_prefix) + '{}')
-    elif "{}middle_block.0.in_layers.0.weight".format(key_prefix) in state_dict_keys:
-        transformer_depth_middle = -1
-    else:
-        transformer_depth_middle = -2
-
-    unet_config["in_channels"] = in_channels
-    unet_config["out_channels"] = out_channels
-    unet_config["model_channels"] = model_channels
-    unet_config["num_res_blocks"] = num_res_blocks
-    unet_config["transformer_depth"] = transformer_depth
-    unet_config["transformer_depth_output"] = transformer_depth_output
-    unet_config["channel_mult"] = channel_mult
-    unet_config["transformer_depth_middle"] = transformer_depth_middle
-    unet_config['use_linear_in_transformer'] = use_linear_in_transformer
-    unet_config["context_dim"] = context_dim
-
-    if video_model:
-        unet_config["extra_ff_mix_layer"] = True
-        unet_config["use_spatial_context"] = True
-        unet_config["merge_strategy"] = "learned_with_images"
-        unet_config["merge_factor"] = 0.0
-        unet_config["video_kernel_size"] = [3, 1, 1]
-        unet_config["use_temporal_resblock"] = True
-        unet_config["use_temporal_attention"] = True
-        unet_config["disable_temporal_crossattention"] = not video_model_cross
-    else:
-        unet_config["use_temporal_resblock"] = False
-        unet_config["use_temporal_attention"] = False
-
-    return unet_config
-
-def model_config_from_unet_config(unet_config, state_dict=None):
-    for model_config in comfy.supported_models.models:
-        if model_config.matches(unet_config, state_dict):
-            return model_config(unet_config)
-
-    logging.error("no match {}".format(unet_config))
-    return None
-
-def model_config_from_unet(state_dict, unet_key_prefix, use_base_if_no_match=False):
-    unet_config = detect_unet_config(state_dict, unet_key_prefix)
-    model_config = model_config_from_unet_config(unet_config, state_dict)
-    if model_config is None and use_base_if_no_match:
-        return comfy.supported_models_base.BASE(unet_config)
-    else:
-        return model_config
-
-def unet_prefix_from_state_dict(state_dict):
-    if "model.model.postprocess_conv.weight" in state_dict: #audio models
-        unet_key_prefix = "model.model."
-    else:
-        unet_key_prefix = "model.diffusion_model."
-    return unet_key_prefix
-
-def convert_config(unet_config):
-    new_config = unet_config.copy()
-    num_res_blocks = new_config.get("num_res_blocks", None)
-    channel_mult = new_config.get("channel_mult", None)
-
-    if isinstance(num_res_blocks, int):
-        num_res_blocks = len(channel_mult) * [num_res_blocks]
-
-    if "attention_resolutions" in new_config:
-        attention_resolutions = new_config.pop("attention_resolutions")
-        transformer_depth = new_config.get("transformer_depth", None)
-        transformer_depth_middle = new_config.get("transformer_depth_middle", None)
-
-        if isinstance(transformer_depth, int):
-            transformer_depth = len(channel_mult) * [transformer_depth]
-        if transformer_depth_middle is None:
-            transformer_depth_middle =  transformer_depth[-1]
-        t_in = []
-        t_out = []
-        s = 1
-        for i in range(len(num_res_blocks)):
-            res = num_res_blocks[i]
-            d = 0
-            if s in attention_resolutions:
-                d = transformer_depth[i]
-
-            t_in += [d] * res
-            t_out += [d] * (res + 1)
-            s *= 2
-        transformer_depth = t_in
-        transformer_depth_output = t_out
-        new_config["transformer_depth"] = t_in
-        new_config["transformer_depth_output"] = t_out
-        new_config["transformer_depth_middle"] = transformer_depth_middle
-
-    new_config["num_res_blocks"] = num_res_blocks
-    return new_config
-
-
-def unet_config_from_diffusers_unet(state_dict, dtype=None):
-    match = {}
-    transformer_depth = []
-
-    attn_res = 1
-    down_blocks = count_blocks(state_dict, "down_blocks.{}")
-    for i in range(down_blocks):
-        attn_blocks = count_blocks(state_dict, "down_blocks.{}.attentions.".format(i) + '{}')
-        res_blocks = count_blocks(state_dict, "down_blocks.{}.resnets.".format(i) + '{}')
-        for ab in range(attn_blocks):
-            transformer_count = count_blocks(state_dict, "down_blocks.{}.attentions.{}.transformer_blocks.".format(i, ab) + '{}')
-            transformer_depth.append(transformer_count)
-            if transformer_count > 0:
-                match["context_dim"] = state_dict["down_blocks.{}.attentions.{}.transformer_blocks.0.attn2.to_k.weight".format(i, ab)].shape[1]
-
-        attn_res *= 2
-        if attn_blocks == 0:
-            for i in range(res_blocks):
-                transformer_depth.append(0)
-
-    match["transformer_depth"] = transformer_depth
-
-    match["model_channels"] = state_dict["conv_in.weight"].shape[0]
-    match["in_channels"] = state_dict["conv_in.weight"].shape[1]
-    match["adm_in_channels"] = None
-    if "class_embedding.linear_1.weight" in state_dict:
-        match["adm_in_channels"] = state_dict["class_embedding.linear_1.weight"].shape[1]
-    elif "add_embedding.linear_1.weight" in state_dict:
-        match["adm_in_channels"] = state_dict["add_embedding.linear_1.weight"].shape[1]
-
-    SDXL = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
-            'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320,
-            'num_res_blocks': [2, 2, 2], 'transformer_depth': [0, 0, 2, 2, 10, 10], 'channel_mult': [1, 2, 4], 'transformer_depth_middle': 10,
-            'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64, 'transformer_depth_output': [0, 0, 0, 2, 2, 2, 10, 10, 10],
-            'use_temporal_attention': False, 'use_temporal_resblock': False}
-
-    SDXL_refiner = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
-                    'num_classes': 'sequential', 'adm_in_channels': 2560, 'dtype': dtype, 'in_channels': 4, 'model_channels': 384,
-                    'num_res_blocks': [2, 2, 2, 2], 'transformer_depth': [0, 0, 4, 4, 4, 4, 0, 0], 'channel_mult': [1, 2, 4, 4], 'transformer_depth_middle': 4,
-                    'use_linear_in_transformer': True, 'context_dim': 1280, 'num_head_channels': 64, 'transformer_depth_output': [0, 0, 0, 4, 4, 4, 4, 4, 4, 0, 0, 0],
-                    'use_temporal_attention': False, 'use_temporal_resblock': False}
-
-    SD21 = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
-            'adm_in_channels': None, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320, 'num_res_blocks': [2, 2, 2, 2],
-            'transformer_depth': [1, 1, 1, 1, 1, 1, 0, 0], 'channel_mult': [1, 2, 4, 4], 'transformer_depth_middle': 1, 'use_linear_in_transformer': True,
-            'context_dim': 1024, 'num_head_channels': 64, 'transformer_depth_output': [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0],
-            'use_temporal_attention': False, 'use_temporal_resblock': False}
-
-    SD21_uncliph = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
-                    'num_classes': 'sequential', 'adm_in_channels': 2048, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320,
-                    'num_res_blocks': [2, 2, 2, 2], 'transformer_depth': [1, 1, 1, 1, 1, 1, 0, 0], 'channel_mult': [1, 2, 4, 4], 'transformer_depth_middle': 1,
-                    'use_linear_in_transformer': True, 'context_dim': 1024, 'num_head_channels': 64, 'transformer_depth_output': [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0],
-                    'use_temporal_attention': False, 'use_temporal_resblock': False}
-
-    SD21_unclipl = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
-                    'num_classes': 'sequential', 'adm_in_channels': 1536, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320,
-                    'num_res_blocks': [2, 2, 2, 2], 'transformer_depth': [1, 1, 1, 1, 1, 1, 0, 0], 'channel_mult': [1, 2, 4, 4], 'transformer_depth_middle': 1,
-                    'use_linear_in_transformer': True, 'context_dim': 1024, 'num_head_channels': 64, 'transformer_depth_output': [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0],
-                    'use_temporal_attention': False, 'use_temporal_resblock': False}
-
-    SD15 = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, 'adm_in_channels': None,
-            'dtype': dtype, 'in_channels': 4, 'model_channels': 320, 'num_res_blocks': [2, 2, 2, 2], 'transformer_depth': [1, 1, 1, 1, 1, 1, 0, 0],
-            'channel_mult': [1, 2, 4, 4], 'transformer_depth_middle': 1, 'use_linear_in_transformer': False, 'context_dim': 768, 'num_heads': 8,
-            'transformer_depth_output': [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0],
-            'use_temporal_attention': False, 'use_temporal_resblock': False}
-
-    SDXL_mid_cnet = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
-                     'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320,
-                     'num_res_blocks': [2, 2, 2], 'transformer_depth': [0, 0, 0, 0, 1, 1], 'channel_mult': [1, 2, 4], 'transformer_depth_middle': 1,
-                     'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64, 'transformer_depth_output': [0, 0, 0, 0, 0, 0, 1, 1, 1],
-                     'use_temporal_attention': False, 'use_temporal_resblock': False}
-
-    SDXL_small_cnet = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
-                       'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320,
-                       'num_res_blocks': [2, 2, 2], 'transformer_depth': [0, 0, 0, 0, 0, 0], 'channel_mult': [1, 2, 4], 'transformer_depth_middle': 0,
-                       'use_linear_in_transformer': True, 'num_head_channels': 64, 'context_dim': 1, 'transformer_depth_output': [0, 0, 0, 0, 0, 0, 0, 0, 0],
-                       'use_temporal_attention': False, 'use_temporal_resblock': False}
-
-    SDXL_diffusers_inpaint = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
-                              'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 9, 'model_channels': 320,
-                              'num_res_blocks': [2, 2, 2], 'transformer_depth': [0, 0, 2, 2, 10, 10], 'channel_mult': [1, 2, 4], 'transformer_depth_middle': 10,
-                              'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64, 'transformer_depth_output': [0, 0, 0, 2, 2, 2, 10, 10, 10],
-                              'use_temporal_attention': False, 'use_temporal_resblock': False}
-
-    SDXL_diffusers_ip2p = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
-                              'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 8, 'model_channels': 320,
-                              'num_res_blocks': [2, 2, 2], 'transformer_depth': [0, 0, 2, 2, 10, 10], 'channel_mult': [1, 2, 4], 'transformer_depth_middle': 10,
-                              'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64, 'transformer_depth_output': [0, 0, 0, 2, 2, 2, 10, 10, 10],
-                              'use_temporal_attention': False, 'use_temporal_resblock': False}
-
-    SSD_1B = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
-              'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320,
-              'num_res_blocks': [2, 2, 2], 'transformer_depth': [0, 0, 2, 2, 4, 4], 'transformer_depth_output': [0, 0, 0, 1, 1, 2, 10, 4, 4],
-              'channel_mult': [1, 2, 4], 'transformer_depth_middle': -1, 'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64,
-              'use_temporal_attention': False, 'use_temporal_resblock': False}
-
-    Segmind_Vega = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
-              'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320,
-              'num_res_blocks': [2, 2, 2], 'transformer_depth': [0, 0, 1, 1, 2, 2], 'transformer_depth_output': [0, 0, 0, 1, 1, 1, 2, 2, 2],
-              'channel_mult': [1, 2, 4], 'transformer_depth_middle': -1, 'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64,
-              'use_temporal_attention': False, 'use_temporal_resblock': False}
-
-    KOALA_700M = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
-              'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320,
-              'num_res_blocks': [1, 1, 1], 'transformer_depth': [0, 2, 5], 'transformer_depth_output': [0, 0, 2, 2, 5, 5],
-              'channel_mult': [1, 2, 4], 'transformer_depth_middle': -2, 'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64,
-              'use_temporal_attention': False, 'use_temporal_resblock': False}
-
-    KOALA_1B = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
-              'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320,
-              'num_res_blocks': [1, 1, 1], 'transformer_depth': [0, 2, 6], 'transformer_depth_output': [0, 0, 2, 2, 6, 6],
-              'channel_mult': [1, 2, 4], 'transformer_depth_middle': 6, 'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64,
-              'use_temporal_attention': False, 'use_temporal_resblock': False}
-
-    SD09_XS = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
-            'adm_in_channels': None, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320, 'num_res_blocks': [1, 1, 1],
-            'transformer_depth': [1, 1, 1], 'channel_mult': [1, 2, 4], 'transformer_depth_middle': -2, 'use_linear_in_transformer': True,
-            'context_dim': 1024, 'num_head_channels': 64, 'transformer_depth_output': [1, 1, 1, 1, 1, 1],
-            'use_temporal_attention': False, 'use_temporal_resblock': False, 'disable_self_attentions': [True, False, False]}
-
-    SD_XS = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
-            'adm_in_channels': None, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320, 'num_res_blocks': [1, 1, 1],
-            'transformer_depth': [0, 1, 1], 'channel_mult': [1, 2, 4], 'transformer_depth_middle': -2, 'use_linear_in_transformer': False,
-            'context_dim': 768, 'num_head_channels': 64, 'transformer_depth_output': [0, 0, 1, 1, 1, 1],
-            'use_temporal_attention': False, 'use_temporal_resblock': False}
-
-
-    supported_models = [SDXL, SDXL_refiner, SD21, SD15, SD21_uncliph, SD21_unclipl, SDXL_mid_cnet, SDXL_small_cnet, SDXL_diffusers_inpaint, SSD_1B, Segmind_Vega, KOALA_700M, KOALA_1B, SD09_XS, SD_XS, SDXL_diffusers_ip2p]
-
-    for unet_config in supported_models:
-        matches = True
-        for k in match:
-            if match[k] != unet_config[k]:
-                matches = False
-                break
-        if matches:
-            return convert_config(unet_config)
-    return None
-
-def model_config_from_diffusers_unet(state_dict):
-    unet_config = unet_config_from_diffusers_unet(state_dict)
-    if unet_config is not None:
-        return model_config_from_unet_config(unet_config)
-    return None
diff --git a/MagicQuill/comfy/model_management.py b/MagicQuill/comfy/model_management.py
deleted file mode 100644
index 047193290fa27199431300679b1dcfc64383ab85..0000000000000000000000000000000000000000
--- a/MagicQuill/comfy/model_management.py
+++ /dev/null
@@ -1,957 +0,0 @@
-import psutil
-import logging
-from enum import Enum
-from comfy.cli_args import args
-import torch
-import sys
-import platform
-
-class VRAMState(Enum):
-    DISABLED = 0    #No vram present: no need to move models to vram
-    NO_VRAM = 1     #Very low vram: enable all the options to save vram
-    LOW_VRAM = 2
-    NORMAL_VRAM = 3
-    HIGH_VRAM = 4
-    SHARED = 5      #No dedicated vram: memory shared between CPU and GPU but models still need to be moved between both.
-
-class CPUState(Enum):
-    GPU = 0
-    CPU = 1
-    MPS = 2
-
-# Determine VRAM State
-vram_state = VRAMState.NORMAL_VRAM
-set_vram_to = VRAMState.NORMAL_VRAM
-cpu_state = CPUState.GPU
-
-total_vram = 0
-
-lowvram_available = True
-xpu_available = False
-
-if args.deterministic:
-    logging.info("Using deterministic algorithms for pytorch")
-    torch.use_deterministic_algorithms(True, warn_only=True)
-
-directml_enabled = False
-if args.directml is not None:
-    import torch_directml
-    directml_enabled = True
-    device_index = args.directml
-    if device_index < 0:
-        directml_device = torch_directml.device()
-    else:
-        directml_device = torch_directml.device(device_index)
-    logging.info("Using directml with device: {}".format(torch_directml.device_name(device_index)))
-    # torch_directml.disable_tiled_resources(True)
-    lowvram_available = False #TODO: need to find a way to get free memory in directml before this can be enabled by default.
-
-try:
-    import intel_extension_for_pytorch as ipex
-    if torch.xpu.is_available():
-        xpu_available = True
-except:
-    pass
-
-try:
-    if torch.backends.mps.is_available():
-        cpu_state = CPUState.MPS
-        import torch.mps
-except:
-    pass
-
-if args.cpu:
-    cpu_state = CPUState.CPU
-
-def is_intel_xpu():
-    global cpu_state
-    global xpu_available
-    if cpu_state == CPUState.GPU:
-        if xpu_available:
-            return True
-    return False
-
-def get_torch_device():
-    global directml_enabled
-    global cpu_state
-    if directml_enabled:
-        global directml_device
-        return directml_device
-    if cpu_state == CPUState.MPS:
-        return torch.device("mps")
-    if cpu_state == CPUState.CPU:
-        return torch.device("cpu")
-    else:
-        if is_intel_xpu():
-            return torch.device("xpu", torch.xpu.current_device())
-        else:
-            return torch.device(torch.cuda.current_device())
-
-def get_total_memory(dev=None, torch_total_too=False):
-    global directml_enabled
-    if dev is None:
-        dev = get_torch_device()
-
-    if hasattr(dev, 'type') and (dev.type == 'cpu' or dev.type == 'mps'):
-        mem_total = psutil.virtual_memory().total
-        mem_total_torch = mem_total
-    else:
-        if directml_enabled:
-            mem_total = 1024 * 1024 * 1024 #TODO
-            mem_total_torch = mem_total
-        elif is_intel_xpu():
-            stats = torch.xpu.memory_stats(dev)
-            mem_reserved = stats['reserved_bytes.all.current']
-            mem_total_torch = mem_reserved
-            mem_total = torch.xpu.get_device_properties(dev).total_memory
-        else:
-            stats = torch.cuda.memory_stats(dev)
-            mem_reserved = stats['reserved_bytes.all.current']
-            _, mem_total_cuda = torch.cuda.mem_get_info(dev)
-            mem_total_torch = mem_reserved
-            mem_total = mem_total_cuda
-
-    if torch_total_too:
-        return (mem_total, mem_total_torch)
-    else:
-        return mem_total
-
-total_vram = get_total_memory(get_torch_device()) / (1024 * 1024)
-total_ram = psutil.virtual_memory().total / (1024 * 1024)
-logging.info("Total VRAM {:0.0f} MB, total RAM {:0.0f} MB".format(total_vram, total_ram))
-
-try:
-    logging.info("pytorch version: {}".format(torch.version.__version__))
-except:
-    pass
-
-try:
-    OOM_EXCEPTION = torch.cuda.OutOfMemoryError
-except:
-    OOM_EXCEPTION = Exception
-
-XFORMERS_VERSION = ""
-XFORMERS_ENABLED_VAE = True
-if args.disable_xformers:
-    XFORMERS_IS_AVAILABLE = False
-else:
-    try:
-        import xformers
-        import xformers.ops
-        XFORMERS_IS_AVAILABLE = True
-        try:
-            XFORMERS_IS_AVAILABLE = xformers._has_cpp_library
-        except:
-            pass
-        try:
-            XFORMERS_VERSION = xformers.version.__version__
-            logging.info("xformers version: {}".format(XFORMERS_VERSION))
-            if XFORMERS_VERSION.startswith("0.0.18"):
-                logging.warning("\nWARNING: This version of xformers has a major bug where you will get black images when generating high resolution images.")
-                logging.warning("Please downgrade or upgrade xformers to a different version.\n")
-                XFORMERS_ENABLED_VAE = False
-        except:
-            pass
-    except:
-        XFORMERS_IS_AVAILABLE = False
-
-def is_nvidia():
-    global cpu_state
-    if cpu_state == CPUState.GPU:
-        if torch.version.cuda:
-            return True
-    return False
-
-ENABLE_PYTORCH_ATTENTION = False
-if args.use_pytorch_cross_attention:
-    ENABLE_PYTORCH_ATTENTION = True
-    XFORMERS_IS_AVAILABLE = False
-
-VAE_DTYPES = [torch.float32]
-
-try:
-    if is_nvidia():
-        torch_version = torch.version.__version__
-        if int(torch_version[0]) >= 2:
-            if ENABLE_PYTORCH_ATTENTION == False and args.use_split_cross_attention == False and args.use_quad_cross_attention == False:
-                ENABLE_PYTORCH_ATTENTION = True
-            if torch.cuda.is_bf16_supported() and torch.cuda.get_device_properties(torch.cuda.current_device()).major >= 8:
-                VAE_DTYPES = [torch.bfloat16] + VAE_DTYPES
-    if is_intel_xpu():
-        if args.use_split_cross_attention == False and args.use_quad_cross_attention == False:
-            ENABLE_PYTORCH_ATTENTION = True
-except:
-    pass
-
-if is_intel_xpu():
-    VAE_DTYPES = [torch.bfloat16] + VAE_DTYPES
-
-if args.cpu_vae:
-    VAE_DTYPES = [torch.float32]
-
-
-if ENABLE_PYTORCH_ATTENTION:
-    torch.backends.cuda.enable_math_sdp(True)
-    torch.backends.cuda.enable_flash_sdp(True)
-    torch.backends.cuda.enable_mem_efficient_sdp(True)
-
-if args.lowvram:
-    set_vram_to = VRAMState.LOW_VRAM
-    lowvram_available = True
-elif args.novram:
-    set_vram_to = VRAMState.NO_VRAM
-elif args.highvram or args.gpu_only:
-    vram_state = VRAMState.HIGH_VRAM
-
-FORCE_FP32 = False
-FORCE_FP16 = False
-if args.force_fp32:
-    logging.info("Forcing FP32, if this improves things please report it.")
-    FORCE_FP32 = True
-
-if args.force_fp16:
-    logging.info("Forcing FP16.")
-    FORCE_FP16 = True
-
-if lowvram_available:
-    if set_vram_to in (VRAMState.LOW_VRAM, VRAMState.NO_VRAM):
-        vram_state = set_vram_to
-
-
-if cpu_state != CPUState.GPU:
-    vram_state = VRAMState.DISABLED
-
-if cpu_state == CPUState.MPS:
-    vram_state = VRAMState.SHARED
-
-logging.info(f"Set vram state to: {vram_state.name}")
-
-DISABLE_SMART_MEMORY = args.disable_smart_memory
-
-if DISABLE_SMART_MEMORY:
-    logging.info("Disabling smart memory management")
-
-def get_torch_device_name(device):
-    if hasattr(device, 'type'):
-        if device.type == "cuda":
-            try:
-                allocator_backend = torch.cuda.get_allocator_backend()
-            except:
-                allocator_backend = ""
-            return "{} {} : {}".format(device, torch.cuda.get_device_name(device), allocator_backend)
-        else:
-            return "{}".format(device.type)
-    elif is_intel_xpu():
-        return "{} {}".format(device, torch.xpu.get_device_name(device))
-    else:
-        return "CUDA {}: {}".format(device, torch.cuda.get_device_name(device))
-
-try:
-    logging.info("Device: {}".format(get_torch_device_name(get_torch_device())))
-except:
-    logging.warning("Could not pick default device.")
-
-
-current_loaded_models = []
-
-def module_size(module):
-    module_mem = 0
-    sd = module.state_dict()
-    for k in sd:
-        t = sd[k]
-        module_mem += t.nelement() * t.element_size()
-    return module_mem
-
-class LoadedModel:
-    def __init__(self, model):
-        self.model = model
-        self.device = model.load_device
-        self.weights_loaded = False
-        self.real_model = None
-        self.currently_used = True
-
-    def model_memory(self):
-        return self.model.model_size()
-
-    def model_memory_required(self, device):
-        if device == self.model.current_device:
-            return 0
-        else:
-            return self.model_memory()
-
-    def model_load(self, lowvram_model_memory=0, force_patch_weights=False):
-        patch_model_to = self.device
-
-        self.model.model_patches_to(self.device)
-        self.model.model_patches_to(self.model.model_dtype())
-
-        load_weights = not self.weights_loaded
-
-        try:
-            if lowvram_model_memory > 0 and load_weights:
-                self.real_model = self.model.patch_model_lowvram(device_to=patch_model_to, lowvram_model_memory=lowvram_model_memory, force_patch_weights=force_patch_weights)
-            else:
-                self.real_model = self.model.patch_model(device_to=patch_model_to, patch_weights=load_weights)
-        except Exception as e:
-            self.model.unpatch_model(self.model.offload_device)
-            self.model_unload()
-            raise e
-
-        if is_intel_xpu() and not args.disable_ipex_optimize:
-            self.real_model = ipex.optimize(self.real_model.eval(), graph_mode=True, concat_linear=True)
-
-        self.weights_loaded = True
-        return self.real_model
-
-    def should_reload_model(self, force_patch_weights=False):
-        if force_patch_weights and self.model.lowvram_patch_counter > 0:
-            return True
-        return False
-
-    def model_unload(self, unpatch_weights=True):
-        self.model.unpatch_model(self.model.offload_device, unpatch_weights=unpatch_weights)
-        self.model.model_patches_to(self.model.offload_device)
-        self.weights_loaded = self.weights_loaded and not unpatch_weights
-        self.real_model = None
-
-    def __eq__(self, other):
-        return self.model is other.model
-
-def minimum_inference_memory():
-    return (1024 * 1024 * 1024)
-
-def unload_model_clones(model, unload_weights_only=True, force_unload=True):
-    to_unload = []
-    for i in range(len(current_loaded_models)):
-        if model.is_clone(current_loaded_models[i].model):
-            to_unload = [i] + to_unload
-
-    if len(to_unload) == 0:
-        return True
-
-    same_weights = 0
-    for i in to_unload:
-        if model.clone_has_same_weights(current_loaded_models[i].model):
-            same_weights += 1
-
-    if same_weights == len(to_unload):
-        unload_weight = False
-    else:
-        unload_weight = True
-
-    if not force_unload:
-        if unload_weights_only and unload_weight == False:
-            return None
-
-    for i in to_unload:
-        logging.debug("unload clone {} {}".format(i, unload_weight))
-        current_loaded_models.pop(i).model_unload(unpatch_weights=unload_weight)
-
-    return unload_weight
-
-def free_memory(memory_required, device, keep_loaded=[]):
-    unloaded_model = []
-    can_unload = []
-
-    for i in range(len(current_loaded_models) -1, -1, -1):
-        shift_model = current_loaded_models[i]
-        if shift_model.device == device:
-            if shift_model not in keep_loaded:
-                can_unload.append((sys.getrefcount(shift_model.model), shift_model.model_memory(), i))
-                shift_model.currently_used = False
-
-    for x in sorted(can_unload):
-        i = x[-1]
-        if not DISABLE_SMART_MEMORY:
-            if get_free_memory(device) > memory_required:
-                break
-        current_loaded_models[i].model_unload()
-        unloaded_model.append(i)
-
-    for i in sorted(unloaded_model, reverse=True):
-        current_loaded_models.pop(i)
-
-    if len(unloaded_model) > 0:
-        soft_empty_cache()
-    else:
-        if vram_state != VRAMState.HIGH_VRAM:
-            mem_free_total, mem_free_torch = get_free_memory(device, torch_free_too=True)
-            if mem_free_torch > mem_free_total * 0.25:
-                soft_empty_cache()
-
-def load_models_gpu(models, memory_required=0, force_patch_weights=False):
-    global vram_state
-
-    inference_memory = minimum_inference_memory()
-    extra_mem = max(inference_memory, memory_required)
-
-    models = set(models)
-
-    models_to_load = []
-    models_already_loaded = []
-    for x in models:
-        loaded_model = LoadedModel(x)
-        loaded = None
-
-        try:
-            loaded_model_index = current_loaded_models.index(loaded_model)
-        except:
-            loaded_model_index = None
-
-        if loaded_model_index is not None:
-            loaded = current_loaded_models[loaded_model_index]
-            if loaded.should_reload_model(force_patch_weights=force_patch_weights): #TODO: cleanup this model reload logic
-                current_loaded_models.pop(loaded_model_index).model_unload(unpatch_weights=True)
-                loaded = None
-            else:
-                loaded.currently_used = True
-                models_already_loaded.append(loaded)
-
-        if loaded is None:
-            if hasattr(x, "model"):
-                logging.info(f"Requested to load {x.model.__class__.__name__}")
-            models_to_load.append(loaded_model)
-
-    if len(models_to_load) == 0:
-        devs = set(map(lambda a: a.device, models_already_loaded))
-        for d in devs:
-            if d != torch.device("cpu"):
-                free_memory(extra_mem, d, models_already_loaded)
-        return
-
-    logging.info(f"Loading {len(models_to_load)} new model{'s' if len(models_to_load) > 1 else ''}")
-
-    total_memory_required = {}
-    for loaded_model in models_to_load:
-        if unload_model_clones(loaded_model.model, unload_weights_only=True, force_unload=False) == True:#unload clones where the weights are different
-            total_memory_required[loaded_model.device] = total_memory_required.get(loaded_model.device, 0) + loaded_model.model_memory_required(loaded_model.device)
-
-    for device in total_memory_required:
-        if device != torch.device("cpu"):
-            free_memory(total_memory_required[device] * 1.3 + extra_mem, device, models_already_loaded)
-
-    for loaded_model in models_to_load:
-        weights_unloaded = unload_model_clones(loaded_model.model, unload_weights_only=False, force_unload=False) #unload the rest of the clones where the weights can stay loaded
-        if weights_unloaded is not None:
-            loaded_model.weights_loaded = not weights_unloaded
-
-    for loaded_model in models_to_load:
-        model = loaded_model.model
-        torch_dev = model.load_device
-        if is_device_cpu(torch_dev):
-            vram_set_state = VRAMState.DISABLED
-        else:
-            vram_set_state = vram_state
-        lowvram_model_memory = 0
-        if lowvram_available and (vram_set_state == VRAMState.LOW_VRAM or vram_set_state == VRAMState.NORMAL_VRAM):
-            model_size = loaded_model.model_memory_required(torch_dev)
-            current_free_mem = get_free_memory(torch_dev)
-            lowvram_model_memory = int(max(64 * (1024 * 1024), (current_free_mem - 1024 * (1024 * 1024)) / 1.3 ))
-            if model_size <= (current_free_mem - inference_memory): #only switch to lowvram if really necessary
-                lowvram_model_memory = 0
-
-        if vram_set_state == VRAMState.NO_VRAM:
-            lowvram_model_memory = 64 * 1024 * 1024
-
-        cur_loaded_model = loaded_model.model_load(lowvram_model_memory, force_patch_weights=force_patch_weights)
-        current_loaded_models.insert(0, loaded_model)
-    return
-
-
-def load_model_gpu(model):
-    return load_models_gpu([model])
-
-def loaded_models(only_currently_used=False):
-    output = []
-    for m in current_loaded_models:
-        if only_currently_used:
-            if not m.currently_used:
-                continue
-
-        output.append(m.model)
-    return output
-
-def cleanup_models(keep_clone_weights_loaded=False):
-    to_delete = []
-    for i in range(len(current_loaded_models)):
-        if sys.getrefcount(current_loaded_models[i].model) <= 2:
-            if not keep_clone_weights_loaded:
-                to_delete = [i] + to_delete
-            #TODO: find a less fragile way to do this.
-            elif sys.getrefcount(current_loaded_models[i].real_model) <= 3: #references from .real_model + the .model
-                to_delete = [i] + to_delete
-
-    for i in to_delete:
-        x = current_loaded_models.pop(i)
-        x.model_unload()
-        del x
-
-def dtype_size(dtype):
-    dtype_size = 4
-    if dtype == torch.float16 or dtype == torch.bfloat16:
-        dtype_size = 2
-    elif dtype == torch.float32:
-        dtype_size = 4
-    else:
-        try:
-            dtype_size = dtype.itemsize
-        except: #Old pytorch doesn't have .itemsize
-            pass
-    return dtype_size
-
-def unet_offload_device():
-    if vram_state == VRAMState.HIGH_VRAM:
-        return get_torch_device()
-    else:
-        return torch.device("cpu")
-
-def unet_inital_load_device(parameters, dtype):
-    torch_dev = get_torch_device()
-    if vram_state == VRAMState.HIGH_VRAM:
-        return torch_dev
-
-    cpu_dev = torch.device("cpu")
-    if DISABLE_SMART_MEMORY:
-        return cpu_dev
-
-    model_size = dtype_size(dtype) * parameters
-
-    mem_dev = get_free_memory(torch_dev)
-    mem_cpu = get_free_memory(cpu_dev)
-    if mem_dev > mem_cpu and model_size < mem_dev:
-        return torch_dev
-    else:
-        return cpu_dev
-
-def unet_dtype(device=None, model_params=0, supported_dtypes=[torch.float16, torch.bfloat16, torch.float32]):
-    if args.bf16_unet:
-        return torch.bfloat16
-    if args.fp16_unet:
-        return torch.float16
-    if args.fp8_e4m3fn_unet:
-        return torch.float8_e4m3fn
-    if args.fp8_e5m2_unet:
-        return torch.float8_e5m2
-    if should_use_fp16(device=device, model_params=model_params, manual_cast=True):
-        if torch.float16 in supported_dtypes:
-            return torch.float16
-    if should_use_bf16(device, model_params=model_params, manual_cast=True):
-        if torch.bfloat16 in supported_dtypes:
-            return torch.bfloat16
-    return torch.float32
-
-# None means no manual cast
-def unet_manual_cast(weight_dtype, inference_device, supported_dtypes=[torch.float16, torch.bfloat16, torch.float32]):
-    if weight_dtype == torch.float32:
-        return None
-
-    fp16_supported = should_use_fp16(inference_device, prioritize_performance=False)
-    if fp16_supported and weight_dtype == torch.float16:
-        return None
-
-    bf16_supported = should_use_bf16(inference_device)
-    if bf16_supported and weight_dtype == torch.bfloat16:
-        return None
-
-    if fp16_supported and torch.float16 in supported_dtypes:
-        return torch.float16
-
-    elif bf16_supported and torch.bfloat16 in supported_dtypes:
-        return torch.bfloat16
-    else:
-        return torch.float32
-
-def text_encoder_offload_device():
-    if args.gpu_only:
-        return get_torch_device()
-    else:
-        return torch.device("cpu")
-
-def text_encoder_device():
-    if args.gpu_only:
-        return get_torch_device()
-    elif vram_state == VRAMState.HIGH_VRAM or vram_state == VRAMState.NORMAL_VRAM:
-        if should_use_fp16(prioritize_performance=False):
-            return get_torch_device()
-        else:
-            return torch.device("cpu")
-    else:
-        return torch.device("cpu")
-
-def text_encoder_dtype(device=None):
-    if args.fp8_e4m3fn_text_enc:
-        return torch.float8_e4m3fn
-    elif args.fp8_e5m2_text_enc:
-        return torch.float8_e5m2
-    elif args.fp16_text_enc:
-        return torch.float16
-    elif args.fp32_text_enc:
-        return torch.float32
-
-    if is_device_cpu(device):
-        return torch.float16
-
-    return torch.float16
-
-
-def intermediate_device():
-    if args.gpu_only:
-        return get_torch_device()
-    else:
-        return torch.device("cpu")
-
-def vae_device():
-    if args.cpu_vae:
-        return torch.device("cpu")
-    return get_torch_device()
-
-def vae_offload_device():
-    if args.gpu_only:
-        return get_torch_device()
-    else:
-        return torch.device("cpu")
-
-def vae_dtype(device=None, allowed_dtypes=[]):
-    global VAE_DTYPES
-    if args.fp16_vae:
-        return torch.float16
-    elif args.bf16_vae:
-        return torch.bfloat16
-    elif args.fp32_vae:
-        return torch.float32
-
-    for d in allowed_dtypes:
-        if d == torch.float16 and should_use_fp16(device, prioritize_performance=False):
-            return d
-        if d in VAE_DTYPES:
-            return d
-
-    return VAE_DTYPES[0]
-
-def get_autocast_device(dev):
-    if hasattr(dev, 'type'):
-        return dev.type
-    return "cuda"
-
-def supports_dtype(device, dtype): #TODO
-    if dtype == torch.float32:
-        return True
-    if is_device_cpu(device):
-        return False
-    if dtype == torch.float16:
-        return True
-    if dtype == torch.bfloat16:
-        return True
-    return False
-
-def supports_cast(device, dtype): #TODO
-    if dtype == torch.float32:
-        return True
-    if dtype == torch.float16:
-        return True
-    if is_device_mps(device):
-        return False
-    if directml_enabled: #TODO: test this
-        return False
-    if dtype == torch.bfloat16:
-        return True
-    if dtype == torch.float8_e4m3fn:
-        return True
-    if dtype == torch.float8_e5m2:
-        return True
-    return False
-
-def device_supports_non_blocking(device):
-    if is_device_mps(device):
-        return False #pytorch bug? mps doesn't support non blocking
-    if is_intel_xpu():
-        return False
-    if args.deterministic: #TODO: figure out why deterministic breaks non blocking from gpu to cpu (previews)
-        return False
-    if directml_enabled:
-        return False
-    return True
-
-def device_should_use_non_blocking(device):
-    if not device_supports_non_blocking(device):
-        return False
-    return False
-    # return True #TODO: figure out why this causes memory issues on Nvidia and possibly others
-
-def force_channels_last():
-    if args.force_channels_last:
-        return True
-
-    #TODO
-    return False
-
-def cast_to_device(tensor, device, dtype, copy=False):
-    device_supports_cast = False
-    if tensor.dtype == torch.float32 or tensor.dtype == torch.float16:
-        device_supports_cast = True
-    elif tensor.dtype == torch.bfloat16:
-        if hasattr(device, 'type') and device.type.startswith("cuda"):
-            device_supports_cast = True
-        elif is_intel_xpu():
-            device_supports_cast = True
-
-    non_blocking = device_should_use_non_blocking(device)
-
-    if device_supports_cast:
-        if copy:
-            if tensor.device == device:
-                return tensor.to(dtype, copy=copy, non_blocking=non_blocking)
-            return tensor.to(device, copy=copy, non_blocking=non_blocking).to(dtype, non_blocking=non_blocking)
-        else:
-            return tensor.to(device, non_blocking=non_blocking).to(dtype, non_blocking=non_blocking)
-    else:
-        return tensor.to(device, dtype, copy=copy, non_blocking=non_blocking)
-
-def xformers_enabled():
-    global directml_enabled
-    global cpu_state
-    if cpu_state != CPUState.GPU:
-        return False
-    if is_intel_xpu():
-        return False
-    if directml_enabled:
-        return False
-    return XFORMERS_IS_AVAILABLE
-
-
-def xformers_enabled_vae():
-    enabled = xformers_enabled()
-    if not enabled:
-        return False
-
-    return XFORMERS_ENABLED_VAE
-
-def pytorch_attention_enabled():
-    global ENABLE_PYTORCH_ATTENTION
-    return ENABLE_PYTORCH_ATTENTION
-
-def pytorch_attention_flash_attention():
-    global ENABLE_PYTORCH_ATTENTION
-    if ENABLE_PYTORCH_ATTENTION:
-        #TODO: more reliable way of checking for flash attention?
-        if is_nvidia(): #pytorch flash attention only works on Nvidia
-            return True
-        if is_intel_xpu():
-            return True
-    return False
-
-def force_upcast_attention_dtype():
-    upcast = args.force_upcast_attention
-    try:
-        if platform.mac_ver()[0] in ['14.5']: #black image bug on OSX Sonoma 14.5
-            upcast = True
-    except:
-        pass
-    if upcast:
-        return torch.float32
-    else:
-        return None
-
-def get_free_memory(dev=None, torch_free_too=False):
-    global directml_enabled
-    if dev is None:
-        dev = get_torch_device()
-
-    if hasattr(dev, 'type') and (dev.type == 'cpu' or dev.type == 'mps'):
-        mem_free_total = psutil.virtual_memory().available
-        mem_free_torch = mem_free_total
-    else:
-        if directml_enabled:
-            mem_free_total = 1024 * 1024 * 1024 #TODO
-            mem_free_torch = mem_free_total
-        elif is_intel_xpu():
-            stats = torch.xpu.memory_stats(dev)
-            mem_active = stats['active_bytes.all.current']
-            mem_reserved = stats['reserved_bytes.all.current']
-            mem_free_torch = mem_reserved - mem_active
-            mem_free_xpu = torch.xpu.get_device_properties(dev).total_memory - mem_reserved
-            mem_free_total = mem_free_xpu + mem_free_torch
-        else:
-            stats = torch.cuda.memory_stats(dev)
-            mem_active = stats['active_bytes.all.current']
-            mem_reserved = stats['reserved_bytes.all.current']
-            mem_free_cuda, _ = torch.cuda.mem_get_info(dev)
-            mem_free_torch = mem_reserved - mem_active
-            mem_free_total = mem_free_cuda + mem_free_torch
-
-    if torch_free_too:
-        return (mem_free_total, mem_free_torch)
-    else:
-        return mem_free_total
-
-def cpu_mode():
-    global cpu_state
-    return cpu_state == CPUState.CPU
-
-def mps_mode():
-    global cpu_state
-    return cpu_state == CPUState.MPS
-
-def is_device_type(device, type):
-    if hasattr(device, 'type'):
-        if (device.type == type):
-            return True
-    return False
-
-def is_device_cpu(device):
-    return is_device_type(device, 'cpu')
-
-def is_device_mps(device):
-    return is_device_type(device, 'mps')
-
-def is_device_cuda(device):
-    return is_device_type(device, 'cuda')
-
-def should_use_fp16(device=None, model_params=0, prioritize_performance=True, manual_cast=False):
-    global directml_enabled
-
-    if device is not None:
-        if is_device_cpu(device):
-            return False
-
-    if FORCE_FP16:
-        return True
-
-    if device is not None:
-        if is_device_mps(device):
-            return True
-
-    if FORCE_FP32:
-        return False
-
-    if directml_enabled:
-        return False
-
-    if mps_mode():
-        return True
-
-    if cpu_mode():
-        return False
-
-    if is_intel_xpu():
-        return True
-
-    if torch.version.hip:
-        return True
-
-    props = torch.cuda.get_device_properties("cuda")
-    if props.major >= 8:
-        return True
-
-    if props.major < 6:
-        return False
-
-    fp16_works = False
-    #FP16 is confirmed working on a 1080 (GP104) but it's a bit slower than FP32 so it should only be enabled
-    #when the model doesn't actually fit on the card
-    #TODO: actually test if GP106 and others have the same type of behavior
-    nvidia_10_series = ["1080", "1070", "titan x", "p3000", "p3200", "p4000", "p4200", "p5000", "p5200", "p6000", "1060", "1050", "p40", "p100", "p6", "p4"]
-    for x in nvidia_10_series:
-        if x in props.name.lower():
-            fp16_works = True
-
-    if fp16_works or manual_cast:
-        free_model_memory = (get_free_memory() * 0.9 - minimum_inference_memory())
-        if (not prioritize_performance) or model_params * 4 > free_model_memory:
-            return True
-
-    if props.major < 7:
-        return False
-
-    #FP16 is just broken on these cards
-    nvidia_16_series = ["1660", "1650", "1630", "T500", "T550", "T600", "MX550", "MX450", "CMP 30HX", "T2000", "T1000", "T1200"]
-    for x in nvidia_16_series:
-        if x in props.name:
-            return False
-
-    return True
-
-def should_use_bf16(device=None, model_params=0, prioritize_performance=True, manual_cast=False):
-    if device is not None:
-        if is_device_cpu(device): #TODO ? bf16 works on CPU but is extremely slow
-            return False
-
-    if device is not None: #TODO not sure about mps bf16 support
-        if is_device_mps(device):
-            return False
-
-    if FORCE_FP32:
-        return False
-
-    if directml_enabled:
-        return False
-
-    if cpu_mode() or mps_mode():
-        return False
-
-    if is_intel_xpu():
-        return True
-
-    if device is None:
-        device = torch.device("cuda")
-
-    props = torch.cuda.get_device_properties(device)
-    if props.major >= 8:
-        return True
-
-    bf16_works = torch.cuda.is_bf16_supported()
-
-    if bf16_works or manual_cast:
-        free_model_memory = (get_free_memory() * 0.9 - minimum_inference_memory())
-        if (not prioritize_performance) or model_params * 4 > free_model_memory:
-            return True
-
-    return False
-
-def soft_empty_cache(force=False):
-    global cpu_state
-    if cpu_state == CPUState.MPS:
-        torch.mps.empty_cache()
-    elif is_intel_xpu():
-        torch.xpu.empty_cache()
-    elif torch.cuda.is_available():
-        if force or is_nvidia(): #This seems to make things worse on ROCm so I only do it for cuda
-            torch.cuda.empty_cache()
-            torch.cuda.ipc_collect()
-
-def unload_all_models():
-    free_memory(1e30, get_torch_device())
-
-
-def resolve_lowvram_weight(weight, model, key): #TODO: remove
-    print("WARNING: The comfy.model_management.resolve_lowvram_weight function will be removed soon, please stop using it.")
-    return weight
-
-#TODO: might be cleaner to put this somewhere else
-import threading
-
-class InterruptProcessingException(Exception):
-    pass
-
-interrupt_processing_mutex = threading.RLock()
-
-interrupt_processing = False
-def interrupt_current_processing(value=True):
-    global interrupt_processing
-    global interrupt_processing_mutex
-    with interrupt_processing_mutex:
-        interrupt_processing = value
-
-def processing_interrupted():
-    global interrupt_processing
-    global interrupt_processing_mutex
-    with interrupt_processing_mutex:
-        return interrupt_processing
-
-def throw_exception_if_processing_interrupted():
-    global interrupt_processing
-    global interrupt_processing_mutex
-    with interrupt_processing_mutex:
-        if interrupt_processing:
-            interrupt_processing = False
-            raise InterruptProcessingException()
diff --git a/MagicQuill/comfy/model_patcher.py b/MagicQuill/comfy/model_patcher.py
deleted file mode 100644
index 44b82795f3000afe134cdbafb3e8ab918982ae0c..0000000000000000000000000000000000000000
--- a/MagicQuill/comfy/model_patcher.py
+++ /dev/null
@@ -1,541 +0,0 @@
-import torch
-import copy
-import inspect
-import logging
-import uuid
-
-import comfy.utils
-import comfy.model_management
-from comfy.types import UnetWrapperFunction
-
-
-def weight_decompose(dora_scale, weight, lora_diff, alpha, strength):
-    dora_scale = comfy.model_management.cast_to_device(dora_scale, weight.device, torch.float32)
-    lora_diff *= alpha
-    weight_calc = weight + lora_diff.type(weight.dtype)
-    weight_norm = (
-        weight_calc.transpose(0, 1)
-        .reshape(weight_calc.shape[1], -1)
-        .norm(dim=1, keepdim=True)
-        .reshape(weight_calc.shape[1], *[1] * (weight_calc.dim() - 1))
-        .transpose(0, 1)
-    )
-
-    weight_calc *= (dora_scale / weight_norm).type(weight.dtype)
-    if strength != 1.0:
-        weight_calc -= weight
-        weight += strength * (weight_calc)
-    else:
-        weight[:] = weight_calc
-    return weight
-
-
-def set_model_options_patch_replace(model_options, patch, name, block_name, number, transformer_index=None):
-    to = model_options["transformer_options"].copy()
-
-    if "patches_replace" not in to:
-        to["patches_replace"] = {}
-    else:
-        to["patches_replace"] = to["patches_replace"].copy()
-
-    if name not in to["patches_replace"]:
-        to["patches_replace"][name] = {}
-    else:
-        to["patches_replace"][name] = to["patches_replace"][name].copy()
-
-    if transformer_index is not None:
-        block = (block_name, number, transformer_index)
-    else:
-        block = (block_name, number)
-    to["patches_replace"][name][block] = patch
-    model_options["transformer_options"] = to
-    return model_options
-
-class ModelPatcher:
-    def __init__(self, model, load_device, offload_device, size=0, current_device=None, weight_inplace_update=False):
-        self.size = size
-        self.model = model
-        self.patches = {}
-        self.backup = {}
-        self.object_patches = {}
-        self.object_patches_backup = {}
-        self.model_options = {"transformer_options":{}}
-        self.model_size()
-        self.load_device = load_device
-        self.offload_device = offload_device
-        if current_device is None:
-            self.current_device = self.offload_device
-        else:
-            self.current_device = current_device
-
-        self.weight_inplace_update = weight_inplace_update
-        self.model_lowvram = False
-        self.lowvram_patch_counter = 0
-        self.patches_uuid = uuid.uuid4()
-
-    def model_size(self):
-        if self.size > 0:
-            return self.size
-        self.size = comfy.model_management.module_size(self.model)
-        return self.size
-
-    def clone(self):
-        n = ModelPatcher(self.model, self.load_device, self.offload_device, self.size, self.current_device, weight_inplace_update=self.weight_inplace_update)
-        n.patches = {}
-        for k in self.patches:
-            n.patches[k] = self.patches[k][:]
-        n.patches_uuid = self.patches_uuid
-
-        n.object_patches = self.object_patches.copy()
-        n.model_options = copy.deepcopy(self.model_options)
-        n.backup = self.backup
-        n.object_patches_backup = self.object_patches_backup
-        return n
-
-    def is_clone(self, other):
-        if hasattr(other, 'model') and self.model is other.model:
-            return True
-        return False
-
-    def clone_has_same_weights(self, clone):
-        if not self.is_clone(clone):
-            return False
-
-        if len(self.patches) == 0 and len(clone.patches) == 0:
-            return True
-
-        if self.patches_uuid == clone.patches_uuid:
-            if len(self.patches) != len(clone.patches):
-                logging.warning("WARNING: something went wrong, same patch uuid but different length of patches.")
-            else:
-                return True
-
-    def memory_required(self, input_shape):
-        return self.model.memory_required(input_shape=input_shape)
-
-    def set_model_sampler_cfg_function(self, sampler_cfg_function, disable_cfg1_optimization=False):
-        if len(inspect.signature(sampler_cfg_function).parameters) == 3:
-            self.model_options["sampler_cfg_function"] = lambda args: sampler_cfg_function(args["cond"], args["uncond"], args["cond_scale"]) #Old way
-        else:
-            self.model_options["sampler_cfg_function"] = sampler_cfg_function
-        if disable_cfg1_optimization:
-            self.model_options["disable_cfg1_optimization"] = True
-
-    def set_model_sampler_post_cfg_function(self, post_cfg_function, disable_cfg1_optimization=False):
-        self.model_options["sampler_post_cfg_function"] = self.model_options.get("sampler_post_cfg_function", []) + [post_cfg_function]
-        if disable_cfg1_optimization:
-            self.model_options["disable_cfg1_optimization"] = True
-
-    def set_model_unet_function_wrapper(self, unet_wrapper_function: UnetWrapperFunction):
-        self.model_options["model_function_wrapper"] = unet_wrapper_function
-
-    def set_model_denoise_mask_function(self, denoise_mask_function):
-        self.model_options["denoise_mask_function"] = denoise_mask_function
-
-    def set_model_patch(self, patch, name):
-        to = self.model_options["transformer_options"]
-        if "patches" not in to:
-            to["patches"] = {}
-        to["patches"][name] = to["patches"].get(name, []) + [patch]
-
-    def set_model_patch_replace(self, patch, name, block_name, number, transformer_index=None):
-        self.model_options = set_model_options_patch_replace(self.model_options, patch, name, block_name, number, transformer_index=transformer_index)
-
-    def set_model_attn1_patch(self, patch):
-        self.set_model_patch(patch, "attn1_patch")
-
-    def set_model_attn2_patch(self, patch):
-        self.set_model_patch(patch, "attn2_patch")
-
-    def set_model_attn1_replace(self, patch, block_name, number, transformer_index=None):
-        self.set_model_patch_replace(patch, "attn1", block_name, number, transformer_index)
-
-    def set_model_attn2_replace(self, patch, block_name, number, transformer_index=None):
-        self.set_model_patch_replace(patch, "attn2", block_name, number, transformer_index)
-
-    def set_model_attn1_output_patch(self, patch):
-        self.set_model_patch(patch, "attn1_output_patch")
-
-    def set_model_attn2_output_patch(self, patch):
-        self.set_model_patch(patch, "attn2_output_patch")
-
-    def set_model_input_block_patch(self, patch):
-        self.set_model_patch(patch, "input_block_patch")
-
-    def set_model_input_block_patch_after_skip(self, patch):
-        self.set_model_patch(patch, "input_block_patch_after_skip")
-
-    def set_model_output_block_patch(self, patch):
-        self.set_model_patch(patch, "output_block_patch")
-
-    def add_object_patch(self, name, obj):
-        self.object_patches[name] = obj
-
-    def get_model_object(self, name):
-        if name in self.object_patches:
-            return self.object_patches[name]
-        else:
-            if name in self.object_patches_backup:
-                return self.object_patches_backup[name]
-            else:
-                return comfy.utils.get_attr(self.model, name)
-
-    def model_patches_to(self, device):
-        to = self.model_options["transformer_options"]
-        if "patches" in to:
-            patches = to["patches"]
-            for name in patches:
-                patch_list = patches[name]
-                for i in range(len(patch_list)):
-                    if hasattr(patch_list[i], "to"):
-                        patch_list[i] = patch_list[i].to(device)
-        if "patches_replace" in to:
-            patches = to["patches_replace"]
-            for name in patches:
-                patch_list = patches[name]
-                for k in patch_list:
-                    if hasattr(patch_list[k], "to"):
-                        patch_list[k] = patch_list[k].to(device)
-        if "model_function_wrapper" in self.model_options:
-            wrap_func = self.model_options["model_function_wrapper"]
-            if hasattr(wrap_func, "to"):
-                self.model_options["model_function_wrapper"] = wrap_func.to(device)
-
-    def model_dtype(self):
-        if hasattr(self.model, "get_dtype"):
-            return self.model.get_dtype()
-
-    def add_patches(self, patches, strength_patch=1.0, strength_model=1.0):
-        p = set()
-        model_sd = self.model.state_dict()
-        for k in patches:
-            offset = None
-            if isinstance(k, str):
-                key = k
-            else:
-                offset = k[1]
-                key = k[0]
-
-            if key in model_sd:
-                p.add(k)
-                current_patches = self.patches.get(key, [])
-                current_patches.append((strength_patch, patches[k], strength_model, offset))
-                self.patches[key] = current_patches
-
-        self.patches_uuid = uuid.uuid4()
-        return list(p)
-
-    def get_key_patches(self, filter_prefix=None):
-        comfy.model_management.unload_model_clones(self)
-        model_sd = self.model_state_dict()
-        p = {}
-        for k in model_sd:
-            if filter_prefix is not None:
-                if not k.startswith(filter_prefix):
-                    continue
-            if k in self.patches:
-                p[k] = [model_sd[k]] + self.patches[k]
-            else:
-                p[k] = (model_sd[k],)
-        return p
-
-    def model_state_dict(self, filter_prefix=None):
-        sd = self.model.state_dict()
-        keys = list(sd.keys())
-        if filter_prefix is not None:
-            for k in keys:
-                if not k.startswith(filter_prefix):
-                    sd.pop(k)
-        return sd
-
-    def patch_weight_to_device(self, key, device_to=None):
-        if key not in self.patches:
-            return
-
-        weight = comfy.utils.get_attr(self.model, key)
-
-        inplace_update = self.weight_inplace_update
-
-        if key not in self.backup:
-            self.backup[key] = weight.to(device=self.offload_device, copy=inplace_update)
-
-        if device_to is not None:
-            temp_weight = comfy.model_management.cast_to_device(weight, device_to, torch.float32, copy=True)
-        else:
-            temp_weight = weight.to(torch.float32, copy=True)
-        out_weight = self.calculate_weight(self.patches[key], temp_weight, key).to(weight.dtype)
-        if inplace_update:
-            comfy.utils.copy_to_param(self.model, key, out_weight)
-        else:
-            comfy.utils.set_attr_param(self.model, key, out_weight)
-
-    def patch_model(self, device_to=None, patch_weights=True):
-        for k in self.object_patches:
-            old = comfy.utils.set_attr(self.model, k, self.object_patches[k])
-            if k not in self.object_patches_backup:
-                self.object_patches_backup[k] = old
-
-        if patch_weights:
-            model_sd = self.model_state_dict()
-            for key in self.patches:
-                if key not in model_sd:
-                    logging.warning("could not patch. key doesn't exist in model: {}".format(key))
-                    continue
-
-                self.patch_weight_to_device(key, device_to)
-
-            if device_to is not None:
-                self.model.to(device_to)
-                self.current_device = device_to
-
-        return self.model
-
-    def patch_model_lowvram(self, device_to=None, lowvram_model_memory=0, force_patch_weights=False):
-        self.patch_model(device_to, patch_weights=False)
-
-        logging.info("loading in lowvram mode {}".format(lowvram_model_memory/(1024 * 1024)))
-        class LowVramPatch:
-            def __init__(self, key, model_patcher):
-                self.key = key
-                self.model_patcher = model_patcher
-            def __call__(self, weight):
-                return self.model_patcher.calculate_weight(self.model_patcher.patches[self.key], weight, self.key)
-
-        mem_counter = 0
-        patch_counter = 0
-        for n, m in self.model.named_modules():
-            lowvram_weight = False
-            if hasattr(m, "comfy_cast_weights"):
-                module_mem = comfy.model_management.module_size(m)
-                if mem_counter + module_mem >= lowvram_model_memory:
-                    lowvram_weight = True
-
-            weight_key = "{}.weight".format(n)
-            bias_key = "{}.bias".format(n)
-
-            if lowvram_weight:
-                if weight_key in self.patches:
-                    if force_patch_weights:
-                        self.patch_weight_to_device(weight_key)
-                    else:
-                        m.weight_function = LowVramPatch(weight_key, self)
-                        patch_counter += 1
-                if bias_key in self.patches:
-                    if force_patch_weights:
-                        self.patch_weight_to_device(bias_key)
-                    else:
-                        m.bias_function = LowVramPatch(bias_key, self)
-                        patch_counter += 1
-
-                m.prev_comfy_cast_weights = m.comfy_cast_weights
-                m.comfy_cast_weights = True
-            else:
-                if hasattr(m, "weight"):
-                    self.patch_weight_to_device(weight_key, device_to)
-                    self.patch_weight_to_device(bias_key, device_to)
-                    m.to(device_to)
-                    mem_counter += comfy.model_management.module_size(m)
-                    logging.debug("lowvram: loaded module regularly {} {}".format(n, m))
-
-        self.model_lowvram = True
-        self.lowvram_patch_counter = patch_counter
-        return self.model
-
-    def calculate_weight(self, patches, weight, key):
-        for p in patches:
-            strength = p[0]
-            v = p[1]
-            strength_model = p[2]
-            offset = p[3]
-
-            old_weight = None
-            if offset is not None:
-                old_weight = weight
-                weight = weight.narrow(offset[0], offset[1], offset[2])
-
-            if strength_model != 1.0:
-                weight *= strength_model
-
-            if isinstance(v, list):
-                v = (self.calculate_weight(v[1:], v[0].clone(), key), )
-
-            if len(v) == 1:
-                patch_type = "diff"
-            elif len(v) == 2:
-                patch_type = v[0]
-                v = v[1]
-
-            if patch_type == "diff":
-                w1 = v[0]
-                if strength != 0.0:
-                    if w1.shape != weight.shape:
-                        logging.warning("WARNING SHAPE MISMATCH {} WEIGHT NOT MERGED {} != {}".format(key, w1.shape, weight.shape))
-                    else:
-                        weight += strength * comfy.model_management.cast_to_device(w1, weight.device, weight.dtype)
-            elif patch_type == "lora": #lora/locon
-                mat1 = comfy.model_management.cast_to_device(v[0], weight.device, torch.float32)
-                mat2 = comfy.model_management.cast_to_device(v[1], weight.device, torch.float32)
-                dora_scale = v[4]
-                if v[2] is not None:
-                    alpha = v[2] / mat2.shape[0]
-                else:
-                    alpha = 1.0
-
-                if v[3] is not None:
-                    #locon mid weights, hopefully the math is fine because I didn't properly test it
-                    mat3 = comfy.model_management.cast_to_device(v[3], weight.device, torch.float32)
-                    final_shape = [mat2.shape[1], mat2.shape[0], mat3.shape[2], mat3.shape[3]]
-                    mat2 = torch.mm(mat2.transpose(0, 1).flatten(start_dim=1), mat3.transpose(0, 1).flatten(start_dim=1)).reshape(final_shape).transpose(0, 1)
-                try:
-                    lora_diff = torch.mm(mat1.flatten(start_dim=1), mat2.flatten(start_dim=1)).reshape(weight.shape)
-                    if dora_scale is not None:
-                        weight = weight_decompose(dora_scale, weight, lora_diff, alpha, strength)
-                    else:
-                        weight += ((strength * alpha) * lora_diff).type(weight.dtype)
-                except Exception as e:
-                    logging.error("ERROR {} {} {}".format(patch_type, key, e))
-            elif patch_type == "lokr":
-                w1 = v[0]
-                w2 = v[1]
-                w1_a = v[3]
-                w1_b = v[4]
-                w2_a = v[5]
-                w2_b = v[6]
-                t2 = v[7]
-                dora_scale = v[8]
-                dim = None
-
-                if w1 is None:
-                    dim = w1_b.shape[0]
-                    w1 = torch.mm(comfy.model_management.cast_to_device(w1_a, weight.device, torch.float32),
-                                  comfy.model_management.cast_to_device(w1_b, weight.device, torch.float32))
-                else:
-                    w1 = comfy.model_management.cast_to_device(w1, weight.device, torch.float32)
-
-                if w2 is None:
-                    dim = w2_b.shape[0]
-                    if t2 is None:
-                        w2 = torch.mm(comfy.model_management.cast_to_device(w2_a, weight.device, torch.float32),
-                                      comfy.model_management.cast_to_device(w2_b, weight.device, torch.float32))
-                    else:
-                        w2 = torch.einsum('i j k l, j r, i p -> p r k l',
-                                          comfy.model_management.cast_to_device(t2, weight.device, torch.float32),
-                                          comfy.model_management.cast_to_device(w2_b, weight.device, torch.float32),
-                                          comfy.model_management.cast_to_device(w2_a, weight.device, torch.float32))
-                else:
-                    w2 = comfy.model_management.cast_to_device(w2, weight.device, torch.float32)
-
-                if len(w2.shape) == 4:
-                    w1 = w1.unsqueeze(2).unsqueeze(2)
-                if v[2] is not None and dim is not None:
-                    alpha = v[2] / dim
-                else:
-                    alpha = 1.0
-
-                try:
-                    lora_diff = torch.kron(w1, w2).reshape(weight.shape)
-                    if dora_scale is not None:
-                        weight = weight_decompose(dora_scale, weight, lora_diff, alpha, strength)
-                    else:
-                        weight += ((strength * alpha) * lora_diff).type(weight.dtype)
-                except Exception as e:
-                    logging.error("ERROR {} {} {}".format(patch_type, key, e))
-            elif patch_type == "loha":
-                w1a = v[0]
-                w1b = v[1]
-                if v[2] is not None:
-                    alpha = v[2] / w1b.shape[0]
-                else:
-                    alpha = 1.0
-
-                w2a = v[3]
-                w2b = v[4]
-                dora_scale = v[7]
-                if v[5] is not None: #cp decomposition
-                    t1 = v[5]
-                    t2 = v[6]
-                    m1 = torch.einsum('i j k l, j r, i p -> p r k l',
-                                      comfy.model_management.cast_to_device(t1, weight.device, torch.float32),
-                                      comfy.model_management.cast_to_device(w1b, weight.device, torch.float32),
-                                      comfy.model_management.cast_to_device(w1a, weight.device, torch.float32))
-
-                    m2 = torch.einsum('i j k l, j r, i p -> p r k l',
-                                      comfy.model_management.cast_to_device(t2, weight.device, torch.float32),
-                                      comfy.model_management.cast_to_device(w2b, weight.device, torch.float32),
-                                      comfy.model_management.cast_to_device(w2a, weight.device, torch.float32))
-                else:
-                    m1 = torch.mm(comfy.model_management.cast_to_device(w1a, weight.device, torch.float32),
-                                  comfy.model_management.cast_to_device(w1b, weight.device, torch.float32))
-                    m2 = torch.mm(comfy.model_management.cast_to_device(w2a, weight.device, torch.float32),
-                                  comfy.model_management.cast_to_device(w2b, weight.device, torch.float32))
-
-                try:
-                    lora_diff = (m1 * m2).reshape(weight.shape)
-                    if dora_scale is not None:
-                        weight = weight_decompose(dora_scale, weight, lora_diff, alpha, strength)
-                    else:
-                        weight += ((strength * alpha) * lora_diff).type(weight.dtype)
-                except Exception as e:
-                    logging.error("ERROR {} {} {}".format(patch_type, key, e))
-            elif patch_type == "glora":
-                if v[4] is not None:
-                    alpha = v[4] / v[0].shape[0]
-                else:
-                    alpha = 1.0
-
-                dora_scale = v[5]
-
-                a1 = comfy.model_management.cast_to_device(v[0].flatten(start_dim=1), weight.device, torch.float32)
-                a2 = comfy.model_management.cast_to_device(v[1].flatten(start_dim=1), weight.device, torch.float32)
-                b1 = comfy.model_management.cast_to_device(v[2].flatten(start_dim=1), weight.device, torch.float32)
-                b2 = comfy.model_management.cast_to_device(v[3].flatten(start_dim=1), weight.device, torch.float32)
-
-                try:
-                    lora_diff = (torch.mm(b2, b1) + torch.mm(torch.mm(weight.flatten(start_dim=1), a2), a1)).reshape(weight.shape)
-                    if dora_scale is not None:
-                        weight = weight_decompose(dora_scale, weight, lora_diff, alpha, strength)
-                    else:
-                        weight += ((strength * alpha) * lora_diff).type(weight.dtype)
-                except Exception as e:
-                    logging.error("ERROR {} {} {}".format(patch_type, key, e))
-            else:
-                logging.warning("patch type not recognized {} {}".format(patch_type, key))
-
-            if old_weight is not None:
-                weight = old_weight
-
-        return weight
-
-    def unpatch_model(self, device_to=None, unpatch_weights=True):
-        if unpatch_weights:
-            if self.model_lowvram:
-                for m in self.model.modules():
-                    if hasattr(m, "prev_comfy_cast_weights"):
-                        m.comfy_cast_weights = m.prev_comfy_cast_weights
-                        del m.prev_comfy_cast_weights
-                    m.weight_function = None
-                    m.bias_function = None
-
-                self.model_lowvram = False
-                self.lowvram_patch_counter = 0
-
-            keys = list(self.backup.keys())
-
-            if self.weight_inplace_update:
-                for k in keys:
-                    comfy.utils.copy_to_param(self.model, k, self.backup[k])
-            else:
-                for k in keys:
-                    comfy.utils.set_attr_param(self.model, k, self.backup[k])
-
-            self.backup.clear()
-
-            if device_to is not None:
-                self.model.to(device_to)
-                self.current_device = device_to
-
-        keys = list(self.object_patches_backup.keys())
-        for k in keys:
-            comfy.utils.set_attr(self.model, k, self.object_patches_backup[k])
-
-        self.object_patches_backup.clear()
diff --git a/MagicQuill/comfy/model_sampling.py b/MagicQuill/comfy/model_sampling.py
deleted file mode 100644
index 6bd3a5d79a5ad466d31fcae278d4f1a94a1b6645..0000000000000000000000000000000000000000
--- a/MagicQuill/comfy/model_sampling.py
+++ /dev/null
@@ -1,272 +0,0 @@
-import torch
-from comfy.ldm.modules.diffusionmodules.util import make_beta_schedule
-import math
-
-class EPS:
-    def calculate_input(self, sigma, noise):
-        sigma = sigma.view(sigma.shape[:1] + (1,) * (noise.ndim - 1))
-        return noise / (sigma ** 2 + self.sigma_data ** 2) ** 0.5
-
-    def calculate_denoised(self, sigma, model_output, model_input):
-        sigma = sigma.view(sigma.shape[:1] + (1,) * (model_output.ndim - 1))
-        return model_input - model_output * sigma
-
-    def noise_scaling(self, sigma, noise, latent_image, max_denoise=False):
-        if max_denoise:
-            noise = noise * torch.sqrt(1.0 + sigma ** 2.0)
-        else:
-            noise = noise * sigma
-
-        noise += latent_image
-        return noise
-
-    def inverse_noise_scaling(self, sigma, latent):
-        return latent
-
-class V_PREDICTION(EPS):
-    def calculate_denoised(self, sigma, model_output, model_input):
-        sigma = sigma.view(sigma.shape[:1] + (1,) * (model_output.ndim - 1))
-        return model_input * self.sigma_data ** 2 / (sigma ** 2 + self.sigma_data ** 2) - model_output * sigma * self.sigma_data / (sigma ** 2 + self.sigma_data ** 2) ** 0.5
-
-class EDM(V_PREDICTION):
-    def calculate_denoised(self, sigma, model_output, model_input):
-        sigma = sigma.view(sigma.shape[:1] + (1,) * (model_output.ndim - 1))
-        return model_input * self.sigma_data ** 2 / (sigma ** 2 + self.sigma_data ** 2) + model_output * sigma * self.sigma_data / (sigma ** 2 + self.sigma_data ** 2) ** 0.5
-
-class CONST:
-    def calculate_input(self, sigma, noise):
-        return noise
-
-    def calculate_denoised(self, sigma, model_output, model_input):
-        sigma = sigma.view(sigma.shape[:1] + (1,) * (model_output.ndim - 1))
-        return model_input - model_output * sigma
-
-    def noise_scaling(self, sigma, noise, latent_image, max_denoise=False):
-        return sigma * noise + (1.0 - sigma) * latent_image
-
-    def inverse_noise_scaling(self, sigma, latent):
-        return latent / (1.0 - sigma)
-
-class ModelSamplingDiscrete(torch.nn.Module):
-    def __init__(self, model_config=None):
-        super().__init__()
-
-        if model_config is not None:
-            sampling_settings = model_config.sampling_settings
-        else:
-            sampling_settings = {}
-
-        beta_schedule = sampling_settings.get("beta_schedule", "linear")
-        linear_start = sampling_settings.get("linear_start", 0.00085)
-        linear_end = sampling_settings.get("linear_end", 0.012)
-
-        self._register_schedule(given_betas=None, beta_schedule=beta_schedule, timesteps=1000, linear_start=linear_start, linear_end=linear_end, cosine_s=8e-3)
-        self.sigma_data = 1.0
-
-    def _register_schedule(self, given_betas=None, beta_schedule="linear", timesteps=1000,
-                          linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
-        if given_betas is not None:
-            betas = given_betas
-        else:
-            betas = make_beta_schedule(beta_schedule, timesteps, linear_start=linear_start, linear_end=linear_end, cosine_s=cosine_s)
-        alphas = 1. - betas
-        alphas_cumprod = torch.cumprod(alphas, dim=0)
-
-        timesteps, = betas.shape
-        self.num_timesteps = int(timesteps)
-        self.linear_start = linear_start
-        self.linear_end = linear_end
-
-        # self.register_buffer('betas', torch.tensor(betas, dtype=torch.float32))
-        # self.register_buffer('alphas_cumprod', torch.tensor(alphas_cumprod, dtype=torch.float32))
-        # self.register_buffer('alphas_cumprod_prev', torch.tensor(alphas_cumprod_prev, dtype=torch.float32))
-
-        sigmas = ((1 - alphas_cumprod) / alphas_cumprod) ** 0.5
-        self.set_sigmas(sigmas)
-
-    def set_sigmas(self, sigmas):
-        self.register_buffer('sigmas', sigmas.float())
-        self.register_buffer('log_sigmas', sigmas.log().float())
-
-    @property
-    def sigma_min(self):
-        return self.sigmas[0]
-
-    @property
-    def sigma_max(self):
-        return self.sigmas[-1]
-
-    def timestep(self, sigma):
-        log_sigma = sigma.log()
-        dists = log_sigma.to(self.log_sigmas.device) - self.log_sigmas[:, None]
-        return dists.abs().argmin(dim=0).view(sigma.shape).to(sigma.device)
-
-    def sigma(self, timestep):
-        t = torch.clamp(timestep.float().to(self.log_sigmas.device), min=0, max=(len(self.sigmas) - 1))
-        low_idx = t.floor().long()
-        high_idx = t.ceil().long()
-        w = t.frac()
-        log_sigma = (1 - w) * self.log_sigmas[low_idx] + w * self.log_sigmas[high_idx]
-        return log_sigma.exp().to(timestep.device)
-
-    def percent_to_sigma(self, percent):
-        if percent <= 0.0:
-            return 999999999.9
-        if percent >= 1.0:
-            return 0.0
-        percent = 1.0 - percent
-        return self.sigma(torch.tensor(percent * 999.0)).item()
-
-class ModelSamplingDiscreteEDM(ModelSamplingDiscrete):
-    def timestep(self, sigma):
-        return 0.25 * sigma.log()
-
-    def sigma(self, timestep):
-        return (timestep / 0.25).exp()
-
-class ModelSamplingContinuousEDM(torch.nn.Module):
-    def __init__(self, model_config=None):
-        super().__init__()
-        if model_config is not None:
-            sampling_settings = model_config.sampling_settings
-        else:
-            sampling_settings = {}
-
-        sigma_min = sampling_settings.get("sigma_min", 0.002)
-        sigma_max = sampling_settings.get("sigma_max", 120.0)
-        sigma_data = sampling_settings.get("sigma_data", 1.0)
-        self.set_parameters(sigma_min, sigma_max, sigma_data)
-
-    def set_parameters(self, sigma_min, sigma_max, sigma_data):
-        self.sigma_data = sigma_data
-        sigmas = torch.linspace(math.log(sigma_min), math.log(sigma_max), 1000).exp()
-
-        self.register_buffer('sigmas', sigmas) #for compatibility with some schedulers
-        self.register_buffer('log_sigmas', sigmas.log())
-
-    @property
-    def sigma_min(self):
-        return self.sigmas[0]
-
-    @property
-    def sigma_max(self):
-        return self.sigmas[-1]
-
-    def timestep(self, sigma):
-        return 0.25 * sigma.log()
-
-    def sigma(self, timestep):
-        return (timestep / 0.25).exp()
-
-    def percent_to_sigma(self, percent):
-        if percent <= 0.0:
-            return 999999999.9
-        if percent >= 1.0:
-            return 0.0
-        percent = 1.0 - percent
-
-        log_sigma_min = math.log(self.sigma_min)
-        return math.exp((math.log(self.sigma_max) - log_sigma_min) * percent + log_sigma_min)
-
-
-class ModelSamplingContinuousV(ModelSamplingContinuousEDM):
-    def timestep(self, sigma):
-        return sigma.atan() / math.pi * 2
-
-    def sigma(self, timestep):
-        return (timestep * math.pi / 2).tan()
-
-
-def time_snr_shift(alpha, t):
-    if alpha == 1.0:
-        return t
-    return alpha * t / (1 + (alpha - 1) * t)
-
-class ModelSamplingDiscreteFlow(torch.nn.Module):
-    def __init__(self, model_config=None):
-        super().__init__()
-        if model_config is not None:
-            sampling_settings = model_config.sampling_settings
-        else:
-            sampling_settings = {}
-
-        self.set_parameters(shift=sampling_settings.get("shift", 1.0))
-
-    def set_parameters(self, shift=1.0, timesteps=1000):
-        self.shift = shift
-        ts = self.sigma(torch.arange(1, timesteps + 1, 1))
-        self.register_buffer('sigmas', ts)
-
-    @property
-    def sigma_min(self):
-        return self.sigmas[0]
-
-    @property
-    def sigma_max(self):
-        return self.sigmas[-1]
-
-    def timestep(self, sigma):
-        return sigma * 1000
-
-    def sigma(self, timestep):
-        return time_snr_shift(self.shift, timestep / 1000)
-
-    def percent_to_sigma(self, percent):
-        if percent <= 0.0:
-            return 1.0
-        if percent >= 1.0:
-            return 0.0
-        return 1.0 - percent
-
-class StableCascadeSampling(ModelSamplingDiscrete):
-    def __init__(self, model_config=None):
-        super().__init__()
-
-        if model_config is not None:
-            sampling_settings = model_config.sampling_settings
-        else:
-            sampling_settings = {}
-
-        self.set_parameters(sampling_settings.get("shift", 1.0))
-
-    def set_parameters(self, shift=1.0, cosine_s=8e-3):
-        self.shift = shift
-        self.cosine_s = torch.tensor(cosine_s)
-        self._init_alpha_cumprod = torch.cos(self.cosine_s / (1 + self.cosine_s) * torch.pi * 0.5) ** 2
-
-        #This part is just for compatibility with some schedulers in the codebase
-        self.num_timesteps = 10000
-        sigmas = torch.empty((self.num_timesteps), dtype=torch.float32)
-        for x in range(self.num_timesteps):
-            t = (x + 1) / self.num_timesteps
-            sigmas[x] = self.sigma(t)
-
-        self.set_sigmas(sigmas)
-
-    def sigma(self, timestep):
-        alpha_cumprod = (torch.cos((timestep + self.cosine_s) / (1 + self.cosine_s) * torch.pi * 0.5) ** 2 / self._init_alpha_cumprod)
-
-        if self.shift != 1.0:
-            var = alpha_cumprod
-            logSNR = (var/(1-var)).log()
-            logSNR += 2 * torch.log(1.0 / torch.tensor(self.shift))
-            alpha_cumprod = logSNR.sigmoid()
-
-        alpha_cumprod = alpha_cumprod.clamp(0.0001, 0.9999)
-        return ((1 - alpha_cumprod) / alpha_cumprod) ** 0.5
-
-    def timestep(self, sigma):
-        var = 1 / ((sigma * sigma) + 1)
-        var = var.clamp(0, 1.0)
-        s, min_var = self.cosine_s.to(var.device), self._init_alpha_cumprod.to(var.device)
-        t = (((var * min_var) ** 0.5).acos() / (torch.pi * 0.5)) * (1 + s) - s
-        return t
-
-    def percent_to_sigma(self, percent):
-        if percent <= 0.0:
-            return 999999999.9
-        if percent >= 1.0:
-            return 0.0
-
-        percent = 1.0 - percent
-        return self.sigma(torch.tensor(percent))
diff --git a/MagicQuill/comfy/ops.py b/MagicQuill/comfy/ops.py
deleted file mode 100644
index 0f1ceb5746356a2c7cc3cd6107449a2ee65fe820..0000000000000000000000000000000000000000
--- a/MagicQuill/comfy/ops.py
+++ /dev/null
@@ -1,204 +0,0 @@
-"""
-    This file is part of ComfyUI.
-    Copyright (C) 2024 Stability AI
-
-    This program is free software: you can redistribute it and/or modify
-    it under the terms of the GNU General Public License as published by
-    the Free Software Foundation, either version 3 of the License, or
-    (at your option) any later version.
-
-    This program is distributed in the hope that it will be useful,
-    but WITHOUT ANY WARRANTY; without even the implied warranty of
-    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-    GNU General Public License for more details.
-
-    You should have received a copy of the GNU General Public License
-    along with this program.  If not, see <https://www.gnu.org/licenses/>.
-"""
-
-import torch
-import comfy.model_management
-
-def cast_bias_weight(s, input):
-    bias = None
-    non_blocking = comfy.model_management.device_should_use_non_blocking(input.device)
-    if s.bias is not None:
-        bias = s.bias.to(device=input.device, dtype=input.dtype, non_blocking=non_blocking)
-        if s.bias_function is not None:
-            bias = s.bias_function(bias)
-    weight = s.weight.to(device=input.device, dtype=input.dtype, non_blocking=non_blocking)
-    if s.weight_function is not None:
-        weight = s.weight_function(weight)
-    return weight, bias
-
-class CastWeightBiasOp:
-    comfy_cast_weights = False
-    weight_function = None
-    bias_function = None
-
-class disable_weight_init:
-    class Linear(torch.nn.Linear, CastWeightBiasOp):
-        def reset_parameters(self):
-            return None
-
-        def forward_comfy_cast_weights(self, input):
-            weight, bias = cast_bias_weight(self, input)
-            return torch.nn.functional.linear(input, weight, bias)
-
-        def forward(self, *args, **kwargs):
-            if self.comfy_cast_weights:
-                return self.forward_comfy_cast_weights(*args, **kwargs)
-            else:
-                return super().forward(*args, **kwargs)
-
-    class Conv1d(torch.nn.Conv1d, CastWeightBiasOp):
-        def reset_parameters(self):
-            return None
-
-        def forward_comfy_cast_weights(self, input):
-            weight, bias = cast_bias_weight(self, input)
-            return self._conv_forward(input, weight, bias)
-
-        def forward(self, *args, **kwargs):
-            if self.comfy_cast_weights:
-                return self.forward_comfy_cast_weights(*args, **kwargs)
-            else:
-                return super().forward(*args, **kwargs)
-
-    class Conv2d(torch.nn.Conv2d, CastWeightBiasOp):
-        def reset_parameters(self):
-            return None
-
-        def forward_comfy_cast_weights(self, input):
-            weight, bias = cast_bias_weight(self, input)
-            return self._conv_forward(input, weight, bias)
-
-        def forward(self, *args, **kwargs):
-            if self.comfy_cast_weights:
-                return self.forward_comfy_cast_weights(*args, **kwargs)
-            else:
-                return super().forward(*args, **kwargs)
-
-    class Conv3d(torch.nn.Conv3d, CastWeightBiasOp):
-        def reset_parameters(self):
-            return None
-
-        def forward_comfy_cast_weights(self, input):
-            weight, bias = cast_bias_weight(self, input)
-            return self._conv_forward(input, weight, bias)
-
-        def forward(self, *args, **kwargs):
-            if self.comfy_cast_weights:
-                return self.forward_comfy_cast_weights(*args, **kwargs)
-            else:
-                return super().forward(*args, **kwargs)
-
-    class GroupNorm(torch.nn.GroupNorm, CastWeightBiasOp):
-        def reset_parameters(self):
-            return None
-
-        def forward_comfy_cast_weights(self, input):
-            weight, bias = cast_bias_weight(self, input)
-            return torch.nn.functional.group_norm(input, self.num_groups, weight, bias, self.eps)
-
-        def forward(self, *args, **kwargs):
-            if self.comfy_cast_weights:
-                return self.forward_comfy_cast_weights(*args, **kwargs)
-            else:
-                return super().forward(*args, **kwargs)
-
-
-    class LayerNorm(torch.nn.LayerNorm, CastWeightBiasOp):
-        def reset_parameters(self):
-            return None
-
-        def forward_comfy_cast_weights(self, input):
-            if self.weight is not None:
-                weight, bias = cast_bias_weight(self, input)
-            else:
-                weight = None
-                bias = None
-            return torch.nn.functional.layer_norm(input, self.normalized_shape, weight, bias, self.eps)
-
-        def forward(self, *args, **kwargs):
-            if self.comfy_cast_weights:
-                return self.forward_comfy_cast_weights(*args, **kwargs)
-            else:
-                return super().forward(*args, **kwargs)
-
-    class ConvTranspose2d(torch.nn.ConvTranspose2d, CastWeightBiasOp):
-        def reset_parameters(self):
-            return None
-
-        def forward_comfy_cast_weights(self, input, output_size=None):
-            num_spatial_dims = 2
-            output_padding = self._output_padding(
-                input, output_size, self.stride, self.padding, self.kernel_size,
-                num_spatial_dims, self.dilation)
-
-            weight, bias = cast_bias_weight(self, input)
-            return torch.nn.functional.conv_transpose2d(
-                input, weight, bias, self.stride, self.padding,
-                output_padding, self.groups, self.dilation)
-
-        def forward(self, *args, **kwargs):
-            if self.comfy_cast_weights:
-                return self.forward_comfy_cast_weights(*args, **kwargs)
-            else:
-                return super().forward(*args, **kwargs)
-
-    class ConvTranspose1d(torch.nn.ConvTranspose1d, CastWeightBiasOp):
-        def reset_parameters(self):
-            return None
-
-        def forward_comfy_cast_weights(self, input, output_size=None):
-            num_spatial_dims = 1
-            output_padding = self._output_padding(
-                input, output_size, self.stride, self.padding, self.kernel_size,
-                num_spatial_dims, self.dilation)
-
-            weight, bias = cast_bias_weight(self, input)
-            return torch.nn.functional.conv_transpose1d(
-                input, weight, bias, self.stride, self.padding,
-                output_padding, self.groups, self.dilation)
-
-        def forward(self, *args, **kwargs):
-            if self.comfy_cast_weights:
-                return self.forward_comfy_cast_weights(*args, **kwargs)
-            else:
-                return super().forward(*args, **kwargs)
-
-    @classmethod
-    def conv_nd(s, dims, *args, **kwargs):
-        if dims == 2:
-            return s.Conv2d(*args, **kwargs)
-        elif dims == 3:
-            return s.Conv3d(*args, **kwargs)
-        else:
-            raise ValueError(f"unsupported dimensions: {dims}")
-
-
-class manual_cast(disable_weight_init):
-    class Linear(disable_weight_init.Linear):
-        comfy_cast_weights = True
-
-    class Conv1d(disable_weight_init.Conv1d):
-        comfy_cast_weights = True
-
-    class Conv2d(disable_weight_init.Conv2d):
-        comfy_cast_weights = True
-
-    class Conv3d(disable_weight_init.Conv3d):
-        comfy_cast_weights = True
-
-    class GroupNorm(disable_weight_init.GroupNorm):
-        comfy_cast_weights = True
-
-    class LayerNorm(disable_weight_init.LayerNorm):
-        comfy_cast_weights = True
-
-    class ConvTranspose2d(disable_weight_init.ConvTranspose2d):
-        comfy_cast_weights = True
-
-    class ConvTranspose1d(disable_weight_init.ConvTranspose1d):
-        comfy_cast_weights = True
diff --git a/MagicQuill/comfy/options.py b/MagicQuill/comfy/options.py
deleted file mode 100644
index f7f8af41ebd8b9669ef0ef21827ea6195bcb4752..0000000000000000000000000000000000000000
--- a/MagicQuill/comfy/options.py
+++ /dev/null
@@ -1,6 +0,0 @@
-
-args_parsing = False
-
-def enable_args_parsing(enable=True):
-    global args_parsing
-    args_parsing = enable
diff --git a/MagicQuill/comfy/sa_t5.py b/MagicQuill/comfy/sa_t5.py
deleted file mode 100644
index 37be5287e22d6e9c458f543beaaba5729a775d13..0000000000000000000000000000000000000000
--- a/MagicQuill/comfy/sa_t5.py
+++ /dev/null
@@ -1,22 +0,0 @@
-from comfy import sd1_clip
-from transformers import T5TokenizerFast
-import comfy.t5
-import os
-
-class T5BaseModel(sd1_clip.SDClipModel):
-    def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None):
-        textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_config_base.json")
-        super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens={"end": 1, "pad": 0}, model_class=comfy.t5.T5, enable_attention_masks=True, zero_out_masked=True)
-
-class T5BaseTokenizer(sd1_clip.SDTokenizer):
-    def __init__(self, embedding_directory=None):
-        tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_tokenizer")
-        super().__init__(tokenizer_path, pad_with_end=False, embedding_size=768, embedding_key='t5base', tokenizer_class=T5TokenizerFast, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=128)
-
-class SAT5Tokenizer(sd1_clip.SD1Tokenizer):
-    def __init__(self, embedding_directory=None):
-        super().__init__(embedding_directory=embedding_directory, clip_name="t5base", tokenizer=T5BaseTokenizer)
-
-class SAT5Model(sd1_clip.SD1ClipModel):
-    def __init__(self, device="cpu", dtype=None, **kwargs):
-        super().__init__(device=device, dtype=dtype, clip_name="t5base", clip_model=T5BaseModel, **kwargs)
diff --git a/MagicQuill/comfy/sample.py b/MagicQuill/comfy/sample.py
deleted file mode 100644
index 98dcaca7f38e76754bdce7fffaccf620fd0ba497..0000000000000000000000000000000000000000
--- a/MagicQuill/comfy/sample.py
+++ /dev/null
@@ -1,50 +0,0 @@
-import torch
-import comfy.model_management
-import comfy.samplers
-import comfy.utils
-import numpy as np
-import logging
-
-def prepare_noise(latent_image, seed, noise_inds=None):
-    """
-    creates random noise given a latent image and a seed.
-    optional arg skip can be used to skip and discard x number of noise generations for a given seed
-    """
-    generator = torch.manual_seed(seed)
-    if noise_inds is None:
-        return torch.randn(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, generator=generator, device="cpu")
-    
-    unique_inds, inverse = np.unique(noise_inds, return_inverse=True)
-    noises = []
-    for i in range(unique_inds[-1]+1):
-        noise = torch.randn([1] + list(latent_image.size())[1:], dtype=latent_image.dtype, layout=latent_image.layout, generator=generator, device="cpu")
-        if i in unique_inds:
-            noises.append(noise)
-    noises = [noises[i] for i in inverse]
-    noises = torch.cat(noises, axis=0)
-    return noises
-
-def fix_empty_latent_channels(model, latent_image):
-    latent_channels = model.get_model_object("latent_format").latent_channels #Resize the empty latent image so it has the right number of channels
-    if latent_channels != latent_image.shape[1] and torch.count_nonzero(latent_image) == 0:
-        latent_image = comfy.utils.repeat_to_batch_size(latent_image, latent_channels, dim=1)
-    return latent_image
-
-def prepare_sampling(model, noise_shape, positive, negative, noise_mask):
-    logging.warning("Warning: comfy.sample.prepare_sampling isn't used anymore and can be removed")
-    return model, positive, negative, noise_mask, []
-
-def cleanup_additional_models(models):
-    logging.warning("Warning: comfy.sample.cleanup_additional_models isn't used anymore and can be removed")
-
-def sample(model, noise, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=1.0, disable_noise=False, start_step=None, last_step=None, force_full_denoise=False, noise_mask=None, sigmas=None, callback=None, disable_pbar=False, seed=None):
-    sampler = comfy.samplers.KSampler(model, steps=steps, device=model.load_device, sampler=sampler_name, scheduler=scheduler, denoise=denoise, model_options=model.model_options)
-
-    samples = sampler.sample(noise, positive, negative, cfg=cfg, latent_image=latent_image, start_step=start_step, last_step=last_step, force_full_denoise=force_full_denoise, denoise_mask=noise_mask, sigmas=sigmas, callback=callback, disable_pbar=disable_pbar, seed=seed)
-    samples = samples.to(comfy.model_management.intermediate_device())
-    return samples
-
-def sample_custom(model, noise, cfg, sampler, sigmas, positive, negative, latent_image, noise_mask=None, callback=None, disable_pbar=False, seed=None):
-    samples = comfy.samplers.sample(model, noise, positive, negative, cfg, model.load_device, sampler, sigmas, model_options=model.model_options, latent_image=latent_image, denoise_mask=noise_mask, callback=callback, disable_pbar=disable_pbar, seed=seed)
-    samples = samples.to(comfy.model_management.intermediate_device())
-    return samples
diff --git a/MagicQuill/comfy/sampler_helpers.py b/MagicQuill/comfy/sampler_helpers.py
deleted file mode 100644
index a18abd9e9c7e82ad3e3b0ca014b2dadcb2127e92..0000000000000000000000000000000000000000
--- a/MagicQuill/comfy/sampler_helpers.py
+++ /dev/null
@@ -1,76 +0,0 @@
-import torch
-import comfy.model_management
-import comfy.conds
-
-def prepare_mask(noise_mask, shape, device):
-    """ensures noise mask is of proper dimensions"""
-    noise_mask = torch.nn.functional.interpolate(noise_mask.reshape((-1, 1, noise_mask.shape[-2], noise_mask.shape[-1])), size=(shape[2], shape[3]), mode="bilinear")
-    noise_mask = torch.cat([noise_mask] * shape[1], dim=1)
-    noise_mask = comfy.utils.repeat_to_batch_size(noise_mask, shape[0])
-    noise_mask = noise_mask.to(device)
-    return noise_mask
-
-def get_models_from_cond(cond, model_type):
-    models = []
-    for c in cond:
-        if model_type in c:
-            models += [c[model_type]]
-    return models
-
-def convert_cond(cond):
-    out = []
-    for c in cond:
-        temp = c[1].copy()
-        model_conds = temp.get("model_conds", {})
-        if c[0] is not None:
-            model_conds["c_crossattn"] = comfy.conds.CONDCrossAttn(c[0]) #TODO: remove
-            temp["cross_attn"] = c[0]
-        temp["model_conds"] = model_conds
-        out.append(temp)
-    return out
-
-def get_additional_models(conds, dtype):
-    """loads additional models in conditioning"""
-    cnets = []
-    gligen = []
-
-    for k in conds:
-        cnets += get_models_from_cond(conds[k], "control")
-        gligen += get_models_from_cond(conds[k], "gligen")
-
-    control_nets = set(cnets)
-
-    inference_memory = 0
-    control_models = []
-    for m in control_nets:
-        control_models += m.get_models()
-        inference_memory += m.inference_memory_requirements(dtype)
-
-    gligen = [x[1] for x in gligen]
-    models = control_models + gligen
-    return models, inference_memory
-
-def cleanup_additional_models(models):
-    """cleanup additional models that were loaded"""
-    for m in models:
-        if hasattr(m, 'cleanup'):
-            m.cleanup()
-
-
-def prepare_sampling(model, noise_shape, conds):
-    device = model.load_device
-    real_model = None
-    models, inference_memory = get_additional_models(conds, model.model_dtype())
-    comfy.model_management.load_models_gpu([model] + models, model.memory_required([noise_shape[0] * 2] + list(noise_shape[1:])) + inference_memory)
-    real_model = model.model
-
-    return real_model, conds, models
-
-def cleanup_models(conds, models):
-    cleanup_additional_models(models)
-
-    control_cleanup = []
-    for k in conds:
-        control_cleanup += get_models_from_cond(conds[k], "control")
-
-    cleanup_additional_models(set(control_cleanup))
diff --git a/MagicQuill/comfy/samplers.py b/MagicQuill/comfy/samplers.py
deleted file mode 100644
index 656e0a28f4a41406420b4182220ec9d7055dd185..0000000000000000000000000000000000000000
--- a/MagicQuill/comfy/samplers.py
+++ /dev/null
@@ -1,794 +0,0 @@
-from .k_diffusion import sampling as k_diffusion_sampling
-from .extra_samplers import uni_pc
-import torch
-import collections
-from comfy import model_management
-import math
-import logging
-import comfy.sampler_helpers
-
-def get_area_and_mult(conds, x_in, timestep_in):
-    dims = tuple(x_in.shape[2:])
-    area = None
-    strength = 1.0
-
-    if 'timestep_start' in conds:
-        timestep_start = conds['timestep_start']
-        if timestep_in[0] > timestep_start:
-            return None
-    if 'timestep_end' in conds:
-        timestep_end = conds['timestep_end']
-        if timestep_in[0] < timestep_end:
-            return None
-    if 'area' in conds:
-        area = list(conds['area'])
-    if 'strength' in conds:
-        strength = conds['strength']
-
-    input_x = x_in
-    if area is not None:
-        for i in range(len(dims)):
-            area[i] = min(input_x.shape[i + 2] - area[len(dims) + i], area[i])
-            input_x = input_x.narrow(i + 2, area[len(dims) + i], area[i])
-
-    if 'mask' in conds:
-        # Scale the mask to the size of the input
-        # The mask should have been resized as we began the sampling process
-        mask_strength = 1.0
-        if "mask_strength" in conds:
-            mask_strength = conds["mask_strength"]
-        mask = conds['mask']
-        assert(mask.shape[1:] == x_in.shape[2:])
-
-        mask = mask[:input_x.shape[0]]
-        if area is not None:
-            for i in range(len(dims)):
-                mask = mask.narrow(i + 1, area[len(dims) + i], area[i])
-
-        mask = mask * mask_strength
-        mask = mask.unsqueeze(1).repeat(input_x.shape[0] // mask.shape[0], input_x.shape[1], 1, 1)
-    else:
-        mask = torch.ones_like(input_x)
-    mult = mask * strength
-
-    if 'mask' not in conds and area is not None:
-        rr = 8
-        for i in range(len(dims)):
-            if area[len(dims) + i] != 0:
-                for t in range(rr):
-                    m = mult.narrow(i + 2, t, 1)
-                    m *= ((1.0/rr) * (t + 1))
-            if (area[i] + area[len(dims) + i]) < x_in.shape[i + 2]:
-                for t in range(rr):
-                    m = mult.narrow(i + 2, area[i] - 1 - t, 1)
-                    m *= ((1.0/rr) * (t + 1))
-
-    conditioning = {}
-    model_conds = conds["model_conds"]
-    for c in model_conds:
-        conditioning[c] = model_conds[c].process_cond(batch_size=x_in.shape[0], device=x_in.device, area=area)
-
-    control = conds.get('control', None)
-
-    patches = None
-    if 'gligen' in conds:
-        gligen = conds['gligen']
-        patches = {}
-        gligen_type = gligen[0]
-        gligen_model = gligen[1]
-        if gligen_type == "position":
-            gligen_patch = gligen_model.model.set_position(input_x.shape, gligen[2], input_x.device)
-        else:
-            gligen_patch = gligen_model.model.set_empty(input_x.shape, input_x.device)
-
-        patches['middle_patch'] = [gligen_patch]
-
-    cond_obj = collections.namedtuple('cond_obj', ['input_x', 'mult', 'conditioning', 'area', 'control', 'patches'])
-    return cond_obj(input_x, mult, conditioning, area, control, patches)
-
-def cond_equal_size(c1, c2):
-    if c1 is c2:
-        return True
-    if c1.keys() != c2.keys():
-        return False
-    for k in c1:
-        if not c1[k].can_concat(c2[k]):
-            return False
-    return True
-
-def can_concat_cond(c1, c2):
-    if c1.input_x.shape != c2.input_x.shape:
-        return False
-
-    def objects_concatable(obj1, obj2):
-        if (obj1 is None) != (obj2 is None):
-            return False
-        if obj1 is not None:
-            if obj1 is not obj2:
-                return False
-        return True
-
-    if not objects_concatable(c1.control, c2.control):
-        return False
-
-    if not objects_concatable(c1.patches, c2.patches):
-        return False
-
-    return cond_equal_size(c1.conditioning, c2.conditioning)
-
-def cond_cat(c_list):
-    c_crossattn = []
-    c_concat = []
-    c_adm = []
-    crossattn_max_len = 0
-
-    temp = {}
-    for x in c_list:
-        for k in x:
-            cur = temp.get(k, [])
-            cur.append(x[k])
-            temp[k] = cur
-
-    out = {}
-    for k in temp:
-        conds = temp[k]
-        out[k] = conds[0].concat(conds[1:])
-
-    return out
-
-def calc_cond_batch(model, conds, x_in, timestep, model_options):
-    out_conds = []
-    out_counts = []
-    to_run = []
-
-    for i in range(len(conds)):
-        out_conds.append(torch.zeros_like(x_in))
-        out_counts.append(torch.ones_like(x_in) * 1e-37)
-
-        cond = conds[i]
-        if cond is not None:
-            for x in cond:
-                p = get_area_and_mult(x, x_in, timestep)
-                if p is None:
-                    continue
-
-                to_run += [(p, i)]
-
-    while len(to_run) > 0:
-        first = to_run[0]
-        first_shape = first[0][0].shape
-        to_batch_temp = []
-        for x in range(len(to_run)):
-            if can_concat_cond(to_run[x][0], first[0]):
-                to_batch_temp += [x]
-
-        to_batch_temp.reverse()
-        to_batch = to_batch_temp[:1]
-
-        free_memory = model_management.get_free_memory(x_in.device)
-        for i in range(1, len(to_batch_temp) + 1):
-            batch_amount = to_batch_temp[:len(to_batch_temp)//i]
-            input_shape = [len(batch_amount) * first_shape[0]] + list(first_shape)[1:]
-            if model.memory_required(input_shape) < free_memory:
-                to_batch = batch_amount
-                break
-
-        input_x = []
-        mult = []
-        c = []
-        cond_or_uncond = []
-        area = []
-        control = None
-        patches = None
-        for x in to_batch:
-            o = to_run.pop(x)
-            p = o[0]
-            input_x.append(p.input_x)
-            mult.append(p.mult)
-            c.append(p.conditioning)
-            area.append(p.area)
-            cond_or_uncond.append(o[1])
-            control = p.control
-            patches = p.patches
-
-        batch_chunks = len(cond_or_uncond)
-        input_x = torch.cat(input_x)
-        c = cond_cat(c)
-        timestep_ = torch.cat([timestep] * batch_chunks)
-
-        if control is not None:
-            c['control'] = control.get_control(input_x, timestep_, c, len(cond_or_uncond))
-
-        transformer_options = {}
-        if 'transformer_options' in model_options:
-            transformer_options = model_options['transformer_options'].copy()
-
-        if patches is not None:
-            if "patches" in transformer_options:
-                cur_patches = transformer_options["patches"].copy()
-                for p in patches:
-                    if p in cur_patches:
-                        cur_patches[p] = cur_patches[p] + patches[p]
-                    else:
-                        cur_patches[p] = patches[p]
-                transformer_options["patches"] = cur_patches
-            else:
-                transformer_options["patches"] = patches
-
-        transformer_options["cond_or_uncond"] = cond_or_uncond[:]
-        transformer_options["sigmas"] = timestep
-
-        c['transformer_options'] = transformer_options
-
-        if 'model_function_wrapper' in model_options:
-            output = model_options['model_function_wrapper'](model.apply_model, {"input": input_x, "timestep": timestep_, "c": c, "cond_or_uncond": cond_or_uncond}).chunk(batch_chunks)
-        else:
-            output = model.apply_model(input_x, timestep_, **c).chunk(batch_chunks)
-
-        for o in range(batch_chunks):
-            cond_index = cond_or_uncond[o]
-            a = area[o]
-            if a is None:
-                out_conds[cond_index] += output[o] * mult[o]
-                out_counts[cond_index] += mult[o]
-            else:
-                out_c = out_conds[cond_index]
-                out_cts = out_counts[cond_index]
-                dims = len(a) // 2
-                for i in range(dims):
-                    out_c = out_c.narrow(i + 2, a[i + dims], a[i])
-                    out_cts = out_cts.narrow(i + 2, a[i + dims], a[i])
-                out_c += output[o] * mult[o]
-                out_cts += mult[o]
-
-    for i in range(len(out_conds)):
-        out_conds[i] /= out_counts[i]
-
-    return out_conds
-
-def calc_cond_uncond_batch(model, cond, uncond, x_in, timestep, model_options): #TODO: remove
-    logging.warning("WARNING: The comfy.samplers.calc_cond_uncond_batch function is deprecated please use the calc_cond_batch one instead.")
-    return tuple(calc_cond_batch(model, [cond, uncond], x_in, timestep, model_options))
-
-def cfg_function(model, cond_pred, uncond_pred, cond_scale, x, timestep, model_options={}, cond=None, uncond=None):
-    if "sampler_cfg_function" in model_options:
-        args = {"cond": x - cond_pred, "uncond": x - uncond_pred, "cond_scale": cond_scale, "timestep": timestep, "input": x, "sigma": timestep,
-                "cond_denoised": cond_pred, "uncond_denoised": uncond_pred, "model": model, "model_options": model_options}
-        cfg_result = x - model_options["sampler_cfg_function"](args)
-    else:
-        cfg_result = uncond_pred + (cond_pred - uncond_pred) * cond_scale
-
-    for fn in model_options.get("sampler_post_cfg_function", []):
-        args = {"denoised": cfg_result, "cond": cond, "uncond": uncond, "model": model, "uncond_denoised": uncond_pred, "cond_denoised": cond_pred,
-                "sigma": timestep, "model_options": model_options, "input": x}
-        cfg_result = fn(args)
-
-    return cfg_result
-
-#The main sampling function shared by all the samplers
-#Returns denoised
-def sampling_function(model, x, timestep, uncond, cond, cond_scale, model_options={}, seed=None):
-    if math.isclose(cond_scale, 1.0) and model_options.get("disable_cfg1_optimization", False) == False:
-        uncond_ = None
-    else:
-        uncond_ = uncond
-
-    conds = [cond, uncond_]
-    out = calc_cond_batch(model, conds, x, timestep, model_options)
-    return cfg_function(model, out[0], out[1], cond_scale, x, timestep, model_options=model_options, cond=cond, uncond=uncond_)
-
-
-class KSamplerX0Inpaint:
-    def __init__(self, model, sigmas):
-        self.inner_model = model
-        self.sigmas = sigmas
-    def __call__(self, x, sigma, denoise_mask, model_options={}, seed=None):
-        if denoise_mask is not None:
-            if "denoise_mask_function" in model_options:
-                denoise_mask = model_options["denoise_mask_function"](sigma, denoise_mask, extra_options={"model": self.inner_model, "sigmas": self.sigmas})
-            latent_mask = 1. - denoise_mask
-            x = x * denoise_mask + self.inner_model.inner_model.model_sampling.noise_scaling(sigma.reshape([sigma.shape[0]] + [1] * (len(self.noise.shape) - 1)), self.noise, self.latent_image) * latent_mask
-        out = self.inner_model(x, sigma, model_options=model_options, seed=seed)
-        if denoise_mask is not None:
-            out = out * denoise_mask + self.latent_image * latent_mask
-        return out
-
-def simple_scheduler(model_sampling, steps):
-    s = model_sampling
-    sigs = []
-    ss = len(s.sigmas) / steps
-    for x in range(steps):
-        sigs += [float(s.sigmas[-(1 + int(x * ss))])]
-    sigs += [0.0]
-    return torch.FloatTensor(sigs)
-
-def ddim_scheduler(model_sampling, steps):
-    s = model_sampling
-    sigs = []
-    ss = max(len(s.sigmas) // steps, 1)
-    x = 1
-    while x < len(s.sigmas):
-        sigs += [float(s.sigmas[x])]
-        x += ss
-    sigs = sigs[::-1]
-    sigs += [0.0]
-    return torch.FloatTensor(sigs)
-
-def normal_scheduler(model_sampling, steps, sgm=False, floor=False):
-    s = model_sampling
-    start = s.timestep(s.sigma_max)
-    end = s.timestep(s.sigma_min)
-
-    if sgm:
-        timesteps = torch.linspace(start, end, steps + 1)[:-1]
-    else:
-        timesteps = torch.linspace(start, end, steps)
-
-    sigs = []
-    for x in range(len(timesteps)):
-        ts = timesteps[x]
-        sigs.append(s.sigma(ts))
-    sigs += [0.0]
-    return torch.FloatTensor(sigs)
-
-def get_mask_aabb(masks):
-    if masks.numel() == 0:
-        return torch.zeros((0, 4), device=masks.device, dtype=torch.int)
-
-    b = masks.shape[0]
-
-    bounding_boxes = torch.zeros((b, 4), device=masks.device, dtype=torch.int)
-    is_empty = torch.zeros((b), device=masks.device, dtype=torch.bool)
-    for i in range(b):
-        mask = masks[i]
-        if mask.numel() == 0:
-            continue
-        if torch.max(mask != 0) == False:
-            is_empty[i] = True
-            continue
-        y, x = torch.where(mask)
-        bounding_boxes[i, 0] = torch.min(x)
-        bounding_boxes[i, 1] = torch.min(y)
-        bounding_boxes[i, 2] = torch.max(x)
-        bounding_boxes[i, 3] = torch.max(y)
-
-    return bounding_boxes, is_empty
-
-def resolve_areas_and_cond_masks_multidim(conditions, dims, device):
-    # We need to decide on an area outside the sampling loop in order to properly generate opposite areas of equal sizes.
-    # While we're doing this, we can also resolve the mask device and scaling for performance reasons
-    for i in range(len(conditions)):
-        c = conditions[i]
-        if 'area' in c:
-            area = c['area']
-            if area[0] == "percentage":
-                modified = c.copy()
-                a = area[1:]
-                a_len = len(a) // 2
-                area = ()
-                for d in range(len(dims)):
-                    area += (max(1, round(a[d] * dims[d])),)
-                for d in range(len(dims)):
-                    area += (round(a[d + a_len] * dims[d]),)
-
-                modified['area'] = area
-                c = modified
-                conditions[i] = c
-
-        if 'mask' in c:
-            mask = c['mask']
-            mask = mask.to(device=device)
-            modified = c.copy()
-            if len(mask.shape) == len(dims):
-                mask = mask.unsqueeze(0)
-            if mask.shape[1:] != dims:
-                mask = torch.nn.functional.interpolate(mask.unsqueeze(1), size=dims, mode='bilinear', align_corners=False).squeeze(1)
-
-            if modified.get("set_area_to_bounds", False): #TODO: handle dim != 2
-                bounds = torch.max(torch.abs(mask),dim=0).values.unsqueeze(0)
-                boxes, is_empty = get_mask_aabb(bounds)
-                if is_empty[0]:
-                    # Use the minimum possible size for efficiency reasons. (Since the mask is all-0, this becomes a noop anyway)
-                    modified['area'] = (8, 8, 0, 0)
-                else:
-                    box = boxes[0]
-                    H, W, Y, X = (box[3] - box[1] + 1, box[2] - box[0] + 1, box[1], box[0])
-                    H = max(8, H)
-                    W = max(8, W)
-                    area = (int(H), int(W), int(Y), int(X))
-                    modified['area'] = area
-
-            modified['mask'] = mask
-            conditions[i] = modified
-
-def resolve_areas_and_cond_masks(conditions, h, w, device):
-    logging.warning("WARNING: The comfy.samplers.resolve_areas_and_cond_masks function is deprecated please use the resolve_areas_and_cond_masks_multidim one instead.")
-    return resolve_areas_and_cond_masks_multidim(conditions, [h, w], device)
-
-def create_cond_with_same_area_if_none(conds, c): #TODO: handle dim != 2
-    if 'area' not in c:
-        return
-
-    c_area = c['area']
-    smallest = None
-    for x in conds:
-        if 'area' in x:
-            a = x['area']
-            if c_area[2] >= a[2] and c_area[3] >= a[3]:
-                if a[0] + a[2] >= c_area[0] + c_area[2]:
-                    if a[1] + a[3] >= c_area[1] + c_area[3]:
-                        if smallest is None:
-                            smallest = x
-                        elif 'area' not in smallest:
-                            smallest = x
-                        else:
-                            if smallest['area'][0] * smallest['area'][1] > a[0] * a[1]:
-                                smallest = x
-        else:
-            if smallest is None:
-                smallest = x
-    if smallest is None:
-        return
-    if 'area' in smallest:
-        if smallest['area'] == c_area:
-            return
-
-    out = c.copy()
-    out['model_conds'] = smallest['model_conds'].copy() #TODO: which fields should be copied?
-    conds += [out]
-
-def calculate_start_end_timesteps(model, conds):
-    s = model.model_sampling
-    for t in range(len(conds)):
-        x = conds[t]
-
-        timestep_start = None
-        timestep_end = None
-        if 'start_percent' in x:
-            timestep_start = s.percent_to_sigma(x['start_percent'])
-        if 'end_percent' in x:
-            timestep_end = s.percent_to_sigma(x['end_percent'])
-
-        if (timestep_start is not None) or (timestep_end is not None):
-            n = x.copy()
-            if (timestep_start is not None):
-                n['timestep_start'] = timestep_start
-            if (timestep_end is not None):
-                n['timestep_end'] = timestep_end
-            conds[t] = n
-
-def pre_run_control(model, conds):
-    s = model.model_sampling
-    for t in range(len(conds)):
-        x = conds[t]
-
-        timestep_start = None
-        timestep_end = None
-        percent_to_timestep_function = lambda a: s.percent_to_sigma(a)
-        if 'control' in x:
-            x['control'].pre_run(model, percent_to_timestep_function)
-
-def apply_empty_x_to_equal_area(conds, uncond, name, uncond_fill_func):
-    cond_cnets = []
-    cond_other = []
-    uncond_cnets = []
-    uncond_other = []
-    for t in range(len(conds)):
-        x = conds[t]
-        if 'area' not in x:
-            if name in x and x[name] is not None:
-                cond_cnets.append(x[name])
-            else:
-                cond_other.append((x, t))
-    for t in range(len(uncond)):
-        x = uncond[t]
-        if 'area' not in x:
-            if name in x and x[name] is not None:
-                uncond_cnets.append(x[name])
-            else:
-                uncond_other.append((x, t))
-
-    if len(uncond_cnets) > 0:
-        return
-
-    for x in range(len(cond_cnets)):
-        temp = uncond_other[x % len(uncond_other)]
-        o = temp[0]
-        if name in o and o[name] is not None:
-            n = o.copy()
-            n[name] = uncond_fill_func(cond_cnets, x)
-            uncond += [n]
-        else:
-            n = o.copy()
-            n[name] = uncond_fill_func(cond_cnets, x)
-            uncond[temp[1]] = n
-
-def encode_model_conds(model_function, conds, noise, device, prompt_type, **kwargs):
-    for t in range(len(conds)):
-        x = conds[t]
-        params = x.copy()
-        params["device"] = device
-        params["noise"] = noise
-        default_width = None
-        if len(noise.shape) >= 4: #TODO: 8 multiple should be set by the model
-            default_width = noise.shape[3] * 8
-        params["width"] = params.get("width", default_width)
-        params["height"] = params.get("height", noise.shape[2] * 8)
-        params["prompt_type"] = params.get("prompt_type", prompt_type)
-        for k in kwargs:
-            if k not in params:
-                params[k] = kwargs[k]
-
-        out = model_function(**params)
-        x = x.copy()
-        model_conds = x['model_conds'].copy()
-        for k in out:
-            model_conds[k] = out[k]
-        x['model_conds'] = model_conds
-        conds[t] = x
-    return conds
-
-class Sampler:
-    def sample(self):
-        pass
-
-    def max_denoise(self, model_wrap, sigmas):
-        max_sigma = float(model_wrap.inner_model.model_sampling.sigma_max)
-        sigma = float(sigmas[0])
-        return math.isclose(max_sigma, sigma, rel_tol=1e-05) or sigma > max_sigma
-
-KSAMPLER_NAMES = ["euler", "euler_ancestral", "heun", "heunpp2","dpm_2", "dpm_2_ancestral",
-                  "lms", "dpm_fast", "dpm_adaptive", "dpmpp_2s_ancestral", "dpmpp_sde", "dpmpp_sde_gpu",
-                  "dpmpp_2m", "dpmpp_2m_sde", "dpmpp_2m_sde_gpu", "dpmpp_3m_sde", "dpmpp_3m_sde_gpu", "ddpm", "lcm"]
-
-class KSAMPLER(Sampler):
-    def __init__(self, sampler_function, extra_options={}, inpaint_options={}):
-        self.sampler_function = sampler_function
-        self.extra_options = extra_options
-        self.inpaint_options = inpaint_options
-
-    def sample(self, model_wrap, sigmas, extra_args, callback, noise, latent_image=None, denoise_mask=None, disable_pbar=False):
-        extra_args["denoise_mask"] = denoise_mask
-        model_k = KSamplerX0Inpaint(model_wrap, sigmas)
-        model_k.latent_image = latent_image
-        if self.inpaint_options.get("random", False): #TODO: Should this be the default?
-            generator = torch.manual_seed(extra_args.get("seed", 41) + 1)
-            model_k.noise = torch.randn(noise.shape, generator=generator, device="cpu").to(noise.dtype).to(noise.device)
-        else:
-            model_k.noise = noise
-
-        noise = model_wrap.inner_model.model_sampling.noise_scaling(sigmas[0], noise, latent_image, self.max_denoise(model_wrap, sigmas))
-
-        k_callback = None
-        total_steps = len(sigmas) - 1
-        if callback is not None:
-            k_callback = lambda x: callback(x["i"], x["denoised"], x["x"], total_steps)
-
-        samples = self.sampler_function(model_k, noise, sigmas, extra_args=extra_args, callback=k_callback, disable=disable_pbar, **self.extra_options)
-        samples = model_wrap.inner_model.model_sampling.inverse_noise_scaling(sigmas[-1], samples)
-        return samples
-
-
-def ksampler(sampler_name, extra_options={}, inpaint_options={}):
-    if sampler_name == "dpm_fast":
-        def dpm_fast_function(model, noise, sigmas, extra_args, callback, disable):
-            if len(sigmas) <= 1:
-                return noise
-
-            sigma_min = sigmas[-1]
-            if sigma_min == 0:
-                sigma_min = sigmas[-2]
-            total_steps = len(sigmas) - 1
-            return k_diffusion_sampling.sample_dpm_fast(model, noise, sigma_min, sigmas[0], total_steps, extra_args=extra_args, callback=callback, disable=disable)
-        sampler_function = dpm_fast_function
-    elif sampler_name == "dpm_adaptive":
-        def dpm_adaptive_function(model, noise, sigmas, extra_args, callback, disable, **extra_options):
-            if len(sigmas) <= 1:
-                return noise
-
-            sigma_min = sigmas[-1]
-            if sigma_min == 0:
-                sigma_min = sigmas[-2]
-            return k_diffusion_sampling.sample_dpm_adaptive(model, noise, sigma_min, sigmas[0], extra_args=extra_args, callback=callback, disable=disable, **extra_options)
-        sampler_function = dpm_adaptive_function
-    else:
-        sampler_function = getattr(k_diffusion_sampling, "sample_{}".format(sampler_name))
-
-    return KSAMPLER(sampler_function, extra_options, inpaint_options)
-
-
-def process_conds(model, noise, conds, device, latent_image=None, denoise_mask=None, seed=None):
-    for k in conds:
-        conds[k] = conds[k][:]
-        resolve_areas_and_cond_masks_multidim(conds[k], noise.shape[2:], device)
-
-    for k in conds:
-        calculate_start_end_timesteps(model, conds[k])
-
-    if hasattr(model, 'extra_conds'):
-        for k in conds:
-            conds[k] = encode_model_conds(model.extra_conds, conds[k], noise, device, k, latent_image=latent_image, denoise_mask=denoise_mask, seed=seed)
-
-    #make sure each cond area has an opposite one with the same area
-    for k in conds:
-        for c in conds[k]:
-            for kk in conds:
-                if k != kk:
-                    create_cond_with_same_area_if_none(conds[kk], c)
-
-    for k in conds:
-        pre_run_control(model, conds[k])
-
-    if "positive" in conds:
-        positive = conds["positive"]
-        for k in conds:
-            if k != "positive":
-                apply_empty_x_to_equal_area(list(filter(lambda c: c.get('control_apply_to_uncond', False) == True, positive)), conds[k], 'control', lambda cond_cnets, x: cond_cnets[x])
-                apply_empty_x_to_equal_area(positive, conds[k], 'gligen', lambda cond_cnets, x: cond_cnets[x])
-
-    return conds
-
-class CFGGuider:
-    def __init__(self, model_patcher):
-        self.model_patcher = model_patcher
-        self.model_options = model_patcher.model_options
-        self.original_conds = {}
-        self.cfg = 1.0
-
-    def set_conds(self, positive, negative):
-        self.inner_set_conds({"positive": positive, "negative": negative})
-
-    def set_cfg(self, cfg):
-        self.cfg = cfg
-
-    def inner_set_conds(self, conds):
-        for k in conds:
-            self.original_conds[k] = comfy.sampler_helpers.convert_cond(conds[k])
-
-    def __call__(self, *args, **kwargs):
-        return self.predict_noise(*args, **kwargs)
-
-    def predict_noise(self, x, timestep, model_options={}, seed=None):
-        return sampling_function(self.inner_model, x, timestep, self.conds.get("negative", None), self.conds.get("positive", None), self.cfg, model_options=model_options, seed=seed)
-
-    def inner_sample(self, noise, latent_image, device, sampler, sigmas, denoise_mask, callback, disable_pbar, seed):
-        if latent_image is not None and torch.count_nonzero(latent_image) > 0: #Don't shift the empty latent image.
-            latent_image = self.inner_model.process_latent_in(latent_image)
-
-        self.conds = process_conds(self.inner_model, noise, self.conds, device, latent_image, denoise_mask, seed)
-
-        extra_args = {"model_options": self.model_options, "seed":seed}
-
-        samples = sampler.sample(self, sigmas, extra_args, callback, noise, latent_image, denoise_mask, disable_pbar)
-        return self.inner_model.process_latent_out(samples.to(torch.float32))
-
-    def sample(self, noise, latent_image, sampler, sigmas, denoise_mask=None, callback=None, disable_pbar=False, seed=None):
-        if sigmas.shape[-1] == 0:
-            return latent_image
-
-        self.conds = {}
-        for k in self.original_conds:
-            self.conds[k] = list(map(lambda a: a.copy(), self.original_conds[k]))
-
-        self.inner_model, self.conds, self.loaded_models = comfy.sampler_helpers.prepare_sampling(self.model_patcher, noise.shape, self.conds)
-        device = self.model_patcher.load_device
-
-        if denoise_mask is not None:
-            denoise_mask = comfy.sampler_helpers.prepare_mask(denoise_mask, noise.shape, device)
-
-        noise = noise.to(device)
-        latent_image = latent_image.to(device)
-        sigmas = sigmas.to(device)
-
-        output = self.inner_sample(noise, latent_image, device, sampler, sigmas, denoise_mask, callback, disable_pbar, seed)
-
-        comfy.sampler_helpers.cleanup_models(self.conds, self.loaded_models)
-        del self.inner_model
-        del self.conds
-        del self.loaded_models
-        return output
-
-
-def sample(model, noise, positive, negative, cfg, device, sampler, sigmas, model_options={}, latent_image=None, denoise_mask=None, callback=None, disable_pbar=False, seed=None):
-    cfg_guider = CFGGuider(model)
-    cfg_guider.set_conds(positive, negative)
-    cfg_guider.set_cfg(cfg)
-    return cfg_guider.sample(noise, latent_image, sampler, sigmas, denoise_mask, callback, disable_pbar, seed)
-
-
-SCHEDULER_NAMES = ["normal", "karras", "exponential", "sgm_uniform", "simple", "ddim_uniform"]
-SAMPLER_NAMES = KSAMPLER_NAMES + ["ddim", "uni_pc", "uni_pc_bh2"]
-
-def calculate_sigmas(model_sampling, scheduler_name, steps):
-    if scheduler_name == "karras":
-        sigmas = k_diffusion_sampling.get_sigmas_karras(n=steps, sigma_min=float(model_sampling.sigma_min), sigma_max=float(model_sampling.sigma_max))
-    elif scheduler_name == "exponential":
-        sigmas = k_diffusion_sampling.get_sigmas_exponential(n=steps, sigma_min=float(model_sampling.sigma_min), sigma_max=float(model_sampling.sigma_max))
-    elif scheduler_name == "normal":
-        sigmas = normal_scheduler(model_sampling, steps)
-    elif scheduler_name == "simple":
-        sigmas = simple_scheduler(model_sampling, steps)
-    elif scheduler_name == "ddim_uniform":
-        sigmas = ddim_scheduler(model_sampling, steps)
-    elif scheduler_name == "sgm_uniform":
-        sigmas = normal_scheduler(model_sampling, steps, sgm=True)
-    else:
-        logging.error("error invalid scheduler {}".format(scheduler_name))
-    return sigmas
-
-def sampler_object(name):
-    if name == "uni_pc":
-        sampler = KSAMPLER(uni_pc.sample_unipc)
-    elif name == "uni_pc_bh2":
-        sampler = KSAMPLER(uni_pc.sample_unipc_bh2)
-    elif name == "ddim":
-        sampler = ksampler("euler", inpaint_options={"random": True})
-    else:
-        sampler = ksampler(name)
-    return sampler
-
-class KSampler:
-    SCHEDULERS = SCHEDULER_NAMES
-    SAMPLERS = SAMPLER_NAMES
-    DISCARD_PENULTIMATE_SIGMA_SAMPLERS = set(('dpm_2', 'dpm_2_ancestral', 'uni_pc', 'uni_pc_bh2'))
-
-    def __init__(self, model, steps, device, sampler=None, scheduler=None, denoise=None, model_options={}):
-        self.model = model
-        self.device = device
-        if scheduler not in self.SCHEDULERS:
-            scheduler = self.SCHEDULERS[0]
-        if sampler not in self.SAMPLERS:
-            sampler = self.SAMPLERS[0]
-        self.scheduler = scheduler
-        self.sampler = sampler
-        self.set_steps(steps, denoise)
-        self.denoise = denoise
-        self.model_options = model_options
-
-    def calculate_sigmas(self, steps):
-        sigmas = None
-
-        discard_penultimate_sigma = False
-        if self.sampler in self.DISCARD_PENULTIMATE_SIGMA_SAMPLERS:
-            steps += 1
-            discard_penultimate_sigma = True
-
-        sigmas = calculate_sigmas(self.model.get_model_object("model_sampling"), self.scheduler, steps)
-
-        if discard_penultimate_sigma:
-            sigmas = torch.cat([sigmas[:-2], sigmas[-1:]])
-        return sigmas
-
-    def set_steps(self, steps, denoise=None):
-        self.steps = steps
-        if denoise is None or denoise > 0.9999:
-            self.sigmas = self.calculate_sigmas(steps).to(self.device)
-        else:
-            if denoise <= 0.0:
-                self.sigmas = torch.FloatTensor([])
-            else:
-                new_steps = int(steps/denoise)
-                sigmas = self.calculate_sigmas(new_steps).to(self.device)
-                self.sigmas = sigmas[-(steps + 1):]
-
-    def sample(self, noise, positive, negative, cfg, latent_image=None, start_step=None, last_step=None, force_full_denoise=False, denoise_mask=None, sigmas=None, callback=None, disable_pbar=False, seed=None):
-        if sigmas is None:
-            sigmas = self.sigmas
-
-        if last_step is not None and last_step < (len(sigmas) - 1):
-            sigmas = sigmas[:last_step + 1]
-            if force_full_denoise:
-                sigmas[-1] = 0
-
-        if start_step is not None:
-            if start_step < (len(sigmas) - 1):
-                sigmas = sigmas[start_step:]
-            else:
-                if latent_image is not None:
-                    return latent_image
-                else:
-                    return torch.zeros_like(noise)
-
-        sampler = sampler_object(self.sampler)
-
-        return sample(self.model, noise, positive, negative, cfg, self.device, sampler, sigmas, self.model_options, latent_image=latent_image, denoise_mask=denoise_mask, callback=callback, disable_pbar=disable_pbar, seed=seed)
diff --git a/MagicQuill/comfy/sd.py b/MagicQuill/comfy/sd.py
deleted file mode 100644
index cfbf8fa4d201cee3f8ee04b662fe35a99d60677b..0000000000000000000000000000000000000000
--- a/MagicQuill/comfy/sd.py
+++ /dev/null
@@ -1,624 +0,0 @@
-import torch
-from enum import Enum
-import logging
-
-from comfy import model_management
-from .ldm.models.autoencoder import AutoencoderKL, AutoencodingEngine
-from .ldm.cascade.stage_a import StageA
-from .ldm.cascade.stage_c_coder import StageC_coder
-from .ldm.audio.autoencoder import AudioOobleckVAE
-import yaml
-
-import comfy.utils
-
-from . import clip_vision
-from . import gligen
-from . import diffusers_convert
-from . import model_detection
-
-from . import sd1_clip
-from . import sd2_clip
-from . import sdxl_clip
-from . import sd3_clip
-from . import sa_t5
-
-import comfy.model_patcher
-import comfy.lora
-import comfy.t2i_adapter.adapter
-import comfy.supported_models_base
-import comfy.taesd.taesd
-
-def load_model_weights(model, sd):
-    m, u = model.load_state_dict(sd, strict=False)
-    m = set(m)
-    unexpected_keys = set(u)
-
-    k = list(sd.keys())
-    for x in k:
-        if x not in unexpected_keys:
-            w = sd.pop(x)
-            del w
-    if len(m) > 0:
-        logging.warning("missing {}".format(m))
-    return model
-
-def load_clip_weights(model, sd):
-    k = list(sd.keys())
-    for x in k:
-        if x.startswith("cond_stage_model.transformer.") and not x.startswith("cond_stage_model.transformer.text_model."):
-            y = x.replace("cond_stage_model.transformer.", "cond_stage_model.transformer.text_model.")
-            sd[y] = sd.pop(x)
-
-    if 'cond_stage_model.transformer.text_model.embeddings.position_ids' in sd:
-        ids = sd['cond_stage_model.transformer.text_model.embeddings.position_ids']
-        if ids.dtype == torch.float32:
-            sd['cond_stage_model.transformer.text_model.embeddings.position_ids'] = ids.round()
-
-    sd = comfy.utils.clip_text_transformers_convert(sd, "cond_stage_model.model.", "cond_stage_model.transformer.")
-    return load_model_weights(model, sd)
-
-
-def load_lora_for_models(model, clip, lora, strength_model, strength_clip):
-    key_map = {}
-    if model is not None:
-        key_map = comfy.lora.model_lora_keys_unet(model.model, key_map)
-    if clip is not None:
-        key_map = comfy.lora.model_lora_keys_clip(clip.cond_stage_model, key_map)
-
-    loaded = comfy.lora.load_lora(lora, key_map)
-    if model is not None:
-        new_modelpatcher = model.clone()
-        k = new_modelpatcher.add_patches(loaded, strength_model)
-    else:
-        k = ()
-        new_modelpatcher = None
-
-    if clip is not None:
-        new_clip = clip.clone()
-        k1 = new_clip.add_patches(loaded, strength_clip)
-    else:
-        k1 = ()
-        new_clip = None
-    k = set(k)
-    k1 = set(k1)
-    for x in loaded:
-        if (x not in k) and (x not in k1):
-            logging.warning("NOT LOADED {}".format(x))
-
-    return (new_modelpatcher, new_clip)
-
-
-class CLIP:
-    def __init__(self, target=None, embedding_directory=None, no_init=False):
-        if no_init:
-            return
-        params = target.params.copy()
-        clip = target.clip
-        tokenizer = target.tokenizer
-
-        load_device = model_management.text_encoder_device()
-        offload_device = model_management.text_encoder_offload_device()
-        params['device'] = offload_device
-        dtype = model_management.text_encoder_dtype(load_device)
-        params['dtype'] = dtype
-
-        self.cond_stage_model = clip(**(params))
-
-        for dt in self.cond_stage_model.dtypes:
-            if not model_management.supports_cast(load_device, dt):
-                load_device = offload_device
-
-        self.tokenizer = tokenizer(embedding_directory=embedding_directory)
-        self.patcher = comfy.model_patcher.ModelPatcher(self.cond_stage_model, load_device=load_device, offload_device=offload_device)
-        self.layer_idx = None
-        logging.debug("CLIP model load device: {}, offload device: {}".format(load_device, offload_device))
-
-    def clone(self):
-        n = CLIP(no_init=True)
-        n.patcher = self.patcher.clone()
-        n.cond_stage_model = self.cond_stage_model
-        n.tokenizer = self.tokenizer
-        n.layer_idx = self.layer_idx
-        return n
-
-    def add_patches(self, patches, strength_patch=1.0, strength_model=1.0):
-        return self.patcher.add_patches(patches, strength_patch, strength_model)
-
-    def clip_layer(self, layer_idx):
-        self.layer_idx = layer_idx
-
-    def tokenize(self, text, return_word_ids=False):
-        return self.tokenizer.tokenize_with_weights(text, return_word_ids)
-
-    def encode_from_tokens(self, tokens, return_pooled=False):
-        self.cond_stage_model.reset_clip_options()
-
-        if self.layer_idx is not None:
-            self.cond_stage_model.set_clip_options({"layer": self.layer_idx})
-
-        if return_pooled == "unprojected":
-            self.cond_stage_model.set_clip_options({"projected_pooled": False})
-
-        self.load_model()
-        cond, pooled = self.cond_stage_model.encode_token_weights(tokens)
-        if return_pooled:
-            return cond, pooled
-        return cond
-
-    def encode(self, text):
-        tokens = self.tokenize(text)
-        return self.encode_from_tokens(tokens)
-
-    def load_sd(self, sd, full_model=False):
-        if full_model:
-            return self.cond_stage_model.load_state_dict(sd, strict=False)
-        else:
-            return self.cond_stage_model.load_sd(sd)
-
-    def get_sd(self):
-        return self.cond_stage_model.state_dict()
-
-    def load_model(self):
-        model_management.load_model_gpu(self.patcher)
-        return self.patcher
-
-    def get_key_patches(self):
-        return self.patcher.get_key_patches()
-
-class VAE:
-    def __init__(self, sd=None, device=None, config=None, dtype=None):
-        if 'decoder.up_blocks.0.resnets.0.norm1.weight' in sd.keys(): #diffusers format
-            sd = diffusers_convert.convert_vae_state_dict(sd)
-
-        self.memory_used_encode = lambda shape, dtype: (1767 * shape[2] * shape[3]) * model_management.dtype_size(dtype) #These are for AutoencoderKL and need tweaking (should be lower)
-        self.memory_used_decode = lambda shape, dtype: (2178 * shape[2] * shape[3] * 64) * model_management.dtype_size(dtype)
-        self.downscale_ratio = 8
-        self.upscale_ratio = 8
-        self.latent_channels = 4
-        self.output_channels = 3
-        self.process_input = lambda image: image * 2.0 - 1.0
-        self.process_output = lambda image: torch.clamp((image + 1.0) / 2.0, min=0.0, max=1.0)
-        self.working_dtypes = [torch.bfloat16, torch.float32]
-
-        if config is None:
-            if "decoder.mid.block_1.mix_factor" in sd:
-                encoder_config = {'double_z': True, 'z_channels': 4, 'resolution': 256, 'in_channels': 3, 'out_ch': 3, 'ch': 128, 'ch_mult': [1, 2, 4, 4], 'num_res_blocks': 2, 'attn_resolutions': [], 'dropout': 0.0}
-                decoder_config = encoder_config.copy()
-                decoder_config["video_kernel_size"] = [3, 1, 1]
-                decoder_config["alpha"] = 0.0
-                self.first_stage_model = AutoencodingEngine(regularizer_config={'target': "comfy.ldm.models.autoencoder.DiagonalGaussianRegularizer"},
-                                                            encoder_config={'target': "comfy.ldm.modules.diffusionmodules.model.Encoder", 'params': encoder_config},
-                                                            decoder_config={'target': "comfy.ldm.modules.temporal_ae.VideoDecoder", 'params': decoder_config})
-            elif "taesd_decoder.1.weight" in sd:
-                self.latent_channels = sd["taesd_decoder.1.weight"].shape[1]
-                self.first_stage_model = comfy.taesd.taesd.TAESD(latent_channels=self.latent_channels)
-            elif "vquantizer.codebook.weight" in sd: #VQGan: stage a of stable cascade
-                self.first_stage_model = StageA()
-                self.downscale_ratio = 4
-                self.upscale_ratio = 4
-                #TODO
-                #self.memory_used_encode
-                #self.memory_used_decode
-                self.process_input = lambda image: image
-                self.process_output = lambda image: image
-            elif "backbone.1.0.block.0.1.num_batches_tracked" in sd: #effnet: encoder for stage c latent of stable cascade
-                self.first_stage_model = StageC_coder()
-                self.downscale_ratio = 32
-                self.latent_channels = 16
-                new_sd = {}
-                for k in sd:
-                    new_sd["encoder.{}".format(k)] = sd[k]
-                sd = new_sd
-            elif "blocks.11.num_batches_tracked" in sd: #previewer: decoder for stage c latent of stable cascade
-                self.first_stage_model = StageC_coder()
-                self.latent_channels = 16
-                new_sd = {}
-                for k in sd:
-                    new_sd["previewer.{}".format(k)] = sd[k]
-                sd = new_sd
-            elif "encoder.backbone.1.0.block.0.1.num_batches_tracked" in sd: #combined effnet and previewer for stable cascade
-                self.first_stage_model = StageC_coder()
-                self.downscale_ratio = 32
-                self.latent_channels = 16
-            elif "decoder.conv_in.weight" in sd:
-                #default SD1.x/SD2.x VAE parameters
-                ddconfig = {'double_z': True, 'z_channels': 4, 'resolution': 256, 'in_channels': 3, 'out_ch': 3, 'ch': 128, 'ch_mult': [1, 2, 4, 4], 'num_res_blocks': 2, 'attn_resolutions': [], 'dropout': 0.0}
-
-                if 'encoder.down.2.downsample.conv.weight' not in sd and 'decoder.up.3.upsample.conv.weight' not in sd: #Stable diffusion x4 upscaler VAE
-                    ddconfig['ch_mult'] = [1, 2, 4]
-                    self.downscale_ratio = 4
-                    self.upscale_ratio = 4
-
-                self.latent_channels = ddconfig['z_channels'] = sd["decoder.conv_in.weight"].shape[1]
-                if 'quant_conv.weight' in sd:
-                    self.first_stage_model = AutoencoderKL(ddconfig=ddconfig, embed_dim=4)
-                else:
-                    self.first_stage_model = AutoencodingEngine(regularizer_config={'target': "comfy.ldm.models.autoencoder.DiagonalGaussianRegularizer"},
-                                                                encoder_config={'target': "comfy.ldm.modules.diffusionmodules.model.Encoder", 'params': ddconfig},
-                                                                decoder_config={'target': "comfy.ldm.modules.diffusionmodules.model.Decoder", 'params': ddconfig})
-            elif "decoder.layers.0.weight_v" in sd:
-                self.first_stage_model = AudioOobleckVAE()
-                self.memory_used_encode = lambda shape, dtype: (1000 * shape[2]) * model_management.dtype_size(dtype)
-                self.memory_used_decode = lambda shape, dtype: (1000 * shape[2] * 2048) * model_management.dtype_size(dtype)
-                self.latent_channels = 64
-                self.output_channels = 2
-                self.upscale_ratio = 2048
-                self.downscale_ratio =  2048
-                self.process_output = lambda audio: audio
-                self.process_input = lambda audio: audio
-                self.working_dtypes = [torch.float16, torch.bfloat16, torch.float32]
-            else:
-                logging.warning("WARNING: No VAE weights detected, VAE not initalized.")
-                self.first_stage_model = None
-                return
-        else:
-            self.first_stage_model = AutoencoderKL(**(config['params']))
-        self.first_stage_model = self.first_stage_model.eval()
-
-        m, u = self.first_stage_model.load_state_dict(sd, strict=False)
-        if len(m) > 0:
-            logging.warning("Missing VAE keys {}".format(m))
-
-        if len(u) > 0:
-            logging.debug("Leftover VAE keys {}".format(u))
-
-        if device is None:
-            device = model_management.vae_device()
-        self.device = device
-        offload_device = model_management.vae_offload_device()
-        if dtype is None:
-            dtype = model_management.vae_dtype(self.device, self.working_dtypes)
-        self.vae_dtype = dtype
-        self.first_stage_model.to(self.vae_dtype)
-        self.output_device = model_management.intermediate_device()
-
-        self.patcher = comfy.model_patcher.ModelPatcher(self.first_stage_model, load_device=self.device, offload_device=offload_device)
-        logging.debug("VAE load device: {}, offload device: {}, dtype: {}".format(self.device, offload_device, self.vae_dtype))
-
-    def vae_encode_crop_pixels(self, pixels):
-        dims = pixels.shape[1:-1]
-        for d in range(len(dims)):
-            x = (dims[d] // self.downscale_ratio) * self.downscale_ratio
-            x_offset = (dims[d] % self.downscale_ratio) // 2
-            if x != dims[d]:
-                pixels = pixels.narrow(d + 1, x_offset, x)
-        return pixels
-
-    def decode_tiled_(self, samples, tile_x=64, tile_y=64, overlap = 16):
-        steps = samples.shape[0] * comfy.utils.get_tiled_scale_steps(samples.shape[3], samples.shape[2], tile_x, tile_y, overlap)
-        steps += samples.shape[0] * comfy.utils.get_tiled_scale_steps(samples.shape[3], samples.shape[2], tile_x // 2, tile_y * 2, overlap)
-        steps += samples.shape[0] * comfy.utils.get_tiled_scale_steps(samples.shape[3], samples.shape[2], tile_x * 2, tile_y // 2, overlap)
-        pbar = comfy.utils.ProgressBar(steps)
-
-        decode_fn = lambda a: self.first_stage_model.decode(a.to(self.vae_dtype).to(self.device)).float()
-        output = self.process_output(
-            (comfy.utils.tiled_scale(samples, decode_fn, tile_x // 2, tile_y * 2, overlap, upscale_amount = self.upscale_ratio, output_device=self.output_device, pbar = pbar) +
-            comfy.utils.tiled_scale(samples, decode_fn, tile_x * 2, tile_y // 2, overlap, upscale_amount = self.upscale_ratio, output_device=self.output_device, pbar = pbar) +
-             comfy.utils.tiled_scale(samples, decode_fn, tile_x, tile_y, overlap, upscale_amount = self.upscale_ratio, output_device=self.output_device, pbar = pbar))
-            / 3.0)
-        return output
-
-    def decode_tiled_1d(self, samples, tile_x=128, overlap=64):
-        output = torch.empty((samples.shape[0], self.output_channels) + tuple(map(lambda a: a * self.upscale_ratio, samples.shape[2:])), device=self.output_device)
-
-        for j in range(samples.shape[0]):
-            for i in range(0, samples.shape[-1], tile_x - overlap):
-                f = i
-                t = i + tile_x
-                output[j:j+1,:,f * self.upscale_ratio:t * self.upscale_ratio] = self.first_stage_model.decode(samples[j:j+1,:,f:t].to(self.vae_dtype).to(self.device)).float()
-
-        return output
-
-    def encode_tiled_(self, pixel_samples, tile_x=512, tile_y=512, overlap = 64):
-        steps = pixel_samples.shape[0] * comfy.utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x, tile_y, overlap)
-        steps += pixel_samples.shape[0] * comfy.utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x // 2, tile_y * 2, overlap)
-        steps += pixel_samples.shape[0] * comfy.utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x * 2, tile_y // 2, overlap)
-        pbar = comfy.utils.ProgressBar(steps)
-
-        encode_fn = lambda a: self.first_stage_model.encode((self.process_input(a)).to(self.vae_dtype).to(self.device)).float()
-        samples = comfy.utils.tiled_scale(pixel_samples, encode_fn, tile_x, tile_y, overlap, upscale_amount = (1/self.downscale_ratio), out_channels=self.latent_channels, output_device=self.output_device, pbar=pbar)
-        samples += comfy.utils.tiled_scale(pixel_samples, encode_fn, tile_x * 2, tile_y // 2, overlap, upscale_amount = (1/self.downscale_ratio), out_channels=self.latent_channels, output_device=self.output_device, pbar=pbar)
-        samples += comfy.utils.tiled_scale(pixel_samples, encode_fn, tile_x // 2, tile_y * 2, overlap, upscale_amount = (1/self.downscale_ratio), out_channels=self.latent_channels, output_device=self.output_device, pbar=pbar)
-        samples /= 3.0
-        return samples
-
-    def decode(self, samples_in):
-        try:
-            memory_used = self.memory_used_decode(samples_in.shape, self.vae_dtype)
-            model_management.load_models_gpu([self.patcher], memory_required=memory_used)
-            free_memory = model_management.get_free_memory(self.device)
-            batch_number = int(free_memory / memory_used)
-            batch_number = max(1, batch_number)
-
-            pixel_samples = torch.empty((samples_in.shape[0], self.output_channels) + tuple(map(lambda a: a * self.upscale_ratio, samples_in.shape[2:])), device=self.output_device)
-            for x in range(0, samples_in.shape[0], batch_number):
-                samples = samples_in[x:x+batch_number].to(self.vae_dtype).to(self.device)
-                pixel_samples[x:x+batch_number] = self.process_output(self.first_stage_model.decode(samples).to(self.output_device).float())
-        except model_management.OOM_EXCEPTION as e:
-            logging.warning("Warning: Ran out of memory when regular VAE decoding, retrying with tiled VAE decoding.")
-            if len(samples_in.shape) == 3:
-                pixel_samples = self.decode_tiled_1d(samples_in)
-            else:
-                pixel_samples = self.decode_tiled_(samples_in)
-
-        pixel_samples = pixel_samples.to(self.output_device).movedim(1,-1)
-        return pixel_samples
-
-    def decode_tiled(self, samples, tile_x=64, tile_y=64, overlap = 16):
-        model_management.load_model_gpu(self.patcher)
-        output = self.decode_tiled_(samples, tile_x, tile_y, overlap)
-        return output.movedim(1,-1)
-
-    def encode(self, pixel_samples):
-        pixel_samples = self.vae_encode_crop_pixels(pixel_samples)
-        pixel_samples = pixel_samples.movedim(-1,1)
-        try:
-            memory_used = self.memory_used_encode(pixel_samples.shape, self.vae_dtype)
-            model_management.load_models_gpu([self.patcher], memory_required=memory_used)
-            free_memory = model_management.get_free_memory(self.device)
-            batch_number = int(free_memory / memory_used)
-            batch_number = max(1, batch_number)
-            samples = torch.empty((pixel_samples.shape[0], self.latent_channels) + tuple(map(lambda a: a // self.downscale_ratio, pixel_samples.shape[2:])), device=self.output_device)
-            for x in range(0, pixel_samples.shape[0], batch_number):
-                pixels_in = self.process_input(pixel_samples[x:x+batch_number]).to(self.vae_dtype).to(self.device)
-                samples[x:x+batch_number] = self.first_stage_model.encode(pixels_in).to(self.output_device).float()
-
-        except model_management.OOM_EXCEPTION as e:
-            logging.warning("Warning: Ran out of memory when regular VAE encoding, retrying with tiled VAE encoding.")
-            samples = self.encode_tiled_(pixel_samples)
-
-        return samples
-
-    def encode_tiled(self, pixel_samples, tile_x=512, tile_y=512, overlap = 64):
-        pixel_samples = self.vae_encode_crop_pixels(pixel_samples)
-        model_management.load_model_gpu(self.patcher)
-        pixel_samples = pixel_samples.movedim(-1,1)
-        samples = self.encode_tiled_(pixel_samples, tile_x=tile_x, tile_y=tile_y, overlap=overlap)
-        return samples
-
-    def get_sd(self):
-        return self.first_stage_model.state_dict()
-
-class StyleModel:
-    def __init__(self, model, device="cpu"):
-        self.model = model
-
-    def get_cond(self, input):
-        return self.model(input.last_hidden_state)
-
-
-def load_style_model(ckpt_path):
-    model_data = comfy.utils.load_torch_file(ckpt_path, safe_load=True)
-    keys = model_data.keys()
-    if "style_embedding" in keys:
-        model = comfy.t2i_adapter.adapter.StyleAdapter(width=1024, context_dim=768, num_head=8, n_layes=3, num_token=8)
-    else:
-        raise Exception("invalid style model {}".format(ckpt_path))
-    model.load_state_dict(model_data)
-    return StyleModel(model)
-
-class CLIPType(Enum):
-    STABLE_DIFFUSION = 1
-    STABLE_CASCADE = 2
-    SD3 = 3
-    STABLE_AUDIO = 4
-
-def load_clip(ckpt_paths, embedding_directory=None, clip_type=CLIPType.STABLE_DIFFUSION):
-    clip_data = []
-    for p in ckpt_paths:
-        clip_data.append(comfy.utils.load_torch_file(p, safe_load=True))
-
-    class EmptyClass:
-        pass
-
-    for i in range(len(clip_data)):
-        if "transformer.resblocks.0.ln_1.weight" in clip_data[i]:
-            clip_data[i] = comfy.utils.clip_text_transformers_convert(clip_data[i], "", "")
-        else:
-            if "text_projection" in clip_data[i]:
-                clip_data[i]["text_projection.weight"] = clip_data[i]["text_projection"].transpose(0, 1) #old models saved with the CLIPSave node
-
-    clip_target = EmptyClass()
-    clip_target.params = {}
-    if len(clip_data) == 1:
-        if "text_model.encoder.layers.30.mlp.fc1.weight" in clip_data[0]:
-            if clip_type == CLIPType.STABLE_CASCADE:
-                clip_target.clip = sdxl_clip.StableCascadeClipModel
-                clip_target.tokenizer = sdxl_clip.StableCascadeTokenizer
-            else:
-                clip_target.clip = sdxl_clip.SDXLRefinerClipModel
-                clip_target.tokenizer = sdxl_clip.SDXLTokenizer
-        elif "text_model.encoder.layers.22.mlp.fc1.weight" in clip_data[0]:
-            clip_target.clip = sd2_clip.SD2ClipModel
-            clip_target.tokenizer = sd2_clip.SD2Tokenizer
-        elif "encoder.block.23.layer.1.DenseReluDense.wi_1.weight" in clip_data[0]:
-            dtype_t5 = clip_data[0]["encoder.block.23.layer.1.DenseReluDense.wi_1.weight"].dtype
-            clip_target.clip = sd3_clip.sd3_clip(clip_l=False, clip_g=False, t5=True, dtype_t5=dtype_t5)
-            clip_target.tokenizer = sd3_clip.SD3Tokenizer
-        elif "encoder.block.0.layer.0.SelfAttention.k.weight" in clip_data[0]:
-            clip_target.clip = sa_t5.SAT5Model
-            clip_target.tokenizer = sa_t5.SAT5Tokenizer
-        else:
-            clip_target.clip = sd1_clip.SD1ClipModel
-            clip_target.tokenizer = sd1_clip.SD1Tokenizer
-    elif len(clip_data) == 2:
-        if clip_type == CLIPType.SD3:
-            clip_target.clip = sd3_clip.sd3_clip(clip_l=True, clip_g=True, t5=False)
-            clip_target.tokenizer = sd3_clip.SD3Tokenizer
-        else:
-            clip_target.clip = sdxl_clip.SDXLClipModel
-            clip_target.tokenizer = sdxl_clip.SDXLTokenizer
-    elif len(clip_data) == 3:
-        clip_target.clip = sd3_clip.SD3ClipModel
-        clip_target.tokenizer = sd3_clip.SD3Tokenizer
-
-    clip = CLIP(clip_target, embedding_directory=embedding_directory)
-    for c in clip_data:
-        m, u = clip.load_sd(c)
-        if len(m) > 0:
-            logging.warning("clip missing: {}".format(m))
-
-        if len(u) > 0:
-            logging.debug("clip unexpected: {}".format(u))
-    return clip
-
-def load_gligen(ckpt_path):
-    data = comfy.utils.load_torch_file(ckpt_path, safe_load=True)
-    model = gligen.load_gligen(data)
-    if model_management.should_use_fp16():
-        model = model.half()
-    return comfy.model_patcher.ModelPatcher(model, load_device=model_management.get_torch_device(), offload_device=model_management.unet_offload_device())
-
-def load_checkpoint(config_path=None, ckpt_path=None, output_vae=True, output_clip=True, embedding_directory=None, state_dict=None, config=None):
-    logging.warning("Warning: The load checkpoint with config function is deprecated and will eventually be removed, please use the other one.")
-    model, clip, vae, _ = load_checkpoint_guess_config(ckpt_path, output_vae=output_vae, output_clip=output_clip, output_clipvision=False, embedding_directory=embedding_directory, output_model=True)
-    #TODO: this function is a mess and should be removed eventually
-    if config is None:
-        with open(config_path, 'r') as stream:
-            config = yaml.safe_load(stream)
-    model_config_params = config['model']['params']
-    clip_config = model_config_params['cond_stage_config']
-    scale_factor = model_config_params['scale_factor']
-
-    if "parameterization" in model_config_params:
-        if model_config_params["parameterization"] == "v":
-            m = model.clone()
-            class ModelSamplingAdvanced(comfy.model_sampling.ModelSamplingDiscrete, comfy.model_sampling.V_PREDICTION):
-                pass
-            m.add_object_patch("model_sampling", ModelSamplingAdvanced(model.model.model_config))
-            model = m
-
-    layer_idx = clip_config.get("params", {}).get("layer_idx", None)
-    if layer_idx is not None:
-        clip.clip_layer(layer_idx)
-
-    return (model, clip, vae)
-
-def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, output_clipvision=False, embedding_directory=None, output_model=True):
-    sd = comfy.utils.load_torch_file(ckpt_path)
-    sd_keys = sd.keys()
-    clip = None
-    clipvision = None
-    vae = None
-    model = None
-    model_patcher = None
-    clip_target = None
-
-    diffusion_model_prefix = model_detection.unet_prefix_from_state_dict(sd)
-    parameters = comfy.utils.calculate_parameters(sd, diffusion_model_prefix)
-    load_device = model_management.get_torch_device()
-
-    model_config = model_detection.model_config_from_unet(sd, diffusion_model_prefix)
-    unet_dtype = model_management.unet_dtype(model_params=parameters, supported_dtypes=model_config.supported_inference_dtypes)
-    manual_cast_dtype = model_management.unet_manual_cast(unet_dtype, load_device, model_config.supported_inference_dtypes)
-    model_config.set_inference_dtype(unet_dtype, manual_cast_dtype)
-
-    if model_config is None:
-        raise RuntimeError("ERROR: Could not detect model type of: {}".format(ckpt_path))
-
-    if model_config.clip_vision_prefix is not None:
-        if output_clipvision:
-            clipvision = clip_vision.load_clipvision_from_sd(sd, model_config.clip_vision_prefix, True)
-
-    if output_model:
-        inital_load_device = model_management.unet_inital_load_device(parameters, unet_dtype)
-        offload_device = model_management.unet_offload_device()
-        model = model_config.get_model(sd, diffusion_model_prefix, device=inital_load_device)
-        model.load_model_weights(sd, diffusion_model_prefix)
-
-    if output_vae:
-        vae_sd = comfy.utils.state_dict_prefix_replace(sd, {k: "" for k in model_config.vae_key_prefix}, filter_keys=True)
-        vae_sd = model_config.process_vae_state_dict(vae_sd)
-        vae = VAE(sd=vae_sd)
-
-    if output_clip:
-        clip_target = model_config.clip_target(state_dict=sd)
-        if clip_target is not None:
-            clip_sd = model_config.process_clip_state_dict(sd)
-            if len(clip_sd) > 0:
-                clip = CLIP(clip_target, embedding_directory=embedding_directory)
-                m, u = clip.load_sd(clip_sd, full_model=True)
-                if len(m) > 0:
-                    m_filter = list(filter(lambda a: ".logit_scale" not in a and ".transformer.text_projection.weight" not in a, m))
-                    if len(m_filter) > 0:
-                        logging.warning("clip missing: {}".format(m))
-                    else:
-                        logging.debug("clip missing: {}".format(m))
-
-                if len(u) > 0:
-                    logging.debug("clip unexpected {}:".format(u))
-            else:
-                logging.warning("no CLIP/text encoder weights in checkpoint, the text encoder model will not be loaded.")
-
-    left_over = sd.keys()
-    if len(left_over) > 0:
-        logging.debug("left over keys: {}".format(left_over))
-
-    if output_model:
-        model_patcher = comfy.model_patcher.ModelPatcher(model, load_device=load_device, offload_device=model_management.unet_offload_device(), current_device=inital_load_device)
-        if inital_load_device != torch.device("cpu"):
-            logging.info("loaded straight to GPU")
-            model_management.load_model_gpu(model_patcher)
-
-    return (model_patcher, clip, vae, clipvision)
-
-
-def load_unet_state_dict(sd): #load unet in diffusers format
-    parameters = comfy.utils.calculate_parameters(sd)
-    unet_dtype = model_management.unet_dtype(model_params=parameters)
-    load_device = model_management.get_torch_device()
-
-    if "input_blocks.0.0.weight" in sd or 'clf.1.weight' in sd: #ldm or stable cascade
-        model_config = model_detection.model_config_from_unet(sd, "")
-        if model_config is None:
-            return None
-        new_sd = sd
-
-    else: #diffusers
-        model_config = model_detection.model_config_from_diffusers_unet(sd)
-        if model_config is None:
-            return None
-
-        diffusers_keys = comfy.utils.unet_to_diffusers(model_config.unet_config)
-
-        new_sd = {}
-        for k in diffusers_keys:
-            if k in sd:
-                new_sd[diffusers_keys[k]] = sd.pop(k)
-            else:
-                logging.warning("{} {}".format(diffusers_keys[k], k))
-
-    offload_device = model_management.unet_offload_device()
-    unet_dtype = model_management.unet_dtype(model_params=parameters, supported_dtypes=model_config.supported_inference_dtypes)
-    manual_cast_dtype = model_management.unet_manual_cast(unet_dtype, load_device, model_config.supported_inference_dtypes)
-    model_config.set_inference_dtype(unet_dtype, manual_cast_dtype)
-    model = model_config.get_model(new_sd, "")
-    model = model.to(offload_device)
-    model.load_model_weights(new_sd, "")
-    left_over = sd.keys()
-    if len(left_over) > 0:
-        logging.info("left over keys in unet: {}".format(left_over))
-    return comfy.model_patcher.ModelPatcher(model, load_device=load_device, offload_device=offload_device)
-
-def load_unet(unet_path):
-    sd = comfy.utils.load_torch_file(unet_path)
-    model = load_unet_state_dict(sd)
-    if model is None:
-        logging.error("ERROR UNSUPPORTED UNET {}".format(unet_path))
-        raise RuntimeError("ERROR: Could not detect model type of: {}".format(unet_path))
-    return model
-
-def save_checkpoint(output_path, model, clip=None, vae=None, clip_vision=None, metadata=None, extra_keys={}):
-    clip_sd = None
-    load_models = [model]
-    if clip is not None:
-        load_models.append(clip.load_model())
-        clip_sd = clip.get_sd()
-
-    model_management.load_models_gpu(load_models, force_patch_weights=True)
-    clip_vision_sd = clip_vision.get_sd() if clip_vision is not None else None
-    sd = model.model.state_dict_for_saving(clip_sd, vae.get_sd(), clip_vision_sd)
-    for k in extra_keys:
-        sd[k] = extra_keys[k]
-
-    comfy.utils.save_torch_file(sd, output_path, metadata=metadata)
diff --git a/MagicQuill/comfy/sd1_clip.py b/MagicQuill/comfy/sd1_clip.py
deleted file mode 100644
index 911af0a7e8c4501cbb9d55d1b43debd43a21ccbd..0000000000000000000000000000000000000000
--- a/MagicQuill/comfy/sd1_clip.py
+++ /dev/null
@@ -1,530 +0,0 @@
-import os
-
-from transformers import CLIPTokenizer
-import comfy.ops
-import torch
-import traceback
-import zipfile
-from . import model_management
-import comfy.clip_model
-import json
-import logging
-
-def gen_empty_tokens(special_tokens, length):
-    start_token = special_tokens.get("start", None)
-    end_token = special_tokens.get("end", None)
-    pad_token = special_tokens.get("pad")
-    output = []
-    if start_token is not None:
-        output.append(start_token)
-    if end_token is not None:
-        output.append(end_token)
-    output += [pad_token] * (length - len(output))
-    return output
-
-class ClipTokenWeightEncoder:
-    def encode_token_weights(self, token_weight_pairs):
-        to_encode = list()
-        max_token_len = 0
-        has_weights = False
-        for x in token_weight_pairs:
-            tokens = list(map(lambda a: a[0], x))
-            max_token_len = max(len(tokens), max_token_len)
-            has_weights = has_weights or not all(map(lambda a: a[1] == 1.0, x))
-            to_encode.append(tokens)
-
-        sections = len(to_encode)
-        if has_weights or sections == 0:
-            to_encode.append(gen_empty_tokens(self.special_tokens, max_token_len))
-
-        out, pooled = self.encode(to_encode)
-        if pooled is not None:
-            first_pooled = pooled[0:1].to(model_management.intermediate_device())
-        else:
-            first_pooled = pooled
-
-        output = []
-        for k in range(0, sections):
-            z = out[k:k+1]
-            if has_weights:
-                z_empty = out[-1]
-                for i in range(len(z)):
-                    for j in range(len(z[i])):
-                        weight = token_weight_pairs[k][j][1]
-                        if weight != 1.0:
-                            z[i][j] = (z[i][j] - z_empty[j]) * weight + z_empty[j]
-            output.append(z)
-
-        if (len(output) == 0):
-            return out[-1:].to(model_management.intermediate_device()), first_pooled
-        return torch.cat(output, dim=-2).to(model_management.intermediate_device()), first_pooled
-
-class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder):
-    """Uses the CLIP transformer encoder for text (from huggingface)"""
-    LAYERS = [
-        "last",
-        "pooled",
-        "hidden"
-    ]
-    def __init__(self, version="openai/clip-vit-large-patch14", device="cpu", max_length=77,
-                 freeze=True, layer="last", layer_idx=None, textmodel_json_config=None, dtype=None, model_class=comfy.clip_model.CLIPTextModel,
-                 special_tokens={"start": 49406, "end": 49407, "pad": 49407}, layer_norm_hidden_state=True, enable_attention_masks=False, zero_out_masked=False,
-                 return_projected_pooled=True):  # clip-vit-base-patch32
-        super().__init__()
-        assert layer in self.LAYERS
-
-        if textmodel_json_config is None:
-            textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "sd1_clip_config.json")
-
-        with open(textmodel_json_config) as f:
-            config = json.load(f)
-
-        self.transformer = model_class(config, dtype, device, comfy.ops.manual_cast)
-        self.num_layers = self.transformer.num_layers
-
-        self.max_length = max_length
-        if freeze:
-            self.freeze()
-        self.layer = layer
-        self.layer_idx = None
-        self.special_tokens = special_tokens
-
-        self.logit_scale = torch.nn.Parameter(torch.tensor(4.6055))
-        self.enable_attention_masks = enable_attention_masks
-        self.zero_out_masked = zero_out_masked
-
-        self.layer_norm_hidden_state = layer_norm_hidden_state
-        self.return_projected_pooled = return_projected_pooled
-
-        if layer == "hidden":
-            assert layer_idx is not None
-            assert abs(layer_idx) < self.num_layers
-            self.set_clip_options({"layer": layer_idx})
-        self.options_default = (self.layer, self.layer_idx, self.return_projected_pooled)
-
-    def freeze(self):
-        self.transformer = self.transformer.eval()
-        #self.train = disabled_train
-        for param in self.parameters():
-            param.requires_grad = False
-
-    def set_clip_options(self, options):
-        layer_idx = options.get("layer", self.layer_idx)
-        self.return_projected_pooled = options.get("projected_pooled", self.return_projected_pooled)
-        if layer_idx is None or abs(layer_idx) > self.num_layers:
-            self.layer = "last"
-        else:
-            self.layer = "hidden"
-            self.layer_idx = layer_idx
-
-    def reset_clip_options(self):
-        self.layer = self.options_default[0]
-        self.layer_idx = self.options_default[1]
-        self.return_projected_pooled = self.options_default[2]
-
-    def set_up_textual_embeddings(self, tokens, current_embeds):
-        out_tokens = []
-        next_new_token = token_dict_size = current_embeds.weight.shape[0] - 1
-        embedding_weights = []
-
-        for x in tokens:
-            tokens_temp = []
-            for y in x:
-                if isinstance(y, int):
-                    if y == token_dict_size: #EOS token
-                        y = -1
-                    tokens_temp += [y]
-                else:
-                    if y.shape[0] == current_embeds.weight.shape[1]:
-                        embedding_weights += [y]
-                        tokens_temp += [next_new_token]
-                        next_new_token += 1
-                    else:
-                        logging.warning("WARNING: shape mismatch when trying to apply embedding, embedding will be ignored {} != {}".format(y.shape[0], current_embeds.weight.shape[1]))
-            while len(tokens_temp) < len(x):
-                tokens_temp += [self.special_tokens["pad"]]
-            out_tokens += [tokens_temp]
-
-        n = token_dict_size
-        if len(embedding_weights) > 0:
-            new_embedding = torch.nn.Embedding(next_new_token + 1, current_embeds.weight.shape[1], device=current_embeds.weight.device, dtype=current_embeds.weight.dtype)
-            new_embedding.weight[:token_dict_size] = current_embeds.weight[:-1]
-            for x in embedding_weights:
-                new_embedding.weight[n] = x
-                n += 1
-            new_embedding.weight[n] = current_embeds.weight[-1] #EOS embedding
-            self.transformer.set_input_embeddings(new_embedding)
-
-        processed_tokens = []
-        for x in out_tokens:
-            processed_tokens += [list(map(lambda a: n if a == -1 else a, x))] #The EOS token should always be the largest one
-
-        return processed_tokens
-
-    def forward(self, tokens):
-        backup_embeds = self.transformer.get_input_embeddings()
-        device = backup_embeds.weight.device
-        tokens = self.set_up_textual_embeddings(tokens, backup_embeds)
-        tokens = torch.LongTensor(tokens).to(device)
-
-        attention_mask = None
-        if self.enable_attention_masks:
-            attention_mask = torch.zeros_like(tokens)
-            end_token = self.special_tokens.get("end", -1)
-            for x in range(attention_mask.shape[0]):
-                for y in range(attention_mask.shape[1]):
-                    attention_mask[x, y] = 1
-                    if tokens[x, y] == end_token:
-                        break
-
-        outputs = self.transformer(tokens, attention_mask, intermediate_output=self.layer_idx, final_layer_norm_intermediate=self.layer_norm_hidden_state)
-        self.transformer.set_input_embeddings(backup_embeds)
-
-        if self.layer == "last":
-            z = outputs[0].float()
-        else:
-            z = outputs[1].float()
-
-        if self.zero_out_masked and attention_mask is not None:
-            z *= attention_mask.unsqueeze(-1).float()
-
-        pooled_output = None
-        if len(outputs) >= 3:
-            if not self.return_projected_pooled and len(outputs) >= 4 and outputs[3] is not None:
-                pooled_output = outputs[3].float()
-            elif outputs[2] is not None:
-                pooled_output = outputs[2].float()
-
-        return z, pooled_output
-
-    def encode(self, tokens):
-        return self(tokens)
-
-    def load_sd(self, sd):
-        return self.transformer.load_state_dict(sd, strict=False)
-
-def parse_parentheses(string):
-    result = []
-    current_item = ""
-    nesting_level = 0
-    for char in string:
-        if char == "(":
-            if nesting_level == 0:
-                if current_item:
-                    result.append(current_item)
-                    current_item = "("
-                else:
-                    current_item = "("
-            else:
-                current_item += char
-            nesting_level += 1
-        elif char == ")":
-            nesting_level -= 1
-            if nesting_level == 0:
-                result.append(current_item + ")")
-                current_item = ""
-            else:
-                current_item += char
-        else:
-            current_item += char
-    if current_item:
-        result.append(current_item)
-    return result
-
-def token_weights(string, current_weight):
-    a = parse_parentheses(string)
-    out = []
-    for x in a:
-        weight = current_weight
-        if len(x) >= 2 and x[-1] == ')' and x[0] == '(':
-            x = x[1:-1]
-            xx = x.rfind(":")
-            weight *= 1.1
-            if xx > 0:
-                try:
-                    weight = float(x[xx+1:])
-                    x = x[:xx]
-                except:
-                    pass
-            out += token_weights(x, weight)
-        else:
-            out += [(x, current_weight)]
-    return out
-
-def escape_important(text):
-    text = text.replace("\\)", "\0\1")
-    text = text.replace("\\(", "\0\2")
-    return text
-
-def unescape_important(text):
-    text = text.replace("\0\1", ")")
-    text = text.replace("\0\2", "(")
-    return text
-
-def safe_load_embed_zip(embed_path):
-    with zipfile.ZipFile(embed_path) as myzip:
-        names = list(filter(lambda a: "data/" in a, myzip.namelist()))
-        names.reverse()
-        for n in names:
-            with myzip.open(n) as myfile:
-                data = myfile.read()
-                number = len(data) // 4
-                length_embed = 1024 #sd2.x
-                if number < 768:
-                    continue
-                if number % 768 == 0:
-                    length_embed = 768 #sd1.x
-                num_embeds = number // length_embed
-                embed = torch.frombuffer(data, dtype=torch.float)
-                out = embed.reshape((num_embeds, length_embed)).clone()
-                del embed
-                return out
-
-def expand_directory_list(directories):
-    dirs = set()
-    for x in directories:
-        dirs.add(x)
-        for root, subdir, file in os.walk(x, followlinks=True):
-            dirs.add(root)
-    return list(dirs)
-
-def load_embed(embedding_name, embedding_directory, embedding_size, embed_key=None):
-    if isinstance(embedding_directory, str):
-        embedding_directory = [embedding_directory]
-
-    embedding_directory = expand_directory_list(embedding_directory)
-
-    valid_file = None
-    for embed_dir in embedding_directory:
-        embed_path = os.path.abspath(os.path.join(embed_dir, embedding_name))
-        embed_dir = os.path.abspath(embed_dir)
-        try:
-            if os.path.commonpath((embed_dir, embed_path)) != embed_dir:
-                continue
-        except:
-            continue
-        if not os.path.isfile(embed_path):
-            extensions = ['.safetensors', '.pt', '.bin']
-            for x in extensions:
-                t = embed_path + x
-                if os.path.isfile(t):
-                    valid_file = t
-                    break
-        else:
-            valid_file = embed_path
-        if valid_file is not None:
-            break
-
-    if valid_file is None:
-        return None
-
-    embed_path = valid_file
-
-    embed_out = None
-
-    try:
-        if embed_path.lower().endswith(".safetensors"):
-            import safetensors.torch
-            embed = safetensors.torch.load_file(embed_path, device="cpu")
-        else:
-            if 'weights_only' in torch.load.__code__.co_varnames:
-                try:
-                    embed = torch.load(embed_path, weights_only=True, map_location="cpu")
-                except:
-                    embed_out = safe_load_embed_zip(embed_path)
-            else:
-                embed = torch.load(embed_path, map_location="cpu")
-    except Exception as e:
-        logging.warning("{}\n\nerror loading embedding, skipping loading: {}".format(traceback.format_exc(), embedding_name))
-        return None
-
-    if embed_out is None:
-        if 'string_to_param' in embed:
-            values = embed['string_to_param'].values()
-            embed_out = next(iter(values))
-        elif isinstance(embed, list):
-            out_list = []
-            for x in range(len(embed)):
-                for k in embed[x]:
-                    t = embed[x][k]
-                    if t.shape[-1] != embedding_size:
-                        continue
-                    out_list.append(t.reshape(-1, t.shape[-1]))
-            embed_out = torch.cat(out_list, dim=0)
-        elif embed_key is not None and embed_key in embed:
-            embed_out = embed[embed_key]
-        else:
-            values = embed.values()
-            embed_out = next(iter(values))
-    return embed_out
-
-class SDTokenizer:
-    def __init__(self, tokenizer_path=None, max_length=77, pad_with_end=True, embedding_directory=None, embedding_size=768, embedding_key='clip_l', tokenizer_class=CLIPTokenizer, has_start_token=True, pad_to_max_length=True, min_length=None):
-        if tokenizer_path is None:
-            tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "sd1_tokenizer")
-        self.tokenizer = tokenizer_class.from_pretrained(tokenizer_path)
-        self.max_length = max_length
-        self.min_length = min_length
-
-        empty = self.tokenizer('')["input_ids"]
-        if has_start_token:
-            self.tokens_start = 1
-            self.start_token = empty[0]
-            self.end_token = empty[1]
-        else:
-            self.tokens_start = 0
-            self.start_token = None
-            self.end_token = empty[0]
-        self.pad_with_end = pad_with_end
-        self.pad_to_max_length = pad_to_max_length
-
-        vocab = self.tokenizer.get_vocab()
-        self.inv_vocab = {v: k for k, v in vocab.items()}
-        self.embedding_directory = embedding_directory
-        self.max_word_length = 8
-        self.embedding_identifier = "embedding:"
-        self.embedding_size = embedding_size
-        self.embedding_key = embedding_key
-
-    def _try_get_embedding(self, embedding_name:str):
-        '''
-        Takes a potential embedding name and tries to retrieve it.
-        Returns a Tuple consisting of the embedding and any leftover string, embedding can be None.
-        '''
-        embed = load_embed(embedding_name, self.embedding_directory, self.embedding_size, self.embedding_key)
-        if embed is None:
-            stripped = embedding_name.strip(',')
-            if len(stripped) < len(embedding_name):
-                embed = load_embed(stripped, self.embedding_directory, self.embedding_size, self.embedding_key)
-                return (embed, embedding_name[len(stripped):])
-        return (embed, "")
-
-
-    def tokenize_with_weights(self, text:str, return_word_ids=False):
-        '''
-        Takes a prompt and converts it to a list of (token, weight, word id) elements.
-        Tokens can both be integer tokens and pre computed CLIP tensors.
-        Word id values are unique per word and embedding, where the id 0 is reserved for non word tokens.
-        Returned list has the dimensions NxM where M is the input size of CLIP
-        '''
-        if self.pad_with_end:
-            pad_token = self.end_token
-        else:
-            pad_token = 0
-
-        text = escape_important(text)
-        parsed_weights = token_weights(text, 1.0)
-
-        #tokenize words
-        tokens = []
-        for weighted_segment, weight in parsed_weights:
-            to_tokenize = unescape_important(weighted_segment).replace("\n", " ").split(' ')
-            to_tokenize = [x for x in to_tokenize if x != ""]
-            for word in to_tokenize:
-                #if we find an embedding, deal with the embedding
-                if word.startswith(self.embedding_identifier) and self.embedding_directory is not None:
-                    embedding_name = word[len(self.embedding_identifier):].strip('\n')
-                    embed, leftover = self._try_get_embedding(embedding_name)
-                    if embed is None:
-                        logging.warning(f"warning, embedding:{embedding_name} does not exist, ignoring")
-                    else:
-                        if len(embed.shape) == 1:
-                            tokens.append([(embed, weight)])
-                        else:
-                            tokens.append([(embed[x], weight) for x in range(embed.shape[0])])
-                    #if we accidentally have leftover text, continue parsing using leftover, else move on to next word
-                    if leftover != "":
-                        word = leftover
-                    else:
-                        continue
-                #parse word
-                tokens.append([(t, weight) for t in self.tokenizer(word)["input_ids"][self.tokens_start:-1]])
-
-        #reshape token array to CLIP input size
-        batched_tokens = []
-        batch = []
-        if self.start_token is not None:
-            batch.append((self.start_token, 1.0, 0))
-        batched_tokens.append(batch)
-        for i, t_group in enumerate(tokens):
-            #determine if we're going to try and keep the tokens in a single batch
-            is_large = len(t_group) >= self.max_word_length
-
-            while len(t_group) > 0:
-                if len(t_group) + len(batch) > self.max_length - 1:
-                    remaining_length = self.max_length - len(batch) - 1
-                    #break word in two and add end token
-                    if is_large:
-                        batch.extend([(t,w,i+1) for t,w in t_group[:remaining_length]])
-                        batch.append((self.end_token, 1.0, 0))
-                        t_group = t_group[remaining_length:]
-                    #add end token and pad
-                    else:
-                        batch.append((self.end_token, 1.0, 0))
-                        if self.pad_to_max_length:
-                            batch.extend([(pad_token, 1.0, 0)] * (remaining_length))
-                    #start new batch
-                    batch = []
-                    if self.start_token is not None:
-                        batch.append((self.start_token, 1.0, 0))
-                    batched_tokens.append(batch)
-                else:
-                    batch.extend([(t,w,i+1) for t,w in t_group])
-                    t_group = []
-
-        #fill last batch
-        batch.append((self.end_token, 1.0, 0))
-        if self.pad_to_max_length:
-            batch.extend([(pad_token, 1.0, 0)] * (self.max_length - len(batch)))
-        if self.min_length is not None and len(batch) < self.min_length:
-            batch.extend([(pad_token, 1.0, 0)] * (self.min_length - len(batch)))
-
-        if not return_word_ids:
-            batched_tokens = [[(t, w) for t, w,_ in x] for x in batched_tokens]
-
-        return batched_tokens
-
-
-    def untokenize(self, token_weight_pair):
-        return list(map(lambda a: (a, self.inv_vocab[a[0]]), token_weight_pair))
-
-
-class SD1Tokenizer:
-    def __init__(self, embedding_directory=None, clip_name="l", tokenizer=SDTokenizer):
-        self.clip_name = clip_name
-        self.clip = "clip_{}".format(self.clip_name)
-        setattr(self, self.clip, tokenizer(embedding_directory=embedding_directory))
-
-    def tokenize_with_weights(self, text:str, return_word_ids=False):
-        out = {}
-        out[self.clip_name] = getattr(self, self.clip).tokenize_with_weights(text, return_word_ids)
-        return out
-
-    def untokenize(self, token_weight_pair):
-        return getattr(self, self.clip).untokenize(token_weight_pair)
-
-
-class SD1ClipModel(torch.nn.Module):
-    def __init__(self, device="cpu", dtype=None, clip_name="l", clip_model=SDClipModel, **kwargs):
-        super().__init__()
-        self.clip_name = clip_name
-        self.clip = "clip_{}".format(self.clip_name)
-        setattr(self, self.clip, clip_model(device=device, dtype=dtype, **kwargs))
-
-        self.dtypes = set()
-        if dtype is not None:
-            self.dtypes.add(dtype)
-
-    def set_clip_options(self, options):
-        getattr(self, self.clip).set_clip_options(options)
-
-    def reset_clip_options(self):
-        getattr(self, self.clip).reset_clip_options()
-
-    def encode_token_weights(self, token_weight_pairs):
-        token_weight_pairs = token_weight_pairs[self.clip_name]
-        out, pooled = getattr(self, self.clip).encode_token_weights(token_weight_pairs)
-        return out, pooled
-
-    def load_sd(self, sd):
-        return getattr(self, self.clip).load_sd(sd)
diff --git a/MagicQuill/comfy/sd1_clip_config.json b/MagicQuill/comfy/sd1_clip_config.json
deleted file mode 100644
index 0158a1fd52727adf22359238285afafb150f66f2..0000000000000000000000000000000000000000
--- a/MagicQuill/comfy/sd1_clip_config.json
+++ /dev/null
@@ -1,25 +0,0 @@
-{
-  "_name_or_path": "openai/clip-vit-large-patch14",
-  "architectures": [
-    "CLIPTextModel"
-  ],
-  "attention_dropout": 0.0,
-  "bos_token_id": 0,
-  "dropout": 0.0,
-  "eos_token_id": 2,
-  "hidden_act": "quick_gelu",
-  "hidden_size": 768,
-  "initializer_factor": 1.0,
-  "initializer_range": 0.02,
-  "intermediate_size": 3072,
-  "layer_norm_eps": 1e-05,
-  "max_position_embeddings": 77,
-  "model_type": "clip_text_model",
-  "num_attention_heads": 12,
-  "num_hidden_layers": 12,
-  "pad_token_id": 1,
-  "projection_dim": 768,
-  "torch_dtype": "float32",
-  "transformers_version": "4.24.0",
-  "vocab_size": 49408
-}
diff --git a/MagicQuill/comfy/sd1_tokenizer/merges.txt b/MagicQuill/comfy/sd1_tokenizer/merges.txt
deleted file mode 100644
index 76e821f1b6f0a9709293c3b6b51ed90980b3166b..0000000000000000000000000000000000000000
--- a/MagicQuill/comfy/sd1_tokenizer/merges.txt
+++ /dev/null
@@ -1,48895 +0,0 @@
-#version: 0.2
-i n
-t h
-a n
-r e
-a r
-e r
-th e</w>
-in g</w>
-o u
-o n
-s t
-o r
-e n
-o n</w>
-a l
-a t
-e r</w>
-i t
-i n</w>
-t o</w>
-r o
-i s</w>
-l e
-i c
-a t</w>
-an d</w>
-e d</w>
-o f</w>
-c h
-o r</w>
-e s</w>
-i l
-e l
-s t</w>
-a c
-o m
-a m
-l o
-a n</w>
-a y</w>
-s h
-r i
-l i
-t i
-f or</w>
-n e
-ð Ł
-r a
-h a
-d e
-o l
-v e</w>
-s i
-u r
-a l</w>
-s e
-' s</w>
-u n
-d i
-b e
-l a
-w h
-o o
-d ay</w>
-e n</w>
-m a
-n o
-l e</w>
-t o
-ou r</w>
-i r
-g h
-w it
-i t</w>
-y o
-a s
-s p
-th is</w>
-t s</w>
-at i
-yo u</w>
-wit h</w>
-a d
-i s
-a b
-l y</w>
-w e
-th e
-t e
-a s</w>
-a g
-v i
-p p
-s u
-h o
-m y</w>
-. .
-b u
-c om
-s e</w>
-er s</w>
-m e
-m e</w>
-al l</w>
-c on
-m o
-k e</w>
-g e
-ou t</w>
-en t</w>
-c o
-f e
-v er
-a r</w>
-f ro
-a u
-p o
-c e</w>
-gh t</w>
-ar e</w>
-s s</w>
-fro m</w>
-c h</w>
-t r
-ou n
-on e</w>
-b y</w>
-d o
-t h</w>
-w or
-er e</w>
-k e
-p ro
-f or
-d s</w>
-b o
-t a
-w e</w>
-g o
-h e
-t er</w>
-in g
-d e</w>
-b e</w>
-ati on</w>
-m or
-a y
-e x
-il l</w>
-p e
-k s</w>
-s c
-l u
-f u
-q u
-v er</w>
-ðŁ ĺ
-j u
-m u
-at e</w>
-an d
-v e
-k ing</w>
-m ar
-o p
-h i
-.. .</w>
-p re
-a d</w>
-r u
-th at</w>
-j o
-o f
-c e
-ne w</w>
-a m</w>
-a p
-g re
-s s
-d u
-no w</w>
-y e
-t ing</w>
-y our</w>
-it y</w>
-n i
-c i
-p ar
-g u
-f i
-a f
-p er
-t er
-u p</w>
-s o</w>
-g i
-on s</w>
-g r
-g e</w>
-b r
-p l
-' t</w>
-m i
-in e</w>
-we e
-b i
-u s</w>
-sh o
-ha ve</w>
-to day</w>
-a v
-m an
-en t
-ac k</w>
-ur e</w>
-ou r
-â Ģ
-c u
-l d</w>
-lo o
-i m
-ic e</w>
-s om
-f in
-re d</w>
-re n
-oo d</w>
-w as</w>
-ti on</w>
-p i
-i r</w>
-th er</w>
-t y</w>
-p h
-ar d</w>
-e c
-! !</w>
-m on
-mor e</w>
-w ill</w>
-t ra
-c an</w>
-c ol
-p u
-t e</w>
-w n</w>
-m b
-s o
-it i
-ju st</w>
-n ing</w>
-h ere</w>
-t u
-p a
-p r
-bu t</w>
-wh at</w>
-al ly</w>
-f ir
-m in
-c a
-an t</w>
-s a
-t ed</w>
-e v
-m ent</w>
-f a
-ge t</w>
-am e</w>
-ab out</w>
-g ra
-no t</w>
-ha pp
-ay s</w>
-m an</w>
-h is</w>
-ti me</w>
-li ke</w>
-g h</w>
-ha s</w>
-th an
-lo ve</w>
-ar t</w>
-st e
-d ing</w>
-h e</w>
-c re
-w s</w>
-w at
-d er</w>
-it e</w>
-s er
-ac e</w>
-ag e</w>
-en d</w>
-st r
-a w
-st or
-r e</w>
-c ar
-el l</w>
-al l
-p s</w>
-f ri
-p ho
-p or
-d o</w>
-a k
-w i
-f re
-wh o</w>
-sh i
-b oo
-s on</w>
-el l
-wh en</w>
-il l
-ho w</w>
-gre at</w>
-w in
-e l</w>
-b l
-s si
-al i
-som e</w>
-ðŁ Ĵ
-t on
-d er
-le s</w>
-p la
-ï ¸
-e d
-s ch
-h u
-on g</w>
-d on</w>
-k i
-s h</w>
-an n
-c or
-. .</w>
-oun d</w>
-a z
-in e
-ar y</w>
-fu l</w>
-st u
-ou ld</w>
-st i
-g o</w>
-se e</w>
-ab le</w>
-ar s</w>
-l l</w>
-m is
-b er
-c k</w>
-w a
-en ts</w>
-n o</w>
-si g
-f e</w>
-fir st</w>
-e t</w>
-sp e
-ac k
-i f</w>
-ou s</w>
-' m</w>
-st er</w>
-a pp
-an g
-an ce</w>
-an s</w>
-g ood</w>
-b re
-e ver
-the y</w>
-t ic
-com e</w>
-of f
-b ack</w>
-as e</w>
-ing s</w>
-ol d</w>
-i ght</w>
-f o
-h er</w>
-happ y</w>
-p ic
-it s</w>
-v ing</w>
-u s
-m at
-h om
-d y</w>
-e m
-s k
-y ing</w>
-the ir</w>
-le d</w>
-r y</w>
-u l
-h ar
-c k
-t on</w>
-on al</w>
-h el
-r ic
-b ir
-vi e
-w ay</w>
-t ri
-d a
-p le
-b ro
-st o
-oo l</w>
-ni ght</w>
-tr u
-b a
-re ad
-re s</w>
-ye ar</w>
-f r
-t or
-al s</w>
-c oun
-c la
-t ure</w>
-v el
-at ed</w>
-le c
-en d
-th ing</w>
-v o
-ic i
-be st</w>
-c an
-wor k</w>
-la st</w>
-af ter</w>
-en ce</w>
-p ri
-p e</w>
-e s
-i l</w>
-âĢ ¦</w>
-d re
-y s</w>
-o ver</w>
-i es</w>
-ðŁ ij
-com m
-t w
-in k</w>
-s un
-c l
-li fe</w>
-t t
-a ch
-l and</w>
-s y
-t re
-t al
-p ol
-s m
-du c
-s al
-f t</w>
-' re</w>
-ch e
-w ar
-t ur
-ati ons</w>
-ac h</w>
-m s</w>
-il e</w>
-p m</w>
-ou gh</w>
-at e
-st ar
-wee k</w>
-! !!</w>
-c lu
-th ere</w>
-n er</w>
-t om
-s el
-ï¸ ı</w>
-wor ld</w>
-v es</w>
-c am
-go t</w>
-in ter
-of f</w>
-u m</w>
-ton ight</w>
-o ther</w>
-h ou
-loo k</w>
-j e
-i d</w>
-si on</w>
-be au
-at t
-el i
-or t</w>
-re c
-f f
-st er
-su pp
-g en
-be en</w>
-il y</w>
-te am</w>
-m m
-i c</w>
-pe op
-it t
-at s</w>
-on ly</w>
-mb er</w>
-en g
-b ri
-m p
-k now</w>
-b ur
-b ar
-in s</w>
-lo w</w>
-sh e</w>
-ro w</w>
-â Ŀ
-t ro
-peop le</w>
-vi a</w>
-lo w
-ag a
-be t
-x t</w>
-f ac
-ch ar
-e ar
-w al
-s en
-f am
-b le</w>
-n ati
-is h</w>
-n or
-g ame</w>
-li ve</w>
-s co
-le y</w>
-d on
-ic k</w>
-b all</w>
-ver y</w>
-the se</w>
-p an
-i a</w>
-at ing</w>
-c r
-a re
-g ir
-ma ke</w>
-st re
-sho w</w>
-. "</w>
-f l
-u p
-d r
-than ks</w>
-il li
-w om
-st s</w>
-i g
-s ur
-ever y
-c ur
-vie w</w>
-le t</w>
-in to</w>
-mo st</w>
-n a
-in di
-g ar
-ha d</w>
-s ou
-v ed</w>
-an t
-iti on</w>
-ma de</w>
-f ol
-un i
-it ed</w>
-ðŁ ı
-ic al</w>
-th r
-read y</w>
-ch ec
-d ra
-k es</w>
-boo k</w>
-e p</w>
-si c</w>
-mor ning</w>
-ne ws</w>
-c au
-c t</w>
-w ell</w>
-an c
-pho to</w>
-th an</w>
-or s</w>
-bir th
-g g
-ou t
-ne xt</w>
-som e
-en ing</w>
-stor y</w>
-ch ri
-do wn</w>
-hom e</w>
-f fe
-fre e</w>
-d a</w>
-b or
-f il
-ci al</w>
-than k</w>
-si de</w>
-le ar
-qu e
-l ine</w>
-t en
-at es</w>
-ye ars</w>
-m y
-pho to
-beau ti
-ri ght</w>
-n u
-for m
-shi p</w>
-b an
-th er
-d ays</w>
-g am
-as on</w>
-g y</w>
-ðŁ İ
-birth day</w>
-se t</w>
-ic k
-e t
-st ill</w>
-com ing</w>
-ta ke</w>
-ðŁ ĩ
-b b
-s ol
-s on
-d en
-e p
-mu sic</w>
-the m</w>
-de n</w>
-wh y</w>
-f oo
-c ra
-am az
-w n
-h ol
-t ting</w>
-w r
-u e</w>
-ma g
-c ro
-l an
-c lo
-b ra
-a k</w>
-s ing</w>
-c al
-re ad</w>
-' ve</w>
-jo h
-b ab
-d ri
-b lo
-bi g</w>
-er ic
-in t</w>
-t or</w>
-tr y</w>
-l a</w>
-le g
-hou se</w>
-m ic
-v al
-beauti ful</w>
-l itt
-chec k</w>
-ne w
-ver s
-s w
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-pla y
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-âĢ ĵ</w>
-w in</w>
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-con gr
-sch ool</w>
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-. @</w>
-he al
-ic h</w>
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-wh ere</w>
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-de sig
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-t y
-sp o
-ne ed</w>
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-be ing</w>
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-l s</w>
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-tom or
-m c
-h on
-w ant</w>
-a ir
-pi c</w>
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-p er</w>
-le ss</w>
-wee k
-ve l</w>
-a h</w>
-c ap
-ch am
-g er
-ti m
-tomor row</w>
-ne ss</w>
-st ate</w>
-h al
-ser v
-z e</w>
-o s</w>
-p at
-v is
-ex c
-s in
-f f</w>
-c ity</w>
-c en
-an y
-b el
-su mm
-t in
-w ould</w>
-loo king</w>
-k o
-ce le
-fam ily</w>
-m er
-po w
-hel p</w>
-bu s
-c o</w>
-c le
-sel f</w>
-en s</w>
-ic s</w>
-th o
-an i
-ch o
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-b s</w>
-t wee
-th ink</w>
-for e</w>
-ch il
-vi de
-di d</w>
-al e</w>
-ch i
-v il
-en ds</w>
-w ing</w>
-p as
-' ll</w>
-v ol
-s a</w>
-g s</w>
-man y</w>
-j ec
-be fore</w>
-gra ph
-n y</w>
-ur ing</w>
-w il
-d d
-bu il
-f av
-st ed</w>
-tr an
-l ing</w>
-ou d</w>
-d ge</w>
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-nati onal</w>
-st a
-c er
-w ere</w>
-in a</w>
-se ason</w>
-c ou
-n ed</w>
-amaz ing</w>
-ti ons</w>
-cele br
-n s</w>
-a th
-he ad</w>
-s day</w>
-d ar
-lo c
-v in
-an other</w>
-g oo
-s at
-n y
-jo in</w>
-pre s
-s es</w>
-s ing
-an a</w>
-in ing</w>
-.. ..</w>
-c our
-ï¸ ı
-ac t</w>
-cau se</w>
-li ght</w>
-am s</w>
-t a</w>
-b al
-f c</w>
-hi gh</w>
-off ici
-t t</w>
-chri st
-d ic
-d ay
-ra l</w>
-h or
-: )</w>
-vi si
-n am
-o b
-ma s</w>
-gh t
-re ally</w>
-t un
-fin d</w>
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-ell ington</w>
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-infin iti</w>
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deleted file mode 100644
index 2c2130b544c0c5a72d5d00da071ba130a9800fb2..0000000000000000000000000000000000000000
--- a/MagicQuill/comfy/sd1_tokenizer/special_tokens_map.json
+++ /dev/null
@@ -1,24 +0,0 @@
-{
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deleted file mode 100644
index 5ba7bf706515bc60487ad0e1816b4929b82542d6..0000000000000000000000000000000000000000
--- a/MagicQuill/comfy/sd1_tokenizer/tokenizer_config.json
+++ /dev/null
@@ -1,34 +0,0 @@
-{
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-  "name_or_path": "openai/clip-vit-large-patch14",
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index 469be27c5c010538f845f518c4f5e8574c78f7c8..0000000000000000000000000000000000000000
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-  "ĭ": 233,
-  "ĭ</w>": 489,
-  "ĭãģ": 36218,
-  "ĭãĤ": 45737,
-  "Į": 234,
-  "Į</w>": 490,
-  "ĮãĤĬãģ": 45969,
-  "ĮãĤĬãģŁãģĦ</w>": 47021,
-  "Įë": 17003,
-  "į": 235,
-  "į</w>": 491,
-  "İ": 236,
-  "İ</w>": 492,
-  "ı": 237,
-  "ı</w>": 493,
-  "IJ": 238,
-  "IJ</w>": 494,
-  "ij": 239,
-  "ij</w>": 495,
-  "Ĵ": 240,
-  "Ĵ</w>": 496,
-  "ĵ": 241,
-  "ĵ</w>": 497,
-  "Ķ": 242,
-  "Ķ</w>": 498,
-  "Ķë": 37978,
-  "Ķï¸ı": 24395,
-  "Ķï¸ı</w>": 7443,
-  "ķ": 243,
-  "ķ</w>": 499,
-  "ķãĤ": 26609,
-  "ķï¸ı</w>": 44853,
-  "ĸ": 244,
-  "ĸ</w>": 500,
-  "ĸï¸ı</w>": 28877,
-  "Ĺ": 245,
-  "Ĺ</w>": 501,
-  "ĺ": 246,
-  "ĺ</w>": 502,
-  "Ļ": 247,
-  "Ļ</w>": 503,
-  "ļ": 248,
-  "ļ</w>": 504,
-  "Ľ": 249,
-  "Ľ</w>": 505,
-  "ľ": 250,
-  "ľ</w>": 506,
-  "ľë": 39810,
-  "Ŀ": 251,
-  "Ŀ</w>": 507,
-  "ŀ": 252,
-  "ŀ</w>": 508,
-  "Ł": 253,
-  "Ł</w>": 509,
-  "ŁãģĦ</w>": 46023,
-  "ł": 254,
-  "ł</w>": 510,
-  "łï¸ı": 27899,
-  "łï¸ı</w>": 12715,
-  "łĪ": 43364,
-  "Ń": 255,
-  "Ń</w>": 511
-}
diff --git a/MagicQuill/comfy/sd2_clip.py b/MagicQuill/comfy/sd2_clip.py
deleted file mode 100644
index d14b445441b393874020df14919a064fad8067b0..0000000000000000000000000000000000000000
--- a/MagicQuill/comfy/sd2_clip.py
+++ /dev/null
@@ -1,23 +0,0 @@
-from comfy import sd1_clip
-import os
-
-class SD2ClipHModel(sd1_clip.SDClipModel):
-    def __init__(self, arch="ViT-H-14", device="cpu", max_length=77, freeze=True, layer="penultimate", layer_idx=None, dtype=None):
-        if layer == "penultimate":
-            layer="hidden"
-            layer_idx=-2
-
-        textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "sd2_clip_config.json")
-        super().__init__(device=device, freeze=freeze, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens={"start": 49406, "end": 49407, "pad": 0})
-
-class SD2ClipHTokenizer(sd1_clip.SDTokenizer):
-    def __init__(self, tokenizer_path=None, embedding_directory=None):
-        super().__init__(tokenizer_path, pad_with_end=False, embedding_directory=embedding_directory, embedding_size=1024)
-
-class SD2Tokenizer(sd1_clip.SD1Tokenizer):
-    def __init__(self, embedding_directory=None):
-        super().__init__(embedding_directory=embedding_directory, clip_name="h", tokenizer=SD2ClipHTokenizer)
-
-class SD2ClipModel(sd1_clip.SD1ClipModel):
-    def __init__(self, device="cpu", dtype=None, **kwargs):
-        super().__init__(device=device, dtype=dtype, clip_name="h", clip_model=SD2ClipHModel, **kwargs)
diff --git a/MagicQuill/comfy/sd2_clip_config.json b/MagicQuill/comfy/sd2_clip_config.json
deleted file mode 100644
index 85cec832be9a1d0957245a8d125af398829f247e..0000000000000000000000000000000000000000
--- a/MagicQuill/comfy/sd2_clip_config.json
+++ /dev/null
@@ -1,23 +0,0 @@
-{
-  "architectures": [
-    "CLIPTextModel"
-  ],
-  "attention_dropout": 0.0,
-  "bos_token_id": 0,
-  "dropout": 0.0,
-  "eos_token_id": 2,
-  "hidden_act": "gelu",
-  "hidden_size": 1024,
-  "initializer_factor": 1.0,
-  "initializer_range": 0.02,
-  "intermediate_size": 4096,
-  "layer_norm_eps": 1e-05,
-  "max_position_embeddings": 77,
-  "model_type": "clip_text_model",
-  "num_attention_heads": 16,
-  "num_hidden_layers": 24,
-  "pad_token_id": 1,
-  "projection_dim": 1024,
-  "torch_dtype": "float32",
-  "vocab_size": 49408
-}
diff --git a/MagicQuill/comfy/sd3_clip.py b/MagicQuill/comfy/sd3_clip.py
deleted file mode 100644
index 0713eb28529469b28ae57445b740e13b5bf8eafa..0000000000000000000000000000000000000000
--- a/MagicQuill/comfy/sd3_clip.py
+++ /dev/null
@@ -1,150 +0,0 @@
-from comfy import sd1_clip
-from comfy import sdxl_clip
-from transformers import T5TokenizerFast
-import comfy.t5
-import torch
-import os
-import comfy.model_management
-import logging
-
-class T5XXLModel(sd1_clip.SDClipModel):
-    def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None):
-        textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_config_xxl.json")
-        super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens={"end": 1, "pad": 0}, model_class=comfy.t5.T5)
-
-class T5XXLTokenizer(sd1_clip.SDTokenizer):
-    def __init__(self, embedding_directory=None):
-        tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_tokenizer")
-        super().__init__(tokenizer_path, pad_with_end=False, embedding_size=4096, embedding_key='t5xxl', tokenizer_class=T5TokenizerFast, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=77)
-
-class SDT5XXLTokenizer(sd1_clip.SD1Tokenizer):
-    def __init__(self, embedding_directory=None):
-        super().__init__(embedding_directory=embedding_directory, clip_name="t5xxl", tokenizer=T5XXLTokenizer)
-
-class SDT5XXLModel(sd1_clip.SD1ClipModel):
-    def __init__(self, device="cpu", dtype=None, **kwargs):
-        super().__init__(device=device, dtype=dtype, clip_name="t5xxl", clip_model=T5XXLModel, **kwargs)
-
-
-
-class SD3Tokenizer:
-    def __init__(self, embedding_directory=None):
-        self.clip_l = sd1_clip.SDTokenizer(embedding_directory=embedding_directory)
-        self.clip_g = sdxl_clip.SDXLClipGTokenizer(embedding_directory=embedding_directory)
-        self.t5xxl = T5XXLTokenizer(embedding_directory=embedding_directory)
-
-    def tokenize_with_weights(self, text:str, return_word_ids=False):
-        out = {}
-        out["g"] = self.clip_g.tokenize_with_weights(text, return_word_ids)
-        out["l"] = self.clip_l.tokenize_with_weights(text, return_word_ids)
-        out["t5xxl"] = self.t5xxl.tokenize_with_weights(text, return_word_ids)
-        return out
-
-    def untokenize(self, token_weight_pair):
-        return self.clip_g.untokenize(token_weight_pair)
-
-class SD3ClipModel(torch.nn.Module):
-    def __init__(self, clip_l=True, clip_g=True, t5=True, dtype_t5=None, device="cpu", dtype=None):
-        super().__init__()
-        self.dtypes = set()
-        if clip_l:
-            self.clip_l = sd1_clip.SDClipModel(layer="hidden", layer_idx=-2, device=device, dtype=dtype, layer_norm_hidden_state=False, return_projected_pooled=False)
-            self.dtypes.add(dtype)
-        else:
-            self.clip_l = None
-
-        if clip_g:
-            self.clip_g = sdxl_clip.SDXLClipG(device=device, dtype=dtype)
-            self.dtypes.add(dtype)
-        else:
-            self.clip_g = None
-
-        if t5:
-            if dtype_t5 is None:
-                dtype_t5 = dtype
-            elif comfy.model_management.dtype_size(dtype_t5) > comfy.model_management.dtype_size(dtype):
-                dtype_t5 = dtype
-
-            if not comfy.model_management.supports_cast(device, dtype_t5):
-                dtype_t5 = dtype
-
-            self.t5xxl = T5XXLModel(device=device, dtype=dtype_t5)
-            self.dtypes.add(dtype_t5)
-        else:
-            self.t5xxl = None
-
-        logging.debug("Created SD3 text encoder with: clip_l {}, clip_g {}, t5xxl {}:{}".format(clip_l, clip_g, t5, dtype_t5))
-
-    def set_clip_options(self, options):
-        if self.clip_l is not None:
-            self.clip_l.set_clip_options(options)
-        if self.clip_g is not None:
-            self.clip_g.set_clip_options(options)
-        if self.t5xxl is not None:
-            self.t5xxl.set_clip_options(options)
-
-    def reset_clip_options(self):
-        if self.clip_l is not None:
-            self.clip_l.reset_clip_options()
-        if self.clip_g is not None:
-            self.clip_g.reset_clip_options()
-        if self.t5xxl is not None:
-            self.t5xxl.reset_clip_options()
-
-    def encode_token_weights(self, token_weight_pairs):
-        token_weight_pairs_l = token_weight_pairs["l"]
-        token_weight_pairs_g = token_weight_pairs["g"]
-        token_weight_pars_t5 = token_weight_pairs["t5xxl"]
-        lg_out = None
-        pooled = None
-        out = None
-
-        if len(token_weight_pairs_g) > 0 or len(token_weight_pairs_l) > 0:
-            if self.clip_l is not None:
-                lg_out, l_pooled = self.clip_l.encode_token_weights(token_weight_pairs_l)
-            else:
-                l_pooled = torch.zeros((1, 768), device=comfy.model_management.intermediate_device())
-
-            if self.clip_g is not None:
-                g_out, g_pooled = self.clip_g.encode_token_weights(token_weight_pairs_g)
-                if lg_out is not None:
-                    lg_out = torch.cat([lg_out, g_out], dim=-1)
-                else:
-                    lg_out = torch.nn.functional.pad(g_out, (768, 0))
-            else:
-                g_out = None
-                g_pooled = torch.zeros((1, 1280), device=comfy.model_management.intermediate_device())
-
-            if lg_out is not None:
-                lg_out = torch.nn.functional.pad(lg_out, (0, 4096 - lg_out.shape[-1]))
-                out = lg_out
-            pooled = torch.cat((l_pooled, g_pooled), dim=-1)
-
-        if self.t5xxl is not None:
-            t5_out, t5_pooled = self.t5xxl.encode_token_weights(token_weight_pars_t5)
-            if lg_out is not None:
-                out = torch.cat([lg_out, t5_out], dim=-2)
-            else:
-                out = t5_out
-
-        if out is None:
-            out = torch.zeros((1, 77, 4096), device=comfy.model_management.intermediate_device())
-
-        if pooled is None:
-            pooled = torch.zeros((1, 768 + 1280), device=comfy.model_management.intermediate_device())
-
-        return out, pooled
-
-    def load_sd(self, sd):
-        if "text_model.encoder.layers.30.mlp.fc1.weight" in sd:
-            return self.clip_g.load_sd(sd)
-        elif "text_model.encoder.layers.1.mlp.fc1.weight" in sd:
-            return self.clip_l.load_sd(sd)
-        else:
-            return self.t5xxl.load_sd(sd)
-
-def sd3_clip(clip_l=True, clip_g=True, t5=True, dtype_t5=None):
-    class SD3ClipModel_(SD3ClipModel):
-        def __init__(self, device="cpu", dtype=None):
-            super().__init__(clip_l=clip_l, clip_g=clip_g, t5=t5, dtype_t5=dtype_t5, device=device, dtype=dtype)
-    return SD3ClipModel_
diff --git a/MagicQuill/comfy/sdxl_clip.py b/MagicQuill/comfy/sdxl_clip.py
deleted file mode 100644
index 1257cba1e4296280db50c04e556ad23f02264267..0000000000000000000000000000000000000000
--- a/MagicQuill/comfy/sdxl_clip.py
+++ /dev/null
@@ -1,89 +0,0 @@
-from comfy import sd1_clip
-import torch
-import os
-
-class SDXLClipG(sd1_clip.SDClipModel):
-    def __init__(self, device="cpu", max_length=77, freeze=True, layer="penultimate", layer_idx=None, dtype=None):
-        if layer == "penultimate":
-            layer="hidden"
-            layer_idx=-2
-
-        textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_config_bigg.json")
-        super().__init__(device=device, freeze=freeze, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype,
-                         special_tokens={"start": 49406, "end": 49407, "pad": 0}, layer_norm_hidden_state=False)
-
-    def load_sd(self, sd):
-        return super().load_sd(sd)
-
-class SDXLClipGTokenizer(sd1_clip.SDTokenizer):
-    def __init__(self, tokenizer_path=None, embedding_directory=None):
-        super().__init__(tokenizer_path, pad_with_end=False, embedding_directory=embedding_directory, embedding_size=1280, embedding_key='clip_g')
-
-
-class SDXLTokenizer:
-    def __init__(self, embedding_directory=None):
-        self.clip_l = sd1_clip.SDTokenizer(embedding_directory=embedding_directory)
-        self.clip_g = SDXLClipGTokenizer(embedding_directory=embedding_directory)
-
-    def tokenize_with_weights(self, text:str, return_word_ids=False):
-        out = {}
-        out["g"] = self.clip_g.tokenize_with_weights(text, return_word_ids)
-        out["l"] = self.clip_l.tokenize_with_weights(text, return_word_ids)
-        return out
-
-    def untokenize(self, token_weight_pair):
-        return self.clip_g.untokenize(token_weight_pair)
-
-class SDXLClipModel(torch.nn.Module):
-    def __init__(self, device="cpu", dtype=None):
-        super().__init__()
-        self.clip_l = sd1_clip.SDClipModel(layer="hidden", layer_idx=-2, device=device, dtype=dtype, layer_norm_hidden_state=False)
-        self.clip_g = SDXLClipG(device=device, dtype=dtype)
-        self.dtypes = set([dtype])
-
-    def set_clip_options(self, options):
-        self.clip_l.set_clip_options(options)
-        self.clip_g.set_clip_options(options)
-
-    def reset_clip_options(self):
-        self.clip_g.reset_clip_options()
-        self.clip_l.reset_clip_options()
-
-    def encode_token_weights(self, token_weight_pairs):
-        token_weight_pairs_g = token_weight_pairs["g"]
-        token_weight_pairs_l = token_weight_pairs["l"]
-        g_out, g_pooled = self.clip_g.encode_token_weights(token_weight_pairs_g)
-        l_out, l_pooled = self.clip_l.encode_token_weights(token_weight_pairs_l)
-        return torch.cat([l_out, g_out], dim=-1), g_pooled
-
-    def load_sd(self, sd):
-        if "text_model.encoder.layers.30.mlp.fc1.weight" in sd:
-            return self.clip_g.load_sd(sd)
-        else:
-            return self.clip_l.load_sd(sd)
-
-class SDXLRefinerClipModel(sd1_clip.SD1ClipModel):
-    def __init__(self, device="cpu", dtype=None):
-        super().__init__(device=device, dtype=dtype, clip_name="g", clip_model=SDXLClipG)
-
-
-class StableCascadeClipGTokenizer(sd1_clip.SDTokenizer):
-    def __init__(self, tokenizer_path=None, embedding_directory=None):
-        super().__init__(tokenizer_path, pad_with_end=True, embedding_directory=embedding_directory, embedding_size=1280, embedding_key='clip_g')
-
-class StableCascadeTokenizer(sd1_clip.SD1Tokenizer):
-    def __init__(self, embedding_directory=None):
-        super().__init__(embedding_directory=embedding_directory, clip_name="g", tokenizer=StableCascadeClipGTokenizer)
-
-class StableCascadeClipG(sd1_clip.SDClipModel):
-    def __init__(self, device="cpu", max_length=77, freeze=True, layer="hidden", layer_idx=-1, dtype=None):
-        textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_config_bigg.json")
-        super().__init__(device=device, freeze=freeze, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype,
-                         special_tokens={"start": 49406, "end": 49407, "pad": 49407}, layer_norm_hidden_state=False, enable_attention_masks=True)
-
-    def load_sd(self, sd):
-        return super().load_sd(sd)
-
-class StableCascadeClipModel(sd1_clip.SD1ClipModel):
-    def __init__(self, device="cpu", dtype=None):
-        super().__init__(device=device, dtype=dtype, clip_name="g", clip_model=StableCascadeClipG)
diff --git a/MagicQuill/comfy/supported_models.py b/MagicQuill/comfy/supported_models.py
deleted file mode 100644
index 761498dbc9e54a2365dbef910363eb2ce3c7756e..0000000000000000000000000000000000000000
--- a/MagicQuill/comfy/supported_models.py
+++ /dev/null
@@ -1,559 +0,0 @@
-import torch
-from . import model_base
-from . import utils
-
-from . import sd1_clip
-from . import sd2_clip
-from . import sdxl_clip
-from . import sd3_clip
-from . import sa_t5
-
-from . import supported_models_base
-from . import latent_formats
-
-from . import diffusers_convert
-
-class SD15(supported_models_base.BASE):
-    unet_config = {
-        "context_dim": 768,
-        "model_channels": 320,
-        "use_linear_in_transformer": False,
-        "adm_in_channels": None,
-        "use_temporal_attention": False,
-    }
-
-    unet_extra_config = {
-        "num_heads": 8,
-        "num_head_channels": -1,
-    }
-
-    latent_format = latent_formats.SD15
-
-    def process_clip_state_dict(self, state_dict):
-        k = list(state_dict.keys())
-        for x in k:
-            if x.startswith("cond_stage_model.transformer.") and not x.startswith("cond_stage_model.transformer.text_model."):
-                y = x.replace("cond_stage_model.transformer.", "cond_stage_model.transformer.text_model.")
-                state_dict[y] = state_dict.pop(x)
-
-        if 'cond_stage_model.transformer.text_model.embeddings.position_ids' in state_dict:
-            ids = state_dict['cond_stage_model.transformer.text_model.embeddings.position_ids']
-            if ids.dtype == torch.float32:
-                state_dict['cond_stage_model.transformer.text_model.embeddings.position_ids'] = ids.round()
-
-        replace_prefix = {}
-        replace_prefix["cond_stage_model."] = "clip_l."
-        state_dict = utils.state_dict_prefix_replace(state_dict, replace_prefix, filter_keys=True)
-        return state_dict
-
-    def process_clip_state_dict_for_saving(self, state_dict):
-        pop_keys = ["clip_l.transformer.text_projection.weight", "clip_l.logit_scale"]
-        for p in pop_keys:
-            if p in state_dict:
-                state_dict.pop(p)
-
-        replace_prefix = {"clip_l.": "cond_stage_model."}
-        return utils.state_dict_prefix_replace(state_dict, replace_prefix)
-
-    def clip_target(self, state_dict={}):
-        return supported_models_base.ClipTarget(sd1_clip.SD1Tokenizer, sd1_clip.SD1ClipModel)
-
-class SD20(supported_models_base.BASE):
-    unet_config = {
-        "context_dim": 1024,
-        "model_channels": 320,
-        "use_linear_in_transformer": True,
-        "adm_in_channels": None,
-        "use_temporal_attention": False,
-    }
-
-    unet_extra_config = {
-        "num_heads": -1,
-        "num_head_channels": 64,
-        "attn_precision": torch.float32,
-    }
-
-    latent_format = latent_formats.SD15
-
-    def model_type(self, state_dict, prefix=""):
-        if self.unet_config["in_channels"] == 4: #SD2.0 inpainting models are not v prediction
-            k = "{}output_blocks.11.1.transformer_blocks.0.norm1.bias".format(prefix)
-            out = state_dict.get(k, None)
-            if out is not None and torch.std(out, unbiased=False) > 0.09: # not sure how well this will actually work. I guess we will find out.
-                return model_base.ModelType.V_PREDICTION
-        return model_base.ModelType.EPS
-
-    def process_clip_state_dict(self, state_dict):
-        replace_prefix = {}
-        replace_prefix["conditioner.embedders.0.model."] = "clip_h." #SD2 in sgm format
-        replace_prefix["cond_stage_model.model."] = "clip_h."
-        state_dict = utils.state_dict_prefix_replace(state_dict, replace_prefix, filter_keys=True)
-        state_dict = utils.clip_text_transformers_convert(state_dict, "clip_h.", "clip_h.transformer.")
-        return state_dict
-
-    def process_clip_state_dict_for_saving(self, state_dict):
-        replace_prefix = {}
-        replace_prefix["clip_h"] = "cond_stage_model.model"
-        state_dict = utils.state_dict_prefix_replace(state_dict, replace_prefix)
-        state_dict = diffusers_convert.convert_text_enc_state_dict_v20(state_dict)
-        return state_dict
-
-    def clip_target(self, state_dict={}):
-        return supported_models_base.ClipTarget(sd2_clip.SD2Tokenizer, sd2_clip.SD2ClipModel)
-
-class SD21UnclipL(SD20):
-    unet_config = {
-        "context_dim": 1024,
-        "model_channels": 320,
-        "use_linear_in_transformer": True,
-        "adm_in_channels": 1536,
-        "use_temporal_attention": False,
-    }
-
-    clip_vision_prefix = "embedder.model.visual."
-    noise_aug_config = {"noise_schedule_config": {"timesteps": 1000, "beta_schedule": "squaredcos_cap_v2"}, "timestep_dim": 768}
-
-
-class SD21UnclipH(SD20):
-    unet_config = {
-        "context_dim": 1024,
-        "model_channels": 320,
-        "use_linear_in_transformer": True,
-        "adm_in_channels": 2048,
-        "use_temporal_attention": False,
-    }
-
-    clip_vision_prefix = "embedder.model.visual."
-    noise_aug_config = {"noise_schedule_config": {"timesteps": 1000, "beta_schedule": "squaredcos_cap_v2"}, "timestep_dim": 1024}
-
-class SDXLRefiner(supported_models_base.BASE):
-    unet_config = {
-        "model_channels": 384,
-        "use_linear_in_transformer": True,
-        "context_dim": 1280,
-        "adm_in_channels": 2560,
-        "transformer_depth": [0, 0, 4, 4, 4, 4, 0, 0],
-        "use_temporal_attention": False,
-    }
-
-    latent_format = latent_formats.SDXL
-
-    def get_model(self, state_dict, prefix="", device=None):
-        return model_base.SDXLRefiner(self, device=device)
-
-    def process_clip_state_dict(self, state_dict):
-        keys_to_replace = {}
-        replace_prefix = {}
-        replace_prefix["conditioner.embedders.0.model."] = "clip_g."
-        state_dict = utils.state_dict_prefix_replace(state_dict, replace_prefix, filter_keys=True)
-
-        state_dict = utils.clip_text_transformers_convert(state_dict, "clip_g.", "clip_g.transformer.")
-        state_dict = utils.state_dict_key_replace(state_dict, keys_to_replace)
-        return state_dict
-
-    def process_clip_state_dict_for_saving(self, state_dict):
-        replace_prefix = {}
-        state_dict_g = diffusers_convert.convert_text_enc_state_dict_v20(state_dict, "clip_g")
-        if "clip_g.transformer.text_model.embeddings.position_ids" in state_dict_g:
-            state_dict_g.pop("clip_g.transformer.text_model.embeddings.position_ids")
-        replace_prefix["clip_g"] = "conditioner.embedders.0.model"
-        state_dict_g = utils.state_dict_prefix_replace(state_dict_g, replace_prefix)
-        return state_dict_g
-
-    def clip_target(self, state_dict={}):
-        return supported_models_base.ClipTarget(sdxl_clip.SDXLTokenizer, sdxl_clip.SDXLRefinerClipModel)
-
-class SDXL(supported_models_base.BASE):
-    unet_config = {
-        "model_channels": 320,
-        "use_linear_in_transformer": True,
-        "transformer_depth": [0, 0, 2, 2, 10, 10],
-        "context_dim": 2048,
-        "adm_in_channels": 2816,
-        "use_temporal_attention": False,
-    }
-
-    latent_format = latent_formats.SDXL
-
-    def model_type(self, state_dict, prefix=""):
-        if 'edm_mean' in state_dict and 'edm_std' in state_dict: #Playground V2.5
-            self.latent_format = latent_formats.SDXL_Playground_2_5()
-            self.sampling_settings["sigma_data"] = 0.5
-            self.sampling_settings["sigma_max"] = 80.0
-            self.sampling_settings["sigma_min"] = 0.002
-            return model_base.ModelType.EDM
-        elif "edm_vpred.sigma_max" in state_dict:
-            self.sampling_settings["sigma_max"] = float(state_dict["edm_vpred.sigma_max"].item())
-            if "edm_vpred.sigma_min" in state_dict:
-                self.sampling_settings["sigma_min"] = float(state_dict["edm_vpred.sigma_min"].item())
-            return model_base.ModelType.V_PREDICTION_EDM
-        elif "v_pred" in state_dict:
-            return model_base.ModelType.V_PREDICTION
-        else:
-            return model_base.ModelType.EPS
-
-    def get_model(self, state_dict, prefix="", device=None):
-        out = model_base.SDXL(self, model_type=self.model_type(state_dict, prefix), device=device)
-        if self.inpaint_model():
-            out.set_inpaint()
-        return out
-
-    def process_clip_state_dict(self, state_dict):
-        keys_to_replace = {}
-        replace_prefix = {}
-
-        replace_prefix["conditioner.embedders.0.transformer.text_model"] = "clip_l.transformer.text_model"
-        replace_prefix["conditioner.embedders.1.model."] = "clip_g."
-        state_dict = utils.state_dict_prefix_replace(state_dict, replace_prefix, filter_keys=True)
-
-        state_dict = utils.state_dict_key_replace(state_dict, keys_to_replace)
-        state_dict = utils.clip_text_transformers_convert(state_dict, "clip_g.", "clip_g.transformer.")
-        return state_dict
-
-    def process_clip_state_dict_for_saving(self, state_dict):
-        replace_prefix = {}
-        keys_to_replace = {}
-        state_dict_g = diffusers_convert.convert_text_enc_state_dict_v20(state_dict, "clip_g")
-        for k in state_dict:
-            if k.startswith("clip_l"):
-                state_dict_g[k] = state_dict[k]
-
-        state_dict_g["clip_l.transformer.text_model.embeddings.position_ids"] = torch.arange(77).expand((1, -1))
-        pop_keys = ["clip_l.transformer.text_projection.weight", "clip_l.logit_scale"]
-        for p in pop_keys:
-            if p in state_dict_g:
-                state_dict_g.pop(p)
-
-        replace_prefix["clip_g"] = "conditioner.embedders.1.model"
-        replace_prefix["clip_l"] = "conditioner.embedders.0"
-        state_dict_g = utils.state_dict_prefix_replace(state_dict_g, replace_prefix)
-        return state_dict_g
-
-    def clip_target(self, state_dict={}):
-        return supported_models_base.ClipTarget(sdxl_clip.SDXLTokenizer, sdxl_clip.SDXLClipModel)
-
-class SSD1B(SDXL):
-    unet_config = {
-        "model_channels": 320,
-        "use_linear_in_transformer": True,
-        "transformer_depth": [0, 0, 2, 2, 4, 4],
-        "context_dim": 2048,
-        "adm_in_channels": 2816,
-        "use_temporal_attention": False,
-    }
-
-class Segmind_Vega(SDXL):
-    unet_config = {
-        "model_channels": 320,
-        "use_linear_in_transformer": True,
-        "transformer_depth": [0, 0, 1, 1, 2, 2],
-        "context_dim": 2048,
-        "adm_in_channels": 2816,
-        "use_temporal_attention": False,
-    }
-
-class KOALA_700M(SDXL):
-    unet_config = {
-        "model_channels": 320,
-        "use_linear_in_transformer": True,
-        "transformer_depth": [0, 2, 5],
-        "context_dim": 2048,
-        "adm_in_channels": 2816,
-        "use_temporal_attention": False,
-    }
-
-class KOALA_1B(SDXL):
-    unet_config = {
-        "model_channels": 320,
-        "use_linear_in_transformer": True,
-        "transformer_depth": [0, 2, 6],
-        "context_dim": 2048,
-        "adm_in_channels": 2816,
-        "use_temporal_attention": False,
-    }
-
-class SVD_img2vid(supported_models_base.BASE):
-    unet_config = {
-        "model_channels": 320,
-        "in_channels": 8,
-        "use_linear_in_transformer": True,
-        "transformer_depth": [1, 1, 1, 1, 1, 1, 0, 0],
-        "context_dim": 1024,
-        "adm_in_channels": 768,
-        "use_temporal_attention": True,
-        "use_temporal_resblock": True
-    }
-
-    unet_extra_config = {
-        "num_heads": -1,
-        "num_head_channels": 64,
-        "attn_precision": torch.float32,
-    }
-
-    clip_vision_prefix = "conditioner.embedders.0.open_clip.model.visual."
-
-    latent_format = latent_formats.SD15
-
-    sampling_settings = {"sigma_max": 700.0, "sigma_min": 0.002}
-
-    def get_model(self, state_dict, prefix="", device=None):
-        out = model_base.SVD_img2vid(self, device=device)
-        return out
-
-    def clip_target(self, state_dict={}):
-        return None
-
-class SV3D_u(SVD_img2vid):
-    unet_config = {
-        "model_channels": 320,
-        "in_channels": 8,
-        "use_linear_in_transformer": True,
-        "transformer_depth": [1, 1, 1, 1, 1, 1, 0, 0],
-        "context_dim": 1024,
-        "adm_in_channels": 256,
-        "use_temporal_attention": True,
-        "use_temporal_resblock": True
-    }
-
-    vae_key_prefix = ["conditioner.embedders.1.encoder."]
-
-    def get_model(self, state_dict, prefix="", device=None):
-        out = model_base.SV3D_u(self, device=device)
-        return out
-
-class SV3D_p(SV3D_u):
-    unet_config = {
-        "model_channels": 320,
-        "in_channels": 8,
-        "use_linear_in_transformer": True,
-        "transformer_depth": [1, 1, 1, 1, 1, 1, 0, 0],
-        "context_dim": 1024,
-        "adm_in_channels": 1280,
-        "use_temporal_attention": True,
-        "use_temporal_resblock": True
-    }
-
-
-    def get_model(self, state_dict, prefix="", device=None):
-        out = model_base.SV3D_p(self, device=device)
-        return out
-
-class Stable_Zero123(supported_models_base.BASE):
-    unet_config = {
-        "context_dim": 768,
-        "model_channels": 320,
-        "use_linear_in_transformer": False,
-        "adm_in_channels": None,
-        "use_temporal_attention": False,
-        "in_channels": 8,
-    }
-
-    unet_extra_config = {
-        "num_heads": 8,
-        "num_head_channels": -1,
-    }
-
-    required_keys = {
-        "cc_projection.weight": None,
-        "cc_projection.bias": None,
-    }
-
-    clip_vision_prefix = "cond_stage_model.model.visual."
-
-    latent_format = latent_formats.SD15
-
-    def get_model(self, state_dict, prefix="", device=None):
-        out = model_base.Stable_Zero123(self, device=device, cc_projection_weight=state_dict["cc_projection.weight"], cc_projection_bias=state_dict["cc_projection.bias"])
-        return out
-
-    def clip_target(self, state_dict={}):
-        return None
-
-class SD_X4Upscaler(SD20):
-    unet_config = {
-        "context_dim": 1024,
-        "model_channels": 256,
-        'in_channels': 7,
-        "use_linear_in_transformer": True,
-        "adm_in_channels": None,
-        "use_temporal_attention": False,
-    }
-
-    unet_extra_config = {
-        "disable_self_attentions": [True, True, True, False],
-        "num_classes": 1000,
-        "num_heads": 8,
-        "num_head_channels": -1,
-    }
-
-    latent_format = latent_formats.SD_X4
-
-    sampling_settings = {
-        "linear_start": 0.0001,
-        "linear_end": 0.02,
-    }
-
-    def get_model(self, state_dict, prefix="", device=None):
-        out = model_base.SD_X4Upscaler(self, device=device)
-        return out
-
-class Stable_Cascade_C(supported_models_base.BASE):
-    unet_config = {
-        "stable_cascade_stage": 'c',
-    }
-
-    unet_extra_config = {}
-
-    latent_format = latent_formats.SC_Prior
-    supported_inference_dtypes = [torch.bfloat16, torch.float32]
-
-    sampling_settings = {
-        "shift": 2.0,
-    }
-
-    vae_key_prefix = ["vae."]
-    text_encoder_key_prefix = ["text_encoder."]
-    clip_vision_prefix = "clip_l_vision."
-
-    def process_unet_state_dict(self, state_dict):
-        key_list = list(state_dict.keys())
-        for y in ["weight", "bias"]:
-            suffix = "in_proj_{}".format(y)
-            keys = filter(lambda a: a.endswith(suffix), key_list)
-            for k_from in keys:
-                weights = state_dict.pop(k_from)
-                prefix = k_from[:-(len(suffix) + 1)]
-                shape_from = weights.shape[0] // 3
-                for x in range(3):
-                    p = ["to_q", "to_k", "to_v"]
-                    k_to = "{}.{}.{}".format(prefix, p[x], y)
-                    state_dict[k_to] = weights[shape_from*x:shape_from*(x + 1)]
-        return state_dict
-
-    def process_clip_state_dict(self, state_dict):
-        state_dict = utils.state_dict_prefix_replace(state_dict, {k: "" for k in self.text_encoder_key_prefix}, filter_keys=True)
-        if "clip_g.text_projection" in state_dict:
-            state_dict["clip_g.transformer.text_projection.weight"] = state_dict.pop("clip_g.text_projection").transpose(0, 1)
-        return state_dict
-
-    def get_model(self, state_dict, prefix="", device=None):
-        out = model_base.StableCascade_C(self, device=device)
-        return out
-
-    def clip_target(self, state_dict={}):
-        return supported_models_base.ClipTarget(sdxl_clip.StableCascadeTokenizer, sdxl_clip.StableCascadeClipModel)
-
-class Stable_Cascade_B(Stable_Cascade_C):
-    unet_config = {
-        "stable_cascade_stage": 'b',
-    }
-
-    unet_extra_config = {}
-
-    latent_format = latent_formats.SC_B
-    supported_inference_dtypes = [torch.float16, torch.bfloat16, torch.float32]
-
-    sampling_settings = {
-        "shift": 1.0,
-    }
-
-    clip_vision_prefix = None
-
-    def get_model(self, state_dict, prefix="", device=None):
-        out = model_base.StableCascade_B(self, device=device)
-        return out
-
-class SD15_instructpix2pix(SD15):
-    unet_config = {
-        "context_dim": 768,
-        "model_channels": 320,
-        "use_linear_in_transformer": False,
-        "adm_in_channels": None,
-        "use_temporal_attention": False,
-        "in_channels": 8,
-    }
-
-    def get_model(self, state_dict, prefix="", device=None):
-        return model_base.SD15_instructpix2pix(self, device=device)
-
-class SDXL_instructpix2pix(SDXL):
-    unet_config = {
-        "model_channels": 320,
-        "use_linear_in_transformer": True,
-        "transformer_depth": [0, 0, 2, 2, 10, 10],
-        "context_dim": 2048,
-        "adm_in_channels": 2816,
-        "use_temporal_attention": False,
-        "in_channels": 8,
-    }
-
-    def get_model(self, state_dict, prefix="", device=None):
-        return model_base.SDXL_instructpix2pix(self, model_type=self.model_type(state_dict, prefix), device=device)
-
-class SD3(supported_models_base.BASE):
-    unet_config = {
-        "in_channels": 16,
-        "pos_embed_scaling_factor": None,
-    }
-
-    sampling_settings = {
-        "shift": 3.0,
-    }
-
-    unet_extra_config = {}
-    latent_format = latent_formats.SD3
-    text_encoder_key_prefix = ["text_encoders."]
-
-    def get_model(self, state_dict, prefix="", device=None):
-        out = model_base.SD3(self, device=device)
-        return out
-
-    def clip_target(self, state_dict={}):
-        clip_l = False
-        clip_g = False
-        t5 = False
-        dtype_t5 = None
-        pref = self.text_encoder_key_prefix[0]
-        if "{}clip_l.transformer.text_model.final_layer_norm.weight".format(pref) in state_dict:
-            clip_l = True
-        if "{}clip_g.transformer.text_model.final_layer_norm.weight".format(pref) in state_dict:
-            clip_g = True
-        t5_key = "{}t5xxl.transformer.encoder.final_layer_norm.weight".format(pref)
-        if t5_key in state_dict:
-            t5 = True
-            dtype_t5 = state_dict[t5_key].dtype
-
-        return supported_models_base.ClipTarget(sd3_clip.SD3Tokenizer, sd3_clip.sd3_clip(clip_l=clip_l, clip_g=clip_g, t5=t5, dtype_t5=dtype_t5))
-
-class StableAudio(supported_models_base.BASE):
-    unet_config = {
-        "audio_model": "dit1.0",
-    }
-
-    sampling_settings = {"sigma_max": 500.0, "sigma_min": 0.03}
-
-    unet_extra_config = {}
-    latent_format = latent_formats.StableAudio1
-
-    text_encoder_key_prefix = ["text_encoders."]
-    vae_key_prefix = ["pretransform.model."]
-
-    def get_model(self, state_dict, prefix="", device=None):
-        seconds_start_sd = utils.state_dict_prefix_replace(state_dict, {"conditioner.conditioners.seconds_start.": ""}, filter_keys=True)
-        seconds_total_sd = utils.state_dict_prefix_replace(state_dict, {"conditioner.conditioners.seconds_total.": ""}, filter_keys=True)
-        return model_base.StableAudio1(self, seconds_start_embedder_weights=seconds_start_sd, seconds_total_embedder_weights=seconds_total_sd, device=device)
-
-
-    def process_unet_state_dict(self, state_dict):
-        for k in list(state_dict.keys()):
-            if k.endswith(".cross_attend_norm.beta") or k.endswith(".ff_norm.beta") or k.endswith(".pre_norm.beta"): #These weights are all zero
-                state_dict.pop(k)
-        return state_dict
-
-    def clip_target(self, state_dict={}):
-        return supported_models_base.ClipTarget(sa_t5.SAT5Tokenizer, sa_t5.SAT5Model)
-
-
-models = [Stable_Zero123, SD15_instructpix2pix, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXL_instructpix2pix, SDXLRefiner, SDXL, SSD1B, KOALA_700M, KOALA_1B, Segmind_Vega, SD_X4Upscaler, Stable_Cascade_C, Stable_Cascade_B, SV3D_u, SV3D_p, SD3, StableAudio]
-
-models += [SVD_img2vid]
diff --git a/MagicQuill/comfy/supported_models_base.py b/MagicQuill/comfy/supported_models_base.py
deleted file mode 100644
index cf7cdff34bff803e6dfa750e84f03d66e06634af..0000000000000000000000000000000000000000
--- a/MagicQuill/comfy/supported_models_base.py
+++ /dev/null
@@ -1,95 +0,0 @@
-import torch
-from . import model_base
-from . import utils
-from . import latent_formats
-
-class ClipTarget:
-    def __init__(self, tokenizer, clip):
-        self.clip = clip
-        self.tokenizer = tokenizer
-        self.params = {}
-
-class BASE:
-    unet_config = {}
-    unet_extra_config = {
-        "num_heads": -1,
-        "num_head_channels": 64,
-    }
-
-    required_keys = {}
-
-    clip_prefix = []
-    clip_vision_prefix = None
-    noise_aug_config = None
-    sampling_settings = {}
-    latent_format = latent_formats.LatentFormat
-    vae_key_prefix = ["first_stage_model."]
-    text_encoder_key_prefix = ["cond_stage_model."]
-    supported_inference_dtypes = [torch.float16, torch.bfloat16, torch.float32]
-
-    manual_cast_dtype = None
-
-    @classmethod
-    def matches(s, unet_config, state_dict=None):
-        for k in s.unet_config:
-            if k not in unet_config or s.unet_config[k] != unet_config[k]:
-                return False
-        if state_dict is not None:
-            for k in s.required_keys:
-                if k not in state_dict:
-                    return False
-        return True
-
-    def model_type(self, state_dict, prefix=""):
-        return model_base.ModelType.EPS
-
-    def inpaint_model(self):
-        return self.unet_config["in_channels"] > 4
-
-    def __init__(self, unet_config):
-        self.unet_config = unet_config.copy()
-        self.sampling_settings = self.sampling_settings.copy()
-        self.latent_format = self.latent_format()
-        for x in self.unet_extra_config:
-            self.unet_config[x] = self.unet_extra_config[x]
-
-    def get_model(self, state_dict, prefix="", device=None):
-        if self.noise_aug_config is not None:
-            out = model_base.SD21UNCLIP(self, self.noise_aug_config, model_type=self.model_type(state_dict, prefix), device=device)
-        else:
-            out = model_base.BaseModel(self, model_type=self.model_type(state_dict, prefix), device=device)
-        if self.inpaint_model():
-            out.set_inpaint()
-        return out
-
-    def process_clip_state_dict(self, state_dict):
-        state_dict = utils.state_dict_prefix_replace(state_dict, {k: "" for k in self.text_encoder_key_prefix}, filter_keys=True)
-        return state_dict
-
-    def process_unet_state_dict(self, state_dict):
-        return state_dict
-
-    def process_vae_state_dict(self, state_dict):
-        return state_dict
-
-    def process_clip_state_dict_for_saving(self, state_dict):
-        replace_prefix = {"": self.text_encoder_key_prefix[0]}
-        return utils.state_dict_prefix_replace(state_dict, replace_prefix)
-
-    def process_clip_vision_state_dict_for_saving(self, state_dict):
-        replace_prefix = {}
-        if self.clip_vision_prefix is not None:
-            replace_prefix[""] = self.clip_vision_prefix
-        return utils.state_dict_prefix_replace(state_dict, replace_prefix)
-
-    def process_unet_state_dict_for_saving(self, state_dict):
-        replace_prefix = {"": "model.diffusion_model."}
-        return utils.state_dict_prefix_replace(state_dict, replace_prefix)
-
-    def process_vae_state_dict_for_saving(self, state_dict):
-        replace_prefix = {"": self.vae_key_prefix[0]}
-        return utils.state_dict_prefix_replace(state_dict, replace_prefix)
-
-    def set_inference_dtype(self, dtype, manual_cast_dtype):
-        self.unet_config['dtype'] = dtype
-        self.manual_cast_dtype = manual_cast_dtype
diff --git a/MagicQuill/comfy/t2i_adapter/__pycache__/adapter.cpython-310.pyc b/MagicQuill/comfy/t2i_adapter/__pycache__/adapter.cpython-310.pyc
deleted file mode 100644
index e15c40eb9fceafadcaa877447c5b4a8a9580533c..0000000000000000000000000000000000000000
Binary files a/MagicQuill/comfy/t2i_adapter/__pycache__/adapter.cpython-310.pyc and /dev/null differ
diff --git a/MagicQuill/comfy/t2i_adapter/adapter.py b/MagicQuill/comfy/t2i_adapter/adapter.py
deleted file mode 100644
index e9a606b1cd67fd9a955a0ea0a86d1bd5498d85e5..0000000000000000000000000000000000000000
--- a/MagicQuill/comfy/t2i_adapter/adapter.py
+++ /dev/null
@@ -1,293 +0,0 @@
-#taken from https://github.com/TencentARC/T2I-Adapter
-import torch
-import torch.nn as nn
-from collections import OrderedDict
-
-
-def conv_nd(dims, *args, **kwargs):
-    """
-    Create a 1D, 2D, or 3D convolution module.
-    """
-    if dims == 1:
-        return nn.Conv1d(*args, **kwargs)
-    elif dims == 2:
-        return nn.Conv2d(*args, **kwargs)
-    elif dims == 3:
-        return nn.Conv3d(*args, **kwargs)
-    raise ValueError(f"unsupported dimensions: {dims}")
-
-
-def avg_pool_nd(dims, *args, **kwargs):
-    """
-    Create a 1D, 2D, or 3D average pooling module.
-    """
-    if dims == 1:
-        return nn.AvgPool1d(*args, **kwargs)
-    elif dims == 2:
-        return nn.AvgPool2d(*args, **kwargs)
-    elif dims == 3:
-        return nn.AvgPool3d(*args, **kwargs)
-    raise ValueError(f"unsupported dimensions: {dims}")
-
-
-class Downsample(nn.Module):
-    """
-    A downsampling layer with an optional convolution.
-    :param channels: channels in the inputs and outputs.
-    :param use_conv: a bool determining if a convolution is applied.
-    :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
-                 downsampling occurs in the inner-two dimensions.
-    """
-
-    def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1):
-        super().__init__()
-        self.channels = channels
-        self.out_channels = out_channels or channels
-        self.use_conv = use_conv
-        self.dims = dims
-        stride = 2 if dims != 3 else (1, 2, 2)
-        if use_conv:
-            self.op = conv_nd(
-                dims, self.channels, self.out_channels, 3, stride=stride, padding=padding
-            )
-        else:
-            assert self.channels == self.out_channels
-            self.op = avg_pool_nd(dims, kernel_size=stride, stride=stride)
-
-    def forward(self, x):
-        assert x.shape[1] == self.channels
-        if not self.use_conv:
-            padding = [x.shape[2] % 2, x.shape[3] % 2]
-            self.op.padding = padding
-
-        x = self.op(x)
-        return x
-
-
-class ResnetBlock(nn.Module):
-    def __init__(self, in_c, out_c, down, ksize=3, sk=False, use_conv=True):
-        super().__init__()
-        ps = ksize // 2
-        if in_c != out_c or sk == False:
-            self.in_conv = nn.Conv2d(in_c, out_c, ksize, 1, ps)
-        else:
-            # print('n_in')
-            self.in_conv = None
-        self.block1 = nn.Conv2d(out_c, out_c, 3, 1, 1)
-        self.act = nn.ReLU()
-        self.block2 = nn.Conv2d(out_c, out_c, ksize, 1, ps)
-        if sk == False:
-            self.skep = nn.Conv2d(in_c, out_c, ksize, 1, ps)
-        else:
-            self.skep = None
-
-        self.down = down
-        if self.down == True:
-            self.down_opt = Downsample(in_c, use_conv=use_conv)
-
-    def forward(self, x):
-        if self.down == True:
-            x = self.down_opt(x)
-        if self.in_conv is not None:  # edit
-            x = self.in_conv(x)
-
-        h = self.block1(x)
-        h = self.act(h)
-        h = self.block2(h)
-        if self.skep is not None:
-            return h + self.skep(x)
-        else:
-            return h + x
-
-
-class Adapter(nn.Module):
-    def __init__(self, channels=[320, 640, 1280, 1280], nums_rb=3, cin=64, ksize=3, sk=False, use_conv=True, xl=True):
-        super(Adapter, self).__init__()
-        self.unshuffle_amount = 8
-        resblock_no_downsample = []
-        resblock_downsample = [3, 2, 1]
-        self.xl = xl
-        if self.xl:
-            self.unshuffle_amount = 16
-            resblock_no_downsample = [1]
-            resblock_downsample = [2]
-
-        self.input_channels = cin // (self.unshuffle_amount * self.unshuffle_amount)
-        self.unshuffle = nn.PixelUnshuffle(self.unshuffle_amount)
-        self.channels = channels
-        self.nums_rb = nums_rb
-        self.body = []
-        for i in range(len(channels)):
-            for j in range(nums_rb):
-                if (i in resblock_downsample) and (j == 0):
-                    self.body.append(
-                        ResnetBlock(channels[i - 1], channels[i], down=True, ksize=ksize, sk=sk, use_conv=use_conv))
-                elif (i in resblock_no_downsample) and (j == 0):
-                    self.body.append(
-                        ResnetBlock(channels[i - 1], channels[i], down=False, ksize=ksize, sk=sk, use_conv=use_conv))
-                else:
-                    self.body.append(
-                        ResnetBlock(channels[i], channels[i], down=False, ksize=ksize, sk=sk, use_conv=use_conv))
-        self.body = nn.ModuleList(self.body)
-        self.conv_in = nn.Conv2d(cin, channels[0], 3, 1, 1)
-
-    def forward(self, x):
-        # unshuffle
-        x = self.unshuffle(x)
-        # extract features
-        features = []
-        x = self.conv_in(x)
-        for i in range(len(self.channels)):
-            for j in range(self.nums_rb):
-                idx = i * self.nums_rb + j
-                x = self.body[idx](x)
-            if self.xl:
-                features.append(None)
-                if i == 0:
-                    features.append(None)
-                    features.append(None)
-                if i == 2:
-                    features.append(None)
-            else:
-                features.append(None)
-                features.append(None)
-            features.append(x)
-
-        return features
-
-
-class LayerNorm(nn.LayerNorm):
-    """Subclass torch's LayerNorm to handle fp16."""
-
-    def forward(self, x: torch.Tensor):
-        orig_type = x.dtype
-        ret = super().forward(x.type(torch.float32))
-        return ret.type(orig_type)
-
-
-class QuickGELU(nn.Module):
-
-    def forward(self, x: torch.Tensor):
-        return x * torch.sigmoid(1.702 * x)
-
-
-class ResidualAttentionBlock(nn.Module):
-
-    def __init__(self, d_model: int, n_head: int, attn_mask: torch.Tensor = None):
-        super().__init__()
-
-        self.attn = nn.MultiheadAttention(d_model, n_head)
-        self.ln_1 = LayerNorm(d_model)
-        self.mlp = nn.Sequential(
-            OrderedDict([("c_fc", nn.Linear(d_model, d_model * 4)), ("gelu", QuickGELU()),
-                         ("c_proj", nn.Linear(d_model * 4, d_model))]))
-        self.ln_2 = LayerNorm(d_model)
-        self.attn_mask = attn_mask
-
-    def attention(self, x: torch.Tensor):
-        self.attn_mask = self.attn_mask.to(dtype=x.dtype, device=x.device) if self.attn_mask is not None else None
-        return self.attn(x, x, x, need_weights=False, attn_mask=self.attn_mask)[0]
-
-    def forward(self, x: torch.Tensor):
-        x = x + self.attention(self.ln_1(x))
-        x = x + self.mlp(self.ln_2(x))
-        return x
-
-
-class StyleAdapter(nn.Module):
-
-    def __init__(self, width=1024, context_dim=768, num_head=8, n_layes=3, num_token=4):
-        super().__init__()
-
-        scale = width ** -0.5
-        self.transformer_layes = nn.Sequential(*[ResidualAttentionBlock(width, num_head) for _ in range(n_layes)])
-        self.num_token = num_token
-        self.style_embedding = nn.Parameter(torch.randn(1, num_token, width) * scale)
-        self.ln_post = LayerNorm(width)
-        self.ln_pre = LayerNorm(width)
-        self.proj = nn.Parameter(scale * torch.randn(width, context_dim))
-
-    def forward(self, x):
-        # x shape [N, HW+1, C]
-        style_embedding = self.style_embedding + torch.zeros(
-            (x.shape[0], self.num_token, self.style_embedding.shape[-1]), device=x.device)
-        x = torch.cat([x, style_embedding], dim=1)
-        x = self.ln_pre(x)
-        x = x.permute(1, 0, 2)  # NLD -> LND
-        x = self.transformer_layes(x)
-        x = x.permute(1, 0, 2)  # LND -> NLD
-
-        x = self.ln_post(x[:, -self.num_token:, :])
-        x = x @ self.proj
-
-        return x
-
-
-class ResnetBlock_light(nn.Module):
-    def __init__(self, in_c):
-        super().__init__()
-        self.block1 = nn.Conv2d(in_c, in_c, 3, 1, 1)
-        self.act = nn.ReLU()
-        self.block2 = nn.Conv2d(in_c, in_c, 3, 1, 1)
-
-    def forward(self, x):
-        h = self.block1(x)
-        h = self.act(h)
-        h = self.block2(h)
-
-        return h + x
-
-
-class extractor(nn.Module):
-    def __init__(self, in_c, inter_c, out_c, nums_rb, down=False):
-        super().__init__()
-        self.in_conv = nn.Conv2d(in_c, inter_c, 1, 1, 0)
-        self.body = []
-        for _ in range(nums_rb):
-            self.body.append(ResnetBlock_light(inter_c))
-        self.body = nn.Sequential(*self.body)
-        self.out_conv = nn.Conv2d(inter_c, out_c, 1, 1, 0)
-        self.down = down
-        if self.down == True:
-            self.down_opt = Downsample(in_c, use_conv=False)
-
-    def forward(self, x):
-        if self.down == True:
-            x = self.down_opt(x)
-        x = self.in_conv(x)
-        x = self.body(x)
-        x = self.out_conv(x)
-
-        return x
-
-
-class Adapter_light(nn.Module):
-    def __init__(self, channels=[320, 640, 1280, 1280], nums_rb=3, cin=64):
-        super(Adapter_light, self).__init__()
-        self.unshuffle_amount = 8
-        self.unshuffle = nn.PixelUnshuffle(self.unshuffle_amount)
-        self.input_channels = cin // (self.unshuffle_amount * self.unshuffle_amount)
-        self.channels = channels
-        self.nums_rb = nums_rb
-        self.body = []
-        self.xl = False
-
-        for i in range(len(channels)):
-            if i == 0:
-                self.body.append(extractor(in_c=cin, inter_c=channels[i]//4, out_c=channels[i], nums_rb=nums_rb, down=False))
-            else:
-                self.body.append(extractor(in_c=channels[i-1], inter_c=channels[i]//4, out_c=channels[i], nums_rb=nums_rb, down=True))
-        self.body = nn.ModuleList(self.body)
-
-    def forward(self, x):
-        # unshuffle
-        x = self.unshuffle(x)
-        # extract features
-        features = []
-        for i in range(len(self.channels)):
-            x = self.body[i](x)
-            features.append(None)
-            features.append(None)
-            features.append(x)
-
-        return features
diff --git a/MagicQuill/comfy/t5.py b/MagicQuill/comfy/t5.py
deleted file mode 100644
index 06dfe47668e6326dfbc761bbc4600fd2db0a66de..0000000000000000000000000000000000000000
--- a/MagicQuill/comfy/t5.py
+++ /dev/null
@@ -1,231 +0,0 @@
-import torch
-import math
-from comfy.ldm.modules.attention import optimized_attention_for_device
-
-class T5LayerNorm(torch.nn.Module):
-    def __init__(self, hidden_size, eps=1e-6, dtype=None, device=None, operations=None):
-        super().__init__()
-        self.weight = torch.nn.Parameter(torch.empty(hidden_size, dtype=dtype, device=device))
-        self.variance_epsilon = eps
-
-    def forward(self, x):
-        variance = x.pow(2).mean(-1, keepdim=True)
-        x = x * torch.rsqrt(variance + self.variance_epsilon)
-        return self.weight.to(device=x.device, dtype=x.dtype) * x
-
-class T5DenseActDense(torch.nn.Module):
-    def __init__(self, model_dim, ff_dim, dtype, device, operations):
-        super().__init__()
-        self.wi = operations.Linear(model_dim, ff_dim, bias=False, dtype=dtype, device=device)
-        self.wo = operations.Linear(ff_dim, model_dim, bias=False, dtype=dtype, device=device)
-        # self.dropout = nn.Dropout(config.dropout_rate)
-
-    def forward(self, x):
-        x = torch.nn.functional.relu(self.wi(x))
-        # x = self.dropout(x)
-        x = self.wo(x)
-        return x
-
-class T5DenseGatedActDense(torch.nn.Module):
-    def __init__(self, model_dim, ff_dim, dtype, device, operations):
-        super().__init__()
-        self.wi_0 = operations.Linear(model_dim, ff_dim, bias=False, dtype=dtype, device=device)
-        self.wi_1 = operations.Linear(model_dim, ff_dim, bias=False, dtype=dtype, device=device)
-        self.wo = operations.Linear(ff_dim, model_dim, bias=False, dtype=dtype, device=device)
-        # self.dropout = nn.Dropout(config.dropout_rate)
-
-    def forward(self, x):
-        hidden_gelu = torch.nn.functional.gelu(self.wi_0(x), approximate="tanh")
-        hidden_linear = self.wi_1(x)
-        x = hidden_gelu * hidden_linear
-        # x = self.dropout(x)
-        x = self.wo(x)
-        return x
-
-class T5LayerFF(torch.nn.Module):
-    def __init__(self, model_dim, ff_dim, ff_activation, dtype, device, operations):
-        super().__init__()
-        if ff_activation == "gelu_pytorch_tanh":
-            self.DenseReluDense = T5DenseGatedActDense(model_dim, ff_dim, dtype, device, operations)
-        elif ff_activation == "relu":
-            self.DenseReluDense = T5DenseActDense(model_dim, ff_dim, dtype, device, operations)
-
-        self.layer_norm = T5LayerNorm(model_dim, dtype=dtype, device=device, operations=operations)
-        # self.dropout = nn.Dropout(config.dropout_rate)
-
-    def forward(self, x):
-        forwarded_states = self.layer_norm(x)
-        forwarded_states = self.DenseReluDense(forwarded_states)
-        # x = x + self.dropout(forwarded_states)
-        x += forwarded_states
-        return x
-
-class T5Attention(torch.nn.Module):
-    def __init__(self, model_dim, inner_dim, num_heads, relative_attention_bias, dtype, device, operations):
-        super().__init__()
-
-        # Mesh TensorFlow initialization to avoid scaling before softmax
-        self.q = operations.Linear(model_dim, inner_dim, bias=False, dtype=dtype, device=device)
-        self.k = operations.Linear(model_dim, inner_dim, bias=False, dtype=dtype, device=device)
-        self.v = operations.Linear(model_dim, inner_dim, bias=False, dtype=dtype, device=device)
-        self.o = operations.Linear(inner_dim, model_dim, bias=False, dtype=dtype, device=device)
-        self.num_heads = num_heads
-
-        self.relative_attention_bias = None
-        if relative_attention_bias:
-            self.relative_attention_num_buckets = 32
-            self.relative_attention_max_distance = 128
-            self.relative_attention_bias = torch.nn.Embedding(self.relative_attention_num_buckets, self.num_heads, device=device)
-
-    @staticmethod
-    def _relative_position_bucket(relative_position, bidirectional=True, num_buckets=32, max_distance=128):
-        """
-        Adapted from Mesh Tensorflow:
-        https://github.com/tensorflow/mesh/blob/0cb87fe07da627bf0b7e60475d59f95ed6b5be3d/mesh_tensorflow/transformer/transformer_layers.py#L593
-
-        Translate relative position to a bucket number for relative attention. The relative position is defined as
-        memory_position - query_position, i.e. the distance in tokens from the attending position to the attended-to
-        position. If bidirectional=False, then positive relative positions are invalid. We use smaller buckets for
-        small absolute relative_position and larger buckets for larger absolute relative_positions. All relative
-        positions >=max_distance map to the same bucket. All relative positions <=-max_distance map to the same bucket.
-        This should allow for more graceful generalization to longer sequences than the model has been trained on
-
-        Args:
-            relative_position: an int32 Tensor
-            bidirectional: a boolean - whether the attention is bidirectional
-            num_buckets: an integer
-            max_distance: an integer
-
-        Returns:
-            a Tensor with the same shape as relative_position, containing int32 values in the range [0, num_buckets)
-        """
-        relative_buckets = 0
-        if bidirectional:
-            num_buckets //= 2
-            relative_buckets += (relative_position > 0).to(torch.long) * num_buckets
-            relative_position = torch.abs(relative_position)
-        else:
-            relative_position = -torch.min(relative_position, torch.zeros_like(relative_position))
-        # now relative_position is in the range [0, inf)
-
-        # half of the buckets are for exact increments in positions
-        max_exact = num_buckets // 2
-        is_small = relative_position < max_exact
-
-        # The other half of the buckets are for logarithmically bigger bins in positions up to max_distance
-        relative_position_if_large = max_exact + (
-            torch.log(relative_position.float() / max_exact)
-            / math.log(max_distance / max_exact)
-            * (num_buckets - max_exact)
-        ).to(torch.long)
-        relative_position_if_large = torch.min(
-            relative_position_if_large, torch.full_like(relative_position_if_large, num_buckets - 1)
-        )
-
-        relative_buckets += torch.where(is_small, relative_position, relative_position_if_large)
-        return relative_buckets
-
-    def compute_bias(self, query_length, key_length, device):
-        """Compute binned relative position bias"""
-        context_position = torch.arange(query_length, dtype=torch.long, device=device)[:, None]
-        memory_position = torch.arange(key_length, dtype=torch.long, device=device)[None, :]
-        relative_position = memory_position - context_position  # shape (query_length, key_length)
-        relative_position_bucket = self._relative_position_bucket(
-            relative_position,  # shape (query_length, key_length)
-            bidirectional=True,
-            num_buckets=self.relative_attention_num_buckets,
-            max_distance=self.relative_attention_max_distance,
-        )
-        values = self.relative_attention_bias(relative_position_bucket)  # shape (query_length, key_length, num_heads)
-        values = values.permute([2, 0, 1]).unsqueeze(0)  # shape (1, num_heads, query_length, key_length)
-        return values
-
-    def forward(self, x, mask=None, past_bias=None, optimized_attention=None):
-        q = self.q(x)
-        k = self.k(x)
-        v = self.v(x)
-        if self.relative_attention_bias is not None:
-            past_bias = self.compute_bias(x.shape[1], x.shape[1], x.device)
-
-        if past_bias is not None:
-            if mask is not None:
-                mask = mask + past_bias
-            else:
-                mask = past_bias
-
-        out = optimized_attention(q, k * ((k.shape[-1] / self.num_heads) ** 0.5), v, self.num_heads, mask)
-        return self.o(out), past_bias
-
-class T5LayerSelfAttention(torch.nn.Module):
-    def __init__(self, model_dim, inner_dim, ff_dim, num_heads, relative_attention_bias, dtype, device, operations):
-        super().__init__()
-        self.SelfAttention = T5Attention(model_dim, inner_dim, num_heads, relative_attention_bias, dtype, device, operations)
-        self.layer_norm = T5LayerNorm(model_dim, dtype=dtype, device=device, operations=operations)
-        # self.dropout = nn.Dropout(config.dropout_rate)
-
-    def forward(self, x, mask=None, past_bias=None, optimized_attention=None):
-        normed_hidden_states = self.layer_norm(x)
-        output, past_bias = self.SelfAttention(self.layer_norm(x), mask=mask, past_bias=past_bias, optimized_attention=optimized_attention)
-        # x = x + self.dropout(attention_output)
-        x += output
-        return x, past_bias
-
-class T5Block(torch.nn.Module):
-    def __init__(self, model_dim, inner_dim, ff_dim, ff_activation, num_heads, relative_attention_bias, dtype, device, operations):
-        super().__init__()
-        self.layer = torch.nn.ModuleList()
-        self.layer.append(T5LayerSelfAttention(model_dim, inner_dim, ff_dim, num_heads, relative_attention_bias, dtype, device, operations))
-        self.layer.append(T5LayerFF(model_dim, ff_dim, ff_activation, dtype, device, operations))
-
-    def forward(self, x, mask=None, past_bias=None, optimized_attention=None):
-        x, past_bias = self.layer[0](x, mask, past_bias, optimized_attention)
-        x = self.layer[-1](x)
-        return x, past_bias
-
-class T5Stack(torch.nn.Module):
-    def __init__(self, num_layers, model_dim, inner_dim, ff_dim, ff_activation, num_heads, dtype, device, operations):
-        super().__init__()
-
-        self.block = torch.nn.ModuleList(
-            [T5Block(model_dim, inner_dim, ff_dim, ff_activation, num_heads, relative_attention_bias=(i == 0), dtype=dtype, device=device, operations=operations) for i in range(num_layers)]
-        )
-        self.final_layer_norm = T5LayerNorm(model_dim, dtype=dtype, device=device, operations=operations)
-        # self.dropout = nn.Dropout(config.dropout_rate)
-
-    def forward(self, x, attention_mask=None, intermediate_output=None, final_layer_norm_intermediate=True):
-        mask = None
-        if attention_mask is not None:
-            mask = 1.0 - attention_mask.to(x.dtype).reshape((attention_mask.shape[0], 1, -1, attention_mask.shape[-1])).expand(attention_mask.shape[0], 1, attention_mask.shape[-1], attention_mask.shape[-1])
-            mask = mask.masked_fill(mask.to(torch.bool), float("-inf"))
-
-        intermediate = None
-        optimized_attention = optimized_attention_for_device(x.device, mask=attention_mask is not None, small_input=True)
-        past_bias = None
-        for i, l in enumerate(self.block):
-            x, past_bias = l(x, mask, past_bias, optimized_attention)
-            if i == intermediate_output:
-                intermediate = x.clone()
-        x = self.final_layer_norm(x)
-        if intermediate is not None and final_layer_norm_intermediate:
-            intermediate = self.final_layer_norm(intermediate)
-        return x, intermediate
-
-class T5(torch.nn.Module):
-    def __init__(self, config_dict, dtype, device, operations):
-        super().__init__()
-        self.num_layers = config_dict["num_layers"]
-        model_dim = config_dict["d_model"]
-
-        self.encoder = T5Stack(self.num_layers, model_dim, model_dim, config_dict["d_ff"], config_dict["dense_act_fn"], config_dict["num_heads"], dtype, device, operations)
-        self.dtype = dtype
-        self.shared = torch.nn.Embedding(config_dict["vocab_size"], model_dim, device=device)
-
-    def get_input_embeddings(self):
-        return self.shared
-
-    def set_input_embeddings(self, embeddings):
-        self.shared = embeddings
-
-    def forward(self, input_ids, *args, **kwargs):
-        x = self.shared(input_ids)
-        return self.encoder(x, *args, **kwargs)
diff --git a/MagicQuill/comfy/t5_config_base.json b/MagicQuill/comfy/t5_config_base.json
deleted file mode 100644
index facd85ef3a9c695d564e40b8c1a7db994e392cd3..0000000000000000000000000000000000000000
--- a/MagicQuill/comfy/t5_config_base.json
+++ /dev/null
@@ -1,21 +0,0 @@
-{
-  "d_ff": 3072,
-  "d_kv": 64,
-  "d_model": 768,
-  "decoder_start_token_id": 0,
-  "dropout_rate": 0.1,
-  "eos_token_id": 1,
-  "dense_act_fn": "relu",
-  "initializer_factor": 1.0,
-  "is_encoder_decoder": true,
-  "layer_norm_epsilon": 1e-06,
-  "model_type": "t5",
-  "num_decoder_layers": 12,
-  "num_heads": 12,
-  "num_layers": 12,
-  "output_past": true,
-  "pad_token_id": 0,
-  "relative_attention_num_buckets": 32,
-  "tie_word_embeddings": false,
-  "vocab_size": 32128
-}
diff --git a/MagicQuill/comfy/t5_config_xxl.json b/MagicQuill/comfy/t5_config_xxl.json
deleted file mode 100644
index bf4feadcf501776e65deeda04789738f08e450f9..0000000000000000000000000000000000000000
--- a/MagicQuill/comfy/t5_config_xxl.json
+++ /dev/null
@@ -1,21 +0,0 @@
-{
-  "d_ff": 10240,
-  "d_kv": 64,
-  "d_model": 4096,
-  "decoder_start_token_id": 0,
-  "dropout_rate": 0.1,
-  "eos_token_id": 1,
-  "dense_act_fn": "gelu_pytorch_tanh",
-  "initializer_factor": 1.0,
-  "is_encoder_decoder": true,
-  "layer_norm_epsilon": 1e-06,
-  "model_type": "t5",
-  "num_decoder_layers": 24,
-  "num_heads": 64,
-  "num_layers": 24,
-  "output_past": true,
-  "pad_token_id": 0,
-  "relative_attention_num_buckets": 32,
-  "tie_word_embeddings": false,
-  "vocab_size": 32128
-}
diff --git a/MagicQuill/comfy/t5_tokenizer/special_tokens_map.json b/MagicQuill/comfy/t5_tokenizer/special_tokens_map.json
deleted file mode 100644
index 17ade346a1042cbe0c1436f5bedcbd85c099d582..0000000000000000000000000000000000000000
--- a/MagicQuill/comfy/t5_tokenizer/special_tokens_map.json
+++ /dev/null
@@ -1,125 +0,0 @@
-{
-  "additional_special_tokens": [
-    "<extra_id_0>",
-    "<extra_id_1>",
-    "<extra_id_2>",
-    "<extra_id_3>",
-    "<extra_id_4>",
-    "<extra_id_5>",
-    "<extra_id_6>",
-    "<extra_id_7>",
-    "<extra_id_8>",
-    "<extra_id_9>",
-    "<extra_id_10>",
-    "<extra_id_11>",
-    "<extra_id_12>",
-    "<extra_id_13>",
-    "<extra_id_14>",
-    "<extra_id_15>",
-    "<extra_id_16>",
-    "<extra_id_17>",
-    "<extra_id_18>",
-    "<extra_id_19>",
-    "<extra_id_20>",
-    "<extra_id_21>",
-    "<extra_id_22>",
-    "<extra_id_23>",
-    "<extra_id_24>",
-    "<extra_id_25>",
-    "<extra_id_26>",
-    "<extra_id_27>",
-    "<extra_id_28>",
-    "<extra_id_29>",
-    "<extra_id_30>",
-    "<extra_id_31>",
-    "<extra_id_32>",
-    "<extra_id_33>",
-    "<extra_id_34>",
-    "<extra_id_35>",
-    "<extra_id_36>",
-    "<extra_id_37>",
-    "<extra_id_38>",
-    "<extra_id_39>",
-    "<extra_id_40>",
-    "<extra_id_41>",
-    "<extra_id_42>",
-    "<extra_id_43>",
-    "<extra_id_44>",
-    "<extra_id_45>",
-    "<extra_id_46>",
-    "<extra_id_47>",
-    "<extra_id_48>",
-    "<extra_id_49>",
-    "<extra_id_50>",
-    "<extra_id_51>",
-    "<extra_id_52>",
-    "<extra_id_53>",
-    "<extra_id_54>",
-    "<extra_id_55>",
-    "<extra_id_56>",
-    "<extra_id_57>",
-    "<extra_id_58>",
-    "<extra_id_59>",
-    "<extra_id_60>",
-    "<extra_id_61>",
-    "<extra_id_62>",
-    "<extra_id_63>",
-    "<extra_id_64>",
-    "<extra_id_65>",
-    "<extra_id_66>",
-    "<extra_id_67>",
-    "<extra_id_68>",
-    "<extra_id_69>",
-    "<extra_id_70>",
-    "<extra_id_71>",
-    "<extra_id_72>",
-    "<extra_id_73>",
-    "<extra_id_74>",
-    "<extra_id_75>",
-    "<extra_id_76>",
-    "<extra_id_77>",
-    "<extra_id_78>",
-    "<extra_id_79>",
-    "<extra_id_80>",
-    "<extra_id_81>",
-    "<extra_id_82>",
-    "<extra_id_83>",
-    "<extra_id_84>",
-    "<extra_id_85>",
-    "<extra_id_86>",
-    "<extra_id_87>",
-    "<extra_id_88>",
-    "<extra_id_89>",
-    "<extra_id_90>",
-    "<extra_id_91>",
-    "<extra_id_92>",
-    "<extra_id_93>",
-    "<extra_id_94>",
-    "<extra_id_95>",
-    "<extra_id_96>",
-    "<extra_id_97>",
-    "<extra_id_98>",
-    "<extra_id_99>"
-  ],
-  "eos_token": {
-    "content": "</s>",
-    "lstrip": false,
-    "normalized": false,
-    "rstrip": false,
-    "single_word": false
-  },
-  "pad_token": {
-    "content": "<pad>",
-    "lstrip": false,
-    "normalized": false,
-    "rstrip": false,
-    "single_word": false
-  },
-  "unk_token": {
-    "content": "<unk>",
-    "lstrip": false,
-    "normalized": false,
-    "rstrip": false,
-    "single_word": false
-  }
-}
diff --git a/MagicQuill/comfy/t5_tokenizer/tokenizer.json b/MagicQuill/comfy/t5_tokenizer/tokenizer.json
deleted file mode 100644
index b11c92d7184d265f0dc857ec5d676aa81aa16262..0000000000000000000000000000000000000000
--- a/MagicQuill/comfy/t5_tokenizer/tokenizer.json
+++ /dev/null
@@ -1,129428 +0,0 @@
-{
-  "version": "1.0",
-  "truncation": null,
-  "padding": null,
-  "added_tokens": [
-    {
-      "id": 0,
-      "content": "<pad>",
-      "single_word": false,
-      "lstrip": false,
-      "rstrip": false,
-      "normalized": false,
-      "special": true
-    },
-    {
-      "id": 1,
-      "content": "</s>",
-      "single_word": false,
-      "lstrip": false,
-      "rstrip": false,
-      "normalized": false,
-      "special": true
-    },
-    {
-      "id": 2,
-      "content": "<unk>",
-      "single_word": false,
-      "lstrip": false,
-      "rstrip": false,
-      "normalized": false,
-      "special": true
-    },
-    {
-      "id": 32000,
-      "content": "<extra_id_99>",
-      "single_word": false,
-      "lstrip": false,
-      "rstrip": false,
-      "normalized": false,
-      "special": true
-    },
-    {
-      "id": 32001,
-      "content": "<extra_id_98>",
-      "single_word": false,
-      "lstrip": false,
-      "rstrip": false,
-      "normalized": false,
-      "special": true
-    },
-    {
-      "id": 32002,
-      "content": "<extra_id_97>",
-      "single_word": false,
-      "lstrip": false,
-      "rstrip": false,
-      "normalized": false,
-      "special": true
-    },
-    {
-      "id": 32003,
-      "content": "<extra_id_96>",
-      "single_word": false,
-      "lstrip": false,
-      "rstrip": false,
-      "normalized": false,
-      "special": true
-    },
-    {
-      "id": 32004,
-      "content": "<extra_id_95>",
-      "single_word": false,
-      "lstrip": false,
-      "rstrip": false,
-      "normalized": false,
-      "special": true
-    },
-    {
-      "id": 32005,
-      "content": "<extra_id_94>",
-      "single_word": false,
-      "lstrip": false,
-      "rstrip": false,
-      "normalized": false,
-      "special": true
-    },
-    {
-      "id": 32006,
-      "content": "<extra_id_93>",
-      "single_word": false,
-      "lstrip": false,
-      "rstrip": false,
-      "normalized": false,
-      "special": true
-    },
-    {
-      "id": 32007,
-      "content": "<extra_id_92>",
-      "single_word": false,
-      "lstrip": false,
-      "rstrip": false,
-      "normalized": false,
-      "special": true
-    },
-    {
-      "id": 32008,
-      "content": "<extra_id_91>",
-      "single_word": false,
-      "lstrip": false,
-      "rstrip": false,
-      "normalized": false,
-      "special": true
-    },
-    {
-      "id": 32009,
-      "content": "<extra_id_90>",
-      "single_word": false,
-      "lstrip": false,
-      "rstrip": false,
-      "normalized": false,
-      "special": true
-    },
-    {
-      "id": 32010,
-      "content": "<extra_id_89>",
-      "single_word": false,
-      "lstrip": false,
-      "rstrip": false,
-      "normalized": false,
-      "special": true
-    },
-    {
-      "id": 32011,
-      "content": "<extra_id_88>",
-      "single_word": false,
-      "lstrip": false,
-      "rstrip": false,
-      "normalized": false,
-      "special": true
-    },
-    {
-      "id": 32012,
-      "content": "<extra_id_87>",
-      "single_word": false,
-      "lstrip": false,
-      "rstrip": false,
-      "normalized": false,
-      "special": true
-    },
-    {
-      "id": 32013,
-      "content": "<extra_id_86>",
-      "single_word": false,
-      "lstrip": false,
-      "rstrip": false,
-      "normalized": false,
-      "special": true
-    },
-    {
-      "id": 32014,
-      "content": "<extra_id_85>",
-      "single_word": false,
-      "lstrip": false,
-      "rstrip": false,
-      "normalized": false,
-      "special": true
-    },
-    {
-      "id": 32015,
-      "content": "<extra_id_84>",
-      "single_word": false,
-      "lstrip": false,
-      "rstrip": false,
-      "normalized": false,
-      "special": true
-    },
-    {
-      "id": 32016,
-      "content": "<extra_id_83>",
-      "single_word": false,
-      "lstrip": false,
-      "rstrip": false,
-      "normalized": false,
-      "special": true
-    },
-    {
-      "id": 32017,
-      "content": "<extra_id_82>",
-      "single_word": false,
-      "lstrip": false,
-      "rstrip": false,
-      "normalized": false,
-      "special": true
-    },
-    {
-      "id": 32018,
-      "content": "<extra_id_81>",
-      "single_word": false,
-      "lstrip": false,
-      "rstrip": false,
-      "normalized": false,
-      "special": true
-    },
-    {
-      "id": 32019,
-      "content": "<extra_id_80>",
-      "single_word": false,
-      "lstrip": false,
-      "rstrip": false,
-      "normalized": false,
-      "special": true
-    },
-    {
-      "id": 32020,
-      "content": "<extra_id_79>",
-      "single_word": false,
-      "lstrip": false,
-      "rstrip": false,
-      "normalized": false,
-      "special": true
-    },
-    {
-      "id": 32021,
-      "content": "<extra_id_78>",
-      "single_word": false,
-      "lstrip": false,
-      "rstrip": false,
-      "normalized": false,
-      "special": true
-    },
-    {
-      "id": 32022,
-      "content": "<extra_id_77>",
-      "single_word": false,
-      "lstrip": false,
-      "rstrip": false,
-      "normalized": false,
-      "special": true
-    },
-    {
-      "id": 32023,
-      "content": "<extra_id_76>",
-      "single_word": false,
-      "lstrip": false,
-      "rstrip": false,
-      "normalized": false,
-      "special": true
-    },
-    {
-      "id": 32024,
-      "content": "<extra_id_75>",
-      "single_word": false,
-      "lstrip": false,
-      "rstrip": false,
-      "normalized": false,
-      "special": true
-    },
-    {
-      "id": 32025,
-      "content": "<extra_id_74>",
-      "single_word": false,
-      "lstrip": false,
-      "rstrip": false,
-      "normalized": false,
-      "special": true
-    },
-    {
-      "id": 32026,
-      "content": "<extra_id_73>",
-      "single_word": false,
-      "lstrip": false,
-      "rstrip": false,
-      "normalized": false,
-      "special": true
-    },
-    {
-      "id": 32027,
-      "content": "<extra_id_72>",
-      "single_word": false,
-      "lstrip": false,
-      "rstrip": false,
-      "normalized": false,
-      "special": true
-    },
-    {
-      "id": 32028,
-      "content": "<extra_id_71>",
-      "single_word": false,
-      "lstrip": false,
-      "rstrip": false,
-      "normalized": false,
-      "special": true
-    },
-    {
-      "id": 32029,
-      "content": "<extra_id_70>",
-      "single_word": false,
-      "lstrip": false,
-      "rstrip": false,
-      "normalized": false,
-      "special": true
-    },
-    {
-      "id": 32030,
-      "content": "<extra_id_69>",
-      "single_word": false,
-      "lstrip": false,
-      "rstrip": false,
-      "normalized": false,
-      "special": true
-    },
-    {
-      "id": 32031,
-      "content": "<extra_id_68>",
-      "single_word": false,
-      "lstrip": false,
-      "rstrip": false,
-      "normalized": false,
-      "special": true
-    },
-    {
-      "id": 32032,
-      "content": "<extra_id_67>",
-      "single_word": false,
-      "lstrip": false,
-      "rstrip": false,
-      "normalized": false,
-      "special": true
-    },
-    {
-      "id": 32033,
-      "content": "<extra_id_66>",
-      "single_word": false,
-      "lstrip": false,
-      "rstrip": false,
-      "normalized": false,
-      "special": true
-    },
-    {
-      "id": 32034,
-      "content": "<extra_id_65>",
-      "single_word": false,
-      "lstrip": false,
-      "rstrip": false,
-      "normalized": false,
-      "special": true
-    },
-    {
-      "id": 32035,
-      "content": "<extra_id_64>",
-      "single_word": false,
-      "lstrip": false,
-      "rstrip": false,
-      "normalized": false,
-      "special": true
-    },
-    {
-      "id": 32036,
-      "content": "<extra_id_63>",
-      "single_word": false,
-      "lstrip": false,
-      "rstrip": false,
-      "normalized": false,
-      "special": true
-    },
-    {
-      "id": 32037,
-      "content": "<extra_id_62>",
-      "single_word": false,
-      "lstrip": false,
-      "rstrip": false,
-      "normalized": false,
-      "special": true
-    },
-    {
-      "id": 32038,
-      "content": "<extra_id_61>",
-      "single_word": false,
-      "lstrip": false,
-      "rstrip": false,
-      "normalized": false,
-      "special": true
-    },
-    {
-      "id": 32039,
-      "content": "<extra_id_60>",
-      "single_word": false,
-      "lstrip": false,
-      "rstrip": false,
-      "normalized": false,
-      "special": true
-    },
-    {
-      "id": 32040,
-      "content": "<extra_id_59>",
-      "single_word": false,
-      "lstrip": false,
-      "rstrip": false,
-      "normalized": false,
-      "special": true
-    },
-    {
-      "id": 32041,
-      "content": "<extra_id_58>",
-      "single_word": false,
-      "lstrip": false,
-      "rstrip": false,
-      "normalized": false,
-      "special": true
-    },
-    {
-      "id": 32042,
-      "content": "<extra_id_57>",
-      "single_word": false,
-      "lstrip": false,
-      "rstrip": false,
-      "normalized": false,
-      "special": true
-    },
-    {
-      "id": 32043,
-      "content": "<extra_id_56>",
-      "single_word": false,
-      "lstrip": false,
-      "rstrip": false,
-      "normalized": false,
-      "special": true
-    },
-    {
-      "id": 32044,
-      "content": "<extra_id_55>",
-      "single_word": false,
-      "lstrip": false,
-      "rstrip": false,
-      "normalized": false,
-      "special": true
-    },
-    {
-      "id": 32045,
-      "content": "<extra_id_54>",
-      "single_word": false,
-      "lstrip": false,
-      "rstrip": false,
-      "normalized": false,
-      "special": true
-    },
-    {
-      "id": 32046,
-      "content": "<extra_id_53>",
-      "single_word": false,
-      "lstrip": false,
-      "rstrip": false,
-      "normalized": false,
-      "special": true
-    },
-    {
-      "id": 32047,
-      "content": "<extra_id_52>",
-      "single_word": false,
-      "lstrip": false,
-      "rstrip": false,
-      "normalized": false,
-      "special": true
-    },
-    {
-      "id": 32048,
-      "content": "<extra_id_51>",
-      "single_word": false,
-      "lstrip": false,
-      "rstrip": false,
-      "normalized": false,
-      "special": true
-    },
-    {
-      "id": 32049,
-      "content": "<extra_id_50>",
-      "single_word": false,
-      "lstrip": false,
-      "rstrip": false,
-      "normalized": false,
-      "special": true
-    },
-    {
-      "id": 32050,
-      "content": "<extra_id_49>",
-      "single_word": false,
-      "lstrip": false,
-      "rstrip": false,
-      "normalized": false,
-      "special": true
-    },
-    {
-      "id": 32051,
-      "content": "<extra_id_48>",
-      "single_word": false,
-      "lstrip": false,
-      "rstrip": false,
-      "normalized": false,
-      "special": true
-    },
-    {
-      "id": 32052,
-      "content": "<extra_id_47>",
-      "single_word": false,
-      "lstrip": false,
-      "rstrip": false,
-      "normalized": false,
-      "special": true
-    },
-    {
-      "id": 32053,
-      "content": "<extra_id_46>",
-      "single_word": false,
-      "lstrip": false,
-      "rstrip": false,
-      "normalized": false,
-      "special": true
-    },
-    {
-      "id": 32054,
-      "content": "<extra_id_45>",
-      "single_word": false,
-      "lstrip": false,
-      "rstrip": false,
-      "normalized": false,
-      "special": true
-    },
-    {
-      "id": 32055,
-      "content": "<extra_id_44>",
-      "single_word": false,
-      "lstrip": false,
-      "rstrip": false,
-      "normalized": false,
-      "special": true
-    },
-    {
-      "id": 32056,
-      "content": "<extra_id_43>",
-      "single_word": false,
-      "lstrip": false,
-      "rstrip": false,
-      "normalized": false,
-      "special": true
-    },
-    {
-      "id": 32057,
-      "content": "<extra_id_42>",
-      "single_word": false,
-      "lstrip": false,
-      "rstrip": false,
-      "normalized": false,
-      "special": true
-    },
-    {
-      "id": 32058,
-      "content": "<extra_id_41>",
-      "single_word": false,
-      "lstrip": false,
-      "rstrip": false,
-      "normalized": false,
-      "special": true
-    },
-    {
-      "id": 32059,
-      "content": "<extra_id_40>",
-      "single_word": false,
-      "lstrip": false,
-      "rstrip": false,
-      "normalized": false,
-      "special": true
-    },
-    {
-      "id": 32060,
-      "content": "<extra_id_39>",
-      "single_word": false,
-      "lstrip": false,
-      "rstrip": false,
-      "normalized": false,
-      "special": true
-    },
-    {
-      "id": 32061,
-      "content": "<extra_id_38>",
-      "single_word": false,
-      "lstrip": false,
-      "rstrip": false,
-      "normalized": false,
-      "special": true
-    },
-    {
-      "id": 32062,
-      "content": "<extra_id_37>",
-      "single_word": false,
-      "lstrip": false,
-      "rstrip": false,
-      "normalized": false,
-      "special": true
-    },
-    {
-      "id": 32063,
-      "content": "<extra_id_36>",
-      "single_word": false,
-      "lstrip": false,
-      "rstrip": false,
-      "normalized": false,
-      "special": true
-    },
-    {
-      "id": 32064,
-      "content": "<extra_id_35>",
-      "single_word": false,
-      "lstrip": false,
-      "rstrip": false,
-      "normalized": false,
-      "special": true
-    },
-    {
-      "id": 32065,
-      "content": "<extra_id_34>",
-      "single_word": false,
-      "lstrip": false,
-      "rstrip": false,
-      "normalized": false,
-      "special": true
-    },
-    {
-      "id": 32066,
-      "content": "<extra_id_33>",
-      "single_word": false,
-      "lstrip": false,
-      "rstrip": false,
-      "normalized": false,
-      "special": true
-    },
-    {
-      "id": 32067,
-      "content": "<extra_id_32>",
-      "single_word": false,
-      "lstrip": false,
-      "rstrip": false,
-      "normalized": false,
-      "special": true
-    },
-    {
-      "id": 32068,
-      "content": "<extra_id_31>",
-      "single_word": false,
-      "lstrip": false,
-      "rstrip": false,
-      "normalized": false,
-      "special": true
-    },
-    {
-      "id": 32069,
-      "content": "<extra_id_30>",
-      "single_word": false,
-      "lstrip": false,
-      "rstrip": false,
-      "normalized": false,
-      "special": true
-    },
-    {
-      "id": 32070,
-      "content": "<extra_id_29>",
-      "single_word": false,
-      "lstrip": false,
-      "rstrip": false,
-      "normalized": false,
-      "special": true
-    },
-    {
-      "id": 32071,
-      "content": "<extra_id_28>",
-      "single_word": false,
-      "lstrip": false,
-      "rstrip": false,
-      "normalized": false,
-      "special": true
-    },
-    {
-      "id": 32072,
-      "content": "<extra_id_27>",
-      "single_word": false,
-      "lstrip": false,
-      "rstrip": false,
-      "normalized": false,
-      "special": true
-    },
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-      "id": 32073,
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-      [
-        "<extra_id_0>",
-        0.0
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-    ],
-    "byte_fallback": false
-  }
-}
\ No newline at end of file
diff --git a/MagicQuill/comfy/t5_tokenizer/tokenizer_config.json b/MagicQuill/comfy/t5_tokenizer/tokenizer_config.json
deleted file mode 100644
index 02020eb6d20746871e1ea93f14c4475cf9368f98..0000000000000000000000000000000000000000
--- a/MagicQuill/comfy/t5_tokenizer/tokenizer_config.json
+++ /dev/null
@@ -1,939 +0,0 @@
-{
-  "added_tokens_decoder": {
-    "0": {
-      "content": "<pad>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "1": {
-      "content": "</s>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "2": {
-      "content": "<unk>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "32000": {
-      "content": "<extra_id_99>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "32001": {
-      "content": "<extra_id_98>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "32002": {
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-      "single_word": false,
-      "special": true
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-      "rstrip": false,
-      "single_word": false,
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-    },
-    "32004": {
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-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "32005": {
-      "content": "<extra_id_94>",
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-      "normalized": false,
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-      "special": true
-    },
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-      "special": true
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-    },
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-      "single_word": false,
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-    },
-    "32009": {
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-      "special": true
-    },
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-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "32011": {
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-      "lstrip": false,
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-      "rstrip": false,
-      "single_word": false,
-      "special": true
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-    "32012": {
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-      "single_word": false,
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-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "32098": {
-      "content": "<extra_id_1>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "32099": {
-      "content": "<extra_id_0>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    }
-  },
-  "additional_special_tokens": [
-    "<extra_id_0>",
-    "<extra_id_1>",
-    "<extra_id_2>",
-    "<extra_id_3>",
-    "<extra_id_4>",
-    "<extra_id_5>",
-    "<extra_id_6>",
-    "<extra_id_7>",
-    "<extra_id_8>",
-    "<extra_id_9>",
-    "<extra_id_10>",
-    "<extra_id_11>",
-    "<extra_id_12>",
-    "<extra_id_13>",
-    "<extra_id_14>",
-    "<extra_id_15>",
-    "<extra_id_16>",
-    "<extra_id_17>",
-    "<extra_id_18>",
-    "<extra_id_19>",
-    "<extra_id_20>",
-    "<extra_id_21>",
-    "<extra_id_22>",
-    "<extra_id_23>",
-    "<extra_id_24>",
-    "<extra_id_25>",
-    "<extra_id_26>",
-    "<extra_id_27>",
-    "<extra_id_28>",
-    "<extra_id_29>",
-    "<extra_id_30>",
-    "<extra_id_31>",
-    "<extra_id_32>",
-    "<extra_id_33>",
-    "<extra_id_34>",
-    "<extra_id_35>",
-    "<extra_id_36>",
-    "<extra_id_37>",
-    "<extra_id_38>",
-    "<extra_id_39>",
-    "<extra_id_40>",
-    "<extra_id_41>",
-    "<extra_id_42>",
-    "<extra_id_43>",
-    "<extra_id_44>",
-    "<extra_id_45>",
-    "<extra_id_46>",
-    "<extra_id_47>",
-    "<extra_id_48>",
-    "<extra_id_49>",
-    "<extra_id_50>",
-    "<extra_id_51>",
-    "<extra_id_52>",
-    "<extra_id_53>",
-    "<extra_id_54>",
-    "<extra_id_55>",
-    "<extra_id_56>",
-    "<extra_id_57>",
-    "<extra_id_58>",
-    "<extra_id_59>",
-    "<extra_id_60>",
-    "<extra_id_61>",
-    "<extra_id_62>",
-    "<extra_id_63>",
-    "<extra_id_64>",
-    "<extra_id_65>",
-    "<extra_id_66>",
-    "<extra_id_67>",
-    "<extra_id_68>",
-    "<extra_id_69>",
-    "<extra_id_70>",
-    "<extra_id_71>",
-    "<extra_id_72>",
-    "<extra_id_73>",
-    "<extra_id_74>",
-    "<extra_id_75>",
-    "<extra_id_76>",
-    "<extra_id_77>",
-    "<extra_id_78>",
-    "<extra_id_79>",
-    "<extra_id_80>",
-    "<extra_id_81>",
-    "<extra_id_82>",
-    "<extra_id_83>",
-    "<extra_id_84>",
-    "<extra_id_85>",
-    "<extra_id_86>",
-    "<extra_id_87>",
-    "<extra_id_88>",
-    "<extra_id_89>",
-    "<extra_id_90>",
-    "<extra_id_91>",
-    "<extra_id_92>",
-    "<extra_id_93>",
-    "<extra_id_94>",
-    "<extra_id_95>",
-    "<extra_id_96>",
-    "<extra_id_97>",
-    "<extra_id_98>",
-    "<extra_id_99>"
-  ],
-  "clean_up_tokenization_spaces": true,
-  "eos_token": "</s>",
-  "extra_ids": 100,
-  "legacy": false,
-  "model_max_length": 512,
-  "pad_token": "<pad>",
-  "sp_model_kwargs": {},
-  "tokenizer_class": "T5Tokenizer",
-  "unk_token": "<unk>"
-}
diff --git a/MagicQuill/comfy/taesd/__pycache__/taesd.cpython-310.pyc b/MagicQuill/comfy/taesd/__pycache__/taesd.cpython-310.pyc
deleted file mode 100644
index 0932688b81b27dafa7b884340ea11b1ed5748839..0000000000000000000000000000000000000000
Binary files a/MagicQuill/comfy/taesd/__pycache__/taesd.cpython-310.pyc and /dev/null differ
diff --git a/MagicQuill/comfy/taesd/taesd.py b/MagicQuill/comfy/taesd/taesd.py
deleted file mode 100644
index ce36f1a84dae599a35e84a8da3462408c0f0ccc6..0000000000000000000000000000000000000000
--- a/MagicQuill/comfy/taesd/taesd.py
+++ /dev/null
@@ -1,79 +0,0 @@
-#!/usr/bin/env python3
-"""
-Tiny AutoEncoder for Stable Diffusion
-(DNN for encoding / decoding SD's latent space)
-"""
-import torch
-import torch.nn as nn
-
-import comfy.utils
-import comfy.ops
-
-def conv(n_in, n_out, **kwargs):
-    return comfy.ops.disable_weight_init.Conv2d(n_in, n_out, 3, padding=1, **kwargs)
-
-class Clamp(nn.Module):
-    def forward(self, x):
-        return torch.tanh(x / 3) * 3
-
-class Block(nn.Module):
-    def __init__(self, n_in, n_out):
-        super().__init__()
-        self.conv = nn.Sequential(conv(n_in, n_out), nn.ReLU(), conv(n_out, n_out), nn.ReLU(), conv(n_out, n_out))
-        self.skip = comfy.ops.disable_weight_init.Conv2d(n_in, n_out, 1, bias=False) if n_in != n_out else nn.Identity()
-        self.fuse = nn.ReLU()
-    def forward(self, x):
-        return self.fuse(self.conv(x) + self.skip(x))
-
-def Encoder(latent_channels=4):
-    return nn.Sequential(
-        conv(3, 64), Block(64, 64),
-        conv(64, 64, stride=2, bias=False), Block(64, 64), Block(64, 64), Block(64, 64),
-        conv(64, 64, stride=2, bias=False), Block(64, 64), Block(64, 64), Block(64, 64),
-        conv(64, 64, stride=2, bias=False), Block(64, 64), Block(64, 64), Block(64, 64),
-        conv(64, latent_channels),
-    )
-
-
-def Decoder(latent_channels=4):
-    return nn.Sequential(
-        Clamp(), conv(latent_channels, 64), nn.ReLU(),
-        Block(64, 64), Block(64, 64), Block(64, 64), nn.Upsample(scale_factor=2), conv(64, 64, bias=False),
-        Block(64, 64), Block(64, 64), Block(64, 64), nn.Upsample(scale_factor=2), conv(64, 64, bias=False),
-        Block(64, 64), Block(64, 64), Block(64, 64), nn.Upsample(scale_factor=2), conv(64, 64, bias=False),
-        Block(64, 64), conv(64, 3),
-    )
-
-class TAESD(nn.Module):
-    latent_magnitude = 3
-    latent_shift = 0.5
-
-    def __init__(self, encoder_path=None, decoder_path=None, latent_channels=4):
-        """Initialize pretrained TAESD on the given device from the given checkpoints."""
-        super().__init__()
-        self.taesd_encoder = Encoder(latent_channels=latent_channels)
-        self.taesd_decoder = Decoder(latent_channels=latent_channels)
-        self.vae_scale = torch.nn.Parameter(torch.tensor(1.0))
-        self.vae_shift = torch.nn.Parameter(torch.tensor(0.0))
-        if encoder_path is not None:
-            self.taesd_encoder.load_state_dict(comfy.utils.load_torch_file(encoder_path, safe_load=True))
-        if decoder_path is not None:
-            self.taesd_decoder.load_state_dict(comfy.utils.load_torch_file(decoder_path, safe_load=True))
-
-    @staticmethod
-    def scale_latents(x):
-        """raw latents -> [0, 1]"""
-        return x.div(2 * TAESD.latent_magnitude).add(TAESD.latent_shift).clamp(0, 1)
-
-    @staticmethod
-    def unscale_latents(x):
-        """[0, 1] -> raw latents"""
-        return x.sub(TAESD.latent_shift).mul(2 * TAESD.latent_magnitude)
-
-    def decode(self, x):
-        x_sample = self.taesd_decoder((x - self.vae_shift) * self.vae_scale)
-        x_sample = x_sample.sub(0.5).mul(2)
-        return x_sample
-
-    def encode(self, x):
-        return (self.taesd_encoder(x * 0.5 + 0.5) / self.vae_scale) + self.vae_shift
diff --git a/MagicQuill/comfy/types.py b/MagicQuill/comfy/types.py
deleted file mode 100644
index 70cf4b158e5f969192c0c11d9bd461964aaea5b5..0000000000000000000000000000000000000000
--- a/MagicQuill/comfy/types.py
+++ /dev/null
@@ -1,32 +0,0 @@
-import torch
-from typing import Callable, Protocol, TypedDict, Optional, List
-
-
-class UnetApplyFunction(Protocol):
-    """Function signature protocol on comfy.model_base.BaseModel.apply_model"""
-
-    def __call__(self, x: torch.Tensor, t: torch.Tensor, **kwargs) -> torch.Tensor:
-        pass
-
-
-class UnetApplyConds(TypedDict):
-    """Optional conditions for unet apply function."""
-
-    c_concat: Optional[torch.Tensor]
-    c_crossattn: Optional[torch.Tensor]
-    control: Optional[torch.Tensor]
-    transformer_options: Optional[dict]
-
-
-class UnetParams(TypedDict):
-    # Tensor of shape [B, C, H, W]
-    input: torch.Tensor
-    # Tensor of shape [B]
-    timestep: torch.Tensor
-    c: UnetApplyConds
-    # List of [0, 1], [0], [1], ...
-    # 0 means conditional, 1 means conditional unconditional
-    cond_or_uncond: List[int]
-
-
-UnetWrapperFunction = Callable[[UnetApplyFunction, UnetParams], torch.Tensor]
diff --git a/MagicQuill/comfy/utils.py b/MagicQuill/comfy/utils.py
deleted file mode 100644
index 884404cceb39ebfafffa2e712f2a95d6b69f6c7e..0000000000000000000000000000000000000000
--- a/MagicQuill/comfy/utils.py
+++ /dev/null
@@ -1,483 +0,0 @@
-import torch
-import math
-import struct
-import comfy.checkpoint_pickle
-import safetensors.torch
-import numpy as np
-from PIL import Image
-import logging
-
-def load_torch_file(ckpt, safe_load=False, device=None):
-    if device is None:
-        device = torch.device("cpu")
-    if ckpt.lower().endswith(".safetensors"):
-        sd = safetensors.torch.load_file(ckpt, device=device.type)
-    else:
-        if safe_load:
-            if not 'weights_only' in torch.load.__code__.co_varnames:
-                logging.warning("Warning torch.load doesn't support weights_only on this pytorch version, loading unsafely.")
-                safe_load = False
-        if safe_load:
-            pl_sd = torch.load(ckpt, map_location=device, weights_only=True)
-        else:
-            pl_sd = torch.load(ckpt, map_location=device, pickle_module=comfy.checkpoint_pickle)
-        if "global_step" in pl_sd:
-            logging.debug(f"Global Step: {pl_sd['global_step']}")
-        if "state_dict" in pl_sd:
-            sd = pl_sd["state_dict"]
-        else:
-            sd = pl_sd
-    return sd
-
-def save_torch_file(sd, ckpt, metadata=None):
-    if metadata is not None:
-        safetensors.torch.save_file(sd, ckpt, metadata=metadata)
-    else:
-        safetensors.torch.save_file(sd, ckpt)
-
-def calculate_parameters(sd, prefix=""):
-    params = 0
-    for k in sd.keys():
-        if k.startswith(prefix):
-            params += sd[k].nelement()
-    return params
-
-def state_dict_key_replace(state_dict, keys_to_replace):
-    for x in keys_to_replace:
-        if x in state_dict:
-            state_dict[keys_to_replace[x]] = state_dict.pop(x)
-    return state_dict
-
-def state_dict_prefix_replace(state_dict, replace_prefix, filter_keys=False):
-    if filter_keys:
-        out = {}
-    else:
-        out = state_dict
-    for rp in replace_prefix:
-        replace = list(map(lambda a: (a, "{}{}".format(replace_prefix[rp], a[len(rp):])), filter(lambda a: a.startswith(rp), state_dict.keys())))
-        for x in replace:
-            w = state_dict.pop(x[0])
-            out[x[1]] = w
-    return out
-
-
-def transformers_convert(sd, prefix_from, prefix_to, number):
-    keys_to_replace = {
-        "{}positional_embedding": "{}embeddings.position_embedding.weight",
-        "{}token_embedding.weight": "{}embeddings.token_embedding.weight",
-        "{}ln_final.weight": "{}final_layer_norm.weight",
-        "{}ln_final.bias": "{}final_layer_norm.bias",
-    }
-
-    for k in keys_to_replace:
-        x = k.format(prefix_from)
-        if x in sd:
-            sd[keys_to_replace[k].format(prefix_to)] = sd.pop(x)
-
-    resblock_to_replace = {
-        "ln_1": "layer_norm1",
-        "ln_2": "layer_norm2",
-        "mlp.c_fc": "mlp.fc1",
-        "mlp.c_proj": "mlp.fc2",
-        "attn.out_proj": "self_attn.out_proj",
-    }
-
-    for resblock in range(number):
-        for x in resblock_to_replace:
-            for y in ["weight", "bias"]:
-                k = "{}transformer.resblocks.{}.{}.{}".format(prefix_from, resblock, x, y)
-                k_to = "{}encoder.layers.{}.{}.{}".format(prefix_to, resblock, resblock_to_replace[x], y)
-                if k in sd:
-                    sd[k_to] = sd.pop(k)
-
-        for y in ["weight", "bias"]:
-            k_from = "{}transformer.resblocks.{}.attn.in_proj_{}".format(prefix_from, resblock, y)
-            if k_from in sd:
-                weights = sd.pop(k_from)
-                shape_from = weights.shape[0] // 3
-                for x in range(3):
-                    p = ["self_attn.q_proj", "self_attn.k_proj", "self_attn.v_proj"]
-                    k_to = "{}encoder.layers.{}.{}.{}".format(prefix_to, resblock, p[x], y)
-                    sd[k_to] = weights[shape_from*x:shape_from*(x + 1)]
-
-    return sd
-
-def clip_text_transformers_convert(sd, prefix_from, prefix_to):
-    sd = transformers_convert(sd, prefix_from, "{}text_model.".format(prefix_to), 32)
-
-    tp = "{}text_projection.weight".format(prefix_from)
-    if tp in sd:
-        sd["{}text_projection.weight".format(prefix_to)] = sd.pop(tp)
-
-    tp = "{}text_projection".format(prefix_from)
-    if tp in sd:
-        sd["{}text_projection.weight".format(prefix_to)] = sd.pop(tp).transpose(0, 1).contiguous()
-    return sd
-
-
-UNET_MAP_ATTENTIONS = {
-    "proj_in.weight",
-    "proj_in.bias",
-    "proj_out.weight",
-    "proj_out.bias",
-    "norm.weight",
-    "norm.bias",
-}
-
-TRANSFORMER_BLOCKS = {
-    "norm1.weight",
-    "norm1.bias",
-    "norm2.weight",
-    "norm2.bias",
-    "norm3.weight",
-    "norm3.bias",
-    "attn1.to_q.weight",
-    "attn1.to_k.weight",
-    "attn1.to_v.weight",
-    "attn1.to_out.0.weight",
-    "attn1.to_out.0.bias",
-    "attn2.to_q.weight",
-    "attn2.to_k.weight",
-    "attn2.to_v.weight",
-    "attn2.to_out.0.weight",
-    "attn2.to_out.0.bias",
-    "ff.net.0.proj.weight",
-    "ff.net.0.proj.bias",
-    "ff.net.2.weight",
-    "ff.net.2.bias",
-}
-
-UNET_MAP_RESNET = {
-    "in_layers.2.weight": "conv1.weight",
-    "in_layers.2.bias": "conv1.bias",
-    "emb_layers.1.weight": "time_emb_proj.weight",
-    "emb_layers.1.bias": "time_emb_proj.bias",
-    "out_layers.3.weight": "conv2.weight",
-    "out_layers.3.bias": "conv2.bias",
-    "skip_connection.weight": "conv_shortcut.weight",
-    "skip_connection.bias": "conv_shortcut.bias",
-    "in_layers.0.weight": "norm1.weight",
-    "in_layers.0.bias": "norm1.bias",
-    "out_layers.0.weight": "norm2.weight",
-    "out_layers.0.bias": "norm2.bias",
-}
-
-UNET_MAP_BASIC = {
-    ("label_emb.0.0.weight", "class_embedding.linear_1.weight"),
-    ("label_emb.0.0.bias", "class_embedding.linear_1.bias"),
-    ("label_emb.0.2.weight", "class_embedding.linear_2.weight"),
-    ("label_emb.0.2.bias", "class_embedding.linear_2.bias"),
-    ("label_emb.0.0.weight", "add_embedding.linear_1.weight"),
-    ("label_emb.0.0.bias", "add_embedding.linear_1.bias"),
-    ("label_emb.0.2.weight", "add_embedding.linear_2.weight"),
-    ("label_emb.0.2.bias", "add_embedding.linear_2.bias"),
-    ("input_blocks.0.0.weight", "conv_in.weight"),
-    ("input_blocks.0.0.bias", "conv_in.bias"),
-    ("out.0.weight", "conv_norm_out.weight"),
-    ("out.0.bias", "conv_norm_out.bias"),
-    ("out.2.weight", "conv_out.weight"),
-    ("out.2.bias", "conv_out.bias"),
-    ("time_embed.0.weight", "time_embedding.linear_1.weight"),
-    ("time_embed.0.bias", "time_embedding.linear_1.bias"),
-    ("time_embed.2.weight", "time_embedding.linear_2.weight"),
-    ("time_embed.2.bias", "time_embedding.linear_2.bias")
-}
-
-def unet_to_diffusers(unet_config):
-    if "num_res_blocks" not in unet_config:
-        return {}
-    num_res_blocks = unet_config["num_res_blocks"]
-    channel_mult = unet_config["channel_mult"]
-    transformer_depth = unet_config["transformer_depth"][:]
-    transformer_depth_output = unet_config["transformer_depth_output"][:]
-    num_blocks = len(channel_mult)
-
-    transformers_mid = unet_config.get("transformer_depth_middle", None)
-
-    diffusers_unet_map = {}
-    for x in range(num_blocks):
-        n = 1 + (num_res_blocks[x] + 1) * x
-        for i in range(num_res_blocks[x]):
-            for b in UNET_MAP_RESNET:
-                diffusers_unet_map["down_blocks.{}.resnets.{}.{}".format(x, i, UNET_MAP_RESNET[b])] = "input_blocks.{}.0.{}".format(n, b)
-            num_transformers = transformer_depth.pop(0)
-            if num_transformers > 0:
-                for b in UNET_MAP_ATTENTIONS:
-                    diffusers_unet_map["down_blocks.{}.attentions.{}.{}".format(x, i, b)] = "input_blocks.{}.1.{}".format(n, b)
-                for t in range(num_transformers):
-                    for b in TRANSFORMER_BLOCKS:
-                        diffusers_unet_map["down_blocks.{}.attentions.{}.transformer_blocks.{}.{}".format(x, i, t, b)] = "input_blocks.{}.1.transformer_blocks.{}.{}".format(n, t, b)
-            n += 1
-        for k in ["weight", "bias"]:
-            diffusers_unet_map["down_blocks.{}.downsamplers.0.conv.{}".format(x, k)] = "input_blocks.{}.0.op.{}".format(n, k)
-
-    i = 0
-    for b in UNET_MAP_ATTENTIONS:
-        diffusers_unet_map["mid_block.attentions.{}.{}".format(i, b)] = "middle_block.1.{}".format(b)
-    for t in range(transformers_mid):
-        for b in TRANSFORMER_BLOCKS:
-            diffusers_unet_map["mid_block.attentions.{}.transformer_blocks.{}.{}".format(i, t, b)] = "middle_block.1.transformer_blocks.{}.{}".format(t, b)
-
-    for i, n in enumerate([0, 2]):
-        for b in UNET_MAP_RESNET:
-            diffusers_unet_map["mid_block.resnets.{}.{}".format(i, UNET_MAP_RESNET[b])] = "middle_block.{}.{}".format(n, b)
-
-    num_res_blocks = list(reversed(num_res_blocks))
-    for x in range(num_blocks):
-        n = (num_res_blocks[x] + 1) * x
-        l = num_res_blocks[x] + 1
-        for i in range(l):
-            c = 0
-            for b in UNET_MAP_RESNET:
-                diffusers_unet_map["up_blocks.{}.resnets.{}.{}".format(x, i, UNET_MAP_RESNET[b])] = "output_blocks.{}.0.{}".format(n, b)
-            c += 1
-            num_transformers = transformer_depth_output.pop()
-            if num_transformers > 0:
-                c += 1
-                for b in UNET_MAP_ATTENTIONS:
-                    diffusers_unet_map["up_blocks.{}.attentions.{}.{}".format(x, i, b)] = "output_blocks.{}.1.{}".format(n, b)
-                for t in range(num_transformers):
-                    for b in TRANSFORMER_BLOCKS:
-                        diffusers_unet_map["up_blocks.{}.attentions.{}.transformer_blocks.{}.{}".format(x, i, t, b)] = "output_blocks.{}.1.transformer_blocks.{}.{}".format(n, t, b)
-            if i == l - 1:
-                for k in ["weight", "bias"]:
-                    diffusers_unet_map["up_blocks.{}.upsamplers.0.conv.{}".format(x, k)] = "output_blocks.{}.{}.conv.{}".format(n, c, k)
-            n += 1
-
-    for k in UNET_MAP_BASIC:
-        diffusers_unet_map[k[1]] = k[0]
-
-    return diffusers_unet_map
-
-def repeat_to_batch_size(tensor, batch_size, dim=0):
-    if tensor.shape[dim] > batch_size:
-        return tensor.narrow(dim, 0, batch_size)
-    elif tensor.shape[dim] < batch_size:
-        return tensor.repeat(dim * [1] + [math.ceil(batch_size / tensor.shape[dim])] + [1] * (len(tensor.shape) - 1 - dim)).narrow(dim, 0, batch_size)
-    return tensor
-
-def resize_to_batch_size(tensor, batch_size):
-    in_batch_size = tensor.shape[0]
-    if in_batch_size == batch_size:
-        return tensor
-
-    if batch_size <= 1:
-        return tensor[:batch_size]
-
-    output = torch.empty([batch_size] + list(tensor.shape)[1:], dtype=tensor.dtype, device=tensor.device)
-    if batch_size < in_batch_size:
-        scale = (in_batch_size - 1) / (batch_size - 1)
-        for i in range(batch_size):
-            output[i] = tensor[min(round(i * scale), in_batch_size - 1)]
-    else:
-        scale = in_batch_size / batch_size
-        for i in range(batch_size):
-            output[i] = tensor[min(math.floor((i + 0.5) * scale), in_batch_size - 1)]
-
-    return output
-
-def convert_sd_to(state_dict, dtype):
-    keys = list(state_dict.keys())
-    for k in keys:
-        state_dict[k] = state_dict[k].to(dtype)
-    return state_dict
-
-def safetensors_header(safetensors_path, max_size=100*1024*1024):
-    with open(safetensors_path, "rb") as f:
-        header = f.read(8)
-        length_of_header = struct.unpack('<Q', header)[0]
-        if length_of_header > max_size:
-            return None
-        return f.read(length_of_header)
-
-def set_attr(obj, attr, value):
-    attrs = attr.split(".")
-    for name in attrs[:-1]:
-        obj = getattr(obj, name)
-    prev = getattr(obj, attrs[-1])
-    setattr(obj, attrs[-1], value)
-    return prev
-
-def set_attr_param(obj, attr, value):
-    return set_attr(obj, attr, torch.nn.Parameter(value, requires_grad=False))
-
-def copy_to_param(obj, attr, value):
-    # inplace update tensor instead of replacing it
-    attrs = attr.split(".")
-    for name in attrs[:-1]:
-        obj = getattr(obj, name)
-    prev = getattr(obj, attrs[-1])
-    prev.data.copy_(value)
-
-def get_attr(obj, attr):
-    attrs = attr.split(".")
-    for name in attrs:
-        obj = getattr(obj, name)
-    return obj
-
-def bislerp(samples, width, height):
-    def slerp(b1, b2, r):
-        '''slerps batches b1, b2 according to ratio r, batches should be flat e.g. NxC'''
-        
-        c = b1.shape[-1]
-
-        #norms
-        b1_norms = torch.norm(b1, dim=-1, keepdim=True)
-        b2_norms = torch.norm(b2, dim=-1, keepdim=True)
-
-        #normalize
-        b1_normalized = b1 / b1_norms
-        b2_normalized = b2 / b2_norms
-
-        #zero when norms are zero
-        b1_normalized[b1_norms.expand(-1,c) == 0.0] = 0.0
-        b2_normalized[b2_norms.expand(-1,c) == 0.0] = 0.0
-
-        #slerp
-        dot = (b1_normalized*b2_normalized).sum(1)
-        omega = torch.acos(dot)
-        so = torch.sin(omega)
-
-        #technically not mathematically correct, but more pleasing?
-        res = (torch.sin((1.0-r.squeeze(1))*omega)/so).unsqueeze(1)*b1_normalized + (torch.sin(r.squeeze(1)*omega)/so).unsqueeze(1) * b2_normalized
-        res *= (b1_norms * (1.0-r) + b2_norms * r).expand(-1,c)
-
-        #edge cases for same or polar opposites
-        res[dot > 1 - 1e-5] = b1[dot > 1 - 1e-5] 
-        res[dot < 1e-5 - 1] = (b1 * (1.0-r) + b2 * r)[dot < 1e-5 - 1]
-        return res
-    
-    def generate_bilinear_data(length_old, length_new, device):
-        coords_1 = torch.arange(length_old, dtype=torch.float32, device=device).reshape((1,1,1,-1))
-        coords_1 = torch.nn.functional.interpolate(coords_1, size=(1, length_new), mode="bilinear")
-        ratios = coords_1 - coords_1.floor()
-        coords_1 = coords_1.to(torch.int64)
-        
-        coords_2 = torch.arange(length_old, dtype=torch.float32, device=device).reshape((1,1,1,-1)) + 1
-        coords_2[:,:,:,-1] -= 1
-        coords_2 = torch.nn.functional.interpolate(coords_2, size=(1, length_new), mode="bilinear")
-        coords_2 = coords_2.to(torch.int64)
-        return ratios, coords_1, coords_2
-
-    orig_dtype = samples.dtype
-    samples = samples.float()
-    n,c,h,w = samples.shape
-    h_new, w_new = (height, width)
-    
-    #linear w
-    ratios, coords_1, coords_2 = generate_bilinear_data(w, w_new, samples.device)
-    coords_1 = coords_1.expand((n, c, h, -1))
-    coords_2 = coords_2.expand((n, c, h, -1))
-    ratios = ratios.expand((n, 1, h, -1))
-
-    pass_1 = samples.gather(-1,coords_1).movedim(1, -1).reshape((-1,c))
-    pass_2 = samples.gather(-1,coords_2).movedim(1, -1).reshape((-1,c))
-    ratios = ratios.movedim(1, -1).reshape((-1,1))
-
-    result = slerp(pass_1, pass_2, ratios)
-    result = result.reshape(n, h, w_new, c).movedim(-1, 1)
-
-    #linear h
-    ratios, coords_1, coords_2 = generate_bilinear_data(h, h_new, samples.device)
-    coords_1 = coords_1.reshape((1,1,-1,1)).expand((n, c, -1, w_new))
-    coords_2 = coords_2.reshape((1,1,-1,1)).expand((n, c, -1, w_new))
-    ratios = ratios.reshape((1,1,-1,1)).expand((n, 1, -1, w_new))
-
-    pass_1 = result.gather(-2,coords_1).movedim(1, -1).reshape((-1,c))
-    pass_2 = result.gather(-2,coords_2).movedim(1, -1).reshape((-1,c))
-    ratios = ratios.movedim(1, -1).reshape((-1,1))
-
-    result = slerp(pass_1, pass_2, ratios)
-    result = result.reshape(n, h_new, w_new, c).movedim(-1, 1)
-    return result.to(orig_dtype)
-
-def lanczos(samples, width, height):
-    images = [Image.fromarray(np.clip(255. * image.movedim(0, -1).cpu().numpy(), 0, 255).astype(np.uint8)) for image in samples]
-    images = [image.resize((width, height), resample=Image.Resampling.LANCZOS) for image in images]
-    images = [torch.from_numpy(np.array(image).astype(np.float32) / 255.0).movedim(-1, 0) for image in images]
-    result = torch.stack(images)
-    return result.to(samples.device, samples.dtype)
-
-def common_upscale(samples, width, height, upscale_method, crop):
-        if crop == "center":
-            old_width = samples.shape[3]
-            old_height = samples.shape[2]
-            old_aspect = old_width / old_height
-            new_aspect = width / height
-            x = 0
-            y = 0
-            if old_aspect > new_aspect:
-                x = round((old_width - old_width * (new_aspect / old_aspect)) / 2)
-            elif old_aspect < new_aspect:
-                y = round((old_height - old_height * (old_aspect / new_aspect)) / 2)
-            s = samples[:,:,y:old_height-y,x:old_width-x]
-        else:
-            s = samples
-
-        if upscale_method == "bislerp":
-            return bislerp(s, width, height)
-        elif upscale_method == "lanczos":
-            return lanczos(s, width, height)
-        else:
-            return torch.nn.functional.interpolate(s, size=(height, width), mode=upscale_method)
-
-def get_tiled_scale_steps(width, height, tile_x, tile_y, overlap):
-    return math.ceil((height / (tile_y - overlap))) * math.ceil((width / (tile_x - overlap)))
-
-@torch.inference_mode()
-def tiled_scale(samples, function, tile_x=64, tile_y=64, overlap = 8, upscale_amount = 4, out_channels = 3, output_device="cpu", pbar = None):
-    output = torch.empty((samples.shape[0], out_channels, round(samples.shape[2] * upscale_amount), round(samples.shape[3] * upscale_amount)), device=output_device)
-    for b in range(samples.shape[0]):
-        s = samples[b:b+1]
-        out = torch.zeros((s.shape[0], out_channels, round(s.shape[2] * upscale_amount), round(s.shape[3] * upscale_amount)), device=output_device)
-        out_div = torch.zeros((s.shape[0], out_channels, round(s.shape[2] * upscale_amount), round(s.shape[3] * upscale_amount)), device=output_device)
-        for y in range(0, s.shape[2], tile_y - overlap):
-            for x in range(0, s.shape[3], tile_x - overlap):
-                x = max(0, min(s.shape[-1] - overlap, x))
-                y = max(0, min(s.shape[-2] - overlap, y))
-                s_in = s[:,:,y:y+tile_y,x:x+tile_x]
-
-                ps = function(s_in).to(output_device)
-                mask = torch.ones_like(ps)
-                feather = round(overlap * upscale_amount)
-                for t in range(feather):
-                        mask[:,:,t:1+t,:] *= ((1.0/feather) * (t + 1))
-                        mask[:,:,mask.shape[2] -1 -t: mask.shape[2]-t,:] *= ((1.0/feather) * (t + 1))
-                        mask[:,:,:,t:1+t] *= ((1.0/feather) * (t + 1))
-                        mask[:,:,:,mask.shape[3]- 1 - t: mask.shape[3]- t] *= ((1.0/feather) * (t + 1))
-                out[:,:,round(y*upscale_amount):round((y+tile_y)*upscale_amount),round(x*upscale_amount):round((x+tile_x)*upscale_amount)] += ps * mask
-                out_div[:,:,round(y*upscale_amount):round((y+tile_y)*upscale_amount),round(x*upscale_amount):round((x+tile_x)*upscale_amount)] += mask
-                if pbar is not None:
-                    pbar.update(1)
-
-        output[b:b+1] = out/out_div
-    return output
-
-PROGRESS_BAR_ENABLED = True
-def set_progress_bar_enabled(enabled):
-    global PROGRESS_BAR_ENABLED
-    PROGRESS_BAR_ENABLED = enabled
-
-PROGRESS_BAR_HOOK = None
-def set_progress_bar_global_hook(function):
-    global PROGRESS_BAR_HOOK
-    PROGRESS_BAR_HOOK = function
-
-class ProgressBar:
-    def __init__(self, total):
-        global PROGRESS_BAR_HOOK
-        self.total = total
-        self.current = 0
-        self.hook = PROGRESS_BAR_HOOK
-
-    def update_absolute(self, value, total=None, preview=None):
-        if total is not None:
-            self.total = total
-        if value > self.total:
-            value = self.total
-        self.current = value
-        if self.hook is not None:
-            self.hook(self.current, self.total, preview)
-
-    def update(self, value):
-        self.update_absolute(self.current + value)
diff --git a/MagicQuill/comfyui_utils.py b/MagicQuill/comfyui_utils.py
deleted file mode 100644
index 718a2417fc60b78bf83c4f449a2c68011a8b3202..0000000000000000000000000000000000000000
--- a/MagicQuill/comfyui_utils.py
+++ /dev/null
@@ -1,403 +0,0 @@
-import os
-import folder_paths
-import comfy.diffusers_load
-import comfy.samplers
-import comfy.sample
-import comfy.sd
-import comfy.utils
-import comfy.controlnet
-import comfy.clip_vision
-import comfy.model_management
-from comfy.cli_args import args
-import torch
-import torch.nn as nn
-import numpy as np
-import latent_preview
-from PIL import Image
-from einops import rearrange
-import scipy.ndimage
-import sys
-import cv2
-from magic_utils import HWC3, apply_color, common_input_validate, resize_image_with_pad
-from pidi import pidinet
-
-
-supported_pt_extensions = set(['.ckpt', '.pt', '.bin', '.pth', '.safetensors', '.pkl'])
-folder_names_and_paths = {}
-
-base_path = os.path.dirname(os.path.realpath(__file__))
-models_dir = os.path.join(base_path, "../models")
-
-folder_names_and_paths["checkpoints"] = ([os.path.join(models_dir, "checkpoints")], supported_pt_extensions)
-folder_names_and_paths["configs"] = ([os.path.join(models_dir, "configs")], [".yaml"])
-
-folder_names_and_paths["loras"] = ([os.path.join(models_dir, "loras")], supported_pt_extensions)
-folder_names_and_paths["vae"] = ([os.path.join(models_dir, "vae")], supported_pt_extensions)
-folder_names_and_paths["clip"] = ([os.path.join(models_dir, "clip")], supported_pt_extensions)
-folder_names_and_paths["unet"] = ([os.path.join(models_dir, "unet")], supported_pt_extensions)
-folder_names_and_paths["clip_vision"] = ([os.path.join(models_dir, "clip_vision")], supported_pt_extensions)
-folder_names_and_paths["style_models"] = ([os.path.join(models_dir, "style_models")], supported_pt_extensions)
-folder_names_and_paths["embeddings"] = ([os.path.join(models_dir, "embeddings")], supported_pt_extensions)
-folder_names_and_paths["diffusers"] = ([os.path.join(models_dir, "diffusers")], ["folder"])
-folder_names_and_paths["vae_approx"] = ([os.path.join(models_dir, "vae_approx")], supported_pt_extensions)
-
-folder_names_and_paths["controlnet"] = ([os.path.join(models_dir, "controlnet"), os.path.join(models_dir, "t2i_adapter")], supported_pt_extensions)
-folder_names_and_paths["gligen"] = ([os.path.join(models_dir, "gligen")], supported_pt_extensions)
-folder_names_and_paths["upscale_models"] = ([os.path.join(models_dir, "upscale_models")], supported_pt_extensions)
-folder_names_and_paths["hypernetworks"] = ([os.path.join(models_dir, "hypernetworks")], supported_pt_extensions)
-folder_names_and_paths["photomaker"] = ([os.path.join(models_dir, "photomaker")], supported_pt_extensions)
-folder_names_and_paths["classifiers"] = ([os.path.join(models_dir, "classifiers")], {""})
-
-def common_annotator_call(model, tensor_image, input_batch=False, show_pbar=True, **kwargs):
-    if "detect_resolution" in kwargs:
-        del kwargs["detect_resolution"] #Prevent weird case?
-
-    if "resolution" in kwargs:
-        detect_resolution = kwargs["resolution"] if type(kwargs["resolution"]) == int and kwargs["resolution"] >= 64 else 512
-        del kwargs["resolution"]
-    else:
-        detect_resolution = 512
-
-    if input_batch:
-        np_images = np.asarray(tensor_image * 255., dtype=np.uint8)
-        np_results = model(np_images, output_type="np", detect_resolution=detect_resolution, **kwargs)
-        return torch.from_numpy(np_results.astype(np.float32) / 255.0)
-
-    batch_size = tensor_image.shape[0]
-    if show_pbar:
-        pbar = comfy.utils.ProgressBar(batch_size)
-    out_tensor = None
-    for i, image in enumerate(tensor_image):
-        np_image = np.asarray(image.cpu() * 255., dtype=np.uint8)
-        np_result = model(np_image, output_type="np", detect_resolution=detect_resolution, **kwargs)
-        out = torch.from_numpy(np_result.astype(np.float32) / 255.0)
-        if out_tensor is None:
-            out_tensor = torch.zeros(batch_size, *out.shape, dtype=torch.float32)
-        out_tensor[i] = out
-        if show_pbar:
-            pbar.update(1)
-    return out_tensor
-
-class CheckpointLoaderSimple:
-    def load_checkpoint(self, ckpt_name):
-        ckpt_path = folder_paths.get_full_path("checkpoints", ckpt_name)
-        print("Loading checkpoint from:", ckpt_path)
-        out = comfy.sd.load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, embedding_directory=folder_paths.get_folder_paths("embeddings"))
-        return out[:3]
-
-class ControlNetLoader:
-    def load_controlnet(self, control_net_name):
-        controlnet_path = folder_paths.get_full_path("controlnet", control_net_name)
-        controlnet = comfy.controlnet.load_controlnet(controlnet_path)
-        return (controlnet, )
-
-class ControlNetApplyAdvanced:
-    def apply_controlnet(self, positive, negative, control_net, image, strength, start_percent, end_percent):
-        if strength == 0:
-            return (positive, negative)
-
-        control_hint = image.movedim(-1,1)
-        cnets = {}
-
-        out = []
-        for conditioning in [positive, negative]:
-            c = []
-            for t in conditioning:
-                d = t[1].copy()
-
-                prev_cnet = d.get('control', None)
-                if prev_cnet in cnets:
-                    c_net = cnets[prev_cnet]
-                else:
-                    c_net = control_net.copy().set_cond_hint(control_hint, strength, (start_percent, end_percent))
-                    c_net.set_previous_controlnet(prev_cnet)
-                    cnets[prev_cnet] = c_net
-
-                d['control'] = c_net
-                d['control_apply_to_uncond'] = False
-                n = [t[0], d]
-                c.append(n)
-            out.append(c)
-        return (out[0], out[1])
-    
-class CLIPTextEncode:
-    def encode(self, clip, text):
-        tokens = clip.tokenize(text)
-        cond, pooled = clip.encode_from_tokens(tokens, return_pooled=True)
-        return ([[cond, {"pooled_output": pooled}]], )
-
-class KSampler:
-    def common_ksampler(self, model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent, denoise, disable_noise=False, start_step=None, last_step=None, force_full_denoise=False):
-        latent_image = latent["samples"]
-        latent_image = comfy.sample.fix_empty_latent_channels(model, latent_image)
-
-        if disable_noise:
-            noise = torch.zeros(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, device="cpu")
-        else:
-            batch_inds = latent["batch_index"] if "batch_index" in latent else None
-            noise = comfy.sample.prepare_noise(latent_image, seed, batch_inds)
-
-        noise_mask = None
-        if "noise_mask" in latent:
-            noise_mask = latent["noise_mask"]
-
-        callback = latent_preview.prepare_callback(model, steps)
-        disable_pbar = not comfy.utils.PROGRESS_BAR_ENABLED
-        samples = comfy.sample.sample(model, noise, steps, cfg, sampler_name, scheduler, positive, negative, latent_image,
-                                    denoise=denoise, disable_noise=disable_noise, start_step=start_step, last_step=last_step,
-                                    force_full_denoise=force_full_denoise, noise_mask=noise_mask, callback=callback, disable_pbar=disable_pbar, seed=seed)
-        out = latent.copy()
-        out["samples"] = samples
-        return (out, )
-
-    def sample(self, model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=1.0):
-        return self.common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=denoise)
-
-class VAEDecode:
-    def decode(self, vae, samples):
-        return (vae.decode(samples["samples"]), )
-
-class ColorDetector:
-    def __call__(self, input_image=None, detect_resolution=2048, output_type=None, **kwargs):
-        input_image, output_type = common_input_validate(input_image, output_type, **kwargs)
-        input_image = HWC3(input_image)
-        detected_map = HWC3(apply_color(input_image, detect_resolution))
-        
-        if output_type == "pil":
-            detected_map = Image.fromarray(detected_map)
-            
-        return detected_map
-
-class Color_Preprocessor:
-    def execute(self, image, resolution=512, **kwargs):
-        return (common_annotator_call(ColorDetector(), image, resolution=resolution), )
-    
-norm_layer = nn.InstanceNorm2d
-class ResidualBlock(nn.Module):
-    def __init__(self, in_features):
-        super(ResidualBlock, self).__init__()
-
-        conv_block = [  nn.ReflectionPad2d(1),
-                        nn.Conv2d(in_features, in_features, 3),
-                        norm_layer(in_features),
-                        nn.ReLU(inplace=True),
-                        nn.ReflectionPad2d(1),
-                        nn.Conv2d(in_features, in_features, 3),
-                        norm_layer(in_features)
-                        ]
-
-        self.conv_block = nn.Sequential(*conv_block)
-
-    def forward(self, x):
-        return x + self.conv_block(x)
-
-class Generator(nn.Module):
-    def __init__(self, input_nc, output_nc, n_residual_blocks=9, sigmoid=True):
-        super(Generator, self).__init__()
-
-        # Initial convolution block
-        model0 = [   nn.ReflectionPad2d(3),
-                    nn.Conv2d(input_nc, 64, 7),
-                    norm_layer(64),
-                    nn.ReLU(inplace=True) ]
-        self.model0 = nn.Sequential(*model0)
-
-        # Downsampling
-        model1 = []
-        in_features = 64
-        out_features = in_features*2
-        for _ in range(2):
-            model1 += [  nn.Conv2d(in_features, out_features, 3, stride=2, padding=1),
-                        norm_layer(out_features),
-                        nn.ReLU(inplace=True) ]
-            in_features = out_features
-            out_features = in_features*2
-        self.model1 = nn.Sequential(*model1)
-
-        model2 = []
-        # Residual blocks
-        for _ in range(n_residual_blocks):
-            model2 += [ResidualBlock(in_features)]
-        self.model2 = nn.Sequential(*model2)
-
-        # Upsampling
-        model3 = []
-        out_features = in_features//2
-        for _ in range(2):
-            model3 += [  nn.ConvTranspose2d(in_features, out_features, 3, stride=2, padding=1, output_padding=1),
-                        norm_layer(out_features),
-                        nn.ReLU(inplace=True) ]
-            in_features = out_features
-            out_features = in_features//2
-        self.model3 = nn.Sequential(*model3)
-
-        # Output layer
-        model4 = [  nn.ReflectionPad2d(3),
-                        nn.Conv2d(64, output_nc, 7)]
-        if sigmoid:
-            model4 += [nn.Sigmoid()]
-
-        self.model4 = nn.Sequential(*model4)
-
-    def forward(self, x, cond=None):
-        out = self.model0(x)
-        out = self.model1(out)
-        out = self.model2(out)
-        out = self.model3(out)
-        out = self.model4(out)
-
-        return out
-
-class LineartDetector:
-    def __init__(self, model, coarse_model):
-        self.model = model
-        self.model_coarse = coarse_model
-        self.device = "cpu"
-
-    @classmethod
-    def from_pretrained(cls):
-        current_dir = os.path.dirname(os.path.abspath(__file__))
-        model_path = os.path.join(current_dir, "../models/preprocessor/sk_model.pth")
-        coarse_model_path = os.path.join(current_dir, "../models/preprocessor/sk_model2.pth")
-
-        # print("model_path:", model_path)
-        model = Generator(3, 1, 3)
-        model.load_state_dict(torch.load(model_path, map_location=torch.device('cpu')))
-        model.eval()
-
-        coarse_model = Generator(3, 1, 3)
-        coarse_model.load_state_dict(torch.load(coarse_model_path, map_location=torch.device('cpu')))
-        coarse_model.eval()
-
-        return cls(model, coarse_model)
-    
-    def to(self, device):
-        self.model.to(device)
-        self.model_coarse.to(device)
-        self.device = device
-        return self
-    
-    def __call__(self, input_image, coarse=False, detect_resolution=512, output_type="pil", upscale_method="INTER_CUBIC", **kwargs):
-        input_image, output_type = common_input_validate(input_image, output_type, **kwargs)
-        detected_map, remove_pad = resize_image_with_pad(input_image, detect_resolution, upscale_method)
-
-        model = self.model_coarse if coarse else self.model
-        assert detected_map.ndim == 3
-        with torch.no_grad():
-            image = torch.from_numpy(detected_map).float().to(self.device)
-            image = image / 255.0
-            image = rearrange(image, 'h w c -> 1 c h w')
-            line = model(image)[0][0]
-
-            line = line.cpu().numpy()
-            line = (line * 255.0).clip(0, 255).astype(np.uint8)
-
-        detected_map = HWC3(line)
-        detected_map = remove_pad(255 - detected_map)
-        
-        if output_type == "pil":
-            detected_map = Image.fromarray(detected_map)
-            
-        return detected_map
-
-class LineArt_Preprocessor:
-    def execute(self, image, resolution=512, **kwargs):
-        model = LineartDetector.from_pretrained().to(comfy.model_management.get_torch_device())
-        print("model.device:", model.device)
-        out = common_annotator_call(model, image, resolution=resolution, apply_filter=False, coarse = kwargs["coarse"] == "enable")
-        del model
-        return (out, )
-    
-def nms(x, t, s):
-    x = cv2.GaussianBlur(x.astype(np.float32), (0, 0), s)
-
-    f1 = np.array([[0, 0, 0], [1, 1, 1], [0, 0, 0]], dtype=np.uint8)
-    f2 = np.array([[0, 1, 0], [0, 1, 0], [0, 1, 0]], dtype=np.uint8)
-    f3 = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]], dtype=np.uint8)
-    f4 = np.array([[0, 0, 1], [0, 1, 0], [1, 0, 0]], dtype=np.uint8)
-
-    y = np.zeros_like(x)
-
-    for f in [f1, f2, f3, f4]:
-        np.putmask(y, cv2.dilate(x, kernel=f) == x, x)
-
-    z = np.zeros_like(y, dtype=np.uint8)
-    z[y > t] = 255
-    return z
-
-class PidiNetDetector:
-    def __init__(self, netNetwork):
-        self.netNetwork = netNetwork
-        self.device = "cpu"
-
-    @classmethod
-    def from_pretrained(cls, filename="table5_pidinet.pth"):
-        current_dir = os.path.dirname(os.path.abspath(__file__))
-        model_path = os.path.join(current_dir, f"../models/preprocessor/{filename}")
-
-        netNetwork = pidinet()
-        netNetwork.load_state_dict({k.replace('module.', ''): v for k, v in torch.load(model_path)['state_dict'].items()})
-        netNetwork.eval()
-
-        return cls(netNetwork)
-
-    def to(self, device):
-        self.netNetwork.to(device)
-        self.device = device
-        return self
-    
-    def __call__(self, input_image, detect_resolution=512, safe=False, output_type="pil", scribble=False, apply_filter=True, upscale_method="INTER_CUBIC", **kwargs):
-        input_image, output_type = common_input_validate(input_image, output_type, **kwargs)
-        detected_map, remove_pad = resize_image_with_pad(input_image, detect_resolution, upscale_method)
-        
-        detected_map = detected_map[:, :, ::-1].copy()
-        with torch.no_grad():
-            image_pidi = torch.from_numpy(detected_map).float().to(self.device)
-            image_pidi = image_pidi / 255.0
-            image_pidi = rearrange(image_pidi, 'h w c -> 1 c h w')
-            edge = self.netNetwork(image_pidi)[-1]
-            edge = edge.cpu().numpy()
-            if apply_filter:
-                edge = edge > 0.5 
-            edge = (edge * 255.0).clip(0, 255).astype(np.uint8)
-
-        detected_map = edge[0, 0]
-
-        if scribble:
-            detected_map = nms(detected_map, 127, 3.0)
-            detected_map = cv2.GaussianBlur(detected_map, (0, 0), 3.0)
-            detected_map[detected_map > 4] = 255
-            detected_map[detected_map < 255] = 0
-
-        detected_map = HWC3(remove_pad(detected_map))
-
-        if output_type == "pil":
-            detected_map = Image.fromarray(detected_map)
-
-        return detected_map
-
-class GrowMask:
-    def expand_mask(self, mask, expand, tapered_corners):
-        c = 0 if tapered_corners else 1
-        kernel = np.array([[c, 1, c],
-                           [1, 1, 1],
-                           [c, 1, c]])
-        mask = mask.reshape((-1, mask.shape[-2], mask.shape[-1]))
-        out = []
-        for m in mask:
-            output = m.numpy()
-            for _ in range(abs(expand)):
-                if expand < 0:
-                    output = scipy.ndimage.grey_erosion(output, footprint=kernel)
-                else:
-                    output = scipy.ndimage.grey_dilation(output, footprint=kernel)
-            output = torch.from_numpy(output)
-            out.append(output)
-        return (torch.stack(out, dim=0),)
-    
-class PIDINET_Preprocessor:
-    def execute(self, image, resolution=512, **kwargs):
-        model = PidiNetDetector.from_pretrained().to(comfy.model_management.get_torch_device())
-        out = common_annotator_call(model, image, resolution=resolution, safe=True)
-        del model
-        return (out, )
\ No newline at end of file
diff --git a/MagicQuill/folder_paths.py b/MagicQuill/folder_paths.py
deleted file mode 100644
index 5e8f6874aa3453d8b1ef22501f91c1841806adf7..0000000000000000000000000000000000000000
--- a/MagicQuill/folder_paths.py
+++ /dev/null
@@ -1,269 +0,0 @@
-import os
-import time
-import logging
-from typing import Set, List, Dict, Tuple
-
-supported_pt_extensions: Set[str] = set(['.ckpt', '.pt', '.bin', '.pth', '.safetensors', '.pkl'])
-
-SupportedFileExtensionsType = Set[str]
-ScanPathType = List[str]
-folder_names_and_paths: Dict[str, Tuple[ScanPathType, SupportedFileExtensionsType]] = {}
-
-base_path = os.path.dirname(os.path.realpath(__file__))
-models_dir = os.path.join(base_path, "../models")
-
-folder_names_and_paths["checkpoints"] = ([os.path.join(models_dir, "checkpoints")], supported_pt_extensions)
-folder_names_and_paths["configs"] = ([os.path.join(models_dir, "configs")], [".yaml"])
-
-folder_names_and_paths["loras"] = ([os.path.join(models_dir, "loras")], supported_pt_extensions)
-folder_names_and_paths["vae"] = ([os.path.join(models_dir, "vae")], supported_pt_extensions)
-folder_names_and_paths["clip"] = ([os.path.join(models_dir, "clip")], supported_pt_extensions)
-folder_names_and_paths["unet"] = ([os.path.join(models_dir, "unet")], supported_pt_extensions)
-folder_names_and_paths["clip_vision"] = ([os.path.join(models_dir, "clip_vision")], supported_pt_extensions)
-folder_names_and_paths["style_models"] = ([os.path.join(models_dir, "style_models")], supported_pt_extensions)
-folder_names_and_paths["embeddings"] = ([os.path.join(models_dir, "embeddings")], supported_pt_extensions)
-folder_names_and_paths["diffusers"] = ([os.path.join(models_dir, "diffusers")], ["folder"])
-folder_names_and_paths["vae_approx"] = ([os.path.join(models_dir, "vae_approx")], supported_pt_extensions)
-
-folder_names_and_paths["controlnet"] = ([os.path.join(models_dir, "controlnet"), os.path.join(models_dir, "t2i_adapter")], supported_pt_extensions)
-folder_names_and_paths["gligen"] = ([os.path.join(models_dir, "gligen")], supported_pt_extensions)
-
-folder_names_and_paths["upscale_models"] = ([os.path.join(models_dir, "upscale_models")], supported_pt_extensions)
-
-folder_names_and_paths["hypernetworks"] = ([os.path.join(models_dir, "hypernetworks")], supported_pt_extensions)
-
-folder_names_and_paths["photomaker"] = ([os.path.join(models_dir, "photomaker")], supported_pt_extensions)
-
-folder_names_and_paths["classifiers"] = ([os.path.join(models_dir, "classifiers")], {""})
-
-output_directory = os.path.join(os.path.dirname(os.path.realpath(__file__)), "output")
-temp_directory = os.path.join(os.path.dirname(os.path.realpath(__file__)), "temp")
-input_directory = os.path.join(os.path.dirname(os.path.realpath(__file__)), "input")
-user_directory = os.path.join(os.path.dirname(os.path.realpath(__file__)), "user")
-
-filename_list_cache = {}
-
-if not os.path.exists(input_directory):
-    try:
-        os.makedirs(input_directory)
-    except:
-        logging.error("Failed to create input directory")
-
-def set_output_directory(output_dir):
-    global output_directory
-    output_directory = output_dir
-
-def set_temp_directory(temp_dir):
-    global temp_directory
-    temp_directory = temp_dir
-
-def set_input_directory(input_dir):
-    global input_directory
-    input_directory = input_dir
-
-def get_output_directory():
-    global output_directory
-    return output_directory
-
-def get_temp_directory():
-    global temp_directory
-    return temp_directory
-
-def get_input_directory():
-    global input_directory
-    return input_directory
-
-
-#NOTE: used in http server so don't put folders that should not be accessed remotely
-def get_directory_by_type(type_name):
-    if type_name == "output":
-        return get_output_directory()
-    if type_name == "temp":
-        return get_temp_directory()
-    if type_name == "input":
-        return get_input_directory()
-    return None
-
-
-# determine base_dir rely on annotation if name is 'filename.ext [annotation]' format
-# otherwise use default_path as base_dir
-def annotated_filepath(name):
-    if name.endswith("[output]"):
-        base_dir = get_output_directory()
-        name = name[:-9]
-    elif name.endswith("[input]"):
-        base_dir = get_input_directory()
-        name = name[:-8]
-    elif name.endswith("[temp]"):
-        base_dir = get_temp_directory()
-        name = name[:-7]
-    else:
-        return name, None
-
-    return name, base_dir
-
-
-def get_annotated_filepath(name, default_dir=None):
-    name, base_dir = annotated_filepath(name)
-
-    if base_dir is None:
-        if default_dir is not None:
-            base_dir = default_dir
-        else:
-            base_dir = get_input_directory()  # fallback path
-
-    return os.path.join(base_dir, name)
-
-
-def exists_annotated_filepath(name):
-    name, base_dir = annotated_filepath(name)
-
-    if base_dir is None:
-        base_dir = get_input_directory()  # fallback path
-
-    filepath = os.path.join(base_dir, name)
-    return os.path.exists(filepath)
-
-
-def add_model_folder_path(folder_name, full_folder_path):
-    global folder_names_and_paths
-    if folder_name in folder_names_and_paths:
-        folder_names_and_paths[folder_name][0].append(full_folder_path)
-    else:
-        folder_names_and_paths[folder_name] = ([full_folder_path], set())
-
-def get_folder_paths(folder_name):
-    return folder_names_and_paths[folder_name][0][:]
-
-def recursive_search(directory, excluded_dir_names=None):
-    if not os.path.isdir(directory):
-        return [], {}
-
-    if excluded_dir_names is None:
-        excluded_dir_names = []
-
-    result = []
-    dirs = {}
-
-    # Attempt to add the initial directory to dirs with error handling
-    try:
-        dirs[directory] = os.path.getmtime(directory)
-    except FileNotFoundError:
-        logging.warning(f"Warning: Unable to access {directory}. Skipping this path.")
-
-    logging.debug("recursive file list on directory {}".format(directory))
-    for dirpath, subdirs, filenames in os.walk(directory, followlinks=True, topdown=True):
-        subdirs[:] = [d for d in subdirs if d not in excluded_dir_names]
-        for file_name in filenames:
-            relative_path = os.path.relpath(os.path.join(dirpath, file_name), directory)
-            result.append(relative_path)
-
-        for d in subdirs:
-            path = os.path.join(dirpath, d)
-            try:
-                dirs[path] = os.path.getmtime(path)
-            except FileNotFoundError:
-                logging.warning(f"Warning: Unable to access {path}. Skipping this path.")
-                continue
-    logging.debug("found {} files".format(len(result)))
-    return result, dirs
-
-def filter_files_extensions(files, extensions):
-    return sorted(list(filter(lambda a: os.path.splitext(a)[-1].lower() in extensions or len(extensions) == 0, files)))
-
-
-
-def get_full_path(folder_name, filename):
-    global folder_names_and_paths
-    if folder_name not in folder_names_and_paths:
-        return None
-    folders = folder_names_and_paths[folder_name]
-    filename = os.path.relpath(os.path.join("/", filename), "/")
-    for x in folders[0]:
-        full_path = os.path.join(x, filename)
-        if os.path.isfile(full_path):
-            return full_path
-        elif os.path.islink(full_path):
-            logging.warning("WARNING path {} exists but doesn't link anywhere, skipping.".format(full_path))
-
-    return None
-
-def get_filename_list_(folder_name):
-    global folder_names_and_paths
-    output_list = set()
-    folders = folder_names_and_paths[folder_name]
-    output_folders = {}
-    for x in folders[0]:
-        files, folders_all = recursive_search(x, excluded_dir_names=[".git"])
-        output_list.update(filter_files_extensions(files, folders[1]))
-        output_folders = {**output_folders, **folders_all}
-
-    return (sorted(list(output_list)), output_folders, time.perf_counter())
-
-def cached_filename_list_(folder_name):
-    global filename_list_cache
-    global folder_names_and_paths
-    if folder_name not in filename_list_cache:
-        return None
-    out = filename_list_cache[folder_name]
-
-    for x in out[1]:
-        time_modified = out[1][x]
-        folder = x
-        if os.path.getmtime(folder) != time_modified:
-            return None
-
-    folders = folder_names_and_paths[folder_name]
-    for x in folders[0]:
-        if os.path.isdir(x):
-            if x not in out[1]:
-                return None
-
-    return out
-
-def get_filename_list(folder_name):
-    out = cached_filename_list_(folder_name)
-    if out is None:
-        out = get_filename_list_(folder_name)
-        global filename_list_cache
-        filename_list_cache[folder_name] = out
-    return list(out[0])
-
-def get_save_image_path(filename_prefix, output_dir, image_width=0, image_height=0):
-    def map_filename(filename):
-        prefix_len = len(os.path.basename(filename_prefix))
-        prefix = filename[:prefix_len + 1]
-        try:
-            digits = int(filename[prefix_len + 1:].split('_')[0])
-        except:
-            digits = 0
-        return (digits, prefix)
-
-    def compute_vars(input, image_width, image_height):
-        input = input.replace("%width%", str(image_width))
-        input = input.replace("%height%", str(image_height))
-        return input
-
-    filename_prefix = compute_vars(filename_prefix, image_width, image_height)
-
-    subfolder = os.path.dirname(os.path.normpath(filename_prefix))
-    filename = os.path.basename(os.path.normpath(filename_prefix))
-
-    full_output_folder = os.path.join(output_dir, subfolder)
-
-    if os.path.commonpath((output_dir, os.path.abspath(full_output_folder))) != output_dir:
-        err = "**** ERROR: Saving image outside the output folder is not allowed." + \
-              "\n full_output_folder: " + os.path.abspath(full_output_folder) + \
-              "\n         output_dir: " + output_dir + \
-              "\n         commonpath: " + os.path.commonpath((output_dir, os.path.abspath(full_output_folder)))
-        logging.error(err)
-        raise Exception(err)
-
-    try:
-        counter = max(filter(lambda a: os.path.normcase(a[1][:-1]) == os.path.normcase(filename) and a[1][-1] == "_", map(map_filename, os.listdir(full_output_folder))))[0] + 1
-    except ValueError:
-        counter = 1
-    except FileNotFoundError:
-        os.makedirs(full_output_folder, exist_ok=True)
-        counter = 1
-    return full_output_folder, filename, counter, subfolder, filename_prefix
diff --git a/MagicQuill/latent_preview.py b/MagicQuill/latent_preview.py
deleted file mode 100644
index 1a6b71e90a2c15b115ffd0eaac5fd321067bdeaf..0000000000000000000000000000000000000000
--- a/MagicQuill/latent_preview.py
+++ /dev/null
@@ -1,99 +0,0 @@
-import torch
-from PIL import Image
-import struct
-import numpy as np
-from comfy.cli_args import args, LatentPreviewMethod
-from comfy.taesd.taesd import TAESD
-import comfy.model_management
-import folder_paths
-import comfy.utils
-import logging
-import gradio as gr
-
-MAX_PREVIEW_RESOLUTION = 512
-
-def preview_to_image(latent_image):
-        latents_ubyte = (((latent_image + 1.0) / 2.0).clamp(0, 1)  # change scale from -1..1 to 0..1
-                            .mul(0xFF)  # to 0..255
-                            ).to(device="cpu", dtype=torch.uint8, non_blocking=comfy.model_management.device_supports_non_blocking(latent_image.device))
-
-        return Image.fromarray(latents_ubyte.numpy())
-
-class LatentPreviewer:
-    def decode_latent_to_preview(self, x0):
-        pass
-
-    def decode_latent_to_preview_image(self, preview_format, x0):
-        preview_image = self.decode_latent_to_preview(x0)
-        return ("JPEG", preview_image, MAX_PREVIEW_RESOLUTION)
-
-class TAESDPreviewerImpl(LatentPreviewer):
-    def __init__(self, taesd):
-        self.taesd = taesd
-
-    def decode_latent_to_preview(self, x0):
-        x_sample = self.taesd.decode(x0[:1])[0].movedim(0, 2)
-        return preview_to_image(x_sample)
-
-
-class Latent2RGBPreviewer(LatentPreviewer):
-    def __init__(self, latent_rgb_factors):
-        self.latent_rgb_factors = torch.tensor(latent_rgb_factors, device="cpu")
-
-    def decode_latent_to_preview(self, x0):
-        self.latent_rgb_factors = self.latent_rgb_factors.to(dtype=x0.dtype, device=x0.device)
-        latent_image = x0[0].permute(1, 2, 0) @ self.latent_rgb_factors
-        return preview_to_image(latent_image)
-
-
-def get_previewer(device, latent_format):
-    previewer = None
-    method = args.preview_method
-    if method != LatentPreviewMethod.NoPreviews:
-        # TODO previewer methods
-        taesd_decoder_path = None
-        if latent_format.taesd_decoder_name is not None:
-            taesd_decoder_path = next(
-                (fn for fn in folder_paths.get_filename_list("vae_approx")
-                    if fn.startswith(latent_format.taesd_decoder_name)),
-                ""
-            )
-            taesd_decoder_path = folder_paths.get_full_path("vae_approx", taesd_decoder_path)
-
-        if method == LatentPreviewMethod.Auto:
-            method = LatentPreviewMethod.Latent2RGB
-
-        if method == LatentPreviewMethod.TAESD:
-            if taesd_decoder_path:
-                taesd = TAESD(None, taesd_decoder_path, latent_channels=latent_format.latent_channels).to(device)
-                previewer = TAESDPreviewerImpl(taesd)
-            else:
-                logging.warning("Warning: TAESD previews enabled, but could not find models/vae_approx/{}".format(latent_format.taesd_decoder_name))
-
-        if previewer is None:
-            if latent_format.latent_rgb_factors is not None:
-                previewer = Latent2RGBPreviewer(latent_format.latent_rgb_factors)
-    return previewer
-
-def prepare_callback(model, steps, x0_output_dict=None):
-    preview_format = "JPEG"
-    if preview_format not in ["JPEG", "PNG"]:
-        preview_format = "JPEG"
-
-    previewer = get_previewer(model.load_device, model.model.latent_format)
-
-    pbar = comfy.utils.ProgressBar(steps)
-    gradio_progress = gr.Progress()
-    gradio_bar = gradio_progress.tqdm(range(steps))
-    # print(type(gradio_bar))
-    def callback(step, x0, x, total_steps):
-        if x0_output_dict is not None:
-            x0_output_dict["x0"] = x0
-
-        preview_bytes = None
-        if previewer:
-            preview_bytes = previewer.decode_latent_to_preview_image(preview_format, x0)
-        pbar.update_absolute(step + 1, total_steps, preview_bytes)
-        gradio_bar.update(1)
-    return callback
-
diff --git a/MagicQuill/llava_new.py b/MagicQuill/llava_new.py
deleted file mode 100644
index 4bbc34f7da7e23855d5cfdc827969fdde03961bf..0000000000000000000000000000000000000000
--- a/MagicQuill/llava_new.py
+++ /dev/null
@@ -1,111 +0,0 @@
-import torch
-from transformers import TextStreamer
-import webcolors
-import os
-import random
-from collections import Counter
-import numpy as np
-from torchvision import transforms
-from .magic_utils import get_colored_contour, find_different_colors, get_bounding_box_from_mask
-from .LLaVA.llava.conversation import conv_templates, SeparatorStyle
-from .LLaVA.llava.model.builder import load_pretrained_model
-from .LLaVA.llava.mm_utils import get_model_name_from_path, expand2square, tokenizer_image_token
-from .LLaVA.llava.constants import (
-    IMAGE_TOKEN_INDEX,
-    DEFAULT_IMAGE_TOKEN,
-    DEFAULT_IM_START_TOKEN,
-    DEFAULT_IM_END_TOKEN,
-    IMAGE_PLACEHOLDER,
-)
-import re
-
-class LLaVAModel:
-    def __init__(self):
-        current_dir = os.path.dirname(os.path.abspath(__file__))
-        model_path = os.path.join(current_dir, "../models/llava-v1.5-7b-finetune-clean")
-        self.tokenizer, self.model, self.image_processor, self.context_len = load_pretrained_model(
-            model_path=model_path,
-            model_base=None,
-            model_name=get_model_name_from_path(model_path),
-            load_4bit=True
-        )
-
-    def generate_description(self, images, question):
-        qs = question
-        image_token_se = DEFAULT_IM_START_TOKEN + DEFAULT_IMAGE_TOKEN + DEFAULT_IM_END_TOKEN
-        if IMAGE_PLACEHOLDER in qs:
-            if self.model.config.mm_use_im_start_end:
-                qs = re.sub(IMAGE_PLACEHOLDER, image_token_se, qs)
-            else:
-                qs = re.sub(IMAGE_PLACEHOLDER, DEFAULT_IMAGE_TOKEN, qs)
-        else:
-            if self.model.config.mm_use_im_start_end:
-                qs = image_token_se + "\n" + qs
-            else:
-                qs = DEFAULT_IMAGE_TOKEN + "\n" + qs
-
-        images_tensor = []
-        image_sizes = []
-        to_pil = transforms.ToPILImage()
-        for image in images:
-            image = image.clone().permute(2, 0, 1).cpu()
-            image = to_pil(image)
-            image_sizes.append(image.size)
-            image = expand2square(image, tuple(int(x) for x in self.image_processor.image_mean))
-            image = self.image_processor.preprocess(image, return_tensors='pt')['pixel_values'][0]
-            images_tensor.append(image.half())
-
-        conv = conv_templates["llava_v1"].copy()
-        conv.append_message(conv.roles[0], qs)
-        conv.append_message(conv.roles[1], None)
-        prompt = conv.get_prompt()
-        
-        input_ids = (
-            tokenizer_image_token(prompt, self.tokenizer, IMAGE_TOKEN_INDEX, return_tensors="pt")
-            .unsqueeze(0)
-            .cuda()
-        )
-
-        with torch.inference_mode():
-            output_ids = self.model.generate(
-                input_ids,
-                images=images_tensor,
-                image_sizes=image_sizes,
-                temperature=0.2,
-                do_sample=True,
-                use_cache=True,
-            )
-        outputs = self.tokenizer.decode(output_ids[0]).strip()
-        outputs = outputs.split('>')[1].split('<')[0]
-        # print(outputs)
-        return outputs
-
-    def process(self, image, colored_image, add_mask):
-        description = ""
-        answer1 = ""
-        answer2 = ""
-        
-        image_with_sketch = image.clone()
-        if torch.sum(add_mask).item() > 0:
-            x_min, y_min, x_max, y_max = get_bounding_box_from_mask(add_mask)
-            # print(x_min, y_min, x_max, y_max)
-            question = f"This is an 'I draw, you guess' game. I will upload an image containing some sketches. To help you locate the sketch, I will give you the normalized bounding box coordinates of the sketch where their original coordinates are divided by the image width and height. The top-left corner of the bounding box is at ({x_min}, {y_min}), and the bottom-right corner is at ({x_max}, {y_max}). Now tell me, what am I trying to draw with these sketches in the image?"
-            # image_with_sketch[add_mask > 0.5] = 1.0
-            bool_add_mask = add_mask > 0.5
-            mean_brightness = image_with_sketch[bool_add_mask].mean()
-            if mean_brightness > 0.8:
-                image_with_sketch[bool_add_mask] = 0.0
-            else:
-                image_with_sketch[bool_add_mask] = 1.0
-            answer1 = self.generate_description([image_with_sketch.squeeze() * 255], question)
-            print(answer1)
-
-        if not torch.equal(image, colored_image):
-            color = find_different_colors(image.squeeze() * 255, colored_image.squeeze() * 255)
-            image_with_bbox, colored_mask = get_colored_contour(colored_image.squeeze() * 255, image.squeeze() * 255)
-            x_min, y_min, x_max, y_max = get_bounding_box_from_mask(colored_mask)
-            question = f"The user will upload an image containing some contours in red color. To help you locate the contour, I will give you the normalized bounding box coordinates where their original coordinates are divided by the image width and height. The top-left corner of the bounding box is at ({x_min}, {y_min}), and the bottom-right corner is at ({x_max}, {y_max}). You need to identify what is inside the contours using a single word or phrase."
-            answer2 = color + ', ' + self.generate_description([image_with_bbox.squeeze() * 255], question)
-            print(answer2)
-
-        return (description, answer1, answer2)
\ No newline at end of file
diff --git a/MagicQuill/magic_utils.py b/MagicQuill/magic_utils.py
deleted file mode 100644
index 61696f913e941aa85dc8232e2fba61ab5e4fe200..0000000000000000000000000000000000000000
--- a/MagicQuill/magic_utils.py
+++ /dev/null
@@ -1,203 +0,0 @@
-import webcolors
-import random
-from collections import Counter
-import numpy as np
-from torchvision import transforms
-import cv2  # OpenCV
-import torch
-import warnings
-import os
-
-
-
-def HWC3(x):
-    assert x.dtype == np.uint8
-    if x.ndim == 2:
-        x = x[:, :, None]
-    assert x.ndim == 3
-    H, W, C = x.shape
-    assert C == 1 or C == 3 or C == 4
-    if C == 3:
-        return x
-    if C == 1:
-        return np.concatenate([x, x, x], axis=2)
-    if C == 4:
-        color = x[:, :, 0:3].astype(np.float32)
-        alpha = x[:, :, 3:4].astype(np.float32) / 255.0
-        y = color * alpha + 255.0 * (1.0 - alpha)
-        y = y.clip(0, 255).astype(np.uint8)
-        return y
-    
-def common_input_validate(input_image, output_type, **kwargs):
-    if "img" in kwargs:
-            warnings.warn("img is deprecated, please use `input_image=...` instead.", DeprecationWarning)
-            input_image = kwargs.pop("img")
-    
-    if "return_pil" in kwargs:
-            warnings.warn("return_pil is deprecated. Use output_type instead.", DeprecationWarning)
-            output_type = "pil" if kwargs["return_pil"] else "np"
-    
-    if type(output_type) is bool:
-        warnings.warn("Passing `True` or `False` to `output_type` is deprecated and will raise an error in future versions")
-        if output_type:
-            output_type = "pil"
-
-    if input_image is None:
-        raise ValueError("input_image must be defined.")
-
-    if not isinstance(input_image, np.ndarray):
-        input_image = np.array(input_image, dtype=np.uint8)
-        output_type = output_type or "pil"
-    else:
-        output_type = output_type or "np"
-    
-    return (input_image, output_type)
-
-def cv2_resize_shortest_edge(image, size):
-    h, w = image.shape[:2]
-    if h < w:
-        new_h = size
-        new_w = int(round(w / h * size))
-    else:
-        new_w = size
-        new_h = int(round(h / w * size))
-    resized_image = cv2.resize(image, (new_w, new_h), interpolation=cv2.INTER_AREA)
-    return resized_image
-
-def apply_color(img, res=512):
-    img = cv2_resize_shortest_edge(img, res)
-    h, w = img.shape[:2]
-
-    input_img_color = cv2.resize(img, (w//64, h//64), interpolation=cv2.INTER_CUBIC)  
-    input_img_color = cv2.resize(input_img_color, (w, h), interpolation=cv2.INTER_NEAREST)
-    return input_img_color
-
-UPSCALE_METHODS = ["INTER_NEAREST", "INTER_LINEAR", "INTER_AREA", "INTER_CUBIC", "INTER_LANCZOS4"]
-def get_upscale_method(method_str):
-    assert method_str in UPSCALE_METHODS, f"Method {method_str} not found in {UPSCALE_METHODS}"
-    return getattr(cv2, method_str)
-
-def pad64(x):
-    return int(np.ceil(float(x) / 64.0) * 64 - x)
-
-def safer_memory(x):
-    # Fix many MAC/AMD problems
-    return np.ascontiguousarray(x.copy()).copy()
-
-def resize_image_with_pad(input_image, resolution, upscale_method = "", skip_hwc3=False, mode='edge'):
-    if skip_hwc3:
-        img = input_image
-    else:
-        img = HWC3(input_image)
-    H_raw, W_raw, _ = img.shape
-    if resolution == 0:
-        return img, lambda x: x
-    k = float(resolution) / float(min(H_raw, W_raw))
-    H_target = int(np.round(float(H_raw) * k))
-    W_target = int(np.round(float(W_raw) * k))
-    img = cv2.resize(img, (W_target, H_target), interpolation=get_upscale_method(upscale_method) if k > 1 else cv2.INTER_AREA)
-    H_pad, W_pad = pad64(H_target), pad64(W_target)
-    img_padded = np.pad(img, [[0, H_pad], [0, W_pad], [0, 0]], mode=mode)
-
-    def remove_pad(x):
-        return safer_memory(x[:H_target, :W_target, ...])
-
-    return safer_memory(img_padded), remove_pad
-
-def draw_contour(img, mask):
-    mask_np = mask.numpy().astype(np.uint8) * 255
-    img_np = img.numpy()
-    img_np = img_np.astype(np.uint8)
-    img_bgr = cv2.cvtColor(img_np, cv2.COLOR_RGB2BGR)
-
-    kernel = np.ones((5, 5), np.uint8)
-    mask_dilated = cv2.dilate(mask_np, kernel, iterations=3)
-    contours, _ = cv2.findContours(mask_np, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
-    for contour in contours:
-        cv2.drawContours(img_bgr, [contour], -1, (0, 0, 255), thickness=10)
-    img_np = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2RGB)
-    transform = transforms.ToTensor()
-    img_tensor = transform(img_np)
-
-    img_tensor = img_tensor.permute(1, 2, 0)
-
-    return img_tensor.unsqueeze(0)
-
-def get_colored_contour(img1, img2, threshold=10):
-    diff = torch.abs(img1 - img2).float()
-    diff_gray = torch.mean(diff, dim=-1)
-    mask = diff_gray > threshold
-
-    return draw_contour(img2, mask), mask
-
-def closest_colour(requested_colour):
-    min_colours = {}
-    for key, name in webcolors.CSS3_HEX_TO_NAMES.items():
-        r_c, g_c, b_c = webcolors.hex_to_rgb(key)
-        rd = (r_c - requested_colour[0].item()) ** 2
-        gd = (g_c - requested_colour[1].item()) ** 2
-        bd = (b_c - requested_colour[2].item()) ** 2
-        min_colours[(rd + gd + bd)] = name
-    return min_colours[min(min_colours.keys())]
-
-def rgb_to_name(rgb_tuple):
-    try:
-        return webcolors.rgb_to_name(rgb_tuple)
-    except ValueError:
-        closest_name = closest_colour(rgb_tuple)
-        return closest_name
-
-def find_different_colors(img1, img2, threshold=10):
-    img1 = img1.to(torch.uint8)
-    img2 = img2.to(torch.uint8)
-    diff = torch.abs(img1 - img2).float().mean(dim=-1)
-    diff_mask = diff > threshold
-    diff_indices = torch.nonzero(diff_mask, as_tuple=True)
-
-    if len(diff_indices[0]) > 100:
-        sampled_indices = random.sample(range(len(diff_indices[0])), 100)
-        sampled_diff_indices = (diff_indices[0][sampled_indices], diff_indices[1][sampled_indices])
-    else:
-        sampled_diff_indices = diff_indices
-
-    diff_colors = img2[sampled_diff_indices[0], sampled_diff_indices[1], :]
-    color_names = [rgb_to_name(tuple(color)) for color in diff_colors]
-    name_counter = Counter(color_names)
-    filtered_colors = {name: count for name, count in name_counter.items() if count > 10}
-    sorted_color_names = [name for name, count in sorted(filtered_colors.items(), key=lambda item: item[1], reverse=True)]
-    if len(sorted_color_names) >= 3:
-        return "colorful"
-    unique_color_names_str = ', '.join(sorted_color_names)
-    return unique_color_names_str
-
-def get_bounding_box_from_mask(mask, padded=False):
-    # Ensure the mask is a binary mask (0s and 1s)
-    mask = mask.squeeze()
-    rows, cols = torch.where(mask > 0.5)
-    if len(rows) == 0 or len(cols) == 0:
-        return (0, 0, 0, 0)
-    height, width = mask.shape
-    if padded:
-        padded_size = max(width, height)
-        if width < height:
-            offset_x = (padded_size - width) / 2
-            offset_y = 0
-        else:
-            offset_y = (padded_size - height) / 2
-            offset_x = 0
-        # Find the bounding box coordinates
-        top_left_x = round(float((torch.min(cols).item() + offset_x) / padded_size), 3)
-        bottom_right_x = round(float((torch.max(cols).item() + offset_x) / padded_size), 3)
-        top_left_y = round(float((torch.min(rows).item() + offset_y) / padded_size), 3)
-        bottom_right_y = round(float((torch.max(rows).item() + offset_y) / padded_size), 3)
-    else:
-        offset_x = 0
-        offset_y = 0
-
-        top_left_x = round(float(torch.min(cols).item() / width), 3)
-        bottom_right_x = round(float(torch.max(cols).item() / width), 3)
-        top_left_y = round(float(torch.min(rows).item() / height), 3)
-        bottom_right_y = round(float(torch.max(rows).item() / height), 3)
-
-    
-    return (top_left_x, top_left_y, bottom_right_x, bottom_right_y)
\ No newline at end of file
diff --git a/MagicQuill/model_patch.py b/MagicQuill/model_patch.py
deleted file mode 100644
index e31113f8a07d0b10749d124e7d0a2e2220f2d212..0000000000000000000000000000000000000000
--- a/MagicQuill/model_patch.py
+++ /dev/null
@@ -1,138 +0,0 @@
-import torch
-import comfy
-
-
-# Check and add 'model_patch' to model.model_options['transformer_options']
-def add_model_patch_option(model):
-    if 'transformer_options' not in model.model_options:
-        model.model_options['transformer_options'] = {}
-    to = model.model_options['transformer_options']
-    if "model_patch" not in to:
-        to["model_patch"] = {}
-    return to
-
-
-# Patch model with model_function_wrapper
-def patch_model_function_wrapper(model, forward_patch, remove=False):
-
-    def brushnet_model_function_wrapper(apply_model_method, options_dict):
-        to = options_dict['c']['transformer_options']
-
-        control = None
-        if 'control' in options_dict['c']:
-            control = options_dict['c']['control']
-        
-        x = options_dict['input']
-        timestep = options_dict['timestep']
-        
-        # check if there are patches to execute
-        if 'model_patch' not in to or 'forward' not in to['model_patch']:
-            return apply_model_method(x, timestep, **options_dict['c'])
-
-        mp = to['model_patch']
-        unet = mp['unet']
-        
-        all_sigmas = mp['all_sigmas']
-        sigma = to['sigmas'][0].item()
-        total_steps = all_sigmas.shape[0] - 1
-        step = torch.argmin((all_sigmas - sigma).abs()).item()
-
-        mp['step'] = step
-        mp['total_steps'] = total_steps
-
-        # comfy.model_base.apply_model
-        xc = model.model.model_sampling.calculate_input(timestep, x)
-        if 'c_concat' in options_dict['c'] and options_dict['c']['c_concat'] is not None:
-            xc = torch.cat([xc] + [options_dict['c']['c_concat']], dim=1)
-        t = model.model.model_sampling.timestep(timestep).float()
-        # execute all patches        
-        for method in mp['forward']:
-            method(unet, xc, t, to, control)
-            
-        return apply_model_method(x, timestep, **options_dict['c'])
-    
-    if "model_function_wrapper" in model.model_options and model.model_options["model_function_wrapper"]:
-        print('BrushNet is going to replace existing model_function_wrapper:', model.model_options["model_function_wrapper"])
-    model.set_model_unet_function_wrapper(brushnet_model_function_wrapper)   
-
-    to = add_model_patch_option(model)
-    mp = to['model_patch']
-
-    if isinstance(model.model.model_config, comfy.supported_models.SD15):
-        mp['SDXL'] = False
-    elif isinstance(model.model.model_config, comfy.supported_models.SDXL):
-        mp['SDXL'] = True
-    else:
-        print('Base model type: ', type(model.model.model_config))
-        raise Exception("Unsupported model type: ", type(model.model.model_config))
-
-    if 'forward' not in mp:
-        mp['forward'] = []
-
-    if remove:
-        if forward_patch in mp['forward']:
-            mp['forward'].remove(forward_patch)
-    else:
-        mp['forward'].append(forward_patch)
-
-    mp['unet'] = model.model.diffusion_model
-    mp['step'] = 0
-    mp['total_steps'] = 1
-
-    # apply patches to code
-    if comfy.samplers.sample.__doc__ is None or 'BrushNet' not in comfy.samplers.sample.__doc__:
-        comfy.samplers.original_sample = comfy.samplers.sample
-        comfy.samplers.sample = modified_sample
-
-    if comfy.ldm.modules.diffusionmodules.openaimodel.apply_control.__doc__ is None or \
-        'BrushNet' not in comfy.ldm.modules.diffusionmodules.openaimodel.apply_control.__doc__:
-        comfy.ldm.modules.diffusionmodules.openaimodel.original_apply_control = comfy.ldm.modules.diffusionmodules.openaimodel.apply_control
-        comfy.ldm.modules.diffusionmodules.openaimodel.apply_control = modified_apply_control
-
-
-# Model needs current step number and cfg at inference step. It is possible to write a custom KSampler but I'd like to use ComfyUI's one.
-# The first versions had modified_common_ksampler, but it broke custom KSampler nodes
-def modified_sample(model, noise, positive, negative, cfg, device, sampler, sigmas, model_options={}, 
-           latent_image=None, denoise_mask=None, callback=None, disable_pbar=False, seed=None):
-    '''
-    Modified by BrushNet nodes
-    '''
-    cfg_guider = comfy.samplers.CFGGuider(model)
-    cfg_guider.set_conds(positive, negative)
-    cfg_guider.set_cfg(cfg)
-
-    ### Modified part ######################################################################
-    #
-    to = add_model_patch_option(model)
-    to['model_patch']['all_sigmas'] = sigmas
-    #
-    #sigma_start = model.get_model_object("model_sampling").percent_to_sigma(start_at)
-    #sigma_end = model.get_model_object("model_sampling").percent_to_sigma(end_at)
-    #
-    #
-    #if math.isclose(cfg, 1.0) and model_options.get("disable_cfg1_optimization", False) == False:
-    #    to['model_patch']['free_guidance'] = False
-    #else:
-    #    to['model_patch']['free_guidance'] = True
-    #
-    #######################################################################################
-       
-    return cfg_guider.sample(noise, latent_image, sampler, sigmas, denoise_mask, callback, disable_pbar, seed)
-
-
-# To use Controlnet with RAUNet it is much easier to modify apply_control a little
-def modified_apply_control(h, control, name):
-    '''
-    Modified by BrushNet nodes
-    '''
-    if control is not None and name in control and len(control[name]) > 0:
-        ctrl = control[name].pop()
-        if ctrl is not None:
-            if h.shape[2] != ctrl.shape[2] or h.shape[3] != ctrl.shape[3]:
-                ctrl = torch.nn.functional.interpolate(ctrl, size=(h.shape[2], h.shape[3]), mode='bicubic').to(h.dtype).to(h.device)                    
-            try:
-                h += ctrl
-            except:
-                print.warning("warning control could not be applied {} {}".format(h.shape, ctrl.shape))
-    return h    
-
diff --git a/MagicQuill/pidi.py b/MagicQuill/pidi.py
deleted file mode 100644
index 13dcaf42a8b2aff722a62d366727e9ab72894cd9..0000000000000000000000000000000000000000
--- a/MagicQuill/pidi.py
+++ /dev/null
@@ -1,667 +0,0 @@
-"""
-Author: Zhuo Su, Wenzhe Liu
-Date: Feb 18, 2021
-"""
-
-import math
-
-import cv2
-import numpy as np
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-
-def img2tensor(imgs, bgr2rgb=True, float32=True):
-    """Numpy array to tensor.
-
-    Args:
-        imgs (list[ndarray] | ndarray): Input images.
-        bgr2rgb (bool): Whether to change bgr to rgb.
-        float32 (bool): Whether to change to float32.
-
-    Returns:
-        list[tensor] | tensor: Tensor images. If returned results only have
-            one element, just return tensor.
-    """
-
-    def _totensor(img, bgr2rgb, float32):
-        if img.shape[2] == 3 and bgr2rgb:
-            if img.dtype == 'float64':
-                img = img.astype('float32')
-            img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
-        img = torch.from_numpy(img.transpose(2, 0, 1))
-        if float32:
-            img = img.float()
-        return img
-
-    if isinstance(imgs, list):
-        return [_totensor(img, bgr2rgb, float32) for img in imgs]
-    else:
-        return _totensor(imgs, bgr2rgb, float32)
-
-nets = {
-    'baseline': {
-        'layer0':  'cv',
-        'layer1':  'cv',
-        'layer2':  'cv',
-        'layer3':  'cv',
-        'layer4':  'cv',
-        'layer5':  'cv',
-        'layer6':  'cv',
-        'layer7':  'cv',
-        'layer8':  'cv',
-        'layer9':  'cv',
-        'layer10': 'cv',
-        'layer11': 'cv',
-        'layer12': 'cv',
-        'layer13': 'cv',
-        'layer14': 'cv',
-        'layer15': 'cv',
-        },
-    'c-v15': {
-        'layer0':  'cd',
-        'layer1':  'cv',
-        'layer2':  'cv',
-        'layer3':  'cv',
-        'layer4':  'cv',
-        'layer5':  'cv',
-        'layer6':  'cv',
-        'layer7':  'cv',
-        'layer8':  'cv',
-        'layer9':  'cv',
-        'layer10': 'cv',
-        'layer11': 'cv',
-        'layer12': 'cv',
-        'layer13': 'cv',
-        'layer14': 'cv',
-        'layer15': 'cv',
-        },
-    'a-v15': {
-        'layer0':  'ad',
-        'layer1':  'cv',
-        'layer2':  'cv',
-        'layer3':  'cv',
-        'layer4':  'cv',
-        'layer5':  'cv',
-        'layer6':  'cv',
-        'layer7':  'cv',
-        'layer8':  'cv',
-        'layer9':  'cv',
-        'layer10': 'cv',
-        'layer11': 'cv',
-        'layer12': 'cv',
-        'layer13': 'cv',
-        'layer14': 'cv',
-        'layer15': 'cv',
-        },
-    'r-v15': {
-        'layer0':  'rd',
-        'layer1':  'cv',
-        'layer2':  'cv',
-        'layer3':  'cv',
-        'layer4':  'cv',
-        'layer5':  'cv',
-        'layer6':  'cv',
-        'layer7':  'cv',
-        'layer8':  'cv',
-        'layer9':  'cv',
-        'layer10': 'cv',
-        'layer11': 'cv',
-        'layer12': 'cv',
-        'layer13': 'cv',
-        'layer14': 'cv',
-        'layer15': 'cv',
-        },
-    'cvvv4': {
-        'layer0':  'cd',
-        'layer1':  'cv',
-        'layer2':  'cv',
-        'layer3':  'cv',
-        'layer4':  'cd',
-        'layer5':  'cv',
-        'layer6':  'cv',
-        'layer7':  'cv',
-        'layer8':  'cd',
-        'layer9':  'cv',
-        'layer10': 'cv',
-        'layer11': 'cv',
-        'layer12': 'cd',
-        'layer13': 'cv',
-        'layer14': 'cv',
-        'layer15': 'cv',
-        },
-    'avvv4': {
-        'layer0':  'ad',
-        'layer1':  'cv',
-        'layer2':  'cv',
-        'layer3':  'cv',
-        'layer4':  'ad',
-        'layer5':  'cv',
-        'layer6':  'cv',
-        'layer7':  'cv',
-        'layer8':  'ad',
-        'layer9':  'cv',
-        'layer10': 'cv',
-        'layer11': 'cv',
-        'layer12': 'ad',
-        'layer13': 'cv',
-        'layer14': 'cv',
-        'layer15': 'cv',
-        },
-    'rvvv4': {
-        'layer0':  'rd',
-        'layer1':  'cv',
-        'layer2':  'cv',
-        'layer3':  'cv',
-        'layer4':  'rd',
-        'layer5':  'cv',
-        'layer6':  'cv',
-        'layer7':  'cv',
-        'layer8':  'rd',
-        'layer9':  'cv',
-        'layer10': 'cv',
-        'layer11': 'cv',
-        'layer12': 'rd',
-        'layer13': 'cv',
-        'layer14': 'cv',
-        'layer15': 'cv',
-        },
-    'cccv4': {
-        'layer0':  'cd',
-        'layer1':  'cd',
-        'layer2':  'cd',
-        'layer3':  'cv',
-        'layer4':  'cd',
-        'layer5':  'cd',
-        'layer6':  'cd',
-        'layer7':  'cv',
-        'layer8':  'cd',
-        'layer9':  'cd',
-        'layer10': 'cd',
-        'layer11': 'cv',
-        'layer12': 'cd',
-        'layer13': 'cd',
-        'layer14': 'cd',
-        'layer15': 'cv',
-        },
-    'aaav4': {
-        'layer0':  'ad',
-        'layer1':  'ad',
-        'layer2':  'ad',
-        'layer3':  'cv',
-        'layer4':  'ad',
-        'layer5':  'ad',
-        'layer6':  'ad',
-        'layer7':  'cv',
-        'layer8':  'ad',
-        'layer9':  'ad',
-        'layer10': 'ad',
-        'layer11': 'cv',
-        'layer12': 'ad',
-        'layer13': 'ad',
-        'layer14': 'ad',
-        'layer15': 'cv',
-        },
-    'rrrv4': {
-        'layer0':  'rd',
-        'layer1':  'rd',
-        'layer2':  'rd',
-        'layer3':  'cv',
-        'layer4':  'rd',
-        'layer5':  'rd',
-        'layer6':  'rd',
-        'layer7':  'cv',
-        'layer8':  'rd',
-        'layer9':  'rd',
-        'layer10': 'rd',
-        'layer11': 'cv',
-        'layer12': 'rd',
-        'layer13': 'rd',
-        'layer14': 'rd',
-        'layer15': 'cv',
-        },
-    'c16': {
-        'layer0':  'cd',
-        'layer1':  'cd',
-        'layer2':  'cd',
-        'layer3':  'cd',
-        'layer4':  'cd',
-        'layer5':  'cd',
-        'layer6':  'cd',
-        'layer7':  'cd',
-        'layer8':  'cd',
-        'layer9':  'cd',
-        'layer10': 'cd',
-        'layer11': 'cd',
-        'layer12': 'cd',
-        'layer13': 'cd',
-        'layer14': 'cd',
-        'layer15': 'cd',
-        },
-    'a16': {
-        'layer0':  'ad',
-        'layer1':  'ad',
-        'layer2':  'ad',
-        'layer3':  'ad',
-        'layer4':  'ad',
-        'layer5':  'ad',
-        'layer6':  'ad',
-        'layer7':  'ad',
-        'layer8':  'ad',
-        'layer9':  'ad',
-        'layer10': 'ad',
-        'layer11': 'ad',
-        'layer12': 'ad',
-        'layer13': 'ad',
-        'layer14': 'ad',
-        'layer15': 'ad',
-        },
-    'r16': {
-        'layer0':  'rd',
-        'layer1':  'rd',
-        'layer2':  'rd',
-        'layer3':  'rd',
-        'layer4':  'rd',
-        'layer5':  'rd',
-        'layer6':  'rd',
-        'layer7':  'rd',
-        'layer8':  'rd',
-        'layer9':  'rd',
-        'layer10': 'rd',
-        'layer11': 'rd',
-        'layer12': 'rd',
-        'layer13': 'rd',
-        'layer14': 'rd',
-        'layer15': 'rd',
-        },
-    'carv4': {
-        'layer0':  'cd',
-        'layer1':  'ad',
-        'layer2':  'rd',
-        'layer3':  'cv',
-        'layer4':  'cd',
-        'layer5':  'ad',
-        'layer6':  'rd',
-        'layer7':  'cv',
-        'layer8':  'cd',
-        'layer9':  'ad',
-        'layer10': 'rd',
-        'layer11': 'cv',
-        'layer12': 'cd',
-        'layer13': 'ad',
-        'layer14': 'rd',
-        'layer15': 'cv',
-        },
-    }
-
-def createConvFunc(op_type):
-    assert op_type in ['cv', 'cd', 'ad', 'rd'], 'unknown op type: %s' % str(op_type)
-    if op_type == 'cv':
-        return F.conv2d
-
-    if op_type == 'cd':
-        def func(x, weights, bias=None, stride=1, padding=0, dilation=1, groups=1):
-            assert dilation in [1, 2], 'dilation for cd_conv should be in 1 or 2'
-            assert weights.size(2) == 3 and weights.size(3) == 3, 'kernel size for cd_conv should be 3x3'
-            assert padding == dilation, 'padding for cd_conv set wrong'
-
-            weights_c = weights.sum(dim=[2, 3], keepdim=True)
-            yc = F.conv2d(x, weights_c, stride=stride, padding=0, groups=groups)
-            y = F.conv2d(x, weights, bias, stride=stride, padding=padding, dilation=dilation, groups=groups)
-            return y - yc
-        return func
-    elif op_type == 'ad':
-        def func(x, weights, bias=None, stride=1, padding=0, dilation=1, groups=1):
-            assert dilation in [1, 2], 'dilation for ad_conv should be in 1 or 2'
-            assert weights.size(2) == 3 and weights.size(3) == 3, 'kernel size for ad_conv should be 3x3'
-            assert padding == dilation, 'padding for ad_conv set wrong'
-
-            shape = weights.shape
-            weights = weights.view(shape[0], shape[1], -1)
-            weights_conv = (weights - weights[:, :, [3, 0, 1, 6, 4, 2, 7, 8, 5]]).view(shape) # clock-wise
-            y = F.conv2d(x, weights_conv, bias, stride=stride, padding=padding, dilation=dilation, groups=groups)
-            return y
-        return func
-    elif op_type == 'rd':
-        def func(x, weights, bias=None, stride=1, padding=0, dilation=1, groups=1):
-            assert dilation in [1, 2], 'dilation for rd_conv should be in 1 or 2'
-            assert weights.size(2) == 3 and weights.size(3) == 3, 'kernel size for rd_conv should be 3x3'
-            padding = 2 * dilation
-
-            shape = weights.shape
-            if weights.is_cuda:
-                buffer = torch.cuda.FloatTensor(shape[0], shape[1], 5 * 5).fill_(0)
-            else:
-                buffer = torch.zeros(shape[0], shape[1], 5 * 5).to(weights.device)
-            weights = weights.view(shape[0], shape[1], -1)
-            buffer[:, :, [0, 2, 4, 10, 14, 20, 22, 24]] = weights[:, :, 1:]
-            buffer[:, :, [6, 7, 8, 11, 13, 16, 17, 18]] = -weights[:, :, 1:]
-            buffer[:, :, 12] = 0
-            buffer = buffer.view(shape[0], shape[1], 5, 5)
-            y = F.conv2d(x, buffer, bias, stride=stride, padding=padding, dilation=dilation, groups=groups)
-            return y
-        return func
-    else:
-        print('impossible to be here unless you force that')
-        return None
-
-class Conv2d(nn.Module):
-    def __init__(self, pdc, in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=False):
-        super(Conv2d, self).__init__()
-        if in_channels % groups != 0:
-            raise ValueError('in_channels must be divisible by groups')
-        if out_channels % groups != 0:
-            raise ValueError('out_channels must be divisible by groups')
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.kernel_size = kernel_size
-        self.stride = stride
-        self.padding = padding
-        self.dilation = dilation
-        self.groups = groups
-        self.weight = nn.Parameter(torch.Tensor(out_channels, in_channels // groups, kernel_size, kernel_size))
-        if bias:
-            self.bias = nn.Parameter(torch.Tensor(out_channels))
-        else:
-            self.register_parameter('bias', None)
-        self.reset_parameters()
-        self.pdc = pdc
-
-    def reset_parameters(self):
-        nn.init.kaiming_uniform_(self.weight, a=math.sqrt(5))
-        if self.bias is not None:
-            fan_in, _ = nn.init._calculate_fan_in_and_fan_out(self.weight)
-            bound = 1 / math.sqrt(fan_in)
-            nn.init.uniform_(self.bias, -bound, bound)
-
-    def forward(self, input):
-
-        return self.pdc(input, self.weight, self.bias, self.stride, self.padding, self.dilation, self.groups)
-
-class CSAM(nn.Module):
-    """
-    Compact Spatial Attention Module
-    """
-    def __init__(self, channels):
-        super(CSAM, self).__init__()
-
-        mid_channels = 4
-        self.relu1 = nn.ReLU()
-        self.conv1 = nn.Conv2d(channels, mid_channels, kernel_size=1, padding=0)
-        self.conv2 = nn.Conv2d(mid_channels, 1, kernel_size=3, padding=1, bias=False)
-        self.sigmoid = nn.Sigmoid()
-        nn.init.constant_(self.conv1.bias, 0)
-
-    def forward(self, x):
-        y = self.relu1(x)
-        y = self.conv1(y)
-        y = self.conv2(y)
-        y = self.sigmoid(y)
-
-        return x * y
-
-class CDCM(nn.Module):
-    """
-    Compact Dilation Convolution based Module
-    """
-    def __init__(self, in_channels, out_channels):
-        super(CDCM, self).__init__()
-
-        self.relu1 = nn.ReLU()
-        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=1, padding=0)
-        self.conv2_1 = nn.Conv2d(out_channels, out_channels, kernel_size=3, dilation=5, padding=5, bias=False)
-        self.conv2_2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, dilation=7, padding=7, bias=False)
-        self.conv2_3 = nn.Conv2d(out_channels, out_channels, kernel_size=3, dilation=9, padding=9, bias=False)
-        self.conv2_4 = nn.Conv2d(out_channels, out_channels, kernel_size=3, dilation=11, padding=11, bias=False)
-        nn.init.constant_(self.conv1.bias, 0)
-
-    def forward(self, x):
-        x = self.relu1(x)
-        x = self.conv1(x)
-        x1 = self.conv2_1(x)
-        x2 = self.conv2_2(x)
-        x3 = self.conv2_3(x)
-        x4 = self.conv2_4(x)
-        return x1 + x2 + x3 + x4
-
-
-class MapReduce(nn.Module):
-    """
-    Reduce feature maps into a single edge map
-    """
-    def __init__(self, channels):
-        super(MapReduce, self).__init__()
-        self.conv = nn.Conv2d(channels, 1, kernel_size=1, padding=0)
-        nn.init.constant_(self.conv.bias, 0)
-
-    def forward(self, x):
-        return self.conv(x)
-
-
-class PDCBlock(nn.Module):
-    def __init__(self, pdc, inplane, ouplane, stride=1):
-        super(PDCBlock, self).__init__()
-        self.stride=stride
-
-        self.stride=stride
-        if self.stride > 1:
-            self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
-            self.shortcut = nn.Conv2d(inplane, ouplane, kernel_size=1, padding=0)
-        self.conv1 = Conv2d(pdc, inplane, inplane, kernel_size=3, padding=1, groups=inplane, bias=False)
-        self.relu2 = nn.ReLU()
-        self.conv2 = nn.Conv2d(inplane, ouplane, kernel_size=1, padding=0, bias=False)
-
-    def forward(self, x):
-        if self.stride > 1:
-            x = self.pool(x)
-        y = self.conv1(x)
-        y = self.relu2(y)
-        y = self.conv2(y)
-        if self.stride > 1:
-            x = self.shortcut(x)
-        y = y + x
-        return y
-
-class PDCBlock_converted(nn.Module):
-    """
-    CPDC, APDC can be converted to vanilla 3x3 convolution
-    RPDC can be converted to vanilla 5x5 convolution
-    """
-    def __init__(self, pdc, inplane, ouplane, stride=1):
-        super(PDCBlock_converted, self).__init__()
-        self.stride=stride
-
-        if self.stride > 1:
-            self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
-            self.shortcut = nn.Conv2d(inplane, ouplane, kernel_size=1, padding=0)
-        if pdc == 'rd':
-            self.conv1 = nn.Conv2d(inplane, inplane, kernel_size=5, padding=2, groups=inplane, bias=False)
-        else:
-            self.conv1 = nn.Conv2d(inplane, inplane, kernel_size=3, padding=1, groups=inplane, bias=False)
-        self.relu2 = nn.ReLU()
-        self.conv2 = nn.Conv2d(inplane, ouplane, kernel_size=1, padding=0, bias=False)
-
-    def forward(self, x):
-        if self.stride > 1:
-            x = self.pool(x)
-        y = self.conv1(x)
-        y = self.relu2(y)
-        y = self.conv2(y)
-        if self.stride > 1:
-            x = self.shortcut(x)
-        y = y + x
-        return y
-
-class PiDiNet(nn.Module):
-    def __init__(self, inplane, pdcs, dil=None, sa=False, convert=False):
-        super(PiDiNet, self).__init__()
-        self.sa = sa
-        if dil is not None:
-            assert isinstance(dil, int), 'dil should be an int'
-        self.dil = dil
-
-        self.fuseplanes = []
-
-        self.inplane = inplane
-        if convert:
-            if pdcs[0] == 'rd':
-                init_kernel_size = 5
-                init_padding = 2
-            else:
-                init_kernel_size = 3
-                init_padding = 1
-            self.init_block = nn.Conv2d(3, self.inplane,
-                    kernel_size=init_kernel_size, padding=init_padding, bias=False)
-            block_class = PDCBlock_converted
-        else:
-            self.init_block = Conv2d(pdcs[0], 3, self.inplane, kernel_size=3, padding=1)
-            block_class = PDCBlock
-
-        self.block1_1 = block_class(pdcs[1], self.inplane, self.inplane)
-        self.block1_2 = block_class(pdcs[2], self.inplane, self.inplane)
-        self.block1_3 = block_class(pdcs[3], self.inplane, self.inplane)
-        self.fuseplanes.append(self.inplane) # C
-
-        inplane = self.inplane
-        self.inplane = self.inplane * 2
-        self.block2_1 = block_class(pdcs[4], inplane, self.inplane, stride=2)
-        self.block2_2 = block_class(pdcs[5], self.inplane, self.inplane)
-        self.block2_3 = block_class(pdcs[6], self.inplane, self.inplane)
-        self.block2_4 = block_class(pdcs[7], self.inplane, self.inplane)
-        self.fuseplanes.append(self.inplane) # 2C
-
-        inplane = self.inplane
-        self.inplane = self.inplane * 2
-        self.block3_1 = block_class(pdcs[8], inplane, self.inplane, stride=2)
-        self.block3_2 = block_class(pdcs[9], self.inplane, self.inplane)
-        self.block3_3 = block_class(pdcs[10], self.inplane, self.inplane)
-        self.block3_4 = block_class(pdcs[11], self.inplane, self.inplane)
-        self.fuseplanes.append(self.inplane) # 4C
-
-        self.block4_1 = block_class(pdcs[12], self.inplane, self.inplane, stride=2)
-        self.block4_2 = block_class(pdcs[13], self.inplane, self.inplane)
-        self.block4_3 = block_class(pdcs[14], self.inplane, self.inplane)
-        self.block4_4 = block_class(pdcs[15], self.inplane, self.inplane)
-        self.fuseplanes.append(self.inplane) # 4C
-
-        self.conv_reduces = nn.ModuleList()
-        if self.sa and self.dil is not None:
-            self.attentions = nn.ModuleList()
-            self.dilations = nn.ModuleList()
-            for i in range(4):
-                self.dilations.append(CDCM(self.fuseplanes[i], self.dil))
-                self.attentions.append(CSAM(self.dil))
-                self.conv_reduces.append(MapReduce(self.dil))
-        elif self.sa:
-            self.attentions = nn.ModuleList()
-            for i in range(4):
-                self.attentions.append(CSAM(self.fuseplanes[i]))
-                self.conv_reduces.append(MapReduce(self.fuseplanes[i]))
-        elif self.dil is not None:
-            self.dilations = nn.ModuleList()
-            for i in range(4):
-                self.dilations.append(CDCM(self.fuseplanes[i], self.dil))
-                self.conv_reduces.append(MapReduce(self.dil))
-        else:
-            for i in range(4):
-                self.conv_reduces.append(MapReduce(self.fuseplanes[i]))
-
-        self.classifier = nn.Conv2d(4, 1, kernel_size=1) # has bias
-        nn.init.constant_(self.classifier.weight, 0.25)
-        nn.init.constant_(self.classifier.bias, 0)
-
-        # print('initialization done')
-
-    def get_weights(self):
-        conv_weights = []
-        bn_weights = []
-        relu_weights = []
-        for pname, p in self.named_parameters():
-            if 'bn' in pname:
-                bn_weights.append(p)
-            elif 'relu' in pname:
-                relu_weights.append(p)
-            else:
-                conv_weights.append(p)
-
-        return conv_weights, bn_weights, relu_weights
-
-    def forward(self, x):
-        H, W = x.size()[2:]
-
-        x = self.init_block(x)
-
-        x1 = self.block1_1(x)
-        x1 = self.block1_2(x1)
-        x1 = self.block1_3(x1)
-
-        x2 = self.block2_1(x1)
-        x2 = self.block2_2(x2)
-        x2 = self.block2_3(x2)
-        x2 = self.block2_4(x2)
-
-        x3 = self.block3_1(x2)
-        x3 = self.block3_2(x3)
-        x3 = self.block3_3(x3)
-        x3 = self.block3_4(x3)
-
-        x4 = self.block4_1(x3)
-        x4 = self.block4_2(x4)
-        x4 = self.block4_3(x4)
-        x4 = self.block4_4(x4)
-
-        x_fuses = []
-        if self.sa and self.dil is not None:
-            for i, xi in enumerate([x1, x2, x3, x4]):
-                x_fuses.append(self.attentions[i](self.dilations[i](xi)))
-        elif self.sa:
-            for i, xi in enumerate([x1, x2, x3, x4]):
-                x_fuses.append(self.attentions[i](xi))
-        elif self.dil is not None:
-            for i, xi in enumerate([x1, x2, x3, x4]):
-                x_fuses.append(self.dilations[i](xi))
-        else:
-            x_fuses = [x1, x2, x3, x4]
-
-        e1 = self.conv_reduces[0](x_fuses[0])
-        e1 = F.interpolate(e1, (H, W), mode="bilinear", align_corners=False)
-
-        e2 = self.conv_reduces[1](x_fuses[1])
-        e2 = F.interpolate(e2, (H, W), mode="bilinear", align_corners=False)
-
-        e3 = self.conv_reduces[2](x_fuses[2])
-        e3 = F.interpolate(e3, (H, W), mode="bilinear", align_corners=False)
-
-        e4 = self.conv_reduces[3](x_fuses[3])
-        e4 = F.interpolate(e4, (H, W), mode="bilinear", align_corners=False)
-
-        outputs = [e1, e2, e3, e4]
-
-        output = self.classifier(torch.cat(outputs, dim=1))
-        #if not self.training:
-        #    return torch.sigmoid(output)
-
-        outputs.append(output)
-        outputs = [torch.sigmoid(r) for r in outputs]
-        return outputs
-
-def config_model(model):
-    model_options = list(nets.keys())
-    assert model in model_options, \
-        'unrecognized model, please choose from %s' % str(model_options)
-
-    # print(str(nets[model]))
-
-    pdcs = []
-    for i in range(16):
-        layer_name = 'layer%d' % i
-        op = nets[model][layer_name]
-        pdcs.append(createConvFunc(op))
-
-    return pdcs
-
-def pidinet():
-    pdcs = config_model('carv4')
-    dil = 24 #if args.dil else None
-    return PiDiNet(60, pdcs, dil=dil, sa=True)
\ No newline at end of file
diff --git a/MagicQuill/scribble_color_edit.py b/MagicQuill/scribble_color_edit.py
deleted file mode 100644
index 5761fb108f37786090a882ce8a76438153146af3..0000000000000000000000000000000000000000
--- a/MagicQuill/scribble_color_edit.py
+++ /dev/null
@@ -1,125 +0,0 @@
-import torch.nn.functional as F
-import torch
-import numpy as np
-from PIL import Image
-import os
-import sys
-sys.path.append(os.path.dirname(os.path.abspath(__file__)))
-
-from .brushnet_nodes import BrushNetLoader, BrushNet, BlendInpaint, get_files_with_extension
-from .comfyui_utils import CheckpointLoaderSimple, ControlNetLoader, ControlNetApplyAdvanced, CLIPTextEncode, KSampler, VAEDecode, GrowMask, PIDINET_Preprocessor, LineArt_Preprocessor, Color_Preprocessor
-
-class ScribbleColorEditModel():
-    def __init__(self):
-        self.checkpoint_loader = CheckpointLoaderSimple()
-        self.clip_text_encoder = CLIPTextEncode()
-        self.mask_processor = GrowMask()
-        self.controlnet_loader = ControlNetLoader()
-        self.scribble_processor = PIDINET_Preprocessor()
-        self.lineart_processor = LineArt_Preprocessor()
-        self.color_processor = Color_Preprocessor()
-        self.brushnet_loader = BrushNetLoader()
-        self.brushnet_node = BrushNet()
-        self.controlnet_apply = ControlNetApplyAdvanced()
-        self.ksampler = KSampler()
-        self.vae_decoder = VAEDecode()
-        self.blender = BlendInpaint()
-        self.ckpt_name = os.path.normpath("SD1.5/realisticVisionV60B1_v51VAE.safetensors")
-        with torch.no_grad():
-            self.model, self.clip, self.vae = self.checkpoint_loader.load_checkpoint(self.ckpt_name)
-        self.load_models('SD1.5', 'float16')
-
-    def load_models(self, base_model_version="SD1.5", dtype='float16'):
-        if base_model_version == "SD1.5":
-            edge_controlnet_name = "control_v11p_sd15_scribble.safetensors"
-            color_controlnet_name = "color_finetune.safetensors"
-            brushnet_name = os.path.normpath("brushnet/random_mask_brushnet_ckpt/diffusion_pytorch_model.safetensors")
-        else:
-            raise ValueError("Invalid base_model_version, not supported yet!!!: {}".format(base_model_version))
-        self.edge_controlnet = self.controlnet_loader.load_controlnet(edge_controlnet_name)[0]
-        self.color_controlnet = self.controlnet_loader.load_controlnet(color_controlnet_name)[0]
-        self.brushnet_loader.inpaint_files = get_files_with_extension('inpaint')
-        print("self.brushnet_loader.inpaint_files: ", get_files_with_extension('inpaint'))
-        self.brushnet = self.brushnet_loader.brushnet_loading(brushnet_name, dtype)[0]
-    
-    def process(self, ckpt_name, image, colored_image, positive_prompt, negative_prompt, mask, add_mask, remove_mask, grow_size, stroke_as_edge, fine_edge, edge_strength, color_strength, inpaint_strength, seed, steps, cfg, sampler_name, scheduler, base_model_version='SD1.5', dtype='float16', palette_resolution=2048):
-        if ckpt_name != self.ckpt_name:
-            self.ckpt_name = ckpt_name
-            with torch.no_grad():
-                self.model, self.clip, self.vae = self.checkpoint_loader.load_checkpoint(ckpt_name)
-        if not hasattr(self, 'edge_controlnet') or not hasattr(self, 'color_controlnet') or not hasattr(self, 'brushnet'):
-            self.load_models(base_model_version, dtype)
-        positive = self.clip_text_encoder.encode(self.clip, positive_prompt)[0]
-        negative = self.clip_text_encoder.encode(self.clip, negative_prompt)[0]        
-        # Grow Mask for Color Editing
-        mask = self.mask_processor.expand_mask(mask, expand=grow_size, tapered_corners=True)[0]
-        # Realistic Lineart
-        image_copy = image.clone()
-        if stroke_as_edge == "disable":
-            bool_add_mask = add_mask > 0.5
-            mean_brightness = image_copy[bool_add_mask].mean()
-            if mean_brightness > 0.8:
-                image_copy[bool_add_mask] = 0.0
-            else:
-                image_copy[bool_add_mask] = 1.0
-                
-
-        if not torch.equal(image, colored_image):
-            print("Apply color controlnet")
-            color_output = self.color_processor.execute(colored_image, resolution=palette_resolution)[0]
-            lineart_output = self.lineart_processor.execute(image, resolution=512, coarse=False)[0]
-            positive, negative = self.controlnet_apply.apply_controlnet(positive, negative, self.color_controlnet, color_output, color_strength, 0.0, 1.0)
-            positive, negative = self.controlnet_apply.apply_controlnet(positive, negative, self.edge_controlnet, lineart_output, 0.8, 0.0, 1.0)
-        else:
-            print("Apply edge controlnet")
-            # Resize masks to match the dimensions of lineart_output
-            color_output = self.color_processor.execute(image, resolution=palette_resolution)[0]
-            if fine_edge == "enable":
-                lineart_output = self.lineart_processor.execute(image, resolution=512, coarse=False)[0]
-            else:
-                lineart_output = self.scribble_processor.execute(image, resolution=512)[0]
-            add_mask_resized = F.interpolate(add_mask.unsqueeze(0).unsqueeze(0).float(), size=(1, lineart_output.shape[1], lineart_output.shape[2]), mode='nearest').squeeze(0).squeeze(0)
-            remove_mask_resized = F.interpolate(remove_mask.unsqueeze(0).unsqueeze(0).float(), size=(1, lineart_output.shape[1], lineart_output.shape[2]), mode='nearest').squeeze(0).squeeze(0)
-
-            bool_add_mask_resized = (add_mask_resized > 0.5)
-            bool_remove_mask_resized = (remove_mask_resized > 0.5)
-
-            if stroke_as_edge == "enable":
-                lineart_output[bool_remove_mask_resized] = 0.0
-                lineart_output[bool_add_mask_resized] = 1.0
-            else:
-                lineart_output[bool_remove_mask_resized & ~bool_add_mask_resized] = 0.0
-            positive, negative = self.controlnet_apply.apply_controlnet(positive, negative, self.edge_controlnet, lineart_output, edge_strength, 0.0, 1.0)
-
-        # BrushNet
-        model, positive, negative, latent = self.brushnet_node.model_update(
-            model=self.model,
-            vae=self.vae,
-            image=image,
-            mask=mask,
-            brushnet=self.brushnet,
-            positive=positive,
-            negative=negative,
-            scale=inpaint_strength,
-            start_at=0,
-            end_at=10000
-        )
-
-        # KSampler Node
-        latent_samples = self.ksampler.sample(
-            model=model, 
-            seed=seed, 
-            steps=steps, 
-            cfg=cfg, 
-            sampler_name=sampler_name, 
-            scheduler=scheduler, 
-            positive=positive, 
-            negative=negative, 
-            latent_image=latent,
-        )[0]
-
-        final_image = self.vae_decoder.decode(self.vae, latent_samples)[0]
-        final_image = self.blender.blend_inpaint(final_image, image, mask, kernel=10, sigma=10.0)[0]
-
-        # Return the final image
-        return (latent_samples, final_image, lineart_output, color_output)