'''simple docstring'''




import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


# =================#
# UNet Conversion #
# =================#

__SCREAMING_SNAKE_CASE			:	List[Any]	=    [
    # (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"""),
]

__SCREAMING_SNAKE_CASE			:	List[str]	=    [
    # (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"""),
]

__SCREAMING_SNAKE_CASE			:	Tuple	=    []
# 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
    __SCREAMING_SNAKE_CASE			:	Optional[Any]	=    F'down_blocks.{i}.resnets.{j}.'
    __SCREAMING_SNAKE_CASE			:	Optional[Any]	=    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
      __SCREAMING_SNAKE_CASE			:	str	=    F'down_blocks.{i}.attentions.{j}.'
      __SCREAMING_SNAKE_CASE			:	Tuple	=    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
    __SCREAMING_SNAKE_CASE			:	Dict	=    F'up_blocks.{i}.resnets.{j}.'
    __SCREAMING_SNAKE_CASE			:	Dict	=    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
      __SCREAMING_SNAKE_CASE			:	List[Any]	=    F'up_blocks.{i}.attentions.{j}.'
      __SCREAMING_SNAKE_CASE			:	List[Any]	=    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
    __SCREAMING_SNAKE_CASE			:	Dict	=    F'down_blocks.{i}.downsamplers.0.conv.'
    __SCREAMING_SNAKE_CASE			:	str	=    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
    __SCREAMING_SNAKE_CASE			:	List[Any]	=    F'up_blocks.{i}.upsamplers.0.'
    __SCREAMING_SNAKE_CASE			:	Tuple	=    F'output_blocks.{3*i + 2}.{1 if i == 0 else 2}.'
    unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))

__SCREAMING_SNAKE_CASE			:	Tuple	=    """mid_block.attentions.0."""
__SCREAMING_SNAKE_CASE			:	Dict	=    """middle_block.1."""
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))

for j in range(2):
  __SCREAMING_SNAKE_CASE			:	Any	=    F'mid_block.resnets.{j}.'
  __SCREAMING_SNAKE_CASE			:	str	=    F'middle_block.{2*j}.'
  unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))

def       UpperCamelCase_				(   _UpperCAmelCase    : int       )       ->       Tuple:


       """simple docstring"""







       _UpperCAmelCase	:    Any				 =       {k: k for k in unet_state_dict.keys()}
       for sd_name, hf_name in unet_conversion_map:
              _UpperCAmelCase	:    int				 =       sd_name
       for k, v in mapping.items():
              if "resnets" in k:
                     for sd_part, hf_part in unet_conversion_map_resnet:
                            _UpperCAmelCase	:    Dict				 =       v.replace(lowerCamelCase_     ,      lowerCamelCase_       )
                     _UpperCAmelCase	:    Dict				 =       v
       for k, v in mapping.items():
              for sd_part, hf_part in unet_conversion_map_layer:
                     _UpperCAmelCase	:    List[Any]				 =       v.replace(lowerCamelCase_     ,      lowerCamelCase_       )
              _UpperCAmelCase	:    Any				 =       v
       _UpperCAmelCase	:    Optional[Any]				 =       {v: unet_state_dict[k] for k, v in mapping.items()}
       return new_state_dict


# ================#
# VAE Conversion #
# ================#

__SCREAMING_SNAKE_CASE			:	Tuple	=    [
    # (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):
    __SCREAMING_SNAKE_CASE			:	Optional[Any]	=    F'encoder.down_blocks.{i}.resnets.{j}.'
    __SCREAMING_SNAKE_CASE			:	Optional[Any]	=    F'encoder.down.{i}.block.{j}.'
    vae_conversion_map.append((sd_down_prefix, hf_down_prefix))

  if i < 3:
    __SCREAMING_SNAKE_CASE			:	List[str]	=    F'down_blocks.{i}.downsamplers.0.'
    __SCREAMING_SNAKE_CASE			:	List[Any]	=    F'down.{i}.downsample.'
    vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))

    __SCREAMING_SNAKE_CASE			:	Optional[Any]	=    F'up_blocks.{i}.upsamplers.0.'
    __SCREAMING_SNAKE_CASE			:	str	=    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):
    __SCREAMING_SNAKE_CASE			:	Dict	=    F'decoder.up_blocks.{i}.resnets.{j}.'
    __SCREAMING_SNAKE_CASE			:	Any	=    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):
  __SCREAMING_SNAKE_CASE			:	Dict	=    F'mid_block.resnets.{i}.'
  __SCREAMING_SNAKE_CASE			:	Dict	=    F'mid.block_{i+1}.'
  vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))


__SCREAMING_SNAKE_CASE			:	Optional[int]	=    [
    # (stable-diffusion, HF Diffusers)
    ("""norm.""", """group_norm."""),
    ("""q.""", """query."""),
    ("""k.""", """key."""),
    ("""v.""", """value."""),
    ("""proj_out.""", """proj_attn."""),
]

def       UpperCamelCase_				(   _UpperCAmelCase    : str       )       ->       Optional[int]:


       """simple docstring"""







       return w.reshape(*w.shape     ,      1     ,      1       )

def       UpperCamelCase_				(   _UpperCAmelCase    : List[str]       )       ->       Tuple:


       """simple docstring"""







       _UpperCAmelCase	:    Optional[Any]				 =       {k: k for k in vae_state_dict.keys()}
       for k, v in mapping.items():
              for sd_part, hf_part in vae_conversion_map:
                     _UpperCAmelCase	:    str				 =       v.replace(lowerCamelCase_     ,      lowerCamelCase_       )
              _UpperCAmelCase	:    str				 =       v
       for k, v in mapping.items():
              if "attentions" in k:
                     for sd_part, hf_part in vae_conversion_map_attn:
                            _UpperCAmelCase	:    str				 =       v.replace(lowerCamelCase_     ,      lowerCamelCase_       )
                     _UpperCAmelCase	:    Any				 =       v
       _UpperCAmelCase	:    List[Any]				 =       {v: vae_state_dict[k] for k, v in mapping.items()}
       _UpperCAmelCase	:    Tuple				 =       ["""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:
                            print(F"""Reshaping {k} for SD format"""       )
                            _UpperCAmelCase	:    Union[str, Any]				 =       reshape_weight_for_sd(lowerCamelCase_       )
       return new_state_dict


# =========================#
# Text Encoder Conversion #
# =========================#


__SCREAMING_SNAKE_CASE			:	Any	=    [
    # (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"""),
]
__SCREAMING_SNAKE_CASE			:	Optional[int]	=    {re.escape(x[1]): x[0] for x in textenc_conversion_lst}
__SCREAMING_SNAKE_CASE			:	Optional[Any]	=    re.compile("""|""".join(protected.keys()))

# Ordering is from https://github.com/pytorch/pytorch/blob/master/test/cpp/api/modules.cpp
__SCREAMING_SNAKE_CASE			:	Union[str, Any]	=    {"""q""": 0, """k""": 1, """v""": 2}

def       UpperCamelCase_				(   _UpperCAmelCase    : Union[str, Any]       )       ->       Union[str, Any]:


       """simple docstring"""







       _UpperCAmelCase	:    int				 =       {}
       _UpperCAmelCase	:    Dict				 =       {}
       _UpperCAmelCase	:    Optional[int]				 =       {}
       for k, v in text_enc_dict.items():
              if (
                  k.endswith(".self_attn.q_proj.weight"       )
                  or k.endswith(".self_attn.k_proj.weight"       )
                  or k.endswith(".self_attn.v_proj.weight"       )
              ):
                     _UpperCAmelCase	:    List[str]				 =       k[: -len(".q_proj.weight"       )]
                     _UpperCAmelCase	:    int				 =       k[-len("q_proj.weight"       )]
                     if k_pre not in capture_qkv_weight:
                            _UpperCAmelCase	:    Optional[Any]				 =       [None, None, None]
                     _UpperCAmelCase	:    str				 =       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"       )
              ):
                     _UpperCAmelCase	:    Dict				 =       k[: -len(".q_proj.bias"       )]
                     _UpperCAmelCase	:    Optional[Any]				 =       k[-len("q_proj.bias"       )]
                     if k_pre not in capture_qkv_bias:
                            _UpperCAmelCase	:    int				 =       [None, None, None]
                     _UpperCAmelCase	:    str				 =       v
                     continue

              _UpperCAmelCase	:    Union[str, Any]				 =       textenc_pattern.sub(lambda _UpperCAmelCase       : protected[re.escape(m.group(0       )       )]     ,      lowerCamelCase_       )
              _UpperCAmelCase	:    Union[str, Any]				 =       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"       )
              _UpperCAmelCase	:    Any				 =       textenc_pattern.sub(lambda _UpperCAmelCase       : protected[re.escape(m.group(0       )       )]     ,      lowerCamelCase_       )
              _UpperCAmelCase	:    str				 =       torch.cat(lowerCamelCase_       )

       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"       )
              _UpperCAmelCase	:    Any				 =       textenc_pattern.sub(lambda _UpperCAmelCase       : protected[re.escape(m.group(0       )       )]     ,      lowerCamelCase_       )
              _UpperCAmelCase	:    List[Any]				 =       torch.cat(lowerCamelCase_       )

       return new_state_dict

def       UpperCamelCase_				(   _UpperCAmelCase    : Optional[Any]       )       ->       Tuple:


       """simple docstring"""







       return text_enc_dict


if __name__ == "__main__":
  __SCREAMING_SNAKE_CASE			:	Any	=    argparse.ArgumentParser()

  parser.add_argument("""--model_path""", default=None, type=str, required=True, help="""Path to the model to convert.""")
  parser.add_argument("""--checkpoint_path""", default=None, type=str, required=True, help="""Path to the output model.""")
  parser.add_argument("""--half""", action="""store_true""", help="""Save weights in half precision.""")
  parser.add_argument(
      """--use_safetensors""", action="""store_true""", help="""Save weights use safetensors, default is ckpt."""
  )

  __SCREAMING_SNAKE_CASE			:	List[str]	=    parser.parse_args()

  assert args.model_path is not None, "Must provide a model path!"

  assert args.checkpoint_path is not None, "Must provide a checkpoint path!"

  # Path for safetensors
  __SCREAMING_SNAKE_CASE			:	List[str]	=    osp.join(args.model_path, """unet""", """diffusion_pytorch_model.safetensors""")
  __SCREAMING_SNAKE_CASE			:	List[Any]	=    osp.join(args.model_path, """vae""", """diffusion_pytorch_model.safetensors""")
  __SCREAMING_SNAKE_CASE			:	Optional[int]	=    osp.join(args.model_path, """text_encoder""", """model.safetensors""")

  # Load models from safetensors if it exists, if it doesn't pytorch
  if osp.exists(unet_path):
    __SCREAMING_SNAKE_CASE			:	Tuple	=    load_file(unet_path, device="""cpu""")
  else:
    __SCREAMING_SNAKE_CASE			:	Any	=    osp.join(args.model_path, """unet""", """diffusion_pytorch_model.bin""")
    __SCREAMING_SNAKE_CASE			:	int	=    torch.load(unet_path, map_location="""cpu""")

  if osp.exists(vae_path):
    __SCREAMING_SNAKE_CASE			:	Optional[int]	=    load_file(vae_path, device="""cpu""")
  else:
    __SCREAMING_SNAKE_CASE			:	Union[str, Any]	=    osp.join(args.model_path, """vae""", """diffusion_pytorch_model.bin""")
    __SCREAMING_SNAKE_CASE			:	str	=    torch.load(vae_path, map_location="""cpu""")

  if osp.exists(text_enc_path):
    __SCREAMING_SNAKE_CASE			:	List[Any]	=    load_file(text_enc_path, device="""cpu""")
  else:
    __SCREAMING_SNAKE_CASE			:	int	=    osp.join(args.model_path, """text_encoder""", """pytorch_model.bin""")
    __SCREAMING_SNAKE_CASE			:	int	=    torch.load(text_enc_path, map_location="""cpu""")

  # Convert the UNet model
  __SCREAMING_SNAKE_CASE			:	Optional[int]	=    convert_unet_state_dict(unet_state_dict)
  __SCREAMING_SNAKE_CASE			:	Dict	=    {"""model.diffusion_model.""" + k: v for k, v in unet_state_dict.items()}

  # Convert the VAE model
  __SCREAMING_SNAKE_CASE			:	int	=    convert_vae_state_dict(vae_state_dict)
  __SCREAMING_SNAKE_CASE			:	List[str]	=    {"""first_stage_model.""" + k: v for k, v in vae_state_dict.items()}

  # Easiest way to identify v2.0 model seems to be that the text encoder (OpenCLIP) is deeper
  __SCREAMING_SNAKE_CASE			:	str	=    """text_model.encoder.layers.22.layer_norm2.bias""" in text_enc_dict

  if is_vaa_model:
    # Need to add the tag 'transformer' in advance so we can knock it out from the final layer-norm
    __SCREAMING_SNAKE_CASE			:	Any	=    {"""transformer.""" + k: v for k, v in text_enc_dict.items()}
    __SCREAMING_SNAKE_CASE			:	Tuple	=    convert_text_enc_state_dict_vaa(text_enc_dict)
    __SCREAMING_SNAKE_CASE			:	Tuple	=    {"""cond_stage_model.model.""" + k: v for k, v in text_enc_dict.items()}
  else:
    __SCREAMING_SNAKE_CASE			:	Optional[Any]	=    convert_text_enc_state_dict(text_enc_dict)
    __SCREAMING_SNAKE_CASE			:	Optional[int]	=    {"""cond_stage_model.transformer.""" + k: v for k, v in text_enc_dict.items()}

  # Put together new checkpoint
  __SCREAMING_SNAKE_CASE			:	Optional[Any]	=    {**unet_state_dict, **vae_state_dict, **text_enc_dict}
  if args.half:
    __SCREAMING_SNAKE_CASE			:	Dict	=    {k: v.half() for k, v in state_dict.items()}

  if args.use_safetensors:
    save_file(state_dict, args.checkpoint_path)
  else:
    __SCREAMING_SNAKE_CASE			:	str	=    {"""state_dict""": state_dict}
    torch.save(state_dict, args.checkpoint_path)








'''simple docstring'''




__UpperCAmelCase            =       """0.18.2"""

from .configuration_utils import ConfigMixin
from .utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_inflect_available,
    is_invisible_watermark_available,
    is_k_diffusion_available,
    is_k_diffusion_version,
    is_librosa_available,
    is_note_seq_available,
    is_onnx_available,
    is_scipy_available,
    is_torch_available,
    is_torchsde_available,
    is_transformers_available,
    is_transformers_version,
    is_unidecode_available,
    logging,
)


try:
							if not is_onnx_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from .utils.dummy_onnx_objects import *  # noqa F403
else:
							from .pipelines import OnnxRuntimeModel

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from .utils.dummy_pt_objects import *  # noqa F403
else:
							from .models import (
							    AutoencoderKL,
							    ControlNetModel,
							    ModelMixin,
							    PriorTransformer,
							    TaFilmDecoder,
							    TransformeraDModel,
							    UNetaDModel,
							    UNetaDConditionModel,
							    UNetaDModel,
							    UNetaDConditionModel,
							    VQModel,
							)
							from .optimization import (
							    get_constant_schedule,
							    get_constant_schedule_with_warmup,
							    get_cosine_schedule_with_warmup,
							    get_cosine_with_hard_restarts_schedule_with_warmup,
							    get_linear_schedule_with_warmup,
							    get_polynomial_decay_schedule_with_warmup,
							    get_scheduler,
							)
							from .pipelines import (
							    AudioPipelineOutput,
							    ConsistencyModelPipeline,
							    DanceDiffusionPipeline,
							    DDIMPipeline,
							    DDPMPipeline,
							    DiffusionPipeline,
							    DiTPipeline,
							    ImagePipelineOutput,
							    KarrasVePipeline,
							    LDMPipeline,
							    LDMSuperResolutionPipeline,
							    PNDMPipeline,
							    RePaintPipeline,
							    ScoreSdeVePipeline,
							)
							from .schedulers import (
							    CMStochasticIterativeScheduler,
							    DDIMInverseScheduler,
							    DDIMParallelScheduler,
							    DDIMScheduler,
							    DDPMParallelScheduler,
							    DDPMScheduler,
							    DEISMultistepScheduler,
							    DPMSolverMultistepInverseScheduler,
							    DPMSolverMultistepScheduler,
							    DPMSolverSinglestepScheduler,
							    EulerAncestralDiscreteScheduler,
							    EulerDiscreteScheduler,
							    HeunDiscreteScheduler,
							    IPNDMScheduler,
							    KarrasVeScheduler,
							    KDPMaAncestralDiscreteScheduler,
							    KDPMaDiscreteScheduler,
							    PNDMScheduler,
							    RePaintScheduler,
							    SchedulerMixin,
							    ScoreSdeVeScheduler,
							    UnCLIPScheduler,
							    UniPCMultistepScheduler,
							    VQDiffusionScheduler,
							)
							from .training_utils import EMAModel

try:
							if not (is_torch_available() and is_scipy_available()):
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from .utils.dummy_torch_and_scipy_objects import *  # noqa F403
else:
							from .schedulers import LMSDiscreteScheduler

try:
							if not (is_torch_available() and is_torchsde_available()):
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from .utils.dummy_torch_and_torchsde_objects import *  # noqa F403
else:
							from .schedulers import DPMSolverSDEScheduler

try:
							if not (is_torch_available() and is_transformers_available()):
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from .utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
							from .pipelines import (
							    AltDiffusionImgaImgPipeline,
							    AltDiffusionPipeline,
							    AudioLDMPipeline,
							    CycleDiffusionPipeline,
							    IFImgaImgPipeline,
							    IFImgaImgSuperResolutionPipeline,
							    IFInpaintingPipeline,
							    IFInpaintingSuperResolutionPipeline,
							    IFPipeline,
							    IFSuperResolutionPipeline,
							    ImageTextPipelineOutput,
							    KandinskyImgaImgPipeline,
							    KandinskyInpaintPipeline,
							    KandinskyPipeline,
							    KandinskyPriorPipeline,
							    KandinskyVaaControlnetImgaImgPipeline,
							    KandinskyVaaControlnetPipeline,
							    KandinskyVaaImgaImgPipeline,
							    KandinskyVaaInpaintPipeline,
							    KandinskyVaaPipeline,
							    KandinskyVaaPriorEmbaEmbPipeline,
							    KandinskyVaaPriorPipeline,
							    LDMTextToImagePipeline,
							    PaintByExamplePipeline,
							    SemanticStableDiffusionPipeline,
							    ShapEImgaImgPipeline,
							    ShapEPipeline,
							    StableDiffusionAttendAndExcitePipeline,
							    StableDiffusionControlNetImgaImgPipeline,
							    StableDiffusionControlNetInpaintPipeline,
							    StableDiffusionControlNetPipeline,
							    StableDiffusionDepthaImgPipeline,
							    StableDiffusionDiffEditPipeline,
							    StableDiffusionImageVariationPipeline,
							    StableDiffusionImgaImgPipeline,
							    StableDiffusionInpaintPipeline,
							    StableDiffusionInpaintPipelineLegacy,
							    StableDiffusionInstructPixaPixPipeline,
							    StableDiffusionLatentUpscalePipeline,
							    StableDiffusionLDMaDPipeline,
							    StableDiffusionModelEditingPipeline,
							    StableDiffusionPanoramaPipeline,
							    StableDiffusionParadigmsPipeline,
							    StableDiffusionPipeline,
							    StableDiffusionPipelineSafe,
							    StableDiffusionPixaPixZeroPipeline,
							    StableDiffusionSAGPipeline,
							    StableDiffusionUpscalePipeline,
							    StableUnCLIPImgaImgPipeline,
							    StableUnCLIPPipeline,
							    TextToVideoSDPipeline,
							    TextToVideoZeroPipeline,
							    UnCLIPImageVariationPipeline,
							    UnCLIPPipeline,
							    UniDiffuserModel,
							    UniDiffuserPipeline,
							    UniDiffuserTextDecoder,
							    VersatileDiffusionDualGuidedPipeline,
							    VersatileDiffusionImageVariationPipeline,
							    VersatileDiffusionPipeline,
							    VersatileDiffusionTextToImagePipeline,
							    VideoToVideoSDPipeline,
							    VQDiffusionPipeline,
							)

try:
							if not (is_torch_available() and is_transformers_available() and is_invisible_watermark_available()):
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from .utils.dummy_torch_and_transformers_and_invisible_watermark_objects import *  # noqa F403
else:
							from .pipelines import StableDiffusionXLImgaImgPipeline, StableDiffusionXLPipeline

try:
							if not (is_torch_available() and is_transformers_available() and is_k_diffusion_available()):
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from .utils.dummy_torch_and_transformers_and_k_diffusion_objects import *  # noqa F403
else:
							from .pipelines import StableDiffusionKDiffusionPipeline

try:
							if not (is_torch_available() and is_transformers_available() and is_onnx_available()):
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from .utils.dummy_torch_and_transformers_and_onnx_objects import *  # noqa F403
else:
							from .pipelines import (
							    OnnxStableDiffusionImgaImgPipeline,
							    OnnxStableDiffusionInpaintPipeline,
							    OnnxStableDiffusionInpaintPipelineLegacy,
							    OnnxStableDiffusionPipeline,
							    OnnxStableDiffusionUpscalePipeline,
							    StableDiffusionOnnxPipeline,
							)

try:
							if not (is_torch_available() and is_librosa_available()):
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from .utils.dummy_torch_and_librosa_objects import *  # noqa F403
else:
							from .pipelines import AudioDiffusionPipeline, Mel

try:
							if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from .utils.dummy_transformers_and_torch_and_note_seq_objects import *  # noqa F403
else:
							from .pipelines import SpectrogramDiffusionPipeline

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from .utils.dummy_flax_objects import *  # noqa F403
else:
							from .models.controlnet_flax import FlaxControlNetModel
							from .models.modeling_flax_utils import FlaxModelMixin
							from .models.unet_ad_condition_flax import FlaxUNetaDConditionModel
							from .models.vae_flax import FlaxAutoencoderKL
							from .pipelines import FlaxDiffusionPipeline
							from .schedulers import (
							    FlaxDDIMScheduler,
							    FlaxDDPMScheduler,
							    FlaxDPMSolverMultistepScheduler,
							    FlaxKarrasVeScheduler,
							    FlaxLMSDiscreteScheduler,
							    FlaxPNDMScheduler,
							    FlaxSchedulerMixin,
							    FlaxScoreSdeVeScheduler,
							)


try:
							if not (is_flax_available() and is_transformers_available()):
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from .utils.dummy_flax_and_transformers_objects import *  # noqa F403
else:
							from .pipelines import (
							    FlaxStableDiffusionControlNetPipeline,
							    FlaxStableDiffusionImgaImgPipeline,
							    FlaxStableDiffusionInpaintPipeline,
							    FlaxStableDiffusionPipeline,
							)

try:
							if not (is_note_seq_available()):
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from .utils.dummy_note_seq_objects import *  # noqa F403
else:
							from .pipelines import MidiProcessor



