code: 
  
  import argparse
  
  import huggingface_hub
  import k_diffusion as K
  import torch
  
  from diffusers import UNetaDConditionModel
  
  
  UpperCAmelCase_       :				int		   =      '''pcuenq/k-upscaler'''
  def        __SCREAMING_SNAKE_CASE    (      a__						:      List[str] ,a__						:      Optional[Any] ,*, a__						:      Union[str, Any] ,a__						:      Any					)				->							Union[str, Any]:
   __A   : Tuple								= {
       # norm1
       f"""{diffusers_resnet_prefix}.norm1.linear.weight""": checkpoint[f"""{resnet_prefix}.main.0.mapper.weight"""],
       f"""{diffusers_resnet_prefix}.norm1.linear.bias""": checkpoint[f"""{resnet_prefix}.main.0.mapper.bias"""],
       # conv1
       f"""{diffusers_resnet_prefix}.conv1.weight""": checkpoint[f"""{resnet_prefix}.main.2.weight"""],
       f"""{diffusers_resnet_prefix}.conv1.bias""": checkpoint[f"""{resnet_prefix}.main.2.bias"""],
       # norm2
       f"""{diffusers_resnet_prefix}.norm2.linear.weight""": checkpoint[f"""{resnet_prefix}.main.4.mapper.weight"""],
       f"""{diffusers_resnet_prefix}.norm2.linear.bias""": checkpoint[f"""{resnet_prefix}.main.4.mapper.bias"""],
       # conv2
       f"""{diffusers_resnet_prefix}.conv2.weight""": checkpoint[f"""{resnet_prefix}.main.6.weight"""],
       f"""{diffusers_resnet_prefix}.conv2.bias""": checkpoint[f"""{resnet_prefix}.main.6.bias"""],
   }
  
   if resnet.conv_shortcut is not None:
    rv.update(
        {
            f"""{diffusers_resnet_prefix}.conv_shortcut.weight""": checkpoint[f"""{resnet_prefix}.skip.weight"""],
        }					)
  
   return rv
  def        __SCREAMING_SNAKE_CASE    (      a__						:      Dict ,*, a__						:      List[str] ,a__						:      List[str]					)				->							List[Any]:
   __A   : List[str]								= checkpoint[f"""{attention_prefix}.qkv_proj.weight"""].chunk(3 ,dim=0					)
   __A   : List[str]								= checkpoint[f"""{attention_prefix}.qkv_proj.bias"""].chunk(3 ,dim=0					)
   __A   : Optional[int]								= {
       # norm
       f"""{diffusers_attention_prefix}.norm1.linear.weight""": checkpoint[f"""{attention_prefix}.norm_in.mapper.weight"""],
       f"""{diffusers_attention_prefix}.norm1.linear.bias""": checkpoint[f"""{attention_prefix}.norm_in.mapper.bias"""],
       # to_q
       f"""{diffusers_attention_prefix}.attn1.to_q.weight""": weight_q.squeeze(-1					).squeeze(-1					),
       f"""{diffusers_attention_prefix}.attn1.to_q.bias""": bias_q,
       # to_k
       f"""{diffusers_attention_prefix}.attn1.to_k.weight""": weight_k.squeeze(-1					).squeeze(-1					),
       f"""{diffusers_attention_prefix}.attn1.to_k.bias""": bias_k,
       # to_v
       f"""{diffusers_attention_prefix}.attn1.to_v.weight""": weight_v.squeeze(-1					).squeeze(-1					),
       f"""{diffusers_attention_prefix}.attn1.to_v.bias""": bias_v,
       # to_out
       f"""{diffusers_attention_prefix}.attn1.to_out.0.weight""": checkpoint[f"""{attention_prefix}.out_proj.weight"""]
       .squeeze(-1					)
       .squeeze(-1					),
       f"""{diffusers_attention_prefix}.attn1.to_out.0.bias""": checkpoint[f"""{attention_prefix}.out_proj.bias"""],
   }
  
   return rv
  def        __SCREAMING_SNAKE_CASE    (      a__						:      int ,*, a__						:      Tuple ,a__						:      Union[str, Any] ,a__						:      List[Any]					)				->							Dict:
   __A   : Tuple								= checkpoint[f"""{attention_prefix}.kv_proj.weight"""].chunk(2 ,dim=0					)
   __A   : Optional[Any]								= checkpoint[f"""{attention_prefix}.kv_proj.bias"""].chunk(2 ,dim=0					)
  
   __A   : Any								= {
       # norm2 (ada groupnorm)
       f"""{diffusers_attention_prefix}.norm{diffusers_attention_index}.linear.weight""": checkpoint[
           f"""{attention_prefix}.norm_dec.mapper.weight"""
       ],
       f"""{diffusers_attention_prefix}.norm{diffusers_attention_index}.linear.bias""": checkpoint[
           f"""{attention_prefix}.norm_dec.mapper.bias"""
       ],
       # layernorm on encoder_hidden_state
       f"""{diffusers_attention_prefix}.attn{diffusers_attention_index}.norm_cross.weight""": checkpoint[
           f"""{attention_prefix}.norm_enc.weight"""
       ],
       f"""{diffusers_attention_prefix}.attn{diffusers_attention_index}.norm_cross.bias""": checkpoint[
           f"""{attention_prefix}.norm_enc.bias"""
       ],
       # to_q
       f"""{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_q.weight""": checkpoint[
           f"""{attention_prefix}.q_proj.weight"""
       ]
       .squeeze(-1					)
       .squeeze(-1					),
       f"""{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_q.bias""": checkpoint[
           f"""{attention_prefix}.q_proj.bias"""
       ],
       # to_k
       f"""{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_k.weight""": weight_k.squeeze(-1					).squeeze(-1					),
       f"""{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_k.bias""": bias_k,
       # to_v
       f"""{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_v.weight""": weight_v.squeeze(-1					).squeeze(-1					),
       f"""{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_v.bias""": bias_v,
       # to_out
       f"""{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_out.0.weight""": checkpoint[
           f"""{attention_prefix}.out_proj.weight"""
       ]
       .squeeze(-1					)
       .squeeze(-1					),
       f"""{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_out.0.bias""": checkpoint[
           f"""{attention_prefix}.out_proj.bias"""
       ],
   }
  
   return rv
  def        __SCREAMING_SNAKE_CASE    (      a__						:      Union[str, Any] ,a__						:      List[Any] ,a__						:      str ,a__						:      str					)				->							Union[str, Any]:
   __A   : Any								= """inner_model.u_net.u_blocks""" if block_type == """up""" else """inner_model.u_net.d_blocks"""
   __A   : Any								= f"""{block_prefix}.{block_idx}"""
  
   __A   : Optional[Any]								= {}
  
   if not hasattr(a__ ,"""attentions"""					):
    __A   : Tuple								= 1  # resnet only
   elif not block.attentions[0].add_self_attention:
    __A   : List[Any]								= 2  # resnet -> cross-attention
   else:
    __A   : int								= 3  # resnet -> self-attention -> cross-attention)
  
   for resnet_idx, resnet in enumerate(block.resnets					):
    # diffusers_resnet_prefix = f"{diffusers_up_block_prefix}.resnets.{resnet_idx}"
    __A   : List[Any]								= f"""{block_type}_blocks.{block_idx}.resnets.{resnet_idx}"""
    __A   : Tuple								= n * resnet_idx if block_type == """up""" else n * resnet_idx + 1
    __A   : Tuple								= f"""{block_prefix}.{idx}""" if block_type == """up""" else f"""{block_prefix}.{idx}"""
  
    diffusers_checkpoint.update(
        resnet_to_diffusers_checkpoint(
            a__ ,a__ ,diffusers_resnet_prefix=a__ ,resnet_prefix=a__					)					)
  
   if hasattr(a__ ,"""attentions"""					):
    for attention_idx, attention in enumerate(block.attentions					):
     __A   : int								= f"""{block_type}_blocks.{block_idx}.attentions.{attention_idx}"""
     __A   : Optional[int]								= n * attention_idx + 1 if block_type == """up""" else n * attention_idx + 2
     __A   : List[str]								= f"""{block_prefix}.{idx}"""
     __A   : Union[str, Any]								= f"""{block_prefix}.{idx }"""
     __A   : Tuple								= 1 if not attention.add_self_attention else 2
     __A   : List[Any]								= (
         n * attention_idx + cross_attention_index
         if block_type == """up"""
         else n * attention_idx + cross_attention_index + 1
     )
     __A   : int								= f"""{block_prefix}.{idx }"""
  
     diffusers_checkpoint.update(
         cross_attn_to_diffusers_checkpoint(
             a__ ,diffusers_attention_prefix=a__ ,diffusers_attention_index=2 ,attention_prefix=a__ ,)					)
  
     if attention.add_self_attention is True:
      diffusers_checkpoint.update(
          self_attn_to_diffusers_checkpoint(
              a__ ,diffusers_attention_prefix=a__ ,attention_prefix=a__ ,)					)
  
   return diffusers_checkpoint
  def        __SCREAMING_SNAKE_CASE    (      a__						:      int ,a__						:      Union[str, Any]					)				->							List[str]:
   __A   : Tuple								= {}
  
   # pre-processing
   diffusers_checkpoint.update(
       {
           """conv_in.weight""": checkpoint["""inner_model.proj_in.weight"""],
           """conv_in.bias""": checkpoint["""inner_model.proj_in.bias"""],
       }					)
  
   # timestep and class embedding
   diffusers_checkpoint.update(
       {
           """time_proj.weight""": checkpoint["""inner_model.timestep_embed.weight"""].squeeze(-1					),
           """time_embedding.linear_1.weight""": checkpoint["""inner_model.mapping.0.weight"""],
           """time_embedding.linear_1.bias""": checkpoint["""inner_model.mapping.0.bias"""],
           """time_embedding.linear_2.weight""": checkpoint["""inner_model.mapping.2.weight"""],
           """time_embedding.linear_2.bias""": checkpoint["""inner_model.mapping.2.bias"""],
           """time_embedding.cond_proj.weight""": checkpoint["""inner_model.mapping_cond.weight"""],
       }					)
  
   # down_blocks
   for down_block_idx, down_block in enumerate(model.down_blocks					):
    diffusers_checkpoint.update(block_to_diffusers_checkpoint(a__ ,a__ ,a__ ,"""down"""					)					)
  
   # up_blocks
   for up_block_idx, up_block in enumerate(model.up_blocks					):
    diffusers_checkpoint.update(block_to_diffusers_checkpoint(a__ ,a__ ,a__ ,"""up"""					)					)
  
   # post-processing
   diffusers_checkpoint.update(
       {
           """conv_out.weight""": checkpoint["""inner_model.proj_out.weight"""],
           """conv_out.bias""": checkpoint["""inner_model.proj_out.bias"""],
       }					)
  
   return diffusers_checkpoint
  def        __SCREAMING_SNAKE_CASE    (      a__						:      Union[str, Any]					)				->							List[str]:
   __A   : Optional[int]								= original_config["""input_channels"""] + original_config["""unet_cond_dim"""]
   __A   : Tuple								= original_config["""input_channels"""] + (1 if original_config["""has_variance"""] else 0)
  
   __A   : Optional[Any]								= original_config["""channels"""]
  
   assert (
       len(set(original_config["""depths"""]					)					) == 1
   ), "UNet2DConditionModel currently do not support blocks with different number of layers"
   __A   : Union[str, Any]								= original_config["""depths"""][0]
  
   __A   : Union[str, Any]								= original_config["""mapping_cond_dim"""]
   __A   : Dict								= original_config["""cross_cond_dim"""]
  
   __A   : int								= []
   __A   : List[Any]								= []
   for s, c in zip(original_config["""self_attn_depths"""] ,original_config["""cross_attn_depths"""]					):
    if s:
     __A   : Union[str, Any]								= """self"""
     __A   : List[Any]								= """cross""" if c else None
    elif c:
     __A   : Any								= """cross"""
     __A   : int								= None
    else:
     __A   : Dict								= None
     __A   : Optional[Any]								= None
    attna_types.append(a__					)
    attna_types.append(a__					)
  
   __A   : Union[str, Any]								= UNetaDConditionModel(
       in_channels=a__ ,out_channels=a__ ,down_block_types=("""KDownBlock2D""", """KCrossAttnDownBlock2D""", """KCrossAttnDownBlock2D""", """KCrossAttnDownBlock2D""") ,mid_block_type=a__ ,up_block_types=("""KCrossAttnUpBlock2D""", """KCrossAttnUpBlock2D""", """KCrossAttnUpBlock2D""", """KUpBlock2D""") ,block_out_channels=a__ ,layers_per_block=a__ ,act_fn="""gelu""" ,norm_num_groups=a__ ,cross_attention_dim=a__ ,attention_head_dim=64 ,time_cond_proj_dim=a__ ,resnet_time_scale_shift="""scale_shift""" ,time_embedding_type="""fourier""" ,timestep_post_act="""gelu""" ,conv_in_kernel=1 ,conv_out_kernel=1 ,)
  
   return unet
  def        __SCREAMING_SNAKE_CASE    (      a__						:      List[Any]					)				->							str:
   __A   : List[Any]								= torch.device("""cuda""" if torch.cuda.is_available() else """cpu"""					)
  
   __A   : int								= huggingface_hub.hf_hub_download(a__ ,"""config_laion_text_cond_latent_upscaler_2.json"""					)
   __A   : Optional[int]								= huggingface_hub.hf_hub_download(
       a__ ,"""laion_text_cond_latent_upscaler_2_1_00470000_slim.pth"""					)
   print(f"""loading original model configuration from {orig_config_path}"""					)
   print(f"""loading original model checkpoint from {orig_weights_path}"""					)
  
   print("""converting to diffusers unet"""					)
   __A   : Dict								= K.config.load_config(open(a__					)					)["""model"""]
   __A   : int								= unet_model_from_original_config(a__					)
  
   __A   : List[str]								= torch.load(a__ ,map_location=a__					)["""model_ema"""]
   __A   : Any								= unet_to_diffusers_checkpoint(a__ ,a__					)
  
   model.load_state_dict(a__ ,strict=a__					)
   model.save_pretrained(args.dump_path					)
   print(f"""saving converted unet model in {args.dump_path}"""					)
  
  
  if __name__ == "__main__":
     UpperCAmelCase_       :				int		   =      argparse.ArgumentParser()
  
     parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')
     UpperCAmelCase_       :				Any		   =      parser.parse_args()
  
     main(args)
  
  
  
  
  
  
style_context: 
  
  # DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
  # and https://github.com/hojonathanho/diffusion
  
  from dataclasses import dataclass
  from typing import Optional, Tuple, Union
  
  import flax
  import jax.numpy as jnp
  
  from ..configuration_utils import ConfigMixin, register_to_config
  from .scheduling_utils_flax import (
      CommonSchedulerState,
      FlaxKarrasDiffusionSchedulers,
      FlaxSchedulerMixin,
      FlaxSchedulerOutput,
      add_noise_common,
      get_velocity_common,
  )
  
  
  
  
  
  
  @flax.struct.dataclass
  class     lowerCamelCase_			:
  	_lowercase		:      CommonSchedulerState
  	_lowercase		:      jnp.ndarray
  
  	# setable values
  	_lowercase		:      jnp.ndarray
  	_lowercase		:      jnp.ndarray
  	_lowercase		:      Optional[int]    				=							None
  
  
  
  
  	@classmethod
  	def 					lowerCAmelCase_					(    cls	:    int				,			__A	:    CommonSchedulerState				,			__A	:    jnp.ndarray				,			__A	:    jnp.ndarray				,			__A	:    jnp.ndarray				,			):
  		return cls(
  		    common=__A				,			final_alpha_cumprod=__A				,			init_noise_sigma=__A				,			timesteps=__A				,			)
  
  
  
  
  
  
  @dataclass
  class     lowerCamelCase_			(  _lowercase  ):
  	_lowercase		:      DDIMSchedulerState
  
  
  
  
  
  
  class     lowerCamelCase_			(  _lowercase	,			_lowercase  ):
  	_lowercase		:      Union[str, Any]    				=							[e.name for e in FlaxKarrasDiffusionSchedulers]
  
  	_lowercase		:      jnp.dtype
  
  
  
  
  	@property
  	def 					lowerCAmelCase_					(    self	:    Tuple       ):
  		return True
  
  
  
  
  	@register_to_config
  	def __init__(    self	:    Any				,			__A	:    int = 1000				,			__A	:    float = 0.0_0_0_1				,			__A	:    float = 0.0_2				,			__A	:    str = "linear"				,			__A	:    Optional[jnp.ndarray] = None				,			__A	:    bool = True				,			__A	:    int = 0				,			__A	:    str = "epsilon"				,			__A	:    jnp.dtype = jnp.floataa				,			):
  		__A   : Optional[int]								= dtype
  
  
  
  
  	def 					lowerCAmelCase_					(    self	:    Union[str, Any]				,			__A	:    Optional[CommonSchedulerState] = None       ):
  		if common is None:
  			__A   : Any								= CommonSchedulerState.create(self       )
  
  		# At every step in ddim, we are looking into the previous alphas_cumprod
  		# For the final step, there is no previous alphas_cumprod because we are already at 0
  		# `set_alpha_to_one` decides whether we set this parameter simply to one or
  		# whether we use the final alpha of the "non-previous" one.
  		__A   : Tuple								= (
  		    jnp.array(1.0				,			dtype=self.dtype       ) if self.config.set_alpha_to_one else common.alphas_cumprod[0]
  		)
  
  		# standard deviation of the initial noise distribution
  		__A   : List[str]								= jnp.array(1.0				,			dtype=self.dtype       )
  
  		__A   : Any								= jnp.arange(0				,			self.config.num_train_timesteps       ).round()[::-1]
  
  		return DDIMSchedulerState.create(
  		    common=__A				,			final_alpha_cumprod=__A				,			init_noise_sigma=__A				,			timesteps=__A				,			)
  
  
  
  
  	def 					lowerCAmelCase_					(    self	:    Union[str, Any]				,			__A	:    DDIMSchedulerState				,			__A	:    jnp.ndarray				,			__A	:    Optional[int] = None       ):
  		return sample
  
  
  
  
  	def 					lowerCAmelCase_					(    self	:    Tuple				,			__A	:    DDIMSchedulerState				,			__A	:    int				,			__A	:    Tuple = ()       ):
  		__A   : Union[str, Any]								= self.config.num_train_timesteps // num_inference_steps
  		# creates integer timesteps by multiplying by ratio
  		# rounding to avoid issues when num_inference_step is power of 3
  		__A   : Optional[Any]								= (jnp.arange(0				,			__A       ) * step_ratio).round()[::-1] + self.config.steps_offset
  
  		return state.replace(
  		    num_inference_steps=__A				,			timesteps=__A				,			)
  
  
  
  
  	def 					lowerCAmelCase_					(    self	:    Any				,			__A	:    DDIMSchedulerState				,			__A	:    str				,			__A	:    List[Any]       ):
  		__A   : Tuple								= state.common.alphas_cumprod[timestep]
  		__A   : Tuple								= jnp.where(
  		    prev_timestep >= 0				,			state.common.alphas_cumprod[prev_timestep]				,			state.final_alpha_cumprod       )
  		__A   : List[Any]								= 1 - alpha_prod_t
  		__A   : Tuple								= 1 - alpha_prod_t_prev
  
  		__A   : List[Any]								= (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)
  
  		return variance
  
  
  
  
  	def 					lowerCAmelCase_					(    self	:    List[Any]				,			__A	:    DDIMSchedulerState				,			__A	:    jnp.ndarray				,			__A	:    int				,			__A	:    jnp.ndarray				,			__A	:    float = 0.0				,			__A	:    bool = True				,			):
  		if state.num_inference_steps is None:
  			raise ValueError(
  			    """Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"""       )
  
  		# See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
  		# Ideally, read DDIM paper in-detail understanding
  
  		# Notation (<variable name> -> <name in paper>
  		# - pred_noise_t -> e_theta(x_t, t)
  		# - pred_original_sample -> f_theta(x_t, t) or x_0
  		# - std_dev_t -> sigma_t
  		# - eta -> η
  		# - pred_sample_direction -> "direction pointing to x_t"
  		# - pred_prev_sample -> "x_t-1"
  
  		# 1. get previous step value (=t-1)
  		__A   : Optional[Any]								= timestep - self.config.num_train_timesteps // state.num_inference_steps
  
  		__A   : Optional[int]								= state.common.alphas_cumprod
  		__A   : Optional[int]								= state.final_alpha_cumprod
  
  		# 2. compute alphas, betas
  		__A   : Optional[int]								= alphas_cumprod[timestep]
  		__A   : str								= jnp.where(prev_timestep >= 0				,			alphas_cumprod[prev_timestep]				,			__A       )
  
  		__A   : Any								= 1 - alpha_prod_t
  
  		# 3. compute predicted original sample from predicted noise also called
  		# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
  		if self.config.prediction_type == "epsilon":
  			__A   : Any								= (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
  			__A   : List[Any]								= model_output
  		elif self.config.prediction_type == "sample":
  			__A   : Dict								= model_output
  			__A   : Tuple								= (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
  		elif self.config.prediction_type == "v_prediction":
  			__A   : int								= (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
  			__A   : str								= (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
  		else:
  			raise ValueError(
  			    F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or"""
  			    """ `v_prediction`"""       )
  
  		# 4. compute variance: "sigma_t(η)" -> see formula (16)
  		# σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
  		__A   : Union[str, Any]								= self._get_variance(__A				,			__A				,			__A       )
  		__A   : Any								= eta * variance ** 0.5
  
  		# 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
  		__A   : Any								= (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * pred_epsilon
  
  		# 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
  		__A   : Optional[int]								= alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction
  
  		if not return_dict:
  			return (prev_sample, state)
  
  		return FlaxDDIMSchedulerOutput(prev_sample=__A				,			state=__A       )
  
  
  
  
  	def 					lowerCAmelCase_					(    self	:    List[str]				,			__A	:    DDIMSchedulerState				,			__A	:    jnp.ndarray				,			__A	:    jnp.ndarray				,			__A	:    jnp.ndarray				,			):
  		return add_noise_common(state.common				,			__A				,			__A				,			__A       )
  
  
  
  
  	def 					lowerCAmelCase_					(    self	:    Optional[int]				,			__A	:    DDIMSchedulerState				,			__A	:    jnp.ndarray				,			__A	:    jnp.ndarray				,			__A	:    jnp.ndarray				,			):
  		return get_velocity_common(state.common				,			__A				,			__A				,			__A       )
  
  
  
  
  	def __len__(    self	:    Union[str, Any]       ):
  		return self.config.num_train_timesteps
  
  
  
  
  
  