




"""simple docstring"""

from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput





class     lowercase_	(					__lowerCAmelCase							):



 '''simple docstring'''
 UpperCAmelCase   :					UNetaDModel
 UpperCAmelCase   :					KarrasVeScheduler

 def __init__(			self	:	Any   ,							_UpperCAmelCase	:	UNetaDModel   ,							_UpperCAmelCase	:	KarrasVeScheduler		):
       super().__init__()
       self.register_modules(unet=_UpperCAmelCase   ,							scheduler=_UpperCAmelCase		)



 @torch.no_grad()
 def __call__(			self	:	Any   ,							_UpperCAmelCase	:	int = 1   ,							_UpperCAmelCase	:	int = 50   ,							_UpperCAmelCase	:	Optional[Union[torch.Generator, List[torch.Generator]]] = None   ,							_UpperCAmelCase	:	Optional[str] = "pil"   ,							_UpperCAmelCase	:	bool = True   ,							**_UpperCAmelCase	:	Optional[int]   ,							):
       _A        =       self.unet.config.sample_size
       _A        =       (batch_size, 3, img_size, img_size)

       _A        =       self.unet

       # sample x_0 ~ N(0, sigma_0^2 * I)
       _A        =       randn_tensor(_UpperCAmelCase   ,							generator=_UpperCAmelCase   ,							device=self.device		) * self.scheduler.init_noise_sigma

       self.scheduler.set_timesteps(_UpperCAmelCase		)

       for t in self.progress_bar(self.scheduler.timesteps		):
             # here sigma_t == t_i from the paper
             _A        =       self.scheduler.schedule[t]
             _A        =       self.scheduler.schedule[t - 1] if t > 0 else 0

             # 1. Select temporarily increased noise level sigma_hat
             # 2. Add new noise to move from sample_i to sample_hat
             _A       ,   _A        =       self.scheduler.add_noise_to_input(_UpperCAmelCase   ,							_UpperCAmelCase   ,							generator=_UpperCAmelCase		)

             # 3. Predict the noise residual given the noise magnitude `sigma_hat`
             # The model inputs and output are adjusted by following eq. (213) in [1].
             _A        =       (sigma_hat / 2) * model((sample_hat + 1) / 2   ,							sigma_hat / 2		).sample

             # 4. Evaluate dx/dt at sigma_hat
             # 5. Take Euler step from sigma to sigma_prev
             _A        =       self.scheduler.step(_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase		)

             if sigma_prev != 0:
                   # 6. Apply 2nd order correction
                   # The model inputs and output are adjusted by following eq. (213) in [1].
                   _A        =       (sigma_prev / 2) * model((step_output.prev_sample + 1) / 2   ,							sigma_prev / 2		).sample
                   _A        =       self.scheduler.step_correct(
                       _UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase   ,							step_output.prev_sample   ,							step_output['derivative']   ,							)
             _A        =       step_output.prev_sample

       _A        =       (sample / 2 + 0.5).clamp(0   ,							1		)
       _A        =       sample.cpu().permute(0   ,							2   ,							3   ,							1		).numpy()
       if output_type == "pil":
             _A        =       self.numpy_to_pil(_UpperCAmelCase		)

       if not return_dict:
             return (image,)

       return ImagePipelineOutput(images=_UpperCAmelCase		)







"""simple docstring"""

import os

from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE, hf_cache_home


a         =      HUGGINGFACE_HUB_CACHE


a         =      '''config.json'''
a         =      '''diffusion_pytorch_model.bin'''
a         =      '''diffusion_flax_model.msgpack'''
a         =      '''model.onnx'''
a         =      '''diffusion_pytorch_model.safetensors'''
a         =      '''weights.pb'''
a         =      '''https://huggingface.co'''
a         =      default_cache_path
a         =      '''diffusers_modules'''
a         =      os.getenv('''HF_MODULES_CACHE''', os.path.join(hf_cache_home, '''modules'''))
a         =      ['''fp16''', '''non-ema''']
a         =      '''.self_attn'''

