class_docstr: Pipeline for video-to-video translation using Stable Diffusion with Rerender Algorithm.
  
  This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
  library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
  
  In addition the pipeline inherits the following loading methods:
      - *Textual-Inversion*: [`loaders.TextualInversionLoaderMixin.load_textual_inversion`]
  
  Args:
      vae ([`AutoencoderKL`]):
          Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
      text_encoder ([`CLIPTextModel`]):
          Frozen text-encoder. Stable Diffusion uses the text portion of
          [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
          the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
      tokenizer (`CLIPTokenizer`):
          Tokenizer of class
          [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
      unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
      controlnet ([`ControlNetModel`] or `List[ControlNetModel]`):
          Provides additional conditioning to the unet during the denoising process. If you set multiple ControlNets
          as a list, the outputs from each ControlNet are added together to create one combined additional
          conditioning.
      scheduler ([`SchedulerMixin`]):
          A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
          [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
      safety_checker ([`StableDiffusionSafetyChecker`]):
          Classification module that estimates whether generated images could be considered offensive or harmful.
          Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
      feature_extractor ([`CLIPImageProcessor`]):
          Model that extracts features from generated images to be used as inputs for the `safety_checker`.
class_docstr_tok: ["Pipeline","for","video-to-video","translation","using","Stable","Diffusion","with","Rerender","Algorithm",".","This","model","inherits","from","[","`","DiffusionPipeline","`","]",".","Check","the","superclass","documentation","for","the","generic","methods","the","library","implements","for","all","the","pipelines","(","such","as","downloading","or","saving",",","running","on","a","particular","device",",","etc",".",")","In","addition","the","pipeline","inherits","the","following","loading","methods",":","-","*","Textual-Inversion","*",":","[","`","loaders.TextualInversionLoaderMixin.load_textual_inversion","`","]","Args",":","vae","(","[","`","AutoencoderKL","`","]",")",":","Variational","Auto-Encoder","(","VAE",")","Model","to","encode","and","decode","images","to","and","from","latent","representations",".","text_encoder","(","[","`","CLIPTextModel","`","]",")",":","Frozen","text-encoder",".","Stable","Diffusion","uses","the","text","portion","of","[","CLIP","]","(","https",":","\/\/huggingface.co\/docs\/transformers\/model_doc\/clip","#","transformers.CLIPTextModel",")",",","specifically","the","[","clip-vit-large-patch14","]","(","https",":","\/\/huggingface.co\/openai\/clip-vit-large-patch14",")","variant",".","tokenizer","(","`","CLIPTokenizer","`",")",":","Tokenizer","of","class","[","CLIPTokenizer","]","(","https",":","\/\/huggingface.co\/docs\/transformers\/v4.21.0\/en\/model_doc\/clip","#","transformers.CLIPTokenizer",")",".","unet","(","[","`","UNet2DConditionModel","`","]",")",":","Conditional","U-Net","architecture","to","denoise","the","encoded","image","latents",".","controlnet","(","[","`","ControlNetModel","`","]","or","`","List","[","ControlNetModel","]","`",")",":","Provides","additional","conditioning","to","the","unet","during","the","denoising","process",".","If","you","set","multiple","ControlNets","as","a","list",",","the","outputs","from","each","ControlNet","are","added","together","to","create","one","combined","additional","conditioning",".","scheduler","(","[","`","SchedulerMixin","`","]",")",":","A","scheduler","to","be","used","in","combination","with","`","unet","`","to","denoise","the","encoded","image","latents",".","Can","be","one","of","[","`","DDIMScheduler","`","]",",","[","`","LMSDiscreteScheduler","`","]",",","or","[","`","PNDMScheduler","`","]",".","safety_checker","(","[","`","StableDiffusionSafetyChecker","`","]",")",":","Classification","module","that","estimates","whether","generated","images","could","be","considered","offensive","or","harmful",".","Please",",","refer","to","the","[","model","card","]","(","https",":","\/\/huggingface.co\/runwayml\/stable-diffusion-v1-5",")","for","details",".","feature_extractor","(","[","`","CLIPImageProcessor","`","]",")",":","Model","that","extracts","features","from","generated","images","to","be","used","as","inputs","for","the","`","safety_checker","`","."]
code: def get_timesteps(self, num_inference_steps, strength, device):
      # get the original timestep using init_timestep
      init_timestep = min(
          int(num_inference_steps * strength), num_inference_steps
      )
  
      t_start = max(num_inference_steps - init_timestep, 0)
      timesteps = self.scheduler.timesteps[
          t_start * self.scheduler.order :
      ]
  
      return timesteps, num_inference_steps - t_start
  
imports: import sys
  from dataclasses import dataclass
  from typing import Any, Callable, Dict, List, Optional, Tuple, Union
  import numpy
  import PIL.Image
  import torch
  import torch.nn.functional
  import torchvision.transforms
  from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
  from diffusers.image_processor import VaeImageProcessor
  from diffusers.models import AutoencoderKL, ControlNetModel, UNet2DConditionModel
  from diffusers.models.attention_processor import Attention, AttnProcessor
  from diffusers.pipelines.controlnet.multicontrolnet import MultiControlNetModel
  from diffusers.pipelines.controlnet.pipeline_controlnet_img2img import StableDiffusionControlNetImg2ImgPipeline
  from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
  from diffusers.schedulers import KarrasDiffusionSchedulers
  from diffusers.utils import BaseOutput, deprecate, logging
  from diffusers.utils.torch_utils import is_compiled_module, randn_tensor
  from gmflow.gmflow import GMFlow
  from utils.utils import InputPadder
label_desc: Use image node_id 4 for calling the RerenderAVideoPipeline obj's underlying member method code with example usage: obj.get_timesteps(num_inference_steps, strength, device) and returns: timesteps,  unknown