class_docstr: Pipeline for text-guided depth-based image-to-image generation using Stable Diffusion.
  
  This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
  implemented for all pipelines (downloading, saving, running on a particular device, etc.).
  
  The pipeline also inherits the following loading methods:
      - [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings
      - [`~loaders.LoraLoaderMixin.load_lora_weights`] for loading LoRA weights
      - [`~loaders.LoraLoaderMixin.save_lora_weights`] for saving LoRA weights
  
  Args:
      vae ([`AutoencoderKL`]):
          Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.
      text_encoder ([`~transformers.CLIPTextModel`]):
          Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
      tokenizer ([`~transformers.CLIPTokenizer`]):
          A `CLIPTokenizer` to tokenize text.
      unet ([`UNet2DConditionModel`]):
          A `UNet2DConditionModel` to denoise the encoded image latents.
      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`].
class_docstr_tok: ["Pipeline","for","text-guided","depth-based","image-to-image","generation","using","Stable","Diffusion",".","This","model","inherits","from","[","`","DiffusionPipeline","`","]",".","Check","the","superclass","documentation","for","the","generic","methods","implemented","for","all","pipelines","(","downloading",",","saving",",","running","on","a","particular","device",",","etc.",")",".","The","pipeline","also","inherits","the","following","loading","methods",":","-","[","`","~loaders.TextualInversionLoaderMixin.load_textual_inversion","`","]","for","loading","textual","inversion","embeddings","-","[","`","~loaders.LoraLoaderMixin.load_lora_weights","`","]","for","loading","LoRA","weights","-","[","`","~loaders.LoraLoaderMixin.save_lora_weights","`","]","for","saving","LoRA","weights","Args",":","vae","(","[","`","AutoencoderKL","`","]",")",":","Variational","Auto-Encoder","(","VAE",")","model","to","encode","and","decode","images","to","and","from","latent","representations",".","text_encoder","(","[","`","~transformers.CLIPTextModel","`","]",")",":","Frozen","text-encoder","(","[","clip-vit-large-patch14","]","(","https",":","\/\/huggingface.co\/openai\/clip-vit-large-patch14",")",")",".","tokenizer","(","[","`","~transformers.CLIPTokenizer","`","]",")",":","A","`","CLIPTokenizer","`","to","tokenize","text",".","unet","(","[","`","UNet2DConditionModel","`","]",")",":","A","`","UNet2DConditionModel","`","to","denoise","the","encoded","image","latents",".","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","`","]","."]
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 contextlib
  import inspect
  from typing import Any, Callable, Dict, List, Optional, Union
  import numpy
  import PIL.Image
  import torch
  from packaging import version
  from transformers import CLIPTextModel, CLIPTokenizer, DPTFeatureExtractor, DPTForDepthEstimation
  from ...configuration_utils import FrozenDict
  from ...image_processor import PipelineImageInput, VaeImageProcessor
  from ...loaders import LoraLoaderMixin, TextualInversionLoaderMixin
  from ...models import AutoencoderKL, UNet2DConditionModel
  from ...models.lora import adjust_lora_scale_text_encoder
  from ...schedulers import KarrasDiffusionSchedulers
  from ...utils import PIL_INTERPOLATION, USE_PEFT_BACKEND, deprecate, logging, scale_lora_layers, unscale_lora_layers
  from ...utils.torch_utils import randn_tensor
  from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
label_desc: Use image node_id 8 for calling the StableDiffusionDepth2ImgPipeline obj's underlying member method code with example usage: obj.get_timesteps(num_inference_steps, strength, device) and returns: timesteps,  unknown