+hSSKJrJrJrJr SSKrSSKJr SSKJ r SSK J r SSK J r SS KJrJr \ "5(a SSKJs Jr S rOS r"S S \5rg))ListOptionalTupleUnionN) UNet2DModel) DDPMScheduler)is_torch_xla_available) randn_tensor)DiffusionPipelineImagePipelineOutputTFc^\rSrSrSrSrS\S\4U4Sjjr\ R"5SS\ S\ \ \ R\\ R4S\ S \ \S \S \ \\44 S jj5rS rU=r$) DDPMPipeline#a  Pipeline for image generation. This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods implemented for all pipelines (downloading, saving, running on a particular device, etc.). Parameters: unet ([`UNet2DModel`]): A `UNet2DModel` to denoise the encoded image latents. scheduler ([`SchedulerMixin`]): A scheduler to be used in combination with `unet` to denoise the encoded image. Can be one of [`DDPMScheduler`], or [`DDIMScheduler`]. unet schedulerc@>[TU]5 URXS9 g)N)rr)super__init__register_modules)selfrr __class__s `/home/james-whalen/.local/lib/python3.13/site-packages/diffusers/pipelines/ddpm/pipeline_ddpm.pyrDDPMPipeline.__init__4s  4= batch_size generatornum_inference_steps output_type return_dictreturnc[URRR[5(aaUURRR URRRURRR4nOBXRRR /URRRQ7nUR RS:Xa:[XbURRS9nURUR 5nO)[XbUR URRS9nURRU5 URURR5HdnURXx5Rn URR!XXrS9R"n[$(dMO[&R("5 Mf US- S-R+SS5nUR-5R/SSS S5R15nUS :XaUR3U5nU(dU4$[5US 9$) ay The call function to the pipeline for generation. Args: batch_size (`int`, *optional*, defaults to 1): The number of images to generate. generator (`torch.Generator`, *optional*): A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation deterministic. num_inference_steps (`int`, *optional*, defaults to 1000): The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference. output_type (`str`, *optional*, defaults to `"pil"`): The output format of the generated image. Choose between `PIL.Image` or `np.array`. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple. Example: ```py >>> from diffusers import DDPMPipeline >>> # load model and scheduler >>> pipe = DDPMPipeline.from_pretrained("google/ddpm-cat-256") >>> # run pipeline in inference (sample random noise and denoise) >>> image = pipe().images[0] >>> # save image >>> image.save("ddpm_generated_image.png") ``` Returns: [`~pipelines.ImagePipelineOutput`] or `tuple`: If `return_dict` is `True`, [`~pipelines.ImagePipelineOutput`] is returned, otherwise a `tuple` is returned where the first element is a list with the generated images mps)rdtype)rdevicer%)rr g?rrpil)images) isinstancerconfig sample_sizeint in_channelsr&typer r%tor set_timesteps progress_bar timestepssamplestep prev_sample XLA_AVAILABLExm mark_stepclampcpupermutenumpy numpy_to_pilr) rrrrr r! image_shapeimaget model_outputs r__call__DDPMPipeline.__call__8s^ dii&&22C 8 8   ,,   ,,   ,, K&yy'7'7'C'CcdiiFVFVFbFbcK ;;  u $ YEHHT[[)E $++]a]f]f]l]lmE $$%89""4>>#;#;[>]> ]]_MQ#'%* T1T1E%//43H"HIJT1! T1 c] T1  T1 "E) *T1T1rr)typingrrrrrLmodelsr schedulersr utilsr utils.torch_utilsr pipeline_utilsr rtorch_xla.core.xla_modelcore xla_modelr8r7rrErrr\sI 0/ !'+-C))MMj1$j1r