+h6nSSKJrJrJrJr SSKrSSKJr SSKJ r SSK J r SSK J r Jr "S S \ 5rg) )ListOptionalTupleUnionN) UNet2DModel) PNDMScheduler) randn_tensor)DiffusionPipelineImagePipelineOutputc^\rSrSr%Sr\\S'\\S'S\S\4U4Sjjr\ R"5SS\ S\ S\ \ \ R\\ R4S \ \S \S \ \\44 S jj5rS rU=r$) PNDMPipelinea Pipeline for unconditional image generation. This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods implemented for all pipelines (downloading, saving, running on a particular device, etc.). Parameters: unet ([`UNet2DModel`]): A `UNet2DModel` to denoise the encoded image latents. scheduler ([`PNDMScheduler`]): A `PNDMScheduler` to be used in combination with `unet` to denoise the encoded image. unet schedulerc>[TU]5 [R"UR5nUR XS9 g)N)rr)super__init__r from_configconfigregister_modules)selfrr __class__s k/home/james-whalen/.local/lib/python3.13/site-packages/diffusers/pipelines/deprecated/pndm/pipeline_pndm.pyrPNDMPipeline.__init__+s5 !--i.>.>?  4= batch_sizenum_inference_steps generator output_type return_dictreturnc [XRRRURRRURRR4UUR S9nUR RU5 URUR R5HDnURXx5Rn UR RXU5RnMF US- S-RSS5nUR5RSSSS5R!5nUS:XaUR#U5nU(dU4$[%US9$) ap The call function to the pipeline for generation. Args: batch_size (`int`, `optional`, defaults to 1): The number of images to generate. num_inference_steps (`int`, `optional`, defaults to 50): The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference. generator (`torch.Generator`, `optional`): A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation deterministic. 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 [`ImagePipelineOutput`] instead of a plain tuple. Example: ```py >>> from diffusers import PNDMPipeline >>> # load model and scheduler >>> pndm = PNDMPipeline.from_pretrained("google/ddpm-cifar10-32") >>> # run pipeline in inference (sample random noise and denoise) >>> image = pndm().images[0] >>> # save image >>> image.save("pndm_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. )r deviceg?rr pil)images)r rr in_channels sample_sizer%r set_timesteps progress_bar timestepssamplestep prev_sampleclampcpupermutenumpy numpy_to_pilr ) rrrr r!r"kwargsimaget model_outputs r__call__PNDMPipeline.__call__2s(f ))55tyy7G7G7S7SUYU^U^UeUeUqUq r;;  $$%89""4>>#;#;[>]> ]]_#%MQ%* F1F1!F1E%//43H"HIJ F1 c] F1  F1 "E) *F1F1rr)typingrrrrrEmodelsr schedulersr utils.torch_utilsr pipeline_utilsr r rr=rrrRs+ 0/ "(.D_1$_1r