3j.SSKrSSKrSSKJr SSKJr SSKJr SSKJ r SSK J r J r \R"\5r\"5(a SSKJs Jr S rOS r"S S \ 5rg) N) UNet2DModel) DDIMScheduler)is_torch_xla_available) randn_tensor)DiffusionPipelineImagePipelineOutputTFc^\rSrSrSrSrS\S\4U4Sjjr\ R"5SS\ S\ R\ \ R-S-S \S \ S \S-S \S-S \S\\-4Sjj5rSrU=r$) DDIMPipeline%a  Pipeline for image generation. This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods implemented for all pipelines (downloading, saving, running on a particular device, etc.). Parameters: unet ([`UNet2DModel`]): A `UNet2DModel` to denoise the encoded image latents. scheduler ([`SchedulerMixin`]): A scheduler to be used in combination with `unet` to denoise the encoded image. Can be one of [`DDPMScheduler`], or [`DDIMScheduler`]. unet schedulerc>[TU]5 [R"UR5nUR XS9 g)N)rr)super__init__r from_configconfigregister_modules)selfrr __class__s `/home/wildlama/miniconda3/lib/python3.13/site-packages/diffusers/pipelines/ddim/pipeline_ddim.pyrDDIMPipeline.__init__6s7 "--i.>.>?  4=N batch_size generatoretanum_inference_stepsuse_clipped_model_output output_type return_dictreturnc [URRR[5(aaUURRR URRRURRR4nOBXRRR /URRRQ7nSUs=::aS::dO [ RSUS35 [U[5(a*[U5U:wa[S[U5SUS35e[XURURRS9n URRU5 UR!URR"5Hen URX5R$n URR'XXXRS 9R(n [*(dMP[,R."5 Mg U S - S -R1S S 5n U R35R5S S SS 5R75n US:XaUR9U 5n U(dU 4$[;U S9$)u 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. eta (`float`, *optional*, defaults to 0.0): Corresponds to parameter eta (η) from the [DDIM](https://huggingface.co/papers/2010.02502) paper. Only applies to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers. A value of `0` corresponds to DDIM and `1` corresponds to DDPM. 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. use_clipped_model_output (`bool`, *optional*, defaults to `None`): If `True` or `False`, see documentation for [`DDIMScheduler.step`]. If `None`, nothing is passed downstream to the scheduler (use `None` for schedulers which don't support this argument). 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 DDIMPipeline >>> import PIL.Image >>> import numpy as np >>> # load model and scheduler >>> pipe = DDIMPipeline.from_pretrained("fusing/ddim-lsun-bedroom") >>> # run pipeline in inference (sample random noise and denoise) >>> image = pipe(eta=0.0, num_inference_steps=50) >>> # process image to PIL >>> image_processed = image.cpu().permute(0, 2, 3, 1) >>> image_processed = (image_processed + 1.0) * 127.5 >>> image_processed = image_processed.numpy().astype(np.uint8) >>> image_pil = PIL.Image.fromarray(image_processed[0]) >>> # save image >>> image_pil.save("test.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 g?z:`eta` should be between 0 and 1 (inclusive), but received zy. A value of 0 corresponds to DDIM and 1 corresponds to DDPM. Unexpected results may occur for values outside this range.z/You have passed a list of generators of length z+, but requested an effective batch size of z@. Make sure the batch size matches the length of the generators.)rdevicedtype)rrrrg?rrpil)images) isinstancerr sample_sizeint in_channelsloggerwarninglistlen ValueErrorr_execution_devicer&r set_timesteps progress_bar timestepssamplestep prev_sample XLA_AVAILABLExm mark_stepclampcpupermutenumpy numpy_to_pilr ) rrrrrrr r! image_shapeimaget model_outputs r__call__DDIMPipeline.__call__>sB dii&&22C 8 8   ,,   ,,   ,, K&yy'7'7'C'CcdiiFVFVFbFbcKc S NNLSERNN  i & &3y>Z+GA#i.AQR&<'gi  [dF\F\dhdmdmdsdst $$%89""4>>#;#;[>]> ]]_DH#%04"' r1r1??T%//%::TAr1 r1 ! r1 #'+ r14Zr1r1 u $r1r1rr )loggingrPmodelsr schedulersrutilsrutils.torch_utilsrpipeline_utilsr r getLoggerrJr.torch_xla.core.xla_modelcore xla_modelr;r:r rHrrrcsX !'+-C   8 $))MML1$L1r