o ۷i@s~ddlZddlmZddlmZddlmZddlmZddl m Z m Z er3ddl m mZd Znd ZGd d d e ZdS) N) UNet2DModel) DDIMScheduler)is_torch_xla_available) randn_tensor)DiffusionPipelineImagePipelineOutputTFcseZdZdZdZdedeffdd Ze     dd e d ej e ej BdBde de dedBdedBdedeeBfddZZS) DDIMPipelinea1 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 schedulercs(tt|j}|j||ddS)N)r r )super__init__r from_configconfigregister_modules)selfr r  __class__\/home/ubuntu/vllm_env/lib/python3.10/site-packages/diffusers/pipelines/ddim/pipeline_ddim.pyr1s  zDDIMPipeline.__init__N2pilT batch_size generatoretanum_inference_stepsuse_clipped_model_output output_type return_dictreturnc Cs6t|jjjtr||jjj|jjj|jjjf}n ||jjjg|jjjR}t|tr=t||kr=tdt|d|dt |||j |jj d} |j |||j jD]} || | j} |j j| | | |||dj} trqtqU| dddd } | ddd d } |d kr|| } |s| fSt| d S) 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 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?rrrr)images) isinstancer r sample_sizeint in_channelslistlen ValueErrorr_execution_devicer$r set_timesteps progress_bar timestepssamplestep prev_sample XLA_AVAILABLExm mark_stepclampcpupermutenumpy numpy_to_pilr ) rrrrrrr r! image_shapeimaget model_outputrrr__call__9s@A     zDDIMPipeline.__call__)rNrrNrT)__name__ __module__ __qualname____doc__model_cpu_offload_seqrrrtorchno_gradr( Generatorr*floatboolstrr tupler@ __classcell__rrrrr s: r )rFmodelsr schedulersrutilsrutils.torch_utilsrpipeline_utilsrr torch_xla.core.xla_modelcore xla_modelr5r4r rrrrs