o ۷i@s~ddlZddlmZddlmZddlmZddlmZddl m Z m Z er3ddl m mZd Znd ZGd d d e ZdS) N) UNet2DModel) DDPMScheduler)is_torch_xla_available) randn_tensor)DiffusionPipelineImagePipelineOutputTFcs|eZdZdZdZdedeffdd Ze    dd e d ej e ej BdBd e de dBdedeeBf ddZZS) DDPMPipelinea1 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 schedulercst|j||ddS)N)r r )super__init__register_modules)selfr r  __class__\/home/ubuntu/vllm_env/lib/python3.10/site-packages/diffusers/pipelines/ddpm/pipeline_ddpm.pyr2s zDDPMPipeline.__init__NpilT batch_size generatornum_inference_steps output_type return_dictreturnc Cs.t|jjjtr||jjj|jjj|jjjf}n ||jjjg|jjjR}|jjdkr;t|||jj d}| |j}n t|||j|jj d}|j || |j jD]}|||j} |j j| |||dj}trmtqS|dddd}|ddd d}|d kr||}|s|fSt|d S) aa 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)rrg?rrrr)images) isinstancer config sample_sizeint in_channelsr typerrtor set_timesteps progress_bar timestepssamplestep prev_sample XLA_AVAILABLExm mark_stepclampcpupermutenumpy numpy_to_pilr ) rrrrrr image_shapeimaget model_outputrrr__call__6s2/    zDDPMPipeline.__call__)rNrrT)__name__ __module__ __qualname____doc__model_cpu_offload_seqrrrtorchno_gradr% Generatorliststrboolr tupler; __classcell__rrrrr !s.r )rAmodelsr schedulersrutilsrutils.torch_utilsrpipeline_utilsrr torch_xla.core.xla_modelcore xla_modelr0r/r rrrrs