o pi7'@sddlmZmZmZmZddlZddlZddl Z ddl m Z ddl m Z ddlmZmZmZddlmZdd lmZmZeeZd eeejje jffd d Zd eeejje jffddZGdddeZdS))ListOptionalTupleUnionN) UNet2DModel)RePaintScheduler)PIL_INTERPOLATION deprecatelogging) randn_tensor)DiffusionPipelineImagePipelineOutputimagecsd}tdd|ddt|tjr|St|tjjr|g}t|dtjjrh|dj\ddfD\fd d |D}tj|dd }t | tj d }| dd dd}d|d}t |}|St|dtjrwtj|dd}|S)NzThe preprocess method is deprecated and will be removed in diffusers 1.0.0. Please use VaeImageProcessor.preprocess(...) instead preprocessz1.0.0F) standard_warnrcs|] }||dVqdS)N.0xrru/home/ubuntu/SoloSpeech/.venv/lib/python3.10/site-packages/diffusers/pipelines/deprecated/repaint/pipeline_repaint.py +z$_preprocess_image..cs4g|]}t|jftdddddfqS)lanczosresampleN)nparrayresizer )rihwrr -s4z%_preprocess_image..axiso@r g@g?dim)r isinstancetorchTensorPILImagesizer concatenater astypefloat32 transpose from_numpycat)rdeprecation_messagerr#r_preprocess_image!s&   r;maskcst|tjr|St|tjjr|g}t|dtjjrY|dj\ddfD\fdd|D}tj|dd}|tj d}d||dk<d ||dk<t |}|St|dtjrhtj |dd }|S) Nrcsr) Nrrrrrr@rz#_preprocess_mask..cs:g|]}t|djftdddddfqS)LnearestrN)rr convertr!r )rmr#rrr&As:z$_preprocess_mask..r'r)?r*r,) r.r/r0r1r2r3rr4r5r6r8r9)r<rr#r_preprocess_mask8s"    rCcseZdZUdZeed<eed<dZfddZe     dd e e j e jjfd e e j e jjfdededededee e jee jfdeedede eeffddZZS)RePaintPipelinea Pipeline for image inpainting using RePaint. 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 ([`RePaintScheduler`]): A `RePaintScheduler` to be used in combination with `unet` to denoise the encoded image. unet schedulercst|j||ddS)N)rErF)super__init__register_modules)selfrErF __class__rrrH^s zRePaintPipeline.__init__ NpilTr mask_imagenum_inference_stepseta jump_length jump_n_sample generator output_type return_dictreturnc  Cs|} t| } | j|j|jjd} t|}|j|j|jjd}| jd} t|tr;t || kr;t dt |d| d| j} t | ||j|jjd}|j ||||j||j _|j jdd} t|trh|dn|}t||j jD]&\}}|| kr|||j}|j |||| ||j}n|j || |}|} qs|dd dd}|ddd d}|d kr||}| s|fSt|d S) a The call function to the pipeline for generation. Args: image (`torch.Tensor` or `PIL.Image.Image`): The original image to inpaint on. mask_image (`torch.Tensor` or `PIL.Image.Image`): The mask_image where 0.0 define which part of the original image to inpaint. 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. eta (`float`): The weight of the added noise in a diffusion step. Its value is between 0.0 and 1.0; 0.0 corresponds to DDIM and 1.0 is the DDPM scheduler. jump_length (`int`, *optional*, defaults to 10): The number of steps taken forward in time before going backward in time for a single jump ("j" in RePaint paper). Take a look at Figure 9 and 10 in the [paper](https://arxiv.org/pdf/2201.09865.pdf). jump_n_sample (`int`, *optional*, defaults to 10): The number of times to make a forward time jump for a given chosen time sample. Take a look at Figure 9 and 10 in the [paper](https://arxiv.org/pdf/2201.09865.pdf). 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 io import BytesIO >>> import torch >>> import PIL >>> import requests >>> from diffusers import RePaintPipeline, RePaintScheduler >>> def download_image(url): ... response = requests.get(url) ... return PIL.Image.open(BytesIO(response.content)).convert("RGB") >>> img_url = "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/celeba_hq_256.png" >>> mask_url = "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png" >>> # Load the original image and the mask as PIL images >>> original_image = download_image(img_url).resize((256, 256)) >>> mask_image = download_image(mask_url).resize((256, 256)) >>> # Load the RePaint scheduler and pipeline based on a pretrained DDPM model >>> scheduler = RePaintScheduler.from_pretrained("google/ddpm-ema-celebahq-256") >>> pipe = RePaintPipeline.from_pretrained("google/ddpm-ema-celebahq-256", scheduler=scheduler) >>> pipe = pipe.to("cuda") >>> generator = torch.Generator(device="cuda").manual_seed(0) >>> output = pipe( ... image=original_image, ... mask_image=mask_image, ... num_inference_steps=250, ... eta=0.0, ... jump_length=10, ... jump_n_sample=10, ... generator=generator, ... ) >>> inpainted_image = output.images[0] ``` 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. )devicedtyperz/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.)rVrZr[r*r+rBr rP)images)r;to_execution_devicerEr[rCshaper.listlen ValueErrorr rF set_timestepsrS timesteps enumerate progress_barsamplestep prev_sample undo_stepclampcpupermutenumpy numpy_to_pilr)rJrrQrRrSrTrUrVrWrXoriginal_image batch_size image_shapet_lastr"t model_outputrrr__call__bs>W    zRePaintPipeline.__call__)rMrNrOrONrPT)__name__ __module__ __qualname____doc__r__annotations__rmodel_cpu_offload_seqrHr/no_gradrr0r1r2intfloatr Generatorrstrboolrrrv __classcell__rrrKrrDLsF      rD)typingrrrrrnr PIL.Imager1r/modelsr schedulersrutilsr r r utils.torch_utilsr pipeline_utilsrr get_loggerrwloggerr2r0r;rCrDrrrrs