bin'^ddlmZmZmZmZddlZddlZddl Z ddl m Z ddl m Z ddlmZmZmZddlmZdd lmZmZej.eZd eeej4j4e j6ffd Zd eeej4j4e j6ffd ZGddeZy))ListOptionalTupleUnionN) UNet2DModel)RePaintScheduler)PIL_INTERPOLATION deprecatelogging) randn_tensor)DiffusionPipelineImagePipelineOutputimagec 2d}tdd|dt|tjr|St|tj j r|g}t|dtj j r|dj \}}d||fD\}}|Dcgc]8}tj|j||ftd dddf:}}tj|d }tj|jtjd z }|jdd d d}d|zdz }tj|}|St|dtjrtj |d}|Scc}w)NzThe preprocess method is deprecated and will be removed in diffusers 1.0.0. Please use VaeImageProcessor.preprocess(...) instead preprocessz1.0.0F) standard_warnrc3,K|] }||dzz yw)N.0xs r/home/cdr/jupyterlab/.venv/lib/python3.12/site-packages/diffusers/pipelines/deprecated/repaint/pipeline_repaint.py z$_preprocess_image..+s*aAE *lanczosresampleaxiso@rg@g?dim)r isinstancetorchTensorPILImagesizenparrayresizer concatenateastypefloat32 transpose from_numpycat)rdeprecation_messagewhis r_preprocess_imager;!sX] lG%8N%& E399?? +%(CIIOO,Qx}}1*Aq6*1ejk`a!((Aq64Ei4P(QRSWYZSZ[kku1-&&rzz2U:1a+e c!  ' L E!Hell + %Q' Lls =Fmaskc t|tjr|St|tjjr|g}t|dtjjr|dj \}}d||fD\}}|Dcgc]G}t j|jdj||ftddddfI}}t j|d}|jt jdz }d||dk<d ||dk\<tj|}|St|dtjrtj|d }|Scc}w) Nrc3,K|] }||dzz yw) Nrrs rrz#_preprocess_mask..@s+qAF +rLnearestrr!r#?r$r&)r(r)r*r+r,r-r.r/convertr0r r1r2r3r5r6)r<r8r9ms r_preprocess_maskrE8s<$ % D#))// *v$q'399??+Aw||1+QF+1quvlm3..1v@QR[@\.]^_cef_fgvv~~d+{{2::&.TCZTS[% K DGU\\ *yy1% KwsA E3ceZdZUdZeed<eed<dZdedeffd Ze j dde e je jjfde e je jjfdeded ed ed ee e j$ee j$fd eed ede eeffdZxZS)RePaintPipelinea Pipeline for image inpainting using RePaint. This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods implemented for all pipelines (downloading, saving, running on a particular device, etc.). Parameters: unet ([`UNet2DModel`]): A `UNet2DModel` to denoise the encoded image latents. scheduler ([`RePaintScheduler`]): A `RePaintScheduler` to be used in combination with `unet` to denoise the encoded image. unet schedulercHt||j||y)N)rHrI)super__init__register_modules)selfrHrI __class__s rrLzRePaintPipeline.__init__^s!  49=r mask_imagenum_inference_stepseta jump_length jump_n_sample generator output_type return_dictreturnc |} t| } | j|j|jj} t |}|j|j|jj}| j d} t|tr)t|| k7rtdt|d| d| j } t| ||j|jj}|jj||||j||j_|jjddz} t|tr|dn|}t!|j#|jjD]p\}}|| krG|j||j$}|jj'|||| ||j(}n|jj+|| |}|} r|dz d zj-dd}|j/j1ddd dj3}|d k(r|j5|}| s|fSt7| 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://huggingface.co/papers/2201.09865). 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://huggingface.co/papers/2201.09865). 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.)rVr[r\r$r%rBrpil)images)r;to_execution_devicerHr\rEshaper(listlen ValueErrorr rI set_timestepsrS timesteps enumerate progress_barsamplestep prev_sample undo_stepclampcpupermutenumpy numpy_to_pilr)rNrrQrRrSrTrUrVrWrXoriginal_image batch_size image_shapet_lastr:t model_outputs r__call__zRePaintPipeline.__call__bsBp*>:'**$2H2HPTPYPYP_P_*`%j1 ]]$*@*@ ]X #))!,  i &3y>Z+GA#i.AQR&<'gi  %** [IdF\F\dhdmdmdsdst $$%8+}VZVlVlm ))!,q0$.y$$?IaLY d//0H0HIJ DAq6z#yy299 ++L!UNT^`ijvv00 JF S''1- ##Aq!Q/557 % %%e,E8O"%00rP)g rzNr]T)__name__ __module__ __qualname____doc__r__annotations__r model_cpu_offload_seqrLr)no_gradrr*r+r,intfloatr Generatorrstrboolrrrx __classcell__)rOs@rrGrGLs(  ">[>5E>U]]_ $'MQ%* D1U\\399??23D1%,, 78D1! D1  D1  D1D1E%//43H"HIJD1c]D1D1 "E) *D1D1rPrG)typingrrrrrpr. PIL.Imager+r)modelsr schedulersr utilsr r r utils.torch_utilsr pipeline_utilsrr get_loggerr{loggerr,r*r;rErGrrPrrs 0/ "+;;.D   H %U4%,,#FG.5syy !DE([1'[1rP