o Gi%@sddlmZddlmZddlZddlZddlmZddl m Z ddl m Z m Z ddlmZdd lmZmZdd lmZmZdd lmZmZmZmZdd lmZd dlmZddlm Z erkddl!m"m#Z$dZ%ndZ%e&e'Z(dZ)eGdddeZ*GdddeZ+dS)) dataclass)AnyN)Image)tqdm) CLIPTextModel CLIPTokenizer)PipelineImageInput) AutoencoderKLUNet2DConditionModel) DDIMScheduler LCMScheduler) BaseOutputis_torch_xla_availableloggingreplace_example_docstring) randn_tensor)DiffusionPipeline)MarigoldImageProcessorTFa Examples: ```py >>> import diffusers >>> import torch >>> pipe = diffusers.MarigoldNormalsPipeline.from_pretrained( ... "prs-eth/marigold-normals-v1-1", variant="fp16", torch_dtype=torch.float16 ... ).to("cuda") >>> image = diffusers.utils.load_image("https://marigoldmonodepth.github.io/images/einstein.jpg") >>> normals = pipe(image) >>> vis = pipe.image_processor.visualize_normals(normals.prediction) >>> vis[0].save("einstein_normals.png") ``` c@sDeZdZUdZejejBed<dejBejBed<dejBed<dS)MarigoldNormalsOutputug Output class for Marigold monocular normals prediction pipeline. Args: prediction (`np.ndarray`, `torch.Tensor`): Predicted normals with values in the range [-1, 1]. The shape is `numimages × 3 × height × width` for `torch.Tensor` or `numimages × height × width × 3` for `np.ndarray`. uncertainty (`None`, `np.ndarray`, `torch.Tensor`): Uncertainty maps computed from the ensemble, with values in the range [0, 1]. The shape is `numimages × 1 × height × width` for `torch.Tensor` or `numimages × height × width × 1` for `np.ndarray`. latent (`None`, `torch.Tensor`): Latent features corresponding to the predictions, compatible with the `latents` argument of the pipeline. The shape is `numimages * numensemble × 4 × latentheight × latentwidth`. predictionN uncertaintylatent) __name__ __module__ __qualname____doc__npndarraytorchTensor__annotations__r$r$j/home/ubuntu/.local/lib/python3.10/site-packages/diffusers/pipelines/marigold/pipeline_marigold_normals.pyrMs rc!s,eZdZdZdZdZ    d9dededee Bd e d e d e dBd e dBd edBdedBffdd Zdedededede de dedee efdBdejdBdejeejBdBde de defddZejjd:d d!Zeee  "   # # "    $ % % d;dededBdededBd&e de de dedee efdBdejeejBdBdejeejBdBde de d'e d(e fd)d*ZdejdejdBdejdBdededeejejff d+d,Z d-ejdejfd.d/Z!e"dMarigoldNormalsPipelinea0 Pipeline for monocular normals estimation using the Marigold method: https://marigoldmonodepth.github.io. This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.) Args: unet (`UNet2DConditionModel`): Conditional U-Net to denoise the normals latent, conditioned on image latent. vae (`AutoencoderKL`): Variational Auto-Encoder (VAE) Model to encode and decode images and predictions to and from latent representations. scheduler (`DDIMScheduler` or `LCMScheduler`): A scheduler to be used in combination with `unet` to denoise the encoded image latents. text_encoder (`CLIPTextModel`): Text-encoder, for empty text embedding. tokenizer (`CLIPTokenizer`): CLIP tokenizer. prediction_type (`str`, *optional*): Type of predictions made by the model. use_full_z_range (`bool`, *optional*): Whether the normals predicted by this model utilize the full range of the Z dimension, or only its positive half. default_denoising_steps (`int`, *optional*): The minimum number of denoising diffusion steps that are required to produce a prediction of reasonable quality with the given model. This value must be set in the model config. When the pipeline is called without explicitly setting `num_inference_steps`, the default value is used. This is required to ensure reasonable results with various model flavors compatible with the pipeline, such as those relying on very short denoising schedules (`LCMScheduler`) and those with full diffusion schedules (`DDIMScheduler`). default_processing_resolution (`int`, *optional*): The recommended value of the `processing_resolution` parameter of the pipeline. This value must be set in the model config. When the pipeline is called without explicitly setting `processing_resolution`, the default value is used. This is required to ensure reasonable results with various model flavors trained with varying optimal processing resolution values. ztext_encoder->unet->vae)normalsNTunetvae scheduler text_encoder tokenizerprediction_typeuse_full_z_rangedefault_denoising_stepsdefault_processing_resolutionc st||jvrtd|d|jd|j|||||d|j|||| dt|ddr;dt|j j j dnd |_ ||_ ||_| |_d|_t|j d |_dS) Nz*Potentially unsupported `prediction_type='z&'`; values supported by the pipeline: .)r(r)r*r+r,)r-r.r/r0r)rr)vae_scale_factor)super__init__supported_prediction_typesloggerwarningregister_modulesregister_to_configgetattrlenr)configblock_out_channelsr3r.r/r0empty_text_embeddingrimage_processor) selfr(r)r*r+r,r-r.r/r0 __class__r$r%r5s4 (z MarigoldNormalsPipeline.__init__imagenum_inference_steps ensemble_sizeprocessing_resolutionresample_method_inputresample_method_output batch_sizeensembling_kwargslatents generator output_typeoutput_uncertaintyreturnc  sdt|jjjd} | |jkrtd|jd| d|dur$td|dkr,td|dkr4td|dkr=td |dkrG| rGtd |durOtd |d krWtd ||jd krgtd|jd|dvrotd|dvrwtd|dkrtd| dvrtd| durdurtd|durt|t stdd|vr|ddvrtdd }d\}}t|t s|g}t |D]{\}}t|t j st|r|jdvrtd|d|jd|jdd\}}d}|jd kr|jd }nt|tjr|j\}}d}n td!|d"t|d|dur||}}n||f||fkr7td#|d$||fd%||f||7}q| durt| sKtd&| d kr[td'| jd|d krt||}|||}|||}|d ks{|d krtd(|d)|d*||}}||jd|j}||jd|j}|||jjj||f}| j|krtd+| jd,|ddurtt rt||krtd-tfd.d/Dstd0|Sttjstd1td|S)2Nrrz/`vae_scale_factor` computed at initialization (z) differs from the actual one (z).zW`num_inference_steps` is not specified and could not be resolved from the model config.z'`num_inference_steps` must be positive.z!`ensemble_size` must be positive.zk`ensemble_size` == 2 results are similar to no ensembling (1); consider increasing the value to at least 3.zpComputing uncertainty by setting `output_uncertainty=True` also requires setting `ensemble_size` greater than 1.zY`processing_resolution` is not specified and could not be resolved from the model config.rzx`processing_resolution` must be non-negative: 0 for native resolution, or any positive value for downsampled processing.z.`processing_resolution` must be a multiple of r1)nearestz nearest-exactbilinearbicubicareazy`resample_method_input` takes string values compatible with PIL library: nearest, nearest-exact, bilinear, bicubic, area.zz`resample_method_output` takes string values compatible with PIL library: nearest, nearest-exact, bilinear, bicubic, area.z`batch_size` must be positive.)ptrz*`output_type` must be one of `pt` or `np`.z2`latents` and `generator` cannot be used together.z)`ensembling_kwargs` must be a dictionary. reductionclosestmeanzG`ensembling_kwargs['reduction']` can be either `'closest'` or `'mean'`.)NN)rrz`image[z(]` has unsupported dimensions or shape: rZzUnsupported `image[z ]` type: z Input `image[z]` has incompatible dimensions z with the previous images z!`latents` must be a torch.Tensor.z/`latents` has unsupported dimensions or shape: z*Extreme aspect ratio of the input image: [z x ]z`latents` has unexpected shape=z expected=z^The number of generators must match the total number of ensemble members for all input images.c3s$|] }|jjdjjkVqdS)rN)devicetype).0grMr$r% 2s"z7MarigoldNormalsPipeline.check_inputs..z;`generator` device placement is not consistent in the list.zUnsupported generator type: )r<r)r=r>r3 ValueErrorr7r8 isinstancedictlist enumeraterr r! is_tensorndimshapersizer^dimmaxlatent_channelsall Generator)rArDrErFrGrHrIrJrKrLrMrNrOactual_vae_scale_factor num_imagesWHiimgH_iW_iN_imax_orignew_Hnew_Wwhshape_expectedr$rar% check_inputss                     z$MarigoldNormalsPipeline.check_inputscCst|ds i|_nt|jtstdt|jdtdi|j}|d||d<|d||d<|dur>t|fi|S|durKtdd|i|Std) N_progress_bar_configz=`self._progress_bar_config` should be of type `dict`, but is r1descleavetotalz/Either `total` or `iterable` has to be defined.r$)hasattrrrdrercr^getr)rAiterablerrrprogress_bar_configr$r$r% progress_bar9s  z$MarigoldNormalsPipeline.progress_barrrRrFmatch_input_resolution output_latent return_dictc$s0j}j}|dur j}|durj}||||||| | |  }jdurEd}j|djjddd}|j |} |d_j |||||\}}} || | |\}~jj ||dd d }g}jtd||dd d D]\}|||}||}|jd}|d|}jj||d jjjd dd D]'} tj||gd d}!j|!| |d dd}"jj|"| || dj}trtq||q{tj|dd~~~~~~~!~"tjfddtdjdDdd|sdj |d}#|d krHj||gjd dRfddt|Dt \}#tjddrFtj|#dd}#nd}#|rlj j!||d d"|#durlrlj j!|#||d d}#| dkrj #|#durrj #|#}#$|s|#fSt%|#dS)a^ Function invoked when calling the pipeline. Args: image (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `list[PIL.Image.Image]`, `list[np.ndarray]`), `list[torch.Tensor]`: An input image or images used as an input for the normals estimation task. For arrays and tensors, the expected value range is between `[0, 1]`. Passing a batch of images is possible by providing a four-dimensional array or a tensor. Additionally, a list of images of two- or three-dimensional arrays or tensors can be passed. In the latter case, all list elements must have the same width and height. num_inference_steps (`int`, *optional*, defaults to `None`): Number of denoising diffusion steps during inference. The default value `None` results in automatic selection. ensemble_size (`int`, defaults to `1`): Number of ensemble predictions. Higher values result in measurable improvements and visual degradation. processing_resolution (`int`, *optional*, defaults to `None`): Effective processing resolution. When set to `0`, matches the larger input image dimension. This produces crisper predictions, but may also lead to the overall loss of global context. The default value `None` resolves to the optimal value from the model config. match_input_resolution (`bool`, *optional*, defaults to `True`): When enabled, the output prediction is resized to match the input dimensions. When disabled, the longer side of the output will equal to `processing_resolution`. resample_method_input (`str`, *optional*, defaults to `"bilinear"`): Resampling method used to resize input images to `processing_resolution`. The accepted values are: `"nearest"`, `"nearest-exact"`, `"bilinear"`, `"bicubic"`, or `"area"`. resample_method_output (`str`, *optional*, defaults to `"bilinear"`): Resampling method used to resize output predictions to match the input resolution. The accepted values are `"nearest"`, `"nearest-exact"`, `"bilinear"`, `"bicubic"`, or `"area"`. batch_size (`int`, *optional*, defaults to `1`): Batch size; only matters when setting `ensemble_size` or passing a tensor of images. ensembling_kwargs (`dict`, *optional*, defaults to `None`) Extra dictionary with arguments for precise ensembling control. The following options are available: - reduction (`str`, *optional*, defaults to `"closest"`): Defines the ensembling function applied in every pixel location, can be either `"closest"` or `"mean"`. latents (`torch.Tensor`, *optional*, defaults to `None`): Latent noise tensors to replace the random initialization. These can be taken from the previous function call's output. generator (`torch.Generator`, or `list[torch.Generator]`, *optional*, defaults to `None`): Random number generator object to ensure reproducibility. output_type (`str`, *optional*, defaults to `"np"`): Preferred format of the output's `prediction` and the optional `uncertainty` fields. The accepted values are: `"np"` (numpy array) or `"pt"` (torch tensor). output_uncertainty (`bool`, *optional*, defaults to `False`): When enabled, the output's `uncertainty` field contains the predictive uncertainty map, provided that the `ensemble_size` argument is set to a value above 2. output_latent (`bool`, *optional*, defaults to `False`): When enabled, the output's `latent` field contains the latent codes corresponding to the predictions within the ensemble. These codes can be saved, modified, and used for subsequent calls with the `latents` argument. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`~pipelines.marigold.MarigoldNormalsOutput`] instead of a plain tuple. Examples: Returns: [`~pipelines.marigold.MarigoldNormalsOutput`] or `tuple`: If `return_dict` is `True`, [`~pipelines.marigold.MarigoldNormalsOutput`] is returned, otherwise a `tuple` is returned where the first element is the prediction, the second element is the uncertainty (or `None`), and the third is the latent (or `None`). N do_not_padTrU)padding max_length truncationreturn_tensorsr)r]dtyperzMarigold predictions...)rr)r]FzDiffusion steps...rl)encoder_hidden_statesrracs"g|] }||qSr$)decode_predictionr_ru)rJ pred_latentrAr$r% sz4MarigoldNormalsPipeline.__call__..cs(g|]}j|fipiqSr$)ensemble_normalsr)rKrOrrAr$r%r&)is_aar)rrr)&_execution_devicerr/r0rr?r,model_max_length input_idstor+r@ preprocessprepare_latentsrepeatrrangerjr* set_timesteps timestepsr!catr(step prev_sample XLA_AVAILABLExm mark_stepappend unpad_imagereshapezipresize_antialiasnormalize_normals pt_to_numpymaybe_free_model_hooksr)$rArDrErFrGrrHrIrJrKrLrMrNrOrrr]rrrprompt text_inputstext_input_idsroriginal_resolution image_latentbatch_empty_text_embedding pred_latentsrubatch_image_latentbatch_pred_latenteffective_batch_sizetextt batch_latentnoiserr$)rJrKrOrrrAr%__call__LsR                 z MarigoldNormalsPipeline.__call__cs~ddtjfddtdjdDdd}|jjj}|j|dd}|}|dur;t|j||j |j d}||fS)NcSs,t|dr |jSt|dr|jStd)N latent_distrLz3Could not access latents of provided encoder_output)rrmoderLAttributeError)encoder_outputr$r$r%retrieve_latents[s   zAMarigoldNormalsPipeline.prepare_latents..retrieve_latentsc s(g|]}j||qSr$)r)encoderrJrDrrAr$r%rdrz;MarigoldNormalsPipeline.prepare_latents..rr)rMr]r) r!rrrjr)r=scaling_factorrepeat_interleaverr]r)rArDrLrMrFrJrrr$rr%rSs$z'MarigoldNormalsPipeline.prepare_latentsrcCs|dks|jd|jjjkrtd|jjjd|jd|jj||jjjddd}t |d d }|j s\|ddd ddddfd 9<|ddd ddddfd 7<| |}|S) NrZrz Expecting 4D tensor of shape [B,z ,H,W]; got r1F)rrgg?rg?) rlrjr)r=rnrcdecoderr!clipr.r)rArrr$r$r%rxs $$ z)MarigoldNormalsPipeline.decode_predictionư>r'epscCsP|dks |jddkrtd|jdtj|ddd}||j|d}|S) NrZrr,Expecting 4D tensor of shape [B,3,H,W]; got r1Trlkeepdim)min)rlrjrcr!normclamp)r'rrr$r$r%rs z)MarigoldNormalsPipeline.normalize_normalsrXrVcCs|dks |jddkrtd|jd|dvr"td|d|jdd d }t|}||jdd d }|d d}d }|rO|}|jdd d t j }|d krW||fS|j dd d }| dddd}t |d|}||fS)a2 Ensembles the normals maps represented by the `normals` tensor with expected shape `(B, 3, H, W)`, where B is the number of ensemble members for a given prediction of size `(H x W)`. Args: normals (`torch.Tensor`): Input ensemble normals maps. output_uncertainty (`bool`, *optional*, defaults to `False`): Whether to output uncertainty map. reduction (`str`, *optional*, defaults to `"closest"`): Reduction method used to ensemble aligned predictions. The accepted values are: `"closest"` and `"mean"`. Returns: A tensor of aligned and ensembled normals maps with shape `(1, 3, H, W)` and optionally a tensor of uncertainties of shape `(1, 1, H, W)`. rZrrrr1rWzUnrecognized reduction method: rTrNrY)rlrjrcrYr&rsumrarccosrpiargmaxrr!gather)r'rOrV mean_normalssim_cosrclosest_indicesclosest_normalsr$r$r%rs$  z(MarigoldNormalsPipeline.ensemble_normals)NTNN)NNNT)NrNTrRrRrNNNrFFT)r)rX)'rrrrmodel_cpu_offload_seqr6r r r r rrstrboolintr5r rerr!r"rprfrcompilerdisablerno_gradrEXAMPLE_DOC_STRINGrtuplerr staticmethodfloatrr __classcell__r$r$rBr%r&cs $   ,             % r&), dataclassesrtypingrnumpyrr!PILr tqdm.autor transformersrrr@r modelsr r schedulersr r utilsrrrrutils.torch_utilsrpipeline_utilsrmarigold_image_processingrtorch_xla.core.xla_modelcore xla_modelrr get_loggerrr7rrr&r$r$r$r%s.