wi&~ddlmZmZmZmZmZddlZddlmZm Z ddl m Z ddl m Z ddlmZmZGd d eZdS) )DictListOptionalTupleUnionN) AutoencoderKLDiTTransformer2DModel)KarrasDiffusionSchedulers) randn_tensor)DiffusionPipelineImagePipelineOutputcNeZdZdZdZ ddedededee e e fffd Z d e e ee fd ee fd Zej ddee dedee ejeejfde dee ded e eeffdZxZS) DiTPipelinea Pipeline for image generation based on a Transformer backbone instead of a UNet. 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: transformer ([`DiTTransformer2DModel`]): A class conditioned `DiTTransformer2DModel` to denoise the encoded image latents. vae ([`AutoencoderKL`]): Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations. scheduler ([`DDIMScheduler`]): A scheduler to be used in combination with `transformer` to denoise the encoded image latents. ztransformer->vaeN transformervae schedulerid2labelct||||i|_||D]X\}}|dD]=}t ||j|<>Ytt|j|_dSdS)N)rrr,) super__init__register_moduleslabelsitemssplitintlstriprstripdictsorted) selfrrrrkeyvaluelabel __class__s t/root/.openclaw/workspace/chatterbox_venv_py311/lib/python3.11/site-packages/diffusers/pipelines/dit/pipeline_dit.pyrzDiTPipeline.__init__1s  +3)TTT  &nn.. D D U"[[--DDE;>s88DK  5 5 7 788Dvdk&7&7&9&9::;;DKKK r&returnct|tst|}|D]%}|jvrt|djd&fd|DS)a0 Map label strings from ImageNet to corresponding class ids. Parameters: label (`str` or `dict` of `str`): Label strings to be mapped to class ids. Returns: `list` of `int`: Class ids to be processed by pipeline. zK does not exist. Please make sure to select one of the following labels: .c*g|]}j|S)r).0lr#s r( z-DiTPipeline.get_label_ids..Zs...1 A...r)) isinstancelistr ValueError)r#r&r0s` r( get_label_idszDiTPipeline.get_label_idsCs%&& KKE  A ## ttfjfqttt$ /.......r)@2pilT class_labelsguidance_scale generatornum_inference_steps output_type return_dictct|}|jjj}|jjj} t || ||f||j|jj} |dkrtj | gdzn| } tj ||j d}tj dg|z|j} |dkrtj || gdn|} |j |||j jD]6}|dkr2| dt| dz}tj ||gd } |j | |} |}tj|sx| jjd k}t)|t*r|r tjn tj}n|r tjn tj}tj |g|| j }n8t|jdkr |d| j}|| jd}|| || j}|dkr|ddd| f|dd| df}}tj|t|dzd \}}||||z zz}tj ||gd }tj ||gd }|jjjdz| krtj|| d \}}n|}|j ||| j!} 8|dkr| "dd \} }n| } d|j#jj$z | z} |j#%| j}|dz d z&dd}|'(dddd)}|dkr|*|}|+|s|fStY|S)a> The call function to the pipeline for generation. Args: class_labels (List[int]): List of ImageNet class labels for the images to be generated. guidance_scale (`float`, *optional*, defaults to 4.0): A higher guidance scale value encourages the model to generate images closely linked to the text `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`. 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 250): 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 [`ImagePipelineOutput`] instead of a plain tuple. Examples: ```py >>> from diffusers import DiTPipeline, DPMSolverMultistepScheduler >>> import torch >>> pipe = DiTPipeline.from_pretrained("facebook/DiT-XL-2-256", torch_dtype=torch.float16) >>> pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config) >>> pipe = pipe.to("cuda") >>> # pick words from Imagenet class labels >>> pipe.labels # to print all available words >>> # pick words that exist in ImageNet >>> words = ["white shark", "umbrella"] >>> class_ids = pipe.get_label_ids(words) >>> generator = torch.manual_seed(33) >>> output = pipe(class_labels=class_ids, num_inference_steps=25, generator=generator) >>> image = output.images[0] # label 'white shark' ``` 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 )shaper;devicedtyper )rAirN)dimmps)rBrA)timestepr9g?rr8)images)-lenrconfig sample_size in_channelsr _execution_devicerBtorchcattensorreshaper set_timesteps progress_bar timestepsscale_model_input is_tensorrAtyper2floatfloat32float64int32int64r@toexpandsampler out_channelsstep prev_samplechunkrscaling_factordecodeclampcpupermutenumpy numpy_to_pilmaybe_free_model_hooksr)r#r9r:r;r<r=r> batch_size latent_sizelatent_channelslatentslatent_model_input class_nullclass_labels_inputthalfrTis_mpsrB noise_predepsrestcond_eps uncond_epshalf_eps model_output_sampless r(__call__zDiTPipeline.__call__\sx&& &-9 *1= [I)"(     :H!9K9KUYy1}555QX|L9OPPPXXY[\\ \4&:"5d>TUUU IWZ[I[I[UY j'A1EEEam $$%8999""4>#;<<* f* fA!!)*HC0B,C,Cq,H*HI%*Yd|%C%C%C"!%!A!ABTVW!X!X I?9-- J,275@i//C-3FEMMEE+1BEKKu{E!L)EJ\Jcddd Y_%%**%dO../A/HII !(();)A!)DEEI))"YEW*  !!&qqq*:?*:':;Z?K[K[H[=\T',{3CA 1'M'M'M$*%(Z:O(PPi8 4!<<<"YT{::: &3q8OKK"'+j/q"Q"Q"Q aa) "&!4!4\1FX!Y!Y!e   A  +11!1;;JGQQ(Gdho44w>(//'**1Q;$++Aq11++--''1a3399;;AACC %  ''00G ##%%% : "'2222r))N)r6Nr7r8T)__name__ __module__ __qualname____doc__model_cpu_offload_seqr r r rrrstrrrrr5rNno_gradrX Generatorboolrrr __classcell__)r's@r(rrs|  /.2 <<*<<- < 4S>* <<<<<<$/5d3i#8/T#Y////2U]__!$MQ#%%* O3O33iO3O3E%/43H"HIJ O3 ! O3 c] O3O3 "E) *O3O3O3_O3O3O3O3O3r)r)typingrrrrrrNmodelsr r schedulersr utils.torch_utilsr pipeline_utilsrrrr.r)r(rs*65555555555555 ::::::::333333------CCCCCCCCM3M3M3M3M3#M3M3M3M3M3r)