+h`QSSKJrJrJrJrJr SSKrSSKrSSK J r J r SSK J r SSKJrJrJrJr SSKJr SS KJrJrJr S S KJr S S KJr \"5(a SSKJs Jr S r!OSr!\RD"\#5r$Sr%"SS\\5r&g))CallableDictListOptionalUnionN) CLIPTextModel CLIPTokenizer)DDPMWuerstchenScheduler) deprecateis_torch_xla_availableloggingreplace_example_docstring) randn_tensor)DeprecatedPipelineMixinDiffusionPipelineImagePipelineOutput) PaellaVQModel)WuerstchenDiffNeXtTFa Examples: ```py >>> import torch >>> from diffusers import WuerstchenPriorPipeline, WuerstchenDecoderPipeline >>> prior_pipe = WuerstchenPriorPipeline.from_pretrained( ... "warp-ai/wuerstchen-prior", torch_dtype=torch.float16 ... ).to("cuda") >>> gen_pipe = WuerstchenDecoderPipeline.from_pretrain("warp-ai/wuerstchen", torch_dtype=torch.float16).to( ... "cuda" ... ) >>> prompt = "an image of a shiba inu, donning a spacesuit and helmet" >>> prior_output = pipe(prompt) >>> images = gen_pipe(prior_output.image_embeddings, prompt=prompt) ``` c^\rSrSrSrSr/SQrS'S\S\S\ S\ S \ S \ S S 4U4S jjjr SrS(Sjr\S5r\S5r\S5r\R*"5\"\5S SS SS SS S SSS S/4 S\\R2\\R24S\\\\4S\S\\\ S\ S\\\\\4S\S \\\R<\\R<4S\\R2S!\\S"\S#\\ \\\!/S 4S$\\4S%jj55r"S&r#U=r$$))WuerstchenDecoderPipeline;a Pipeline for generating images from the Wuerstchen model. 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: tokenizer (`CLIPTokenizer`): The CLIP tokenizer. text_encoder (`CLIPTextModel`): The CLIP text encoder. decoder ([`WuerstchenDiffNeXt`]): The WuerstchenDiffNeXt unet decoder. vqgan ([`PaellaVQModel`]): The VQGAN model. scheduler ([`DDPMWuerstchenScheduler`]): A scheduler to be used in combination with `prior` to generate image embedding. latent_dim_scale (float, `optional`, defaults to 10.67): Multiplier to determine the VQ latent space size from the image embeddings. If the image embeddings are height=24 and width=24, the VQ latent shape needs to be height=int(24*10.67)=256 and width=int(24*10.67)=256 in order to match the training conditions. ztext_encoder->decoder->vqgan)latentstext_encoder_hidden_statesnegative_prompt_embedsimage_embeddings tokenizer text_encoderdecoder schedulervqganlatent_dim_scalereturnNcf>[TU]5 URUUUUUS9 URUS9 g)N)rr r!r"r#)r$)super__init__register_modulesregister_to_config)selfrr r!r"r#r$ __class__s l/home/james-whalen/.local/lib/python3.13/site-packages/diffusers/pipelines/wuerstchen/pipeline_wuerstchen.pyr("WuerstchenDecoderPipeline.__init__[sF  %   1ABcUc [XX2S9nO>66q$..JiJilmJmprJrGr7stL NNNN334Il^M ,A/P1P1P/P,PQN+A/P1P1P/P,PQN"//0A0A&0IZhZkZklrZs/t%8%J%J"%?%Q%QRgmn%Q%o",0) &&!#z 1 fT/%::UVZ[jVkUlmV ~Q(OS11!0 1 s?33 )/)::J3K_J`ax/ |<33 !0 >>$>>::# *L:>9J9J&&))&1,B]B]B`B`agBh:K: 61[0l0l -8==a@G0Q0X0XYZ\qst0u -0Q0V0V2GR1 -*LLr/cUR$N_guidance_scaler+s r-guidance_scale(WuerstchenDecoderPipeline.guidance_scales###r/c URS:$)Nrrkrms r-rZ5WuerstchenDecoderPipeline.do_classifier_free_guidances##a''r/cUR$rj)_num_timestepsrms r- num_timesteps'WuerstchenDecoderPipeline.num_timestepss"""r/ grpilTrrrXnum_inference_steps timestepsrnr[rYr1 output_type return_dictcallback_on_step_end"callback_on_step_end_tensor_inputsc ^URSS5nURSS5nUb [SSS5 Ub [SSS5 U bX[U4SjU 55(d>[STRS U Vs/sHnUTR;dMUPM sn35eTR nTR RnUTl[U[5(d1[U[5(aU/nO[S [U5S 35eTR(aIUbF[U[5(d1[U[5(aU/nO[S [U5S 35e[U[5(a[R "US S9n[U["R$5(a"[R&"UUS9R)US9n[U[R&5(d[S[U5S 35e[U[*5(d[S[U5S35eTR-UUUR/S 5U-TRU5unnUb[R "UU/5OUnTR(a,[R "U[R0"U5/5OUn[+UR/S5TR2R4-5n[+UR/S5TR2R4-5nUR/S 5U-SUU4nUb<TR6R9UUS9 TR6R:n[=U5nO0TR6R9UUS9 TR6R:nTR?UUUXTR65n [=USS5Tl [CTREUSS55GHunnURGU R/S 55R)U5nTR TR(a[R "U /S-5OU TR(a[R "U/S-5OUUUS9nTR(a6URIS5unn [RJ"U UTRL5nTR6ROUUU US9RPn U b\0n!U Hn[S5UU!U'M U "TUUU!5n"U"RSU 5n U"RSU5nU"RSU5nUb-UU-S :Xa$UTR6SS5-n#U"U#UU 5 [V(dGM[XRZ"5 GM U S ;a[S!U 35eU S":XdTR\R2R^U -n TR\RaU 5RbReS S5n$U S#:Xa?U$RgS SSS5Ri5Rk5Rm5n$OXU S$:XaOU$RgS SSS5Ri5Rk5Rm5n$TRoU$5n$OU n$TRq5 U (dU$$[sU$5$s snf)%a Function invoked when calling the pipeline for generation. Args: image_embedding (`torch.Tensor` or `List[torch.Tensor]`): Image Embeddings either extracted from an image or generated by a Prior Model. prompt (`str` or `List[str]`): The prompt or prompts to guide the image generation. num_inference_steps (`int`, *optional*, defaults to 12): The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference. timesteps (`List[int]`, *optional*): Custom timesteps to use for the denoising process. If not defined, equal spaced `num_inference_steps` timesteps are used. Must be in descending order. guidance_scale (`float`, *optional*, defaults to 0.0): Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://huggingface.co/papers/2207.12598). `decoder_guidance_scale` is defined as `w` of equation 2. of [Imagen Paper](https://huggingface.co/papers/2205.11487). Guidance scale is enabled by setting `decoder_guidance_scale > 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, usually at the expense of lower image quality. negative_prompt (`str` or `List[str]`, *optional*): The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored if `decoder_guidance_scale` is less than `1`). num_images_per_prompt (`int`, *optional*, defaults to 1): The number of images to generate per prompt. generator (`torch.Generator` or `List[torch.Generator]`, *optional*): One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation deterministic. latents (`torch.Tensor`, *optional*): Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image generation. Can be used to tweak the same generation with different prompts. If not provided, a latents tensor will ge generated by sampling using the supplied random `generator`. output_type (`str`, *optional*, defaults to `"pil"`): The output format of the generate image. Choose between: `"pil"` (`PIL.Image.Image`), `"np"` (`np.array`) or `"pt"` (`torch.Tensor`). return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple. callback_on_step_end (`Callable`, *optional*): A function that calls at the end of each denoising steps during the inference. The function is called with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int, callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by `callback_on_step_end_tensor_inputs`. callback_on_step_end_tensor_inputs (`List`, *optional*): The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the `._callback_tensor_inputs` attribute of your pipeline class. Examples: Returns: [`~pipelines.ImagePipelineOutput`] or `tuple` [`~pipelines.ImagePipelineOutput`] if `return_dict` is True, otherwise a `tuple`. When returning a tuple, the first element is a list with the generated image embeddings. callbackNcallback_stepsz1.0.0zhPassing `callback` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`znPassing `callback_steps` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`c3@># UHoTR;v M g7frj)_callback_tensor_inputs).0kr+s r- 5WuerstchenDecoderPipeline.__call__..2sF 7Y!-- -7Ysz2`callback_on_step_end_tensor_inputs` has to be in z , but found z2'prompt' must be of type 'list' or 'str', but got rFz;'negative_prompt' must be of type 'list' or 'str', but got rrC)r2)r3zI'image_embeddings' must be of type 'torch.Tensor' or 'np.array', but got z5'num_inference_steps' must be of type 'int', but got zo In Case you want to provide explicit timesteps, please use the 'timesteps' argument.rr )ryr2rA)reffnetclip) model_outputtimestepsampler1rrrorderr)r<nprwlatentzSOnly the output types `pt`, `np`, `pil` and `latent` are supported not output_type=rrrw):popr allr5r_execution_devicer!r3rlrGrHrUrTrSrZrLcatrndarrayTensorr6intrgsize zeros_likeconfigr$r" set_timestepsryrIr8rs enumerate progress_barexpandchunklerprnstep prev_samplelocalsgetattr XLA_AVAILABLExm mark_stepr# scale_factordecoderclamppermutecpufloatnumpy numpy_to_pilmaybe_free_model_hooksr)%r+rrXrxryrnr[rYr1rrzr{r|r}kwargsrrrr2r3 prompt_embedsrrr latent_height latent_widthlatent_features_shapeitratiopredicted_latentspredicted_latents_textpredicted_latents_uncondcallback_kwargscallback_outputsstep_idximagess%` r-__call__"WuerstchenDecoderPipeline.__call__sT::j$/$4d;   z   %  A  . 9#F 7YF C C DTEaEaDbbn|^pH|^vwbckokGkGbGpq|^pHoIJ  '' ""-&$''&#&& "TUYZ`UaTbbc dee  + +*:ot3T3Tos33'6&7O#UVZ[jVkUllmn & - -$yy)9qA  & 3 3$||,jw # // II')9)9:J)KL M! ,11!4t{{7S7SST +003dkk6R6RRS !1!6!6q!9GZZ&&w/66<rss98 51ZZ-\\3<))MM   H %(F+ 79JF+r/