+h.ASSKJrJrJrJrJr SSKrSSKJrJ r SSK J r SSK J r Jr SSKJrJr S S KJr S S KJr S S KJr S S KJr S SKJr Sr"SS\\5rg))CallableDictListOptionalUnionN) CLIPTextModel CLIPTokenizer)DDPMWuerstchenScheduler) deprecatereplace_example_docstring)DeprecatedPipelineMixinDiffusionPipeline) PaellaVQModel)WuerstchenDiffNeXt)WuerstchenPrior)WuerstchenDecoderPipeline)WuerstchenPriorPipelineax Examples: ```py >>> from diffusions import WuerstchenCombinedPipeline >>> pipe = WuerstchenCombinedPipeline.from_pretrained("warp-ai/Wuerstchen", torch_dtype=torch.float16).to( ... "cuda" ... ) >>> prompt = "an image of a shiba inu, donning a spacesuit and helmet" >>> images = pipe(prompt=prompt) ``` c/^\rSrSrSrSrSrS\S\S\ S\ S \ S \S \S \ S \ 4U4Sjjr S6S\\4SjjrS7S\\S\\R(\44SjjrS7S\\S\\R(\44SjjrS7SjrSr\R4"5\"\5SSSSSSSSSSSSSSSSSSS/SS/4S \\\\\4S!\S"\S#\S$\\\S%\S&\S'\\\S(\S)\\\\\4S*\\R>S+\\R>S,\S-\\\R@\\R@4S\\R>S.\\S/\!S0\\\\\"/S4S1\\S2\\\\\"/S4S3\\4*S4jj55r#S5r$U=r%$)8WuerstchenCombinedPipeline+a Combined Pipeline for text-to-image generation using Wuerstchen 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 decoder tokenizer to be used for text inputs. text_encoder (`CLIPTextModel`): The decoder text encoder to be used for text inputs. decoder (`WuerstchenDiffNeXt`): The decoder model to be used for decoder image generation pipeline. scheduler (`DDPMWuerstchenScheduler`): The scheduler to be used for decoder image generation pipeline. vqgan (`PaellaVQModel`): The VQGAN model to be used for decoder image generation pipeline. prior_tokenizer (`CLIPTokenizer`): The prior tokenizer to be used for text inputs. prior_text_encoder (`CLIPTextModel`): The prior text encoder to be used for text inputs. prior_prior (`WuerstchenPrior`): The prior model to be used for prior pipeline. prior_scheduler (`DDPMWuerstchenScheduler`): The scheduler to be used for prior pipeline. z0.33.1T tokenizer text_encoderdecoder schedulervqganprior_tokenizerprior_text_encoder prior_priorprior_schedulerc >[T U]5 URUUUUUUUUU S9 [UUUU S9Ul[ UUUUUS9Ulg)N) rrrrrr!r rr")priorrrr)rrrrr)super__init__register_modulesr prior_piper decoder_pipe) selfrrrrrrr r!r" __class__s u/home/james-whalen/.local/lib/python3.13/site-packages/diffusers/pipelines/wuerstchen/pipeline_wuerstchen_combined.pyr&#WuerstchenCombinedPipeline.__init__Jsv  %#1++  2+%%   6%  N attention_opc:URRU5 gN)r)*enable_xformers_memory_efficient_attention)r*r/s r,r2EWuerstchenCombinedPipeline.enable_xformers_memory_efficient_attentionqs DD\Rr.gpu_iddevicechURRXS9 URRXS9 g)a Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared to `enable_sequential_cpu_offload`, this method moves one whole model at a time to the GPU when its `forward` method is called, and the model remains in GPU until the next model runs. Memory savings are lower than with `enable_sequential_cpu_offload`, but performance is much better due to the iterative execution of the `unet`. r4r5N)r(enable_model_cpu_offloadr)r*r4r5s r,r83WuerstchenCombinedPipeline.enable_model_cpu_offloadts0 000N 22&2Pr.chURRXS9 URRXS9 g)u Offloads all models (`unet`, `text_encoder`, `vae`, and `safety checker` state dicts) to CPU using 🤗 Accelerate, significantly reducing memory usage. Models are moved to a `torch.device('meta')` and loaded on a GPU only when their specific submodule's `forward` method is called. Offloading happens on a submodule basis. Memory savings are higher than using `enable_model_cpu_offload`, but performance is lower. r7N)r(enable_sequential_cpu_offloadr)r9s r,r<8WuerstchenCombinedPipeline.enable_sequential_cpu_offload~s0 55V5S 77v7Ur.chURRXS9 URRXS9 g)N)iterabletotal)r( progress_barr))r*r?r@s r,rA'WuerstchenCombinedPipeline.progress_bars. $$h$D &&&Fr.c tURR"S0UD6 URR"S0UD6 g)N)r(set_progress_bar_configr))r*kwargss r,rE2WuerstchenCombinedPipeline.set_progress_bar_configs. //9&9 11;F;r.i<g@ grpillatentspromptheightwidthprior_num_inference_stepsprior_timestepsprior_guidance_scalenum_inference_stepsdecoder_timestepsdecoder_guidance_scalenegative_prompt prompt_embedsnegative_prompt_embedsnum_images_per_prompt generator output_type return_dictprior_callback_on_step_end(prior_callback_on_step_end_tensor_inputscallback_on_step_end"callback_on_step_end_tensor_inputsc 0nURSS5b!URS5US'[SSS5 URSS5b![SSS5 URS5US'UR"S0S U cUOS_S U_S U_S U_S U_SU_SU cU OS_SU _SU _SU _SU_SU_SS_SS_SU_SU_UD6nUSnUR"SUUbUOSUUU U UUUUUS. UD6nU$)a Function invoked when calling the pipeline for generation. Args: prompt (`str` or `List[str]`): The prompt or prompts to guide the image generation for the prior and decoder. 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 `guidance_scale` is less than `1`). prompt_embeds (`torch.Tensor`, *optional*): Pre-generated text embeddings for the prior. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, text embeddings will be generated from `prompt` input argument. negative_prompt_embeds (`torch.Tensor`, *optional*): Pre-generated negative text embeddings for the prior. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input argument. num_images_per_prompt (`int`, *optional*, defaults to 1): The number of images to generate per prompt. height (`int`, *optional*, defaults to 512): The height in pixels of the generated image. width (`int`, *optional*, defaults to 512): The width in pixels of the generated image. prior_guidance_scale (`float`, *optional*, defaults to 4.0): Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://huggingface.co/papers/2207.12598). `prior_guidance_scale` is defined as `w` of equation 2. of [Imagen Paper](https://huggingface.co/papers/2205.11487). Guidance scale is enabled by setting `prior_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. prior_num_inference_steps (`Union[int, Dict[float, int]]`, *optional*, defaults to 60): The number of prior denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference. For more specific timestep spacing, you can pass customized `prior_timesteps` num_inference_steps (`int`, *optional*, defaults to 12): The number of decoder denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference. For more specific timestep spacing, you can pass customized `timesteps` prior_timesteps (`List[float]`, *optional*): Custom timesteps to use for the denoising process for the prior. If not defined, equal spaced `prior_num_inference_steps` timesteps are used. Must be in descending order. decoder_timesteps (`List[float]`, *optional*): Custom timesteps to use for the denoising process for the decoder. If not defined, equal spaced `num_inference_steps` timesteps are used. Must be in descending order. decoder_guidance_scale (`float`, *optional*, defaults to 0.0): Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://huggingface.co/papers/2207.12598). `guidance_scale` is defined as `w` of equation 2. of [Imagen Paper](https://huggingface.co/papers/2205.11487). Guidance scale is enabled by setting `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. 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. prior_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: `prior_callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int, callback_kwargs: Dict)`. prior_callback_on_step_end_tensor_inputs (`List`, *optional*): The list of tensor inputs for the `prior_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. 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 images. prior_callbackNcallbackz1.0.0ztPassing `prior_callback` as an input argument to `__call__` is deprecated, consider use `prior_callback_on_step_end`prior_callback_stepszzPassing `prior_callback_steps` as an input argument to `__call__` is deprecated, consider use `prior_callback_on_step_end`callback_stepsrLrMrNrR timestepsguidance_scalerUrVrWrXrYrKrZptr[Fr^r_r) image_embeddingsrLrRrerfrUrYrZr[r^r_rD)getpopr r(r))r*rLrMrNrOrPrQrRrSrTrUrVrWrXrYrKrZr[r\r]r^r_rF prior_kwargs prior_outputsrioutputss r,__call__#WuerstchenCombinedPipeline.__call__s^ ::& - 9'-zz2B'CL $  G  ::,d 3 ? &M  .4ZZ8N-OL) * *26   !:  &  0  0F/MOSW ( $: #8      "< 0X# &)+##  -#/6R 3'1+##!5/Q    r.)r)r(r1)NN)&__name__ __module__ __qualname____firstlineno____doc___last_supported_version_load_connected_pipesr rrr rrr&rrr2intrtorchr5strr8r<rArEno_gradr TEXT2IMAGE_EXAMPLE_DOC_STRINGrfloatTensor Generatorboolrro__static_attributes__ __classcell__)r+s@r,rr+s6' %  % $% $ % + %  % '% *% %% 1% NSxPXGYSQx}QUSXS_S_adSdMeQVHSMVRWX]XdXdfiXiRjVG< ]]_<=37)+15&)#%37(+;?049=%&MQ*.%* QU?HkKO9B -asDI~./aa a $' a "$u+. a$a!a$DK0a!&a"%T#Y"78a  -a!) 6a #aE%//43H"HIJa %,,'!a"c]#a$%a&%-XsC6F6L-M$N'a(37s))a*'xc40@$0F'GH+a,-1I-a>ar.r)typingrrrrrry transformersrr schedulersr utilsr r pipeline_utilsrrmodeling_paella_vq_modelrmodeling_wuerstchen_diffnextrmodeling_wuerstchen_priorrpipeline_wuerstchenrpipeline_wuerstchen_priorrr|rrDr.r,rsE98 519G3<6:> !H!8:KHr.