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SanaPipeline)StableAudioPipeline)StableAudio3Pipeline)StableCascadeCombinedPipelineStableCascadeDecoderPipeline)StableDiffusionImg2ImgPipelineStableDiffusionInpaintPipelineStableDiffusionPipeline)StableDiffusion3Img2ImgPipelineStableDiffusion3InpaintPipelineStableDiffusion3Pipeline) StableDiffusionXLImg2ImgPipeline StableDiffusionXLInpaintPipelineStableDiffusionXLPipeline)WanImageToVideoPipeline WanPipelineWanVideoToVideoPipeline)ZImageControlNetInpaintPipelineZImageControlNetPipelineZImageImg2ImgPipelineZImageInpaintPipelineZImageOmniPipelineZImagePipelinezstable-diffusionzstable-diffusion-xlzstable-diffusion-3zstable-diffusion-3-pagifhunyuanz hunyuan-pag kandinsky kandinsky22 kandinsky3zstable-diffusion-controlnetzstable-diffusion-xl-controlnetz$stable-diffusion-xl-controlnet-unionzstable-diffusion-3-controlnet wuerstchencascadelcmz pixart-alphaz 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*_AUTO_TEXT2IMAGE_DECODER_PIPELINES_MAPPINGvalues_get_task_class!AUTO_TEXT2IMAGE_PIPELINES_MAPPING__name__+_AUTO_IMAGE2IMAGE_DECODER_PIPELINES_MAPPING"AUTO_IMAGE2IMAGE_PIPELINES_MAPPING'_AUTO_INPAINT_DECODER_PIPELINES_MAPPINGAUTO_INPAINT_PIPELINES_MAPPING) pipeline_clss گC:\Users\johnn\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.13_qbz5n2kfra8p0\LocalCache\local-packages\Python313\site-packages\diffusers/pipelines/auto_pipeline.py_get_connected_pipelinerTsAHHJJ -|/D/D_d  BIIKK . 0E0E`e  >EEGG=|?T?TotuuHc|[H2nUR5Hup#URU:XdMUs s $ M4 g)N)SUPPORTED_TASKS_MAPPINGSitemsr)pipeline_class_name task_mapping model_namepipelines r _get_modelrbs70 $0$6$6$8 J  $77!!%91rrc~[U5nUbURUS5nUbU$U(a[SUSU35eg)Nz-AutoPipeline can't find a pipeline linked to z for )rget ValueError)mappingrrr task_classs rrrisU/0J[[T2  ! HI\H]]bcmbnopp rcL\rSrSrSrSrSr\\S55r \S5r Sr g) AutoPipelineForText2Imageiua [`AutoPipelineForText2Image`] is a generic pipeline class that instantiates a text-to-image pipeline class. The specific underlying pipeline class is automatically selected from either the [`~AutoPipelineForText2Image.from_pretrained`] or [`~AutoPipelineForText2Image.from_pipe`] methods. This class cannot be instantiated using `__init__()` (throws an error). Class attributes: - **config_name** (`str`) -- The configuration filename that stores the class and module names of all the diffusion pipeline's components. model_index.jsonc[URRSURRSURRS35eNz+ is designed to be instantiated using the `z5.from_pretrained(pretrained_model_name_or_path)` or `z.from_pipe(pipeline)` methods.EnvironmentError __class__rselfargskwargss r__init__"AutoPipelineForText2Image.__init__R~~&&'(..1123''((F H  rc vURSS5nURSS5nURSS5nURSS5nURSS5nURSS5nUUUUUUS .n UR"U40U D6n U S n S U ;aS n OS n S U;aC[US [5(aU S R U S5n OU S R U S5n SU;a*URS5n U (aU R U S5n [ [ U 5n0U EUEnUR"U40UD6$)u Instantiates a text-to-image Pytorch diffusion pipeline from pretrained pipeline weight. The from_pretrained() method takes care of returning the correct pipeline class instance by: 1. Detect the pipeline class of the pretrained_model_or_path based on the _class_name property of its config object 2. Find the text-to-image pipeline linked to the pipeline class using pattern matching on pipeline class name. If a `controlnet` argument is passed, it will instantiate a [`StableDiffusionControlNetPipeline`] object. The pipeline is set in evaluation mode (`model.eval()`) by default. If you get the error message below, you need to finetune the weights for your downstream task: ``` Some weights of UNet2DConditionModel were not initialized from the model checkpoint at stable-diffusion-v1-5/stable-diffusion-v1-5 and are newly initialized because the shapes did not match: - conv_in.weight: found shape torch.Size([320, 4, 3, 3]) in the checkpoint and torch.Size([320, 9, 3, 3]) in the model instantiated You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. ``` Parameters: pretrained_model_or_path (`str` or `os.PathLike`, *optional*): Can be either: - A string, the *repo id* (for example `CompVis/ldm-text2im-large-256`) of a pretrained pipeline hosted on the Hub. - A path to a *directory* (for example `./my_pipeline_directory/`) containing pipeline weights saved using [`~DiffusionPipeline.save_pretrained`]. dtype (`torch.dtype`, *optional*): Override the default `torch.dtype` and load the model with another dtype. force_download (`bool`, *optional*, defaults to `False`): Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist. cache_dir (`str | os.PathLike`, *optional*): Path to a directory where a downloaded pretrained model configuration is cached if the standard cache is not used. proxies (`dict[str, str]`, *optional*): A dictionary of proxy servers to use by protocol or endpoint, for example, `{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request. output_loading_info(`bool`, *optional*, defaults to `False`): Whether or not to also return a dictionary containing missing keys, unexpected keys and error messages. local_files_only (`bool`, *optional*, defaults to `False`): Whether to only load local model weights and configuration files or not. If set to `True`, the model won't be downloaded from the Hub. token (`str` or *bool*, *optional*): The token to use as HTTP bearer authorization for remote files. If `True`, the token generated from `diffusers-cli login` (stored in `~/.huggingface`) is used. revision (`str`, *optional*, defaults to `"main"`): The specific model version to use. It can be a branch name, a tag name, a commit id, or any identifier allowed by Git. custom_revision (`str`, *optional*, defaults to `"main"`): The specific model version to use. It can be a branch name, a tag name, or a commit id similar to `revision` when loading a custom pipeline from the Hub. It can be a 🤗 Diffusers version when loading a custom pipeline from GitHub, otherwise it defaults to `"main"` when loading from the Hub. mirror (`str`, *optional*): Mirror source to resolve accessibility issues if you’re downloading a model in China. We do not guarantee the timeliness or safety of the source, and you should refer to the mirror site for more information. device_map (`str` or `dict[str, int | str | torch.device]`, *optional*): A map that specifies where each submodule should go. It doesn’t need to be defined for each parameter/buffer name; once a given module name is inside, every submodule of it will be sent to the same device. Set `device_map="auto"` to have 🤗 Accelerate automatically compute the most optimized `device_map`. For more information about each option see [designing a device map](https://hf.co/docs/accelerate/main/en/usage_guides/big_modeling#designing-a-device-map). max_memory (`Dict`, *optional*): A dictionary device identifier for the maximum memory. Will default to the maximum memory available for each GPU and the available CPU RAM if unset. offload_folder (`str` or `os.PathLike`, *optional*): The path to offload weights if device_map contains the value `"disk"`. offload_state_dict (`bool`, *optional*): If `True`, temporarily offloads the CPU state dict to the hard drive to avoid running out of CPU RAM if the weight of the CPU state dict + the biggest shard of the checkpoint does not fit. Defaults to `True` when there is some disk offload. low_cpu_mem_usage (`bool`, *optional*, defaults to `True` if torch version >= 1.9.0 else `False`): Speed up model loading only loading the pretrained weights and not initializing the weights. This also tries to not use more than 1x model size in CPU memory (including peak memory) while loading the model. Only supported for PyTorch >= 1.9.0. If you are using an older version of PyTorch, setting this argument to `True` will raise an error. use_safetensors (`bool`, *optional*, defaults to `None`): If set to `None`, the safetensors weights are downloaded if they're available **and** if the safetensors library is installed. If set to `True`, the model is forcibly loaded from safetensors weights. If set to `False`, safetensors weights are not loaded. kwargs (remaining dictionary of keyword arguments, *optional*): Can be used to overwrite load and saveable variables (the pipeline components of the specific pipeline class). The overwritten components are passed directly to the pipelines `__init__` method. See example below for more information. variant (`str`, *optional*): Load weights from a specified variant filename such as `"fp16"` or `"ema"`. > [!TIP] > To use private or [gated](https://huggingface.co/docs/hub/models-gated#gated-models) models, log-in with `hf > auth login`. Examples: ```py >>> from diffusers import AutoPipelineForText2Image >>> pipeline = AutoPipelineForText2Image.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5") >>> image = pipeline(prompt).images[0] ``` cache_dirNforce_downloadFproxiestokenlocal_files_onlyrevisionrrrrrr _class_nameControlPipelinePipeline controlnetControlNetUnionPipelineControlNetPipeline enable_pag PAGPipeline)pop load_config isinstancerreplacerrfrom_pretrained)clspretrained_model_or_pathrrrrrrrload_config_kwargsconfigorig_class_name to_replacertext_2_image_clss rr)AutoPipelineForText2Image.from_pretrainedsbZJJ{D1 $4e<**Y- 7D)!::&8%@::j$/#, 0  !9P=OP /  /*J#J 6 !&.0DEE"("7"?"? Le"f"("7"?"? L`"a 6 !L1J"1"9"9*m"T*+Lo^1&1&1//0HSFSSrc [UR5nURRn[ [ U5nSU;aUSbRSUR;aSOSn[ [ URR SS5R USU-55nO*[ [ URR SS55nSU;a}URS5nU(a;[ [ URR SS5R SS55nO*[ [ URR SS55nURU5upURS S5n UV s0sHoU;dM XRU 5_M n n URR5V V s0sH"upX;dM X;dMXRU _M$ nn n U V s0sHoU;dM XRU 5_M nn UR5V V s0sHupX;dM X;dMXU _M nn n UR5V s/sH6n U RS 5(dMU S SU ;dM&U S SU;dM1U S SPM8 nn UHn URS U 35UU 'M 0U EUEUEUEnUR5V V s0sH.upU U;dM U RS 5(aSOS U 3X;_M0 nn n [U5[UR5- [UR55- n[U5S :aO[!S USUS[[#U R55[#UR55-5S35eU"S0UD6nUR%U S9 UR$"S0UD6 U$s sn fs sn n fs sn fs sn n fs sn fs sn n f)a Instantiates a text-to-image Pytorch diffusion pipeline from another instantiated diffusion pipeline class. The from_pipe() method takes care of returning the correct pipeline class instance by finding the text-to-image pipeline linked to the pipeline class using pattern matching on pipeline class name. All the modules the pipeline contains will be used to initialize the new pipeline without reallocating additional memory. The pipeline is set in evaluation mode (`model.eval()`) by default. Parameters: pipeline (`DiffusionPipeline`): an instantiated `DiffusionPipeline` object ```py >>> from diffusers import AutoPipelineForText2Image, AutoPipelineForImage2Image >>> pipe_i2i = AutoPipelineForImage2Image.from_pretrained( ... "stable-diffusion-v1-5/stable-diffusion-v1-5", requires_safety_checker=False ... ) >>> pipe_t2i = AutoPipelineForText2Image.from_pipe(pipe_i2i) >>> image = pipe_t2i(prompt).images[0] ``` rNPAGrr ControlNetr _name_or_path_r r Pipeline expected , but only were passedr)dictrrrrrrr_get_signature_keys componentsrkeys startswithset_optional_componentslenrlistregister_to_config)rrroriginal_configoriginal_cls_namerrrexpected_modulesoptional_kwargspretrained_model_name_or_pathkpassed_class_objvoriginal_class_objpassed_pipe_kwargsoriginal_pipe_kwargsadditional_pipe_kwargstext_2_image_kwargsunused_original_configmissing_modulesmodels r from_pipe#AutoPipelineForText2Image.from_pipe!ss:x/$..77++LN_` 6 !l#/.37G7P7P.P]V` #25$--55lBGOOPZ\hku\uv$ $35$--55lBG$ 6 !L1J#25$--55eR@HHUbc$ $35$--55eR@$ -=,P,PQa,b)(7(;(;OT(R%7GV6Fv+,Azz!},6FV!++113 3$ &)*)B &A""1% %3  9HW1PV;.aA.W(--/ /# "()(C "Aq! !/  %))+" +||C  %&qrUo%= BCAB%OaBa AabE+ " (A&5&9&9AaS'&B  #(w!1v5GvK]vauv (--/" /++ IQ\\#&&rC0 4o6H H/ "   !C(8(M(M$N NQTUhUmUmUoQp p   ! #,-Z8H7IUXY]^n^s^s^uYvy}QVVXzYZYVZU[[gh !7#67   /L M   :#9: _W X " " s`0 O=O1 O O O ! O.O O'O. O O*O5O O O%OrN r __module__ __qualname____firstlineno____doc__ config_namer classmethodrrr__static_attributes__rrrrrusJ %K OTOTbqqrrcL\rSrSrSrSrSr\\S55r \S5r Sr g) AutoPipelineForImage2Imageia [`AutoPipelineForImage2Image`] is a generic pipeline class that instantiates an image-to-image pipeline class. The specific underlying pipeline class is automatically selected from either the [`~AutoPipelineForImage2Image.from_pretrained`] or [`~AutoPipelineForImage2Image.from_pipe`] methods. This class cannot be instantiated using `__init__()` (throws an error). Class attributes: - **config_name** (`str`) -- The configuration filename that stores the class and module names of all the diffusion pipeline's components. rc[URRSURRSURRS35errrs rr#AutoPipelineForImage2Image.__init__rrc URSS5nURSS5nURSS5nURSS5nURSS5nURSS5nUUUUUUS .n UR"U40U D6n U S n S U ;aS n O S U ;aS n OSn SU;aC[US[5(aU R U SU -5n OU R U SU -5n SU;a-URS5n U (aU R U SU -5n U S :XaU R U S5n [ [ U 5n0U EUEnUR"U40UD6$)u Instantiates a image-to-image Pytorch diffusion pipeline from pretrained pipeline weight. The from_pretrained() method takes care of returning the correct pipeline class instance by: 1. Detect the pipeline class of the pretrained_model_or_path based on the _class_name property of its config object 2. Find the image-to-image pipeline linked to the pipeline class using pattern matching on pipeline class name. If a `controlnet` argument is passed, it will instantiate a [`StableDiffusionControlNetImg2ImgPipeline`] object. The pipeline is set in evaluation mode (`model.eval()`) by default. If you get the error message below, you need to finetune the weights for your downstream task: ``` Some weights of UNet2DConditionModel were not initialized from the model checkpoint at stable-diffusion-v1-5/stable-diffusion-v1-5 and are newly initialized because the shapes did not match: - conv_in.weight: found shape torch.Size([320, 4, 3, 3]) in the checkpoint and torch.Size([320, 9, 3, 3]) in the model instantiated You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. ``` Parameters: pretrained_model_or_path (`str` or `os.PathLike`, *optional*): Can be either: - A string, the *repo id* (for example `CompVis/ldm-text2im-large-256`) of a pretrained pipeline hosted on the Hub. - A path to a *directory* (for example `./my_pipeline_directory/`) containing pipeline weights saved using [`~DiffusionPipeline.save_pretrained`]. dtype (`str` or `torch.dtype`, *optional*): Override the default `torch.dtype` and load the model with another dtype. force_download (`bool`, *optional*, defaults to `False`): Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist. cache_dir (`str | os.PathLike`, *optional*): Path to a directory where a downloaded pretrained model configuration is cached if the standard cache is not used. proxies (`dict[str, str]`, *optional*): A dictionary of proxy servers to use by protocol or endpoint, for example, `{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request. output_loading_info(`bool`, *optional*, defaults to `False`): Whether or not to also return a dictionary containing missing keys, unexpected keys and error messages. local_files_only (`bool`, *optional*, defaults to `False`): Whether to only load local model weights and configuration files or not. If set to `True`, the model won't be downloaded from the Hub. token (`str` or *bool*, *optional*): The token to use as HTTP bearer authorization for remote files. If `True`, the token generated from `diffusers-cli login` (stored in `~/.huggingface`) is used. revision (`str`, *optional*, defaults to `"main"`): The specific model version to use. It can be a branch name, a tag name, a commit id, or any identifier allowed by Git. custom_revision (`str`, *optional*, defaults to `"main"`): The specific model version to use. It can be a branch name, a tag name, or a commit id similar to `revision` when loading a custom pipeline from the Hub. It can be a 🤗 Diffusers version when loading a custom pipeline from GitHub, otherwise it defaults to `"main"` when loading from the Hub. mirror (`str`, *optional*): Mirror source to resolve accessibility issues if you’re downloading a model in China. We do not guarantee the timeliness or safety of the source, and you should refer to the mirror site for more information. device_map (`str` or `dict[str, int | str | torch.device]`, *optional*): A map that specifies where each submodule should go. It doesn’t need to be defined for each parameter/buffer name; once a given module name is inside, every submodule of it will be sent to the same device. Set `device_map="auto"` to have 🤗 Accelerate automatically compute the most optimized `device_map`. For more information about each option see [designing a device map](https://hf.co/docs/accelerate/main/en/usage_guides/big_modeling#designing-a-device-map). max_memory (`Dict`, *optional*): A dictionary device identifier for the maximum memory. Will default to the maximum memory available for each GPU and the available CPU RAM if unset. offload_folder (`str` or `os.PathLike`, *optional*): The path to offload weights if device_map contains the value `"disk"`. offload_state_dict (`bool`, *optional*): If `True`, temporarily offloads the CPU state dict to the hard drive to avoid running out of CPU RAM if the weight of the CPU state dict + the biggest shard of the checkpoint does not fit. Defaults to `True` when there is some disk offload. low_cpu_mem_usage (`bool`, *optional*, defaults to `True` if torch version >= 1.9.0 else `False`): Speed up model loading only loading the pretrained weights and not initializing the weights. This also tries to not use more than 1x model size in CPU memory (including peak memory) while loading the model. Only supported for PyTorch >= 1.9.0. If you are using an older version of PyTorch, setting this argument to `True` will raise an error. use_safetensors (`bool`, *optional*, defaults to `None`): If set to `None`, the safetensors weights are downloaded if they're available **and** if the safetensors library is installed. If set to `True`, the model is forcibly loaded from safetensors weights. If set to `False`, safetensors weights are not loaded. kwargs (remaining dictionary of keyword arguments, *optional*): Can be used to overwrite load and saveable variables (the pipeline components of the specific pipeline class). The overwritten components are passed directly to the pipelines `__init__` method. See example below for more information. variant (`str`, *optional*): Load weights from a specified variant filename such as `"fp16"` or `"ema"`. > [!TIP] > To use private or [gated](https://huggingface.co/docs/hub/models-gated#gated-models) models, log-in with `hf > auth login`. Examples: ```py >>> from diffusers import AutoPipelineForImage2Image >>> pipeline = AutoPipelineForImage2Image.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5") >>> image = pipeline(prompt, image).images[0] ``` rNrFrrrrrrImg2ImgImg2ImgPipelinerrrControlNetUnionrrrControlImg2ImgPipeline)rrrrrrrr)rrrrrrrrrrrrrrimage_2_image_clss rr*AutoPipelineForImage2Image.from_pretraineds\JJ{D1 $4e<**Y- 7D)!::&8%@::j$/#, 0  !9P=OP /  '*J / 1*J#J 6 !&.0DEE"1"9"9*FWZdFd"e"1"9"9*lU_F_"` 6 !L1J"1"9"9*ejFX"Y * *-55jBZ[O+,NP_`1&1&1 001ITVTTrc [UR5nURRn[ [ U5nSU;aUSbUSnSUR;aSU-n[ [ URR SS5R USU-55nO*[ [ URR SS55nSU;a}URS5nU(a;[ [ URR SS5R SS55nO*[ [ URR SS55nURU5upURS S5n UV s0sHoU;dM XRU 5_M n n URR5V V s0sH"upX;dM X;dMXRU _M$ nn n U V s0sHoU;dM XRU 5_M nn UR5V V s0sHupX;dM X;dMXU _M nn n UR5V s/sH6n U RS 5(dMU S SU ;dM&U S SU;dM1U S SPM8 nn UHn URS U 35UU 'M 0U EUEUEUEnUR5V V s0sH.upU U;dM U RS 5(aSOS U 3X;_M0 nn n [U5[UR5- [UR55- n[U5S :aO[!S USUS[[#U R55[#UR55-5S35eU"S0UD6nUR%U S9 UR$"S0UD6 U$s sn fs sn n fs sn fs sn n fs sn fs sn n f)a Instantiates a image-to-image Pytorch diffusion pipeline from another instantiated diffusion pipeline class. The from_pipe() method takes care of returning the correct pipeline class instance by finding the image-to-image pipeline linked to the pipeline class using pattern matching on pipeline class name. All the modules the pipeline contains will be used to initialize the new pipeline without reallocating additional memory. The pipeline is set in evaluation mode (`model.eval()`) by default. Parameters: pipeline (`DiffusionPipeline`): an instantiated `DiffusionPipeline` object Examples: ```py >>> from diffusers import AutoPipelineForText2Image, AutoPipelineForImage2Image >>> pipe_t2i = AutoPipelineForText2Image.from_pretrained( ... "stable-diffusion-v1-5/stable-diffusion-v1-5", requires_safety_checker=False ... ) >>> pipe_i2i = AutoPipelineForImage2Image.from_pipe(pipe_t2i) >>> image = pipe_i2i(prompt, image).images[0] ``` rNrrrrrPAGImg2ImgPipelinerrr rrrrrrr)rrrrrrrrrrrrrrrrrrr)rrrrrrrrrrrrrrrrrrimage_2_image_kwargsrrrs rr$AutoPipelineForImage2Image.from_pipeMsz>x/$..77+,NPab 6 !l#/. -666!&!3J$36%..66|RHPP"L:$=%!%46%..66|RH%! 6 !L1J$36%..66ubAIIJ[]qr%! %46%..66ubA%! ->,Q,QRc,d)(7(;(;OT(R%7GV6Fv+,Azz!},6FV!++113 3$ &)*)B &A""1% %3  9HW1PV;.aA.W(--/ /# "()(C "Aq! !/  %))+" +||C  %&qrUo%= BCAB%OaBa AabE+ " (A&5&9&9AaS'&B  #( x"2w6HwL^wbvw (--/" /,, IQ\\#&&rC0 4o6H H/ "   !C(9(N(N$O ORUVjVoVoVqRr r   ! #-.j9I8J+VYZ^_o_t_t_vZwz~@R@W@W@Y{Z[ZW[V\\hi "9$89   /L M   :#9: _W X " " s`3 OO4 O O  O $ O1O O*O1 OO-O8O O O"%O"rNr rrrrrsJ %K ZUZUxvvrrcL\rSrSrSrSrSr\\S55r \S5r Sr g) AutoPipelineForInpaintingia [`AutoPipelineForInpainting`] is a generic pipeline class that instantiates an inpainting pipeline class. The specific underlying pipeline class is automatically selected from either the [`~AutoPipelineForInpainting.from_pretrained`] or [`~AutoPipelineForInpainting.from_pipe`] methods. This class cannot be instantiated using `__init__()` (throws an error). Class attributes: - **config_name** (`str`) -- The configuration filename that stores the class and module names of all the diffusion pipeline's components. rc[URRSURRSURRS35errrs rr"AutoPipelineForInpainting.__init__rrc URSS5nURSS5nURSS5nURSS5nURSS5nURSS5nUUUUUUS .n UR"U40U D6n U S n S U ;aS n O S U ;aS n OSn SU;aC[US[5(aU R U SU -5n OU R U SU -5n SU;a-URS5n U (aU R U SU -5n U S :XaU R U S5n [ [ U 5n0U EUEnUR"U40UD6$)u Instantiates a inpainting Pytorch diffusion pipeline from pretrained pipeline weight. The from_pretrained() method takes care of returning the correct pipeline class instance by: 1. Detect the pipeline class of the pretrained_model_or_path based on the _class_name property of its config object 2. Find the inpainting pipeline linked to the pipeline class using pattern matching on pipeline class name. If a `controlnet` argument is passed, it will instantiate a [`StableDiffusionControlNetInpaintPipeline`] object. The pipeline is set in evaluation mode (`model.eval()`) by default. If you get the error message below, you need to finetune the weights for your downstream task: ``` Some weights of UNet2DConditionModel were not initialized from the model checkpoint at stable-diffusion-v1-5/stable-diffusion-v1-5 and are newly initialized because the shapes did not match: - conv_in.weight: found shape torch.Size([320, 4, 3, 3]) in the checkpoint and torch.Size([320, 9, 3, 3]) in the model instantiated You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. ``` Parameters: pretrained_model_or_path (`str` or `os.PathLike`, *optional*): Can be either: - A string, the *repo id* (for example `CompVis/ldm-text2im-large-256`) of a pretrained pipeline hosted on the Hub. - A path to a *directory* (for example `./my_pipeline_directory/`) containing pipeline weights saved using [`~DiffusionPipeline.save_pretrained`]. dtype (`str` or `torch.dtype`, *optional*): Override the default `torch.dtype` and load the model with another dtype. force_download (`bool`, *optional*, defaults to `False`): Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist. cache_dir (`str | os.PathLike`, *optional*): Path to a directory where a downloaded pretrained model configuration is cached if the standard cache is not used. proxies (`dict[str, str]`, *optional*): A dictionary of proxy servers to use by protocol or endpoint, for example, `{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request. output_loading_info(`bool`, *optional*, defaults to `False`): Whether or not to also return a dictionary containing missing keys, unexpected keys and error messages. local_files_only (`bool`, *optional*, defaults to `False`): Whether to only load local model weights and configuration files or not. If set to `True`, the model won't be downloaded from the Hub. token (`str` or *bool*, *optional*): The token to use as HTTP bearer authorization for remote files. If `True`, the token generated from `diffusers-cli login` (stored in `~/.huggingface`) is used. revision (`str`, *optional*, defaults to `"main"`): The specific model version to use. It can be a branch name, a tag name, a commit id, or any identifier allowed by Git. custom_revision (`str`, *optional*, defaults to `"main"`): The specific model version to use. It can be a branch name, a tag name, or a commit id similar to `revision` when loading a custom pipeline from the Hub. It can be a 🤗 Diffusers version when loading a custom pipeline from GitHub, otherwise it defaults to `"main"` when loading from the Hub. mirror (`str`, *optional*): Mirror source to resolve accessibility issues if you’re downloading a model in China. We do not guarantee the timeliness or safety of the source, and you should refer to the mirror site for more information. device_map (`str` or `dict[str, int | str | torch.device]`, *optional*): A map that specifies where each submodule should go. It doesn’t need to be defined for each parameter/buffer name; once a given module name is inside, every submodule of it will be sent to the same device. Set `device_map="auto"` to have 🤗 Accelerate automatically compute the most optimized `device_map`. For more information about each option see [designing a device map](https://hf.co/docs/accelerate/main/en/usage_guides/big_modeling#designing-a-device-map). max_memory (`Dict`, *optional*): A dictionary device identifier for the maximum memory. Will default to the maximum memory available for each GPU and the available CPU RAM if unset. offload_folder (`str` or `os.PathLike`, *optional*): The path to offload weights if device_map contains the value `"disk"`. offload_state_dict (`bool`, *optional*): If `True`, temporarily offloads the CPU state dict to the hard drive to avoid running out of CPU RAM if the weight of the CPU state dict + the biggest shard of the checkpoint does not fit. Defaults to `True` when there is some disk offload. low_cpu_mem_usage (`bool`, *optional*, defaults to `True` if torch version >= 1.9.0 else `False`): Speed up model loading only loading the pretrained weights and not initializing the weights. This also tries to not use more than 1x model size in CPU memory (including peak memory) while loading the model. Only supported for PyTorch >= 1.9.0. If you are using an older version of PyTorch, setting this argument to `True` will raise an error. use_safetensors (`bool`, *optional*, defaults to `None`): If set to `None`, the safetensors weights are downloaded if they're available **and** if the safetensors library is installed. If set to `True`, the model is forcibly loaded from safetensors weights. If set to `False`, safetensors weights are not loaded. kwargs (remaining dictionary of keyword arguments, *optional*): Can be used to overwrite load and saveable variables (the pipeline components of the specific pipeline class). The overwritten components are passed directly to the pipelines `__init__` method. See example below for more information. variant (`str`, *optional*): Load weights from a specified variant filename such as `"fp16"` or `"ema"`. > [!TIP] > To use private or [gated](https://huggingface.co/docs/hub/models-gated#gated-models) models, log-in with `hf > auth login`. Examples: ```py >>> from diffusers import AutoPipelineForInpainting >>> pipeline = AutoPipelineForInpainting.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5") >>> image = pipeline(prompt, image=init_image, mask_image=mask_image).images[0] ``` rNrFrrrrrrInpaintInpaintPipelinerrrrrrrControlInpaintPipeline)rrrrrrrr)rrrrrrrrrrrrrrinpainting_clss rr)AutoPipelineForInpainting.from_pretrainedsZJJ{D1 $4e<**Y- 7D)!::&8%@::j$/#, 0  !9P=OP /  '*J / 1*J#J 6 !&.0DEE"1"9"9*FWZdFd"e"1"9"9*lU_F_"` 6 !L1J"1"9"9*ejFX"Y * *-55jBZ[O()GY1&1&1--.FQ&QQrc [UR5nURRn[ [ U5nSU;akUSb;[ [ URR SS5R SS55nO*[ [ URR SS55nSU;a}URS5nU(a;[ [ URR SS5R SS 55nO*[ [ URR S S55nURU5upxURS S5n UV s0sHoU;dM XRU 5_M n n URR5V V s0sH"upX;dM X;dMXRU _M$ n n n UV s0sHoU;dM XRU 5_M nn UR5V V s0sHupX;dM X;dMXU _M nn n UR5V s/sH6n U RS 5(dMU S SU;dM&U S SU;dM1U S SPM8 nn UHn URS U 35X'M 0U EU EUEUEnUR5V V s0sH.upU U;dM U RS 5(aSOS U 3X:_M0 nn n [U5[UR5- [UR55- n[U5S :aO[!SUSUS[[#U R55[#U R55-5S35eU"S0UD6nUR%U S9 UR$"S0UD6 U$s sn fs sn n fs sn fs sn n fs sn fs sn n f)a Instantiates a inpainting Pytorch diffusion pipeline from another instantiated diffusion pipeline class. The from_pipe() method takes care of returning the correct pipeline class instance by finding the inpainting pipeline linked to the pipeline class using pattern matching on pipeline class name. All the modules the pipeline class contain will be used to initialize the new pipeline without reallocating additional memory. The pipeline is set in evaluation mode (`model.eval()`) by default. Parameters: pipeline (`DiffusionPipeline`): an instantiated `DiffusionPipeline` object Examples: ```py >>> from diffusers import AutoPipelineForText2Image, AutoPipelineForInpainting >>> pipe_t2i = AutoPipelineForText2Image.from_pretrained( ... "DeepFloyd/IF-I-XL-v1.0", requires_safety_checker=False ... ) >>> pipe_inpaint = AutoPipelineForInpainting.from_pipe(pipe_t2i) >>> image = pipe_inpaint(prompt, image=init_image, mask_image=mask_image).images[0] ``` rNrrr'ControlNetInpaintPipelinerrPAGInpaintPipelinerrr rrrrrrr)rrrrrrrrrrrrrrrrrrr)rrrrrr)rrrrrrrrrrrinpainting_kwargsrrrs rr#AutoPipelineForInpainting.from_pipe{sS<x/$..77))GIZ[ 6 !l#/!02"++33L"EMM)+F""12"++334OQbc" 6 !L1J!02"++33E2>FFGXZno" "12"++334HJ[\" -;,N,N~,^)(7(;(;OT(R%7GV6Fv+,Azz!},6FV!++113 3$ &)*)B &A""1% %3  9HW1PV;.aA.W(--/ /# "()(C "Aq! !/  %))+" +||C  %&qrUo%= BCAB%OaBa AabE+ " (A&5&9&9AaS'&B #(u/t3EtI[t_st (--/" /)) IQ\\#&&rC0 4o6H H/ "   !C(K(K$L LsSdSiSiSkOl l   ! #N+:6F5G{SVW[\l\q\q\sWtw{}O}T}T}VxWXWTXSYYef 3!23   /L M   :#9: _W X " " s` N,&N, N1)N10N1 N7N7 N<N< N<5OOO) O0 O%OrNr rrrr"r"sJ %K WRWRrssrr"cL\rSrSrSrSrSr\\S55r \S5r Sr g) AutoPipelineForText2Audioia [`AutoPipelineForText2Audio`] is a generic pipeline class that instantiates a text-to-audio pipeline class. The specific underlying pipeline class is automatically selected from either the [`~AutoPipelineForText2Audio.from_pretrained`] or [`~AutoPipelineForText2Audio.from_pipe`] methods. This class cannot be instantiated using `__init__()` (throws an error). Class attributes: - **config_name** (`str`) -- The configuration filename that stores the class and module names of all the diffusion pipeline's components. rc[URRSURRSURRS35errrs rr"AutoPipelineForText2Audio.__init__rrc nURSS5nURSS5nURSS5nURSS5nURSS5nURSS5nUUUUUUS .n UR"U40U D6n U S n [[U 5n 0U EUEnU R"U40UD6$) u Instantiates a text-to-audio Pytorch diffusion pipeline from pretrained pipeline weight. The from_pretrained() method takes care of returning the correct pipeline class instance by: 1. Detect the pipeline class of the pretrained_model_or_path based on the _class_name property of its config object 2. Find the text-to-audio pipeline linked to the pipeline class using pattern matching on pipeline class name. The pipeline is set in evaluation mode (`model.eval()`) by default. Parameters: pretrained_model_or_path (`str` or `os.PathLike`, *optional*): Can be either: - A string, the *repo id* (for example `stabilityai/stable-audio-open-1.0`) of a pretrained pipeline hosted on the Hub. - A path to a *directory* (for example `./my_pipeline_directory/`) containing pipeline weights saved using [`~DiffusionPipeline.save_pretrained`]. dtype (`torch.dtype`, *optional*): Override the default `torch.dtype` and load the model with another dtype. force_download (`bool`, *optional*, defaults to `False`): Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist. cache_dir (`str | os.PathLike`, *optional*): Path to a directory where a downloaded pretrained model configuration is cached if the standard cache is not used. proxies (`dict[str, str]`, *optional*): A dictionary of proxy servers to use by protocol or endpoint, for example, `{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request. output_loading_info(`bool`, *optional*, defaults to `False`): Whether or not to also return a dictionary containing missing keys, unexpected keys and error messages. local_files_only (`bool`, *optional*, defaults to `False`): Whether to only load local model weights and configuration files or not. If set to `True`, the model won't be downloaded from the Hub. token (`str` or *bool*, *optional*): The token to use as HTTP bearer authorization for remote files. If `True`, the token generated from `diffusers-cli login` (stored in `~/.huggingface`) is used. revision (`str`, *optional*, defaults to `"main"`): The specific model version to use. It can be a branch name, a tag name, a commit id, or any identifier allowed by Git. custom_revision (`str`, *optional*, defaults to `"main"`): The specific model version to use. It can be a branch name, a tag name, or a commit id similar to `revision` when loading a custom pipeline from the Hub. It can be a 🤗 Diffusers version when loading a custom pipeline from GitHub, otherwise it defaults to `"main"` when loading from the Hub. mirror (`str`, *optional*): Mirror source to resolve accessibility issues if you're downloading a model in China. We do not guarantee the timeliness or safety of the source, and you should refer to the mirror site for more information. device_map (`str` or `dict[str, int | str | torch.device]`, *optional*): A map that specifies where each submodule should go. It doesn't need to be defined for each parameter/buffer name; once a given module name is inside, every submodule of it will be sent to the same device. Set `device_map="auto"` to have 🤗 Accelerate automatically compute the most optimized `device_map`. For more information about each option see [designing a device map](https://hf.co/docs/accelerate/main/en/usage_guides/big_modeling#designing-a-device-map). max_memory (`Dict`, *optional*): A dictionary device identifier for the maximum memory. Will default to the maximum memory available for each GPU and the available CPU RAM if unset. offload_folder (`str` or `os.PathLike`, *optional*): The path to offload weights if device_map contains the value `"disk"`. offload_state_dict (`bool`, *optional*): If `True`, temporarily offloads the CPU state dict to the hard drive to avoid running out of CPU RAM if the weight of the CPU state dict + the biggest shard of the checkpoint does not fit. Defaults to `True` when there is some disk offload. low_cpu_mem_usage (`bool`, *optional*, defaults to `True` if torch version >= 1.9.0 else `False`): Speed up model loading only loading the pretrained weights and not initializing the weights. This also tries to not use more than 1x model size in CPU memory (including peak memory) while loading the model. Only supported for PyTorch >= 1.9.0. If you are using an older version of PyTorch, setting this argument to `True` will raise an error. use_safetensors (`bool`, *optional*, defaults to `None`): If set to `None`, the safetensors weights are downloaded if they're available **and** if the safetensors library is installed. If set to `True`, the model is forcibly loaded from safetensors weights. If set to `False`, safetensors weights are not loaded. kwargs (remaining dictionary of keyword arguments, *optional*): Can be used to overwrite load and saveable variables (the pipeline components of the specific pipeline class). The overwritten components are passed directly to the pipelines `__init__` method. See example below for more information. variant (`str`, *optional*): Load weights from a specified variant filename such as `"fp16"` or `"ema"`. > [!TIP] > To use private or [gated](https://huggingface.co/docs/hub/models-gated#gated-models) models, log-in with `hf > auth login`. Examples: ```py >>> import torch >>> import soundfile as sf >>> from diffusers import AutoPipelineForText2Audio >>> pipeline = AutoPipelineForText2Audio.from_pretrained( ... "stabilityai/stable-audio-open-1.0", torch_dtype=torch.float16 ... ) >>> pipeline = pipeline.to("cuda") >>> output = pipeline( ... "Generate a male voice reading a paragraph", ... num_inference_steps=200, ... audio_end_in_s=10.0, ... ) >>> audio = output.audios[0].T.float().cpu().numpy() >>> sf.write("audio.wav", audio, pipeline.vae.sampling_rate) ``` rNrFrrrrrr)rrr!AUTO_TEXT2AUDIO_PIPELINES_MAPPINGr) rrrrrrrrrrrrtext_2_audio_clss rr)AutoPipelineForText2Audio.from_pretrained s^JJ{D1 $4e<**Y- 7D)!::&8%@::j$/#, 0  !9P=OP /*+Lo^1&1&1//0HSFSSrc [UR5nURRn[ [ U5nUR U5upgURSS5nUV s0sHoU;dM XRU 5_M n n URR5V V s0sH"upX;dM X;dMXRU _M$ n n n UV s0sHoU;dM XRU 5_M n n UR5V V s0sHupX;dM X;dMXU _M nn n UR5V s/sH6n U RS5(dMU SSU;dM&U SSU ;dM1U SSPM8 nn UHn URSU 35X'M 0U EU EU EUEnUR5V V s0sH.upU U;dM U RS5(aSOSU 3X9_M0 nn n [U5[UR5- [UR55- n[U5S:aO[SUSUS [[!U R55[!U R55-5S 35eU"S 0UD6nUR#US 9 UR""S 0UD6 U$s sn fs sn n fs sn fs sn n fs sn fs sn n f) ap Instantiates a text-to-audio Pytorch diffusion pipeline from another instantiated diffusion pipeline class. The from_pipe() method takes care of returning the correct pipeline class instance by finding the text-to-audio pipeline linked to the pipeline class using pattern matching on pipeline class name. All the modules the pipeline contains will be used to initialize the new pipeline without reallocating additional memory. The pipeline is set in evaluation mode (`model.eval()`) by default. Parameters: pipeline (`DiffusionPipeline`): an instantiated `DiffusionPipeline` object ```py >>> import torch >>> import soundfile as sf >>> from diffusers import AutoPipelineForText2Audio, StableAudioPipeline >>> pipe = StableAudioPipeline.from_pretrained("stabilityai/stable-audio-open-1.0", torch_dtype=torch.float16) >>> pipe_audio = AutoPipelineForText2Audio.from_pipe(pipe) >>> output = pipe_audio( ... "Generate a sound", ... num_inference_steps=200, ... audio_end_in_s=10.0, ... ) >>> audio = output.audios[0].T.float().cpu().numpy() >>> sf.write("audio.wav", audio, pipe_audio.vae.sampling_rate) ``` rNrr rrrrrrrr)rrrrrr5rrrrrrrrrrrr)rrrrrr6rrrrrrrrrrtext_2_audio_kwargsrrrs rr#AutoPipelineForText2Audio.from_pipespFx/$..77*+LN_`,<,P,PQa,b)(7(;(;OT(R%6FV6Fv+,Azz!},6FV!++113 3$ &)*)B &A""1% %3  9HW1PV;.aA.W(--/ /# "()(C "Aq! !/  %))+" +||C  %&qrUo%= BCAB%OaBa AabE+ " (A&5&9&9AaS'&B #(w!1v5GvK]vauv(--/" /++ IQ\\#&&rC0 4o6H H/ "   !C(8(M(M$N NQTUhUmUmUoQp p   ! #,-Z8H7IUXY]^n^s^s^uYvy}QVVXzYZYVZU[[gh !7#67   /L M   :#9: WW X " " s`% J82J8& J=5J=<J= K#K KK# KKK*K5 K< K %KrNr rrrr1r1sJ %K CTCTJVVrr1N)T) collectionsrhuggingface_hub.utilsrconfiguration_utilsrmodels.controlnetsrutilsrrr r rr aura_flowr rrrrrrrrrrrrrrrrrcontrolnet_sd3rrcosmosr deepfloyd_ifrrr deprecated.wuerstchenr!r"rr#r$r%r&r'r(r)r*r+r,rr-r.rr/rr0r1 hunyuanditr2rr3r}r4r5r6r7r8r9 kandinsky2_2r:r;r<r=r>r?rr@rArrBlatent_consistency_modelsrCrDlongcat_audio_ditrErrFrrGnucleusmoe_imagerH ovis_imagerIpagrJrKrLrMrNrOrPrQrRrSrTrUrVrWrX pixart_alpharYrZrr[rr\r]r^r_r`rarbrcrrd stable_audiorestable_audio_3rfstable_cascadergrhstable_diffusionrirjrkstable_diffusion_3rlrmrnstable_diffusion_xlrorprqrrrrsrtz_imagerurvrwrxryrzrr5rr!AUTO_TEXT2VIDEO_PIPELINES_MAPPING&AUTO_CONDITION2VIDEO_PIPELINES_MAPPING"AUTO_IMAGE2VIDEO_PIPELINES_MAPPING"AUTO_VIDEO2VIDEO_PIPELINES_MAPPINGrrrrrrrrrrboolrrrr"r1rrrrYs* $6-5.8('"*?   (MMX   5'9*(F l6"$5)"C   -0W   ON%06 456  9:6 786 "#>? 6 z 6 &' 6 -.6 /06 456 )*6 '(IJ6 *+NO6 01YZ6 )*LM6 126 12!6" ./#6$ ,-%6& ,-'6( )6* _%+6, !;<-6. +,PQ/60 #$@A162 ./UV364 34566 %&768 96: ,-;6< 23=6> ,-?6@ *+A6B - C6D '(E6F - G6H >"I6J O$K6L >"M6N )*O6P %&Q6R &'S6T >"U6V 01W6X 45Y6Z 45[6\ '(]6^ !<=_6` N#a6b 78c6d &'FGe6f +,g6h "#i6j  k68%!t%0 '( ,- /0 56 %!&1 ;<  @A >? !#EF  ! 67 ;< 01 &(PQ !BC )+UV /1`a "$GH -/\] 56 $% 9: 34 ,- *+ - ./ 01  9: 67 )*5&">"- ;<  @A >? #$ 67 ;< &(PQ *,WX )+UV /1`a (*VW "$GH $% 9: 34 !BC ./ 78 )*'"0%0 O$  %!*5 ,-*& &1 *+ +,&"&1 *+ '(&".9 '( ,- 01 01 .*/: ./ 34/+ +6 ./ 34+'=&2@%h/6G%l33H&x0&&"%*&&%./+  v" qD q^ ^B nnb h hV w wr