# ONNX Runtime Diffusion Pipelines

## Generic ORT Diffusion Pipeline classes

The following classes are available for instantiating a diffusion pipeline class without needing to specify the task or architecture.

### ORTDiffusionPipeline[[optimum.onnxruntime.ORTDiffusionPipeline]]

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<docstring><name>class optimum.onnxruntime.ORTDiffusionPipeline</name><anchor>optimum.onnxruntime.ORTDiffusionPipeline</anchor><source>https://github.com/huggingface/optimum-onnx/blob/v0.0.2/optimum/onnxruntime/modeling_diffusion.py#L87</source><parameters>[{"name": "unet_session", "val": ": InferenceSession | None = None"}, {"name": "transformer_session", "val": ": InferenceSession | None = None"}, {"name": "vae_decoder_session", "val": ": InferenceSession | None = None"}, {"name": "vae_encoder_session", "val": ": InferenceSession | None = None"}, {"name": "text_encoder_session", "val": ": InferenceSession | None = None"}, {"name": "text_encoder_2_session", "val": ": InferenceSession | None = None"}, {"name": "text_encoder_3_session", "val": ": InferenceSession | None = None"}, {"name": "scheduler", "val": ": SchedulerMixin | None = None"}, {"name": "tokenizer", "val": ": CLIPTokenizer | None = None"}, {"name": "tokenizer_2", "val": ": CLIPTokenizer | None = None"}, {"name": "tokenizer_3", "val": ": CLIPTokenizer | None = None"}, {"name": "feature_extractor", "val": ": CLIPFeatureExtractor | None = None"}, {"name": "force_zeros_for_empty_prompt", "val": ": bool = True"}, {"name": "requires_aesthetics_score", "val": ": bool = False"}, {"name": "add_watermarker", "val": ": bool | None = None"}, {"name": "use_io_binding", "val": ": bool | None = None"}, {"name": "model_save_dir", "val": ": str | Path | TemporaryDirectory | None = None"}, {"name": "**kwargs", "val": ""}]</parameters></docstring>
Base class for all ONNX Runtime Pipelines.

`ORTDiffusionPipeline` stores all components (models, schedulers, and processors) for diffusion pipelines and
provides methods for exporting, loading, downloading and saving models. It also includes methods to:

- move all ONNX Runtime sessions to the device of your choice
- enable/disable the progress bar for the denoising iteration
- handle ONNX Runtime io binding if used

Class attributes:

- **config_name** (`str`) -- The configuration filename that stores the class and module names of all the
  diffusion pipeline's components.
- **task** (`str`) -- A string that identifies the pipeline's task.
- **library** (`str`) -- The library the pipeline is compatible with.
- **auto_model_class** (`Type[DiffusionPipeline]`) -- The corresponding/equivalent Diffusers pipeline class.



<div class="docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8">


<docstring><name>from_pretrained</name><anchor>optimum.onnxruntime.ORTDiffusionPipeline.from_pretrained</anchor><source>https://github.com/huggingface/optimum-onnx/blob/v0.0.2/optimum/onnxruntime/modeling_diffusion.py#L261</source><parameters>[{"name": "model_name_or_path", "val": ": str | Path"}, {"name": "export", "val": ": bool | None = None"}, {"name": "provider", "val": ": str = 'CPUExecutionProvider'"}, {"name": "providers", "val": ": Sequence[str] | None = None"}, {"name": "provider_options", "val": ": Sequence[dict[str, Any]] | dict[str, Any] | None = None"}, {"name": "session_options", "val": ": SessionOptions | None = None"}, {"name": "use_io_binding", "val": ": bool | None = None"}, {"name": "**kwargs", "val": ""}]</parameters><paramsdesc>- **model_name_or_path** (`str` or `os.PathLike`) --
  Path to a folder containing the model files or a hub repository id.
- **export** (`bool`, *optional*, defaults to `None`) --
  Whether to export the model from Diffusers to ONNX. If left to `None`, the model is exported only if no
  ONNX files are found in the `model_name_or_path` folder. If set to `True`, the model is always exported. If set to
  `False`, the model is never exported.
- **provider** (`str`, *optional*, defaults to `"CPUExecutionProvider"`) --
  The execution provider for ONNX Runtime. Can be `"CUDAExecutionProvider"`, `"DmlExecutionProvider"`,
  etc.
- **providers** (`Sequence[str]`, *optional*) --
  A list of execution providers for ONNX Runtime. Overrides `provider`.
- **provider_options** (`Union[Sequence[Dict[str, Any]], Dict[str, Any]]`, *optional*) --
  Options for each execution provider. Can be a single dictionary for the first provider or a list of
  dictionaries for each provider. The order of the dictionaries should match the order of the providers.
- **session_options** (`SessionOptions`, *optional*) --
  Options for the ONNX Runtime session. Can be used to set optimization levels, graph optimization,
  etc.
- **use_io_binding** (`bool`, *optional*) --
  Whether to use IOBinding for the ONNX Runtime session. If set to `True`, it will use IOBinding for
  input and output tensors.
- ****kwargs** --
  Can include the following:
  - Export arguments (e.g., `slim`, `dtype`, `device`, `no_dynamic_axes`, etc.).
  - Hugging Face Hub arguments (e.g., `revision`, `cache_dir`, `force_download`, etc.).
  - Preloaded models or sessions for the different components of the pipeline (e.g., `vae_encoder_session`,
  `vae_decoder_session`, `unet_session`, `transformer_session`, `image_encoder`, `safety_checker`, etc.).</paramsdesc><paramgroups>0</paramgroups><rettype>`ORTDiffusionPipeline`</rettype><retdesc>The loaded pipeline with ONNX Runtime sessions.</retdesc></docstring>
Instantiates a `ORTDiffusionPipeline` with ONNX Runtime sessions from a pretrained pipeline repo or directory.
This method can be used to export a diffusion pipeline to ONNX and/or load a pipeline with ONNX Runtime from a repo or a directory.








</div></div>

### ORTPipelineForText2Image[[optimum.onnxruntime.ORTPipelineForText2Image]]

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<docstring><name>class optimum.onnxruntime.ORTPipelineForText2Image</name><anchor>optimum.onnxruntime.ORTPipelineForText2Image</anchor><source>https://github.com/huggingface/optimum-onnx/blob/v0.0.2/optimum/onnxruntime/modeling_diffusion.py#L1223</source><parameters>[]</parameters></docstring>
`ORTPipelineForText2Image` is a generic pipeline class that instantiates a text-to-image pipeline class.
The specific underlying pipeline class is automatically selected from either the
`~ORTPipelineForText2Image.from_pretrained` or `~ORTPipelineForText2Image.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.
- **auto_model_class** (`Type[DiffusionPipeline]`) -- The corresponding/equivalent Diffusers pipeline class.
- **ort_pipelines_mapping** (`OrderedDict`) -- The mapping between the model names/architectures and the
corresponding ORT pipeline class.



</div>

### ORTPipelineForImage2Image[[optimum.onnxruntime.ORTPipelineForImage2Image]]

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<docstring><name>class optimum.onnxruntime.ORTPipelineForImage2Image</name><anchor>optimum.onnxruntime.ORTPipelineForImage2Image</anchor><source>https://github.com/huggingface/optimum-onnx/blob/v0.0.2/optimum/onnxruntime/modeling_diffusion.py#L1245</source><parameters>[]</parameters></docstring>
`ORTPipelineForImage2Image` is a generic pipeline class that instantiates an image-to-image pipeline class. The
specific underlying pipeline class is automatically selected from either the
`~ORTPipelineForImage2Image.from_pretrained` or `~ORTPipelineForImage2Image.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.
- **auto_model_class** (`Type[DiffusionPipeline]`) -- The corresponding/equivalent Diffusers pipeline class.
- **ort_pipelines_mapping** (`OrderedDict`) -- The mapping between the model names/architectures and the
  corresponding ORT pipeline class.


</div>

### ORTPipelineForInpainting[[optimum.onnxruntime.ORTPipelineForInpainting]]

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<docstring><name>class optimum.onnxruntime.ORTPipelineForInpainting</name><anchor>optimum.onnxruntime.ORTPipelineForInpainting</anchor><source>https://github.com/huggingface/optimum-onnx/blob/v0.0.2/optimum/onnxruntime/modeling_diffusion.py#L1266</source><parameters>[]</parameters></docstring>
`ORTPipelineForInpainting` is a generic pipeline class that instantiates an inpainting pipeline class. The
specific underlying pipeline class is automatically selected from either the
`~ORTPipelineForInpainting.from_pretrained` or `~ORTPipelineForInpainting.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.
- **auto_model_class** (`Type[DiffusionPipeline]`) -- The corresponding/equivalent Diffusers pipeline class.
- **ort_pipelines_mapping** (`OrderedDict`) -- The mapping between the model names/architectures and the
  corresponding ORT pipeline class.



</div>

## Supported ORT Diffusion Pipeline classes

The following classes are available for instantiating a diffusion pipeline class for a specific task and architecture.

### ORTStableDiffusionPipeline[[optimum.onnxruntime.ORTStableDiffusionPipeline]]

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<docstring><name>class optimum.onnxruntime.ORTStableDiffusionPipeline</name><anchor>optimum.onnxruntime.ORTStableDiffusionPipeline</anchor><source>https://github.com/huggingface/optimum-onnx/blob/v0.0.2/optimum/onnxruntime/modeling_diffusion.py#L901</source><parameters>[{"name": "unet_session", "val": ": InferenceSession | None = None"}, {"name": "transformer_session", "val": ": InferenceSession | None = None"}, {"name": "vae_decoder_session", "val": ": InferenceSession | None = None"}, {"name": "vae_encoder_session", "val": ": InferenceSession | None = None"}, {"name": "text_encoder_session", "val": ": InferenceSession | None = None"}, {"name": "text_encoder_2_session", "val": ": InferenceSession | None = None"}, {"name": "text_encoder_3_session", "val": ": InferenceSession | None = None"}, {"name": "scheduler", "val": ": SchedulerMixin | None = None"}, {"name": "tokenizer", "val": ": CLIPTokenizer | None = None"}, {"name": "tokenizer_2", "val": ": CLIPTokenizer | None = None"}, {"name": "tokenizer_3", "val": ": CLIPTokenizer | None = None"}, {"name": "feature_extractor", "val": ": CLIPFeatureExtractor | None = None"}, {"name": "force_zeros_for_empty_prompt", "val": ": bool = True"}, {"name": "requires_aesthetics_score", "val": ": bool = False"}, {"name": "add_watermarker", "val": ": bool | None = None"}, {"name": "use_io_binding", "val": ": bool | None = None"}, {"name": "model_save_dir", "val": ": str | Path | TemporaryDirectory | None = None"}, {"name": "**kwargs", "val": ""}]</parameters></docstring>
ONNX Runtime-powered Pipeline for text-to-image generation using Stable Diffusion and corresponding to [StableDiffusionPipeline]
(https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/text2img#diffusers.StableDiffusionPipeline).

This Pipeline inherits from `ORTDiffusionPipeline` and is used to run inference with the ONNX Runtime.
The pipeline can be loaded from a pretrained pipeline using the generic `ORTDiffusionPipeline.from_pretrained` method.



<div class="docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8">


<docstring><name>__call__</name><anchor>optimum.onnxruntime.ORTStableDiffusionPipeline.__call__</anchor><source>https://github.com/huggingface/optimum-onnx/blob/v0.0.2/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py#L778</source><parameters>[{"name": "prompt", "val": ": typing.Union[str, typing.List[str]] = None"}, {"name": "height", "val": ": typing.Optional[int] = None"}, {"name": "width", "val": ": typing.Optional[int] = None"}, {"name": "num_inference_steps", "val": ": int = 50"}, {"name": "timesteps", "val": ": typing.List[int] = None"}, {"name": "sigmas", "val": ": typing.List[float] = None"}, {"name": "guidance_scale", "val": ": float = 7.5"}, {"name": "negative_prompt", "val": ": typing.Union[str, typing.List[str], NoneType] = None"}, {"name": "num_images_per_prompt", "val": ": typing.Optional[int] = 1"}, {"name": "eta", "val": ": float = 0.0"}, {"name": "generator", "val": ": typing.Union[torch._C.Generator, typing.List[torch._C.Generator], NoneType] = None"}, {"name": "latents", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "prompt_embeds", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "negative_prompt_embeds", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "ip_adapter_image", "val": ": typing.Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, typing.List[PIL.Image.Image], typing.List[numpy.ndarray], typing.List[torch.Tensor], NoneType] = None"}, {"name": "ip_adapter_image_embeds", "val": ": typing.Optional[typing.List[torch.Tensor]] = None"}, {"name": "output_type", "val": ": typing.Optional[str] = 'pil'"}, {"name": "return_dict", "val": ": bool = True"}, {"name": "cross_attention_kwargs", "val": ": typing.Optional[typing.Dict[str, typing.Any]] = None"}, {"name": "guidance_rescale", "val": ": float = 0.0"}, {"name": "clip_skip", "val": ": typing.Optional[int] = None"}, {"name": "callback_on_step_end", "val": ": typing.Union[typing.Callable[[int, int, typing.Dict], NoneType], diffusers.callbacks.PipelineCallback, diffusers.callbacks.MultiPipelineCallbacks, NoneType] = None"}, {"name": "callback_on_step_end_tensor_inputs", "val": ": typing.List[str] = ['latents']"}, {"name": "**kwargs", "val": ""}]</parameters><paramsdesc>- **prompt** (`str` or `List[str]`, *optional*) --
  The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.
- **height** (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`) --
  The height in pixels of the generated image.
- **width** (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`) --
  The width in pixels of the generated image.
- **num_inference_steps** (`int`, *optional*, defaults to 50) --
  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 with schedulers which support a `timesteps` argument
  in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is
  passed will be used. Must be in descending order.
- **sigmas** (`List[float]`, *optional*) --
  Custom sigmas to use for the denoising process with schedulers which support a `sigmas` argument in
  their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is passed
  will be used.
- **guidance_scale** (`float`, *optional*, defaults to 7.5) --
  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`.
- **negative_prompt** (`str` or `List[str]`, *optional*) --
  The prompt or prompts to guide what to not include in image generation. If not defined, you need to
  pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).
- **num_images_per_prompt** (`int`, *optional*, defaults to 1) --
  The number of images to generate per prompt.
- **eta** (`float`, *optional*, defaults to 0.0) --
  Corresponds to parameter eta (η) from the [DDIM](https://huggingface.co/papers/2010.02502) paper. Only
  applies to the `~schedulers.DDIMScheduler`, and is ignored in other schedulers.
- **generator** (`torch.Generator` or `List[torch.Generator]`, *optional*) --
  A [`torch.Generator`](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 is generated by sampling using the supplied random `generator`.
- **prompt_embeds** (`torch.Tensor`, *optional*) --
  Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
  provided, text embeddings are generated from the `prompt` input argument.
- **negative_prompt_embeds** (`torch.Tensor`, *optional*) --
  Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
  not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.
- **ip_adapter_image** -- (`PipelineImageInput`, *optional*): Optional image input to work with IP Adapters.
- **ip_adapter_image_embeds** (`List[torch.Tensor]`, *optional*) --
  Pre-generated image embeddings for IP-Adapter. It should be a list of length same as number of
  IP-adapters. Each element should be a tensor of shape `(batch_size, num_images, emb_dim)`. It should
  contain the negative image embedding if `do_classifier_free_guidance` is set to `True`. If not
  provided, embeddings are computed from the `ip_adapter_image` input argument.
- **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 `diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput` instead of a
  plain tuple.
- **cross_attention_kwargs** (`dict`, *optional*) --
  A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined in
  [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
- **guidance_rescale** (`float`, *optional*, defaults to 0.0) --
  Guidance rescale factor from [Common Diffusion Noise Schedules and Sample Steps are
  Flawed](https://huggingface.co/papers/2305.08891). Guidance rescale factor should fix overexposure when
  using zero terminal SNR.
- **clip_skip** (`int`, *optional*) --
  Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
  the output of the pre-final layer will be used for computing the prompt embeddings.
- **callback_on_step_end** (`Callable`, `PipelineCallback`, `MultiPipelineCallbacks`, *optional*) --
  A function or a subclass of `PipelineCallback` or `MultiPipelineCallbacks` that is called at the end of
  each denoising step during the inference. 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.</paramsdesc><paramgroups>0</paramgroups><rettype>`diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput` or `tuple`</rettype><retdesc>If `return_dict` is `True`, `diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput` is returned,
otherwise a `tuple` is returned where the first element is a list with the generated images and the
second element is a list of `bool`s indicating whether the corresponding generated image contains
"not-safe-for-work" (nsfw) content.</retdesc></docstring>

The call function to the pipeline for generation.



<ExampleCodeBlock anchor="optimum.onnxruntime.ORTStableDiffusionPipeline.__call__.example">

Examples:
```py
>>> import torch
>>> from optimum.onnxruntime import ORTStableDiffusionPipeline

>>> pipe = ORTStableORTDiffusionPipeline.from_pretrained(
...     "stable-diffusion-v1-5/stable-diffusion-v1-5", torch_dtype=torch.float16
... )
>>> pipe = pipe.to("cuda")

>>> prompt = "a photo of an astronaut riding a horse on mars"
>>> image = pipe(prompt).images[0]
```

</ExampleCodeBlock>







</div></div>

### ORTStableDiffusionImg2ImgPipeline[[optimum.onnxruntime.ORTStableDiffusionImg2ImgPipeline]]

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<docstring><name>class optimum.onnxruntime.ORTStableDiffusionImg2ImgPipeline</name><anchor>optimum.onnxruntime.ORTStableDiffusionImg2ImgPipeline</anchor><source>https://github.com/huggingface/optimum-onnx/blob/v0.0.2/optimum/onnxruntime/modeling_diffusion.py#L912</source><parameters>[{"name": "unet_session", "val": ": InferenceSession | None = None"}, {"name": "transformer_session", "val": ": InferenceSession | None = None"}, {"name": "vae_decoder_session", "val": ": InferenceSession | None = None"}, {"name": "vae_encoder_session", "val": ": InferenceSession | None = None"}, {"name": "text_encoder_session", "val": ": InferenceSession | None = None"}, {"name": "text_encoder_2_session", "val": ": InferenceSession | None = None"}, {"name": "text_encoder_3_session", "val": ": InferenceSession | None = None"}, {"name": "scheduler", "val": ": SchedulerMixin | None = None"}, {"name": "tokenizer", "val": ": CLIPTokenizer | None = None"}, {"name": "tokenizer_2", "val": ": CLIPTokenizer | None = None"}, {"name": "tokenizer_3", "val": ": CLIPTokenizer | None = None"}, {"name": "feature_extractor", "val": ": CLIPFeatureExtractor | None = None"}, {"name": "force_zeros_for_empty_prompt", "val": ": bool = True"}, {"name": "requires_aesthetics_score", "val": ": bool = False"}, {"name": "add_watermarker", "val": ": bool | None = None"}, {"name": "use_io_binding", "val": ": bool | None = None"}, {"name": "model_save_dir", "val": ": str | Path | TemporaryDirectory | None = None"}, {"name": "**kwargs", "val": ""}]</parameters></docstring>
ONNX Runtime-powered Pipeline for text-guided image-to-image generation using Stable Diffusion and corresponding to [StableDiffusionImg2ImgPipeline]
(https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/img2img#diffusers.StableDiffusionImg2ImgPipeline).

This Pipeline inherits from `ORTDiffusionPipeline` and is used to run inference with the ONNX Runtime.
The pipeline can be loaded from a pretrained pipeline using the generic `ORTDiffusionPipeline.from_pretrained` method.



<div class="docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8">


<docstring><name>__call__</name><anchor>optimum.onnxruntime.ORTStableDiffusionImg2ImgPipeline.__call__</anchor><source>https://github.com/huggingface/optimum-onnx/blob/v0.0.2/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_img2img.py#L858</source><parameters>[{"name": "prompt", "val": ": typing.Union[str, typing.List[str]] = None"}, {"name": "image", "val": ": typing.Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, typing.List[PIL.Image.Image], typing.List[numpy.ndarray], typing.List[torch.Tensor]] = None"}, {"name": "strength", "val": ": float = 0.8"}, {"name": "num_inference_steps", "val": ": typing.Optional[int] = 50"}, {"name": "timesteps", "val": ": typing.List[int] = None"}, {"name": "sigmas", "val": ": typing.List[float] = None"}, {"name": "guidance_scale", "val": ": typing.Optional[float] = 7.5"}, {"name": "negative_prompt", "val": ": typing.Union[str, typing.List[str], NoneType] = None"}, {"name": "num_images_per_prompt", "val": ": typing.Optional[int] = 1"}, {"name": "eta", "val": ": typing.Optional[float] = 0.0"}, {"name": "generator", "val": ": typing.Union[torch._C.Generator, typing.List[torch._C.Generator], NoneType] = None"}, {"name": "prompt_embeds", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "negative_prompt_embeds", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "ip_adapter_image", "val": ": typing.Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, typing.List[PIL.Image.Image], typing.List[numpy.ndarray], typing.List[torch.Tensor], NoneType] = None"}, {"name": "ip_adapter_image_embeds", "val": ": typing.Optional[typing.List[torch.Tensor]] = None"}, {"name": "output_type", "val": ": typing.Optional[str] = 'pil'"}, {"name": "return_dict", "val": ": bool = True"}, {"name": "cross_attention_kwargs", "val": ": typing.Optional[typing.Dict[str, typing.Any]] = None"}, {"name": "clip_skip", "val": ": int = None"}, {"name": "callback_on_step_end", "val": ": typing.Union[typing.Callable[[int, int, typing.Dict], NoneType], diffusers.callbacks.PipelineCallback, diffusers.callbacks.MultiPipelineCallbacks, NoneType] = None"}, {"name": "callback_on_step_end_tensor_inputs", "val": ": typing.List[str] = ['latents']"}, {"name": "**kwargs", "val": ""}]</parameters><paramsdesc>- **prompt** (`str` or `List[str]`, *optional*) --
  The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.
- **image** (`torch.Tensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.Tensor]`, `List[PIL.Image.Image]`, or `List[np.ndarray]`) --
  `Image`, numpy array or tensor representing an image batch to be used as the starting point. For both
  numpy array and pytorch tensor, the expected value range is between `[0, 1]` If it's a tensor or a list
  or tensors, the expected shape should be `(B, C, H, W)` or `(C, H, W)`. If it is a numpy array or a
  list of arrays, the expected shape should be `(B, H, W, C)` or `(H, W, C)` It can also accept image
  latents as `image`, but if passing latents directly it is not encoded again.
- **strength** (`float`, *optional*, defaults to 0.8) --
  Indicates extent to transform the reference `image`. Must be between 0 and 1. `image` is used as a
  starting point and more noise is added the higher the `strength`. The number of denoising steps depends
  on the amount of noise initially added. When `strength` is 1, added noise is maximum and the denoising
  process runs for the full number of iterations specified in `num_inference_steps`. A value of 1
  essentially ignores `image`.
- **num_inference_steps** (`int`, *optional*, defaults to 50) --
  The number of denoising steps. More denoising steps usually lead to a higher quality image at the
  expense of slower inference. This parameter is modulated by `strength`.
- **timesteps** (`List[int]`, *optional*) --
  Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument
  in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is
  passed will be used. Must be in descending order.
- **sigmas** (`List[float]`, *optional*) --
  Custom sigmas to use for the denoising process with schedulers which support a `sigmas` argument in
  their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is passed
  will be used.
- **guidance_scale** (`float`, *optional*, defaults to 7.5) --
  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`.
- **negative_prompt** (`str` or `List[str]`, *optional*) --
  The prompt or prompts to guide what to not include in image generation. If not defined, you need to
  pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).
- **num_images_per_prompt** (`int`, *optional*, defaults to 1) --
  The number of images to generate per prompt.
- **eta** (`float`, *optional*, defaults to 0.0) --
  Corresponds to parameter eta (η) from the [DDIM](https://huggingface.co/papers/2010.02502) paper. Only
  applies to the `~schedulers.DDIMScheduler`, and is ignored in other schedulers.
- **generator** (`torch.Generator` or `List[torch.Generator]`, *optional*) --
  A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
  generation deterministic.
- **prompt_embeds** (`torch.Tensor`, *optional*) --
  Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
  provided, text embeddings are generated from the `prompt` input argument.
- **negative_prompt_embeds** (`torch.Tensor`, *optional*) --
  Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
  not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.
- **ip_adapter_image** -- (`PipelineImageInput`, *optional*): Optional image input to work with IP Adapters.
- **ip_adapter_image_embeds** (`List[torch.Tensor]`, *optional*) --
  Pre-generated image embeddings for IP-Adapter. It should be a list of length same as number of
  IP-adapters. Each element should be a tensor of shape `(batch_size, num_images, emb_dim)`. It should
  contain the negative image embedding if `do_classifier_free_guidance` is set to `True`. If not
  provided, embeddings are computed from the `ip_adapter_image` input argument.
- **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 `diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput` instead of a
  plain tuple.
- **cross_attention_kwargs** (`dict`, *optional*) --
  A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined in
  [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
- **clip_skip** (`int`, *optional*) --
  Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
  the output of the pre-final layer will be used for computing the prompt embeddings.
- **callback_on_step_end** (`Callable`, `PipelineCallback`, `MultiPipelineCallbacks`, *optional*) --
  A function or a subclass of `PipelineCallback` or `MultiPipelineCallbacks` that is called at the end of
  each denoising step during the inference. 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.</paramsdesc><paramgroups>0</paramgroups><rettype>`diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput` or `tuple`</rettype><retdesc>If `return_dict` is `True`, `diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput` is returned,
otherwise a `tuple` is returned where the first element is a list with the generated images and the
second element is a list of `bool`s indicating whether the corresponding generated image contains
"not-safe-for-work" (nsfw) content.</retdesc></docstring>

The call function to the pipeline for generation.



<ExampleCodeBlock anchor="optimum.onnxruntime.ORTStableDiffusionImg2ImgPipeline.__call__.example">

Examples:
```py
>>> import requests
>>> import torch
>>> from PIL import Image
>>> from io import BytesIO

>>> from optimum.onnxruntime import ORTStableDiffusionImg2ImgPipeline

>>> device = "cuda"
>>> model_id_or_path = "stable-diffusion-v1-5/stable-diffusion-v1-5"
>>> pipe = ORTStableDiffusionImg2ImgPipeline.from_pretrained(model_id_or_path, torch_dtype=torch.float16)
>>> pipe = pipe.to(device)

>>> url = "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg"

>>> response = requests.get(url)
>>> init_image = Image.open(BytesIO(response.content)).convert("RGB")
>>> init_image = init_image.resize((768, 512))

>>> prompt = "A fantasy landscape, trending on artstation"

>>> images = pipe(prompt=prompt, image=init_image, strength=0.75, guidance_scale=7.5).images
>>> images[0].save("fantasy_landscape.png")
```

</ExampleCodeBlock>







</div></div>

### ORTStableDiffusionInpaintPipeline[[optimum.onnxruntime.ORTStableDiffusionInpaintPipeline]]

<div class="docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8">


<docstring><name>class optimum.onnxruntime.ORTStableDiffusionInpaintPipeline</name><anchor>optimum.onnxruntime.ORTStableDiffusionInpaintPipeline</anchor><source>https://github.com/huggingface/optimum-onnx/blob/v0.0.2/optimum/onnxruntime/modeling_diffusion.py#L923</source><parameters>[{"name": "unet_session", "val": ": InferenceSession | None = None"}, {"name": "transformer_session", "val": ": InferenceSession | None = None"}, {"name": "vae_decoder_session", "val": ": InferenceSession | None = None"}, {"name": "vae_encoder_session", "val": ": InferenceSession | None = None"}, {"name": "text_encoder_session", "val": ": InferenceSession | None = None"}, {"name": "text_encoder_2_session", "val": ": InferenceSession | None = None"}, {"name": "text_encoder_3_session", "val": ": InferenceSession | None = None"}, {"name": "scheduler", "val": ": SchedulerMixin | None = None"}, {"name": "tokenizer", "val": ": CLIPTokenizer | None = None"}, {"name": "tokenizer_2", "val": ": CLIPTokenizer | None = None"}, {"name": "tokenizer_3", "val": ": CLIPTokenizer | None = None"}, {"name": "feature_extractor", "val": ": CLIPFeatureExtractor | None = None"}, {"name": "force_zeros_for_empty_prompt", "val": ": bool = True"}, {"name": "requires_aesthetics_score", "val": ": bool = False"}, {"name": "add_watermarker", "val": ": bool | None = None"}, {"name": "use_io_binding", "val": ": bool | None = None"}, {"name": "model_save_dir", "val": ": str | Path | TemporaryDirectory | None = None"}, {"name": "**kwargs", "val": ""}]</parameters></docstring>
ONNX Runtime-powered Pipeline for text-guided image inpainting using Stable Diffusion and corresponding to [StableDiffusionInpaintPipeline]
(https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/inpaint#diffusers.StableDiffusionInpaintPipeline).

This Pipeline inherits from `ORTDiffusionPipeline` and is used to run inference with the ONNX Runtime.
The pipeline can be loaded from a pretrained pipeline using the generic `ORTDiffusionPipeline.from_pretrained` method.



<div class="docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8">


<docstring><name>__call__</name><anchor>optimum.onnxruntime.ORTStableDiffusionInpaintPipeline.__call__</anchor><source>https://github.com/huggingface/optimum-onnx/blob/v0.0.2/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint.py#L880</source><parameters>[{"name": "prompt", "val": ": typing.Union[str, typing.List[str]] = None"}, {"name": "image", "val": ": typing.Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, typing.List[PIL.Image.Image], typing.List[numpy.ndarray], typing.List[torch.Tensor]] = None"}, {"name": "mask_image", "val": ": typing.Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, typing.List[PIL.Image.Image], typing.List[numpy.ndarray], typing.List[torch.Tensor]] = None"}, {"name": "masked_image_latents", "val": ": Tensor = None"}, {"name": "height", "val": ": typing.Optional[int] = None"}, {"name": "width", "val": ": typing.Optional[int] = None"}, {"name": "padding_mask_crop", "val": ": typing.Optional[int] = None"}, {"name": "strength", "val": ": float = 1.0"}, {"name": "num_inference_steps", "val": ": int = 50"}, {"name": "timesteps", "val": ": typing.List[int] = None"}, {"name": "sigmas", "val": ": typing.List[float] = None"}, {"name": "guidance_scale", "val": ": float = 7.5"}, {"name": "negative_prompt", "val": ": typing.Union[str, typing.List[str], NoneType] = None"}, {"name": "num_images_per_prompt", "val": ": typing.Optional[int] = 1"}, {"name": "eta", "val": ": float = 0.0"}, {"name": "generator", "val": ": typing.Union[torch._C.Generator, typing.List[torch._C.Generator], NoneType] = None"}, {"name": "latents", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "prompt_embeds", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "negative_prompt_embeds", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "ip_adapter_image", "val": ": typing.Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, typing.List[PIL.Image.Image], typing.List[numpy.ndarray], typing.List[torch.Tensor], NoneType] = None"}, {"name": "ip_adapter_image_embeds", "val": ": typing.Optional[typing.List[torch.Tensor]] = None"}, {"name": "output_type", "val": ": typing.Optional[str] = 'pil'"}, {"name": "return_dict", "val": ": bool = True"}, {"name": "cross_attention_kwargs", "val": ": typing.Optional[typing.Dict[str, typing.Any]] = None"}, {"name": "clip_skip", "val": ": int = None"}, {"name": "callback_on_step_end", "val": ": typing.Union[typing.Callable[[int, int, typing.Dict], NoneType], diffusers.callbacks.PipelineCallback, diffusers.callbacks.MultiPipelineCallbacks, NoneType] = None"}, {"name": "callback_on_step_end_tensor_inputs", "val": ": typing.List[str] = ['latents']"}, {"name": "**kwargs", "val": ""}]</parameters><paramsdesc>- **prompt** (`str` or `List[str]`, *optional*) --
  The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.
- **image** (`torch.Tensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.Tensor]`, `List[PIL.Image.Image]`, or `List[np.ndarray]`) --
  `Image`, numpy array or tensor representing an image batch to be inpainted (which parts of the image to
  be masked out with `mask_image` and repainted according to `prompt`). For both numpy array and pytorch
  tensor, the expected value range is between `[0, 1]` If it's a tensor or a list or tensors, the
  expected shape should be `(B, C, H, W)` or `(C, H, W)`. If it is a numpy array or a list of arrays, the
  expected shape should be `(B, H, W, C)` or `(H, W, C)` It can also accept image latents as `image`, but
  if passing latents directly it is not encoded again.
- **mask_image** (`torch.Tensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.Tensor]`, `List[PIL.Image.Image]`, or `List[np.ndarray]`) --
  `Image`, numpy array or tensor representing an image batch to mask `image`. White pixels in the mask
  are repainted while black pixels are preserved. If `mask_image` is a PIL image, it is converted to a
  single channel (luminance) before use. If it's a numpy array or pytorch tensor, it should contain one
  color channel (L) instead of 3, so the expected shape for pytorch tensor would be `(B, 1, H, W)`, `(B,
  H, W)`, `(1, H, W)`, `(H, W)`. And for numpy array would be for `(B, H, W, 1)`, `(B, H, W)`, `(H, W,
  1)`, or `(H, W)`.
- **height** (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`) --
  The height in pixels of the generated image.
- **width** (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`) --
  The width in pixels of the generated image.
- **padding_mask_crop** (`int`, *optional*, defaults to `None`) --
  The size of margin in the crop to be applied to the image and masking. If `None`, no crop is applied to
  image and mask_image. If `padding_mask_crop` is not `None`, it will first find a rectangular region
  with the same aspect ration of the image and contains all masked area, and then expand that area based
  on `padding_mask_crop`. The image and mask_image will then be cropped based on the expanded area before
  resizing to the original image size for inpainting. This is useful when the masked area is small while
  the image is large and contain information irrelevant for inpainting, such as background.
- **strength** (`float`, *optional*, defaults to 1.0) --
  Indicates extent to transform the reference `image`. Must be between 0 and 1. `image` is used as a
  starting point and more noise is added the higher the `strength`. The number of denoising steps depends
  on the amount of noise initially added. When `strength` is 1, added noise is maximum and the denoising
  process runs for the full number of iterations specified in `num_inference_steps`. A value of 1
  essentially ignores `image`.
- **num_inference_steps** (`int`, *optional*, defaults to 50) --
  The number of denoising steps. More denoising steps usually lead to a higher quality image at the
  expense of slower inference. This parameter is modulated by `strength`.
- **timesteps** (`List[int]`, *optional*) --
  Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument
  in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is
  passed will be used. Must be in descending order.
- **sigmas** (`List[float]`, *optional*) --
  Custom sigmas to use for the denoising process with schedulers which support a `sigmas` argument in
  their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is passed
  will be used.
- **guidance_scale** (`float`, *optional*, defaults to 7.5) --
  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`.
- **negative_prompt** (`str` or `List[str]`, *optional*) --
  The prompt or prompts to guide what to not include in image generation. If not defined, you need to
  pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).
- **num_images_per_prompt** (`int`, *optional*, defaults to 1) --
  The number of images to generate per prompt.
- **eta** (`float`, *optional*, defaults to 0.0) --
  Corresponds to parameter eta (η) from the [DDIM](https://huggingface.co/papers/2010.02502) paper. Only
  applies to the `~schedulers.DDIMScheduler`, and is ignored in other schedulers.
- **generator** (`torch.Generator` or `List[torch.Generator]`, *optional*) --
  A [`torch.Generator`](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 is generated by sampling using the supplied random `generator`.
- **prompt_embeds** (`torch.Tensor`, *optional*) --
  Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
  provided, text embeddings are generated from the `prompt` input argument.
- **negative_prompt_embeds** (`torch.Tensor`, *optional*) --
  Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
  not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.
- **ip_adapter_image** -- (`PipelineImageInput`, *optional*): Optional image input to work with IP Adapters.
- **ip_adapter_image_embeds** (`List[torch.Tensor]`, *optional*) --
  Pre-generated image embeddings for IP-Adapter. It should be a list of length same as number of
  IP-adapters. Each element should be a tensor of shape `(batch_size, num_images, emb_dim)`. It should
  contain the negative image embedding if `do_classifier_free_guidance` is set to `True`. If not
  provided, embeddings are computed from the `ip_adapter_image` input argument.
- **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 `diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput` instead of a
  plain tuple.
- **cross_attention_kwargs** (`dict`, *optional*) --
  A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined in
  [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
- **clip_skip** (`int`, *optional*) --
  Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
  the output of the pre-final layer will be used for computing the prompt embeddings.
- **callback_on_step_end** (`Callable`, `PipelineCallback`, `MultiPipelineCallbacks`, *optional*) --
  A function or a subclass of `PipelineCallback` or `MultiPipelineCallbacks` that is called at the end of
  each denoising step during the inference. 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.</paramsdesc><paramgroups>0</paramgroups><rettype>`diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput` or `tuple`</rettype><retdesc>If `return_dict` is `True`, `diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput` is returned,
otherwise a `tuple` is returned where the first element is a list with the generated images and the
second element is a list of `bool`s indicating whether the corresponding generated image contains
"not-safe-for-work" (nsfw) content.</retdesc></docstring>

The call function to the pipeline for generation.



<ExampleCodeBlock anchor="optimum.onnxruntime.ORTStableDiffusionInpaintPipeline.__call__.example">

Examples:

```py
>>> import PIL
>>> import requests
>>> import torch
>>> from io import BytesIO

>>> from optimum.onnxruntime import ORTStableDiffusionInpaintPipeline


>>> def download_image(url):
...     response = requests.get(url)
...     return PIL.Image.open(BytesIO(response.content)).convert("RGB")


>>> img_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png"
>>> mask_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png"

>>> init_image = download_image(img_url).resize((512, 512))
>>> mask_image = download_image(mask_url).resize((512, 512))

>>> pipe = ORTStableDiffusionInpaintPipeline.from_pretrained(
...     "stable-diffusion-v1-5/stable-diffusion-inpainting", torch_dtype=torch.float16
... )
>>> pipe = pipe.to("cuda")

>>> prompt = "Face of a yellow cat, high resolution, sitting on a park bench"
>>> image = pipe(prompt=prompt, image=init_image, mask_image=mask_image).images[0]
```

</ExampleCodeBlock>






</div></div>

### ORTStableDiffusionXLPipeline[[optimum.onnxruntime.ORTStableDiffusionXLPipeline]]

<div class="docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8">


<docstring><name>class optimum.onnxruntime.ORTStableDiffusionXLPipeline</name><anchor>optimum.onnxruntime.ORTStableDiffusionXLPipeline</anchor><source>https://github.com/huggingface/optimum-onnx/blob/v0.0.2/optimum/onnxruntime/modeling_diffusion.py#L934</source><parameters>[{"name": "unet_session", "val": ": InferenceSession | None = None"}, {"name": "transformer_session", "val": ": InferenceSession | None = None"}, {"name": "vae_decoder_session", "val": ": InferenceSession | None = None"}, {"name": "vae_encoder_session", "val": ": InferenceSession | None = None"}, {"name": "text_encoder_session", "val": ": InferenceSession | None = None"}, {"name": "text_encoder_2_session", "val": ": InferenceSession | None = None"}, {"name": "text_encoder_3_session", "val": ": InferenceSession | None = None"}, {"name": "scheduler", "val": ": SchedulerMixin | None = None"}, {"name": "tokenizer", "val": ": CLIPTokenizer | None = None"}, {"name": "tokenizer_2", "val": ": CLIPTokenizer | None = None"}, {"name": "tokenizer_3", "val": ": CLIPTokenizer | None = None"}, {"name": "feature_extractor", "val": ": CLIPFeatureExtractor | None = None"}, {"name": "force_zeros_for_empty_prompt", "val": ": bool = True"}, {"name": "requires_aesthetics_score", "val": ": bool = False"}, {"name": "add_watermarker", "val": ": bool | None = None"}, {"name": "use_io_binding", "val": ": bool | None = None"}, {"name": "model_save_dir", "val": ": str | Path | TemporaryDirectory | None = None"}, {"name": "**kwargs", "val": ""}]</parameters></docstring>
ONNX Runtime-powered Pipeline for text-to-image generation using Stable Diffusion XL and corresponding to [StableDiffusionXLPipeline]
(https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/stable_diffusion_xl#diffusers.StableDiffusionXLPipeline).

This Pipeline inherits from `ORTDiffusionPipeline` and is used to run inference with the ONNX Runtime.
The pipeline can be loaded from a pretrained pipeline using the generic `ORTDiffusionPipeline.from_pretrained` method.



<div class="docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8">


<docstring><name>__call__</name><anchor>optimum.onnxruntime.ORTStableDiffusionXLPipeline.__call__</anchor><source>https://github.com/huggingface/optimum-onnx/blob/v0.0.2/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl.py#L836</source><parameters>[{"name": "prompt", "val": ": typing.Union[str, typing.List[str]] = None"}, {"name": "prompt_2", "val": ": typing.Union[str, typing.List[str], NoneType] = None"}, {"name": "height", "val": ": typing.Optional[int] = None"}, {"name": "width", "val": ": typing.Optional[int] = None"}, {"name": "num_inference_steps", "val": ": int = 50"}, {"name": "timesteps", "val": ": typing.List[int] = None"}, {"name": "sigmas", "val": ": typing.List[float] = None"}, {"name": "denoising_end", "val": ": typing.Optional[float] = None"}, {"name": "guidance_scale", "val": ": float = 5.0"}, {"name": "negative_prompt", "val": ": typing.Union[str, typing.List[str], NoneType] = None"}, {"name": "negative_prompt_2", "val": ": typing.Union[str, typing.List[str], NoneType] = None"}, {"name": "num_images_per_prompt", "val": ": typing.Optional[int] = 1"}, {"name": "eta", "val": ": float = 0.0"}, {"name": "generator", "val": ": typing.Union[torch._C.Generator, typing.List[torch._C.Generator], NoneType] = None"}, {"name": "latents", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "prompt_embeds", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "negative_prompt_embeds", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "pooled_prompt_embeds", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "negative_pooled_prompt_embeds", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "ip_adapter_image", "val": ": typing.Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, typing.List[PIL.Image.Image], typing.List[numpy.ndarray], typing.List[torch.Tensor], NoneType] = None"}, {"name": "ip_adapter_image_embeds", "val": ": typing.Optional[typing.List[torch.Tensor]] = None"}, {"name": "output_type", "val": ": typing.Optional[str] = 'pil'"}, {"name": "return_dict", "val": ": bool = True"}, {"name": "cross_attention_kwargs", "val": ": typing.Optional[typing.Dict[str, typing.Any]] = None"}, {"name": "guidance_rescale", "val": ": float = 0.0"}, {"name": "original_size", "val": ": typing.Optional[typing.Tuple[int, int]] = None"}, {"name": "crops_coords_top_left", "val": ": typing.Tuple[int, int] = (0, 0)"}, {"name": "target_size", "val": ": typing.Optional[typing.Tuple[int, int]] = None"}, {"name": "negative_original_size", "val": ": typing.Optional[typing.Tuple[int, int]] = None"}, {"name": "negative_crops_coords_top_left", "val": ": typing.Tuple[int, int] = (0, 0)"}, {"name": "negative_target_size", "val": ": typing.Optional[typing.Tuple[int, int]] = None"}, {"name": "clip_skip", "val": ": typing.Optional[int] = None"}, {"name": "callback_on_step_end", "val": ": typing.Union[typing.Callable[[int, int, typing.Dict], NoneType], diffusers.callbacks.PipelineCallback, diffusers.callbacks.MultiPipelineCallbacks, NoneType] = None"}, {"name": "callback_on_step_end_tensor_inputs", "val": ": typing.List[str] = ['latents']"}, {"name": "**kwargs", "val": ""}]</parameters><paramsdesc>- **prompt** (`str` or `List[str]`, *optional*) --
  The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
  instead.
- **prompt_2** (`str` or `List[str]`, *optional*) --
  The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is
  used in both text-encoders
- **height** (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor) --
  The height in pixels of the generated image. This is set to 1024 by default for the best results.
  Anything below 512 pixels won't work well for
  [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0)
  and checkpoints that are not specifically fine-tuned on low resolutions.
- **width** (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor) --
  The width in pixels of the generated image. This is set to 1024 by default for the best results.
  Anything below 512 pixels won't work well for
  [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0)
  and checkpoints that are not specifically fine-tuned on low resolutions.
- **num_inference_steps** (`int`, *optional*, defaults to 50) --
  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 with schedulers which support a `timesteps` argument
  in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is
  passed will be used. Must be in descending order.
- **sigmas** (`List[float]`, *optional*) --
  Custom sigmas to use for the denoising process with schedulers which support a `sigmas` argument in
  their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is passed
  will be used.
- **denoising_end** (`float`, *optional*) --
  When specified, determines the fraction (between 0.0 and 1.0) of the total denoising process to be
  completed before it is intentionally prematurely terminated. As a result, the returned sample will
  still retain a substantial amount of noise as determined by the discrete timesteps selected by the
  scheduler. The denoising_end parameter should ideally be utilized when this pipeline forms a part of a
  "Mixture of Denoisers" multi-pipeline setup, as elaborated in [**Refining the Image
  Output**](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/stable_diffusion_xl#refining-the-image-output)
- **guidance_scale** (`float`, *optional*, defaults to 5.0) --
  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.
- **negative_prompt** (`str` or `List[str]`, *optional*) --
  The prompt or prompts not to guide the image generation. If not defined, one has to pass
  `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
  less than `1`).
- **negative_prompt_2** (`str` or `List[str]`, *optional*) --
  The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and
  `text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders
- **num_images_per_prompt** (`int`, *optional*, defaults to 1) --
  The number of images to generate per prompt.
- **eta** (`float`, *optional*, defaults to 0.0) --
  Corresponds to parameter eta (η) in the DDIM paper: https://huggingface.co/papers/2010.02502. Only
  applies to `schedulers.DDIMScheduler`, will be ignored for others.
- **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`.
- **prompt_embeds** (`torch.Tensor`, *optional*) --
  Pre-generated text embeddings. 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. 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.
- **pooled_prompt_embeds** (`torch.Tensor`, *optional*) --
  Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.
  If not provided, pooled text embeddings will be generated from `prompt` input argument.
- **negative_pooled_prompt_embeds** (`torch.Tensor`, *optional*) --
  Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
  weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt`
  input argument.
- **ip_adapter_image** -- (`PipelineImageInput`, *optional*): Optional image input to work with IP Adapters.
- **ip_adapter_image_embeds** (`List[torch.Tensor]`, *optional*) --
  Pre-generated image embeddings for IP-Adapter. It should be a list of length same as number of
  IP-adapters. Each element should be a tensor of shape `(batch_size, num_images, emb_dim)`. It should
  contain the negative image embedding if `do_classifier_free_guidance` is set to `True`. If not
  provided, embeddings are computed from the `ip_adapter_image` input argument.
- **output_type** (`str`, *optional*, defaults to `"pil"`) --
  The output format of the generate image. Choose between
  [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
- **return_dict** (`bool`, *optional*, defaults to `True`) --
  Whether or not to return a `diffusers.pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput` instead
  of a plain tuple.
- **cross_attention_kwargs** (`dict`, *optional*) --
  A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
  `self.processor` in
  [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
- **guidance_rescale** (`float`, *optional*, defaults to 0.0) --
  Guidance rescale factor proposed by [Common Diffusion Noise Schedules and Sample Steps are
  Flawed](https://huggingface.co/papers/2305.08891) `guidance_scale` is defined as `φ` in equation 16. of
  [Common Diffusion Noise Schedules and Sample Steps are
  Flawed](https://huggingface.co/papers/2305.08891). Guidance rescale factor should fix overexposure when
  using zero terminal SNR.
- **original_size** (`Tuple[int]`, *optional*, defaults to (1024, 1024)) --
  If `original_size` is not the same as `target_size` the image will appear to be down- or upsampled.
  `original_size` defaults to `(height, width)` if not specified. Part of SDXL's micro-conditioning as
  explained in section 2.2 of
  [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
- **crops_coords_top_left** (`Tuple[int]`, *optional*, defaults to (0, 0)) --
  `crops_coords_top_left` can be used to generate an image that appears to be "cropped" from the position
  `crops_coords_top_left` downwards. Favorable, well-centered images are usually achieved by setting
  `crops_coords_top_left` to (0, 0). Part of SDXL's micro-conditioning as explained in section 2.2 of
  [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
- **target_size** (`Tuple[int]`, *optional*, defaults to (1024, 1024)) --
  For most cases, `target_size` should be set to the desired height and width of the generated image. If
  not specified it will default to `(height, width)`. Part of SDXL's micro-conditioning as explained in
  section 2.2 of [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
- **negative_original_size** (`Tuple[int]`, *optional*, defaults to (1024, 1024)) --
  To negatively condition the generation process based on a specific image resolution. Part of SDXL's
  micro-conditioning as explained in section 2.2 of
  [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more
  information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
- **negative_crops_coords_top_left** (`Tuple[int]`, *optional*, defaults to (0, 0)) --
  To negatively condition the generation process based on a specific crop coordinates. Part of SDXL's
  micro-conditioning as explained in section 2.2 of
  [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more
  information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
- **negative_target_size** (`Tuple[int]`, *optional*, defaults to (1024, 1024)) --
  To negatively condition the generation process based on a target image resolution. It should be as same
  as the `target_size` for most cases. Part of SDXL's micro-conditioning as explained in section 2.2 of
  [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more
  information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
- **callback_on_step_end** (`Callable`, `PipelineCallback`, `MultiPipelineCallbacks`, *optional*) --
  A function or a subclass of `PipelineCallback` or `MultiPipelineCallbacks` that is called at the end of
  each denoising step during the inference. 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.</paramsdesc><paramgroups>0</paramgroups><rettype>`diffusers.pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput` or `tuple`</rettype><retdesc>`diffusers.pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput` if `return_dict` is True, otherwise a
`tuple`. When returning a tuple, the first element is a list with the generated images.</retdesc></docstring>

Function invoked when calling the pipeline for generation.



<ExampleCodeBlock anchor="optimum.onnxruntime.ORTStableDiffusionXLPipeline.__call__.example">

Examples:
```py
>>> import torch
>>> from optimum.onnxruntime import ORTStableDiffusionXLPipeline

>>> pipe = ORTStableDiffusionXLPipeline.from_pretrained(
...     "stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16
... )
>>> pipe = pipe.to("cuda")

>>> prompt = "a photo of an astronaut riding a horse on mars"
>>> image = pipe(prompt).images[0]
```

</ExampleCodeBlock>







</div></div>

### ORTStableDiffusionXLImg2ImgPipeline[[optimum.onnxruntime.ORTStableDiffusionXLImg2ImgPipeline]]

<div class="docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8">


<docstring><name>class optimum.onnxruntime.ORTStableDiffusionXLImg2ImgPipeline</name><anchor>optimum.onnxruntime.ORTStableDiffusionXLImg2ImgPipeline</anchor><source>https://github.com/huggingface/optimum-onnx/blob/v0.0.2/optimum/onnxruntime/modeling_diffusion.py#L957</source><parameters>[{"name": "unet_session", "val": ": InferenceSession | None = None"}, {"name": "transformer_session", "val": ": InferenceSession | None = None"}, {"name": "vae_decoder_session", "val": ": InferenceSession | None = None"}, {"name": "vae_encoder_session", "val": ": InferenceSession | None = None"}, {"name": "text_encoder_session", "val": ": InferenceSession | None = None"}, {"name": "text_encoder_2_session", "val": ": InferenceSession | None = None"}, {"name": "text_encoder_3_session", "val": ": InferenceSession | None = None"}, {"name": "scheduler", "val": ": SchedulerMixin | None = None"}, {"name": "tokenizer", "val": ": CLIPTokenizer | None = None"}, {"name": "tokenizer_2", "val": ": CLIPTokenizer | None = None"}, {"name": "tokenizer_3", "val": ": CLIPTokenizer | None = None"}, {"name": "feature_extractor", "val": ": CLIPFeatureExtractor | None = None"}, {"name": "force_zeros_for_empty_prompt", "val": ": bool = True"}, {"name": "requires_aesthetics_score", "val": ": bool = False"}, {"name": "add_watermarker", "val": ": bool | None = None"}, {"name": "use_io_binding", "val": ": bool | None = None"}, {"name": "model_save_dir", "val": ": str | Path | TemporaryDirectory | None = None"}, {"name": "**kwargs", "val": ""}]</parameters></docstring>
ONNX Runtime-powered Pipeline for text-guided image-to-image generation using Stable Diffusion XL and corresponding to [StableDiffusionXLImg2ImgPipeline]
(https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/stable_diffusion_xl#diffusers.StableDiffusionXLImg2ImgPipeline).

This Pipeline inherits from `ORTDiffusionPipeline` and is used to run inference with the ONNX Runtime.
The pipeline can be loaded from a pretrained pipeline using the generic `ORTDiffusionPipeline.from_pretrained` method.



<div class="docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8">


<docstring><name>__call__</name><anchor>optimum.onnxruntime.ORTStableDiffusionXLImg2ImgPipeline.__call__</anchor><source>https://github.com/huggingface/optimum-onnx/blob/v0.0.2/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_img2img.py#L986</source><parameters>[{"name": "prompt", "val": ": typing.Union[str, typing.List[str]] = None"}, {"name": "prompt_2", "val": ": typing.Union[str, typing.List[str], NoneType] = None"}, {"name": "image", "val": ": typing.Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, typing.List[PIL.Image.Image], typing.List[numpy.ndarray], typing.List[torch.Tensor]] = None"}, {"name": "strength", "val": ": float = 0.3"}, {"name": "num_inference_steps", "val": ": int = 50"}, {"name": "timesteps", "val": ": typing.List[int] = None"}, {"name": "sigmas", "val": ": typing.List[float] = None"}, {"name": "denoising_start", "val": ": typing.Optional[float] = None"}, {"name": "denoising_end", "val": ": typing.Optional[float] = None"}, {"name": "guidance_scale", "val": ": float = 5.0"}, {"name": "negative_prompt", "val": ": typing.Union[str, typing.List[str], NoneType] = None"}, {"name": "negative_prompt_2", "val": ": typing.Union[str, typing.List[str], NoneType] = None"}, {"name": "num_images_per_prompt", "val": ": typing.Optional[int] = 1"}, {"name": "eta", "val": ": float = 0.0"}, {"name": "generator", "val": ": typing.Union[torch._C.Generator, typing.List[torch._C.Generator], NoneType] = None"}, {"name": "latents", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "prompt_embeds", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "negative_prompt_embeds", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "pooled_prompt_embeds", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "negative_pooled_prompt_embeds", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "ip_adapter_image", "val": ": typing.Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, typing.List[PIL.Image.Image], typing.List[numpy.ndarray], typing.List[torch.Tensor], NoneType] = None"}, {"name": "ip_adapter_image_embeds", "val": ": typing.Optional[typing.List[torch.Tensor]] = None"}, {"name": "output_type", "val": ": typing.Optional[str] = 'pil'"}, {"name": "return_dict", "val": ": bool = True"}, {"name": "cross_attention_kwargs", "val": ": typing.Optional[typing.Dict[str, typing.Any]] = None"}, {"name": "guidance_rescale", "val": ": float = 0.0"}, {"name": "original_size", "val": ": typing.Tuple[int, int] = None"}, {"name": "crops_coords_top_left", "val": ": typing.Tuple[int, int] = (0, 0)"}, {"name": "target_size", "val": ": typing.Tuple[int, int] = None"}, {"name": "negative_original_size", "val": ": typing.Optional[typing.Tuple[int, int]] = None"}, {"name": "negative_crops_coords_top_left", "val": ": typing.Tuple[int, int] = (0, 0)"}, {"name": "negative_target_size", "val": ": typing.Optional[typing.Tuple[int, int]] = None"}, {"name": "aesthetic_score", "val": ": float = 6.0"}, {"name": "negative_aesthetic_score", "val": ": float = 2.5"}, {"name": "clip_skip", "val": ": typing.Optional[int] = None"}, {"name": "callback_on_step_end", "val": ": typing.Union[typing.Callable[[int, int, typing.Dict], NoneType], diffusers.callbacks.PipelineCallback, diffusers.callbacks.MultiPipelineCallbacks, NoneType] = None"}, {"name": "callback_on_step_end_tensor_inputs", "val": ": typing.List[str] = ['latents']"}, {"name": "**kwargs", "val": ""}]</parameters><paramsdesc>- **prompt** (`str` or `List[str]`, *optional*) --
  The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
  instead.
- **prompt_2** (`str` or `List[str]`, *optional*) --
  The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is
  used in both text-encoders
- **image** (`torch.Tensor` or `PIL.Image.Image` or `np.ndarray` or `List[torch.Tensor]` or `List[PIL.Image.Image]` or `List[np.ndarray]`) --
  The image(s) to modify with the pipeline.
- **strength** (`float`, *optional*, defaults to 0.3) --
  Conceptually, indicates how much to transform the reference `image`. Must be between 0 and 1. `image`
  will be used as a starting point, adding more noise to it the larger the `strength`. The number of
  denoising steps depends on the amount of noise initially added. When `strength` is 1, added noise will
  be maximum and the denoising process will run for the full number of iterations specified in
  `num_inference_steps`. A value of 1, therefore, essentially ignores `image`. Note that in the case of
  `denoising_start` being declared as an integer, the value of `strength` will be ignored.
- **num_inference_steps** (`int`, *optional*, defaults to 50) --
  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 with schedulers which support a `timesteps` argument
  in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is
  passed will be used. Must be in descending order.
- **sigmas** (`List[float]`, *optional*) --
  Custom sigmas to use for the denoising process with schedulers which support a `sigmas` argument in
  their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is passed
  will be used.
- **denoising_start** (`float`, *optional*) --
  When specified, indicates the fraction (between 0.0 and 1.0) of the total denoising process to be
  bypassed before it is initiated. Consequently, the initial part of the denoising process is skipped and
  it is assumed that the passed `image` is a partly denoised image. Note that when this is specified,
  strength will be ignored. The `denoising_start` parameter is particularly beneficial when this pipeline
  is integrated into a "Mixture of Denoisers" multi-pipeline setup, as detailed in [**Refine Image
  Quality**](https://huggingface.co/docs/diffusers/using-diffusers/sdxl#refine-image-quality).
- **denoising_end** (`float`, *optional*) --
  When specified, determines the fraction (between 0.0 and 1.0) of the total denoising process to be
  completed before it is intentionally prematurely terminated. As a result, the returned sample will
  still retain a substantial amount of noise (ca. final 20% of timesteps still needed) and should be
  denoised by a successor pipeline that has `denoising_start` set to 0.8 so that it only denoises the
  final 20% of the scheduler. The denoising_end parameter should ideally be utilized when this pipeline
  forms a part of a "Mixture of Denoisers" multi-pipeline setup, as elaborated in [**Refine Image
  Quality**](https://huggingface.co/docs/diffusers/using-diffusers/sdxl#refine-image-quality).
- **guidance_scale** (`float`, *optional*, defaults to 7.5) --
  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.
- **negative_prompt** (`str` or `List[str]`, *optional*) --
  The prompt or prompts not to guide the image generation. If not defined, one has to pass
  `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
  less than `1`).
- **negative_prompt_2** (`str` or `List[str]`, *optional*) --
  The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and
  `text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders
- **num_images_per_prompt** (`int`, *optional*, defaults to 1) --
  The number of images to generate per prompt.
- **eta** (`float`, *optional*, defaults to 0.0) --
  Corresponds to parameter eta (η) in the DDIM paper: https://huggingface.co/papers/2010.02502. Only
  applies to `schedulers.DDIMScheduler`, will be ignored for others.
- **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`.
- **prompt_embeds** (`torch.Tensor`, *optional*) --
  Pre-generated text embeddings. 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. 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.
- **pooled_prompt_embeds** (`torch.Tensor`, *optional*) --
  Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.
  If not provided, pooled text embeddings will be generated from `prompt` input argument.
- **negative_pooled_prompt_embeds** (`torch.Tensor`, *optional*) --
  Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
  weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt`
  input argument.
- **ip_adapter_image** -- (`PipelineImageInput`, *optional*): Optional image input to work with IP Adapters.
- **ip_adapter_image_embeds** (`List[torch.Tensor]`, *optional*) --
  Pre-generated image embeddings for IP-Adapter. It should be a list of length same as number of
  IP-adapters. Each element should be a tensor of shape `(batch_size, num_images, emb_dim)`. It should
  contain the negative image embedding if `do_classifier_free_guidance` is set to `True`. If not
  provided, embeddings are computed from the `ip_adapter_image` input argument.
- **output_type** (`str`, *optional*, defaults to `"pil"`) --
  The output format of the generate image. Choose between
  [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
- **return_dict** (`bool`, *optional*, defaults to `True`) --
  Whether or not to return a `diffusers.pipelines.stable_diffusion.StableDiffusionXLPipelineOutput` instead of a
  plain tuple.
- **cross_attention_kwargs** (`dict`, *optional*) --
  A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
  `self.processor` in
  [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
- **guidance_rescale** (`float`, *optional*, defaults to 0.0) --
  Guidance rescale factor proposed by [Common Diffusion Noise Schedules and Sample Steps are
  Flawed](https://huggingface.co/papers/2305.08891) `guidance_scale` is defined as `φ` in equation 16. of
  [Common Diffusion Noise Schedules and Sample Steps are
  Flawed](https://huggingface.co/papers/2305.08891). Guidance rescale factor should fix overexposure when
  using zero terminal SNR.
- **original_size** (`Tuple[int]`, *optional*, defaults to (1024, 1024)) --
  If `original_size` is not the same as `target_size` the image will appear to be down- or upsampled.
  `original_size` defaults to `(height, width)` if not specified. Part of SDXL's micro-conditioning as
  explained in section 2.2 of
  [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
- **crops_coords_top_left** (`Tuple[int]`, *optional*, defaults to (0, 0)) --
  `crops_coords_top_left` can be used to generate an image that appears to be "cropped" from the position
  `crops_coords_top_left` downwards. Favorable, well-centered images are usually achieved by setting
  `crops_coords_top_left` to (0, 0). Part of SDXL's micro-conditioning as explained in section 2.2 of
  [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
- **target_size** (`Tuple[int]`, *optional*, defaults to (1024, 1024)) --
  For most cases, `target_size` should be set to the desired height and width of the generated image. If
  not specified it will default to `(height, width)`. Part of SDXL's micro-conditioning as explained in
  section 2.2 of [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
- **negative_original_size** (`Tuple[int]`, *optional*, defaults to (1024, 1024)) --
  To negatively condition the generation process based on a specific image resolution. Part of SDXL's
  micro-conditioning as explained in section 2.2 of
  [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more
  information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
- **negative_crops_coords_top_left** (`Tuple[int]`, *optional*, defaults to (0, 0)) --
  To negatively condition the generation process based on a specific crop coordinates. Part of SDXL's
  micro-conditioning as explained in section 2.2 of
  [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more
  information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
- **negative_target_size** (`Tuple[int]`, *optional*, defaults to (1024, 1024)) --
  To negatively condition the generation process based on a target image resolution. It should be as same
  as the `target_size` for most cases. Part of SDXL's micro-conditioning as explained in section 2.2 of
  [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more
  information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
- **aesthetic_score** (`float`, *optional*, defaults to 6.0) --
  Used to simulate an aesthetic score of the generated image by influencing the positive text condition.
  Part of SDXL's micro-conditioning as explained in section 2.2 of
  [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
- **negative_aesthetic_score** (`float`, *optional*, defaults to 2.5) --
  Part of SDXL's micro-conditioning as explained in section 2.2 of
  [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). Can be used to
  simulate an aesthetic score of the generated image by influencing the negative text condition.
- **clip_skip** (`int`, *optional*) --
  Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
  the output of the pre-final layer will be used for computing the prompt embeddings.
- **callback_on_step_end** (`Callable`, `PipelineCallback`, `MultiPipelineCallbacks`, *optional*) --
  A function or a subclass of `PipelineCallback` or `MultiPipelineCallbacks` that is called at the end of
  each denoising step during the inference. 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.</paramsdesc><paramgroups>0</paramgroups><rettype>`diffusers.pipelines.stable_diffusion.StableDiffusionXLPipelineOutput` or `tuple`</rettype><retdesc>`diffusers.pipelines.stable_diffusion.StableDiffusionXLPipelineOutput` if `return_dict` is True, otherwise a
`tuple. When returning a tuple, the first element is a list with the generated images.</retdesc></docstring>

Function invoked when calling the pipeline for generation.



<ExampleCodeBlock anchor="optimum.onnxruntime.ORTStableDiffusionXLImg2ImgPipeline.__call__.example">

Examples:
```py
>>> import torch
>>> from optimum.onnxruntime import ORTStableDiffusionXLImg2ImgPipeline
>>> from diffusers.utils import load_image

>>> pipe = ORTStableDiffusionXLImg2ImgPipeline.from_pretrained(
...     "stabilityai/stable-diffusion-xl-refiner-1.0", torch_dtype=torch.float16
... )
>>> pipe = pipe.to("cuda")
>>> url = "https://huggingface.co/datasets/patrickvonplaten/images/resolve/main/aa_xl/000000009.png"

>>> init_image = load_image(url).convert("RGB")
>>> prompt = "a photo of an astronaut riding a horse on mars"
>>> image = pipe(prompt, image=init_image).images[0]
```

</ExampleCodeBlock>







</div></div>

### ORTStableDiffusionXLInpaintPipeline[[optimum.onnxruntime.ORTStableDiffusionXLInpaintPipeline]]

<div class="docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8">


<docstring><name>class optimum.onnxruntime.ORTStableDiffusionXLInpaintPipeline</name><anchor>optimum.onnxruntime.ORTStableDiffusionXLInpaintPipeline</anchor><source>https://github.com/huggingface/optimum-onnx/blob/v0.0.2/optimum/onnxruntime/modeling_diffusion.py#L995</source><parameters>[{"name": "unet_session", "val": ": InferenceSession | None = None"}, {"name": "transformer_session", "val": ": InferenceSession | None = None"}, {"name": "vae_decoder_session", "val": ": InferenceSession | None = None"}, {"name": "vae_encoder_session", "val": ": InferenceSession | None = None"}, {"name": "text_encoder_session", "val": ": InferenceSession | None = None"}, {"name": "text_encoder_2_session", "val": ": InferenceSession | None = None"}, {"name": "text_encoder_3_session", "val": ": InferenceSession | None = None"}, {"name": "scheduler", "val": ": SchedulerMixin | None = None"}, {"name": "tokenizer", "val": ": CLIPTokenizer | None = None"}, {"name": "tokenizer_2", "val": ": CLIPTokenizer | None = None"}, {"name": "tokenizer_3", "val": ": CLIPTokenizer | None = None"}, {"name": "feature_extractor", "val": ": CLIPFeatureExtractor | None = None"}, {"name": "force_zeros_for_empty_prompt", "val": ": bool = True"}, {"name": "requires_aesthetics_score", "val": ": bool = False"}, {"name": "add_watermarker", "val": ": bool | None = None"}, {"name": "use_io_binding", "val": ": bool | None = None"}, {"name": "model_save_dir", "val": ": str | Path | TemporaryDirectory | None = None"}, {"name": "**kwargs", "val": ""}]</parameters></docstring>
ONNX Runtime-powered Pipeline for text-guided image inpainting using Stable Diffusion XL and corresponding to [StableDiffusionXLInpaintPipeline]
(https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/stable_diffusion_xl#diffusers.StableDiffusionXLInpaintPipeline).

This Pipeline inherits from `ORTDiffusionPipeline` and is used to run inference with the ONNX Runtime.
The pipeline can be loaded from a pretrained pipeline using the generic `ORTDiffusionPipeline.from_pretrained` method.



<div class="docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8">


<docstring><name>__call__</name><anchor>optimum.onnxruntime.ORTStableDiffusionXLInpaintPipeline.__call__</anchor><source>https://github.com/huggingface/optimum-onnx/blob/v0.0.2/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_inpaint.py#L1091</source><parameters>[{"name": "prompt", "val": ": typing.Union[str, typing.List[str]] = None"}, {"name": "prompt_2", "val": ": typing.Union[str, typing.List[str], NoneType] = None"}, {"name": "image", "val": ": typing.Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, typing.List[PIL.Image.Image], typing.List[numpy.ndarray], typing.List[torch.Tensor]] = None"}, {"name": "mask_image", "val": ": typing.Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, typing.List[PIL.Image.Image], typing.List[numpy.ndarray], typing.List[torch.Tensor]] = None"}, {"name": "masked_image_latents", "val": ": Tensor = None"}, {"name": "height", "val": ": typing.Optional[int] = None"}, {"name": "width", "val": ": typing.Optional[int] = None"}, {"name": "padding_mask_crop", "val": ": typing.Optional[int] = None"}, {"name": "strength", "val": ": float = 0.9999"}, {"name": "num_inference_steps", "val": ": int = 50"}, {"name": "timesteps", "val": ": typing.List[int] = None"}, {"name": "sigmas", "val": ": typing.List[float] = None"}, {"name": "denoising_start", "val": ": typing.Optional[float] = None"}, {"name": "denoising_end", "val": ": typing.Optional[float] = None"}, {"name": "guidance_scale", "val": ": float = 7.5"}, {"name": "negative_prompt", "val": ": typing.Union[str, typing.List[str], NoneType] = None"}, {"name": "negative_prompt_2", "val": ": typing.Union[str, typing.List[str], NoneType] = None"}, {"name": "num_images_per_prompt", "val": ": typing.Optional[int] = 1"}, {"name": "eta", "val": ": float = 0.0"}, {"name": "generator", "val": ": typing.Union[torch._C.Generator, typing.List[torch._C.Generator], NoneType] = None"}, {"name": "latents", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "prompt_embeds", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "negative_prompt_embeds", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "pooled_prompt_embeds", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "negative_pooled_prompt_embeds", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "ip_adapter_image", "val": ": typing.Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, typing.List[PIL.Image.Image], typing.List[numpy.ndarray], typing.List[torch.Tensor], NoneType] = None"}, {"name": "ip_adapter_image_embeds", "val": ": typing.Optional[typing.List[torch.Tensor]] = None"}, {"name": "output_type", "val": ": typing.Optional[str] = 'pil'"}, {"name": "return_dict", "val": ": bool = True"}, {"name": "cross_attention_kwargs", "val": ": typing.Optional[typing.Dict[str, typing.Any]] = None"}, {"name": "guidance_rescale", "val": ": float = 0.0"}, {"name": "original_size", "val": ": typing.Tuple[int, int] = None"}, {"name": "crops_coords_top_left", "val": ": typing.Tuple[int, int] = (0, 0)"}, {"name": "target_size", "val": ": typing.Tuple[int, int] = None"}, {"name": "negative_original_size", "val": ": typing.Optional[typing.Tuple[int, int]] = None"}, {"name": "negative_crops_coords_top_left", "val": ": typing.Tuple[int, int] = (0, 0)"}, {"name": "negative_target_size", "val": ": typing.Optional[typing.Tuple[int, int]] = None"}, {"name": "aesthetic_score", "val": ": float = 6.0"}, {"name": "negative_aesthetic_score", "val": ": float = 2.5"}, {"name": "clip_skip", "val": ": typing.Optional[int] = None"}, {"name": "callback_on_step_end", "val": ": typing.Union[typing.Callable[[int, int, typing.Dict], NoneType], diffusers.callbacks.PipelineCallback, diffusers.callbacks.MultiPipelineCallbacks, NoneType] = None"}, {"name": "callback_on_step_end_tensor_inputs", "val": ": typing.List[str] = ['latents']"}, {"name": "**kwargs", "val": ""}]</parameters><paramsdesc>- **prompt** (`str` or `List[str]`, *optional*) --
  The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
  instead.
- **prompt_2** (`str` or `List[str]`, *optional*) --
  The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is
  used in both text-encoders
- **image** (`PIL.Image.Image`) --
  `Image`, or tensor representing an image batch which will be inpainted, *i.e.* parts of the image will
  be masked out with `mask_image` and repainted according to `prompt`.
- **mask_image** (`PIL.Image.Image`) --
  `Image`, or tensor representing an image batch, to mask `image`. White pixels in the mask will be
  repainted, while black pixels will be preserved. If `mask_image` is a PIL image, it will be converted
  to a single channel (luminance) before use. If it's a tensor, it should contain one color channel (L)
  instead of 3, so the expected shape would be `(B, H, W, 1)`.
- **height** (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor) --
  The height in pixels of the generated image. This is set to 1024 by default for the best results.
  Anything below 512 pixels won't work well for
  [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0)
  and checkpoints that are not specifically fine-tuned on low resolutions.
- **width** (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor) --
  The width in pixels of the generated image. This is set to 1024 by default for the best results.
  Anything below 512 pixels won't work well for
  [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0)
  and checkpoints that are not specifically fine-tuned on low resolutions.
- **padding_mask_crop** (`int`, *optional*, defaults to `None`) --
  The size of margin in the crop to be applied to the image and masking. If `None`, no crop is applied to
  image and mask_image. If `padding_mask_crop` is not `None`, it will first find a rectangular region
  with the same aspect ration of the image and contains all masked area, and then expand that area based
  on `padding_mask_crop`. The image and mask_image will then be cropped based on the expanded area before
  resizing to the original image size for inpainting. This is useful when the masked area is small while
  the image is large and contain information irrelevant for inpainting, such as background.
- **strength** (`float`, *optional*, defaults to 0.9999) --
  Conceptually, indicates how much to transform the masked portion of the reference `image`. Must be
  between 0 and 1. `image` will be used as a starting point, adding more noise to it the larger the
  `strength`. The number of denoising steps depends on the amount of noise initially added. When
  `strength` is 1, added noise will be maximum and the denoising process will run for the full number of
  iterations specified in `num_inference_steps`. A value of 1, therefore, essentially ignores the masked
  portion of the reference `image`. Note that in the case of `denoising_start` being declared as an
  integer, the value of `strength` will be ignored.
- **num_inference_steps** (`int`, *optional*, defaults to 50) --
  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 with schedulers which support a `timesteps` argument
  in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is
  passed will be used. Must be in descending order.
- **sigmas** (`List[float]`, *optional*) --
  Custom sigmas to use for the denoising process with schedulers which support a `sigmas` argument in
  their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is passed
  will be used.
- **denoising_start** (`float`, *optional*) --
  When specified, indicates the fraction (between 0.0 and 1.0) of the total denoising process to be
  bypassed before it is initiated. Consequently, the initial part of the denoising process is skipped and
  it is assumed that the passed `image` is a partly denoised image. Note that when this is specified,
  strength will be ignored. The `denoising_start` parameter is particularly beneficial when this pipeline
  is integrated into a "Mixture of Denoisers" multi-pipeline setup, as detailed in [**Refining the Image
  Output**](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/stable_diffusion_xl#refining-the-image-output).
- **denoising_end** (`float`, *optional*) --
  When specified, determines the fraction (between 0.0 and 1.0) of the total denoising process to be
  completed before it is intentionally prematurely terminated. As a result, the returned sample will
  still retain a substantial amount of noise (ca. final 20% of timesteps still needed) and should be
  denoised by a successor pipeline that has `denoising_start` set to 0.8 so that it only denoises the
  final 20% of the scheduler. The denoising_end parameter should ideally be utilized when this pipeline
  forms a part of a "Mixture of Denoisers" multi-pipeline setup, as elaborated in [**Refining the Image
  Output**](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/stable_diffusion_xl#refining-the-image-output).
- **guidance_scale** (`float`, *optional*, defaults to 7.5) --
  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.
- **negative_prompt** (`str` or `List[str]`, *optional*) --
  The prompt or prompts not to guide the image generation. If not defined, one has to pass
  `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
  less than `1`).
- **negative_prompt_2** (`str` or `List[str]`, *optional*) --
  The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and
  `text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders
- **prompt_embeds** (`torch.Tensor`, *optional*) --
  Pre-generated text embeddings. 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. 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.
- **pooled_prompt_embeds** (`torch.Tensor`, *optional*) --
  Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.
  If not provided, pooled text embeddings will be generated from `prompt` input argument.
- **negative_pooled_prompt_embeds** (`torch.Tensor`, *optional*) --
  Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
  weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt`
  input argument.
- **ip_adapter_image** -- (`PipelineImageInput`, *optional*): Optional image input to work with IP Adapters.
- **ip_adapter_image_embeds** (`List[torch.Tensor]`, *optional*) --
  Pre-generated image embeddings for IP-Adapter. It should be a list of length same as number of
  IP-adapters. Each element should be a tensor of shape `(batch_size, num_images, emb_dim)`. It should
  contain the negative image embedding if `do_classifier_free_guidance` is set to `True`. If not
  provided, embeddings are computed from the `ip_adapter_image` input argument.
- **num_images_per_prompt** (`int`, *optional*, defaults to 1) --
  The number of images to generate per prompt.
- **eta** (`float`, *optional*, defaults to 0.0) --
  Corresponds to parameter eta (η) in the DDIM paper: https://huggingface.co/papers/2010.02502. Only
  applies to `schedulers.DDIMScheduler`, will be ignored for others.
- **generator** (`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](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
- **return_dict** (`bool`, *optional*, defaults to `True`) --
  Whether or not to return a `diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput` instead of a
  plain tuple.
- **cross_attention_kwargs** (`dict`, *optional*) --
  A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
  `self.processor` in
  [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
- **original_size** (`Tuple[int]`, *optional*, defaults to (1024, 1024)) --
  If `original_size` is not the same as `target_size` the image will appear to be down- or upsampled.
  `original_size` defaults to `(height, width)` if not specified. Part of SDXL's micro-conditioning as
  explained in section 2.2 of
  [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
- **crops_coords_top_left** (`Tuple[int]`, *optional*, defaults to (0, 0)) --
  `crops_coords_top_left` can be used to generate an image that appears to be "cropped" from the position
  `crops_coords_top_left` downwards. Favorable, well-centered images are usually achieved by setting
  `crops_coords_top_left` to (0, 0). Part of SDXL's micro-conditioning as explained in section 2.2 of
  [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
- **target_size** (`Tuple[int]`, *optional*, defaults to (1024, 1024)) --
  For most cases, `target_size` should be set to the desired height and width of the generated image. If
  not specified it will default to `(height, width)`. Part of SDXL's micro-conditioning as explained in
  section 2.2 of [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
- **negative_original_size** (`Tuple[int]`, *optional*, defaults to (1024, 1024)) --
  To negatively condition the generation process based on a specific image resolution. Part of SDXL's
  micro-conditioning as explained in section 2.2 of
  [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more
  information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
- **negative_crops_coords_top_left** (`Tuple[int]`, *optional*, defaults to (0, 0)) --
  To negatively condition the generation process based on a specific crop coordinates. Part of SDXL's
  micro-conditioning as explained in section 2.2 of
  [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more
  information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
- **negative_target_size** (`Tuple[int]`, *optional*, defaults to (1024, 1024)) --
  To negatively condition the generation process based on a target image resolution. It should be as same
  as the `target_size` for most cases. Part of SDXL's micro-conditioning as explained in section 2.2 of
  [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more
  information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
- **aesthetic_score** (`float`, *optional*, defaults to 6.0) --
  Used to simulate an aesthetic score of the generated image by influencing the positive text condition.
  Part of SDXL's micro-conditioning as explained in section 2.2 of
  [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
- **negative_aesthetic_score** (`float`, *optional*, defaults to 2.5) --
  Part of SDXL's micro-conditioning as explained in section 2.2 of
  [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). Can be used to
  simulate an aesthetic score of the generated image by influencing the negative text condition.
- **clip_skip** (`int`, *optional*) --
  Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
  the output of the pre-final layer will be used for computing the prompt embeddings.
- **callback_on_step_end** (`Callable`, `PipelineCallback`, `MultiPipelineCallbacks`, *optional*) --
  A function or a subclass of `PipelineCallback` or `MultiPipelineCallbacks` that is called at the end of
  each denoising step during the inference. 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.</paramsdesc><paramgroups>0</paramgroups><rettype>`diffusers.pipelines.stable_diffusion.StableDiffusionXLPipelineOutput` or `tuple`</rettype><retdesc>`diffusers.pipelines.stable_diffusion.StableDiffusionXLPipelineOutput` if `return_dict` is True, otherwise a
`tuple. `tuple. When returning a tuple, the first element is a list with the generated images.</retdesc></docstring>

Function invoked when calling the pipeline for generation.



<ExampleCodeBlock anchor="optimum.onnxruntime.ORTStableDiffusionXLInpaintPipeline.__call__.example">

Examples:
```py
>>> import torch
>>> from optimum.onnxruntime import ORTStableDiffusionXLInpaintPipeline
>>> from diffusers.utils import load_image

>>> pipe = ORTStableDiffusionXLInpaintPipeline.from_pretrained(
...     "stabilityai/stable-diffusion-xl-base-1.0",
...     torch_dtype=torch.float16,
...     variant="fp16",
...     use_safetensors=True,
... )
>>> pipe.to("cuda")

>>> img_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png"
>>> mask_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png"

>>> init_image = load_image(img_url).convert("RGB")
>>> mask_image = load_image(mask_url).convert("RGB")

>>> prompt = "A majestic tiger sitting on a bench"
>>> image = pipe(
...     prompt=prompt, image=init_image, mask_image=mask_image, num_inference_steps=50, strength=0.80
... ).images[0]
```

</ExampleCodeBlock>







</div></div>

### ORTStableDiffusionXLImg2ImgPipeline[[optimum.onnxruntime.ORTStableDiffusionXLImg2ImgPipeline]]

<div class="docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8">


<docstring><name>class optimum.onnxruntime.ORTStableDiffusionXLImg2ImgPipeline</name><anchor>optimum.onnxruntime.ORTStableDiffusionXLImg2ImgPipeline</anchor><source>https://github.com/huggingface/optimum-onnx/blob/v0.0.2/optimum/onnxruntime/modeling_diffusion.py#L957</source><parameters>[{"name": "unet_session", "val": ": InferenceSession | None = None"}, {"name": "transformer_session", "val": ": InferenceSession | None = None"}, {"name": "vae_decoder_session", "val": ": InferenceSession | None = None"}, {"name": "vae_encoder_session", "val": ": InferenceSession | None = None"}, {"name": "text_encoder_session", "val": ": InferenceSession | None = None"}, {"name": "text_encoder_2_session", "val": ": InferenceSession | None = None"}, {"name": "text_encoder_3_session", "val": ": InferenceSession | None = None"}, {"name": "scheduler", "val": ": SchedulerMixin | None = None"}, {"name": "tokenizer", "val": ": CLIPTokenizer | None = None"}, {"name": "tokenizer_2", "val": ": CLIPTokenizer | None = None"}, {"name": "tokenizer_3", "val": ": CLIPTokenizer | None = None"}, {"name": "feature_extractor", "val": ": CLIPFeatureExtractor | None = None"}, {"name": "force_zeros_for_empty_prompt", "val": ": bool = True"}, {"name": "requires_aesthetics_score", "val": ": bool = False"}, {"name": "add_watermarker", "val": ": bool | None = None"}, {"name": "use_io_binding", "val": ": bool | None = None"}, {"name": "model_save_dir", "val": ": str | Path | TemporaryDirectory | None = None"}, {"name": "**kwargs", "val": ""}]</parameters></docstring>
ONNX Runtime-powered Pipeline for text-guided image-to-image generation using Stable Diffusion XL and corresponding to [StableDiffusionXLImg2ImgPipeline]
(https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/stable_diffusion_xl#diffusers.StableDiffusionXLImg2ImgPipeline).

This Pipeline inherits from `ORTDiffusionPipeline` and is used to run inference with the ONNX Runtime.
The pipeline can be loaded from a pretrained pipeline using the generic `ORTDiffusionPipeline.from_pretrained` method.



<div class="docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8">


<docstring><name>__call__</name><anchor>optimum.onnxruntime.ORTStableDiffusionXLImg2ImgPipeline.__call__</anchor><source>https://github.com/huggingface/optimum-onnx/blob/v0.0.2/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_img2img.py#L986</source><parameters>[{"name": "prompt", "val": ": typing.Union[str, typing.List[str]] = None"}, {"name": "prompt_2", "val": ": typing.Union[str, typing.List[str], NoneType] = None"}, {"name": "image", "val": ": typing.Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, typing.List[PIL.Image.Image], typing.List[numpy.ndarray], typing.List[torch.Tensor]] = None"}, {"name": "strength", "val": ": float = 0.3"}, {"name": "num_inference_steps", "val": ": int = 50"}, {"name": "timesteps", "val": ": typing.List[int] = None"}, {"name": "sigmas", "val": ": typing.List[float] = None"}, {"name": "denoising_start", "val": ": typing.Optional[float] = None"}, {"name": "denoising_end", "val": ": typing.Optional[float] = None"}, {"name": "guidance_scale", "val": ": float = 5.0"}, {"name": "negative_prompt", "val": ": typing.Union[str, typing.List[str], NoneType] = None"}, {"name": "negative_prompt_2", "val": ": typing.Union[str, typing.List[str], NoneType] = None"}, {"name": "num_images_per_prompt", "val": ": typing.Optional[int] = 1"}, {"name": "eta", "val": ": float = 0.0"}, {"name": "generator", "val": ": typing.Union[torch._C.Generator, typing.List[torch._C.Generator], NoneType] = None"}, {"name": "latents", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "prompt_embeds", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "negative_prompt_embeds", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "pooled_prompt_embeds", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "negative_pooled_prompt_embeds", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "ip_adapter_image", "val": ": typing.Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, typing.List[PIL.Image.Image], typing.List[numpy.ndarray], typing.List[torch.Tensor], NoneType] = None"}, {"name": "ip_adapter_image_embeds", "val": ": typing.Optional[typing.List[torch.Tensor]] = None"}, {"name": "output_type", "val": ": typing.Optional[str] = 'pil'"}, {"name": "return_dict", "val": ": bool = True"}, {"name": "cross_attention_kwargs", "val": ": typing.Optional[typing.Dict[str, typing.Any]] = None"}, {"name": "guidance_rescale", "val": ": float = 0.0"}, {"name": "original_size", "val": ": typing.Tuple[int, int] = None"}, {"name": "crops_coords_top_left", "val": ": typing.Tuple[int, int] = (0, 0)"}, {"name": "target_size", "val": ": typing.Tuple[int, int] = None"}, {"name": "negative_original_size", "val": ": typing.Optional[typing.Tuple[int, int]] = None"}, {"name": "negative_crops_coords_top_left", "val": ": typing.Tuple[int, int] = (0, 0)"}, {"name": "negative_target_size", "val": ": typing.Optional[typing.Tuple[int, int]] = None"}, {"name": "aesthetic_score", "val": ": float = 6.0"}, {"name": "negative_aesthetic_score", "val": ": float = 2.5"}, {"name": "clip_skip", "val": ": typing.Optional[int] = None"}, {"name": "callback_on_step_end", "val": ": typing.Union[typing.Callable[[int, int, typing.Dict], NoneType], diffusers.callbacks.PipelineCallback, diffusers.callbacks.MultiPipelineCallbacks, NoneType] = None"}, {"name": "callback_on_step_end_tensor_inputs", "val": ": typing.List[str] = ['latents']"}, {"name": "**kwargs", "val": ""}]</parameters><paramsdesc>- **prompt** (`str` or `List[str]`, *optional*) --
  The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
  instead.
- **prompt_2** (`str` or `List[str]`, *optional*) --
  The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is
  used in both text-encoders
- **image** (`torch.Tensor` or `PIL.Image.Image` or `np.ndarray` or `List[torch.Tensor]` or `List[PIL.Image.Image]` or `List[np.ndarray]`) --
  The image(s) to modify with the pipeline.
- **strength** (`float`, *optional*, defaults to 0.3) --
  Conceptually, indicates how much to transform the reference `image`. Must be between 0 and 1. `image`
  will be used as a starting point, adding more noise to it the larger the `strength`. The number of
  denoising steps depends on the amount of noise initially added. When `strength` is 1, added noise will
  be maximum and the denoising process will run for the full number of iterations specified in
  `num_inference_steps`. A value of 1, therefore, essentially ignores `image`. Note that in the case of
  `denoising_start` being declared as an integer, the value of `strength` will be ignored.
- **num_inference_steps** (`int`, *optional*, defaults to 50) --
  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 with schedulers which support a `timesteps` argument
  in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is
  passed will be used. Must be in descending order.
- **sigmas** (`List[float]`, *optional*) --
  Custom sigmas to use for the denoising process with schedulers which support a `sigmas` argument in
  their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is passed
  will be used.
- **denoising_start** (`float`, *optional*) --
  When specified, indicates the fraction (between 0.0 and 1.0) of the total denoising process to be
  bypassed before it is initiated. Consequently, the initial part of the denoising process is skipped and
  it is assumed that the passed `image` is a partly denoised image. Note that when this is specified,
  strength will be ignored. The `denoising_start` parameter is particularly beneficial when this pipeline
  is integrated into a "Mixture of Denoisers" multi-pipeline setup, as detailed in [**Refine Image
  Quality**](https://huggingface.co/docs/diffusers/using-diffusers/sdxl#refine-image-quality).
- **denoising_end** (`float`, *optional*) --
  When specified, determines the fraction (between 0.0 and 1.0) of the total denoising process to be
  completed before it is intentionally prematurely terminated. As a result, the returned sample will
  still retain a substantial amount of noise (ca. final 20% of timesteps still needed) and should be
  denoised by a successor pipeline that has `denoising_start` set to 0.8 so that it only denoises the
  final 20% of the scheduler. The denoising_end parameter should ideally be utilized when this pipeline
  forms a part of a "Mixture of Denoisers" multi-pipeline setup, as elaborated in [**Refine Image
  Quality**](https://huggingface.co/docs/diffusers/using-diffusers/sdxl#refine-image-quality).
- **guidance_scale** (`float`, *optional*, defaults to 7.5) --
  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.
- **negative_prompt** (`str` or `List[str]`, *optional*) --
  The prompt or prompts not to guide the image generation. If not defined, one has to pass
  `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
  less than `1`).
- **negative_prompt_2** (`str` or `List[str]`, *optional*) --
  The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and
  `text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders
- **num_images_per_prompt** (`int`, *optional*, defaults to 1) --
  The number of images to generate per prompt.
- **eta** (`float`, *optional*, defaults to 0.0) --
  Corresponds to parameter eta (η) in the DDIM paper: https://huggingface.co/papers/2010.02502. Only
  applies to `schedulers.DDIMScheduler`, will be ignored for others.
- **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`.
- **prompt_embeds** (`torch.Tensor`, *optional*) --
  Pre-generated text embeddings. 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. 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.
- **pooled_prompt_embeds** (`torch.Tensor`, *optional*) --
  Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.
  If not provided, pooled text embeddings will be generated from `prompt` input argument.
- **negative_pooled_prompt_embeds** (`torch.Tensor`, *optional*) --
  Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
  weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt`
  input argument.
- **ip_adapter_image** -- (`PipelineImageInput`, *optional*): Optional image input to work with IP Adapters.
- **ip_adapter_image_embeds** (`List[torch.Tensor]`, *optional*) --
  Pre-generated image embeddings for IP-Adapter. It should be a list of length same as number of
  IP-adapters. Each element should be a tensor of shape `(batch_size, num_images, emb_dim)`. It should
  contain the negative image embedding if `do_classifier_free_guidance` is set to `True`. If not
  provided, embeddings are computed from the `ip_adapter_image` input argument.
- **output_type** (`str`, *optional*, defaults to `"pil"`) --
  The output format of the generate image. Choose between
  [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
- **return_dict** (`bool`, *optional*, defaults to `True`) --
  Whether or not to return a `diffusers.pipelines.stable_diffusion.StableDiffusionXLPipelineOutput` instead of a
  plain tuple.
- **cross_attention_kwargs** (`dict`, *optional*) --
  A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
  `self.processor` in
  [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
- **guidance_rescale** (`float`, *optional*, defaults to 0.0) --
  Guidance rescale factor proposed by [Common Diffusion Noise Schedules and Sample Steps are
  Flawed](https://huggingface.co/papers/2305.08891) `guidance_scale` is defined as `φ` in equation 16. of
  [Common Diffusion Noise Schedules and Sample Steps are
  Flawed](https://huggingface.co/papers/2305.08891). Guidance rescale factor should fix overexposure when
  using zero terminal SNR.
- **original_size** (`Tuple[int]`, *optional*, defaults to (1024, 1024)) --
  If `original_size` is not the same as `target_size` the image will appear to be down- or upsampled.
  `original_size` defaults to `(height, width)` if not specified. Part of SDXL's micro-conditioning as
  explained in section 2.2 of
  [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
- **crops_coords_top_left** (`Tuple[int]`, *optional*, defaults to (0, 0)) --
  `crops_coords_top_left` can be used to generate an image that appears to be "cropped" from the position
  `crops_coords_top_left` downwards. Favorable, well-centered images are usually achieved by setting
  `crops_coords_top_left` to (0, 0). Part of SDXL's micro-conditioning as explained in section 2.2 of
  [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
- **target_size** (`Tuple[int]`, *optional*, defaults to (1024, 1024)) --
  For most cases, `target_size` should be set to the desired height and width of the generated image. If
  not specified it will default to `(height, width)`. Part of SDXL's micro-conditioning as explained in
  section 2.2 of [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
- **negative_original_size** (`Tuple[int]`, *optional*, defaults to (1024, 1024)) --
  To negatively condition the generation process based on a specific image resolution. Part of SDXL's
  micro-conditioning as explained in section 2.2 of
  [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more
  information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
- **negative_crops_coords_top_left** (`Tuple[int]`, *optional*, defaults to (0, 0)) --
  To negatively condition the generation process based on a specific crop coordinates. Part of SDXL's
  micro-conditioning as explained in section 2.2 of
  [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more
  information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
- **negative_target_size** (`Tuple[int]`, *optional*, defaults to (1024, 1024)) --
  To negatively condition the generation process based on a target image resolution. It should be as same
  as the `target_size` for most cases. Part of SDXL's micro-conditioning as explained in section 2.2 of
  [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more
  information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
- **aesthetic_score** (`float`, *optional*, defaults to 6.0) --
  Used to simulate an aesthetic score of the generated image by influencing the positive text condition.
  Part of SDXL's micro-conditioning as explained in section 2.2 of
  [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
- **negative_aesthetic_score** (`float`, *optional*, defaults to 2.5) --
  Part of SDXL's micro-conditioning as explained in section 2.2 of
  [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). Can be used to
  simulate an aesthetic score of the generated image by influencing the negative text condition.
- **clip_skip** (`int`, *optional*) --
  Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
  the output of the pre-final layer will be used for computing the prompt embeddings.
- **callback_on_step_end** (`Callable`, `PipelineCallback`, `MultiPipelineCallbacks`, *optional*) --
  A function or a subclass of `PipelineCallback` or `MultiPipelineCallbacks` that is called at the end of
  each denoising step during the inference. 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.</paramsdesc><paramgroups>0</paramgroups><rettype>`diffusers.pipelines.stable_diffusion.StableDiffusionXLPipelineOutput` or `tuple`</rettype><retdesc>`diffusers.pipelines.stable_diffusion.StableDiffusionXLPipelineOutput` if `return_dict` is True, otherwise a
`tuple. When returning a tuple, the first element is a list with the generated images.</retdesc></docstring>

Function invoked when calling the pipeline for generation.



<ExampleCodeBlock anchor="optimum.onnxruntime.ORTStableDiffusionXLImg2ImgPipeline.__call__.example">

Examples:
```py
>>> import torch
>>> from optimum.onnxruntime import ORTStableDiffusionXLImg2ImgPipeline
>>> from diffusers.utils import load_image

>>> pipe = ORTStableDiffusionXLImg2ImgPipeline.from_pretrained(
...     "stabilityai/stable-diffusion-xl-refiner-1.0", torch_dtype=torch.float16
... )
>>> pipe = pipe.to("cuda")
>>> url = "https://huggingface.co/datasets/patrickvonplaten/images/resolve/main/aa_xl/000000009.png"

>>> init_image = load_image(url).convert("RGB")
>>> prompt = "a photo of an astronaut riding a horse on mars"
>>> image = pipe(prompt, image=init_image).images[0]
```

</ExampleCodeBlock>







</div></div>

### ORTLatentConsistencyModelPipeline[[optimum.onnxruntime.ORTLatentConsistencyModelPipeline]]

<div class="docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8">


<docstring><name>class optimum.onnxruntime.ORTLatentConsistencyModelPipeline</name><anchor>optimum.onnxruntime.ORTLatentConsistencyModelPipeline</anchor><source>https://github.com/huggingface/optimum-onnx/blob/v0.0.2/optimum/onnxruntime/modeling_diffusion.py#L1033</source><parameters>[{"name": "unet_session", "val": ": InferenceSession | None = None"}, {"name": "transformer_session", "val": ": InferenceSession | None = None"}, {"name": "vae_decoder_session", "val": ": InferenceSession | None = None"}, {"name": "vae_encoder_session", "val": ": InferenceSession | None = None"}, {"name": "text_encoder_session", "val": ": InferenceSession | None = None"}, {"name": "text_encoder_2_session", "val": ": InferenceSession | None = None"}, {"name": "text_encoder_3_session", "val": ": InferenceSession | None = None"}, {"name": "scheduler", "val": ": SchedulerMixin | None = None"}, {"name": "tokenizer", "val": ": CLIPTokenizer | None = None"}, {"name": "tokenizer_2", "val": ": CLIPTokenizer | None = None"}, {"name": "tokenizer_3", "val": ": CLIPTokenizer | None = None"}, {"name": "feature_extractor", "val": ": CLIPFeatureExtractor | None = None"}, {"name": "force_zeros_for_empty_prompt", "val": ": bool = True"}, {"name": "requires_aesthetics_score", "val": ": bool = False"}, {"name": "add_watermarker", "val": ": bool | None = None"}, {"name": "use_io_binding", "val": ": bool | None = None"}, {"name": "model_save_dir", "val": ": str | Path | TemporaryDirectory | None = None"}, {"name": "**kwargs", "val": ""}]</parameters></docstring>
ONNX Runtime-powered Pipeline for text-to-image generation using a Latent Consistency Model and corresponding to [LatentConsistencyModelPipeline]
(https://huggingface.co/docs/diffusers/api/pipelines/latent_consistency_models#diffusers.LatentConsistencyModelPipeline).

This Pipeline inherits from `ORTDiffusionPipeline` and is used to run inference with the ONNX Runtime.
The pipeline can be loaded from a pretrained pipeline using the generic `ORTDiffusionPipeline.from_pretrained` method.



<div class="docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8">


<docstring><name>__call__</name><anchor>optimum.onnxruntime.ORTLatentConsistencyModelPipeline.__call__</anchor><source>https://github.com/huggingface/optimum-onnx/blob/v0.0.2/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_text2img.py#L640</source><parameters>[{"name": "prompt", "val": ": typing.Union[str, typing.List[str]] = None"}, {"name": "height", "val": ": typing.Optional[int] = None"}, {"name": "width", "val": ": typing.Optional[int] = None"}, {"name": "num_inference_steps", "val": ": int = 4"}, {"name": "original_inference_steps", "val": ": int = None"}, {"name": "timesteps", "val": ": typing.List[int] = None"}, {"name": "guidance_scale", "val": ": float = 8.5"}, {"name": "num_images_per_prompt", "val": ": typing.Optional[int] = 1"}, {"name": "generator", "val": ": typing.Union[torch._C.Generator, typing.List[torch._C.Generator], NoneType] = None"}, {"name": "latents", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "prompt_embeds", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "ip_adapter_image", "val": ": typing.Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, typing.List[PIL.Image.Image], typing.List[numpy.ndarray], typing.List[torch.Tensor], NoneType] = None"}, {"name": "ip_adapter_image_embeds", "val": ": typing.Optional[typing.List[torch.Tensor]] = None"}, {"name": "output_type", "val": ": typing.Optional[str] = 'pil'"}, {"name": "return_dict", "val": ": bool = True"}, {"name": "cross_attention_kwargs", "val": ": typing.Optional[typing.Dict[str, typing.Any]] = None"}, {"name": "clip_skip", "val": ": typing.Optional[int] = None"}, {"name": "callback_on_step_end", "val": ": typing.Optional[typing.Callable[[int, int, typing.Dict], NoneType]] = None"}, {"name": "callback_on_step_end_tensor_inputs", "val": ": typing.List[str] = ['latents']"}, {"name": "**kwargs", "val": ""}]</parameters><paramsdesc>- **prompt** (`str` or `List[str]`, *optional*) --
  The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.
- **height** (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`) --
  The height in pixels of the generated image.
- **width** (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`) --
  The width in pixels of the generated image.
- **num_inference_steps** (`int`, *optional*, defaults to 50) --
  The number of denoising steps. More denoising steps usually lead to a higher quality image at the
  expense of slower inference.
- **original_inference_steps** (`int`, *optional*) --
  The original number of inference steps use to generate a linearly-spaced timestep schedule, from which
  we will draw `num_inference_steps` evenly spaced timesteps from as our final timestep schedule,
  following the Skipping-Step method in the paper (see Section 4.3). If not set this will default to the
  scheduler's `original_inference_steps` attribute.
- **timesteps** (`List[int]`, *optional*) --
  Custom timesteps to use for the denoising process. If not defined, equal spaced `num_inference_steps`
  timesteps on the original LCM training/distillation timestep schedule are used. Must be in descending
  order.
- **guidance_scale** (`float`, *optional*, defaults to 7.5) --
  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`.
  Note that the original latent consistency models paper uses a different CFG formulation where the
  guidance scales are decreased by 1 (so in the paper formulation CFG is enabled when `guidance_scale >
  0`).
- **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*) --
  A [`torch.Generator`](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 is generated by sampling using the supplied random `generator`.
- **prompt_embeds** (`torch.Tensor`, *optional*) --
  Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
  provided, text embeddings are generated from the `prompt` input argument.
- **ip_adapter_image** -- (`PipelineImageInput`, *optional*):
  Optional image input to work with IP Adapters.
- **ip_adapter_image_embeds** (`List[torch.Tensor]`, *optional*) --
  Pre-generated image embeddings for IP-Adapter. It should be a list of length same as number of
  IP-adapters. Each element should be a tensor of shape `(batch_size, num_images, emb_dim)`. It should
  contain the negative image embedding if `do_classifier_free_guidance` is set to `True`. If not
  provided, embeddings are computed from the `ip_adapter_image` input argument.
- **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 `diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput` instead of a
  plain tuple.
- **cross_attention_kwargs** (`dict`, *optional*) --
  A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined in
  [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
- **clip_skip** (`int`, *optional*) --
  Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
  the output of the pre-final layer will be used for computing the prompt embeddings.
- **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.</paramsdesc><paramgroups>0</paramgroups><rettype>`diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput` or `tuple`</rettype><retdesc>If `return_dict` is `True`, `diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput` is returned,
otherwise a `tuple` is returned where the first element is a list with the generated images and the
second element is a list of `bool`s indicating whether the corresponding generated image contains
"not-safe-for-work" (nsfw) content.</retdesc></docstring>

The call function to the pipeline for generation.



<ExampleCodeBlock anchor="optimum.onnxruntime.ORTLatentConsistencyModelPipeline.__call__.example">

Examples:
```py
>>> from optimum.onnxruntime import ORTDiffusionPipeline
>>> import torch

>>> pipe = ORTDiffusionPipeline.from_pretrained("SimianLuo/LCM_Dreamshaper_v7")
>>> # To save GPU memory, torch.float16 can be used, but it may compromise image quality.
>>> pipe.to(torch_device="cuda", torch_dtype=torch.float32)

>>> prompt = "Self-portrait oil painting, a beautiful cyborg with golden hair, 8k"

>>> # Can be set to 1~50 steps. LCM support fast inference even <= 4 steps. Recommend: 1~8 steps.
>>> num_inference_steps = 4
>>> images = pipe(prompt=prompt, num_inference_steps=num_inference_steps, guidance_scale=8.0).images
>>> images[0].save("image.png")
```

</ExampleCodeBlock>







</div></div>

### ORTLatentConsistencyModelImg2ImgPipeline[[optimum.onnxruntime.ORTLatentConsistencyModelImg2ImgPipeline]]

<div class="docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8">


<docstring><name>class optimum.onnxruntime.ORTLatentConsistencyModelImg2ImgPipeline</name><anchor>optimum.onnxruntime.ORTLatentConsistencyModelImg2ImgPipeline</anchor><source>https://github.com/huggingface/optimum-onnx/blob/v0.0.2/optimum/onnxruntime/modeling_diffusion.py#L1044</source><parameters>[{"name": "unet_session", "val": ": InferenceSession | None = None"}, {"name": "transformer_session", "val": ": InferenceSession | None = None"}, {"name": "vae_decoder_session", "val": ": InferenceSession | None = None"}, {"name": "vae_encoder_session", "val": ": InferenceSession | None = None"}, {"name": "text_encoder_session", "val": ": InferenceSession | None = None"}, {"name": "text_encoder_2_session", "val": ": InferenceSession | None = None"}, {"name": "text_encoder_3_session", "val": ": InferenceSession | None = None"}, {"name": "scheduler", "val": ": SchedulerMixin | None = None"}, {"name": "tokenizer", "val": ": CLIPTokenizer | None = None"}, {"name": "tokenizer_2", "val": ": CLIPTokenizer | None = None"}, {"name": "tokenizer_3", "val": ": CLIPTokenizer | None = None"}, {"name": "feature_extractor", "val": ": CLIPFeatureExtractor | None = None"}, {"name": "force_zeros_for_empty_prompt", "val": ": bool = True"}, {"name": "requires_aesthetics_score", "val": ": bool = False"}, {"name": "add_watermarker", "val": ": bool | None = None"}, {"name": "use_io_binding", "val": ": bool | None = None"}, {"name": "model_save_dir", "val": ": str | Path | TemporaryDirectory | None = None"}, {"name": "**kwargs", "val": ""}]</parameters></docstring>
ONNX Runtime-powered Pipeline for text-guided image-to-image generation using a Latent Consistency Model and corresponding to [LatentConsistencyModelImg2ImgPipeline]
(https://huggingface.co/docs/diffusers/api/pipelines/latent_consistency_models#diffusers.LatentConsistencyModelImg2ImgPipeline).

This Pipeline inherits from `ORTDiffusionPipeline` and is used to run inference with the ONNX Runtime.
The pipeline can be loaded from a pretrained pipeline using the generic `ORTDiffusionPipeline.from_pretrained` method.



<div class="docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8">


<docstring><name>__call__</name><anchor>optimum.onnxruntime.ORTLatentConsistencyModelImg2ImgPipeline.__call__</anchor><source>https://github.com/huggingface/optimum-onnx/blob/v0.0.2/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_img2img.py#L709</source><parameters>[{"name": "prompt", "val": ": typing.Union[str, typing.List[str]] = None"}, {"name": "image", "val": ": typing.Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, typing.List[PIL.Image.Image], typing.List[numpy.ndarray], typing.List[torch.Tensor]] = None"}, {"name": "num_inference_steps", "val": ": int = 4"}, {"name": "strength", "val": ": float = 0.8"}, {"name": "original_inference_steps", "val": ": int = None"}, {"name": "timesteps", "val": ": typing.List[int] = None"}, {"name": "guidance_scale", "val": ": float = 8.5"}, {"name": "num_images_per_prompt", "val": ": typing.Optional[int] = 1"}, {"name": "generator", "val": ": typing.Union[torch._C.Generator, typing.List[torch._C.Generator], NoneType] = None"}, {"name": "latents", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "prompt_embeds", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "ip_adapter_image", "val": ": typing.Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, typing.List[PIL.Image.Image], typing.List[numpy.ndarray], typing.List[torch.Tensor], NoneType] = None"}, {"name": "ip_adapter_image_embeds", "val": ": typing.Optional[typing.List[torch.Tensor]] = None"}, {"name": "output_type", "val": ": typing.Optional[str] = 'pil'"}, {"name": "return_dict", "val": ": bool = True"}, {"name": "cross_attention_kwargs", "val": ": typing.Optional[typing.Dict[str, typing.Any]] = None"}, {"name": "clip_skip", "val": ": typing.Optional[int] = None"}, {"name": "callback_on_step_end", "val": ": typing.Optional[typing.Callable[[int, int, typing.Dict], NoneType]] = None"}, {"name": "callback_on_step_end_tensor_inputs", "val": ": typing.List[str] = ['latents']"}, {"name": "**kwargs", "val": ""}]</parameters><paramsdesc>- **prompt** (`str` or `List[str]`, *optional*) --
  The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.
- **height** (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`) --
  The height in pixels of the generated image.
- **width** (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`) --
  The width in pixels of the generated image.
- **num_inference_steps** (`int`, *optional*, defaults to 50) --
  The number of denoising steps. More denoising steps usually lead to a higher quality image at the
  expense of slower inference.
- **original_inference_steps** (`int`, *optional*) --
  The original number of inference steps use to generate a linearly-spaced timestep schedule, from which
  we will draw `num_inference_steps` evenly spaced timesteps from as our final timestep schedule,
  following the Skipping-Step method in the paper (see Section 4.3). If not set this will default to the
  scheduler's `original_inference_steps` attribute.
- **timesteps** (`List[int]`, *optional*) --
  Custom timesteps to use for the denoising process. If not defined, equal spaced `num_inference_steps`
  timesteps on the original LCM training/distillation timestep schedule are used. Must be in descending
  order.
- **guidance_scale** (`float`, *optional*, defaults to 7.5) --
  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`.
  Note that the original latent consistency models paper uses a different CFG formulation where the
  guidance scales are decreased by 1 (so in the paper formulation CFG is enabled when `guidance_scale >
  0`).
- **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*) --
  A [`torch.Generator`](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 is generated by sampling using the supplied random `generator`.
- **prompt_embeds** (`torch.Tensor`, *optional*) --
  Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
  provided, text embeddings are generated from the `prompt` input argument.
- **ip_adapter_image** -- (`PipelineImageInput`, *optional*):
  Optional image input to work with IP Adapters.
- **ip_adapter_image_embeds** (`List[torch.Tensor]`, *optional*) --
  Pre-generated image embeddings for IP-Adapter. It should be a list of length same as number of
  IP-adapters. Each element should be a tensor of shape `(batch_size, num_images, emb_dim)`. It should
  contain the negative image embedding if `do_classifier_free_guidance` is set to `True`. If not
  provided, embeddings are computed from the `ip_adapter_image` input argument.
- **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 `diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput` instead of a
  plain tuple.
- **cross_attention_kwargs** (`dict`, *optional*) --
  A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined in
  [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
- **clip_skip** (`int`, *optional*) --
  Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
  the output of the pre-final layer will be used for computing the prompt embeddings.
- **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.</paramsdesc><paramgroups>0</paramgroups><rettype>`diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput` or `tuple`</rettype><retdesc>If `return_dict` is `True`, `diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput` is returned,
otherwise a `tuple` is returned where the first element is a list with the generated images and the
second element is a list of `bool`s indicating whether the corresponding generated image contains
"not-safe-for-work" (nsfw) content.</retdesc></docstring>

The call function to the pipeline for generation.



<ExampleCodeBlock anchor="optimum.onnxruntime.ORTLatentConsistencyModelImg2ImgPipeline.__call__.example">

Examples:
```py
>>> from optimum.onnxruntime import ORTPipelineForImage2Image
>>> import torch
>>> import PIL

>>> pipe = ORTPipelineForImage2Image.from_pretrained("SimianLuo/LCM_Dreamshaper_v7")
>>> # To save GPU memory, torch.float16 can be used, but it may compromise image quality.
>>> pipe.to(torch_device="cuda", torch_dtype=torch.float32)

>>> prompt = "High altitude snowy mountains"
>>> image = PIL.Image.open("./snowy_mountains.png")

>>> # Can be set to 1~50 steps. LCM support fast inference even <= 4 steps. Recommend: 1~8 steps.
>>> num_inference_steps = 4
>>> images = pipe(
...     prompt=prompt, image=image, num_inference_steps=num_inference_steps, guidance_scale=8.0
... ).images

>>> images[0].save("image.png")
```

</ExampleCodeBlock>








</div></div>

### ORTStableDiffusion3Pipeline[[optimum.onnxruntime.ORTStableDiffusion3Pipeline]]

<div class="docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8">


<docstring><name>class optimum.onnxruntime.ORTStableDiffusion3Pipeline</name><anchor>optimum.onnxruntime.ORTStableDiffusion3Pipeline</anchor><source>https://github.com/huggingface/optimum-onnx/blob/v0.0.2/optimum/onnxruntime/modeling_diffusion.py#L1067</source><parameters>[{"name": "unet_session", "val": ": InferenceSession | None = None"}, {"name": "transformer_session", "val": ": InferenceSession | None = None"}, {"name": "vae_decoder_session", "val": ": InferenceSession | None = None"}, {"name": "vae_encoder_session", "val": ": InferenceSession | None = None"}, {"name": "text_encoder_session", "val": ": InferenceSession | None = None"}, {"name": "text_encoder_2_session", "val": ": InferenceSession | None = None"}, {"name": "text_encoder_3_session", "val": ": InferenceSession | None = None"}, {"name": "scheduler", "val": ": SchedulerMixin | None = None"}, {"name": "tokenizer", "val": ": CLIPTokenizer | None = None"}, {"name": "tokenizer_2", "val": ": CLIPTokenizer | None = None"}, {"name": "tokenizer_3", "val": ": CLIPTokenizer | None = None"}, {"name": "feature_extractor", "val": ": CLIPFeatureExtractor | None = None"}, {"name": "force_zeros_for_empty_prompt", "val": ": bool = True"}, {"name": "requires_aesthetics_score", "val": ": bool = False"}, {"name": "add_watermarker", "val": ": bool | None = None"}, {"name": "use_io_binding", "val": ": bool | None = None"}, {"name": "model_save_dir", "val": ": str | Path | TemporaryDirectory | None = None"}, {"name": "**kwargs", "val": ""}]</parameters></docstring>
ONNX Runtime-powered Pipeline for text-to-image generation using Stable Diffusion 3 and corresponding to [StableDiffusion3Pipeline](https://huggingface.co/docs/diffusers/en/api/pipelines/stable_diffusion/stable_diffusion_3#diffusers.StableDiffusion3Pipeline).
This Pipeline inherits from `ORTDiffusionPipeline` and is used to run inference with the ONNX Runtime.
The pipeline can be loaded from a pretrained pipeline using the generic `ORTDiffusionPipeline.from_pretrained` method.



<div class="docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8">


<docstring><name>__call__</name><anchor>optimum.onnxruntime.ORTStableDiffusion3Pipeline.__call__</anchor><source>https://github.com/huggingface/optimum-onnx/blob/v0.0.2/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3.py#L772</source><parameters>[{"name": "prompt", "val": ": typing.Union[str, typing.List[str]] = None"}, {"name": "prompt_2", "val": ": typing.Union[str, typing.List[str], NoneType] = None"}, {"name": "prompt_3", "val": ": typing.Union[str, typing.List[str], NoneType] = None"}, {"name": "height", "val": ": typing.Optional[int] = None"}, {"name": "width", "val": ": typing.Optional[int] = None"}, {"name": "num_inference_steps", "val": ": int = 28"}, {"name": "sigmas", "val": ": typing.Optional[typing.List[float]] = None"}, {"name": "guidance_scale", "val": ": float = 7.0"}, {"name": "negative_prompt", "val": ": typing.Union[str, typing.List[str], NoneType] = None"}, {"name": "negative_prompt_2", "val": ": typing.Union[str, typing.List[str], NoneType] = None"}, {"name": "negative_prompt_3", "val": ": typing.Union[str, typing.List[str], NoneType] = None"}, {"name": "num_images_per_prompt", "val": ": typing.Optional[int] = 1"}, {"name": "generator", "val": ": typing.Union[torch._C.Generator, typing.List[torch._C.Generator], NoneType] = None"}, {"name": "latents", "val": ": typing.Optional[torch.FloatTensor] = None"}, {"name": "prompt_embeds", "val": ": typing.Optional[torch.FloatTensor] = None"}, {"name": "negative_prompt_embeds", "val": ": typing.Optional[torch.FloatTensor] = None"}, {"name": "pooled_prompt_embeds", "val": ": typing.Optional[torch.FloatTensor] = None"}, {"name": "negative_pooled_prompt_embeds", "val": ": typing.Optional[torch.FloatTensor] = None"}, {"name": "ip_adapter_image", "val": ": typing.Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, typing.List[PIL.Image.Image], typing.List[numpy.ndarray], typing.List[torch.Tensor], NoneType] = None"}, {"name": "ip_adapter_image_embeds", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "output_type", "val": ": typing.Optional[str] = 'pil'"}, {"name": "return_dict", "val": ": bool = True"}, {"name": "joint_attention_kwargs", "val": ": typing.Optional[typing.Dict[str, typing.Any]] = None"}, {"name": "clip_skip", "val": ": typing.Optional[int] = None"}, {"name": "callback_on_step_end", "val": ": typing.Optional[typing.Callable[[int, int, typing.Dict], NoneType]] = None"}, {"name": "callback_on_step_end_tensor_inputs", "val": ": typing.List[str] = ['latents']"}, {"name": "max_sequence_length", "val": ": int = 256"}, {"name": "skip_guidance_layers", "val": ": typing.List[int] = None"}, {"name": "skip_layer_guidance_scale", "val": ": float = 2.8"}, {"name": "skip_layer_guidance_stop", "val": ": float = 0.2"}, {"name": "skip_layer_guidance_start", "val": ": float = 0.01"}, {"name": "mu", "val": ": typing.Optional[float] = None"}]</parameters><paramsdesc>- **prompt** (`str` or `List[str]`, *optional*) --
  The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
  instead.
- **prompt_2** (`str` or `List[str]`, *optional*) --
  The prompt or prompts to be sent to `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is
  will be used instead
- **prompt_3** (`str` or `List[str]`, *optional*) --
  The prompt or prompts to be sent to `tokenizer_3` and `text_encoder_3`. If not defined, `prompt` is
  will be used instead
- **height** (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor) --
  The height in pixels of the generated image. This is set to 1024 by default for the best results.
- **width** (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor) --
  The width in pixels of the generated image. This is set to 1024 by default for the best results.
- **num_inference_steps** (`int`, *optional*, defaults to 50) --
  The number of denoising steps. More denoising steps usually lead to a higher quality image at the
  expense of slower inference.
- **sigmas** (`List[float]`, *optional*) --
  Custom sigmas to use for the denoising process with schedulers which support a `sigmas` argument in
  their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is passed
  will be used.
- **guidance_scale** (`float`, *optional*, defaults to 7.0) --
  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.
- **negative_prompt** (`str` or `List[str]`, *optional*) --
  The prompt or prompts not to guide the image generation. If not defined, one has to pass
  `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
  less than `1`).
- **negative_prompt_2** (`str` or `List[str]`, *optional*) --
  The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and
  `text_encoder_2`. If not defined, `negative_prompt` is used instead
- **negative_prompt_3** (`str` or `List[str]`, *optional*) --
  The prompt or prompts not to guide the image generation to be sent to `tokenizer_3` and
  `text_encoder_3`. If not defined, `negative_prompt` is used instead
- **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.FloatTensor`, *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`.
- **prompt_embeds** (`torch.FloatTensor`, *optional*) --
  Pre-generated text embeddings. 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.FloatTensor`, *optional*) --
  Pre-generated negative text embeddings. 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.
- **pooled_prompt_embeds** (`torch.FloatTensor`, *optional*) --
  Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.
  If not provided, pooled text embeddings will be generated from `prompt` input argument.
- **negative_pooled_prompt_embeds** (`torch.FloatTensor`, *optional*) --
  Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
  weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt`
  input argument.
- **ip_adapter_image** (`PipelineImageInput`, *optional*) --
  Optional image input to work with IP Adapters.
- **ip_adapter_image_embeds** (`torch.Tensor`, *optional*) --
  Pre-generated image embeddings for IP-Adapter. Should be a tensor of shape `(batch_size, num_images,
  emb_dim)`. It should contain the negative image embedding if `do_classifier_free_guidance` is set to
  `True`. If not provided, embeddings are computed from the `ip_adapter_image` input argument.
- **output_type** (`str`, *optional*, defaults to `"pil"`) --
  The output format of the generate image. Choose between
  [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
- **return_dict** (`bool`, *optional*, defaults to `True`) --
  Whether or not to return a `diffusers.pipelines.stable_diffusion_3.StableDiffusion3PipelineOutput` instead of
  a plain tuple.
- **joint_attention_kwargs** (`dict`, *optional*) --
  A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
  `self.processor` in
  [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
- **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.
- **max_sequence_length** (`int` defaults to 256) -- Maximum sequence length to use with the `prompt`.
- **skip_guidance_layers** (`List[int]`, *optional*) --
  A list of integers that specify layers to skip during guidance. If not provided, all layers will be
  used for guidance. If provided, the guidance will only be applied to the layers specified in the list.
  Recommended value by StabiltyAI for Stable Diffusion 3.5 Medium is [7, 8, 9].
- **skip_layer_guidance_scale** (`int`, *optional*) -- The scale of the guidance for the layers specified in
  `skip_guidance_layers`. The guidance will be applied to the layers specified in `skip_guidance_layers`
  with a scale of `skip_layer_guidance_scale`. The guidance will be applied to the rest of the layers
  with a scale of `1`.
- **skip_layer_guidance_stop** (`int`, *optional*) -- The step at which the guidance for the layers specified in
  `skip_guidance_layers` will stop. The guidance will be applied to the layers specified in
  `skip_guidance_layers` until the fraction specified in `skip_layer_guidance_stop`. Recommended value by
  StabiltyAI for Stable Diffusion 3.5 Medium is 0.2.
- **skip_layer_guidance_start** (`int`, *optional*) -- The step at which the guidance for the layers specified in
  `skip_guidance_layers` will start. The guidance will be applied to the layers specified in
  `skip_guidance_layers` from the fraction specified in `skip_layer_guidance_start`. Recommended value by
  StabiltyAI for Stable Diffusion 3.5 Medium is 0.01.
- **mu** (`float`, *optional*) -- `mu` value used for `dynamic_shifting`.</paramsdesc><paramgroups>0</paramgroups><rettype>`diffusers.pipelines.stable_diffusion_3.StableDiffusion3PipelineOutput` or `tuple`</rettype><retdesc>`diffusers.pipelines.stable_diffusion_3.StableDiffusion3PipelineOutput` if `return_dict` is True, otherwise a
`tuple`. When returning a tuple, the first element is a list with the generated images.</retdesc></docstring>

Function invoked when calling the pipeline for generation.



<ExampleCodeBlock anchor="optimum.onnxruntime.ORTStableDiffusion3Pipeline.__call__.example">

Examples:
```py
>>> import torch
>>> from optimum.onnxruntime import ORTStableDiffusion3Pipeline

>>> pipe = ORTStableDiffusion3Pipeline.from_pretrained(
...     "stabilityai/stable-diffusion-3-medium-diffusers", torch_dtype=torch.float16
... )
>>> pipe.to("cuda")
>>> prompt = "A cat holding a sign that says hello world"
>>> image = pipe(prompt).images[0]
>>> image.save("sd3.png")
```

</ExampleCodeBlock>







</div></div>

### ORTStableDiffusion3Img2ImgPipeline[[optimum.onnxruntime.ORTStableDiffusion3Img2ImgPipeline]]

<div class="docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8">


<docstring><name>class optimum.onnxruntime.ORTStableDiffusion3Img2ImgPipeline</name><anchor>optimum.onnxruntime.ORTStableDiffusion3Img2ImgPipeline</anchor><source>https://github.com/huggingface/optimum-onnx/blob/v0.0.2/optimum/onnxruntime/modeling_diffusion.py#L1075</source><parameters>[{"name": "unet_session", "val": ": InferenceSession | None = None"}, {"name": "transformer_session", "val": ": InferenceSession | None = None"}, {"name": "vae_decoder_session", "val": ": InferenceSession | None = None"}, {"name": "vae_encoder_session", "val": ": InferenceSession | None = None"}, {"name": "text_encoder_session", "val": ": InferenceSession | None = None"}, {"name": "text_encoder_2_session", "val": ": InferenceSession | None = None"}, {"name": "text_encoder_3_session", "val": ": InferenceSession | None = None"}, {"name": "scheduler", "val": ": SchedulerMixin | None = None"}, {"name": "tokenizer", "val": ": CLIPTokenizer | None = None"}, {"name": "tokenizer_2", "val": ": CLIPTokenizer | None = None"}, {"name": "tokenizer_3", "val": ": CLIPTokenizer | None = None"}, {"name": "feature_extractor", "val": ": CLIPFeatureExtractor | None = None"}, {"name": "force_zeros_for_empty_prompt", "val": ": bool = True"}, {"name": "requires_aesthetics_score", "val": ": bool = False"}, {"name": "add_watermarker", "val": ": bool | None = None"}, {"name": "use_io_binding", "val": ": bool | None = None"}, {"name": "model_save_dir", "val": ": str | Path | TemporaryDirectory | None = None"}, {"name": "**kwargs", "val": ""}]</parameters></docstring>
ONNX Runtime-powered Pipeline for text-guided image-to-image generation using Stable Diffusion 3 and corresponding to [StableDiffusion3Img2ImgPipeline](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/stable_diffusion_3#diffusers.StableDiffusion3Img2ImgPipeline).
This Pipeline inherits from `ORTDiffusionPipeline` and is used to run inference with the ONNX Runtime.
The pipeline can be loaded from a pretrained pipeline using the generic `ORTDiffusionPipeline.from_pretrained` method.



<div class="docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8">


<docstring><name>__call__</name><anchor>optimum.onnxruntime.ORTStableDiffusion3Img2ImgPipeline.__call__</anchor><source>https://github.com/huggingface/optimum-onnx/blob/v0.0.2/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3_img2img.py#L829</source><parameters>[{"name": "prompt", "val": ": typing.Union[str, typing.List[str]] = None"}, {"name": "prompt_2", "val": ": typing.Union[str, typing.List[str], NoneType] = None"}, {"name": "prompt_3", "val": ": typing.Union[str, typing.List[str], NoneType] = None"}, {"name": "height", "val": ": typing.Optional[int] = None"}, {"name": "width", "val": ": typing.Optional[int] = None"}, {"name": "image", "val": ": typing.Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, typing.List[PIL.Image.Image], typing.List[numpy.ndarray], typing.List[torch.Tensor]] = None"}, {"name": "strength", "val": ": float = 0.6"}, {"name": "num_inference_steps", "val": ": int = 50"}, {"name": "sigmas", "val": ": typing.Optional[typing.List[float]] = None"}, {"name": "guidance_scale", "val": ": float = 7.0"}, {"name": "negative_prompt", "val": ": typing.Union[str, typing.List[str], NoneType] = None"}, {"name": "negative_prompt_2", "val": ": typing.Union[str, typing.List[str], NoneType] = None"}, {"name": "negative_prompt_3", "val": ": typing.Union[str, typing.List[str], NoneType] = None"}, {"name": "num_images_per_prompt", "val": ": typing.Optional[int] = 1"}, {"name": "generator", "val": ": typing.Union[torch._C.Generator, typing.List[torch._C.Generator], NoneType] = None"}, {"name": "latents", "val": ": typing.Optional[torch.FloatTensor] = None"}, {"name": "prompt_embeds", "val": ": typing.Optional[torch.FloatTensor] = None"}, {"name": "negative_prompt_embeds", "val": ": typing.Optional[torch.FloatTensor] = None"}, {"name": "pooled_prompt_embeds", "val": ": typing.Optional[torch.FloatTensor] = None"}, {"name": "negative_pooled_prompt_embeds", "val": ": typing.Optional[torch.FloatTensor] = None"}, {"name": "output_type", "val": ": typing.Optional[str] = 'pil'"}, {"name": "ip_adapter_image", "val": ": typing.Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, typing.List[PIL.Image.Image], typing.List[numpy.ndarray], typing.List[torch.Tensor], NoneType] = None"}, {"name": "ip_adapter_image_embeds", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "return_dict", "val": ": bool = True"}, {"name": "joint_attention_kwargs", "val": ": typing.Optional[typing.Dict[str, typing.Any]] = None"}, {"name": "clip_skip", "val": ": typing.Optional[int] = None"}, {"name": "callback_on_step_end", "val": ": typing.Optional[typing.Callable[[int, int, typing.Dict], NoneType]] = None"}, {"name": "callback_on_step_end_tensor_inputs", "val": ": typing.List[str] = ['latents']"}, {"name": "max_sequence_length", "val": ": int = 256"}, {"name": "mu", "val": ": typing.Optional[float] = None"}]</parameters><paramsdesc>- **prompt** (`str` or `List[str]`, *optional*) --
  The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
  instead.
- **prompt_2** (`str` or `List[str]`, *optional*) --
  The prompt or prompts to be sent to `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is
  will be used instead
- **prompt_3** (`str` or `List[str]`, *optional*) --
  The prompt or prompts to be sent to `tokenizer_3` and `text_encoder_3`. If not defined, `prompt` is
  will be used instead
- **height** (`int`, *optional*, defaults to self.transformer.config.sample_size * self.vae_scale_factor) --
  The height in pixels of the generated image. This is set to 1024 by default for the best results.
- **width** (`int`, *optional*, defaults to self.transformer.config.sample_size * self.vae_scale_factor) --
  The width in pixels of the generated image. This is set to 1024 by default for the best results.
- **num_inference_steps** (`int`, *optional*, defaults to 50) --
  The number of denoising steps. More denoising steps usually lead to a higher quality image at the
  expense of slower inference.
- **sigmas** (`List[float]`, *optional*) --
  Custom sigmas to use for the denoising process with schedulers which support a `sigmas` argument in
  their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is passed
  will be used.
- **guidance_scale** (`float`, *optional*, defaults to 7.0) --
  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.
- **negative_prompt** (`str` or `List[str]`, *optional*) --
  The prompt or prompts not to guide the image generation. If not defined, one has to pass
  `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
  less than `1`).
- **negative_prompt_2** (`str` or `List[str]`, *optional*) --
  The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and
  `text_encoder_2`. If not defined, `negative_prompt` is used instead
- **negative_prompt_3** (`str` or `List[str]`, *optional*) --
  The prompt or prompts not to guide the image generation to be sent to `tokenizer_3` and
  `text_encoder_3`. If not defined, `negative_prompt` is used instead
- **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.FloatTensor`, *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`.
- **prompt_embeds** (`torch.FloatTensor`, *optional*) --
  Pre-generated text embeddings. 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.FloatTensor`, *optional*) --
  Pre-generated negative text embeddings. 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.
- **pooled_prompt_embeds** (`torch.FloatTensor`, *optional*) --
  Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.
  If not provided, pooled text embeddings will be generated from `prompt` input argument.
- **negative_pooled_prompt_embeds** (`torch.FloatTensor`, *optional*) --
  Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
  weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt`
  input argument.
- **ip_adapter_image** (`PipelineImageInput`, *optional*) --
  Optional image input to work with IP Adapters.
- **ip_adapter_image_embeds** (`torch.Tensor`, *optional*) --
  Pre-generated image embeddings for IP-Adapter. Should be a tensor of shape `(batch_size, num_images,
  emb_dim)`. It should contain the negative image embedding if `do_classifier_free_guidance` is set to
  `True`. If not provided, embeddings are computed from the `ip_adapter_image` input argument.
- **output_type** (`str`, *optional*, defaults to `"pil"`) --
  The output format of the generate image. Choose between
  [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
- **return_dict** (`bool`, *optional*, defaults to `True`) --
  Whether or not to return a `diffusers.pipelines.stable_diffusion_3.StableDiffusion3PipelineOutput` instead of
  a plain tuple.
- **joint_attention_kwargs** (`dict`, *optional*) --
  A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
  `self.processor` in
  [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
- **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.
- **max_sequence_length** (`int` defaults to 256) -- Maximum sequence length to use with the `prompt`.
- **mu** (`float`, *optional*) -- `mu` value used for `dynamic_shifting`.</paramsdesc><paramgroups>0</paramgroups><rettype>`diffusers.pipelines.stable_diffusion_3.StableDiffusion3PipelineOutput` or `tuple`</rettype><retdesc>`diffusers.pipelines.stable_diffusion_3.StableDiffusion3PipelineOutput` if `return_dict` is True, otherwise a
`tuple`. When returning a tuple, the first element is a list with the generated images.</retdesc></docstring>

Function invoked when calling the pipeline for generation.



<ExampleCodeBlock anchor="optimum.onnxruntime.ORTStableDiffusion3Img2ImgPipeline.__call__.example">

Examples:
```py
>>> import torch

>>> from optimum.onnxruntime import ORTPipelineForImage2Image
>>> from diffusers.utils import load_image

>>> device = "cuda"
>>> model_id_or_path = "stabilityai/stable-diffusion-3-medium-diffusers"
>>> pipe = ORTPipelineForImage2Image.from_pretrained(model_id_or_path, torch_dtype=torch.float16)
>>> pipe = pipe.to(device)

>>> url = "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg"
>>> init_image = load_image(url).resize((1024, 1024))

>>> prompt = "cat wizard, gandalf, lord of the rings, detailed, fantasy, cute, adorable, Pixar, Disney, 8k"

>>> images = pipe(prompt=prompt, image=init_image, strength=0.95, guidance_scale=7.5).images[0]
```

</ExampleCodeBlock>







</div></div>

### ORTStableDiffusion3InpaintPipeline[[optimum.onnxruntime.ORTStableDiffusion3InpaintPipeline]]

<div class="docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8">


<docstring><name>class optimum.onnxruntime.ORTStableDiffusion3InpaintPipeline</name><anchor>optimum.onnxruntime.ORTStableDiffusion3InpaintPipeline</anchor><source>https://github.com/huggingface/optimum-onnx/blob/v0.0.2/optimum/onnxruntime/modeling_diffusion.py#L1094</source><parameters>[{"name": "unet_session", "val": ": InferenceSession | None = None"}, {"name": "transformer_session", "val": ": InferenceSession | None = None"}, {"name": "vae_decoder_session", "val": ": InferenceSession | None = None"}, {"name": "vae_encoder_session", "val": ": InferenceSession | None = None"}, {"name": "text_encoder_session", "val": ": InferenceSession | None = None"}, {"name": "text_encoder_2_session", "val": ": InferenceSession | None = None"}, {"name": "text_encoder_3_session", "val": ": InferenceSession | None = None"}, {"name": "scheduler", "val": ": SchedulerMixin | None = None"}, {"name": "tokenizer", "val": ": CLIPTokenizer | None = None"}, {"name": "tokenizer_2", "val": ": CLIPTokenizer | None = None"}, {"name": "tokenizer_3", "val": ": CLIPTokenizer | None = None"}, {"name": "feature_extractor", "val": ": CLIPFeatureExtractor | None = None"}, {"name": "force_zeros_for_empty_prompt", "val": ": bool = True"}, {"name": "requires_aesthetics_score", "val": ": bool = False"}, {"name": "add_watermarker", "val": ": bool | None = None"}, {"name": "use_io_binding", "val": ": bool | None = None"}, {"name": "model_save_dir", "val": ": str | Path | TemporaryDirectory | None = None"}, {"name": "**kwargs", "val": ""}]</parameters></docstring>
ONNX Runtime-powered Pipeline for text-guided image inpainting using Stable Diffusion 3 and corresponding to [StableDiffusion3InpaintPipeline](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/stable_diffusion_3#diffusers.StableDiffusion3InpaintPipeline).
This Pipeline inherits from `ORTDiffusionPipeline` and is used to run inference with the ONNX Runtime.
The pipeline can be loaded from a pretrained pipeline using the generic `ORTDiffusionPipeline.from_pretrained` method.



<div class="docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8">


<docstring><name>__call__</name><anchor>optimum.onnxruntime.ORTStableDiffusion3InpaintPipeline.__call__</anchor><source>https://github.com/huggingface/optimum-onnx/blob/v0.0.2/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3_inpaint.py#L921</source><parameters>[{"name": "prompt", "val": ": typing.Union[str, typing.List[str]] = None"}, {"name": "prompt_2", "val": ": typing.Union[str, typing.List[str], NoneType] = None"}, {"name": "prompt_3", "val": ": typing.Union[str, typing.List[str], NoneType] = None"}, {"name": "image", "val": ": typing.Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, typing.List[PIL.Image.Image], typing.List[numpy.ndarray], typing.List[torch.Tensor]] = None"}, {"name": "mask_image", "val": ": typing.Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, typing.List[PIL.Image.Image], typing.List[numpy.ndarray], typing.List[torch.Tensor]] = None"}, {"name": "masked_image_latents", "val": ": typing.Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, typing.List[PIL.Image.Image], typing.List[numpy.ndarray], typing.List[torch.Tensor]] = None"}, {"name": "height", "val": ": int = None"}, {"name": "width", "val": ": int = None"}, {"name": "padding_mask_crop", "val": ": typing.Optional[int] = None"}, {"name": "strength", "val": ": float = 0.6"}, {"name": "num_inference_steps", "val": ": int = 50"}, {"name": "sigmas", "val": ": typing.Optional[typing.List[float]] = None"}, {"name": "guidance_scale", "val": ": float = 7.0"}, {"name": "negative_prompt", "val": ": typing.Union[str, typing.List[str], NoneType] = None"}, {"name": "negative_prompt_2", "val": ": typing.Union[str, typing.List[str], NoneType] = None"}, {"name": "negative_prompt_3", "val": ": typing.Union[str, typing.List[str], NoneType] = None"}, {"name": "num_images_per_prompt", "val": ": typing.Optional[int] = 1"}, {"name": "generator", "val": ": typing.Union[torch._C.Generator, typing.List[torch._C.Generator], NoneType] = None"}, {"name": "latents", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "prompt_embeds", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "negative_prompt_embeds", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "pooled_prompt_embeds", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "negative_pooled_prompt_embeds", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "ip_adapter_image", "val": ": typing.Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, typing.List[PIL.Image.Image], typing.List[numpy.ndarray], typing.List[torch.Tensor], NoneType] = None"}, {"name": "ip_adapter_image_embeds", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "output_type", "val": ": typing.Optional[str] = 'pil'"}, {"name": "return_dict", "val": ": bool = True"}, {"name": "joint_attention_kwargs", "val": ": typing.Optional[typing.Dict[str, typing.Any]] = None"}, {"name": "clip_skip", "val": ": typing.Optional[int] = None"}, {"name": "callback_on_step_end", "val": ": typing.Optional[typing.Callable[[int, int, typing.Dict], NoneType]] = None"}, {"name": "callback_on_step_end_tensor_inputs", "val": ": typing.List[str] = ['latents']"}, {"name": "max_sequence_length", "val": ": int = 256"}, {"name": "mu", "val": ": typing.Optional[float] = None"}]</parameters><paramsdesc>- **prompt** (`str` or `List[str]`, *optional*) --
  The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
  instead.
- **prompt_2** (`str` or `List[str]`, *optional*) --
  The prompt or prompts to be sent to `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is
  will be used instead
- **prompt_3** (`str` or `List[str]`, *optional*) --
  The prompt or prompts to be sent to `tokenizer_3` and `text_encoder_3`. If not defined, `prompt` is
  will be used instead
- **image** (`torch.Tensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.Tensor]`, `List[PIL.Image.Image]`, or `List[np.ndarray]`) --
  `Image`, numpy array or tensor representing an image batch to be used as the starting point. For both
  numpy array and pytorch tensor, the expected value range is between `[0, 1]` If it's a tensor or a list
  or tensors, the expected shape should be `(B, C, H, W)` or `(C, H, W)`. If it is a numpy array or a
  list of arrays, the expected shape should be `(B, H, W, C)` or `(H, W, C)` It can also accept image
  latents as `image`, but if passing latents directly it is not encoded again.
- **mask_image** (`torch.Tensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.Tensor]`, `List[PIL.Image.Image]`, or `List[np.ndarray]`) --
  `Image`, numpy array or tensor representing an image batch to mask `image`. White pixels in the mask
  are repainted while black pixels are preserved. If `mask_image` is a PIL image, it is converted to a
  single channel (luminance) before use. If it's a numpy array or pytorch tensor, it should contain one
  color channel (L) instead of 3, so the expected shape for pytorch tensor would be `(B, 1, H, W)`, `(B,
  H, W)`, `(1, H, W)`, `(H, W)`. And for numpy array would be for `(B, H, W, 1)`, `(B, H, W)`, `(H, W,
  1)`, or `(H, W)`.
- **mask_image_latent** (`torch.Tensor`, `List[torch.Tensor]`) --
  `Tensor` representing an image batch to mask `image` generated by VAE. If not provided, the mask
  latents tensor will ge generated by `mask_image`.
- **height** (`int`, *optional*, defaults to self.transformer.config.sample_size * self.vae_scale_factor) --
  The height in pixels of the generated image. This is set to 1024 by default for the best results.
- **width** (`int`, *optional*, defaults to self.transformer.config.sample_size * self.vae_scale_factor) --
  The width in pixels of the generated image. This is set to 1024 by default for the best results.
- **padding_mask_crop** (`int`, *optional*, defaults to `None`) --
  The size of margin in the crop to be applied to the image and masking. If `None`, no crop is applied to
  image and mask_image. If `padding_mask_crop` is not `None`, it will first find a rectangular region
  with the same aspect ration of the image and contains all masked area, and then expand that area based
  on `padding_mask_crop`. The image and mask_image will then be cropped based on the expanded area before
  resizing to the original image size for inpainting. This is useful when the masked area is small while
  the image is large and contain information irrelevant for inpainting, such as background.
- **strength** (`float`, *optional*, defaults to 1.0) --
  Indicates extent to transform the reference `image`. Must be between 0 and 1. `image` is used as a
  starting point and more noise is added the higher the `strength`. The number of denoising steps depends
  on the amount of noise initially added. When `strength` is 1, added noise is maximum and the denoising
  process runs for the full number of iterations specified in `num_inference_steps`. A value of 1
  essentially ignores `image`.
- **num_inference_steps** (`int`, *optional*, defaults to 50) --
  The number of denoising steps. More denoising steps usually lead to a higher quality image at the
  expense of slower inference.
- **sigmas** (`List[float]`, *optional*) --
  Custom sigmas to use for the denoising process with schedulers which support a `sigmas` argument in
  their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is passed
  will be used.
- **guidance_scale** (`float`, *optional*, defaults to 7.0) --
  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.
- **negative_prompt** (`str` or `List[str]`, *optional*) --
  The prompt or prompts not to guide the image generation. If not defined, one has to pass
  `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
  less than `1`).
- **negative_prompt_2** (`str` or `List[str]`, *optional*) --
  The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and
  `text_encoder_2`. If not defined, `negative_prompt` is used instead
- **negative_prompt_3** (`str` or `List[str]`, *optional*) --
  The prompt or prompts not to guide the image generation to be sent to `tokenizer_3` and
  `text_encoder_3`. If not defined, `negative_prompt` is used instead
- **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.FloatTensor`, *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`.
- **prompt_embeds** (`torch.FloatTensor`, *optional*) --
  Pre-generated text embeddings. 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.FloatTensor`, *optional*) --
  Pre-generated negative text embeddings. 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.
- **pooled_prompt_embeds** (`torch.FloatTensor`, *optional*) --
  Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.
  If not provided, pooled text embeddings will be generated from `prompt` input argument.
- **negative_pooled_prompt_embeds** (`torch.FloatTensor`, *optional*) --
  Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
  weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt`
  input argument.
- **ip_adapter_image** (`PipelineImageInput`, *optional*) --
  Optional image input to work with IP Adapters.
- **ip_adapter_image_embeds** (`torch.Tensor`, *optional*) --
  Pre-generated image embeddings for IP-Adapter. Should be a tensor of shape `(batch_size, num_images,
  emb_dim)`. It should contain the negative image embedding if `do_classifier_free_guidance` is set to
  `True`. If not provided, embeddings are computed from the `ip_adapter_image` input argument.
- **output_type** (`str`, *optional*, defaults to `"pil"`) --
  The output format of the generate image. Choose between
  [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
- **return_dict** (`bool`, *optional*, defaults to `True`) --
  Whether or not to return a `diffusers.pipelines.stable_diffusion_3.StableDiffusion3PipelineOutput` instead of
  a plain tuple.
- **joint_attention_kwargs** (`dict`, *optional*) --
  A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
  `self.processor` in
  [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
- **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.
- **max_sequence_length** (`int` defaults to 256) -- Maximum sequence length to use with the `prompt`.
- **mu** (`float`, *optional*) -- `mu` value used for `dynamic_shifting`.</paramsdesc><paramgroups>0</paramgroups><rettype>`diffusers.pipelines.stable_diffusion_3.StableDiffusion3PipelineOutput` or `tuple`</rettype><retdesc>`diffusers.pipelines.stable_diffusion_3.StableDiffusion3PipelineOutput` if `return_dict` is True, otherwise a
`tuple`. When returning a tuple, the first element is a list with the generated images.</retdesc></docstring>

Function invoked when calling the pipeline for generation.



<ExampleCodeBlock anchor="optimum.onnxruntime.ORTStableDiffusion3InpaintPipeline.__call__.example">

Examples:
```py
>>> import torch
>>> from optimum.onnxruntime import ORTStableDiffusion3InpaintPipeline
>>> from diffusers.utils import load_image

>>> pipe = ORTStableDiffusion3InpaintPipeline.from_pretrained(
...     "stabilityai/stable-diffusion-3-medium-diffusers", torch_dtype=torch.float16
... )
>>> pipe.to("cuda")
>>> prompt = "Face of a yellow cat, high resolution, sitting on a park bench"
>>> img_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png"
>>> mask_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png"
>>> source = load_image(img_url)
>>> mask = load_image(mask_url)
>>> image = pipe(prompt=prompt, image=source, mask_image=mask).images[0]
>>> image.save("sd3_inpainting.png")
```

</ExampleCodeBlock>







</div></div>

### ORTFluxPipeline[[optimum.onnxruntime.ORTFluxPipeline]]

<div class="docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8">


<docstring><name>class optimum.onnxruntime.ORTFluxPipeline</name><anchor>optimum.onnxruntime.ORTFluxPipeline</anchor><source>https://github.com/huggingface/optimum-onnx/blob/v0.0.2/optimum/onnxruntime/modeling_diffusion.py#L1102</source><parameters>[{"name": "unet_session", "val": ": InferenceSession | None = None"}, {"name": "transformer_session", "val": ": InferenceSession | None = None"}, {"name": "vae_decoder_session", "val": ": InferenceSession | None = None"}, {"name": "vae_encoder_session", "val": ": InferenceSession | None = None"}, {"name": "text_encoder_session", "val": ": InferenceSession | None = None"}, {"name": "text_encoder_2_session", "val": ": InferenceSession | None = None"}, {"name": "text_encoder_3_session", "val": ": InferenceSession | None = None"}, {"name": "scheduler", "val": ": SchedulerMixin | None = None"}, {"name": "tokenizer", "val": ": CLIPTokenizer | None = None"}, {"name": "tokenizer_2", "val": ": CLIPTokenizer | None = None"}, {"name": "tokenizer_3", "val": ": CLIPTokenizer | None = None"}, {"name": "feature_extractor", "val": ": CLIPFeatureExtractor | None = None"}, {"name": "force_zeros_for_empty_prompt", "val": ": bool = True"}, {"name": "requires_aesthetics_score", "val": ": bool = False"}, {"name": "add_watermarker", "val": ": bool | None = None"}, {"name": "use_io_binding", "val": ": bool | None = None"}, {"name": "model_save_dir", "val": ": str | Path | TemporaryDirectory | None = None"}, {"name": "**kwargs", "val": ""}]</parameters></docstring>
ONNX Runtime-powered Pipeline for text-to-image generation using Flux and corresponding to [FluxPipeline](https://huggingface.co/docs/diffusers/api/pipelines/flux#diffusers.FluxPipeline).
This Pipeline inherits from `ORTDiffusionPipeline` and is used to run inference with the ONNX Runtime.
The pipeline can be loaded from a pretrained pipeline using the generic `ORTDiffusionPipeline.from_pretrained` method.



<div class="docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8">


<docstring><name>__call__</name><anchor>optimum.onnxruntime.ORTFluxPipeline.__call__</anchor><source>https://github.com/huggingface/optimum-onnx/blob/v0.0.2/diffusers/pipelines/flux/pipeline_flux.py#L627</source><parameters>[{"name": "prompt", "val": ": typing.Union[str, typing.List[str]] = None"}, {"name": "prompt_2", "val": ": typing.Union[str, typing.List[str], NoneType] = None"}, {"name": "negative_prompt", "val": ": typing.Union[str, typing.List[str]] = None"}, {"name": "negative_prompt_2", "val": ": typing.Union[str, typing.List[str], NoneType] = None"}, {"name": "true_cfg_scale", "val": ": float = 1.0"}, {"name": "height", "val": ": typing.Optional[int] = None"}, {"name": "width", "val": ": typing.Optional[int] = None"}, {"name": "num_inference_steps", "val": ": int = 28"}, {"name": "sigmas", "val": ": typing.Optional[typing.List[float]] = None"}, {"name": "guidance_scale", "val": ": float = 3.5"}, {"name": "num_images_per_prompt", "val": ": typing.Optional[int] = 1"}, {"name": "generator", "val": ": typing.Union[torch._C.Generator, typing.List[torch._C.Generator], NoneType] = None"}, {"name": "latents", "val": ": typing.Optional[torch.FloatTensor] = None"}, {"name": "prompt_embeds", "val": ": typing.Optional[torch.FloatTensor] = None"}, {"name": "pooled_prompt_embeds", "val": ": typing.Optional[torch.FloatTensor] = None"}, {"name": "ip_adapter_image", "val": ": typing.Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, typing.List[PIL.Image.Image], typing.List[numpy.ndarray], typing.List[torch.Tensor], NoneType] = None"}, {"name": "ip_adapter_image_embeds", "val": ": typing.Optional[typing.List[torch.Tensor]] = None"}, {"name": "negative_ip_adapter_image", "val": ": typing.Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, typing.List[PIL.Image.Image], typing.List[numpy.ndarray], typing.List[torch.Tensor], NoneType] = None"}, {"name": "negative_ip_adapter_image_embeds", "val": ": typing.Optional[typing.List[torch.Tensor]] = None"}, {"name": "negative_prompt_embeds", "val": ": typing.Optional[torch.FloatTensor] = None"}, {"name": "negative_pooled_prompt_embeds", "val": ": typing.Optional[torch.FloatTensor] = None"}, {"name": "output_type", "val": ": typing.Optional[str] = 'pil'"}, {"name": "return_dict", "val": ": bool = True"}, {"name": "joint_attention_kwargs", "val": ": typing.Optional[typing.Dict[str, typing.Any]] = None"}, {"name": "callback_on_step_end", "val": ": typing.Optional[typing.Callable[[int, int, typing.Dict], NoneType]] = None"}, {"name": "callback_on_step_end_tensor_inputs", "val": ": typing.List[str] = ['latents']"}, {"name": "max_sequence_length", "val": ": int = 512"}]</parameters><paramsdesc>- **prompt** (`str` or `List[str]`, *optional*) --
  The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
  instead.
- **prompt_2** (`str` or `List[str]`, *optional*) --
  The prompt or prompts to be sent to `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is
  will be used instead.
- **negative_prompt** (`str` or `List[str]`, *optional*) --
  The prompt or prompts not to guide the image generation. If not defined, one has to pass
  `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `true_cfg_scale` is
  not greater than `1`).
- **negative_prompt_2** (`str` or `List[str]`, *optional*) --
  The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and
  `text_encoder_2`. If not defined, `negative_prompt` is used in all the text-encoders.
- **true_cfg_scale** (`float`, *optional*, defaults to 1.0) --
  True classifier-free guidance (guidance scale) is enabled when `true_cfg_scale` > 1 and
  `negative_prompt` is provided.
- **height** (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor) --
  The height in pixels of the generated image. This is set to 1024 by default for the best results.
- **width** (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor) --
  The width in pixels of the generated image. This is set to 1024 by default for the best results.
- **num_inference_steps** (`int`, *optional*, defaults to 50) --
  The number of denoising steps. More denoising steps usually lead to a higher quality image at the
  expense of slower inference.
- **sigmas** (`List[float]`, *optional*) --
  Custom sigmas to use for the denoising process with schedulers which support a `sigmas` argument in
  their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is passed
  will be used.
- **guidance_scale** (`float`, *optional*, defaults to 3.5) --
  Embedded guiddance scale is enabled by setting `guidance_scale` > 1. Higher `guidance_scale` encourages
  a model to generate images more aligned with `prompt` at the expense of lower image quality.

  Guidance-distilled models approximates true classifer-free guidance for `guidance_scale` > 1. Refer to
  the [paper](https://huggingface.co/papers/2210.03142) to learn more.
- **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.FloatTensor`, *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 be generated by sampling using the supplied random `generator`.
- **prompt_embeds** (`torch.FloatTensor`, *optional*) --
  Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
  provided, text embeddings will be generated from `prompt` input argument.
- **pooled_prompt_embeds** (`torch.FloatTensor`, *optional*) --
  Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.
  If not provided, pooled text embeddings will be generated from `prompt` input argument.
- **ip_adapter_image** -- (`PipelineImageInput`, *optional*): Optional image input to work with IP Adapters.
- **ip_adapter_image_embeds** (`List[torch.Tensor]`, *optional*) --
  Pre-generated image embeddings for IP-Adapter. It should be a list of length same as number of
  IP-adapters. Each element should be a tensor of shape `(batch_size, num_images, emb_dim)`. If not
  provided, embeddings are computed from the `ip_adapter_image` input argument.
- **negative_ip_adapter_image** --
  (`PipelineImageInput`, *optional*): Optional image input to work with IP Adapters.
- **negative_ip_adapter_image_embeds** (`List[torch.Tensor]`, *optional*) --
  Pre-generated image embeddings for IP-Adapter. It should be a list of length same as number of
  IP-adapters. Each element should be a tensor of shape `(batch_size, num_images, emb_dim)`. If not
  provided, embeddings are computed from the `ip_adapter_image` input argument.
- **negative_prompt_embeds** (`torch.FloatTensor`, *optional*) --
  Pre-generated negative text embeddings. 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.
- **negative_pooled_prompt_embeds** (`torch.FloatTensor`, *optional*) --
  Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
  weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt`
  input argument.
- **output_type** (`str`, *optional*, defaults to `"pil"`) --
  The output format of the generate image. Choose between
  [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
- **return_dict** (`bool`, *optional*, defaults to `True`) --
  Whether or not to return a `diffusers.pipelines.flux.FluxPipelineOutput` instead of a plain tuple.
- **joint_attention_kwargs** (`dict`, *optional*) --
  A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
  `self.processor` in
  [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
- **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.
- **max_sequence_length** (`int` defaults to 512) -- Maximum sequence length to use with the `prompt`.</paramsdesc><paramgroups>0</paramgroups><rettype>`diffusers.pipelines.flux.FluxPipelineOutput` or `tuple`</rettype><retdesc>`diffusers.pipelines.flux.FluxPipelineOutput` if `return_dict`
is True, otherwise a `tuple`. When returning a tuple, the first element is a list with the generated
images.</retdesc></docstring>

Function invoked when calling the pipeline for generation.



<ExampleCodeBlock anchor="optimum.onnxruntime.ORTFluxPipeline.__call__.example">

Examples:
```py
>>> import torch
>>> from optimum.onnxruntime import ORTFluxPipeline

>>> pipe = ORTFluxPipeline.from_pretrained("black-forest-labs/FLUX.1-schnell")
>>> pipe.to("cuda")
>>> prompt = "A cat holding a sign that says hello world"
>>> # Depending on the variant being used, the pipeline call will slightly vary.
>>> # Refer to the pipeline documentation for more details.
>>> image = pipe(prompt, num_inference_steps=4, guidance_scale=0.0).images[0]
>>> image.save("flux.png")
```

</ExampleCodeBlock>







</div></div>