The NeuronBaseModel
class is available for instantiating a base Neuron model without a specific head.
It is used as the base class for all tasks but text generation.
( model: ScriptModule config: PretrainedConfig model_save_dir: Union = None model_file_name: Optional = None preprocessors: Optional = None neuron_config: Optional = None **kwargs )
Base class running compiled and optimized models on Neuron devices.
It implements generic methods for interacting with the Hugging Face Hub as well as compiling vanilla
transformers models to neuron-optimized TorchScript module and export it using optimum.exporters.neuron
toolchain.
Class attributes:
str
, optional, defaults to "neuron_model"
) — The name of the model type to use when
registering the NeuronBaseModel classes.Type
, optional, defaults to AutoModel
) — The AutoModel
class to be represented by the
current NeuronBaseModel class.Common attributes:
torch.jit._script.ScriptModule
) — The loaded ScriptModule
compiled for neuron devices.Path
) — The directory where a neuron compiled model is saved.
By default, if the loaded model is local, the directory where the original model will be used. Otherwise, the
cache directory will be used.Gets a dictionary of inputs with their valid static shapes.
( path: Union )
Loads a TorchScript module compiled by neuron(x)-cc compiler. It will be first loaded onto CPU and then moved to one or multiple NeuronCore.
( outputs: List dims: List indices: List padding_side: Literal = 'right' )
Parameters
List[torch.Tensor]
) —
List of torch tensors which are inference output. List[int]
) —
List of dimensions in which we slice a tensor. List[int]
) —
List of indices in which we slice a tensor along an axis. Literal["right", "left"]
, defaults to “right”) —
The side on which the padding has been applied. Removes padding from output tensors.
The NeuronDecoderModel
class is the base class for text generation models.
( model: Module config: PretrainedConfig model_path: Union generation_config: Optional = None )
Base class to convert and run pre-trained transformers decoder models on Neuron devices.
It implements the methods to convert a pre-trained transformers decoder model into a Neuron transformer model by:
Common attributes:
torch.nn.Module
) — The decoder model with a graph optimized for neuron devices.generate()
.The following Neuron model classes are available for natural language processing tasks.
( model: ScriptModule config: PretrainedConfig model_save_dir: Union = None model_file_name: Optional = None preprocessors: Optional = None neuron_config: Optional = None **kwargs )
Parameters
transformers.PretrainedConfig
) — PretrainedConfig is the Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the optimum.neuron.modeling.NeuronBaseModel.from_pretrained
method to load the model weights. torch.jit._script.ScriptModule
) — torch.jit._script.ScriptModule is the TorchScript graph compiled by neuron(x) compiler. Neuron Model with a BaseModelOutput for feature-extraction tasks.
This model inherits from ~neuron.modeling.NeuronBaseModel
. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving)
Feature Extraction model on Neuron devices.
( input_ids: Tensor attention_mask: Tensor token_type_ids: Optional = None **kwargs )
Parameters
torch.Tensor
of shape (batch_size, sequence_length)
) —
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using AutoTokenizer
.
See PreTrainedTokenizer.encode
and
PreTrainedTokenizer.__call__
for details.
What are input IDs? Union[torch.Tensor, None]
of shape (batch_size, sequence_length)
, defaults to None
) —
Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]
:Union[torch.Tensor, None]
of shape (batch_size, sequence_length)
, defaults to None
) —
Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0, 1]
:The NeuronModelForFeatureExtraction forward method, overrides the __call__
special method.
Although the recipe for forward pass needs to be defined within this function, one should call the Module
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.
Example of feature extraction: (Following model is compiled with neuronx compiler and can only be run on INF2. Replace “neuronx” with “neuron” if you are using INF1.)
>>> from transformers import AutoTokenizer
>>> from optimum.neuron import NeuronModelForFeatureExtraction
>>> tokenizer = AutoTokenizer.from_pretrained("optimum/all-MiniLM-L6-v2-neuronx")
>>> model = NeuronModelForFeatureExtraction.from_pretrained("optimum/all-MiniLM-L6-v2-neuronx")
>>> inputs = tokenizer("Dear Evan Hansen is the winner of six Tony Awards.", return_tensors="pt")
>>> outputs = model(**inputs)
>>> last_hidden_state = outputs.last_hidden_state
>>> list(last_hidden_state.shape)
[1, 13, 384]
( model: ScriptModule config: PretrainedConfig model_save_dir: Union = None model_file_name: Optional = None preprocessors: Optional = None neuron_config: Optional = None **kwargs )
Parameters
transformers.PretrainedConfig
) — PretrainedConfig is the Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the optimum.neuron.modeling.NeuronBaseModel.from_pretrained
method to load the model weights. torch.jit._script.ScriptModule
) — torch.jit._script.ScriptModule is the TorchScript graph compiled by neuron(x) compiler. Neuron Model with a MaskedLMOutput for masked language modeling tasks.
This model inherits from ~neuron.modeling.NeuronBaseModel
. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving)
Masked language model for on Neuron devices.
( input_ids: Tensor attention_mask: Tensor token_type_ids: Optional = None **kwargs )
Parameters
torch.Tensor
of shape (batch_size, sequence_length)
) —
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using AutoTokenizer
.
See PreTrainedTokenizer.encode
and
PreTrainedTokenizer.__call__
for details.
What are input IDs? Union[torch.Tensor, None]
of shape (batch_size, sequence_length)
, defaults to None
) —
Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]
:Union[torch.Tensor, None]
of shape (batch_size, sequence_length)
, defaults to None
) —
Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0, 1]
:The NeuronModelForMaskedLM forward method, overrides the __call__
special method.
Although the recipe for forward pass needs to be defined within this function, one should call the Module
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.
Example of fill mask: (Following model is compiled with neuronx compiler and can only be run on INF2. Replace “neuronx” with “neuron” if you are using INF1.)
>>> from transformers import AutoTokenizer
>>> from optimum.neuron import NeuronModelForMaskedLM
>>> import torch
>>> tokenizer = AutoTokenizer.from_pretrained("optimum/legal-bert-base-uncased-neuronx")
>>> model = NeuronModelForMaskedLM.from_pretrained("optimum/legal-bert-base-uncased-neuronx")
>>> inputs = tokenizer("This [MASK] Agreement is between General Motors and John Murray.", return_tensors="pt")
>>> outputs = model(**inputs)
>>> logits = outputs.logits
>>> list(logits.shape)
[1, 13, 30522]
( model: ScriptModule config: PretrainedConfig model_save_dir: Union = None model_file_name: Optional = None preprocessors: Optional = None neuron_config: Optional = None **kwargs )
Parameters
transformers.PretrainedConfig
) — PretrainedConfig is the Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the optimum.neuron.modeling.NeuronBaseModel.from_pretrained
method to load the model weights. torch.jit._script.ScriptModule
) — torch.jit._script.ScriptModule is the TorchScript graph compiled by neuron(x) compiler. Neuron Model with a sequence classification/regression head on top (a linear layer on top of the pooled output) e.g. for GLUE tasks.
This model inherits from ~neuron.modeling.NeuronBaseModel
. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving)
Sequence Classification model on Neuron devices.
( input_ids: Tensor attention_mask: Tensor token_type_ids: Optional = None **kwargs )
Parameters
torch.Tensor
of shape (batch_size, sequence_length)
) —
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using AutoTokenizer
.
See PreTrainedTokenizer.encode
and
PreTrainedTokenizer.__call__
for details.
What are input IDs? Union[torch.Tensor, None]
of shape (batch_size, sequence_length)
, defaults to None
) —
Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]
:Union[torch.Tensor, None]
of shape (batch_size, sequence_length)
, defaults to None
) —
Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0, 1]
:The NeuronModelForSequenceClassification forward method, overrides the __call__
special method.
Although the recipe for forward pass needs to be defined within this function, one should call the Module
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.
Example of single-label classification: (Following model is compiled with neuronx compiler and can only be run on INF2.)
>>> from transformers import AutoTokenizer
>>> from optimum.neuron import NeuronModelForSequenceClassification
>>> tokenizer = AutoTokenizer.from_pretrained("optimum/distilbert-base-uncased-finetuned-sst-2-english-neuronx")
>>> model = NeuronModelForSequenceClassification.from_pretrained("optimum/distilbert-base-uncased-finetuned-sst-2-english-neuronx")
>>> inputs = tokenizer("Hamilton is considered to be the best musical of human history.", return_tensors="pt")
>>> outputs = model(**inputs)
>>> logits = outputs.logits
>>> list(logits.shape)
[1, 2]
( model: ScriptModule config: PretrainedConfig model_save_dir: Union = None model_file_name: Optional = None preprocessors: Optional = None neuron_config: Optional = None **kwargs )
Parameters
transformers.PretrainedConfig
) — PretrainedConfig is the Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the optimum.neuron.modeling.NeuronBaseModel.from_pretrained
method to load the model weights. torch.jit._script.ScriptModule
) — torch.jit._script.ScriptModule is the TorchScript graph compiled by neuron(x) compiler. Neuron Model with a QuestionAnsweringModelOutput for extractive question-answering tasks like SQuAD.
This model inherits from ~neuron.modeling.NeuronBaseModel
. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving)
Question Answering model on Neuron devices.
( input_ids: Tensor attention_mask: Tensor token_type_ids: Optional = None **kwargs )
Parameters
torch.Tensor
of shape (batch_size, sequence_length)
) —
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using AutoTokenizer
.
See PreTrainedTokenizer.encode
and
PreTrainedTokenizer.__call__
for details.
What are input IDs? Union[torch.Tensor, None]
of shape (batch_size, sequence_length)
, defaults to None
) —
Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]
:Union[torch.Tensor, None]
of shape (batch_size, sequence_length)
, defaults to None
) —
Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0, 1]
:The NeuronModelForQuestionAnswering forward method, overrides the __call__
special method.
Although the recipe for forward pass needs to be defined within this function, one should call the Module
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.
Example of question answering: (Following model is compiled with neuronx compiler and can only be run on INF2.)
>>> import torch
>>> from transformers import AutoTokenizer
>>> from optimum.neuron import NeuronModelForQuestionAnswering
>>> tokenizer = AutoTokenizer.from_pretrained("optimum/roberta-base-squad2-neuronx")
>>> model = NeuronModelForQuestionAnswering.from_pretrained("optimum/roberta-base-squad2-neuronx")
>>> question, text = "Are there wheelchair spaces in the theatres?", "Yes, we have reserved wheelchair spaces with a good view."
>>> inputs = tokenizer(question, text, return_tensors="pt")
>>> start_positions = torch.tensor([1])
>>> end_positions = torch.tensor([12])
>>> outputs = model(**inputs, start_positions=start_positions, end_positions=end_positions)
>>> start_scores = outputs.start_logits
>>> end_scores = outputs.end_logits
( model: ScriptModule config: PretrainedConfig model_save_dir: Union = None model_file_name: Optional = None preprocessors: Optional = None neuron_config: Optional = None **kwargs )
Parameters
transformers.PretrainedConfig
) — PretrainedConfig is the Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the optimum.neuron.modeling.NeuronBaseModel.from_pretrained
method to load the model weights. torch.jit._script.ScriptModule
) — torch.jit._script.ScriptModule is the TorchScript graph compiled by neuron(x) compiler. Neuron Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks.
This model inherits from ~neuron.modeling.NeuronBaseModel
. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving)
Token Classification model on Neuron devices.
( input_ids: Tensor attention_mask: Tensor token_type_ids: Optional = None **kwargs )
Parameters
torch.Tensor
of shape (batch_size, sequence_length)
) —
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using AutoTokenizer
.
See PreTrainedTokenizer.encode
and
PreTrainedTokenizer.__call__
for details.
What are input IDs? Union[torch.Tensor, None]
of shape (batch_size, sequence_length)
, defaults to None
) —
Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]
:Union[torch.Tensor, None]
of shape (batch_size, sequence_length)
, defaults to None
) —
Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0, 1]
:The NeuronModelForTokenClassification forward method, overrides the __call__
special method.
Although the recipe for forward pass needs to be defined within this function, one should call the Module
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.
Example of token classification: (Following model is compiled with neuronx compiler and can only be run on INF2.)
>>> from transformers import AutoTokenizer
>>> from optimum.neuron import NeuronModelForTokenClassification
>>> tokenizer = AutoTokenizer.from_pretrained("optimum/bert-base-NER-neuronx")
>>> model = NeuronModelForTokenClassification.from_pretrained("optimum/bert-base-NER-neuronx")
>>> inputs = tokenizer("Lin-Manuel Miranda is an American songwriter, actor, singer, filmmaker, and playwright.", return_tensors="pt")
>>> outputs = model(**inputs)
>>> logits = outputs.logits
>>> list(logits.shape)
[1, 20, 9]
( model: ScriptModule config: PretrainedConfig model_save_dir: Union = None model_file_name: Optional = None preprocessors: Optional = None neuron_config: Optional = None **kwargs )
Parameters
transformers.PretrainedConfig
) — PretrainedConfig is the Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the optimum.neuron.modeling.NeuronBaseModel.from_pretrained
method to load the model weights. torch.jit._script.ScriptModule
) — torch.jit._script.ScriptModule is the TorchScript graph compiled by neuron(x) compiler. Neuron Model with a multiple choice classification head on top (a linear layer on top of the pooled output and a softmax) e.g. for RocStories/SWAG tasks.
This model inherits from ~neuron.modeling.NeuronBaseModel
. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving)
Multiple choice model on Neuron devices.
( input_ids: Tensor attention_mask: Tensor token_type_ids: Optional = None **kwargs )
Parameters
torch.Tensor
of shape (batch_size, num_choices, sequence_length)
) —
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using AutoTokenizer
.
See PreTrainedTokenizer.encode
and
PreTrainedTokenizer.__call__
for details.
What are input IDs? Union[torch.Tensor, None]
of shape (batch_size, num_choices, sequence_length)
, defaults to None
) —
Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]
:Union[torch.Tensor, None]
of shape (batch_size, num_choices, sequence_length)
, defaults to None
) —
Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0, 1]
:The NeuronModelForMultipleChoice forward method, overrides the __call__
special method.
Although the recipe for forward pass needs to be defined within this function, one should call the Module
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.
Example of mutliple choice: (Following model is compiled with neuronx compiler and can only be run on INF2.)
>>> from transformers import AutoTokenizer
>>> from optimum.neuron import NeuronModelForMultipleChoice
>>> tokenizer = AutoTokenizer.from_pretrained("optimum/bert-base-uncased_SWAG-neuronx")
>>> model = NeuronModelForMultipleChoice.from_pretrained("optimum/bert-base-uncased_SWAG-neuronx", export=True)
>>> num_choices = 4
>>> first_sentence = ["Members of the procession walk down the street holding small horn brass instruments."] * num_choices
>>> second_sentence = [
... "A drum line passes by walking down the street playing their instruments.",
... "A drum line has heard approaching them.",
... "A drum line arrives and they're outside dancing and asleep.",
... "A drum line turns the lead singer watches the performance."
... ]
>>> inputs = tokenizer(first_sentence, second_sentence, truncation=True, padding=True)
# Unflatten the inputs values expanding it to the shape [batch_size, num_choices, seq_length]
>>> for k, v in inputs.items():
... inputs[k] = [v[i: i + num_choices] for i in range(0, len(v), num_choices)]
>>> inputs = dict(inputs.convert_to_tensors(tensor_type="pt"))
>>> outputs = model(**inputs)
>>> logits = outputs.logits
>>> logits.shape
[1, 4]
( model: Module config: PretrainedConfig model_path: Union generation_config: Optional = None )
Parameters
torch.nn.Module
) — torch.nn.Module is the neuron decoder graph. transformers.PretrainedConfig
) — PretrainedConfig is the Model configuration class with all the parameters of the model. Path
) — The directory where the compiled artifacts for the model are stored.
It can be a temporary directory if the model has never been saved locally before. transformers.GenerationConfig
) — GenerationConfig holds the configuration for the model generation task. Neuron model with a causal language modeling head for inference on Neuron devices.
This model inherits from ~neuron.modeling.NeuronDecoderModel
. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving)
Returns True to validate the check made in GenerationMixin.generate()
.
( input_ids: Tensor cache_ids: Tensor start_ids: Tensor = None return_dict: bool = True )
Parameters
torch.LongTensor
) —
Indices of decoder input sequence tokens in the vocabulary of shape (batch_size, sequence_length)
. torch.LongTensor
) — The indices at which the cached key and value for the current inputs need to be stored. torch.LongTensor
) — The indices of the first tokens to be processed, deduced form the attention masks. The NeuronModelForCausalLM forward method, overrides the __call__
special method.
Although the recipe for forward pass needs to be defined within this function, one should call the Module
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.
Example of text generation:
>>> from transformers import AutoTokenizer
>>> from optimum.neuron import NeuronModelForCausalLM
>>> import torch
>>> tokenizer = AutoTokenizer.from_pretrained("gpt2")
>>> model = NeuronModelForCausalLM.from_pretrained("gpt2", export=True)
>>> inputs = tokenizer("My favorite moment of the day is", return_tensors="pt")
>>> gen_tokens = model.generate(**inputs, do_sample=True, temperature=0.9, min_length=20, max_length=20)
>>> tokenizer.batch_decode(gen_tokens)
( input_ids: Tensor attention_mask: Optional = None generation_config: Optional = None **kwargs ) → torch.Tensor
Parameters
torch.Tensor
of shape (batch_size, sequence_length)
) —
The sequence used as a prompt for the generation. torch.Tensor
of shape (batch_size, sequence_length)
, optional) —
Mask to avoid performing attention on padding token indices. ~transformers.generation.GenerationConfig
, optional) —
The generation configuration to be used as base parametrization for the generation call. **kwargs
passed to generate matching the attributes of generation_config
will override them. If
generation_config
is not provided, default will be used, which had the following loading
priority: 1) from the generation_config.json
model file, if it exists; 2) from the model
configuration. Please note that unspecified parameters will inherit GenerationConfig
’s
default values, whose documentation should be checked to parameterize generation. Returns
torch.Tensor
A torch.FloatTensor
.
A streamlined generate() method overriding the transformers.GenerationMixin.generate() method.
This method uses the same logits processors/warpers and stopping criterias as the transformers library
generate()
method but restricts the generation to greedy search and sampling.
It does not support transformers generate()
advanced options.
Please refer to https://huggingface.co/docs/transformers/en/main_classes/text_generation#transformers.GenerationMixin.generate for details on generation configuration.
( input_ids: LongTensor selector: TokenSelector batch_size: int attention_mask: Optional = None **model_kwargs ) → torch.LongTensor
Parameters
torch.LongTensor
of shape (batch_size, sequence_length)
) —
The sequence used as a prompt for the generation. TokenSelector
) —
The object implementing the generation logic based on transformers processors and stopping criterias. int
) —
The actual input batch size. Used to avoid generating tokens for padded inputs. torch.Tensor
of shape (batch_size, sequence_length)
, optional) —
Mask to avoid performing attention on padding token indices.
model_kwargs —
Additional model specific kwargs will be forwarded to the forward
function of the model. Returns
torch.LongTensor
A torch.LongTensor
containing the generated tokens.
Generate tokens using sampling or greedy search.
( text_encoder: ScriptModule unet: ScriptModule vae_decoder: Union config: Dict tokenizer: CLIPTokenizer scheduler: Union data_parallel_mode: str vae_encoder: Union = None text_encoder_2: Union = None tokenizer_2: Optional = None feature_extractor: Optional = None configs: Optional = None neuron_configs: Optional = None model_save_dir: Union = None model_and_config_save_paths: Optional = None )
( data_parallel_mode: Optional text_encoder_path: Union unet_path: Union vae_decoder_path: Union = None vae_encoder_path: Union = None text_encoder_2_path: Union = None dynamic_batch_size: bool = False )
Parameters
Optional[str]
) —
Mode to decide what components to load into both NeuronCores of a Neuron device. Can be “none”(no data parallel), “unet”(only
load unet into both cores of each device), “all”(load the whole pipeline into both cores). Union[str, Path]
) —
Path of the compiled text encoder. Union[str, Path]
) —
Path of the compiled U-NET. Optional[Union[str, Path]]
, defaults to None
) —
Path of the compiled VAE decoder. Optional[Union[str, Path]]
, defaults to None
) —
Path of the compiled VAE encoder. It is optional, only used for tasks taking images as input. Optional[Union[str, Path]]
, defaults to None
) —
Path of the compiled second frozen text encoder. SDXL only. bool
, defaults to False
) —
Whether enable dynamic batch size for neuron compiled model. If True
, the input batch size can be a multiple of the batch size during the compilation. Loads Stable Diffusion TorchScript modules compiled by neuron(x)-cc compiler. It will be first loaded onto CPU and then moved to one or multiple NeuronCore.
( text_encoder: ScriptModule unet: ScriptModule vae_decoder: Union config: Dict tokenizer: CLIPTokenizer scheduler: Union data_parallel_mode: str vae_encoder: Union = None text_encoder_2: Union = None tokenizer_2: Optional = None feature_extractor: Optional = None configs: Optional = None neuron_configs: Optional = None model_save_dir: Union = None model_and_config_save_paths: Optional = None )
( prompt: Union = None num_inference_steps: int = 50 guidance_scale: float = 7.5 negative_prompt: Union = None num_images_per_prompt: int = 1 eta: float = 0.0 generator: Union = None latents: Optional = None prompt_embeds: Optional = None negative_prompt_embeds: Optional = None output_type: Optional = 'pil' return_dict: bool = True callback: Optional = None callback_steps: int = 1 cross_attention_kwargs: Optional = None guidance_rescale: float = 0.0 ) → diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput
or tuple
Parameters
Optional[Union[str, List[str]]]
, defaults to None
) —
The prompt or prompts to guide image generation. If not defined, you need to pass prompt_embeds
. int
, defaults to 50) —
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference. float
, 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
. Optional[Union[str, List[str]]]
, defaults to None
) —
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
). int
, defaults to 1) —
The number of images to generate per prompt. If it is different from the batch size used for the compiltaion,
it will be overriden by the static batch size of neuron (except for dynamic batching). float
, defaults to 0.0) —
Corresponds to parameter eta (η) from the DDIM paper. Only applies
to the diffusers.schedulers.DDIMScheduler
, and is ignored in other schedulers. Optional[Union[torch.Generator, List[torch.Generator]]]
, defaults to None
) —
A torch.Generator
to make
generation deterministic. Optional[torch.FloatTensor]
, defaults to None
) —
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
. Optional[torch.FloatTensor]
, defaults to None
) —
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. Optional[torch.FloatTensor]
, defaults to None
) —
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. Optional[str]
, defaults to "pil"
) —
The output format of the generated image. Choose between PIL.Image
or np.array
. bool
, defaults to True
) —
Whether or not to return a diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput
instead of a
plain tuple. Optional[Callable]
, defaults to None
) —
A function that calls every callback_steps
steps during inference. The function is called with the
following arguments: callback(step: int, timestep: int, latents: torch.FloatTensor)
. int
, defaults to 1) —
The frequency at which the callback
function is called. If not specified, the callback is called at
every step. dict
, defaults to None
) —
A kwargs dictionary that if specified is passed along to the AttentionProcessor
as defined in
self.processor
. float
, defaults to 0.0) —
Guidance rescale factor from Common Diffusion Noise Schedules and Sample Steps are
Flawed. Guidance rescale factor should fix overexposure when
using zero terminal SNR. Returns
diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput
or tuple
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.
The call function to the pipeline for generation.
Examples:
>>> from optimum.neuron import NeuronStableDiffusionPipeline
>>> compiler_args = {"auto_cast": "matmul", "auto_cast_type": "bf16"}
>>> input_shapes = {"batch_size": 1, "height": 512, "width": 512}
>>> stable_diffusion = NeuronStableDiffusionPipeline.from_pretrained(
... "runwayml/stable-diffusion-v1-5", export=True, **compiler_args, **input_shapes
... )
>>> stable_diffusion.save_pretrained("sd_neuron/")
>>> prompt = "a photo of an astronaut riding a horse on mars"
>>> image = stable_diffusion(prompt).images[0]
( text_encoder: ScriptModule unet: ScriptModule vae_decoder: Union config: Dict tokenizer: CLIPTokenizer scheduler: Union data_parallel_mode: str vae_encoder: Union = None text_encoder_2: Union = None tokenizer_2: Optional = None feature_extractor: Optional = None configs: Optional = None neuron_configs: Optional = None model_save_dir: Union = None model_and_config_save_paths: Optional = None )
( prompt: Union = None image: Optional = None strength: float = 0.8 num_inference_steps: int = 50 guidance_scale: float = 7.5 negative_prompt: Union = None num_images_per_prompt: int = 1 eta: float = 0.0 generator: Optional = None prompt_embeds: Optional = None negative_prompt_embeds: Optional = None output_type: str = 'pil' return_dict: bool = True callback: Optional = None callback_steps: int = 1 cross_attention_kwargs: Optional = None ) → diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput
or tuple
Parameters
Optional[Union[str, List[str]]]
, defaults to None
) —
The prompt or prompts to guide image generation. If not defined, you need to pass prompt_embeds
. Optional["PipelineImageInput"]
, defaults to None
) —
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. float
, 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
. int
, 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
. float
, 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
. Optional[Union[str, List[str]
, defaults to None
) —
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
). int
, defaults to 1) —
The number of images to generate per prompt. If it is different from the batch size used for the compiltaion,
it will be overriden by the static batch size of neuron (except for dynamic batching). float
, defaults to 0.0) —
Corresponds to parameter eta (η) from the DDIM paper. Only applies
to the diffusers.schedulers.DDIMScheduler
, and is ignored in other schedulers. Optional[Union[torch.Generator, List[torch.Generator]]]
, defaults to None
) —
A torch.Generator
to make
generation deterministic. Optional[torch.FloatTensor]
, defaults to None
) —
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. Optional[torch.FloatTensor]
, defaults to None
) —
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. Optional[str]
, defaults to "pil"
) —
The output format of the generated image. Choose between PIL.Image
or np.array
. bool
, defaults to True
) —
Whether or not to return a diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput
instead of a
plain tuple. Optional[Callable]
, defaults to None
) —
A function that calls every callback_steps
steps during inference. The function is called with the
following arguments: callback(step: int, timestep: int, latents: torch.FloatTensor)
. int
, defaults to 1) —
The frequency at which the callback
function is called. If not specified, the callback is called at
every step. dict
, defaults to None
) —
A kwargs dictionary that if specified is passed along to the AttentionProcessor
as defined in
self.processor
. Returns
diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput
or tuple
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.
The call function to the pipeline for generation.
Examples:
>>> from optimum.neuron import NeuronStableDiffusionImg2ImgPipeline
>>> from diffusers.utils import load_image
>>> url = "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg"
>>> init_image = load_image(url).convert("RGB")
>>> compiler_args = {"auto_cast": "matmul", "auto_cast_type": "bf16"}
>>> input_shapes = {"batch_size": 1, "height": 512, "width": 512}
>>> pipeline = NeuronStableDiffusionImg2ImgPipeline.from_pretrained(
... "nitrosocke/Ghibli-Diffusion", export=True, **compiler_args, **input_shapes,
... )
>>> pipeline.save_pretrained("sd_img2img/")
>>> prompt = "ghibli style, a fantasy landscape with snowcapped mountains, trees, lake with detailed reflection."
>>> image = pipeline(prompt=prompt, image=init_image, strength=0.75, guidance_scale=7.5).images[0]
( text_encoder: ScriptModule unet: ScriptModule vae_decoder: Union config: Dict tokenizer: CLIPTokenizer scheduler: Union data_parallel_mode: str vae_encoder: Union = None text_encoder_2: Union = None tokenizer_2: Optional = None feature_extractor: Optional = None configs: Optional = None neuron_configs: Optional = None model_save_dir: Union = None model_and_config_save_paths: Optional = None )
( prompt: Union = None image: Optional = None mask_image: Optional = None masked_image_latents: Optional = None strength: float = 1.0 num_inference_steps: int = 50 guidance_scale: float = 7.5 negative_prompt: Union = None num_images_per_prompt: Optional = 1 eta: float = 0.0 generator: Union = None latents: Optional = None prompt_embeds: Optional = None negative_prompt_embeds: Optional = None output_type: Optional = 'pil' return_dict: bool = True callback: Optional = None callback_steps: int = 1 cross_attention_kwargs: Optional = None clip_skip: int = None ) → diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput
or tuple
Parameters
Optional[Union[str, List[str]]]
, defaults to None
) —
The prompt or prompts to guide image generation. If not defined, you need to pass prompt_embeds
. Optional["PipelineImageInput"]
, defaults to None
) —
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. Optional["PipelineImageInput"]
, defaults to None
) —
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)
. float
, 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
. int
, 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
. float
, 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
. Optional[Union[str, List[str]
, defaults to None
) —
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
). int
, defaults to 1) —
The number of images to generate per prompt. If it is different from the batch size used for the compiltaion,
it will be overriden by the static batch size of neuron (except for dynamic batching). float
, defaults to 0.0) —
Corresponds to parameter eta (η) from the DDIM paper. Only applies
to the diffusers.schedulers.DDIMScheduler
, and is ignored in other schedulers. Optional[Union[torch.Generator, List[torch.Generator]]]
, defaults to None
) —
A torch.Generator
to make
generation deterministic. Optional[torch.FloatTensor]
, defaults to None
) —
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
. Optional[torch.FloatTensor]
, defaults to None
) —
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. Optional[torch.FloatTensor]
, defaults to None
) —
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. Optional[str]
, defaults to "pil"
) —
The output format of the generated image. Choose between PIL.Image
or np.array
. bool
, defaults to True
) —
Whether or not to return a diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput
instead of a
plain tuple. Optional[Callable]
, defaults to None
) —
A function that calls every callback_steps
steps during inference. The function is called with the
following arguments: callback(step: int, timestep: int, latents: torch.FloatTensor)
. int
, defaults to 1) —
The frequency at which the callback
function is called. If not specified, the callback is called at
every step. dict
, defaults to None
) —
A kwargs dictionary that if specified is passed along to the AttentionProcessor
as defined in
self.processor
. int
, defaults to None
) —
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. Returns
diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput
or tuple
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.
The call function to the pipeline for generation.
Examples:
>>> from optimum.neuron import NeuronStableDiffusionInpaintPipeline
>>> from diffusers.utils import load_image
>>> 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")
>>> compiler_args = {"auto_cast": "matmul", "auto_cast_type": "bf16"}
>>> input_shapes = {"batch_size": 1, "height": 1024, "width": 1024}
>>> pipeline = NeuronStableDiffusionInpaintPipeline.from_pretrained(
... "runwayml/stable-diffusion-inpainting", export=True, **compiler_args, **input_shapes,
... )
>>> pipeline.save_pretrained("sd_inpaint/")
>>> prompt = "Face of a yellow cat, high resolution, sitting on a park bench"
>>> image = pipeline(prompt=prompt, image=init_image, mask_image=mask_image).images[0]
( text_encoder: ScriptModule unet: ScriptModule vae_decoder: Union config: Dict tokenizer: CLIPTokenizer scheduler: Union data_parallel_mode: str vae_encoder: Union = None text_encoder_2: Union = None tokenizer_2: Optional = None feature_extractor: Optional = None configs: Optional = None neuron_configs: Optional = None model_save_dir: Union = None model_and_config_save_paths: Optional = None )
( prompt: Union = None num_inference_steps: int = 50 original_inference_steps: Optional = None guidance_scale: float = 8.5 num_images_per_prompt: int = 1 generator: Union = None latents: Optional = None prompt_embeds: Optional = None output_type: str = 'pil' return_dict: bool = True cross_attention_kwargs: Optional = None clip_skip: Optional = None callback_on_step_end: Optional = None callback_on_step_end_tensor_inputs: List = ['latents'] ) → diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput
or tuple
Parameters
Optional[Union[str, List[str]]]
, defaults to None
) —
The prompt or prompts to guide image generation. If not defined, you need to pass prompt_embeds
. int
, defaults to 50) —
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference. Optional[int]
, defaults to None
) —
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. float
, defaults to 8.5) —
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
). int
, defaults to 1) —
The number of images to generate per prompt. Optional[Union[torch.Generator, List[torch.Generator]]]
, defaults to None
) —
A torch.Generator
to make
generation deterministic. Optional[torch.FloatTensor]
, defaults to None
) —
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
. Optional[torch.FloatTensor]
, defaults to None
) —
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. str
, defaults to "pil"
) —
The output format of the generated image. Choose between PIL.Image
or np.array
. bool
, defaults to True
) —
Whether or not to return a ~pipelines.stable_diffusion.StableDiffusionPipelineOutput
instead of a
plain tuple. Optional[Dict[str, Any]]
, defaults to None
) —
A kwargs dictionary that if specified is passed along to the AttentionProcessor
as defined in
self.processor
. Optional[int]
, defaults to None
) —
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. Optional[Callable]
, defaults to None
) —
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
. List[str]
, defaults to ["latents"]
) —
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 pipeine class. Returns
diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput
or tuple
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.
The call function to the pipeline for generation.
Examples:
( text_encoder: ScriptModule unet: ScriptModule vae_decoder: ScriptModule config: Dict tokenizer: CLIPTokenizer scheduler: Union data_parallel_mode: str vae_encoder: Optional = None text_encoder_2: Optional = None tokenizer_2: Optional = None feature_extractor: Optional = None configs: Optional = None neuron_configs: Optional = None model_save_dir: Union = None model_and_config_save_paths: Optional = None add_watermarker: Optional = None )
( prompt: Union = None prompt_2: Union = None num_inference_steps: int = 50 denoising_end: Optional = None guidance_scale: float = 5.0 negative_prompt: Union = None negative_prompt_2: Union = None num_images_per_prompt: int = 1 eta: float = 0.0 generator: Union = None latents: Optional = None prompt_embeds: Optional = None negative_prompt_embeds: Optional = None pooled_prompt_embeds: Optional = None negative_pooled_prompt_embeds: Optional = None output_type: Optional = 'pil' return_dict: bool = True callback: Optional = None callback_steps: int = 1 cross_attention_kwargs: Optional = None guidance_rescale: float = 0.0 original_size: Optional = None crops_coords_top_left: Tuple = (0, 0) target_size: Optional = None negative_original_size: Optional = None negative_crops_coords_top_left: Tuple = (0, 0) negative_target_size: Optional = None clip_skip: Optional = None ) → diffusers.pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput
or tuple
Parameters
Optional[Union[str, List[str]]]
, defaults to None
) —
The prompt or prompts to guide the image generation. If not defined, one has to pass prompt_embeds
.
instead. Optional[Union[str, List[str]]]
, defaults to None
) —
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 int
, defaults to 50) —
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference. Optional[float]
, defaults to None
) —
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 float
, defaults to 5.0) —
Guidance scale as defined in Classifier-Free Diffusion Guidance.
guidance_scale
is defined as w
of equation 2. of Imagen
Paper. 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. Optional[Union[str, List[str]]]
, defaults to None
) —
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
). Optional[Union[str, List[str]]]
, defaults to None
) —
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 int
, defaults to 1) —
The number of images to generate per prompt. If it is different from the batch size used for the compiltaion,
it will be overriden by the static batch size of neuron (except for dynamic batching). float
, defaults to 0.0) —
Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
schedulers.DDIMScheduler
, will be ignored for others. Optional[Union[torch.Generator, List[torch.Generator]]]
, defaults to None
) —
One or a list of torch generator(s)
to make generation deterministic. Optional[torch.FloatTensor]
, defaults to None
) —
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
. Optional[torch.FloatTensor]
, defaults to None
) —
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. Optional[torch.FloatTensor]
, defaults to None
) —
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. Optional[torch.FloatTensor]
, defaults to None
) —
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. Optional[torch.FloatTensor]
, defaults to None
) —
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. Optional[str]
, defaults to "pil"
) —
The output format of the generate image. Choose between
PIL: PIL.Image.Image
or np.array
. bool
, defaults to True
) —
Whether or not to return a diffusers.pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput
instead
of a plain tuple. Optional[Callable]
, defaults to None
) —
A function that will be called every callback_steps
steps during inference. The function will be
called with the following arguments: callback(step: int, timestep: int, latents: torch.FloatTensor)
. int
, defaults to 1) —
The frequency at which the callback
function will be called. If not specified, the callback will be
called at every step. dict
, defaults to None
) —
A kwargs dictionary that if specified is passed along to the AttentionProcessor
as defined under
self.processor
in
diffusers.models.attention_processor. float
, optional, defaults to 0.0) —
Guidance rescale factor proposed by Common Diffusion Noise Schedules and Sample Steps are
Flawed guidance_scale
is defined as φ
in equation 16. of
Common Diffusion Noise Schedules and Sample Steps are Flawed.
Guidance rescale factor should fix overexposure when using zero terminal SNR. Optional[Tuple[int, int]]
, 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 (width, height)
if not specified. Part of SDXL’s micro-conditioning as
explained in section 2.2 of
https://huggingface.co/papers/2307.01952. Tuple[int]
, 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. Tuple[int]
,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 (width, height)
. Part of SDXL’s micro-conditioning as explained in
section 2.2 of https://huggingface.co/papers/2307.01952. Tuple[int]
, 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. For more
information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208. Tuple[int]
, 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. For more
information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208. Tuple[int]
, 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. For more
information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208. Optional[int]
, defaults to None
) —
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. Returns
diffusers.pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput
or tuple
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.
Function invoked when calling the pipeline for generation.
Examples:
>>> from optimum.neuron import NeuronStableDiffusionXLPipeline
>>> compiler_args = {"auto_cast": "matmul", "auto_cast_type": "bf16"}
>>> input_shapes = {"batch_size": 1, "height": 1024, "width": 1024}
>>> stable_diffusion_xl = NeuronStableDiffusionXLPipeline.from_pretrained(
... "stabilityai/stable-diffusion-xl-base-1.0", export=True, **compiler_args, **input_shapes)
... )
>>> stable_diffusion_xl.save_pretrained("sd_neuron_xl/")
>>> prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
>>> image = stable_diffusion_xl(prompt).images[0]
( text_encoder: ScriptModule unet: ScriptModule vae_decoder: ScriptModule config: Dict tokenizer: CLIPTokenizer scheduler: Union data_parallel_mode: str vae_encoder: Optional = None text_encoder_2: Optional = None tokenizer_2: Optional = None feature_extractor: Optional = None configs: Optional = None neuron_configs: Optional = None model_save_dir: Union = None model_and_config_save_paths: Optional = None add_watermarker: Optional = None )
( prompt: Union = None prompt_2: Union = None image: Optional = None strength: float = 0.3 num_inference_steps: int = 50 denoising_start: Optional = None denoising_end: Optional = None guidance_scale: float = 5.0 negative_prompt: Union = None negative_prompt_2: Union = None num_images_per_prompt: Optional = 1 eta: float = 0.0 generator: Union = None latents: Optional = None prompt_embeds: Optional = None negative_prompt_embeds: Optional = None pooled_prompt_embeds: Optional = None negative_pooled_prompt_embeds: Optional = None output_type: Optional = 'pil' return_dict: bool = True callback: Optional = None callback_steps: int = 1 cross_attention_kwargs: Optional = None guidance_rescale: float = 0.0 original_size: Tuple = None crops_coords_top_left: Tuple = (0, 0) target_size: Tuple = None negative_original_size: Optional = None negative_crops_coords_top_left: Tuple = (0, 0) negative_target_size: Optional = None aesthetic_score: float = 6.0 negative_aesthetic_score: float = 2.5 clip_skip: Optional = None ) → diffusers.pipelines.stable_diffusion.StableDiffusionXLPipelineOutput
or tuple
Parameters
Optional[Union[str, List[str]]]
, defaults to None
) —
The prompt or prompts to guide the image generation. If not defined, one has to pass prompt_embeds
.
instead. Optional[Union[str, List[str]]]
, defaults to None
) —
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 Optional["PipelineImageInput"]
, defaults to None
) —
The image(s) to modify with the pipeline. float
, 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. int
, defaults to 50) —
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference. Optional[float]
, defaults to None
) —
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. Optional[float]
, defaults to None
) —
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. float
, defaults to 7.5) —
Guidance scale as defined in Classifier-Free Diffusion Guidance.
guidance_scale
is defined as w
of equation 2. of Imagen
Paper. 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. Optional[Union[str, List[str]]]
, defaults to None
) —
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
). Optional[Union[str, List[str]]]
, defaults to None
) —
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 int
, defaults to 1) —
The number of images to generate per prompt. If it is different from the batch size used for the compiltaion,
it will be overriden by the static batch size of neuron (except for dynamic batching). float
, defaults to 0.0) —
Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
schedulers.DDIMScheduler
, will be ignored for others. Optional[Union[torch.Generator, List[torch.Generator]]]
, defaults to None
) —
One or a list of torch generator(s)
to make generation deterministic. Optional[torch.FloatTensor]
, defaults to None
) —
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
. Optional[torch.FloatTensor]
, defaults to None
) —
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. Optional[torch.FloatTensor]
, defaults to None
) —
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. Optional[torch.FloatTensor]
, defaults to None
) —
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. Optional[torch.FloatTensor]
, defaults to None
) —
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. Optional[str]
, defaults to "pil"
) —
The output format of the generate image. Choose between
PIL: PIL.Image.Image
or np.array
. bool
, defaults to True
) —
Whether or not to return a diffusers.pipelines.stable_diffusion.StableDiffusionXLPipelineOutput
instead of a
plain tuple. Optional[Callable]
, defaults to None
) —
A function that will be called every callback_steps
steps during inference. The function will be
called with the following arguments: callback(step: int, timestep: int, latents: torch.FloatTensor)
. int
, defaults to 1) —
The frequency at which the callback
function will be called. If not specified, the callback will be
called at every step. Optional[Dict[str, Any]]
, defaults to None
) —
A kwargs dictionary that if specified is passed along to the AttentionProcessor
as defined under
self.processor
in
diffusers.models.attention_processor. float
, defaults to 0.0) —
Guidance rescale factor proposed by Common Diffusion Noise Schedules and Sample Steps are
Flawed guidance_scale
is defined as φ
in equation 16. of
Common Diffusion Noise Schedules and Sample Steps are Flawed.
Guidance rescale factor should fix overexposure when using zero terminal SNR. Optional[Tuple[int, int]]
, 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 (width, height)
if not specified. Part of SDXL’s micro-conditioning as
explained in section 2.2 of
https://huggingface.co/papers/2307.01952. Tuple[int]
, 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. Tuple[int]
,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 (width, height)
. Part of SDXL’s micro-conditioning as explained in
section 2.2 of https://huggingface.co/papers/2307.01952. Tuple[int]
, 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. For more
information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208. Tuple[int]
, 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. For more
information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208. Tuple[int]
, 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. For more
information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208. float
, 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. float
, defaults to 2.5) —
Part of SDXL’s micro-conditioning as explained in section 2.2 of
https://huggingface.co/papers/2307.01952. Can be used to
simulate an aesthetic score of the generated image by influencing the negative text condition. Optional[int]
, defaults to None
) —
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. Returns
diffusers.pipelines.stable_diffusion.StableDiffusionXLPipelineOutput
or tuple
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.
Function invoked when calling the pipeline for generation.
Examples:
>>> from optimum.neuron import NeuronStableDiffusionXLImg2ImgPipeline
>>> from diffusers.utils import load_image
>>> url = "https://huggingface.co/datasets/optimum/documentation-images/resolve/main/intel/openvino/sd_xl/castle_friedrich.png"
>>> init_image = load_image(url).convert("RGB")
>>> compiler_args = {"auto_cast": "matmul", "auto_cast_type": "bf16"}
>>> input_shapes = {"batch_size": 1, "height": 512, "width": 512}
>>> pipeline = NeuronStableDiffusionXLImg2ImgPipeline.from_pretrained(
... "stabilityai/stable-diffusion-xl-base-1.0", export=True, **compiler_args, **input_shapes,
... )
>>> pipeline.save_pretrained("sdxl_img2img/")
>>> prompt = "a dog running, lake, moat"
>>> image = pipeline(prompt=prompt, image=init_image).images[0]
( text_encoder: ScriptModule unet: ScriptModule vae_decoder: ScriptModule config: Dict tokenizer: CLIPTokenizer scheduler: Union data_parallel_mode: str vae_encoder: Optional = None text_encoder_2: Optional = None tokenizer_2: Optional = None feature_extractor: Optional = None configs: Optional = None neuron_configs: Optional = None model_save_dir: Union = None model_and_config_save_paths: Optional = None add_watermarker: Optional = None )
( prompt: Union = None prompt_2: Union = None image: Optional = None mask_image: Optional = None masked_image_latents: Optional = None strength: float = 0.9999 num_inference_steps: int = 50 denoising_start: Optional = None denoising_end: Optional = None guidance_scale: float = 7.5 negative_prompt: Union = None negative_prompt_2: Union = None num_images_per_prompt: Optional = 1 eta: float = 0.0 generator: Union = None latents: Optional = None prompt_embeds: Optional = None negative_prompt_embeds: Optional = None pooled_prompt_embeds: Optional = None negative_pooled_prompt_embeds: Optional = None output_type: Optional = 'pil' return_dict: bool = True callback: Optional = None callback_steps: int = 1 cross_attention_kwargs: Optional = None guidance_rescale: float = 0.0 original_size: Tuple = None crops_coords_top_left: Tuple = (0, 0) target_size: Tuple = None negative_original_size: Optional = None negative_crops_coords_top_left: Tuple = (0, 0) negative_target_size: Optional = None aesthetic_score: float = 6.0 negative_aesthetic_score: float = 2.5 clip_skip: Optional = None ) → diffusers.pipelines.stable_diffusion.StableDiffusionXLPipelineOutput
or tuple
Parameters
Optional[Union[str, List[str]]]
, defaults to None
) —
The prompt or prompts to guide the image generation. If not defined, one has to pass prompt_embeds
.
instead. Optional[Union[str, List[str]]]
, defaults to None
) —
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 Optional["PipelineImageInput"]
, defaults to None
) —
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
. Optional["PipelineImageInput"]
, defaults to None
) —
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)
. float
, 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. int
, defaults to 50) —
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference. Optional[float]
, defaults to None
) —
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. Optional[float]
, defaults to None
) —
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. float
, defaults to 7.5) —
Guidance scale as defined in Classifier-Free Diffusion Guidance.
guidance_scale
is defined as w
of equation 2. of Imagen
Paper. 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. Optional[Union[str, List[str]]]
, defaults to None
) —
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
). Optional[Union[str, List[str]]]
, defaults to None
) —
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 Optional[torch.FloatTensor]
, defaults to None
) —
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. Optional[torch.FloatTensor]
, defaults to None
) —
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. Optional[torch.FloatTensor]
, defaults to None
) —
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. Optional[torch.FloatTensor]
, defaults to None
) —
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. int
, defaults to 1) —
The number of images to generate per prompt. float
, defaults to 0.0) —
Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
schedulers.DDIMScheduler
, will be ignored for others. Optional[Union[torch.Generator, List[torch.Generator]]]
, defaults to None
) —
One or a list of torch generator(s)
to make generation deterministic. Optional[torch.FloatTensor]
, defaults to None
) —
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
. Optional[str]
, defaults to "pil"
) —
The output format of the generate image. Choose between
PIL: PIL.Image.Image
or np.array
. bool
, defaults to True
) —
Whether or not to return a ~pipelines.stable_diffusion.StableDiffusionPipelineOutput
instead of a
plain tuple. Optional[Callable]
, defaults to None
) —
A function that will be called every callback_steps
steps during inference. The function will be
called with the following arguments: callback(step: int, timestep: int, latents: torch.FloatTensor)
. int
, defaults to 1) —
The frequency at which the callback
function will be called. If not specified, the callback will be
called at every step. Optional[Dict[str, Any]]
, defaults to None
) —
A kwargs dictionary that if specified is passed along to the AttentionProcessor
as defined under
self.processor
in
diffusers.models.attention_processor. Tuple[int]
, 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. Tuple[int]
, 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. Tuple[int]
, 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. Tuple[int]
, 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. For more
information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208. Tuple[int]
, 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. For more
information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208. Tuple[int]
, 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. For more
information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208. float
, 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. float
, defaults to 2.5) —
Part of SDXL’s micro-conditioning as explained in section 2.2 of
https://huggingface.co/papers/2307.01952. Can be used to
simulate an aesthetic score of the generated image by influencing the negative text condition. Optional[int]
, defaults to None
) —
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. Returns
diffusers.pipelines.stable_diffusion.StableDiffusionXLPipelineOutput
or tuple
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.
Function invoked when calling the pipeline for generation.
Examples:
>>> from optimum.neuron import NeuronStableDiffusionXLInpaintPipeline
>>> from diffusers.utils import load_image
>>> img_url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/sdxl-text2img.png" (
>>> mask_url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/sdxl-inpaint-mask.png"
>>> init_image = load_image(img_url).convert("RGB")
>>> mask_image = load_image(mask_url).convert("RGB")
>>> compiler_args = {"auto_cast": "matmul", "auto_cast_type": "bf16"}
>>> input_shapes = {"batch_size": 1, "height": 1024, "width": 1024}
>>> pipeline = NeuronStableDiffusionXLInpaintPipeline.from_pretrained(
... "stabilityai/stable-diffusion-xl-base-1.0", export=True, **compiler_args, **input_shapes,
... )
>>> pipeline.save_pretrained("sdxl_inpaint/")
>>> prompt = "A deep sea diver floating"
>>> image = pipeline(prompt=prompt, image=init_image, mask_image=mask_image, strength=0.85, guidance_scale=12.5).images[0]