The GaudiStableDiffusionPipeline
class enables to perform text-to-image generation on HPUs.
It inherits from the GaudiDiffusionPipeline
class that is the parent to any kind of diffuser pipeline.
To get the most out of it, it should be associated with a scheduler that is optimized for HPUs like GaudiDDIMScheduler
.
( vae: AutoencoderKL text_encoder: CLIPTextModel tokenizer: CLIPTokenizer unet: UNet2DConditionModel scheduler: KarrasDiffusionSchedulers safety_checker: StableDiffusionSafetyChecker feature_extractor: CLIPImageProcessor image_encoder: CLIPVisionModelWithProjection = None requires_safety_checker: bool = True use_habana: bool = False use_hpu_graphs: bool = False gaudi_config: Union = None bf16_full_eval: bool = False )
Parameters
AutoencoderKL
) —
Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations. ~transformers.CLIPTokenizer
) —
A CLIPTokenizer
to tokenize text. UNet2DConditionModel
) —
A UNet2DConditionModel
to denoise the encoded image latents. SchedulerMixin
) —
A scheduler to be used in combination with unet
to denoise the encoded image latents. Can be one of
DDIMScheduler
, LMSDiscreteScheduler
, or PNDMScheduler
. StableDiffusionSafetyChecker
) —
Classification module that estimates whether generated images could be considered offensive or harmful.
Please refer to the model card for more details
about a model’s potential harms. CLIPImageProcessor
to extract features from generated images; used as inputs to the safety_checker
. False
) —
Whether to use Gaudi (True
) or CPU (False
). False
) —
Whether to use HPU graphs or not. None
) —
Gaudi configuration to use. Can be a string to download it from the Hub.
Or a previously initialized config can be passed. False
) —
Whether to use full bfloat16 evaluation instead of 32-bit.
This will be faster and save memory compared to fp32/mixed precision but can harm generated images. mark_step()
were added to add support for lazy mode( prompt: Union = None height: Optional = None width: Optional = None num_inference_steps: int = 50 timesteps: List = None guidance_scale: float = 7.5 negative_prompt: Union = None num_images_per_prompt: Optional = 1 batch_size: int = 1 eta: float = 0.0 generator: Union = None latents: Optional = None prompt_embeds: Optional = None negative_prompt_embeds: Optional = None ip_adapter_image: Union = 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 clip_skip: Optional = None callback_on_step_end: Optional = None callback_on_step_end_tensor_inputs: List = ['latents'] profiling_warmup_steps: Optional = 0 profiling_steps: Optional = 0 **kwargs ) → GaudiStableDiffusionPipelineOutput
or tuple
Parameters
str
or List[str]
, optional) —
The prompt or prompts to guide image generation. If not defined, you need to pass prompt_embeds
. int
, optional, defaults to self.unet.config.sample_size * self.vae_scale_factor
) —
The height in pixels of the generated images. int
, optional, defaults to self.unet.config.sample_size * self.vae_scale_factor
) —
The width in pixels of the generated images. 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. 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. 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
. 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
). int
, optional, defaults to 1) —
The number of images to generate per prompt. int
, optional, defaults to 1) —
The number of images in a batch. float
, optional, defaults to 0.0) —
Corresponds to parameter eta (η) from the DDIM paper. Only applies
to the ~schedulers.DDIMScheduler
, and is ignored in other schedulers. torch.Generator
or List[torch.Generator]
, optional) —
A torch.Generator
to make
generation deterministic. 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 is generated by sampling using the supplied random generator
. torch.FloatTensor
, 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. torch.FloatTensor
, 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. str
, optional, defaults to "pil"
) —
The output format of the generated image. Choose between PIL.Image
or np.array
. bool
, optional, defaults to True
) —
Whether or not to return a GaudiStableDiffusionPipelineOutput
instead of a
plain tuple. dict
, optional) —
A kwargs dictionary that if specified is passed along to the AttentionProcessor
as defined in
self.processor
. float
, optional, 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. 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. 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
. 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. int
, optional) —
Number of steps to ignore for profling. int
, optional) —
Number of steps to be captured when enabling profiling. Returns
GaudiStableDiffusionPipelineOutput
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.
( use_habana: bool = False use_hpu_graphs: bool = False gaudi_config: Union = None bf16_full_eval: bool = False )
Parameters
False
) —
Whether to use Gaudi (True
) or CPU (False
). False
) —
Whether to use HPU graphs or not. None
) —
Gaudi configuration to use. Can be a string to download it from the Hub.
Or a previously initialized config can be passed. False
) —
Whether to use full bfloat16 evaluation instead of 32-bit.
This will be faster and save memory compared to fp32/mixed precision but can harm generated images. Extends the DiffusionPipeline
class:
use_habana=True
.More information here.
( save_directory: Union safe_serialization: bool = True variant: Optional = None push_to_hub: bool = False **kwargs )
Parameters
str
or os.PathLike
) —
Directory to which to save. Will be created if it doesn’t exist. bool
, optional, defaults to True
) —
Whether to save the model using safetensors
or the traditional PyTorch way (that uses pickle
). str
, optional) —
If specified, weights are saved in the format pytorch_model.bool
, optional, defaults to False
) —
Whether or not to push your model to the Hugging Face model hub after saving it. You can specify the
repository you want to push to with repo_id
(will default to the name of save_directory
in your
namespace). Dict[str, Any]
, optional) —
Additional keyword arguments passed along to the ~utils.PushToHubMixin.push_to_hub
method. Save the pipeline and Gaudi configurations. More information here.
( num_train_timesteps: int = 1000 beta_start: float = 0.0001 beta_end: float = 0.02 beta_schedule: str = 'linear' trained_betas: Union = None clip_sample: bool = True set_alpha_to_one: bool = True steps_offset: int = 0 prediction_type: str = 'epsilon' thresholding: bool = False dynamic_thresholding_ratio: float = 0.995 clip_sample_range: float = 1.0 sample_max_value: float = 1.0 timestep_spacing: str = 'leading' rescale_betas_zero_snr: bool = False )
Parameters
int
, defaults to 1000) —
The number of diffusion steps to train the model. float
, defaults to 0.0001) —
The starting beta
value of inference. float
, defaults to 0.02) —
The final beta
value. str
, defaults to "linear"
) —
The beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from
linear
, scaled_linear
, or squaredcos_cap_v2
. np.ndarray
, optional) —
Pass an array of betas directly to the constructor to bypass beta_start
and beta_end
. bool
, defaults to True
) —
Clip the predicted sample for numerical stability. float
, defaults to 1.0) —
The maximum magnitude for sample clipping. Valid only when clip_sample=True
. bool
, defaults to True
) —
Each diffusion step uses the alphas product value at that step and at the previous one. For the final step
there is no previous alpha. When this option is True
the previous alpha product is fixed to 1
,
otherwise it uses the alpha value at step 0. int
, defaults to 0) —
An offset added to the inference steps. You can use a combination of offset=1
and
set_alpha_to_one=False
to make the last step use step 0 for the previous alpha product like in Stable
Diffusion. str
, defaults to epsilon
, optional) —
Prediction type of the scheduler function; can be epsilon
(predicts the noise of the diffusion process),
sample
(directly predicts the noisy sample) or
v_prediction` (see section 2.4 of Imagen
Video paper). bool
, defaults to False
) —
Whether to use the “dynamic thresholding” method. This is unsuitable for latent-space diffusion models such
as Stable Diffusion. float
, defaults to 0.995) —
The ratio for the dynamic thresholding method. Valid only when thresholding=True
. float
, defaults to 1.0) —
The threshold value for dynamic thresholding. Valid only when thresholding=True
. str
, defaults to "leading"
) —
The way the timesteps should be scaled. Refer to Table 2 of the Common Diffusion Noise Schedules and
Sample Steps are Flawed for more information. bool
, defaults to False
) —
Whether to rescale the betas to have zero terminal SNR. This enables the model to generate very bright and
dark samples instead of limiting it to samples with medium brightness. Loosely related to
--offset_noise
. Extends Diffusers’ DDIMScheduler to run optimally on Gaudi:
( timestep: Optional = None )
Initialize the time-dependent parameters, and retrieve the time-dependent parameters at each timestep. The tensors are rolled in a separate function at the end of the scheduler step in case parameters are retrieved multiple times in a timestep, e.g., when scaling model inputs and in the scheduler step.
Roll tensors to update the values of the time-dependent parameters at each timestep.
( model_output: FloatTensor timestep: int sample: FloatTensor eta: float = 0.0 use_clipped_model_output: bool = False generator = None variance_noise: Optional = None return_dict: bool = True ) → diffusers.schedulers.scheduling_utils.DDIMSchedulerOutput
or tuple
Parameters
torch.FloatTensor
) —
The direct output from learned diffusion model. torch.FloatTensor
) —
A current instance of a sample created by the diffusion process. float
) —
The weight of noise for added noise in diffusion step. bool
, defaults to False
) —
If True
, computes “corrected” model_output
from the clipped predicted original sample. Necessary
because predicted original sample is clipped to [-1, 1] when self.config.clip_sample
is True
. If no
clipping has happened, “corrected” model_output
would coincide with the one provided as input and
use_clipped_model_output
has no effect. torch.Generator
, optional) —
A random number generator. torch.FloatTensor
) —
Alternative to generating noise with generator
by directly providing the noise for the variance
itself. Useful for methods such as CycleDiffusion
. bool
, optional, defaults to True
) —
Whether or not to return a DDIMSchedulerOutput
or tuple
. Returns
diffusers.schedulers.scheduling_utils.DDIMSchedulerOutput
or tuple
If return_dict is True
, DDIMSchedulerOutput
is returned, otherwise a
tuple is returned where the first element is the sample tensor.
Predict the sample from the previous timestep by reversing the SDE. This function propagates the diffusion process from the learned model outputs (most often the predicted noise).
The GaudiStableDiffusionXLPipeline
class enables to perform text-to-image generation on HPUs using SDXL models.
It inherits from the GaudiDiffusionPipeline
class that is the parent to any kind of diffuser pipeline.
To get the most out of it, it should be associated with a scheduler that is optimized for HPUs like GaudiDDIMScheduler
.
Recommended schedulers are GaudiEulerDiscreteScheduler
for SDXL base and GaudiEulerAncestralDiscreteScheduler
for SDXL turbo.
( vae: AutoencoderKL text_encoder: CLIPTextModel text_encoder_2: CLIPTextModelWithProjection tokenizer: CLIPTokenizer tokenizer_2: CLIPTokenizer unet: UNet2DConditionModel scheduler: KarrasDiffusionSchedulers image_encoder: CLIPVisionModelWithProjection = None feature_extractor: CLIPImageProcessor = None force_zeros_for_empty_prompt: bool = True use_habana: bool = False use_hpu_graphs: bool = False gaudi_config: Union = None bf16_full_eval: bool = False )
Parameters
AutoencoderKL
) —
Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations. CLIPTextModel
) —
Frozen text-encoder. Stable Diffusion XL uses the text portion of
CLIP, specifically
the clip-vit-large-patch14 variant. CLIPTextModelWithProjection
) —
Second frozen text-encoder. Stable Diffusion XL uses the text and pool portion of
CLIP,
specifically the
laion/CLIP-ViT-bigG-14-laion2B-39B-b160k
variant. CLIPTokenizer
) —
Tokenizer of class
CLIPTokenizer. CLIPTokenizer
) —
Second Tokenizer of class
CLIPTokenizer. UNet2DConditionModel
) — Conditional U-Net architecture to denoise the encoded image latents. SchedulerMixin
) —
A scheduler to be used in combination with unet
to denoise the encoded image latents. Can be one of
DDIMScheduler
, LMSDiscreteScheduler
, or PNDMScheduler
. bool
, optional, defaults to "True"
) —
Whether the negative prompt embeddings shall be forced to always be set to 0. Also see the config of
stabilityai/stable-diffusion-xl-base-1-0
. False
) —
Whether to use Gaudi (True
) or CPU (False
). False
) —
Whether to use HPU graphs or not. None
) —
Gaudi configuration to use. Can be a string to download it from the Hub.
Or a previously initialized config can be passed. False
) —
Whether to use full bfloat16 evaluation instead of 32-bit.
This will be faster and save memory compared to fp32/mixed precision but can harm generated images. Pipeline for text-to-image generation using Stable Diffusion XL on Gaudi devices Adapted from: https://github.com/huggingface/diffusers/blob/v0.23.1/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl.py#L96
Extends the StableDiffusionXLPipeline
class:
mark_step()
were added to add support for lazy mode( prompt: Union = None prompt_2: Union = None height: Optional = None width: Optional = None num_inference_steps: int = 50 timesteps: List = 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 batch_size: 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 ip_adapter_image: Union = 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 callback_on_step_end: Optional = None callback_on_step_end_tensor_inputs: List = ['latents', 'prompt_embeds', 'negative_prompt_embeds', 'add_text_embeds', 'add_time_ids', 'negative_pooled_prompt_embeds', 'negative_add_time_ids'] profiling_warmup_steps: Optional = 0 profiling_steps: Optional = 0 **kwargs ) → #~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput
or tuple
Parameters
str
or List[str]
, optional) —
The prompt or prompts to guide the image generation. If not defined, one has to pass prompt_embeds
.
instead. 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 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
and checkpoints that are not specifically fine-tuned on low resolutions. 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
and checkpoints that are not specifically fine-tuned on low resolutions. 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. 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. 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 float
, optional, 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. 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
). 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 int
, optional, defaults to 1) —
The number of images to generate per prompt. int
, optional, defaults to 1) —
The number of images in a batch. float
, optional, 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. torch.Generator
or List[torch.Generator]
, optional) —
One or a list of torch generator(s)
to make generation deterministic. 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
. 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. 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. 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. 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. str
, optional, defaults to "pil"
) —
The output format of the generate image. Choose between
PIL: PIL.Image.Image
or np.array
. bool
, optional, defaults to True
) —
#Whether or not to return a ~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput
instead
Whether or not to return a GaudiStableDiffusionXLPipelineOutput
instead
of a plain tuple. dict
, optional) —
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. 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. 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. 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. 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. For more
information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208. 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. For more
information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208. 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. For more
information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208. 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
. 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. int
, optional) —
Number of steps to ignore for profling. int
, optional) —
Number of steps to be captured when enabling profiling. Returns
#~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput
or tuple
#~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput
if return_dict
is True, otherwise a
GaudiStableDiffusionXLPipelineOutput
or tuple
:
GaudiStableDiffusionXLPipelineOutput
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:
( num_train_timesteps: int = 1000 beta_start: float = 0.0001 beta_end: float = 0.02 beta_schedule: str = 'linear' trained_betas: Union = None prediction_type: str = 'epsilon' interpolation_type: str = 'linear' use_karras_sigmas: Optional = False sigma_min: Optional = None sigma_max: Optional = None timestep_spacing: str = 'linspace' timestep_type: str = 'discrete' steps_offset: int = 0 rescale_betas_zero_snr: bool = False )
Parameters
int
, defaults to 1000) —
The number of diffusion steps to train the model. float
, defaults to 0.0001) —
The starting beta
value of inference. float
, defaults to 0.02) —
The final beta
value. str
, defaults to "linear"
) —
The beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from
linear
or scaled_linear
. np.ndarray
, optional) —
Pass an array of betas directly to the constructor to bypass beta_start
and beta_end
. str
, defaults to epsilon
, optional) —
Prediction type of the scheduler function; can be epsilon
(predicts the noise of the diffusion process),
sample
(directly predicts the noisy sample) or
v_prediction` (see section 2.4 of Imagen
Video paper). str
, defaults to "linear"
, optional) —
The interpolation type to compute intermediate sigmas for the scheduler denoising steps. Should be on of
"linear"
or "log_linear"
. bool
, optional, defaults to False
) —
Whether to use Karras sigmas for step sizes in the noise schedule during the sampling process. If True
,
the sigmas are determined according to a sequence of noise levels {σi}. str
, defaults to "linspace"
) —
The way the timesteps should be scaled. Refer to Table 2 of the Common Diffusion Noise Schedules and
Sample Steps are Flawed for more information. int
, defaults to 0) —
An offset added to the inference steps. You can use a combination of offset=1
and
set_alpha_to_one=False
to make the last step use step 0 for the previous alpha product like in Stable
Diffusion. bool
, defaults to False
) —
Whether to rescale the betas to have zero terminal SNR. This enables the model to generate very bright and
dark samples instead of limiting it to samples with medium brightness. Loosely related to
--offset_noise
. Extends Diffusers’ EulerDiscreteScheduler to run optimally on Gaudi:
Roll tensors to update the values of the time-dependent parameters at each timestep.
( sample: FloatTensor timestep: Union ) → torch.FloatTensor
Ensures interchangeability with schedulers that need to scale the denoising model input depending on the
current timestep. Scales the denoising model input by (sigma**2 + 1) ** 0.5
to match the Euler algorithm.
( model_output: FloatTensor timestep: Union sample: FloatTensor s_churn: float = 0.0 s_tmin: float = 0.0 s_tmax: float = inf s_noise: float = 1.0 generator: Optional = None return_dict: bool = True ) → ~schedulers.scheduling_euler_discrete.EulerDiscreteSchedulerOutput
or tuple
Parameters
torch.FloatTensor
) —
The direct output from learned diffusion model. float
) —
The current discrete timestep in the diffusion chain. torch.FloatTensor
) —
A current instance of a sample created by the diffusion process. float
) — float
) — float
) — float
, defaults to 1.0) —
Scaling factor for noise added to the sample. torch.Generator
, optional) —
A random number generator. bool
) —
Whether or not to return a ~schedulers.scheduling_euler_discrete.EulerDiscreteSchedulerOutput
or
tuple. Returns
~schedulers.scheduling_euler_discrete.EulerDiscreteSchedulerOutput
or tuple
If return_dict is True
, ~schedulers.scheduling_euler_discrete.EulerDiscreteSchedulerOutput
is
returned, otherwise a tuple is returned where the first element is the sample tensor.
Predict the sample from the previous timestep by reversing the SDE. This function propagates the diffusion process from the learned model outputs (most often the predicted noise).
( num_train_timesteps: int = 1000 beta_start: float = 0.0001 beta_end: float = 0.02 beta_schedule: str = 'linear' trained_betas: Union = None prediction_type: str = 'epsilon' timestep_spacing: str = 'linspace' steps_offset: int = 0 rescale_betas_zero_snr: bool = False )
Parameters
int
, defaults to 1000) —
The number of diffusion steps to train the model. float
, defaults to 0.0001) —
The starting beta
value of inference. float
, defaults to 0.02) —
The final beta
value. str
, defaults to "linear"
) —
The beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from
linear
or scaled_linear
. np.ndarray
, optional) —
Pass an array of betas directly to the constructor to bypass beta_start
and beta_end
. str
, defaults to epsilon
, optional) —
Prediction type of the scheduler function; can be epsilon
(predicts the noise of the diffusion process),
sample
(directly predicts the noisy sample) or
v_prediction` (see section 2.4 of Imagen
Video paper). str
, defaults to "linspace"
) —
The way the timesteps should be scaled. Refer to Table 2 of the Common Diffusion Noise Schedules and
Sample Steps are Flawed for more information. int
, defaults to 0) —
An offset added to the inference steps. You can use a combination of offset=1
and
set_alpha_to_one=False
to make the last step use step 0 for the previous alpha product like in Stable
Diffusion. bool
, defaults to False
) —
Whether to rescale the betas to have zero terminal SNR. This enables the model to generate very bright and
dark samples instead of limiting it to samples with medium brightness. Loosely related to
--offset_noise
. Extends Diffusers’ EulerAncestralDiscreteScheduler to run optimally on Gaudi:
( timestep: Union )
Initialize the time-dependent parameters, and retrieve the time-dependent parameters at each timestep. The tensors are rolled in a separate function at the end of the scheduler step in case parameters are retrieved multiple times in a timestep, e.g., when scaling model inputs and in the scheduler step.
Roll tensors to update the values of the time-dependent parameters at each timestep.
( sample: FloatTensor timestep: Union ) → torch.FloatTensor
Ensures interchangeability with schedulers that need to scale the denoising model input depending on the
current timestep. Scales the denoising model input by (sigma**2 + 1) ** 0.5
to match the Euler algorithm.
( model_output: FloatTensor timestep: Union sample: FloatTensor generator: Optional = None return_dict: bool = True ) → ~schedulers.scheduling_euler_ancestral_discrete.EulerAncestralDiscreteSchedulerOutput
or tuple
Parameters
torch.FloatTensor
) —
The direct output from learned diffusion model. float
) —
The current discrete timestep in the diffusion chain. torch.FloatTensor
) —
A current instance of a sample created by the diffusion process. torch.Generator
, optional) —
A random number generator. bool
) —
Whether or not to return a
~schedulers.scheduling_euler_ancestral_discrete.EulerAncestralDiscreteSchedulerOutput
or tuple. Returns
~schedulers.scheduling_euler_ancestral_discrete.EulerAncestralDiscreteSchedulerOutput
or tuple
If return_dict is True
,
~schedulers.scheduling_euler_ancestral_discrete.EulerAncestralDiscreteSchedulerOutput
is returned,
otherwise a tuple is returned where the first element is the sample tensor.
Predict the sample from the previous timestep by reversing the SDE. This function propagates the diffusion process from the learned model outputs (most often the predicted noise).
The GaudiStableDiffusionUpscalePipeline
is used to enhance the resolution of input images by a factor of 4 on HPUs.
It inherits from the GaudiDiffusionPipeline
class that is the parent to any kind of diffuser pipeline.
( vae: AutoencoderKL text_encoder: CLIPTextModel tokenizer: CLIPTokenizer unet: UNet2DConditionModel low_res_scheduler: DDPMScheduler scheduler: KarrasDiffusionSchedulers safety_checker: Optional = None feature_extractor: Optional = None watermarker: Optional = None max_noise_level: int = 350 use_habana: bool = False use_hpu_graphs: bool = False gaudi_config: Union = None bf16_full_eval: bool = False )
Parameters
AutoencoderKL
) —
Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations. CLIPTextModel
) —
Frozen text-encoder. Stable Diffusion uses the text portion of
CLIP, specifically
the clip-vit-large-patch14 variant. CLIPTokenizer
) —
Tokenizer of class
CLIPTokenizer. UNet2DConditionModel
) — Conditional U-Net architecture to denoise the encoded image latents. SchedulerMixin
) —
A scheduler used to add initial noise to the low resolution conditioning image. It must be an instance of
DDPMScheduler
. SchedulerMixin
) —
A scheduler to be used in combination with unet
to denoise the encoded image latents. Can be one of
DDIMScheduler
, LMSDiscreteScheduler
, or PNDMScheduler
. StableDiffusionSafetyChecker
) —
Classification module that estimates whether generated images could be considered offensive or harmful.
Please, refer to the model card for details. CLIPImageProcessor
) —
Model that extracts features from generated images to be used as inputs for the safety_checker
. False
) —
Whether to use Gaudi (True
) or CPU (False
). False
) —
Whether to use HPU graphs or not. None
) —
Gaudi configuration to use. Can be a string to download it from the Hub.
Or a previously initialized config can be passed. False
) —
Whether to use full bfloat16 evaluation instead of 32-bit.
This will be faster and save memory compared to fp32/mixed precision but can harm generated images. Pipeline for text-guided image super-resolution using Stable Diffusion 2.
mark_step()
were added to add support for lazy mode( prompt: Union = None image: Union = None num_inference_steps: int = 75 guidance_scale: float = 9.0 noise_level: int = 20 negative_prompt: Union = None num_images_per_prompt: Optional = 1 batch_size: 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 clip_skip: int = None **kwargs ) → GaudiStableDiffusionPipelineOutput
or tuple
Parameters
str
or List[str]
, optional) —
The prompt or prompts to guide the image generation. If not defined, one has to pass prompt_embeds
.
instead. torch.FloatTensor
, PIL.Image.Image
, np.ndarray
, List[torch.FloatTensor]
, List[PIL.Image.Image]
, or List[np.ndarray]
) —
Image
or tensor representing an image batch to be upscaled. 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. float
, optional, 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. 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
). int
, optional, defaults to 1) —
The number of images to generate per prompt. int
, optional, defaults to 1) —
The number of images in a batch. float
, optional, 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. torch.Generator
or List[torch.Generator]
, optional) —
One or a list of torch generator(s)
to make generation deterministic. 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 randomly. 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. 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. str
, optional, defaults to "pil"
) —
The output format of the generate image. Choose between
PIL: PIL.Image.Image
or np.array
. bool
, optional, defaults to True
) —
Whether or not to return a GaudiStableDiffusionPipelineOutput
instead of a
plain tuple. Callable
, optional) —
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
, optional, 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
, optional) —
A kwargs dictionary that if specified is passed along to the AttentionProcessor
as defined under
self.processor
in
diffusers.cross_attention. 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. Returns
GaudiStableDiffusionPipelineOutput
or tuple
GaudiStableDiffusionPipelineOutput
if return_dict
is True, otherwise a tuple
.
When returning a tuple, the first element is a list with the generated images, and the second element is a
list of bool
s denoting whether the corresponding generated image likely represents “not-safe-for-work”
(nsfw) content, according to the safety_checker
.
Function invoked when calling the pipeline for generation.
Examples:
>>> import requests #TODO to test?
>>> from PIL import Image
>>> from io import BytesIO
>>> from optimum.habana.diffusers import GaudiStableDiffusionUpscalePipeline
>>> import torch
>>> # load model and scheduler
>>> model_id = "stabilityai/stable-diffusion-x4-upscaler"
>>> pipeline = GaudiStableDiffusionUpscalePipeline.from_pretrained(
... model_id, revision="fp16", torch_dtype=torch.bfloat16
... )
>>> pipeline = pipeline.to("cuda")
>>> # let's download an image
>>> url = "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale/low_res_cat.png"
>>> response = requests.get(url)
>>> low_res_img = Image.open(BytesIO(response.content)).convert("RGB")
>>> low_res_img = low_res_img.resize((128, 128))
>>> prompt = "a white cat"
>>> upscaled_image = pipeline(prompt=prompt, image=low_res_img).images[0]
>>> upscaled_image.save("upsampled_cat.png")
The GaudiDDPMPipeline
is to enable unconditional image generations on HPUs. It has similar APIs as the regular DiffusionPipeline
.
It shares a common parent class, GaudiDiffusionPipeline
, with other existing Gaudi pipelines. It now supports both DDPM and DDIM scheduler.
It is recommended to use the optimized scheduler, GaudiDDIMScheduler
, to obtain the best performance and image outputs.