sayakpaul/diffusers-qa-chatbot-artifacts · Datasets at Hugging Face

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A CLIPTokenizer to tokenize text. unet (UNet2DConditionModel) —
A UNet2DConditionModel to denoise the encoded image latents. scheduler (SchedulerMixin) —
A scheduler to be used in combination with unet to denoise the encoded image latents. Can be one of
DDIMScheduler, LMSDiscreteScheduler, or PNDMScheduler. safety_checker (Q16SafetyChecker) —
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. feature_extractor (CLIPImageProcessor) —
A CLIPImageProcessor to extract features from generated images; used as inputs to the safety_checker. Pipeline for text-to-image generation using Stable Diffusion with latent editing. This model inherits from DiffusionPipeline and builds on the StableDiffusionPipeline. Check the superclass
documentation for the generic methods implemented for all pipelines (downloading, saving, running on a particular
device, etc.). __call__ < source > ( prompt: Union height: Optional = None width: Optional = 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 output_type: Optional = 'pil' return_dict: bool = True callback: Optional = None callback_steps: int = 1 editing_prompt: Union = None editing_prompt_embeddings: Optional = None reverse_editing_direction: Union = False edit_guidance_scale: Union = 5 edit_warmup_steps: Union = 10 edit_cooldown_steps: Union = None edit_threshold: Union = 0.9 edit_momentum_scale: Optional = 0.1 edit_mom_beta: Optional = 0.4 edit_weights: Optional = None sem_guidance: Optional = None ) → SemanticStableDiffusionPipelineOutput or tuple Parameters prompt (str or List[str]) —
The prompt or prompts to guide image generation. height (int, optional, defaults to self.unet.config.sample_size * self.vae_scale_factor) —
The height in pixels of the generated image. width (int, optional, defaults to self.unet.config.sample_size * self.vae_scale_factor) —
The width in pixels of the generated image. num_inference_steps (int, optional, defaults to 50) —
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference. guidance_scale (float, optional, defaults to 7.5) —
A higher guidance scale value encourages the model to generate images closely linked to the text
prompt at the expense of lower image quality. Guidance scale is enabled when guidance_scale > 1. negative_prompt (str or List[str], optional) —
The prompt or prompts to guide what to not include in image generation. If not defined, you need to
pass negative_prompt_embeds instead. Ignored when not using guidance (guidance_scale < 1). num_images_per_prompt (int, optional, defaults to 1) —
The number of images to generate per prompt. eta (float, optional, defaults to 0.0) —
Corresponds to parameter eta (η) from the DDIM paper. Only applies
to the DDIMScheduler, and is ignored in other schedulers. generator (torch.Generator or List[torch.Generator], optional) —
A torch.Generator to make
generation deterministic. latents (torch.FloatTensor, optional) —
Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor is generated by sampling using the supplied random generator. output_type (str, optional, defaults to "pil") —
The output format of the generated image. Choose between PIL.Image or np.array. return_dict (bool, optional, defaults to True) —
Whether or not to return a StableDiffusionPipelineOutput instead of a
plain tuple. callback (Callable, optional) —
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). callback_steps (int, optional, defaults to 1) —
The frequency at which the callback function is called. If not specified, the callback is called at
every step. editing_prompt (str or List[str], optional) —
The prompt or prompts to use for semantic guidance. Semantic guidance is disabled by setting
editing_prompt = None. Guidance direction of prompt should be specified via
reverse_editing_direction. editing_prompt_embeddings (torch.Tensor, optional) —
Pre-computed embeddings to use for semantic guidance. Guidance direction of embedding should be
specified via reverse_editing_direction. reverse_editing_direction (bool or List[bool], optional, defaults to False) —
Whether the corresponding prompt in editing_prompt should be increased or decreased. edit_guidance_scale (float or List[float], optional, defaults to 5) —
Guidance scale for semantic guidance. If provided as a list, values should correspond to
editing_prompt. edit_warmup_steps (float or List[float], optional, defaults to 10) —
Number of diffusion steps (for each prompt) for which semantic guidance is not applied. Momentum is
calculated for those steps and applied once all warmup periods are over. edit_cooldown_steps (float or List[float], optional, defaults to None) —
Number of diffusion steps (for each prompt) after which semantic guidance is longer applied. edit_threshold (float or List[float], optional, defaults to 0.9) —
Threshold of semantic guidance. edit_momentum_scale (float, optional, defaults to 0.1) —
Scale of the momentum to be added to the semantic guidance at each diffusion step. If set to 0.0,
momentum is disabled. Momentum is already built up during warmup (for diffusion steps smaller than
sld_warmup_steps). Momentum is only added to latent guidance once all warmup periods are finished. edit_mom_beta (float, optional, defaults to 0.4) —
Defines how semantic guidance momentum builds up. edit_mom_beta indicates how much of the previous
momentum is kept. Momentum is already built up during warmup (for diffusion steps smaller than
edit_warmup_steps). edit_weights (List[float], optional, defaults to None) —
Indicates how much each individual concept should influence the overall guidance. If no weights are
provided all concepts are applied equally. sem_guidance (List[torch.Tensor], optional) —
List of pre-generated guidance vectors to be applied at generation. Length of the list has to
correspond to num_inference_steps. Returns
SemanticStableDiffusionPipelineOutput or tuple

If return_dict is True,
SemanticStableDiffusionPipelineOutput 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 bools indicating whether the corresponding generated image contains “not-safe-for-work”
(nsfw) content.
The call function to the pipeline for generation. Examples: Copied >>> import torch
>>> from diffusers import SemanticStableDiffusionPipeline

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

>>> out = pipe(
... prompt="a photo of the face of a woman",
... num_images_per_prompt=1,
... guidance_scale=7,
... editing_prompt=[
... "smiling, smile", # Concepts to apply
... "glasses, wearing glasses",
... "curls, wavy hair, curly hair",
... "beard, full beard, mustache",
... ],
... reverse_editing_direction=[
... False,
... False,
... False,
... False,
... ], # Direction of guidance i.e. increase all concepts
... edit_warmup_steps=[10, 10, 10, 10], # Warmup period for each concept
... edit_guidance_scale=[4, 5, 5, 5.4], # Guidance scale for each concept
... edit_threshold=[
... 0.99,
... 0.975,
... 0.925,
... 0.96,
... ], # Threshold for each concept. Threshold equals the percentile of the latent space that will be discarded. I.e. threshold=0.99 uses 1% of the latent dimensions
... edit_momentum_scale=0.3, # Momentum scale that will be added to the latent guidance
... edit_mom_beta=0.6, # Momentum beta
... edit_weights=[1, 1, 1, 1, 1], # Weights of the individual concepts against each other
... )
>>> image = out.images[0] StableDiffusionSafePipelineOutput class diffusers.pipelines.semantic_stable_diffusion.SemanticStableDiffusionPipelineOutput < source > ( images: Union nsfw_content_detected: Optional ) Parameters images (List[PIL.Image.Image] or np.ndarray) —

A CLIPTokenizer to tokenize text. unet (UNet2DConditionModel) —

A UNet2DConditionModel to denoise the encoded image latents. scheduler (SchedulerMixin) —

A scheduler to be used in combination with unet to denoise the encoded image latents. Can be one of

DDIMScheduler, LMSDiscreteScheduler, or PNDMScheduler. safety_checker (Q16SafetyChecker) —

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. feature_extractor (CLIPImageProcessor) —

A CLIPImageProcessor to extract features from generated images; used as inputs to the safety_checker. Pipeline for text-to-image generation using Stable Diffusion with latent editing. This model inherits from DiffusionPipeline and builds on the StableDiffusionPipeline. Check the superclass

documentation for the generic methods implemented for all pipelines (downloading, saving, running on a particular

device, etc.). __call__ < source > ( prompt: Union height: Optional = None width: Optional = 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 output_type: Optional = 'pil' return_dict: bool = True callback: Optional = None callback_steps: int = 1 editing_prompt: Union = None editing_prompt_embeddings: Optional = None reverse_editing_direction: Union = False edit_guidance_scale: Union = 5 edit_warmup_steps: Union = 10 edit_cooldown_steps: Union = None edit_threshold: Union = 0.9 edit_momentum_scale: Optional = 0.1 edit_mom_beta: Optional = 0.4 edit_weights: Optional = None sem_guidance: Optional = None ) → SemanticStableDiffusionPipelineOutput or tuple Parameters prompt (str or List[str]) —

The prompt or prompts to guide image generation. height (int, optional, defaults to self.unet.config.sample_size * self.vae_scale_factor) —

The height in pixels of the generated image. width (int, optional, defaults to self.unet.config.sample_size * self.vae_scale_factor) —

The width in pixels of the generated image. num_inference_steps (int, optional, defaults to 50) —

The number of denoising steps. More denoising steps usually lead to a higher quality image at the

expense of slower inference. guidance_scale (float, optional, defaults to 7.5) —

A higher guidance scale value encourages the model to generate images closely linked to the text

prompt at the expense of lower image quality. Guidance scale is enabled when guidance_scale > 1. negative_prompt (str or List[str], optional) —

The prompt or prompts to guide what to not include in image generation. If not defined, you need to

pass negative_prompt_embeds instead. Ignored when not using guidance (guidance_scale < 1). num_images_per_prompt (int, optional, defaults to 1) —

The number of images to generate per prompt. eta (float, optional, defaults to 0.0) —

Corresponds to parameter eta (η) from the DDIM paper. Only applies

to the DDIMScheduler, and is ignored in other schedulers. generator (torch.Generator or List[torch.Generator], optional) —

A torch.Generator to make

generation deterministic. latents (torch.FloatTensor, optional) —

Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image

generation. Can be used to tweak the same generation with different prompts. If not provided, a latents

tensor is generated by sampling using the supplied random generator. output_type (str, optional, defaults to "pil") —

The output format of the generated image. Choose between PIL.Image or np.array. return_dict (bool, optional, defaults to True) —

Whether or not to return a StableDiffusionPipelineOutput instead of a

plain tuple. callback (Callable, optional) —

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). callback_steps (int, optional, defaults to 1) —

The frequency at which the callback function is called. If not specified, the callback is called at

every step. editing_prompt (str or List[str], optional) —

The prompt or prompts to use for semantic guidance. Semantic guidance is disabled by setting

editing_prompt = None. Guidance direction of prompt should be specified via

reverse_editing_direction. editing_prompt_embeddings (torch.Tensor, optional) —

Pre-computed embeddings to use for semantic guidance. Guidance direction of embedding should be

specified via reverse_editing_direction. reverse_editing_direction (bool or List[bool], optional, defaults to False) —

Whether the corresponding prompt in editing_prompt should be increased or decreased. edit_guidance_scale (float or List[float], optional, defaults to 5) —

Guidance scale for semantic guidance. If provided as a list, values should correspond to

editing_prompt. edit_warmup_steps (float or List[float], optional, defaults to 10) —

Number of diffusion steps (for each prompt) for which semantic guidance is not applied. Momentum is

calculated for those steps and applied once all warmup periods are over. edit_cooldown_steps (float or List[float], optional, defaults to None) —

Number of diffusion steps (for each prompt) after which semantic guidance is longer applied. edit_threshold (float or List[float], optional, defaults to 0.9) —

Threshold of semantic guidance. edit_momentum_scale (float, optional, defaults to 0.1) —

Scale of the momentum to be added to the semantic guidance at each diffusion step. If set to 0.0,

momentum is disabled. Momentum is already built up during warmup (for diffusion steps smaller than

sld_warmup_steps). Momentum is only added to latent guidance once all warmup periods are finished. edit_mom_beta (float, optional, defaults to 0.4) —

Defines how semantic guidance momentum builds up. edit_mom_beta indicates how much of the previous

momentum is kept. Momentum is already built up during warmup (for diffusion steps smaller than

edit_warmup_steps). edit_weights (List[float], optional, defaults to None) —

Indicates how much each individual concept should influence the overall guidance. If no weights are

provided all concepts are applied equally. sem_guidance (List[torch.Tensor], optional) —

List of pre-generated guidance vectors to be applied at generation. Length of the list has to

correspond to num_inference_steps. Returns

SemanticStableDiffusionPipelineOutput or tuple

If return_dict is True,

SemanticStableDiffusionPipelineOutput 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 bools indicating whether the corresponding generated image contains “not-safe-for-work”

(nsfw) content.

The call function to the pipeline for generation. Examples: Copied >>> import torch

>>> from diffusers import SemanticStableDiffusionPipeline

>>> pipe = SemanticStableDiffusionPipeline.from_pretrained(

... "runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16

... )

>>> pipe = pipe.to("cuda")

>>> out = pipe(

... prompt="a photo of the face of a woman",

... num_images_per_prompt=1,

... guidance_scale=7,

... editing_prompt=[

... "smiling, smile", # Concepts to apply

... "glasses, wearing glasses",

... "curls, wavy hair, curly hair",

... "beard, full beard, mustache",

... ],

... reverse_editing_direction=[

... False,

... ], # Direction of guidance i.e. increase all concepts

... edit_warmup_steps=[10, 10, 10, 10], # Warmup period for each concept

... edit_guidance_scale=[4, 5, 5, 5.4], # Guidance scale for each concept

... edit_threshold=[

... 0.99,

... 0.975,

... 0.925,

... 0.96,

... ], # Threshold for each concept. Threshold equals the percentile of the latent space that will be discarded. I.e. threshold=0.99 uses 1% of the latent dimensions

... edit_momentum_scale=0.3, # Momentum scale that will be added to the latent guidance

... edit_mom_beta=0.6, # Momentum beta

... edit_weights=[1, 1, 1, 1, 1], # Weights of the individual concepts against each other

... )

>>> image = out.images[0] StableDiffusionSafePipelineOutput class diffusers.pipelines.semantic_stable_diffusion.SemanticStableDiffusionPipelineOutput < source > ( images: Union nsfw_content_detected: Optional ) Parameters images (List[PIL.Image.Image] or np.ndarray) —