Datasets:
sayakpaul
/
diffusers-qa-chatbot-artifacts

Dataset card Data Studio Files
xet
 Community
1
text
stringlengths
0
5.54k
which adds large negative values to the attention scores corresponding to β€œdiscard” tokens. return_dict (bool, optional, defaults to True) β€”
Whether or not to return a UNet2DConditionOutput instead of a plain
tuple. cross_attention_kwargs (dict, optional) β€”
A kwargs dictionary that if specified is passed along to the AttnProcessor. encoder_hidden_states_1 (torch.FloatTensor, optional) β€”
A second set of encoder hidden states with shape (batch, sequence_length_2, feature_dim_2). Can be
used to condition the model on a different set of embeddings to encoder_hidden_states. encoder_attention_mask_1 (torch.Tensor, optional) β€”
A cross-attention mask of shape (batch, sequence_length_2) is applied to encoder_hidden_states_1.
If True the mask is kept, otherwise if False it is discarded. Mask will be converted into a bias,
which adds large negative values to the attention scores corresponding to β€œdiscard” tokens. Returns
UNet2DConditionOutput or tuple
If return_dict is True, an UNet2DConditionOutput is returned, otherwise
a tuple is returned where the first element is the sample tensor.
The AudioLDM2UNet2DConditionModel forward method. AudioPipelineOutput class diffusers.AudioPipelineOutput < source > ( audios: ndarray ) Parameters audios (np.ndarray) β€”
List of denoised audio samples of a NumPy array of shape (batch_size, num_channels, sample_rate). Output class for audio pipelines.
Semantic Guidance Semantic Guidance for Diffusion Models was proposed in SEGA: Instructing Text-to-Image Models using Semantic Guidance and provides strong semantic control over image generation.
Small changes to the text prompt usually result in entirely different output images. However, with SEGA a variety of changes to the image are enabled that can be controlled easily and intuitively, while staying true to the original image composition. The abstract from the paper is: Text-to-image diffusion models have r...
Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations. text_encoder (CLIPTextModel) β€”
Frozen text-encoder (clip-vit-large-patch14). tokenizer (CLIPTokenizer) β€”
A CLIPTokenizer to tokenize text. unet (UNet2DConditionModel) β€”
A UNet2DConditionModel to denoise the encoded image latents. scheduler (SchedulerMixin) β€”
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) β€”
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) β€”
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' retur...
The prompt or prompts to guide image generation. height (int, optional, defaults to self.unet.config.sample_size * self.vae_scale_factor) β€”
The height in pixels of the generated image. width (int, optional, defaults to self.unet.config.sample_size * self.vae_scale_factor) β€”
The width in pixels of the generated image. num_inference_steps (int, optional, defaults to 50) β€”
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) β€”
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) β€”
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) β€”
The number of images to generate per prompt. eta (float, optional, defaults to 0.0) β€”
Corresponds to parameter eta (Ξ·) from the DDIM paper. Only applies
to the DDIMScheduler, and is ignored in other schedulers. generator (torch.Generator or List[torch.Generator], optional) β€”
A torch.Generator to make
generation deterministic. latents (torch.FloatTensor, optional) β€”
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") β€”
The output format of the generated image. Choose between PIL.Image or np.array. return_dict (bool, optional, defaults to True) β€”
Whether or not to return a StableDiffusionPipelineOutput instead of a
plain tuple. callback (Callable, optional) β€”
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) β€”
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) β€”
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) β€”
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) β€”
Whether the corresponding prompt in editing_prompt should be increased or decreased. edit_guidance_scale (float or List[float], optional, defaults to 5) β€”
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) β€”
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) β€”
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) β€”
Threshold of semantic guidance. edit_momentum_scale (float, optional, defaults to 0.1) β€”
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) β€”
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) β€”
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) β€”
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 β€œnot-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",
... ],