Datasets:
sayakpaul
/
diffusers-qa-chatbot-artifacts

Dataset card Data Studio Files
xet
Community
1
text
stringlengths
0
5.54k
Frozen text-encoder. Stable Diffusion uses the text portion of
CLIP, specifically
the clip-vit-large-patch14 variant.
tokenizer (CLIPTokenizer) —
Tokenizer of class
CLIPTokenizer.
unet (UNet2DConditionModel) — Conditional U-Net architecture to denoise the encoded image latents.
scheduler (SchedulerMixin) —
A scheduler to be used in combination with unet to denoise the encoded image latens. 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 details.
feature_extractor (CLIPImageProcessor) —
Model that extracts features from generated images to be used as inputs for the safety_checker.
Pipeline for text-to-image generation with latent editing.
This model inherits from DiffusionPipeline. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
This model builds on the implementation of [‘StableDiffusionPipeline’]
__call__
<
source
>
(
prompt: typing.Union[str, typing.List[str]]
height: typing.Optional[int] = None
width: typing.Optional[int] = None
num_inference_steps: int = 50
guidance_scale: float = 7.5
negative_prompt: typing.Union[str, typing.List[str], NoneType] = None
num_images_per_prompt: int = 1
eta: float = 0.0
generator: typing.Union[torch._C.Generator, typing.List[torch._C.Generator], NoneType] = None
latents: typing.Optional[torch.FloatTensor] = None
output_type: typing.Optional[str] = 'pil'
return_dict: bool = True
callback: typing.Union[typing.Callable[[int, int, torch.FloatTensor], NoneType], NoneType] = None
callback_steps: int = 1
editing_prompt: typing.Union[str, typing.List[str], NoneType] = None
editing_prompt_embeddings: typing.Optional[torch.Tensor] = None
reverse_editing_direction: typing.Union[bool, typing.List[bool], NoneType] = False
edit_guidance_scale: typing.Union[float, typing.List[float], NoneType] = 5
edit_warmup_steps: typing.Union[int, typing.List[int], NoneType] = 10
edit_cooldown_steps: typing.Union[int, typing.List[int], NoneType] = None
edit_threshold: typing.Union[float, typing.List[float], NoneType] = 0.9
edit_momentum_scale: typing.Optional[float] = 0.1
edit_mom_beta: typing.Optional[float] = 0.4
edit_weights: typing.Optional[typing.List[float]] = None
sem_guidance: typing.Optional[typing.List[torch.Tensor]] = None
)
→
SemanticStableDiffusionPipelineOutput or tuple
Parameters
prompt (str or List[str]) —
The prompt or prompts to guide the 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) —
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.
negative_prompt (str or List[str], optional) —
The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
if guidance_scale is less than 1).