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nsfw_content_detected (List[bool]) — |
List of flags denoting whether the corresponding generated image likely represents “not-safe-for-work” |
(nsfw) content, or None if safety checking could not be performed. |
Output class for Alt Diffusion pipelines. |
__call__ |
( |
*args |
**kwargs |
) |
Call self as a function. |
AltDiffusionPipeline |
class diffusers.AltDiffusionPipeline |
< |
source |
> |
( |
vae: AutoencoderKL |
text_encoder: RobertaSeriesModelWithTransformation |
tokenizer: XLMRobertaTokenizer |
unet: UNet2DConditionModel |
scheduler: KarrasDiffusionSchedulers |
safety_checker: StableDiffusionSafetyChecker |
feature_extractor: CLIPFeatureExtractor |
requires_safety_checker: bool = True |
) |
Parameters |
vae (AutoencoderKL) — |
Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations. |
text_encoder (RobertaSeriesModelWithTransformation) — |
Frozen text-encoder. Alt Diffusion uses the text portion of |
CLIP, |
specifically the clip-vit-large-patch14 variant. |
tokenizer (XLMRobertaTokenizer) — |
Tokenizer of class |
XLMRobertaTokenizer. |
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 latents. Can be one of |
DDIMScheduler, LMSDiscreteScheduler, or PNDMScheduler. |
safety_checker (StableDiffusionSafetyChecker) — |
Classification module that estimates whether generated images could be considered offensive or harmful. |
Please, refer to the model card for details. |
feature_extractor (CLIPFeatureExtractor) — |
Model that extracts features from generated images to be used as inputs for the safety_checker. |
Pipeline for text-to-image generation using Alt Diffusion. |
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.) |
__call__ |
< |
source |
> |
( |
prompt: typing.Union[str, typing.List[str]] = None |
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: typing.Optional[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 |
prompt_embeds: typing.Optional[torch.FloatTensor] = None |
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