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**kwargs |
) |
Call self as a function. |
StableDiffusionPipelineSafe |
class diffusers.StableDiffusionPipelineSafe |
< |
source |
> |
( |
vae: AutoencoderKL |
text_encoder: CLIPTextModel |
tokenizer: CLIPTokenizer |
unet: UNet2DConditionModel |
scheduler: KarrasDiffusionSchedulers |
safety_checker: SafeStableDiffusionSafetyChecker |
feature_extractor: CLIPImageProcessor |
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 (CLIPTextModel) — |
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 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 (CLIPImageProcessor) — |
Model that extracts features from generated images to be used as inputs for the safety_checker. |
Pipeline for text-to-image generation using Safe Latent Diffusion. |
The implementation is based on the StableDiffusionPipeline |
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]] |
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 |
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 |
sld_guidance_scale: typing.Optional[float] = 1000 |
sld_warmup_steps: typing.Optional[int] = 10 |
sld_threshold: typing.Optional[float] = 0.01 |
sld_momentum_scale: typing.Optional[float] = 0.3 |
sld_mom_beta: typing.Optional[float] = 0.4 |
) |
→ |
StableDiffusionPipelineOutput or tuple |
Parameters |
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