Datasets:
sayakpaul
/
diffusers-qa-chatbot-artifacts

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Enable tiled VAE decoding.
When this option is enabled, the VAE will split the input tensor into tiles to compute decoding and encoding in
several steps. This is useful to save a large amount of memory and to allow the processing of larger images.
AltDiffusionImg2ImgPipeline
class diffusers.AltDiffusionImg2ImgPipeline
<
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-guided image 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
image: typing.Union[torch.FloatTensor, PIL.Image.Image] = None
strength: float = 0.8
num_inference_steps: typing.Optional[int] = 50
guidance_scale: typing.Optional[float] = 7.5
negative_prompt: typing.Union[str, typing.List[str], NoneType] = None
num_images_per_prompt: typing.Optional[int] = 1
eta: typing.Optional[float] = 0.0
generator: typing.Union[torch._C.Generator, typing.List[torch._C.Generator], NoneType] = None
prompt_embeds: typing.Optional[torch.FloatTensor] = None
negative_prompt_embeds: 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
)
→
~pipelines.stable_diffusion.AltDiffusionPipelineOutput or tuple
Parameters