Datasets:
sayakpaul
/
diffusers-qa-chatbot-artifacts

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Encodes the prompt into text encoder hidden states.
device: (torch.device, optional):
torch device to place the resulting embeddings on
num_images_per_prompt (int, optional, defaults to 1):
number of images that should be generated per prompt
do_classifier_free_guidance (bool, optional, defaults to True):
whether to use classifier free guidance or not
negative_prompt (str or List[str], optional):
The prompt or prompts not to guide the image generation. If not defined, one has to pass
negative_prompt_embeds. instead. If not defined, one has to pass negative_prompt_embeds. instead.
Ignored when not using guidance (i.e., ignored if guidance_scale is less than 1).
prompt_embeds (torch.FloatTensor, optional):
Pre-generated text embeddings. Can be used to easily tweak text inputs, e.g. prompt weighting. If not
provided, text embeddings will be generated from prompt input argument.
negative_prompt_embeds (torch.FloatTensor, optional):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, e.g. prompt
weighting. If not provided, negative_prompt_embeds will be generated from negative_prompt input
argument.
IFImg2ImgPipeline
class diffusers.IFImg2ImgPipeline
<
source
>
(
tokenizer: T5Tokenizer
text_encoder: T5EncoderModel
unet: UNet2DConditionModel
scheduler: DDPMScheduler
safety_checker: typing.Optional[diffusers.pipelines.deepfloyd_if.safety_checker.IFSafetyChecker]
feature_extractor: typing.Optional[transformers.models.clip.image_processing_clip.CLIPImageProcessor]
watermarker: typing.Optional[diffusers.pipelines.deepfloyd_if.watermark.IFWatermarker]
requires_safety_checker: bool = True
)
__call__
<
source
>
(
prompt: typing.Union[str, typing.List[str]] = None
image: typing.Union[PIL.Image.Image, torch.Tensor, numpy.ndarray, typing.List[PIL.Image.Image], typing.List[torch.Tensor], typing.List[numpy.ndarray]] = None
strength: float = 0.7
num_inference_steps: int = 80
timesteps: typing.List[int] = None
guidance_scale: float = 10.0
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
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
clean_caption: bool = True
cross_attention_kwargs: typing.Union[typing.Dict[str, typing.Any], NoneType] = None
)
→
~pipelines.stable_diffusion.IFPipelineOutput or tuple
Parameters
prompt (str or List[str], optional) —
The prompt or prompts to guide the image generation. If not defined, one has to pass prompt_embeds.
instead.
image (torch.FloatTensor or PIL.Image.Image) —
Image, or tensor representing an image batch, that will be used as the starting point for the
process.
strength (float, optional, defaults to 0.8) —
Conceptually, indicates how much to transform the reference image. Must be between 0 and 1. image
will be used as a starting point, adding more noise to it the larger the strength. The number of
denoising steps depends on the amount of noise initially added. When strength is 1, added noise will
be maximum and the denoising process will run for the full number of iterations specified in
num_inference_steps. A value of 1, therefore, essentially ignores 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.
timesteps (List[int], optional) —
Custom timesteps to use for the denoising process. If not defined, equal spaced num_inference_steps