Datasets:
sayakpaul
/
diffusers-qa-chatbot-artifacts

Dataset card Data Studio Files
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feature_extractor: DPTFeatureExtractor
)
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.
Pipeline for text-guided image to image generation using Stable 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
depth_map: typing.Optional[torch.FloatTensor] = 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
)
→
StableDiffusionPipelineOutput 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. This parameter will be modulated by strength.
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. 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).