Datasets:
sayakpaul
/
diffusers-qa-chatbot-artifacts

Dataset card Data Studio Files
xet
 Community
1
text
stringlengths
0
5.54k
prompt (str or List[str]) β€”
The prompt or prompts to guide the image generation.
height (int, optional, defaults to self.unet.config.sample_size * self.vae_scale_factor) β€”
The height in pixels of the generated image.
width (int, optional, defaults to self.unet.config.sample_size * self.vae_scale_factor) β€”
The width in pixels of the generated image.
num_inference_steps (int, optional, defaults to 50) β€”
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
guidance_scale (float, optional, defaults to 7.5) β€”
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) β€”
The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
if guidance_scale is less than 1).
num_images_per_prompt (int, optional, defaults to 1) β€”
The number of images to generate per prompt.
eta (float, optional, defaults to 0.0) β€”
Corresponds to parameter eta (Ξ·) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
schedulers.DDIMScheduler, will be ignored for others.
generator (torch.Generator, optional) β€”
One or a list of torch generator(s)
to make generation deterministic.
latents (torch.FloatTensor, optional) β€”
Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor will ge generated by sampling using the supplied random generator.
output_type (str, optional, defaults to "pil") β€”
The output format of the generate image. Choose between
PIL: PIL.Image.Image or np.array.
return_dict (bool, optional, defaults to True) β€”
Whether or not to return a StableDiffusionPipelineOutput instead of a
plain tuple.
callback (Callable, optional) β€”
A function that will be called every callback_steps steps during inference. The function will be
called with the following arguments: callback(step: int, timestep: int, latents: torch.FloatTensor).
callback_steps (int, optional, defaults to 1) β€”
The frequency at which the callback function will be called. If not specified, the callback will be
called at every step.
sld_guidance_scale (float, optional, defaults to 1000) β€”
Safe latent guidance as defined in Safe Latent Diffusion.
sld_guidance_scale is defined as sS of Eq. 6. If set to be less than 1, safety guidance will be
disabled.
sld_warmup_steps (int, optional, defaults to 10) β€”
Number of warmup steps for safety guidance. SLD will only be applied for diffusion steps greater than
sld_warmup_steps. sld_warmup_steps is defined as delta of Safe Latent
Diffusion.
sld_threshold (float, optional, defaults to 0.01) β€”
Threshold that separates the hyperplane between appropriate and inappropriate images. sld_threshold
is defined as lamda of Eq. 5 in Safe Latent Diffusion.
sld_momentum_scale (float, optional, defaults to 0.3) β€”
Scale of the SLD momentum to be added to the safety guidance at each diffusion step. If set to 0.0
momentum will be disabled. Momentum is already built up during warmup, i.e. for diffusion steps smaller
than sld_warmup_steps. sld_momentum_scale is defined as sm of Eq. 7 in Safe Latent
Diffusion.
sld_mom_beta (float, optional, defaults to 0.4) β€”
Defines how safety guidance momentum builds up. sld_mom_beta indicates how much of the previous
momentum will be kept. Momentum is already built up during warmup, i.e. for diffusion steps smaller
than sld_warmup_steps. sld_mom_beta is defined as beta m of Eq. 8 in Safe Latent
Diffusion.