Datasets:
sayakpaul
/
diffusers-qa-chatbot-artifacts

Dataset card Data Studio Files
xet
 Community
1
text
stringlengths
0
5.54k
Parameters
vqvae (VQModel) —
Vector-quantized (VQ) VAE Model to encode and decode images to and from latent representations.
unet (UNet2DModel) — U-Net architecture to denoise the encoded image.
scheduler (SchedulerMixin) —
A scheduler to be used in combination with unet to denoise the encoded image latens. Can be one of
DDIMScheduler, LMSDiscreteScheduler, EulerDiscreteScheduler,
EulerAncestralDiscreteScheduler, DPMSolverMultistepScheduler, or PNDMScheduler.
A pipeline for image super-resolution using Latent
This class 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
>
(
image: typing.Union[torch.Tensor, PIL.Image.Image] = None
batch_size: typing.Optional[int] = 1
num_inference_steps: typing.Optional[int] = 100
eta: typing.Optional[float] = 0.0
generator: typing.Union[torch._C.Generator, typing.List[torch._C.Generator], NoneType] = None
output_type: typing.Optional[str] = 'pil'
return_dict: bool = True
)
→
ImagePipelineOutput or tuple
Parameters
image (torch.Tensor or PIL.Image.Image) —
Image, or tensor representing an image batch, that will be used as the starting point for the
process.
batch_size (int, optional, defaults to 1) —
Number of images to generate.
num_inference_steps (int, optional, defaults to 100) —
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
eta (float, optional, defaults to 0.0) —
Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
schedulers.DDIMScheduler, will be ignored for others.
generator (torch.Generator, optional) —
One or a list of torch generator(s)
to make generation deterministic.
output_type (str, optional, defaults to "pil") —
The output format of the generate image. Choose between
PIL: PIL.Image.Image or np.array.
return_dict (bool, optional) —
Whether or not to return a ImagePipelineOutput instead of a plain tuple.
Returns
ImagePipelineOutput or tuple
~pipelines.utils.ImagePipelineOutput if return_dict is
True, otherwise a `tuple. When returning a tuple, the first element is a list with the generated images.
Stable Diffusion 2 Stable Diffusion 2 is a text-to-image latent diffusion model built upon the work of the original Stable Diffusion, and it was led by Robin Rombach and Katherine Crowson from Stability AI and LAION. The Stable Diffusion 2.0 release includes robust text-to-image models trained using a brand new text en...
These models are trained on an aesthetic subset of the LAION-5B dataset created by the DeepFloyd team at Stability AI, which is then further filtered to remove adult content using LAION’s NSFW filter. For more details about how Stable Diffusion 2 works and how it differs from the original Stable Diffusion, please refer...
import torch
repo_id = "stabilityai/stable-diffusion-2-base"
pipe = DiffusionPipeline.from_pretrained(repo_id, torch_dtype=torch.float16, revision="fp16")
pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
pipe = pipe.to("cuda")
prompt = "High quality photo of an astronaut riding a horse in space"
image = pipe(prompt, num_inference_steps=25).images[0]
image Inpainting Copied import torch
from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler
from diffusers.utils import load_image, make_image_grid
img_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png"
mask_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png"