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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]] |
height: typing.Optional[int] = None |
width: typing.Optional[int] = None |
num_inference_steps: typing.Optional[int] = 50 |
guidance_scale: typing.Optional[float] = 1.0 |
eta: typing.Optional[float] = 0.0 |
generator: typing.Union[torch._C.Generator, typing.List[torch._C.Generator], NoneType] = None |
latents: typing.Optional[torch.FloatTensor] = None |
output_type: typing.Optional[str] = 'pil' |
return_dict: bool = True |
**kwargs |
) |
→ |
ImagePipelineOutput or tuple |
Parameters |
prompt (str or List[str]) — |
The prompt or prompts to guide the image generation. |
height (int, optional, defaults to self.unet.config.sample_size * self.vae_scale_factor) — |
The height in pixels of the generated image. |
width (int, optional, defaults to self.unet.config.sample_size * self.vae_scale_factor) — |
The width in pixels of the generated 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. |
guidance_scale (float, optional, defaults to 1.0) — |
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 at |
the, usually at the expense of lower image quality. |
generator (torch.Generator, optional) — |
One or a list of torch generator(s) |
to make generation deterministic. |
latents (torch.FloatTensor, optional) — |
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") — |
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. |
LDMSuperResolutionPipeline |
class diffusers.LDMSuperResolutionPipeline |
< |
source |
> |
( |
vqvae: VQModel |
unet: UNet2DModel |
scheduler: typing.Union[diffusers.schedulers.scheduling_ddim.DDIMScheduler, diffusers.schedulers.scheduling_pndm.PNDMScheduler, diffusers.schedulers.scheduling_lms_discrete.LMSDiscreteScheduler, diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler, diffusers.schedulers.scheduling_euler_ancestral_discrete.EulerAncestralDiscreteScheduler, diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler] |
) |
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