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(
gpu_id = 0
)
Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared
to enable_sequential_cpu_offload, this method moves one whole model at a time to the GPU when its forward
method is called, and the model remains in GPU until the next model runs. Memory savings are lower than with
enable_sequential_cpu_offload, but performance is much better due to the iterative execution of the unet.
enable_sequential_cpu_offload
<
source
>
(
gpu_id = 0
)
Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet,
text_encoder, vae and safety checker have their state dicts saved to CPU and then are moved to a
torch.device('meta') and loaded to GPU only when their specific submodule has its forwardmethod called. Note that offloading happens on a submodule basis. Memory savings are higher than withenable_model_cpu_offload`, but performance is lower.
generate_caption
<
source
>
(
images
)
Generates caption for a given image.
invert
<
source
>
(
prompt: typing.Optional[str] = None
image: typing.Union[torch.FloatTensor, PIL.Image.Image] = None
num_inference_steps: int = 50
guidance_scale: float = 1
generator: typing.Union[torch._C.Generator, typing.List[torch._C.Generator], NoneType] = None
latents: typing.Optional[torch.FloatTensor] = None
prompt_embeds: typing.Optional[torch.FloatTensor] = None
cross_attention_guidance_amount: float = 0.1
output_type: typing.Optional[str] = 'pil'
return_dict: bool = True
callback: typing.Union[typing.Callable[[int, int, torch.FloatTensor], NoneType], NoneType] = None
callback_steps: typing.Optional[int] = 1
cross_attention_kwargs: typing.Union[typing.Dict[str, typing.Any], NoneType] = None
lambda_auto_corr: float = 20.0
lambda_kl: float = 20.0
num_reg_steps: int = 5
num_auto_corr_rolls: int = 5
)
Parameters
prompt (str or List[str], optional) β€”
The prompt or prompts to guide the image generation. If not defined, one has to pass prompt_embeds.
instead.
image (PIL.Image.Image, optional) β€”
Image, or tensor representing an image batch which will be used for conditioning.
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.
generator (torch.Generator or List[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