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The list of tensor inputs for the prior_callback_on_step_end function. The tensors specified in the |
list will be passed as callback_kwargs argument. You will only be able to include variables listed in |
the ._callback_tensor_inputs attribute of your pipeline class. callback_on_step_end (Callable, optional) — |
A function that calls at the end of each denoising steps during the inference. The function is called |
with the following arguments: callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int, callback_kwargs: Dict). callback_kwargs will include a list of all tensors as specified by |
callback_on_step_end_tensor_inputs. callback_on_step_end_tensor_inputs (List, optional) — |
The list of tensor inputs for the callback_on_step_end function. The tensors specified in the list |
will be passed as callback_kwargs argument. You will only be able to include variables listed in the |
._callback_tensor_inputs attribute of your pipeline class. Function invoked when calling the pipeline for generation. Examples: Copied >>> from diffusions import WuerstchenCombinedPipeline |
>>> pipe = WuerstchenCombinedPipeline.from_pretrained("warp-ai/Wuerstchen", torch_dtype=torch.float16).to( |
... "cuda" |
... ) |
>>> prompt = "an image of a shiba inu, donning a spacesuit and helmet" |
>>> images = pipe(prompt=prompt) enable_model_cpu_offload < source > ( 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 (unet, text_encoder, vae, and safety checker state dicts) to CPU using 🤗 |
Accelerate, significantly reducing memory usage. Models are moved to a torch.device('meta') and loaded on a |
GPU only when their specific submodule’s forward method is called. Offloading happens on a submodule basis. |
Memory savings are higher than using enable_model_cpu_offload, but performance is lower. WuerstchenPriorPipeline class diffusers.WuerstchenPriorPipeline < source > ( tokenizer: CLIPTokenizer text_encoder: CLIPTextModel prior: WuerstchenPrior scheduler: DDPMWuerstchenScheduler latent_mean: float = 42.0 latent_std: float = 1.0 resolution_multiple: float = 42.67 ) Parameters prior (Prior) — |
The canonical unCLIP prior to approximate the image embedding from the text embedding. text_encoder (CLIPTextModelWithProjection) — |
Frozen text-encoder. tokenizer (CLIPTokenizer) — |
Tokenizer of class |
CLIPTokenizer. scheduler (DDPMWuerstchenScheduler) — |
A scheduler to be used in combination with prior to generate image embedding. latent_mean (‘float’, optional, defaults to 42.0) — |
Mean value for latent diffusers. latent_std (‘float’, optional, defaults to 1.0) — |
Standard value for latent diffusers. resolution_multiple (‘float’, optional, defaults to 42.67) — |
Default resolution for multiple images generated. Pipeline for generating image prior for Wuerstchen. 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.) The pipeline also inherits the following loading methods: load_lora_weights() for loading LoRA weights save_lora_weights() for saving LoRA weights __call__ < source > ( prompt: Union = None height: int = 1024 width: int = 1024 num_inference_steps: int = 60 timesteps: List = None guidance_scale: float = 8.0 negative_prompt: Union = None prompt_embeds: Optional = None negative_prompt_embeds: Optional = None num_images_per_prompt: Optional = 1 generator: Union = None latents: Optional = None output_type: Optional = 'pt' return_dict: bool = True callback_on_step_end: Optional = None callback_on_step_end_tensor_inputs: List = ['latents'] **kwargs ) Parameters prompt (str or List[str]) — |
The prompt or prompts to guide the image generation. height (int, optional, defaults to 1024) — |
The height in pixels of the generated image. width (int, optional, defaults to 1024) — |
The width in pixels of the generated image. num_inference_steps (int, optional, defaults to 60) — |
The number of denoising steps. More denoising steps usually lead to a higher quality image at the |
expense of slower inference. timesteps (List[int], optional) — |
Custom timesteps to use for the denoising process. If not defined, equal spaced num_inference_steps |
timesteps are used. Must be in descending order. guidance_scale (float, optional, defaults to 8.0) — |
Guidance scale as defined in Classifier-Free Diffusion Guidance. |
decoder_guidance_scale is defined as w of equation 2. of Imagen |
Paper. Guidance scale is enabled by setting |
decoder_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. Ignored when not using guidance (i.e., ignored |
if decoder_guidance_scale is less than 1). prompt_embeds (torch.FloatTensor, optional) — |
Pre-generated text embeddings. Can be used to easily tweak text inputs, e.g. prompt weighting. If not |
provided, text embeddings will be generated from prompt input argument. negative_prompt_embeds (torch.FloatTensor, optional) — |
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, e.g. prompt |
weighting. If not provided, negative_prompt_embeds will be generated from negative_prompt input |
argument. num_images_per_prompt (int, optional, defaults to 1) — |
The number of images to generate per prompt. generator (torch.Generator or List[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), "np" |
(np.array) or "pt" (torch.Tensor). return_dict (bool, optional, defaults to True) — |
Whether or not to return a ImagePipelineOutput instead of a plain tuple. callback_on_step_end (Callable, optional) — |
A function that calls at the end of each denoising steps during the inference. The function is called |
with the following arguments: callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int, callback_kwargs: Dict). callback_kwargs will include a list of all tensors as specified by |
callback_on_step_end_tensor_inputs. callback_on_step_end_tensor_inputs (List, optional) — |
The list of tensor inputs for the callback_on_step_end function. The tensors specified in the list |
will be passed as callback_kwargs argument. You will only be able to include variables listed in the |
._callback_tensor_inputs attribute of your pipeline class. Function invoked when calling the pipeline for generation. Examples: Copied >>> import torch |
>>> from diffusers import WuerstchenPriorPipeline |
>>> prior_pipe = WuerstchenPriorPipeline.from_pretrained( |
... "warp-ai/wuerstchen-prior", torch_dtype=torch.float16 |
... ).to("cuda") |
>>> prompt = "an image of a shiba inu, donning a spacesuit and helmet" |
>>> prior_output = pipe(prompt) WuerstchenPriorPipelineOutput class diffusers.pipelines.wuerstchen.pipeline_wuerstchen_prior.WuerstchenPriorPipelineOutput < source > ( image_embeddings: Union ) Parameters image_embeddings (torch.FloatTensor or np.ndarray) — |
Prior image embeddings for text prompt Output class for WuerstchenPriorPipeline. WuerstchenDecoderPipeline class diffusers.WuerstchenDecoderPipeline < source > ( tokenizer: CLIPTokenizer text_encoder: CLIPTextModel decoder: WuerstchenDiffNeXt scheduler: DDPMWuerstchenScheduler vqgan: PaellaVQModel latent_dim_scale: float = 10.67 ) Parameters tokenizer (CLIPTokenizer) — |
The CLIP tokenizer. text_encoder (CLIPTextModel) — |
The CLIP text encoder. decoder (WuerstchenDiffNeXt) — |
The WuerstchenDiffNeXt unet decoder. vqgan (PaellaVQModel) — |
The VQGAN model. scheduler (DDPMWuerstchenScheduler) — |
A scheduler to be used in combination with prior to generate image embedding. latent_dim_scale (float, optional, defaults to 10.67) — |
Multiplier to determine the VQ latent space size from the image embeddings. If the image embeddings are |
height=24 and width=24, the VQ latent shape needs to be height=int(2410.67)=256 and |
width=int(2410.67)=256 in order to match the training conditions. Pipeline for generating images from the Wuerstchen model. 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 > ( image_embeddings: Union prompt: Union = None num_inference_steps: int = 12 timesteps: Optional = None guidance_scale: float = 0.0 negative_prompt: Union = None num_images_per_prompt: int = 1 generator: Union = None latents: Optional = None output_type: Optional = 'pil' return_dict: bool = True callback_on_step_end: Optional = None callback_on_step_end_tensor_inputs: List = ['latents'] **kwargs ) Parameters image_embedding (torch.FloatTensor or List[torch.FloatTensor]) — |
Image Embeddings either extracted from an image or generated by a Prior Model. prompt (str or List[str]) — |
The prompt or prompts to guide the image generation. num_inference_steps (int, optional, defaults to 12) — |
The number of denoising steps. More denoising steps usually lead to a higher quality image at the |
expense of slower inference. timesteps (List[int], optional) — |
Custom timesteps to use for the denoising process. If not defined, equal spaced num_inference_steps |
timesteps are used. Must be in descending order. guidance_scale (float, optional, defaults to 0.0) — |
Guidance scale as defined in Classifier-Free Diffusion Guidance. |
decoder_guidance_scale is defined as w of equation 2. of Imagen |
Paper. Guidance scale is enabled by setting |
decoder_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. Ignored when not using guidance (i.e., ignored |
if decoder_guidance_scale is less than 1). num_images_per_prompt (int, optional, defaults to 1) — |
The number of images to generate per prompt. generator (torch.Generator or List[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 |
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