sayakpaul/diffusers-qa-chatbot-artifacts · Datasets at Hugging Face

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expense of slower inference. 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. negative_prior_prompt (str, optional) —
The prompt not to guide the prior diffusion process. Ignored when not using guidance (i.e., ignored if
guidance_scale is less than 1). negative_prompt (str or List[str], optional) —
The prompt not to guide the image generation. Ignored when not using guidance (i.e., ignored if
guidance_scale is less than 1). guidance_scale (float, optional, defaults to 4.0) —
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,
usually at the expense of lower image quality. Returns
KandinskyPriorPipelineOutput or tuple
Function invoked when using the prior pipeline for interpolation. Examples: Copied >>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22Pipeline
>>> from diffusers.utils import load_image
>>> import PIL
>>> import torch
>>> from torchvision import transforms

>>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(
... "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16
... )
>>> pipe_prior.to("cuda")
>>> img1 = load_image(
... "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
... "/kandinsky/cat.png"
... )
>>> img2 = load_image(
... "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
... "/kandinsky/starry_night.jpeg"
... )
>>> images_texts = ["a cat", img1, img2]
>>> weights = [0.3, 0.3, 0.4]
>>> out = pipe_prior.interpolate(images_texts, weights)
>>> pipe = KandinskyV22Pipeline.from_pretrained(
... "kandinsky-community/kandinsky-2-2-decoder", torch_dtype=torch.float16
... )
>>> pipe.to("cuda")
>>> image = pipe(
... image_embeds=out.image_embeds,
... negative_image_embeds=out.negative_image_embeds,
... height=768,
... width=768,
... num_inference_steps=50,
... ).images[0]
>>> image.save("starry_cat.png") KandinskyV22Pipeline class diffusers.KandinskyV22Pipeline < source > ( unet: UNet2DConditionModel scheduler: DDPMScheduler movq: VQModel ) Parameters scheduler (Union[DDIMScheduler,DDPMScheduler]) —
A scheduler to be used in combination with unet to generate image latents. unet (UNet2DConditionModel) —
Conditional U-Net architecture to denoise the image embedding. movq (VQModel) —
MoVQ Decoder to generate the image from the latents. Pipeline for text-to-image generation using Kandinsky 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_embeds: Union negative_image_embeds: Union height: int = 512 width: int = 512 num_inference_steps: int = 100 guidance_scale: float = 4.0 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 ) → ImagePipelineOutput or tuple Parameters image_embeds (torch.FloatTensor or List[torch.FloatTensor]) —
The clip image embeddings for text prompt, that will be used to condition the image generation. negative_image_embeds (torch.FloatTensor or List[torch.FloatTensor]) —
The clip image embeddings for negative text prompt, will be used to condition the image generation. height (int, optional, defaults to 512) —
The height in pixels of the generated image. width (int, optional, defaults to 512) —
The width in pixels of the generated image. 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. guidance_scale (float, optional, defaults to 4.0) —
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,
usually at the expense of lower image quality. 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. Returns
ImagePipelineOutput or tuple
Function invoked when calling the pipeline for generation. Examples: Copied >>> from diffusers import KandinskyV22Pipeline, KandinskyV22PriorPipeline
>>> import torch

>>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained("kandinsky-community/kandinsky-2-2-prior")
>>> pipe_prior.to("cuda")
>>> prompt = "red cat, 4k photo"
>>> out = pipe_prior(prompt)
>>> image_emb = out.image_embeds
>>> zero_image_emb = out.negative_image_embeds
>>> pipe = KandinskyV22Pipeline.from_pretrained("kandinsky-community/kandinsky-2-2-decoder")
>>> pipe.to("cuda")
>>> image = pipe(
... image_embeds=image_emb,
... negative_image_embeds=zero_image_emb,
... height=768,
... width=768,
... num_inference_steps=50,
... ).images
>>> image[0].save("cat.png") KandinskyV22CombinedPipeline class diffusers.KandinskyV22CombinedPipeline < source > ( unet: UNet2DConditionModel scheduler: DDPMScheduler movq: VQModel prior_prior: PriorTransformer prior_image_encoder: CLIPVisionModelWithProjection prior_text_encoder: CLIPTextModelWithProjection prior_tokenizer: CLIPTokenizer prior_scheduler: UnCLIPScheduler prior_image_processor: CLIPImageProcessor ) Parameters scheduler (Union[DDIMScheduler,DDPMScheduler]) —
A scheduler to be used in combination with unet to generate image latents. unet (UNet2DConditionModel) —
Conditional U-Net architecture to denoise the image embedding. movq (VQModel) —
MoVQ Decoder to generate the image from the latents. prior_prior (PriorTransformer) —

expense of slower inference. 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. negative_prior_prompt (str, optional) —

The prompt not to guide the prior diffusion process. Ignored when not using guidance (i.e., ignored if

guidance_scale is less than 1). negative_prompt (str or List[str], optional) —

The prompt not to guide the image generation. Ignored when not using guidance (i.e., ignored if

guidance_scale is less than 1). guidance_scale (float, optional, defaults to 4.0) —

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,

usually at the expense of lower image quality. Returns

KandinskyPriorPipelineOutput or tuple

Function invoked when using the prior pipeline for interpolation. Examples: Copied >>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22Pipeline

>>> from diffusers.utils import load_image

>>> import PIL

>>> import torch

>>> from torchvision import transforms

>>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(

... "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16

... )

>>> pipe_prior.to("cuda")

>>> img1 = load_image(

... "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"

... "/kandinsky/cat.png"

... )

>>> img2 = load_image(

... "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"

... "/kandinsky/starry_night.jpeg"

... )

>>> images_texts = ["a cat", img1, img2]

>>> weights = [0.3, 0.3, 0.4]

>>> out = pipe_prior.interpolate(images_texts, weights)

>>> pipe = KandinskyV22Pipeline.from_pretrained(

... "kandinsky-community/kandinsky-2-2-decoder", torch_dtype=torch.float16

... )

>>> pipe.to("cuda")

>>> image = pipe(

... image_embeds=out.image_embeds,

... negative_image_embeds=out.negative_image_embeds,

... height=768,

... width=768,

... num_inference_steps=50,

... ).images[0]

>>> image.save("starry_cat.png") KandinskyV22Pipeline class diffusers.KandinskyV22Pipeline < source > ( unet: UNet2DConditionModel scheduler: DDPMScheduler movq: VQModel ) Parameters scheduler (Union[DDIMScheduler,DDPMScheduler]) —

A scheduler to be used in combination with unet to generate image latents. unet (UNet2DConditionModel) —

Conditional U-Net architecture to denoise the image embedding. movq (VQModel) —

MoVQ Decoder to generate the image from the latents. Pipeline for text-to-image generation using Kandinsky 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_embeds: Union negative_image_embeds: Union height: int = 512 width: int = 512 num_inference_steps: int = 100 guidance_scale: float = 4.0 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 ) → ImagePipelineOutput or tuple Parameters image_embeds (torch.FloatTensor or List[torch.FloatTensor]) —

The clip image embeddings for text prompt, that will be used to condition the image generation. negative_image_embeds (torch.FloatTensor or List[torch.FloatTensor]) —

The clip image embeddings for negative text prompt, will be used to condition the image generation. height (int, optional, defaults to 512) —

The height in pixels of the generated image. width (int, optional, defaults to 512) —

The width in pixels of the generated image. 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. guidance_scale (float, optional, defaults to 4.0) —

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,

usually at the expense of lower image quality. 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. Returns

ImagePipelineOutput or tuple

Function invoked when calling the pipeline for generation. Examples: Copied >>> from diffusers import KandinskyV22Pipeline, KandinskyV22PriorPipeline

>>> import torch

>>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained("kandinsky-community/kandinsky-2-2-prior")

>>> pipe_prior.to("cuda")

>>> prompt = "red cat, 4k photo"

>>> out = pipe_prior(prompt)

>>> image_emb = out.image_embeds

>>> zero_image_emb = out.negative_image_embeds

>>> pipe = KandinskyV22Pipeline.from_pretrained("kandinsky-community/kandinsky-2-2-decoder")

>>> pipe.to("cuda")

>>> image = pipe(

... image_embeds=image_emb,

... negative_image_embeds=zero_image_emb,

... height=768,

... width=768,

... num_inference_steps=50,

... ).images

>>> image[0].save("cat.png") KandinskyV22CombinedPipeline class diffusers.KandinskyV22CombinedPipeline < source > ( unet: UNet2DConditionModel scheduler: DDPMScheduler movq: VQModel prior_prior: PriorTransformer prior_image_encoder: CLIPVisionModelWithProjection prior_text_encoder: CLIPTextModelWithProjection prior_tokenizer: CLIPTokenizer prior_scheduler: UnCLIPScheduler prior_image_processor: CLIPImageProcessor ) Parameters scheduler (Union[DDIMScheduler,DDPMScheduler]) —

A scheduler to be used in combination with unet to generate image latents. unet (UNet2DConditionModel) —

Conditional U-Net architecture to denoise the image embedding. movq (VQModel) —

MoVQ Decoder to generate the image from the latents. prior_prior (PriorTransformer) —