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Conceptually, indicates how much to transform the reference emb. Must be between 0 and 1. image |
will be used as a starting point, adding more noise to it the larger the strength. The number of |
denoising steps depends on the amount of noise initially added. emb (torch.FloatTensor) — |
The image embedding. 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 guidance_scale is less than 1). num_images_per_prompt (int, optional, defaults to 1) — |
The number of images to generate per prompt. 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. generator (torch.Generator or List[torch.Generator], optional) — |
One or a list of torch generator(s) |
to make generation deterministic. 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. output_type (str, optional, defaults to "pt") — |
The output format of the generate image. Choose between: "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. Returns |
KandinskyPriorPipelineOutput or tuple |
Function invoked when calling the pipeline for generation. Examples: Copied >>> from diffusers import KandinskyV22Pipeline, KandinskyV22PriorEmb2EmbPipeline |
>>> import torch |
>>> pipe_prior = KandinskyPriorPipeline.from_pretrained( |
... "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16 |
... ) |
>>> pipe_prior.to("cuda") |
>>> prompt = "red cat, 4k photo" |
>>> img = load_image( |
... "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" |
... "/kandinsky/cat.png" |
... ) |
>>> image_emb, nagative_image_emb = pipe_prior(prompt, image=img, strength=0.2).to_tuple() |
>>> pipe = KandinskyPipeline.from_pretrained( |
... "kandinsky-community/kandinsky-2-2-decoder, torch_dtype=torch.float16" |
... ) |
>>> pipe.to("cuda") |
>>> image = pipe( |
... image_embeds=image_emb, |
... negative_image_embeds=negative_image_emb, |
... height=768, |
... width=768, |
... num_inference_steps=100, |
... ).images |
>>> image[0].save("cat.png") interpolate < source > ( images_and_prompts: List weights: List num_images_per_prompt: int = 1 num_inference_steps: int = 25 generator: Union = None latents: Optional = None negative_prior_prompt: Optional = None negative_prompt: str = '' guidance_scale: float = 4.0 device = None ) → KandinskyPriorPipelineOutput or tuple Parameters images_and_prompts (List[Union[str, PIL.Image.Image, torch.FloatTensor]]) — |
list of prompts and images to guide the image generation. |
weights — (List[float]): |
list of weights for each condition in images_and_prompts num_images_per_prompt (int, optional, defaults to 1) — |
The number of images to generate per prompt. 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. 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 KandinskyV22PriorEmb2EmbPipeline, 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] |
>>> image_emb, zero_image_emb = 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( |
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