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expense of slower inference. generator (torch.Generator or List[torch.Generator], optional) β
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One or a list of torch generator(s)
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to make generation deterministic. latents (torch.FloatTensor, optional) β
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Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
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generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
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tensor will ge generated by sampling using the supplied random generator. negative_prior_prompt (str, optional) β
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The prompt not to guide the prior diffusion process. Ignored when not using guidance (i.e., ignored if
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guidance_scale is less than 1). negative_prompt (str or List[str], optional) β
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The prompt not to guide the image generation. Ignored when not using guidance (i.e., ignored if
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guidance_scale is less than 1). guidance_scale (float, optional, defaults to 4.0) β
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Guidance scale as defined in Classifier-Free Diffusion Guidance.
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guidance_scale is defined as w of equation 2. of Imagen
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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,
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usually at the expense of lower image quality. Returns
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KandinskyPriorPipelineOutput or tuple
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Function invoked when using the prior pipeline for interpolation. Examples: Copied >>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22Pipeline
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>>> from diffusers.utils import load_image
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>>> import PIL
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>>> import torch
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>>> from torchvision import transforms
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>>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(
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... "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16
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... )
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>>> pipe_prior.to("cuda")
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>>> img1 = load_image(
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... "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
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... "/kandinsky/cat.png"
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... )
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>>> img2 = load_image(
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... "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
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... "/kandinsky/starry_night.jpeg"
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... )
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>>> images_texts = ["a cat", img1, img2]
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>>> weights = [0.3, 0.3, 0.4]
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>>> out = pipe_prior.interpolate(images_texts, weights)
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>>> pipe = KandinskyV22Pipeline.from_pretrained(
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... "kandinsky-community/kandinsky-2-2-decoder", torch_dtype=torch.float16
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... )
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>>> pipe.to("cuda")
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>>> image = pipe(
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... image_embeds=out.image_embeds,
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... negative_image_embeds=out.negative_image_embeds,
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... height=768,
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... width=768,
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... num_inference_steps=50,
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... ).images[0]
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>>> image.save("starry_cat.png") KandinskyV22Pipeline class diffusers.KandinskyV22Pipeline < source > ( unet: UNet2DConditionModel scheduler: DDPMScheduler movq: VQModel ) Parameters scheduler (Union[DDIMScheduler,DDPMScheduler]) β
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A scheduler to be used in combination with unet to generate image latents. unet (UNet2DConditionModel) β
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Conditional U-Net architecture to denoise the image embedding. movq (VQModel) β
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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
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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 ) β ImagePipelineOutput or tuple Parameters image_embeds (torch.FloatTensor or List[torch.FloatTensor]) β
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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]) β
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The clip image embeddings for negative text prompt, will be used to condition the image generation. height (int, optional, defaults to 512) β
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The height in pixels of the generated image. width (int, optional, defaults to 512) β
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The width in pixels of the generated image. num_inference_steps (int, optional, defaults to 100) β
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The number of denoising steps. More denoising steps usually lead to a higher quality image at the
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expense of slower inference. guidance_scale (float, optional, defaults to 4.0) β
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Guidance scale as defined in Classifier-Free Diffusion Guidance.
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guidance_scale is defined as w of equation 2. of Imagen
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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,
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usually at the expense of lower image quality. num_images_per_prompt (int, optional, defaults to 1) β
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The number of images to generate per prompt. generator (torch.Generator or List[torch.Generator], optional) β
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One or a list of torch generator(s)
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to make generation deterministic. latents (torch.FloatTensor, optional) β
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Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
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generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
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tensor will ge generated by sampling using the supplied random generator. output_type (str, optional, defaults to "pil") β
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The output format of the generate image. Choose between: "pil" (PIL.Image.Image), "np"
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(np.array) or "pt" (torch.Tensor). return_dict (bool, optional, defaults to True) β
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Whether or not to return a ImagePipelineOutput instead of a plain tuple. callback_on_step_end (Callable, optional) β
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A function that calls at the end of each denoising steps during the inference. The function is called
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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
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callback_on_step_end_tensor_inputs. callback_on_step_end_tensor_inputs (List, optional) β
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The list of tensor inputs for the callback_on_step_end function. The tensors specified in the list
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will be passed as callback_kwargs argument. You will only be able to include variables listed in the
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._callback_tensor_inputs attribute of your pipeline class. Returns
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ImagePipelineOutput or tuple
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Function invoked when calling the pipeline for generation. Examples: Copied >>> from diffusers import KandinskyV22Pipeline, KandinskyV22PriorPipeline
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>>> import torch
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>>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained("kandinsky-community/kandinsky-2-2-prior")
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>>> pipe_prior.to("cuda")
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>>> prompt = "red cat, 4k photo"
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>>> out = pipe_prior(prompt)
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>>> image_emb = out.image_embeds
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>>> zero_image_emb = out.negative_image_embeds
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>>> pipe = KandinskyV22Pipeline.from_pretrained("kandinsky-community/kandinsky-2-2-decoder")
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>>> pipe.to("cuda")
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>>> image = pipe(
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... image_embeds=image_emb,
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... negative_image_embeds=zero_image_emb,
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... height=768,
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... width=768,
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... num_inference_steps=50,
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... ).images
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>>> 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]) β
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A scheduler to be used in combination with unet to generate image latents. unet (UNet2DConditionModel) β
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Conditional U-Net architecture to denoise the image embedding. movq (VQModel) β
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MoVQ Decoder to generate the image from the latents. prior_prior (PriorTransformer) β
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