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Kandinsky 2.1

Kandinsky 2.1 is created by Arseniy Shakhmatov, Anton Razzhigaev, Aleksandr Nikolich, Vladimir Arkhipkin, Igor Pavlov, Andrey Kuznetsov, and Denis Dimitrov.

The description from it's GitHub page is:

Kandinsky 2.1 inherits best practicies from Dall-E 2 and Latent diffusion, while introducing some new ideas. As text and image encoder it uses CLIP model and diffusion image prior (mapping) between latent spaces of CLIP modalities. This approach increases the visual performance of the model and unveils new horizons in blending images and text-guided image manipulation.

The original codebase can be found at ai-forever/Kandinsky-2.

Check out the Kandinsky Community organization on the Hub for the official model checkpoints for tasks like text-to-image, image-to-image, and inpainting.

Make sure to check out the Schedulers guide to learn how to explore the tradeoff between scheduler speed and quality, and see the reuse components across pipelines section to learn how to efficiently load the same components into multiple pipelines.

KandinskyPriorPipeline[[diffusers.KandinskyPriorPipeline]]

diffusers.KandinskyPriorPipeline[[diffusers.KandinskyPriorPipeline]]

Source

Pipeline for generating image prior for 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__diffusers.KandinskyPriorPipeline.__call__https://github.com/huggingface/diffusers/blob/vr_11739/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_prior.py#L406[{"name": "prompt", "val": ": typing.Union[str, typing.List[str]]"}, {"name": "negative_prompt", "val": ": typing.Union[str, typing.List[str], NoneType] = None"}, {"name": "num_images_per_prompt", "val": ": int = 1"}, {"name": "num_inference_steps", "val": ": int = 25"}, {"name": "generator", "val": ": typing.Union[torch._C.Generator, typing.List[torch._C.Generator], NoneType] = None"}, {"name": "latents", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "guidance_scale", "val": ": float = 4.0"}, {"name": "output_type", "val": ": typing.Optional[str] = 'pt'"}, {"name": "return_dict", "val": ": bool = True"}]- prompt (str or List[str]) -- The prompt or prompts to guide the image generation.

  • 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 25) -- 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.Tensor, 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 be generated by sampling using the supplied random generator.
  • 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.0KandinskyPriorPipelineOutput or tuple

Function invoked when calling the pipeline for generation.

Examples:

>>> from diffusers import KandinskyPipeline, KandinskyPriorPipeline
>>> import torch

>>> pipe_prior = KandinskyPriorPipeline.from_pretrained("kandinsky-community/kandinsky-2-1-prior")
>>> pipe_prior.to("cuda")

>>> prompt = "red cat, 4k photo"
>>> out = pipe_prior(prompt)
>>> image_emb = out.image_embeds
>>> negative_image_emb = out.negative_image_embeds

>>> pipe = KandinskyPipeline.from_pretrained("kandinsky-community/kandinsky-2-1")
>>> pipe.to("cuda")

>>> image = pipe(
...     prompt,
...     image_embeds=image_emb,
...     negative_image_embeds=negative_image_emb,
...     height=768,
...     width=768,
...     num_inference_steps=100,
... ).images

>>> image[0].save("cat.png")

Parameters:

prior (PriorTransformer) : The canonical unCLIP prior to approximate the image embedding from the text embedding.

image_encoder (CLIPVisionModelWithProjection) : Frozen image-encoder.

text_encoder (CLIPTextModelWithProjection) : Frozen text-encoder.

tokenizer (CLIPTokenizer) : Tokenizer of class CLIPTokenizer.

scheduler (UnCLIPScheduler) : A scheduler to be used in combination with prior to generate image embedding.

Returns:

KandinskyPriorPipelineOutput` or `tuple

interpolate[[diffusers.KandinskyPriorPipeline.interpolate]]

Source

Function invoked when using the prior pipeline for interpolation.

Examples:

>>> from diffusers import KandinskyPriorPipeline, KandinskyPipeline
>>> from diffusers.utils import load_image
>>> import PIL

>>> import torch
>>> from torchvision import transforms

>>> pipe_prior = KandinskyPriorPipeline.from_pretrained(
...     "kandinsky-community/kandinsky-2-1-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 = KandinskyPipeline.from_pretrained("kandinsky-community/kandinsky-2-1", torch_dtype=torch.float16)
>>> pipe.to("cuda")

>>> image = pipe(
...     "",
...     image_embeds=image_emb,
...     negative_image_embeds=zero_image_emb,
...     height=768,
...     width=768,
...     num_inference_steps=150,
... ).images[0]

>>> image.save("starry_cat.png")

Parameters:

images_and_prompts (List[Union[str, PIL.Image.Image, torch.Tensor]]) : 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 25) : 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.Tensor, 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 be 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

KandinskyPipeline[[diffusers.KandinskyPipeline]]

diffusers.KandinskyPipeline[[diffusers.KandinskyPipeline]]

Source

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__diffusers.KandinskyPipeline.__call__https://github.com/huggingface/diffusers/blob/vr_11739/src/diffusers/pipelines/kandinsky/pipeline_kandinsky.py#L236[{"name": "prompt", "val": ": typing.Union[str, typing.List[str]]"}, {"name": "image_embeds", "val": ": typing.Union[torch.Tensor, typing.List[torch.Tensor]]"}, {"name": "negative_image_embeds", "val": ": typing.Union[torch.Tensor, typing.List[torch.Tensor]]"}, {"name": "negative_prompt", "val": ": typing.Union[str, typing.List[str], NoneType] = None"}, {"name": "height", "val": ": int = 512"}, {"name": "width", "val": ": int = 512"}, {"name": "num_inference_steps", "val": ": int = 100"}, {"name": "guidance_scale", "val": ": float = 4.0"}, {"name": "num_images_per_prompt", "val": ": int = 1"}, {"name": "generator", "val": ": typing.Union[torch._C.Generator, typing.List[torch._C.Generator], NoneType] = None"}, {"name": "latents", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "output_type", "val": ": typing.Optional[str] = 'pil'"}, {"name": "callback", "val": ": typing.Optional[typing.Callable[[int, int, torch.Tensor], NoneType]] = None"}, {"name": "callback_steps", "val": ": int = 1"}, {"name": "return_dict", "val": ": bool = True"}]- prompt (str or List[str]) -- The prompt or prompts to guide the image generation.

  • image_embeds (torch.Tensor or List[torch.Tensor]) -- The clip image embeddings for text prompt, that will be used to condition the image generation.
  • negative_image_embeds (torch.Tensor or List[torch.Tensor]) -- The clip image embeddings for negative text prompt, will be used to condition the image generation.
  • 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).
  • 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.Tensor, 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 be 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).
  • callback (Callable, optional) -- A function that calls every callback_steps steps during inference. The function is called with the following arguments: callback(step: int, timestep: int, latents: torch.Tensor).
  • callback_steps (int, optional, defaults to 1) -- The frequency at which the callback function is called. If not specified, the callback is called at every step.
  • return_dict (bool, optional, defaults to True) -- Whether or not to return a ImagePipelineOutput instead of a plain tuple.0ImagePipelineOutput or tuple

Function invoked when calling the pipeline for generation.

Examples:

>>> from diffusers import KandinskyPipeline, KandinskyPriorPipeline
>>> import torch

>>> pipe_prior = KandinskyPriorPipeline.from_pretrained("kandinsky-community/Kandinsky-2-1-prior")
>>> pipe_prior.to("cuda")

>>> prompt = "red cat, 4k photo"
>>> out = pipe_prior(prompt)
>>> image_emb = out.image_embeds
>>> negative_image_emb = out.negative_image_embeds

>>> pipe = KandinskyPipeline.from_pretrained("kandinsky-community/kandinsky-2-1")
>>> pipe.to("cuda")

>>> image = pipe(
...     prompt,
...     image_embeds=image_emb,
...     negative_image_embeds=negative_image_emb,
...     height=768,
...     width=768,
...     num_inference_steps=100,
... ).images

>>> image[0].save("cat.png")

Parameters:

text_encoder (MultilingualCLIP) : Frozen text-encoder.

tokenizer (XLMRobertaTokenizer) : Tokenizer of class

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.

Returns:

[ImagePipelineOutput](/docs/diffusers/pr_11739/en/api/pipelines/stable_unclip#diffusers.ImagePipelineOutput) or tuple``

KandinskyCombinedPipeline[[diffusers.KandinskyCombinedPipeline]]

diffusers.KandinskyCombinedPipeline[[diffusers.KandinskyCombinedPipeline]]

Source

Combined 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__diffusers.KandinskyCombinedPipeline.__call__https://github.com/huggingface/diffusers/blob/vr_11739/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_combined.py#L215[{"name": "prompt", "val": ": typing.Union[str, typing.List[str]]"}, {"name": "negative_prompt", "val": ": typing.Union[str, typing.List[str], NoneType] = None"}, {"name": "num_inference_steps", "val": ": int = 100"}, {"name": "guidance_scale", "val": ": float = 4.0"}, {"name": "num_images_per_prompt", "val": ": int = 1"}, {"name": "height", "val": ": int = 512"}, {"name": "width", "val": ": int = 512"}, {"name": "prior_guidance_scale", "val": ": float = 4.0"}, {"name": "prior_num_inference_steps", "val": ": int = 25"}, {"name": "generator", "val": ": typing.Union[torch._C.Generator, typing.List[torch._C.Generator], NoneType] = None"}, {"name": "latents", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "output_type", "val": ": typing.Optional[str] = 'pil'"}, {"name": "callback", "val": ": typing.Optional[typing.Callable[[int, int, torch.Tensor], NoneType]] = None"}, {"name": "callback_steps", "val": ": int = 1"}, {"name": "return_dict", "val": ": bool = True"}]- prompt (str or List[str]) -- The prompt or prompts to guide the image generation.

  • 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.
  • 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.
  • prior_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.
  • prior_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.
  • generator (torch.Generator or List[torch.Generator], optional) -- One or a list of torch generator(s) to make generation deterministic.
  • latents (torch.Tensor, 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 be 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).
  • callback (Callable, optional) -- A function that calls every callback_steps steps during inference. The function is called with the following arguments: callback(step: int, timestep: int, latents: torch.Tensor).
  • callback_steps (int, optional, defaults to 1) -- The frequency at which the callback function is called. If not specified, the callback is called at every step.
  • return_dict (bool, optional, defaults to True) -- Whether or not to return a ImagePipelineOutput instead of a plain tuple.0ImagePipelineOutput or tuple

Function invoked when calling the pipeline for generation.

Examples:

from diffusers import AutoPipelineForText2Image
import torch

pipe = AutoPipelineForText2Image.from_pretrained(
    "kandinsky-community/kandinsky-2-1", torch_dtype=torch.float16
)
pipe.enable_model_cpu_offload()

prompt = "A lion in galaxies, spirals, nebulae, stars, smoke, iridescent, intricate detail, octane render, 8k"

image = pipe(prompt=prompt, num_inference_steps=25).images[0]

Parameters:

text_encoder (MultilingualCLIP) : Frozen text-encoder.

tokenizer (XLMRobertaTokenizer) : Tokenizer of class

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) : The canonical unCLIP prior to approximate the image embedding from the text embedding.

prior_image_encoder (CLIPVisionModelWithProjection) : Frozen image-encoder.

prior_text_encoder (CLIPTextModelWithProjection) : Frozen text-encoder.

prior_tokenizer (CLIPTokenizer) : Tokenizer of class CLIPTokenizer.

prior_scheduler (UnCLIPScheduler) : A scheduler to be used in combination with prior to generate image embedding.

Returns:

[ImagePipelineOutput](/docs/diffusers/pr_11739/en/api/pipelines/stable_unclip#diffusers.ImagePipelineOutput) or tuple``

enable_sequential_cpu_offload[[diffusers.KandinskyCombinedPipeline.enable_sequential_cpu_offload]]

Source

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.

KandinskyImg2ImgPipeline[[diffusers.KandinskyImg2ImgPipeline]]

diffusers.KandinskyImg2ImgPipeline[[diffusers.KandinskyImg2ImgPipeline]]

Source

Pipeline for image-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__diffusers.KandinskyImg2ImgPipeline.__call__https://github.com/huggingface/diffusers/blob/vr_11739/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_img2img.py#L297[{"name": "prompt", "val": ": typing.Union[str, typing.List[str]]"}, {"name": "image", "val": ": typing.Union[torch.Tensor, PIL.Image.Image, typing.List[torch.Tensor], typing.List[PIL.Image.Image]]"}, {"name": "image_embeds", "val": ": Tensor"}, {"name": "negative_image_embeds", "val": ": Tensor"}, {"name": "negative_prompt", "val": ": typing.Union[str, typing.List[str], NoneType] = None"}, {"name": "height", "val": ": int = 512"}, {"name": "width", "val": ": int = 512"}, {"name": "num_inference_steps", "val": ": int = 100"}, {"name": "strength", "val": ": float = 0.3"}, {"name": "guidance_scale", "val": ": float = 7.0"}, {"name": "num_images_per_prompt", "val": ": int = 1"}, {"name": "generator", "val": ": typing.Union[torch._C.Generator, typing.List[torch._C.Generator], NoneType] = None"}, {"name": "output_type", "val": ": typing.Optional[str] = 'pil'"}, {"name": "callback", "val": ": typing.Optional[typing.Callable[[int, int, torch.Tensor], NoneType]] = None"}, {"name": "callback_steps", "val": ": int = 1"}, {"name": "return_dict", "val": ": bool = True"}]- prompt (str or List[str]) -- The prompt or prompts to guide the image generation.

  • image (torch.Tensor, PIL.Image.Image) -- Image, or tensor representing an image batch, that will be used as the starting point for the process.
  • image_embeds (torch.Tensor or List[torch.Tensor]) -- The clip image embeddings for text prompt, that will be used to condition the image generation.
  • negative_image_embeds (torch.Tensor or List[torch.Tensor]) -- The clip image embeddings for negative text prompt, will be used to condition the image generation.
  • 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).
  • 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.
  • strength (float, optional, defaults to 0.3) -- Conceptually, indicates how much to transform the reference image. 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. When strength is 1, added noise will be maximum and the denoising process will run for the full number of iterations specified in num_inference_steps. A value of 1, therefore, essentially ignores image.
  • 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.
  • 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).
  • callback (Callable, optional) -- A function that calls every callback_steps steps during inference. The function is called with the following arguments: callback(step: int, timestep: int, latents: torch.Tensor).
  • callback_steps (int, optional, defaults to 1) -- The frequency at which the callback function is called. If not specified, the callback is called at every step.
  • return_dict (bool, optional, defaults to True) -- Whether or not to return a ImagePipelineOutput instead of a plain tuple.0ImagePipelineOutput or tuple

Function invoked when calling the pipeline for generation.

Examples:

>>> from diffusers import KandinskyImg2ImgPipeline, KandinskyPriorPipeline
>>> from diffusers.utils import load_image
>>> import torch

>>> pipe_prior = KandinskyPriorPipeline.from_pretrained(
...     "kandinsky-community/kandinsky-2-1-prior", torch_dtype=torch.float16
... )
>>> pipe_prior.to("cuda")

>>> prompt = "A red cartoon frog, 4k"
>>> image_emb, zero_image_emb = pipe_prior(prompt, return_dict=False)

>>> pipe = KandinskyImg2ImgPipeline.from_pretrained(
...     "kandinsky-community/kandinsky-2-1", torch_dtype=torch.float16
... )
>>> pipe.to("cuda")

>>> init_image = load_image(
...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
...     "/kandinsky/frog.png"
... )

>>> image = pipe(
...     prompt,
...     image=init_image,
...     image_embeds=image_emb,
...     negative_image_embeds=zero_image_emb,
...     height=768,
...     width=768,
...     num_inference_steps=100,
...     strength=0.2,
... ).images

>>> image[0].save("red_frog.png")

Parameters:

text_encoder (MultilingualCLIP) : Frozen text-encoder.

tokenizer (XLMRobertaTokenizer) : Tokenizer of class

scheduler (DDIMScheduler) : 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 image encoder and decoder

Returns:

[ImagePipelineOutput](/docs/diffusers/pr_11739/en/api/pipelines/stable_unclip#diffusers.ImagePipelineOutput) or tuple``

KandinskyImg2ImgCombinedPipeline[[diffusers.KandinskyImg2ImgCombinedPipeline]]

diffusers.KandinskyImg2ImgCombinedPipeline[[diffusers.KandinskyImg2ImgCombinedPipeline]]

Source

Combined Pipeline for image-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__diffusers.KandinskyImg2ImgCombinedPipeline.__call__https://github.com/huggingface/diffusers/blob/vr_11739/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_combined.py#L434[{"name": "prompt", "val": ": typing.Union[str, typing.List[str]]"}, {"name": "image", "val": ": typing.Union[torch.Tensor, PIL.Image.Image, typing.List[torch.Tensor], typing.List[PIL.Image.Image]]"}, {"name": "negative_prompt", "val": ": typing.Union[str, typing.List[str], NoneType] = None"}, {"name": "num_inference_steps", "val": ": int = 100"}, {"name": "guidance_scale", "val": ": float = 4.0"}, {"name": "num_images_per_prompt", "val": ": int = 1"}, {"name": "strength", "val": ": float = 0.3"}, {"name": "height", "val": ": int = 512"}, {"name": "width", "val": ": int = 512"}, {"name": "prior_guidance_scale", "val": ": float = 4.0"}, {"name": "prior_num_inference_steps", "val": ": int = 25"}, {"name": "generator", "val": ": typing.Union[torch._C.Generator, typing.List[torch._C.Generator], NoneType] = None"}, {"name": "latents", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "output_type", "val": ": typing.Optional[str] = 'pil'"}, {"name": "callback", "val": ": typing.Optional[typing.Callable[[int, int, torch.Tensor], NoneType]] = None"}, {"name": "callback_steps", "val": ": int = 1"}, {"name": "return_dict", "val": ": bool = True"}]- prompt (str or List[str]) -- The prompt or prompts to guide the image generation.

  • image (torch.Tensor, PIL.Image.Image, np.ndarray, List[torch.Tensor], List[PIL.Image.Image], or List[np.ndarray]) -- Image, or tensor representing an image batch, that will be used as the starting point for the process. Can also accept image latents as image, if passing latents directly, it will not be encoded again.
  • 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.
  • 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.
  • strength (float, optional, defaults to 0.3) -- Conceptually, indicates how much to transform the reference image. 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. When strength is 1, added noise will be maximum and the denoising process will run for the full number of iterations specified in num_inference_steps. A value of 1, therefore, essentially ignores image.
  • prior_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.
  • prior_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.
  • generator (torch.Generator or List[torch.Generator], optional) -- One or a list of torch generator(s) to make generation deterministic.
  • latents (torch.Tensor, 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 be 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).
  • callback (Callable, optional) -- A function that calls every callback_steps steps during inference. The function is called with the following arguments: callback(step: int, timestep: int, latents: torch.Tensor).
  • callback_steps (int, optional, defaults to 1) -- The frequency at which the callback function is called. If not specified, the callback is called at every step.
  • return_dict (bool, optional, defaults to True) -- Whether or not to return a ImagePipelineOutput instead of a plain tuple.0ImagePipelineOutput or tuple

Function invoked when calling the pipeline for generation.

Examples:

from diffusers import AutoPipelineForImage2Image
import torch
import requests
from io import BytesIO
from PIL import Image
import os

pipe = AutoPipelineForImage2Image.from_pretrained(
    "kandinsky-community/kandinsky-2-1", torch_dtype=torch.float16
)
pipe.enable_model_cpu_offload()

prompt = "A fantasy landscape, Cinematic lighting"
negative_prompt = "low quality, bad quality"

url = "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg"

response = requests.get(url)
image = Image.open(BytesIO(response.content)).convert("RGB")
image.thumbnail((768, 768))

image = pipe(prompt=prompt, image=original_image, num_inference_steps=25).images[0]

Parameters:

text_encoder (MultilingualCLIP) : Frozen text-encoder.

tokenizer (XLMRobertaTokenizer) : Tokenizer of class

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) : The canonical unCLIP prior to approximate the image embedding from the text embedding.

prior_image_encoder (CLIPVisionModelWithProjection) : Frozen image-encoder.

prior_text_encoder (CLIPTextModelWithProjection) : Frozen text-encoder.

prior_tokenizer (CLIPTokenizer) : Tokenizer of class CLIPTokenizer.

prior_scheduler (UnCLIPScheduler) : A scheduler to be used in combination with prior to generate image embedding.

Returns:

[ImagePipelineOutput](/docs/diffusers/pr_11739/en/api/pipelines/stable_unclip#diffusers.ImagePipelineOutput) or tuple``

enable_sequential_cpu_offload[[diffusers.KandinskyImg2ImgCombinedPipeline.enable_sequential_cpu_offload]]

Source

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.

KandinskyInpaintPipeline[[diffusers.KandinskyInpaintPipeline]]

diffusers.KandinskyInpaintPipeline[[diffusers.KandinskyInpaintPipeline]]

Source

Pipeline for text-guided image inpainting using Kandinsky2.1

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__diffusers.KandinskyInpaintPipeline.__call__https://github.com/huggingface/diffusers/blob/vr_11739/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_inpaint.py#L401[{"name": "prompt", "val": ": typing.Union[str, typing.List[str]]"}, {"name": "image", "val": ": typing.Union[torch.Tensor, PIL.Image.Image]"}, {"name": "mask_image", "val": ": typing.Union[torch.Tensor, PIL.Image.Image, numpy.ndarray]"}, {"name": "image_embeds", "val": ": Tensor"}, {"name": "negative_image_embeds", "val": ": Tensor"}, {"name": "negative_prompt", "val": ": typing.Union[str, typing.List[str], NoneType] = None"}, {"name": "height", "val": ": int = 512"}, {"name": "width", "val": ": int = 512"}, {"name": "num_inference_steps", "val": ": int = 100"}, {"name": "guidance_scale", "val": ": float = 4.0"}, {"name": "num_images_per_prompt", "val": ": int = 1"}, {"name": "generator", "val": ": typing.Union[torch._C.Generator, typing.List[torch._C.Generator], NoneType] = None"}, {"name": "latents", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "output_type", "val": ": typing.Optional[str] = 'pil'"}, {"name": "callback", "val": ": typing.Optional[typing.Callable[[int, int, torch.Tensor], NoneType]] = None"}, {"name": "callback_steps", "val": ": int = 1"}, {"name": "return_dict", "val": ": bool = True"}]- prompt (str or List[str]) -- The prompt or prompts to guide the image generation.

  • image (torch.Tensor, PIL.Image.Image or np.ndarray) -- Image, or tensor representing an image batch, that will be used as the starting point for the process.
  • mask_image (PIL.Image.Image,torch.Tensor or np.ndarray) -- Image, or a tensor representing an image batch, to mask image. White pixels in the mask will be repainted, while black pixels will be preserved. You can pass a pytorch tensor as mask only if the image you passed is a pytorch tensor, and it should contain one color channel (L) instead of 3, so the expected shape would be either (B, 1, H, W,), (B, H, W), (1, H, W) or (H, W) If image is an PIL image or numpy array, mask should also be a either PIL image or numpy array. If it is a PIL image, it will be converted to a single channel (luminance) before use. If it is a nummpy array, the expected shape is (H, W).
  • image_embeds (torch.Tensor or List[torch.Tensor]) -- The clip image embeddings for text prompt, that will be used to condition the image generation.
  • negative_image_embeds (torch.Tensor or List[torch.Tensor]) -- The clip image embeddings for negative text prompt, will be used to condition the image generation.
  • 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).
  • 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.Tensor, 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 be 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).
  • callback (Callable, optional) -- A function that calls every callback_steps steps during inference. The function is called with the following arguments: callback(step: int, timestep: int, latents: torch.Tensor).
  • callback_steps (int, optional, defaults to 1) -- The frequency at which the callback function is called. If not specified, the callback is called at every step.
  • return_dict (bool, optional, defaults to True) -- Whether or not to return a ImagePipelineOutput instead of a plain tuple.0ImagePipelineOutput or tuple

Function invoked when calling the pipeline for generation.

Examples:

>>> from diffusers import KandinskyInpaintPipeline, KandinskyPriorPipeline
>>> from diffusers.utils import load_image
>>> import torch
>>> import numpy as np

>>> pipe_prior = KandinskyPriorPipeline.from_pretrained(
...     "kandinsky-community/kandinsky-2-1-prior", torch_dtype=torch.float16
... )
>>> pipe_prior.to("cuda")

>>> prompt = "a hat"
>>> image_emb, zero_image_emb = pipe_prior(prompt, return_dict=False)

>>> pipe = KandinskyInpaintPipeline.from_pretrained(
...     "kandinsky-community/kandinsky-2-1-inpaint", torch_dtype=torch.float16
... )
>>> pipe.to("cuda")

>>> init_image = load_image(
...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
...     "/kandinsky/cat.png"
... )

>>> mask = np.zeros((768, 768), dtype=np.float32)
>>> mask[:250, 250:-250] = 1

>>> out = pipe(
...     prompt,
...     image=init_image,
...     mask_image=mask,
...     image_embeds=image_emb,
...     negative_image_embeds=zero_image_emb,
...     height=768,
...     width=768,
...     num_inference_steps=50,
... )

>>> image = out.images[0]
>>> image.save("cat_with_hat.png")

Parameters:

text_encoder (MultilingualCLIP) : Frozen text-encoder.

tokenizer (XLMRobertaTokenizer) : Tokenizer of class

scheduler (DDIMScheduler) : 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 image encoder and decoder

Returns:

[ImagePipelineOutput](/docs/diffusers/pr_11739/en/api/pipelines/stable_unclip#diffusers.ImagePipelineOutput) or tuple``

KandinskyInpaintCombinedPipeline[[diffusers.KandinskyInpaintCombinedPipeline]]

diffusers.KandinskyInpaintCombinedPipeline[[diffusers.KandinskyInpaintCombinedPipeline]]

Source

Combined Pipeline for 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__diffusers.KandinskyInpaintCombinedPipeline.__call__https://github.com/huggingface/diffusers/blob/vr_11739/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_combined.py#L675[{"name": "prompt", "val": ": typing.Union[str, typing.List[str]]"}, {"name": "image", "val": ": typing.Union[torch.Tensor, PIL.Image.Image, typing.List[torch.Tensor], typing.List[PIL.Image.Image]]"}, {"name": "mask_image", "val": ": typing.Union[torch.Tensor, PIL.Image.Image, typing.List[torch.Tensor], typing.List[PIL.Image.Image]]"}, {"name": "negative_prompt", "val": ": typing.Union[str, typing.List[str], NoneType] = None"}, {"name": "num_inference_steps", "val": ": int = 100"}, {"name": "guidance_scale", "val": ": float = 4.0"}, {"name": "num_images_per_prompt", "val": ": int = 1"}, {"name": "height", "val": ": int = 512"}, {"name": "width", "val": ": int = 512"}, {"name": "prior_guidance_scale", "val": ": float = 4.0"}, {"name": "prior_num_inference_steps", "val": ": int = 25"}, {"name": "generator", "val": ": typing.Union[torch._C.Generator, typing.List[torch._C.Generator], NoneType] = None"}, {"name": "latents", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "output_type", "val": ": typing.Optional[str] = 'pil'"}, {"name": "callback", "val": ": typing.Optional[typing.Callable[[int, int, torch.Tensor], NoneType]] = None"}, {"name": "callback_steps", "val": ": int = 1"}, {"name": "return_dict", "val": ": bool = True"}]- prompt (str or List[str]) -- The prompt or prompts to guide the image generation.

  • image (torch.Tensor, PIL.Image.Image, np.ndarray, List[torch.Tensor], List[PIL.Image.Image], or List[np.ndarray]) -- Image, or tensor representing an image batch, that will be used as the starting point for the process. Can also accept image latents as image, if passing latents directly, it will not be encoded again.
  • mask_image (np.array) -- Tensor representing an image batch, to mask image. White pixels in the mask will be repainted, while black pixels will be preserved. If mask_image is a PIL image, it will be converted to a single channel (luminance) before use. If it's a tensor, it should contain one color channel (L) instead of 3, so the expected shape would be (B, H, W, 1).
  • 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.
  • 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.
  • prior_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.
  • prior_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.
  • generator (torch.Generator or List[torch.Generator], optional) -- One or a list of torch generator(s) to make generation deterministic.
  • latents (torch.Tensor, 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 be 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).
  • callback (Callable, optional) -- A function that calls every callback_steps steps during inference. The function is called with the following arguments: callback(step: int, timestep: int, latents: torch.Tensor).
  • callback_steps (int, optional, defaults to 1) -- The frequency at which the callback function is called. If not specified, the callback is called at every step.
  • return_dict (bool, optional, defaults to True) -- Whether or not to return a ImagePipelineOutput instead of a plain tuple.0ImagePipelineOutput or tuple

Function invoked when calling the pipeline for generation.

Examples:

from diffusers import AutoPipelineForInpainting
from diffusers.utils import load_image
import torch
import numpy as np

pipe = AutoPipelineForInpainting.from_pretrained(
    "kandinsky-community/kandinsky-2-1-inpaint", torch_dtype=torch.float16
)
pipe.enable_model_cpu_offload()

prompt = "A fantasy landscape, Cinematic lighting"
negative_prompt = "low quality, bad quality"

original_image = load_image(
    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png"
)

mask = np.zeros((768, 768), dtype=np.float32)
# Let's mask out an area above the cat's head
mask[:250, 250:-250] = 1

image = pipe(prompt=prompt, image=original_image, mask_image=mask, num_inference_steps=25).images[0]

Parameters:

text_encoder (MultilingualCLIP) : Frozen text-encoder.

tokenizer (XLMRobertaTokenizer) : Tokenizer of class

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) : The canonical unCLIP prior to approximate the image embedding from the text embedding.

prior_image_encoder (CLIPVisionModelWithProjection) : Frozen image-encoder.

prior_text_encoder (CLIPTextModelWithProjection) : Frozen text-encoder.

prior_tokenizer (CLIPTokenizer) : Tokenizer of class CLIPTokenizer.

prior_scheduler (UnCLIPScheduler) : A scheduler to be used in combination with prior to generate image embedding.

Returns:

[ImagePipelineOutput](/docs/diffusers/pr_11739/en/api/pipelines/stable_unclip#diffusers.ImagePipelineOutput) or tuple``

enable_sequential_cpu_offload[[diffusers.KandinskyInpaintCombinedPipeline.enable_sequential_cpu_offload]]

Source

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.

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