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
class diffusers.KandinskyPriorPipeline
< source >( prior: PriorTransformer image_encoder: CLIPVisionModelWithProjection text_encoder: CLIPTextModelWithProjection tokenizer: CLIPTokenizer scheduler: UnCLIPScheduler image_processor: CLIPImageProcessor )
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 withprior
to generate image embedding.
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__
< source >( prompt: Union negative_prompt: Union = None num_images_per_prompt: int = 1 num_inference_steps: int = 25 generator: Union = None latents: Optional = None guidance_scale: float = 4.0 output_type: Optional = 'pt' return_dict: bool = True ) → KandinskyPriorPipelineOutput
or tuple
Parameters
- prompt (
str
orList[str]
) — The prompt or prompts to guide the image generation. - negative_prompt (
str
orList[str]
, optional) — The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored ifguidance_scale
is less than1
). - 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
orList[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 ge generated by sampling using the supplied randomgenerator
. - guidance_scale (
float
, optional, defaults to 4.0) — Guidance scale as defined in Classifier-Free Diffusion Guidance.guidance_scale
is defined asw
of equation 2. of Imagen Paper. Guidance scale is enabled by settingguidance_scale > 1
. Higher guidance scale encourages to generate images that are closely linked to the textprompt
, 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 toTrue
) — 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:
>>> 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")
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.Tensor]]
) — list of prompts and images to guide the image generation. weights — (List[float]
): list of weights for each condition inimages_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
orList[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 ge generated by sampling using the supplied randomgenerator
. - negative_prior_prompt (
str
, optional) — The prompt not to guide the prior diffusion process. Ignored when not using guidance (i.e., ignored ifguidance_scale
is less than1
). - negative_prompt (
str
orList[str]
, optional) — The prompt not to guide the image generation. Ignored when not using guidance (i.e., ignored ifguidance_scale
is less than1
). - guidance_scale (
float
, optional, defaults to 4.0) — Guidance scale as defined in Classifier-Free Diffusion Guidance.guidance_scale
is defined asw
of equation 2. of Imagen Paper. Guidance scale is enabled by settingguidance_scale > 1
. Higher guidance scale encourages to generate images that are closely linked to the textprompt
, usually at the expense of lower image quality.
Returns
KandinskyPriorPipelineOutput
or tuple
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")
KandinskyPipeline
class diffusers.KandinskyPipeline
< source >( text_encoder: MultilingualCLIP tokenizer: XLMRobertaTokenizer unet: UNet2DConditionModel scheduler: Union movq: VQModel )
Parameters
- text_encoder (
MultilingualCLIP
) — Frozen text-encoder. - tokenizer (
XLMRobertaTokenizer
) — Tokenizer of class - scheduler (Union[
DDIMScheduler
,DDPMScheduler
]) — A scheduler to be used in combination withunet
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 >( prompt: Union image_embeds: Union negative_image_embeds: Union negative_prompt: Union = None 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' callback: Optional = None callback_steps: int = 1 return_dict: bool = True ) → ImagePipelineOutput or tuple
Parameters
- prompt (
str
orList[str]
) — The prompt or prompts to guide the image generation. - image_embeds (
torch.Tensor
orList[torch.Tensor]
) — The clip image embeddings for text prompt, that will be used to condition the image generation. - negative_image_embeds (
torch.Tensor
orList[torch.Tensor]
) — The clip image embeddings for negative text prompt, will be used to condition the image generation. - negative_prompt (
str
orList[str]
, optional) — The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored ifguidance_scale
is less than1
). - 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 asw
of equation 2. of Imagen Paper. Guidance scale is enabled by settingguidance_scale > 1
. Higher guidance scale encourages to generate images that are closely linked to the textprompt
, 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
orList[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 ge generated by sampling using the supplied randomgenerator
. - 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 everycallback_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 thecallback
function is called. If not specified, the callback is called at every step. - return_dict (
bool
, optional, defaults toTrue
) — Whether or not to return a ImagePipelineOutput instead of a plain tuple.
Returns
ImagePipelineOutput 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")
KandinskyCombinedPipeline
class diffusers.KandinskyCombinedPipeline
< source >( text_encoder: MultilingualCLIP tokenizer: XLMRobertaTokenizer unet: UNet2DConditionModel scheduler: Union movq: VQModel prior_prior: PriorTransformer prior_image_encoder: CLIPVisionModelWithProjection prior_text_encoder: CLIPTextModelWithProjection prior_tokenizer: CLIPTokenizer prior_scheduler: UnCLIPScheduler prior_image_processor: CLIPImageProcessor )
Parameters
- text_encoder (
MultilingualCLIP
) — Frozen text-encoder. - tokenizer (
XLMRobertaTokenizer
) — Tokenizer of class - scheduler (Union[
DDIMScheduler
,DDPMScheduler
]) — A scheduler to be used in combination withunet
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 withprior
to generate image embedding.
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__
< source >( prompt: Union negative_prompt: Union = None num_inference_steps: int = 100 guidance_scale: float = 4.0 num_images_per_prompt: int = 1 height: int = 512 width: int = 512 prior_guidance_scale: float = 4.0 prior_num_inference_steps: int = 25 generator: Union = None latents: Optional = None output_type: Optional = 'pil' callback: Optional = None callback_steps: int = 1 return_dict: bool = True ) → ImagePipelineOutput or tuple
Parameters
- prompt (
str
orList[str]
) — The prompt or prompts to guide the image generation. - negative_prompt (
str
orList[str]
, optional) — The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored ifguidance_scale
is less than1
). - 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 asw
of equation 2. of Imagen Paper. Guidance scale is enabled by settingguidance_scale > 1
. Higher guidance scale encourages to generate images that are closely linked to the textprompt
, 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 asw
of equation 2. of Imagen Paper. Guidance scale is enabled by settingguidance_scale > 1
. Higher guidance scale encourages to generate images that are closely linked to the textprompt
, usually at the expense of lower image quality. - generator (
torch.Generator
orList[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 ge generated by sampling using the supplied randomgenerator
. - 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 everycallback_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 thecallback
function is called. If not specified, the callback is called at every step. - return_dict (
bool
, optional, defaults toTrue
) — Whether or not to return a ImagePipelineOutput instead of a plain tuple.
Returns
ImagePipelineOutput 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]
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
class diffusers.KandinskyImg2ImgPipeline
< source >( text_encoder: MultilingualCLIP movq: VQModel tokenizer: XLMRobertaTokenizer unet: UNet2DConditionModel scheduler: DDIMScheduler )
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
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__
< source >( prompt: Union image: Union image_embeds: Tensor negative_image_embeds: Tensor negative_prompt: Union = None height: int = 512 width: int = 512 num_inference_steps: int = 100 strength: float = 0.3 guidance_scale: float = 7.0 num_images_per_prompt: int = 1 generator: Union = None output_type: Optional = 'pil' callback: Optional = None callback_steps: int = 1 return_dict: bool = True ) → ImagePipelineOutput or tuple
Parameters
- prompt (
str
orList[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
orList[torch.Tensor]
) — The clip image embeddings for text prompt, that will be used to condition the image generation. - negative_image_embeds (
torch.Tensor
orList[torch.Tensor]
) — The clip image embeddings for negative text prompt, will be used to condition the image generation. - negative_prompt (
str
orList[str]
, optional) — The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored ifguidance_scale
is less than1
). - 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 referenceimage
. Must be between 0 and 1.image
will be used as a starting point, adding more noise to it the larger thestrength
. The number of denoising steps depends on the amount of noise initially added. Whenstrength
is 1, added noise will be maximum and the denoising process will run for the full number of iterations specified innum_inference_steps
. A value of 1, therefore, essentially ignoresimage
. - guidance_scale (
float
, optional, defaults to 4.0) — Guidance scale as defined in Classifier-Free Diffusion Guidance.guidance_scale
is defined asw
of equation 2. of Imagen Paper. Guidance scale is enabled by settingguidance_scale > 1
. Higher guidance scale encourages to generate images that are closely linked to the textprompt
, 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
orList[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 everycallback_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 thecallback
function is called. If not specified, the callback is called at every step. - return_dict (
bool
, optional, defaults toTrue
) — Whether or not to return a ImagePipelineOutput instead of a plain tuple.
Returns
ImagePipelineOutput 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")
KandinskyImg2ImgCombinedPipeline
class diffusers.KandinskyImg2ImgCombinedPipeline
< source >( text_encoder: MultilingualCLIP tokenizer: XLMRobertaTokenizer unet: UNet2DConditionModel scheduler: Union movq: VQModel prior_prior: PriorTransformer prior_image_encoder: CLIPVisionModelWithProjection prior_text_encoder: CLIPTextModelWithProjection prior_tokenizer: CLIPTokenizer prior_scheduler: UnCLIPScheduler prior_image_processor: CLIPImageProcessor )
Parameters
- text_encoder (
MultilingualCLIP
) — Frozen text-encoder. - tokenizer (
XLMRobertaTokenizer
) — Tokenizer of class - scheduler (Union[
DDIMScheduler
,DDPMScheduler
]) — A scheduler to be used in combination withunet
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 withprior
to generate image embedding.
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__
< source >( prompt: Union image: Union negative_prompt: Union = None num_inference_steps: int = 100 guidance_scale: float = 4.0 num_images_per_prompt: int = 1 strength: float = 0.3 height: int = 512 width: int = 512 prior_guidance_scale: float = 4.0 prior_num_inference_steps: int = 25 generator: Union = None latents: Optional = None output_type: Optional = 'pil' callback: Optional = None callback_steps: int = 1 return_dict: bool = True ) → ImagePipelineOutput or tuple
Parameters
- prompt (
str
orList[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]
, orList[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 asimage
, if passing latents directly, it will not be encoded again. - negative_prompt (
str
orList[str]
, optional) — The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored ifguidance_scale
is less than1
). - 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 referenceimage
. Must be between 0 and 1.image
will be used as a starting point, adding more noise to it the larger thestrength
. The number of denoising steps depends on the amount of noise initially added. Whenstrength
is 1, added noise will be maximum and the denoising process will run for the full number of iterations specified innum_inference_steps
. A value of 1, therefore, essentially ignoresimage
. - prior_guidance_scale (
float
, optional, defaults to 4.0) — Guidance scale as defined in Classifier-Free Diffusion Guidance.guidance_scale
is defined asw
of equation 2. of Imagen Paper. Guidance scale is enabled by settingguidance_scale > 1
. Higher guidance scale encourages to generate images that are closely linked to the textprompt
, 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 asw
of equation 2. of Imagen Paper. Guidance scale is enabled by settingguidance_scale > 1
. Higher guidance scale encourages to generate images that are closely linked to the textprompt
, usually at the expense of lower image quality. - generator (
torch.Generator
orList[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 ge generated by sampling using the supplied randomgenerator
. - 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 everycallback_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 thecallback
function is called. If not specified, the callback is called at every step. - return_dict (
bool
, optional, defaults toTrue
) — Whether or not to return a ImagePipelineOutput instead of a plain tuple.
Returns
ImagePipelineOutput 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]
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 with
enable_model_cpu_offload`, but performance is lower.
KandinskyInpaintPipeline
class diffusers.KandinskyInpaintPipeline
< source >( text_encoder: MultilingualCLIP movq: VQModel tokenizer: XLMRobertaTokenizer unet: UNet2DConditionModel scheduler: DDIMScheduler )
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
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__
< source >( prompt: Union image: Union mask_image: Union image_embeds: Tensor negative_image_embeds: Tensor negative_prompt: Union = None 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' callback: Optional = None callback_steps: int = 1 return_dict: bool = True ) → ImagePipelineOutput or tuple
Parameters
- prompt (
str
orList[str]
) — The prompt or prompts to guide the image generation. - image (
torch.Tensor
,PIL.Image.Image
ornp.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
ornp.ndarray
) —Image
, or a tensor representing an image batch, to maskimage
. 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
orList[torch.Tensor]
) — The clip image embeddings for text prompt, that will be used to condition the image generation. - negative_image_embeds (
torch.Tensor
orList[torch.Tensor]
) — The clip image embeddings for negative text prompt, will be used to condition the image generation. - negative_prompt (
str
orList[str]
, optional) — The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored ifguidance_scale
is less than1
). - 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 asw
of equation 2. of Imagen Paper. Guidance scale is enabled by settingguidance_scale > 1
. Higher guidance scale encourages to generate images that are closely linked to the textprompt
, 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
orList[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 ge generated by sampling using the supplied randomgenerator
. - 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 everycallback_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 thecallback
function is called. If not specified, the callback is called at every step. - return_dict (
bool
, optional, defaults toTrue
) — Whether or not to return a ImagePipelineOutput instead of a plain tuple.
Returns
ImagePipelineOutput 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")
KandinskyInpaintCombinedPipeline
class diffusers.KandinskyInpaintCombinedPipeline
< source >( text_encoder: MultilingualCLIP tokenizer: XLMRobertaTokenizer unet: UNet2DConditionModel scheduler: Union movq: VQModel prior_prior: PriorTransformer prior_image_encoder: CLIPVisionModelWithProjection prior_text_encoder: CLIPTextModelWithProjection prior_tokenizer: CLIPTokenizer prior_scheduler: UnCLIPScheduler prior_image_processor: CLIPImageProcessor )
Parameters
- text_encoder (
MultilingualCLIP
) — Frozen text-encoder. - tokenizer (
XLMRobertaTokenizer
) — Tokenizer of class - scheduler (Union[
DDIMScheduler
,DDPMScheduler
]) — A scheduler to be used in combination withunet
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 withprior
to generate image embedding.
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__
< source >( prompt: Union image: Union mask_image: Union negative_prompt: Union = None num_inference_steps: int = 100 guidance_scale: float = 4.0 num_images_per_prompt: int = 1 height: int = 512 width: int = 512 prior_guidance_scale: float = 4.0 prior_num_inference_steps: int = 25 generator: Union = None latents: Optional = None output_type: Optional = 'pil' callback: Optional = None callback_steps: int = 1 return_dict: bool = True ) → ImagePipelineOutput or tuple
Parameters
- prompt (
str
orList[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]
, orList[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 asimage
, if passing latents directly, it will not be encoded again. - mask_image (
np.array
) — Tensor representing an image batch, to maskimage
. White pixels in the mask will be repainted, while black pixels will be preserved. Ifmask_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
orList[str]
, optional) — The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored ifguidance_scale
is less than1
). - 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 asw
of equation 2. of Imagen Paper. Guidance scale is enabled by settingguidance_scale > 1
. Higher guidance scale encourages to generate images that are closely linked to the textprompt
, 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 asw
of equation 2. of Imagen Paper. Guidance scale is enabled by settingguidance_scale > 1
. Higher guidance scale encourages to generate images that are closely linked to the textprompt
, usually at the expense of lower image quality. - generator (
torch.Generator
orList[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 ge generated by sampling using the supplied randomgenerator
. - 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 everycallback_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 thecallback
function is called. If not specified, the callback is called at every step. - return_dict (
bool
, optional, defaults toTrue
) — Whether or not to return a ImagePipelineOutput instead of a plain tuple.
Returns
ImagePipelineOutput 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]
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 with
enable_model_cpu_offload`, but performance is lower.