#
Kandinsky 5.0 Image

Kandinsky 5.0 is a family of diffusion models for Video & Image generation.

Kandinsky 5.0 Image Lite is a lightweight image generation model (6B parameters)

The model introduces several key innovations:

- Latent diffusion pipeline with Flow Matching for improved training stability
- Diffusion Transformer (DiT) as the main generative backbone with cross-attention to text embeddings
- Dual text encoding using Qwen2.5-VL and CLIP for comprehensive text understanding
- Flux VAE for efficient image encoding and decoding

The original codebase can be found at kandinskylab/Kandinsky-5.

##
Available Models

Kandinsky 5.0 Image Lite: | model_id | Description | Use Cases | |------------|-------------|-----------| | kandinskylab/Kandinsky-5.0-T2I-Lite-sft-Diffusers | 6B image Supervised Fine-Tuned model | Highest generation quality | | kandinskylab/Kandinsky-5.0-I2I-Lite-sft-Diffusers | 6B image editing Supervised Fine-Tuned model | Highest generation quality | | kandinskylab/Kandinsky-5.0-T2I-Lite-pretrain-Diffusers | 6B image Base pretrained model | Research and fine-tuning | | kandinskylab/Kandinsky-5.0-I2I-Lite-pretrain-Diffusers | 6B image editing Base pretrained model | Research and fine-tuning |

##
Usage Examples

###
Basic Text-to-Image Generation

    import torch
    from diffusers import Kandinsky5T2IPipeline

    # Load the pipeline
    model_id = "kandinskylab/Kandinsky-5.0-T2I-Lite-sft-Diffusers"
    pipe = Kandinsky5T2IPipeline.from_pretrained(model_id)
    _ = pipe.to(device='cuda',dtype=torch.bfloat16)

    # Generate image
    prompt = "A fluffy, expressive cat wearing a bright red hat with a soft, slightly textured fabric. The hat should look cozy and well-fitted on the cat’s head. On the front of the hat, add clean, bold white text that reads “SWEET”, clearly visible and neatly centered. Ensure the overall lighting highlights the hat’s color and the cat’s fur details."

    output = pipe(
        prompt=prompt,
        negative_prompt="",
        height=1024,
        width=1024,
        num_inference_steps=50,
        guidance_scale=3.5,
    ).image[0]

###
Basic Image-to-Image Generation

    import torch
    from diffusers import Kandinsky5I2IPipeline
    from diffusers.utils import load_image
    # Load the pipeline
    model_id = "kandinskylab/Kandinsky-5.0-I2I-Lite-sft-Diffusers"
    pipe = Kandinsky5I2IPipeline.from_pretrained(model_id)

    _ = pipe.to(device='cuda',dtype=torch.bfloat16)
    pipe.enable_model_cpu_offload()                                               # <--- Enable CPU offloading for single GPU inference

    # Edit the input image
    image = load_image(
        "https://huggingface.co/kandinsky-community/kandinsky-3/resolve/main/assets/title.jpg?download=true"
    )

    prompt = "Change the background from a winter night scene to a bright summer day. Place the character on a sandy beach with clear blue sky, soft sunlight, and gentle waves in the distance. Replace the winter clothing with a light short-sleeved T-shirt (in soft pastel colors) and casual shorts. Ensure the character’s fur reflects warm daylight instead of cold winter tones. Add small beach details such as seashells, footprints in the sand, and a few scattered beach toys nearby. Keep the oranges in the scene, but place them naturally on the sand."
    negative_prompt = ""

    output = pipe(
        image=image,
        prompt=prompt,
        negative_prompt=negative_prompt,
        guidance_scale=3.5,
    ).image[0]

##
Kandinsky5T2IPipeline

###
class diffusers.Kandinsky5T2IPipeline

< source >

( transformer: Kandinsky5Transformer3DModel vae: AutoencoderKL text_encoder: Qwen2_5_VLForConditionalGeneration tokenizer: Qwen2VLProcessor text_encoder_2: CLIPTextModel tokenizer_2: CLIPTokenizer scheduler: FlowMatchEulerDiscreteScheduler )

Parameters

- transformer (`Kandinsky5Transformer3DModel`) — Conditional Transformer to denoise the encoded image latents.
- vae (AutoencoderKL) — Variational Auto-Encoder Model black-forest-labs/FLUX.1-dev (vae) to encode and decode videos to and from latent representations.
- text_encoder (`Qwen2_5_VLForConditionalGeneration`) — Frozen text-encoder Qwen2.5-VL.
- tokenizer (`AutoProcessor`) — Tokenizer for Qwen2.5-VL.
- text_encoder_2 (`CLIPTextModel`) — Frozen CLIP, specifically the clip-vit-large-patch14 variant.
- tokenizer_2 (`CLIPTokenizer`) — Tokenizer for CLIP.
- scheduler (FlowMatchEulerDiscreteScheduler) — A scheduler to be used in combination with `transformer` to denoise the encoded image latents.

Pipeline for text-to-image generation using Kandinsky 5.0.

This model inherits from DiffusionPipeline. Check the superclass documentation for the generic methods implemented for all pipelines (downloading, saving, running on a particular device, etc.).

####
__call__

< source >

( prompt: typing.Union[str, typing.List[str]] = None negative_prompt: typing.Union[str, typing.List[str], NoneType] = None height: int = 1024 width: int = 1024 num_inference_steps: int = 50 guidance_scale: float = 3.5 num_images_per_prompt: typing.Optional[int] = 1 generator: typing.Union[torch._C.Generator, typing.List[torch._C.Generator], NoneType] = None latents: typing.Optional[torch.Tensor] = None prompt_embeds_qwen: typing.Optional[torch.Tensor] = None prompt_embeds_clip: typing.Optional[torch.Tensor] = None negative_prompt_embeds_qwen: typing.Optional[torch.Tensor] = None negative_prompt_embeds_clip: typing.Optional[torch.Tensor] = None prompt_cu_seqlens: typing.Optional[torch.Tensor] = None negative_prompt_cu_seqlens: typing.Optional[torch.Tensor] = None output_type: typing.Optional[str] = 'pil' return_dict: bool = True callback_on_step_end: typing.Union[typing.Callable[[int, int, typing.Dict], NoneType], diffusers.callbacks.PipelineCallback, diffusers.callbacks.MultiPipelineCallbacks, NoneType] = None callback_on_step_end_tensor_inputs: typing.List[str] = ['latents'] max_sequence_length: int = 512 ) →
`~KandinskyImagePipelineOutput` or `tuple`

Parameters

- prompt (`str` or `List[str]`, optional) — The prompt or prompts to guide the image generation. If not defined, pass `prompt_embeds` instead.
- negative_prompt (`str` or `List[str]`, optional) — The prompt or prompts to avoid during image generation. If not defined, pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale` < `1`).
- height (`int`, defaults to `1024`) — The height in pixels of the generated image.
- width (`int`, defaults to `1024`) — The width in pixels of the generated image.
- num_inference_steps (`int`, defaults to `50`) — The number of denoising steps.
- guidance_scale (`float`, defaults to `5.0`) — Guidance scale as defined in classifier-free guidance.
- 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) — A torch generator to make generation deterministic.
- latents (`torch.Tensor`, optional) — Pre-generated noisy latents.
- prompt_embeds_qwen (`torch.Tensor`, optional) — Pre-generated Qwen text embeddings.
- prompt_embeds_clip (`torch.Tensor`, optional) — Pre-generated CLIP text embeddings.
- negative_prompt_embeds_qwen (`torch.Tensor`, optional) — Pre-generated Qwen negative text embeddings.
- negative_prompt_embeds_clip (`torch.Tensor`, optional) — Pre-generated CLIP negative text embeddings.
- prompt_cu_seqlens (`torch.Tensor`, optional) — Pre-generated cumulative sequence lengths for Qwen positive prompt.
- negative_prompt_cu_seqlens (`torch.Tensor`, optional) — Pre-generated cumulative sequence lengths for Qwen negative prompt.
- output_type (`str`, optional, defaults to `"pil"`) — The output format of the generated image.
- return_dict (`bool`, optional, defaults to `True`) — Whether or not to return a `KandinskyImagePipelineOutput`.
- callback_on_step_end (`Callable`, `PipelineCallback`, `MultiPipelineCallbacks`, optional) — A function that is called at the end of each denoising step.
- callback_on_step_end_tensor_inputs (`List`, optional) — The list of tensor inputs for the `callback_on_step_end` function.
- max_sequence_length (`int`, defaults to `512`) — The maximum sequence length for text encoding.

Returns

`~KandinskyImagePipelineOutput` or `tuple`

If `return_dict` is `True`, `KandinskyImagePipelineOutput` is returned, otherwise a `tuple` is returned where the first element is a list with the generated images.

The call function to the pipeline for text-to-image generation.

Examples:

    >>> import torch
    >>> from diffusers import Kandinsky5T2IPipeline

    >>> # Available models:
    >>> # kandinskylab/Kandinsky-5.0-T2I-Lite-sft-Diffusers
    >>> # kandinskylab/Kandinsky-5.0-T2I-Lite-pretrain-Diffusers

    >>> model_id = "kandinskylab/Kandinsky-5.0-T2I-Lite-sft-Diffusers"
    >>> pipe = Kandinsky5T2IPipeline.from_pretrained(model_id, torch_dtype=torch.bfloat16)
    >>> pipe = pipe.to("cuda")

    >>> prompt = "A cat and a dog baking a cake together in a kitchen."

    >>> output = pipe(
    ...     prompt=prompt,
    ...     negative_prompt="",
    ...     height=1024,
    ...     width=1024,
    ...     num_inference_steps=50,
    ...     guidance_scale=3.5,
    ... ).frames[0]

####
check_inputs

< source >

( prompt negative_prompt height width prompt_embeds_qwen = None prompt_embeds_clip = None negative_prompt_embeds_qwen = None negative_prompt_embeds_clip = None prompt_cu_seqlens = None negative_prompt_cu_seqlens = None callback_on_step_end_tensor_inputs = None max_sequence_length = None )

Parameters

- prompt — Input prompt
- negative_prompt — Negative prompt for guidance
- height — Image height
- width — Image width
- prompt_embeds_qwen — Pre-computed Qwen prompt embeddings
- prompt_embeds_clip — Pre-computed CLIP prompt embeddings
- negative_prompt_embeds_qwen — Pre-computed Qwen negative prompt embeddings
- negative_prompt_embeds_clip — Pre-computed CLIP negative prompt embeddings
- prompt_cu_seqlens — Pre-computed cumulative sequence lengths for Qwen positive prompt
- negative_prompt_cu_seqlens — Pre-computed cumulative sequence lengths for Qwen negative prompt
- callback_on_step_end_tensor_inputs — Callback tensor inputs

Raises

`ValueError`

- `ValueError` — If inputs are invalid

Validate input parameters for the pipeline.

####
encode_prompt

< source >

( prompt: typing.Union[str, typing.List[str]] num_images_per_prompt: int = 1 max_sequence_length: int = 512 device: typing.Optional[torch.device] = None dtype: typing.Optional[torch.dtype] = None ) →
Tuple[torch.Tensor, torch.Tensor, torch.Tensor]

Parameters

- prompt (`str` or `List[str]`) — Prompt to be encoded.
- num_images_per_prompt (`int`, optional, defaults to 1) — Number of images to generate per prompt.
- max_sequence_length (`int`, optional, defaults to 512) — Maximum sequence length for text encoding. Must be less than 1024
- device (`torch.device`, optional) — Torch device.
- dtype (`torch.dtype`, optional) — Torch dtype.

Returns

Tuple[torch.Tensor, torch.Tensor, torch.Tensor]

- Qwen text embeddings of shape (batch_size * num_images_per_prompt, sequence_length, embedding_dim)
- CLIP pooled embeddings of shape (batch_size * num_images_per_prompt, clip_embedding_dim)
- Cumulative sequence lengths (`cu_seqlens`) for Qwen embeddings of shape (batch_size * num_images_per_prompt + 1,)

Encodes a single prompt (positive or negative) into text encoder hidden states.

This method combines embeddings from both Qwen2.5-VL and CLIP text encoders to create comprehensive text representations for image generation.

####
prepare_latents

< source >

( batch_size: int num_channels_latents: int = 16 height: int = 1024 width: int = 1024 dtype: typing.Optional[torch.dtype] = None device: typing.Optional[torch.device] = None generator: typing.Union[torch._C.Generator, typing.List[torch._C.Generator], NoneType] = None latents: typing.Optional[torch.Tensor] = None ) →
torch.Tensor

Parameters

- batch_size (int) — Number of images to generate
- num_channels_latents (int) — Number of channels in latent space
- height (int) — Height of generated image
- width (int) — Width of generated image
- dtype (torch.dtype) — Data type for latents
- device (torch.device) — Device to create latents on
- generator (torch.Generator) — Random number generator
- latents (torch.Tensor) — Pre-existing latents to use

Returns

torch.Tensor

Prepared latent tensor

Prepare initial latent variables for text-to-image generation.

This method creates random noise latents

##
Kandinsky5I2IPipeline

###
class diffusers.Kandinsky5I2IPipeline

< source >

( transformer: Kandinsky5Transformer3DModel vae: AutoencoderKL text_encoder: Qwen2_5_VLForConditionalGeneration tokenizer: Qwen2VLProcessor text_encoder_2: CLIPTextModel tokenizer_2: CLIPTokenizer scheduler: FlowMatchEulerDiscreteScheduler )

Parameters

- transformer (`Kandinsky5Transformer3DModel`) — Conditional Transformer to denoise the encoded image latents.
- vae (AutoencoderKL) — Variational Auto-Encoder Model black-forest-labs/FLUX.1-dev (vae) to encode and decode videos to and from latent representations.
- text_encoder (`Qwen2_5_VLForConditionalGeneration`) — Frozen text-encoder Qwen2.5-VL.
- tokenizer (`AutoProcessor`) — Tokenizer for Qwen2.5-VL.
- text_encoder_2 (`CLIPTextModel`) — Frozen CLIP, specifically the clip-vit-large-patch14 variant.
- tokenizer_2 (`CLIPTokenizer`) — Tokenizer for CLIP.
- scheduler (FlowMatchEulerDiscreteScheduler) — A scheduler to be used in combination with `transformer` to denoise the encoded image latents.

Pipeline for image-to-image generation using Kandinsky 5.0.

This model inherits from DiffusionPipeline. Check the superclass documentation for the generic methods implemented for all pipelines (downloading, saving, running on a particular device, etc.).

####
__call__

< source >

( image: typing.Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, typing.List[PIL.Image.Image], typing.List[numpy.ndarray], typing.List[torch.Tensor]] prompt: typing.Union[str, typing.List[str]] = None negative_prompt: typing.Union[str, typing.List[str], NoneType] = None height: typing.Optional[int] = None width: typing.Optional[int] = None num_inference_steps: int = 50 guidance_scale: float = 3.5 num_images_per_prompt: typing.Optional[int] = 1 generator: typing.Union[torch._C.Generator, typing.List[torch._C.Generator], NoneType] = None latents: typing.Optional[torch.Tensor] = None prompt_embeds_qwen: typing.Optional[torch.Tensor] = None prompt_embeds_clip: typing.Optional[torch.Tensor] = None negative_prompt_embeds_qwen: typing.Optional[torch.Tensor] = None negative_prompt_embeds_clip: typing.Optional[torch.Tensor] = None prompt_cu_seqlens: typing.Optional[torch.Tensor] = None negative_prompt_cu_seqlens: typing.Optional[torch.Tensor] = None output_type: typing.Optional[str] = 'pil' return_dict: bool = True callback_on_step_end: typing.Union[typing.Callable[[int, int, typing.Dict], NoneType], diffusers.callbacks.PipelineCallback, diffusers.callbacks.MultiPipelineCallbacks, NoneType] = None callback_on_step_end_tensor_inputs: typing.List[str] = ['latents'] max_sequence_length: int = 1024 ) →
`~KandinskyImagePipelineOutput` or `tuple`

Parameters

- image (`PipelineImageInput`) — The input image to condition the generation on. Must be an image, a list of images or a `torch.Tensor`.
- prompt (`str` or `List[str]`, optional) — The prompt or prompts to guide the image generation. If not defined, pass `prompt_embeds` instead.
- negative_prompt (`str` or `List[str]`, optional) — The prompt or prompts to avoid during image generation. If not defined, pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale` < `1`).
- height (`int`) — The height in pixels of the generated image.
- width (`int`) — The width in pixels of the generated image.
- num_inference_steps (`int`, defaults to `50`) — The number of denoising steps.
- guidance_scale (`float`, defaults to `5.0`) — Guidance scale as defined in classifier-free guidance.
- 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) — A torch generator to make generation deterministic.
- latents (`torch.Tensor`, optional) — Pre-generated noisy latents.
- prompt_embeds_qwen (`torch.Tensor`, optional) — Pre-generated Qwen text embeddings.
- prompt_embeds_clip (`torch.Tensor`, optional) — Pre-generated CLIP text embeddings.
- negative_prompt_embeds_qwen (`torch.Tensor`, optional) — Pre-generated Qwen negative text embeddings.
- negative_prompt_embeds_clip (`torch.Tensor`, optional) — Pre-generated CLIP negative text embeddings.
- prompt_cu_seqlens (`torch.Tensor`, optional) — Pre-generated cumulative sequence lengths for Qwen positive prompt.
- negative_prompt_cu_seqlens (`torch.Tensor`, optional) — Pre-generated cumulative sequence lengths for Qwen negative prompt.
- output_type (`str`, optional, defaults to `"pil"`) — The output format of the generated image.
- return_dict (`bool`, optional, defaults to `True`) — Whether or not to return a `KandinskyImagePipelineOutput`.
- callback_on_step_end (`Callable`, `PipelineCallback`, `MultiPipelineCallbacks`, optional) — A function that is called at the end of each denoising step.
- callback_on_step_end_tensor_inputs (`List`, optional) — The list of tensor inputs for the `callback_on_step_end` function.
- max_sequence_length (`int`, defaults to `1024`) — The maximum sequence length for text and image qwen encoding. Must be less than 1024

Returns

`~KandinskyImagePipelineOutput` or `tuple`

If `return_dict` is `True`, `KandinskyImagePipelineOutput` is returned, otherwise a `tuple` is returned where the first element is a list with the generated images.

The call function to the pipeline for image-to-image generation.

Examples:

    >>> import torch
    >>> from diffusers import Kandinsky5I2IPipeline

    >>> # Available models:
    >>> # kandinskylab/Kandinsky-5.0-I2I-Lite-sft-Diffusers
    >>> # kandinskylab/Kandinsky-5.0-I2I-Lite-pretrain-Diffusers

    >>> model_id = "kandinskylab/Kandinsky-5.0-I2I-Lite-sft-Diffusers"
    >>> pipe = Kandinsky5I2IPipeline.from_pretrained(model_id, torch_dtype=torch.bfloat16)
    >>> pipe = pipe.to("cuda")

    >>> prompt = "A cat and a dog baking a cake together in a kitchen."

    >>> output = pipe(
    ...     prompt=prompt,
    ...     negative_prompt="",
    ...     height=1024,
    ...     width=1024,
    ...     num_inference_steps=50,
    ...     guidance_scale=3.5,
    ... ).frames[0]

####
check_inputs

< source >

( prompt negative_prompt image height width prompt_embeds_qwen = None prompt_embeds_clip = None negative_prompt_embeds_qwen = None negative_prompt_embeds_clip = None prompt_cu_seqlens = None negative_prompt_cu_seqlens = None callback_on_step_end_tensor_inputs = None max_sequence_length = None )

Parameters

- prompt — Input prompt
- negative_prompt — Negative prompt for guidance
- image — Input image for conditioning
- height — Image height
- width — Image width
- prompt_embeds_qwen — Pre-computed Qwen prompt embeddings
- prompt_embeds_clip — Pre-computed CLIP prompt embeddings
- negative_prompt_embeds_qwen — Pre-computed Qwen negative prompt embeddings
- negative_prompt_embeds_clip — Pre-computed CLIP negative prompt embeddings
- prompt_cu_seqlens — Pre-computed cumulative sequence lengths for Qwen positive prompt
- negative_prompt_cu_seqlens — Pre-computed cumulative sequence lengths for Qwen negative prompt
- callback_on_step_end_tensor_inputs — Callback tensor inputs

Raises

`ValueError`

- `ValueError` — If inputs are invalid

Validate input parameters for the pipeline.

####
encode_prompt

< source >

( prompt: typing.Union[str, typing.List[str]] image: Tensor num_images_per_prompt: int = 1 max_sequence_length: int = 1024 device: typing.Optional[torch.device] = None dtype: typing.Optional[torch.dtype] = None ) →
Tuple[torch.Tensor, torch.Tensor, torch.Tensor]

Parameters

- prompt (`str` or `List[str]`) — Prompt to be encoded.
- num_images_per_prompt (`int`, optional, defaults to 1) — Number of images to generate per prompt.
- max_sequence_length (`int`, optional, defaults to 1024) — Maximum sequence length for text encoding. Must be less than 1024
- device (`torch.device`, optional) — Torch device.
- dtype (`torch.dtype`, optional) — Torch dtype.

Returns

Tuple[torch.Tensor, torch.Tensor, torch.Tensor]

- Qwen text embeddings of shape (batch_size * num_images_per_prompt, sequence_length, embedding_dim)
- CLIP pooled embeddings of shape (batch_size * num_images_per_prompt, clip_embedding_dim)
- Cumulative sequence lengths (`cu_seqlens`) for Qwen embeddings of shape (batch_size * num_images_per_prompt + 1,)

Encodes a single prompt (positive or negative) into text encoder hidden states.

This method combines embeddings from both Qwen2.5-VL and CLIP text encoders to create comprehensive text representations for image generation.

####
prepare_latents

< source >

( image: typing.Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, typing.List[PIL.Image.Image], typing.List[numpy.ndarray], typing.List[torch.Tensor]] batch_size: int num_channels_latents: int = 16 height: int = 1024 width: int = 1024 dtype: typing.Optional[torch.dtype] = None device: typing.Optional[torch.device] = None generator: typing.Union[torch._C.Generator, typing.List[torch._C.Generator], NoneType] = None latents: typing.Optional[torch.Tensor] = None ) →
torch.Tensor

Parameters

- image (PipelineImageInput) — Input image to condition the generation on
- batch_size (int) — Number of images to generate
- num_channels_latents (int) — Number of channels in latent space
- height (int) — Height of generated image
- width (int) — Width of generated image
- dtype (torch.dtype) — Data type for latents
- device (torch.device) — Device to create latents on
- generator (torch.Generator) — Random number generator
- latents (torch.Tensor) — Pre-existing latents to use

Returns

torch.Tensor

Prepared latent tensor with encoded image

Prepare initial latent variables for image-to-image generation.

This method creates random noise latents with encoded image,

##
Citation

    @misc{kandinsky2025,
        author = {Alexander Belykh and Alexander Varlamov and Alexey Letunovskiy and Anastasia Aliaskina and Anastasia Maltseva and Anastasiia Kargapoltseva and Andrey Shutkin and Anna Averchenkova and Anna Dmitrienko and Bulat Akhmatov and Denis Dimitrov and Denis Koposov and Denis Parkhomenko and Dmitrii and Ilya Vasiliev and Ivan Kirillov and Julia Agafonova and Kirill Chernyshev and Kormilitsyn Semen and Lev Novitskiy and Maria Kovaleva and Mikhail Mamaev and Mikhailov and Nikita Kiselev and Nikita Osterov and Nikolai Gerasimenko and Nikolai Vaulin and Olga Kim and Olga Vdovchenko and Polina Gavrilova and Polina Mikhailova and Tatiana Nikulina and Viacheslav Vasilev and Vladimir Arkhipkin and Vladimir Korviakov and Vladimir Polovnikov and Yury Kolabushin},
        title = {Kandinsky 5.0: A family of diffusion models for Video & Image generation},
        howpublished = {\url{https://github.com/kandinskylab/Kandinsky-5}},
        year = 2025
    }

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