diffusers/pr_11739/en/api/pipelines/kandinsky5_image.md

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.

Check out the Kandinsky Lab organization on the Hub for the official model checkpoints for text-to-video generation, including pretrained, SFT, no-CFG, and distilled variants.

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()                                               # >> 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]

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.

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.

check_inputs[[diffusers.Kandinsky5T2IPipeline.check_inputs]]

Source

Validate input parameters for the pipeline.

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

encode_prompt[[diffusers.Kandinsky5T2IPipeline.encode_prompt]]

Source

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.

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,)

prepare_latents[[diffusers.Kandinsky5T2IPipeline.prepare_latents]]

Source

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

This method creates random noise latents

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

Kandinsky5I2IPipeline[[diffusers.Kandinsky5I2IPipeline]]

diffusers.Kandinsky5I2IPipeline[[diffusers.Kandinsky5I2IPipeline]]

Source

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__diffusers.Kandinsky5I2IPipeline.__call__https://github.com/huggingface/diffusers/blob/vr_11739/src/diffusers/pipelines/kandinsky5/pipeline_kandinsky_i2i.py#L566[{"name": "image", "val": ": typing.Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, typing.List[PIL.Image.Image], typing.List[numpy.ndarray], typing.List[torch.Tensor]]"}, {"name": "prompt", "val": ": typing.Union[str, typing.List[str]] = None"}, {"name": "negative_prompt", "val": ": typing.Union[str, typing.List[str], NoneType] = None"}, {"name": "height", "val": ": typing.Optional[int] = None"}, {"name": "width", "val": ": typing.Optional[int] = None"}, {"name": "num_inference_steps", "val": ": int = 50"}, {"name": "guidance_scale", "val": ": float = 3.5"}, {"name": "num_images_per_prompt", "val": ": typing.Optional[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": "prompt_embeds_qwen", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "prompt_embeds_clip", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "negative_prompt_embeds_qwen", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "negative_prompt_embeds_clip", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "prompt_cu_seqlens", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "negative_prompt_cu_seqlens", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "output_type", "val": ": typing.Optional[str] = 'pil'"}, {"name": "return_dict", "val": ": bool = True"}, {"name": "callback_on_step_end", "val": ": typing.Union[typing.Callable[[int, int, typing.Dict], NoneType], diffusers.callbacks.PipelineCallback, diffusers.callbacks.MultiPipelineCallbacks, NoneType] = None"}, {"name": "callback_on_step_end_tensor_inputs", "val": ": typing.List[str] = ['latents']"}, {"name": "max_sequence_length", "val": ": int = 1024"}]- 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 0~KandinskyImagePipelineOutput or tupleIf 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]

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.

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.

check_inputs[[diffusers.Kandinsky5I2IPipeline.check_inputs]]

Source

Validate input parameters for the pipeline.

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

encode_prompt[[diffusers.Kandinsky5I2IPipeline.encode_prompt]]

Source

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.

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,)

prepare_latents[[diffusers.Kandinsky5I2IPipeline.prepare_latents]]

Source

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

This method creates random noise latents with encoded image,

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

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
}