#
Kandinsky 5.0 Video

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

Kandinsky 5.0 Lite line-up of lightweight video generation models (2B parameters) that ranks #1 among open-source models in its class. It outperforms larger models and offers the best understanding of Russian concepts in the open-source ecosystem.

Kandinsky 5.0 Pro line-up of large high quality video generation models (19B parameters). It offers high qualty generation in HD and more generation formats like I2V.

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
- HunyuanVideo 3D VAE for efficient video encoding and decoding
- Sparse attention mechanisms (NABLA) for efficient long-sequence processing

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 T2V Pro: | model_id | Description | Use Cases | |------------|-------------|-----------| | kandinskylab/Kandinsky-5.0-T2V-Pro-sft-5s-Diffusers | 5 second Text-to-Video Pro model | High-quality text-to-video generation | | kandinskylab/Kandinsky-5.0-I2V-Pro-sft-5s-Diffusers | 5 second Image-to-Video Pro model | High-quality image-to-video generation |

Kandinsky 5.0 T2V Lite: | model_id | Description | Use Cases | |------------|-------------|-----------| | kandinskylab/Kandinsky-5.0-T2V-Lite-sft-5s-Diffusers | 5 second Supervised Fine-Tuned model | Highest generation quality | | kandinskylab/Kandinsky-5.0-T2V-Lite-sft-10s-Diffusers | 10 second Supervised Fine-Tuned model | Highest generation quality | | kandinskylab/Kandinsky-5.0-T2V-Lite-nocfg-5s-Diffusers | 5 second Classifier-Free Guidance distilled | 2× faster inference | | kandinskylab/Kandinsky-5.0-T2V-Lite-nocfg-10s-Diffusers | 10 second Classifier-Free Guidance distilled | 2× faster inference | | kandinskylab/Kandinsky-5.0-T2V-Lite-distilled16steps-5s-Diffusers | 5 second Diffusion distilled to 16 steps | 6× faster inference, minimal quality loss | | kandinskylab/Kandinsky-5.0-T2V-Lite-distilled16steps-10s-Diffusers | 10 second Diffusion distilled to 16 steps | 6× faster inference, minimal quality loss | | kandinskylab/Kandinsky-5.0-T2V-Lite-pretrain-5s-Diffusers | 5 second Base pretrained model | Research and fine-tuning | | kandinskylab/Kandinsky-5.0-T2V-Lite-pretrain-10s-Diffusers | 10 second Base pretrained model | Research and fine-tuning |

##
Usage Examples

###
Basic Text-to-Video Generation

####
Pro

⚠️ Warning! all Pro models should be infered with pipeline.enable_model_cpu_offload()

    import torch
    from diffusers import Kandinsky5T2VPipeline
    from diffusers.utils import export_to_video

    # Load the pipeline
    model_id = "kandinskylab/Kandinsky-5.0-T2V-Pro-sft-5s-Diffusers"
    pipe = Kandinsky5T2VPipeline.from_pretrained(model_id, torch_dtype=torch.bfloat16)

    pipe = pipe.to("cuda")
    pipeline.transformer.set_attention_backend("flex")                            # <--- Set attention bakend to Flex
    pipeline.enable_model_cpu_offload()                                           # <--- Enable cpu offloading for single GPU inference
    pipeline.transformer.compile(mode="max-autotune-no-cudagraphs", dynamic=True) # <--- Compile with max-autotune-no-cudagraphs

    # Generate video
    prompt = "A cat and a dog baking a cake together in a kitchen."
    negative_prompt = "Static, 2D cartoon, cartoon, 2d animation, paintings, images, worst quality, low quality, ugly, deformed, walking backwards"

    output = pipe(
        prompt=prompt,
        negative_prompt=negative_prompt,
        height=768,
        width=1024,
        num_frames=121,  # ~5 seconds at 24fps
        num_inference_steps=50,
        guidance_scale=5.0,
    ).frames[0]

    export_to_video(output, "output.mp4", fps=24, quality=9)

####
Lite

    import torch
    from diffusers import Kandinsky5T2VPipeline
    from diffusers.utils import export_to_video

    # Load the pipeline
    model_id = "kandinskylab/Kandinsky-5.0-T2V-Lite-sft-5s-Diffusers"
    pipe = Kandinsky5T2VPipeline.from_pretrained(model_id, torch_dtype=torch.bfloat16)
    pipe = pipe.to("cuda")

    # Generate video
    prompt = "A cat and a dog baking a cake together in a kitchen."
    negative_prompt = "Static, 2D cartoon, cartoon, 2d animation, paintings, images, worst quality, low quality, ugly, deformed, walking backwards"

    output = pipe(
        prompt=prompt,
        negative_prompt=negative_prompt,
        height=512,
        width=768,
        num_frames=121,  # ~5 seconds at 24fps
        num_inference_steps=50,
        guidance_scale=5.0,
    ).frames[0]

    export_to_video(output, "output.mp4", fps=24, quality=9)

###
10 second Models

⚠️ Warning! all 10 second models should be used with Flex attention and max-autotune-no-cudagraphs compilation:

    pipe = Kandinsky5T2VPipeline.from_pretrained(
        "kandinskylab/Kandinsky-5.0-T2V-Lite-sft-10s-Diffusers",
        torch_dtype=torch.bfloat16
    )
    pipe = pipe.to("cuda")

    pipe.transformer.set_attention_backend(
        "flex"
    )                                       # <--- Set attention bakend to Flex
    pipe.transformer.compile(
        mode="max-autotune-no-cudagraphs",
        dynamic=True
    )                                       # <--- Compile with max-autotune-no-cudagraphs

    prompt = "A cat and a dog baking a cake together in a kitchen."
    negative_prompt = "Static, 2D cartoon, cartoon, 2d animation, paintings, images, worst quality, low quality, ugly, deformed, walking backwards"

    output = pipe(
        prompt=prompt,
        negative_prompt=negative_prompt,
        height=512,
        width=768,
        num_frames=241,
        num_inference_steps=50,
        guidance_scale=5.0,
    ).frames[0]

    export_to_video(output, "output.mp4", fps=24, quality=9)

###
Diffusion Distilled model

⚠️ Warning! all nocfg and diffusion distilled models should be infered wothout CFG (`guidance_scale=1.0`):

    model_id = "kandinskylab/Kandinsky-5.0-T2V-Lite-distilled16steps-5s-Diffusers"
    pipe = Kandinsky5T2VPipeline.from_pretrained(model_id, torch_dtype=torch.bfloat16)
    pipe = pipe.to("cuda")

    output = pipe(
        prompt="A beautiful sunset over mountains",
        num_inference_steps=16,  # <--- Model is distilled in 16 steps
        guidance_scale=1.0,      # <--- no CFG
    ).frames[0]

    export_to_video(output, "output.mp4", fps=24, quality=9)

###
Basic Image-to-Video Generation

⚠️ Warning! all Pro models should be infered with pipeline.enable_model_cpu_offload()

    import torch
    from diffusers import Kandinsky5T2VPipeline
    from diffusers.utils import export_to_video

    # Load the pipeline
    model_id = "kandinskylab/Kandinsky-5.0-I2V-Pro-sft-5s-Diffusers"
    pipe = Kandinsky5T2VPipeline.from_pretrained(model_id, torch_dtype=torch.bfloat16)

    pipe = pipe.to("cuda")
    pipeline.transformer.set_attention_backend("flex")                            # <--- Set attention bakend to Flex
    pipeline.enable_model_cpu_offload()                                           # <--- Enable cpu offloading for single GPU inference
    pipeline.transformer.compile(mode="max-autotune-no-cudagraphs", dynamic=True) # <--- Compile with max-autotune-no-cudagraphs

    # Generate video
    image = load_image(
        "https://huggingface.co/kandinsky-community/kandinsky-3/resolve/main/assets/title.jpg?download=true"
    )
    height = 896
    width = 896
    image = image.resize((width, height))

    prompt = "An funny furry creture smiles happily and holds a sign that says 'Kandinsky'"
    negative_prompt = ""

    output = pipe(
        prompt=prompt,
        negative_prompt=negative_prompt,
        height=height,
        width=width,
        num_frames=121,  # ~5 seconds at 24fps
        num_inference_steps=50,
        guidance_scale=5.0,
    ).frames[0]

    export_to_video(output, "output.mp4", fps=24, quality=9)

##
Kandinsky 5.0 Pro Side-by-Side evaluation

Comparison with Veo 3 | Comparison with Veo 3 fast
Comparison with Wan 2.2 A14B Text-to-Video mode | Comparison with Wan 2.2 A14B Image-to-Video mode

##
Kandinsky 5.0 Lite Side-by-Side evaluation

The evaluation is based on the expanded prompts from the Movie Gen benchmark, which are available in the expanded_prompt column of the benchmark/moviegen_bench.csv file.

##
Kandinsky 5.0 Lite Distill Side-by-Side evaluation

##
Kandinsky5T2VPipeline

###
class diffusers.Kandinsky5T2VPipeline

< source >

( transformer: Kandinsky5Transformer3DModel vae: AutoencoderKLHunyuanVideo 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 video latents.
- vae (AutoencoderKLHunyuanVideo) — Variational Auto-Encoder Model hunyuanvideo-community/HunyuanVideo (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 video latents.

Pipeline for text-to-video 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 = 512 width: int = 768 num_frames: int = 121 num_inference_steps: int = 50 guidance_scale: float = 5.0 num_videos_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 ) →
`~KandinskyPipelineOutput` or `tuple`

Parameters

- prompt (`str` or `List[str]`, optional) — The prompt or prompts to guide the video generation. If not defined, pass `prompt_embeds` instead.
- negative_prompt (`str` or `List[str]`, optional) — The prompt or prompts to avoid during video generation. If not defined, pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale` < `1`).
- height (`int`, defaults to `512`) — The height in pixels of the generated video.
- width (`int`, defaults to `768`) — The width in pixels of the generated video.
- num_frames (`int`, defaults to `25`) — The number of frames in the generated video.
- 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_videos_per_prompt (`int`, optional, defaults to 1) — The number of videos 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 (`torch.Tensor`, optional) — Pre-generated text embeddings.
- negative_prompt_embeds (`torch.Tensor`, optional) — Pre-generated negative text embeddings.
- output_type (`str`, optional, defaults to `"pil"`) — The output format of the generated video.
- return_dict (`bool`, optional, defaults to `True`) — Whether or not to return a `KandinskyPipelineOutput`.
- 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

`~KandinskyPipelineOutput` or `tuple`

If `return_dict` is `True`, `KandinskyPipelineOutput` 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 generation.

Examples:

    >>> import torch
    >>> from diffusers import Kandinsky5T2VPipeline
    >>> from diffusers.utils import export_to_video

    >>> # Available models:
    >>> # kandinskylab/Kandinsky-5.0-T2V-Pro-sft-5s-Diffusers
    >>> # kandinskylab/Kandinsky-5.0-T2V-Lite-sft-5s-Diffusers
    >>> # kandinskylab/Kandinsky-5.0-T2V-Lite-nocfg-5s-Diffusers
    >>> # kandinskylab/Kandinsky-5.0-T2V-Lite-distilled16steps-5s-Diffusers
    >>> # kandinskylab/Kandinsky-5.0-T2V-Lite-pretrain-5s-Diffusers
    >>> # kandinskylab/Kandinsky-5.0-T2V-Lite-sft-10s-Diffusers
    >>> # kandinskylab/Kandinsky-5.0-T2V-Lite-nocfg-10s-Diffusers
    >>> # kandinskylab/Kandinsky-5.0-T2V-Lite-distilled16steps-10s-Diffusers
    >>> # kandinskylab/Kandinsky-5.0-T2V-Lite-pretrain-10s-Diffusers

    >>> model_id = "kandinskylab/Kandinsky-5.0-T2V-Lite-sft-5s-Diffusers"
    >>> pipe = Kandinsky5T2VPipeline.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."
    >>> negative_prompt = "Static, 2D cartoon, cartoon, 2d animation, paintings, images, worst quality, low quality, ugly, deformed, walking backwards"

    >>> output = pipe(
    ...     prompt=prompt,
    ...     negative_prompt=negative_prompt,
    ...     height=512,
    ...     width=768,
    ...     num_frames=121,
    ...     num_inference_steps=50,
    ...     guidance_scale=5.0,
    ... ).frames[0]

    >>> export_to_video(output, "output.mp4", fps=24, quality=9)

####
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 — Video height
- width — Video 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_videos_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_videos_per_prompt (`int`, optional, defaults to 1) — Number of videos to generate per prompt.
- max_sequence_length (`int`, optional, defaults to 512) — Maximum sequence length for text encoding.
- 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_videos_per_prompt, sequence_length, embedding_dim)
- CLIP pooled embeddings of shape (batch_size * num_videos_per_prompt, clip_embedding_dim)
- Cumulative sequence lengths (`cu_seqlens`) for Qwen embeddings of shape (batch_size * num_videos_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 video generation.

####
fast_sta_nabla

< source >

( T: int H: int W: int wT: int = 3 wH: int = 3 wW: int = 3 device = 'cuda' ) →
torch.Tensor

Parameters

- T (int) — Number of temporal frames
- H (int) — Height in latent space
- W (int) — Width in latent space
- wT (int) — Temporal attention window size
- wH (int) — Height attention window size
- wW (int) — Width attention window size
- device (str) — Device to create tensor on

Returns

torch.Tensor

Sparse attention mask of shape (THW, THW)

Create a sparse temporal attention (STA) mask for efficient video generation.

This method generates a mask that limits attention to nearby frames and spatial positions, reducing computational complexity for video generation.

####
get_sparse_params

< source >

( sample device ) →
Dict

Parameters

- sample (torch.Tensor) — Input sample tensor
- device (torch.device) — Device to place tensors on

Returns

Dict

Dictionary containing sparse attention parameters

Generate sparse attention parameters for the transformer based on sample dimensions.

This method computes the sparse attention configuration needed for efficient video processing in the transformer model.

####
prepare_latents

< source >

( batch_size: int num_channels_latents: int = 16 height: int = 480 width: int = 832 num_frames: int = 81 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 videos to generate
- num_channels_latents (int) — Number of channels in latent space
- height (int) — Height of generated video
- width (int) — Width of generated video
- num_frames (int) — Number of frames in video
- 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 video generation.

This method creates random noise latents or uses provided latents as starting point for the denoising process.

##
Kandinsky5I2VPipeline

###
class diffusers.Kandinsky5I2VPipeline

< source >

( transformer: Kandinsky5Transformer3DModel vae: AutoencoderKLHunyuanVideo 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 video latents.
- vae (AutoencoderKLHunyuanVideo) — Variational Auto-Encoder Model hunyuanvideo-community/HunyuanVideo (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 video latents.

Pipeline for image-to-video 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: int = 512 width: int = 768 num_frames: int = 121 num_inference_steps: int = 50 guidance_scale: float = 5.0 num_videos_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 ) →
`~KandinskyPipelineOutput` 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 video generation. If not defined, pass `prompt_embeds` instead.
- negative_prompt (`str` or `List[str]`, optional) — The prompt or prompts to avoid during video generation. If not defined, pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale` < `1`).
- height (`int`, defaults to `512`) — The height in pixels of the generated video.
- width (`int`, defaults to `768`) — The width in pixels of the generated video.
- num_frames (`int`, defaults to `121`) — The number of frames in the generated video.
- 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_videos_per_prompt (`int`, optional, defaults to 1) — The number of videos 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 video.
- return_dict (`bool`, optional, defaults to `True`) — Whether or not to return a `KandinskyPipelineOutput`.
- 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

`~KandinskyPipelineOutput` or `tuple`

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

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

Examples:

    >>> import torch
    >>> from diffusers import Kandinsky5I2VPipeline
    >>> from diffusers.utils import export_to_video, load_image

    >>> # Available models:
    >>> # kandinskylab/Kandinsky-5.0-I2V-Pro-sft-5s-Diffusers

    >>> model_id = "kandinskylab/Kandinsky-5.0-I2V-Pro-sft-5s-Diffusers"
    >>> pipe = Kandinsky5I2VPipeline.from_pretrained(model_id, torch_dtype=torch.bfloat16)
    >>> pipe = pipe.to("cuda")

    >>> image = load_image(
    ...     "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/astronaut.jpg"
    ... )
    >>> prompt = "An astronaut floating in space with Earth in the background, cinematic shot"
    >>> negative_prompt = "Static, 2D cartoon, cartoon, 2d animation, paintings, images, worst quality, low quality, ugly, deformed, walking backwards"

    >>> output = pipe(
    ...     image=image,
    ...     prompt=prompt,
    ...     negative_prompt=negative_prompt,
    ...     height=512,
    ...     width=768,
    ...     num_frames=121,
    ...     num_inference_steps=50,
    ...     guidance_scale=5.0,
    ... ).frames[0]

    >>> export_to_video(output, "output.mp4", fps=24, quality=9)

####
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 — Video height
- width — Video 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_videos_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_videos_per_prompt (`int`, optional, defaults to 1) — Number of videos to generate per prompt.
- max_sequence_length (`int`, optional, defaults to 512) — Maximum sequence length for text encoding.
- 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_videos_per_prompt, sequence_length, embedding_dim)
- CLIP pooled embeddings of shape (batch_size * num_videos_per_prompt, clip_embedding_dim)
- Cumulative sequence lengths (`cu_seqlens`) for Qwen embeddings of shape (batch_size * num_videos_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 video generation.

####
fast_sta_nabla

< source >

( T: int H: int W: int wT: int = 3 wH: int = 3 wW: int = 3 device = 'cuda' ) →
torch.Tensor

Parameters

- T (int) — Number of temporal frames
- H (int) — Height in latent space
- W (int) — Width in latent space
- wT (int) — Temporal attention window size
- wH (int) — Height attention window size
- wW (int) — Width attention window size
- device (str) — Device to create tensor on

Returns

torch.Tensor

Sparse attention mask of shape (THW, THW)

Create a sparse temporal attention (STA) mask for efficient video generation.

This method generates a mask that limits attention to nearby frames and spatial positions, reducing computational complexity for video generation.

####
get_sparse_params

< source >

( sample device ) →
Dict

Parameters

- sample (torch.Tensor) — Input sample tensor
- device (torch.device) — Device to place tensors on

Returns

Dict

Dictionary containing sparse attention parameters

Generate sparse attention parameters for the transformer based on sample dimensions.

This method computes the sparse attention configuration needed for efficient video processing in the transformer model.

####
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 = 480 width: int = 832 num_frames: int = 81 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 videos to generate
- num_channels_latents (int) — Number of channels in latent space
- height (int) — Height of generated video
- width (int) — Width of generated video
- num_frames (int) — Number of frames in video
- 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 first frame as encoded image

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

This method creates random noise latents for all frames except the first frame, which is replaced with the encoded input 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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