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Kandinsky 6

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Kandinsky 6

Kandinsky 6 is a family of video generation models from Kandinsky Lab. The main model generates video and synchronized audio from text or a reference image with a single multimodal diffusion transformer: video and audio latents are denoised together through fused blocks that cross-attend between the two modalities, each conditioned on its own Qwen2.5-VL text branch and a CLIP pooled embedding. A separate super-resolution model upscales the generated video tile by tile in the latent space of a causal 3D K-VAE.

Check out the Kandinsky Lab organization on the Hub for the full set of official checkpoints, including flow-matching and distilled variants of both the base and super-resolution models.

Distilled checkpoints ship with the few-step PiflowScheduler and must be run with guidance_scale=1.0.

Available models

ModelPipelineNotes
kandinskylab/Kandinsky-6.0-Pro-5s-DiffusersKandinsky6TI2VAPipelineFlow matching, guidance_scale=5.0, 50 steps
kandinskylab/Kandinsky-6.0-Pro-distill-5s-DiffusersKandinsky6TI2VAPipelineDistilled, guidance_scale=1.0, 10 steps
kandinskylab/Kandinsky-6.0-Lite-5s-DiffusersKandinsky6TI2VAPipelineFlow matching, guidance_scale=5.0, 50 steps
kandinskylab/Kandinsky-6.0-Lite-distill-5s-DiffusersKandinsky6TI2VAPipelineDistilled, guidance_scale=1.0, 10 steps
kandinskylab/Kandinsky-6.0-Pro-pretrain-5s-DiffusersKandinsky6TI2VAPipelineFlow matching, guidance_scale=5.0, 50 steps
kandinskylab/Kandinsky-6.0-Lite-pretrain-5s-DiffusersKandinsky6TI2VAPipelineFlow matching, guidance_scale=5.0, 50 steps
kandinskylab/Kandinsky-6.0-VSR-5s-DiffusersKandinsky6SRPipelineFlow matching super-resolution
kandinskylab/Kandinsky-6.0-VSR-distilled2steps-5s-DiffusersKandinsky6SRPipelineDistilled super-resolution, 2 steps

Text/image-to-video-and-audio

import torch
from diffusers import Kandinsky6TI2VAPipeline
from diffusers.utils import encode_video

pipe = Kandinsky6TI2VAPipeline.from_pretrained(
    "kandinskylab/Kandinsky-6.0-Pro-distill-5s-Diffusers", torch_dtype=torch.bfloat16
)
pipe.enable_model_cpu_offload()

output = pipe(
    prompt="A cat and a dog baking a cake together in a kitchen.",
    height=480,
    width=864,
    num_frames=121,
    num_inference_steps=10,
    guidance_scale=1.0,
)
encode_video(
    output.frames[0],
    fps=24,
    output_path="output.mp4",
    audio=output.audio[0][None],
    audio_sample_rate=pipe.audio_sample_rate,
)

Pass image= to condition the first frame on a reference image, sample_audio=False to generate video only, and expand_prompts=True to let the Qwen2.5-VL text encoder rewrite short prompts into detailed ones first.

Video super-resolution

Kandinsky6SRPipeline takes the frames produced by Kandinsky6TI2VAPipeline and upscales them by 2, 4, or 2.25 (a 1.125x bilinear pre-upscale followed by the 2x path). The video is split into overlapping tiles, every tile is refined at one of the tile sizes the SR transformer was trained on, and the tiles are blended back with Hann windows.

# required: lets inductor pick flex-attention tiles that fit the SR block mask
torch._inductor.config.max_autotune = True  

sr_pipe = Kandinsky6SRPipeline.from_pretrained(
    "kandinskylab/Kandinsky-6.0-VSR-distilled2steps-5s-Diffusers", torch_dtype=torch.bfloat16
)
# The SR transformer always runs NABLA sparse attention on the `flex` backend. Compile it, otherwise flex falls
# back to an eager implementation that needs far more memory at video resolutions.
sr_pipe.enable_model_cpu_offload()
sr_pipe.transformer.set_attention_backend("flex")
sr_pipe.transformer.compile_repeated_blocks(fullgraph=True)

upscaled = sr_pipe(video=output.frames[0], resolution_scale=2.25, num_inference_steps=2).frames[0]

Memory optimization

Refer to the Reduce memory usage guide for the general set of techniques. Both Kandinsky6TI2VAPipeline and Kandinsky6SRPipeline support model offloading (used above) and, for a smaller footprint at the cost of speed, sequential CPU offloading:

pipe.enable_sequential_cpu_offload()

Kandinsky6TI2VAPipeline’s video VAE also supports tiled decoding for high resolutions or long videos:

pipe.vae.enable_tiling()

Notes

  • height and width must be divisible by the video VAE’s spatial compression ratio times the transformer’s patch size — 16 with the default Kandinsky6TI2VAPipeline configuration (AutoencoderKLHunyuanVideo at a compression ratio of 8, patch_size=(1, 2, 2)). 480x864, used in the example above, satisfies this.
  • Kandinsky6SRPipeline’s input video must have 1 + k * vae_scale_factor_temporal frames for some integer k (a temporal compression ratio of 4 with the default K-VAE configuration, so 121 frames works but 120 doesn’t) — trim or pad a video that doesn’t already satisfy this before upscaling it.
  • expand_prompts=True reuses the already-loaded Qwen2.5-VL text encoder for an extra generation pass before denoising, so it adds latency but no extra model weights.
  • Compile the repeated transformer blocks for faster repeated inference: 
    pipe.transformer.compile_repeated_blocks(fullgraph=True)

Kandinsky6TI2VAPipeline

class diffusers.Kandinsky6TI2VAPipeline

< >

( transformer: Kandinsky6Transformer3DModelvae: AutoencoderKLHunyuanVideotext_encoder: Qwen2_5_VLForConditionalGenerationtokenizer: Qwen2_5_VLProcessortext_encoder_2: CLIPTextModeltokenizer_2: CLIPTokenizerscheduler: diffusers.schedulers.scheduling_flow_match_euler_discrete.FlowMatchEulerDiscreteScheduler | diffusers.schedulers.scheduling_piflow.PiflowScheduleraudio_vae: diffusers.models.autoencoders.autoencoder_mmaudio.MMAudioVAE | None = Nonevocoder: diffusers.models.autoencoders.mmaudio_vocoder.MMAudioVocoder | None = None )

Parameters

  • transformer (Kandinsky6Transformer3DModel) — Multimodal transformer that denoises the video and audio latents.
  • vae (AutoencoderKLHunyuanVideo) — Video VAE used to encode the reference image and decode the generated video.
  • text_encoder (Qwen2_5_VLForConditionalGeneration) — Qwen2.5-VL model providing the token-level text embeddings and, optionally, prompt expansion.
  • tokenizer (Qwen2_5_VLProcessor) — Processor of text_encoder.
  • text_encoder_2 (CLIPTextModel) — CLIP text encoder providing the pooled text embedding.
  • tokenizer_2 (CLIPTokenizer) — Tokenizer of text_encoder_2.
  • scheduler (FlowMatchEulerDiscreteScheduler or PiflowScheduler) — Scheduler used with transformer to denoise the latents. Distilled checkpoints ship with a PiflowScheduler and must be run with guidance_scale=1.0.
  • audio_vae (MMAudioVAE, optional) — Audio VAE used to decode the generated audio latents into a mel spectrogram. Only needed when sample_audio=True.
  • vocoder (MMAudioVocoder, optional) — Vocoder used to turn the mel spectrogram audio_vae decodes into a waveform. Only needed when sample_audio=True.

Pipeline for text/image-to-video-and-audio generation with Kandinsky 6.

Video and audio latents are denoised together by a single multimodal transformer, conditioned on Qwen2.5-VL text tokens and a CLIP pooled embedding. An optional reference image conditions the first frame.

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__

< >

( prompt: str | list[str] | None = Noneimage: typing.Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, list[PIL.Image.Image], list[numpy.ndarray], list[torch.Tensor], NoneType] = Nonenegative_prompt: str | list[str] | None = Noneheight: int = 512width: int = 768num_frames: int = 121frame_rate: float = 24.0num_inference_steps: int = 50timesteps: list[int] | None = Nonesigmas: list[float] | None = Noneguidance_scale: float = 5.0num_videos_per_prompt: int = 1generator: typing.Union[torch.Generator, list[torch.Generator], NoneType] = Nonelatents: typing.Optional[torch.Tensor] = Noneaudio_latents: typing.Optional[torch.Tensor] = Noneprompt_embeds: typing.Optional[torch.Tensor] = Nonepooled_prompt_embeds: typing.Optional[torch.Tensor] = Nonenegative_prompt_embeds: typing.Optional[torch.Tensor] = Nonenegative_pooled_prompt_embeds: typing.Optional[torch.Tensor] = Nonesample_audio: bool = Trueexpand_prompts: bool = Falsemax_sequence_length: int = 1024output_type: str = 'pil'return_dict: bool = Truecallback_on_step_end: collections.abc.Callable[[int, int, dict], None] | None = Nonecallback_on_step_end_tensor_inputs: list = ['latents'] ) → Kandinsky6TI2VAPipelineOutput or tuple

Parameters

  • prompt (str or list[str], optional) — The prompt or prompts to guide the generation. Required unless prompt_embeds is given.
  • image (PipelineImageInput, optional) — Reference image(s) conditioning the first frame (image-to-video-and-audio).
  • negative_prompt (str or list[str], optional) — The prompt or prompts not to guide the generation. Defaults to the Kandinsky 6 negative prompt.
  • height (int, defaults to 512) — Height of the generated video in pixels.
  • width (int, defaults to 768) — Width of the generated video in pixels.
  • num_frames (int, defaults to 121) — Number of generated frames.
  • frame_rate (float, defaults to 24.0) — Frame rate the video is generated at; sets the length of the synchronized audio.
  • num_inference_steps (int, defaults to 50) — The number of denoising steps. Use 16 with the distilled checkpoints.
  • timesteps (list[int], optional) — Custom timesteps for schedulers that support them.
  • sigmas (list[float], optional) — Custom sigmas for schedulers that support them.
  • guidance_scale (float, defaults to 5.0) — Classifier-free guidance scale. Must be 1.0 with a PiflowScheduler.
  • num_videos_per_prompt (int, defaults to 1) — The number of videos to generate per prompt.
  • generator (torch.Generator or list[torch.Generator], optional) — Generator(s) used for the initial noise and the reference image encoding.
  • latents (torch.Tensor, optional) — Pre-generated video latents of shape (batch_size, channels, num_latent_frames, latent_height, latent_width).
  • audio_latents (torch.Tensor, optional) — Pre-generated audio latents of shape (batch_size, channels, audio_length).
  • prompt_embeds (torch.Tensor, optional) — Pre-generated Qwen2.5-VL text embeddings.
  • pooled_prompt_embeds (torch.Tensor, optional) — Pre-generated CLIP pooled text embeddings.
  • negative_prompt_embeds (torch.Tensor, optional) — Pre-generated negative Qwen2.5-VL text embeddings.
  • negative_pooled_prompt_embeds (torch.Tensor, optional) — Pre-generated negative CLIP pooled text embeddings.
  • sample_audio (bool, defaults to True) — Whether to generate synchronized audio. Requires the pipeline to have an audio_vae and a vocoder.
  • expand_prompts (bool, defaults to False) — Whether to rewrite the prompts with expand_prompts() before encoding.
  • max_sequence_length (int, defaults to 1024) — Maximum number of prompt tokens after the chat template.
  • output_type (str, defaults to "pil") — The output format of the generated video: "pil", "np", "pt" or "latent".
  • return_dict (bool, defaults to True) — Whether or not to return a Kandinsky6TI2VAPipelineOutput instead of a plain tuple.
  • callback_on_step_end (Callable, optional) — A function called at the end of each denoising step with callback_on_step_end(self, step, timestep, callback_kwargs). It may return a dict overriding the listed tensors.
  • callback_on_step_end_tensor_inputs (list[str], defaults to ["latents"]) — Tensor inputs passed to callback_on_step_end; a subset of _callback_tensor_inputs.

Returns

Kandinsky6TI2VAPipelineOutput or tuple

The generated video and audio; a (frames, audio) tuple when return_dict=False.

The call function to the pipeline for generation.

Examples:

>>> import torch
>>> from diffusers import Kandinsky6TI2VAPipeline
>>> from diffusers.utils import encode_video

>>> pipe = Kandinsky6TI2VAPipeline.from_pretrained(
...     "kandinskylab/Kandinsky-6.0-Pro-distill-5s-Diffusers", torch_dtype=torch.bfloat16
... )
>>> pipe.enable_model_cpu_offload()

>>> output = pipe(
...     prompt="A cat and a dog baking a cake together in a kitchen.",
...     height=480,
...     width=864,
...     num_frames=121,
...     num_inference_steps=16,
...     guidance_scale=1.0,
... )
>>> encode_video(
...     output.frames[0],
...     fps=24,
...     output_path="output.mp4",
...     audio=output.audio[0][None],
...     audio_sample_rate=pipe.audio_sample_rate,
... )

encode_image

< >

( image: typing.Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, list[PIL.Image.Image], list[numpy.ndarray], list[torch.Tensor]]height: intwidth: intdevice: devicedtype: dtypenum_videos_per_prompt: int = 1generator: typing.Optional[torch.Generator] = None )

Encodes the reference image(s) into first-frame latents of shape (batch_size, latent_height, latent_width, latent_channels), scaled by the VAE scaling_factor. PIL images are resized and center-cropped to height x width; tensors and arrays must already have that size. The latents are repeated num_videos_per_prompt times along the batch dimension.

encode_prompt

< >

( prompt: str | list[str]negative_prompt: str | list[str] | None = Nonedo_classifier_free_guidance: bool = Truenum_videos_per_prompt: int = 1prompt_embeds: typing.Optional[torch.Tensor] = Nonepooled_prompt_embeds: typing.Optional[torch.Tensor] = Noneprompt_attention_mask: typing.Optional[torch.Tensor] = Nonenegative_prompt_embeds: typing.Optional[torch.Tensor] = Nonenegative_pooled_prompt_embeds: typing.Optional[torch.Tensor] = Nonenegative_prompt_attention_mask: typing.Optional[torch.Tensor] = Nonemax_sequence_length: int = 1024device: typing.Optional[torch.device] = Nonedtype: typing.Optional[torch.dtype] = None )

Parameters

  • prompt (str or list[str]) — Prompt to be encoded.
  • negative_prompt (str or list[str], optional) — The prompt not to guide the generation. Ignored when do_classifier_free_guidance is False.
  • do_classifier_free_guidance (bool, defaults to True) — Whether to also encode the negative prompt.
  • num_videos_per_prompt (int, defaults to 1) — Number of videos generated per prompt; the embeddings are repeated accordingly.
  • prompt_embeds (torch.Tensor, optional) — Pre-generated Qwen2.5-VL text embeddings. Skips encoding prompt.
  • pooled_prompt_embeds (torch.Tensor, optional) — Pre-generated CLIP pooled text embeddings. Must be given together with prompt_embeds.
  • prompt_attention_mask (torch.Tensor, optional) — Boolean padding mask of prompt_embeds.
  • negative_prompt_embeds (torch.Tensor, optional) — Pre-generated negative Qwen2.5-VL text embeddings.
  • negative_pooled_prompt_embeds (torch.Tensor, optional) — Pre-generated negative CLIP pooled text embeddings.
  • negative_prompt_attention_mask (torch.Tensor, optional) — Boolean padding mask of negative_prompt_embeds.
  • max_sequence_length (int, defaults to 1024) — Maximum number of prompt tokens after the chat template.
  • device (torch.device, optional) — Device to run the text encoders on.
  • dtype (torch.dtype, optional) — Dtype of the returned embeddings.

Encodes the prompt into text encoder hidden states.

expand_prompts

< >

( prompt: str | list[str]tokenizertext_encoderdevice: deviceimage: PIL.Image.Image | list[PIL.Image.Image] | None = Nonemax_sequence_length: int = 1024generator: typing.Union[torch.Generator, list[torch.Generator], NoneType] = None ) → str or list[str]

Parameters

  • prompt (str or list[str]) — Prompt or prompts to expand.
  • tokenizer — The Qwen2.5-VL processor, e.g. pipe.tokenizer.
  • text_encoder — The Qwen2.5-VL model, e.g. pipe.text_encoder.
  • device (torch.device) — Device to run the text encoder on.
  • image (PIL.Image.Image or list[PIL.Image.Image], optional) — Reference image(s) of an image-to-video call.
  • max_sequence_length (int, defaults to 1024) — Maximum number of generated tokens per prompt.
  • generator (torch.Generator or list[torch.Generator], optional) — Seeds the sampled expansion; a list must match prompt’s length, one generator per item. generate draws from the global RNG, so the global RNG is seeded from this generator’s seed; later randn_tensor calls keep using generator directly.

Returns

str or list[str]

The expanded prompt(s).

Rewrites short prompts into detailed video+audio prompts with the Qwen2.5-VL text encoder, grounding them on the reference image when one is given. A staticmethod so it can be used standalone, before running the pipeline.

prepare_audio_latents

< >

( batch_size: intnum_channels_latents: intaudio_length: intdtype: dtypedevice: devicegenerator: typing.Union[torch.Generator, list[torch.Generator], NoneType]audio_latents: typing.Optional[torch.Tensor] = None )

Returns audio latents in the transformer’s (batch_size, audio_length, channels) layout. A user-provided audio_latents tensor is expected in the (batch_size, channels, audio_length) layout.

prepare_latents

< >

( batch_size: intnum_channels_latents: intheight: intwidth: intnum_frames: intdtype: dtypedevice: devicegenerator: typing.Union[torch.Generator, list[torch.Generator], NoneType]latents: typing.Optional[torch.Tensor] = None )

Returns video latents in the transformer’s (batch_size, num_frames, height, width, channels) layout. A user-provided latents tensor is expected in the (batch_size, channels, num_frames, height, width) layout.

Kandinsky6SRPipeline

class diffusers.Kandinsky6SRPipeline

< >

( transformer: Kandinsky6SRTransformer3DModelvae: Kandinsky6SRVAEscheduler: diffusers.schedulers.scheduling_flow_match_euler_discrete.FlowMatchEulerDiscreteScheduler | diffusers.schedulers.scheduling_piflow.PiflowSchedulerlatent_upscaler: diffusers.models.latent_upscaler.latent_upscaler_kandinsky6_sr.Kandinsky6SRLatentUpscalerBank | None = None )

Parameters

Pipeline for video super-resolution with Kandinsky 6.

The video is split into overlapping spatial tiles, every tile is refined by the SR transformer at one of the tile sizes the model was trained on (transformer.config.tile_sizes), and the refined tiles are blended back with Hann windows. When the pipeline has a latent_upscaler, the tiles are cut from the K-VAE latents of the whole video and upscaled in latent space; otherwise the pixel tiles are bilinearly upscaled and encoded.

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__

< >

( video: typing.Union[list[PIL.Image.Image], list[list[PIL.Image.Image]], numpy.ndarray, torch.Tensor]resolution_scale: float = 2.25num_inference_steps: int = 4timesteps: list[int] | None = Nonesigmas: list[float] | None = Nonelq_noise_scale: float = 0.7min_overlap: float = 0.2tiles_batch_size: int = 1generator: typing.Union[torch.Generator, list[torch.Generator], NoneType] = Noneoutput_type: str = 'pil'return_dict: bool = True ) → Kandinsky6SRPipelineOutput or tuple

Parameters

  • video (list[PIL.Image.Image], np.ndarray or torch.Tensor) — The low-resolution video(s), in any format preprocess_video() accepts, with 1 + k * 4 frames. Sizes are rounded down to a multiple of the VAE spatial factor.
  • resolution_scale (float, defaults to 2.25) — Total spatial upscale: 2, 4, or 2.25 (a 1.125x bilinear pre-upscale followed by the 2x path).
  • num_inference_steps (int, defaults to 4) — The number of denoising steps per tile. Use 2 with the distilled checkpoints.
  • timesteps (list[int], optional) — Custom timesteps for schedulers that support them.
  • sigmas (list[float], optional) — Custom sigmas for schedulers that support them.
  • lq_noise_scale (float, defaults to 0.7) — Amount of Gaussian noise mixed into the low-resolution latents (variance preserving) before denoising.
  • min_overlap (float, defaults to 0.2) — Minimum overlap between neighbouring tiles as a fraction of the tile size.
  • tiles_batch_size (int, defaults to 1) — Number of tiles denoised per transformer call.
  • generator (torch.Generator or list[torch.Generator], optional) — Generator(s) used for the noise mixed into the tiles.
  • output_type (str, defaults to "pil") — The output format of the generated video: "pil", "np" or "pt".
  • return_dict (bool, defaults to True) — Whether or not to return a Kandinsky6SRPipelineOutput instead of a plain tuple.

Returns

Kandinsky6SRPipelineOutput or tuple

The super-resolved video; a one-element tuple when return_dict=False.

The call function to the pipeline for super-resolution.

Examples:

>>> import torch
>>> from diffusers import Kandinsky6SRPipeline, Kandinsky6TI2VAPipeline
>>> from diffusers.utils import export_to_video

>>> pipe = Kandinsky6TI2VAPipeline.from_pretrained(
...     "kandinskylab/Kandinsky-6.0-Pro-distill-5s-Diffusers", torch_dtype=torch.bfloat16
... )
>>> pipe.enable_model_cpu_offload()
>>> video = pipe(
...     prompt="A cat and a dog baking a cake together in a kitchen.",
...     height=480,
...     width=864,
...     num_inference_steps=16,
...     guidance_scale=1.0,
...     sample_audio=False,
... ).frames[0]

>>> sr_pipe = Kandinsky6SRPipeline.from_pretrained(
...     "kandinskylab/Kandinsky-6.0-VSR-distilled2steps-5s-Diffusers", torch_dtype=torch.bfloat16
... )
>>> # The transformer always runs attention through the `flex` backend; compiling avoids the eager
>>> # fallback's much higher memory use at video resolutions.
>>> sr_pipe.transformer.compile_repeated_blocks(fullgraph=True)
>>> sr_pipe.enable_model_cpu_offload()
>>> output = sr_pipe(video=video, resolution_scale=2.25, num_inference_steps=2)
>>> export_to_video(output.frames[0], "output_sr.mp4", fps=24)

decode_latents

< >

( latents: Tensor )

Decodes scaled K-VAE latents into a (batch_size, channels, num_frames, height, width) video in [-1, 1].

encode_video

< >

( video: Tensor )

Encodes a (batch_size, channels, num_frames, height, width) video in [-1, 1] into K-VAE latents scaled by the VAE scaling_factor.

Kandinsky6SRLatentUpscalerBank

class diffusers.Kandinsky6SRLatentUpscalerBank

< >

( in_channels: int = 64stage_channels: tuple[int, int, int] = (2048, 1024, 512)num_pre_blocks: int = 5num_mid_blocks: int = 3num_post_blocks: int = 3num_x2_adapter_blocks: int = 2scales: tuple[int, ...] = (2, 4)scaling_factor: float = 0.910344004631042 )

Parameters

  • in_channels (int, defaults to 64) — Number of latent channels.
  • stage_channels (tuple[int, int, int], defaults to (2048, 1024, 512)) — Feature widths of the three stages of the cascade.
  • num_pre_blocks (int, defaults to 5) — Residual blocks before the first upsample of the x4 model.
  • num_mid_blocks (int, defaults to 3) — Residual blocks between the two upsamples.
  • num_post_blocks (int, defaults to 3) — Residual blocks after the last upsample.
  • num_x2_adapter_blocks (int, defaults to 2) — Residual blocks of the x2 model’s adapter.
  • scales (tuple[int, ...], defaults to (2, 4)) — Spatial scales the bank provides an upscaler for.
  • scaling_factor (float, defaults to 0.910344) — Scale the input latents are expected to carry (the K-VAE scaling_factor).

Bank of latent upscalers used by Kandinsky6SRPipeline: one Kandinsky6SRLatentUpscaler per supported spatial scale, operating on K-VAE latents.

forward

< >

( latents: Tensorscale: intreturn_dict: bool = True )

Parameters

  • latents (torch.Tensor of shape (batch_size, in_channels, num_frames, height, width)) — K-VAE latents scaled by scaling_factor.
  • scale (int) — Spatial upscale factor; one of scales.
  • return_dict (bool, defaults to True) — Whether to return a ~models.autoencoder_kl.DecoderOutput instead of a plain tuple.

Kandinsky6TI2VAPipelineOutput

class diffusers.Kandinsky6TI2VAPipelineOutput

< >

( frames: typing.Union[torch.Tensor, numpy.ndarray, list[list[PIL.Image.Image]]]audio: typing.Union[torch.Tensor, numpy.ndarray, NoneType] = None )

Parameters

  • frames (torch.Tensor, np.ndarray, or list[list[PIL.Image.Image]]) — The generated video. A nested list of length batch_size holding num_frames PIL images each, or a NumPy array or torch tensor of shape (batch_size, num_frames, height, width, channels) / (batch_size, num_frames, channels, height, width). With output_type="latent", the video latents of shape (batch_size, channels, num_latent_frames, latent_height, latent_width).
  • audio (torch.Tensor or np.ndarray, optional) — The generated waveforms of shape (batch_size, num_samples) in [-1, 1] at the audio VAE’s sample rate, or None when audio was not sampled. With output_type="latent", the audio latents of shape (batch_size, channels, audio_length).

Output class for Kandinsky6TI2VAPipeline.

Kandinsky6SRPipelineOutput

class diffusers.Kandinsky6SRPipelineOutput

< >

( frames: typing.Union[torch.Tensor, numpy.ndarray, list[list[PIL.Image.Image]]] )

Parameters

  • frames (torch.Tensor, np.ndarray, or list[list[PIL.Image.Image]]) — The super-resolved video. A nested list of length batch_size holding num_frames PIL images each, or a NumPy array or torch tensor of shape (batch_size, num_frames, height, width, channels) / (batch_size, num_frames, channels, height, width).

Output class for Kandinsky6SRPipeline.

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