sayakpaul · Oct 6, 2026 · Sep 16, 2026 · Sep 16, 2026 · Sep 16, 2026 · Sep 16, 2026
diff --git a/docs/source/en/_toctree.yml b/docs/source/en/_toctree.yml
@@ -375,6 +375,8 @@
         title: JoyImageEditPlusTransformer3DModel
       - local: api/models/transformer_joyimage
         title: JoyImageEditTransformer3DModel
+      - local: api/models/kandinsky6_transformer
+        title: Kandinsky 6 Transformers
       - local: api/models/krea2_transformer2d
         title: Krea2Transformer2DModel
       - local: api/models/latte_transformer3d
@@ -501,6 +503,8 @@
         title: AutoencoderSAME
       - local: api/models/consistency_decoder_vae
         title: ConsistencyDecoderVAE
+      - local: api/models/kandinsky6_vae
+        title: Kandinsky 6 VAEs
       - local: api/models/ltx2_diffusion_decoder
         title: LTX2VideoDiffusionDecoderModel
       - local: api/models/autoencoder_oobleck
@@ -725,6 +729,8 @@
         title: HunyuanVideo1.5
       - local: api/pipelines/kandinsky5_video
         title: Kandinsky 5.0 Video
+      - local: api/pipelines/kandinsky6
+        title: Kandinsky 6
       - local: api/pipelines/latte
         title: Latte
       - local: api/pipelines/ltx2
@@ -816,6 +822,8 @@
       title: LMSDiscreteScheduler
     - local: api/schedulers/minimax_h3
       title: MiniMaxH3Scheduler
+    - local: api/schedulers/piflow
+      title: PiflowScheduler
     - local: api/schedulers/pndm
       title: PNDMScheduler
     - local: api/schedulers/repaint

diff --git a/docs/source/en/api/models/kandinsky6_transformer.md b/docs/source/en/api/models/kandinsky6_transformer.md
@@ -0,0 +1,53 @@
+<!--Copyright 2026 The Kandinsky Team and The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+-->
+
+# Kandinsky 6 Transformers
+
+Kandinsky 6 uses a multimodal diffusion transformer that denoises video and audio latents together for
+text/image-to-video-and-audio generation, and a text-free diffusion transformer for video super-resolution.
+
+## Kandinsky6Transformer3DModel
+
+The multimodal transformer used by [`Kandinsky6TI2VAPipeline`].
+
+```python
+import torch
+from diffusers import Kandinsky6Transformer3DModel
+
+transformer = Kandinsky6Transformer3DModel.from_pretrained(
+    "kandinskylab/Kandinsky-6.0-Pro-distill-5s-Diffusers", subfolder="transformer", torch_dtype=torch.bfloat16
+)
+```
+
+[[autodoc]] Kandinsky6Transformer3DModel
+  - all
+  - forward
+
+## Kandinsky6SRTransformer3DModel
+
+The text-free transformer used by [`Kandinsky6SRPipeline`] to refine one tile of the upscaled video at a time.
+
+```python
+import torch
+from diffusers import Kandinsky6SRTransformer3DModel
+
+transformer = Kandinsky6SRTransformer3DModel.from_pretrained(
+    "kandinskylab/Kandinsky-6.0-VSR-distilled2steps-5s-Diffusers", subfolder="transformer", torch_dtype=torch.bfloat16
+)
+# The transformer always runs NABLA sparse attention (`nabla_threshold`, 0.8 by default) on the `flex` backend.
+# Compile it, otherwise flex falls back to an eager implementation that needs far more memory at video resolutions.
+transformer.compile_repeated_blocks(fullgraph=True)
+```
+
+[[autodoc]] Kandinsky6SRTransformer3DModel
+  - all
+  - forward
diff --git a/docs/source/en/api/models/kandinsky6_vae.md b/docs/source/en/api/models/kandinsky6_vae.md
@@ -0,0 +1,66 @@
+<!--Copyright 2026 The Kandinsky Team and The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+-->
+
+# Kandinsky 6 VAEs
+
+Kandinsky 6 uses a causal 3D K-VAE for video super-resolution and the MMAudio mel-spectrogram VAE, paired with a
+separate BigVGAN [`MMAudioVocoder`], for synchronized audio generation.
+
+## Kandinsky6SRVAE
+
+The causal 3D K-VAE used by [`Kandinsky6SRPipeline`]. It processes arbitrarily long videos in bounded-memory
+segments while reproducing the exact output of a single, non-segmented pass.
+
+```python
+import torch
+from diffusers import Kandinsky6SRVAE
+
+vae = Kandinsky6SRVAE.from_pretrained(
+    "kandinskylab/Kandinsky-6.0-VSR-5s-Diffusers", subfolder="vae", torch_dtype=torch.bfloat16
+)
+```
+
+[[autodoc]] Kandinsky6SRVAE
+  - encode
+  - decode
+  - all
+
+## MMAudioVAE
+
+The mel-spectrogram VAE used by [`Kandinsky6TI2VAPipeline`] when `sample_audio=True`. Its `decode` output is a mel
+spectrogram; pass it through [`MMAudioVocoder`] to get a waveform.
+
+The reference implementation can be found at [hkchengrex/MMAudio](https://github.com/hkchengrex/MMAudio) (MIT
+license).
+
+```python
+import torch
+from diffusers import MMAudioVAE
+
+audio_vae = MMAudioVAE.from_pretrained(
+    "kandinskylab/Kandinsky-6.0-Pro-distill-5s-Diffusers", subfolder="audio_vae", torch_dtype=torch.bfloat16
+)
+```
+
+[[autodoc]] MMAudioVAE
+  - encode
+  - decode
+  - all
+
+## MMAudioVocoder
+
+Adapted from the BigVGAN-v2 vocoder MMAudio bundles, itself from
+[NVIDIA/BigVGAN](https://github.com/NVIDIA/BigVGAN) (MIT license), with the anti-aliased Snake activations of
+[alias-free-torch](https://github.com/junjun3518/alias-free-torch) (Apache License 2.0).
+
+[[autodoc]] MMAudioVocoder
+  - forward
diff --git a/docs/source/en/api/pipelines/kandinsky6.md b/docs/source/en/api/pipelines/kandinsky6.md
@@ -0,0 +1,150 @@
+<!--Copyright 2026 The Kandinsky Team and The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+-->
+
+# Kandinsky 6
+
+Kandinsky 6 is a family of video generation models from [Kandinsky Lab](https://huggingface.co/kandinskylab). 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.
+
+> [!TIP]
+> Check out the [Kandinsky Lab](https://huggingface.co/kandinskylab) 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
+
+| Model | Pipeline | Notes |
+|---|---|---|
+| [`kandinskylab/Kandinsky-6.0-Pro-5s-Diffusers`](https://huggingface.co/kandinskylab/Kandinsky-6.0-Pro-5s-Diffusers) | [`Kandinsky6TI2VAPipeline`] | Flow matching, `guidance_scale=5.0`, 50 steps |
+| [`kandinskylab/Kandinsky-6.0-Pro-distill-5s-Diffusers`](https://huggingface.co/kandinskylab/Kandinsky-6.0-Pro-distill-5s-Diffusers) | [`Kandinsky6TI2VAPipeline`] | Distilled, `guidance_scale=1.0`, 10 steps |
+| [`kandinskylab/Kandinsky-6.0-Lite-5s-Diffusers`](https://huggingface.co/kandinskylab/Kandinsky-6.0-Lite-5s-Diffusers) | [`Kandinsky6TI2VAPipeline`] | Flow matching, `guidance_scale=5.0`, 50 steps |
+| [`kandinskylab/Kandinsky-6.0-Lite-distill-5s-Diffusers`](https://huggingface.co/kandinskylab/Kandinsky-6.0-Lite-distill-5s-Diffusers) | [`Kandinsky6TI2VAPipeline`] | Distilled, `guidance_scale=1.0`, 10 steps |
+| [`kandinskylab/Kandinsky-6.0-Pro-pretrain-5s-Diffusers`](https://huggingface.co/kandinskylab/Kandinsky-6.0-Pro-pretrain-5s-Diffusers) | [`Kandinsky6TI2VAPipeline`] | Flow matching, `guidance_scale=5.0`, 50 steps |
+| [`kandinskylab/Kandinsky-6.0-Lite-pretrain-5s-Diffusers`](https://huggingface.co/kandinskylab/Kandinsky-6.0-Lite-pretrain-5s-Diffusers) | [`Kandinsky6TI2VAPipeline`] | Flow matching, `guidance_scale=5.0`, 50 steps |
+| [`kandinskylab/Kandinsky-6.0-VSR-5s-Diffusers`](https://huggingface.co/kandinskylab/Kandinsky-6.0-VSR-5s-Diffusers) | [`Kandinsky6SRPipeline`] | Flow matching super-resolution |
+| [`kandinskylab/Kandinsky-6.0-VSR-distilled2steps-5s-Diffusers`](https://huggingface.co/kandinskylab/Kandinsky-6.0-VSR-distilled2steps-5s-Diffusers) | [`Kandinsky6SRPipeline`] | Distilled super-resolution, 2 steps |
+
+## Text/image-to-video-and-audio
+
+```python
+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.
+
+```python
+# 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](../../optimization/memory) guide for the general set of techniques. Both
+[`Kandinsky6TI2VAPipeline`] and [`Kandinsky6SRPipeline`] support [model offloading](../../optimization/memory#model-offloading)
+(used above) and, for a smaller footprint at the cost of speed, [sequential CPU offloading](../../optimization/memory#cpu-offloading):
+
+```python
+pipe.enable_sequential_cpu_offload()
+```
+
+[`Kandinsky6TI2VAPipeline`]'s video VAE also supports [tiled decoding](../../optimization/memory#vae-tiling) for high
+resolutions or long videos:
+
+```python
+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:
+  ```python
+  pipe.transformer.compile_repeated_blocks(fullgraph=True)
+  ```
+
+## Kandinsky6TI2VAPipeline
+
+[[autodoc]] Kandinsky6TI2VAPipeline
+  - all
+  - __call__
+
+## Kandinsky6SRPipeline
+
+[[autodoc]] Kandinsky6SRPipeline
+  - all
+  - __call__
+
+## Kandinsky6SRLatentUpscalerBank
+
+[[autodoc]] Kandinsky6SRLatentUpscalerBank
+  - forward
+
+## Kandinsky6TI2VAPipelineOutput
+
+[[autodoc]] pipelines.kandinsky6.pipeline_output.Kandinsky6TI2VAPipelineOutput
+
+## Kandinsky6SRPipelineOutput
+
+[[autodoc]] pipelines.kandinsky6.pipeline_output.Kandinsky6SRPipelineOutput
diff --git a/docs/source/en/api/schedulers/piflow.md b/docs/source/en/api/schedulers/piflow.md
@@ -0,0 +1,30 @@
+<!--Copyright 2026 The Kandinsky Team and The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+-->
+
+# PiflowScheduler
+
+`PiflowScheduler` is the few-step scheduler of the distilled Kandinsky 6 checkpoints, both the
+text/image-to-video-and-audio model and the video super-resolution model. It implements
+[π-Flow](https://huggingface.co/papers/2510.14974): the transformer predicts `n_grid` denoised estimates per latent
+channel at a small number of grid points, and the scheduler integrates a network-free policy between them.
+
+The reference implementation can be found at [Lakonik/LakonLab](https://github.com/Lakonik/LakonLab).
+
+## PiflowScheduler
+
+[[autodoc]] PiflowScheduler
+  - set_timesteps
+  - step
+
+## PiflowSchedulerOutput
+
+[[autodoc]] schedulers.scheduling_piflow.PiflowSchedulerOutput