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Sponsored by
Vmake Labs Video Enhancer
kandinskylab / Kandinsky-6.0-VSR-distilled2steps-5s-Diffusers
Model's Last Updated: October 12 2026
any-to-any
Introduction of Kandinsky-6.0-VSR-distilled2steps-5s-Diffusers
Model Details of Kandinsky-6.0-VSR-distilled2steps-5s-Diffusers
Kandinsky 6.0: A family of diffusion models for Video + Audio generation
We present Kandinsky 6.0 Video, a family of foundation diffusion models for synchronized text-to-audio-video generation, comprising Kandinsky 6.0 Video Lite (3B parameters) and Kandinsky 6.0 Video Pro (29B parameters). Both models generate 5-second video clips with synchronized 44 kHz audio, including lip-sync, in text-to-audio-video (T2AV) and image-to-audio-video (TI2AV) modes; a plug-in super-resolution model raises the output resolution to Full-HD (1920×1080).
The complete Kandinsky 6.0 video super-resolution pipeline as a single 🧨 Diffusers bundle: the 1.4B SR DiT distilled with π-Flow DX down to 2 model evaluations per tile , the KVAE video VAE and the x2 / x4 latent-upscaler bank. It upscales a video by x2, x2.25 or x4 with tiled diffusion in KVAE latent space. The Diffusers pipeline is video-only; mux the source audio back in if you need it in the output.
This repository contains all components needed for inference with
from_pretrained
. For the flow-matching teacher with 4 Euler steps per tile, see
the flow-matching Diffusers bundle
.
Usage with Diffusers
import torch
from diffusers import Kandinsky6SRPipeline
from diffusers.utils import export_to_video, load_video
torch._inductor.config.max_autotune = True # required: lets inductor pick flex-attention tiles that fit the SR block mask
pipe = Kandinsky6SRPipeline.from_pretrained("kandinskylab/Kandinsky-6.0-VSR-distilled2steps-5s-Diffusers", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
pipe.transformer.set_attention_backend("flex")
pipe.transformer.compile_repeated_blocks(fullgraph=True)
video = load_video("lq.mp4") # -> list[PIL.Image.Image]
output = pipe(
video=video,
resolution_scale=2.25, # 2, 2.25 or 4
num_inference_steps=2, # must be set explicitly -- the pipeline's own default (4) is for the flow-matching checkpoint
generator=torch.Generator("cuda").manual_seed(1137),
)
export_to_video(output.frames[0], "lq_sr.mp4", fps=24)
Video Super Resolution Models
| Component | Repository |
|---|---|
| Diffusers bundle, π-Flow distilled (2 evaluations per tile) (this repository) | Kandinsky-6.0-VSR-distilled2steps-5s-Diffusers |
| Diffusers bundle, flow matching | Kandinsky-6.0-VSR-5s-Diffusers |
Components
| Folder | Class | Parameters | Contents |
|---|---|---|---|
transformer/
|
Kandinsky6SRTransformer3DModel
|
1.41B | π-Flow DX distilled SR DiT (2 evaluations per tile) |
vae/
|
Kandinsky6SRVAE
|
1.74B | Video VAE (included in this bundle) |
latent_upscaler/
|
Kandinsky6SRLatentUpscalerBank
(x2 + x4)
|
3.65B | x2 and x4 latent upscalers (included in this bundle) |
scheduler/
|
PiflowScheduler
|
— |
π-Flow DX policy rollout:
nfe = 2
,
shift = 3.5
, 10 grid points, 128 policy substeps
|
model_index.json
names the pipeline class
Kandinsky6SRPipeline
.
Runs of kandinskylab Kandinsky-6.0-VSR-distilled2steps-5s-Diffusers on huggingface.co
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More Information About Kandinsky-6.0-VSR-distilled2steps-5s-Diffusers huggingface.co Model
Url of Kandinsky-6.0-VSR-distilled2steps-5s-Diffusers
Kandinsky-6.0-VSR-distilled2steps-5s-Diffusers huggingface.co Url
Provider of Kandinsky-6.0-VSR-distilled2steps-5s-Diffusers huggingface.co
Other API from kandinskylab
Updated:October 12 2026
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