Fractalyze/kandinsky6-rtx5090-showcase

text-to-video model by Fractalyze

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Kandinsky 6.0 Pro-distill (29B joint video+audio DiT, 56 GiB in BF16) generating 864x480, 121 frames at 24 fps with audio on a single RTX 5090 (32 GB) in a 60 GB host, with vLLM-Omni. Upstream cannot serve this checkpoint on that machine; this branch does it in 151 s, 36% faster than the BF16 reference configuration, at LPIPS 0.11 against that reference. A fast, lossy mode takes 105 s (Results). Every change is behind a switch, off by default. W1: 864x480, 121 frames, 10 PiFlow steps, guidance 1.0, audio on, one RTX 5090, batch 1, seed 42. Speed: median of a mirrored A B B A session. Quality: LPIPS mean / worst frame over nine prompts, against a compiled BF16 reference generated on the same code and host. The reference's wall time is from its own gate run, not the same session. W2 (Pro-5s, 50 steps, CFG 5.0): 1611 s…

Fractalyze/kandinsky6-rtx5090-showcase on Hugging Face A short preview, not the full document.

Read the full model card ↗ · Preview checked 2026-10-08T08:17:14.625Z

Inside the original model card — Document outline
  1. Kandinsky 6 Pro with audio on one RTX 5090: a 5-second clip in 151 s
  2. What's inside
  3. Results
  4. Quick start
  5. Samples
  6. Limitations
  7. Links

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Model overview

Fractalyze/kandinsky6-rtx5090-showcase is a text-to-video model repository published by Fractalyze on Hugging Face. A library was not reported.

This page summarizes Hub metadata. For intended use, training data, evaluation results and limitations, consult the original model card.

Model facts

Task
text-to-video
Library
Not reported
Recent downloads (30 days)
0
Cumulative likes
0
Hugging Face trending score
0
Reported safetensors parameters
Not reported
Architecture
Not reported
License
mit
Access
Not gated by Hugging Face
Created
2026-10-08T06:14:46.000Z
Last modified
2026-10-08T06:15:05.000Z

Compatibility and lineage

Language coverage was not reported.

Reported base models: kandinskylab/Kandinsky-6.0-Pro-distill-5s-Diffusers

Parameter count is not a RAM/VRAM requirement. Check precision, quantization, context length and runtime compatibility in the model card; no hardware or API-cost claim is inferred here.

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Source: Hugging Face Hub. Metadata observed 2026-10-08T06:40:12.812Z. Daily imports are snapshots, not real-time monitoring.

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