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Severian 
posted an update about 22 hours ago
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1363
I’ve been working on a new mathematical approach to real-time video compositing and background removal, and I wanted to share a live demo.

Traditionally, real-time keyers either use 3D color-space bounding boxes (which struggle with semi-transparent hair and motion blur) or heavy Machine Learning models (which require massive GPU compute and often suffer from temporal "jitter" on the edges).

I wanted to see if I could solve this using purely deterministic math so it could run client-side in a standard browser.

The engine uses a custom mathematical framework I call CMT SRL SEFA. Instead of looking at raw color values or guessing semantics like an AI, it treats the video feed as complex-encoded sequences. It uses harmonic frequencies to map phase geometry and applies a "Stability Cost Function" to find the global minimum stability. In short: it isolates the foreground from the background by measuring signal complexity and structural contradictions.

Give it a try using your own messy plates and such. As I am not a VFX artist, I am curious to hear thoughts and what should be improved upon and made better

https://severian-cmt-sefa-realtime-vfx-keyer.hf.space/
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fffiloni 
posted an update 8 days ago
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3905
I brought DALL·E mini back to life 🤖🎨

You can try it here:
fffiloni/dalle-mini-reboot

And I also built a batch version using Hugging Face Jobs (up to 50 images per prompt):
fffiloni/dalle-mini-via-jobs

The goal was to stay close to the original JAX/Flax pipeline, while integrating it with modern tooling (Gradio + Jobs).

It ended up being a fun way to revisit this model — still weird, still fun 😄
  • 3 replies
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fffiloni 
posted an update 13 days ago
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456
A clearer demo for TADA (now multilingual) 🔊🌍

I improved the public demo for TADA — a generative framework for speech modeling via text–acoustic dual alignment.

TADA models speech as a joint sequence of text tokens and acoustic tokens, using a transformer backbone to keep text and audio synchronized during generation.

The original demo already exposed these mechanisms, but the workflow made the pipeline hard to understand.

This updated demo makes the process clearer:

• load the model
• prepare a reference voice (optionally with transcript or Whisper auto-transcription)
• generate speech conditioned on that reference

It also adds multilingual support.

Presets are included for a few languages, but the model supports more:

English, French, Spanish, German, Arabic, Mandarin Chinese, Italian, Japanese, Polish, Portuguese

Feel free to try different voices, accents, or languages and see how the alignment behaves.

👉 fffiloni/tada-dual-alignment-tts-demo

Paper
TADA: A Generative Framework for Speech Modeling via Text-Acoustic Dual Alignment (2602.23068)
Tonic 
posted an update about 1 month ago
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3389
🤔 Who would win ?

- a fully subsidized ai lab
OR
- 3 random students named
kurakurai
?

demo : Tonic/fr-on-device

if you like it give the demo a little star and send a shoutout to : @MaxLSB @jddqd and @GAD-cell for absolutely obliterating the pareto frontier of the french language understanding .
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Tonic 
posted an update about 1 month ago
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3307
🙋🏻‍♂️hello my lovelies ,

it is with great pleasure i present to you my working one-click deploy 16GB ram completely free huggingface spaces deployment.

repo : Tonic/hugging-claw (use git clone to inspect)
literally the one-click link : Tonic/hugging-claw

you can also run it locally and see for yourself :

docker run -it -p 7860:7860 --platform=linux/amd64 \
-e HF_TOKEN="YOUR_VALUE_HERE" \
-e OPENCLAW_GATEWAY_TRUSTED_PROXIES="YOUR_VALUE_HERE" \
-e OPENCLAW_GATEWAY_PASSWORD="YOUR_VALUE_HERE" \
-e OPENCLAW_CONTROL_UI_ALLOWED_ORIGINS="YOUR_VALUE_HERE" \
registry.hf.space/tonic-hugging-claw:latest


just a few quite minor details i'll take care of but i wanted to share here first
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KingNish 
posted an update 4 months ago
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3317
Muon vs MuonClip vs Muon+Adamw

Muon has gone from an experiment to a mainstream optimizer, but does it hold up for fine‑tuning? We ran head‑to‑head tests on Qwen3‑4B (10k+ high‑quality instruction rows) to find out.

Short story: Pure Muon converged fastest at the start, but its gradient‑norm spikes made training unstable. MuonClip (Kimi K2’s clipping) stabilizes long pretraining runs, yet in our small‑scale fine‑tune it underperformed, lower token accuracy and slower convergence. The winner was the hybrid: Muon for 2D layers + AdamW for 1D layers. It delivered the best balance of stability and final performance and even beat vanilla AdamW.

Takeaway: for small-scale fine-tuning, hybrid = practical and reliable.

Next Step: scale to larger models/datasets to see if Muon’s spikes become catastrophic or if clipping wins out.

Full Blog Link: https://huggingface.co/blog/KingNish/optimizer-part1
KingNish 
posted an update 4 months ago