A Frozen Pixel-Space Diffusion Model Can Guide Itself with Its Own Samples
Paper • 2607.29122 • Published • 2
Dev Mode Explorers
community
AI & ML interests
None defined yet.
Recent Activity
View all activity
nielsr
submitted a
paper to Daily Papers about 19 hours ago
nielsr
submitted a
paper to Daily Papers 8 days ago
Post
3987
who's working on an NVFP4 version of Kimi-K3?
- 4 replies
Post
5489
Introducing Inflect-v2, two exceptionally small, open-weight English TTS models at just 3.9M and 9.3M parameters. Both generate speech multiple times faster than real-time on CPU. Despite their size, Inflect-v2 delivers quality that is competitive with much larger lightweight TTS systems, including KittenTTS, Piper, and Supertonic-3.
CPU, CUDA, PyTorch, and ONNX are supported. Apache 2.0.
See it for yourselves:
owensong/Inflect-Micro-v2
owensong/Inflect-Nano-v2
Try the Demos:
Nymbo/Inflect-TTS (unlimited CPU usage)
owensong/Inflect-v2 (ultra-fast ZeroGPU usage)
CPU, CUDA, PyTorch, and ONNX are supported. Apache 2.0.
See it for yourselves:
owensong/Inflect-Micro-v2
owensong/Inflect-Nano-v2
Try the Demos:
Nymbo/Inflect-TTS (unlimited CPU usage)
owensong/Inflect-v2 (ultra-fast ZeroGPU usage)
- 6 replies
nielsr
submitted a
paper to Daily Papers 21 days ago
nielsr
submitted a
paper to Daily Papers 22 days ago
nielsr
submitted 2
papers to Daily Papers 27 days ago
GeorgeBredis
submitted a
paper to Daily Papers 28 days ago
nielsr
submitted 2
papers to Daily Papers 28 days ago
nielsr
submitted a
paper to Daily Papers 29 days ago
DongfuJiang
authored 2
papers about 1 month ago
Post
2395
Post
1858
I made a Hugging Face Space for SCAIL-2 🤗
Reference character + driving motion → animated result.
A simple demo to explore the paper’s core workflow with curated examples.
👉 fffiloni/SCAIL-2
Reference character + driving motion → animated result.
A simple demo to explore the paper’s core workflow with curated examples.
👉 fffiloni/SCAIL-2
- 1 reply
Post
830
⏱️ Built a small Space for Visual Chronometer / Pulse of Motion.
Upload a video and estimate its Physical FPS: the frame rate implied by visual motion, independent of metadata.
Useful to inspect “chronometric hallucination” in generated videos: clips that look smooth, but move with the wrong physical time scale.
Try it here: fffiloni/Pulse-of-Motion
Upload a video and estimate its Physical FPS: the frame rate implied by visual motion, independent of metadata.
Useful to inspect “chronometric hallucination” in generated videos: clips that look smooth, but move with the wrong physical time scale.
Try it here: fffiloni/Pulse-of-Motion
- 1 reply
Post
1594
A few weeks ago, @victor opened the door: coding agents can now ship Hugging Face Spaces autonomously.
I pulled on that thread.
As someone who builds and ships Gradio demos regularly, I didn’t just want to reproduce the loop. I wanted to see what happens when that loop is plugged into the whole Hugging Face stack.
The interesting part is not only that an agent can ship a Space.
It’s what happens when Space generation becomes a first-class Hugging Face workflow.
That became Agentic Space Factory.
More soon. 🤗
I pulled on that thread.
As someone who builds and ships Gradio demos regularly, I didn’t just want to reproduce the loop. I wanted to see what happens when that loop is plugged into the whole Hugging Face stack.
The interesting part is not only that an agent can ship a Space.
It’s what happens when Space generation becomes a first-class Hugging Face workflow.
That became Agentic Space Factory.
More soon. 🤗
- 1 reply
nielsr
submitted a
paper to Daily Papers about 2 months ago
Post
4648
We trained an open-source Mythos like cybersecurity LLM for the Build Small Hackathon meet OpenMythos
Trained in two stages: SFT on ~1.84K filtered ArXiv cs.CR papers + real CVE data, then RLVR using paired with past vulnerabilities GitHub repos with a verifier model checking outputs against ground truth.
Trained on: H100s from Modal
The RLVR stage made the biggest difference responses got more precise and less prone to confusing similar vulnerability classes.
Everything is open:
🤖 Demo → build-small-hackathon/OpenMythos
🧠 Model → build-small-hackathon/OpenMythos
📦 CVE Dataset → build-small-hackathon/CVE_Vulnerailities_Detailed
📄 ArXiv Dataset → himanshu17HF/ArvixImport-Filtered-Final
Try it out and let us know where it breaks 🙏
Trained in two stages: SFT on ~1.84K filtered ArXiv cs.CR papers + real CVE data, then RLVR using paired with past vulnerabilities GitHub repos with a verifier model checking outputs against ground truth.
Trained on: H100s from Modal
The RLVR stage made the biggest difference responses got more precise and less prone to confusing similar vulnerability classes.
Everything is open:
🤖 Demo → build-small-hackathon/OpenMythos
🧠 Model → build-small-hackathon/OpenMythos
📦 CVE Dataset → build-small-hackathon/CVE_Vulnerailities_Detailed
📄 ArXiv Dataset → himanshu17HF/ArvixImport-Filtered-Final
Try it out and let us know where it breaks 🙏
- 2 replies