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AIOpsInSpace

AIOpsInSpace
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reacted to wop's post with 🔥 2 days ago
# PixelModel v1 Last month we released PixelModel — a neural network whose weights are literally the pixels of a PNG. It was a toy: 202,752 parameters, welded to 32×32 output, trained on six solid-color swatches. It scored FID 566.84 on the Tiny-T2I-Leaderboard, mostly by producing the same yellow noise for every prompt. Today we're releasing PixelModel v1. It is 8.5× smaller — 23,747 parameters — and it beats v0 on both benchmark metrics while being trained on 20,000 real MS-COCO caption/image pairs instead of six color swatches. The entire model now fits in a 160×149 PNG. That image is not a visualization of the model. It is the model. All 23,747 weights, one per pixel. ## links https://huggingface.co/bench-labs Blog post [read it here ⇗](https://huggingface.co/spaces/bench-labs/blog?post=pixelmodel-v1.html) See us on the [Leaderboard ⇗](https://huggingface.co/spaces/FlameF0X/Tiny-T2I-Leaderboard) Model card [here](https://huggingface.co/bench-labs/pixelmodel-v1) ## The catch A 23K-parameter model does not draw sandwiches. With ~1 parameter per training image, the loss-minimizing behavior is to output the average of all plausible images for a caption — caption-conditioned color, light, and layout statistics. Food prompts come out warm and brown; sky prompts come out cool and bright. That is the ceiling for this size class, and we'd rather show it than crop around it. # cherry on top 🍒 The model generates 600 images (cpu) in 5 (five) seconds. Thats 5000 images in 24 seconds on cpu. The model trained on cpu for just 30 minutes.
reacted to wop's post with 🤗 2 days ago
# PixelModel v1 Last month we released PixelModel — a neural network whose weights are literally the pixels of a PNG. It was a toy: 202,752 parameters, welded to 32×32 output, trained on six solid-color swatches. It scored FID 566.84 on the Tiny-T2I-Leaderboard, mostly by producing the same yellow noise for every prompt. Today we're releasing PixelModel v1. It is 8.5× smaller — 23,747 parameters — and it beats v0 on both benchmark metrics while being trained on 20,000 real MS-COCO caption/image pairs instead of six color swatches. The entire model now fits in a 160×149 PNG. That image is not a visualization of the model. It is the model. All 23,747 weights, one per pixel. ## links https://huggingface.co/bench-labs Blog post [read it here ⇗](https://huggingface.co/spaces/bench-labs/blog?post=pixelmodel-v1.html) See us on the [Leaderboard ⇗](https://huggingface.co/spaces/FlameF0X/Tiny-T2I-Leaderboard) Model card [here](https://huggingface.co/bench-labs/pixelmodel-v1) ## The catch A 23K-parameter model does not draw sandwiches. With ~1 parameter per training image, the loss-minimizing behavior is to output the average of all plausible images for a caption — caption-conditioned color, light, and layout statistics. Food prompts come out warm and brown; sky prompts come out cool and bright. That is the ceiling for this size class, and we'd rather show it than crop around it. # cherry on top 🍒 The model generates 600 images (cpu) in 5 (five) seconds. Thats 5000 images in 24 seconds on cpu. The model trained on cpu for just 30 minutes.
reacted to danielhanchen's post with 🚀 2 days ago
Gemma 4 is now faster and much more accurate! 🚀 Google made huge improvements to tool-calling and chat accuracy, reliability + speed. To get fixes, re-download our updated GGUF, MLX, NVFP4 quants! Unsloth quants: https://huggingface.co/collections/unsloth/gemma-4 Gemma 4 Guide: https://unsloth.ai/docs/models/gemma-4
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