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NVIDIA's PiD is a new pixel diffusion decoder for high-res image models. It skips decode-then-upscale stop, making sharper outputs faster. > Directly generates 4K images > Up to 5.9x faster than SeedVR2 > Free & open weights

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13,071 просмотров • 21 дней назад •via X (Twitter)

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NVidia just released PiD: super resolution in pixel space directly from model latents 🔎 4X resolution for any generated image, FAST! 🏎️💨 FLUX.1, 2 and Z-Image (Qwen Image coming) of course, i built a demo: generate 4K images with Z-Image
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NVidia just released PiD: super resolution in pixel space directly from model latents 🔎 4X resolution for any generated image, FAST! 🏎️💨 FLUX.1, 2 and Z-Image (Qwen Image coming) of course, i built a demo: generate 4K images with Z-Image

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Diffusion models are sensitive to small changes in the input noise. We introduce Alias-Free Latent Diffusion Models (𝗔𝗙-𝗟𝗗𝗠) at #CVPR2025. It achieves shift-equivariance and generates consistent outputs. Project: arXiv:
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Diffusion models are sensitive to small changes in the input noise. We introduce Alias-Free Latent Diffusion Models (𝗔𝗙-𝗟𝗗𝗠) at #CVPR2025. It achieves shift-equivariance and generates consistent outputs. Project: arXiv:

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Here's the result of Codex 5.3 ! > Upload a reference animation (IDLE) > Prompt what new animation you want > Sends image + prompt to Nano Banana Pro > Generates new animation sprite sheet The app looks good and functions well! > Next.js 16.1 > Convex db
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Here's the result of Codex 5.3 ! > Upload a reference animation (IDLE) > Prompt what new animation you want > Sends image + prompt to Nano Banana Pro > Generates new animation sprite sheet The app looks good and functions well! > Next.js 16.1 > Convex db

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Making the 50/50 “reactable” for a tricky mix-up >>>>
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1. nano banana is great for editing images go to Canvas -> Nano Banana -> upload image -> upload product swap face, hold product, change clothes.. and use Veo3 or Kling 2.1 to generate video it's free and unlimited for 24h now link:

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Selected as a best paper finalist at #CVPR2026: PixelDiT from NVIDIA Research In most image generation models, a pretrained autoencoder compresses the image before any diffusion happens, causing quality loss that accumulates across the entire pipeline. PixelDiT, or Pixel Diffusion Transformers, removes this step entirely. It's a single-stage model that learns the diffusion process directly in pixel space, end-to-end.

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