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Meet Iris-3B: a pixel-space T2I model and general vision learner that generates every pixel directly. No VAE. Pre-trained from scratch, 3B, open weights. We also explore its generative prior on detail-critical vision tasks: depth estimation and image restoration. Fully open: weights, code and demo (Apache 2.0)

Speridlabs

1,026 subscribers

80,397 просмотров • 3 дней назад •via X (Twitter)

Образование Наука и технологии

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Комментарии: 34

Фото профиля Speridlabs

Speridlabs3 дней назад

Weights: Code: Try it:

Фото профиля Speridlabs

Speridlabs3 дней назад

We're hiring

Фото профиля Borja Perez

Borja Perez3 дней назад

@barckcode nunca te pido nada 🥹 y encima son de españita

Фото профиля Alexey Fateev

Alexey Fateev3 дней назад

@ComfyUI workflow guys? sounds incredible!

Фото профиля Armin Catovic

Armin Catovic3 дней назад

Can it work on low res images, like 64x64 pixels? This is the limitation of todays imagegen models - even for pixel art they produce high res images, when scaled down look awful.

Фото профиля alejandro

alejandro3 дней назад

🚀🚀🚀

Фото профиля Jorge Dobón

Jorge Dobón3 дней назад

Go go go @speridlabs 🙌🏼

Фото профиля Julien Blanchon 🇺🇦

Julien Blanchon 🇺🇦3 дней назад

@AntonObukhov1 Interesting as a prior

Фото профиля Buswe

Buswe3 дней назад

Dropping the VAE is what makes depth and restoration work here: no latent decode step to smooth away the thin, high frequency detail.

Фото профиля Alice The Ai Expert

Alice The Ai Expert2 дней назад

Incredible work 3B pixels directly with no VAE and fully open weights is exactly the kind of open research the community needs!

Фото профиля Javier Álvarez

Javier Álvarez2 дней назад

no VAE here... top my friend @hanqiulicai 🚀 🚀

Фото профиля FRareDom

FRareDom3 дней назад

Open weights is incredible! THANK YOU!!

Фото профиля ander

ander2 дней назад

Congrats!

Фото профиля Amelia | AI & Tech

Amelia | AI & Tech2 дней назад

Generating pixels directly without a VAE is boldOpen weights on a no-VAE generator is the real flex.

Фото профиля A.W.E.S.O.M.-O 4000

A.W.E.S.O.M.-O 40003 дней назад

ngl open weights at 3b makes this easy for people to actually try fr

Фото профиля Salar 🇪🇸🇮🇷

Salar 🇪🇸🇮🇷3 дней назад

Neat

Фото профиля RAZA | AI EXPLORER

RAZA | AI EXPLORER2 дней назад

Impressive work! Open-weight pixel-space models open up exciting possibilities for image generation and vision research.

Фото профиля ZenithAi

ZenithAi2 дней назад

Open weights and pixel level generation make this research especially promising

Фото профиля Pongphat

Pongphat2 дней назад

Interesting!

Фото профиля Aaliya

Aaliya2 дней назад

Using it to fix images sounds really useful too.

Фото профиля Elise Tech Ai

Elise Tech Ai2 дней назад

This is fantastic Iris 3B directly in pixel space with no VAE is a bold and impressive approach love that its fully open

Фото профиля 드럼통청약당첨

드럼통청약당첨2 дней назад

Pixel Space with optimized architecture is future! Thank you for your work!

Фото профиля Adria Blancafort

Adria Blancafort3 дней назад

Nice!

Фото профиля AI路子野

AI路子野2 дней назад

Still using a 4B VL addition. Not fully portable

Фото профиля Vantix AI Agency

Vantix AI Agency2 дней назад

No VAE and direct pixel generation at 3B thats a bold architecture bet love that its fully open with weights and code

Фото профиля Jack

Jack3 дней назад

pixel space models deserve more love

Фото профиля Anherutowa Calvo

Anherutowa Calvo3 дней назад

Lets go!!

Фото профиля Ella Tech & Tool

Ella Tech & Tool2 дней назад

Love this. Pixel-space generation with open weights will push T2I and vision research forward fast.

Фото профиля Sani Ai Tech

Sani Ai Tech2 дней назад

Impressive to see pixel-native generation paired with open weights and broader vision capabilities

Фото профиля Elara AI

Elara AI1 день назад

Iris-3B pixel-space T2I model generates pixels directly without VAE openly released now

Фото профиля Aria Tech

Aria Tech2 дней назад

No VAE pixel-space T2I is bold Iris-3B looks super promising

Фото профиля Pablo

Pablo3 дней назад

@bot

Фото профиля Zara Tech & Tools

Zara Tech & Tools2 дней назад

Exciting open release pushing direct pixel generation

Фото профиля Martii

Martii3 дней назад

🔥🔥🔥🔥🔥

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Tyler Wayne

78,042 просмотров • 1 месяц назад

InternLM-XComposer-2.5 A Versatile Large Vision Language Model Supporting Long-Contextual Input and Output We present InternLM-XComposer-2.5 (IXC-2.5), a versatile large-vision language model that supports long-contextual input and output. IXC-2.5 excels in various text-image comprehension and composition applications, achieving GPT-4V level capabilities with merely 7B LLM backend. Trained with 24K interleaved image-text contexts, it can seamlessly extend to 96K long contexts via RoPE extrapolation. This long-context capability allows IXC-2.5 to excel in tasks requiring extensive input and output contexts. Compared to its previous 2.0 version, InternLM-XComposer-2.5 features three major upgrades in vision-language comprehension: (1) Ultra-High Resolution Understanding, (2) Fine-Grained Video Understanding, and (3) Multi-Turn Multi-Image Dialogue. In addition to comprehension, IXC-2.5 extends to two compelling applications using extra LoRA parameters for text-image composition: (1) Crafting Webpages and (2) Composing High-Quality Text-Image Articles. IXC-2.5 has been evaluated on 28 benchmarks, outperforming existing open-source state-of-the-art models on 16 benchmarks. It also surpasses or competes closely with GPT-4V and Gemini Pro on 16 key tasks.

InternLM-XComposer-2.5 A Versatile Large Vision Language Model Supporting Long-Contextual Input and Output We present InternLM-XComposer-2.5 (IXC-2.5), a versatile large-vision language model that supports long-contextual input and output. IXC-2.5 excels in various text-image comprehension and composition applications, achieving GPT-4V level capabilities with merely 7B LLM backend. Trained with 24K interleaved image-text contexts, it can seamlessly extend to 96K long contexts via RoPE extrapolation. This long-context capability allows IXC-2.5 to excel in tasks requiring extensive input and output contexts. Compared to its previous 2.0 version, InternLM-XComposer-2.5 features three major upgrades in vision-language comprehension: (1) Ultra-High Resolution Understanding, (2) Fine-Grained Video Understanding, and (3) Multi-Turn Multi-Image Dialogue. In addition to comprehension, IXC-2.5 extends to two compelling applications using extra LoRA parameters for text-image composition: (1) Crafting Webpages and (2) Composing High-Quality Text-Image Articles. IXC-2.5 has been evaluated on 28 benchmarks, outperforming existing open-source state-of-the-art models on 16 benchmarks. It also surpasses or competes closely with GPT-4V and Gemini Pro on 16 key tasks.

AK

66,476 просмотров • 2 лет назад

Check out our #PAMI paper with code "Dense Continuous-Time Optical Flow from Event Cameras," where we show how to regress *continuous-time* trajectories of every pixel from event cameras alone or events plus frames! The key idea is to iteratively estimate per-pixel polynomials using a recurrent lookup and update scheme. Paper: Code: DOI: We present a method for estimating dense continuous-time optical flow from event data. Traditional dense optical flow methods compute the pixel displacement between two images. Due to missing information, these approaches cannot recover the pixel trajectories in the blind time between two images. We show that it is possible to compute per-pixel, continuous-time optical flow using events from an event camera. Events provide temporally fine-grained information about movement in pixel space due to their asynchronous nature and microsecond response time. We leverage these benefits to predict pixel trajectories densely in continuous time via parameterized Bézier curves. To achieve this, we build a neural network with strong inductive biases for this task: First, we build multiple sequential correlation volumes in time using event data. Second, we use Bézier curves to index these correlation volumes at multiple timestamps along the trajectory. Third, we use the retrieved correlation to update the Bézier curve representations iteratively. Our method can optionally include image pairs to boost performance further. To train and evaluate our model, we introduce a synthetic dataset (MultiFlow) that features moving objects and ground truth trajectories for every pixel. Our quantitative experiments suggest that our method successfully predicts pixel trajectories in continuous time and is competitive in the traditional two-view pixel displacement metric on MultiFlow and DSEC-Flow. Open source code and datasets are released to the public. Kudos to Mathias Gehrig Manasi Muglikar

Check out our #PAMI paper with code "Dense Continuous-Time Optical Flow from Event Cameras," where we show how to regress continuous-time trajectories of every pixel from event cameras alone or events plus frames! The key idea is to iteratively estimate per-pixel polynomials using a recurrent lookup and update scheme. Paper: Code: DOI: We present a method for estimating dense continuous-time optical flow from event data. Traditional dense optical flow methods compute the pixel displacement between two images. Due to missing information, these approaches cannot recover the pixel trajectories in the blind time between two images. We show that it is possible to compute per-pixel, continuous-time optical flow using events from an event camera. Events provide temporally fine-grained information about movement in pixel space due to their asynchronous nature and microsecond response time. We leverage these benefits to predict pixel trajectories densely in continuous time via parameterized Bézier curves. To achieve this, we build a neural network with strong inductive biases for this task: First, we build multiple sequential correlation volumes in time using event data. Second, we use Bézier curves to index these correlation volumes at multiple timestamps along the trajectory. Third, we use the retrieved correlation to update the Bézier curve representations iteratively. Our method can optionally include image pairs to boost performance further. To train and evaluate our model, we introduce a synthetic dataset (MultiFlow) that features moving objects and ground truth trajectories for every pixel. Our quantitative experiments suggest that our method successfully predicts pixel trajectories in continuous time and is competitive in the traditional two-view pixel displacement metric on MultiFlow and DSEC-Flow. Open source code and datasets are released to the public. Kudos to Mathias Gehrig Manasi Muglikar

Davide Scaramuzza

12,692 просмотров • 2 лет назад

how to evolve generative art 🦠✨👇 over the past weeks I built this toy 'proof-of-concept' example to explore and better understand how open-form generative art works, and discover some of the new archetypes it enables. In open-form genart each mint at the root of a project can define its own lineage: properties, traits, and characteristics that carry over to new editions evolved from it. For example, in this demo, the first parent defines the color palette that's used for the pixel pattern — these colors are then inherited by subsequent evolutions (with a random chance to alter a color or introduce a new one). But that's just scratching the tip of the iceberg — the REALLY COOL thing is that the root mint also specifies the rules that define how the pixel pattern morphs and changes over the course of its lineage! Meaning that each branch in this collection is its own unconstrained system, that evolves, and is explored by minting and evolving new editions. in this setting collectors play a huge role to explore the parametric space of the project ✨ on fxhash the UI will be much cleaner and more intuitive 🫧

how to evolve generative art 🦠✨👇 over the past weeks I built this toy 'proof-of-concept' example to explore and better understand how open-form generative art works, and discover some of the new archetypes it enables. In open-form genart each mint at the root of a project can define its own lineage: properties, traits, and characteristics that carry over to new editions evolved from it. For example, in this demo, the first parent defines the color palette that's used for the pixel pattern — these colors are then inherited by subsequent evolutions (with a random chance to alter a color or introduce a new one). But that's just scratching the tip of the iceberg — the REALLY COOL thing is that the root mint also specifies the rules that define how the pixel pattern morphs and changes over the course of its lineage! Meaning that each branch in this collection is its own unconstrained system, that evolves, and is explored by minting and evolving new editions. in this setting collectors play a huge role to explore the parametric space of the project ✨ on fxhash the UI will be much cleaner and more intuitive 🫧

Ahmad Moussa || Gorilla Sun

13,100 просмотров • 1 год назад

In collaboration with Intel, our Depth Fusion showcases the power of our LDM3D diffusion model in generating 360° views from text prompts provided by the user. The LDM3D diffusion model generates a 2D RGB image and its corresponding relative depth map providing a complete RGBD representation corresponding to the text prompt. The LDM 3D model is a specialized version of the stable diffusion V 1.4 model that has been modified to fit both image and depth map data.The model was then fine tuned on a subset of the Laion400M data set - large scale image caption data set. The depth maps used to fine tune our model were generated by the DPTBeiT large 512 depth estimation model that provides highly accurate relative depth estimates for each pixel. We take the generated 2D RGB image and depth map and use them to compute a 360° projection using touchdesigner. Touchdesigner is a versatile platform that allows for the creation of immersive and interactive multimedia experiences. Our application harnesses the power of touchdesigner to bring the generated 360° views to life, providing users with a unique and engaging way to experience their text prompts, whether it’s a description of a tranquil forest, a noisy cityscape or a futuristic sci fi world. Our depth fusion can bring these concepts to life in a vivid and immersive detail. - Scottie Fox, VP Engineering Blockade Labs ScottieFox #AI #VR #3D #gamedev #stablediffusion

In collaboration with Intel, our Depth Fusion showcases the power of our LDM3D diffusion model in generating 360° views from text prompts provided by the user. The LDM3D diffusion model generates a 2D RGB image and its corresponding relative depth map providing a complete RGBD representation corresponding to the text prompt. The LDM 3D model is a specialized version of the stable diffusion V 1.4 model that has been modified to fit both image and depth map data.The model was then fine tuned on a subset of the Laion400M data set - large scale image caption data set. The depth maps used to fine tune our model were generated by the DPTBeiT large 512 depth estimation model that provides highly accurate relative depth estimates for each pixel. We take the generated 2D RGB image and depth map and use them to compute a 360° projection using touchdesigner. Touchdesigner is a versatile platform that allows for the creation of immersive and interactive multimedia experiences. Our application harnesses the power of touchdesigner to bring the generated 360° views to life, providing users with a unique and engaging way to experience their text prompts, whether it’s a description of a tranquil forest, a noisy cityscape or a futuristic sci fi world. Our depth fusion can bring these concepts to life in a vivid and immersive detail. - Scottie Fox, VP Engineering Blockade Labs ScottieFox #AI #VR #3D #gamedev #stablediffusion

Blockade Labs

11,458 просмотров • 3 лет назад