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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 次观看 • 3 天前 •via X (Twitter)

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34 条评论

Speridlabs 的头像

Speridlabs3 天前

Weights: Code: Try it:

Speridlabs 的头像

Speridlabs3 天前

We're hiring

Borja Perez 的头像

Borja Perez3 天前

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

Alexey Fateev 的头像

Alexey Fateev3 天前

@ComfyUI workflow guys? sounds incredible!

Armin Catovic 的头像

Armin Catovic3 天前

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.

alejandro 的头像

alejandro3 天前

🚀🚀🚀

Jorge Dobón 的头像

Jorge Dobón3 天前

Go go go @speridlabs 🙌🏼

Julien Blanchon 🇺🇦 的头像

Julien Blanchon 🇺🇦3 天前

@AntonObukhov1 Interesting as a prior

Buswe 的头像

Buswe3 天前

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

Alice The Ai Expert 的头像

Alice The Ai Expert2 天前

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

Javier Álvarez 的头像

Javier Álvarez2 天前

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

FRareDom 的头像

FRareDom3 天前

Open weights is incredible! THANK YOU!!

ander 的头像

ander2 天前

Congrats!

Amelia | AI & Tech 的头像

Amelia | AI & Tech2 天前

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

A.W.E.S.O.M.-O 4000 的头像

A.W.E.S.O.M.-O 40003 天前

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

Salar 🇪🇸🇮🇷 的头像

Salar 🇪🇸🇮🇷3 天前

Neat

RAZA | AI EXPLORER 的头像

RAZA | AI EXPLORER2 天前

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

ZenithAi 的头像

ZenithAi2 天前

Open weights and pixel level generation make this research especially promising

Pongphat 的头像

Pongphat2 天前

Interesting!

Aaliya 的头像

Aaliya2 天前

Using it to fix images sounds really useful too.

Elise Tech Ai 的头像

Elise Tech Ai2 天前

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

드럼통청약당첨 的头像

드럼통청약당첨2 天前

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

Adria Blancafort 的头像

Adria Blancafort3 天前

Nice!

AI路子野的头像

AI路子野2 天前

Still using a 4B VL addition. Not fully portable

Vantix AI Agency 的头像

Vantix AI Agency2 天前

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

Jack 的头像

Jack3 天前

pixel space models deserve more love

Anherutowa Calvo 的头像

Anherutowa Calvo3 天前

Lets go!!

Ella Tech & Tool 的头像

Ella Tech & Tool2 天前

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

Sani Ai Tech 的头像

Sani Ai Tech2 天前

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

Elara AI 的头像

Elara AI1 天前

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

Aria Tech 的头像

Aria Tech2 天前

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

Pablo 的头像

Pablo3 天前

@bot

Zara Tech & Tools 的头像

Zara Tech & Tools2 天前

Exciting open release pushing direct pixel generation

Martii 的头像

Martii3 天前

🔥🔥🔥🔥🔥

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78,042 次观看 • 1 个月前

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 次观看 • 2 年前

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 次观看 • 2 年前

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 次观看 • 1 年前

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 次观看 • 3 年前