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🚀 Pixel Diffusion Decoder (PiD) v1.5 is out 🎨 No color-shifting problem, better 4K visual quality 📦 Undistilled checkpoint and full training code 🧩 Support FLUX, FLUX2, Qwen-Image, Z-Image, ... Feel free to use it, reproduce it, and build on top of it 💻 Code: 🔗 Demo & comparison:

Xuanchi Ren

1,516 subscribers

51,736 次观看 • 3 个月前 •via X (Twitter)

Anya Rossi• Live Now

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6 条评论

Pausecatito 的头像

Pausecatito3 个月前

@kohya_tech No color shifting problem when there is massive color shift in every example🤔

Yarin Didi 的头像

Yarin Didi3 个月前

Thing is for references based image generation/editing, we will need conditional pid/upscaler or generating already at naitive resolution. Great work

franky 的头像

franky3 个月前

请问未来是否会支持Krea2？

Diego 的头像

Diego3 个月前

@bdsqlsz Amazing

Ares 的头像

Ares3 个月前

There is already Topaz for this...

AI Mastery Guide 的头像

AI Mastery Guide3 个月前

Fixing color shifting alone makes this worth trying

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Image-to-Pixel 3.0 is out 🎨 Turn images and video into pixel art right in your browser. Free, MIT open source. Try it: Or use it in code: npm i image-to-pixel

Image-to-Pixel 3.0 is out 🎨 Turn images and video into pixel art right in your browser. Free, MIT open source. Try it: Or use it in code: npm i image-to-pixel

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🎨 Qwen-Image-Layered is LIVE — native image decomposition, fully open-sourced! ✨ Why it stands out ✅ Photoshop-grade layering Physically isolated RGBA layers with true native editability ✅ Prompt-controlled structure Explicitly specify 3–10 layers — from coarse layouts to fine-grained details ✅ Infinite decomposition Keep drilling down: layers within layers, to any depth of detail 🤗 Hugging Face: 🧩 ModelScope: 💻 GitHub: 📝 Blog: 📄 Technical Report: 🚀 Demo (HF): 🚀 Demo (ModelScope):

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Qwen

1,212,750 次观看 • 10 个月前

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36,190 次观看 • 6 个月前

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Yukang Chen

31,876 次观看 • 1 年前

FREE UNLIMITED GPT-6 LUNA. NO CARD. NO LOGIN. > DuckDuckGo just opened Luna to everyone on DuckAi, with no limits. What you get: - chat with Luna, nothing to sign up for - image generation - full voice conversation, works great on mobile How to start: open and type. No account, no payment details. Use it for drafts, code review and image ideas, so your paid quota is saved for the heavy stuff.

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Atenov int.

108,549 次观看 • 8 天前

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24,968 次观看 • 1 个月前

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54,586 次观看 • 1 个月前

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Peter Yang

17,210 次观看 • 3 个月前

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Enze Xie

70,944 次观看 • 8 天前

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Santiago

101,068 次观看 • 2 年前

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Bunsen, Beaker, Bernoulli & Brix

14,011 次观看 • 4 个月前

🚀 New Paper: Pixel Reasoner 🧠🖼️ How can Vision-Language Models (VLMs) perform chain-of-thought reasoning within the image itself? We introduce Pixel Reasoner, the first open-source framework that enables VLMs to “think in pixel space” through curiosity-driven reinforcement learning. Current VLMs reason only in text — even when grounded in rich images or videos, their logical steps are verbalized in natural language. This restricts their ability to interrogate visual evidence and demonstrate how conclusions are drawn. 🔍 So we ask: What if we could make VLMs "show their work" by reasoning directly in the pixel space? Inspired by GPT-o3’s "think-in-image" ability, we propose a framework where VLMs use interactive visual operations — zoom, select-frame, highlight — to reason through complex visual inputs. To do this, we design a two-stage training process: Instruction tuning with synthesized visual reasoning traces. Reinforcement learning with curiosity-driven reward to balance exploration between pixel and text reasoning ✨ With this, Pixel Reasoner achieves near-SoTA performance on many information-rich multimodal benchmarks: 📊 84% on InfographicsVQA 🧠 84% on V* benchmark 🧩 74% on TallyQA-Complex It also achieves strong accuracy of 68% on MVBench (a video benchmark). Website: Paper: Code: Demo: (coming soon)

🚀 New Paper: Pixel Reasoner 🧠🖼️ How can Vision-Language Models (VLMs) perform chain-of-thought reasoning within the image itself? We introduce Pixel Reasoner, the first open-source framework that enables VLMs to “think in pixel space” through curiosity-driven reinforcement learning. Current VLMs reason only in text — even when grounded in rich images or videos, their logical steps are verbalized in natural language. This restricts their ability to interrogate visual evidence and demonstrate how conclusions are drawn. 🔍 So we ask: What if we could make VLMs "show their work" by reasoning directly in the pixel space? Inspired by GPT-o3’s "think-in-image" ability, we propose a framework where VLMs use interactive visual operations — zoom, select-frame, highlight — to reason through complex visual inputs. To do this, we design a two-stage training process: Instruction tuning with synthesized visual reasoning traces. Reinforcement learning with curiosity-driven reward to balance exploration between pixel and text reasoning ✨ With this, Pixel Reasoner achieves near-SoTA performance on many information-rich multimodal benchmarks: 📊 84% on InfographicsVQA 🧠 84% on V* benchmark 🧩 74% on TallyQA-Complex It also achieves strong accuracy of 68% on MVBench (a video benchmark). Website: Paper: Code: Demo: (coming soon)

Wenhu Chen

82,829 次观看 • 1 年前

Today, we introduce HunyuanImage 3.0-Instruct, a native multimodal model focusing on image-editing by integrating visual understanding with precise image synthesis! 🚀 It understands input images and reasons before generating images. Built on an 80B-parameter MoE architecture (13B activated), it natively unifies deep multimodal comprehension and high-fidelity generation. 🧠 A "Thinking" Model with Native CoT & MixGRPO: The model doesn’t just execute commands, it processes them through a Native Chain-of-Thought (CoT) schema. Enhanced by our self-developed MixGRPO algorithm, it reasons through complex instructions to achieve flawless intent alignment and human-preference consistency. 🎨 Precise Editing & Multi-Image Fusion: The model enables accurate image editing by adding, removing, or modifying elements while keeping non-target areas perfectly intact. It also excels at seamless multi-image fusion, synthesizing complex scenes by extracting and blending elements from multiple sources into a unified, consistent output. 🏆 SOTA Performance: HunyuanImage 3.0-Instruct sets a new benchmark in visual quality and alignment, delivering performance that matches leading proprietary models. We aim to enable the community to explore new ideas with a state-of-the-art foundation model, fostering a dynamic and vibrant image generation ecosystem. 🛠️🎨 💻Try it at (PC only):

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Tencent Hy

126,668 次观看 • 8 个月前

🚀 Excited to release LongLive 2.0! 🎬 An end-to-end infrastructure for long video generation, with FP4 and parallelism at the core of both training and inference. ⚡45.7 FPS generation speed on 5B model⚡ ✨ LongLive 2.0 supports real-video training, few-step distillation, multi-shot training/inference, sequence-parallel acceleration, NVFP4 KV cache, and async VAE decoding deployment. 🧩 To our knowledge, this is the first open-source 4-bit long video generation infra that covers both training and inference. 🙌 Welcome to check it out, try it, and share feedback! 🔗 Code: 📰 Paper: 🎥 Demo: #LongVideoGeneration #VideoGeneration #Realtime #AIInfra #EfficientAI #FP4 #Parallel #NVIDIA

🚀 Excited to release LongLive 2.0! 🎬 An end-to-end infrastructure for long video generation, with FP4 and parallelism at the core of both training and inference. ⚡45.7 FPS generation speed on 5B model⚡ ✨ LongLive 2.0 supports real-video training, few-step distillation, multi-shot training/inference, sequence-parallel acceleration, NVFP4 KV cache, and async VAE decoding deployment. 🧩 To our knowledge, this is the first open-source 4-bit long video generation infra that covers both training and inference. 🙌 Welcome to check it out, try it, and share feedback! 🔗 Code: 📰 Paper: 🎥 Demo: #LongVideoGeneration #VideoGeneration #Realtime #AIInfra #EfficientAI #FP4 #Parallel #NVIDIA

Yukang Chen

60,053 次观看 • 5 个月前

🚀 Exciting News! We're thrilled to announce the launch of Qwen Chat ( ) – your new go-to Web UI for interacting with Qwen models! 🌟 💬 Chat effortlessly with our flagship model Qwen2.5-Plus , explore vision-language capabilities with Qwen2-VL-Max , and dive into reasoning models like QwQ and QVQ, code with coding expert Qwen2.5-Coder-32B-Instruct, etc. ✨ Key Features: * Choose & compare multiple models in one interface * Upload documents & get answers based on them * Preview mode with HTML support * Upload images for visual understanding 🌟 Coming Soon: Web search, image generation, voice mode, and more! 🔗 Ready to experience the future? Start to chat with Qwen today 💬 Feel free to send us your feedback and suggestions!

🚀 Exciting News! We're thrilled to announce the launch of Qwen Chat ( ) – your new go-to Web UI for interacting with Qwen models! 🌟 💬 Chat effortlessly with our flagship model Qwen2.5-Plus , explore vision-language capabilities with Qwen2-VL-Max , and dive into reasoning models like QwQ and QVQ, code with coding expert Qwen2.5-Coder-32B-Instruct, etc. ✨ Key Features: * Choose & compare multiple models in one interface * Upload documents & get answers based on them * Preview mode with HTML support * Upload images for visual understanding 🌟 Coming Soon: Web search, image generation, voice mode, and more! 🔗 Ready to experience the future? Start to chat with Qwen today 💬 Feel free to send us your feedback and suggestions!

Qwen

215,564 次观看 • 1 年前

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✨ To inspire more people to go build something now that we have AI to help us (especially non-tech people, cause I still know so many who are scared of building something): I added a [ BUILD IT ] button to It's like a mini-Lovable/Replit/v0: Any idea you see you like, you can click [ BUILD IT ], and it will use Opus 4.6 to build a landing page for it And then you can download the code it generated It's not a full startup of course, but a nice preview of what it can be, to give you an idea and inspire you to build it out further The code is live streamed also so you can see it being built 😊 Ironically this itself took me 1 hour to build with AI too Completely free and I pay for the tokens (please don't abuse it :D)

@levelsio

277,832 次观看 • 6 个月前

StyleDrop: Text-to-Image Generation in Any Style introduce StyleDrop, a method that enables the synthesis of images that faithfully follow a specific style using a text-to-image model. The proposed method is extremely versatile and captures nuances and details of a user-provided style, such as color schemes, shading, design patterns, and local and global effects. It efficiently learns a new style by fine-tuning very few trainable parameters (less than 1% of total model parameters) and improving the quality via iterative training with either human or automated feedback. Better yet, StyleDrop is able to deliver impressive results even when the user supplies only a single image that specifies the desired style. An extensive study shows that, for the task of style tuning text-to-image models, StyleDrop implemented on Muse convincingly outperforms other methods, including DreamBooth and textual inversion on Imagen or Stable Diffusion. paper page:

StyleDrop: Text-to-Image Generation in Any Style introduce StyleDrop, a method that enables the synthesis of images that faithfully follow a specific style using a text-to-image model. The proposed method is extremely versatile and captures nuances and details of a user-provided style, such as color schemes, shading, design patterns, and local and global effects. It efficiently learns a new style by fine-tuning very few trainable parameters (less than 1% of total model parameters) and improving the quality via iterative training with either human or automated feedback. Better yet, StyleDrop is able to deliver impressive results even when the user supplies only a single image that specifies the desired style. An extensive study shows that, for the task of style tuning text-to-image models, StyleDrop implemented on Muse convincingly outperforms other methods, including DreamBooth and textual inversion on Imagen or Stable Diffusion. paper page:

AK

56,408 次观看 • 3 年前

I feel like we haven't seen enough of what Gemini 4 Argon can actually do ❤️‍🔥 I gave it one image of a Gothic Victorian carriage and asked it to build the whole thing as an interactive 3D experience. Just one image and this is what you get. A 174,000 face model from scratch, complete with opening doors, hinged windows, working wheel physics, suspension, flickering lanterns and even custom sculpted skulls. All of this generated through code. No blender. Argon absolutely delivered with this one too. Many more such experiments are on the way :D

I feel like we haven't seen enough of what Gemini 4 Argon can actually do ❤️‍🔥 I gave it one image of a Gothic Victorian carriage and asked it to build the whole thing as an interactive 3D experience. Just one image and this is what you get. A 174,000 face model from scratch, complete with opening doors, hinged windows, working wheel physics, suspension, flickering lanterns and even custom sculpted skulls. All of this generated through code. No blender. Argon absolutely delivered with this one too. Many more such experiments are on the way :D

The Bugged Dev

126,670 次观看 • 6 天前

Your data is only as valuable as what you build on top of it. We now integrate with Databricks to let enterprise teams vibe code living applications and deploy them directly into Databricks Apps: with governance, security, and observability built in. 👷🏼‍♂️ More builders. 🛠️ Better tools. 🎮 No compromises on control.

Your data is only as valuable as what you build on top of it. We now integrate with Databricks to let enterprise teams vibe code living applications and deploy them directly into Databricks Apps: with governance, security, and observability built in. 👷🏼‍♂️ More builders. 🛠️ Better tools. 🎮 No compromises on control.

Replit ⠕

42,008 次观看 • 8 个月前